diff --git a/.gitattributes b/.gitattributes index bed0738c7eeb449bca98b5d2f33c89a1ee56349a..fc3751507353ade90e85fef0cce212d3041be175 100644 --- a/.gitattributes +++ b/.gitattributes @@ -58,3 +58,22 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text # Video files - compressed *.mp4 filter=lfs diff=lfs merge=lfs -text *.webm filter=lfs diff=lfs merge=lfs -text +transfer/code/CCFM/logs/ccfm_1gpu_online_5404417.out filter=lfs diff=lfs merge=lfs -text +transfer/code/CCFM/logs/ccfm_v2_5404735.out filter=lfs diff=lfs merge=lfs -text +transfer/code/CCFM/logs/ccfm_v2_resume_5406605.out filter=lfs diff=lfs merge=lfs -text +transfer/code/CCFM/result/ccfm-fusion_differential_perceiver-norman-cascaded-predict_y-gamma_0.5-perturbation_function_crisper-lr_5e-05-dim_model_128-infer_top_gene_1000-split_method_additive-use_mmd_loss_True-fold_1-latent_weight_1.0-choose_latent_p_0.4/iteration_110000/pred.h5ad filter=lfs diff=lfs merge=lfs -text 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merge=lfs -text +transfer/code/CCFM/result/ccfm-norman-f1-topk30-negTrue-d128-lr5e-05-lw1.0-lp0.4-ema0.9999-ln-wu2000-rk4-online/loss_curve.csv filter=lfs diff=lfs merge=lfs -text +transfer/code/CCFM/result_joint_generate/ccfm-norman-f1-topk30-negTrue-d128-lr5e-05-lw1.0-lp0.4-online/eval_only/pred.h5ad filter=lfs diff=lfs merge=lfs -text +transfer/code/CCFM/result_joint_generate/ccfm-norman-f1-topk30-negTrue-d128-lr5e-05-lw1.0-lp0.4-online/eval_only/real.h5ad filter=lfs diff=lfs merge=lfs -text +transfer/code/CCFM/test_result/ccfm-fusion_differential_perceiver-norman-cascaded-predict_y-gamma_0.5-perturbation_function_crisper-lr_5e-05-dim_model_128-infer_top_gene_1000-split_method_additive-use_mmd_loss_True-fold_1-latent_weight_1.0-choose_latent_p_0.4/iteration_0/pred.h5ad filter=lfs diff=lfs merge=lfs -text +transfer/code/CCFM/test_result/ccfm-fusion_differential_perceiver-norman-cascaded-predict_y-gamma_0.5-perturbation_function_crisper-lr_5e-05-dim_model_128-infer_top_gene_1000-split_method_additive-use_mmd_loss_True-fold_1-latent_weight_1.0-choose_latent_p_0.4/iteration_0/real.h5ad filter=lfs diff=lfs merge=lfs -text +transfer/code/ori_scDFM/data/norman.h5ad filter=lfs diff=lfs merge=lfs -text +transfer/code/ori_scDFM/logs/ccfm_baseline_5404238.out filter=lfs diff=lfs merge=lfs -text +transfer/code/scDFM/data/norman/go.csv filter=lfs diff=lfs merge=lfs -text +transfer/code/scDFM/data/norman/perturb_processed.h5ad filter=lfs diff=lfs merge=lfs -text +transfer/code/scDFM/data/norman.h5ad filter=lfs diff=lfs merge=lfs -text diff --git a/transfer/code/CCFM/CCFM_Architecture.md b/transfer/code/CCFM/CCFM_Architecture.md new file mode 100644 index 0000000000000000000000000000000000000000..18460c52125f32f8150b8b9425d584a35232e774 --- /dev/null +++ b/transfer/code/CCFM/CCFM_Architecture.md @@ -0,0 +1,437 @@ +# CCFM (Cascaded Conditioned Flow Matching) 架构文档 + +## 一、项目概述 + +CCFM 是一个**级联流匹配**框架，融合三个模型的优势来做单细胞扰动预测： + +- **scDFM**：基础流匹配架构（backbone、数据加载、训练范式） +- **LatentForcing**：级联双流思想（latent 流 + 表达流分阶段生成） +- **scGPT**：冻结的预训练模型，提供 per-gene 上下文化特征 + +### 核心创新 + +借鉴 LatentForcing 的双流架构，将其从图像域迁移到单细胞域： + +| LatentForcing (图像) | CCFM (单细胞) | +|---|---| +| 像素值 | 基因表达值 | +| DINO-v2 特征（辅助生成目标） | scGPT 上下文特征（辅助生成目标） | +| 类别标签（条件信号） | control 表达 + perturbation_id（条件信号） | + +**关键区分**：scGPT 特征是从 target（扰动细胞）提取的**辅助生成目标**，不是条件信号。推理时模型从噪声生成 scGPT 特征（Stage 1），再用生成的特征引导表达值生成（Stage 2）。真正的条件信号是 control 表达和 perturbation_id，它们在推理时始终可获取。 + +### 文件结构 + +``` +CCFM/ +├── _bootstrap_scdfm.py # Bootstrap scDFM 模块，命名空间隔离 +├── config/ +│ └── config_cascaded.py # CascadedFlowConfig dataclass (tyro CLI) +├── src/ +│ ├── __init__.py +│ ├── utils.py # Re-exports scDFM utils +│ ├── _scdfm_imports.py # scDFM 模块集中导入 +│ ├── denoiser.py # CascadedDenoiser (训练/推理逻辑) +│ ├── model/ +│ │ ├── model.py # CascadedFlowModel 双流模型 +│ │ └── layers.py # LatentEmbedder, LatentDecoder +│ └── data/ +│ ├── data.py # scDFM 数据加载集成 +│ └── scgpt_extractor.py # FrozenScGPTExtractor +├── scripts/ +│ ├── run_cascaded.py # 训练/评估入口 +│ └── download_scgpt.py # 下载 scGPT 预训练模型 +├── run.sh # pjsub 模板 +└── run_topk30_neg.sh # 完整参数化 job 脚本 +``` + +### 默认超参数 + +| 参数 | 值 | 说明 | +|---|---|---| +| `B` | 48 | batch size | +| `G_full` | 5000 | HVG 总数 | +| `G` | 1000 | 训练时随机基因子集 | +| `d_model` | 128 | 隐藏维度 | +| `scgpt_dim` | 512 | scGPT 输出特征维度 | +| `bottleneck_dim` | 128 | LatentEmbedder 瓶颈维度 | +| `nhead` | 8 | 注意力头数 | +| `nlayers` | 4 | Transformer 层数 | +| `dh_depth` | 2 | LatentDecoder block 数 | +| `choose_latent_p` | 0.4 | 训练 latent 流的概率 | +| `latent_weight` | 1.0 | latent loss 权重 | +| `gamma` | 0.5 | MMD loss 权重 | +| `lr` | 5e-5 | 学习率 | +| `steps` | 200000 | 训练总步数 | +| `latent_steps` | 20 | 推理 ODE 步数（latent） | +| `expr_steps` | 20 | 推理 ODE 步数（表达） | +| `warmup_batches` | 200 | scGPT running stats 预热 | + +--- + +## 二、训练流程 Tensor 数据流 + +### 2.1 数据准备 + +``` +输入: + source: (B, G_full=5000) 控制组表达 + target: (B, G_full=5000) 扰动后表达 + perturbation_id: (B, 2) 扰动 token ID + gene_ids: (G_full=5000,) 全部基因的 vocab ID + +随机采样 1000 个基因: + input_gene_ids = randperm(5000)[:1000] → (1000,) + source_sub = source[:, input_gene_ids] → (48, 1000) + target_sub = target[:, input_gene_ids] → (48, 1000) + gene_input = gene_ids[input_gene_ids].expand → (48, 1000) +``` + +### 2.2 冻结 scGPT 提取辅助生成目标（类似 LatentForcing 的 DINO 特征提取） + +``` +scgpt_extractor.extract(target_sub, gene_indices=input_gene_ids): + + target_sub: (48, 1000) 输入表达 + hvg_ids: (1000,) HVG → scGPT vocab ID 映射 + valid_mask: (1000,) bool, 过滤在 scGPT vocab 中的基因 + expr_valid: (48, G_valid) 有效基因的表达值 + + 若 G_valid+1 > max_seq_len(1200): + 随机采样 1199 个基因 + seq_len = 1200 + 否则: + seq_len = G_valid + 1 + + 拼接 CLS token: + src = [cls_id | gene_ids] → (48, seq_len) long + values = [0 | expr_valid] → (48, seq_len) float + + scGPT frozen forward: + encoder_out = scgpt._encode(src, values, mask) → (48, seq_len, 512) + + 去掉 CLS, scatter 回固定位置: + gene_features = encoder_out[:, 1:, :] → (48, seq_len-1, 512) + output = zeros(48, 1000, 512) + output.scatter_(1, idx, gene_features) → (48, 1000, 512) + + 归一化 (running mean/var, warmup 200 batches 后冻结): + output = (output - running_mean) / sqrt(running_var) * target_std + + z_target: (48, 1000, 512) +``` + +### 2.3 级联时间采样 + +``` +t_latent = rand(48) → (48,) +t_expr = rand(48) → (48,) +choose_latent_mask = rand(48) < 0.4 → (48,) bool + +对每个样本二选一: + 若 mask=True (40%概率): t_latent 保留, t_expr=0, w_expr=0, w_latent=1 → 训练 latent 流 + 若 mask=False (60%概率): t_expr 保留, t_latent=1, w_expr=1, w_latent=0 → 训练表达流 + +输出: + t_expr: (48,) 表达流时间步 + t_latent: (48,) 潜变量流时间步 + w_expr: (48,) 表达 loss 权重 (0 或 1) + w_latent: (48,) 潜变量 loss 权重 (0 或 1) +``` + +### 2.4 Flow Path 采样（线性插值 + 速度） + +``` +表达流: + noise_expr = randn_like(source_sub) → (48, 1000) + path_expr = AffineProbPath.sample(t_expr, noise_expr, target_sub) + path_expr.x_t = (1-t)*noise + t*target → (48, 1000) 插值后的噪声表达 + path_expr.dx_t = target - noise → (48, 1000) 目标速度 (ground truth) + +潜变量流: + noise_latent = randn_like(z_target) → (48, 1000, 512) + 展平: + z_target_flat = z_target.reshape(48, 512000) → (48, 512000) + noise_latent_flat = noise_latent.reshape(48, 512000) → (48, 512000) + path_latent_flat = AffineProbPath.sample(t_latent, noise_latent_flat, z_target_flat) + 还原: + path_latent.x_t = reshape → (48, 1000, 512) 插值后的噪声 latent + path_latent.dx_t = reshape → (48, 1000, 512) 目标速度 +``` + +### 2.5 模型前向传播 (`CascadedFlowModel.forward`) + +``` +输入: + gene_input: (48, 1000) 基因 token ID + source_sub: (48, 1000) 源表达 + path_expr.x_t: (48, 1000) 噪声表达 + path_latent.x_t: (48, 1000, 512) 噪声 latent + t_expr: (48,) + t_latent: (48,) + perturbation_id: (48, 2) +``` + +#### Step 5a: 表达流 Embedding + +``` +gene_emb = GeneEncoder(gene_input) → (48, 1000, 128) +val_emb_1 = ContinuousValueEncoder(x_t) → (48, 1000, 128) encoder_1 = 噪声 target (同 scDFM) +val_emb_2 = ContinuousValueEncoder(source_sub) → (48, 1000, 128) encoder_2 = control (同 scDFM) +expr_tokens = fusion_layer(cat[val_emb_1, val_emb_2]) + gene_emb + = Linear(256→128) → GELU → Linear(128→128) → LN + gene_emb → (48, 1000, 128) +``` + +> **设计说明**（与 scDFM 对齐）： +> - `value_encoder_1` 编码噪声 target（`x_t`），`value_encoder_2` 编码 control（`source`），与 scDFM 角色一致 +> - `gene_emb` 只在融合后加一次（去冗余），而非分别加到两个 encoder 的输出上 +> - `val_emb_2`（control 嵌入）传入 backbone 的 DiffPerceiverBlock 作为交叉注意力 KV（稳定参考基线） + +#### Step 5b: 潜变量流 Embedding + +``` +latent_tokens = LatentEmbedder(path_latent.x_t) + = Linear(512→128) → GELU → Linear(128→128) → (48, 1000, 128) +``` + +#### Step 5c: 双流融合 + +``` +x = expr_tokens + latent_tokens → (48, 1000, 128) +``` + +#### Step 5d: 条件向量 + +``` +t_expr_emb = TimestepEmbedder(t_expr) → (48, 128) +t_latent_emb = TimestepEmbedder(t_latent) → (48, 128) +pert_emb = GeneEncoder(perturbation_id).mean(dim=1) → (48, 128) + # perturbation_id: (48,2) → encoder → (48,2,128) → mean → (48,128) +c = t_expr_emb + t_latent_emb + pert_emb → (48, 128) +``` + +#### Step 5e: 共享 Backbone (4 层 DiffPerceiverBlock) + +``` +for i in range(4): + # GeneadaLN: 用 gene_emb 调制 x + x = gene_adaLN[i](gene_emb, x) → (48, 1000, 128) + + # Adapter: 拼接 pert_emb 后降维 + pert_exp = pert_emb[:, None, :].expand(-1, 1000, -1) → (48, 1000, 128) + x = cat[x, pert_exp] → (48, 1000, 256) + x = adapter_layer[i](x) + = Linear(256→128) → LeakyReLU → Linear(128→128) → LeakyReLU + → (48, 1000, 128) + + # DiffPerceiverBlock: attention + MLP, 用 c 做 AdaLN 条件 + x = DiffPerceiverBlock(x, val_emb_2, c) → (48, 1000, 128) +``` + +#### Step 5f: 表达 Decoder Head + +``` +x_with_pert = cat[x, pert_exp] → (48, 1000, 256) +pred_v_expr = ExprDecoder(x_with_pert)["pred"] → (48, 1000) +``` + +#### Step 5g: Latent Decoder Head + +``` +h = dh_proj(x) # Linear(128→128) → (48, 1000, 128) + +for j in range(2): # dh_depth=2 + # AdaLN: c → SiLU → Linear(128→768) → chunk 6 + shift_msa, scale_msa, gate_msa, + shift_mlp, scale_mlp, gate_mlp = adaLN_modulation(c) 各 (48, 128) + + # Self-Attention with AdaLN + h_norm = LayerNorm(h) * (1+scale_msa[:,None,:]) + shift_msa[:,None,:] + → (48, 1000, 128) + h_attn = MultiheadAttention(h_norm, h_norm, h_norm) → (48, 1000, 128) + h = h + gate_msa[:,None,:] * h_attn → (48, 1000, 128) + + # MLP with AdaLN + h_norm = LayerNorm(h) * (1+scale_mlp[:,None,:]) + shift_mlp[:,None,:] + → (48, 1000, 128) + h_mlp = Linear(128→512) → GELU → Linear(512→128) → (48, 1000, 128) + h = h + gate_mlp[:,None,:] * h_mlp → (48, 1000, 128) + +pred_v_latent = final(h) + = LN → Linear(128→128) → GELU → Linear(128→512) → (48, 1000, 512) +``` + +### 2.6 Loss 计算 + +``` +loss_expr = MSE(pred_v_expr - path_expr.dx_t) * w_expr[:, None] + = ((48,1000) - (48,1000))^2 * (48,1) → mean → scalar + 只对 w_expr=1 的样本有贡献 (约60%) + +loss_latent = MSE(pred_v_latent - path_latent.dx_t) * w_latent[:, None, None] + = ((48,1000,512) - (48,1000,512))^2 * (48,1,1) → mean → scalar + 只对 w_latent=1 的样本有贡献 (约40%) + +loss = loss_expr + latent_weight * loss_latent + +可选 MMD loss (对 w_expr>0 的样本): + x1_hat = x_t + pred_v_expr * (1-t_expr) # 单步重建 → (N_expr, 1000) + mmd_loss = mmd2_unbiased_multi_sigma(x1_hat, target_sub) → scalar + loss += gamma * mmd_loss +``` + +### 2.7 训练循环 + +``` +for iteration in range(200000): + batch → source(48, 5000), target(48, 5000), pert_id(48, 2) + loss = denoiser.train_step(source, target, pert_id, gene_ids, infer_top_gene=1000) + optimizer.zero_grad() + accelerator.backward(loss) + optimizer.step() + scheduler.step() # CosineAnnealing + + if iteration % 5000 == 0: + save checkpoint + run evaluation +``` + +--- + +## 三、推理流程 Tensor 数据流 + +推理时**不调用 scGPT 编码器**，模型从噪声自行生成 latent（辅助语义表征）。用**全部 5000 个基因**，不做子集采样。 +条件信号（control 表达 + perturbation_id）在每个 ODE 步中持续提供。 + +``` +输入: + source: (B, 5000) 控制组表达 + perturbation_id: (B, 2) 扰动 ID + gene_ids: (B, 5000) 基因 vocab ID +``` + +### 3.1 Stage 1: 生成 Latent（t_latent: 0→1, t_expr 固定=0） + +``` +z_noise = randn(B, 5000, 512) 初始噪声 + +ODE 求解 (RK4, 20步, t ∈ [0,1]): + 每一步调用 latent_vf(t, z_t): + t_latent = t.expand(B) → (B,) 当前时间步 + t_expr = zeros(B) → (B,) 固定为 0 + + model.forward( + gene_ids, (B, 5000) + source, (B, 5000) 源表达 + source, (B, 5000) ← 注意: x_t 用 source (因为 t_expr=0) + z_t, (B, 5000, 512) 当前 latent 状态 + t_expr=0, (B,) + t_latent=t, (B,) + perturbation_id, (B, 2) + ) + → _, v_latent (B, 5000, 512) latent 速度场 + + return v_latent + + z_traj = odeint(latent_vf, z_noise, linspace(0,1,20)) + → (20, B, 5000, 512) + z_generated = z_traj[-1] → (B, 5000, 512) ★ 生成的 latent +``` + +### 3.2 Stage 2: 用生成的 Latent 引导表达生成（t_expr: 0→1, t_latent 固定=1） + +``` +expr_noise = randn_like(source) → (B, 5000) 高斯噪声 (或 Poisson) + +ODE 求解 (RK4, 20步, t ∈ [0,1]): + 每一步调用 expr_vf(t, x_t): + t_expr = t.expand(B) → (B,) 当前时间步 + t_latent = ones(B) → (B,) 固定为 1 (latent 已完成) + + model.forward( + gene_ids, (B, 5000) + source, (B, 5000) + x_t, (B, 5000) ← 当前噪声表达 + z_generated, (B, 5000, 512) ★ Stage 1 的结果 (固定) + t_expr=t, (B,) + t_latent=1, (B,) + perturbation_id, (B, 2) + ) + → v_expr, _ (B, 5000) 表达速度场 + + return v_expr + + x_traj = odeint(expr_vf, expr_noise, linspace(0,1,20)) + → (20, B, 5000) + x_generated = clamp(x_traj[-1], min=0) → (B, 5000) ★ 最终预测表达 +``` + +### 3.3 评估流程 + +``` +对每个扰动条件: + source = 随机采样 128 个控制细胞 → (128, 5000) + 按 batch_size 调用 generate: + pred = denoiser.generate(source, pert_id, gene_ids) → (128, 5000) + +汇总: + pred_adata = AnnData(X=所有预测表达, obs=扰动标签) + real_adata = AnnData(X=所有真实表达, obs=扰动标签) + MetricsEvaluator(pred_adata, real_adata) → results.csv, agg_results.csv +``` + +--- + +## 四、训练 vs 推理 对比总结 + +| 维度 | 训练 | 推理 | +|---|---|---| +| 基因数 | 随机采样 G=1000 | 全量 G=5000 | +| scGPT 特征（辅助生成目标） | 冻结提取 target 特征 `z_target` 作为 latent 流的生成目标 | **不使用**，模型从噪声自行生成 latent（Stage 1） | +| 时间步 | 级联采样：40% 训练 latent, 60% 训练表达 | 两阶段串行：先 latent(0→1), 再表达(0→1) | +| 流采样 | 单步：`x_t = (1-t)*noise + t*target` | ODE 积分：RK4, 20 步 | +| Loss | MSE(velocity) + MMD | 无 | +| 输出 | scalar loss | `(B, G_full)` 表达矩阵 | +| 条件信号 | control + pert_id（真条件）；scGPT 特征是生成目标 | control + pert_id（每步输入）；latent 由 Stage1 生成 | +| 时间步 | `t_expr` 和 `t_latent` 随机，互斥激活 | Stage1: `t_expr=0, t_latent∈[0,1]`; Stage2: `t_latent=1, t_expr∈[0,1]` | + +--- + +## 五、模型架构总览图 + +``` + ┌──────────────────────────────────────────────┐ + │ CascadedFlowModel │ + │ │ + gene_id (B,G) ───────→│ GeneEncoder ──→ gene_emb (B,G,d) │ + │ │ │ + x_t (B,G) ──────────→│ ValEnc1 ──→ val_emb_1 (B,G,d) 噪声target │ + source (B,G) ────────→│ ValEnc2 ──→ val_emb_2 (B,G,d) control │ + │ │ │ + │ fusion_layer(cat[val_emb_1, val_emb_2]) │ + │ + gene_emb (融合后加一次) │ + │ ↓ │ + │ expr_tokens (B,G,d) │ + │ │ │ + z_t (B,G,512) ──────→│ LatentEmbedder(512→128→d) │ + │ ↓ │ + │ latent_tokens (B,G,d) │ + │ │ │ + │ x = expr_tokens + latent_tokens (B,G,d) │ + │ │ + t_expr (B,) ────────→│ TimestepEmb ──┐ │ + t_latent (B,) ──────→│ TimestepEmb ──┼→ c = sum (B,d) │ + pert_id (B,2) ──────→│ PertEmb ──────┘ │ + │ │ + │ ┌─ 4x DiffPerceiverBlock ──────────────┐ │ + │ │ gene_adaLN(gene_emb, x) │ │ + │ │ cat[x, pert_emb] → adapter → x │ │ + │ │ DiffPerceiverBlock(x, val_emb_2, c) │ │ + │ └──────────────────────────────────────┘ │ + │ │ │ + │ ├──→ ExprDecoder(cat[x, pert]) ──→ pred_v_expr (B,G) + │ │ │ + │ └──→ LatentDecoder(x, c) │ + │ dh_proj → 2x AdaLN Block │ + │ → final proj ──→ pred_v_latent (B,G,512) + └──────────────────────────────────────────────┘ +``` diff --git a/transfer/code/CCFM/_bootstrap_scdfm.py b/transfer/code/CCFM/_bootstrap_scdfm.py new file mode 100644 index 0000000000000000000000000000000000000000..b1d12fa3d457aae2b33c29c7760450052c82c8a7 --- /dev/null +++ b/transfer/code/CCFM/_bootstrap_scdfm.py @@ -0,0 +1,101 @@ +""" +Bootstrap scDFM imports by creating missing __init__.py files and loading +its modules under a 'scdfm_src' prefix in sys.modules. + +This module MUST be imported before any CCFM src imports. +""" + +import sys +import os +import types + +_SCDFM_ROOT = os.path.normpath( + os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "scDFM") +) + +# Directories in scDFM that need __init__.py to be proper packages +_DIRS_NEEDING_INIT = [ + "src", + "src/models", + "src/models/origin", + "src/data_process", + "src/tokenizer", + "src/script", + "src/models/perturbation", +] + + +def _ensure_init_files(): + """Create missing __init__.py files in scDFM so it can be imported as packages.""" + created = [] + for d in _DIRS_NEEDING_INIT: + init_path = os.path.join(_SCDFM_ROOT, d, "__init__.py") + if not os.path.exists(init_path): + with open(init_path, "w") as f: + f.write("# Auto-created by CCFM bootstrap\n") + created.append(init_path) + return created + + +def bootstrap(): + """Load scDFM's src package as 'scdfm_src' in sys.modules.""" + if "scdfm_src" in sys.modules: + return # Already bootstrapped + + # Create missing __init__.py files + _ensure_init_files() + + # Save CCFM's src modules + saved = {} + for key in list(sys.modules.keys()): + if key == "src" or key.startswith("src."): + saved[key] = sys.modules.pop(key) + + # Add scDFM root to path + sys.path.insert(0, _SCDFM_ROOT) + + try: + # Import scDFM modules (their relative imports work now) + import src as scdfm_src_pkg + import src.models + import src.models.origin + import src.models.origin.blocks + import src.models.origin.layers + import src.models.origin.model + import src.flow_matching + import src.flow_matching.path + import src.flow_matching.path.path + import src.flow_matching.path.path_sample + import src.flow_matching.path.affine + import src.flow_matching.path.scheduler + import src.flow_matching.path.scheduler.scheduler + # Skip src.flow_matching.ot (requires 'ot' package, not needed for CCFM) + import src.utils + import src.utils.utils + import src.tokenizer + import src.tokenizer.gene_tokenizer + # Skip src.data_process (has heavy deps like bs4, rdkit) + # We handle data loading separately in CCFM + + # Re-register all under scdfm_src.* prefix + for key in list(sys.modules.keys()): + if key == "src" or key.startswith("src."): + new_key = "scdfm_" + key + sys.modules[new_key] = sys.modules[key] + + finally: + # Remove scDFM's src.* entries + for key in list(sys.modules.keys()): + if (key == "src" or key.startswith("src.")) and not key.startswith("scdfm_"): + del sys.modules[key] + + # Restore CCFM's src modules + for key, mod in saved.items(): + sys.modules[key] = mod + + # Remove scDFM from front of path + if _SCDFM_ROOT in sys.path: + sys.path.remove(_SCDFM_ROOT) + + +bootstrap() diff --git a/transfer/code/CCFM/cell-eval-outdir/agg_results.csv b/transfer/code/CCFM/cell-eval-outdir/agg_results.csv new file mode 100644 index 0000000000000000000000000000000000000000..b41fbb26f0495b4e7c1dc6d9777fa26c16c9876b --- /dev/null +++ b/transfer/code/CCFM/cell-eval-outdir/agg_results.csv @@ -0,0 +1,10 @@ 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b/transfer/code/CCFM/config/config_cascaded.py new file mode 100644 index 0000000000000000000000000000000000000000..ede045ade06584c0f24f585a0663ee723548ea30 --- /dev/null +++ b/transfer/code/CCFM/config/config_cascaded.py @@ -0,0 +1,92 @@ +from dataclasses import dataclass +import os + + +@dataclass +class CascadedFlowConfig: + # === Base (same as scDFM FlowConfig) === + model_type: str = "cascaded" + batch_size: int = 48 + ntoken: int = 512 + d_model: int = 128 + nhead: int = 8 + nlayers: int = 4 + lr: float = 5e-5 + steps: int = 200000 + eta_min: float = 1e-6 + devices: str = "1" + test_only: bool = False + + data_name: str = "norman" + perturbation_function: str = "crisper" + noise_type: str = "Gaussian" + poisson_alpha: float = 0.8 + poisson_target_sum: int = -1 + + print_every: int = 5000 + mode: str = "predict_y" + result_path: str = "./result_online" + fusion_method: str = "differential_perceiver" + infer_top_gene: int = 1000 + n_top_genes: int = 5000 + checkpoint_path: str = "" + gamma: float = 0.5 + split_method: str = "additive" + use_mmd_loss: bool = True + fold: int = 1 + use_negative_edge: bool = True + topk: int = 30 + + # === Cascaded / Latent specific === + scgpt_dim: int = 512 + bottleneck_dim: int = 128 + latent_weight: float = 1.0 + choose_latent_p: float = 0.4 + target_std: float = 1.0 + dh_depth: int = 2 + warmup_batches: int = 200 # batches to collect running stats + + # === Cascaded noise (LatentForcing dino_first_cascaded_noised) === + noise_beta: float = 0.25 # when training expr flow, t_latent ~ U[1-beta, 1] instead of 1.0 + + # === EMA === + ema_decay: float = 0.9999 + + # === Logit-normal time-step sampling === + t_sample_mode: str = "logit_normal" # "uniform" or "logit_normal" + t_expr_mean: float = 0.0 # expression flow logit-normal mu + t_expr_std: float = 1.0 # expression flow logit-normal sigma + t_latent_mean: float = 0.0 # latent flow logit-normal mu + t_latent_std: float = 1.0 # latent flow logit-normal sigma + + # === LR warmup === + warmup_steps: int = 2000 + + # === scGPT paths === + scgpt_model_dir: str = "transfer/data/scGPT_pretrained" + scgpt_max_seq_len: int = 1200 + scgpt_cache_path: str = "" # Pre-extracted HDF5 path. Empty = online extraction (default) + + # === Inference === + latent_steps: int = 20 + expr_steps: int = 20 + ode_method: str = "rk4" # "euler" or "rk4" + + def __post_init__(self): + if self.data_name == "norman_umi_go_filtered": + self.n_top_genes = 5054 + if self.data_name == "norman": + self.n_top_genes = 5000 + + def make_path(self): + scgpt_mode = "cached" if self.scgpt_cache_path else "online" + t_mode = "ln" if self.t_sample_mode == "logit_normal" else "uni" + exp_name = ( + f"ccfm-{self.data_name}-f{self.fold}" + f"-topk{self.topk}-neg{self.use_negative_edge}" + f"-d{self.d_model}-lr{self.lr}" + f"-lw{self.latent_weight}-lp{self.choose_latent_p}" + f"-ema{self.ema_decay}-{t_mode}-wu{self.warmup_steps}" + f"-{self.ode_method}-{scgpt_mode}" + ) + return os.path.join(self.result_path, exp_name) diff --git a/transfer/code/CCFM/config/config_cascaded.py.bak b/transfer/code/CCFM/config/config_cascaded.py.bak new file mode 100644 index 0000000000000000000000000000000000000000..4561b2c1c52916ad2d8b1b13dcb5063e5b4cdefd --- /dev/null +++ b/transfer/code/CCFM/config/config_cascaded.py.bak @@ -0,0 +1,89 @@ +from dataclasses import dataclass +import os + + +@dataclass +class CascadedFlowConfig: + # === Base (same as scDFM FlowConfig) === + model_type: str = "cascaded" + batch_size: int = 48 + ntoken: int = 512 + d_model: int = 128 + nhead: int = 8 + nlayers: int = 4 + lr: float = 5e-5 + steps: int = 200000 + eta_min: float = 1e-6 + devices: str = "1" + test_only: bool = False + + data_name: str = "norman" + perturbation_function: str = "crisper" + noise_type: str = "Gaussian" + poisson_alpha: float = 0.8 + poisson_target_sum: int = -1 + + print_every: int = 5000 + mode: str = "predict_y" + result_path: str = "./result_online" + fusion_method: str = "differential_perceiver" + infer_top_gene: int = 1000 + n_top_genes: int = 5000 + checkpoint_path: str = "" + gamma: float = 0.5 + split_method: str = "additive" + use_mmd_loss: bool = True + fold: int = 1 + use_negative_edge: bool = True + topk: int = 30 + + # === Cascaded / Latent specific === + scgpt_dim: int = 512 + bottleneck_dim: int = 128 + latent_weight: float = 1.0 + choose_latent_p: float = 0.4 + target_std: float = 1.0 + dh_depth: int = 2 + warmup_batches: int = 200 # batches to collect running stats + + # === EMA === + ema_decay: float = 0.9999 + + # === Logit-normal time-step sampling === + t_sample_mode: str = "logit_normal" # "uniform" or "logit_normal" + t_expr_mean: float = 0.0 # expression flow logit-normal mu + t_expr_std: float = 1.0 # expression flow logit-normal sigma + t_latent_mean: float = 0.0 # latent flow logit-normal mu + t_latent_std: float = 1.0 # latent flow logit-normal sigma + + # === LR warmup === + warmup_steps: int = 2000 + + # === scGPT paths === + scgpt_model_dir: str = "transfer/data/scGPT_pretrained" + scgpt_max_seq_len: int = 1200 + scgpt_cache_path: str = "" # Pre-extracted HDF5 path. Empty = online extraction (default) + + # === Inference === + latent_steps: int = 20 + expr_steps: int = 20 + ode_method: str = "rk4" # "euler" or "rk4" + + def __post_init__(self): + if self.data_name == "norman_umi_go_filtered": + self.n_top_genes = 5054 + if self.data_name == "norman": + self.n_top_genes = 5000 + + def make_path(self): + scgpt_mode = "cached" if self.scgpt_cache_path else "online" + t_mode = "ln" if self.t_sample_mode == "logit_normal" else "uni" + exp_name = ( + f"ccfm-{self.data_name}-f{self.fold}" + f"-topk{self.topk}-neg{self.use_negative_edge}" + f"-d{self.d_model}-lr{self.lr}" + f"-lw{self.latent_weight}-lp{self.choose_latent_p}" + f"-ema{self.ema_decay}-{t_mode}-wu{self.warmup_steps}" + f"-{self.ode_method}-{scgpt_mode}" + ) + return os.path.join(self.result_path, exp_name) diff --git a/transfer/code/CCFM/eval_ccfm_v2.sh b/transfer/code/CCFM/eval_ccfm_v2.sh new file mode 100644 index 0000000000000000000000000000000000000000..7e4209370ea575319f7881cc55538955fa3c2734 --- /dev/null +++ b/transfer/code/CCFM/eval_ccfm_v2.sh @@ -0,0 +1,68 @@ +#!/bin/sh +#PJM -L rscgrp=b-batch +#PJM -L gpu=1 +#PJM -L elapse=3:00:00 +#PJM -N eval_ccfm_v2 +#PJM -j +#PJM -o logs/eval_ccfm_v2_%j.out + +module load cuda/12.2.2 +module load cudnn/8.9.7 +module load gcc-toolset/12 + +source /home/hp250092/ku50001222/qian/aivc/lfj/stack_env/bin/activate +cd /home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM + +export PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:256 + +CKPT="/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/result/ccfm-norman-f1-topk30-negTrue-d128-lr5e-05-lw1.0-lp0.4-ema0.9999-ln-wu2000-rk4-online/iteration_100000/checkpoint.pt" + +echo "==========================================" +echo "Job ID: $PJM_JOBID" +echo "Job Name: $PJM_JOBNAME" +echo "Start: $(date)" +echo "Node: $(hostname)" +echo "GPU: $(nvidia-smi --query-gpu=name,memory.total --format=csv,noheader 2>/dev/null || echo 'N/A')" +echo "Eval: CCFM v2 (online) iteration_100000" +echo "==========================================" + +python scripts/run_cascaded.py \ + --data-name norman \ + --d-model 128 \ + --nhead 8 \ + --nlayers 4 \ + --batch-size 48 \ + --lr 5e-5 \ + --steps 200000 \ + --fusion-method differential_perceiver \ + --perturbation-function crisper \ + --noise-type Gaussian \ + --infer-top-gene 1000 \ + --n-top-genes 5000 \ + --use-mmd-loss \ + --gamma 0.5 \ + --split-method additive \ + --fold 1 \ + --scgpt-dim 512 \ + --bottleneck-dim 128 \ + --latent-weight 1.0 \ + --choose-latent-p 0.4 \ + --dh-depth 2 \ + --print-every 10000 \ + --topk 30 \ + --use-negative-edge \ + --ema-decay 0.9999 \ + --t-sample-mode logit_normal \ + --t-expr-mean 0.0 \ + --t-expr-std 1.0 \ + --t-latent-mean 0.0 \ + --t-latent-std 1.0 \ + --warmup-steps 2000 \ + --ode-method rk4 \ + --result-path ./result \ + --checkpoint-path "$CKPT" \ + --test-only + +echo "==========================================" +echo "Finished: $(date)" +echo "==========================================" diff --git a/transfer/code/CCFM/eval_joint_generate.sh b/transfer/code/CCFM/eval_joint_generate.sh new file mode 100644 index 0000000000000000000000000000000000000000..c89804a6b0b3ee3df5153479e493b5eced5987b5 --- /dev/null +++ b/transfer/code/CCFM/eval_joint_generate.sh @@ -0,0 +1,56 @@ +#!/bin/bash +#PJM -L rscgrp=b-batch +#PJM -L gpu=1 +#PJM -L elapse=3:00:00 +#PJM -j +#PJM -S +#PJM -o /home/hp250092/ku50001222/qian/aivc/lfj/transfer/logs/ccfm_eval_joint_%j.out + +# Evaluate CCFM with joint-generation inference (LatentForcing style) +# using the checkpoint from two-stage ODE training. + +source /home/hp250092/ku50001222/qian/aivc/lfj/stack_env/bin/activate +cd /home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM + +export PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:256 + +echo "==========================================" +echo "Job ID: $PJM_JOBID" +echo "Start: $(date)" +echo "Node: $(hostname)" +echo "Eval mode: joint-generate (LatentForcing style)" +echo "==========================================" + +CHECKPOINT="/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/result/ccfm-fusion_differential_perceiver-norman-cascaded-predict_y-gamma_0.5-perturbation_function_crisper-lr_5e-05-dim_model_128-infer_top_gene_1000-split_method_additive-use_mmd_loss_True-fold_1-latent_weight_1.0-choose_latent_p_0.4/iteration_110000/checkpoint.pt" + +python scripts/run_cascaded.py \ + --data-name norman \ + --d-model 128 \ + --nhead 8 \ + --nlayers 4 \ + --batch-size 128 \ + --lr 5e-5 \ + --steps 200000 \ + --fusion-method differential_perceiver \ + --perturbation-function crisper \ + --noise-type Gaussian \ + --infer-top-gene 1000 \ + --n-top-genes 5000 \ + --use-mmd-loss \ + --gamma 0.5 \ + --split-method additive \ + --fold 1 \ + --scgpt-dim 512 \ + --bottleneck-dim 128 \ + --latent-weight 1.0 \ + --choose-latent-p 0.4 \ + --dh-depth 2 \ + --latent-steps 20 \ + --expr-steps 20 \ + --result-path ./result_joint_generate \ + --checkpoint-path "$CHECKPOINT" \ + --test-only + +echo "==========================================" +echo "Finished: $(date)" +echo "==========================================" diff --git a/transfer/code/CCFM/eval_joint_generate.sh.5404217.stats b/transfer/code/CCFM/eval_joint_generate.sh.5404217.stats new file mode 100644 index 0000000000000000000000000000000000000000..ce26e6f0b93c2f1194fc5eba2c6ec3395e9cd800 --- /dev/null +++ b/transfer/code/CCFM/eval_joint_generate.sh.5404217.stats @@ -0,0 +1,467 @@ +Job Statistical Information + + JOB ID : 5404217 + SUB JOB NUM : - + START BULKNO : - + END BULKNO : - + HOST NAME : genkai0001 + JOB NAME : eval_joint_generate.sh + JOB TYPE : BATCH + JOB MODEL : NM + USER : ku50001222 + GROUP : hp250092 + RESOURCE UNIT : rscunit_pg01 + RESOURCE GROUP : b-batch + APRIORITY : 127 + PRIORITY : 127 + SHELL : /bin/bash + MAIL SEND FLAG : ---- + MAIL ADDRESS : ku50001222 + 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a/transfer/code/CCFM/logs/ccfm_1gpu_cached_5404438.out b/transfer/code/CCFM/logs/ccfm_1gpu_cached_5404438.out new file mode 100644 index 0000000000000000000000000000000000000000..56dcabff5d9d0296a4a69057b8bcd2952396c906 --- /dev/null +++ b/transfer/code/CCFM/logs/ccfm_1gpu_cached_5404438.out @@ -0,0 +1,57 @@ +========================================== +Job ID: 5404438 +Job Name: ccfm_1gpu_cached +Start: Sun Mar 15 00:10:32 JST 2026 +Node: b0031 +GPU: NVIDIA H100, 95830 MiB +Run: CCFM 1GPU cached scGPT +========================================== +The following values were not passed to `accelerate launch` and had defaults used instead: + `--num_machines` was set to a value of `1` + `--mixed_precision` was set to a value of `'no'` + `--dynamo_backend` was set to a value of `'no'` +To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`. +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scGPT/scgpt/model/model.py:21: UserWarning: flash_attn is not installed + warnings.warn("flash_attn is not installed") +WARNING:accelerate.utils.other:[RANK 0] Detected kernel version 4.18.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher. +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:185: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control') +/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scanpy/preprocessing/_highly_variable_genes.py:806: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. + adata.uns["hvg"] = {"flavor": flavor} +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:277: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:329: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_extractor.py:109: UserWarning: FrozenScGPTExtractor: 498/5035 HVG genes not found in scGPT vocab, will use zero vectors. + warnings.warn( +CascadedFlowConfig(model_type='cascaded', batch_size=48, ntoken=512, d_model=128, nhead=8, nlayers=4, lr=5e-05, steps=200000, eta_min=1e-06, devices='1', test_only=False, data_name='norman', perturbation_function='crisper', noise_type='Gaussian', poisson_alpha=0.8, poisson_target_sum=-1, print_every=5000, mode='predict_y', result_path='./result_scgpt', fusion_method='differential_perceiver', infer_top_gene=1000, n_top_genes=5000, checkpoint_path='', gamma=0.5, split_method='additive', use_mmd_loss=True, fold=1, use_negative_edge=True, topk=30, scgpt_dim=512, bottleneck_dim=128, latent_weight=1.0, choose_latent_p=0.4, target_std=1.0, dh_depth=2, warmup_batches=200, scgpt_model_dir='transfer/data/scGPT_pretrained', scgpt_max_seq_len=1200, scgpt_cache_path='scgpt_cache_norman.h5', latent_steps=20, expr_steps=20) +Converted var_names to gene symbols, sample: ['RP11-34P13.8', 'RP11-54O7.3', 'SAMD11', 'PERM1', 'HES4'] +Warning: ctrl is not in the gene names +##### loading vocab from file ##### +Loaded 159/163 pretrained parameters +Using pre-extracted scGPT cache: scgpt_cache_norman.h5 + Cache shape: (91205, 5035, 512), cells: 91205 + 0%| | 0/200000 [00:00 + cached_z_target = scgpt_cache.lookup(tgt_cell_names, input_gene_ids, device=device) + File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_cache.py", line 50, in lookup + row_indices = [self.name_to_idx[n] for n in cell_names] + ~~~~~~~~~~~~~~~~^^^ +KeyError: ('TGAGCCGCAGTAAGAT-8',) + 0%| | 0/200000 [00:42 + sys.exit(main()) + ~~~~^^ + File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/accelerate/commands/accelerate_cli.py", line 50, in main + args.func(args) + ~~~~~~~~~^^^^^^ + File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/accelerate/commands/launch.py", line 1405, in launch_command + simple_launcher(args) + ~~~~~~~~~~~~~~~^^^^^^ + File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/accelerate/commands/launch.py", line 993, in simple_launcher + raise subprocess.CalledProcessError(returncode=process.returncode, cmd=cmd) +subprocess.CalledProcessError: Command '['/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/bin/python', 'scripts/run_cascaded.py', '--data-name', 'norman', '--d-model', '128', '--nhead', '8', '--nlayers', '4', '--batch-size', '48', '--lr', '5e-5', '--steps', '200000', '--fusion-method', 'differential_perceiver', '--perturbation-function', 'crisper', '--noise-type', 'Gaussian', '--infer-top-gene', '1000', '--n-top-genes', '5000', '--use-mmd-loss', '--gamma', '0.5', '--split-method', 'additive', '--fold', '1', '--scgpt-dim', '512', '--bottleneck-dim', '128', '--latent-weight', '1.0', '--choose-latent-p', '0.4', '--dh-depth', '2', '--print-every', '5000', '--topk', '30', '--use-negative-edge', '--scgpt-cache-path', 'scgpt_cache_norman.h5', '--result-path', './result_scgpt']' returned non-zero exit status 1. +========================================== +Finished: Sun Mar 15 00:12:56 JST 2026 +========================================== diff --git a/transfer/code/CCFM/logs/ccfm_1gpu_cached_5404526.out b/transfer/code/CCFM/logs/ccfm_1gpu_cached_5404526.out new file mode 100644 index 0000000000000000000000000000000000000000..b49f14a697793743a690c90082b97580cc91e913 --- /dev/null +++ b/transfer/code/CCFM/logs/ccfm_1gpu_cached_5404526.out @@ -0,0 +1,68 @@ +========================================== +Job ID: 5404526 +Job Name: ccfm_1gpu_cached +Start: Sun Mar 15 01:17:06 JST 2026 +Node: b0027 +GPU: NVIDIA H100, 95830 MiB +Run: CCFM 1GPU cached scGPT +========================================== +The following values were not passed to `accelerate launch` and had defaults used instead: + `--num_machines` was set to a value of `1` + `--mixed_precision` was set to a value of `'no'` + `--dynamo_backend` was set to a value of `'no'` +To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`. +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scGPT/scgpt/model/model.py:21: UserWarning: flash_attn is not installed + warnings.warn("flash_attn is not installed") +WARNING:accelerate.utils.other:[RANK 0] Detected kernel version 4.18.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher. +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:185: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control') +/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scanpy/preprocessing/_highly_variable_genes.py:806: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. + adata.uns["hvg"] = {"flavor": flavor} +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:277: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:329: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_extractor.py:109: UserWarning: FrozenScGPTExtractor: 498/5035 HVG genes not found in scGPT vocab, will use zero vectors. + warnings.warn( +CascadedFlowConfig(model_type='cascaded', batch_size=48, ntoken=512, d_model=128, nhead=8, nlayers=4, lr=5e-05, steps=200000, eta_min=1e-06, devices='1', test_only=False, data_name='norman', perturbation_function='crisper', noise_type='Gaussian', poisson_alpha=0.8, poisson_target_sum=-1, print_every=5000, mode='predict_y', result_path='./result_scgpt', fusion_method='differential_perceiver', infer_top_gene=1000, n_top_genes=5000, checkpoint_path='', gamma=0.5, split_method='additive', use_mmd_loss=True, fold=1, use_negative_edge=True, topk=30, scgpt_dim=512, bottleneck_dim=128, latent_weight=1.0, choose_latent_p=0.4, target_std=1.0, dh_depth=2, warmup_batches=200, scgpt_model_dir='transfer/data/scGPT_pretrained', scgpt_max_seq_len=1200, scgpt_cache_path='scgpt_cache_norman.h5', latent_steps=20, expr_steps=20) +Converted var_names to gene symbols, sample: ['RP11-34P13.8', 'RP11-54O7.3', 'SAMD11', 'PERM1', 'HES4'] +Warning: ctrl is not in the gene names +##### loading vocab from file ##### +Loaded 159/163 pretrained parameters +Using pre-extracted scGPT cache: scgpt_cache_norman.h5 + Cache shape: (91205, 5035, 512), cells: 91205 + 0%| | 0/200000 [00:00 + cached_z_target = scgpt_cache.lookup(tgt_cell_names, input_gene_ids, device=device) + File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_cache.py", line 55, in lookup + raw = self.features[row_indices] # (B, G_full, D) as numpy + ~~~~~~~~~~~~~^^^^^^^^^^^^^ + File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper + File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper + File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/dataset.py", line 945, in __getitem__ + selection = sel.select(self.shape, args, dataset=self) + File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/selections.py", line 85, in select + return selector.make_selection(args) + ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^ + File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper + File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper + File "h5py/_selector.pyx", line 297, in h5py._selector.Selector.make_selection + File "h5py/_selector.pyx", line 216, in h5py._selector.Selector.apply_args +TypeError: Indexing elements must be in increasing order + 0%| | 0/200000 [00:42 + sys.exit(main()) + ~~~~^^ + File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/accelerate/commands/accelerate_cli.py", line 50, in main + args.func(args) + ~~~~~~~~~^^^^^^ + File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/accelerate/commands/launch.py", line 1405, in launch_command + simple_launcher(args) + ~~~~~~~~~~~~~~~^^^^^^ + File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/accelerate/commands/launch.py", line 993, in simple_launcher + raise subprocess.CalledProcessError(returncode=process.returncode, cmd=cmd) +subprocess.CalledProcessError: Command '['/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/bin/python', 'scripts/run_cascaded.py', '--data-name', 'norman', '--d-model', '128', '--nhead', '8', '--nlayers', '4', '--batch-size', '48', '--lr', '5e-5', '--steps', '200000', '--fusion-method', 'differential_perceiver', '--perturbation-function', 'crisper', '--noise-type', 'Gaussian', '--infer-top-gene', '1000', '--n-top-genes', '5000', '--use-mmd-loss', '--gamma', '0.5', '--split-method', 'additive', '--fold', '1', '--scgpt-dim', '512', '--bottleneck-dim', '128', '--latent-weight', '1.0', '--choose-latent-p', '0.4', '--dh-depth', '2', '--print-every', '5000', '--topk', '30', '--use-negative-edge', '--scgpt-cache-path', 'scgpt_cache_norman.h5', '--result-path', './result_scgpt']' returned non-zero exit status 1. +========================================== +Finished: Sun Mar 15 01:19:22 JST 2026 +========================================== diff --git a/transfer/code/CCFM/logs/ccfm_1gpu_online_5404417.out b/transfer/code/CCFM/logs/ccfm_1gpu_online_5404417.out new file mode 100644 index 0000000000000000000000000000000000000000..f7ece61d8f6946e49a5a7856e1523cd1129646dd --- /dev/null +++ b/transfer/code/CCFM/logs/ccfm_1gpu_online_5404417.out @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cf48151b252a3896e8ae3132cae1e9fafbd433de265dd15f653765d9919a5ea1 +size 34640252 diff --git a/transfer/code/CCFM/logs/ccfm_topk30_neg_5402619.out b/transfer/code/CCFM/logs/ccfm_topk30_neg_5402619.out new file mode 100644 index 0000000000000000000000000000000000000000..adde88147eb22a2cb8b615533a682508154e18da --- /dev/null +++ b/transfer/code/CCFM/logs/ccfm_topk30_neg_5402619.out @@ -0,0 +1,111 @@ +========================================== +Job ID: 5402619 +Job Name: ccfm_topk30_neg +Start: Sat Mar 14 14:34:35 JST 2026 +Node: b0018 +GPU: NVIDIA H100, 95830 MiB +NVIDIA H100, 95830 MiB +NVIDIA H100, 95830 MiB +NVIDIA H100, 95830 MiB +Run: CCFM (topk=30, use_negative_edge=True) +========================================== +The following values were not passed to `accelerate launch` and had defaults used instead: + More than one GPU was found, enabling multi-GPU training. + If this was unintended please pass in `--num_processes=1`. + `--num_machines` was set to a value of `1` + `--mixed_precision` was set to a value of `'no'` + `--dynamo_backend` was set to a value of `'no'` +To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`. +[W314 14:35:31.809931854 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol). +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scGPT/scgpt/model/model.py:21: UserWarning: flash_attn is not installed + warnings.warn("flash_attn is not installed") +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scGPT/scgpt/model/model.py:21: UserWarning: flash_attn is not installed + warnings.warn("flash_attn is not installed") +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scGPT/scgpt/model/model.py:21: UserWarning: flash_attn is not installed + warnings.warn("flash_attn is not installed") +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scGPT/scgpt/model/model.py:21: UserWarning: flash_attn is not installed + warnings.warn("flash_attn is not installed") +[W314 14:36:13.805735826 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol). +[W314 14:36:13.808311201 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol). +[W314 14:36:13.810035709 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol). +[W314 14:36:13.815100046 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol). +WARNING:accelerate.utils.other:[RANK 0] Detected kernel version 4.18.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher. +CascadedFlowConfig(model_type='cascaded', batch_size=48, ntoken=512, d_model=128, nhead=8, nlayers=4, lr=5e-05, steps=200000, eta_min=1e-06, devices='1', test_only=False, data_name='norman', perturbation_function='crisper', noise_type='Gaussian', poisson_alpha=0.8, poisson_target_sum=-1, print_every=10000, mode='predict_y', result_path='./result', fusion_method='differential_perceiver', infer_top_gene=1000, n_top_genes=5000, checkpoint_path='', gamma=0.5, split_method='additive', use_mmd_loss=True, fold=1, use_negative_edge=True, topk=30, scgpt_dim=512, bottleneck_dim=128, latent_weight=1.0, choose_latent_p=0.4, target_std=1.0, dh_depth=2, warmup_batches=200, scgpt_model_dir='transfer/data/scGPT_pretrained', scgpt_max_seq_len=1200, scgpt_cache_path='', latent_steps=20, expr_steps=20) +[rank0]: Traceback (most recent call last): +[rank0]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/scripts/run_cascaded.py", line 146, in +[rank0]: os.makedirs(save_path, exist_ok=True) +[rank0]: ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank0]: File "", line 227, in makedirs +[rank0]: OSError: [Errno 36] File name too long: './result/ccfm-fusion_differential_perceiver-norman-cascaded-predict_y-gamma_0.5-perturbation_function_crisper-lr_5e-05-dim_model_128-infer_top_gene_1000-split_method_additive-use_mmd_loss_True-fold_1-latent_weight_1.0-choose_latent_p_0.4-use_negative_edge_True-topk_30-scgpt_online' +[rank0]:[W314 14:36:18.955909336 ProcessGroupNCCL.cpp:1553] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator()) +W0314 14:36:24.320000 128 torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 201 closing signal SIGTERM +W0314 14:36:24.322000 128 torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 202 closing signal SIGTERM +W0314 14:36:24.322000 128 torch/distributed/elastic/multiprocessing/api.py:1010] Sending process 203 closing signal SIGTERM +E0314 14:36:24.687000 128 torch/distributed/elastic/multiprocessing/api.py:984] failed (exitcode: 1) local_rank: 0 (pid: 200) of binary: /home/hp250092/ku50001222/qian/aivc/lfj/stack_env/bin/python +Traceback (most recent call last): + File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/bin/accelerate", line 8, in + sys.exit(main()) + ~~~~^^ + File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/accelerate/commands/accelerate_cli.py", line 50, in main + args.func(args) + ~~~~~~~~~^^^^^^ + File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/accelerate/commands/launch.py", line 1396, in launch_command + multi_gpu_launcher(args) + ~~~~~~~~~~~~~~~~~~^^^^^^ + File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/accelerate/commands/launch.py", line 1023, in multi_gpu_launcher + distrib_run.run(args) + ~~~~~~~~~~~~~~~^^^^^^ + File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/distributed/run.py", line 982, in run + elastic_launch( + ~~~~~~~~~~~~~~~ + config=config, + ~~~~~~~~~~~~~~ + entrypoint=cmd, + ~~~~~~~~~~~~~~~ + )(*cmd_args) + ~^^^^^^^^^^^ + File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/distributed/launcher/api.py", line 170, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/distributed/launcher/api.py", line 317, in launch_agent + raise ChildFailedError( + ...<2 lines>... + ) +torch.distributed.elastic.multiprocessing.errors.ChildFailedError: +============================================================ +scripts/run_cascaded.py FAILED +------------------------------------------------------------ +Failures: +[1]: + time : 2026-03-14_14:36:24 + host : b0018 + rank : 1 (local_rank: 1) + exitcode : -15 (pid: 201) + error_file: + traceback : Signal 15 (SIGTERM) received by PID 201 +[2]: + time : 2026-03-14_14:36:24 + host : b0018 + rank : 2 (local_rank: 2) + exitcode : -15 (pid: 202) + error_file: + traceback : Signal 15 (SIGTERM) received by PID 202 +[3]: + time : 2026-03-14_14:36:24 + host : b0018 + rank : 3 (local_rank: 3) + exitcode : -15 (pid: 203) + error_file: + traceback : Signal 15 (SIGTERM) received by PID 203 +------------------------------------------------------------ +Root Cause (first observed failure): +[0]: + time : 2026-03-14_14:36:24 + host : b0018 + rank : 0 (local_rank: 0) + exitcode : 1 (pid: 200) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +============================================================ +========================================== +Finished: Sat Mar 14 14:36:25 JST 2026 +========================================== diff --git a/transfer/code/CCFM/logs/ccfm_v2_5404727.out b/transfer/code/CCFM/logs/ccfm_v2_5404727.out new file mode 100644 index 0000000000000000000000000000000000000000..0e6adbf174e84ac0f0c6d18a89b18624c5968662 --- /dev/null +++ b/transfer/code/CCFM/logs/ccfm_v2_5404727.out @@ -0,0 +1,214 @@ +========================================== +Job ID: 5404727 +Job Name: ccfm_v2 +Start: Sun Mar 15 04:49:31 JST 2026 +Node: b0019 +GPU: NVIDIA H100, 95830 MiB +NVIDIA H100, 95830 MiB +NVIDIA H100, 95830 MiB +NVIDIA H100, 95830 MiB +Run: CCFM v2 (KV fix + loss fix + EMA + RK4 + logit-normal + warmup) +========================================== +The following values were not passed to `accelerate launch` and had defaults used instead: + More than one GPU was found, enabling multi-GPU training. + If this was unintended please pass in `--num_processes=1`. + `--num_machines` was set to a value of `1` + `--mixed_precision` was set to a value of `'no'` + `--dynamo_backend` was set to a value of `'no'` +To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`. +[W315 04:50:08.654423221 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol). +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scGPT/scgpt/model/model.py:21: UserWarning: flash_attn is not installed + warnings.warn("flash_attn is not installed") +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scGPT/scgpt/model/model.py:21: UserWarning: flash_attn is not installed + warnings.warn("flash_attn is not installed") +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scGPT/scgpt/model/model.py:21: UserWarning: flash_attn is not installed + warnings.warn("flash_attn is not installed") +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scGPT/scgpt/model/model.py:21: UserWarning: flash_attn is not installed + warnings.warn("flash_attn is not installed") +[W315 04:50:33.963867213 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol). +[W315 04:50:33.963864886 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol). +[W315 04:50:33.963886032 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol). +[W315 04:50:33.963888124 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol). +WARNING:accelerate.utils.other:[RANK 0] Detected kernel version 4.18.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher. +CascadedFlowConfig(model_type='cascaded', batch_size=48, ntoken=512, d_model=128, nhead=8, nlayers=4, lr=5e-05, steps=200000, eta_min=1e-06, devices='1', test_only=False, data_name='norman', perturbation_function='crisper', noise_type='Gaussian', poisson_alpha=0.8, poisson_target_sum=-1, print_every=10000, mode='predict_y', result_path='./result', fusion_method='differential_perceiver', infer_top_gene=1000, n_top_genes=5000, checkpoint_path='', gamma=0.5, split_method='additive', use_mmd_loss=True, fold=1, use_negative_edge=True, topk=30, scgpt_dim=512, bottleneck_dim=128, latent_weight=1.0, choose_latent_p=0.4, target_std=1.0, dh_depth=2, warmup_batches=200, ema_decay=0.9999, t_sample_mode='logit_normal', t_expr_mean=0.0, t_expr_std=1.0, t_latent_mean=0.0, t_latent_std=1.0, warmup_steps=2000, scgpt_model_dir='transfer/data/scGPT_pretrained', scgpt_max_seq_len=1200, scgpt_cache_path='', latent_steps=20, expr_steps=20, ode_method='rk4') +Converted var_names to gene symbols, sample: ['RP11-34P13.8', 'RP11-54O7.3', 'SAMD11', 'PERM1', 'HES4'] +Warning: ctrl is not in the gene namesWarning: ctrl is not in the gene names + +Warning: ctrl is not in the gene names +Warning: ctrl is not in the gene names +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:185: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control') +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:185: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control') +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:185: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control') +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:185: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control') +/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scanpy/preprocessing/_highly_variable_genes.py:806: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. + adata.uns["hvg"] = {"flavor": flavor} +/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scanpy/preprocessing/_highly_variable_genes.py:806: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. + adata.uns["hvg"] = {"flavor": flavor} +/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scanpy/preprocessing/_highly_variable_genes.py:806: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. + adata.uns["hvg"] = {"flavor": flavor} +/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scanpy/preprocessing/_highly_variable_genes.py:806: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. + adata.uns["hvg"] = {"flavor": flavor} +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:277: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:277: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:277: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:277: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:329: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:329: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:329: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:329: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +##### loading vocab from file ##### +##### loading vocab from file ##### +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_extractor.py:109: UserWarning: FrozenScGPTExtractor: 498/5035 HVG genes not found in scGPT vocab, will use zero vectors. + warnings.warn( +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_extractor.py:109: UserWarning: FrozenScGPTExtractor: 498/5035 HVG genes not found in scGPT vocab, will use zero vectors. + warnings.warn( +##### loading vocab from file ##### +##### loading vocab from file ##### +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_extractor.py:109: UserWarning: FrozenScGPTExtractor: 498/5035 HVG genes not found in scGPT vocab, will use zero vectors. + warnings.warn( +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_extractor.py:109: UserWarning: FrozenScGPTExtractor: 498/5035 HVG genes not found in scGPT vocab, will use zero vectors. + warnings.warn( +Loaded 159/163 pretrained parameters +Loaded 159/163 pretrained parameters +Loaded 159/163 pretrained parameters +Loaded 159/163 pretrained parameters + 0%| | 0/200000 [00:00 +[rank2]: ema_p.lerp_(model_p.data, 1 - decay) +[rank2]: ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank2]: RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:2 and cpu! +[rank1]: Traceback (most recent call last): +[rank1]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/scripts/run_cascaded.py", line 372, in +[rank1]: ema_p.lerp_(model_p.data, 1 - decay) +[rank1]: ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank1]: RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:1 and cpu! +[rank0]: Traceback (most recent call last): +[rank0]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/scripts/run_cascaded.py", line 372, in +[rank0]: ema_p.lerp_(model_p.data, 1 - decay) +[rank0]: ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank0]: RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu! +[rank3]: Traceback (most recent call last): +[rank3]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/scripts/run_cascaded.py", line 372, in +[rank3]: ema_p.lerp_(model_p.data, 1 - decay) +[rank3]: ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank3]: RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:3 and cpu! +[rank0]:[W315 04:52:01.076850828 ProcessGroupNCCL.cpp:1553] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator()) + 0%| | 0/200000 [00:53 + sys.exit(main()) + ~~~~^^ + File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/accelerate/commands/accelerate_cli.py", line 50, in main + args.func(args) + ~~~~~~~~~^^^^^^ + File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/accelerate/commands/launch.py", line 1396, in launch_command + multi_gpu_launcher(args) + ~~~~~~~~~~~~~~~~~~^^^^^^ + File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/accelerate/commands/launch.py", line 1023, in multi_gpu_launcher + distrib_run.run(args) + ~~~~~~~~~~~~~~~^^^^^^ + File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/distributed/run.py", line 982, in run + elastic_launch( + ~~~~~~~~~~~~~~~ + config=config, + ~~~~~~~~~~~~~~ + entrypoint=cmd, + ~~~~~~~~~~~~~~~ + )(*cmd_args) + ~^^^^^^^^^^^ + File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/distributed/launcher/api.py", line 170, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/distributed/launcher/api.py", line 317, in launch_agent + raise ChildFailedError( + ...<2 lines>... + ) +torch.distributed.elastic.multiprocessing.errors.ChildFailedError: +============================================================ +scripts/run_cascaded.py FAILED +------------------------------------------------------------ +Failures: +[1]: + time : 2026-03-15_04:52:08 + host : b0019 + rank : 0 (local_rank: 0) + exitcode : 1 (pid: 200) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[2]: + time : 2026-03-15_04:52:08 + host : b0019 + rank : 1 (local_rank: 1) + exitcode : 1 (pid: 201) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[3]: + time : 2026-03-15_04:52:08 + host : b0019 + rank : 2 (local_rank: 2) + exitcode : 1 (pid: 202) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +------------------------------------------------------------ +Root Cause (first observed failure): +[0]: + time : 2026-03-15_04:52:07 + host : b0019 + rank : 3 (local_rank: 3) + exitcode : 1 (pid: 203) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +============================================================ +========================================== +Finished: Sun Mar 15 04:52:09 JST 2026 +========================================== diff --git a/transfer/code/CCFM/logs/ccfm_v2_5404735.out b/transfer/code/CCFM/logs/ccfm_v2_5404735.out new file mode 100644 index 0000000000000000000000000000000000000000..1c91d9ee5d197d74bfdac4e80487de2137101c43 --- /dev/null +++ b/transfer/code/CCFM/logs/ccfm_v2_5404735.out @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:78ae507c08bcf68993af70a80ee5dc04ea4c1b5236c845f4c2da3d8cb3356573 +size 81641352 diff --git a/transfer/code/CCFM/logs/ccfm_v2_cached_5404728.out b/transfer/code/CCFM/logs/ccfm_v2_cached_5404728.out new file mode 100644 index 0000000000000000000000000000000000000000..9bf08f6963a9e117d1beedeadac9a48c24af0809 --- /dev/null +++ b/transfer/code/CCFM/logs/ccfm_v2_cached_5404728.out @@ -0,0 +1,236 @@ +========================================== +Job ID: 5404728 +Job Name: ccfm_v2_cached +Start: Sun Mar 15 04:52:33 JST 2026 +Node: b0021 +GPU: NVIDIA H100, 95830 MiB +NVIDIA H100, 95830 MiB +NVIDIA H100, 95830 MiB +NVIDIA H100, 95830 MiB +Run: CCFM v2 cached (KV fix + loss fix + EMA + RK4 + logit-normal + warmup + scGPT cache) +========================================== +The following values were not passed to `accelerate launch` and had defaults used instead: + More than one GPU was found, enabling multi-GPU training. + If this was unintended please pass in `--num_processes=1`. + `--num_machines` was set to a value of `1` + `--mixed_precision` was set to a value of `'no'` + `--dynamo_backend` was set to a value of `'no'` +To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`. +[W315 04:52:57.426142473 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol). +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scGPT/scgpt/model/model.py:21: UserWarning: flash_attn is not installed + warnings.warn("flash_attn is not installed") +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scGPT/scgpt/model/model.py:21: UserWarning: flash_attn is not installed + warnings.warn("flash_attn is not installed") +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scGPT/scgpt/model/model.py:21: UserWarning: flash_attn is not installed + warnings.warn("flash_attn is not installed") +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scGPT/scgpt/model/model.py:21: UserWarning: flash_attn is not installed + warnings.warn("flash_attn is not installed") +[W315 04:53:16.185740786 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol). +[W315 04:53:16.441379224 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol). +[W315 04:53:16.447651346 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol). +[W315 04:53:16.490639280 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol). +WARNING:accelerate.utils.other:[RANK 0] Detected kernel version 4.18.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher. +CascadedFlowConfig(model_type='cascaded', batch_size=48, ntoken=512, d_model=128, nhead=8, nlayers=4, lr=5e-05, steps=200000, eta_min=1e-06, devices='1', test_only=False, data_name='norman', perturbation_function='crisper', noise_type='Gaussian', poisson_alpha=0.8, poisson_target_sum=-1, print_every=10000, mode='predict_y', result_path='./result', fusion_method='differential_perceiver', infer_top_gene=1000, n_top_genes=5000, checkpoint_path='', gamma=0.5, split_method='additive', use_mmd_loss=True, fold=1, use_negative_edge=True, topk=30, scgpt_dim=512, bottleneck_dim=128, latent_weight=1.0, choose_latent_p=0.4, target_std=1.0, dh_depth=2, warmup_batches=200, ema_decay=0.9999, t_sample_mode='logit_normal', t_expr_mean=0.0, t_expr_std=1.0, t_latent_mean=0.0, t_latent_std=1.0, warmup_steps=2000, scgpt_model_dir='transfer/data/scGPT_pretrained', scgpt_max_seq_len=1200, scgpt_cache_path='scgpt_cache_norman.h5', latent_steps=20, expr_steps=20, ode_method='rk4') +Converted var_names to gene symbols, sample: ['RP11-34P13.8', 'RP11-54O7.3', 'SAMD11', 'PERM1', 'HES4'] +Warning: ctrl is not in the gene namesWarning: ctrl is not in the gene names +Warning: ctrl is not in the gene names + +Warning: ctrl is not in the gene names +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:185: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control') +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:185: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control') +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:185: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control') +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:185: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control') +/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scanpy/preprocessing/_highly_variable_genes.py:806: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. + adata.uns["hvg"] = {"flavor": flavor} +/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scanpy/preprocessing/_highly_variable_genes.py:806: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. + adata.uns["hvg"] = {"flavor": flavor} +/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scanpy/preprocessing/_highly_variable_genes.py:806: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. + adata.uns["hvg"] = {"flavor": flavor} +/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scanpy/preprocessing/_highly_variable_genes.py:806: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. + adata.uns["hvg"] = {"flavor": flavor} +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:277: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:277: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:277: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:277: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:329: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:329: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:329: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:329: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +##### loading vocab from file ##### +##### loading vocab from file ##### +##### loading vocab from file ##### +##### loading vocab from file ##### +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_extractor.py:109: UserWarning: FrozenScGPTExtractor: 498/5035 HVG genes not found in scGPT vocab, will use zero vectors. + warnings.warn( +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_extractor.py:109: UserWarning: FrozenScGPTExtractor: 498/5035 HVG genes not found in scGPT vocab, will use zero vectors. + warnings.warn( +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_extractor.py:109: UserWarning: FrozenScGPTExtractor: 498/5035 HVG genes not found in scGPT vocab, will use zero vectors. + warnings.warn( +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_extractor.py:109: UserWarning: FrozenScGPTExtractor: 498/5035 HVG genes not found in scGPT vocab, will use zero vectors. + warnings.warn( +Loaded 159/163 pretrained parameters +Loaded 159/163 pretrained parameters +Loaded 159/163 pretrained parameters +Loaded 159/163 pretrained parameters +Using pre-extracted scGPT cache: scgpt_cache_norman.h5 + Cache shape: (91205, 5035, 512), cells: 91205 + 0%| | 0/200000 [00:00 +[rank3]: cached_z_target = scgpt_cache.lookup(tgt_cell_names, input_gene_ids, device=device) +[rank3]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_cache.py", line 55, in lookup +[rank3]: raw = self.features[row_indices] # (B, G_full, D) as numpy +[rank3]: ~~~~~~~~~~~~~^^^^^^^^^^^^^ +[rank3]: File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper +[rank3]: File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper +[rank3]: File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/dataset.py", line 945, in __getitem__ +[rank3]: selection = sel.select(self.shape, args, dataset=self) +[rank3]: File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/selections.py", line 85, in select +[rank3]: return selector.make_selection(args) +[rank3]: ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^ +[rank3]: File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper +[rank3]: File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper +[rank3]: File "h5py/_selector.pyx", line 297, in h5py._selector.Selector.make_selection +[rank3]: File "h5py/_selector.pyx", line 216, in h5py._selector.Selector.apply_args +[rank3]: TypeError: Indexing elements must be in increasing order +[rank0]: Traceback (most recent call last): +[rank0]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/scripts/run_cascaded.py", line 350, in +[rank0]: cached_z_target = scgpt_cache.lookup(tgt_cell_names, input_gene_ids, device=device) +[rank0]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_cache.py", line 55, in lookup +[rank0]: raw = self.features[row_indices] # (B, G_full, D) as numpy +[rank0]: ~~~~~~~~~~~~~^^^^^^^^^^^^^ +[rank0]: File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper +[rank0]: File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper +[rank0]: File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/dataset.py", line 945, in __getitem__ +[rank0]: selection = sel.select(self.shape, args, dataset=self) +[rank0]: File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/selections.py", line 85, in select +[rank0]: return selector.make_selection(args) +[rank0]: ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^ +[rank0]: File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper +[rank0]: File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper +[rank0]: File "h5py/_selector.pyx", line 297, in h5py._selector.Selector.make_selection +[rank0]: File "h5py/_selector.pyx", line 216, in h5py._selector.Selector.apply_args +[rank0]: TypeError: Indexing elements must be in increasing order +[rank1]: Traceback (most recent call last): +[rank1]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/scripts/run_cascaded.py", line 350, in +[rank1]: cached_z_target = scgpt_cache.lookup(tgt_cell_names, input_gene_ids, device=device) +[rank1]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_cache.py", line 55, in lookup +[rank1]: raw = self.features[row_indices] # (B, G_full, D) as numpy +[rank1]: ~~~~~~~~~~~~~^^^^^^^^^^^^^ +[rank1]: File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper +[rank1]: File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper +[rank1]: File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/dataset.py", line 945, in __getitem__ +[rank1]: selection = sel.select(self.shape, args, dataset=self) +[rank1]: File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/selections.py", line 85, in select +[rank1]: return selector.make_selection(args) +[rank1]: ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^ +[rank1]: File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper +[rank1]: File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper +[rank1]: File "h5py/_selector.pyx", line 297, in h5py._selector.Selector.make_selection +[rank1]: File "h5py/_selector.pyx", line 216, in h5py._selector.Selector.apply_args +[rank1]: TypeError: Indexing elements must be in increasing order +[rank2]: Traceback (most recent call last): +[rank2]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/scripts/run_cascaded.py", line 350, in +[rank2]: cached_z_target = scgpt_cache.lookup(tgt_cell_names, input_gene_ids, device=device) +[rank2]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_cache.py", line 55, in lookup +[rank2]: raw = self.features[row_indices] # (B, G_full, D) as numpy +[rank2]: ~~~~~~~~~~~~~^^^^^^^^^^^^^ +[rank2]: File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper +[rank2]: File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper +[rank2]: File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/dataset.py", line 945, in __getitem__ +[rank2]: selection = sel.select(self.shape, args, dataset=self) +[rank2]: File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/selections.py", line 85, in select +[rank2]: return selector.make_selection(args) +[rank2]: ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^ +[rank2]: File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper +[rank2]: File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper +[rank2]: File "h5py/_selector.pyx", line 297, in h5py._selector.Selector.make_selection +[rank2]: File "h5py/_selector.pyx", line 216, in h5py._selector.Selector.apply_args +[rank2]: TypeError: Indexing elements must be in increasing order +[rank0]:[W315 04:54:29.650127266 ProcessGroupNCCL.cpp:1553] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator()) + 0%| | 0/200000 [00:42 + sys.exit(main()) + ~~~~^^ + File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/accelerate/commands/accelerate_cli.py", line 50, in main + args.func(args) + ~~~~~~~~~^^^^^^ + File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/accelerate/commands/launch.py", line 1396, in launch_command + multi_gpu_launcher(args) + ~~~~~~~~~~~~~~~~~~^^^^^^ + File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/accelerate/commands/launch.py", line 1023, in multi_gpu_launcher + distrib_run.run(args) + ~~~~~~~~~~~~~~~^^^^^^ + File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/distributed/run.py", line 982, in run + elastic_launch( + ~~~~~~~~~~~~~~~ + config=config, + ~~~~~~~~~~~~~~ + entrypoint=cmd, + ~~~~~~~~~~~~~~~ + )(*cmd_args) + ~^^^^^^^^^^^ + File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/distributed/launcher/api.py", line 170, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/distributed/launcher/api.py", line 317, in launch_agent + raise ChildFailedError( + ...<2 lines>... + ) +torch.distributed.elastic.multiprocessing.errors.ChildFailedError: +============================================================ +scripts/run_cascaded.py FAILED +------------------------------------------------------------ +Failures: +[1]: + time : 2026-03-15_04:54:34 + host : b0021 + rank : 0 (local_rank: 0) + exitcode : 1 (pid: 201) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[2]: + time : 2026-03-15_04:54:34 + host : b0021 + rank : 1 (local_rank: 1) + exitcode : 1 (pid: 202) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[3]: + time : 2026-03-15_04:54:34 + host : b0021 + rank : 3 (local_rank: 3) + exitcode : 1 (pid: 204) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +------------------------------------------------------------ +Root Cause (first observed failure): +[0]: + time : 2026-03-15_04:54:34 + host : b0021 + rank : 2 (local_rank: 2) + exitcode : 1 (pid: 203) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +============================================================ +========================================== +Finished: Sun Mar 15 04:54:35 JST 2026 +========================================== diff --git a/transfer/code/CCFM/logs/ccfm_v2_cached_5404736.out b/transfer/code/CCFM/logs/ccfm_v2_cached_5404736.out new file mode 100644 index 0000000000000000000000000000000000000000..4deb602179e921ef7f8a515321adabe9f4b58d52 --- /dev/null +++ b/transfer/code/CCFM/logs/ccfm_v2_cached_5404736.out @@ -0,0 +1,236 @@ +========================================== +Job ID: 5404736 +Job Name: ccfm_v2_cached +Start: Sun Mar 15 04:58:13 JST 2026 +Node: b0021 +GPU: NVIDIA H100, 95830 MiB +NVIDIA H100, 95830 MiB +NVIDIA H100, 95830 MiB +NVIDIA H100, 95830 MiB +Run: CCFM v2 cached (KV fix + loss fix + EMA + RK4 + logit-normal + warmup + scGPT cache) +========================================== +The following values were not passed to `accelerate launch` and had defaults used instead: + More than one GPU was found, enabling multi-GPU training. + If this was unintended please pass in `--num_processes=1`. + `--num_machines` was set to a value of `1` + `--mixed_precision` was set to a value of `'no'` + `--dynamo_backend` was set to a value of `'no'` +To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`. +[W315 04:58:44.021644370 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol). +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scGPT/scgpt/model/model.py:21: UserWarning: flash_attn is not installed + warnings.warn("flash_attn is not installed") +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scGPT/scgpt/model/model.py:21: UserWarning: flash_attn is not installed + warnings.warn("flash_attn is not installed") +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scGPT/scgpt/model/model.py:21: UserWarning: flash_attn is not installed + warnings.warn("flash_attn is not installed") +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scGPT/scgpt/model/model.py:21: UserWarning: flash_attn is not installed + warnings.warn("flash_attn is not installed") +[W315 04:59:02.163293234 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol). +[W315 04:59:02.432132628 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol). +[W315 04:59:02.432621699 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol). +[W315 04:59:02.442458875 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol). +WARNING:accelerate.utils.other:[RANK 0] Detected kernel version 4.18.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher. +CascadedFlowConfig(model_type='cascaded', batch_size=48, ntoken=512, d_model=128, nhead=8, nlayers=4, lr=5e-05, steps=200000, eta_min=1e-06, devices='1', test_only=False, data_name='norman', perturbation_function='crisper', noise_type='Gaussian', poisson_alpha=0.8, poisson_target_sum=-1, print_every=10000, mode='predict_y', result_path='./result', fusion_method='differential_perceiver', infer_top_gene=1000, n_top_genes=5000, checkpoint_path='', gamma=0.5, split_method='additive', use_mmd_loss=True, fold=1, use_negative_edge=True, topk=30, scgpt_dim=512, bottleneck_dim=128, latent_weight=1.0, choose_latent_p=0.4, target_std=1.0, dh_depth=2, warmup_batches=200, ema_decay=0.9999, t_sample_mode='logit_normal', t_expr_mean=0.0, t_expr_std=1.0, t_latent_mean=0.0, t_latent_std=1.0, warmup_steps=2000, scgpt_model_dir='transfer/data/scGPT_pretrained', scgpt_max_seq_len=1200, scgpt_cache_path='scgpt_cache_norman.h5', latent_steps=20, expr_steps=20, ode_method='rk4') +Converted var_names to gene symbols, sample: ['RP11-34P13.8', 'RP11-54O7.3', 'SAMD11', 'PERM1', 'HES4'] +Warning: ctrl is not in the gene names +Warning: ctrl is not in the gene namesWarning: ctrl is not in the gene names + +Warning: ctrl is not in the gene names +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:185: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control') +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:185: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control') +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:185: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control') +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:185: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control') +/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scanpy/preprocessing/_highly_variable_genes.py:806: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. + adata.uns["hvg"] = {"flavor": flavor} +/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scanpy/preprocessing/_highly_variable_genes.py:806: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. + adata.uns["hvg"] = {"flavor": flavor} +/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scanpy/preprocessing/_highly_variable_genes.py:806: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. + adata.uns["hvg"] = {"flavor": flavor} +/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scanpy/preprocessing/_highly_variable_genes.py:806: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. + adata.uns["hvg"] = {"flavor": flavor} +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:277: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:277: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:277: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:277: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:329: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:329: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:329: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +##### loading vocab from file ##### +##### loading vocab from file ##### +##### loading vocab from file ##### +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_extractor.py:109: UserWarning: FrozenScGPTExtractor: 498/5035 HVG genes not found in scGPT vocab, will use zero vectors. + warnings.warn( +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_extractor.py:109: UserWarning: FrozenScGPTExtractor: 498/5035 HVG genes not found in scGPT vocab, will use zero vectors. + warnings.warn( +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_extractor.py:109: UserWarning: FrozenScGPTExtractor: 498/5035 HVG genes not found in scGPT vocab, will use zero vectors. + warnings.warn( +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:329: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +##### loading vocab from file ##### +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_extractor.py:109: UserWarning: FrozenScGPTExtractor: 498/5035 HVG genes not found in scGPT vocab, will use zero vectors. + warnings.warn( +Loaded 159/163 pretrained parameters +Loaded 159/163 pretrained parameters +Loaded 159/163 pretrained parameters +Using pre-extracted scGPT cache: scgpt_cache_norman.h5 + Cache shape: (91205, 5035, 512), cells: 91205 +Loaded 159/163 pretrained parameters + 0%| | 0/200000 [00:00 +[rank0]: cached_z_target = scgpt_cache.lookup(tgt_cell_names, input_gene_ids, device=device) +[rank0]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_cache.py", line 55, in lookup +[rank0]: raw = self.features[row_indices] # (B, G_full, D) as numpy +[rank0]: ~~~~~~~~~~~~~^^^^^^^^^^^^^ +[rank0]: File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper +[rank0]: File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper +[rank0]: File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/dataset.py", line 945, in __getitem__ +[rank0]: selection = sel.select(self.shape, args, dataset=self) +[rank0]: File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/selections.py", line 85, in select +[rank0]: return selector.make_selection(args) +[rank0]: ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^ +[rank0]: File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper +[rank0]: File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper +[rank0]: File "h5py/_selector.pyx", line 297, in h5py._selector.Selector.make_selection +[rank0]: File "h5py/_selector.pyx", line 216, in h5py._selector.Selector.apply_args +[rank0]: TypeError: Indexing elements must be in increasing order +[rank2]: Traceback (most recent call last): +[rank2]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/scripts/run_cascaded.py", line 350, in +[rank2]: cached_z_target = scgpt_cache.lookup(tgt_cell_names, input_gene_ids, device=device) +[rank2]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_cache.py", line 55, in lookup +[rank2]: raw = self.features[row_indices] # (B, G_full, D) as numpy +[rank2]: ~~~~~~~~~~~~~^^^^^^^^^^^^^ +[rank2]: File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper +[rank2]: File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper +[rank2]: File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/dataset.py", line 945, in __getitem__ +[rank2]: selection = sel.select(self.shape, args, dataset=self) +[rank2]: File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/selections.py", line 85, in select +[rank2]: return selector.make_selection(args) +[rank2]: ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^ +[rank2]: File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper +[rank2]: File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper +[rank2]: File "h5py/_selector.pyx", line 297, in h5py._selector.Selector.make_selection +[rank2]: File "h5py/_selector.pyx", line 216, in h5py._selector.Selector.apply_args +[rank2]: TypeError: Indexing elements must be in increasing order +[rank1]: Traceback (most recent call last): +[rank1]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/scripts/run_cascaded.py", line 350, in +[rank1]: cached_z_target = scgpt_cache.lookup(tgt_cell_names, input_gene_ids, device=device) +[rank1]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_cache.py", line 55, in lookup +[rank1]: raw = self.features[row_indices] # (B, G_full, D) as numpy +[rank1]: ~~~~~~~~~~~~~^^^^^^^^^^^^^ +[rank1]: File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper +[rank1]: File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper +[rank1]: File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/dataset.py", line 945, in __getitem__ +[rank1]: selection = sel.select(self.shape, args, dataset=self) +[rank1]: File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/selections.py", line 85, in select +[rank1]: return selector.make_selection(args) +[rank1]: ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^ +[rank1]: File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper +[rank1]: File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper +[rank1]: File "h5py/_selector.pyx", line 297, in h5py._selector.Selector.make_selection +[rank1]: File "h5py/_selector.pyx", line 216, in h5py._selector.Selector.apply_args +[rank1]: TypeError: Indexing elements must be in increasing order +[rank3]: Traceback (most recent call last): +[rank3]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/scripts/run_cascaded.py", line 350, in +[rank3]: cached_z_target = scgpt_cache.lookup(tgt_cell_names, input_gene_ids, device=device) +[rank3]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_cache.py", line 55, in lookup +[rank3]: raw = self.features[row_indices] # (B, G_full, D) as numpy +[rank3]: ~~~~~~~~~~~~~^^^^^^^^^^^^^ +[rank3]: File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper +[rank3]: File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper +[rank3]: File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/dataset.py", line 945, in __getitem__ +[rank3]: selection = sel.select(self.shape, args, dataset=self) +[rank3]: File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/selections.py", line 85, in select +[rank3]: return selector.make_selection(args) +[rank3]: ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^ +[rank3]: File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper +[rank3]: File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper +[rank3]: File "h5py/_selector.pyx", line 297, in h5py._selector.Selector.make_selection +[rank3]: File "h5py/_selector.pyx", line 216, in h5py._selector.Selector.apply_args +[rank3]: TypeError: Indexing elements must be in increasing order +[rank0]:[W315 05:00:15.940375045 ProcessGroupNCCL.cpp:1553] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator()) + 0%| | 0/200000 [00:42 + sys.exit(main()) + ~~~~^^ + File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/accelerate/commands/accelerate_cli.py", line 50, in main + args.func(args) + ~~~~~~~~~^^^^^^ + File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/accelerate/commands/launch.py", line 1396, in launch_command + multi_gpu_launcher(args) + ~~~~~~~~~~~~~~~~~~^^^^^^ + File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/accelerate/commands/launch.py", line 1023, in multi_gpu_launcher + distrib_run.run(args) + ~~~~~~~~~~~~~~~^^^^^^ + File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/distributed/run.py", line 982, in run + elastic_launch( + ~~~~~~~~~~~~~~~ + config=config, + ~~~~~~~~~~~~~~ + entrypoint=cmd, + ~~~~~~~~~~~~~~~ + )(*cmd_args) + ~^^^^^^^^^^^ + File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/distributed/launcher/api.py", line 170, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/distributed/launcher/api.py", line 317, in launch_agent + raise ChildFailedError( + ...<2 lines>... + ) +torch.distributed.elastic.multiprocessing.errors.ChildFailedError: +============================================================ +scripts/run_cascaded.py FAILED +------------------------------------------------------------ +Failures: +[1]: + time : 2026-03-15_05:00:20 + host : b0021 + rank : 0 (local_rank: 0) + exitcode : 1 (pid: 200) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[2]: + time : 2026-03-15_05:00:20 + host : b0021 + rank : 1 (local_rank: 1) + exitcode : 1 (pid: 201) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[3]: + time : 2026-03-15_05:00:20 + host : b0021 + rank : 3 (local_rank: 3) + exitcode : 1 (pid: 203) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +------------------------------------------------------------ +Root Cause (first observed failure): +[0]: + time : 2026-03-15_05:00:19 + host : b0021 + rank : 2 (local_rank: 2) + exitcode : 1 (pid: 202) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +============================================================ +========================================== +Finished: Sun Mar 15 05:00:21 JST 2026 +========================================== diff --git a/transfer/code/CCFM/logs/ccfm_v2_cached_5404737.out b/transfer/code/CCFM/logs/ccfm_v2_cached_5404737.out new file mode 100644 index 0000000000000000000000000000000000000000..d145b61a4fe8e39315fad218acdee96494c609be --- /dev/null +++ b/transfer/code/CCFM/logs/ccfm_v2_cached_5404737.out @@ -0,0 +1,236 @@ +========================================== +Job ID: 5404737 +Job Name: ccfm_v2_cached +Start: Sun Mar 15 05:02:01 JST 2026 +Node: b0021 +GPU: NVIDIA H100, 95830 MiB +NVIDIA H100, 95830 MiB +NVIDIA H100, 95830 MiB +NVIDIA H100, 95830 MiB +Run: CCFM v2 cached (KV fix + loss fix + EMA + RK4 + logit-normal + warmup + scGPT cache) +========================================== +The following values were not passed to `accelerate launch` and had defaults used instead: + More than one GPU was found, enabling multi-GPU training. + If this was unintended please pass in `--num_processes=1`. + `--num_machines` was set to a value of `1` + `--mixed_precision` was set to a value of `'no'` + `--dynamo_backend` was set to a value of `'no'` +To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`. +[W315 05:02:27.190711515 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol). +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scGPT/scgpt/model/model.py:21: UserWarning: flash_attn is not installed + warnings.warn("flash_attn is not installed") +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scGPT/scgpt/model/model.py:21: UserWarning: flash_attn is not installed + warnings.warn("flash_attn is not installed") +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scGPT/scgpt/model/model.py:21: UserWarning: flash_attn is not installed + warnings.warn("flash_attn is not installed") +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scGPT/scgpt/model/model.py:21: UserWarning: flash_attn is not installed + warnings.warn("flash_attn is not installed") +[W315 05:02:46.969095263 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol). +[W315 05:02:46.996806598 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol). +[W315 05:02:46.020582775 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol). +[W315 05:02:46.024342649 socket.cpp:764] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol). +WARNING:accelerate.utils.other:[RANK 0] Detected kernel version 4.18.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher. +CascadedFlowConfig(model_type='cascaded', batch_size=48, ntoken=512, d_model=128, nhead=8, nlayers=4, lr=5e-05, steps=200000, eta_min=1e-06, devices='1', test_only=False, data_name='norman', perturbation_function='crisper', noise_type='Gaussian', poisson_alpha=0.8, poisson_target_sum=-1, print_every=10000, mode='predict_y', result_path='./result', fusion_method='differential_perceiver', infer_top_gene=1000, n_top_genes=5000, checkpoint_path='', gamma=0.5, split_method='additive', use_mmd_loss=True, fold=1, use_negative_edge=True, topk=30, scgpt_dim=512, bottleneck_dim=128, latent_weight=1.0, choose_latent_p=0.4, target_std=1.0, dh_depth=2, warmup_batches=200, ema_decay=0.9999, t_sample_mode='logit_normal', t_expr_mean=0.0, t_expr_std=1.0, t_latent_mean=0.0, t_latent_std=1.0, warmup_steps=2000, scgpt_model_dir='transfer/data/scGPT_pretrained', scgpt_max_seq_len=1200, scgpt_cache_path='scgpt_cache_norman.h5', latent_steps=20, expr_steps=20, ode_method='rk4') +Converted var_names to gene symbols, sample: ['RP11-34P13.8', 'RP11-54O7.3', 'SAMD11', 'PERM1', 'HES4'] +Warning: ctrl is not in the gene namesWarning: ctrl is not in the gene namesWarning: ctrl is not in the gene names + + +Warning: ctrl is not in the gene names +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:185: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control') +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:185: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control') +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:185: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control') +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:185: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control') +/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scanpy/preprocessing/_highly_variable_genes.py:806: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. + adata.uns["hvg"] = {"flavor": flavor} +/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scanpy/preprocessing/_highly_variable_genes.py:806: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. + adata.uns["hvg"] = {"flavor": flavor} +/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scanpy/preprocessing/_highly_variable_genes.py:806: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. + adata.uns["hvg"] = {"flavor": flavor} +/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scanpy/preprocessing/_highly_variable_genes.py:806: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. + adata.uns["hvg"] = {"flavor": flavor} +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:277: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:277: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:277: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:277: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:329: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:329: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:329: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:329: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +##### loading vocab from file ##### +##### loading vocab from file ##### +##### loading vocab from file ##### +##### loading vocab from file ##### +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_extractor.py:109: UserWarning: FrozenScGPTExtractor: 498/5035 HVG genes not found in scGPT vocab, will use zero vectors. + warnings.warn( +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_extractor.py:109: UserWarning: FrozenScGPTExtractor: 498/5035 HVG genes not found in scGPT vocab, will use zero vectors. + warnings.warn( +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_extractor.py:109: UserWarning: FrozenScGPTExtractor: 498/5035 HVG genes not found in scGPT vocab, will use zero vectors. + warnings.warn( +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_extractor.py:109: UserWarning: FrozenScGPTExtractor: 498/5035 HVG genes not found in scGPT vocab, will use zero vectors. + warnings.warn( +Loaded 159/163 pretrained parameters +Loaded 159/163 pretrained parameters +Loaded 159/163 pretrained parameters +Loaded 159/163 pretrained parameters +Using pre-extracted scGPT cache: scgpt_cache_norman.h5 + Cache shape: (91205, 5035, 512), cells: 91205 + 0%| | 0/200000 [00:00 +[rank0]: cached_z_target = scgpt_cache.lookup(tgt_cell_names, input_gene_ids, device=device) +[rank0]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_cache.py", line 55, in lookup +[rank0]: raw = self.features[sorted_indices.tolist()] # (B, G_full, D) as numpy +[rank0]: ~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank0]: File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper +[rank0]: File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper +[rank0]: File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/dataset.py", line 945, in __getitem__ +[rank0]: selection = sel.select(self.shape, args, dataset=self) +[rank0]: File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/selections.py", line 85, in select +[rank0]: return selector.make_selection(args) +[rank0]: ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^ +[rank0]: File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper +[rank0]: File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper +[rank0]: File "h5py/_selector.pyx", line 297, in h5py._selector.Selector.make_selection +[rank0]: File "h5py/_selector.pyx", line 216, in h5py._selector.Selector.apply_args +[rank0]: TypeError: Indexing elements must be in increasing order +[rank1]: Traceback (most recent call last): +[rank1]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/scripts/run_cascaded.py", line 350, in +[rank1]: cached_z_target = scgpt_cache.lookup(tgt_cell_names, input_gene_ids, device=device) +[rank1]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_cache.py", line 55, in lookup +[rank1]: raw = self.features[sorted_indices.tolist()] # (B, G_full, D) as numpy +[rank1]: ~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank1]: File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper +[rank1]: File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper +[rank1]: File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/dataset.py", line 945, in __getitem__ +[rank1]: selection = sel.select(self.shape, args, dataset=self) +[rank1]: File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/selections.py", line 85, in select +[rank1]: return selector.make_selection(args) +[rank1]: ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^ +[rank1]: File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper +[rank1]: File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper +[rank1]: File "h5py/_selector.pyx", line 297, in h5py._selector.Selector.make_selection +[rank1]: File "h5py/_selector.pyx", line 216, in h5py._selector.Selector.apply_args +[rank1]: TypeError: Indexing elements must be in increasing order +[rank2]: Traceback (most recent call last): +[rank2]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/scripts/run_cascaded.py", line 350, in +[rank2]: cached_z_target = scgpt_cache.lookup(tgt_cell_names, input_gene_ids, device=device) +[rank2]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_cache.py", line 55, in lookup +[rank2]: raw = self.features[sorted_indices.tolist()] # (B, G_full, D) as numpy +[rank2]: ~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank2]: File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper +[rank2]: File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper +[rank2]: File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/dataset.py", line 945, in __getitem__ +[rank2]: selection = sel.select(self.shape, args, dataset=self) +[rank2]: File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/selections.py", line 85, in select +[rank2]: return selector.make_selection(args) +[rank2]: ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^ +[rank2]: File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper +[rank2]: File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper +[rank2]: File "h5py/_selector.pyx", line 297, in h5py._selector.Selector.make_selection +[rank2]: File "h5py/_selector.pyx", line 216, in h5py._selector.Selector.apply_args +[rank2]: TypeError: Indexing elements must be in increasing order +[rank3]: Traceback (most recent call last): +[rank3]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/scripts/run_cascaded.py", line 350, in +[rank3]: cached_z_target = scgpt_cache.lookup(tgt_cell_names, input_gene_ids, device=device) +[rank3]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_cache.py", line 55, in lookup +[rank3]: raw = self.features[sorted_indices.tolist()] # (B, G_full, D) as numpy +[rank3]: ~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank3]: File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper +[rank3]: File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper +[rank3]: File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/dataset.py", line 945, in __getitem__ +[rank3]: selection = sel.select(self.shape, args, dataset=self) +[rank3]: File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/h5py/_hl/selections.py", line 85, in select +[rank3]: return selector.make_selection(args) +[rank3]: ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^ +[rank3]: File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper +[rank3]: File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper +[rank3]: File "h5py/_selector.pyx", line 297, in h5py._selector.Selector.make_selection +[rank3]: File "h5py/_selector.pyx", line 216, in h5py._selector.Selector.apply_args +[rank3]: TypeError: Indexing elements must be in increasing order +[rank0]:[W315 05:04:02.931689961 ProcessGroupNCCL.cpp:1553] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator()) + 0%| | 0/200000 [00:42 + sys.exit(main()) + ~~~~^^ + File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/accelerate/commands/accelerate_cli.py", line 50, in main + args.func(args) + ~~~~~~~~~^^^^^^ + File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/accelerate/commands/launch.py", line 1396, in launch_command + multi_gpu_launcher(args) + ~~~~~~~~~~~~~~~~~~^^^^^^ + File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/accelerate/commands/launch.py", line 1023, in multi_gpu_launcher + distrib_run.run(args) + ~~~~~~~~~~~~~~~^^^^^^ + File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/distributed/run.py", line 982, in run + elastic_launch( + ~~~~~~~~~~~~~~~ + config=config, + ~~~~~~~~~~~~~~ + entrypoint=cmd, + ~~~~~~~~~~~~~~~ + )(*cmd_args) + ~^^^^^^^^^^^ + File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/distributed/launcher/api.py", line 170, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + File "/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/torch/distributed/launcher/api.py", line 317, in launch_agent + raise ChildFailedError( + ...<2 lines>... + ) +torch.distributed.elastic.multiprocessing.errors.ChildFailedError: +============================================================ +scripts/run_cascaded.py FAILED +------------------------------------------------------------ +Failures: +[1]: + time : 2026-03-15_05:04:06 + host : b0021 + rank : 0 (local_rank: 0) + exitcode : 1 (pid: 200) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[2]: + time : 2026-03-15_05:04:06 + host : b0021 + rank : 1 (local_rank: 1) + exitcode : 1 (pid: 201) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +[3]: + time : 2026-03-15_05:04:06 + host : b0021 + rank : 3 (local_rank: 3) + exitcode : 1 (pid: 203) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +------------------------------------------------------------ +Root Cause (first observed failure): +[0]: + time : 2026-03-15_05:04:06 + host : b0021 + rank : 2 (local_rank: 2) + exitcode : 1 (pid: 202) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +============================================================ +========================================== +Finished: Sun Mar 15 05:04:07 JST 2026 +========================================== diff --git a/transfer/code/CCFM/logs/ccfm_v2_cached_5405774.out b/transfer/code/CCFM/logs/ccfm_v2_cached_5405774.out new file mode 100644 index 0000000000000000000000000000000000000000..beafa4948a63b2c0d49c74753c6360b45f3bff3c --- /dev/null +++ b/transfer/code/CCFM/logs/ccfm_v2_cached_5405774.out @@ -0,0 +1,689 @@ +========================================== +Job ID: 5405774 +Job Name: ccfm_v2_cached +Start: Sun Mar 15 16:58:15 JST 2026 +Node: b0034 +GPU: NVIDIA H100, 95830 MiB +Run: CCFM v2 cached (KV fix + loss fix + EMA + RK4 + logit-normal + warmup + scGPT cache) +========================================== +The following values were not passed to `accelerate launch` and had defaults used instead: + `--num_machines` was set to a value of `1` + `--mixed_precision` was set to a value of `'no'` + `--dynamo_backend` was set to a value of `'no'` +To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`. +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scGPT/scgpt/model/model.py:21: UserWarning: flash_attn is not installed + warnings.warn("flash_attn is not installed") +WARNING:accelerate.utils.other:[RANK 0] Detected kernel version 4.18.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher. +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:185: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control') +/home/hp250092/ku50001222/qian/aivc/lfj/stack_env/lib/python3.13/site-packages/scanpy/preprocessing/_highly_variable_genes.py:806: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. + adata.uns["hvg"] = {"flavor": flavor} +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:277: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/scDFM/src/data_process/data.py:329: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/src/data/scgpt_extractor.py:109: UserWarning: FrozenScGPTExtractor: 498/5035 HVG genes not found in scGPT vocab, will use zero vectors. + warnings.warn( +CascadedFlowConfig(model_type='cascaded', batch_size=48, ntoken=512, d_model=128, nhead=8, nlayers=4, lr=5e-05, steps=200000, eta_min=1e-06, devices='1', test_only=False, data_name='norman', perturbation_function='crisper', noise_type='Gaussian', poisson_alpha=0.8, poisson_target_sum=-1, print_every=10000, mode='predict_y', result_path='./result', fusion_method='differential_perceiver', infer_top_gene=1000, n_top_genes=5000, checkpoint_path='', gamma=0.5, split_method='additive', use_mmd_loss=True, fold=1, use_negative_edge=True, topk=30, scgpt_dim=512, bottleneck_dim=128, latent_weight=1.0, choose_latent_p=0.4, target_std=1.0, dh_depth=2, warmup_batches=200, ema_decay=0.9999, t_sample_mode='logit_normal', t_expr_mean=0.0, t_expr_std=1.0, t_latent_mean=0.0, t_latent_std=1.0, warmup_steps=2000, scgpt_model_dir='transfer/data/scGPT_pretrained', scgpt_max_seq_len=1200, scgpt_cache_path='scgpt_cache_norman.h5', latent_steps=20, expr_steps=20, ode_method='rk4') +Converted var_names to gene symbols, sample: ['RP11-34P13.8', 'RP11-54O7.3', 'SAMD11', 'PERM1', 'HES4'] +Warning: ctrl is not in the gene names +##### loading vocab from file ##### +Loaded 159/163 pretrained parameters +Using pre-extracted scGPT cache: scgpt_cache_norman.h5 + Cache shape: (91205, 5035, 512), cells: 91205 + 0%| | 0/200000 [00:00` token） +- 需要用 gene ID 映射将变长输出 **scatter** 回固定的 G 个基因位置 +- 基因对齐: scDFM 使用 gene symbol (如 "MAPK1")，scGPT 使用自己的 vocab。在 `__init__` 中建立 `hvg_name → scGPT_vocab_id` 的映射 +- 不在 scGPT vocab 中的基因: 用零向量替代 + +### 1.4 全局归一化与方差匹配 (CRITICAL) + +在 `FrozenScGPTExtractor` 内部实现: + +```python +# 方案: 使用 running statistics (类似 BatchNorm 的 running_mean/var) +# 在前 N 个 batch 中收集统计量，之后固定 + +# Step 1: 标准化到零均值单位方差 +z_normalized = (z_raw - running_mean) / (running_std + 1e-6) + +# Step 2: 方差匹配 — 缩放到与表达嵌入相近的量级 +z_matched = z_normalized * target_std # target_std 默认 1.0 +``` + +这对应 LatentForcing 中 `dinov2_hf.py` 的归一化: +```python +z = (z - latent_mean) / sqrt(latent_var + eps) * match_pixel_norm +``` + +--- + +## 2. 网络架构 + +### 2.1 核心设计 — 严格参照 LatentForcing CoT (`model_cot.py`) + +**LatentForcing 的做法 (model_cot.py:398-446)**: +1. **双流嵌入 element-wise 相加** (line 414): + ```python + x = pixel_embedder(x_pixel) + dino_embedder(x_dino) + ``` +2. **条件向量求和通过 AdaLN** (line 409): + ```python + c = t_emb_pixel + t_emb_dino + y_emb + ``` +3. **共享 Transformer backbone**: 所有 block 处理融合后的 tokens +4. **分离解码头** (line 429-441): `dh_blocks_pixel` + `dh_blocks_dino` 分别解码 + +**CCFM 的对应实现**: +1. `x = expr_embedder(x_t_expr, gene_emb) + latent_embedder(z_t_latent)` +2. `c = t_expr_emb + t_latent_emb + pert_emb` +3. 复用 scDFM 的 DiffPerceiverBlock / PerceiverBlock +4. ExprDecoder (复用) + LatentDecoder (新增) + +### 2.2 CascadedFlowModel 架构 (`src/model/model.py`) + +``` +输入: + - gene_id: (B, G) 基因 token IDs + - cell_1 (source): (B, G) control 细胞表达值 + - x_t: (B, G) 加噪后的扰动细胞表达值 (expression flow) + - z_t: (B, G, 512) 加噪后的 scGPT per-gene 特征 (latent flow) + - t_expr: (B,) 表达流的时间步 + - t_latent: (B,) 潜变量流的时间步 + - perturbation_id: (B, 2) 扰动 token IDs + +数据流 (参照 model_cot.py 的 forward): + ┌──────────────────────────────────────────────────────┐ + │ 1. 表达流嵌入 (复用 scDFM 的 origin 层) │ + │ gene_emb = GeneEncoder(gene_id) (B,G,d) │ + │ val_emb_1 = ValueEncoder_1(cell_1) + gene_emb │ + │ val_emb_2 = ValueEncoder_2(x_t) + gene_emb │ + │ expr_tokens = fusion_layer(cat(val_1, val_2)) │ + │ → (B, G, d_model) │ + ├──────────────────────────────────────────────────────┤ + │ 2. Latent 流嵌入 (新增, 对应 dino_embedder) │ + │ latent_tokens = LatentEmbedder(z_t) │ + │ → (B, G, d_model) [bottleneck: 512 → d_model] │ + ├──────────────────────────────────────────────────────┤ + │ 3. Element-wise 相加 (对应 model_cot.py line 414) │ + │ x = expr_tokens + latent_tokens │ + │ → (B, G, d_model) │ + ├──────────────────────────────────────────────────────┤ + │ 4. 条件向量 (对应 model_cot.py line 409) │ + │ c = t_expr_emb + t_latent_emb + pert_emb │ + │ → (B, d_model) │ + ├──────────────────────────────────────────────────────┤ + │ 5. 共享 Backbone (复用 scDFM blocks) │ + │ for i, block in enumerate(blocks): │ + │ x = gene_adaLN[i](gene_emb, x) │ + │ x = cat([x, pert_emb.expand], dim=-1) │ + │ x = adapter_layer[i](x) │ + │ x = block(x, val_emb_2, c) │ + │ → (B, G, d_model) │ + ├──────────────────────────────────────────────────────┤ + │ 6a. 表达解码头 (复用 ExprDecoder) │ + │ → pred_v_expr: (B, G) │ + ├──────────────────────────────────────────────────────┤ + │ 6b. Latent 解码头 (新增, 对应 dh_blocks_dino) │ + │ latent_feats = dh_latent_proj(x) │ + │ for block in dh_blocks_latent: │ + │ latent_feats = block(latent_feats, c) │ + │ pred_v_latent = latent_final_layer(latent_feats) │ + │ → (B, G, 512) │ + └──────────────────────────────────────────────────────┘ + +输出: (pred_v_expr, pred_v_latent) +``` + +### 2.3 新增模块 (`src/model/layers.py`) + +```python +class LatentEmbedder(nn.Module): + """ + 对应 LatentForcing 的 dino_embedder (BottleneckPatchEmbed)。 + 将 per-gene scGPT 特征 (B, G, 512) 投影到 (B, G, d_model)。 + """ + def __init__(self, scgpt_dim=512, bottleneck_dim=128, d_model=512): + self.proj = nn.Sequential( + nn.Linear(scgpt_dim, bottleneck_dim), + nn.GELU(), + nn.Linear(bottleneck_dim, d_model), + ) + + def forward(self, z): # z: (B, G, scgpt_dim) + return self.proj(z) # (B, G, d_model) + +class LatentDecoder(nn.Module): + """ + 对应 LatentForcing 的 final_layer_dino + dh_blocks_dino。 + 从 backbone 输出 (B, G, d_model) 解码回 (B, G, scgpt_dim)。 + """ + def __init__(self, d_model=512, scgpt_dim=512, dh_depth=2): + # 可选: 额外的 decoder head blocks + if dh_depth > 0: + self.dh_blocks = nn.ModuleList([...]) + self.final = nn.Sequential( + nn.LayerNorm(d_model), + nn.Linear(d_model, d_model), + nn.GELU(), + nn.Linear(d_model, scgpt_dim), + ) +``` + +### 2.4 复用的现有模块 + +| 模块 | 来源 | 文件路径 | +|------|------|---------| +| `TimestepEmbedder` | scDFM | `scDFM/src/models/origin/layers.py:157` | +| `ContinuousValueEncoder` | scDFM | `scDFM/src/models/origin/layers.py:26` | +| `GeneEncoder` | scDFM | `scDFM/src/models/origin/layers.py:55` | +| `BatchLabelEncoder` | scDFM | `scDFM/src/models/origin/layers.py:98` | +| `ExprDecoder` | scDFM | `scDFM/src/models/origin/layers.py:116` | +| `GeneadaLN` | scDFM | `scDFM/src/models/origin/layers.py:10` | +| `DiffPerceiverBlock` / `PerceiverBlock` | scDFM | `scDFM/src/models/origin/model.py:55,92` | +| `modulate` | scDFM | `scDFM/src/models/origin/blocks.py` | +| `AffineProbPath` + `CondOTScheduler` | scDFM | `scDFM/src/flow_matching/path/` | +| `make_lognorm_poisson_noise` | scDFM | `scDFM/src/utils/utils.py` | +| `TransformerModel` | scGPT | `scGPT/scgpt/model/model.py` | +| `GeneVocab` | scGPT | `scGPT/scgpt/tokenizer/gene_tokenizer.py` | +| `load_pretrained` | scGPT | `scGPT/scgpt/utils/util.py` | +| `DataCollator` | scGPT | `scGPT/scgpt/data_collator.py` | + +--- + +## 3. 训练逻辑 + +### 3.1 CascadedDenoiser (`src/denoiser.py`) + +封装 `CascadedFlowModel` + `FrozenScGPTExtractor`。 + +### 3.2 级联时间步采样 — 参照 `denoiser_cot.py:121-132` + +```python +def sample_t(self, n, device): + """ + dino_first_cascaded 模式 (denoiser_cot.py line 121-132): + - choose_latent_mask=True → 训练 latent 流 + t_latent 随机, t_expr=0, loss_weight_expr=0 + - choose_latent_mask=False → 训练 expression 流 + t_expr 随机, t_latent=1 (clean latent), loss_weight_latent=0 + """ + t_latent = torch.rand(n, device=device) + t_expr = torch.rand(n, device=device) + choose_latent_mask = torch.rand(n, device=device) < self.choose_latent_p + + t_latent = torch.where(choose_latent_mask, t_latent, torch.ones_like(t_latent)) + t_expr = torch.where(choose_latent_mask, torch.zeros_like(t_expr), t_expr) + + w_expr = (~choose_latent_mask).float() + w_latent = choose_latent_mask.float() + return t_expr, t_latent, w_expr, w_latent +``` + +### 3.3 训练步骤 + +```python +def train_step(self, source, target, perturbation_id, gene_ids): + B = source.shape[0] + + # 1. On-the-fly 提取 scGPT per-gene 特征 (冻结) + z_target = self.scgpt_extractor.extract(target) # (B, G, 512) + + # 2. 时间采样 + t_expr, t_latent, w_expr, w_latent = self.sample_t(B, device) + + # 3. Expression path (同 scDFM) + noise_expr = make_noise(source, noise_type) + path_expr = affine_path.sample(t=t_expr, x_0=noise_expr, x_1=target) + + # 4. Latent path + noise_latent = torch.randn_like(z_target) + path_latent = affine_path.sample( + t=t_latent.unsqueeze(-1).unsqueeze(-1), # broadcast to (B,1,1) + x_0=noise_latent, x_1=z_target + ) + + # 5. Model forward + pred_v_expr, pred_v_latent = model( + gene_ids, source, path_expr.x_t, path_latent.x_t, + t_expr, t_latent, perturbation_id + ) + + # 6. Losses + loss_expr = ((pred_v_expr - path_expr.dx_t)**2 + * w_expr[:, None]).mean() + loss_latent = ((pred_v_latent - path_latent.dx_t)**2 + * w_latent[:, None, None]).mean() + loss = loss_expr + latent_weight * loss_latent + + # Optional MMD (同 scDFM) + if use_mmd_loss: + x1_hat = path_expr.x_t + pred_v_expr * (1 - t_expr).unsqueeze(-1) + loss += gamma * mmd_loss(x1_hat, target) + + return loss +``` + +### 3.4 训练入口 (`scripts/run_cascaded.py`) + +基于 scDFM 的 `run.py` 改造: +- 初始化 `FrozenScGPTExtractor` + `CascadedFlowModel` +- 封装进 `CascadedDenoiser` +- 训练循环: 使用 `denoiser.train_step()` 替代 `train_step()` +- 评估: 使用 `denoiser.generate()` + `MetricsEvaluator` +- 其余（optimizer, scheduler, checkpoint）保持不变 + +--- + +## 4. 推理逻辑 — 两阶段级联生成 + +参照 LatentForcing `denoiser_cot.py:224-247`: + +```python +@torch.no_grad() +def generate(self, source, perturbation_id, gene_ids, + latent_steps=20, expr_steps=20): + B = source.shape[0] + G = source.shape[1] + + # ═══ Stage 1: 生成 Latent (per-gene scGPT features) ═══ + # t_latent: 0→1, t_expr=0 (表达流不参与) + z_noise = torch.randn(B, G, scgpt_dim, device=device) + + def latent_vf(t, z_t): + t_latent = t.expand(B) + t_expr = torch.zeros(B, device=device) + _, v_latent = model(gene_ids, source, source, z_t, + t_expr, t_latent, perturbation_id) + return v_latent + + z_generated = torchdiffeq.odeint( + latent_vf, z_noise, + torch.linspace(0, 1, latent_steps, device=device), + method='rk4' + )[-1] # (B, G, 512) + + # ═══ Stage 2: 用生成的 Latent 引导生成表达谱 ═══ + # t_expr: 0→1, t_latent=1 (latent "clean") + expr_noise = make_noise(source, noise_type) + + def expr_vf(t, x_t): + t_expr = t.expand(B) + t_latent = torch.ones(B, device=device) + v_expr, _ = model(gene_ids, source, x_t, z_generated, + t_expr, t_latent, perturbation_id) + return v_expr + + x_generated = torchdiffeq.odeint( + expr_vf, expr_noise, + torch.linspace(0, 1, expr_steps, device=device), + method='rk4' + )[-1] # (B, G) + + return torch.clamp(x_generated, min=0) +``` + +**关键设计** (与 LatentForcing 的 `dino_first_cascaded` 推理完全对应): +- Stage 1: `t_expr=0` → 表达流为纯噪声，模型聚焦 latent 预测 +- Stage 2: `t_latent=1` → latent 已解析，`z_generated` 通过 `LatentEmbedder` 投影后与表达 tokens 相加，引导表达生成 +- 评估: 复用 scDFM 的 `test()` 函数结构 + `cell-eval MetricsEvaluator` + +--- + +## 5. 配置 (`config/config_cascaded.py`) + +```python +@dataclass +class CascadedFlowConfig: + # === 基础 (同 scDFM FlowConfig) === + model_type: str = 'cascaded' + batch_size: int = 48 + d_model: int = 128 + nhead: int = 8 + nlayers: int = 4 + lr: float = 5e-5 + steps: int = 200000 + eta_min: float = 1e-6 + data_name: str = 'norman' + fusion_method: str = 'differential_perceiver' + perturbation_function: str = 'crisper' + noise_type: str = 'Gaussian' + infer_top_gene: int = 1000 + n_top_genes: int = 5000 + print_every: int = 5000 + gamma: float = 0.5 + use_mmd_loss: bool = True + split_method: str = 'additive' + fold: int = 1 + + # === 级联/Latent 特有 === + scgpt_dim: int = 512 # scGPT embedding 维度 + bottleneck_dim: int = 128 # LatentEmbedder 瓶颈维度 + latent_weight: float = 1.0 # latent 流损失权重 + choose_latent_p: float = 0.4 # 训练 latent 流的概率 + target_std: float = 1.0 # 方差匹配目标标准差 + dh_depth: int = 2 # latent decoder head 层数 + + # === scGPT 路径 === + scgpt_model_dir: str = 'transfer/data/scGPT_pretrained' + + # === 推理 === + latent_steps: int = 20 + expr_steps: int = 20 +``` + +--- + +## 6. 实现顺序 + +### Step 1: 项目初始化 + 下载 scGPT +- 创建 `transfer/code/CCFM/` 目录结构 +- 下载 scGPT `whole-human` 预训练模型 +- 验证: 加载 scGPT 模型成功 + +### Step 2: FrozenScGPTExtractor +- 实现 `src/data/scgpt_extractor.py` +- 处理 scDFM ↔ scGPT 的基因名对齐 +- 实现 per-gene feature scatter 和归一化 +- 验证: 输入 (48, 1000) expression → 输出 (48, 1000, 512) features + +### Step 3: 数据加载 +- 实现 `src/data/data.py`，复用 scDFM 的 Data 类 +- 验证: DataLoader 正确返回 expression + perturbation_id + +### Step 4: CascadedFlowModel +- 实现 `src/model/model.py` 和 `src/model/layers.py` +- 验证: dummy forward pass → (pred_v_expr, pred_v_latent) 形状正确 + +### Step 5: CascadedDenoiser +- 实现 `src/denoiser.py` +- 验证: `train_step()` 返回 scalar loss, `generate()` 返回正确形状 + +### Step 6: 训练入口 + 配置 +- 实现 `scripts/run_cascaded.py` + `config/config_cascaded.py` +- 编写 `run.sh` (pjsub 脚本) +- 验证: 完整训练循环运行，loss 下降 + +### Step 7: 端到端验证 +- Norman 数据集上训练（先小 step 数） +- 检查 loss_expr 和 loss_latent 分别下降 +- 推理并通过 cell-eval 评估 +- 与 baseline scDFM 对比 + +--- + +## 7. 关键技术决策 + +| 决策点 | 选择 | 理由 | +|--------|------|------| +| Latent 注入方式 | Element-wise add (token-level) | 严格对标 LatentForcing `model_cot.py:414` | +| 条件注入 | AdaLN (c = sum of all embeddings) | 对标 `model_cot.py:409` | +| scGPT 特征提取 | On-the-fly (冻结) | 存储不可行 (~1TB); 对标 LatentForcing 冻结 DINO-v2 | +| 时间步采样 | dino_first_cascaded | 对标 `denoiser_cot.py:121-132` | +| 方差匹配 | 全局 running statistics + scaling | 对标 `dinov2_hf.py` 的归一化 | +| Backbone | 复用 scDFM 的 DiffPerceiver/Perceiver | 保持底盘一致 | +| 解码头 | 分离: ExprDecoder + LatentDecoder | 对标 `model_cot.py:429-441` 的分离头 | diff --git a/transfer/code/CCFM/preextract_scgpt.5402631.stats 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a/transfer/code/CCFM/result_scgpt/ccfm-norman-f1-topk30-negTrue-d128-lr5e-05-lw1.0-lp0.4-cached/tb_logs/events.out.tfevents.1773505111.b0027.161.0 b/transfer/code/CCFM/result_scgpt/ccfm-norman-f1-topk30-negTrue-d128-lr5e-05-lw1.0-lp0.4-cached/tb_logs/events.out.tfevents.1773505111.b0027.161.0 new file mode 100644 index 0000000000000000000000000000000000000000..773122915fe472187d7e123cc9b5d9f040f60dd1 --- /dev/null +++ b/transfer/code/CCFM/result_scgpt/ccfm-norman-f1-topk30-negTrue-d128-lr5e-05-lw1.0-lp0.4-cached/tb_logs/events.out.tfevents.1773505111.b0027.161.0 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b4cc1cf09bb4d6fdba98b794940c752e774380c25ee771738d5d7d3809cb0278 +size 88 diff --git a/transfer/code/CCFM/run.sh b/transfer/code/CCFM/run.sh new file mode 100644 index 0000000000000000000000000000000000000000..0ffee9d87bf28d13598d9da9f1974b15a0dd2844 --- /dev/null +++ b/transfer/code/CCFM/run.sh @@ -0,0 +1,39 @@ +#!/bin/bash +#PJM -L rscgrp=b-batch +#PJM -L gpu=4 +#PJM -L elapse=24:00:00 +#PJM -j +#PJM -S +#PJM -o /home/hp250092/ku50001222/qian/aivc/lfj/transfer/logs/ccfm_%j.out + +# Activate environment +source /home/hp250092/ku50001222/qian/aivc/lfj/stack_env/bin/activate + +# Navigate to CCFM +cd /home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM + +# Run training with accelerate (multi-GPU) +accelerate launch --num_processes=4 scripts/run_cascaded.py \ + --data-name norman \ + --d-model 128 \ + --nhead 8 \ + --nlayers 4 \ + --batch-size 48 \ + --lr 5e-5 \ + --steps 200000 \ + --fusion-method differential_perceiver \ + --perturbation-function crisper \ + --noise-type Gaussian \ + --infer-top-gene 1000 \ + --n-top-genes 5000 \ + --use-mmd-loss \ + --gamma 0.5 \ + --split-method additive \ + --fold 1 \ + --scgpt-dim 512 \ + --bottleneck-dim 128 \ + --latent-weight 1.0 \ + --choose-latent-p 0.4 \ + --dh-depth 2 \ + --print-every 10000 \ + --result-path ./result diff --git a/transfer/code/CCFM/run_1gpu_cached.sh b/transfer/code/CCFM/run_1gpu_cached.sh new file mode 100644 index 0000000000000000000000000000000000000000..c4e410eff5e17f7d3fdafd447adc65e8be458d1d --- /dev/null +++ b/transfer/code/CCFM/run_1gpu_cached.sh @@ -0,0 +1,58 @@ +#!/bin/bash +#PJM -L rscgrp=b-batch +#PJM -L gpu=1 +#PJM -L elapse=24:00:00 +#PJM -N ccfm_1gpu_cached +#PJM -j +#PJM -o logs/ccfm_1gpu_cached_%j.out + +module load cuda/12.2.2 +module load cudnn/8.9.7 +module load gcc-toolset/12 + +source /home/hp250092/ku50001222/qian/aivc/lfj/stack_env/bin/activate + +cd /home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM + +export PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:256 + +echo "==========================================" +echo "Job ID: $PJM_JOBID" +echo "Job Name: $PJM_JOBNAME" +echo "Start: $(date)" +echo "Node: $(hostname)" +echo "GPU: $(nvidia-smi --query-gpu=name,memory.total --format=csv,noheader 2>/dev/null || echo 'N/A')" +echo "Run: CCFM 1GPU cached scGPT" +echo "==========================================" + +accelerate launch --num_processes=1 scripts/run_cascaded.py \ + --data-name norman \ + --d-model 128 \ + --nhead 8 \ + --nlayers 4 \ + --batch-size 48 \ + --lr 5e-5 \ + --steps 200000 \ + --fusion-method differential_perceiver \ + --perturbation-function crisper \ + --noise-type Gaussian \ + --infer-top-gene 1000 \ + --n-top-genes 5000 \ + --use-mmd-loss \ + --gamma 0.5 \ + --split-method additive \ + --fold 1 \ + --scgpt-dim 512 \ + --bottleneck-dim 128 \ + --latent-weight 1.0 \ + --choose-latent-p 0.4 \ + --dh-depth 2 \ + --print-every 5000 \ + --topk 30 \ + --use-negative-edge \ + --scgpt-cache-path scgpt_cache_norman.h5 \ + --result-path ./result_scgpt + +echo "==========================================" +echo "Finished: $(date)" +echo "==========================================" diff --git a/transfer/code/CCFM/run_1gpu_online.sh b/transfer/code/CCFM/run_1gpu_online.sh new file mode 100644 index 0000000000000000000000000000000000000000..fc91ea844d9913bbdb236d199826eca5b194c32f --- /dev/null +++ b/transfer/code/CCFM/run_1gpu_online.sh @@ -0,0 +1,56 @@ +#!/bin/bash +#PJM -L rscgrp=b-batch +#PJM -L gpu=1 +#PJM -L elapse=24:00:00 +#PJM -N ccfm_1gpu_online +#PJM -j +#PJM -o logs/ccfm_1gpu_online_%j.out + +module load cuda/12.2.2 +module load cudnn/8.9.7 +module load gcc-toolset/12 + +source /home/hp250092/ku50001222/qian/aivc/lfj/stack_env/bin/activate + +cd /home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM + +export PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:256 + +echo "==========================================" +echo "Job ID: $PJM_JOBID" +echo "Job Name: $PJM_JOBNAME" +echo "Start: $(date)" +echo "Node: $(hostname)" +echo "GPU: $(nvidia-smi --query-gpu=name,memory.total --format=csv,noheader 2>/dev/null || echo 'N/A')" +echo "Run: CCFM 1GPU online" +echo "==========================================" + +accelerate launch --num_processes=1 scripts/run_cascaded.py \ + --data-name norman \ + --d-model 128 \ + --nhead 8 \ + --nlayers 4 \ + --batch-size 48 \ + --lr 5e-5 \ + --steps 200000 \ + --fusion-method differential_perceiver \ + --perturbation-function crisper \ + --noise-type Gaussian \ + --infer-top-gene 1000 \ + --n-top-genes 5000 \ + --use-mmd-loss \ + --gamma 0.5 \ + --split-method additive \ + --fold 1 \ + --scgpt-dim 512 \ + --bottleneck-dim 128 \ + --latent-weight 1.0 \ + --choose-latent-p 0.4 \ + --dh-depth 2 \ + --print-every 5000 \ + --topk 30 \ + --use-negative-edge + +echo "==========================================" +echo "Finished: $(date)" +echo "==========================================" diff --git a/transfer/code/CCFM/run_cached.sh b/transfer/code/CCFM/run_cached.sh new file mode 100644 index 0000000000000000000000000000000000000000..0f652cbb970f97d3a7e29c23393673a34444dc98 --- /dev/null +++ b/transfer/code/CCFM/run_cached.sh @@ -0,0 +1,52 @@ +#!/bin/bash +#PJM -L rscgrp=b-batch +#PJM -L gpu=4 +#PJM -L elapse=24:00:00 +#PJM -j +#PJM -S +#PJM -o /home/hp250092/ku50001222/qian/aivc/lfj/transfer/logs/ccfm_cached_%j.out + +# Activate environment +source /home/hp250092/ku50001222/qian/aivc/lfj/stack_env/bin/activate + +# Navigate to CCFM +cd /home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM + +export PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:256 + +echo "==========================================" +echo "Job ID: $PJM_JOBID" +echo "Start: $(date)" +echo "Node: $(hostname)" +echo "==========================================" + +# Run training with accelerate (multi-GPU) + scGPT cache +accelerate launch --num_processes=4 scripts/run_cascaded.py \ + --data-name norman \ + --d-model 128 \ + --nhead 8 \ + --nlayers 4 \ + --batch-size 48 \ + --lr 5e-5 \ + --steps 200000 \ + --fusion-method differential_perceiver \ + --perturbation-function crisper \ + --noise-type Gaussian \ + --infer-top-gene 1000 \ + --n-top-genes 5000 \ + --use-mmd-loss \ + --gamma 0.5 \ + --split-method additive \ + --fold 1 \ + --scgpt-dim 512 \ + --bottleneck-dim 128 \ + --latent-weight 1.0 \ + --choose-latent-p 0.4 \ + --dh-depth 2 \ + --print-every 10000 \ + --result-path ./result \ + --scgpt-cache-path /home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/scgpt_cache_norman.h5 + +echo "==========================================" +echo "Finished: $(date)" +echo "==========================================" diff --git a/transfer/code/CCFM/run_topk30_neg.sh b/transfer/code/CCFM/run_topk30_neg.sh new file mode 100644 index 0000000000000000000000000000000000000000..6764a8d2736f76ae16ca6f390b7f7a3f8394b442 --- /dev/null +++ b/transfer/code/CCFM/run_topk30_neg.sh @@ -0,0 +1,57 @@ +#!/bin/bash +#PJM -L rscgrp=b-batch +#PJM -L gpu=4 +#PJM -L elapse=24:00:00 +#PJM -N ccfm_topk30_neg +#PJM -j +#PJM -o logs/ccfm_topk30_neg_%j.out + +module load cuda/12.2.2 +module load cudnn/8.9.7 +module load gcc-toolset/12 + +source /home/hp250092/ku50001222/qian/aivc/lfj/stack_env/bin/activate + +cd /home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM + +export PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:256 + +echo "==========================================" +echo "Job ID: $PJM_JOBID" +echo "Job Name: $PJM_JOBNAME" +echo "Start: $(date)" +echo "Node: $(hostname)" +echo "GPU: $(nvidia-smi --query-gpu=name,memory.total --format=csv,noheader 2>/dev/null || echo 'N/A')" +echo "Run: CCFM (topk=30, use_negative_edge=True)" +echo "==========================================" + +accelerate launch --num_processes=4 scripts/run_cascaded.py \ + --data-name norman \ + --d-model 128 \ + --nhead 8 \ + --nlayers 4 \ + --batch-size 48 \ + --lr 5e-5 \ + --steps 200000 \ + --fusion-method differential_perceiver \ + --perturbation-function crisper \ + --noise-type Gaussian \ + --infer-top-gene 1000 \ + --n-top-genes 5000 \ + --use-mmd-loss \ + --gamma 0.5 \ + --split-method additive \ + --fold 1 \ + --scgpt-dim 512 \ + --bottleneck-dim 128 \ + --latent-weight 1.0 \ + --choose-latent-p 0.4 \ + --dh-depth 2 \ + --print-every 10000 \ + --topk 30 \ + --use-negative-edge \ + --result-path ./result + +echo "==========================================" +echo "Finished: $(date)" +echo "==========================================" diff --git a/transfer/code/CCFM/run_v2_cached.sh b/transfer/code/CCFM/run_v2_cached.sh new file mode 100644 index 0000000000000000000000000000000000000000..f29055119452cae4002034e4eb38dcaf84d8d7e3 --- /dev/null +++ b/transfer/code/CCFM/run_v2_cached.sh @@ -0,0 +1,66 @@ +#!/bin/bash +#PJM -L rscgrp=b-batch +#PJM -L gpu=1 +#PJM -L elapse=24:00:00 +#PJM -N ccfm_v2_cached +#PJM -j +#PJM -o logs/ccfm_v2_cached_%j.out + +module load cuda/12.2.2 +module load cudnn/8.9.7 +module load gcc-toolset/12 + +source /home/hp250092/ku50001222/qian/aivc/lfj/stack_env/bin/activate + +cd /home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM + +export PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:256 + +echo "==========================================" +echo "Job ID: $PJM_JOBID" +echo "Job Name: $PJM_JOBNAME" +echo "Start: $(date)" +echo "Node: $(hostname)" +echo "GPU: $(nvidia-smi --query-gpu=name,memory.total --format=csv,noheader 2>/dev/null || echo 'N/A')" +echo "Run: CCFM v2 cached (KV fix + loss fix + EMA + RK4 + logit-normal + warmup + scGPT cache)" +echo "==========================================" + +accelerate launch --num_processes=1 scripts/run_cascaded.py \ + --data-name norman \ + --d-model 128 \ + --nhead 8 \ + --nlayers 4 \ + --batch-size 48 \ + --lr 5e-5 \ + --steps 200000 \ + --fusion-method differential_perceiver \ + --perturbation-function crisper \ + --noise-type Gaussian \ + --infer-top-gene 1000 \ + --n-top-genes 5000 \ + --use-mmd-loss \ + --gamma 0.5 \ + --split-method additive \ + --fold 1 \ + --scgpt-dim 512 \ + --bottleneck-dim 128 \ + --latent-weight 1.0 \ + --choose-latent-p 0.4 \ + --dh-depth 2 \ + --print-every 10000 \ + --topk 30 \ + --use-negative-edge \ + --ema-decay 0.9999 \ + --t-sample-mode logit_normal \ + --t-expr-mean 0.0 \ + --t-expr-std 1.0 \ + --t-latent-mean 0.0 \ + --t-latent-std 1.0 \ + --warmup-steps 2000 \ + --ode-method rk4 \ + --scgpt-cache-path scgpt_cache_norman.h5 \ + --result-path ./result + +echo "==========================================" +echo "Finished: $(date)" +echo "==========================================" diff --git a/transfer/code/CCFM/run_v2_ema_rk4_ln.sh b/transfer/code/CCFM/run_v2_ema_rk4_ln.sh new file mode 100644 index 0000000000000000000000000000000000000000..6fcb21797e0321e6b38c9785d80d6c71724488d5 --- /dev/null +++ b/transfer/code/CCFM/run_v2_ema_rk4_ln.sh @@ -0,0 +1,65 @@ +#!/bin/bash +#PJM -L rscgrp=b-batch +#PJM -L gpu=4 +#PJM -L elapse=24:00:00 +#PJM -N ccfm_v2 +#PJM -j +#PJM -o logs/ccfm_v2_%j.out + +module load cuda/12.2.2 +module load cudnn/8.9.7 +module load gcc-toolset/12 + +source /home/hp250092/ku50001222/qian/aivc/lfj/stack_env/bin/activate + +cd /home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM + +export PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:256 + +echo "==========================================" +echo "Job ID: $PJM_JOBID" +echo "Job Name: $PJM_JOBNAME" +echo "Start: $(date)" +echo "Node: $(hostname)" +echo "GPU: $(nvidia-smi --query-gpu=name,memory.total --format=csv,noheader 2>/dev/null || echo 'N/A')" +echo "Run: CCFM v2 (KV fix + loss fix + EMA + RK4 + logit-normal + warmup)" +echo "==========================================" + +accelerate launch --num_processes=4 scripts/run_cascaded.py \ + --data-name norman \ + --d-model 128 \ + --nhead 8 \ + --nlayers 4 \ + --batch-size 48 \ + --lr 5e-5 \ + --steps 200000 \ + --fusion-method differential_perceiver \ + --perturbation-function crisper \ + --noise-type Gaussian \ + --infer-top-gene 1000 \ + --n-top-genes 5000 \ + --use-mmd-loss \ + --gamma 0.5 \ + --split-method additive \ + --fold 1 \ + --scgpt-dim 512 \ + --bottleneck-dim 128 \ + --latent-weight 1.0 \ + --choose-latent-p 0.4 \ + --dh-depth 2 \ + --print-every 10000 \ + --topk 30 \ + --use-negative-edge \ + --ema-decay 0.9999 \ + --t-sample-mode logit_normal \ + --t-expr-mean 0.0 \ + --t-expr-std 1.0 \ + --t-latent-mean 0.0 \ + --t-latent-std 1.0 \ + --warmup-steps 2000 \ + --ode-method rk4 \ + --result-path ./result + +echo "==========================================" +echo "Finished: $(date)" +echo "==========================================" diff --git a/transfer/code/CCFM/run_v2_resume.sh b/transfer/code/CCFM/run_v2_resume.sh new file mode 100644 index 0000000000000000000000000000000000000000..17dac0b41823d4c0f90909c8c09a31c887086a70 --- /dev/null +++ b/transfer/code/CCFM/run_v2_resume.sh @@ -0,0 +1,66 @@ +#!/bin/bash +#PJM -L rscgrp=b-batch +#PJM -L gpu=4 +#PJM -L elapse=24:00:00 +#PJM -N ccfm_v2_resume +#PJM -j +#PJM -o logs/ccfm_v2_resume_%j.out + +module load cuda/12.2.2 +module load cudnn/8.9.7 +module load gcc-toolset/12 + +source /home/hp250092/ku50001222/qian/aivc/lfj/stack_env/bin/activate + +cd /home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM + +export PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:256 + +echo "==========================================" +echo "Job ID: $PJM_JOBID" +echo "Job Name: $PJM_JOBNAME" +echo "Start: $(date)" +echo "Node: $(hostname)" +echo "GPU: $(nvidia-smi --query-gpu=name,memory.total --format=csv,noheader 2>/dev/null || echo 'N/A')" +echo "Run: CCFM v2 RESUME from iteration_100000" +echo "==========================================" + +accelerate launch --num_processes=4 scripts/run_cascaded.py \ + --data-name norman \ + --d-model 128 \ + --nhead 8 \ + --nlayers 4 \ + --batch-size 48 \ + --lr 5e-5 \ + --steps 200000 \ + --fusion-method differential_perceiver \ + --perturbation-function crisper \ + --noise-type Gaussian \ + --infer-top-gene 1000 \ + --n-top-genes 5000 \ + --use-mmd-loss \ + --gamma 0.5 \ + --split-method additive \ + --fold 1 \ + --scgpt-dim 512 \ + --bottleneck-dim 128 \ + --latent-weight 1.0 \ + --choose-latent-p 0.4 \ + --dh-depth 2 \ + --print-every 10000 \ + --topk 30 \ + --use-negative-edge \ + --ema-decay 0.9999 \ + --t-sample-mode logit_normal \ + --t-expr-mean 0.0 \ + --t-expr-std 1.0 \ + --t-latent-mean 0.0 \ + --t-latent-std 1.0 \ + --warmup-steps 2000 \ + --ode-method rk4 \ + --result-path ./result \ + --checkpoint-path ./result/ccfm-norman-f1-topk30-negTrue-d128-lr5e-05-lw1.0-lp0.4-ema0.9999-ln-wu2000-rk4-online/iteration_100000/checkpoint.pt + +echo "==========================================" +echo "Finished: $(date)" +echo "==========================================" diff --git a/transfer/code/CCFM/scripts/diagnose_distributions.py b/transfer/code/CCFM/scripts/diagnose_distributions.py new file mode 100644 index 0000000000000000000000000000000000000000..884c9ccf3d42957290426e757df9a22c137d0480 --- /dev/null +++ b/transfer/code/CCFM/scripts/diagnose_distributions.py @@ -0,0 +1,152 @@ +""" +Diagnostic script: check expression vs latent value distributions in CCFM training. +Loads data + scGPT cache, runs a few batches, prints distribution stats. + +Usage (login node is fine — no GPU needed for cached features): + cd /home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM + python scripts/diagnose_distributions.py +""" + +import sys +import os + +_PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) +sys.path.insert(0, _PROJECT_ROOT) + +import _bootstrap_scdfm # noqa: F401 + +import torch +import numpy as np +from torch.utils.data import DataLoader + +from src.data.data import get_data_classes +from src.data.scgpt_cache import ScGPTFeatureCache +from src._scdfm_imports import AffineProbPath, CondOTScheduler, process_vocab +from src.utils import GeneVocab + +_REPO_ROOT = os.path.dirname(_PROJECT_ROOT) # transfer/code/ + + +def describe(name, t): + """Print distribution stats for a tensor.""" + t_flat = t.float().flatten() + nonzero = t_flat[t_flat.abs() > 1e-8] + print(f" {name:30s} | shape {str(list(t.shape)):20s} | " + f"mean={t_flat.mean():.4f} std={t_flat.std():.4f} " + f"min={t_flat.min():.4f} max={t_flat.max():.4f} " + f"median={t_flat.median():.4f} " + f"zero_frac={1 - len(nonzero)/len(t_flat):.2%}") + if len(nonzero) > 0 and len(nonzero) < len(t_flat): + print(f" {' (nonzero only)':30s} | " + f"mean={nonzero.mean():.4f} std={nonzero.std():.4f} " + f"min={nonzero.min():.4f} max={nonzero.max():.4f}") + + +def main(): + device = torch.device("cpu") + data_name = "norman" + n_top_genes = 5000 + infer_top_gene = 1000 + batch_size = 48 + cache_path = os.path.join(_PROJECT_ROOT, "scgpt_cache_norman.h5") + + # --- Load data (reuse scDFM) --- + Data, PerturbationDataset, TrainSampler, TestDataset = get_data_classes() + scdfm_data_path = os.path.join(_REPO_ROOT, "scDFM", "data") + data_manager = Data(scdfm_data_path) + data_manager.load_data(data_name) + + if "gene_name" in data_manager.adata.var.columns and data_manager.adata.var_names[0].startswith("ENSG"): + data_manager.adata.var_names = data_manager.adata.var["gene_name"].values + data_manager.adata.var_names_make_unique() + + data_manager.process_data( + n_top_genes=n_top_genes, split_method="additive", + fold=1, use_negative_edge=True, k=30, + ) + train_sampler, _, _ = data_manager.load_flow_data(batch_size=batch_size) + train_dataset = PerturbationDataset(train_sampler, batch_size) + dataloader = DataLoader(train_dataset, batch_size=1, shuffle=False, num_workers=0) + + # --- Load scGPT cache --- + scgpt_cache = ScGPTFeatureCache(cache_path, target_std=1.0) + print(f"Cache shape: {scgpt_cache.features.shape}") + print(f"Cache norm_mean: mean={scgpt_cache.norm_mean.mean():.4f}, std={scgpt_cache.norm_mean.std():.4f}") + print(f"Cache norm_var: mean={scgpt_cache.norm_var.mean():.4f}, std={scgpt_cache.norm_var.std():.4f}") + + # --- Flow path --- + flow_path = AffineProbPath(scheduler=CondOTScheduler()) + + # --- Run a few batches --- + n_batches = 5 + print(f"\n{'='*90}") + print(f"Running {n_batches} batches (batch_size={batch_size}, infer_top_gene={infer_top_gene})") + print(f"{'='*90}") + + for i, batch_data in enumerate(dataloader): + if i >= n_batches: + break + + source = batch_data["src_cell_data"].squeeze(0) # (B, G_full) + target = batch_data["tgt_cell_data"].squeeze(0) + tgt_cell_names = [n[0] if isinstance(n, (tuple, list)) else n for n in batch_data["tgt_cell_id"]] + + # Random gene subset + input_gene_ids = torch.randperm(source.shape[-1])[:infer_top_gene] + source_sub = source[:, input_gene_ids] + target_sub = target[:, input_gene_ids] + + # scGPT latent features (from cache) + z_target = scgpt_cache.lookup(tgt_cell_names, input_gene_ids, device=device) + + # Noise + noise_expr = torch.randn_like(source_sub) + noise_latent = torch.randn_like(z_target) + + # Sample time steps + t = torch.sigmoid(torch.randn(source_sub.shape[0])) # logit-normal + + # Flow path: expression + path_expr = flow_path.sample(t=t, x_0=noise_expr, x_1=target_sub) + + # Flow path: latent (flatten for AffineProbPath) + B, G, D = z_target.shape + z_target_flat = z_target.reshape(B, G * D) + noise_latent_flat = noise_latent.reshape(B, G * D) + path_latent_flat = flow_path.sample(t=t, x_0=noise_latent_flat, x_1=z_target_flat) + + dx_t_expr = path_expr.dx_t + dx_t_latent = path_latent_flat.dx_t.reshape(B, G, D) + + print(f"\n--- Batch {i} ---") + print(f"[Raw Values]") + describe("source_sub (control expr)", source_sub) + describe("target_sub (perturbed expr)", target_sub) + describe("z_target (scGPT latent)", z_target) + + print(f"\n[Noise]") + describe("noise_expr", noise_expr) + describe("noise_latent", noise_latent) + + print(f"\n[Flow Path x_t (interpolated)]") + describe("x_t_expr", path_expr.x_t) + describe("z_t_latent", path_latent_flat.x_t.reshape(B, G, D)) + + print(f"\n[Velocity Targets dx_t]") + describe("dx_t_expr", dx_t_expr) + describe("dx_t_latent", dx_t_latent) + + print(f"\n[Velocity MSE (hypothetical)]") + mse_expr = (dx_t_expr ** 2).mean().item() + mse_latent = (dx_t_latent ** 2).mean().item() + print(f" mean(dx_t_expr^2) = {mse_expr:.4f}") + print(f" mean(dx_t_latent^2) = {mse_latent:.4f}") + print(f" ratio expr/latent = {mse_expr / max(mse_latent, 1e-8):.2f}x") + + scgpt_cache.close() + print(f"\n{'='*90}") + print("Done.") + + +if __name__ == "__main__": + main() diff --git a/transfer/code/CCFM/scripts/diagnose_trained_model.py b/transfer/code/CCFM/scripts/diagnose_trained_model.py new file mode 100644 index 0000000000000000000000000000000000000000..f38b7b536966248b5a1385adc9b2fa9e6ad7d0b4 --- /dev/null +++ b/transfer/code/CCFM/scripts/diagnose_trained_model.py @@ -0,0 +1,208 @@ +""" +Diagnostic: load trained checkpoint, run forward on a few batches, +print expression vs latent prediction MSE and velocity distributions. + +Uses synthetic latent features (same distribution as normalized scGPT features) +to avoid loading the 44GB cache file. + +Usage (login node, CPU): + cd /home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM + python scripts/diagnose_trained_model.py +""" + +import sys +import os + +_PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) +sys.path.insert(0, _PROJECT_ROOT) + +import _bootstrap_scdfm # noqa: F401 + +import torch +import numpy as np +from torch.utils.data import DataLoader + +from src.data.data import get_data_classes +from src._scdfm_imports import AffineProbPath, CondOTScheduler, process_vocab +from src.utils import GeneVocab +from src.model.model import CascadedFlowModel + +_REPO_ROOT = os.path.dirname(_PROJECT_ROOT) + + +def describe(name, t): + t_flat = t.float().flatten() + print(f" {name:35s} | mean={t_flat.mean():.4f} std={t_flat.std():.4f} " + f"min={t_flat.min():.4f} max={t_flat.max():.4f} median={t_flat.median():.4f}") + + +def main(): + device = torch.device("cpu") + data_name = "norman" + n_top_genes = 5000 + infer_top_gene = 1000 + batch_size = 48 + scgpt_dim = 512 + ckpt_path = os.path.join( + _PROJECT_ROOT, + "result/ccfm-norman-f1-topk30-negTrue-d128-lr5e-05-lw1.0-lp0.4-ema0.9999-ln-wu2000-rk4-online", + "iteration_100000/checkpoint.pt", + ) + + # --- Load data --- + Data, PerturbationDataset, TrainSampler, TestDataset = get_data_classes() + scdfm_data_path = os.path.join(_REPO_ROOT, "scDFM", "data") + data_manager = Data(scdfm_data_path) + data_manager.load_data(data_name) + + if "gene_name" in data_manager.adata.var.columns and data_manager.adata.var_names[0].startswith("ENSG"): + data_manager.adata.var_names = data_manager.adata.var["gene_name"].values + data_manager.adata.var_names_make_unique() + + data_manager.process_data( + n_top_genes=n_top_genes, split_method="additive", + fold=1, use_negative_edge=True, k=30, + ) + train_sampler, _, _ = data_manager.load_flow_data(batch_size=batch_size) + train_dataset = PerturbationDataset(train_sampler, batch_size) + dataloader = DataLoader(train_dataset, batch_size=1, shuffle=False, num_workers=0) + + # --- Vocab --- + orig_cwd = os.getcwd() + os.chdir(os.path.join(_REPO_ROOT, "scDFM")) + _fc = type("_FakeConfig", (), { + "perturbation_function": "crisper", + "data_name": data_name, + "n_top_genes": n_top_genes, + })() + vocab = process_vocab(data_manager, _fc) + os.chdir(orig_cwd) + + gene_ids = vocab.encode(list(data_manager.adata.var_names)) + gene_ids = torch.tensor(gene_ids, dtype=torch.long, device=device) + + # --- Build mask path --- + mask_path = os.path.join( + data_manager.data_path, data_manager.data_name, + "mask_fold_1topk_30additive_negative_edge.pt", + ) + + # --- Load model --- + model = CascadedFlowModel( + ntoken=len(vocab), d_model=128, nhead=8, d_hid=512, nlayers=4, + fusion_method="differential_perceiver", + perturbation_function="crisper", + mask_path=mask_path, + scgpt_dim=scgpt_dim, bottleneck_dim=128, dh_depth=2, + ) + + ckpt = torch.load(ckpt_path, map_location="cpu") + model.load_state_dict(ckpt["model_state_dict"]) + model.eval() + print(f"Loaded checkpoint: {ckpt_path}") + print(f" iteration: {ckpt.get('iteration', '?')}") + + # --- Flow path --- + flow_path = AffineProbPath(scheduler=CondOTScheduler()) + inverse_dict = {v: str(k) for k, v in data_manager.perturbation_dict.items()} + + # --- Run batches --- + n_batches = 5 + all_mse_expr = [] + all_mse_latent = [] + + print(f"\n{'='*90}") + print(f"Running {n_batches} batches with trained model") + print(f"Note: using synthetic latent (N(0,1.1)) to match normalized scGPT distribution") + print(f"{'='*90}") + + for i, batch_data in enumerate(dataloader): + if i >= n_batches: + break + + source = batch_data["src_cell_data"].squeeze(0) + target = batch_data["tgt_cell_data"].squeeze(0) + + # Perturbation ID + perturbation_id = batch_data["condition_id"].squeeze(0).to(device) + perturbation_name = [ + inverse_dict[int(p_id)] for p_id in perturbation_id[0].cpu().numpy() + ] + perturbation_id = torch.tensor( + vocab.encode(perturbation_name), dtype=torch.long, device=device + ) + perturbation_id = perturbation_id.repeat(source.shape[0], 1) + + # Gene subset + input_gene_ids = torch.randperm(source.shape[-1])[:infer_top_gene] + source_sub = source[:, input_gene_ids] + target_sub = target[:, input_gene_ids] + gene_input = gene_ids[input_gene_ids].unsqueeze(0).expand(source.shape[0], -1) + + B = source_sub.shape[0] + + # Synthetic latent target (matches normalized scGPT: ~N(0, 1.1)) + z_target = torch.randn(B, infer_top_gene, scgpt_dim) * 1.1 + + # Sample time steps (logit-normal) + t = torch.sigmoid(torch.randn(B)) + + # Expression flow path + noise_expr = torch.randn_like(source_sub) + path_expr = flow_path.sample(t=t, x_0=noise_expr, x_1=target_sub) + + # Latent flow path + noise_latent = torch.randn_like(z_target) + z_target_flat = z_target.reshape(B, -1) + noise_latent_flat = noise_latent.reshape(B, -1) + path_latent_flat = flow_path.sample(t=t, x_0=noise_latent_flat, x_1=z_target_flat) + + z_t = path_latent_flat.x_t.reshape(B, infer_top_gene, scgpt_dim) + dx_t_latent = path_latent_flat.dx_t.reshape(B, infer_top_gene, scgpt_dim) + + # Model forward + with torch.no_grad(): + pred_v_expr, pred_v_latent = model( + gene_input, source_sub, path_expr.x_t, z_t, + t, t, perturbation_id, + ) + + # Compute errors + err_expr = (pred_v_expr - path_expr.dx_t) ** 2 + err_latent = (pred_v_latent - dx_t_latent) ** 2 + mse_expr = err_expr.mean().item() + mse_latent = err_latent.mean().item() + all_mse_expr.append(mse_expr) + all_mse_latent.append(mse_latent) + + print(f"\n--- Batch {i} ---") + print(f"[Velocity Targets]") + describe("dx_t_expr (ground truth)", path_expr.dx_t) + describe("dx_t_latent (ground truth)", dx_t_latent) + + print(f"[Model Predictions]") + describe("pred_v_expr", pred_v_expr) + describe("pred_v_latent", pred_v_latent) + + print(f"[Prediction Error]") + describe("error_expr (pred - gt)^2", err_expr) + describe("error_latent (pred - gt)^2", err_latent) + + print(f"[MSE Summary]") + print(f" MSE_expr = {mse_expr:.4f}") + print(f" MSE_latent = {mse_latent:.4f}") + print(f" ratio expr/latent = {mse_expr / max(mse_latent, 1e-8):.2f}x") + + # --- Overall summary --- + avg_expr = np.mean(all_mse_expr) + avg_latent = np.mean(all_mse_latent) + print(f"\n{'='*90}") + print(f"OVERALL AVERAGE ({n_batches} batches):") + print(f" avg MSE_expr = {avg_expr:.4f}") + print(f" avg MSE_latent = {avg_latent:.4f}") + print(f" ratio expr/latent = {avg_expr / max(avg_latent, 1e-8):.2f}x") + print(f"{'='*90}") + + +if __name__ == "__main__": + main() diff --git a/transfer/code/CCFM/scripts/download_scgpt.py b/transfer/code/CCFM/scripts/download_scgpt.py new file mode 100644 index 0000000000000000000000000000000000000000..c4533940c113eb58214adb1d4e25ac05d5e85055 --- /dev/null +++ b/transfer/code/CCFM/scripts/download_scgpt.py @@ -0,0 +1,49 @@ +""" +Download scGPT whole-human pretrained model. +Run on login node: python scripts/download_scgpt.py +""" + +import os +import sys + + +def download_scgpt_model(output_dir: str = "transfer/data/scGPT_pretrained"): + """Download scGPT whole-human model files.""" + os.makedirs(output_dir, exist_ok=True) + + required_files = ["best_model.pt", "vocab.json", "args.json"] + missing = [f for f in required_files if not os.path.exists(os.path.join(output_dir, f))] + + if not missing: + print(f"All scGPT pretrained files already exist in {output_dir}") + return + + print(f"Missing files: {missing}") + print(f"Please download the scGPT whole-human pretrained model to: {output_dir}") + print() + print("Option 1: From the scGPT release (if available):") + print(" Download from: https://github.com/bowang-lab/scGPT/releases") + print(f" Place files in: {output_dir}/") + print() + print("Option 2: Manual setup:") + print(" Required files:") + for f in required_files: + print(f" - {f}") + print() + print(" args.json example content:") + print(' {"embsize": 512, "nheads": 8, "d_hid": 512, "nlayers": 12,') + print(' "n_layers_cls": 3, "dropout": 0.2, "pad_token": "",') + print(' "pad_value": 0, "mask_value": -1}') + + +if __name__ == "__main__": + # Default path relative to repo root + repo_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) + default_dir = os.path.join(repo_root, "data", "scGPT_pretrained") + + import argparse + parser = argparse.ArgumentParser() + parser.add_argument("--output_dir", default=default_dir) + args = parser.parse_args() + + download_scgpt_model(args.output_dir) diff --git a/transfer/code/CCFM/scripts/preextract_job.sh b/transfer/code/CCFM/scripts/preextract_job.sh new file mode 100644 index 0000000000000000000000000000000000000000..24313d0d4d0939ea898e34db9f7b85e700ac8a89 --- /dev/null +++ b/transfer/code/CCFM/scripts/preextract_job.sh @@ -0,0 +1,31 @@ +#!/bin/bash +#PJM -L rscgrp=b-batch +#PJM -L gpu=1 +#PJM -L elapse=3:00:00 +#PJM -N preextract_scgpt +#PJM -j +#PJM -S +#PJM -o /home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/logs/preextract_%j.out + +source /home/hp250092/ku50001222/qian/aivc/lfj/stack_env/bin/activate + +cd /home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM + +export PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:256 + +echo "==========================================" +echo "Job ID: $PJM_JOBID" +echo "Job Name: $PJM_JOBNAME" +echo "Start: $(date)" +echo "Node: $(hostname)" +echo "GPU: $(nvidia-smi --query-gpu=name,memory.total --format=csv,noheader 2>/dev/null || echo 'N/A')" +echo "==========================================" + +python scripts/preextract_scgpt.py \ + --data_name norman \ + --batch_size 256 \ + --output /home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/scgpt_cache_norman.h5 + +echo "==========================================" +echo "Finished: $(date)" +echo "==========================================" diff --git a/transfer/code/CCFM/scripts/preextract_scgpt.py b/transfer/code/CCFM/scripts/preextract_scgpt.py new file mode 100644 index 0000000000000000000000000000000000000000..0e3f820ead77ba91aefbe93ea6f1189299c6667a --- /dev/null +++ b/transfer/code/CCFM/scripts/preextract_scgpt.py @@ -0,0 +1,186 @@ +""" +Pre-extract scGPT per-gene features for all training cells. + +Saves to HDF5 for use with ScGPTFeatureCache during training. +Must run on GPU node via pjsub. + +Usage: + python scripts/preextract_scgpt.py --data_name norman --batch_size 256 --output scgpt_cache_norman.h5 +""" + +import sys +import os +import argparse + +# Set up paths +_PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) +sys.path.insert(0, _PROJECT_ROOT) + +# Bootstrap scDFM imports +import _bootstrap_scdfm # noqa: F401 + +import torch +import numpy as np +import h5py +from tqdm import tqdm + +from src.data.data import get_data_classes +from src.data.scgpt_extractor import FrozenScGPTExtractor + +_REPO_ROOT = os.path.dirname(_PROJECT_ROOT) # transfer/code/ + + +def main(): + parser = argparse.ArgumentParser(description="Pre-extract scGPT features") + parser.add_argument("--data_name", type=str, default="norman") + parser.add_argument("--n_top_genes", type=int, default=5000) + parser.add_argument("--split_method", type=str, default="additive") + parser.add_argument("--fold", type=int, default=1) + parser.add_argument("--topk", type=int, default=15) + parser.add_argument("--use_negative_edge", action="store_true") + parser.add_argument("--scgpt_model_dir", type=str, default="transfer/data/scGPT_pretrained") + parser.add_argument("--batch_size", type=int, default=256) + parser.add_argument("--output", type=str, default="scgpt_cache_norman.h5") + args = parser.parse_args() + + if args.data_name == "norman": + args.n_top_genes = 5000 + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + print(f"Device: {device}") + + # === Load data (same as run_cascaded.py) === + Data, PerturbationDataset, TrainSampler, TestDataset = get_data_classes() + + scdfm_data_path = os.path.join(_REPO_ROOT, "scDFM", "data") + data_manager = Data(scdfm_data_path) + data_manager.load_data(args.data_name) + + # Convert var_names from Ensembl IDs to gene symbols if needed + if "gene_name" in data_manager.adata.var.columns and data_manager.adata.var_names[0].startswith("ENSG"): + data_manager.adata.var_names = data_manager.adata.var["gene_name"].values + data_manager.adata.var_names_make_unique() + print(f"Converted var_names to gene symbols, sample: {list(data_manager.adata.var_names[:5])}") + + data_manager.process_data( + n_top_genes=args.n_top_genes, + split_method=args.split_method, + fold=args.fold, + use_negative_edge=args.use_negative_edge, + k=args.topk, + ) + + # Get all cells from the adata (train + valid + test all share the same adata genes) + adata = data_manager.adata + n_cells = adata.n_obs + n_genes = adata.n_vars + hvg_gene_names = list(adata.var_names) + cell_names = list(adata.obs_names) + + print(f"Cells: {n_cells}, Genes: {n_genes}") + print(f"HVG gene names sample: {hvg_gene_names[:5]}") + + # === Build FrozenScGPTExtractor with large max_seq_len === + scgpt_model_dir = os.path.join( + os.path.dirname(_REPO_ROOT), # transfer/ + args.scgpt_model_dir.replace("transfer/", ""), + ) + + # Count valid genes to set max_seq_len + import json + vocab_path = os.path.join(scgpt_model_dir, "vocab.json") + with open(vocab_path, "r") as f: + scgpt_vocab = json.load(f) + n_valid = sum(1 for g in hvg_gene_names if g in scgpt_vocab) + max_seq_len = n_valid + 2 # +1 CLS, +1 margin + print(f"Valid genes in scGPT vocab: {n_valid}/{n_genes}, max_seq_len={max_seq_len}") + + extractor = FrozenScGPTExtractor( + model_dir=scgpt_model_dir, + hvg_gene_names=hvg_gene_names, + device=device, + max_seq_len=max_seq_len, + target_std=1.0, + warmup_batches=0, # no warmup needed, we compute global stats + ) + extractor = extractor.to(device) + extractor.eval() + + scgpt_dim = extractor.scgpt_d_model + print(f"scGPT d_model: {scgpt_dim}") + + # === Get expression matrix === + # adata.X may be sparse + import scipy.sparse as sp + if sp.issparse(adata.X): + X = torch.from_numpy(adata.X.toarray()).float() + else: + X = torch.from_numpy(np.array(adata.X)).float() + + # === Create HDF5 output === + print(f"Output: {args.output}") + print(f"Features shape: ({n_cells}, {n_genes}, {scgpt_dim}) float16") + + h5 = h5py.File(args.output, "w") + feat_ds = h5.create_dataset( + "features", + shape=(n_cells, n_genes, scgpt_dim), + dtype=np.float16, + chunks=(min(args.batch_size, n_cells), n_genes, scgpt_dim), + ) + + # === Extract features in batches === + # We pass gene_indices=None so extract() uses all genes + # We do NOT apply normalization yet — store raw features, compute stats after + # Temporarily disable normalization by setting running_mean=0, running_var=1 + extractor.running_mean.zero_() + extractor.running_var.fill_(1.0) + extractor._stats_frozen = True # don't update stats during extraction + + running_sum = torch.zeros(scgpt_dim, dtype=torch.float64) + running_sq_sum = torch.zeros(scgpt_dim, dtype=torch.float64) + total_valid_count = 0 + + for start in tqdm(range(0, n_cells, args.batch_size), desc="Extracting"): + end = min(start + args.batch_size, n_cells) + batch_expr = X[start:end].to(device) # (B, G) + + # Extract with target_std=1.0 and identity normalization → raw features + with torch.no_grad(): + feats = extractor.extract(batch_expr, gene_indices=None) # (B, G, D) + + feats_cpu = feats.cpu() + + # Accumulate stats on non-zero features (genes with valid scGPT mapping) + nonzero_mask = feats_cpu.abs().sum(-1) > 0 # (B, G) + if nonzero_mask.any(): + valid_feats = feats_cpu[nonzero_mask].double() # (K, D) + running_sum += valid_feats.sum(dim=0) + running_sq_sum += (valid_feats ** 2).sum(dim=0) + total_valid_count += valid_feats.shape[0] + + # Store raw (un-normalized) features as float16 + feat_ds[start:end] = feats_cpu.numpy().astype(np.float16) + + # === Compute global normalization statistics === + global_mean = (running_sum / total_valid_count).float() + global_var = ((running_sq_sum / total_valid_count) - global_mean.double() ** 2).float().clamp_min(0) + print(f"Global mean range: [{global_mean.min():.4f}, {global_mean.max():.4f}]") + print(f"Global var range: [{global_var.min():.4f}, {global_var.max():.4f}]") + + # Save stats and cell names + h5.create_dataset("norm_mean", data=global_mean.numpy()) + h5.create_dataset("norm_var", data=global_var.numpy()) + + # Save cell names as variable-length strings + dt = h5py.string_dtype() + h5.create_dataset("cell_names", data=np.array(cell_names, dtype=object), dtype=dt) + + h5.close() + print(f"Done! Saved to {args.output}") + print(f" Features: ({n_cells}, {n_genes}, {scgpt_dim}) float16") + print(f" Valid features counted: {total_valid_count}") + + +if __name__ == "__main__": + main() diff --git a/transfer/code/CCFM/scripts/run_cascaded.py b/transfer/code/CCFM/scripts/run_cascaded.py new file mode 100644 index 0000000000000000000000000000000000000000..1ab24018207bcdb5165d348eb04e27052dd6e291 --- /dev/null +++ b/transfer/code/CCFM/scripts/run_cascaded.py @@ -0,0 +1,448 @@ +""" +Training and evaluation entry point for CCFM (Cascaded Conditioned Flow Matching). +Based on scDFM's run.py with cascaded denoiser integration. + +Conditioning signals: control expression + perturbation_id. +scGPT latent features are an auxiliary generation target (like DINO in LatentForcing), +generated from noise at inference — not a conditioning signal. +""" + +import sys +import os + +# Set up paths — CCFM project root must be on sys.path for config/ and src/ imports +_PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) +sys.path.insert(0, _PROJECT_ROOT) + +# Bootstrap scDFM imports (must happen before any CCFM src imports) +import _bootstrap_scdfm # noqa: F401 + +import copy +import torch +import torch.nn as nn +import tyro +import tqdm +import numpy as np +import pandas as pd +import anndata as ad +import scanpy as sc +from torch.utils.data import DataLoader +from tqdm import trange +from accelerate import Accelerator, DistributedDataParallelKwargs +from torch.optim.lr_scheduler import LinearLR, CosineAnnealingLR, SequentialLR + +from config.config_cascaded import CascadedFlowConfig as Config +from src.data.data import get_data_classes +from src.model.model import CascadedFlowModel +from src.data.scgpt_extractor import FrozenScGPTExtractor +from src.data.scgpt_cache import ScGPTFeatureCache +from src.denoiser import CascadedDenoiser +from src.utils import ( + save_checkpoint, + load_checkpoint, + pick_eval_score, + process_vocab, + set_requires_grad_for_p_only, + GeneVocab, +) + +from cell_eval import MetricsEvaluator + +# Resolve scDFM directory paths +_REPO_ROOT = os.path.dirname(_PROJECT_ROOT) # transfer/code/ + + +@torch.inference_mode() +def test(data_sampler, denoiser, accelerator, config, vocab, data_manager, + batch_size=128, path_dir="./"): + """Evaluate: generate predictions and compute cell-eval metrics.""" + device = accelerator.device + gene_ids_test = vocab.encode(list(data_sampler.adata.var_names)) + gene_ids_test = torch.tensor(gene_ids_test, dtype=torch.long, device=device) + + perturbation_name_list = data_sampler._perturbation_covariates + control_data = data_sampler.get_control_data() + inverse_dict = {v: str(k) for k, v in data_manager.perturbation_dict.items()} + + all_pred_expressions = [control_data["src_cell_data"]] + obs_perturbation_name_pred = ["control"] * control_data["src_cell_data"].shape[0] + all_target_expressions = [control_data["src_cell_data"]] + obs_perturbation_name_real = ["control"] * control_data["src_cell_data"].shape[0] + + print("perturbation_name_list:", len(perturbation_name_list)) + for perturbation_name in perturbation_name_list: + perturbation_data = data_sampler.get_perturbation_data(perturbation_name) + target = perturbation_data["tgt_cell_data"] + perturbation_id = perturbation_data["condition_id"] + source = control_data["src_cell_data"].to(device) + perturbation_id = perturbation_id.to(device) + + if config.perturbation_function == "crisper": + perturbation_name_crisper = [ + inverse_dict[int(p_id)] for p_id in perturbation_id[0].cpu().numpy() + ] + perturbation_id = torch.tensor( + vocab.encode(perturbation_name_crisper), dtype=torch.long, device=device + ) + perturbation_id = perturbation_id.repeat(source.shape[0], 1) + + idx = torch.randperm(source.shape[0]) + source = source[idx] + N = 128 + source = source[:N] + + pred_expressions = [] + for i in trange(0, N, batch_size, desc=perturbation_name): + batch_source = source[i : i + batch_size] + batch_pert_id = perturbation_id[0].repeat(batch_source.shape[0], 1).to(device) + + # Get the underlying model for generation + model = denoiser.module if hasattr(denoiser, "module") else denoiser + + pred = model.generate( + batch_source, + batch_pert_id, + gene_ids_test, + latent_steps=config.latent_steps, + expr_steps=config.expr_steps, + method=config.ode_method, + ) + pred_expressions.append(pred) + + pred_expressions = torch.cat(pred_expressions, dim=0).cpu().numpy() + all_pred_expressions.append(pred_expressions) + all_target_expressions.append(target) + obs_perturbation_name_pred.extend([perturbation_name] * pred_expressions.shape[0]) + obs_perturbation_name_real.extend([perturbation_name] * target.shape[0]) + + all_pred_expressions = np.concatenate(all_pred_expressions, axis=0) + all_target_expressions = np.concatenate(all_target_expressions, axis=0) + obs_pred = pd.DataFrame({"perturbation": obs_perturbation_name_pred}) + obs_real = pd.DataFrame({"perturbation": obs_perturbation_name_real}) + pred_adata = ad.AnnData(X=all_pred_expressions, obs=obs_pred) + real_adata = ad.AnnData(X=all_target_expressions, obs=obs_real) + + eval_score = None + if accelerator.is_main_process: + evaluator = MetricsEvaluator( + adata_pred=pred_adata, + adata_real=real_adata, + control_pert="control", + pert_col="perturbation", + num_threads=32, + ) + results, agg_results = evaluator.compute() + results.write_csv(os.path.join(path_dir, "results.csv")) + agg_results.write_csv(os.path.join(path_dir, "agg_results.csv")) + pred_adata.write_h5ad(os.path.join(path_dir, "pred.h5ad")) + real_adata.write_h5ad(os.path.join(path_dir, "real.h5ad")) + eval_score = pick_eval_score(agg_results, "mse") + print(f"Current evaluation score: {eval_score:.4f}") + + return eval_score + + +if __name__ == "__main__": + config = tyro.cli(Config) + + ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=True) + accelerator = Accelerator(kwargs_handlers=[ddp_kwargs]) + if accelerator.is_main_process: + print(config) + save_path = config.make_path() + os.makedirs(save_path, exist_ok=True) + device = accelerator.device + + # === Data loading (reuse scDFM) === + Data, PerturbationDataset, TrainSampler, TestDataset = get_data_classes() + + scdfm_data_path = os.path.join(_REPO_ROOT, "scDFM", "data") + data_manager = Data(scdfm_data_path) + data_manager.load_data(config.data_name) + + # Convert var_names from Ensembl IDs to gene symbols if needed. + # scDFM vocab and perturbation encoding both expect gene symbols as var_names. + if "gene_name" in data_manager.adata.var.columns and data_manager.adata.var_names[0].startswith("ENSG"): + data_manager.adata.var_names = data_manager.adata.var["gene_name"].values + data_manager.adata.var_names_make_unique() + if accelerator.is_main_process: + print(f"Converted var_names to gene symbols, sample: {list(data_manager.adata.var_names[:5])}") + + data_manager.process_data( + n_top_genes=config.n_top_genes, + split_method=config.split_method, + fold=config.fold, + use_negative_edge=config.use_negative_edge, + k=config.topk, + ) + train_sampler, valid_sampler, _ = data_manager.load_flow_data(batch_size=config.batch_size) + + train_dataset = PerturbationDataset(train_sampler, config.batch_size) + dataloader = DataLoader( + train_dataset, batch_size=1, shuffle=False, + num_workers=8, pin_memory=True, persistent_workers=True, + ) + + # === Build mask path === + if config.use_negative_edge: + mask_path = os.path.join( + data_manager.data_path, data_manager.data_name, + f"mask_fold_{config.fold}topk_{config.topk}{config.split_method}_negative_edge.pt", + ) + else: + mask_path = os.path.join( + data_manager.data_path, data_manager.data_name, + f"mask_fold_{config.fold}topk_{config.topk}{config.split_method}.pt", + ) + + # === Vocab === + orig_cwd = os.getcwd() + os.chdir(os.path.join(_REPO_ROOT, "scDFM")) + vocab = process_vocab(data_manager, config) + os.chdir(orig_cwd) + + # Vocab is built from var_names (may be Ensembl IDs or gene symbols) + gene_ids = vocab.encode(list(data_manager.adata.var_names)) + gene_ids = torch.tensor(gene_ids, dtype=torch.long, device=device) + + # === Build CascadedFlowModel === + vf = CascadedFlowModel( + ntoken=len(vocab), + d_model=config.d_model, + nhead=config.nhead, + d_hid=config.d_model * 4, + nlayers=config.nlayers, + fusion_method=config.fusion_method, + perturbation_function=config.perturbation_function, + mask_path=mask_path, + scgpt_dim=config.scgpt_dim, + bottleneck_dim=config.bottleneck_dim, + dh_depth=config.dh_depth, + ) + + # === Build FrozenScGPTExtractor === + # var_names have been converted to gene symbols above, matching scGPT vocab. + hvg_gene_names = list(data_manager.adata.var_names) + scgpt_model_dir = os.path.join( + os.path.dirname(_REPO_ROOT), # transfer/ + config.scgpt_model_dir.replace("transfer/", ""), + ) + scgpt_extractor = FrozenScGPTExtractor( + model_dir=scgpt_model_dir, + hvg_gene_names=hvg_gene_names, + device=device, + max_seq_len=config.scgpt_max_seq_len, + target_std=config.target_std, + warmup_batches=config.warmup_batches, + ) + scgpt_extractor = scgpt_extractor.to(device) + + # === Build CascadedDenoiser === + denoiser = CascadedDenoiser( + model=vf, + scgpt_extractor=scgpt_extractor, + choose_latent_p=config.choose_latent_p, + latent_weight=config.latent_weight, + noise_type=config.noise_type, + use_mmd_loss=config.use_mmd_loss, + gamma=config.gamma, + poisson_alpha=config.poisson_alpha, + poisson_target_sum=config.poisson_target_sum, + t_sample_mode=config.t_sample_mode, + t_expr_mean=config.t_expr_mean, + t_expr_std=config.t_expr_std, + t_latent_mean=config.t_latent_mean, + t_latent_std=config.t_latent_std, + noise_beta=config.noise_beta, + ) + + # === Load scGPT cache if configured === + scgpt_cache = None + if config.scgpt_cache_path: + scgpt_cache = ScGPTFeatureCache( + config.scgpt_cache_path, + target_std=config.target_std, + ) + if accelerator.is_main_process: + print(f"Using pre-extracted scGPT cache: {config.scgpt_cache_path}") + print(f" Cache shape: {scgpt_cache.features.shape}, cells: {len(scgpt_cache.name_to_idx)}") + + # === EMA model (on same device as training model) === + ema_model = copy.deepcopy(vf).to(device) + ema_model.eval() + ema_model.requires_grad_(False) + + # === Optimizer & Scheduler (with warmup) === + save_path = config.make_path() + optimizer = torch.optim.Adam(vf.parameters(), lr=config.lr) + warmup_scheduler = LinearLR( + optimizer, start_factor=1e-3, end_factor=1.0, total_iters=config.warmup_steps, + ) + cosine_scheduler = CosineAnnealingLR( + optimizer, T_max=max(config.steps - config.warmup_steps, 1), eta_min=config.eta_min, + ) + scheduler = SequentialLR( + optimizer, [warmup_scheduler, cosine_scheduler], milestones=[config.warmup_steps], + ) + + start_iteration = 0 + if config.checkpoint_path != "": + start_iteration, _ = load_checkpoint(config.checkpoint_path, vf, optimizer, scheduler) + # Sync EMA with loaded weights + ema_model.load_state_dict(vf.state_dict()) + + # === Prepare with accelerator === + denoiser = accelerator.prepare(denoiser) + optimizer, scheduler, dataloader = accelerator.prepare(optimizer, scheduler, dataloader) + + inverse_dict = {v: str(k) for k, v in data_manager.perturbation_dict.items()} + + # === Test-only mode === + if config.test_only: + eval_path = os.path.join(save_path, "eval_only") + os.makedirs(eval_path, exist_ok=True) + if accelerator.is_main_process: + print(f"Test-only mode. Saving results to {eval_path}") + eval_score = test( + valid_sampler, denoiser, accelerator, config, vocab, data_manager, + batch_size=config.batch_size, path_dir=eval_path, + ) + if accelerator.is_main_process and eval_score is not None: + print(f"Final evaluation score: {eval_score:.4f}") + sys.exit(0) + + # === Loss logging (CSV + TensorBoard) === + import csv + from torch.utils.tensorboard import SummaryWriter + if accelerator.is_main_process: + os.makedirs(save_path, exist_ok=True) + csv_path = os.path.join(save_path, 'loss_curve.csv') + if start_iteration > 0 and os.path.exists(csv_path): + csv_file = open(csv_path, 'a', newline='') + csv_writer = csv.writer(csv_file) + else: + csv_file = open(csv_path, 'w', newline='') + csv_writer = csv.writer(csv_file) + csv_writer.writerow(['iteration', 'loss', 'loss_expr', 'loss_latent', 'loss_mmd', 'lr']) + tb_writer = SummaryWriter(log_dir=os.path.join(save_path, 'tb_logs')) + + # === Training loop === + pbar = tqdm.tqdm(total=config.steps, initial=start_iteration) + iteration = start_iteration + + while iteration < config.steps: + for batch_data in dataloader: + source = batch_data["src_cell_data"].squeeze(0) + target = batch_data["tgt_cell_data"].squeeze(0) + perturbation_id = batch_data["condition_id"].squeeze(0).to(device) + + if config.perturbation_function == "crisper": + perturbation_name = [ + inverse_dict[int(p_id)] for p_id in perturbation_id[0].cpu().numpy() + ] + perturbation_id = torch.tensor( + vocab.encode(perturbation_name), dtype=torch.long, device=device + ) + perturbation_id = perturbation_id.repeat(source.shape[0], 1) + + # Get the underlying denoiser for train_step + base_denoiser = denoiser.module if hasattr(denoiser, "module") else denoiser + base_denoiser.model.train() + + if scgpt_cache is not None: + # Cache mode: sample gene subset here, look up pre-extracted features + # DataLoader collate wraps strings in tuples; unwrap them + tgt_cell_names = [n[0] if isinstance(n, (tuple, list)) else n for n in batch_data["tgt_cell_id"]] + input_gene_ids = torch.randperm(source.shape[-1], device=device)[:config.infer_top_gene] + cached_z_target = scgpt_cache.lookup(tgt_cell_names, input_gene_ids, device=device) + loss_dict = base_denoiser.train_step( + source, target, perturbation_id, gene_ids, + infer_top_gene=config.infer_top_gene, + cached_z_target=cached_z_target, + cached_gene_ids=input_gene_ids, + ) + else: + loss_dict = base_denoiser.train_step( + source, target, perturbation_id, gene_ids, + infer_top_gene=config.infer_top_gene, + ) + + loss = loss_dict["loss"] + optimizer.zero_grad(set_to_none=True) + accelerator.backward(loss) + optimizer.step() + scheduler.step() + + # === EMA update === + with torch.no_grad(): + decay = config.ema_decay + for ema_p, model_p in zip(ema_model.parameters(), vf.parameters()): + ema_p.lerp_(model_p.data, 1 - decay) + + if iteration % config.print_every == 0: + save_path_ = os.path.join(save_path, f"iteration_{iteration}") + os.makedirs(save_path_, exist_ok=True) + if accelerator.is_main_process: + print(f"Saving iteration {iteration} checkpoint...") + # Save EMA model (used for inference) and training state + save_checkpoint( + model=ema_model, + optimizer=optimizer, + scheduler=scheduler, + iteration=iteration, + eval_score=None, + save_path=save_path_, + is_best=False, + ) + # Evaluate with EMA weights + # Only evaluate at the start and the last checkpoint + if iteration == 0 or iteration + config.print_every >= config.steps: + # Swap EMA weights into denoiser for evaluation + orig_state = copy.deepcopy(vf.state_dict()) + vf.load_state_dict(ema_model.state_dict()) + + eval_score = test( + valid_sampler, denoiser, accelerator, config, vocab, data_manager, + batch_size=config.batch_size, path_dir=save_path_, + ) + + # Restore training weights + vf.load_state_dict(orig_state) + + if accelerator.is_main_process and eval_score is not None: + tb_writer.add_scalar('eval/score', eval_score, iteration) + + # --- Per-iteration loss logging --- + if accelerator.is_main_process: + current_lr = scheduler.get_last_lr()[0] + csv_writer.writerow([ + iteration, loss.item(), + loss_dict["loss_expr"].item(), + loss_dict["loss_latent"].item(), + loss_dict["loss_mmd"].item(), + current_lr, + ]) + if iteration % 100 == 0: + csv_file.flush() + tb_writer.add_scalar('loss/train', loss.item(), iteration) + tb_writer.add_scalar('loss/expr', loss_dict["loss_expr"].item(), iteration) + tb_writer.add_scalar('loss/latent', loss_dict["loss_latent"].item(), iteration) + tb_writer.add_scalar('loss/mmd', loss_dict["loss_mmd"].item(), iteration) + tb_writer.add_scalar('lr', current_lr, iteration) + + accelerator.wait_for_everyone() + + pbar.update(1) + pbar.set_description( + f"loss: {loss.item():.4f} (expr: {loss_dict['loss_expr'].item():.4f}, " + f"latent: {loss_dict['loss_latent'].item():.4f}, " + f"mmd: {loss_dict['loss_mmd'].item():.4f}), iter: {iteration}" + ) + iteration += 1 + if iteration >= config.steps: + break + + # === Close logging === + if accelerator.is_main_process: + csv_file.close() + tb_writer.close() diff --git a/transfer/code/CCFM/src/__init__.py b/transfer/code/CCFM/src/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/transfer/code/CCFM/src/_scdfm_imports.py b/transfer/code/CCFM/src/_scdfm_imports.py new file mode 100644 index 0000000000000000000000000000000000000000..d8e7e583ca294464d0a972635354c88f874399d5 --- /dev/null +++ b/transfer/code/CCFM/src/_scdfm_imports.py @@ -0,0 +1,50 @@ +""" +Central import hub for scDFM modules. +Requires _bootstrap_scdfm to have been imported first (at script entry point). +""" + +import sys + +# Ensure bootstrap has run +if "scdfm_src" not in sys.modules: + import os + sys.path.insert(0, os.path.normpath(os.path.join(os.path.dirname(__file__), ".."))) + import _bootstrap_scdfm + +import scdfm_src.models.origin.layers as _layers +import scdfm_src.models.origin.model as _model +import scdfm_src.flow_matching.path as _fm_path +import scdfm_src.flow_matching.path.scheduler.scheduler as _scheduler +import scdfm_src.utils.utils as _utils +import scdfm_src.tokenizer.gene_tokenizer as _tokenizer +# === scDFM Layers === +GeneadaLN = _layers.GeneadaLN +ContinuousValueEncoder = _layers.ContinuousValueEncoder +GeneEncoder = _layers.GeneEncoder +BatchLabelEncoder = _layers.BatchLabelEncoder +TimestepEmbedder = _layers.TimestepEmbedder +ExprDecoder = _layers.ExprDecoder + +# === scDFM Blocks === +DifferentialTransformerBlock = _model.DifferentialTransformerBlock +PerceiverBlock = _model.PerceiverBlock +DiffPerceiverBlock = _model.DiffPerceiverBlock + +# === scDFM Flow Matching === +AffineProbPath = _fm_path.AffineProbPath +CondOTScheduler = _scheduler.CondOTScheduler + +# === scDFM Utils === +save_checkpoint = _utils.save_checkpoint +load_checkpoint = _utils.load_checkpoint +make_lognorm_poisson_noise = _utils.make_lognorm_poisson_noise +pick_eval_score = _utils.pick_eval_score +process_vocab = _utils.process_vocab +set_requires_grad_for_p_only = _utils.set_requires_grad_for_p_only +get_perturbation_emb = _utils.get_perturbation_emb + +# === scDFM Tokenizer === +GeneVocab = _tokenizer.GeneVocab + +# === scDFM Data === +# Data loading handled separately in CCFM (scDFM data module has heavy deps) diff --git a/transfer/code/CCFM/src/backup_two_stage_ode/config_cascaded.py b/transfer/code/CCFM/src/backup_two_stage_ode/config_cascaded.py new file mode 100644 index 0000000000000000000000000000000000000000..7c02a7bf146b039d3dc801aa98fa9094e10db442 --- /dev/null +++ b/transfer/code/CCFM/src/backup_two_stage_ode/config_cascaded.py @@ -0,0 +1,73 @@ +from dataclasses import dataclass +import os + + +@dataclass +class CascadedFlowConfig: + # === Base (same as scDFM FlowConfig) === + model_type: str = "cascaded" + batch_size: int = 48 + ntoken: int = 512 + d_model: int = 128 + nhead: int = 8 + nlayers: int = 4 + lr: float = 5e-5 + steps: int = 200000 + eta_min: float = 1e-6 + devices: str = "1" + test_only: bool = False + + data_name: str = "norman" + perturbation_function: str = "crisper" + noise_type: str = "Gaussian" + poisson_alpha: float = 0.8 + poisson_target_sum: int = -1 + + print_every: int = 5000 + mode: str = "predict_y" + result_path: str = "./result_online" + fusion_method: str = "differential_perceiver" + infer_top_gene: int = 1000 + n_top_genes: int = 5000 + checkpoint_path: str = "" + gamma: float = 0.5 + split_method: str = "additive" + use_mmd_loss: bool = True + fold: int = 1 + use_negative_edge: bool = True + topk: int = 30 + + # === Cascaded / Latent specific === + scgpt_dim: int = 512 + bottleneck_dim: int = 128 + latent_weight: float = 1.0 + choose_latent_p: float = 0.4 + target_std: float = 1.0 + dh_depth: int = 2 + warmup_batches: int = 200 # batches to collect running stats + + # === scGPT paths === + scgpt_model_dir: str = "transfer/data/scGPT_pretrained" + scgpt_max_seq_len: int = 1200 + scgpt_cache_path: str = "" # Pre-extracted HDF5 path. Empty = online extraction (default) + + # === Inference === + latent_steps: int = 20 + expr_steps: int = 20 + + def __post_init__(self): + if self.data_name == "norman_umi_go_filtered": + self.n_top_genes = 5054 + if self.data_name == "norman": + self.n_top_genes = 5000 + + def make_path(self): + scgpt_mode = "cached" if self.scgpt_cache_path else "online" + exp_name = ( + f"ccfm-{self.data_name}-f{self.fold}" + f"-topk{self.topk}-neg{self.use_negative_edge}" + f"-d{self.d_model}-lr{self.lr}" + f"-lw{self.latent_weight}-lp{self.choose_latent_p}" + f"-{scgpt_mode}" + ) + return os.path.join(self.result_path, exp_name) diff --git a/transfer/code/CCFM/src/backup_two_stage_ode/denoiser.py b/transfer/code/CCFM/src/backup_two_stage_ode/denoiser.py new file mode 100644 index 0000000000000000000000000000000000000000..46423a0cb13b174d8284245b5f8005f038af3c3f --- /dev/null +++ b/transfer/code/CCFM/src/backup_two_stage_ode/denoiser.py @@ -0,0 +1,256 @@ +""" +CascadedDenoiser — Wraps CascadedFlowModel + FrozenScGPTExtractor. + +Implements cascaded time-step sampling (from LatentForcing denoiser_cot.py:121-132) +and two-stage cascaded generation (denoiser_cot.py:224-247). +""" + +import torch +import torch.nn as nn +import torchdiffeq + +from ._scdfm_imports import AffineProbPath, CondOTScheduler, make_lognorm_poisson_noise +from .model.model import CascadedFlowModel +from .data.scgpt_extractor import FrozenScGPTExtractor + + +# Shared flow matching path +flow_path = AffineProbPath(scheduler=CondOTScheduler()) + + +def pairwise_sq_dists(X, Y): + return torch.cdist(X, Y, p=2) ** 2 + + +@torch.no_grad() +def median_sigmas(X, scales=(0.5, 1.0, 2.0, 4.0)): + D2 = pairwise_sq_dists(X, X) + tri = D2[~torch.eye(D2.size(0), dtype=bool, device=D2.device)] + m = torch.median(tri).clamp_min(1e-12) + s2 = torch.tensor(scales, device=X.device) * m + return [float(s.item()) for s in torch.sqrt(s2)] + + +def mmd2_unbiased_multi_sigma(X, Y, sigmas): + m, n = X.size(0), Y.size(0) + Dxx = pairwise_sq_dists(X, X) + Dyy = pairwise_sq_dists(Y, Y) + Dxy = pairwise_sq_dists(X, Y) + vals = [] + for sigma in sigmas: + beta = 1.0 / (2.0 * (sigma ** 2) + 1e-12) + Kxx = torch.exp(-beta * Dxx) + Kyy = torch.exp(-beta * Dyy) + Kxy = torch.exp(-beta * Dxy) + term_xx = (Kxx.sum() - Kxx.diag().sum()) / (m * (m - 1) + 1e-12) + term_yy = (Kyy.sum() - Kyy.diag().sum()) / (n * (n - 1) + 1e-12) + term_xy = Kxy.mean() + vals.append(term_xx + term_yy - 2.0 * term_xy) + return torch.stack(vals).mean() + + +class CascadedDenoiser(nn.Module): + """ + Cascaded denoiser combining CascadedFlowModel with FrozenScGPTExtractor. + + Training: Cascaded time-step sampling (dino_first_cascaded mode). + Inference: Two-stage cascaded generation (latent first, then expression). + """ + + def __init__( + self, + model: CascadedFlowModel, + scgpt_extractor: FrozenScGPTExtractor, + choose_latent_p: float = 0.4, + latent_weight: float = 1.0, + noise_type: str = "Gaussian", + use_mmd_loss: bool = True, + gamma: float = 0.5, + poisson_alpha: float = 0.8, + poisson_target_sum: float = 1e4, + ): + super().__init__() + self.model = model + self.scgpt_extractor = scgpt_extractor + self.choose_latent_p = choose_latent_p + self.latent_weight = latent_weight + self.noise_type = noise_type + self.use_mmd_loss = use_mmd_loss + self.gamma = gamma + self.poisson_alpha = poisson_alpha + self.poisson_target_sum = poisson_target_sum + + def sample_t(self, n: int, device: torch.device): + """ + Cascaded time-step sampling — dino_first_cascaded mode. + (LatentForcing denoiser_cot.py:121-132) + + With probability choose_latent_p: + - Train latent flow: t_latent ~ U(0,1), t_expr = 0, loss_weight_expr = 0 + Otherwise: + - Train expression flow: t_expr ~ U(0,1), t_latent = 1 (clean), loss_weight_latent = 0 + """ + t_latent = torch.rand(n, device=device) + t_expr = torch.rand(n, device=device) + + choose_latent_mask = torch.rand(n, device=device) < self.choose_latent_p + + t_latent = torch.where(choose_latent_mask, t_latent, torch.ones_like(t_latent)) + t_expr = torch.where(choose_latent_mask, torch.zeros_like(t_expr), t_expr) + + w_expr = (~choose_latent_mask).float() + w_latent = choose_latent_mask.float() + + return t_expr, t_latent, w_expr, w_latent + + def _make_expr_noise(self, source: torch.Tensor) -> torch.Tensor: + """Create noise for expression flow.""" + if self.noise_type == "Gaussian": + return torch.randn_like(source) + elif self.noise_type == "Poisson": + return make_lognorm_poisson_noise( + target_log=source, + alpha=self.poisson_alpha, + per_cell_L=self.poisson_target_sum, + ) + else: + raise ValueError(f"Unknown noise_type: {self.noise_type}") + + def train_step( + self, + source: torch.Tensor, # (B, G_full) + target: torch.Tensor, # (B, G_full) + perturbation_id: torch.Tensor, # (B, 2) + gene_ids: torch.Tensor, # (G_full,) + infer_top_gene: int = 1000, + cached_z_target: torch.Tensor = None, # (B, G_sub, scgpt_dim) pre-extracted + cached_gene_ids: torch.Tensor = None, # (G_sub,) gene indices used for cache + ) -> torch.Tensor: + """ + Single training step with cascaded time sampling. + Returns: scalar loss + """ + B = source.shape[0] + device = source.device + + # Random gene subset (same as scDFM) + if cached_gene_ids is not None: + input_gene_ids = cached_gene_ids + else: + input_gene_ids = torch.randperm(source.shape[-1], device=device)[:infer_top_gene] + source_sub = source[:, input_gene_ids] + target_sub = target[:, input_gene_ids] + gene_input = gene_ids[input_gene_ids].unsqueeze(0).expand(B, -1) + + # 1. scGPT features: use cache if available, otherwise extract on-the-fly + if cached_z_target is not None: + z_target = cached_z_target + else: + z_target = self.scgpt_extractor.extract(target_sub, gene_indices=input_gene_ids) # (B, G, scgpt_dim) + + # 2. Cascaded time sampling + t_expr, t_latent, w_expr, w_latent = self.sample_t(B, device) + + # 3. Expression flow path + noise_expr = self._make_expr_noise(source_sub) + path_expr = flow_path.sample(t=t_expr, x_0=noise_expr, x_1=target_sub) + + # 4. Latent flow path + # AffineProbPath.sample expects t as (B,) and broadcasts internally. + # But z_target is (B, G, scgpt_dim) — we need to flatten to 2D, sample, then reshape. + B_l, G_l, D_l = z_target.shape + noise_latent = torch.randn_like(z_target) + z_target_flat = z_target.reshape(B_l, G_l * D_l) + noise_latent_flat = noise_latent.reshape(B_l, G_l * D_l) + path_latent_flat = flow_path.sample(t=t_latent, x_0=noise_latent_flat, x_1=z_target_flat) + + # Wrap path_latent with reshaped tensors + class _LatentPath: + pass + path_latent = _LatentPath() + path_latent.x_t = path_latent_flat.x_t.reshape(B_l, G_l, D_l) + path_latent.dx_t = path_latent_flat.dx_t.reshape(B_l, G_l, D_l) + + # 5. Model forward + pred_v_expr, pred_v_latent = self.model( + gene_input, source_sub, path_expr.x_t, path_latent.x_t, + t_expr, t_latent, perturbation_id, + ) + + # 6. Losses (weighted by cascaded mask) + loss_expr = ((pred_v_expr - path_expr.dx_t) ** 2 * w_expr[:, None]).mean() + loss_latent = ((pred_v_latent - path_latent.dx_t) ** 2 * w_latent[:, None, None]).mean() + loss = loss_expr + self.latent_weight * loss_latent + + # Optional MMD loss on expression (same as scDFM) + if self.use_mmd_loss and w_expr.sum() > 0: + expr_mask = w_expr > 0 + if expr_mask.any(): + x1_hat = ( + path_expr.x_t[expr_mask] + + pred_v_expr[expr_mask] * (1 - t_expr[expr_mask]).unsqueeze(-1) + ) + sigmas = median_sigmas(target_sub[expr_mask], scales=(0.5, 1.0, 2.0, 4.0)) + _mmd_loss = mmd2_unbiased_multi_sigma(x1_hat, target_sub[expr_mask], sigmas) + loss = loss + _mmd_loss * self.gamma + + return loss + + @torch.no_grad() + def generate( + self, + source: torch.Tensor, # (B, G) + perturbation_id: torch.Tensor, # (B, 2) + gene_ids: torch.Tensor, # (B, G) or (G,) + latent_steps: int = 20, + expr_steps: int = 20, + ) -> torch.Tensor: + """ + Joint cascaded generation (LatentForcing denoiser_cot.py:224-280 style). + + Single-loop joint evolution of latent and expression flows: + First half (latent_steps): t_latent 0->1, t_expr=0 (latent evolves) + Second half (expr_steps): t_latent=1, t_expr 0->1 (expression evolves, + latent still updated with near-zero dt) + + Returns: (B, G) generated expression values + """ + B, G = source.shape + device = source.device + scgpt_dim = self.scgpt_extractor.scgpt_d_model + + if gene_ids.dim() == 1: + gene_ids = gene_ids.unsqueeze(0).expand(B, -1) + + # === Timestep schedules (LatentForcing denoiser_cot.py:189-195) === + t_latent_schedule = torch.cat([ + torch.linspace(0, 1, latent_steps + 1, device=device), + torch.ones(expr_steps, device=device), + ]) # length = latent_steps + expr_steps + 1 + t_expr_schedule = torch.cat([ + torch.zeros(latent_steps + 1, device=device), + torch.linspace(0, 1, expr_steps + 1, device=device)[1:], + ]) # length = latent_steps + expr_steps + 1 + + total_steps = latent_steps + expr_steps + + # === Initialize both noise states === + z_t = torch.randn(B, G, scgpt_dim, device=device) + x_t = self._make_expr_noise(source) + + # === Single-loop joint Euler steps (LatentForcing denoiser_cot.py:276-280) === + for i in range(total_steps): + t_lat = t_latent_schedule[i] + t_lat_next = t_latent_schedule[i + 1] + t_exp = t_expr_schedule[i] + t_exp_next = t_expr_schedule[i + 1] + + v_expr, v_latent = self.model( + gene_ids, source, x_t, z_t, + t_exp.expand(B), t_lat.expand(B), perturbation_id, + ) + + x_t = x_t + (t_exp_next - t_exp) * v_expr + z_t = z_t + (t_lat_next - t_lat) * v_latent + + return torch.clamp(x_t, min=0) diff --git a/transfer/code/CCFM/src/backup_two_stage_ode/denoiser_two_stage.py b/transfer/code/CCFM/src/backup_two_stage_ode/denoiser_two_stage.py new file mode 100644 index 0000000000000000000000000000000000000000..dc4f342edcd0adf0bd75c992e2b96c8a867ef759 --- /dev/null +++ b/transfer/code/CCFM/src/backup_two_stage_ode/denoiser_two_stage.py @@ -0,0 +1,269 @@ +""" +CascadedDenoiser — Wraps CascadedFlowModel + FrozenScGPTExtractor. + +Implements cascaded time-step sampling (from LatentForcing denoiser_cot.py:121-132) +and two-stage cascaded generation (denoiser_cot.py:224-247). +""" + +import torch +import torch.nn as nn +import torchdiffeq + +from ._scdfm_imports import AffineProbPath, CondOTScheduler, make_lognorm_poisson_noise +from .model.model import CascadedFlowModel +from .data.scgpt_extractor import FrozenScGPTExtractor + + +# Shared flow matching path +flow_path = AffineProbPath(scheduler=CondOTScheduler()) + + +def pairwise_sq_dists(X, Y): + return torch.cdist(X, Y, p=2) ** 2 + + +@torch.no_grad() +def median_sigmas(X, scales=(0.5, 1.0, 2.0, 4.0)): + D2 = pairwise_sq_dists(X, X) + tri = D2[~torch.eye(D2.size(0), dtype=bool, device=D2.device)] + m = torch.median(tri).clamp_min(1e-12) + s2 = torch.tensor(scales, device=X.device) * m + return [float(s.item()) for s in torch.sqrt(s2)] + + +def mmd2_unbiased_multi_sigma(X, Y, sigmas): + m, n = X.size(0), Y.size(0) + Dxx = pairwise_sq_dists(X, X) + Dyy = pairwise_sq_dists(Y, Y) + Dxy = pairwise_sq_dists(X, Y) + vals = [] + for sigma in sigmas: + beta = 1.0 / (2.0 * (sigma ** 2) + 1e-12) + Kxx = torch.exp(-beta * Dxx) + Kyy = torch.exp(-beta * Dyy) + Kxy = torch.exp(-beta * Dxy) + term_xx = (Kxx.sum() - Kxx.diag().sum()) / (m * (m - 1) + 1e-12) + term_yy = (Kyy.sum() - Kyy.diag().sum()) / (n * (n - 1) + 1e-12) + term_xy = Kxy.mean() + vals.append(term_xx + term_yy - 2.0 * term_xy) + return torch.stack(vals).mean() + + +class CascadedDenoiser(nn.Module): + """ + Cascaded denoiser combining CascadedFlowModel with FrozenScGPTExtractor. + + Training: Cascaded time-step sampling (dino_first_cascaded mode). + Inference: Two-stage cascaded generation (latent first, then expression). + """ + + def __init__( + self, + model: CascadedFlowModel, + scgpt_extractor: FrozenScGPTExtractor, + choose_latent_p: float = 0.4, + latent_weight: float = 1.0, + noise_type: str = "Gaussian", + use_mmd_loss: bool = True, + gamma: float = 0.5, + poisson_alpha: float = 0.8, + poisson_target_sum: float = 1e4, + ): + super().__init__() + self.model = model + self.scgpt_extractor = scgpt_extractor + self.choose_latent_p = choose_latent_p + self.latent_weight = latent_weight + self.noise_type = noise_type + self.use_mmd_loss = use_mmd_loss + self.gamma = gamma + self.poisson_alpha = poisson_alpha + self.poisson_target_sum = poisson_target_sum + + def sample_t(self, n: int, device: torch.device): + """ + Cascaded time-step sampling — dino_first_cascaded mode. + (LatentForcing denoiser_cot.py:121-132) + + With probability choose_latent_p: + - Train latent flow: t_latent ~ U(0,1), t_expr = 0, loss_weight_expr = 0 + Otherwise: + - Train expression flow: t_expr ~ U(0,1), t_latent = 1 (clean), loss_weight_latent = 0 + """ + t_latent = torch.rand(n, device=device) + t_expr = torch.rand(n, device=device) + + choose_latent_mask = torch.rand(n, device=device) < self.choose_latent_p + + t_latent = torch.where(choose_latent_mask, t_latent, torch.ones_like(t_latent)) + t_expr = torch.where(choose_latent_mask, torch.zeros_like(t_expr), t_expr) + + w_expr = (~choose_latent_mask).float() + w_latent = choose_latent_mask.float() + + return t_expr, t_latent, w_expr, w_latent + + def _make_expr_noise(self, source: torch.Tensor) -> torch.Tensor: + """Create noise for expression flow.""" + if self.noise_type == "Gaussian": + return torch.randn_like(source) + elif self.noise_type == "Poisson": + return make_lognorm_poisson_noise( + target_log=source, + alpha=self.poisson_alpha, + per_cell_L=self.poisson_target_sum, + ) + else: + raise ValueError(f"Unknown noise_type: {self.noise_type}") + + def train_step( + self, + source: torch.Tensor, # (B, G_full) + target: torch.Tensor, # (B, G_full) + perturbation_id: torch.Tensor, # (B, 2) + gene_ids: torch.Tensor, # (G_full,) + infer_top_gene: int = 1000, + cached_z_target: torch.Tensor = None, # (B, G_sub, scgpt_dim) pre-extracted + cached_gene_ids: torch.Tensor = None, # (G_sub,) gene indices used for cache + ) -> torch.Tensor: + """ + Single training step with cascaded time sampling. + Returns: scalar loss + """ + B = source.shape[0] + device = source.device + + # Random gene subset (same as scDFM) + if cached_gene_ids is not None: + input_gene_ids = cached_gene_ids + else: + input_gene_ids = torch.randperm(source.shape[-1], device=device)[:infer_top_gene] + source_sub = source[:, input_gene_ids] + target_sub = target[:, input_gene_ids] + gene_input = gene_ids[input_gene_ids].unsqueeze(0).expand(B, -1) + + # 1. scGPT features: use cache if available, otherwise extract on-the-fly + if cached_z_target is not None: + z_target = cached_z_target + else: + z_target = self.scgpt_extractor.extract(target_sub, gene_indices=input_gene_ids) # (B, G, scgpt_dim) + + # 2. Cascaded time sampling + t_expr, t_latent, w_expr, w_latent = self.sample_t(B, device) + + # 3. Expression flow path + noise_expr = self._make_expr_noise(source_sub) + path_expr = flow_path.sample(t=t_expr, x_0=noise_expr, x_1=target_sub) + + # 4. Latent flow path + # AffineProbPath.sample expects t as (B,) and broadcasts internally. + # But z_target is (B, G, scgpt_dim) — we need to flatten to 2D, sample, then reshape. + B_l, G_l, D_l = z_target.shape + noise_latent = torch.randn_like(z_target) + z_target_flat = z_target.reshape(B_l, G_l * D_l) + noise_latent_flat = noise_latent.reshape(B_l, G_l * D_l) + path_latent_flat = flow_path.sample(t=t_latent, x_0=noise_latent_flat, x_1=z_target_flat) + + # Wrap path_latent with reshaped tensors + class _LatentPath: + pass + path_latent = _LatentPath() + path_latent.x_t = path_latent_flat.x_t.reshape(B_l, G_l, D_l) + path_latent.dx_t = path_latent_flat.dx_t.reshape(B_l, G_l, D_l) + + # 5. Model forward + pred_v_expr, pred_v_latent = self.model( + gene_input, source_sub, path_expr.x_t, path_latent.x_t, + t_expr, t_latent, perturbation_id, + ) + + # 6. Losses (weighted by cascaded mask) + loss_expr = ((pred_v_expr - path_expr.dx_t) ** 2 * w_expr[:, None]).mean() + loss_latent = ((pred_v_latent - path_latent.dx_t) ** 2 * w_latent[:, None, None]).mean() + loss = loss_expr + self.latent_weight * loss_latent + + # Optional MMD loss on expression (same as scDFM) + if self.use_mmd_loss and w_expr.sum() > 0: + expr_mask = w_expr > 0 + if expr_mask.any(): + x1_hat = ( + path_expr.x_t[expr_mask] + + pred_v_expr[expr_mask] * (1 - t_expr[expr_mask]).unsqueeze(-1) + ) + sigmas = median_sigmas(target_sub[expr_mask], scales=(0.5, 1.0, 2.0, 4.0)) + _mmd_loss = mmd2_unbiased_multi_sigma(x1_hat, target_sub[expr_mask], sigmas) + loss = loss + _mmd_loss * self.gamma + + return loss + + @torch.no_grad() + def generate( + self, + source: torch.Tensor, # (B, G) + perturbation_id: torch.Tensor, # (B, 2) + gene_ids: torch.Tensor, # (B, G) or (G,) + latent_steps: int = 20, + expr_steps: int = 20, + ) -> torch.Tensor: + """ + Two-stage cascaded generation (LatentForcing denoiser_cot.py:224-247). + + Stage 1: Generate latent (per-gene scGPT features) + t_latent: 0->1, t_expr=0 (expression flow inactive) + Stage 2: Use generated latent to guide expression generation + t_expr: 0->1, t_latent=1 (latent is "clean"/generated) + + Returns: (B, G) generated expression values + """ + B, G = source.shape + device = source.device + scgpt_dim = self.scgpt_extractor.scgpt_d_model + + if gene_ids.dim() == 1: + gene_ids = gene_ids.unsqueeze(0).expand(B, -1) + + # === Stage 1: Generate Latent === + z_noise = torch.randn(B, G, scgpt_dim, device=device) + + def latent_vf(t, z_t): + t_latent = t.expand(B) + t_expr = torch.zeros(B, device=device) + _, v_latent = self.model( + gene_ids, source, source, z_t, + t_expr, t_latent, perturbation_id, + ) + return v_latent + + z_traj = torchdiffeq.odeint( + latent_vf, + z_noise, + torch.linspace(0, 1, latent_steps, device=device), + atol=1e-4, + rtol=1e-4, + method="rk4", + ) + z_generated = z_traj[-1] # (B, G, scgpt_dim) + + # === Stage 2: Generate Expression guided by latent === + expr_noise = self._make_expr_noise(source) + + def expr_vf(t, x_t): + t_expr = t.expand(B) + t_latent = torch.ones(B, device=device) + v_expr, _ = self.model( + gene_ids, source, x_t, z_generated, + t_expr, t_latent, perturbation_id, + ) + return v_expr + + x_traj = torchdiffeq.odeint( + expr_vf, + expr_noise, + torch.linspace(0, 1, expr_steps, device=device), + atol=1e-4, + rtol=1e-4, + method="rk4", + ) + x_generated = x_traj[-1] # (B, G) + + return torch.clamp(x_generated, min=0) diff --git a/transfer/code/CCFM/src/backup_two_stage_ode/model.py b/transfer/code/CCFM/src/backup_two_stage_ode/model.py new file mode 100644 index 0000000000000000000000000000000000000000..d7b38974ec0e79c11f5a89263d930d13d3125963 --- /dev/null +++ b/transfer/code/CCFM/src/backup_two_stage_ode/model.py @@ -0,0 +1,223 @@ +""" +CascadedFlowModel — Main backbone for Cascaded Conditioned Flow Matching. + +Architecture mirrors LatentForcing's model_cot.py: +1. Dual-stream embedding (expression + latent) with element-wise addition +2. Unified conditioning via AdaLN: c = t_expr_emb + t_latent_emb + pert_emb +3. Shared backbone (reusing scDFM's DiffPerceiver/Perceiver blocks) +4. Separate decoder heads: ExprDecoder (reused) + LatentDecoder (new) +""" + +import torch +import torch.nn as nn +from torch import Tensor +from typing import Optional, Tuple + +from .layers import LatentEmbedder, LatentDecoder +from .._scdfm_imports import ( + GeneadaLN, + ContinuousValueEncoder, + GeneEncoder, + BatchLabelEncoder, + TimestepEmbedder, + ExprDecoder, + DifferentialTransformerBlock, + PerceiverBlock, + DiffPerceiverBlock, +) + + +class CascadedFlowModel(nn.Module): + """ + Cascaded Flow Model for single-cell perturbation prediction. + + Inputs: + gene_id: (B, G) gene token IDs + cell_1: (B, G) source (control) expression + x_t: (B, G) noised target expression (expression flow) + z_t: (B, G, scgpt_dim) noised scGPT features (latent flow) + t_expr: (B,) expression flow timestep + t_latent: (B,) latent flow timestep + perturbation_id: (B, 2) perturbation token IDs + + Outputs: + pred_v_expr: (B, G) predicted expression velocity + pred_v_latent: (B, G, scgpt_dim) predicted latent velocity + """ + + def __init__( + self, + ntoken: int = 6000, + d_model: int = 128, + nhead: int = 8, + d_hid: int = 512, + nlayers: int = 4, + dropout: float = 0.1, + fusion_method: str = "differential_perceiver", + perturbation_function: str = "crisper", + use_perturbation_interaction: bool = True, + mask_path: str = None, + # Latent-specific + scgpt_dim: int = 512, + bottleneck_dim: int = 128, + dh_depth: int = 2, + ): + super().__init__() + self.d_model = d_model + self.fusion_method = fusion_method + self.perturbation_function = perturbation_function + + # === Timestep embedders (separate for expr and latent, like model_cot.py) === + self.t_expr_embedder = TimestepEmbedder(d_model) + self.t_latent_embedder = TimestepEmbedder(d_model) + + # === Perturbation embedder === + self.perturbation_embedder = BatchLabelEncoder(ntoken, d_model) + + # === Expression stream (reused from scDFM) === + self.value_encoder_1 = ContinuousValueEncoder(d_model, dropout) + self.value_encoder_2 = ContinuousValueEncoder(d_model, dropout) + self.encoder = GeneEncoder( + ntoken, d_model, + use_perturbation_interaction=use_perturbation_interaction, + mask_path=mask_path, + ) + self.use_perturbation_interaction = use_perturbation_interaction + self.fusion_layer = nn.Sequential( + nn.Linear(2 * d_model, d_model), + nn.GELU(), + nn.Linear(d_model, d_model), + nn.LayerNorm(d_model), + ) + + # === Latent stream embedder (new, analogous to dino_embedder) === + self.latent_embedder = LatentEmbedder(scgpt_dim, bottleneck_dim, d_model) + + # === Shared backbone blocks === + if fusion_method == "differential_transformer": + self.blocks = nn.ModuleList([ + DifferentialTransformerBlock(d_model, nhead, i, mlp_ratio=4.0) + for i in range(nlayers) + ]) + elif fusion_method == "differential_perceiver": + self.blocks = nn.ModuleList([ + DiffPerceiverBlock(d_model, nhead, i, mlp_ratio=4.0) + for i in range(nlayers) + ]) + elif fusion_method == "perceiver": + self.blocks = nn.ModuleList([ + PerceiverBlock(d_model, d_model, heads=nhead, mlp_ratio=4.0, dropout=0.1) + for _ in range(nlayers) + ]) + else: + raise ValueError(f"Invalid fusion method: {fusion_method}") + + # === Per-layer gene AdaLN + adapter === + self.gene_adaLN = nn.ModuleList([ + GeneadaLN(d_model, dropout) for _ in range(nlayers) + ]) + self.adapter_layer = nn.ModuleList([ + nn.Sequential( + nn.Linear(2 * d_model, d_model), + nn.LeakyReLU(), + nn.Dropout(dropout), + nn.Linear(d_model, d_model), + nn.LeakyReLU(), + ) + for _ in range(nlayers) + ]) + + # === Expression decoder head (reused from scDFM) === + self.final_layer = ExprDecoder(d_model, explicit_zero_prob=False, use_batch_labels=True) + + # === Latent decoder head (new, analogous to dh_blocks_dino + final_layer_dino) === + self.latent_decoder = LatentDecoder( + d_model=d_model, + scgpt_dim=scgpt_dim, + dh_depth=dh_depth, + num_heads=max(nhead // 2, 1), + hidden_size_c=d_model, + ) + + self.initialize_weights() + + def initialize_weights(self): + def _basic_init(module): + if isinstance(module, nn.Linear): + torch.nn.init.xavier_uniform_(module.weight) + if module.bias is not None: + nn.init.constant_(module.bias, 0) + self.apply(_basic_init) + + def get_perturbation_emb( + self, + perturbation_id: Optional[Tensor] = None, + perturbation_emb: Optional[Tensor] = None, + cell_1: Optional[Tensor] = None, + ) -> Tensor: + """Get perturbation embedding, replicating scDFM logic.""" + assert perturbation_emb is None or perturbation_id is None + if perturbation_id is not None: + if self.perturbation_function == "crisper": + perturbation_emb = self.encoder(perturbation_id) + else: + perturbation_emb = self.perturbation_embedder(perturbation_id) + perturbation_emb = perturbation_emb.mean(1) # (B, d) + elif perturbation_emb is not None: + perturbation_emb = perturbation_emb.to(cell_1.device, dtype=cell_1.dtype) + if perturbation_emb.dim() == 1: + perturbation_emb = perturbation_emb.unsqueeze(0) + if perturbation_emb.size(0) == 1: + perturbation_emb = perturbation_emb.expand(cell_1.shape[0], -1).contiguous() + perturbation_emb = self.perturbation_embedder.enc_norm(perturbation_emb) + return perturbation_emb + + def forward( + self, + gene_id: Tensor, # (B, G) + cell_1: Tensor, # (B, G) source expression + x_t: Tensor, # (B, G) noised expression + z_t: Tensor, # (B, G, scgpt_dim) noised latent features + t_expr: Tensor, # (B,) + t_latent: Tensor, # (B,) + perturbation_id: Optional[Tensor] = None, # (B, 2) + ) -> Tuple[Tensor, Tensor]: + if t_expr.dim() == 0: + t_expr = t_expr.repeat(cell_1.size(0)) + if t_latent.dim() == 0: + t_latent = t_latent.repeat(cell_1.size(0)) + + # === 1. Expression stream embedding (reused from scDFM) === + gene_emb = self.encoder(gene_id) # (B, G, d) + val_emb_1 = self.value_encoder_1(cell_1) + gene_emb # (B, G, d) + val_emb_2 = self.value_encoder_2(x_t) + gene_emb # (B, G, d) + expr_tokens = self.fusion_layer(torch.cat([val_emb_1, val_emb_2], dim=-1)) # (B, G, d) + + # === 2. Latent stream embedding (new, analogous to dino_embedder) === + latent_tokens = self.latent_embedder(z_t) # (B, G, d) + + # === 3. Element-wise addition (model_cot.py line 414) === + x = expr_tokens + latent_tokens # (B, G, d) + + # === 4. Conditioning vector (model_cot.py line 409) === + t_expr_emb = self.t_expr_embedder(t_expr) # (B, d) + t_latent_emb = self.t_latent_embedder(t_latent) # (B, d) + pert_emb = self.get_perturbation_emb(perturbation_id, cell_1=cell_1) # (B, d) + c = t_expr_emb + t_latent_emb + pert_emb # (B, d) + + # === 5. Shared backbone (reused scDFM blocks) === + for i, block in enumerate(self.blocks): + x = self.gene_adaLN[i](gene_emb, x) + pert_exp = pert_emb[:, None, :].expand(-1, x.size(1), -1) + x = torch.cat([x, pert_exp], dim=-1) + x = self.adapter_layer[i](x) + x = block(x, val_emb_2, c) + + # === 6a. Expression decoder head (reused) === + x_with_pert = torch.cat([x, pert_emb[:, None, :].expand(-1, x.size(1), -1)], dim=-1) + pred_v_expr = self.final_layer(x_with_pert)["pred"] # (B, G) + + # === 6b. Latent decoder head (new, analogous to dh_blocks_dino) === + pred_v_latent = self.latent_decoder(x, c) # (B, G, scgpt_dim) + + return pred_v_expr, pred_v_latent diff --git a/transfer/code/CCFM/src/backup_two_stage_ode/run_cascaded.py b/transfer/code/CCFM/src/backup_two_stage_ode/run_cascaded.py new file mode 100644 index 0000000000000000000000000000000000000000..e5bdb395cfee43e8e7cf14d5661ea125669dbff1 --- /dev/null +++ b/transfer/code/CCFM/src/backup_two_stage_ode/run_cascaded.py @@ -0,0 +1,391 @@ +""" +Training and evaluation entry point for CCFM (Cascaded Conditioned Flow Matching). +Based on scDFM's run.py with cascaded denoiser integration. + +Conditioning signals: control expression + perturbation_id. +scGPT latent features are an auxiliary generation target (like DINO in LatentForcing), +generated from noise at inference — not a conditioning signal. +""" + +import sys +import os + +# Set up paths — CCFM project root must be on sys.path for config/ and src/ imports +_PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) +sys.path.insert(0, _PROJECT_ROOT) + +# Bootstrap scDFM imports (must happen before any CCFM src imports) +import _bootstrap_scdfm # noqa: F401 + +import torch +import torch.nn as nn +import tyro +import tqdm +import numpy as np +import pandas as pd +import anndata as ad +import scanpy as sc +from torch.utils.data import DataLoader +from tqdm import trange +from accelerate import Accelerator, DistributedDataParallelKwargs + +from config.config_cascaded import CascadedFlowConfig as Config +from src.data.data import get_data_classes +from src.model.model import CascadedFlowModel +from src.data.scgpt_extractor import FrozenScGPTExtractor +from src.data.scgpt_cache import ScGPTFeatureCache +from src.denoiser import CascadedDenoiser +from src.utils import ( + save_checkpoint, + load_checkpoint, + pick_eval_score, + process_vocab, + set_requires_grad_for_p_only, + GeneVocab, +) + +from cell_eval import MetricsEvaluator + +# Resolve scDFM directory paths +_REPO_ROOT = os.path.dirname(_PROJECT_ROOT) # transfer/code/ + + +@torch.inference_mode() +def test(data_sampler, denoiser, accelerator, config, vocab, data_manager, + batch_size=128, path_dir="./"): + """Evaluate: generate predictions and compute cell-eval metrics.""" + device = accelerator.device + gene_ids_test = vocab.encode(list(data_sampler.adata.var_names)) + gene_ids_test = torch.tensor(gene_ids_test, dtype=torch.long, device=device) + + perturbation_name_list = data_sampler._perturbation_covariates + control_data = data_sampler.get_control_data() + inverse_dict = {v: str(k) for k, v in data_manager.perturbation_dict.items()} + + all_pred_expressions = [control_data["src_cell_data"]] + obs_perturbation_name_pred = ["control"] * control_data["src_cell_data"].shape[0] + all_target_expressions = [control_data["src_cell_data"]] + obs_perturbation_name_real = ["control"] * control_data["src_cell_data"].shape[0] + + print("perturbation_name_list:", len(perturbation_name_list)) + for perturbation_name in perturbation_name_list: + perturbation_data = data_sampler.get_perturbation_data(perturbation_name) + target = perturbation_data["tgt_cell_data"] + perturbation_id = perturbation_data["condition_id"] + source = control_data["src_cell_data"].to(device) + perturbation_id = perturbation_id.to(device) + + if config.perturbation_function == "crisper": + perturbation_name_crisper = [ + inverse_dict[int(p_id)] for p_id in perturbation_id[0].cpu().numpy() + ] + perturbation_id = torch.tensor( + vocab.encode(perturbation_name_crisper), dtype=torch.long, device=device + ) + perturbation_id = perturbation_id.repeat(source.shape[0], 1) + + idx = torch.randperm(source.shape[0]) + source = source[idx] + N = 128 + source = source[:N] + + pred_expressions = [] + for i in trange(0, N, batch_size, desc=perturbation_name): + batch_source = source[i : i + batch_size] + batch_pert_id = perturbation_id[0].repeat(batch_source.shape[0], 1).to(device) + + # Get the underlying model for generation + model = denoiser.module if hasattr(denoiser, "module") else denoiser + + pred = model.generate( + batch_source, + batch_pert_id, + gene_ids_test, + latent_steps=config.latent_steps, + expr_steps=config.expr_steps, + ) + pred_expressions.append(pred) + + pred_expressions = torch.cat(pred_expressions, dim=0).cpu().numpy() + all_pred_expressions.append(pred_expressions) + all_target_expressions.append(target) + obs_perturbation_name_pred.extend([perturbation_name] * pred_expressions.shape[0]) + obs_perturbation_name_real.extend([perturbation_name] * target.shape[0]) + + all_pred_expressions = np.concatenate(all_pred_expressions, axis=0) + all_target_expressions = np.concatenate(all_target_expressions, axis=0) + obs_pred = pd.DataFrame({"perturbation": obs_perturbation_name_pred}) + obs_real = pd.DataFrame({"perturbation": obs_perturbation_name_real}) + pred_adata = ad.AnnData(X=all_pred_expressions, obs=obs_pred) + real_adata = ad.AnnData(X=all_target_expressions, obs=obs_real) + + eval_score = None + if accelerator.is_main_process: + evaluator = MetricsEvaluator( + adata_pred=pred_adata, + adata_real=real_adata, + control_pert="control", + pert_col="perturbation", + num_threads=32, + ) + results, agg_results = evaluator.compute() + results.write_csv(os.path.join(path_dir, "results.csv")) + agg_results.write_csv(os.path.join(path_dir, "agg_results.csv")) + pred_adata.write_h5ad(os.path.join(path_dir, "pred.h5ad")) + real_adata.write_h5ad(os.path.join(path_dir, "real.h5ad")) + eval_score = pick_eval_score(agg_results, "mse") + print(f"Current evaluation score: {eval_score:.4f}") + + return eval_score + + +if __name__ == "__main__": + config = tyro.cli(Config) + + ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=True) + accelerator = Accelerator(kwargs_handlers=[ddp_kwargs]) + if accelerator.is_main_process: + print(config) + save_path = config.make_path() + os.makedirs(save_path, exist_ok=True) + device = accelerator.device + + # === Data loading (reuse scDFM) === + Data, PerturbationDataset, TrainSampler, TestDataset = get_data_classes() + + scdfm_data_path = os.path.join(_REPO_ROOT, "scDFM", "data") + data_manager = Data(scdfm_data_path) + data_manager.load_data(config.data_name) + + # Convert var_names from Ensembl IDs to gene symbols if needed. + # scDFM vocab and perturbation encoding both expect gene symbols as var_names. + if "gene_name" in data_manager.adata.var.columns and data_manager.adata.var_names[0].startswith("ENSG"): + data_manager.adata.var_names = data_manager.adata.var["gene_name"].values + data_manager.adata.var_names_make_unique() + if accelerator.is_main_process: + print(f"Converted var_names to gene symbols, sample: {list(data_manager.adata.var_names[:5])}") + + data_manager.process_data( + n_top_genes=config.n_top_genes, + split_method=config.split_method, + fold=config.fold, + use_negative_edge=config.use_negative_edge, + k=config.topk, + ) + train_sampler, valid_sampler, _ = data_manager.load_flow_data(batch_size=config.batch_size) + + train_dataset = PerturbationDataset(train_sampler, config.batch_size) + dataloader = DataLoader( + train_dataset, batch_size=1, shuffle=False, + num_workers=8, pin_memory=True, persistent_workers=True, + ) + + # === Build mask path === + if config.use_negative_edge: + mask_path = os.path.join( + data_manager.data_path, data_manager.data_name, + f"mask_fold_{config.fold}topk_{config.topk}{config.split_method}_negative_edge.pt", + ) + else: + mask_path = os.path.join( + data_manager.data_path, data_manager.data_name, + f"mask_fold_{config.fold}topk_{config.topk}{config.split_method}.pt", + ) + + # === Vocab === + orig_cwd = os.getcwd() + os.chdir(os.path.join(_REPO_ROOT, "scDFM")) + vocab = process_vocab(data_manager, config) + os.chdir(orig_cwd) + + # Vocab is built from var_names (may be Ensembl IDs or gene symbols) + gene_ids = vocab.encode(list(data_manager.adata.var_names)) + gene_ids = torch.tensor(gene_ids, dtype=torch.long, device=device) + + # === Build CascadedFlowModel === + vf = CascadedFlowModel( + ntoken=len(vocab), + d_model=config.d_model, + nhead=config.nhead, + d_hid=config.d_model * 4, + nlayers=config.nlayers, + fusion_method=config.fusion_method, + perturbation_function=config.perturbation_function, + mask_path=mask_path, + scgpt_dim=config.scgpt_dim, + bottleneck_dim=config.bottleneck_dim, + dh_depth=config.dh_depth, + ) + + # === Build FrozenScGPTExtractor === + # var_names have been converted to gene symbols above, matching scGPT vocab. + hvg_gene_names = list(data_manager.adata.var_names) + scgpt_model_dir = os.path.join( + os.path.dirname(_REPO_ROOT), # transfer/ + config.scgpt_model_dir.replace("transfer/", ""), + ) + scgpt_extractor = FrozenScGPTExtractor( + model_dir=scgpt_model_dir, + hvg_gene_names=hvg_gene_names, + device=device, + max_seq_len=config.scgpt_max_seq_len, + target_std=config.target_std, + warmup_batches=config.warmup_batches, + ) + scgpt_extractor = scgpt_extractor.to(device) + + # === Build CascadedDenoiser === + denoiser = CascadedDenoiser( + model=vf, + scgpt_extractor=scgpt_extractor, + choose_latent_p=config.choose_latent_p, + latent_weight=config.latent_weight, + noise_type=config.noise_type, + use_mmd_loss=config.use_mmd_loss, + gamma=config.gamma, + poisson_alpha=config.poisson_alpha, + poisson_target_sum=config.poisson_target_sum, + ) + + # === Load scGPT cache if configured === + scgpt_cache = None + if config.scgpt_cache_path: + scgpt_cache = ScGPTFeatureCache( + config.scgpt_cache_path, + target_std=config.target_std, + ) + if accelerator.is_main_process: + print(f"Using pre-extracted scGPT cache: {config.scgpt_cache_path}") + print(f" Cache shape: {scgpt_cache.features.shape}, cells: {len(scgpt_cache.name_to_idx)}") + + # === Optimizer & Scheduler (only for trainable params) === + save_path = config.make_path() + optimizer = torch.optim.Adam(vf.parameters(), lr=config.lr) + scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( + optimizer, T_max=config.steps, eta_min=config.eta_min, + ) + + if config.checkpoint_path != "": + _, _ = load_checkpoint(config.checkpoint_path, vf, optimizer, scheduler) + + # === Prepare with accelerator === + denoiser = accelerator.prepare(denoiser) + optimizer, scheduler, dataloader = accelerator.prepare(optimizer, scheduler, dataloader) + + inverse_dict = {v: str(k) for k, v in data_manager.perturbation_dict.items()} + + # === Test-only mode === + if config.test_only: + eval_path = os.path.join(save_path, "eval_only") + os.makedirs(eval_path, exist_ok=True) + if accelerator.is_main_process: + print(f"Test-only mode. Saving results to {eval_path}") + eval_score = test( + valid_sampler, denoiser, accelerator, config, vocab, data_manager, + batch_size=config.batch_size, path_dir=eval_path, + ) + if accelerator.is_main_process and eval_score is not None: + print(f"Final evaluation score: {eval_score:.4f}") + sys.exit(0) + + # === Loss logging (CSV + TensorBoard) === + import csv + from torch.utils.tensorboard import SummaryWriter + if accelerator.is_main_process: + os.makedirs(save_path, exist_ok=True) + csv_file = open(os.path.join(save_path, 'loss_curve.csv'), 'w', newline='') + csv_writer = csv.writer(csv_file) + csv_writer.writerow(['iteration', 'loss', 'lr']) + tb_writer = SummaryWriter(log_dir=os.path.join(save_path, 'tb_logs')) + + # === Training loop === + pbar = tqdm.tqdm(total=config.steps) + iteration = 0 + + while iteration < config.steps: + for batch_data in dataloader: + source = batch_data["src_cell_data"].squeeze(0) + target = batch_data["tgt_cell_data"].squeeze(0) + perturbation_id = batch_data["condition_id"].squeeze(0).to(device) + + if config.perturbation_function == "crisper": + perturbation_name = [ + inverse_dict[int(p_id)] for p_id in perturbation_id[0].cpu().numpy() + ] + perturbation_id = torch.tensor( + vocab.encode(perturbation_name), dtype=torch.long, device=device + ) + perturbation_id = perturbation_id.repeat(source.shape[0], 1) + + # Get the underlying denoiser for train_step + base_denoiser = denoiser.module if hasattr(denoiser, "module") else denoiser + base_denoiser.model.train() + + if scgpt_cache is not None: + # Cache mode: sample gene subset here, look up pre-extracted features + # DataLoader collate wraps strings in tuples; unwrap them + tgt_cell_names = [n[0] if isinstance(n, (tuple, list)) else n for n in batch_data["tgt_cell_id"]] + input_gene_ids = torch.randperm(source.shape[-1], device=device)[:config.infer_top_gene] + cached_z_target = scgpt_cache.lookup(tgt_cell_names, input_gene_ids, device=device) + loss = base_denoiser.train_step( + source, target, perturbation_id, gene_ids, + infer_top_gene=config.infer_top_gene, + cached_z_target=cached_z_target, + cached_gene_ids=input_gene_ids, + ) + else: + loss = base_denoiser.train_step( + source, target, perturbation_id, gene_ids, + infer_top_gene=config.infer_top_gene, + ) + + optimizer.zero_grad(set_to_none=True) + accelerator.backward(loss) + optimizer.step() + scheduler.step() + + if iteration % config.print_every == 0: + save_path_ = os.path.join(save_path, f"iteration_{iteration}") + os.makedirs(save_path_, exist_ok=True) + if accelerator.is_main_process: + print(f"Saving iteration {iteration} checkpoint...") + unwrapped_model = accelerator.unwrap_model(denoiser).model + save_checkpoint( + model=unwrapped_model, + optimizer=optimizer, + scheduler=scheduler, + iteration=iteration, + eval_score=None, + save_path=save_path_, + is_best=False, + ) + # Only evaluate at the start and the last checkpoint + if iteration == 0 or iteration + config.print_every >= config.steps: + eval_score = test( + valid_sampler, denoiser, accelerator, config, vocab, data_manager, + batch_size=config.batch_size, path_dir=save_path_, + ) + if accelerator.is_main_process and eval_score is not None: + tb_writer.add_scalar('eval/score', eval_score, iteration) + + # --- Per-iteration loss logging --- + if accelerator.is_main_process: + current_lr = scheduler.get_last_lr()[0] + csv_writer.writerow([iteration, loss.item(), current_lr]) + if iteration % 100 == 0: + csv_file.flush() + tb_writer.add_scalar('loss/train', loss.item(), iteration) + tb_writer.add_scalar('lr', current_lr, iteration) + + accelerator.wait_for_everyone() + + pbar.update(1) + pbar.set_description(f"loss: {loss.item():.4f}, iter: {iteration}") + iteration += 1 + if iteration >= config.steps: + break + + # === Close logging === + if accelerator.is_main_process: + csv_file.close() + tb_writer.close() diff --git a/transfer/code/CCFM/src/data/__init__.py b/transfer/code/CCFM/src/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/transfer/code/CCFM/src/data/data.py b/transfer/code/CCFM/src/data/data.py new file mode 100644 index 0000000000000000000000000000000000000000..404599834bdc064535c00cb8967e9967d1c9e43f --- /dev/null +++ b/transfer/code/CCFM/src/data/data.py @@ -0,0 +1,63 @@ +""" +Data loading for CCFM. +Imports scDFM Data/PerturbationDataset by temporarily swapping sys.modules +so that scDFM's 'src.*' packages are visible during import. +""" + +import sys +import os +import types + +_SCDFM_ROOT = os.path.normpath( + os.path.join(os.path.dirname(__file__), "..", "..", "..", "scDFM") +) + +# Cache to avoid repeated imports +_cached_classes = {} + + +def get_data_classes(): + """Lazily import scDFM data classes with proper module isolation.""" + if _cached_classes: + return ( + _cached_classes["Data"], + _cached_classes["PerturbationDataset"], + _cached_classes["TrainSampler"], + _cached_classes["TestDataset"], + ) + + # Save CCFM's src modules + saved = {} + for key in list(sys.modules.keys()): + if key == "src" or key.startswith("src."): + saved[key] = sys.modules.pop(key) + + # Ensure __init__.py exists for scDFM data_process + for d in ["src", "src/data_process", "src/utils", "src/tokenizer"]: + init_path = os.path.join(_SCDFM_ROOT, d, "__init__.py") + if not os.path.exists(init_path): + os.makedirs(os.path.dirname(init_path), exist_ok=True) + with open(init_path, "w") as f: + f.write("# Auto-created by CCFM\n") + + sys.path.insert(0, _SCDFM_ROOT) + try: + from src.data_process.data import Data, PerturbationDataset, TrainSampler, TestDataset + _cached_classes["Data"] = Data + _cached_classes["PerturbationDataset"] = PerturbationDataset + _cached_classes["TrainSampler"] = TrainSampler + _cached_classes["TestDataset"] = TestDataset + finally: + # Remove scDFM's src.* entries + for key in list(sys.modules.keys()): + if (key == "src" or key.startswith("src.")) and not key.startswith("scdfm_"): + del sys.modules[key] + + # Restore CCFM's src modules + for key, mod in saved.items(): + sys.modules[key] = mod + + if _SCDFM_ROOT in sys.path: + sys.path.remove(_SCDFM_ROOT) + + return Data, PerturbationDataset, TrainSampler, TestDataset diff --git a/transfer/code/CCFM/src/data/scgpt_cache.py b/transfer/code/CCFM/src/data/scgpt_cache.py new file mode 100644 index 0000000000000000000000000000000000000000..6d34a38a4a0c1ca9e48f55d24e31cabac91c1e0c --- /dev/null +++ b/transfer/code/CCFM/src/data/scgpt_cache.py @@ -0,0 +1,82 @@ +""" +ScGPTFeatureCache — Load pre-extracted scGPT features from HDF5. + +Replaces on-the-fly FrozenScGPTExtractor during training when a cache file exists. +""" + +import h5py +import numpy as np +import torch + + +class ScGPTFeatureCache: + """ + Loads pre-extracted per-gene scGPT features from HDF5 and provides + batch lookup by cell name and gene indices. + + HDF5 layout: + /features (N, G_full, scgpt_dim) float16 + /norm_mean (scgpt_dim,) float32 + /norm_var (scgpt_dim,) float32 + /cell_names (N,) string + """ + + def __init__(self, h5_path: str, target_std: float = 1.0): + self.h5_path = h5_path + self.target_std = target_std + + self.h5 = h5py.File(h5_path, "r") + self.features = self.h5["features"] # lazy dataset, shape (N, G, D) + self.norm_mean = torch.from_numpy(self.h5["norm_mean"][:]).float() # (D,) + self.norm_var = torch.from_numpy(self.h5["norm_var"][:]).float() # (D,) + + # Build cell_name -> row index mapping + cell_names = self.h5["cell_names"].asstr()[:] + self.name_to_idx = {name: i for i, name in enumerate(cell_names)} + + def lookup(self, cell_names, gene_indices, device=None) -> torch.Tensor: + """ + Retrieve pre-extracted features for a batch. + + Args: + cell_names: list of str, cell identifiers from batch + gene_indices: (G_sub,) tensor, gene subset indices + device: target torch device + + Returns: + (B, G_sub, D) tensor, normalized features + """ + # Map cell names to HDF5 row indices + row_indices = np.array([self.name_to_idx[n] for n in cell_names]) + + # h5py fancy indexing requires sorted, unique indices + unique_indices, inverse = np.unique(row_indices, return_inverse=True) + raw = self.features[unique_indices.tolist()] # (U, G_full, D) as numpy + + # Map back to original batch order (handles duplicates) + raw = raw[inverse] + + # Select gene subset + gene_idx_np = gene_indices.cpu().numpy() + raw = raw[:, gene_idx_np, :] # (B, G_sub, D) + + z = torch.from_numpy(raw.astype(np.float32)) + + # Normalize: (x - mean) / sqrt(var) * target_std + eps = 1e-6 + z = (z - self.norm_mean) / (self.norm_var.sqrt() + eps) + z = z * self.target_std + + if device is not None: + z = z.to(device) + + return z + + def close(self): + self.h5.close() + + def __del__(self): + try: + self.h5.close() + except Exception: + pass diff --git a/transfer/code/CCFM/src/data/scgpt_extractor.py b/transfer/code/CCFM/src/data/scgpt_extractor.py new file mode 100644 index 0000000000000000000000000000000000000000..54b0265091ad08eeeb408b1e4cb6eecefe4b96dd --- /dev/null +++ b/transfer/code/CCFM/src/data/scgpt_extractor.py @@ -0,0 +1,284 @@ +""" +FrozenScGPTExtractor — Frozen scGPT model for on-the-fly per-gene feature extraction. + +Analogous to LatentForcing's dinov2_hf.py RAE class: +- Frozen encoder (no gradients) +- Running statistics for normalization +- Variance matching to align scale with expression embeddings +""" + +import sys +import os +import json +import logging +import warnings +from typing import List, Optional + +import torch +import torch.nn as nn +import numpy as np +import types + +# Set up scGPT imports — create minimal package stubs to avoid scgpt/__init__.py +# pulling in heavy dependencies (datasets, scbank, etc.) +_SCGPT_ROOT = os.path.normpath(os.path.join(os.path.dirname(__file__), "..", "..", "..", "scGPT")) +if _SCGPT_ROOT not in sys.path: + sys.path.insert(0, _SCGPT_ROOT) + +# Create minimal package stubs +for pkg, subdir in [ + ("scgpt", "scgpt"), + ("scgpt.model", "scgpt/model"), + ("scgpt.utils", "scgpt/utils"), +]: + if pkg not in sys.modules: + mod = types.ModuleType(pkg) + mod.__path__ = [os.path.join(_SCGPT_ROOT, subdir)] + sys.modules[pkg] = mod + +# Add logger stub +if not hasattr(sys.modules["scgpt"], "logger"): + sys.modules["scgpt"].logger = logging.getLogger("scgpt") + +from scgpt.model.dsbn import DomainSpecificBatchNorm1d # noqa: F401 (dependency of model.py) +from scgpt.model.grad_reverse import grad_reverse # noqa: F401 (dependency of model.py) +from scgpt.model.model import TransformerModel + + +def _load_pretrained_safe(model, pretrained_params, verbose=False): + """Load pretrained weights with non-strict matching (simplified from scGPT).""" + model_dict = model.state_dict() + loaded = 0 + for key, val in pretrained_params.items(): + # Handle flash attention -> standard attention key mapping + new_key = key.replace("Wqkv.", "in_proj_").replace("inner_attn.out_proj", "out_proj") + if new_key in model_dict and model_dict[new_key].shape == val.shape: + model_dict[new_key] = val + loaded += 1 + elif key in model_dict and model_dict[key].shape == val.shape: + model_dict[key] = val + loaded += 1 + model.load_state_dict(model_dict) + if verbose: + print(f"Loaded {loaded}/{len(pretrained_params)} pretrained parameters") + + +class FrozenScGPTExtractor(nn.Module): + """ + Wraps a frozen scGPT TransformerModel for on-the-fly per-gene feature extraction. + Similar to LatentForcing's RAE (frozen DINO-v2 encoder). + + Given expression values for G HVG genes, extracts contextualized per-gene features + from scGPT's transformer encoder, then scatters them back to a fixed G-length tensor. + + Output: (B, G, scgpt_d_model) normalized features. + """ + + def __init__( + self, + model_dir: str, + hvg_gene_names: List[str], + device: torch.device = torch.device("cpu"), + max_seq_len: int = 1200, + target_std: float = 1.0, + warmup_batches: int = 200, + ): + super().__init__() + self.device = device + self.max_seq_len = max_seq_len + self.target_std = target_std + self.warmup_batches = warmup_batches + self.n_hvg = len(hvg_gene_names) + + # Load scGPT vocab as a simple dict (avoid torchtext dependency) + vocab_path = os.path.join(model_dir, "vocab.json") + with open(vocab_path, "r") as f: + self.scgpt_vocab = json.load(f) # {gene_name: index} + + # Build HVG -> scGPT vocab ID mapping + self.hvg_gene_names = hvg_gene_names + hvg_to_scgpt_id = [] + missing_count = 0 + for gene in hvg_gene_names: + if gene in self.scgpt_vocab: + hvg_to_scgpt_id.append(self.scgpt_vocab[gene]) + else: + hvg_to_scgpt_id.append(-1) + missing_count += 1 + if missing_count > 0: + warnings.warn( + f"FrozenScGPTExtractor: {missing_count}/{len(hvg_gene_names)} HVG genes " + f"not found in scGPT vocab, will use zero vectors." + ) + self.register_buffer( + "hvg_to_scgpt_id", + torch.tensor(hvg_to_scgpt_id, dtype=torch.long), + ) + + # Load scGPT model config + args_path = os.path.join(model_dir, "args.json") + with open(args_path, "r") as f: + model_args = json.load(f) + + self.scgpt_d_model = model_args.get("embsize", 512) + + # Build scGPT model (using a simple Vocab-like wrapper) + pad_token = model_args.get("pad_token", "") + pad_value = model_args.get("pad_value", 0) + vocab_size = len(self.scgpt_vocab) + pad_token_id = self.scgpt_vocab.get(pad_token, 0) + + # Create a minimal vocab-like object that TransformerModel needs + class _SimpleVocab: + def __getitem__(self, token): + return self._map.get(token, 0) + def __len__(self): + return self._size + def __contains__(self, token): + return token in self._map + + simple_vocab = _SimpleVocab() + simple_vocab._map = self.scgpt_vocab + simple_vocab._size = vocab_size + + self.scgpt_model = TransformerModel( + ntoken=vocab_size, + d_model=self.scgpt_d_model, + nhead=model_args.get("nheads", 8), + d_hid=model_args.get("d_hid", 512), + nlayers=model_args.get("nlayers", 12), + vocab=simple_vocab, + dropout=0.0, + pad_token=pad_token, + pad_value=pad_value, + input_emb_style="continuous", + use_fast_transformer=False, + ) + + # Load pretrained weights + model_file = os.path.join(model_dir, "best_model.pt") + pretrained_params = torch.load(model_file, map_location="cpu") + _load_pretrained_safe(self.scgpt_model, pretrained_params, verbose=True) + + # Freeze all parameters + self.scgpt_model.eval() + for p in self.scgpt_model.parameters(): + p.requires_grad_(False) + + # Pad/CLS token IDs + self.pad_token_id = pad_token_id + self.cls_token_id = self.scgpt_vocab.get("", pad_token_id) + + # Running statistics for normalization (like dinov2_hf.py) + self.register_buffer("running_mean", torch.zeros(self.scgpt_d_model)) + self.register_buffer("running_var", torch.ones(self.scgpt_d_model)) + self.register_buffer("n_batches_seen", torch.tensor(0, dtype=torch.long)) + self._stats_frozen = False + + def _update_running_stats(self, z: torch.Tensor): + """Update running mean/var from a batch of features. z: (total_genes, d_model)""" + if self._stats_frozen or z.numel() == 0: + return + + batch_mean = z.mean(dim=0) + batch_var = z.var(dim=0, unbiased=False) + n = self.n_batches_seen.item() + + # Exponential moving average + momentum = 1.0 / (n + 1) + self.running_mean.lerp_(batch_mean, momentum) + self.running_var.lerp_(batch_var, momentum) + self.n_batches_seen += 1 + + if self.n_batches_seen.item() >= self.warmup_batches: + self._stats_frozen = True + + @torch.no_grad() + def extract(self, expression_values: torch.Tensor, gene_indices: Optional[torch.Tensor] = None) -> torch.Tensor: + """ + Extract per-gene contextualized features from frozen scGPT. + + Args: + expression_values: (B, G) expression values for G genes + gene_indices: (G,) optional indices into the full HVG list. + If provided, selects the corresponding subset of + hvg_to_scgpt_id mapping. If None, assumes expression_values + covers all n_hvg genes. + + Returns: + (B, G, scgpt_d_model) normalized per-gene features + """ + B, G = expression_values.shape + device = expression_values.device + + # Select the appropriate scGPT ID mapping + if gene_indices is not None: + hvg_ids = self.hvg_to_scgpt_id[gene_indices] # (G,) + else: + hvg_ids = self.hvg_to_scgpt_id # (n_hvg,) + + # Valid mask: genes that have a scGPT vocab mapping + valid_mask = hvg_ids >= 0 # (G,) + valid_scgpt_ids = hvg_ids[valid_mask] # (G_valid,) + n_valid = valid_scgpt_ids.shape[0] + + # Get expression values for valid genes only + expr_valid = expression_values[:, valid_mask] # (B, G_valid) + + # Limit sequence length + if n_valid + 1 > self.max_seq_len: # +1 for CLS + perm = torch.randperm(n_valid, device=device)[:self.max_seq_len - 1] + perm, _ = perm.sort() + selected_scgpt_ids = valid_scgpt_ids[perm] + selected_expr = expr_valid[:, perm] + seq_len = self.max_seq_len + selected_valid_idx = torch.where(valid_mask)[0][perm] + else: + selected_scgpt_ids = valid_scgpt_ids + selected_expr = expr_valid + seq_len = n_valid + 1 + selected_valid_idx = torch.where(valid_mask)[0] + + # Build input: prepend CLS token + cls_ids = torch.full((B, 1), self.cls_token_id, dtype=torch.long, device=device) + gene_ids = selected_scgpt_ids.unsqueeze(0).expand(B, -1) + src = torch.cat([cls_ids, gene_ids], dim=1) # (B, seq_len) + + cls_val = torch.zeros(B, 1, device=device) + values = torch.cat([cls_val, selected_expr], dim=1) # (B, seq_len) + + # Padding mask + src_key_padding_mask = torch.zeros(B, seq_len, dtype=torch.bool, device=device) + + # Run frozen scGPT encoder + encoder_out = self.scgpt_model._encode( + src, values, src_key_padding_mask + ) # (B, seq_len, d_model) + + # Skip CLS token, get per-gene features + gene_features = encoder_out[:, 1:, :] # (B, seq-1, d_model) + + # Scatter back to fixed G positions + output = torch.zeros(B, G, self.scgpt_d_model, device=device, dtype=gene_features.dtype) + idx = selected_valid_idx.unsqueeze(0).unsqueeze(-1).expand(B, -1, self.scgpt_d_model) + output.scatter_(1, idx, gene_features) + + # Update running statistics (only during training warmup) + if self.training and not self._stats_frozen: + nonzero_mask = output.abs().sum(-1) > 0 + if nonzero_mask.any(): + nonzero_feats = output[nonzero_mask] + self._update_running_stats(nonzero_feats) + + # Normalize: zero mean, unit variance, then scale + eps = 1e-6 + output = (output - self.running_mean) / (self.running_var.sqrt() + eps) + output = output * self.target_std + + return output + + def train(self, mode: bool = True): + """Override to keep scGPT always in eval mode.""" + super().train(mode) + self.scgpt_model.eval() + return self diff --git a/transfer/code/CCFM/src/denoiser.py b/transfer/code/CCFM/src/denoiser.py new file mode 100644 index 0000000000000000000000000000000000000000..2b9990a88b842cac43d7f227dee54cb299ac4192 --- /dev/null +++ b/transfer/code/CCFM/src/denoiser.py @@ -0,0 +1,318 @@ +""" +CascadedDenoiser — Wraps CascadedFlowModel + FrozenScGPTExtractor. + +Implements cascaded time-step sampling (from LatentForcing denoiser_cot.py:121-132) +and two-stage cascaded generation (denoiser_cot.py:224-247). +""" + +import torch +import torch.nn as nn +import torchdiffeq + +from ._scdfm_imports import AffineProbPath, CondOTScheduler, make_lognorm_poisson_noise +from .model.model import CascadedFlowModel +from .data.scgpt_extractor import FrozenScGPTExtractor + + +# Shared flow matching path +flow_path = AffineProbPath(scheduler=CondOTScheduler()) + + +def pairwise_sq_dists(X, Y): + return torch.cdist(X, Y, p=2) ** 2 + + +@torch.no_grad() +def median_sigmas(X, scales=(0.5, 1.0, 2.0, 4.0)): + D2 = pairwise_sq_dists(X, X) + tri = D2[~torch.eye(D2.size(0), dtype=bool, device=D2.device)] + m = torch.median(tri).clamp_min(1e-12) + s2 = torch.tensor(scales, device=X.device) * m + return [float(s.item()) for s in torch.sqrt(s2)] + + +def mmd2_unbiased_multi_sigma(X, Y, sigmas): + m, n = X.size(0), Y.size(0) + Dxx = pairwise_sq_dists(X, X) + Dyy = pairwise_sq_dists(Y, Y) + Dxy = pairwise_sq_dists(X, Y) + vals = [] + for sigma in sigmas: + beta = 1.0 / (2.0 * (sigma ** 2) + 1e-12) + Kxx = torch.exp(-beta * Dxx) + Kyy = torch.exp(-beta * Dyy) + Kxy = torch.exp(-beta * Dxy) + term_xx = (Kxx.sum() - Kxx.diag().sum()) / (m * (m - 1) + 1e-12) + term_yy = (Kyy.sum() - Kyy.diag().sum()) / (n * (n - 1) + 1e-12) + term_xy = Kxy.mean() + vals.append(term_xx + term_yy - 2.0 * term_xy) + return torch.stack(vals).mean() + + +class CascadedDenoiser(nn.Module): + """ + Cascaded denoiser combining CascadedFlowModel with FrozenScGPTExtractor. + + Training: Cascaded time-step sampling (dino_first_cascaded mode). + Inference: Two-stage cascaded generation (latent first, then expression). + """ + + def __init__( + self, + model: CascadedFlowModel, + scgpt_extractor: FrozenScGPTExtractor, + choose_latent_p: float = 0.4, + latent_weight: float = 1.0, + noise_type: str = "Gaussian", + use_mmd_loss: bool = True, + gamma: float = 0.5, + poisson_alpha: float = 0.8, + poisson_target_sum: float = 1e4, + # Logit-normal time-step sampling + t_sample_mode: str = "logit_normal", + t_expr_mean: float = 0.0, + t_expr_std: float = 1.0, + t_latent_mean: float = 0.0, + t_latent_std: float = 1.0, + # Cascaded noise (LatentForcing dino_first_cascaded_noised) + noise_beta: float = 0.25, + ): + super().__init__() + self.model = model + self.scgpt_extractor = scgpt_extractor + self.choose_latent_p = choose_latent_p + self.latent_weight = latent_weight + self.noise_type = noise_type + self.use_mmd_loss = use_mmd_loss + self.gamma = gamma + self.poisson_alpha = poisson_alpha + self.poisson_target_sum = poisson_target_sum + self.t_sample_mode = t_sample_mode + self.t_expr_mean = t_expr_mean + self.t_expr_std = t_expr_std + self.t_latent_mean = t_latent_mean + self.t_latent_std = t_latent_std + self.noise_beta = noise_beta + + def sample_t(self, n: int, device: torch.device): + """ + Cascaded time-step sampling — dino_first_cascaded mode. + (LatentForcing denoiser_cot.py:121-132) + + Supports uniform or logit-normal sampling per flow. + + With probability choose_latent_p: + - Train latent flow: t_latent sampled, t_expr = 0, loss_weight_expr = 0 + Otherwise: + - Train expression flow: t_expr sampled, t_latent = 1 (clean), loss_weight_latent = 0 + """ + if self.t_sample_mode == "logit_normal": + t_latent = torch.sigmoid(torch.randn(n, device=device) * self.t_latent_std + self.t_latent_mean) + t_expr = torch.sigmoid(torch.randn(n, device=device) * self.t_expr_std + self.t_expr_mean) + else: + t_latent = torch.rand(n, device=device) + t_expr = torch.rand(n, device=device) + + choose_latent_mask = torch.rand(n, device=device) < self.choose_latent_p + + # When training expr flow: t_latent ~ U[1-noise_beta, 1] (LatentForcing dino_first_cascaded_noised) + # noise_beta=0 recovers original behavior (t_latent=1, no noise) + t_latent_expr = torch.rand_like(t_latent) * self.noise_beta + (1.0 - self.noise_beta) + t_latent = torch.where(choose_latent_mask, t_latent, t_latent_expr) + t_expr = torch.where(choose_latent_mask, torch.zeros_like(t_expr), t_expr) + + w_expr = (~choose_latent_mask).float() + w_latent = choose_latent_mask.float() + + return t_expr, t_latent, w_expr, w_latent + + def _make_expr_noise(self, source: torch.Tensor) -> torch.Tensor: + """Create noise for expression flow.""" + if self.noise_type == "Gaussian": + return torch.randn_like(source) + elif self.noise_type == "Poisson": + return make_lognorm_poisson_noise( + target_log=source, + alpha=self.poisson_alpha, + per_cell_L=self.poisson_target_sum, + ) + else: + raise ValueError(f"Unknown noise_type: {self.noise_type}") + + def train_step( + self, + source: torch.Tensor, # (B, G_full) + target: torch.Tensor, # (B, G_full) + perturbation_id: torch.Tensor, # (B, 2) + gene_ids: torch.Tensor, # (G_full,) + infer_top_gene: int = 1000, + cached_z_target: torch.Tensor = None, # (B, G_sub, scgpt_dim) pre-extracted + cached_gene_ids: torch.Tensor = None, # (G_sub,) gene indices used for cache + ) -> torch.Tensor: + """ + Single training step with cascaded time sampling. + Returns: scalar loss + """ + B = source.shape[0] + device = source.device + + # Random gene subset (same as scDFM) + if cached_gene_ids is not None: + input_gene_ids = cached_gene_ids + else: + input_gene_ids = torch.randperm(source.shape[-1], device=device)[:infer_top_gene] + source_sub = source[:, input_gene_ids] + target_sub = target[:, input_gene_ids] + gene_input = gene_ids[input_gene_ids].unsqueeze(0).expand(B, -1) + + # 1. scGPT features: use cache if available, otherwise extract on-the-fly + if cached_z_target is not None: + z_target = cached_z_target + else: + z_target = self.scgpt_extractor.extract(target_sub, gene_indices=input_gene_ids) # (B, G, scgpt_dim) + + # 2. Cascaded time sampling + t_expr, t_latent, w_expr, w_latent = self.sample_t(B, device) + + # 3. Expression flow path + noise_expr = self._make_expr_noise(source_sub) + path_expr = flow_path.sample(t=t_expr, x_0=noise_expr, x_1=target_sub) + + # 4. Latent flow path + # AffineProbPath.sample expects t as (B,) and broadcasts internally. + # But z_target is (B, G, scgpt_dim) — we need to flatten to 2D, sample, then reshape. + B_l, G_l, D_l = z_target.shape + noise_latent = torch.randn_like(z_target) + z_target_flat = z_target.reshape(B_l, G_l * D_l) + noise_latent_flat = noise_latent.reshape(B_l, G_l * D_l) + path_latent_flat = flow_path.sample(t=t_latent, x_0=noise_latent_flat, x_1=z_target_flat) + + # Wrap path_latent with reshaped tensors + class _LatentPath: + pass + path_latent = _LatentPath() + path_latent.x_t = path_latent_flat.x_t.reshape(B_l, G_l, D_l) + path_latent.dx_t = path_latent_flat.dx_t.reshape(B_l, G_l, D_l) + + # 5. Model forward + pred_v_expr, pred_v_latent = self.model( + gene_input, source_sub, path_expr.x_t, path_latent.x_t, + t_expr, t_latent, perturbation_id, + ) + + # 6. Losses — per-sample MSE first, then weight by cascaded mask + # (aligns with LatentForcing: eliminates dimension mismatch, decouples choose_latent_p from loss weight) + loss_expr_per_sample = ((pred_v_expr - path_expr.dx_t) ** 2).mean(dim=-1) # (B,) + loss_expr = (loss_expr_per_sample * w_expr).sum() / w_expr.sum().clamp(min=1) + + loss_latent_per_sample = ((pred_v_latent - path_latent.dx_t) ** 2).mean(dim=(-1, -2)) # (B,) + loss_latent = (loss_latent_per_sample * w_latent).sum() / w_latent.sum().clamp(min=1) + + loss = loss_expr + self.latent_weight * loss_latent + + # Optional MMD loss on expression (same as scDFM) + _mmd_loss = torch.tensor(0.0, device=device) + if self.use_mmd_loss and w_expr.sum() > 0: + expr_mask = w_expr > 0 + if expr_mask.any(): + x1_hat = ( + path_expr.x_t[expr_mask] + + pred_v_expr[expr_mask] * (1 - t_expr[expr_mask]).unsqueeze(-1) + ) + sigmas = median_sigmas(target_sub[expr_mask], scales=(0.5, 1.0, 2.0, 4.0)) + _mmd_loss = mmd2_unbiased_multi_sigma(x1_hat, target_sub[expr_mask], sigmas) + loss = loss + _mmd_loss * self.gamma + + return { + "loss": loss, + "loss_expr": loss_expr.detach(), + "loss_latent": loss_latent.detach(), + "loss_mmd": _mmd_loss.detach(), + } + + @torch.no_grad() + def generate( + self, + source: torch.Tensor, # (B, G) + perturbation_id: torch.Tensor, # (B, 2) + gene_ids: torch.Tensor, # (B, G) or (G,) + latent_steps: int = 20, + expr_steps: int = 20, + method: str = "rk4", + ) -> torch.Tensor: + """ + Two-stage cascaded generation. + + method="euler": Single-loop joint Euler steps (LatentForcing style). + method="rk4": Two-stage serial ODE with torchdiffeq RK4 (scDFM style, higher accuracy). + + Returns: (B, G) generated expression values + """ + B, G = source.shape + device = source.device + scgpt_dim = self.scgpt_extractor.scgpt_d_model + + if gene_ids.dim() == 1: + gene_ids = gene_ids.unsqueeze(0).expand(B, -1) + + # === Initialize both noise states === + z_t = torch.randn(B, G, scgpt_dim, device=device) + x_t = self._make_expr_noise(source) + + if method == "rk4": + # === Stage 1: Latent generation (t_latent: 0->1, t_expr=0) === + t_zero = torch.zeros(B, device=device) + t_one = torch.ones(B, device=device) + + def latent_vf(t, z): + v_expr, v_latent = self.model( + gene_ids, source, x_t, z, + t_zero, t.expand(B), perturbation_id, + ) + return v_latent + + z_t = torchdiffeq.odeint( + latent_vf, z_t, + torch.linspace(0, 1, latent_steps + 1, device=device), + method="rk4", atol=1e-4, rtol=1e-4, + )[-1] + + # === Stage 2: Expression generation (t_expr: 0->1, t_latent=1) === + def expr_vf(t, x): + v_expr, v_latent = self.model( + gene_ids, source, x, z_t, + t.expand(B), t_one, perturbation_id, + ) + return v_expr + + x_t = torchdiffeq.odeint( + expr_vf, x_t, + torch.linspace(0, 1, expr_steps + 1, device=device), + method="rk4", atol=1e-4, rtol=1e-4, + )[-1] + + else: # euler — joint loop (LatentForcing style) + t_latent_schedule = torch.cat([ + torch.linspace(0, 1, latent_steps + 1, device=device), + torch.ones(expr_steps, device=device), + ]) + t_expr_schedule = torch.cat([ + torch.zeros(latent_steps + 1, device=device), + torch.linspace(0, 1, expr_steps + 1, device=device)[1:], + ]) + + for i in range(latent_steps + expr_steps): + t_lat = t_latent_schedule[i] + t_lat_next = t_latent_schedule[i + 1] + t_exp = t_expr_schedule[i] + t_exp_next = t_expr_schedule[i + 1] + + v_expr, v_latent = self.model( + gene_ids, source, x_t, z_t, + t_exp.expand(B), t_lat.expand(B), perturbation_id, + ) + + x_t = x_t + (t_exp_next - t_exp) * v_expr + z_t = z_t + (t_lat_next - t_lat) * v_latent + + return torch.clamp(x_t, min=0) diff --git a/transfer/code/CCFM/src/denoiser.py.bak b/transfer/code/CCFM/src/denoiser.py.bak new file mode 100644 index 0000000000000000000000000000000000000000..6c80470ddf73e34fd461aa8645b9fd1a7e4ecbb1 --- /dev/null +++ b/transfer/code/CCFM/src/denoiser.py.bak @@ -0,0 +1,312 @@ +""" +CascadedDenoiser — Wraps CascadedFlowModel + FrozenScGPTExtractor. + +Implements cascaded time-step sampling (from LatentForcing denoiser_cot.py:121-132) +and two-stage cascaded generation (denoiser_cot.py:224-247). +""" + +import torch +import torch.nn as nn +import torchdiffeq + +from ._scdfm_imports import AffineProbPath, CondOTScheduler, make_lognorm_poisson_noise +from .model.model import CascadedFlowModel +from .data.scgpt_extractor import FrozenScGPTExtractor + + +# Shared flow matching path +flow_path = AffineProbPath(scheduler=CondOTScheduler()) + + +def pairwise_sq_dists(X, Y): + return torch.cdist(X, Y, p=2) ** 2 + + +@torch.no_grad() +def median_sigmas(X, scales=(0.5, 1.0, 2.0, 4.0)): + D2 = pairwise_sq_dists(X, X) + tri = D2[~torch.eye(D2.size(0), dtype=bool, device=D2.device)] + m = torch.median(tri).clamp_min(1e-12) + s2 = torch.tensor(scales, device=X.device) * m + return [float(s.item()) for s in torch.sqrt(s2)] + + +def mmd2_unbiased_multi_sigma(X, Y, sigmas): + m, n = X.size(0), Y.size(0) + Dxx = pairwise_sq_dists(X, X) + Dyy = pairwise_sq_dists(Y, Y) + Dxy = pairwise_sq_dists(X, Y) + vals = [] + for sigma in sigmas: + beta = 1.0 / (2.0 * (sigma ** 2) + 1e-12) + Kxx = torch.exp(-beta * Dxx) + Kyy = torch.exp(-beta * Dyy) + Kxy = torch.exp(-beta * Dxy) + term_xx = (Kxx.sum() - Kxx.diag().sum()) / (m * (m - 1) + 1e-12) + term_yy = (Kyy.sum() - Kyy.diag().sum()) / (n * (n - 1) + 1e-12) + term_xy = Kxy.mean() + vals.append(term_xx + term_yy - 2.0 * term_xy) + return torch.stack(vals).mean() + + +class CascadedDenoiser(nn.Module): + """ + Cascaded denoiser combining CascadedFlowModel with FrozenScGPTExtractor. + + Training: Cascaded time-step sampling (dino_first_cascaded mode). + Inference: Two-stage cascaded generation (latent first, then expression). + """ + + def __init__( + self, + model: CascadedFlowModel, + scgpt_extractor: FrozenScGPTExtractor, + choose_latent_p: float = 0.4, + latent_weight: float = 1.0, + noise_type: str = "Gaussian", + use_mmd_loss: bool = True, + gamma: float = 0.5, + poisson_alpha: float = 0.8, + poisson_target_sum: float = 1e4, + # Logit-normal time-step sampling + t_sample_mode: str = "logit_normal", + t_expr_mean: float = 0.0, + t_expr_std: float = 1.0, + t_latent_mean: float = 0.0, + t_latent_std: float = 1.0, + ): + super().__init__() + self.model = model + self.scgpt_extractor = scgpt_extractor + self.choose_latent_p = choose_latent_p + self.latent_weight = latent_weight + self.noise_type = noise_type + self.use_mmd_loss = use_mmd_loss + self.gamma = gamma + self.poisson_alpha = poisson_alpha + self.poisson_target_sum = poisson_target_sum + self.t_sample_mode = t_sample_mode + self.t_expr_mean = t_expr_mean + self.t_expr_std = t_expr_std + self.t_latent_mean = t_latent_mean + self.t_latent_std = t_latent_std + + def sample_t(self, n: int, device: torch.device): + """ + Cascaded time-step sampling — dino_first_cascaded mode. + (LatentForcing denoiser_cot.py:121-132) + + Supports uniform or logit-normal sampling per flow. + + With probability choose_latent_p: + - Train latent flow: t_latent sampled, t_expr = 0, loss_weight_expr = 0 + Otherwise: + - Train expression flow: t_expr sampled, t_latent = 1 (clean), loss_weight_latent = 0 + """ + if self.t_sample_mode == "logit_normal": + t_latent = torch.sigmoid(torch.randn(n, device=device) * self.t_latent_std + self.t_latent_mean) + t_expr = torch.sigmoid(torch.randn(n, device=device) * self.t_expr_std + self.t_expr_mean) + else: + t_latent = torch.rand(n, device=device) + t_expr = torch.rand(n, device=device) + + choose_latent_mask = torch.rand(n, device=device) < self.choose_latent_p + + t_latent = torch.where(choose_latent_mask, t_latent, torch.ones_like(t_latent)) + t_expr = torch.where(choose_latent_mask, torch.zeros_like(t_expr), t_expr) + + w_expr = (~choose_latent_mask).float() + w_latent = choose_latent_mask.float() + + return t_expr, t_latent, w_expr, w_latent + + def _make_expr_noise(self, source: torch.Tensor) -> torch.Tensor: + """Create noise for expression flow.""" + if self.noise_type == "Gaussian": + return torch.randn_like(source) + elif self.noise_type == "Poisson": + return make_lognorm_poisson_noise( + target_log=source, + alpha=self.poisson_alpha, + per_cell_L=self.poisson_target_sum, + ) + else: + raise ValueError(f"Unknown noise_type: {self.noise_type}") + + def train_step( + self, + source: torch.Tensor, # (B, G_full) + target: torch.Tensor, # (B, G_full) + perturbation_id: torch.Tensor, # (B, 2) + gene_ids: torch.Tensor, # (G_full,) + infer_top_gene: int = 1000, + cached_z_target: torch.Tensor = None, # (B, G_sub, scgpt_dim) pre-extracted + cached_gene_ids: torch.Tensor = None, # (G_sub,) gene indices used for cache + ) -> torch.Tensor: + """ + Single training step with cascaded time sampling. + Returns: scalar loss + """ + B = source.shape[0] + device = source.device + + # Random gene subset (same as scDFM) + if cached_gene_ids is not None: + input_gene_ids = cached_gene_ids + else: + input_gene_ids = torch.randperm(source.shape[-1], device=device)[:infer_top_gene] + source_sub = source[:, input_gene_ids] + target_sub = target[:, input_gene_ids] + gene_input = gene_ids[input_gene_ids].unsqueeze(0).expand(B, -1) + + # 1. scGPT features: use cache if available, otherwise extract on-the-fly + if cached_z_target is not None: + z_target = cached_z_target + else: + z_target = self.scgpt_extractor.extract(target_sub, gene_indices=input_gene_ids) # (B, G, scgpt_dim) + + # 2. Cascaded time sampling + t_expr, t_latent, w_expr, w_latent = self.sample_t(B, device) + + # 3. Expression flow path + noise_expr = self._make_expr_noise(source_sub) + path_expr = flow_path.sample(t=t_expr, x_0=noise_expr, x_1=target_sub) + + # 4. Latent flow path + # AffineProbPath.sample expects t as (B,) and broadcasts internally. + # But z_target is (B, G, scgpt_dim) — we need to flatten to 2D, sample, then reshape. + B_l, G_l, D_l = z_target.shape + noise_latent = torch.randn_like(z_target) + z_target_flat = z_target.reshape(B_l, G_l * D_l) + noise_latent_flat = noise_latent.reshape(B_l, G_l * D_l) + path_latent_flat = flow_path.sample(t=t_latent, x_0=noise_latent_flat, x_1=z_target_flat) + + # Wrap path_latent with reshaped tensors + class _LatentPath: + pass + path_latent = _LatentPath() + path_latent.x_t = path_latent_flat.x_t.reshape(B_l, G_l, D_l) + path_latent.dx_t = path_latent_flat.dx_t.reshape(B_l, G_l, D_l) + + # 5. Model forward + pred_v_expr, pred_v_latent = self.model( + gene_input, source_sub, path_expr.x_t, path_latent.x_t, + t_expr, t_latent, perturbation_id, + ) + + # 6. Losses — per-sample MSE first, then weight by cascaded mask + # (aligns with LatentForcing: eliminates dimension mismatch, decouples choose_latent_p from loss weight) + loss_expr_per_sample = ((pred_v_expr - path_expr.dx_t) ** 2).mean(dim=-1) # (B,) + loss_expr = (loss_expr_per_sample * w_expr).sum() / w_expr.sum().clamp(min=1) + + loss_latent_per_sample = ((pred_v_latent - path_latent.dx_t) ** 2).mean(dim=(-1, -2)) # (B,) + loss_latent = (loss_latent_per_sample * w_latent).sum() / w_latent.sum().clamp(min=1) + + loss = loss_expr + self.latent_weight * loss_latent + + # Optional MMD loss on expression (same as scDFM) + _mmd_loss = torch.tensor(0.0, device=device) + if self.use_mmd_loss and w_expr.sum() > 0: + expr_mask = w_expr > 0 + if expr_mask.any(): + x1_hat = ( + path_expr.x_t[expr_mask] + + pred_v_expr[expr_mask] * (1 - t_expr[expr_mask]).unsqueeze(-1) + ) + sigmas = median_sigmas(target_sub[expr_mask], scales=(0.5, 1.0, 2.0, 4.0)) + _mmd_loss = mmd2_unbiased_multi_sigma(x1_hat, target_sub[expr_mask], sigmas) + loss = loss + _mmd_loss * self.gamma + + return { + "loss": loss, + "loss_expr": loss_expr.detach(), + "loss_latent": loss_latent.detach(), + "loss_mmd": _mmd_loss.detach(), + } + + @torch.no_grad() + def generate( + self, + source: torch.Tensor, # (B, G) + perturbation_id: torch.Tensor, # (B, 2) + gene_ids: torch.Tensor, # (B, G) or (G,) + latent_steps: int = 20, + expr_steps: int = 20, + method: str = "rk4", + ) -> torch.Tensor: + """ + Two-stage cascaded generation. + + method="euler": Single-loop joint Euler steps (LatentForcing style). + method="rk4": Two-stage serial ODE with torchdiffeq RK4 (scDFM style, higher accuracy). + + Returns: (B, G) generated expression values + """ + B, G = source.shape + device = source.device + scgpt_dim = self.scgpt_extractor.scgpt_d_model + + if gene_ids.dim() == 1: + gene_ids = gene_ids.unsqueeze(0).expand(B, -1) + + # === Initialize both noise states === + z_t = torch.randn(B, G, scgpt_dim, device=device) + x_t = self._make_expr_noise(source) + + if method == "rk4": + # === Stage 1: Latent generation (t_latent: 0->1, t_expr=0) === + t_zero = torch.zeros(B, device=device) + t_one = torch.ones(B, device=device) + + def latent_vf(t, z): + v_expr, v_latent = self.model( + gene_ids, source, x_t, z, + t_zero, t.expand(B), perturbation_id, + ) + return v_latent + + z_t = torchdiffeq.odeint( + latent_vf, z_t, + torch.linspace(0, 1, latent_steps + 1, device=device), + method="rk4", atol=1e-4, rtol=1e-4, + )[-1] + + # === Stage 2: Expression generation (t_expr: 0->1, t_latent=1) === + def expr_vf(t, x): + v_expr, v_latent = self.model( + gene_ids, source, x, z_t, + t.expand(B), t_one, perturbation_id, + ) + return v_expr + + x_t = torchdiffeq.odeint( + expr_vf, x_t, + torch.linspace(0, 1, expr_steps + 1, device=device), + method="rk4", atol=1e-4, rtol=1e-4, + )[-1] + + else: # euler — joint loop (LatentForcing style) + t_latent_schedule = torch.cat([ + torch.linspace(0, 1, latent_steps + 1, device=device), + torch.ones(expr_steps, device=device), + ]) + t_expr_schedule = torch.cat([ + torch.zeros(latent_steps + 1, device=device), + torch.linspace(0, 1, expr_steps + 1, device=device)[1:], + ]) + + for i in range(latent_steps + expr_steps): + t_lat = t_latent_schedule[i] + t_lat_next = t_latent_schedule[i + 1] + t_exp = t_expr_schedule[i] + t_exp_next = t_expr_schedule[i + 1] + + v_expr, v_latent = self.model( + gene_ids, source, x_t, z_t, + t_exp.expand(B), t_lat.expand(B), perturbation_id, + ) + + x_t = x_t + (t_exp_next - t_exp) * v_expr + z_t = z_t + (t_lat_next - t_lat) * v_latent + + return torch.clamp(x_t, min=0) diff --git a/transfer/code/CCFM/src/model/__init__.py b/transfer/code/CCFM/src/model/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/transfer/code/CCFM/src/model/layers.py b/transfer/code/CCFM/src/model/layers.py new file mode 100644 index 0000000000000000000000000000000000000000..34a93007b208e3613631329896f6c8f7b5f48829 --- /dev/null +++ b/transfer/code/CCFM/src/model/layers.py @@ -0,0 +1,114 @@ +""" +New layers for CCFM: LatentEmbedder and LatentDecoder. +Analogous to LatentForcing's dino_embedder (BottleneckPatchEmbed) and +final_layer_dino + dh_blocks_dino. +""" + +import torch +import torch.nn as nn + + +class LatentEmbedder(nn.Module): + """ + Projects per-gene scGPT features (B, G, scgpt_dim) to (B, G, d_model). + Analogous to LatentForcing's dino_embedder (BottleneckPatchEmbed with kernel=1). + Uses a bottleneck projection: scgpt_dim -> bottleneck_dim -> d_model. + """ + + def __init__(self, scgpt_dim: int = 512, bottleneck_dim: int = 128, d_model: int = 128): + super().__init__() + self.proj = nn.Sequential( + nn.Linear(scgpt_dim, bottleneck_dim), + nn.GELU(), + nn.Linear(bottleneck_dim, d_model), + ) + + def forward(self, z: torch.Tensor) -> torch.Tensor: + """z: (B, G, scgpt_dim) -> (B, G, d_model)""" + return self.proj(z) + + +class LatentDecoderBlock(nn.Module): + """ + A simple transformer-like block for the latent decoder head. + Uses AdaLN conditioning (analogous to LatentForcing's dh_blocks_dino). + """ + + def __init__(self, hidden_size: int, num_heads: int = 4, mlp_ratio: float = 4.0, + hidden_size_c: int = None): + super().__init__() + hidden_size_c = hidden_size_c or hidden_size + self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.attn = nn.MultiheadAttention(hidden_size, num_heads, batch_first=True) + self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + mlp_hidden = int(hidden_size * mlp_ratio) + self.mlp = nn.Sequential( + nn.Linear(hidden_size, mlp_hidden), + nn.GELU(), + nn.Linear(mlp_hidden, hidden_size), + ) + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(hidden_size_c, 6 * hidden_size, bias=True), + ) + + def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor: + """ + x: (B, G, hidden_size) + c: (B, hidden_size_c) — conditioning vector + """ + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = ( + self.adaLN_modulation(c).chunk(6, dim=1) + ) + # Self-attention with AdaLN + h = self.norm1(x) + h = h * (1 + scale_msa.unsqueeze(1)) + shift_msa.unsqueeze(1) + h = self.attn(h, h, h)[0] + x = x + gate_msa.unsqueeze(1) * h + # MLP with AdaLN + h = self.norm2(x) + h = h * (1 + scale_mlp.unsqueeze(1)) + shift_mlp.unsqueeze(1) + h = self.mlp(h) + x = x + gate_mlp.unsqueeze(1) * h + return x + + +class LatentDecoder(nn.Module): + """ + Decodes backbone output (B, G, d_model) back to (B, G, scgpt_dim). + Analogous to LatentForcing's final_layer_dino + dh_blocks_dino. + """ + + def __init__(self, d_model: int = 128, scgpt_dim: int = 512, + dh_depth: int = 2, num_heads: int = 4, + hidden_size_c: int = None): + super().__init__() + hidden_size_c = hidden_size_c or d_model + + self.dh_proj = nn.Linear(d_model, d_model) + + if dh_depth > 0: + self.dh_blocks = nn.ModuleList([ + LatentDecoderBlock(d_model, num_heads=num_heads, hidden_size_c=hidden_size_c) + for _ in range(dh_depth) + ]) + else: + self.dh_blocks = nn.ModuleList() + + self.final = nn.Sequential( + nn.LayerNorm(d_model), + nn.Linear(d_model, d_model), + nn.GELU(), + nn.Linear(d_model, scgpt_dim), + ) + + def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor: + """ + x: (B, G, d_model) — backbone output + c: (B, d_model) — conditioning vector + Returns: (B, G, scgpt_dim) + """ + h = self.dh_proj(x) + for block in self.dh_blocks: + h = block(h, c) + return self.final(h) diff --git a/transfer/code/CCFM/src/model/model.py b/transfer/code/CCFM/src/model/model.py new file mode 100644 index 0000000000000000000000000000000000000000..8c9cce9a4ac2ee2e32358eeb04dd065ce8a0257a --- /dev/null +++ b/transfer/code/CCFM/src/model/model.py @@ -0,0 +1,226 @@ +""" +CascadedFlowModel — Main backbone for Cascaded Conditioned Flow Matching (CCFM). + +Architecture mirrors LatentForcing's model_cot.py: +1. Dual-stream embedding (expression + latent) with element-wise addition +2. Conditioning via AdaLN: c = t_expr_emb + t_latent_emb + pert_emb + - True conditioning signals: control expression + perturbation_id (available at inference) + - scGPT latent features are an auxiliary generation target (like DINO in LatentForcing), + NOT a conditioning signal — they are generated from noise at inference time. +3. Shared backbone (reusing scDFM's DiffPerceiver/Perceiver blocks) +4. Separate decoder heads: ExprDecoder (reused) + LatentDecoder (new) +""" + +import torch +import torch.nn as nn +from torch import Tensor +from typing import Optional, Tuple + +from .layers import LatentEmbedder, LatentDecoder +from .._scdfm_imports import ( + GeneadaLN, + ContinuousValueEncoder, + GeneEncoder, + BatchLabelEncoder, + TimestepEmbedder, + ExprDecoder, + DifferentialTransformerBlock, + PerceiverBlock, + DiffPerceiverBlock, +) + + +class CascadedFlowModel(nn.Module): + """ + Cascaded Flow Model for single-cell perturbation prediction. + + Inputs: + gene_id: (B, G) gene token IDs + cell_1: (B, G) source (control) expression + x_t: (B, G) noised target expression (expression flow) + z_t: (B, G, scgpt_dim) noised scGPT features (latent flow) + t_expr: (B,) expression flow timestep + t_latent: (B,) latent flow timestep + perturbation_id: (B, 2) perturbation token IDs + + Outputs: + pred_v_expr: (B, G) predicted expression velocity + pred_v_latent: (B, G, scgpt_dim) predicted latent velocity + """ + + def __init__( + self, + ntoken: int = 6000, + d_model: int = 128, + nhead: int = 8, + d_hid: int = 512, + nlayers: int = 4, + dropout: float = 0.1, + fusion_method: str = "differential_perceiver", + perturbation_function: str = "crisper", + use_perturbation_interaction: bool = True, + mask_path: str = None, + # Latent-specific + scgpt_dim: int = 512, + bottleneck_dim: int = 128, + dh_depth: int = 2, + ): + super().__init__() + self.d_model = d_model + self.fusion_method = fusion_method + self.perturbation_function = perturbation_function + + # === Timestep embedders (separate for expr and latent, like model_cot.py) === + self.t_expr_embedder = TimestepEmbedder(d_model) + self.t_latent_embedder = TimestepEmbedder(d_model) + + # === Perturbation embedder === + self.perturbation_embedder = BatchLabelEncoder(ntoken, d_model) + + # === Expression stream (reused from scDFM) === + self.value_encoder_1 = ContinuousValueEncoder(d_model, dropout) + self.value_encoder_2 = ContinuousValueEncoder(d_model, dropout) + self.encoder = GeneEncoder( + ntoken, d_model, + use_perturbation_interaction=use_perturbation_interaction, + mask_path=mask_path, + ) + self.use_perturbation_interaction = use_perturbation_interaction + self.fusion_layer = nn.Sequential( + nn.Linear(2 * d_model, d_model), + nn.GELU(), + nn.Linear(d_model, d_model), + nn.LayerNorm(d_model), + ) + + # === Latent stream embedder (new, analogous to dino_embedder) === + self.latent_embedder = LatentEmbedder(scgpt_dim, bottleneck_dim, d_model) + + # === Shared backbone blocks === + if fusion_method == "differential_transformer": + self.blocks = nn.ModuleList([ + DifferentialTransformerBlock(d_model, nhead, i, mlp_ratio=4.0) + for i in range(nlayers) + ]) + elif fusion_method == "differential_perceiver": + self.blocks = nn.ModuleList([ + DiffPerceiverBlock(d_model, nhead, i, mlp_ratio=4.0) + for i in range(nlayers) + ]) + elif fusion_method == "perceiver": + self.blocks = nn.ModuleList([ + PerceiverBlock(d_model, d_model, heads=nhead, mlp_ratio=4.0, dropout=0.1) + for _ in range(nlayers) + ]) + else: + raise ValueError(f"Invalid fusion method: {fusion_method}") + + # === Per-layer gene AdaLN + adapter === + self.gene_adaLN = nn.ModuleList([ + GeneadaLN(d_model, dropout) for _ in range(nlayers) + ]) + self.adapter_layer = nn.ModuleList([ + nn.Sequential( + nn.Linear(2 * d_model, d_model), + nn.LeakyReLU(), + nn.Dropout(dropout), + nn.Linear(d_model, d_model), + nn.LeakyReLU(), + ) + for _ in range(nlayers) + ]) + + # === Expression decoder head (reused from scDFM) === + self.final_layer = ExprDecoder(d_model, explicit_zero_prob=False, use_batch_labels=True) + + # === Latent decoder head (new, analogous to dh_blocks_dino + final_layer_dino) === + self.latent_decoder = LatentDecoder( + d_model=d_model, + scgpt_dim=scgpt_dim, + dh_depth=dh_depth, + num_heads=max(nhead // 2, 1), + hidden_size_c=d_model, + ) + + self.initialize_weights() + + def initialize_weights(self): + def _basic_init(module): + if isinstance(module, nn.Linear): + torch.nn.init.xavier_uniform_(module.weight) + if module.bias is not None: + nn.init.constant_(module.bias, 0) + self.apply(_basic_init) + + def get_perturbation_emb( + self, + perturbation_id: Optional[Tensor] = None, + perturbation_emb: Optional[Tensor] = None, + cell_1: Optional[Tensor] = None, + ) -> Tensor: + """Get perturbation embedding, replicating scDFM logic.""" + assert perturbation_emb is None or perturbation_id is None + if perturbation_id is not None: + if self.perturbation_function == "crisper": + perturbation_emb = self.encoder(perturbation_id) + else: + perturbation_emb = self.perturbation_embedder(perturbation_id) + perturbation_emb = perturbation_emb.mean(1) # (B, d) + elif perturbation_emb is not None: + perturbation_emb = perturbation_emb.to(cell_1.device, dtype=cell_1.dtype) + if perturbation_emb.dim() == 1: + perturbation_emb = perturbation_emb.unsqueeze(0) + if perturbation_emb.size(0) == 1: + perturbation_emb = perturbation_emb.expand(cell_1.shape[0], -1).contiguous() + perturbation_emb = self.perturbation_embedder.enc_norm(perturbation_emb) + return perturbation_emb + + def forward( + self, + gene_id: Tensor, # (B, G) + cell_1: Tensor, # (B, G) source expression + x_t: Tensor, # (B, G) noised expression + z_t: Tensor, # (B, G, scgpt_dim) noised latent features + t_expr: Tensor, # (B,) + t_latent: Tensor, # (B,) + perturbation_id: Optional[Tensor] = None, # (B, 2) + ) -> Tuple[Tensor, Tensor]: + if t_expr.dim() == 0: + t_expr = t_expr.repeat(cell_1.size(0)) + if t_latent.dim() == 0: + t_latent = t_latent.repeat(cell_1.size(0)) + + # === 1. Expression stream embedding (aligned with scDFM) === + gene_emb = self.encoder(gene_id) # (B, G, d) + val_emb_1 = self.value_encoder_1(x_t) # (B, G, d) encoder_1 = noisy target (same role as scDFM) + val_emb_2 = self.value_encoder_2(cell_1) # (B, G, d) encoder_2 = control (same role as scDFM) + expr_tokens = self.fusion_layer(torch.cat([val_emb_1, val_emb_2], dim=-1)) + gene_emb # (B, G, d) + + # === 2. Latent stream embedding (new, analogous to dino_embedder) === + latent_tokens = self.latent_embedder(z_t) # (B, G, d) + + # === 3. Element-wise addition (model_cot.py line 414) === + x = expr_tokens + latent_tokens # (B, G, d) + + # === 4. Conditioning vector (model_cot.py line 409) === + t_expr_emb = self.t_expr_embedder(t_expr) # (B, d) + t_latent_emb = self.t_latent_embedder(t_latent) # (B, d) + pert_emb = self.get_perturbation_emb(perturbation_id, cell_1=cell_1) # (B, d) + c = t_expr_emb + t_latent_emb + pert_emb # (B, d) + + # === 5. Shared backbone (reused scDFM blocks) === + for i, block in enumerate(self.blocks): + x = self.gene_adaLN[i](gene_emb, x) + pert_exp = pert_emb[:, None, :].expand(-1, x.size(1), -1) + x = torch.cat([x, pert_exp], dim=-1) + x = self.adapter_layer[i](x) + x = block(x, val_emb_2, c) + + # === 6a. Expression decoder head (reused) === + x_with_pert = torch.cat([x, pert_emb[:, None, :].expand(-1, x.size(1), -1)], dim=-1) + pred_v_expr = self.final_layer(x_with_pert)["pred"] # (B, G) + + # === 6b. Latent decoder head (new, analogous to dh_blocks_dino) === + pred_v_latent = self.latent_decoder(x, c) # (B, G, scgpt_dim) + + return pred_v_expr, pred_v_latent diff --git a/transfer/code/CCFM/src/utils.py b/transfer/code/CCFM/src/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..58d807f5c05c1be3067f849c6aad71b03ff60f27 --- /dev/null +++ b/transfer/code/CCFM/src/utils.py @@ -0,0 +1,14 @@ +""" +Re-export scDFM utility functions from the central import module. +""" + +from ._scdfm_imports import ( + save_checkpoint, + load_checkpoint, + make_lognorm_poisson_noise, + pick_eval_score, + process_vocab, + set_requires_grad_for_p_only, + get_perturbation_emb, + GeneVocab, +) diff --git 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/transfer/code/FOCUS/README.md b/transfer/code/FOCUS/README.md new file mode 100644 index 0000000000000000000000000000000000000000..e81848a2b9258159241e3638917f1e053f598522 --- /dev/null +++ b/transfer/code/FOCUS/README.md @@ -0,0 +1,76 @@ +# FOCUS: Efficient Keyframe Selection for Long Video Understanding + +> 🎉 **NEWS**: Our paper has been accepted by ICLR 2026! +> +> 📄 [Read the paper on OpenReview](https://openreview.net/forum?id=1OQKqLFcbB) + +![FOCUS Framework](fig/framework.png) + +Multimodal large language models (MLLMs) represent images and video frames as visual tokens. Scaling from single images to hour-long videos, however, inflates the token budget far beyond practical limits. Popular pipelines therefore either uniformly subsample or apply keyframe selection with retrieval-style scoring using smaller vision-language models. However, these keyframe selection methods still rely on pre-filtering before selection to reduce the inference cost and can miss the most informative moments. + +We propose **FOCUS**, *Frame-Optimistic Confidence Upper-bound Selection*, a training-free, model-agnostic keyframe selection module that selects query-relevant frames under a strict token budget. FOCUS formulates keyframe selection as a combinatorial pure-exploration (CPE) problem in multi-armed bandits: it treats short temporal clips as arms, and uses empirical means and Bernstein confidence radius to identify informative regions while preserving exploration of uncertain areas. The resulting two-stage exploration-exploitation procedure reduces from a sequential policy with theoretical guarantees, first identifying high-value temporal regions, then selecting top-scoring frames within each region. + +On two long-video question-answering benchmarks, FOCUS delivers substantial accuracy improvements while processing less than 2% of video frames. For videos longer than 20 minutes, it achieves an 11.9% gain in accuracy on LongVideoBench, demonstrating its effectiveness as a keyframe selection method and providing a simple and general solution for scalable long-video understanding with MLLMs. + + +## Installation + +1. First, follow the installation instructions from the [AKS repository](https://github.com/ncTimTang/AKS) to set up the environment and dependencies. + +2. Then install the additional requirements: +```bash +pip install -r requirements.txt +``` + +## Usage + +Run FOCUS keyframe extraction on LongVideoBench: + +```bash +python select_keyframe.py \ + --dataset_name longvideobench \ + --dataset_path ./datasets/longvideobench \ + --output_dir focus_blip \ + --num_keyframes 64 \ + --batch_size 32 \ + --blip_model large +``` + +## Evaluation + +For evaluation, please follow the evaluation setup from the [lmms-eval repository](https://github.com/EvolvingLMMs-Lab/lmms-eval) and use the evaluation scripts provided in the [AKS repository](https://github.com/ncTimTang/AKS). + +## Output + +FOCUS generates the following outputs: + +- `selected_frames.json`: Selected keyframe indices for each video +- `sampling_details.json`: Detailed sampling information including: + - Coarse and fine sampling results + - Arm information and FOCUS scores + - Arm selection probabilities + - Video metadata +- `extraction_stats.json`: Statistics about the extraction process + + +## Citation + +If you find FOCUS useful for your research, please cite our paper: + +```bibtex +@inproceedings{ +ziruiz2026focus, +title={{FOCUS}: Efficient Keyframe Selection for Long Video Understanding}, +author={Zirui Zhu and Hailun Xu and Yang Luo and Yong Liu and Kanchan Sarkar and Zhenheng Yang and Yang You}, +booktitle={The Fourteenth International Conference on Learning Representations}, +year={2026}, +url={https://openreview.net/forum?id=1OQKqLFcbB} +} +``` + +## Acknowledgments + +This work builds upon the excellent research from: +- [AKS: Adaptive Keyframe Sampling](https://github.com/ncTimTang/AKS) for the evaluation framework +- [lmms-eval](https://github.com/EvolvingLMMs-Lab/lmms-eval) for multimodal evaluation + diff --git a/transfer/code/FOCUS/fig/framework.png b/transfer/code/FOCUS/fig/framework.png new file mode 100644 index 0000000000000000000000000000000000000000..9d5c797af947298a2305a89f4f32a7645c7600ce --- /dev/null +++ b/transfer/code/FOCUS/fig/framework.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5c2c369bc78a1c8ee2afdedc27c848083ede572e8bb7a803596d2aa4f9a4c2fc +size 395634 diff --git a/transfer/code/FOCUS/focus.py b/transfer/code/FOCUS/focus.py new file mode 100644 index 0000000000000000000000000000000000000000..ad325e41537b04f91b3abfa1fe17ce8c8b6cdd8a --- /dev/null +++ b/transfer/code/FOCUS/focus.py @@ -0,0 +1,849 @@ +""" +FOCUS: Frame-Optimistic Confidence Upper-bound Selection + +Core algorithm implementation for keyframe extraction using confidence upper-bound bandit approach. +This module contains only the algorithm logic, without data processing or I/O operations. +""" + +import numpy as np +import math +from typing import List, Dict, Tuple, Optional, Callable +from scipy.interpolate import Rbf +from decord import VideoReader + + +# ============================================================================ +# Module-level Helper Functions (Pure Mathematical Operations) +# ============================================================================ + +def estimate_arm_scores( + sampled_indices: List[int], + sampled_scores: List[float], + arm_start: int, + arm_end: int, + interpolation_method: str = 'nearest' +) -> Dict[int, float]: + """ + Estimate similarity scores for all frames within a single arm using interpolation. + + This is a pure function that performs mathematical interpolation without side effects. + + Args: + sampled_indices: List of sampled frame indices within the arm + sampled_scores: List of corresponding similarity scores + arm_start: Start frame index of the arm (inclusive) + arm_end: End frame index of the arm (inclusive) + interpolation_method: Method for interpolation ('nearest', 'linear', 'rbf', 'uniform') + + Returns: + Dictionary mapping frame indices to estimated scores + """ + if len(sampled_indices) < 1 or not sampled_scores: + return {} + + # Create candidate indices for all frames within this arm + arm_candidates = list(range(arm_start, arm_end + 1)) + if not arm_candidates: + return {} + + sampled_indices_arr = np.array(sampled_indices, dtype=float) + sampled_scores_arr = np.array(sampled_scores, dtype=float) + arm_candidates_arr = np.array(arm_candidates, dtype=float) + + try: + if interpolation_method == 'nearest': + # Nearest neighbor interpolation + estimated_scores = [] + for candidate in arm_candidates_arr: + nearest_idx = np.argmin(np.abs(sampled_indices_arr - candidate)) + estimated_scores.append(sampled_scores_arr[nearest_idx]) + estimated_scores_arr = np.array(estimated_scores) + + elif interpolation_method == 'linear': + # Linear interpolation + if len(sampled_indices) >= 2: + estimated_scores_arr = np.interp(arm_candidates_arr, sampled_indices_arr, sampled_scores_arr) + else: + # Fallback to constant value if only one sample + estimated_scores_arr = np.full_like(arm_candidates_arr, sampled_scores_arr[0]) + + elif interpolation_method == 'rbf': + # RBF interpolation using scipy + if len(sampled_indices) >= 2: + rbf = Rbf(sampled_indices_arr, sampled_scores_arr, function='gaussian', smooth=0.0) + estimated_scores_arr = rbf(arm_candidates_arr) + else: + # Fallback to constant value if only one sample + estimated_scores_arr = np.full_like(arm_candidates_arr, sampled_scores_arr[0]) + else: + raise ValueError(f"Unknown interpolation method: {interpolation_method}") + + # Clamp results to reasonable range [0, 1] + estimated_scores_arr = np.clip(estimated_scores_arr, 0.0, 1.0) + + # Convert to dictionary + result = {} + for candidate_idx, estimated_score in zip(arm_candidates, estimated_scores_arr): + result[candidate_idx] = float(estimated_score) + return result + + except Exception as e: + print(f"Interpolation failed in arm [{arm_start}, {arm_end}] with method {interpolation_method}: {e}") + # Fallback: use mean score for all candidates + mean_score = np.mean(sampled_scores) + result = {} + for candidate_idx in arm_candidates: + result[candidate_idx] = float(mean_score) + return result + + +# ============================================================================ +# FOCUS Algorithm Class +# ============================================================================ + +class FOCUS: + """ + FOCUS: Frame-Optimistic Confidence Upper-bound Selection + + A bandit-based keyframe extraction algorithm that uses confidence upper bounds + to balance exploration and exploitation across temporal segments of the video. + + The algorithm operates in several stages: + 1. Setup arms: Partition video into temporal segments + 2. Coarse sampling: Sample center + random points in each arm + 3. Fine sampling: Densely sample in promising arms based on FOCUS scores + 4. Final selection: Combine top-ranked frames with FOCUS-guided arm allocation + """ + + def __init__( + self, + similarity_fn: Callable[[VideoReader, str, List[int]], List[float]], + coarse_every_sec: float = 16.0, + fine_every_sec: float = 1.0, + zoom_ratio: float = 0.25, + final_min_arms: int = 4, + final_max_arms: int = 32, + min_coarse_segments: int = 8, + min_zoom_segments: int = 4, + extra_samples_per_region: int = 2, + min_variance_threshold: float = 1e-6, + fine_uniform_ratio: float = 0.5, + interpolation_method: str = 'nearest', + top_ratio: float = 0.2, + temperature: float = 0.06, + region_half_window_sec: Optional[float] = None + ): + """ + Initialize FOCUS keyframe selector. + + Args: + similarity_fn: Function with signature (video, query, frame_indices) -> similarity_scores + coarse_every_sec: Coarse level sampling interval in seconds + fine_every_sec: Fine level sampling interval in seconds + zoom_ratio: Fraction of arms to select for fine sampling and final selection + final_min_arms: Minimum number of arms to use in final allocation + final_max_arms: Maximum number of arms to use in final allocation + min_coarse_segments: Minimum number of coarse segments + min_zoom_segments: Minimum number of segments to zoom into for fine sampling + extra_samples_per_region: Extra random samples per region for variance estimation + min_variance_threshold: Minimum variance threshold for confidence bounds + fine_uniform_ratio: Ratio of uniform sampling in fine stage (0~1) + interpolation_method: Method for score interpolation ('nearest', 'linear', 'rbf', 'uniform') + top_ratio: Ratio of top-ranked frames to select directly + temperature: Temperature for softmax sampling within arms + region_half_window_sec: Half window size for fine sampling (defaults to coarse_every_sec/2) + """ + self.similarity_fn = similarity_fn + self.coarse_every_sec = coarse_every_sec + self.fine_every_sec = fine_every_sec + self.zoom_ratio = zoom_ratio + self.final_min_arms = final_min_arms + self.final_max_arms = final_max_arms + self.min_coarse_segments = min_coarse_segments + self.min_zoom_segments = min_zoom_segments + self.extra_samples_per_region = extra_samples_per_region + self.min_variance_threshold = min_variance_threshold + self.fine_uniform_ratio = fine_uniform_ratio + self.interpolation_method = interpolation_method + self.top_ratio = top_ratio + self.temperature = temperature + self.region_half_window_sec = region_half_window_sec + + # ======================================================================== + # Public Interface + # ======================================================================== + + def select_keyframes( + self, + video: VideoReader, + query: str, + k: int, + min_gap_sec: float = 0.0, + rng: Optional[np.random.Generator] = None + ) -> Tuple[List[int], Dict]: + """ + Select keyframes from video based on query using FOCUS algorithm. + + Args: + video: VideoReader object + query: Query text + k: Number of keyframes to select + min_gap_sec: Minimum temporal gap between selections in seconds + rng: Random number generator (optional, will create default if None) + + Returns: + Tuple of (selected_frames, sampling_details) + - selected_frames: List of selected frame indices (sorted) + - sampling_details: Dictionary containing detailed sampling information + """ + # Extract video metadata + total_frames = len(video) + fps = float(video.get_avg_fps()) + video_duration = float(total_frames) / max(1.0, fps) + + if rng is None: + rng = np.random.default_rng() + + # Calculate strides and parameters + coarse_stride = max(1, int(round(self.coarse_every_sec * fps))) + desired_coarse = max(self.min_coarse_segments, int(np.ceil(video_duration / max(1e-6, self.coarse_every_sec)))) + if desired_coarse > 0: + coarse_stride = max(1, int(np.floor(total_frames / desired_coarse))) + + fine_stride = max(1, int(round(self.fine_every_sec * fps))) + fine_stride = min(fine_stride, coarse_stride) + + if self.region_half_window_sec is None: + effective_coarse_sec = max(1e-6, video_duration / max(1, desired_coarse)) + region_half_window_sec = max(1.0 / fps, effective_coarse_sec / 2.0) + else: + region_half_window_sec = self.region_half_window_sec + region_half_window = max(1, int(round(region_half_window_sec * fps))) + + # Stage 0: Setup arms (temporal partitioning) + arms = self._setup_arms(total_frames, coarse_stride) + + # Stage 1: Coarse sampling + all_coarse_indices = self._coarse_sampling_in_arms(arms, rng) + all_coarse_similarities = self.similarity_fn(video, query, all_coarse_indices) + + # Update arms with coarse results + self._update_arms_with_scores(arms, all_coarse_indices, all_coarse_similarities) + self._update_focus_scores(arms) + for arm in arms: + arm['focus_after_coarse'] = float(arm['focus_score']) + + # Stage 2: Choose promising arms for fine sampling + selected_arms = self._choose_promising_arms(arms) + + # Stage 3: Fine sampling in promising arms + coarse_indices_set = set(all_coarse_indices) + fine_indices = self._fine_sampling_in_arms( + selected_arms, total_frames, fine_stride, + region_half_window, coarse_indices_set, rng + ) + + # Compute similarities for fine samples + all_fine_indices = [] + all_fine_similarities = [] + if fine_indices: + all_fine_indices = fine_indices + all_fine_similarities = self.similarity_fn(video, query, fine_indices) + + # Update arms with fine results + self._update_arms_with_scores(arms, all_fine_indices, all_fine_similarities) + self._update_focus_scores(arms) + for arm in arms: + arm['focus_after_fine'] = float(arm['focus_score']) + + # Merge all sampled scores + all_sampled_scores = {idx: sim for idx, sim in zip(all_coarse_indices, all_coarse_similarities)} + if all_fine_indices: + all_sampled_scores.update({idx: sim for idx, sim in zip(all_fine_indices, all_fine_similarities)}) + + # Stage 4: Final keyframe selection + selected_frames = [] + arm_selection_probs = [] + arm_selection_counts = [] + + if all_sampled_scores and k > 0: + # 4a. Select top-ranked frames + selected_frames = self._select_top_frames(all_sampled_scores, k) + + # 4b. Select remaining frames using FOCUS arm selection + remaining_count = max(0, k - len(selected_frames)) + if remaining_count > 0: + additional_frames, arm_selection_probs, arm_selection_counts = self._select_remaining_frames( + arms=arms, + remaining_count=remaining_count, + all_sampled_scores=all_sampled_scores, + selected_frames=selected_frames, + fps=fps, + min_gap_sec=min_gap_sec, + rng=rng + ) + selected_frames.extend(additional_frames) + + # Finalize: deduplicate and sort + selected_frames = sorted(list(dict.fromkeys(selected_frames)))[:k] if k > 0 else sorted(list(dict.fromkeys(selected_frames))) + + # Prepare sampling details + sampling_details = self._prepare_sampling_details( + all_coarse_indices, all_coarse_similarities, + all_fine_indices, all_fine_similarities, + arms, selected_frames, total_frames, fps, + len(all_coarse_indices) + len(all_fine_indices), + arm_selection_probs, arm_selection_counts + ) + + return selected_frames, sampling_details + + # ======================================================================== + # Internal Methods (Algorithm Stages) + # ======================================================================== + + def _setup_arms(self, total_frames: int, coarse_stride: int) -> List[Dict]: + """ + Stage 0: Setup arms (temporal partitioning). + + Partition the video into temporal segments (arms). Each arm represents + a temporal region that can be independently sampled and evaluated. + + Args: + total_frames: Total number of frames in the video + coarse_stride: Stride for coarse sampling (determines arm size) + + Returns: + List of arm dictionaries, each containing: + - arm_id: Unique identifier + - start, end: Frame range (inclusive) + - samples, mean_sim, variance: Statistics + - focus_score: Confidence upper-bound score + - sampled_indices, sampled_scores: Tracking of samples + """ + num_regions = max(1, total_frames // coarse_stride) + region_size = max(1, total_frames // num_regions) + + arms = [] + for region_idx in range(num_regions): + region_start = region_idx * region_size + region_end = min((region_idx + 1) * region_size, total_frames) + if region_end > region_start: + arms.append({ + 'arm_id': region_idx, + 'start': region_start, + 'end': region_end - 1, # inclusive end + 'samples': 0, + 'total_reward': 0.0, + 'sum_squared_reward': 0.0, + 'mean_sim': 0.0, + 'variance': 0.0, + 'focus_score': 0.0, + 'focus_after_coarse': None, + 'focus_after_fine': None, + 'sampled_indices': [], + 'sampled_scores': [] + }) + return arms + + def _coarse_sampling_in_arms( + self, + arms: List[Dict], + rng: np.random.Generator + ) -> List[int]: + """ + Stage 1a: Coarse sampling in each arm. + + Sample the center point of each arm plus extra random points for + variance estimation. This provides initial exploration of all arms. + + Args: + arms: List of arm dictionaries + rng: Random number generator + + Returns: + List of frame indices to sample (will be passed to similarity_fn) + """ + all_coarse_indices = [] + for arm in arms: + start, end = arm['start'], arm['end'] + # Sample center point + center_idx = (start + end) // 2 + sampled_indices = [center_idx] + + # Sample extra random points in this arm for variance estimation + available_indices = list(range(start, end + 1)) + if center_idx in available_indices: + available_indices.remove(center_idx) + + if len(available_indices) > 0: + extra_count = min(self.extra_samples_per_region, len(available_indices)) + extra_indices = rng.choice(available_indices, size=extra_count, replace=False) + sampled_indices.extend(extra_indices.tolist()) + + arm['sampled_indices'] = sampled_indices + all_coarse_indices.extend(sampled_indices) + + return all_coarse_indices + + def _update_arms_with_scores( + self, + arms: List[Dict], + indices: List[int], + similarities: List[float] + ) -> None: + """ + Update arms with new sampling results and compute statistics. + + This method updates each arm's sampled_scores and recomputes + the mean similarity and variance based on all samples collected so far. + + Args: + arms: List of arm dictionaries + indices: List of sampled frame indices + similarities: List of corresponding similarity scores + """ + # Create mapping from index to score + idx_to_score = {idx: score for idx, score in zip(indices, similarities)} + + for arm in arms: + # Update with new scores (avoid duplicates) + for idx in arm['sampled_indices']: + if idx in idx_to_score: + score = idx_to_score[idx] + if idx not in [s[0] for s in arm['sampled_scores']]: + arm['sampled_scores'].append((idx, score)) + + # Recompute statistics for this arm + if arm['sampled_scores']: + all_arm_scores = [score for _, score in arm['sampled_scores']] + arm['samples'] = len(all_arm_scores) + arm['mean_sim'] = float(np.mean(all_arm_scores)) + arm['variance'] = float(np.var(all_arm_scores)) if len(all_arm_scores) > 1 else 0.0 + + def _update_focus_scores(self, arms: List[Dict]) -> None: + """ + Stage 1b: Compute FOCUS confidence upper-bound scores for all arms. + + FOCUS score combines: + - Mean similarity (exploitation) + - Variance-based uncertainty term (exploration) + - Sample count penalty (exploration) + + Formula: mean + sqrt(2*log(N)*var/n) + 3*log(N)/n + where N = total samples, n = samples in this arm, var = variance + + Args: + arms: List of arm dictionaries + """ + total_samples = sum(arm['samples'] for arm in arms) + + for arm in arms: + n_i = arm['samples'] + mean = arm['mean_sim'] + var = max(arm['variance'], self.min_variance_threshold) + + focus_score = mean + if total_samples > 1 and n_i > 0: + # Variance-based confidence term + focus_score += math.sqrt(max(0.0, 2 * math.log(total_samples) * var / n_i)) + # Sample count penalty + focus_score += (3 * math.log(total_samples) / n_i) + + arm['focus_score'] = focus_score + + def _choose_promising_arms(self, arms: List[Dict]) -> List[Dict]: + """ + Stage 2: Choose promising arms using FOCUS confidence upper-bound scores. + + Select the top arms based on FOCUS scores for fine-grained sampling. + This focuses computational budget on the most promising temporal regions. + + Args: + arms: List of arm dictionaries + + Returns: + List of selected promising arms + """ + arms_sorted = sorted(arms, key=lambda x: x['focus_score'], reverse=True) + zoom_count = max(self.min_zoom_segments, int(np.ceil(len(arms) * self.zoom_ratio))) + zoom_count = min(zoom_count, len(arms)) + + selected_arms = arms_sorted[:zoom_count] + return selected_arms + + def _fine_sampling_in_arms( + self, + selected_arms: List[Dict], + total_frames: int, + fine_stride: int, + region_half_window: int, + coarse_indices_set: set, + rng: np.random.Generator + ) -> List[int]: + """ + Stage 3: Fine sampling in promising arms with mixed uniform/random strategy. + + For each selected arm, densely sample a window around its center using + a mix of uniform sampling (for coverage) and random sampling (for exploration). + + Args: + selected_arms: List of selected promising arms + total_frames: Total number of frames in video + fine_stride: Stride for fine sampling + region_half_window: Half window size around arm centers (in frames) + coarse_indices_set: Set of already sampled coarse indices (to avoid duplicates) + rng: Random number generator + + Returns: + List of frame indices for fine sampling + """ + candidate_fine = set() + + for arm in selected_arms: + arm_center = (arm['start'] + arm['end']) // 2 + start = max(0, arm_center - region_half_window) + end = min(total_frames - 1, arm_center + region_half_window) + + window_size = end - start + 1 + required_samples = max(1, window_size // fine_stride) + + # Split into uniform and random sampling + uniform_count = int(round(required_samples * self.fine_uniform_ratio)) + random_count = required_samples - uniform_count + + # Uniform sampling for coverage + if uniform_count > 0: + if window_size <= uniform_count: + # If window is small, just take center + center_idx = (start + end) // 2 + if center_idx not in coarse_indices_set and center_idx not in candidate_fine: + candidate_fine.add(center_idx) + else: + # Uniform spacing across window + interval_size = window_size / uniform_count + for i in range(uniform_count): + uniform_idx = start + int(i * interval_size + interval_size / 2) + uniform_idx = min(uniform_idx, end) + if uniform_idx not in coarse_indices_set and uniform_idx not in candidate_fine: + candidate_fine.add(uniform_idx) + + # Random sampling for exploration + if random_count > 0: + available_indices = [i for i in range(start, end + 1) + if i not in coarse_indices_set and i not in candidate_fine] + if available_indices: + actual_random_count = min(random_count, len(available_indices)) + random_indices = rng.choice(available_indices, size=actual_random_count, replace=False) + for idx in random_indices: + candidate_fine.add(int(idx)) + + return sorted(list(candidate_fine)) + + def _select_top_frames( + self, + all_sampled_scores: Dict[int, float], + k: int + ) -> List[int]: + """ + Stage 4a: Select top-ranked frames from all sampled scores. + + Directly select the highest scoring frames based on top_ratio. + This ensures that the best discovered frames are always included. + + Args: + all_sampled_scores: Dictionary mapping frame indices to similarity scores + k: Total number of keyframes to select + + Returns: + List of selected top frame indices + """ + if not all_sampled_scores or k <= 0: + return [] + + num_computed_frames = len(all_sampled_scores) + k_top = int(round(self.top_ratio * min(k, num_computed_frames))) + + sorted_sampled = sorted(all_sampled_scores.items(), key=lambda x: x[1], reverse=True) + top_frames = [idx for idx, _ in sorted_sampled[:k_top]] + + return top_frames + + def _select_remaining_frames( + self, + arms: List[Dict], + remaining_count: int, + all_sampled_scores: Dict[int, float], + selected_frames: List[int], + fps: float, + min_gap_sec: float, + rng: np.random.Generator + ) -> Tuple[List[int], List[float], List[int]]: + """ + Stage 4b: Select remaining frames using FOCUS arm selection strategy. + + Strategy: + 1. Select top S = clamp(zoom_ratio * num_arms, min_arms, max_arms) arms + 2. Evenly distribute remaining_count across these S arms + 3. Within each arm, use interpolation or random sampling based on interpolation_method + 4. Respect minimum temporal gap constraints if specified + + Args: + arms: List of arm dictionaries + remaining_count: Number of remaining frames to select + all_sampled_scores: Dictionary of all sampled scores + selected_frames: List of already selected frames + fps: Video frame rate + min_gap_sec: Minimum temporal gap between selections in seconds + rng: Random number generator + + Returns: + Tuple of (new_frames, arm_selection_probs, arm_selection_counts) + - new_frames: List of newly selected frame indices + - arm_selection_probs: Probability of each arm being selected + - arm_selection_counts: Number of frames selected from each arm + """ + if remaining_count <= 0 or not arms: + return [], [], [] + + gap_frames = int(round(min_gap_sec * fps)) if min_gap_sec > 0 else 0 + selected_set = set(selected_frames) + + def respects_gap(cand: int, chosen_set: set) -> bool: + """Check if candidate respects minimum gap constraint.""" + if gap_frames <= 0: + return True + for frame in chosen_set: + if abs(cand - frame) < gap_frames: + return False + return True + + # Compute number of top arms using zoom_ratio with hard bounds + total_arms = len(arms) + S = int(np.ceil(total_arms * max(0.0, min(1.0, float(self.zoom_ratio))))) + S = max(self.final_min_arms, S) + S = min(S, self.final_max_arms) + S = min(S, total_arms) + + arms_sorted = sorted([(i, arm) for i, arm in enumerate(arms)], + key=lambda x: x[1]['focus_score'], reverse=True) + top_arm_entries = arms_sorted[:S] + + # Even allocation across top S arms + base = remaining_count // S + rem = remaining_count % S + per_arm_need = [base + (1 if i < rem else 0) for i in range(S)] + + arm_selection_counts = [0 for _ in range(total_arms)] + new_frames = [] + current_selected_set = selected_set.copy() + + for rank, (arm_idx, arm) in enumerate(top_arm_entries): + needed_count = per_arm_need[rank] + if needed_count == 0: + continue + + arm_start, arm_end = arm['start'], arm['end'] + all_arm_candidates = list(range(arm_start, arm_end + 1)) + available_candidates = [c for c in all_arm_candidates if c not in current_selected_set] + if not available_candidates: + continue + + if self.interpolation_method == 'uniform': + # Random sampling within arm (no interpolation) + if gap_frames > 0: + gap_candidates = [c for c in available_candidates if respects_gap(c, current_selected_set)] + candidates_to_use = gap_candidates if gap_candidates else available_candidates + else: + candidates_to_use = available_candidates + + actual_count = min(needed_count, len(candidates_to_use)) + if actual_count > 0: + sampled_indices = rng.choice(candidates_to_use, size=actual_count, replace=False) + for idx in sampled_indices: + new_frames.append(int(idx)) + current_selected_set.add(int(idx)) + arm_selection_counts[arm_idx] += 1 + else: + # Interpolation-based sampling within arm + arm_sampled_indices = [idx for idx, _ in arm['sampled_scores']] + arm_sampled_similarities = [score for _, score in arm['sampled_scores']] + + arm_estimated_scores = estimate_arm_scores( + arm_sampled_indices, arm_sampled_similarities, + arm_start, arm_end, self.interpolation_method + ) + + scored_candidates = [] + for cand in available_candidates: + if cand in arm_estimated_scores: + score = arm_estimated_scores[cand] + if gap_frames == 0 or respects_gap(cand, current_selected_set): + scored_candidates.append((cand, score)) + + if not scored_candidates: + # Fallback: ignore gap if necessary + for cand in available_candidates: + if cand in arm_estimated_scores: + scored_candidates.append((cand, arm_estimated_scores[cand])) + + if scored_candidates: + candidates, scores = zip(*scored_candidates) + candidates = list(candidates) + scores = np.array(scores, dtype=np.float64) + + # Normalize scores to [0, 1] + if scores.max() > scores.min(): + scores = (scores - scores.min()) / (scores.max() - scores.min()) + else: + scores = np.ones_like(scores) + + # Apply temperature-based softmax + logits = scores / max(1e-12, self.temperature) + logits = logits - logits.max() # numerical stability + probs = np.exp(logits) + probs = probs / probs.sum() + + actual_count = min(needed_count, len(candidates)) + if actual_count > 0: + sampled_positions = rng.choice(len(candidates), size=actual_count, p=probs, replace=False) + for pos in sampled_positions: + idx = candidates[pos] + new_frames.append(int(idx)) + current_selected_set.add(int(idx)) + arm_selection_counts[arm_idx] += 1 + else: + # Fallback: random sampling + candidates_to_use = available_candidates + if gap_frames > 0: + gap_candidates = [c for c in available_candidates if respects_gap(c, current_selected_set)] + candidates_to_use = gap_candidates if gap_candidates else available_candidates + actual_count = min(needed_count, len(candidates_to_use)) + if actual_count > 0: + sampled_indices = rng.choice(candidates_to_use, size=actual_count, replace=False) + for idx in sampled_indices: + new_frames.append(int(idx)) + current_selected_set.add(int(idx)) + arm_selection_counts[arm_idx] += 1 + + # Derive per-arm probabilities from counts + if remaining_count > 0: + arm_selection_probs = [c / remaining_count for c in arm_selection_counts] + else: + arm_selection_probs = [0.0 for _ in arm_selection_counts] + + return new_frames, arm_selection_probs, arm_selection_counts + + def _prepare_sampling_details( + self, + coarse_indices: List[int], + coarse_similarities: List[float], + fine_indices: List[int], + fine_similarities: List[float], + arms: List[Dict], + selected_frames: List[int], + total_frames: int, + fps: float, + budget_used: int, + arm_selection_probs: List[float], + arm_selection_counts: List[int] + ) -> Dict: + """ + Prepare detailed sampling information for export and analysis. + + This creates a comprehensive record of the sampling process including: + - Coarse and fine sampling statistics + - Arm information and statistics + - Final selection results + - Video metadata + + Args: + coarse_indices: Frame indices sampled in coarse stage + coarse_similarities: Corresponding similarity scores + fine_indices: Frame indices sampled in fine stage + fine_similarities: Corresponding similarity scores + arms: List of arm dictionaries with statistics + selected_frames: Final selected frame indices + total_frames: Total frames in video + fps: Video frame rate + budget_used: Total number of similarity computations + arm_selection_probs: Probability of each arm being selected + arm_selection_counts: Number of frames selected from each arm + + Returns: + Dictionary containing all sampling details + """ + + def create_temporal_order(frame_indices: List[int], similarities: List[float], fps: float) -> List[Dict]: + """Helper: Create temporal order list with timestamps.""" + if not frame_indices: + return [] + + combined = list(zip(frame_indices, similarities)) + combined.sort(key=lambda x: x[0]) + + temporal_order = [] + for frame_idx, score in combined: + temporal_order.append({ + "frame_idx": int(frame_idx), + "score": float(score), + "timestamp": float(frame_idx / max(1.0, fps)) + }) + return temporal_order + + # Prepare coarse sampling info + coarse_sampling = { + "frame_indices": [int(idx) for idx in coarse_indices], + "relevance_scores": [float(score) for score in coarse_similarities], + "temporal_order": create_temporal_order(coarse_indices, coarse_similarities, fps), + "budget_used": int(len(coarse_indices)) + } + + # Prepare fine sampling info + fine_sampling = { + "frame_indices": [int(idx) for idx in fine_indices], + "relevance_scores": [float(score) for score in fine_similarities], + "temporal_order": create_temporal_order(fine_indices, fine_similarities, fps), + "budget_used": int(len(fine_indices)) + } + + # Prepare arms info + arms_info = { + "total_arms": len(arms), + "frames_per_arm": total_frames // max(1, len(arms)), + "arms": [] + } + + for i, arm in enumerate(arms): + times_selected = arm_selection_counts[i] if i < len(arm_selection_counts) else 0 + arm_info = { + "arm_id": int(arm['arm_id']), + "start_frame": int(arm['start']), + "end_frame": int(arm['end']), + "focus_score": float(arm['focus_score']), + "focus_after_coarse": float(arm['focus_after_coarse']) if arm.get('focus_after_coarse') is not None else None, + "focus_after_fine": float(arm['focus_after_fine']) if arm.get('focus_after_fine') is not None else None, + "times_selected": int(times_selected), + "mean_similarity": float(arm['mean_sim']), + "variance": float(arm['variance']), + "samples_count": int(arm['samples']) + } + arms_info["arms"].append(arm_info) + + # Prepare video metadata + video_metadata = { + "total_frames": int(total_frames), + "fps": float(fps), + "duration_seconds": float(total_frames / max(1.0, fps)), + "budget_used": int(budget_used) + } + + return { + "coarse_sampling": coarse_sampling, + "fine_sampling": fine_sampling, + "arms_info": arms_info, + "arm_selection_probabilities": [float(prob) for prob in arm_selection_probs], + "final_selected_frames": [int(idx) for idx in selected_frames], + "video_metadata": video_metadata + } + diff --git a/transfer/code/FOCUS/requirements.txt b/transfer/code/FOCUS/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..c8c60c6bc3be1983d40803d1e8d404902c1c335f --- /dev/null +++ b/transfer/code/FOCUS/requirements.txt @@ -0,0 +1,3 @@ +opencv-python +ray +scikit-learn \ No newline at end of file diff --git a/transfer/code/FOCUS/run_focus_example.sh b/transfer/code/FOCUS/run_focus_example.sh new file mode 100644 index 0000000000000000000000000000000000000000..6441dafe2bcc5b982291f89f2f36348172705a06 --- /dev/null +++ b/transfer/code/FOCUS/run_focus_example.sh @@ -0,0 +1,95 @@ +#!/bin/bash + +# FOCUS Keyframe Extraction Example Script +# This script demonstrates how to use FOCUS for keyframe extraction + +echo "FOCUS: Frame-Optimistic Confidence Upper-bound Selection" +echo "========================================================" + +# Set default parameters +DATASET_NAME="longvideobench" +DATASET_PATH="./datasets/longvideobench" +OUTPUT_DIR="focus_example" +NUM_KEYFRAMES=64 +BATCH_SIZE=32 +BLIP_MODEL="large" + +# FOCUS-specific parameters +COARSE_EVERY_SEC=16.0 +FINE_EVERY_SEC=1.0 +ZOOM_RATIO=0.25 +INTERPOLATION_METHOD="nearest" +FINAL_MIN_ARMS=4 +FINAL_MAX_ARMS=32 +TOP_RATIO=0.2 +TEMPERATURE=0.06 + +echo "Configuration:" +echo " Dataset: $DATASET_NAME" +echo " Dataset Path: $DATASET_PATH" +echo " Output Directory: $OUTPUT_DIR" +echo " Number of Keyframes: $NUM_KEYFRAMES" +echo " Batch Size: $BATCH_SIZE" +echo " BLIP Model: $BLIP_MODEL" +echo "" +echo "FOCUS Parameters:" +echo " Coarse Sampling Interval: ${COARSE_EVERY_SEC}s" +echo " Fine Sampling Interval: ${FINE_EVERY_SEC}s" +echo " Zoom Ratio: $ZOOM_RATIO" +echo " Interpolation Method: $INTERPOLATION_METHOD" +echo " Final Arm Bounds: [$FINAL_MIN_ARMS, $FINAL_MAX_ARMS]" +echo " Top Ratio: $TOP_RATIO" +echo " Temperature: $TEMPERATURE" +echo "" + +# Check if dataset exists +if [ ! -d "$DATASET_PATH" ]; then + echo "Error: Dataset path '$DATASET_PATH' does not exist!" + echo "Please download the dataset first following the AKS instructions." + exit 1 +fi + +# Check if select_keyframe.py exists +if [ ! -f "select_keyframe.py" ]; then + echo "Error: select_keyframe.py not found in current directory!" + echo "Please make sure you're running this script from the opensource directory." + exit 1 +fi + +echo "Starting FOCUS keyframe extraction..." +echo "" + +# Run FOCUS +python select_keyframe.py \ + --dataset_name "$DATASET_NAME" \ + --dataset_path "$DATASET_PATH" \ + --output_dir "$OUTPUT_DIR" \ + --num_keyframes "$NUM_KEYFRAMES" \ + --batch_size "$BATCH_SIZE" \ + --blip_model "$BLIP_MODEL" \ + --coarse_every_sec "$COARSE_EVERY_SEC" \ + --fine_every_sec "$FINE_EVERY_SEC" \ + --zoom_ratio "$ZOOM_RATIO" \ + --interpolation_method "$INTERPOLATION_METHOD" \ + --final_min_arms "$FINAL_MIN_ARMS" \ + --final_max_arms "$FINAL_MAX_ARMS" \ + --top_ratio "$TOP_RATIO" \ + --temperature "$TEMPERATURE" \ + --seed 42 + +# Check if extraction was successful +if [ $? -eq 0 ]; then + echo "" + echo "FOCUS keyframe extraction completed successfully!" + echo "" + echo "Output files:" + echo " - ./selected_frames/$DATASET_NAME/$OUTPUT_DIR/selected_frames.json" + echo " - ./selected_frames/$DATASET_NAME/$OUTPUT_DIR/sampling_details.json" + echo " - ./selected_frames/$DATASET_NAME/$OUTPUT_DIR/extraction_stats.json" + echo "" + echo "You can now use these results for evaluation with lmms-eval." +else + echo "" + echo "FOCUS keyframe extraction failed!" + echo "Please check the error messages above and ensure all dependencies are installed." +fi diff --git a/transfer/code/FOCUS/select_keyframe.py b/transfer/code/FOCUS/select_keyframe.py new file mode 100644 index 0000000000000000000000000000000000000000..e63276a72d84f0adcb06cf3c7301beec1aadd4bd --- /dev/null +++ b/transfer/code/FOCUS/select_keyframe.py @@ -0,0 +1,497 @@ +""" +FOCUS Data Processing and I/O Module + +This module handles data processing, video loading, and result output +for the FOCUS keyframe extraction algorithm. +""" + +import os +import json +import argparse +import datetime +import random +import time +from typing import Optional, List, Tuple, Dict + +import numpy as np +import torch +import ray +from decord import VideoReader, cpu +from PIL import Image +from tqdm import tqdm + +from lavis.models import load_model_and_preprocess +from focus import FOCUS + + +# ============================================================================ +# Video Processing Functions +# ============================================================================ + +def create_blip_similarity_fn(vr: VideoReader, vis_processors, text_processors, + model, device: str, batch_size: int): + """ + Create a BLIP-based similarity function for FOCUS algorithm. + + This function creates a closure that captures the BLIP model and processors, + providing a clean interface for the FOCUS algorithm. + + Args: + vr: VideoReader object + vis_processors: Vision processors for BLIP + text_processors: Text processors for BLIP + model: BLIP model + device: Device to run inference on + batch_size: Batch size for processing + + Returns: + Function with signature (video, query, frame_indices) -> similarity_scores + """ + def similarity_fn(video: VideoReader, query: str, frame_indices: List[int]) -> List[float]: + """ + Compute BLIP similarity scores for a batch of frame indices. + + Args: + video: VideoReader object (same as vr from closure) + query: Query text + frame_indices: List of frame indices to compute similarity for + + Returns: + List of similarity scores + """ + txt = text_processors["eval"](query) + similarities = [] + + for i in range(0, len(frame_indices), batch_size): + batch_indices = frame_indices[i:i+batch_size] + batch_images = [] + for idx in batch_indices: + raw_image = vr[idx].numpy() + raw_image = Image.fromarray(raw_image) + img = vis_processors["eval"](raw_image).unsqueeze(0) + batch_images.append(img) + + if batch_images: + batch_tensor = torch.cat(batch_images, dim=0).to(device) + with torch.no_grad(): + blip_output, _ = model({"image": batch_tensor, "text_input": txt}, match_head="itm-e") + blip_scores = torch.nn.functional.softmax(blip_output, dim=1) + batch_similarities = [blip_scores[j, 1].item() for j in range(len(batch_indices))] + similarities.extend(batch_similarities) + + return similarities + + return similarity_fn + + +# ============================================================================ +# Ray Worker Functions +# ============================================================================ + +@ray.remote(num_gpus=1) +def ray_worker(dp_rank: int, output_json_base_prefix: str, data_slice, args_dict): + """Ray worker for distributed processing.""" + worker_start_time = time.time() + + class Args: pass + args = Args() + for k, v in args_dict.items(): + setattr(args, k, v) + + device = 'cuda:0' + full_output_dir = os.path.join('./selected_frames', args.dataset_name, args.output_dir) + os.makedirs(full_output_dir, exist_ok=True) + output_json = os.path.join(full_output_dir, f"{output_json_base_prefix}_rank{dp_rank}.json") + + model, vis_processors, text_processors = load_model_and_preprocess("blip_image_text_matching", args.blip_model, + device=device, is_eval=True) + + video_root = (args.dataset_path + '/videos' if args.dataset_name == 'longvideobench' else args.dataset_path + '/data') + rng = np.random.default_rng(args.seed + dp_rank) + + results = [] + budget_stats = [] + sampling_details_results = [] + + pbar = tqdm(data_slice, desc=f"Rank {dp_rank}", ncols=100) + for original_idx, data in pbar: + try: + text = data['question'] + video_file = (os.path.join(video_root, data['video_path']) + if args.dataset_name == 'longvideobench' + else os.path.join(video_root, data['videoID'] + '.mp4')) + + if not os.path.exists(video_file): + selected = [] + budget_used = 0 + total_frames = 0 + video_duration = 0.0 + sampling_details = { + "coarse_sampling": {"frame_indices": [], "relevance_scores": [], "temporal_order": [], "budget_used": 0}, + "fine_sampling": {"frame_indices": [], "relevance_scores": [], "temporal_order": [], "budget_used": 0}, + "arms_info": {"total_arms": 0, "frames_per_arm": 0, "arms": []}, + "arm_selection_probabilities": [], + "final_selected_frames": [], + "video_metadata": {"total_frames": 0, "fps": 0.0, "duration_seconds": 0.0, "budget_used": 0} + } + else: + vr = VideoReader(video_file, ctx=cpu(0)) + fps = float(vr.get_avg_fps()) + total_frames = len(vr) + video_duration = float(total_frames) / max(1.0, fps) + + # Adaptive min-gap calculation + avg_spacing_sec = video_duration / max(1, args.num_keyframes) + if avg_spacing_sec <= float(args.disable_gap_below_sec): + auto_min_gap_sec = 0.0 + else: + gap_from_ratio = float(args.gap_ratio_of_avg) * avg_spacing_sec + auto_min_gap_sec = min(gap_from_ratio, float(args.min_gap_sec)) + + # Create BLIP similarity function + similarity_fn = create_blip_similarity_fn( + vr, vis_processors, text_processors, model, device, args.batch_size + ) + + # Create FOCUS instance + focus = FOCUS( + similarity_fn=similarity_fn, + coarse_every_sec=args.coarse_every_sec, + fine_every_sec=args.fine_every_sec, + zoom_ratio=args.zoom_ratio, + final_min_arms=args.final_min_arms, + final_max_arms=args.final_max_arms, + min_coarse_segments=args.min_coarse_segments, + min_zoom_segments=args.min_zoom_segments, + extra_samples_per_region=args.extra_samples_per_region, + min_variance_threshold=args.min_variance_threshold, + fine_uniform_ratio=args.fine_uniform_ratio, + interpolation_method=args.interpolation_method, + top_ratio=args.top_ratio, + temperature=args.temperature, + region_half_window_sec=args.region_half_window_sec + ) + + # Select keyframes using FOCUS algorithm + selected, sampling_details = focus.select_keyframes( + video=vr, + query=text, + k=args.num_keyframes, + min_gap_sec=auto_min_gap_sec, + rng=rng + ) + + budget_used = sampling_details["video_metadata"]["budget_used"] + + results.append({"original_idx": original_idx, "selected_frames": [int(x) for x in selected]}) + budget_stats.append({ + "original_idx": original_idx, + "budget_used": int(budget_used), + "total_frames": int(total_frames), + "video_duration": float(video_duration) + }) + sampling_details_results.append({ + "original_idx": original_idx, + **sampling_details + }) + + with open(output_json, 'w') as f: + json.dump(results, f) + pbar.set_postfix({"processed": len(results), "last_selected": len(selected)}) + + except Exception as e: + print(f"Error on video {original_idx}: {e}") + results.append({"original_idx": original_idx, "selected_frames": []}) + budget_stats.append({ + "original_idx": original_idx, + "budget_used": 0, + "total_frames": 0, + "video_duration": 0.0 + }) + sampling_details_results.append({ + "original_idx": original_idx, + "coarse_sampling": {"frame_indices": [], "relevance_scores": [], "temporal_order": [], "budget_used": 0}, + "fine_sampling": {"frame_indices": [], "relevance_scores": [], "temporal_order": [], "budget_used": 0}, + "arms_info": {"total_arms": 0, "frames_per_arm": 0, "arms": []}, + "arm_selection_probabilities": [], + "final_selected_frames": [], + "video_metadata": {"total_frames": 0, "fps": 0.0, "duration_seconds": 0.0, "budget_used": 0} + }) + with open(output_json, 'w') as f: + json.dump(results, f) + + worker_end_time = time.time() + worker_runtime_hours = (worker_end_time - worker_start_time) / 3600 + + return output_json, budget_stats, worker_runtime_hours, sampling_details_results + + +# ============================================================================ +# File I/O Functions +# ============================================================================ + +def merge_json_files(output_dir: str, output_json_base_prefix: str, dp_size: int, merged_output_path: str): + """Merge results from multiple workers.""" + all_results = {} + for dp_rank in range(dp_size): + fname = os.path.join(output_dir, f"{output_json_base_prefix}_rank{dp_rank}.json") + if os.path.exists(fname): + with open(fname, 'r') as f: + rank_results = json.load(f) + for result in rank_results: + all_results[result["original_idx"]] = result["selected_frames"] + else: + print(f"Warning: File {fname} not found") + + total_videos = max(all_results.keys()) + 1 if all_results else 0 + final_results = [] + for i in range(total_videos): + if i in all_results: + final_results.append(all_results[i]) + else: + final_results.append([]) + + with open(merged_output_path, 'w') as f: + json.dump(final_results, f) + print(f"Merged results saved to {merged_output_path}") + + for dp_rank in range(dp_size): + fname = os.path.join(output_dir, f"{output_json_base_prefix}_rank{dp_rank}.json") + if os.path.exists(fname): + os.remove(fname) + + +def merge_sampling_details_files(sampling_details_results: List[List[Dict]], output_dir: str, merged_sampling_details_path: str): + """Merge sampling details from multiple workers.""" + all_sampling_details = {} + for worker_details in sampling_details_results: + for detail in worker_details: + all_sampling_details[detail["original_idx"]] = detail + + total_videos = max(all_sampling_details.keys()) + 1 if all_sampling_details else 0 + final_sampling_details = [] + for i in range(total_videos): + if i in all_sampling_details: + final_sampling_details.append(all_sampling_details[i]) + else: + final_sampling_details.append({ + "original_idx": i, + "coarse_sampling": {"frame_indices": [], "relevance_scores": [], "temporal_order": [], "budget_used": 0}, + "fine_sampling": {"frame_indices": [], "relevance_scores": [], "temporal_order": [], "budget_used": 0}, + "arms_info": {"total_arms": 0, "frames_per_arm": 0, "arms": []}, + "arm_selection_probabilities": [], + "final_selected_frames": [], + "video_metadata": {"total_frames": 0, "fps": 0.0, "duration_seconds": 0.0, "budget_used": 0} + }) + + with open(merged_sampling_details_path, 'w') as f: + json.dump(final_sampling_details, f, indent=2) + print(f"Merged sampling details saved to {merged_sampling_details_path}") + + +# ============================================================================ +# CLI Interface +# ============================================================================ + +def parse_arguments(): + """Parse command line arguments.""" + parser = argparse.ArgumentParser(description='Keyframe extraction with FOCUS approach (Frame-Optimistic Confidence Upper-bound Selection)') + parser.add_argument('--dataset_name', type=str, default='longvideobench', + help='support longvideobench and videomme') + parser.add_argument('--dataset_path', type=str, default='./datasets/longvideobench', + help='path to the dataset root') + parser.add_argument('--output_dir', type=str, default='focus_blip', + help='algorithm name folder under ./selected_frames/{dataset_name}/') + parser.add_argument('--num_keyframes', type=int, default=64, + help='number of keyframes to select') + parser.add_argument('--batch_size', type=int, default=32, + help='batch size for BLIP processing') + parser.add_argument('--blip_model', type=str, default='large', + help='BLIP model type (base, large)') + + # Hybrid selection parameters + parser.add_argument('--top_ratio', type=float, default=0.2, + help='Ratio to determine top-ranked selection count: top_count = ratio * min(num_keyframes, computed_frames) (0~1)') + parser.add_argument('--temperature', type=float, default=0.06, help='Softmax temperature for within-arm sampling when using interpolation') + parser.add_argument('--min_gap_sec', type=float, default=1.0, help='Fixed minimum temporal gap between selections (sec)') + + # Adaptive min-gap controls + parser.add_argument('--disable_gap_below_sec', type=float, default=0.2, + help='Disable min-gap if average spacing <= this (sec)') + parser.add_argument('--gap_ratio_of_avg', type=float, default=0.25, + help='min-gap = min(fixed, ratio * average spacing) when not disabled') + + # Proportional zooming controls (coarse -> fine) + parser.add_argument('--coarse_every_sec', type=float, default=16.0, + help='Coarse level: sample 1 frame every X seconds') + parser.add_argument('--fine_every_sec', type=float, default=1.0, + help='Fine level: sample 1 frame every Y seconds in zoomed regions') + parser.add_argument('--zoom_ratio', type=float, default=0.25, + help='Fraction of coarse segments to zoom into (0~1) and also used in final arm selection') + parser.add_argument('--min_coarse_segments', type=int, default=8, + help='Ensure at least this many coarse segments') + parser.add_argument('--min_zoom_segments', type=int, default=4, + help='Ensure at least this many zoomed regions') + parser.add_argument('--region_half_window_sec', type=float, default=None, + help='Half window size (sec) around each coarse center; default=coarse_every_sec/2') + + # FOCUS shared parameters + parser.add_argument('--extra_samples_per_region', type=int, default=2, + help='Extra random samples per region for initial variance estimation') + parser.add_argument('--min_variance_threshold', type=float, default=1e-6, + help='Minimum variance threshold to avoid division by zero issues in confidence upper-bound') + + # FOCUS specifics + parser.add_argument('--fine_uniform_ratio', type=float, default=0.5, + help='Ratio of uniform sampling in fine sampling stage (0~1). Rest will be random sampling.') + parser.add_argument('--interpolation_method', type=str, default='nearest', choices=['nearest', 'linear', 'rbf', 'uniform'], + help='Interpolation method for estimating scores within arms') + parser.add_argument('--final_min_arms', type=int, default=4, + help='Minimum number of arms to use in final allocation (after zoom_ratio)') + parser.add_argument('--final_max_arms', type=int, default=32, + help='Maximum number of arms to use in final allocation (after zoom_ratio)') + + parser.add_argument('--seed', type=int, default=42, help='random seed') + return parser.parse_args() + + +def main(): + """Main function for running FOCUS keyframe extraction.""" + args = parse_arguments() + + if not torch.cuda.is_available(): + print("CUDA is not available!") + return + + random.seed(args.seed) + np.random.seed(args.seed) + + gpu_count = torch.cuda.device_count() + print(f"Available GPUs: {gpu_count}") + + ray.init() + DP_SIZE = min(8, gpu_count) + print(f"Using {DP_SIZE} workers") + + time_stamp = datetime.datetime.now().strftime("%Y%m%d%H%M%S") + output_json_base_prefix = f'keyframe_focus_{args.dataset_name}_{time_stamp}' + + output_dir = os.path.join('./selected_frames', args.dataset_name, args.output_dir) + os.makedirs(output_dir, exist_ok=True) + merged_output_path = os.path.join(output_dir, 'selected_frames.json') + merged_sampling_details_path = os.path.join(output_dir, 'sampling_details.json') + + if args.dataset_name == 'longvideobench': + label_path = os.path.join(args.dataset_path, 'lvb_val.json') + elif args.dataset_name == 'videomme': + label_path = os.path.join(args.dataset_path, 'videomme.json') + else: + raise ValueError('dataset_name: longvideobench or videomme') + + if not os.path.exists(label_path): + raise OSError('the label file does not exist') + with open(label_path, 'r') as f: + datas = json.load(f) + print(f"Total videos to process: {len(datas)}") + + total = len(datas) + per_rank = (total + DP_SIZE - 1) // DP_SIZE + + original_indices = list(range(total)) + shuffled_indices = original_indices.copy() + random.shuffle(shuffled_indices) + print(f"Shuffled data indices for load balancing across {DP_SIZE} workers") + + args_dict = vars(args) + ray_tasks = [] + for dp_rank in range(DP_SIZE): + start = dp_rank * per_rank + end = min(start + per_rank, total) + shuffled_slice_indices = shuffled_indices[start:end] + data_slice = [(orig_idx, datas[orig_idx]) for orig_idx in shuffled_slice_indices] + if len(data_slice) > 0: + ray_tasks.append(ray_worker.remote(dp_rank, output_json_base_prefix, data_slice, args_dict)) + + print("Processing videos in parallel...") + ray_results = ray.get(ray_tasks) + + all_budget_stats = [] + all_sampling_details = [] + total_gpu_hours = 0.0 + for _, stats, worker_hours, sampling_details in ray_results: + all_budget_stats.extend(stats) + all_sampling_details.append(sampling_details) + total_gpu_hours += worker_hours + + print("Merging results...") + merge_json_files(output_dir, output_json_base_prefix, DP_SIZE, merged_output_path) + print("Merging sampling details...") + merge_sampling_details_files(all_sampling_details, output_dir, merged_sampling_details_path) + + total_budget_used = sum(s.get('budget_used', 0) for s in all_budget_stats) + total_frames = sum(s.get('total_frames', 0) for s in all_budget_stats) + total_duration = sum(s.get('video_duration', 0.0) for s in all_budget_stats) + + frame_speedup = (total_frames / total_budget_used) if total_budget_used > 0 else 0.0 + time_speedup = (total_duration / total_budget_used) if total_budget_used > 0 else 0.0 + + print("\n" + "=" * 60) + print("BUDGET USAGE STATISTICS") + print("=" * 60) + print(f"Total videos processed: {len(all_budget_stats)}") + print(f"Total budget used (BLIP forward passes): {total_budget_used:,}") + print(f"Total frames in all videos: {total_frames:,}") + print(f"Total video duration: {total_duration:.1f} seconds ({total_duration/3600:.2f} hours)") + print(f" Frame-based speedup: {frame_speedup:.2f}x") + print(f" Time-based speedup: {time_speedup:.2f}x") + print("=" * 60) + print("Method: FOCUS (Frame-Optimistic Confidence Upper-bound Selection)") + print(f" Extra samples per region: {args.extra_samples_per_region}") + print(f" Min variance threshold: {args.min_variance_threshold}") + print(f" Fine uniform ratio: {args.fine_uniform_ratio:.2f}") + print(f" Interpolation method: {args.interpolation_method}") + print(f" Top-ranked ratio: {args.top_ratio:.2f}") + print(f" Final selection arms: zoom_ratio={args.zoom_ratio}, bounds=[{args.final_min_arms}, {args.final_max_arms}]") + print("=" * 60) + + stats_output_path = os.path.join(output_dir, "extraction_stats.json") + extraction_stats = { + "gpu_usage": { + "total_gpu_hours": total_gpu_hours, + "num_workers": DP_SIZE, + "avg_gpu_hours_per_worker": (total_gpu_hours / DP_SIZE) if DP_SIZE > 0 else 0.0 + }, + "budget_usage": { + "total_budget_used": total_budget_used, + "total_videos_processed": len(all_budget_stats), + "total_frames": total_frames, + "total_duration_sec": total_duration, + "total_duration_hours": total_duration / 3600 if total_duration else 0.0, + "frame_speedup": frame_speedup, + "time_speedup": time_speedup + }, + "algorithm_params": { + "blip_model": args.blip_model, + "top_ratio": args.top_ratio, + "extra_samples_per_region": args.extra_samples_per_region, + "min_variance_threshold": args.min_variance_threshold, + "temperature": args.temperature, + "fine_uniform_ratio": args.fine_uniform_ratio, + "interpolation_method": args.interpolation_method, + "final_min_arms": args.final_min_arms, + "final_max_arms": args.final_max_arms + }, + "timestamp": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"), + "output_path": merged_output_path, + "experiment_name": args.output_dir + } + with open(stats_output_path, 'w') as f: + json.dump(extraction_stats, f, indent=2) + + print(f"\nExtraction statistics saved to: {stats_output_path}") + print(f"\nFOCUS keyframe extraction completed. Results saved to {merged_output_path}") + ray.shutdown() + + +if __name__ == '__main__': + main() diff --git a/transfer/code/LatentForcing/.gitignore b/transfer/code/LatentForcing/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..46dd7155bb6f50e03dfe58a68e77cd4cffcb93c0 --- /dev/null +++ b/transfer/code/LatentForcing/.gitignore @@ -0,0 +1,3 @@ +*.pyc +ALAN_LOG.md +.vscode* \ No newline at end of file diff --git a/transfer/code/LatentForcing/LICENSE b/transfer/code/LatentForcing/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..376972eeb10ae0cd4e0756aea1fe8811b3697f08 --- /dev/null +++ b/transfer/code/LatentForcing/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2026 Alan Baade + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. \ No newline at end of file diff --git a/transfer/code/LatentForcing/README.md b/transfer/code/LatentForcing/README.md new file mode 100644 index 0000000000000000000000000000000000000000..253a257d3fc3489494f446dff97691b39f13d154 --- /dev/null +++ b/transfer/code/LatentForcing/README.md @@ -0,0 +1,126 @@ +## Latent Forcing: Reordering the Diffusion Trajectory for Pixel-Space Image Generation + +[![arXiv](https://img.shields.io/badge/arXiv%20paper-2602.11401-b31b1b.svg)](https://arxiv.org/abs/2602.11401)  + + + + +Code for Latent Forcing + +``` +@article{baade2026latentforcing, + title={Latent Forcing: Reordering the Diffusion Trajectory for Pixel-Space Image Generation}, + author={Alan Baade and Eric Ryan Chan and Kyle Sargent and Changan Chen and Justin Johnson and Ehsan Adeli and Li Fei-Fei}, + journal={arXiv preprint arXiv:2602.11401}, + year={2026}, +} +``` + +Our code is based on JiT: https://github.com/LTH14/JiT.git + +

+ +

+ +### Dataset +We use [ImageNet](http://image-net.org/download) dataset, and Webdataset. + +### Installation + +Download the code: +``` +git clone https://github.com/AlanBaade/LatentForcing.git +cd LatentForcing +``` + +Create the conda environment. uv is recommended, but not required. +```bash +conda create -n latentforcing python=3.10 +conda activate latentforcing +uv pip install opencv-python==4.11.0.86 numpy==1.23 timm==0.9.12 tensorboard==2.10.0 scipy==1.9.1 einops==0.8.1 gdown==5.2.0 matplotlib==3.10.8 transformers==4.57.3 webdataset==1.0.2 +uv pip install torch==2.5.1 --index-url https://download.pytorch.org/whl/cu124 +uv pip install "torch-fidelity @ git+https://github.com/LTH14/torch-fidelity.git@master" +``` + +### Training +Example script for training LatentForcing-L on ImageNet 200 epochs: + +``` +torchrun --nproc_per_node=8 --standalone \ +main_jit.py \ +--model JiTCoT-LM/16 \ +--D_mean -1.2 --D_std 1.0 \ +--P_mean -0.4 --P_std 0.8 \ +--batch_size 128 --blr 5e-5 \ +--epochs 200 --warmup_epochs 5 \ +--gen_bsz 256 --num_images 10000 \ +--cfg 1.0 --cfg_dino 1.0 \ +--interval_min 0.0 --interval_max 1.0 \ +--dino_weight 0.333 --choose_dino_p 0.4 \ +--sample_mode dino_first_cascaded_noised \ +--dh_depth 2 --dh_hidden_size 1024 \ +--output_dir ${OUTPUT_DIR} \ +--resume ${OUTPUT_DIR} \ +--data_path ${DATA_PATH} \ +--online_eval +``` + +For unconditional training and generation, set ```--label_drop_prob 1.0``` + +To train a Multi-Schedule model set ```--sample_mode shifted_independent_uniform``` + +### Evaluation + +PyTorch pre-trained models are WIP + +Evaluate LatentForcing-L with Autoguidance (Default Evaluation Setting) +``` +torchrun --nproc_per_node=8 --standalone \ +main_jit.py \ +--model JiTCoT-LM/16 \ +--dh_depth 2 --dh_hidden_size 1024 \ +--gen_bsz 1536 --num_images 50000 \ +--cfg 1.5 --cfg_dino 1.5 \ +--interval_min 0.0 --interval_max 1.0 \ +--interval_min_dino 0.0 --interval_max_dino 1.0 \ +--sample_mode dino_first_cascaded_noised \ +--output_dir ${OUTPUT_DIR_EVAL} \ +--resume ${OUTPUT_DIR} \ +--data_path ${DATA_PATH} \ +--evaluate_gen --num_sampling_steps 50 \ +--sampling_method heun \ +--guidance_method autoguidance \ +--autoguidance_ckpt ${AUTOGUIDANCE_CKPT}$ +``` + +Evaluate LatentForcing-L with Interval CFG (Used in the System-Level comparison only) +``` +torchrun --nproc_per_node=8 --standalone \ +main_jit.py \ +--model JiTCoT-LM/16 \ +--dh_depth 2 --dh_hidden_size 1024 \ +--gen_bsz 1536 --num_images 50000 \ +--cfg 1.5 --cfg_dino 2.9 \ +--interval_min 0.0 --interval_max 1.0 \ +--interval_min_dino 0.06 --interval_max_dino 1.0 \ +--sample_mode dino_first_cascaded_noised \ +--output_dir ${OUTPUT_DIR_EVAL} \ +--resume ${OUTPUT_DIR} \ +--data_path ${DATA_PATH} \ +--evaluate_gen --num_sampling_steps 50 \ +--gen_shift_dino 0.575 --sampling_method heun \ +--guidance_method cfg_interval \ +--autoguidance_ckpt ${AUTOGUIDANCE_CKPT}$ +``` + +We use the same customized FID eval as JiT: [```torch-fidelity```](https://github.com/LTH14/torch-fidelity) + +### Contact + +You can contact me at baade@stanford.edu for questions. + + + + diff --git a/transfer/code/LatentForcing/assets/ConceptDiagram.png b/transfer/code/LatentForcing/assets/ConceptDiagram.png new file mode 100644 index 0000000000000000000000000000000000000000..e403e65175b6438383c6aa67e073b0de8da0e726 --- /dev/null +++ b/transfer/code/LatentForcing/assets/ConceptDiagram.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9dc20edff93609a18513af5de4d8dcfea2e76582c738b0763024d7ddd9d7d844 +size 1824619 diff --git a/transfer/code/LatentForcing/data2vec2.py b/transfer/code/LatentForcing/data2vec2.py new file mode 100644 index 0000000000000000000000000000000000000000..9fb1fe9a6b362e1c8bc84af93d24c7fe5bb6feb1 --- /dev/null +++ b/transfer/code/LatentForcing/data2vec2.py @@ -0,0 +1,2571 @@ +## Single file reimplementation of +# https://github.com/facebookresearch/fairseq/blob/main/examples/data2vec/README.md +# Modified from SyllableLM: https://github.com/AlanBaade/SyllableLM + +from typing import List, Tuple + +import torch +from torch import nn +import math +import torch.nn.functional as F + + +class Fp32GroupNorm(nn.GroupNorm): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def forward(self, input): + output = F.group_norm( + input.float(), + self.num_groups, + self.weight.float() if self.weight is not None else None, + self.bias.float() if self.bias is not None else None, + self.eps, + ) + return output.type_as(input) + + +class Fp32LayerNorm(nn.LayerNorm): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def forward(self, input): + output = F.layer_norm( + input.float(), + self.normalized_shape, + self.weight.float() if self.weight is not None else None, + self.bias.float() if self.bias is not None else None, + self.eps, + ) + return output.type_as(input) + + +class TransposeLast(nn.Module): + def __init__(self, deconstruct_idx=None, tranpose_dim=-2): + super().__init__() + self.deconstruct_idx = deconstruct_idx + self.tranpose_dim = tranpose_dim + + def forward(self, x): + if self.deconstruct_idx is not None: + x = x[self.deconstruct_idx] + return x.transpose(self.tranpose_dim, -1) + + +def norm_block(is_layer_norm, dim, affine=True): + if is_layer_norm: + mod = nn.Sequential( + TransposeLast(), + Fp32LayerNorm(dim, elementwise_affine=affine), + TransposeLast(), + ) + else: + mod = Fp32GroupNorm(1, dim, affine=affine) + + return mod + + +class SamePad(nn.Module): + def __init__(self, kernel_size, causal=False): + super().__init__() + if causal: + self.remove = kernel_size - 1 + else: + self.remove = 1 if kernel_size % 2 == 0 else 0 + + def forward(self, x): + if self.remove > 0: + x = x[:, :, : -self.remove] + return x + + +try: + from apex.normalization import FusedLayerNorm as _FusedLayerNorm + + has_fused_layernorm = True + + + class FusedLayerNorm(_FusedLayerNorm): + @torch.jit.unused + def forward(self, x): + if not x.is_cuda: + return super().forward(x) + else: + with torch.cuda.device(x.device): + return super().forward(x) +except ImportError: + has_fused_layernorm = False + + +def LayerNorm(normalized_shape, eps=1e-5, elementwise_affine=True, export=False): + if torch.jit.is_scripting() or torch.jit.is_tracing(): + export = True + if not export and torch.cuda.is_available() and has_fused_layernorm: + return FusedLayerNorm(normalized_shape, eps, elementwise_affine) + return torch.nn.LayerNorm(normalized_shape, eps, elementwise_affine) + + +class ConvFeatureExtractionModel(nn.Module): + def __init__( + self, + conv_layers: List[Tuple[int, int, int]], + dropout: float = 0.0, + mode: str = "default", + conv_bias: bool = False, + ): + super().__init__() + + assert mode in {"default", "layer_norm"} + + def block( + n_in, + n_out, + k, + stride, + is_layer_norm=False, + is_group_norm=False, + conv_bias=False, + ): + def make_conv(): + conv = nn.Conv1d(n_in, n_out, k, stride=stride, bias=conv_bias) + nn.init.kaiming_normal_(conv.weight) + return conv + + assert ( + is_layer_norm and is_group_norm + ) == False, "layer norm and group norm are exclusive" + + if is_layer_norm: + return nn.Sequential( + make_conv(), + nn.Dropout(p=dropout), + nn.Sequential( + TransposeLast(), + Fp32LayerNorm(dim, elementwise_affine=True), + TransposeLast(), + ), + nn.GELU(), + ) + elif is_group_norm: + return nn.Sequential( + make_conv(), + nn.Dropout(p=dropout), + Fp32GroupNorm(dim, dim, affine=True), + nn.GELU(), + ) + else: + return nn.Sequential(make_conv(), nn.Dropout(p=dropout), nn.GELU()) + + in_d = 1 + self.conv_layers = nn.ModuleList() + for i, cl in enumerate(conv_layers): + assert len(cl) == 3, "invalid conv definition: " + str(cl) + (dim, k, stride) = cl + + self.conv_layers.append( + block( + in_d, + dim, + k, + stride, + is_layer_norm=mode == "layer_norm", + is_group_norm=mode == "default" and i == 0, + conv_bias=conv_bias, + ) + ) + in_d = dim + + def forward(self, x): + + # BxT -> BxCxT + x = x.unsqueeze(1) + + for conv in self.conv_layers: + x = conv(x) + + return x + +class SamePad2d(nn.Module): + def __init__(self, kernel_size): + super().__init__() + self.remove = 1 if kernel_size % 2 == 0 else 0 + + def forward(self, x): + assert len(x.size()) == 4 + if self.remove > 0: + x = x[:, :, : -self.remove, : -self.remove] + return x + + +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np +from dataclasses import dataclass + + +@dataclass +class D2vDecoderConfig: + decoder_dim: int = 384 + decoder_groups: int = 16 + decoder_kernel: int = 5 + decoder_layers: int = 5 + input_dropout: float = 0.1 + + add_positions_masked: bool = False + add_positions_all: bool = False + + decoder_residual: bool = True + projection_layers: int = 1 + projection_ratio: float = 2.0 + + +class FixedPositionalEncoder(nn.Module): + def __init__(self, pos_embed): + super().__init__() + self.positions = pos_embed + + def forward(self, x, padding_mask): + return self.positions + + +class TextFeatPositionalEncoder(nn.Module): + """ + Original encoder expects (B, T) long input. This module wraps it to take + local_encoder output which are (B, T, D) float tensors + """ + + def __init__(self, pos_encoder): + super().__init__() + self.pos_encoder = pos_encoder + + def forward(self, x, padding_mask): + # assume padded token embeddings are 0s + # TODO: consider using padding_mask as input + return self.pos_encoder(x[..., 0]) + + +class BlockEncoder(nn.Module): + def __init__(self, blocks, norm_layer, layer_norm_first, layerdrop, dropout): + super().__init__() + self.blocks = blocks + self.norm = norm_layer + self.layer_norm_first = layer_norm_first + self.layerdrop = layerdrop + self.dropout = nn.Dropout(dropout, inplace=True) + + def forward(self, x, padding_mask, alibi_bias, alibi_scale): + if self.norm is not None and not self.layer_norm_first: + x = self.norm(x) + + x = self.dropout(x) + + for i, blk in enumerate(self.blocks): + if ( + not self.training + or self.layerdrop == 0 + or (np.random.random() > self.layerdrop) + ): + ab = alibi_bias + if ab is not None and alibi_scale is not None: + scale = ( + alibi_scale[i] + if alibi_scale.size(0) > 1 + else alibi_scale.squeeze(0) + ) + ab = ab * scale.type_as(ab) + x, _ = blk(x, padding_mask, ab) + + if self.norm is not None and self.layer_norm_first: + x = self.norm(x) + + return x + + +class DecoderBase(nn.Module): + decoder_cfg: D2vDecoderConfig + + def __init__(self, cfg: D2vDecoderConfig): + super().__init__() + + self.decoder_cfg = cfg + + def reset_parameters(self): + for mod in self.proj.modules(): + if isinstance(mod, nn.Linear): + mod.reset_parameters() + + def add_residual(self, x, residual, i, mask_info): + if ( + residual is None + or not self.decoder_cfg.decoder_residual + or residual.size(1) != x.size(1) + ): + return x + + ret = x + residual + + return ret + + +class Decoder1d(DecoderBase): + def __init__(self, cfg: D2vDecoderConfig, input_dim): + super().__init__(cfg) + + def make_block(in_dim): + block = [ + nn.Conv1d( + in_dim, + cfg.decoder_dim, + kernel_size=cfg.decoder_kernel, + padding=cfg.decoder_kernel // 2, + groups=cfg.decoder_groups, + ), + SamePad(cfg.decoder_kernel), + TransposeLast(), + LayerNorm(cfg.decoder_dim, elementwise_affine=False), + TransposeLast(), + nn.GELU(), + ] + + return nn.Sequential(*block) + + self.blocks = nn.Sequential( + *[ + make_block(input_dim if i == 0 else cfg.decoder_dim) + for i in range(cfg.decoder_layers) + ] + ) + + projs = [] + curr_dim = cfg.decoder_dim + for i in range(cfg.projection_layers - 1): + next_dim = int(curr_dim * cfg.projection_ratio) if i == 0 else curr_dim + projs.append(nn.Linear(curr_dim, next_dim)) + projs.append(nn.GELU()) + curr_dim = next_dim + projs.append(nn.Linear(curr_dim, input_dim)) + if len(projs) == 1: + self.proj = projs[0] + else: + self.proj = nn.Sequential(*projs) + + def forward(self, x, mask_info): + + x = x.transpose(1, 2) + + residual = x + + for i, layer in enumerate(self.blocks): + x = layer(x) + x = self.add_residual(x, residual, i, mask_info) + residual = x + + x = x.transpose(1, 2) + x = self.proj(x) + return x + + +class Decoder2d(DecoderBase): + def __init__(self, cfg: D2vDecoderConfig, input_dim, h_size, w_size): + super().__init__(cfg) + + self.h_size = h_size + self.w_size = w_size + + def make_block(in_dim): + block = [ + nn.Conv2d( + in_dim, + cfg.decoder_dim, + kernel_size=cfg.decoder_kernel, + padding=cfg.decoder_kernel // 2, + groups=cfg.decoder_groups, + ), + SamePad2d(cfg.decoder_kernel), + TransposeLast(tranpose_dim=-3), + LayerNorm(cfg.decoder_dim, elementwise_affine=False), + TransposeLast(tranpose_dim=-3), + nn.GELU(), + ] + + return nn.Sequential(*block) + + self.blocks = nn.Sequential( + *[ + make_block(input_dim if i == 0 else cfg.decoder_dim) + for i in range(cfg.decoder_layers) + ] + ) + + self.proj = nn.Linear(cfg.decoder_dim, input_dim) + + def forward(self, x, mask_info): + B, T, C = x.shape + + x = x.transpose(1, 2).reshape(B, C, self.h_size, self.w_size) + + residual = x + + for i, layer in enumerate(self.blocks): + x = layer(x) + x = self.add_residual(x, residual, i, mask_info) + residual = x + + x = x.reshape(B, -1, T).transpose(1, 2) + x = self.proj(x) + return x + + +class TransformerDecoder(nn.Module): + decoder_cfg: D2vDecoderConfig + + def __init__(self, cfg: D2vDecoderConfig, input_dim, encoder): + super().__init__() + + self.decoder_cfg = cfg + + self.input_proj = nn.Linear(input_dim, cfg.decoder_dim) + + self.encoder = encoder + + self.proj = nn.Linear(cfg.decoder_dim, input_dim) + + def reset_parameters(self): + from fairseq.modules.transformer_sentence_encoder import init_bert_params + + self.apply(init_bert_params) + + def forward(self, x, mask_info): + x = self.input_proj(x) + x = self.encoder(x, None, None, 1) + x = self.proj(x) + return x + + +class AltBlock(nn.Module): + def __init__( + self, + dim, + num_heads, + mlp_ratio=4.0, + qkv_bias=False, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + mlp_drop=0.0, + post_mlp_drop=0.0, + drop_path=0.0, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + layer_norm_first=True, + ffn_targets=False, + cosine_attention=False, + ): + super().__init__() + + self.layer_norm_first = layer_norm_first + self.ffn_targets = ffn_targets + + from timm.models.vision_transformer import DropPath, Mlp + + self.norm1 = norm_layer(dim) + self.attn = AltAttention( + dim, + num_heads=num_heads, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + attn_drop=attn_drop, + proj_drop=drop, + cosine_attention=cosine_attention, + ) + + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp( + in_features=dim, + hidden_features=mlp_hidden_dim, + act_layer=act_layer, + drop=mlp_drop, + ) + self.post_mlp_dropout = nn.Dropout(post_mlp_drop, inplace=False) + + def forward(self, x, padding_mask=None, alibi_bias=None): + if self.layer_norm_first: + x = x + self.drop_path(self.attn(self.norm1(x), padding_mask, alibi_bias)) + r = x = self.mlp(self.norm2(x)) # LatentForcing Authors: Lol og d2v2 is bugged + t = x + x = r + self.drop_path(self.post_mlp_dropout(x)) + if not self.ffn_targets: + t = x + else: + x = x + self.drop_path(self.attn(x, padding_mask, alibi_bias)) + r = x = self.norm1(x) + x = self.mlp(x) + t = x + x = self.norm2(r + self.drop_path(self.post_mlp_dropout(x))) + if not self.ffn_targets: + t = x + + return x, t + + +class AltAttention(nn.Module): + def __init__( + self, + dim, + num_heads=8, + qkv_bias=False, + qk_scale=None, + attn_drop=0.0, + proj_drop=0.0, + cosine_attention=False, + ): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim ** -0.5 + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + self.cosine_attention = cosine_attention + + if cosine_attention: + self.logit_scale = nn.Parameter( + torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True + ) + + def forward(self, x, padding_mask=None, alibi_bias=None): + B, N, C = x.shape + qkv = ( + self.qkv(x) + .reshape(B, N, 3, self.num_heads, C // self.num_heads) + .permute(2, 0, 3, 1, 4) # qkv x B x H x L x D + ) + q, k, v = ( + qkv[0], + qkv[1], + qkv[2], + ) # make torchscript happy (cannot use tensor as tuple) + + dtype = q.dtype + + if self.cosine_attention: + # cosine attention + attn = F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1) + logit_scale = torch.clamp( + self.logit_scale, max=torch.log(torch.tensor(1.0 / 0.01)) + ).exp() + attn = attn * logit_scale + else: + q = q * self.scale + attn = q @ k.transpose(-2, -1) + + if alibi_bias is not None: + attn = attn.type_as(alibi_bias) + attn[:, : alibi_bias.size(1)] += alibi_bias + + if padding_mask is not None and padding_mask.any(): + attn = attn.masked_fill( + padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool), + float("-inf"), + ) + + attn = attn.softmax(dim=-1, dtype=torch.float32).to(dtype=dtype) + attn = self.attn_drop(attn) + x = (attn @ v).transpose(1, 2) # + x = x.reshape(B, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class EncDecAttention(nn.Module): + def __init__( + self, + q_dim, + kv_dim, + num_heads=8, + qkv_bias=False, + qk_scale=None, + attn_drop=0.0, + proj_drop=0.0, + cosine_attention=False, + ): + super().__init__() + self.num_heads = num_heads + head_dim = q_dim // num_heads + self.scale = qk_scale or head_dim ** -0.5 + + self.q_proj = nn.Linear(q_dim, q_dim, bias=qkv_bias) + self.kv_proj = nn.Linear(kv_dim, 2 * q_dim, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(q_dim, q_dim) + self.proj_drop = nn.Dropout(proj_drop) + + self.cosine_attention = cosine_attention + + if cosine_attention: + self.logit_scale = nn.Parameter( + torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True + ) + + def forward(self, q, kv, padding_mask=None, alibi_bias=None): + B, N, C = q.shape + + q = ( + self.q_proj(q) + .reshape(B, N, self.num_heads, C // self.num_heads) + .permute(0, 2, 1, 3) + ) # B x H x L x D + kv = ( + self.kv_proj(kv) + .reshape(B, -1, 2, self.num_heads, C // self.num_heads) + .permute(2, 0, 3, 1, 4) + ) # kv x B x H x L x D + k, v = ( + kv[0], + kv[1], + ) # make torchscript happy (cannot use tensor as tuple) + + dtype = q.dtype + + if self.cosine_attention: + # cosine attention + attn = F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1) + logit_scale = torch.clamp( + self.logit_scale, max=torch.log(torch.tensor(1.0 / 0.01)) + ).exp() + attn = attn * logit_scale + else: + q = q * self.scale + attn = q @ k.transpose(-2, -1) + + if alibi_bias is not None: + attn = attn.type_as(alibi_bias) + attn[:, : alibi_bias.size(1)] += alibi_bias + + if padding_mask is not None and padding_mask.any(): + attn = attn.masked_fill( + padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool), + float("-inf"), + ) + + attn = attn.softmax(dim=-1, dtype=torch.float32).to(dtype=dtype) + attn = self.attn_drop(attn) + x = (attn @ v).transpose(1, 2) # + x = x.reshape(B, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class EncDecBlock(nn.Module): + def __init__( + self, + q_dim, + kv_dim, + num_heads, + mlp_ratio=4.0, + qkv_bias=False, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + mlp_drop=0.0, + post_mlp_drop=0.0, + drop_path=0.0, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + layer_norm_first=True, + cosine_attention=False, + first_residual=True, + ): + super().__init__() + + self.layer_norm_first = layer_norm_first + + from timm.models.vision_transformer import DropPath, Mlp + + self.norm1 = norm_layer(q_dim) + self.attn = EncDecAttention( + q_dim, + kv_dim, + num_heads=num_heads, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + attn_drop=attn_drop, + proj_drop=drop, + cosine_attention=cosine_attention, + ) + + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.norm2 = norm_layer(q_dim) + mlp_hidden_dim = int(q_dim * mlp_ratio) + self.mlp = Mlp( + in_features=q_dim, + hidden_features=mlp_hidden_dim, + act_layer=act_layer, + drop=mlp_drop, + ) + self.post_mlp_dropout = nn.Dropout(post_mlp_drop, inplace=False) + self.first_residual = first_residual + + def forward(self, q, kv, padding_mask=None, alibi_bias=None): + r = q if self.first_residual else 0 + if self.layer_norm_first: + x = r + self.drop_path( + self.attn(self.norm1(q), kv, padding_mask, alibi_bias) + ) + r = x = self.mlp(self.norm2(x)) + x = r + self.drop_path(self.post_mlp_dropout(x)) + else: + x = r + self.drop_path(self.attn(q, kv, padding_mask, alibi_bias)) + r = x = self.norm1(x) + x = self.mlp(x) + x = self.norm2(r + self.drop_path(self.post_mlp_dropout(x))) + + return x + + +class EncDecTransformerDecoder(nn.Module): + def __init__(self, cfg: D2vDecoderConfig, input_dim): + super().__init__() + + self.input_proj = nn.Linear(input_dim, cfg.decoder_dim) + + self.blocks = nn.Sequential( + *[ + EncDecBlock( + q_dim=cfg.decoder_dim, + kv_dim=input_dim, + num_heads=8, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + mlp_drop=0.0, + post_mlp_drop=0.0, + drop_path=0.0, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + layer_norm_first=False, + cosine_attention=False, + first_residual=i > 0, + ) + for i in range(cfg.decoder_layers) + ] + ) + + self.proj = nn.Linear(cfg.decoder_dim, input_dim) + + def reset_parameters(self): + from fairseq.modules.transformer_sentence_encoder import init_bert_params + + self.apply(init_bert_params) + + def forward(self, x, kv): + x = self.input_proj(x) + for i, layer in enumerate(self.blocks): + x = layer(x, kv) + + x = self.proj(x) + return x + +from enum import Enum, auto + +class Modality(Enum): + AUDIO = auto() + IMAGE = auto() + TEXT = auto() + + +@dataclass +class D2vDecoderConfig: + decoder_dim: int = 384 + decoder_groups: int = 16 + decoder_kernel: int = 5 + decoder_layers: int = 5 + input_dropout: float = 0.1 + + add_positions_masked: bool = False + add_positions_all: bool = False + + decoder_residual: bool = True + projection_layers: int = 1 + projection_ratio: float = 2.0 + + channel_mult: object = (1, 0.5, 0.25, 0.25, 0.25) # tuple[float] + decoder_transformer_layers: int = 4 + + +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +import math +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from collections import namedtuple +from dataclasses import dataclass +from functools import partial +from omegaconf import MISSING, II +from typing import Optional, Callable +# from fairseq.data.data_utils import compute_mask_indices +# from fairseq.modules import GradMultiply +# from fairseq.utils import index_put + +logger = logging.getLogger(__name__) + + +@dataclass +class D2vModalityConfig: + type: Modality = MISSING + prenet_depth: int = 4 + prenet_layerdrop: float = 0 + prenet_dropout: float = 0 + start_drop_path_rate: float = 0 + end_drop_path_rate: float = 0 + + num_extra_tokens: int = 0 + init_extra_token_zero: bool = True + + mask_noise_std: float = 0.01 + mask_prob_min: Optional[float] = None + mask_prob: float = 0.7 + inverse_mask: bool = False + mask_prob_adjust: float = 0 + keep_masked_pct: float = 0 + + mask_length: int = 5 + add_masks: bool = False + remove_masks: bool = False + mask_dropout: float = 0.0 + encoder_zero_mask: bool = True + + mask_channel_prob: float = 0.0 + mask_channel_length: int = 64 + + ema_local_encoder: bool = False # used in data2vec_multi + local_grad_mult: float = 1.0 + + use_alibi_encoder: bool = False + alibi_scale: float = 1.0 + learned_alibi: bool = False + alibi_max_pos: Optional[int] = None + learned_alibi_scale: bool = False + learned_alibi_scale_per_head: bool = False + learned_alibi_scale_per_layer: bool = False + + num_alibi_heads: int = II("model.num_heads") + model_depth: int = II("model.depth") + + decoder: Optional[D2vDecoderConfig] = D2vDecoderConfig() + + +MaskSeed = namedtuple("MaskSeed", ["seed", "update", "ids"]) +MaskInfo = namedtuple("MaskInfo", ["x_unmasked", "mask", "ids_restore", "ids_keep"]) + + +class ModalitySpecificEncoder(nn.Module): + def __init__( + self, + modality_cfg: D2vModalityConfig, + embed_dim: int, + local_encoder: nn.Module, + project_features: nn.Module, + fixed_positional_encoder: Optional[nn.Module], + relative_positional_encoder: Optional[nn.Module], + context_encoder: nn.Module, + decoder: nn.Module, + get_alibi_bias: Optional[Callable[[int, int, str, str], torch.Tensor]], + ): + super().__init__() + + self.modality_cfg = modality_cfg + self.local_encoder = local_encoder + self.project_features = project_features + self.fixed_positional_encoder = fixed_positional_encoder + self.relative_positional_encoder = relative_positional_encoder + self.context_encoder = context_encoder + + self.decoder = decoder + self.get_alibi_bias = get_alibi_bias if modality_cfg.use_alibi_encoder else None + + self.local_grad_mult = self.modality_cfg.local_grad_mult + + self.extra_tokens = None + if modality_cfg.num_extra_tokens > 0: + self.extra_tokens = nn.Parameter( + torch.zeros(1, modality_cfg.num_extra_tokens, embed_dim) + ) + if not modality_cfg.init_extra_token_zero: + nn.init.normal_(self.extra_tokens) + elif self.extra_tokens.size(1) > 1: + nn.init.normal_(self.extra_tokens[:, 1:]) + + self.alibi_scale = None + if self.get_alibi_bias is not None: + self.alibi_scale = nn.Parameter( + torch.full( + ( + (modality_cfg.prenet_depth + modality_cfg.model_depth) + if modality_cfg.learned_alibi_scale_per_layer + else 1, + 1, + self.modality_cfg.num_alibi_heads + if modality_cfg.learned_alibi_scale_per_head + else 1, + 1, + 1, + ), + modality_cfg.alibi_scale, + dtype=torch.float, + ), + requires_grad=modality_cfg.learned_alibi_scale, + ) + + if modality_cfg.learned_alibi and self.get_alibi_bias is not None: + assert modality_cfg.alibi_max_pos is not None + alibi_bias = self.get_alibi_bias( + batch_size=1, + time_steps=modality_cfg.alibi_max_pos, + heads=modality_cfg.num_alibi_heads, + scale=1.0, + dtype=torch.float, + device="cpu", + ) + self.alibi_bias = nn.Parameter(alibi_bias) + self.get_alibi_bias = partial( + _learned_alibi_bias, alibi_bias=self.alibi_bias + ) + + def upgrade_state_dict_named(self, state_dict, name): + k = f"{name}.alibi_scale" + if k in state_dict and state_dict[k].dim() == 4: + state_dict[k] = state_dict[k].unsqueeze(0) + + return state_dict + + def convert_padding_mask(self, x, padding_mask): + return padding_mask + + def decoder_input(self, x, mask_info: MaskInfo): + inp_drop = self.modality_cfg.decoder.input_dropout + if inp_drop > 0: + x = F.dropout(x, inp_drop, training=self.training, inplace=True) + + num_extra = self.modality_cfg.num_extra_tokens + + if mask_info is not None: + num_masked = mask_info.ids_restore.shape[1] - x.shape[1] + num_extra + + mask_tokens = x.new_empty( + x.size(0), + num_masked, + x.size(-1), + ).normal_(0, self.modality_cfg.mask_noise_std) + + x_ = torch.cat([x[:, num_extra:], mask_tokens], dim=1) + x = torch.gather(x_, dim=1, index=mask_info.ids_restore) + + if self.modality_cfg.decoder.add_positions_masked: + assert self.fixed_positional_encoder is not None + pos = self.fixed_positional_encoder(x, None) + x = x + (pos * mask_info.mask.unsqueeze(-1)) + else: + x = x[:, num_extra:] + + if self.modality_cfg.decoder.add_positions_all: + assert self.fixed_positional_encoder is not None + x = x + self.fixed_positional_encoder(x, None) + + return x, mask_info + + def local_features(self, features): + if self.local_grad_mult > 0: + if self.local_grad_mult == 1.0: + x = self.local_encoder(features) + else: + x = GradMultiply.apply( + self.local_encoder(features), self.local_grad_mult + ) + else: + with torch.no_grad(): + x = self.local_encoder(features) + + x = self.project_features(x) + return x + + def contextualized_features( + self, + x, + padding_mask, + mask, + remove_masked, + clone_batch: int = 1, + mask_seeds: Optional[torch.Tensor] = None, + precomputed_mask=None, + ): + + if padding_mask is not None: + padding_mask = self.convert_padding_mask(x, padding_mask) + + local_features = x + if mask and clone_batch == 1: + local_features = local_features.clone() + + orig_B, orig_T, _ = x.shape + pre_mask_B = orig_B + mask_info = None + + x_pos = None + if self.fixed_positional_encoder is not None: + x = x + self.fixed_positional_encoder(x, padding_mask) + + if mask: + if clone_batch > 1: + x = x.repeat_interleave(clone_batch, 0) + if mask_seeds is not None: + clone_hash = [ + int(hash((mask_seeds.seed, ind)) % 1e10) + for ind in range(clone_batch - 1) + ] + clone_hash = torch.tensor([0] + clone_hash).long().view(1, -1) + + id = mask_seeds.ids + id = id.repeat_interleave(clone_batch, 0) + id = id.view(-1, clone_batch) + clone_hash.to(id) + id = id.view(-1) + mask_seeds = MaskSeed( + seed=mask_seeds.seed, update=mask_seeds.update, ids=id + ) + if padding_mask is not None: + padding_mask = padding_mask.repeat_interleave(clone_batch, 0) + + x, mask_info = self.compute_mask( + x, + padding_mask, + mask_seed=mask_seeds, + apply=self.relative_positional_encoder is not None or not remove_masked, + precomputed_mask=precomputed_mask, + ) + + if self.relative_positional_encoder is not None: + x_pos = self.relative_positional_encoder(x) + + masked_padding_mask = padding_mask + if mask and remove_masked: + x = mask_info.x_unmasked + if x_pos is not None: + x = x + gather_unmasked(x_pos, mask_info) + + if padding_mask is not None and padding_mask.any(): + masked_padding_mask = gather_unmasked_mask(padding_mask, mask_info) + if not masked_padding_mask.any(): + masked_padding_mask = None + else: + masked_padding_mask = None + + elif x_pos is not None: + x = x + x_pos + + alibi_bias = orig_alibi_bias = None + alibi_scale = self.alibi_scale + + if self.get_alibi_bias is not None: + orig_alibi_bias = alibi_bias = self.get_alibi_bias( + batch_size=pre_mask_B, + time_steps=orig_T, + heads=self.modality_cfg.num_alibi_heads, + dtype=torch.float32, + device=x.device, + ) + + if alibi_scale is not None: + alibi_scale = alibi_scale.clamp_min(0) + if alibi_scale.size(0) == 1: + alibi_bias = alibi_bias * alibi_scale.squeeze(0).type_as(alibi_bias) + alibi_scale = None + + if clone_batch > 1: + alibi_bias = alibi_bias.repeat_interleave(clone_batch, 0) + + if mask_info is not None and remove_masked: + alibi_bias = masked_alibi(alibi_bias, mask_info) + + if self.extra_tokens is not None: + num = self.extra_tokens.size(1) + x = torch.cat([self.extra_tokens.expand(x.size(0), -1, -1), x], dim=1) + if masked_padding_mask is not None: + # B x T + masked_padding_mask = F.pad(masked_padding_mask, (num, 0)) + if alibi_bias is not None: + # B x H x T x T + alibi_bias = F.pad(alibi_bias, (num, 0, num, 0)) + + x = self.context_encoder( + x, + masked_padding_mask, + alibi_bias, + alibi_scale[: self.modality_cfg.prenet_depth] + if alibi_scale is not None + else None, + ) + + return { + "x": x, + "local_features": local_features, + "padding_mask": masked_padding_mask, + "alibi_bias": alibi_bias, + "orig_alibi_bias": orig_alibi_bias, + "alibi_scale": alibi_scale[self.modality_cfg.prenet_depth:] + if alibi_scale is not None and alibi_scale.size(0) > 1 + else alibi_scale, + "encoder_mask": mask_info, + } + + def forward( + self, + features, + padding_mask, + mask: bool, + remove_masked: bool, + clone_batch: int = 1, + mask_seeds: Optional[torch.Tensor] = None, + precomputed_mask=None, + ): + x = self.local_features(features) + return self.contextualized_features( + x, + padding_mask, + mask, + remove_masked, + clone_batch, + mask_seeds, + precomputed_mask, + ) + + def reset_parameters(self): + pass + + def compute_mask( + self, + x, + padding_mask, + mask_seed: Optional[MaskSeed], + apply, + precomputed_mask, + ): + if precomputed_mask is not None: + mask = precomputed_mask + mask_info = self.make_maskinfo(x, mask) + else: + B, T, C = x.shape + cfg = self.modality_cfg + + mask_prob = cfg.mask_prob + + if ( + cfg.mask_prob_min is not None + and cfg.mask_prob_min >= 0 + and cfg.mask_prob_min < mask_prob + ): + mask_prob = np.random.uniform(cfg.mask_prob_min, mask_prob) + + if mask_prob > 0: + if cfg.mask_length == 1: + mask_info = random_masking(x, mask_prob, mask_seed) + else: + if self.modality_cfg.inverse_mask: + mask_prob = 1 - mask_prob + + mask = compute_mask_indices( + (B, T), + padding_mask, + mask_prob, + cfg.mask_length, + min_masks=1, + require_same_masks=True, + mask_dropout=cfg.mask_dropout, + add_masks=cfg.add_masks, + seed=mask_seed.seed if mask_seed is not None else None, + epoch=mask_seed.update if mask_seed is not None else None, + indices=mask_seed.ids if mask_seed is not None else None, + ) + + mask = torch.from_numpy(mask).to(device=x.device) + if self.modality_cfg.inverse_mask: + mask = 1 - mask + mask_info = self.make_maskinfo(x, mask) + else: + mask_info = None + + if apply: + x = self.apply_mask(x, mask_info) + + return x, mask_info + + def make_maskinfo(self, x, mask, shape=None): + if shape is None: + B, T, D = x.shape + else: + B, T, D = shape + + mask = mask.to(torch.uint8) + ids_shuffle = mask.argsort(dim=1) + ids_restore = ids_shuffle.argsort(dim=1).unsqueeze(-1).expand(-1, -1, D) + + len_keep = T - mask[0].sum() + if self.modality_cfg.keep_masked_pct > 0: + len_keep += round((T - int(len_keep)) * self.modality_cfg.keep_masked_pct) + + ids_keep = ids_shuffle[:, :len_keep] + + mask = mask.new_zeros(mask.shape) # Alan addition, mask should update to represent new kept + mask.scatter_(index=ids_shuffle[:, len_keep:], dim=1, value=1) + + if shape is not None: + x_unmasked = None + else: + ids_keep = ids_keep.unsqueeze(-1).expand(-1, -1, D) + x_unmasked = torch.gather(x, dim=1, index=ids_keep) + + mask_info = MaskInfo( + x_unmasked=x_unmasked, + mask=mask, + ids_restore=ids_restore, + ids_keep=ids_keep, + ) + return mask_info + + def apply_mask(self, x, mask_info): + cfg = self.modality_cfg + B, T, C = x.shape + + if mask_info is not None: + mask = mask_info.mask + if cfg.encoder_zero_mask: + x = x * (1 - mask.type_as(x).unsqueeze(-1)) + else: + num_masks = mask.sum().item() + masks = x.new_empty(num_masks, x.size(-1)).normal_( + 0, cfg.mask_noise_std + ) + x = index_put(x, mask, masks) + if cfg.mask_channel_prob > 0: + mask_channel = compute_mask_indices( + (B, C), + None, + cfg.mask_channel_prob, + cfg.mask_channel_length, + ) + mask_channel = ( + torch.from_numpy(mask_channel) + .to(x.device) + .unsqueeze(1) + .expand(-1, T, -1) + ) + x = index_put(x, mask_channel, 0) + return x + + def remove_pretraining_modules(self, keep_decoder=False): + if not keep_decoder: + self.decoder = None + + +def get_annealed_rate(start, end, curr_step, total_steps): + if curr_step >= total_steps: + return end + r = end - start + pct_remaining = 1 - curr_step / total_steps + return end - r * pct_remaining + + +# adapted from MAE +def random_masking(x, mask_ratio, mask_seed: Optional[MaskSeed]): + N, L, D = x.shape # batch, length, dim + len_keep = int(L * (1 - mask_ratio)) + + generator = None + if mask_seed is not None: + seed = int( + hash((mask_seed.seed, mask_seed.update, mask_seed.ids.sum().item())) % 1e6 + ) + generator = torch.Generator(device=x.device) + generator.manual_seed(seed) + + noise = torch.rand(N, L, generator=generator, device=x.device) # noise in [0, 1] + + # sort noise for each sample + ids_shuffle = noise.argsort(dim=1) # ascend: small is keep, large is remove + ids_restore = ids_shuffle.argsort(dim=1) + + # keep the first subset + ids_keep = ids_shuffle[:, :len_keep] + ids_keep = ids_keep.unsqueeze(-1).expand(-1, -1, D) + x_unmasked = torch.gather(x, dim=1, index=ids_keep) + + # generate the binary mask: 0 is keep, 1 is remove + mask = torch.ones([N, L], dtype=x.dtype, device=x.device) + mask[:, :len_keep] = 0 + # unshuffle to get the binary mask + mask = torch.gather(mask, dim=1, index=ids_restore) + + ids_restore = ids_restore.unsqueeze(-1).expand(-1, -1, D) + + return MaskInfo( + x_unmasked=x_unmasked, mask=mask, ids_restore=ids_restore, ids_keep=ids_keep + ) + + +def gather_unmasked(x: torch.Tensor, mask_info: MaskInfo) -> torch.Tensor: + return torch.gather( + x, + dim=1, + index=mask_info.ids_keep, + ) + + +def gather_unmasked_mask(x: torch.Tensor, mask_info: MaskInfo) -> torch.Tensor: + return torch.gather( + x, + dim=1, + index=mask_info.ids_keep[..., 0], # ignore the feature dimension + ) + + +def get_alibi( + max_positions: int, + attention_heads: int, + dims: int = 1, + distance: str = "manhattan", +): + def get_slopes(n): + def get_slopes_power_of_2(n): + start = 2 ** (-(2 ** -(math.log2(n) - 3))) + ratio = start + return [start * ratio ** i for i in range(n)] + + # In the paper, we only train models that have 2^a heads for some + # a. This function has some good properties that only occur when + # the input is a power of 2. To maintain that even when the number + # of heads is not a power of 2, we use this workaround. + if math.log2(n).is_integer(): + return get_slopes_power_of_2(n) + else: + closest_power_of_2 = 2 ** math.floor(math.log2(n)) + return ( + get_slopes_power_of_2(closest_power_of_2) + + get_slopes(2 * closest_power_of_2)[0::2][: n - closest_power_of_2] + ) + + maxpos = max_positions + attn_heads = attention_heads + slopes = torch.Tensor(get_slopes(attn_heads)) + + if dims == 1: + # prepare alibi position linear bias. Note that wav2vec2 is non + # autoregressive model so we want a symmetric mask with 0 on the + # diagonal and other wise linear decreasing valuees + pos_bias = ( + torch.abs( + torch.arange(maxpos).unsqueeze(0) - torch.arange(maxpos).unsqueeze(1) + ) + * -1 + ) + elif dims == 2: + if distance == "manhattan": + df = lambda x1, y1, x2, y2: abs(x1 - x2) + abs(y1 - y2) + elif distance == "euclidean": + df = lambda x1, y1, x2, y2: math.sqrt((x1 - x2) ** 2 + (y1 - y2) ** 2) + + n = math.sqrt(max_positions) + assert n.is_integer(), n + n = int(n) + + pos_bias = torch.zeros((max_positions, max_positions)) + + for i in range(n): + for j in range(n): + for k in range(n): + for l in range(n): + new_x = i * n + j + new_y = k * n + l + pos_bias[new_x, new_y] = -df(i, j, k, l) + + else: + raise Exception(f"unsupported number of alibi dims: {dims}") + + alibi_bias = slopes.unsqueeze(1).unsqueeze(1) * pos_bias.unsqueeze(0).expand( + attn_heads, -1, -1 + ) + + return alibi_bias + + +def get_alibi_bias( + alibi_biases, + batch_size, + time_steps, + heads, + dtype, + device, + dims=1, + distance="manhattan", +): + cache_key = f"{dims}_{heads}_{distance}" + + buffered = alibi_biases.get(cache_key, None) + + target_size = heads * batch_size + if ( + buffered is None + or buffered.size(0) < target_size + or buffered.size(1) < time_steps + or buffered.dtype != dtype + or buffered.device != device + ): + bt = max(time_steps, buffered.size(1) if buffered is not None else 0) + bn = max(target_size, buffered.size(0) if buffered is not None else 0) // heads + + buffered = ( + get_alibi(bt, heads, dims=dims, distance=distance) + .to(dtype=dtype, device=device) + .repeat(bn, 1, 1) + ) + + alibi_biases[cache_key] = buffered + + b = buffered[:target_size, :time_steps, :time_steps] + b = b.view(batch_size, heads, time_steps, time_steps) + return b + + +def _learned_alibi_bias( + alibi_bias, + batch_size, + time_steps, + heads, + scale, + dtype, + device, +): + assert alibi_bias.size(1) == heads, alibi_bias.shape + assert alibi_bias.dtype == dtype, alibi_bias.dtype + assert alibi_bias.device == device, alibi_bias.device + + if alibi_bias.size(-1) < time_steps: + psz = math.ceil((time_steps - alibi_bias.size(-1)) / 2) + alibi_bias = F.pad(alibi_bias, (psz, psz, psz, psz), mode="replicate") + + alibi_bias = alibi_bias.expand(batch_size, -1, -1, -1) * scale + return alibi_bias[..., :time_steps, :time_steps] + + +def masked_alibi(alibi_bias, mask_info): + H = alibi_bias.size(1) + + orig_bias = alibi_bias + + index = mask_info.ids_keep.unsqueeze(1)[..., 0].unsqueeze(-1) + alibi_bias = torch.gather( + orig_bias, + dim=-2, + index=index.expand(-1, H, -1, mask_info.ids_restore.size(1)), + ) + alibi_bias = torch.gather( + alibi_bias, + dim=-1, + index=index.transpose(-1, -2).expand(-1, H, alibi_bias.size(-2), -1), + ) + + return alibi_bias + + +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np +from functools import partial +from dataclasses import dataclass +from typing import Callable, Dict, Optional + +def to_2tuple(x): + return (x,x) + +class PatchEmbed(nn.Module): + """ 2D Image to Patch Embedding + """ + def __init__( + self, + img_size=224, + patch_size=16, + in_chans=3, + embed_dim=768, + norm_layer=None, + flatten=True, + bias=True, + ): + super().__init__() + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + self.img_size = img_size + self.patch_size = patch_size + self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1]) + self.num_patches = self.grid_size[0] * self.grid_size[1] + self.flatten = flatten + + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias) + self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() + + def forward(self, x): + B, C, H, W = x.shape + x = self.proj(x) + if self.flatten: + x = x.flatten(2).transpose(1, 2) # BCHW -> BNC + x = self.norm(x) + return x + + + + +def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False): + """ + grid_size: int of the grid height and width + return: + pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token) + """ + grid_h = np.arange(grid_size, dtype=np.float32) + grid_w = np.arange(grid_size, dtype=np.float32) + grid = np.meshgrid(grid_w, grid_h) # here w goes first + grid = np.stack(grid, axis=0) + + grid = grid.reshape([2, 1, grid_size, grid_size]) + pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid) + if cls_token: + pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0) + return pos_embed + + +def get_2d_sincos_pos_embed_from_grid(embed_dim, grid): + assert embed_dim % 2 == 0 + + # use half of dimensions to encode grid_h + emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2) + emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2) + + emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D) + return emb + + +def get_1d_sincos_pos_embed_from_grid(embed_dim, pos): + """ + embed_dim: output dimension for each position + pos: a list of positions to be encoded: size (M,) + out: (M, D) + """ + assert embed_dim % 2 == 0 + omega = np.arange(embed_dim // 2, dtype=np.float) + omega /= embed_dim / 2.0 + omega = 1.0 / 10000 ** omega # (D/2,) + + pos = pos.reshape(-1) # (M,) + out = np.einsum("m,d->md", pos, omega) # (M, D/2), outer product + + emb_sin = np.sin(out) # (M, D/2) + emb_cos = np.cos(out) # (M, D/2) + + emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D) + return emb + +@dataclass +class D2vImageConfig(D2vModalityConfig): + type: Modality = Modality.IMAGE + + input_size: int = 224 + in_chans: int = 3 + patch_size: int = 16 + embed_dim: int = 768 + + alibi_dims: int = 2 + alibi_distance: str = "manhattan" + + fixed_positions: bool = True + + transformer_decoder: bool = False + enc_dec_transformer: bool = False + + +class ImageEncoder(ModalitySpecificEncoder): + + modality_cfg: D2vImageConfig + + def __init__( + self, + modality_cfg: D2vImageConfig, + embed_dim: int, + make_block: Callable[[float, Optional[int], Optional[int]], nn.ModuleList], + norm_layer: Callable[[int], nn.LayerNorm], + layer_norm_first: bool, + alibi_biases: Dict, + task, #Optional[FairseqTask], + ): + + img_size = to_2tuple(modality_cfg.input_size) + patch_size = to_2tuple(modality_cfg.patch_size) + num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0]) + + local_encoder = PatchEmbed( + modality_cfg.input_size, + modality_cfg.patch_size, + modality_cfg.in_chans, + modality_cfg.embed_dim, + ) + + w = local_encoder.proj.weight.data + torch.nn.init.xavier_uniform_(w.view([w.shape[0], -1])) + + if modality_cfg.embed_dim != embed_dim: + local_encoder = nn.Sequential( + local_encoder, + nn.Linear(modality_cfg.embed_dim, embed_dim), + ) + + project_features = nn.Identity() + + pos_embed = nn.Parameter( + torch.zeros(1, num_patches, embed_dim), requires_grad=False + ) + + side_n = int(num_patches ** 0.5) + + emb = get_2d_sincos_pos_embed( + pos_embed.shape[-1], + side_n, + cls_token=False, + ) + pos_embed.data.copy_(torch.from_numpy(emb).float().unsqueeze(0)) + fixed_positional_encoder = ( + FixedPositionalEncoder(pos_embed) if modality_cfg.fixed_positions else None + ) + + dpr = np.linspace( + modality_cfg.start_drop_path_rate, + modality_cfg.end_drop_path_rate, + modality_cfg.prenet_depth, + ) + + context_encoder = BlockEncoder( + nn.ModuleList(make_block(dpr[i]) for i in range(modality_cfg.prenet_depth)), + norm_layer(embed_dim) if not layer_norm_first else None, + layer_norm_first, + modality_cfg.prenet_layerdrop, + modality_cfg.prenet_dropout, + ) + + if modality_cfg.transformer_decoder: + if modality_cfg.enc_dec_transformer: + decoder = EncDecTransformerDecoder(modality_cfg.decoder, embed_dim) + else: + dec_enc = BlockEncoder( + nn.ModuleList( + make_block(0, modality_cfg.decoder.decoder_dim, 8) + for _ in range(modality_cfg.decoder.decoder_layers) + ), + None, + layer_norm_first, + 0, + 0, + ) + decoder = TransformerDecoder(modality_cfg.decoder, embed_dim, dec_enc) + else: + decoder = ( + Decoder2d(modality_cfg.decoder, embed_dim, side_n, side_n) + if modality_cfg.decoder is not None + else None + ) + + alibi_bias_fn = partial( + get_alibi_bias, + alibi_biases=alibi_biases, + heads=modality_cfg.num_alibi_heads, + dims=modality_cfg.alibi_dims, + distance=modality_cfg.alibi_distance, + ) + + super().__init__( + modality_cfg=modality_cfg, + embed_dim=embed_dim, + local_encoder=local_encoder, + project_features=project_features, + fixed_positional_encoder=fixed_positional_encoder, + relative_positional_encoder=None, + context_encoder=context_encoder, + decoder=decoder, + get_alibi_bias=alibi_bias_fn, + ) + + def reset_parameters(self): + super().reset_parameters() + if self.decoder is not None: + self.decoder.reset_parameters() + + @torch.no_grad() + def patchify(self, imgs): + """ + imgs: (N, 3, H, W) + x: (N, L, patch_size**2 *3) + """ + p = self.modality_cfg.patch_size + h = w = imgs.shape[2] // p + x = imgs.reshape(shape=(imgs.shape[0], 3, h, p, w, p)) + x = torch.einsum("nchpwq->nhwpqc", x) + x = x.reshape(shape=(imgs.shape[0], h * w, p ** 2 * 3)) + + return x + + @torch.no_grad() + def unpatchify(self, x): + """ + x: (N, L, patch_size**2 *3) + imgs: (N, 3, H, W) + """ + p = self.modality_cfg.patch_size + h = w = int(x.shape[1] ** 0.5) + assert h * w == x.shape[1] + + x = x.reshape(shape=(x.shape[0], h, w, p, p, 3)) + x = torch.einsum("nhwpqc->nchpwq", x) + imgs = x.reshape(shape=(x.shape[0], 3, h * p, h * p)) + return imgs + + def compute_mask( + self, + x, + padding_mask, + mask_seed: Optional[MaskSeed], + apply, + shape=None, + precomputed_mask=None, + ): + mlen = self.modality_cfg.mask_length + if mlen <= 1: + return super().compute_mask( + x, padding_mask, mask_seed, apply, precomputed_mask + ) + + if precomputed_mask is not None: + mask = precomputed_mask + else: + from fairseq.data.data_utils import compute_block_mask_2d + + if shape is not None: + B, L, D = shape + else: + B, L, D = x.shape + + mask = compute_block_mask_2d( + shape=(B, L), + mask_prob=self.modality_cfg.mask_prob, + mask_length=self.modality_cfg.mask_length, + mask_prob_adjust=self.modality_cfg.mask_prob_adjust, + inverse_mask=self.modality_cfg.inverse_mask, + require_same_masks=True, + mask_dropout=self.modality_cfg.mask_dropout, + ) + + mask_info = self.make_maskinfo(x, mask, shape) + if apply: + x = self.apply_mask(x, mask_info) + + return x, mask_info + + def decoder_input(self, x, mask_info): + if ( + not self.modality_cfg.transformer_decoder + or not self.modality_cfg.enc_dec_transformer + ): + return super().decoder_input(x, mask_info) + + inp_drop = self.modality_cfg.decoder.input_dropout + if inp_drop > 0: + x = F.dropout(x, inp_drop, training=self.training, inplace=True) + + kv = x[:, self.modality_cfg.num_extra_tokens :] + + assert self.fixed_positional_encoder is not None + pos = self.fixed_positional_encoder(x, None).expand(x.size(0), -1, -1) + + mask = mask_info.mask.bool() + if self.modality_cfg.decoder.add_positions_all: + kv = kv + pos[~mask].view(kv.shape) + + q = pos[mask].view(x.size(0), -1, x.size(-1)) + + return q, kv + + +# Copyright (c) Facebook, Inc. and its affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import logging +from dataclasses import dataclass, field +from typing import Optional, Callable +from functools import partial +import numpy as np + +from omegaconf import II + +import torch +import torch.nn as nn + +# from syllablelm.data2vec.data.modality import Modality + +# from syllablelm.data2vec.models.modalities.base import ( +# MaskSeed, +# D2vModalityConfig, +# ModalitySpecificEncoder, +# get_annealed_rate, +# ) +# from syllablelm.data2vec.models.modalities.modules import ( +# D2vDecoderConfig, +# AltBlock, +# ) + + +# from syllablelm.data2vec.models.modalities.audio import ( +# D2vAudioConfig, +# AudioEncoder, +# ) + + +@dataclass +class D2vModalitiesConfig: + image: D2vImageConfig = D2vImageConfig() + + +@dataclass +class Data2VecMultiConfig: + + loss_beta: float = field( + default=0, metadata={"help": "beta for smooth l1 loss. 0 means use l2 loss"} + ) + loss_scale: Optional[float] = field( + default=None, + metadata={ + "help": "scale the reconstruction loss by this constant. if None then scales by 1/sqrt(dim)" + }, + ) + + depth: int = 8 + start_drop_path_rate: float = 0 + end_drop_path_rate: float = 0 + num_heads: int = 12 + norm_eps: float = 1e-6 + norm_affine: bool = True + encoder_dropout: float = 0.1 + post_mlp_drop: float = 0.1 + attention_dropout: float = 0.1 + activation_dropout: float = 0.0 + dropout_input: float = 0.0 + layerdrop: float = 0.0 + embed_dim: int = 768 + mlp_ratio: float = 4 + layer_norm_first: bool = False + + average_top_k_layers: int = field( + default=8, metadata={"help": "how many layers to average"} + ) + + end_of_block_targets: bool = False + + clone_batch: int = 1 + + layer_norm_target_layer: bool = False + batch_norm_target_layer: bool = False + instance_norm_target_layer: bool = False + instance_norm_targets: bool = False + layer_norm_targets: bool = False + + ema_decay: float = field(default=0.999, metadata={"help": "initial ema decay rate"}) + ema_same_dtype: bool = True + log_norms: bool = True + ema_end_decay: float = field( + default=0.9999, metadata={"help": "final ema decay rate"} + ) + + # when to finish annealing ema decay rate + ema_anneal_end_step: int = II("optimization.max_update") + + ema_encoder_only: bool = field( + default=True, + metadata={ + "help": "whether to momentum update only the shared transformer encoder" + }, + ) + + max_update: int = II("optimization.max_update") + + modalities: D2vModalitiesConfig = D2vModalitiesConfig() + + shared_decoder: Optional[D2vDecoderConfig] = None + + min_target_var: float = field( + default=0.1, metadata={"help": "stop training if target var falls below this"} + ) + min_pred_var: float = field( + default=0.01, + metadata={"help": "stop training if prediction var falls below this"}, + ) + + supported_modality: Optional[Modality] = None + mae_init: bool = False + + seed: int = II("common.seed") + + skip_ema: bool = False + + cls_loss: float = 0 + recon_loss: float = 0 + d2v_loss: float = 1 + + decoder_group: bool = False + + +class Data2VecMultiModel(nn.Module): + def make_modality_encoder( + self, + cfg: D2vModalityConfig, + embed_dim: int, + make_block: Callable[[float], nn.ModuleList], + norm_layer: Callable[[int], nn.LayerNorm], + layer_norm_first: bool, + alibi_biases, + task, + ) -> ModalitySpecificEncoder: + # if cfg.type == Modality.AUDIO: + # enc_cls = AudioEncoder + # elif cfg.type == Modality.IMAGE: + enc_cls = ImageEncoder + # elif cfg.type == Modality.TEXT: + # enc_cls = TextEncoder + # if hasattr(task, "text_task"): + # task = task.text_task + # else: + # raise Exception(f"unsupported modality {cfg.type}") + + return enc_cls( + cfg, + embed_dim, + make_block, + norm_layer, + layer_norm_first, + alibi_biases, + task, + ) + + def __init__(self, cfg: Data2VecMultiConfig, modalities, skip_ema=False, task=None): + super().__init__() + self.cfg = cfg + self.modalities = modalities + self.task = task + + make_layer_norm = partial( + nn.LayerNorm, eps=cfg.norm_eps, elementwise_affine=cfg.norm_affine + ) + + def make_block(drop_path, dim=None, heads=None): + return AltBlock( + cfg.embed_dim if dim is None else dim, + cfg.num_heads if heads is None else heads, + cfg.mlp_ratio, + qkv_bias=True, + drop=cfg.encoder_dropout, + attn_drop=cfg.attention_dropout, + mlp_drop=cfg.activation_dropout, + post_mlp_drop=cfg.post_mlp_drop, + drop_path=drop_path, + norm_layer=make_layer_norm, + layer_norm_first=cfg.layer_norm_first, + ffn_targets=not cfg.end_of_block_targets, + ) + + self.alibi_biases = {} + self.modality_encoders = nn.ModuleDict() + for mod in self.modalities: + mod_cfg = getattr(cfg.modalities, mod.name.lower()) + enc = self.make_modality_encoder( + mod_cfg, + cfg.embed_dim, + make_block, + make_layer_norm, + cfg.layer_norm_first, + self.alibi_biases, + task, + ) + self.modality_encoders[mod.name] = enc + + self.ema = None + + self.average_top_k_layers = cfg.average_top_k_layers + self.loss_beta = cfg.loss_beta + self.loss_scale = cfg.loss_scale + + self.dropout_input = nn.Dropout(cfg.dropout_input) + + dpr = np.linspace(cfg.start_drop_path_rate, cfg.end_drop_path_rate, cfg.depth) + + self.blocks = nn.ModuleList([make_block(dpr[i]) for i in range(cfg.depth)]) + + self.norm = None + if cfg.layer_norm_first: + self.norm = make_layer_norm(cfg.embed_dim) + + # if self.cfg.mae_init: + # self.apply(self._init_weights) + # else: + # from fairseq.modules.transformer_sentence_encoder import init_bert_params + # + # self.apply(init_bert_params) + + # for mod_enc in self.modality_encoders.values(): + # mod_enc.reset_parameters() + + # if not skip_ema: + # self.ema = self.make_ema_teacher(cfg.ema_decay) + # self.shared_decoder = ( + # Decoder1d(cfg.shared_decoder, cfg.embed_dim) + # if self.cfg.shared_decoder is not None + # else None + # ) + # if self.shared_decoder is not None: + # self.shared_decoder.apply(self._init_weights) + # + # self.recon_proj = None + # if cfg.recon_loss > 0: + # self.recon_proj = nn.Linear(cfg.embed_dim, cfg.embed_dim) + + for pn, p in self.named_parameters(): + if len(p.shape) == 1 or pn.endswith(".bias") or "alibi_scale" in pn: + p.optim_overrides = {"optimizer": {"weight_decay_scale": 0}} + if cfg.decoder_group and "decoder" in pn: + p.param_group = "decoder" + + self.num_updates = 0 + + def _init_weights(self, m): + + try: + from apex.normalization import FusedLayerNorm + + fn = FusedLayerNorm + except: + fn = nn.LayerNorm + + if isinstance(m, nn.Linear): + torch.nn.init.xavier_uniform_(m.weight) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm) or isinstance(m, fn): + if m.bias is not None: + nn.init.constant_(m.bias, 0) + if m.weight is not None: + nn.init.constant_(m.weight, 1.0) + + # @torch.no_grad() + # def make_ema_teacher(self, ema_decay): + # ema_config = EMAModuleConfig( + # ema_decay=ema_decay, + # ema_fp32=True, + # log_norms=self.cfg.log_norms, + # add_missing_params=False, + # ) + # + # model_copy = self.make_target_model() + # + # return EMAModule( + # model_copy, + # ema_config, + # copy_model=False, + # ) + + def make_target_model(self): + logger.info("making target model") + + model_copy = Data2VecMultiModel( + self.cfg, self.modalities, skip_ema=True, task=self.task + ) + + if self.cfg.ema_encoder_only: + model_copy = model_copy.blocks + for p_s, p_t in zip(self.blocks.parameters(), model_copy.parameters()): + p_t.data.copy_(p_s.data) + else: + for p_s, p_t in zip(self.parameters(), model_copy.parameters()): + p_t.data.copy_(p_s.data) + + for mod_enc in model_copy.modality_encoders.values(): + mod_enc.decoder = None + if not mod_enc.modality_cfg.ema_local_encoder: + mod_enc.local_encoder = None + mod_enc.project_features = None + + model_copy.requires_grad_(False) + return model_copy + + def set_num_updates(self, num_updates): + super().set_num_updates(num_updates) + + if self.ema is not None and ( + (self.num_updates == 0 and num_updates > 1) + or self.num_updates >= num_updates + ): + pass + elif self.training and self.ema is not None: + ema_weight_decay = None + if self.cfg.ema_decay != self.cfg.ema_end_decay: + if num_updates >= self.cfg.ema_anneal_end_step: + decay = self.cfg.ema_end_decay + else: + decay = get_annealed_rate( + self.cfg.ema_decay, + self.cfg.ema_end_decay, + num_updates, + self.cfg.ema_anneal_end_step, + ) + self.ema.set_decay(decay, weight_decay=ema_weight_decay) + if self.ema.get_decay() < 1: + self.ema.step(self.blocks if self.cfg.ema_encoder_only else self) + + self.num_updates = num_updates + + def state_dict(self, destination=None, prefix="", keep_vars=False): + state = super().state_dict(destination, prefix, keep_vars) + + if self.ema is not None: + state[prefix + "_ema"] = self.ema.fp32_params + + return state + + def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs): + k = prefix + "_ema" + if self.ema is not None: + assert k in state_dict + self.ema.restore(state_dict[k], True) + del state_dict[k] + elif k in state_dict: + del state_dict[k] + + return super()._load_from_state_dict(state_dict, prefix, *args, **kwargs) + + @classmethod + def build_model(cls, cfg: Data2VecMultiConfig, task=None): + """Build a new model instance.""" + if task is None or not hasattr(task, "supported_modalities"): + modalities = ( + [cfg.supported_modality] + if cfg.supported_modality is not None + else [ + Modality.AUDIO, + Modality.IMAGE, + Modality.TEXT, + ] + ) + else: + modalities = task.supported_modalities + return cls(cfg, modalities, task=task, skip_ema=cfg.skip_ema) + + def forward( + self, + source, + target=None, + id=None, + mode=None, + padding_mask=None, + mask=True, + features_only=False, + force_remove_masked=False, + remove_extra_tokens=True, + precomputed_mask=None, + out_layer=None, ## NEGATIVE + ): + if mode is None: + assert self.cfg.supported_modality is not None + mode = self.cfg.supported_modality + + if isinstance(mode, Modality): + mode = mode.name + + feature_extractor = self.modality_encoders[mode] + + mask_seeds = None + if id is not None: + mask_seeds = MaskSeed(seed=self.cfg.seed, update=self.num_updates, ids=id) + + extractor_out = feature_extractor( + source, + padding_mask, + mask, + remove_masked=not features_only or force_remove_masked, + clone_batch=self.cfg.clone_batch if not features_only else 1, + mask_seeds=mask_seeds, + precomputed_mask=precomputed_mask, + ) + + x = extractor_out["x"] + encoder_mask = extractor_out["encoder_mask"] + masked_padding_mask = extractor_out["padding_mask"] + masked_alibi_bias = extractor_out.get("alibi_bias", None) + alibi_scale = extractor_out.get("alibi_scale", None) + + if self.dropout_input is not None: + x = self.dropout_input(x) + + layer_results = [] + xs = [] + for i, blk in enumerate(self.blocks): + if ( + not self.training + or self.cfg.layerdrop == 0 + or (np.random.random() > self.cfg.layerdrop) + ): + ab = masked_alibi_bias + if ab is not None and alibi_scale is not None: + scale = ( + alibi_scale[i] + if alibi_scale.size(0) > 1 + else alibi_scale.squeeze(0) + ) + ab = ab * scale.type_as(ab) + + x, lr = blk( + x, + padding_mask=masked_padding_mask, + alibi_bias=ab, + ) + if features_only: + layer_results.append(lr) + xs.append(x) + if out_layer is not None and i == len(self.blocks) + out_layer: + break + + if self.norm is not None: + x = self.norm(x) + + if features_only: + if remove_extra_tokens: + x = x[:, feature_extractor.modality_cfg.num_extra_tokens :] + if masked_padding_mask is not None: + masked_padding_mask = masked_padding_mask[ + :, feature_extractor.modality_cfg.num_extra_tokens : + ] + + return { + "x": x, + "padding_mask": masked_padding_mask, + "layer_results": layer_results, + "mask": encoder_mask, + "xs": xs + } + + +from types import SimpleNamespace + + +d2v2_config = SimpleNamespace(**{'_name': 'data2vec_multi', + 'cosine_loss_temp': 0.0, + 'loss_beta': 0.0, + 'loss_scale': None, + 'mean_loss': False, + 'reconstruct_all': False, + 'depth': 24, + 'start_drop_path_rate': 0.0, + 'end_drop_path_rate': 0.0, + 'num_heads': 16, + 'norm_eps': 1e-06, + 'norm_affine': True, + 'encoder_dropout': 0.0, + 'post_mlp_drop': 0.0, + 'attention_dropout': 0.0, + 'activation_dropout': 0.0, + 'dropout_input': 0.0, + 'layerdrop': 0.0, + 'embed_dim': 1024, + 'mlp_ratio': 4.0, + 'layer_norm_first': False, + 'average_top_k_layers': 18, + 'end_of_block_targets': False, + 'clone_batch': 16, + 'layer_norm_target_layer': False, + 'batch_norm_target_layer': False, + 'instance_norm_target_layer': True, + 'instance_norm_targets': False, + 'layer_norm_targets': True, + 'ema_decay': 0.9998, + 'ema_same_dtype': True, + 'log_norms': True, + 'ema_end_decay': 1.0, + 'ema_anneal_end_step': 500000, + 'ema_encoder_only': False, + 'max_update': 750000, + 'modalities': SimpleNamespace(**{'audio': SimpleNamespace(**{'type': 'AUDIO', + 'prenet_depth': 4, + 'prenet_layerdrop': 0.0, + 'prenet_dropout': 0.0, + 'start_drop_path_rate': 0.0, + 'end_drop_path_rate': 0.0, + 'num_extra_tokens': 0, + 'init_extra_token_zero': True, + 'mask_from_extra': False, + 'mask_from_extra_detached': False, + 'mask_noise_std': 0.01, + 'mask_prob_min': None, + 'mask_prob': 0.7, + 'inverse_mask': False, + 'mask_prob_adjust': 0.0, + 'keep_masked_pct': 0.0, + 'mask_length': 5, + 'add_masks': False, + 'remove_masks': False, + 'mask_dropout': 0.0, + 'encoder_zero_mask': True, + 'mask_channel_prob': 0.0, + 'mask_channel_length': 64, + 'ema_local_encoder': False, + 'local_grad_mult': 1.0, + 'use_alibi_encoder': False, + 'alibi_scale': 1.0, + 'learned_alibi': False, + 'alibi_max_pos': None, + 'learned_alibi_scale': False, + 'learned_alibi_scale_per_head': False, + 'learned_alibi_scale_per_layer': False, + 'num_alibi_heads': 16, + 'model_depth': 24, + 'decoder': None, + 'max_alibi_scale': 0.0, + 'max_alibi_grad': 0.0, + 'max_alibi_val': 0.0, + 'extractor_mode': 'layer_norm', + 'feature_encoder_spec': '[(512, 10, 5)] + [(512, 3, 2)] * 4 + [(512,2,2)] + [(512,2,2)]', + 'conv_pos_width': 95, + 'conv_pos_groups': 16, + 'conv_pos_depth': 5, + 'conv_pos_pre_ln': False, + 'mlp_encoder': False, + 'mlp_n_in': 320, + 'mlp_dim': None, + 'mlp_layers': 9}), + 'image': SimpleNamespace(**{'type': 'IMAGE', + 'prenet_depth': 0, + 'prenet_layerdrop': 0.0, + 'prenet_dropout': 0.0, + 'start_drop_path_rate': 0.0, + 'end_drop_path_rate': 0.0, + 'num_extra_tokens': 1, + 'init_extra_token_zero': False, + 'mask_from_extra': False, + 'mask_from_extra_detached': False, + 'mask_noise_std': 0.01, + 'mask_prob_min': None, + 'mask_prob': 0.75, + 'inverse_mask': True, + 'mask_prob_adjust': 0.1, + 'keep_masked_pct': 0.0, + 'mask_length': 3, + 'add_masks': False, + 'remove_masks': False, + 'mask_dropout': 0.0, + 'encoder_zero_mask': True, + 'mask_channel_prob': 0.0, + 'mask_channel_length': 64, + 'ema_local_encoder': True, + 'local_grad_mult': 1.0, + 'use_alibi_encoder': False, + 'alibi_scale': 1.0, + 'learned_alibi': False, + 'alibi_max_pos': None, + 'learned_alibi_scale': False, + 'learned_alibi_scale_per_head': False, + 'learned_alibi_scale_per_layer': False, + 'num_alibi_heads': 16, + 'model_depth': 24, + 'decoder': SimpleNamespace(**{'decoder_dim': 1024, + 'decoder_groups': 16, + 'decoder_kernel': 5, + 'decoder_layers': 3, + 'input_dropout': 0.0, + 'add_positions_masked': False, + 'add_positions_all': False, + 'final_layer_norm': False, + 'tanh_scale': 0.0, + 'project_first_residual': False, + 'decoder_residual': True, + 'projection_layers': 1, + 'projection_ratio': 2.0, + 'residual_scale': 1.0, + 'remove_residual_noise': False, + 'post_residual_ln': False}), + 'max_alibi_scale': 0.0, + 'max_alibi_grad': 0.0, + 'max_alibi_val': 0.0, + 'input_size': 224, + 'in_chans': 3, + 'patch_size': 16, + 'embed_dim': 1024, + 'fix_masks': False, + 'exact_mask_pct': False, + 'unmask_focal': False, + 'focal_length': 1, + 'alibi_dims': 2, + 'alibi_distance': 'manhattan', + 'fixed_positions': True, + 'conv_pos_cfg': None, + 'transformer_decoder': False, + 'enc_dec_transformer': False, + 'conv_mae': False, + 'conv_mae_multiscale': True, + 'conv_mae_masking': True}), + 'text': SimpleNamespace(**{'type': 'TEXT', + 'prenet_depth': 4, + 'prenet_layerdrop': 0.0, + 'prenet_dropout': 0.0, + 'start_drop_path_rate': 0.0, + 'end_drop_path_rate': 0.0, + 'num_extra_tokens': 0, + 'init_extra_token_zero': True, + 'mask_from_extra': False, + 'mask_from_extra_detached': False, + 'mask_noise_std': 0.01, + 'mask_prob_min': None, + 'mask_prob': 0.7, + 'inverse_mask': False, + 'mask_prob_adjust': 0.0, + 'keep_masked_pct': 0.0, + 'mask_length': 5, + 'add_masks': False, + 'remove_masks': False, + 'mask_dropout': 0.0, + 'encoder_zero_mask': True, + 'mask_channel_prob': 0.0, + 'mask_channel_length': 64, + 'ema_local_encoder': False, + 'local_grad_mult': 1.0, + 'use_alibi_encoder': False, + 'alibi_scale': 1.0, + 'learned_alibi': False, + 'alibi_max_pos': None, + 'learned_alibi_scale': False, + 'learned_alibi_scale_per_head': False, + 'learned_alibi_scale_per_layer': False, + 'num_alibi_heads': 16, + 'model_depth': 24, + 'decoder': None, + 'max_alibi_scale': 0.0, + 'max_alibi_grad': 0.0, + 'max_alibi_val': 0.0, + 'max_source_positions': 512, + 'learned_pos': True, + 'dropout': 0.1, + 'no_scale_embedding': True, + 'layernorm_embedding': True, + 'no_token_positional_embeddings': False})}), + 'shared_decoder': None, + 'min_target_var': 0.0, + 'min_pred_var': 0.0, + 'supported_modality': 'IMAGE', + 'mae_init': False, + 'bert_init': True, + 'seed': 1, + 'skip_ema': False, + 'cls_loss': 0.01, + 'alt_cls_targets': False, + 'recon_loss': 0.0, + 'recon_dim': 0, + 'd2v_loss': 1.0, + 'qk_scale': None, + 'cosine_attention': False, + 'decoder_group': False, + 'extra_tokens_group': False, + 'shift_targets_down_updates': 0, + 'shift_targets_down_scale': 1.0, + 'modality_discrim_weight': 1.0, + 'modality_discrim_ema': 0.0, + 'modality_discrim_depth': 0} +) + +d2v2_12_std = torch.tensor([1.7143195867538452, 0.5259653329849243, 1.5721157789230347, 0.4863377809524536, 0.6252964735031128, 0.4615776240825653, 0.5931997299194336, 2.6008753776550293, 0.4766261577606201, 0.7057560086250305, 0.4928278625011444, 0.5091769099235535, 0.4916788935661316, 0.5836999416351318, 0.6225807666778564, 0.7759029269218445, 0.4879513382911682, 0.4539593458175659, 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-0.10871358215808868, 0.12677182257175446, -0.07570789754390717, -0.07952741533517838, -0.026477813720703125, 0.06984280794858932, -0.22945941984653473, 0.015926821157336235, -0.14885887503623962, -0.011332892812788486, 0.08493077754974365, -0.15522490441799164, -0.06845367699861526, -0.23213626444339752, -0.07301405817270279, -0.11880737543106079, 0.003320008981972933, 0.17502792179584503, 0.41226688027381897, 0.04537675902247429, 0.014825945720076561, 0.09765269607305527, 0.02394934371113777, -0.1532425433397293, 0.1607235074043274, 0.08105559647083282, -0.22956547141075134, -0.0916622057557106, -0.06985598057508469, 0.18260440230369568, 0.3137093484401703, 0.09711585938930511, 0.17369917035102844, 0.0399029515683651, -0.2348783016204834]) + +import os +class Data2Vec2Encoder(nn.Module): + def __init__( + self, + model_ckpt_path: str = '/path/to/your/latentforcing/checkpoint/', + match_pixel_norm: float = 0.485, + ): + super().__init__() + + self.register_buffer("latent_std", d2v2_12_std.clone().float()) + self.register_buffer("latent_mean", d2v2_12_mean.clone().float()) + + self.register_buffer("pixel_std", torch.tensor((0.229, 0.224, 0.225))) + self.register_buffer("pixel_mean", torch.tensor((0.485, 0.456, 0.406))) + + self.match_pixel_norm = match_pixel_norm + + if not os.path.exists(model_ckpt_path): # TODO should be home dir local + assert False, "Download D2V2 https://dl.fbaipublicfiles.com/fairseq/data2vec2/large_imagenet.pt" + state_dict = torch.load(model_ckpt_path, map_location="cpu", weights_only=False) + d2v2_model = Data2VecMultiModel(d2v2_config, [Modality.IMAGE]) + d2v2_model.load_state_dict(state_dict['model']) + + d2v2_pos_196 = d2v2_model.modality_encoders["IMAGE"].fixed_positional_encoder.positions + d2v2_pos_256 = d2v2_pos_196.clone().reshape(1, 14, 14, -1).permute(0, 3, 1, 2) + d2v2_pos_256 = torch.nn.functional.interpolate(d2v2_pos_256, size=(16, 16), mode='bicubic', align_corners=False) + d2v2_pos_256 = d2v2_pos_256.permute(0, 2, 3, 1).flatten(1, 2) # Returns (1, 256, 1024) + d2v2_model.modality_encoders["IMAGE"].fixed_positional_encoder.positions = nn.Parameter(d2v2_pos_256) + + d2v2_model.requires_grad_(False) + d2v2_model.eval() + + self.d2v2_model = d2v2_model + + @torch.compile() + @torch.no_grad() + def encode(self, x: torch.Tensor) -> torch.Tensor: + # normalize input + # x : b c h w + + x = (x - self.pixel_mean.view(1,3,1,1)) / self.pixel_std.view(1,3,1,1) + + z = self.d2v2_model(x, mode=None, mask=False, features_only=True, remove_extra_tokens=True, out_layer=-12) + z = z['xs'][12][:,1:] + z = (z - self.latent_mean.view(1,1,-1)) / self.latent_std.view(1,1,-1) + z = z.clamp(-5, 5) + + z = z * self.match_pixel_norm + z = z.view(-1,16,16,1024).permute(0,3,1,2) # b hw d --> b d h w + + return z diff --git a/transfer/code/LatentForcing/denoiser.py b/transfer/code/LatentForcing/denoiser.py new file mode 100644 index 0000000000000000000000000000000000000000..a7b6256872289e73356cd9e7fed283886c165f7d --- /dev/null +++ b/transfer/code/LatentForcing/denoiser.py @@ -0,0 +1,145 @@ +import torch +import torch.nn as nn +from model_jit import JiT_models + + +class Denoiser(nn.Module): + def __init__( + self, + args + ): + super().__init__() + self.net = JiT_models[args.model]( + input_size=args.img_size, + in_channels=3, + num_classes=args.class_num, + attn_drop=args.attn_dropout, + proj_drop=args.proj_dropout, + ) + self.img_size = args.img_size + self.num_classes = args.class_num + + self.label_drop_prob = args.label_drop_prob + self.P_mean = args.P_mean + self.P_std = args.P_std + self.t_eps = args.t_eps + self.noise_scale = args.noise_scale + + # ema + self.ema_decay1 = args.ema_decay1 + self.ema_decay2 = args.ema_decay2 + self.ema_params1 = None + self.ema_params2 = None + + # generation hyper params + self.method = args.sampling_method + self.steps = args.num_sampling_steps + self.cfg_scale = args.cfg + self.cfg_interval = (args.interval_min, args.interval_max) + + # Alan change: Autoguidance + self.ag_net = None + if args.autoguidance_ckpt != "": + self.ag_net = JiT_models['JiT-S/16']( + input_size=args.img_size, + in_channels=3, + num_classes=args.class_num, + attn_drop=args.attn_dropout, + proj_drop=args.proj_dropout, + ) + self.ag_net.requires_grad_(False) + + def drop_labels(self, labels): + drop = torch.rand(labels.shape[0], device=labels.device) < self.label_drop_prob + out = torch.where(drop, torch.full_like(labels, self.num_classes), labels) + return out + + def sample_t(self, n: int, device=None): + z = torch.randn(n, device=device) * self.P_std + self.P_mean + return torch.sigmoid(z) + + def forward(self, x, labels): + labels_dropped = self.drop_labels(labels) if self.training else labels + + t = self.sample_t(x.size(0), device=x.device).view(-1, *([1] * (x.ndim - 1))) + e = torch.randn_like(x) * self.noise_scale + + z = t * x + (1 - t) * e + v = (x - z) / (1 - t).clamp_min(self.t_eps) + + x_pred = self.net(z, t.flatten(), labels_dropped) + v_pred = (x_pred - z) / (1 - t).clamp_min(self.t_eps) + + # l2 loss + loss = (v - v_pred) ** 2 + loss = loss.mean(dim=(1, 2, 3)).mean() + + return loss, {} + + @torch.no_grad() + def generate(self, labels): + device = labels.device + bsz = labels.size(0) + z = self.noise_scale * torch.randn(bsz, 3, self.img_size, self.img_size, device=device) + timesteps = torch.linspace(0.0, 1.0, self.steps+1, device=device).view(-1, *([1] * z.ndim)).expand(-1, bsz, -1, -1, -1) + + if self.method == "euler": + stepper = self._euler_step + elif self.method == "heun": + stepper = self._heun_step + else: + raise NotImplementedError + + # ode + for i in range(self.steps - 1): + t = timesteps[i] + t_next = timesteps[i + 1] + z = stepper(z, t, t_next, labels) + # last step euler + z = self._euler_step(z, timesteps[-2], timesteps[-1], labels) + return z + + @torch.no_grad() + def _forward_sample(self, z, t, labels): + # conditional + x_cond = self.net(z, t.flatten(), labels) + v_cond = (x_cond - z) / (1.0 - t).clamp_min(self.t_eps) + + # unconditional + if self.ag_net is not None: + x_uncond = self.ag_net(z, t.flatten(), torch.full_like(labels, self.num_classes)) + else: + x_uncond = self.net(z, t.flatten(), torch.full_like(labels, self.num_classes)) + v_uncond = (x_uncond - z) / (1.0 - t).clamp_min(self.t_eps) + + # cfg interval + low, high = self.cfg_interval + interval_mask = (t < high) & ((low == 0) | (t > low)) + cfg_scale_interval = torch.where(interval_mask, self.cfg_scale, 1.0) + + return v_uncond + cfg_scale_interval * (v_cond - v_uncond) + + @torch.no_grad() + def _euler_step(self, z, t, t_next, labels): + v_pred = self._forward_sample(z, t, labels) + z_next = z + (t_next - t) * v_pred + return z_next + + @torch.no_grad() + def _heun_step(self, z, t, t_next, labels): + v_pred_t = self._forward_sample(z, t, labels) + + z_next_euler = z + (t_next - t) * v_pred_t + v_pred_t_next = self._forward_sample(z_next_euler, t_next, labels) + + v_pred = 0.5 * (v_pred_t + v_pred_t_next) + z_next = z + (t_next - t) * v_pred + return z_next + + @torch.no_grad() + def update_ema(self): + source_params = list(self.parameters()) + for targ, src in zip(self.ema_params1, source_params): + targ.detach().mul_(self.ema_decay1).add_(src, alpha=1 - self.ema_decay1) + for targ, src in zip(self.ema_params2, source_params): + targ.detach().mul_(self.ema_decay2).add_(src, alpha=1 - self.ema_decay2) diff --git a/transfer/code/LatentForcing/denoiser_cot.py b/transfer/code/LatentForcing/denoiser_cot.py new file mode 100644 index 0000000000000000000000000000000000000000..62b53a5ed493609ece38dbf6de38a76c2b61731a --- /dev/null +++ b/transfer/code/LatentForcing/denoiser_cot.py @@ -0,0 +1,299 @@ +import torch +import torch.nn as nn +from model_cot import JiTCoT_models +from dinov2_hf import RAE +from mocov3 import MocoV3 +from data2vec2 import Data2Vec2Encoder +from pixel_downsample import PixelDownsampleEncoder + +class DenoiserCoT(nn.Module): + def __init__( + self, + args + ): + super().__init__() + self.net = JiTCoT_models[args.model]( + input_size=args.img_size, + in_channels=3, + num_classes=args.class_num, + attn_drop=args.attn_dropout, + proj_drop=args.proj_dropout, + bottleneck_dim_dino=args.bottleneck_dim_dino, + dh_depth=args.dh_depth, + dh_hidden_size=args.dh_hidden_size, + dino_in_channels=args.dino_in_channels, + ) + self.img_size = args.img_size + self.num_classes = args.class_num + + self.label_drop_prob = args.label_drop_prob + self.P_mean = args.P_mean + self.P_std = args.P_std + self.D_mean = args.D_mean + self.D_std = args.D_std + self.t_eps = args.t_eps + self.t_eps_inference = args.t_eps_inference + self.noise_scale = args.noise_scale + + if args.latent_model == "dino": + self.dino = RAE() + self.dino_hidden_dim = 768 + elif args.latent_model == "mocov3": + self.dino = MocoV3() + self.dino_hidden_dim = 768 + elif args.latent_model == "d2v2": + self.dino = Data2Vec2Encoder() + self.dino_hidden_dim = 1024 + elif args.latent_model == "pixeldownsample": + self.dino = PixelDownsampleEncoder() + self.dino_hidden_dim = 48 + else: + raise NotImplementedError() + + + self.dino_max_t = args.dino_max_t + self.dino_weight = args.dino_weight + self.sample_mode = args.sample_mode + self.choose_dino_p = args.choose_dino_p + self.dino_pixel_offset = args.dino_pixel_offset + self.dino_pixel_shift = args.dino_pixel_shift + + # ema + self.ema_decay1 = args.ema_decay1 + self.ema_decay2 = args.ema_decay2 + self.ema_params1 = None + self.ema_params2 = None + + # generation hyper params + self.method = args.sampling_method + self.steps = args.num_sampling_steps + self.cfg_scale = args.cfg + self.cfg_scale_dino = args.cfg_dino + self.cfg_interval = (args.interval_min, args.interval_max) + self.cfg_interval_dino = (args.interval_min_dino, args.interval_max_dino) + self.gen_shift_pixel = args.gen_shift_pixel + self.gen_shift_dino = args.gen_shift_dino + self.guidance_method = args.guidance_method + + self.mask_p = args.mask_p + + self.ag_net = None + if args.autoguidance_ckpt != "": + self.ag_net = JiTCoT_models['JiTCoT-S/16']( + input_size=args.img_size, + in_channels=3, + num_classes=args.class_num, + attn_drop=args.attn_dropout, + proj_drop=args.proj_dropout, + bottleneck_dim_dino=args.bottleneck_dim_dino, + dh_depth=2, + dh_hidden_size=1024, + ) + self.ag_net.requires_grad_(False) + + def drop_labels(self, labels): + drop = torch.rand(labels.shape[0], device=labels.device) < self.label_drop_prob + out = torch.where(drop, torch.full_like(labels, self.num_classes), labels) + return out + + @torch.autocast('cuda', enabled=False) + def sample_t(self, n: int, device=None, dtype=torch.bfloat16): + if self.sample_mode in {"shifted_independent_uniform", "shifted_independent_uniform_masked"}: + ts = 0.5 + t = torch.rand((2, n), device=device) + t = t * ts / (1 + (ts-1) * t) + loss_weight = [torch.ones(n, device=device), torch.ones(n, device=device)] + return [t[0], t[1]], loss_weight + elif self.sample_mode == "dino_only": + t_dino = torch.sigmoid(torch.randn(n, device=device) * self.D_std + self.D_mean) + t_pixel = torch.zeros(n, device=device) + + t = [t_pixel.to(dtype), t_dino.to(dtype)] + loss_weight = [torch.zeros(n, device=device, dtype=dtype), torch.ones(n, device=device, dtype=dtype)] + return t, loss_weight + elif self.sample_mode == "pixel_only": + t_pixel = torch.sigmoid(torch.randn(n, device=device) * self.P_std + self.P_mean) + t_dino = torch.zeros(n, device=device) + + t = [t_pixel.to(dtype), t_dino.to(dtype)] + loss_weight = [torch.ones(n, device=device, dtype=dtype), torch.zeros(n, device=device, dtype=dtype)] + return t, loss_weight + elif self.sample_mode == "dino_first_cascaded": + t_dino = torch.sigmoid(torch.randn(n, device=device) * self.D_std + self.D_mean) + t_pixel = torch.sigmoid(torch.randn(n, device=device) * self.P_std + self.P_mean) + t_pixel = torch.where(torch.rand(n, device=device) < 0.1, torch.rand(n, device=device) * 0.5, t_pixel) # nonzero t=0 + + choose_dino_mask = torch.rand(n, device=device) < self.choose_dino_p + t_dino = torch.where(choose_dino_mask, t_dino, torch.ones_like(t_dino)) + t_pixel = torch.where(choose_dino_mask, torch.zeros_like(t_pixel), t_pixel) + + t = [t_pixel.to(dtype), t_dino.to(dtype)] + loss_weight = [1.0*(~choose_dino_mask), 1.0*choose_dino_mask] + return t, loss_weight + elif self.sample_mode == "dino_first_cascaded_noised": + t_dino = torch.sigmoid(torch.randn(n, device=device) * self.D_std + self.D_mean) + t_pixel = torch.sigmoid(torch.randn(n, device=device) * self.P_std + self.P_mean) + t_pixel = torch.where(torch.rand(n, device=device) < 0.1, torch.rand(n, device=device) * 0.5, t_pixel) # nonzero t=0 + + choose_dino_mask = torch.rand(n, device=device) < self.choose_dino_p + t_dino = torch.where(choose_dino_mask, t_dino, torch.rand_like(t_dino) * 0.25 + 0.75) # Noise beta = 0.25 + t_pixel = torch.where(choose_dino_mask, torch.zeros_like(t_pixel), t_pixel) + + t = [t_pixel.to(dtype), t_dino.to(dtype)] + loss_weight = [1.0*(~choose_dino_mask), 1.0*choose_dino_mask] + return t, loss_weight + elif self.sample_mode == "dino_first_shifted_aligned": + t_dino = torch.sigmoid(torch.randn(n, device=device) * self.D_std + self.D_mean) + t_pixel = torch.sigmoid(torch.randn(n, device=device) * self.P_std + self.P_mean) + + def local_shift(t,a): + return t * a / (1 + (a-1)*t) + + choose_dino_mask = torch.rand(n, device=device) < self.choose_dino_p + t_dino = torch.where(choose_dino_mask, t_dino, local_shift(t_pixel, self.dino_pixel_shift)) + t_pixel = torch.where(choose_dino_mask, local_shift(t_dino, 1/self.dino_pixel_shift), t_pixel) + + t = [t_pixel.to(dtype), t_dino.to(dtype)] + loss_weight = [1.0*(~choose_dino_mask), 1.0*choose_dino_mask] # There's a lower variance way to do this + return t, loss_weight + + def get_generate_timesteps(self, labels, z): + device = labels.device + bsz = labels.size(0) + if self.sample_mode in {"eval_shifted", "dino_first_shifted_aligned", "shifted_independent_uniform"}: + # Balances the diffusion steps along the curved trajectory, inverts this to get dino/pixel timesteps + # Input using shift**0.5. This code creates a variance shift of $alpha$ by multipliying and dividing by sqrt(alpha) for pixel and dino + def get_schedule(n, off, s, dev): + y = torch.linspace(0, 1, n + 1, device=dev) + t_d = y / (s - (s - 1) * y) + t_p = y / ((1/s) - ((1/s) - 1) * y) + + t_d = t_d * (1 + off) - off if off < 0 else t_d + t_p = t_p * (1 - off) + off if off >= 0 else t_p + + t = torch.sort(torch.cat([t_d, t_p]))[0][::2] + + def warp(v, p, o, dino): + dly = (o < 0 and dino) or (o > 0 and not dino) + vn = (v - abs(o)).clamp(0) / (1 - abs(o)) if dly else v + return (p * vn) / (1 + (p - 1) * vn) + + return torch.stack([warp(ti, 1/s, off, False) for ti in t]), \ + torch.stack([warp(ti, s, off, True) for ti in t]) + + tp, td = get_schedule(self.steps, self.dino_pixel_offset, self.dino_pixel_shift**0.5 if self.sample_mode == "dino_first_shifted_aligned" else self.dino_pixel_shift, device) + + timesteps_pixel = tp.view(-1, *([1] * z[0].ndim)).expand(-1, bsz, -1, -1, -1) + timesteps_dino = td.view(-1, *([1] * z[1].ndim)).expand(-1, bsz, -1, -1, -1) + timesteps = list(zip(timesteps_pixel, timesteps_dino)) + elif self.sample_mode in {"dino_first_cascaded", "dino_first_cascaded_noised"}: + timesteps_pixel = torch.cat([torch.zeros(self.steps//2+1, device=device), torch.linspace(0.0, 1.0, self.steps//2+1, device=device)[1:]], dim=0).view(-1, *([1] * z[1].ndim)).expand(-1, bsz, -1, -1, -1) + timesteps_dino = torch.cat([torch.linspace(0.0, self.dino_max_t, self.steps//2+1, device=device), torch.full((self.steps//2+1,), self.dino_max_t, device=device)[1:]], dim=0).view(-1, *([1] * z[0].ndim)).expand(-1, bsz, -1, -1, -1) + + timesteps_pixel = timesteps_pixel * self.gen_shift_pixel / (1 + (self.gen_shift_pixel - 1) * timesteps_pixel) + timesteps_dino = timesteps_dino * self.gen_shift_dino / (1 + (self.gen_shift_dino - 1) * timesteps_dino) + timesteps = list(zip(timesteps_pixel, timesteps_dino)) + return timesteps + + def forward(self, x, labels): + # x: [-1,1] min,max; Image-shape N 3 H W + labels_dropped = self.drop_labels(labels) if self.training else labels + + bsz, device = x.size(0), x.device + x_pixel = x + x_dino = self.dino.encode(x * 0.5 + 0.5) + x = [x_pixel, x_dino] + t, loss_weight = self.sample_t(bsz, device=device) + t = [t[i].view(-1, *([1] * (x[i].ndim - 1))) for i in [0,1]] + e = [torch.randn_like(x[i]) * self.noise_scale for i in [0,1]] + + z = [t[i] * x[i] + (1 - t[i]) * e[i] for i in [0,1]] + v = [(x[i] - z[i]) / (1 - t[i]).clamp_min(self.t_eps) for i in [0,1]] + + x_pred = self.net(z, [t[i].flatten() for i in [0,1]], labels_dropped) + v_pred = [(x_pred[i] - z[i]) / (1 - t[i]).clamp_min(self.t_eps) for i in [0,1]] + + # l2 loss + losses = [((v[i] - v_pred[i]).pow(2).mean(dim=(1, 2, 3)) * loss_weight[i]).mean() for i in [0,1]] + loss = losses[0] + losses[1] * self.dino_weight + results_dict = {'loss_pixel': losses[0].item(), 'loss_dino': losses[1].item()} + + return loss, results_dict + + @torch.no_grad() + def generate(self, labels): + device = labels.device + bsz = labels.size(0) + z_pixel = self.noise_scale * torch.randn(bsz, 3, self.img_size, self.img_size, device=device) + z_dino = self.noise_scale * torch.randn(bsz, self.dino_hidden_dim, 16, 16, device=device) + z = [z_pixel, z_dino] + timesteps = self.get_generate_timesteps(labels, z) + + if self.method == "euler": + stepper = self._euler_step + elif self.method == "heun": + stepper = self._heun_step + else: + raise NotImplementedError + + # ode + for i in range(self.steps - 1): + t = timesteps[i] + t_next = timesteps[i + 1] + z = stepper(z, t, t_next, labels) + # last step euler + z = self._euler_step(z, timesteps[-2], timesteps[-1], labels) + z_pixel, z_dino = z + return z_pixel + + @torch.no_grad() + def _forward_sample(self, z, t, labels): + # conditional + x_cond = self.net(z, [t[i].flatten() for i in [0,1]], labels) + v_cond = [(x_cond[i] - z[i]) / (1.0 - t[i]).clamp_min(self.t_eps_inference) for i in [0,1]] + + # unconditional + if self.guidance_method == "autoguidance": + x_uncond = self.ag_net(z, [t[i].flatten() for i in [0,1]], torch.full_like(labels, self.num_classes)) + elif self.guidance_method == "cfg": + x_uncond = self.net(z, [t[i].flatten() for i in [0,1]], torch.full_like(labels, self.num_classes)) + elif self.guidance_method == "cfg_interval": + if self.ag_net is not None and t[1].flatten()[1].item() >= 0.999: + x_uncond = self.ag_net(z, [t[i].flatten() for i in [0,1]], torch.full_like(labels, self.num_classes)) + else: + x_uncond = self.net(z, [t[i].flatten() for i in [0,1]], torch.full_like(labels, self.num_classes)) + + v_uncond = [(x_uncond[i] - z[i]) / (1.0 - t[i]).clamp_min(self.t_eps_inference) for i in [0,1]] + + # cfg interval + low, high = self.cfg_interval + low_dino, high_dino = self.cfg_interval_dino + interval_mask = [(t[0] < high) & ((low == 0) | (t[0] > low)), (t[1] < high_dino) & ((low_dino == 0) | (t[1] > low_dino))] + cfg_scale_interval = [torch.where(interval_mask[i], [self.cfg_scale, self.cfg_scale_dino][i], 1.0) for i in [0,1]] + + return [v_uncond[i] + cfg_scale_interval[i] * (v_cond[i] - v_uncond[i]) for i in [0,1]] + + @torch.no_grad() + def _euler_step(self, z, t, t_next, labels): + v_pred = self._forward_sample(z, t, labels) + z_next = [z[i] + (t_next[i] - t[i]) * v_pred[i] for i in [0,1]] + return z_next + + @torch.no_grad() + def _heun_step(self, z, t, t_next, labels): + v_pred_t = self._forward_sample(z, t, labels) + + z_next_euler = [z[i] + (t_next[i] - t[i]) * v_pred_t[i] for i in [0,1]] + v_pred_t_next = self._forward_sample(z_next_euler, t_next, labels) + + v_pred = [0.5 * (v_pred_t[i] + v_pred_t_next[i]) for i in [0,1]] + z_next = [z[i] + (t_next[i] - t[i]) * v_pred[i] for i in [0,1]] + return z_next + + @torch.no_grad() + def update_ema(self): + source_params = list(self.parameters()) + for targ, src in zip(self.ema_params1, source_params): + targ.detach().mul_(self.ema_decay1).add_(src, alpha=1 - self.ema_decay1) + for targ, src in zip(self.ema_params2, source_params): + targ.detach().mul_(self.ema_decay2).add_(src, alpha=1 - self.ema_decay2) diff --git a/transfer/code/LatentForcing/denoiser_repa.py b/transfer/code/LatentForcing/denoiser_repa.py new file mode 100644 index 0000000000000000000000000000000000000000..9e8365d2bd070364559c03523f931018c8d7a537 --- /dev/null +++ b/transfer/code/LatentForcing/denoiser_repa.py @@ -0,0 +1,139 @@ +import torch +import torch.nn as nn +from model_repa import JiT_models +from dinov2_hf import RAE + +class DenoiserRepa(nn.Module): + def __init__( + self, + args + ): + super().__init__() + self.net = JiT_models[args.model]( + input_size=args.img_size, + in_channels=3, + num_classes=args.class_num, + attn_drop=args.attn_dropout, + proj_drop=args.proj_dropout, + ) + self.img_size = args.img_size + self.num_classes = args.class_num + + self.label_drop_prob = args.label_drop_prob + self.P_mean = args.P_mean + self.P_std = args.P_std + self.t_eps = args.t_eps + self.noise_scale = args.noise_scale + + # ema + self.ema_decay1 = args.ema_decay1 + self.ema_decay2 = args.ema_decay2 + self.ema_params1 = None + self.ema_params2 = None + + # generation hyper params + self.method = args.sampling_method + self.steps = args.num_sampling_steps + self.cfg_scale = args.cfg + self.cfg_interval = (args.interval_min, args.interval_max) + + # repa target + self.dino = RAE() + self.repa_weight = args.dino_weight + + def drop_labels(self, labels): + drop = torch.rand(labels.shape[0], device=labels.device) < self.label_drop_prob + out = torch.where(drop, torch.full_like(labels, self.num_classes), labels) + return out + + def sample_t(self, n: int, device=None): + z = torch.randn(n, device=device) * self.P_std + self.P_mean + return torch.sigmoid(z) + + def forward(self, x, labels): + labels_dropped = self.drop_labels(labels) if self.training else labels + + t = self.sample_t(x.size(0), device=x.device).view(-1, *([1] * (x.ndim - 1))) + e = torch.randn_like(x) * self.noise_scale + + z = t * x + (1 - t) * e + v = (x - z) / (1 - t).clamp_min(self.t_eps) + + x_pred, repa_out = self.net(z, t.flatten(), labels_dropped, return_repa=True) + v_pred = (x_pred - z) / (1 - t).clamp_min(self.t_eps) + + # l2 loss + loss = (v - v_pred) ** 2 + loss_pixel = loss.mean(dim=(1, 2, 3)).mean() + + repa_target = self.dino.encode(x * 0.5 + 0.5) + loss_repa = (1 - torch.nn.functional.cosine_similarity(repa_out, repa_target.view(-1,768,256).permute(0,2,1), dim=-1).mean()) + + loss = loss_pixel + loss_repa * self.repa_weight + + return loss, {'loss_pixel': loss_pixel.item(), 'loss_repa': loss_repa.item()} + + @torch.no_grad() + def generate(self, labels): + device = labels.device + bsz = labels.size(0) + z = self.noise_scale * torch.randn(bsz, 3, self.img_size, self.img_size, device=device) + timesteps = torch.linspace(0.0, 1.0, self.steps+1, device=device).view(-1, *([1] * z.ndim)).expand(-1, bsz, -1, -1, -1) + + if self.method == "euler": + stepper = self._euler_step + elif self.method == "heun": + stepper = self._heun_step + else: + raise NotImplementedError + + # ode + for i in range(self.steps - 1): + t = timesteps[i] + t_next = timesteps[i + 1] + z = stepper(z, t, t_next, labels) + # last step euler + z = self._euler_step(z, timesteps[-2], timesteps[-1], labels) + return z + + @torch.no_grad() + def _forward_sample(self, z, t, labels): + # conditional + x_cond = self.net(z, t.flatten(), labels) + v_cond = (x_cond - z) / (1.0 - t).clamp_min(self.t_eps) + + # unconditional + x_uncond = self.net(z, t.flatten(), torch.full_like(labels, self.num_classes)) + v_uncond = (x_uncond - z) / (1.0 - t).clamp_min(self.t_eps) + + # cfg interval + low, high = self.cfg_interval + interval_mask = (t < high) & ((low == 0) | (t > low)) + cfg_scale_interval = torch.where(interval_mask, self.cfg_scale, 1.0) + + return v_uncond + cfg_scale_interval * (v_cond - v_uncond) + + @torch.no_grad() + def _euler_step(self, z, t, t_next, labels): + v_pred = self._forward_sample(z, t, labels) + z_next = z + (t_next - t) * v_pred + return z_next + + @torch.no_grad() + def _heun_step(self, z, t, t_next, labels): + v_pred_t = self._forward_sample(z, t, labels) + + z_next_euler = z + (t_next - t) * v_pred_t + v_pred_t_next = self._forward_sample(z_next_euler, t_next, labels) + + v_pred = 0.5 * (v_pred_t + v_pred_t_next) + z_next = z + (t_next - t) * v_pred + return z_next + + @torch.no_grad() + def update_ema(self): + source_params = list(self.parameters()) + for targ, src in zip(self.ema_params1, source_params): + targ.detach().mul_(self.ema_decay1).add_(src, alpha=1 - self.ema_decay1) + for targ, src in zip(self.ema_params2, source_params): + targ.detach().mul_(self.ema_decay2).add_(src, alpha=1 - self.ema_decay2) diff --git a/transfer/code/LatentForcing/dinov2_hf.py b/transfer/code/LatentForcing/dinov2_hf.py new file mode 100644 index 0000000000000000000000000000000000000000..8fd9748344229c0c4bd9eea3f7501e876ed21bfe --- /dev/null +++ b/transfer/code/LatentForcing/dinov2_hf.py @@ -0,0 +1,125 @@ +# -------------------------------------------------------- +# Code adapted from: +# RAE: https://github.com/bytetriper/RAE +# -------------------------------------------------------- + +from transformers import Dinov2WithRegistersModel +from torch import nn +import torch +from math import * +from transformers import AutoConfig, AutoImageProcessor +from typing import Optional +from math import sqrt +from typing import Protocol +from PIL import Image +import torch.nn.functional as F +import os + +# !wget https://huggingface.co/nyu-visionx/RAE-collections/resolve/main/stats/dinov2/wReg_base/imagenet1k/stat.pt +# !wget https://huggingface.co/facebook/dinov2-with-registers-base/resolve/main/model.safetensors + +class Dinov2withNorm(nn.Module): + def __init__( + self, + dinov2_path: str, + normalize: bool = True, + ): + super().__init__() + # Support both local paths and HuggingFace model IDs + try: + self.encoder = Dinov2WithRegistersModel.from_pretrained(dinov2_path, local_files_only=True) + except (OSError, ValueError, AttributeError): + self.encoder = Dinov2WithRegistersModel.from_pretrained(dinov2_path, local_files_only=False) + + # self.encoder = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitb14_reg') + + self.encoder.requires_grad_(False) + self.encoder.eval() + if normalize: + self.encoder.layernorm.elementwise_affine = False + self.encoder.layernorm.weight = None + self.encoder.layernorm.bias = None + self.patch_size = self.encoder.config.patch_size + self.hidden_size = self.encoder.config.hidden_size + + def dinov2_forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.encoder(x, output_hidden_states=True) + unused_token_num = 5 # 1 CLS + 4 register tokens + image_features = x.last_hidden_state[:, unused_token_num:] + return image_features + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.dinov2_forward(x) + +class RAE(nn.Module): + def __init__( + self, + # ---- encoder configs ---- + encoder_cls: str = 'Dinov2withNorm', + encoder_config_path: str = 'facebook/dinov2-with-registers-base', + encoder_input_size: int = 224, + encoder_params: dict = {'dinov2_path': 'facebook/dinov2-with-registers-base', 'normalize': True}, + # ---- noising, reshaping and normalization----- + noise_tau: float = 0.0, + reshape_to_2d: bool = True, + normalization_stat_path: Optional[str] = '/path/to/latentforcing/normalization_stat_path.pt', + match_pixel_norm: float = 0.485, # Empirically calculated + eps: float = 1e-5, + ): + super().__init__() + self.encoder = Dinov2withNorm(**encoder_params) + print(f"encoder_config_path: {encoder_config_path}") + proc = AutoImageProcessor.from_pretrained(encoder_config_path) + self.encoder_mean = torch.tensor(proc.image_mean).view(1, 3, 1, 1) + self.encoder_std = torch.tensor(proc.image_std).view(1, 3, 1, 1) + encoder_config = AutoConfig.from_pretrained(encoder_config_path) + # see if the encoder has patch size attribute + self.encoder_input_size = encoder_input_size + self.encoder_patch_size = self.encoder.patch_size + self.latent_dim = self.encoder.hidden_size + assert self.encoder_input_size % self.encoder_patch_size == 0, f"encoder_input_size {self.encoder_input_size} must be divisible by encoder_patch_size {self.encoder_patch_size}" + self.base_patches = (self.encoder_input_size // self.encoder_patch_size) ** 2 # number of patches of the latent + + # noising + self.noise_tau = noise_tau + self.reshape_to_2d = reshape_to_2d + + if normalization_stat_path is not None: + if not os.path.exists(normalization_stat_path): + assert False, "wget RAE stats: wget https://huggingface.co/nyu-visionx/RAE-collections/resolve/main/stats/dinov2/wReg_base/imagenet1k/stat.pt" + stats = torch.load(normalization_stat_path, map_location='cpu', weights_only=True) + self.latent_mean = stats.get('mean', None) + self.latent_var = stats.get('var', None) + self.do_normalization = True + self.eps = eps + print(f"Loaded normalization stats from {normalization_stat_path}") + else: + self.do_normalization = False + self.match_pixel_norm = match_pixel_norm + + def noising(self, x: torch.Tensor) -> torch.Tensor: + noise_sigma = self.noise_tau * torch.rand((x.size(0),) + (1,) * (len(x.shape) - 1), device=x.device) + noise = noise_sigma * torch.randn_like(x) + return x + noise + + @torch.compile() + @torch.no_grad() + def encode(self, x: torch.Tensor) -> torch.Tensor: + # normalize input + _, _, h, w = x.shape + if h != self.encoder_input_size or w != self.encoder_input_size: + x = nn.functional.interpolate(x, size=(self.encoder_input_size, self.encoder_input_size), mode='bicubic', align_corners=False) + x = (x - self.encoder_mean.to(x.device)) / self.encoder_std.to(x.device) + z = self.encoder(x) + if self.training and self.noise_tau > 0: + z = self.noising(z) + if self.reshape_to_2d: + b, n, c = z.shape + h = w = int(sqrt(n)) + z = z.transpose(1, 2).view(b, c, h, w) + if self.do_normalization: + latent_mean = self.latent_mean.to(z.device) if self.latent_mean is not None else 0 + latent_var = self.latent_var.to(z.device) if self.latent_var is not None else 1 + z = (z - latent_mean) / torch.sqrt(latent_var + self.eps) + z = z * self.match_pixel_norm + return z \ No newline at end of file diff --git a/transfer/code/LatentForcing/engine_jit.py b/transfer/code/LatentForcing/engine_jit.py new file mode 100644 index 0000000000000000000000000000000000000000..0fb08c3ee790955d922f68c4fcef42bee7335c18 --- /dev/null +++ b/transfer/code/LatentForcing/engine_jit.py @@ -0,0 +1,177 @@ +import math +import sys +import os +import shutil + +import torch +import numpy as np +import cv2 + +import util.misc as misc +import util.lr_sched as lr_sched +import torch_fidelity +import copy +import random + + +def train_one_epoch(model, model_without_ddp, data_loader, optimizer, device, epoch, log_writer=None, args=None): + model.train(True) + metric_logger = misc.MetricLogger(delimiter=" ") + metric_logger.add_meter('lr', misc.SmoothedValue(window_size=1, fmt='{value:.6f}')) + header = 'Epoch: [{}]'.format(epoch) + print_freq = 100 + + optimizer.zero_grad() + + if log_writer is not None: + print('log_dir: {}'.format(log_writer.log_dir)) + + for data_iter_step, (x, labels) in enumerate(metric_logger.log_every(data_loader, print_freq, header)): + # per iteration (instead of per epoch) lr scheduler + lr_sched.adjust_learning_rate(optimizer, data_iter_step / len(data_loader) + epoch, args) + + # normalize image to [-1, 1] + x = x.to(device, non_blocking=True).to(torch.float32).div_(255) + x = x * 2.0 - 1.0 + labels = labels.to(device, non_blocking=True) + + with torch.amp.autocast('cuda', dtype=torch.bfloat16): + loss, results_dict = model(x, labels) + + loss_value = loss.item() + if not math.isfinite(loss_value): + print("Loss is {}, stopping training".format(loss_value)) + sys.exit(1) + + optimizer.zero_grad() + loss.backward() + # print(torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=float('inf')).detach().cpu().item()) + optimizer.step() + + torch.cuda.synchronize() + + model_without_ddp.update_ema() + + metric_logger.update(loss=loss_value) + metric_logger.update(**results_dict) + lr = optimizer.param_groups[0]["lr"] + metric_logger.update(lr=lr) + + loss_value_reduce = misc.all_reduce_mean(loss_value) + reduced_results = {} + for k, v in sorted(results_dict.items()): + reduced_results[k] = misc.all_reduce_mean(v) + + if log_writer is not None: + # Use epoch_1000x as the x-axis in TensorBoard to calibrate curves. + epoch_1000x = int((data_iter_step / len(data_loader) + epoch) * 1000) + if data_iter_step % args.log_freq == 0: + log_writer.add_scalar('train_loss', loss_value_reduce, epoch_1000x) + log_writer.add_scalar('lr', lr, epoch_1000x) + for k, v in reduced_results.items(): + log_writer.add_scalar(k, v, epoch_1000x) + + +def evaluate(model_without_ddp, args, epoch, batch_size=64, log_writer=None): + + model_without_ddp.eval() + world_size = misc.get_world_size() + local_rank = misc.get_rank() + num_images = 1000 if epoch == 0 else args.num_images + num_steps = num_images // (batch_size * world_size) + 1 + + # Construct the folder name for saving generated images. + save_folder = os.path.join( + args.output_dir, + "{}-steps{}-cfg{}-interval{}-{}-image{}-res{}".format( + model_without_ddp.method, model_without_ddp.steps, model_without_ddp.cfg_scale, + model_without_ddp.cfg_interval[0], model_without_ddp.cfg_interval[1], num_images, args.img_size + ) + ) + print("Save to:", save_folder) + if misc.get_rank() == 0 and not os.path.exists(save_folder): + os.makedirs(save_folder) + + # switch to ema params, hard-coded to be the first one + if args.generation_ema != 'none': + model_state_dict = copy.deepcopy(model_without_ddp.state_dict()) + ema_state_dict = copy.deepcopy(model_without_ddp.state_dict()) + for i, (name, _value) in enumerate(model_without_ddp.named_parameters()): + assert name in ema_state_dict + maybe_ema_state_dict = { + '1': model_without_ddp.ema_params1, + '2': model_without_ddp.ema_params2, + }[args.generation_ema] + ema_state_dict[name] = maybe_ema_state_dict[i] + print("Switch to ema") + model_without_ddp.load_state_dict(ema_state_dict) + + # ensure that the number of images per class is equal. + class_num = args.class_num + assert num_images % class_num == 0, "Number of images per class must be the same" + class_label_gen_world = np.arange(0, class_num).repeat(num_images // class_num) + class_label_gen_world = np.hstack([class_label_gen_world, np.zeros(50000)]) + + for i in range(num_steps): + print("Generation step {}/{}".format(i, num_steps)) + + start_idx = world_size * batch_size * i + local_rank * batch_size + end_idx = start_idx + batch_size + labels_gen = class_label_gen_world[start_idx:end_idx] + labels_gen = torch.Tensor(labels_gen).long().cuda() + if args.label_drop_prob == 1.0: + labels_gen = labels_gen * 0 + 1000 + + with torch.amp.autocast('cuda', dtype=torch.bfloat16): + sampled_images = model_without_ddp.generate(labels_gen) + + torch.distributed.barrier() + + # denormalize images + sampled_images = (sampled_images + 1) / 2 + sampled_images = sampled_images.detach().cpu() + + # distributed save images + for b_id in range(sampled_images.size(0)): + img_id = i * sampled_images.size(0) * world_size + local_rank * sampled_images.size(0) + b_id + if img_id >= num_images: + break + gen_img = np.round(np.clip(sampled_images[b_id].numpy().transpose([1, 2, 0]) * 255, 0, 255)) + gen_img = gen_img.astype(np.uint8)[:, :, ::-1] + cv2.imwrite(os.path.join(save_folder, '{}.png'.format(str(img_id).zfill(5))), gen_img) + + torch.distributed.barrier() + + # back to no ema + print("Switch back from ema") + model_without_ddp.load_state_dict(model_state_dict) + + # compute FID and IS + if log_writer is not None: + if args.img_size == 256: + fid_statistics_file = 'fid_stats/jit_in256_stats.npz' + elif args.img_size == 512: + fid_statistics_file = 'fid_stats/jit_in512_stats.npz' + else: + raise NotImplementedError + metrics_dict = torch_fidelity.calculate_metrics( + input1=save_folder, + input2=None, + fid_statistics_file=fid_statistics_file, + cuda=True, + isc=True, + fid=True, + kid=False, + prc=False, + verbose=False, + ) + fid = metrics_dict['frechet_inception_distance'] + inception_score = metrics_dict['inception_score_mean'] + postfix = "_cfg{}_res{}".format(model_without_ddp.cfg_scale, args.img_size) + log_writer.add_scalar('fid{}'.format(postfix), fid, epoch) + log_writer.add_scalar('is{}'.format(postfix), inception_score, epoch) + print("FID: {:.4f}, Inception Score: {:.4f}".format(fid, inception_score)) + if not args.keep_images: + shutil.rmtree(save_folder) + + torch.distributed.barrier() diff --git a/transfer/code/LatentForcing/fid_stats/jit_in256_stats.npz b/transfer/code/LatentForcing/fid_stats/jit_in256_stats.npz new file mode 100644 index 0000000000000000000000000000000000000000..18c6d238ef70128ef0322f87a225d62b81081dfc --- /dev/null +++ b/transfer/code/LatentForcing/fid_stats/jit_in256_stats.npz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:412046720c0d496dc6d72a30eac3e22b9ef6ddac3501cce7f996674ad227ba4c +size 33563124 diff --git a/transfer/code/LatentForcing/fid_stats/jit_in512_stats.npz b/transfer/code/LatentForcing/fid_stats/jit_in512_stats.npz new file mode 100644 index 0000000000000000000000000000000000000000..59382274cb3706a4323732b6f80a9780e234d652 --- /dev/null +++ b/transfer/code/LatentForcing/fid_stats/jit_in512_stats.npz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cdd15b54f9d1f26a881fcdc920a3410fa7a934047d6c4f5b8395b87182a9ab42 +size 33563124 diff --git a/transfer/code/LatentForcing/main_jit.py b/transfer/code/LatentForcing/main_jit.py new file mode 100644 index 0000000000000000000000000000000000000000..f12c28618a66e1a11038ad4c397ba54a20f085ca --- /dev/null +++ b/transfer/code/LatentForcing/main_jit.py @@ -0,0 +1,322 @@ +import argparse +import datetime +import glob +import numpy as np +import os +import time +from pathlib import Path + +import torch +import torch.backends.cudnn as cudnn +from torch.utils.tensorboard import SummaryWriter +import torchvision.transforms as transforms +import webdataset as wds + +from util.crop import center_crop_arr +import util.misc as misc + +import copy +from engine_jit import train_one_epoch, evaluate + +from denoiser import Denoiser +from denoiser_cot import DenoiserCoT +from denoiser_repa import DenoiserRepa + + +def get_args_parser(): + parser = argparse.ArgumentParser('JiT', add_help=False) + + # architecture + parser.add_argument('--model', default='JiT-B/16', type=str, metavar='MODEL', + help='Name of the model to train') + parser.add_argument('--img_size', default=256, type=int, help='Image size') + parser.add_argument('--attn_dropout', type=float, default=0.0, help='Attention dropout rate') + parser.add_argument('--proj_dropout', type=float, default=0.0, help='Projection dropout rate') + + # training + parser.add_argument('--epochs', default=200, type=int) + parser.add_argument('--warmup_epochs', type=int, default=5, metavar='N', + help='Epochs to warm up LR') + parser.add_argument('--batch_size', default=128, type=int, + help='Batch size per GPU (effective batch size = batch_size * # GPUs)') + parser.add_argument('--lr', type=float, default=None, metavar='LR', + help='Learning rate (absolute)') + parser.add_argument('--blr', type=float, default=5e-5, metavar='LR', + help='Base learning rate: absolute_lr = base_lr * total_batch_size / 256') + parser.add_argument('--min_lr', type=float, default=0., metavar='LR', + help='Minimum LR for cyclic schedulers that hit 0') + parser.add_argument('--lr_schedule', type=str, default='constant', + help='Learning rate schedule') + parser.add_argument('--weight_decay', type=float, default=0.0, + help='Weight decay (default: 0.0)') + parser.add_argument('--ema_decay1', type=float, default=0.9999, + help='The first ema to track. Use the first ema for sampling by default.') + parser.add_argument('--ema_decay2', type=float, default=0.9998, + help='The second ema to track') + parser.add_argument('--P_mean', default=-0.8, type=float) + parser.add_argument('--P_std', default=0.8, type=float) + parser.add_argument('--D_mean', default=-0.8, type=float) + parser.add_argument('--D_std', default=0.8, type=float) + parser.add_argument('--dino_pixel_offset', default=0.0, type=float) + parser.add_argument('--dino_pixel_shift', default=1.0, type=float) + parser.add_argument('--noise_scale', default=1.0, type=float) + parser.add_argument('--t_eps', default=5e-2, type=float) + parser.add_argument('--label_drop_prob', default=0.1, type=float) + + parser.add_argument('--seed', default=0, type=int) + parser.add_argument('--start_epoch', default=0, type=int, metavar='N', + help='Starting epoch') + parser.add_argument('--num_workers', default=12, type=int) + parser.add_argument('--prefetch_factor', default=6, type=int) + parser.add_argument('--pin_mem', action='store_true', + help='Pin CPU memory in DataLoader for faster GPU transfers') + parser.add_argument('--no_pin_mem', action='store_false', dest='pin_mem') + parser.set_defaults(pin_mem=True) + + # dino + parser.add_argument('--latent_model', default='dino', type=str) # dino, mocov3 + parser.add_argument('--dino_max_t', default=1.0, type=float) + parser.add_argument('--dino_weight', default=1.0, type=float) + parser.add_argument('--sample_mode', default='default', type=str) # dino_first, + parser.add_argument('--choose_dino_p', default=0.0, type=float) # dino_first, + parser.add_argument('--bottleneck_dim_dino', default=128, type=int) + parser.add_argument('--dino_in_channels', default=768, type=int) + parser.add_argument('--dh_depth', default=0, type=int) + parser.add_argument('--dh_hidden_size', default=2048, type=int) + parser.add_argument('--mask_p', default=0.0, type=float) + parser.add_argument('--override_guidance', action='store_true') + + + # sampling + parser.add_argument('--sampling_method', default='heun', type=str, + help='ODE samping method') + parser.add_argument('--num_sampling_steps', default=50, type=int, + help='Sampling steps') + parser.add_argument('--cfg', default=1.0, type=float, + help='Classifier-free guidance factor') + parser.add_argument('--cfg_dino', default=1.0, type=float) + parser.add_argument('--interval_min', default=0.0, type=float, + help='CFG interval min') + parser.add_argument('--interval_max', default=1.0, type=float, + help='CFG interval max') + parser.add_argument('--interval_min_dino', default=0.0, type=float) + parser.add_argument('--interval_max_dino', default=1.0, type=float) + parser.add_argument('--num_images', default=50000, type=int, + help='Number of images to generate') + parser.add_argument('--eval_freq', type=int, default=40, + help='Frequency (in epochs) for evaluation') + parser.add_argument('--online_eval', action='store_true') + parser.add_argument('--evaluate_gen', action='store_true') + parser.add_argument('--keep_images', action='store_true') + parser.add_argument('--gen_bsz', type=int, default=256, + help='Generation batch size') + parser.add_argument('--autoguidance_ckpt', default='', type=str) + parser.add_argument('--autoguidance_ema', default='1', type=str) # 'none', '1', '2' + parser.add_argument('--generation_ema', default='1', type=str) # 'none', '1', '2' + parser.add_argument('--t_eps_inference', default=0.05, type=float) + parser.add_argument('--gen_shift_pixel', default=1.0, type=float) + parser.add_argument('--gen_shift_dino', default=1.0, type=float) + parser.add_argument('--guidance_method', default='cfg', type=str, help='cfg autoguidance cfg_interval') + + # dataset + parser.add_argument('--data_path', default='/path/to/ImageNet_2012_webdataset', type=str, + help='Path to the WebDataset shards') + parser.add_argument('--dataset_size', default=1281167, type=int, help='Total number of images in dataset') + parser.add_argument('--class_num', default=1000, type=int) + + # checkpointing + parser.add_argument('--output_dir', default='./output_dir', + help='Directory to save outputs (empty for no saving)') + parser.add_argument('--resume', default='', + help='Folder that contains checkpoint to resume from') + parser.add_argument('--save_last_freq', type=int, default=5, + help='Frequency (in epochs) to save checkpoints') + parser.add_argument('--log_freq', default=100, type=int) + parser.add_argument('--device', default='cuda', + help='Device to use for training/testing') + parser.add_argument('--checkpoint_keep_freq', default=100, type=int) + + + # distributed training + parser.add_argument('--world_size', default=1, type=int, + help='Number of distributed processes') + parser.add_argument('--local_rank', default=-1, type=int) + parser.add_argument('--dist_on_itp', action='store_true') + parser.add_argument('--dist_url', default='env://', + help='URL used to set up distributed training') + + return parser + + +def main(args): + misc.init_distributed_mode(args) + print('Job directory:', os.path.dirname(os.path.realpath(__file__))) + print("Arguments:\n{}".format(args).replace(', ', ',\n')) + + device = torch.device(args.device) + + # Set seeds for reproducibility + seed = args.seed + misc.get_rank() + torch.manual_seed(seed) + np.random.seed(seed) + + cudnn.benchmark = True + + num_tasks = misc.get_world_size() + global_rank = misc.get_rank() + + # Set up TensorBoard logging (only on main process) + if global_rank == 0 and args.output_dir is not None: + os.makedirs(args.output_dir, exist_ok=True) + log_writer = SummaryWriter(log_dir=args.output_dir) + else: + log_writer = None + + # Data augmentation transforms + transform_train = transforms.Compose([ + transforms.Lambda(lambda img: center_crop_arr(img, args.img_size)), + transforms.RandomHorizontalFlip(), + transforms.PILToTensor() + ]) + + # WebDataset Pipeline + + train_paths_pattern = os.path.join(args.data_path, 'train', '*.tar') + train_paths = glob.glob(train_paths_pattern) + dataset_train = ( + wds.WebDataset(train_paths, shardshuffle=len(train_paths), nodesplitter=wds.split_by_node, workersplitter=wds.split_by_worker) + .repeat() + .shuffle(1000) + .decode("pil") + .to_tuple("jpg;png;jpeg", "cls") + .map_tuple(transform_train, lambda x: x) + .batched(args.batch_size, partial=False) + ) + + # Compute number of batches per epoch for the loader + batches_per_epoch = args.dataset_size // (args.batch_size * num_tasks) + + data_loader_train = wds.WebLoader( + dataset_train, + batch_size=None, + shuffle=False, + num_workers=args.num_workers, + pin_memory=args.pin_mem, + prefetch_factor=args.prefetch_factor, + ).with_epoch(batches_per_epoch).with_length(batches_per_epoch) + + print(f"WebDataset loaded from {args.data_path}") + + torch._dynamo.config.cache_size_limit = 128 + torch._dynamo.config.optimize_ddp = False + + # Create denoiser + if "CoT" in args.model: + model = DenoiserCoT(args) + elif "Repa" in args.model: + model = DenoiserRepa(args) + else: + model = Denoiser(args) + + print("Model =", model) + n_params = sum(p.numel() for p in model.parameters() if p.requires_grad) + print("Number of trainable parameters: {:.6f}M".format(n_params / 1e6)) + + model.to(device) + + eff_batch_size = args.batch_size * misc.get_world_size() + if args.lr is None: # only base_lr (blr) is specified + args.lr = args.blr * eff_batch_size / 256 + + print("Base lr: {:.2e}".format(args.lr * 256 / eff_batch_size)) + print("Actual lr: {:.2e}".format(args.lr)) + print("Effective batch size: %d" % eff_batch_size) + + model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu]) + model_without_ddp = model.module + + # Set up optimizer with weight decay adjustment for bias and norm layers + param_groups = misc.add_weight_decay(model_without_ddp, args.weight_decay) + optimizer = torch.optim.AdamW(param_groups, lr=args.lr, betas=(0.9, 0.95)) + print(optimizer) + + # Resume from checkpoint if provided + checkpoint_path = os.path.join(args.resume, "checkpoint-last.pth") if args.resume else None + if checkpoint_path and os.path.exists(checkpoint_path): + checkpoint = torch.load(checkpoint_path, map_location='cpu', weights_only=False) + if args.autoguidance_ckpt: + autoguidance_checkpoint = torch.load(args.autoguidance_ckpt, map_location='cpu', weights_only=False) + ag_key = {'none': 'model', '1': 'model_ema1', '2': 'model_ema2'}[args.autoguidance_ema] + for model_key in ['model', 'model_ema1', 'model_ema2']: + checkpoint[model_key].update({'ag_' + k:v for k,v in autoguidance_checkpoint[ag_key].items() if k.startswith('net.')}) + + model_without_ddp.load_state_dict(checkpoint['model'], strict=False) + + ema_state_dict1 = checkpoint['model_ema1'] + ema_state_dict2 = checkpoint['model_ema2'] + model_without_ddp.ema_params1 = [ema_state_dict1[name].cuda() for name, _ in model_without_ddp.named_parameters()] + model_without_ddp.ema_params2 = [ema_state_dict2[name].cuda() for name, _ in model_without_ddp.named_parameters()] + print("Resumed checkpoint from", args.resume) + + if 'optimizer' in checkpoint and 'epoch' in checkpoint: + optimizer.load_state_dict(checkpoint['optimizer']) + args.start_epoch = checkpoint['epoch'] + 1 + print("Loaded optimizer & scaler state!") + del checkpoint + else: + model_without_ddp.ema_params1 = copy.deepcopy(list(model_without_ddp.parameters())) + model_without_ddp.ema_params2 = copy.deepcopy(list(model_without_ddp.parameters())) + print("Training from scratch") + + # Evaluate generation + if args.evaluate_gen: + print("Evaluating checkpoint at {} epoch".format(args.start_epoch)) + with torch.random.fork_rng(): + torch.manual_seed(seed) + with torch.no_grad(): + evaluate(model_without_ddp, args, 1, batch_size=args.gen_bsz, log_writer=log_writer) + return + + # Training loop + print(f"Start training for {args.epochs} epochs") + start_time = time.time() + for epoch in range(args.start_epoch, args.epochs): + train_one_epoch(model, model_without_ddp, data_loader_train, optimizer, device, epoch, log_writer=log_writer, args=args) + + # Save checkpoint periodically + if epoch % args.save_last_freq == 0 or epoch + 1 == args.epochs: + misc.save_model( + args=args, + model_without_ddp=model_without_ddp, + optimizer=optimizer, + epoch=epoch, + epoch_name="last" + ) + + if epoch % args.checkpoint_keep_freq == 0 and epoch > 0: + misc.save_model( + args=args, + model_without_ddp=model_without_ddp, + optimizer=optimizer, + epoch=epoch + ) + + # Perform online evaluation at specified intervals + if args.online_eval and (epoch % args.eval_freq == 0 or epoch + 1 == args.epochs): + torch.cuda.empty_cache() + with torch.no_grad(): + evaluate(model_without_ddp, args, epoch, batch_size=args.gen_bsz, log_writer=log_writer) + torch.cuda.empty_cache() + + if misc.is_main_process() and log_writer is not None: + log_writer.flush() + + total_time = time.time() - start_time + total_time_str = str(datetime.timedelta(seconds=int(total_time))) + print('Training time:', total_time_str) + + +if __name__ == '__main__': + args = get_args_parser().parse_args() + Path(args.output_dir).mkdir(parents=True, exist_ok=True) + main(args) \ No newline at end of file diff --git a/transfer/code/LatentForcing/mocov3.py b/transfer/code/LatentForcing/mocov3.py new file mode 100644 index 0000000000000000000000000000000000000000..b1e7212e52666736ad2cd7482e5069c1cc70250a --- /dev/null +++ b/transfer/code/LatentForcing/mocov3.py @@ -0,0 +1,411 @@ +import torch +import torch.nn as nn +import timm +import torch.nn.functional as F + +mocov3_std = torch.tensor([0.0365, 0.0384, 0.0333, 0.0364, 0.0177, 0.0388, 0.0418, 0.0400, 0.0347, + 0.0327, 0.0478, 0.0385, 0.0384, 0.0396, 0.0361, 0.0347, 0.0443, 0.0342, + 0.0383, 0.0374, 0.0365, 0.0453, 0.0352, 0.0315, 0.0384, 0.0534, 0.0374, + 0.0358, 0.0355, 0.0349, 0.0350, 0.0392, 0.0360, 0.0369, 0.0356, 0.0332, + 0.0372, 0.0349, 0.0358, 0.0332, 0.0352, 0.0387, 0.0328, 0.0358, 0.0381, + 0.0373, 0.0359, 0.0326, 0.0342, 0.0338, 0.0347, 0.0725, 0.0400, 0.0345, + 0.0377, 0.0376, 0.0368, 0.0339, 0.0371, 0.0341, 0.0380, 0.0353, 0.0350, + 0.0389, 0.0363, 0.0347, 0.0363, 0.0363, 0.0354, 0.0354, 0.0369, 0.0538, + 0.0358, 0.0384, 0.0339, 0.0362, 0.0354, 0.0381, 0.0357, 0.0370, 0.0349, + 0.0394, 0.0355, 0.0344, 0.0372, 0.0379, 0.0361, 0.0376, 0.0344, 0.0354, + 0.0309, 0.0360, 0.0382, 0.0349, 0.0386, 0.0375, 0.0344, 0.0325, 0.0354, + 0.0392, 0.0329, 0.0361, 0.0358, 0.0364, 0.0361, 0.0345, 0.0361, 0.0347, + 0.0384, 0.0399, 0.0328, 0.0511, 0.0371, 0.0400, 0.0343, 0.0375, 0.0388, + 0.0417, 0.0373, 0.0392, 0.0352, 0.0483, 0.0374, 0.0373, 0.0347, 0.0357, + 0.0359, 0.0375, 0.0361, 0.0646, 0.0412, 0.0375, 0.0347, 0.0353, 0.0311, + 0.0371, 0.0352, 0.0376, 0.0372, 0.0411, 0.0349, 0.0338, 0.0398, 0.0341, + 0.0386, 0.0348, 0.0383, 0.0366, 0.0379, 0.0336, 0.0343, 0.0357, 0.0341, + 0.0348, 0.0331, 0.0382, 0.0374, 0.0355, 0.0378, 0.0336, 0.0376, 0.0362, + 0.0347, 0.0336, 0.0317, 0.0351, 0.0329, 0.0736, 0.0382, 0.0388, 0.0353, + 0.0357, 0.0381, 0.0406, 0.0390, 0.0349, 0.0392, 0.0341, 0.0358, 0.0398, + 0.0335, 0.0391, 0.0385, 0.0342, 0.0345, 0.0347, 0.0386, 0.0348, 0.0357, + 0.0345, 0.0357, 0.0368, 0.0394, 0.0391, 0.0397, 0.0328, 0.0366, 0.0351, + 0.0395, 0.0418, 0.0389, 0.0397, 0.0360, 0.0407, 0.0697, 0.1027, 0.0411, + 0.0194, 0.0363, 0.0398, 0.0371, 0.0362, 0.0372, 0.0310, 0.0338, 0.0425, + 0.0355, 0.0462, 0.0662, 0.0540, 0.0347, 0.0671, 0.0341, 0.0348, 0.0375, + 0.0374, 0.0334, 0.0372, 0.0346, 0.0376, 0.0372, 0.0412, 0.0353, 0.0411, + 0.0333, 0.0346, 0.0360, 0.0397, 0.0313, 0.0369, 0.0354, 0.0311, 0.0395, + 0.0510, 0.0361, 0.0366, 0.0364, 0.0392, 0.0405, 0.0348, 0.0339, 0.0509, + 0.0379, 0.0365, 0.0790, 0.0370, 0.0355, 0.0350, 0.0347, 0.0398, 0.0335, + 0.0357, 0.0356, 0.0311, 0.0380, 0.0338, 0.0385, 0.0358, 0.0360, 0.0355, + 0.0407, 0.0383, 0.0387, 0.0344, 0.0365, 0.0307, 0.0383, 0.0365, 0.0369, + 0.0343, 0.0341, 0.0363, 0.0387, 0.0361, 0.0384, 0.0362, 0.0440, 0.0352, + 0.0381, 0.0357, 0.0342, 0.0379, 0.0348, 0.0394, 0.0380, 0.0351, 0.0357, + 0.0334, 0.0357, 0.0368, 0.0356, 0.0406, 0.0365, 0.0380, 0.0384, 0.0388, + 0.0354, 0.0355, 0.0317, 0.0368, 0.0377, 0.0353, 0.0345, 0.0373, 0.0348, + 0.1193, 0.0546, 0.0362, 0.0352, 0.0354, 0.0381, 0.0387, 0.0455, 0.0358, + 0.0338, 0.0345, 0.0369, 0.0342, 0.0334, 0.0384, 0.0405, 0.0361, 0.0353, + 0.0364, 0.0347, 0.0335, 0.0430, 0.0375, 0.0371, 0.0374, 0.0350, 0.0358, + 0.0325, 0.0359, 0.0817, 0.0406, 0.0356, 0.0358, 0.0349, 0.0385, 0.0323, + 0.0361, 0.0372, 0.0405, 0.0346, 0.0349, 0.0361, 0.0371, 0.0387, 0.0402, + 0.0373, 0.0370, 0.0368, 0.0369, 0.0337, 0.0353, 0.0344, 0.0361, 0.0352, + 0.0369, 0.0357, 0.0370, 0.0359, 0.0361, 0.0335, 0.0351, 0.0351, 0.0365, + 0.0401, 0.0336, 0.0335, 0.0357, 0.0386, 0.0374, 0.0357, 0.0376, 0.0369, + 0.0369, 0.1032, 0.0365, 0.0823, 0.0367, 0.0459, 0.0823, 0.0397, 0.0364, + 0.0343, 0.0357, 0.0401, 0.0357, 0.0388, 0.0345, 0.0334, 0.0359, 0.0331, + 0.0362, 0.0388, 0.0586, 0.0402, 0.0385, 0.0373, 0.0368, 0.0332, 0.0357, + 0.0366, 0.0378, 0.0343, 0.0348, 0.0338, 0.0381, 0.0359, 0.0342, 0.0385, + 0.0347, 0.0359, 0.0396, 0.0366, 0.0374, 0.0305, 0.0689, 0.0389, 0.0359, + 0.0373, 0.0359, 0.0377, 0.0367, 0.0367, 0.0454, 0.0364, 0.0327, 0.0347, + 0.0406, 0.0374, 0.0360, 0.0336, 0.0363, 0.0364, 0.0413, 0.0364, 0.0348, + 0.0591, 0.0390, 0.0327, 0.0424, 0.0369, 0.0356, 0.0350, 0.0369, 0.0362, + 0.0346, 0.0479, 0.0319, 0.0370, 0.0406, 0.0371, 0.0387, 0.0346, 0.0389, + 0.0394, 0.0363, 0.0331, 0.0371, 0.0391, 0.0365, 0.0389, 0.0362, 0.0380, + 0.0378, 0.0369, 0.0353, 0.0365, 0.0363, 0.0374, 0.0362, 0.0642, 0.0399, + 0.0349, 0.0349, 0.0381, 0.0312, 0.0544, 0.0330, 0.0346, 0.0359, 0.0428, + 0.0365, 0.0380, 0.0370, 0.0377, 0.0378, 0.0377, 0.0370, 0.0364, 0.0365, + 0.0368, 0.0375, 0.0416, 0.0382, 0.0348, 0.0373, 0.0353, 0.0372, 0.0357, + 0.0338, 0.0395, 0.0358, 0.0359, 0.0382, 0.0352, 0.0356, 0.0340, 0.0396, + 0.0424, 0.0387, 0.0304, 0.0364, 0.0401, 0.0511, 0.0392, 0.0370, 0.0363, + 0.0405, 0.0341, 0.0348, 0.0334, 0.0346, 0.0349, 0.0392, 0.0370, 0.0406, + 0.0372, 0.0369, 0.0364, 0.0357, 0.0385, 0.0387, 0.0366, 0.0307, 0.0350, + 0.0357, 0.0422, 0.0404, 0.0334, 0.0345, 0.0532, 0.0368, 0.0333, 0.0374, + 0.0371, 0.0381, 0.0397, 0.0384, 0.0354, 0.0353, 0.0343, 0.0415, 0.0368, + 0.0351, 0.0398, 0.0395, 0.0365, 0.0370, 0.0367, 0.0370, 0.0362, 0.0349, + 0.0372, 0.0327, 0.0367, 0.0373, 0.0426, 0.0348, 0.0349, 0.0473, 0.0331, + 0.0365, 0.0376, 0.0352, 0.0366, 0.0410, 0.0381, 0.0373, 0.0407, 0.0331, + 0.0364, 0.0497, 0.0375, 0.0378, 0.0349, 0.0413, 0.0348, 0.0379, 0.0354, + 0.0364, 0.0347, 0.0356, 0.0347, 0.0383, 0.0714, 0.0392, 0.0452, 0.0353, + 0.0373, 0.0361, 0.0358, 0.0348, 0.0362, 0.0377, 0.0350, 0.0354, 0.0365, + 0.0360, 0.0367, 0.0366, 0.0358, 0.0357, 0.0357, 0.0508, 0.0368, 0.0353, + 0.0419, 0.0344, 0.0380, 0.0338, 0.0363, 0.0370, 0.0355, 0.0358, 0.0367, + 0.0375, 0.0375, 0.0559, 0.0361, 0.0378, 0.0381, 0.0343, 0.0379, 0.0390, + 0.0396, 0.0360, 0.0388, 0.0351, 0.0362, 0.0351, 0.0357, 0.0349, 0.0336, + 0.0371, 0.0344, 0.0358, 0.0354, 0.0382, 0.0386, 0.0406, 0.0834, 0.0361, + 0.0360, 0.0361, 0.0351, 0.0379, 0.0355, 0.0390, 0.0364, 0.0351, 0.0374, + 0.0436, 0.0375, 0.0363, 0.0353, 0.0388, 0.0355, 0.0348, 0.0364, 0.0325, + 0.0340, 0.0343, 0.0389, 0.0358, 0.0348, 0.0349, 0.0373, 0.0361, 0.0364, + 0.0367, 0.0373, 0.0377, 0.0322, 0.0379, 0.0333, 0.0442, 0.0389, 0.0324, + 0.0367, 0.0356, 0.0345, 0.0393, 0.0349, 0.0450, 0.0382, 0.0376, 0.0463, + 0.0363, 0.0328, 0.0356, 0.0379, 0.0360, 0.0342, 0.0371, 0.0356, 0.0373, + 0.0355, 0.0367, 0.0313, 0.0425, 0.0366, 0.0352, 0.0366, 0.0363, 0.0323, + 0.0328, 0.0335, 0.0337, 0.0402, 0.0369, 0.0390, 0.0363, 0.0416, 0.0592, + 0.0343, 0.0338, 0.0371, 0.0722, 0.0449, 0.0350, 0.0356, 0.0352, 0.0361, + 0.0366, 0.0362, 0.0463, 0.0347, 0.0400, 0.0327, 0.0362, 0.0375, 0.0466, + 0.0341, 0.0332, 0.0325, 0.0369, 0.0326, 0.0373, 0.0374, 0.0367, 0.0365, + 0.0344, 0.0398, 0.0378]) +mocov3_mean = torch.tensor([-4.9909e-03, 5.8531e-02, -8.0204e-02, 1.4484e-02, 6.5256e-04, + 3.1926e-02, 5.2389e-02, -4.6138e-02, -2.9104e-02, -1.0310e-03, + 1.4314e-02, 4.3464e-02, 5.4860e-02, -3.8034e-03, 9.6628e-02, + 6.7566e-02, -2.0503e-01, -5.7046e-02, -8.4732e-02, -5.1926e-02, + 2.8064e-02, -7.4545e-02, -3.0411e-02, -2.1032e-02, 1.0223e-02, + -3.9128e-02, -1.0685e-01, -4.2874e-02, 7.4012e-02, -8.5295e-02, + -5.1053e-02, 1.1215e-01, -3.4985e-02, -1.9459e-02, -5.4159e-02, + -3.3352e-02, -2.7664e-02, 6.8211e-02, -5.2040e-02, 1.4412e-02, + -5.8436e-02, 2.2623e-02, 1.6369e-02, -2.6669e-02, 7.5853e-03, + -1.7022e-02, 1.9521e-02, 1.7904e-02, -1.7904e-02, -5.8781e-02, + -5.1144e-02, -5.0436e-03, -2.6308e-02, 3.3595e-03, 2.5913e-02, + 2.7867e-03, -9.1458e-02, -4.5019e-02, -3.1314e-02, -4.4559e-02, + -5.5143e-02, 1.8014e-02, 2.7575e-02, -4.7217e-02, 4.3467e-02, + -8.2260e-02, -6.7334e-03, -5.6354e-02, 7.9308e-02, -2.9664e-02, + -1.8751e-02, -9.8325e-02, 7.9536e-02, -1.3846e-02, 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+#https://github.com/huggingface/pytorch-image-models/blob/main/timm/models/vision_transformer.py +#https://github.com/huggingface/pytorch-image-models/blob/main/timm/layers/pos_embed.py#L19 +# + +# weight_url = "https://dl.fbaipublicfiles.com/moco-v3/vit-b-300ep/vit-b-300ep.pth.tar" +# checkpoint_path = "vit-b-300ep.pth.tar" + +# if not os.path.exists(checkpoint_path): +# print(f"Downloading weights from {weight_url}...") +# torch.hub.download_url_to_file(weight_url, checkpoint_path) + +from typing import Optional, Union, Tuple, List + +@torch.fx.wrap +def resample_abs_pos_embed( + posemb: torch.Tensor, + new_size: List[int], + old_size: Optional[List[int]] = None, + num_prefix_tokens: int = 1, + interpolation: str = 'bicubic', + antialias: bool = True, + verbose: bool = False, +): + # sort out sizes, assume square if old size not provided + num_pos_tokens = posemb.shape[1] + num_new_tokens = new_size[0] * new_size[1] + num_prefix_tokens + if num_new_tokens == num_pos_tokens and new_size[0] == new_size[1]: + return posemb + + if old_size is None: + hw = int(math.sqrt(num_pos_tokens - num_prefix_tokens)) + old_size = hw, hw + + if num_prefix_tokens: + posemb_prefix, posemb = posemb[:, :num_prefix_tokens], posemb[:, num_prefix_tokens:] + else: + posemb_prefix, posemb = None, posemb + + # do the interpolation + embed_dim = posemb.shape[-1] + orig_dtype = posemb.dtype + posemb = posemb.float() # interpolate needs float32 + posemb = posemb.reshape(1, old_size[0], old_size[1], -1).permute(0, 3, 1, 2) + posemb = F.interpolate(posemb, size=new_size, mode=interpolation, antialias=antialias) + posemb = posemb.permute(0, 2, 3, 1).reshape(1, -1, embed_dim) + posemb = posemb.to(orig_dtype) + + # add back extra (class, etc) prefix tokens + if posemb_prefix is not None: + posemb = torch.cat([posemb_prefix, posemb], dim=1) + + return posemb + + +def to_2tuple(x): + return x,x + +def _init_img_size(self, img_size: Union[int, Tuple[int, int]]): + assert self.patch_size + if img_size is None: + return None, None, None + img_size = to_2tuple(img_size) + grid_size = tuple([s // p for s, p in zip(img_size, self.patch_size)]) + num_patches = grid_size[0] * grid_size[1] + return img_size, grid_size, num_patches + +def set_input_size_patchembed( + self, + img_size: Optional[Union[int, Tuple[int, int]]] = None, + patch_size: Optional[Union[int, Tuple[int, int]]] = None, +): + new_patch_size = None + if patch_size is not None: + new_patch_size = to_2tuple(patch_size) + if new_patch_size is not None and new_patch_size != self.patch_size: + with torch.no_grad(): + new_proj = nn.Conv2d( + self.proj.in_channels, + self.proj.out_channels, + kernel_size=new_patch_size, + stride=new_patch_size, + bias=self.proj.bias is not None, + device=self.proj.weight.device, + dtype=self.proj.weight.dtype, + ) + new_proj.weight.copy_(resample_patch_embed(self.proj.weight, new_patch_size, verbose=True)) + if self.proj.bias is not None: + new_proj.bias.copy_(self.proj.bias) + self.proj = new_proj + self.patch_size = new_patch_size + img_size = img_size or self.img_size + if img_size != self.img_size or new_patch_size is not None: + self.img_size, self.grid_size, self.num_patches = _init_img_size(self, img_size) + +def set_input_size( + self, + img_size: Optional[Union[int, Tuple[int, int]]] = None, + patch_size: Optional[Union[int, Tuple[int, int]]] = None, + ) -> None: + """Update the input image resolution and patch size. + + Args: + img_size: New input resolution, if None current resolution is used. + patch_size: New patch size, if None existing patch size is used. + """ + prev_grid_size = self.patch_embed.grid_size + set_input_size_patchembed(self.patch_embed, img_size=img_size, patch_size=patch_size) + if self.pos_embed is not None: + num_prefix_tokens = 0 if self.no_embed_class else self.num_prefix_tokens + num_new_tokens = self.patch_embed.num_patches + num_prefix_tokens + if num_new_tokens != self.pos_embed.shape[1]: + self.pos_embed = nn.Parameter(resample_abs_pos_embed( + self.pos_embed, + new_size=self.patch_embed.grid_size, + old_size=prev_grid_size, + num_prefix_tokens=num_prefix_tokens, + verbose=True, + )) + + +class MocoV3(nn.Module): + def __init__( + self, + model_ckpt_path: str = '/path/to/latentforcing/mocov3b.pth.tar', + match_pixel_norm: float = 0.485, + ): + super().__init__() + + self.register_buffer("latent_std", mocov3_std.clone().float()) + self.register_buffer("latent_mean", mocov3_mean.clone().float()) + + self.register_buffer("pixel_std", torch.tensor((0.229, 0.224, 0.225))) + self.register_buffer("pixel_mean", torch.tensor((0.485, 0.456, 0.406))) + + self.match_pixel_norm = match_pixel_norm + + + checkpoint = torch.load(model_ckpt_path, map_location="cpu") + state_dict = checkpoint['state_dict'] + new_state_dict = {} + for k, v in state_dict.items(): + if k.startswith("module.base_encoder."): + new_k = k.replace("module.base_encoder.", "") + new_state_dict[new_k] = v + + self.mocov3 = timm.create_model("vit_base_patch16_224", num_classes=0) + self.mocov3.load_state_dict(new_state_dict, strict=False) + set_input_size(self.mocov3, 256) + self.mocov3.eval() + self.mocov3.requires_grad_(False) + + @torch.compile() + @torch.no_grad() + def encode(self, x: torch.Tensor) -> torch.Tensor: + # normalize input + # x : b c h w + + x = (x - self.pixel_mean.view(1,3,1,1)) / self.pixel_std.view(1,3,1,1) + z = self.mocov3.forward_features(x) # b 1+n d + z = z[:,1:] # remove cls + z = (z - self.latent_mean.view(1,1,-1)) / self.latent_std.view(1,1,-1) + z = z * self.match_pixel_norm + z = z.view(-1,16,16,768).permute(0,3,1,2) # b hw d --> b d h w + + return z diff --git a/transfer/code/LatentForcing/model_cot.py b/transfer/code/LatentForcing/model_cot.py new file mode 100644 index 0000000000000000000000000000000000000000..93c8f647422b1ac812e8a94dba2ddce6271467eb --- /dev/null +++ b/transfer/code/LatentForcing/model_cot.py @@ -0,0 +1,495 @@ +# -------------------------------------------------------- +# References: +# SiT: https://github.com/willisma/SiT +# Lightning-DiT: https://github.com/hustvl/LightningDiT +# JiT: https://github.com/LTH14/JiT +# REPA: https://github.com/sihyun-yu/REPA +# RAE: https://github.com/bytetriper/RAE +# -------------------------------------------------------- +import torch +import torch.nn as nn +import math +import torch.nn.functional as F +from util.model_util import VisionRotaryEmbeddingFast, get_2d_sincos_pos_embed, RMSNorm + + +def modulate(x, shift, scale): + return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + + +class BottleneckPatchEmbed(nn.Module): + """ Image to Patch Embedding + """ + def __init__(self, img_size=224, patch_size=16, in_chans=3, pca_dim=768, embed_dim=768, bias=True): + super().__init__() + img_size = (img_size, img_size) + patch_size = (patch_size, patch_size) + num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0]) + self.img_size = img_size + self.patch_size = patch_size + self.num_patches = num_patches + + self.proj1 = nn.Conv2d(in_chans, pca_dim, kernel_size=patch_size, stride=patch_size, bias=False) + self.proj2 = nn.Conv2d(pca_dim, embed_dim, kernel_size=1, stride=1, bias=bias) + + def forward(self, x): + B, C, H, W = x.shape + assert H == self.img_size[0] and W == self.img_size[1], \ + f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})." + x = self.proj2(self.proj1(x)).flatten(2).transpose(1, 2) + return x + +class NoBottleneckPatchEmbed(nn.Module): + """ Image to Patch Embedding + """ + def __init__(self, img_size=224, patch_size=16, in_chans=3, pca_dim=768, embed_dim=768, bias=True): + super().__init__() + img_size = (img_size, img_size) + patch_size = (patch_size, patch_size) + num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0]) + self.img_size = img_size + self.patch_size = patch_size + self.num_patches = num_patches + + self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias) + + def forward(self, x): + B, C, H, W = x.shape + assert H == self.img_size[0] and W == self.img_size[1], \ + f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})." + x = self.proj(x).flatten(2).transpose(1, 2) + return x + + +class TimestepEmbedder(nn.Module): + """ + Embeds scalar timesteps into vector representations. + """ + def __init__(self, hidden_size, frequency_embedding_size=256): + super().__init__() + self.mlp = nn.Sequential( + nn.Linear(frequency_embedding_size, hidden_size, bias=True), + nn.SiLU(), + nn.Linear(hidden_size, hidden_size, bias=True), + ) + self.frequency_embedding_size = frequency_embedding_size + + @staticmethod + def timestep_embedding(t, dim, max_period=10000): + """ + Create sinusoidal timestep embeddings. + :param t: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an (N, D) Tensor of positional embeddings. + """ + # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py + half = dim // 2 + freqs = torch.exp( + -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half + ).to(device=t.device) + args = t[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + return embedding + + def forward(self, t): + t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(t.dtype) + t_emb = self.mlp(t_freq) + return t_emb + + +class LabelEmbedder(nn.Module): + """ + Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance. + """ + def __init__(self, num_classes, hidden_size): + super().__init__() + self.embedding_table = nn.Embedding(num_classes + 1, hidden_size) + self.num_classes = num_classes + + def forward(self, labels): + embeddings = self.embedding_table(labels) + return embeddings + + +def scaled_dot_product_attention(query, key, value, dropout_p=0.0): + return F.scaled_dot_product_attention( + query, + key, + value, + attn_mask=None, + dropout_p=dropout_p, + is_causal=False + ) + + +class Attention(nn.Module): + def __init__(self, dim, num_heads=8, qkv_bias=True, qk_norm=True, attn_drop=0., proj_drop=0.): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + + self.q_norm = RMSNorm(head_dim) if qk_norm else nn.Identity() + self.k_norm = RMSNorm(head_dim) if qk_norm else nn.Identity() + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x, rope): + B, N, C = x.shape + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + q = self.q_norm(q) + k = self.k_norm(k) + + q = rope(q) + k = rope(k) + + x = scaled_dot_product_attention(q, k, v, dropout_p=self.attn_drop.p if self.training else 0.) + + x = x.transpose(1, 2).reshape(B, N, C) + + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class SwiGLUFFN(nn.Module): + def __init__( + self, + dim: int, + hidden_dim: int, + drop=0.0, + bias=True + ) -> None: + super().__init__() + hidden_dim = int(hidden_dim * 2 / 3) + self.w12 = nn.Linear(dim, 2 * hidden_dim, bias=bias) + self.w3 = nn.Linear(hidden_dim, dim, bias=bias) + self.ffn_dropout = nn.Dropout(drop) + + def forward(self, x): + x12 = self.w12(x) + x1, x2 = x12.chunk(2, dim=-1) + hidden = F.silu(x1) * x2 + return self.w3(self.ffn_dropout(hidden)) + + +class FinalLayer(nn.Module): + """ + The final layer of JiT. + """ + def __init__(self, hidden_size, out_channels, hidden_size_c=None): + super().__init__() + self.norm_final = RMSNorm(hidden_size) + self.linear = nn.Linear(hidden_size, out_channels, bias=True) + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(hidden_size_c or hidden_size, 2 * hidden_size, bias=True) + ) + + @torch.compile + def forward(self, x, c): + shift, scale = self.adaLN_modulation(c).chunk(2, dim=1) + x = modulate(self.norm_final(x), shift, scale) + x = self.linear(x) + return x + + +class JiTBlock(nn.Module): + def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, attn_drop=0.0, proj_drop=0.0, hidden_size_c=None): + super().__init__() + self.norm1 = RMSNorm(hidden_size, eps=1e-6) + self.attn = Attention(hidden_size, num_heads=num_heads, qkv_bias=True, qk_norm=True, + attn_drop=attn_drop, proj_drop=proj_drop) + self.norm2 = RMSNorm(hidden_size, eps=1e-6) + mlp_hidden_dim = int(hidden_size * mlp_ratio) + self.mlp = SwiGLUFFN(hidden_size, mlp_hidden_dim, drop=proj_drop) + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(hidden_size_c or hidden_size, 6 * hidden_size, bias=True) + ) + + @torch.compile + def forward(self, x, c, feat_rope=None): + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(6, dim=-1) + x = x + gate_msa.unsqueeze(1) * self.attn(modulate(self.norm1(x), shift_msa, scale_msa), rope=feat_rope) + x = x + gate_mlp.unsqueeze(1) * self.mlp(modulate(self.norm2(x), shift_mlp, scale_mlp)) + return x + + +class JiTCoT(nn.Module): + """ + Just image Transformer. + """ + def __init__( + self, + input_size=256, + patch_size=16, + in_channels=3, + hidden_size=1024, + depth=24, + num_heads=16, + mlp_ratio=4.0, + attn_drop=0.0, + proj_drop=0.0, + num_classes=1000, + bottleneck_dim=128, + bottleneck_dim_dino=128, + in_context_len=32, + in_context_start=8, + dino_in_channels=768, + dh_depth=0, + dh_hidden_size=1536, + ): + super().__init__() + self.in_channels = in_channels + self.out_channels_pixel = in_channels * patch_size * patch_size + self.out_channels_dino = dino_in_channels + self.out_channels = self.out_channels_pixel + self.out_channels_dino + self.patch_size = patch_size + self.num_heads = num_heads + self.hidden_size = hidden_size + self.input_size = input_size + self.in_context_len = in_context_len + self.in_context_start = in_context_start + self.num_classes = num_classes + + # time and class embed + self.t_pixel_embedder = TimestepEmbedder(hidden_size) + self.t_dino_embedder = TimestepEmbedder(hidden_size) + self.y_embedder = LabelEmbedder(num_classes, hidden_size) + + # linear embed + self.pixel_embedder = BottleneckPatchEmbed(input_size, patch_size, in_channels, bottleneck_dim, hidden_size, bias=True) + self.dino_embedder = BottleneckPatchEmbed(input_size//patch_size, 1, dino_in_channels, bottleneck_dim_dino, hidden_size, bias=True) + + # use fixed sin-cos embedding + num_patches = self.pixel_embedder.num_patches + self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, hidden_size), requires_grad=False) + + # in-context cls token + if self.in_context_len > 0: + self.in_context_posemb = nn.Parameter(torch.zeros(1, self.in_context_len, hidden_size), requires_grad=True) + torch.nn.init.normal_(self.in_context_posemb, std=.02) + + # rope + half_head_dim = hidden_size // num_heads // 2 + hw_seq_len = input_size // patch_size + self.feat_rope = VisionRotaryEmbeddingFast( + dim=half_head_dim, + pt_seq_len=hw_seq_len, + num_cls_token=0 + ) + self.feat_rope_incontext = VisionRotaryEmbeddingFast( + dim=half_head_dim, + pt_seq_len=hw_seq_len, + num_cls_token=self.in_context_len + ) + + # transformer + self.blocks = nn.ModuleList([ + JiTBlock(hidden_size, num_heads, mlp_ratio=mlp_ratio, + attn_drop=attn_drop if (depth // 4 * 3 > i >= depth // 4) else 0.0, + proj_drop=proj_drop if (depth // 4 * 3 > i >= depth // 4) else 0.0) + for i in range(depth) + ]) + + # linear predict + self.dh_depth = dh_depth + if self.dh_depth > 0: + self.dh_pixel_proj = nn.Linear(hidden_size, dh_hidden_size) + self.dh_dino_proj = nn.Linear(hidden_size, dh_hidden_size) + + self.dh_blocks_pixel = nn.ModuleList([ + JiTBlock(dh_hidden_size, num_heads * dh_hidden_size // hidden_size , mlp_ratio=mlp_ratio, hidden_size_c=hidden_size) for _ in range(dh_depth) + ]) + self.dh_blocks_dino = nn.ModuleList([ + JiTBlock(dh_hidden_size, num_heads * dh_hidden_size // hidden_size , mlp_ratio=mlp_ratio, hidden_size_c=hidden_size) for _ in range(dh_depth) + ]) + + self.final_layer_pixel = FinalLayer(dh_hidden_size, self.out_channels_pixel, hidden_size_c=hidden_size) + self.final_layer_dino = FinalLayer(dh_hidden_size, self.out_channels_dino, hidden_size_c=hidden_size) + else: + self.final_layer = FinalLayer(hidden_size, self.out_channels) + + self.initialize_weights() + + def initialize_weights(self): + # Initialize transformer layers: + def _basic_init(module): + if isinstance(module, nn.Linear): + torch.nn.init.xavier_uniform_(module.weight) + if module.bias is not None: + nn.init.constant_(module.bias, 0) + self.apply(_basic_init) + + # Initialize (and freeze) pos_embed by sin-cos embedding: + pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1], int(self.pixel_embedder.num_patches ** 0.5)) + self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0)) + + # Initialize patch_embed like nn.Linear (instead of nn.Conv2d): + w1 = self.pixel_embedder.proj1.weight.data + nn.init.xavier_uniform_(w1.view([w1.shape[0], -1])) + w2 = self.pixel_embedder.proj2.weight.data + nn.init.xavier_uniform_(w2.view([w2.shape[0], -1])) + nn.init.constant_(self.pixel_embedder.proj2.bias, 0) + + w1 = self.dino_embedder.proj1.weight.data + nn.init.xavier_uniform_(w1.view([w1.shape[0], -1])) + w2 = self.dino_embedder.proj2.weight.data + nn.init.xavier_uniform_(w2.view([w2.shape[0], -1])) + nn.init.constant_(self.dino_embedder.proj2.bias, 0) + + # Initialize label embedding table: + nn.init.normal_(self.y_embedder.embedding_table.weight, std=0.02) + + nn.init.normal_(self.t_pixel_embedder.mlp[0].weight, std=0.02) + nn.init.normal_(self.t_pixel_embedder.mlp[2].weight, std=0.02) + nn.init.normal_(self.t_dino_embedder.mlp[0].weight, std=0.02) + nn.init.normal_(self.t_dino_embedder.mlp[2].weight, std=0.02) + + # Zero-out adaLN modulation layers: + for block in self.blocks: + nn.init.constant_(block.adaLN_modulation[-1].weight, 0) + nn.init.constant_(block.adaLN_modulation[-1].bias, 0) + + if self.dh_depth > 0: + for block in self.dh_blocks_pixel: + nn.init.constant_(block.adaLN_modulation[-1].weight, 0) + nn.init.constant_(block.adaLN_modulation[-1].bias, 0) + for block in self.dh_blocks_dino: + nn.init.constant_(block.adaLN_modulation[-1].weight, 0) + nn.init.constant_(block.adaLN_modulation[-1].bias, 0) + + # w1 = self.pixel_embedder_dh.proj.weight.data + # nn.init.xavier_uniform_(w1.view([w1.shape[0], -1])) + # nn.init.constant_(self.pixel_embedder_dh.proj.bias, 0) + # w1 = self.dino_embedder_dh.proj.weight.data + # nn.init.xavier_uniform_(w1.view([w1.shape[0], -1])) + # nn.init.constant_(self.dino_embedder_dh.proj.bias, 0) + + # Zero-out output layers: + for final_layer in [self.final_layer] if self.dh_depth == 0 else [self.final_layer_pixel, self.final_layer_dino]: + nn.init.constant_(final_layer.adaLN_modulation[-1].weight, 0) + nn.init.constant_(final_layer.adaLN_modulation[-1].bias, 0) + nn.init.constant_(final_layer.linear.weight, 0) + nn.init.constant_(final_layer.linear.bias, 0) + + def unpatchify(self, x, p, c): + """ + x: (N, T, patch_size**2 * C) + imgs: (N, H, W, C) + """ + h = w = int(x.shape[1] ** 0.5) + assert h * w == x.shape[1] + + x = x.reshape(shape=(x.shape[0], h, w, p, p, c)) + x = torch.einsum('nhwpqc->nchpwq', x) + imgs = x.reshape(shape=(x.shape[0], c, h * p, h * p)) + return imgs + + def forward(self, x, t, y, m=None): + """ + x: (N, C, H, W), (N D HP WP) + t: (N,), (N,) + y: (N,) + m: Optional (N, 256), (N, 256) + """ + # class and time embeddings + t_emb_pixel = self.t_pixel_embedder(t[0]) + t_emb_dino = self.t_dino_embedder(t[1]) + y_emb = self.y_embedder(y) + c = t_emb_pixel + t_emb_dino + y_emb # Todo try tokens? + + # forward JiT + x_pixel, x_dino = x + if m is None: + x = self.pixel_embedder(x_pixel) + self.dino_embedder(x_dino) + else: + x = self.pixel_embedder(x_pixel) * (~m[0][...,None]) + self.dino_embedder(x_dino) * (~m[1][...,None]) + x += self.pos_embed + + for i, block in enumerate(self.blocks): + # in-context + if self.in_context_len > 0 and i == self.in_context_start: + in_context_tokens = y_emb.unsqueeze(1).repeat(1, self.in_context_len, 1) + in_context_tokens += self.in_context_posemb + x = torch.cat([in_context_tokens, x], dim=1) + x = block(x, c, self.feat_rope if i < self.in_context_start else self.feat_rope_incontext) + + x = x[:, self.in_context_len:] + + if self.dh_depth == 0: + output_pixel, output_dino = self.final_layer(x, c).split([self.out_channels_pixel, self.out_channels_dino], dim=-1) + else: + dh_feats_pixel = self.dh_pixel_proj(x) + dh_feats_dino = self.dh_dino_proj(x) + + for i, block in enumerate(self.dh_blocks_pixel): + dh_feats_pixel = block(dh_feats_pixel, c, self.feat_rope) + for i, block in enumerate(self.dh_blocks_dino): + dh_feats_dino = block(dh_feats_dino, c, self.feat_rope) + + output_pixel = self.final_layer_pixel(dh_feats_pixel, c) + output_dino = self.final_layer_dino(dh_feats_dino, c) + + output_pixel = self.unpatchify(output_pixel, self.patch_size, self.in_channels) + output_dino = self.unpatchify(output_dino, 1, self.out_channels_dino) + output = [output_pixel, output_dino] + + return output + + +def JiTCoT_S_16(**kwargs): + return JiTCoT(depth=12, hidden_size=384, num_heads=6, + bottleneck_dim=128, in_context_len=32, in_context_start=4, patch_size=16, **kwargs) + + +def JiTCoT_B_16(**kwargs): + return JiTCoT(depth=12, hidden_size=768, num_heads=12, + bottleneck_dim=128, in_context_len=32, in_context_start=4, patch_size=16, **kwargs) + +def JiTCoT_B_32(**kwargs): + return JiTCoT(depth=12, hidden_size=768, num_heads=12, + bottleneck_dim=128, in_context_len=32, in_context_start=4, patch_size=32, **kwargs) + +def JiTCoT_L_16(**kwargs): + return JiTCoT(depth=24, hidden_size=1024, num_heads=16, + bottleneck_dim=128, in_context_len=32, in_context_start=8, patch_size=16, **kwargs) + +def JiTCoT_LM_16(**kwargs): + return JiTCoT(depth=20, hidden_size=1024, num_heads=16, + bottleneck_dim=128, in_context_len=32, in_context_start=8, patch_size=16, **kwargs) + +def JiTCoT_L_32(**kwargs): + return JiTCoT(depth=24, hidden_size=1024, num_heads=16, + bottleneck_dim=128, in_context_len=32, in_context_start=8, patch_size=32, **kwargs) + +def JiTCoT_H_16(**kwargs): + return JiTCoT(depth=32, hidden_size=1280, num_heads=16, + bottleneck_dim=256, in_context_len=32, in_context_start=10, patch_size=16, **kwargs) + +def JiTCoT_H_32(**kwargs): + return JiTCoT(depth=32, hidden_size=1280, num_heads=16, + bottleneck_dim=256, in_context_len=32, in_context_start=10, patch_size=32, **kwargs) + + +JiTCoT_models = { + 'JiTCoT-S/16': JiTCoT_S_16, + 'JiTCoT-B/16': JiTCoT_B_16, + 'JiTCoT-B/32': JiTCoT_B_32, + 'JiTCoT-L/16': JiTCoT_L_16, + 'JiTCoT-LM/16': JiTCoT_LM_16, + 'JiTCoT-L/32': JiTCoT_L_32, + 'JiTCoT-H/16': JiTCoT_H_16, + 'JiTCoT-H/32': JiTCoT_H_32, +} + +# TODO halve heads diff --git a/transfer/code/LatentForcing/model_jit.py b/transfer/code/LatentForcing/model_jit.py new file mode 100644 index 0000000000000000000000000000000000000000..f2ea8af4685e3e12d71462c3a8d3ceb37e6b53c1 --- /dev/null +++ b/transfer/code/LatentForcing/model_jit.py @@ -0,0 +1,410 @@ +# -------------------------------------------------------- +# References: +# SiT: https://github.com/willisma/SiT +# Lightning-DiT: https://github.com/hustvl/LightningDiT +# JiT: https://github.com/LTH14/JiT +# REPA: https://github.com/sihyun-yu/REPA +# -------------------------------------------------------- +import torch +import torch.nn as nn +import math +import torch.nn.functional as F +from util.model_util import VisionRotaryEmbeddingFast, get_2d_sincos_pos_embed, RMSNorm + + +def modulate(x, shift, scale): + return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + + +class BottleneckPatchEmbed(nn.Module): + """ Image to Patch Embedding + """ + def __init__(self, img_size=224, patch_size=16, in_chans=3, pca_dim=768, embed_dim=768, bias=True): + super().__init__() + img_size = (img_size, img_size) + patch_size = (patch_size, patch_size) + num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0]) + self.img_size = img_size + self.patch_size = patch_size + self.num_patches = num_patches + + self.proj1 = nn.Conv2d(in_chans, pca_dim, kernel_size=patch_size, stride=patch_size, bias=False) + self.proj2 = nn.Conv2d(pca_dim, embed_dim, kernel_size=1, stride=1, bias=bias) + + def forward(self, x): + B, C, H, W = x.shape + assert H == self.img_size[0] and W == self.img_size[1], \ + f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})." + x = self.proj2(self.proj1(x)).flatten(2).transpose(1, 2) + return x + + +class TimestepEmbedder(nn.Module): + """ + Embeds scalar timesteps into vector representations. + """ + def __init__(self, hidden_size, frequency_embedding_size=256): + super().__init__() + self.mlp = nn.Sequential( + nn.Linear(frequency_embedding_size, hidden_size, bias=True), + nn.SiLU(), + nn.Linear(hidden_size, hidden_size, bias=True), + ) + self.frequency_embedding_size = frequency_embedding_size + + @staticmethod + def timestep_embedding(t, dim, max_period=10000): + """ + Create sinusoidal timestep embeddings. + :param t: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an (N, D) Tensor of positional embeddings. + """ + # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py + half = dim // 2 + freqs = torch.exp( + -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half + ).to(device=t.device) + args = t[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + return embedding + + def forward(self, t): + t_freq = self.timestep_embedding(t, self.frequency_embedding_size) + t_emb = self.mlp(t_freq) + return t_emb + + +class LabelEmbedder(nn.Module): + """ + Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance. + """ + def __init__(self, num_classes, hidden_size): + super().__init__() + self.embedding_table = nn.Embedding(num_classes + 1, hidden_size) + self.num_classes = num_classes + + def forward(self, labels): + embeddings = self.embedding_table(labels) + return embeddings + + +def scaled_dot_product_attention(query, key, value, dropout_p=0.0) -> torch.Tensor: + # L, S = query.size(-2), key.size(-2) + # scale_factor = 1 / math.sqrt(query.size(-1)) + # attn_bias = torch.zeros(query.size(0), 1, L, S, dtype=query.dtype).cuda() + + # with torch.cuda.amp.autocast(enabled=False): + # attn_weight = query.float() @ key.float().transpose(-2, -1) * scale_factor + # attn_weight += attn_bias + # attn_weight = torch.softmax(attn_weight, dim=-1) + # attn_weight = torch.dropout(attn_weight, dropout_p, train=True) + # return attn_weight @ value + + return F.scaled_dot_product_attention( + query, + key, + value, + attn_mask=None, + dropout_p=dropout_p, + is_causal=False + ) + + +class Attention(nn.Module): + def __init__(self, dim, num_heads=8, qkv_bias=True, qk_norm=True, attn_drop=0., proj_drop=0.): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + + self.q_norm = RMSNorm(head_dim) if qk_norm else nn.Identity() + self.k_norm = RMSNorm(head_dim) if qk_norm else nn.Identity() + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x, rope): + B, N, C = x.shape + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + q = self.q_norm(q) + k = self.k_norm(k) + + q = rope(q) + k = rope(k) + + x = scaled_dot_product_attention(q, k, v, dropout_p=self.attn_drop.p if self.training else 0.) + + x = x.transpose(1, 2).reshape(B, N, C) + + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class SwiGLUFFN(nn.Module): + def __init__( + self, + dim: int, + hidden_dim: int, + drop=0.0, + bias=True + ) -> None: + super().__init__() + hidden_dim = int(hidden_dim * 2 / 3) + self.w12 = nn.Linear(dim, 2 * hidden_dim, bias=bias) + self.w3 = nn.Linear(hidden_dim, dim, bias=bias) + self.ffn_dropout = nn.Dropout(drop) + + def forward(self, x): + x12 = self.w12(x) + x1, x2 = x12.chunk(2, dim=-1) + hidden = F.silu(x1) * x2 + return self.w3(self.ffn_dropout(hidden)) + + +class FinalLayer(nn.Module): + """ + The final layer of JiT. + """ + def __init__(self, hidden_size, patch_size, out_channels): + super().__init__() + self.norm_final = RMSNorm(hidden_size) + self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True) + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(hidden_size, 2 * hidden_size, bias=True) + ) + + @torch.compile + def forward(self, x, c): + shift, scale = self.adaLN_modulation(c).chunk(2, dim=1) + x = modulate(self.norm_final(x), shift, scale) + x = self.linear(x) + return x + + +class JiTBlock(nn.Module): + def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, attn_drop=0.0, proj_drop=0.0): + super().__init__() + self.norm1 = RMSNorm(hidden_size, eps=1e-6) + self.attn = Attention(hidden_size, num_heads=num_heads, qkv_bias=True, qk_norm=True, + attn_drop=attn_drop, proj_drop=proj_drop) + self.norm2 = RMSNorm(hidden_size, eps=1e-6) + mlp_hidden_dim = int(hidden_size * mlp_ratio) + self.mlp = SwiGLUFFN(hidden_size, mlp_hidden_dim, drop=proj_drop) + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(hidden_size, 6 * hidden_size, bias=True) + ) + + @torch.compile + def forward(self, x, c, feat_rope=None): + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(6, dim=-1) + x = x + gate_msa.unsqueeze(1) * self.attn(modulate(self.norm1(x), shift_msa, scale_msa), rope=feat_rope) + x = x + gate_mlp.unsqueeze(1) * self.mlp(modulate(self.norm2(x), shift_mlp, scale_mlp)) + return x + + +class JiT(nn.Module): + """ + Just image Transformer. + """ + def __init__( + self, + input_size=256, + patch_size=16, + in_channels=3, + hidden_size=1024, + depth=24, + num_heads=16, + mlp_ratio=4.0, + attn_drop=0.0, + proj_drop=0.0, + num_classes=1000, + bottleneck_dim=128, + in_context_len=32, + in_context_start=8 + ): + super().__init__() + self.in_channels = in_channels + self.out_channels = in_channels + self.patch_size = patch_size + self.num_heads = num_heads + self.hidden_size = hidden_size + self.input_size = input_size + self.in_context_len = in_context_len + self.in_context_start = in_context_start + self.num_classes = num_classes + + # time and class embed + self.t_embedder = TimestepEmbedder(hidden_size) + self.y_embedder = LabelEmbedder(num_classes, hidden_size) + + # linear embed + self.x_embedder = BottleneckPatchEmbed(input_size, patch_size, in_channels, bottleneck_dim, hidden_size, bias=True) + + # use fixed sin-cos embedding + num_patches = self.x_embedder.num_patches + self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, hidden_size), requires_grad=False) + + # in-context cls token + if self.in_context_len > 0: + self.in_context_posemb = nn.Parameter(torch.zeros(1, self.in_context_len, hidden_size), requires_grad=True) + torch.nn.init.normal_(self.in_context_posemb, std=.02) + + # rope + half_head_dim = hidden_size // num_heads // 2 + hw_seq_len = input_size // patch_size + self.feat_rope = VisionRotaryEmbeddingFast( + dim=half_head_dim, + pt_seq_len=hw_seq_len, + num_cls_token=0 + ) + self.feat_rope_incontext = VisionRotaryEmbeddingFast( + dim=half_head_dim, + pt_seq_len=hw_seq_len, + num_cls_token=self.in_context_len + ) + + # transformer + self.blocks = nn.ModuleList([ + JiTBlock(hidden_size, num_heads, mlp_ratio=mlp_ratio, + attn_drop=attn_drop if (depth // 4 * 3 > i >= depth // 4) else 0.0, + proj_drop=proj_drop if (depth // 4 * 3 > i >= depth // 4) else 0.0) + for i in range(depth) + ]) + + # linear predict + self.final_layer = FinalLayer(hidden_size, patch_size, self.out_channels) + + self.initialize_weights() + + def initialize_weights(self): + # Initialize transformer layers: + def _basic_init(module): + if isinstance(module, nn.Linear): + torch.nn.init.xavier_uniform_(module.weight) + if module.bias is not None: + nn.init.constant_(module.bias, 0) + self.apply(_basic_init) + + # Initialize (and freeze) pos_embed by sin-cos embedding: + pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1], int(self.x_embedder.num_patches ** 0.5)) + self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0)) + + # Initialize patch_embed like nn.Linear (instead of nn.Conv2d): + w1 = self.x_embedder.proj1.weight.data + nn.init.xavier_uniform_(w1.view([w1.shape[0], -1])) + w2 = self.x_embedder.proj2.weight.data + nn.init.xavier_uniform_(w2.view([w2.shape[0], -1])) + nn.init.constant_(self.x_embedder.proj2.bias, 0) + + # Initialize label embedding table: + nn.init.normal_(self.y_embedder.embedding_table.weight, std=0.02) + + nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02) + nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02) + + # Zero-out adaLN modulation layers: + for block in self.blocks: + nn.init.constant_(block.adaLN_modulation[-1].weight, 0) + nn.init.constant_(block.adaLN_modulation[-1].bias, 0) + + # Zero-out output layers: + nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0) + nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0) + + nn.init.constant_(self.final_layer.linear.weight, 0) + nn.init.constant_(self.final_layer.linear.bias, 0) + + def unpatchify(self, x, p): + """ + x: (N, T, patch_size**2 * C) + imgs: (N, H, W, C) + """ + c = self.out_channels + h = w = int(x.shape[1] ** 0.5) + assert h * w == x.shape[1] + + x = x.reshape(shape=(x.shape[0], h, w, p, p, c)) + x = torch.einsum('nhwpqc->nchpwq', x) + imgs = x.reshape(shape=(x.shape[0], c, h * p, h * p)) + return imgs + + def forward(self, x, t, y): + """ + x: (N, C, H, W) + t: (N,) + y: (N,) + """ + # class and time embeddings + t_emb = self.t_embedder(t) + y_emb = self.y_embedder(y) + c = t_emb + y_emb + + # forward JiT + x = self.x_embedder(x) + x += self.pos_embed + + for i, block in enumerate(self.blocks): + # in-context + if self.in_context_len > 0 and i == self.in_context_start: + in_context_tokens = y_emb.unsqueeze(1).repeat(1, self.in_context_len, 1) + in_context_tokens += self.in_context_posemb + x = torch.cat([in_context_tokens, x], dim=1) + x = block(x, c, self.feat_rope if i < self.in_context_start else self.feat_rope_incontext) + + x = x[:, self.in_context_len:] + + x = self.final_layer(x, c) + output = self.unpatchify(x, self.patch_size) + + return output + + +def JiT_S_16(**kwargs): + return JiT(depth=12, hidden_size=384, num_heads=6, + bottleneck_dim=128, in_context_len=32, in_context_start=4, patch_size=16, **kwargs) + +def JiT_B_16(**kwargs): + return JiT(depth=12, hidden_size=768, num_heads=12, + bottleneck_dim=128, in_context_len=32, in_context_start=4, patch_size=16, **kwargs) + +def JiT_B_32(**kwargs): + return JiT(depth=12, hidden_size=768, num_heads=12, + bottleneck_dim=128, in_context_len=32, in_context_start=4, patch_size=32, **kwargs) + +def JiT_L_16(**kwargs): + return JiT(depth=24, hidden_size=1024, num_heads=16, + bottleneck_dim=128, in_context_len=32, in_context_start=8, patch_size=16, **kwargs) + +def JiT_L_32(**kwargs): + return JiT(depth=24, hidden_size=1024, num_heads=16, + bottleneck_dim=128, in_context_len=32, in_context_start=8, patch_size=32, **kwargs) + +def JiT_H_16(**kwargs): + return JiT(depth=32, hidden_size=1280, num_heads=16, + bottleneck_dim=256, in_context_len=32, in_context_start=10, patch_size=16, **kwargs) + +def JiT_H_32(**kwargs): + return JiT(depth=32, hidden_size=1280, num_heads=16, + bottleneck_dim=256, in_context_len=32, in_context_start=10, patch_size=32, **kwargs) + + +JiT_models = { + 'JiT-S/16': JiT_S_16, + 'JiT-B/16': JiT_B_16, + 'JiT-B/32': JiT_B_32, + 'JiT-L/16': JiT_L_16, + 'JiT-L/32': JiT_L_32, + 'JiT-H/16': JiT_H_16, + 'JiT-H/32': JiT_H_32, +} diff --git a/transfer/code/LatentForcing/model_repa.py b/transfer/code/LatentForcing/model_repa.py new file mode 100644 index 0000000000000000000000000000000000000000..1c8cb8c1e08e164845b4478a80428173b5637614 --- /dev/null +++ b/transfer/code/LatentForcing/model_repa.py @@ -0,0 +1,422 @@ +# -------------------------------------------------------- +# References: +# SiT: https://github.com/willisma/SiT +# Lightning-DiT: https://github.com/hustvl/LightningDiT +# JiT: https://github.com/LTH14/JiT +# REPA: https://github.com/sihyun-yu/REPA +# -------------------------------------------------------- +import torch +import torch.nn as nn +import math +import torch.nn.functional as F +from util.model_util import VisionRotaryEmbeddingFast, get_2d_sincos_pos_embed, RMSNorm + + +def modulate(x, shift, scale): + return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + + +def build_mlp_repa(hidden_size, projector_dim, z_dim): + return nn.Sequential( + nn.Linear(hidden_size, projector_dim), + nn.SiLU(), + nn.Linear(projector_dim, projector_dim), + nn.SiLU(), + nn.Linear(projector_dim, z_dim), + ) + + +class BottleneckPatchEmbed(nn.Module): + """ Image to Patch Embedding + """ + def __init__(self, img_size=224, patch_size=16, in_chans=3, pca_dim=768, embed_dim=768, bias=True): + super().__init__() + img_size = (img_size, img_size) + patch_size = (patch_size, patch_size) + num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0]) + self.img_size = img_size + self.patch_size = patch_size + self.num_patches = num_patches + + self.proj1 = nn.Conv2d(in_chans, pca_dim, kernel_size=patch_size, stride=patch_size, bias=False) + self.proj2 = nn.Conv2d(pca_dim, embed_dim, kernel_size=1, stride=1, bias=bias) + + def forward(self, x): + B, C, H, W = x.shape + assert H == self.img_size[0] and W == self.img_size[1], \ + f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})." + x = self.proj2(self.proj1(x)).flatten(2).transpose(1, 2) + return x + + +class TimestepEmbedder(nn.Module): + """ + Embeds scalar timesteps into vector representations. + """ + def __init__(self, hidden_size, frequency_embedding_size=256): + super().__init__() + self.mlp = nn.Sequential( + nn.Linear(frequency_embedding_size, hidden_size, bias=True), + nn.SiLU(), + nn.Linear(hidden_size, hidden_size, bias=True), + ) + self.frequency_embedding_size = frequency_embedding_size + + @staticmethod + def timestep_embedding(t, dim, max_period=10000): + """ + Create sinusoidal timestep embeddings. + :param t: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an (N, D) Tensor of positional embeddings. + """ + # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py + half = dim // 2 + freqs = torch.exp( + -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half + ).to(device=t.device) + args = t[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + return embedding + + def forward(self, t): + t_freq = self.timestep_embedding(t, self.frequency_embedding_size) + t_emb = self.mlp(t_freq) + return t_emb + + +class LabelEmbedder(nn.Module): + """ + Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance. + """ + def __init__(self, num_classes, hidden_size): + super().__init__() + self.embedding_table = nn.Embedding(num_classes + 1, hidden_size) + self.num_classes = num_classes + + def forward(self, labels): + embeddings = self.embedding_table(labels) + return embeddings + + +def scaled_dot_product_attention(query, key, value, dropout_p=0.0) -> torch.Tensor: + # L, S = query.size(-2), key.size(-2) + # scale_factor = 1 / math.sqrt(query.size(-1)) + # attn_bias = torch.zeros(query.size(0), 1, L, S, dtype=query.dtype).cuda() + + # with torch.cuda.amp.autocast(enabled=False): + # attn_weight = query.float() @ key.float().transpose(-2, -1) * scale_factor + # attn_weight += attn_bias + # attn_weight = torch.softmax(attn_weight, dim=-1) + # attn_weight = torch.dropout(attn_weight, dropout_p, train=True) + # return attn_weight @ value + + return F.scaled_dot_product_attention( + query, + key, + value, + attn_mask=None, + dropout_p=dropout_p, + is_causal=False + ) + + +class Attention(nn.Module): + def __init__(self, dim, num_heads=8, qkv_bias=True, qk_norm=True, attn_drop=0., proj_drop=0.): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + + self.q_norm = RMSNorm(head_dim) if qk_norm else nn.Identity() + self.k_norm = RMSNorm(head_dim) if qk_norm else nn.Identity() + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x, rope): + B, N, C = x.shape + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + q = self.q_norm(q) + k = self.k_norm(k) + + q = rope(q) + k = rope(k) + + x = scaled_dot_product_attention(q, k, v, dropout_p=self.attn_drop.p if self.training else 0.) + + x = x.transpose(1, 2).reshape(B, N, C) + + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class SwiGLUFFN(nn.Module): + def __init__( + self, + dim: int, + hidden_dim: int, + drop=0.0, + bias=True + ) -> None: + super().__init__() + hidden_dim = int(hidden_dim * 2 / 3) + self.w12 = nn.Linear(dim, 2 * hidden_dim, bias=bias) + self.w3 = nn.Linear(hidden_dim, dim, bias=bias) + self.ffn_dropout = nn.Dropout(drop) + + def forward(self, x): + x12 = self.w12(x) + x1, x2 = x12.chunk(2, dim=-1) + hidden = F.silu(x1) * x2 + return self.w3(self.ffn_dropout(hidden)) + + +class FinalLayer(nn.Module): + """ + The final layer of JiT. + """ + def __init__(self, hidden_size, patch_size, out_channels): + super().__init__() + self.norm_final = RMSNorm(hidden_size) + self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True) + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(hidden_size, 2 * hidden_size, bias=True) + ) + + @torch.compile + def forward(self, x, c): + shift, scale = self.adaLN_modulation(c).chunk(2, dim=1) + x = modulate(self.norm_final(x), shift, scale) + x = self.linear(x) + return x + + +class JiTBlock(nn.Module): + def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, attn_drop=0.0, proj_drop=0.0): + super().__init__() + self.norm1 = RMSNorm(hidden_size, eps=1e-6) + self.attn = Attention(hidden_size, num_heads=num_heads, qkv_bias=True, qk_norm=True, + attn_drop=attn_drop, proj_drop=proj_drop) + self.norm2 = RMSNorm(hidden_size, eps=1e-6) + mlp_hidden_dim = int(hidden_size * mlp_ratio) + self.mlp = SwiGLUFFN(hidden_size, mlp_hidden_dim, drop=proj_drop) + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(hidden_size, 6 * hidden_size, bias=True) + ) + + @torch.compile + def forward(self, x, c, feat_rope=None): + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(6, dim=-1) + x = x + gate_msa.unsqueeze(1) * self.attn(modulate(self.norm1(x), shift_msa, scale_msa), rope=feat_rope) + x = x + gate_mlp.unsqueeze(1) * self.mlp(modulate(self.norm2(x), shift_mlp, scale_mlp)) + return x + + +class JiT(nn.Module): + """ + Just image Transformer. + """ + def __init__( + self, + input_size=256, + patch_size=16, + in_channels=3, + hidden_size=1024, + depth=24, + num_heads=16, + mlp_ratio=4.0, + attn_drop=0.0, + proj_drop=0.0, + num_classes=1000, + bottleneck_dim=128, + in_context_len=32, + in_context_start=8 + ): + super().__init__() + self.in_channels = in_channels + self.out_channels = in_channels + self.patch_size = patch_size + self.num_heads = num_heads + self.hidden_size = hidden_size + self.input_size = input_size + self.in_context_len = in_context_len + self.in_context_start = in_context_start + self.num_classes = num_classes + + # time and class embed + self.t_embedder = TimestepEmbedder(hidden_size) + self.y_embedder = LabelEmbedder(num_classes, hidden_size) + + # linear embed + self.x_embedder = BottleneckPatchEmbed(input_size, patch_size, in_channels, bottleneck_dim, hidden_size, bias=True) + + # use fixed sin-cos embedding + num_patches = self.x_embedder.num_patches + self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, hidden_size), requires_grad=False) + + # in-context cls token + if self.in_context_len > 0: + self.in_context_posemb = nn.Parameter(torch.zeros(1, self.in_context_len, hidden_size), requires_grad=True) + torch.nn.init.normal_(self.in_context_posemb, std=.02) + + # rope + half_head_dim = hidden_size // num_heads // 2 + hw_seq_len = input_size // patch_size + self.feat_rope = VisionRotaryEmbeddingFast( + dim=half_head_dim, + pt_seq_len=hw_seq_len, + num_cls_token=0 + ) + self.feat_rope_incontext = VisionRotaryEmbeddingFast( + dim=half_head_dim, + pt_seq_len=hw_seq_len, + num_cls_token=self.in_context_len + ) + + # transformer + self.blocks = nn.ModuleList([ + JiTBlock(hidden_size, num_heads, mlp_ratio=mlp_ratio, + attn_drop=attn_drop if (depth // 4 * 3 > i >= depth // 4) else 0.0, + proj_drop=proj_drop if (depth // 4 * 3 > i >= depth // 4) else 0.0) + for i in range(depth) + ]) + + # repa + self.repa_projection = build_mlp_repa(hidden_size, 2048, 768) + + # linear predict + self.final_layer = FinalLayer(hidden_size, patch_size, self.out_channels) + + self.initialize_weights() + + def initialize_weights(self): + # Initialize transformer layers: + def _basic_init(module): + if isinstance(module, nn.Linear): + torch.nn.init.xavier_uniform_(module.weight) + if module.bias is not None: + nn.init.constant_(module.bias, 0) + self.apply(_basic_init) + + # Initialize (and freeze) pos_embed by sin-cos embedding: + pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1], int(self.x_embedder.num_patches ** 0.5)) + self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0)) + + # Initialize patch_embed like nn.Linear (instead of nn.Conv2d): + w1 = self.x_embedder.proj1.weight.data + nn.init.xavier_uniform_(w1.view([w1.shape[0], -1])) + w2 = self.x_embedder.proj2.weight.data + nn.init.xavier_uniform_(w2.view([w2.shape[0], -1])) + nn.init.constant_(self.x_embedder.proj2.bias, 0) + + # Initialize label embedding table: + nn.init.normal_(self.y_embedder.embedding_table.weight, std=0.02) + + nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02) + nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02) + + # Zero-out adaLN modulation layers: + for block in self.blocks: + nn.init.constant_(block.adaLN_modulation[-1].weight, 0) + nn.init.constant_(block.adaLN_modulation[-1].bias, 0) + + # Zero-out output layers: + nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0) + nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0) + + nn.init.constant_(self.final_layer.linear.weight, 0) + nn.init.constant_(self.final_layer.linear.bias, 0) + + def unpatchify(self, x, p): + """ + x: (N, T, patch_size**2 * C) + imgs: (N, H, W, C) + """ + c = self.out_channels + h = w = int(x.shape[1] ** 0.5) + assert h * w == x.shape[1] + + x = x.reshape(shape=(x.shape[0], h, w, p, p, c)) + x = torch.einsum('nhwpqc->nchpwq', x) + imgs = x.reshape(shape=(x.shape[0], c, h * p, h * p)) + return imgs + + def forward(self, x, t, y, return_repa=False): + """ + x: (N, C, H, W) + t: (N,) + y: (N,) + """ + # class and time embeddings + t_emb = self.t_embedder(t) + y_emb = self.y_embedder(y) + c = t_emb + y_emb + + # forward JiT + x = self.x_embedder(x) + x += self.pos_embed + + for i, block in enumerate(self.blocks): + # in-context + if self.in_context_len > 0 and i == self.in_context_start: + repa_out = self.repa_projection(x) + + in_context_tokens = y_emb.unsqueeze(1).repeat(1, self.in_context_len, 1) + in_context_tokens += self.in_context_posemb + x = torch.cat([in_context_tokens, x], dim=1) + x = block(x, c, self.feat_rope if i < self.in_context_start else self.feat_rope_incontext) + + x = x[:, self.in_context_len:] + + x = self.final_layer(x, c) + output = self.unpatchify(x, self.patch_size) + + if return_repa: + return output, repa_out + return output + + +def JiTRepa_B_16(**kwargs): + return JiT(depth=12, hidden_size=768, num_heads=12, + bottleneck_dim=128, in_context_len=32, in_context_start=4, patch_size=16, **kwargs) + +def JiTRepa_B_32(**kwargs): + return JiT(depth=12, hidden_size=768, num_heads=12, + bottleneck_dim=128, in_context_len=32, in_context_start=4, patch_size=32, **kwargs) + +def JiTRepa_L_16(**kwargs): + return JiT(depth=24, hidden_size=1024, num_heads=16, + bottleneck_dim=128, in_context_len=32, in_context_start=8, patch_size=16, **kwargs) + +def JiTRepa_L_32(**kwargs): + return JiT(depth=24, hidden_size=1024, num_heads=16, + bottleneck_dim=128, in_context_len=32, in_context_start=8, patch_size=32, **kwargs) + +def JiTRepa_H_16(**kwargs): + return JiT(depth=32, hidden_size=1280, num_heads=16, + bottleneck_dim=256, in_context_len=32, in_context_start=10, patch_size=16, **kwargs) + +def JiTRepa_H_32(**kwargs): + return JiT(depth=32, hidden_size=1280, num_heads=16, + bottleneck_dim=256, in_context_len=32, in_context_start=10, patch_size=32, **kwargs) + + +JiT_models = { + 'JiTRepa-B/16': JiTRepa_B_16, + 'JiTRepa-B/32': JiTRepa_B_32, + 'JiTRepa-L/16': JiTRepa_L_16, + 'JiTRepa-L/32': JiTRepa_L_32, + 'JiTRepa-H/16': JiTRepa_H_16, + 'JiTRepa-H/32': JiTRepa_H_32, +} diff --git a/transfer/code/LatentForcing/pixel_downsample.py b/transfer/code/LatentForcing/pixel_downsample.py new file mode 100644 index 0000000000000000000000000000000000000000..62819e4a43230b8f8e41cc35769c52f420037458 --- /dev/null +++ b/transfer/code/LatentForcing/pixel_downsample.py @@ -0,0 +1,24 @@ +## UTILS + +import torch +from torch import nn +import torch.nn.functional as F + +class PixelDownsampleEncoder(nn.Module): + def __init__( + self, + ): + super().__init__() + + @torch.compile() + @torch.no_grad() + def encode(self, x: torch.Tensor) -> torch.Tensor: + # normalize input + # x : b c h w + + x = F.interpolate(x, size=(64, 64), mode='area') + B, C, H, W = x.shape + p = 4 # patch size + z = x.view(B, C, H // p, p, W // p, p).permute(0, 1, 3, 5, 2, 4).reshape(B, 48, 16, 16) + + return z diff --git a/transfer/code/LatentForcing/prepare_ref.py b/transfer/code/LatentForcing/prepare_ref.py new file mode 100644 index 0000000000000000000000000000000000000000..77640695014fa31b16fa18249e25b23b00b76c4d --- /dev/null +++ b/transfer/code/LatentForcing/prepare_ref.py @@ -0,0 +1,61 @@ +import os +import argparse +from torchvision import transforms, datasets +from torch.utils.data import DataLoader +from util.crop import center_crop_arr + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('--data_path', type=str, required=True, + help='Path to ImageNet root directory') + parser.add_argument('--output_path', type=str, default='imagenet-train-256', + help='Folder where transformed images will be saved') + parser.add_argument('--img_size', type=int, default=256, + help='Resolution to center-crop and resize') + args = parser.parse_args() + + transform_train = transforms.Compose([ + transforms.Lambda(lambda pil_image: center_crop_arr(pil_image, args.img_size)), + transforms.CenterCrop(args.img_size), + transforms.ToTensor(), + ]) + + dataset_train = datasets.ImageFolder( + os.path.join(args.data_path, 'train'), + transform=transform_train + ) + + data_loader = DataLoader( + dataset_train, + batch_size=256, + num_workers=32, + shuffle=False, + pin_memory=False + ) + + os.makedirs(args.output_path, exist_ok=True) + + to_pil = transforms.ToPILImage() + global_idx = 0 + + from tqdm import tqdm + for batch_images, batch_labels in tqdm(data_loader): + for i in range(batch_images.size(0)): + img_tensor = batch_images[i] + + pil_img = to_pil(img_tensor) + out_path = os.path.join( + args.output_path, + f"transformed_{global_idx:08d}.png" + ) + pil_img.save(out_path, format='PNG', compress_level=0) + global_idx += 1 + + print(f"Saved batch up to index={global_idx} ...") + + print("Finished saving all images.") + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/transfer/code/LatentForcing/util/crop.py b/transfer/code/LatentForcing/util/crop.py new file mode 100644 index 0000000000000000000000000000000000000000..7582690e6ebbbc5ce397906d5e3724f21b8755be --- /dev/null +++ b/transfer/code/LatentForcing/util/crop.py @@ -0,0 +1,23 @@ +import numpy as np +from PIL import Image + + +def center_crop_arr(pil_image, image_size): + """ + Center cropping implementation from ADM. + https://github.com/openai/guided-diffusion/blob/8fb3ad9197f16bbc40620447b2742e13458d2831/guided_diffusion/image_datasets.py#L126 + """ + while min(*pil_image.size) >= 2 * image_size: + pil_image = pil_image.resize( + tuple(x // 2 for x in pil_image.size), resample=Image.BOX + ) + + scale = image_size / min(*pil_image.size) + pil_image = pil_image.resize( + tuple(round(x * scale) for x in pil_image.size), resample=Image.BICUBIC + ) + + arr = np.array(pil_image) + crop_y = (arr.shape[0] - image_size) // 2 + crop_x = (arr.shape[1] - image_size) // 2 + return Image.fromarray(arr[crop_y: crop_y + image_size, crop_x: crop_x + image_size]) diff --git a/transfer/code/LatentForcing/util/lr_sched.py b/transfer/code/LatentForcing/util/lr_sched.py new file mode 100644 index 0000000000000000000000000000000000000000..73565c235d834f40ea4aeb67fe706d46bbc1f957 --- /dev/null +++ b/transfer/code/LatentForcing/util/lr_sched.py @@ -0,0 +1,24 @@ +import math + + +def adjust_learning_rate(optimizer, epoch, args): + """Decay the learning rate with half-cycle cosine after warmup""" + if epoch < args.warmup_epochs: + lr = args.lr * epoch / args.warmup_epochs + else: + if args.lr_schedule == "constant": + lr = args.lr + elif args.lr_schedule == "cosine": + lr = args.min_lr + (args.lr - args.min_lr) * 0.5 * \ + (1. + math.cos(math.pi * (epoch - args.warmup_epochs) / (args.epochs - args.warmup_epochs))) + elif args.lr_schedule == "linear": + lr_end = args.lr * 0.1 + lr = args.lr - (args.lr - lr_end) * (epoch - args.warmup_epochs) / (args.epochs - args.warmup_epochs) + else: + raise NotImplementedError + for param_group in optimizer.param_groups: + if "lr_scale" in param_group: + param_group["lr"] = lr * param_group["lr_scale"] + else: + param_group["lr"] = lr + return lr diff --git a/transfer/code/LatentForcing/util/misc.py b/transfer/code/LatentForcing/util/misc.py new file mode 100644 index 0000000000000000000000000000000000000000..b468619590b7b7b6f5d9abad2a55c4f1a68a187f --- /dev/null +++ b/transfer/code/LatentForcing/util/misc.py @@ -0,0 +1,289 @@ +import builtins +import datetime +import os +import time +from collections import defaultdict, deque +from pathlib import Path +import copy + +import torch +import torch.distributed as dist + + +class SmoothedValue(object): + """Track a series of values and provide access to smoothed values over a + window or the global series average. + """ + + def __init__(self, window_size=20, fmt=None): + if fmt is None: + fmt = "{median:.4f} ({global_avg:.4f})" + self.deque = deque(maxlen=window_size) + self.total = 0.0 + self.count = 0 + self.fmt = fmt + + def update(self, value, n=1): + self.deque.append(value) + self.count += n + self.total += value * n + + def synchronize_between_processes(self): + """ + Warning: does not synchronize the deque! + """ + if not is_dist_avail_and_initialized(): + return + t = torch.tensor([self.count, self.total], dtype=torch.float64, device='cuda') + dist.barrier() + dist.all_reduce(t) + t = t.tolist() + self.count = int(t[0]) + self.total = t[1] + + @property + def median(self): + d = torch.tensor(list(self.deque)) + return d.median().item() + + @property + def avg(self): + d = torch.tensor(list(self.deque), dtype=torch.float32) + return d.mean().item() + + @property + def global_avg(self): + return self.total / self.count + + @property + def max(self): + return max(self.deque) + + @property + def value(self): + return self.deque[-1] + + def __str__(self): + return self.fmt.format( + median=self.median, + avg=self.avg, + global_avg=self.global_avg, + max=self.max, + value=self.value) + + +class MetricLogger(object): + def __init__(self, delimiter="\t"): + self.meters = defaultdict(SmoothedValue) + self.delimiter = delimiter + + def update(self, **kwargs): + for k, v in kwargs.items(): + if v is None: + continue + if isinstance(v, torch.Tensor): + v = v.item() + assert isinstance(v, (float, int)) + self.meters[k].update(v) + + def __getattr__(self, attr): + if attr in self.meters: + return self.meters[attr] + if attr in self.__dict__: + return self.__dict__[attr] + raise AttributeError("'{}' object has no attribute '{}'".format( + type(self).__name__, attr)) + + def __str__(self): + loss_str = [] + for name, meter in self.meters.items(): + loss_str.append( + "{}: {}".format(name, str(meter)) + ) + return self.delimiter.join(loss_str) + + def synchronize_between_processes(self): + for meter in self.meters.values(): + meter.synchronize_between_processes() + + def add_meter(self, name, meter): + self.meters[name] = meter + + def log_every(self, iterable, print_freq, header=None): + i = 0 + if not header: + header = '' + start_time = time.time() + end = time.time() + iter_time = SmoothedValue(fmt='{avg:.4f}') + data_time = SmoothedValue(fmt='{avg:.4f}') + space_fmt = ':' + str(len(str(len(iterable)))) + 'd' + log_msg = [ + header, + '[{0' + space_fmt + '}/{1}]', + 'eta: {eta}', + '{meters}', + 'time: {time}', + 'data: {data}' + ] + if torch.cuda.is_available(): + log_msg.append('max mem: {memory:.0f}') + log_msg = self.delimiter.join(log_msg) + MB = 1024.0 * 1024.0 + for obj in iterable: + data_time.update(time.time() - end) + yield obj + iter_time.update(time.time() - end) + if i % print_freq == 0 or i == len(iterable) - 1: + eta_seconds = iter_time.global_avg * (len(iterable) - i) + eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) + if torch.cuda.is_available(): + print(log_msg.format( + i, len(iterable), eta=eta_string, + meters=str(self), + time=str(iter_time), data=str(data_time), + memory=torch.cuda.max_memory_allocated() / MB)) + else: + print(log_msg.format( + i, len(iterable), eta=eta_string, + meters=str(self), + time=str(iter_time), data=str(data_time))) + i += 1 + end = time.time() + total_time = time.time() - start_time + total_time_str = str(datetime.timedelta(seconds=int(total_time))) + print('{} Total time: {} ({:.4f} s / it)'.format( + header, total_time_str, total_time / len(iterable))) + + +def setup_for_distributed(is_master): + """ + This function disables printing when not in master process + """ + builtin_print = builtins.print + + def print(*args, **kwargs): + force = kwargs.pop('force', False) + force = force or (get_world_size() > 8) + if is_master or force: + now = datetime.datetime.now().time() + builtin_print('[{}] '.format(now), end='') # print with time stamp + builtin_print(*args, **kwargs) + + builtins.print = print + + +def is_dist_avail_and_initialized(): + if not dist.is_available(): + return False + if not dist.is_initialized(): + return False + return True + + +def get_world_size(): + if not is_dist_avail_and_initialized(): + return 1 + return dist.get_world_size() + + +def get_rank(): + if not is_dist_avail_and_initialized(): + return 0 + return dist.get_rank() + + +def is_main_process(): + return get_rank() == 0 + + +def save_on_master(*args, **kwargs): + if is_main_process(): + torch.save(*args, **kwargs) + + +def init_distributed_mode(args): + if args.dist_on_itp: + args.rank = int(os.environ['OMPI_COMM_WORLD_RANK']) + args.world_size = int(os.environ['OMPI_COMM_WORLD_SIZE']) + args.gpu = int(os.environ['OMPI_COMM_WORLD_LOCAL_RANK']) + args.dist_url = "tcp://%s:%s" % (os.environ['MASTER_ADDR'], os.environ['MASTER_PORT']) + os.environ['LOCAL_RANK'] = str(args.gpu) + os.environ['RANK'] = str(args.rank) + os.environ['WORLD_SIZE'] = str(args.world_size) + # ["RANK", "WORLD_SIZE", "MASTER_ADDR", "MASTER_PORT", "LOCAL_RANK"] + elif 'RANK' in os.environ and 'WORLD_SIZE' in os.environ: + args.rank = int(os.environ["RANK"]) + args.world_size = int(os.environ['WORLD_SIZE']) + args.gpu = int(os.environ['LOCAL_RANK']) + elif 'SLURM_PROCID' in os.environ: + args.rank = int(os.environ['SLURM_PROCID']) + args.gpu = args.rank % torch.cuda.device_count() + else: + print('Not using distributed mode') + setup_for_distributed(is_master=True) # hack + args.distributed = False + return + + args.distributed = True + + torch.cuda.set_device(args.gpu) + args.dist_backend = 'nccl' + print('| distributed init (rank {}): {}, gpu {}'.format( + args.rank, args.dist_url, args.gpu), flush=True) + torch.distributed.init_process_group(backend=args.dist_backend, init_method=args.dist_url, + world_size=args.world_size, rank=args.rank) + torch.distributed.barrier() + setup_for_distributed(args.rank == 0) + + +def add_weight_decay(model, weight_decay=0, skip_list=()): + decay = [] + no_decay = [] + for name, param in model.named_parameters(): + if not param.requires_grad: + continue # frozen weights + if len(param.shape) == 1 or name.endswith(".bias") or name in skip_list or 'diffloss' in name: + no_decay.append(param) # no weight decay on bias, norm and diffloss + else: + decay.append(param) + return [ + {'params': no_decay, 'weight_decay': 0.}, + {'params': decay, 'weight_decay': weight_decay}] + + +def save_model(args, model_without_ddp, optimizer, epoch, epoch_name=None): + if epoch_name is None: + epoch_name = str(epoch) + output_dir = Path(args.output_dir) + checkpoint_path = output_dir / ('checkpoint-%s.pth' % epoch_name) + + to_save = { + 'model': model_without_ddp.state_dict(), + 'optimizer': optimizer.state_dict(), + 'epoch': epoch, + 'args': args, + } + + # ema + ema_state_dict1 = copy.deepcopy(model_without_ddp.state_dict()) + ema_state_dict2 = copy.deepcopy(model_without_ddp.state_dict()) + for i, (name, _value) in enumerate(model_without_ddp.named_parameters()): + assert name in ema_state_dict1 and name in ema_state_dict2 + ema_state_dict1[name] = model_without_ddp.ema_params1[i] + ema_state_dict2[name] = model_without_ddp.ema_params2[i] + to_save['model_ema1'] = ema_state_dict1 + to_save['model_ema2'] = ema_state_dict2 + + save_on_master(to_save, checkpoint_path) + + +def all_reduce_mean(x): + world_size = get_world_size() + if world_size > 1: + x_reduce = torch.tensor(x).cuda() + dist.all_reduce(x_reduce) + x_reduce /= world_size + return x_reduce.item() + else: + return x \ No newline at end of file diff --git a/transfer/code/LatentForcing/util/model_util.py b/transfer/code/LatentForcing/util/model_util.py new file mode 100644 index 0000000000000000000000000000000000000000..174a51f15a5b5f86a62b8374532899a8551ed347 --- /dev/null +++ b/transfer/code/LatentForcing/util/model_util.py @@ -0,0 +1,201 @@ +# -------------------------------------------------------- +# References: +# Lightning-DiT: https://github.com/hustvl/LightningDiT +# -------------------------------------------------------- + +from math import pi + +import torch +from torch import nn +import numpy as np + +from einops import rearrange, repeat + + +def broadcat(tensors, dim = -1): + num_tensors = len(tensors) + shape_lens = set(list(map(lambda t: len(t.shape), tensors))) + assert len(shape_lens) == 1, 'tensors must all have the same number of dimensions' + shape_len = list(shape_lens)[0] + dim = (dim + shape_len) if dim < 0 else dim + dims = list(zip(*map(lambda t: list(t.shape), tensors))) + expandable_dims = [(i, val) for i, val in enumerate(dims) if i != dim] + assert all([*map(lambda t: len(set(t[1])) <= 2, expandable_dims)]), 'invalid dimensions for broadcastable concatentation' + max_dims = list(map(lambda t: (t[0], max(t[1])), expandable_dims)) + expanded_dims = list(map(lambda t: (t[0], (t[1],) * num_tensors), max_dims)) + expanded_dims.insert(dim, (dim, dims[dim])) + expandable_shapes = list(zip(*map(lambda t: t[1], expanded_dims))) + tensors = list(map(lambda t: t[0].expand(*t[1]), zip(tensors, expandable_shapes))) + return torch.cat(tensors, dim = dim) + + +def rotate_half(x): + x = rearrange(x, '... (d r) -> ... d r', r = 2) + x1, x2 = x.unbind(dim = -1) + x = torch.stack((-x2, x1), dim = -1) + return rearrange(x, '... d r -> ... (d r)') + + +class VisionRotaryEmbedding(nn.Module): + def __init__( + self, + dim, + pt_seq_len, + ft_seq_len=None, + custom_freqs = None, + freqs_for = 'lang', + theta = 10000, + max_freq = 10, + num_freqs = 1, + ): + super().__init__() + if custom_freqs: + freqs = custom_freqs + elif freqs_for == 'lang': + freqs = 1. / (theta ** (torch.arange(0, dim, 2)[:(dim // 2)].float() / dim)) + elif freqs_for == 'pixel': + freqs = torch.linspace(1., max_freq / 2, dim // 2) * pi + elif freqs_for == 'constant': + freqs = torch.ones(num_freqs).float() + else: + raise ValueError(f'unknown modality {freqs_for}') + + if ft_seq_len is None: ft_seq_len = pt_seq_len + t = torch.arange(ft_seq_len) / ft_seq_len * pt_seq_len + + freqs_h = torch.einsum('..., f -> ... f', t, freqs) + freqs_h = repeat(freqs_h, '... n -> ... (n r)', r = 2) + + freqs_w = torch.einsum('..., f -> ... f', t, freqs) + freqs_w = repeat(freqs_w, '... n -> ... (n r)', r = 2) + + freqs = broadcat((freqs_h[:, None, :], freqs_w[None, :, :]), dim = -1) + + self.register_buffer("freqs_cos", freqs.cos()) + self.register_buffer("freqs_sin", freqs.sin()) + + def forward(self, t, start_index = 0): + rot_dim = self.freqs_cos.shape[-1] + end_index = start_index + rot_dim + assert rot_dim <= t.shape[-1], f'feature dimension {t.shape[-1]} is not of sufficient size to rotate in all the positions {rot_dim}' + t_left, t, t_right = t[..., :start_index], t[..., start_index:end_index], t[..., end_index:] + t = (t * self.freqs_cos) + (rotate_half(t) * self.freqs_sin) + return torch.cat((t_left, t, t_right), dim = -1) + + +class VisionRotaryEmbeddingFast(nn.Module): + def __init__( + self, + dim, + pt_seq_len=16, + ft_seq_len=None, + custom_freqs = None, + freqs_for = 'lang', + theta = 10000, + max_freq = 10, + num_freqs = 1, + num_cls_token = 0 + ): + super().__init__() + if custom_freqs: + freqs = custom_freqs + elif freqs_for == 'lang': + freqs = 1. / (theta ** (torch.arange(0, dim, 2)[:(dim // 2)].float() / dim)) + elif freqs_for == 'pixel': + freqs = torch.linspace(1., max_freq / 2, dim // 2) * pi + elif freqs_for == 'constant': + freqs = torch.ones(num_freqs).float() + else: + raise ValueError(f'unknown modality {freqs_for}') + + if ft_seq_len is None: ft_seq_len = pt_seq_len + t = torch.arange(ft_seq_len) / ft_seq_len * pt_seq_len + + freqs = torch.einsum('..., f -> ... f', t, freqs) + freqs = repeat(freqs, '... n -> ... (n r)', r = 2) + freqs = broadcat((freqs[:, None, :], freqs[None, :, :]), dim = -1) + + if num_cls_token > 0: + freqs_flat = freqs.view(-1, freqs.shape[-1]) # [N_img, D] + cos_img = freqs_flat.cos() + sin_img = freqs_flat.sin() + + # prepend in-context cls token + N_img, D = cos_img.shape + cos_pad = torch.ones(num_cls_token, D, dtype=cos_img.dtype, device=cos_img.device) + sin_pad = torch.zeros(num_cls_token, D, dtype=sin_img.dtype, device=sin_img.device) + + self.freqs_cos = torch.cat([cos_pad, cos_img], dim=0).cuda() # [N_cls+N_img, D] + self.freqs_sin = torch.cat([sin_pad, sin_img], dim=0).cuda() + else: + self.freqs_cos = freqs.cos().view(-1, freqs.shape[-1]).cuda() + self.freqs_sin = freqs.sin().view(-1, freqs.shape[-1]).cuda() + + def forward(self, t): return (t * self.freqs_cos + rotate_half(t) * self.freqs_sin).to(t.dtype) + + +class RMSNorm(nn.Module): + def __init__(self, hidden_size, eps=1e-6): + """ + LlamaRMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states): + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + variance = hidden_states.pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + return (self.weight * hidden_states).to(input_dtype) + + +def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, extra_tokens=0): + """ + grid_size: int of the grid height and width + return: + pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token) + """ + grid_h = np.arange(grid_size, dtype=np.float32) + grid_w = np.arange(grid_size, dtype=np.float32) + grid = np.meshgrid(grid_w, grid_h) # here w goes first + grid = np.stack(grid, axis=0) + + grid = grid.reshape([2, 1, grid_size, grid_size]) + pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid) + if cls_token and extra_tokens > 0: + pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0) + return pos_embed + + +def get_2d_sincos_pos_embed_from_grid(embed_dim, grid): + assert embed_dim % 2 == 0 + + # use half of dimensions to encode grid_h + emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2) + emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2) + + emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D) + return emb + + +def get_1d_sincos_pos_embed_from_grid(embed_dim, pos): + """ + embed_dim: output dimension for each position + pos: a list of positions to be encoded: size (M,) + out: (M, D) + """ + assert embed_dim % 2 == 0 + omega = np.arange(embed_dim // 2, dtype=np.float64) + omega /= embed_dim / 2. + omega = 1. / 10000**omega # (D/2,) + + pos = pos.reshape(-1) # (M,) + out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product + + emb_sin = np.sin(out) # (M, D/2) + emb_cos = np.cos(out) # (M, D/2) + + emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D) + return emb \ No newline at end of file diff --git a/transfer/code/adaptive_prompt_selection/__init__.py b/transfer/code/adaptive_prompt_selection/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6b778b70851af8747e7a25e155ecf2289a36f240 --- /dev/null +++ b/transfer/code/adaptive_prompt_selection/__init__.py @@ -0,0 +1,10 @@ +"""Adaptive prompt selection via FOCUS-inspired multi-armed bandit.""" + +try: + from .cell_bandit import CellBandit + from .adaptive_prompt import adaptive_prompt_selection, run_baseline, setup_cell_arms +except ImportError: + from cell_bandit import CellBandit + from adaptive_prompt import adaptive_prompt_selection, run_baseline, setup_cell_arms + +__all__ = ["CellBandit", "adaptive_prompt_selection", "run_baseline", "setup_cell_arms"] diff --git a/transfer/code/adaptive_prompt_selection/adaptive_prompt.py b/transfer/code/adaptive_prompt_selection/adaptive_prompt.py new file mode 100644 index 0000000000000000000000000000000000000000..2abadb2a120403c85f09f843e4875bd288f447e7 --- /dev/null +++ b/transfer/code/adaptive_prompt_selection/adaptive_prompt.py @@ -0,0 +1,500 @@ +""" +Adaptive Prompt Selection pipeline for Stack. + +Orchestrates the full pipeline: + Step 0: Data splitting + Step 1: Embedding extraction + Step 2: Arm construction (cell type x sub-cluster) + Step 3: CellBandit selection of unperturbed prompt cells + Step 4: Bridge prediction (unperturbed -> predicted perturbed) + Step 5: TopK retrieval of real perturbed cells + Step 6: Final generation with selected prompts +""" + +from __future__ import annotations + +import logging +from typing import Dict, List, Optional, Tuple + +import anndata as ad +import numpy as np +from scipy.sparse import issparse +from sklearn.cluster import KMeans +from sklearn.metrics.pairwise import cosine_similarity + +try: + from .cell_bandit import CellBandit +except ImportError: + from cell_bandit import CellBandit + +LOGGER = logging.getLogger("adaptive_prompt_selection") + + +# --------------------------------------------------------------------------- +# Similarity function factory +# --------------------------------------------------------------------------- + +def create_cell_similarity_fn(precomputed_embeddings: np.ndarray): + """Create a closure that computes cosine similarity to query embedding. + + Pre-normalizes all embeddings once. Each call is just index lookup + dot product. + + Args: + precomputed_embeddings: (n_cells, embedding_dim) array. + + Returns: + fn(query_embedding, cell_indices) -> List[float] + """ + norms = np.linalg.norm(precomputed_embeddings, axis=1, keepdims=True) + norms = np.maximum(norms, 1e-12) + normed = precomputed_embeddings / norms + + def similarity_fn(query_embedding: np.ndarray, cell_indices: List[int]) -> List[float]: + q = query_embedding.ravel() + q_norm = np.linalg.norm(q) + if q_norm < 1e-12: + return [0.0] * len(cell_indices) + q_normed = q / q_norm + subset = normed[cell_indices] # (n, dim) + sims = subset @ q_normed # (n,) + # Clamp to [0, 1] for bandit + sims = np.clip(sims, 0.0, 1.0) + return sims.tolist() + + return similarity_fn + + +# --------------------------------------------------------------------------- +# Arm construction +# --------------------------------------------------------------------------- + +def setup_cell_arms( + pool_adata: ad.AnnData, + pool_embeddings: np.ndarray, + cell_type_col: str = "cell_type", + n_clusters_per_type: int = 5, + random_state: int = 42, +) -> Tuple[List[Dict], np.ndarray]: + """Cluster unperturbed pool cells by cell type then k-means. + + Each (cell_type, cluster_id) = one arm. + + Args: + pool_adata: AnnData of unperturbed pool cells. + pool_embeddings: (n_cells, dim) embeddings aligned with pool_adata. + cell_type_col: Column in pool_adata.obs for cell type. + n_clusters_per_type: Max sub-clusters per cell type. + random_state: Random seed for k-means. + + Returns: + (arms, cluster_labels) where cluster_labels[i] = arm_id for cell i. + """ + cell_types = pool_adata.obs[cell_type_col].values + unique_types = sorted(set(cell_types)) + + arms: List[Dict] = [] + cluster_labels = np.full(len(pool_adata), -1, dtype=int) + arm_id = 0 + + for ct in unique_types: + ct_mask = cell_types == ct + ct_indices = np.where(ct_mask)[0] + n_cells_ct = len(ct_indices) + + if n_cells_ct == 0: + continue + + # Adaptive cluster count to avoid degenerate clusters + n_clust = min(n_clusters_per_type, max(1, n_cells_ct // 10)) + + ct_embeddings = pool_embeddings[ct_indices] + + if n_clust <= 1: + # Single cluster for this cell type + arms.append({ + "arm_id": arm_id, + "cell_type": str(ct), + "cluster_id": 0, + "cell_indices": ct_indices.tolist(), + "samples": 0, + "mean_sim": 0.0, + "variance": 0.0, + "focus_score": 0.0, + "sampled_indices": [], + "sampled_scores": [], + }) + cluster_labels[ct_indices] = arm_id + arm_id += 1 + else: + km = KMeans(n_clusters=n_clust, random_state=random_state, n_init=10) + km_labels = km.fit_predict(ct_embeddings) + + for cid in range(n_clust): + members = ct_indices[km_labels == cid] + if len(members) == 0: + continue + arms.append({ + "arm_id": arm_id, + "cell_type": str(ct), + "cluster_id": int(cid), + "cell_indices": members.tolist(), + "samples": 0, + "mean_sim": 0.0, + "variance": 0.0, + "focus_score": 0.0, + "sampled_indices": [], + "sampled_scores": [], + }) + cluster_labels[members] = arm_id + arm_id += 1 + + LOGGER.info( + "Created %d arms from %d cell types (%d total pool cells)", + len(arms), len(unique_types), len(pool_adata), + ) + return arms, cluster_labels + + +# --------------------------------------------------------------------------- +# Main pipeline +# --------------------------------------------------------------------------- + +def adaptive_prompt_selection( + model, + full_adata: ad.AnnData, + genelist_path: str, + query_cell_type: str, + perturbation: str, + control_name: str = "Dimethyl Sulfoxide", + cell_type_col: str = "cell_type", + perturbation_col: str = "sm_name", + control_col: str = "control", + # Bandit params + n_clusters_per_type: int = 5, + zoom_ratio: float = 0.25, + coarse_samples_per_arm: int = 10, + coarse_ratio: float = 0.2, + extra_fine_samples_per_arm: int = 10, + exploration_weight: float = 1.0, + top_ratio: float = 0.2, + temperature: float = 0.06, + # Prediction params + n_prompt_cells: Optional[int] = None, + top_k_real: Optional[int] = None, + prompt_ratio: float = 0.25, + context_ratio: float = 0.4, + context_ratio_min: float = 0.2, + num_steps: int = 5, + mode: str = "mdm", + gene_name_col: Optional[str] = None, + batch_size: int = 16, + num_workers: int = 4, + random_seed: int = 42, + show_progress: bool = True, +) -> Tuple[ad.AnnData, Dict]: + """Full adaptive prompt selection pipeline. + + Args: + model: Loaded Stack model (ICL_FinetunedModel). + full_adata: Complete dataset with all cell types and conditions. + genelist_path: Path to gene list pickle. + query_cell_type: Cell type to predict for (e.g. "B cells"). + perturbation: Target perturbation (e.g. drug name). + control_name: Name of control condition in perturbation_col. + cell_type_col: obs column for cell type. + perturbation_col: obs column for perturbation name. + control_col: obs column for control flag (bool). + + Returns: + (pred_adata, details) — predicted AnnData and bandit selection details. + """ + import torch + + # ----- Step 0: Data splitting ----- + LOGGER.info("Step 0: Splitting data...") + obs = full_adata.obs + + query_mask = (obs[cell_type_col] == query_cell_type) & (obs[control_col] == True) + query_cells = full_adata[query_mask].copy() + LOGGER.info(" Query cells (%s, control): %d", query_cell_type, query_cells.n_obs) + + unperturbed_pool_mask = (obs[cell_type_col] != query_cell_type) & (obs[control_col] == True) + unperturbed_pool = full_adata[unperturbed_pool_mask].copy() + LOGGER.info(" Unperturbed pool (non-%s, control): %d", query_cell_type, unperturbed_pool.n_obs) + + perturbed_pool_mask = (obs[cell_type_col] != query_cell_type) & (obs[perturbation_col] == perturbation) + perturbed_pool = full_adata[perturbed_pool_mask].copy() + LOGGER.info(" Perturbed pool (non-%s, %s): %d", query_cell_type, perturbation, perturbed_pool.n_obs) + + if query_cells.n_obs == 0: + raise ValueError(f"No query cells found for {query_cell_type} under control") + if unperturbed_pool.n_obs == 0: + raise ValueError("No unperturbed pool cells found") + if perturbed_pool.n_obs == 0: + raise ValueError(f"No perturbed pool cells found for {perturbation}") + + # Default prompt cell count + # Stack's get_incontext_prediction uses n_base_cells = n_cells * (prompt_ratio + context_ratio) + # per batch, and cycles through base_adata. We need enough cells to avoid + # excessive repetition. Use multiplier to provide diversity across batches. + n_base_per_batch = int(model.n_cells * (prompt_ratio + context_ratio)) + n_test_per_batch = model.n_cells - n_base_per_batch + num_batches = max(1, int(np.ceil(query_cells.n_obs / n_test_per_batch))) + if n_prompt_cells is None: + # Match Stack's per-batch ratio: each batch needs n_base_per_batch prompts. + # Scale linearly with num_batches so every batch gets unique prompts. + n_prompt_cells = min(unperturbed_pool.n_obs, n_base_per_batch * num_batches) + if top_k_real is None: + top_k_real = min(perturbed_pool.n_obs, n_base_per_batch * num_batches) + LOGGER.info(" n_base_per_batch=%d, n_test_per_batch=%d, num_batches=%d", + n_base_per_batch, n_test_per_batch, num_batches) + LOGGER.info(" n_prompt_cells=%d, top_k_real=%d", n_prompt_cells, top_k_real) + + # ----- Step 1: Extract embeddings ----- + LOGGER.info("Step 1: Extracting embeddings...") + query_emb, _ = model.get_latent_representation( + adata_path=query_cells, genelist_path=genelist_path, + gene_name_col=gene_name_col, batch_size=batch_size, + show_progress=show_progress, num_workers=num_workers, + ) + query_mean = query_emb.mean(axis=0) + LOGGER.info(" Query mean embedding shape: %s", query_mean.shape) + + pool_emb, _ = model.get_latent_representation( + adata_path=unperturbed_pool, genelist_path=genelist_path, + gene_name_col=gene_name_col, batch_size=batch_size, + show_progress=show_progress, num_workers=num_workers, + ) + LOGGER.info(" Pool embeddings shape: %s", pool_emb.shape) + + # ----- Step 2: Build arms ----- + LOGGER.info("Step 2: Building arms (clustering)...") + arms, cluster_labels = setup_cell_arms( + unperturbed_pool, pool_emb, cell_type_col, + n_clusters_per_type=n_clusters_per_type, random_state=random_seed, + ) + + # ----- Step 3: Bandit selection ----- + LOGGER.info("Step 3: Running CellBandit selection...") + sim_fn = create_cell_similarity_fn(pool_emb) + bandit = CellBandit( + similarity_fn=sim_fn, + zoom_ratio=zoom_ratio, + coarse_samples_per_arm=coarse_samples_per_arm, + coarse_ratio=coarse_ratio, + extra_fine_samples_per_arm=extra_fine_samples_per_arm, + exploration_weight=exploration_weight, + top_ratio=top_ratio, + temperature=temperature, + ) + selected_indices, bandit_details = bandit.select_cells( + query_embedding=query_mean, + arms=arms, + k=n_prompt_cells, + rng=np.random.default_rng(random_seed), + ) + matched_unperturbed = unperturbed_pool[selected_indices].copy() + LOGGER.info(" Bandit selected %d unperturbed cells", len(selected_indices)) + + # ----- Step 4: Bridge prediction ----- + LOGGER.info("Step 4: Bridge prediction (unperturbed -> predicted perturbed)...") + intermediate_pred = model.get_incontext_prediction( + base_adata_or_path=perturbed_pool, + test_adata_or_path=matched_unperturbed, + genelist_path=genelist_path, + prompt_ratio=prompt_ratio, + context_ratio=context_ratio, + mode="predict", + gene_name_col=gene_name_col, + batch_size=batch_size, + show_progress=show_progress, + num_workers=num_workers, + random_seed=random_seed, + ) + # intermediate_pred is a sparse matrix + if issparse(intermediate_pred): + intermediate_pred_dense = intermediate_pred.toarray() + else: + intermediate_pred_dense = np.asarray(intermediate_pred) + LOGGER.info(" Intermediate prediction shape: %s", intermediate_pred_dense.shape) + + # ----- Step 5: Find real perturbed prompts ----- + LOGGER.info("Step 5: Retrieving TopK real perturbed cells...") + + # Build AnnData from intermediate predictions for embedding extraction + pred_adata_tmp = ad.AnnData( + X=intermediate_pred_dense, + obs=matched_unperturbed.obs.copy(), + var=matched_unperturbed.var.copy() if hasattr(matched_unperturbed, 'var') else None, + ) + + pred_emb, _ = model.get_latent_representation( + adata_path=pred_adata_tmp, genelist_path=genelist_path, + gene_name_col=gene_name_col, batch_size=batch_size, + show_progress=show_progress, num_workers=num_workers, + ) + + perturbed_emb, _ = model.get_latent_representation( + adata_path=perturbed_pool, genelist_path=genelist_path, + gene_name_col=gene_name_col, batch_size=batch_size, + show_progress=show_progress, num_workers=num_workers, + ) + + # Per-cluster mean of predicted embeddings, then find closest real perturbed + # Group selected cells by which arm they came from + selected_arm_map: Dict[int, List[int]] = {} # arm_id -> list of positions in selected_indices + for pos, cell_idx in enumerate(selected_indices): + arm_label = cluster_labels[cell_idx] + selected_arm_map.setdefault(arm_label, []).append(pos) + + # Per-arm retrieval: each arm computes its own mean predicted embedding, + # then retrieves its proportional share of real perturbed cells. + # This preserves the diversity that the bandit selection identified. + n_arms_with_cells = len(selected_arm_map) + LOGGER.info(" %d arms contributed to selection", n_arms_with_cells) + + # Allocate top_k_real proportionally to arm size + arm_sizes = {aid: len(positions) for aid, positions in selected_arm_map.items()} + total_selected = sum(arm_sizes.values()) + arm_quotas: Dict[int, int] = {} + allocated = 0 + sorted_arms = sorted(arm_sizes.items(), key=lambda x: x[1], reverse=True) + for i, (aid, size) in enumerate(sorted_arms): + if i == len(sorted_arms) - 1: + # Last arm gets remainder to ensure exact total + arm_quotas[aid] = top_k_real - allocated + else: + quota = max(1, int(round(top_k_real * size / total_selected))) + arm_quotas[aid] = quota + allocated += quota + + # Retrieve real perturbed cells per arm + all_retrieved_idx: List[int] = [] + all_retrieved_sims: List[float] = [] + already_selected: set = set() + per_arm_retrieval_info = [] + + for aid, positions in selected_arm_map.items(): + quota = arm_quotas[aid] + # Mean embedding of this arm's predicted perturbed profiles + arm_pred_emb = pred_emb[positions] # (n_arm_cells, dim) + arm_mean = arm_pred_emb.mean(axis=0, keepdims=True) # (1, dim) + + sims = cosine_similarity(arm_mean, perturbed_emb)[0] # (n_perturbed,) + # Exclude already-selected cells + for idx in already_selected: + sims[idx] = -1.0 + top_idx = np.argsort(sims)[-quota:] + retrieved_sims = sims[top_idx] + + all_retrieved_idx.extend(top_idx.tolist()) + all_retrieved_sims.extend(retrieved_sims.tolist()) + already_selected.update(top_idx.tolist()) + + arm_ct = arms[aid]["cell_type"] if aid < len(arms) else "unknown" + per_arm_retrieval_info.append({ + "arm_id": int(aid), + "cell_type": arm_ct, + "n_selected_unperturbed": len(positions), + "n_retrieved_perturbed": int(quota), + "mean_similarity": float(np.mean(retrieved_sims)), + }) + LOGGER.info(" Arm %d (%s): %d unperturbed → %d perturbed (mean_sim=%.4f)", + aid, arm_ct, len(positions), quota, float(np.mean(retrieved_sims))) + + final_prompts = perturbed_pool[all_retrieved_idx].copy() + LOGGER.info(" Selected %d real perturbed cells as final prompts", final_prompts.n_obs) + + bandit_details["n_final_prompts"] = final_prompts.n_obs + bandit_details["per_arm_retrieval"] = per_arm_retrieval_info + bandit_details["top_k_similarities"] = all_retrieved_sims + + # ----- Step 6: Final generation ----- + LOGGER.info("Step 6: Final generation with adaptive prompts...") + result = model.get_incontext_generation( + base_adata_or_path=final_prompts, + test_adata_or_path=query_cells, + genelist_path=genelist_path, + prompt_ratio=prompt_ratio, + context_ratio=context_ratio, + context_ratio_min=context_ratio_min, + num_steps=num_steps, + mode=mode, + gene_name_col=gene_name_col, + batch_size=batch_size, + show_progress=show_progress, + num_workers=num_workers, + random_seed=random_seed, + ) + + if isinstance(result, tuple): + predictions, test_logit = result + else: + predictions, test_logit = result, None + + pred_adata = ad.AnnData( + X=predictions, + obs=query_cells.obs.copy(), + var=query_cells.var.copy(), + ) + if test_logit is not None: + pred_adata.obs["gen_logit"] = np.asarray(test_logit) + + LOGGER.info("Adaptive prompt selection complete. Prediction shape: %s", pred_adata.shape) + return pred_adata, bandit_details + + +# --------------------------------------------------------------------------- +# Baseline: random prompt generation +# --------------------------------------------------------------------------- + +def run_baseline( + model, + context_adata: ad.AnnData, + query_adata: ad.AnnData, + genelist_path: str, + prompt_ratio: float = 0.25, + context_ratio: float = 0.4, + context_ratio_min: float = 0.2, + num_steps: int = 5, + mode: str = "mdm", + gene_name_col: Optional[str] = None, + batch_size: int = 16, + num_workers: int = 4, + random_seed: int = 42, + show_progress: bool = True, +) -> ad.AnnData: + """Run standard random-prompt Stack generation for comparison.""" + LOGGER.info("Running baseline (random prompt) generation...") + + result = model.get_incontext_generation( + base_adata_or_path=context_adata, + test_adata_or_path=query_adata, + genelist_path=genelist_path, + prompt_ratio=prompt_ratio, + context_ratio=context_ratio, + context_ratio_min=context_ratio_min, + num_steps=num_steps, + mode=mode, + gene_name_col=gene_name_col, + batch_size=batch_size, + show_progress=show_progress, + num_workers=num_workers, + random_seed=random_seed, + ) + + if isinstance(result, tuple): + predictions, test_logit = result + else: + predictions, test_logit = result, None + + pred_adata = ad.AnnData( + X=predictions, + obs=query_adata.obs.copy(), + var=query_adata.var.copy(), + ) + if test_logit is not None: + pred_adata.obs["gen_logit"] = np.asarray(test_logit) + + LOGGER.info("Baseline generation complete. Prediction shape: %s", pred_adata.shape) + return pred_adata diff --git a/transfer/code/adaptive_prompt_selection/cell_bandit.py b/transfer/code/adaptive_prompt_selection/cell_bandit.py new file mode 100644 index 0000000000000000000000000000000000000000..304e2383a98288f12c24fd55c29510fbe29fb227 --- /dev/null +++ b/transfer/code/adaptive_prompt_selection/cell_bandit.py @@ -0,0 +1,355 @@ +""" +CellBandit: FOCUS-inspired multi-armed bandit for cell prompt selection. + +Adapted from FOCUS (Frame-Optimistic Confidence Upper-bound Selection). +Removes all temporal/video concepts; keeps the Bernstein UCB formula +and coarse-fine-select three-stage structure. +""" + +import math +from typing import Callable, Dict, List, Optional, Tuple + +import numpy as np + + +class CellBandit: + """ + Multi-armed bandit for selecting optimal cells from a candidate pool. + + Each arm is a (cell_type, cluster_id) group. The algorithm: + 1. Coarse: sample a few cells per arm, score via similarity_fn + 2. UCB: rank arms by mean + confidence bound (Bernstein) + 3. Fine: sample more cells from top arms + 4. Select: top_ratio direct picks + softmax-weighted sampling + """ + + def __init__( + self, + similarity_fn: Callable[[np.ndarray, List[int]], List[float]], + zoom_ratio: float = 0.25, + min_zoom_arms: int = 2, + coarse_samples_per_arm: int = 10, + coarse_ratio: float = 0.2, + extra_fine_samples_per_arm: int = 10, + min_variance_threshold: float = 1e-6, + exploration_weight: float = 1.0, + top_ratio: float = 0.2, + temperature: float = 0.06, + ): + """ + Args: + similarity_fn: fn(query_embedding, cell_indices) -> List[float] + zoom_ratio: Fraction of arms to zoom into for fine sampling. + min_zoom_arms: Minimum number of arms to zoom into. + coarse_samples_per_arm: Minimum coarse samples per arm. + coarse_ratio: Minimum fraction of each arm to sample in coarse phase. + extra_fine_samples_per_arm: Extra samples per arm in fine phase. + min_variance_threshold: Floor for variance in UCB. + exploration_weight: Scales the exploration bonus (lower = more exploitation). + top_ratio: Fraction of k selected directly from top scores. + temperature: Softmax temperature for within-arm sampling. + """ + self.similarity_fn = similarity_fn + self.zoom_ratio = zoom_ratio + self.min_zoom_arms = min_zoom_arms + self.coarse_samples_per_arm = coarse_samples_per_arm + self.coarse_ratio = coarse_ratio + self.extra_fine_samples_per_arm = extra_fine_samples_per_arm + self.min_variance_threshold = min_variance_threshold + self.exploration_weight = exploration_weight + self.top_ratio = top_ratio + self.temperature = temperature + + def select_cells( + self, + query_embedding: np.ndarray, + arms: List[Dict], + k: int, + rng: Optional[np.random.Generator] = None, + ) -> Tuple[List[int], Dict]: + """ + Select k cells from the pool using FOCUS-style bandit. + + Args: + query_embedding: Mean embedding of query cells. + arms: List of arm dicts, each with 'cell_indices' and metadata. + k: Number of cells to select. + rng: Random number generator. + + Returns: + (selected_indices, details) where selected_indices are pool indices. + """ + if rng is None: + rng = np.random.default_rng() + + # Reset arm statistics + for arm in arms: + arm["samples"] = 0 + arm["mean_sim"] = 0.0 + arm["variance"] = 0.0 + arm["focus_score"] = 0.0 + arm["sampled_indices"] = [] + arm["sampled_scores"] = [] + + # Stage 1: Coarse sampling + coarse_indices = self._coarse_sampling(arms, rng) + if coarse_indices: + coarse_scores = self.similarity_fn(query_embedding, coarse_indices) + self._update_arms_with_scores(arms, coarse_indices, coarse_scores) + self._update_focus_scores(arms) + + for arm in arms: + arm["focus_after_coarse"] = float(arm["focus_score"]) + + # Stage 2: Choose promising arms + selected_arms = self._choose_promising_arms(arms) + + # Stage 3: Fine sampling in promising arms + coarse_set = set(coarse_indices) + fine_indices = self._fine_sampling(selected_arms, coarse_set, rng) + if fine_indices: + fine_scores = self.similarity_fn(query_embedding, fine_indices) + self._update_arms_with_scores(arms, fine_indices, fine_scores) + self._update_focus_scores(arms) + + for arm in arms: + arm["focus_after_fine"] = float(arm["focus_score"]) + + # Merge all scores + all_scores: Dict[int, float] = {} + for arm in arms: + for idx, score in arm["sampled_scores"]: + all_scores[idx] = score + + # Stage 4a: Top picks + selected = self._select_top_cells(all_scores, k) + + # Stage 4b: Remaining via softmax within top arms + remaining = k - len(selected) + if remaining > 0: + additional = self._select_remaining_cells( + arms, remaining, all_scores, set(selected), rng + ) + selected.extend(additional) + + selected = selected[:k] + + details = self._prepare_details(arms, selected, coarse_indices, fine_indices) + return selected, details + + # ------------------------------------------------------------------ + # Internal stages + # ------------------------------------------------------------------ + + def _coarse_sampling(self, arms: List[Dict], rng: np.random.Generator) -> List[int]: + """Sample cells from each arm. Uses max(coarse_samples_per_arm, coarse_ratio * arm_size).""" + all_indices: List[int] = [] + for arm in arms: + cell_indices = arm["cell_indices"] + # Adaptive: sample at least coarse_ratio of each arm + n_from_ratio = max(1, int(np.ceil(len(cell_indices) * self.coarse_ratio))) + n_sample = min(max(self.coarse_samples_per_arm, n_from_ratio), len(cell_indices)) + if n_sample == 0: + continue + sampled = rng.choice(cell_indices, size=n_sample, replace=False).tolist() + arm["sampled_indices"] = sampled + all_indices.extend(sampled) + return all_indices + + def _update_arms_with_scores( + self, arms: List[Dict], indices: List[int], scores: List[float] + ) -> None: + """Update arms with new scores and recompute statistics.""" + idx_to_score = dict(zip(indices, scores)) + + for arm in arms: + existing_idx_set = {idx for idx, _ in arm["sampled_scores"]} + for idx in arm["sampled_indices"]: + if idx in idx_to_score and idx not in existing_idx_set: + arm["sampled_scores"].append((idx, idx_to_score[idx])) + existing_idx_set.add(idx) + + if arm["sampled_scores"]: + all_arm_scores = [s for _, s in arm["sampled_scores"]] + arm["samples"] = len(all_arm_scores) + arm["mean_sim"] = float(np.mean(all_arm_scores)) + arm["variance"] = ( + float(np.var(all_arm_scores)) if len(all_arm_scores) > 1 else 0.0 + ) + + def _update_focus_scores(self, arms: List[Dict]) -> None: + """Compute FOCUS UCB scores (Bernstein confidence bound). + + Formula: mean + w * [sqrt(2*log(N)*var/n) + 3*log(N)/n] + Based on FOCUS focus.py:424-453, with exploration_weight (w) scaling. + """ + total_samples = sum(arm["samples"] for arm in arms) + w = self.exploration_weight + + for arm in arms: + n_i = arm["samples"] + mean = arm["mean_sim"] + var = max(arm["variance"], self.min_variance_threshold) + + focus_score = mean + if total_samples > 1 and n_i > 0: + focus_score += w * math.sqrt( + max(0.0, 2 * math.log(total_samples) * var / n_i) + ) + focus_score += w * 3 * math.log(total_samples) / n_i + arm["focus_score"] = focus_score + + def _choose_promising_arms(self, arms: List[Dict]) -> List[Dict]: + """Select top zoom_ratio fraction of arms by FOCUS score.""" + arms_sorted = sorted(arms, key=lambda x: x["focus_score"], reverse=True) + n_select = max(self.min_zoom_arms, int(np.ceil(len(arms) * self.zoom_ratio))) + n_select = min(n_select, len(arms)) + return arms_sorted[:n_select] + + def _fine_sampling( + self, + selected_arms: List[Dict], + coarse_set: set, + rng: np.random.Generator, + ) -> List[int]: + """Sample extra cells from promising arms.""" + fine: List[int] = [] + for arm in selected_arms: + available = [i for i in arm["cell_indices"] if i not in coarse_set] + n_sample = min(self.extra_fine_samples_per_arm, len(available)) + if n_sample > 0: + sampled = rng.choice(available, size=n_sample, replace=False).tolist() + arm["sampled_indices"].extend(sampled) + fine.extend(sampled) + return fine + + def _select_top_cells( + self, all_scores: Dict[int, float], k: int + ) -> List[int]: + """Select top_ratio * k cells with highest scores.""" + if not all_scores or k <= 0: + return [] + k_top = int(round(self.top_ratio * min(k, len(all_scores)))) + sorted_scores = sorted(all_scores.items(), key=lambda x: x[1], reverse=True) + return [idx for idx, _ in sorted_scores[:k_top]] + + def _select_remaining_cells( + self, + arms: List[Dict], + count: int, + all_scores: Dict[int, float], + selected_set: set, + rng: np.random.Generator, + ) -> List[int]: + """Softmax-weighted sampling within top arms (no temporal gap). + + Final selection ranks arms by mean_sim (exploitation-focused), + not focus_score, since we want the best cells, not exploration. + """ + if count <= 0 or not arms: + return [] + + total_arms = len(arms) + S = int(np.ceil(total_arms * max(0.0, min(1.0, self.zoom_ratio)))) + S = max(self.min_zoom_arms, S) + S = min(S, total_arms) + + # Use mean_sim for final arm ranking (exploitation) + arms_sorted = sorted( + enumerate(arms), key=lambda x: x[1]["mean_sim"], reverse=True + ) + top_arm_entries = arms_sorted[:S] + + # Even allocation across top arms + base_alloc = count // S + rem = count % S + per_arm_need = [base_alloc + (1 if i < rem else 0) for i in range(S)] + + new_cells: List[int] = [] + current_selected = set(selected_set) + + for rank, (_, arm) in enumerate(top_arm_entries): + needed = per_arm_need[rank] + if needed == 0: + continue + + available = [i for i in arm["cell_indices"] if i not in current_selected] + if not available: + continue + + # Collect scores for available candidates + scored = [(c, all_scores[c]) for c in available if c in all_scores] + unscored = [c for c in available if c not in all_scores] + + if scored: + candidates, scores_arr = zip(*scored) + candidates = list(candidates) + scores_arr = np.array(scores_arr, dtype=np.float64) + + # Normalize to [0, 1] + if scores_arr.max() > scores_arr.min(): + scores_arr = (scores_arr - scores_arr.min()) / ( + scores_arr.max() - scores_arr.min() + ) + else: + scores_arr = np.ones_like(scores_arr) + + # Softmax with temperature + logits = scores_arr / max(1e-12, self.temperature) + logits = logits - logits.max() + probs = np.exp(logits) + probs = probs / probs.sum() + + actual = min(needed, len(candidates)) + if actual > 0: + chosen_pos = rng.choice( + len(candidates), size=actual, p=probs, replace=False + ) + for pos in chosen_pos: + idx = candidates[pos] + new_cells.append(idx) + current_selected.add(idx) + needed -= actual + + # Fill remaining from unscored candidates randomly + if needed > 0 and unscored: + actual = min(needed, len(unscored)) + chosen = rng.choice(unscored, size=actual, replace=False).tolist() + new_cells.extend(chosen) + for c in chosen: + current_selected.add(c) + + return new_cells + + def _prepare_details( + self, + arms: List[Dict], + selected: List[int], + coarse_indices: List[int], + fine_indices: List[int], + ) -> Dict: + """Prepare sampling details for analysis.""" + arms_info = [] + for arm in arms: + arms_info.append( + { + "arm_id": arm["arm_id"], + "cell_type": arm.get("cell_type", ""), + "cluster_id": arm.get("cluster_id", -1), + "n_cells_in_arm": len(arm["cell_indices"]), + "focus_score": float(arm["focus_score"]), + "focus_after_coarse": arm.get("focus_after_coarse"), + "focus_after_fine": arm.get("focus_after_fine"), + "mean_similarity": float(arm["mean_sim"]), + "variance": float(arm["variance"]), + "samples_count": int(arm["samples"]), + } + ) + + return { + "n_coarse_samples": len(coarse_indices), + "n_fine_samples": len(fine_indices), + "n_selected": len(selected), + "arms_info": arms_info, + "selected_indices": selected, + } diff --git a/transfer/code/adaptive_prompt_selection/run_bcell_experiment.py b/transfer/code/adaptive_prompt_selection/run_bcell_experiment.py new file mode 100644 index 0000000000000000000000000000000000000000..711e8de5ad4f3d7fd53838ffa3719508c10b6502 --- /dev/null +++ b/transfer/code/adaptive_prompt_selection/run_bcell_experiment.py @@ -0,0 +1,360 @@ +#!/usr/bin/env python3 +""" +B-cell experiment: Adaptive Prompt Selection vs Random Baseline. + +Uses B cells (lymphocyte of b lineage) as query, other cells as prompt. +Compares adaptive prompt selection vs random baseline. +Evaluates with cell-eval metrics suite. + +Usage: + python code/adaptive_prompt_selection/run_bcell_experiment.py \ + --checkpoint data/tutorial-pred-model/bc_large_aligned.ckpt \ + --data data/tutorial-pred-data/openproblems_donor1.h5ad \ + --genelist data/tutorial-pred-model/basecount_1000per_15000max.pkl \ + --output-dir data/bcell_experiment_results \ + --show-progress +""" + +from __future__ import annotations + +import argparse +import json +import logging +import sys +from pathlib import Path + +import anndata as ad +import numpy as np +import pandas as pd +from scipy.sparse import issparse + +# --------------------------------------------------------------------------- +# Path setup +# --------------------------------------------------------------------------- +REPO_ROOT = Path(__file__).resolve().parents[2] # lfj/ +for _p in [ + str(REPO_ROOT / "code" / "stack" / "src"), + str(Path(__file__).resolve().parent), + str(REPO_ROOT / "code" / "cell-eval" / "src"), +]: + if _p not in sys.path: + sys.path.insert(0, _p) + +from stack.model_loading import load_model_from_checkpoint +from adaptive_prompt import adaptive_prompt_selection, run_baseline + +LOGGER = logging.getLogger("bcell_experiment") + +# --------------------------------------------------------------------------- +# Constants for the openproblems_donor1 dataset +# --------------------------------------------------------------------------- +QUERY_CELL_TYPE_BROAD = "lymphocyte of b lineage" # broad_cell_class value +QUERY_CELL_TYPE_FINE = "B cells" # cell_type value +CONTROL_NAME = "Dimethyl Sulfoxide" +CELL_TYPE_COL = "broad_cell_class" # broad classes for arm grouping +PERTURBATION_COL = "sm_name" +CONTROL_COL = "control" + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + +def align_genes(adata_pred: ad.AnnData, adata_real: ad.AnnData): + """Ensure pred and real have identical var_names in the same order.""" + common = adata_pred.var_names.intersection(adata_real.var_names) + if len(common) == 0: + raise ValueError("No common genes between predicted and real data") + LOGGER.info("Gene alignment: pred=%d, real=%d, common=%d", + adata_pred.n_vars, adata_real.n_vars, len(common)) + return adata_pred[:, common].copy(), adata_real[:, common].copy() + + +def run_celleval(adata_pred, adata_real, outdir, label, num_threads=4): + """Run cell-eval MetricsEvaluator and return (results, agg_results).""" + from cell_eval import MetricsEvaluator + + eval_dir = str(Path(outdir) / f"celleval_{label}") + evaluator = MetricsEvaluator( + adata_pred=adata_pred, + adata_real=adata_real, + control_pert=CONTROL_NAME, + pert_col=PERTURBATION_COL, + outdir=eval_dir, + allow_discrete=True, + num_threads=num_threads, + ) + results, agg_results = evaluator.compute( + profile="full", + write_csv=True, + break_on_error=False, + ) + return results, agg_results + + +# --------------------------------------------------------------------------- +# Main +# --------------------------------------------------------------------------- + +def build_parser(): + p = argparse.ArgumentParser( + description="B-cell adaptive prompt experiment with cell-eval evaluation" + ) + p.add_argument("--checkpoint", required=True, + help="Path to Stack-Large-Aligned checkpoint (.ckpt)") + p.add_argument("--data", required=True, + help="Path to openproblems_donor1.h5ad") + p.add_argument("--genelist", required=True, + help="Path to gene list pickle") + p.add_argument("--output-dir", required=True, + help="Output directory") + p.add_argument("--drugs", nargs="*", default=None, + help="Specific drugs to test (default: all non-DMSO)") + # Stack generation params + p.add_argument("--batch-size", type=int, default=16) + p.add_argument("--num-steps", type=int, default=5) + p.add_argument("--mode", default="mdm") + p.add_argument("--random-seed", type=int, default=42) + p.add_argument("--show-progress", action="store_true") + # Bandit params + p.add_argument("--n-clusters-per-type", type=int, default=5) + p.add_argument("--zoom-ratio", type=float, default=0.25) + p.add_argument("--top-ratio", type=float, default=0.2) + p.add_argument("--temperature", type=float, default=0.06) + # cell-eval params + p.add_argument("--num-threads", type=int, default=4, + help="Threads for cell-eval DE computation") + p.add_argument("--celleval-profile", default="full", + choices=["full", "minimal", "vcc", "de", "anndata"]) + return p + + +def main(): + parsed = build_parser().parse_args() + output_dir = Path(parsed.output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + logging.basicConfig( + level=logging.INFO, + format="%(asctime)s %(name)s %(levelname)s %(message)s", + handlers=[ + logging.StreamHandler(), + logging.FileHandler(output_dir / "experiment.log"), + ], + ) + + # ---- Load model ---- + LOGGER.info("Loading model from %s", parsed.checkpoint) + model = load_model_from_checkpoint( + parsed.checkpoint, model_class="ICL_FinetunedModel" + ) + LOGGER.info("Model n_cells=%d", model.n_cells) + + # ---- Load data ---- + LOGGER.info("Loading data from %s", parsed.data) + full_adata = ad.read_h5ad(parsed.data) + obs = full_adata.obs + LOGGER.info("Data shape: %s", full_adata.shape) + LOGGER.info("Cell types (broad): %s", + obs[CELL_TYPE_COL].value_counts().to_dict()) + LOGGER.info("Cell types (fine): %s", + obs["cell_type"].value_counts().to_dict()) + + # ---- Identify drugs ---- + all_drugs = sorted( + d for d in obs[PERTURBATION_COL].unique() if d != CONTROL_NAME + ) + drugs = ( + [d for d in parsed.drugs if d in all_drugs] + if parsed.drugs else all_drugs + ) + LOGGER.info("Testing %d drugs: %s", len(drugs), drugs) + + # ---- B-cell subsets ---- + b_mask = obs["cell_type"] == QUERY_CELL_TYPE_FINE + control_B_mask = b_mask & (obs[CONTROL_COL] == True) + control_B = full_adata[control_B_mask].copy() + LOGGER.info("Control B cells: %d", control_B.n_obs) + + # ---- Per-drug prediction loop ---- + adaptive_preds = {} # drug -> AnnData + baseline_preds = {} # drug -> AnnData + ground_truths = {} # drug -> AnnData + bandit_details = {} + + for drug in drugs: + LOGGER.info("=" * 60) + LOGGER.info("Drug: %s", drug) + LOGGER.info("=" * 60) + + # Ground truth: perturbed B cells + gt_mask = b_mask & (obs[PERTURBATION_COL] == drug) + if gt_mask.sum() == 0: + LOGGER.warning("No ground truth B cells for %s, skipping", drug) + continue + LOGGER.info("Ground truth B cells: %d", gt_mask.sum()) + ground_truths[drug] = full_adata[gt_mask].copy() + + # ---- Adaptive ---- + try: + LOGGER.info("[Adaptive] Starting...") + pred_a, details = adaptive_prompt_selection( + model=model, + full_adata=full_adata, + genelist_path=parsed.genelist, + query_cell_type=QUERY_CELL_TYPE_BROAD, + perturbation=drug, + control_name=CONTROL_NAME, + cell_type_col=CELL_TYPE_COL, + perturbation_col=PERTURBATION_COL, + control_col=CONTROL_COL, + n_clusters_per_type=parsed.n_clusters_per_type, + zoom_ratio=parsed.zoom_ratio, + top_ratio=parsed.top_ratio, + temperature=parsed.temperature, + batch_size=parsed.batch_size, + num_steps=parsed.num_steps, + mode=parsed.mode, + random_seed=parsed.random_seed, + show_progress=parsed.show_progress, + ) + pred_a.obs[PERTURBATION_COL] = drug + pred_a.obs[CONTROL_COL] = False + adaptive_preds[drug] = pred_a + bandit_details[drug] = details + LOGGER.info("[Adaptive] Done: %d cells", pred_a.n_obs) + except Exception as e: + LOGGER.error("[Adaptive] Failed: %s", e, exc_info=True) + + # ---- Baseline (random prompt) ---- + try: + LOGGER.info("[Baseline] Starting...") + ctx_mask = (~b_mask) & (obs[PERTURBATION_COL] == drug) + context = full_adata[ctx_mask].copy() + LOGGER.info("[Baseline] Context cells (non-B, %s): %d", + drug, context.n_obs) + + pred_b = run_baseline( + model=model, + context_adata=context, + query_adata=control_B.copy(), + genelist_path=parsed.genelist, + batch_size=parsed.batch_size, + num_steps=parsed.num_steps, + mode=parsed.mode, + random_seed=parsed.random_seed, + show_progress=parsed.show_progress, + ) + pred_b.obs[PERTURBATION_COL] = drug + pred_b.obs[CONTROL_COL] = False + baseline_preds[drug] = pred_b + LOGGER.info("[Baseline] Done: %d cells", pred_b.n_obs) + except Exception as e: + LOGGER.error("[Baseline] Failed: %s", e, exc_info=True) + + # ---- Keep only drugs where both methods succeeded ---- + ok_drugs = sorted( + set(adaptive_preds) & set(baseline_preds) & set(ground_truths) + ) + LOGGER.info("Drugs with both methods OK: %d / %d: %s", + len(ok_drugs), len(drugs), ok_drugs) + if not ok_drugs: + LOGGER.error("No drugs succeeded for both methods, exiting") + return + + # ---- Save bandit details ---- + for drug, det in bandit_details.items(): + safe = drug.replace(" ", "_") + with open(output_dir / f"bandit_{safe}.json", "w") as f: + json.dump(det, f, indent=2, default=str) + + # ---- Assemble combined AnnData for cell-eval ---- + LOGGER.info("Assembling combined AnnData for cell-eval...") + + real_combined = ad.concat( + [control_B] + [ground_truths[d] for d in ok_drugs], join="inner" + ) + adaptive_combined = ad.concat( + [control_B] + [adaptive_preds[d] for d in ok_drugs], join="inner" + ) + baseline_combined = ad.concat( + [control_B] + [baseline_preds[d] for d in ok_drugs], join="inner" + ) + + LOGGER.info("Real: %d cells x %d genes, perts=%s", + real_combined.n_obs, real_combined.n_vars, + real_combined.obs[PERTURBATION_COL].value_counts().to_dict()) + LOGGER.info("Adaptive: %d cells x %d genes", + adaptive_combined.n_obs, adaptive_combined.n_vars) + LOGGER.info("Baseline: %d cells x %d genes", + baseline_combined.n_obs, baseline_combined.n_vars) + + # ---- Align genes between pred and real ---- + adaptive_al, real_for_a = align_genes(adaptive_combined, real_combined) + baseline_al, real_for_b = align_genes(baseline_combined, real_combined) + + # ---- Save intermediate h5ad ---- + adaptive_al.write_h5ad(output_dir / "adaptive_combined.h5ad") + baseline_al.write_h5ad(output_dir / "baseline_combined.h5ad") + real_for_a.write_h5ad(output_dir / "real_combined.h5ad") + + # ---- Run cell-eval ---- + agg_a, agg_b = None, None + + LOGGER.info("Running cell-eval for ADAPTIVE...") + try: + _, agg_a = run_celleval( + adaptive_al, real_for_a, output_dir, "adaptive", + num_threads=parsed.num_threads, + ) + except Exception as e: + LOGGER.error("cell-eval (adaptive) failed: %s", e, exc_info=True) + + LOGGER.info("Running cell-eval for BASELINE...") + try: + _, agg_b = run_celleval( + baseline_al, real_for_b, output_dir, "baseline", + num_threads=parsed.num_threads, + ) + except Exception as e: + LOGGER.error("cell-eval (baseline) failed: %s", e, exc_info=True) + + # ---- Print & save comparison ---- + print("\n" + "=" * 70) + print("CELL-EVAL RESULTS") + print("=" * 70) + + if agg_a is not None: + print("\n--- Adaptive (aggregated) ---") + print(agg_a) + agg_a.write_csv(str(output_dir / "agg_adaptive.csv")) + + if agg_b is not None: + print("\n--- Baseline (aggregated) ---") + print(agg_b) + agg_b.write_csv(str(output_dir / "agg_baseline.csv")) + + if agg_a is not None and agg_b is not None: + # Build side-by-side comparison on mean column + import polars as pl + try: + mean_a = agg_a.filter(pl.col("statistic") == "mean").drop("statistic") + mean_b = agg_b.filter(pl.col("statistic") == "mean").drop("statistic") + # Melt to long format for comparison + a_long = mean_a.unpivot(variable_name="metric", value_name="adaptive") + b_long = mean_b.unpivot(variable_name="metric", value_name="baseline") + comparison = a_long.join(b_long, on="metric") + comparison = comparison.with_columns( + (pl.col("adaptive") - pl.col("baseline")).alias("diff") + ) + print("\n--- Comparison (mean across perturbations) ---") + print(comparison) + comparison.write_csv(str(output_dir / "comparison_mean.csv")) + except Exception as e: + LOGGER.warning("Could not build comparison table: %s", e) + + LOGGER.info("Experiment complete. Results saved to %s", output_dir) + + +if __name__ == "__main__": + main() diff --git a/transfer/code/adaptive_prompt_selection/run_experiment.py b/transfer/code/adaptive_prompt_selection/run_experiment.py new file mode 100644 index 0000000000000000000000000000000000000000..8f50b57cb6a68eeb78d297ff1b1efb7b3da9fe65 --- /dev/null +++ b/transfer/code/adaptive_prompt_selection/run_experiment.py @@ -0,0 +1,356 @@ +#!/usr/bin/env python3 +""" +Experiment script: Adaptive Prompt Selection vs. Random Baseline. + +Runs both methods on openproblems_donor1.h5ad and compares predictions +against ground truth perturbed cells. + +Usage: + source stack_env/bin/activate + python code/adaptive_prompt_selection/run_experiment.py \ + --checkpoint data/tutorial-pred-model/bc_large_aligned.ckpt \ + --data data/tutorial-pred-data/openproblems_donor1.h5ad \ + --genelist data/tutorial-pred-model/basecount_1000per_15000max.pkl \ + --output-dir data/adaptive_prompt_results \ + --show-progress +""" + +from __future__ import annotations + +import argparse +import json +import logging +import sys +from pathlib import Path +from typing import Dict, List, Optional + +import anndata as ad +import numpy as np +import pandas as pd +from scipy.sparse import issparse +from scipy.stats import pearsonr + +# Ensure stack package is importable +REPO_ROOT = Path(__file__).resolve().parents[2] # lfj/ +STACK_SRC = REPO_ROOT / "code" / "stack" / "src" +THIS_DIR = Path(__file__).resolve().parent +for p in [str(STACK_SRC), str(THIS_DIR)]: + if p not in sys.path: + sys.path.insert(0, p) + +from stack.model_loading import load_model_from_checkpoint +from adaptive_prompt import adaptive_prompt_selection, run_baseline + +LOGGER = logging.getLogger("run_experiment") + + +def compute_metrics( + pred_adata: ad.AnnData, + real_adata: ad.AnnData, +) -> Dict[str, float]: + """Compute evaluation metrics between predicted and real expression. + + Aligns genes by var index, then computes: + - mean_pearson: mean per-gene Pearson correlation + - mean_mse: mean squared error across all genes + - mean_mae: mean absolute error across all genes + """ + # Get dense matrices + pred_X = pred_adata.X + if issparse(pred_X): + pred_X = pred_X.toarray() + pred_X = np.asarray(pred_X, dtype=np.float64) + + real_X = real_adata.X + if issparse(real_X): + real_X = real_X.toarray() + real_X = np.asarray(real_X, dtype=np.float64) + + # Align genes + pred_genes = pred_adata.var_names + real_genes = real_adata.var_names + common_genes = pred_genes.intersection(real_genes) + + if len(common_genes) == 0: + LOGGER.warning("No common genes found between predicted and real data") + return {"mean_pearson": float("nan"), "mean_mse": float("nan"), "mean_mae": float("nan")} + + pred_X = pred_X[:, pred_genes.isin(common_genes)] + real_X = real_X[:, real_genes.isin(common_genes)] + + # Mean expression per gene (average across cells) + pred_mean = pred_X.mean(axis=0) + real_mean = real_X.mean(axis=0) + + # Per-gene Pearson on mean expression profiles + valid_mask = (np.std(pred_mean) > 0) and (np.std(real_mean) > 0) + if valid_mask: + overall_pearson, _ = pearsonr(pred_mean, real_mean) + else: + overall_pearson = float("nan") + + # MSE and MAE on mean expression + mse = float(np.mean((pred_mean - real_mean) ** 2)) + mae = float(np.mean(np.abs(pred_mean - real_mean))) + + return { + "mean_pearson": float(overall_pearson), + "mean_mse": mse, + "mean_mae": mae, + "n_common_genes": len(common_genes), + "n_pred_cells": pred_adata.n_obs, + "n_real_cells": real_adata.n_obs, + } + + +def run_experiment( + model, + full_adata: ad.AnnData, + genelist_path: str, + output_dir: Path, + query_cell_types: List[str], + perturbations: Optional[List[str]] = None, + cell_type_col: str = "cell_type", + perturbation_col: str = "sm_name", + control_col: str = "control", + control_name: str = "Dimethyl Sulfoxide", + gene_name_col: Optional[str] = None, + batch_size: int = 16, + num_steps: int = 5, + mode: str = "mdm", + random_seed: int = 42, + show_progress: bool = True, + # Bandit params + n_clusters_per_type: int = 5, + zoom_ratio: float = 0.25, + top_ratio: float = 0.2, + temperature: float = 0.06, +) -> pd.DataFrame: + """Run adaptive vs baseline comparison across conditions.""" + + output_dir.mkdir(parents=True, exist_ok=True) + + # Determine perturbations to test + if perturbations is None: + all_perts = full_adata.obs[perturbation_col].unique() + perturbations = [p for p in all_perts if p != control_name] + LOGGER.info("Auto-detected %d perturbations: %s", len(perturbations), perturbations) + + results_rows = [] + + for query_ct in query_cell_types: + for drug in perturbations: + LOGGER.info("=" * 60) + LOGGER.info("Experiment: query=%s, perturbation=%s", query_ct, drug) + LOGGER.info("=" * 60) + + # Check if ground truth exists + obs = full_adata.obs + gt_mask = (obs[cell_type_col] == query_ct) & (obs[perturbation_col] == drug) + if gt_mask.sum() == 0: + LOGGER.warning("No ground truth cells for %s + %s, skipping", query_ct, drug) + continue + ground_truth = full_adata[gt_mask].copy() + + # Check if perturbed pool exists for this drug (non-query cell types) + perturbed_pool_mask = (obs[cell_type_col] != query_ct) & (obs[perturbation_col] == drug) + if perturbed_pool_mask.sum() == 0: + LOGGER.warning("No perturbed pool for %s (non-%s), skipping", drug, query_ct) + continue + + safe_name = f"{query_ct}_{drug}".replace(" ", "_") + + # --- Adaptive --- + try: + adaptive_pred, bandit_details = adaptive_prompt_selection( + model=model, + full_adata=full_adata, + genelist_path=genelist_path, + query_cell_type=query_ct, + perturbation=drug, + control_name=control_name, + cell_type_col=cell_type_col, + perturbation_col=perturbation_col, + control_col=control_col, + n_clusters_per_type=n_clusters_per_type, + zoom_ratio=zoom_ratio, + top_ratio=top_ratio, + temperature=temperature, + batch_size=batch_size, + num_steps=num_steps, + mode=mode, + gene_name_col=gene_name_col, + random_seed=random_seed, + show_progress=show_progress, + ) + adaptive_metrics = compute_metrics(adaptive_pred, ground_truth) + LOGGER.info("Adaptive metrics: %s", adaptive_metrics) + + # Save adaptive prediction + adaptive_pred.write_h5ad(output_dir / f"adaptive_{safe_name}.h5ad") + + # Save bandit details + with open(output_dir / f"bandit_details_{safe_name}.json", "w") as f: + json.dump(bandit_details, f, indent=2, default=str) + + except Exception as e: + LOGGER.error("Adaptive failed for %s + %s: %s", query_ct, drug, e, exc_info=True) + adaptive_metrics = { + "mean_pearson": float("nan"), + "mean_mse": float("nan"), + "mean_mae": float("nan"), + } + + # --- Baseline --- + try: + # Baseline uses all perturbed non-query cells as context + baseline_context = full_adata[perturbed_pool_mask].copy() + query_mask = (obs[cell_type_col] == query_ct) & (obs[control_col] == True) + query_cells = full_adata[query_mask].copy() + + baseline_pred = run_baseline( + model=model, + context_adata=baseline_context, + query_adata=query_cells, + genelist_path=genelist_path, + batch_size=batch_size, + num_steps=num_steps, + mode=mode, + gene_name_col=gene_name_col, + random_seed=random_seed, + show_progress=show_progress, + ) + baseline_metrics = compute_metrics(baseline_pred, ground_truth) + LOGGER.info("Baseline metrics: %s", baseline_metrics) + + baseline_pred.write_h5ad(output_dir / f"baseline_{safe_name}.h5ad") + + except Exception as e: + LOGGER.error("Baseline failed for %s + %s: %s", query_ct, drug, e, exc_info=True) + baseline_metrics = { + "mean_pearson": float("nan"), + "mean_mse": float("nan"), + "mean_mae": float("nan"), + } + + # Record results + row = { + "query_cell_type": query_ct, + "perturbation": drug, + "n_ground_truth": gt_mask.sum(), + } + for key in ["mean_pearson", "mean_mse", "mean_mae"]: + row[f"adaptive_{key}"] = adaptive_metrics.get(key, float("nan")) + row[f"baseline_{key}"] = baseline_metrics.get(key, float("nan")) + results_rows.append(row) + + results_df = pd.DataFrame(results_rows) + results_df.to_csv(output_dir / "comparison_results.csv", index=False) + LOGGER.info("Results saved to %s", output_dir / "comparison_results.csv") + return results_df + + +def build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser( + description="Adaptive Prompt Selection experiment for Stack" + ) + parser.add_argument("--checkpoint", required=True, help="Path to Stack checkpoint (.ckpt)") + parser.add_argument("--data", required=True, help="Path to full AnnData (.h5ad)") + parser.add_argument("--genelist", required=True, help="Path to gene list pickle") + parser.add_argument("--output-dir", required=True, help="Output directory") + parser.add_argument("--cell-type-col", default="cell_type") + parser.add_argument("--perturbation-col", default="sm_name") + parser.add_argument("--control-col", default="control") + parser.add_argument("--control-name", default="Dimethyl Sulfoxide") + parser.add_argument("--gene-name-col", default=None) + parser.add_argument( + "--query-cell-types", nargs="*", default=None, + help="Cell types to use as query (default: all non-T cell types)", + ) + parser.add_argument( + "--perturbations", nargs="*", default=None, + help="Perturbations to test (default: all non-control)", + ) + parser.add_argument("--batch-size", type=int, default=16) + parser.add_argument("--num-steps", type=int, default=5) + parser.add_argument("--mode", default="mdm") + parser.add_argument("--random-seed", type=int, default=42) + parser.add_argument("--show-progress", action="store_true") + # Bandit params + parser.add_argument("--n-clusters-per-type", type=int, default=5) + parser.add_argument("--zoom-ratio", type=float, default=0.25) + parser.add_argument("--top-ratio", type=float, default=0.2) + parser.add_argument("--temperature", type=float, default=0.06) + return parser + + +def main(args=None): + parser = build_parser() + parsed = parser.parse_args(args=args) + logging.basicConfig(level=logging.INFO, format="%(asctime)s %(name)s %(levelname)s %(message)s") + + LOGGER.info("Loading model from %s", parsed.checkpoint) + model = load_model_from_checkpoint(parsed.checkpoint, model_class="ICL_FinetunedModel") + + LOGGER.info("Loading data from %s", parsed.data) + full_adata = ad.read_h5ad(parsed.data) + + # Default query cell types: non-T cell types + if parsed.query_cell_types is None: + all_types = full_adata.obs[parsed.cell_type_col].unique().tolist() + # Exclude T-cell-like types (those used as context in the tutorial) + t_cell_keywords = ["t cell", "t regulatory", "nk"] + query_cell_types = [ + ct for ct in all_types + if not any(kw in ct.lower() for kw in t_cell_keywords) + ] + if not query_cell_types: + query_cell_types = all_types[:1] + LOGGER.info("Auto-selected query cell types: %s", query_cell_types) + else: + query_cell_types = parsed.query_cell_types + + output_dir = Path(parsed.output_dir) + + results = run_experiment( + model=model, + full_adata=full_adata, + genelist_path=parsed.genelist, + output_dir=output_dir, + query_cell_types=query_cell_types, + perturbations=parsed.perturbations, + cell_type_col=parsed.cell_type_col, + perturbation_col=parsed.perturbation_col, + control_col=parsed.control_col, + control_name=parsed.control_name, + gene_name_col=parsed.gene_name_col, + batch_size=parsed.batch_size, + num_steps=parsed.num_steps, + mode=parsed.mode, + random_seed=parsed.random_seed, + show_progress=parsed.show_progress, + n_clusters_per_type=parsed.n_clusters_per_type, + zoom_ratio=parsed.zoom_ratio, + top_ratio=parsed.top_ratio, + temperature=parsed.temperature, + ) + + print("\n" + "=" * 70) + print("RESULTS SUMMARY") + print("=" * 70) + print(results.to_string(index=False)) + + # Summary statistics + if len(results) > 0: + print("\n--- Aggregated ---") + for metric in ["mean_pearson", "mean_mse", "mean_mae"]: + a_col = f"adaptive_{metric}" + b_col = f"baseline_{metric}" + if a_col in results.columns and b_col in results.columns: + a_mean = results[a_col].mean() + b_mean = results[b_col].mean() + print(f" {metric}: adaptive={a_mean:.4f} baseline={b_mean:.4f} diff={a_mean - b_mean:+.4f}") + + +if __name__ == "__main__": + main() diff --git a/transfer/code/adaptive_prompt_selection/visualize_results.py b/transfer/code/adaptive_prompt_selection/visualize_results.py new file mode 100644 index 0000000000000000000000000000000000000000..cd043f9481a6eafb128b68ef26b56300645cd2d0 --- /dev/null +++ b/transfer/code/adaptive_prompt_selection/visualize_results.py @@ -0,0 +1,457 @@ +#!/usr/bin/env python3 +""" +Visualize B-cell experiment results: Adaptive Prompt Selection vs Stack Baseline. + +Reads cell-eval CSV outputs and generates comparison charts. + +Usage: + python code/adaptive_prompt_selection/visualize_results.py \ + --results-dir data/bcell_test_results \ + --output-dir data/bcell_test_results/figures +""" + +import argparse +from pathlib import Path + +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import matplotlib.ticker as mticker +import numpy as np +import pandas as pd + + +# --------------------------------------------------------------------------- +# Metric metadata +# --------------------------------------------------------------------------- + +# Higher is better for these metrics; lower is better for the rest +HIGHER_IS_BETTER = { + "overlap_at_N", "overlap_at_50", "overlap_at_100", "overlap_at_200", "overlap_at_500", + "precision_at_N", "precision_at_50", "precision_at_100", "precision_at_200", "precision_at_500", + "de_spearman_sig", "de_direction_match", "de_spearman_lfc_sig", "de_sig_genes_recall", + "pr_auc", "roc_auc", "pearson_delta", + "discrimination_score_l1", "discrimination_score_l2", "discrimination_score_cosine", +} +LOWER_IS_BETTER = {"mse", "mae", "mse_delta", "mae_delta"} + +# Metrics to skip in visualizations (uninformative or broken) +SKIP_METRICS = { + "de_nsig_counts_real", "de_nsig_counts_pred", + "pearson_edistance", # both -1 + "de_spearman_sig", # both -1 +} + +# Group metrics for multi-panel display +METRIC_GROUPS = { + "DE Gene Overlap": ["overlap_at_50", "overlap_at_100", "overlap_at_200", "overlap_at_500", "overlap_at_N"], + "DE Precision": ["precision_at_50", "precision_at_100", "precision_at_200", "precision_at_500", "precision_at_N"], + "DE Quality": ["de_direction_match", "de_spearman_lfc_sig", "de_sig_genes_recall"], + "Classification": ["pr_auc", "roc_auc"], + "Expression Error": ["pearson_delta", "mse", "mae", "mse_delta", "mae_delta"], + "Discrimination": ["discrimination_score_l1", "discrimination_score_l2", "discrimination_score_cosine"], +} + +NICE_NAMES = { + "overlap_at_N": "Overlap@N", + "overlap_at_50": "Overlap@50", + "overlap_at_100": "Overlap@100", + "overlap_at_200": "Overlap@200", + "overlap_at_500": "Overlap@500", + "precision_at_N": "Precision@N", + "precision_at_50": "Precision@50", + "precision_at_100": "Precision@100", + "precision_at_200": "Precision@200", + "precision_at_500": "Precision@500", + "de_direction_match": "DE Direction Match", + "de_spearman_lfc_sig": "DE Spearman LFC (sig)", + "de_sig_genes_recall": "DE Sig Genes Recall", + "pr_auc": "PR-AUC", + "roc_auc": "ROC-AUC", + "pearson_delta": "Pearson (delta)", + "mse": "MSE", + "mae": "MAE", + "mse_delta": "MSE (delta)", + "mae_delta": "MAE (delta)", + "discrimination_score_l1": "Discrim L1", + "discrimination_score_l2": "Discrim L2", + "discrimination_score_cosine": "Discrim Cosine", +} + + +def load_per_pert_results(results_dir: Path): + """Load per-perturbation results for both methods.""" + ada = pd.read_csv(results_dir / "celleval_adaptive" / "results.csv") + bas = pd.read_csv(results_dir / "celleval_baseline" / "results.csv") + return ada, bas + + +def load_comparison(results_dir: Path): + """Load the mean comparison CSV.""" + return pd.read_csv(results_dir / "comparison_mean.csv") + + +# --------------------------------------------------------------------------- +# Figure 1: Per-drug side-by-side bars (key metrics only) +# --------------------------------------------------------------------------- + +def plot_per_drug_bars(ada_df, bas_df, output_dir: Path, drug: str): + """Side-by-side bar chart for a single drug across key metrics.""" + ada_row = ada_df[ada_df["perturbation"] == drug].iloc[0] + bas_row = bas_df[bas_df["perturbation"] == drug].iloc[0] + + key_metrics = [ + "pearson_delta", "mse", "mae", + "de_direction_match", "de_spearman_lfc_sig", "de_sig_genes_recall", + "overlap_at_N", "pr_auc", "roc_auc", + ] + # Filter out metrics with identical perfect or broken values + filtered = [] + for m in key_metrics: + a, b = ada_row[m], bas_row[m] + if abs(a) > 1e-10 or abs(b) > 1e-10: # skip all-zero + if a != -1.0 and b != -1.0: # skip broken + filtered.append(m) + metrics = filtered + + ada_vals = [ada_row[m] for m in metrics] + bas_vals = [bas_row[m] for m in metrics] + labels = [NICE_NAMES.get(m, m) for m in metrics] + + x = np.arange(len(metrics)) + width = 0.35 + + fig, ax = plt.subplots(figsize=(12, 5)) + bars_a = ax.bar(x - width / 2, ada_vals, width, label="Adaptive", color="#2196F3", alpha=0.85) + bars_b = ax.bar(x + width / 2, bas_vals, width, label="Baseline (Random)", color="#FF9800", alpha=0.85) + + ax.set_ylabel("Metric Value") + ax.set_title(f"Adaptive vs Baseline — {drug}", fontsize=14, fontweight="bold") + ax.set_xticks(x) + ax.set_xticklabels(labels, rotation=35, ha="right", fontsize=9) + ax.legend(fontsize=11) + ax.grid(axis="y", alpha=0.3) + + # Annotate differences + for i, m in enumerate(metrics): + a, b = ada_vals[i], bas_vals[i] + diff = a - b + if m in LOWER_IS_BETTER: + better = "A" if diff < 0 else "B" + else: + better = "A" if diff > 0 else "B" + color = "#2196F3" if better == "A" else "#FF9800" + sign = "+" if diff > 0 else "" + ax.annotate(f"{sign}{diff:.4f}", xy=(x[i], max(a, b)), + fontsize=7, ha="center", va="bottom", color=color, fontweight="bold") + + fig.tight_layout() + fig.savefig(output_dir / f"per_drug_{drug.replace(' ', '_')}.png", dpi=150) + plt.close(fig) + print(f" Saved per_drug_{drug.replace(' ', '_')}.png") + + +# --------------------------------------------------------------------------- +# Figure 2: Radar / Spider chart for key metrics +# --------------------------------------------------------------------------- + +def plot_radar(ada_row, bas_row, output_dir: Path, drug: str): + """Radar chart comparing the two methods on a single drug.""" + metrics = [ + "pearson_delta", "de_direction_match", "de_spearman_lfc_sig", + "de_sig_genes_recall", "overlap_at_N", "pr_auc", "roc_auc", + ] + # Filter broken + metrics = [m for m in metrics if ada_row[m] != -1.0 and bas_row[m] != -1.0] + if len(metrics) < 3: + return + + labels = [NICE_NAMES.get(m, m) for m in metrics] + ada_vals = [ada_row[m] for m in metrics] + bas_vals = [bas_row[m] for m in metrics] + + angles = np.linspace(0, 2 * np.pi, len(metrics), endpoint=False).tolist() + ada_vals_c = ada_vals + [ada_vals[0]] + bas_vals_c = bas_vals + [bas_vals[0]] + angles_c = angles + [angles[0]] + + fig, ax = plt.subplots(figsize=(7, 7), subplot_kw=dict(polar=True)) + ax.plot(angles_c, ada_vals_c, "o-", linewidth=2, label="Adaptive", color="#2196F3") + ax.fill(angles_c, ada_vals_c, alpha=0.15, color="#2196F3") + ax.plot(angles_c, bas_vals_c, "s-", linewidth=2, label="Baseline", color="#FF9800") + ax.fill(angles_c, bas_vals_c, alpha=0.15, color="#FF9800") + + ax.set_thetagrids(np.degrees(angles), labels, fontsize=9) + ax.set_title(f"Adaptive vs Baseline — {drug}", fontsize=13, fontweight="bold", pad=20) + ax.legend(loc="upper right", bbox_to_anchor=(1.3, 1.1), fontsize=10) + ax.set_ylim(0, 1.05) + + fig.tight_layout() + fig.savefig(output_dir / f"radar_{drug.replace(' ', '_')}.png", dpi=150) + plt.close(fig) + print(f" Saved radar_{drug.replace(' ', '_')}.png") + + +# --------------------------------------------------------------------------- +# Figure 3: Grouped bar chart for all metric groups (mean comparison) +# --------------------------------------------------------------------------- + +def plot_grouped_comparison(comparison_df, output_dir: Path): + """Multi-panel grouped bar chart from the mean comparison.""" + # Filter out skip metrics and trivial ones + comp = comparison_df[~comparison_df["metric"].isin(SKIP_METRICS)].copy() + # Remove metrics where both are exactly equal (e.g. discrimination=1.0) + comp = comp[comp["diff"].abs() > 1e-12] + + if comp.empty: + print(" No non-trivial metric differences to plot.") + return + + metrics = comp["metric"].tolist() + ada_vals = comp["adaptive"].tolist() + bas_vals = comp["baseline"].tolist() + diffs = comp["diff"].tolist() + labels = [NICE_NAMES.get(m, m) for m in metrics] + + x = np.arange(len(metrics)) + width = 0.35 + + fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(14, 8), gridspec_kw={"height_ratios": [3, 1]}) + + # Top: absolute values + ax1.bar(x - width / 2, ada_vals, width, label="Adaptive", color="#2196F3", alpha=0.85) + ax1.bar(x + width / 2, bas_vals, width, label="Baseline", color="#FF9800", alpha=0.85) + ax1.set_ylabel("Metric Value (mean across perturbations)") + ax1.set_title("Adaptive Prompt Selection vs Random Baseline — Mean Comparison", fontsize=13, fontweight="bold") + ax1.set_xticks(x) + ax1.set_xticklabels(labels, rotation=40, ha="right", fontsize=8) + ax1.legend(fontsize=10) + ax1.grid(axis="y", alpha=0.3) + + # Bottom: difference (adaptive - baseline) + colors = [] + for m, d in zip(metrics, diffs): + if m in LOWER_IS_BETTER: + colors.append("#2196F3" if d < 0 else "#FF9800") # lower = adaptive wins + else: + colors.append("#2196F3" if d > 0 else "#FF9800") # higher = adaptive wins + ax2.bar(x, diffs, 0.5, color=colors, alpha=0.85) + ax2.axhline(0, color="black", linewidth=0.8) + ax2.set_ylabel("Diff (Adaptive - Baseline)") + ax2.set_xticks(x) + ax2.set_xticklabels(labels, rotation=40, ha="right", fontsize=8) + ax2.grid(axis="y", alpha=0.3) + + # Color legend for diff chart + from matplotlib.patches import Patch + legend_elements = [ + Patch(facecolor="#2196F3", alpha=0.85, label="Adaptive wins"), + Patch(facecolor="#FF9800", alpha=0.85, label="Baseline wins"), + ] + ax2.legend(handles=legend_elements, fontsize=9, loc="upper right") + + fig.tight_layout() + fig.savefig(output_dir / "comparison_mean_bars.png", dpi=150) + plt.close(fig) + print(" Saved comparison_mean_bars.png") + + +# --------------------------------------------------------------------------- +# Figure 4: Dabrafenib-focused detailed comparison +# --------------------------------------------------------------------------- + +def plot_dabrafenib_detail(ada_df, bas_df, output_dir: Path): + """Detailed comparison for Dabrafenib only (the real test drug).""" + if "Dabrafenib" not in ada_df["perturbation"].values: + print(" No Dabrafenib data found, skipping detail plot.") + return + + ada = ada_df[ada_df["perturbation"] == "Dabrafenib"].iloc[0] + bas = bas_df[bas_df["perturbation"] == "Dabrafenib"].iloc[0] + + # All informative metrics + all_metrics = [] + for group_name, group_metrics in METRIC_GROUPS.items(): + for m in group_metrics: + if m in SKIP_METRICS: + continue + a, b = ada.get(m, None), bas.get(m, None) + if a is None or b is None: + continue + if a == -1.0 and b == -1.0: + continue + all_metrics.append((group_name, m, a, b)) + + if not all_metrics: + return + + fig, ax = plt.subplots(figsize=(10, max(6, len(all_metrics) * 0.4))) + + y_pos = np.arange(len(all_metrics)) + labels = [] + ada_vals = [] + bas_vals = [] + win_colors = [] + + for group, m, a, b in all_metrics: + labels.append(f"[{group}] {NICE_NAMES.get(m, m)}") + ada_vals.append(a) + bas_vals.append(b) + diff = a - b + if m in LOWER_IS_BETTER: + win_colors.append("#2196F3" if diff < 0 else "#FF9800") + else: + win_colors.append("#2196F3" if diff > 0 else "#FF9800") + + # Horizontal bar chart: show absolute values + h = 0.35 + ax.barh(y_pos - h / 2, ada_vals, h, label="Adaptive", color="#2196F3", alpha=0.8) + ax.barh(y_pos + h / 2, bas_vals, h, label="Baseline", color="#FF9800", alpha=0.8) + + ax.set_yticks(y_pos) + ax.set_yticklabels(labels, fontsize=8) + ax.invert_yaxis() + ax.set_xlabel("Metric Value") + ax.set_title("Dabrafenib — Detailed Metric Comparison", fontsize=13, fontweight="bold") + ax.legend(fontsize=10, loc="lower right") + ax.grid(axis="x", alpha=0.3) + + # Annotate diffs on the right + for i, (_, m, a, b) in enumerate(all_metrics): + diff = a - b + sign = "+" if diff > 0 else "" + ax.annotate(f"{sign}{diff:.4f}", xy=(max(a, b) + 0.01, y_pos[i]), + fontsize=7, va="center", color=win_colors[i], fontweight="bold") + + fig.tight_layout() + fig.savefig(output_dir / "dabrafenib_detail.png", dpi=150) + plt.close(fig) + print(" Saved dabrafenib_detail.png") + + +# --------------------------------------------------------------------------- +# Summary table +# --------------------------------------------------------------------------- + +def print_summary_table(ada_df, bas_df): + """Print a text summary of per-drug results.""" + drugs = sorted(set(ada_df["perturbation"]) & set(bas_df["perturbation"])) + + key_metrics = [ + ("pearson_delta", True), # higher is better + ("mse", False), # lower is better + ("mae", False), + ("de_direction_match", True), + ("de_spearman_lfc_sig", True), + ("de_sig_genes_recall", True), + ("overlap_at_N", True), + ("pr_auc", True), + ("roc_auc", True), + ] + + print("\n" + "=" * 80) + print("EXPERIMENT RESULTS SUMMARY") + print("=" * 80) + + for drug in drugs: + ada = ada_df[ada_df["perturbation"] == drug].iloc[0] + bas = bas_df[bas_df["perturbation"] == drug].iloc[0] + + print(f"\n--- {drug} ---") + print(f"{'Metric':<25} {'Adaptive':>12} {'Baseline':>12} {'Diff':>12} {'Winner':>10}") + print("-" * 75) + + a_wins = 0 + b_wins = 0 + for m, higher_better in key_metrics: + a_val, b_val = ada[m], bas[m] + if a_val == -1.0 and b_val == -1.0: + continue + diff = a_val - b_val + if higher_better: + winner = "Adaptive" if diff > 0 else "Baseline" if diff < 0 else "Tie" + else: + winner = "Adaptive" if diff < 0 else "Baseline" if diff > 0 else "Tie" + + if winner == "Adaptive": + a_wins += 1 + elif winner == "Baseline": + b_wins += 1 + + sign = "+" if diff > 0 else "" + print(f"{NICE_NAMES.get(m, m):<25} {a_val:>12.6f} {b_val:>12.6f} {sign}{diff:>11.6f} {winner:>10}") + + print(f"\n Score: Adaptive {a_wins} — Baseline {b_wins}") + + # Overall summary across non-trivial drugs + nontrivial_drugs = [] + for drug in drugs: + ada = ada_df[ada_df["perturbation"] == drug].iloc[0] + # Skip drugs where pearson_delta ≈ 1.0 (trivial) + if ada.get("pearson_delta", 0) < 0.999: + nontrivial_drugs.append(drug) + + if nontrivial_drugs: + print("\n" + "=" * 80) + print(f"NON-TRIVIAL DRUGS ({len(nontrivial_drugs)}): {nontrivial_drugs}") + print("=" * 80) + for m, higher_better in key_metrics: + a_mean = np.mean([ada_df[ada_df["perturbation"] == d].iloc[0][m] for d in nontrivial_drugs]) + b_mean = np.mean([bas_df[bas_df["perturbation"] == d].iloc[0][m] for d in nontrivial_drugs]) + if a_mean == -1.0 and b_mean == -1.0: + continue + diff = a_mean - b_mean + if higher_better: + winner = "Adaptive" if diff > 0 else "Baseline" + else: + winner = "Adaptive" if diff < 0 else "Baseline" + sign = "+" if diff > 0 else "" + print(f" {NICE_NAMES.get(m, m):<25} A={a_mean:.6f} B={b_mean:.6f} diff={sign}{diff:.6f} -> {winner}") + + +# --------------------------------------------------------------------------- +# Main +# --------------------------------------------------------------------------- + +def main(): + parser = argparse.ArgumentParser(description="Visualize B-cell experiment results") + parser.add_argument("--results-dir", required=True, help="Path to bcell_test_results") + parser.add_argument("--output-dir", default=None, help="Output dir for figures (default: results-dir/figures)") + args = parser.parse_args() + + results_dir = Path(args.results_dir) + output_dir = Path(args.output_dir) if args.output_dir else results_dir / "figures" + output_dir.mkdir(parents=True, exist_ok=True) + + print(f"Results dir: {results_dir}") + print(f"Output dir: {output_dir}") + + # Load data + ada_df, bas_df = load_per_pert_results(results_dir) + comparison_df = load_comparison(results_dir) + + # Print text summary + print_summary_table(ada_df, bas_df) + + # Generate figures + print("\nGenerating figures...") + + # Per-drug bar charts + drugs = sorted(set(ada_df["perturbation"]) & set(bas_df["perturbation"])) + for drug in drugs: + plot_per_drug_bars(ada_df, bas_df, output_dir, drug) + ada_row = ada_df[ada_df["perturbation"] == drug].iloc[0] + bas_row = bas_df[bas_df["perturbation"] == drug].iloc[0] + plot_radar(ada_row, bas_row, output_dir, drug) + + # Mean comparison + plot_grouped_comparison(comparison_df, output_dir) + + # Dabrafenib detail + plot_dabrafenib_detail(ada_df, bas_df, output_dir) + + print(f"\nAll figures saved to {output_dir}") + + +if __name__ == "__main__": + main() diff --git a/transfer/code/cell-eval/.github/workflows/CI.yml b/transfer/code/cell-eval/.github/workflows/CI.yml new file mode 100644 index 0000000000000000000000000000000000000000..105b8ccd25c75eb3640ae3ff351f8a312d3976b3 --- /dev/null +++ b/transfer/code/cell-eval/.github/workflows/CI.yml @@ -0,0 +1,97 @@ +name: "CI" + +on: [push, pull_request] + +jobs: + all_jobs: + runs-on: ubuntu-latest + needs: [formatting, pytest, cli-test] + steps: + - name: Complete + run: echo "Complete" + + install-job: + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v4 + + - name: install uv + uses: astral-sh/setup-uv@v5 + with: + enable-cache: true + cache-dependency-glob: "pyproject.toml" + python-version: "3.12" + + - name: install dependencies + run: | + uv sync --all-extras --dev + + formatting: + runs-on: ubuntu-latest + + needs: [install-job] + + steps: + - uses: actions/checkout@v4 + + - name: install uv + uses: astral-sh/setup-uv@v5 + with: + enable-cache: true + cache-dependency-glob: "pyproject.toml" + python-version: "3.12" + + - name: install dependencies + run: | + uv sync --all-extras --dev + + - name: run formatting + run: | + uv run ruff format --check + + pytest: + runs-on: ubuntu-latest + + needs: [install-job] + + steps: + - uses: actions/checkout@v4 + + - name: install uv + uses: astral-sh/setup-uv@v5 + with: + enable-cache: true + cache-dependency-glob: "pyproject.toml" + python-version: "3.12" + + - name: install dependencies + run: | + uv sync --all-extras --dev + + - name: run pytest + run: | + uv run pytest -v + + cli-test: + runs-on: ubuntu-latest + + needs: [install-job] + + steps: + - uses: actions/checkout@v4 + + - name: install uv + uses: astral-sh/setup-uv@v5 + with: + enable-cache: true + cache-dependency-glob: "pyproject.toml" + python-version: "3.12" + + - name: install dependencies + run: | + uv sync --all-extras --dev + + - name: run cli tests + run: | + uv run cell-eval --help diff --git a/transfer/code/cell-eval/.github/workflows/release.yml b/transfer/code/cell-eval/.github/workflows/release.yml new file mode 100644 index 0000000000000000000000000000000000000000..18802c853d39cb2f3ba4e2b4fd935967d222e30c --- /dev/null +++ b/transfer/code/cell-eval/.github/workflows/release.yml @@ -0,0 +1,32 @@ +name: Publish to PyPI + +on: + release: + types: [published] + +jobs: + publish: + runs-on: ubuntu-latest + environment: pypi + permissions: + id-token: write # Required for trusted publishing + + steps: + - uses: actions/checkout@v4 + + - name: Install uv + uses: astral-sh/setup-uv@v4 + with: + version: "latest" + + - name: Set up Python + run: uv python install + + - name: Install build dependencies + run: uv sync --all-extras + + - name: Build the project + run: uv build + + - name: Publish to PyPI + run: uv publish diff --git a/transfer/code/cell-eval/.gitignore b/transfer/code/cell-eval/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..42fb9194ad291a16f4f602a5c2261bb3cf1af2ea --- /dev/null +++ b/transfer/code/cell-eval/.gitignore @@ -0,0 +1,16 @@ +uv.lock +**/__pycache__ +eval_output/ +**/celltype*csv +.DS_Store +.idea/* + + +## Custom State-Eval Ignores +notebooks/* +output/* +*.h5ad +dist/ +cell-eval-outdir/* +TEST_OUTPUT_DIRECTORY/* +**.csv diff --git a/transfer/code/cell-eval/.python-version b/transfer/code/cell-eval/.python-version new file mode 100644 index 0000000000000000000000000000000000000000..e4fba2183587225f216eeada4c78dfab6b2e65f5 --- /dev/null +++ b/transfer/code/cell-eval/.python-version @@ -0,0 +1 @@ +3.12 diff --git a/transfer/code/cell-eval/LICENSE b/transfer/code/cell-eval/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..58bb917460dd2a651519164bc0ce42439f982299 --- /dev/null +++ b/transfer/code/cell-eval/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2025 Arc Research Institute + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/transfer/code/cell-eval/README.md b/transfer/code/cell-eval/README.md new file mode 100644 index 0000000000000000000000000000000000000000..875c2b5bc3acbb1302d27caec044d378c72d3f50 --- /dev/null +++ b/transfer/code/cell-eval/README.md @@ -0,0 +1,124 @@ +# cell-eval + +## Description + +This package provides a comprehensive suite of metrics for evaluating the performance of models that predict cellular responses to perturbations at the single-cell level. It can be used either as a command-line tool or as a Python module. + +## Installation + +Distribution with [`uv`](https://docs.astral.sh/uv/) + +```bash +# install from pypi +uv pip install -U cell-eval + +# install from github directly +uv pip install -U git+https://github.com/arcinstitute/cell-eval + +# install cli with uv tool +uv tool install -U git+https://github.com/arcinstitute/cell-eval + +# Check installation +cell-eval --help +``` + +## Usage + +To get started you'll need to have two anndata files. + +1. a predicted anndata (`adata_pred`). +2. a real anndata to compare against (`adata_real`). + +### Prep (VCC) + +To prepare an anndata for [VCC evaluation](https://virtualcellchallenge.org/) you can use the `cell-eval prep` command. +This will strip the anndata to bare essentials, compress it, adjust naming conventions, and ensure compatibility with the evaluation framework. + +This step is optional for downstream usage, but recommended for optimal performance and compatibility. + +Run this on your predicted anndata: + +```bash +cell-eval prep \ + -i .h5ad \ + -g +``` + +### Run + +To run an evaluation between two anndatas you can use the `cell-eval run` command. + +This will run [differential expression](https://github.com/arcinstitute/pdex) for each anndata and then run a suite of +evaluation metrics to compare the two (select your suite of metrics with the `--profile` flag). + +To save time you can submit precomputed differential expression results, see the `cell-eval run --help` menu for more information. + +```bash +cell-eval run \ + -ap .h5ad \ + -ar .h5ad \ + --num-threads 64 \ + --profile full +``` + +To run this as a python module you will need to use the `MetricsEvaluator` class. + +```python +from cell_eval import MetricsEvaluator +from cell_eval.data import build_random_anndata, downsample_cells + +adata_real = build_random_anndata() +adata_pred = downsample_cells(adata_real, fraction=0.5) +evaluator = MetricsEvaluator( + adata_pred=adata_pred, + adata_real=adata_real, + control_pert="control", + pert_col="perturbation", + num_threads=64, +) +(results, agg_results) = evaluator.compute() +``` + +This will give you metric evaluations for each perturbation individually (`results`) and aggregated results over all perturbations (`agg_results`). + +### Score + +To normalize your scores against a baseline you can run the `cell-eval score` command. + +This accepts two `agg_results.csv` (or `agg_results` objects in python) as input. + +```bash +cell-eval score \ + --user-input /agg_results.csv \ + --base-input /agg_results.csv +``` + +Or from python: + +```python +from cell_eval import score_agg_metrics + +user_input = "./cell-eval-user/agg_results.csv" +base_input = "./cell-eval-base/agg_results.csv" +output_path = "./score.csv" + +score_agg_metrics( + results_user=user_input, + results_base=base_input, + output=output_path, +) +``` + +## Library Design + +The metrics are built using the python registry pattern. This allows for easy extension for new metrics with a well-typed interface. + +Take a look at existing metrics in `cell_eval.metrics` to get started. + +## Development + +This work is open-source and welcomes contributions. Feel free to submit a pull request or open an issue. + +## Citation + +Any publication that uses this source code should cite the [State paper](https://arcinstitute.org/manuscripts/State). diff --git a/transfer/code/cell-eval/config/eval_config.yaml b/transfer/code/cell-eval/config/eval_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ec4eca01261483c4fd8fb31069c3b08dc643e179 --- /dev/null +++ b/transfer/code/cell-eval/config/eval_config.yaml @@ -0,0 +1,9 @@ +include_dist_metrics: true +control_pert: "[('DMSO_TF', 0.0, 'uM')]" +pert_col: "pert_name" +celltype_col: "celltype_name" +output_space: "gene" +shared_perts: null +outdir: "./eval_output/" +de_metric: true +class_score: true diff --git a/transfer/code/cell-eval/config/replogle_config.yaml b/transfer/code/cell-eval/config/replogle_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..456518d01b45453414a6d43fad7cda8a00f100af --- /dev/null +++ b/transfer/code/cell-eval/config/replogle_config.yaml @@ -0,0 +1,9 @@ +include_dist_metrics: true +control_pert: "non-targeting" +pert_col: "pert_name" +celltype_col: "celltype_name" +output_space: "gene" +shared_perts: null +outdir: "./output" +de_metric: true +class_score: true diff --git a/transfer/code/cell-eval/config/tahoe_config.yaml b/transfer/code/cell-eval/config/tahoe_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f3b42ee17bb4f03f66d9232e5632ce2881ed221b --- /dev/null +++ b/transfer/code/cell-eval/config/tahoe_config.yaml @@ -0,0 +1,9 @@ +include_dist_metrics: true +control_pert: "[('DMSO_TF', 0.0, 'uM')]" +pert_col: "pert_name" +celltype_col: "celltype_name" +output_space: "gene" +shared_perts: null +outdir: "/home/yhr/cell-eval/output/" +de_metric: true +class_score: true diff --git a/transfer/code/cell-eval/pyproject.toml b/transfer/code/cell-eval/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..142bed3e6179abc3467c0bec416bb1f2cc38d75d --- /dev/null +++ b/transfer/code/cell-eval/pyproject.toml @@ -0,0 +1,34 @@ +[project] +name = "cell-eval" +version = "0.6.8" +description = "Evaluation metrics for single-cell perturbation predictions" +readme = "README.md" +authors = [ + { name = "Noam Teyssier", email = "noam.teyssier@arcinstitute.org" }, + { name = "Abhinav Adduri", email = "abhinav.adduri@arcinstitute.org" }, + { name = "Yusuf Roohani", email = "yusuf.roohani@arcinstitute.org" }, +] +requires-python = ">=3.10,<3.14" +dependencies = [ + "igraph>=0.11.8", + "pdex>=0.1.26", + "polars>=1.30.0", + "pyyaml>=6.0.2", + "scanpy>=1.10.3", + "pyarrow>=18.0.0", + "tqdm>=4.67.1", +] + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[dependency-groups] +dev = ["ipykernel>=6.29.5", "pytest>=8.3.5", "ruff>=0.11.8"] + +[project.scripts] +cell-eval = "cell_eval.__main__:main" + +[tool.pyright] +venvPath = "." +venv = ".venv" diff --git a/transfer/code/cell-eval/ruff.toml b/transfer/code/cell-eval/ruff.toml new file mode 100644 index 0000000000000000000000000000000000000000..235fc08d50bb76e3662a696c568cf9dac980345d --- /dev/null +++ b/transfer/code/cell-eval/ruff.toml @@ -0,0 +1,5 @@ +[lint] +select = ["E", "F", "ERA"] + +[lint.pycodestyle] +max-line-length = 120 diff --git a/transfer/code/cell-eval/src/cell_eval/__init__.py b/transfer/code/cell-eval/src/cell_eval/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..90d72af7d1955d17e6fef8ecebc95f7d20bcead1 --- /dev/null +++ b/transfer/code/cell-eval/src/cell_eval/__init__.py @@ -0,0 +1,38 @@ +from ._baseline import build_base_mean_adata +from ._evaluator import MetricsEvaluator +from ._pipeline import KNOWN_PROFILES, MetricPipeline +from ._score import score_agg_metrics +from ._types import ( + BulkArrays, + CellArrays, + DEComparison, + DEResults, + DESortBy, + MetricType, + PerturbationAnndataPair, + initialize_de_comparison, +) +from .metrics import metrics_registry + +__all__ = [ + # Evaluation + "MetricsEvaluator", + # Baseline + "build_base_mean_adata", + # Scoring + "score_agg_metrics", + # Types + "DEComparison", + "DEResults", + "DESortBy", + "MetricType", + "PerturbationAnndataPair", + "BulkArrays", + "CellArrays", + "initialize_de_comparison", + # Pipeline + "MetricPipeline", + "KNOWN_PROFILES", + # Global registry + "metrics_registry", +] diff --git a/transfer/code/cell-eval/src/cell_eval/__main__.py b/transfer/code/cell-eval/src/cell_eval/__main__.py new file mode 100644 index 0000000000000000000000000000000000000000..7390b14f163f6485e944e5136f3fed3524568e82 --- /dev/null +++ b/transfer/code/cell-eval/src/cell_eval/__main__.py @@ -0,0 +1,45 @@ +import argparse as ap +import logging + +from ._cli import ( + parse_args_baseline, + parse_args_prep, + parse_args_run, + parse_args_score, + run_baseline, + run_evaluation, + run_prep, + run_score, +) + +logger = logging.getLogger(__name__) + + +def get_args(): + parser = ap.ArgumentParser() + subparsers = parser.add_subparsers(required=True, dest="subcommand") + parse_args_prep(subparsers.add_parser("prep")) + parse_args_run(subparsers.add_parser("run")) + parse_args_baseline(subparsers.add_parser("baseline")) + parse_args_score(subparsers.add_parser("score")) + return parser.parse_args() + + +def main(): + """ + Main function to run the evaluation. + """ + args = get_args() + match args.subcommand: + case "prep": + run_prep(args) + case "run": + run_evaluation(args) + case "baseline": + run_baseline(args) + case "score": + run_score(args) + + +if __name__ == "__main__": + main() diff --git a/transfer/code/cell-eval/src/cell_eval/_baseline.py b/transfer/code/cell-eval/src/cell_eval/_baseline.py new file mode 100644 index 0000000000000000000000000000000000000000..7ad1107b3930dd302a4f909f52410ae7c1d5d491 --- /dev/null +++ b/transfer/code/cell-eval/src/cell_eval/_baseline.py @@ -0,0 +1,196 @@ +import logging +from typing import Any + +import anndata as ad +import numpy as np +import polars as pl +from numpy.typing import NDArray +from pdex import parallel_differential_expression +from scipy.sparse import issparse + +from ._evaluator import _build_pdex_kwargs, _convert_to_normlog + +logger = logging.getLogger(__name__) + + +def build_base_mean_adata( + adata: ad.AnnData | str, + counts_df: pl.DataFrame | str | None = None, + pert_col: str = "target_gene", + control_pert: str = "non-targeting", + counts_col: str = "n_cells", + as_delta: bool = False, + allow_discrete: bool = False, + output_path: str | None = None, + output_de_path: str | None = None, + batch_size: int = 1000, + num_threads: int = 1, + de_method: str = "wilcoxon", + pdex_kwargs: dict[str, Any] = {}, +) -> ad.AnnData: + if isinstance(adata, str): + logger.info(f"Reading adata from path: {adata}") + adata = ad.read_h5ad(adata) + + # Convert to normalized log space if necessary + _convert_to_normlog(adata=adata, allow_discrete=allow_discrete) + + counts = ( + _load_counts_df( + counts_df=counts_df, + pert_col=pert_col, + control_pert=control_pert, + counts_col=counts_col, + ) + if counts_df is not None + else _build_counts_df_from_adata( + adata=adata, + pert_col=pert_col, + control_pert=control_pert, + counts_col=counts_col, + ) + ) + baseline = _build_pert_baseline( + adata=adata, pert_col=pert_col, control_pert=control_pert, as_delta=as_delta + ) + + obs = ( + counts.select([pl.col(pert_col).repeat_by(counts_col)]) + .explode(pert_col) + .to_pandas() + ) + obs.index = obs.index.astype(str).str.replace("^", "p.", regex=True) + + logger.info("Assembling baseline adata from perturbation mean") + baseline_adata = ad.AnnData( + X=np.full( + (int(counts[counts_col].sum()), baseline.size), + baseline, + ), + var=adata.var, + obs=obs, + ) + + logger.info("Concatenating baseline adata with controls from original adata") + baseline_adata = ad.concat( + [baseline_adata, adata[adata.obs[pert_col] == control_pert]] + ) + + if output_path is not None: + logger.info(f"Saving baseline data to {output_path}") + baseline_adata.write_h5ad(output_path) + + if output_de_path is not None: + logger.info("Calculating differential expression") + pdex_kwargs = _build_pdex_kwargs( + groupby_key=pert_col, + reference=control_pert, + num_workers=num_threads, + metric=de_method, + batch_size=batch_size, + allow_discrete=allow_discrete, + pdex_kwargs=pdex_kwargs, + ) + frame = parallel_differential_expression( + adata=baseline_adata, + **pdex_kwargs, + ) + logger.info(f"Saving differential expression results to {output_de_path}") + frame.write_csv(output_de_path) + + return baseline_adata + + +def _load_counts_df( + counts_df: pl.DataFrame | str, + pert_col: str = "target_gene", + control_pert: str = "non-targeting", + counts_col: str = "n_cells", +) -> pl.DataFrame: + if isinstance(counts_df, str): + logger.info(f"Loading counts from {counts_df}") + counts_df = pl.read_csv(counts_df) + + if pert_col not in counts_df.columns: + raise ValueError( + f"Column '{pert_col}' not found in counts_df: {counts_df.columns}" + ) + + if counts_col not in counts_df.columns: + raise ValueError( + f"Column '{counts_col}' not found in counts_df: {counts_df.columns}" + ) + + logger.info(f"Filtering out counts from {control_pert}") + return counts_df.filter( + pl.col(pert_col) != control_pert # drop control pert + ) + + +def _build_counts_df_from_adata( + adata: ad.AnnData, + pert_col: str = "target_gene", + control_pert: str = "non-targeting", + counts_col: str = "n_cells", +) -> pl.DataFrame: + if pert_col not in adata.obs.columns: + raise ValueError( + f"Column '{pert_col}' not found in adata.obs: {adata.obs.columns}" + ) + if control_pert not in adata.obs[pert_col].unique(): + raise ValueError( + f"Control pert '{control_pert}' not found in adata.obs[{pert_col}]: {adata.obs[pert_col].unique()}" + ) + logger.info("Building counts DataFrame from adata") + return ( + pl.DataFrame(adata.obs) + .group_by(pert_col) + .len() + .rename({"len": counts_col}) + .filter(pl.col(pert_col) != control_pert) + ) + + +def _build_pert_baseline( + adata: ad.AnnData, + pert_col: str = "target_gene", + control_pert: str = "non-targeting", + as_delta: bool = False, +) -> NDArray[np.float64]: + if pert_col not in adata.obs.columns: + raise ValueError( + f"Column '{pert_col}' not found in adata.obs: {adata.obs.columns}" + ) + unique_perts = adata.obs[pert_col].unique() + if control_pert not in unique_perts: + raise ValueError( + f"Control pert '{control_pert}' not found in unique_perts: {unique_perts}" + ) + + logger.info("Building perturbation-level means") + pert_means = ( + pl.DataFrame( + adata.X if not issparse(adata.X) else adata.X.toarray() # type: ignore + ) + .with_columns(pl.Series(pert_col, adata.obs[pert_col])) + .group_by(pert_col) + .mean() + ) + + names = pert_means.drop_in_place(pert_col).to_numpy() + pert_mask = names != control_pert + pert_matrix = pert_means.to_numpy() + + if as_delta: + logger.info("Calculating delta from control means") + delta = pert_matrix[pert_mask] - pert_matrix[~pert_mask] + + logger.info("Calculating mean delta") + mean_delta = delta.mean(axis=0) + + return mean_delta + else: + logger.info("Calculating mean of perturbation-level means") + mean_pert = pert_matrix.mean(axis=0) + + return mean_pert diff --git a/transfer/code/cell-eval/src/cell_eval/_cli/__init__.py b/transfer/code/cell-eval/src/cell_eval/_cli/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a03070f29a0c5c80277798c8017904b2bcde0441 --- /dev/null +++ b/transfer/code/cell-eval/src/cell_eval/_cli/__init__.py @@ -0,0 +1,15 @@ +from ._baseline import parse_args_baseline, run_baseline +from ._prep import parse_args_prep, run_prep +from ._run import parse_args_run, run_evaluation +from ._score import parse_args_score, run_score + +__all__ = [ + "parse_args_run", + "run_evaluation", + "parse_args_prep", + "run_prep", + "parse_args_baseline", + "run_baseline", + "parse_args_score", + "run_score", +] diff --git a/transfer/code/cell-eval/src/cell_eval/_cli/_baseline.py b/transfer/code/cell-eval/src/cell_eval/_cli/_baseline.py new file mode 100644 index 0000000000000000000000000000000000000000..a8a22f13aa82eefee67440a5dd123d76adea5e1b --- /dev/null +++ b/transfer/code/cell-eval/src/cell_eval/_cli/_baseline.py @@ -0,0 +1,103 @@ +import argparse as ap +import importlib.metadata +import logging + +from ._const import DEFAULT_COUNTS_COL, DEFAULT_CTRL, DEFAULT_PERT_COL + +logger = logging.getLogger(__name__) + + +def parse_args_baseline(parser: ap.ArgumentParser): + """ + CLI for evaluation + """ + parser.add_argument( + "-a", + "--adata", + type=str, + help="Path to the anndata", + required=True, + ) + parser.add_argument( + "-c", + "--counts", + type=str, + help="Path to the perturbation cell counts", + ) + parser.add_argument( + "-o", + "--output-path", + type=str, + help="Path to save the baseline anndata [default: %(default)s]", + default="./baseline.h5ad", + ) + parser.add_argument( + "-O", + "--output-de-path", + type=str, + help="Path to save the baseline differential expression table [default: %(default)s]", + default="./baseline_de.csv", + ) + parser.add_argument( + "--control-pert", + type=str, + default=DEFAULT_CTRL, + help="Name of the control perturbation [default: %(default)s]", + ) + parser.add_argument( + "--pert-col", + type=str, + default=DEFAULT_PERT_COL, + help="Name of the column designated perturbations [default: %(default)s]", + ) + parser.add_argument( + "--counts-col", + type=str, + default=DEFAULT_COUNTS_COL, + help="Name of the column designated counts in input csv (if provided) [default: %(default)s]", + ) + parser.add_argument( + "-t", + "--num-threads", + type=int, + default=1, + help="Number of threads to use [default: %(default)s]", + ) + parser.add_argument( + "--allow-discrete", + action="store_true", + help="Bypass log normalization in case we incorrectly guess the data is discrete", + ) + parser.add_argument( + "--skip-de", + action="store_true", + help="Whether to skip differential expression analysis", + ) + parser.add_argument( + "--version", + action="version", + version="%(prog)s {version}".format( + version=importlib.metadata.version("cell_eval") + ), + ) + + +def run_baseline(args: ap.Namespace): + from .. import build_base_mean_adata + + pdex_kwargs = { + "clip_value": 2**20, + "is_log1p": True, # Enforces log normalization internally + } + build_base_mean_adata( + adata=args.adata, + counts_df=args.counts, + control_pert=args.control_pert, + pert_col=args.pert_col, + counts_col=args.counts_col, + output_path=args.output_path, + output_de_path=args.output_de_path if not args.skip_de else None, + num_threads=args.num_threads, + allow_discrete=args.allow_discrete, + pdex_kwargs=pdex_kwargs, + ) diff --git a/transfer/code/cell-eval/src/cell_eval/_cli/_const.py b/transfer/code/cell-eval/src/cell_eval/_cli/_const.py new file mode 100644 index 0000000000000000000000000000000000000000..21e4fff3c7e97df897227a31743f3e11937ba638 --- /dev/null +++ b/transfer/code/cell-eval/src/cell_eval/_cli/_const.py @@ -0,0 +1,7 @@ +DEFAULT_PERT_COL = "target_gene" +DEFAULT_CTRL = "non-targeting" +DEFAULT_COUNTS_COL = "n_cells" +DEFAULT_CELLTYPE_COL = "celltype" +DEFAULT_NTC_NAME = "non-targeting" + +DEFAULT_OUTDIR = "./cell-eval-outdir" diff --git a/transfer/code/cell-eval/src/cell_eval/_cli/_prep.py b/transfer/code/cell-eval/src/cell_eval/_cli/_prep.py new file mode 100644 index 0000000000000000000000000000000000000000..aba2e98bd472ff1820102b87977f5f1a1b7637df --- /dev/null +++ b/transfer/code/cell-eval/src/cell_eval/_cli/_prep.py @@ -0,0 +1,290 @@ +import argparse as ap +import importlib.metadata +import logging +import os +import shutil +import subprocess +from tempfile import TemporaryDirectory + +import anndata as ad +import numpy as np +import pandas as pd +import polars as pl +from scipy.sparse import csr_matrix, issparse + +from .._evaluator import _convert_to_normlog +from ._const import DEFAULT_CELLTYPE_COL, DEFAULT_NTC_NAME, DEFAULT_PERT_COL + +logger = logging.getLogger(__name__) + +VALID_ENCODINGS = [64, 32] +EXPECTED_GENE_DIM = 18080 +MAX_CELL_DIM = 200000 + + +def parse_args_prep(parser: ap.ArgumentParser): + parser.add_argument( + "-i", + "--input", + type=str, + help="Path to h5ad to read", + required=True, + ) + parser.add_argument( + "-g", + "--genes", + type=str, + required=True, + help="CSV file containing expected gene names and order", + ) + parser.add_argument( + "-o", + "--output", + type=str, + help="Path to vcc to write [default: .prep.vcc]", + ) + parser.add_argument( + "-p", + "--pert-col", + type=str, + default=DEFAULT_PERT_COL, + help="Name of the column designated perturbations [default: %(default)s]", + ) + parser.add_argument( + "-c", + "--celltype-col", + type=str, + help="Name of the column designated celltype (optional)", + ) + parser.add_argument( + "-n", + "--ntc-name", + type=str, + default=DEFAULT_NTC_NAME, + help="Name of the column designated negative control (optional) [default: %(default)s]", + ) + parser.add_argument( + "-P", + "--output-pert-col", + type=str, + default=DEFAULT_PERT_COL, + help="Name of the column designated perturbations in the output [default: %(default)s]", + ) + parser.add_argument( + "-C", + "--output-celltype-col", + type=str, + default=DEFAULT_CELLTYPE_COL, + help="Name of the column designated celltype in the output [default: %(default)s]", + ) + parser.add_argument( + "-e", + "--encoding", + type=int, + help=f"Bit size to encode ({VALID_ENCODINGS}) [default: %(default)s]", + default=32, + ) + parser.add_argument( + "--allow-discrete", + action="store_true", + help="Bypass log normalization in case we incorrectly guess the data is discrete", + ) + parser.add_argument( + "--expected-gene-dim", + type=int, + help=f"Expected gene dimension ({EXPECTED_GENE_DIM}) [default: %(default)s]. Set to -1 to disable.", + default=EXPECTED_GENE_DIM, + ) + parser.add_argument( + "--max-cell-dim", + type=int, + help=f"Maximum cell dimension ({MAX_CELL_DIM}) [default: %(default)s]. Set to -1 to disable.", + default=MAX_CELL_DIM, + ) + parser.add_argument( + "--version", + action="version", + version="%(prog)s {version}".format( + version=importlib.metadata.version("cell_eval") + ), + ) + + +def strip_anndata( + adata: ad.AnnData, + output_path: str, + genelist: list[str], + pert_col: str = "target_gene", + celltype_col: str | None = None, + output_pert_col: str = DEFAULT_PERT_COL, + output_celltype_col: str = DEFAULT_CELLTYPE_COL, + ntc_name: str = DEFAULT_NTC_NAME, + encoding: int = 64, + allow_discrete: bool = False, + max_cell_dim: int | None = MAX_CELL_DIM, + exp_gene_dim: int | None = EXPECTED_GENE_DIM, +): + # Force anndata var to string + adata.var.index = adata.var.index.astype(str) + + if pert_col not in adata.obs: + raise ValueError( + f"Provided perturbation column: '{pert_col}' missing from anndata: {adata.obs.columns}" + ) + if celltype_col: + if celltype_col not in adata.obs: + raise ValueError( + f"Provided celltype column: '{celltype_col}' missing from anndata: {adata.obs.columns}" + ) + if ntc_name not in adata.obs[pert_col].unique(): + raise ValueError( + f"Provided negative control name: '{ntc_name}' missing from anndata: {adata.obs[pert_col].unique()}" + ) + + # Check if expected dimension is provided and matches the length of the genelist + if exp_gene_dim and len(genelist) != exp_gene_dim: + logger.warning( + f"Provided gene dimension: {len(genelist)} does not match expected gene dimension: {exp_gene_dim}." + ) + logger.info(f"Setting expected gene dimension to {len(genelist)}") + exp_gene_dim = len(genelist) + + if adata.var_names.tolist() != genelist: + missing_genes = set(genelist) - set(adata.var_names.tolist()) + extra_genes = set(adata.var_names.tolist()) - set(genelist) + if len(missing_genes) == 0 and len(extra_genes) == 0: + logger.warning( + "Provided anndata contains all expected genes but they are out of order." + ) + logger.info("Reordering genes...") + adata = adata[:, np.array(genelist)] + else: + raise ValueError( + "Provided gene list does not match anndata gene names:\n" + f"Missing genes: {missing_genes}\n" + f"Extra genes: {extra_genes}" + ) + + if exp_gene_dim and adata.shape[1] != exp_gene_dim: + raise ValueError( + f"Provided gene dimension: {adata.shape[1]} does not match expected gene dimension: {exp_gene_dim}" + ) + + if max_cell_dim and adata.shape[0] > max_cell_dim: + raise ValueError( + f"Provided cell dimension: {adata.shape[0]} exceeds maximum cell dimension: {max_cell_dim}" + ) + + if encoding not in VALID_ENCODINGS: + raise ValueError(f"Encoding must be in {VALID_ENCODINGS}") + + dtype = np.dtype(np.float64) # force bound + match encoding: + case 64: + logger.info("Using 64-bit float encoding") + dtype = np.dtype(np.float64) + case 32: + logger.info("Using 32-bit float encoding") + dtype = np.dtype(np.float32) + + logger.info("Setting data to sparse if not already") + new_x = ( + adata.X.astype(dtype) # type: ignore + if issparse(adata.X) + else csr_matrix(adata.X.astype(dtype)) # type: ignore + ) + + logger.info("Simplifying obs dataframe") + new_obs = pd.DataFrame( + {output_pert_col: adata.obs[pert_col].values}, + index=np.arange(adata.shape[0]).astype(str), + ) + if celltype_col: + new_obs[output_celltype_col] = adata.obs[celltype_col].values + + logger.info("Simplifying var dataframe") + new_var = pd.DataFrame( + index=adata.var.index.values, + ) + + logger.info("Creating final minimal AnnData object") + minimal = ad.AnnData( + X=new_x, + obs=new_obs, + var=new_var, + ) + + logger.info("Applying normlog transformation if required") + _convert_to_normlog(minimal, allow_discrete=allow_discrete) + + # Create a temporary directory to work in + with TemporaryDirectory() as temp_dir: + # Create temp files with specific names + tmp_h5ad = os.path.join(temp_dir, "pred.h5ad") + tmp_watermark = os.path.join(temp_dir, "watermark.txt") + + # Write the h5ad file + logger.info(f"Writing h5ad output to {tmp_h5ad}") + minimal.write_h5ad(tmp_h5ad) + + # Zstd compress the h5ad file (will create pred.h5ad.zst) + logger.info(f"Zstd compressing {tmp_h5ad}") + subprocess.run(["zstd", "-T0", "-f", "--rm", tmp_h5ad]) + + # Write the watermark file + with open(tmp_watermark, "w") as f: + f.write("vcc-prep") + + # Pack the files into a tarball + logger.info(f"Packing files into {output_path}") + subprocess.run( + [ + "tar", + "-cf", + output_path, + "-C", + temp_dir, + "pred.h5ad.zst", + "watermark.txt", + ] + ) + + logger.info("Done") + + +def _validate_tools_in_path(): + if shutil.which("tar") is None: + raise ValueError("tar is not installed") + if shutil.which("zstd") is None: + raise ValueError("zstd is not installed") + return True + + +def run_prep(args: ap.Namespace): + _validate_tools_in_path() + + logger.info("Reading input anndata") + adata = ad.read_h5ad(args.input) + + logger.info("Reading gene list") + genelist = ( + pl.read_csv(args.genes, has_header=False).to_series(0).cast(str).to_list() + ) + + logger.info("Preparing anndata") + strip_anndata( + adata, + genelist=genelist, + output_path=args.output + if args.output + else args.input.replace(".h5ad", ".prep.vcc"), + pert_col=args.pert_col, + celltype_col=args.celltype_col, + output_pert_col=args.output_pert_col, + output_celltype_col=args.output_celltype_col, + ntc_name=args.ntc_name, + encoding=args.encoding, + allow_discrete=args.allow_discrete, + exp_gene_dim=args.expected_gene_dim if args.expected_gene_dim != -1 else None, + max_cell_dim=args.max_cell_dim if args.max_cell_dim != -1 else None, + ) diff --git a/transfer/code/cell-eval/src/cell_eval/_cli/_run.py b/transfer/code/cell-eval/src/cell_eval/_cli/_run.py new file mode 100644 index 0000000000000000000000000000000000000000..d40f541504d9eedcbef86319cea3d32439c3c1b9 --- /dev/null +++ b/transfer/code/cell-eval/src/cell_eval/_cli/_run.py @@ -0,0 +1,204 @@ +import argparse as ap +import importlib.metadata +import logging +import os + +from .. import KNOWN_PROFILES +from ._const import DEFAULT_CTRL, DEFAULT_OUTDIR, DEFAULT_PERT_COL + +logger = logging.getLogger(__name__) + + +def parse_args_run(parser: ap.ArgumentParser): + """ + CLI for evaluation + """ + parser.add_argument( + "-ap", + "--adata-pred", + type=str, + help="Path to the predicted adata object to evaluate", + required=True, + ) + parser.add_argument( + "-ar", + "--adata-real", + type=str, + help="Path to the real adata object to evaluate against", + required=True, + ) + parser.add_argument( + "-dp", + "--de-pred", + type=str, + help="Path to the predicted DE results " + f"(computed with pdex from adata-pred if not provided and saved to {DEFAULT_OUTDIR}/pred_de.csv)", + required=False, + ) + parser.add_argument( + "-dr", + "--de-real", + type=str, + help="Path to the real DE results " + f"(computed with pdex from adata-real if not provided and saved to {DEFAULT_OUTDIR}/real_de.csv)", + required=False, + ) + parser.add_argument( + "--control-pert", + type=str, + default=DEFAULT_CTRL, + help="Name of the control perturbation [default: %(default)s]", + ) + parser.add_argument( + "--pert-col", + type=str, + default=DEFAULT_PERT_COL, + help="Name of the column designated perturbations [default: %(default)s]", + ) + parser.add_argument( + "--celltype-col", + type=str, + help="Name of the column designated celltype to split results by (optional)", + ) + parser.add_argument( + "--embed-key", + type=str, + default=None, + help="Key for embedded data (.obsm) in the AnnData object used in some metrics (evaluated over .X otherwise)", + ) + parser.add_argument( + "-o", + "--outdir", + type=str, + default=DEFAULT_OUTDIR, + help="Output directory to write to [default: %(default)s]", + ) + parser.add_argument( + "--num-threads", + type=int, + default=1, + help="Number of threads to use for parallel processing [default: %(default)s]", + ) + parser.add_argument( + "--batch-size", + type=int, + default=100, + help="Batch size for parallel processing [default: %(default)s]", + ) + parser.add_argument( + "--de-method", + type=str, + default="wilcoxon", + help="Method to use for differential expression analysis [default: %(default)s]", + ) + parser.add_argument( + "--allow-discrete", + action="store_true", + help="Allow discrete data to be evaluated (usually expected to be norm-logged inputs)", + ) + parser.add_argument( + "--profile", + type=str, + default="full", + help="Profile of metrics to compute [default: %(default)s]", + choices=KNOWN_PROFILES, + ) + parser.add_argument( + "--skip-metrics", + type=str, + help="Metrics to skip (comma-separated for multiple) (see docs for more details)", + ) + parser.add_argument( + "--version", + action="version", + version="%(prog)s {version}".format( + version=importlib.metadata.version("cell_eval") + ), + ) + + +def build_outdir(outdir: str): + if os.path.exists(outdir): + logger.warning( + f"Output directory {outdir} already exists, potential overwrite occurring" + ) + os.makedirs(outdir, exist_ok=True) + + +def run_evaluation(args: ap.Namespace): + import anndata as ad + + from cell_eval import MetricsEvaluator + from cell_eval.utils import split_anndata_on_celltype + + # Set metric config for embed key if provided + metric_kwargs = ( + { + "discrimination_score_l2": {"embed_key": args.embed_key}, + "discrimination_score_cosine": {"embed_key": args.embed_key}, + "pearson_edistance": {"n_jobs": args.num_threads}, + } + if args.embed_key is not None + else {} + ) + + skip_metrics = args.skip_metrics.split(",") if args.skip_metrics else None + + if args.celltype_col is not None: + real = ad.read_h5ad(args.adata_real) + pred = ad.read_h5ad(args.adata_pred) + + real_split = split_anndata_on_celltype(real, args.celltype_col) + pred_split = split_anndata_on_celltype(pred, args.celltype_col) + + assert len(real_split) == len(pred_split), ( + f"Number of celltypes in real and pred anndata must match: {len(real_split)} != {len(pred_split)}" + ) + + for ct in real_split.keys(): + real_ct = real_split[ct] + pred_ct = pred_split[ct] + + evaluator = MetricsEvaluator( + adata_pred=pred_ct, + adata_real=real_ct, + de_pred=args.de_pred, + de_real=args.de_real, + control_pert=args.control_pert, + pert_col=args.pert_col, + de_method=args.de_method, + num_threads=args.num_threads, + batch_size=args.batch_size, + outdir=args.outdir, + allow_discrete=args.allow_discrete, + prefix=ct, + skip_de=args.profile == "pds", + ) + evaluator.compute( + profile=args.profile, + metric_configs=metric_kwargs, + skip_metrics=skip_metrics, + basename="results.csv", + ) + + else: + evaluator = MetricsEvaluator( + adata_pred=args.adata_pred, + adata_real=args.adata_real, + de_pred=args.de_pred, + de_real=args.de_real, + control_pert=args.control_pert, + pert_col=args.pert_col, + de_method=args.de_method, + num_threads=args.num_threads, + batch_size=args.batch_size, + outdir=args.outdir, + allow_discrete=args.allow_discrete, + skip_de=args.profile == "pds", + ) + evaluator.compute( + profile=args.profile, + metric_configs=metric_kwargs, + skip_metrics=skip_metrics, + basename="results.csv", + ) diff --git a/transfer/code/cell-eval/src/cell_eval/_cli/_score.py b/transfer/code/cell-eval/src/cell_eval/_cli/_score.py new file mode 100644 index 0000000000000000000000000000000000000000..6d1b8e327e684c773f12409a5c3ed076ca7c7d8d --- /dev/null +++ b/transfer/code/cell-eval/src/cell_eval/_cli/_score.py @@ -0,0 +1,46 @@ +import argparse as ap +import importlib.metadata +import logging + +logger = logging.getLogger(__name__) + + +def parse_args_score(parser: ap.ArgumentParser): + parser.add_argument( + "-i", + "--user-input", + type=str, + help="Path to aggregated results for user model", + required=True, + ) + parser.add_argument( + "-I", + "--base-input", + type=str, + help="Path to aggregated results for base model", + required=True, + ) + parser.add_argument( + "-o", + "--output", + type=str, + default="./baseline_diff.csv", + help="Path to csv to write [default: %(default)s]", + ) + parser.add_argument( + "--version", + action="version", + version="%(prog)s {version}".format( + version=importlib.metadata.version("cell_eval") + ), + ) + + +def run_score(args: ap.Namespace): + from .. import score_agg_metrics + + score_agg_metrics( + results_user=args.user_input, + results_base=args.base_input, + output=args.output, + ) diff --git a/transfer/code/cell-eval/src/cell_eval/_evaluator.py b/transfer/code/cell-eval/src/cell_eval/_evaluator.py new file mode 100644 index 0000000000000000000000000000000000000000..0e86ce8721965d67f43b47de9877c5a6f72e622a --- /dev/null +++ b/transfer/code/cell-eval/src/cell_eval/_evaluator.py @@ -0,0 +1,350 @@ +import logging +import multiprocessing as mp +import os +from typing import Any, Literal + +import anndata as ad +import pandas as pd +import polars as pl +import scanpy as sc +from pdex import parallel_differential_expression + +from cell_eval.utils import guess_is_lognorm + +from ._pipeline import MetricPipeline +from ._types import PerturbationAnndataPair, initialize_de_comparison + +logger = logging.getLogger(__name__) + + +class MetricsEvaluator: + """ + Evaluates benchmarking metrics of a predicted and real anndata object. + + Arguments + ========= + + adata_pred: ad.AnnData | str + Predicted anndata object or path to anndata object. + adata_real: ad.AnnData | str + Real anndata object or path to anndata object. + de_pred: pl.DataFrame | str | None = None + Predicted differential expression results or path to differential expression results. + If `None`, differential expression will be computed using parallel_differential_expression + de_real: pl.DataFrame | str | None = None + Real differential expression results or path to differential expression results. + If `None`, differential expression will be computed using parallel_differential_expression + control_pert: str = "non-targeting" + Control perturbation name. + pert_col: str = "target" + Perturbation column name. + de_method: str = "wilcoxon" + Differential expression method. + num_threads: int = -1 + Number of threads for parallel differential expression. + batch_size: int = 100 + Batch size for parallel differential expression. + outdir: str = "./cell-eval-outdir" + Output directory. + allow_discrete: bool = False + Allow discrete data. + prefix: str | None = None + Prefix for output files. + pdex_kwargs: dict[str, Any] | None = None + Keyword arguments for parallel_differential_expression. + These will overwrite arguments passed to MetricsEvaluator.__init__ if they conflict. + """ + + def __init__( + self, + adata_pred: ad.AnnData | str, + adata_real: ad.AnnData | str, + de_pred: pl.DataFrame | str | None = None, + de_real: pl.DataFrame | str | None = None, + control_pert: str = "non-targeting", + pert_col: str = "target", + de_method: str = "wilcoxon", + num_threads: int = -1, + batch_size: int = 100, + outdir: str = "./cell-eval-outdir", + allow_discrete: bool = False, + prefix: str | None = None, + pdex_kwargs: dict[str, Any] | None = None, + skip_de: bool = False, + ): + # Enable a global string cache for categorical columns + pl.enable_string_cache() + + if os.path.exists(outdir): + logger.warning( + f"Output directory {outdir} already exists, potential overwrite occurring" + ) + os.makedirs(outdir, exist_ok=True) + + self.anndata_pair = _build_anndata_pair( + real=adata_real, + pred=adata_pred, + control_pert=control_pert, + pert_col=pert_col, + allow_discrete=allow_discrete, + ) + + if skip_de: + self.de_comparison = None + else: + self.de_comparison = _build_de_comparison( + anndata_pair=self.anndata_pair, + de_pred=de_pred, + de_real=de_real, + de_method=de_method, + num_threads=num_threads if num_threads != -1 else mp.cpu_count(), + batch_size=batch_size, + allow_discrete=allow_discrete, + outdir=outdir, + prefix=prefix, + pdex_kwargs=pdex_kwargs or {}, + ) + + self.outdir = outdir + self.prefix = prefix + + def compute( + self, + profile: Literal["full", "vcc", "minimal", "de", "anndata"] = "full", + metric_configs: dict[str, dict[str, Any]] | None = None, + skip_metrics: list[str] | None = None, + basename: str = "results.csv", + write_csv: bool = True, + break_on_error: bool = False, + ) -> tuple[pl.DataFrame, pl.DataFrame]: + pipeline = MetricPipeline( + profile=profile, + metric_configs=metric_configs, + break_on_error=break_on_error, + ) + if skip_metrics is not None: + pipeline.skip_metrics(skip_metrics) + pipeline.compute_de_metrics(self.de_comparison) + pipeline.compute_anndata_metrics(self.anndata_pair) + results = pipeline.get_results() + agg_results = pipeline.get_agg_results() + + if write_csv: + if self.prefix is not None: + self.prefix = self.prefix.replace( + "/", "-" + ) # some prefixes (e.g. HepG2/C3A) may have slashes in them + if basename is not None: + basename = basename.replace( + "/", "-" + ) # some basenames (e.g. HepG2/C3A_results.csv) may have slashes in them + outpath = os.path.join( + self.outdir, + f"{self.prefix}_{basename}" if self.prefix else basename, + ) + agg_outpath = os.path.join( + self.outdir, + f"{self.prefix}_agg_{basename}" if self.prefix else f"agg_{basename}", + ) + + logger.info(f"Writing perturbation level metrics to {outpath}") + results.write_csv(outpath) + + logger.info(f"Writing aggregate metrics to {agg_outpath}") + agg_results.write_csv(agg_outpath) + + return results, agg_results + + +def _build_anndata_pair( + real: ad.AnnData | str, + pred: ad.AnnData | str, + control_pert: str, + pert_col: str, + allow_discrete: bool = False, +): + if isinstance(real, str): + logger.info(f"Reading real anndata from {real}") + real = ad.read_h5ad(real) + if isinstance(pred, str): + logger.info(f"Reading pred anndata from {pred}") + pred = ad.read_h5ad(pred) + + # Validate that the input is normalized and log-transformed + _convert_to_normlog(real, which="real", allow_discrete=allow_discrete) + _convert_to_normlog(pred, which="pred", allow_discrete=allow_discrete) + + # Build the anndata pair + return PerturbationAnndataPair( + real=real, pred=pred, control_pert=control_pert, pert_col=pert_col + ) + + +def _convert_to_normlog( + adata: ad.AnnData, + which: str | None = None, + allow_discrete: bool = False, +): + """Performs a norm-log conversion if the input is integer data (inplace). + + Will skip if the input is not integer data. + """ + if guess_is_lognorm(adata=adata, validate=not allow_discrete): + logger.info( + "Input is found to be log-normalized already - skipping transformation." + ) + return # Input is already log-normalized + + # User specified that they want to allow discrete data + if allow_discrete: + if which: + logger.info( + f"Discovered integer data for {which}. Configuration set to allow discrete. " + "Make sure this is intentional." + ) + else: + logger.info( + "Discovered integer data. Configuration set to allow discrete. " + "Make sure this is intentional." + ) + return # proceed without conversion + + # Convert the data to norm-log + if which: + logger.info(f"Discovered integer data for {which}. Converting to norm-log.") + sc.pp.normalize_total(adata=adata, inplace=True) # normalize to median + sc.pp.log1p(adata) # log-transform (log1p) + + +def _build_de_comparison( + anndata_pair: PerturbationAnndataPair | None = None, + de_pred: pl.DataFrame | str | None = None, + de_real: pl.DataFrame | str | None = None, + de_method: str = "wilcoxon", + num_threads: int = 1, + batch_size: int = 100, + allow_discrete: bool = False, + outdir: str | None = None, + prefix: str | None = None, + pdex_kwargs: dict[str, Any] | None = None, +): + return initialize_de_comparison( + real=_load_or_build_de( + mode="real", + de_path=de_real, + anndata_pair=anndata_pair, + de_method=de_method, + num_threads=num_threads, + batch_size=batch_size, + allow_discrete=allow_discrete, + outdir=outdir, + prefix=prefix, + pdex_kwargs=pdex_kwargs or {}, + ), + pred=_load_or_build_de( + mode="pred", + de_path=de_pred, + anndata_pair=anndata_pair, + de_method=de_method, + num_threads=num_threads, + batch_size=batch_size, + allow_discrete=allow_discrete, + outdir=outdir, + prefix=prefix, + pdex_kwargs=pdex_kwargs or {}, + ), + ) + + +def _build_pdex_kwargs( + reference: str, + groupby_key: str, + num_workers: int, + batch_size: int, + metric: str, + allow_discrete: bool, + pdex_kwargs: dict[str, Any] | None = None, +) -> dict[str, Any]: + pdex_kwargs = pdex_kwargs or {} + if "reference" not in pdex_kwargs: + pdex_kwargs["reference"] = reference + if "groupby_key" not in pdex_kwargs: + pdex_kwargs["groupby_key"] = groupby_key + if "num_workers" not in pdex_kwargs: + pdex_kwargs["num_workers"] = num_workers + if "batch_size" not in pdex_kwargs: + pdex_kwargs["batch_size"] = batch_size + if "metric" not in pdex_kwargs: + pdex_kwargs["metric"] = metric + if "is_log1p" not in pdex_kwargs: + if allow_discrete: + pdex_kwargs["is_log1p"] = False + else: + pdex_kwargs["is_log1p"] = True + + # always return polars DataFrames + pdex_kwargs["as_polars"] = True + return pdex_kwargs + + +def _load_or_build_de( + mode: Literal["pred", "real"], + de_path: pl.DataFrame | str | None = None, + anndata_pair: PerturbationAnndataPair | None = None, + de_method: str = "wilcoxon", + num_threads: int = 1, + batch_size: int = 100, + outdir: str | None = None, + prefix: str | None = None, + allow_discrete: bool = False, + pdex_kwargs: dict[str, Any] | None = None, +) -> pl.DataFrame: + if de_path is None: + if anndata_pair is None: + raise ValueError("anndata_pair must be provided if de_path is not provided") + logger.info(f"Computing DE for {mode} data") + pdex_kwargs = _build_pdex_kwargs( + reference=anndata_pair.control_pert, + groupby_key=anndata_pair.pert_col, + num_workers=num_threads, + metric=de_method, + batch_size=batch_size, + allow_discrete=allow_discrete, + pdex_kwargs=pdex_kwargs or {}, + ) + logger.info(f"Using the following pdex kwargs: {pdex_kwargs}") + frame = parallel_differential_expression( + adata=anndata_pair.real if mode == "real" else anndata_pair.pred, + **pdex_kwargs, + ) + if outdir is not None: + if prefix is not None: + prefix = prefix.replace( + "/", "-" + ) # some prefixes (e.g. HepG2/C3A) may have slashes in them + pathname = f"{mode}_de.csv" if not prefix else f"{prefix}_{mode}_de.csv" + logger.info(f"Writing {mode} DE results to: {pathname}") + frame.write_csv(os.path.join(outdir, pathname)) + + return frame # type: ignore + elif isinstance(de_path, str): + logger.info(f"Reading {mode} DE results from {de_path}") + if pdex_kwargs: + logger.warning("pdex_kwargs are ignored when reading from a CSV file") + return pl.read_csv( + de_path, + schema_overrides={ + "target": pl.Utf8, + "feature": pl.Utf8, + }, + ) + elif isinstance(de_path, pl.DataFrame): + if pdex_kwargs: + logger.warning("pdex_kwargs are ignored when reading from a CSV file") + return de_path + elif isinstance(de_path, pd.DataFrame): + if pdex_kwargs: + logger.warning("pdex_kwargs are ignored when reading from a CSV file") + return pl.from_pandas(de_path) + else: + raise TypeError(f"Unexpected type for de_path: {type(de_path)}") diff --git a/transfer/code/cell-eval/src/cell_eval/_pipeline/__init__.py b/transfer/code/cell-eval/src/cell_eval/_pipeline/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b554b92b32cd6bb6ce7e4fed064aa4120cd68b6a --- /dev/null +++ b/transfer/code/cell-eval/src/cell_eval/_pipeline/__init__.py @@ -0,0 +1,3 @@ +from ._runner import KNOWN_PROFILES, MetricPipeline + +__all__ = ["MetricPipeline", "KNOWN_PROFILES"] diff --git a/transfer/code/cell-eval/src/cell_eval/_pipeline/_runner.py b/transfer/code/cell-eval/src/cell_eval/_pipeline/_runner.py new file mode 100644 index 0000000000000000000000000000000000000000..1174a8e892948cb6ff14bc63426da1e95694f3cf --- /dev/null +++ b/transfer/code/cell-eval/src/cell_eval/_pipeline/_runner.py @@ -0,0 +1,238 @@ +import logging +from typing import Any, Callable, Literal + +import polars as pl + +from .._types import DEComparison, MetricBestValue, MetricType, PerturbationAnndataPair +from ..metrics import MetricResult, metrics_registry + +logger = logging.getLogger(__name__) + +MINIMAL_METRICS = [ + "pearson_delta", + "mse", + "mae", + "discrimination_score_l1", + "overlap_at_N", + "precision_at_N", + "de_nsig_counts", +] + +VCC_METRICS = [ + "mae", + "discrimination_score_l1", + "overlap_at_N", +] + +KNOWN_PROFILES = [ + "full", + "minimal", + "vcc", + "de", + "anndata", + "pds", +] + + +class MetricPipeline: + """Pipeline for computing metrics.""" + + def __init__( + self, + profile: Literal["full", "minimal", "vcc", "de", "anndata", "pds"] + | None = "full", + metric_configs: dict[str, dict[str, Any]] | None = None, + break_on_error: bool = False, + ) -> None: + """Initialize pipeline. + + Args: + profile: Which set of metrics to compute ('full', 'de', 'anndata', or None) + metric_configs: Dictionary mapping metric names to their configuration kwargs + break_on_error: Whether to stop the pipeline on error + """ + self._metrics: list[str] = [] + self._results: list[MetricResult] = [] + self._metric_configs = metric_configs or {} + self._break_on_error = break_on_error + self._results_df = None + + match profile: + case "full": + self._metrics.extend(metrics_registry.list_metrics(MetricType.DE)) + self._metrics.extend( + metrics_registry.list_metrics(MetricType.ANNDATA_PAIR) + ) + case "de": + self._metrics.extend(metrics_registry.list_metrics(MetricType.DE)) + case "anndata": + self._metrics.extend( + metrics_registry.list_metrics(MetricType.ANNDATA_PAIR) + ) + case "minimal": + self._metrics.extend(MINIMAL_METRICS) + case "vcc": + self._metrics.extend(VCC_METRICS) + case "pds": + self._metrics.extend(["discrimination_score_l1"]) + case None: + pass + case _: + raise ValueError(f"Unrecognized profile: {profile}") + + # Apply metric configurations + for metric_name, config in self._metric_configs.items(): + if metric_name in metrics_registry.list_metrics(): + metrics_registry.update_metric_kwargs(metric_name, config) + + def add_metrics( + self, metrics: list[str], configs: dict[str, dict[str, Any]] | None = None + ) -> None: + """Add metrics to pipeline. + + Args: + metrics: List of metric names to add + configs: Optional dictionary mapping metric names to their configuration kwargs + """ + self._metrics.extend(metrics) + if configs: + self._metric_configs.update(configs) + for metric_name, config in configs.items(): + if metric_name in metrics_registry.list_metrics(): + metrics_registry.update_metric_kwargs(metric_name, config) + + def add_metric( + self, + name: str, + metric_type: MetricType, + description: str, + func: Callable[ + [PerturbationAnndataPair | DEComparison], float | dict[str, float] + ], + best_value: MetricBestValue, + is_class: bool = False, + kwargs: dict[str, Any] | None = None, + ) -> None: + """Register a new metric and add it to the pipeline. + + Args: + name: Unique name for the metric + metric_type: Type of metric being registered + description: Description of what the metric computes + func: Function to compute the metric + is_class: Whether the metric is a class that needs instantiation + kwargs: Optional keyword arguments for the metric + """ + if name in metrics_registry.list_metrics(): + logger.warning( + f"Metric '{name}' already registered, skipping re-registering" + ) + return + # Register the metric with the registry + metrics_registry.register( + name=name, + metric_type=metric_type, + description=description, + func=func, + is_class=is_class, + best_value=best_value, + kwargs=kwargs, + ) + # Add it to the pipeline's metrics list + self._metrics.append(name) + # Add any kwargs to the metric configs + if kwargs: + self._metric_configs[name] = kwargs + + def skip_metrics( + self, + to_skip: list[str] | str, + ): + if isinstance(to_skip, str): + to_skip = [to_skip] + self._metrics = [metric for metric in self._metrics if metric not in to_skip] + + def _compute_metric( + self, + name: str, + data: DEComparison | PerturbationAnndataPair, + ): + """Compute a specific metric.""" + try: + logger.info(f"Computing metric '{name}'") + # Get any runtime config for this metric + runtime_config = self._metric_configs.get(name, {}) + value = metrics_registry.compute(name, data, kwargs=runtime_config) + if isinstance(value, dict): + # Add each perturbation result separately + for pert, pert_value in value.items(): + # if the return value is a dictionary add the sub-metric + # as a separate result + if isinstance(pert_value, dict): + for sub_name, value in pert_value.items(): + self._results.append( + MetricResult( + name=f"{name}_{sub_name}", + value=value, + perturbation=pert, + ) + ) + else: + self._results.append( + MetricResult( + name=name, + value=pert_value, + perturbation=pert, + ) + ) + else: + # Add single result to all perturbations + for pert in data.get_perts(include_control=False): + self._results.append( + MetricResult( + name=name, + value=value, + perturbation=pert, + ) + ) + except Exception as error: + logger.error(f"Error computing metric '{name}': {error}") + if self._break_on_error: + raise error + + def compute_de_metrics(self, data: DEComparison | None) -> None: + """Compute DE metrics.""" + for name in self._metrics: + if name not in metrics_registry.list_metrics(MetricType.DE): + continue + if data is None: + continue + self._compute_metric(name, data) + + def compute_anndata_metrics( + self, + data: PerturbationAnndataPair, + ) -> None: + """Compute perturbation metrics.""" + for name in self._metrics: + if name not in metrics_registry.list_metrics(MetricType.ANNDATA_PAIR): + continue + self._compute_metric(name, data) + + def get_results(self) -> pl.DataFrame: + """Get results as a DataFrame.""" + if self._results_df is None: + self._results_df = ( + pl.DataFrame() + if not self._results + else pl.DataFrame([r.to_dict() for r in self._results]).pivot( + index="perturbation", + on="metric", + values="value", + ) + ) + return self._results_df + + def get_agg_results(self) -> pl.DataFrame: + """Get aggregated results as a DataFrame.""" + return self.get_results().drop("perturbation").describe() diff --git a/transfer/code/cell-eval/src/cell_eval/_score.py b/transfer/code/cell-eval/src/cell_eval/_score.py new file mode 100644 index 0000000000000000000000000000000000000000..150e2a2e1658d91374f326a838e8d0e5ff345da0 --- /dev/null +++ b/transfer/code/cell-eval/src/cell_eval/_score.py @@ -0,0 +1,124 @@ +import logging + +import numpy as np +import polars as pl +from numpy.typing import NDArray + +from ._types import MetricBestValue +from .metrics import metrics_registry + +logger = logging.getLogger(__name__) + + +def score_agg_metrics( + results_user: pl.DataFrame | str, + results_base: pl.DataFrame | str, + output: str | None = None, + comparison_statistic: str = "mean", +) -> pl.DataFrame: + """Scores the aggregated results between a user's model and a base model. + + Files are expected to be in CSV format. + + Expects the aggregated results to be in the format output by the `get_agg_results` method (i.e. pl.describe). + """ + if isinstance(results_user, str): + logger.info(f"Reading user results from {results_user}") + results_user = pl.read_csv(results_user) + if isinstance(results_base, str): + logger.info(f"Reading base results from {results_base}") + results_base = pl.read_csv(results_base) + + if results_user.columns != results_base.columns: + raise ValueError( + f"Columns do not match: {results_user.columns} != {results_base.columns}" + ) + if "statistic" not in results_user.columns: + raise ValueError( + "Missing 'statistic' column in agg results (likely wrong file input)" + ) + + # Determine the statistic to be compared + comp_stats = results_user["statistic"].unique() + if comparison_statistic not in comp_stats: + raise ValueError( + f"Comparison statistic '{comparison_statistic}' not found in results: {comp_stats}" + ) + + # Determine the necessary normalization strategy for each metric + idx_norm_by_one = [] + idx_norm_by_zero = [] + metric_names = np.array(results_user.columns[1:]) + for idx, m in enumerate(metric_names): + try: + info = metrics_registry.get_metric(m) + norm_type = info.best_value + if norm_type == MetricBestValue.ZERO: + idx_norm_by_zero.append(idx) + elif norm_type == MetricBestValue.ONE: + idx_norm_by_one.append(idx) + except KeyError: + logger.warning(f"Metric '{m}' not found in registry (skipping index {idx})") + idx_norm_by_zero = np.array(idx_norm_by_zero) + idx_norm_by_one = np.array(idx_norm_by_one) + + # Determine the row index for the comparison statistic + u_row_idx = np.flatnonzero(results_user["statistic"] == comparison_statistic)[0] + b_row_idx = np.flatnonzero(results_base["statistic"] == comparison_statistic)[0] + scores_user = results_user.drop("statistic").to_numpy()[u_row_idx] + scores_base = results_base.drop("statistic").to_numpy()[b_row_idx] + logger.info(f"user score: {scores_user}") + logger.info(f"base score: {scores_base}") + + metrics_by_zero = metric_names[idx_norm_by_zero] + logger.info( + f"Calculating norm by zero for {len(metrics_by_zero)} metrics: {', '.join(metrics_by_zero)}" + ) + norm_by_zero = _calc_norm_by_zero( + scores_user[idx_norm_by_zero], + scores_base[idx_norm_by_zero], + ) + + metrics_by_one = metric_names[idx_norm_by_one] + logger.info( + f"Calculating norm by one for {len(metrics_by_one)} metrics: {', '.join(metrics_by_one)}" + ) + norm_by_one = _calc_norm_by_one( + scores_user[idx_norm_by_one], + scores_base[idx_norm_by_one], + ) + + # Concatenate the two norm modes, replace nan, and clip lower bound to zero + all_scores = np.concatenate([norm_by_zero, norm_by_one]) + all_scores[np.isnan(all_scores)] = 0.0 + all_scores = np.clip(all_scores, 0, None) + + # Build output dataframe + results = pl.DataFrame( + { + "metric": np.concatenate( + [metrics_by_zero, metrics_by_one, np.array(["avg_score"])] + ), + "from_baseline": np.concatenate([all_scores, [all_scores.mean()]]), + } + ) + + if output is not None: + logger.info(f"Writing results to {output}") + results.write_csv(output) + + return results + + +def _calc_norm_by_zero( + user: NDArray[np.float64], + base: NDArray[np.float64], +) -> NDArray[np.float64]: + return 1.0 - (user / base) + + +def _calc_norm_by_one( + user: NDArray[np.float64], + base: NDArray[np.float64], +) -> NDArray[np.float64]: + return (user - base) / (1 - base) diff --git a/transfer/code/cell-eval/src/cell_eval/_types/__init__.py b/transfer/code/cell-eval/src/cell_eval/_types/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..262e0342d066e0ec7eeaef06167c25304e24be82 --- /dev/null +++ b/transfer/code/cell-eval/src/cell_eval/_types/__init__.py @@ -0,0 +1,15 @@ +from ._anndata import BulkArrays, CellArrays, PerturbationAnndataPair +from ._de import DEComparison, DEResults, initialize_de_comparison +from ._enums import DESortBy, MetricBestValue, MetricType + +__all__ = [ + "DESortBy", + "MetricBestValue", + "MetricType", + "DEComparison", + "DEResults", + "initialize_de_comparison", + "PerturbationAnndataPair", + "BulkArrays", + "CellArrays", +] diff --git a/transfer/code/cell-eval/src/cell_eval/_types/_anndata.py b/transfer/code/cell-eval/src/cell_eval/_types/_anndata.py new file mode 100644 index 0000000000000000000000000000000000000000..c8a1bfd1cf02de9690da29b883117e225de218b0 --- /dev/null +++ b/transfer/code/cell-eval/src/cell_eval/_types/_anndata.py @@ -0,0 +1,295 @@ +import logging +from dataclasses import dataclass, field +from typing import Iterator, Literal + +import anndata as ad +import numpy as np +import polars as pl +from numpy.typing import NDArray +from scipy.sparse import issparse +from tqdm import tqdm + +logger = logging.getLogger(__name__) + + +@dataclass(frozen=True) +class PerturbationAnndataPair: + """Pair of AnnData objects with perturbation information.""" + + real: ad.AnnData + pred: ad.AnnData + pert_col: str + control_pert: str + embed_key: str | None = None + perts: NDArray[np.str_] = field(init=False) + genes: NDArray[np.str_] = field(init=False) + + # Masks of indices for each perturbation + pert_mask_real: dict[str, np.ndarray] = field(init=False) + pert_mask_pred: dict[str, np.ndarray] = field(init=False) + + # Bulk anndata by embedding key and perturbation + bulk_real: dict[str, tuple[NDArray[np.str_], NDArray[np.float64]]] | None = field( + init=False + ) + bulk_pred: dict[str, tuple[NDArray[np.str_], NDArray[np.float64]]] | None = field( + init=False + ) + + def __post_init__(self) -> None: + if self.real.shape[1] != self.pred.shape[1]: + raise ValueError( + f"Shape mismatch: real {self.real.shape[1]} != pred {self.pred.shape[1]}" + " Expected to be the same number of genes" + ) + + if self.embed_key: + if self.embed_key not in self.real.obsm: + raise ValueError( + f"Embed key {self.embed_key} not found in real AnnData" + ) + if self.embed_key not in self.pred.obsm: + raise ValueError( + f"Embed key {self.embed_key} not found in pred AnnData" + ) + + var_names_real = np.array(self.real.var.index.values) + var_names_pred = np.array(self.pred.var.index.values) + if not np.array_equal(var_names_real, var_names_pred): + raise ValueError( + f"Gene names order mismatch: real {var_names_real} != pred {var_names_pred}" + ) + object.__setattr__(self, "genes", var_names_real) + + if self.pert_col not in self.real.obs.columns: + raise ValueError( + f"Perturbation column ({self.pert_col}) not found in real AnnData: {self.real.obs.columns}" + ) + if self.pert_col not in self.pred.obs.columns: + raise ValueError( + f"Perturbation column ({self.pert_col}) not found in pred AnnData: {self.pred.obs.columns}" + ) + + perts_real = np.unique(self.real.obs[self.pert_col].to_numpy(str)) + perts_pred = np.unique(self.pred.obs[self.pert_col].to_numpy(str)) + if not np.array_equal(perts_real, perts_pred): + raise ValueError( + f"Perturbation mismatch: real {perts_real} != pred {perts_pred}" + ) + + if self.control_pert not in perts_real: + raise ValueError( + f"Control perturbation ({self.control_pert}) not found in real AnnData: {perts_real}" + ) + if self.control_pert not in perts_pred: + raise ValueError( + f"Control perturbation ({self.control_pert}) not found in pred AnnData: {perts_pred}" + ) + + perts = np.union1d(perts_real, perts_pred) + perts = np.array([p for p in perts if p != self.control_pert]) + + pert_mask_real = self.pert_mask( + self.real.obs[self.pert_col].to_numpy(str), + ) + pert_mask_pred = self.pert_mask( + self.pred.obs[self.pert_col].to_numpy(str), + ) + + object.__setattr__(self, "perts", perts) + object.__setattr__(self, "pert_mask_real", pert_mask_real) + object.__setattr__(self, "pert_mask_pred", pert_mask_pred) + + # Initialize bulk anndata + object.__setattr__(self, "bulk_real", {}) + object.__setattr__(self, "bulk_pred", {}) + + @staticmethod + def _bulk_anndata( + adata: ad.AnnData, + groupby_key: str, + embed_key: str | None = None, + ) -> tuple[NDArray[np.str_], NDArray[np.float64]]: + """Get bulk anndata for a groupby key.""" + + matrix = adata.X if not embed_key else adata.obsm[embed_key] + if issparse(matrix): + # Convert sparse matrix to dense array + logger.info("Converting sparse matrix to dense array for bulk calculation") + matrix = matrix.toarray() # type: ignore + + matrix = np.asarray(matrix, dtype=np.float64) + + # Use numpy for groupby-mean (avoids polars crash with wide DataFrames) + groups = adata.obs[groupby_key].to_numpy(str) + unique_groups = np.unique(groups) + unique_groups.sort() + + bulk_values = np.zeros((len(unique_groups), matrix.shape[1]), dtype=np.float64) + for i, g in enumerate(unique_groups): + mask = groups == g + bulk_values[i] = matrix[mask].mean(axis=0) + + return (unique_groups, bulk_values) + + @staticmethod + def pert_mask(perts: NDArray[np.str_]) -> dict[str, NDArray[np.int_]]: + unique_perts, inverse = np.unique(perts, return_inverse=True) + return {pert: np.where(inverse == i)[0] for i, pert in enumerate(unique_perts)} + + def get_perts(self, include_control: bool = False) -> NDArray[np.str_]: + """Get all perturbations.""" + if include_control: + return self.perts + return self.perts[self.perts != self.control_pert] + + def _initialize_bulk_arrays(self, embed_key: str | None = None): + """Initialize bulk arrays if necessary (memoized)""" + if embed_key is None: + embed_key = "_default" + rebuilt = False + assert self.bulk_real is not None + if embed_key not in self.bulk_real: + logger.info( + "Building pseudobulk embeddings for real anndata on: {}".format( + embed_key if embed_key != "_default" else ".X" + ) + ) + self.bulk_real[embed_key] = self._bulk_anndata( + self.real, + self.pert_col, + embed_key=embed_key if embed_key != "_default" else None, + ) + rebuilt = True + + assert self.bulk_pred is not None + if embed_key not in self.bulk_pred: + logger.info( + "Building pseudobulk embeddings for predicted anndata on: {}".format( + embed_key if embed_key != "_default" else ".X" + ) + ) + self.bulk_pred[embed_key] = self._bulk_anndata( + self.pred, + self.pert_col, + embed_key=embed_key if embed_key != "_default" else None, + ) + rebuilt = True + + if rebuilt: + real_id = self.bulk_real[embed_key][0] + pred_id = self.bulk_pred[embed_key][0] + if not np.array_equal(real_id, pred_id): + raise ValueError( + f"Real and predicted embeddings are missing perturbations for {embed_key}" + ) + if self.control_pert not in real_id: + raise ValueError( + f"Control perturbation {self.control_pert} is missing in embeddings for {embed_key}" + ) + + def build_bulk_array(self, pert: str, embed_key: str | None = None) -> "BulkArrays": + """Build bulk array for a perturbation.""" + if not embed_key: + embed_key = "_default" + + assert self.bulk_real is not None, "Bulk real data is missing" + assert self.bulk_pred is not None, "Bulk pred data is missing" + + # Get the perturbation indices + pert_pos = np.flatnonzero(self.bulk_real[embed_key][0] == pert)[0] + ctrl_pos = np.flatnonzero(self.bulk_real[embed_key][0] == self.control_pert)[0] + return BulkArrays( + key=pert, + pert_real=self.bulk_real[embed_key][1][pert_pos], + pert_pred=self.bulk_pred[embed_key][1][pert_pos], + ctrl_real=self.bulk_real[embed_key][1][ctrl_pos], + ctrl_pred=self.bulk_pred[embed_key][1][ctrl_pos], + ) + + def build_cell_array(self, pert: str, embed_key: str | None = None) -> "CellArrays": + """Build cell array for a perturbation.""" + + if not embed_key: + matrix_real = self.real.X + matrix_pred = self.pred.X + else: + matrix_real = self.real.obsm[embed_key] + matrix_pred = self.pred.obsm[embed_key] + + pert_pos_real = self.pert_mask_real[pert] + pert_pos_pred = self.pert_mask_pred[pert] + ctrl_pos_real = self.pert_mask_real[self.control_pert] + ctrl_pos_pred = self.pert_mask_pred[self.control_pert] + + return CellArrays( + key=pert, + pert_real=matrix_real[pert_pos_real], # type: ignore + pert_pred=matrix_pred[pert_pos_pred], # type: ignore + ctrl_real=matrix_real[ctrl_pos_real], # type: ignore + ctrl_pred=matrix_pred[ctrl_pos_pred], # type: ignore + ) + + def iter_bulk_arrays(self, embed_key: str | None = None) -> Iterator["BulkArrays"]: + """Iterate over bulk arrays for all perturbations.""" + self._initialize_bulk_arrays(embed_key) + for pert in tqdm(self.perts, desc="Iterating over perturbations..."): + yield self.build_bulk_array(pert, embed_key=embed_key) + + def iter_cell_arrays(self, embed_key: str | None = None) -> Iterator["CellArrays"]: + """Iterate over subarrays of cells for all perturbations""" + for pert in tqdm(self.perts, desc="Iterating over perturbations..."): + yield self.build_cell_array(pert, embed_key=embed_key) + + def ctrl_matrix( + self, which: Literal["real", "pred"], embed_key: str | None = None + ) -> np.ndarray: + """Build a CellArrays object for the control perturbation.""" + if not embed_key: + matrix = self.real.X if which == "real" else self.pred.X + else: + matrix = ( + self.real.obsm[embed_key] + if which == "real" + else self.pred.obsm[embed_key] + ) + mask_lookup = self.pert_mask_real if which == "real" else self.pert_mask_pred + return matrix[mask_lookup[self.control_pert]] # type: ignore + + +@dataclass(frozen=True) +class BulkArrays: + """Arrays of bulk results for a perturbation.""" + + key: str + pert_real: np.ndarray + pert_pred: np.ndarray + ctrl_real: np.ndarray + ctrl_pred: np.ndarray + + def perturbation_effect( + self, + which: Literal["real", "pred"], + abs: bool = False, + ) -> np.ndarray: + match which: + case "real": + effect = self.pert_real - self.ctrl_real + case "pred": + effect = self.pert_pred - self.ctrl_pred + case _: + raise ValueError(f"Invalid value for `which`: {which}") + if abs: + effect = np.abs(effect) + return effect + + +@dataclass(frozen=True) +class CellArrays: + """Arrays of single-cell results for a perturbation.""" + + key: str + pert_real: np.ndarray + pert_pred: np.ndarray + ctrl_real: np.ndarray + ctrl_pred: np.ndarray diff --git a/transfer/code/cell-eval/src/cell_eval/_types/_de.py b/transfer/code/cell-eval/src/cell_eval/_types/_de.py new file mode 100644 index 0000000000000000000000000000000000000000..72d94501e576224bb6973a4e3dd79e55f4038512 --- /dev/null +++ b/transfer/code/cell-eval/src/cell_eval/_types/_de.py @@ -0,0 +1,278 @@ +import logging +from dataclasses import dataclass, field +from functools import partial +from typing import Iterator, Literal + +import numpy as np +import polars as pl +from numpy.typing import NDArray + +from ._enums import DESortBy + +logger = logging.getLogger(__name__) + + +def initialize_de_comparison( + real: pl.DataFrame, + pred: pl.DataFrame, + target_col: str = "target", + feature_col: str = "feature", + fold_change_col: str = "fold_change", + log2_fold_change_col: str = "log2_fold_change", + abs_log2_fold_change_col: str = "abs_log2_fold_change", + pvalue_col: str = "p_value", + fdr_col: str = "fdr", +) -> "DEComparison": + partial_de_result = partial( + DEResults, + target_col=target_col, + feature_col=feature_col, + fold_change_col=fold_change_col, + log2_fold_change_col=log2_fold_change_col, + abs_log2_fold_change_col=abs_log2_fold_change_col, + ) + with pl.StringCache(): + return DEComparison( + real=partial_de_result(real, name="real"), + pred=partial_de_result(pred, name="pred"), + ) + + +@dataclass(frozen=False) +class DEResults: + """Raw differential expression results with sorting and filtering capabilities.""" + + data: pl.DataFrame + + # Column names configuration + target_col: str = "target" + feature_col: str = "feature" + fold_change_col: str = "fold_change" + log2_fold_change_col: str = "log2_fold_change" + abs_log2_fold_change_col: str = "abs_log2_fold_change" + pvalue_col: str = "p_value" + fdr_col: str = "fdr" + name: str = "de" + + def __post_init__(self) -> None: + required_cols = { + self.target_col, + self.feature_col, + self.fold_change_col, + self.pvalue_col, + self.fdr_col, + } + missing = required_cols - set(self.data.columns) + if missing: + raise ValueError(f"Missing required columns: {missing}") + + numeric_cols = [ + self.fold_change_col, + self.pvalue_col, + self.fdr_col, + self.log2_fold_change_col, + self.abs_log2_fold_change_col, + ] + + categorical_cols = [ + self.target_col, + self.feature_col, + ] + + logger.info(f"Checking DE data integrity... ({self.name})") + fc_num_null = self.data.filter(pl.col(self.fold_change_col).is_null()).height + fc_num_inf = self.data.filter(pl.col(self.fold_change_col).is_infinite()).height + fc_num_nan = self.data.filter(pl.col(self.fold_change_col).is_nan()).height + if fc_num_null > 0: + logger.warning( + f"Identified {fc_num_null} missing fold change values ({self.name})" + ) + if fc_num_inf > 0: + logger.warning( + f"Identified {fc_num_inf} infinite fold change values ({self.name})" + ) + if fc_num_nan > 0: + logger.warning( + f"Identified {fc_num_nan} NaN fold change values ({self.name})" + ) + logger.info(f"DE data integrity check complete. ({self.name})") + + # Add log2 fold change columns if not present + if self.log2_fold_change_col not in self.data.columns: + self.data = self.data.with_columns( + pl.col(self.fold_change_col) + .log(base=2) + .alias(self.log2_fold_change_col) + .fill_nan(0.0) + ).with_columns( + pl.col(self.log2_fold_change_col) + .abs() + .alias(self.abs_log2_fold_change_col) + ) + + # Enforce types + self.data = self.data.with_columns( + [pl.col(c).cast(pl.Float32) for c in numeric_cols] + + [pl.col(c).cast(pl.Categorical) for c in categorical_cols] + ).drop( + [c for c in self.data.columns if c not in numeric_cols + categorical_cols] + ) + + def get_perts(self) -> NDArray[np.str_]: + """Get perturbations.""" + perts = self.data[self.target_col].unique().to_numpy() + perts.sort() + return perts + + def get_significant_genes( + self, pert: str, fdr_threshold: float = 0.05 + ) -> NDArray[np.str_]: + """Get significant genes for a perturbation.""" + return ( + self.data.filter( + (pl.col(self.target_col) == pert) + & (pl.col(self.fdr_col) < fdr_threshold) + ) + .select(self.feature_col) + .to_numpy() + ) + + def filter_to_significant( + self, + fdr_threshold: float = 0.05, + ) -> pl.DataFrame: + """Filter DE results to significant genes.""" + return self.data.filter(pl.col(self.fdr_col) < fdr_threshold) + + def get_top_genes( + self, + sort_by: DESortBy, + fdr_threshold: float | None = None, + ) -> pl.DataFrame: + """Get top genes per perturbation, optionally filtered by FDR.""" + # Set FDR threshold if not provided + fdr_threshold = fdr_threshold if fdr_threshold is not None else 0.05 + + descending = sort_by in {DESortBy.FOLD_CHANGE, DESortBy.ABS_FOLD_CHANGE} + + # Create a rank matrix where each row is the ordinal rank of a gene and each column is a perturbation. + # The rank is sensitive to the sort-by column and is computed post-filtering for FDR. + rank_matrix = ( + self.data.filter(pl.col(self.fdr_col) < fdr_threshold) + .with_columns( + rank=pl.struct(sort_by.value) + .rank("ordinal", descending=descending) + .over("target") + - 1 + ) + .pivot( + index="rank", + on="target", + values="feature", + ) + .sort("rank") + ) + + # Add perturbations that are missing from the rank matrix (no significant genes) + missing_perts = set(self.get_perts()) - set(rank_matrix.columns) + if missing_perts: + rank_matrix = rank_matrix.with_columns( + [pl.lit(None).alias(p) for p in missing_perts] + ) + + return rank_matrix + + +@dataclass +class DEComparison: + """Comparison between real and predicted DE results.""" + + real: DEResults + pred: DEResults + + perturbations: NDArray[np.str_] = field(init=False) + n_perts: int = field(init=False) + + def __post_init__(self) -> None: + real_perts = self.real.get_perts() + pred_perts = self.pred.get_perts() + if not np.array_equal(real_perts, pred_perts): + raise ValueError( + f"Perturbation mismatch: real {real_perts} != pred {pred_perts}" + ) + + object.__setattr__(self, "perturbations", list(real_perts)) + object.__setattr__(self, "n_perts", len(real_perts)) + + def iter_perturbations(self) -> Iterator[str]: + for pert in self.perturbations: + yield pert + + def get_perts(self, include_control: bool = False) -> NDArray[np.str_]: + """Get perturbations.""" + if include_control: + logger.warning("DEComparison should not include control perturbation") + return self.perturbations + + def compute_overlap( + self, + k: int | None, + metric: Literal["overlap", "precision"] = "overlap", + fdr_threshold: float | None = None, + sort_by: DESortBy = DESortBy.ABS_FOLD_CHANGE, + ) -> dict[str, float]: + """ + Compute overlap metrics across perturbations. + + Args: + k: If specified, use top k genes from real data + fdr_threshold: If specified, only consider genes below this FDR + sort_by: Metric to sort genes by + + Returns: + Dictionary mapping perturbation names to overlap scores + """ + real_sig_rank_matrix = self.real.get_top_genes( + sort_by=sort_by, fdr_threshold=fdr_threshold + ) + pred_sig_rank_matrix = self.pred.get_top_genes( + sort_by=sort_by, fdr_threshold=fdr_threshold + ) + + if real_sig_rank_matrix.shape[0] == 0 or pred_sig_rank_matrix.shape[0] == 0: + # No significant genes in either real or predicted DE. + # Cannot evaluate in this case so setting all perturbations to 0.0 + return {pert: 0.0 for pert in self.iter_perturbations()} + + overlaps = {} + for pert in self.iter_perturbations(): + # If perturbation is not in either real or pred, set overlap to 0.0 + if ( + pert not in real_sig_rank_matrix.columns + or pert not in pred_sig_rank_matrix.columns + ): + overlaps[pert] = 0.0 + continue + + # Get sorted gene lists + real_genes = real_sig_rank_matrix[pert].drop_nulls().to_numpy() + pred_genes = pred_sig_rank_matrix[pert].drop_nulls().to_numpy() + + match metric: + case "overlap": + k_eff = real_genes.size if not k else k + k_eff = min(k_eff, real_genes.size) + case "precision": + k_eff = pred_genes.size if not k else k + k_eff = min(k_eff, pred_genes.size) + case _: + raise ValueError(f"Invalid metric: {metric}") + + if k_eff == 0: + overlaps[pert] = 0.0 + else: + real_subset = real_genes[:k_eff] + pred_subset = pred_genes[:k_eff] + overlaps[pert] = np.intersect1d(real_subset, pred_subset).size / k_eff + + return overlaps diff --git a/transfer/code/cell-eval/src/cell_eval/_types/_enums.py b/transfer/code/cell-eval/src/cell_eval/_types/_enums.py new file mode 100644 index 0000000000000000000000000000000000000000..2d72a6b0729bc88ec583da244b2fef707420e783 --- /dev/null +++ b/transfer/code/cell-eval/src/cell_eval/_types/_enums.py @@ -0,0 +1,32 @@ +import enum + + +class DESortBy(enum.Enum): + """Sorting options for differential expression results.""" + + FOLD_CHANGE = "log2_fold_change" + ABS_FOLD_CHANGE = "abs_log2_fold_change" + PVALUE = "p_value" + FDR = "fdr" + + +class MetricType(enum.Enum): + """Types of metrics supported by the registry.""" + + DE = "de" + ANNDATA_PAIR = "anndata_pair" + + +class MetricBestValue(enum.Enum): + """How to comparse this metric w.r.t the baseline. + + Design your metrics to be bounded between 0 and 1. + + If the metric's best value is zero, set this to `ZERO` + If the metric's best value is one, set this to `ONE` + If the metric should not be compared to baseline, set this to `NONE` + """ + + ZERO = "zero" + ONE = "one" + NONE = "none" diff --git a/transfer/code/cell-eval/src/cell_eval/data.py b/transfer/code/cell-eval/src/cell_eval/data.py new file mode 100644 index 0000000000000000000000000000000000000000..5c9c691d979b802150a5c27cf190efb791976300 --- /dev/null +++ b/transfer/code/cell-eval/src/cell_eval/data.py @@ -0,0 +1,81 @@ +import anndata as ad +import numpy as np +import pandas as pd +from scipy.sparse import csr_matrix + +PERT_COL = "perturbation" +CELLTYPE_COL = "celltype" +CONTROL_VAR = "control" + +N_CELLS = 1000 +N_GENES = 100 +N_PERTS = 10 +N_CELLTYPES = 3 +MAX_UMI = 1e6 +NORM_TOTAL = 1e4 + +RANDOM_SEED = 42 + +OUTDIR = "TEST_OUTPUT_DIRECTORY" + + +def build_random_anndata( + n_cells: int = N_CELLS, + n_genes: int = N_GENES, + n_perts: int = N_PERTS, + n_celltypes: int = N_CELLTYPES, + pert_col: str = PERT_COL, + celltype_col: str = CELLTYPE_COL, + control_var: str = CONTROL_VAR, + random_state: int = RANDOM_SEED, + as_sparse: bool = False, + normlog: bool = True, + normtotal: int | float = NORM_TOTAL, +) -> ad.AnnData: + """Sample a random AnnData object.""" + if random_state is not None: + np.random.seed(random_state) + + # Randomly sample a matrix + matrix = np.random.randint(0, int(MAX_UMI), size=(n_cells, n_genes)) + + # Normalize and log transform if required + if normlog: + matrix = matrix / matrix.sum(axis=1, keepdims=True) * normtotal + matrix = np.log1p(matrix) + + # Convert to sparse if required + if as_sparse: + matrix = csr_matrix(matrix) + + return ad.AnnData( + X=matrix, + obs=pd.DataFrame( + { + pert_col: np.random.choice( + [f"pert_{i}" for i in range(n_perts)] + [control_var], + size=n_cells, + replace=True, + ), + celltype_col: np.random.choice( + [f"celltype_{i}" for i in range(n_celltypes)], + size=n_cells, + replace=True, + ), + }, + index=np.arange(n_cells).astype(str), + ), + ) + + +def downsample_cells( + adata: ad.AnnData, + fraction: float = 0.5, +) -> ad.AnnData: + """Downsample cells in an AnnData object. + + Copies the output to avoid memory overlaps. + """ + assert 0 <= fraction <= 1, "Fraction must be between 0 and 1" + mask = np.random.rand(adata.shape[0]) < fraction + return adata[mask, :].copy() diff --git a/transfer/code/cell-eval/src/cell_eval/metrics/__init__.py b/transfer/code/cell-eval/src/cell_eval/metrics/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..350a365bc7578d93688096299777651f932c55c5 --- /dev/null +++ b/transfer/code/cell-eval/src/cell_eval/metrics/__init__.py @@ -0,0 +1,49 @@ +"""Metrics package for evaluating cell perturbation predictions.""" + +from ._anndata import ( + ClusteringAgreement, + discrimination_score, + mae, + mae_delta, + mse, + mse_delta, + pearson_delta, +) +from ._de import ( + DEDirectionMatch, + DENsigCounts, + DESigGenesRecall, + DESpearmanLFC, + DESpearmanSignificant, + compute_pr_auc, + compute_roc_auc, + de_overlap_metric, +) +from ._impl import metrics_registry +from .base import Metric, MetricInfo, MetricResult + +__all__ = [ + # Array metrics + "ClusteringAgreement", + "pearson_delta", + "mse", + "mae", + "mse_delta", + "mae_delta", + "discrimination_score", + # DE metrics + "DEDirectionMatch", + "DESpearmanSignificant", + "de_overlap_metric", + "DESpearmanLFC", + "compute_pr_auc", + "compute_roc_auc", + "DESigGenesRecall", + "DENsigCounts", + # Global registry + "metrics_registry", + # Base Classes + "Metric", + "MetricResult", + "MetricInfo", +] diff --git a/transfer/code/cell-eval/src/cell_eval/metrics/_anndata.py b/transfer/code/cell-eval/src/cell_eval/metrics/_anndata.py new file mode 100644 index 0000000000000000000000000000000000000000..8bcdf7d7b583e7db522b55b3ad25f76150ff4ef0 --- /dev/null +++ b/transfer/code/cell-eval/src/cell_eval/metrics/_anndata.py @@ -0,0 +1,345 @@ +"""Array metrics module.""" + +from logging import getLogger +from typing import Callable, Literal, Sequence + +import anndata as ad +import numpy as np +import pandas as pd +import scanpy as sc +import sklearn.metrics as skm +from scipy.sparse import issparse +from scipy.stats import pearsonr +from sklearn.metrics import ( + adjusted_mutual_info_score, + adjusted_rand_score, + normalized_mutual_info_score, +) + +from .._types import PerturbationAnndataPair + +logger = getLogger(__name__) + + +def pearson_delta( + data: PerturbationAnndataPair, embed_key: str | None = None +) -> dict[str, float]: + """Compute Pearson correlation between mean differences from control.""" + return _generic_evaluation( + data, + pearsonr, # type: ignore + use_delta=True, + embed_key=embed_key, + ) + + +def mse( + data: PerturbationAnndataPair, embed_key: str | None = None +) -> dict[str, float]: + """Compute mean squared error of each perturbation from control.""" + return _generic_evaluation( + data, skm.mean_squared_error, use_delta=False, embed_key=embed_key + ) + + +def mae( + data: PerturbationAnndataPair, embed_key: str | None = None +) -> dict[str, float]: + """Compute mean absolute error of each perturbation from control.""" + return _generic_evaluation( + data, skm.mean_absolute_error, use_delta=False, embed_key=embed_key + ) + + +def mse_delta( + data: PerturbationAnndataPair, embed_key: str | None = None +) -> dict[str, float]: + """Compute mean squared error of each perturbation-control delta.""" + return _generic_evaluation( + data, skm.mean_squared_error, use_delta=True, embed_key=embed_key + ) + + +def mae_delta( + data: PerturbationAnndataPair, embed_key: str | None = None +) -> dict[str, float]: + """Compute mean absolute error of each perturbation-control delta.""" + return _generic_evaluation( + data, skm.mean_absolute_error, use_delta=True, embed_key=embed_key + ) + + +def edistance( + data: PerturbationAnndataPair, + embed_key: str | None = None, + metric: str = "euclidean", + **kwargs, +) -> float: + """Compute Euclidean distance of each perturbation-control delta.""" + + def _edistance( + x: np.ndarray, + y: np.ndarray, + metric: str = "euclidean", + precomp_sigma_y: float | None = None, + **kwargs, + ) -> float: + sigma_x = skm.pairwise_distances(x, metric=metric, **kwargs).mean() + sigma_y = ( + precomp_sigma_y + if precomp_sigma_y is not None + else skm.pairwise_distances(y, metric=metric, **kwargs).mean() + ) + delta = skm.pairwise_distances(x, y, metric=metric, **kwargs).mean() + return 2 * delta - sigma_x - sigma_y + + d_real = np.zeros(data.perts.size) + d_pred = np.zeros(data.perts.size) + + # Precompute sigma for control data (reused by all perturbations) + logger.info("Precomputing sigma for control data (real)") + precomp_sigma_real = skm.pairwise_distances( + data.ctrl_matrix(which="real", embed_key=embed_key), metric=metric, **kwargs + ).mean() + + logger.info("Precomputing sigma for control data (pred)") + precomp_sigma_pred = skm.pairwise_distances( + data.ctrl_matrix(which="pred", embed_key=embed_key), metric=metric, **kwargs + ).mean() + + for idx, delta in enumerate(data.iter_cell_arrays(embed_key=embed_key)): + d_real[idx] = _edistance( + delta.pert_real, + delta.ctrl_real, + precomp_sigma_y=precomp_sigma_real, + metric=metric, + **kwargs, + ) + d_pred[idx] = _edistance( + delta.pert_pred, + delta.ctrl_pred, + precomp_sigma_y=precomp_sigma_pred, + metric=metric, + **kwargs, + ) + + return pearsonr(d_real, d_pred).correlation + + +def discrimination_score( + data: PerturbationAnndataPair, + metric: str = "l1", + embed_key: str | None = None, + exclude_target_gene: bool = True, +) -> dict[str, float]: + """Base implementation for discrimination score computation. + + Best score is 1.0 - worst score is 0.0. + + Args: + data: PerturbationAnndataPair containing real and predicted data + embed_key: Key for embedding data in obsm, None for expression data + metric: Metric for distance calculation (e.g., "l1", "l2", see `scipy.metrics.pairwise.distance_metrics`) + exclude_target_gene: Whether to exclude target gene from calculation + + Returns: + Dictionary mapping perturbation names to normalized ranks + """ + if metric == "l1" or metric == "manhattan" or metric == "cityblock": + # Ignore the embedding key for L1 + embed_key = None + + # Compute perturbation effects for all perturbations + real_effects = np.vstack( + [ + d.perturbation_effect(which="real", abs=False) + for d in data.iter_bulk_arrays(embed_key=embed_key) + ] + ) + pred_effects = np.vstack( + [ + d.perturbation_effect(which="pred", abs=False) + for d in data.iter_bulk_arrays(embed_key=embed_key) + ] + ) + + norm_ranks = {} + for p_idx, p in enumerate(data.perts): + # Determine which features to include in the comparison + if exclude_target_gene and not embed_key: + # For expression data, exclude the target gene + include_mask = np.flatnonzero(data.genes != p) + else: + # For embedding data or when not excluding target gene, use all features + include_mask = np.ones(real_effects.shape[1], dtype=bool) + + # Compute distances to all real effects + distances = skm.pairwise_distances( + real_effects[ + :, include_mask + ], # compare to all real effects across perturbations + pred_effects[p_idx, include_mask].reshape( + 1, -1 + ), # select pred effect for current perturbation + metric=metric, + ).flatten() + + # Sort by distance (ascending - lower distance = better match) + sorted_indices = np.argsort(distances) + + # Find rank of the correct perturbation + p_index = np.flatnonzero(data.perts == p)[0] + rank = np.flatnonzero(sorted_indices == p_index)[0] + + # Normalize rank by total number of perturbations + norm_rank = rank / data.perts.size + norm_ranks[str(p)] = 1 - norm_rank + + return norm_ranks + + +def _generic_evaluation( + data: PerturbationAnndataPair, + func: Callable[[np.ndarray, np.ndarray], float], + use_delta: bool = False, + embed_key: str | None = None, +) -> dict[str, float]: + """Generic evaluation function for anndata pair.""" + res = {} + for bulk_array in data.iter_bulk_arrays(embed_key=embed_key): + if use_delta: + x = bulk_array.perturbation_effect(which="pred", abs=False) + y = bulk_array.perturbation_effect(which="real", abs=False) + else: + x = bulk_array.pert_pred + y = bulk_array.pert_real + + result = func(x, y) + if isinstance(result, tuple): + result = result[0] + + res[bulk_array.key] = float(result) + + return res + + +# TODO: clean up this implementation +class ClusteringAgreement: + """Compute clustering agreement between real and predicted perturbation centroids.""" + + def __init__( + self, + embed_key: str | None = None, + real_resolution: float = 1.0, + pred_resolutions: tuple[float, ...] = (0.2, 0.4, 0.6, 0.8, 1.0, 1.5, 2.0), + metric: Literal["ami", "nmi", "ari"] = "ami", + n_neighbors: int = 15, + ) -> None: + self.embed_key = embed_key + self.real_resolution = real_resolution + self.pred_resolutions = pred_resolutions + self.metric = metric + self.n_neighbors = n_neighbors + + @staticmethod + def _score( + labels_real: Sequence[int], + labels_pred: Sequence[int], + metric: Literal["ami", "nmi", "ari"], + ) -> float: + if metric == "ami": + return adjusted_mutual_info_score(labels_real, labels_pred) + if metric == "nmi": + return normalized_mutual_info_score(labels_real, labels_pred) + if metric == "ari": + return (adjusted_rand_score(labels_real, labels_pred) + 1) / 2 + raise ValueError(f"Unknown metric: {metric}") + + @staticmethod + def _cluster_leiden( + adata: ad.AnnData, + resolution: float, + key_added: str, + n_neighbors: int = 15, + ) -> None: + if key_added in adata.obs: + return + if "neighbors" not in adata.uns: + sc.pp.neighbors( + adata, n_neighbors=min(n_neighbors, adata.n_obs - 1), use_rep="X" + ) + sc.tl.leiden( + adata, + resolution=resolution, + key_added=key_added, + flavor="igraph", + n_iterations=2, + ) + + @staticmethod + def _centroid_ann( + adata: ad.AnnData, + category_key: str, + control_pert: str, + embed_key: str | None = None, + ) -> ad.AnnData: + # Isolate the features + feats = adata.obsm.get(embed_key, adata.X) # type: ignore + + # Convert to float if not already + if feats.dtype != np.dtype("float64"): # type: ignore + feats = feats.astype(np.float64) # type: ignore + + # Densify if required + if issparse(feats): + feats = feats.toarray() # type: ignore + + cats = adata.obs[category_key].values + uniq, inv = np.unique(cats, return_inverse=True) # type: ignore + centroids = np.zeros((uniq.size, feats.shape[1]), dtype=feats.dtype) # type: ignore + + for i, cat in enumerate(uniq): + mask = cats == cat + if np.any(mask): + centroids[i] = feats[mask].mean(axis=0) # type: ignore + + adc = ad.AnnData(X=centroids) + adc.obs[category_key] = uniq + return adc[adc.obs[category_key] != control_pert] + + def __call__(self, data: PerturbationAnndataPair) -> float: + cats_sorted = sorted([c for c in data.perts if c != data.control_pert]) + + # 2. build centroids + ad_real_cent = self._centroid_ann( + adata=data.real, + category_key=data.pert_col, + control_pert=data.control_pert, + embed_key=self.embed_key, + ) + ad_pred_cent = self._centroid_ann( + adata=data.pred, + category_key=data.pert_col, + control_pert=data.control_pert, + embed_key=self.embed_key, + ) + + # 3. cluster real once + real_key = "real_clusters" + self._cluster_leiden( + ad_real_cent, self.real_resolution, real_key, self.n_neighbors + ) + ad_real_cent.obs = ad_real_cent.obs.set_index(data.pert_col).loc[cats_sorted] + real_labels = pd.Categorical(ad_real_cent.obs[real_key]) + + # 4. sweep predicted resolutions + best_score = 0.0 + ad_pred_cent.obs = ad_pred_cent.obs.set_index(data.pert_col).loc[cats_sorted] + for r in self.pred_resolutions: + pred_key = f"pred_clusters_{r}" + self._cluster_leiden(ad_pred_cent, r, pred_key, self.n_neighbors) + pred_labels = pd.Categorical(ad_pred_cent.obs[pred_key]) + score = self._score(real_labels, pred_labels, self.metric) # type: ignore + best_score = max(best_score, score) + + return float(best_score) diff --git a/transfer/code/cell-eval/src/cell_eval/metrics/_de.py b/transfer/code/cell-eval/src/cell_eval/metrics/_de.py new file mode 100644 index 0000000000000000000000000000000000000000..1f2ca00b95a8106536e1bb201f5b99b8164d63f8 --- /dev/null +++ b/transfer/code/cell-eval/src/cell_eval/metrics/_de.py @@ -0,0 +1,263 @@ +"""DE metrics module.""" + +from typing import Literal + +import polars as pl +from sklearn.metrics import auc, average_precision_score, roc_curve + +from .._types import DEComparison, DESortBy + + +def de_overlap_metric( + data: DEComparison, + k: int | None, + metric: Literal["precision", "overlap"] = "overlap", + fdr_threshold: float = 0.05, + sort_by: DESortBy = DESortBy.ABS_FOLD_CHANGE, +) -> dict[str, float]: + """Compute overlap between real and predicted DE genes. + + Note: use `k` argument for measuring recall and use `topk` argument for measuring precision. + + """ + return data.compute_overlap( + k=k, + metric=metric, + fdr_threshold=fdr_threshold, + sort_by=sort_by, + ) + + +class DESpearmanSignificant: + """Compute Spearman correlation on number of significant DE genes.""" + + def __init__(self, fdr_threshold: float = 0.05) -> None: + self.fdr_threshold = fdr_threshold + + def __call__(self, data: DEComparison) -> float: + """Compute correlation between number of significant genes in real and predicted DE.""" + + filt_real = ( + data.real.filter_to_significant(fdr_threshold=self.fdr_threshold) + .group_by(data.real.target_col) + .len() + ) + filt_pred = ( + data.pred.filter_to_significant(fdr_threshold=self.fdr_threshold) + .group_by(data.pred.target_col) + .len() + ) + + merged = filt_real.join( + filt_pred, + left_on=data.real.target_col, + right_on=data.pred.target_col, + suffix="_pred", + how="left", + coalesce=True, + ).fill_null(0) + + # No significant genes in either real or predicted DE. Set to 1.0 since perfect + # agreement but will fail spearman test + if merged.shape[0] == 0: + return 1.0 + + return float( + merged.select( + pl.corr( + pl.col("len"), + pl.col("len_pred"), + method="spearman", + ).alias("spearman_corr_nsig") + ) + .to_numpy() + .flatten()[0] + ) + + +class DEDirectionMatch: + """Compute agreement in direction of DE gene changes.""" + + def __init__(self, fdr_threshold: float = 0.05) -> None: + self.fdr_threshold = fdr_threshold + + def __call__(self, data: DEComparison) -> dict[str, float]: + """Compute directional agreement between real and predicted DE genes.""" + matches = {} + + merged = data.real.filter_to_significant(fdr_threshold=self.fdr_threshold).join( + data.pred.data, + on=[data.real.target_col, data.real.feature_col], + suffix="_pred", + how="inner", + ) + for row in ( + merged.with_columns( + direction_match=pl.col(data.real.log2_fold_change_col).sign() + == pl.col(f"{data.real.log2_fold_change_col}_pred").sign() + ) + .group_by( + data.real.target_col, + ) + .agg(pl.mean("direction_match")) + .iter_rows() + ): + matches.update({row[0]: row[1]}) + return matches + + +class DESpearmanLFC: + """Compute Spearman correlation on log fold changes of significant genes.""" + + def __init__(self, fdr_threshold: float = 0.05) -> None: + self.fdr_threshold = fdr_threshold + + def __call__(self, data: DEComparison) -> dict[str, float]: + """Compute correlation between log fold changes of significant genes.""" + correlations = {} + + merged = data.real.filter_to_significant(fdr_threshold=self.fdr_threshold).join( + data.pred.data, + on=[data.real.target_col, data.real.feature_col], + suffix="_pred", + how="inner", + ) + + for row in ( + merged.group_by( + data.real.target_col, + ) + .agg( + pl.corr( + pl.col(data.real.fold_change_col).cast(pl.Float64), + pl.col(f"{data.real.fold_change_col}_pred").cast(pl.Float64), + method="spearman", + ).alias("spearman_corr"), + ) + .iter_rows() + ): + correlations.update({row[0]: row[1]}) + + return correlations + + +class DESigGenesRecall: + """Compute recall of significant genes.""" + + def __init__(self, fdr_threshold: float = 0.05) -> None: + self.fdr_threshold = fdr_threshold + + def __call__(self, data: DEComparison) -> dict[str, float]: + """Compute recall of significant genes between real and predicted DE.""" + + filt_real = data.real.filter_to_significant(fdr_threshold=self.fdr_threshold) + filt_pred = data.pred.filter_to_significant(fdr_threshold=self.fdr_threshold) + + recall_frame = ( + filt_real.join( + filt_pred, + on=[data.real.target_col, data.real.feature_col], + how="inner", + coalesce=True, + ) + .group_by(data.real.target_col) + .len() + .join( + filt_real.group_by(data.real.target_col).len(), + on=data.real.target_col, + how="full", + suffix="_expected", + coalesce=True, + ) + .fill_null(0) + .with_columns(recall=pl.col("len") / pl.col("len_expected")) + .select([data.real.target_col, "recall"]) + ) + + return {row[0]: row[1] for row in recall_frame.iter_rows()} + + +class DENsigCounts: + """Compute counts of significant genes.""" + + def __init__(self, fdr_threshold: float = 0.05) -> None: + self.fdr_threshold = fdr_threshold + + def __call__(self, data: DEComparison) -> dict[str, dict[str, int]]: + """Compute counts of significant genes in real and predicted DE.""" + counts = {} + + for pert in data.iter_perturbations(): + real_sig = data.real.get_significant_genes(pert, self.fdr_threshold) + pred_sig = data.pred.get_significant_genes(pert, self.fdr_threshold) + + counts[pert] = { + "real": int(real_sig.size), + "pred": int(pred_sig.size), + } + + return counts + + +def compute_pr_auc(data: DEComparison) -> dict[str, float]: + """Compute precision-recall AUC per perturbation for significant recovery.""" + return compute_generic_auc(data, method="pr") + + +def compute_roc_auc(data: DEComparison) -> dict[str, float]: + """Compute ROC AUC per perturbation for significant recovery.""" + return compute_generic_auc(data, method="roc") + + +def compute_generic_auc( + data: DEComparison, + method: Literal["pr", "roc"] = "pr", +) -> dict[str, float]: + """Compute AUC values for significant recovery per perturbation.""" + + target_col = data.real.target_col + feature_col = data.real.feature_col + real_fdr_col = data.real.fdr_col + pred_fdr_col = data.pred.fdr_col + + labeled_real = data.real.data.with_columns( + (pl.col(real_fdr_col) < 0.05).cast(pl.Float32).alias("label") + ).select([target_col, feature_col, "label"]) + + merged = ( + data.pred.data.select([target_col, feature_col, pred_fdr_col]) + .join( + labeled_real, + on=[target_col, feature_col], + how="inner", + coalesce=True, + ) + .drop_nulls(["label"]) + .with_columns((-pl.col(pred_fdr_col).replace(0, 1e-10).log10()).alias("nlp")) + .drop_nulls(["nlp"]) + ) + + results: dict[str, float] = {} + for pert in data.iter_perturbations(): + pert_data = merged.filter(pl.col(target_col) == pert) + if pert_data.shape[0] == 0: + results[pert] = float("nan") + continue + + labels = pert_data["label"].to_numpy() + scores = pert_data["nlp"].to_numpy() + + if not (0 < labels.sum() < len(labels)): + results[pert] = float("nan") + continue + + match method: + case "pr": + results[pert] = float(average_precision_score(labels, scores)) + case "roc": + fpr, tpr, _ = roc_curve(labels, scores) + results[pert] = float(auc(fpr, tpr)) + case _: + raise ValueError(f"Invalid AUC method: {method}") + + return results diff --git a/transfer/code/cell-eval/src/cell_eval/metrics/_impl.py b/transfer/code/cell-eval/src/cell_eval/metrics/_impl.py new file mode 100644 index 0000000000000000000000000000000000000000..667f08af4a743d9b8968ff023250ce49982fdf30 --- /dev/null +++ b/transfer/code/cell-eval/src/cell_eval/metrics/_impl.py @@ -0,0 +1,165 @@ +from .._types import MetricBestValue, MetricType +from ._anndata import ( + ClusteringAgreement, + discrimination_score, + edistance, + mae, + mae_delta, + mse, + mse_delta, + pearson_delta, +) +from ._de import ( + DEDirectionMatch, + DENsigCounts, + DESigGenesRecall, + DESpearmanLFC, + DESpearmanSignificant, + compute_pr_auc, + compute_roc_auc, + de_overlap_metric, +) +from ._registry import MetricRegistry + +metrics_registry = MetricRegistry() + +metrics_registry.register( + name="pearson_delta", + metric_type=MetricType.ANNDATA_PAIR, + description="Pearson correlation between mean differences from control", + best_value=MetricBestValue.ONE, + func=pearson_delta, +) + +metrics_registry.register( + name="mse", + metric_type=MetricType.ANNDATA_PAIR, + description="Mean squared error of each perturbation from control.", + best_value=MetricBestValue.ZERO, + func=mse, +) +metrics_registry.register( + name="mae", + metric_type=MetricType.ANNDATA_PAIR, + description="Mean absolute error of each perturbation from control.", + best_value=MetricBestValue.ZERO, + func=mae, +) + +metrics_registry.register( + name="mse_delta", + metric_type=MetricType.ANNDATA_PAIR, + description="Mean squared error of each perturbation-control delta.", + best_value=MetricBestValue.ZERO, + func=mse_delta, +) + +metrics_registry.register( + name="mae_delta", + metric_type=MetricType.ANNDATA_PAIR, + description="Mean squared error of each perturbation-control delta.", + best_value=MetricBestValue.ZERO, + func=mae_delta, +) + +for distance_metric in ["l1", "l2", "cosine"]: + metrics_registry.register( + name=f"discrimination_score_{distance_metric}", + metric_type=MetricType.ANNDATA_PAIR, + description=f"Determines similarity of each pred representation to real via normalized rank: {distance_metric}", + best_value=MetricBestValue.ONE, + func=discrimination_score, + kwargs={"metric": distance_metric}, + ) + +metrics_registry.register( + name="pearson_edistance", + metric_type=MetricType.ANNDATA_PAIR, + best_value=MetricBestValue.ONE, + description="Calculates the pearson correlation coefficient between all pred and real edistance from controls", + func=edistance, +) + + +for metric in ["overlap", "precision"]: + for n in [None, 50, 100, 200, 500]: + repr = n if n else "N" + metrics_registry.register( + name=f"{metric}_at_{repr}", + metric_type=MetricType.DE, + description=f"Overlap metric ({metric}) of top {repr} DE genes", + best_value=MetricBestValue.ONE, + func=de_overlap_metric, + kwargs={"k": n, "metric": metric}, + ) + + +metrics_registry.register( + name="de_spearman_sig", + metric_type=MetricType.DE, + description="Spearman correlation on number of significant DE genes", + best_value=MetricBestValue.ONE, + func=DESpearmanSignificant, # type: ignore + is_class=True, +) + +metrics_registry.register( + name="de_direction_match", + metric_type=MetricType.DE, + description="Agreement in direction of DE gene changes", + best_value=MetricBestValue.ONE, + func=DEDirectionMatch, # type: ignore + is_class=True, +) + +metrics_registry.register( + name="de_spearman_lfc_sig", + metric_type=MetricType.DE, + description="Spearman correlation on log fold changes of significant genes", + best_value=MetricBestValue.ONE, + func=DESpearmanLFC, # type: ignore + is_class=True, +) + +metrics_registry.register( + name="de_sig_genes_recall", + metric_type=MetricType.DE, + description="Recall of significant genes", + best_value=MetricBestValue.ONE, + func=DESigGenesRecall, # type: ignore + is_class=True, +) + +metrics_registry.register( + name="de_nsig_counts", + metric_type=MetricType.DE, + description="Counts of significant genes", + best_value=MetricBestValue.NONE, + func=DENsigCounts, # type: ignore + is_class=True, +) + +metrics_registry.register( + name="pr_auc", + metric_type=MetricType.DE, + description="Computes precision-recall for significant recovery", + best_value=MetricBestValue.ONE, + func=compute_pr_auc, +) + +metrics_registry.register( + name="roc_auc", + metric_type=MetricType.DE, + description="Computes ROC AUC for significant recovery", + best_value=MetricBestValue.ONE, + func=compute_roc_auc, +) + +metrics_registry.register( + name="clustering_agreement", + metric_type=MetricType.ANNDATA_PAIR, + description="Clustering agreement between real and predicted perturbation centroids", + best_value=MetricBestValue.ONE, + func=ClusteringAgreement, # type: ignore + is_class=True, +) diff --git a/transfer/code/cell-eval/src/cell_eval/metrics/_registry.py b/transfer/code/cell-eval/src/cell_eval/metrics/_registry.py new file mode 100644 index 0000000000000000000000000000000000000000..a24802fc2b770519ec4afa7f77ac04938f330bb1 --- /dev/null +++ b/transfer/code/cell-eval/src/cell_eval/metrics/_registry.py @@ -0,0 +1,124 @@ +from typing import Any, Callable, Dict, List, Optional + +from .._types import DEComparison, MetricBestValue, MetricType, PerturbationAnndataPair +from .base import MetricInfo + +METRIC_FUNC_ADATA_KWARGS = Callable[ + [PerturbationAnndataPair, Any], float | dict[str, float] +] +METRIC_FUNC_ADATA = Callable[[PerturbationAnndataPair], float | dict[str, float]] +METRIC_FUNC_DE_KWARGS = Callable[[DEComparison, Any], float | dict[str, float]] +METRIC_FUNC_DE = Callable[[DEComparison], float | dict[str, float]] + +METRIC_FUNC = ( + METRIC_FUNC_ADATA + | METRIC_FUNC_DE + | METRIC_FUNC_DE_KWARGS + | METRIC_FUNC_ADATA_KWARGS +) + + +class MetricRegistry: + """Registry for managing and accessing metrics.""" + + def __init__(self) -> None: + self.metrics: Dict[str, MetricInfo] = {} + + def register( + self, + name: str, + metric_type: MetricType, + description: str, + func: METRIC_FUNC, + best_value: MetricBestValue, + is_class: bool = False, + kwargs: dict[str, Any] | None = None, + ): + """ + Register a new metric. + + Args: + name: Unique name for the metric + metric_type: Type of metric being registered + description: Description of what the metric computes + func: Function to compute the metric + best_value: Best value for the metric + is_class: Whether the metric is a class that needs instantiation + kwargs: Optional keyword arguments for the metric + """ + if name in self.metrics: + raise ValueError(f"Metric '{name}' already registered") + self.metrics[name] = MetricInfo( + name=name, + type=metric_type, + func=func, + description=description, + best_value=best_value, + is_class=is_class, + kwargs=kwargs, + ) + + def update_metric_kwargs(self, name: str, kwargs: dict[str, Any]) -> None: + """ + Update the keyword arguments for a registered metric. + + Args: + name: Name of the metric to update + kwargs: New keyword arguments to use + """ + if name not in self.metrics: + raise KeyError(f"Metric '{name}' not found in registry") + + if self.metrics[name].kwargs is None: + self.metrics[name].kwargs = {} + + self.metrics[name].kwargs.update(kwargs) # type: ignore + + def get_metric(self, name: str) -> MetricInfo: + """Get information about a registered metric.""" + if name not in self.metrics: + raise KeyError(f"Metric '{name}' not found in registry") + return self.metrics[name] + + def list_metrics(self, metric_type: Optional[MetricType] = None) -> List[str]: + """ + List registered metrics, optionally filtered by type. + + Args: + metric_type: If provided, only list metrics of this type + + Returns: + List of metric names + """ + if metric_type is None: + return list(self.metrics.keys()) + return [name for name, info in self.metrics.items() if info.type == metric_type] + + def compute( + self, + name: str, + data: PerturbationAnndataPair | DEComparison, + kwargs: dict[str, Any] | None = None, + ) -> float | dict[str, float]: + """ + Compute a metric on the provided data. + + Args: + name: Name of the metric to compute + data: Data to compute the metric on + kwargs: Optional keyword arguments to override stored kwargs + + Returns: + Metric result, either a single float or dictionary of values + """ + metric = self.get_metric(name) + # Merge stored kwargs with any provided kwargs + merged_kwargs = metric.kwargs.copy() # type: ignore + if kwargs: + merged_kwargs.update(kwargs) + + if metric.is_class: + # Instantiate the class before calling + instance = metric.func(**merged_kwargs) + return instance(data) + return metric.func(data, **merged_kwargs) diff --git a/transfer/code/cell-eval/src/cell_eval/metrics/base.py b/transfer/code/cell-eval/src/cell_eval/metrics/base.py new file mode 100644 index 0000000000000000000000000000000000000000..e8e77c87cde922ea365479ebb36aca28d693ebcd --- /dev/null +++ b/transfer/code/cell-eval/src/cell_eval/metrics/base.py @@ -0,0 +1,48 @@ +from dataclasses import dataclass +from typing import Any, Callable, Protocol + +from .._types import DEComparison, MetricBestValue, MetricType, PerturbationAnndataPair + + +class Metric(Protocol): + """Protocol for metric functions.""" + + def __call__( + self, + data: PerturbationAnndataPair | DEComparison, + **kwargs, + ) -> float | dict[str, float]: ... + + +@dataclass +class MetricResult: + """Result of a metric computation.""" + + name: str + value: float | str + perturbation: str | None = None + + def to_dict(self) -> dict[str, float | str]: + """Convert result to dictionary.""" + return { + "perturbation": self.perturbation, # type: ignore + "metric": self.name, + "value": self.value, + } + + +@dataclass +class MetricInfo: + """Information about a registered metric.""" + + name: str + type: MetricType + func: Callable + description: str + best_value: MetricBestValue + is_class: bool = False + kwargs: dict[str, Any] | None = None + + def __post_init__(self): + if self.kwargs is None: + self.kwargs = {} diff --git a/transfer/code/cell-eval/src/cell_eval/py.typed b/transfer/code/cell-eval/src/cell_eval/py.typed new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/transfer/code/cell-eval/src/cell_eval/utils.py b/transfer/code/cell-eval/src/cell_eval/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..f3f5ad100532b30372803c69e639ccff25ba8c44 --- /dev/null +++ b/transfer/code/cell-eval/src/cell_eval/utils.py @@ -0,0 +1,107 @@ +import logging + +import anndata as ad +import numpy as np +from scipy.sparse import csc_matrix, csr_matrix + +logger = logging.getLogger(__name__) + + +def guess_is_lognorm( + adata: ad.AnnData, + epsilon: float = 1e-3, + max_threshold: float = 15.0, + validate: bool = True, +) -> bool: + """Guess if the input is integer counts or log-normalized. + + This is an _educated guess_ based on whether there is a fractional component of values. + Checks that data with decimal values is in expected log1p range. + + Args: + adata: AnnData object to check + epsilon: Threshold for detecting fractional values (default 1e-3) + max_threshold: Maximum valid value for log1p normalized data (default 15.0) + validate: Whether to validate the data is in valid log1p range (default True) + + Returns: + bool: True if the input is lognorm, False if integer counts + + Raises: + ValueError: If data has decimal values but falls outside + valid log1p range (min < 0 or max >= max_threshold), indicating mixed or invalid scales + """ + if adata.X is None: + raise ValueError("adata.X is None") + + # Check for fractional values + if isinstance(adata.X, csr_matrix) or isinstance(adata.X, csc_matrix): + frac, _ = np.modf(adata.X.data) + elif adata.isview: + frac, _ = np.modf(adata.X.toarray()) + elif adata.X is None: + raise ValueError("adata.X is None") + else: + frac, _ = np.modf(adata.X) # type: ignore + + has_decimals = bool(np.any(frac > epsilon)) + + if not has_decimals: + # All integer values - assume raw counts + logger.info("Data appears to be integer counts (no decimal values detected)") + return False + + # Data has decimals - perform validation if requested + # Validate it's in valid log1p range + if isinstance(adata.X, csr_matrix) or isinstance(adata.X, csc_matrix): + max_val = adata.X.max() + min_val = adata.X.min() + else: + max_val = float(np.max(adata.X)) + min_val = float(np.min(adata.X)) + + # Validate range + if min_val < 0: + raise ValueError( + f"Invalid scale: min value {min_val:.2f} is negative. " + f"Both Natural or Log1p normalized data must have all values >= 0." + ) + + if validate and max_val >= max_threshold: + raise ValueError( + f"Invalid scale: max value {max_val:.2f} exceeds log1p threshold of {max_threshold}. " + f"Expected log1p normalized values in range [0, {max_threshold}), but found values suggesting " + f"raw counts or incorrect normalization. Values above {max_threshold} indicate mixed scales " + f"(some cells with raw counts, some with log1p values)." + ) + + # Valid log1p data + logger.info( + f"Data appears to be log1p normalized (decimals detected, range [{min_val:.2f}, {max_val:.2f}])" + ) + + return True + + +def split_anndata_on_celltype( + adata: ad.AnnData, + celltype_col: str, +) -> dict[str, ad.AnnData]: + """Split anndata on celltype column. + + Args: + adata: AnnData object + celltype_col: Column name in adata.obs that contains the celltype labels + + Returns: + dict[str, AnnData]: Dictionary of AnnData objects, keyed by celltype + """ + if celltype_col not in adata.obs.columns: + raise ValueError( + f"Celltype column {celltype_col} not found in adata.obs: {adata.obs.columns}" + ) + + return { + ct: adata[adata.obs[celltype_col] == ct] + for ct in adata.obs[celltype_col].unique() + } diff --git a/transfer/code/cell-eval/tests/test_de_float_types.py b/transfer/code/cell-eval/tests/test_de_float_types.py new file mode 100644 index 0000000000000000000000000000000000000000..8fd18ef7b38cbc8a91dee64d0b07b7daca5bc3a4 --- /dev/null +++ b/transfer/code/cell-eval/tests/test_de_float_types.py @@ -0,0 +1,29 @@ +import polars as pl + +from cell_eval._types import DEComparison, DEResults +from cell_eval.metrics._de import DESpearmanLFC + + +def test_de_spearman_lfc_mixed_float_types() -> None: + """Regression test: DESpearmanLFC handles mixed Float32/Float64 columns.""" + real_df = pl.DataFrame( + { + "target": ["pert1", "pert1", "pert2", "pert2"], + "feature": ["gene1", "gene2", "gene1", "gene2"], + "fold_change": [1.5, 2.0, 0.5, 1.2], + "p_value": [0.01, 0.02, 0.03, 0.04], + "fdr": [0.01, 0.02, 0.03, 0.04], + } + ) + + pred_df = real_df.with_columns(pl.col("fold_change").cast(pl.Float64)) + + comparison = DEComparison( + real=DEResults(real_df, name="real"), + pred=DEResults(pred_df, name="pred"), + ) + + result = DESpearmanLFC(fdr_threshold=0.05)(comparison) + + assert isinstance(result, dict) + assert all(isinstance(value, (int, float)) for value in result.values()) diff --git a/transfer/code/cell-eval/tests/test_eval.py b/transfer/code/cell-eval/tests/test_eval.py new file mode 100644 index 0000000000000000000000000000000000000000..968a701daa8e7fd2be99f9b0a373fc4d7e8470ea --- /dev/null +++ b/transfer/code/cell-eval/tests/test_eval.py @@ -0,0 +1,409 @@ +import os +import shutil +from typing import Literal + +import numpy as np +import pytest + +from cell_eval import MetricsEvaluator +from cell_eval.data import ( + CONTROL_VAR, + PERT_COL, + build_random_anndata, + downsample_cells, +) + +OUTDIR = "TEST_OUTPUT_DIRECTORY" +KNOWN_PROFILES: list[Literal["full", "vcc", "minimal", "de", "anndata"]] = [ + "full", + "vcc", + "minimal", + "de", + "anndata", +] + + +def test_broken_adata_mismatched_var_size(): + adata_real = build_random_anndata(normlog=False) + adata_pred = adata_real.copy() + + # Randomly subset genes on pred + var_mask = np.random.random(adata_real.shape[1]) < 0.8 + adata_pred = adata_pred[:, var_mask] + + with pytest.raises(Exception): + MetricsEvaluator( + adata_pred=adata_pred, + adata_real=adata_real, + control_pert=CONTROL_VAR, + pert_col=PERT_COL, + outdir=OUTDIR, + ) + + +def test_broken_adata_mismatched_var_ordering(): + adata_real = build_random_anndata(normlog=False) + adata_pred = adata_real.copy() + + # Randomly subset genes on pred + indices = np.arange(adata_real.shape[1]) + np.random.shuffle(indices) + adata_pred = adata_pred[:, indices] + + with pytest.raises(Exception): + MetricsEvaluator( + adata_pred=adata_pred, + adata_real=adata_real, + control_pert=CONTROL_VAR, + pert_col=PERT_COL, + outdir=OUTDIR, + ) + + +def test_broken_adata_not_normlog(): + adata_real = build_random_anndata(normlog=False) + adata_pred = adata_real.copy() + evaluator = MetricsEvaluator( + adata_pred=adata_pred, + adata_real=adata_real, + control_pert=CONTROL_VAR, + pert_col=PERT_COL, + outdir=OUTDIR, + ) + evaluator.compute( + break_on_error=True, + ) + + +def test_broken_adata_not_normlog_skip_check(): + adata_real = build_random_anndata(normlog=False) + adata_pred = adata_real.copy() + evaluator = MetricsEvaluator( + adata_pred=adata_pred, + adata_real=adata_real, + control_pert=CONTROL_VAR, + pert_col=PERT_COL, + outdir=OUTDIR, + allow_discrete=True, + ) + evaluator.compute( + break_on_error=True, + ) + + +def test_broken_adata_invalid_pred_scale(): + """Test that predicted data with invalid scale is rejected.""" + adata_real = build_random_anndata(normlog=True) + adata_pred = adata_real.copy() + + # Create invalid predicted data: mix of raw counts and log1p + adata_pred.X = np.random.uniform( + 0, + 5000, + size=adata_pred.X.shape, # type: ignore + ) + + with pytest.raises(ValueError, match="Invalid scale.*exceeds log1p threshold"): + MetricsEvaluator( + adata_pred=adata_pred, + adata_real=adata_real, + control_pert=CONTROL_VAR, + pert_col=PERT_COL, + outdir=OUTDIR, + ) + + +def test_broken_adata_missing_pertcol_in_real(): + adata_real = build_random_anndata() + adata_pred = adata_real.copy() + + # Remove pert_col from adata_real + adata_real.obs.drop(columns=[PERT_COL], inplace=True) + + with pytest.raises(Exception): + MetricsEvaluator( + adata_pred=adata_pred, + adata_real=adata_real, + control_pert=CONTROL_VAR, + pert_col=PERT_COL, + outdir=OUTDIR, + ) + + +def test_broken_adata_missing_pertcol_in_pred(): + adata_real = build_random_anndata() + adata_pred = adata_real.copy() + + # Remove pert_col from adata_pred + adata_pred.obs.drop(columns=[PERT_COL], inplace=True) + + with pytest.raises(Exception): + MetricsEvaluator( + adata_pred=adata_pred, + adata_real=adata_real, + control_pert=CONTROL_VAR, + pert_col=PERT_COL, + outdir=OUTDIR, + ) + + +def test_broken_adata_missing_control_in_real(): + adata_real = build_random_anndata() + adata_pred = adata_real.copy() + + # Remove control_pert from adata_real + adata_real = adata_real[adata_real.obs[PERT_COL] != CONTROL_VAR].copy() + + with pytest.raises(Exception): + MetricsEvaluator( + adata_pred=adata_pred, + adata_real=adata_real, + control_pert=CONTROL_VAR, + pert_col=PERT_COL, + outdir=OUTDIR, + ) + + +def test_broken_adata_missing_control_in_pred(): + adata_real = build_random_anndata() + adata_pred = adata_real.copy() + + # Remove control_pert from adata_pred + adata_pred = adata_pred[adata_pred.obs[PERT_COL] != CONTROL_VAR].copy() + + with pytest.raises(Exception): + MetricsEvaluator( + adata_pred=adata_pred, + adata_real=adata_real, + control_pert=CONTROL_VAR, + pert_col=PERT_COL, + outdir=OUTDIR, + ) + + +def test_unknown_alternative_de_metric(): + adata_real = build_random_anndata() + adata_pred = adata_real.copy() + + # Remove control_pert from adata_pred + adata_pred = adata_pred[adata_pred.obs[PERT_COL] != CONTROL_VAR].copy() + + with pytest.raises(Exception): + MetricsEvaluator( + adata_pred=adata_pred, + adata_real=adata_real, + control_pert=CONTROL_VAR, + pert_col=PERT_COL, + outdir=OUTDIR, + de_method="unknown", + ).compute() + + +def test_eval_simple(): + adata_real = build_random_anndata() + adata_pred = downsample_cells(adata_real, fraction=0.5) + evaluator = MetricsEvaluator( + adata_pred=adata_pred, + adata_real=adata_real, + control_pert="control", + pert_col="perturbation", + ) + evaluator.compute( + break_on_error=True, + ) + + +def test_eval_simple_profiles(): + adata_real = build_random_anndata() + adata_pred = downsample_cells(adata_real, fraction=0.5) + evaluator = MetricsEvaluator( + adata_pred=adata_pred, + adata_real=adata_real, + control_pert="control", + pert_col="perturbation", + ) + for profile in KNOWN_PROFILES: + evaluator.compute( + profile=profile, + break_on_error=True, + ) + + with pytest.raises(ValueError): + evaluator.compute( + profile="unknown", # type: ignore + break_on_error=True, + ) + + +def test_eval_missing_celltype_col(): + adata_real = build_random_anndata() + adata_pred = downsample_cells(adata_real, fraction=0.5) + + adata_real.obs.drop(columns="celltype", inplace=True) + adata_pred.obs.drop(columns="celltype", inplace=True) + + assert "celltype" not in adata_real.obs.columns + assert "celltype" not in adata_pred.obs.columns + + evaluator = MetricsEvaluator( + adata_pred=adata_pred, + adata_real=adata_real, + control_pert="control", + pert_col="perturbation", + ) + evaluator.compute( + break_on_error=True, + ) + + +def test_eval_pdex_kwargs(): + adata_real = build_random_anndata() + adata_pred = downsample_cells(adata_real, fraction=0.5) + evaluator = MetricsEvaluator( + adata_pred=adata_pred, + adata_real=adata_real, + control_pert="control", + pert_col="perturbation", + pdex_kwargs={ + "exp_post_agg": True, + }, + ) + evaluator.compute( + break_on_error=True, + ) + + +def test_eval_pdex_kwargs_duplicated(): + adata_real = build_random_anndata() + adata_pred = downsample_cells(adata_real, fraction=0.5) + evaluator = MetricsEvaluator( + adata_pred=adata_pred, + adata_real=adata_real, + control_pert="control", + pert_col="perturbation", + pdex_kwargs={ + "exp_post_agg": True, + "num_workers": 4, + }, + ) + evaluator.compute( + break_on_error=True, + ) + + +def validate_expected_files( + outdir: str, prefix: str | None = None, remove: bool = True +): + base_real_de = "real_de.csv" if not prefix else f"{prefix}_real_de.csv" + base_pred_de = "pred_de.csv" if not prefix else f"{prefix}_pred_de.csv" + base_results = "results.csv" if not prefix else f"{prefix}_results.csv" + assert os.path.exists(f"{outdir}/{base_real_de}"), ( + "Expected file for real DE results missing" + ) + assert os.path.exists(f"{outdir}/{base_pred_de}"), ( + "Expected file for predicted DE results missing" + ) + assert os.path.exists(f"{outdir}/{base_results}"), ( + "Expected file for results missing" + ) + if remove: + shutil.rmtree(outdir) + + +def test_eval(): + adata_real = build_random_anndata() + adata_pred = adata_real.copy() + evaluator = MetricsEvaluator( + adata_pred=adata_pred, + adata_real=adata_real, + control_pert=CONTROL_VAR, + pert_col=PERT_COL, + outdir=OUTDIR, + ) + evaluator.compute( + break_on_error=True, + ) + validate_expected_files(OUTDIR) + + +def test_eval_prefix(): + adata_real = build_random_anndata() + adata_pred = adata_real.copy() + evaluator = MetricsEvaluator( + adata_pred=adata_pred, + adata_real=adata_real, + control_pert=CONTROL_VAR, + pert_col=PERT_COL, + outdir=OUTDIR, + prefix="arbitrary", + ) + evaluator.compute( + break_on_error=True, + ) + validate_expected_files(OUTDIR, prefix="arbitrary") + + +def test_minimal_eval(): + adata_real = build_random_anndata() + adata_pred = adata_real.copy() + evaluator = MetricsEvaluator( + adata_pred=adata_pred, + adata_real=adata_real, + control_pert=CONTROL_VAR, + pert_col=PERT_COL, + outdir=OUTDIR, + ) + evaluator.compute( + profile="minimal", + break_on_error=True, + ) + validate_expected_files(OUTDIR) + + +def test_eval_sparse(): + adata_real = build_random_anndata(as_sparse=True) + adata_pred = adata_real.copy() + evaluator = MetricsEvaluator( + adata_pred=adata_pred, + adata_real=adata_real, + control_pert=CONTROL_VAR, + pert_col=PERT_COL, + outdir=OUTDIR, + ) + evaluator.compute( + break_on_error=True, + ) + validate_expected_files(OUTDIR) + + +def test_eval_downsampled_cells(): + adata_real = build_random_anndata() + adata_pred = downsample_cells(adata_real, fraction=0.5) + evaluator = MetricsEvaluator( + adata_pred=adata_pred, + adata_real=adata_real, + control_pert=CONTROL_VAR, + pert_col=PERT_COL, + outdir=OUTDIR, + ) + evaluator.compute( + break_on_error=True, + ) + validate_expected_files(OUTDIR) + + +def test_eval_alt_metric(): + adata_real = build_random_anndata() + adata_pred = downsample_cells(adata_real, fraction=0.5) + evaluator = MetricsEvaluator( + adata_pred=adata_pred, + adata_real=adata_real, + control_pert=CONTROL_VAR, + pert_col=PERT_COL, + outdir=OUTDIR, + de_method="anderson", + ) + evaluator.compute( + break_on_error=True, + ) + validate_expected_files(OUTDIR) diff --git a/transfer/code/cell-eval/tests/test_utils.py b/transfer/code/cell-eval/tests/test_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..bc9af0e57a127e67a2bdef39ea7d00496d616aff --- /dev/null +++ b/transfer/code/cell-eval/tests/test_utils.py @@ -0,0 +1,109 @@ +import numpy as np +import pytest + +from cell_eval.data import build_random_anndata +from cell_eval.utils import guess_is_lognorm + + +def test_is_lognorm_true(): + data = build_random_anndata(normlog=True) + assert guess_is_lognorm(data) + + +def test_is_lognorm_view(): + data = build_random_anndata(normlog=True) + sub = data[:100] + assert guess_is_lognorm(sub) + + +def test_is_lognorm_false(): + data = build_random_anndata(normlog=False) + assert not guess_is_lognorm(data) + + +def test_guess_is_lognorm_valid_lognorm(): + """Test that valid log1p normalized data returns True.""" + data = build_random_anndata(normlog=True, random_state=42) + # Should return True without raising exception + assert guess_is_lognorm( + data, + ) + + +def test_guess_is_lognorm_valid_lognorm_sparse(): + """Test that valid log1p normalized sparse data returns True.""" + data = build_random_anndata(normlog=True, as_sparse=True, random_state=42) + # Should return True without raising exception + assert guess_is_lognorm( + data, + ) + + +def test_guess_is_lognorm_integer_data(): + """Test that integer data (raw counts) returns False.""" + data = build_random_anndata(normlog=False, random_state=42) + # Should return False - integer data indicates raw counts + assert not guess_is_lognorm( + data, + ) + + +def test_guess_is_lognorm_edge_case_near_threshold(): + """Test that values near but below threshold return True.""" + data = build_random_anndata(normlog=True, random_state=42) + # Modify data to have values near threshold (10.9) + data.X = np.random.uniform( + 0, + 14.9, + size=data.X.shape, # type: ignore + ) + # Should return True without raising exception + assert guess_is_lognorm( + data, + ) + + +def test_guess_is_lognorm_exceeds_threshold(): + """Test that data with max value > 11.0 raises ValueError when .""" + data = build_random_anndata(normlog=True, random_state=42) + # Modify data to exceed threshold (mix of valid and invalid) + data.X = np.random.uniform( + 0, + 15.1, + size=data.X.shape, # type: ignore + ) + + with pytest.raises(ValueError, match="Invalid scale.*exceeds log1p threshold"): + guess_is_lognorm( + data, + ) + + +def test_guess_is_lognorm_negative_values(): + """Test that data with negative values raises ValueError when .""" + data = build_random_anndata(normlog=True, random_state=42) + # Modify data to include negative values + data.X = np.random.uniform( + -1, + 9, + size=data.X.shape, # type: ignore + ) + + with pytest.raises(ValueError, match="Invalid scale.*is negative"): + guess_is_lognorm( + data, + ) + + +def test_guess_is_lognorm_mixed_scales(): + """Test mixed scenario: some cells with raw counts, some with log1p.""" + data = build_random_anndata(normlog=True, random_state=42) + n_cells = data.X.shape[0] # type: ignore + half = n_cells // 2 + data.X[:half] = np.random.uniform(0, 9, size=(half, data.X.shape[1])) # type: ignore + data.X[half:] = np.random.uniform(100, 5000, size=(n_cells - half, data.X.shape[1])) # type: ignore + + with pytest.raises(ValueError, match="Invalid scale.*exceeds log1p threshold"): + guess_is_lognorm( + data, + ) diff --git a/transfer/code/cell-eval/tutorials/vcc/vcc.ipynb b/transfer/code/cell-eval/tutorials/vcc/vcc.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..5f023c248d5055c637d489c647dc172562bcede3 --- /dev/null +++ b/transfer/code/cell-eval/tutorials/vcc/vcc.ipynb @@ -0,0 +1,339 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "837a8ecd", + "metadata": {}, + "source": [ + "# VCC Submission Notebook\n", + "\n", + "Hello! \n", + "\n", + "This is a notebook that will help you prepare your predicted AnnData to be ready to be scored by `cell-eval` against a validation dataset.\n", + "\n", + "Before we begin you will need a few things:\n", + "\n", + "1. `cell-eval` installed and in your `$PATH` (see our [installation guide](https://github.com/ArcInstitute/cell-eval?tab=readme-ov-file#installation))\n", + "2. The number of expected cells / perturbation in the validation dataset (CSV) ([download](https://virtualcellchallenge.org/app))\n", + "3. The gene names to predict (CSV) ([download](https://virtualcellchallenge.org/app))\n", + "4. Your model predictions in an AnnData (h5ad)\n", + "5. (Optional) The training AnnData (if you are not predicting Non-Targeting Controls) ([download](https://virtualcellchallenge.org/app))\n", + "\n", + "\n", + "> Note: Your model predictions **may not exceed 100K cells total**" + ] + }, + { + "cell_type": "markdown", + "id": "b5cc204d", + "metadata": {}, + "source": [ + "## Building an Example Submission\n", + "\n", + "For the purposes of this tutorial we will be generating **random predictions** and preparing them to be evaluated.\n", + "\n", + "We will create an AnnData with *random gene abundances* for each cell, where the number of cells for each perturbation matches the number of cells in the validation dataset." + ] + }, + { + "cell_type": "markdown", + "id": "3e9c543f", + "metadata": {}, + "source": [ + "### Load in our Expected Counts" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "2d172eea", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dimensions: (50, 3)\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "shape: (5, 3)
target_genen_cellsmedian_umi_per_cell
stri64f64
"SH3BP4"292554551.0
"ZNF581"250253803.5
"ANXA6"249655175.0
"PACSIN3"210154088.0
"MGST1"209654217.5
" + ], + "text/plain": [ + "shape: (5, 3)\n", + "┌─────────────┬─────────┬─────────────────────┐\n", + "│ target_gene ┆ n_cells ┆ median_umi_per_cell │\n", + "│ --- ┆ --- ┆ --- │\n", + "│ str ┆ i64 ┆ f64 │\n", + "╞═════════════╪═════════╪═════════════════════╡\n", + "│ SH3BP4 ┆ 2925 ┆ 54551.0 │\n", + "│ ZNF581 ┆ 2502 ┆ 53803.5 │\n", + "│ ANXA6 ┆ 2496 ┆ 55175.0 │\n", + "│ PACSIN3 ┆ 2101 ┆ 54088.0 │\n", + "│ MGST1 ┆ 2096 ┆ 54217.5 │\n", + "└─────────────┴─────────┴─────────────────────┘" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import polars as pl\n", + "\n", + "# Define our path\n", + "pert_counts_path = \"./pert_counts_Validation.csv\"\n", + "\n", + "# Read in the csv\n", + "pert_counts = pl.read_csv(pert_counts_path)\n", + "\n", + "# Show the dimensions\n", + "print(f\"Dimensions: {pert_counts.shape}\")\n", + "pert_counts.head()" + ] + }, + { + "cell_type": "markdown", + "id": "34164d85", + "metadata": {}, + "source": [ + "### Load in our Expected Gene Names" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "04e3cfad", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['SAMD11', 'NOC2L', 'KLHL17', ..., 'MT-ND5', 'MT-ND6', 'MT-CYB'],\n", + " shape=(18080,), dtype=object)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gene_names_path = \"./gene_names.csv\"\n", + "\n", + "# Read this in and immediately convert to array\n", + "gene_names = pl.read_csv(gene_names_path, has_header=False).to_numpy().flatten()\n", + "\n", + "gene_names" + ] + }, + { + "cell_type": "markdown", + "id": "b6895f5b", + "metadata": {}, + "source": [ + "### Define our random predictor" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "0262449f", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from numpy.typing import NDArray\n", + "import anndata as ad\n", + "\n", + "\n", + "def random_predictor(\n", + " pert_names: NDArray[np.str_],\n", + " cell_counts: NDArray[np.int64],\n", + " gene_names: NDArray[np.str_],\n", + " max_count: int | float = 1e4,\n", + " log1p: bool = True,\n", + ") -> ad.AnnData:\n", + " \"\"\"Generate a random AnnData with the expected number of cells / perturbation.\n", + "\n", + " This is a dummy function that is meant to stand-in for a perturbation model.\n", + " \"\"\"\n", + " matrix = np.random.randint(\n", + " 0,\n", + " int(max_count),\n", + " size=(\n", + " cell_counts.sum(),\n", + " gene_names.size,\n", + " ),\n", + " )\n", + " if log1p:\n", + " matrix = np.log1p(matrix)\n", + " return ad.AnnData(\n", + " X=matrix,\n", + " obs=pd.DataFrame(\n", + " {\n", + " \"target_gene\": np.repeat(pert_names, cell_counts),\n", + " },\n", + " index=np.arange(cell_counts.sum()).astype(str),\n", + " ),\n", + " var=pd.DataFrame(index=gene_names),\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "d5dbd56d", + "metadata": {}, + "source": [ + "### Run our random predictor" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "1273bea1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AnnData object with n_obs × n_vars = 60751 × 18080\n", + " obs: 'target_gene'" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "adata = random_predictor(\n", + " pert_names=pert_counts[\"target_gene\"].to_numpy(),\n", + " cell_counts=pert_counts[\"n_cells\"].to_numpy(),\n", + " gene_names=gene_names,\n", + ")\n", + "adata" + ] + }, + { + "cell_type": "markdown", + "id": "9f9de9dd", + "metadata": {}, + "source": [ + "### Adding in Non-Targeting Controls if you are not predicting them\n", + "\n", + "Our evaluation framework expects non-targeting controls to be included in the predicted AnnData, but not all models may try to predict non-targeting controls.\n", + "If you are not predicting non-targeting controls, you can take the non-targeting from the training AnnData and just copy them over into your predicted AnnData for validation." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "3879267c", + "metadata": {}, + "outputs": [], + "source": [ + "# Define our path to the training anndata\n", + "tr_adata_path = \"./adata_Training.h5ad\"\n", + "\n", + "# Read in the anndata\n", + "tr_adata = ad.read_h5ad(tr_adata_path)\n", + "\n", + "# Filter for non-targeting\n", + "ntc_adata = tr_adata[tr_adata.obs[\"target_gene\"] == \"non-targeting\"]\n", + "\n", + "# Append the non-targeting controls to the example anndata if they're missing\n", + "if \"non-targeting\" not in adata.obs[\"target_gene\"].unique():\n", + " assert np.all(adata.var_names.values == ntc_adata.var_names.values), (\n", + " \"Gene-Names are out of order or unequal\"\n", + " )\n", + " adata = ad.concat(\n", + " [\n", + " adata,\n", + " ntc_adata,\n", + " ]\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "6f19ac72", + "metadata": {}, + "source": [ + "### Write our predictions to some output path" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "386ee994", + "metadata": {}, + "outputs": [], + "source": [ + "adata.write_h5ad(\"./example.h5ad\")" + ] + }, + { + "cell_type": "markdown", + "id": "29cd57ef", + "metadata": {}, + "source": [ + "## Running `cell-eval prep`\n", + "\n", + "Now that we have our predictions, we will run `cell-eval` to prepare our AnnData for competition scoring." + ] + }, + { + "cell_type": "markdown", + "id": "7716a83e", + "metadata": {}, + "source": [ + "```bash\n", + "cell-eval prep \\\n", + " -i ./example.h5ad \\\n", + " --genes ./gene_names.csv\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "6f938a60", + "metadata": {}, + "source": [ + "And that's it! Your model outputs will be output to path: `./example.prep.vcc` are ready for scoring." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/transfer/code/ori_scDFM/.gitignore b/transfer/code/ori_scDFM/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..f5b69b22867a1c7d862ec13fdb23b5539bf8f8d3 --- /dev/null +++ b/transfer/code/ori_scDFM/.gitignore @@ -0,0 +1,6 @@ +__pycache__/ +test.md +*.h5ad +*.zip +result +data/norman \ No newline at end of file diff --git a/transfer/code/ori_scDFM/LICENSE b/transfer/code/ori_scDFM/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..46e3f8d7061455f8b427f4e13ae30c617bd29a68 --- /dev/null +++ b/transfer/code/ori_scDFM/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2025 AI4Science-WestlakeU + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/transfer/code/ori_scDFM/README.md b/transfer/code/ori_scDFM/README.md new file mode 100644 index 0000000000000000000000000000000000000000..624aab3bb4f46fdcc8d82a055f1d10e285c65f98 --- /dev/null +++ b/transfer/code/ori_scDFM/README.md @@ -0,0 +1,81 @@ +

+ scDFN logo +

+ +# scDFM: Distributional Flow Matching for Robust Single-Cell Perturbation Prediction (ICLR 2026) + +[![arXiv](https://img.shields.io/badge/arXiv-2601.01829-b31b1b?logo=arxiv)](https://openreview.net/forum?id=QSGanMEcUV) +[![Codebase](https://img.shields.io/badge/Codebase-GitHub-181717?logo=github)](https://github.com/AI4Science-WestlakeU/scDFM) +[![License: MIT](https://img.shields.io/badge/License-MIT-yellow?logo=open-source-initiative&logoColor=white)](LICENSE) + + +Official repo for the paper [scDFM](URL), ICLR 2026.
+Chenglei Yu∗1,2, [Chuanrui Wang](https://wang-cr.github.io/)∗1, Bangyan Liao∗1,2 & [Tailin Wu](https://tailin.org/)†1.
+ +1School of Engineering, Westlake University; +2Zhejaing University; + +*Equal contribution, †Corresponding authors + +---- + +## Overview +We propose a novel distributional flow matching framework (scDFM) for robust single-cell perturbation prediction, which models the full distribution of perturbed cellular expression profiles conditioned on control states, thereby overcoming limitations of existing methods that rely on cell-level correspondences and fail to capture population-level transcriptional shifts. + +Framework of paper: + + + +## Install dependencies +``` +conda env create -f environment.yml +``` + +## ⏬ Dataset download + +Put dataset into data file: + +- [Norman](https://figshare.com/articles/dataset/Norman_et_al_2019_Science_labeled_Perturb-seq_data/24688110) +- [Combosciplex subset of sciplex v3](https://figshare.com/articles/dataset/combosciplex/25062230?file=44229635) +### Alternative Data Access + +We also provide the datasets via [Google Drive](https://drive.google.com/drive/folders/1cNpYAt9jVWZN82miNZtkP10YeSo7hufL?usp=sharing). This folder contains: +- The **Norman** dataset and its corresponding data splits. +- The **ComboSciPlex** dataset. + +Example directory layout after download (relative to repo root): +``` +scDFM/ +├─ data/ +│ ├─ norman.h5ad +│ └─ combosciplex.h5ad +├─ src/ +│ └─ ... +└─ run.sh +``` + + + +## 📥 Training + +An example on additive task. +```bash +bash run.sh +``` + +## 🫡 Citation + +If you find our work and/or our code useful, please cite us via: + +```bibtex +@article{yu2026scdfm, + title={scDFM: Distributional Flow Matching Model for Robust Single-Cell Perturbation Prediction}, + author={Yu, Chenglei and Wang, Chuanrui and Liao, Bangyan and Wu, Tailin}, + journal={arXiv preprint arXiv:2602.07103}, + year={2026} +} +``` + +## 📚 Related Resources + +- AI for Scientific Simulation and Discovery Lab: https://github.com/AI4Science-WestlakeU diff --git a/transfer/code/ori_scDFM/assets/fig1.png b/transfer/code/ori_scDFM/assets/fig1.png new file mode 100644 index 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+ZC3HAV1+CEBPE,0.08641975308641975,0.06,0.08,0.08641975308641975,0.08641975308641975,0.16227180527383367,0.06,0.08,0.095,0.118,0.33029815543559937,0.8209876543209876,0.7384795473575855,0.9876543209876543,162.0,986.0,0.29926723572605585,0.505257278771344,0.3948254883289337,0.16430911421775818,0.23621705174446106,0.16430911421775818,0.23621705174446106,0.8461538461538461,0.8717948717948718,0.9487179487179487,0.5894095251440064,0.5539158017707626 diff --git a/transfer/code/ori_scDFM/config/config_flow.py b/transfer/code/ori_scDFM/config/config_flow.py new file mode 100644 index 0000000000000000000000000000000000000000..000d61bbe1cf29464b55ebc84ef51611855943a9 --- /dev/null +++ b/transfer/code/ori_scDFM/config/config_flow.py @@ -0,0 +1,65 @@ + +from dataclasses import dataclass +import os +@dataclass +class FlowConfig: + # Flow model type + model_type: str = 'hierarchical' + + # Flow Matching specific parameters + batch_size: int = 32 + ntoken: int = 512 + d_model: int = 512 + lr: float = 1e-5 + steps: int = 5000 + eta_min: float = 1e-7 + devices: str = "1" + test_only: bool = False + # Perturbation related parameters + data_name: str = "combosciplex" + perturbation_function: str = 'crisper' + noise_type: str = "Gaussian" + poisson_alpha: float = 0.8 + poisson_target_sum: int = -1 + + print_every: int = 5000 + mode: str = 'predict_y' # predict_y, predict_p + result_path: str = './result' + perturbation_fusion_method: str = 'sum' # mlp, sum + fusion_method: str = 'cross' # cross , concat, add + infer_top_gene: int = 1000 + n_top_genes: int = 5000 + checkpoint_path: str = '' + gamma: float = 0.0 + split_method: str = 'additive' + use_mmd_loss: bool = False + fold: int = 0 + use_negative_edge: bool = False + topk: int = 15 + gene_noise_scale: bool = False + + def __post_init__(self): + if self.data_name == 'norman_umi_go_filtered': + self.n_top_genes = 5054 + if self.data_name == 'norman': + self.n_top_genes = 5000 + path = self.make_path() + + def make_path(self): + exp_name = '-'.join(['flow', + f'fusion_{self.fusion_method}', + f'{self.data_name}', + self.model_type, + self.mode, + f'gamma_{self.gamma}', + f'perturbation_function_{self.perturbation_function}', + f'lr_{self.lr}', + f'dim_model_{self.d_model}', + f'infer_top_gene_{self.infer_top_gene}', + f'split_method_{self.split_method}', + f'use_mmd_loss_{self.use_mmd_loss}', + f'fold_{self.fold}', + f'use_negative_edge_{self.use_negative_edge}', + f'topk_{self.topk}', + ] + ([f'gene_noise_scale'] if self.gene_noise_scale else [])) + return os.path.join(self.result_path, exp_name) \ No newline at end of file diff --git a/transfer/code/ori_scDFM/data/norman.h5ad b/transfer/code/ori_scDFM/data/norman.h5ad new file mode 100644 index 0000000000000000000000000000000000000000..0d60f54804ab63d9dd24562cdf1696c697ce91d4 --- /dev/null +++ b/transfer/code/ori_scDFM/data/norman.h5ad @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:23ffb0fac6a847ff927cf7509d80d85052bfefbfb97610786a2dafaaefa0b6a0 +size 2228610012 diff --git a/transfer/code/ori_scDFM/environment.yml b/transfer/code/ori_scDFM/environment.yml new file mode 100644 index 0000000000000000000000000000000000000000..88802032db069b901c887ad0750e4336d8bb34df --- /dev/null +++ b/transfer/code/ori_scDFM/environment.yml @@ -0,0 +1,318 @@ +name: sc +channels: + - conda-forge +dependencies: + - _libgcc_mutex=0.1 + - _openmp_mutex=4.5 + - asttokens=3.0.0 + - bzip2=1.0.8 + - ca-certificates=2025.8.3 + - comm=0.2.3 + - decorator=5.2.1 + - exceptiongroup=1.3.0 + - executing=2.2.0 + - importlib-metadata=8.7.0 + - ipykernel=6.30.1 + - ipython=9.4.0 + - ipython_pygments_lexers=1.1.1 + - jedi=0.19.2 + - jupyter_client=8.6.3 + - jupyter_core=5.8.1 + - keyutils=1.6.1 + - krb5=1.21.3 + - ld_impl_linux-64=2.44 + - libedit=3.1.20250104 + - libexpat=2.7.1 + - libffi=3.4.6 + - libgcc=15.1.0 + - libgcc-ng=15.1.0 + - libgomp=15.1.0 + - liblzma=5.8.1 + - libnsl=2.0.1 + - libsodium=1.0.20 + - libsqlite=3.50.4 + - libstdcxx=15.1.0 + - libstdcxx-ng=15.1.0 + - libuuid=2.38.1 + - libxcrypt=4.4.36 + - libzlib=1.3.1 + - matplotlib-inline=0.1.7 + - ncurses=6.5 + - nest-asyncio=1.6.0 + - openssl=3.5.2 + - packaging=25.0 + - parso=0.8.4 + - pexpect=4.9.0 + - pickleshare=0.7.5 + - pip=25.2 + - platformdirs=4.3.8 + - prompt-toolkit=3.0.51 + - ptyprocess=0.7.0 + - pure_eval=0.2.3 + - pygments=2.19.2 + - python=3.11.13 + - python-dateutil=2.9.0.post0 + - python_abi=3.11 + - readline=8.2 + - setuptools=80.9.0 + - six=1.17.0 + - stack_data=0.6.3 + - tk=8.6.13 + - traitlets=5.14.3 + - typing_extensions=4.14.1 + - wcwidth=0.2.13 + - wheel=0.45.1 + - zeromq=4.3.5 + - zipp=3.23.0 + - pip: + - absl-py==2.3.1 + - accelerate==1.9.0 + - adjusttext==1.3.0 + - adpbulk==0.1.4 + - aiofiles==24.1.0 + - aiohappyeyeballs==2.6.1 + - aiohttp==3.12.15 + - aiosignal==1.4.0 + - anndata==0.12.1 + - antlr4-python3-runtime==4.9.3 + - anyio==4.10.0 + - argon2-cffi==25.1.0 + - argon2-cffi-bindings==25.1.0 + - array-api-compat==1.12.0 + - arrow==1.3.0 + - arviz==0.22.0 + - async-lru==2.0.5 + - attrs==25.3.0 + - babel==2.17.0 + - beautifulsoup4==4.13.4 + - bleach==6.2.0 + - blitzgsea==1.3.54 + - cached-property==2.0.1 + - cell-eval==0.5.42 + - cell-gears==0.1.2 + - certifi==2025.8.3 + - cffi==1.17.1 + - charset-normalizer==3.4.2 + - chex==0.1.90 + - click==8.2.1 + - contourpy==1.3.3 + - cpa==0.4.1 + - crc32c==2.7.1 + - cycler==0.12.1 + - datasets==2.21.0 + - dcor==0.6 + - debugpy==1.8.15 + - defusedxml==0.7.1 + - deprecated==1.2.18 + - dill==0.3.8 + - docrep==0.3.2 + - docstring-parser==0.17.0 + - donfig==0.8.1.post1 + - einops==0.8.1 + - equinox==0.13.0 + - et-xmlfile==2.0.0 + - etils==1.13.0 + - fast-array-utils==1.2.1 + - fastjsonschema==2.21.1 + - filelock==3.18.0 + - flax==0.11.0 + - fonttools==4.59.0 + - formulaic==1.2.0 + - formulaic-contrasts==1.0.0 + - fqdn==1.5.1 + - frozenlist==1.7.0 + - fsspec==2024.6.1 + - grpcio==1.74.0 + - h11==0.16.0 + - h5netcdf==1.6.3 + - h5py==3.14.0 + - hf-xet==1.1.5 + - httpcore==1.0.9 + - httpx==0.28.1 + - huggingface-hub==0.34.3 + - humanize==4.12.3 + - idna==3.10 + - igraph==0.11.9 + - importlib-resources==6.5.2 + - iniconfig==2.1.0 + - interface-meta==1.3.0 + - ipywidgets==8.1.7 + - isoduration==20.11.0 + - jax==0.6.2 + - jaxlib==0.6.2 + - jaxopt==0.8.5 + - jaxtyping==0.3.2 + - jinja2==3.1.6 + - joblib==1.5.1 + - json5==0.12.0 + - jsonpointer==3.0.0 + - jsonschema==4.25.0 + - jsonschema-specifications==2025.4.1 + - jupyter==1.1.1 + - jupyter-console==6.6.3 + - jupyter-events==0.12.0 + - jupyter-lsp==2.2.6 + - jupyter-server==2.16.0 + - jupyter-server-terminals==0.5.3 + - jupyterlab==4.4.5 + - jupyterlab-pygments==0.3.0 + - jupyterlab-server==2.27.3 + - jupyterlab-widgets==3.0.15 + - kiwisolver==1.4.8 + - lamin-utils==0.15.0 + - lark==1.2.2 + - legacy-api-wrap==1.4.1 + - leidenalg==0.10.2 + - lightning==2.5.2 + - lightning-utilities==0.15.0 + - lineax==0.0.8 + - llvmlite==0.44.0 + - markdown==3.8.2 + - markdown-it-py==3.0.0 + - markupsafe==3.0.2 + - matplotlib==3.10.5 + - mdurl==0.1.2 + - mistune==3.1.3 + - ml-collections==1.1.0 + - ml-dtypes==0.5.3 + - mpmath==1.3.0 + - msgpack==1.1.1 + - mudata==0.3.2 + - multidict==6.6.3 + - multipledispatch==1.0.0 + - multiprocess==0.70.16 + - narwhals==2.0.1 + - natsort==8.4.0 + - nbclient==0.10.2 + - nbconvert==7.16.6 + - nbformat==5.10.4 + - networkx==3.5 + - notebook==7.4.5 + - notebook-shim==0.2.4 + - numba==0.61.2 + - numcodecs==0.16.1 + - numpy==2.2.6 + - numpyro==0.18.0 + - nvidia-cublas-cu12==12.6.4.1 + - nvidia-cuda-cupti-cu12==12.6.80 + - nvidia-cuda-nvrtc-cu12==12.6.77 + - nvidia-cuda-runtime-cu12==12.6.77 + - nvidia-cudnn-cu12==9.5.1.17 + - nvidia-cufft-cu12==11.3.0.4 + - nvidia-cufile-cu12==1.11.1.6 + - nvidia-curand-cu12==10.3.7.77 + - nvidia-cusolver-cu12==11.7.1.2 + - nvidia-cusparse-cu12==12.5.4.2 + - nvidia-cusparselt-cu12==0.6.3 + - nvidia-nccl-cu12==2.26.2 + - nvidia-nvjitlink-cu12==12.6.85 + - nvidia-nvtx-cu12==12.6.77 + - omegaconf==2.3.0 + - openpyxl==3.1.5 + - opt-einsum==3.4.0 + - optax==0.2.5 + - orbax==0.1.7 + - orbax-checkpoint==0.11.20 + - ott-jax==0.5.1 + - overrides==7.7.0 + - pandas==2.3.1 + - pandocfilters==1.5.1 + - patsy==1.0.1 + - pdex==0.1.21 + - pertpy==1.0.1 + - pillow==11.3.0 + - pluggy==1.6.0 + - ply==3.11 + - polars==1.32.0 + - pot==0.9.5 + - prometheus-client==0.22.1 + - propcache==0.3.2 + - protobuf==6.31.1 + - psutil==7.0.0 + - pubchempy==1.0.4 + - pyarrow==21.0.0 + - pycparser==2.22 + - pydeseq2==0.5.2 + - pydot==4.0.1 + - pynndescent==0.5.13 + - pyomo==6.9.2 + - pyparsing==3.2.3 + - pyro-api==0.1.2 + - pyro-ppl==1.9.1 + - pytest==8.4.1 + - python-json-logger==3.3.0 + - pytorch-lightning==1.9.5 + - pytz==2025.2 + - pyyaml==6.0.2 + - pyzmq==27.0.1 + - rdkit==2025.3.5 + - referencing==0.36.2 + - requests==2.32.4 + - rfc3339-validator==0.1.4 + - rfc3986-validator==0.1.1 + - rfc3987-syntax==1.1.0 + - rich==14.1.0 + - rpds-py==0.26.0 + - safetensors==0.5.3 + - scanpy==1.11.4 + - scgpt==0.2.4 + - scib==1.1.7 + - scikit-learn==1.7.1 + - scikit-misc==0.5.1 + - scipy==1.16.1 + - scvi-tools==0.20.3 + - seaborn==0.13.2 + - send2trash==1.8.3 + - session-info==1.0.1 + - session-info2==0.2 + - shtab==1.7.2 + - simplejson==3.20.1 + - sniffio==1.3.1 + - soupsieve==2.7 + - sparse==0.17.0 + - sparsecca==0.3.1 + - statsmodels==0.14.5 + - stdlib-list==0.11.1 + - sympy==1.14.0 + - tensorboard==2.20.0 + - tensorboard-data-server==0.7.2 + - tensorstore==0.1.76 + - terminado==0.18.1 + - texttable==1.7.0 + - threadpoolctl==3.6.0 + - timm==1.0.19 + - tinycss2==1.4.0 + - tomlkit==0.13.3 + - toolz==1.0.0 + - torch==2.7.1 + - torch-geometric==2.6.1 + - torchaudio==2.7.1 + - torchdiffeq==0.2.5 + - torchinfo==1.8.0 + - torchmetrics==1.8.0 + - torchtext==0.18.0 + - torchvision==0.22.1 + - tornado==6.5.1 + - tqdm==4.67.1 + - treescope==0.1.9 + - triton==3.3.1 + - typeguard==4.4.4 + - types-python-dateutil==2.9.0.20250708 + - tyro==0.9.27 + - tzdata==2025.2 + - umap-learn==0.5.9.post2 + - uri-template==1.3.0 + - urllib3==2.5.0 + - wadler-lindig==0.1.7 + - webcolors==24.11.1 + - webencodings==0.5.1 + - websocket-client==1.8.0 + - werkzeug==3.1.3 + - widgetsnbextension==4.0.14 + - wrapt==1.17.2 + - xarray==2025.7.1 + - xarray-einstats==0.9.1 + - xxhash==3.5.0 + - yarl==1.20.1 + - zarr==3.1.1 +prefix: /opt/conda/envs/sc diff --git a/transfer/code/ori_scDFM/eval_baseline.sh b/transfer/code/ori_scDFM/eval_baseline.sh new file mode 100644 index 0000000000000000000000000000000000000000..0da66982d88086f052fa6ae08e012555a52c3122 --- /dev/null +++ b/transfer/code/ori_scDFM/eval_baseline.sh @@ -0,0 +1,58 @@ +#!/bin/sh +#PJM -L rscgrp=b-batch +#PJM -L gpu=1 +#PJM -L elapse=3:00:00 +#PJM -N eval_baseline +#PJM -j +#PJM -o logs/eval_baseline_%j.out + +module load cuda/12.2.2 +module load cudnn/8.9.7 +module load gcc-toolset/12 + +source /home/hp250092/ku50001222/qian/aivc/lfj/ori_scDFM_env/bin/activate +cd /home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM + +export PYTHONPATH=./ +export PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:256 + +CKPT="/home/hp250092/ku50001222/qian/aivc/lfj/transfer/CCFM_result/flow-fusion_differential_perceiver-norman-origin-predict_y-gamma_0.5-perturbation_function_crisper-lr_5e-05-dim_model_128-infer_top_gene_1000-split_method_additive-use_mmd_loss_True-fold_1-use_negative_edge_True-topk_30/iteration_100000/checkpoint.pt" + +echo "==========================================" +echo "Job ID: $PJM_JOBID" +echo "Job Name: $PJM_JOBNAME" +echo "Start: $(date)" +echo "Node: $(hostname)" +echo "GPU: $(nvidia-smi --query-gpu=name,memory.total --format=csv,noheader 2>/dev/null || echo 'N/A')" +echo "Eval: Baseline (ori_scDFM) iteration_100000" +echo "==========================================" + +python src/script/run.py \ + --batch_size=48 \ + --devices='0' \ + --model_type=origin \ + --lr=5e-5 \ + --steps=200000 \ + --data_name=norman \ + --d_model=128 \ + --eta_min=1e-6 \ + --fusion_method=differential_perceiver \ + --infer_top_gene=1000 \ + --n_top_genes=5000 \ + --perturbation_function=crisper \ + --noise_type=Gaussian \ + --mode=predict_y \ + --gamma=0.5 \ + --split_method=additive \ + --use_mmd_loss \ + --fold=1 \ + --topk=30 \ + --use_negative_edge \ + --print_every=10000 \ + --result_path=/home/hp250092/ku50001222/qian/aivc/lfj/transfer/CCFM_result \ + --checkpoint_path="$CKPT" \ + --test_only + +echo "==========================================" +echo "Finished: $(date)" +echo "==========================================" diff --git a/transfer/code/ori_scDFM/logs/baseline_ccfm_5402617.out b/transfer/code/ori_scDFM/logs/baseline_ccfm_5402617.out new file mode 100644 index 0000000000000000000000000000000000000000..f603fda09a9620d51585d2235a00e457748a6e60 --- /dev/null +++ b/transfer/code/ori_scDFM/logs/baseline_ccfm_5402617.out @@ -0,0 +1,579 @@ +========================================== +Job ID: 5402617 +Job Name: baseline_ccfm +Start: Sat Mar 14 14:29:41 JST 2026 +Node: b0018 +GPU: NVIDIA H100, 95830 MiB +NVIDIA H100, 95830 MiB +NVIDIA H100, 95830 MiB +NVIDIA H100, 95830 MiB +Run: baseline_ccfm_aligned (topk=15, no negative edge) +========================================== +ipex flag is deprecated, will be removed in Accelerate v1.10. From 2.7.0, PyTorch has all needed optimizations for Intel CPU and XPU. +The following values were not passed to `accelerate launch` and had defaults used instead: + More than one GPU was found, enabling multi-GPU training. + If this was unintended please pass in `--num_processes=1`. + `--num_machines` was set to a value of `1` + `--mixed_precision` was set to a value of `'no'` + `--dynamo_backend` was set to a value of `'no'` +To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`. +[W314 14:31:08.382724459 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol). +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/models/scGPT/model.py:1392: UserWarning: flash_attn is not installed + warnings.warn("flash_attn is not installed") +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/models/scGPT/model.py:1392: UserWarning: flash_attn is not installed + warnings.warn("flash_attn is not installed") +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/models/scGPT/model.py:1392: UserWarning: flash_attn is not installed + warnings.warn("flash_attn is not installed") +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/models/scGPT/model.py:1392: UserWarning: flash_attn is not installed + warnings.warn("flash_attn is not installed") +[W314 14:31:54.254519617 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol). +[W314 14:31:54.267059682 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol). +[W314 14:31:54.276467349 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol). +[W314 14:31:54.276713777 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol). +WARNING:accelerate.utils.other:Detected kernel version 4.18.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher. +FlowConfig(model_type='origin', batch_size=48, ntoken=512, d_model=128, lr=5e-05, steps=200000, eta_min=1e-06, devices='1', test_only=False, data_name='norman', perturbation_function='crisper', noise_type='Gaussian', poisson_alpha=0.8, poisson_target_sum=-1, print_every=10000, mode='predict_y', result_path='./result/baseline_ccfm_aligned', perturbation_fusion_method='sum', fusion_method='differential_perceiver', infer_top_gene=1000, n_top_genes=5000, checkpoint_path='', gamma=0.5, split_method='additive', use_mmd_loss=True, fold=1, use_negative_edge=False, topk=15) +Warning: AHR is not in the gene namesWarning: AHR is not in the gene namesWarning: AHR is not in the gene names + + +Warning: ARID1A is not in the gene namesWarning: AHR is not in the gene namesWarning: ARID1A is not in the gene namesWarning: ARID1A is not in the gene names + + + +Warning: ARRDC3 is not in the gene namesWarning: ARID1A is not in the gene namesWarning: ARRDC3 is not in the gene namesWarning: ARRDC3 is not in the gene names + + + +Warning: ATL1 is not in the gene namesWarning: ARRDC3 is not in the gene namesWarning: ATL1 is not in the gene namesWarning: ATL1 is not in the gene names + + + +Warning: BAK1 is not in the gene namesWarning: ATL1 is not in the gene namesWarning: BAK1 is not in the gene namesWarning: BAK1 is not in the gene names + + + +Warning: BCL2L11 is not in the gene namesWarning: BAK1 is not in the gene namesWarning: BCL2L11 is not in the gene namesWarning: BCL2L11 is not in the gene names + + + +Warning: BCORL1 is not in the gene namesWarning: BCORL1 is not in the gene namesWarning: BCL2L11 is not in the gene namesWarning: BCORL1 is not in the gene names + + + +Warning: BPGM is not in the gene namesWarning: BPGM is not in the gene names +Warning: BCORL1 is not in the gene names +Warning: BPGM is not in the gene namesWarning: C19orf26 is not in the gene names +Warning: C19orf26 is not in the gene names + + +Warning: C19orf26 is not in the gene namesWarning: BPGM is not in the gene namesWarning: C3orf72 is not in the gene namesWarning: C3orf72 is not in the gene names + + + +Warning: C3orf72 is not in the gene namesWarning: C19orf26 is not in the gene namesWarning: CBFA2T3 is not in the gene namesWarning: CBFA2T3 is not in the gene names + + + +Warning: CBFA2T3 is not in the gene namesWarning: CBL is not in the gene namesWarning: C3orf72 is not in the gene namesWarning: CBL is not in the gene names + + + +Warning: CBL is not in the gene namesWarning: CDKN1A is not in the gene namesWarning: CDKN1A is not in the gene names +Warning: CBFA2T3 is not in the gene names + +Warning: CDKN1A is not in the gene names +Warning: CDKN1B is not in the gene namesWarning: CDKN1B is not in the gene names + + +Warning: CBL is not in the gene namesWarning: CDKN1B is not in the gene namesWarning: CDKN1C is not in the gene namesWarning: CDKN1C is not in the gene names + + + +Warning: CDKN1C is not in the gene namesWarning: CEBPA is not in the gene namesWarning: CEBPA is not in the gene names +Warning: CDKN1A is not in the gene names + +Warning: CEBPA is not in the gene names +Warning: CEBPB is not in the gene namesWarning: CEBPB is not in the gene names + + +Warning: CDKN1B is not in the gene namesWarning: CEBPB is not in the gene namesWarning: CEBPE is not in the gene namesWarning: CEBPE is not in the gene names + + + +Warning: CEBPE is not in the gene namesWarning: CDKN1C is not in the gene namesWarning: CELF2 is not in the gene namesWarning: CELF2 is not in the gene names + + + +Warning: CELF2 is not in the gene namesWarning: CEBPA is not in the gene namesWarning: CITED1 is not in the gene namesWarning: CITED1 is not in the gene names + + + +Warning: CITED1 is not in the gene namesWarning: CEBPB is not in the gene namesWarning: CKS1B is not in the gene namesWarning: CKS1B is not in the gene names + + + +Warning: CKS1B is not in the gene namesWarning: CEBPE is not in the gene namesWarning: CLDN6 is not in the gene namesWarning: CLDN6 is not in the gene names + + + +Warning: CLDN6 is not in the gene namesWarning: CELF2 is not in the gene namesWarning: CNN1 is not in the gene namesWarning: CNN1 is not in the gene names + + + +Warning: CNN1 is not in the gene namesWarning: CITED1 is not in the gene namesWarning: CNNM4 is not in the gene namesWarning: CNNM4 is not in the gene names + + + +Warning: CNNM4 is not in the gene namesWarning: COL1A1 is not in the gene namesWarning: CKS1B is not in the gene namesWarning: COL1A1 is not in the gene names + + + +Warning: COL1A1 is not in the gene namesWarning: COL2A1 is not in the gene namesWarning: CLDN6 is not in the gene namesWarning: COL2A1 is not in the gene names + + + +Warning: COL2A1 is not in the gene namesWarning: CSRNP1 is not in the gene namesWarning: CSRNP1 is not in the gene names +Warning: CNN1 is not in the gene names + +Warning: CSRNP1 is not in the gene names +Warning: DLX2 is not in the gene namesWarning: DLX2 is not in the gene names +Warning: CNNM4 is not in the gene names + +Warning: DLX2 is not in the gene names +Warning: DUSP9 is not in the gene namesWarning: DUSP9 is not in the gene names +Warning: COL1A1 is not in the gene names + +Warning: DUSP9 is not in the gene names +Warning: EGR1 is not in the gene namesWarning: EGR1 is not in the gene names + +Warning: COL2A1 is not in the gene names +Warning: EGR1 is not in the gene namesWarning: ELMSAN1 is not in the gene names +Warning: ELMSAN1 is not in the gene names + +Warning: CSRNP1 is not in the gene names +Warning: ELMSAN1 is not in the gene namesWarning: ETS2 is not in the gene names +Warning: ETS2 is not in the gene names + +Warning: DLX2 is not in the gene names +Warning: ETS2 is not in the gene namesWarning: FEV is not in the gene names +Warning: FEV is not in the gene names + +Warning: DUSP9 is not in the gene names +Warning: FEV is not in the gene namesWarning: FOSB is not in the gene names +Warning: FOSB is not in the gene names + +Warning: EGR1 is not in the gene names +Warning: FOSB is not in the gene namesWarning: FOXA1 is not in the gene names +Warning: FOXA1 is not in the gene names + + +Warning: ELMSAN1 is not in the gene namesWarning: FOXA1 is not in the gene namesWarning: FOXA3 is not in the gene namesWarning: FOXA3 is not in the gene names + + + +Warning: ETS2 is not in the gene namesWarning: FOXA3 is not in the gene namesWarning: FOXF1 is not in the gene namesWarning: FOXF1 is not in the gene names + + + +Warning: FEV is not in the gene namesWarning: FOXF1 is not in the gene namesWarning: FOXL2 is not in the gene namesWarning: FOXL2 is not in the gene names + + + +Warning: FOXL2 is not in the gene namesWarning: FOSB is not in the gene namesWarning: FOXO4 is not in the gene namesWarning: FOXO4 is not in the gene names + + + +Warning: FOXO4 is not in the gene namesWarning: FOXA1 is not in the gene namesWarning: GLB1L2 is not in the gene namesWarning: GLB1L2 is not in the gene names + + + +Warning: GLB1L2 is not in the gene namesWarning: FOXA3 is not in the gene namesWarning: HES7 is not in the gene namesWarning: HES7 is not in the gene names + + + +Warning: HES7 is not in the gene namesWarning: FOXF1 is not in the gene namesWarning: HK2 is not in the gene namesWarning: HK2 is not in the gene names + + + +Warning: HK2 is not in the gene namesWarning: HNF4A is not in the gene namesWarning: FOXL2 is not in the gene namesWarning: HNF4A is not in the gene names + + + +Warning: HNF4A is not in the gene namesWarning: HOXA13 is not in the gene namesWarning: FOXO4 is not in the gene namesWarning: HOXA13 is not in the gene names + + + +Warning: HOXA13 is not in the gene namesWarning: HOXB9 is not in the gene namesWarning: GLB1L2 is not in the gene namesWarning: HOXB9 is not in the gene names + + + +Warning: HOXB9 is not in the gene namesWarning: HOXC13 is not in the gene namesWarning: HES7 is not in the gene namesWarning: HOXC13 is not in the gene names + + + +Warning: HOXC13 is not in the gene namesWarning: IER5L is not in the gene namesWarning: IER5L is not in the gene namesWarning: HK2 is not in the gene names + + + +Warning: IER5L is not in the gene namesWarning: IGDCC3 is not in the gene namesWarning: IGDCC3 is not in the gene namesWarning: HNF4A is not in the gene names + + + +Warning: IGDCC3 is not in the gene namesWarning: IKZF3 is not in the gene namesWarning: IKZF3 is not in the gene namesWarning: HOXA13 is not in the gene names + + + +Warning: IKZF3 is not in the gene namesWarning: IRF1 is not in the gene namesWarning: IRF1 is not in the gene namesWarning: HOXB9 is not in the gene names + + + +Warning: IRF1 is not in the gene namesWarning: ISL2 is not in the gene namesWarning: ISL2 is not in the gene namesWarning: HOXC13 is not in the gene names + + + +Warning: ISL2 is not in the gene namesWarning: JUN is not in the gene namesWarning: JUN is not in the gene namesWarning: IER5L is not in the gene names + + + +Warning: JUN is not in the gene namesWarning: KIAA1804 is not in the gene namesWarning: KIAA1804 is not in the gene namesWarning: IGDCC3 is not in the gene names + + + +Warning: KIAA1804 is not in the gene namesWarning: KIF18B is not in the gene namesWarning: KIF18B is not in the gene namesWarning: IKZF3 is not in the gene names + + + +Warning: KIF18B is not in the gene namesWarning: KIF2C is not in the gene namesWarning: KIF2C is not in the gene names + +Warning: IRF1 is not in the gene names +Warning: KIF2C is not in the gene namesWarning: KLF1 is not in the gene names +Warning: KLF1 is not in the gene names + +Warning: ISL2 is not in the gene names +Warning: KLF1 is not in the gene namesWarning: KMT2A is not in the gene names +Warning: KMT2A is not in the gene names + +Warning: JUN is not in the gene names +Warning: KMT2A is not in the gene namesWarning: LHX1 is not in the gene names +Warning: LHX1 is not in the gene namesWarning: KIAA1804 is not in the gene names + + + +Warning: LHX1 is not in the gene namesWarning: LYL1 is not in the gene namesWarning: LYL1 is not in the gene namesWarning: KIF18B is not in the gene names + + + +Warning: LYL1 is not in the gene namesWarning: MAML2 is not in the gene namesWarning: MAML2 is not in the gene namesWarning: KIF2C is not in the gene names + + + +Warning: MAML2 is not in the gene namesWarning: MAP2K3 is not in the gene namesWarning: MAP2K3 is not in the gene namesWarning: KLF1 is not in the gene names + + + +Warning: MAP2K3 is not in the gene namesWarning: MAP2K6 is not in the gene namesWarning: MAP2K6 is not in the gene namesWarning: KMT2A is not in the gene names + + + +Warning: MAP2K6 is not in the gene namesWarning: MAP4K3 is not in the gene namesWarning: MAP4K3 is not in the gene namesWarning: LHX1 is not in the gene names + + + +Warning: MAP4K3 is not in the gene namesWarning: MAP4K5 is not in the gene namesWarning: MAP4K5 is not in the gene namesWarning: LYL1 is not in the gene names + + + +Warning: MAP4K5 is not in the gene namesWarning: MAP7D1 is not in the gene namesWarning: MAP7D1 is not in the gene namesWarning: MAML2 is not in the gene names + + + +Warning: MAP7D1 is not in the gene namesWarning: MAPK1 is not in the gene namesWarning: MAPK1 is not in the gene namesWarning: MAP2K3 is not in the gene names + + + +Warning: MAPK1 is not in the gene namesWarning: MEIS1 is not in the gene namesWarning: MEIS1 is not in the gene namesWarning: MAP2K6 is not in the gene names + + + +Warning: MEIS1 is not in the gene namesWarning: MIDN is not in the gene namesWarning: MIDN is not in the gene names +Warning: MAP4K3 is not in the gene names + +Warning: MIDN is not in the gene names +Warning: NCL is not in the gene namesWarning: NCL is not in the gene names +Warning: MAP4K5 is not in the gene names + +Warning: NCL is not in the gene names +Warning: NIT1 is not in the gene namesWarning: NIT1 is not in the gene names +Warning: MAP7D1 is not in the gene names + +Warning: NIT1 is not in the gene names +Warning: OSR2 is not in the gene namesWarning: OSR2 is not in the gene names + +Warning: MAPK1 is not in the gene names +Warning: OSR2 is not in the gene namesWarning: PLK4 is not in the gene names +Warning: PLK4 is not in the gene names + +Warning: MEIS1 is not in the gene names +Warning: PLK4 is not in the gene namesWarning: POU3F2 is not in the gene names +Warning: POU3F2 is not in the gene names + +Warning: MIDN is not in the gene names +Warning: POU3F2 is not in the gene namesWarning: PRDM1 is not in the gene names +Warning: PRDM1 is not in the gene names + +Warning: NCL is not in the gene names +Warning: PRDM1 is not in the gene namesWarning: PRTG is not in the gene names +Warning: PRTG is not in the gene names + +Warning: NIT1 is not in the gene names +Warning: PRTG is not in the gene namesWarning: PTPN1 is not in the gene names +Warning: PTPN1 is not in the gene names + +Warning: OSR2 is not in the gene names +Warning: PTPN1 is not in the gene namesWarning: PTPN12 is not in the gene names +Warning: PTPN12 is not in the gene names + +Warning: PLK4 is not in the gene names +Warning: PTPN12 is not in the gene namesWarning: PTPN13 is not in the gene names +Warning: PTPN13 is not in the gene names + + +Warning: POU3F2 is not in the gene namesWarning: PTPN13 is not in the gene namesWarning: PTPN9 is not in the gene namesWarning: PTPN9 is not in the gene names + + + +Warning: PRDM1 is not in the gene namesWarning: PTPN9 is not in the gene namesWarning: RHOXF2BB is not in the gene namesWarning: RHOXF2BB is not in the gene names + + + +Warning: PRTG is not in the gene namesWarning: RHOXF2BB is not in the gene namesWarning: RREB1 is not in the gene namesWarning: RREB1 is not in the gene names + + + +Warning: PTPN1 is not in the gene namesWarning: RREB1 is not in the gene namesWarning: RUNX1T1 is not in the gene namesWarning: RUNX1T1 is not in the gene names + + + +Warning: PTPN12 is not in the gene namesWarning: RUNX1T1 is not in the gene namesWarning: S1PR2 is not in the gene namesWarning: S1PR2 is not in the gene names + + + +Warning: PTPN13 is not in the gene namesWarning: S1PR2 is not in the gene namesWarning: SAMD1 is not in the gene namesWarning: SAMD1 is not in the gene names + + + +Warning: PTPN9 is not in the gene namesWarning: SAMD1 is not in the gene namesWarning: SET is not in the gene namesWarning: SET is not in the gene names + + + +Warning: RHOXF2BB is not in the gene namesWarning: SET is not in the gene namesWarning: SGK1 is not in the gene namesWarning: SGK1 is not in the gene names + + + +Warning: RREB1 is not in the gene namesWarning: SGK1 is not in the gene namesWarning: SLC38A2 is not in the gene namesWarning: SLC38A2 is not in the gene names + + + +Warning: RUNX1T1 is not in the gene namesWarning: SLC38A2 is not in the gene namesWarning: SLC4A1 is not in the gene namesWarning: SLC4A1 is not in the gene names + + + +Warning: S1PR2 is not in the gene namesWarning: SLC4A1 is not in the gene namesWarning: SLC6A9 is not in the gene namesWarning: SLC6A9 is not in the gene names + + + +Warning: SAMD1 is not in the gene namesWarning: SLC6A9 is not in the gene namesWarning: SNAI1 is not in the gene namesWarning: SNAI1 is not in the gene names + + + +Warning: SET is not in the gene namesWarning: SNAI1 is not in the gene namesWarning: SPI1 is not in the gene namesWarning: SPI1 is not in the gene names + + + +Warning: SGK1 is not in the gene namesWarning: SPI1 is not in the gene namesWarning: STIL is not in the gene namesWarning: STIL is not in the gene names + + + +Warning: SLC38A2 is not in the gene namesWarning: STIL is not in the gene namesWarning: TBX2 is not in the gene namesWarning: TBX2 is not in the gene names + + + +Warning: SLC4A1 is not in the gene namesWarning: TBX2 is not in the gene namesWarning: TBX3 is not in the gene namesWarning: TBX3 is not in the gene names + + + +Warning: SLC6A9 is not in the gene namesWarning: TBX3 is not in the gene namesWarning: TGFBR2 is not in the gene namesWarning: TGFBR2 is not in the gene names + + + +Warning: SNAI1 is not in the gene namesWarning: TGFBR2 is not in the gene namesWarning: TMSB4X is not in the gene namesWarning: TMSB4X is not in the gene names + + + +Warning: SPI1 is not in the gene namesWarning: TMSB4X is not in the gene namesWarning: TP73 is not in the gene namesWarning: TP73 is not in the gene names + + + +Warning: STIL is not in the gene namesWarning: TP73 is not in the gene namesWarning: TSC22D1 is not in the gene namesWarning: TSC22D1 is not in the gene names + + + +Warning: TBX2 is not in the gene namesWarning: TSC22D1 is not in the gene namesWarning: UBASH3A is not in the gene namesWarning: UBASH3A is not in the gene names + + + +Warning: TBX3 is not in the gene namesWarning: UBASH3A is not in the gene namesWarning: UBASH3B is not in the gene namesWarning: UBASH3B is not in the gene names + + + +Warning: TGFBR2 is not in the gene namesWarning: UBASH3B is not in the gene namesWarning: ZBTB1 is not in the gene namesWarning: ZBTB1 is not in the gene names + + + +Warning: TMSB4X is not in the gene namesWarning: ZBTB1 is not in the gene namesWarning: ZBTB10 is not in the gene namesWarning: ZBTB10 is not in the gene names + + + +Warning: TP73 is not in the gene namesWarning: ZBTB10 is not in the gene namesWarning: ZBTB25 is not in the gene namesWarning: ZBTB25 is not in the gene names + + + +Warning: TSC22D1 is not in the gene namesWarning: ZBTB25 is not in the gene namesWarning: ZC3HAV1 is not in the gene namesWarning: ZC3HAV1 is not in the gene names + + + +Warning: UBASH3A is not in the gene namesWarning: ZC3HAV1 is not in the gene namesWarning: ZNF318 is not in the gene namesWarning: ZNF318 is not in the gene names + + + +Warning: UBASH3B is not in the gene namesWarning: ZNF318 is not in the gene namesWarning: ctrl is not in the gene namesWarning: ctrl is not in the gene names + + + +Warning: ZBTB1 is not in the gene namesWarning: ctrl is not in the gene names + +Warning: ZBTB10 is not in the gene names +Warning: ZBTB25 is not in the gene names +Warning: ZC3HAV1 is not in the gene names +Warning: ZNF318 is not in the gene names +Warning: ctrl is not in the gene names +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/data_process/data.py:185: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control') +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/data_process/data.py:185: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control') +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/data_process/data.py:185: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control') +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/data_process/data.py:185: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control') +/home/hp250092/ku50001222/qian/aivc/lfj/ori_scDFM_env/lib64/python3.11/site-packages/scanpy/preprocessing/_highly_variable_genes.py:705: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. + adata.uns["hvg"] = {"flavor": flavor} +/home/hp250092/ku50001222/qian/aivc/lfj/ori_scDFM_env/lib64/python3.11/site-packages/scanpy/preprocessing/_highly_variable_genes.py:705: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. + adata.uns["hvg"] = {"flavor": flavor} +/home/hp250092/ku50001222/qian/aivc/lfj/ori_scDFM_env/lib64/python3.11/site-packages/scanpy/preprocessing/_highly_variable_genes.py:705: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. + adata.uns["hvg"] = {"flavor": flavor} +/home/hp250092/ku50001222/qian/aivc/lfj/ori_scDFM_env/lib64/python3.11/site-packages/scanpy/preprocessing/_highly_variable_genes.py:705: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. + adata.uns["hvg"] = {"flavor": flavor} +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/data_process/data.py:277: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/data_process/data.py:277: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/data_process/data.py:277: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/data_process/data.py:277: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/data_process/data.py:329: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/data_process/data.py:329: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/data_process/data.py:329: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/data_process/data.py:329: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +##### loading vocab from file ##### +[rank3]: Traceback (most recent call last): +[rank3]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/script/run.py", line 279, in +[rank3]: gene_ids = vocab.encode(list(data_manager.adata.var_names)) +[rank3]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank3]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/tokenizer/gene_tokenizer.py", line 67, in encode +[rank3]: return [self.stoi[token] for token in tokens] +[rank3]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank3]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/tokenizer/gene_tokenizer.py", line 67, in +[rank3]: return [self.stoi[token] for token in tokens] +[rank3]: ~~~~~~~~~^^^^^^^ +[rank3]: KeyError: 'ENSG00000239945' +##### loading vocab from file ##### +[rank1]: Traceback (most recent call last): +[rank1]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/script/run.py", line 279, in +[rank1]: gene_ids = vocab.encode(list(data_manager.adata.var_names)) +[rank1]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank1]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/tokenizer/gene_tokenizer.py", line 67, in encode +[rank1]: return [self.stoi[token] for token in tokens] +[rank1]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank1]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/tokenizer/gene_tokenizer.py", line 67, in +[rank1]: return [self.stoi[token] for token in tokens] +[rank1]: ~~~~~~~~~^^^^^^^ +[rank1]: KeyError: 'ENSG00000239945' +##### loading vocab from file ##### +[rank0]: Traceback (most recent call last): +[rank0]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/script/run.py", line 279, in +[rank0]: gene_ids = vocab.encode(list(data_manager.adata.var_names)) +[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank0]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/tokenizer/gene_tokenizer.py", line 67, in encode +[rank0]: return [self.stoi[token] for token in tokens] +[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank0]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/tokenizer/gene_tokenizer.py", line 67, in +[rank0]: return [self.stoi[token] for token in tokens] +[rank0]: ~~~~~~~~~^^^^^^^ +[rank0]: KeyError: 'ENSG00000239945' +##### loading vocab from file ##### +[rank2]: Traceback (most recent call last): +[rank2]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/script/run.py", line 279, in +[rank2]: gene_ids = vocab.encode(list(data_manager.adata.var_names)) +[rank2]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank2]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/tokenizer/gene_tokenizer.py", line 67, in encode +[rank2]: return [self.stoi[token] for token in tokens] +[rank2]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank2]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/tokenizer/gene_tokenizer.py", line 67, in +[rank2]: return [self.stoi[token] for token in tokens] +[rank2]: ~~~~~~~~~^^^^^^^ +[rank2]: KeyError: 'ENSG00000239945' +[rank0]:[W314 14:32:14.929098959 ProcessGroupNCCL.cpp:1479] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator()) +W0314 14:32:20.274000 126 torch/distributed/elastic/multiprocessing/api.py:900] Sending process 199 closing signal SIGTERM +W0314 14:32:20.275000 126 torch/distributed/elastic/multiprocessing/api.py:900] Sending process 200 closing signal SIGTERM +W0314 14:32:20.275000 126 torch/distributed/elastic/multiprocessing/api.py:900] Sending process 201 closing signal SIGTERM +E0314 14:32:20.492000 126 torch/distributed/elastic/multiprocessing/api.py:874] failed (exitcode: 1) local_rank: 0 (pid: 198) of binary: /home/hp250092/ku50001222/qian/aivc/lfj/ori_scDFM_env/bin/python3.11 +Traceback (most recent call last): + File "/home/hp250092/ku50001222/qian/aivc/lfj/ori_scDFM_env/bin/accelerate", line 6, in + sys.exit(main()) + ^^^^^^ + File "/home/hp250092/ku50001222/qian/aivc/lfj/ori_scDFM_env/lib64/python3.11/site-packages/accelerate/commands/accelerate_cli.py", line 50, in main + args.func(args) + File "/home/hp250092/ku50001222/qian/aivc/lfj/ori_scDFM_env/lib64/python3.11/site-packages/accelerate/commands/launch.py", line 1190, in launch_command + multi_gpu_launcher(args) + File "/home/hp250092/ku50001222/qian/aivc/lfj/ori_scDFM_env/lib64/python3.11/site-packages/accelerate/commands/launch.py", line 815, in multi_gpu_launcher + distrib_run.run(args) + File "/home/hp250092/ku50001222/qian/aivc/lfj/ori_scDFM_env/lib64/python3.11/site-packages/torch/distributed/run.py", line 883, in run + elastic_launch( + File "/home/hp250092/ku50001222/qian/aivc/lfj/ori_scDFM_env/lib64/python3.11/site-packages/torch/distributed/launcher/api.py", line 139, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/hp250092/ku50001222/qian/aivc/lfj/ori_scDFM_env/lib64/python3.11/site-packages/torch/distributed/launcher/api.py", line 270, in launch_agent + raise ChildFailedError( +torch.distributed.elastic.multiprocessing.errors.ChildFailedError: +============================================================ +src/script/run.py FAILED +------------------------------------------------------------ +Failures: + +------------------------------------------------------------ +Root Cause (first observed failure): +[0]: + time : 2026-03-14_14:32:20 + host : b0018 + rank : 0 (local_rank: 0) + exitcode : 1 (pid: 198) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +============================================================ +========================================== +Finished: Sat Mar 14 14:32:21 JST 2026 +========================================== diff --git a/transfer/code/ori_scDFM/logs/baseline_original_5402618.out b/transfer/code/ori_scDFM/logs/baseline_original_5402618.out new file mode 100644 index 0000000000000000000000000000000000000000..6e01bedd9a2033a275294e1e4e477e8f42eb2a3d --- /dev/null +++ b/transfer/code/ori_scDFM/logs/baseline_original_5402618.out @@ -0,0 +1,583 @@ +========================================== +Job ID: 5402618 +Job Name: baseline_original +Start: Sat Mar 14 14:29:47 JST 2026 +Node: b0020 +GPU: NVIDIA H100, 95830 MiB +NVIDIA H100, 95830 MiB +NVIDIA H100, 95830 MiB +NVIDIA H100, 95830 MiB +Run: baseline_original (topk=30, negative edge) +========================================== +ipex flag is deprecated, will be removed in Accelerate v1.10. From 2.7.0, PyTorch has all needed optimizations for Intel CPU and XPU. +The following values were not passed to `accelerate launch` and had defaults used instead: + More than one GPU was found, enabling multi-GPU training. + If this was unintended please pass in `--num_processes=1`. + `--num_machines` was set to a value of `1` + `--mixed_precision` was set to a value of `'no'` + `--dynamo_backend` was set to a value of `'no'` +To avoid this warning pass in values for each of the problematic parameters or run `accelerate config`. +[W314 14:31:08.365914653 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol). +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/models/scGPT/model.py:1392: UserWarning: flash_attn is not installed + warnings.warn("flash_attn is not installed") +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/models/scGPT/model.py:1392: UserWarning: flash_attn is not installed + warnings.warn("flash_attn is not installed") +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/models/scGPT/model.py:1392: UserWarning: flash_attn is not installed + warnings.warn("flash_attn is not installed") +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/models/scGPT/model.py:1392: UserWarning: flash_attn is not installed + warnings.warn("flash_attn is not installed") +[W314 14:31:54.195356166 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol). +[W314 14:31:54.212635237 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol). +[W314 14:31:54.616293686 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol). +[W314 14:31:54.623997463 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [localhost]:29500 (errno: 97 - Address family not supported by protocol). +WARNING:accelerate.utils.other:Detected kernel version 4.18.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher. +FlowConfig(model_type='origin', batch_size=48, ntoken=512, d_model=128, lr=5e-05, steps=200000, eta_min=1e-06, devices='1', test_only=False, data_name='norman', perturbation_function='crisper', noise_type='Gaussian', poisson_alpha=0.8, poisson_target_sum=-1, print_every=10000, mode='predict_y', result_path='./result/baseline_original', perturbation_fusion_method='sum', fusion_method='differential_perceiver', infer_top_gene=1000, n_top_genes=5000, checkpoint_path='', gamma=0.5, split_method='additive', use_mmd_loss=True, fold=1, use_negative_edge=True, topk=30) +Warning: AHR is not in the gene namesWarning: AHR is not in the gene names +Warning: AHR is not in the gene namesWarning: AHR is not in the gene names +Warning: ARID1A is not in the gene names + +Warning: ARID1A is not in the gene names +Warning: ARID1A is not in the gene names +Warning: ARRDC3 is not in the gene names +Warning: ARRDC3 is not in the gene names +Warning: ARID1A is not in the gene namesWarning: ARRDC3 is not in the gene names +Warning: ATL1 is not in the gene names + +Warning: ATL1 is not in the gene names +Warning: ATL1 is not in the gene names +Warning: BAK1 is not in the gene namesWarning: ARRDC3 is not in the gene names +Warning: BAK1 is not in the gene names + +Warning: BAK1 is not in the gene names +Warning: BCL2L11 is not in the gene namesWarning: ATL1 is not in the gene names +Warning: BCL2L11 is not in the gene names + + +Warning: BCL2L11 is not in the gene namesWarning: BCORL1 is not in the gene namesWarning: BAK1 is not in the gene namesWarning: BCORL1 is not in the gene names + + + +Warning: BCORL1 is not in the gene namesWarning: BPGM is not in the gene names + +Warning: BPGM is not in the gene namesWarning: BCL2L11 is not in the gene namesWarning: C19orf26 is not in the gene names + +Warning: BPGM is not in the gene names +Warning: C19orf26 is not in the gene names +Warning: C3orf72 is not in the gene names +Warning: C19orf26 is not in the gene names +Warning: BCORL1 is not in the gene namesWarning: C3orf72 is not in the gene names +Warning: CBFA2T3 is not in the gene names + +Warning: C3orf72 is not in the gene names +Warning: CBFA2T3 is not in the gene names +Warning: BPGM is not in the gene namesWarning: CBL is not in the gene names +Warning: CBFA2T3 is not in the gene names + +Warning: CBL is not in the gene names +Warning: CDKN1A is not in the gene namesWarning: C19orf26 is not in the gene names +Warning: CBL is not in the gene names + +Warning: CDKN1A is not in the gene names +Warning: CDKN1B is not in the gene namesWarning: C3orf72 is not in the gene names +Warning: CDKN1A is not in the gene names + +Warning: CDKN1B is not in the gene names +Warning: CDKN1C is not in the gene namesWarning: CBFA2T3 is not in the gene names +Warning: CDKN1B is not in the gene names + +Warning: CDKN1C is not in the gene names +Warning: CEBPA is not in the gene names +Warning: CBL is not in the gene namesWarning: CDKN1C is not in the gene names +Warning: CEBPA is not in the gene names + +Warning: CEBPB is not in the gene names +Warning: CEBPA is not in the gene namesWarning: CDKN1A is not in the gene names +Warning: CEBPB is not in the gene names + +Warning: CEBPE is not in the gene names +Warning: CEBPB is not in the gene namesWarning: CDKN1B is not in the gene names +Warning: CEBPE is not in the gene names + +Warning: CELF2 is not in the gene names +Warning: CEBPE is not in the gene namesWarning: CDKN1C is not in the gene names +Warning: CELF2 is not in the gene names + +Warning: CITED1 is not in the gene names +Warning: CELF2 is not in the gene names +Warning: CEBPA is not in the gene namesWarning: CITED1 is not in the gene names +Warning: CKS1B is not in the gene names + +Warning: CITED1 is not in the gene names +Warning: CEBPB is not in the gene namesWarning: CKS1B is not in the gene names +Warning: CLDN6 is not in the gene names + +Warning: CKS1B is not in the gene names +Warning: CEBPE is not in the gene namesWarning: CLDN6 is not in the gene names +Warning: CNN1 is not in the gene names + +Warning: CLDN6 is not in the gene names +Warning: CELF2 is not in the gene namesWarning: CNN1 is not in the gene names +Warning: CNNM4 is not in the gene names + +Warning: CNN1 is not in the gene names +Warning: CNNM4 is not in the gene namesWarning: CITED1 is not in the gene names +Warning: COL1A1 is not in the gene names + +Warning: CNNM4 is not in the gene names +Warning: COL1A1 is not in the gene namesWarning: CKS1B is not in the gene names +Warning: COL2A1 is not in the gene names + +Warning: COL1A1 is not in the gene names +Warning: COL2A1 is not in the gene names +Warning: CLDN6 is not in the gene namesWarning: CSRNP1 is not in the gene names +Warning: COL2A1 is not in the gene names + +Warning: CSRNP1 is not in the gene names +Warning: CNN1 is not in the gene namesWarning: DLX2 is not in the gene names +Warning: CSRNP1 is not in the gene names + +Warning: DLX2 is not in the gene names +Warning: DUSP9 is not in the gene namesWarning: CNNM4 is not in the gene names +Warning: DLX2 is not in the gene names + +Warning: DUSP9 is not in the gene names +Warning: EGR1 is not in the gene names +Warning: COL1A1 is not in the gene namesWarning: DUSP9 is not in the gene names +Warning: EGR1 is not in the gene names + +Warning: ELMSAN1 is not in the gene names +Warning: COL2A1 is not in the gene namesWarning: EGR1 is not in the gene names +Warning: ELMSAN1 is not in the gene names + +Warning: ETS2 is not in the gene names +Warning: CSRNP1 is not in the gene namesWarning: ELMSAN1 is not in the gene names +Warning: ETS2 is not in the gene names + +Warning: FEV is not in the gene names +Warning: DLX2 is not in the gene names +Warning: ETS2 is not in the gene namesWarning: FEV is not in the gene names +Warning: FOSB is not in the gene names + + +Warning: DUSP9 is not in the gene namesWarning: FEV is not in the gene namesWarning: FOSB is not in the gene namesWarning: FOXA1 is not in the gene names + + + +Warning: EGR1 is not in the gene namesWarning: FOSB is not in the gene namesWarning: FOXA1 is not in the gene namesWarning: FOXA3 is not in the gene names + + + +Warning: ELMSAN1 is not in the gene namesWarning: FOXA1 is not in the gene namesWarning: FOXA3 is not in the gene namesWarning: FOXF1 is not in the gene names + + + +Warning: FOXA3 is not in the gene namesWarning: ETS2 is not in the gene namesWarning: FOXF1 is not in the gene namesWarning: FOXL2 is not in the gene names + + + +Warning: FOXF1 is not in the gene namesWarning: FEV is not in the gene namesWarning: FOXL2 is not in the gene namesWarning: FOXO4 is not in the gene names + + + +Warning: FOXL2 is not in the gene namesWarning: FOSB is not in the gene namesWarning: FOXO4 is not in the gene namesWarning: GLB1L2 is not in the gene names + + + +Warning: FOXO4 is not in the gene namesWarning: FOXA1 is not in the gene namesWarning: GLB1L2 is not in the gene namesWarning: HES7 is not in the gene names + + + +Warning: GLB1L2 is not in the gene namesWarning: HES7 is not in the gene namesWarning: FOXA3 is not in the gene namesWarning: HK2 is not in the gene names + + + +Warning: HES7 is not in the gene namesWarning: HK2 is not in the gene namesWarning: FOXF1 is not in the gene namesWarning: HNF4A is not in the gene names + + + +Warning: HK2 is not in the gene namesWarning: HNF4A is not in the gene namesWarning: FOXL2 is not in the gene namesWarning: HOXA13 is not in the gene names + + + +Warning: HNF4A is not in the gene namesWarning: HOXA13 is not in the gene namesWarning: HOXB9 is not in the gene namesWarning: FOXO4 is not in the gene names + + + +Warning: HOXA13 is not in the gene namesWarning: HOXB9 is not in the gene namesWarning: HOXC13 is not in the gene namesWarning: GLB1L2 is not in the gene names + + + +Warning: HOXB9 is not in the gene namesWarning: HOXC13 is not in the gene namesWarning: IER5L is not in the gene namesWarning: HES7 is not in the gene names + + + +Warning: HOXC13 is not in the gene namesWarning: IER5L is not in the gene namesWarning: IGDCC3 is not in the gene namesWarning: HK2 is not in the gene names + + + +Warning: IER5L is not in the gene namesWarning: IGDCC3 is not in the gene namesWarning: IKZF3 is not in the gene namesWarning: HNF4A is not in the gene names + + + +Warning: IGDCC3 is not in the gene namesWarning: IKZF3 is not in the gene namesWarning: IRF1 is not in the gene namesWarning: HOXA13 is not in the gene names + + + +Warning: IKZF3 is not in the gene namesWarning: HOXB9 is not in the gene namesWarning: IRF1 is not in the gene namesWarning: ISL2 is not in the gene names + + + +Warning: IRF1 is not in the gene namesWarning: HOXC13 is not in the gene namesWarning: ISL2 is not in the gene namesWarning: JUN is not in the gene names + + + +Warning: ISL2 is not in the gene namesWarning: IER5L is not in the gene namesWarning: JUN is not in the gene namesWarning: KIAA1804 is not in the gene names + + + +Warning: JUN is not in the gene namesWarning: IGDCC3 is not in the gene namesWarning: KIAA1804 is not in the gene namesWarning: KIF18B is not in the gene names + + + +Warning: KIAA1804 is not in the gene namesWarning: IKZF3 is not in the gene namesWarning: KIF18B is not in the gene namesWarning: KIF2C is not in the gene names + + + +Warning: KIF18B is not in the gene namesWarning: KIF2C is not in the gene namesWarning: IRF1 is not in the gene namesWarning: KLF1 is not in the gene names + + + +Warning: KIF2C is not in the gene namesWarning: KLF1 is not in the gene namesWarning: ISL2 is not in the gene namesWarning: KMT2A is not in the gene names + + + +Warning: KLF1 is not in the gene namesWarning: KMT2A is not in the gene namesWarning: JUN is not in the gene namesWarning: LHX1 is not in the gene names + + + +Warning: KMT2A is not in the gene namesWarning: LHX1 is not in the gene namesWarning: KIAA1804 is not in the gene namesWarning: LYL1 is not in the gene names + + + +Warning: LHX1 is not in the gene namesWarning: LYL1 is not in the gene namesWarning: KIF18B is not in the gene namesWarning: MAML2 is not in the gene names + + + +Warning: LYL1 is not in the gene namesWarning: MAML2 is not in the gene namesWarning: KIF2C is not in the gene namesWarning: MAP2K3 is not in the gene names + + + +Warning: MAML2 is not in the gene namesWarning: MAP2K3 is not in the gene namesWarning: KLF1 is not in the gene namesWarning: MAP2K6 is not in the gene names + + + +Warning: MAP2K3 is not in the gene namesWarning: MAP2K6 is not in the gene namesWarning: KMT2A is not in the gene namesWarning: MAP4K3 is not in the gene names + + + +Warning: MAP2K6 is not in the gene namesWarning: MAP4K3 is not in the gene namesWarning: LHX1 is not in the gene namesWarning: MAP4K5 is not in the gene names + + + +Warning: MAP4K3 is not in the gene namesWarning: MAP4K5 is not in the gene namesWarning: LYL1 is not in the gene namesWarning: MAP7D1 is not in the gene names + + + +Warning: MAP4K5 is not in the gene namesWarning: MAP7D1 is not in the gene namesWarning: MAML2 is not in the gene namesWarning: MAPK1 is not in the gene names + + + +Warning: MAP7D1 is not in the gene namesWarning: MAPK1 is not in the gene namesWarning: MAP2K3 is not in the gene namesWarning: MEIS1 is not in the gene names + + + +Warning: MAPK1 is not in the gene namesWarning: MEIS1 is not in the gene namesWarning: MAP2K6 is not in the gene namesWarning: MIDN is not in the gene names + + + +Warning: MEIS1 is not in the gene namesWarning: MIDN is not in the gene namesWarning: MAP4K3 is not in the gene namesWarning: NCL is not in the gene names + + + +Warning: MIDN is not in the gene namesWarning: NCL is not in the gene namesWarning: MAP4K5 is not in the gene namesWarning: NIT1 is not in the gene names + + + +Warning: NCL is not in the gene namesWarning: NIT1 is not in the gene namesWarning: MAP7D1 is not in the gene namesWarning: OSR2 is not in the gene names + + + +Warning: NIT1 is not in the gene namesWarning: OSR2 is not in the gene namesWarning: MAPK1 is not in the gene namesWarning: PLK4 is not in the gene names + + + +Warning: OSR2 is not in the gene namesWarning: PLK4 is not in the gene namesWarning: POU3F2 is not in the gene namesWarning: MEIS1 is not in the gene names + + + +Warning: PLK4 is not in the gene namesWarning: POU3F2 is not in the gene namesWarning: PRDM1 is not in the gene namesWarning: MIDN is not in the gene names + + + +Warning: POU3F2 is not in the gene namesWarning: PRDM1 is not in the gene namesWarning: PRTG is not in the gene namesWarning: NCL is not in the gene names + + + +Warning: PRDM1 is not in the gene namesWarning: PRTG is not in the gene namesWarning: PTPN1 is not in the gene names +Warning: NIT1 is not in the gene names + +Warning: PRTG is not in the gene names +Warning: PTPN1 is not in the gene namesWarning: PTPN12 is not in the gene names +Warning: OSR2 is not in the gene names + +Warning: PTPN1 is not in the gene names +Warning: PTPN12 is not in the gene namesWarning: PTPN13 is not in the gene names +Warning: PLK4 is not in the gene names + +Warning: PTPN12 is not in the gene names +Warning: PTPN13 is not in the gene namesWarning: PTPN9 is not in the gene names +Warning: POU3F2 is not in the gene names + +Warning: PTPN13 is not in the gene names +Warning: PTPN9 is not in the gene namesWarning: RHOXF2BB is not in the gene names +Warning: PRDM1 is not in the gene names + +Warning: PTPN9 is not in the gene names +Warning: RHOXF2BB is not in the gene namesWarning: RREB1 is not in the gene names +Warning: PRTG is not in the gene names + +Warning: RHOXF2BB is not in the gene names +Warning: RREB1 is not in the gene namesWarning: RUNX1T1 is not in the gene names +Warning: PTPN1 is not in the gene names + +Warning: RREB1 is not in the gene names +Warning: RUNX1T1 is not in the gene namesWarning: S1PR2 is not in the gene names +Warning: PTPN12 is not in the gene names + +Warning: RUNX1T1 is not in the gene names +Warning: S1PR2 is not in the gene namesWarning: SAMD1 is not in the gene names +Warning: PTPN13 is not in the gene names + +Warning: S1PR2 is not in the gene names +Warning: SAMD1 is not in the gene namesWarning: SET is not in the gene names +Warning: PTPN9 is not in the gene names + +Warning: SAMD1 is not in the gene names +Warning: SET is not in the gene namesWarning: SGK1 is not in the gene names +Warning: RHOXF2BB is not in the gene names + +Warning: SET is not in the gene names +Warning: SGK1 is not in the gene namesWarning: SLC38A2 is not in the gene names +Warning: RREB1 is not in the gene names + +Warning: SGK1 is not in the gene names +Warning: SLC38A2 is not in the gene namesWarning: SLC4A1 is not in the gene names +Warning: RUNX1T1 is not in the gene names + +Warning: SLC38A2 is not in the gene names +Warning: SLC4A1 is not in the gene namesWarning: SLC6A9 is not in the gene names +Warning: S1PR2 is not in the gene names + +Warning: SLC4A1 is not in the gene names +Warning: SLC6A9 is not in the gene namesWarning: SNAI1 is not in the gene names +Warning: SAMD1 is not in the gene names + +Warning: SLC6A9 is not in the gene names +Warning: SNAI1 is not in the gene namesWarning: SPI1 is not in the gene names +Warning: SET is not in the gene names + +Warning: SNAI1 is not in the gene names +Warning: SPI1 is not in the gene namesWarning: STIL is not in the gene names +Warning: SGK1 is not in the gene names + +Warning: SPI1 is not in the gene names +Warning: STIL is not in the gene namesWarning: TBX2 is not in the gene names +Warning: SLC38A2 is not in the gene names + +Warning: STIL is not in the gene names +Warning: TBX2 is not in the gene namesWarning: TBX3 is not in the gene names +Warning: SLC4A1 is not in the gene names + +Warning: TBX2 is not in the gene names +Warning: TBX3 is not in the gene namesWarning: TGFBR2 is not in the gene names +Warning: SLC6A9 is not in the gene names + +Warning: TBX3 is not in the gene names +Warning: TGFBR2 is not in the gene namesWarning: TMSB4X is not in the gene names +Warning: SNAI1 is not in the gene names + +Warning: TGFBR2 is not in the gene names +Warning: TMSB4X is not in the gene namesWarning: TP73 is not in the gene names +Warning: SPI1 is not in the gene names + +Warning: TMSB4X is not in the gene names +Warning: TP73 is not in the gene namesWarning: TSC22D1 is not in the gene names +Warning: STIL is not in the gene names + +Warning: TP73 is not in the gene names +Warning: TSC22D1 is not in the gene namesWarning: UBASH3A is not in the gene names +Warning: TBX2 is not in the gene names + +Warning: TSC22D1 is not in the gene names +Warning: UBASH3A is not in the gene namesWarning: UBASH3B is not in the gene names +Warning: TBX3 is not in the gene names + +Warning: UBASH3A is not in the gene names +Warning: UBASH3B is not in the gene namesWarning: ZBTB1 is not in the gene names +Warning: TGFBR2 is not in the gene names + +Warning: UBASH3B is not in the gene names +Warning: ZBTB1 is not in the gene namesWarning: ZBTB10 is not in the gene names +Warning: TMSB4X is not in the gene names + +Warning: ZBTB1 is not in the gene names +Warning: ZBTB10 is not in the gene namesWarning: ZBTB25 is not in the gene names +Warning: TP73 is not in the gene names + +Warning: ZBTB10 is not in the gene names +Warning: ZBTB25 is not in the gene namesWarning: ZC3HAV1 is not in the gene names +Warning: TSC22D1 is not in the gene names + +Warning: ZBTB25 is not in the gene names +Warning: ZC3HAV1 is not in the gene namesWarning: ZNF318 is not in the gene names + +Warning: UBASH3A is not in the gene names +Warning: ZC3HAV1 is not in the gene namesWarning: ZNF318 is not in the gene names +Warning: ctrl is not in the gene names + +Warning: UBASH3B is not in the gene names +Warning: ZNF318 is not in the gene namesWarning: ctrl is not in the gene names + + +Warning: ZBTB1 is not in the gene namesWarning: ctrl is not in the gene names + +Warning: ZBTB10 is not in the gene names +Warning: ZBTB25 is not in the gene names +Warning: ZC3HAV1 is not in the gene names +Warning: ZNF318 is not in the gene names +Warning: ctrl is not in the gene names +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/data_process/data.py:185: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control') +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/data_process/data.py:185: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control') +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/data_process/data.py:185: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control') +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/data_process/data.py:185: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control') +/home/hp250092/ku50001222/qian/aivc/lfj/ori_scDFM_env/lib64/python3.11/site-packages/scanpy/preprocessing/_highly_variable_genes.py:705: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. + adata.uns["hvg"] = {"flavor": flavor} +/home/hp250092/ku50001222/qian/aivc/lfj/ori_scDFM_env/lib64/python3.11/site-packages/scanpy/preprocessing/_highly_variable_genes.py:705: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. + adata.uns["hvg"] = {"flavor": flavor} +/home/hp250092/ku50001222/qian/aivc/lfj/ori_scDFM_env/lib64/python3.11/site-packages/scanpy/preprocessing/_highly_variable_genes.py:705: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. + adata.uns["hvg"] = {"flavor": flavor} +/home/hp250092/ku50001222/qian/aivc/lfj/ori_scDFM_env/lib64/python3.11/site-packages/scanpy/preprocessing/_highly_variable_genes.py:705: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. + adata.uns["hvg"] = {"flavor": flavor} +mask saved +mask saved +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/data_process/data.py:277: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/data_process/data.py:277: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +mask saved +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/data_process/data.py:277: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +mask saved +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/data_process/data.py:277: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/data_process/data.py:329: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/data_process/data.py:329: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/data_process/data.py:329: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +##### loading vocab from file ##### +[rank1]: Traceback (most recent call last): +[rank1]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/script/run.py", line 279, in +[rank1]: gene_ids = vocab.encode(list(data_manager.adata.var_names)) +[rank1]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank1]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/tokenizer/gene_tokenizer.py", line 67, in encode +[rank1]: return [self.stoi[token] for token in tokens] +[rank1]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank1]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/tokenizer/gene_tokenizer.py", line 67, in +[rank1]: return [self.stoi[token] for token in tokens] +[rank1]: ~~~~~~~~~^^^^^^^ +[rank1]: KeyError: 'ENSG00000239945' +##### loading vocab from file ##### +[rank0]: Traceback (most recent call last): +[rank0]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/script/run.py", line 279, in +[rank0]: gene_ids = vocab.encode(list(data_manager.adata.var_names)) +[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank0]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/tokenizer/gene_tokenizer.py", line 67, in encode +[rank0]: return [self.stoi[token] for token in tokens] +[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank0]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/tokenizer/gene_tokenizer.py", line 67, in +[rank0]: return [self.stoi[token] for token in tokens] +[rank0]: ~~~~~~~~~^^^^^^^ +[rank0]: KeyError: 'ENSG00000239945' +[rank0]:[W314 14:33:01.737475875 ProcessGroupNCCL.cpp:1479] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator()) +##### loading vocab from file ##### +[rank2]: Traceback (most recent call last): +[rank2]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/script/run.py", line 279, in +[rank2]: gene_ids = vocab.encode(list(data_manager.adata.var_names)) +[rank2]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank2]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/tokenizer/gene_tokenizer.py", line 67, in encode +[rank2]: return [self.stoi[token] for token in tokens] +[rank2]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank2]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/tokenizer/gene_tokenizer.py", line 67, in +[rank2]: return [self.stoi[token] for token in tokens] +[rank2]: ~~~~~~~~~^^^^^^^ +[rank2]: KeyError: 'ENSG00000239945' +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/data_process/data.py:329: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +##### loading vocab from file ##### +[rank3]: Traceback (most recent call last): +[rank3]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/script/run.py", line 279, in +[rank3]: gene_ids = vocab.encode(list(data_manager.adata.var_names)) +[rank3]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank3]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/tokenizer/gene_tokenizer.py", line 67, in encode +[rank3]: return [self.stoi[token] for token in tokens] +[rank3]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +[rank3]: File "/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/tokenizer/gene_tokenizer.py", line 67, in +[rank3]: return [self.stoi[token] for token in tokens] +[rank3]: ~~~~~~~~~^^^^^^^ +[rank3]: KeyError: 'ENSG00000239945' +W0314 14:33:05.293000 126 torch/distributed/elastic/multiprocessing/api.py:900] Sending process 199 closing signal SIGTERM +W0314 14:33:05.294000 126 torch/distributed/elastic/multiprocessing/api.py:900] Sending process 200 closing signal SIGTERM +W0314 14:33:05.294000 126 torch/distributed/elastic/multiprocessing/api.py:900] Sending process 201 closing signal SIGTERM +E0314 14:33:05.658000 126 torch/distributed/elastic/multiprocessing/api.py:874] failed (exitcode: 1) local_rank: 0 (pid: 198) of binary: /home/hp250092/ku50001222/qian/aivc/lfj/ori_scDFM_env/bin/python3.11 +Traceback (most recent call last): + File "/home/hp250092/ku50001222/qian/aivc/lfj/ori_scDFM_env/bin/accelerate", line 6, in + sys.exit(main()) + ^^^^^^ + File "/home/hp250092/ku50001222/qian/aivc/lfj/ori_scDFM_env/lib64/python3.11/site-packages/accelerate/commands/accelerate_cli.py", line 50, in main + args.func(args) + File "/home/hp250092/ku50001222/qian/aivc/lfj/ori_scDFM_env/lib64/python3.11/site-packages/accelerate/commands/launch.py", line 1190, in launch_command + multi_gpu_launcher(args) + File "/home/hp250092/ku50001222/qian/aivc/lfj/ori_scDFM_env/lib64/python3.11/site-packages/accelerate/commands/launch.py", line 815, in multi_gpu_launcher + distrib_run.run(args) + File "/home/hp250092/ku50001222/qian/aivc/lfj/ori_scDFM_env/lib64/python3.11/site-packages/torch/distributed/run.py", line 883, in run + elastic_launch( + File "/home/hp250092/ku50001222/qian/aivc/lfj/ori_scDFM_env/lib64/python3.11/site-packages/torch/distributed/launcher/api.py", line 139, in __call__ + return launch_agent(self._config, self._entrypoint, list(args)) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + File "/home/hp250092/ku50001222/qian/aivc/lfj/ori_scDFM_env/lib64/python3.11/site-packages/torch/distributed/launcher/api.py", line 270, in launch_agent + raise ChildFailedError( +torch.distributed.elastic.multiprocessing.errors.ChildFailedError: +============================================================ +src/script/run.py FAILED +------------------------------------------------------------ +Failures: + +------------------------------------------------------------ +Root Cause (first observed failure): +[0]: + time : 2026-03-14_14:33:05 + host : b0020 + rank : 0 (local_rank: 0) + exitcode : 1 (pid: 198) + error_file: + traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html +============================================================ +========================================== +Finished: Sat Mar 14 14:33:06 JST 2026 +========================================== diff --git a/transfer/code/ori_scDFM/logs/ccfm_baseline_5404238.out b/transfer/code/ori_scDFM/logs/ccfm_baseline_5404238.out new file mode 100644 index 0000000000000000000000000000000000000000..c8d031f715fcd9b1c6e5a50e599815aa95fbf025 --- /dev/null +++ b/transfer/code/ori_scDFM/logs/ccfm_baseline_5404238.out @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f1f64ce68e36ad97b8add960daf01b047b8341edd0dbc7647e72ac39f7aee9a7 +size 23112344 diff --git a/transfer/code/ori_scDFM/logs/eval_baseline_5406213.out b/transfer/code/ori_scDFM/logs/eval_baseline_5406213.out new file mode 100644 index 0000000000000000000000000000000000000000..63f58feccdd081e18c33fb9fdee910918552de1b --- /dev/null +++ b/transfer/code/ori_scDFM/logs/eval_baseline_5406213.out @@ -0,0 +1,150 @@ +========================================== +Job ID: 5406213 +Job Name: eval_baseline +Start: Mon Mar 16 00:43:23 JST 2026 +Node: b0028 +GPU: NVIDIA H100, 95830 MiB +Eval: Baseline (ori_scDFM) iteration_100000 +========================================== +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/models/scGPT/model.py:1392: UserWarning: flash_attn is not installed + warnings.warn("flash_attn is not installed") +WARNING:accelerate.utils.other:Detected kernel version 4.18.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher. +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/data_process/data.py:189: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control') +/home/hp250092/ku50001222/qian/aivc/lfj/ori_scDFM_env/lib64/python3.11/site-packages/scanpy/preprocessing/_highly_variable_genes.py:705: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual. + adata.uns["hvg"] = {"flavor": flavor} +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/data_process/data.py:281: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM/src/data_process/data.py:333: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual. + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) +FlowConfig(model_type='origin', batch_size=48, ntoken=512, d_model=128, lr=5e-05, steps=200000, eta_min=1e-06, devices='0', test_only=True, data_name='norman', perturbation_function='crisper', noise_type='Gaussian', poisson_alpha=0.8, poisson_target_sum=-1, print_every=10000, mode='predict_y', result_path='/home/hp250092/ku50001222/qian/aivc/lfj/transfer/CCFM_result', perturbation_fusion_method='sum', fusion_method='differential_perceiver', infer_top_gene=1000, n_top_genes=5000, checkpoint_path='/home/hp250092/ku50001222/qian/aivc/lfj/transfer/CCFM_result/flow-fusion_differential_perceiver-norman-origin-predict_y-gamma_0.5-perturbation_function_crisper-lr_5e-05-dim_model_128-infer_top_gene_1000-split_method_additive-use_mmd_loss_True-fold_1-use_negative_edge_True-topk_30/iteration_100000/checkpoint.pt', gamma=0.5, split_method='additive', use_mmd_loss=True, fold=1, use_negative_edge=True, topk=30) +Warning: ctrl is not in the gene names +##### loading vocab from file ##### +loading /home/hp250092/ku50001222/qian/aivc/lfj/transfer/CCFM_result/flow-fusion_differential_perceiver-norman-origin-predict_y-gamma_0.5-perturbation_function_crisper-lr_5e-05-dim_model_128-infer_top_gene_1000-split_method_additive-use_mmd_loss_True-fold_1-use_negative_edge_True-topk_30/iteration_100000/checkpoint.pt checkpoint, iteration: 100000, eval_score: None +Test-only mode. Saving results to /home/hp250092/ku50001222/qian/aivc/lfj/transfer/CCFM_result/flow-fusion_differential_perceiver-norman-origin-predict_y-gamma_0.5-perturbation_function_crisper-lr_5e-05-dim_model_128-infer_top_gene_1000-split_method_additive-use_mmd_loss_True-fold_1-use_negative_edge_True-topk_30/eval_only +perturbation_name_list: 39 + 0%| | 0/3 [00:00/dev/null || echo 'N/A')" +echo "Run: baseline_ccfm_aligned (topk=15, no negative edge)" +echo "==========================================" + +accelerate launch --num_processes=4 src/script/run.py \ + --batch_size=48 \ + --model_type=origin \ + --lr=5e-5 \ + --steps=200000 \ + --data_name=norman \ + --d_model=128 \ + --eta_min=1e-6 \ + --fusion_method=differential_perceiver \ + --infer_top_gene=1000 \ + --n_top_genes=5000 \ + --perturbation_function=crisper \ + --noise_type=Gaussian \ + --mode=predict_y \ + --gamma=0.5 \ + --split_method=additive \ + --use_mmd_loss \ + --fold=1 \ + --print_every=10000 \ + --topk=15 \ + --result_path=./result/baseline_ccfm_aligned + +echo "==========================================" +echo "Finished: $(date)" +echo "==========================================" diff --git a/transfer/code/ori_scDFM/run_baseline_original.sh b/transfer/code/ori_scDFM/run_baseline_original.sh new file mode 100644 index 0000000000000000000000000000000000000000..dfb0fceed83e9d44df867b9de00b6c41e8947e75 --- /dev/null +++ b/transfer/code/ori_scDFM/run_baseline_original.sh @@ -0,0 +1,54 @@ +#!/bin/sh +#PJM -L rscgrp=b-batch +#PJM -L gpu=4 +#PJM -L elapse=24:00:00 +#PJM -N baseline_original +#PJM -j +#PJM -o logs/baseline_original_%j.out + +module load cuda/12.2.2 +module load cudnn/8.9.7 +module load gcc-toolset/12 + +source /home/hp250092/ku50001222/qian/aivc/lfj/ori_scDFM_env/bin/activate + +cd /home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM + +export PYTHONPATH=./ +export PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:256 + +echo "==========================================" +echo "Job ID: $PJM_JOBID" +echo "Job Name: $PJM_JOBNAME" +echo "Start: $(date)" +echo "Node: $(hostname)" +echo "GPU: $(nvidia-smi --query-gpu=name,memory.total --format=csv,noheader 2>/dev/null || echo 'N/A')" +echo "Run: baseline_original (topk=30, negative edge)" +echo "==========================================" + +accelerate launch --num_processes=4 src/script/run.py \ + --batch_size=48 \ + --model_type=origin \ + --lr=5e-5 \ + --steps=200000 \ + --data_name=norman \ + --d_model=128 \ + --eta_min=1e-6 \ + --fusion_method=differential_perceiver \ + --infer_top_gene=1000 \ + --n_top_genes=5000 \ + --perturbation_function=crisper \ + --noise_type=Gaussian \ + --mode=predict_y \ + --gamma=0.5 \ + --split_method=additive \ + --use_mmd_loss \ + --fold=1 \ + --print_every=10000 \ + --topk=30 \ + --use_negative_edge \ + --result_path=./result/baseline_original + +echo "==========================================" +echo "Finished: $(date)" +echo "==========================================" diff --git a/transfer/code/ori_scDFM/run_ccfm_baseline.sh b/transfer/code/ori_scDFM/run_ccfm_baseline.sh new file mode 100644 index 0000000000000000000000000000000000000000..9a908d2b939e856fb8e11225d576a49afc999fc6 --- /dev/null +++ b/transfer/code/ori_scDFM/run_ccfm_baseline.sh @@ -0,0 +1,41 @@ +#!/bin/sh +#PJM -L rscgrp=b-batch +#PJM -L gpu=1 +#PJM -L elapse=24:00:00 +#PJM -N ccfm_baseline_ori_scdfm +#PJM -j +#PJM -o logs/ccfm_baseline_%j.out + +module load cuda/12.2.2 +module load cudnn/8.9.7 +module load gcc-toolset/12 + +source /home/hp250092/ku50001222/qian/aivc/lfj/ori_scDFM_env/bin/activate +cd /home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/ori_scDFM + +export PYTHONPATH=./ +export PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:256 + +python src/script/run.py \ + --batch_size=48 \ + --devices='0' \ + --model_type=origin \ + --lr=5e-5 \ + --steps=200000 \ + --data_name=norman \ + --d_model=128 \ + --eta_min=1e-6 \ + --fusion_method=differential_perceiver \ + --infer_top_gene=1000 \ + --n_top_genes=5000 \ + --perturbation_function=crisper \ + --noise_type=Gaussian \ + --mode=predict_y \ + --gamma=0.5 \ + --split_method=additive \ + --use_mmd_loss \ + --fold=1 \ + --topk=30 \ + --use_negative_edge \ + --print_every=10000 \ + --result_path=/home/hp250092/ku50001222/qian/aivc/lfj/transfer/CCFM_result diff --git a/transfer/code/ori_scDFM/src/data_process/_data.py b/transfer/code/ori_scDFM/src/data_process/_data.py new file mode 100644 index 0000000000000000000000000000000000000000..21ff590fd61e2583f58b9a128fa91dfde4c15a52 --- /dev/null +++ b/transfer/code/ori_scDFM/src/data_process/_data.py @@ -0,0 +1,218 @@ +from collections.abc import Callable +from dataclasses import dataclass +from typing import Any + +import jax +import jax.numpy as jnp + +from src.utils._types import ArrayLike + +__all__ = [ + "BaseDataMixin", + "ConditionData", + "PredictionData", + "TrainingData", + "ValidationData", +] + + +@dataclass +class ReturnData: # TODO: this should rather be a NamedTuple + split_covariates_mask: jnp.ndarray | None + split_idx_to_covariates: dict[int, tuple[Any, ...]] + perturbation_covariates_mask: jnp.ndarray | None + perturbation_idx_to_covariates: dict[int, tuple[Any, ...]] + perturbation_idx_to_id: dict[int, Any] + condition_data: dict[str, ArrayLike] + control_to_perturbation: dict[int, ArrayLike] + max_combination_length: int + condition_data_id: jnp.ndarray # mapping from perturbation_idx to condition_id + + +class BaseDataMixin: + """Base class for data containers.""" + + @property + def n_controls(self) -> int: + """Returns the number of control covariate values.""" + return len(self.split_idx_to_covariates) # type: ignore[attr-defined] + + @property + def n_perturbations(self) -> int: + """Returns the number of perturbation covariate combinations.""" + return len(self.perturbation_idx_to_covariates) # type: ignore[attr-defined] + + @property + def n_perturbation_covariates(self) -> int: + """Returns the number of perturbation covariates.""" + return len(self.condition_data) # type: ignore[attr-defined] + + def _format_params(self, fmt: Callable[[Any], str]) -> str: + params = { + "n_controls": self.n_controls, + "n_perturbations": self.n_perturbations, + } + return ", ".join(f"{name}={fmt(val)}" for name, val in params.items()) + + def __repr__(self) -> str: + return f"{self.__class__.__name__}[{self._format_params(repr)}]" + + +@dataclass +class ConditionData(BaseDataMixin): + """Data container containing condition embeddings. + + Parameters + ---------- + condition_data + Dictionary with embeddings for conditions. + max_combination_length + Maximum number of covariates in a combination. + null_value + Token to use for masking `null_value`. + data_manager + Data manager used to generate the data. + condition_data_id + Mapping from perturbation index to condition ID. + """ + + condition_data: dict[str, ArrayLike] + max_combination_length: int + perturbation_idx_to_covariates: dict[int, tuple[str, ...]] + perturbation_idx_to_id: dict[int, Any] + null_value: Any + data_manager: Any + condition_data_id: jnp.ndarray + +@dataclass +class TrainingData(BaseDataMixin): + """Training data. + + Parameters + ---------- + cell_data + The representation of cell data, e.g. PCA of gene expression data. + split_covariates_mask + Mask of the split covariates. + split_idx_to_covariates + Dictionary explaining values in ``split_covariates_mask``. + perturbation_covariates_mask + Mask of the perturbation covariates. + perturbation_idx_to_covariates + Dictionary explaining values in ``perturbation_covariates_mask``. + condition_data + Dictionary with embeddings for conditions. + control_to_perturbation + Mapping from control index to target distribution indices. + max_combination_length + Maximum number of covariates in a combination. + data_manager + The data manager + condition_data_id + Mapping from perturbation index to condition ID. + """ + + cell_data: jax.Array # (n_cells, n_features) + split_covariates_mask: jax.Array # (n_cells,), which cell assigned to which source distribution + split_idx_to_covariates: dict[int, tuple[Any, ...]] # (n_sources,) dictionary explaining split_covariates_mask + perturbation_covariates_mask: jax.Array # (n_cells,), which cell assigned to which target distribution + perturbation_idx_to_covariates: dict[ + int, tuple[str, ...] + ] # (n_targets,), dictionary explaining perturbation_covariates_mask + perturbation_idx_to_id: dict[int, Any] + condition_data: dict[str, ArrayLike] # (n_targets,) all embeddings for conditions + control_to_perturbation: dict[int, ArrayLike] # mapping from control idx to target distribution idcs + max_combination_length: int + null_value: Any + data_manager: Any + condition_data_id: jnp.ndarray + cell_data_id: Any + + +@dataclass +class ValidationData(BaseDataMixin): + """Data container for the validation data. + + Parameters + ---------- + cell_data + The representation of cell data, e.g. PCA of gene expression data. + split_covariates_mask + Mask of the split covariates. + split_idx_to_covariates + Dictionary explaining values in ``split_covariates_mask``. + perturbation_covariates_mask + Mask of the perturbation covariates. + perturbation_idx_to_covariates + Dictionary explaining values in ``perturbation_covariates_mask``. + condition_data + Dictionary with embeddings for conditions. + control_to_perturbation + Mapping from control index to target distribution indices. + max_combination_length + Maximum number of covariates in a combination. + data_manager + The data manager + condition_data_id + Mapping from perturbation index to condition ID. + n_conditions_on_log_iteration + Number of conditions to use for computation callbacks at each logged iteration. + If :obj:`None`, use all conditions. + n_conditions_on_train_end + Number of conditions to use for computation callbacks at the end of training. + If :obj:`None`, use all conditions. + """ + + cell_data: jax.Array # (n_cells, n_features) + split_covariates_mask: jax.Array # (n_cells,), which cell assigned to which source distribution + split_idx_to_covariates: dict[int, tuple[Any, ...]] # (n_sources,) dictionary explaining split_covariates_mask + perturbation_covariates_mask: jax.Array # (n_cells,), which cell assigned to which target distribution + perturbation_idx_to_covariates: dict[ + int, tuple[str, ...] + ] # (n_targets,), dictionary explaining perturbation_covariates_mask + perturbation_idx_to_id: dict[int, Any] + condition_data: dict[str, ArrayLike] # (n_targets,) all embeddings for conditions + control_to_perturbation: dict[int, jax.Array] # mapping from control idx to target distribution idcs + max_combination_length: int + null_value: Any + data_manager: Any + condition_data_id: jnp.ndarray + n_conditions_on_log_iteration: int | None = None + n_conditions_on_train_end: int | None = None + + +@dataclass +class PredictionData(BaseDataMixin): + """Data container to perform prediction. + + Parameters + ---------- + src_data + Dictionary with data for source cells. + condition_data + Dictionary with embeddings for conditions. + control_to_perturbation + Mapping from control index to target distribution indices. + covariate_encoder + Encoder for the primary covariate. + max_combination_length + Maximum number of covariates in a combination. + null_value + Token to use for masking ``null_value``. + condition_data_id + Mapping from condition name to condition ID embedding. + """ + + cell_data: jax.Array # (n_cells, n_features) + split_covariates_mask: jax.Array # (n_cells,), which cell assigned to which source distribution + split_idx_to_covariates: dict[int, tuple[Any, ...]] # (n_sources,) dictionary explaining split_covariates_mask + perturbation_idx_to_covariates: dict[ + int, tuple[str, ...] + ] # (n_targets,), dictionary explaining perturbation_covariates_mask + perturbation_idx_to_id: dict[int, Any] + condition_data: dict[str, ArrayLike] # (n_targets,) all embeddings for conditions + control_to_perturbation: dict[int, ArrayLike] + max_combination_length: int + null_value: Any + data_manager: Any + condition_data_id: jnp.ndarray diff --git a/transfer/code/ori_scDFM/src/data_process/_dataloader.py b/transfer/code/ori_scDFM/src/data_process/_dataloader.py new file mode 100644 index 0000000000000000000000000000000000000000..be30c54b45b3b8c6367e12a4e474df6bcdcf5d4b --- /dev/null +++ b/transfer/code/ori_scDFM/src/data_process/_dataloader.py @@ -0,0 +1,235 @@ +import abc +from typing import Any, Literal + +import jax +import jax.numpy as jnp +import numpy as np + +from src.data_process._data import PredictionData, TrainingData, ValidationData +import pdb +import torch + +__all__ = ["TrainSampler", "ValidationSampler", "PredictionSampler"] + + +class TrainSampler: + """Data sampler for :class:`~cellflow.data.TrainingData`. + + Parameters + ---------- + data + The training data. + batch_size + The batch size. + + """ + + def __init__(self, data: TrainingData, batch_size: int = 1024): + self._data = data + self._data_idcs = jnp.arange(data.cell_data.shape[0]) + self.batch_size = batch_size + self.n_source_dists = data.n_controls + self.n_target_dists = data.n_perturbations + self.conditional_samplings = [ + lambda key, i=i: jax.random.choice(key, self._data.control_to_perturbation[i]) + for i in range(self.n_source_dists) + ] + self.get_embeddings = lambda idx: { + pert_cov: jnp.expand_dims(arr[idx], 0) for pert_cov, arr in self._data.condition_data.items() + } + # @jax.jit + def _sample(rng: jax.Array) -> Any: + + rng_1, rng_2, rng_3, rng_4 = jax.random.split(rng, 4) + source_dist_idx = jax.random.choice(rng_1, self.n_source_dists) + source_cells_mask = self._data.split_covariates_mask == source_dist_idx + src_cond_p = source_cells_mask / jnp.count_nonzero(source_cells_mask) + source_batch_idcs = jax.random.choice(rng_2, self._data_idcs, [self.batch_size], replace=True, p=src_cond_p) + source_batch = self._data.cell_data[source_batch_idcs] + + target_dist_idx = jax.lax.switch(source_dist_idx, self.conditional_samplings, rng_3) + target_cells_mask = self._data.perturbation_covariates_mask == target_dist_idx + tgt_cond_p = target_cells_mask / jnp.count_nonzero(target_cells_mask) + target_batch_idcs = jax.random.choice( + rng_4, + self._data_idcs, + [self.batch_size], + replace=True, + p=tgt_cond_p, + ) + target_batch = self._data.cell_data[target_batch_idcs] + if self._data.condition_data is None: + return {"src_cell_data": source_batch, "tgt_cell_data": target_batch} + + condition_batch = self.get_embeddings(target_dist_idx) + + return { + "src_cell_data": source_batch, + "tgt_cell_data": target_batch, + "condition": condition_batch, + "condition_id": self._data.condition_data_id[target_dist_idx], + "src_cell_id": self._data.cell_data_id[source_batch_idcs], + "tgt_cell_id": self._data.cell_data_id[target_batch_idcs] + } + + self.sample = _sample + + @property + def data(self) -> TrainingData: + """The training data.""" + return self._data + + def precompute_latent_embedding(self, model,): + """ + compute latent representation + + return + ---- + latent_embedding : torch.Tensor + """ + + cell_data = self._data.cell_data + with torch.no_grad(): + self._data.cell_embedding = model(cell_data) + + +class BaseValidSampler(abc.ABC): + @abc.abstractmethod + def sample(*args, **kwargs): + pass + + def _get_key(self, cond_idx: int) -> tuple[str, ...]: + if len(self._data.perturbation_idx_to_id): # type: ignore[attr-defined] + return self._data.perturbation_idx_to_id[cond_idx] # type: ignore[attr-defined] + cov_combination = self._data.perturbation_idx_to_covariates[cond_idx] # type: ignore[attr-defined] + return tuple(cov_combination[i] for i in range(len(cov_combination))) + + def _get_perturbation_to_control(self, data: ValidationData | PredictionData) -> dict[int, int]: + d = {} + for k, v in data.control_to_perturbation.items(): + for el in v: + d[el] = k + return d + + def _get_condition_data(self, cond_idx: int) -> jnp.ndarray: + return {k: v[[cond_idx], ...] for k, v in self._data.condition_data.items()} # type: ignore[attr-defined] + + +class ValidationSampler(BaseValidSampler): + """Data sampler for :class:`~cellflow.data.ValidationData`. + + Parameters + ---------- + val_data + The validation data. + seed + Random seed. + """ + + def __init__(self, val_data: ValidationData, seed: int = 0) -> None: + self._data = val_data + self.perturbation_to_control = self._get_perturbation_to_control(val_data) + self.n_conditions_on_log_iteration = ( + val_data.n_conditions_on_log_iteration + if val_data.n_conditions_on_log_iteration is not None + else val_data.n_perturbations + ) + self.n_conditions_on_train_end = ( + val_data.n_conditions_on_train_end + if val_data.n_conditions_on_train_end is not None + else val_data.n_perturbations + ) + self.rng = np.random.default_rng(seed) + if self._data.condition_data is None: + raise NotImplementedError("Validation data must have condition data.") + + def sample(self, mode: Literal["on_log_iteration", "on_train_end"]) -> Any: + """Sample data for validation. + + Parameters + ---------- + mode + Sampling mode. Either ``"on_log_iteration"`` or ``"on_train_end"``. + + Returns + ------- + Dictionary with source, condition, and target data from the validation data. + """ + import pdb; pdb.set_trace() + size = self.n_conditions_on_log_iteration if mode == "on_log_iteration" else self.n_conditions_on_train_end + condition_idcs = self.rng.choice(self._data.n_perturbations, size=(size,), replace=False) + + source_idcs = [self.perturbation_to_control[cond_idx] for cond_idx in condition_idcs] + source_cells_mask = [self._data.split_covariates_mask == source_idx for source_idx in source_idcs] + source_cells = [self._data.cell_data[mask] for mask in source_cells_mask] + target_cells_mask = [cond_idx == self._data.perturbation_covariates_mask for cond_idx in condition_idcs] + target_cells = [self._data.cell_data[mask] for mask in target_cells_mask] + conditions = [self._get_condition_data(cond_idx) for cond_idx in condition_idcs] + cell_rep_dict = {} + cond_dict = {} + true_dict = {} + for i in range(len(condition_idcs)): + k = self._get_key(condition_idcs[i]) + cell_rep_dict[k] = source_cells[i] + cond_dict[k] = conditions[i] + true_dict[k] = target_cells[i] + + return {"source": cell_rep_dict, "condition": cond_dict, "target": true_dict} + + @property + def data(self) -> ValidationData: + """The validation data.""" + return self._data + + +class PredictionSampler(BaseValidSampler): + """Data sampler for :class:`~cellflow.data.PredictionData`. + + Parameters + ---------- + pred_data + The prediction data. + + """ + + def __init__(self, pred_data: PredictionData) -> None: + self._data = pred_data + self.perturbation_to_control = self._get_perturbation_to_control(pred_data) + if self._data.condition_data is None: + raise NotImplementedError("Validation data must have condition data.") + + def sample(self) -> Any: + """Sample data for prediction. + + Returns + ------- + Dictionary with source and condition data from the prediction data. + """ + condition_idcs = range(self._data.n_perturbations) + + source_idcs = [self.perturbation_to_control[cond_idx] for cond_idx in condition_idcs] + source_cells_mask = [self._data.split_covariates_mask == source_idx for source_idx in source_idcs] + source_cells = [self._data.cell_data[mask] for mask in source_cells_mask] + target_cells_mask = [cond_idx == self._data.perturbation_covariates_mask for cond_idx in condition_idcs] + target_cells = [self._data.cell_data[mask] for mask in target_cells_mask] + + conditions = [self._get_condition_data(cond_idx) for cond_idx in condition_idcs] + cell_rep_dict = {} + cond_dict = {} + true_dict = {} + for i in range(len(condition_idcs)): + k = self._get_key(condition_idcs[i]) + cell_rep_dict[k] = source_cells[i] + cond_dict[k] = conditions[i] + true_dict[k] = target_cells[i] + + return { + "source": cell_rep_dict, + "target": true_dict, + "condition": cond_dict, + } + + @property + def data(self) -> PredictionData: + """The training data.""" + return self._data diff --git a/transfer/code/ori_scDFM/src/data_process/_datamanager.py b/transfer/code/ori_scDFM/src/data_process/_datamanager.py new file mode 100644 index 0000000000000000000000000000000000000000..9d1000417ae97bbdc35a1556e7c5bb4d288883ad --- /dev/null +++ b/transfer/code/ori_scDFM/src/data_process/_datamanager.py @@ -0,0 +1,988 @@ +from collections.abc import Sequence +from typing import Any + +import anndata +import jax +import jax.numpy as jnp +import numpy as np +import pandas as pd +import scipy.sparse as sp +import sklearn.preprocessing as preprocessing +from pandas.api.types import is_numeric_dtype +from tqdm import tqdm + +from src.utils._logging import logger +from src.utils._types import ArrayLike +from src.data_process._data import ConditionData, PredictionData, ReturnData, TrainingData, ValidationData +import pdb +from src.utils._utils import _flatten_list, _to_list + +__all__ = ["DataManager"] + + +class DataManager: + """Data manager for handling perturbation data. + + Parameters + ---------- + adata + An :class:`~anndata.AnnData` object. + covariate_encoder + Encoder for the primary covariate. + categorical + Whether the primary covariate is categorical. + max_combination_length + Maximum number of combinations of primary ``perturbation_covariates``. + sample_rep + Key in :attr:`~anndata.AnnData.obsm` where the sample representation is stored or ``'X'`` + to use :attr:`~anndata.AnnData.X`. + covariate_data + Dataframe with covariates. If :obj:`None`, :attr:`~anndata.AnnData.obs` is used. + condition_id_key + Key in :attr:`~anndata.AnnData.obs` that defines the condition id. + perturbation_covariates + A dictionary where the keys indicate the name of the covariate group + and the values are keys in :attr:`~anndata.AnnData.uns`. The corresponding + columns should be either boolean (presence/abscence of the perturbation) or + numeric (concentration or magnitude of the perturbation). If multiple groups + are provided, the first is interpreted as the primary perturbation and the + others as covariates corresponding to these perturbations, e.g. + ``{"drug":("drugA", "drugB"), "time":("drugA_time", "drugB_time")}``. + perturbation_covariate_reps + A dictionary where the keys indicate the name of the covariate group and the + values are keys in :attr:`~anndata.AnnData.uns` storing a dictionary with + the representation of the covariates. E.g. ``{"drug":"drug_embeddings"}`` + with ``adata.uns["drug_embeddings"] = {"drugA": np.array, "drugB": np.array}``. + sample_covariates + Keys in :attr:`~anndata.AnnData.obs` indicating sample covatiates to be taken + into account for training and prediction, e.g. ``["age", "cell_type"]``. + sample_covariate_reps + A dictionary where the keys indicate the name of the covariate group and the + values are keys in :attr:`~anndata.AnnData.uns` storing a dictionary with the + representation of the covariates. E.g. ``{"cell_type": "cell_type_embeddings"}`` with + ``adata.uns["cell_type_embeddings"] = {"cell_typeA": np.array, "cell_typeB": np.array}``. + split_covariates + Covariates in :attr:`~anndata.AnnData.obs` to split all control cells into + different control populations. The perturbed cells are also split according to these + columns, but if these covariates should also be encoded in the model, the corresponding + column should also be used in ``perturbation_covariates`` or ``sample_covariates``. + null_value + Value to use for padding to ``max_combination_length``. + """ + + def __init__( + self, + adata: anndata.AnnData, + sample_rep: str | dict[str, str], + control_key: str, + perturbation_covariates: dict[str, Sequence[str]] | None = None, + perturbation_covariate_reps: dict[str, str] | None = None, + sample_covariates: Sequence[str] | None = None, + sample_covariate_reps: dict[str, str] | None = None, + split_covariates: Sequence[str] | None = None, + max_combination_length: int | None = None, + null_value: float = 0.0, + ): + self._adata = adata + self._sample_rep = self._verify_sample_rep(sample_rep) + self._control_key = control_key + self._perturbation_covariates = self._verify_perturbation_covariates(perturbation_covariates) + self._perturbation_covariate_reps = self._verify_perturbation_covariate_reps( + adata, + perturbation_covariate_reps, + self._perturbation_covariates, + ) + self._sample_covariates = self._verify_sample_covariates(sample_covariates) + self._sample_covariate_reps = self._verify_sample_covariate_reps( + adata, sample_covariate_reps, self._sample_covariates + ) + self._split_covariates = self._verify_split_covariates(adata, split_covariates, control_key) + self._max_combination_length = self._get_max_combination_length( + self._perturbation_covariates, max_combination_length + ) + self._null_value = null_value + self._primary_one_hot_encoder, self._is_categorical = self._get_primary_covar_encoder( + self._adata, + self._perturbation_covariates, + self._perturbation_covariate_reps, + ) + self._linked_perturb_covars = self._get_linked_perturbation_covariates(self._perturbation_covariates) + sample_cov_groups = {covar: _to_list(covar) for covar in self._sample_covariates} + covariate_groups = self._perturbation_covariates | sample_cov_groups + self._covariate_reps = (self._perturbation_covariate_reps or {}) | (self._sample_covariate_reps or {}) + + self._covar_to_idx = self._get_covar_to_idx(covariate_groups) # type: ignore[arg-type] + perturb_covar_keys = _flatten_list(self._perturbation_covariates.values()) + list(self._sample_covariates) + perturb_covar_keys += [col for col in self._split_covariates if col not in perturb_covar_keys] + self._perturb_covar_keys = [k for k in perturb_covar_keys if k is not None] + + # get the unique values of the split covariates + condition = np.unique(list(self.adata.obs['condition'])) + unique_drug = [] + np.array([unique_drug.extend(perturbation.split('+')) for perturbation in condition]) + unique_drug = np.unique(unique_drug) + unique_drug.sort() + self.unique_drug = unique_drug + self.drug_dict = {drug: i for i, drug in enumerate(unique_drug)} + + def get_train_data(self, adata: anndata.AnnData, one_hot: bool = False) -> Any: + """Get training data for the model. + + Parameters + ---------- + adata + An :class:`~anndata.AnnData` object. + + Returns + ------- + Training data for the model. + """ + # create a + split_cov_combs = self._get_split_cov_combs(adata.obs) + cond_data = self._get_condition_data(split_cov_combs=split_cov_combs, adata=adata, one_hot=one_hot) + cell_data = self._get_cell_data(adata) + return TrainingData( + cell_data=cell_data, + split_covariates_mask=cond_data.split_covariates_mask, + split_idx_to_covariates=cond_data.split_idx_to_covariates, + perturbation_covariates_mask=cond_data.perturbation_covariates_mask, + perturbation_idx_to_covariates=cond_data.perturbation_idx_to_covariates, + perturbation_idx_to_id=cond_data.perturbation_idx_to_id, + condition_data=cond_data.condition_data, + control_to_perturbation=cond_data.control_to_perturbation, + max_combination_length=cond_data.max_combination_length, + null_value=self._null_value, + data_manager=self, + condition_data_id=cond_data.condition_data_id, + cell_data_id=adata.obs.index, + ) + + def get_validation_data( + self, + adata: anndata.AnnData, + n_conditions_on_log_iteration: int | None = None, + n_conditions_on_train_end: int | None = None, + one_hot: bool = False, + ) -> ValidationData: + """Get validation data for the model. + + Parameters + ---------- + adata + An :class:`~anndata.AnnData` object. + n_conditions_on_log_iteration + Number of conditions to validate on during logging. + n_conditions_on_train_end + Number of conditions to validate on at the end of training. + + Returns + ------- + Validation data for the model. + """ + split_cov_combs = self._get_split_cov_combs(adata.obs) + cond_data = self._get_condition_data(split_cov_combs=split_cov_combs, adata=adata, one_hot=one_hot) + cell_data = self._get_cell_data(adata) + return ValidationData( + cell_data=cell_data, + split_covariates_mask=cond_data.split_covariates_mask, + split_idx_to_covariates=cond_data.split_idx_to_covariates, + perturbation_covariates_mask=cond_data.perturbation_covariates_mask, + perturbation_idx_to_covariates=cond_data.perturbation_idx_to_covariates, + perturbation_idx_to_id=cond_data.perturbation_idx_to_id, + condition_data=cond_data.condition_data, + control_to_perturbation=cond_data.control_to_perturbation, + max_combination_length=cond_data.max_combination_length, + null_value=self._null_value, + data_manager=self, + condition_data_id=cond_data.condition_data_id, + n_conditions_on_log_iteration=n_conditions_on_log_iteration, + n_conditions_on_train_end=n_conditions_on_train_end, + ) + + def get_prediction_data( + self, + adata: anndata.AnnData, + sample_rep: str, + covariate_data: pd.DataFrame, + rep_dict: dict[str, Any] | None = None, + condition_id_key: str | None = None, + one_hot: bool = False, + ) -> Any: + """Get predictions for control cells. + + Extracts source distributions from ``'adata'`` and simulates cells perturbed + with covariates defined in ``'covariate_data'``. + + Parameters + ---------- + adata + An :class:`~anndata.AnnData` object to extract control cells from. + sample_rep + Key in :attr:`~anndata.AnnData.obsm` where the sample representation of the control + is stored or ``'X'`` to use :attr:`~anndata.AnnData.X`. + covariate_data + A :class:`~pandas.DataFrame` with columns defining the covariates as + in :meth:`cellflow.model.CellFlow.prepare_data` and stored in + :attr:`cellflow.model.CellFlow.data_manager`. + rep_dict + Dictionary with representations of the covariates. + If not provided, :attr:`~anndata.AnnData.uns` is used. + condition_id_key + Key in :class:`~pandas.DataFrame` that defines the condition names. + + Returns + ------- + Training data for the model. + """ + self._verify_prediction_data(adata) + split_cov_combs = self._get_split_cov_combs(covariate_data=covariate_data) + + # adata is None since we don't extract cell masks for predicted covariates + cond_data = self._get_condition_data( + split_cov_combs=split_cov_combs, + adata=None, + covariate_data=covariate_data, + rep_dict=adata.uns if rep_dict is None else rep_dict, + condition_id_key=condition_id_key, + one_hot=one_hot, + ) + + cell_data = self._get_cell_data(adata, sample_rep) + split_covariates_mask, split_idx_to_covariates = self._get_split_covariates_mask( + adata=adata, split_cov_combs=split_cov_combs + ) + + return PredictionData( + cell_data=cell_data, + split_covariates_mask=split_covariates_mask, + split_idx_to_covariates=split_idx_to_covariates, + condition_data=cond_data.condition_data, + control_to_perturbation=cond_data.control_to_perturbation, + perturbation_idx_to_covariates=cond_data.perturbation_idx_to_covariates, + perturbation_idx_to_id=cond_data.perturbation_idx_to_id, + max_combination_length=cond_data.max_combination_length, + null_value=self._null_value, + data_manager=self, + condition_data_id=cond_data.condition_data_id, + ) + + def get_condition_data( + self, + covariate_data: pd.DataFrame, + rep_dict: dict[str, Any] | None = None, + condition_id_key: str | None = None, + one_hot: bool = False, + ) -> ConditionData: + """Get condition data for the model. + + Parameters + ---------- + covariate_data + Dataframe with covariates. + condition_id_key + Key in ``covariate_data`` that defines the condition id. + rep_dict + + Returns + ------- + Condition data for the model. + """ + self._verify_covariate_data(covariate_data, self._perturb_covar_keys) + split_cov_combs = self._get_split_cov_combs(covariate_data) + cond_data = self._get_condition_data( + split_cov_combs=split_cov_combs, + adata=None, + covariate_data=covariate_data, + rep_dict=rep_dict, + condition_id_key=condition_id_key, + one_hot=one_hot, + ) + return ConditionData( + condition_data=cond_data.condition_data, + max_combination_length=cond_data.max_combination_length, + perturbation_idx_to_covariates=cond_data.perturbation_idx_to_covariates, + perturbation_idx_to_id=cond_data.perturbation_idx_to_id, + null_value=self._null_value, + data_manager=self, + condition_data_id=cond_data.condition_data_id, + ) + + def _get_split_cov_combs(self, covariate_data: pd.DataFrame) -> np.ndarray | list[list[Any]]: # type: ignore[type-arg] + if len(self._split_covariates) > 0: + return covariate_data[self._split_covariates].drop_duplicates().values + else: + return [[]] + + def _get_condition_data( + self, + split_cov_combs: np.ndarray | list[list[Any]], # type: ignore[type-arg] + adata: anndata.AnnData | None, + covariate_data: pd.DataFrame | None = None, + rep_dict: dict[str, Any] | None = None, + condition_id_key: str | None = None, + one_hot: bool = False, + ) -> ReturnData: + # for prediction: adata is None, covariate_data is provided + # for training/validation: adata is provided and used to get cell masks, covariate_data is None + if adata is None and covariate_data is None: + raise ValueError("Either `adata` or `covariate_data` must be provided.") + covariate_data = covariate_data if covariate_data is not None else adata.obs # type: ignore[union-attr] + if rep_dict is None: + rep_dict = adata.uns if adata is not None else {} + # check if all perturbation/split covariates and control cells are present in the input + self._verify_covariate_data( + covariate_data, + {covar: _to_list(covar) for covar in self._sample_covariates}, + ) + self._verify_control_data(adata) + self._verify_covariate_data(covariate_data, _to_list(self._split_covariates)) + + # extract unique combinations of perturbation covariates + if condition_id_key is not None: + self._verify_condition_id_key(covariate_data, condition_id_key) + select_keys = self._perturb_covar_keys + [condition_id_key] + else: + select_keys = self._perturb_covar_keys + perturb_covar_df = covariate_data[select_keys].drop_duplicates() + if condition_id_key is not None: + perturb_covar_df = perturb_covar_df.set_index(condition_id_key) + else: + perturb_covar_df = perturb_covar_df.reset_index() + + # get indices of cells belonging to each unique condition + _perturb_covar_df, _covariate_data = ( + perturb_covar_df[self._perturb_covar_keys], + covariate_data[self._perturb_covar_keys], + ) + _perturb_covar_df["row_id"] = range(len(perturb_covar_df)) + _covariate_data["cell_index"] = _covariate_data.index + _perturb_covar_merged = _perturb_covar_df.merge(_covariate_data, on=self._perturb_covar_keys, how="inner") + perturb_covar_to_cells = _perturb_covar_merged.groupby("row_id")["cell_index"].apply(list).to_list() + + # intialize data containers + if adata is not None: + split_covariates_mask = np.full((len(adata),), -1, dtype=jnp.int32) + perturbation_covariates_mask = np.full((len(adata),), -1, dtype=jnp.int32) + control_mask = covariate_data[self._control_key] + else: + split_covariates_mask = None + perturbation_covariates_mask = None + control_mask = jnp.ones((len(covariate_data),)) + + condition_data: dict[str, list[jnp.ndarray]] = {i: [] for i in self._covar_to_idx.keys()} + + control_to_perturbation: dict[int, ArrayLike] = {} + split_idx_to_covariates: dict[int, tuple[Any]] = {} + perturbation_idx_to_covariates: dict[int, tuple[Any]] = {} + perturbation_idx_to_id: dict[int, Any] = {} + condition_data_id_list: list[jnp.ndarray] = [] + + src_counter = 0 + tgt_counter = 0 + + # iterate over unique split covariate combinations + for split_combination in split_cov_combs: + # get masks for split covariates; for prediction, it's done outside this method + if adata is not None: + split_covariates_mask, split_idx_to_covariates, split_cov_mask = self._get_split_combination_mask( + covariate_data=adata.obs, + split_covariates_mask=split_covariates_mask, # type: ignore[arg-type] + split_combination=split_combination, # type: ignore[arg-type] + split_idx_to_covariates=split_idx_to_covariates, + control_mask=control_mask, + src_counter=src_counter, + ) + conditional_distributions = [] + + # iterate over target conditions + filter_dict = dict(zip(self.split_covariates, split_combination, strict=False)) + pc_df = perturb_covar_df[ + (perturb_covar_df[list(filter_dict.keys())] == list(filter_dict.values())).all(axis=1) + ] + pbar = tqdm(pc_df.iterrows(), total=pc_df.shape[0]) + + for i, tgt_cond in pbar: + tgt_cond = tgt_cond[self._perturb_covar_keys] + # for train/validation, only extract covariate combinations that are present in adata + if adata is not None: + mask = covariate_data.index.isin(perturb_covar_to_cells[i]) + mask *= (1 - control_mask) * split_cov_mask + mask = np.array(mask == 1) + if mask.sum() == 0: + continue + # map unique condition id to target id + perturbation_covariates_mask[mask] = tgt_counter # type: ignore[index] + + # map target id to unique conditions and their ids + conditional_distributions.append(tgt_counter) + perturbation_idx_to_covariates[tgt_counter] = tgt_cond.values + if condition_id_key is not None: + perturbation_idx_to_id[tgt_counter] = i + + # get embeddings for conditions + embedding = self._get_perturbation_covariates( + condition_data=tgt_cond, + rep_dict=rep_dict, + perturb_covariates={k: _to_list(v) for k, v in self._perturbation_covariates.items()}, + ) + for pert_cov, emb in embedding.items(): + condition_data[pert_cov].append(emb) + + condition_name, condition_id_emb = self._generate_condition_name_and_embedding(tgt_cond, tgt_counter, embedding, one_hot=one_hot) + condition_data_id_list.append(condition_id_emb) + tgt_counter += 1 + + # map source (control) to target condition ids + control_to_perturbation[src_counter] = np.array(conditional_distributions) + src_counter += 1 + # convert outputs to jax arrays + for pert_cov, emb in condition_data.items(): + condition_data[pert_cov] = jnp.array(emb) + condition_data_id = jnp.stack(condition_data_id_list, axis=0) # shape: (N, D) + split_covariates_mask = jnp.asarray(split_covariates_mask) if split_covariates_mask is not None else None + perturbation_covariates_mask = ( + jnp.asarray(perturbation_covariates_mask) if perturbation_covariates_mask is not None else None + ) + return ReturnData( + split_covariates_mask=split_covariates_mask, + split_idx_to_covariates=split_idx_to_covariates, + perturbation_covariates_mask=perturbation_covariates_mask, + perturbation_idx_to_covariates=perturbation_idx_to_covariates, + perturbation_idx_to_id=perturbation_idx_to_id, + condition_data=condition_data, # type: ignore[arg-type] + control_to_perturbation=control_to_perturbation, + max_combination_length=self._max_combination_length, + condition_data_id=condition_data_id, + ) + + def _generate_condition_name_and_embedding( + self, + tgt_cond: pd.Series, + tgt_counter: int, + embedding: dict[str, jnp.ndarray], + one_hot: bool = False, + ) -> tuple[str, jnp.ndarray]: + """ + 基于 self.drug_dict 映射 tgt_cond 中的药物组合，生成唯一名称和 embedding。 + """ + # 展开所有 perturbation covariates（如 Drug1, Drug2） + drug_names = [] + for covar_group in self._perturbation_covariates.values(): + for col in covar_group: + val = tgt_cond[col] + if isinstance(val, str): + drug_names.extend(val.split('+')) + else: + drug_names.append(str(val)) + drug_names = sorted(set(drug_names)) # 去重 + 排序 + drug_ids = [self.drug_dict[name] for name in drug_names] + + condition_name = 'drug_' + '_'.join(str(i) for i in drug_ids) + if one_hot: + one_hots = [] + for idx in drug_ids: + vec = jnp.zeros((num_drugs,), dtype=jnp.float32).at[idx].set(1.0) + one_hots.append(vec) + condition_id_emb = jnp.concatenate(one_hots, axis=-1) + else: + condition_id_emb = jnp.array(drug_ids, dtype=jnp.int32) + return condition_name, condition_id_emb + + @staticmethod + def _verify_condition_id_key(covariate_data: pd.DataFrame, condition_id_key: str | None) -> None: + if condition_id_key is not None and condition_id_key not in covariate_data.columns: + raise ValueError(f"The `condition_id_key` column ('{condition_id_key}') was not found in `adata.obs`.") + if not len(covariate_data[condition_id_key].unique()) == len(covariate_data): + raise ValueError(f"The `condition_id_key` column ('{condition_id_key}') must contain unique values.") + + @staticmethod + def _verify_sample_rep(sample_rep: str | dict[str, str]) -> str | dict[str, str]: + if not (isinstance(sample_rep, str) or isinstance(sample_rep, dict)): + raise ValueError( + f"`sample_rep` should be of type `str` or `dict`, found {sample_rep} to be of type {type(sample_rep)}." + ) + return sample_rep + + def _get_cell_data( + self, + adata: anndata.AnnData, + sample_rep: str | None = None, + ) -> jax.Array: + sample_rep = self._sample_rep if sample_rep is None else sample_rep + if sample_rep == "X": + sample_rep = adata.X + if isinstance(sample_rep, sp.csr_matrix): + return jnp.asarray(sample_rep.toarray()) + else: + return jnp.asarray(sample_rep) + if isinstance(self._sample_rep, str): + if self._sample_rep not in adata.obsm: + raise KeyError(f"Sample representation '{self._sample_rep}' not found in `adata.obsm`.") + return jnp.asarray(adata.obsm[self._sample_rep]) + attr, key = next(iter(sample_rep.items())) # type: ignore[union-attr] + return jnp.asarray(getattr(adata, attr)[key]) + + def _verify_control_data(self, adata: anndata.AnnData | None) -> None: + if adata is None: + return None + if self._control_key not in adata.obs: + raise ValueError(f"Control column '{self._control_key}' not found in adata.obs.") + if not isinstance(adata.obs[self._control_key].dtype, pd.BooleanDtype): + try: + adata.obs[self._control_key] = adata.obs[self._control_key].astype("boolean") + except TypeError as e: + raise ValueError(f"Control column '{self._control_key}' could not be converted to boolean.") from e + if adata.obs[self._control_key].sum() == 0: + raise ValueError("No control cells found in adata.") + + def _verify_prediction_data(self, adata: anndata.AnnData) -> None: + if self._control_key not in adata.obs: + raise ValueError(f"Control column '{self._control_key}' not found in adata.obs.") + if not isinstance(adata.obs[self._control_key].dtype, pd.BooleanDtype): + try: + adata.obs[self._control_key] = adata.obs[self._control_key].astype("boolean") + except ValueError as e: + raise ValueError(f"Control column '{self._control_key}' could not be converted to boolean.") from e + if not adata.obs[self._control_key].all(): + raise ValueError( + f"For prediction, all cells in `adata` should be from control condition. Ensure that '{self._control_key}' is `True` for all cells, even if you're setting `.obs` to predicted condition." + ) + + def _get_split_combination_mask( + self, + covariate_data: pd.DataFrame, + split_covariates_mask: ArrayLike, + split_combination: ArrayLike, + split_idx_to_covariates: dict[int, tuple[Any]], + control_mask: ArrayLike, + src_counter: int, + ) -> tuple[ArrayLike, dict[int, tuple[Any]], ArrayLike]: + filter_dict = dict(zip(self.split_covariates, split_combination, strict=False)) + split_cov_mask = (covariate_data[list(filter_dict.keys())] == list(filter_dict.values())).all(axis=1) + mask = jnp.array(control_mask * split_cov_mask).astype(bool) + split_covariates_mask[mask] = src_counter + split_idx_to_covariates[src_counter] = tuple(split_combination) + return split_covariates_mask, split_idx_to_covariates, split_cov_mask + + def _get_split_covariates_mask( + self, + adata: anndata.AnnData, + split_cov_combs: np.ndarray | list[list[Any]], # type: ignore[type-arg] + ) -> tuple[ArrayLike, dict[int, tuple[Any]]]: + # here we assume that adata only contains source cells + if len(self.split_covariates) == 0: + return jnp.full((len(adata),), 0, dtype=jnp.int32), {} + split_covariates_mask = np.full((len(adata),), -1, dtype=jnp.int32) + split_idx_to_covariates: dict[int, Any] = {} + src_counter = 0 + for split_combination in split_cov_combs: + split_covariates_mask_previous = split_covariates_mask.copy() + split_covariates_mask, split_idx_to_covariates, _ = self._get_split_combination_mask( + covariate_data=adata.obs, + split_covariates_mask=split_covariates_mask, + split_combination=split_combination, # type: ignore[arg-type] + split_idx_to_covariates=split_idx_to_covariates, + control_mask=jnp.ones((adata.n_obs,)), + src_counter=src_counter, + ) + if (split_covariates_mask == split_covariates_mask_previous).all(): + raise ValueError(f"No cells found in `adata` for split covariates {split_combination}.") + src_counter += 1 + return jnp.asarray(split_covariates_mask), split_idx_to_covariates + + @staticmethod + def _verify_perturbation_covariates(data: dict[str, Sequence[str]] | None) -> dict[str, list[str]]: + if data is None: + return {} + if not isinstance(data, dict): + raise ValueError( + f"`perturbation_covariates` should be a dictionary, found {data} to be of type {type(data)}." + ) + if len(data) == 0: + raise ValueError("No perturbation covariates provided.") + for key, covars in data.items(): + if not isinstance(key, str): + raise ValueError(f"Key should be a string, found {key} to be of type {type(key)}.") + if not isinstance(covars, tuple | list): + raise ValueError(f"Value should be a tuple, found {covars} to be of type {type(covars)}.") + if len(covars) == 0: + raise ValueError(f"No covariates provided for perturbation group {key}.") + lengths = [len(covs) for covs in data.values()] + if len(set(lengths)) != 1: + raise ValueError(f"Length of perturbation covariate groups must match, found lengths {lengths}.") + return {k: list(el) for k, el in data.items()} + + @staticmethod + def _verify_sample_covariates( + sample_covariates: Sequence[str] | None, + ) -> list[str]: + if sample_covariates is None: + return [] + if not isinstance(sample_covariates, tuple | list): + raise ValueError( + f"`sample_covariates` should be a tuple or list, found {sample_covariates} to be of type {type(sample_covariates)}." + ) + for covar in sample_covariates: + if not isinstance(covar, str): + raise ValueError(f"Key should be a string, found {covar} to be of type {type(covar)}.") + return list(sample_covariates) + + @staticmethod + def _verify_split_covariates( + adata: anndata.AnnData, + data: Sequence[str] | None, + control_key: str, + ) -> Sequence[str]: + if data is None: + return [] + if not isinstance(data, tuple | list): + raise ValueError(f"`split_covariates` should be a tuple or list, found {data} to be of type {type(data)}.") + for covar in data: + if not isinstance(covar, str): + raise ValueError(f"Key should be a string, found {covar} to be of type {type(covar)}.") + source_splits = adata.obs[adata.obs[control_key]][data].drop_duplicates() + source_splits = map(tuple, source_splits.values) + target_splits = adata.obs[~adata.obs[control_key]][data].drop_duplicates() + target_splits = map(tuple, target_splits.values) + source_without_targets = set(source_splits) - set(target_splits) + if len(source_without_targets) > 0: + raise ValueError( + f"Source distribution with split covariate values {source_without_targets} do not have a corresponding target distribution." + ) + return data + + @staticmethod + def _verify_covariate_data(covariate_data: pd.DataFrame, covars) -> None: + for covariate in covars: + if covariate is not None and covariate not in covariate_data: + raise ValueError(f"Covariate {covariate} not found in adata.obs or covariate_data.") + + @staticmethod + def _get_linked_perturbation_covariates(perturb_covariates: dict[str, list[str]]) -> dict[str, dict[Any, Any]]: + primary_group, primary_covars = next(iter(perturb_covariates.items())) + linked_perturb_covars: dict[str, dict[Any, Any]] = {k: {} for k in primary_covars} + for cov_group, covars in list(perturb_covariates.items())[1:]: + for primary_cov, linked_cov in zip(primary_covars, covars, strict=False): + linked_perturb_covars[primary_cov][cov_group] = linked_cov + + return linked_perturb_covars + + @staticmethod + def _verify_perturbation_covariate_reps( + adata: anndata.AnnData, + perturbation_covariate_reps: dict[str, str] | None, + perturbation_covariates: dict[str, list[str]], + ) -> dict[str, str]: + if perturbation_covariate_reps is None: + return {} + for key, value in perturbation_covariate_reps.items(): + if key not in perturbation_covariates: + raise ValueError(f"Key '{key}' not found in covariates.") + if value not in adata.uns: + raise ValueError(f"Perturbation covariate representation '{value}' not found in `adata.uns`.") + if not isinstance(adata.uns[value], dict): + raise ValueError( + f"Perturbation covariate representation '{value}' in `adata.uns` should be of type `dict`, found {type(adata.uns[value])}." + ) + return perturbation_covariate_reps + + @staticmethod + def _verify_sample_covariate_reps( + adata: anndata.AnnData, + sample_covariate_reps: dict[str, str] | None, + covariates: list[str], + ) -> dict[str, str]: + if sample_covariate_reps is None: + return {} + for key, value in sample_covariate_reps.items(): + if key not in covariates: + raise ValueError(f"Key '{key}' not found in covariates.") + if value not in adata.uns: + raise ValueError(f"Sample covariate representation '{value}' not found in `adata.uns`.") + if not isinstance(adata.uns[value], dict): + raise ValueError( + f"Sample covariate representation '{value}' in `adata.uns` should be of type `dict`, found {type(adata.uns[value])}." + ) + return sample_covariate_reps + + @staticmethod + def _get_max_combination_length( + perturbation_covariates: dict[str, list[str]], + max_combination_length: int | None, + ) -> int: + obs_max_combination_length = max(len(comb) for comb in perturbation_covariates.values()) + if max_combination_length is None: + return obs_max_combination_length + elif max_combination_length < obs_max_combination_length: + logger.warning( + f"Provided `max_combination_length` is smaller than the observed maximum combination length of the perturbation covariates. Setting maximum combination length to {obs_max_combination_length}.", + stacklevel=2, + ) + return obs_max_combination_length + else: + return max_combination_length + + def _get_primary_covar_encoder( + self, + adata: anndata.AnnData, + perturbation_covariates: dict[str, list[str]], + perturbation_covariate_reps: dict[str, str], + ) -> tuple[preprocessing.OneHotEncoder | None, bool]: + primary_group, primary_covars = next(iter(perturbation_covariates.items())) + is_categorical = self._check_covariate_type(adata, primary_covars) + if perturbation_covariate_reps and primary_group in perturbation_covariate_reps: + return None, is_categorical + if is_categorical: + encoder = preprocessing.OneHotEncoder(sparse_output=False) + all_values = np.unique(adata.obs[primary_covars].values.flatten()) + encoder.fit(all_values.reshape(-1, 1)) + return encoder, is_categorical + encoder = preprocessing.OneHotEncoder(sparse_output=False) + encoder.fit(np.array(primary_covars).reshape(-1, 1)) + return encoder, is_categorical + + @staticmethod + def _check_covariate_type(adata: anndata.AnnData, covars: Sequence[str]) -> bool: + col_is_cat = [] + for covariate in covars: + if is_numeric_dtype(adata.obs[covariate]): + col_is_cat.append(False) + continue + if adata.obs[covariate].isin(["True", "False", True, False]).all(): + adata.obs[covariate] = adata.obs[covariate].astype(int) + col_is_cat.append(False) + continue + try: + adata.obs[covariate] = adata.obs[covariate].astype("category") + col_is_cat.append(True) + except ValueError as e: + raise ValueError( + f"Perturbation covariates `{covariate}` should be either numeric/boolean or categorical." + ) from e + + if max(col_is_cat) != min(col_is_cat): + raise ValueError( + f"Groups of perturbation covariates `{covariate}` should be either all numeric/boolean or all categorical." + ) + + return max(col_is_cat) + + @staticmethod + def _verify_covariate_type(covariate_data: pd.DataFrame, covars: Sequence[str], categorical: bool) -> None: + for covariate in covars: + if is_numeric_dtype(covariate_data[covariate]): + if categorical: + raise ValueError(f"Perturbation covariates `{covariate}` should be categorical, found numeric.") + continue + if covariate_data[covariate].isin(["True", "False", True, False]).all(): + if categorical: + raise ValueError(f"Perturbation covariates `{covariate}` should be categorical, found boolean.") + continue + try: + covariate_data[covariate] = covariate_data[covariate].astype("category") + except ValueError as e: + raise ValueError( + f"Perturbation covariates `{covariate}` should be either numeric/boolean or categorical." + ) from e + else: + if not categorical: + raise ValueError( + f"Perturbation covariates `{covariate}` should be numeric/boolean, found categorical." + ) + + @staticmethod + def _check_shape(arr: float | ArrayLike) -> ArrayLike: + if not hasattr(arr, "shape") or len(arr.shape) == 0: + return jnp.ones((1, 1)) * arr + if arr.ndim == 1: # type: ignore[union-attr] + return jnp.expand_dims(arr, 0) + elif arr.ndim == 2: # type: ignore[union-attr] + if arr.shape[0] == 1: + return arr # type: ignore[return-value] + if arr.shape[1] == 1: + return jnp.transpose(arr) + raise ValueError( + "Condition representation has an unexpected shape. Should be (1, n_features) or (n_features, )." + ) + elif arr.ndim > 2: # type: ignore[union-attr] + raise ValueError("Condition representation has too many dimensions. Should be 1 or 2.") + + raise ValueError( + "Condition representation as an unexpected format. Expected an array of shape (1, n_features) or (n_features, )." + ) + + @staticmethod + def _get_covar_to_idx(covariate_groups: dict[str, Sequence[str]]) -> dict[str, int]: + idx_to_covar = {} + for idx, cov_group in enumerate(covariate_groups): + idx_to_covar[idx] = cov_group + covar_to_idx = {v: k for k, v in idx_to_covar.items()} + return covar_to_idx + + @staticmethod + def _pad_to_max_length(arr: jax.Array, max_combination_length: int, null_value: Any) -> jax.Array: + if arr.shape[0] < max_combination_length: + null_arr = jnp.full((max_combination_length - arr.shape[0], arr.shape[1]), null_value) + arr = jnp.concatenate([arr, null_arr], axis=0) + return arr + + def _get_perturbation_covariates( + self, + condition_data: pd.DataFrame, + rep_dict: dict[str, dict[str, ArrayLike]], + perturb_covariates: Any, # TODO: check if we can save as attribtue + ) -> dict[str, jax.Array]: + primary_group, primary_covars = next(iter(perturb_covariates.items())) + + perturb_covar_emb: dict[str, list[jax.Array]] = {group: [] for group in perturb_covariates} + for primary_cov in primary_covars: + value = condition_data[primary_cov] + cov_name = value if self.is_categorical else primary_cov + if primary_group in self._covariate_reps: + rep_key = self._covariate_reps[primary_group] + try: + prim_arr = rep_dict[rep_key][cov_name] + except KeyError as err: + raise ValueError( + f"Representation for '{cov_name}' not found in `adata.uns['{rep_key}']` or `rep_dict`." + ) from err + prim_arr = jnp.asarray(prim_arr) + else: + prim_arr = jnp.asarray( + self.primary_one_hot_encoder.transform( # type: ignore[union-attr] + np.array(cov_name).reshape(-1, 1) + ) + ) + + if not self.is_categorical: + prim_arr *= value + + prim_arr = self._check_shape(prim_arr) + perturb_covar_emb[primary_group].append(prim_arr) + + for linked_covar in self._linked_perturb_covars[primary_cov].items(): + linked_group, linked_cov = list(linked_covar) + + if linked_cov is None: + linked_arr = jnp.full((1, 1), self._null_value) + linked_arr = self._check_shape(linked_arr) + perturb_covar_emb[linked_group].append(linked_arr) + continue + + cov_name = condition_data[linked_cov] + + if linked_group in self._covariate_reps: + rep_key = self._covariate_reps[linked_group] + if cov_name not in rep_dict[rep_key]: + raise ValueError(f"Representation for '{cov_name}' not found in `adata.uns['{rep_key}']`.") + linked_arr = jnp.asarray(rep_dict[rep_key][cov_name]) + else: + linked_arr = jnp.asarray(condition_data[linked_cov]) + + linked_arr = self._check_shape(linked_arr) + perturb_covar_emb[linked_group].append(linked_arr) + + perturb_covar_emb = { + k: self._pad_to_max_length( + jnp.concatenate(v, axis=0), + self._max_combination_length, + self._null_value, + ) + for k, v in perturb_covar_emb.items() + } + + sample_covar_emb: dict[str, jax.Array] = {} + for sample_cov in self._sample_covariates: + value = condition_data[sample_cov] + if sample_cov in self._covariate_reps: + rep_key = self._covariate_reps[sample_cov] + if value not in rep_dict[rep_key]: + raise ValueError(f"Representation for '{value}' not found in `adata.uns['{sample_cov}']`.") + cov_arr = jnp.asarray(rep_dict[rep_key][value]) + else: + cov_arr = jnp.asarray(value) + + cov_arr = self._check_shape(cov_arr) + sample_covar_emb[sample_cov] = jnp.tile(cov_arr, (self._max_combination_length, 1)) + + return perturb_covar_emb | sample_covar_emb + + @property + def is_categorical(self) -> bool: + """Whether the primary covariate is categorical.""" + return self._is_categorical + + @property + def adata(self) -> anndata.AnnData: + """An :class:`~anndata.AnnData` object used for instantiating the DataManager.""" + return self._adata + + @property + def control_key(self) -> str: + """Boolean key in :attr:`~anndata.AnnData.obs` indicating whether belongs to control group.""" + return self._control_key + + @property + def perturbation_covariates(self) -> dict[str, list[str]]: + """Dictionary with keys indicating the name of the covariate group and values are keys in :attr:`~anndata.AnnData.obs` which together define the covariates.""" + return self._perturbation_covariates + + @property + def perturbation_covariate_reps(self) -> dict[str, str]: + """Dictionary with keys indicating the name of the covariate group and values are keys in :attr:`~anndata.AnnData.uns` storing a dictionary with the representation of the covariates.""" + return self._perturbation_covariate_reps + + @property + def sample_covariates(self) -> Sequence[str]: + """Keys in :attr:`~anndata.AnnData.obs` indicating which sample the cell belongs to (e.g. cell line).""" + return self._sample_covariates + + @property + def sample_covariate_reps(self) -> dict[str, str]: + """Dictionary with keys indicating the name of the sample covariate group and values are keys in :attr:`~anndata.AnnData.uns` storing a dictionary with the representation of the sample covariates.""" + return self._sample_covariate_reps + + @property + def split_covariates(self) -> Sequence[str]: + """Covariates in :attr:`~anndata.AnnData.obs` to split all control cells into different control populations.""" + return self._split_covariates + + @property + def max_combination_length(self) -> int: + """Maximum combination length of perturbation covariates.""" + return self._max_combination_length + + @property + def null_value(self) -> float: + """Value to use for padding to :attr:`~max_combination_length`.""" + return self._null_value + + @property + def primary_one_hot_encoder(self) -> preprocessing.OneHotEncoder | None: + """One-hot encoder for the primary covariate.""" + return self._primary_one_hot_encoder + + @property + def linked_perturb_covars(self) -> dict[str, dict[Any, Any]]: + """Dictionary with keys indicating the name of the primary covariate and values are dictionaries with keys indicating the name of the linked covariate group and values are the linked covariates.""" + return self._linked_perturb_covars + + @property + def covariate_reps(self) -> dict[str, str]: + """Dictionary which stores representation of covariates, i.e. the union of ``sample_covariate_reps`` and ``perturbation_covariate_reps``.""" + return self._covariate_reps + + @property + def covar_to_idx(self) -> dict[str, int]: + """TODO: add description""" + return self._covar_to_idx + + @property + def perturb_covar_keys(self) -> list[str]: + """List of all perturbation covariates.""" + return self._perturb_covar_keys + + @property + def sample_rep(self) -> str | dict[str, str]: + """Key of the sample representation.""" + return self._sample_rep diff --git a/transfer/code/ori_scDFM/src/data_process/data.py b/transfer/code/ori_scDFM/src/data_process/data.py new file mode 100644 index 0000000000000000000000000000000000000000..5fa20f33e6581d3fba95316bbd302b4f785a4f8e --- /dev/null +++ b/transfer/code/ori_scDFM/src/data_process/data.py @@ -0,0 +1,596 @@ +import scanpy as sc +import pandas as pd +import numpy as np +import json +import torch +import pickle +from typing import Union, Optional +from pathlib import Path +import os +from src.utils._preprocessing import annotate_compounds, get_molecular_fingerprints +from src.data_process._datamanager import DataManager +import jax +import torch.nn as nn +from torch.utils.data import Dataset, DataLoader +import pdb +import tqdm +from random import shuffle +from src.utils.utils import build_gene_coexpression_graph,sorted_pad_mask +# combosciplex url: https://figshare.com/articles/dataset/combosciplex/25062230?file=44229635 +# 'norman' url = 'https://dataverse.harvard.edu/api/access/datafile/6154020' + +class Data: + def __init__(self, data_path='../../data'): + self.data_path = data_path + if not os.path.exists(data_path): + raise ValueError(data_path + ' does not exist') + # os.makedirs(data_path) + + + def load_data(self, data_name = None, data_path = None): + self.data_name = data_name + if data_name in ['norman', 'norman_umi_go_filtered',]: + self.adata = sc.read_h5ad(os.path.join(self.data_path, data_name + '.h5ad')) + # Ensure var_names are gene symbols (not Ensembl IDs) so they + # match perturbation names and the cached vocabulary. + if 'gene_name' in self.adata.var.columns and self.adata.var_names[0].startswith('ENSG'): + self.adata.var_names = self.adata.var['gene_name'].astype(str).values + elif data_name in ['combosciplex', ]: + self.adata = sc.read_h5ad(os.path.join(self.data_path, data_name + '.h5ad')) + else: + raise ValueError(data_name + ' is not a valid data name') + + def process_data(self, n_top_genes = 2000,infer_top_gene=1000,split_method='additive', + use_negative_edge=True, k=30, + **kwargs): + os.makedirs(os.path.join(self.data_path, self.data_name), exist_ok=True) + if self.data_name == 'combosciplex': + + if os.path.exists(os.path.join(self.data_path, self.data_name, 'processed.h5ad')): + self.adata = sc.read_h5ad(os.path.join(self.data_path, self.data_name, 'processed.h5ad')) + else: + + self.adata.obs["condition"] = self.adata.obs.apply( + lambda x: "control" if x["condition"] == "control+control" else x["condition"], axis=1 + ) + + self.adata.obs["is_control"] = self.adata.obs.apply( + lambda x: True if x["condition"] == "control" else False, axis=1 + ) + + annotate_compounds(self.adata, compound_keys=["Drug1", "Drug2"]) + get_molecular_fingerprints(self.adata, compound_keys=["Drug1", "Drug2"]) + self.adata.uns["fingerprints"]["control"] = np.zeros(1024) + + self.adata.write(os.path.join(self.data_path, self.data_name, 'processed.h5ad')) + + self.adata.X = self.adata.layers["counts"].copy() + sc.pp.normalize_total(self.adata) + sc.pp.log1p(self.adata) + sc.pp.highly_variable_genes(self.adata, inplace=True, n_top_genes=n_top_genes) + + if 'test_conditions' in kwargs.keys(): + test_conditions = kwargs['test_conditions'] + else: + test_conditions = ['Panobinostat+Crizotinib', + 'Panobinostat+Curcumin', + 'Panobinostat+SRT1720', + 'Panobinostat+Sorafenib', + 'SRT2104+Alvespimycin', + 'control+Alvespimycin', + 'control+Dacinostat'] + + self.adata = self.adata[:,self.adata.var['highly_variable']] # filter out low variable genes + + self.adata.obs["mode"] = self.adata.obs.apply(lambda x: "test" if x["condition"] in test_conditions else "train", axis=1) + self.adata_train = self.adata[self.adata.obs["mode"] == "train"] + self.adata_test = self.adata[(self.adata.obs["mode"] == "test") | (self.adata.obs["condition"]=="control")] + + sc.pp.highly_variable_genes(self.adata_test, inplace=True, n_top_genes=infer_top_gene) + self.adata_test = self.adata_test[:,self.adata_test.var['highly_variable']] + + condition = np.unique(list(self.adata.obs['condition'])) + unique_perturbation = [] + np.array([unique_perturbation.extend(perturbation.split('+')) for perturbation in condition]) + unique_perturbation = np.unique(unique_perturbation) + unique_perturbation.sort() + self.unique_perturbation = unique_perturbation + self.perturbation_dict = {perturbation: i for i, perturbation in enumerate(unique_perturbation)} + # self._val_manager = + elif self.data_name == 'norman' or self.data_name == 'norman_umi_go_filtered': + + sc.pp.highly_variable_genes(self.adata, inplace=True, n_top_genes=n_top_genes) + unique_perturbation = [] + [unique_perturbation.extend(perturbation.split('+')) for perturbation in self.adata.obs['condition'].unique()] + unique_perturbation = np.unique(unique_perturbation) + + if self.data_name == 'norman': + for perturbation in unique_perturbation: + if perturbation in self.adata.var_names: + self.adata.var.loc[perturbation, 'highly_variable'] = True + else: + print(f"Warning: {perturbation} is not in the gene names") + self.adata = self.adata[:,self.adata.var['highly_variable']] + elif self.data_name == 'norman_umi_go_filtered': + all_gene_names = list(self.adata.var['gene_name']) + ['ctrl'] + for perturbation in unique_perturbation: + if perturbation not in all_gene_names: + print(f"Warning: {perturbation} is not in the gene names") + self.adata.var['highly_variable'] = True + + + #### for split five times + if split_method == 'additive' or 'combinations': + split_file = os.path.join(self.data_path, self.data_name, 'split_results.pkl') + if os.path.exists(split_file): + with open(split_file, 'rb') as f: + self.split_results = pickle.load(f) + else: + perturbations = np.unique(self.adata.obs['condition']) + double_perturbation = [p for p in perturbations if 'ctrl' not in p] + double_perturbation = np.array(double_perturbation) + + self.split_results = [] + + for i in range(5): + np.random.seed(42 + i) + shuffled = double_perturbation.copy() + np.random.shuffle(shuffled) + + split_idx = int(len(shuffled) * 0.3) + test_double = shuffled[:split_idx] + train_double = shuffled[split_idx:] + self.split_results.append({ + 'train': train_double.tolist(), + 'test': test_double.tolist() + }) + + with open(split_file, 'wb') as f: + pickle.dump(self.split_results, f) + print('split results saved') + + elif split_method == 'unseen': + split_file = os.path.join(self.data_path, self.data_name, 'split_results_unseen.pkl') + if os.path.exists(split_file): + with open(split_file, 'rb') as f: + self.split_results = pickle.load(f) + else: + self.split_results = [] + for i in range(5): + perturbations = np.unique(self.adata.obs['condition']) + double_perturbation = [p for p in perturbations if 'ctrl' not in p] + single = [] + [single.extend(p.split('+')) for p in double_perturbation] + single = list(set(single)) + + shuffle(single) + remove_genes = single[:12] + p_count = {} + for p in double_perturbation: + ps = p.split('+') + count = int(ps[0] in remove_genes) + int(ps[1] in remove_genes) + p_count[p] = count + double_perturbation = [p for p, count in p_count.items() if count > 0] + double_perturbation = list(double_perturbation) + remove_genes_condition = [p+'+control' for p in remove_genes] + double_perturbation.extend(remove_genes_condition) + self.split_results.append({ + 'p_count': p_count, + 'test': double_perturbation + }) + with open(split_file, 'wb') as f: + pickle.dump(self.split_results, f) + print('split results unseen saved') + + if 'fold' in kwargs.keys(): + fold = kwargs['fold'] + else: + fold = 0 + self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control') + self.adata.obs['Drug1'] = self.adata.obs['condition'].str.split('+').apply(lambda x: x[0]) + self.adata.obs['Drug2'] = self.adata.obs['condition'].str.split('+').apply(lambda x: x[-1]) + self.adata.obs['is_control'] = False + self.adata.obs.loc[self.adata.obs['control'] == 1, 'is_control'] = True + self.adata.obs['mode'] = 'train' + + + if split_method == 'combinations': + self.split_results[fold]['test'] = self.split_results[fold]['test'][:15] + remove_genes = [] + [remove_genes.extend(p.split('+')) for p in self.split_results[fold]['test']] + remove_genes = set(remove_genes) + remove_genes_condition = [p+'+control' for p in remove_genes] + + self.split_results[fold]['test'].extend(remove_genes_condition) + + + self.adata.obs.loc[self.adata.obs['condition'].isin(self.split_results[fold]['test']), 'mode'] = 'test' + + self.adata_train = self.adata[self.adata.obs['mode'] == 'train'] + self.adata_test = self.adata[(self.adata.obs['mode'] == 'test') | (self.adata.obs['control'] == 1)] + + + + sc.pp.highly_variable_genes(self.adata_test, inplace=True, n_top_genes=infer_top_gene) + self.adata_test = self.adata_test[:,self.adata_test.var['highly_variable']] + + condition = np.unique(list(self.adata.obs['condition'])) + unique_perturbation = [] + np.array([unique_perturbation.extend(perturbation.split('+')) for perturbation in condition]) + unique_perturbation = np.unique(unique_perturbation) + unique_perturbation.sort() + self.unique_perturbation = unique_perturbation + self.perturbation_dict = {perturbation: i for i, perturbation in enumerate(unique_perturbation)} + + else: + raise ValueError(self.data_name + ' is not a valid data name') + + if 'fold' in kwargs.keys(): + fold = kwargs['fold'] + else: + fold = 0 + if use_negative_edge: + mask_path = os.path.join(self.data_path, self.data_name,'mask_fold_'+str(fold)+'topk_'+str(k)+split_method+'_negative_edge'+'.pt') + else: + mask_path = os.path.join(self.data_path, self.data_name,'mask_fold_'+str(fold)+'topk_'+str(k)+split_method+'.pt') + if os.path.exists(mask_path): + self.mask = torch.load(mask_path) + else: + X = self.adata_train.X.toarray() + mask = build_gene_coexpression_graph(X, + method="pearson", + wgcna_beta=None, + sparsify="topk", + k=k, + use_negative_edge=use_negative_edge) + mask = sorted_pad_mask(mask, pad_size=4, gene_names=list(self.adata_train.var_names)) + torch.save(mask, mask_path) + print('mask saved') + + + def load_flow_data(self, batch_size = 128): + if self.data_name == 'combosciplex': + train_sampler = TrainSampler(self.data_name, self.adata_train, ["Drug1", "Drug2"], self.perturbation_dict) + test_sampler = TestDataset(self.data_name, self.adata_test, ["Drug1", "Drug2"], self.perturbation_dict) + + return train_sampler , test_sampler, [] + elif self.data_name == 'norman' or self.data_name == 'norman_umi_go_filtered': + train_sampler = TrainSampler(self.data_name, self.adata_train, ["Drug1", "Drug2"], self.perturbation_dict) + test_sampler = TestDataset(self.data_name, self.adata_test, ["Drug1", "Drug2"], self.perturbation_dict) + return train_sampler , test_sampler, [] + else: + raise ValueError(self.data_name + ' is not a valid data name') + + + def pretrain_data(self, batch_size = 128): + if self.data_name == 'combosciplex': + + self.pretrain_train_data = PretrainData(self.adata_train, self.perturbation_dict) + self.pretrain_train_data_loader = DataLoader(self.pretrain_train_data, batch_size=batch_size, shuffle=True, pin_memory=True, num_workers=4) + self.pretrain_test_data = PretrainData(self.adata_test, self.perturbation_dict) + self.pretrain_test_data_loader = DataLoader(self.pretrain_test_data, batch_size=batch_size, shuffle=False, pin_memory=True, num_workers=4) + return self.pretrain_train_data_loader, self.pretrain_test_data_loader + else: + raise ValueError(self.data_name + ' is not a valid data name') + +class TrainSampler: + def __init__(self, data_name, adata: sc.AnnData, perturbation_covariates: list[str], perturbation_dict: dict,): + self.data_name = data_name + self.adata = adata + self.perturbation_covariates = perturbation_covariates + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) + self._perturbation_covariates = adata.obs['perturbation_covariates'].unique() + + self._perturbation_covariates = self._perturbation_covariates[self._perturbation_covariates != 'control+control'] + + self._perturbation_covariates.sort() + self.perturbation_covariates_dict = {perturbation: i for i, perturbation in enumerate(self._perturbation_covariates)} + + perturbation_covariates_id = [adata.obs[perturbation_covariates[i]].apply(lambda x: perturbation_dict[x]) + for i in range(len(perturbation_covariates))] + self.perturbation_covariates_id = np.array(perturbation_covariates_id).T + + + self.cells_name = self.adata.obs_names + + + def get_batch(self, batch_size: int, same_perturbation: bool = True): + if same_perturbation: + # random sample a perturbation from self._perturbation_covariates. + # the last one is control + perturbation_idx = np.random.choice(len(self._perturbation_covariates), 1)[0] + + perturbation_id = self._perturbation_covariates[perturbation_idx] + + # get the target data + tgt_idx = (self.adata.obs['perturbation_covariates'] == perturbation_id).to_numpy().nonzero()[0] + tgt_batch_idx = np.random.choice(tgt_idx, batch_size) + + tgt_batch = torch.from_numpy(self.adata.X[tgt_batch_idx].toarray()) + + # get data from control + src_idx = (self.adata.obs['perturbation_covariates'] == 'control+control').to_numpy().nonzero()[0] + src_batch_idx = np.random.choice(src_idx, batch_size) + + src_batch = torch.from_numpy(self.adata.X[src_batch_idx].toarray()) + + return { + 'src_cell_data': src_batch, + 'tgt_cell_data': tgt_batch, + 'src_cell_id': self.cells_name[src_batch_idx], + 'tgt_cell_id': self.cells_name[tgt_batch_idx], + 'condition_id': self.perturbation_covariates_id[tgt_batch_idx], + } + + else: + raise ValueError('same_perturbation must be True') + +class TestDataset: + def __init__(self, data_name,adata: sc.AnnData, perturbation_covariates: list[str], perturbation_dict: dict,): + self.data_name = data_name + self.adata = adata + self.perturbation_covariates = perturbation_covariates + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) + self._perturbation_covariates = adata.obs['perturbation_covariates'].unique() + + self._perturbation_covariates = self._perturbation_covariates[self._perturbation_covariates != 'control+control'] + + self._perturbation_covariates.sort() + self.perturbation_covariates_dict = {perturbation: i for i, perturbation in enumerate(self._perturbation_covariates)} + + perturbation_covariates_id = [adata.obs[perturbation_covariates[i]].apply(lambda x: perturbation_dict[x]) + for i in range(len(perturbation_covariates))] + self.perturbation_covariates_id = np.array(perturbation_covariates_id).T + + + self.cells_name = self.adata.obs_names + + def get_control_data(self,): + control_data = self.adata[self.adata.obs['is_control']] + return { + 'src_cell_data': torch.from_numpy(control_data.X.toarray()), + 'src_cell_id': control_data.obs_names, + 'condition_id': torch.tensor(self.perturbation_covariates_id[self.adata.obs['is_control']]), + } + + def get_perturbation_data(self, perturbation: str): + perturbation_data = self.adata[self.adata.obs['perturbation_covariates'] == perturbation] + return { + 'tgt_cell_data': torch.from_numpy(perturbation_data.X.toarray()), + 'tgt_cell_id': perturbation_data.obs_names, + 'condition_id': torch.tensor(self.perturbation_covariates_id[self.adata.obs['perturbation_covariates'] == perturbation]), + } + + + +class PerturbationDataset(Dataset): + def __init__(self, sampler: TrainSampler, batch_size: int): + self.sampler = sampler + self.batch_size = batch_size + self.perturbations = sampler._perturbation_covariates + + self.control_idx = (sampler.adata.obs['perturbation_covariates'] == 'control+control').to_numpy().nonzero()[0] + + def __len__(self): + + return len(self.perturbations) * 1000 + + def __getitem__(self, idx): + # 随机选一个 perturbation + perturbation_idx = np.random.choice(len(self.perturbations), 1)[0] + perturbation_id = self.perturbations[perturbation_idx] + + # target batch + tgt_idx = (self.sampler.adata.obs['perturbation_covariates'] == perturbation_id).to_numpy().nonzero()[0] + tgt_batch_idx = np.random.choice(tgt_idx, self.batch_size) + + # source (control) batch + src_batch_idx = np.random.choice(self.control_idx, self.batch_size) + if hasattr(self.sampler.adata.X[src_batch_idx], "toarray"): + src_batch = torch.from_numpy(self.sampler.adata.X[src_batch_idx].toarray()) + tgt_batch = torch.from_numpy(self.sampler.adata.X[tgt_batch_idx].toarray()) + else: + src_batch = torch.from_numpy(self.sampler.adata.X[src_batch_idx]) + tgt_batch = torch.from_numpy(self.sampler.adata.X[tgt_batch_idx]) + + return { + 'src_cell_data': src_batch, + 'tgt_cell_data': tgt_batch, + 'src_cell_id': list(self.sampler.cells_name[src_batch_idx]), + 'tgt_cell_id': list(self.sampler.cells_name[tgt_batch_idx]), + 'condition_id': torch.tensor(self.sampler.perturbation_covariates_id[tgt_batch_idx]), + } +class BinDiscretizer: + """ + data = np.random.exponential(scale=2.0, size=1000) + + bd = BinDiscretizer(n_bins=200) + bd.fit(data) + + bd.save_edges('./data/combosciplex/bin_discretizer_edges.pkl') + + new_bd = BinDiscretizer(n_bins=200) + new_bd.load_edges('./data/combosciplex/bin_discretizer_edges.pkl') + + binned = bd.transform(data) + + recon = bd.inverse_transform(binned, random=False) + + Note: 0 is treated as a separate class (class 0), and non-zero values are discretized into classes 1 to n_bins. + """ + def __init__(self, n_bins: int, strategy: str = "quantile"): + self.n_bins = n_bins + self.strategy = strategy + self.edges = None # will be (n_bins + 1, ) array + + def fit(self, data: Union[np.ndarray, torch.Tensor]): + if isinstance(data, torch.Tensor): + data = data.detach().cpu().numpy() + data = data.flatten() + data = data[data > 0] # exclude zeros from fitting + + if len(data) == 0: + raise ValueError("No non-zero entries in data to fit.") + + if self.strategy == "quantile": + self.edges = np.quantile(data, np.linspace(0, 1, self.n_bins + 1)) + elif self.strategy == "uniform": + self.edges = np.linspace(data.min(), data.max(), self.n_bins + 1) + else: + raise ValueError(f"Unknown strategy {self.strategy}") + + def transform(self, data: Union[np.ndarray, torch.Tensor]) -> Union[np.ndarray, torch.Tensor]: + if self.edges is None: + raise RuntimeError("Call fit() before transform().") + + is_torch = isinstance(data, torch.Tensor) + if is_torch: + orig_dtype = data.dtype + data = data.detach().cpu().numpy() + + out = np.zeros_like(data, dtype=np.int64) + mask = data > 0 + + # Digitize non-zero entries: 0 remains 0, non-zero values get classes 1 to n_bins + if np.any(mask): + # np.digitize returns 0-based indices for the bins + # We want to map these to 1-based class indices + digitized = np.digitize(data[mask], self.edges[1:-1]) + # Convert 0-based bin indices to 1-based class indices + # digitized=0 means it's in the first bin, which should be class 1 + # digitized=1 means it's in the second bin, which should be class 2, etc. + out[mask] = digitized + 1 + + if is_torch: + return torch.from_numpy(out).to(dtype=torch.int64) + return out + + def inverse_transform(self, digitized: Union[np.ndarray, torch.Tensor], random: bool = False) -> Union[np.ndarray, torch.Tensor]: + if self.edges is None: + raise RuntimeError("Call fit() before inverse_transform().") + + is_torch = isinstance(digitized, torch.Tensor) + if is_torch: + orig_dtype = digitized.dtype + digitized = digitized.detach().cpu().numpy() + + out = np.zeros_like(digitized, dtype=np.float64) + mask = digitized > 0 + + if np.any(mask): + ids = digitized[mask] + # Ensure ids are within valid range (1 to n_bins) + ids = np.clip(ids, 1, self.n_bins) + # Convert 1-based class indices back to 0-based bin indices + bin_ids = ids - 1 + lefts = self.edges[bin_ids] + rights = self.edges[bin_ids + 1] + + if random: + out[mask] = np.random.uniform(lefts, rights) + else: + out[mask] = (lefts + rights) / 2 + + if is_torch: + return torch.from_numpy(out).to(dtype=torch.float64) + return out + + def save_edges(self, filepath: Union[str, Path]): + """Save edges to a file""" + if self.edges is None: + raise RuntimeError("No edges to save. Call fit() first.") + + filepath = Path(filepath) + with open(filepath, 'wb') as f: + pickle.dump({'edges': self.edges, 'n_bins': self.n_bins}, f) + + def load_edges(self, filepath: Union[str, Path]): + """Load edges from a file""" + filepath = Path(filepath) + if not filepath.exists(): + raise FileNotFoundError(f"File {filepath} not found") + + with open(filepath, 'rb') as f: + data = pickle.load(f) + loaded_n_bins = data['n_bins'] + if loaded_n_bins != self.n_bins: + raise ValueError(f"Loaded n_bins ({loaded_n_bins}) does not match initialized n_bins ({self.n_bins})") + self.edges = data['edges'] + +class PretrainData(Dataset): + def __init__(self, adata: sc.AnnData, drug_dict: dict): + self.adata = adata + self.drug_dict = drug_dict + self.X = torch.from_numpy(adata.X.toarray()) + self.cell_id = adata.obs_names + # self.drug1 = torch.tensor(np.array(adata.obs['Drug1'].apply(lambda x: drug_dict[x]))) + # self.drug2 = torch.tensor(np.array(adata.obs['Drug2'].apply(lambda x: drug_dict[x]))) + + def __len__(self): + return len(self.adata) + + def __getitem__(self, idx): + return { + 'values' : self.X[idx], + 'cell_id': self.cell_id[idx], + } + + +class FlowMatchingDataset(Dataset): + """PyTorch Dataset for flow matching training data""" + + def __init__(self, jax_sampler, num_samples=10000, seed=42): + """ + Args: + jax_sampler: JAX-based TrainSampler + num_samples: Number of samples to generate per epoch + seed: Random seed + """ + self.jax_sampler = jax_sampler + self.num_samples = num_samples + self.rng = jax.random.PRNGKey(seed) + + def __len__(self): + return self.num_samples + + def __getitem__(self, idx): + """Sample a batch from the JAX sampler""" + # Generate new random key for each sample + self.rng, sample_key = jax.random.split(self.rng) + + # Sample from JAX sampler + sample = self.jax_sampler.sample(sample_key) + + # Convert JAX arrays to PyTorch tensors + src_cell_data = torch.from_numpy(np.array(sample['src_cell_data'])).float() + tgt_cell_data = torch.from_numpy(np.array(sample['tgt_cell_data'])).float() + + sample['src_cell_id'] + sample['tgt_cell_id'] + # Convert condition embedding if available + condition_data = None + if 'condition' in sample: + condition_data = { + key: torch.from_numpy(np.array(val)).float() + for key, val in sample['condition'].items() + } + + # Convert condition_id embedding if available + condition_id = None + if 'condition_id' in sample: + condition_id = torch.from_numpy(np.array(sample['condition_id'])).long() + + return { + 'src_cell_data': src_cell_data, + 'tgt_cell_data': tgt_cell_data, + 'condition': condition_data, + 'condition_id': condition_id, + } + +if __name__ == "__main__": + data = Data(data_path='./data') + data.load_data(data_name='combosciplex') + data.process_data() + + + \ No newline at end of file diff --git a/transfer/code/ori_scDFM/src/data_process/scgpt_preprocess.py b/transfer/code/ori_scDFM/src/data_process/scgpt_preprocess.py new file mode 100644 index 0000000000000000000000000000000000000000..c4b1f8b9aa422f0dac7d9efc514545aecf1f4c8d --- /dev/null +++ b/transfer/code/ori_scDFM/src/data_process/scgpt_preprocess.py @@ -0,0 +1,303 @@ +from typing import Dict, Optional, Union + +import numpy as np +import torch +from scipy.sparse import issparse +import scanpy as sc +from scanpy.get import _get_obs_rep, _set_obs_rep +from anndata import AnnData + +# from scgpt import logger + + +class Preprocessor: + """ + Prepare data into training, valid and test split. Normalize raw expression + values, binning or using other transform into the preset model input format. + """ + + def __init__( + self, + use_key: Optional[str] = None, + filter_gene_by_counts: Union[int, bool] = False, + filter_cell_by_counts: Union[int, bool] = False, + normalize_total: Union[float, bool] = 1e4, + result_normed_key: Optional[str] = "X_normed", + log1p: bool = False, + result_log1p_key: str = "X_log1p", + subset_hvg: Union[int, bool] = False, + hvg_use_key: Optional[str] = None, + hvg_flavor: str = "seurat_v3", + binning: Optional[int] = None, + result_binned_key: str = "X_binned", + ): + r""" + Set up the preprocessor, use the args to config the workflow steps. + + Args: + + use_key (:class:`str`, optional): + The key of :class:`~anndata.AnnData` to use for preprocessing. + filter_gene_by_counts (:class:`int` or :class:`bool`, default: ``False``): + Whther to filter genes by counts, if :class:`int`, filter genes with counts + filter_cell_by_counts (:class:`int` or :class:`bool`, default: ``False``): + Whther to filter cells by counts, if :class:`int`, filter cells with counts + normalize_total (:class:`float` or :class:`bool`, default: ``1e4``): + Whether to normalize the total counts of each cell to a specific value. + result_normed_key (:class:`str`, default: ``"X_normed"``): + The key of :class:`~anndata.AnnData` to store the normalized data. If + :class:`None`, will use normed data to replce the :attr:`use_key`. + log1p (:class:`bool`, default: ``True``): + Whether to apply log1p transform to the normalized data. + result_log1p_key (:class:`str`, default: ``"X_log1p"``): + The key of :class:`~anndata.AnnData` to store the log1p transformed data. + subset_hvg (:class:`int` or :class:`bool`, default: ``False``): + Whether to subset highly variable genes. + hvg_use_key (:class:`str`, optional): + The key of :class:`~anndata.AnnData` to use for calculating highly variable + genes. If :class:`None`, will use :attr:`adata.X`. + hvg_flavor (:class:`str`, default: ``"seurat_v3"``): + The flavor of highly variable genes selection. See + :func:`scanpy.pp.highly_variable_genes` for more details. + binning (:class:`int`, optional): + Whether to bin the data into discrete values of number of bins provided. + result_binned_key (:class:`str`, default: ``"X_binned"``): + The key of :class:`~anndata.AnnData` to store the binned data. + """ + self.use_key = use_key + self.filter_gene_by_counts = filter_gene_by_counts + self.filter_cell_by_counts = filter_cell_by_counts + self.normalize_total = normalize_total + self.result_normed_key = result_normed_key + self.log1p = log1p + self.result_log1p_key = result_log1p_key + self.subset_hvg = subset_hvg + self.hvg_use_key = hvg_use_key + self.hvg_flavor = hvg_flavor + self.binning = binning + self.result_binned_key = result_binned_key + + def __call__(self, adata: AnnData, batch_key: Optional[str] = None) -> Dict: + """ + format controls the different input value wrapping, including categorical + binned style, fixed-sum normalized counts, log1p fixed-sum normalized counts, etc. + + Args: + + adata (:class:`AnnData`): + The :class:`AnnData` object to preprocess. + batch_key (:class:`str`, optional): + The key of :class:`AnnData.obs` to use for batch information. This arg + is used in the highly variable gene selection step. + """ + key_to_process = self.use_key + # preliminary checks, will use later + if key_to_process == "X": + key_to_process = None # the following scanpy apis use arg None to use X + is_logged = self.check_logged(adata, obs_key=key_to_process) + + # step 1: filter genes + if self.filter_gene_by_counts: + print("Filtering genes by counts ...") + sc.pp.filter_genes( + adata, + min_counts=self.filter_gene_by_counts + if isinstance(self.filter_gene_by_counts, int) + else None, + ) + + # step 2: filter cells + if ( + isinstance(self.filter_cell_by_counts, int) + and self.filter_cell_by_counts > 0 + ): + print("Filtering cells by counts ...") + sc.pp.filter_cells( + adata, + min_counts=self.filter_cell_by_counts + if isinstance(self.filter_cell_by_counts, int) + else None, + ) + + # step 3: normalize total + if self.normalize_total: + print("Normalizing total counts ...") + normed_ = sc.pp.normalize_total( + adata, + target_sum=self.normalize_total + if isinstance(self.normalize_total, float) + else None, + layer=key_to_process, + inplace=False, + )["X"] + key_to_process = self.result_normed_key or key_to_process + _set_obs_rep(adata, normed_, layer=key_to_process) + + # step 4: log1p + if self.log1p: + print("Log1p transforming ...") + if is_logged: + print( + "The input data seems to be already log1p transformed. " + "Set `log1p=False` to avoid double log1p transform." + ) + if self.result_log1p_key: + _set_obs_rep( + adata, + _get_obs_rep(adata, layer=key_to_process), + layer=self.result_log1p_key, + ) + key_to_process = self.result_log1p_key + sc.pp.log1p(adata, layer=key_to_process) + + # step 5: subset hvg + if self.subset_hvg: + print("Subsetting highly variable genes ...") + if batch_key is None: + print( + "No batch_key is provided, will use all cells for HVG selection." + ) + sc.pp.highly_variable_genes( + adata, + layer=self.hvg_use_key, + n_top_genes=self.subset_hvg + if isinstance(self.subset_hvg, int) + else None, + batch_key=batch_key, + flavor=self.hvg_flavor, + subset=True, + ) + + # step 6: binning + if self.binning: + print("Binning data ...") + if not isinstance(self.binning, int): + raise ValueError( + "Binning arg must be an integer, but got {}.".format(self.binning) + ) + n_bins = self.binning # NOTE: the first bin is always a spectial for zero + binned_rows = [] + bin_edges = [] + layer_data = _get_obs_rep(adata, layer=key_to_process) + layer_data = layer_data.toarray() if issparse(layer_data) else layer_data + if layer_data.min() < 0: + raise ValueError( + f"Assuming non-negative data, but got min value {layer_data.min()}." + ) + for row in layer_data: + if row.max() == 0: + print( + "The input data contains all zero rows. Please make sure " + "this is expected. You can use the `filter_cell_by_counts` " + "arg to filter out all zero rows." + ) + binned_rows.append(np.zeros_like(row, dtype=np.int64)) + bin_edges.append(np.array([0] * n_bins)) + continue + non_zero_ids = row.nonzero() + non_zero_row = row[non_zero_ids] + bins = np.quantile(non_zero_row, np.linspace(0, 1, n_bins - 1)) + # bins = np.sort(np.unique(bins)) + # NOTE: comment this line for now, since this will make the each category + # has different relative meaning across datasets + non_zero_digits = _digitize(non_zero_row, bins) + assert non_zero_digits.min() >= 1 + assert non_zero_digits.max() <= n_bins - 1 + binned_row = np.zeros_like(row, dtype=np.int64) + binned_row[non_zero_ids] = non_zero_digits + binned_rows.append(binned_row) + bin_edges.append(np.concatenate([[0], bins])) + adata.layers[self.result_binned_key] = np.stack(binned_rows) + adata.obsm["bin_edges"] = np.stack(bin_edges) + + def check_logged(self, adata: AnnData, obs_key: Optional[str] = None) -> bool: + """ + Check if the data is already log1p transformed. + + Args: + + adata (:class:`AnnData`): + The :class:`AnnData` object to preprocess. + obs_key (:class:`str`, optional): + The key of :class:`AnnData.obs` to use for batch information. This arg + is used in the highly variable gene selection step. + """ + data = _get_obs_rep(adata, layer=obs_key) + max_, min_ = data.max(), data.min() + if max_ > 30: + return False + if min_ < 0: + return False + + non_zero_min = data[data > 0].min() + if non_zero_min >= 1: + return False + + return True + + +def _digitize(x: np.ndarray, bins: np.ndarray, side="both") -> np.ndarray: + """ + Digitize the data into bins. This method spreads data uniformly when bins + have same values. + + Args: + + x (:class:`np.ndarray`): + The data to digitize. + bins (:class:`np.ndarray`): + The bins to use for digitization, in increasing order. + side (:class:`str`, optional): + The side to use for digitization. If "one", the left side is used. If + "both", the left and right side are used. Default to "one". + + Returns: + + :class:`np.ndarray`: + The digitized data. + """ + assert x.ndim == 1 and bins.ndim == 1 + + left_digits = np.digitize(x, bins) + if side == "one": + return left_digits + + right_difits = np.digitize(x, bins, right=True) + + rands = np.random.rand(len(x)) # uniform random numbers + + digits = rands * (right_difits - left_digits) + left_digits + digits = np.ceil(digits).astype(np.int64) + return digits + + +def binning( + row: Union[np.ndarray, torch.Tensor], n_bins: int +) -> Union[np.ndarray, torch.Tensor]: + """Binning the row into n_bins.""" + dtype = row.dtype + return_np = False if isinstance(row, torch.Tensor) else True + row = row.cpu().numpy() if isinstance(row, torch.Tensor) else row + # TODO: use torch.quantile and torch.bucketize + + if row.max() == 0: + print( + "The input data contains row of zeros. Please make sure this is expected." + ) + return ( + np.zeros_like(row, dtype=dtype) + if return_np + else torch.zeros_like(row, dtype=dtype) + ) + + if row.min() <= 0: + non_zero_ids = row.nonzero() + non_zero_row = row[non_zero_ids] + bins = np.quantile(non_zero_row, np.linspace(0, 1, n_bins - 1)) + non_zero_digits = _digitize(non_zero_row, bins) + binned_row = np.zeros_like(row, dtype=np.int64) + binned_row[non_zero_ids] = non_zero_digits + else: + bins = np.quantile(row, np.linspace(0, 1, n_bins - 1)) + binned_row = _digitize(row, bins) + return torch.from_numpy(binned_row) if not return_np else binned_row.astype(dtype) \ No newline at end of file diff --git a/transfer/code/ori_scDFM/src/data_process/utils.py b/transfer/code/ori_scDFM/src/data_process/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..71d7de92f507e726d731cb07ebf669e3de33e8bc --- /dev/null +++ b/transfer/code/ori_scDFM/src/data_process/utils.py @@ -0,0 +1,35 @@ +import anndata as ad +import pandas as pd +import numpy as np + +def build_generated_anndata( + embeddings: np.ndarray, # shape: (N, D) + condition_id: np.ndarray, # shape: (N, 2) + pert_col: str = "perturbation", # first perturbation column name + pert_col2: str = "perturbation_2", # second perturbation (if combo) + combo_col: str = "pert_combo", # combined label +) -> ad.AnnData: + """ + Construct AnnData from model-generated embeddings and condition IDs. + """ + N = embeddings.shape[0] + if condition_id.shape[0] != N: + raise ValueError("condition_id and embeddings must have same first dimension.") + + # obs: per-cell annotation + obs = pd.DataFrame({ + pert_col: condition_id[:, 0], + pert_col2: condition_id[:, 1], + combo_col: [f"{a}_{b}" for a, b in condition_id], + }, index=[f"cell_{i}" for i in range(N)]) + + # construct AnnData + adata = ad.AnnData( + X=embeddings, # could also store in obsm + obs=obs + ) + + # also store embedding explicitly for plotting + adata.obsm["embedding"] = embeddings + + return adata \ No newline at end of file diff --git a/transfer/code/ori_scDFM/src/flow_matching/__init__.py b/transfer/code/ori_scDFM/src/flow_matching/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..719ae055c6a13d749beca75c7a496f3cd7cd8948 --- /dev/null +++ b/transfer/code/ori_scDFM/src/flow_matching/__init__.py @@ -0,0 +1,7 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +__version__ = "1.0.10" diff --git a/transfer/code/ori_scDFM/src/flow_matching/ot/__init__.py b/transfer/code/ori_scDFM/src/flow_matching/ot/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4a7eee1fa298559ee8d944804ac860cfb4767a25 --- /dev/null +++ b/transfer/code/ori_scDFM/src/flow_matching/ot/__init__.py @@ -0,0 +1,5 @@ +from .optimal_transport import OTPlanSampler + +__all__ = [ + "OTPlanSampler", +] \ No newline at end of file diff --git a/transfer/code/ori_scDFM/src/flow_matching/ot/optimal_transport.py b/transfer/code/ori_scDFM/src/flow_matching/ot/optimal_transport.py new file mode 100644 index 0000000000000000000000000000000000000000..e0cd855a572274e82de4f1c563f6be592a5cc2a8 --- /dev/null +++ b/transfer/code/ori_scDFM/src/flow_matching/ot/optimal_transport.py @@ -0,0 +1,291 @@ +import math +import warnings +from functools import partial +from typing import Optional, Union + +import numpy as np +import ot as pot +import torch + + +class OTPlanSampler: + """OTPlanSampler implements sampling coordinates according to an OT plan (wrt squared Euclidean + cost) with different implementations of the plan calculation.""" + + def __init__( + self, + method: str, + reg: float = 0.05, + reg_m: float = 1.0, + normalize_cost: bool = False, + num_threads: Union[int, str] = 1, + warn: bool = True, + ) -> None: + """Initialize the OTPlanSampler class. + + Parameters + ---------- + method: str + choose which optimal transport solver you would like to use. + Currently supported are ["exact", "sinkhorn", "unbalanced", + "partial"] OT solvers. + reg: float, optional + regularization parameter to use for Sinkhorn-based iterative solvers. + reg_m: float, optional + regularization weight for unbalanced Sinkhorn-knopp solver. + normalize_cost: bool, optional + normalizes the cost matrix so that the maximum cost is 1. Helps + stabilize Sinkhorn-based solvers. Should not be used in the vast + majority of cases. + num_threads: int or str, optional + number of threads to use for the "exact" OT solver. If "max", uses + the maximum number of threads. + warn: bool, optional + if True, raises a warning if the algorithm does not converge + """ + # ot_fn should take (a, b, M) as arguments where a, b are marginals and + # M is a cost matrix + if method == "exact": + self.ot_fn = partial(pot.emd, numThreads=num_threads) + elif method == "sinkhorn": + self.ot_fn = partial(pot.sinkhorn, reg=reg) + elif method == "unbalanced": + self.ot_fn = partial(pot.unbalanced.sinkhorn_knopp_unbalanced, reg=reg, reg_m=reg_m) + elif method == "partial": + self.ot_fn = partial(pot.partial.entropic_partial_wasserstein, reg=reg) + else: + raise ValueError(f"Unknown method: {method}") + self.reg = reg + self.reg_m = reg_m + self.normalize_cost = normalize_cost + self.warn = warn + + def get_map(self, x0, x1): + """Compute the OT plan (wrt squared Euclidean cost) between a source and a target + minibatch. + + Parameters + ---------- + x0 : Tensor, shape (bs, *dim) + represents the source minibatch + x1 : Tensor, shape (bs, *dim) + represents the source minibatch + + Returns + ------- + p : numpy array, shape (bs, bs) + represents the OT plan between minibatches + """ + a, b = pot.unif(x0.shape[0]), pot.unif(x1.shape[0]) + if x0.dim() > 2: + x0 = x0.reshape(x0.shape[0], -1) + if x1.dim() > 2: + x1 = x1.reshape(x1.shape[0], -1) + M = torch.cdist(x0, x1) ** 2 + if self.normalize_cost: + M = M / M.max() # should not be normalized when using minibatches + p = self.ot_fn(a, b, M.detach().cpu().numpy()) + if not np.all(np.isfinite(p)): + print("ERROR: p is not finite") + print(p) + print("Cost mean, max", M.mean(), M.max()) + print(x0, x1) + if np.abs(p.sum()) < 1e-8: + if self.warn: + warnings.warn("Numerical errors in OT plan, reverting to uniform plan.") + p = np.ones_like(p) / p.size + return p + + def sample_map(self, pi, batch_size, replace=True): + r"""Draw source and target samples from pi $(x,z) \sim \pi$ + + Parameters + ---------- + pi : numpy array, shape (bs, bs) + represents the source minibatch + batch_size : int + represents the OT plan between minibatches + replace : bool + represents sampling or without replacement from the OT plan + + Returns + ------- + (i_s, i_j) : tuple of numpy arrays, shape (bs, bs) + represents the indices of source and target data samples from $\pi$ + """ + p = pi.flatten() + p = p / p.sum() + choices = np.random.choice( + pi.shape[0] * pi.shape[1], p=p, size=batch_size, replace=replace + ) + return np.divmod(choices, pi.shape[1]) + + def sample_plan(self, x0, x1, replace=True): + r"""Compute the OT plan $\pi$ (wrt squared Euclidean cost) between a source and a target + minibatch and draw source and target samples from pi $(x,z) \sim \pi$ + + Parameters + ---------- + x0 : Tensor, shape (bs, *dim) + represents the source minibatch + x1 : Tensor, shape (bs, *dim) + represents the source minibatch + replace : bool + represents sampling or without replacement from the OT plan + + Returns + ------- + x0[i] : Tensor, shape (bs, *dim) + represents the source minibatch drawn from $\pi$ + x1[j] : Tensor, shape (bs, *dim) + represents the source minibatch drawn from $\pi$ + """ + pi = self.get_map(x0, x1) + i, j = self.sample_map(pi, x0.shape[0], replace=replace) + return x0[i], x1[j] + + def sample_plan_fix_x0(self, x0, x1, replace=True): + """ + Sample matched samples from x1 according to the OT plan, keeping the order of x0 unchanged. + + Args: + x0 : Tensor, shape (bs, *dim) - kept unchanged + x1 : Tensor, shape (bs, *dim) - to be sampled + replace : bool - whether to sample with replacement + + Returns: + x0 : original x0, unchanged + x1_matched : matched samples sampled from x1 + """ + pi = self.get_map(x0, x1) + matched_indices = [] + + for i in range(pi.shape[0]): + prob = pi[i] / pi[i].sum() + j = np.random.choice(pi.shape[1], p=prob) + matched_indices.append(j) + + matched_indices = torch.tensor(matched_indices, dtype=torch.long, device=x1.device) + x1_matched = x1[matched_indices] + return x0, x1_matched + + def sample_plan_with_labels(self, x0, x1, y0=None, y1=None, replace=True): + r"""Compute the OT plan $\pi$ (wrt squared Euclidean cost) between a source and a target + minibatch and draw source and target labeled samples from pi $(x,z) \sim \pi$ + + Parameters + ---------- + x0 : Tensor, shape (bs, *dim) + represents the source minibatch + x1 : Tensor, shape (bs, *dim) + represents the target minibatch + y0 : Tensor, shape (bs) + represents the source label minibatch + y1 : Tensor, shape (bs) + represents the target label minibatch + replace : bool + represents sampling or without replacement from the OT plan + + Returns + ------- + x0[i] : Tensor, shape (bs, *dim) + represents the source minibatch drawn from $\pi$ + x1[j] : Tensor, shape (bs, *dim) + represents the target minibatch drawn from $\pi$ + y0[i] : Tensor, shape (bs, *dim) + represents the source label minibatch drawn from $\pi$ + y1[j] : Tensor, shape (bs, *dim) + represents the target label minibatch drawn from $\pi$ + """ + pi = self.get_map(x0, x1) + i, j = self.sample_map(pi, x0.shape[0], replace=replace) + return ( + x0[i], + x1[j], + y0[i] if y0 is not None else None, + y1[j] if y1 is not None else None, + ) + + def sample_trajectory(self, X): + """Compute the OT trajectories between different sample populations moving from the source + to the target distribution. + + Parameters + ---------- + X : Tensor, (bs, times, *dim) + different populations of samples moving from the source to the target distribution. + + Returns + ------- + to_return : Tensor, (bs, times, *dim) + represents the OT sampled trajectories over time. + """ + times = X.shape[1] + pis = [] + for t in range(times - 1): + pis.append(self.get_map(X[:, t], X[:, t + 1])) + + indices = [np.arange(X.shape[0])] + for pi in pis: + j = [] + for i in indices[-1]: + j.append(np.random.choice(pi.shape[1], p=pi[i] / pi[i].sum())) + indices.append(np.array(j)) + + to_return = [] + for t in range(times): + to_return.append(X[:, t][indices[t]]) + to_return = np.stack(to_return, axis=1) + return to_return + + +def wasserstein( + x0: torch.Tensor, + x1: torch.Tensor, + method: Optional[str] = None, + reg: float = 0.05, + power: int = 2, + **kwargs, +) -> float: + """Compute the Wasserstein (1 or 2) distance (wrt Euclidean cost) between a source and a target + distributions. + + Parameters + ---------- + x0 : Tensor, shape (bs, *dim) + represents the source minibatch + x1 : Tensor, shape (bs, *dim) + represents the source minibatch + method : str (default : None) + Use exact Wasserstein or an entropic regularization + reg : float (default : 0.05) + Entropic regularization coefficients + power : int (default : 2) + power of the Wasserstein distance (1 or 2) + Returns + ------- + ret : float + Wasserstein distance + """ + assert power == 1 or power == 2 + # ot_fn should take (a, b, M) as arguments where a, b are marginals and + # M is a cost matrix + if method == "exact" or method is None: + ot_fn = pot.emd2 + elif method == "sinkhorn": + ot_fn = partial(pot.sinkhorn2, reg=reg) + else: + raise ValueError(f"Unknown method: {method}") + + a, b = pot.unif(x0.shape[0]), pot.unif(x1.shape[0]) + if x0.dim() > 2: + x0 = x0.reshape(x0.shape[0], -1) + if x1.dim() > 2: + x1 = x1.reshape(x1.shape[0], -1) + M = torch.cdist(x0, x1) + if power == 2: + M = M**2 + ret = ot_fn(a, b, M.detach().cpu().numpy(), numItermax=int(1e7)) + if power == 2: + ret = math.sqrt(ret) + return ret diff --git a/transfer/code/ori_scDFM/src/flow_matching/path/__init__.py b/transfer/code/ori_scDFM/src/flow_matching/path/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..88d29a2575c1be14d2ec10bd9eda799e7599dd1d --- /dev/null +++ b/transfer/code/ori_scDFM/src/flow_matching/path/__init__.py @@ -0,0 +1,22 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from .affine import AffineProbPath, CondOTProbPath +from .geodesic import GeodesicProbPath +from .mixture import MixtureDiscreteProbPath +from .path import ProbPath +from .path_sample import DiscretePathSample, PathSample + + +__all__ = [ + "ProbPath", + "AffineProbPath", + "CondOTProbPath", + "MixtureDiscreteProbPath", + "GeodesicProbPath", + "PathSample", + "DiscretePathSample", +] diff --git a/transfer/code/ori_scDFM/src/flow_matching/path/affine.py b/transfer/code/ori_scDFM/src/flow_matching/path/affine.py new file mode 100644 index 0000000000000000000000000000000000000000..90bb06a22f84055fe009c0f6257eb61493ed6f1c --- /dev/null +++ b/transfer/code/ori_scDFM/src/flow_matching/path/affine.py @@ -0,0 +1,260 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from torch import Tensor + +from src.flow_matching.path.path import ProbPath +from src.flow_matching.path.path_sample import PathSample +from src.flow_matching.path.scheduler.scheduler import CondOTScheduler, Scheduler +from src.flow_matching.utils import expand_tensor_like + + +class AffineProbPath(ProbPath): + r"""The ``AffineProbPath`` class represents a specific type of probability path where the transformation between distributions is affine. + An affine transformation can be represented as: + + .. math:: + + X_t = \alpha_t X_1 + \sigma_t X_0, + + where :math:`X_t` is the transformed data point at time `t`. :math:`X_0` and :math:`X_1` are the source and target data points, respectively. :math:`\alpha_t` and :math:`\sigma_t` are the parameters of the affine transformation at time `t`. + + The scheduler is responsible for providing the time-dependent parameters :math:`\alpha_t` and :math:`\sigma_t`, as well as their derivatives, which define the affine transformation at any given time `t`. + + Using ``AffineProbPath`` in the flow matching framework: + + .. code-block:: python + + # Instantiates a probability path + my_path = AffineProbPath(...) + mse_loss = torch.nn.MSELoss() + + for x_1 in dataset: + # Sets x_0 to random noise + x_0 = torch.randn() + + # Sets t to a random value in [0,1] + t = torch.rand() + + # Samples the conditional path X_t ~ p_t(X_t|X_0,X_1) + path_sample = my_path.sample(x_0=x_0, x_1=x_1, t=t) + + # Computes the MSE loss w.r.t. the velocity + loss = mse_loss(path_sample.dx_t, my_model(x_t, t)) + loss.backward() + + Args: + scheduler (Scheduler): An instance of a scheduler that provides the parameters :math:`\alpha_t`, :math:`\sigma_t`, and their derivatives over time. + + """ + + def __init__(self, scheduler: Scheduler): + self.scheduler = scheduler + + def sample(self, x_0: Tensor, x_1: Tensor, t: Tensor) -> PathSample: + r"""Sample from the affine probability path: + + | given :math:`(X_0,X_1) \sim \pi(X_0,X_1)` and a scheduler :math:`(\alpha_t,\sigma_t)`. + | return :math:`X_0, X_1, X_t = \alpha_t X_1 + \sigma_t X_0`, and the conditional velocity at :math:`X_t, \dot{X}_t = \dot{\alpha}_t X_1 + \dot{\sigma}_t X_0`. + + Args: + x_0 (Tensor): source data point, shape (batch_size, ...). + x_1 (Tensor): target data point, shape (batch_size, ...). + t (Tensor): times in [0,1], shape (batch_size). + + Returns: + PathSample: a conditional sample at :math:`X_t \sim p_t`. + """ + self.assert_sample_shape(x_0=x_0, x_1=x_1, t=t) + + scheduler_output = self.scheduler(t) + + alpha_t = expand_tensor_like( + input_tensor=scheduler_output.alpha_t, expand_to=x_1 + ) + sigma_t = expand_tensor_like( + input_tensor=scheduler_output.sigma_t, expand_to=x_1 + ) + d_alpha_t = expand_tensor_like( + input_tensor=scheduler_output.d_alpha_t, expand_to=x_1 + ) + d_sigma_t = expand_tensor_like( + input_tensor=scheduler_output.d_sigma_t, expand_to=x_1 + ) + + # construct xt ~ p_t(x|x1). + x_t = sigma_t * x_0 + alpha_t * x_1 + dx_t = d_sigma_t * x_0 + d_alpha_t * x_1 + + return PathSample(x_t=x_t, dx_t=dx_t, x_1=x_1, x_0=x_0, t=t) + + def target_to_velocity(self, x_1: Tensor, x_t: Tensor, t: Tensor) -> Tensor: + r"""Convert from x_1 representation to velocity. + + | given :math:`X_1`. + | return :math:`\dot{X}_t`. + + Args: + x_1 (Tensor): target data point. + x_t (Tensor): path sample at time t. + t (Tensor): time in [0,1]. + + Returns: + Tensor: velocity. + """ + scheduler_output = self.scheduler(t) + + alpha_t = scheduler_output.alpha_t + d_alpha_t = scheduler_output.d_alpha_t + sigma_t = scheduler_output.sigma_t + d_sigma_t = scheduler_output.d_sigma_t + + a_t = d_sigma_t / sigma_t + b_t = (d_alpha_t * sigma_t - d_sigma_t * alpha_t) / sigma_t + + return a_t * x_t + b_t * x_1 + + def epsilon_to_velocity(self, epsilon: Tensor, x_t: Tensor, t: Tensor) -> Tensor: + r"""Convert from epsilon representation to velocity. + + | given :math:`\epsilon`. + | return :math:`\dot{X}_t`. + + Args: + epsilon (Tensor): noise in the path sample. + x_t (Tensor): path sample at time t. + t (Tensor): time in [0,1]. + + Returns: + Tensor: velocity. + """ + scheduler_output = self.scheduler(t) + + alpha_t = scheduler_output.alpha_t + d_alpha_t = scheduler_output.d_alpha_t + sigma_t = scheduler_output.sigma_t + d_sigma_t = scheduler_output.d_sigma_t + + a_t = d_alpha_t / alpha_t + b_t = (d_sigma_t * alpha_t - d_alpha_t * sigma_t) / alpha_t + + return a_t * x_t + b_t * epsilon + + def velocity_to_target(self, velocity: Tensor, x_t: Tensor, t: Tensor) -> Tensor: + r"""Convert from velocity to x_1 representation. + + | given :math:`\dot{X}_t`. + | return :math:`X_1`. + + Args: + velocity (Tensor): velocity at the path sample. + x_t (Tensor): path sample at time t. + t (Tensor): time in [0,1]. + + Returns: + Tensor: target data point. + """ + scheduler_output = self.scheduler(t) + + alpha_t = scheduler_output.alpha_t + d_alpha_t = scheduler_output.d_alpha_t + sigma_t = scheduler_output.sigma_t + d_sigma_t = scheduler_output.d_sigma_t + + a_t = -d_sigma_t / (d_alpha_t * sigma_t - d_sigma_t * alpha_t) + b_t = sigma_t / (d_alpha_t * sigma_t - d_sigma_t * alpha_t) + + return a_t * x_t + b_t * velocity + + def epsilon_to_target(self, epsilon: Tensor, x_t: Tensor, t: Tensor) -> Tensor: + r"""Convert from epsilon representation to x_1 representation. + + | given :math:`\epsilon`. + | return :math:`X_1`. + + Args: + epsilon (Tensor): noise in the path sample. + x_t (Tensor): path sample at time t. + t (Tensor): time in [0,1]. + + Returns: + Tensor: target data point. + """ + scheduler_output = self.scheduler(t) + + alpha_t = scheduler_output.alpha_t + sigma_t = scheduler_output.sigma_t + + a_t = 1 / alpha_t + b_t = -sigma_t / alpha_t + + return a_t * x_t + b_t * epsilon + + def velocity_to_epsilon(self, velocity: Tensor, x_t: Tensor, t: Tensor) -> Tensor: + r"""Convert from velocity to noise representation. + + | given :math:`\dot{X}_t`. + | return :math:`\epsilon`. + + Args: + velocity (Tensor): velocity at the path sample. + x_t (Tensor): path sample at time t. + t (Tensor): time in [0,1]. + + Returns: + Tensor: noise in the path sample. + """ + scheduler_output = self.scheduler(t) + + alpha_t = scheduler_output.alpha_t + d_alpha_t = scheduler_output.d_alpha_t + sigma_t = scheduler_output.sigma_t + d_sigma_t = scheduler_output.d_sigma_t + + a_t = -d_alpha_t / (d_sigma_t * alpha_t - d_alpha_t * sigma_t) + b_t = alpha_t / (d_sigma_t * alpha_t - d_alpha_t * sigma_t) + + return a_t * x_t + b_t * velocity + + def target_to_epsilon(self, x_1: Tensor, x_t: Tensor, t: Tensor) -> Tensor: + r"""Convert from x_1 representation to velocity. + + | given :math:`X_1`. + | return :math:`\epsilon`. + + Args: + x_1 (Tensor): target data point. + x_t (Tensor): path sample at time t. + t (Tensor): time in [0,1]. + + Returns: + Tensor: noise in the path sample. + """ + scheduler_output = self.scheduler(t) + + alpha_t = scheduler_output.alpha_t + sigma_t = scheduler_output.sigma_t + + a_t = 1 / sigma_t + b_t = -alpha_t / sigma_t + + return a_t * x_t + b_t * x_1 + + +class CondOTProbPath(AffineProbPath): + r"""The ``CondOTProbPath`` class represents a conditional optimal transport probability path. + + This class is a specialized version of the ``AffineProbPath`` that uses a conditional optimal transport scheduler to determine the parameters of the affine transformation. + + The parameters :math:`\alpha_t` and :math:`\sigma_t` for the conditional optimal transport path are defined as: + + .. math:: + + \alpha_t = t \quad \text{and} \quad \sigma_t = 1 - t. + """ + + def __init__(self): + self.scheduler = CondOTScheduler() diff --git a/transfer/code/ori_scDFM/src/flow_matching/path/geodesic.py b/transfer/code/ori_scDFM/src/flow_matching/path/geodesic.py new file mode 100644 index 0000000000000000000000000000000000000000..23144cc842e72caa79797044dbac3738b8245977 --- /dev/null +++ b/transfer/code/ori_scDFM/src/flow_matching/path/geodesic.py @@ -0,0 +1,100 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +import torch + +from torch import Tensor +from torch.func import jvp, vmap + +from src.flow_matching.path.path import ProbPath + +from src.flow_matching.path.path_sample import PathSample +from src.flow_matching.path.scheduler import ConvexScheduler +from src.flow_matching.utils import expand_tensor_like + +from src.flow_matching.utils.manifolds import geodesic, Manifold + + +class GeodesicProbPath(ProbPath): + r"""The ``GeodesicProbPath`` class represents a specific type of probability path where the transformation between distributions is defined through the geodesic path. + Mathematically, a geodesic path can be represented as: + + .. math:: + + X_t = \psi_t(X_0 | X_1) = \exp_{X_1}(\kappa_t \log_{X_1}(X_0)), + + where :math:`X_t` is the transformed data point at time `t`, :math:`X_0` and :math:`X_1` are the source and target data points, respectively, and :math:`\kappa_t` is a scheduler. + + The scheduler is responsible for providing the time-dependent :math:`\kappa_t` and must be differentiable. + + Using ``GeodesicProbPath`` in the flow matching framework: + + .. code-block:: python + # Instantiates a manifold + manifold = FlatTorus() + + # Instantiates a scheduler + scheduler = CondOTScheduler() + + # Instantiates a probability path + my_path = GeodesicProbPath(scheduler, manifold) + mse_loss = torch.nn.MSELoss() + + for x_1 in dataset: + # Sets x_0 to random noise + x_0 = torch.randn() + + # Sets t to a random value in [0,1] + t = torch.rand() + + # Samples the conditional path :math:`X_t \sim p_t(X_t|X_0,X_1)` + path_sample = my_path.sample(x_0=x_0, x_1=x_1, t=t) + + # Computes the MSE loss w.r.t. the velocity + loss = mse_loss(path_sample.dx_t, my_model(x_t, t)) + loss.backward() + + Args: + scheduler (ConvexScheduler): The scheduler that provides :math:`\kappa_t`. + manifold (Manifold): The manifold on which the probability path is defined. + + """ + + def __init__(self, scheduler: ConvexScheduler, manifold: Manifold): + self.scheduler = scheduler + self.manifold = manifold + + def sample(self, x_0: Tensor, x_1: Tensor, t: Tensor) -> PathSample: + r"""Sample from the Riemannian probability path with geodesic interpolation: + + | given :math:`(X_0,X_1) \sim \pi(X_0,X_1)` and a scheduler :math:`\kappa_t`. + | return :math:`X_0, X_1, X_t = \exp_{X_1}(\kappa_t \log_{X_1}(X_0))`, and the conditional velocity at :math:`X_t, \dot{X}_t`. + + Args: + x_0 (Tensor): source data point, shape (batch_size, ...). + x_1 (Tensor): target data point, shape (batch_size, ...). + t (Tensor): times in [0,1], shape (batch_size). + + Returns: + PathSample: A conditional sample at :math:`X_t \sim p_t`. + """ + self.assert_sample_shape(x_0=x_0, x_1=x_1, t=t) + t = expand_tensor_like(input_tensor=t, expand_to=x_1[..., 0:1]).clone() + + def cond_u(x_0, x_1, t): + path = geodesic(self.manifold, x_0, x_1) + x_t, dx_t = jvp( + lambda t: path(self.scheduler(t).alpha_t), + (t,), + (torch.ones_like(t).to(t),), + ) + return x_t, dx_t + + x_t, dx_t = vmap(cond_u)(x_0, x_1, t) + x_t = x_t.reshape_as(x_1) + dx_t = dx_t.reshape_as(x_1) + + return PathSample(x_t=x_t, dx_t=dx_t, x_1=x_1, x_0=x_0, t=t) diff --git a/transfer/code/ori_scDFM/src/flow_matching/path/mixture.py b/transfer/code/ori_scDFM/src/flow_matching/path/mixture.py new file mode 100644 index 0000000000000000000000000000000000000000..3c5b5f841be04f5f213cb21889df7df486770545 --- /dev/null +++ b/transfer/code/ori_scDFM/src/flow_matching/path/mixture.py @@ -0,0 +1,117 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn.functional as F + +from torch import Tensor + +from src.flow_matching.path.path import ProbPath + +from src.flow_matching.path.path_sample import DiscretePathSample +from src.flow_matching.path.scheduler import ConvexScheduler +from src.flow_matching.utils import expand_tensor_like, unsqueeze_to_match + + +class MixtureDiscreteProbPath(ProbPath): + r"""The ``MixtureDiscreteProbPath`` class defines a factorized discrete probability path. + + This path remains constant at the source data point :math:`X_0` until a random time, determined by the scheduler, when it flips to the target data point :math:`X_1`. + The scheduler determines the flip probability using the parameter :math:`\sigma_t`, which is a function of time `t`. Specifically, :math:`\sigma_t` represents the probability of remaining at :math:`X_0`, while :math:`1 - \sigma_t` is the probability of flipping to :math:`X_1`: + + .. math:: + + P(X_t = X_0) = \sigma_t \quad \text{and} \quad P(X_t = X_1) = 1 - \sigma_t, + + where :math:`\sigma_t` is provided by the scheduler. + + Example: + + .. code-block:: python + + >>> x_0 = torch.zeros((1, 3, 3)) + >>> x_1 = torch.ones((1, 3, 3)) + + >>> path = MixtureDiscreteProbPath(PolynomialConvexScheduler(n=1.0)) + >>> result = path.sample(x_0, x_1, t=torch.tensor([0.1])).x_t + >>> result + tensor([[[0.0, 0.0, 0.0], + [0.0, 0.0, 1.0], + [0.0, 0.0, 0.0]]]) + + >>> result = path.sample(x_0, x_1, t=torch.tensor([0.5])).x_t + >>> result + tensor([[[1.0, 0.0, 1.0], + [0.0, 1.0, 0.0], + [0.0, 1.0, 0.0]]]) + + >>> result = path.sample(x_0, x_1, t=torch.tensor([1.0])).x_t + >>> result + tensor([[[1.0, 1.0, 1.0], + [1.0, 1.0, 1.0], + [1.0, 1.0, 1.0]]]) + + Args: + scheduler (ConvexScheduler): The scheduler that provides :math:`\sigma_t`. + """ + + def __init__(self, scheduler: ConvexScheduler): + assert isinstance( + scheduler, ConvexScheduler + ), "Scheduler for ConvexProbPath must be a ConvexScheduler." + + self.scheduler = scheduler + + def sample(self, x_0: Tensor, x_1: Tensor, t: Tensor) -> DiscretePathSample: + r"""Sample from the affine probability path: + | given :math:`(X_0,X_1) \sim \pi(X_0,X_1)` and a scheduler :math:`(\alpha_t,\sigma_t)`. + | return :math:`X_0, X_1, t`, and :math:`X_t \sim p_t`. + Args: + x_0 (Tensor): source data point, shape (batch_size, ...). + x_1 (Tensor): target data point, shape (batch_size, ...). + t (Tensor): times in [0,1], shape (batch_size). + + Returns: + DiscretePathSample: a conditional sample at :math:`X_t ~ p_t`. + """ + self.assert_sample_shape(x_0=x_0, x_1=x_1, t=t) + + sigma_t = self.scheduler(t).sigma_t + + sigma_t = expand_tensor_like(input_tensor=sigma_t, expand_to=x_1) + + source_indices = torch.rand(size=x_1.shape, device=x_1.device) < sigma_t + x_t = torch.where(condition=source_indices, input=x_0, other=x_1) + + return DiscretePathSample(x_t=x_t, x_1=x_1, x_0=x_0, t=t) + + def posterior_to_velocity( + self, posterior_logits: Tensor, x_t: Tensor, t: Tensor + ) -> Tensor: + r"""Convert the factorized posterior to velocity. + + | given :math:`p(X_1|X_t)`. In the factorized case: :math:`\prod_i p(X_1^i | X_t)`. + | return :math:`u_t`. + + Args: + posterior_logits (Tensor): logits of the x_1 posterior conditional on x_t, shape (..., vocab size). + x_t (Tensor): path sample at time t, shape (...). + t (Tensor): time in [0,1]. + + Returns: + Tensor: velocity. + """ + posterior = torch.softmax(posterior_logits, dim=-1) + vocabulary_size = posterior.shape[-1] + x_t = F.one_hot(x_t, num_classes=vocabulary_size) + t = unsqueeze_to_match(source=t, target=x_t) + + scheduler_output = self.scheduler(t) + + kappa_t = scheduler_output.alpha_t + d_kappa_t = scheduler_output.d_alpha_t + + return (d_kappa_t / (1 - kappa_t)) * (posterior - x_t) diff --git a/transfer/code/ori_scDFM/src/flow_matching/path/path.py b/transfer/code/ori_scDFM/src/flow_matching/path/path.py new file mode 100644 index 0000000000000000000000000000000000000000..0dfcd477eb0d4986c0022cee14b176aaa6e76e6d --- /dev/null +++ b/transfer/code/ori_scDFM/src/flow_matching/path/path.py @@ -0,0 +1,61 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from abc import ABC, abstractmethod + +from torch import Tensor + +from src.flow_matching.path.path_sample import PathSample + + +class ProbPath(ABC): + r"""Abstract class, representing a probability path. + + A probability path transforms the distribution :math:`p(X_0)` into :math:`p(X_1)` over :math:`t=0\rightarrow 1`. + + The ``ProbPath`` class is designed to support model training in the flow matching framework. It supports two key functionalities: (1) sampling the conditional probability path and (2) conversion between various training objectives. + Here is a high-level example + + .. code-block:: python + + # Instantiate a probability path + my_path = ProbPath(...) + + for x_0, x_1 in dataset: + # Sets t to a random value in [0,1] + t = torch.rand() + + # Samples the conditional path X_t ~ p_t(X_t|X_0,X_1) + path_sample = my_path.sample(x_0=x_0, x_1=x_1, t=t) + + # Optimizes the model. The loss function varies, depending on model and path. + loss(path_sample, my_model(x_t, t)).backward() + + """ + + @abstractmethod + def sample(self, x_0: Tensor, x_1: Tensor, t: Tensor) -> PathSample: + r"""Sample from an abstract probability path: + + | given :math:`(X_0,X_1) \sim \pi(X_0,X_1)`. + | returns :math:`X_0, X_1, X_t \sim p_t(X_t)`, and a conditional target :math:`Y`, all objects are under ``PathSample``. + + Args: + x_0 (Tensor): source data point, shape (batch_size, ...). + x_1 (Tensor): target data point, shape (batch_size, ...). + t (Tensor): times in [0,1], shape (batch_size). + + Returns: + PathSample: a conditional sample. + """ + + def assert_sample_shape(self, x_0: Tensor, x_1: Tensor, t: Tensor): + assert ( + t.ndim == 1 + ), f"The time vector t must have shape [batch_size]. Got {t.shape}." + assert ( + t.shape[0] == x_0.shape[0] == x_1.shape[0] + ), f"Time t dimension must match the batch size [{x_1.shape[0]}]. Got {t.shape}" diff --git a/transfer/code/ori_scDFM/src/flow_matching/path/path_sample.py b/transfer/code/ori_scDFM/src/flow_matching/path/path_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..867032e3dfe06fc5926316c9c4f133f57a025082 --- /dev/null +++ b/transfer/code/ori_scDFM/src/flow_matching/path/path_sample.py @@ -0,0 +1,53 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from dataclasses import dataclass, field + +from torch import Tensor + + +@dataclass +class PathSample: + r"""Represents a sample of a conditional-flow generated probability path. + + Attributes: + x_1 (Tensor): the target sample :math:`X_1`. + x_0 (Tensor): the source sample :math:`X_0`. + t (Tensor): the time sample :math:`t`. + x_t (Tensor): samples :math:`X_t \sim p_t(X_t)`, shape (batch_size, ...). + dx_t (Tensor): conditional target :math:`\frac{\partial X}{\partial t}`, shape: (batch_size, ...). + + """ + + x_1: Tensor = field(metadata={"help": "target samples X_1 (batch_size, ...)."}) + x_0: Tensor = field(metadata={"help": "source samples X_0 (batch_size, ...)."}) + t: Tensor = field(metadata={"help": "time samples t (batch_size, ...)."}) + x_t: Tensor = field( + metadata={"help": "samples x_t ~ p_t(X_t), shape (batch_size, ...)."} + ) + dx_t: Tensor = field( + metadata={"help": "conditional target dX_t, shape: (batch_size, ...)."} + ) + + +@dataclass +class DiscretePathSample: + """ + Represents a sample of a conditional-flow generated discrete probability path. + + Attributes: + x_1 (Tensor): the target sample :math:`X_1`. + x_0 (Tensor): the source sample :math:`X_0`. + t (Tensor): the time sample :math:`t`. + x_t (Tensor): the sample along the path :math:`X_t \sim p_t`. + """ + + x_1: Tensor = field(metadata={"help": "target samples X_1 (batch_size, ...)."}) + x_0: Tensor = field(metadata={"help": "source samples X_0 (batch_size, ...)."}) + t: Tensor = field(metadata={"help": "time samples t (batch_size, ...)."}) + x_t: Tensor = field( + metadata={"help": "samples X_t ~ p_t(X_t), shape (batch_size, ...)."} + ) diff --git a/transfer/code/ori_scDFM/src/flow_matching/path/scheduler/__init__.py b/transfer/code/ori_scDFM/src/flow_matching/path/scheduler/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f3b1a43f08b3c85a9c69ebe87b4fdeaf9eff29b1 --- /dev/null +++ b/transfer/code/ori_scDFM/src/flow_matching/path/scheduler/__init__.py @@ -0,0 +1,29 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from .schedule_transform import ScheduleTransformedModel +from .scheduler import ( + CondOTScheduler, + ConvexScheduler, + CosineScheduler, + LinearVPScheduler, + PolynomialConvexScheduler, + Scheduler, + SchedulerOutput, + VPScheduler, +) + +__all__ = [ + "CondOTScheduler", + "CosineScheduler", + "ConvexScheduler", + "PolynomialConvexScheduler", + "ScheduleTransformedModel", + "Scheduler", + "VPScheduler", + "LinearVPScheduler", + "SchedulerOutput", +] diff --git a/transfer/code/ori_scDFM/src/flow_matching/path/scheduler/schedule_transform.py b/transfer/code/ori_scDFM/src/flow_matching/path/scheduler/schedule_transform.py new file mode 100644 index 0000000000000000000000000000000000000000..8e2e436ee959c30d52500a422dd60b41b06f065e --- /dev/null +++ b/transfer/code/ori_scDFM/src/flow_matching/path/scheduler/schedule_transform.py @@ -0,0 +1,148 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from torch import Tensor + +from src.flow_matching.path.scheduler.scheduler import Scheduler +from src.flow_matching.utils import ModelWrapper + + +class ScheduleTransformedModel(ModelWrapper): + """ + Change of scheduler for a velocity model. + + This class wraps a given velocity model and transforms its scheduling + to a new scheduler function. It modifies the time + dynamics of the model according to the new scheduler while maintaining + the original model's behavior. + + Example: + + .. code-block:: python + + import torch + from flow_matching.path.scheduler import CondOTScheduler, CosineScheduler, ScheduleTransformedModel + from flow_matching.solver import ODESolver + + # Initialize the model and schedulers + model = ... + + original_scheduler = CondOTScheduler() + new_scheduler = CosineScheduler() + + # Create the transformed model + transformed_model = ScheduleTransformedModel( + velocity_model=model, + original_scheduler=original_scheduler, + new_scheduler=new_scheduler + ) + + # Set up the solver + solver = ODESolver(velocity_model=transformed_model) + + x_0 = torch.randn([10, 2]) # Example initial condition + + x_1 = solver.sample( + time_steps=torch.tensor([0.0, 1.0]), + x_init=x_0, + step_size=1/1000 + )[1] + + Args: + velocity_model (ModelWrapper): The original velocity model to be transformed. + original_scheduler (Scheduler): The scheduler used by the original model. Must implement the snr_inverse function. + new_scheduler (Scheduler): The new scheduler to be applied to the model. + """ + + def __init__( + self, + velocity_model: ModelWrapper, + original_scheduler: Scheduler, + new_scheduler: Scheduler, + ): + super().__init__(model=velocity_model) + self.original_scheduler = original_scheduler + self.new_scheduler = new_scheduler + + assert hasattr(self.original_scheduler, "snr_inverse") and callable( + getattr(self.original_scheduler, "snr_inverse") + ), "The original scheduler must have a callable 'snr_inverse' method." + + def forward(self, x: Tensor, t: Tensor, **extras) -> Tensor: + r""" + Compute the transformed marginal velocity field for a new scheduler. + This method implements a post-training velocity scheduler change for + affine conditional flows. It transforms a generating marginal velocity + field :math:`u_t(x)` based on an original scheduler to a new marginal velocity + field :math:`\bar{u}_r(x)` based on a different scheduler, while maintaining + the same data coupling. + The transformation is based on the scale-time (ST) transformation + between the two conditional flows, defined as: + + .. math:: + + \bar{X}_r = s_r X_{t_r}, + + where :math:`X_t` and :math:`\bar{X}_r` are defined by their respective schedulers. + The ST transformation is computed as: + + .. math:: + + t_r = \rho^{-1}(\bar{\rho}(r)) \quad \text{and} \quad s_r = \frac{\bar{\sigma}_r}{\sigma_{t_r}}. + + Here, :math:`\rho(t)` is the signal-to-noise ratio (SNR) defined as: + + .. math:: + + \rho(t) = \frac{\alpha_t}{\sigma_t}. + + :math:`\bar{\rho}(r)` is similarly defined for the new scheduler. + The marginal velocity for the new scheduler is then given by: + + .. math:: + + \bar{u}_r(x) = \left(\frac{\dot{s}_r}{s_r}\right) x + s_r \dot{t}_r u_{t_r}\left(\frac{x}{s_r}\right). + + Args: + x (Tensor): :math:`x_t`, the input tensor. + t (Tensor): The time tensor (denoted as :math:`r` above). + **extras: Additional arguments for the model. + Returns: + Tensor: The transformed velocity. + """ + r = t + + r_scheduler_output = self.new_scheduler(t=r) + + alpha_r = r_scheduler_output.alpha_t + sigma_r = r_scheduler_output.sigma_t + d_alpha_r = r_scheduler_output.d_alpha_t + d_sigma_r = r_scheduler_output.d_sigma_t + + t = self.original_scheduler.snr_inverse(alpha_r / sigma_r) + + t_scheduler_output = self.original_scheduler(t=t) + + alpha_t = t_scheduler_output.alpha_t + sigma_t = t_scheduler_output.sigma_t + d_alpha_t = t_scheduler_output.d_alpha_t + d_sigma_t = t_scheduler_output.d_sigma_t + + s_r = sigma_r / sigma_t + + dt_r = ( + sigma_t + * sigma_t + * (sigma_r * d_alpha_r - alpha_r * d_sigma_r) + / (sigma_r * sigma_r * (sigma_t * d_alpha_t - alpha_t * d_sigma_t)) + ) + + ds_r = (sigma_t * d_sigma_r - sigma_r * d_sigma_t * dt_r) / (sigma_t * sigma_t) + + u_t = self.model(x=x / s_r, t=t, **extras) + u_r = ds_r * x / s_r + dt_r * s_r * u_t + + return u_r diff --git a/transfer/code/ori_scDFM/src/flow_matching/path/scheduler/scheduler.py b/transfer/code/ori_scDFM/src/flow_matching/path/scheduler/scheduler.py new file mode 100644 index 0000000000000000000000000000000000000000..422618acd255bcc50779f1df60ce4f2666cea3f6 --- /dev/null +++ b/transfer/code/ori_scDFM/src/flow_matching/path/scheduler/scheduler.py @@ -0,0 +1,199 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from abc import ABC, abstractmethod +from dataclasses import dataclass, field + +from typing import Union + +import torch + +from torch import Tensor + + +@dataclass +class SchedulerOutput: + r"""Represents a sample of a conditional-flow generated probability path. + + Attributes: + alpha_t (Tensor): :math:`\alpha_t`, shape (...). + sigma_t (Tensor): :math:`\sigma_t`, shape (...). + d_alpha_t (Tensor): :math:`\frac{\partial}{\partial t}\alpha_t`, shape (...). + d_sigma_t (Tensor): :math:`\frac{\partial}{\partial t}\sigma_t`, shape (...). + + """ + + alpha_t: Tensor = field(metadata={"help": "alpha_t"}) + sigma_t: Tensor = field(metadata={"help": "sigma_t"}) + d_alpha_t: Tensor = field(metadata={"help": "Derivative of alpha_t."}) + d_sigma_t: Tensor = field(metadata={"help": "Derivative of sigma_t."}) + + +class Scheduler(ABC): + """Base Scheduler class.""" + + @abstractmethod + def __call__(self, t: Tensor) -> SchedulerOutput: + r""" + Args: + t (Tensor): times in [0,1], shape (...). + + Returns: + SchedulerOutput: :math:`\alpha_t,\sigma_t,\frac{\partial}{\partial t}\alpha_t,\frac{\partial}{\partial t}\sigma_t` + """ + ... + + @abstractmethod + def snr_inverse(self, snr: Tensor) -> Tensor: + r""" + Computes :math:`t` from the signal-to-noise ratio :math:`\frac{\alpha_t}{\sigma_t}`. + + Args: + snr (Tensor): The signal-to-noise, shape (...) + + Returns: + Tensor: t, shape (...) + """ + ... + + +class ConvexScheduler(Scheduler): + @abstractmethod + def __call__(self, t: Tensor) -> SchedulerOutput: + """Scheduler for convex paths. + + Args: + t (Tensor): times in [0,1], shape (...). + + Returns: + SchedulerOutput: :math:`\alpha_t,\sigma_t,\frac{\partial}{\partial t}\alpha_t,\frac{\partial}{\partial t}\sigma_t` + """ + ... + + @abstractmethod + def kappa_inverse(self, kappa: Tensor) -> Tensor: + """ + Computes :math:`t` from :math:`\kappa_t`. + + Args: + kappa (Tensor): :math:`\kappa`, shape (...) + + Returns: + Tensor: t, shape (...) + """ + ... + + def snr_inverse(self, snr: Tensor) -> Tensor: + r""" + Computes :math:`t` from the signal-to-noise ratio :math:`\frac{\alpha_t}{\sigma_t}`. + + Args: + snr (Tensor): The signal-to-noise, shape (...) + + Returns: + Tensor: t, shape (...) + """ + kappa_t = snr / (1.0 + snr) + + return self.kappa_inverse(kappa=kappa_t) + + +class CondOTScheduler(ConvexScheduler): + """CondOT Scheduler.""" + + def __call__(self, t: Tensor) -> SchedulerOutput: + return SchedulerOutput( + alpha_t=t, + sigma_t=1 - t, + d_alpha_t=torch.ones_like(t), + d_sigma_t=-torch.ones_like(t), + ) + + def kappa_inverse(self, kappa: Tensor) -> Tensor: + return kappa + + +class PolynomialConvexScheduler(ConvexScheduler): + """Polynomial Scheduler.""" + + def __init__(self, n: Union[float, int]) -> None: + assert isinstance( + n, (float, int) + ), f"`n` must be a float or int. Got {type(n)=}." + assert n > 0, f"`n` must be positive. Got {n=}." + + self.n = n + + def __call__(self, t: Tensor) -> SchedulerOutput: + return SchedulerOutput( + alpha_t=t**self.n, + sigma_t=1 - t**self.n, + d_alpha_t=self.n * (t ** (self.n - 1)), + d_sigma_t=-self.n * (t ** (self.n - 1)), + ) + + def kappa_inverse(self, kappa: Tensor) -> Tensor: + return torch.pow(kappa, 1.0 / self.n) + + +class VPScheduler(Scheduler): + """Variance Preserving Scheduler.""" + + def __init__(self, beta_min: float = 0.1, beta_max: float = 20.0) -> None: + self.beta_min = beta_min + self.beta_max = beta_max + super().__init__() + + def __call__(self, t: Tensor) -> SchedulerOutput: + b = self.beta_min + B = self.beta_max + T = 0.5 * (1 - t) ** 2 * (B - b) + (1 - t) * b + dT = -(1 - t) * (B - b) - b + + return SchedulerOutput( + alpha_t=torch.exp(-0.5 * T), + sigma_t=torch.sqrt(1 - torch.exp(-T)), + d_alpha_t=-0.5 * dT * torch.exp(-0.5 * T), + d_sigma_t=0.5 * dT * torch.exp(-T) / torch.sqrt(1 - torch.exp(-T)), + ) + + def snr_inverse(self, snr: Tensor) -> Tensor: + T = -torch.log(snr**2 / (snr**2 + 1)) + b = self.beta_min + B = self.beta_max + t = 1 - ((-b + torch.sqrt(b**2 + 2 * (B - b) * T)) / (B - b)) + return t + + +class LinearVPScheduler(Scheduler): + """Linear Variance Preserving Scheduler.""" + + def __call__(self, t: Tensor) -> SchedulerOutput: + return SchedulerOutput( + alpha_t=t, + sigma_t=(1 - t**2) ** 0.5, + d_alpha_t=torch.ones_like(t), + d_sigma_t=-t / (1 - t**2) ** 0.5, + ) + + def snr_inverse(self, snr: Tensor) -> Tensor: + return torch.sqrt(snr**2 / (1 + snr**2)) + + +class CosineScheduler(Scheduler): + """Cosine Scheduler.""" + + def __call__(self, t: Tensor) -> SchedulerOutput: + pi = torch.pi + return SchedulerOutput( + alpha_t=torch.sin(pi / 2 * t), + sigma_t=torch.cos(pi / 2 * t), + d_alpha_t=pi / 2 * torch.cos(pi / 2 * t), + d_sigma_t=-pi / 2 * torch.sin(pi / 2 * t), + ) + + def snr_inverse(self, snr: Tensor) -> Tensor: + return 2.0 * torch.atan(snr) / torch.pi diff --git a/transfer/code/ori_scDFM/src/flow_matching/solver/__init__.py b/transfer/code/ori_scDFM/src/flow_matching/solver/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6bd7b01c0992b5469d35885961f81da1bc9fe745 --- /dev/null +++ b/transfer/code/ori_scDFM/src/flow_matching/solver/__init__.py @@ -0,0 +1,18 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from .discrete_solver import MixtureDiscreteEulerSolver +from .ode_solver import ODESolver +from .riemannian_ode_solver import RiemannianODESolver +from .solver import Solver + +__all__ = [ + "ODESolver", + "Solver", + "ModelWrapper", + "MixtureDiscreteEulerSolver", + "RiemannianODESolver", +] diff --git a/transfer/code/ori_scDFM/src/flow_matching/solver/discrete_solver.py b/transfer/code/ori_scDFM/src/flow_matching/solver/discrete_solver.py new file mode 100644 index 0000000000000000000000000000000000000000..2d0b04fbf5cb3622d809226c111d4cd38980ac5a --- /dev/null +++ b/transfer/code/ori_scDFM/src/flow_matching/solver/discrete_solver.py @@ -0,0 +1,260 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from contextlib import nullcontext +from math import ceil +from typing import Callable, Optional, Union + +import torch +from torch import Tensor + +from torch.nn import functional as F + +from src.flow_matching.path import MixtureDiscreteProbPath + +from src.flow_matching.solver.solver import Solver +from src.flow_matching.utils import categorical, ModelWrapper +from .utils import get_nearest_times + +try: + from tqdm import tqdm + + TQDM_AVAILABLE = True +except ImportError: + TQDM_AVAILABLE = False + + +class MixtureDiscreteEulerSolver(Solver): + r"""Solver that simulates the CTMC process :math:`(X_t)_{t_{\text{init}}\leq t\leq t_{\text{final}}}` defined by :math:`p_t` the marginal probability path of ``path``. + Given :math:`X_t \sim p_t`, the algorithm of solver step from :math:`t` to :math:`t+h` for the i-th coordinate is: + + .. math:: + + \begin{align*} + & X_1^i \sim p_{1|t}^i(\cdot|X_t)\\ + & \lambda^i \gets \sum_{x^i\ne X_t^i} u_t^i(x^i, X_t^i|X_1^i)\\ + & Z^i_{\text{change}} \sim U[0,1]\\ + & X_{t+h}^i \sim \begin{cases} + \frac{u_t^i(\cdot, X_t^i|X_1^i)}{\lambda^i}(1-\delta_{X_t^i}(\cdot)) \text{ if $Z^i_{\text{change}}\le 1-e^{-h\lambda^i}$}\\ + \delta_{X_t^i}(\cdot) \text{ else } + \end{cases} + \end{align*} + + Where :math:`p_{1|t}(\cdot|X_t)` is the output of ``model``, and the conditional probability velocity is of the mixture probability path is: + + .. math:: + + u_t^i(x^i, y^i|x_1^i) = \hat{u}_t^i(x^i, y^i|x_1^i) + c_{\text{div\_free}}\left[\hat{u}_t^i(x^i, y^i|x_1^i) - \check{u}_t^i(x^i, y^i|x_1^i) \right], + + where + + .. math:: + \hat{u}_t^i(x^i, y^i|x_1^i) = \frac{\dot{\kappa}_t}{1-\kappa_t} \left[ \delta_{x_1^i}(x^i) - \delta_{y^i}(x^i) \right], + + and + + .. math:: + + \check{u}_t^i(x^i, y^i|x_1^i) = \frac{\dot{\kappa}_t}{\kappa_t}\left[ \delta_{y^i}(x^i) - p(x^i) \right]. + + The source distribution :math:`p(x^i)` is given by ``p``. + + Args: + model (ModelWrapper): trained with x-prediction, outputting posterior probabilities (in the range :math:`[0,1]`), output must be [..., vocabulary_size]. + path (MixtureDiscreteProbPath): Probability path used for x-prediction training. + vocabulary_size (int): size of the discrete vocabulary. + source_distribution_p (Optional[Tensor], optional): Source distribution, must be of shape [vocabulary_size]. Required only when divergence-free term for the probability velocity is non-zero. Defaults to None. + """ + + def __init__( + self, + model: ModelWrapper, + path: MixtureDiscreteProbPath, + vocabulary_size: int, + source_distribution_p: Optional[Tensor] = None, + ): + super().__init__() + self.model = model + self.path = path + self.vocabulary_size = vocabulary_size + + if source_distribution_p is not None: + assert source_distribution_p.shape == torch.Size( + [vocabulary_size] + ), f"Source distribution p dimension must match the vocabulary size {vocabulary_size}. Got {source_distribution_p.shape}." + + self.source_distribution_p = source_distribution_p + + @torch.no_grad() + def sample( + self, + x_init: Tensor, + step_size: Optional[float], + div_free: Union[float, Callable[[float], float]] = 0.0, + dtype_categorical: torch.dtype = torch.float32, + time_grid: Tensor = torch.tensor([0.0, 1.0]), + return_intermediates: bool = False, + verbose: bool = False, + **model_extras, + ) -> Tensor: + """ + Sample a sequence of discrete values from the given model. + + .. code-block:: python + + import torch + from flow_matching.utils import ModelWrapper + from flow_matching.solver import MixtureDiscreteEulerSolver + + class DummyModel(ModelWrapper): + def __init__(self): + super().__init__(None) + def forward(self, x: torch.Tensor, t: torch.Tensor, **extras) -> torch.Tensor: + return ... + + model = DummyModel() + solver = MixtureDiscreteEulerSolver(model=model) + + x_init = torch.LongTensor([122, 725]) + step_size = 0.001 + time_grid = torch.tensor([0.0, 1.0]) + + result = solver.sample(x_init=x_init, step_size=step_size, time_grid=time_grid) + + Args: + x_init (Tensor): The initial state. + step_size (Optional[float]): If float then time discretization is uniform with the given step size. If None then time discretization is set to be time_grid. + div_free (Union[float, Callable[[float], float]]): The coefficient of the divergence-free term in the probability velocity. Can be either a float or a time dependent function. Defaults to 0.0. + dtype_categorical (torch.dtype): Precision to use for categorical sampler. Defaults to torch.float32. + time_grid (Tensor): The CTMC process is solved in the interval [time_grid[0], time_grid[-1]] and if step_size is None then time discretization is set by the time grid. Defaults to torch.tensor([0.0,1.0]). + return_intermediates (bool): If True then return intermediate time steps according to time_grid. Defaults to False. + verbose (bool): Whether to print progress bars. Defaults to False. + **model_extras: Additional input for the model. + + Returns: + Tensor: The sampled sequence of discrete values. + + Raises: + ImportError: To run in verbose mode, tqdm must be installed. + """ + if not div_free == 0.0: + assert ( + self.source_distribution_p is not None + ), "Source distribution p must be specified in order to add a divergence-free term to the probability velocity." + + # Initialize the current state `x_t` with the initial state `X_0`. + time_grid = time_grid.to(device=x_init.device) + + if step_size is None: + # If step_size is None then set the t discretization to time_grid. + t_discretization = time_grid + n_steps = len(time_grid) - 1 + else: + # If step_size is float then t discretization is uniform with step size set by step_size. + t_init = time_grid[0].item() + t_final = time_grid[-1].item() + assert ( + t_final - t_init + ) > step_size, f"Time interval [time_grid[0], time_grid[-1]] must be larger than step_size. Got a time interval [{t_init}, {t_final}] and step_size {step_size}." + + n_steps = ceil((t_final - t_init) / step_size) + t_discretization = torch.tensor( + [t_init + step_size * i for i in range(n_steps)] + [t_final], + device=x_init.device, + ) + + if return_intermediates: + # get order of intermediate steps: + order = torch.argsort(time_grid) + # Compute intermediate steps to return via nearest points in t_discretization to time_grid. + time_grid = get_nearest_times( + time_grid=time_grid, t_discretization=t_discretization + ) + + x_t = x_init.clone() + steps_counter = 0 + res = [] + + if return_intermediates: + res = [x_init.clone()] + + if verbose: + if not TQDM_AVAILABLE: + raise ImportError( + "tqdm is required for verbose mode. Please install it." + ) + ctx = tqdm(total=t_final, desc=f"NFE: {steps_counter}") + else: + ctx = nullcontext() + + with ctx: + for i in range(n_steps): + t = t_discretization[i : i + 1] + h = t_discretization[i + 1 : i + 2] - t_discretization[i : i + 1] + + # Sample x_1 ~ p_1|t( \cdot |x_t) + p_1t = self.model(x=x_t, t=t.repeat(x_t.shape[0]), **model_extras) + x_1 = categorical(p_1t.to(dtype=dtype_categorical)) + + # Checks if final step + if i == n_steps - 1: + x_t = x_1 + else: + # Compute u_t(x|x_t,x_1) + scheduler_output = self.path.scheduler(t=t) + + k_t = scheduler_output.alpha_t + d_k_t = scheduler_output.d_alpha_t + + delta_1 = F.one_hot(x_1, num_classes=self.vocabulary_size).to( + k_t.dtype + ) + u = d_k_t / (1 - k_t) * delta_1 + + # Add divergence-free part + div_free_t = div_free(t) if callable(div_free) else div_free + + if div_free_t > 0: + p_0 = self.source_distribution_p[(None,) * x_t.dim()] + u = u + div_free_t * d_k_t / (k_t * (1 - k_t)) * ( + (1 - k_t) * p_0 + k_t * delta_1 + ) + + # Set u_t(x_t|x_t,x_1) = 0 + delta_t = F.one_hot(x_t, num_classes=self.vocabulary_size) + u = torch.where( + delta_t.to(dtype=torch.bool), torch.zeros_like(u), u + ) + + # Sample x_t ~ u_t( \cdot |x_t,x_1) + intensity = u.sum(dim=-1) # Assuming u_t(xt|xt,x1) := 0 + mask_jump = torch.rand( + size=x_t.shape, device=x_t.device + ) < 1 - torch.exp(-h * intensity) + + if mask_jump.sum() > 0: + x_t[mask_jump] = categorical( + u[mask_jump].to(dtype=dtype_categorical) + ) + + steps_counter += 1 + t = t + h + + if return_intermediates and (t in time_grid): + res.append(x_t.clone()) + + if verbose: + ctx.n = t.item() + ctx.refresh() + ctx.set_description(f"NFE: {steps_counter}") + + if return_intermediates: + if step_size is None: + return torch.stack(res, dim=0) + else: + return torch.stack(res, dim=0)[order] + else: + return x_t diff --git a/transfer/code/ori_scDFM/src/flow_matching/solver/ode_solver.py b/transfer/code/ori_scDFM/src/flow_matching/solver/ode_solver.py new file mode 100644 index 0000000000000000000000000000000000000000..60cf30268735368e91e1f4fa746f27e4b672bf6a --- /dev/null +++ b/transfer/code/ori_scDFM/src/flow_matching/solver/ode_solver.py @@ -0,0 +1,197 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Callable, Optional, Sequence, Tuple, Union + +import torch +from torch import Tensor +from torchdiffeq import odeint + +from src.flow_matching.solver.solver import Solver +from src.flow_matching.utils import gradient, ModelWrapper + + +class ODESolver(Solver): + """A class to solve ordinary differential equations (ODEs) using a specified velocity model. + + This class utilizes a velocity field model to solve ODEs over a given time grid using numerical ode solvers. + + Args: + velocity_model (Union[ModelWrapper, Callable]): a velocity field model receiving :math:`(x,t)` and returning :math:`u_t(x)` + """ + + def __init__(self, velocity_model: Union[ModelWrapper, Callable]): + super().__init__() + self.velocity_model = velocity_model + + def sample( + self, + x_init: Tensor, + step_size: Optional[float], + method: str = "euler", + atol: float = 1e-5, + rtol: float = 1e-5, + time_grid: Tensor = torch.tensor([0.0, 1.0]), + return_intermediates: bool = False, + enable_grad: bool = False, + **model_extras, + ) -> Union[Tensor, Sequence[Tensor]]: + r"""Solve the ODE with the velocity field. + + Example: + + .. code-block:: python + + import torch + from flow_matching.utils import ModelWrapper + from flow_matching.solver import ODESolver + + class DummyModel(ModelWrapper): + def __init__(self): + super().__init__(None) + + def forward(self, x: torch.Tensor, t: torch.Tensor, **extras) -> torch.Tensor: + return torch.ones_like(x) * 3.0 * t**2 + + velocity_model = DummyModel() + solver = ODESolver(velocity_model=velocity_model) + x_init = torch.tensor([0.0, 0.0]) + step_size = 0.001 + time_grid = torch.tensor([0.0, 1.0]) + + result = solver.sample(x_init=x_init, step_size=step_size, time_grid=time_grid) + + Args: + x_init (Tensor): initial conditions (e.g., source samples :math:`X_0 \sim p`). Shape: [batch_size, ...]. + step_size (Optional[float]): The step size. Must be None for adaptive step solvers. + method (str): A method supported by torchdiffeq. Defaults to "euler". Other commonly used solvers are "dopri5", "midpoint" and "heun3". For a complete list, see torchdiffeq. + atol (float): Absolute tolerance, used for adaptive step solvers. + rtol (float): Relative tolerance, used for adaptive step solvers. + time_grid (Tensor): The process is solved in the interval [min(time_grid, max(time_grid)] and if step_size is None then time discretization is set by the time grid. May specify a descending time_grid to solve in the reverse direction. Defaults to torch.tensor([0.0, 1.0]). + return_intermediates (bool, optional): If True then return intermediate time steps according to time_grid. Defaults to False. + enable_grad (bool, optional): Whether to compute gradients during sampling. Defaults to False. + **model_extras: Additional input for the model. + + Returns: + Union[Tensor, Sequence[Tensor]]: The last timestep when return_intermediates=False, otherwise all values specified in time_grid. + """ + + time_grid = time_grid.to(x_init.device) + + def ode_func(t, x): + return self.velocity_model(x=x, t=t, **model_extras) + + ode_opts = {"step_size": step_size} if step_size is not None else {} + + with torch.set_grad_enabled(enable_grad): + # Approximate ODE solution with numerical ODE solver + sol = odeint( + ode_func, + x_init, + time_grid, + method=method, + options=ode_opts, + atol=atol, + rtol=rtol, + ) + + if return_intermediates: + return sol + else: + return sol[-1] + + def compute_likelihood( + self, + x_1: Tensor, + log_p0: Callable[[Tensor], Tensor], + step_size: Optional[float], + method: str = "euler", + atol: float = 1e-5, + rtol: float = 1e-5, + time_grid: Tensor = torch.tensor([1.0, 0.0]), + return_intermediates: bool = False, + exact_divergence: bool = False, + enable_grad: bool = False, + **model_extras, + ) -> Union[Tuple[Tensor, Tensor], Tuple[Sequence[Tensor], Tensor]]: + r"""Solve for log likelihood given a target sample at :math:`t=0`. + + Works similarly to sample, but solves the ODE in reverse to compute the log-likelihood. The velocity model must be differentiable with respect to x. + The function assumes log_p0 is the log probability of the source distribution at :math:`t=0`. + + Args: + x_1 (Tensor): target sample (e.g., samples :math:`X_1 \sim p_1`). + log_p0 (Callable[[Tensor], Tensor]): Log probability function of the source distribution. + step_size (Optional[float]): The step size. Must be None for adaptive step solvers. + method (str): A method supported by torchdiffeq. Defaults to "euler". Other commonly used solvers are "dopri5", "midpoint" and "heun3". For a complete list, see torchdiffeq. + atol (float): Absolute tolerance, used for adaptive step solvers. + rtol (float): Relative tolerance, used for adaptive step solvers. + time_grid (Tensor): If step_size is None then time discretization is set by the time grid. Must start at 1.0 and end at 0.0, otherwise the likelihood computation is not valid. Defaults to torch.tensor([1.0, 0.0]). + return_intermediates (bool, optional): If True then return intermediate time steps according to time_grid. Otherwise only return the final sample. Defaults to False. + exact_divergence (bool): Whether to compute the exact divergence or use the Hutchinson estimator. + enable_grad (bool, optional): Whether to compute gradients during sampling. Defaults to False. + **model_extras: Additional input for the model. + + Returns: + Union[Tuple[Tensor, Tensor], Tuple[Sequence[Tensor], Tensor]]: Samples at time_grid and log likelihood values of given x_1. + """ + assert ( + time_grid[0] == 1.0 and time_grid[-1] == 0.0 + ), f"Time grid must start at 1.0 and end at 0.0. Got {time_grid}" + + # Fix the random projection for the Hutchinson divergence estimator + if not exact_divergence: + z = (torch.randn_like(x_1).to(x_1.device) < 0) * 2.0 - 1.0 + + def ode_func(x, t): + return self.velocity_model(x=x, t=t, **model_extras) + + def dynamics_func(t, states): + xt = states[0] + with torch.set_grad_enabled(True): + xt.requires_grad_() + ut = ode_func(xt, t) + + if exact_divergence: + # Compute exact divergence + div = 0 + for i in range(ut.flatten(1).shape[1]): + div += gradient(ut[:, i], xt, create_graph=True)[:, i] + else: + # Compute Hutchinson divergence estimator E[z^T D_x(ut) z] + ut_dot_z = torch.einsum( + "ij,ij->i", ut.flatten(start_dim=1), z.flatten(start_dim=1) + ) + grad_ut_dot_z = gradient(ut_dot_z, xt) + div = torch.einsum( + "ij,ij->i", + grad_ut_dot_z.flatten(start_dim=1), + z.flatten(start_dim=1), + ) + + return ut.detach(), div.detach() + + y_init = (x_1, torch.zeros(x_1.shape[0], device=x_1.device)) + ode_opts = {"step_size": step_size} if step_size is not None else {} + + with torch.set_grad_enabled(enable_grad): + sol, log_det = odeint( + dynamics_func, + y_init, + time_grid, + method=method, + options=ode_opts, + atol=atol, + rtol=rtol, + ) + + x_source = sol[-1] + source_log_p = log_p0(x_source) + + if return_intermediates: + return sol, source_log_p + log_det[-1] + else: + return sol[-1], source_log_p + log_det[-1] diff --git a/transfer/code/ori_scDFM/src/flow_matching/solver/riemannian_ode_solver.py b/transfer/code/ori_scDFM/src/flow_matching/solver/riemannian_ode_solver.py new file mode 100644 index 0000000000000000000000000000000000000000..eaeb682e9bab49fd2625d77f6b71a19250a24d71 --- /dev/null +++ b/transfer/code/ori_scDFM/src/flow_matching/solver/riemannian_ode_solver.py @@ -0,0 +1,261 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +import math +from typing import Callable + +import torch +from torch import Tensor + +from src.flow_matching.solver.solver import Solver +from src.flow_matching.utils import ModelWrapper +from src.flow_matching.utils.manifolds import geodesic, Manifold + +try: + from tqdm import tqdm + + TQDM_AVAILABLE = True +except ImportError: + TQDM_AVAILABLE = False + + +class RiemannianODESolver(Solver): + r"""Riemannian ODE solver + Initialize the ``RiemannianODESolver``. + + Args: + manifold (Manifold): the manifold to solve on. + velocity_model (ModelWrapper): a velocity field model receiving :math:`(x,t)` + and returning :math:`u_t(x)` which is assumed to lie on the tangent plane at `x`. + """ + + def __init__(self, manifold: Manifold, velocity_model: ModelWrapper): + super().__init__() + self.manifold = manifold + self.velocity_model = velocity_model + + def sample( + self, + x_init: Tensor, + step_size: float, + projx: bool = True, + proju: bool = True, + method: str = "euler", + time_grid: Tensor = torch.tensor([0.0, 1.0]), + return_intermediates: bool = False, + verbose: bool = False, + enable_grad: bool = False, + **model_extras, + ) -> Tensor: + r"""Solve the ODE with the `velocity_field` on the manifold. + + Args: + x_init (Tensor): initial conditions (e.g., source samples :math:`X_0 \sim p`). + step_size (float): The step size. + projx (bool): Whether to project the point onto the manifold at each step. Defaults to True. + proju (bool): Whether to project the vector field onto the tangent plane at each step. Defaults to True. + method (str): One of ["euler", "midpoint", "rk4"]. Defaults to "euler". + time_grid (Tensor, optional): The process is solved in the interval [min(time_grid, max(time_grid)] and if step_size is None then time discretization is set by the time grid. Defaults to torch.tensor([0.0,1.0]). + return_intermediates (bool, optional): If True then return intermediate time steps according to time_grid. Defaults to False. + verbose (bool, optional): Whether to print progress bars. Defaults to False. + enable_grad (bool, optional): Whether to compute gradients during sampling. Defaults to False. + **model_extras: Additional input for the model. + + Returns: + Tensor: The sampled sequence. Defaults to returning samples at :math:`t=1`. + + Raises: + ImportError: To run in verbose mode, tqdm must be installed. + """ + step_fns = { + "euler": _euler_step, + "midpoint": _midpoint_step, + "rk4": _rk4_step, + } + assert method in step_fns.keys(), f"Unknown method {method}" + step_fn = step_fns[method] + + def velocity_func(x, t): + return self.velocity_model(x=x, t=t, **model_extras) + + # --- Factor this out. + time_grid = torch.sort(time_grid.to(device=x_init.device)).values + + if step_size is None: + # If step_size is None then set the t discretization to time_grid. + t_discretization = time_grid + n_steps = len(time_grid) - 1 + else: + # If step_size is float then t discretization is uniform with step size set by step_size. + t_init = time_grid[0].item() + t_final = time_grid[-1].item() + assert ( + t_final - t_init + ) > step_size, f"Time interval [min(time_grid), max(time_grid)] must be larger than step_size. Got a time interval [{t_init}, {t_final}] and step_size {step_size}." + + n_steps = math.ceil((t_final - t_init) / step_size) + t_discretization = torch.tensor( + [step_size * i for i in range(n_steps)] + [t_final], + device=x_init.device, + ) + # --- + t0s = t_discretization[:-1] + + if verbose: + if not TQDM_AVAILABLE: + raise ImportError( + "tqdm is required for verbose mode. Please install it." + ) + t0s = tqdm(t0s) + + if return_intermediates: + xts = [] + i_ret = 0 + + with torch.set_grad_enabled(enable_grad): + xt = x_init + for t0, t1 in zip(t0s, t_discretization[1:]): + dt = t1 - t0 + xt_next = step_fn( + velocity_func, + xt, + t0, + dt, + manifold=self.manifold, + projx=projx, + proju=proju, + ) + if return_intermediates: + while ( + i_ret < len(time_grid) + and t0 <= time_grid[i_ret] + and time_grid[i_ret] <= t1 + ): + xts.append( + interp(self.manifold, xt, xt_next, t0, t1, time_grid[i_ret]) + ) + i_ret += 1 + xt = xt_next + + if return_intermediates: + return torch.stack(xts, dim=0) + else: + return xt + + +def interp(manifold, xt, xt_next, t, t_next, t_ret): + return geodesic(manifold, xt, xt_next)( + (t_ret - t) / (t_next - t).reshape(1) + ).reshape_as(xt) + + +def _euler_step( + velocity_model: Callable, + xt: Tensor, + t0: Tensor, + dt: Tensor, + manifold: Manifold, + projx: bool = True, + proju: bool = True, +) -> Tensor: + r"""Perform an Euler step on a manifold. + + Args: + velocity_model (Callable): the velocity model + xt (Tensor): tensor containing the state at time t0 + t0 (Tensor): the time at which this step is taken + dt (Tensor): the step size + manifold (Manifold): a manifold object + projx (bool, optional): whether to project the state onto the manifold. Defaults to True. + proju (bool, optional): whether to project the velocity onto the tangent plane. Defaults to True. + + Returns: + Tensor: tensor containing the state after the step + """ + velocity_fn = lambda x, t: ( + manifold.proju(x, velocity_model(x, t)) if proju else velocity_model(x, t) + ) + projx_fn = lambda x: manifold.projx(x) if projx else x + + vt = velocity_fn(xt, t0) + + xt = xt + dt * vt + + return projx_fn(xt) + + +def _midpoint_step( + velocity_model: Callable, + xt: Tensor, + t0: Tensor, + dt: Tensor, + manifold: Manifold, + projx: bool = True, + proju: bool = True, +) -> Tensor: + r"""Perform a midpoint step on a manifold. + + Args: + velocity_model (Callable): the velocity model + xt (Tensor): tensor containing the state at time t0 + t0 (Tensor): the time at which this step is taken + dt (Tensor): the step size + manifold (Manifold): a manifold object + projx (bool, optional): whether to project the state onto the manifold. Defaults to True. + proju (bool, optional): whether to project the velocity onto the tangent plane. Defaults to True. + + Returns: + Tensor: tensor containing the state after the step + """ + velocity_fn = lambda x, t: ( + manifold.proju(x, velocity_model(x, t)) if proju else velocity_model(x, t) + ) + projx_fn = lambda x: manifold.projx(x) if projx else x + + half_dt = 0.5 * dt + vt = velocity_fn(xt, t0) + x_mid = xt + half_dt * vt + x_mid = projx_fn(x_mid) + + xt = xt + dt * velocity_fn(x_mid, t0 + half_dt) + + return projx_fn(xt) + + +def _rk4_step( + velocity_model: Callable, + xt: Tensor, + t0: Tensor, + dt: Tensor, + manifold: Manifold, + projx: bool = True, + proju: bool = True, +) -> Tensor: + r"""Perform an RK4 step on a manifold. + + Args: + velocity_model (Callable): the velocity model + xt (Tensor): tensor containing the state at time t0 + t0 (Tensor): the time at which this step is taken + dt (Tensor): the step size + manifold (Manifold): a manifold object + projx (bool, optional): whether to project the state onto the manifold. Defaults to True. + proju (bool, optional): whether to project the velocity onto the tangent plane. Defaults to True. + + Returns: + Tensor: tensor containing the state after the step + """ + velocity_fn = lambda x, t: ( + manifold.proju(x, velocity_model(x, t)) if proju else velocity_model(x, t) + ) + projx_fn = lambda x: manifold.projx(x) if projx else x + + k1 = velocity_fn(xt, t0) + k2 = velocity_fn(projx_fn(xt + dt * k1 / 3), t0 + dt / 3) + k3 = velocity_fn(projx_fn(xt + dt * (k2 - k1 / 3)), t0 + dt * 2 / 3) + k4 = velocity_fn(projx_fn(xt + dt * (k1 - k2 + k3)), t0 + dt) + + return projx_fn(xt + (k1 + 3 * (k2 + k3) + k4) * dt * 0.125) diff --git a/transfer/code/ori_scDFM/src/flow_matching/solver/solver.py b/transfer/code/ori_scDFM/src/flow_matching/solver/solver.py new file mode 100644 index 0000000000000000000000000000000000000000..4819e1c0ff148c411648c6e248380f471efd0a6c --- /dev/null +++ b/transfer/code/ori_scDFM/src/flow_matching/solver/solver.py @@ -0,0 +1,17 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from abc import ABC, abstractmethod + +from torch import nn, Tensor + + +class Solver(ABC, nn.Module): + """Abstract base class for solvers.""" + + @abstractmethod + def sample(self, x_0: Tensor = None) -> Tensor: + ... diff --git a/transfer/code/ori_scDFM/src/flow_matching/solver/utils.py b/transfer/code/ori_scDFM/src/flow_matching/solver/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..f3a34ee4b0ca4c3ef64dfa1b55cb13da1187b965 --- /dev/null +++ b/transfer/code/ori_scDFM/src/flow_matching/solver/utils.py @@ -0,0 +1,19 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from torch import Tensor + + +def get_nearest_times(time_grid: Tensor, t_discretization: Tensor) -> Tensor: + distances = torch.cdist( + time_grid.unsqueeze(1), + t_discretization.unsqueeze(1), + compute_mode="donot_use_mm_for_euclid_dist", + ) + nearest_indices = distances.argmin(dim=1) + + return t_discretization[nearest_indices] diff --git a/transfer/code/ori_scDFM/src/flow_matching/utils/__init__.py b/transfer/code/ori_scDFM/src/flow_matching/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..0085c44c5e044d5a7dd0c2bde9755e9d3c18c0b1 --- /dev/null +++ b/transfer/code/ori_scDFM/src/flow_matching/utils/__init__.py @@ -0,0 +1,17 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from .categorical_sampler import categorical +from .model_wrapper import ModelWrapper +from .utils import expand_tensor_like, gradient, unsqueeze_to_match + +__all__ = [ + "unsqueeze_to_match", + "expand_tensor_like", + "gradient", + "categorical", + "ModelWrapper", +] diff --git a/transfer/code/ori_scDFM/src/flow_matching/utils/categorical_sampler.py b/transfer/code/ori_scDFM/src/flow_matching/utils/categorical_sampler.py new file mode 100644 index 0000000000000000000000000000000000000000..70937af5af1da4c3c2b18a85745197e240724a96 --- /dev/null +++ b/transfer/code/ori_scDFM/src/flow_matching/utils/categorical_sampler.py @@ -0,0 +1,23 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from torch import Tensor + + +def categorical(probs: Tensor) -> Tensor: + r"""Categorical sampler according to weights in the last dimension of ``probs`` using :func:`torch.multinomial`. + + Args: + probs (Tensor): probabilities. + + Returns: + Tensor: Samples. + """ + + return torch.multinomial(probs.flatten(0, -2), 1, replacement=True).view( + *probs.shape[:-1] + ) diff --git a/transfer/code/ori_scDFM/src/flow_matching/utils/manifolds/__init__.py b/transfer/code/ori_scDFM/src/flow_matching/utils/manifolds/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..1148872f29c5e42e2578eab31751abaf6def51da --- /dev/null +++ b/transfer/code/ori_scDFM/src/flow_matching/utils/manifolds/__init__.py @@ -0,0 +1,18 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from .manifold import Euclidean, Manifold +from .sphere import Sphere +from .torus import FlatTorus +from .utils import geodesic + +__all__ = [ + "Euclidean", + "Manifold", + "Sphere", + "FlatTorus", + "geodesic", +] diff --git a/transfer/code/ori_scDFM/src/flow_matching/utils/manifolds/manifold.py b/transfer/code/ori_scDFM/src/flow_matching/utils/manifolds/manifold.py new file mode 100644 index 0000000000000000000000000000000000000000..52a6a1bc6ea7d83e3aeefd335edb19a842aeca21 --- /dev/null +++ b/transfer/code/ori_scDFM/src/flow_matching/utils/manifolds/manifold.py @@ -0,0 +1,93 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +import abc + +import torch.nn as nn +from torch import Tensor + + +class Manifold(nn.Module, metaclass=abc.ABCMeta): + """A manifold class that contains projection operations and logarithm and exponential maps.""" + + @abc.abstractmethod + def expmap(self, x: Tensor, u: Tensor) -> Tensor: + r"""Computes exponential map :math:`\exp_x(u)`. + + Args: + x (Tensor): point on the manifold + u (Tensor): tangent vector at point :math:`x` + + Raises: + NotImplementedError: if not implemented + + Returns: + Tensor: transported point + """ + raise NotImplementedError + + @abc.abstractmethod + def logmap(self, x: Tensor, y: Tensor) -> Tensor: + r"""Computes logarithmic map :math:`\log_x(y)`. + + Args: + x (Tensor): point on the manifold + y (Tensor): point on the manifold + + Raises: + NotImplementedError: if not implemented + + Returns: + Tensor: tangent vector at point :math:`x` + """ + raise NotImplementedError + + @abc.abstractmethod + def projx(self, x: Tensor) -> Tensor: + """Project point :math:`x` on the manifold. + + Args: + x (Tensor): point to be projected + + Raises: + NotImplementedError: if not implemented + + Returns: + Tensor: projected point on the manifold + """ + raise NotImplementedError + + @abc.abstractmethod + def proju(self, x: Tensor, u: Tensor) -> Tensor: + """Project vector :math:`u` on a tangent space for :math:`x`. + + Args: + x (Tensor): point on the manifold + u (Tensor): vector to be projected + + Raises: + NotImplementedError: if not implemented + + Returns: + Tensor: projected tangent vector + """ + raise NotImplementedError + + +class Euclidean(Manifold): + """The Euclidean manifold.""" + + def expmap(self, x: Tensor, u: Tensor) -> Tensor: + return x + u + + def logmap(self, x: Tensor, y: Tensor) -> Tensor: + return y - x + + def projx(self, x: Tensor) -> Tensor: + return x + + def proju(self, x: Tensor, u: Tensor) -> Tensor: + return u diff --git a/transfer/code/ori_scDFM/src/flow_matching/utils/manifolds/sphere.py b/transfer/code/ori_scDFM/src/flow_matching/utils/manifolds/sphere.py new file mode 100644 index 0000000000000000000000000000000000000000..23122926530add5b72420f31011476c5e0c5e7ad --- /dev/null +++ b/transfer/code/ori_scDFM/src/flow_matching/utils/manifolds/sphere.py @@ -0,0 +1,45 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from torch import Tensor + +from src.flow_matching.utils.manifolds import Manifold + + +class Sphere(Manifold): + """Represents a hyperpshere in :math:`R^D`. Isometric to the product of 1-D spheres.""" + + EPS = {torch.float32: 1e-4, torch.float64: 1e-7} + + def expmap(self, x: Tensor, u: Tensor) -> Tensor: + norm_u = u.norm(dim=-1, keepdim=True) + exp = x * torch.cos(norm_u) + u * torch.sin(norm_u) / norm_u + retr = self.projx(x + u) + cond = norm_u > self.EPS[norm_u.dtype] + + return torch.where(cond, exp, retr) + + def logmap(self, x: Tensor, y: Tensor) -> Tensor: + u = self.proju(x, y - x) + dist = self.dist(x, y, keepdim=True) + cond = dist.gt(self.EPS[x.dtype]) + result = torch.where( + cond, + u * dist / u.norm(dim=-1, keepdim=True).clamp_min(self.EPS[x.dtype]), + u, + ) + return result + + def projx(self, x: Tensor) -> Tensor: + return x / x.norm(dim=-1, keepdim=True) + + def proju(self, x: Tensor, u: Tensor) -> Tensor: + return u - (x * u).sum(dim=-1, keepdim=True) * x + + def dist(self, x: Tensor, y: Tensor, *, keepdim=False) -> Tensor: + inner = (x * y).sum(-1, keepdim=keepdim) + return torch.acos(inner) diff --git a/transfer/code/ori_scDFM/src/flow_matching/utils/manifolds/torus.py b/transfer/code/ori_scDFM/src/flow_matching/utils/manifolds/torus.py new file mode 100644 index 0000000000000000000000000000000000000000..8c97703b6b565e5d106b0d61def6470acb2f60de --- /dev/null +++ b/transfer/code/ori_scDFM/src/flow_matching/utils/manifolds/torus.py @@ -0,0 +1,28 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +import math + +import torch +from torch import Tensor + +from src.flow_matching.utils.manifolds import Manifold + + +class FlatTorus(Manifold): + r"""Represents a flat torus on the :math:`[0, 2\pi]^D` subspace. Isometric to the product of 1-D spheres.""" + + def expmap(self, x: Tensor, u: Tensor) -> Tensor: + return (x + u) % (2 * math.pi) + + def logmap(self, x: Tensor, y: Tensor) -> Tensor: + return torch.atan2(torch.sin(y - x), torch.cos(y - x)) + + def projx(self, x: Tensor) -> Tensor: + return x % (2 * math.pi) + + def proju(self, x: Tensor, u: Tensor) -> Tensor: + return u diff --git a/transfer/code/ori_scDFM/src/flow_matching/utils/manifolds/utils.py b/transfer/code/ori_scDFM/src/flow_matching/utils/manifolds/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..af19591931b45831b70b71af02090aded937c422 --- /dev/null +++ b/transfer/code/ori_scDFM/src/flow_matching/utils/manifolds/utils.py @@ -0,0 +1,45 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Callable + +import torch +from torch import Tensor + +from src.flow_matching.utils.manifolds import Manifold + + +def geodesic( + manifold: Manifold, start_point: Tensor, end_point: Tensor +) -> Callable[[Tensor], Tensor]: + """Generate parameterized function for geodesic curve. + + Args: + manifold (Manifold): the manifold to compute geodesic on. + start_point (Tensor): point on the manifold at :math:`t=0`. + end_point (Tensor): point on the manifold at :math:`t=1`. + + Returns: + Callable[[Tensor], Tensor]: a function that takes in :math:`t` and outputs the geodesic at time :math:`t`. + """ + + shooting_tangent_vec = manifold.logmap(start_point, end_point) + + def path(t: Tensor) -> Tensor: + """Generate parameterized function for geodesic curve. + + Args: + t (Tensor): Times at which to compute points of the geodesics. + + Returns: + Tensor: geodesic path evaluated at time t. + """ + tangent_vecs = torch.einsum("i,...k->...ik", t, shooting_tangent_vec) + points_at_time_t = manifold.expmap(start_point.unsqueeze(-2), tangent_vecs) + + return points_at_time_t + + return path diff --git a/transfer/code/ori_scDFM/src/flow_matching/utils/model_wrapper.py b/transfer/code/ori_scDFM/src/flow_matching/utils/model_wrapper.py new file mode 100644 index 0000000000000000000000000000000000000000..ac7d932c851b3d2194bd63ce597ef924dc819a7f --- /dev/null +++ b/transfer/code/ori_scDFM/src/flow_matching/utils/model_wrapper.py @@ -0,0 +1,43 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from abc import ABC + +from torch import nn, Tensor + + +class ModelWrapper(ABC, nn.Module): + """ + This class is used to wrap around another model, adding custom forward pass logic. + """ + + def __init__(self, model: nn.Module): + super().__init__() + self.model = model + + def forward(self, x: Tensor, t: Tensor, **extras) -> Tensor: + r""" + This method defines how inputs should be passed through the wrapped model. + Here, we're assuming that the wrapped model takes both :math:`x` and :math:`t` as input, + along with any additional keyword arguments. + + Optional things to do here: + - check that t is in the dimensions that the model is expecting. + - add a custom forward pass logic. + - call the wrapped model. + + | given x, t + | returns the model output for input x at time t, with extra information `extra`. + + Args: + x (Tensor): input data to the model (batch_size, ...). + t (Tensor): time (batch_size). + **extras: additional information forwarded to the model, e.g., text condition. + + Returns: + Tensor: model output. + """ + return self.model(x=x, t=t, **extras) diff --git a/transfer/code/ori_scDFM/src/flow_matching/utils/utils.py b/transfer/code/ori_scDFM/src/flow_matching/utils/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..beb31ff27343c40955ec52513c44f75a6ab5d665 --- /dev/null +++ b/transfer/code/ori_scDFM/src/flow_matching/utils/utils.py @@ -0,0 +1,90 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Optional + +import torch +from torch import Tensor + + +def unsqueeze_to_match(source: Tensor, target: Tensor, how: str = "suffix") -> Tensor: + """ + Unsqueeze the source tensor to match the dimensionality of the target tensor. + + Args: + source (Tensor): The source tensor to be unsqueezed. + target (Tensor): The target tensor to match the dimensionality of. + how (str, optional): Whether to unsqueeze the source tensor at the beginning + ("prefix") or end ("suffix"). Defaults to "suffix". + + Returns: + Tensor: The unsqueezed source tensor. + """ + assert ( + how == "prefix" or how == "suffix" + ), f"{how} is not supported, only 'prefix' and 'suffix' are supported." + + dim_diff = target.dim() - source.dim() + + for _ in range(dim_diff): + if how == "prefix": + source = source.unsqueeze(0) + elif how == "suffix": + source = source.unsqueeze(-1) + + return source + + +def expand_tensor_like(input_tensor: Tensor, expand_to: Tensor) -> Tensor: + """`input_tensor` is a 1d vector of length equal to the batch size of `expand_to`, + expand `input_tensor` to have the same shape as `expand_to` along all remaining dimensions. + + Args: + input_tensor (Tensor): (batch_size,). + expand_to (Tensor): (batch_size, ...). + + Returns: + Tensor: (batch_size, ...). + """ + assert input_tensor.ndim == 1, "Input tensor must be a 1d vector." + assert ( + input_tensor.shape[0] == expand_to.shape[0] + ), f"The first (batch_size) dimension must match. Got shape {input_tensor.shape} and {expand_to.shape}." + + dim_diff = expand_to.ndim - input_tensor.ndim + + t_expanded = input_tensor.clone() + t_expanded = t_expanded.reshape(-1, *([1] * dim_diff)) + + return t_expanded.expand_as(expand_to) + + +def gradient( + output: Tensor, + x: Tensor, + grad_outputs: Optional[Tensor] = None, + create_graph: bool = False, +) -> Tensor: + """ + Compute the gradient of the inner product of output and grad_outputs w.r.t :math:`x`. + + Args: + output (Tensor): [N, D] Output of the function. + x (Tensor): [N, d_1, d_2, ... ] input + grad_outputs (Optional[Tensor]): [N, D] Gradient of outputs, if `None`, + then will use a tensor of ones + create_graph (bool): If True, graph of the derivative will be constructed, allowing + to compute higher order derivative products. Defaults to False. + Returns: + Tensor: [N, d_1, d_2, ... ]. the gradient w.r.t x. + """ + + if grad_outputs is None: + grad_outputs = torch.ones_like(output).detach() + grad = torch.autograd.grad( + output, x, grad_outputs=grad_outputs, create_graph=create_graph + )[0] + return grad diff --git a/transfer/code/ori_scDFM/src/loss/metrics.py b/transfer/code/ori_scDFM/src/loss/metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..d0ba0e2e24560286bffd75ff225c9080196f1d6c --- /dev/null +++ b/transfer/code/ori_scDFM/src/loss/metrics.py @@ -0,0 +1,328 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np +from scipy import stats +from scipy.spatial.distance import jensenshannon +from sklearn.metrics import r2_score +from sklearn.decomposition import PCA +from sklearn.neighbors import NearestNeighbors +import warnings +warnings.filterwarnings('ignore') + + +# TODO: To be implemented into flow generation +class FlowMatchingMetrics: + + def __init__(self, device='cuda'): + self.device = device + + def reconstruction_metrics(self, generated, target): + + if isinstance(generated, torch.Tensor): + generated = generated.detach().cpu().numpy() + if isinstance(target, torch.Tensor): + target = target.detach().cpu().numpy() + + mae = np.mean(np.abs(generated - target)) + mse = np.mean((generated - target) ** 2) + rmse = np.sqrt(mse) + + + pos_mask = target > 0 + zero_mask = target == 0 + + pos_mae = np.mean(np.abs(generated[pos_mask] - target[pos_mask])) if pos_mask.any() else 0.0 + zero_mae = np.mean(np.abs(generated[zero_mask] - target[zero_mask])) if zero_mask.any() else 0.0 + + + r2 = r2_score(target.flatten(), generated.flatten()) + + rel_error = np.mean(np.abs((generated - target) / (target + 1e-8))) + + return { + 'mae': mae, + 'mse': mse, + 'rmse': rmse, + 'pos_mae': pos_mae, + 'zero_mae': zero_mae, + 'r2_score': r2, + 'relative_error': rel_error + } + + def distribution_metrics(self, generated, target): + if isinstance(generated, torch.Tensor): + generated = generated.detach().cpu().numpy() + if isinstance(target, torch.Tensor): + target = target.detach().cpu().numpy() + + metrics = {} + + mean_diff = np.mean(np.abs(np.mean(generated, axis=0) - np.mean(target, axis=0))) + var_diff = np.mean(np.abs(np.var(generated, axis=0) - np.var(target, axis=0))) + + metrics['mean_difference'] = mean_diff + metrics['variance_difference'] = var_diff + + + correlations = [] + for i in range(generated.shape[1]): + if np.var(generated[:, i]) > 1e-8 and np.var(target[:, i]) > 1e-8: + corr, _ = stats.pearsonr(generated[:, i], target[:, i]) + if not np.isnan(corr): + correlations.append(corr) + + metrics['mean_correlation'] = np.mean(correlations) if correlations else 0.0 + + + kl_divs = [] + for i in range(min(generated.shape[1], 100)): + try: + bins = np.linspace( + min(generated[:, i].min(), target[:, i].min()), + max(generated[:, i].max(), target[:, i].max()), + 50 + ) + hist_gen, _ = np.histogram(generated[:, i], bins=bins, density=True) + hist_target, _ = np.histogram(target[:, i], bins=bins, density=True) + + + hist_gen = hist_gen + 1e-8 + hist_target = hist_target + 1e-8 + + + hist_gen = hist_gen / np.sum(hist_gen) + hist_target = hist_target / np.sum(hist_target) + + kl_div = stats.entropy(hist_gen, hist_target) + if not np.isnan(kl_div) and not np.isinf(kl_div): + kl_divs.append(kl_div) + except: + continue + + metrics['mean_kl_divergence'] = np.mean(kl_divs) if kl_divs else float('inf') + + js_divs = [] + for i in range(min(generated.shape[1], 100)): + try: + bins = np.linspace( + min(generated[:, i].min(), target[:, i].min()), + max(generated[:, i].max(), target[:, i].max()), + 50 + ) + hist_gen, _ = np.histogram(generated[:, i], bins=bins, density=True) + hist_target, _ = np.histogram(target[:, i], bins=bins, density=True) + + hist_gen = hist_gen + 1e-8 + hist_target = hist_target + 1e-8 + hist_gen = hist_gen / np.sum(hist_gen) + hist_target = hist_target / np.sum(hist_target) + + js_div = jensenshannon(hist_gen, hist_target) + if not np.isnan(js_div): + js_divs.append(js_div) + except: + continue + + metrics['mean_js_divergence'] = np.mean(js_divs) if js_divs else 1.0 + + return metrics + + def embedding_metrics(self, generated, target): + + if isinstance(generated, torch.Tensor): + generated = generated.detach().cpu().numpy() + if isinstance(target, torch.Tensor): + target = target.detach().cpu().numpy() + + metrics = {} + + + cosine_sims = [] + for i in range(generated.shape[0]): + sim = np.dot(generated[i], target[i]) / ( + np.linalg.norm(generated[i]) * np.linalg.norm(target[i]) + 1e-8 + ) + cosine_sims.append(sim) + + metrics['mean_cosine_similarity'] = np.mean(cosine_sims) + + + euclidean_dists = np.linalg.norm(generated - target, axis=1) + metrics['mean_euclidean_distance'] = np.mean(euclidean_dists) + + + if generated.shape[0] > 10: + k = min(5, generated.shape[0] // 2) + + nn_target = NearestNeighbors(n_neighbors=k) + nn_target.fit(target) + _, target_indices = nn_target.kneighbors(target) + + nn_generated = NearestNeighbors(n_neighbors=k) + nn_generated.fit(generated) + _, generated_indices = nn_generated.kneighbors(generated) + + preservation = 0 + for i in range(generated.shape[0]): + intersection = len(set(target_indices[i]) & set(generated_indices[i])) + preservation += intersection / k + + metrics['neighbor_preservation'] = preservation / generated.shape[0] + else: + metrics['neighbor_preservation'] = 0.0 + + return metrics + + def flow_specific_metrics(self, generated, target, flow_model=None): + + metrics = {} + + if isinstance(generated, torch.Tensor): + generated = generated.detach().cpu().numpy() + if isinstance(target, torch.Tensor): + target = target.detach().cpu().numpy() + + + pairwise_dists = [] + for i in range(min(generated.shape[0], 100)): + for j in range(i + 1, min(generated.shape[0], 100)): + dist = np.linalg.norm(generated[i] - generated[j]) + pairwise_dists.append(dist) + + metrics['generation_diversity'] = np.mean(pairwise_dists) if pairwise_dists else 0.0 + + if generated.shape[0] > 10 and target.shape[0] > 10: + + combined = np.vstack([generated, target]) + pca = PCA(n_components=min(10, combined.shape[1])) + combined_pca = pca.fit_transform(combined) + + generated_pca = combined_pca[:generated.shape[0]] + target_pca = combined_pca[generated.shape[0]:] + + + coverage = 0 + for dim in range(generated_pca.shape[1]): + gen_min, gen_max = generated_pca[:, dim].min(), generated_pca[:, dim].max() + target_min, target_max = target_pca[:, dim].min(), target_pca[:, dim].max() + + if target_max > target_min: + overlap = max(0, min(gen_max, target_max) - max(gen_min, target_min)) + coverage += overlap / (target_max - target_min) + + metrics['mode_coverage'] = coverage / generated_pca.shape[1] + else: + metrics['mode_coverage'] = 0.0 + + return metrics + + def comprehensive_evaluation(self, generated, target, embeddings_generated=None, embeddings_target=None): + + all_metrics = {} + + recon_metrics = self.reconstruction_metrics(generated, target) + all_metrics.update({f'recon_{k}': v for k, v in recon_metrics.items()}) + + + dist_metrics = self.distribution_metrics(generated, target) + all_metrics.update({f'dist_{k}': v for k, v in dist_metrics.items()}) + + flow_metrics = self.flow_specific_metrics(generated, target) + all_metrics.update({f'flow_{k}': v for k, v in flow_metrics.items()}) + + if embeddings_generated is not None and embeddings_target is not None: + emb_metrics = self.embedding_metrics(embeddings_generated, embeddings_target) + all_metrics.update({f'emb_{k}': v for k, v in emb_metrics.items()}) + + return all_metrics + +def evaluate_flow_generation(generated_data, target_data, generated_embeddings=None, target_embeddings=None, device='cuda'): + + metrics = FlowMatchingMetrics(device=device) + return metrics.comprehensive_evaluation( + generated_data, target_data, + generated_embeddings, target_embeddings + ) + +def print_metrics(metrics_dict, title="Metrics"): + + print(f"\n{'='*50}") + print(f"{title:^50}") + print(f"{'='*50}") + + + categories = { + 'Reconstruction': [k for k in metrics_dict.keys() if k.startswith('recon_')], + 'Distribution': [k for k in metrics_dict.keys() if k.startswith('dist_')], + 'Flow-specific': [k for k in metrics_dict.keys() if k.startswith('flow_')], + 'Embedding': [k for k in metrics_dict.keys() if k.startswith('emb_')] + } + + for category, keys in categories.items(): + if keys: + print(f"\n{category}:") + print("-" * 20) + for key in keys: + clean_key = key.replace(f"{category.lower().replace('-', '_')}_", "") + value = metrics_dict[key] + if isinstance(value, float): + print(f" {clean_key:<20}: {value:.6f}") + else: + print(f" {clean_key:<20}: {value}") + + print(f"{'='*50}\n") + + +def test_metrics(): + + batch_size, n_features = 100, 2000 + generated = torch.randn(batch_size, n_features) + target = torch.randn(batch_size, n_features) + + metrics = evaluate_flow_generation(generated, target) + + print_metrics(metrics, "Flow Matching Evaluation") + + return metrics + + +def pairwise_sq_dists(X, Y): + # X:[m,d], Y:[n,d] -> [m,n] + return torch.cdist(X, Y, p=2)**2 + +@torch.no_grad() +def median_sigmas(X, scales=(0.5, 1.0, 2.0, 4.0)): + Z = X + D2 = pairwise_sq_dists(Z, Z) + tri = D2[~torch.eye(D2.size(0), dtype=bool, device=D2.device)] + m = torch.median(tri).clamp_min(1e-12) + s2 = torch.tensor(scales, device=Z.device) * m + sigmas = torch.sqrt(s2) + return [float(s.item()) for s in sigmas] + +def mmd2_unbiased_multi_sigma(X, Y, sigmas): + """ + """ + m, n = X.size(0), Y.size(0) + Dxx = pairwise_sq_dists(X, X) # [m,m] + Dyy = pairwise_sq_dists(Y, Y) # [n,n] + Dxy = pairwise_sq_dists(X, Y) # [m,n] + + vals = [] + for sigma in sigmas: + beta = 1.0 / (2.0 * (sigma ** 2) + 1e-12) + Kxx = torch.exp(-beta * Dxx) + Kyy = torch.exp(-beta * Dyy) + Kxy = torch.exp(-beta * Dxy) + + + term_xx = (Kxx.sum() - Kxx.diag().sum()) / (m * (m - 1) + 1e-12) + term_yy = (Kyy.sum() - Kyy.diag().sum()) / (n * (n - 1) + 1e-12) + term_xy = Kxy.mean() # / (m*n) + vals.append(term_xx + term_yy - 2.0 * term_xy) + + return torch.stack(vals).mean() + +if __name__ == "__main__": + test_metrics() diff --git a/transfer/code/ori_scDFM/src/models/instantiate_model.py b/transfer/code/ori_scDFM/src/models/instantiate_model.py new file mode 100644 index 0000000000000000000000000000000000000000..3dc8b2f8d82c057dcc2e56492669e18e6a7832f5 --- /dev/null +++ b/transfer/code/ori_scDFM/src/models/instantiate_model.py @@ -0,0 +1,36 @@ +from src.models.scGPT.model import TransformerModel +from src.models.perturbation.model import Model as FlowModel +from src.models.perturbation.model import TimedTransformer +from src.models.origin.model import model as OriginModel +import torch + +def instantiate_model(model_type: str, **kwargs): + + if model_type == 'origin': + if kwargs['fusion_method'] == 'differential_transformer': + layers = 8 + elif kwargs['fusion_method'] == 'differential_perceiver': + layers = 4 + else: + layers = 8 + d_model = kwargs.get('d_model', 512) + ntoken = kwargs.get('ntoken', 6000) + d_hid = int(4.0 * d_model) + return OriginModel( + ntoken=ntoken, + d_model=d_model, + d_hid=d_hid, + nlayers=layers, + fusion_method=kwargs['fusion_method'], + perturbation_function=kwargs['perturbation_function'], + mask_path=kwargs['mask_path'], + ) + else: + raise ValueError(f"Invalid model type: {model_type}") + +if __name__ == "__main__": + model = instantiate_model("punet128") + x = torch.randn(32, 128, 128) + t = torch.randn(32) + out = model( x,t) + print(out.shape) \ No newline at end of file diff --git a/transfer/code/ori_scDFM/src/models/origin/blocks.py b/transfer/code/ori_scDFM/src/models/origin/blocks.py new file mode 100644 index 0000000000000000000000000000000000000000..3fab34c6e0656af4287463607ad976e36871b60c --- /dev/null +++ b/transfer/code/ori_scDFM/src/models/origin/blocks.py @@ -0,0 +1,355 @@ +import pdb +import torch +import torch.nn as nn +from torch import Tensor +from typing import Optional, Dict, Tuple +import math +from .rotary import apply_rotary_emb + +def lambda_init_fn(depth): + return 0.8 - 0.6 * math.exp(-0.3 * depth) + +def repeat_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor: + """torch.repeat_interleave(x, dim=1, repeats=n_rep)""" + bs, n_kv_heads, slen, head_dim = x.shape + if n_rep == 1: + return x + return ( + x[:, :, None, :, :] + .expand(bs, n_kv_heads, n_rep, slen, head_dim) + .reshape(bs, n_kv_heads * n_rep, slen, head_dim) + ) + +def modulate(x, shift, scale): + return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + +class RMSNorm(nn.Module): + def __init__(self, dim: int, eps: float = 1e-6, elementwise_affine=True, memory_efficient=False): + super().__init__() + self.dim = dim + self.eps = eps + self.elementwise_affine = elementwise_affine + if self.elementwise_affine: + self.weight = nn.Parameter(torch.ones(dim)) + else: + self.register_parameter('weight', None) + + def _norm(self, x): + return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) + + def forward(self, x): + output = self._norm(x.float()).type_as(x) + if self.weight is not None: + output = output * self.weight + return output + + def extra_repr(self) -> str: + return f'dim={self.dim}, eps={self.eps}, elementwise_affine={self.elementwise_affine}' + + +class MultiheadDiffAttn_Origin(nn.Module): + def __init__( + self, + embed_dim, + depth, # current layer index + num_heads, + num_kv_heads=None, + ): + super().__init__() + self.embed_dim = embed_dim + + # arg num_heads set to half of baseline Transformer's num_heads + # for e.g., to compare with a baseline Transformer with 16 heads, pass in num_heads=8 for DIFF Transformer + self.num_heads = num_heads + + # arg num_kv_heads set to half of baseline Transformer's num_kv_heads if use GQA + # for e.g., to compare with a baseline Transformer with 16 heads and 8 kv_heads, + # pass in num_heads=8, num_kv_heads=4 for DIFF Transformer + # if use MHA, pass in num_kv_heads=None + self.num_kv_heads = num_kv_heads if num_kv_heads is not None else num_heads + self.n_rep = self.num_heads // self.num_kv_heads + + self.head_dim = embed_dim // num_heads // 2 + self.scaling = self.head_dim ** -0.5 + + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=False) + self.k_proj = nn.Linear(embed_dim, embed_dim // self.n_rep, bias=False) + self.v_proj = nn.Linear(embed_dim, embed_dim // self.n_rep, bias=False) + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=False) + + # depth means current layer index + self.lambda_init = lambda_init_fn(depth) + self.lambda_q1 = nn.Parameter(torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0,std=0.1)) + self.lambda_k1 = nn.Parameter(torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0,std=0.1)) + self.lambda_q2 = nn.Parameter(torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0,std=0.1)) + self.lambda_k2 = nn.Parameter(torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0,std=0.1)) + + self.subln = RMSNorm(2 * self.head_dim, eps=1e-5, elementwise_affine=True) + + def forward( + self, + x, + rel_pos, + attn_mask=None, + ): + bsz, tgt_len, embed_dim = x.size() + src_len = tgt_len + + q = self.q_proj(x) + k = self.k_proj(x) + v = self.v_proj(x) + + q = q.view(bsz, tgt_len, 2 * self.num_heads, self.head_dim) + k = k.view(bsz, src_len, 2 * self.num_kv_heads, self.head_dim) + v = v.view(bsz, src_len, self.num_kv_heads, 2 * self.head_dim) + + q = apply_rotary_emb(q, *rel_pos, interleaved=True) + k = apply_rotary_emb(k, *rel_pos, interleaved=True) + + offset = src_len - tgt_len + q = q.transpose(1, 2) + k = repeat_kv(k.transpose(1, 2), self.n_rep) + v = repeat_kv(v.transpose(1, 2), self.n_rep) + q *= self.scaling + attn_weights = torch.matmul(q, k.transpose(-1, -2)) + attn_weights = torch.nan_to_num(attn_weights) + attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).type_as( + attn_weights + ) + + lambda_1 = torch.exp(torch.sum(self.lambda_q1 * self.lambda_k1, dim=-1).float()).type_as(q) + lambda_2 = torch.exp(torch.sum(self.lambda_q2 * self.lambda_k2, dim=-1).float()).type_as(q) + lambda_full = lambda_1 - lambda_2 + self.lambda_init + attn_weights = attn_weights.view(bsz, self.num_heads, 2, tgt_len, src_len) + attn_weights = attn_weights[:, :, 0] - lambda_full * attn_weights[:, :, 1] + + attn = torch.matmul(attn_weights, v) + attn = self.subln(attn) + attn = attn * (1 - self.lambda_init) + attn = attn.transpose(1, 2).reshape(bsz, tgt_len, self.num_heads * 2 * self.head_dim) + + attn = self.out_proj(attn) + return attn + +class MultiheadDiffAttn(nn.Module): + def __init__( + self, + embed_dim, + num_heads, + depth, # current layer index + cross=False, + ): + super().__init__() + self.embed_dim = embed_dim + self.cross = cross + # The code `pdb.set_trace()` is setting a breakpoint in the code using the Python debugger + # (pdb). When the code is executed, it will pause the program's execution at that point and + # allow you to interactively debug the program. + # pdb.set_trace() + self.num_heads = num_heads + self.head_dim = embed_dim // num_heads + self.scaling = self.head_dim ** -0.5 + + self.q_proj_1 = nn.Linear(embed_dim, embed_dim, bias=False) + self.k_proj_1 = nn.Linear(embed_dim, embed_dim, bias=False) + self.q_proj_2 = nn.Linear(embed_dim, embed_dim, bias=False) + self.k_proj_2 = nn.Linear(embed_dim, embed_dim, bias=False) + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=False) + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=False) + + # depth means current layer index + self.lambda_init = lambda_init_fn(depth) + self.lambda_q1 = nn.Parameter(torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0,std=0.1)) + self.lambda_k1 = nn.Parameter(torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0,std=0.1)) + self.lambda_q2 = nn.Parameter(torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0,std=0.1)) + self.lambda_k2 = nn.Parameter(torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0,std=0.1)) + + self.subln = RMSNorm(self.head_dim, eps=1e-5, elementwise_affine=True) + + def forward( + self, + noisy_y, + x, + ): + bsz, tgt_len, embed_dim = x.size() + src_len = tgt_len + + if self.cross: + q_1 = self.q_proj_1(noisy_y) + k_1 = self.k_proj_1(x) + q_2 = self.q_proj_2(noisy_y) + k_2 = self.k_proj_2(x) + else: + q_1 = self.q_proj_1(noisy_y) + k_1 = self.k_proj_1(noisy_y) + q_2 = self.q_proj_2(x) + k_2 = self.k_proj_2(x) + v = self.v_proj(noisy_y) + + q_1 = q_1.view(bsz, tgt_len, self.num_heads, self.head_dim) + k_1 = k_1.view(bsz, src_len, self.num_heads, self.head_dim) + q_2 = q_2.view(bsz, tgt_len, self.num_heads, self.head_dim) + k_2 = k_2.view(bsz, src_len, self.num_heads, self.head_dim) + + v = v.view(bsz, src_len, self.num_heads, self.head_dim) + + + q_1 = q_1.transpose(1, 2) + q_2 = q_2.transpose(1, 2) + k_1 = k_1.transpose(1, 2) + k_2 = k_2.transpose(1, 2) + q_1 *= self.scaling + q_2 *= self.scaling + + attn_weights_1 = torch.matmul(q_1, k_1.transpose(-1, -2)) + attn_weights_2 = torch.matmul(q_2, k_2.transpose(-1, -2)) + + attn_weights_1 = torch.nn.functional.softmax(attn_weights_1, dim=-1, dtype=torch.float32).type_as( + attn_weights_1 + ) + attn_weights_2 = torch.nn.functional.softmax(attn_weights_2, dim=-1, dtype=torch.float32).type_as( + attn_weights_2 + ) + + lambda_1 = torch.exp(torch.sum(self.lambda_q1 * self.lambda_k1, dim=-1).float()).type_as(q_1) + lambda_2 = torch.exp(torch.sum(self.lambda_q2 * self.lambda_k2, dim=-1).float()).type_as(q_1) + + lambda_full = lambda_1 - lambda_2 + self.lambda_init + attn_weights = attn_weights_1 - lambda_full * attn_weights_2 + + + attn = torch.matmul(attn_weights, v.transpose(1,2)) + # attn: (bsz, num_heads, tgt_len, head_dim) + attn = self.subln(attn) + attn = attn * (1 - self.lambda_init) + attn = attn.transpose(1, 2).reshape(bsz, tgt_len, self.num_heads * self.head_dim) + + attn = self.out_proj(attn) + return attn + + + +class CrossAttentionTransformerLayer(nn.Module): + """ + One-layer Transformer block with ONLY cross-attention: + - Pre-norm -> Multihead cross-attention (Q=tgt, K/V=memory) + - Residual + - Pre-norm -> FeedForward (MLP) + - Residual + + Shapes (batch_first=True): + tgt: (B, T_tgt, d_model) + memory: (B, T_mem, d_model) + """ + def __init__( + self, + d_model: int, + nhead: int, + mlp_ratio: int = 4, + dropout: float = 0.1, + activation: str = "gelu", + batch_first: bool = True + ): + super().__init__() + self.batch_first = batch_first + + # Cross attention: Q from tgt, K/V from memory + self.cross_attn = nn.MultiheadAttention( + embed_dim=d_model, num_heads=nhead, dropout=dropout, batch_first=batch_first + ) + self.cross_attn_dropout = nn.Dropout(dropout) + self.norm1 = nn.LayerNorm(d_model) + + # Feedforward + self.linear1 = nn.Linear(d_model, int(mlp_ratio * d_model)) + self.linear2 = nn.Linear(int(mlp_ratio * d_model), d_model) + self.ffn_dropout = nn.Dropout(dropout) + self.norm2 = nn.LayerNorm(d_model) + + if activation == "relu": + self.activation = nn.ReLU() + elif activation == "gelu": + self.activation = nn.GELU() + else: + raise ValueError(f"Unsupported activation: {activation}") + + def forward( + self, + tgt: torch.Tensor, + memory: torch.Tensor, + attn_mask: Optional[torch.Tensor] = None, + memory_key_padding_mask: Optional[torch.Tensor] = None, + need_weights: bool = False, + average_attn_weights: bool = True, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: + """ + Args: + tgt: (B, T_tgt, d_model) + memory: (B, T_mem, d_model) + attn_mask: optional mask applied to (tgt_len, mem_len) or (B*nhead, tgt_len, mem_len) + memory_key_padding_mask: (B, T_mem) with True for positions to mask + need_weights: return attention weights + average_attn_weights: average heads if True + + Returns: + out: (B, T_tgt, d_model) + attn: (B, T_tgt, T_mem) or (B, nhead, T_tgt, T_mem) if need_weights else None + """ + # --- Cross Attention (pre-norm) --- + x = self.norm1(tgt) + attn_out, attn_w = self.cross_attn( + query=x, + key=memory, + value=memory, + attn_mask=attn_mask, + key_padding_mask=memory_key_padding_mask, + need_weights=need_weights, + average_attn_weights=average_attn_weights, + ) + x = tgt + self.cross_attn_dropout(attn_out) + + # --- Feedforward (pre-norm) --- + y = self.norm2(x) + y = self.linear2(self.ffn_dropout(self.activation(self.linear1(y)))) + out = x + self.ffn_dropout(y) + + return out, attn_w if need_weights else None +# class PerceiverDiffTransBlock(nn.Module): +# def __init__(self, d_in, d_latent, heads=8, mlp_ratio=4): +# super().__init__() + +# self.ln_z1 = nn.LayerNorm(d_latent) +# self.q = nn.Linear(d_latent, d_latent) +# self.k = nn.Linear(d_in, d_latent) +# self.v = nn.Linear(d_in, d_latent) + +# self.q2 = nn.Linear(d_latent, d_latent) +# self.k2 = nn.Linear(d_latent, d_latent) +# self.v2 = nn.Linear(d_latent, d_latent) +# self.cross = nn.MultiheadAttention(d_latent, heads, dropout=0.1, batch_first=True) + +# self.ln_z2 = nn.LayerNorm(d_latent) +# self.self_attn = nn.MultiheadAttention(d_latent, heads, dropout=0.1, batch_first=True) +# self.ln_z3 = nn.LayerNorm(d_latent) +# self.mlp = nn.Sequential( +# nn.Linear(d_latent, int(mlp_ratio * d_latent)), nn.GELU(), +# nn.Dropout(0.1), +# nn.Linear(int(mlp_ratio * d_latent), d_latent), +# nn.Dropout(0.1), +# ) +# self.adaLN_modulation = nn.Sequential( +# nn.SiLU(), +# nn.Linear(d_latent, 6 * d_latent, bias=True) +# ) + + +# def forward(self, z, x, t): +# shift_self, scale_self, gate_self, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(t).chunk(6, dim=1) +# z = z + self.cross(self.q(self.ln_z1(z)), +# self.k(x), self.v(x))[0] + +# z = modulate(self.ln_z2(z), shift_self, scale_self) +# z = z + gate_self.unsqueeze(1) * self.self_attn(self.q2(z), self.k2(z), self.v2(z))[0] + +# z = z + gate_mlp.unsqueeze(1) * self.mlp(modulate(self.ln_z3(z), shift_mlp, scale_mlp)) +# return z \ No newline at end of file diff --git a/transfer/code/ori_scDFM/src/models/origin/layers.py b/transfer/code/ori_scDFM/src/models/origin/layers.py new file mode 100644 index 0000000000000000000000000000000000000000..23af1b23d78d2ae17a5919dca93e6d8408feab03 --- /dev/null +++ b/transfer/code/ori_scDFM/src/models/origin/layers.py @@ -0,0 +1,194 @@ +import pdb +from re import X +import torch +import torch.nn as nn +from torch import Tensor +from typing import Optional, Dict +import math +from .blocks import CrossAttentionTransformerLayer +import copy +class GeneadaLN(nn.Module): + def __init__(self, hidden_size: int, dropout: float = 0.1): + super().__init__() + + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(hidden_size, 3 * hidden_size, bias=True) + ) + self.norm = nn.LayerNorm(hidden_size) + + def forward(self, gene_emb: Tensor, value_emb: Tensor) -> Tensor: + shift, gate, scale = self.adaLN_modulation(gene_emb).chunk(3, dim=-1) + + x = value_emb + gate * (self.norm(value_emb) * scale + shift ) + return x + +class ContinuousValueEncoder(nn.Module): + """ + Encode real number values to a vector using neural nets projection. + """ + + def __init__(self, d_model: int, dropout: float = 0.1, max_value: int = 512): + super().__init__() + self.dropout = nn.Dropout(p=dropout) + self.linear1 = nn.Linear(1, d_model) + self.activation = nn.ReLU() + self.linear2 = nn.Linear(d_model, d_model) + self.norm = nn.LayerNorm(d_model) + self.max_value = max_value + + def forward(self, x: Tensor) -> Tensor: + """ + Args: + x: Tensor, shape [batch_size, seq_len] + """ + # TODO: test using actual embedding layer if input is categorical + # expand last dimension + x = x.unsqueeze(-1) + # clip x to [-inf, max_value] + x = torch.clamp(x, max=self.max_value) + x = self.activation(self.linear1(x)) + x = self.linear2(x) + x = self.norm(x) + return self.dropout(x) + +class GeneEncoder(nn.Module): + def __init__( + self, + num_embeddings: int, + embedding_dim: int, + padding_idx: Optional[int] = None, + nhead:int = 8 , + use_perturbation_interaction: bool = False, + dropout:float = 0.1, + mask_path: str = None, + + ): + super().__init__() + self.embedding = nn.Embedding( + num_embeddings, embedding_dim, padding_idx=padding_idx + ) + self.enc_norm = nn.LayerNorm(embedding_dim) + self.use_perturbation_interaction = use_perturbation_interaction + if use_perturbation_interaction: + self.data_name = mask_path.split('/')[-2] + self.perturbation_interaction = CrossAttentionTransformerLayer(embedding_dim, nhead, mlp_ratio=4.0, dropout=dropout) + self.mask_padded = torch.load(mask_path) + self.mask_num = self.mask_padded.shape[0] + def forward(self, x: Tensor) -> Tensor: + if self.use_perturbation_interaction: + # NOTE using the same perturbation and gene names + if self.mask_padded.device != x.device: + + self.mask_padded = self.mask_padded.to(x.device) + + mask = self.mask_padded[x[0]] + + x = self.embedding(x) # (batch, seq_len, embsize) + + x = self.enc_norm(x) + if self.use_perturbation_interaction: + + memory_id = torch.arange(self.mask_num,device=x.device) + memory_emb = self.embedding(memory_id) + memory_emb = self.enc_norm(memory_emb).expand(x.shape[0],-1,-1) + x = self.perturbation_interaction(x, memory_emb,mask)[0] + return x + +class BatchLabelEncoder(nn.Module): + def __init__( + self, + num_embeddings: int, + embedding_dim: int, + padding_idx: Optional[int] = None, + ): + super().__init__() + self.embedding = nn.Embedding( + num_embeddings, embedding_dim, padding_idx=padding_idx + ) + self.enc_norm = nn.LayerNorm(embedding_dim) + + def forward(self, x: Tensor) -> Tensor: + x = self.embedding(x) # (batch, embsize) + x = self.enc_norm(x) + return x + +class ExprDecoder(nn.Module): + def __init__( + self, + d_model: int, + explicit_zero_prob: bool = False, + use_batch_labels: bool = False, + ): + super().__init__() + d_in = d_model * 2 if use_batch_labels else d_model + self.fc = nn.Sequential( + nn.Linear(d_in, d_model), + nn.LeakyReLU(), + nn.Linear(d_model, d_model), + nn.LeakyReLU(), + nn.Linear(d_model, 1), + ) + self.explicit_zero_prob = explicit_zero_prob + if explicit_zero_prob: + self.zero_logit = nn.Sequential( + nn.Linear(d_in, d_model), + nn.LeakyReLU(), + nn.Linear(d_model, d_model), + nn.LeakyReLU(), + nn.Linear(d_model, 1), + ) + + def forward(self, x: Tensor) -> Dict[str, Tensor]: + """x is the output of the transformer, (batch, seq_len, d_model)""" + pred_value = self.fc(x).squeeze(-1) # (batch, seq_len) + + if not self.explicit_zero_prob: + return dict(pred=pred_value) + zero_logits = self.zero_logit(x).squeeze(-1) # (batch, seq_len) + zero_probs = torch.sigmoid(zero_logits) + return dict(pred=pred_value, zero_probs=zero_probs) + # TODO: note that the return currently is only for training. Since decoder + # is not used in the test setting for the integration task, the eval/inference + # logic is not implemented yet. However, remember to implement it when + # the decoder is used in any test setting. The inference logic will need + # to sample from the bernoulli distribution with the zero_probs. + +class TimestepEmbedder(nn.Module): + """ + Embeds scalar timesteps into vector representations. + """ + def __init__(self, hidden_size, frequency_embedding_size=256): + super().__init__() + self.mlp = nn.Sequential( + nn.Linear(frequency_embedding_size, hidden_size, bias=True), + nn.SiLU(), + nn.Linear(hidden_size, hidden_size, bias=True), + ) + self.frequency_embedding_size = frequency_embedding_size + + @staticmethod + def timestep_embedding(t, dim, max_period=10000): + """ + Create sinusoidal timestep embeddings. + :param t: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an (N, D) Tensor of positional embeddings. + """ + # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py + half = dim // 2 + freqs = torch.exp( + -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half + ).to(device=t.device) + args = t[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + return embedding + + def forward(self, t): + t_freq = self.timestep_embedding(t, self.frequency_embedding_size) + t_emb = self.mlp(t_freq) + return t_emb \ No newline at end of file diff --git a/transfer/code/ori_scDFM/src/models/origin/model.py b/transfer/code/ori_scDFM/src/models/origin/model.py new file mode 100644 index 0000000000000000000000000000000000000000..a51d87851f25f6cfd424e4365a12dca522c9e046 --- /dev/null +++ b/transfer/code/ori_scDFM/src/models/origin/model.py @@ -0,0 +1,255 @@ +import torch +import torch.nn as nn +from torch import Tensor +from typing import Optional, Dict +import math +from timm.models.vision_transformer import PatchEmbed, Attention, Mlp + +import pdb + +from .layers import GeneadaLN, ContinuousValueEncoder, GeneEncoder, BatchLabelEncoder, TimestepEmbedder, ExprDecoder +from .blocks import MultiheadDiffAttn, modulate , CrossAttentionTransformerLayer +class Block(nn.Module): + """ + A DiT block with adaptive layer norm zero (adaLN-Zero) conditioning. + """ + def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, **block_kwargs): + super().__init__() + self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.attn = Attention(hidden_size, num_heads=num_heads, qkv_bias=True, **block_kwargs) + self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + mlp_hidden_dim = int(hidden_size * mlp_ratio) + approx_gelu = lambda: nn.GELU(approximate="tanh") + self.mlp = Mlp(in_features=hidden_size, hidden_features=mlp_hidden_dim, act_layer=approx_gelu, drop=0) + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(hidden_size, 6 * hidden_size, bias=True) + ) + + def forward(self, x, c): + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(6, dim=1) + x = x + gate_msa.unsqueeze(1) * self.attn(modulate(self.norm1(x), shift_msa, scale_msa)) + x = x + gate_mlp.unsqueeze(1) * self.mlp(modulate(self.norm2(x), shift_mlp, scale_mlp)) + return x + +class DifferentialTransformerBlock(nn.Module): + def __init__(self, hidden_size, num_heads, depth, mlp_ratio=4.0, cross=False, **block_kwargs): + super().__init__() + self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.attn = MultiheadDiffAttn(hidden_size, num_heads, depth, cross=cross) + self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + mlp_hidden_dim = int(hidden_size * mlp_ratio) + approx_gelu = lambda: nn.GELU(approximate="tanh") + self.mlp = Mlp(in_features=hidden_size, hidden_features=mlp_hidden_dim, act_layer=approx_gelu, drop=0) + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(hidden_size, 6 * hidden_size, bias=True) + ) + + def forward(self, y, x, c): + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(6, dim=1) + y = y + gate_msa.unsqueeze(1) * self.attn(modulate(self.norm1(y), shift_msa, scale_msa), x) + y = y + gate_mlp.unsqueeze(1) * self.mlp(modulate(self.norm2(y), shift_mlp, scale_mlp)) + return y + +class PerceiverBlock(nn.Module): + def __init__(self, d_in, d_latent, heads=8, mlp_ratio=4, dropout=0.0): + super().__init__() + self.ln_z1 = nn.LayerNorm(d_latent) + self.q = nn.Linear(d_latent, d_latent) + self.k = nn.Linear(d_in, d_latent) + self.v = nn.Linear(d_in, d_latent) + + self.q2 = nn.Linear(d_latent, d_latent) + self.k2 = nn.Linear(d_latent, d_latent) + self.v2 = nn.Linear(d_latent, d_latent) + self.cross = nn.MultiheadAttention(d_latent, heads, dropout=dropout, batch_first=True) + + self.ln_z2 = nn.LayerNorm(d_latent) + self.self_attn = nn.MultiheadAttention(d_latent, heads, dropout=dropout, batch_first=True) + self.ln_z3 = nn.LayerNorm(d_latent) + self.mlp = nn.Sequential( + nn.Linear(d_latent, int(mlp_ratio * d_latent)), nn.GELU(), + nn.Linear(int(mlp_ratio * d_latent), d_latent) + ) + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(d_latent, 6 * d_latent, bias=True) + ) + + + def forward(self, z, x, t): + shift_self, scale_self, gate_self, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(t).chunk(6, dim=1) + z = z + self.cross(self.q(self.ln_z1(z)), + self.k(x), self.v(x))[0] + + z = modulate(self.ln_z2(z), shift_self, scale_self) + z = z + gate_self.unsqueeze(1) * self.self_attn(self.q2(z), self.k2(z), self.v2(z))[0] + + z = z + gate_mlp.unsqueeze(1) * self.mlp(modulate(self.ln_z3(z), shift_mlp, scale_mlp)) + return z + +class DiffPerceiverBlock(nn.Module): + def __init__(self,hidden_size, num_heads, depth, mlp_ratio=4.0): + super().__init__() + self.diff_self_attn = DifferentialTransformerBlock(hidden_size, num_heads, depth, mlp_ratio=mlp_ratio,cross=True) + self.diff_cross_attn = DifferentialTransformerBlock(hidden_size, num_heads, depth, mlp_ratio=mlp_ratio,cross=False) + + def forward(self, y, x, c): + y = self.diff_self_attn(y, y, c) + y = self.diff_cross_attn(y, x, c) + return y + +class model(nn.Module): + def __init__(self, + ntoken: int = 6000, + d_model: int = 512, + nhead: int = 8, + d_hid: int = 2048, + nlayers: int = 8, # 8 + dropout: float = 0.1, + fusion_method: str = 'cross', # concat, add, cross + perturbation_function: str = 'crisper', + use_perturbation_interaction: bool = True, + mask_path: str = None, + ): + super().__init__() + self.t_embedder = TimestepEmbedder(d_model) + self.perturbation_embedder = BatchLabelEncoder(ntoken, d_model,) + self.fusion_method = fusion_method + self.perturbation_function = perturbation_function + self.fusion_layer = nn.Sequential(nn.Linear(2*d_model, d_model), + nn.GELU(), + nn.Linear(d_model, d_model), + nn.LayerNorm(d_model)) + self.value_encoder_1 = ContinuousValueEncoder(d_model, dropout) + self.value_encoder_2 = ContinuousValueEncoder(d_model, dropout) + self.encoder = GeneEncoder(ntoken, d_model,use_perturbation_interaction=use_perturbation_interaction,mask_path=mask_path) + self.use_perturbation_interaction = use_perturbation_interaction + # if use_perturbation_interaction: + # self.perturbation_interaction = CrossAttentionTransformerLayer(d_model, nhead, mlp_ratio=4.0, dropout=dropout) + + if self.fusion_method == 'differential_transformer': + self.blocks = nn.ModuleList([ + DifferentialTransformerBlock(d_model, nhead, i, mlp_ratio=4.0) for i in range(nlayers) + ]) + elif self.fusion_method == 'differential_perceiver': + self.blocks = nn.ModuleList([ + DiffPerceiverBlock(d_model, nhead, i, mlp_ratio=4.0) for i in range(nlayers) + ]) + elif self.fusion_method == 'perceiver': + self.blocks = nn.ModuleList([ + PerceiverBlock(d_model, d_model, heads=nhead, mlp_ratio=4.0, dropout=0.1) for _ in range(nlayers) + ]) + else: + raise ValueError(f"Invalid fusion method: {self.fusion_method}") + + + self.gene_adaLN = nn.ModuleList([ + GeneadaLN(d_model, dropout) for _ in range(nlayers) + ]) + self.adapter_layer = nn.ModuleList([ + nn.Sequential( + nn.Linear(2*d_model, d_model), + nn.LeakyReLU(), + nn.Dropout(dropout), + nn.Linear(d_model, d_model), + nn.LeakyReLU()) for _ in range(nlayers) + ]) + + # predict_p task with embedding prediction + self.p_mask_embed = nn.Parameter(torch.randn(d_model)) # (d_model,) + self.p_head = nn.Sequential( + nn.LayerNorm(d_model), + nn.Linear(d_model, d_model) + ) + + self.final_layer = ExprDecoder(d_model, explicit_zero_prob=False, use_batch_labels=True) + self.initialize_weights() + + def initialize_weights(self): + # Initialize transformer layers: + def _basic_init(module): + if isinstance(module, nn.Linear): + torch.nn.init.xavier_uniform_(module.weight) + if module.bias is not None: + nn.init.constant_(module.bias, 0) + self.apply(_basic_init) + + + def get_perturbation_emb(self, perturbation_id=None, perturbation_emb=None, + cell_1=None, use_mask: bool=False): + if use_mask: + B = cell_1.size(0) + return self.p_mask_embed[None, :].expand(B, -1).to(cell_1.device, dtype=cell_1.dtype) + + assert perturbation_emb is None or perturbation_id is None + if perturbation_id is not None: + if self.perturbation_function == 'crisper': + perturbation_emb = self.encoder(perturbation_id) + + else: + perturbation_emb = self.perturbation_embedder(perturbation_id) + perturbation_emb = perturbation_emb.mean(1) # (B,d) + elif perturbation_emb is not None: + perturbation_emb = perturbation_emb.to(cell_1.device, dtype=cell_1.dtype) + if perturbation_emb.dim() == 1: + perturbation_emb = perturbation_emb.unsqueeze(0) + if perturbation_emb.size(0) == 1: + perturbation_emb = perturbation_emb.expand(cell_1.shape[0], -1).contiguous() + perturbation_emb = self.perturbation_embedder.enc_norm(perturbation_emb) + + return perturbation_emb + + def forward(self,gene_id, cell_1, t, cell_2, perturbation_id=None, gene_id_all=None, perturbation_emb=None, mode="predict_y"): + if t.dim() == 0: + t = t.repeat(cell_1.size(0)) + + gene_emb = self.encoder(gene_id) + # gene_emb_all = self.encoder(gene_id_all) + gene_emb_all = gene_emb + value_emb_1 = self.value_encoder_1(cell_1) + value_emb_2 = self.value_encoder_2(cell_2) + + value_emb_1 = value_emb_1 + gene_emb + value_emb_2 = value_emb_2 + gene_emb_all + + value_emb = torch.cat([value_emb_1, value_emb_2], dim=-1) + value_emb = self.fusion_layer(value_emb) + + t_emb = self.t_embedder(t) + + x = value_emb + + perturbation_emb = self.get_perturbation_emb(perturbation_id, perturbation_emb, cell_1) + + for i,block in enumerate(self.blocks): + x = self.gene_adaLN[i](gene_emb, x) + perturbation_exp = perturbation_emb[:, None, :].expand(-1, x.size(1), -1) # (B, T, emb) + x = torch.cat([x, perturbation_exp], dim=-1) + x = self.adapter_layer[i](x) + x = block(x, value_emb_2, t_emb) + + + if mode=="predict_p": + x_pooling = x.mean(dim=1) + return self.p_head(x_pooling) + + x = torch.cat([x, perturbation_emb[:, None, :].expand(-1, x.size(1), -1)], dim=-1) + x = self.final_layer(x) + + return x['pred'] + + + +if __name__ == "__main__": + model = model(ntoken=100, d_model=128, nhead=8, d_hid=128, nlayers=12) + batch_size = 10 + gene_id = torch.randint(0, 100, (batch_size, 10)) + x = torch.randn(batch_size, 10) + t = torch.randint(0, 100, (batch_size,)) + y = torch.randn(batch_size, 10) + perturbation = torch.randint(0, 100, (batch_size,2)) + + output = model(gene_id, x, t, y, perturbation) + pdb.set_trace() \ No newline at end of file diff --git a/transfer/code/ori_scDFM/src/models/origin/rotary.py b/transfer/code/ori_scDFM/src/models/origin/rotary.py new file mode 100644 index 0000000000000000000000000000000000000000..1f4e6fb77dfb37bc431c8de63798e42cf2510b9b --- /dev/null +++ b/transfer/code/ori_scDFM/src/models/origin/rotary.py @@ -0,0 +1,333 @@ +# Copyright (c) 2023, Tri Dao. +# Adapted from Diff-Transformer codebase + +from typing import Optional, Union + +import torch + +import triton +import triton.language as tl + + +# @triton.autotune( +# configs=[ +# triton.Config({"BLOCK_M": 2}), +# triton.Config({"BLOCK_M": 4}), +# triton.Config({"BLOCK_M": 8}), +# triton.Config({"BLOCK_M": 16}), +# ], +# key=["CACHE_KEY_SEQLEN", "BLOCK_K", "INTERLEAVED"], +# ) +@triton.jit +def rotary_kernel( + OUT, # Pointers to matrices + X, + COS, + SIN, + CU_SEQLENS, + SEQLEN_OFFSETS, # this could be int or a pointer + # Matrix dimensions + seqlen, + nheads, + rotary_dim, + seqlen_ro, + CACHE_KEY_SEQLEN, + # strides + stride_out_batch, + stride_out_seqlen, + stride_out_nheads, + stride_out_headdim, + stride_x_batch, + stride_x_seqlen, + stride_x_nheads, + stride_x_headdim, + # Meta-parameters + BLOCK_K: tl.constexpr, + IS_SEQLEN_OFFSETS_TENSOR: tl.constexpr, + IS_VARLEN: tl.constexpr, + INTERLEAVED: tl.constexpr, + CONJUGATE: tl.constexpr, + BLOCK_M: tl.constexpr, +): + pid_m = tl.program_id(axis=0) + pid_batch = tl.program_id(axis=1) + pid_head = tl.program_id(axis=2) + rotary_dim_half = rotary_dim // 2 + + if not IS_VARLEN: + X = X + pid_batch * stride_x_batch + pid_head * stride_x_nheads + OUT = OUT + pid_batch * stride_out_batch + pid_head * stride_out_nheads + else: + start_idx = tl.load(CU_SEQLENS + pid_batch) + seqlen = tl.load(CU_SEQLENS + pid_batch + 1) - start_idx + X = X + start_idx * stride_x_seqlen + pid_head * stride_x_nheads + OUT = OUT + start_idx * stride_out_seqlen + pid_head * stride_out_nheads + + if pid_m * BLOCK_M >= seqlen: + return + rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M) + if not IS_SEQLEN_OFFSETS_TENSOR: + rm_cs = rm + SEQLEN_OFFSETS + else: + rm_cs = rm + tl.load(SEQLEN_OFFSETS + pid_batch) + rk = tl.arange(0, BLOCK_K) + rk_half = tl.arange(0, BLOCK_K // 2) + + if not INTERLEAVED: + # Load the 1st and 2nd halves of X, do calculation, then store to 1st and 2nd halves of OUT + X = X + (rm[:, None] * stride_x_seqlen + rk_half[None, :] * stride_x_headdim) + COS = COS + (rm_cs[:, None] * rotary_dim_half + rk_half[None, :]) + SIN = SIN + (rm_cs[:, None] * rotary_dim_half + rk_half[None, :]) + cos = tl.load( + COS, mask=(rm_cs[:, None] < seqlen_ro) & (rk_half[None, :] < rotary_dim_half), other=1.0 + ).to(tl.float32) + sin = tl.load( + SIN, mask=(rm_cs[:, None] < seqlen_ro) & (rk_half[None, :] < rotary_dim_half), other=0.0 + ).to(tl.float32) + x0 = tl.load( + X, mask=(rm[:, None] < seqlen) & (rk_half[None, :] < rotary_dim_half), other=0.0 + ).to(tl.float32) + x1 = tl.load( + X + rotary_dim_half * stride_x_headdim, + mask=(rm[:, None] < seqlen) & (rk_half[None, :] < rotary_dim_half), + other=0.0, + ).to(tl.float32) + if CONJUGATE: + sin = -sin + o0 = x0 * cos - x1 * sin + o1 = x0 * sin + x1 * cos + # write back result + OUT = OUT + (rm[:, None] * stride_out_seqlen + rk_half[None, :] * stride_out_headdim) + tl.store(OUT, o0, mask=(rm[:, None] < seqlen) & (rk_half[None, :] < rotary_dim_half)) + tl.store( + OUT + rotary_dim_half * stride_out_headdim, + o1, + mask=(rm[:, None] < seqlen) & (rk_half[None, :] < rotary_dim_half), + ) + else: + # We don't want to load X[0, 2, 4, ...] and X[1, 3, 5, ...] separately since both are slow. + # Instead, we load x0 = X[0, 1, 2, 3, ...] and x1 = X[1, 0, 3, 2, ...]. + # Loading x0 will be fast but x1 will be slow. + # Then we load cos = COS[0, 0, 1, 1, ...] and sin = SIN[0, 0, 1, 1, ...]. + # Then we do the calculation and use tl.where to pick put the right outputs for the even + # and for the odd indices. + rk_swap = rk + ((rk + 1) % 2) * 2 - 1 # 1, 0, 3, 2, 5, 4, ... + rk_repeat = tl.arange(0, BLOCK_K) // 2 + X0 = X + (rm[:, None] * stride_x_seqlen + rk[None, :] * stride_x_headdim) + X1 = X + (rm[:, None] * stride_x_seqlen + rk_swap[None, :] * stride_x_headdim) + COS = COS + (rm_cs[:, None] * rotary_dim_half + rk_repeat[None, :]) + SIN = SIN + (rm_cs[:, None] * rotary_dim_half + rk_repeat[None, :]) + cos = tl.load( + COS, + mask=(rm_cs[:, None] < seqlen_ro) & (rk_repeat[None, :] < rotary_dim_half), + other=1.0, + ).to(tl.float32) + sin = tl.load( + SIN, + mask=(rm_cs[:, None] < seqlen_ro) & (rk_repeat[None, :] < rotary_dim_half), + other=0.0, + ).to(tl.float32) + x0 = tl.load(X0, mask=(rm[:, None] < seqlen) & (rk[None, :] < rotary_dim), other=0.0).to( + tl.float32 + ) + x1 = tl.load( + X1, mask=(rm[:, None] < seqlen) & (rk_swap[None, :] < rotary_dim), other=0.0 + ).to(tl.float32) + if CONJUGATE: + sin = -sin + x0_cos = x0 * cos + x1_sin = x1 * sin + out = tl.where(rk[None, :] % 2 == 0, x0_cos - x1_sin, x0_cos + x1_sin) + OUT = OUT + (rm[:, None] * stride_out_seqlen + rk[None, :] * stride_out_headdim) + tl.store(OUT, out, mask=(rm[:, None] < seqlen) & (rk[None, :] < rotary_dim)) + + +def apply_rotary( + x: torch.Tensor, + cos: torch.Tensor, + sin: torch.Tensor, + seqlen_offsets: Union[int, torch.Tensor] = 0, + cu_seqlens: Optional[torch.Tensor] = None, + max_seqlen: Optional[int] = None, + interleaved=False, + inplace=False, + conjugate=False, +) -> torch.Tensor: + """ + Arguments: + x: (batch, seqlen, nheads, headdim) if cu_seqlens is None + else (total_seqlen, nheads, headdim). + cos: (seqlen_ro, rotary_dim / 2) + sin: (seqlen_ro, rotary_dim / 2) + seqlen_offsets: integer or integer tensor of size (batch,) + cu_seqlens: (batch + 1,) or None + max_seqlen: int + Returns: + y: (batch, seqlen, nheads, headdim) + """ + is_varlen = cu_seqlens is not None + if not is_varlen: + batch, seqlen, nheads, headdim = x.shape + else: + assert max_seqlen is not None, "If cu_seqlens is passed in, then max_seqlen must be passed" + total_seqlen, nheads, headdim = x.shape + batch_p_1 = cu_seqlens.shape[0] + batch = batch_p_1 - 1 + seqlen = max_seqlen + seqlen_ro, rotary_dim = cos.shape + assert sin.shape == cos.shape + rotary_dim *= 2 + assert rotary_dim <= headdim, "rotary_dim must be <= headdim" + assert headdim <= 256, "Only support headdim <= 256" + assert seqlen_ro >= seqlen, "seqlen_ro must be >= seqlen" + + assert ( + cos.dtype == sin.dtype + ), f"cos and sin must have the same dtype, got {cos.dtype} and {sin.dtype}" + assert ( + x.dtype == cos.dtype + ), f"Input and cos/sin must have the same dtype, got {x.dtype} and {cos.dtype}" + + cos, sin = cos.contiguous(), sin.contiguous() + if isinstance(seqlen_offsets, torch.Tensor): + assert seqlen_offsets.shape == (batch,) + assert seqlen_offsets.dtype in [torch.int32, torch.int64] + seqlen_offsets = seqlen_offsets.contiguous() + else: + assert seqlen_offsets + seqlen <= seqlen_ro + + output = torch.empty_like(x) if not inplace else x + if rotary_dim < headdim and not inplace: + output[..., rotary_dim:].copy_(x[..., rotary_dim:]) + + BLOCK_K = ( + 32 + if rotary_dim <= 32 + else (64 if rotary_dim <= 64 else (128 if rotary_dim <= 128 else 256)) + ) + grid = lambda META: (triton.cdiv(seqlen, META["BLOCK_M"]), batch, nheads) # noqa + BLOCK_M = 4 if interleaved else (8 if rotary_dim <= 64 else 4) + + # Need this, otherwise Triton tries to launch from cuda:0 and we get + # ValueError: Pointer argument (at 0) cannot be accessed from Triton (cpu tensor?) + with torch.cuda.device(x.device.index): + rotary_kernel[grid]( + output, # data ptrs + x, + cos, + sin, + cu_seqlens, + seqlen_offsets, + seqlen, # shapes + nheads, + rotary_dim, + seqlen_ro, + seqlen // 128, # key for triton cache (limit number of compilations) + output.stride(0) if not is_varlen else 0, # batch_strides if not varlen else 0 + output.stride(-3), # seqlen_stride or total_seqlen_stride + output.stride(-2), # nheads_stride + output.stride(-1), # headdim_stride + x.stride(0) if not is_varlen else 0, # batch_strides if not varlen else 0 + x.stride(-3), # seqlen stride or total_seqlen_stride + x.stride(-2), # nheads stride + x.stride(-1), # headdim stride + BLOCK_K, + isinstance(seqlen_offsets, torch.Tensor), + is_varlen, + interleaved, + conjugate, + BLOCK_M, + ) + return output + +class ApplyRotaryEmb(torch.autograd.Function): + @staticmethod + def forward( + ctx, + x, + cos, + sin, + interleaved=False, + inplace=False, + seqlen_offsets: Union[int, torch.Tensor] = 0, + cu_seqlens: Optional[torch.Tensor] = None, + max_seqlen: Optional[int] = None, + ): + out = apply_rotary( + x, + cos, + sin, + seqlen_offsets=seqlen_offsets, + cu_seqlens=cu_seqlens, + max_seqlen=max_seqlen, + interleaved=interleaved, + inplace=inplace, + ) + if isinstance(seqlen_offsets, int): + # Can't save int with save_for_backward + ctx.save_for_backward(cos, sin, cu_seqlens) + ctx.seqlen_offsets = seqlen_offsets + else: + ctx.save_for_backward(cos, sin, cu_seqlens, seqlen_offsets) + ctx.seqlen_offsets = None + ctx.interleaved = interleaved + ctx.inplace = inplace + ctx.max_seqlen = max_seqlen + return out if not inplace else x + + @staticmethod + def backward(ctx, do): + seqlen_offsets = ctx.seqlen_offsets + if seqlen_offsets is None: + cos, sin, cu_seqlens, seqlen_offsets = ctx.saved_tensors + else: + cos, sin, cu_seqlens = ctx.saved_tensors + # TD [2023-09-02]: For some reason Triton (2.0.0.post1) errors with + # "[CUDA]: invalid device context", and cloning makes it work. Idk why. Triton 2.1.0 works. + if not ctx.interleaved and not ctx.inplace: + do = do.clone() + dx = apply_rotary( + do, + cos, + sin, + seqlen_offsets=seqlen_offsets, + cu_seqlens=cu_seqlens, + max_seqlen=ctx.max_seqlen, + interleaved=ctx.interleaved, + inplace=ctx.inplace, + conjugate=True, + ) + return dx, None, None, None, None, None, None, None + + +def apply_rotary_emb( + x, + cos, + sin, + interleaved=False, + inplace=False, + seqlen_offsets: Union[int, torch.Tensor] = 0, + cu_seqlens: Optional[torch.Tensor] = None, + max_seqlen: Optional[int] = None, +): + """ + Arguments: + x: (batch_size, seqlen, nheads, headdim) if cu_seqlens is None + else (total_seqlen, nheads, headdim) + cos, sin: (seqlen_rotary, rotary_dim / 2) + interleaved: if True, rotate pairs of even and odd dimensions (GPT-J style) instead + of 1st half and 2nd half (GPT-NeoX style). + inplace: if True, apply rotary embedding in-place. + seqlen_offsets: (batch_size,) or int. Each sequence in x is shifted by this amount. + Most commonly used in inference when we have KV cache. + cu_seqlens: (batch + 1,) or None + max_seqlen: int + Return: + out: (batch_size, seqlen, nheads, headdim) if cu_seqlens is None + else (total_seqlen, nheads, headdim) + rotary_dim must be <= headdim + Apply rotary embedding to the first rotary_dim of x. + """ + return ApplyRotaryEmb.apply( + x, cos, sin, interleaved, inplace, seqlen_offsets, cu_seqlens, max_seqlen + ) \ No newline at end of file diff --git a/transfer/code/ori_scDFM/src/models/perturbation/model.py b/transfer/code/ori_scDFM/src/models/perturbation/model.py new file mode 100644 index 0000000000000000000000000000000000000000..d0e9c6fc236f7d9a6d08a3056da1402ae0c5ceed --- /dev/null +++ b/transfer/code/ori_scDFM/src/models/perturbation/model.py @@ -0,0 +1,393 @@ +import torch +import torch.nn as nn +import numpy as np +import math +from typing import Dict, Mapping, Optional, Tuple, Any, Union +import pdb +from timm.models.vision_transformer import PatchEmbed, Attention, Mlp +from src.flow_matching.utils.model_wrapper import ModelWrapper + +def modulate(x, shift, scale): + return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + + +################################################################################# +# Embedding Layers for Timesteps and Class Labels # +################################################################################# + +class TimestepEmbedder(nn.Module): + """ + Embeds scalar timesteps into vector representations. + """ + def __init__(self, hidden_size, frequency_embedding_size=256): + super().__init__() + self.mlp = nn.Sequential( + nn.Linear(frequency_embedding_size, hidden_size, bias=True), + nn.SiLU(), + nn.Linear(hidden_size, hidden_size, bias=True), + ) + self.frequency_embedding_size = frequency_embedding_size + + @staticmethod + def timestep_embedding(t, dim, max_period=10000): + """ + Create sinusoidal timestep embeddings. + :param t: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an (N, D) Tensor of positional embeddings. + """ + # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py + half = dim // 2 + freqs = torch.exp( + -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half + ).to(device=t.device) + args = t[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + return embedding + + def forward(self, t): + t_freq = self.timestep_embedding(t, self.frequency_embedding_size) + t_emb = self.mlp(t_freq) + return t_emb + + +class PerturbationEmbedder(nn.Module): + """ + Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance. + """ + def __init__(self, num_classes, hidden_size, dropout_prob): + super().__init__() + use_cfg_embedding = dropout_prob > 0 + self.embedding_table = nn.Embedding(num_classes + use_cfg_embedding, hidden_size) + self.num_classes = num_classes + self.dropout_prob = dropout_prob + + def token_drop(self, labels, force_drop_ids=None): + """ + Drops labels to enable classifier-free guidance. + """ + if force_drop_ids is None: + drop_ids = torch.rand(labels.shape[0], device=labels.device) < self.dropout_prob + else: + drop_ids = force_drop_ids == 1 + labels = torch.where(drop_ids, self.num_classes, labels) + return labels + + def forward(self, labels, train=True, force_drop_ids=None): + use_dropout = self.dropout_prob > 0 + if (train and use_dropout) or (force_drop_ids is not None): + labels = self.token_drop(labels, force_drop_ids) + embeddings = self.embedding_table(labels) + return embeddings + +class CrossAttentionModule(nn.Module): + def __init__(self, embed_dim, num_heads, dropout=0.0): + super().__init__() + self.attn = nn.MultiheadAttention(embed_dim=embed_dim, + num_heads=num_heads, + dropout=dropout, + batch_first=True) # using (batch, seq, dim) + self.norm = nn.LayerNorm(embed_dim) + self.ff = nn.Sequential( + nn.Linear(embed_dim, embed_dim * 4), + nn.GELU(), + nn.Linear(embed_dim * 4, embed_dim), + nn.Dropout(dropout), + ) + + def forward(self, x1, x2, attn_mask=None, key_padding_mask=None): + # x1: (b, l1, dim), x2: (b, l2, dim) + residual = x1 + attn_out, attn_weights = self.attn(query=x1, key=x2, value=x2, + attn_mask=attn_mask, + key_padding_mask=key_padding_mask) + x = self.norm(attn_out + residual) + + # add FFN + residual2 = x + x = self.ff(x) + x = self.norm(x + residual2) + + return x, attn_weights + + +################################################################################# +# Core DiT Model # +################################################################################# + +class DiTBlock(nn.Module): + """ + A DiT block with adaptive layer norm zero (adaLN-Zero) conditioning. + """ + def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, **block_kwargs): + super().__init__() + self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.attn = Attention(hidden_size, num_heads=num_heads, qkv_bias=True, **block_kwargs) + self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + mlp_hidden_dim = int(hidden_size * mlp_ratio) + approx_gelu = lambda: nn.GELU(approximate="tanh") + self.mlp = Mlp(in_features=hidden_size, hidden_features=mlp_hidden_dim, act_layer=approx_gelu, drop=0) + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(hidden_size, 6 * hidden_size, bias=True) + ) + + def forward(self, x, c): + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(6, dim=1) + x = x + gate_msa.unsqueeze(1) * self.attn(modulate(self.norm1(x), shift_msa, scale_msa)) + x = x + gate_mlp.unsqueeze(1) * self.mlp(modulate(self.norm2(x), shift_mlp, scale_mlp)) + return x + +class HierarchicalBlock(nn.Module): + def __init__(self,d_model,nhead,dropout): + super().__init__() + self.cell1_block = DiTBlock(d_model,nhead,dropout) + self.cell2_block = DiTBlock(d_model,nhead,dropout) + self.cross_attention = CrossAttentionModule(d_model,nhead,dropout) + + def forward(self,cell1,cell2,perturbation,t): + cell1 = self.cell1_block(cell1,perturbation+t) + cell2 = self.cell2_block(cell2,t) + cell1, _ = self.cross_attention(cell2,cell1) + + return cell1,cell2, + +class SymmetricBlock(nn.Module): + def __init__(self,d_model,nhead,dropout): + super().__init__() + # self.hierarchical_block1 = HierarchicalBlock(d_model,nhead,dropout) + # self.hierarchical_block2 = HierarchicalBlock(d_model,nhead,dropout) + self.hierarchical_block = HierarchicalBlock(d_model,nhead,dropout) + self.norm = nn.LayerNorm(d_model) + + def forward(self,cell1,cell2,perturbation,t): + # cell1_1,cell2_1 = self.hierarchical_block1(cell1,cell2,perturbation,t) + # cell2_2,cell1_2 = self.hierarchical_block2(cell2,cell1,-perturbation,t) + cell1_1,cell2_1 = self.hierarchical_block(cell1,cell2,perturbation,t) + cell2_2,cell1_2 = self.hierarchical_block(cell2,cell1,-perturbation,t) + cell1 = self.norm(cell1_1 + cell1_2) + cell2 = self.norm(cell2_1 + cell2_2) + return cell1,cell2 + +class Model(nn.Module): + def __init__( + self, + model_type: str = 'hierarchical', + ntoken: int = 512, + d_model: int = 512, + nhead: int = 8, + nlayers: int = 6 , + n_combination: int = 2, + nlayers_cls: int = 3, + d_perturbation: int = 512, + vocab: Any = None, + dropout: float = 0.5, + ): + super().__init__() + self.n_combination = n_combination + self.d_model = d_model + self.model_type = model_type + self.ntoken = ntoken + self.d_perturbation = d_perturbation + # self.p_embedder = PerturbationEmbedder(n_perturbation, d_model, dropout_prob=0) + self.p_embedder = nn.Sequential( + nn.Linear(d_perturbation,d_model), + nn.SiLU(), + nn.Linear(d_model,d_model), + ) + self.t_embedder = TimestepEmbedder(d_model) + self.blocks = nn.ModuleList([ + DiTBlock(d_model, nhead, dropout) for _ in range(nlayers) + ]) + + if model_type == 'hierarchical': + self.blocks = nn.ModuleList([ + SymmetricBlock(d_model, nhead, dropout) for _ in range(nlayers) + ]) + else: + self.blocks = nn.ModuleList([ + DiTBlock(d_model, nhead, dropout) for _ in range(nlayers) + ]) + self.perturbation_decoder = nn.Sequential( + nn.Linear(d_model,d_model*4), + nn.SiLU(), + nn.Linear(d_model*4,d_model), + ) + + + def forward(self,cell1,cell2,t,perturbation=None): + """_summary_ + + Args: + cell1 (tensor): (B,ntoken,d_model) for control cell + cell2 (tensor): (B,ntoken,d_model) for perturbed cell + t (long): (B,) when inference is a number + perturbation (long): (B,n_combination) + """ + t = self.t_embedder(t) # (N, D) + # pdb.set_trace() + if perturbation is not None: + perturbation_ = perturbation.reshape(-1,self.d_perturbation) + p = self.p_embedder(perturbation_) # (N, D) + # p = p.reshape(-1,self.n_combination,self.d_model) + # p = p.sum(dim=1) + c = t + p + else: + c = t + cells = torch.cat([cell1,cell2],dim=1) + for block in self.blocks: + if self.model_type == 'hierarchical': + cell1,cell2 = block(cell1,cell2,p,t) + else: + cells = block(cells,c) + if self.model_type != 'hierarchical': + cells = cells.reshape(-1,2,self.ntoken,self.d_model) + cell1 = cells[:,0,:,:] + cell2 = cells[:,1,:,:] + p = self.perturbation_decoder((cell1 + cell2).mean(dim=1)) + # p = p.reshape(-1,self.n_combination) + + return cell1, cell2, p + + + +class TimedTransformer(nn.Module): + def __init__( + self, + model_type: str = 'hierarchical', + ntoken: int = 512, + d_model: int = 512, + nhead: int = 8, + nlayers: int = 8 , + n_combination: int = 2, + nlayers_cls: int = 3, + d_perturbation: int = 512, + vocab: Any = None, + dropout: float = 0.5, + ): + super().__init__() + self.n_combination = n_combination + self.d_model = d_model + self.model_type = model_type + self.ntoken = ntoken + # self.p_embedder = PerturbationEmbedder(n_perturbation, d_model, dropout_prob=0) + self.t_embedder = TimestepEmbedder(d_model) + self.blocks = nn.ModuleList([ + DiTBlock(d_model, nhead, dropout) for _ in range(nlayers) + ]) + + + + def forward(self,cell1,t): + """_summary_ + + Args: + cell1 (tensor): (B,ntoken,d_model) for control cell + cell2 (tensor): (B,ntoken,d_model) for perturbed cell + t (long): (B,) when inference is a number + perturbation (long): (B,n_combination) + """ + t = self.t_embedder(t) # (N, D) + c = t + cells = cell1 + for block in self.blocks: + cells = block(cells,c) + return cells + + +# class WrappedModel(ModelWrapper): +# def __init__(self, model, mode="noisy_y"): +# super().__init__(model) +# assert mode in ["noisy_y", "noisy_p"] +# self.mode = mode + +# def forward(self, x: torch.Tensor, t: torch.Tensor, x_t: torch.Tensor, condition_vec: torch.Tensor, mode=None): +# """ +# x: x_0 +# x_t: path sample at time t +# t: time step +# condition_vec: condition vector +# """ +# if mode is not None: +# self.mode = mode +# if self.mode == "noisy_y": +# _, predicted_x_t, _ = self.model(x, x_t, t, condition_vec) +# return (x, predicted_x_t, condition_vec) +# elif self.mode == "noisy_p": +# _, _, predicted_p_t = self.model(x, x_t, t, condition_vec) +# return (x, x_t, predicted_p_t) +# else: +# raise ValueError(f"Unsupported mode: {self.mode}") + +class WrappedModel(ModelWrapper): + def __init__(self, model, mode="noisy_y"): + super().__init__(model) + assert mode in ["noisy_y", "noisy_p"] + self.mode = mode + self.model = model + + def change_mode(self, mode): + self.mode = mode + + def forward(self, x: torch.Tensor, t: torch.Tensor, **extras): + if t.ndim == 0: + t = t.expand(x.shape[0]).to(x.device) + + if self.mode == "noisy_y": + condition_vec = extras["condition_vec"] + x_0 = extras["x_0"] + _, predicted_x_t, _ = self.model(x_0, x, t, condition_vec) + return predicted_x_t + elif self.mode == "noisy_p": + target_y = extras["target_y"] + x_0 = extras["x_0"] + _, _, predicted_p_t = self.model(x_0, target_y, t, x) + return predicted_p_t + else: + raise ValueError(f"Unsupported mode: {self.mode}") + +if __name__ == '__main__': + batch_size = 128 + model = Model(model_type='hs',ntoken=512).cuda() + cell1 = torch.randn(batch_size, 1024,512).cuda() + cell2 = torch.randn(batch_size, 1024,512).cuda() + t = torch.randint(0, 1000, (batch_size,)).cuda() + # p = torch.randint(0,2,(5,2)).cuda() + p = torch.randn(batch_size,512).cuda() + model = nn.DataParallel(model,device_ids=[0,1,2,3,4,5,6,7]) + + from tqdm import tqdm + for i in tqdm(range(5000)): + out = model(cell1,cell2, t,p) + out[0].sum().backward() + pdb.set_trace() + print(out.shape) + + # from accelerate import Accelerator + # import time + + # accelerator = Accelerator() + # batch_size = 128 + # model = Model(model_type='hs', ntoken=512) + # cell1 = torch.randn(batch_size, 1024, 512) + # cell2 = torch.randn(batch_size, 1024, 512) + # t = torch.randint(0, 1000, (batch_size,)) + # p = torch.randn(batch_size, 512) + + # # 用 accelerator.prepare 包装 + # model, cell1, cell2, t, p = accelerator.prepare(model, cell1, cell2, t, p) + + # from tqdm import tqdm + # start = time.time() + # for i in tqdm(range(5000)): + # out = model(cell1, cell2, t, p) + # loss = out[0].sum() + out[1].sum() + out[2].sum() + # accelerator.backward(loss) + # end = time.time() + # print(f"Total time: {end - start:.2f} seconds") + # import pdb; pdb.set_trace() + # print(out[0].shape) \ No newline at end of file diff --git a/transfer/code/ori_scDFM/src/models/perturbation/moduls.py b/transfer/code/ori_scDFM/src/models/perturbation/moduls.py new file mode 100644 index 0000000000000000000000000000000000000000..660c8de1134cc22cf78cb67b0a74e98ee7d69670 --- /dev/null +++ b/transfer/code/ori_scDFM/src/models/perturbation/moduls.py @@ -0,0 +1,136 @@ +import torch.nn as nn +from typing import Optional +from torch import nn, Tensor +import pdb +class CategoryValueEncoder(nn.Module): + def __init__( + self, + num_embeddings: int, + embedding_dim: int, + padding_idx: Optional[int] = None, + ): + super().__init__() + self.embedding = nn.Embedding( + num_embeddings, embedding_dim, padding_idx=padding_idx + ) + self.enc_norm = nn.LayerNorm(embedding_dim) + + def forward(self, x: Tensor) -> Tensor: + x = x.long() + x = self.embedding(x) # (batch, seq_len, embsize) + x = self.enc_norm(x) + return x + +class GeneEncoder(nn.Module): + def __init__( + self, + num_embeddings: int, + embedding_dim: int, + padding_idx: Optional[int] = None, + ): + super().__init__() + self.embedding = nn.Embedding( + num_embeddings, embedding_dim, padding_idx=padding_idx + ) + self.enc_norm = nn.LayerNorm(embedding_dim) + + def forward(self, x: Tensor) -> Tensor: + x = self.embedding(x) # (batch, seq_len, embsize) + x = self.enc_norm(x) + return x + +class PerturbationEmbedding(nn.Module): + def __init__(self, num_perturbations, emb_dim, max_comb_len=2, fusion_method='mlp', output_matrix=False): + super().__init__() + self.embedding = nn.Embedding(num_perturbations, emb_dim) + self.fusion_method = fusion_method + self.max_comb_len = max_comb_len + self.output_matrix = output_matrix + self.output_dim = emb_dim if not output_matrix else emb_dim * emb_dim + + if fusion_method == 'mlp': + self.fusion = nn.Sequential( + nn.Linear(emb_dim * max_comb_len, emb_dim * 2), + nn.ReLU(), + nn.Linear(emb_dim * 2, self.output_dim) + ) + elif fusion_method == 'sum': + self.fusion = None + else: + raise ValueError(f"Unsupported fusion method: {fusion_method}") + + def forward(self, ids): + emb = self.embedding(ids) # [B, C, D] + + if self.fusion_method == 'mlp': + emb = emb.view(emb.size(0), -1) # [B, C*D] + fused = self.fusion(emb) # [B, D] or [B, D*D] + + if self.output_matrix: + B = fused.size(0) + D = int(self.output_dim ** 0.5) + return fused.view(B, D, D) # [B, D, D] + else: + return fused + + elif self.fusion_method == 'sum': + out = emb.sum(dim=1) # [B, D] + if self.output_matrix: + B = out.size(0) + D = out.size(1) + return out.view(B, D, 1).expand(B, D, D) # dummy expansion + return out + +# class PerturbationEmbedding(nn.Module): +# def __init__(self, num_perturbations, emb_dim, max_comb_len=2, fusion_method='mlp'): +# """ +# Args: +# num_perturbations: 词表大小 +# emb_dim: 嵌入维度 +# max_comb_len: 每个 condition 最多包含的 token 数量（如 drug1, drug2） +# fusion_method: 'mlp' 或 'sum' +# """ +# super().__init__() +# self.embedding = nn.Embedding(num_perturbations, emb_dim) +# self.fusion_method = fusion_method +# self.max_comb_len = max_comb_len + +# if fusion_method == 'mlp': +# self.fusion = nn.Sequential( +# nn.Linear(emb_dim * max_comb_len, emb_dim), +# nn.ReLU(), +# nn.Linear(emb_dim, emb_dim) +# ) +# elif fusion_method == 'sum': +# self.fusion = None +# else: +# raise ValueError(f"Unsupported fusion method: {fusion_method}") +# def init_weights(self, m): +# if isinstance(m, nn.Linear): +# nn.init.xavier_uniform_(m.weight) +# nn.init.zeros_(m.bias) + +# def initialize_weights(self): +# self.apply(self.init_weights) + +# def forward(self, ids): +# """ +# Args: +# ids: LongTensor of shape [B, max_comb_len] +# Returns: +# fused: Tensor of shape [B, emb_dim] +# """ + +# emb = self.embedding(ids) # [B, C, D] + +# if self.fusion_method == 'mlp': +# emb = emb.view(emb.size(0), -1) # [B, C*D] +# return self.fusion(emb) # [B, D] + +# elif self.fusion_method == 'sum': +# if emb.dim() == 2: +# return emb.sum(dim=0) # [B, D] +# elif emb.dim() == 3: +# return emb.sum(dim=1) # [B, C, D] +# else: +# raise ValueError(f"Unsupported dimension: {ids.dim()}") \ No newline at end of file diff --git a/transfer/code/ori_scDFM/src/models/perturbation/timed_transformer.py b/transfer/code/ori_scDFM/src/models/perturbation/timed_transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..5f43b4deee5291b99144d6538043268df5bb4e3e --- /dev/null +++ b/transfer/code/ori_scDFM/src/models/perturbation/timed_transformer.py @@ -0,0 +1,187 @@ +import torch +import torch.nn as nn +import numpy as np +import math + +#### Adapted from Diffusion Transformer codebase https://github.com/facebookresearch/DiT ### + +def modulate(x, shift, scale): + return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + + +# TODO: How to embed perturbation? +# Since they are mainly labels, we could easily embed them via a trained embedding layer + +class DiTAttention(nn.Module): + def __init__(self, cfg): + super().__init__() + self.t_embedder = TimestepEmbedder(cfg.hidden_size) + self.trans_conv = nn.ModuleList([ + DiTBlock(cfg) for _ in range(cfg.num_layers) + ]) + + def forward(self, t, x, mask=None): + t = self.t_embedder(t) + for block in self.trans_conv: + x = block(x, t, mask) + return x + + + +class TimestepEmbedder(nn.Module): + """ + Embeds scalar timesteps into vector representations. + """ + def __init__(self, hidden_size, frequency_embedding_size=256): + super().__init__() + self.mlp = nn.Sequential( + nn.Linear(frequency_embedding_size, hidden_size, bias=True), + nn.SiLU(), + nn.Linear(hidden_size, hidden_size, bias=True), + ) + self.frequency_embedding_size = frequency_embedding_size + + @staticmethod + def timestep_embedding(t, dim, max_period=10000): + """ + Create sinusoidal timestep embeddings. + :param t: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an (N, D) Tensor of positional embeddings. + """ + # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py + half = dim // 2 + freqs = torch.exp( + -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half + ).to(device=t.device) + args = t[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + return embedding + + def forward(self, t): + t_freq = self.timestep_embedding(t, self.frequency_embedding_size) + t_emb = self.mlp(t_freq) + return t_emb + + + +class Mlp(nn.Module): + """ MLP as used in Vision Transformer, MLP-Mixer and related networks + + NOTE: When use_conv=True, expects 2D NCHW tensors, otherwise N*C expected. + """ + def __init__( + self, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.GELU, + norm_layer=None, + bias=True, + drop=0., + ): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + linear_layer = nn.Linear + + self.fc1 = linear_layer(in_features, hidden_features, bias=bias) + self.act = act_layer() + self.drop1 = nn.Dropout(drop) + self.norm = norm_layer(hidden_features) if norm_layer is not None else nn.Identity() + self.fc2 = linear_layer(hidden_features, out_features, bias=bias) + self.drop2 = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop1(x) + x = self.norm(x) + x = self.fc2(x) + x = self.drop2(x) + return x + +class DiTBlock(nn.Module): + """ + A DiT block with adaptive layer norm zero (adaLN-Zero) conditioning. + """ + def __init__(self, cfg): + super().__init__() + hidden_size = cfg.model.hidden_channels + cfg.model.edge_dim + num_heads = cfg.model.trans_num_heads + dropout = cfg.model.trans_dropout + mlp_ratio= 4.0 + + self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.attn = BertSelfAttention(num_attention_heads=num_heads, hidden_size=hidden_size, dropout_prob=dropout) + self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + mlp_hidden_dim = int(hidden_size * mlp_ratio) + approx_gelu = lambda: nn.GELU(approximate="tanh") + self.mlp = Mlp(in_features=hidden_size, hidden_features=mlp_hidden_dim, act_layer=approx_gelu, drop=dropout) + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(hidden_size, 6 * hidden_size, bias=True) + ) + + def forward(self, x, c, mask): + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(6, dim=-1) + x = x + gate_msa * self.attn(modulate(self.norm1(x), shift_msa, scale_msa), mask) + x = x + gate_mlp * self.mlp(modulate(self.norm2(x), shift_mlp, scale_mlp)) + return x + + +class BertSelfAttention(nn.Module): + def __init__(self, num_attention_heads, hidden_size, dropout_prob): + super().__init__() + self.num_attention_heads = num_attention_heads + self.attention_head_size = int(hidden_size / num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(hidden_size, hidden_size) + self.key = nn.Linear(hidden_size, hidden_size) + self.value = nn.Linear(hidden_size, hidden_size) + self.dropout = nn.Dropout(dropout_prob) + + def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor: + new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) + x = x.view(new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: None, + ): + mixed_query_layer = self.query(hidden_states) + + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + query_layer = self.transpose_for_scores(mixed_query_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + + if attention_mask is not None: + # our attention mask is 1 for positions we want to attend and 0 for ones we want to ignore + # let the mask have -10000.0 where we want to ignore + attention_mask = torch.einsum('bi,bj->bij', attention_mask, attention_mask).unsqueeze(1) + attention_mask = attention_mask.masked_fill(attention_mask == 0, -10000.0) + attention_scores = attention_scores + attention_mask + + # Normalize the attention scores to probabilities. + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + context_layer = torch.matmul(attention_probs, value_layer) + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(new_context_layer_shape) + + return context_layer \ No newline at end of file diff --git a/transfer/code/ori_scDFM/src/models/scGPT/dsbn.py b/transfer/code/ori_scDFM/src/models/scGPT/dsbn.py new file mode 100644 index 0000000000000000000000000000000000000000..9b904f30bb317a46457f5d35c0974ebb2880163d --- /dev/null +++ b/transfer/code/ori_scDFM/src/models/scGPT/dsbn.py @@ -0,0 +1,82 @@ +from typing import Optional, Tuple + +import torch +from torch import nn + +# The code is modified from https://github.com/wgchang/DSBN/blob/master/model/dsbn.py +class _DomainSpecificBatchNorm(nn.Module): + _version = 2 + + def __init__( + self, + num_features: int, + num_domains: int, + eps: float = 1e-5, + momentum: float = 0.1, + affine: bool = True, + track_running_stats: bool = True, + ): + super(_DomainSpecificBatchNorm, self).__init__() + self._cur_domain = None + self.num_domains = num_domains + self.bns = nn.ModuleList( + [ + self.bn_handle(num_features, eps, momentum, affine, track_running_stats) + for _ in range(num_domains) + ] + ) + + @property + def bn_handle(self) -> nn.Module: + raise NotImplementedError + + @property + def cur_domain(self) -> Optional[int]: + return self._cur_domain + + @cur_domain.setter + def cur_domain(self, domain_label: int): + self._cur_domain = domain_label + + def reset_running_stats(self): + for bn in self.bns: + bn.reset_running_stats() + + def reset_parameters(self): + for bn in self.bns: + bn.reset_parameters() + + def _check_input_dim(self, input: torch.Tensor): + raise NotImplementedError + + def forward(self, x: torch.Tensor, domain_label: int) -> torch.Tensor: + self._check_input_dim(x) + if domain_label >= self.num_domains: + raise ValueError( + f"Domain label {domain_label} exceeds the number of domains {self.num_domains}" + ) + bn = self.bns[domain_label] + self.cur_domain = domain_label + return bn(x) + + +class DomainSpecificBatchNorm1d(_DomainSpecificBatchNorm): + @property + def bn_handle(self) -> nn.Module: + return nn.BatchNorm1d + + def _check_input_dim(self, input: torch.Tensor): + if input.dim() > 3: + raise ValueError( + "expected at most 3D input (got {}D input)".format(input.dim()) + ) + + +class DomainSpecificBatchNorm2d(_DomainSpecificBatchNorm): + @property + def bn_handle(self) -> nn.Module: + return nn.BatchNorm2d + + def _check_input_dim(self, input: torch.Tensor): + if input.dim() != 4: + raise ValueError("expected 4D input (got {}D input)".format(input.dim())) \ No newline at end of file diff --git a/transfer/code/ori_scDFM/src/models/scGPT/model.py b/transfer/code/ori_scDFM/src/models/scGPT/model.py new file mode 100644 index 0000000000000000000000000000000000000000..e71c38dfe7881366ab1dc539d2c9921d5f17fcf1 --- /dev/null +++ b/transfer/code/ori_scDFM/src/models/scGPT/model.py @@ -0,0 +1,1393 @@ +import gc +import math +from typing import Dict, Mapping, Optional, Tuple, Any, Union +import pdb +import torch +import numpy as np +from torch import nn, Tensor +import torch.distributed as dist +import torch.nn.functional as F +from torch.nn import TransformerEncoder, TransformerEncoderLayer +from torch.distributions import Bernoulli +from tqdm import trange +import torch +from torch.autograd import Function +from .dsbn import DomainSpecificBatchNorm1d + + +class TransformerModel(nn.Module): + def __init__( + self, + ntoken: int, + d_model: int, + nhead: int, + d_hid: int, + nlayers: int, + nlayers_cls: int = 3, + n_cls: int = 1, + vocab: Any = None, + dropout: float = 0.5, + pad_token: str = "", + pad_value: int = 0, + do_mvc: bool = False, + do_dab: bool = False, + use_batch_labels: bool = False, + num_batch_labels: Optional[int] = None, + domain_spec_batchnorm: Union[bool, str] = False, + input_emb_style: str = "continuous", + n_input_bins: Optional[int] = None, + cell_emb_style: str = "avg-pool", + mvc_decoder_style: str = "inner product", + ecs_threshold: float = 0.3, + explicit_zero_prob: bool = False, + use_fast_transformer: bool = False, + fast_transformer_backend: str = "flash", + pre_norm: bool = False, + bin_output: bool = False, + use_down_up_transformer: bool = False, + n_top_genes: int = 2048, + bottleneck_dim: int = 128, + ): + super().__init__() + self.model_type = "Transformer" + self.d_model = d_model + self.do_dab = do_dab + self.ecs_threshold = ecs_threshold + self.use_batch_labels = use_batch_labels + self.domain_spec_batchnorm = domain_spec_batchnorm + self.input_emb_style = input_emb_style + self.cell_emb_style = cell_emb_style + self.explicit_zero_prob = explicit_zero_prob + self.norm_scheme = "pre" if pre_norm else "post" + self.bin_output = bin_output + self.n_top_genes = n_top_genes + self.use_down_up_transformer = use_down_up_transformer + if self.input_emb_style not in ["category", "continuous", "scaling"]: + raise ValueError( + f"input_emb_style should be one of category, continuous, scaling, " + f"got {input_emb_style}" + ) + if cell_emb_style not in ["cls", "avg-pool", "w-pool"]: + raise ValueError(f"Unknown cell_emb_style: {cell_emb_style}") + if use_fast_transformer: + if not flash_attn_available: + warnings.warn( + "flash-attn is not installed, using pytorch transformer instead. " + "Set use_fast_transformer=False to avoid this warning. " + "Installing flash-attn is highly recommended." + ) + use_fast_transformer = False + self.use_fast_transformer = use_fast_transformer + + # TODO: add dropout in the GeneEncoder + + self.encoder = GeneEncoder(ntoken, d_model, padding_idx=vocab[pad_token]) + + # Value Encoder, NOTE: the scaling style is also handled in _encode method + if input_emb_style == "continuous": + self.value_encoder = ContinuousValueEncoder(d_model, dropout) + elif input_emb_style == "category": + assert n_input_bins > 0 + self.value_encoder = CategoryValueEncoder( + n_input_bins, d_model, padding_idx=pad_value + ) + else: + self.value_encoder = nn.Identity() # nn.Softmax(dim=1) + # TODO: consider row-wise normalization or softmax + # TODO: Correct handle the mask_value when using scaling + if self.bin_output and n_input_bins > 0: + self.value_decode = CategoryValueDecoder(d_model, n_input_bins, use_batch_labels=use_batch_labels,) + else: + self.value_decode = nn.Identity() + + # Batch Encoder + if use_batch_labels: + self.batch_encoder = BatchLabelEncoder(num_batch_labels, d_model) + + if domain_spec_batchnorm is True or domain_spec_batchnorm == "dsbn": + use_affine = True if domain_spec_batchnorm == "do_affine" else False + print(f"Use domain specific batchnorm with affine={use_affine}") + self.dsbn = DomainSpecificBatchNorm1d( + d_model, num_batch_labels, eps=6.1e-5, affine=use_affine + ) + elif domain_spec_batchnorm == "batchnorm": + print("Using simple batchnorm instead of domain specific batchnorm") + self.bn = nn.BatchNorm1d(d_model, eps=6.1e-5) + + if use_fast_transformer: + if fast_transformer_backend == "linear": + self.transformer_encoder = FastTransformerEncoderWrapper( + d_model, nhead, d_hid, nlayers, dropout + ) + elif fast_transformer_backend == "flash": + encoder_layers = FlashTransformerEncoderLayer( + d_model, + nhead, + d_hid, + dropout, + batch_first=True, + norm_scheme=self.norm_scheme, + ) + self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers) + elif self.use_down_up_transformer: + encoder_layers = TransformerEncoderLayer( + d_model, nhead, d_hid, dropout, batch_first=True + ) + self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers) + + # self.attention_pooling = AttentionPooling(n=bottleneck_dim, d_model=d_model) + # self.cross_attention_module = nn.ModuleList([CrossAttentionModule(d_model, nhead, dropout) for _ in range(nlayers)]) + self.MLP_pooling = nn.Sequential( + nn.Linear(n_top_genes, d_model), + nn.ReLU(), + nn.Linear(d_model, bottleneck_dim),) + self.ln1 = nn.LayerNorm(n_top_genes,) + self.ln2 = nn.LayerNorm(bottleneck_dim,) + self.ln3 = nn.LayerNorm(bottleneck_dim,) + self.batch_norm = nn.BatchNorm1d(bottleneck_dim, momentum=0.01, eps=0.001) + + self.cross_x_in = nn.Parameter(torch.randn(512, 512)) + self.encoder_cross_x = GeneEncoder(ntoken, d_model, padding_idx=vocab[pad_token]) + self.feature_MLP = nn.Sequential( + nn.Linear(d_model, d_model), + nn.ReLU(), + nn.Linear(d_model, d_model),) + self.MLP_upsampling = nn.Sequential( + nn.Linear(bottleneck_dim, d_model), + nn.ReLU(), + nn.Linear(d_model, n_top_genes),) + else: + encoder_layers = TransformerEncoderLayer( + d_model, nhead, d_hid, dropout, batch_first=True + ) + self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers) + + self.decoder = ExprDecoder( + d_model, + explicit_zero_prob=explicit_zero_prob, + use_batch_labels=use_batch_labels, + ) + self.cls_decoder = ClsDecoder(d_model, n_cls, nlayers=nlayers_cls) + if do_mvc: + self.mvc_decoder = MVCDecoder( + d_model, + arch_style=mvc_decoder_style, + explicit_zero_prob=explicit_zero_prob, + use_batch_labels=use_batch_labels, + ) + + if do_dab: + self.grad_reverse_discriminator = AdversarialDiscriminator( + d_model, + n_cls=num_batch_labels, + reverse_grad=True, + ) + + self.sim = Similarity(temp=0.5) # TODO: auto set temp + self.creterion_cce = nn.CrossEntropyLoss() + + self.init_weights() + + def init_weights(self) -> None: + initrange = 0.1 + # TODO: check if this initialization is helpful and shall we apply to all? + self.encoder.embedding.weight.data.uniform_(-initrange, initrange) + + def bin_decode(self, x: Tensor) -> Tensor: + if self.bin_output: + return self.value_decode(x) + else: + return x + + def _decode(self, x: Tensor) -> Tensor: + + x = self.MLP_upsampling(x).transpose(-1,-2) + bin_output = self.value_decode(x) + return bin_output + + def _encode( + self, + src: Tensor, + values: Tensor, + src_key_padding_mask: Tensor, + batch_labels: Optional[Tensor] = None, # (batch,) + ) -> Tensor: + self._check_batch_labels(batch_labels) + + src = self.encoder(src) # (batch, seq_len, embsize) + self.cur_gene_token_embs = src + + values = self.value_encoder(values) # (batch, seq_len, embsize) + if self.input_emb_style == "scaling": + values = values.unsqueeze(2) + total_embs = src * values + else: + total_embs = src + values + + if getattr(self, "dsbn", None) is not None: + batch_label = int(batch_labels[0].item()) + total_embs = self.dsbn(total_embs.permute(0, 2, 1), batch_label).permute( + 0, 2, 1 + ) # the batch norm always works on dim 1 + elif getattr(self, "bn", None) is not None: + total_embs = self.bn(total_embs.permute(0, 2, 1)).permute(0, 2, 1) + + output = self.transformer_encoder( + total_embs, src_key_padding_mask=src_key_padding_mask + ) + + if self.use_down_up_transformer: + + output = self.ln1(output.transpose(-1,-2)) #[B, D, L] ln on L + output = self.MLP_pooling(output) #[B, D, L] -> [B, D, n] + output = self.ln2(output) # ln on n + + output = self.feature_MLP(output.transpose(-1,-2)) # [B, n, D] -> [B, n, D] + output = self.ln3(output.transpose(-1,-2)) # [B, D, n] ln again on n + output = torch.clip(output, min=-1, max=1) + # [B, L, D] + ######## + # output = self.attention_pooling(output) + + # output = self.feature_MLP(output.transpose(-1,-2)) # [B, n, D] -> [B, n, D] + # output = self.ln3(output.transpose(-1,-2)) # [B, D, n] ln again on n + # output = self.MLP_upsampling(output).transpose(-1,-2) + + return output # (batch, seq_len, embsize) + + def _get_cell_emb_from_layer( + self, layer_output: Tensor, weights: Tensor = None + ) -> Tensor: + """ + Args: + layer_output(:obj:`Tensor`): shape (batch, seq_len, embsize) + weights(:obj:`Tensor`): shape (batch, seq_len), optional and only used + when :attr:`self.cell_emb_style` is "w-pool". + + Returns: + :obj:`Tensor`: shape (batch, embsize) + """ + if self.cell_emb_style == "cls": + cell_emb = layer_output[:, 0, :] # (batch, embsize) + elif self.cell_emb_style == "avg-pool": + cell_emb = torch.mean(layer_output, dim=1) + elif self.cell_emb_style == "w-pool": + if weights is None: + raise ValueError("weights is required when cell_emb_style is w-pool") + if weights.dim() != 2: + raise ValueError("weights should be 2D") + cell_emb = torch.sum(layer_output * weights.unsqueeze(2), dim=1) + cell_emb = F.normalize(cell_emb, p=2, dim=1) # (batch, embsize) + + return cell_emb + + def _check_batch_labels(self, batch_labels: Tensor) -> None: + if self.use_batch_labels or self.domain_spec_batchnorm: + assert batch_labels is not None + elif batch_labels is not None: + raise ValueError( + "batch_labels should only be provided when `self.use_batch_labels`" + " or `self.domain_spec_batchnorm` is True" + ) + + def generate( + self, + cell_emb: Tensor, + src: Tensor, + values: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + gen_iters: int = 1, + batch_labels: Optional[Tensor] = None, # (batch,) + ) -> Tensor: + """ + Args: + cell_emb(:obj:`Tensor`): shape (batch, embsize) + src(:obj:`Tensor`): shape (batch, seq_len) + values(:obj:`Tensor`): shape (batch, seq_len), optional + src_key_padding_mask(:obj:`Tensor`): shape (batch, seq_len), optional + gen_iters(:obj:`int`): number of generation iterations + batch_labels(:obj:`Tensor`): shape (batch,), optional + """ + # TODO: should have a tag indicate the generation mode + # TODO: if gen_iters > 1, should have a tag indicate the current iteration + try: + self._check_batch_labels(batch_labels) + except: + import warnings + + warnings.warn( + "batch_labels is required but not provided, using zeros instead" + ) + batch_labels = torch.zeros( + cell_emb.shape[0], dtype=torch.long, device=cell_emb.device + ) + + src = self.encoder(src) # (batch, seq_len, embsize) + + if values is not None: + values = self.value_encoder(values) # (batch, seq_len, embsize) + if self.input_emb_style == "scaling": + values = values.unsqueeze(2) + total_embs = src * values + else: + total_embs = src + values + else: + total_embs = src + + if getattr(self, "dsbn", None) is not None: + batch_label = int(batch_labels[0].item()) + total_embs = self.dsbn(total_embs.permute(0, 2, 1), batch_label).permute( + 0, 2, 1 + ) # the batch norm always works on dim 1 + elif getattr(self, "bn", None) is not None: + total_embs = self.bn(total_embs.permute(0, 2, 1)).permute(0, 2, 1) + + total_embs[:, 0, :] = cell_emb + + if src_key_padding_mask is None: + src_key_padding_mask = torch.zeros( + total_embs.shape[:2], dtype=torch.bool, device=total_embs.device + ) + transformer_output = self.transformer_encoder( + total_embs, src_key_padding_mask=src_key_padding_mask + ) + + if self.use_batch_labels: + batch_emb = self.batch_encoder(batch_labels) # (batch, embsize) + mlm_output = self.decoder( + transformer_output + if not self.use_batch_labels + else torch.cat( + [ + transformer_output, + batch_emb.unsqueeze(1).repeat(1, transformer_output.shape[1], 1), + ], + dim=2, + ), + # else transformer_output + batch_emb.unsqueeze(1), + ) + output = mlm_output["pred"] # (batch, seq_len) + + return output # (batch, seq_len) + + def forward( + self, + src: Tensor, + values: Tensor, + src_key_padding_mask: Tensor, + condition: Optional[Tensor] = None, + batch_labels: Optional[Tensor] = None, + CLS: bool = False, + CCE: bool = False, + MVC: bool = False, + ECS: bool = False, + do_sample: bool = False, + ) -> Mapping[str, Tensor]: + """ + Args: + src (:obj:`Tensor`): token ids, shape [batch_size, seq_len] + values (:obj:`Tensor`): token values, shape [batch_size, seq_len] + src_key_padding_mask (:obj:`Tensor`): mask for src, shape [batch_size, + seq_len] + batch_labels (:obj:`Tensor`): batch labels, shape [batch_size] + CLS (:obj:`bool`): if True, return the celltype classification objective + (CLS) output + CCE (:obj:`bool`): if True, return the contrastive cell embedding objective + (CCE) output + MVC (:obj:`bool`): if True, return the masked value prediction for cell + embedding MVC output + ECS (:obj:`bool`): if True, return the elastic cell similarity objective + (ECS) output. + + Returns: + dict of output Tensors. + """ + transformer_output = self._encode( + src, values, src_key_padding_mask, batch_labels + ) + if self.use_down_up_transformer: + transformer_output = self.MLP_upsampling(transformer_output).transpose(-1,-2) + # transformer_output = self._decode(src,transformer_output) + if self.use_batch_labels: + batch_emb = self.batch_encoder(batch_labels) # (batch, embsize) + + output = {} + mlm_output = self.decoder( + transformer_output + if not self.use_batch_labels + else torch.cat( + [ + transformer_output, + batch_emb.unsqueeze(1).repeat(1, transformer_output.shape[1], 1), + ], + dim=2, + ), + # else transformer_output + batch_emb.unsqueeze(1), + ) + + if self.bin_output: + output["bin_output"] = self.value_decode( + transformer_output + if not self.use_batch_labels + else torch.cat( + [ + transformer_output, + batch_emb.unsqueeze(1).repeat(1, transformer_output.shape[1], 1), + ], + dim=2, + ), + ) + + if self.explicit_zero_prob and do_sample: + bernoulli = Bernoulli(probs=mlm_output["zero_probs"]) + output["mlm_output"] = bernoulli.sample() * mlm_output["pred"] + else: + output["mlm_output"] = mlm_output["pred"] # (batch, seq_len) + if self.explicit_zero_prob: + output["mlm_zero_probs"] = mlm_output["zero_probs"] + + cell_emb = self._get_cell_emb_from_layer(transformer_output, values) + output["cell_emb"] = cell_emb + + if CLS: + output["cls_output"] = self.cls_decoder(cell_emb) # (batch, n_cls) + if CCE: + cell1 = cell_emb + transformer_output2 = self._encode( + src, values, src_key_padding_mask, batch_labels + ) + cell2 = self._get_cell_emb_from_layer(transformer_output2) + + # Gather embeddings from all devices if distributed training + if dist.is_initialized() and self.training: + cls1_list = [ + torch.zeros_like(cell1) for _ in range(dist.get_world_size()) + ] + cls2_list = [ + torch.zeros_like(cell2) for _ in range(dist.get_world_size()) + ] + dist.all_gather(tensor_list=cls1_list, tensor=cell1.contiguous()) + dist.all_gather(tensor_list=cls2_list, tensor=cell2.contiguous()) + + # NOTE: all_gather results have no gradients, so replace the item + # of the current rank with the original tensor to keep gradients. + # See https://github.com/princeton-nlp/SimCSE/blob/main/simcse/models.py#L186 + cls1_list[dist.get_rank()] = cell1 + cls2_list[dist.get_rank()] = cell2 + + cell1 = torch.cat(cls1_list, dim=0) + cell2 = torch.cat(cls2_list, dim=0) + # TODO: should detach the second run cls2? Can have a try + cos_sim = self.sim(cell1.unsqueeze(1), cell2.unsqueeze(0)) # (batch, batch) + labels = torch.arange(cos_sim.size(0)).long().to(cell1.device) + output["loss_cce"] = self.creterion_cce(cos_sim, labels) + if MVC: + mvc_output = self.mvc_decoder( + cell_emb + if not self.use_batch_labels + else torch.cat([cell_emb, batch_emb], dim=1), + # else cell_emb + batch_emb, + self.cur_gene_token_embs, + ) + if self.explicit_zero_prob and do_sample: + bernoulli = Bernoulli(probs=mvc_output["zero_probs"]) + output["mvc_output"] = bernoulli.sample() * mvc_output["pred"] + else: + output["mvc_output"] = mvc_output["pred"] # (batch, seq_len) + if self.explicit_zero_prob: + output["mvc_zero_probs"] = mvc_output["zero_probs"] + if ECS: + # Here using customized cosine similarity instead of F.cosine_similarity + # to avoid the pytorch issue of similarity larger than 1.0, pytorch # 78064 + # normalize the embedding + cell_emb_normed = F.normalize(cell_emb, p=2, dim=1) + cos_sim = torch.mm(cell_emb_normed, cell_emb_normed.t()) # (batch, batch) + + # mask out diagnal elements + mask = torch.eye(cos_sim.size(0)).bool().to(cos_sim.device) + cos_sim = cos_sim.masked_fill(mask, 0.0) + # only optimize positive similarities + cos_sim = F.relu(cos_sim) + + output["loss_ecs"] = torch.mean(1 - (cos_sim - self.ecs_threshold) ** 2) + + + if self.do_dab: + output["dab_output"] = self.grad_reverse_discriminator(cell_emb) + + return output + + def bin_encode(self, src: Tensor, values: Tensor, src_key_padding_mask: Tensor, batch_labels: Optional[Tensor] = None) -> Tensor: + transformer_output = self._encode( + src, values, src_key_padding_mask, batch_labels + ) + return transformer_output + + def bin_decode(self, transformer_output: Tensor, batch_emb: Optional[Tensor] = None) -> Tensor: + if self.bin_output: + return self.value_decode( + transformer_output + if not self.use_batch_labels + else torch.cat( + [ + transformer_output, + batch_emb.unsqueeze(1).repeat(1, transformer_output.shape[1], 1), + ], + dim=2, + ), + ) + else: + raise ValueError("bin_output is not enabled") + + def encode_batch( + self, + src: Tensor, + values: Tensor, + src_key_padding_mask: Tensor, + batch_size: int, + batch_labels: Optional[Tensor] = None, + output_to_cpu: bool = True, + time_step: Optional[int] = None, + return_np: bool = False, + ) -> Tensor: + """ + Args: + src (Tensor): shape [N, seq_len] + values (Tensor): shape [N, seq_len] + src_key_padding_mask (Tensor): shape [N, seq_len] + batch_size (int): batch size for encoding + batch_labels (Tensor): shape [N, n_batch_labels] + output_to_cpu (bool): whether to move the output to cpu + time_step (int): the time step index in the transformer output to return. + The time step is along the second dimenstion. If None, return all. + return_np (bool): whether to return numpy array + + Returns: + output Tensor of shape [N, seq_len, embsize] + """ + N = src.size(0) + device = next(self.parameters()).device + + # initialize the output tensor + array_func = np.zeros if return_np else torch.zeros + float32_ = np.float32 if return_np else torch.float32 + shape = ( + (N, self.d_model) + if time_step is not None + else (N, src.size(1), self.d_model) + ) + outputs = array_func(shape, dtype=float32_) + + for i in trange(0, N, batch_size): + raw_output = self._encode( + src[i : i + batch_size].to(device), + values[i : i + batch_size].to(device), + src_key_padding_mask[i : i + batch_size].to(device), + batch_labels[i : i + batch_size].to(device) + if batch_labels is not None + else None, + ) + output = raw_output.detach() + if output_to_cpu: + output = output.cpu() + if return_np: + output = output.numpy() + if time_step is not None: + output = output[:, time_step, :] + outputs[i : i + batch_size] = output + + return outputs + + +def generate_square_subsequent_mask(sz: int) -> Tensor: + """Generates an upper-triangular matrix of -inf, with zeros on diag.""" + return torch.triu(torch.ones(sz, sz) * float("-inf"), diagonal=1) + + +class FastTransformerEncoderWrapper(nn.Module): + def __init__( + self, + d_model: int, + nhead: int, + d_hid: int, + nlayers: int, + dropout: float = 0.5, + ): + super().__init__() + self.fast_transformer_encoder = self.build_fast_transformer_encoder( + d_model, nhead, d_hid, nlayers, dropout + ) + + @staticmethod + def build_fast_transformer_encoder( + d_model: int, nhead: int, d_hid: int, nlayers: int, dropout: float + ) -> nn.Module: + from fast_transformers.builders import TransformerEncoderBuilder + + if d_model % nhead != 0: + raise ValueError( + f"d_model must be divisible by nhead, " + f"got d_model={d_model} and nhead={nhead}" + ) + builder = TransformerEncoderBuilder.from_kwargs( + n_layers=nlayers, + n_heads=nhead, + query_dimensions=d_model // nhead, + value_dimensions=d_model // nhead, + feed_forward_dimensions=d_hid, + attention_type="linear", + attention_dropout=dropout, + dropout=dropout, + activation="gelu", + ) + assert builder.attention_type == "linear" + return builder.get() + + @staticmethod + def build_length_mask( + src: Tensor, + src_key_padding_mask: torch.BoolTensor, + ) -> "LengthMask": + from fast_transformers.masking import LengthMask + + seq_len = src.shape[1] + num_paddings = src_key_padding_mask.sum(dim=1) + actual_seq_len = seq_len - num_paddings # (N,) + length_mask = LengthMask(actual_seq_len, max_len=seq_len, device=src.device) + + if src_key_padding_mask[length_mask.bool_matrix].sum() != 0: + raise ValueError( + "Found padding tokens in the middle of the sequence. " + "src_key_padding_mask and length_mask are not compatible." + ) + return length_mask + + def forward( + self, + src: Tensor, + src_key_padding_mask: torch.BoolTensor, + ) -> Tensor: + """ + Args: + src: Tensor, shape [N, seq_len, embsize] + src_key_padding_mask: Tensor, shape [N, seq_len] + + Returns: + output Tensor of shape [N, seq_len, embsize] + """ + if src_key_padding_mask.shape != src.shape[:2]: + raise ValueError( + f"src_key_padding_mask shape {src_key_padding_mask.shape} " + f"does not match first two dims of src shape {src.shape[:2]}" + ) + + if src_key_padding_mask.dtype != torch.bool: + raise ValueError( + f"src_key_padding_mask needs to be of type torch.bool, " + f"got {src_key_padding_mask.dtype}" + ) + + length_mask = self.build_length_mask(src, src_key_padding_mask) + output = self.fast_transformer_encoder(src, length_mask=length_mask) + return output + +class AttentionPooling(nn.Module): + def __init__(self, n, d_model): + super().__init__() + self.n = n + self.d_model = d_model + self.query = nn.Parameter(torch.randn(n, d_model)) # learnable query + self.K = nn.Linear(d_model, d_model) + self.V = nn.Linear(d_model, d_model) + self.ln = nn.LayerNorm(d_model) + print(f"using attention pooling") + self.ff = nn.Sequential( + nn.Linear(d_model, d_model * 4), + nn.GELU(), + nn.Linear(d_model * 4, d_model), + nn.Dropout(0.5), + ) + + def forward(self, hidden): # hidden: (B, 512, d_model) or (512, d_model) + if hidden.dim() == 2: # (512, d_model) + hidden = hidden.unsqueeze(0) # -> (1, 512, d_model) + + B, L, D = hidden.shape # B=batch size, L=512, D=d_model + k = self.K(hidden) + v = self.V(hidden) + attn_score = torch.matmul(self.query, k.transpose(1, 2)) / (D ** 0.5) # (B, n, 512) + attn_weights = F.softmax(attn_score, dim=-1) # (B, n, 512) + + pooled = torch.matmul(attn_weights, v) # (B, n, d_model) + residual = pooled + pooled = self.ff(pooled) + pooled = self.ln(pooled + residual) + # pooled = hidden.view(B, self.n, -1, D).mean(-2) + return pooled.squeeze(0) if pooled.size(0) == 1 else pooled # -> (n, d_model) or (B, n, d_model) +class FlashMultiheadAttention(nn.Module): + """ + Multi-head self-attention using flash-attn backend. + Only supports self-attention (q=k=v) and batch_first=True. + """ + def __init__(self, embed_dim, num_heads, dropout=0.0, batch_first=True, **factory_kwargs): + super().__init__() + assert batch_first, "Only batch_first=True is supported." + self.embed_dim = embed_dim + self.num_heads = num_heads + self.dropout = dropout + self.head_dim = embed_dim // num_heads + assert ( + self.head_dim * num_heads == embed_dim + ), "embed_dim must be divisible by num_heads" + self.qkv_proj = nn.Linear(embed_dim, 3 * embed_dim, **factory_kwargs) + self.out_proj = nn.Linear(embed_dim, embed_dim, **factory_kwargs) + + def forward(self, x, key_padding_mask=None): + # x: (batch, seq, embed_dim) + B, S, C = x.shape + qkv = self.qkv_proj(x) # (B, S, 3*embed_dim) + qkv = qkv.view(B, S, 3, self.num_heads, self.head_dim) + q = qkv[:, :, 0] + k = qkv[:, :, 1] + v = qkv[:, :, 2] + # flash_attn_func expects (B, S, nH, dH) + # key_padding_mask: (B, S) with True for PAD + # flash_attn_func does not support key_padding_mask directly, so we mask input + if key_padding_mask is not None: + # set PAD positions to zero + mask = ~key_padding_mask # invert: True for keep + q = q * mask.unsqueeze(-1).unsqueeze(-1) + k = k * mask.unsqueeze(-1).unsqueeze(-1) + v = v * mask.unsqueeze(-1).unsqueeze(-1) + attn_output = flash_attn_func( + q, k, v, dropout_p=self.dropout, causal=False + ) # (B, S, nH, dH) + attn_output = attn_output.reshape(B, S, C) + return self.out_proj(attn_output), None # match nn.MultiheadAttention API + + +class FlashTransformerEncoderLayer(nn.Module): + r"""TransformerEncoderLayer is made up of self-attn and feedforward network. + The class is modified from torch.nn.TransformerEncoderLayer to support the + FlashAttention. + + Args: + d_model: the number of expected features in the input (required). + nhead: the number of heads in the multiheadattention models (required). + dim_feedforward: the dimension of the feedforward network model (default=2048). + dropout: the dropout value (default=0.1). + activation: the activation function of intermediate layer, relu or gelu (default=relu). + layer_norm_eps: the eps value in layer normalization components (default=1e-5). + batch_first: If ``True``, then the input and output tensors are provided + as (batch, seq, feature). Default: ``False``. + + Examples:: + >>> encoder_layer = nn.TransformerEncoderLayer(d_model=512, nhead=8) + >>> src = torch.rand(10, 32, 512) + >>> out = encoder_layer(src) + + Alternatively, when ``batch_first`` is ``True``: + >>> encoder_layer = nn.TransformerEncoderLayer(d_model=512, nhead=8, batch_first=True) + >>> src = torch.rand(32, 10, 512) + >>> out = encoder_layer(src) + """ + __constants__ = ["batch_first"] + + def __init__( + self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + activation="relu", + layer_norm_eps=1e-5, + batch_first=True, + device=None, + dtype=None, + norm_scheme="post", # "pre" or "post" + ) -> None: + factory_kwargs = {"device": device, "dtype": dtype} + super().__init__() + self.self_attn = FlashMultiheadAttention( + embed_dim=d_model, + num_heads=nhead, + batch_first=batch_first, + dropout=dropout, + **factory_kwargs, + ) + + # Version compatibility workaround + if not hasattr(self.self_attn, "batch_first"): + self.self_attn.batch_first = batch_first + # Implementation of Feedforward model + self.linear1 = nn.Linear(d_model, dim_feedforward, **factory_kwargs) + self.dropout = nn.Dropout(dropout) + self.linear2 = nn.Linear(dim_feedforward, d_model, **factory_kwargs) + + self.norm1 = nn.LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs) + self.norm2 = nn.LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.activation = self._get_activation_fn(activation) + self.norm_scheme = norm_scheme + if self.norm_scheme not in ["pre", "post"]: + raise ValueError(f"norm_scheme should be pre or post, not {norm_scheme}") + + @staticmethod + def _get_activation_fn(activation): + if activation == "relu": + return F.relu + elif activation == "gelu": + return F.gelu + + raise RuntimeError("activation should be relu/gelu, not {}".format(activation)) + + def __setstate__(self, state): + if "activation" not in state: + state["activation"] = F.relu + super().__setstate__(state) + + def forward( + self, + src: Tensor, + src_mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + **kwargs, + ) -> Tensor: + r"""Pass the input through the encoder layer. + + Args: + src: the sequence to the encoder layer (required). + src_mask: the mask for the src sequence (optional). + src_key_padding_mask: the mask for the src keys per batch (optional). + + Shape: + see the docs in Transformer class. + """ + print('FlashTransformerEncoderLayer forward') + if src_mask is not None: + raise ValueError("FlashTransformerEncoderLayer does not support src_mask") + + if not src_key_padding_mask.any().item(): + # no padding tokens in src + src_key_padding_mask_ = None + else: + if src_key_padding_mask.dtype != torch.bool: + src_key_padding_mask = src_key_padding_mask.bool() + # NOTE: the FlashMHA uses mask 0 for padding tokens, which is the opposite + src_key_padding_mask_ = ~src_key_padding_mask + + if self.norm_scheme == "pre": + src = self.norm1(src) + src2 = self.self_attn(src, key_padding_mask=src_key_padding_mask_)[0] + src = src + self.dropout1(src2) + src = self.norm2(src) + src2 = self.linear2(self.dropout(self.activation(self.linear1(src)))) + src = src + self.dropout2(src2) + else: + src2 = self.self_attn(src, key_padding_mask=src_key_padding_mask_)[0] + src = src + self.dropout1(src2) + src = self.norm1(src) + src2 = self.linear2(self.dropout(self.activation(self.linear1(src)))) + src = src + self.dropout2(src2) + src = self.norm2(src) + + return src + +class DownTransformerEncoder(nn.Module): + def __init__(self, d_model, n_top_genes, nhead, d_hid, nlayers, dropout): + super().__init__() + self.nlayers = nlayers // 2 + gene_d_model = [n_top_genes // 2 ** i for i in range(0,self.nlayers+1)] + self.transformer_gene = [] # gene encoder + self.transformer_feature = [] # feature encoder + self.down_transfer = [] # down transformer + + for i in range(self.nlayers): + self.transformer_gene.append(TransformerEncoderLayer(gene_d_model[i+1], nhead, d_hid, dropout, batch_first=True)) + self.transformer_feature.append(nn.Sequential(TransformerEncoderLayer(d_model, nhead, d_hid, dropout, batch_first=True), + TransformerEncoderLayer(d_model, nhead, d_hid, dropout, batch_first=True))) + self.down_transfer.append(nn.Sequential( + nn.Linear(gene_d_model[i], gene_d_model[i+1]), + nn.ReLU(), + nn.LayerNorm(gene_d_model[i+1]), + )) + self.down_transfer = nn.ModuleList(self.down_transfer) + self.transformer_gene = nn.ModuleList(self.transformer_gene) + self.transformer_feature = nn.ModuleList(self.transformer_feature) + + def forward(self, src, src_key_padding_mask=None): + for i in range(self.nlayers): + src = self.transformer_feature[i](src) + # src = src.transpose(1,2) + # src = self.down_transfer[i](src) + # src = self.transformer_gene[i](src) + # src = src.transpose(1,2) + return src + +class UpTransformerDecoder(nn.Module): + def __init__(self, d_model, n_top_genes, nhead, d_hid, nlayers, dropout): + super().__init__() + self.nlayers = nlayers // 2 + gene_d_model = [n_top_genes // 2 ** i for i in range(self.nlayers,-1,-1)] + self.transformer_gene = [] # gene encoder + self.transformer_feature = [] # feature encoder + self.up_transfer = [] # up transformer + + for i in range(self.nlayers): + self.transformer_gene.append(TransformerEncoderLayer(gene_d_model[i+1], nhead, d_hid, dropout, batch_first=True)) + self.transformer_feature.append(nn.Sequential(TransformerEncoderLayer(d_model, nhead, d_hid, dropout, batch_first=True), + TransformerEncoderLayer(d_model, nhead, d_hid, dropout, batch_first=True))) + self.up_transfer.append(nn.Sequential( + nn.Linear(gene_d_model[i], gene_d_model[i+1]), + nn.ReLU(), + nn.LayerNorm(gene_d_model[i+1]), + )) + self.up_transfer = nn.ModuleList(self.up_transfer) + self.transformer_gene = nn.ModuleList(self.transformer_gene) + self.transformer_feature = nn.ModuleList(self.transformer_feature) + + def forward(self, src, src_key_padding_mask=None): + for i in range(self.nlayers): + + src = self.transformer_feature[i](src) + # src = src.transpose(1,2) + # src = self.up_transfer[i](src) + # src = self.transformer_gene[i](src) + # src = src.transpose(1,2) + return src + + +class CrossAttentionModule(nn.Module): + def __init__(self, embed_dim, num_heads, dropout=0.0): + super().__init__() + self.attn = nn.MultiheadAttention(embed_dim=embed_dim, + num_heads=num_heads, + dropout=dropout, + batch_first=True) # using (batch, seq, dim) + self.norm1 = nn.LayerNorm(embed_dim) + self.norm2 = nn.LayerNorm(embed_dim) + self.ff = nn.Sequential( + nn.Linear(embed_dim, embed_dim * 4), + nn.GELU(), + nn.Linear(embed_dim * 4, embed_dim), + nn.Dropout(dropout), + ) + self.selfattn = nn.MultiheadAttention(embed_dim=embed_dim, + num_heads=num_heads, + dropout=dropout, + batch_first=True) # using (batch, seq, dim) + self.norm3 = nn.LayerNorm(embed_dim) + self.norm4 = nn.LayerNorm(embed_dim) + self.ff2 = nn.Sequential( + nn.Linear(embed_dim, embed_dim * 4), + nn.GELU(), + nn.Linear(embed_dim * 4, embed_dim), + nn.Dropout(dropout), + ) + + def forward(self, x1, x2, attn_mask=None, key_padding_mask=None): + # x1: (b, l1, dim), x2: (b, l2, dim) + residual = x1 + attn_out, attn_weights = self.attn(query=x1, key=x2, value=x2, + attn_mask=attn_mask, + key_padding_mask=key_padding_mask) + x = self.norm1(attn_out + residual) + + # add FFN + residual2 = x + x = self.ff(x) + x = self.norm2(x + residual2) + + residual = x + attn_out, _ = self.selfattn(query=x, key=x, value=x, + attn_mask=attn_mask, + key_padding_mask=key_padding_mask) + x = self.norm3(attn_out + residual) + + # add FFN + residual2 = x + x = self.ff2(x) + x = self.norm4(x + residual2) + return x, attn_weights +class GeneEncoder(nn.Module): + def __init__( + self, + num_embeddings: int, + embedding_dim: int, + padding_idx: Optional[int] = None, + ): + super().__init__() + self.embedding = nn.Embedding( + num_embeddings, embedding_dim, padding_idx=padding_idx + ) + self.enc_norm = nn.LayerNorm(embedding_dim) + + def forward(self, x: Tensor) -> Tensor: + x = self.embedding(x) # (batch, seq_len, embsize) + x = self.enc_norm(x) + return x + + +class PositionalEncoding(nn.Module): + def __init__(self, d_model: int, dropout: float = 0.1, max_len: int = 5000): + super().__init__() + self.dropout = nn.Dropout(p=dropout) + + position = torch.arange(max_len).unsqueeze(1) + div_term = torch.exp( + torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model) + ) + pe = torch.zeros(max_len, 1, d_model) + pe[:, 0, 0::2] = torch.sin(position * div_term) + pe[:, 0, 1::2] = torch.cos(position * div_term) + self.register_buffer("pe", pe) + + def forward(self, x: Tensor) -> Tensor: + """ + Args: + x: Tensor, shape [seq_len, batch_size, embedding_dim] + """ + x = x + self.pe[: x.size(0)] + return self.dropout(x) + + +class ContinuousValueEncoder(nn.Module): + """ + Encode real number values to a vector using neural nets projection. + """ + + def __init__(self, d_model: int, dropout: float = 0.1, max_value: int = 512): + super().__init__() + self.dropout = nn.Dropout(p=dropout) + self.linear1 = nn.Linear(1, d_model) + self.activation = nn.ReLU() + self.linear2 = nn.Linear(d_model, d_model) + self.norm = nn.LayerNorm(d_model) + self.max_value = max_value + + def forward(self, x: Tensor) -> Tensor: + """ + Args: + x: Tensor, shape [batch_size, seq_len] + """ + # TODO: test using actual embedding layer if input is categorical + # expand last dimension + x = x.unsqueeze(-1) + # clip x to [-inf, max_value] + x = torch.clamp(x, max=self.max_value) + x = self.activation(self.linear1(x)) + x = self.linear2(x) + x = self.norm(x) + return self.dropout(x) + + +class CategoryValueEncoder(nn.Module): + def __init__( + self, + num_embeddings: int, + embedding_dim: int, + padding_idx: Optional[int] = None, + ): + super().__init__() + self.embedding = nn.Embedding( + num_embeddings, embedding_dim, padding_idx=padding_idx + ) + self.enc_norm = nn.LayerNorm(embedding_dim) + + def forward(self, x: Tensor) -> Tensor: + x = x.long() + x = self.embedding(x) # (batch, seq_len, embsize) + x = self.enc_norm(x) + return x + + +class CategoryValueDecoder(nn.Module): + def __init__( + self, + d_model: int, + n_bins: int, + use_batch_labels: bool = False, + nlayers: int = 3, + activation: callable = nn.ReLU, + ): + super().__init__() + self._decoder = nn.ModuleList() + d_in = d_model * 2 if use_batch_labels else d_model + if use_batch_labels: + self._decoder.append(nn.Linear(d_in, d_model)) + self._decoder.append(activation()) + self._decoder.append(nn.LayerNorm(d_model)) + for i in range(nlayers - 1): + self._decoder.append(nn.Linear(d_model, d_model)) + self._decoder.append(activation()) + self._decoder.append(nn.LayerNorm(d_model)) + self.out_layer = nn.Linear(d_model, n_bins) + + def forward(self, x: Tensor) -> Tensor: + """ + Args: + x: Tensor, shape [batch_size, embsize] + output: Tensor, shape [batch_size, n_bins] + """ + for layer in self._decoder: + x = layer(x) + return self.out_layer(x) + + +class BatchLabelEncoder(nn.Module): + def __init__( + self, + num_embeddings: int, + embedding_dim: int, + padding_idx: Optional[int] = None, + ): + super().__init__() + self.embedding = nn.Embedding( + num_embeddings, embedding_dim, padding_idx=padding_idx + ) + self.enc_norm = nn.LayerNorm(embedding_dim) + + def forward(self, x: Tensor) -> Tensor: + x = self.embedding(x) # (batch, embsize) + x = self.enc_norm(x) + return x + + +class Similarity(nn.Module): + """ + Dot product or cosine similarity + """ + + def __init__(self, temp): + super().__init__() + self.temp = temp + self.cos = nn.CosineSimilarity(dim=-1) + + def forward(self, x, y): + return self.cos(x, y) / self.temp + + +class ExprDecoder(nn.Module): + def __init__( + self, + d_model: int, + explicit_zero_prob: bool = False, + use_batch_labels: bool = False, + ): + super().__init__() + d_in = d_model * 2 if use_batch_labels else d_model + self.fc = nn.Sequential( + nn.Linear(d_in, d_model), + nn.LeakyReLU(), + nn.Linear(d_model, d_model), + nn.LeakyReLU(), + nn.Linear(d_model, 1), + ) + self.explicit_zero_prob = explicit_zero_prob + if explicit_zero_prob: + self.zero_logit = nn.Sequential( + nn.Linear(d_in, d_model), + nn.LeakyReLU(), + nn.Linear(d_model, d_model), + nn.LeakyReLU(), + nn.Linear(d_model, 1), + ) + + def forward(self, x: Tensor) -> Dict[str, Tensor]: + """x is the output of the transformer, (batch, seq_len, d_model)""" + pred_value = self.fc(x).squeeze(-1) # (batch, seq_len) + + if not self.explicit_zero_prob: + return dict(pred=pred_value) + zero_logits = self.zero_logit(x).squeeze(-1) # (batch, seq_len) + zero_probs = torch.sigmoid(zero_logits) + return dict(pred=pred_value, zero_probs=zero_probs) + # TODO: note that the return currently is only for training. Since decoder + # is not used in the test setting for the integration task, the eval/inference + # logic is not implemented yet. However, remember to implement it when + # the decoder is used in any test setting. The inference logic will need + # to sample from the bernoulli distribution with the zero_probs. + + +class ClsDecoder(nn.Module): + """ + Decoder for classification task. + """ + + def __init__( + self, + d_model: int, + n_cls: int, + nlayers: int = 3, + activation: callable = nn.ReLU, + ): + super().__init__() + # module list + self._decoder = nn.ModuleList() + for i in range(nlayers - 1): + self._decoder.append(nn.Linear(d_model, d_model)) + self._decoder.append(activation()) + self._decoder.append(nn.LayerNorm(d_model)) + self.out_layer = nn.Linear(d_model, n_cls) + + def forward(self, x: Tensor) -> Tensor: + """ + Args: + x: Tensor, shape [batch_size, embsize] + """ + for layer in self._decoder: + x = layer(x) + return self.out_layer(x) + + +class MVCDecoder(nn.Module): + """ + Decoder for the masked value prediction for cell embeddings. + """ + + def __init__( + self, + d_model: int, + arch_style: str = "inner product", + query_activation: nn.Module = nn.Sigmoid, + hidden_activation: nn.Module = nn.PReLU, + explicit_zero_prob: bool = False, + use_batch_labels: bool = False, + ) -> None: + """ + Args: + d_model (:obj:`int`): dimension of the gene embedding. + arch_style (:obj:`str`): architecture style of the decoder, choice from + 1. "inner product" or 2. "concat query" or 3. "sum query". + query_activation (:obj:`nn.Module`): activation function for the query + vectors. + hidden_activation (:obj:`nn.Module`): activation function for the hidden + layers. + """ + super().__init__() + d_in = d_model * 2 if use_batch_labels else d_model + if arch_style in ["inner product", "inner product, detach"]: + self.gene2query = nn.Linear(d_model, d_model) + self.query_activation = query_activation() + self.W = nn.Linear(d_model, d_in, bias=False) + if explicit_zero_prob: # by default, gene-wise prob rate + self.W_zero_logit = nn.Linear(d_model, d_in) + elif arch_style == "concat query": + self.gene2query = nn.Linear(d_model, 64) + self.query_activation = query_activation() + self.fc1 = nn.Linear(d_model + 64, 64) + self.hidden_activation = hidden_activation() + self.fc2 = nn.Linear(64, 1) + elif arch_style == "sum query": + self.gene2query = nn.Linear(d_model, d_model) + self.query_activation = query_activation() + self.fc1 = nn.Linear(d_model, 64) + self.hidden_activation = hidden_activation() + self.fc2 = nn.Linear(64, 1) + else: + raise ValueError(f"Unknown arch_style: {arch_style}") + + self.arch_style = arch_style + self.do_detach = arch_style.endswith("detach") + self.explicit_zero_prob = explicit_zero_prob + + def forward( + self, cell_emb: Tensor, gene_embs: Tensor + ) -> Union[Tensor, Dict[str, Tensor]]: + """ + Args: + cell_emb: Tensor, shape (batch, embsize=d_model) + gene_embs: Tensor, shape (batch, seq_len, embsize=d_model) + """ + gene_embs = gene_embs.detach() if self.do_detach else gene_embs + if self.arch_style in ["inner product", "inner product, detach"]: + query_vecs = self.query_activation(self.gene2query(gene_embs)) + cell_emb = cell_emb.unsqueeze(2) # (batch, embsize, 1) + # the pred gene expr values, # (batch, seq_len) + pred_value = torch.bmm(self.W(query_vecs), cell_emb).squeeze(2) + if not self.explicit_zero_prob: + return dict(pred=pred_value) + # zero logits need to based on the cell_emb, because of input exprs + zero_logits = torch.bmm(self.W_zero_logit(query_vecs), cell_emb).squeeze(2) + zero_probs = torch.sigmoid(zero_logits) + return dict(pred=pred_value, zero_probs=zero_probs) + elif self.arch_style == "concat query": + query_vecs = self.query_activation(self.gene2query(gene_embs)) + # expand cell_emb to (batch, seq_len, embsize) + cell_emb = cell_emb.unsqueeze(1).expand(-1, gene_embs.shape[1], -1) + + h = self.hidden_activation( + self.fc1(torch.cat([cell_emb, query_vecs], dim=2)) + ) + if self.explicit_zero_prob: + raise NotImplementedError + return self.fc2(h).squeeze(2) # (batch, seq_len) + elif self.arch_style == "sum query": + query_vecs = self.query_activation(self.gene2query(gene_embs)) + cell_emb = cell_emb.unsqueeze(1) + + h = self.hidden_activation(self.fc1(cell_emb + query_vecs)) + if self.explicit_zero_prob: + raise NotImplementedError + return self.fc2(h).squeeze(2) # (batch, seq_len) + + +class AdversarialDiscriminator(nn.Module): + """ + Discriminator for the adversarial training for batch correction. + """ + + def __init__( + self, + d_model: int, + n_cls: int, + nlayers: int = 3, + activation: callable = nn.LeakyReLU, + reverse_grad: bool = False, + ): + super().__init__() + # module list + self._decoder = nn.ModuleList() + for i in range(nlayers - 1): + self._decoder.append(nn.Linear(d_model, d_model)) + self._decoder.append(activation()) + self._decoder.append(nn.LayerNorm(d_model)) + self.out_layer = nn.Linear(d_model, n_cls) + self.reverse_grad = reverse_grad + + def forward(self, x: Tensor) -> Tensor: + """ + Args: + x: Tensor, shape [batch_size, embsize] + """ + if self.reverse_grad: + x = grad_reverse(x, lambd=1.0) + for layer in self._decoder: + x = layer(x) + return self.out_layer(x) + +class GradReverse(Function): + @staticmethod + def forward(ctx, x: torch.Tensor, lambd: float) -> torch.Tensor: + ctx.lambd = lambd + return x.view_as(x) + + @staticmethod + def backward(ctx, grad_output: torch.Tensor) -> torch.Tensor: + return grad_output.neg() * ctx.lambd, None + + +def grad_reverse(x: torch.Tensor, lambd: float = 1.0) -> torch.Tensor: + return GradReverse.apply(x, lambd) + +try: + from flash_attn.flash_attention import FlashMHA + # from flash_attn import flash_attn_qkvpacked_func, flash_attn_func + flash_attn_available = True +except ImportError: + import warnings + + warnings.warn("flash_attn is not installed") + flash_attn_available = False \ No newline at end of file diff --git a/transfer/code/ori_scDFM/src/models/scGPT/modules.py b/transfer/code/ori_scDFM/src/models/scGPT/modules.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/transfer/code/ori_scDFM/src/script/run.py b/transfer/code/ori_scDFM/src/script/run.py new file mode 100644 index 0000000000000000000000000000000000000000..4c21dd4f4bdc405a60543a56137ac1377a5e97ec --- /dev/null +++ b/transfer/code/ori_scDFM/src/script/run.py @@ -0,0 +1,417 @@ +import accelerate +import torch +import torch.nn as nn +import tyro +from config.config_flow import FlowConfig as Config +import torch.nn.functional as F +import time +from torch.utils.data import Dataset, DataLoader +import jax +import random +from src.data_process.data import Data, PerturbationDataset +from src.flow_matching.ot import OTPlanSampler +from src.flow_matching.path import AffineProbPath +from src.flow_matching.solver import ODESolver +from src.models.instantiate_model import instantiate_model +from src.tokenizer.gene_tokenizer import GeneVocab +from src.models.perturbation.moduls import PerturbationEmbedding +import pdb +import tqdm +from src.flow_matching.path.scheduler import CondOTScheduler +import scanpy as sc +import os +from src.data_process.utils import build_generated_anndata + +import json +from accelerate import Accelerator,DistributedDataParallelKwargs +import torchdiffeq +from tqdm import trange +import numpy as np +from cell_eval import MetricsEvaluator +import anndata as ad +import pandas as pd +from src.utils.utils import save_checkpoint, load_checkpoint, make_lognorm_poisson_noise, pick_eval_score, process_vocab, set_requires_grad_for_p_only, get_perturbation_emb + +ot_sampler = OTPlanSampler(method="exact") +path = AffineProbPath(scheduler=CondOTScheduler()) + +def gaussian_kernel(x, y, sigma=1.0): + beta = 1.0 / (2.0 * sigma**2) + dist = torch.cdist(x, y, p=2) ** 2 + return torch.exp(-beta * dist) + +def mmd_loss(pred, tgt, sigma=1.0): + xx = gaussian_kernel(pred, pred, sigma).mean(dim=(1)) + yy = gaussian_kernel(tgt, tgt, sigma).mean(dim=(1)) + xy = gaussian_kernel(pred, tgt, sigma).mean(dim=(1)) + return (xx + yy - 2 * xy).mean() + +def pairwise_sq_dists(X, Y): + # X:[m,d], Y:[n,d] -> [m,n] + return torch.cdist(X, Y, p=2)**2 + +@torch.no_grad() +def median_sigmas(X, scales=(0.5, 1.0, 2.0, 4.0)): + Z = X + D2 = pairwise_sq_dists(Z, Z) + tri = D2[~torch.eye(D2.size(0), dtype=bool, device=D2.device)] + m = torch.median(tri).clamp_min(1e-12) + s2 = torch.tensor(scales, device=Z.device) * m + sigmas = torch.sqrt(s2) + return [float(s.item()) for s in sigmas] + +def mmd2_unbiased_multi_sigma(X, Y, sigmas): + """ + """ + m, n = X.size(0), Y.size(0) + Dxx = pairwise_sq_dists(X, X) # [m,m] + Dyy = pairwise_sq_dists(Y, Y) # [n,n] + Dxy = pairwise_sq_dists(X, Y) # [m,n] + + vals = [] + for sigma in sigmas: + beta = 1.0 / (2.0 * (sigma ** 2) + 1e-12) + Kxx = torch.exp(-beta * Dxx) + Kyy = torch.exp(-beta * Dyy) + Kxy = torch.exp(-beta * Dxy) + + term_xx = (Kxx.sum() - Kxx.diag().sum()) / (m * (m - 1) + 1e-12) + term_yy = (Kyy.sum() - Kyy.diag().sum()) / (n * (n - 1) + 1e-12) + term_xy = Kxy.mean() # / (m*n) + vals.append(term_xx + term_yy - 2.0 * term_xy) + + return torch.stack(vals).mean() + +def train_step(source, target, perturbation_id, vf, criterion, accelerator, noise_type='Poisson', mode="predict_y"): + B = source.shape[0] + device = accelerator.device + + input_gene_ids = torch.randperm(source.shape[-1], device=device)[:config.infer_top_gene] + source = source[:,input_gene_ids] + target = target[:,input_gene_ids] + gene = gene_ids.repeat(B,1).to(device) + gene_input = gene[:,input_gene_ids] + + if mode=="predict_y": + # source, target = ot_sampler.sample_plan(source, target) + t = torch.rand(B, device=device) + if noise_type=="Gaussian": + target_noise = torch.randn_like(source) + if gene_noise_weight is not None: + target_noise = target_noise * gene_noise_weight[input_gene_ids] + elif noise_type=="Poisson": + target_noise = make_lognorm_poisson_noise( + target_log=source, + alpha=getattr(config, "poisson_alpha", 0.8), + per_cell_L=getattr(config, "poisson_target_sum", 1e4), # e.g., 1e4 or None + ) + path_x1 = path.sample(t=t, x_0=target_noise, x_1=target) + predicted_x_t_velocity = vf(gene_input,path_x1.x_t, path_x1.t,source,perturbation_id, gene_input, mode=mode) + loss = ((predicted_x_t_velocity - path_x1.dx_t)**2).mean() + + if config.use_mmd_loss: + x1_hat = path_x1.x_t + predicted_x_t_velocity*(1-t).unsqueeze(-1) + sigmas = median_sigmas(target, scales=(0.5,1.0,2.0,4.0)) + + _mmd_loss = mmd2_unbiased_multi_sigma(x1_hat, target, sigmas) + # _mmd_loss = mmd_loss(x1_hat, target) + loss = loss + _mmd_loss * config.gamma + + elif mode=="predict_p": + t_p = torch.ones(B, device=device) # Or uniform(0.7,1.0) + predicted_p_embed = vf(gene_input, target, t_p, source, perturbation_id, gene_input, mode=mode) + if hasattr(vf, "module"): + base_vf = vf.module + else: + base_vf = vf + p_embed_gt = base_vf.get_perturbation_emb(perturbation_id=perturbation_id, cell_1=source) + pred = F.normalize(predicted_p_embed, dim=-1) + tgt = F.normalize(p_embed_gt.detach(), dim=-1) + loss = 1 - (pred * tgt).sum(dim=-1).mean() # cosine distance + + return loss + +@torch.inference_mode() +def test(data_sampler, vf, accelerator, batch_size=128, path='./',vocab=None,scheme='mse'): + gene_ids_test = vocab.encode(list(data_sampler.adata.var_names)) + + gene_ids_test = torch.tensor(gene_ids_test, dtype=torch.long, device=device) + perturbation_name_list = data_sampler._perturbation_covariates + control_data = data_sampler.get_control_data() + all_pred_expressions = [control_data['src_cell_data']] + obs_perturbation_name_pred = ['control']*control_data['src_cell_data'].shape[0] + all_target_expressions = [control_data['src_cell_data']] + obs_perturbation_name_real = ['control']*control_data['src_cell_data'].shape[0] + count = 0 + print('perturbation_name_list:',len(perturbation_name_list)) + for perturbation_name in perturbation_name_list: + perturbation_data = data_sampler.get_perturbation_data(perturbation_name) + target = perturbation_data['tgt_cell_data'] + perturbation_id = perturbation_data['condition_id'] + source = control_data['src_cell_data'] + source = source.to(device) + perturbation_id = perturbation_id.to(device) + if config.perturbation_function == 'crisper': + perturbation_name_crisper = [inverse_dict[int(p_id)] for p_id in perturbation_id[0].cpu().numpy()] + perturbation_id = torch.tensor(vocab.encode(perturbation_name_crisper), dtype=torch.long, device=device) + perturbation_id = perturbation_id.repeat(source.shape[0],1) + + idx = torch.randperm(source.shape[0]) + source = source[idx] + N = 128 + source = source[:N] + + pred_expressions = [] + for i in trange(0, N, batch_size): + batch_perturbation_id = perturbation_id[0].repeat(source[i:i+batch_size].shape[0],1) + + batch_perturbation_id = batch_perturbation_id.to(accelerator.device) + + pred_expression = generate_sample(wrapped_vf,source[i:i+batch_size],batch_perturbation_id,vf,gene_ids=gene_ids_test,gene_all=gene_ids_test) + pred_expressions.append(pred_expression) + + pred_expressions = torch.cat(pred_expressions, dim=0).cpu().numpy() + all_pred_expressions.append(pred_expressions) + all_target_expressions.append(target) + obs_perturbation_name_pred.extend([perturbation_name] * pred_expressions.shape[0]) + obs_perturbation_name_real.extend([perturbation_name] * target.shape[0]) + # count += 1 + # if count > 3: + # break + + all_pred_expressions = np.concatenate(all_pred_expressions, axis=0) + all_target_expressions = np.concatenate(all_target_expressions, axis=0) + obs_pred = pd.DataFrame({'perturbation':obs_perturbation_name_pred}) + obs_real = pd.DataFrame({'perturbation':obs_perturbation_name_real}) + pred = ad.AnnData(X=all_pred_expressions, obs=obs_pred) + real = ad.AnnData(X=all_target_expressions, obs=obs_real) + + + eval_score = None + if accelerator.is_main_process: + evaluator = MetricsEvaluator( + adata_pred=pred, + adata_real=real, + control_pert="control", + pert_col="perturbation", + num_threads=32, + ) + (results, agg_results) = evaluator.compute() + + results.write_csv(os.path.join(path, 'results.csv')) + agg_results.write_csv(os.path.join(path, 'agg_results.csv')) + pred.write_h5ad(os.path.join(path, 'pred.h5ad')) + real.write_h5ad(os.path.join(path, 'real.h5ad')) + + eval_score = pick_eval_score(agg_results, scheme) + print(f"Current evaluation score: {eval_score:.4f}") + + return eval_score + +def wrapped_vf(target,t,source,perturbation_id,vf,gene_ids, gene_all): + + gene = gene_ids.repeat(source.shape[0],1).to(device) + predicted_x_t_velocity = vf(gene,target,t,source,perturbation_id,gene_all) + + return predicted_x_t_velocity + +@torch.no_grad() +def generate_sample(wrapped_vf,source,condition_vec=None,vf=None,gene_ids=None,gene_all=None,steps=20,method="rk4"): + + noise_type = config.noise_type + if noise_type=="Gaussian": + target_noise = torch.randn(source.shape[0],config.infer_top_gene,device=source.device) + if gene_noise_weight is not None: + target_noise = target_noise * gene_noise_weight[:config.infer_top_gene] + elif noise_type=="Poisson": + target_noise = make_lognorm_poisson_noise( + target_log=source, + alpha=getattr(config, "poisson_alpha", 0.8), + per_cell_L=getattr(config, "poisson_target_sum", 1e4), + ) + + traj = torchdiffeq.odeint(lambda t,x: wrapped_vf(x,t,source,condition_vec,vf,gene_ids,gene_all), + target_noise, + torch.linspace(0,1,steps).to(source.device), + atol=1e-4, + rtol=1e-4, + method=method) + # t = torch.linspace(0,1,steps).to(source.device) + # traj = [target_noise + 0.8*wrapped_vf(target_noise,t,source,condition_vec,vf,gene_ids,gene_all)] + + return torch.clamp(traj[-1], min=0) + +if __name__ == "__main__": + config = tyro.cli(Config) + + ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=True) + + accelerator = Accelerator( + kwargs_handlers=[ddp_kwargs] + ) + if accelerator.is_main_process: + print(config) + save_path = config.make_path() + os.makedirs(save_path, exist_ok=True) + device = accelerator.device + + data_manager = Data('./data') + + data_manager.load_data(config.data_name) + data_manager.process_data(n_top_genes=config.n_top_genes, split_method=config.split_method, fold=config.fold, use_negative_edge=config.use_negative_edge, k=config.topk) + train_sampler, valid_sampler, test_dl = data_manager.load_flow_data(batch_size=config.batch_size) + + train_dataset = PerturbationDataset(train_sampler, config.batch_size) + dataloader = DataLoader(train_dataset, batch_size=1, shuffle=False,num_workers=8,pin_memory=True,persistent_workers=True) # batch_size=1 因为每个getitem本身就是一个batch + if config.use_negative_edge: + mask_path = os.path.join(data_manager.data_path, data_manager.data_name,'mask_fold_'+str(config.fold)+'topk_'+str(config.topk)+config.split_method+'_negative_edge'+'.pt') + else: + mask_path = os.path.join(data_manager.data_path, data_manager.data_name,'mask_fold_'+str(config.fold)+'topk_'+str(config.topk)+config.split_method+'.pt') + + vocab = process_vocab(data_manager, config) + gene_ids = vocab.encode(list(data_manager.adata.var_names)) + gene_ids = torch.tensor(gene_ids, dtype=torch.long, device=device) + ntoken = len(vocab) + + vf = instantiate_model(config.model_type, + ntoken = ntoken, + d_model = config.d_model, + d_perturbation = config.d_model, + fusion_method = config.fusion_method, + perturbation_function = config.perturbation_function, + mask_path = mask_path + ) + if accelerator.is_main_process: + total_params = sum(p.numel() for p in vf.parameters()) + print(f"[DIM CHECK] ntoken={ntoken}, d_model={config.d_model}, " + f"d_hid={int(4*config.d_model)}, nhead=8, total_params={total_params:,}") + for name, p in vf.named_parameters(): + if p.dim() >= 2: + print(f" {name}: {tuple(p.shape)}") + + model_path = config.make_path() + + save_path = config.make_path() + best_loss = float('inf') + + # === Gene-specific noise scale === + gene_noise_weight = None + if config.gene_noise_scale: + if accelerator.is_main_process: + print("Computing per-gene noise scale from training data...") + _deltas = [] + _count = 0 + for _batch in dataloader: + _src = _batch['src_cell_data'].squeeze(0) + _tgt = _batch['tgt_cell_data'].squeeze(0) + _deltas.append((_tgt - _src).cpu()) + _count += 1 + if _count >= 100: + break + _deltas = torch.cat(_deltas, 0) # (N, G) + _gene_std = _deltas.std(dim=0).clamp(min=1e-6) # (G,) + gene_noise_weight = (_gene_std / _gene_std.median()).clamp(0.1, 3.0).to(device) + if accelerator.is_main_process: + print(f" gene_noise_weight: min={gene_noise_weight.min():.3f}, " + f"max={gene_noise_weight.max():.3f}, " + f"median={gene_noise_weight.median():.3f}, " + f"genes>1.5: {(gene_noise_weight > 1.5).sum().item()}, " + f"genes<0.5: {(gene_noise_weight < 0.5).sum().item()}") + del _deltas, _gene_std + + criterion = nn.MSELoss() + optimizer = torch.optim.Adam(vf.parameters(), lr=config.lr) + scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=config.steps, eta_min=config.eta_min) + + if config.checkpoint_path != '': + _, _ = load_checkpoint(config.checkpoint_path, vf, optimizer, scheduler) + start_iteration = 0 + vf = accelerator.prepare(vf) + optimizer, scheduler, dataloader = accelerator.prepare(optimizer,scheduler,dataloader) + inverse_dict = {v: str(k) for k, v in data_manager.perturbation_dict.items()} + + # === Test-only mode === + if config.test_only: + eval_path = os.path.join(save_path, "eval_only") + os.makedirs(eval_path, exist_ok=True) + if accelerator.is_main_process: + print(f"Test-only mode. Saving results to {eval_path}") + eval_score = test(valid_sampler, vf, accelerator, batch_size=config.batch_size, path=eval_path, vocab=vocab) + if accelerator.is_main_process and eval_score is not None: + print(f"Final evaluation score: {eval_score:.4f}") + import sys + sys.exit(0) + + # --- Loss logging (CSV + TensorBoard) --- + import csv + from torch.utils.tensorboard import SummaryWriter + if accelerator.is_main_process: + csv_file = open(os.path.join(save_path, 'loss_curve.csv'), 'w', newline='') + csv_writer = csv.writer(csv_file) + csv_writer.writerow(['iteration', 'loss', 'lr']) + tb_writer = SummaryWriter(log_dir=os.path.join(save_path, 'tb_logs')) + + pbar = tqdm.tqdm(total=config.steps, initial=start_iteration) + iteration = start_iteration + while iteration < config.steps: + for batch_data in dataloader: + + source = batch_data['src_cell_data'].squeeze(0) + target = batch_data['tgt_cell_data'].squeeze(0) + perturbation_id = batch_data['condition_id'].squeeze(0).to(device) + if config.perturbation_function == 'crisper': + perturbation_name = [inverse_dict[int(p_id)] for p_id in perturbation_id[0].cpu().numpy()] + perturbation_id = torch.tensor(vocab.encode(perturbation_name), dtype=torch.long, device=device) + perturbation_id = perturbation_id.repeat(source.shape[0],1) + + + set_requires_grad_for_p_only(vf, p_only=config.mode) + loss = train_step(source, target, perturbation_id, vf, criterion, accelerator, noise_type=config.noise_type, mode=config.mode) + optimizer.zero_grad(set_to_none=True) + accelerator.backward(loss) + optimizer.step() + scheduler.step() + + + # --- Per-iteration loss logging --- + if accelerator.is_main_process: + current_lr = scheduler.get_last_lr()[0] + csv_writer.writerow([iteration, loss.item(), current_lr]) + if iteration % 100 == 0: + csv_file.flush() + tb_writer.add_scalar('loss/train', loss.item(), iteration) + tb_writer.add_scalar('lr', current_lr, iteration) + + if iteration % config.print_every == 0 and iteration > 0: + save_path_ = os.path.join(save_path, f'iteration_{iteration}') + os.makedirs(save_path_, exist_ok=True) + if accelerator.is_main_process: + print(f"saving {iteration}'s checkpoint...") + + save_checkpoint( + model=accelerator.unwrap_model(vf), + optimizer=optimizer, + scheduler=scheduler, + iteration=iteration, + eval_score=None, + save_path=save_path_, + is_best=False + ) + # Only evaluate at the last checkpoint + is_last = (iteration + config.print_every >= config.steps) + if is_last: + eval_score = test(valid_sampler, vf, accelerator, batch_size=config.batch_size, path=save_path_,vocab=vocab) + if accelerator.is_main_process and eval_score is not None: + tb_writer.add_scalar('eval/score', eval_score, iteration) + + accelerator.wait_for_everyone() + + pbar.update(1) + pbar.set_description(f'loss: {loss.item():.4f}, iteration: {iteration}') + iteration += 1 + + # --- Close logging --- + if accelerator.is_main_process: + csv_file.close() + tb_writer.close() + \ No newline at end of file diff --git a/transfer/code/ori_scDFM/src/tokenizer/combosciplex_5000_highly_vocab.json b/transfer/code/ori_scDFM/src/tokenizer/combosciplex_5000_highly_vocab.json new file mode 100644 index 0000000000000000000000000000000000000000..44e9c9325d015ba43d2eb271f91b68a57fe05aeb --- /dev/null +++ b/transfer/code/ori_scDFM/src/tokenizer/combosciplex_5000_highly_vocab.json @@ -0,0 +1,5006 @@ +{ + "": 0, + "": 1, + "": 2, + "control": 3, + "A1CF": 4, + "A2MP1": 5, + "A3GALT2": 6, + "AACSP1": 7, + "AADAC": 8, + "AADACL2-AS1": 9, + "AADACP1": 10, + "AAMDC": 11, + "ABCA13": 12, + "ABCA7": 13, + "ABCB1": 14, + "ABCC2": 15, + "ABCC3": 16, + "ABCC6": 17, + "ABCC9": 18, + "ABCG2": 19, + "ABCG5": 20, + "ABHD14A": 21, + "ABHD17C": 22, + "ABI3BP": 23, + "ABTB2": 24, + "AC000095.2": 25, + "AC000403.1": 26, + "AC002066.1": 27, + "AC002070.1": 28, + "AC002347.2": 29, + "AC002350.2": 30, + "AC002383.1": 31, + "AC002451.2": 32, + "AC002463.1": 33, + "AC002472.1": 34, + "AC002546.2": 35, + "AC003035.1": 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Tuple, Union +from typing_extensions import Self + +import numpy as np +import pandas as pd +import torch + +# Remove torchtext dependency and create custom Vocab class +class Vocab: + """ + Custom vocabulary implementation to replace torchtext.vocab.Vocab + """ + + def __init__(self, token2idx: Dict[str, int]): + self.stoi = token2idx + self.itos = {idx: token for token, idx in token2idx.items()} + self.default_index = None + self.default_token = None + + def __getitem__(self, token: str) -> int: + return self.stoi[token] + + def __contains__(self, token: str) -> bool: + return token in self.stoi + + def __len__(self) -> int: + return len(self.stoi) + + def get_stoi(self) -> Dict[str, int]: + return self.stoi + + def get_itos(self) -> Dict[int, str]: + return self.itos + + def insert_token(self, token: str, index: int) -> None: + """Insert a token at a specific index""" + self.stoi[token] = index + self.itos[index] = token + + def set_default_index(self, index: int) -> None: + """Set the default index for unknown tokens""" + self.default_index = index + + def set_default_token(self, token: str) -> None: + """Set the default token""" + if token in self.stoi: + self.default_token = token + self.default_index = self.stoi[token] + + def encode(self, tokens: Union[str, List[str], List[List[str]]]) -> Union[int, List[int], List[List[int]]]: + """ + Convert tokens to indices. + + Args: + tokens: Single token (str) or list of tokens + + Returns: + Single index (int) or list of indices + """ + if isinstance(tokens, str): + return self.stoi[tokens] + elif isinstance(tokens, list) and not isinstance(tokens[0], list): + return [self.stoi[token] for token in tokens] + elif isinstance(tokens, list) and isinstance(tokens[0], list): + return [[self.stoi[token] for token in token_list] for token_list in tokens] + else: + raise TypeError(f"Expected str or list of str, got {type(tokens)}") + + def decode(self, indices: Union[int, List[int], torch.Tensor, np.ndarray]) -> Union[str, List[str], torch.Tensor, np.ndarray]: + """ + Convert indices to tokens. + + Args: + indices: Single index (int) or list of indices + + Returns: + Single token (str) or list of tokens + """ + if isinstance(indices, int): + return self.itos[indices] + elif isinstance(indices, list): + return [self.itos[idx] for idx in indices] + elif isinstance(indices, torch.Tensor) or isinstance(indices, np.ndarray): + if len(indices.shape) > 1: + return [self.decode(indices[i]) for i in range(indices.shape[0])] + else: + return [self.itos[idx] for idx in indices] + else: + raise TypeError(f"Expected int or list of int, got {type(indices)}") + + def encode_batch(self, batch_tokens: List[List[str]]) -> List[List[int]]: + """ + Convert a batch of token lists to indices. + + Args: + batch_tokens: List of token lists + + Returns: + List of index lists + """ + return [self.encode(tokens) for tokens in batch_tokens] + + def decode_batch(self, batch_indices: List[List[int]]) -> List[List[str]]: + """ + Convert a batch of index lists to tokens. + + Args: + batch_indices: List of index lists + + Returns: + List of token lists + """ + return [self.decode(indices) for indices in batch_indices] + + def lookup_indices(self, tokens: List[str]) -> List[int]: + """ + Look up indices for a list of tokens, handling unknown tokens. + + Args: + tokens: List of tokens + + Returns: + List of indices, using default_index for unknown tokens + """ + indices = [] + for token in tokens: + if token in self.stoi: + indices.append(self.stoi[token]) + elif self.default_index is not None: + indices.append(self.default_index) + else: + raise KeyError(f"Token '{token}' not found in vocabulary and no default index set") + return indices + + def lookup_tokens(self, indices: List[int]) -> List[str]: + """ + Look up tokens for a list of indices. + + Args: + indices: List of indices + + Returns: + List of tokens + """ + tokens = [] + for idx in indices: + if idx in self.itos: + tokens.append(self.itos[idx]) + else: + raise KeyError(f"Index {idx} not found in vocabulary") + return tokens + + +class GeneVocab(Vocab): + """ + Vocabulary for genes. + """ + + def __init__( + self, + gene_list_or_vocab: Union[List[str], Vocab], + specials: Optional[List[str]] = None, + special_first: bool = True, + default_token: Optional[str] = "", + ) -> None: + """ + Initialize the vocabulary. + Note: add specials only works when init from a gene list. + + Args: + gene_list_or_vocab (List[str] or Vocab): List of gene names or a + Vocab object. + specials (List[str]): List of special tokens. + special_first (bool): Whether to add special tokens to the beginning + of the vocabulary. + default_token (str): Default token, by default will set to "", + if "" is in the vocabulary. + """ + if isinstance(gene_list_or_vocab, Vocab): + _vocab = gene_list_or_vocab + if specials is not None: + raise ValueError( + "receive non-empty specials when init from a Vocab object." + ) + elif isinstance(gene_list_or_vocab, list): + _vocab = self._build_vocab_from_iterator( + gene_list_or_vocab, + specials=specials, + special_first=special_first, + ) + else: + raise ValueError( + "gene_list_or_vocab must be a list of gene names or a Vocab object." + ) + super().__init__(_vocab.stoi) + if default_token is not None and default_token in self: + self.set_default_token(default_token) + + @classmethod + def from_file(cls, file_path: Union[Path, str]) -> Self: + """ + Load the vocabulary from a file. The file should be either a pickle or a + json file of token to index mapping. + """ + if isinstance(file_path, str): + file_path = Path(file_path) + if file_path.suffix == ".pkl": + with file_path.open("rb") as f: + vocab = pickle.load(f) + return cls(vocab) + elif file_path.suffix == ".json": + with file_path.open("r") as f: + token2idx = json.load(f) + return cls.from_dict(token2idx) + else: + raise ValueError( + f"{file_path} is not a valid file type. " + "Only .pkl and .json are supported." + ) + + @classmethod + def from_dict( + cls, + token2idx: Dict[str, int], + default_token: Optional[str] = "", + ) -> Self: + """ + Load the vocabulary from a dictionary. + + Args: + token2idx (Dict[str, int]): Dictionary mapping tokens to indices. + """ + # initiate an empty vocabulary first + _vocab = cls([]) + + # add the tokens to the vocabulary, GeneVocab requires consecutive indices + for t, i in sorted(token2idx.items(), key=lambda x: x[1]): + _vocab.insert_token(t, i) + + if default_token is not None and default_token in _vocab: + _vocab.set_default_token(default_token) + + return _vocab + + def _build_vocab_from_iterator( + self, + iterator: Iterable, + min_freq: int = 1, + specials: Optional[List[str]] = None, + special_first: bool = True, + ) -> Vocab: + """ + Build a Vocab from an iterator. This function is modified from + torchtext.vocab.build_vocab_from_iterator. The original function always + splits tokens into characters, which is not what we want. + + Args: + iterator (Iterable): Iterator used to build Vocab. Must yield list + or iterator of tokens. + min_freq (int): The minimum frequency needed to include a token in + the vocabulary. + specials (List[str]): Special symbols to add. The order of supplied + tokens will be preserved. + special_first (bool): Whether to add special tokens to the beginning + + Returns: + Vocab: A `Vocab` object + """ + + counter = Counter() + counter.update(iterator) + + if specials is not None: + for tok in specials: + del counter[tok] + + sorted_by_freq_tuples = sorted(counter.items(), key=lambda x: x[0]) + sorted_by_freq_tuples.sort(key=lambda x: x[1], reverse=True) + ordered_dict = OrderedDict(sorted_by_freq_tuples) + + if specials is not None: + if special_first: + specials = specials[::-1] + for symbol in specials: + ordered_dict.update({symbol: min_freq}) + ordered_dict.move_to_end(symbol, last=not special_first) + + # Create token2idx mapping + token2idx = {} + for i, (token, _) in enumerate(ordered_dict.items()): + if ordered_dict[token] >= min_freq: + token2idx[token] = i + + return Vocab(token2idx) + + @property + def pad_token(self) -> Optional[str]: + """ + Get the pad token. + """ + if getattr(self, "_pad_token", None) is None: + self._pad_token = None + return self._pad_token + + @pad_token.setter + def pad_token(self, pad_token: str) -> None: + """ + Set the pad token. Will not add the pad token to the vocabulary. + + Args: + pad_token (str): Pad token, should be in the vocabulary. + """ + if pad_token not in self: + raise ValueError(f"{pad_token} is not in the vocabulary.") + self._pad_token = pad_token + + def save_json(self, file_path: Union[Path, str]) -> None: + """ + Save the vocabulary to a json file. + """ + if isinstance(file_path, str): + file_path = Path(file_path) + with file_path.open("w") as f: + json.dump(self.get_stoi(), f, indent=2) + + def set_default_token(self, default_token: str) -> None: + """ + Set the default token. + + Args: + default_token (str): Default token. + """ + if default_token not in self: + raise ValueError(f"{default_token} is not in the vocabulary.") + self.set_default_index(self[default_token]) + + def get_gene_list(self) -> List[str]: + """ + Get the list of all genes in the vocabulary. + + Returns: + List of gene names + """ + return list(self.stoi.keys()) + + def get_gene_indices(self) -> List[int]: + """ + Get the list of all gene indices in the vocabulary. + + Returns: + List of gene indices + """ + return list(self.itos.keys()) + + def add_gene(self, gene_name: str) -> int: + """ + Add a new gene to the vocabulary. + + Args: + gene_name: Name of the gene to add + + Returns: + Index assigned to the gene + """ + if gene_name in self.stoi: + return self.stoi[gene_name] + + new_index = len(self.stoi) + self.insert_token(gene_name, new_index) + return new_index + + def add_genes(self, gene_names: List[str]) -> List[int]: + """ + Add multiple genes to the vocabulary. + + Args: + gene_names: List of gene names to add + + Returns: + List of indices assigned to the genes + """ + indices = [] + for gene_name in gene_names: + indices.append(self.add_gene(gene_name)) + return indices + + def filter_genes(self, gene_names: List[str]) -> 'GeneVocab': + """ + Create a new vocabulary containing only the specified genes. + + Args: + gene_names: List of gene names to keep + + Returns: + New GeneVocab with filtered genes + """ + filtered_stoi = {gene: self.stoi[gene] for gene in gene_names if gene in self.stoi} + return GeneVocab.from_dict(filtered_stoi, default_token=self.default_token) + + def get_gene_frequency(self, gene_name: str) -> int: + """ + Get the frequency of a gene in the vocabulary. + Note: This is a placeholder method since we don't track frequencies in this implementation. + + Args: + gene_name: Name of the gene + + Returns: + Frequency of the gene (always 1 in this implementation) + """ + if gene_name in self.stoi: + return 1 + return 0 + + +def get_default_gene_vocab() -> GeneVocab: + """ + Get the default gene vocabulary, consisting of gene symbols and ids. + """ + vocab_file = Path(__file__).parent / "default_gene_vocab.json" + if not vocab_file.exists(): + print( + f"No existing default vocab, will build one and save to {vocab_file}" + ) + return _build_default_gene_vocab(save_vocab_to=vocab_file) + print(f"Loading gene vocabulary from {vocab_file}") + return GeneVocab.from_file(vocab_file) + + +def _build_default_gene_vocab( + download_source_to: str = "/tmp", + save_vocab_to: Union[Path, str, None] = None, +) -> GeneVocab: + """ + Build the default gene vocabulary from HGNC gene symbols. + + Args: + download_source_to (str): Directory to download the source data. + save_vocab_to (Path or str): Path to save the vocabulary. If None, + the vocabulary will not be saved. Default to None. + """ + gene_collection_file = ( + Path(download_source_to) / "human.gene_name_symbol.from_genenames.org.tsv" + ) + if not gene_collection_file.exists(): + # download and save file from url + url = ( + "https://www.genenames.org/cgi-bin/download/custom?col=gd_app_sym&" + "col=md_ensembl_id&status=Approved&status=Entry%20Withdrawn&hgnc_dbtag" + "=on&order_by=gd_app_sym_sort&format=text&submit=submit" + ) + import requests + + r = requests.get(url) + gene_collection_file.write_text(r.text) + + print(f"Building gene vocabulary from {gene_collection_file}") + df = pd.read_csv(gene_collection_file, sep="\t") + gene_list = df["Approved symbol"].dropna().unique().tolist() + gene_vocab = GeneVocab(gene_list) # no special tokens set in default vocab + if save_vocab_to is not None: + gene_vocab.save_json(Path(save_vocab_to)) + return gene_vocab + + +def tokenize_batch( + data: np.ndarray, + gene_ids: np.ndarray, + return_pt: bool = True, + append_cls: bool = True, + include_zero_gene: bool = False, + cls_id: int = "", + mod_type: np.ndarray = None, + cls_id_mod_type: int = None, +) -> List[Tuple[Union[torch.Tensor, np.ndarray]]]: + """ + Tokenize a batch of data. Returns a list of tuple (gene_id, count). + + Args: + data (array-like): A batch of data, with shape (batch_size, n_features). + n_features equals the number of all genes. + gene_ids (array-like): A batch of gene ids, with shape (n_features,). + return_pt (bool): Whether to return torch tensors of gene_ids and counts, + default to True. + + Returns: + list: A list of tuple (gene_id, count) of non zero gene expressions. + """ + if data.shape[1] != len(gene_ids): + raise ValueError( + f"Number of features in data ({data.shape[1]}) does not match " + f"number of gene_ids ({len(gene_ids)})." + ) + if mod_type is not None and data.shape[1] != len(mod_type): + raise ValueError( + f"Number of features in data ({data.shape[1]}) does not match " + f"number of mod_type ({len(mod_type)})." + ) + + tokenized_data = [] + for i in range(len(data)): + row = data[i] + mod_types = None + if include_zero_gene: + values = row + genes = gene_ids + if mod_type is not None: + mod_types = mod_type + else: + idx = np.nonzero(row)[0] + values = row[idx] + genes = gene_ids[idx] + if mod_type is not None: + mod_types = mod_type[idx] + if append_cls: + genes = np.insert(genes, 0, cls_id) + values = np.insert(values, 0, 0) + if mod_type is not None: + mod_types = np.insert(mod_types, 0, cls_id_mod_type) + if return_pt: + genes = torch.from_numpy(genes).long() + values = torch.from_numpy(values).float() + if mod_type is not None: + mod_types = torch.from_numpy(mod_types).long() + tokenized_data.append((genes, values, mod_types)) + return tokenized_data + + +def pad_batch( + batch: List[Tuple], + max_len: int, + vocab: Vocab, + pad_token: str = "", + pad_value: int = 0, + cls_appended: bool = True, + vocab_mod: Vocab = None, +) -> Dict[str, torch.Tensor]: + """ + Pad a batch of data. Returns a list of Dict[gene_id, count]. + + Args: + batch (list): A list of tuple (gene_id, count). + max_len (int): The maximum length of the batch. + vocab (Vocab): The vocabulary containing the pad token. + pad_token (str): The token to pad with. + + Returns: + Dict[str, torch.Tensor]: A dictionary of gene_id and count. + """ + max_ori_len = max(len(batch[i][0]) for i in range(len(batch))) + max_len = min(max_ori_len, max_len) + + pad_id = vocab[pad_token] + if vocab_mod is not None: + mod_pad_id = vocab_mod[pad_token] + gene_ids_list = [] + values_list = [] + mod_types_list = [] + + for i in range(len(batch)): + gene_ids, values, mod_types = batch[i] + + if len(gene_ids) > max_len: + # sample max_len genes + if not cls_appended: + idx = np.random.choice(len(gene_ids), max_len, replace=False) + else: + idx = np.random.choice(len(gene_ids) - 1, max_len - 1, replace=False) + idx = idx + 1 + idx = np.insert(idx, 0, 0) + gene_ids = gene_ids[idx] + values = values[idx] + if mod_types is not None: + mod_types = mod_types[idx] + if len(gene_ids) < max_len: + gene_ids = torch.cat( + [ + gene_ids, + torch.full( + (max_len - len(gene_ids),), pad_id, dtype=gene_ids.dtype + ), + ] + ) + values = torch.cat( + [ + values, + torch.full((max_len - len(values),), pad_value, dtype=values.dtype), + ] + ) + if mod_types is not None: + mod_types = torch.cat( + [ + mod_types, + torch.full( + (max_len - len(mod_types),), + mod_pad_id, + dtype=mod_types.dtype, + ), + ] + ) + + gene_ids_list.append(gene_ids) + values_list.append(values) + if mod_types is not None: + mod_types_list.append(mod_types) + + batch_padded = { + "genes": torch.stack(gene_ids_list, dim=0), + "values": torch.stack(values_list, dim=0), + } + if mod_types is not None: + batch_padded["mod_types"] = torch.stack(mod_types_list, dim=0) + return batch_padded + + +def tokenize_and_pad_batch( + data: np.ndarray, + gene_ids: np.ndarray, + max_len: int, + vocab: Vocab, + pad_token: str, + pad_value: int, + append_cls: bool = True, + include_zero_gene: bool = False, + cls_token: str = "", + return_pt: bool = True, + mod_type: np.ndarray = None, + vocab_mod: Vocab = None, +) -> Dict[str, torch.Tensor]: + """ + Tokenize and pad a batch of data. Returns a list of tuple (gene_id, count). + """ + cls_id = vocab[cls_token] + if mod_type is not None: + cls_id_mod_type = vocab_mod[cls_token] + tokenized_data = tokenize_batch( + data, + gene_ids, + return_pt=return_pt, + append_cls=append_cls, + include_zero_gene=include_zero_gene, + cls_id=cls_id, + mod_type=mod_type, + cls_id_mod_type=cls_id_mod_type if mod_type is not None else None, + ) + + batch_padded = pad_batch( + tokenized_data, + max_len, + vocab, + pad_token, + pad_value, + cls_appended=append_cls, + vocab_mod=vocab_mod, + ) + return batch_padded + + +def random_mask_value( + values: Union[torch.Tensor, np.ndarray], + mask_ratio: float = 0.15, + mask_value: int = -1, + pad_value: int = 0, + random_mask_zero_gene: bool = False, +) -> torch.Tensor: + """ + Randomly mask a batch of data. + + Args: + values (array-like): + A batch of tokenized data, with shape (batch_size, n_features). + mask_ratio (float): The ratio of genes to mask, default to 0.15. + mask_value (int): The value to mask with, default to -1. + pad_value (int): The value of padding in the values, will be kept unchanged. + + Returns: + torch.Tensor: A tensor of masked data. + """ + if isinstance(values, torch.Tensor): + # it is crutial to clone the tensor, otherwise it changes the original tensor + values = values.clone().detach().numpy() + else: + values = values.copy() + + for i in range(len(values)): + row = values[i] + non_padding_idx = np.nonzero(row - pad_value)[0] + n_mask = int(len(non_padding_idx) * mask_ratio) + mask_idx = np.random.choice(non_padding_idx, n_mask, replace=False) + if random_mask_zero_gene: + zero_idx = np.where(row == 0)[0] + if len(zero_idx) > 0: + n_mask_zero = min(n_mask, len(zero_idx)) + mask_zero_idx = np.random.choice(zero_idx, n_mask_zero, replace=False) + row[mask_zero_idx] = mask_value + row[mask_idx] = mask_value + return torch.from_numpy(values).float() + +def category_noise(values: Union[torch.Tensor, np.ndarray], mask_ratio: float = 0.15, pad_value: int = 0, n_bins: int = 200): + """ + Randomly replace values with noise from [0, n_bins] range and return both noisy values and mask. + + Args: + values (array-like): A batch of tokenized data, with shape (batch_size, n_features) + mask_ratio (float): The ratio of values to replace with noise, default to 0.15 + pad_value (int): The value of padding in the values, will be kept unchanged + n_bins (int): The maximum bin value (exclusive) for noise generation + + Returns: + Tuple[torch.Tensor, torch.Tensor]: A tuple containing: + - noisy data tensor with same shape as input + - binary mask tensor indicating which positions were noised (1 for noised, 0 for unchanged) + """ + if isinstance(values, torch.Tensor): + values = values.clone().detach().numpy() + else: + values = values.copy() + + # Initialize mask array with same shape as values + mask = np.zeros_like(values) + + for i in range(len(values)): + row = values[i] + row_mask = mask[i] + + # Get indices of non-zero and non-padding values + nonzero_idx = np.where((row != 0) & (row != pad_value))[0] + zero_idx = np.where(row == 0)[0] + + # Calculate number of values to noise for each category + n_noise_nonzero = int(len(nonzero_idx) * mask_ratio) + n_noise_zero = int(len(zero_idx) * mask_ratio) + # n_noise_zero = int(n_noise_nonzero/10) + # Randomly select indices to noise + if len(nonzero_idx) > 0: + noise_nonzero_idx = np.random.choice(nonzero_idx, n_noise_nonzero, replace=False) + row[noise_nonzero_idx] = np.random.randint(0, n_bins, size=n_noise_nonzero) + row_mask[noise_nonzero_idx] = 1 + + if len(zero_idx) > 0: + noise_zero_idx = np.random.choice(zero_idx, n_noise_zero, replace=False) + row[noise_zero_idx] = np.random.randint(0, n_bins, size=n_noise_zero) + row_mask[noise_zero_idx] = 1 + + return torch.from_numpy(values).float(), torch.from_numpy(mask).bool() + +def batch_encode_genes( + gene_lists: List[List[str]], + vocab: Vocab, + max_length: Optional[int] = None, + pad_token: str = "", + truncate: bool = True +) -> Dict[str, torch.Tensor]: + """ + Encode a batch of gene lists to indices with padding. + + Args: + gene_lists: List of gene name lists + vocab: Vocabulary to use for encoding + max_length: Maximum length for padding/truncation + pad_token: Token to use for padding + truncate: Whether to truncate sequences longer than max_length + + Returns: + Dictionary with 'gene_ids' tensor and 'attention_mask' tensor + """ + if max_length is None: + max_length = max(len(genes) for genes in gene_lists) + + batch_size = len(gene_lists) + gene_ids = [] + attention_mask = [] + + for genes in gene_lists: + # Encode genes to indices + indices = vocab.encode(genes) + + # Truncate if necessary + if truncate and len(indices) > max_length: + indices = indices[:max_length] + + # Pad if necessary + if len(indices) < max_length: + pad_length = max_length - len(indices) + indices.extend([vocab[pad_token]] * pad_length) + mask = [1] * len(genes) + [0] * pad_length + else: + mask = [1] * max_length + + gene_ids.append(indices) + attention_mask.append(mask) + + return { + 'gene_ids': torch.tensor(gene_ids, dtype=torch.long), + 'attention_mask': torch.tensor(attention_mask, dtype=torch.long) + } + + +def batch_decode_genes( + gene_ids: torch.Tensor, + vocab: Vocab, + attention_mask: Optional[torch.Tensor] = None, + pad_token: str = "" +) -> List[List[str]]: + """ + Decode a batch of gene indices back to gene names. + + Args: + gene_ids: Tensor of shape (batch_size, seq_length) containing gene indices + vocab: Vocabulary to use for decoding + attention_mask: Optional attention mask to ignore padding tokens + pad_token: Token to ignore during decoding + + Returns: + List of gene name lists + """ + batch_size = gene_ids.shape[0] + decoded_genes = [] + + for i in range(batch_size): + indices = gene_ids[i].tolist() + genes = [] + + for j, idx in enumerate(indices): + # Skip padding tokens + if attention_mask is not None and attention_mask[i][j] == 0: + continue + if idx == vocab[pad_token]: + continue + + gene_name = vocab.decode(idx) + genes.append(gene_name) + + decoded_genes.append(genes) + + return decoded_genes + + +def create_gene_vocab_from_data( + gene_data: List[List[str]], + min_freq: int = 1, + special_tokens: Optional[List[str]] = None, + default_token: str = "" +) -> GeneVocab: + """ + Create a gene vocabulary from gene data. + + Args: + gene_data: List of gene lists + min_freq: Minimum frequency for a gene to be included + special_tokens: Special tokens to add to vocabulary + default_token: Default token for unknown genes + + Returns: + GeneVocab object + """ + # Flatten all gene lists + all_genes = [] + for gene_list in gene_data: + all_genes.extend(gene_list) + + # Count frequencies + gene_counts = Counter(all_genes) + + # Filter by minimum frequency + filtered_genes = [gene for gene, count in gene_counts.items() if count >= min_freq] + + # Add special tokens if provided + if special_tokens: + filtered_genes = special_tokens + filtered_genes + + return GeneVocab(filtered_genes, specials=special_tokens, default_token=default_token) + + +def merge_gene_vocabs( + vocab1: GeneVocab, + vocab2: GeneVocab, + default_token: str = "" +) -> GeneVocab: + """ + Merge two gene vocabularies. + + Args: + vocab1: First vocabulary + vocab2: Second vocabulary + default_token: Default token for the merged vocabulary + + Returns: + Merged GeneVocab object + """ + # Get all genes from both vocabularies + all_genes = list(vocab1.get_gene_list()) + list(vocab2.get_gene_list()) + + # Remove duplicates while preserving order + unique_genes = [] + seen = set() + for gene in all_genes: + if gene not in seen: + unique_genes.append(gene) + seen.add(gene) + + return GeneVocab(unique_genes, default_token=default_token) + +if __name__ == "__main__": + + vocab = GeneVocab(['GENE1', 'GENE2', 'GENE3'], specials=['', '']) + + indices = vocab.encode(['GENE1', 'GENE2']) + genes = vocab.decode(indices) + + gene_lists = [['GENE1', 'GENE2'], ['GENE3']] + batch_result = batch_encode_genes(gene_lists, vocab, max_length=5) + + gene_data = [['GENE1', 'GENE2'], ['GENE2', 'GENE3']] + vocab_from_data = create_gene_vocab_from_data(gene_data, min_freq=2) \ No newline at end of file diff --git a/transfer/code/ori_scDFM/src/tokenizer/norman_5000_highly_vocab.json b/transfer/code/ori_scDFM/src/tokenizer/norman_5000_highly_vocab.json new file mode 100644 index 0000000000000000000000000000000000000000..ce59caf349e949b11db2e3a6e9e86bb51ac35747 --- /dev/null +++ b/transfer/code/ori_scDFM/src/tokenizer/norman_5000_highly_vocab.json @@ -0,0 +1,5041 @@ +{ + "": 0, + "": 1, + "": 2, + "control": 3, + "A2M": 4, + "A2M-AS1": 5, + "A2ML1": 6, + "AAK1": 7, + "AASS": 8, + "AATBC": 9, + "ABAT": 10, + "ABC11-4932300O16.1": 11, + "ABC12-49244600F4.4": 12, + "ABCA1": 13, + "ABCA13": 14, + "ABCA4": 15, + "ABCA5": 16, + "ABCA6": 17, + "ABCA9": 18, + "ABCB1": 19, + "ABCB11": 20, + "ABCG1": 21, + "ABCG2": 22, + "ABHD1": 23, + "ABHD11-AS1": 24, + "ABHD12B": 25, + "ABHD17C": 26, + 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"ZMAT4": 4995, + "ZMIZ1-AS1": 4996, + "ZMYND12": 4997, + "ZMYND15": 4998, + "ZNF117": 4999, + "ZNF30-AS1": 5000, + "ZNF318": 5001, + "ZNF32-AS3": 5002, + "ZNF366": 5003, + "ZNF396": 5004, + "ZNF397": 5005, + "ZNF425": 5006, + "ZNF431": 5007, + "ZNF436-AS1": 5008, + "ZNF454": 5009, + "ZNF467": 5010, + "ZNF483": 5011, + "ZNF492": 5012, + "ZNF503": 5013, + "ZNF503-AS1": 5014, + "ZNF536": 5015, + "ZNF541": 5016, + "ZNF556": 5017, + "ZNF570": 5018, + "ZNF578": 5019, + "ZNF610": 5020, + "ZNF620": 5021, + "ZNF625": 5022, + "ZNF658": 5023, + "ZNF677": 5024, + "ZNF683": 5025, + "ZNF738": 5026, + "ZNF763": 5027, + "ZNF780B": 5028, + "ZNF83": 5029, + "ZNF843": 5030, + "ZNF844": 5031, + "ZNF85": 5032, + "ZNF862": 5033, + "ZNF879": 5034, + "ZSWIM1": 5035, + "ZSWIM4": 5036, + "ZSWIM8": 5037, + "ZYX": 5038 +} \ No newline at end of file diff --git a/transfer/code/ori_scDFM/src/utils/__init__.py b/transfer/code/ori_scDFM/src/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/transfer/code/ori_scDFM/src/utils/_logging.py b/transfer/code/ori_scDFM/src/utils/_logging.py new file mode 100644 index 0000000000000000000000000000000000000000..aab86cc25df2ad3c1552a6c2523d51d0ab80a5cf --- /dev/null +++ b/transfer/code/ori_scDFM/src/utils/_logging.py @@ -0,0 +1,23 @@ +import logging + +__all__ = ["logger"] + + +def _setup_logger() -> "logging.Logger": + from rich.console import Console + from rich.logging import RichHandler + + logger = logging.getLogger(__name__) + logger.setLevel(logging.INFO) + console = Console(force_terminal=True) + if console.is_jupyter is True: + console.is_jupyter = False + ch = RichHandler(show_path=False, console=console, show_time=False) + logger.addHandler(ch) + + # this prevents double outputs + logger.propagate = False + return logger + + +logger = _setup_logger() diff --git a/transfer/code/ori_scDFM/src/utils/_preprocessing.py b/transfer/code/ori_scDFM/src/utils/_preprocessing.py new file mode 100644 index 0000000000000000000000000000000000000000..8c6c7cc2a9abf0d2e304633b99d5df072a3c8d44 --- /dev/null +++ b/transfer/code/ori_scDFM/src/utils/_preprocessing.py @@ -0,0 +1,284 @@ +from collections.abc import Sequence +from typing import Any, Literal + +import anndata as ad +import numpy as np +import sklearn.preprocessing as preprocessing + +from src.utils._logging import logger +from src.utils._types import ArrayLike +from src.utils._utils import _to_list +import requests +import xml.etree.ElementTree as ET +from bs4 import BeautifulSoup + +__all__ = ["encode_onehot", "annotate_compounds", "get_molecular_fingerprints"] + + +def get_smiles_pubchemlite(cid): + url = f"https://pubchemlite.lcsb.uni.lu/e/compound/{cid}" + r = requests.get(url) + soup = BeautifulSoup(r.text, 'html.parser') + label = soup.find(text="SMILES") + try: + return label.find_next().text.strip() + except: + return None + + +def get_smiles_via_http(cid): + url = f"https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi" + params = { + 'db': 'pccompound', + 'id': cid, + 'rettype': 'docsum', + 'retmode': 'xml' + } + r = requests.get(url, params=params) + r.raise_for_status() + root = ET.fromstring(r.text) + for item in root.findall('.//Item'): + if item.get('Name') in ('IsomericSmiles', 'CanonicalSMILES'): + return item.text + return None + +def annotate_compounds( + adata: ad.AnnData, + compound_keys: str | Sequence[str], + control_category: str = "control", + query_id_type: Literal["name", "cid"] = "name", + obs_key_prefixes: str | Sequence[str] | None = None, + copy: bool = False, +) -> None | ad.AnnData: + """Annotates compounds in ``adata`` using pertpy and PubChem. + + Parameters + ---------- + adata + An :class:`~anndata.AnnData` object. + compound_keys + Key(s) in :attr:`~anndata.AnnData.obs` containing the compound identifiers. + control_category + Category to exclude from the annotation. + query_id_type + Type of the compound identifiers. Either ``'name'`` or ``'cid'``. + obs_key_prefixes + Prefix for the keys in :attr:`~anndata.AnnData.obs` to store the annotations. If :obj:`None`, + uses ``compound_keys`` as prefixes. + copy + Return a copy of ``adata`` instead of updating it in place. + + Returns + ------- + If ``copy`` is :obj:`True`, returns a new :class:`~anndata.AnnData` object with the compound + annotations stored in :attr:`~anndata.AnnData.obs`. Otherwise, updates ``adata`` in place. + + Sets the following fields for each value in ``compound_keys``: + + - ``.obs[f"{obs_key_prefix}_pubchem_name"]``: Name of the compound. + - ``.obs[f"{obs_key_prefix}_pubchem_ID"]``: PubChem CID of the compound. + - ``.obs[f"{obs_key_prefix}_smiles"]``: SMILES representation of the compound. + """ + try: + import pertpy as pt + except ImportError as e: + raise ImportError( + "pertpy is not installed. To annotate compounds, please install it via `pip install pertpy`." + ) from e + + adata = adata.copy() if copy else adata + + compound_keys = _to_list(compound_keys) + obs_key_prefixes = _to_list(obs_key_prefixes) if obs_key_prefixes is not None else compound_keys + + if len(compound_keys) != len(obs_key_prefixes): + raise ValueError("The number of `compound_keys` must match the number of values in `obs_key_prefixes`.") + + # Annotate compounds in each query column + not_found = set() + c_meta = pt.metadata.Compound() + for query_key, prefix in zip(compound_keys, obs_key_prefixes, strict=False): + adata.obs[query_key] = adata.obs[query_key].replace(control_category, np.nan) + c_meta.annotate_compounds( + adata, + query_id=query_key, + query_id_type=query_id_type, + verbosity=0, + copy=False, + ) + + # if adata.obs['smiles'] is None, using get_smiles_pubchemlite to get smiles + # missing = adata.obs.loc[adata.obs["smiles"].isna(), query_key].dropna().unique().tolist() + # not_found.update(missing) + missing_pubchem_ID = adata.obs.loc[~adata.obs["pubchem_ID"].isna(), 'pubchem_ID'].dropna().unique().tolist() + + for cid in missing_pubchem_ID: + adata.obs.loc[adata.obs['pubchem_ID'] == cid, "smiles"] = get_smiles_via_http(cid) + + adata.obs[[query_key, "pubchem_name"]] = adata.obs[[query_key, "pubchem_name"]].fillna(control_category) + + # Drop columns with new annotations + adata.obs.rename( + columns={ + "pubchem_name": f"{prefix}_pubchem_name", + "pubchem_ID": f"{prefix}_pubchem_ID", + "smiles": f"{prefix}_smiles", + }, + inplace=True, + ) + + # if not_found: + # logger.warning(f"Could not find annotations for the following compounds: {', '.join(map(str, not_found))}") + + return adata if copy else None + + +def _get_fingerprint(smiles: str, radius: int = 4, n_bits: int = 1024) -> ArrayLike | None: + """Computes Morgan fingerprints for a given SMILES string.""" + try: + import rdkit.Chem.rdFingerprintGenerator as rfg + from rdkit import Chem + except ImportError: + raise ImportError( + "rdkit is not installed. To compute fingerprints, please install it via `pip install rdkit`." + ) from None + + mmol = Chem.MolFromSmiles(str(smiles), sanitize=True) + + # Check if molecule is valid, MolFromSmiles returns None if error occurs + if mmol is None: + return None + + mfpgen = rfg.GetMorganGenerator(radius=radius, fpSize=n_bits) + return np.array(mfpgen.GetFingerprint(mmol)) + + +def get_molecular_fingerprints( + adata, + compound_keys: str | list[str], + smiles_keys: str | None = None, + control_value: str = "control", + uns_key_added: str = "fingerprints", + radius: int = 4, + n_bits: int = 1024, + copy: bool = False, +) -> None | ad.AnnData: + """Computes Morgan fingerprints for compounds in ``adata`` and stores them in :attr:`~anndata.AnnData.uns`. + + Parameters + ---------- + adata + An :class:`~anndata.AnnData` object. + compound_keys + Key(s) in :attr:`~anndata.AnnData.obs` containing the compound identifiers. + control_value + Skip `control_value` (and :obj:`None` values). + smiles_keys + Key(s) in :attr:`~anndata.AnnData.obs` containing the SMILES strings. If :obj:`None`, uses + ``f"{compound_key}_smiles"``. + uns_key_added + Key in :attr:`~anndata.AnnData.uns` to store the fingerprints. + radius + Radius of the Morgan fingerprints. + n_bits + Number of bits in the fingerprint. + copy + Return a copy of ``adata`` instead of updating it in place. + + Returns + ------- + Updates :attr:`~anndata.AnnData.uns` with the computed fingerprints. + + Sets the following fields: + + - ``.uns[uns_key_added]``: Dictionary containing the fingerprints for each compound. + """ + adata = adata.copy() if copy else adata + + compound_keys = _to_list(compound_keys) + + if smiles_keys is None: + smiles_keys = [f"{key}_smiles" for key in compound_keys] + + smiles_keys = _to_list(smiles_keys) + + # Get dict with SMILES for each compound + smiles_dict = {} + for compound_key, smiles_key in zip(compound_keys, smiles_keys, strict=False): # type: ignore[arg-type] + if compound_key not in adata.obs: + raise KeyError(f"Key {compound_key} not found in `adata.obs`.") + + if smiles_key not in adata.obs: + raise KeyError(f"Key {smiles_key} not found in `adata.obs`.") + smiles_dict.update(adata.obs.set_index(compound_key)[smiles_key].to_dict()) + + # Compute fingerprints for each compound + valid_fingerprints = {} + not_found = [] + for comp, smiles in smiles_dict.items(): + if not isinstance(comp, str) or comp == control_value: + continue + comp_fp = _get_fingerprint(smiles, radius=radius, n_bits=n_bits) + if comp_fp is not None: + valid_fingerprints[comp] = comp_fp + else: + not_found.append(str(comp)) + + if not_found: + logger.warning(f"Could not compute fingerprints for the following compounds: {', '.join(not_found)}") + + adata.uns[uns_key_added] = valid_fingerprints + + if copy: + return adata + + +def encode_onehot( + adata: ad.AnnData, + covariate_keys: str | Sequence[str], + uns_key_added: str, + exclude_values: str | Sequence[Any] = None, + copy: bool = False, +) -> None | ad.AnnData: + """Encodes covariates :attr:`~anndata.AnnData.obs` as one-hot vectors and stores them in :attr:`~anndata.AnnData.uns`. + + Parameters + ---------- + adata + An :class:`~anndata.AnnData` object. + covariate_keys + Key(s) in :attr:`~anndata.AnnData.obs` containing the covariate(s) to encode. + uns_key_added + Key in :attr:`~anndata.AnnData.uns` to store the one-hot encodings. + exclude_values + Value(s) to exclude from the one-hot encoding. + copy + Return a copy of ``adata`` instead of updating it in place. + + Returns + ------- + If ``copy`` is :obj:`True`, returns a new :class:`~anndata.AnnData` object with the one-hot + encodings stored in :attr:`~anndata.AnnData.uns`. Otherwise, updates ``adata`` in place. + + Sets the following fields: + + - ``.uns[uns_key_added]``: Dictionary containing the one-hot encodings for each covariate. + """ + adata = adata.copy() if copy else adata + + covariate_keys = _to_list(covariate_keys) + exclude_values = _to_list(exclude_values) + + # Get unique values from all columns + all_values = np.unique(adata.obs[covariate_keys].values.flatten()) + values_encode = np.setdiff1d(all_values, exclude_values).reshape(-1, 1) + encoder = preprocessing.OneHotEncoder(sparse_output=False) + encodings = encoder.fit_transform(values_encode) + + # Store encodings in adata.uns + adata.uns[uns_key_added] = {} + for value, encoding in zip(values_encode, encodings, strict=False): + adata.uns[uns_key_added][value[0]] = encoding + + if copy: + return adata diff --git a/transfer/code/ori_scDFM/src/utils/_types.py b/transfer/code/ori_scDFM/src/utils/_types.py new file mode 100644 index 0000000000000000000000000000000000000000..6485eac76a0067ce1e5516fe9d577ca743bb77dd --- /dev/null +++ b/transfer/code/ori_scDFM/src/utils/_types.py @@ -0,0 +1,22 @@ +import os +from collections.abc import Callable, Sequence +from typing import Any + +import numpy as np + +# TODO(michalk8): polish + +try: + from numpy.typing import NDArray + + ArrayLike = NDArray[np.float64] +except (ImportError, TypeError): + ArrayLike = np.ndarray # type: ignore[misc] + DTypeLike = np.dtype + +ComputationCallback_t = Callable[[dict[str, ArrayLike], dict[str, ArrayLike]], dict[str, Any]] +LoggingCallback_t = Callable[[dict[str, ArrayLike]], dict[str, Any]] + +Layers_t = Sequence[dict[str, Any]] +Layers_separate_input_t = dict[str, Layers_t] +PathLike = os.PathLike | str # type: ignore[type-arg] diff --git a/transfer/code/ori_scDFM/src/utils/_utils.py b/transfer/code/ori_scDFM/src/utils/_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..c22d50bbaee7fd8343926a618fe00f5e085e49db --- /dev/null +++ b/transfer/code/ori_scDFM/src/utils/_utils.py @@ -0,0 +1,14 @@ +from collections.abc import Iterable +from typing import Any + + +def _to_list(x: list[Any] | tuple[Any] | Any) -> list[Any] | tuple[Any]: + """Converts x to a list if it is not already a list or tuple.""" + if isinstance(x, (list | tuple)): + return x + return [x] + + +def _flatten_list(x: Iterable[Iterable[Any]]) -> list[Any]: + """Flattens a list of lists.""" + return [item for sublist in x for item in sublist] diff --git a/transfer/code/ori_scDFM/src/utils/utils.py b/transfer/code/ori_scDFM/src/utils/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..5c76489997c65597da0b56c1217852a24b500c89 --- /dev/null +++ b/transfer/code/ori_scDFM/src/utils/utils.py @@ -0,0 +1,536 @@ +import random +import numpy as np +import torch +import os +from src.tokenizer.gene_tokenizer import GeneVocab +from typing import Optional, Dict +import numpy as np +import pandas as pd +from scipy import stats, sparse +import networkx as nx + +def set_seed(seed): + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + + +def pick_eval_score(agg_results, scheme): + df = agg_results.to_pandas() + + if scheme in ["pearson_delta", "mse", "mae", "mse_delta"]: + return float(df[scheme].iloc[0]) + + if scheme == "reverse": + return float(df["pr_auc"].iloc[0]) + + if scheme == "forward": + pear = float(df["pearson_delta"].iloc[0]) + mse = float(df["mse_delta"].iloc[0]) + alpha = 0.05 + return pear - alpha * mse + + if scheme == "de": + keys = ["de_spearman_sig", "de_direction_match", "de_sig_genes_recall"] + return float(df[keys].iloc[0].mean()) + + if scheme == "composite": + pear = float(df["pearson_delta"].iloc[0]) + mse = float(df["mse_delta"].iloc[0]) + pra = float(df["pr_auc"].iloc[0]) + de = float(df[["de_spearman_sig","de_direction_match","de_sig_genes_recall"]].iloc[0].mean()) + return 0.4*pra + 0.3*de + 0.3*(pear - 0.05*mse) + + raise ValueError("unknown scheme") + +def make_lognorm_poisson_noise(target_log, alpha=1.0, per_cell_L=None, eps=1e-8): + """ + target_log: log1p(normalized_counts) + alpha: noise intensity (0.3~1.0; smaller is more conservative) + per_cell_L: if specified (e.g. 1e4), normalize expected total per cell to L + """ + base = torch.expm1(target_log) + if per_cell_L is not None or per_cell_L == -1 : + scale = per_cell_L / (base.sum(dim=1, keepdim=True) + eps) + lam = (base * scale).clamp_min(1e-8) + else: + lam = (alpha * base).clamp_min(1e-8) + x0_counts = torch.poisson(lam) + x0_log = torch.log1p(x0_counts) + return x0_log + + +def save_checkpoint(model, optimizer, scheduler, iteration, eval_score, save_path, is_best=False): + """save checkpoint""" + checkpoint = { + 'iteration': iteration, + 'model_state_dict': model.state_dict(), + 'optimizer_state_dict': optimizer.state_dict(), + 'scheduler_state_dict': scheduler.state_dict(), + 'eval_score': eval_score, + } + + checkpoint_path = os.path.join(save_path, f'checkpoint.pt') + torch.save(checkpoint, checkpoint_path) + print(f"save checkpoint to: {checkpoint_path}") + + # If this is the best model, save an extra copy + if is_best: + best_path = os.path.join(save_path, 'best_checkpoint.pt') + torch.save(checkpoint, best_path) + print(f"save best checkpoint: {best_path}") + +def load_checkpoint(checkpoint_path, model, optimizer, scheduler): + """load checkpoint""" + if os.path.exists(checkpoint_path): + checkpoint = torch.load(checkpoint_path, map_location='cpu') + model.load_state_dict(checkpoint['model_state_dict']) + optimizer.load_state_dict(checkpoint['optimizer_state_dict']) + scheduler.load_state_dict(checkpoint['scheduler_state_dict']) + iteration = checkpoint['iteration'] + eval_score = checkpoint.get('eval_score', float('-inf')) + print(f"loading {checkpoint_path} checkpoint, iteration: {iteration}, eval_score: {eval_score}") + return iteration, eval_score + else: + print(f"Checkpoint file not found: {checkpoint_path}") + return 0, float('-inf') + + +def process_vocab(data_manager, config): + vocab_path = os.path.join('src/tokenizer',config.data_name+'_'+str(config.n_top_genes)+'_highly_vocab.json') + if os.path.exists(vocab_path): + print('##### loading vocab from file #####') + vocab = GeneVocab.from_file(vocab_path) + else: + print('##### building vocab #####') + highly_gene = data_manager.adata.var[data_manager.adata_train.var['highly_variable']] + vocab = GeneVocab(list(highly_gene.index), specials=['', '', '', 'control']) + vocab.save_json(vocab_path) + vocab = GeneVocab.from_file(vocab_path) + return vocab + +def perturbation_id_to_emb_id(perturbation_id, vocab, perturbation_dict=None): + device = perturbation_id.device + inverse_dict = {v: str(k) for k, v in perturbation_dict.items()} + perturbation_name = [inverse_dict[int(p_id)] for p_id in perturbation_id.cpu().numpy()] + emb_id = vocab.encode(perturbation_name) + emb_id = torch.tensor(emb_id, dtype=torch.long, device=device) + return emb_id + +def emb_id_to_perturbation_id(perturbation_id, vocab, perturbation_dict=None): + device = perturbation_id.device + inverse_dict = {v: str(k) for k, v in perturbation_dict.items()} + perturbation_name = [inverse_dict[int(p_id)] for p_id in perturbation_id.cpu().numpy()] + emb_id = vocab.encode(perturbation_name) + emb_id = torch.tensor(emb_id, dtype=torch.long, device=device) + return emb_id + + +@torch.no_grad() +def approximate_with_two_perturbations( + model, + perturb_vec: torch.Tensor, # target perturbation vector, shape (d,) or (1,d) or (B,d) + candidate_ids: Optional[torch.Tensor]=None,# If None and not 'crisper', use all ntoken as candidates + topk: int = 64, # preselect top-k candidates by cosine similarity + nonneg_renorm: bool = True, # clamp weights to non-negative and normalize to sum=1 + return_reconstruction: bool = True, + topn_output: int = 10, # number of top results to return + mode: str = "pair", # "pair" = combinations of two, "single" = single candidate +): + device = next(model.parameters()).device + dtype = next(model.parameters()).dtype + + # 1) Normalize target vector to shape (B, d) + if perturb_vec.dim() == 1: + perturb_vec = perturb_vec.unsqueeze(0) + perturb_vec = perturb_vec.to(device=device, dtype=dtype) # (B, d) + B, d = perturb_vec.shape + + # 2) Build candidate embedding matrix E: (N, d) + if model.perturbation_function == 'crisper': + assert candidate_ids is not None, "In 'crisper' mode, candidate_ids (N, L) must be provided." + # Encode and average over sequence length + E = model.encoder(candidate_ids.to(device=device)).to(dtype=dtype).mean(dim=1) + cand_index = torch.arange(E.size(0), device=device) + else: + if candidate_ids is None: + all_ids = torch.arange(model.perturbation_embedder.embedding.num_embeddings, device=device) + E = model.perturbation_embedder(all_ids).to(dtype=dtype) + cand_index = all_ids + else: + if candidate_ids.dim() == 2 and candidate_ids.size(1) == 1: + candidate_ids = candidate_ids.squeeze(1) + E = model.perturbation_embedder(candidate_ids.to(device=device)).to(dtype=dtype) + cand_index = candidate_ids.to(device) + + N = E.size(0) + assert N >= 1, "Need at least 1 candidate." + + # Mode: single perturbation search + if mode == "single": + E_norm = torch.nn.functional.normalize(E, dim=-1) + P_norm = torch.nn.functional.normalize(perturb_vec, dim=-1) + sims = P_norm @ E_norm.t() # cosine similarity (B, N) + k = min(topn_output, N) + topn_val, topn_idx = torch.topk(sims, k=k, dim=-1) + return { + "topn_ids": cand_index[topn_idx], # (B, k) + "topn_scores": topn_val, # (B, k) + } + + # Mode: pairwise search + elif mode == "pair": + assert N >= 2, "Pair mode requires at least 2 candidates." + + # Preselect top-k candidates by cosine similarity + E_norm = torch.nn.functional.normalize(E, dim=-1) + P_norm = torch.nn.functional.normalize(perturb_vec, dim=-1) + sims = P_norm @ E_norm.t() # (B, N) + k = min(topk, N) + _, topk_idx = torch.topk(sims, k=k, dim=-1) # (B, k) + + all_pairs = [] + I2 = torch.eye(2, device=device, dtype=dtype) + + for b in range(B): + cand = E[topk_idx[b]] # (k, d) + idxs = topk_idx[b] # (k,) + pair_results = [] + + for i in range(k): + e_i = cand[i] + for j in range(i + 1, k): + e_j = cand[j] + # Stack into matrix A: (d, 2) + A = torch.stack([e_i, e_j], dim=1) + ATA = A.t() @ A + ATp = A.t() @ perturb_vec[b] + # Solve least squares with small regularization for stability + w = torch.linalg.solve(ATA + 1e-6 * I2, ATp) + + if nonneg_renorm: + w = torch.clamp(w, min=0) + s = w.sum() + if s > 0: + w = w / s + + recon = A @ w + err = torch.norm(perturb_vec[b] - recon, p=2) + + pair_results.append(( + err.item(), + cand_index[idxs[i]].item(), + cand_index[idxs[j]].item(), + w.detach().clone().cpu(), + recon.detach().clone().cpu() if return_reconstruction else None + )) + + # Sort by error and keep top N + pair_results.sort(key=lambda x: x[0]) + pair_results = pair_results[:min(topn_output, len(pair_results))] + all_pairs.append(pair_results) + + return { + "topn_pairs": all_pairs, # length B; each is a list of up to topn_output tuples + } + + else: + raise ValueError("mode must be 'pair' or 'single'") + +@torch.no_grad() +def get_perturbation_embedding(model, perturbation_id: torch.Tensor) -> torch.Tensor: + device = next(model.parameters()).device + dtype = next(model.parameters()).dtype + + if model.perturbation_function == 'crisper': + # (B, L) -> (B, L, d) -> mean over L -> (B, d) + emb = model.encoder(perturbation_id.to(device=device)).to(dtype=dtype) + emb = emb.mean(dim=1) + else: + # (B,) or (B,1) -> (B, d) + if perturbation_id.dim() == 2 and perturbation_id.size(1) == 1: + perturbation_id = perturbation_id.squeeze(1) + emb = model.perturbation_embedder(perturbation_id.to(device=device)).to(dtype=dtype) + return emb # (B, d_model) + +def freeze_backbone_for_p(vf): + for name, p in vf.named_parameters(): + if name.startswith('p_head.') or name == 'p_mask_embed': + p.requires_grad_(True) + else: + p.requires_grad_(False) + +def unfreeze_all(vf): + for _, p in vf.named_parameters(): + p.requires_grad_(True) + +def set_requires_grad_for_p_only(vf, p_only: str): + if hasattr(vf, "module"): + base_vf = vf.module + else: + base_vf = vf + for name, p in base_vf.named_parameters(): + if p_only == 'predict_p': + if name.startswith("p_head.") or name == "p_mask_embed": + p.requires_grad_(True) + else: + p.requires_grad_(False) + else: + p.requires_grad_(True) + +@torch.no_grad() +def get_perturbation_emb(vf, perturbation_id=None, perturbation_emb=None, + cell_1=None, use_mask: bool=False): + if use_mask: + B = cell_1.size(0) + return vf.p_mask_embed[None, :].expand(B, -1).to(cell_1.device, dtype=cell_1.dtype) + + assert perturbation_emb is None or perturbation_id is None + if perturbation_id is not None: + if vf.perturbation_function == 'crisper': + perturbation_emb = vf.encoder(perturbation_id) + else: + perturbation_emb = vf.perturbation_embedder(perturbation_id) + perturbation_emb = perturbation_emb.mean(1) # (B,d) + elif perturbation_emb is not None: + perturbation_emb = perturbation_emb.to(cell_1.device, dtype=cell_1.dtype) + if perturbation_emb.dim() == 1: + perturbation_emb = perturbation_emb.unsqueeze(0) + if perturbation_emb.size(0) == 1: + perturbation_emb = perturbation_emb.expand(cell_1.shape[0], -1).contiguous() + perturbation_emb = vf.perturbation_embedder.enc_norm(perturbation_emb) + return perturbation_emb + + +import numpy as np +import pandas as pd +from scipy import stats, sparse +import networkx as nx +import torch +from scipy import sparse + + +def preprocess_expression(X, log1p=False, zscore_per_gene=True): + + if isinstance(X, pd.DataFrame): + X = X.values + X = X.astype(np.float64, copy=False) + if log1p: + X = np.log1p(np.clip(X, a_min=0, a_max=None)) + if zscore_per_gene: + # z-score per gene (column) + mean = X.mean(axis=0, keepdims=True) + std = X.std(axis=0, ddof=1, keepdims=True) + std[std == 0] = 1.0 + X = (X - mean) / std + return X + +# ============ 1) Correlation matrix calculation ============ +def correlation_matrix(X, method="pearson"): + """ + X: (cells, genes) + return: (genes, genes) correlation matrix in [-1, 1] + """ + if method == "pearson": + # np.corrcoef computes correlation by row by default, so transpose to compute by column (gene) + C = np.corrcoef(X.T) + elif method == "spearman": + # Rank transform each gene, then Pearson + X_rank = np.apply_along_axis(stats.rankdata, 0, X) + C = np.corrcoef(X_rank.T) + else: + raise ValueError("method must be 'pearson' or 'spearman'") + # Numerical stability + np.fill_diagonal(C, 1.0) + C = np.clip(C, -1.0, 1.0) + return C +def safe_correlation_matrix(X, method="pearson"): + """ + X: (cells, genes). Returns (genes, genes) correlation matrix, no NaN/Inf. + Rules: + - If any gene has variance 0, set correlation with any other gene to 0 (self-correlation to 1) + - If data contains NaN/Inf, replace with 0 (you can change to median/mean) + """ + X = np.asarray(X, dtype=np.float64) + + # 1) Clean NaN/Inf + X = np.nan_to_num(X, nan=0.0, posinf=0.0, neginf=0.0) + + # 2) Spearman: rank transform per column + if method == "spearman": + X = np.apply_along_axis(stats.rankdata, 0, X) + + n, g = X.shape + + # 3) Center + Xc = X - X.mean(axis=0, keepdims=True) + + # 4) Column std (ddof=1), mark zero-variance columns + std = Xc.std(axis=0, ddof=1) + zero_var = std == 0.0 + + # 5) Covariance matrix + cov = (Xc.T @ Xc) / max(n - 1, 1) + + # 6) Build denominator and safe division + denom = np.outer(std, std) + with np.errstate(divide="ignore", invalid="ignore"): + C = cov / denom + + # 7) Handle zero variance: wherever denominator is 0, set to 0; self-correlation to 1 + C[np.isnan(C)] = 0.0 + C[np.isinf(C)] = 0.0 + C[zero_var, :] = 0.0 + C[:, zero_var] = 0.0 + np.fill_diagonal(C, 1.0) + + # Clip + np.clip(C, -1.0, 1.0, out=C) + return C +# ============ 2) WGCNA-style soft threshold (optional) ============ +def soft_threshold_weights(C, beta=6, use_abs=True): + """ + C: correlation matrix (genes, genes) + return: weighted adjacency (non-negative) + """ + W = np.abs(C) if use_abs else np.maximum(C, 0.0) + W = np.power(W, beta) + np.fill_diagonal(W, 0.0) + return W + +# ============ 3A) Sparsification: Top-k ============ +def sparsify_topk(W, k=10, keep_symmetry=True): + """ + For each gene, keep k edges with largest absolute value. + W: dense (genes, genes) + return: csr sparse adjacency + """ + n = W.shape[0] + rows, cols, data = [], [], [] + for i in range(n): + # Remove self-loop + row = W[i].copy() + row[i] = 0.0 + if k >= n - 1: + idx = np.where(row != 0)[0] + else: + idx = np.argpartition(-np.abs(row), kth=min(k, n-2))[:k] + vals = row[idx] + mask = vals != 0 + rows.extend([i]*mask.sum()) + cols.extend(idx[mask]) + data.extend(vals[mask]) + + A = sparse.csr_matrix((data, (rows, cols)), shape=W.shape) + if keep_symmetry: + # Take symmetric max or sum; here take max + A = A.maximum(A.T) + return A + +# ============ 3B) Sparsification: threshold ============ +def sparsify_threshold(W, tau=0.3, keep_symmetry=True): + """ + Only keep edges where |W_ij| >= tau + W: dense (genes, genes) + """ + M = np.abs(W) >= tau + np.fill_diagonal(M, False) + rows, cols = np.where(M) + data = W[rows, cols] + A = sparse.csr_matrix((data, (rows, cols)), shape=W.shape) + if keep_symmetry: + A = A.maximum(A.T) + return A + +# ============ 4) GCN normalization ============ +def gcn_normalize(A, add_self_loops=True): + """ + Kipf & Welling GCN symmetric normalization: \hat A = D^{-1/2} (A + I) D^{-1/2} + A: csr adjacency + """ + if add_self_loops: + A = A + sparse.eye(A.shape[0], format='csr') + deg = np.array(A.sum(axis=1)).flatten() + deg[deg == 0] = 1.0 + D_inv_sqrt = sparse.diags(1.0 / np.sqrt(deg)) + A_hat = D_inv_sqrt @ A @ D_inv_sqrt + return A_hat.tocsr() + +# ============ 5) Build graph (optional, for visualization/export) ============ +def to_networkx(A, gene_names=None, weight_attr="weight"): + """ + A: csr adjacency + """ + # Compatible with networkx versions + if hasattr(nx, "from_scipy_sparse_array"): + G = nx.from_scipy_sparse_array(A, edge_attribute=weight_attr) + else: + G = nx.from_scipy_sparse_matrix(A, edge_attribute=weight_attr) + + if gene_names is not None: + mapping = {i: name for i, name in enumerate(gene_names)} + G = nx.relabel_nodes(G, mapping) + return G + + +def adjacency_to_mha_mask(A_csr: sparse.csr_matrix, allow_self=True): + # Sparse adjacency -> dense 0/1 allowed matrix + A = A_csr.tolil(copy=True) + if allow_self: + A.setdiag(1) + A = A.tocsr().toarray().astype(bool) # True=allowed edge + disallow = ~A # True=disallowed (PyTorch bool mask semantics) + # For MultiheadAttention, attn_mask shape can be (L, S) + attn_mask_bool = torch.from_numpy(disallow) + return attn_mask_bool # True means masked + +# ============ 6) One-stop pipeline ============ +def build_gene_coexpression_graph( + X, + method="pearson", + wgcna_beta=None, # int or None; if given, use soft threshold weights + sparsify="topk", # "topk" or "threshold" + k=10, # top-k + tau=0.3, # threshold + log1p=True, + zscore_per_gene=True, + use_negative_edge=False +): + Xp = preprocess_expression(X, log1p=log1p, zscore_per_gene=zscore_per_gene) + # C = correlation_matrix(Xp, method=method) + C = safe_correlation_matrix(Xp, method=method) + # Choose weight matrix source: use correlation directly or WGCNA style + + + if wgcna_beta is not None: + W = soft_threshold_weights(C, beta=wgcna_beta, use_abs=True) + else: + W = C + + sign_matrix = np.sign(W) + if use_negative_edge: + W = np.abs(W) + # Sparsification + if sparsify == "topk": + A = sparsify_topk(W, k=k, keep_symmetry=True) + elif sparsify == "threshold": + A = sparsify_threshold(W, tau=tau, keep_symmetry=True) + else: + raise ValueError("sparsify must be 'topk' or 'threshold'") + mask = adjacency_to_mha_mask(A) + # return A, A_hat, G, sign_matrix + return mask + +def sorted_pad_mask(mask, pad_size=4, gene_names=None): + sorted_gene_names = sorted(list(gene_names)) + reorder_idx = [gene_names.index(g) for g in sorted_gene_names] + mask_sorted = mask[reorder_idx][:, reorder_idx] + orig_shape = mask_sorted.shape + pad_row = torch.ones((pad_size, orig_shape[1]), dtype=mask_sorted.dtype) + pad_col = torch.ones((orig_shape[0] + pad_size, pad_size), dtype=mask_sorted.dtype) + mask_padded = torch.cat([pad_row, mask_sorted], dim=0) + mask_padded = torch.cat([pad_col, mask_padded], dim=1) + length = mask.shape[0] + mask_padded[torch.arange(length), torch.arange(length)] = False + return mask_padded \ No newline at end of file diff --git a/transfer/code/prompt_selection/__init__.py b/transfer/code/prompt_selection/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..66fdbc0d7bf00abc10300f2b0704e308f61c2bcc --- /dev/null +++ b/transfer/code/prompt_selection/__init__.py @@ -0,0 +1 @@ +"Embedding-based prompt selection pipeline for Stack generation." diff --git a/transfer/code/prompt_selection/__main__.py b/transfer/code/prompt_selection/__main__.py new file mode 100644 index 0000000000000000000000000000000000000000..40d44b1e5a7e61fd409a71f6ce99160267644540 --- /dev/null +++ b/transfer/code/prompt_selection/__main__.py @@ -0,0 +1,4 @@ +"""Allow ``python -m prompt_selection.run_pipeline`` from code/ directory.""" +from prompt_selection.run_pipeline import main + +main() diff --git a/transfer/code/prompt_selection/aggregate_results.py b/transfer/code/prompt_selection/aggregate_results.py new file mode 100644 index 0000000000000000000000000000000000000000..e7691208774cf6736ad22e35948d2a9d9daf78d7 --- /dev/null +++ b/transfer/code/prompt_selection/aggregate_results.py @@ -0,0 +1,102 @@ +#!/usr/bin/env python3 +"""Aggregate cell-eval comparison results across all perturbation conditions. + +Reads per-perturbation comparison_mean.csv files and produces: + 1. all_comparison.csv — full table with perturbation column + 2. Summary statistics printed to stdout + +Usage: + python code/prompt_selection/aggregate_results.py +""" +from __future__ import annotations + +import logging +import sys +from pathlib import Path + +_THIS_DIR = Path(__file__).resolve().parent +if str(_THIS_DIR.parent) not in sys.path: + sys.path.insert(0, str(_THIS_DIR.parent)) + +import pandas as pd + +from prompt_selection import config as cfg + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s [%(name)s] %(levelname)s: %(message)s", +) +LOGGER = logging.getLogger("aggregate_results") + + +def main(): + all_dfs = [] + + for pert_name in cfg.ALL_PERTURBATIONS: + pcfg = cfg.get_pert_config(pert_name) + csv_path = pcfg.eval_dir / "comparison_mean.csv" + + if not csv_path.exists(): + LOGGER.warning("No comparison_mean.csv for %s, skipping.", pert_name) + continue + + df = pd.read_csv(csv_path) + df["perturbation"] = pert_name + all_dfs.append(df) + LOGGER.info("Loaded %s (%d metrics)", pert_name, len(df)) + + if not all_dfs: + LOGGER.error("No comparison results found. Run evaluation first.") + return + + combined = pd.concat(all_dfs, ignore_index=True) + + # Save full table + output_path = cfg.EVAL_DIR / "all_comparison.csv" + cfg.EVAL_DIR.mkdir(parents=True, exist_ok=True) + combined.to_csv(output_path, index=False) + LOGGER.info("Saved aggregated results: %s (%d rows)", output_path, len(combined)) + + # Summary: mean across perturbations per metric + print("\n" + "=" * 80) + print("SUMMARY: Mean across all perturbations") + print("=" * 80) + + summary = combined.groupby("metric")[["prompt_selection", "random_baseline", "diff"]].agg( + ["mean", "std"] + ) + summary.columns = [f"{col}_{stat}" for col, stat in summary.columns] + summary = summary.sort_values("diff_mean", ascending=False) + + print(summary.to_string()) + + summary_path = cfg.EVAL_DIR / "summary_statistics.csv" + summary.to_csv(summary_path) + LOGGER.info("Saved summary statistics: %s", summary_path) + + # Count wins + print("\n" + "=" * 80) + print("WIN COUNTS (per metric, across perturbations)") + print("=" * 80) + + lower_is_better = {"mse", "mae", "mse_delta", "mae_delta"} + for metric_name, group in combined.groupby("metric"): + ps_wins = 0 + bl_wins = 0 + ties = 0 + for _, row in group.iterrows(): + diff = row["diff"] + if metric_name in lower_is_better: + diff = -diff + if abs(diff) < 1e-12: + ties += 1 + elif diff > 0: + ps_wins += 1 + else: + bl_wins += 1 + total = len(group) + print(f" {metric_name:35s} PS wins: {ps_wins}/{total} BL wins: {bl_wins}/{total} Ties: {ties}/{total}") + + +if __name__ == "__main__": + main() diff --git a/transfer/code/prompt_selection/config.py b/transfer/code/prompt_selection/config.py new file mode 100644 index 0000000000000000000000000000000000000000..e007cd4e543afb9e77c3a437ab5b70519bc107ab --- /dev/null +++ b/transfer/code/prompt_selection/config.py @@ -0,0 +1,98 @@ +"""Paths and hyperparameter configuration for prompt selection pipeline.""" +from __future__ import annotations + +from dataclasses import dataclass +from pathlib import Path + +# ---- Root directories ---- +PROJECT_ROOT = Path("/home/hp250092/ku50001222/qian/aivc/lfj/transfer") +DATA_DIR = PROJECT_ROOT / "data" +RESULTS_DIR = DATA_DIR / "prompt_selection_results" +BASELINE_DIR = DATA_DIR / "baseline_results" +EVAL_DIR = DATA_DIR / "eval_results" + +# ---- Input data ---- +SOURCE_ADATA = DATA_DIR / "tutorial-pred-data" / "openproblems_donor1.h5ad" + +# ---- Model checkpoints ---- +EMBED_MODEL_DIR = DATA_DIR / "tutorial-embed-model" +EMBED_CKPT = EMBED_MODEL_DIR / "bc_large.ckpt" +GENELIST_PATH = DATA_DIR / "tutorial-pred-model" / "basecount_1000per_15000max.pkl" +ALIGNED_CKPT = DATA_DIR / "tutorial-pred-model" / "bc_large_aligned.ckpt" + +# ---- HuggingFace repo for embedding checkpoint ---- +HF_EMBED_REPO = "arcinstitute/Stack-Large" + +# ---- Cell subset filters (shared across all perturbations) ---- +QUERY_FILTER = {"broad_cell_class": "lymphocyte of b lineage", "sm_name": "Dimethyl Sulfoxide"} +PROMPT_CTRL_FILTER = {"broad_cell_class": "t cell", "sm_name": "Dimethyl Sulfoxide"} + +# ---- Control perturbation name ---- +CONTROL_NAME = "Dimethyl Sulfoxide" + +# ---- Shared intermediate file names (inside RESULTS_DIR root) ---- +QUERY_CTRL_H5AD = "query_ctrl.h5ad" +PROMPT_CTRL_H5AD = "prompt_ctrl.h5ad" +QUERY_EMB_NPY = "query_embeddings.npy" +PROMPT_CTRL_EMB_NPY = "prompt_ctrl_embeddings.npy" + +# ---- Per-perturbation intermediate file names (inside RESULTS_DIR / pert_name) ---- +PROMPT_PERT_H5AD = "prompt_pert.h5ad" +PREDICTED_PERT_H5AD = "predicted_pert.h5ad" +PROMPT_PERT_EMB_NPY = "prompt_pert_embeddings.npy" +PREDICTED_PERT_EMB_NPY = "predicted_pert_embeddings.npy" + +# ---- Generation hyperparameters ---- +NUM_STEPS = 5 +PROMPT_RATIO = 0.25 +CONTEXT_RATIO = 0.4 +CONTEXT_RATIO_MIN = 0.2 +BATCH_SIZE = 32 +NUM_WORKERS = 4 + +# ---- Prompt selection hyperparameters ---- +TOP_K1 = 512 # Stage 1: number of ctrl prompts to shortlist + +# ---- All perturbation conditions ---- +ALL_PERTURBATIONS = [ + "Belinostat", + "CHIR-99021", + "Crizotinib", + "Dabrafenib", + "Dactolisib", + "Foretinib", + "Idelalisib", + "LDN 193189", + "Linagliptin", + "O-Demethylated Adapalene", + "Palbociclib", + "Penfluridol", + "Porcn Inhibitor III", + "R428", +] + + +@dataclass +class PertConfig: + """Per-perturbation path configuration.""" + + perturbation_name: str + prompt_pert_filter: dict + results_dir: Path + baseline_dir: Path + eval_dir: Path + final_result_h5ad: str + baseline_result_h5ad: str + + +def get_pert_config(pert_name: str) -> PertConfig: + """Return path configuration for a specific perturbation condition.""" + return PertConfig( + perturbation_name=pert_name, + prompt_pert_filter={"broad_cell_class": "t cell", "sm_name": pert_name}, + results_dir=RESULTS_DIR / pert_name, + baseline_dir=BASELINE_DIR / pert_name, + eval_dir=EVAL_DIR / pert_name, + final_result_h5ad=f"predicted_bcells_{pert_name}.h5ad", + baseline_result_h5ad=f"baseline_random_{pert_name}.h5ad", + ) diff --git a/transfer/code/prompt_selection/custom_generation.py b/transfer/code/prompt_selection/custom_generation.py new file mode 100644 index 0000000000000000000000000000000000000000..c93bb5f847b5446814c79643102b9813bfda4110 --- /dev/null +++ b/transfer/code/prompt_selection/custom_generation.py @@ -0,0 +1,223 @@ +"""Custom generation loop with per-step embedding-based prompt selection. + +Re-implements the MDM loop from ``get_incontext_generation`` but replaces +random prompt selection with the two-stage embedding similarity approach +at every generation step. +""" +from __future__ import annotations + +import logging +import math +from typing import Optional, Tuple + +import anndata as ad +import numpy as np +import pandas as pd +from scipy.sparse import csr_matrix + +from stack.models.utils import align_result_to_adata_numpy + +from prompt_selection.prompt_selector import select_prompt_indices + +LOGGER = logging.getLogger("prompt_selection.custom_generation") + + +def custom_generation_loop( + model, + query_adata: ad.AnnData, + prompt_pert_adata: ad.AnnData, + genelist_path: str, + query_embeddings: np.ndarray, + prompt_ctrl_embeddings: np.ndarray, + predicted_pert_embeddings: np.ndarray, + prompt_pert_embeddings: np.ndarray, + *, + num_steps: int = 5, + prompt_ratio: float = 0.25, + context_ratio: float = 0.4, + context_ratio_min: float = 0.2, + top_k1: int = 512, + batch_size: int = 32, + num_workers: int = 4, + gene_name_col: Optional[str] = None, +) -> Tuple[csr_matrix, np.ndarray]: + """Run MDM generation with per-step prompt re-selection. + + Parameters + ---------- + model : StateICLModel (ICL_FinetunedModel) + Loaded ``bc_large_aligned`` model in eval mode. + query_adata : AnnData + Query cells (B cells, DMSO). Will be mutated in-place each step. + prompt_pert_adata : AnnData + Real perturbed prompt cells (T cells, Dabrafenib). + genelist_path : str + Path to pickled gene list. + query_embeddings : (N_query, D) + prompt_ctrl_embeddings : (N_ctrl, D) + predicted_pert_embeddings : (N_ctrl, D) + prompt_pert_embeddings : (N_pert, D) + num_steps, prompt_ratio, context_ratio, context_ratio_min, top_k1 : + Generation hyperparameters. + batch_size, num_workers : DataLoader settings. + gene_name_col : Optional gene name column. + + Returns + ------- + result : csr_matrix + Final predicted expression (aligned to query_adata gene space). + final_logit : np.ndarray + Per-cell logit scores from the last step. + """ + n_cells = model.n_cells # 512 + cell_names = query_adata.obs_names.copy() + N = len(cell_names) + + # Global state indexed by obs_names + is_masked = pd.Series(np.ones(N, dtype=bool), index=cell_names) + test_logit = pd.Series(np.zeros(N, dtype=np.float32), index=cell_names) + + # MDM schedule (matches get_incontext_generation lines 869-875) + t = (np.arange(num_steps, dtype=np.float32) + 1) / num_steps + cr_list = np.linspace(context_ratio_min, context_ratio, num_steps, dtype=np.float32) + mr_list = 1 - t + mr_list[-1] = 0.0 + + LOGGER.info("Masking ratio schedule: %s", mr_list) + LOGGER.info("Context ratio schedule: %s", cr_list) + + result = None + + for step_idx, (mr, cr) in enumerate(zip(mr_list, cr_list)): + ratio = prompt_ratio + cr + n_test_cells = max(1, int(n_cells * (1 - ratio))) + n_base_cells = n_cells - n_test_cells + num_batches = math.ceil(N / n_test_cells) + + LOGGER.info( + "Step %d/%d: mr=%.3f cr=%.3f n_test=%d n_base=%d batches=%d", + step_idx + 1, num_steps, mr, cr, n_test_cells, n_base_cells, num_batches, + ) + + mixed_adata_list = [] + is_test_cell_parts = [] + flat_idx_to_cell_name = [] + + for i in range(num_batches): + start = i * n_test_cells + end = min((i + 1) * n_test_cells, N) + batch_cell_names = cell_names[start:end] + actual_len = len(batch_cell_names) + + # Pad last batch if needed + if actual_len < n_test_cells: + pad_names = cell_names[:n_test_cells - actual_len] + current_test_slice = query_adata[list(batch_cell_names) + list(pad_names)] + else: + current_test_slice = query_adata[list(batch_cell_names)] + + # Two-stage prompt selection + batch_global_indices = np.arange(start, end) + selected_base_idx = select_prompt_indices( + query_embeddings=query_embeddings, + batch_global_indices=batch_global_indices, + prompt_ctrl_embeddings=prompt_ctrl_embeddings, + predicted_pert_embeddings=predicted_pert_embeddings, + prompt_pert_embeddings=prompt_pert_embeddings, + n_base_cells=n_base_cells, + top_k1=top_k1, + ) + + actual_base = len(selected_base_idx) + + # Build mixed sample: [base_cells, test_cells] + sample_adata = ad.concat( + [prompt_pert_adata[selected_base_idx], current_test_slice], + join="inner", + axis=0, + ) + mixed_adata_list.append(sample_adata) + + # Track test cell positions (only real cells, not padding) + batch_mask = np.zeros(actual_base + n_test_cells, dtype=bool) + batch_mask[actual_base : actual_base + actual_len] = True + is_test_cell_parts.append(batch_mask) + flat_idx_to_cell_name.extend(batch_cell_names) + + is_test_cell_mask = np.concatenate(is_test_cell_parts) + + # Concatenate all batches + full_mixed_adata = ad.concat(mixed_adata_list, axis=0, join="inner") + + # Model forward pass + mean_preds, disp_preds, count_preds, logit_preds = model.get_prediction( + adata_path=full_mixed_adata, + genelist_path=genelist_path, + gene_name_col=gene_name_col, + cell_ratio=prompt_ratio, + context_ratio=cr, + batch_size=batch_size, + num_workers=num_workers, + ) + + # Extract test cell results + result_counts = count_preds[is_test_cell_mask] # (N, n_model_genes) + new_logit = logit_preds[is_test_cell_mask] # (N,) + result_cell_names = np.array(flat_idx_to_cell_name) # (N,) + + # ---- MDM unmasking logic (mirrors get_incontext_prediction lines 694-703) ---- + cur_is_masked = is_masked.loc[result_cell_names].values.copy() + cur_new_logit = new_logit.copy() + + cell_indices_to_keep = np.zeros(N, dtype=bool) + # 1) Already unmasked cells keep their previous prediction + cell_indices_to_keep[~cur_is_masked] = True + + # 2) Compute unmask_rate + n_masked = cur_is_masked.sum() + n_total = len(cur_is_masked) + if n_masked > 0: + unmask_rate = (n_masked / n_total - mr) * n_total / n_masked + unmask_rate = np.clip(unmask_rate, 0.0, 1.0) + else: + unmask_rate = 1.0 + + # 3) Unmask cells with logit above quantile threshold + if n_masked > 0: + masked_logits = cur_new_logit[cur_is_masked] + threshold = np.quantile(masked_logits, unmask_rate) + cell_indices_to_keep[cur_is_masked] = cur_new_logit[cur_is_masked] > threshold + + # 4) Update global is_masked + new_is_masked = cur_is_masked.copy() + new_is_masked[~cell_indices_to_keep] = False + new_is_masked[cur_new_logit > 0] = True + is_masked.loc[result_cell_names] = new_is_masked + + # 5) Update global test_logit + test_logit.loc[result_cell_names] = cur_new_logit + + LOGGER.info( + "Step %d: unmasked fraction = %.3f", + step_idx + 1, + (cur_new_logit < 0).sum() / len(cur_new_logit), + ) + + # Align to query_adata gene space + aligned_result = align_result_to_adata_numpy( + result_counts, + query_adata, + genelist_path, + gene_name_col, + cell_indices_to_keep=cell_indices_to_keep, + ) + result = csr_matrix(aligned_result) + + # Update query_adata for next step (consistent with original code) + if query_adata.raw is not None: + query_adata.raw.X = result + else: + query_adata.X = result + + final_logit = test_logit.loc[cell_names].values + return result, final_logit diff --git a/transfer/code/prompt_selection/evaluate_results.py b/transfer/code/prompt_selection/evaluate_results.py new file mode 100644 index 0000000000000000000000000000000000000000..0a24299203b52986128a2fc8e4630fc7918fb8f1 --- /dev/null +++ b/transfer/code/prompt_selection/evaluate_results.py @@ -0,0 +1,252 @@ +#!/usr/bin/env python3 +"""Evaluate Prompt Selection vs Random Baseline using cell-eval. + +Compares predicted B-cell perturbation results from two methods: + 1. Prompt Selection (embedding-based prompt ordering) + 2. Random Baseline (default Stack random prompt ordering) + +Usage: + python code/prompt_selection/evaluate_results.py --perturbation Dabrafenib +""" +from __future__ import annotations + +import gc +import logging +import sys +from pathlib import Path + +_THIS_DIR = Path(__file__).resolve().parent +_REPO_ROOT = _THIS_DIR.parents[1] # lfj/transfer/ +for _p in [ + str(_REPO_ROOT / "code" / "cell-eval" / "src"), + str(_THIS_DIR.parent), +]: + if _p not in sys.path: + sys.path.insert(0, _p) + +import argparse + +import anndata as ad +import numpy as np +import polars as pl +from scipy.sparse import issparse + +from cell_eval import MetricsEvaluator +from prompt_selection import config as cfg + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s [%(name)s] %(levelname)s: %(message)s", +) +LOGGER = logging.getLogger("evaluate_results") + +# --------------------------------------------------------------------------- +# Constants +# --------------------------------------------------------------------------- +PERTURBATION_COL = "sm_name" +CONTROL_NAME = "Dimethyl Sulfoxide" +CELL_TYPE_FILTER = {"broad_cell_class": "lymphocyte of b lineage"} + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + +def build_real_combined(pert_name: str, output_dir: Path) -> ad.AnnData: + """Extract ground truth B cells (control + perturbation) from source data.""" + LOGGER.info("Loading source data: %s", cfg.SOURCE_ADATA) + source = ad.read_h5ad(str(cfg.SOURCE_ADATA)) + LOGGER.info("Source shape: %s", source.shape) + + broad = source.obs["broad_cell_class"] + sm = source.obs[PERTURBATION_COL] + b_lineage = CELL_TYPE_FILTER["broad_cell_class"] + + ctrl_mask = (broad == b_lineage) & (sm == CONTROL_NAME) + pert_mask = (broad == b_lineage) & (sm == pert_name) + + control_B = source[ctrl_mask].copy() + real_pert_B = source[pert_mask].copy() + del source + gc.collect() + + LOGGER.info("Control B cells (DMSO): %d", control_B.n_obs) + LOGGER.info("Real perturbed B cells (%s): %d", pert_name, real_pert_B.n_obs) + + if real_pert_B.n_obs == 0: + raise ValueError(f"No ground truth B cells found for {pert_name}") + + real_combined = ad.concat([control_B, real_pert_B], join="inner") + LOGGER.info( + "real_combined: %d cells, sm_name: %s", + real_combined.n_obs, + real_combined.obs[PERTURBATION_COL].value_counts().to_dict(), + ) + real_path = output_dir / "real_combined.h5ad" + real_combined.write_h5ad(real_path) + LOGGER.info("Saved real_combined to %s", real_path) + return real_combined + + +def build_pred_combined(pred_path: Path, label: str) -> ad.AnnData: + """Combine control B cells with predictions into a single AnnData.""" + ctrl = ad.read_h5ad(str(cfg.RESULTS_DIR / cfg.QUERY_CTRL_H5AD)) + pred = ad.read_h5ad(str(pred_path)) + LOGGER.info("[%s] Control: %d cells, Pred: %d cells", label, ctrl.n_obs, pred.n_obs) + + combined = ad.concat([ctrl, pred], join="inner") + combined.obs_names_make_unique() + LOGGER.info( + "[%s] combined: %d cells, sm_name: %s", + label, combined.n_obs, + combined.obs[PERTURBATION_COL].value_counts().to_dict(), + ) + return combined + + +def align_genes(adata_pred: ad.AnnData, adata_real: ad.AnnData): + """Ensure pred and real have identical var_names in the same order.""" + common = adata_pred.var_names.intersection(adata_real.var_names) + if len(common) == 0: + raise ValueError("No common genes between predicted and real data") + LOGGER.info( + "Gene alignment: pred=%d, real=%d, common=%d", + adata_pred.n_vars, adata_real.n_vars, len(common), + ) + return adata_pred[:, common].copy(), adata_real[:, common].copy() + + +def densify_X(adata: ad.AnnData) -> None: + """Convert sparse X to dense in-place to avoid repeated sparse→dense.""" + if issparse(adata.X): + adata.X = np.asarray(adata.X.todense(), dtype=np.float32) + + +def evaluate_one(pred_path, real_combined, label, output_dir): + """Build pred_combined, align genes, run cell-eval, free memory.""" + LOGGER.info("=" * 60) + LOGGER.info("Evaluating: %s", label) + LOGGER.info("=" * 60) + + pred_combined = build_pred_combined(pred_path, label) + pred_al, real_al = align_genes(pred_combined, real_combined) + del pred_combined + gc.collect() + + LOGGER.info("[%s] Densifying expression matrices...", label) + densify_X(pred_al) + densify_X(real_al) + + eval_dir = str(output_dir / f"celleval_{label}") + evaluator = MetricsEvaluator( + adata_pred=pred_al, + adata_real=real_al, + control_pert=CONTROL_NAME, + pert_col=PERTURBATION_COL, + outdir=eval_dir, + allow_discrete=True, + num_threads=4, + ) + results, agg_results = evaluator.compute( + profile="full", + write_csv=True, + break_on_error=False, + ) + + del pred_al, real_al, evaluator + gc.collect() + + return results, agg_results + + +# --------------------------------------------------------------------------- +# Main +# --------------------------------------------------------------------------- + +def main(): + parser = argparse.ArgumentParser(description="Evaluate Prompt Selection vs Random Baseline") + parser.add_argument( + "--perturbation", type=str, required=True, + help="Perturbation name (e.g., Dabrafenib).", + ) + parser.add_argument( + "--output-dir", type=Path, default=None, + help="Output directory for evaluation results (default: eval_results/).", + ) + args = parser.parse_args() + + pert_name = args.perturbation + pcfg = cfg.get_pert_config(pert_name) + + output_dir = args.output_dir if args.output_dir else pcfg.eval_dir + output_dir.mkdir(parents=True, exist_ok=True) + + pred_ps_path = pcfg.results_dir / pcfg.final_result_h5ad + pred_bl_path = pcfg.baseline_dir / pcfg.baseline_result_h5ad + + # ---- Step 1: Build ground truth ---- + LOGGER.info("Step 1: Preparing ground truth data for %s", pert_name) + real_combined = build_real_combined(pert_name, output_dir) + + # ---- Step 2+3: Evaluate each method ---- + agg_ps = None + agg_bl = None + + if pred_ps_path.exists(): + try: + _, agg_ps = evaluate_one(pred_ps_path, real_combined, "prompt_selection", output_dir) + except Exception as e: + LOGGER.error("Evaluation (prompt_selection) failed: %s", e, exc_info=True) + else: + LOGGER.warning("Prompt selection result not found: %s", pred_ps_path) + + if pred_bl_path.exists(): + try: + _, agg_bl = evaluate_one(pred_bl_path, real_combined, "baseline", output_dir) + except Exception as e: + LOGGER.error("Evaluation (baseline) failed: %s", e, exc_info=True) + else: + LOGGER.warning("Baseline result not found: %s", pred_bl_path) + + # ---- Step 4: Compare results ---- + LOGGER.info("=" * 60) + LOGGER.info("Comparing results for %s", pert_name) + LOGGER.info("=" * 60) + + if agg_ps is not None: + print(f"\n--- Prompt Selection ({pert_name}, aggregated) ---") + print(agg_ps) + + if agg_bl is not None: + print(f"\n--- Random Baseline ({pert_name}, aggregated) ---") + print(agg_bl) + + if agg_ps is not None and agg_bl is not None: + try: + mean_ps = agg_ps.filter(pl.col("statistic") == "mean").drop("statistic") + mean_bl = agg_bl.filter(pl.col("statistic") == "mean").drop("statistic") + + ps_long = mean_ps.unpivot(variable_name="metric", value_name="prompt_selection") + bl_long = mean_bl.unpivot(variable_name="metric", value_name="random_baseline") + + comparison = ps_long.join(bl_long, on="metric") + comparison = comparison.with_columns( + (pl.col("prompt_selection") - pl.col("random_baseline")).alias("diff") + ) + + comparison_path = output_dir / "comparison_mean.csv" + comparison.write_csv(str(comparison_path)) + + print("\n" + "=" * 70) + print(f"COMPARISON ({pert_name}): Prompt Selection vs Random Baseline (mean)") + print("=" * 70) + print(comparison) + print(f"\nSaved to: {comparison_path}") + except Exception as e: + LOGGER.warning("Could not build comparison table: %s", e) + + LOGGER.info("Evaluation complete for %s. Results in %s", pert_name, output_dir) + + +if __name__ == "__main__": + main() diff --git a/transfer/code/prompt_selection/prompt_selector.py b/transfer/code/prompt_selection/prompt_selector.py new file mode 100644 index 0000000000000000000000000000000000000000..c5b9b1327280011000a528b6a12ecde29a44fd1d --- /dev/null +++ b/transfer/code/prompt_selection/prompt_selector.py @@ -0,0 +1,64 @@ +"""Two-stage embedding-based prompt selection. + +Stage 1: For each query batch, find top-K1 most similar control prompts + based on cosine similarity in embedding space. +Stage 2: Map those K1 control prompts to their predicted perturbation + embeddings, average, and find top-n_base_cells real perturbed + prompts closest to that average. +""" +from __future__ import annotations + +import numpy as np +from sklearn.metrics.pairwise import cosine_similarity + + +def select_prompt_indices( + query_embeddings: np.ndarray, + batch_global_indices: np.ndarray, + prompt_ctrl_embeddings: np.ndarray, + predicted_pert_embeddings: np.ndarray, + prompt_pert_embeddings: np.ndarray, + n_base_cells: int, + top_k1: int = 512, +) -> np.ndarray: + """Select best prompt indices for a single query batch. + + Parameters + ---------- + query_embeddings : (N_query, D) + Precomputed embeddings for all query cells. + batch_global_indices : (batch_len,) + Indices into query_embeddings for the current batch. + prompt_ctrl_embeddings : (N_ctrl, D) + Embeddings of control prompt cells. + predicted_pert_embeddings : (N_ctrl, D) + Embeddings of predicted perturbation cells (1-to-1 with ctrl). + prompt_pert_embeddings : (N_pert, D) + Embeddings of real perturbed prompt cells. + n_base_cells : int + Number of prompt cells to select. + top_k1 : int + Number of control prompts to shortlist in stage 1. + + Returns + ------- + selected_idx : (n_base_cells,) + Indices into the prompt_pert AnnData for the selected prompts. + """ + # Mean embedding of current query batch + mean_query_emb = query_embeddings[batch_global_indices].mean(axis=0, keepdims=True) + + # Stage 1: cosine similarity against control prompts + sim_ctrl = cosine_similarity(mean_query_emb, prompt_ctrl_embeddings)[0] + k1 = min(top_k1, len(sim_ctrl)) + top_k1_idx = np.argpartition(sim_ctrl, -k1)[-k1:] + + # Stage 2: average predicted perturbation embeddings of stage-1 hits + pred_emb_mean = predicted_pert_embeddings[top_k1_idx].mean(axis=0, keepdims=True) + + # Cosine similarity against real perturbed prompts + sim_pert = cosine_similarity(pred_emb_mean, prompt_pert_embeddings)[0] + n_select = min(n_base_cells, len(sim_pert)) + selected_idx = np.argpartition(sim_pert, -n_select)[-n_select:] + + return selected_idx diff --git a/transfer/code/prompt_selection/run.sh b/transfer/code/prompt_selection/run.sh new file mode 100644 index 0000000000000000000000000000000000000000..3cf61519cb699aaa959737af8c68e1f56c467921 --- /dev/null +++ b/transfer/code/prompt_selection/run.sh @@ -0,0 +1,31 @@ +#!/bin/sh +#PJM -L rscgrp=b-batch +#PJM -L gpu=1 +#PJM -L elapse=3:00:00 +#PJM -N prompt_selection +#PJM -j +#PJM -o logs/prompt_selection_%j.out + +module load cuda/12.2.2 +module load cudnn/8.9.7 +module load gcc-toolset/12 + +source /home/hp250092/ku50001222/qian/aivc/lfj/stack_env/bin/activate + +cd /home/hp250092/ku50001222/qian/aivc/lfj/transfer + +export PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:256 + +echo "==========================================" +echo "Job ID: $PJM_JOBID" +echo "Job Name: $PJM_JOBNAME" +echo "Start: $(date)" +echo "Node: $(hostname)" +echo "GPU: $(nvidia-smi --query-gpu=name,memory.total --format=csv,noheader 2>/dev/null || echo 'N/A')" +echo "==========================================" + +python code/prompt_selection/run_pipeline.py + +echo "==========================================" +echo "Finished: $(date)" +echo "==========================================" diff --git a/transfer/code/prompt_selection/run_baseline.py b/transfer/code/prompt_selection/run_baseline.py new file mode 100644 index 0000000000000000000000000000000000000000..37dd8b517f81312e180aef0c95413249e854503b --- /dev/null +++ b/transfer/code/prompt_selection/run_baseline.py @@ -0,0 +1,161 @@ +"""Random baseline generation using Stack's get_incontext_generation(). + +Uses random prompt ordering (the default Stack behaviour) instead of +embedding-based selection. Provides a baseline for comparison. + +Usage: + python code/prompt_selection/run_baseline.py --perturbation Dabrafenib +""" +from __future__ import annotations + +import argparse +import gc +import logging +import sys +from pathlib import Path + +_THIS_DIR = Path(__file__).resolve().parent +if str(_THIS_DIR.parent) not in sys.path: + sys.path.insert(0, str(_THIS_DIR.parent)) + +import anndata as ad +import numpy as np +import torch +from scipy.sparse import csr_matrix, issparse + +from stack.model_loading import load_model_from_checkpoint + +from prompt_selection import config as cfg + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s [%(name)s] %(levelname)s: %(message)s", +) +LOGGER = logging.getLogger("prompt_selection.baseline") + + +def _filter_adata(adata: ad.AnnData, filters: dict) -> ad.AnnData: + """Subset AnnData by column-value filters.""" + mask = np.ones(adata.n_obs, dtype=bool) + for col, val in filters.items(): + mask &= (adata.obs[col] == val).values + return adata[mask].copy() + + +def ensure_prompt_pert(pcfg: cfg.PertConfig): + """Extract prompt_pert.h5ad if it doesn't exist yet.""" + pcfg.results_dir.mkdir(parents=True, exist_ok=True) + pert_path = pcfg.results_dir / cfg.PROMPT_PERT_H5AD + + if pert_path.exists(): + return True + + LOGGER.info("prompt_pert not found, extracting from source data...") + adata = ad.read_h5ad(str(cfg.SOURCE_ADATA)) + pert = _filter_adata(adata, pcfg.prompt_pert_filter) + LOGGER.info("prompt_pert (%s): %d cells", pcfg.perturbation_name, pert.n_obs) + + if pert.n_obs == 0: + LOGGER.warning("No T cells found for '%s'. Skipping.", pcfg.perturbation_name) + del adata, pert + gc.collect() + return False + + pert.write_h5ad(pert_path) + del adata, pert + gc.collect() + return True + + +def main(): + parser = argparse.ArgumentParser(description="Random Baseline Generation") + parser.add_argument( + "--perturbation", type=str, required=True, + help="Perturbation name (e.g., Dabrafenib).", + ) + args = parser.parse_args() + + pert_name = args.perturbation + pcfg = cfg.get_pert_config(pert_name) + + LOGGER.info("=" * 60) + LOGGER.info("Random Baseline Generation — %s", pert_name) + LOGGER.info("=" * 60) + + pcfg.baseline_dir.mkdir(parents=True, exist_ok=True) + output_path = pcfg.baseline_dir / pcfg.baseline_result_h5ad + + if output_path.exists(): + LOGGER.info("Baseline result already exists: %s — skipping.", output_path) + return + + # Ensure prompt_pert data exists + has_data = ensure_prompt_pert(pcfg) + if not has_data: + LOGGER.warning("Skipping baseline for %s (no T cell data).", pert_name) + return + + # --- Load model --- + LOGGER.info("Loading model: %s", cfg.ALIGNED_CKPT) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + model = load_model_from_checkpoint( + str(cfg.ALIGNED_CKPT), + model_class="ICL_FinetunedModel", + device=device, + ) + + # --- Load data --- + query_ctrl_path = str(cfg.RESULTS_DIR / cfg.QUERY_CTRL_H5AD) + prompt_pert_path = str(pcfg.results_dir / cfg.PROMPT_PERT_H5AD) + + LOGGER.info("Query (test): %s", query_ctrl_path) + LOGGER.info("Prompt (base): %s", prompt_pert_path) + + # --- Run random-prompt generation --- + LOGGER.info("Running get_incontext_generation (random prompt baseline)...") + result = model.get_incontext_generation( + base_adata_or_path=prompt_pert_path, + test_adata_or_path=query_ctrl_path, + genelist_path=str(cfg.GENELIST_PATH), + mode="mdm", + num_steps=cfg.NUM_STEPS, + prompt_ratio=cfg.PROMPT_RATIO, + context_ratio=cfg.CONTEXT_RATIO, + context_ratio_min=cfg.CONTEXT_RATIO_MIN, + batch_size=cfg.BATCH_SIZE, + num_workers=cfg.NUM_WORKERS, + ) + + if isinstance(result, tuple): + predictions, test_logit = result + else: + predictions, test_logit = result, None + + # --- Build output AnnData --- + query_ctrl = ad.read_h5ad(query_ctrl_path) + + if issparse(predictions): + pred_X = predictions + else: + pred_X = csr_matrix(np.asarray(predictions, dtype=np.float32)) + + result_adata = ad.AnnData( + X=pred_X, + obs=query_ctrl.obs.copy(), + var=query_ctrl.var.copy(), + ) + result_adata.obs["sm_name"] = pert_name + result_adata.obs["control"] = False + if test_logit is not None: + result_adata.obs["gen_logit"] = np.asarray(test_logit) + + result_adata.write_h5ad(output_path) + LOGGER.info("Saved baseline result: %s shape=%s", output_path, result_adata.shape) + + LOGGER.info("=" * 60) + LOGGER.info("Random Baseline Generation — %s — Done", pert_name) + LOGGER.info("=" * 60) + + +if __name__ == "__main__": + main() diff --git a/transfer/code/prompt_selection/run_baseline.sh b/transfer/code/prompt_selection/run_baseline.sh new file mode 100644 index 0000000000000000000000000000000000000000..96949bcd27bb2bce45da5d5a750776eea5e10a52 --- /dev/null +++ b/transfer/code/prompt_selection/run_baseline.sh @@ -0,0 +1,31 @@ +#!/bin/sh +#PJM -L rscgrp=b-batch +#PJM -L gpu=1 +#PJM -L elapse=1:00:00 +#PJM -N baseline_random +#PJM -j +#PJM -o logs/baseline_random_%j.out + +module load cuda/12.2.2 +module load cudnn/8.9.7 +module load gcc-toolset/12 + +source /home/hp250092/ku50001222/qian/aivc/lfj/stack_env/bin/activate + +cd /home/hp250092/ku50001222/qian/aivc/lfj/transfer + +export PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:256 + +echo "==========================================" +echo "Job ID: $PJM_JOBID" +echo "Job Name: $PJM_JOBNAME" +echo "Start: $(date)" +echo "Node: $(hostname)" +echo "GPU: $(nvidia-smi --query-gpu=name,memory.total --format=csv,noheader 2>/dev/null || echo 'N/A')" +echo "==========================================" + +python code/prompt_selection/run_baseline.py + +echo "==========================================" +echo "Finished: $(date)" +echo "==========================================" diff --git a/transfer/code/prompt_selection/run_baseline_pert.sh b/transfer/code/prompt_selection/run_baseline_pert.sh new file mode 100644 index 0000000000000000000000000000000000000000..299f8dad12ec6e89e1135387cdff5e5849e69f0d --- /dev/null +++ b/transfer/code/prompt_selection/run_baseline_pert.sh @@ -0,0 +1,33 @@ +#!/bin/sh +#PJM -L rscgrp=b-batch +#PJM -L gpu=1 +#PJM -L elapse=1:00:00 +#PJM -N bl_pert +#PJM -j +#PJM -o logs/bl_pert_%j.out + +module load cuda/12.2.2 +module load cudnn/8.9.7 +module load gcc-toolset/12 + +source /home/hp250092/ku50001222/qian/aivc/lfj/stack_env/bin/activate + +cd /home/hp250092/ku50001222/qian/aivc/lfj/transfer + +export PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:256 +export HDF5_USE_FILE_LOCKING=FALSE + +echo "==========================================" +echo "Job ID: $PJM_JOBID" +echo "Job Name: $PJM_JOBNAME" +echo "Perturbation: $PERT_NAME" +echo "Start: $(date)" +echo "Node: $(hostname)" +echo "GPU: $(nvidia-smi --query-gpu=name,memory.total --format=csv,noheader 2>/dev/null || echo 'N/A')" +echo "==========================================" + +python code/prompt_selection/run_baseline.py --perturbation "$PERT_NAME" + +echo "==========================================" +echo "Finished: $(date)" +echo "==========================================" diff --git a/transfer/code/prompt_selection/run_eval_all.sh b/transfer/code/prompt_selection/run_eval_all.sh new file mode 100644 index 0000000000000000000000000000000000000000..3975505aa5ffe412f2bed695894e622ed5987139 --- /dev/null +++ b/transfer/code/prompt_selection/run_eval_all.sh @@ -0,0 +1,42 @@ +#!/bin/bash +# Evaluate all perturbation conditions and aggregate results. +# Run on login node (no GPU needed). + +source /home/hp250092/ku50001222/qian/aivc/lfj/stack_env/bin/activate +cd /home/hp250092/ku50001222/qian/aivc/lfj/transfer + +PERTURBATIONS=( + "Belinostat" + "CHIR-99021" + "Crizotinib" + "Dabrafenib" + "Dactolisib" + "Foretinib" + "Idelalisib" + "LDN 193189" + "Linagliptin" + "O-Demethylated Adapalene" + "Palbociclib" + "Penfluridol" + "Porcn Inhibitor III" + "R428" +) + +echo "==========================================" +echo "Evaluating all perturbation conditions" +echo "Start: $(date)" +echo "==========================================" + +for PERT in "${PERTURBATIONS[@]}"; do + echo "" + echo "--- Evaluating: $PERT ---" + python code/prompt_selection/evaluate_results.py --perturbation "$PERT" || echo "WARNING: Evaluation failed for $PERT" +done + +echo "" +echo "--- Aggregating results ---" +python code/prompt_selection/aggregate_results.py + +echo "==========================================" +echo "All evaluations complete: $(date)" +echo "==========================================" diff --git a/transfer/code/prompt_selection/run_pert.sh b/transfer/code/prompt_selection/run_pert.sh new file mode 100644 index 0000000000000000000000000000000000000000..5328e73e6c138d653f3b3f8950bd5fb348bd1748 --- /dev/null +++ b/transfer/code/prompt_selection/run_pert.sh @@ -0,0 +1,33 @@ +#!/bin/sh +#PJM -L rscgrp=b-batch +#PJM -L gpu=1 +#PJM -L elapse=3:00:00 +#PJM -N ps_pert +#PJM -j +#PJM -o logs/ps_pert_%j.out + +module load cuda/12.2.2 +module load cudnn/8.9.7 +module load gcc-toolset/12 + +source /home/hp250092/ku50001222/qian/aivc/lfj/stack_env/bin/activate + +cd /home/hp250092/ku50001222/qian/aivc/lfj/transfer + +export PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:256 +export HDF5_USE_FILE_LOCKING=FALSE + +echo "==========================================" +echo "Job ID: $PJM_JOBID" +echo "Job Name: $PJM_JOBNAME" +echo "Perturbation: $PERT_NAME" +echo "Start: $(date)" +echo "Node: $(hostname)" +echo "GPU: $(nvidia-smi --query-gpu=name,memory.total --format=csv,noheader 2>/dev/null || echo 'N/A')" +echo "==========================================" + +python code/prompt_selection/run_pipeline.py --perturbation "$PERT_NAME" + +echo "==========================================" +echo "Finished: $(date)" +echo "==========================================" diff --git a/transfer/code/prompt_selection/run_pipeline.py b/transfer/code/prompt_selection/run_pipeline.py new file mode 100644 index 0000000000000000000000000000000000000000..69a0248345b103616b758e67f0fb6d8e0d14073c --- /dev/null +++ b/transfer/code/prompt_selection/run_pipeline.py @@ -0,0 +1,426 @@ +"""Main pipeline: embedding-based prompt selection for Stack generation. + +Supports two modes: + --shared-only : Run shared steps only (download ckpt, extract query_ctrl + & prompt_ctrl, compute their embeddings). + --perturbation X: Run per-perturbation steps (extract prompt_pert, bridge + prediction, pert embeddings, custom generation). + +Execution order for per-perturbation (3 model loads): + Step 0' : Extract prompt_pert from source AnnData + Load 1 : bc_large_aligned.ckpt → bridge prediction → release + Load 2 : bc_large.ckpt → pert embeddings → release + Load 3 : bc_large_aligned.ckpt → custom generation → release +""" +from __future__ import annotations + +import argparse +import gc +import logging +import sys +from pathlib import Path + +_THIS_DIR = Path(__file__).resolve().parent +if str(_THIS_DIR.parent) not in sys.path: + sys.path.insert(0, str(_THIS_DIR.parent)) + +import anndata as ad +import numpy as np +import torch +from scipy.sparse import csr_matrix, issparse + +from stack.model_loading import load_model_from_checkpoint + +from prompt_selection import config as cfg +from prompt_selection.custom_generation import custom_generation_loop + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s [%(name)s] %(levelname)s: %(message)s", +) +LOGGER = logging.getLogger("prompt_selection.pipeline") + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + +def _release_model(model): + """Delete model and free GPU memory.""" + del model + gc.collect() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + + +def _filter_adata(adata: ad.AnnData, filters: dict) -> ad.AnnData: + """Subset AnnData by column-value filters.""" + mask = np.ones(adata.n_obs, dtype=bool) + for col, val in filters.items(): + mask &= (adata.obs[col] == val).values + return adata[mask].copy() + + +# --------------------------------------------------------------------------- +# Shared steps +# --------------------------------------------------------------------------- + +def step0_download_checkpoint(): + """Download bc_large.ckpt from HuggingFace if not present.""" + if cfg.EMBED_CKPT.exists(): + LOGGER.info("Embedding checkpoint already exists: %s", cfg.EMBED_CKPT) + return + + LOGGER.info("Downloading %s from HuggingFace...", cfg.HF_EMBED_REPO) + from huggingface_hub import snapshot_download + + snapshot_download( + repo_id=cfg.HF_EMBED_REPO, + local_dir=str(cfg.EMBED_MODEL_DIR), + allow_patterns=["bc_large.ckpt"], + ) + if not cfg.EMBED_CKPT.exists(): + import glob + matches = glob.glob(str(cfg.EMBED_MODEL_DIR / "**" / "bc_large.ckpt"), recursive=True) + if matches: + Path(matches[0]).rename(cfg.EMBED_CKPT) + assert cfg.EMBED_CKPT.exists(), f"Failed to download {cfg.EMBED_CKPT}" + LOGGER.info("Downloaded embedding checkpoint to %s", cfg.EMBED_CKPT) + + +def step0_extract_shared_subsets(): + """Extract query_ctrl and prompt_ctrl (shared across all perturbations).""" + cfg.RESULTS_DIR.mkdir(parents=True, exist_ok=True) + + query_path = cfg.RESULTS_DIR / cfg.QUERY_CTRL_H5AD + ctrl_path = cfg.RESULTS_DIR / cfg.PROMPT_CTRL_H5AD + + if query_path.exists() and ctrl_path.exists(): + LOGGER.info("Shared cell subsets already extracted, skipping.") + return + + LOGGER.info("Loading source AnnData: %s", cfg.SOURCE_ADATA) + adata = ad.read_h5ad(str(cfg.SOURCE_ADATA)) + + if not query_path.exists(): + query = _filter_adata(adata, cfg.QUERY_FILTER) + LOGGER.info("query_ctrl: %d cells", query.n_obs) + query.write_h5ad(query_path) + del query + + if not ctrl_path.exists(): + ctrl = _filter_adata(adata, cfg.PROMPT_CTRL_FILTER) + LOGGER.info("prompt_ctrl: %d cells", ctrl.n_obs) + ctrl.write_h5ad(ctrl_path) + del ctrl + + del adata + gc.collect() + + +def step_extract_shared_embeddings(): + """Extract embeddings for query_ctrl and prompt_ctrl (shared).""" + emb_files = [ + (cfg.QUERY_EMB_NPY, cfg.QUERY_CTRL_H5AD), + (cfg.PROMPT_CTRL_EMB_NPY, cfg.PROMPT_CTRL_H5AD), + ] + + all_exist = all((cfg.RESULTS_DIR / npy).exists() for npy, _ in emb_files) + if all_exist: + LOGGER.info("Shared embeddings already exist, skipping.") + return + + LOGGER.info("=== Loading bc_large.ckpt for shared embedding extraction ===") + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + model = load_model_from_checkpoint(str(cfg.EMBED_CKPT), device=device) + + for npy_name, h5ad_name in emb_files: + npy_path = cfg.RESULTS_DIR / npy_name + if npy_path.exists(): + LOGGER.info(" %s already exists, skipping.", npy_name) + continue + + h5ad_path = str(cfg.RESULTS_DIR / h5ad_name) + LOGGER.info(" Extracting embeddings: %s -> %s", h5ad_name, npy_name) + + embeddings, _ = model.get_latent_representation( + adata_path=h5ad_path, + genelist_path=str(cfg.GENELIST_PATH), + batch_size=cfg.BATCH_SIZE, + num_workers=cfg.NUM_WORKERS, + show_progress=True, + ) + + np.save(npy_path, embeddings) + LOGGER.info(" Saved: %s, shape=%s", npy_path, embeddings.shape) + + _release_model(model) + + +def run_shared_steps(): + """Run all shared steps (checkpoint download, subset extraction, embeddings).""" + LOGGER.info("=" * 60) + LOGGER.info("Running shared steps") + LOGGER.info("=" * 60) + + step0_download_checkpoint() + step0_extract_shared_subsets() + step_extract_shared_embeddings() + + LOGGER.info("Shared steps complete.") + + +# --------------------------------------------------------------------------- +# Per-perturbation steps +# --------------------------------------------------------------------------- + +def step_extract_prompt_pert(pcfg: cfg.PertConfig): + """Extract prompt_pert (T cells + drug X) for a specific perturbation.""" + pcfg.results_dir.mkdir(parents=True, exist_ok=True) + pert_path = pcfg.results_dir / cfg.PROMPT_PERT_H5AD + + if pert_path.exists(): + LOGGER.info("prompt_pert already exists for %s, skipping.", pcfg.perturbation_name) + return True + + LOGGER.info("Loading source AnnData: %s", cfg.SOURCE_ADATA) + adata = ad.read_h5ad(str(cfg.SOURCE_ADATA)) + + pert = _filter_adata(adata, pcfg.prompt_pert_filter) + LOGGER.info("prompt_pert (%s): %d cells", pcfg.perturbation_name, pert.n_obs) + + if pert.n_obs == 0: + LOGGER.warning("No T cells found for perturbation '%s'. Skipping.", pcfg.perturbation_name) + (pcfg.results_dir / "SKIPPED_no_tcells.txt").touch() + del adata, pert + gc.collect() + return False + + pert.write_h5ad(pert_path) + + del adata, pert + gc.collect() + return True + + +def step_bridge_prediction(pcfg: cfg.PertConfig): + """Generate predicted perturbation for control T cells using aligned model.""" + pred_path = pcfg.results_dir / cfg.PREDICTED_PERT_H5AD + if pred_path.exists(): + LOGGER.info("Predicted perturbation already exists for %s, skipping.", pcfg.perturbation_name) + return + + LOGGER.info("=== Load 1: bc_large_aligned.ckpt for bridge prediction (%s) ===", pcfg.perturbation_name) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + model = load_model_from_checkpoint( + str(cfg.ALIGNED_CKPT), + model_class="ICL_FinetunedModel", + device=device, + ) + + prompt_pert_path = str(pcfg.results_dir / cfg.PROMPT_PERT_H5AD) + prompt_ctrl_path = str(cfg.RESULTS_DIR / cfg.PROMPT_CTRL_H5AD) + + LOGGER.info("Running bridge prediction: prompt_pert → prompt_ctrl") + result = model.get_incontext_generation( + base_adata_or_path=prompt_pert_path, + test_adata_or_path=prompt_ctrl_path, + genelist_path=str(cfg.GENELIST_PATH), + mode="mdm", + num_steps=cfg.NUM_STEPS, + prompt_ratio=cfg.PROMPT_RATIO, + context_ratio=cfg.CONTEXT_RATIO, + context_ratio_min=cfg.CONTEXT_RATIO_MIN, + batch_size=cfg.BATCH_SIZE, + num_workers=cfg.NUM_WORKERS, + ) + pred_X, _ = result + + _release_model(model) + + ctrl_adata = ad.read_h5ad(prompt_ctrl_path) + if issparse(pred_X): + pred_X_dense = pred_X.toarray() + else: + pred_X_dense = np.asarray(pred_X) + + pred_adata = ad.AnnData( + X=csr_matrix(pred_X_dense.astype(np.float32)), + obs=ctrl_adata.obs.copy(), + var=ctrl_adata.var.copy(), + ) + pred_adata.obs["sm_name"] = pcfg.perturbation_name + pred_adata.write_h5ad(pred_path) + + LOGGER.info("Saved predicted perturbation: %s (%d cells)", pred_path, pred_adata.n_obs) + del ctrl_adata, pred_adata, pred_X, pred_X_dense + gc.collect() + + +def step_extract_pert_embeddings(pcfg: cfg.PertConfig): + """Extract embeddings for prompt_pert and predicted_pert.""" + emb_files = [ + (cfg.PROMPT_PERT_EMB_NPY, cfg.PROMPT_PERT_H5AD), + (cfg.PREDICTED_PERT_EMB_NPY, cfg.PREDICTED_PERT_H5AD), + ] + + all_exist = all((pcfg.results_dir / npy).exists() for npy, _ in emb_files) + if all_exist: + LOGGER.info("Pert embeddings already exist for %s, skipping.", pcfg.perturbation_name) + return + + LOGGER.info("=== Load 2: bc_large.ckpt for pert embedding extraction (%s) ===", pcfg.perturbation_name) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + model = load_model_from_checkpoint(str(cfg.EMBED_CKPT), device=device) + + for npy_name, h5ad_name in emb_files: + npy_path = pcfg.results_dir / npy_name + if npy_path.exists(): + LOGGER.info(" %s already exists, skipping.", npy_name) + continue + + h5ad_path = str(pcfg.results_dir / h5ad_name) + LOGGER.info(" Extracting embeddings: %s -> %s", h5ad_name, npy_name) + + embeddings, _ = model.get_latent_representation( + adata_path=h5ad_path, + genelist_path=str(cfg.GENELIST_PATH), + batch_size=cfg.BATCH_SIZE, + num_workers=cfg.NUM_WORKERS, + show_progress=True, + ) + + np.save(npy_path, embeddings) + LOGGER.info(" Saved: %s, shape=%s", npy_path, embeddings.shape) + + _release_model(model) + + +def step_custom_generation(pcfg: cfg.PertConfig): + """Run MDM generation with per-step embedding-based prompt selection.""" + final_path = pcfg.results_dir / pcfg.final_result_h5ad + if final_path.exists(): + LOGGER.info("Final result already exists for %s, skipping.", pcfg.perturbation_name) + return + + LOGGER.info("=== Load 3: bc_large_aligned.ckpt for custom generation (%s) ===", pcfg.perturbation_name) + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + model = load_model_from_checkpoint( + str(cfg.ALIGNED_CKPT), + model_class="ICL_FinetunedModel", + device=device, + ) + + # Load data + query_adata = ad.read_h5ad(str(cfg.RESULTS_DIR / cfg.QUERY_CTRL_H5AD)) + prompt_pert_adata = ad.read_h5ad(str(pcfg.results_dir / cfg.PROMPT_PERT_H5AD)) + + # Load precomputed embeddings (shared + per-pert) + query_emb = np.load(cfg.RESULTS_DIR / cfg.QUERY_EMB_NPY) + prompt_ctrl_emb = np.load(cfg.RESULTS_DIR / cfg.PROMPT_CTRL_EMB_NPY) + predicted_pert_emb = np.load(pcfg.results_dir / cfg.PREDICTED_PERT_EMB_NPY) + prompt_pert_emb = np.load(pcfg.results_dir / cfg.PROMPT_PERT_EMB_NPY) + + LOGGER.info("Embeddings loaded: query=%s, ctrl=%s, pred_pert=%s, pert=%s", + query_emb.shape, prompt_ctrl_emb.shape, + predicted_pert_emb.shape, prompt_pert_emb.shape) + + result, final_logit = custom_generation_loop( + model=model, + query_adata=query_adata, + prompt_pert_adata=prompt_pert_adata, + genelist_path=str(cfg.GENELIST_PATH), + query_embeddings=query_emb, + prompt_ctrl_embeddings=prompt_ctrl_emb, + predicted_pert_embeddings=predicted_pert_emb, + prompt_pert_embeddings=prompt_pert_emb, + num_steps=cfg.NUM_STEPS, + prompt_ratio=cfg.PROMPT_RATIO, + context_ratio=cfg.CONTEXT_RATIO, + context_ratio_min=cfg.CONTEXT_RATIO_MIN, + top_k1=cfg.TOP_K1, + batch_size=cfg.BATCH_SIZE, + num_workers=cfg.NUM_WORKERS, + ) + + _release_model(model) + + query_ctrl_clean = ad.read_h5ad(str(cfg.RESULTS_DIR / cfg.QUERY_CTRL_H5AD)) + result_adata = ad.AnnData( + X=result, + obs=query_ctrl_clean.obs.copy(), + var=query_ctrl_clean.var.copy(), + ) + result_adata.obs["sm_name"] = pcfg.perturbation_name + result_adata.obs["control"] = False + result_adata.obs["gen_logit"] = np.asarray(final_logit) + + result_adata.write_h5ad(final_path) + LOGGER.info("Saved final result: %s (%d cells)", final_path, result_adata.n_obs) + + +def run_perturbation(pert_name: str): + """Run all per-perturbation steps for a given drug.""" + pcfg = cfg.get_pert_config(pert_name) + + LOGGER.info("=" * 60) + LOGGER.info("Running perturbation: %s", pert_name) + LOGGER.info("=" * 60) + + # Verify shared files exist + for shared_file in [cfg.QUERY_CTRL_H5AD, cfg.PROMPT_CTRL_H5AD, + cfg.QUERY_EMB_NPY, cfg.PROMPT_CTRL_EMB_NPY]: + if not (cfg.RESULTS_DIR / shared_file).exists(): + raise FileNotFoundError( + f"Shared file {shared_file} not found. Run --shared-only first." + ) + + # Extract prompt_pert + has_data = step_extract_prompt_pert(pcfg) + if not has_data: + LOGGER.warning("Skipping %s (no T cell data).", pert_name) + return + + # Bridge prediction + step_bridge_prediction(pcfg) + + # Pert embeddings + step_extract_pert_embeddings(pcfg) + + # Custom generation + step_custom_generation(pcfg) + + LOGGER.info("=" * 60) + LOGGER.info("Perturbation %s complete!", pert_name) + LOGGER.info("Final result: %s", pcfg.results_dir / pcfg.final_result_h5ad) + LOGGER.info("=" * 60) + + +# --------------------------------------------------------------------------- +# Main +# --------------------------------------------------------------------------- + +def main(): + parser = argparse.ArgumentParser( + description="Prompt Selection Pipeline for Stack generation." + ) + parser.add_argument( + "--perturbation", type=str, default=None, + help="Perturbation name (e.g., Dabrafenib). Required unless --shared-only.", + ) + parser.add_argument( + "--shared-only", action="store_true", + help="Only run shared steps (extract query_ctrl, prompt_ctrl, their embeddings).", + ) + args = parser.parse_args() + + if args.shared_only: + run_shared_steps() + elif args.perturbation: + run_perturbation(args.perturbation) + else: + parser.error("Either --perturbation or --shared-only is required.") + + +if __name__ == "__main__": + main() diff --git a/transfer/code/prompt_selection/run_shared.sh b/transfer/code/prompt_selection/run_shared.sh new file mode 100644 index 0000000000000000000000000000000000000000..ea7f7fb1512f4f4bb92d1368ef9f3ec7800aaa7c --- /dev/null +++ b/transfer/code/prompt_selection/run_shared.sh @@ -0,0 +1,32 @@ +#!/bin/sh +#PJM -L rscgrp=b-batch +#PJM -L gpu=1 +#PJM -L elapse=1:00:00 +#PJM -N ps_shared +#PJM -j +#PJM -o logs/ps_shared_%j.out + +module load cuda/12.2.2 +module load cudnn/8.9.7 +module load gcc-toolset/12 + +source /home/hp250092/ku50001222/qian/aivc/lfj/stack_env/bin/activate + +cd /home/hp250092/ku50001222/qian/aivc/lfj/transfer + +export PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:256 +export HDF5_USE_FILE_LOCKING=FALSE + +echo "==========================================" +echo "Job ID: $PJM_JOBID" +echo "Job Name: $PJM_JOBNAME" +echo "Start: $(date)" +echo "Node: $(hostname)" +echo "GPU: $(nvidia-smi --query-gpu=name,memory.total --format=csv,noheader 2>/dev/null || echo 'N/A')" +echo "==========================================" + +python code/prompt_selection/run_pipeline.py --shared-only + +echo "==========================================" +echo "Finished: $(date)" +echo "==========================================" diff --git a/transfer/code/prompt_selection/submit_all.sh b/transfer/code/prompt_selection/submit_all.sh new file mode 100644 index 0000000000000000000000000000000000000000..2e977a70348716ce49f124c8f5e9774ae3bebf80 --- /dev/null +++ b/transfer/code/prompt_selection/submit_all.sh @@ -0,0 +1,65 @@ +#!/bin/bash +# Master submission script for all perturbation conditions. +# +# Usage: +# Phase 1: bash code/prompt_selection/submit_all.sh shared +# (Submit shared steps, wait for completion) +# +# Phase 2: bash code/prompt_selection/submit_all.sh perturbations +# (Submit all per-perturbation pipeline + baseline jobs) +# +# Phase 3: bash code/prompt_selection/run_eval_all.sh +# (Run evaluation on login node after all GPU jobs complete) + +cd /home/hp250092/ku50001222/qian/aivc/lfj/transfer +mkdir -p logs + +PERTURBATIONS=( + "Belinostat" + "CHIR-99021" + "Crizotinib" + "Dabrafenib" + "Dactolisib" + "Foretinib" + "Idelalisib" + "LDN 193189" + "Linagliptin" + "O-Demethylated Adapalene" + "Palbociclib" + "Penfluridol" + "Porcn Inhibitor III" + "R428" +) + +case "$1" in + shared) + echo "=== Submitting shared steps ===" + pjsub code/prompt_selection/run_shared.sh + echo "" + echo "Wait for the shared job to complete, then run:" + echo " bash code/prompt_selection/submit_all.sh perturbations" + ;; + + perturbations) + echo "=== Submitting per-perturbation jobs ===" + for PERT in "${PERTURBATIONS[@]}"; do + echo "Submitting: $PERT" + # Pipeline (prompt selection) + pjsub -x PERT_NAME="$PERT" code/prompt_selection/run_pert.sh + # Baseline (random prompt) + pjsub -x PERT_NAME="$PERT" code/prompt_selection/run_baseline_pert.sh + done + echo "" + echo "Submitted ${#PERTURBATIONS[@]} pipeline + ${#PERTURBATIONS[@]} baseline jobs." + echo "After all jobs complete, run evaluation:" + echo " bash code/prompt_selection/run_eval_all.sh" + ;; + + *) + echo "Usage: $0 {shared|perturbations}" + echo "" + echo " shared Submit shared steps (run first)" + echo " perturbations Submit all per-perturbation jobs (run after shared completes)" + exit 1 + ;; +esac diff --git a/transfer/code/prompt_selection/visualize_comparison.py b/transfer/code/prompt_selection/visualize_comparison.py new file mode 100644 index 0000000000000000000000000000000000000000..5286ff0e54d08efc44e4699c9beee854b5c70065 --- /dev/null +++ b/transfer/code/prompt_selection/visualize_comparison.py @@ -0,0 +1,218 @@ +#!/usr/bin/env python3 +"""Visualize cell-eval comparison: Prompt Selection vs Random Baseline. + +Modes: + --perturbation X : Single perturbation bar chart + --all : Heatmap across all perturbations +""" +from __future__ import annotations + +import argparse +import sys +from pathlib import Path + +_THIS_DIR = Path(__file__).resolve().parent +if str(_THIS_DIR.parent) not in sys.path: + sys.path.insert(0, str(_THIS_DIR.parent)) + +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd + +from prompt_selection import config as cfg + +# Metrics to exclude (uninformative: all zero, NaN, or identical) +EXCLUDE = { + "overlap_at_50", "overlap_at_100", "overlap_at_200", + "precision_at_50", "precision_at_100", "precision_at_200", + "de_spearman_sig", + "discrimination_score_l1", "discrimination_score_l2", "discrimination_score_cosine", + "de_nsig_counts_real", "de_nsig_counts_pred", +} + +DISPLAY_NAMES = { + "overlap_at_N": "Overlap@N", + "overlap_at_500": "Overlap@500", + "precision_at_N": "Precision@N", + "precision_at_500": "Precision@500", + "de_direction_match": "DE Direction\nMatch", + "de_spearman_lfc_sig": "DE Spearman\nLFC", + "de_sig_genes_recall": "DE Sig Genes\nRecall", + "pr_auc": "PR AUC", + "roc_auc": "ROC AUC", + "pearson_delta": "Pearson\nDelta", + "mse": "MSE", + "mae": "MAE", + "mse_delta": "MSE Delta", + "mae_delta": "MAE Delta", +} + +LOWER_IS_BETTER = {"mse", "mae", "mse_delta", "mae_delta"} + + +def visualize_single(pert_name: str): + """Generate bar chart for a single perturbation.""" + pcfg = cfg.get_pert_config(pert_name) + csv_path = pcfg.eval_dir / "comparison_mean.csv" + output_path = pcfg.eval_dir / "comparison_chart.png" + + if not csv_path.exists(): + print(f"No comparison_mean.csv found for {pert_name}: {csv_path}") + return + + df = pd.read_csv(csv_path) + df = df[~df["metric"].isin(EXCLUDE)].copy() + df = df.dropna(subset=["prompt_selection", "random_baseline"]).reset_index(drop=True) + df["display"] = df["metric"].map(DISPLAY_NAMES).fillna(df["metric"]) + df["pct_diff"] = np.where( + df["random_baseline"].abs() > 1e-12, + (df["prompt_selection"] - df["random_baseline"]) / df["random_baseline"].abs() * 100, + 0.0, + ) + + error_metrics = df[df["metric"].isin(LOWER_IS_BETTER)].reset_index(drop=True) + quality_metrics = df[~df["metric"].isin(LOWER_IS_BETTER)].reset_index(drop=True) + + fig, axes = plt.subplots( + 2, 1, figsize=(14, 10), + gridspec_kw={"height_ratios": [max(len(quality_metrics), 1), max(len(error_metrics), 1)]}, + ) + fig.suptitle( + f"Cell-Eval: Prompt Selection vs Random Baseline ({pert_name} B-cells)", + fontsize=15, fontweight="bold", y=0.98, + ) + + colors_ps = "#4C72B0" + colors_bl = "#DD8452" + + for ax, subset, title, lower_better in [ + (axes[0], quality_metrics, "Quality Metrics (higher is better)", False), + (axes[1], error_metrics, "Error Metrics (lower is better)", True), + ]: + n = len(subset) + if n == 0: + ax.set_visible(False) + continue + + y = np.arange(n) + bar_h = 0.35 + + ax.barh(y - bar_h / 2, subset["prompt_selection"], bar_h, + label="Prompt Selection", color=colors_ps, edgecolor="white", linewidth=0.5) + ax.barh(y + bar_h / 2, subset["random_baseline"], bar_h, + label="Random Baseline", color=colors_bl, edgecolor="white", linewidth=0.5) + + ax.set_yticks(y) + ax.set_yticklabels(subset["display"], fontsize=11) + ax.invert_yaxis() + ax.set_title(title, fontsize=12, fontweight="bold", pad=10) + ax.legend(loc="lower right", fontsize=10) + ax.grid(axis="x", alpha=0.3, linestyle="--") + ax.set_axisbelow(True) + + for i, row in subset.iterrows(): + idx = subset.index.get_loc(i) + pct = row["pct_diff"] + max_val = max(row["prompt_selection"], row["random_baseline"]) + if abs(pct) < 0.01: + label = "0%" + color = "gray" + else: + sign = "+" if pct > 0 else "" + label = f"{sign}{pct:.1f}%" + if lower_better: + color = "#D32F2F" if pct > 0 else "#388E3C" + else: + color = "#388E3C" if pct > 0 else "#D32F2F" + ax.text(max_val * 1.02, idx, label, + va="center", ha="left", fontsize=10, fontweight="bold", color=color) + + x_max = subset[["prompt_selection", "random_baseline"]].max().max() + ax.set_xlim(right=x_max * 1.18) + + plt.tight_layout(rect=[0, 0, 1, 0.96]) + fig.savefig(output_path, dpi=150, bbox_inches="tight", facecolor="white") + print(f"Saved: {output_path}") + + +def visualize_all(): + """Generate heatmap across all perturbations.""" + csv_path = cfg.EVAL_DIR / "all_comparison.csv" + output_path = cfg.EVAL_DIR / "all_comparison_heatmap.png" + + if not csv_path.exists(): + print(f"No all_comparison.csv found: {csv_path}") + print("Run aggregate_results.py first.") + return + + df = pd.read_csv(csv_path) + df = df[~df["metric"].isin(EXCLUDE)].copy() + df = df.dropna(subset=["prompt_selection", "random_baseline"]) + + # Compute percentage difference + df["pct_diff"] = np.where( + df["random_baseline"].abs() > 1e-12, + (df["prompt_selection"] - df["random_baseline"]) / df["random_baseline"].abs() * 100, + 0.0, + ) + + # For lower-is-better metrics, negate so positive = PS better + for m in LOWER_IS_BETTER: + mask = df["metric"] == m + df.loc[mask, "pct_diff"] = -df.loc[mask, "pct_diff"] + + df["display"] = df["metric"].map(DISPLAY_NAMES).fillna(df["metric"]) + + # Pivot: perturbation × metric + pivot = df.pivot_table(index="perturbation", columns="display", values="pct_diff", aggfunc="first") + pivot = pivot.reindex(sorted(pivot.index)) + + fig, ax = plt.subplots(figsize=(16, max(8, len(pivot) * 0.6))) + vmax = max(abs(pivot.values[~np.isnan(pivot.values)].min()), abs(pivot.values[~np.isnan(pivot.values)].max()), 10) + im = ax.imshow(pivot.values, cmap="RdYlGn", aspect="auto", vmin=-vmax, vmax=vmax) + + ax.set_xticks(range(len(pivot.columns))) + ax.set_xticklabels(pivot.columns, rotation=45, ha="right", fontsize=10) + ax.set_yticks(range(len(pivot.index))) + ax.set_yticklabels(pivot.index, fontsize=11) + + # Annotate cells + for i in range(len(pivot.index)): + for j in range(len(pivot.columns)): + val = pivot.values[i, j] + if not np.isnan(val): + color = "white" if abs(val) > vmax * 0.6 else "black" + ax.text(j, i, f"{val:+.1f}%", ha="center", va="center", + fontsize=8, color=color, fontweight="bold") + + cbar = plt.colorbar(im, ax=ax, shrink=0.8, pad=0.02) + cbar.set_label("% Difference (positive = PS better)", fontsize=11) + + ax.set_title( + "Prompt Selection vs Random Baseline: % Improvement per Perturbation\n" + "(green = PS better, red = Baseline better; error metrics negated)", + fontsize=13, fontweight="bold", pad=15, + ) + + plt.tight_layout() + fig.savefig(output_path, dpi=150, bbox_inches="tight", facecolor="white") + print(f"Saved: {output_path}") + + +def main(): + parser = argparse.ArgumentParser(description="Visualize cell-eval comparison results") + group = parser.add_mutually_exclusive_group(required=True) + group.add_argument("--perturbation", type=str, help="Single perturbation name") + group.add_argument("--all", action="store_true", help="All perturbations heatmap") + args = parser.parse_args() + + if args.all: + visualize_all() + else: + visualize_single(args.perturbation) + + +if __name__ == "__main__": + main() diff --git a/transfer/code/prompt_selection/visualize_per_metric.py b/transfer/code/prompt_selection/visualize_per_metric.py new file mode 100644 index 0000000000000000000000000000000000000000..6456bd176dba687896c1b2422c0eb2edbc452f36 --- /dev/null +++ b/transfer/code/prompt_selection/visualize_per_metric.py @@ -0,0 +1,152 @@ +#!/usr/bin/env python3 +"""Per-metric bar charts: PS vs BL across all perturbations, with % difference.""" +from __future__ import annotations + +import sys +from pathlib import Path + +_THIS_DIR = Path(__file__).resolve().parent +if str(_THIS_DIR.parent) not in sys.path: + sys.path.insert(0, str(_THIS_DIR.parent)) + +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd + +from prompt_selection import config as cfg + +CSV_PATH = cfg.EVAL_DIR / "all_comparison.csv" +OUTPUT_DIR = cfg.EVAL_DIR / "per_metric_charts" + +# Metrics to visualize +SELECTED_METRICS = [ + "pearson_delta", + "de_direction_match", + "de_sig_genes_recall", + "roc_auc", + "mse", + "mae", +] + +DISPLAY_NAMES = { + "pearson_delta": "Pearson Delta", + "de_direction_match": "DE Direction Match", + "de_sig_genes_recall": "DE Sig Genes Recall", + "roc_auc": "ROC AUC", + "mse": "MSE", + "mae": "MAE", +} + +LOWER_IS_BETTER = {"mse", "mae"} + + +def plot_one_metric(df_metric: pd.DataFrame, metric_name: str, output_dir: Path): + """Generate a grouped bar chart for one metric across all perturbations.""" + display_name = DISPLAY_NAMES.get(metric_name, metric_name) + lower_better = metric_name in LOWER_IS_BETTER + + # Sort by PS value (descending for quality, ascending for error) + df_metric = df_metric.sort_values("prompt_selection", ascending=lower_better).reset_index(drop=True) + + # Shorten long perturbation names for display + short_names = { + "O-Demethylated Adapalene": "O-Demeth. Adapalene", + "Porcn Inhibitor III": "Porcn Inhib. III", + "Dimethyl Sulfoxide": "DMSO", + } + df_metric["display_pert"] = df_metric["perturbation"].map(short_names).fillna(df_metric["perturbation"]) + + n = len(df_metric) + y = np.arange(n) + bar_h = 0.35 + + fig, ax = plt.subplots(figsize=(12, max(6, n * 0.55))) + + bars_ps = ax.barh(y - bar_h / 2, df_metric["prompt_selection"], bar_h, + label="Prompt Selection", color="#4C72B0", edgecolor="white", linewidth=0.5) + bars_bl = ax.barh(y + bar_h / 2, df_metric["random_baseline"], bar_h, + label="Random Baseline", color="#DD8452", edgecolor="white", linewidth=0.5) + + ax.set_yticks(y) + ax.set_yticklabels(df_metric["display_pert"], fontsize=11) + ax.invert_yaxis() + ax.set_xlabel(display_name, fontsize=12) + ax.legend(loc="lower right", fontsize=10, framealpha=0.9) + ax.grid(axis="x", alpha=0.3, linestyle="--") + ax.set_axisbelow(True) + + if lower_better: + subtitle = "(lower is better)" + else: + subtitle = "(higher is better)" + ax.set_title(f"{display_name} — Prompt Selection vs Random Baseline\n{subtitle}", + fontsize=14, fontweight="bold", pad=12) + + # Annotate percentage difference + for idx, row in df_metric.iterrows(): + ps_val = row["prompt_selection"] + bl_val = row["random_baseline"] + max_val = max(abs(ps_val), abs(bl_val)) + + if abs(bl_val) > 1e-12: + pct = (ps_val - bl_val) / abs(bl_val) * 100 + else: + pct = 0.0 + + if abs(pct) < 0.01: + label = "0%" + color = "gray" + else: + sign = "+" if pct > 0 else "" + label = f"{sign}{pct:.1f}%" + if lower_better: + color = "#388E3C" if pct < 0 else "#D32F2F" # green if lower (better) + else: + color = "#388E3C" if pct > 0 else "#D32F2F" # green if higher (better) + + # Position label to the right of the longer bar + text_x = max(ps_val, bl_val) + if text_x < 0: + text_x = min(ps_val, bl_val) + ax.text(text_x * 1.02, idx, label, + va="center", ha="right", fontsize=10, fontweight="bold", color=color) + else: + ax.text(text_x * 1.02 + max_val * 0.01, idx, label, + va="center", ha="left", fontsize=10, fontweight="bold", color=color) + + # Add margin for labels + x_vals = pd.concat([df_metric["prompt_selection"], df_metric["random_baseline"]]) + x_min, x_max = x_vals.min(), x_vals.max() + margin = (x_max - x_min) * 0.2 if x_max > x_min else abs(x_max) * 0.3 + if x_min < 0: + ax.set_xlim(left=x_min - margin * 0.5) + ax.set_xlim(right=x_max + margin) + + plt.tight_layout() + out_path = output_dir / f"{metric_name}.png" + fig.savefig(out_path, dpi=150, bbox_inches="tight", facecolor="white") + plt.close(fig) + print(f"Saved: {out_path}") + + +def main(): + df = pd.read_csv(CSV_PATH) + OUTPUT_DIR.mkdir(parents=True, exist_ok=True) + + for metric in SELECTED_METRICS: + df_metric = df[df["metric"] == metric].copy() + df_metric = df_metric.dropna(subset=["prompt_selection", "random_baseline"]) + + if df_metric.empty: + print(f"No data for {metric}, skipping.") + continue + + plot_one_metric(df_metric, metric, OUTPUT_DIR) + + print(f"\nAll charts saved to: {OUTPUT_DIR}") + + +if __name__ == "__main__": + main() diff --git a/transfer/code/scDFM/.gitignore b/transfer/code/scDFM/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..f5b69b22867a1c7d862ec13fdb23b5539bf8f8d3 --- /dev/null +++ b/transfer/code/scDFM/.gitignore @@ -0,0 +1,6 @@ +__pycache__/ +test.md +*.h5ad +*.zip +result +data/norman \ No newline at end of file diff --git a/transfer/code/scDFM/LICENSE b/transfer/code/scDFM/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..46e3f8d7061455f8b427f4e13ae30c617bd29a68 --- /dev/null +++ b/transfer/code/scDFM/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2025 AI4Science-WestlakeU + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/transfer/code/scDFM/README.md b/transfer/code/scDFM/README.md new file mode 100644 index 0000000000000000000000000000000000000000..624aab3bb4f46fdcc8d82a055f1d10e285c65f98 --- /dev/null +++ b/transfer/code/scDFM/README.md @@ -0,0 +1,81 @@ +

+ scDFN logo +

+ +# scDFM: Distributional Flow Matching for Robust Single-Cell Perturbation Prediction (ICLR 2026) + +[![arXiv](https://img.shields.io/badge/arXiv-2601.01829-b31b1b?logo=arxiv)](https://openreview.net/forum?id=QSGanMEcUV) +[![Codebase](https://img.shields.io/badge/Codebase-GitHub-181717?logo=github)](https://github.com/AI4Science-WestlakeU/scDFM) +[![License: MIT](https://img.shields.io/badge/License-MIT-yellow?logo=open-source-initiative&logoColor=white)](LICENSE) + + +Official repo for the paper [scDFM](URL), ICLR 2026.
+Chenglei Yu∗1,2, [Chuanrui Wang](https://wang-cr.github.io/)∗1, Bangyan Liao∗1,2 & [Tailin Wu](https://tailin.org/)†1.
+ +1School of Engineering, Westlake University; +2Zhejaing University; + +*Equal contribution, †Corresponding authors + +---- + +## Overview +We propose a novel distributional flow matching framework (scDFM) for robust single-cell perturbation prediction, which models the full distribution of perturbed cellular expression profiles conditioned on control states, thereby overcoming limitations of existing methods that rely on cell-level correspondences and fail to capture population-level transcriptional shifts. + +Framework of paper: + + + +## Install dependencies +``` +conda env create -f environment.yml +``` + +## ⏬ Dataset download + +Put dataset into data file: + +- [Norman](https://figshare.com/articles/dataset/Norman_et_al_2019_Science_labeled_Perturb-seq_data/24688110) +- [Combosciplex subset of sciplex v3](https://figshare.com/articles/dataset/combosciplex/25062230?file=44229635) +### Alternative Data Access + +We also provide the datasets via [Google Drive](https://drive.google.com/drive/folders/1cNpYAt9jVWZN82miNZtkP10YeSo7hufL?usp=sharing). This folder contains: +- The **Norman** dataset and its corresponding data splits. +- The **ComboSciPlex** dataset. + +Example directory layout after download (relative to repo root): +``` +scDFM/ +├─ data/ +│ ├─ norman.h5ad +│ └─ combosciplex.h5ad +├─ src/ +│ └─ ... +└─ run.sh +``` + + + +## 📥 Training + +An example on additive task. +```bash +bash run.sh +``` + +## 🫡 Citation + +If you find our work and/or our code useful, please cite us via: + +```bibtex +@article{yu2026scdfm, + title={scDFM: Distributional Flow Matching Model for Robust Single-Cell Perturbation Prediction}, + author={Yu, Chenglei and Wang, Chuanrui and Liao, Bangyan and Wu, Tailin}, + journal={arXiv preprint arXiv:2602.07103}, + year={2026} +} +``` + +## 📚 Related Resources + +- AI for Scientific Simulation and Discovery Lab: https://github.com/AI4Science-WestlakeU diff --git a/transfer/code/scDFM/assets/fig1.png b/transfer/code/scDFM/assets/fig1.png new file mode 100644 index 0000000000000000000000000000000000000000..524547e8735dade267ec824cba6f4cb9e79870d2 --- /dev/null +++ b/transfer/code/scDFM/assets/fig1.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:56e7b45649aa2268576eed5923db5f84a2782c7288fd538a0dc37d8e87bb587b +size 931506 diff --git a/transfer/code/scDFM/assets/logo.png b/transfer/code/scDFM/assets/logo.png new file mode 100644 index 0000000000000000000000000000000000000000..c9a04c36372c1403af3e0ddb369498feb673a697 --- /dev/null +++ b/transfer/code/scDFM/assets/logo.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8f0851a69c4456af600fd6044c2b2e37147ab532d6b8dab709a50c117a15b9cd +size 176734 diff --git a/transfer/code/scDFM/config/config_flow.py b/transfer/code/scDFM/config/config_flow.py new file mode 100644 index 0000000000000000000000000000000000000000..28a5bd567f062dac5d935769d93c7b5360b4f284 --- /dev/null +++ b/transfer/code/scDFM/config/config_flow.py @@ -0,0 +1,64 @@ + +from dataclasses import dataclass +import os +@dataclass +class FlowConfig: + # Flow model type + model_type: str = 'hierarchical' + + # Flow Matching specific parameters + batch_size: int = 32 + ntoken: int = 512 + d_model: int = 512 + lr: float = 1e-5 + steps: int = 5000 + eta_min: float = 1e-7 + devices: str = "1" + test_only: bool = False + # Perturbation related parameters + data_name: str = "combosciplex" + perturbation_function: str = 'crisper' + noise_type: str = "Gaussian" + poisson_alpha: float = 0.8 + poisson_target_sum: int = -1 + + print_every: int = 5000 + mode: str = 'predict_y' # predict_y, predict_p + result_path: str = './result' + perturbation_fusion_method: str = 'sum' # mlp, sum + fusion_method: str = 'cross' # cross , concat, add + infer_top_gene: int = 1000 + n_top_genes: int = 5000 + checkpoint_path: str = '' + gamma: float = 0.0 + split_method: str = 'additive' + use_mmd_loss: bool = False + fold: int = 0 + use_negative_edge: bool = False + topk: int = 15 + + def __post_init__(self): + if self.data_name == 'norman_umi_go_filtered': + self.n_top_genes = 5054 + if self.data_name == 'norman': + self.n_top_genes = 5000 + path = self.make_path() + + def make_path(self): + exp_name = '-'.join(['flow', + f'fusion_{self.fusion_method}', + f'{self.data_name}', + self.model_type, + self.mode, + f'gamma_{self.gamma}', + f'perturbation_function_{self.perturbation_function}', + f'lr_{self.lr}', + f'dim_model_{self.d_model}', + f'infer_top_gene_{self.infer_top_gene}', + f'split_method_{self.split_method}', + f'use_mmd_loss_{self.use_mmd_loss}', + f'fold_{self.fold}', + f'use_negative_edge_{self.use_negative_edge}', + f'topk_{self.topk}', + ]) + return os.path.join(self.result_path, exp_name) \ No newline at end of file diff --git a/transfer/code/scDFM/data/norman.h5ad b/transfer/code/scDFM/data/norman.h5ad new file mode 100644 index 0000000000000000000000000000000000000000..0d60f54804ab63d9dd24562cdf1696c697ce91d4 --- /dev/null +++ b/transfer/code/scDFM/data/norman.h5ad @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:23ffb0fac6a847ff927cf7509d80d85052bfefbfb97610786a2dafaaefa0b6a0 +size 2228610012 diff --git a/transfer/code/scDFM/data/norman/go.csv b/transfer/code/scDFM/data/norman/go.csv new file mode 100644 index 0000000000000000000000000000000000000000..d381bfe31c9dc44bb4d0b07dc6a41eac9fe99cd7 --- /dev/null +++ b/transfer/code/scDFM/data/norman/go.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e0cdc85330b468c0b99fa4929f18c411124bca60a88153566ad1dd858d73a806 +size 19700177 diff --git a/transfer/code/scDFM/data/norman/mask_fold_1topk_15additive.pt b/transfer/code/scDFM/data/norman/mask_fold_1topk_15additive.pt new file mode 100644 index 0000000000000000000000000000000000000000..dbff282acbf4cefb38200a5ef97d3622b810d1cd --- /dev/null +++ b/transfer/code/scDFM/data/norman/mask_fold_1topk_15additive.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e2424c302c05f526846772c179161941b5a7019afd02143768f19bc21dc76249 +size 25393262 diff --git a/transfer/code/scDFM/data/norman/mask_fold_1topk_30additive_negative_edge.pt b/transfer/code/scDFM/data/norman/mask_fold_1topk_30additive_negative_edge.pt new file mode 100644 index 0000000000000000000000000000000000000000..32fecda3963f06f182278f4796d246a2976c092c --- /dev/null +++ b/transfer/code/scDFM/data/norman/mask_fold_1topk_30additive_negative_edge.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7ff78a9fac525dce4f351edde2a1ccbf018f25c1a3da92ef5d07bf4109bec283 +size 25393488 diff --git a/transfer/code/scDFM/data/norman/perturb_processed.h5ad b/transfer/code/scDFM/data/norman/perturb_processed.h5ad new file mode 100644 index 0000000000000000000000000000000000000000..0d60f54804ab63d9dd24562cdf1696c697ce91d4 --- /dev/null +++ b/transfer/code/scDFM/data/norman/perturb_processed.h5ad @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:23ffb0fac6a847ff927cf7509d80d85052bfefbfb97610786a2dafaaefa0b6a0 +size 2228610012 diff --git a/transfer/code/scDFM/data/norman/split_results.pkl b/transfer/code/scDFM/data/norman/split_results.pkl new file mode 100644 index 0000000000000000000000000000000000000000..62ef03e52beb9d7c17c79f30164fc463df5aec82 --- /dev/null +++ b/transfer/code/scDFM/data/norman/split_results.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:55fd4bc2fde120ae2dfcb4d8524fa502524053bde0d2e0367354bbc8a4b20b04 +size 9407 diff --git a/transfer/code/scDFM/environment.yml b/transfer/code/scDFM/environment.yml new file mode 100644 index 0000000000000000000000000000000000000000..88802032db069b901c887ad0750e4336d8bb34df --- /dev/null +++ b/transfer/code/scDFM/environment.yml @@ -0,0 +1,318 @@ +name: sc +channels: + - conda-forge +dependencies: + - _libgcc_mutex=0.1 + - _openmp_mutex=4.5 + - asttokens=3.0.0 + - bzip2=1.0.8 + - ca-certificates=2025.8.3 + - comm=0.2.3 + - decorator=5.2.1 + - exceptiongroup=1.3.0 + - executing=2.2.0 + - importlib-metadata=8.7.0 + - ipykernel=6.30.1 + - ipython=9.4.0 + - ipython_pygments_lexers=1.1.1 + - jedi=0.19.2 + - jupyter_client=8.6.3 + - jupyter_core=5.8.1 + - keyutils=1.6.1 + - krb5=1.21.3 + - ld_impl_linux-64=2.44 + - libedit=3.1.20250104 + - libexpat=2.7.1 + - libffi=3.4.6 + - libgcc=15.1.0 + - libgcc-ng=15.1.0 + - libgomp=15.1.0 + - liblzma=5.8.1 + - libnsl=2.0.1 + - libsodium=1.0.20 + - libsqlite=3.50.4 + - libstdcxx=15.1.0 + - libstdcxx-ng=15.1.0 + - libuuid=2.38.1 + - libxcrypt=4.4.36 + - libzlib=1.3.1 + - matplotlib-inline=0.1.7 + - ncurses=6.5 + - nest-asyncio=1.6.0 + - openssl=3.5.2 + - packaging=25.0 + - parso=0.8.4 + - pexpect=4.9.0 + - pickleshare=0.7.5 + - pip=25.2 + - platformdirs=4.3.8 + - prompt-toolkit=3.0.51 + - ptyprocess=0.7.0 + - pure_eval=0.2.3 + - pygments=2.19.2 + - python=3.11.13 + - python-dateutil=2.9.0.post0 + - python_abi=3.11 + - readline=8.2 + - setuptools=80.9.0 + - six=1.17.0 + - stack_data=0.6.3 + - tk=8.6.13 + - traitlets=5.14.3 + - typing_extensions=4.14.1 + - wcwidth=0.2.13 + - wheel=0.45.1 + - zeromq=4.3.5 + - zipp=3.23.0 + - pip: + - absl-py==2.3.1 + - accelerate==1.9.0 + - adjusttext==1.3.0 + - adpbulk==0.1.4 + - aiofiles==24.1.0 + - aiohappyeyeballs==2.6.1 + - aiohttp==3.12.15 + - aiosignal==1.4.0 + - anndata==0.12.1 + - antlr4-python3-runtime==4.9.3 + - anyio==4.10.0 + - argon2-cffi==25.1.0 + - argon2-cffi-bindings==25.1.0 + - array-api-compat==1.12.0 + - arrow==1.3.0 + - arviz==0.22.0 + - async-lru==2.0.5 + - attrs==25.3.0 + - babel==2.17.0 + - beautifulsoup4==4.13.4 + - bleach==6.2.0 + - blitzgsea==1.3.54 + - cached-property==2.0.1 + - cell-eval==0.5.42 + - cell-gears==0.1.2 + - certifi==2025.8.3 + - cffi==1.17.1 + - charset-normalizer==3.4.2 + - chex==0.1.90 + - click==8.2.1 + - contourpy==1.3.3 + - cpa==0.4.1 + - crc32c==2.7.1 + - cycler==0.12.1 + - datasets==2.21.0 + - dcor==0.6 + - debugpy==1.8.15 + - defusedxml==0.7.1 + - deprecated==1.2.18 + - dill==0.3.8 + - docrep==0.3.2 + - docstring-parser==0.17.0 + - donfig==0.8.1.post1 + - einops==0.8.1 + - equinox==0.13.0 + - et-xmlfile==2.0.0 + - etils==1.13.0 + - fast-array-utils==1.2.1 + - fastjsonschema==2.21.1 + - filelock==3.18.0 + - flax==0.11.0 + - fonttools==4.59.0 + - formulaic==1.2.0 + - formulaic-contrasts==1.0.0 + - fqdn==1.5.1 + - frozenlist==1.7.0 + - fsspec==2024.6.1 + - grpcio==1.74.0 + - h11==0.16.0 + - h5netcdf==1.6.3 + - h5py==3.14.0 + - hf-xet==1.1.5 + - httpcore==1.0.9 + - httpx==0.28.1 + - huggingface-hub==0.34.3 + - humanize==4.12.3 + - idna==3.10 + - igraph==0.11.9 + - importlib-resources==6.5.2 + - iniconfig==2.1.0 + - interface-meta==1.3.0 + - ipywidgets==8.1.7 + - isoduration==20.11.0 + - jax==0.6.2 + - jaxlib==0.6.2 + - jaxopt==0.8.5 + - jaxtyping==0.3.2 + - jinja2==3.1.6 + - joblib==1.5.1 + - json5==0.12.0 + - jsonpointer==3.0.0 + - jsonschema==4.25.0 + - jsonschema-specifications==2025.4.1 + - jupyter==1.1.1 + - jupyter-console==6.6.3 + - jupyter-events==0.12.0 + - jupyter-lsp==2.2.6 + - jupyter-server==2.16.0 + - jupyter-server-terminals==0.5.3 + - jupyterlab==4.4.5 + - jupyterlab-pygments==0.3.0 + - jupyterlab-server==2.27.3 + - jupyterlab-widgets==3.0.15 + - kiwisolver==1.4.8 + - lamin-utils==0.15.0 + - lark==1.2.2 + - legacy-api-wrap==1.4.1 + - leidenalg==0.10.2 + - lightning==2.5.2 + - lightning-utilities==0.15.0 + - lineax==0.0.8 + - llvmlite==0.44.0 + - markdown==3.8.2 + - markdown-it-py==3.0.0 + - markupsafe==3.0.2 + - matplotlib==3.10.5 + - mdurl==0.1.2 + - mistune==3.1.3 + - ml-collections==1.1.0 + - ml-dtypes==0.5.3 + - mpmath==1.3.0 + - msgpack==1.1.1 + - mudata==0.3.2 + - multidict==6.6.3 + - multipledispatch==1.0.0 + - multiprocess==0.70.16 + - narwhals==2.0.1 + - natsort==8.4.0 + - nbclient==0.10.2 + - nbconvert==7.16.6 + - nbformat==5.10.4 + - networkx==3.5 + - notebook==7.4.5 + - notebook-shim==0.2.4 + - numba==0.61.2 + - numcodecs==0.16.1 + - numpy==2.2.6 + - numpyro==0.18.0 + - nvidia-cublas-cu12==12.6.4.1 + - nvidia-cuda-cupti-cu12==12.6.80 + - nvidia-cuda-nvrtc-cu12==12.6.77 + - nvidia-cuda-runtime-cu12==12.6.77 + - nvidia-cudnn-cu12==9.5.1.17 + - nvidia-cufft-cu12==11.3.0.4 + - nvidia-cufile-cu12==1.11.1.6 + - nvidia-curand-cu12==10.3.7.77 + - nvidia-cusolver-cu12==11.7.1.2 + - nvidia-cusparse-cu12==12.5.4.2 + - nvidia-cusparselt-cu12==0.6.3 + - nvidia-nccl-cu12==2.26.2 + - nvidia-nvjitlink-cu12==12.6.85 + - nvidia-nvtx-cu12==12.6.77 + - omegaconf==2.3.0 + - openpyxl==3.1.5 + - opt-einsum==3.4.0 + - optax==0.2.5 + - orbax==0.1.7 + - orbax-checkpoint==0.11.20 + - ott-jax==0.5.1 + - overrides==7.7.0 + - pandas==2.3.1 + - pandocfilters==1.5.1 + - patsy==1.0.1 + - pdex==0.1.21 + - pertpy==1.0.1 + - pillow==11.3.0 + - pluggy==1.6.0 + - ply==3.11 + - polars==1.32.0 + - pot==0.9.5 + - prometheus-client==0.22.1 + - propcache==0.3.2 + - protobuf==6.31.1 + - psutil==7.0.0 + - pubchempy==1.0.4 + - pyarrow==21.0.0 + - pycparser==2.22 + - pydeseq2==0.5.2 + - pydot==4.0.1 + - pynndescent==0.5.13 + - pyomo==6.9.2 + - pyparsing==3.2.3 + - pyro-api==0.1.2 + - pyro-ppl==1.9.1 + - pytest==8.4.1 + - python-json-logger==3.3.0 + - pytorch-lightning==1.9.5 + - pytz==2025.2 + - pyyaml==6.0.2 + - pyzmq==27.0.1 + - rdkit==2025.3.5 + - referencing==0.36.2 + - requests==2.32.4 + - rfc3339-validator==0.1.4 + - rfc3986-validator==0.1.1 + - rfc3987-syntax==1.1.0 + - rich==14.1.0 + - rpds-py==0.26.0 + - safetensors==0.5.3 + - scanpy==1.11.4 + - scgpt==0.2.4 + - scib==1.1.7 + - scikit-learn==1.7.1 + - scikit-misc==0.5.1 + - scipy==1.16.1 + - scvi-tools==0.20.3 + - seaborn==0.13.2 + - send2trash==1.8.3 + - session-info==1.0.1 + - session-info2==0.2 + - shtab==1.7.2 + - simplejson==3.20.1 + - sniffio==1.3.1 + - soupsieve==2.7 + - sparse==0.17.0 + - sparsecca==0.3.1 + - statsmodels==0.14.5 + - stdlib-list==0.11.1 + - sympy==1.14.0 + - tensorboard==2.20.0 + - tensorboard-data-server==0.7.2 + - tensorstore==0.1.76 + - terminado==0.18.1 + - texttable==1.7.0 + - threadpoolctl==3.6.0 + - timm==1.0.19 + - tinycss2==1.4.0 + - tomlkit==0.13.3 + - toolz==1.0.0 + - torch==2.7.1 + - torch-geometric==2.6.1 + - torchaudio==2.7.1 + - torchdiffeq==0.2.5 + - torchinfo==1.8.0 + - torchmetrics==1.8.0 + - torchtext==0.18.0 + - torchvision==0.22.1 + - tornado==6.5.1 + - tqdm==4.67.1 + - treescope==0.1.9 + - triton==3.3.1 + - typeguard==4.4.4 + - types-python-dateutil==2.9.0.20250708 + - tyro==0.9.27 + - tzdata==2025.2 + - umap-learn==0.5.9.post2 + - uri-template==1.3.0 + - urllib3==2.5.0 + - wadler-lindig==0.1.7 + - webcolors==24.11.1 + - webencodings==0.5.1 + - websocket-client==1.8.0 + - werkzeug==3.1.3 + - widgetsnbextension==4.0.14 + - wrapt==1.17.2 + - xarray==2025.7.1 + - xarray-einstats==0.9.1 + - xxhash==3.5.0 + - yarl==1.20.1 + - zarr==3.1.1 +prefix: /opt/conda/envs/sc diff --git a/transfer/code/scDFM/run.sh b/transfer/code/scDFM/run.sh new file mode 100644 index 0000000000000000000000000000000000000000..c7a0fbf33509be7a0c171ad75b4dff9024683d91 --- /dev/null +++ b/transfer/code/scDFM/run.sh @@ -0,0 +1,24 @@ +export PYTHONPATH=./ + +python src/script/run.py \ +--batch_size=48 \ +--devices='0' \ +--model_type=origin \ +--lr=5e-5 \ +--steps=200000 \ +--data_name=norman \ +--d_model=128 \ +--eta_min=1e-6 \ +--fusion_method=differential_perceiver \ +--infer_top_gene=1000 \ +--n_top_genes=5000 \ +--result_path=./result/additive \ +--perturbation_function=crisper \ +--noise_type=Gaussian \ +--mode=predict_y \ +--gamma=0.5 \ +--split_method=additive \ +--use_mmd_loss \ +--fold=1 \ +--topk=30 \ +--use_negative_edge \ diff --git a/transfer/code/scDFM/src/__init__.py b/transfer/code/scDFM/src/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d185c87108adb9a0c174b4f18213d11b855ad732 --- /dev/null +++ b/transfer/code/scDFM/src/__init__.py @@ -0,0 +1 @@ +# Auto-created by CCFM bootstrap diff --git a/transfer/code/scDFM/src/data_process/__init__.py b/transfer/code/scDFM/src/data_process/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d185c87108adb9a0c174b4f18213d11b855ad732 --- /dev/null +++ b/transfer/code/scDFM/src/data_process/__init__.py @@ -0,0 +1 @@ +# Auto-created by CCFM bootstrap diff --git a/transfer/code/scDFM/src/data_process/_data.py b/transfer/code/scDFM/src/data_process/_data.py new file mode 100644 index 0000000000000000000000000000000000000000..21ff590fd61e2583f58b9a128fa91dfde4c15a52 --- /dev/null +++ b/transfer/code/scDFM/src/data_process/_data.py @@ -0,0 +1,218 @@ +from collections.abc import Callable +from dataclasses import dataclass +from typing import Any + +import jax +import jax.numpy as jnp + +from src.utils._types import ArrayLike + +__all__ = [ + "BaseDataMixin", + "ConditionData", + "PredictionData", + "TrainingData", + "ValidationData", +] + + +@dataclass +class ReturnData: # TODO: this should rather be a NamedTuple + split_covariates_mask: jnp.ndarray | None + split_idx_to_covariates: dict[int, tuple[Any, ...]] + perturbation_covariates_mask: jnp.ndarray | None + perturbation_idx_to_covariates: dict[int, tuple[Any, ...]] + perturbation_idx_to_id: dict[int, Any] + condition_data: dict[str, ArrayLike] + control_to_perturbation: dict[int, ArrayLike] + max_combination_length: int + condition_data_id: jnp.ndarray # mapping from perturbation_idx to condition_id + + +class BaseDataMixin: + """Base class for data containers.""" + + @property + def n_controls(self) -> int: + """Returns the number of control covariate values.""" + return len(self.split_idx_to_covariates) # type: ignore[attr-defined] + + @property + def n_perturbations(self) -> int: + """Returns the number of perturbation covariate combinations.""" + return len(self.perturbation_idx_to_covariates) # type: ignore[attr-defined] + + @property + def n_perturbation_covariates(self) -> int: + """Returns the number of perturbation covariates.""" + return len(self.condition_data) # type: ignore[attr-defined] + + def _format_params(self, fmt: Callable[[Any], str]) -> str: + params = { + "n_controls": self.n_controls, + "n_perturbations": self.n_perturbations, + } + return ", ".join(f"{name}={fmt(val)}" for name, val in params.items()) + + def __repr__(self) -> str: + return f"{self.__class__.__name__}[{self._format_params(repr)}]" + + +@dataclass +class ConditionData(BaseDataMixin): + """Data container containing condition embeddings. + + Parameters + ---------- + condition_data + Dictionary with embeddings for conditions. + max_combination_length + Maximum number of covariates in a combination. + null_value + Token to use for masking `null_value`. + data_manager + Data manager used to generate the data. + condition_data_id + Mapping from perturbation index to condition ID. + """ + + condition_data: dict[str, ArrayLike] + max_combination_length: int + perturbation_idx_to_covariates: dict[int, tuple[str, ...]] + perturbation_idx_to_id: dict[int, Any] + null_value: Any + data_manager: Any + condition_data_id: jnp.ndarray + +@dataclass +class TrainingData(BaseDataMixin): + """Training data. + + Parameters + ---------- + cell_data + The representation of cell data, e.g. PCA of gene expression data. + split_covariates_mask + Mask of the split covariates. + split_idx_to_covariates + Dictionary explaining values in ``split_covariates_mask``. + perturbation_covariates_mask + Mask of the perturbation covariates. + perturbation_idx_to_covariates + Dictionary explaining values in ``perturbation_covariates_mask``. + condition_data + Dictionary with embeddings for conditions. + control_to_perturbation + Mapping from control index to target distribution indices. + max_combination_length + Maximum number of covariates in a combination. + data_manager + The data manager + condition_data_id + Mapping from perturbation index to condition ID. + """ + + cell_data: jax.Array # (n_cells, n_features) + split_covariates_mask: jax.Array # (n_cells,), which cell assigned to which source distribution + split_idx_to_covariates: dict[int, tuple[Any, ...]] # (n_sources,) dictionary explaining split_covariates_mask + perturbation_covariates_mask: jax.Array # (n_cells,), which cell assigned to which target distribution + perturbation_idx_to_covariates: dict[ + int, tuple[str, ...] + ] # (n_targets,), dictionary explaining perturbation_covariates_mask + perturbation_idx_to_id: dict[int, Any] + condition_data: dict[str, ArrayLike] # (n_targets,) all embeddings for conditions + control_to_perturbation: dict[int, ArrayLike] # mapping from control idx to target distribution idcs + max_combination_length: int + null_value: Any + data_manager: Any + condition_data_id: jnp.ndarray + cell_data_id: Any + + +@dataclass +class ValidationData(BaseDataMixin): + """Data container for the validation data. + + Parameters + ---------- + cell_data + The representation of cell data, e.g. PCA of gene expression data. + split_covariates_mask + Mask of the split covariates. + split_idx_to_covariates + Dictionary explaining values in ``split_covariates_mask``. + perturbation_covariates_mask + Mask of the perturbation covariates. + perturbation_idx_to_covariates + Dictionary explaining values in ``perturbation_covariates_mask``. + condition_data + Dictionary with embeddings for conditions. + control_to_perturbation + Mapping from control index to target distribution indices. + max_combination_length + Maximum number of covariates in a combination. + data_manager + The data manager + condition_data_id + Mapping from perturbation index to condition ID. + n_conditions_on_log_iteration + Number of conditions to use for computation callbacks at each logged iteration. + If :obj:`None`, use all conditions. + n_conditions_on_train_end + Number of conditions to use for computation callbacks at the end of training. + If :obj:`None`, use all conditions. + """ + + cell_data: jax.Array # (n_cells, n_features) + split_covariates_mask: jax.Array # (n_cells,), which cell assigned to which source distribution + split_idx_to_covariates: dict[int, tuple[Any, ...]] # (n_sources,) dictionary explaining split_covariates_mask + perturbation_covariates_mask: jax.Array # (n_cells,), which cell assigned to which target distribution + perturbation_idx_to_covariates: dict[ + int, tuple[str, ...] + ] # (n_targets,), dictionary explaining perturbation_covariates_mask + perturbation_idx_to_id: dict[int, Any] + condition_data: dict[str, ArrayLike] # (n_targets,) all embeddings for conditions + control_to_perturbation: dict[int, jax.Array] # mapping from control idx to target distribution idcs + max_combination_length: int + null_value: Any + data_manager: Any + condition_data_id: jnp.ndarray + n_conditions_on_log_iteration: int | None = None + n_conditions_on_train_end: int | None = None + + +@dataclass +class PredictionData(BaseDataMixin): + """Data container to perform prediction. + + Parameters + ---------- + src_data + Dictionary with data for source cells. + condition_data + Dictionary with embeddings for conditions. + control_to_perturbation + Mapping from control index to target distribution indices. + covariate_encoder + Encoder for the primary covariate. + max_combination_length + Maximum number of covariates in a combination. + null_value + Token to use for masking ``null_value``. + condition_data_id + Mapping from condition name to condition ID embedding. + """ + + cell_data: jax.Array # (n_cells, n_features) + split_covariates_mask: jax.Array # (n_cells,), which cell assigned to which source distribution + split_idx_to_covariates: dict[int, tuple[Any, ...]] # (n_sources,) dictionary explaining split_covariates_mask + perturbation_idx_to_covariates: dict[ + int, tuple[str, ...] + ] # (n_targets,), dictionary explaining perturbation_covariates_mask + perturbation_idx_to_id: dict[int, Any] + condition_data: dict[str, ArrayLike] # (n_targets,) all embeddings for conditions + control_to_perturbation: dict[int, ArrayLike] + max_combination_length: int + null_value: Any + data_manager: Any + condition_data_id: jnp.ndarray diff --git a/transfer/code/scDFM/src/data_process/_dataloader.py b/transfer/code/scDFM/src/data_process/_dataloader.py new file mode 100644 index 0000000000000000000000000000000000000000..be30c54b45b3b8c6367e12a4e474df6bcdcf5d4b --- /dev/null +++ b/transfer/code/scDFM/src/data_process/_dataloader.py @@ -0,0 +1,235 @@ +import abc +from typing import Any, Literal + +import jax +import jax.numpy as jnp +import numpy as np + +from src.data_process._data import PredictionData, TrainingData, ValidationData +import pdb +import torch + +__all__ = ["TrainSampler", "ValidationSampler", "PredictionSampler"] + + +class TrainSampler: + """Data sampler for :class:`~cellflow.data.TrainingData`. + + Parameters + ---------- + data + The training data. + batch_size + The batch size. + + """ + + def __init__(self, data: TrainingData, batch_size: int = 1024): + self._data = data + self._data_idcs = jnp.arange(data.cell_data.shape[0]) + self.batch_size = batch_size + self.n_source_dists = data.n_controls + self.n_target_dists = data.n_perturbations + self.conditional_samplings = [ + lambda key, i=i: jax.random.choice(key, self._data.control_to_perturbation[i]) + for i in range(self.n_source_dists) + ] + self.get_embeddings = lambda idx: { + pert_cov: jnp.expand_dims(arr[idx], 0) for pert_cov, arr in self._data.condition_data.items() + } + # @jax.jit + def _sample(rng: jax.Array) -> Any: + + rng_1, rng_2, rng_3, rng_4 = jax.random.split(rng, 4) + source_dist_idx = jax.random.choice(rng_1, self.n_source_dists) + source_cells_mask = self._data.split_covariates_mask == source_dist_idx + src_cond_p = source_cells_mask / jnp.count_nonzero(source_cells_mask) + source_batch_idcs = jax.random.choice(rng_2, self._data_idcs, [self.batch_size], replace=True, p=src_cond_p) + source_batch = self._data.cell_data[source_batch_idcs] + + target_dist_idx = jax.lax.switch(source_dist_idx, self.conditional_samplings, rng_3) + target_cells_mask = self._data.perturbation_covariates_mask == target_dist_idx + tgt_cond_p = target_cells_mask / jnp.count_nonzero(target_cells_mask) + target_batch_idcs = jax.random.choice( + rng_4, + self._data_idcs, + [self.batch_size], + replace=True, + p=tgt_cond_p, + ) + target_batch = self._data.cell_data[target_batch_idcs] + if self._data.condition_data is None: + return {"src_cell_data": source_batch, "tgt_cell_data": target_batch} + + condition_batch = self.get_embeddings(target_dist_idx) + + return { + "src_cell_data": source_batch, + "tgt_cell_data": target_batch, + "condition": condition_batch, + "condition_id": self._data.condition_data_id[target_dist_idx], + "src_cell_id": self._data.cell_data_id[source_batch_idcs], + "tgt_cell_id": self._data.cell_data_id[target_batch_idcs] + } + + self.sample = _sample + + @property + def data(self) -> TrainingData: + """The training data.""" + return self._data + + def precompute_latent_embedding(self, model,): + """ + compute latent representation + + return + ---- + latent_embedding : torch.Tensor + """ + + cell_data = self._data.cell_data + with torch.no_grad(): + self._data.cell_embedding = model(cell_data) + + +class BaseValidSampler(abc.ABC): + @abc.abstractmethod + def sample(*args, **kwargs): + pass + + def _get_key(self, cond_idx: int) -> tuple[str, ...]: + if len(self._data.perturbation_idx_to_id): # type: ignore[attr-defined] + return self._data.perturbation_idx_to_id[cond_idx] # type: ignore[attr-defined] + cov_combination = self._data.perturbation_idx_to_covariates[cond_idx] # type: ignore[attr-defined] + return tuple(cov_combination[i] for i in range(len(cov_combination))) + + def _get_perturbation_to_control(self, data: ValidationData | PredictionData) -> dict[int, int]: + d = {} + for k, v in data.control_to_perturbation.items(): + for el in v: + d[el] = k + return d + + def _get_condition_data(self, cond_idx: int) -> jnp.ndarray: + return {k: v[[cond_idx], ...] for k, v in self._data.condition_data.items()} # type: ignore[attr-defined] + + +class ValidationSampler(BaseValidSampler): + """Data sampler for :class:`~cellflow.data.ValidationData`. + + Parameters + ---------- + val_data + The validation data. + seed + Random seed. + """ + + def __init__(self, val_data: ValidationData, seed: int = 0) -> None: + self._data = val_data + self.perturbation_to_control = self._get_perturbation_to_control(val_data) + self.n_conditions_on_log_iteration = ( + val_data.n_conditions_on_log_iteration + if val_data.n_conditions_on_log_iteration is not None + else val_data.n_perturbations + ) + self.n_conditions_on_train_end = ( + val_data.n_conditions_on_train_end + if val_data.n_conditions_on_train_end is not None + else val_data.n_perturbations + ) + self.rng = np.random.default_rng(seed) + if self._data.condition_data is None: + raise NotImplementedError("Validation data must have condition data.") + + def sample(self, mode: Literal["on_log_iteration", "on_train_end"]) -> Any: + """Sample data for validation. + + Parameters + ---------- + mode + Sampling mode. Either ``"on_log_iteration"`` or ``"on_train_end"``. + + Returns + ------- + Dictionary with source, condition, and target data from the validation data. + """ + import pdb; pdb.set_trace() + size = self.n_conditions_on_log_iteration if mode == "on_log_iteration" else self.n_conditions_on_train_end + condition_idcs = self.rng.choice(self._data.n_perturbations, size=(size,), replace=False) + + source_idcs = [self.perturbation_to_control[cond_idx] for cond_idx in condition_idcs] + source_cells_mask = [self._data.split_covariates_mask == source_idx for source_idx in source_idcs] + source_cells = [self._data.cell_data[mask] for mask in source_cells_mask] + target_cells_mask = [cond_idx == self._data.perturbation_covariates_mask for cond_idx in condition_idcs] + target_cells = [self._data.cell_data[mask] for mask in target_cells_mask] + conditions = [self._get_condition_data(cond_idx) for cond_idx in condition_idcs] + cell_rep_dict = {} + cond_dict = {} + true_dict = {} + for i in range(len(condition_idcs)): + k = self._get_key(condition_idcs[i]) + cell_rep_dict[k] = source_cells[i] + cond_dict[k] = conditions[i] + true_dict[k] = target_cells[i] + + return {"source": cell_rep_dict, "condition": cond_dict, "target": true_dict} + + @property + def data(self) -> ValidationData: + """The validation data.""" + return self._data + + +class PredictionSampler(BaseValidSampler): + """Data sampler for :class:`~cellflow.data.PredictionData`. + + Parameters + ---------- + pred_data + The prediction data. + + """ + + def __init__(self, pred_data: PredictionData) -> None: + self._data = pred_data + self.perturbation_to_control = self._get_perturbation_to_control(pred_data) + if self._data.condition_data is None: + raise NotImplementedError("Validation data must have condition data.") + + def sample(self) -> Any: + """Sample data for prediction. + + Returns + ------- + Dictionary with source and condition data from the prediction data. + """ + condition_idcs = range(self._data.n_perturbations) + + source_idcs = [self.perturbation_to_control[cond_idx] for cond_idx in condition_idcs] + source_cells_mask = [self._data.split_covariates_mask == source_idx for source_idx in source_idcs] + source_cells = [self._data.cell_data[mask] for mask in source_cells_mask] + target_cells_mask = [cond_idx == self._data.perturbation_covariates_mask for cond_idx in condition_idcs] + target_cells = [self._data.cell_data[mask] for mask in target_cells_mask] + + conditions = [self._get_condition_data(cond_idx) for cond_idx in condition_idcs] + cell_rep_dict = {} + cond_dict = {} + true_dict = {} + for i in range(len(condition_idcs)): + k = self._get_key(condition_idcs[i]) + cell_rep_dict[k] = source_cells[i] + cond_dict[k] = conditions[i] + true_dict[k] = target_cells[i] + + return { + "source": cell_rep_dict, + "target": true_dict, + "condition": cond_dict, + } + + @property + def data(self) -> PredictionData: + """The training data.""" + return self._data diff --git a/transfer/code/scDFM/src/data_process/_datamanager.py b/transfer/code/scDFM/src/data_process/_datamanager.py new file mode 100644 index 0000000000000000000000000000000000000000..9d1000417ae97bbdc35a1556e7c5bb4d288883ad --- /dev/null +++ b/transfer/code/scDFM/src/data_process/_datamanager.py @@ -0,0 +1,988 @@ +from collections.abc import Sequence +from typing import Any + +import anndata +import jax +import jax.numpy as jnp +import numpy as np +import pandas as pd +import scipy.sparse as sp +import sklearn.preprocessing as preprocessing +from pandas.api.types import is_numeric_dtype +from tqdm import tqdm + +from src.utils._logging import logger +from src.utils._types import ArrayLike +from src.data_process._data import ConditionData, PredictionData, ReturnData, TrainingData, ValidationData +import pdb +from src.utils._utils import _flatten_list, _to_list + +__all__ = ["DataManager"] + + +class DataManager: + """Data manager for handling perturbation data. + + Parameters + ---------- + adata + An :class:`~anndata.AnnData` object. + covariate_encoder + Encoder for the primary covariate. + categorical + Whether the primary covariate is categorical. + max_combination_length + Maximum number of combinations of primary ``perturbation_covariates``. + sample_rep + Key in :attr:`~anndata.AnnData.obsm` where the sample representation is stored or ``'X'`` + to use :attr:`~anndata.AnnData.X`. + covariate_data + Dataframe with covariates. If :obj:`None`, :attr:`~anndata.AnnData.obs` is used. + condition_id_key + Key in :attr:`~anndata.AnnData.obs` that defines the condition id. + perturbation_covariates + A dictionary where the keys indicate the name of the covariate group + and the values are keys in :attr:`~anndata.AnnData.uns`. The corresponding + columns should be either boolean (presence/abscence of the perturbation) or + numeric (concentration or magnitude of the perturbation). If multiple groups + are provided, the first is interpreted as the primary perturbation and the + others as covariates corresponding to these perturbations, e.g. + ``{"drug":("drugA", "drugB"), "time":("drugA_time", "drugB_time")}``. + perturbation_covariate_reps + A dictionary where the keys indicate the name of the covariate group and the + values are keys in :attr:`~anndata.AnnData.uns` storing a dictionary with + the representation of the covariates. E.g. ``{"drug":"drug_embeddings"}`` + with ``adata.uns["drug_embeddings"] = {"drugA": np.array, "drugB": np.array}``. + sample_covariates + Keys in :attr:`~anndata.AnnData.obs` indicating sample covatiates to be taken + into account for training and prediction, e.g. ``["age", "cell_type"]``. + sample_covariate_reps + A dictionary where the keys indicate the name of the covariate group and the + values are keys in :attr:`~anndata.AnnData.uns` storing a dictionary with the + representation of the covariates. E.g. ``{"cell_type": "cell_type_embeddings"}`` with + ``adata.uns["cell_type_embeddings"] = {"cell_typeA": np.array, "cell_typeB": np.array}``. + split_covariates + Covariates in :attr:`~anndata.AnnData.obs` to split all control cells into + different control populations. The perturbed cells are also split according to these + columns, but if these covariates should also be encoded in the model, the corresponding + column should also be used in ``perturbation_covariates`` or ``sample_covariates``. + null_value + Value to use for padding to ``max_combination_length``. + """ + + def __init__( + self, + adata: anndata.AnnData, + sample_rep: str | dict[str, str], + control_key: str, + perturbation_covariates: dict[str, Sequence[str]] | None = None, + perturbation_covariate_reps: dict[str, str] | None = None, + sample_covariates: Sequence[str] | None = None, + sample_covariate_reps: dict[str, str] | None = None, + split_covariates: Sequence[str] | None = None, + max_combination_length: int | None = None, + null_value: float = 0.0, + ): + self._adata = adata + self._sample_rep = self._verify_sample_rep(sample_rep) + self._control_key = control_key + self._perturbation_covariates = self._verify_perturbation_covariates(perturbation_covariates) + self._perturbation_covariate_reps = self._verify_perturbation_covariate_reps( + adata, + perturbation_covariate_reps, + self._perturbation_covariates, + ) + self._sample_covariates = self._verify_sample_covariates(sample_covariates) + self._sample_covariate_reps = self._verify_sample_covariate_reps( + adata, sample_covariate_reps, self._sample_covariates + ) + self._split_covariates = self._verify_split_covariates(adata, split_covariates, control_key) + self._max_combination_length = self._get_max_combination_length( + self._perturbation_covariates, max_combination_length + ) + self._null_value = null_value + self._primary_one_hot_encoder, self._is_categorical = self._get_primary_covar_encoder( + self._adata, + self._perturbation_covariates, + self._perturbation_covariate_reps, + ) + self._linked_perturb_covars = self._get_linked_perturbation_covariates(self._perturbation_covariates) + sample_cov_groups = {covar: _to_list(covar) for covar in self._sample_covariates} + covariate_groups = self._perturbation_covariates | sample_cov_groups + self._covariate_reps = (self._perturbation_covariate_reps or {}) | (self._sample_covariate_reps or {}) + + self._covar_to_idx = self._get_covar_to_idx(covariate_groups) # type: ignore[arg-type] + perturb_covar_keys = _flatten_list(self._perturbation_covariates.values()) + list(self._sample_covariates) + perturb_covar_keys += [col for col in self._split_covariates if col not in perturb_covar_keys] + self._perturb_covar_keys = [k for k in perturb_covar_keys if k is not None] + + # get the unique values of the split covariates + condition = np.unique(list(self.adata.obs['condition'])) + unique_drug = [] + np.array([unique_drug.extend(perturbation.split('+')) for perturbation in condition]) + unique_drug = np.unique(unique_drug) + unique_drug.sort() + self.unique_drug = unique_drug + self.drug_dict = {drug: i for i, drug in enumerate(unique_drug)} + + def get_train_data(self, adata: anndata.AnnData, one_hot: bool = False) -> Any: + """Get training data for the model. + + Parameters + ---------- + adata + An :class:`~anndata.AnnData` object. + + Returns + ------- + Training data for the model. + """ + # create a + split_cov_combs = self._get_split_cov_combs(adata.obs) + cond_data = self._get_condition_data(split_cov_combs=split_cov_combs, adata=adata, one_hot=one_hot) + cell_data = self._get_cell_data(adata) + return TrainingData( + cell_data=cell_data, + split_covariates_mask=cond_data.split_covariates_mask, + split_idx_to_covariates=cond_data.split_idx_to_covariates, + perturbation_covariates_mask=cond_data.perturbation_covariates_mask, + perturbation_idx_to_covariates=cond_data.perturbation_idx_to_covariates, + perturbation_idx_to_id=cond_data.perturbation_idx_to_id, + condition_data=cond_data.condition_data, + control_to_perturbation=cond_data.control_to_perturbation, + max_combination_length=cond_data.max_combination_length, + null_value=self._null_value, + data_manager=self, + condition_data_id=cond_data.condition_data_id, + cell_data_id=adata.obs.index, + ) + + def get_validation_data( + self, + adata: anndata.AnnData, + n_conditions_on_log_iteration: int | None = None, + n_conditions_on_train_end: int | None = None, + one_hot: bool = False, + ) -> ValidationData: + """Get validation data for the model. + + Parameters + ---------- + adata + An :class:`~anndata.AnnData` object. + n_conditions_on_log_iteration + Number of conditions to validate on during logging. + n_conditions_on_train_end + Number of conditions to validate on at the end of training. + + Returns + ------- + Validation data for the model. + """ + split_cov_combs = self._get_split_cov_combs(adata.obs) + cond_data = self._get_condition_data(split_cov_combs=split_cov_combs, adata=adata, one_hot=one_hot) + cell_data = self._get_cell_data(adata) + return ValidationData( + cell_data=cell_data, + split_covariates_mask=cond_data.split_covariates_mask, + split_idx_to_covariates=cond_data.split_idx_to_covariates, + perturbation_covariates_mask=cond_data.perturbation_covariates_mask, + perturbation_idx_to_covariates=cond_data.perturbation_idx_to_covariates, + perturbation_idx_to_id=cond_data.perturbation_idx_to_id, + condition_data=cond_data.condition_data, + control_to_perturbation=cond_data.control_to_perturbation, + max_combination_length=cond_data.max_combination_length, + null_value=self._null_value, + data_manager=self, + condition_data_id=cond_data.condition_data_id, + n_conditions_on_log_iteration=n_conditions_on_log_iteration, + n_conditions_on_train_end=n_conditions_on_train_end, + ) + + def get_prediction_data( + self, + adata: anndata.AnnData, + sample_rep: str, + covariate_data: pd.DataFrame, + rep_dict: dict[str, Any] | None = None, + condition_id_key: str | None = None, + one_hot: bool = False, + ) -> Any: + """Get predictions for control cells. + + Extracts source distributions from ``'adata'`` and simulates cells perturbed + with covariates defined in ``'covariate_data'``. + + Parameters + ---------- + adata + An :class:`~anndata.AnnData` object to extract control cells from. + sample_rep + Key in :attr:`~anndata.AnnData.obsm` where the sample representation of the control + is stored or ``'X'`` to use :attr:`~anndata.AnnData.X`. + covariate_data + A :class:`~pandas.DataFrame` with columns defining the covariates as + in :meth:`cellflow.model.CellFlow.prepare_data` and stored in + :attr:`cellflow.model.CellFlow.data_manager`. + rep_dict + Dictionary with representations of the covariates. + If not provided, :attr:`~anndata.AnnData.uns` is used. + condition_id_key + Key in :class:`~pandas.DataFrame` that defines the condition names. + + Returns + ------- + Training data for the model. + """ + self._verify_prediction_data(adata) + split_cov_combs = self._get_split_cov_combs(covariate_data=covariate_data) + + # adata is None since we don't extract cell masks for predicted covariates + cond_data = self._get_condition_data( + split_cov_combs=split_cov_combs, + adata=None, + covariate_data=covariate_data, + rep_dict=adata.uns if rep_dict is None else rep_dict, + condition_id_key=condition_id_key, + one_hot=one_hot, + ) + + cell_data = self._get_cell_data(adata, sample_rep) + split_covariates_mask, split_idx_to_covariates = self._get_split_covariates_mask( + adata=adata, split_cov_combs=split_cov_combs + ) + + return PredictionData( + cell_data=cell_data, + split_covariates_mask=split_covariates_mask, + split_idx_to_covariates=split_idx_to_covariates, + condition_data=cond_data.condition_data, + control_to_perturbation=cond_data.control_to_perturbation, + perturbation_idx_to_covariates=cond_data.perturbation_idx_to_covariates, + perturbation_idx_to_id=cond_data.perturbation_idx_to_id, + max_combination_length=cond_data.max_combination_length, + null_value=self._null_value, + data_manager=self, + condition_data_id=cond_data.condition_data_id, + ) + + def get_condition_data( + self, + covariate_data: pd.DataFrame, + rep_dict: dict[str, Any] | None = None, + condition_id_key: str | None = None, + one_hot: bool = False, + ) -> ConditionData: + """Get condition data for the model. + + Parameters + ---------- + covariate_data + Dataframe with covariates. + condition_id_key + Key in ``covariate_data`` that defines the condition id. + rep_dict + + Returns + ------- + Condition data for the model. + """ + self._verify_covariate_data(covariate_data, self._perturb_covar_keys) + split_cov_combs = self._get_split_cov_combs(covariate_data) + cond_data = self._get_condition_data( + split_cov_combs=split_cov_combs, + adata=None, + covariate_data=covariate_data, + rep_dict=rep_dict, + condition_id_key=condition_id_key, + one_hot=one_hot, + ) + return ConditionData( + condition_data=cond_data.condition_data, + max_combination_length=cond_data.max_combination_length, + perturbation_idx_to_covariates=cond_data.perturbation_idx_to_covariates, + perturbation_idx_to_id=cond_data.perturbation_idx_to_id, + null_value=self._null_value, + data_manager=self, + condition_data_id=cond_data.condition_data_id, + ) + + def _get_split_cov_combs(self, covariate_data: pd.DataFrame) -> np.ndarray | list[list[Any]]: # type: ignore[type-arg] + if len(self._split_covariates) > 0: + return covariate_data[self._split_covariates].drop_duplicates().values + else: + return [[]] + + def _get_condition_data( + self, + split_cov_combs: np.ndarray | list[list[Any]], # type: ignore[type-arg] + adata: anndata.AnnData | None, + covariate_data: pd.DataFrame | None = None, + rep_dict: dict[str, Any] | None = None, + condition_id_key: str | None = None, + one_hot: bool = False, + ) -> ReturnData: + # for prediction: adata is None, covariate_data is provided + # for training/validation: adata is provided and used to get cell masks, covariate_data is None + if adata is None and covariate_data is None: + raise ValueError("Either `adata` or `covariate_data` must be provided.") + covariate_data = covariate_data if covariate_data is not None else adata.obs # type: ignore[union-attr] + if rep_dict is None: + rep_dict = adata.uns if adata is not None else {} + # check if all perturbation/split covariates and control cells are present in the input + self._verify_covariate_data( + covariate_data, + {covar: _to_list(covar) for covar in self._sample_covariates}, + ) + self._verify_control_data(adata) + self._verify_covariate_data(covariate_data, _to_list(self._split_covariates)) + + # extract unique combinations of perturbation covariates + if condition_id_key is not None: + self._verify_condition_id_key(covariate_data, condition_id_key) + select_keys = self._perturb_covar_keys + [condition_id_key] + else: + select_keys = self._perturb_covar_keys + perturb_covar_df = covariate_data[select_keys].drop_duplicates() + if condition_id_key is not None: + perturb_covar_df = perturb_covar_df.set_index(condition_id_key) + else: + perturb_covar_df = perturb_covar_df.reset_index() + + # get indices of cells belonging to each unique condition + _perturb_covar_df, _covariate_data = ( + perturb_covar_df[self._perturb_covar_keys], + covariate_data[self._perturb_covar_keys], + ) + _perturb_covar_df["row_id"] = range(len(perturb_covar_df)) + _covariate_data["cell_index"] = _covariate_data.index + _perturb_covar_merged = _perturb_covar_df.merge(_covariate_data, on=self._perturb_covar_keys, how="inner") + perturb_covar_to_cells = _perturb_covar_merged.groupby("row_id")["cell_index"].apply(list).to_list() + + # intialize data containers + if adata is not None: + split_covariates_mask = np.full((len(adata),), -1, dtype=jnp.int32) + perturbation_covariates_mask = np.full((len(adata),), -1, dtype=jnp.int32) + control_mask = covariate_data[self._control_key] + else: + split_covariates_mask = None + perturbation_covariates_mask = None + control_mask = jnp.ones((len(covariate_data),)) + + condition_data: dict[str, list[jnp.ndarray]] = {i: [] for i in self._covar_to_idx.keys()} + + control_to_perturbation: dict[int, ArrayLike] = {} + split_idx_to_covariates: dict[int, tuple[Any]] = {} + perturbation_idx_to_covariates: dict[int, tuple[Any]] = {} + perturbation_idx_to_id: dict[int, Any] = {} + condition_data_id_list: list[jnp.ndarray] = [] + + src_counter = 0 + tgt_counter = 0 + + # iterate over unique split covariate combinations + for split_combination in split_cov_combs: + # get masks for split covariates; for prediction, it's done outside this method + if adata is not None: + split_covariates_mask, split_idx_to_covariates, split_cov_mask = self._get_split_combination_mask( + covariate_data=adata.obs, + split_covariates_mask=split_covariates_mask, # type: ignore[arg-type] + split_combination=split_combination, # type: ignore[arg-type] + split_idx_to_covariates=split_idx_to_covariates, + control_mask=control_mask, + src_counter=src_counter, + ) + conditional_distributions = [] + + # iterate over target conditions + filter_dict = dict(zip(self.split_covariates, split_combination, strict=False)) + pc_df = perturb_covar_df[ + (perturb_covar_df[list(filter_dict.keys())] == list(filter_dict.values())).all(axis=1) + ] + pbar = tqdm(pc_df.iterrows(), total=pc_df.shape[0]) + + for i, tgt_cond in pbar: + tgt_cond = tgt_cond[self._perturb_covar_keys] + # for train/validation, only extract covariate combinations that are present in adata + if adata is not None: + mask = covariate_data.index.isin(perturb_covar_to_cells[i]) + mask *= (1 - control_mask) * split_cov_mask + mask = np.array(mask == 1) + if mask.sum() == 0: + continue + # map unique condition id to target id + perturbation_covariates_mask[mask] = tgt_counter # type: ignore[index] + + # map target id to unique conditions and their ids + conditional_distributions.append(tgt_counter) + perturbation_idx_to_covariates[tgt_counter] = tgt_cond.values + if condition_id_key is not None: + perturbation_idx_to_id[tgt_counter] = i + + # get embeddings for conditions + embedding = self._get_perturbation_covariates( + condition_data=tgt_cond, + rep_dict=rep_dict, + perturb_covariates={k: _to_list(v) for k, v in self._perturbation_covariates.items()}, + ) + for pert_cov, emb in embedding.items(): + condition_data[pert_cov].append(emb) + + condition_name, condition_id_emb = self._generate_condition_name_and_embedding(tgt_cond, tgt_counter, embedding, one_hot=one_hot) + condition_data_id_list.append(condition_id_emb) + tgt_counter += 1 + + # map source (control) to target condition ids + control_to_perturbation[src_counter] = np.array(conditional_distributions) + src_counter += 1 + # convert outputs to jax arrays + for pert_cov, emb in condition_data.items(): + condition_data[pert_cov] = jnp.array(emb) + condition_data_id = jnp.stack(condition_data_id_list, axis=0) # shape: (N, D) + split_covariates_mask = jnp.asarray(split_covariates_mask) if split_covariates_mask is not None else None + perturbation_covariates_mask = ( + jnp.asarray(perturbation_covariates_mask) if perturbation_covariates_mask is not None else None + ) + return ReturnData( + split_covariates_mask=split_covariates_mask, + split_idx_to_covariates=split_idx_to_covariates, + perturbation_covariates_mask=perturbation_covariates_mask, + perturbation_idx_to_covariates=perturbation_idx_to_covariates, + perturbation_idx_to_id=perturbation_idx_to_id, + condition_data=condition_data, # type: ignore[arg-type] + control_to_perturbation=control_to_perturbation, + max_combination_length=self._max_combination_length, + condition_data_id=condition_data_id, + ) + + def _generate_condition_name_and_embedding( + self, + tgt_cond: pd.Series, + tgt_counter: int, + embedding: dict[str, jnp.ndarray], + one_hot: bool = False, + ) -> tuple[str, jnp.ndarray]: + """ + 基于 self.drug_dict 映射 tgt_cond 中的药物组合，生成唯一名称和 embedding。 + """ + # 展开所有 perturbation covariates（如 Drug1, Drug2） + drug_names = [] + for covar_group in self._perturbation_covariates.values(): + for col in covar_group: + val = tgt_cond[col] + if isinstance(val, str): + drug_names.extend(val.split('+')) + else: + drug_names.append(str(val)) + drug_names = sorted(set(drug_names)) # 去重 + 排序 + drug_ids = [self.drug_dict[name] for name in drug_names] + + condition_name = 'drug_' + '_'.join(str(i) for i in drug_ids) + if one_hot: + one_hots = [] + for idx in drug_ids: + vec = jnp.zeros((num_drugs,), dtype=jnp.float32).at[idx].set(1.0) + one_hots.append(vec) + condition_id_emb = jnp.concatenate(one_hots, axis=-1) + else: + condition_id_emb = jnp.array(drug_ids, dtype=jnp.int32) + return condition_name, condition_id_emb + + @staticmethod + def _verify_condition_id_key(covariate_data: pd.DataFrame, condition_id_key: str | None) -> None: + if condition_id_key is not None and condition_id_key not in covariate_data.columns: + raise ValueError(f"The `condition_id_key` column ('{condition_id_key}') was not found in `adata.obs`.") + if not len(covariate_data[condition_id_key].unique()) == len(covariate_data): + raise ValueError(f"The `condition_id_key` column ('{condition_id_key}') must contain unique values.") + + @staticmethod + def _verify_sample_rep(sample_rep: str | dict[str, str]) -> str | dict[str, str]: + if not (isinstance(sample_rep, str) or isinstance(sample_rep, dict)): + raise ValueError( + f"`sample_rep` should be of type `str` or `dict`, found {sample_rep} to be of type {type(sample_rep)}." + ) + return sample_rep + + def _get_cell_data( + self, + adata: anndata.AnnData, + sample_rep: str | None = None, + ) -> jax.Array: + sample_rep = self._sample_rep if sample_rep is None else sample_rep + if sample_rep == "X": + sample_rep = adata.X + if isinstance(sample_rep, sp.csr_matrix): + return jnp.asarray(sample_rep.toarray()) + else: + return jnp.asarray(sample_rep) + if isinstance(self._sample_rep, str): + if self._sample_rep not in adata.obsm: + raise KeyError(f"Sample representation '{self._sample_rep}' not found in `adata.obsm`.") + return jnp.asarray(adata.obsm[self._sample_rep]) + attr, key = next(iter(sample_rep.items())) # type: ignore[union-attr] + return jnp.asarray(getattr(adata, attr)[key]) + + def _verify_control_data(self, adata: anndata.AnnData | None) -> None: + if adata is None: + return None + if self._control_key not in adata.obs: + raise ValueError(f"Control column '{self._control_key}' not found in adata.obs.") + if not isinstance(adata.obs[self._control_key].dtype, pd.BooleanDtype): + try: + adata.obs[self._control_key] = adata.obs[self._control_key].astype("boolean") + except TypeError as e: + raise ValueError(f"Control column '{self._control_key}' could not be converted to boolean.") from e + if adata.obs[self._control_key].sum() == 0: + raise ValueError("No control cells found in adata.") + + def _verify_prediction_data(self, adata: anndata.AnnData) -> None: + if self._control_key not in adata.obs: + raise ValueError(f"Control column '{self._control_key}' not found in adata.obs.") + if not isinstance(adata.obs[self._control_key].dtype, pd.BooleanDtype): + try: + adata.obs[self._control_key] = adata.obs[self._control_key].astype("boolean") + except ValueError as e: + raise ValueError(f"Control column '{self._control_key}' could not be converted to boolean.") from e + if not adata.obs[self._control_key].all(): + raise ValueError( + f"For prediction, all cells in `adata` should be from control condition. Ensure that '{self._control_key}' is `True` for all cells, even if you're setting `.obs` to predicted condition." + ) + + def _get_split_combination_mask( + self, + covariate_data: pd.DataFrame, + split_covariates_mask: ArrayLike, + split_combination: ArrayLike, + split_idx_to_covariates: dict[int, tuple[Any]], + control_mask: ArrayLike, + src_counter: int, + ) -> tuple[ArrayLike, dict[int, tuple[Any]], ArrayLike]: + filter_dict = dict(zip(self.split_covariates, split_combination, strict=False)) + split_cov_mask = (covariate_data[list(filter_dict.keys())] == list(filter_dict.values())).all(axis=1) + mask = jnp.array(control_mask * split_cov_mask).astype(bool) + split_covariates_mask[mask] = src_counter + split_idx_to_covariates[src_counter] = tuple(split_combination) + return split_covariates_mask, split_idx_to_covariates, split_cov_mask + + def _get_split_covariates_mask( + self, + adata: anndata.AnnData, + split_cov_combs: np.ndarray | list[list[Any]], # type: ignore[type-arg] + ) -> tuple[ArrayLike, dict[int, tuple[Any]]]: + # here we assume that adata only contains source cells + if len(self.split_covariates) == 0: + return jnp.full((len(adata),), 0, dtype=jnp.int32), {} + split_covariates_mask = np.full((len(adata),), -1, dtype=jnp.int32) + split_idx_to_covariates: dict[int, Any] = {} + src_counter = 0 + for split_combination in split_cov_combs: + split_covariates_mask_previous = split_covariates_mask.copy() + split_covariates_mask, split_idx_to_covariates, _ = self._get_split_combination_mask( + covariate_data=adata.obs, + split_covariates_mask=split_covariates_mask, + split_combination=split_combination, # type: ignore[arg-type] + split_idx_to_covariates=split_idx_to_covariates, + control_mask=jnp.ones((adata.n_obs,)), + src_counter=src_counter, + ) + if (split_covariates_mask == split_covariates_mask_previous).all(): + raise ValueError(f"No cells found in `adata` for split covariates {split_combination}.") + src_counter += 1 + return jnp.asarray(split_covariates_mask), split_idx_to_covariates + + @staticmethod + def _verify_perturbation_covariates(data: dict[str, Sequence[str]] | None) -> dict[str, list[str]]: + if data is None: + return {} + if not isinstance(data, dict): + raise ValueError( + f"`perturbation_covariates` should be a dictionary, found {data} to be of type {type(data)}." + ) + if len(data) == 0: + raise ValueError("No perturbation covariates provided.") + for key, covars in data.items(): + if not isinstance(key, str): + raise ValueError(f"Key should be a string, found {key} to be of type {type(key)}.") + if not isinstance(covars, tuple | list): + raise ValueError(f"Value should be a tuple, found {covars} to be of type {type(covars)}.") + if len(covars) == 0: + raise ValueError(f"No covariates provided for perturbation group {key}.") + lengths = [len(covs) for covs in data.values()] + if len(set(lengths)) != 1: + raise ValueError(f"Length of perturbation covariate groups must match, found lengths {lengths}.") + return {k: list(el) for k, el in data.items()} + + @staticmethod + def _verify_sample_covariates( + sample_covariates: Sequence[str] | None, + ) -> list[str]: + if sample_covariates is None: + return [] + if not isinstance(sample_covariates, tuple | list): + raise ValueError( + f"`sample_covariates` should be a tuple or list, found {sample_covariates} to be of type {type(sample_covariates)}." + ) + for covar in sample_covariates: + if not isinstance(covar, str): + raise ValueError(f"Key should be a string, found {covar} to be of type {type(covar)}.") + return list(sample_covariates) + + @staticmethod + def _verify_split_covariates( + adata: anndata.AnnData, + data: Sequence[str] | None, + control_key: str, + ) -> Sequence[str]: + if data is None: + return [] + if not isinstance(data, tuple | list): + raise ValueError(f"`split_covariates` should be a tuple or list, found {data} to be of type {type(data)}.") + for covar in data: + if not isinstance(covar, str): + raise ValueError(f"Key should be a string, found {covar} to be of type {type(covar)}.") + source_splits = adata.obs[adata.obs[control_key]][data].drop_duplicates() + source_splits = map(tuple, source_splits.values) + target_splits = adata.obs[~adata.obs[control_key]][data].drop_duplicates() + target_splits = map(tuple, target_splits.values) + source_without_targets = set(source_splits) - set(target_splits) + if len(source_without_targets) > 0: + raise ValueError( + f"Source distribution with split covariate values {source_without_targets} do not have a corresponding target distribution." + ) + return data + + @staticmethod + def _verify_covariate_data(covariate_data: pd.DataFrame, covars) -> None: + for covariate in covars: + if covariate is not None and covariate not in covariate_data: + raise ValueError(f"Covariate {covariate} not found in adata.obs or covariate_data.") + + @staticmethod + def _get_linked_perturbation_covariates(perturb_covariates: dict[str, list[str]]) -> dict[str, dict[Any, Any]]: + primary_group, primary_covars = next(iter(perturb_covariates.items())) + linked_perturb_covars: dict[str, dict[Any, Any]] = {k: {} for k in primary_covars} + for cov_group, covars in list(perturb_covariates.items())[1:]: + for primary_cov, linked_cov in zip(primary_covars, covars, strict=False): + linked_perturb_covars[primary_cov][cov_group] = linked_cov + + return linked_perturb_covars + + @staticmethod + def _verify_perturbation_covariate_reps( + adata: anndata.AnnData, + perturbation_covariate_reps: dict[str, str] | None, + perturbation_covariates: dict[str, list[str]], + ) -> dict[str, str]: + if perturbation_covariate_reps is None: + return {} + for key, value in perturbation_covariate_reps.items(): + if key not in perturbation_covariates: + raise ValueError(f"Key '{key}' not found in covariates.") + if value not in adata.uns: + raise ValueError(f"Perturbation covariate representation '{value}' not found in `adata.uns`.") + if not isinstance(adata.uns[value], dict): + raise ValueError( + f"Perturbation covariate representation '{value}' in `adata.uns` should be of type `dict`, found {type(adata.uns[value])}." + ) + return perturbation_covariate_reps + + @staticmethod + def _verify_sample_covariate_reps( + adata: anndata.AnnData, + sample_covariate_reps: dict[str, str] | None, + covariates: list[str], + ) -> dict[str, str]: + if sample_covariate_reps is None: + return {} + for key, value in sample_covariate_reps.items(): + if key not in covariates: + raise ValueError(f"Key '{key}' not found in covariates.") + if value not in adata.uns: + raise ValueError(f"Sample covariate representation '{value}' not found in `adata.uns`.") + if not isinstance(adata.uns[value], dict): + raise ValueError( + f"Sample covariate representation '{value}' in `adata.uns` should be of type `dict`, found {type(adata.uns[value])}." + ) + return sample_covariate_reps + + @staticmethod + def _get_max_combination_length( + perturbation_covariates: dict[str, list[str]], + max_combination_length: int | None, + ) -> int: + obs_max_combination_length = max(len(comb) for comb in perturbation_covariates.values()) + if max_combination_length is None: + return obs_max_combination_length + elif max_combination_length < obs_max_combination_length: + logger.warning( + f"Provided `max_combination_length` is smaller than the observed maximum combination length of the perturbation covariates. Setting maximum combination length to {obs_max_combination_length}.", + stacklevel=2, + ) + return obs_max_combination_length + else: + return max_combination_length + + def _get_primary_covar_encoder( + self, + adata: anndata.AnnData, + perturbation_covariates: dict[str, list[str]], + perturbation_covariate_reps: dict[str, str], + ) -> tuple[preprocessing.OneHotEncoder | None, bool]: + primary_group, primary_covars = next(iter(perturbation_covariates.items())) + is_categorical = self._check_covariate_type(adata, primary_covars) + if perturbation_covariate_reps and primary_group in perturbation_covariate_reps: + return None, is_categorical + if is_categorical: + encoder = preprocessing.OneHotEncoder(sparse_output=False) + all_values = np.unique(adata.obs[primary_covars].values.flatten()) + encoder.fit(all_values.reshape(-1, 1)) + return encoder, is_categorical + encoder = preprocessing.OneHotEncoder(sparse_output=False) + encoder.fit(np.array(primary_covars).reshape(-1, 1)) + return encoder, is_categorical + + @staticmethod + def _check_covariate_type(adata: anndata.AnnData, covars: Sequence[str]) -> bool: + col_is_cat = [] + for covariate in covars: + if is_numeric_dtype(adata.obs[covariate]): + col_is_cat.append(False) + continue + if adata.obs[covariate].isin(["True", "False", True, False]).all(): + adata.obs[covariate] = adata.obs[covariate].astype(int) + col_is_cat.append(False) + continue + try: + adata.obs[covariate] = adata.obs[covariate].astype("category") + col_is_cat.append(True) + except ValueError as e: + raise ValueError( + f"Perturbation covariates `{covariate}` should be either numeric/boolean or categorical." + ) from e + + if max(col_is_cat) != min(col_is_cat): + raise ValueError( + f"Groups of perturbation covariates `{covariate}` should be either all numeric/boolean or all categorical." + ) + + return max(col_is_cat) + + @staticmethod + def _verify_covariate_type(covariate_data: pd.DataFrame, covars: Sequence[str], categorical: bool) -> None: + for covariate in covars: + if is_numeric_dtype(covariate_data[covariate]): + if categorical: + raise ValueError(f"Perturbation covariates `{covariate}` should be categorical, found numeric.") + continue + if covariate_data[covariate].isin(["True", "False", True, False]).all(): + if categorical: + raise ValueError(f"Perturbation covariates `{covariate}` should be categorical, found boolean.") + continue + try: + covariate_data[covariate] = covariate_data[covariate].astype("category") + except ValueError as e: + raise ValueError( + f"Perturbation covariates `{covariate}` should be either numeric/boolean or categorical." + ) from e + else: + if not categorical: + raise ValueError( + f"Perturbation covariates `{covariate}` should be numeric/boolean, found categorical." + ) + + @staticmethod + def _check_shape(arr: float | ArrayLike) -> ArrayLike: + if not hasattr(arr, "shape") or len(arr.shape) == 0: + return jnp.ones((1, 1)) * arr + if arr.ndim == 1: # type: ignore[union-attr] + return jnp.expand_dims(arr, 0) + elif arr.ndim == 2: # type: ignore[union-attr] + if arr.shape[0] == 1: + return arr # type: ignore[return-value] + if arr.shape[1] == 1: + return jnp.transpose(arr) + raise ValueError( + "Condition representation has an unexpected shape. Should be (1, n_features) or (n_features, )." + ) + elif arr.ndim > 2: # type: ignore[union-attr] + raise ValueError("Condition representation has too many dimensions. Should be 1 or 2.") + + raise ValueError( + "Condition representation as an unexpected format. Expected an array of shape (1, n_features) or (n_features, )." + ) + + @staticmethod + def _get_covar_to_idx(covariate_groups: dict[str, Sequence[str]]) -> dict[str, int]: + idx_to_covar = {} + for idx, cov_group in enumerate(covariate_groups): + idx_to_covar[idx] = cov_group + covar_to_idx = {v: k for k, v in idx_to_covar.items()} + return covar_to_idx + + @staticmethod + def _pad_to_max_length(arr: jax.Array, max_combination_length: int, null_value: Any) -> jax.Array: + if arr.shape[0] < max_combination_length: + null_arr = jnp.full((max_combination_length - arr.shape[0], arr.shape[1]), null_value) + arr = jnp.concatenate([arr, null_arr], axis=0) + return arr + + def _get_perturbation_covariates( + self, + condition_data: pd.DataFrame, + rep_dict: dict[str, dict[str, ArrayLike]], + perturb_covariates: Any, # TODO: check if we can save as attribtue + ) -> dict[str, jax.Array]: + primary_group, primary_covars = next(iter(perturb_covariates.items())) + + perturb_covar_emb: dict[str, list[jax.Array]] = {group: [] for group in perturb_covariates} + for primary_cov in primary_covars: + value = condition_data[primary_cov] + cov_name = value if self.is_categorical else primary_cov + if primary_group in self._covariate_reps: + rep_key = self._covariate_reps[primary_group] + try: + prim_arr = rep_dict[rep_key][cov_name] + except KeyError as err: + raise ValueError( + f"Representation for '{cov_name}' not found in `adata.uns['{rep_key}']` or `rep_dict`." + ) from err + prim_arr = jnp.asarray(prim_arr) + else: + prim_arr = jnp.asarray( + self.primary_one_hot_encoder.transform( # type: ignore[union-attr] + np.array(cov_name).reshape(-1, 1) + ) + ) + + if not self.is_categorical: + prim_arr *= value + + prim_arr = self._check_shape(prim_arr) + perturb_covar_emb[primary_group].append(prim_arr) + + for linked_covar in self._linked_perturb_covars[primary_cov].items(): + linked_group, linked_cov = list(linked_covar) + + if linked_cov is None: + linked_arr = jnp.full((1, 1), self._null_value) + linked_arr = self._check_shape(linked_arr) + perturb_covar_emb[linked_group].append(linked_arr) + continue + + cov_name = condition_data[linked_cov] + + if linked_group in self._covariate_reps: + rep_key = self._covariate_reps[linked_group] + if cov_name not in rep_dict[rep_key]: + raise ValueError(f"Representation for '{cov_name}' not found in `adata.uns['{rep_key}']`.") + linked_arr = jnp.asarray(rep_dict[rep_key][cov_name]) + else: + linked_arr = jnp.asarray(condition_data[linked_cov]) + + linked_arr = self._check_shape(linked_arr) + perturb_covar_emb[linked_group].append(linked_arr) + + perturb_covar_emb = { + k: self._pad_to_max_length( + jnp.concatenate(v, axis=0), + self._max_combination_length, + self._null_value, + ) + for k, v in perturb_covar_emb.items() + } + + sample_covar_emb: dict[str, jax.Array] = {} + for sample_cov in self._sample_covariates: + value = condition_data[sample_cov] + if sample_cov in self._covariate_reps: + rep_key = self._covariate_reps[sample_cov] + if value not in rep_dict[rep_key]: + raise ValueError(f"Representation for '{value}' not found in `adata.uns['{sample_cov}']`.") + cov_arr = jnp.asarray(rep_dict[rep_key][value]) + else: + cov_arr = jnp.asarray(value) + + cov_arr = self._check_shape(cov_arr) + sample_covar_emb[sample_cov] = jnp.tile(cov_arr, (self._max_combination_length, 1)) + + return perturb_covar_emb | sample_covar_emb + + @property + def is_categorical(self) -> bool: + """Whether the primary covariate is categorical.""" + return self._is_categorical + + @property + def adata(self) -> anndata.AnnData: + """An :class:`~anndata.AnnData` object used for instantiating the DataManager.""" + return self._adata + + @property + def control_key(self) -> str: + """Boolean key in :attr:`~anndata.AnnData.obs` indicating whether belongs to control group.""" + return self._control_key + + @property + def perturbation_covariates(self) -> dict[str, list[str]]: + """Dictionary with keys indicating the name of the covariate group and values are keys in :attr:`~anndata.AnnData.obs` which together define the covariates.""" + return self._perturbation_covariates + + @property + def perturbation_covariate_reps(self) -> dict[str, str]: + """Dictionary with keys indicating the name of the covariate group and values are keys in :attr:`~anndata.AnnData.uns` storing a dictionary with the representation of the covariates.""" + return self._perturbation_covariate_reps + + @property + def sample_covariates(self) -> Sequence[str]: + """Keys in :attr:`~anndata.AnnData.obs` indicating which sample the cell belongs to (e.g. cell line).""" + return self._sample_covariates + + @property + def sample_covariate_reps(self) -> dict[str, str]: + """Dictionary with keys indicating the name of the sample covariate group and values are keys in :attr:`~anndata.AnnData.uns` storing a dictionary with the representation of the sample covariates.""" + return self._sample_covariate_reps + + @property + def split_covariates(self) -> Sequence[str]: + """Covariates in :attr:`~anndata.AnnData.obs` to split all control cells into different control populations.""" + return self._split_covariates + + @property + def max_combination_length(self) -> int: + """Maximum combination length of perturbation covariates.""" + return self._max_combination_length + + @property + def null_value(self) -> float: + """Value to use for padding to :attr:`~max_combination_length`.""" + return self._null_value + + @property + def primary_one_hot_encoder(self) -> preprocessing.OneHotEncoder | None: + """One-hot encoder for the primary covariate.""" + return self._primary_one_hot_encoder + + @property + def linked_perturb_covars(self) -> dict[str, dict[Any, Any]]: + """Dictionary with keys indicating the name of the primary covariate and values are dictionaries with keys indicating the name of the linked covariate group and values are the linked covariates.""" + return self._linked_perturb_covars + + @property + def covariate_reps(self) -> dict[str, str]: + """Dictionary which stores representation of covariates, i.e. the union of ``sample_covariate_reps`` and ``perturbation_covariate_reps``.""" + return self._covariate_reps + + @property + def covar_to_idx(self) -> dict[str, int]: + """TODO: add description""" + return self._covar_to_idx + + @property + def perturb_covar_keys(self) -> list[str]: + """List of all perturbation covariates.""" + return self._perturb_covar_keys + + @property + def sample_rep(self) -> str | dict[str, str]: + """Key of the sample representation.""" + return self._sample_rep diff --git a/transfer/code/scDFM/src/data_process/data.py b/transfer/code/scDFM/src/data_process/data.py new file mode 100644 index 0000000000000000000000000000000000000000..7851cdcfc8d6b994b2041c5b694153d67167a844 --- /dev/null +++ b/transfer/code/scDFM/src/data_process/data.py @@ -0,0 +1,592 @@ +import scanpy as sc +import pandas as pd +import numpy as np +import json +import torch +import pickle +from typing import Union, Optional +from pathlib import Path +import os +from src.utils._preprocessing import annotate_compounds, get_molecular_fingerprints +from src.data_process._datamanager import DataManager +import jax +import torch.nn as nn +from torch.utils.data import Dataset, DataLoader +import pdb +import tqdm +from random import shuffle +from src.utils.utils import build_gene_coexpression_graph,sorted_pad_mask +# combosciplex url: https://figshare.com/articles/dataset/combosciplex/25062230?file=44229635 +# 'norman' url = 'https://dataverse.harvard.edu/api/access/datafile/6154020' + +class Data: + def __init__(self, data_path='../../data'): + self.data_path = data_path + if not os.path.exists(data_path): + raise ValueError(data_path + ' does not exist') + # os.makedirs(data_path) + + + def load_data(self, data_name = None, data_path = None): + self.data_name = data_name + if data_name in ['norman', 'norman_umi_go_filtered',]: + self.adata = sc.read_h5ad(os.path.join(self.data_path, data_name + '.h5ad')) + elif data_name in ['combosciplex', ]: + self.adata = sc.read_h5ad(os.path.join(self.data_path, data_name + '.h5ad')) + else: + raise ValueError(data_name + ' is not a valid data name') + + def process_data(self, n_top_genes = 2000,infer_top_gene=1000,split_method='additive', + use_negative_edge=True, k=30, + **kwargs): + os.makedirs(os.path.join(self.data_path, self.data_name), exist_ok=True) + if self.data_name == 'combosciplex': + + if os.path.exists(os.path.join(self.data_path, self.data_name, 'processed.h5ad')): + self.adata = sc.read_h5ad(os.path.join(self.data_path, self.data_name, 'processed.h5ad')) + else: + + self.adata.obs["condition"] = self.adata.obs.apply( + lambda x: "control" if x["condition"] == "control+control" else x["condition"], axis=1 + ) + + self.adata.obs["is_control"] = self.adata.obs.apply( + lambda x: True if x["condition"] == "control" else False, axis=1 + ) + + annotate_compounds(self.adata, compound_keys=["Drug1", "Drug2"]) + get_molecular_fingerprints(self.adata, compound_keys=["Drug1", "Drug2"]) + self.adata.uns["fingerprints"]["control"] = np.zeros(1024) + + self.adata.write(os.path.join(self.data_path, self.data_name, 'processed.h5ad')) + + self.adata.X = self.adata.layers["counts"].copy() + sc.pp.normalize_total(self.adata) + sc.pp.log1p(self.adata) + sc.pp.highly_variable_genes(self.adata, inplace=True, n_top_genes=n_top_genes) + + if 'test_conditions' in kwargs.keys(): + test_conditions = kwargs['test_conditions'] + else: + test_conditions = ['Panobinostat+Crizotinib', + 'Panobinostat+Curcumin', + 'Panobinostat+SRT1720', + 'Panobinostat+Sorafenib', + 'SRT2104+Alvespimycin', + 'control+Alvespimycin', + 'control+Dacinostat'] + + self.adata = self.adata[:,self.adata.var['highly_variable']] # filter out low variable genes + + self.adata.obs["mode"] = self.adata.obs.apply(lambda x: "test" if x["condition"] in test_conditions else "train", axis=1) + self.adata_train = self.adata[self.adata.obs["mode"] == "train"] + self.adata_test = self.adata[(self.adata.obs["mode"] == "test") | (self.adata.obs["condition"]=="control")] + + sc.pp.highly_variable_genes(self.adata_test, inplace=True, n_top_genes=infer_top_gene) + self.adata_test = self.adata_test[:,self.adata_test.var['highly_variable']] + + condition = np.unique(list(self.adata.obs['condition'])) + unique_perturbation = [] + np.array([unique_perturbation.extend(perturbation.split('+')) for perturbation in condition]) + unique_perturbation = np.unique(unique_perturbation) + unique_perturbation.sort() + self.unique_perturbation = unique_perturbation + self.perturbation_dict = {perturbation: i for i, perturbation in enumerate(unique_perturbation)} + # self._val_manager = + elif self.data_name == 'norman' or self.data_name == 'norman_umi_go_filtered': + + sc.pp.highly_variable_genes(self.adata, inplace=True, n_top_genes=n_top_genes) + unique_perturbation = [] + [unique_perturbation.extend(perturbation.split('+')) for perturbation in self.adata.obs['condition'].unique()] + unique_perturbation = np.unique(unique_perturbation) + + if self.data_name == 'norman': + for perturbation in unique_perturbation: + if perturbation in self.adata.var_names: + self.adata.var.loc[perturbation, 'highly_variable'] = True + else: + print(f"Warning: {perturbation} is not in the gene names") + self.adata = self.adata[:,self.adata.var['highly_variable']] + elif self.data_name == 'norman_umi_go_filtered': + all_gene_names = list(self.adata.var['gene_name']) + ['ctrl'] + for perturbation in unique_perturbation: + if perturbation not in all_gene_names: + print(f"Warning: {perturbation} is not in the gene names") + self.adata.var['highly_variable'] = True + + + #### for split five times + if split_method == 'additive' or 'combinations': + split_file = os.path.join(self.data_path, self.data_name, 'split_results.pkl') + if os.path.exists(split_file): + with open(split_file, 'rb') as f: + self.split_results = pickle.load(f) + else: + perturbations = np.unique(self.adata.obs['condition']) + double_perturbation = [p for p in perturbations if 'ctrl' not in p] + double_perturbation = np.array(double_perturbation) + + self.split_results = [] + + for i in range(5): + np.random.seed(42 + i) + shuffled = double_perturbation.copy() + np.random.shuffle(shuffled) + + split_idx = int(len(shuffled) * 0.3) + test_double = shuffled[:split_idx] + train_double = shuffled[split_idx:] + self.split_results.append({ + 'train': train_double.tolist(), + 'test': test_double.tolist() + }) + + with open(split_file, 'wb') as f: + pickle.dump(self.split_results, f) + print('split results saved') + + elif split_method == 'unseen': + split_file = os.path.join(self.data_path, self.data_name, 'split_results_unseen.pkl') + if os.path.exists(split_file): + with open(split_file, 'rb') as f: + self.split_results = pickle.load(f) + else: + self.split_results = [] + for i in range(5): + perturbations = np.unique(self.adata.obs['condition']) + double_perturbation = [p for p in perturbations if 'ctrl' not in p] + single = [] + [single.extend(p.split('+')) for p in double_perturbation] + single = list(set(single)) + + shuffle(single) + remove_genes = single[:12] + p_count = {} + for p in double_perturbation: + ps = p.split('+') + count = int(ps[0] in remove_genes) + int(ps[1] in remove_genes) + p_count[p] = count + double_perturbation = [p for p, count in p_count.items() if count > 0] + double_perturbation = list(double_perturbation) + remove_genes_condition = [p+'+control' for p in remove_genes] + double_perturbation.extend(remove_genes_condition) + self.split_results.append({ + 'p_count': p_count, + 'test': double_perturbation + }) + with open(split_file, 'wb') as f: + pickle.dump(self.split_results, f) + print('split results unseen saved') + + if 'fold' in kwargs.keys(): + fold = kwargs['fold'] + else: + fold = 0 + self.adata.obs['condition'] = self.adata.obs['condition'].str.replace('ctrl', 'control') + self.adata.obs['Drug1'] = self.adata.obs['condition'].str.split('+').apply(lambda x: x[0]) + self.adata.obs['Drug2'] = self.adata.obs['condition'].str.split('+').apply(lambda x: x[-1]) + self.adata.obs['is_control'] = False + self.adata.obs.loc[self.adata.obs['control'] == 1, 'is_control'] = True + self.adata.obs['mode'] = 'train' + + + if split_method == 'combinations': + self.split_results[fold]['test'] = self.split_results[fold]['test'][:15] + remove_genes = [] + [remove_genes.extend(p.split('+')) for p in self.split_results[fold]['test']] + remove_genes = set(remove_genes) + remove_genes_condition = [p+'+control' for p in remove_genes] + + self.split_results[fold]['test'].extend(remove_genes_condition) + + + self.adata.obs.loc[self.adata.obs['condition'].isin(self.split_results[fold]['test']), 'mode'] = 'test' + + self.adata_train = self.adata[self.adata.obs['mode'] == 'train'] + self.adata_test = self.adata[(self.adata.obs['mode'] == 'test') | (self.adata.obs['control'] == 1)] + + + + sc.pp.highly_variable_genes(self.adata_test, inplace=True, n_top_genes=infer_top_gene) + self.adata_test = self.adata_test[:,self.adata_test.var['highly_variable']] + + condition = np.unique(list(self.adata.obs['condition'])) + unique_perturbation = [] + np.array([unique_perturbation.extend(perturbation.split('+')) for perturbation in condition]) + unique_perturbation = np.unique(unique_perturbation) + unique_perturbation.sort() + self.unique_perturbation = unique_perturbation + self.perturbation_dict = {perturbation: i for i, perturbation in enumerate(unique_perturbation)} + + else: + raise ValueError(self.data_name + ' is not a valid data name') + + if 'fold' in kwargs.keys(): + fold = kwargs['fold'] + else: + fold = 0 + if use_negative_edge: + mask_path = os.path.join(self.data_path, self.data_name,'mask_fold_'+str(fold)+'topk_'+str(k)+split_method+'_negative_edge'+'.pt') + else: + mask_path = os.path.join(self.data_path, self.data_name,'mask_fold_'+str(fold)+'topk_'+str(k)+split_method+'.pt') + if os.path.exists(mask_path): + self.mask = torch.load(mask_path) + else: + X = self.adata_train.X.toarray() + mask = build_gene_coexpression_graph(X, + method="pearson", + wgcna_beta=None, + sparsify="topk", + k=k, + use_negative_edge=use_negative_edge) + mask = sorted_pad_mask(mask, pad_size=4, gene_names=list(self.adata_train.var_names)) + torch.save(mask, mask_path) + print('mask saved') + + + def load_flow_data(self, batch_size = 128): + if self.data_name == 'combosciplex': + train_sampler = TrainSampler(self.data_name, self.adata_train, ["Drug1", "Drug2"], self.perturbation_dict) + test_sampler = TestDataset(self.data_name, self.adata_test, ["Drug1", "Drug2"], self.perturbation_dict) + + return train_sampler , test_sampler, [] + elif self.data_name == 'norman' or self.data_name == 'norman_umi_go_filtered': + train_sampler = TrainSampler(self.data_name, self.adata_train, ["Drug1", "Drug2"], self.perturbation_dict) + test_sampler = TestDataset(self.data_name, self.adata_test, ["Drug1", "Drug2"], self.perturbation_dict) + return train_sampler , test_sampler, [] + else: + raise ValueError(self.data_name + ' is not a valid data name') + + + def pretrain_data(self, batch_size = 128): + if self.data_name == 'combosciplex': + + self.pretrain_train_data = PretrainData(self.adata_train, self.perturbation_dict) + self.pretrain_train_data_loader = DataLoader(self.pretrain_train_data, batch_size=batch_size, shuffle=True, pin_memory=True, num_workers=4) + self.pretrain_test_data = PretrainData(self.adata_test, self.perturbation_dict) + self.pretrain_test_data_loader = DataLoader(self.pretrain_test_data, batch_size=batch_size, shuffle=False, pin_memory=True, num_workers=4) + return self.pretrain_train_data_loader, self.pretrain_test_data_loader + else: + raise ValueError(self.data_name + ' is not a valid data name') + +class TrainSampler: + def __init__(self, data_name, adata: sc.AnnData, perturbation_covariates: list[str], perturbation_dict: dict,): + self.data_name = data_name + self.adata = adata + self.perturbation_covariates = perturbation_covariates + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) + self._perturbation_covariates = adata.obs['perturbation_covariates'].unique() + + self._perturbation_covariates = self._perturbation_covariates[self._perturbation_covariates != 'control+control'] + + self._perturbation_covariates.sort() + self.perturbation_covariates_dict = {perturbation: i for i, perturbation in enumerate(self._perturbation_covariates)} + + perturbation_covariates_id = [adata.obs[perturbation_covariates[i]].apply(lambda x: perturbation_dict[x]) + for i in range(len(perturbation_covariates))] + self.perturbation_covariates_id = np.array(perturbation_covariates_id).T + + + self.cells_name = self.adata.obs_names + + + def get_batch(self, batch_size: int, same_perturbation: bool = True): + if same_perturbation: + # random sample a perturbation from self._perturbation_covariates. + # the last one is control + perturbation_idx = np.random.choice(len(self._perturbation_covariates), 1)[0] + + perturbation_id = self._perturbation_covariates[perturbation_idx] + + # get the target data + tgt_idx = (self.adata.obs['perturbation_covariates'] == perturbation_id).to_numpy().nonzero()[0] + tgt_batch_idx = np.random.choice(tgt_idx, batch_size) + + tgt_batch = torch.from_numpy(self.adata.X[tgt_batch_idx].toarray()) + + # get data from control + src_idx = (self.adata.obs['perturbation_covariates'] == 'control+control').to_numpy().nonzero()[0] + src_batch_idx = np.random.choice(src_idx, batch_size) + + src_batch = torch.from_numpy(self.adata.X[src_batch_idx].toarray()) + + return { + 'src_cell_data': src_batch, + 'tgt_cell_data': tgt_batch, + 'src_cell_id': self.cells_name[src_batch_idx], + 'tgt_cell_id': self.cells_name[tgt_batch_idx], + 'condition_id': self.perturbation_covariates_id[tgt_batch_idx], + } + + else: + raise ValueError('same_perturbation must be True') + +class TestDataset: + def __init__(self, data_name,adata: sc.AnnData, perturbation_covariates: list[str], perturbation_dict: dict,): + self.data_name = data_name + self.adata = adata + self.perturbation_covariates = perturbation_covariates + self.adata.obs['perturbation_covariates'] = self.adata.obs[perturbation_covariates].apply(lambda x: '+'.join(x), axis=1) + self._perturbation_covariates = adata.obs['perturbation_covariates'].unique() + + self._perturbation_covariates = self._perturbation_covariates[self._perturbation_covariates != 'control+control'] + + self._perturbation_covariates.sort() + self.perturbation_covariates_dict = {perturbation: i for i, perturbation in enumerate(self._perturbation_covariates)} + + perturbation_covariates_id = [adata.obs[perturbation_covariates[i]].apply(lambda x: perturbation_dict[x]) + for i in range(len(perturbation_covariates))] + self.perturbation_covariates_id = np.array(perturbation_covariates_id).T + + + self.cells_name = self.adata.obs_names + + def get_control_data(self,): + control_data = self.adata[self.adata.obs['is_control']] + return { + 'src_cell_data': torch.from_numpy(control_data.X.toarray()), + 'src_cell_id': control_data.obs_names, + 'condition_id': torch.tensor(self.perturbation_covariates_id[self.adata.obs['is_control']]), + } + + def get_perturbation_data(self, perturbation: str): + perturbation_data = self.adata[self.adata.obs['perturbation_covariates'] == perturbation] + return { + 'tgt_cell_data': torch.from_numpy(perturbation_data.X.toarray()), + 'tgt_cell_id': perturbation_data.obs_names, + 'condition_id': torch.tensor(self.perturbation_covariates_id[self.adata.obs['perturbation_covariates'] == perturbation]), + } + + + +class PerturbationDataset(Dataset): + def __init__(self, sampler: TrainSampler, batch_size: int): + self.sampler = sampler + self.batch_size = batch_size + self.perturbations = sampler._perturbation_covariates + + self.control_idx = (sampler.adata.obs['perturbation_covariates'] == 'control+control').to_numpy().nonzero()[0] + + def __len__(self): + + return len(self.perturbations) * 1000 + + def __getitem__(self, idx): + # 随机选一个 perturbation + perturbation_idx = np.random.choice(len(self.perturbations), 1)[0] + perturbation_id = self.perturbations[perturbation_idx] + + # target batch + tgt_idx = (self.sampler.adata.obs['perturbation_covariates'] == perturbation_id).to_numpy().nonzero()[0] + tgt_batch_idx = np.random.choice(tgt_idx, self.batch_size) + + # source (control) batch + src_batch_idx = np.random.choice(self.control_idx, self.batch_size) + if hasattr(self.sampler.adata.X[src_batch_idx], "toarray"): + src_batch = torch.from_numpy(self.sampler.adata.X[src_batch_idx].toarray()) + tgt_batch = torch.from_numpy(self.sampler.adata.X[tgt_batch_idx].toarray()) + else: + src_batch = torch.from_numpy(self.sampler.adata.X[src_batch_idx]) + tgt_batch = torch.from_numpy(self.sampler.adata.X[tgt_batch_idx]) + + return { + 'src_cell_data': src_batch, + 'tgt_cell_data': tgt_batch, + 'src_cell_id': list(self.sampler.cells_name[src_batch_idx]), + 'tgt_cell_id': list(self.sampler.cells_name[tgt_batch_idx]), + 'condition_id': torch.tensor(self.sampler.perturbation_covariates_id[tgt_batch_idx]), + } +class BinDiscretizer: + """ + data = np.random.exponential(scale=2.0, size=1000) + + bd = BinDiscretizer(n_bins=200) + bd.fit(data) + + bd.save_edges('./data/combosciplex/bin_discretizer_edges.pkl') + + new_bd = BinDiscretizer(n_bins=200) + new_bd.load_edges('./data/combosciplex/bin_discretizer_edges.pkl') + + binned = bd.transform(data) + + recon = bd.inverse_transform(binned, random=False) + + Note: 0 is treated as a separate class (class 0), and non-zero values are discretized into classes 1 to n_bins. + """ + def __init__(self, n_bins: int, strategy: str = "quantile"): + self.n_bins = n_bins + self.strategy = strategy + self.edges = None # will be (n_bins + 1, ) array + + def fit(self, data: Union[np.ndarray, torch.Tensor]): + if isinstance(data, torch.Tensor): + data = data.detach().cpu().numpy() + data = data.flatten() + data = data[data > 0] # exclude zeros from fitting + + if len(data) == 0: + raise ValueError("No non-zero entries in data to fit.") + + if self.strategy == "quantile": + self.edges = np.quantile(data, np.linspace(0, 1, self.n_bins + 1)) + elif self.strategy == "uniform": + self.edges = np.linspace(data.min(), data.max(), self.n_bins + 1) + else: + raise ValueError(f"Unknown strategy {self.strategy}") + + def transform(self, data: Union[np.ndarray, torch.Tensor]) -> Union[np.ndarray, torch.Tensor]: + if self.edges is None: + raise RuntimeError("Call fit() before transform().") + + is_torch = isinstance(data, torch.Tensor) + if is_torch: + orig_dtype = data.dtype + data = data.detach().cpu().numpy() + + out = np.zeros_like(data, dtype=np.int64) + mask = data > 0 + + # Digitize non-zero entries: 0 remains 0, non-zero values get classes 1 to n_bins + if np.any(mask): + # np.digitize returns 0-based indices for the bins + # We want to map these to 1-based class indices + digitized = np.digitize(data[mask], self.edges[1:-1]) + # Convert 0-based bin indices to 1-based class indices + # digitized=0 means it's in the first bin, which should be class 1 + # digitized=1 means it's in the second bin, which should be class 2, etc. + out[mask] = digitized + 1 + + if is_torch: + return torch.from_numpy(out).to(dtype=torch.int64) + return out + + def inverse_transform(self, digitized: Union[np.ndarray, torch.Tensor], random: bool = False) -> Union[np.ndarray, torch.Tensor]: + if self.edges is None: + raise RuntimeError("Call fit() before inverse_transform().") + + is_torch = isinstance(digitized, torch.Tensor) + if is_torch: + orig_dtype = digitized.dtype + digitized = digitized.detach().cpu().numpy() + + out = np.zeros_like(digitized, dtype=np.float64) + mask = digitized > 0 + + if np.any(mask): + ids = digitized[mask] + # Ensure ids are within valid range (1 to n_bins) + ids = np.clip(ids, 1, self.n_bins) + # Convert 1-based class indices back to 0-based bin indices + bin_ids = ids - 1 + lefts = self.edges[bin_ids] + rights = self.edges[bin_ids + 1] + + if random: + out[mask] = np.random.uniform(lefts, rights) + else: + out[mask] = (lefts + rights) / 2 + + if is_torch: + return torch.from_numpy(out).to(dtype=torch.float64) + return out + + def save_edges(self, filepath: Union[str, Path]): + """Save edges to a file""" + if self.edges is None: + raise RuntimeError("No edges to save. Call fit() first.") + + filepath = Path(filepath) + with open(filepath, 'wb') as f: + pickle.dump({'edges': self.edges, 'n_bins': self.n_bins}, f) + + def load_edges(self, filepath: Union[str, Path]): + """Load edges from a file""" + filepath = Path(filepath) + if not filepath.exists(): + raise FileNotFoundError(f"File {filepath} not found") + + with open(filepath, 'rb') as f: + data = pickle.load(f) + loaded_n_bins = data['n_bins'] + if loaded_n_bins != self.n_bins: + raise ValueError(f"Loaded n_bins ({loaded_n_bins}) does not match initialized n_bins ({self.n_bins})") + self.edges = data['edges'] + +class PretrainData(Dataset): + def __init__(self, adata: sc.AnnData, drug_dict: dict): + self.adata = adata + self.drug_dict = drug_dict + self.X = torch.from_numpy(adata.X.toarray()) + self.cell_id = adata.obs_names + # self.drug1 = torch.tensor(np.array(adata.obs['Drug1'].apply(lambda x: drug_dict[x]))) + # self.drug2 = torch.tensor(np.array(adata.obs['Drug2'].apply(lambda x: drug_dict[x]))) + + def __len__(self): + return len(self.adata) + + def __getitem__(self, idx): + return { + 'values' : self.X[idx], + 'cell_id': self.cell_id[idx], + } + + +class FlowMatchingDataset(Dataset): + """PyTorch Dataset for flow matching training data""" + + def __init__(self, jax_sampler, num_samples=10000, seed=42): + """ + Args: + jax_sampler: JAX-based TrainSampler + num_samples: Number of samples to generate per epoch + seed: Random seed + """ + self.jax_sampler = jax_sampler + self.num_samples = num_samples + self.rng = jax.random.PRNGKey(seed) + + def __len__(self): + return self.num_samples + + def __getitem__(self, idx): + """Sample a batch from the JAX sampler""" + # Generate new random key for each sample + self.rng, sample_key = jax.random.split(self.rng) + + # Sample from JAX sampler + sample = self.jax_sampler.sample(sample_key) + + # Convert JAX arrays to PyTorch tensors + src_cell_data = torch.from_numpy(np.array(sample['src_cell_data'])).float() + tgt_cell_data = torch.from_numpy(np.array(sample['tgt_cell_data'])).float() + + sample['src_cell_id'] + sample['tgt_cell_id'] + # Convert condition embedding if available + condition_data = None + if 'condition' in sample: + condition_data = { + key: torch.from_numpy(np.array(val)).float() + for key, val in sample['condition'].items() + } + + # Convert condition_id embedding if available + condition_id = None + if 'condition_id' in sample: + condition_id = torch.from_numpy(np.array(sample['condition_id'])).long() + + return { + 'src_cell_data': src_cell_data, + 'tgt_cell_data': tgt_cell_data, + 'condition': condition_data, + 'condition_id': condition_id, + } + +if __name__ == "__main__": + data = Data(data_path='./data') + data.load_data(data_name='combosciplex') + data.process_data() + + + \ No newline at end of file diff --git a/transfer/code/scDFM/src/data_process/scgpt_preprocess.py b/transfer/code/scDFM/src/data_process/scgpt_preprocess.py new file mode 100644 index 0000000000000000000000000000000000000000..c4b1f8b9aa422f0dac7d9efc514545aecf1f4c8d --- /dev/null +++ b/transfer/code/scDFM/src/data_process/scgpt_preprocess.py @@ -0,0 +1,303 @@ +from typing import Dict, Optional, Union + +import numpy as np +import torch +from scipy.sparse import issparse +import scanpy as sc +from scanpy.get import _get_obs_rep, _set_obs_rep +from anndata import AnnData + +# from scgpt import logger + + +class Preprocessor: + """ + Prepare data into training, valid and test split. Normalize raw expression + values, binning or using other transform into the preset model input format. + """ + + def __init__( + self, + use_key: Optional[str] = None, + filter_gene_by_counts: Union[int, bool] = False, + filter_cell_by_counts: Union[int, bool] = False, + normalize_total: Union[float, bool] = 1e4, + result_normed_key: Optional[str] = "X_normed", + log1p: bool = False, + result_log1p_key: str = "X_log1p", + subset_hvg: Union[int, bool] = False, + hvg_use_key: Optional[str] = None, + hvg_flavor: str = "seurat_v3", + binning: Optional[int] = None, + result_binned_key: str = "X_binned", + ): + r""" + Set up the preprocessor, use the args to config the workflow steps. + + Args: + + use_key (:class:`str`, optional): + The key of :class:`~anndata.AnnData` to use for preprocessing. + filter_gene_by_counts (:class:`int` or :class:`bool`, default: ``False``): + Whther to filter genes by counts, if :class:`int`, filter genes with counts + filter_cell_by_counts (:class:`int` or :class:`bool`, default: ``False``): + Whther to filter cells by counts, if :class:`int`, filter cells with counts + normalize_total (:class:`float` or :class:`bool`, default: ``1e4``): + Whether to normalize the total counts of each cell to a specific value. + result_normed_key (:class:`str`, default: ``"X_normed"``): + The key of :class:`~anndata.AnnData` to store the normalized data. If + :class:`None`, will use normed data to replce the :attr:`use_key`. + log1p (:class:`bool`, default: ``True``): + Whether to apply log1p transform to the normalized data. + result_log1p_key (:class:`str`, default: ``"X_log1p"``): + The key of :class:`~anndata.AnnData` to store the log1p transformed data. + subset_hvg (:class:`int` or :class:`bool`, default: ``False``): + Whether to subset highly variable genes. + hvg_use_key (:class:`str`, optional): + The key of :class:`~anndata.AnnData` to use for calculating highly variable + genes. If :class:`None`, will use :attr:`adata.X`. + hvg_flavor (:class:`str`, default: ``"seurat_v3"``): + The flavor of highly variable genes selection. See + :func:`scanpy.pp.highly_variable_genes` for more details. + binning (:class:`int`, optional): + Whether to bin the data into discrete values of number of bins provided. + result_binned_key (:class:`str`, default: ``"X_binned"``): + The key of :class:`~anndata.AnnData` to store the binned data. + """ + self.use_key = use_key + self.filter_gene_by_counts = filter_gene_by_counts + self.filter_cell_by_counts = filter_cell_by_counts + self.normalize_total = normalize_total + self.result_normed_key = result_normed_key + self.log1p = log1p + self.result_log1p_key = result_log1p_key + self.subset_hvg = subset_hvg + self.hvg_use_key = hvg_use_key + self.hvg_flavor = hvg_flavor + self.binning = binning + self.result_binned_key = result_binned_key + + def __call__(self, adata: AnnData, batch_key: Optional[str] = None) -> Dict: + """ + format controls the different input value wrapping, including categorical + binned style, fixed-sum normalized counts, log1p fixed-sum normalized counts, etc. + + Args: + + adata (:class:`AnnData`): + The :class:`AnnData` object to preprocess. + batch_key (:class:`str`, optional): + The key of :class:`AnnData.obs` to use for batch information. This arg + is used in the highly variable gene selection step. + """ + key_to_process = self.use_key + # preliminary checks, will use later + if key_to_process == "X": + key_to_process = None # the following scanpy apis use arg None to use X + is_logged = self.check_logged(adata, obs_key=key_to_process) + + # step 1: filter genes + if self.filter_gene_by_counts: + print("Filtering genes by counts ...") + sc.pp.filter_genes( + adata, + min_counts=self.filter_gene_by_counts + if isinstance(self.filter_gene_by_counts, int) + else None, + ) + + # step 2: filter cells + if ( + isinstance(self.filter_cell_by_counts, int) + and self.filter_cell_by_counts > 0 + ): + print("Filtering cells by counts ...") + sc.pp.filter_cells( + adata, + min_counts=self.filter_cell_by_counts + if isinstance(self.filter_cell_by_counts, int) + else None, + ) + + # step 3: normalize total + if self.normalize_total: + print("Normalizing total counts ...") + normed_ = sc.pp.normalize_total( + adata, + target_sum=self.normalize_total + if isinstance(self.normalize_total, float) + else None, + layer=key_to_process, + inplace=False, + )["X"] + key_to_process = self.result_normed_key or key_to_process + _set_obs_rep(adata, normed_, layer=key_to_process) + + # step 4: log1p + if self.log1p: + print("Log1p transforming ...") + if is_logged: + print( + "The input data seems to be already log1p transformed. " + "Set `log1p=False` to avoid double log1p transform." + ) + if self.result_log1p_key: + _set_obs_rep( + adata, + _get_obs_rep(adata, layer=key_to_process), + layer=self.result_log1p_key, + ) + key_to_process = self.result_log1p_key + sc.pp.log1p(adata, layer=key_to_process) + + # step 5: subset hvg + if self.subset_hvg: + print("Subsetting highly variable genes ...") + if batch_key is None: + print( + "No batch_key is provided, will use all cells for HVG selection." + ) + sc.pp.highly_variable_genes( + adata, + layer=self.hvg_use_key, + n_top_genes=self.subset_hvg + if isinstance(self.subset_hvg, int) + else None, + batch_key=batch_key, + flavor=self.hvg_flavor, + subset=True, + ) + + # step 6: binning + if self.binning: + print("Binning data ...") + if not isinstance(self.binning, int): + raise ValueError( + "Binning arg must be an integer, but got {}.".format(self.binning) + ) + n_bins = self.binning # NOTE: the first bin is always a spectial for zero + binned_rows = [] + bin_edges = [] + layer_data = _get_obs_rep(adata, layer=key_to_process) + layer_data = layer_data.toarray() if issparse(layer_data) else layer_data + if layer_data.min() < 0: + raise ValueError( + f"Assuming non-negative data, but got min value {layer_data.min()}." + ) + for row in layer_data: + if row.max() == 0: + print( + "The input data contains all zero rows. Please make sure " + "this is expected. You can use the `filter_cell_by_counts` " + "arg to filter out all zero rows." + ) + binned_rows.append(np.zeros_like(row, dtype=np.int64)) + bin_edges.append(np.array([0] * n_bins)) + continue + non_zero_ids = row.nonzero() + non_zero_row = row[non_zero_ids] + bins = np.quantile(non_zero_row, np.linspace(0, 1, n_bins - 1)) + # bins = np.sort(np.unique(bins)) + # NOTE: comment this line for now, since this will make the each category + # has different relative meaning across datasets + non_zero_digits = _digitize(non_zero_row, bins) + assert non_zero_digits.min() >= 1 + assert non_zero_digits.max() <= n_bins - 1 + binned_row = np.zeros_like(row, dtype=np.int64) + binned_row[non_zero_ids] = non_zero_digits + binned_rows.append(binned_row) + bin_edges.append(np.concatenate([[0], bins])) + adata.layers[self.result_binned_key] = np.stack(binned_rows) + adata.obsm["bin_edges"] = np.stack(bin_edges) + + def check_logged(self, adata: AnnData, obs_key: Optional[str] = None) -> bool: + """ + Check if the data is already log1p transformed. + + Args: + + adata (:class:`AnnData`): + The :class:`AnnData` object to preprocess. + obs_key (:class:`str`, optional): + The key of :class:`AnnData.obs` to use for batch information. This arg + is used in the highly variable gene selection step. + """ + data = _get_obs_rep(adata, layer=obs_key) + max_, min_ = data.max(), data.min() + if max_ > 30: + return False + if min_ < 0: + return False + + non_zero_min = data[data > 0].min() + if non_zero_min >= 1: + return False + + return True + + +def _digitize(x: np.ndarray, bins: np.ndarray, side="both") -> np.ndarray: + """ + Digitize the data into bins. This method spreads data uniformly when bins + have same values. + + Args: + + x (:class:`np.ndarray`): + The data to digitize. + bins (:class:`np.ndarray`): + The bins to use for digitization, in increasing order. + side (:class:`str`, optional): + The side to use for digitization. If "one", the left side is used. If + "both", the left and right side are used. Default to "one". + + Returns: + + :class:`np.ndarray`: + The digitized data. + """ + assert x.ndim == 1 and bins.ndim == 1 + + left_digits = np.digitize(x, bins) + if side == "one": + return left_digits + + right_difits = np.digitize(x, bins, right=True) + + rands = np.random.rand(len(x)) # uniform random numbers + + digits = rands * (right_difits - left_digits) + left_digits + digits = np.ceil(digits).astype(np.int64) + return digits + + +def binning( + row: Union[np.ndarray, torch.Tensor], n_bins: int +) -> Union[np.ndarray, torch.Tensor]: + """Binning the row into n_bins.""" + dtype = row.dtype + return_np = False if isinstance(row, torch.Tensor) else True + row = row.cpu().numpy() if isinstance(row, torch.Tensor) else row + # TODO: use torch.quantile and torch.bucketize + + if row.max() == 0: + print( + "The input data contains row of zeros. Please make sure this is expected." + ) + return ( + np.zeros_like(row, dtype=dtype) + if return_np + else torch.zeros_like(row, dtype=dtype) + ) + + if row.min() <= 0: + non_zero_ids = row.nonzero() + non_zero_row = row[non_zero_ids] + bins = np.quantile(non_zero_row, np.linspace(0, 1, n_bins - 1)) + non_zero_digits = _digitize(non_zero_row, bins) + binned_row = np.zeros_like(row, dtype=np.int64) + binned_row[non_zero_ids] = non_zero_digits + else: + bins = np.quantile(row, np.linspace(0, 1, n_bins - 1)) + binned_row = _digitize(row, bins) + return torch.from_numpy(binned_row) if not return_np else binned_row.astype(dtype) \ No newline at end of file diff --git a/transfer/code/scDFM/src/data_process/utils.py b/transfer/code/scDFM/src/data_process/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..71d7de92f507e726d731cb07ebf669e3de33e8bc --- /dev/null +++ b/transfer/code/scDFM/src/data_process/utils.py @@ -0,0 +1,35 @@ +import anndata as ad +import pandas as pd +import numpy as np + +def build_generated_anndata( + embeddings: np.ndarray, # shape: (N, D) + condition_id: np.ndarray, # shape: (N, 2) + pert_col: str = "perturbation", # first perturbation column name + pert_col2: str = "perturbation_2", # second perturbation (if combo) + combo_col: str = "pert_combo", # combined label +) -> ad.AnnData: + """ + Construct AnnData from model-generated embeddings and condition IDs. + """ + N = embeddings.shape[0] + if condition_id.shape[0] != N: + raise ValueError("condition_id and embeddings must have same first dimension.") + + # obs: per-cell annotation + obs = pd.DataFrame({ + pert_col: condition_id[:, 0], + pert_col2: condition_id[:, 1], + combo_col: [f"{a}_{b}" for a, b in condition_id], + }, index=[f"cell_{i}" for i in range(N)]) + + # construct AnnData + adata = ad.AnnData( + X=embeddings, # could also store in obsm + obs=obs + ) + + # also store embedding explicitly for plotting + adata.obsm["embedding"] = embeddings + + return adata \ No newline at end of file diff --git a/transfer/code/scDFM/src/flow_matching/__init__.py b/transfer/code/scDFM/src/flow_matching/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..719ae055c6a13d749beca75c7a496f3cd7cd8948 --- /dev/null +++ b/transfer/code/scDFM/src/flow_matching/__init__.py @@ -0,0 +1,7 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +__version__ = "1.0.10" diff --git a/transfer/code/scDFM/src/flow_matching/ot/__init__.py b/transfer/code/scDFM/src/flow_matching/ot/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4a7eee1fa298559ee8d944804ac860cfb4767a25 --- /dev/null +++ b/transfer/code/scDFM/src/flow_matching/ot/__init__.py @@ -0,0 +1,5 @@ +from .optimal_transport import OTPlanSampler + +__all__ = [ + "OTPlanSampler", +] \ No newline at end of file diff --git a/transfer/code/scDFM/src/flow_matching/ot/optimal_transport.py b/transfer/code/scDFM/src/flow_matching/ot/optimal_transport.py new file mode 100644 index 0000000000000000000000000000000000000000..e0cd855a572274e82de4f1c563f6be592a5cc2a8 --- /dev/null +++ b/transfer/code/scDFM/src/flow_matching/ot/optimal_transport.py @@ -0,0 +1,291 @@ +import math +import warnings +from functools import partial +from typing import Optional, Union + +import numpy as np +import ot as pot +import torch + + +class OTPlanSampler: + """OTPlanSampler implements sampling coordinates according to an OT plan (wrt squared Euclidean + cost) with different implementations of the plan calculation.""" + + def __init__( + self, + method: str, + reg: float = 0.05, + reg_m: float = 1.0, + normalize_cost: bool = False, + num_threads: Union[int, str] = 1, + warn: bool = True, + ) -> None: + """Initialize the OTPlanSampler class. + + Parameters + ---------- + method: str + choose which optimal transport solver you would like to use. + Currently supported are ["exact", "sinkhorn", "unbalanced", + "partial"] OT solvers. + reg: float, optional + regularization parameter to use for Sinkhorn-based iterative solvers. + reg_m: float, optional + regularization weight for unbalanced Sinkhorn-knopp solver. + normalize_cost: bool, optional + normalizes the cost matrix so that the maximum cost is 1. Helps + stabilize Sinkhorn-based solvers. Should not be used in the vast + majority of cases. + num_threads: int or str, optional + number of threads to use for the "exact" OT solver. If "max", uses + the maximum number of threads. + warn: bool, optional + if True, raises a warning if the algorithm does not converge + """ + # ot_fn should take (a, b, M) as arguments where a, b are marginals and + # M is a cost matrix + if method == "exact": + self.ot_fn = partial(pot.emd, numThreads=num_threads) + elif method == "sinkhorn": + self.ot_fn = partial(pot.sinkhorn, reg=reg) + elif method == "unbalanced": + self.ot_fn = partial(pot.unbalanced.sinkhorn_knopp_unbalanced, reg=reg, reg_m=reg_m) + elif method == "partial": + self.ot_fn = partial(pot.partial.entropic_partial_wasserstein, reg=reg) + else: + raise ValueError(f"Unknown method: {method}") + self.reg = reg + self.reg_m = reg_m + self.normalize_cost = normalize_cost + self.warn = warn + + def get_map(self, x0, x1): + """Compute the OT plan (wrt squared Euclidean cost) between a source and a target + minibatch. + + Parameters + ---------- + x0 : Tensor, shape (bs, *dim) + represents the source minibatch + x1 : Tensor, shape (bs, *dim) + represents the source minibatch + + Returns + ------- + p : numpy array, shape (bs, bs) + represents the OT plan between minibatches + """ + a, b = pot.unif(x0.shape[0]), pot.unif(x1.shape[0]) + if x0.dim() > 2: + x0 = x0.reshape(x0.shape[0], -1) + if x1.dim() > 2: + x1 = x1.reshape(x1.shape[0], -1) + M = torch.cdist(x0, x1) ** 2 + if self.normalize_cost: + M = M / M.max() # should not be normalized when using minibatches + p = self.ot_fn(a, b, M.detach().cpu().numpy()) + if not np.all(np.isfinite(p)): + print("ERROR: p is not finite") + print(p) + print("Cost mean, max", M.mean(), M.max()) + print(x0, x1) + if np.abs(p.sum()) < 1e-8: + if self.warn: + warnings.warn("Numerical errors in OT plan, reverting to uniform plan.") + p = np.ones_like(p) / p.size + return p + + def sample_map(self, pi, batch_size, replace=True): + r"""Draw source and target samples from pi $(x,z) \sim \pi$ + + Parameters + ---------- + pi : numpy array, shape (bs, bs) + represents the source minibatch + batch_size : int + represents the OT plan between minibatches + replace : bool + represents sampling or without replacement from the OT plan + + Returns + ------- + (i_s, i_j) : tuple of numpy arrays, shape (bs, bs) + represents the indices of source and target data samples from $\pi$ + """ + p = pi.flatten() + p = p / p.sum() + choices = np.random.choice( + pi.shape[0] * pi.shape[1], p=p, size=batch_size, replace=replace + ) + return np.divmod(choices, pi.shape[1]) + + def sample_plan(self, x0, x1, replace=True): + r"""Compute the OT plan $\pi$ (wrt squared Euclidean cost) between a source and a target + minibatch and draw source and target samples from pi $(x,z) \sim \pi$ + + Parameters + ---------- + x0 : Tensor, shape (bs, *dim) + represents the source minibatch + x1 : Tensor, shape (bs, *dim) + represents the source minibatch + replace : bool + represents sampling or without replacement from the OT plan + + Returns + ------- + x0[i] : Tensor, shape (bs, *dim) + represents the source minibatch drawn from $\pi$ + x1[j] : Tensor, shape (bs, *dim) + represents the source minibatch drawn from $\pi$ + """ + pi = self.get_map(x0, x1) + i, j = self.sample_map(pi, x0.shape[0], replace=replace) + return x0[i], x1[j] + + def sample_plan_fix_x0(self, x0, x1, replace=True): + """ + Sample matched samples from x1 according to the OT plan, keeping the order of x0 unchanged. + + Args: + x0 : Tensor, shape (bs, *dim) - kept unchanged + x1 : Tensor, shape (bs, *dim) - to be sampled + replace : bool - whether to sample with replacement + + Returns: + x0 : original x0, unchanged + x1_matched : matched samples sampled from x1 + """ + pi = self.get_map(x0, x1) + matched_indices = [] + + for i in range(pi.shape[0]): + prob = pi[i] / pi[i].sum() + j = np.random.choice(pi.shape[1], p=prob) + matched_indices.append(j) + + matched_indices = torch.tensor(matched_indices, dtype=torch.long, device=x1.device) + x1_matched = x1[matched_indices] + return x0, x1_matched + + def sample_plan_with_labels(self, x0, x1, y0=None, y1=None, replace=True): + r"""Compute the OT plan $\pi$ (wrt squared Euclidean cost) between a source and a target + minibatch and draw source and target labeled samples from pi $(x,z) \sim \pi$ + + Parameters + ---------- + x0 : Tensor, shape (bs, *dim) + represents the source minibatch + x1 : Tensor, shape (bs, *dim) + represents the target minibatch + y0 : Tensor, shape (bs) + represents the source label minibatch + y1 : Tensor, shape (bs) + represents the target label minibatch + replace : bool + represents sampling or without replacement from the OT plan + + Returns + ------- + x0[i] : Tensor, shape (bs, *dim) + represents the source minibatch drawn from $\pi$ + x1[j] : Tensor, shape (bs, *dim) + represents the target minibatch drawn from $\pi$ + y0[i] : Tensor, shape (bs, *dim) + represents the source label minibatch drawn from $\pi$ + y1[j] : Tensor, shape (bs, *dim) + represents the target label minibatch drawn from $\pi$ + """ + pi = self.get_map(x0, x1) + i, j = self.sample_map(pi, x0.shape[0], replace=replace) + return ( + x0[i], + x1[j], + y0[i] if y0 is not None else None, + y1[j] if y1 is not None else None, + ) + + def sample_trajectory(self, X): + """Compute the OT trajectories between different sample populations moving from the source + to the target distribution. + + Parameters + ---------- + X : Tensor, (bs, times, *dim) + different populations of samples moving from the source to the target distribution. + + Returns + ------- + to_return : Tensor, (bs, times, *dim) + represents the OT sampled trajectories over time. + """ + times = X.shape[1] + pis = [] + for t in range(times - 1): + pis.append(self.get_map(X[:, t], X[:, t + 1])) + + indices = [np.arange(X.shape[0])] + for pi in pis: + j = [] + for i in indices[-1]: + j.append(np.random.choice(pi.shape[1], p=pi[i] / pi[i].sum())) + indices.append(np.array(j)) + + to_return = [] + for t in range(times): + to_return.append(X[:, t][indices[t]]) + to_return = np.stack(to_return, axis=1) + return to_return + + +def wasserstein( + x0: torch.Tensor, + x1: torch.Tensor, + method: Optional[str] = None, + reg: float = 0.05, + power: int = 2, + **kwargs, +) -> float: + """Compute the Wasserstein (1 or 2) distance (wrt Euclidean cost) between a source and a target + distributions. + + Parameters + ---------- + x0 : Tensor, shape (bs, *dim) + represents the source minibatch + x1 : Tensor, shape (bs, *dim) + represents the source minibatch + method : str (default : None) + Use exact Wasserstein or an entropic regularization + reg : float (default : 0.05) + Entropic regularization coefficients + power : int (default : 2) + power of the Wasserstein distance (1 or 2) + Returns + ------- + ret : float + Wasserstein distance + """ + assert power == 1 or power == 2 + # ot_fn should take (a, b, M) as arguments where a, b are marginals and + # M is a cost matrix + if method == "exact" or method is None: + ot_fn = pot.emd2 + elif method == "sinkhorn": + ot_fn = partial(pot.sinkhorn2, reg=reg) + else: + raise ValueError(f"Unknown method: {method}") + + a, b = pot.unif(x0.shape[0]), pot.unif(x1.shape[0]) + if x0.dim() > 2: + x0 = x0.reshape(x0.shape[0], -1) + if x1.dim() > 2: + x1 = x1.reshape(x1.shape[0], -1) + M = torch.cdist(x0, x1) + if power == 2: + M = M**2 + ret = ot_fn(a, b, M.detach().cpu().numpy(), numItermax=int(1e7)) + if power == 2: + ret = math.sqrt(ret) + return ret diff --git a/transfer/code/scDFM/src/flow_matching/path/__init__.py b/transfer/code/scDFM/src/flow_matching/path/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..88d29a2575c1be14d2ec10bd9eda799e7599dd1d --- /dev/null +++ b/transfer/code/scDFM/src/flow_matching/path/__init__.py @@ -0,0 +1,22 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from .affine import AffineProbPath, CondOTProbPath +from .geodesic import GeodesicProbPath +from .mixture import MixtureDiscreteProbPath +from .path import ProbPath +from .path_sample import DiscretePathSample, PathSample + + +__all__ = [ + "ProbPath", + "AffineProbPath", + "CondOTProbPath", + "MixtureDiscreteProbPath", + "GeodesicProbPath", + "PathSample", + "DiscretePathSample", +] diff --git a/transfer/code/scDFM/src/flow_matching/path/affine.py b/transfer/code/scDFM/src/flow_matching/path/affine.py new file mode 100644 index 0000000000000000000000000000000000000000..90bb06a22f84055fe009c0f6257eb61493ed6f1c --- /dev/null +++ b/transfer/code/scDFM/src/flow_matching/path/affine.py @@ -0,0 +1,260 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from torch import Tensor + +from src.flow_matching.path.path import ProbPath +from src.flow_matching.path.path_sample import PathSample +from src.flow_matching.path.scheduler.scheduler import CondOTScheduler, Scheduler +from src.flow_matching.utils import expand_tensor_like + + +class AffineProbPath(ProbPath): + r"""The ``AffineProbPath`` class represents a specific type of probability path where the transformation between distributions is affine. + An affine transformation can be represented as: + + .. math:: + + X_t = \alpha_t X_1 + \sigma_t X_0, + + where :math:`X_t` is the transformed data point at time `t`. :math:`X_0` and :math:`X_1` are the source and target data points, respectively. :math:`\alpha_t` and :math:`\sigma_t` are the parameters of the affine transformation at time `t`. + + The scheduler is responsible for providing the time-dependent parameters :math:`\alpha_t` and :math:`\sigma_t`, as well as their derivatives, which define the affine transformation at any given time `t`. + + Using ``AffineProbPath`` in the flow matching framework: + + .. code-block:: python + + # Instantiates a probability path + my_path = AffineProbPath(...) + mse_loss = torch.nn.MSELoss() + + for x_1 in dataset: + # Sets x_0 to random noise + x_0 = torch.randn() + + # Sets t to a random value in [0,1] + t = torch.rand() + + # Samples the conditional path X_t ~ p_t(X_t|X_0,X_1) + path_sample = my_path.sample(x_0=x_0, x_1=x_1, t=t) + + # Computes the MSE loss w.r.t. the velocity + loss = mse_loss(path_sample.dx_t, my_model(x_t, t)) + loss.backward() + + Args: + scheduler (Scheduler): An instance of a scheduler that provides the parameters :math:`\alpha_t`, :math:`\sigma_t`, and their derivatives over time. + + """ + + def __init__(self, scheduler: Scheduler): + self.scheduler = scheduler + + def sample(self, x_0: Tensor, x_1: Tensor, t: Tensor) -> PathSample: + r"""Sample from the affine probability path: + + | given :math:`(X_0,X_1) \sim \pi(X_0,X_1)` and a scheduler :math:`(\alpha_t,\sigma_t)`. + | return :math:`X_0, X_1, X_t = \alpha_t X_1 + \sigma_t X_0`, and the conditional velocity at :math:`X_t, \dot{X}_t = \dot{\alpha}_t X_1 + \dot{\sigma}_t X_0`. + + Args: + x_0 (Tensor): source data point, shape (batch_size, ...). + x_1 (Tensor): target data point, shape (batch_size, ...). + t (Tensor): times in [0,1], shape (batch_size). + + Returns: + PathSample: a conditional sample at :math:`X_t \sim p_t`. + """ + self.assert_sample_shape(x_0=x_0, x_1=x_1, t=t) + + scheduler_output = self.scheduler(t) + + alpha_t = expand_tensor_like( + input_tensor=scheduler_output.alpha_t, expand_to=x_1 + ) + sigma_t = expand_tensor_like( + input_tensor=scheduler_output.sigma_t, expand_to=x_1 + ) + d_alpha_t = expand_tensor_like( + input_tensor=scheduler_output.d_alpha_t, expand_to=x_1 + ) + d_sigma_t = expand_tensor_like( + input_tensor=scheduler_output.d_sigma_t, expand_to=x_1 + ) + + # construct xt ~ p_t(x|x1). + x_t = sigma_t * x_0 + alpha_t * x_1 + dx_t = d_sigma_t * x_0 + d_alpha_t * x_1 + + return PathSample(x_t=x_t, dx_t=dx_t, x_1=x_1, x_0=x_0, t=t) + + def target_to_velocity(self, x_1: Tensor, x_t: Tensor, t: Tensor) -> Tensor: + r"""Convert from x_1 representation to velocity. + + | given :math:`X_1`. + | return :math:`\dot{X}_t`. + + Args: + x_1 (Tensor): target data point. + x_t (Tensor): path sample at time t. + t (Tensor): time in [0,1]. + + Returns: + Tensor: velocity. + """ + scheduler_output = self.scheduler(t) + + alpha_t = scheduler_output.alpha_t + d_alpha_t = scheduler_output.d_alpha_t + sigma_t = scheduler_output.sigma_t + d_sigma_t = scheduler_output.d_sigma_t + + a_t = d_sigma_t / sigma_t + b_t = (d_alpha_t * sigma_t - d_sigma_t * alpha_t) / sigma_t + + return a_t * x_t + b_t * x_1 + + def epsilon_to_velocity(self, epsilon: Tensor, x_t: Tensor, t: Tensor) -> Tensor: + r"""Convert from epsilon representation to velocity. + + | given :math:`\epsilon`. + | return :math:`\dot{X}_t`. + + Args: + epsilon (Tensor): noise in the path sample. + x_t (Tensor): path sample at time t. + t (Tensor): time in [0,1]. + + Returns: + Tensor: velocity. + """ + scheduler_output = self.scheduler(t) + + alpha_t = scheduler_output.alpha_t + d_alpha_t = scheduler_output.d_alpha_t + sigma_t = scheduler_output.sigma_t + d_sigma_t = scheduler_output.d_sigma_t + + a_t = d_alpha_t / alpha_t + b_t = (d_sigma_t * alpha_t - d_alpha_t * sigma_t) / alpha_t + + return a_t * x_t + b_t * epsilon + + def velocity_to_target(self, velocity: Tensor, x_t: Tensor, t: Tensor) -> Tensor: + r"""Convert from velocity to x_1 representation. + + | given :math:`\dot{X}_t`. + | return :math:`X_1`. + + Args: + velocity (Tensor): velocity at the path sample. + x_t (Tensor): path sample at time t. + t (Tensor): time in [0,1]. + + Returns: + Tensor: target data point. + """ + scheduler_output = self.scheduler(t) + + alpha_t = scheduler_output.alpha_t + d_alpha_t = scheduler_output.d_alpha_t + sigma_t = scheduler_output.sigma_t + d_sigma_t = scheduler_output.d_sigma_t + + a_t = -d_sigma_t / (d_alpha_t * sigma_t - d_sigma_t * alpha_t) + b_t = sigma_t / (d_alpha_t * sigma_t - d_sigma_t * alpha_t) + + return a_t * x_t + b_t * velocity + + def epsilon_to_target(self, epsilon: Tensor, x_t: Tensor, t: Tensor) -> Tensor: + r"""Convert from epsilon representation to x_1 representation. + + | given :math:`\epsilon`. + | return :math:`X_1`. + + Args: + epsilon (Tensor): noise in the path sample. + x_t (Tensor): path sample at time t. + t (Tensor): time in [0,1]. + + Returns: + Tensor: target data point. + """ + scheduler_output = self.scheduler(t) + + alpha_t = scheduler_output.alpha_t + sigma_t = scheduler_output.sigma_t + + a_t = 1 / alpha_t + b_t = -sigma_t / alpha_t + + return a_t * x_t + b_t * epsilon + + def velocity_to_epsilon(self, velocity: Tensor, x_t: Tensor, t: Tensor) -> Tensor: + r"""Convert from velocity to noise representation. + + | given :math:`\dot{X}_t`. + | return :math:`\epsilon`. + + Args: + velocity (Tensor): velocity at the path sample. + x_t (Tensor): path sample at time t. + t (Tensor): time in [0,1]. + + Returns: + Tensor: noise in the path sample. + """ + scheduler_output = self.scheduler(t) + + alpha_t = scheduler_output.alpha_t + d_alpha_t = scheduler_output.d_alpha_t + sigma_t = scheduler_output.sigma_t + d_sigma_t = scheduler_output.d_sigma_t + + a_t = -d_alpha_t / (d_sigma_t * alpha_t - d_alpha_t * sigma_t) + b_t = alpha_t / (d_sigma_t * alpha_t - d_alpha_t * sigma_t) + + return a_t * x_t + b_t * velocity + + def target_to_epsilon(self, x_1: Tensor, x_t: Tensor, t: Tensor) -> Tensor: + r"""Convert from x_1 representation to velocity. + + | given :math:`X_1`. + | return :math:`\epsilon`. + + Args: + x_1 (Tensor): target data point. + x_t (Tensor): path sample at time t. + t (Tensor): time in [0,1]. + + Returns: + Tensor: noise in the path sample. + """ + scheduler_output = self.scheduler(t) + + alpha_t = scheduler_output.alpha_t + sigma_t = scheduler_output.sigma_t + + a_t = 1 / sigma_t + b_t = -alpha_t / sigma_t + + return a_t * x_t + b_t * x_1 + + +class CondOTProbPath(AffineProbPath): + r"""The ``CondOTProbPath`` class represents a conditional optimal transport probability path. + + This class is a specialized version of the ``AffineProbPath`` that uses a conditional optimal transport scheduler to determine the parameters of the affine transformation. + + The parameters :math:`\alpha_t` and :math:`\sigma_t` for the conditional optimal transport path are defined as: + + .. math:: + + \alpha_t = t \quad \text{and} \quad \sigma_t = 1 - t. + """ + + def __init__(self): + self.scheduler = CondOTScheduler() diff --git a/transfer/code/scDFM/src/flow_matching/path/geodesic.py b/transfer/code/scDFM/src/flow_matching/path/geodesic.py new file mode 100644 index 0000000000000000000000000000000000000000..23144cc842e72caa79797044dbac3738b8245977 --- /dev/null +++ b/transfer/code/scDFM/src/flow_matching/path/geodesic.py @@ -0,0 +1,100 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +import torch + +from torch import Tensor +from torch.func import jvp, vmap + +from src.flow_matching.path.path import ProbPath + +from src.flow_matching.path.path_sample import PathSample +from src.flow_matching.path.scheduler import ConvexScheduler +from src.flow_matching.utils import expand_tensor_like + +from src.flow_matching.utils.manifolds import geodesic, Manifold + + +class GeodesicProbPath(ProbPath): + r"""The ``GeodesicProbPath`` class represents a specific type of probability path where the transformation between distributions is defined through the geodesic path. + Mathematically, a geodesic path can be represented as: + + .. math:: + + X_t = \psi_t(X_0 | X_1) = \exp_{X_1}(\kappa_t \log_{X_1}(X_0)), + + where :math:`X_t` is the transformed data point at time `t`, :math:`X_0` and :math:`X_1` are the source and target data points, respectively, and :math:`\kappa_t` is a scheduler. + + The scheduler is responsible for providing the time-dependent :math:`\kappa_t` and must be differentiable. + + Using ``GeodesicProbPath`` in the flow matching framework: + + .. code-block:: python + # Instantiates a manifold + manifold = FlatTorus() + + # Instantiates a scheduler + scheduler = CondOTScheduler() + + # Instantiates a probability path + my_path = GeodesicProbPath(scheduler, manifold) + mse_loss = torch.nn.MSELoss() + + for x_1 in dataset: + # Sets x_0 to random noise + x_0 = torch.randn() + + # Sets t to a random value in [0,1] + t = torch.rand() + + # Samples the conditional path :math:`X_t \sim p_t(X_t|X_0,X_1)` + path_sample = my_path.sample(x_0=x_0, x_1=x_1, t=t) + + # Computes the MSE loss w.r.t. the velocity + loss = mse_loss(path_sample.dx_t, my_model(x_t, t)) + loss.backward() + + Args: + scheduler (ConvexScheduler): The scheduler that provides :math:`\kappa_t`. + manifold (Manifold): The manifold on which the probability path is defined. + + """ + + def __init__(self, scheduler: ConvexScheduler, manifold: Manifold): + self.scheduler = scheduler + self.manifold = manifold + + def sample(self, x_0: Tensor, x_1: Tensor, t: Tensor) -> PathSample: + r"""Sample from the Riemannian probability path with geodesic interpolation: + + | given :math:`(X_0,X_1) \sim \pi(X_0,X_1)` and a scheduler :math:`\kappa_t`. + | return :math:`X_0, X_1, X_t = \exp_{X_1}(\kappa_t \log_{X_1}(X_0))`, and the conditional velocity at :math:`X_t, \dot{X}_t`. + + Args: + x_0 (Tensor): source data point, shape (batch_size, ...). + x_1 (Tensor): target data point, shape (batch_size, ...). + t (Tensor): times in [0,1], shape (batch_size). + + Returns: + PathSample: A conditional sample at :math:`X_t \sim p_t`. + """ + self.assert_sample_shape(x_0=x_0, x_1=x_1, t=t) + t = expand_tensor_like(input_tensor=t, expand_to=x_1[..., 0:1]).clone() + + def cond_u(x_0, x_1, t): + path = geodesic(self.manifold, x_0, x_1) + x_t, dx_t = jvp( + lambda t: path(self.scheduler(t).alpha_t), + (t,), + (torch.ones_like(t).to(t),), + ) + return x_t, dx_t + + x_t, dx_t = vmap(cond_u)(x_0, x_1, t) + x_t = x_t.reshape_as(x_1) + dx_t = dx_t.reshape_as(x_1) + + return PathSample(x_t=x_t, dx_t=dx_t, x_1=x_1, x_0=x_0, t=t) diff --git a/transfer/code/scDFM/src/flow_matching/path/mixture.py b/transfer/code/scDFM/src/flow_matching/path/mixture.py new file mode 100644 index 0000000000000000000000000000000000000000..3c5b5f841be04f5f213cb21889df7df486770545 --- /dev/null +++ b/transfer/code/scDFM/src/flow_matching/path/mixture.py @@ -0,0 +1,117 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn.functional as F + +from torch import Tensor + +from src.flow_matching.path.path import ProbPath + +from src.flow_matching.path.path_sample import DiscretePathSample +from src.flow_matching.path.scheduler import ConvexScheduler +from src.flow_matching.utils import expand_tensor_like, unsqueeze_to_match + + +class MixtureDiscreteProbPath(ProbPath): + r"""The ``MixtureDiscreteProbPath`` class defines a factorized discrete probability path. + + This path remains constant at the source data point :math:`X_0` until a random time, determined by the scheduler, when it flips to the target data point :math:`X_1`. + The scheduler determines the flip probability using the parameter :math:`\sigma_t`, which is a function of time `t`. Specifically, :math:`\sigma_t` represents the probability of remaining at :math:`X_0`, while :math:`1 - \sigma_t` is the probability of flipping to :math:`X_1`: + + .. math:: + + P(X_t = X_0) = \sigma_t \quad \text{and} \quad P(X_t = X_1) = 1 - \sigma_t, + + where :math:`\sigma_t` is provided by the scheduler. + + Example: + + .. code-block:: python + + >>> x_0 = torch.zeros((1, 3, 3)) + >>> x_1 = torch.ones((1, 3, 3)) + + >>> path = MixtureDiscreteProbPath(PolynomialConvexScheduler(n=1.0)) + >>> result = path.sample(x_0, x_1, t=torch.tensor([0.1])).x_t + >>> result + tensor([[[0.0, 0.0, 0.0], + [0.0, 0.0, 1.0], + [0.0, 0.0, 0.0]]]) + + >>> result = path.sample(x_0, x_1, t=torch.tensor([0.5])).x_t + >>> result + tensor([[[1.0, 0.0, 1.0], + [0.0, 1.0, 0.0], + [0.0, 1.0, 0.0]]]) + + >>> result = path.sample(x_0, x_1, t=torch.tensor([1.0])).x_t + >>> result + tensor([[[1.0, 1.0, 1.0], + [1.0, 1.0, 1.0], + [1.0, 1.0, 1.0]]]) + + Args: + scheduler (ConvexScheduler): The scheduler that provides :math:`\sigma_t`. + """ + + def __init__(self, scheduler: ConvexScheduler): + assert isinstance( + scheduler, ConvexScheduler + ), "Scheduler for ConvexProbPath must be a ConvexScheduler." + + self.scheduler = scheduler + + def sample(self, x_0: Tensor, x_1: Tensor, t: Tensor) -> DiscretePathSample: + r"""Sample from the affine probability path: + | given :math:`(X_0,X_1) \sim \pi(X_0,X_1)` and a scheduler :math:`(\alpha_t,\sigma_t)`. + | return :math:`X_0, X_1, t`, and :math:`X_t \sim p_t`. + Args: + x_0 (Tensor): source data point, shape (batch_size, ...). + x_1 (Tensor): target data point, shape (batch_size, ...). + t (Tensor): times in [0,1], shape (batch_size). + + Returns: + DiscretePathSample: a conditional sample at :math:`X_t ~ p_t`. + """ + self.assert_sample_shape(x_0=x_0, x_1=x_1, t=t) + + sigma_t = self.scheduler(t).sigma_t + + sigma_t = expand_tensor_like(input_tensor=sigma_t, expand_to=x_1) + + source_indices = torch.rand(size=x_1.shape, device=x_1.device) < sigma_t + x_t = torch.where(condition=source_indices, input=x_0, other=x_1) + + return DiscretePathSample(x_t=x_t, x_1=x_1, x_0=x_0, t=t) + + def posterior_to_velocity( + self, posterior_logits: Tensor, x_t: Tensor, t: Tensor + ) -> Tensor: + r"""Convert the factorized posterior to velocity. + + | given :math:`p(X_1|X_t)`. In the factorized case: :math:`\prod_i p(X_1^i | X_t)`. + | return :math:`u_t`. + + Args: + posterior_logits (Tensor): logits of the x_1 posterior conditional on x_t, shape (..., vocab size). + x_t (Tensor): path sample at time t, shape (...). + t (Tensor): time in [0,1]. + + Returns: + Tensor: velocity. + """ + posterior = torch.softmax(posterior_logits, dim=-1) + vocabulary_size = posterior.shape[-1] + x_t = F.one_hot(x_t, num_classes=vocabulary_size) + t = unsqueeze_to_match(source=t, target=x_t) + + scheduler_output = self.scheduler(t) + + kappa_t = scheduler_output.alpha_t + d_kappa_t = scheduler_output.d_alpha_t + + return (d_kappa_t / (1 - kappa_t)) * (posterior - x_t) diff --git a/transfer/code/scDFM/src/flow_matching/path/path.py b/transfer/code/scDFM/src/flow_matching/path/path.py new file mode 100644 index 0000000000000000000000000000000000000000..0dfcd477eb0d4986c0022cee14b176aaa6e76e6d --- /dev/null +++ b/transfer/code/scDFM/src/flow_matching/path/path.py @@ -0,0 +1,61 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from abc import ABC, abstractmethod + +from torch import Tensor + +from src.flow_matching.path.path_sample import PathSample + + +class ProbPath(ABC): + r"""Abstract class, representing a probability path. + + A probability path transforms the distribution :math:`p(X_0)` into :math:`p(X_1)` over :math:`t=0\rightarrow 1`. + + The ``ProbPath`` class is designed to support model training in the flow matching framework. It supports two key functionalities: (1) sampling the conditional probability path and (2) conversion between various training objectives. + Here is a high-level example + + .. code-block:: python + + # Instantiate a probability path + my_path = ProbPath(...) + + for x_0, x_1 in dataset: + # Sets t to a random value in [0,1] + t = torch.rand() + + # Samples the conditional path X_t ~ p_t(X_t|X_0,X_1) + path_sample = my_path.sample(x_0=x_0, x_1=x_1, t=t) + + # Optimizes the model. The loss function varies, depending on model and path. + loss(path_sample, my_model(x_t, t)).backward() + + """ + + @abstractmethod + def sample(self, x_0: Tensor, x_1: Tensor, t: Tensor) -> PathSample: + r"""Sample from an abstract probability path: + + | given :math:`(X_0,X_1) \sim \pi(X_0,X_1)`. + | returns :math:`X_0, X_1, X_t \sim p_t(X_t)`, and a conditional target :math:`Y`, all objects are under ``PathSample``. + + Args: + x_0 (Tensor): source data point, shape (batch_size, ...). + x_1 (Tensor): target data point, shape (batch_size, ...). + t (Tensor): times in [0,1], shape (batch_size). + + Returns: + PathSample: a conditional sample. + """ + + def assert_sample_shape(self, x_0: Tensor, x_1: Tensor, t: Tensor): + assert ( + t.ndim == 1 + ), f"The time vector t must have shape [batch_size]. Got {t.shape}." + assert ( + t.shape[0] == x_0.shape[0] == x_1.shape[0] + ), f"Time t dimension must match the batch size [{x_1.shape[0]}]. Got {t.shape}" diff --git a/transfer/code/scDFM/src/flow_matching/path/path_sample.py b/transfer/code/scDFM/src/flow_matching/path/path_sample.py new file mode 100644 index 0000000000000000000000000000000000000000..867032e3dfe06fc5926316c9c4f133f57a025082 --- /dev/null +++ b/transfer/code/scDFM/src/flow_matching/path/path_sample.py @@ -0,0 +1,53 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from dataclasses import dataclass, field + +from torch import Tensor + + +@dataclass +class PathSample: + r"""Represents a sample of a conditional-flow generated probability path. + + Attributes: + x_1 (Tensor): the target sample :math:`X_1`. + x_0 (Tensor): the source sample :math:`X_0`. + t (Tensor): the time sample :math:`t`. + x_t (Tensor): samples :math:`X_t \sim p_t(X_t)`, shape (batch_size, ...). + dx_t (Tensor): conditional target :math:`\frac{\partial X}{\partial t}`, shape: (batch_size, ...). + + """ + + x_1: Tensor = field(metadata={"help": "target samples X_1 (batch_size, ...)."}) + x_0: Tensor = field(metadata={"help": "source samples X_0 (batch_size, ...)."}) + t: Tensor = field(metadata={"help": "time samples t (batch_size, ...)."}) + x_t: Tensor = field( + metadata={"help": "samples x_t ~ p_t(X_t), shape (batch_size, ...)."} + ) + dx_t: Tensor = field( + metadata={"help": "conditional target dX_t, shape: (batch_size, ...)."} + ) + + +@dataclass +class DiscretePathSample: + """ + Represents a sample of a conditional-flow generated discrete probability path. + + Attributes: + x_1 (Tensor): the target sample :math:`X_1`. + x_0 (Tensor): the source sample :math:`X_0`. + t (Tensor): the time sample :math:`t`. + x_t (Tensor): the sample along the path :math:`X_t \sim p_t`. + """ + + x_1: Tensor = field(metadata={"help": "target samples X_1 (batch_size, ...)."}) + x_0: Tensor = field(metadata={"help": "source samples X_0 (batch_size, ...)."}) + t: Tensor = field(metadata={"help": "time samples t (batch_size, ...)."}) + x_t: Tensor = field( + metadata={"help": "samples X_t ~ p_t(X_t), shape (batch_size, ...)."} + ) diff --git a/transfer/code/scDFM/src/flow_matching/path/scheduler/__init__.py b/transfer/code/scDFM/src/flow_matching/path/scheduler/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f3b1a43f08b3c85a9c69ebe87b4fdeaf9eff29b1 --- /dev/null +++ b/transfer/code/scDFM/src/flow_matching/path/scheduler/__init__.py @@ -0,0 +1,29 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from .schedule_transform import ScheduleTransformedModel +from .scheduler import ( + CondOTScheduler, + ConvexScheduler, + CosineScheduler, + LinearVPScheduler, + PolynomialConvexScheduler, + Scheduler, + SchedulerOutput, + VPScheduler, +) + +__all__ = [ + "CondOTScheduler", + "CosineScheduler", + "ConvexScheduler", + "PolynomialConvexScheduler", + "ScheduleTransformedModel", + "Scheduler", + "VPScheduler", + "LinearVPScheduler", + "SchedulerOutput", +] diff --git a/transfer/code/scDFM/src/flow_matching/path/scheduler/schedule_transform.py b/transfer/code/scDFM/src/flow_matching/path/scheduler/schedule_transform.py new file mode 100644 index 0000000000000000000000000000000000000000..8e2e436ee959c30d52500a422dd60b41b06f065e --- /dev/null +++ b/transfer/code/scDFM/src/flow_matching/path/scheduler/schedule_transform.py @@ -0,0 +1,148 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from torch import Tensor + +from src.flow_matching.path.scheduler.scheduler import Scheduler +from src.flow_matching.utils import ModelWrapper + + +class ScheduleTransformedModel(ModelWrapper): + """ + Change of scheduler for a velocity model. + + This class wraps a given velocity model and transforms its scheduling + to a new scheduler function. It modifies the time + dynamics of the model according to the new scheduler while maintaining + the original model's behavior. + + Example: + + .. code-block:: python + + import torch + from flow_matching.path.scheduler import CondOTScheduler, CosineScheduler, ScheduleTransformedModel + from flow_matching.solver import ODESolver + + # Initialize the model and schedulers + model = ... + + original_scheduler = CondOTScheduler() + new_scheduler = CosineScheduler() + + # Create the transformed model + transformed_model = ScheduleTransformedModel( + velocity_model=model, + original_scheduler=original_scheduler, + new_scheduler=new_scheduler + ) + + # Set up the solver + solver = ODESolver(velocity_model=transformed_model) + + x_0 = torch.randn([10, 2]) # Example initial condition + + x_1 = solver.sample( + time_steps=torch.tensor([0.0, 1.0]), + x_init=x_0, + step_size=1/1000 + )[1] + + Args: + velocity_model (ModelWrapper): The original velocity model to be transformed. + original_scheduler (Scheduler): The scheduler used by the original model. Must implement the snr_inverse function. + new_scheduler (Scheduler): The new scheduler to be applied to the model. + """ + + def __init__( + self, + velocity_model: ModelWrapper, + original_scheduler: Scheduler, + new_scheduler: Scheduler, + ): + super().__init__(model=velocity_model) + self.original_scheduler = original_scheduler + self.new_scheduler = new_scheduler + + assert hasattr(self.original_scheduler, "snr_inverse") and callable( + getattr(self.original_scheduler, "snr_inverse") + ), "The original scheduler must have a callable 'snr_inverse' method." + + def forward(self, x: Tensor, t: Tensor, **extras) -> Tensor: + r""" + Compute the transformed marginal velocity field for a new scheduler. + This method implements a post-training velocity scheduler change for + affine conditional flows. It transforms a generating marginal velocity + field :math:`u_t(x)` based on an original scheduler to a new marginal velocity + field :math:`\bar{u}_r(x)` based on a different scheduler, while maintaining + the same data coupling. + The transformation is based on the scale-time (ST) transformation + between the two conditional flows, defined as: + + .. math:: + + \bar{X}_r = s_r X_{t_r}, + + where :math:`X_t` and :math:`\bar{X}_r` are defined by their respective schedulers. + The ST transformation is computed as: + + .. math:: + + t_r = \rho^{-1}(\bar{\rho}(r)) \quad \text{and} \quad s_r = \frac{\bar{\sigma}_r}{\sigma_{t_r}}. + + Here, :math:`\rho(t)` is the signal-to-noise ratio (SNR) defined as: + + .. math:: + + \rho(t) = \frac{\alpha_t}{\sigma_t}. + + :math:`\bar{\rho}(r)` is similarly defined for the new scheduler. + The marginal velocity for the new scheduler is then given by: + + .. math:: + + \bar{u}_r(x) = \left(\frac{\dot{s}_r}{s_r}\right) x + s_r \dot{t}_r u_{t_r}\left(\frac{x}{s_r}\right). + + Args: + x (Tensor): :math:`x_t`, the input tensor. + t (Tensor): The time tensor (denoted as :math:`r` above). + **extras: Additional arguments for the model. + Returns: + Tensor: The transformed velocity. + """ + r = t + + r_scheduler_output = self.new_scheduler(t=r) + + alpha_r = r_scheduler_output.alpha_t + sigma_r = r_scheduler_output.sigma_t + d_alpha_r = r_scheduler_output.d_alpha_t + d_sigma_r = r_scheduler_output.d_sigma_t + + t = self.original_scheduler.snr_inverse(alpha_r / sigma_r) + + t_scheduler_output = self.original_scheduler(t=t) + + alpha_t = t_scheduler_output.alpha_t + sigma_t = t_scheduler_output.sigma_t + d_alpha_t = t_scheduler_output.d_alpha_t + d_sigma_t = t_scheduler_output.d_sigma_t + + s_r = sigma_r / sigma_t + + dt_r = ( + sigma_t + * sigma_t + * (sigma_r * d_alpha_r - alpha_r * d_sigma_r) + / (sigma_r * sigma_r * (sigma_t * d_alpha_t - alpha_t * d_sigma_t)) + ) + + ds_r = (sigma_t * d_sigma_r - sigma_r * d_sigma_t * dt_r) / (sigma_t * sigma_t) + + u_t = self.model(x=x / s_r, t=t, **extras) + u_r = ds_r * x / s_r + dt_r * s_r * u_t + + return u_r diff --git a/transfer/code/scDFM/src/flow_matching/path/scheduler/scheduler.py b/transfer/code/scDFM/src/flow_matching/path/scheduler/scheduler.py new file mode 100644 index 0000000000000000000000000000000000000000..422618acd255bcc50779f1df60ce4f2666cea3f6 --- /dev/null +++ b/transfer/code/scDFM/src/flow_matching/path/scheduler/scheduler.py @@ -0,0 +1,199 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from abc import ABC, abstractmethod +from dataclasses import dataclass, field + +from typing import Union + +import torch + +from torch import Tensor + + +@dataclass +class SchedulerOutput: + r"""Represents a sample of a conditional-flow generated probability path. + + Attributes: + alpha_t (Tensor): :math:`\alpha_t`, shape (...). + sigma_t (Tensor): :math:`\sigma_t`, shape (...). + d_alpha_t (Tensor): :math:`\frac{\partial}{\partial t}\alpha_t`, shape (...). + d_sigma_t (Tensor): :math:`\frac{\partial}{\partial t}\sigma_t`, shape (...). + + """ + + alpha_t: Tensor = field(metadata={"help": "alpha_t"}) + sigma_t: Tensor = field(metadata={"help": "sigma_t"}) + d_alpha_t: Tensor = field(metadata={"help": "Derivative of alpha_t."}) + d_sigma_t: Tensor = field(metadata={"help": "Derivative of sigma_t."}) + + +class Scheduler(ABC): + """Base Scheduler class.""" + + @abstractmethod + def __call__(self, t: Tensor) -> SchedulerOutput: + r""" + Args: + t (Tensor): times in [0,1], shape (...). + + Returns: + SchedulerOutput: :math:`\alpha_t,\sigma_t,\frac{\partial}{\partial t}\alpha_t,\frac{\partial}{\partial t}\sigma_t` + """ + ... + + @abstractmethod + def snr_inverse(self, snr: Tensor) -> Tensor: + r""" + Computes :math:`t` from the signal-to-noise ratio :math:`\frac{\alpha_t}{\sigma_t}`. + + Args: + snr (Tensor): The signal-to-noise, shape (...) + + Returns: + Tensor: t, shape (...) + """ + ... + + +class ConvexScheduler(Scheduler): + @abstractmethod + def __call__(self, t: Tensor) -> SchedulerOutput: + """Scheduler for convex paths. + + Args: + t (Tensor): times in [0,1], shape (...). + + Returns: + SchedulerOutput: :math:`\alpha_t,\sigma_t,\frac{\partial}{\partial t}\alpha_t,\frac{\partial}{\partial t}\sigma_t` + """ + ... + + @abstractmethod + def kappa_inverse(self, kappa: Tensor) -> Tensor: + """ + Computes :math:`t` from :math:`\kappa_t`. + + Args: + kappa (Tensor): :math:`\kappa`, shape (...) + + Returns: + Tensor: t, shape (...) + """ + ... + + def snr_inverse(self, snr: Tensor) -> Tensor: + r""" + Computes :math:`t` from the signal-to-noise ratio :math:`\frac{\alpha_t}{\sigma_t}`. + + Args: + snr (Tensor): The signal-to-noise, shape (...) + + Returns: + Tensor: t, shape (...) + """ + kappa_t = snr / (1.0 + snr) + + return self.kappa_inverse(kappa=kappa_t) + + +class CondOTScheduler(ConvexScheduler): + """CondOT Scheduler.""" + + def __call__(self, t: Tensor) -> SchedulerOutput: + return SchedulerOutput( + alpha_t=t, + sigma_t=1 - t, + d_alpha_t=torch.ones_like(t), + d_sigma_t=-torch.ones_like(t), + ) + + def kappa_inverse(self, kappa: Tensor) -> Tensor: + return kappa + + +class PolynomialConvexScheduler(ConvexScheduler): + """Polynomial Scheduler.""" + + def __init__(self, n: Union[float, int]) -> None: + assert isinstance( + n, (float, int) + ), f"`n` must be a float or int. Got {type(n)=}." + assert n > 0, f"`n` must be positive. Got {n=}." + + self.n = n + + def __call__(self, t: Tensor) -> SchedulerOutput: + return SchedulerOutput( + alpha_t=t**self.n, + sigma_t=1 - t**self.n, + d_alpha_t=self.n * (t ** (self.n - 1)), + d_sigma_t=-self.n * (t ** (self.n - 1)), + ) + + def kappa_inverse(self, kappa: Tensor) -> Tensor: + return torch.pow(kappa, 1.0 / self.n) + + +class VPScheduler(Scheduler): + """Variance Preserving Scheduler.""" + + def __init__(self, beta_min: float = 0.1, beta_max: float = 20.0) -> None: + self.beta_min = beta_min + self.beta_max = beta_max + super().__init__() + + def __call__(self, t: Tensor) -> SchedulerOutput: + b = self.beta_min + B = self.beta_max + T = 0.5 * (1 - t) ** 2 * (B - b) + (1 - t) * b + dT = -(1 - t) * (B - b) - b + + return SchedulerOutput( + alpha_t=torch.exp(-0.5 * T), + sigma_t=torch.sqrt(1 - torch.exp(-T)), + d_alpha_t=-0.5 * dT * torch.exp(-0.5 * T), + d_sigma_t=0.5 * dT * torch.exp(-T) / torch.sqrt(1 - torch.exp(-T)), + ) + + def snr_inverse(self, snr: Tensor) -> Tensor: + T = -torch.log(snr**2 / (snr**2 + 1)) + b = self.beta_min + B = self.beta_max + t = 1 - ((-b + torch.sqrt(b**2 + 2 * (B - b) * T)) / (B - b)) + return t + + +class LinearVPScheduler(Scheduler): + """Linear Variance Preserving Scheduler.""" + + def __call__(self, t: Tensor) -> SchedulerOutput: + return SchedulerOutput( + alpha_t=t, + sigma_t=(1 - t**2) ** 0.5, + d_alpha_t=torch.ones_like(t), + d_sigma_t=-t / (1 - t**2) ** 0.5, + ) + + def snr_inverse(self, snr: Tensor) -> Tensor: + return torch.sqrt(snr**2 / (1 + snr**2)) + + +class CosineScheduler(Scheduler): + """Cosine Scheduler.""" + + def __call__(self, t: Tensor) -> SchedulerOutput: + pi = torch.pi + return SchedulerOutput( + alpha_t=torch.sin(pi / 2 * t), + sigma_t=torch.cos(pi / 2 * t), + d_alpha_t=pi / 2 * torch.cos(pi / 2 * t), + d_sigma_t=-pi / 2 * torch.sin(pi / 2 * t), + ) + + def snr_inverse(self, snr: Tensor) -> Tensor: + return 2.0 * torch.atan(snr) / torch.pi diff --git a/transfer/code/scDFM/src/flow_matching/solver/__init__.py b/transfer/code/scDFM/src/flow_matching/solver/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6bd7b01c0992b5469d35885961f81da1bc9fe745 --- /dev/null +++ b/transfer/code/scDFM/src/flow_matching/solver/__init__.py @@ -0,0 +1,18 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from .discrete_solver import MixtureDiscreteEulerSolver +from .ode_solver import ODESolver +from .riemannian_ode_solver import RiemannianODESolver +from .solver import Solver + +__all__ = [ + "ODESolver", + "Solver", + "ModelWrapper", + "MixtureDiscreteEulerSolver", + "RiemannianODESolver", +] diff --git a/transfer/code/scDFM/src/flow_matching/solver/discrete_solver.py b/transfer/code/scDFM/src/flow_matching/solver/discrete_solver.py new file mode 100644 index 0000000000000000000000000000000000000000..2d0b04fbf5cb3622d809226c111d4cd38980ac5a --- /dev/null +++ b/transfer/code/scDFM/src/flow_matching/solver/discrete_solver.py @@ -0,0 +1,260 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from contextlib import nullcontext +from math import ceil +from typing import Callable, Optional, Union + +import torch +from torch import Tensor + +from torch.nn import functional as F + +from src.flow_matching.path import MixtureDiscreteProbPath + +from src.flow_matching.solver.solver import Solver +from src.flow_matching.utils import categorical, ModelWrapper +from .utils import get_nearest_times + +try: + from tqdm import tqdm + + TQDM_AVAILABLE = True +except ImportError: + TQDM_AVAILABLE = False + + +class MixtureDiscreteEulerSolver(Solver): + r"""Solver that simulates the CTMC process :math:`(X_t)_{t_{\text{init}}\leq t\leq t_{\text{final}}}` defined by :math:`p_t` the marginal probability path of ``path``. + Given :math:`X_t \sim p_t`, the algorithm of solver step from :math:`t` to :math:`t+h` for the i-th coordinate is: + + .. math:: + + \begin{align*} + & X_1^i \sim p_{1|t}^i(\cdot|X_t)\\ + & \lambda^i \gets \sum_{x^i\ne X_t^i} u_t^i(x^i, X_t^i|X_1^i)\\ + & Z^i_{\text{change}} \sim U[0,1]\\ + & X_{t+h}^i \sim \begin{cases} + \frac{u_t^i(\cdot, X_t^i|X_1^i)}{\lambda^i}(1-\delta_{X_t^i}(\cdot)) \text{ if $Z^i_{\text{change}}\le 1-e^{-h\lambda^i}$}\\ + \delta_{X_t^i}(\cdot) \text{ else } + \end{cases} + \end{align*} + + Where :math:`p_{1|t}(\cdot|X_t)` is the output of ``model``, and the conditional probability velocity is of the mixture probability path is: + + .. math:: + + u_t^i(x^i, y^i|x_1^i) = \hat{u}_t^i(x^i, y^i|x_1^i) + c_{\text{div\_free}}\left[\hat{u}_t^i(x^i, y^i|x_1^i) - \check{u}_t^i(x^i, y^i|x_1^i) \right], + + where + + .. math:: + \hat{u}_t^i(x^i, y^i|x_1^i) = \frac{\dot{\kappa}_t}{1-\kappa_t} \left[ \delta_{x_1^i}(x^i) - \delta_{y^i}(x^i) \right], + + and + + .. math:: + + \check{u}_t^i(x^i, y^i|x_1^i) = \frac{\dot{\kappa}_t}{\kappa_t}\left[ \delta_{y^i}(x^i) - p(x^i) \right]. + + The source distribution :math:`p(x^i)` is given by ``p``. + + Args: + model (ModelWrapper): trained with x-prediction, outputting posterior probabilities (in the range :math:`[0,1]`), output must be [..., vocabulary_size]. + path (MixtureDiscreteProbPath): Probability path used for x-prediction training. + vocabulary_size (int): size of the discrete vocabulary. + source_distribution_p (Optional[Tensor], optional): Source distribution, must be of shape [vocabulary_size]. Required only when divergence-free term for the probability velocity is non-zero. Defaults to None. + """ + + def __init__( + self, + model: ModelWrapper, + path: MixtureDiscreteProbPath, + vocabulary_size: int, + source_distribution_p: Optional[Tensor] = None, + ): + super().__init__() + self.model = model + self.path = path + self.vocabulary_size = vocabulary_size + + if source_distribution_p is not None: + assert source_distribution_p.shape == torch.Size( + [vocabulary_size] + ), f"Source distribution p dimension must match the vocabulary size {vocabulary_size}. Got {source_distribution_p.shape}." + + self.source_distribution_p = source_distribution_p + + @torch.no_grad() + def sample( + self, + x_init: Tensor, + step_size: Optional[float], + div_free: Union[float, Callable[[float], float]] = 0.0, + dtype_categorical: torch.dtype = torch.float32, + time_grid: Tensor = torch.tensor([0.0, 1.0]), + return_intermediates: bool = False, + verbose: bool = False, + **model_extras, + ) -> Tensor: + """ + Sample a sequence of discrete values from the given model. + + .. code-block:: python + + import torch + from flow_matching.utils import ModelWrapper + from flow_matching.solver import MixtureDiscreteEulerSolver + + class DummyModel(ModelWrapper): + def __init__(self): + super().__init__(None) + def forward(self, x: torch.Tensor, t: torch.Tensor, **extras) -> torch.Tensor: + return ... + + model = DummyModel() + solver = MixtureDiscreteEulerSolver(model=model) + + x_init = torch.LongTensor([122, 725]) + step_size = 0.001 + time_grid = torch.tensor([0.0, 1.0]) + + result = solver.sample(x_init=x_init, step_size=step_size, time_grid=time_grid) + + Args: + x_init (Tensor): The initial state. + step_size (Optional[float]): If float then time discretization is uniform with the given step size. If None then time discretization is set to be time_grid. + div_free (Union[float, Callable[[float], float]]): The coefficient of the divergence-free term in the probability velocity. Can be either a float or a time dependent function. Defaults to 0.0. + dtype_categorical (torch.dtype): Precision to use for categorical sampler. Defaults to torch.float32. + time_grid (Tensor): The CTMC process is solved in the interval [time_grid[0], time_grid[-1]] and if step_size is None then time discretization is set by the time grid. Defaults to torch.tensor([0.0,1.0]). + return_intermediates (bool): If True then return intermediate time steps according to time_grid. Defaults to False. + verbose (bool): Whether to print progress bars. Defaults to False. + **model_extras: Additional input for the model. + + Returns: + Tensor: The sampled sequence of discrete values. + + Raises: + ImportError: To run in verbose mode, tqdm must be installed. + """ + if not div_free == 0.0: + assert ( + self.source_distribution_p is not None + ), "Source distribution p must be specified in order to add a divergence-free term to the probability velocity." + + # Initialize the current state `x_t` with the initial state `X_0`. + time_grid = time_grid.to(device=x_init.device) + + if step_size is None: + # If step_size is None then set the t discretization to time_grid. + t_discretization = time_grid + n_steps = len(time_grid) - 1 + else: + # If step_size is float then t discretization is uniform with step size set by step_size. + t_init = time_grid[0].item() + t_final = time_grid[-1].item() + assert ( + t_final - t_init + ) > step_size, f"Time interval [time_grid[0], time_grid[-1]] must be larger than step_size. Got a time interval [{t_init}, {t_final}] and step_size {step_size}." + + n_steps = ceil((t_final - t_init) / step_size) + t_discretization = torch.tensor( + [t_init + step_size * i for i in range(n_steps)] + [t_final], + device=x_init.device, + ) + + if return_intermediates: + # get order of intermediate steps: + order = torch.argsort(time_grid) + # Compute intermediate steps to return via nearest points in t_discretization to time_grid. + time_grid = get_nearest_times( + time_grid=time_grid, t_discretization=t_discretization + ) + + x_t = x_init.clone() + steps_counter = 0 + res = [] + + if return_intermediates: + res = [x_init.clone()] + + if verbose: + if not TQDM_AVAILABLE: + raise ImportError( + "tqdm is required for verbose mode. Please install it." + ) + ctx = tqdm(total=t_final, desc=f"NFE: {steps_counter}") + else: + ctx = nullcontext() + + with ctx: + for i in range(n_steps): + t = t_discretization[i : i + 1] + h = t_discretization[i + 1 : i + 2] - t_discretization[i : i + 1] + + # Sample x_1 ~ p_1|t( \cdot |x_t) + p_1t = self.model(x=x_t, t=t.repeat(x_t.shape[0]), **model_extras) + x_1 = categorical(p_1t.to(dtype=dtype_categorical)) + + # Checks if final step + if i == n_steps - 1: + x_t = x_1 + else: + # Compute u_t(x|x_t,x_1) + scheduler_output = self.path.scheduler(t=t) + + k_t = scheduler_output.alpha_t + d_k_t = scheduler_output.d_alpha_t + + delta_1 = F.one_hot(x_1, num_classes=self.vocabulary_size).to( + k_t.dtype + ) + u = d_k_t / (1 - k_t) * delta_1 + + # Add divergence-free part + div_free_t = div_free(t) if callable(div_free) else div_free + + if div_free_t > 0: + p_0 = self.source_distribution_p[(None,) * x_t.dim()] + u = u + div_free_t * d_k_t / (k_t * (1 - k_t)) * ( + (1 - k_t) * p_0 + k_t * delta_1 + ) + + # Set u_t(x_t|x_t,x_1) = 0 + delta_t = F.one_hot(x_t, num_classes=self.vocabulary_size) + u = torch.where( + delta_t.to(dtype=torch.bool), torch.zeros_like(u), u + ) + + # Sample x_t ~ u_t( \cdot |x_t,x_1) + intensity = u.sum(dim=-1) # Assuming u_t(xt|xt,x1) := 0 + mask_jump = torch.rand( + size=x_t.shape, device=x_t.device + ) < 1 - torch.exp(-h * intensity) + + if mask_jump.sum() > 0: + x_t[mask_jump] = categorical( + u[mask_jump].to(dtype=dtype_categorical) + ) + + steps_counter += 1 + t = t + h + + if return_intermediates and (t in time_grid): + res.append(x_t.clone()) + + if verbose: + ctx.n = t.item() + ctx.refresh() + ctx.set_description(f"NFE: {steps_counter}") + + if return_intermediates: + if step_size is None: + return torch.stack(res, dim=0) + else: + return torch.stack(res, dim=0)[order] + else: + return x_t diff --git a/transfer/code/scDFM/src/flow_matching/solver/ode_solver.py b/transfer/code/scDFM/src/flow_matching/solver/ode_solver.py new file mode 100644 index 0000000000000000000000000000000000000000..60cf30268735368e91e1f4fa746f27e4b672bf6a --- /dev/null +++ b/transfer/code/scDFM/src/flow_matching/solver/ode_solver.py @@ -0,0 +1,197 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Callable, Optional, Sequence, Tuple, Union + +import torch +from torch import Tensor +from torchdiffeq import odeint + +from src.flow_matching.solver.solver import Solver +from src.flow_matching.utils import gradient, ModelWrapper + + +class ODESolver(Solver): + """A class to solve ordinary differential equations (ODEs) using a specified velocity model. + + This class utilizes a velocity field model to solve ODEs over a given time grid using numerical ode solvers. + + Args: + velocity_model (Union[ModelWrapper, Callable]): a velocity field model receiving :math:`(x,t)` and returning :math:`u_t(x)` + """ + + def __init__(self, velocity_model: Union[ModelWrapper, Callable]): + super().__init__() + self.velocity_model = velocity_model + + def sample( + self, + x_init: Tensor, + step_size: Optional[float], + method: str = "euler", + atol: float = 1e-5, + rtol: float = 1e-5, + time_grid: Tensor = torch.tensor([0.0, 1.0]), + return_intermediates: bool = False, + enable_grad: bool = False, + **model_extras, + ) -> Union[Tensor, Sequence[Tensor]]: + r"""Solve the ODE with the velocity field. + + Example: + + .. code-block:: python + + import torch + from flow_matching.utils import ModelWrapper + from flow_matching.solver import ODESolver + + class DummyModel(ModelWrapper): + def __init__(self): + super().__init__(None) + + def forward(self, x: torch.Tensor, t: torch.Tensor, **extras) -> torch.Tensor: + return torch.ones_like(x) * 3.0 * t**2 + + velocity_model = DummyModel() + solver = ODESolver(velocity_model=velocity_model) + x_init = torch.tensor([0.0, 0.0]) + step_size = 0.001 + time_grid = torch.tensor([0.0, 1.0]) + + result = solver.sample(x_init=x_init, step_size=step_size, time_grid=time_grid) + + Args: + x_init (Tensor): initial conditions (e.g., source samples :math:`X_0 \sim p`). Shape: [batch_size, ...]. + step_size (Optional[float]): The step size. Must be None for adaptive step solvers. + method (str): A method supported by torchdiffeq. Defaults to "euler". Other commonly used solvers are "dopri5", "midpoint" and "heun3". For a complete list, see torchdiffeq. + atol (float): Absolute tolerance, used for adaptive step solvers. + rtol (float): Relative tolerance, used for adaptive step solvers. + time_grid (Tensor): The process is solved in the interval [min(time_grid, max(time_grid)] and if step_size is None then time discretization is set by the time grid. May specify a descending time_grid to solve in the reverse direction. Defaults to torch.tensor([0.0, 1.0]). + return_intermediates (bool, optional): If True then return intermediate time steps according to time_grid. Defaults to False. + enable_grad (bool, optional): Whether to compute gradients during sampling. Defaults to False. + **model_extras: Additional input for the model. + + Returns: + Union[Tensor, Sequence[Tensor]]: The last timestep when return_intermediates=False, otherwise all values specified in time_grid. + """ + + time_grid = time_grid.to(x_init.device) + + def ode_func(t, x): + return self.velocity_model(x=x, t=t, **model_extras) + + ode_opts = {"step_size": step_size} if step_size is not None else {} + + with torch.set_grad_enabled(enable_grad): + # Approximate ODE solution with numerical ODE solver + sol = odeint( + ode_func, + x_init, + time_grid, + method=method, + options=ode_opts, + atol=atol, + rtol=rtol, + ) + + if return_intermediates: + return sol + else: + return sol[-1] + + def compute_likelihood( + self, + x_1: Tensor, + log_p0: Callable[[Tensor], Tensor], + step_size: Optional[float], + method: str = "euler", + atol: float = 1e-5, + rtol: float = 1e-5, + time_grid: Tensor = torch.tensor([1.0, 0.0]), + return_intermediates: bool = False, + exact_divergence: bool = False, + enable_grad: bool = False, + **model_extras, + ) -> Union[Tuple[Tensor, Tensor], Tuple[Sequence[Tensor], Tensor]]: + r"""Solve for log likelihood given a target sample at :math:`t=0`. + + Works similarly to sample, but solves the ODE in reverse to compute the log-likelihood. The velocity model must be differentiable with respect to x. + The function assumes log_p0 is the log probability of the source distribution at :math:`t=0`. + + Args: + x_1 (Tensor): target sample (e.g., samples :math:`X_1 \sim p_1`). + log_p0 (Callable[[Tensor], Tensor]): Log probability function of the source distribution. + step_size (Optional[float]): The step size. Must be None for adaptive step solvers. + method (str): A method supported by torchdiffeq. Defaults to "euler". Other commonly used solvers are "dopri5", "midpoint" and "heun3". For a complete list, see torchdiffeq. + atol (float): Absolute tolerance, used for adaptive step solvers. + rtol (float): Relative tolerance, used for adaptive step solvers. + time_grid (Tensor): If step_size is None then time discretization is set by the time grid. Must start at 1.0 and end at 0.0, otherwise the likelihood computation is not valid. Defaults to torch.tensor([1.0, 0.0]). + return_intermediates (bool, optional): If True then return intermediate time steps according to time_grid. Otherwise only return the final sample. Defaults to False. + exact_divergence (bool): Whether to compute the exact divergence or use the Hutchinson estimator. + enable_grad (bool, optional): Whether to compute gradients during sampling. Defaults to False. + **model_extras: Additional input for the model. + + Returns: + Union[Tuple[Tensor, Tensor], Tuple[Sequence[Tensor], Tensor]]: Samples at time_grid and log likelihood values of given x_1. + """ + assert ( + time_grid[0] == 1.0 and time_grid[-1] == 0.0 + ), f"Time grid must start at 1.0 and end at 0.0. Got {time_grid}" + + # Fix the random projection for the Hutchinson divergence estimator + if not exact_divergence: + z = (torch.randn_like(x_1).to(x_1.device) < 0) * 2.0 - 1.0 + + def ode_func(x, t): + return self.velocity_model(x=x, t=t, **model_extras) + + def dynamics_func(t, states): + xt = states[0] + with torch.set_grad_enabled(True): + xt.requires_grad_() + ut = ode_func(xt, t) + + if exact_divergence: + # Compute exact divergence + div = 0 + for i in range(ut.flatten(1).shape[1]): + div += gradient(ut[:, i], xt, create_graph=True)[:, i] + else: + # Compute Hutchinson divergence estimator E[z^T D_x(ut) z] + ut_dot_z = torch.einsum( + "ij,ij->i", ut.flatten(start_dim=1), z.flatten(start_dim=1) + ) + grad_ut_dot_z = gradient(ut_dot_z, xt) + div = torch.einsum( + "ij,ij->i", + grad_ut_dot_z.flatten(start_dim=1), + z.flatten(start_dim=1), + ) + + return ut.detach(), div.detach() + + y_init = (x_1, torch.zeros(x_1.shape[0], device=x_1.device)) + ode_opts = {"step_size": step_size} if step_size is not None else {} + + with torch.set_grad_enabled(enable_grad): + sol, log_det = odeint( + dynamics_func, + y_init, + time_grid, + method=method, + options=ode_opts, + atol=atol, + rtol=rtol, + ) + + x_source = sol[-1] + source_log_p = log_p0(x_source) + + if return_intermediates: + return sol, source_log_p + log_det[-1] + else: + return sol[-1], source_log_p + log_det[-1] diff --git a/transfer/code/scDFM/src/flow_matching/solver/riemannian_ode_solver.py b/transfer/code/scDFM/src/flow_matching/solver/riemannian_ode_solver.py new file mode 100644 index 0000000000000000000000000000000000000000..eaeb682e9bab49fd2625d77f6b71a19250a24d71 --- /dev/null +++ b/transfer/code/scDFM/src/flow_matching/solver/riemannian_ode_solver.py @@ -0,0 +1,261 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +import math +from typing import Callable + +import torch +from torch import Tensor + +from src.flow_matching.solver.solver import Solver +from src.flow_matching.utils import ModelWrapper +from src.flow_matching.utils.manifolds import geodesic, Manifold + +try: + from tqdm import tqdm + + TQDM_AVAILABLE = True +except ImportError: + TQDM_AVAILABLE = False + + +class RiemannianODESolver(Solver): + r"""Riemannian ODE solver + Initialize the ``RiemannianODESolver``. + + Args: + manifold (Manifold): the manifold to solve on. + velocity_model (ModelWrapper): a velocity field model receiving :math:`(x,t)` + and returning :math:`u_t(x)` which is assumed to lie on the tangent plane at `x`. + """ + + def __init__(self, manifold: Manifold, velocity_model: ModelWrapper): + super().__init__() + self.manifold = manifold + self.velocity_model = velocity_model + + def sample( + self, + x_init: Tensor, + step_size: float, + projx: bool = True, + proju: bool = True, + method: str = "euler", + time_grid: Tensor = torch.tensor([0.0, 1.0]), + return_intermediates: bool = False, + verbose: bool = False, + enable_grad: bool = False, + **model_extras, + ) -> Tensor: + r"""Solve the ODE with the `velocity_field` on the manifold. + + Args: + x_init (Tensor): initial conditions (e.g., source samples :math:`X_0 \sim p`). + step_size (float): The step size. + projx (bool): Whether to project the point onto the manifold at each step. Defaults to True. + proju (bool): Whether to project the vector field onto the tangent plane at each step. Defaults to True. + method (str): One of ["euler", "midpoint", "rk4"]. Defaults to "euler". + time_grid (Tensor, optional): The process is solved in the interval [min(time_grid, max(time_grid)] and if step_size is None then time discretization is set by the time grid. Defaults to torch.tensor([0.0,1.0]). + return_intermediates (bool, optional): If True then return intermediate time steps according to time_grid. Defaults to False. + verbose (bool, optional): Whether to print progress bars. Defaults to False. + enable_grad (bool, optional): Whether to compute gradients during sampling. Defaults to False. + **model_extras: Additional input for the model. + + Returns: + Tensor: The sampled sequence. Defaults to returning samples at :math:`t=1`. + + Raises: + ImportError: To run in verbose mode, tqdm must be installed. + """ + step_fns = { + "euler": _euler_step, + "midpoint": _midpoint_step, + "rk4": _rk4_step, + } + assert method in step_fns.keys(), f"Unknown method {method}" + step_fn = step_fns[method] + + def velocity_func(x, t): + return self.velocity_model(x=x, t=t, **model_extras) + + # --- Factor this out. + time_grid = torch.sort(time_grid.to(device=x_init.device)).values + + if step_size is None: + # If step_size is None then set the t discretization to time_grid. + t_discretization = time_grid + n_steps = len(time_grid) - 1 + else: + # If step_size is float then t discretization is uniform with step size set by step_size. + t_init = time_grid[0].item() + t_final = time_grid[-1].item() + assert ( + t_final - t_init + ) > step_size, f"Time interval [min(time_grid), max(time_grid)] must be larger than step_size. Got a time interval [{t_init}, {t_final}] and step_size {step_size}." + + n_steps = math.ceil((t_final - t_init) / step_size) + t_discretization = torch.tensor( + [step_size * i for i in range(n_steps)] + [t_final], + device=x_init.device, + ) + # --- + t0s = t_discretization[:-1] + + if verbose: + if not TQDM_AVAILABLE: + raise ImportError( + "tqdm is required for verbose mode. Please install it." + ) + t0s = tqdm(t0s) + + if return_intermediates: + xts = [] + i_ret = 0 + + with torch.set_grad_enabled(enable_grad): + xt = x_init + for t0, t1 in zip(t0s, t_discretization[1:]): + dt = t1 - t0 + xt_next = step_fn( + velocity_func, + xt, + t0, + dt, + manifold=self.manifold, + projx=projx, + proju=proju, + ) + if return_intermediates: + while ( + i_ret < len(time_grid) + and t0 <= time_grid[i_ret] + and time_grid[i_ret] <= t1 + ): + xts.append( + interp(self.manifold, xt, xt_next, t0, t1, time_grid[i_ret]) + ) + i_ret += 1 + xt = xt_next + + if return_intermediates: + return torch.stack(xts, dim=0) + else: + return xt + + +def interp(manifold, xt, xt_next, t, t_next, t_ret): + return geodesic(manifold, xt, xt_next)( + (t_ret - t) / (t_next - t).reshape(1) + ).reshape_as(xt) + + +def _euler_step( + velocity_model: Callable, + xt: Tensor, + t0: Tensor, + dt: Tensor, + manifold: Manifold, + projx: bool = True, + proju: bool = True, +) -> Tensor: + r"""Perform an Euler step on a manifold. + + Args: + velocity_model (Callable): the velocity model + xt (Tensor): tensor containing the state at time t0 + t0 (Tensor): the time at which this step is taken + dt (Tensor): the step size + manifold (Manifold): a manifold object + projx (bool, optional): whether to project the state onto the manifold. Defaults to True. + proju (bool, optional): whether to project the velocity onto the tangent plane. Defaults to True. + + Returns: + Tensor: tensor containing the state after the step + """ + velocity_fn = lambda x, t: ( + manifold.proju(x, velocity_model(x, t)) if proju else velocity_model(x, t) + ) + projx_fn = lambda x: manifold.projx(x) if projx else x + + vt = velocity_fn(xt, t0) + + xt = xt + dt * vt + + return projx_fn(xt) + + +def _midpoint_step( + velocity_model: Callable, + xt: Tensor, + t0: Tensor, + dt: Tensor, + manifold: Manifold, + projx: bool = True, + proju: bool = True, +) -> Tensor: + r"""Perform a midpoint step on a manifold. + + Args: + velocity_model (Callable): the velocity model + xt (Tensor): tensor containing the state at time t0 + t0 (Tensor): the time at which this step is taken + dt (Tensor): the step size + manifold (Manifold): a manifold object + projx (bool, optional): whether to project the state onto the manifold. Defaults to True. + proju (bool, optional): whether to project the velocity onto the tangent plane. Defaults to True. + + Returns: + Tensor: tensor containing the state after the step + """ + velocity_fn = lambda x, t: ( + manifold.proju(x, velocity_model(x, t)) if proju else velocity_model(x, t) + ) + projx_fn = lambda x: manifold.projx(x) if projx else x + + half_dt = 0.5 * dt + vt = velocity_fn(xt, t0) + x_mid = xt + half_dt * vt + x_mid = projx_fn(x_mid) + + xt = xt + dt * velocity_fn(x_mid, t0 + half_dt) + + return projx_fn(xt) + + +def _rk4_step( + velocity_model: Callable, + xt: Tensor, + t0: Tensor, + dt: Tensor, + manifold: Manifold, + projx: bool = True, + proju: bool = True, +) -> Tensor: + r"""Perform an RK4 step on a manifold. + + Args: + velocity_model (Callable): the velocity model + xt (Tensor): tensor containing the state at time t0 + t0 (Tensor): the time at which this step is taken + dt (Tensor): the step size + manifold (Manifold): a manifold object + projx (bool, optional): whether to project the state onto the manifold. Defaults to True. + proju (bool, optional): whether to project the velocity onto the tangent plane. Defaults to True. + + Returns: + Tensor: tensor containing the state after the step + """ + velocity_fn = lambda x, t: ( + manifold.proju(x, velocity_model(x, t)) if proju else velocity_model(x, t) + ) + projx_fn = lambda x: manifold.projx(x) if projx else x + + k1 = velocity_fn(xt, t0) + k2 = velocity_fn(projx_fn(xt + dt * k1 / 3), t0 + dt / 3) + k3 = velocity_fn(projx_fn(xt + dt * (k2 - k1 / 3)), t0 + dt * 2 / 3) + k4 = velocity_fn(projx_fn(xt + dt * (k1 - k2 + k3)), t0 + dt) + + return projx_fn(xt + (k1 + 3 * (k2 + k3) + k4) * dt * 0.125) diff --git a/transfer/code/scDFM/src/flow_matching/solver/solver.py b/transfer/code/scDFM/src/flow_matching/solver/solver.py new file mode 100644 index 0000000000000000000000000000000000000000..4819e1c0ff148c411648c6e248380f471efd0a6c --- /dev/null +++ b/transfer/code/scDFM/src/flow_matching/solver/solver.py @@ -0,0 +1,17 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from abc import ABC, abstractmethod + +from torch import nn, Tensor + + +class Solver(ABC, nn.Module): + """Abstract base class for solvers.""" + + @abstractmethod + def sample(self, x_0: Tensor = None) -> Tensor: + ... diff --git a/transfer/code/scDFM/src/flow_matching/solver/utils.py b/transfer/code/scDFM/src/flow_matching/solver/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..f3a34ee4b0ca4c3ef64dfa1b55cb13da1187b965 --- /dev/null +++ b/transfer/code/scDFM/src/flow_matching/solver/utils.py @@ -0,0 +1,19 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from torch import Tensor + + +def get_nearest_times(time_grid: Tensor, t_discretization: Tensor) -> Tensor: + distances = torch.cdist( + time_grid.unsqueeze(1), + t_discretization.unsqueeze(1), + compute_mode="donot_use_mm_for_euclid_dist", + ) + nearest_indices = distances.argmin(dim=1) + + return t_discretization[nearest_indices] diff --git a/transfer/code/scDFM/src/flow_matching/utils/__init__.py b/transfer/code/scDFM/src/flow_matching/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..0085c44c5e044d5a7dd0c2bde9755e9d3c18c0b1 --- /dev/null +++ b/transfer/code/scDFM/src/flow_matching/utils/__init__.py @@ -0,0 +1,17 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from .categorical_sampler import categorical +from .model_wrapper import ModelWrapper +from .utils import expand_tensor_like, gradient, unsqueeze_to_match + +__all__ = [ + "unsqueeze_to_match", + "expand_tensor_like", + "gradient", + "categorical", + "ModelWrapper", +] diff --git a/transfer/code/scDFM/src/flow_matching/utils/categorical_sampler.py b/transfer/code/scDFM/src/flow_matching/utils/categorical_sampler.py new file mode 100644 index 0000000000000000000000000000000000000000..70937af5af1da4c3c2b18a85745197e240724a96 --- /dev/null +++ b/transfer/code/scDFM/src/flow_matching/utils/categorical_sampler.py @@ -0,0 +1,23 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from torch import Tensor + + +def categorical(probs: Tensor) -> Tensor: + r"""Categorical sampler according to weights in the last dimension of ``probs`` using :func:`torch.multinomial`. + + Args: + probs (Tensor): probabilities. + + Returns: + Tensor: Samples. + """ + + return torch.multinomial(probs.flatten(0, -2), 1, replacement=True).view( + *probs.shape[:-1] + ) diff --git a/transfer/code/scDFM/src/flow_matching/utils/manifolds/__init__.py b/transfer/code/scDFM/src/flow_matching/utils/manifolds/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..1148872f29c5e42e2578eab31751abaf6def51da --- /dev/null +++ b/transfer/code/scDFM/src/flow_matching/utils/manifolds/__init__.py @@ -0,0 +1,18 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from .manifold import Euclidean, Manifold +from .sphere import Sphere +from .torus import FlatTorus +from .utils import geodesic + +__all__ = [ + "Euclidean", + "Manifold", + "Sphere", + "FlatTorus", + "geodesic", +] diff --git a/transfer/code/scDFM/src/flow_matching/utils/manifolds/manifold.py b/transfer/code/scDFM/src/flow_matching/utils/manifolds/manifold.py new file mode 100644 index 0000000000000000000000000000000000000000..52a6a1bc6ea7d83e3aeefd335edb19a842aeca21 --- /dev/null +++ b/transfer/code/scDFM/src/flow_matching/utils/manifolds/manifold.py @@ -0,0 +1,93 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +import abc + +import torch.nn as nn +from torch import Tensor + + +class Manifold(nn.Module, metaclass=abc.ABCMeta): + """A manifold class that contains projection operations and logarithm and exponential maps.""" + + @abc.abstractmethod + def expmap(self, x: Tensor, u: Tensor) -> Tensor: + r"""Computes exponential map :math:`\exp_x(u)`. + + Args: + x (Tensor): point on the manifold + u (Tensor): tangent vector at point :math:`x` + + Raises: + NotImplementedError: if not implemented + + Returns: + Tensor: transported point + """ + raise NotImplementedError + + @abc.abstractmethod + def logmap(self, x: Tensor, y: Tensor) -> Tensor: + r"""Computes logarithmic map :math:`\log_x(y)`. + + Args: + x (Tensor): point on the manifold + y (Tensor): point on the manifold + + Raises: + NotImplementedError: if not implemented + + Returns: + Tensor: tangent vector at point :math:`x` + """ + raise NotImplementedError + + @abc.abstractmethod + def projx(self, x: Tensor) -> Tensor: + """Project point :math:`x` on the manifold. + + Args: + x (Tensor): point to be projected + + Raises: + NotImplementedError: if not implemented + + Returns: + Tensor: projected point on the manifold + """ + raise NotImplementedError + + @abc.abstractmethod + def proju(self, x: Tensor, u: Tensor) -> Tensor: + """Project vector :math:`u` on a tangent space for :math:`x`. + + Args: + x (Tensor): point on the manifold + u (Tensor): vector to be projected + + Raises: + NotImplementedError: if not implemented + + Returns: + Tensor: projected tangent vector + """ + raise NotImplementedError + + +class Euclidean(Manifold): + """The Euclidean manifold.""" + + def expmap(self, x: Tensor, u: Tensor) -> Tensor: + return x + u + + def logmap(self, x: Tensor, y: Tensor) -> Tensor: + return y - x + + def projx(self, x: Tensor) -> Tensor: + return x + + def proju(self, x: Tensor, u: Tensor) -> Tensor: + return u diff --git a/transfer/code/scDFM/src/flow_matching/utils/manifolds/sphere.py b/transfer/code/scDFM/src/flow_matching/utils/manifolds/sphere.py new file mode 100644 index 0000000000000000000000000000000000000000..23122926530add5b72420f31011476c5e0c5e7ad --- /dev/null +++ b/transfer/code/scDFM/src/flow_matching/utils/manifolds/sphere.py @@ -0,0 +1,45 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from torch import Tensor + +from src.flow_matching.utils.manifolds import Manifold + + +class Sphere(Manifold): + """Represents a hyperpshere in :math:`R^D`. Isometric to the product of 1-D spheres.""" + + EPS = {torch.float32: 1e-4, torch.float64: 1e-7} + + def expmap(self, x: Tensor, u: Tensor) -> Tensor: + norm_u = u.norm(dim=-1, keepdim=True) + exp = x * torch.cos(norm_u) + u * torch.sin(norm_u) / norm_u + retr = self.projx(x + u) + cond = norm_u > self.EPS[norm_u.dtype] + + return torch.where(cond, exp, retr) + + def logmap(self, x: Tensor, y: Tensor) -> Tensor: + u = self.proju(x, y - x) + dist = self.dist(x, y, keepdim=True) + cond = dist.gt(self.EPS[x.dtype]) + result = torch.where( + cond, + u * dist / u.norm(dim=-1, keepdim=True).clamp_min(self.EPS[x.dtype]), + u, + ) + return result + + def projx(self, x: Tensor) -> Tensor: + return x / x.norm(dim=-1, keepdim=True) + + def proju(self, x: Tensor, u: Tensor) -> Tensor: + return u - (x * u).sum(dim=-1, keepdim=True) * x + + def dist(self, x: Tensor, y: Tensor, *, keepdim=False) -> Tensor: + inner = (x * y).sum(-1, keepdim=keepdim) + return torch.acos(inner) diff --git a/transfer/code/scDFM/src/flow_matching/utils/manifolds/torus.py b/transfer/code/scDFM/src/flow_matching/utils/manifolds/torus.py new file mode 100644 index 0000000000000000000000000000000000000000..8c97703b6b565e5d106b0d61def6470acb2f60de --- /dev/null +++ b/transfer/code/scDFM/src/flow_matching/utils/manifolds/torus.py @@ -0,0 +1,28 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +import math + +import torch +from torch import Tensor + +from src.flow_matching.utils.manifolds import Manifold + + +class FlatTorus(Manifold): + r"""Represents a flat torus on the :math:`[0, 2\pi]^D` subspace. Isometric to the product of 1-D spheres.""" + + def expmap(self, x: Tensor, u: Tensor) -> Tensor: + return (x + u) % (2 * math.pi) + + def logmap(self, x: Tensor, y: Tensor) -> Tensor: + return torch.atan2(torch.sin(y - x), torch.cos(y - x)) + + def projx(self, x: Tensor) -> Tensor: + return x % (2 * math.pi) + + def proju(self, x: Tensor, u: Tensor) -> Tensor: + return u diff --git a/transfer/code/scDFM/src/flow_matching/utils/manifolds/utils.py b/transfer/code/scDFM/src/flow_matching/utils/manifolds/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..af19591931b45831b70b71af02090aded937c422 --- /dev/null +++ b/transfer/code/scDFM/src/flow_matching/utils/manifolds/utils.py @@ -0,0 +1,45 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Callable + +import torch +from torch import Tensor + +from src.flow_matching.utils.manifolds import Manifold + + +def geodesic( + manifold: Manifold, start_point: Tensor, end_point: Tensor +) -> Callable[[Tensor], Tensor]: + """Generate parameterized function for geodesic curve. + + Args: + manifold (Manifold): the manifold to compute geodesic on. + start_point (Tensor): point on the manifold at :math:`t=0`. + end_point (Tensor): point on the manifold at :math:`t=1`. + + Returns: + Callable[[Tensor], Tensor]: a function that takes in :math:`t` and outputs the geodesic at time :math:`t`. + """ + + shooting_tangent_vec = manifold.logmap(start_point, end_point) + + def path(t: Tensor) -> Tensor: + """Generate parameterized function for geodesic curve. + + Args: + t (Tensor): Times at which to compute points of the geodesics. + + Returns: + Tensor: geodesic path evaluated at time t. + """ + tangent_vecs = torch.einsum("i,...k->...ik", t, shooting_tangent_vec) + points_at_time_t = manifold.expmap(start_point.unsqueeze(-2), tangent_vecs) + + return points_at_time_t + + return path diff --git a/transfer/code/scDFM/src/flow_matching/utils/model_wrapper.py b/transfer/code/scDFM/src/flow_matching/utils/model_wrapper.py new file mode 100644 index 0000000000000000000000000000000000000000..ac7d932c851b3d2194bd63ce597ef924dc819a7f --- /dev/null +++ b/transfer/code/scDFM/src/flow_matching/utils/model_wrapper.py @@ -0,0 +1,43 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from abc import ABC + +from torch import nn, Tensor + + +class ModelWrapper(ABC, nn.Module): + """ + This class is used to wrap around another model, adding custom forward pass logic. + """ + + def __init__(self, model: nn.Module): + super().__init__() + self.model = model + + def forward(self, x: Tensor, t: Tensor, **extras) -> Tensor: + r""" + This method defines how inputs should be passed through the wrapped model. + Here, we're assuming that the wrapped model takes both :math:`x` and :math:`t` as input, + along with any additional keyword arguments. + + Optional things to do here: + - check that t is in the dimensions that the model is expecting. + - add a custom forward pass logic. + - call the wrapped model. + + | given x, t + | returns the model output for input x at time t, with extra information `extra`. + + Args: + x (Tensor): input data to the model (batch_size, ...). + t (Tensor): time (batch_size). + **extras: additional information forwarded to the model, e.g., text condition. + + Returns: + Tensor: model output. + """ + return self.model(x=x, t=t, **extras) diff --git a/transfer/code/scDFM/src/flow_matching/utils/utils.py b/transfer/code/scDFM/src/flow_matching/utils/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..beb31ff27343c40955ec52513c44f75a6ab5d665 --- /dev/null +++ b/transfer/code/scDFM/src/flow_matching/utils/utils.py @@ -0,0 +1,90 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the CC-by-NC license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Optional + +import torch +from torch import Tensor + + +def unsqueeze_to_match(source: Tensor, target: Tensor, how: str = "suffix") -> Tensor: + """ + Unsqueeze the source tensor to match the dimensionality of the target tensor. + + Args: + source (Tensor): The source tensor to be unsqueezed. + target (Tensor): The target tensor to match the dimensionality of. + how (str, optional): Whether to unsqueeze the source tensor at the beginning + ("prefix") or end ("suffix"). Defaults to "suffix". + + Returns: + Tensor: The unsqueezed source tensor. + """ + assert ( + how == "prefix" or how == "suffix" + ), f"{how} is not supported, only 'prefix' and 'suffix' are supported." + + dim_diff = target.dim() - source.dim() + + for _ in range(dim_diff): + if how == "prefix": + source = source.unsqueeze(0) + elif how == "suffix": + source = source.unsqueeze(-1) + + return source + + +def expand_tensor_like(input_tensor: Tensor, expand_to: Tensor) -> Tensor: + """`input_tensor` is a 1d vector of length equal to the batch size of `expand_to`, + expand `input_tensor` to have the same shape as `expand_to` along all remaining dimensions. + + Args: + input_tensor (Tensor): (batch_size,). + expand_to (Tensor): (batch_size, ...). + + Returns: + Tensor: (batch_size, ...). + """ + assert input_tensor.ndim == 1, "Input tensor must be a 1d vector." + assert ( + input_tensor.shape[0] == expand_to.shape[0] + ), f"The first (batch_size) dimension must match. Got shape {input_tensor.shape} and {expand_to.shape}." + + dim_diff = expand_to.ndim - input_tensor.ndim + + t_expanded = input_tensor.clone() + t_expanded = t_expanded.reshape(-1, *([1] * dim_diff)) + + return t_expanded.expand_as(expand_to) + + +def gradient( + output: Tensor, + x: Tensor, + grad_outputs: Optional[Tensor] = None, + create_graph: bool = False, +) -> Tensor: + """ + Compute the gradient of the inner product of output and grad_outputs w.r.t :math:`x`. + + Args: + output (Tensor): [N, D] Output of the function. + x (Tensor): [N, d_1, d_2, ... ] input + grad_outputs (Optional[Tensor]): [N, D] Gradient of outputs, if `None`, + then will use a tensor of ones + create_graph (bool): If True, graph of the derivative will be constructed, allowing + to compute higher order derivative products. Defaults to False. + Returns: + Tensor: [N, d_1, d_2, ... ]. the gradient w.r.t x. + """ + + if grad_outputs is None: + grad_outputs = torch.ones_like(output).detach() + grad = torch.autograd.grad( + output, x, grad_outputs=grad_outputs, create_graph=create_graph + )[0] + return grad diff --git a/transfer/code/scDFM/src/loss/metrics.py b/transfer/code/scDFM/src/loss/metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..d0ba0e2e24560286bffd75ff225c9080196f1d6c --- /dev/null +++ b/transfer/code/scDFM/src/loss/metrics.py @@ -0,0 +1,328 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np +from scipy import stats +from scipy.spatial.distance import jensenshannon +from sklearn.metrics import r2_score +from sklearn.decomposition import PCA +from sklearn.neighbors import NearestNeighbors +import warnings +warnings.filterwarnings('ignore') + + +# TODO: To be implemented into flow generation +class FlowMatchingMetrics: + + def __init__(self, device='cuda'): + self.device = device + + def reconstruction_metrics(self, generated, target): + + if isinstance(generated, torch.Tensor): + generated = generated.detach().cpu().numpy() + if isinstance(target, torch.Tensor): + target = target.detach().cpu().numpy() + + mae = np.mean(np.abs(generated - target)) + mse = np.mean((generated - target) ** 2) + rmse = np.sqrt(mse) + + + pos_mask = target > 0 + zero_mask = target == 0 + + pos_mae = np.mean(np.abs(generated[pos_mask] - target[pos_mask])) if pos_mask.any() else 0.0 + zero_mae = np.mean(np.abs(generated[zero_mask] - target[zero_mask])) if zero_mask.any() else 0.0 + + + r2 = r2_score(target.flatten(), generated.flatten()) + + rel_error = np.mean(np.abs((generated - target) / (target + 1e-8))) + + return { + 'mae': mae, + 'mse': mse, + 'rmse': rmse, + 'pos_mae': pos_mae, + 'zero_mae': zero_mae, + 'r2_score': r2, + 'relative_error': rel_error + } + + def distribution_metrics(self, generated, target): + if isinstance(generated, torch.Tensor): + generated = generated.detach().cpu().numpy() + if isinstance(target, torch.Tensor): + target = target.detach().cpu().numpy() + + metrics = {} + + mean_diff = np.mean(np.abs(np.mean(generated, axis=0) - np.mean(target, axis=0))) + var_diff = np.mean(np.abs(np.var(generated, axis=0) - np.var(target, axis=0))) + + metrics['mean_difference'] = mean_diff + metrics['variance_difference'] = var_diff + + + correlations = [] + for i in range(generated.shape[1]): + if np.var(generated[:, i]) > 1e-8 and np.var(target[:, i]) > 1e-8: + corr, _ = stats.pearsonr(generated[:, i], target[:, i]) + if not np.isnan(corr): + correlations.append(corr) + + metrics['mean_correlation'] = np.mean(correlations) if correlations else 0.0 + + + kl_divs = [] + for i in range(min(generated.shape[1], 100)): + try: + bins = np.linspace( + min(generated[:, i].min(), target[:, i].min()), + max(generated[:, i].max(), target[:, i].max()), + 50 + ) + hist_gen, _ = np.histogram(generated[:, i], bins=bins, density=True) + hist_target, _ = np.histogram(target[:, i], bins=bins, density=True) + + + hist_gen = hist_gen + 1e-8 + hist_target = hist_target + 1e-8 + + + hist_gen = hist_gen / np.sum(hist_gen) + hist_target = hist_target / np.sum(hist_target) + + kl_div = stats.entropy(hist_gen, hist_target) + if not np.isnan(kl_div) and not np.isinf(kl_div): + kl_divs.append(kl_div) + except: + continue + + metrics['mean_kl_divergence'] = np.mean(kl_divs) if kl_divs else float('inf') + + js_divs = [] + for i in range(min(generated.shape[1], 100)): + try: + bins = np.linspace( + min(generated[:, i].min(), target[:, i].min()), + max(generated[:, i].max(), target[:, i].max()), + 50 + ) + hist_gen, _ = np.histogram(generated[:, i], bins=bins, density=True) + hist_target, _ = np.histogram(target[:, i], bins=bins, density=True) + + hist_gen = hist_gen + 1e-8 + hist_target = hist_target + 1e-8 + hist_gen = hist_gen / np.sum(hist_gen) + hist_target = hist_target / np.sum(hist_target) + + js_div = jensenshannon(hist_gen, hist_target) + if not np.isnan(js_div): + js_divs.append(js_div) + except: + continue + + metrics['mean_js_divergence'] = np.mean(js_divs) if js_divs else 1.0 + + return metrics + + def embedding_metrics(self, generated, target): + + if isinstance(generated, torch.Tensor): + generated = generated.detach().cpu().numpy() + if isinstance(target, torch.Tensor): + target = target.detach().cpu().numpy() + + metrics = {} + + + cosine_sims = [] + for i in range(generated.shape[0]): + sim = np.dot(generated[i], target[i]) / ( + np.linalg.norm(generated[i]) * np.linalg.norm(target[i]) + 1e-8 + ) + cosine_sims.append(sim) + + metrics['mean_cosine_similarity'] = np.mean(cosine_sims) + + + euclidean_dists = np.linalg.norm(generated - target, axis=1) + metrics['mean_euclidean_distance'] = np.mean(euclidean_dists) + + + if generated.shape[0] > 10: + k = min(5, generated.shape[0] // 2) + + nn_target = NearestNeighbors(n_neighbors=k) + nn_target.fit(target) + _, target_indices = nn_target.kneighbors(target) + + nn_generated = NearestNeighbors(n_neighbors=k) + nn_generated.fit(generated) + _, generated_indices = nn_generated.kneighbors(generated) + + preservation = 0 + for i in range(generated.shape[0]): + intersection = len(set(target_indices[i]) & set(generated_indices[i])) + preservation += intersection / k + + metrics['neighbor_preservation'] = preservation / generated.shape[0] + else: + metrics['neighbor_preservation'] = 0.0 + + return metrics + + def flow_specific_metrics(self, generated, target, flow_model=None): + + metrics = {} + + if isinstance(generated, torch.Tensor): + generated = generated.detach().cpu().numpy() + if isinstance(target, torch.Tensor): + target = target.detach().cpu().numpy() + + + pairwise_dists = [] + for i in range(min(generated.shape[0], 100)): + for j in range(i + 1, min(generated.shape[0], 100)): + dist = np.linalg.norm(generated[i] - generated[j]) + pairwise_dists.append(dist) + + metrics['generation_diversity'] = np.mean(pairwise_dists) if pairwise_dists else 0.0 + + if generated.shape[0] > 10 and target.shape[0] > 10: + + combined = np.vstack([generated, target]) + pca = PCA(n_components=min(10, combined.shape[1])) + combined_pca = pca.fit_transform(combined) + + generated_pca = combined_pca[:generated.shape[0]] + target_pca = combined_pca[generated.shape[0]:] + + + coverage = 0 + for dim in range(generated_pca.shape[1]): + gen_min, gen_max = generated_pca[:, dim].min(), generated_pca[:, dim].max() + target_min, target_max = target_pca[:, dim].min(), target_pca[:, dim].max() + + if target_max > target_min: + overlap = max(0, min(gen_max, target_max) - max(gen_min, target_min)) + coverage += overlap / (target_max - target_min) + + metrics['mode_coverage'] = coverage / generated_pca.shape[1] + else: + metrics['mode_coverage'] = 0.0 + + return metrics + + def comprehensive_evaluation(self, generated, target, embeddings_generated=None, embeddings_target=None): + + all_metrics = {} + + recon_metrics = self.reconstruction_metrics(generated, target) + all_metrics.update({f'recon_{k}': v for k, v in recon_metrics.items()}) + + + dist_metrics = self.distribution_metrics(generated, target) + all_metrics.update({f'dist_{k}': v for k, v in dist_metrics.items()}) + + flow_metrics = self.flow_specific_metrics(generated, target) + all_metrics.update({f'flow_{k}': v for k, v in flow_metrics.items()}) + + if embeddings_generated is not None and embeddings_target is not None: + emb_metrics = self.embedding_metrics(embeddings_generated, embeddings_target) + all_metrics.update({f'emb_{k}': v for k, v in emb_metrics.items()}) + + return all_metrics + +def evaluate_flow_generation(generated_data, target_data, generated_embeddings=None, target_embeddings=None, device='cuda'): + + metrics = FlowMatchingMetrics(device=device) + return metrics.comprehensive_evaluation( + generated_data, target_data, + generated_embeddings, target_embeddings + ) + +def print_metrics(metrics_dict, title="Metrics"): + + print(f"\n{'='*50}") + print(f"{title:^50}") + print(f"{'='*50}") + + + categories = { + 'Reconstruction': [k for k in metrics_dict.keys() if k.startswith('recon_')], + 'Distribution': [k for k in metrics_dict.keys() if k.startswith('dist_')], + 'Flow-specific': [k for k in metrics_dict.keys() if k.startswith('flow_')], + 'Embedding': [k for k in metrics_dict.keys() if k.startswith('emb_')] + } + + for category, keys in categories.items(): + if keys: + print(f"\n{category}:") + print("-" * 20) + for key in keys: + clean_key = key.replace(f"{category.lower().replace('-', '_')}_", "") + value = metrics_dict[key] + if isinstance(value, float): + print(f" {clean_key:<20}: {value:.6f}") + else: + print(f" {clean_key:<20}: {value}") + + print(f"{'='*50}\n") + + +def test_metrics(): + + batch_size, n_features = 100, 2000 + generated = torch.randn(batch_size, n_features) + target = torch.randn(batch_size, n_features) + + metrics = evaluate_flow_generation(generated, target) + + print_metrics(metrics, "Flow Matching Evaluation") + + return metrics + + +def pairwise_sq_dists(X, Y): + # X:[m,d], Y:[n,d] -> [m,n] + return torch.cdist(X, Y, p=2)**2 + +@torch.no_grad() +def median_sigmas(X, scales=(0.5, 1.0, 2.0, 4.0)): + Z = X + D2 = pairwise_sq_dists(Z, Z) + tri = D2[~torch.eye(D2.size(0), dtype=bool, device=D2.device)] + m = torch.median(tri).clamp_min(1e-12) + s2 = torch.tensor(scales, device=Z.device) * m + sigmas = torch.sqrt(s2) + return [float(s.item()) for s in sigmas] + +def mmd2_unbiased_multi_sigma(X, Y, sigmas): + """ + """ + m, n = X.size(0), Y.size(0) + Dxx = pairwise_sq_dists(X, X) # [m,m] + Dyy = pairwise_sq_dists(Y, Y) # [n,n] + Dxy = pairwise_sq_dists(X, Y) # [m,n] + + vals = [] + for sigma in sigmas: + beta = 1.0 / (2.0 * (sigma ** 2) + 1e-12) + Kxx = torch.exp(-beta * Dxx) + Kyy = torch.exp(-beta * Dyy) + Kxy = torch.exp(-beta * Dxy) + + + term_xx = (Kxx.sum() - Kxx.diag().sum()) / (m * (m - 1) + 1e-12) + term_yy = (Kyy.sum() - Kyy.diag().sum()) / (n * (n - 1) + 1e-12) + term_xy = Kxy.mean() # / (m*n) + vals.append(term_xx + term_yy - 2.0 * term_xy) + + return torch.stack(vals).mean() + +if __name__ == "__main__": + test_metrics() diff --git a/transfer/code/scDFM/src/models/__init__.py b/transfer/code/scDFM/src/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d185c87108adb9a0c174b4f18213d11b855ad732 --- /dev/null +++ b/transfer/code/scDFM/src/models/__init__.py @@ -0,0 +1 @@ +# Auto-created by CCFM bootstrap diff --git a/transfer/code/scDFM/src/models/instantiate_model.py b/transfer/code/scDFM/src/models/instantiate_model.py new file mode 100644 index 0000000000000000000000000000000000000000..f651aae1e82a71017143f24d23f9488959207c32 --- /dev/null +++ b/transfer/code/scDFM/src/models/instantiate_model.py @@ -0,0 +1,25 @@ +from src.models.scGPT.model import TransformerModel +from src.models.perturbation.model import Model as FlowModel +from src.models.perturbation.model import TimedTransformer +from src.models.origin.model import model as OriginModel +import torch + +def instantiate_model(model_type: str, **kwargs): + + if model_type == 'origin': + if kwargs['fusion_method'] == 'differential_transformer': + layers = 8 + elif kwargs['fusion_method'] == 'differential_perceiver': + layers = 4 + else: + layers = 8 + return OriginModel(fusion_method=kwargs['fusion_method'], nlayers=layers, perturbation_function=kwargs['perturbation_function'],mask_path=kwargs['mask_path']) + else: + raise ValueError(f"Invalid model type: {model_type}") + +if __name__ == "__main__": + model = instantiate_model("punet128") + x = torch.randn(32, 128, 128) + t = torch.randn(32) + out = model( x,t) + print(out.shape) \ No newline at end of file diff --git a/transfer/code/scDFM/src/models/origin/__init__.py b/transfer/code/scDFM/src/models/origin/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d185c87108adb9a0c174b4f18213d11b855ad732 --- /dev/null +++ b/transfer/code/scDFM/src/models/origin/__init__.py @@ -0,0 +1 @@ +# Auto-created by CCFM bootstrap diff --git a/transfer/code/scDFM/src/models/origin/blocks.py b/transfer/code/scDFM/src/models/origin/blocks.py new file mode 100644 index 0000000000000000000000000000000000000000..4281ad0f0fc1833779893fa926d79ef0d0ff0212 --- /dev/null +++ b/transfer/code/scDFM/src/models/origin/blocks.py @@ -0,0 +1,354 @@ +import pdb +import torch +import torch.nn as nn +from torch import Tensor +from typing import Optional, Dict, Tuple +import math +from .rotary import apply_rotary_emb + +def lambda_init_fn(depth): + return 0.8 - 0.6 * math.exp(-0.3 * depth) + +def repeat_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor: + """torch.repeat_interleave(x, dim=1, repeats=n_rep)""" + bs, n_kv_heads, slen, head_dim = x.shape + if n_rep == 1: + return x + return ( + x[:, :, None, :, :] + .expand(bs, n_kv_heads, n_rep, slen, head_dim) + .reshape(bs, n_kv_heads * n_rep, slen, head_dim) + ) + +def modulate(x, shift, scale): + return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + +class RMSNorm(nn.Module): + def __init__(self, dim: int, eps: float = 1e-6, elementwise_affine=True, memory_efficient=False): + super().__init__() + self.dim = dim + self.eps = eps + self.elementwise_affine = elementwise_affine + if self.elementwise_affine: + self.weight = nn.Parameter(torch.ones(dim)) + else: + self.register_parameter('weight', None) + + def _norm(self, x): + return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) + + def forward(self, x): + output = self._norm(x.float()).type_as(x) + if self.weight is not None: + output = output * self.weight + return output + + def extra_repr(self) -> str: + return f'dim={self.dim}, eps={self.eps}, elementwise_affine={self.elementwise_affine}' + + +class MultiheadDiffAttn_Origin(nn.Module): + def __init__( + self, + embed_dim, + depth, # current layer index + num_heads, + num_kv_heads=None, + ): + super().__init__() + self.embed_dim = embed_dim + + # arg num_heads set to half of baseline Transformer's num_heads + # for e.g., to compare with a baseline Transformer with 16 heads, pass in num_heads=8 for DIFF Transformer + self.num_heads = num_heads + + # arg num_kv_heads set to half of baseline Transformer's num_kv_heads if use GQA + # for e.g., to compare with a baseline Transformer with 16 heads and 8 kv_heads, + # pass in num_heads=8, num_kv_heads=4 for DIFF Transformer + # if use MHA, pass in num_kv_heads=None + self.num_kv_heads = num_kv_heads if num_kv_heads is not None else num_heads + self.n_rep = self.num_heads // self.num_kv_heads + + self.head_dim = embed_dim // num_heads // 2 + self.scaling = self.head_dim ** -0.5 + + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=False) + self.k_proj = nn.Linear(embed_dim, embed_dim // self.n_rep, bias=False) + self.v_proj = nn.Linear(embed_dim, embed_dim // self.n_rep, bias=False) + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=False) + + # depth means current layer index + self.lambda_init = lambda_init_fn(depth) + self.lambda_q1 = nn.Parameter(torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0,std=0.1)) + self.lambda_k1 = nn.Parameter(torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0,std=0.1)) + self.lambda_q2 = nn.Parameter(torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0,std=0.1)) + self.lambda_k2 = nn.Parameter(torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0,std=0.1)) + + self.subln = RMSNorm(2 * self.head_dim, eps=1e-5, elementwise_affine=True) + + def forward( + self, + x, + rel_pos, + attn_mask=None, + ): + bsz, tgt_len, embed_dim = x.size() + src_len = tgt_len + + q = self.q_proj(x) + k = self.k_proj(x) + v = self.v_proj(x) + + q = q.view(bsz, tgt_len, 2 * self.num_heads, self.head_dim) + k = k.view(bsz, src_len, 2 * self.num_kv_heads, self.head_dim) + v = v.view(bsz, src_len, self.num_kv_heads, 2 * self.head_dim) + + q = apply_rotary_emb(q, *rel_pos, interleaved=True) + k = apply_rotary_emb(k, *rel_pos, interleaved=True) + + offset = src_len - tgt_len + q = q.transpose(1, 2) + k = repeat_kv(k.transpose(1, 2), self.n_rep) + v = repeat_kv(v.transpose(1, 2), self.n_rep) + q *= self.scaling + attn_weights = torch.matmul(q, k.transpose(-1, -2)) + attn_weights = torch.nan_to_num(attn_weights) + attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).type_as( + attn_weights + ) + + lambda_1 = torch.exp(torch.sum(self.lambda_q1 * self.lambda_k1, dim=-1).float()).type_as(q) + lambda_2 = torch.exp(torch.sum(self.lambda_q2 * self.lambda_k2, dim=-1).float()).type_as(q) + lambda_full = lambda_1 - lambda_2 + self.lambda_init + attn_weights = attn_weights.view(bsz, self.num_heads, 2, tgt_len, src_len) + attn_weights = attn_weights[:, :, 0] - lambda_full * attn_weights[:, :, 1] + + attn = torch.matmul(attn_weights, v) + attn = self.subln(attn) + attn = attn * (1 - self.lambda_init) + attn = attn.transpose(1, 2).reshape(bsz, tgt_len, self.num_heads * 2 * self.head_dim) + + attn = self.out_proj(attn) + return attn + +class MultiheadDiffAttn(nn.Module): + def __init__( + self, + embed_dim, + num_heads, + depth, # current layer index + cross=False, + ): + super().__init__() + self.embed_dim = embed_dim + self.cross = cross + # The code `pdb.set_trace()` is setting a breakpoint in the code using the Python debugger + # (pdb). When the code is executed, it will pause the program's execution at that point and + # allow you to interactively debug the program. + # pdb.set_trace() + self.num_heads = num_heads + self.head_dim = embed_dim // num_heads + self.scaling = self.head_dim ** -0.5 + + self.q_proj_1 = nn.Linear(embed_dim, embed_dim, bias=False) + self.k_proj_1 = nn.Linear(embed_dim, embed_dim, bias=False) + self.q_proj_2 = nn.Linear(embed_dim, embed_dim, bias=False) + self.k_proj_2 = nn.Linear(embed_dim, embed_dim, bias=False) + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=False) + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=False) + + # depth means current layer index + self.lambda_init = lambda_init_fn(depth) + self.lambda_q1 = nn.Parameter(torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0,std=0.1)) + self.lambda_k1 = nn.Parameter(torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0,std=0.1)) + self.lambda_q2 = nn.Parameter(torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0,std=0.1)) + self.lambda_k2 = nn.Parameter(torch.zeros(self.head_dim, dtype=torch.float32).normal_(mean=0,std=0.1)) + + self.subln = RMSNorm(self.head_dim, eps=1e-5, elementwise_affine=True) + + def forward( + self, + noisy_y, + x, + ): + bsz, tgt_len, embed_dim = x.size() + src_len = tgt_len + + if self.cross: + q_1 = self.q_proj_1(noisy_y) + k_1 = self.k_proj_1(x) + q_2 = self.q_proj_2(noisy_y) + k_2 = self.k_proj_2(x) + else: + q_1 = self.q_proj_1(noisy_y) + k_1 = self.k_proj_1(noisy_y) + q_2 = self.q_proj_2(x) + k_2 = self.k_proj_2(x) + v = self.v_proj(noisy_y) + + q_1 = q_1.view(bsz, tgt_len, self.num_heads, self.head_dim) + k_1 = k_1.view(bsz, src_len, self.num_heads, self.head_dim) + q_2 = q_2.view(bsz, tgt_len, self.num_heads, self.head_dim) + k_2 = k_2.view(bsz, src_len, self.num_heads, self.head_dim) + + v = v.view(bsz, src_len, self.num_heads, self.head_dim) + + + q_1 = q_1.transpose(1, 2) + q_2 = q_2.transpose(1, 2) + k_1 = k_1.transpose(1, 2) + k_2 = k_2.transpose(1, 2) + q_1 *= self.scaling + q_2 *= self.scaling + + attn_weights_1 = torch.matmul(q_1, k_1.transpose(-1, -2)) + attn_weights_2 = torch.matmul(q_2, k_2.transpose(-1, -2)) + + with torch.amp.autocast('cuda', enabled=False): + attn_weights_1 = torch.nn.functional.softmax(attn_weights_1, dim=-1) + attn_weights_2 = torch.nn.functional.softmax(attn_weights_2, dim=-1) + + lambda_1 = torch.exp(torch.sum(self.lambda_q1 * self.lambda_k1, dim=-1).float()).type_as(q_1) + lambda_2 = torch.exp(torch.sum(self.lambda_q2 * self.lambda_k2, dim=-1).float()).type_as(q_1) + + lambda_full = lambda_1 - lambda_2 + self.lambda_init + + # Fuse differential subtraction with value projection via distributive law: + # (A - λB) @ v = A@v - λ * (B@v) + # Avoids materializing the combined (B, H, N, N) attention matrix. + vt = v.transpose(1, 2) + attn = torch.matmul(attn_weights_1, vt) - lambda_full * torch.matmul(attn_weights_2, vt) + # attn: (bsz, num_heads, tgt_len, head_dim) + attn = self.subln(attn) + attn = attn * (1 - self.lambda_init) + attn = attn.transpose(1, 2).reshape(bsz, tgt_len, self.num_heads * self.head_dim) + + attn = self.out_proj(attn) + return attn + + + +class CrossAttentionTransformerLayer(nn.Module): + """ + One-layer Transformer block with ONLY cross-attention: + - Pre-norm -> Multihead cross-attention (Q=tgt, K/V=memory) + - Residual + - Pre-norm -> FeedForward (MLP) + - Residual + + Shapes (batch_first=True): + tgt: (B, T_tgt, d_model) + memory: (B, T_mem, d_model) + """ + def __init__( + self, + d_model: int, + nhead: int, + mlp_ratio: int = 4, + dropout: float = 0.1, + activation: str = "gelu", + batch_first: bool = True + ): + super().__init__() + self.batch_first = batch_first + + # Cross attention: Q from tgt, K/V from memory + self.cross_attn = nn.MultiheadAttention( + embed_dim=d_model, num_heads=nhead, dropout=dropout, batch_first=batch_first + ) + self.cross_attn_dropout = nn.Dropout(dropout) + self.norm1 = nn.LayerNorm(d_model) + + # Feedforward + self.linear1 = nn.Linear(d_model, int(mlp_ratio * d_model)) + self.linear2 = nn.Linear(int(mlp_ratio * d_model), d_model) + self.ffn_dropout = nn.Dropout(dropout) + self.norm2 = nn.LayerNorm(d_model) + + if activation == "relu": + self.activation = nn.ReLU() + elif activation == "gelu": + self.activation = nn.GELU() + else: + raise ValueError(f"Unsupported activation: {activation}") + + def forward( + self, + tgt: torch.Tensor, + memory: torch.Tensor, + attn_mask: Optional[torch.Tensor] = None, + memory_key_padding_mask: Optional[torch.Tensor] = None, + need_weights: bool = False, + average_attn_weights: bool = True, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: + """ + Args: + tgt: (B, T_tgt, d_model) + memory: (B, T_mem, d_model) + attn_mask: optional mask applied to (tgt_len, mem_len) or (B*nhead, tgt_len, mem_len) + memory_key_padding_mask: (B, T_mem) with True for positions to mask + need_weights: return attention weights + average_attn_weights: average heads if True + + Returns: + out: (B, T_tgt, d_model) + attn: (B, T_tgt, T_mem) or (B, nhead, T_tgt, T_mem) if need_weights else None + """ + # --- Cross Attention (pre-norm) --- + x = self.norm1(tgt) + attn_out, attn_w = self.cross_attn( + query=x, + key=memory, + value=memory, + attn_mask=attn_mask, + key_padding_mask=memory_key_padding_mask, + need_weights=need_weights, + average_attn_weights=average_attn_weights, + ) + x = tgt + self.cross_attn_dropout(attn_out) + + # --- Feedforward (pre-norm) --- + y = self.norm2(x) + y = self.linear2(self.ffn_dropout(self.activation(self.linear1(y)))) + out = x + self.ffn_dropout(y) + + return out, attn_w if need_weights else None +# class PerceiverDiffTransBlock(nn.Module): +# def __init__(self, d_in, d_latent, heads=8, mlp_ratio=4): +# super().__init__() + +# self.ln_z1 = nn.LayerNorm(d_latent) +# self.q = nn.Linear(d_latent, d_latent) +# self.k = nn.Linear(d_in, d_latent) +# self.v = nn.Linear(d_in, d_latent) + +# self.q2 = nn.Linear(d_latent, d_latent) +# self.k2 = nn.Linear(d_latent, d_latent) +# self.v2 = nn.Linear(d_latent, d_latent) +# self.cross = nn.MultiheadAttention(d_latent, heads, dropout=0.1, batch_first=True) + +# self.ln_z2 = nn.LayerNorm(d_latent) +# self.self_attn = nn.MultiheadAttention(d_latent, heads, dropout=0.1, batch_first=True) +# self.ln_z3 = nn.LayerNorm(d_latent) +# self.mlp = nn.Sequential( +# nn.Linear(d_latent, int(mlp_ratio * d_latent)), nn.GELU(), +# nn.Dropout(0.1), +# nn.Linear(int(mlp_ratio * d_latent), d_latent), +# nn.Dropout(0.1), +# ) +# self.adaLN_modulation = nn.Sequential( +# nn.SiLU(), +# nn.Linear(d_latent, 6 * d_latent, bias=True) +# ) + + +# def forward(self, z, x, t): +# shift_self, scale_self, gate_self, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(t).chunk(6, dim=1) +# z = z + self.cross(self.q(self.ln_z1(z)), +# self.k(x), self.v(x))[0] + +# z = modulate(self.ln_z2(z), shift_self, scale_self) +# z = z + gate_self.unsqueeze(1) * self.self_attn(self.q2(z), self.k2(z), self.v2(z))[0] + +# z = z + gate_mlp.unsqueeze(1) * self.mlp(modulate(self.ln_z3(z), shift_mlp, scale_mlp)) +# return z \ No newline at end of file diff --git a/transfer/code/scDFM/src/models/origin/layers.py b/transfer/code/scDFM/src/models/origin/layers.py new file mode 100644 index 0000000000000000000000000000000000000000..23af1b23d78d2ae17a5919dca93e6d8408feab03 --- /dev/null +++ b/transfer/code/scDFM/src/models/origin/layers.py @@ -0,0 +1,194 @@ +import pdb +from re import X +import torch +import torch.nn as nn +from torch import Tensor +from typing import Optional, Dict +import math +from .blocks import CrossAttentionTransformerLayer +import copy +class GeneadaLN(nn.Module): + def __init__(self, hidden_size: int, dropout: float = 0.1): + super().__init__() + + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(hidden_size, 3 * hidden_size, bias=True) + ) + self.norm = nn.LayerNorm(hidden_size) + + def forward(self, gene_emb: Tensor, value_emb: Tensor) -> Tensor: + shift, gate, scale = self.adaLN_modulation(gene_emb).chunk(3, dim=-1) + + x = value_emb + gate * (self.norm(value_emb) * scale + shift ) + return x + +class ContinuousValueEncoder(nn.Module): + """ + Encode real number values to a vector using neural nets projection. + """ + + def __init__(self, d_model: int, dropout: float = 0.1, max_value: int = 512): + super().__init__() + self.dropout = nn.Dropout(p=dropout) + self.linear1 = nn.Linear(1, d_model) + self.activation = nn.ReLU() + self.linear2 = nn.Linear(d_model, d_model) + self.norm = nn.LayerNorm(d_model) + self.max_value = max_value + + def forward(self, x: Tensor) -> Tensor: + """ + Args: + x: Tensor, shape [batch_size, seq_len] + """ + # TODO: test using actual embedding layer if input is categorical + # expand last dimension + x = x.unsqueeze(-1) + # clip x to [-inf, max_value] + x = torch.clamp(x, max=self.max_value) + x = self.activation(self.linear1(x)) + x = self.linear2(x) + x = self.norm(x) + return self.dropout(x) + +class GeneEncoder(nn.Module): + def __init__( + self, + num_embeddings: int, + embedding_dim: int, + padding_idx: Optional[int] = None, + nhead:int = 8 , + use_perturbation_interaction: bool = False, + dropout:float = 0.1, + mask_path: str = None, + + ): + super().__init__() + self.embedding = nn.Embedding( + num_embeddings, embedding_dim, padding_idx=padding_idx + ) + self.enc_norm = nn.LayerNorm(embedding_dim) + self.use_perturbation_interaction = use_perturbation_interaction + if use_perturbation_interaction: + self.data_name = mask_path.split('/')[-2] + self.perturbation_interaction = CrossAttentionTransformerLayer(embedding_dim, nhead, mlp_ratio=4.0, dropout=dropout) + self.mask_padded = torch.load(mask_path) + self.mask_num = self.mask_padded.shape[0] + def forward(self, x: Tensor) -> Tensor: + if self.use_perturbation_interaction: + # NOTE using the same perturbation and gene names + if self.mask_padded.device != x.device: + + self.mask_padded = self.mask_padded.to(x.device) + + mask = self.mask_padded[x[0]] + + x = self.embedding(x) # (batch, seq_len, embsize) + + x = self.enc_norm(x) + if self.use_perturbation_interaction: + + memory_id = torch.arange(self.mask_num,device=x.device) + memory_emb = self.embedding(memory_id) + memory_emb = self.enc_norm(memory_emb).expand(x.shape[0],-1,-1) + x = self.perturbation_interaction(x, memory_emb,mask)[0] + return x + +class BatchLabelEncoder(nn.Module): + def __init__( + self, + num_embeddings: int, + embedding_dim: int, + padding_idx: Optional[int] = None, + ): + super().__init__() + self.embedding = nn.Embedding( + num_embeddings, embedding_dim, padding_idx=padding_idx + ) + self.enc_norm = nn.LayerNorm(embedding_dim) + + def forward(self, x: Tensor) -> Tensor: + x = self.embedding(x) # (batch, embsize) + x = self.enc_norm(x) + return x + +class ExprDecoder(nn.Module): + def __init__( + self, + d_model: int, + explicit_zero_prob: bool = False, + use_batch_labels: bool = False, + ): + super().__init__() + d_in = d_model * 2 if use_batch_labels else d_model + self.fc = nn.Sequential( + nn.Linear(d_in, d_model), + nn.LeakyReLU(), + nn.Linear(d_model, d_model), + nn.LeakyReLU(), + nn.Linear(d_model, 1), + ) + self.explicit_zero_prob = explicit_zero_prob + if explicit_zero_prob: + self.zero_logit = nn.Sequential( + nn.Linear(d_in, d_model), + nn.LeakyReLU(), + nn.Linear(d_model, d_model), + nn.LeakyReLU(), + nn.Linear(d_model, 1), + ) + + def forward(self, x: Tensor) -> Dict[str, Tensor]: + """x is the output of the transformer, (batch, seq_len, d_model)""" + pred_value = self.fc(x).squeeze(-1) # (batch, seq_len) + + if not self.explicit_zero_prob: + return dict(pred=pred_value) + zero_logits = self.zero_logit(x).squeeze(-1) # (batch, seq_len) + zero_probs = torch.sigmoid(zero_logits) + return dict(pred=pred_value, zero_probs=zero_probs) + # TODO: note that the return currently is only for training. Since decoder + # is not used in the test setting for the integration task, the eval/inference + # logic is not implemented yet. However, remember to implement it when + # the decoder is used in any test setting. The inference logic will need + # to sample from the bernoulli distribution with the zero_probs. + +class TimestepEmbedder(nn.Module): + """ + Embeds scalar timesteps into vector representations. + """ + def __init__(self, hidden_size, frequency_embedding_size=256): + super().__init__() + self.mlp = nn.Sequential( + nn.Linear(frequency_embedding_size, hidden_size, bias=True), + nn.SiLU(), + nn.Linear(hidden_size, hidden_size, bias=True), + ) + self.frequency_embedding_size = frequency_embedding_size + + @staticmethod + def timestep_embedding(t, dim, max_period=10000): + """ + Create sinusoidal timestep embeddings. + :param t: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an (N, D) Tensor of positional embeddings. + """ + # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py + half = dim // 2 + freqs = torch.exp( + -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half + ).to(device=t.device) + args = t[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + return embedding + + def forward(self, t): + t_freq = self.timestep_embedding(t, self.frequency_embedding_size) + t_emb = self.mlp(t_freq) + return t_emb \ No newline at end of file diff --git a/transfer/code/scDFM/src/models/origin/model.py b/transfer/code/scDFM/src/models/origin/model.py new file mode 100644 index 0000000000000000000000000000000000000000..a51d87851f25f6cfd424e4365a12dca522c9e046 --- /dev/null +++ b/transfer/code/scDFM/src/models/origin/model.py @@ -0,0 +1,255 @@ +import torch +import torch.nn as nn +from torch import Tensor +from typing import Optional, Dict +import math +from timm.models.vision_transformer import PatchEmbed, Attention, Mlp + +import pdb + +from .layers import GeneadaLN, ContinuousValueEncoder, GeneEncoder, BatchLabelEncoder, TimestepEmbedder, ExprDecoder +from .blocks import MultiheadDiffAttn, modulate , CrossAttentionTransformerLayer +class Block(nn.Module): + """ + A DiT block with adaptive layer norm zero (adaLN-Zero) conditioning. + """ + def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, **block_kwargs): + super().__init__() + self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.attn = Attention(hidden_size, num_heads=num_heads, qkv_bias=True, **block_kwargs) + self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + mlp_hidden_dim = int(hidden_size * mlp_ratio) + approx_gelu = lambda: nn.GELU(approximate="tanh") + self.mlp = Mlp(in_features=hidden_size, hidden_features=mlp_hidden_dim, act_layer=approx_gelu, drop=0) + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(hidden_size, 6 * hidden_size, bias=True) + ) + + def forward(self, x, c): + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(6, dim=1) + x = x + gate_msa.unsqueeze(1) * self.attn(modulate(self.norm1(x), shift_msa, scale_msa)) + x = x + gate_mlp.unsqueeze(1) * self.mlp(modulate(self.norm2(x), shift_mlp, scale_mlp)) + return x + +class DifferentialTransformerBlock(nn.Module): + def __init__(self, hidden_size, num_heads, depth, mlp_ratio=4.0, cross=False, **block_kwargs): + super().__init__() + self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.attn = MultiheadDiffAttn(hidden_size, num_heads, depth, cross=cross) + self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + mlp_hidden_dim = int(hidden_size * mlp_ratio) + approx_gelu = lambda: nn.GELU(approximate="tanh") + self.mlp = Mlp(in_features=hidden_size, hidden_features=mlp_hidden_dim, act_layer=approx_gelu, drop=0) + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(hidden_size, 6 * hidden_size, bias=True) + ) + + def forward(self, y, x, c): + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(6, dim=1) + y = y + gate_msa.unsqueeze(1) * self.attn(modulate(self.norm1(y), shift_msa, scale_msa), x) + y = y + gate_mlp.unsqueeze(1) * self.mlp(modulate(self.norm2(y), shift_mlp, scale_mlp)) + return y + +class PerceiverBlock(nn.Module): + def __init__(self, d_in, d_latent, heads=8, mlp_ratio=4, dropout=0.0): + super().__init__() + self.ln_z1 = nn.LayerNorm(d_latent) + self.q = nn.Linear(d_latent, d_latent) + self.k = nn.Linear(d_in, d_latent) + self.v = nn.Linear(d_in, d_latent) + + self.q2 = nn.Linear(d_latent, d_latent) + self.k2 = nn.Linear(d_latent, d_latent) + self.v2 = nn.Linear(d_latent, d_latent) + self.cross = nn.MultiheadAttention(d_latent, heads, dropout=dropout, batch_first=True) + + self.ln_z2 = nn.LayerNorm(d_latent) + self.self_attn = nn.MultiheadAttention(d_latent, heads, dropout=dropout, batch_first=True) + self.ln_z3 = nn.LayerNorm(d_latent) + self.mlp = nn.Sequential( + nn.Linear(d_latent, int(mlp_ratio * d_latent)), nn.GELU(), + nn.Linear(int(mlp_ratio * d_latent), d_latent) + ) + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(d_latent, 6 * d_latent, bias=True) + ) + + + def forward(self, z, x, t): + shift_self, scale_self, gate_self, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(t).chunk(6, dim=1) + z = z + self.cross(self.q(self.ln_z1(z)), + self.k(x), self.v(x))[0] + + z = modulate(self.ln_z2(z), shift_self, scale_self) + z = z + gate_self.unsqueeze(1) * self.self_attn(self.q2(z), self.k2(z), self.v2(z))[0] + + z = z + gate_mlp.unsqueeze(1) * self.mlp(modulate(self.ln_z3(z), shift_mlp, scale_mlp)) + return z + +class DiffPerceiverBlock(nn.Module): + def __init__(self,hidden_size, num_heads, depth, mlp_ratio=4.0): + super().__init__() + self.diff_self_attn = DifferentialTransformerBlock(hidden_size, num_heads, depth, mlp_ratio=mlp_ratio,cross=True) + self.diff_cross_attn = DifferentialTransformerBlock(hidden_size, num_heads, depth, mlp_ratio=mlp_ratio,cross=False) + + def forward(self, y, x, c): + y = self.diff_self_attn(y, y, c) + y = self.diff_cross_attn(y, x, c) + return y + +class model(nn.Module): + def __init__(self, + ntoken: int = 6000, + d_model: int = 512, + nhead: int = 8, + d_hid: int = 2048, + nlayers: int = 8, # 8 + dropout: float = 0.1, + fusion_method: str = 'cross', # concat, add, cross + perturbation_function: str = 'crisper', + use_perturbation_interaction: bool = True, + mask_path: str = None, + ): + super().__init__() + self.t_embedder = TimestepEmbedder(d_model) + self.perturbation_embedder = BatchLabelEncoder(ntoken, d_model,) + self.fusion_method = fusion_method + self.perturbation_function = perturbation_function + self.fusion_layer = nn.Sequential(nn.Linear(2*d_model, d_model), + nn.GELU(), + nn.Linear(d_model, d_model), + nn.LayerNorm(d_model)) + self.value_encoder_1 = ContinuousValueEncoder(d_model, dropout) + self.value_encoder_2 = ContinuousValueEncoder(d_model, dropout) + self.encoder = GeneEncoder(ntoken, d_model,use_perturbation_interaction=use_perturbation_interaction,mask_path=mask_path) + self.use_perturbation_interaction = use_perturbation_interaction + # if use_perturbation_interaction: + # self.perturbation_interaction = CrossAttentionTransformerLayer(d_model, nhead, mlp_ratio=4.0, dropout=dropout) + + if self.fusion_method == 'differential_transformer': + self.blocks = nn.ModuleList([ + DifferentialTransformerBlock(d_model, nhead, i, mlp_ratio=4.0) for i in range(nlayers) + ]) + elif self.fusion_method == 'differential_perceiver': + self.blocks = nn.ModuleList([ + DiffPerceiverBlock(d_model, nhead, i, mlp_ratio=4.0) for i in range(nlayers) + ]) + elif self.fusion_method == 'perceiver': + self.blocks = nn.ModuleList([ + PerceiverBlock(d_model, d_model, heads=nhead, mlp_ratio=4.0, dropout=0.1) for _ in range(nlayers) + ]) + else: + raise ValueError(f"Invalid fusion method: {self.fusion_method}") + + + self.gene_adaLN = nn.ModuleList([ + GeneadaLN(d_model, dropout) for _ in range(nlayers) + ]) + self.adapter_layer = nn.ModuleList([ + nn.Sequential( + nn.Linear(2*d_model, d_model), + nn.LeakyReLU(), + nn.Dropout(dropout), + nn.Linear(d_model, d_model), + nn.LeakyReLU()) for _ in range(nlayers) + ]) + + # predict_p task with embedding prediction + self.p_mask_embed = nn.Parameter(torch.randn(d_model)) # (d_model,) + self.p_head = nn.Sequential( + nn.LayerNorm(d_model), + nn.Linear(d_model, d_model) + ) + + self.final_layer = ExprDecoder(d_model, explicit_zero_prob=False, use_batch_labels=True) + self.initialize_weights() + + def initialize_weights(self): + # Initialize transformer layers: + def _basic_init(module): + if isinstance(module, nn.Linear): + torch.nn.init.xavier_uniform_(module.weight) + if module.bias is not None: + nn.init.constant_(module.bias, 0) + self.apply(_basic_init) + + + def get_perturbation_emb(self, perturbation_id=None, perturbation_emb=None, + cell_1=None, use_mask: bool=False): + if use_mask: + B = cell_1.size(0) + return self.p_mask_embed[None, :].expand(B, -1).to(cell_1.device, dtype=cell_1.dtype) + + assert perturbation_emb is None or perturbation_id is None + if perturbation_id is not None: + if self.perturbation_function == 'crisper': + perturbation_emb = self.encoder(perturbation_id) + + else: + perturbation_emb = self.perturbation_embedder(perturbation_id) + perturbation_emb = perturbation_emb.mean(1) # (B,d) + elif perturbation_emb is not None: + perturbation_emb = perturbation_emb.to(cell_1.device, dtype=cell_1.dtype) + if perturbation_emb.dim() == 1: + perturbation_emb = perturbation_emb.unsqueeze(0) + if perturbation_emb.size(0) == 1: + perturbation_emb = perturbation_emb.expand(cell_1.shape[0], -1).contiguous() + perturbation_emb = self.perturbation_embedder.enc_norm(perturbation_emb) + + return perturbation_emb + + def forward(self,gene_id, cell_1, t, cell_2, perturbation_id=None, gene_id_all=None, perturbation_emb=None, mode="predict_y"): + if t.dim() == 0: + t = t.repeat(cell_1.size(0)) + + gene_emb = self.encoder(gene_id) + # gene_emb_all = self.encoder(gene_id_all) + gene_emb_all = gene_emb + value_emb_1 = self.value_encoder_1(cell_1) + value_emb_2 = self.value_encoder_2(cell_2) + + value_emb_1 = value_emb_1 + gene_emb + value_emb_2 = value_emb_2 + gene_emb_all + + value_emb = torch.cat([value_emb_1, value_emb_2], dim=-1) + value_emb = self.fusion_layer(value_emb) + + t_emb = self.t_embedder(t) + + x = value_emb + + perturbation_emb = self.get_perturbation_emb(perturbation_id, perturbation_emb, cell_1) + + for i,block in enumerate(self.blocks): + x = self.gene_adaLN[i](gene_emb, x) + perturbation_exp = perturbation_emb[:, None, :].expand(-1, x.size(1), -1) # (B, T, emb) + x = torch.cat([x, perturbation_exp], dim=-1) + x = self.adapter_layer[i](x) + x = block(x, value_emb_2, t_emb) + + + if mode=="predict_p": + x_pooling = x.mean(dim=1) + return self.p_head(x_pooling) + + x = torch.cat([x, perturbation_emb[:, None, :].expand(-1, x.size(1), -1)], dim=-1) + x = self.final_layer(x) + + return x['pred'] + + + +if __name__ == "__main__": + model = model(ntoken=100, d_model=128, nhead=8, d_hid=128, nlayers=12) + batch_size = 10 + gene_id = torch.randint(0, 100, (batch_size, 10)) + x = torch.randn(batch_size, 10) + t = torch.randint(0, 100, (batch_size,)) + y = torch.randn(batch_size, 10) + perturbation = torch.randint(0, 100, (batch_size,2)) + + output = model(gene_id, x, t, y, perturbation) + pdb.set_trace() \ No newline at end of file diff --git a/transfer/code/scDFM/src/models/origin/rotary.py b/transfer/code/scDFM/src/models/origin/rotary.py new file mode 100644 index 0000000000000000000000000000000000000000..1f4e6fb77dfb37bc431c8de63798e42cf2510b9b --- /dev/null +++ b/transfer/code/scDFM/src/models/origin/rotary.py @@ -0,0 +1,333 @@ +# Copyright (c) 2023, Tri Dao. +# Adapted from Diff-Transformer codebase + +from typing import Optional, Union + +import torch + +import triton +import triton.language as tl + + +# @triton.autotune( +# configs=[ +# triton.Config({"BLOCK_M": 2}), +# triton.Config({"BLOCK_M": 4}), +# triton.Config({"BLOCK_M": 8}), +# triton.Config({"BLOCK_M": 16}), +# ], +# key=["CACHE_KEY_SEQLEN", "BLOCK_K", "INTERLEAVED"], +# ) +@triton.jit +def rotary_kernel( + OUT, # Pointers to matrices + X, + COS, + SIN, + CU_SEQLENS, + SEQLEN_OFFSETS, # this could be int or a pointer + # Matrix dimensions + seqlen, + nheads, + rotary_dim, + seqlen_ro, + CACHE_KEY_SEQLEN, + # strides + stride_out_batch, + stride_out_seqlen, + stride_out_nheads, + stride_out_headdim, + stride_x_batch, + stride_x_seqlen, + stride_x_nheads, + stride_x_headdim, + # Meta-parameters + BLOCK_K: tl.constexpr, + IS_SEQLEN_OFFSETS_TENSOR: tl.constexpr, + IS_VARLEN: tl.constexpr, + INTERLEAVED: tl.constexpr, + CONJUGATE: tl.constexpr, + BLOCK_M: tl.constexpr, +): + pid_m = tl.program_id(axis=0) + pid_batch = tl.program_id(axis=1) + pid_head = tl.program_id(axis=2) + rotary_dim_half = rotary_dim // 2 + + if not IS_VARLEN: + X = X + pid_batch * stride_x_batch + pid_head * stride_x_nheads + OUT = OUT + pid_batch * stride_out_batch + pid_head * stride_out_nheads + else: + start_idx = tl.load(CU_SEQLENS + pid_batch) + seqlen = tl.load(CU_SEQLENS + pid_batch + 1) - start_idx + X = X + start_idx * stride_x_seqlen + pid_head * stride_x_nheads + OUT = OUT + start_idx * stride_out_seqlen + pid_head * stride_out_nheads + + if pid_m * BLOCK_M >= seqlen: + return + rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M) + if not IS_SEQLEN_OFFSETS_TENSOR: + rm_cs = rm + SEQLEN_OFFSETS + else: + rm_cs = rm + tl.load(SEQLEN_OFFSETS + pid_batch) + rk = tl.arange(0, BLOCK_K) + rk_half = tl.arange(0, BLOCK_K // 2) + + if not INTERLEAVED: + # Load the 1st and 2nd halves of X, do calculation, then store to 1st and 2nd halves of OUT + X = X + (rm[:, None] * stride_x_seqlen + rk_half[None, :] * stride_x_headdim) + COS = COS + (rm_cs[:, None] * rotary_dim_half + rk_half[None, :]) + SIN = SIN + (rm_cs[:, None] * rotary_dim_half + rk_half[None, :]) + cos = tl.load( + COS, mask=(rm_cs[:, None] < seqlen_ro) & (rk_half[None, :] < rotary_dim_half), other=1.0 + ).to(tl.float32) + sin = tl.load( + SIN, mask=(rm_cs[:, None] < seqlen_ro) & (rk_half[None, :] < rotary_dim_half), other=0.0 + ).to(tl.float32) + x0 = tl.load( + X, mask=(rm[:, None] < seqlen) & (rk_half[None, :] < rotary_dim_half), other=0.0 + ).to(tl.float32) + x1 = tl.load( + X + rotary_dim_half * stride_x_headdim, + mask=(rm[:, None] < seqlen) & (rk_half[None, :] < rotary_dim_half), + other=0.0, + ).to(tl.float32) + if CONJUGATE: + sin = -sin + o0 = x0 * cos - x1 * sin + o1 = x0 * sin + x1 * cos + # write back result + OUT = OUT + (rm[:, None] * stride_out_seqlen + rk_half[None, :] * stride_out_headdim) + tl.store(OUT, o0, mask=(rm[:, None] < seqlen) & (rk_half[None, :] < rotary_dim_half)) + tl.store( + OUT + rotary_dim_half * stride_out_headdim, + o1, + mask=(rm[:, None] < seqlen) & (rk_half[None, :] < rotary_dim_half), + ) + else: + # We don't want to load X[0, 2, 4, ...] and X[1, 3, 5, ...] separately since both are slow. + # Instead, we load x0 = X[0, 1, 2, 3, ...] and x1 = X[1, 0, 3, 2, ...]. + # Loading x0 will be fast but x1 will be slow. + # Then we load cos = COS[0, 0, 1, 1, ...] and sin = SIN[0, 0, 1, 1, ...]. + # Then we do the calculation and use tl.where to pick put the right outputs for the even + # and for the odd indices. + rk_swap = rk + ((rk + 1) % 2) * 2 - 1 # 1, 0, 3, 2, 5, 4, ... + rk_repeat = tl.arange(0, BLOCK_K) // 2 + X0 = X + (rm[:, None] * stride_x_seqlen + rk[None, :] * stride_x_headdim) + X1 = X + (rm[:, None] * stride_x_seqlen + rk_swap[None, :] * stride_x_headdim) + COS = COS + (rm_cs[:, None] * rotary_dim_half + rk_repeat[None, :]) + SIN = SIN + (rm_cs[:, None] * rotary_dim_half + rk_repeat[None, :]) + cos = tl.load( + COS, + mask=(rm_cs[:, None] < seqlen_ro) & (rk_repeat[None, :] < rotary_dim_half), + other=1.0, + ).to(tl.float32) + sin = tl.load( + SIN, + mask=(rm_cs[:, None] < seqlen_ro) & (rk_repeat[None, :] < rotary_dim_half), + other=0.0, + ).to(tl.float32) + x0 = tl.load(X0, mask=(rm[:, None] < seqlen) & (rk[None, :] < rotary_dim), other=0.0).to( + tl.float32 + ) + x1 = tl.load( + X1, mask=(rm[:, None] < seqlen) & (rk_swap[None, :] < rotary_dim), other=0.0 + ).to(tl.float32) + if CONJUGATE: + sin = -sin + x0_cos = x0 * cos + x1_sin = x1 * sin + out = tl.where(rk[None, :] % 2 == 0, x0_cos - x1_sin, x0_cos + x1_sin) + OUT = OUT + (rm[:, None] * stride_out_seqlen + rk[None, :] * stride_out_headdim) + tl.store(OUT, out, mask=(rm[:, None] < seqlen) & (rk[None, :] < rotary_dim)) + + +def apply_rotary( + x: torch.Tensor, + cos: torch.Tensor, + sin: torch.Tensor, + seqlen_offsets: Union[int, torch.Tensor] = 0, + cu_seqlens: Optional[torch.Tensor] = None, + max_seqlen: Optional[int] = None, + interleaved=False, + inplace=False, + conjugate=False, +) -> torch.Tensor: + """ + Arguments: + x: (batch, seqlen, nheads, headdim) if cu_seqlens is None + else (total_seqlen, nheads, headdim). + cos: (seqlen_ro, rotary_dim / 2) + sin: (seqlen_ro, rotary_dim / 2) + seqlen_offsets: integer or integer tensor of size (batch,) + cu_seqlens: (batch + 1,) or None + max_seqlen: int + Returns: + y: (batch, seqlen, nheads, headdim) + """ + is_varlen = cu_seqlens is not None + if not is_varlen: + batch, seqlen, nheads, headdim = x.shape + else: + assert max_seqlen is not None, "If cu_seqlens is passed in, then max_seqlen must be passed" + total_seqlen, nheads, headdim = x.shape + batch_p_1 = cu_seqlens.shape[0] + batch = batch_p_1 - 1 + seqlen = max_seqlen + seqlen_ro, rotary_dim = cos.shape + assert sin.shape == cos.shape + rotary_dim *= 2 + assert rotary_dim <= headdim, "rotary_dim must be <= headdim" + assert headdim <= 256, "Only support headdim <= 256" + assert seqlen_ro >= seqlen, "seqlen_ro must be >= seqlen" + + assert ( + cos.dtype == sin.dtype + ), f"cos and sin must have the same dtype, got {cos.dtype} and {sin.dtype}" + assert ( + x.dtype == cos.dtype + ), f"Input and cos/sin must have the same dtype, got {x.dtype} and {cos.dtype}" + + cos, sin = cos.contiguous(), sin.contiguous() + if isinstance(seqlen_offsets, torch.Tensor): + assert seqlen_offsets.shape == (batch,) + assert seqlen_offsets.dtype in [torch.int32, torch.int64] + seqlen_offsets = seqlen_offsets.contiguous() + else: + assert seqlen_offsets + seqlen <= seqlen_ro + + output = torch.empty_like(x) if not inplace else x + if rotary_dim < headdim and not inplace: + output[..., rotary_dim:].copy_(x[..., rotary_dim:]) + + BLOCK_K = ( + 32 + if rotary_dim <= 32 + else (64 if rotary_dim <= 64 else (128 if rotary_dim <= 128 else 256)) + ) + grid = lambda META: (triton.cdiv(seqlen, META["BLOCK_M"]), batch, nheads) # noqa + BLOCK_M = 4 if interleaved else (8 if rotary_dim <= 64 else 4) + + # Need this, otherwise Triton tries to launch from cuda:0 and we get + # ValueError: Pointer argument (at 0) cannot be accessed from Triton (cpu tensor?) + with torch.cuda.device(x.device.index): + rotary_kernel[grid]( + output, # data ptrs + x, + cos, + sin, + cu_seqlens, + seqlen_offsets, + seqlen, # shapes + nheads, + rotary_dim, + seqlen_ro, + seqlen // 128, # key for triton cache (limit number of compilations) + output.stride(0) if not is_varlen else 0, # batch_strides if not varlen else 0 + output.stride(-3), # seqlen_stride or total_seqlen_stride + output.stride(-2), # nheads_stride + output.stride(-1), # headdim_stride + x.stride(0) if not is_varlen else 0, # batch_strides if not varlen else 0 + x.stride(-3), # seqlen stride or total_seqlen_stride + x.stride(-2), # nheads stride + x.stride(-1), # headdim stride + BLOCK_K, + isinstance(seqlen_offsets, torch.Tensor), + is_varlen, + interleaved, + conjugate, + BLOCK_M, + ) + return output + +class ApplyRotaryEmb(torch.autograd.Function): + @staticmethod + def forward( + ctx, + x, + cos, + sin, + interleaved=False, + inplace=False, + seqlen_offsets: Union[int, torch.Tensor] = 0, + cu_seqlens: Optional[torch.Tensor] = None, + max_seqlen: Optional[int] = None, + ): + out = apply_rotary( + x, + cos, + sin, + seqlen_offsets=seqlen_offsets, + cu_seqlens=cu_seqlens, + max_seqlen=max_seqlen, + interleaved=interleaved, + inplace=inplace, + ) + if isinstance(seqlen_offsets, int): + # Can't save int with save_for_backward + ctx.save_for_backward(cos, sin, cu_seqlens) + ctx.seqlen_offsets = seqlen_offsets + else: + ctx.save_for_backward(cos, sin, cu_seqlens, seqlen_offsets) + ctx.seqlen_offsets = None + ctx.interleaved = interleaved + ctx.inplace = inplace + ctx.max_seqlen = max_seqlen + return out if not inplace else x + + @staticmethod + def backward(ctx, do): + seqlen_offsets = ctx.seqlen_offsets + if seqlen_offsets is None: + cos, sin, cu_seqlens, seqlen_offsets = ctx.saved_tensors + else: + cos, sin, cu_seqlens = ctx.saved_tensors + # TD [2023-09-02]: For some reason Triton (2.0.0.post1) errors with + # "[CUDA]: invalid device context", and cloning makes it work. Idk why. Triton 2.1.0 works. + if not ctx.interleaved and not ctx.inplace: + do = do.clone() + dx = apply_rotary( + do, + cos, + sin, + seqlen_offsets=seqlen_offsets, + cu_seqlens=cu_seqlens, + max_seqlen=ctx.max_seqlen, + interleaved=ctx.interleaved, + inplace=ctx.inplace, + conjugate=True, + ) + return dx, None, None, None, None, None, None, None + + +def apply_rotary_emb( + x, + cos, + sin, + interleaved=False, + inplace=False, + seqlen_offsets: Union[int, torch.Tensor] = 0, + cu_seqlens: Optional[torch.Tensor] = None, + max_seqlen: Optional[int] = None, +): + """ + Arguments: + x: (batch_size, seqlen, nheads, headdim) if cu_seqlens is None + else (total_seqlen, nheads, headdim) + cos, sin: (seqlen_rotary, rotary_dim / 2) + interleaved: if True, rotate pairs of even and odd dimensions (GPT-J style) instead + of 1st half and 2nd half (GPT-NeoX style). + inplace: if True, apply rotary embedding in-place. + seqlen_offsets: (batch_size,) or int. Each sequence in x is shifted by this amount. + Most commonly used in inference when we have KV cache. + cu_seqlens: (batch + 1,) or None + max_seqlen: int + Return: + out: (batch_size, seqlen, nheads, headdim) if cu_seqlens is None + else (total_seqlen, nheads, headdim) + rotary_dim must be <= headdim + Apply rotary embedding to the first rotary_dim of x. + """ + return ApplyRotaryEmb.apply( + x, cos, sin, interleaved, inplace, seqlen_offsets, cu_seqlens, max_seqlen + ) \ No newline at end of file diff --git a/transfer/code/scDFM/src/models/perturbation/__init__.py b/transfer/code/scDFM/src/models/perturbation/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d185c87108adb9a0c174b4f18213d11b855ad732 --- /dev/null +++ b/transfer/code/scDFM/src/models/perturbation/__init__.py @@ -0,0 +1 @@ +# Auto-created by CCFM bootstrap diff --git a/transfer/code/scDFM/src/models/perturbation/model.py b/transfer/code/scDFM/src/models/perturbation/model.py new file mode 100644 index 0000000000000000000000000000000000000000..d0e9c6fc236f7d9a6d08a3056da1402ae0c5ceed --- /dev/null +++ b/transfer/code/scDFM/src/models/perturbation/model.py @@ -0,0 +1,393 @@ +import torch +import torch.nn as nn +import numpy as np +import math +from typing import Dict, Mapping, Optional, Tuple, Any, Union +import pdb +from timm.models.vision_transformer import PatchEmbed, Attention, Mlp +from src.flow_matching.utils.model_wrapper import ModelWrapper + +def modulate(x, shift, scale): + return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + + +################################################################################# +# Embedding Layers for Timesteps and Class Labels # +################################################################################# + +class TimestepEmbedder(nn.Module): + """ + Embeds scalar timesteps into vector representations. + """ + def __init__(self, hidden_size, frequency_embedding_size=256): + super().__init__() + self.mlp = nn.Sequential( + nn.Linear(frequency_embedding_size, hidden_size, bias=True), + nn.SiLU(), + nn.Linear(hidden_size, hidden_size, bias=True), + ) + self.frequency_embedding_size = frequency_embedding_size + + @staticmethod + def timestep_embedding(t, dim, max_period=10000): + """ + Create sinusoidal timestep embeddings. + :param t: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an (N, D) Tensor of positional embeddings. + """ + # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py + half = dim // 2 + freqs = torch.exp( + -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half + ).to(device=t.device) + args = t[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + return embedding + + def forward(self, t): + t_freq = self.timestep_embedding(t, self.frequency_embedding_size) + t_emb = self.mlp(t_freq) + return t_emb + + +class PerturbationEmbedder(nn.Module): + """ + Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance. + """ + def __init__(self, num_classes, hidden_size, dropout_prob): + super().__init__() + use_cfg_embedding = dropout_prob > 0 + self.embedding_table = nn.Embedding(num_classes + use_cfg_embedding, hidden_size) + self.num_classes = num_classes + self.dropout_prob = dropout_prob + + def token_drop(self, labels, force_drop_ids=None): + """ + Drops labels to enable classifier-free guidance. + """ + if force_drop_ids is None: + drop_ids = torch.rand(labels.shape[0], device=labels.device) < self.dropout_prob + else: + drop_ids = force_drop_ids == 1 + labels = torch.where(drop_ids, self.num_classes, labels) + return labels + + def forward(self, labels, train=True, force_drop_ids=None): + use_dropout = self.dropout_prob > 0 + if (train and use_dropout) or (force_drop_ids is not None): + labels = self.token_drop(labels, force_drop_ids) + embeddings = self.embedding_table(labels) + return embeddings + +class CrossAttentionModule(nn.Module): + def __init__(self, embed_dim, num_heads, dropout=0.0): + super().__init__() + self.attn = nn.MultiheadAttention(embed_dim=embed_dim, + num_heads=num_heads, + dropout=dropout, + batch_first=True) # using (batch, seq, dim) + self.norm = nn.LayerNorm(embed_dim) + self.ff = nn.Sequential( + nn.Linear(embed_dim, embed_dim * 4), + nn.GELU(), + nn.Linear(embed_dim * 4, embed_dim), + nn.Dropout(dropout), + ) + + def forward(self, x1, x2, attn_mask=None, key_padding_mask=None): + # x1: (b, l1, dim), x2: (b, l2, dim) + residual = x1 + attn_out, attn_weights = self.attn(query=x1, key=x2, value=x2, + attn_mask=attn_mask, + key_padding_mask=key_padding_mask) + x = self.norm(attn_out + residual) + + # add FFN + residual2 = x + x = self.ff(x) + x = self.norm(x + residual2) + + return x, attn_weights + + +################################################################################# +# Core DiT Model # +################################################################################# + +class DiTBlock(nn.Module): + """ + A DiT block with adaptive layer norm zero (adaLN-Zero) conditioning. + """ + def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, **block_kwargs): + super().__init__() + self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.attn = Attention(hidden_size, num_heads=num_heads, qkv_bias=True, **block_kwargs) + self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + mlp_hidden_dim = int(hidden_size * mlp_ratio) + approx_gelu = lambda: nn.GELU(approximate="tanh") + self.mlp = Mlp(in_features=hidden_size, hidden_features=mlp_hidden_dim, act_layer=approx_gelu, drop=0) + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(hidden_size, 6 * hidden_size, bias=True) + ) + + def forward(self, x, c): + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(6, dim=1) + x = x + gate_msa.unsqueeze(1) * self.attn(modulate(self.norm1(x), shift_msa, scale_msa)) + x = x + gate_mlp.unsqueeze(1) * self.mlp(modulate(self.norm2(x), shift_mlp, scale_mlp)) + return x + +class HierarchicalBlock(nn.Module): + def __init__(self,d_model,nhead,dropout): + super().__init__() + self.cell1_block = DiTBlock(d_model,nhead,dropout) + self.cell2_block = DiTBlock(d_model,nhead,dropout) + self.cross_attention = CrossAttentionModule(d_model,nhead,dropout) + + def forward(self,cell1,cell2,perturbation,t): + cell1 = self.cell1_block(cell1,perturbation+t) + cell2 = self.cell2_block(cell2,t) + cell1, _ = self.cross_attention(cell2,cell1) + + return cell1,cell2, + +class SymmetricBlock(nn.Module): + def __init__(self,d_model,nhead,dropout): + super().__init__() + # self.hierarchical_block1 = HierarchicalBlock(d_model,nhead,dropout) + # self.hierarchical_block2 = HierarchicalBlock(d_model,nhead,dropout) + self.hierarchical_block = HierarchicalBlock(d_model,nhead,dropout) + self.norm = nn.LayerNorm(d_model) + + def forward(self,cell1,cell2,perturbation,t): + # cell1_1,cell2_1 = self.hierarchical_block1(cell1,cell2,perturbation,t) + # cell2_2,cell1_2 = self.hierarchical_block2(cell2,cell1,-perturbation,t) + cell1_1,cell2_1 = self.hierarchical_block(cell1,cell2,perturbation,t) + cell2_2,cell1_2 = self.hierarchical_block(cell2,cell1,-perturbation,t) + cell1 = self.norm(cell1_1 + cell1_2) + cell2 = self.norm(cell2_1 + cell2_2) + return cell1,cell2 + +class Model(nn.Module): + def __init__( + self, + model_type: str = 'hierarchical', + ntoken: int = 512, + d_model: int = 512, + nhead: int = 8, + nlayers: int = 6 , + n_combination: int = 2, + nlayers_cls: int = 3, + d_perturbation: int = 512, + vocab: Any = None, + dropout: float = 0.5, + ): + super().__init__() + self.n_combination = n_combination + self.d_model = d_model + self.model_type = model_type + self.ntoken = ntoken + self.d_perturbation = d_perturbation + # self.p_embedder = PerturbationEmbedder(n_perturbation, d_model, dropout_prob=0) + self.p_embedder = nn.Sequential( + nn.Linear(d_perturbation,d_model), + nn.SiLU(), + nn.Linear(d_model,d_model), + ) + self.t_embedder = TimestepEmbedder(d_model) + self.blocks = nn.ModuleList([ + DiTBlock(d_model, nhead, dropout) for _ in range(nlayers) + ]) + + if model_type == 'hierarchical': + self.blocks = nn.ModuleList([ + SymmetricBlock(d_model, nhead, dropout) for _ in range(nlayers) + ]) + else: + self.blocks = nn.ModuleList([ + DiTBlock(d_model, nhead, dropout) for _ in range(nlayers) + ]) + self.perturbation_decoder = nn.Sequential( + nn.Linear(d_model,d_model*4), + nn.SiLU(), + nn.Linear(d_model*4,d_model), + ) + + + def forward(self,cell1,cell2,t,perturbation=None): + """_summary_ + + Args: + cell1 (tensor): (B,ntoken,d_model) for control cell + cell2 (tensor): (B,ntoken,d_model) for perturbed cell + t (long): (B,) when inference is a number + perturbation (long): (B,n_combination) + """ + t = self.t_embedder(t) # (N, D) + # pdb.set_trace() + if perturbation is not None: + perturbation_ = perturbation.reshape(-1,self.d_perturbation) + p = self.p_embedder(perturbation_) # (N, D) + # p = p.reshape(-1,self.n_combination,self.d_model) + # p = p.sum(dim=1) + c = t + p + else: + c = t + cells = torch.cat([cell1,cell2],dim=1) + for block in self.blocks: + if self.model_type == 'hierarchical': + cell1,cell2 = block(cell1,cell2,p,t) + else: + cells = block(cells,c) + if self.model_type != 'hierarchical': + cells = cells.reshape(-1,2,self.ntoken,self.d_model) + cell1 = cells[:,0,:,:] + cell2 = cells[:,1,:,:] + p = self.perturbation_decoder((cell1 + cell2).mean(dim=1)) + # p = p.reshape(-1,self.n_combination) + + return cell1, cell2, p + + + +class TimedTransformer(nn.Module): + def __init__( + self, + model_type: str = 'hierarchical', + ntoken: int = 512, + d_model: int = 512, + nhead: int = 8, + nlayers: int = 8 , + n_combination: int = 2, + nlayers_cls: int = 3, + d_perturbation: int = 512, + vocab: Any = None, + dropout: float = 0.5, + ): + super().__init__() + self.n_combination = n_combination + self.d_model = d_model + self.model_type = model_type + self.ntoken = ntoken + # self.p_embedder = PerturbationEmbedder(n_perturbation, d_model, dropout_prob=0) + self.t_embedder = TimestepEmbedder(d_model) + self.blocks = nn.ModuleList([ + DiTBlock(d_model, nhead, dropout) for _ in range(nlayers) + ]) + + + + def forward(self,cell1,t): + """_summary_ + + Args: + cell1 (tensor): (B,ntoken,d_model) for control cell + cell2 (tensor): (B,ntoken,d_model) for perturbed cell + t (long): (B,) when inference is a number + perturbation (long): (B,n_combination) + """ + t = self.t_embedder(t) # (N, D) + c = t + cells = cell1 + for block in self.blocks: + cells = block(cells,c) + return cells + + +# class WrappedModel(ModelWrapper): +# def __init__(self, model, mode="noisy_y"): +# super().__init__(model) +# assert mode in ["noisy_y", "noisy_p"] +# self.mode = mode + +# def forward(self, x: torch.Tensor, t: torch.Tensor, x_t: torch.Tensor, condition_vec: torch.Tensor, mode=None): +# """ +# x: x_0 +# x_t: path sample at time t +# t: time step +# condition_vec: condition vector +# """ +# if mode is not None: +# self.mode = mode +# if self.mode == "noisy_y": +# _, predicted_x_t, _ = self.model(x, x_t, t, condition_vec) +# return (x, predicted_x_t, condition_vec) +# elif self.mode == "noisy_p": +# _, _, predicted_p_t = self.model(x, x_t, t, condition_vec) +# return (x, x_t, predicted_p_t) +# else: +# raise ValueError(f"Unsupported mode: {self.mode}") + +class WrappedModel(ModelWrapper): + def __init__(self, model, mode="noisy_y"): + super().__init__(model) + assert mode in ["noisy_y", "noisy_p"] + self.mode = mode + self.model = model + + def change_mode(self, mode): + self.mode = mode + + def forward(self, x: torch.Tensor, t: torch.Tensor, **extras): + if t.ndim == 0: + t = t.expand(x.shape[0]).to(x.device) + + if self.mode == "noisy_y": + condition_vec = extras["condition_vec"] + x_0 = extras["x_0"] + _, predicted_x_t, _ = self.model(x_0, x, t, condition_vec) + return predicted_x_t + elif self.mode == "noisy_p": + target_y = extras["target_y"] + x_0 = extras["x_0"] + _, _, predicted_p_t = self.model(x_0, target_y, t, x) + return predicted_p_t + else: + raise ValueError(f"Unsupported mode: {self.mode}") + +if __name__ == '__main__': + batch_size = 128 + model = Model(model_type='hs',ntoken=512).cuda() + cell1 = torch.randn(batch_size, 1024,512).cuda() + cell2 = torch.randn(batch_size, 1024,512).cuda() + t = torch.randint(0, 1000, (batch_size,)).cuda() + # p = torch.randint(0,2,(5,2)).cuda() + p = torch.randn(batch_size,512).cuda() + model = nn.DataParallel(model,device_ids=[0,1,2,3,4,5,6,7]) + + from tqdm import tqdm + for i in tqdm(range(5000)): + out = model(cell1,cell2, t,p) + out[0].sum().backward() + pdb.set_trace() + print(out.shape) + + # from accelerate import Accelerator + # import time + + # accelerator = Accelerator() + # batch_size = 128 + # model = Model(model_type='hs', ntoken=512) + # cell1 = torch.randn(batch_size, 1024, 512) + # cell2 = torch.randn(batch_size, 1024, 512) + # t = torch.randint(0, 1000, (batch_size,)) + # p = torch.randn(batch_size, 512) + + # # 用 accelerator.prepare 包装 + # model, cell1, cell2, t, p = accelerator.prepare(model, cell1, cell2, t, p) + + # from tqdm import tqdm + # start = time.time() + # for i in tqdm(range(5000)): + # out = model(cell1, cell2, t, p) + # loss = out[0].sum() + out[1].sum() + out[2].sum() + # accelerator.backward(loss) + # end = time.time() + # print(f"Total time: {end - start:.2f} seconds") + # import pdb; pdb.set_trace() + # print(out[0].shape) \ No newline at end of file diff --git a/transfer/code/scDFM/src/models/perturbation/moduls.py b/transfer/code/scDFM/src/models/perturbation/moduls.py new file mode 100644 index 0000000000000000000000000000000000000000..660c8de1134cc22cf78cb67b0a74e98ee7d69670 --- /dev/null +++ b/transfer/code/scDFM/src/models/perturbation/moduls.py @@ -0,0 +1,136 @@ +import torch.nn as nn +from typing import Optional +from torch import nn, Tensor +import pdb +class CategoryValueEncoder(nn.Module): + def __init__( + self, + num_embeddings: int, + embedding_dim: int, + padding_idx: Optional[int] = None, + ): + super().__init__() + self.embedding = nn.Embedding( + num_embeddings, embedding_dim, padding_idx=padding_idx + ) + self.enc_norm = nn.LayerNorm(embedding_dim) + + def forward(self, x: Tensor) -> Tensor: + x = x.long() + x = self.embedding(x) # (batch, seq_len, embsize) + x = self.enc_norm(x) + return x + +class GeneEncoder(nn.Module): + def __init__( + self, + num_embeddings: int, + embedding_dim: int, + padding_idx: Optional[int] = None, + ): + super().__init__() + self.embedding = nn.Embedding( + num_embeddings, embedding_dim, padding_idx=padding_idx + ) + self.enc_norm = nn.LayerNorm(embedding_dim) + + def forward(self, x: Tensor) -> Tensor: + x = self.embedding(x) # (batch, seq_len, embsize) + x = self.enc_norm(x) + return x + +class PerturbationEmbedding(nn.Module): + def __init__(self, num_perturbations, emb_dim, max_comb_len=2, fusion_method='mlp', output_matrix=False): + super().__init__() + self.embedding = nn.Embedding(num_perturbations, emb_dim) + self.fusion_method = fusion_method + self.max_comb_len = max_comb_len + self.output_matrix = output_matrix + self.output_dim = emb_dim if not output_matrix else emb_dim * emb_dim + + if fusion_method == 'mlp': + self.fusion = nn.Sequential( + nn.Linear(emb_dim * max_comb_len, emb_dim * 2), + nn.ReLU(), + nn.Linear(emb_dim * 2, self.output_dim) + ) + elif fusion_method == 'sum': + self.fusion = None + else: + raise ValueError(f"Unsupported fusion method: {fusion_method}") + + def forward(self, ids): + emb = self.embedding(ids) # [B, C, D] + + if self.fusion_method == 'mlp': + emb = emb.view(emb.size(0), -1) # [B, C*D] + fused = self.fusion(emb) # [B, D] or [B, D*D] + + if self.output_matrix: + B = fused.size(0) + D = int(self.output_dim ** 0.5) + return fused.view(B, D, D) # [B, D, D] + else: + return fused + + elif self.fusion_method == 'sum': + out = emb.sum(dim=1) # [B, D] + if self.output_matrix: + B = out.size(0) + D = out.size(1) + return out.view(B, D, 1).expand(B, D, D) # dummy expansion + return out + +# class PerturbationEmbedding(nn.Module): +# def __init__(self, num_perturbations, emb_dim, max_comb_len=2, fusion_method='mlp'): +# """ +# Args: +# num_perturbations: 词表大小 +# emb_dim: 嵌入维度 +# max_comb_len: 每个 condition 最多包含的 token 数量（如 drug1, drug2） +# fusion_method: 'mlp' 或 'sum' +# """ +# super().__init__() +# self.embedding = nn.Embedding(num_perturbations, emb_dim) +# self.fusion_method = fusion_method +# self.max_comb_len = max_comb_len + +# if fusion_method == 'mlp': +# self.fusion = nn.Sequential( +# nn.Linear(emb_dim * max_comb_len, emb_dim), +# nn.ReLU(), +# nn.Linear(emb_dim, emb_dim) +# ) +# elif fusion_method == 'sum': +# self.fusion = None +# else: +# raise ValueError(f"Unsupported fusion method: {fusion_method}") +# def init_weights(self, m): +# if isinstance(m, nn.Linear): +# nn.init.xavier_uniform_(m.weight) +# nn.init.zeros_(m.bias) + +# def initialize_weights(self): +# self.apply(self.init_weights) + +# def forward(self, ids): +# """ +# Args: +# ids: LongTensor of shape [B, max_comb_len] +# Returns: +# fused: Tensor of shape [B, emb_dim] +# """ + +# emb = self.embedding(ids) # [B, C, D] + +# if self.fusion_method == 'mlp': +# emb = emb.view(emb.size(0), -1) # [B, C*D] +# return self.fusion(emb) # [B, D] + +# elif self.fusion_method == 'sum': +# if emb.dim() == 2: +# return emb.sum(dim=0) # [B, D] +# elif emb.dim() == 3: +# return emb.sum(dim=1) # [B, C, D] +# else: +# raise ValueError(f"Unsupported dimension: {ids.dim()}") \ No newline at end of file diff --git a/transfer/code/scDFM/src/models/perturbation/timed_transformer.py b/transfer/code/scDFM/src/models/perturbation/timed_transformer.py new file mode 100644 index 0000000000000000000000000000000000000000..5f43b4deee5291b99144d6538043268df5bb4e3e --- /dev/null +++ b/transfer/code/scDFM/src/models/perturbation/timed_transformer.py @@ -0,0 +1,187 @@ +import torch +import torch.nn as nn +import numpy as np +import math + +#### Adapted from Diffusion Transformer codebase https://github.com/facebookresearch/DiT ### + +def modulate(x, shift, scale): + return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + + +# TODO: How to embed perturbation? +# Since they are mainly labels, we could easily embed them via a trained embedding layer + +class DiTAttention(nn.Module): + def __init__(self, cfg): + super().__init__() + self.t_embedder = TimestepEmbedder(cfg.hidden_size) + self.trans_conv = nn.ModuleList([ + DiTBlock(cfg) for _ in range(cfg.num_layers) + ]) + + def forward(self, t, x, mask=None): + t = self.t_embedder(t) + for block in self.trans_conv: + x = block(x, t, mask) + return x + + + +class TimestepEmbedder(nn.Module): + """ + Embeds scalar timesteps into vector representations. + """ + def __init__(self, hidden_size, frequency_embedding_size=256): + super().__init__() + self.mlp = nn.Sequential( + nn.Linear(frequency_embedding_size, hidden_size, bias=True), + nn.SiLU(), + nn.Linear(hidden_size, hidden_size, bias=True), + ) + self.frequency_embedding_size = frequency_embedding_size + + @staticmethod + def timestep_embedding(t, dim, max_period=10000): + """ + Create sinusoidal timestep embeddings. + :param t: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an (N, D) Tensor of positional embeddings. + """ + # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py + half = dim // 2 + freqs = torch.exp( + -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half + ).to(device=t.device) + args = t[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + return embedding + + def forward(self, t): + t_freq = self.timestep_embedding(t, self.frequency_embedding_size) + t_emb = self.mlp(t_freq) + return t_emb + + + +class Mlp(nn.Module): + """ MLP as used in Vision Transformer, MLP-Mixer and related networks + + NOTE: When use_conv=True, expects 2D NCHW tensors, otherwise N*C expected. + """ + def __init__( + self, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.GELU, + norm_layer=None, + bias=True, + drop=0., + ): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + linear_layer = nn.Linear + + self.fc1 = linear_layer(in_features, hidden_features, bias=bias) + self.act = act_layer() + self.drop1 = nn.Dropout(drop) + self.norm = norm_layer(hidden_features) if norm_layer is not None else nn.Identity() + self.fc2 = linear_layer(hidden_features, out_features, bias=bias) + self.drop2 = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop1(x) + x = self.norm(x) + x = self.fc2(x) + x = self.drop2(x) + return x + +class DiTBlock(nn.Module): + """ + A DiT block with adaptive layer norm zero (adaLN-Zero) conditioning. + """ + def __init__(self, cfg): + super().__init__() + hidden_size = cfg.model.hidden_channels + cfg.model.edge_dim + num_heads = cfg.model.trans_num_heads + dropout = cfg.model.trans_dropout + mlp_ratio= 4.0 + + self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + self.attn = BertSelfAttention(num_attention_heads=num_heads, hidden_size=hidden_size, dropout_prob=dropout) + self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) + mlp_hidden_dim = int(hidden_size * mlp_ratio) + approx_gelu = lambda: nn.GELU(approximate="tanh") + self.mlp = Mlp(in_features=hidden_size, hidden_features=mlp_hidden_dim, act_layer=approx_gelu, drop=dropout) + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(hidden_size, 6 * hidden_size, bias=True) + ) + + def forward(self, x, c, mask): + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(6, dim=-1) + x = x + gate_msa * self.attn(modulate(self.norm1(x), shift_msa, scale_msa), mask) + x = x + gate_mlp * self.mlp(modulate(self.norm2(x), shift_mlp, scale_mlp)) + return x + + +class BertSelfAttention(nn.Module): + def __init__(self, num_attention_heads, hidden_size, dropout_prob): + super().__init__() + self.num_attention_heads = num_attention_heads + self.attention_head_size = int(hidden_size / num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(hidden_size, hidden_size) + self.key = nn.Linear(hidden_size, hidden_size) + self.value = nn.Linear(hidden_size, hidden_size) + self.dropout = nn.Dropout(dropout_prob) + + def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor: + new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) + x = x.view(new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: None, + ): + mixed_query_layer = self.query(hidden_states) + + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + query_layer = self.transpose_for_scores(mixed_query_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + + if attention_mask is not None: + # our attention mask is 1 for positions we want to attend and 0 for ones we want to ignore + # let the mask have -10000.0 where we want to ignore + attention_mask = torch.einsum('bi,bj->bij', attention_mask, attention_mask).unsqueeze(1) + attention_mask = attention_mask.masked_fill(attention_mask == 0, -10000.0) + attention_scores = attention_scores + attention_mask + + # Normalize the attention scores to probabilities. + attention_probs = nn.functional.softmax(attention_scores, dim=-1) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs = self.dropout(attention_probs) + context_layer = torch.matmul(attention_probs, value_layer) + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(new_context_layer_shape) + + return context_layer \ No newline at end of file diff --git a/transfer/code/scDFM/src/models/scGPT/dsbn.py b/transfer/code/scDFM/src/models/scGPT/dsbn.py new file mode 100644 index 0000000000000000000000000000000000000000..9b904f30bb317a46457f5d35c0974ebb2880163d --- /dev/null +++ b/transfer/code/scDFM/src/models/scGPT/dsbn.py @@ -0,0 +1,82 @@ +from typing import Optional, Tuple + +import torch +from torch import nn + +# The code is modified from https://github.com/wgchang/DSBN/blob/master/model/dsbn.py +class _DomainSpecificBatchNorm(nn.Module): + _version = 2 + + def __init__( + self, + num_features: int, + num_domains: int, + eps: float = 1e-5, + momentum: float = 0.1, + affine: bool = True, + track_running_stats: bool = True, + ): + super(_DomainSpecificBatchNorm, self).__init__() + self._cur_domain = None + self.num_domains = num_domains + self.bns = nn.ModuleList( + [ + self.bn_handle(num_features, eps, momentum, affine, track_running_stats) + for _ in range(num_domains) + ] + ) + + @property + def bn_handle(self) -> nn.Module: + raise NotImplementedError + + @property + def cur_domain(self) -> Optional[int]: + return self._cur_domain + + @cur_domain.setter + def cur_domain(self, domain_label: int): + self._cur_domain = domain_label + + def reset_running_stats(self): + for bn in self.bns: + bn.reset_running_stats() + + def reset_parameters(self): + for bn in self.bns: + bn.reset_parameters() + + def _check_input_dim(self, input: torch.Tensor): + raise NotImplementedError + + def forward(self, x: torch.Tensor, domain_label: int) -> torch.Tensor: + self._check_input_dim(x) + if domain_label >= self.num_domains: + raise ValueError( + f"Domain label {domain_label} exceeds the number of domains {self.num_domains}" + ) + bn = self.bns[domain_label] + self.cur_domain = domain_label + return bn(x) + + +class DomainSpecificBatchNorm1d(_DomainSpecificBatchNorm): + @property + def bn_handle(self) -> nn.Module: + return nn.BatchNorm1d + + def _check_input_dim(self, input: torch.Tensor): + if input.dim() > 3: + raise ValueError( + "expected at most 3D input (got {}D input)".format(input.dim()) + ) + + +class DomainSpecificBatchNorm2d(_DomainSpecificBatchNorm): + @property + def bn_handle(self) -> nn.Module: + return nn.BatchNorm2d + + def _check_input_dim(self, input: torch.Tensor): + if input.dim() != 4: + raise ValueError("expected 4D input (got {}D input)".format(input.dim())) \ No newline at end of file diff --git a/transfer/code/scDFM/src/models/scGPT/model.py b/transfer/code/scDFM/src/models/scGPT/model.py new file mode 100644 index 0000000000000000000000000000000000000000..e71c38dfe7881366ab1dc539d2c9921d5f17fcf1 --- /dev/null +++ b/transfer/code/scDFM/src/models/scGPT/model.py @@ -0,0 +1,1393 @@ +import gc +import math +from typing import Dict, Mapping, Optional, Tuple, Any, Union +import pdb +import torch +import numpy as np +from torch import nn, Tensor +import torch.distributed as dist +import torch.nn.functional as F +from torch.nn import TransformerEncoder, TransformerEncoderLayer +from torch.distributions import Bernoulli +from tqdm import trange +import torch +from torch.autograd import Function +from .dsbn import DomainSpecificBatchNorm1d + + +class TransformerModel(nn.Module): + def __init__( + self, + ntoken: int, + d_model: int, + nhead: int, + d_hid: int, + nlayers: int, + nlayers_cls: int = 3, + n_cls: int = 1, + vocab: Any = None, + dropout: float = 0.5, + pad_token: str = "", + pad_value: int = 0, + do_mvc: bool = False, + do_dab: bool = False, + use_batch_labels: bool = False, + num_batch_labels: Optional[int] = None, + domain_spec_batchnorm: Union[bool, str] = False, + input_emb_style: str = "continuous", + n_input_bins: Optional[int] = None, + cell_emb_style: str = "avg-pool", + mvc_decoder_style: str = "inner product", + ecs_threshold: float = 0.3, + explicit_zero_prob: bool = False, + use_fast_transformer: bool = False, + fast_transformer_backend: str = "flash", + pre_norm: bool = False, + bin_output: bool = False, + use_down_up_transformer: bool = False, + n_top_genes: int = 2048, + bottleneck_dim: int = 128, + ): + super().__init__() + self.model_type = "Transformer" + self.d_model = d_model + self.do_dab = do_dab + self.ecs_threshold = ecs_threshold + self.use_batch_labels = use_batch_labels + self.domain_spec_batchnorm = domain_spec_batchnorm + self.input_emb_style = input_emb_style + self.cell_emb_style = cell_emb_style + self.explicit_zero_prob = explicit_zero_prob + self.norm_scheme = "pre" if pre_norm else "post" + self.bin_output = bin_output + self.n_top_genes = n_top_genes + self.use_down_up_transformer = use_down_up_transformer + if self.input_emb_style not in ["category", "continuous", "scaling"]: + raise ValueError( + f"input_emb_style should be one of category, continuous, scaling, " + f"got {input_emb_style}" + ) + if cell_emb_style not in ["cls", "avg-pool", "w-pool"]: + raise ValueError(f"Unknown cell_emb_style: {cell_emb_style}") + if use_fast_transformer: + if not flash_attn_available: + warnings.warn( + "flash-attn is not installed, using pytorch transformer instead. " + "Set use_fast_transformer=False to avoid this warning. " + "Installing flash-attn is highly recommended." + ) + use_fast_transformer = False + self.use_fast_transformer = use_fast_transformer + + # TODO: add dropout in the GeneEncoder + + self.encoder = GeneEncoder(ntoken, d_model, padding_idx=vocab[pad_token]) + + # Value Encoder, NOTE: the scaling style is also handled in _encode method + if input_emb_style == "continuous": + self.value_encoder = ContinuousValueEncoder(d_model, dropout) + elif input_emb_style == "category": + assert n_input_bins > 0 + self.value_encoder = CategoryValueEncoder( + n_input_bins, d_model, padding_idx=pad_value + ) + else: + self.value_encoder = nn.Identity() # nn.Softmax(dim=1) + # TODO: consider row-wise normalization or softmax + # TODO: Correct handle the mask_value when using scaling + if self.bin_output and n_input_bins > 0: + self.value_decode = CategoryValueDecoder(d_model, n_input_bins, use_batch_labels=use_batch_labels,) + else: + self.value_decode = nn.Identity() + + # Batch Encoder + if use_batch_labels: + self.batch_encoder = BatchLabelEncoder(num_batch_labels, d_model) + + if domain_spec_batchnorm is True or domain_spec_batchnorm == "dsbn": + use_affine = True if domain_spec_batchnorm == "do_affine" else False + print(f"Use domain specific batchnorm with affine={use_affine}") + self.dsbn = DomainSpecificBatchNorm1d( + d_model, num_batch_labels, eps=6.1e-5, affine=use_affine + ) + elif domain_spec_batchnorm == "batchnorm": + print("Using simple batchnorm instead of domain specific batchnorm") + self.bn = nn.BatchNorm1d(d_model, eps=6.1e-5) + + if use_fast_transformer: + if fast_transformer_backend == "linear": + self.transformer_encoder = FastTransformerEncoderWrapper( + d_model, nhead, d_hid, nlayers, dropout + ) + elif fast_transformer_backend == "flash": + encoder_layers = FlashTransformerEncoderLayer( + d_model, + nhead, + d_hid, + dropout, + batch_first=True, + norm_scheme=self.norm_scheme, + ) + self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers) + elif self.use_down_up_transformer: + encoder_layers = TransformerEncoderLayer( + d_model, nhead, d_hid, dropout, batch_first=True + ) + self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers) + + # self.attention_pooling = AttentionPooling(n=bottleneck_dim, d_model=d_model) + # self.cross_attention_module = nn.ModuleList([CrossAttentionModule(d_model, nhead, dropout) for _ in range(nlayers)]) + self.MLP_pooling = nn.Sequential( + nn.Linear(n_top_genes, d_model), + nn.ReLU(), + nn.Linear(d_model, bottleneck_dim),) + self.ln1 = nn.LayerNorm(n_top_genes,) + self.ln2 = nn.LayerNorm(bottleneck_dim,) + self.ln3 = nn.LayerNorm(bottleneck_dim,) + self.batch_norm = nn.BatchNorm1d(bottleneck_dim, momentum=0.01, eps=0.001) + + self.cross_x_in = nn.Parameter(torch.randn(512, 512)) + self.encoder_cross_x = GeneEncoder(ntoken, d_model, padding_idx=vocab[pad_token]) + self.feature_MLP = nn.Sequential( + nn.Linear(d_model, d_model), + nn.ReLU(), + nn.Linear(d_model, d_model),) + self.MLP_upsampling = nn.Sequential( + nn.Linear(bottleneck_dim, d_model), + nn.ReLU(), + nn.Linear(d_model, n_top_genes),) + else: + encoder_layers = TransformerEncoderLayer( + d_model, nhead, d_hid, dropout, batch_first=True + ) + self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers) + + self.decoder = ExprDecoder( + d_model, + explicit_zero_prob=explicit_zero_prob, + use_batch_labels=use_batch_labels, + ) + self.cls_decoder = ClsDecoder(d_model, n_cls, nlayers=nlayers_cls) + if do_mvc: + self.mvc_decoder = MVCDecoder( + d_model, + arch_style=mvc_decoder_style, + explicit_zero_prob=explicit_zero_prob, + use_batch_labels=use_batch_labels, + ) + + if do_dab: + self.grad_reverse_discriminator = AdversarialDiscriminator( + d_model, + n_cls=num_batch_labels, + reverse_grad=True, + ) + + self.sim = Similarity(temp=0.5) # TODO: auto set temp + self.creterion_cce = nn.CrossEntropyLoss() + + self.init_weights() + + def init_weights(self) -> None: + initrange = 0.1 + # TODO: check if this initialization is helpful and shall we apply to all? + self.encoder.embedding.weight.data.uniform_(-initrange, initrange) + + def bin_decode(self, x: Tensor) -> Tensor: + if self.bin_output: + return self.value_decode(x) + else: + return x + + def _decode(self, x: Tensor) -> Tensor: + + x = self.MLP_upsampling(x).transpose(-1,-2) + bin_output = self.value_decode(x) + return bin_output + + def _encode( + self, + src: Tensor, + values: Tensor, + src_key_padding_mask: Tensor, + batch_labels: Optional[Tensor] = None, # (batch,) + ) -> Tensor: + self._check_batch_labels(batch_labels) + + src = self.encoder(src) # (batch, seq_len, embsize) + self.cur_gene_token_embs = src + + values = self.value_encoder(values) # (batch, seq_len, embsize) + if self.input_emb_style == "scaling": + values = values.unsqueeze(2) + total_embs = src * values + else: + total_embs = src + values + + if getattr(self, "dsbn", None) is not None: + batch_label = int(batch_labels[0].item()) + total_embs = self.dsbn(total_embs.permute(0, 2, 1), batch_label).permute( + 0, 2, 1 + ) # the batch norm always works on dim 1 + elif getattr(self, "bn", None) is not None: + total_embs = self.bn(total_embs.permute(0, 2, 1)).permute(0, 2, 1) + + output = self.transformer_encoder( + total_embs, src_key_padding_mask=src_key_padding_mask + ) + + if self.use_down_up_transformer: + + output = self.ln1(output.transpose(-1,-2)) #[B, D, L] ln on L + output = self.MLP_pooling(output) #[B, D, L] -> [B, D, n] + output = self.ln2(output) # ln on n + + output = self.feature_MLP(output.transpose(-1,-2)) # [B, n, D] -> [B, n, D] + output = self.ln3(output.transpose(-1,-2)) # [B, D, n] ln again on n + output = torch.clip(output, min=-1, max=1) + # [B, L, D] + ######## + # output = self.attention_pooling(output) + + # output = self.feature_MLP(output.transpose(-1,-2)) # [B, n, D] -> [B, n, D] + # output = self.ln3(output.transpose(-1,-2)) # [B, D, n] ln again on n + # output = self.MLP_upsampling(output).transpose(-1,-2) + + return output # (batch, seq_len, embsize) + + def _get_cell_emb_from_layer( + self, layer_output: Tensor, weights: Tensor = None + ) -> Tensor: + """ + Args: + layer_output(:obj:`Tensor`): shape (batch, seq_len, embsize) + weights(:obj:`Tensor`): shape (batch, seq_len), optional and only used + when :attr:`self.cell_emb_style` is "w-pool". + + Returns: + :obj:`Tensor`: shape (batch, embsize) + """ + if self.cell_emb_style == "cls": + cell_emb = layer_output[:, 0, :] # (batch, embsize) + elif self.cell_emb_style == "avg-pool": + cell_emb = torch.mean(layer_output, dim=1) + elif self.cell_emb_style == "w-pool": + if weights is None: + raise ValueError("weights is required when cell_emb_style is w-pool") + if weights.dim() != 2: + raise ValueError("weights should be 2D") + cell_emb = torch.sum(layer_output * weights.unsqueeze(2), dim=1) + cell_emb = F.normalize(cell_emb, p=2, dim=1) # (batch, embsize) + + return cell_emb + + def _check_batch_labels(self, batch_labels: Tensor) -> None: + if self.use_batch_labels or self.domain_spec_batchnorm: + assert batch_labels is not None + elif batch_labels is not None: + raise ValueError( + "batch_labels should only be provided when `self.use_batch_labels`" + " or `self.domain_spec_batchnorm` is True" + ) + + def generate( + self, + cell_emb: Tensor, + src: Tensor, + values: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + gen_iters: int = 1, + batch_labels: Optional[Tensor] = None, # (batch,) + ) -> Tensor: + """ + Args: + cell_emb(:obj:`Tensor`): shape (batch, embsize) + src(:obj:`Tensor`): shape (batch, seq_len) + values(:obj:`Tensor`): shape (batch, seq_len), optional + src_key_padding_mask(:obj:`Tensor`): shape (batch, seq_len), optional + gen_iters(:obj:`int`): number of generation iterations + batch_labels(:obj:`Tensor`): shape (batch,), optional + """ + # TODO: should have a tag indicate the generation mode + # TODO: if gen_iters > 1, should have a tag indicate the current iteration + try: + self._check_batch_labels(batch_labels) + except: + import warnings + + warnings.warn( + "batch_labels is required but not provided, using zeros instead" + ) + batch_labels = torch.zeros( + cell_emb.shape[0], dtype=torch.long, device=cell_emb.device + ) + + src = self.encoder(src) # (batch, seq_len, embsize) + + if values is not None: + values = self.value_encoder(values) # (batch, seq_len, embsize) + if self.input_emb_style == "scaling": + values = values.unsqueeze(2) + total_embs = src * values + else: + total_embs = src + values + else: + total_embs = src + + if getattr(self, "dsbn", None) is not None: + batch_label = int(batch_labels[0].item()) + total_embs = self.dsbn(total_embs.permute(0, 2, 1), batch_label).permute( + 0, 2, 1 + ) # the batch norm always works on dim 1 + elif getattr(self, "bn", None) is not None: + total_embs = self.bn(total_embs.permute(0, 2, 1)).permute(0, 2, 1) + + total_embs[:, 0, :] = cell_emb + + if src_key_padding_mask is None: + src_key_padding_mask = torch.zeros( + total_embs.shape[:2], dtype=torch.bool, device=total_embs.device + ) + transformer_output = self.transformer_encoder( + total_embs, src_key_padding_mask=src_key_padding_mask + ) + + if self.use_batch_labels: + batch_emb = self.batch_encoder(batch_labels) # (batch, embsize) + mlm_output = self.decoder( + transformer_output + if not self.use_batch_labels + else torch.cat( + [ + transformer_output, + batch_emb.unsqueeze(1).repeat(1, transformer_output.shape[1], 1), + ], + dim=2, + ), + # else transformer_output + batch_emb.unsqueeze(1), + ) + output = mlm_output["pred"] # (batch, seq_len) + + return output # (batch, seq_len) + + def forward( + self, + src: Tensor, + values: Tensor, + src_key_padding_mask: Tensor, + condition: Optional[Tensor] = None, + batch_labels: Optional[Tensor] = None, + CLS: bool = False, + CCE: bool = False, + MVC: bool = False, + ECS: bool = False, + do_sample: bool = False, + ) -> Mapping[str, Tensor]: + """ + Args: + src (:obj:`Tensor`): token ids, shape [batch_size, seq_len] + values (:obj:`Tensor`): token values, shape [batch_size, seq_len] + src_key_padding_mask (:obj:`Tensor`): mask for src, shape [batch_size, + seq_len] + batch_labels (:obj:`Tensor`): batch labels, shape [batch_size] + CLS (:obj:`bool`): if True, return the celltype classification objective + (CLS) output + CCE (:obj:`bool`): if True, return the contrastive cell embedding objective + (CCE) output + MVC (:obj:`bool`): if True, return the masked value prediction for cell + embedding MVC output + ECS (:obj:`bool`): if True, return the elastic cell similarity objective + (ECS) output. + + Returns: + dict of output Tensors. + """ + transformer_output = self._encode( + src, values, src_key_padding_mask, batch_labels + ) + if self.use_down_up_transformer: + transformer_output = self.MLP_upsampling(transformer_output).transpose(-1,-2) + # transformer_output = self._decode(src,transformer_output) + if self.use_batch_labels: + batch_emb = self.batch_encoder(batch_labels) # (batch, embsize) + + output = {} + mlm_output = self.decoder( + transformer_output + if not self.use_batch_labels + else torch.cat( + [ + transformer_output, + batch_emb.unsqueeze(1).repeat(1, transformer_output.shape[1], 1), + ], + dim=2, + ), + # else transformer_output + batch_emb.unsqueeze(1), + ) + + if self.bin_output: + output["bin_output"] = self.value_decode( + transformer_output + if not self.use_batch_labels + else torch.cat( + [ + transformer_output, + batch_emb.unsqueeze(1).repeat(1, transformer_output.shape[1], 1), + ], + dim=2, + ), + ) + + if self.explicit_zero_prob and do_sample: + bernoulli = Bernoulli(probs=mlm_output["zero_probs"]) + output["mlm_output"] = bernoulli.sample() * mlm_output["pred"] + else: + output["mlm_output"] = mlm_output["pred"] # (batch, seq_len) + if self.explicit_zero_prob: + output["mlm_zero_probs"] = mlm_output["zero_probs"] + + cell_emb = self._get_cell_emb_from_layer(transformer_output, values) + output["cell_emb"] = cell_emb + + if CLS: + output["cls_output"] = self.cls_decoder(cell_emb) # (batch, n_cls) + if CCE: + cell1 = cell_emb + transformer_output2 = self._encode( + src, values, src_key_padding_mask, batch_labels + ) + cell2 = self._get_cell_emb_from_layer(transformer_output2) + + # Gather embeddings from all devices if distributed training + if dist.is_initialized() and self.training: + cls1_list = [ + torch.zeros_like(cell1) for _ in range(dist.get_world_size()) + ] + cls2_list = [ + torch.zeros_like(cell2) for _ in range(dist.get_world_size()) + ] + dist.all_gather(tensor_list=cls1_list, tensor=cell1.contiguous()) + dist.all_gather(tensor_list=cls2_list, tensor=cell2.contiguous()) + + # NOTE: all_gather results have no gradients, so replace the item + # of the current rank with the original tensor to keep gradients. + # See https://github.com/princeton-nlp/SimCSE/blob/main/simcse/models.py#L186 + cls1_list[dist.get_rank()] = cell1 + cls2_list[dist.get_rank()] = cell2 + + cell1 = torch.cat(cls1_list, dim=0) + cell2 = torch.cat(cls2_list, dim=0) + # TODO: should detach the second run cls2? Can have a try + cos_sim = self.sim(cell1.unsqueeze(1), cell2.unsqueeze(0)) # (batch, batch) + labels = torch.arange(cos_sim.size(0)).long().to(cell1.device) + output["loss_cce"] = self.creterion_cce(cos_sim, labels) + if MVC: + mvc_output = self.mvc_decoder( + cell_emb + if not self.use_batch_labels + else torch.cat([cell_emb, batch_emb], dim=1), + # else cell_emb + batch_emb, + self.cur_gene_token_embs, + ) + if self.explicit_zero_prob and do_sample: + bernoulli = Bernoulli(probs=mvc_output["zero_probs"]) + output["mvc_output"] = bernoulli.sample() * mvc_output["pred"] + else: + output["mvc_output"] = mvc_output["pred"] # (batch, seq_len) + if self.explicit_zero_prob: + output["mvc_zero_probs"] = mvc_output["zero_probs"] + if ECS: + # Here using customized cosine similarity instead of F.cosine_similarity + # to avoid the pytorch issue of similarity larger than 1.0, pytorch # 78064 + # normalize the embedding + cell_emb_normed = F.normalize(cell_emb, p=2, dim=1) + cos_sim = torch.mm(cell_emb_normed, cell_emb_normed.t()) # (batch, batch) + + # mask out diagnal elements + mask = torch.eye(cos_sim.size(0)).bool().to(cos_sim.device) + cos_sim = cos_sim.masked_fill(mask, 0.0) + # only optimize positive similarities + cos_sim = F.relu(cos_sim) + + output["loss_ecs"] = torch.mean(1 - (cos_sim - self.ecs_threshold) ** 2) + + + if self.do_dab: + output["dab_output"] = self.grad_reverse_discriminator(cell_emb) + + return output + + def bin_encode(self, src: Tensor, values: Tensor, src_key_padding_mask: Tensor, batch_labels: Optional[Tensor] = None) -> Tensor: + transformer_output = self._encode( + src, values, src_key_padding_mask, batch_labels + ) + return transformer_output + + def bin_decode(self, transformer_output: Tensor, batch_emb: Optional[Tensor] = None) -> Tensor: + if self.bin_output: + return self.value_decode( + transformer_output + if not self.use_batch_labels + else torch.cat( + [ + transformer_output, + batch_emb.unsqueeze(1).repeat(1, transformer_output.shape[1], 1), + ], + dim=2, + ), + ) + else: + raise ValueError("bin_output is not enabled") + + def encode_batch( + self, + src: Tensor, + values: Tensor, + src_key_padding_mask: Tensor, + batch_size: int, + batch_labels: Optional[Tensor] = None, + output_to_cpu: bool = True, + time_step: Optional[int] = None, + return_np: bool = False, + ) -> Tensor: + """ + Args: + src (Tensor): shape [N, seq_len] + values (Tensor): shape [N, seq_len] + src_key_padding_mask (Tensor): shape [N, seq_len] + batch_size (int): batch size for encoding + batch_labels (Tensor): shape [N, n_batch_labels] + output_to_cpu (bool): whether to move the output to cpu + time_step (int): the time step index in the transformer output to return. + The time step is along the second dimenstion. If None, return all. + return_np (bool): whether to return numpy array + + Returns: + output Tensor of shape [N, seq_len, embsize] + """ + N = src.size(0) + device = next(self.parameters()).device + + # initialize the output tensor + array_func = np.zeros if return_np else torch.zeros + float32_ = np.float32 if return_np else torch.float32 + shape = ( + (N, self.d_model) + if time_step is not None + else (N, src.size(1), self.d_model) + ) + outputs = array_func(shape, dtype=float32_) + + for i in trange(0, N, batch_size): + raw_output = self._encode( + src[i : i + batch_size].to(device), + values[i : i + batch_size].to(device), + src_key_padding_mask[i : i + batch_size].to(device), + batch_labels[i : i + batch_size].to(device) + if batch_labels is not None + else None, + ) + output = raw_output.detach() + if output_to_cpu: + output = output.cpu() + if return_np: + output = output.numpy() + if time_step is not None: + output = output[:, time_step, :] + outputs[i : i + batch_size] = output + + return outputs + + +def generate_square_subsequent_mask(sz: int) -> Tensor: + """Generates an upper-triangular matrix of -inf, with zeros on diag.""" + return torch.triu(torch.ones(sz, sz) * float("-inf"), diagonal=1) + + +class FastTransformerEncoderWrapper(nn.Module): + def __init__( + self, + d_model: int, + nhead: int, + d_hid: int, + nlayers: int, + dropout: float = 0.5, + ): + super().__init__() + self.fast_transformer_encoder = self.build_fast_transformer_encoder( + d_model, nhead, d_hid, nlayers, dropout + ) + + @staticmethod + def build_fast_transformer_encoder( + d_model: int, nhead: int, d_hid: int, nlayers: int, dropout: float + ) -> nn.Module: + from fast_transformers.builders import TransformerEncoderBuilder + + if d_model % nhead != 0: + raise ValueError( + f"d_model must be divisible by nhead, " + f"got d_model={d_model} and nhead={nhead}" + ) + builder = TransformerEncoderBuilder.from_kwargs( + n_layers=nlayers, + n_heads=nhead, + query_dimensions=d_model // nhead, + value_dimensions=d_model // nhead, + feed_forward_dimensions=d_hid, + attention_type="linear", + attention_dropout=dropout, + dropout=dropout, + activation="gelu", + ) + assert builder.attention_type == "linear" + return builder.get() + + @staticmethod + def build_length_mask( + src: Tensor, + src_key_padding_mask: torch.BoolTensor, + ) -> "LengthMask": + from fast_transformers.masking import LengthMask + + seq_len = src.shape[1] + num_paddings = src_key_padding_mask.sum(dim=1) + actual_seq_len = seq_len - num_paddings # (N,) + length_mask = LengthMask(actual_seq_len, max_len=seq_len, device=src.device) + + if src_key_padding_mask[length_mask.bool_matrix].sum() != 0: + raise ValueError( + "Found padding tokens in the middle of the sequence. " + "src_key_padding_mask and length_mask are not compatible." + ) + return length_mask + + def forward( + self, + src: Tensor, + src_key_padding_mask: torch.BoolTensor, + ) -> Tensor: + """ + Args: + src: Tensor, shape [N, seq_len, embsize] + src_key_padding_mask: Tensor, shape [N, seq_len] + + Returns: + output Tensor of shape [N, seq_len, embsize] + """ + if src_key_padding_mask.shape != src.shape[:2]: + raise ValueError( + f"src_key_padding_mask shape {src_key_padding_mask.shape} " + f"does not match first two dims of src shape {src.shape[:2]}" + ) + + if src_key_padding_mask.dtype != torch.bool: + raise ValueError( + f"src_key_padding_mask needs to be of type torch.bool, " + f"got {src_key_padding_mask.dtype}" + ) + + length_mask = self.build_length_mask(src, src_key_padding_mask) + output = self.fast_transformer_encoder(src, length_mask=length_mask) + return output + +class AttentionPooling(nn.Module): + def __init__(self, n, d_model): + super().__init__() + self.n = n + self.d_model = d_model + self.query = nn.Parameter(torch.randn(n, d_model)) # learnable query + self.K = nn.Linear(d_model, d_model) + self.V = nn.Linear(d_model, d_model) + self.ln = nn.LayerNorm(d_model) + print(f"using attention pooling") + self.ff = nn.Sequential( + nn.Linear(d_model, d_model * 4), + nn.GELU(), + nn.Linear(d_model * 4, d_model), + nn.Dropout(0.5), + ) + + def forward(self, hidden): # hidden: (B, 512, d_model) or (512, d_model) + if hidden.dim() == 2: # (512, d_model) + hidden = hidden.unsqueeze(0) # -> (1, 512, d_model) + + B, L, D = hidden.shape # B=batch size, L=512, D=d_model + k = self.K(hidden) + v = self.V(hidden) + attn_score = torch.matmul(self.query, k.transpose(1, 2)) / (D ** 0.5) # (B, n, 512) + attn_weights = F.softmax(attn_score, dim=-1) # (B, n, 512) + + pooled = torch.matmul(attn_weights, v) # (B, n, d_model) + residual = pooled + pooled = self.ff(pooled) + pooled = self.ln(pooled + residual) + # pooled = hidden.view(B, self.n, -1, D).mean(-2) + return pooled.squeeze(0) if pooled.size(0) == 1 else pooled # -> (n, d_model) or (B, n, d_model) +class FlashMultiheadAttention(nn.Module): + """ + Multi-head self-attention using flash-attn backend. + Only supports self-attention (q=k=v) and batch_first=True. + """ + def __init__(self, embed_dim, num_heads, dropout=0.0, batch_first=True, **factory_kwargs): + super().__init__() + assert batch_first, "Only batch_first=True is supported." + self.embed_dim = embed_dim + self.num_heads = num_heads + self.dropout = dropout + self.head_dim = embed_dim // num_heads + assert ( + self.head_dim * num_heads == embed_dim + ), "embed_dim must be divisible by num_heads" + self.qkv_proj = nn.Linear(embed_dim, 3 * embed_dim, **factory_kwargs) + self.out_proj = nn.Linear(embed_dim, embed_dim, **factory_kwargs) + + def forward(self, x, key_padding_mask=None): + # x: (batch, seq, embed_dim) + B, S, C = x.shape + qkv = self.qkv_proj(x) # (B, S, 3*embed_dim) + qkv = qkv.view(B, S, 3, self.num_heads, self.head_dim) + q = qkv[:, :, 0] + k = qkv[:, :, 1] + v = qkv[:, :, 2] + # flash_attn_func expects (B, S, nH, dH) + # key_padding_mask: (B, S) with True for PAD + # flash_attn_func does not support key_padding_mask directly, so we mask input + if key_padding_mask is not None: + # set PAD positions to zero + mask = ~key_padding_mask # invert: True for keep + q = q * mask.unsqueeze(-1).unsqueeze(-1) + k = k * mask.unsqueeze(-1).unsqueeze(-1) + v = v * mask.unsqueeze(-1).unsqueeze(-1) + attn_output = flash_attn_func( + q, k, v, dropout_p=self.dropout, causal=False + ) # (B, S, nH, dH) + attn_output = attn_output.reshape(B, S, C) + return self.out_proj(attn_output), None # match nn.MultiheadAttention API + + +class FlashTransformerEncoderLayer(nn.Module): + r"""TransformerEncoderLayer is made up of self-attn and feedforward network. + The class is modified from torch.nn.TransformerEncoderLayer to support the + FlashAttention. + + Args: + d_model: the number of expected features in the input (required). + nhead: the number of heads in the multiheadattention models (required). + dim_feedforward: the dimension of the feedforward network model (default=2048). + dropout: the dropout value (default=0.1). + activation: the activation function of intermediate layer, relu or gelu (default=relu). + layer_norm_eps: the eps value in layer normalization components (default=1e-5). + batch_first: If ``True``, then the input and output tensors are provided + as (batch, seq, feature). Default: ``False``. + + Examples:: + >>> encoder_layer = nn.TransformerEncoderLayer(d_model=512, nhead=8) + >>> src = torch.rand(10, 32, 512) + >>> out = encoder_layer(src) + + Alternatively, when ``batch_first`` is ``True``: + >>> encoder_layer = nn.TransformerEncoderLayer(d_model=512, nhead=8, batch_first=True) + >>> src = torch.rand(32, 10, 512) + >>> out = encoder_layer(src) + """ + __constants__ = ["batch_first"] + + def __init__( + self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + activation="relu", + layer_norm_eps=1e-5, + batch_first=True, + device=None, + dtype=None, + norm_scheme="post", # "pre" or "post" + ) -> None: + factory_kwargs = {"device": device, "dtype": dtype} + super().__init__() + self.self_attn = FlashMultiheadAttention( + embed_dim=d_model, + num_heads=nhead, + batch_first=batch_first, + dropout=dropout, + **factory_kwargs, + ) + + # Version compatibility workaround + if not hasattr(self.self_attn, "batch_first"): + self.self_attn.batch_first = batch_first + # Implementation of Feedforward model + self.linear1 = nn.Linear(d_model, dim_feedforward, **factory_kwargs) + self.dropout = nn.Dropout(dropout) + self.linear2 = nn.Linear(dim_feedforward, d_model, **factory_kwargs) + + self.norm1 = nn.LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs) + self.norm2 = nn.LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.activation = self._get_activation_fn(activation) + self.norm_scheme = norm_scheme + if self.norm_scheme not in ["pre", "post"]: + raise ValueError(f"norm_scheme should be pre or post, not {norm_scheme}") + + @staticmethod + def _get_activation_fn(activation): + if activation == "relu": + return F.relu + elif activation == "gelu": + return F.gelu + + raise RuntimeError("activation should be relu/gelu, not {}".format(activation)) + + def __setstate__(self, state): + if "activation" not in state: + state["activation"] = F.relu + super().__setstate__(state) + + def forward( + self, + src: Tensor, + src_mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + **kwargs, + ) -> Tensor: + r"""Pass the input through the encoder layer. + + Args: + src: the sequence to the encoder layer (required). + src_mask: the mask for the src sequence (optional). + src_key_padding_mask: the mask for the src keys per batch (optional). + + Shape: + see the docs in Transformer class. + """ + print('FlashTransformerEncoderLayer forward') + if src_mask is not None: + raise ValueError("FlashTransformerEncoderLayer does not support src_mask") + + if not src_key_padding_mask.any().item(): + # no padding tokens in src + src_key_padding_mask_ = None + else: + if src_key_padding_mask.dtype != torch.bool: + src_key_padding_mask = src_key_padding_mask.bool() + # NOTE: the FlashMHA uses mask 0 for padding tokens, which is the opposite + src_key_padding_mask_ = ~src_key_padding_mask + + if self.norm_scheme == "pre": + src = self.norm1(src) + src2 = self.self_attn(src, key_padding_mask=src_key_padding_mask_)[0] + src = src + self.dropout1(src2) + src = self.norm2(src) + src2 = self.linear2(self.dropout(self.activation(self.linear1(src)))) + src = src + self.dropout2(src2) + else: + src2 = self.self_attn(src, key_padding_mask=src_key_padding_mask_)[0] + src = src + self.dropout1(src2) + src = self.norm1(src) + src2 = self.linear2(self.dropout(self.activation(self.linear1(src)))) + src = src + self.dropout2(src2) + src = self.norm2(src) + + return src + +class DownTransformerEncoder(nn.Module): + def __init__(self, d_model, n_top_genes, nhead, d_hid, nlayers, dropout): + super().__init__() + self.nlayers = nlayers // 2 + gene_d_model = [n_top_genes // 2 ** i for i in range(0,self.nlayers+1)] + self.transformer_gene = [] # gene encoder + self.transformer_feature = [] # feature encoder + self.down_transfer = [] # down transformer + + for i in range(self.nlayers): + self.transformer_gene.append(TransformerEncoderLayer(gene_d_model[i+1], nhead, d_hid, dropout, batch_first=True)) + self.transformer_feature.append(nn.Sequential(TransformerEncoderLayer(d_model, nhead, d_hid, dropout, batch_first=True), + TransformerEncoderLayer(d_model, nhead, d_hid, dropout, batch_first=True))) + self.down_transfer.append(nn.Sequential( + nn.Linear(gene_d_model[i], gene_d_model[i+1]), + nn.ReLU(), + nn.LayerNorm(gene_d_model[i+1]), + )) + self.down_transfer = nn.ModuleList(self.down_transfer) + self.transformer_gene = nn.ModuleList(self.transformer_gene) + self.transformer_feature = nn.ModuleList(self.transformer_feature) + + def forward(self, src, src_key_padding_mask=None): + for i in range(self.nlayers): + src = self.transformer_feature[i](src) + # src = src.transpose(1,2) + # src = self.down_transfer[i](src) + # src = self.transformer_gene[i](src) + # src = src.transpose(1,2) + return src + +class UpTransformerDecoder(nn.Module): + def __init__(self, d_model, n_top_genes, nhead, d_hid, nlayers, dropout): + super().__init__() + self.nlayers = nlayers // 2 + gene_d_model = [n_top_genes // 2 ** i for i in range(self.nlayers,-1,-1)] + self.transformer_gene = [] # gene encoder + self.transformer_feature = [] # feature encoder + self.up_transfer = [] # up transformer + + for i in range(self.nlayers): + self.transformer_gene.append(TransformerEncoderLayer(gene_d_model[i+1], nhead, d_hid, dropout, batch_first=True)) + self.transformer_feature.append(nn.Sequential(TransformerEncoderLayer(d_model, nhead, d_hid, dropout, batch_first=True), + TransformerEncoderLayer(d_model, nhead, d_hid, dropout, batch_first=True))) + self.up_transfer.append(nn.Sequential( + nn.Linear(gene_d_model[i], gene_d_model[i+1]), + nn.ReLU(), + nn.LayerNorm(gene_d_model[i+1]), + )) + self.up_transfer = nn.ModuleList(self.up_transfer) + self.transformer_gene = nn.ModuleList(self.transformer_gene) + self.transformer_feature = nn.ModuleList(self.transformer_feature) + + def forward(self, src, src_key_padding_mask=None): + for i in range(self.nlayers): + + src = self.transformer_feature[i](src) + # src = src.transpose(1,2) + # src = self.up_transfer[i](src) + # src = self.transformer_gene[i](src) + # src = src.transpose(1,2) + return src + + +class CrossAttentionModule(nn.Module): + def __init__(self, embed_dim, num_heads, dropout=0.0): + super().__init__() + self.attn = nn.MultiheadAttention(embed_dim=embed_dim, + num_heads=num_heads, + dropout=dropout, + batch_first=True) # using (batch, seq, dim) + self.norm1 = nn.LayerNorm(embed_dim) + self.norm2 = nn.LayerNorm(embed_dim) + self.ff = nn.Sequential( + nn.Linear(embed_dim, embed_dim * 4), + nn.GELU(), + nn.Linear(embed_dim * 4, embed_dim), + nn.Dropout(dropout), + ) + self.selfattn = nn.MultiheadAttention(embed_dim=embed_dim, + num_heads=num_heads, + dropout=dropout, + batch_first=True) # using (batch, seq, dim) + self.norm3 = nn.LayerNorm(embed_dim) + self.norm4 = nn.LayerNorm(embed_dim) + self.ff2 = nn.Sequential( + nn.Linear(embed_dim, embed_dim * 4), + nn.GELU(), + nn.Linear(embed_dim * 4, embed_dim), + nn.Dropout(dropout), + ) + + def forward(self, x1, x2, attn_mask=None, key_padding_mask=None): + # x1: (b, l1, dim), x2: (b, l2, dim) + residual = x1 + attn_out, attn_weights = self.attn(query=x1, key=x2, value=x2, + attn_mask=attn_mask, + key_padding_mask=key_padding_mask) + x = self.norm1(attn_out + residual) + + # add FFN + residual2 = x + x = self.ff(x) + x = self.norm2(x + residual2) + + residual = x + attn_out, _ = self.selfattn(query=x, key=x, value=x, + attn_mask=attn_mask, + key_padding_mask=key_padding_mask) + x = self.norm3(attn_out + residual) + + # add FFN + residual2 = x + x = self.ff2(x) + x = self.norm4(x + residual2) + return x, attn_weights +class GeneEncoder(nn.Module): + def __init__( + self, + num_embeddings: int, + embedding_dim: int, + padding_idx: Optional[int] = None, + ): + super().__init__() + self.embedding = nn.Embedding( + num_embeddings, embedding_dim, padding_idx=padding_idx + ) + self.enc_norm = nn.LayerNorm(embedding_dim) + + def forward(self, x: Tensor) -> Tensor: + x = self.embedding(x) # (batch, seq_len, embsize) + x = self.enc_norm(x) + return x + + +class PositionalEncoding(nn.Module): + def __init__(self, d_model: int, dropout: float = 0.1, max_len: int = 5000): + super().__init__() + self.dropout = nn.Dropout(p=dropout) + + position = torch.arange(max_len).unsqueeze(1) + div_term = torch.exp( + torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model) + ) + pe = torch.zeros(max_len, 1, d_model) + pe[:, 0, 0::2] = torch.sin(position * div_term) + pe[:, 0, 1::2] = torch.cos(position * div_term) + self.register_buffer("pe", pe) + + def forward(self, x: Tensor) -> Tensor: + """ + Args: + x: Tensor, shape [seq_len, batch_size, embedding_dim] + """ + x = x + self.pe[: x.size(0)] + return self.dropout(x) + + +class ContinuousValueEncoder(nn.Module): + """ + Encode real number values to a vector using neural nets projection. + """ + + def __init__(self, d_model: int, dropout: float = 0.1, max_value: int = 512): + super().__init__() + self.dropout = nn.Dropout(p=dropout) + self.linear1 = nn.Linear(1, d_model) + self.activation = nn.ReLU() + self.linear2 = nn.Linear(d_model, d_model) + self.norm = nn.LayerNorm(d_model) + self.max_value = max_value + + def forward(self, x: Tensor) -> Tensor: + """ + Args: + x: Tensor, shape [batch_size, seq_len] + """ + # TODO: test using actual embedding layer if input is categorical + # expand last dimension + x = x.unsqueeze(-1) + # clip x to [-inf, max_value] + x = torch.clamp(x, max=self.max_value) + x = self.activation(self.linear1(x)) + x = self.linear2(x) + x = self.norm(x) + return self.dropout(x) + + +class CategoryValueEncoder(nn.Module): + def __init__( + self, + num_embeddings: int, + embedding_dim: int, + padding_idx: Optional[int] = None, + ): + super().__init__() + self.embedding = nn.Embedding( + num_embeddings, embedding_dim, padding_idx=padding_idx + ) + self.enc_norm = nn.LayerNorm(embedding_dim) + + def forward(self, x: Tensor) -> Tensor: + x = x.long() + x = self.embedding(x) # (batch, seq_len, embsize) + x = self.enc_norm(x) + return x + + +class CategoryValueDecoder(nn.Module): + def __init__( + self, + d_model: int, + n_bins: int, + use_batch_labels: bool = False, + nlayers: int = 3, + activation: callable = nn.ReLU, + ): + super().__init__() + self._decoder = nn.ModuleList() + d_in = d_model * 2 if use_batch_labels else d_model + if use_batch_labels: + self._decoder.append(nn.Linear(d_in, d_model)) + self._decoder.append(activation()) + self._decoder.append(nn.LayerNorm(d_model)) + for i in range(nlayers - 1): + self._decoder.append(nn.Linear(d_model, d_model)) + self._decoder.append(activation()) + self._decoder.append(nn.LayerNorm(d_model)) + self.out_layer = nn.Linear(d_model, n_bins) + + def forward(self, x: Tensor) -> Tensor: + """ + Args: + x: Tensor, shape [batch_size, embsize] + output: Tensor, shape [batch_size, n_bins] + """ + for layer in self._decoder: + x = layer(x) + return self.out_layer(x) + + +class BatchLabelEncoder(nn.Module): + def __init__( + self, + num_embeddings: int, + embedding_dim: int, + padding_idx: Optional[int] = None, + ): + super().__init__() + self.embedding = nn.Embedding( + num_embeddings, embedding_dim, padding_idx=padding_idx + ) + self.enc_norm = nn.LayerNorm(embedding_dim) + + def forward(self, x: Tensor) -> Tensor: + x = self.embedding(x) # (batch, embsize) + x = self.enc_norm(x) + return x + + +class Similarity(nn.Module): + """ + Dot product or cosine similarity + """ + + def __init__(self, temp): + super().__init__() + self.temp = temp + self.cos = nn.CosineSimilarity(dim=-1) + + def forward(self, x, y): + return self.cos(x, y) / self.temp + + +class ExprDecoder(nn.Module): + def __init__( + self, + d_model: int, + explicit_zero_prob: bool = False, + use_batch_labels: bool = False, + ): + super().__init__() + d_in = d_model * 2 if use_batch_labels else d_model + self.fc = nn.Sequential( + nn.Linear(d_in, d_model), + nn.LeakyReLU(), + nn.Linear(d_model, d_model), + nn.LeakyReLU(), + nn.Linear(d_model, 1), + ) + self.explicit_zero_prob = explicit_zero_prob + if explicit_zero_prob: + self.zero_logit = nn.Sequential( + nn.Linear(d_in, d_model), + nn.LeakyReLU(), + nn.Linear(d_model, d_model), + nn.LeakyReLU(), + nn.Linear(d_model, 1), + ) + + def forward(self, x: Tensor) -> Dict[str, Tensor]: + """x is the output of the transformer, (batch, seq_len, d_model)""" + pred_value = self.fc(x).squeeze(-1) # (batch, seq_len) + + if not self.explicit_zero_prob: + return dict(pred=pred_value) + zero_logits = self.zero_logit(x).squeeze(-1) # (batch, seq_len) + zero_probs = torch.sigmoid(zero_logits) + return dict(pred=pred_value, zero_probs=zero_probs) + # TODO: note that the return currently is only for training. Since decoder + # is not used in the test setting for the integration task, the eval/inference + # logic is not implemented yet. However, remember to implement it when + # the decoder is used in any test setting. The inference logic will need + # to sample from the bernoulli distribution with the zero_probs. + + +class ClsDecoder(nn.Module): + """ + Decoder for classification task. + """ + + def __init__( + self, + d_model: int, + n_cls: int, + nlayers: int = 3, + activation: callable = nn.ReLU, + ): + super().__init__() + # module list + self._decoder = nn.ModuleList() + for i in range(nlayers - 1): + self._decoder.append(nn.Linear(d_model, d_model)) + self._decoder.append(activation()) + self._decoder.append(nn.LayerNorm(d_model)) + self.out_layer = nn.Linear(d_model, n_cls) + + def forward(self, x: Tensor) -> Tensor: + """ + Args: + x: Tensor, shape [batch_size, embsize] + """ + for layer in self._decoder: + x = layer(x) + return self.out_layer(x) + + +class MVCDecoder(nn.Module): + """ + Decoder for the masked value prediction for cell embeddings. + """ + + def __init__( + self, + d_model: int, + arch_style: str = "inner product", + query_activation: nn.Module = nn.Sigmoid, + hidden_activation: nn.Module = nn.PReLU, + explicit_zero_prob: bool = False, + use_batch_labels: bool = False, + ) -> None: + """ + Args: + d_model (:obj:`int`): dimension of the gene embedding. + arch_style (:obj:`str`): architecture style of the decoder, choice from + 1. "inner product" or 2. "concat query" or 3. "sum query". + query_activation (:obj:`nn.Module`): activation function for the query + vectors. + hidden_activation (:obj:`nn.Module`): activation function for the hidden + layers. + """ + super().__init__() + d_in = d_model * 2 if use_batch_labels else d_model + if arch_style in ["inner product", "inner product, detach"]: + self.gene2query = nn.Linear(d_model, d_model) + self.query_activation = query_activation() + self.W = nn.Linear(d_model, d_in, bias=False) + if explicit_zero_prob: # by default, gene-wise prob rate + self.W_zero_logit = nn.Linear(d_model, d_in) + elif arch_style == "concat query": + self.gene2query = nn.Linear(d_model, 64) + self.query_activation = query_activation() + self.fc1 = nn.Linear(d_model + 64, 64) + self.hidden_activation = hidden_activation() + self.fc2 = nn.Linear(64, 1) + elif arch_style == "sum query": + self.gene2query = nn.Linear(d_model, d_model) + self.query_activation = query_activation() + self.fc1 = nn.Linear(d_model, 64) + self.hidden_activation = hidden_activation() + self.fc2 = nn.Linear(64, 1) + else: + raise ValueError(f"Unknown arch_style: {arch_style}") + + self.arch_style = arch_style + self.do_detach = arch_style.endswith("detach") + self.explicit_zero_prob = explicit_zero_prob + + def forward( + self, cell_emb: Tensor, gene_embs: Tensor + ) -> Union[Tensor, Dict[str, Tensor]]: + """ + Args: + cell_emb: Tensor, shape (batch, embsize=d_model) + gene_embs: Tensor, shape (batch, seq_len, embsize=d_model) + """ + gene_embs = gene_embs.detach() if self.do_detach else gene_embs + if self.arch_style in ["inner product", "inner product, detach"]: + query_vecs = self.query_activation(self.gene2query(gene_embs)) + cell_emb = cell_emb.unsqueeze(2) # (batch, embsize, 1) + # the pred gene expr values, # (batch, seq_len) + pred_value = torch.bmm(self.W(query_vecs), cell_emb).squeeze(2) + if not self.explicit_zero_prob: + return dict(pred=pred_value) + # zero logits need to based on the cell_emb, because of input exprs + zero_logits = torch.bmm(self.W_zero_logit(query_vecs), cell_emb).squeeze(2) + zero_probs = torch.sigmoid(zero_logits) + return dict(pred=pred_value, zero_probs=zero_probs) + elif self.arch_style == "concat query": + query_vecs = self.query_activation(self.gene2query(gene_embs)) + # expand cell_emb to (batch, seq_len, embsize) + cell_emb = cell_emb.unsqueeze(1).expand(-1, gene_embs.shape[1], -1) + + h = self.hidden_activation( + self.fc1(torch.cat([cell_emb, query_vecs], dim=2)) + ) + if self.explicit_zero_prob: + raise NotImplementedError + return self.fc2(h).squeeze(2) # (batch, seq_len) + elif self.arch_style == "sum query": + query_vecs = self.query_activation(self.gene2query(gene_embs)) + cell_emb = cell_emb.unsqueeze(1) + + h = self.hidden_activation(self.fc1(cell_emb + query_vecs)) + if self.explicit_zero_prob: + raise NotImplementedError + return self.fc2(h).squeeze(2) # (batch, seq_len) + + +class AdversarialDiscriminator(nn.Module): + """ + Discriminator for the adversarial training for batch correction. + """ + + def __init__( + self, + d_model: int, + n_cls: int, + nlayers: int = 3, + activation: callable = nn.LeakyReLU, + reverse_grad: bool = False, + ): + super().__init__() + # module list + self._decoder = nn.ModuleList() + for i in range(nlayers - 1): + self._decoder.append(nn.Linear(d_model, d_model)) + self._decoder.append(activation()) + self._decoder.append(nn.LayerNorm(d_model)) + self.out_layer = nn.Linear(d_model, n_cls) + self.reverse_grad = reverse_grad + + def forward(self, x: Tensor) -> Tensor: + """ + Args: + x: Tensor, shape [batch_size, embsize] + """ + if self.reverse_grad: + x = grad_reverse(x, lambd=1.0) + for layer in self._decoder: + x = layer(x) + return self.out_layer(x) + +class GradReverse(Function): + @staticmethod + def forward(ctx, x: torch.Tensor, lambd: float) -> torch.Tensor: + ctx.lambd = lambd + return x.view_as(x) + + @staticmethod + def backward(ctx, grad_output: torch.Tensor) -> torch.Tensor: + return grad_output.neg() * ctx.lambd, None + + +def grad_reverse(x: torch.Tensor, lambd: float = 1.0) -> torch.Tensor: + return GradReverse.apply(x, lambd) + +try: + from flash_attn.flash_attention import FlashMHA + # from flash_attn import flash_attn_qkvpacked_func, flash_attn_func + flash_attn_available = True +except ImportError: + import warnings + + warnings.warn("flash_attn is not installed") + flash_attn_available = False \ No newline at end of file diff --git a/transfer/code/scDFM/src/models/scGPT/modules.py b/transfer/code/scDFM/src/models/scGPT/modules.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/transfer/code/scDFM/src/script/__init__.py b/transfer/code/scDFM/src/script/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d185c87108adb9a0c174b4f18213d11b855ad732 --- /dev/null +++ b/transfer/code/scDFM/src/script/__init__.py @@ -0,0 +1 @@ +# Auto-created by CCFM bootstrap diff --git a/transfer/code/scDFM/src/script/run.py b/transfer/code/scDFM/src/script/run.py new file mode 100644 index 0000000000000000000000000000000000000000..e756bb84d0ab912754846479f7debcab81a631b8 --- /dev/null +++ b/transfer/code/scDFM/src/script/run.py @@ -0,0 +1,341 @@ +import accelerate +import torch +import torch.nn as nn +import tyro +from config.config_flow import FlowConfig as Config +import torch.nn.functional as F +import time +from torch.utils.data import Dataset, DataLoader +import jax +import random +from src.data_process.data import Data, PerturbationDataset +from src.flow_matching.ot import OTPlanSampler +from src.flow_matching.path import AffineProbPath +from src.flow_matching.solver import ODESolver +from src.models.instantiate_model import instantiate_model +from src.tokenizer.gene_tokenizer import GeneVocab +from src.models.perturbation.moduls import PerturbationEmbedding +import pdb +import tqdm +from src.flow_matching.path.scheduler import CondOTScheduler +import scanpy as sc +import os +from src.data_process.utils import build_generated_anndata + +import json +from accelerate import Accelerator,DistributedDataParallelKwargs +import torchdiffeq +from tqdm import trange +import numpy as np +from cell_eval import MetricsEvaluator +import anndata as ad +import pandas as pd +from src.utils.utils import save_checkpoint, load_checkpoint, make_lognorm_poisson_noise, pick_eval_score, process_vocab, set_requires_grad_for_p_only, get_perturbation_emb + +ot_sampler = OTPlanSampler(method="exact") +path = AffineProbPath(scheduler=CondOTScheduler()) + +def gaussian_kernel(x, y, sigma=1.0): + beta = 1.0 / (2.0 * sigma**2) + dist = torch.cdist(x, y, p=2) ** 2 + return torch.exp(-beta * dist) + +def mmd_loss(pred, tgt, sigma=1.0): + xx = gaussian_kernel(pred, pred, sigma).mean(dim=(1)) + yy = gaussian_kernel(tgt, tgt, sigma).mean(dim=(1)) + xy = gaussian_kernel(pred, tgt, sigma).mean(dim=(1)) + return (xx + yy - 2 * xy).mean() + +def pairwise_sq_dists(X, Y): + # X:[m,d], Y:[n,d] -> [m,n] + return torch.cdist(X, Y, p=2)**2 + +@torch.no_grad() +def median_sigmas(X, scales=(0.5, 1.0, 2.0, 4.0)): + Z = X + D2 = pairwise_sq_dists(Z, Z) + tri = D2[~torch.eye(D2.size(0), dtype=bool, device=D2.device)] + m = torch.median(tri).clamp_min(1e-12) + s2 = torch.tensor(scales, device=Z.device) * m + sigmas = torch.sqrt(s2) + return [float(s.item()) for s in sigmas] + +def mmd2_unbiased_multi_sigma(X, Y, sigmas): + """ + """ + m, n = X.size(0), Y.size(0) + Dxx = pairwise_sq_dists(X, X) # [m,m] + Dyy = pairwise_sq_dists(Y, Y) # [n,n] + Dxy = pairwise_sq_dists(X, Y) # [m,n] + + vals = [] + for sigma in sigmas: + beta = 1.0 / (2.0 * (sigma ** 2) + 1e-12) + Kxx = torch.exp(-beta * Dxx) + Kyy = torch.exp(-beta * Dyy) + Kxy = torch.exp(-beta * Dxy) + + term_xx = (Kxx.sum() - Kxx.diag().sum()) / (m * (m - 1) + 1e-12) + term_yy = (Kyy.sum() - Kyy.diag().sum()) / (n * (n - 1) + 1e-12) + term_xy = Kxy.mean() # / (m*n) + vals.append(term_xx + term_yy - 2.0 * term_xy) + + return torch.stack(vals).mean() + +def train_step(source, target, perturbation_id, vf, criterion, accelerator, noise_type='Poisson', mode="predict_y"): + B = source.shape[0] + device = accelerator.device + + input_gene_ids = torch.randperm(source.shape[-1], device=device)[:config.infer_top_gene] + source = source[:,input_gene_ids] + target = target[:,input_gene_ids] + gene = gene_ids.repeat(B,1).to(device) + gene_input = gene[:,input_gene_ids] + + if mode=="predict_y": + # source, target = ot_sampler.sample_plan(source, target) + t = torch.rand(B, device=device) + if noise_type=="Gaussian": + target_noise = torch.randn_like(source) + elif noise_type=="Poisson": + target_noise = make_lognorm_poisson_noise( + target_log=source, + alpha=getattr(config, "poisson_alpha", 0.8), + per_cell_L=getattr(config, "poisson_target_sum", 1e4), # e.g., 1e4 or None + ) + path_x1 = path.sample(t=t, x_0=target_noise, x_1=target) + predicted_x_t_velocity = vf(gene_input,path_x1.x_t, path_x1.t,source,perturbation_id, gene_input, mode=mode) + loss = ((predicted_x_t_velocity - path_x1.dx_t)**2).mean() + + if config.use_mmd_loss: + x1_hat = path_x1.x_t + predicted_x_t_velocity*(1-t).unsqueeze(-1) + sigmas = median_sigmas(target, scales=(0.5,1.0,2.0,4.0)) + + _mmd_loss = mmd2_unbiased_multi_sigma(x1_hat, target, sigmas) + # _mmd_loss = mmd_loss(x1_hat, target) + loss = loss + _mmd_loss * config.gamma + + elif mode=="predict_p": + t_p = torch.ones(B, device=device) # Or uniform(0.7,1.0) + predicted_p_embed = vf(gene_input, target, t_p, source, perturbation_id, gene_input, mode=mode) + if hasattr(vf, "module"): + base_vf = vf.module + else: + base_vf = vf + p_embed_gt = base_vf.get_perturbation_emb(perturbation_id=perturbation_id, cell_1=source) + pred = F.normalize(predicted_p_embed, dim=-1) + tgt = F.normalize(p_embed_gt.detach(), dim=-1) + loss = 1 - (pred * tgt).sum(dim=-1).mean() # cosine distance + + return loss + +@torch.inference_mode() +def test(data_sampler, vf, accelerator, batch_size=128, path='./',vocab=None,scheme='mse'): + gene_ids_test = vocab.encode(list(data_sampler.adata.var_names)) + + gene_ids_test = torch.tensor(gene_ids_test, dtype=torch.long, device=device) + perturbation_name_list = data_sampler._perturbation_covariates + control_data = data_sampler.get_control_data() + all_pred_expressions = [control_data['src_cell_data']] + obs_perturbation_name_pred = ['control']*control_data['src_cell_data'].shape[0] + all_target_expressions = [control_data['src_cell_data']] + obs_perturbation_name_real = ['control']*control_data['src_cell_data'].shape[0] + count = 0 + print('perturbation_name_list:',len(perturbation_name_list)) + for perturbation_name in perturbation_name_list: + perturbation_data = data_sampler.get_perturbation_data(perturbation_name) + target = perturbation_data['tgt_cell_data'] + perturbation_id = perturbation_data['condition_id'] + source = control_data['src_cell_data'] + source = source.to(device) + perturbation_id = perturbation_id.to(device) + if config.perturbation_function == 'crisper': + perturbation_name_crisper = [inverse_dict[int(p_id)] for p_id in perturbation_id[0].cpu().numpy()] + perturbation_id = torch.tensor(vocab.encode(perturbation_name_crisper), dtype=torch.long, device=device) + perturbation_id = perturbation_id.repeat(source.shape[0],1) + + idx = torch.randperm(source.shape[0]) + source = source[idx] + N = 128 + source = source[:N] + + pred_expressions = [] + for i in trange(0, N, batch_size): + batch_perturbation_id = perturbation_id[0].repeat(source[i:i+batch_size].shape[0],1) + + batch_perturbation_id = batch_perturbation_id.to(accelerator.device) + + pred_expression = generate_sample(wrapped_vf,source[i:i+batch_size],batch_perturbation_id,vf,gene_ids=gene_ids_test,gene_all=gene_ids_test) + pred_expressions.append(pred_expression) + + pred_expressions = torch.cat(pred_expressions, dim=0).cpu().numpy() + all_pred_expressions.append(pred_expressions) + all_target_expressions.append(target) + obs_perturbation_name_pred.extend([perturbation_name] * pred_expressions.shape[0]) + obs_perturbation_name_real.extend([perturbation_name] * target.shape[0]) + # count += 1 + # if count > 3: + # break + + all_pred_expressions = np.concatenate(all_pred_expressions, axis=0) + all_target_expressions = np.concatenate(all_target_expressions, axis=0) + obs_pred = pd.DataFrame({'perturbation':obs_perturbation_name_pred}) + obs_real = pd.DataFrame({'perturbation':obs_perturbation_name_real}) + pred = ad.AnnData(X=all_pred_expressions, obs=obs_pred) + real = ad.AnnData(X=all_target_expressions, obs=obs_real) + + + eval_score = None + if accelerator.is_main_process: + evaluator = MetricsEvaluator( + adata_pred=pred, + adata_real=real, + control_pert="control", + pert_col="perturbation", + num_threads=32, + ) + (results, agg_results) = evaluator.compute() + + results.write_csv(os.path.join(path, 'results.csv')) + agg_results.write_csv(os.path.join(path, 'agg_results.csv')) + pred.write_h5ad(os.path.join(path, 'pred.h5ad')) + real.write_h5ad(os.path.join(path, 'real.h5ad')) + + eval_score = pick_eval_score(agg_results, scheme) + print(f"Current evaluation score: {eval_score:.4f}") + + return eval_score + +def wrapped_vf(target,t,source,perturbation_id,vf,gene_ids, gene_all): + + gene = gene_ids.repeat(source.shape[0],1).to(device) + predicted_x_t_velocity = vf(gene,target,t,source,perturbation_id,gene_all) + + return predicted_x_t_velocity + +@torch.no_grad() +def generate_sample(wrapped_vf,source,condition_vec=None,vf=None,gene_ids=None,gene_all=None,steps=20,method="rk4"): + + noise_type = config.noise_type + if noise_type=="Gaussian": + target_noise = torch.randn(source.shape[0],config.infer_top_gene,device=source.device) + elif noise_type=="Poisson": + target_noise = make_lognorm_poisson_noise( + target_log=source, + alpha=getattr(config, "poisson_alpha", 0.8), + per_cell_L=getattr(config, "poisson_target_sum", 1e4), + ) + + traj = torchdiffeq.odeint(lambda t,x: wrapped_vf(x,t,source,condition_vec,vf,gene_ids,gene_all), + target_noise, + torch.linspace(0,1,steps).to(source.device), + atol=1e-4, + rtol=1e-4, + method=method) + # t = torch.linspace(0,1,steps).to(source.device) + # traj = [target_noise + 0.8*wrapped_vf(target_noise,t,source,condition_vec,vf,gene_ids,gene_all)] + + return torch.clamp(traj[-1], min=0) + +if __name__ == "__main__": + config = tyro.cli(Config) + + ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=True) + + accelerator = Accelerator( + kwargs_handlers=[ddp_kwargs] + ) + if accelerator.is_main_process: + print(config) + save_path = config.make_path() + os.makedirs(save_path, exist_ok=True) + device = accelerator.device + + data_manager = Data('./data') + + data_manager.load_data(config.data_name) + data_manager.process_data(n_top_genes=config.n_top_genes, split_method=config.split_method, fold=config.fold, use_negative_edge=config.use_negative_edge, k=config.topk) + train_sampler, valid_sampler, test_dl = data_manager.load_flow_data(batch_size=config.batch_size) + + train_dataset = PerturbationDataset(train_sampler, config.batch_size) + dataloader = DataLoader(train_dataset, batch_size=1, shuffle=False,num_workers=8,pin_memory=True,persistent_workers=True) # batch_size=1 因为每个getitem本身就是一个batch + if config.use_negative_edge: + mask_path = os.path.join(data_manager.data_path, data_manager.data_name,'mask_fold_'+str(config.fold)+'topk_'+str(config.topk)+config.split_method+'_negative_edge'+'.pt') + else: + mask_path = os.path.join(data_manager.data_path, data_manager.data_name,'mask_fold_'+str(config.fold)+'topk_'+str(config.topk)+config.split_method+'.pt') + vf = instantiate_model(config.model_type, + ntoken = config.ntoken, + d_model = config.d_model, + d_perturbation = config.d_model, + fusion_method = config.fusion_method, + perturbation_function = config.perturbation_function, + mask_path = mask_path + ) + + model_path = config.make_path() + + vocab = process_vocab(data_manager, config) + + gene_ids = vocab.encode(list(data_manager.adata.var_names)) + + gene_ids = torch.tensor(gene_ids, dtype=torch.long, device=device) + + save_path = config.make_path() + best_loss = float('inf') + + criterion = nn.MSELoss() + optimizer = torch.optim.Adam(vf.parameters(), lr=config.lr) + scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=config.steps, eta_min=config.eta_min) + + if config.checkpoint_path != '': + _, _ = load_checkpoint(config.checkpoint_path, vf, optimizer, scheduler) + start_iteration = 0 + vf = accelerator.prepare(vf) + optimizer, scheduler, dataloader = accelerator.prepare(optimizer,scheduler,dataloader) + inverse_dict = {v: str(k) for k, v in data_manager.perturbation_dict.items()} + pbar = tqdm.tqdm(total=config.steps, initial=start_iteration) + iteration = start_iteration + while iteration < config.steps: + for batch_data in dataloader: + + source = batch_data['src_cell_data'].squeeze(0) + target = batch_data['tgt_cell_data'].squeeze(0) + perturbation_id = batch_data['condition_id'].squeeze(0).to(device) + if config.perturbation_function == 'crisper': + perturbation_name = [inverse_dict[int(p_id)] for p_id in perturbation_id[0].cpu().numpy()] + perturbation_id = torch.tensor(vocab.encode(perturbation_name), dtype=torch.long, device=device) + perturbation_id = perturbation_id.repeat(source.shape[0],1) + + + set_requires_grad_for_p_only(vf, p_only=config.mode) + loss = train_step(source, target, perturbation_id, vf, criterion, accelerator, noise_type=config.noise_type, mode=config.mode) + optimizer.zero_grad(set_to_none=True) + accelerator.backward(loss) + optimizer.step() + scheduler.step() + + + if iteration % config.print_every == 0: + save_path_ = os.path.join(save_path, f'iteration_{iteration}') + os.makedirs(save_path_, exist_ok=True) + if accelerator.is_main_process: + print(f"svaing {iteration}'s checkpoint...") + + save_checkpoint( + model=accelerator.unwrap_model(vf), + optimizer=optimizer, + scheduler=scheduler, + iteration=iteration, + eval_score=None, # 不需要评估分数 + save_path=save_path_, + is_best=False + ) + eval_score = test(valid_sampler, vf, accelerator, batch_size=config.batch_size, path=save_path_,vocab=vocab) + + accelerator.wait_for_everyone() + + pbar.update(1) + pbar.set_description(f'loss: {loss.item():.4f}, iteration: {iteration}') + iteration += 1 + + \ No newline at end of file diff --git a/transfer/code/scDFM/src/tokenizer/__init__.py b/transfer/code/scDFM/src/tokenizer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d185c87108adb9a0c174b4f18213d11b855ad732 --- /dev/null +++ b/transfer/code/scDFM/src/tokenizer/__init__.py @@ -0,0 +1 @@ +# Auto-created by CCFM bootstrap diff --git a/transfer/code/scDFM/src/tokenizer/combosciplex_5000_highly_vocab.json b/transfer/code/scDFM/src/tokenizer/combosciplex_5000_highly_vocab.json new file mode 100644 index 0000000000000000000000000000000000000000..44e9c9325d015ba43d2eb271f91b68a57fe05aeb --- /dev/null +++ b/transfer/code/scDFM/src/tokenizer/combosciplex_5000_highly_vocab.json @@ -0,0 +1,5006 @@ +{ + "": 0, + "": 1, + "": 2, + "control": 3, + "A1CF": 4, + "A2MP1": 5, + "A3GALT2": 6, + "AACSP1": 7, + "AADAC": 8, + "AADACL2-AS1": 9, + "AADACP1": 10, + "AAMDC": 11, + "ABCA13": 12, + "ABCA7": 13, + "ABCB1": 14, + "ABCC2": 15, + "ABCC3": 16, + "ABCC6": 17, + "ABCC9": 18, + "ABCG2": 19, + "ABCG5": 20, + "ABHD14A": 21, + "ABHD17C": 22, + "ABI3BP": 23, + "ABTB2": 24, + "AC000095.2": 25, + "AC000403.1": 26, + "AC002066.1": 27, + "AC002070.1": 28, + "AC002347.2": 29, + "AC002350.2": 30, + "AC002383.1": 31, + "AC002451.2": 32, + "AC002463.1": 33, + "AC002472.1": 34, + "AC002546.2": 35, + "AC003035.1": 36, + "AC003092.1": 37, + "AC003666.1": 38, + "AC003682.1": 39, + "AC003973.3": 40, + "AC003991.1": 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torchtext dependency and create custom Vocab class +class Vocab: + """ + Custom vocabulary implementation to replace torchtext.vocab.Vocab + """ + + def __init__(self, token2idx: Dict[str, int]): + self.stoi = token2idx + self.itos = {idx: token for token, idx in token2idx.items()} + self.default_index = None + self.default_token = None + + def __getitem__(self, token: str) -> int: + return self.stoi[token] + + def __contains__(self, token: str) -> bool: + return token in self.stoi + + def __len__(self) -> int: + return len(self.stoi) + + def get_stoi(self) -> Dict[str, int]: + return self.stoi + + def get_itos(self) -> Dict[int, str]: + return self.itos + + def insert_token(self, token: str, index: int) -> None: + """Insert a token at a specific index""" + self.stoi[token] = index + self.itos[index] = token + + def set_default_index(self, index: int) -> None: + """Set the default index for unknown tokens""" + self.default_index = index + + def set_default_token(self, token: str) -> None: + """Set the default token""" + if token in self.stoi: + self.default_token = token + self.default_index = self.stoi[token] + + def encode(self, tokens: Union[str, List[str], List[List[str]]]) -> Union[int, List[int], List[List[int]]]: + """ + Convert tokens to indices. + + Args: + tokens: Single token (str) or list of tokens + + Returns: + Single index (int) or list of indices + """ + if isinstance(tokens, str): + return self.stoi[tokens] + elif isinstance(tokens, list) and not isinstance(tokens[0], list): + return [self.stoi[token] for token in tokens] + elif isinstance(tokens, list) and isinstance(tokens[0], list): + return [[self.stoi[token] for token in token_list] for token_list in tokens] + else: + raise TypeError(f"Expected str or list of str, got {type(tokens)}") + + def decode(self, indices: Union[int, List[int], torch.Tensor, np.ndarray]) -> Union[str, List[str], torch.Tensor, np.ndarray]: + """ + Convert indices to tokens. + + Args: + indices: Single index (int) or list of indices + + Returns: + Single token (str) or list of tokens + """ + if isinstance(indices, int): + return self.itos[indices] + elif isinstance(indices, list): + return [self.itos[idx] for idx in indices] + elif isinstance(indices, torch.Tensor) or isinstance(indices, np.ndarray): + if len(indices.shape) > 1: + return [self.decode(indices[i]) for i in range(indices.shape[0])] + else: + return [self.itos[idx] for idx in indices] + else: + raise TypeError(f"Expected int or list of int, got {type(indices)}") + + def encode_batch(self, batch_tokens: List[List[str]]) -> List[List[int]]: + """ + Convert a batch of token lists to indices. + + Args: + batch_tokens: List of token lists + + Returns: + List of index lists + """ + return [self.encode(tokens) for tokens in batch_tokens] + + def decode_batch(self, batch_indices: List[List[int]]) -> List[List[str]]: + """ + Convert a batch of index lists to tokens. + + Args: + batch_indices: List of index lists + + Returns: + List of token lists + """ + return [self.decode(indices) for indices in batch_indices] + + def lookup_indices(self, tokens: List[str]) -> List[int]: + """ + Look up indices for a list of tokens, handling unknown tokens. + + Args: + tokens: List of tokens + + Returns: + List of indices, using default_index for unknown tokens + """ + indices = [] + for token in tokens: + if token in self.stoi: + indices.append(self.stoi[token]) + elif self.default_index is not None: + indices.append(self.default_index) + else: + raise KeyError(f"Token '{token}' not found in vocabulary and no default index set") + return indices + + def lookup_tokens(self, indices: List[int]) -> List[str]: + """ + Look up tokens for a list of indices. + + Args: + indices: List of indices + + Returns: + List of tokens + """ + tokens = [] + for idx in indices: + if idx in self.itos: + tokens.append(self.itos[idx]) + else: + raise KeyError(f"Index {idx} not found in vocabulary") + return tokens + + +class GeneVocab(Vocab): + """ + Vocabulary for genes. + """ + + def __init__( + self, + gene_list_or_vocab: Union[List[str], Vocab], + specials: Optional[List[str]] = None, + special_first: bool = True, + default_token: Optional[str] = "", + ) -> None: + """ + Initialize the vocabulary. + Note: add specials only works when init from a gene list. + + Args: + gene_list_or_vocab (List[str] or Vocab): List of gene names or a + Vocab object. + specials (List[str]): List of special tokens. + special_first (bool): Whether to add special tokens to the beginning + of the vocabulary. + default_token (str): Default token, by default will set to "", + if "" is in the vocabulary. + """ + if isinstance(gene_list_or_vocab, Vocab): + _vocab = gene_list_or_vocab + if specials is not None: + raise ValueError( + "receive non-empty specials when init from a Vocab object." + ) + elif isinstance(gene_list_or_vocab, list): + _vocab = self._build_vocab_from_iterator( + gene_list_or_vocab, + specials=specials, + special_first=special_first, + ) + else: + raise ValueError( + "gene_list_or_vocab must be a list of gene names or a Vocab object." + ) + super().__init__(_vocab.stoi) + if default_token is not None and default_token in self: + self.set_default_token(default_token) + + @classmethod + def from_file(cls, file_path: Union[Path, str]) -> Self: + """ + Load the vocabulary from a file. The file should be either a pickle or a + json file of token to index mapping. + """ + if isinstance(file_path, str): + file_path = Path(file_path) + if file_path.suffix == ".pkl": + with file_path.open("rb") as f: + vocab = pickle.load(f) + return cls(vocab) + elif file_path.suffix == ".json": + with file_path.open("r") as f: + token2idx = json.load(f) + return cls.from_dict(token2idx) + else: + raise ValueError( + f"{file_path} is not a valid file type. " + "Only .pkl and .json are supported." + ) + + @classmethod + def from_dict( + cls, + token2idx: Dict[str, int], + default_token: Optional[str] = "", + ) -> Self: + """ + Load the vocabulary from a dictionary. + + Args: + token2idx (Dict[str, int]): Dictionary mapping tokens to indices. + """ + # initiate an empty vocabulary first + _vocab = cls([]) + + # add the tokens to the vocabulary, GeneVocab requires consecutive indices + for t, i in sorted(token2idx.items(), key=lambda x: x[1]): + _vocab.insert_token(t, i) + + if default_token is not None and default_token in _vocab: + _vocab.set_default_token(default_token) + + return _vocab + + def _build_vocab_from_iterator( + self, + iterator: Iterable, + min_freq: int = 1, + specials: Optional[List[str]] = None, + special_first: bool = True, + ) -> Vocab: + """ + Build a Vocab from an iterator. This function is modified from + torchtext.vocab.build_vocab_from_iterator. The original function always + splits tokens into characters, which is not what we want. + + Args: + iterator (Iterable): Iterator used to build Vocab. Must yield list + or iterator of tokens. + min_freq (int): The minimum frequency needed to include a token in + the vocabulary. + specials (List[str]): Special symbols to add. The order of supplied + tokens will be preserved. + special_first (bool): Whether to add special tokens to the beginning + + Returns: + Vocab: A `Vocab` object + """ + + counter = Counter() + counter.update(iterator) + + if specials is not None: + for tok in specials: + del counter[tok] + + sorted_by_freq_tuples = sorted(counter.items(), key=lambda x: x[0]) + sorted_by_freq_tuples.sort(key=lambda x: x[1], reverse=True) + ordered_dict = OrderedDict(sorted_by_freq_tuples) + + if specials is not None: + if special_first: + specials = specials[::-1] + for symbol in specials: + ordered_dict.update({symbol: min_freq}) + ordered_dict.move_to_end(symbol, last=not special_first) + + # Create token2idx mapping + token2idx = {} + for i, (token, _) in enumerate(ordered_dict.items()): + if ordered_dict[token] >= min_freq: + token2idx[token] = i + + return Vocab(token2idx) + + @property + def pad_token(self) -> Optional[str]: + """ + Get the pad token. + """ + if getattr(self, "_pad_token", None) is None: + self._pad_token = None + return self._pad_token + + @pad_token.setter + def pad_token(self, pad_token: str) -> None: + """ + Set the pad token. Will not add the pad token to the vocabulary. + + Args: + pad_token (str): Pad token, should be in the vocabulary. + """ + if pad_token not in self: + raise ValueError(f"{pad_token} is not in the vocabulary.") + self._pad_token = pad_token + + def save_json(self, file_path: Union[Path, str]) -> None: + """ + Save the vocabulary to a json file. + """ + if isinstance(file_path, str): + file_path = Path(file_path) + with file_path.open("w") as f: + json.dump(self.get_stoi(), f, indent=2) + + def set_default_token(self, default_token: str) -> None: + """ + Set the default token. + + Args: + default_token (str): Default token. + """ + if default_token not in self: + raise ValueError(f"{default_token} is not in the vocabulary.") + self.set_default_index(self[default_token]) + + def get_gene_list(self) -> List[str]: + """ + Get the list of all genes in the vocabulary. + + Returns: + List of gene names + """ + return list(self.stoi.keys()) + + def get_gene_indices(self) -> List[int]: + """ + Get the list of all gene indices in the vocabulary. + + Returns: + List of gene indices + """ + return list(self.itos.keys()) + + def add_gene(self, gene_name: str) -> int: + """ + Add a new gene to the vocabulary. + + Args: + gene_name: Name of the gene to add + + Returns: + Index assigned to the gene + """ + if gene_name in self.stoi: + return self.stoi[gene_name] + + new_index = len(self.stoi) + self.insert_token(gene_name, new_index) + return new_index + + def add_genes(self, gene_names: List[str]) -> List[int]: + """ + Add multiple genes to the vocabulary. + + Args: + gene_names: List of gene names to add + + Returns: + List of indices assigned to the genes + """ + indices = [] + for gene_name in gene_names: + indices.append(self.add_gene(gene_name)) + return indices + + def filter_genes(self, gene_names: List[str]) -> 'GeneVocab': + """ + Create a new vocabulary containing only the specified genes. + + Args: + gene_names: List of gene names to keep + + Returns: + New GeneVocab with filtered genes + """ + filtered_stoi = {gene: self.stoi[gene] for gene in gene_names if gene in self.stoi} + return GeneVocab.from_dict(filtered_stoi, default_token=self.default_token) + + def get_gene_frequency(self, gene_name: str) -> int: + """ + Get the frequency of a gene in the vocabulary. + Note: This is a placeholder method since we don't track frequencies in this implementation. + + Args: + gene_name: Name of the gene + + Returns: + Frequency of the gene (always 1 in this implementation) + """ + if gene_name in self.stoi: + return 1 + return 0 + + +def get_default_gene_vocab() -> GeneVocab: + """ + Get the default gene vocabulary, consisting of gene symbols and ids. + """ + vocab_file = Path(__file__).parent / "default_gene_vocab.json" + if not vocab_file.exists(): + print( + f"No existing default vocab, will build one and save to {vocab_file}" + ) + return _build_default_gene_vocab(save_vocab_to=vocab_file) + print(f"Loading gene vocabulary from {vocab_file}") + return GeneVocab.from_file(vocab_file) + + +def _build_default_gene_vocab( + download_source_to: str = "/tmp", + save_vocab_to: Union[Path, str, None] = None, +) -> GeneVocab: + """ + Build the default gene vocabulary from HGNC gene symbols. + + Args: + download_source_to (str): Directory to download the source data. + save_vocab_to (Path or str): Path to save the vocabulary. If None, + the vocabulary will not be saved. Default to None. + """ + gene_collection_file = ( + Path(download_source_to) / "human.gene_name_symbol.from_genenames.org.tsv" + ) + if not gene_collection_file.exists(): + # download and save file from url + url = ( + "https://www.genenames.org/cgi-bin/download/custom?col=gd_app_sym&" + "col=md_ensembl_id&status=Approved&status=Entry%20Withdrawn&hgnc_dbtag" + "=on&order_by=gd_app_sym_sort&format=text&submit=submit" + ) + import requests + + r = requests.get(url) + gene_collection_file.write_text(r.text) + + print(f"Building gene vocabulary from {gene_collection_file}") + df = pd.read_csv(gene_collection_file, sep="\t") + gene_list = df["Approved symbol"].dropna().unique().tolist() + gene_vocab = GeneVocab(gene_list) # no special tokens set in default vocab + if save_vocab_to is not None: + gene_vocab.save_json(Path(save_vocab_to)) + return gene_vocab + + +def tokenize_batch( + data: np.ndarray, + gene_ids: np.ndarray, + return_pt: bool = True, + append_cls: bool = True, + include_zero_gene: bool = False, + cls_id: int = "", + mod_type: np.ndarray = None, + cls_id_mod_type: int = None, +) -> List[Tuple[Union[torch.Tensor, np.ndarray]]]: + """ + Tokenize a batch of data. Returns a list of tuple (gene_id, count). + + Args: + data (array-like): A batch of data, with shape (batch_size, n_features). + n_features equals the number of all genes. + gene_ids (array-like): A batch of gene ids, with shape (n_features,). + return_pt (bool): Whether to return torch tensors of gene_ids and counts, + default to True. + + Returns: + list: A list of tuple (gene_id, count) of non zero gene expressions. + """ + if data.shape[1] != len(gene_ids): + raise ValueError( + f"Number of features in data ({data.shape[1]}) does not match " + f"number of gene_ids ({len(gene_ids)})." + ) + if mod_type is not None and data.shape[1] != len(mod_type): + raise ValueError( + f"Number of features in data ({data.shape[1]}) does not match " + f"number of mod_type ({len(mod_type)})." + ) + + tokenized_data = [] + for i in range(len(data)): + row = data[i] + mod_types = None + if include_zero_gene: + values = row + genes = gene_ids + if mod_type is not None: + mod_types = mod_type + else: + idx = np.nonzero(row)[0] + values = row[idx] + genes = gene_ids[idx] + if mod_type is not None: + mod_types = mod_type[idx] + if append_cls: + genes = np.insert(genes, 0, cls_id) + values = np.insert(values, 0, 0) + if mod_type is not None: + mod_types = np.insert(mod_types, 0, cls_id_mod_type) + if return_pt: + genes = torch.from_numpy(genes).long() + values = torch.from_numpy(values).float() + if mod_type is not None: + mod_types = torch.from_numpy(mod_types).long() + tokenized_data.append((genes, values, mod_types)) + return tokenized_data + + +def pad_batch( + batch: List[Tuple], + max_len: int, + vocab: Vocab, + pad_token: str = "", + pad_value: int = 0, + cls_appended: bool = True, + vocab_mod: Vocab = None, +) -> Dict[str, torch.Tensor]: + """ + Pad a batch of data. Returns a list of Dict[gene_id, count]. + + Args: + batch (list): A list of tuple (gene_id, count). + max_len (int): The maximum length of the batch. + vocab (Vocab): The vocabulary containing the pad token. + pad_token (str): The token to pad with. + + Returns: + Dict[str, torch.Tensor]: A dictionary of gene_id and count. + """ + max_ori_len = max(len(batch[i][0]) for i in range(len(batch))) + max_len = min(max_ori_len, max_len) + + pad_id = vocab[pad_token] + if vocab_mod is not None: + mod_pad_id = vocab_mod[pad_token] + gene_ids_list = [] + values_list = [] + mod_types_list = [] + + for i in range(len(batch)): + gene_ids, values, mod_types = batch[i] + + if len(gene_ids) > max_len: + # sample max_len genes + if not cls_appended: + idx = np.random.choice(len(gene_ids), max_len, replace=False) + else: + idx = np.random.choice(len(gene_ids) - 1, max_len - 1, replace=False) + idx = idx + 1 + idx = np.insert(idx, 0, 0) + gene_ids = gene_ids[idx] + values = values[idx] + if mod_types is not None: + mod_types = mod_types[idx] + if len(gene_ids) < max_len: + gene_ids = torch.cat( + [ + gene_ids, + torch.full( + (max_len - len(gene_ids),), pad_id, dtype=gene_ids.dtype + ), + ] + ) + values = torch.cat( + [ + values, + torch.full((max_len - len(values),), pad_value, dtype=values.dtype), + ] + ) + if mod_types is not None: + mod_types = torch.cat( + [ + mod_types, + torch.full( + (max_len - len(mod_types),), + mod_pad_id, + dtype=mod_types.dtype, + ), + ] + ) + + gene_ids_list.append(gene_ids) + values_list.append(values) + if mod_types is not None: + mod_types_list.append(mod_types) + + batch_padded = { + "genes": torch.stack(gene_ids_list, dim=0), + "values": torch.stack(values_list, dim=0), + } + if mod_types is not None: + batch_padded["mod_types"] = torch.stack(mod_types_list, dim=0) + return batch_padded + + +def tokenize_and_pad_batch( + data: np.ndarray, + gene_ids: np.ndarray, + max_len: int, + vocab: Vocab, + pad_token: str, + pad_value: int, + append_cls: bool = True, + include_zero_gene: bool = False, + cls_token: str = "", + return_pt: bool = True, + mod_type: np.ndarray = None, + vocab_mod: Vocab = None, +) -> Dict[str, torch.Tensor]: + """ + Tokenize and pad a batch of data. Returns a list of tuple (gene_id, count). + """ + cls_id = vocab[cls_token] + if mod_type is not None: + cls_id_mod_type = vocab_mod[cls_token] + tokenized_data = tokenize_batch( + data, + gene_ids, + return_pt=return_pt, + append_cls=append_cls, + include_zero_gene=include_zero_gene, + cls_id=cls_id, + mod_type=mod_type, + cls_id_mod_type=cls_id_mod_type if mod_type is not None else None, + ) + + batch_padded = pad_batch( + tokenized_data, + max_len, + vocab, + pad_token, + pad_value, + cls_appended=append_cls, + vocab_mod=vocab_mod, + ) + return batch_padded + + +def random_mask_value( + values: Union[torch.Tensor, np.ndarray], + mask_ratio: float = 0.15, + mask_value: int = -1, + pad_value: int = 0, + random_mask_zero_gene: bool = False, +) -> torch.Tensor: + """ + Randomly mask a batch of data. + + Args: + values (array-like): + A batch of tokenized data, with shape (batch_size, n_features). + mask_ratio (float): The ratio of genes to mask, default to 0.15. + mask_value (int): The value to mask with, default to -1. + pad_value (int): The value of padding in the values, will be kept unchanged. + + Returns: + torch.Tensor: A tensor of masked data. + """ + if isinstance(values, torch.Tensor): + # it is crutial to clone the tensor, otherwise it changes the original tensor + values = values.clone().detach().numpy() + else: + values = values.copy() + + for i in range(len(values)): + row = values[i] + non_padding_idx = np.nonzero(row - pad_value)[0] + n_mask = int(len(non_padding_idx) * mask_ratio) + mask_idx = np.random.choice(non_padding_idx, n_mask, replace=False) + if random_mask_zero_gene: + zero_idx = np.where(row == 0)[0] + if len(zero_idx) > 0: + n_mask_zero = min(n_mask, len(zero_idx)) + mask_zero_idx = np.random.choice(zero_idx, n_mask_zero, replace=False) + row[mask_zero_idx] = mask_value + row[mask_idx] = mask_value + return torch.from_numpy(values).float() + +def category_noise(values: Union[torch.Tensor, np.ndarray], mask_ratio: float = 0.15, pad_value: int = 0, n_bins: int = 200): + """ + Randomly replace values with noise from [0, n_bins] range and return both noisy values and mask. + + Args: + values (array-like): A batch of tokenized data, with shape (batch_size, n_features) + mask_ratio (float): The ratio of values to replace with noise, default to 0.15 + pad_value (int): The value of padding in the values, will be kept unchanged + n_bins (int): The maximum bin value (exclusive) for noise generation + + Returns: + Tuple[torch.Tensor, torch.Tensor]: A tuple containing: + - noisy data tensor with same shape as input + - binary mask tensor indicating which positions were noised (1 for noised, 0 for unchanged) + """ + if isinstance(values, torch.Tensor): + values = values.clone().detach().numpy() + else: + values = values.copy() + + # Initialize mask array with same shape as values + mask = np.zeros_like(values) + + for i in range(len(values)): + row = values[i] + row_mask = mask[i] + + # Get indices of non-zero and non-padding values + nonzero_idx = np.where((row != 0) & (row != pad_value))[0] + zero_idx = np.where(row == 0)[0] + + # Calculate number of values to noise for each category + n_noise_nonzero = int(len(nonzero_idx) * mask_ratio) + n_noise_zero = int(len(zero_idx) * mask_ratio) + # n_noise_zero = int(n_noise_nonzero/10) + # Randomly select indices to noise + if len(nonzero_idx) > 0: + noise_nonzero_idx = np.random.choice(nonzero_idx, n_noise_nonzero, replace=False) + row[noise_nonzero_idx] = np.random.randint(0, n_bins, size=n_noise_nonzero) + row_mask[noise_nonzero_idx] = 1 + + if len(zero_idx) > 0: + noise_zero_idx = np.random.choice(zero_idx, n_noise_zero, replace=False) + row[noise_zero_idx] = np.random.randint(0, n_bins, size=n_noise_zero) + row_mask[noise_zero_idx] = 1 + + return torch.from_numpy(values).float(), torch.from_numpy(mask).bool() + +def batch_encode_genes( + gene_lists: List[List[str]], + vocab: Vocab, + max_length: Optional[int] = None, + pad_token: str = "", + truncate: bool = True +) -> Dict[str, torch.Tensor]: + """ + Encode a batch of gene lists to indices with padding. + + Args: + gene_lists: List of gene name lists + vocab: Vocabulary to use for encoding + max_length: Maximum length for padding/truncation + pad_token: Token to use for padding + truncate: Whether to truncate sequences longer than max_length + + Returns: + Dictionary with 'gene_ids' tensor and 'attention_mask' tensor + """ + if max_length is None: + max_length = max(len(genes) for genes in gene_lists) + + batch_size = len(gene_lists) + gene_ids = [] + attention_mask = [] + + for genes in gene_lists: + # Encode genes to indices + indices = vocab.encode(genes) + + # Truncate if necessary + if truncate and len(indices) > max_length: + indices = indices[:max_length] + + # Pad if necessary + if len(indices) < max_length: + pad_length = max_length - len(indices) + indices.extend([vocab[pad_token]] * pad_length) + mask = [1] * len(genes) + [0] * pad_length + else: + mask = [1] * max_length + + gene_ids.append(indices) + attention_mask.append(mask) + + return { + 'gene_ids': torch.tensor(gene_ids, dtype=torch.long), + 'attention_mask': torch.tensor(attention_mask, dtype=torch.long) + } + + +def batch_decode_genes( + gene_ids: torch.Tensor, + vocab: Vocab, + attention_mask: Optional[torch.Tensor] = None, + pad_token: str = "" +) -> List[List[str]]: + """ + Decode a batch of gene indices back to gene names. + + Args: + gene_ids: Tensor of shape (batch_size, seq_length) containing gene indices + vocab: Vocabulary to use for decoding + attention_mask: Optional attention mask to ignore padding tokens + pad_token: Token to ignore during decoding + + Returns: + List of gene name lists + """ + batch_size = gene_ids.shape[0] + decoded_genes = [] + + for i in range(batch_size): + indices = gene_ids[i].tolist() + genes = [] + + for j, idx in enumerate(indices): + # Skip padding tokens + if attention_mask is not None and attention_mask[i][j] == 0: + continue + if idx == vocab[pad_token]: + continue + + gene_name = vocab.decode(idx) + genes.append(gene_name) + + decoded_genes.append(genes) + + return decoded_genes + + +def create_gene_vocab_from_data( + gene_data: List[List[str]], + min_freq: int = 1, + special_tokens: Optional[List[str]] = None, + default_token: str = "" +) -> GeneVocab: + """ + Create a gene vocabulary from gene data. + + Args: + gene_data: List of gene lists + min_freq: Minimum frequency for a gene to be included + special_tokens: Special tokens to add to vocabulary + default_token: Default token for unknown genes + + Returns: + GeneVocab object + """ + # Flatten all gene lists + all_genes = [] + for gene_list in gene_data: + all_genes.extend(gene_list) + + # Count frequencies + gene_counts = Counter(all_genes) + + # Filter by minimum frequency + filtered_genes = [gene for gene, count in gene_counts.items() if count >= min_freq] + + # Add special tokens if provided + if special_tokens: + filtered_genes = special_tokens + filtered_genes + + return GeneVocab(filtered_genes, specials=special_tokens, default_token=default_token) + + +def merge_gene_vocabs( + vocab1: GeneVocab, + vocab2: GeneVocab, + default_token: str = "" +) -> GeneVocab: + """ + Merge two gene vocabularies. + + Args: + vocab1: First vocabulary + vocab2: Second vocabulary + default_token: Default token for the merged vocabulary + + Returns: + Merged GeneVocab object + """ + # Get all genes from both vocabularies + all_genes = list(vocab1.get_gene_list()) + list(vocab2.get_gene_list()) + + # Remove duplicates while preserving order + unique_genes = [] + seen = set() + for gene in all_genes: + if gene not in seen: + unique_genes.append(gene) + seen.add(gene) + + return GeneVocab(unique_genes, default_token=default_token) + +if __name__ == "__main__": + + vocab = GeneVocab(['GENE1', 'GENE2', 'GENE3'], specials=['', '']) + + indices = vocab.encode(['GENE1', 'GENE2']) + genes = vocab.decode(indices) + + gene_lists = [['GENE1', 'GENE2'], ['GENE3']] + batch_result = batch_encode_genes(gene_lists, vocab, max_length=5) + + gene_data = [['GENE1', 'GENE2'], ['GENE2', 'GENE3']] + vocab_from_data = create_gene_vocab_from_data(gene_data, min_freq=2) \ No newline at end of file diff --git a/transfer/code/scDFM/src/tokenizer/norman_5000_highly_vocab.json b/transfer/code/scDFM/src/tokenizer/norman_5000_highly_vocab.json new file mode 100644 index 0000000000000000000000000000000000000000..ce59caf349e949b11db2e3a6e9e86bb51ac35747 --- /dev/null +++ b/transfer/code/scDFM/src/tokenizer/norman_5000_highly_vocab.json @@ -0,0 +1,5041 @@ +{ + "": 0, + "": 1, + "": 2, + "control": 3, + "A2M": 4, + "A2M-AS1": 5, + "A2ML1": 6, + "AAK1": 7, + "AASS": 8, + "AATBC": 9, + "ABAT": 10, + "ABC11-4932300O16.1": 11, + "ABC12-49244600F4.4": 12, + "ABCA1": 13, + "ABCA13": 14, + "ABCA4": 15, + "ABCA5": 16, + "ABCA6": 17, + "ABCA9": 18, + "ABCB1": 19, + "ABCB11": 20, + "ABCG1": 21, + "ABCG2": 22, + "ABHD1": 23, + "ABHD11-AS1": 24, + "ABHD12B": 25, + "ABHD17C": 26, + "ABHD4": 27, + 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4995, + "ZMIZ1-AS1": 4996, + "ZMYND12": 4997, + "ZMYND15": 4998, + "ZNF117": 4999, + "ZNF30-AS1": 5000, + "ZNF318": 5001, + "ZNF32-AS3": 5002, + "ZNF366": 5003, + "ZNF396": 5004, + "ZNF397": 5005, + "ZNF425": 5006, + "ZNF431": 5007, + "ZNF436-AS1": 5008, + "ZNF454": 5009, + "ZNF467": 5010, + "ZNF483": 5011, + "ZNF492": 5012, + "ZNF503": 5013, + "ZNF503-AS1": 5014, + "ZNF536": 5015, + "ZNF541": 5016, + "ZNF556": 5017, + "ZNF570": 5018, + "ZNF578": 5019, + "ZNF610": 5020, + "ZNF620": 5021, + "ZNF625": 5022, + "ZNF658": 5023, + "ZNF677": 5024, + "ZNF683": 5025, + "ZNF738": 5026, + "ZNF763": 5027, + "ZNF780B": 5028, + "ZNF83": 5029, + "ZNF843": 5030, + "ZNF844": 5031, + "ZNF85": 5032, + "ZNF862": 5033, + "ZNF879": 5034, + "ZSWIM1": 5035, + "ZSWIM4": 5036, + "ZSWIM8": 5037, + "ZYX": 5038 +} \ No newline at end of file diff --git a/transfer/code/scDFM/src/utils/__init__.py b/transfer/code/scDFM/src/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/transfer/code/scDFM/src/utils/_logging.py b/transfer/code/scDFM/src/utils/_logging.py new file mode 100644 index 0000000000000000000000000000000000000000..aab86cc25df2ad3c1552a6c2523d51d0ab80a5cf --- /dev/null +++ b/transfer/code/scDFM/src/utils/_logging.py @@ -0,0 +1,23 @@ +import logging + +__all__ = ["logger"] + + +def _setup_logger() -> "logging.Logger": + from rich.console import Console + from rich.logging import RichHandler + + logger = logging.getLogger(__name__) + logger.setLevel(logging.INFO) + console = Console(force_terminal=True) + if console.is_jupyter is True: + console.is_jupyter = False + ch = RichHandler(show_path=False, console=console, show_time=False) + logger.addHandler(ch) + + # this prevents double outputs + logger.propagate = False + return logger + + +logger = _setup_logger() diff --git a/transfer/code/scDFM/src/utils/_preprocessing.py b/transfer/code/scDFM/src/utils/_preprocessing.py new file mode 100644 index 0000000000000000000000000000000000000000..8c6c7cc2a9abf0d2e304633b99d5df072a3c8d44 --- /dev/null +++ b/transfer/code/scDFM/src/utils/_preprocessing.py @@ -0,0 +1,284 @@ +from collections.abc import Sequence +from typing import Any, Literal + +import anndata as ad +import numpy as np +import sklearn.preprocessing as preprocessing + +from src.utils._logging import logger +from src.utils._types import ArrayLike +from src.utils._utils import _to_list +import requests +import xml.etree.ElementTree as ET +from bs4 import BeautifulSoup + +__all__ = ["encode_onehot", "annotate_compounds", "get_molecular_fingerprints"] + + +def get_smiles_pubchemlite(cid): + url = f"https://pubchemlite.lcsb.uni.lu/e/compound/{cid}" + r = requests.get(url) + soup = BeautifulSoup(r.text, 'html.parser') + label = soup.find(text="SMILES") + try: + return label.find_next().text.strip() + except: + return None + + +def get_smiles_via_http(cid): + url = f"https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi" + params = { + 'db': 'pccompound', + 'id': cid, + 'rettype': 'docsum', + 'retmode': 'xml' + } + r = requests.get(url, params=params) + r.raise_for_status() + root = ET.fromstring(r.text) + for item in root.findall('.//Item'): + if item.get('Name') in ('IsomericSmiles', 'CanonicalSMILES'): + return item.text + return None + +def annotate_compounds( + adata: ad.AnnData, + compound_keys: str | Sequence[str], + control_category: str = "control", + query_id_type: Literal["name", "cid"] = "name", + obs_key_prefixes: str | Sequence[str] | None = None, + copy: bool = False, +) -> None | ad.AnnData: + """Annotates compounds in ``adata`` using pertpy and PubChem. + + Parameters + ---------- + adata + An :class:`~anndata.AnnData` object. + compound_keys + Key(s) in :attr:`~anndata.AnnData.obs` containing the compound identifiers. + control_category + Category to exclude from the annotation. + query_id_type + Type of the compound identifiers. Either ``'name'`` or ``'cid'``. + obs_key_prefixes + Prefix for the keys in :attr:`~anndata.AnnData.obs` to store the annotations. If :obj:`None`, + uses ``compound_keys`` as prefixes. + copy + Return a copy of ``adata`` instead of updating it in place. + + Returns + ------- + If ``copy`` is :obj:`True`, returns a new :class:`~anndata.AnnData` object with the compound + annotations stored in :attr:`~anndata.AnnData.obs`. Otherwise, updates ``adata`` in place. + + Sets the following fields for each value in ``compound_keys``: + + - ``.obs[f"{obs_key_prefix}_pubchem_name"]``: Name of the compound. + - ``.obs[f"{obs_key_prefix}_pubchem_ID"]``: PubChem CID of the compound. + - ``.obs[f"{obs_key_prefix}_smiles"]``: SMILES representation of the compound. + """ + try: + import pertpy as pt + except ImportError as e: + raise ImportError( + "pertpy is not installed. To annotate compounds, please install it via `pip install pertpy`." + ) from e + + adata = adata.copy() if copy else adata + + compound_keys = _to_list(compound_keys) + obs_key_prefixes = _to_list(obs_key_prefixes) if obs_key_prefixes is not None else compound_keys + + if len(compound_keys) != len(obs_key_prefixes): + raise ValueError("The number of `compound_keys` must match the number of values in `obs_key_prefixes`.") + + # Annotate compounds in each query column + not_found = set() + c_meta = pt.metadata.Compound() + for query_key, prefix in zip(compound_keys, obs_key_prefixes, strict=False): + adata.obs[query_key] = adata.obs[query_key].replace(control_category, np.nan) + c_meta.annotate_compounds( + adata, + query_id=query_key, + query_id_type=query_id_type, + verbosity=0, + copy=False, + ) + + # if adata.obs['smiles'] is None, using get_smiles_pubchemlite to get smiles + # missing = adata.obs.loc[adata.obs["smiles"].isna(), query_key].dropna().unique().tolist() + # not_found.update(missing) + missing_pubchem_ID = adata.obs.loc[~adata.obs["pubchem_ID"].isna(), 'pubchem_ID'].dropna().unique().tolist() + + for cid in missing_pubchem_ID: + adata.obs.loc[adata.obs['pubchem_ID'] == cid, "smiles"] = get_smiles_via_http(cid) + + adata.obs[[query_key, "pubchem_name"]] = adata.obs[[query_key, "pubchem_name"]].fillna(control_category) + + # Drop columns with new annotations + adata.obs.rename( + columns={ + "pubchem_name": f"{prefix}_pubchem_name", + "pubchem_ID": f"{prefix}_pubchem_ID", + "smiles": f"{prefix}_smiles", + }, + inplace=True, + ) + + # if not_found: + # logger.warning(f"Could not find annotations for the following compounds: {', '.join(map(str, not_found))}") + + return adata if copy else None + + +def _get_fingerprint(smiles: str, radius: int = 4, n_bits: int = 1024) -> ArrayLike | None: + """Computes Morgan fingerprints for a given SMILES string.""" + try: + import rdkit.Chem.rdFingerprintGenerator as rfg + from rdkit import Chem + except ImportError: + raise ImportError( + "rdkit is not installed. To compute fingerprints, please install it via `pip install rdkit`." + ) from None + + mmol = Chem.MolFromSmiles(str(smiles), sanitize=True) + + # Check if molecule is valid, MolFromSmiles returns None if error occurs + if mmol is None: + return None + + mfpgen = rfg.GetMorganGenerator(radius=radius, fpSize=n_bits) + return np.array(mfpgen.GetFingerprint(mmol)) + + +def get_molecular_fingerprints( + adata, + compound_keys: str | list[str], + smiles_keys: str | None = None, + control_value: str = "control", + uns_key_added: str = "fingerprints", + radius: int = 4, + n_bits: int = 1024, + copy: bool = False, +) -> None | ad.AnnData: + """Computes Morgan fingerprints for compounds in ``adata`` and stores them in :attr:`~anndata.AnnData.uns`. + + Parameters + ---------- + adata + An :class:`~anndata.AnnData` object. + compound_keys + Key(s) in :attr:`~anndata.AnnData.obs` containing the compound identifiers. + control_value + Skip `control_value` (and :obj:`None` values). + smiles_keys + Key(s) in :attr:`~anndata.AnnData.obs` containing the SMILES strings. If :obj:`None`, uses + ``f"{compound_key}_smiles"``. + uns_key_added + Key in :attr:`~anndata.AnnData.uns` to store the fingerprints. + radius + Radius of the Morgan fingerprints. + n_bits + Number of bits in the fingerprint. + copy + Return a copy of ``adata`` instead of updating it in place. + + Returns + ------- + Updates :attr:`~anndata.AnnData.uns` with the computed fingerprints. + + Sets the following fields: + + - ``.uns[uns_key_added]``: Dictionary containing the fingerprints for each compound. + """ + adata = adata.copy() if copy else adata + + compound_keys = _to_list(compound_keys) + + if smiles_keys is None: + smiles_keys = [f"{key}_smiles" for key in compound_keys] + + smiles_keys = _to_list(smiles_keys) + + # Get dict with SMILES for each compound + smiles_dict = {} + for compound_key, smiles_key in zip(compound_keys, smiles_keys, strict=False): # type: ignore[arg-type] + if compound_key not in adata.obs: + raise KeyError(f"Key {compound_key} not found in `adata.obs`.") + + if smiles_key not in adata.obs: + raise KeyError(f"Key {smiles_key} not found in `adata.obs`.") + smiles_dict.update(adata.obs.set_index(compound_key)[smiles_key].to_dict()) + + # Compute fingerprints for each compound + valid_fingerprints = {} + not_found = [] + for comp, smiles in smiles_dict.items(): + if not isinstance(comp, str) or comp == control_value: + continue + comp_fp = _get_fingerprint(smiles, radius=radius, n_bits=n_bits) + if comp_fp is not None: + valid_fingerprints[comp] = comp_fp + else: + not_found.append(str(comp)) + + if not_found: + logger.warning(f"Could not compute fingerprints for the following compounds: {', '.join(not_found)}") + + adata.uns[uns_key_added] = valid_fingerprints + + if copy: + return adata + + +def encode_onehot( + adata: ad.AnnData, + covariate_keys: str | Sequence[str], + uns_key_added: str, + exclude_values: str | Sequence[Any] = None, + copy: bool = False, +) -> None | ad.AnnData: + """Encodes covariates :attr:`~anndata.AnnData.obs` as one-hot vectors and stores them in :attr:`~anndata.AnnData.uns`. + + Parameters + ---------- + adata + An :class:`~anndata.AnnData` object. + covariate_keys + Key(s) in :attr:`~anndata.AnnData.obs` containing the covariate(s) to encode. + uns_key_added + Key in :attr:`~anndata.AnnData.uns` to store the one-hot encodings. + exclude_values + Value(s) to exclude from the one-hot encoding. + copy + Return a copy of ``adata`` instead of updating it in place. + + Returns + ------- + If ``copy`` is :obj:`True`, returns a new :class:`~anndata.AnnData` object with the one-hot + encodings stored in :attr:`~anndata.AnnData.uns`. Otherwise, updates ``adata`` in place. + + Sets the following fields: + + - ``.uns[uns_key_added]``: Dictionary containing the one-hot encodings for each covariate. + """ + adata = adata.copy() if copy else adata + + covariate_keys = _to_list(covariate_keys) + exclude_values = _to_list(exclude_values) + + # Get unique values from all columns + all_values = np.unique(adata.obs[covariate_keys].values.flatten()) + values_encode = np.setdiff1d(all_values, exclude_values).reshape(-1, 1) + encoder = preprocessing.OneHotEncoder(sparse_output=False) + encodings = encoder.fit_transform(values_encode) + + # Store encodings in adata.uns + adata.uns[uns_key_added] = {} + for value, encoding in zip(values_encode, encodings, strict=False): + adata.uns[uns_key_added][value[0]] = encoding + + if copy: + return adata diff --git a/transfer/code/scDFM/src/utils/_types.py b/transfer/code/scDFM/src/utils/_types.py new file mode 100644 index 0000000000000000000000000000000000000000..6485eac76a0067ce1e5516fe9d577ca743bb77dd --- /dev/null +++ b/transfer/code/scDFM/src/utils/_types.py @@ -0,0 +1,22 @@ +import os +from collections.abc import Callable, Sequence +from typing import Any + +import numpy as np + +# TODO(michalk8): polish + +try: + from numpy.typing import NDArray + + ArrayLike = NDArray[np.float64] +except (ImportError, TypeError): + ArrayLike = np.ndarray # type: ignore[misc] + DTypeLike = np.dtype + +ComputationCallback_t = Callable[[dict[str, ArrayLike], dict[str, ArrayLike]], dict[str, Any]] +LoggingCallback_t = Callable[[dict[str, ArrayLike]], dict[str, Any]] + +Layers_t = Sequence[dict[str, Any]] +Layers_separate_input_t = dict[str, Layers_t] +PathLike = os.PathLike | str # type: ignore[type-arg] diff --git a/transfer/code/scDFM/src/utils/_utils.py b/transfer/code/scDFM/src/utils/_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..c22d50bbaee7fd8343926a618fe00f5e085e49db --- /dev/null +++ b/transfer/code/scDFM/src/utils/_utils.py @@ -0,0 +1,14 @@ +from collections.abc import Iterable +from typing import Any + + +def _to_list(x: list[Any] | tuple[Any] | Any) -> list[Any] | tuple[Any]: + """Converts x to a list if it is not already a list or tuple.""" + if isinstance(x, (list | tuple)): + return x + return [x] + + +def _flatten_list(x: Iterable[Iterable[Any]]) -> list[Any]: + """Flattens a list of lists.""" + return [item for sublist in x for item in sublist] diff --git a/transfer/code/scDFM/src/utils/utils.py b/transfer/code/scDFM/src/utils/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..5c76489997c65597da0b56c1217852a24b500c89 --- /dev/null +++ b/transfer/code/scDFM/src/utils/utils.py @@ -0,0 +1,536 @@ +import random +import numpy as np +import torch +import os +from src.tokenizer.gene_tokenizer import GeneVocab +from typing import Optional, Dict +import numpy as np +import pandas as pd +from scipy import stats, sparse +import networkx as nx + +def set_seed(seed): + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + + +def pick_eval_score(agg_results, scheme): + df = agg_results.to_pandas() + + if scheme in ["pearson_delta", "mse", "mae", "mse_delta"]: + return float(df[scheme].iloc[0]) + + if scheme == "reverse": + return float(df["pr_auc"].iloc[0]) + + if scheme == "forward": + pear = float(df["pearson_delta"].iloc[0]) + mse = float(df["mse_delta"].iloc[0]) + alpha = 0.05 + return pear - alpha * mse + + if scheme == "de": + keys = ["de_spearman_sig", "de_direction_match", "de_sig_genes_recall"] + return float(df[keys].iloc[0].mean()) + + if scheme == "composite": + pear = float(df["pearson_delta"].iloc[0]) + mse = float(df["mse_delta"].iloc[0]) + pra = float(df["pr_auc"].iloc[0]) + de = float(df[["de_spearman_sig","de_direction_match","de_sig_genes_recall"]].iloc[0].mean()) + return 0.4*pra + 0.3*de + 0.3*(pear - 0.05*mse) + + raise ValueError("unknown scheme") + +def make_lognorm_poisson_noise(target_log, alpha=1.0, per_cell_L=None, eps=1e-8): + """ + target_log: log1p(normalized_counts) + alpha: noise intensity (0.3~1.0; smaller is more conservative) + per_cell_L: if specified (e.g. 1e4), normalize expected total per cell to L + """ + base = torch.expm1(target_log) + if per_cell_L is not None or per_cell_L == -1 : + scale = per_cell_L / (base.sum(dim=1, keepdim=True) + eps) + lam = (base * scale).clamp_min(1e-8) + else: + lam = (alpha * base).clamp_min(1e-8) + x0_counts = torch.poisson(lam) + x0_log = torch.log1p(x0_counts) + return x0_log + + +def save_checkpoint(model, optimizer, scheduler, iteration, eval_score, save_path, is_best=False): + """save checkpoint""" + checkpoint = { + 'iteration': iteration, + 'model_state_dict': model.state_dict(), + 'optimizer_state_dict': optimizer.state_dict(), + 'scheduler_state_dict': scheduler.state_dict(), + 'eval_score': eval_score, + } + + checkpoint_path = os.path.join(save_path, f'checkpoint.pt') + torch.save(checkpoint, checkpoint_path) + print(f"save checkpoint to: {checkpoint_path}") + + # If this is the best model, save an extra copy + if is_best: + best_path = os.path.join(save_path, 'best_checkpoint.pt') + torch.save(checkpoint, best_path) + print(f"save best checkpoint: {best_path}") + +def load_checkpoint(checkpoint_path, model, optimizer, scheduler): + """load checkpoint""" + if os.path.exists(checkpoint_path): + checkpoint = torch.load(checkpoint_path, map_location='cpu') + model.load_state_dict(checkpoint['model_state_dict']) + optimizer.load_state_dict(checkpoint['optimizer_state_dict']) + scheduler.load_state_dict(checkpoint['scheduler_state_dict']) + iteration = checkpoint['iteration'] + eval_score = checkpoint.get('eval_score', float('-inf')) + print(f"loading {checkpoint_path} checkpoint, iteration: {iteration}, eval_score: {eval_score}") + return iteration, eval_score + else: + print(f"Checkpoint file not found: {checkpoint_path}") + return 0, float('-inf') + + +def process_vocab(data_manager, config): + vocab_path = os.path.join('src/tokenizer',config.data_name+'_'+str(config.n_top_genes)+'_highly_vocab.json') + if os.path.exists(vocab_path): + print('##### loading vocab from file #####') + vocab = GeneVocab.from_file(vocab_path) + else: + print('##### building vocab #####') + highly_gene = data_manager.adata.var[data_manager.adata_train.var['highly_variable']] + vocab = GeneVocab(list(highly_gene.index), specials=['', '', '', 'control']) + vocab.save_json(vocab_path) + vocab = GeneVocab.from_file(vocab_path) + return vocab + +def perturbation_id_to_emb_id(perturbation_id, vocab, perturbation_dict=None): + device = perturbation_id.device + inverse_dict = {v: str(k) for k, v in perturbation_dict.items()} + perturbation_name = [inverse_dict[int(p_id)] for p_id in perturbation_id.cpu().numpy()] + emb_id = vocab.encode(perturbation_name) + emb_id = torch.tensor(emb_id, dtype=torch.long, device=device) + return emb_id + +def emb_id_to_perturbation_id(perturbation_id, vocab, perturbation_dict=None): + device = perturbation_id.device + inverse_dict = {v: str(k) for k, v in perturbation_dict.items()} + perturbation_name = [inverse_dict[int(p_id)] for p_id in perturbation_id.cpu().numpy()] + emb_id = vocab.encode(perturbation_name) + emb_id = torch.tensor(emb_id, dtype=torch.long, device=device) + return emb_id + + +@torch.no_grad() +def approximate_with_two_perturbations( + model, + perturb_vec: torch.Tensor, # target perturbation vector, shape (d,) or (1,d) or (B,d) + candidate_ids: Optional[torch.Tensor]=None,# If None and not 'crisper', use all ntoken as candidates + topk: int = 64, # preselect top-k candidates by cosine similarity + nonneg_renorm: bool = True, # clamp weights to non-negative and normalize to sum=1 + return_reconstruction: bool = True, + topn_output: int = 10, # number of top results to return + mode: str = "pair", # "pair" = combinations of two, "single" = single candidate +): + device = next(model.parameters()).device + dtype = next(model.parameters()).dtype + + # 1) Normalize target vector to shape (B, d) + if perturb_vec.dim() == 1: + perturb_vec = perturb_vec.unsqueeze(0) + perturb_vec = perturb_vec.to(device=device, dtype=dtype) # (B, d) + B, d = perturb_vec.shape + + # 2) Build candidate embedding matrix E: (N, d) + if model.perturbation_function == 'crisper': + assert candidate_ids is not None, "In 'crisper' mode, candidate_ids (N, L) must be provided." + # Encode and average over sequence length + E = model.encoder(candidate_ids.to(device=device)).to(dtype=dtype).mean(dim=1) + cand_index = torch.arange(E.size(0), device=device) + else: + if candidate_ids is None: + all_ids = torch.arange(model.perturbation_embedder.embedding.num_embeddings, device=device) + E = model.perturbation_embedder(all_ids).to(dtype=dtype) + cand_index = all_ids + else: + if candidate_ids.dim() == 2 and candidate_ids.size(1) == 1: + candidate_ids = candidate_ids.squeeze(1) + E = model.perturbation_embedder(candidate_ids.to(device=device)).to(dtype=dtype) + cand_index = candidate_ids.to(device) + + N = E.size(0) + assert N >= 1, "Need at least 1 candidate." + + # Mode: single perturbation search + if mode == "single": + E_norm = torch.nn.functional.normalize(E, dim=-1) + P_norm = torch.nn.functional.normalize(perturb_vec, dim=-1) + sims = P_norm @ E_norm.t() # cosine similarity (B, N) + k = min(topn_output, N) + topn_val, topn_idx = torch.topk(sims, k=k, dim=-1) + return { + "topn_ids": cand_index[topn_idx], # (B, k) + "topn_scores": topn_val, # (B, k) + } + + # Mode: pairwise search + elif mode == "pair": + assert N >= 2, "Pair mode requires at least 2 candidates." + + # Preselect top-k candidates by cosine similarity + E_norm = torch.nn.functional.normalize(E, dim=-1) + P_norm = torch.nn.functional.normalize(perturb_vec, dim=-1) + sims = P_norm @ E_norm.t() # (B, N) + k = min(topk, N) + _, topk_idx = torch.topk(sims, k=k, dim=-1) # (B, k) + + all_pairs = [] + I2 = torch.eye(2, device=device, dtype=dtype) + + for b in range(B): + cand = E[topk_idx[b]] # (k, d) + idxs = topk_idx[b] # (k,) + pair_results = [] + + for i in range(k): + e_i = cand[i] + for j in range(i + 1, k): + e_j = cand[j] + # Stack into matrix A: (d, 2) + A = torch.stack([e_i, e_j], dim=1) + ATA = A.t() @ A + ATp = A.t() @ perturb_vec[b] + # Solve least squares with small regularization for stability + w = torch.linalg.solve(ATA + 1e-6 * I2, ATp) + + if nonneg_renorm: + w = torch.clamp(w, min=0) + s = w.sum() + if s > 0: + w = w / s + + recon = A @ w + err = torch.norm(perturb_vec[b] - recon, p=2) + + pair_results.append(( + err.item(), + cand_index[idxs[i]].item(), + cand_index[idxs[j]].item(), + w.detach().clone().cpu(), + recon.detach().clone().cpu() if return_reconstruction else None + )) + + # Sort by error and keep top N + pair_results.sort(key=lambda x: x[0]) + pair_results = pair_results[:min(topn_output, len(pair_results))] + all_pairs.append(pair_results) + + return { + "topn_pairs": all_pairs, # length B; each is a list of up to topn_output tuples + } + + else: + raise ValueError("mode must be 'pair' or 'single'") + +@torch.no_grad() +def get_perturbation_embedding(model, perturbation_id: torch.Tensor) -> torch.Tensor: + device = next(model.parameters()).device + dtype = next(model.parameters()).dtype + + if model.perturbation_function == 'crisper': + # (B, L) -> (B, L, d) -> mean over L -> (B, d) + emb = model.encoder(perturbation_id.to(device=device)).to(dtype=dtype) + emb = emb.mean(dim=1) + else: + # (B,) or (B,1) -> (B, d) + if perturbation_id.dim() == 2 and perturbation_id.size(1) == 1: + perturbation_id = perturbation_id.squeeze(1) + emb = model.perturbation_embedder(perturbation_id.to(device=device)).to(dtype=dtype) + return emb # (B, d_model) + +def freeze_backbone_for_p(vf): + for name, p in vf.named_parameters(): + if name.startswith('p_head.') or name == 'p_mask_embed': + p.requires_grad_(True) + else: + p.requires_grad_(False) + +def unfreeze_all(vf): + for _, p in vf.named_parameters(): + p.requires_grad_(True) + +def set_requires_grad_for_p_only(vf, p_only: str): + if hasattr(vf, "module"): + base_vf = vf.module + else: + base_vf = vf + for name, p in base_vf.named_parameters(): + if p_only == 'predict_p': + if name.startswith("p_head.") or name == "p_mask_embed": + p.requires_grad_(True) + else: + p.requires_grad_(False) + else: + p.requires_grad_(True) + +@torch.no_grad() +def get_perturbation_emb(vf, perturbation_id=None, perturbation_emb=None, + cell_1=None, use_mask: bool=False): + if use_mask: + B = cell_1.size(0) + return vf.p_mask_embed[None, :].expand(B, -1).to(cell_1.device, dtype=cell_1.dtype) + + assert perturbation_emb is None or perturbation_id is None + if perturbation_id is not None: + if vf.perturbation_function == 'crisper': + perturbation_emb = vf.encoder(perturbation_id) + else: + perturbation_emb = vf.perturbation_embedder(perturbation_id) + perturbation_emb = perturbation_emb.mean(1) # (B,d) + elif perturbation_emb is not None: + perturbation_emb = perturbation_emb.to(cell_1.device, dtype=cell_1.dtype) + if perturbation_emb.dim() == 1: + perturbation_emb = perturbation_emb.unsqueeze(0) + if perturbation_emb.size(0) == 1: + perturbation_emb = perturbation_emb.expand(cell_1.shape[0], -1).contiguous() + perturbation_emb = vf.perturbation_embedder.enc_norm(perturbation_emb) + return perturbation_emb + + +import numpy as np +import pandas as pd +from scipy import stats, sparse +import networkx as nx +import torch +from scipy import sparse + + +def preprocess_expression(X, log1p=False, zscore_per_gene=True): + + if isinstance(X, pd.DataFrame): + X = X.values + X = X.astype(np.float64, copy=False) + if log1p: + X = np.log1p(np.clip(X, a_min=0, a_max=None)) + if zscore_per_gene: + # z-score per gene (column) + mean = X.mean(axis=0, keepdims=True) + std = X.std(axis=0, ddof=1, keepdims=True) + std[std == 0] = 1.0 + X = (X - mean) / std + return X + +# ============ 1) Correlation matrix calculation ============ +def correlation_matrix(X, method="pearson"): + """ + X: (cells, genes) + return: (genes, genes) correlation matrix in [-1, 1] + """ + if method == "pearson": + # np.corrcoef computes correlation by row by default, so transpose to compute by column (gene) + C = np.corrcoef(X.T) + elif method == "spearman": + # Rank transform each gene, then Pearson + X_rank = np.apply_along_axis(stats.rankdata, 0, X) + C = np.corrcoef(X_rank.T) + else: + raise ValueError("method must be 'pearson' or 'spearman'") + # Numerical stability + np.fill_diagonal(C, 1.0) + C = np.clip(C, -1.0, 1.0) + return C +def safe_correlation_matrix(X, method="pearson"): + """ + X: (cells, genes). Returns (genes, genes) correlation matrix, no NaN/Inf. + Rules: + - If any gene has variance 0, set correlation with any other gene to 0 (self-correlation to 1) + - If data contains NaN/Inf, replace with 0 (you can change to median/mean) + """ + X = np.asarray(X, dtype=np.float64) + + # 1) Clean NaN/Inf + X = np.nan_to_num(X, nan=0.0, posinf=0.0, neginf=0.0) + + # 2) Spearman: rank transform per column + if method == "spearman": + X = np.apply_along_axis(stats.rankdata, 0, X) + + n, g = X.shape + + # 3) Center + Xc = X - X.mean(axis=0, keepdims=True) + + # 4) Column std (ddof=1), mark zero-variance columns + std = Xc.std(axis=0, ddof=1) + zero_var = std == 0.0 + + # 5) Covariance matrix + cov = (Xc.T @ Xc) / max(n - 1, 1) + + # 6) Build denominator and safe division + denom = np.outer(std, std) + with np.errstate(divide="ignore", invalid="ignore"): + C = cov / denom + + # 7) Handle zero variance: wherever denominator is 0, set to 0; self-correlation to 1 + C[np.isnan(C)] = 0.0 + C[np.isinf(C)] = 0.0 + C[zero_var, :] = 0.0 + C[:, zero_var] = 0.0 + np.fill_diagonal(C, 1.0) + + # Clip + np.clip(C, -1.0, 1.0, out=C) + return C +# ============ 2) WGCNA-style soft threshold (optional) ============ +def soft_threshold_weights(C, beta=6, use_abs=True): + """ + C: correlation matrix (genes, genes) + return: weighted adjacency (non-negative) + """ + W = np.abs(C) if use_abs else np.maximum(C, 0.0) + W = np.power(W, beta) + np.fill_diagonal(W, 0.0) + return W + +# ============ 3A) Sparsification: Top-k ============ +def sparsify_topk(W, k=10, keep_symmetry=True): + """ + For each gene, keep k edges with largest absolute value. + W: dense (genes, genes) + return: csr sparse adjacency + """ + n = W.shape[0] + rows, cols, data = [], [], [] + for i in range(n): + # Remove self-loop + row = W[i].copy() + row[i] = 0.0 + if k >= n - 1: + idx = np.where(row != 0)[0] + else: + idx = np.argpartition(-np.abs(row), kth=min(k, n-2))[:k] + vals = row[idx] + mask = vals != 0 + rows.extend([i]*mask.sum()) + cols.extend(idx[mask]) + data.extend(vals[mask]) + + A = sparse.csr_matrix((data, (rows, cols)), shape=W.shape) + if keep_symmetry: + # Take symmetric max or sum; here take max + A = A.maximum(A.T) + return A + +# ============ 3B) Sparsification: threshold ============ +def sparsify_threshold(W, tau=0.3, keep_symmetry=True): + """ + Only keep edges where |W_ij| >= tau + W: dense (genes, genes) + """ + M = np.abs(W) >= tau + np.fill_diagonal(M, False) + rows, cols = np.where(M) + data = W[rows, cols] + A = sparse.csr_matrix((data, (rows, cols)), shape=W.shape) + if keep_symmetry: + A = A.maximum(A.T) + return A + +# ============ 4) GCN normalization ============ +def gcn_normalize(A, add_self_loops=True): + """ + Kipf & Welling GCN symmetric normalization: \hat A = D^{-1/2} (A + I) D^{-1/2} + A: csr adjacency + """ + if add_self_loops: + A = A + sparse.eye(A.shape[0], format='csr') + deg = np.array(A.sum(axis=1)).flatten() + deg[deg == 0] = 1.0 + D_inv_sqrt = sparse.diags(1.0 / np.sqrt(deg)) + A_hat = D_inv_sqrt @ A @ D_inv_sqrt + return A_hat.tocsr() + +# ============ 5) Build graph (optional, for visualization/export) ============ +def to_networkx(A, gene_names=None, weight_attr="weight"): + """ + A: csr adjacency + """ + # Compatible with networkx versions + if hasattr(nx, "from_scipy_sparse_array"): + G = nx.from_scipy_sparse_array(A, edge_attribute=weight_attr) + else: + G = nx.from_scipy_sparse_matrix(A, edge_attribute=weight_attr) + + if gene_names is not None: + mapping = {i: name for i, name in enumerate(gene_names)} + G = nx.relabel_nodes(G, mapping) + return G + + +def adjacency_to_mha_mask(A_csr: sparse.csr_matrix, allow_self=True): + # Sparse adjacency -> dense 0/1 allowed matrix + A = A_csr.tolil(copy=True) + if allow_self: + A.setdiag(1) + A = A.tocsr().toarray().astype(bool) # True=allowed edge + disallow = ~A # True=disallowed (PyTorch bool mask semantics) + # For MultiheadAttention, attn_mask shape can be (L, S) + attn_mask_bool = torch.from_numpy(disallow) + return attn_mask_bool # True means masked + +# ============ 6) One-stop pipeline ============ +def build_gene_coexpression_graph( + X, + method="pearson", + wgcna_beta=None, # int or None; if given, use soft threshold weights + sparsify="topk", # "topk" or "threshold" + k=10, # top-k + tau=0.3, # threshold + log1p=True, + zscore_per_gene=True, + use_negative_edge=False +): + Xp = preprocess_expression(X, log1p=log1p, zscore_per_gene=zscore_per_gene) + # C = correlation_matrix(Xp, method=method) + C = safe_correlation_matrix(Xp, method=method) + # Choose weight matrix source: use correlation directly or WGCNA style + + + if wgcna_beta is not None: + W = soft_threshold_weights(C, beta=wgcna_beta, use_abs=True) + else: + W = C + + sign_matrix = np.sign(W) + if use_negative_edge: + W = np.abs(W) + # Sparsification + if sparsify == "topk": + A = sparsify_topk(W, k=k, keep_symmetry=True) + elif sparsify == "threshold": + A = sparsify_threshold(W, tau=tau, keep_symmetry=True) + else: + raise ValueError("sparsify must be 'topk' or 'threshold'") + mask = adjacency_to_mha_mask(A) + # return A, A_hat, G, sign_matrix + return mask + +def sorted_pad_mask(mask, pad_size=4, gene_names=None): + sorted_gene_names = sorted(list(gene_names)) + reorder_idx = [gene_names.index(g) for g in sorted_gene_names] + mask_sorted = mask[reorder_idx][:, reorder_idx] + orig_shape = mask_sorted.shape + pad_row = torch.ones((pad_size, orig_shape[1]), dtype=mask_sorted.dtype) + pad_col = torch.ones((orig_shape[0] + pad_size, pad_size), dtype=mask_sorted.dtype) + mask_padded = torch.cat([pad_row, mask_sorted], dim=0) + mask_padded = torch.cat([pad_col, mask_padded], dim=1) + length = mask.shape[0] + mask_padded[torch.arange(length), torch.arange(length)] = False + return mask_padded \ No newline at end of file diff --git a/transfer/code/scGPT/.github/workflows/claude-code-review.yml b/transfer/code/scGPT/.github/workflows/claude-code-review.yml new file mode 100644 index 0000000000000000000000000000000000000000..4caf96a2fa44143e69c338aa8cd58f02382f3e66 --- /dev/null +++ b/transfer/code/scGPT/.github/workflows/claude-code-review.yml @@ -0,0 +1,54 @@ +name: Claude Code Review + +on: + pull_request: + types: [opened, synchronize] + # Optional: Only run on specific file changes + # paths: + # - "src/**/*.ts" + # - "src/**/*.tsx" + # - "src/**/*.js" + # - "src/**/*.jsx" + +jobs: + claude-review: + # Optional: Filter by PR author + # if: | + # github.event.pull_request.user.login == 'external-contributor' || + # github.event.pull_request.user.login == 'new-developer' || + # github.event.pull_request.author_association == 'FIRST_TIME_CONTRIBUTOR' + + runs-on: ubuntu-latest + permissions: + contents: read + pull-requests: read + issues: read + id-token: write + + steps: + - name: Checkout repository + uses: actions/checkout@v4 + with: + fetch-depth: 1 + + - name: Run Claude Code Review + id: claude-review + uses: anthropics/claude-code-action@v1 + with: + claude_code_oauth_token: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }} + prompt: | + Please review this pull request and provide feedback on: + - Code quality and best practices + - Potential bugs or issues + - Performance considerations + - Security concerns + - Test coverage + + Use the repository's CLAUDE.md for guidance on style and conventions. Be constructive and helpful in your feedback. + + Use `gh pr comment` with your Bash tool to leave your review as a comment on the PR. + + # See https://github.com/anthropics/claude-code-action/blob/main/docs/usage.md + # or https://docs.anthropic.com/en/docs/claude-code/sdk#command-line for available options + claude_args: '--allowed-tools "Bash(gh issue view:*),Bash(gh search:*),Bash(gh issue list:*),Bash(gh pr comment:*),Bash(gh pr diff:*),Bash(gh pr view:*),Bash(gh pr list:*)"' + diff --git a/transfer/code/scGPT/.github/workflows/claude.yml b/transfer/code/scGPT/.github/workflows/claude.yml new file mode 100644 index 0000000000000000000000000000000000000000..ae36c007f36934936df72862cfa10b577d692f54 --- /dev/null +++ b/transfer/code/scGPT/.github/workflows/claude.yml @@ -0,0 +1,50 @@ +name: Claude Code + +on: + issue_comment: + types: [created] + pull_request_review_comment: + types: [created] + issues: + types: [opened, assigned] + pull_request_review: + types: [submitted] + +jobs: + claude: + if: | + (github.event_name == 'issue_comment' && contains(github.event.comment.body, '@claude')) || + (github.event_name == 'pull_request_review_comment' && contains(github.event.comment.body, '@claude')) || + (github.event_name == 'pull_request_review' && contains(github.event.review.body, '@claude')) || + (github.event_name == 'issues' && (contains(github.event.issue.body, '@claude') || contains(github.event.issue.title, '@claude'))) + runs-on: ubuntu-latest + permissions: + contents: read + pull-requests: read + issues: read + id-token: write + actions: read # Required for Claude to read CI results on PRs + steps: + - name: Checkout repository + uses: actions/checkout@v4 + with: + fetch-depth: 1 + + - name: Run Claude Code + id: claude + uses: anthropics/claude-code-action@v1 + with: + claude_code_oauth_token: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }} + + # This is an optional setting that allows Claude to read CI results on PRs + additional_permissions: | + actions: read + + # Optional: Give a custom prompt to Claude. If this is not specified, Claude will perform the instructions specified in the comment that tagged it. + # prompt: 'Update the pull request description to include a summary of changes.' + + # Optional: Add claude_args to customize behavior and configuration + # See https://github.com/anthropics/claude-code-action/blob/main/docs/usage.md + # or https://docs.anthropic.com/en/docs/claude-code/sdk#command-line for available options + # claude_args: '--model claude-opus-4-1-20250805 --allowed-tools Bash(gh pr:*)' + diff --git a/transfer/code/scGPT/.gitignore b/transfer/code/scGPT/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..18994437609def016386ba24f522a27a7a272525 --- /dev/null +++ b/transfer/code/scGPT/.gitignore @@ -0,0 +1,155 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# custom +*.pt +*.pth +*.pdf +*.pkl +*.RData +*.Rhistory +*.csv +*.out +*.h5ad +*.npy +events.out.tfevents.* +tmp/ +data/ +save/ +saved/ +output/ +runs/ +logs/ +external/ +wandb/ +references/ +examples/save +examples/modality_pred/data +figures/ diff --git a/transfer/code/scGPT/.readthedocs.yaml b/transfer/code/scGPT/.readthedocs.yaml new file mode 100644 index 0000000000000000000000000000000000000000..43d4354bb700d2d0824cebc42b70dccb011c1ed1 --- /dev/null +++ b/transfer/code/scGPT/.readthedocs.yaml @@ -0,0 +1,34 @@ +# .readthedocs.yaml +# Read the Docs configuration file +# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details + +# Required +version: 2 + +# Set the OS, Python version and other tools you might need +build: + os: ubuntu-22.04 + tools: + python: "mambaforge-4.10" + # You can also specify other tool versions: + # nodejs: "19" + # rust: "1.64" + # golang: "1.19" + +# Build documentation in the "docs/" directory with Sphinx +sphinx: + configuration: docs/conf.py +# Optionally build your docs in additional formats such as PDF and ePub +# formats: +# - pdf +# - epub + +# Optional but recommended, declare the Python requirements required +# to build your documentation +# See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html +# python: +# install: +# - requirements: docs/requirements.txt + +conda: + environment: docs/environment.yml diff --git a/transfer/code/scGPT/.vscode/launch.json b/transfer/code/scGPT/.vscode/launch.json new file mode 100644 index 0000000000000000000000000000000000000000..4e712bca21964194150737f693278356f16b873d --- /dev/null +++ b/transfer/code/scGPT/.vscode/launch.json @@ -0,0 +1,25 @@ +{ + // Use IntelliSense to learn about possible attributes. + // Hover to view descriptions of existing attributes. + // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 + "version": "0.2.0", + "configurations": [ + { + "name": "Python: Current File Run in File Directory", + "type": "python", + "request": "launch", + "program": "${file}", + "console": "integratedTerminal", + "justMyCode": false, + "cwd": "${fileDirname}" + }, + { + "name": "Python: Current File", + "type": "python", + "request": "launch", + "program": "${file}", + "console": "integratedTerminal", + "justMyCode": false + } + ] +} diff --git a/transfer/code/scGPT/.vscode/settings.json b/transfer/code/scGPT/.vscode/settings.json new file mode 100644 index 0000000000000000000000000000000000000000..4a4a62f27f68ba8d4cee43ce913765b5391f9fb7 --- /dev/null +++ b/transfer/code/scGPT/.vscode/settings.json @@ -0,0 +1,8 @@ +{ + "python.formatting.provider": "black", + "files.watcherExclude": { + "**/__pycache__/**": true, + "**/save/**": true, + "**/wandb/**": true + } +} diff --git a/transfer/code/scGPT/LICENSE b/transfer/code/scGPT/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..39fc81d0fb21c8df5c25235b12766bafcfbbfc8c --- /dev/null +++ b/transfer/code/scGPT/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2022 suber + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/transfer/code/scGPT/README.md b/transfer/code/scGPT/README.md new file mode 100644 index 0000000000000000000000000000000000000000..321cd2343c1e4ed4f54bcc973b548947952b2cba --- /dev/null +++ b/transfer/code/scGPT/README.md @@ -0,0 +1,113 @@ +# scGPT + +This is the official codebase for **scGPT: Towards Building a Foundation Model for Single-Cell Multi-omics Using Generative AI**. + +[![Preprint](https://img.shields.io/badge/preprint-available-brightgreen)](https://www.biorxiv.org/content/10.1101/2023.04.30.538439)   +[![Documentation](https://img.shields.io/badge/docs-available-brightgreen)](https://scgpt.readthedocs.io/en/latest/)   +[![PyPI version](https://badge.fury.io/py/scgpt.svg)](https://badge.fury.io/py/scgpt)   +[![Downloads](https://pepy.tech/badge/scgpt)](https://pepy.tech/project/scgpt)   +![Webapp](https://img.shields.io/website?url=https%3A%2F%2Fscgpthub.org&up_color=chartreuse%20&logo=gotomeeting&logoColor=%23FFB3FF&label=WebApp&labelColor=%2300CBFF)   +[![License](https://img.shields.io/badge/license-MIT-blue)](https://github.com/username/repo/blob/main/LICENSE) + +**!UPDATE**: We have released several new pretrained scGPT checkpoints. Please see the [Pretrained scGPT checkpoints](#pretrained-scGPT-checkpoints) section for more details. + +**[2024.02.26]** We have provided a priliminary support for running the pretraining workflow with HuggingFace at the [integrate-huggingface-model](https://github.com/bowang-lab/scGPT/tree/integrate-huggingface-model) branch. We will conduct further testing and merge it to the main branch soon. + +**[2023.12.31]** New tutorials about zero-shot applications are now available! Please see find them in the [tutorials/zero-shot](tutorials/zero-shot) directory. We also provide a new continual pretrained model checkpoint for cell embedding related tasks. Please see the [notebook](tutorials/zero-shot/Tutorial_ZeroShot_Integration_Continual_Pretraining.ipynb) for more details. + +**[2023.11.07]** As requested by many, now we have made flash-attention an optional dependency. The pretrained weights can be loaded on pytorch CPU, GPU, and flash-attn backends using the same [load_pretrained](https://github.com/bowang-lab/scGPT/blob/f6097112fe5175cd4e221890ed2e2b1815f54010/scgpt/utils/util.py#L304) function, `load_pretrained(target_model, torch.load("path_to_ckpt.pt"))`. An example usage is also [here](https://github.com/bowang-lab/scGPT/blob/f6097112fe5175cd4e221890ed2e2b1815f54010/scgpt/tasks/cell_emb.py#L258). + +**[2023.09.05]** We have release a new feature for reference mapping samples to a custom reference dataset or to all the millions of cells collected from CellXGene! With the help of the [faiss](https://github.com/facebookresearch/faiss) library, we achieved a great time and memory efficiency. The index of over 33 millions cells only takes less than 1GB of memory and the similarity search takes less than **1 second for 10,000 query cells on GPU**. Please see the [Reference mapping tutorial](https://github.com/bowang-lab/scGPT/blob/main/tutorials/Tutorial_Reference_Mapping.ipynb) for more details. + +### Online apps + +scGPT is now available at the following online apps as well, so you can get started simply with your browser! + +- Run the [reference mapping app](https://app.superbio.ai/apps/299?id=6548f339a9ed6f6e5560b07d), [cell annotation app](https://app.superbio.ai/apps/274?id=64d205cb980ff714de831ee0) and the [GRN inference app](https://app.superbio.ai/apps/270?id=64b804fb823bc93b64c10a76) with cloud gpus. Thanks to the [Superbio.ai](https://app.superbio.ai/) team for helping create and host the interactive tools. + +## Installation + +scGPT works with Python >= 3.7.13 and R >=3.6.1. Please make sure you have the correct version of Python and R installed pre-installation. + +scGPT is available on PyPI. To install scGPT, run the following command: + +```bash +pip install scgpt "flash-attn<1.0.5" # optional, recommended +# As of 2023.09, pip install may not run with new versions of the google orbax package, if you encounter related issues, please use the following command instead: +# pip install scgpt "flash-attn<1.0.5" "orbax<0.1.8" +``` + +[Optional] We recommend using [wandb](https://wandb.ai/) for logging and visualization. + +```bash +pip install wandb +``` + +The poetry installation is out of sync. Please use pip install instead. ~~For developing, we are using the [Poetry](https://python-poetry.org/) package manager. To install Poetry, follow the instructions [here](https://python-poetry.org/docs/#installation).~~ + +```bash +$ git clone this-repo-url +$ cd scGPT +$ poetry install +``` + +**Note**: The `flash-attn` dependency usually requires specific GPU and CUDA version. If you encounter any issues, please refer to the [flash-attn](https://github.com/HazyResearch/flash-attention/tree/main) repository for installation instructions. For now, May 2023, we recommend using CUDA 11.7 and flash-attn<1.0.5 due to various issues reported about installing new versions of flash-attn. + +## Pretrained scGPT Model Zoo + +Here is the list of pretrained models. Please find the links for downloading the checkpoint folders. We recommend using the `whole-human` model for most applications by default. If your fine-tuning dataset shares similar cell type context with the training data of the organ-specific models, these models can usually demonstrate competitive performance as well. A paired vocabulary file mapping gene names to ids is provided in each checkpoint folder. If ENSEMBL ids are needed, please find the conversion at [gene_info.csv](https://github.com/bowang-lab/scGPT/files/13243634/gene_info.csv). + +| Model name | Description | Download | +| :------------------------ | :------------------------------------------------------ | :------------------------------------------------------------------------------------------- | +| whole-human (recommended) | Pretrained on 33 million normal human cells. | [link](https://drive.google.com/drive/folders/1oWh_-ZRdhtoGQ2Fw24HP41FgLoomVo-y?usp=sharing) | +| continual pretrained | For zero-shot cell embedding related tasks. | [link](https://drive.google.com/drive/folders/1_GROJTzXiAV8HB4imruOTk6PEGuNOcgB?usp=sharing) | +| brain | Pretrained on 13.2 million brain cells. | [link](https://drive.google.com/drive/folders/1vf1ijfQSk7rGdDGpBntR5bi5g6gNt-Gx?usp=sharing) | +| blood | Pretrained on 10.3 million blood and bone marrow cells. | [link](https://drive.google.com/drive/folders/1kkug5C7NjvXIwQGGaGoqXTk_Lb_pDrBU?usp=sharing) | +| heart | Pretrained on 1.8 million heart cells | [link](https://drive.google.com/drive/folders/1GcgXrd7apn6y4Ze_iSCncskX3UsWPY2r?usp=sharing) | +| lung | Pretrained on 2.1 million lung cells | [link](https://drive.google.com/drive/folders/16A1DJ30PT6bodt4bWLa4hpS7gbWZQFBG?usp=sharing) | +| kidney | Pretrained on 814 thousand kidney cells | [link](https://drive.google.com/drive/folders/1S-1AR65DF120kNFpEbWCvRHPhpkGK3kK?usp=sharing) | +| pan-cancer | Pretrained on 5.7 million cells of various cancer types | [link](https://drive.google.com/drive/folders/13QzLHilYUd0v3HTwa_9n4G4yEF-hdkqa?usp=sharing) | + +## Fine-tune scGPT for scRNA-seq integration + +Please see our example code in [examples/finetune_integration.py](examples/finetune_integration.py). By default, the script assumes the scGPT checkpoint folder stored in the `examples/save` directory. + +## To-do-list + +- [x] Upload the pretrained model checkpoint +- [x] Publish to pypi +- [ ] Provide the pretraining code with generative attention masking +- [ ] Finetuning examples for multi-omics integration, cell type annotation, perturbation prediction, cell generation +- [x] Example code for Gene Regulatory Network analysis +- [x] Documentation website with readthedocs +- [x] Bump up to pytorch 2.0 +- [x] New pretraining on larger datasets +- [x] Reference mapping example +- [ ] Publish to huggingface model hub + +## Contributing + +We greatly welcome contributions to scGPT. Please submit a pull request if you have any ideas or bug fixes. We also welcome any issues you encounter while using scGPT. + +## Acknowledgements + +We sincerely thank the authors of following open-source projects: + +- [flash-attention](https://github.com/HazyResearch/flash-attention) +- [scanpy](https://github.com/scverse/scanpy) +- [scvi-tools](https://github.com/scverse/scvi-tools) +- [scib](https://github.com/theislab/scib) +- [datasets](https://github.com/huggingface/datasets) +- [transformers](https://github.com/huggingface/transformers) + +## Citing scGPT + +```bibtex +@article{cui2023scGPT, +title={scGPT: Towards Building a Foundation Model for Single-Cell Multi-omics Using Generative AI}, +author={Cui, Haotian and Wang, Chloe and Maan, Hassaan and Pang, Kuan and Luo, Fengning and Wang, Bo}, +journal={bioRxiv}, +year={2023}, +publisher={Cold Spring Harbor Laboratory} +} +``` diff --git a/transfer/code/scGPT/data/README.md b/transfer/code/scGPT/data/README.md new file mode 100644 index 0000000000000000000000000000000000000000..ad30b7fe12d3cca62e0bf5f9c44ad22f998a5344 --- /dev/null +++ b/transfer/code/scGPT/data/README.md @@ -0,0 +1,54 @@ +# Available datasets + +The full descriptions of the datasets and the studies of origin can be found in the manuscript. Here we provide the links to access the processed datasets. + +## Pretraining data + +To download data from CellXGene and build for pretraining, go to the folder [cellxgene](cellxgene) and follow the instructions. + +## Datasets for cell type annotation + +- Multiple Sclerosis (M.S.) dataset: [link](https://drive.google.com/drive/folders/1Qd42YNabzyr2pWt9xoY4cVMTAxsNBt4v?usp=sharing) + +- Myeloid (Mye.) dataset: [link](https://drive.google.com/drive/folders/1VbpApQufZq8efFGakW3y8QDDpY9MBoDS?usp=drive_link) + +- hPancreas dataset: [link](https://drive.google.com/drive/folders/1s9XjcSiPC-FYV3VeHrEa7SeZetrthQVV?usp=drive_link) + +## Datasets for multi-batch integration + +- PBMC 10K: [link](https://docs.scvi-tools.org/en/stable/api/reference/scvi.data.pbmc_dataset.html) + +- Perirhinal Cortex dataset: [link](https://drive.google.com/file/d/1rDAxDtvWx1GpJaNhlKBi71f8-psUNppE/view?usp=drive_link) + +- COVID-19 dataset: [link](https://drive.google.com/file/d/1eD9LbxNJ35YUde3VtdVcjkwm-f4iyJ6x/view?usp=drive_link) + +## Datasets for multi-omics integration + +- BMMC dataset: [link](https://drive.google.com/drive/folders/1VRsVugg6vgCq8GG0gGajYsyXfrEtP0jK?usp=sharing) + +- 10x Multiome PBMC dataset: [link](https://drive.google.com/drive/folders/163J4Qi7R-awuLiHnWCh-eJD7RPMnb_yK?usp=sharing) + +## Datasets for perturbation prediction + +- Adamson dataset: [link](https://dataverse.harvard.edu/api/access/datafile/6154417) + +- Norman dataset: [link](https://dataverse.harvard.edu/api/access/datafile/6154020) + +## Datasets for the GRN analysis + +- Immune Human dataset [link](https://figshare.com/ndownloader/files/25717328) + +## Datasets for zero-shot integration + +- Lung-Kim dataset: [link](https://drive.google.com/file/d/1z_0vWYMhRuRiD1EyhuFtY9ReIR0msWaL/view?usp=sharing) + +- COVID-19 dataset: [link](https://drive.google.com/file/d/1eD9LbxNJ35YUde3VtdVcjkwm-f4iyJ6x/view?usp=drive_link) + +- Multiple Sclerosis (M.S.) dataset: [link](https://drive.google.com/drive/folders/1Qd42YNabzyr2pWt9xoY4cVMTAxsNBt4v?usp=sharing) + + +## Datasets for zero-shot integration + +- COVID-19 dataset(splitted) : [link](https://drive.google.com/drive/folders/1jSPoPunGQOmd71vDsK0FS7UvmDhGdhQS?usp=sharing) + +- Lung-Kim dataset(splitted): [link](https://drive.google.com/drive/folders/1gbfO7VqxCOkfzgHAih6hO88zFv6pd8wO?usp=sharing) diff --git a/transfer/code/scGPT/data/cellxgene/README.md b/transfer/code/scGPT/data/cellxgene/README.md new file mode 100644 index 0000000000000000000000000000000000000000..1c9ab6e48b3ce32abb3c868d20af5b9d38f0e60d --- /dev/null +++ b/transfer/code/scGPT/data/cellxgene/README.md @@ -0,0 +1,58 @@ +# Build Training Cell Corpus from Cellxgene Census + +- This documentation describes the procedure for building the pre-training cell corpus from the cellxgene census. +- Please note that this script is designed to run on a cluster with the SLURM workload manager for parallelization. +- You may need to modify the scripts to run on your own system. +- Internet access is required for querying the cellxgene census dataset. +- The scripts referred to in this document are located in the `/data/cellxgene` directory. + +## General Workflow for Cell Corpus Construction +- The general workflow is: + + 0. (Optional) Configure the query list and query conditions. + 1. Build the cell index files based on query + 2. Download the dataset in `h5ad` chunks + 3. Transform the `h5ad` into `scb` (single-cell bank for high-performance IO) + +## (Optional) Configure the Query List and Query Conditions +- If you wish to customize your pre-training dataset, you may modify the `data_config.py` file and `query_list.txt` file. +- In the `data_config.py` file, + - `MAJOR_TISSUE_LIST` refers to the general organ system defined in the cellxgene census; it defines the resolution we used to store the cells. + - `VERSION` refers to the version of the cellxgene census; we used the version `2023-05-08` for our experiments. You may change it to the latest/LTS version. Check out the [cellxgene census release plan](https://chanzuckerberg.github.io/cellxgene-census/cellxgene_census_docsite_data_release_info.html) for more information. + - As we only use normal cells for pre-training, we filter the dataset by the `DISEASE` column in the cellxgene census. + - For the `pan-cancer` model, we filter the dataset by the `DISEASE` column in the cellxgene census. The filtered cancer list is defined in the `cancer_list.txt` file. You may modify it according to your own needs. + +## Build the Cell Index Files Based on Query + +- We first query cells from the cellxgene census and filter the cells according to our needs. + - `INDEX_PATH` is the path to the cell index file (to be generated), cell index is the SOMA id (unique index in cellxgene census) for each cell in the cellxgene census. + - `QUERY_PATH` is the path to the query file; each line in the query file is a general organ system defined in the cellxgene census. +- Replace them in the following command and run it to generate the cell index file: + +```{bash} +INDEX_PATH="path/to/index" +QUERY_PATH="path/to/query" + +./build_soma_idx.sh $INDEX_PATH $QUERY_PATH +``` + +## Download the Dataset in Chunks +- We download the dataset in chunks; each chunk contains a maximum of 200000 cells, and the chunk size can be modified by changing the `MAX_PARTITION_SIZE` in the `download_partition.sh` file. +- Before running the script, you need to modify the `DATA_PATH`, `QUERY_PATH` and `INDEX_PATH` in the `array_download_partition.sh` file. + - Keep the `INDEX_PATH` and `QUERY_PATH` consistent with the previous step. + - `DATA_PATH` is the path to the directory to store the downloaded dataset. The resulting dataset will be stored in the `h5ad` format. +- Submit it to download the dataset (each compute node will need internet access): +```{bash} +sbatch array_download_partition.sh +``` + +## Build the `scb` Files +- We preprocess the dataset and then transform the `h5ad` into `scb` (single-cell bank for high-performance I/O). +- Before running the script, you need to modify the `DATA_PATH`, `OUTPUT_PATH`, `QUERY_PATH`, and `VOCAB_PATH` in the `array_build_scb.sh` file. + - Keep the `DATA_PATH` and `QUERY_PATH` consistent with the previous step. + - `OUTPUT_PATH` is the path to store the `scb` files. + - `VOCAB_PATH` is the path to the vocabulary file, which is used to map the gene id to token id. +- Then simply submit the job to the cluster by: +```{bash} +sbatch array_build_scb.sh +``` \ No newline at end of file diff --git a/transfer/code/scGPT/data/cellxgene/array_build_scb.sh b/transfer/code/scGPT/data/cellxgene/array_build_scb.sh new file mode 100644 index 0000000000000000000000000000000000000000..55c722b780faabd7d9afcd89f132ae372b543454 --- /dev/null +++ b/transfer/code/scGPT/data/cellxgene/array_build_scb.sh @@ -0,0 +1,31 @@ +#!/bin/bash +#SBATCH --time=8:00:00 +#SBATCH --cpus-per-task=8 +#SBATCH --array=1-9 +#SBATCH --mem=96G +#SBATCH --qos=nopreemption +#SBATCH -p cpu + + +QUERY_PATH="path/to/query.txt" + + +query_name=$(sed -n "${SLURM_ARRAY_TASK_ID}p" $QUERY_PATH) + +DATA_PATH="path/to/data/${query_name}" +OUTPUT_PATH="path/to/output/${query_name}" +VOCAB_PATH="path/to/vocab" + +echo "processing ${query_name}" +N=200000 + + +mkdir -p $OUTPUT_PATH + +echo "downloading to ${OUTPUT_PATH}" + +python build_large_scale_data.py \ + --input-dir ${DATA_PATH} \ + --output-dir ${OUTPUT_PATH} \ + --vocab-file ${VOCAB_PATH} \ + --N ${N} diff --git a/transfer/code/scGPT/data/cellxgene/array_download_partition.sh b/transfer/code/scGPT/data/cellxgene/array_download_partition.sh new file mode 100644 index 0000000000000000000000000000000000000000..09c40f187730e2c67fd6304a890011fcff7825f7 --- /dev/null +++ b/transfer/code/scGPT/data/cellxgene/array_download_partition.sh @@ -0,0 +1,21 @@ +#!/bin/bash +#SBATCH --time=8:00:00 +#SBATCH --cpus-per-task=4 +#SBATCH --array=1-9 +#SBATCH --mem=48G +#SBATCH --qos=nopreemption +#SBATCH -p cpu + + + +INDEX_PATH="path/to/index" +QUERY_PATH="path/to/query" +DATA_PATH="path/to/data" + +cd $DATA_PATH + +query_name=$(sed -n "${SLURM_ARRAY_TASK_ID}p" $QUERY_PATH) + +echo "downloading ${query_name}" + +./download_partition.sh ${query_name} ${INDEX_PATH} ${DATA_PATH} \ No newline at end of file diff --git a/transfer/code/scGPT/data/cellxgene/array_process_allcounts.sh b/transfer/code/scGPT/data/cellxgene/array_process_allcounts.sh new file mode 100644 index 0000000000000000000000000000000000000000..508a4992c950a8d19d68916ff40222f215a620b8 --- /dev/null +++ b/transfer/code/scGPT/data/cellxgene/array_process_allcounts.sh @@ -0,0 +1,53 @@ +#!/bin/sh +#SBATCH --time=8:00:00 +#SBATCH --cpus-per-task=8 +#SBATCH --array=1,2,4,5,7,8 +#SBATCH --mem=48G +#SBATCH --qos=nopreemption +#SBATCH -p cpu + +QUERY_PATH="query_list.txt" + +query_name=$(sed -n "${SLURM_ARRAY_TASK_ID}p" $QUERY_PATH) + +echo "processing ${query_name}" + +DATASET="/scratch/ssd004/datasets/cellxgene/scb_strict/${query_name}/all_counts" +VOCAB_PATH="/scratch/ssd004/datasets/cellxgene/scFormer/scformer/tokenizer/default_census_vocab.json" +bash ~/.bashrc + +NPROC=$SLURM_GPUS_ON_NODE +JOB_NAME="cellxgene_census_${QUERY_NAME}" +LOG_INTERVAL=2000 +VALID_SIZE_OR_RATIO=0.03 +MAX_LENGTH=1200 +per_proc_batch_size=32 +LAYERS=12 +MODEL_SCALE=8 +SAVE_DIR="/scratch/ssd004/datasets/cellxgene/profile_tmp" +# others, pancreas, lung, kidney, heart, blood +alias python_=~/.cache/pypoetry/virtualenvs/scformer-9yG_XnDJ-py3.9/bin/python +python_ -c "import torch; print(torch.version.cuda)" +python_ process_allcounts.py \ + --data-source $DATASET \ + --save-dir ${SAVE_DIR}/${JOB_NAME}-$(date +%b%d-%H-%M-%Y) \ + --vocab-path ${VOCAB_PATH} \ + --valid-size-or-ratio $VALID_SIZE_OR_RATIO \ + --max-seq-len $MAX_LENGTH \ + --batch-size $per_proc_batch_size \ + --eval-batch-size $(($per_proc_batch_size * 2)) \ + --nlayers $LAYERS \ + --nheads 8 \ + --embsize $((MODEL_SCALE * 64)) \ + --d-hid $((MODEL_SCALE * 64)) \ + --grad-accu-steps $((128 / $per_proc_batch_size)) \ + --epochs 2 \ + --lr 0.0001 \ + --warmup-ratio-or-step 10000 \ + --log-interval $LOG_INTERVAL \ + --save-interval $(($LOG_INTERVAL * 3)) \ + --trunc-by-sample \ + --no-cls \ + --no-cce \ + --fp16 | + awk '{ print strftime("[%Y-%m-%d %H:%M:%S]"), $0; fflush(); }' diff --git a/transfer/code/scGPT/data/cellxgene/build_large_scale_data.py b/transfer/code/scGPT/data/cellxgene/build_large_scale_data.py new file mode 100644 index 0000000000000000000000000000000000000000..78f219d8842f72ffbaa0c583797fb63a97c3b381 --- /dev/null +++ b/transfer/code/scGPT/data/cellxgene/build_large_scale_data.py @@ -0,0 +1,224 @@ +# build large-scale data in scBank format from a group of AnnData objects +# %% +import gc +import json +from pathlib import Path +import argparse +import shutil +import traceback +from typing import Dict, List, Optional +import warnings +import numpy as np +import os + +import scanpy as sc + +import sys + +sys.path.insert(0, "../../") +import scgpt as scg +from scgpt import scbank + +# %% +parser = argparse.ArgumentParser( + description="Build large-scale data in scBank format from a group of AnnData objects" +) +parser.add_argument( + "--input-dir", + type=str, + required=True, + help="Directory containing AnnData objects", +) +parser.add_argument( + "--output-dir", + type=str, + default="./data.scb", + help="Directory to save scBank data, by default will make a directory named " + "data.scb in the current directory", +) +parser.add_argument( + "--include-files", + type=str, + nargs="*", + help="Space separated file names to include, default to all files in input_dir", +) +parser.add_argument( + "--metainfo", + type=str, + default=None, + help="Json file containing meta information for each dataset, default to None.", +) + +# vocabulary +parser.add_argument( + "--vocab-file", + type=str, + default=None, + help="File containing the gene vocabulary, default to None. If None, will " + "use the default gene vocabulary from scGPT, which use HGNC gene symbols.", +) + +parser.add_argument( + "--N", + type=int, + default=10000, + help="Hyperparam for filtering genes, default to 10000.", +) + + +# if scg.utils.isnotebook(): +# args = parser.parse_args( +# [ +# "--input-dir", +# "./datasets/", +# "--output-dir", +# "./databanks/", +# "--include-files", +# "f72958f5-7f42-4ebb-98da-445b0c6de516.h5ad", +# "--metainfo", +# "./metainfo.json", +# "--vocab-file", +# "../../scgpt/tokenizer/default_cellxgene_vocab.json", +# ] +# ) +# else: +args = parser.parse_args() + +"""command line example +python build_large_scale_data.py \ + --input-dir ./datasets/ \ + --output-dir ./databanks/ \ + --metainfo ./metainfo.json \ + --vocab-file ../../scgpt/tokenizer/default_cellxgene_vocab.json +""" + +# %% +print(args) + +input_dir = Path(args.input_dir) +output_dir = Path(args.output_dir) +files = [f for f in input_dir.glob("*.h5ad")] +print(f"Found {len(files)} files in {input_dir}") +if args.include_files is not None: + files = [f for f in files if f.name in args.include_files] +if args.metainfo is not None: + metainfo = json.load(open(args.metainfo)) + files = [f for f in files if f.stem in metainfo] + include_obs = { + f.stem: {"disease": metainfo[f.stem]["include_disease"]} + for f in files + if "include_disease" in metainfo[f.stem] + } + +if args.vocab_file is None: + vocab = scg.tokenizer.get_default_gene_vocab() +else: + vocab = scg.tokenizer.GeneVocab.from_file(args.vocab_file) + +# %% [markdown] +# # preprocessing data + + +def preprocess( + adata: sc.AnnData, + main_table_key: str = "counts", + include_obs: Optional[Dict[str, List[str]]] = None, + N=10000, +) -> sc.AnnData: + """ + Preprocess the data for scBank. This function will modify the AnnData object in place. + + Args: + adata: AnnData object to preprocess + main_table_key: key in adata.layers to store the main table + include_obs: dict of column names and values to include in the main table + + Returns: + The preprocessed AnnData object + """ + if include_obs is not None: + # include only cells that have the specified values in the specified columns + for col, values in include_obs.items(): + adata = adata[adata.obs[col].isin(values)] + + # filter genes + sc.pp.filter_genes(adata, min_counts=(3 / 10000) * N) + + # TODO: add binning in sparse matrix and save in separate datatable + # preprocessor = Preprocessor( + # use_key="X", # the key in adata.layers to use as raw data + # filter_gene_by_counts=False, # step 1 + # filter_cell_by_counts=False, # step 2 + # normalize_total=False, # 3. whether to normalize the raw data and to what sum + # log1p=False, # 4. whether to log1p the normalized data + # binning=51, # 6. whether to bin the raw data and to what number of bins + # result_binned_key="X_binned", # the key in adata.layers to store the binned data + # ) + # preprocessor(adata) + + adata.layers[main_table_key] = adata.X.copy() # preserve counts + # sc.pp.normalize_total(adata, target_sum=1e4) + # sc.pp.log1p(adata) + # adata.raw = adata # freeze the state in `.raw` + + # apply a hard clip to the data for now + print( + f"original mean and max of counts: {adata.layers[main_table_key].mean():.2f}, " + f"{adata.layers[main_table_key].max():.2f}" + ) + # if isinstance(adata.layers[main_table_key], np.ndarray): + # adata.layers[main_table_key] = adata.layers[main_table_key].clip(0, 30) + # else: # assume it is a sparse matrix + # adata.layers[main_table_key].data = adata.layers[main_table_key].data.clip(0, 30) + + return adata + + +# %% +main_table_key = "counts" +token_col = "feature_name" +for f in files: + try: + adata = sc.read(f, cache=True) + adata = preprocess(adata, main_table_key, N=args.N) + print(f"read {adata.shape} valid data from {f.name}") + + # TODO: CHECK AND EXPAND VOCABULARY IF NEEDED + # NOTE: do not simply expand, need to check whether to use the same style of gene names + + # BUILD SCBANK DATA + db = scbank.DataBank.from_anndata( + adata, + vocab=vocab, + to=output_dir / f"{f.stem}.scb", + main_table_key=main_table_key, + token_col=token_col, + immediate_save=False, + ) + db.meta_info.on_disk_format = "parquet" + # sync all to disk + db.sync() + + # clean up + del adata + del db + gc.collect() + except Exception as e: + traceback.print_exc() + warnings.warn(f"failed to process {f.name}: {e}") + shutil.rmtree(output_dir / f"{f.stem}.scb", ignore_errors=True) + +# or run scbank.DataBank.batch_from_anndata(files, to=args.output_dir) +# %% +# test loading from disk +# db = scbank.DataBank.from_path(args.output_dir) + +# %% run this to copy all parquet datatables to a single directory +target_dir = output_dir / f"all_{main_table_key}" +target_dir.mkdir(exist_ok=True) +for f in files: + output_parquet_dt = ( + output_dir / f"{f.stem}.scb" / f"{main_table_key}.datatable.parquet" + ) + if output_parquet_dt.exists(): + os.symlink(output_parquet_dt, target_dir / f"{f.stem}.datatable.parquet") diff --git a/transfer/code/scGPT/data/cellxgene/build_soma_idx.py b/transfer/code/scGPT/data/cellxgene/build_soma_idx.py new file mode 100644 index 0000000000000000000000000000000000000000..e2add15bbab6abc4214c0302207a4c1abaa9b88a --- /dev/null +++ b/transfer/code/scGPT/data/cellxgene/build_soma_idx.py @@ -0,0 +1,71 @@ +### This script is used to retrieve cell soma ids from cellxgene census + +import cellxgene_census +from data_config import VALUE_FILTER, VERSION +from typing import List +import os +import argparse + +parser = argparse.ArgumentParser( + description='Build soma index list based on query') + + +parser.add_argument("--query-name", + type=str, + required=True, + help="query name to build the index", +) + +parser.add_argument("--output-dir", + type=str, + required=True, + help="Directory to store the output idx file", +) + +args = parser.parse_args() +# print(args) + + +def retrieve_soma_idx(query_name) -> List[str]: + """ + This function is used to retrieve cell soma ids from cellxgene census based on the query name + """ + + with cellxgene_census.open_soma(census_version=VERSION) as census: + cell_metadata = census["census_data"]["homo_sapiens"].obs.read( + value_filter = VALUE_FILTER[query_name], + column_names = ["soma_joinid"] + ) + cell_metadata = cell_metadata.concat() + cell_metadata = cell_metadata.to_pandas() + return cell_metadata["soma_joinid"].to_list() + +def convert2file(idx_list: List[str], query_name: str, output_dir: str) -> None: + """ + This function is used to convert the retrieved idx_list to file by query_name + """ + + # set up the dir + if not os.path.exists(output_dir): + os.makedirs(output_dir) + file_path = os.path.join(output_dir, f"{query_name}.idx") + + # write to the file + with open(file_path, 'w') as fp: + for item in idx_list: + fp.write("%s\n" % item) + +def build_soma_idx(query_name, output_dir) -> None: + """ + This function is used to build the soma idx for cells under query_name + """ + idx_list = retrieve_soma_idx(query_name) + convert2file(idx_list, query_name, output_dir) + + +# if __name__ == "__main__": +# build_soma_idx("heart") + +build_soma_idx(args.query_name, args.output_dir) + + diff --git a/transfer/code/scGPT/data/cellxgene/build_soma_idx.sh b/transfer/code/scGPT/data/cellxgene/build_soma_idx.sh new file mode 100644 index 0000000000000000000000000000000000000000..c6486f94236c7388fa38163f329df778fdff3cad --- /dev/null +++ b/transfer/code/scGPT/data/cellxgene/build_soma_idx.sh @@ -0,0 +1,10 @@ +#!/bin/sh +# output directory for the index +OUTPUT_DIR=$1 +QUERY_LIST=$2 + +while read QUERY; do + echo "building index for ${QUERY}" + python3 ./build_soma_idx.py --query-name ${QUERY} --output-dir ${OUTPUT_DIR} +done < ${QUERY_LIST} + diff --git a/transfer/code/scGPT/data/cellxgene/cancer_list.txt b/transfer/code/scGPT/data/cellxgene/cancer_list.txt new file mode 100644 index 0000000000000000000000000000000000000000..727adc1ca308f6938b95b8c14e0644979d673bf3 --- /dev/null +++ b/transfer/code/scGPT/data/cellxgene/cancer_list.txt @@ -0,0 +1,21 @@ +malignant ovarian serous tumor +glioblastoma +lung adenocarcinoma +squamous cell lung carcinoma +small cell lung carcinoma +non-small cell lung carcinoma +B-cell non-Hodgkin lymphoma +follicular lymphoma +gastric cancer +blastoma +pilocytic astrocytoma +acute myeloid leukemia +tubular adenoma +clear cell renal carcinoma +adenocarcinoma +tubulovillous adenoma +colorectal cancer +Wilms tumor +acute promyelocytic leukemia +neuroendocrine carcinoma +chromophobe renal cell carcinoma diff --git a/transfer/code/scGPT/data/cellxgene/data_config.py b/transfer/code/scGPT/data/cellxgene/data_config.py new file mode 100644 index 0000000000000000000000000000000000000000..ae46283995b2d3d9168192c7b84c7aaa6842e6de --- /dev/null +++ b/transfer/code/scGPT/data/cellxgene/data_config.py @@ -0,0 +1,33 @@ + + + +MAJOR_TISSUE_LIST = ["heart", "blood", "brain", "lung", "kidney", "intestine", "pancreas"] +VERSION = "2023-05-08" + +CANCER_LIST_PATH = "./cancer_list.txt" +with open(CANCER_LIST_PATH) as f: + CANCER_LIST = [line.rstrip('\n') for line in f] + +# build the value filter dict for each tissue +VALUE_FILTER = { + tissue : f"suspension_type != 'na' and disease == 'normal' and tissue_general == '{tissue}'" for tissue in MAJOR_TISSUE_LIST +} +# build the value filter dict for cells related with other tissues +# since tileDB does not support `not in ` operator, we will just use `!=` to filter out the other tissues +VALUE_FILTER["others"] = f"suspension_type != 'na' and disease == 'normal'" +for tissue in MAJOR_TISSUE_LIST: + VALUE_FILTER["others"] = f"{VALUE_FILTER['others']} and (tissue_general != '{tissue}')" + +VALUE_FILTER['pan-cancer'] = f"suspension_type != 'na'" +cancer_condition = "" +for disease in CANCER_LIST: + if cancer_condition == "": + cancer_condition = f"(disease == '{disease}')" + else: + cancer_condition = f"{cancer_condition} or (disease == '{disease}')" +VALUE_FILTER['pan-cancer'] = f"(suspension_type != 'na') and ({cancer_condition})" + +if __name__ == "__main__": + # print(VALUE_FILTER["others"]) + # print(MAJOR_TISSUE_LIST) + print(VALUE_FILTER['pan-cancer']) diff --git a/transfer/code/scGPT/data/cellxgene/download_partition.py b/transfer/code/scGPT/data/cellxgene/download_partition.py new file mode 100644 index 0000000000000000000000000000000000000000..32df29b636c5ae6f5088b75297ce162835e48c23 --- /dev/null +++ b/transfer/code/scGPT/data/cellxgene/download_partition.py @@ -0,0 +1,103 @@ +import cellxgene_census +import pandas as pd +import numpy as np +from data_config import VERSION +from typing import List +import os +import argparse + + +parser = argparse.ArgumentParser( + description='Download a given partition cell of the query in h5ad') + +parser.add_argument("--query-name", + type=str, + required=True, + help="query name to build the index", +) + +parser.add_argument("--partition-idx", + type=int, + required=True, + help="partition index to download", +) +parser.add_argument("--output-dir", + type=str, + required=True, + help="Directory to store the output h4ad file", +) + +parser.add_argument("--index-dir", + type=str, + required=True, + help="Directory to find the index file", +) + +parser.add_argument("--max-partition-size", + type=int, + required=True, + help="The max partition size for each partition(chunk)", +) + + +args = parser.parse_args() + +# print(args) + + + + +def define_partition(partition_idx, id_list, partition_size) -> List[str]: + """ + This function is used to define the partition for each job + + partition_idx is the partition index, which is an integer, and 0 <= partition_idx <= len(id_list) // MAX_PARTITION_SIZE + """ + i = partition_idx * partition_size + return id_list[i:i + partition_size] + + +def load2list(query_name, soma_id_dir) -> List[int]: + """ + This function is used to load the idx list from file + """ + file_path = os.path.join(soma_id_dir, f"{query_name}.idx") + with open(file_path, 'r') as fp: + idx_list = fp.readlines() + idx_list = [int(x.strip()) for x in idx_list] + return idx_list + +def download_partition(partition_idx, query_name, output_dir, index_dir, partition_size): + """ + This function is used to download the partition_idx partition of the query_name + """ + # define id partition + id_list = load2list(query_name, index_dir) + id_partition = define_partition(partition_idx, id_list, partition_size) + with cellxgene_census.open_soma(census_version=VERSION) as census: + adata = cellxgene_census.get_anndata(census, + organism="Homo sapiens", + obs_coords=id_partition, + ) + # prepare the query dir if not exist + query_dir = os.path.join(output_dir, query_name) + if not os.path.exists(query_dir): + os.makedirs(query_dir) + query_adata_path = os.path.join(query_dir, f"partition_{partition_idx}.h5ad") + adata.write_h5ad(query_adata_path) + return query_adata_path + +def del_partition(partition_idx, query_name, output_dir, index_dir, partition_size): + query_dir = os.path.join(output_dir, query_name) + query_adata_path = os.path.join(query_dir, f"partition_{partition_idx}.h5ad") + os.remove(query_adata_path) + + +if __name__ == "__main__": + + download_partition(partition_idx=args.partition_idx, + query_name=args.query_name, + output_dir=args.output_dir, + index_dir=args.index_dir, + partition_size=args.max_partition_size + ) \ No newline at end of file diff --git a/transfer/code/scGPT/data/cellxgene/download_partition.sh b/transfer/code/scGPT/data/cellxgene/download_partition.sh new file mode 100644 index 0000000000000000000000000000000000000000..344da6f9502dcee2fd99c4b4ef721e7a5e3457e8 --- /dev/null +++ b/transfer/code/scGPT/data/cellxgene/download_partition.sh @@ -0,0 +1,22 @@ +#!/bin/sh +QUERY=$1 +INDEX_DIR=$2 +OUTPUT_DIR=$3 + +MAX_PARTITION_SIZE=200000 + +total_num=`wc -l ${INDEX_DIR}/${QUERY}.idx | awk '{ print $1 }'` +total_partition=$(($total_num / $MAX_PARTITION_SIZE)) +# echo $total_num +# echo $total_partition" + +for i in $(seq 0 $total_partition) +do + echo "downloading partition ${i}/${total_partition} for ${QUERY}" + python3 ./download_partition.py \ + --query-name ${QUERY} \ + --index-dir ${INDEX_DIR} \ + --output-dir ${OUTPUT_DIR} \ + --partition-idx ${i} \ + --max-partition-size ${MAX_PARTITION_SIZE} +done \ No newline at end of file diff --git a/transfer/code/scGPT/data/cellxgene/expand_gene_list.py b/transfer/code/scGPT/data/cellxgene/expand_gene_list.py new file mode 100644 index 0000000000000000000000000000000000000000..86b0c7b68c78f59b9ff6870b3f5c9a26aa34f630 --- /dev/null +++ b/transfer/code/scGPT/data/cellxgene/expand_gene_list.py @@ -0,0 +1,41 @@ +import cellxgene_census +import json + +VERSION = "2023-05-08" + +with cellxgene_census.open_soma(census_version=VERSION) as census: + meta_data = ( + census["census_data"]["homo_sapiens"] + .ms["RNA"] + .var.read( + column_names=[ + "feature_name", + ], + ) + ) + new_gene_list = meta_data.concat().to_pandas()["feature_name"].to_list() + # print(gene_name) + +with open("../../scgpt/tokenizer/default_cellxgene_vocab.json", "r") as f: + old_gene_dict = json.load(f) + +print("old gene list length:", len(old_gene_dict)) + +expanded_dict = old_gene_dict.copy() + +# count the genes in old but not in new: +# for gene, num in old_gene_dict.items(): +# if gene not in new_gene_list: +# print(f"diff at {gene}") + +starting_num = max(old_gene_dict.values()) + 1 +for new_gene in new_gene_list: + if new_gene not in old_gene_dict.keys(): + expanded_dict[new_gene] = starting_num + starting_num += 1 +print("new gene dict length:", len(expanded_dict)) + +dump_path = "../../scgpt/tokenizer/default_census_vocab.json" + +with open(dump_path, "w") as f: + json.dump(expanded_dict, f, indent=2) diff --git a/transfer/code/scGPT/data/cellxgene/metainfo.json b/transfer/code/scGPT/data/cellxgene/metainfo.json new file mode 100644 index 0000000000000000000000000000000000000000..186fa7f3409a1a426874fe7cef3a15c909bfd1b1 --- /dev/null +++ b/transfer/code/scGPT/data/cellxgene/metainfo.json @@ -0,0 +1,244 @@ +{ + "f72958f5-7f42-4ebb-98da-445b0c6de516": { + "name": "Azimuth meta-analysis of 10 datasets of healthy and diseased human lung", + "url": "https://cellxgene.cziscience.com/e/f72958f5-7f42-4ebb-98da-445b0c6de516.cxg/", + "include_disease": ["normal", "COVID-19"] + }, + "d0c12af4-c0e4-4c7b-873a-70752b449689": { + "name": "Stromal cells (all non-immune cells)", + "url": "https://cellxgene.cziscience.com/e/d0c12af4-c0e4-4c7b-873a-70752b449689.cxg/" + }, + "804d9a85-665b-45c6-b204-13457fdcc7ac": { + "name": "Megakaryocyte/erythroid cells", + "url": "https://cellxgene.cziscience.com/e/804d9a85-665b-45c6-b204-13457fdcc7ac.cxg/" + }, + "6a30bf44-c490-41ac-965b-0bb58432b10a": { + "name": "HSC/progenitor cells", + "url": "https://cellxgene.cziscience.com/e/6a30bf44-c490-41ac-965b-0bb58432b10a.cxg/" + }, + "2aa1c93c-4ef3-4e9a-98e7-0bd37933953c": { + "name": "Myeloid cells", + "url": "https://cellxgene.cziscience.com/e/2aa1c93c-4ef3-4e9a-98e7-0bd37933953c.cxg/" + }, + "3affa268-8a74-460a-ac9b-a984c0832469": { + "name": "Lymphoid cells", + "url": "https://cellxgene.cziscience.com/e/3affa268-8a74-460a-ac9b-a984c0832469.cxg/" + }, + "fd072bc3-2dfb-46f8-b4e3-467cb3223182": { + "name": "Full dataset of single-cell RNA-seq profiles from 9 developmental tissues across gestation (4-17 pcw)", + "url": "https://cellxgene.cziscience.com/e/fd072bc3-2dfb-46f8-b4e3-467cb3223182.cxg/" + }, + "48101fa2-1a63-4514-b892-53ea1d3a8657": { + "name": "HSC/immune cells (all hematopoietic-derived cells)", + "url": "https://cellxgene.cziscience.com/e/48101fa2-1a63-4514-b892-53ea1d3a8657.cxg/" + }, + "aa633105-e8e5-4dcc-b72d-6da6c191b3e9": { + "name": "NK/T cells", + "url": "https://cellxgene.cziscience.com/e/aa633105-e8e5-4dcc-b72d-6da6c191b3e9.cxg/" + }, + "1b9d8702-5af8-4142-85ed-020eb06ec4f6": { + "name": "Global", + "url": "https://cellxgene.cziscience.com/e/1b9d8702-5af8-4142-85ed-020eb06ec4f6.cxg/" + }, + "71be997d-ff75-41b9-8a9f-1288c865f921": { + "name": "B cell compartment", + "url": "https://cellxgene.cziscience.com/e/71be997d-ff75-41b9-8a9f-1288c865f921.cxg/" + }, + "fe52003e-1460-4a65-a213-2bb1a508332f": { + "name": "Myeloid compartment", + "url": "https://cellxgene.cziscience.com/e/fe52003e-1460-4a65-a213-2bb1a508332f.cxg/" + }, + "53d208b0-2cfd-4366-9866-c3c6114081bc": { + "name": "Tabula Sapiens - All Cells", + "url": "https://cellxgene.cziscience.com/e/53d208b0-2cfd-4366-9866-c3c6114081bc.cxg/" + }, + "c5d88abe-f23a-45fa-a534-788985e93dad": { + "name": "Tabula Sapiens - Immune", + "url": "https://cellxgene.cziscience.com/e/c5d88abe-f23a-45fa-a534-788985e93dad.cxg/" + }, + "ae29ebd0-1973-40a4-a6af-d15a5f77a80f": { + "name": "T & innate lymphoid cells", + "url": "https://cellxgene.cziscience.com/e/ae29ebd0-1973-40a4-a6af-d15a5f77a80f.cxg/" + }, + "218acb0f-9f2f-4f76-b90b-15a4b7c7f629": { + "name": "multiplexed scRNA-seq of 1.2 million PBMCs from adult lupus samples", + "url": "https://cellxgene.cziscience.com/e/218acb0f-9f2f-4f76-b90b-15a4b7c7f629.cxg/", + "include_disease": ["normal"] + }, + "3faad104-2ab8-4434-816d-474d8d2641db": { + "name": "Single-cell eQTL mapping identifies cell type specific genetic control of autoimmune disease", + "url": "https://cellxgene.cziscience.com/e/3faad104-2ab8-4434-816d-474d8d2641db.cxg/" + }, + "ebc2e1ff-c8f9-466a-acf4-9d291afaf8b3": { + "name": "COMBAT project: single cell gene expression data from COVID-19, sepsis and flu patient PBMCs", + "url": "https://cellxgene.cziscience.com/e/ebc2e1ff-c8f9-466a-acf4-9d291afaf8b3.cxg/" + }, + "2a498ace-872a-4935-984b-1afa70fd9886": { + "name": "PBMC", + "url": "https://cellxgene.cziscience.com/e/2a498ace-872a-4935-984b-1afa70fd9886.cxg/" + }, + "c05fb583-eb2f-4e3a-8e74-f9bd6414e418": { + "name": "healthy young bone marrow donor", + "url": "https://cellxgene.cziscience.com/e/c05fb583-eb2f-4e3a-8e74-f9bd6414e418.cxg/" + }, + "cd4c96bb-ad66-4e83-ba9e-a7df8790eb12": { + "name": "3 healthy young and 3 healthy old bone marrow donors (Reference sample)", + "url": "https://cellxgene.cziscience.com/e/cd4c96bb-ad66-4e83-ba9e-a7df8790eb12.cxg/" + }, + "d3566d6a-a455-4a15-980f-45eb29114cab": { + "name": "blood and bone marrow from a healthy young donor", + "url": "https://cellxgene.cziscience.com/e/d3566d6a-a455-4a15-980f-45eb29114cab.cxg/" + }, + "1a2e3350-28a8-4f49-b33c-5b67ceb001f6": { + "name": "Fetal Bone Marrow (10x)", + "url": "https://cellxgene.cziscience.com/e/1a2e3350-28a8-4f49-b33c-5b67ceb001f6.cxg/" + }, + "343ff97c-85df-494b-8400-beb937618611": { + "name": "Human Fetal Bone Marrow (CITE-seq)", + "url": "https://cellxgene.cziscience.com/e/343ff97c-85df-494b-8400-beb937618611.cxg/" + }, + "471647b3-04fe-4c76-8372-3264feb950e8": { + "name": "CD34+ Fetal Bone Marrow, Fetal Liver, Cord Blood (CITE-seq)", + "url": "https://cellxgene.cziscience.com/e/471647b3-04fe-4c76-8372-3264feb950e8.cxg/" + }, + "4c4cd77c-8fee-4836-9145-16562a8782fe": { + "name": "Individual Single-Cell RNA-seq PBMC Data from Lee et al.", + "url": "https://cellxgene.cziscience.com/e/4c4cd77c-8fee-4836-9145-16562a8782fe.cxg/" + }, + "db0752b9-f20e-40b8-8997-992f3ae0bb2e": { + "name": "Classical Monocyte sub_clusters of COVID-19 Immune Altas: Integration of 5 public COVID-19 PBMC single-cell datasets", + "url": "https://cellxgene.cziscience.com/e/db0752b9-f20e-40b8-8997-992f3ae0bb2e.cxg/" + }, + "e763ed0d-0e5a-4b8e-9514-6da3d9e47956": { + "name": "Platelet sub_clusters of COVID-19 Immune Altas: Integration of 5 public COVID-19 PBMC single-cell datasets", + "url": "https://cellxgene.cziscience.com/e/e763ed0d-0e5a-4b8e-9514-6da3d9e47956.cxg/" + }, + "59b69042-47c2-47fd-ad03-d21beb99818f": { + "name": "Individual Single-Cell RNA-seq PBMC Data from Arunachalam et al.", + "url": "https://cellxgene.cziscience.com/e/59b69042-47c2-47fd-ad03-d21beb99818f.cxg/" + }, + "d9b4bc69-ed90-4f5f-99b2-61b0681ba436": { + "name": "B Cell/Plasmablast Sub_clusters of COVID-19 Immune Altas: Integration of 5 public COVID-19 PBMC single-cell datasets", + "url": "https://cellxgene.cziscience.com/e/d9b4bc69-ed90-4f5f-99b2-61b0681ba436.cxg/" + }, + "96a3f64b-0ee9-40d8-91e9-813ce38261c9": { + "name": "COVID-19 Immune Altas: Integration of 5 public COVID-19 PBMC single-cell datasets", + "url": "https://cellxgene.cziscience.com/e/96a3f64b-0ee9-40d8-91e9-813ce38261c9.cxg/" + }, + "bc2a7b3d-f04e-477e-96c9-9d5367d5425c": { + "name": "T Cell and NK Cell Subtypes of COVID-19 Immune Altas: Integration of 5 public COVID-19 PBMC single-cell datasets", + "url": "https://cellxgene.cziscience.com/e/bc2a7b3d-f04e-477e-96c9-9d5367d5425c.cxg/" + }, + "055ca631-6ffb-40de-815e-b931e10718c0": { + "name": "Individual Single-Cell RNA-seq PBMC Data from Wilk et al.", + "url": "https://cellxgene.cziscience.com/e/055ca631-6ffb-40de-815e-b931e10718c0.cxg/" + }, + "ae5341b8-60fb-4fac-86db-86e49ee66287": { + "name": "Individual Single-Cell RNA-seq PBMC Data from Guo et al.", + "url": "https://cellxgene.cziscience.com/e/ae5341b8-60fb-4fac-86db-86e49ee66287.cxg/" + }, + "5e717147-0f75-4de1-8bd2-6fda01b8d75f": { + "name": "Individual Single-Cell RNA-seq PBMC Data from Schulte-Schrepping et al.", + "url": "https://cellxgene.cziscience.com/e/5e717147-0f75-4de1-8bd2-6fda01b8d75f.cxg/" + }, + "01ad3cd7-3929-4654-84c0-6db05bd5fd59": { + "name": "Type I interferon autoantibodies are associated with systemic immune alterations in patients with COVID-19", + "url": "https://cellxgene.cziscience.com/e/01ad3cd7-3929-4654-84c0-6db05bd5fd59.cxg/" + }, + "ed5d841d-6346-47d4-ab2f-7119ad7e3a35": { + "name": "nygc multimodal pbmc", + "url": "https://cellxgene.cziscience.com/e/ed5d841d-6346-47d4-ab2f-7119ad7e3a35.cxg/" + }, + "c7775e88-49bf-4ba2-a03b-93f00447c958": { + "name": "Single-cell multi-omics analysis of the immune response in COVID-19", + "url": "https://cellxgene.cziscience.com/e/c7775e88-49bf-4ba2-a03b-93f00447c958.cxg/" + }, + "30cd5311-6c09-46c9-94f1-71fe4b91813c": { + "name": "Time-resolved Systems Immunology Reveals a Late Juncture Linked to Fatal COVID-19: Innate Cells", + "url": "https://cellxgene.cziscience.com/e/30cd5311-6c09-46c9-94f1-71fe4b91813c.cxg/" + }, + "c874f155-9bf9-4928-b821-f52c876b3e48": { + "name": "49 years old male - Fresh PBMCs (1 day post-intubation)", + "url": "https://cellxgene.cziscience.com/e/c874f155-9bf9-4928-b821-f52c876b3e48.cxg/" + }, + "8a554710-08bc-4005-87cd-da9675bdc2e7": { + "name": "82 years old female - Fresh PBMCs (1 day post-intubation)", + "url": "https://cellxgene.cziscience.com/e/8a554710-08bc-4005-87cd-da9675bdc2e7.cxg/" + }, + "881fe679-c6e0-45a3-9427-c4e81be6921f": { + "name": "66 years old female - Fresh PBMCs (2 days post-intubation)", + "url": "https://cellxgene.cziscience.com/e/881fe679-c6e0-45a3-9427-c4e81be6921f.cxg/" + }, + "eeacb0c1-2217-4cf6-b8ce-1f0fedf1b569": { + "name": "49 years old male - Fresh PBMCs (3 days post-intubation)", + "url": "https://cellxgene.cziscience.com/e/eeacb0c1-2217-4cf6-b8ce-1f0fedf1b569.cxg/" + }, + "ed9e9f96-4f08-49d2-bef5-b2c29adf3edc": { + "name": "66 years old female - Fresh PBMCs (4 days post-intubation)", + "url": "https://cellxgene.cziscience.com/e/ed9e9f96-4f08-49d2-bef5-b2c29adf3edc.cxg/" + }, + "01c93cf6-b695-4e30-a26e-121ae8b16a9e": { + "name": "66 years old female - Fresh PBMCs (7 days post-intubation)", + "url": "https://cellxgene.cziscience.com/e/01c93cf6-b695-4e30-a26e-121ae8b16a9e.cxg/" + }, + "db59611b-42de-4035-93aa-1ed39f38b467": { + "name": "49 years old male - Fresh PBMCs (2 days post-intubation)", + "url": "https://cellxgene.cziscience.com/e/db59611b-42de-4035-93aa-1ed39f38b467.cxg/" + }, + "ea786a06-5855-48b7-80d7-0313a21a2044": { + "name": "66 years old female - Fresh PBMCs (3 days post-intubation)", + "url": "https://cellxgene.cziscience.com/e/ea786a06-5855-48b7-80d7-0313a21a2044.cxg/" + }, + "84230ea4-998d-4aa8-8456-81dd54ce23af": { + "name": "74 years old female - Fresh PBMCs (3 days post-intubation)", + "url": "https://cellxgene.cziscience.com/e/84230ea4-998d-4aa8-8456-81dd54ce23af.cxg/" + }, + "50eb1e23-b8d4-4f76-a184-44e5541fa05a": { + "name": "74 years old female - Fresh PBMCs (8 days post-intubation)", + "url": "https://cellxgene.cziscience.com/e/50eb1e23-b8d4-4f76-a184-44e5541fa05a.cxg/" + }, + "79ef1959-a6b4-4cac-82ca-30feaec48df1": { + "name": "74 years old female - Fresh PBMCs (7 days post-intubation)", + "url": "https://cellxgene.cziscience.com/e/79ef1959-a6b4-4cac-82ca-30feaec48df1.cxg/" + }, + "9dbab10c-118d-496b-966a-67f1763a6b7d": { + "name": "Large-scale single-cell analysis reveals critical immune characteristics of COVID-19 patients", + "url": "https://cellxgene.cziscience.com/e/9dbab10c-118d-496b-966a-67f1763a6b7d.cxg/" + }, + "krasnow_lab_human_lung_cell_atlas_10x-1-remixed": { + "name": "Krasnow Lab Human Lung Cell Atlas, 10X", + "url": "https://cellxgene.cziscience.com/e/krasnow_lab_human_lung_cell_atlas_10x-1-remixed.cxg/" + }, + "krasnow_lab_human_lung_cell_atlas_smartseq2-2-remixed": { + "name": "Krasnow Lab Human Lung Cell Atlas, Smart-seq2", + "url": "https://cellxgene.cziscience.com/e/krasnow_lab_human_lung_cell_atlas_smartseq2-2-remixed.cxg/" + }, + "c2a461b1-0c15-4047-9fcb-1f966fe55100": { + "name": "Autoimmunity PBMCs", + "url": "https://cellxgene.cziscience.com/e/c2a461b1-0c15-4047-9fcb-1f966fe55100.cxg/" + }, + "fa8605cf-f27e-44af-ac2a-476bee4410d3": { + "name": "PBMCs", + "url": "https://cellxgene.cziscience.com/e/fa8605cf-f27e-44af-ac2a-476bee4410d3.cxg/" + }, + "Single_cell_atlas_of_peripheral_immune_response_to_SARS_CoV_2_infection": { + "name": "Single-cell atlas of peripheral immune response to SARS-CoV-2 infection", + "url": "https://cellxgene.cziscience.com/e/Single_cell_atlas_of_peripheral_immune_response_to_SARS_CoV_2_infection.cxg/" + }, + "human_cell_landscape": { + "name": "Construction of a human cell landscape at single-cell level", + "url": "https://cellxgene.cziscience.com/e/human_cell_landscape.cxg/" + }, + "01209dce-3575-4bed-b1df-129f57fbc031": { + "name": "Single-cell transcriptomics of human T cells reveals tissue and activation signatures in health and disease", + "url": "https://cellxgene.cziscience.com/e/01209dce-3575-4bed-b1df-129f57fbc031.cxg/" + }, + "5bc42b88-bb76-4954-927b-8bb7369adc64": { + "name": "Pregnant Uterus (All)", + "url": "https://cellxgene.cziscience.com/e/5bc42b88-bb76-4954-927b-8bb7369adc64.cxg/" + }, + "de2c780c-1747-40bd-9ccf-9588ec186cee": { + "name": "Immunophenotyping of COVID-19 and influenza highlights the role of type I interferons in development of severe COVID-19", + "url": "https://cellxgene.cziscience.com/e/de2c780c-1747-40bd-9ccf-9588ec186cee.cxg/" + } +} diff --git a/transfer/code/scGPT/data/cellxgene/process_allcounts.py b/transfer/code/scGPT/data/cellxgene/process_allcounts.py new file mode 100644 index 0000000000000000000000000000000000000000..09943395d5cfd7c20a892c0b00677a3dee239f56 --- /dev/null +++ b/transfer/code/scGPT/data/cellxgene/process_allcounts.py @@ -0,0 +1,315 @@ +import argparse +from pathlib import Path +from scgpt.tokenizer import GeneVocab, random_mask_value +import sys +from datasets import Dataset, load_dataset +import os + +sys.path.insert(0, "../") + +parser = argparse.ArgumentParser() +parser.add_argument( + "-d", + "--data-source", + type=str, + required=True, + help='The name of the data source (currently support "scvi" datasets), or the ' + "path to the data file.", +) +parser.add_argument( + "-s", + "--save-dir", + type=str, + required=True, + help="The directory to save the trained model and the results.", +) +parser.add_argument( + "--load-model", + type=str, + default=None, + help="The directory containing the model and configs to load and continue training.", +) + +# settings for data +parser.add_argument( + "--n-hvg", + type=int, + default=None, + help="The number of highly variable genes. If set to 0, will use all genes. " + "Default is None, which will determine the n_hvg automatically.", +) +parser.add_argument( + "--valid-size-or-ratio", + type=float, + default=0.1, + help="The ratio or size of the validation set size if split the dataset. " + "If value is between 0 and 1, will be parsed as the ratio. If value is " + "greater than 1 and be an integer, will be parsed as the size. If value " + "is 0, will not split the dataset.", +) + +parser.add_argument( + "--grad-accu-steps", + type=int, + default=1, + help="The number of gradient accumulation steps. Default is 1.", +) + +# settings for tokenizer +parser.add_argument( + "--pad-token", + type=str, + default="", + help="The token to use for padding. Default is .", +) +parser.add_argument( + "--input-style", + type=str, + choices=["normed_raw", "log1p", "binned"], + default="binned", + help="The style of the input data. Default is binned.", +) +parser.add_argument( + "--input-emb-style", + type=str, + choices=["category", "continuous", "scaling"], + default="continuous", + help="The style of the input embedding. Default is continuous.", +) +parser.add_argument( + "--n-bins", + type=int, + default=51, + help="The number of bins to use for the binned input style. Default is 51.", +) +parser.add_argument( + "--max-seq-len", + type=int, + default=1536, + help="The maximum length of the sequence. Default is 1000. The actual used " + "max length would be the minimum of this value and the length of the longest " + "sequence in the data.", +) +# omit the args for MLM and MVC, will always use them by default +parser.add_argument( + "--training-tasks", # choices of "mlm", "gen", "both" + type=str, + default="both", + choices=["pcpt", "gen", "both"], + help="The tasks to use for training. pcpt: perception training with maked token " + "learning. gen: generation. Default is both.", +) +parser.add_argument( + "--mask-ratio", + type=float, + default=0.40, + help="The ratio of masked values in the training data. Default is 0.40. This" + "value will be ignored if --training-tasks is set to gen or both.", +) +parser.add_argument( + "--trunc-by-sample", + action="store_true", + help="Whether to truncate the input by sampling rather than cutting off if " + "sequence length > max_seq_length. Default is False.", +) +parser.add_argument( + "--vocab-path", + type=str, + help="Path to the vocabulary file.", +) +# settings for training +parser.add_argument( + "--local-rank", + type=int, + default=-1, + help="The local rank of the process for using the torch.distributed.launch " + "utility. Will be -1 if not running in distributed model.", +) +parser.add_argument( + "--batch-size", + type=int, + default=32, + help="The batch size for training. Default is 32.", +) +parser.add_argument( + "--eval-batch-size", + type=int, + default=32, + help="The batch size for evaluation. Default is 32.", +) +parser.add_argument( + "--epochs", + type=int, + default=10, + help="The number of epochs for training.", +) +parser.add_argument( + "--lr", + type=float, + default=1e-3, + help="The learning rate for training. Default is 1e-3.", +) +parser.add_argument( + "--scheduler-interval", + type=int, + default=100, + help="The interval iterations for updating the learning rate. Default is 100. " + "This will only be used when warmup-ratio is 0.", +) +parser.add_argument( + "--scheduler-factor", + type=float, + default=0.99, + help="The factor for updating the learning rate. Default is 0.99. " + "This will only be used when warmup-ratio is 0.", +) +parser.add_argument( + "--warmup-ratio-or-step", + type=float, + default=0.1, + help="The ratio of warmup steps out of the total training steps. Default is 0.1. " + "If warmup-ratio is above 0, will use a cosine scheduler with warmup. If " + "the value is above 1, will use it as the number of warmup steps.", +) +parser.add_argument( + "--no-cls", + action="store_true", + help="Whether to deactivate the classification loss. Default is False.", +) +parser.add_argument( + "--no-cce", + action="store_true", + help="Whether to deactivate the contrastive cell embedding objective. " + "Default is False.", +) +parser.add_argument( + "--fp16", + action="store_true", + help="Whether to train in automatic mixed precision. Default is False.", +) +parser.add_argument( + "--fast-transformer", + type=bool, + default=True, + help="Whether to use the fast transformer. Default is True.", +) + +# settings for model +parser.add_argument( + "--nlayers", + type=int, + default=4, + help="The number of layers for the transformer. Default is 4.", +) +parser.add_argument( + "--nheads", + type=int, + default=4, + help="The number of heads for the transformer. Default is 4.", +) +parser.add_argument( + "--embsize", + type=int, + default=64, + help="The embedding size for the transformer. Default is 64.", +) +parser.add_argument( + "--d-hid", + type=int, + default=64, + help="dimension of the feedforward network model in the transformer. " + "Default is 64.", +) +parser.add_argument( + "--dropout", + type=float, + default=0.2, + help="The dropout rate. Default is 0.2.", +) +parser.add_argument( + "--n-layers-cls", + type=int, + default=3, + help="The number of layers for the classification network, including the " + "output layer. Default is 3.", +) + +# settings for logging +parser.add_argument( + "--log-interval", + type=int, + default=100, + help="The interval for logging. Default is 100.", +) +parser.add_argument( + "--save-interval", + type=int, + default=1000, + help="The interval for saving the model. Default is 1000.", +) + +args = parser.parse_args() +# args.pad_value = -2 + +if args.input_style == "binned": + if args.input_emb_style == "scaling": + raise ValueError("input_emb_style `scaling` is not supported for binned input.") +elif args.input_style == "log1p" or args.input_style == "normed_raw": + if args.input_emb_style == "category": + raise ValueError( + "input_emb_style `category` is not supported for log1p or normed_raw input." + ) + +if args.input_emb_style == "category": + args.mask_value = args.n_bins + 1 + args.pad_value = args.n_bins # for padding gene expr values + n_input_bins = args.n_bins + 2 +else: + args.mask_value = -1 + args.pad_value = -2 + n_input_bins = args.n_bins + + +def _map_append_cls(dataset: Dataset) -> Dataset: + dataset = dataset.map( + lambda example: { + "genes": [vocab[""]] + example["genes"], + "expressions": [args.pad_value] + example["expressions"], + }, + # batched=True, # not using since then the map func needs to loop + num_proc=len(os.sched_getaffinity(0)), + ) + + return dataset + + +special_tokens = [args.pad_token, "", ""] + +parquet_files = [str(f) for f in Path(args.data_source).glob("*.parquet")] +cache_dir = Path(args.data_source).parent / "cache" +vocab = GeneVocab.from_file(Path(args.vocab_path)) +for s in special_tokens: + if s not in vocab: + vocab.append_token(s) + + +# load or make the dataset w/ appended at the beginning +cls_prefix_datatable = Path(args.data_source) / "cls_prefix_data.parquet" +if not cls_prefix_datatable.exists(): + print("preparing cls prefix dataset") + raw_dataset = load_dataset( + "parquet", + data_files=parquet_files, + split="train", + cache_dir=str(cache_dir), + ) + raw_dataset = _map_append_cls(raw_dataset) + raw_dataset.to_parquet(str(cls_prefix_datatable)) +raw_dataset = load_dataset( + "parquet", + data_files=str(cls_prefix_datatable), + split="train", + cache_dir=str(cache_dir), +) + +# others, pancreas, lung, kidney, heart, blood diff --git a/transfer/code/scGPT/data/cellxgene/query_list.txt b/transfer/code/scGPT/data/cellxgene/query_list.txt new file mode 100644 index 0000000000000000000000000000000000000000..19232a3ef7c8fb8458c16698b0bf2da052a7ed4d --- /dev/null +++ b/transfer/code/scGPT/data/cellxgene/query_list.txt @@ -0,0 +1,9 @@ +heart +blood +brain +lung +kidney +intestine +pancreas +others +pan-cancer diff --git a/transfer/code/scGPT/docs/Makefile b/transfer/code/scGPT/docs/Makefile new file mode 100644 index 0000000000000000000000000000000000000000..d4bb2cbb9eddb1bb1b4f366623044af8e4830919 --- /dev/null +++ b/transfer/code/scGPT/docs/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SOURCEDIR = . +BUILDDIR = _build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/transfer/code/scGPT/docs/conf.py b/transfer/code/scGPT/docs/conf.py new file mode 100644 index 0000000000000000000000000000000000000000..1334d81ffbcbfe60f0be94c57e4af5b74ec488e4 --- /dev/null +++ b/transfer/code/scGPT/docs/conf.py @@ -0,0 +1,45 @@ +# Configuration file for the Sphinx documentation builder. +# +# For the full list of built-in configuration values, see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Project information ----------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information + +import os +import sys + +sys.path.insert(0, os.path.abspath("..")) + +project = "scGPT" +copyright = "2023, Wanglab, Haotian Cui" +author = "Haotian Cui" +release = "0.2.4" + +# -- General configuration --------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration + +extensions = [ + "sphinx.ext.autodoc", + "sphinx.ext.napoleon", + "sphinx.ext.viewcode", + "nbsphinx", + "nbsphinx_link", + # "myst_nb", +] + +templates_path = ["_templates"] +exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] + + +# -- Options for HTML output ------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output + +html_theme = "sphinx_rtd_theme" +html_static_path = ["_static"] + + +# -- Options for nbshpinx ---------------------------------------------------- +# https://nbsphinx.readthedocs.io/en/0.8.0/configure.html + +nbsphinx_execute = "never" diff --git a/transfer/code/scGPT/docs/environment.yml b/transfer/code/scGPT/docs/environment.yml new file mode 100644 index 0000000000000000000000000000000000000000..61d5920474d5111d13f01c3e55e19303bd6a2dd9 --- /dev/null +++ b/transfer/code/scGPT/docs/environment.yml @@ -0,0 +1,29 @@ +name: rtd +channels: + - conda-forge + - defaults +dependencies: + - r-base[version='>=4.2'] + - r-devtools + - python=3.9 + - pip>=22.3,<=23.2 + - pip: + - datasets>=2.3.2 + - leidenalg>=0.8.10 + - numba>=0.55.2 + - pandas>=1.3.5 + - scanpy>=1.9.1 + - scvi-tools>=0.16.4 + - scib>=1.0.3 + - torch>=1.13.0 + - torchtext + - tqdm>=4.64.0 + - typing-extensions>=4.2.0 + - umap-learn>=0.5.3 + - sphinx + - sphinx_rtd_theme + - orbax<0.1.8 + - ipykernel + - nbsphinx + - nbsphinx-link + - myst_nb diff --git a/transfer/code/scGPT/docs/faq.rst b/transfer/code/scGPT/docs/faq.rst new file mode 100644 index 0000000000000000000000000000000000000000..4515e7cfedcc1278c6f9c0012664f56b21a40e97 --- /dev/null +++ b/transfer/code/scGPT/docs/faq.rst @@ -0,0 +1,2 @@ +FAQ +=== \ No newline at end of file diff --git a/transfer/code/scGPT/docs/index.rst b/transfer/code/scGPT/docs/index.rst new file mode 100644 index 0000000000000000000000000000000000000000..8d09d29f4fe5077639e129f184c181a8d1de6260 --- /dev/null +++ b/transfer/code/scGPT/docs/index.rst @@ -0,0 +1,61 @@ +.. scGPT documentation master file, created by + sphinx-quickstart on Sun Jul 2 07:16:12 2023. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Welcome to scGPT's documentation! + +.. image:: https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat + :target: https://scgpt.readthedocs.io/en/latest/?badge=latest + :alt: Documentation Status + +.. image:: https://img.shields.io/pypi/v/scgpt.svg + :target: https://pypi.python.org/pypi/scgpt + :alt: PyPI version + +.. image:: https://img.shields.io/pypi/pyversions/scgpt.svg + :target: https://pypi.python.org/pypi/scgpt + :alt: PyPI supported Python versions + +Welcome to the documentation for scGPT, a Python package for single-cell multi-omic data analysis using pretrained foundation models. This package is based on the work of scGPT (`GitHub `_, `preprint `_) and provides a set of functions for data preprocessing, visualization, and model evaluation. + +================================= + +.. toctree:: + :maxdepth: 2 + :caption: Getting Started: + + introduction + installation + +.. toctree:: + :maxdepth: 1 + :caption: Tutorials: + + tutorial_grn + tutorial_integraion + tutorial_annotation + tutorial_perturbation + tutorial_reference_mapping + +.. toctree:: + :maxdepth: 2 + :caption: API Reference: + + scgpt + +.. toctree:: + :maxdepth: 2 + :caption: References: + + faq + references + + + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` diff --git a/transfer/code/scGPT/docs/installation.rst b/transfer/code/scGPT/docs/installation.rst new file mode 100644 index 0000000000000000000000000000000000000000..fbf660e91072bd0f93eab0cfbb99920beb26a4ca --- /dev/null +++ b/transfer/code/scGPT/docs/installation.rst @@ -0,0 +1,4 @@ +installation +================ + +Please follow the instructions on the project [GitHub page](https://github.com/bowang-lab/scGPT). \ No newline at end of file diff --git a/transfer/code/scGPT/docs/introduction.rst b/transfer/code/scGPT/docs/introduction.rst new file mode 100644 index 0000000000000000000000000000000000000000..b12fe4e5207bee5dc8bdfa1a680e47296dff76cd --- /dev/null +++ b/transfer/code/scGPT/docs/introduction.rst @@ -0,0 +1,10 @@ +Introduction +============ + +Welcome to the documentation for scGPT, a Python package for single-cell multi-omic data analysis using pretrained foundation models. This package is based on the work of scGPT (`GitHub `_, `preprint `_) and provides a set of functions for data preprocessing, visualization, and model evaluation. + +Our package is built on the foundation of the scGPT model, which is the first single-cell foundation model built through generative pre-training on over 33 million cells. The scGPT model incorporates innovative techniques to overcome methodology and engineering challenges specific to pre-training on large-scale single-cell omic data. By adapting the transformer architecture, we enable the simultaneous learning of cell and gene representations, facilitating a comprehensive understanding of cellular characteristics based on gene expression. + +Our package provides a set of functions for data preprocessing, visualization, and model evaluation that are compatible with the scikit-learn library. These functions enable users to preprocess their single-cell RNA-seq data, visualize the results, and evaluate the performance of the scGPT model on downstream tasks such as multi-batch integration, multi-omic integration, cell-type annotation, genetic perturbation prediction, and gene network inference. + +In addition to the functions provided by our package, we also provide a tutorial that demonstrates how to use the scGPT model for single-cell RNA-seq data analysis. This tutorial includes step-by-step instructions for preprocessing the data, training the scGPT model, and evaluating its performance on downstream tasks. \ No newline at end of file diff --git a/transfer/code/scGPT/docs/make.bat b/transfer/code/scGPT/docs/make.bat new file mode 100644 index 0000000000000000000000000000000000000000..32bb24529f92346af26219baed295b7488b77534 --- /dev/null +++ b/transfer/code/scGPT/docs/make.bat @@ -0,0 +1,35 @@ +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=. +set BUILDDIR=_build + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.https://www.sphinx-doc.org/ + exit /b 1 +) + +if "%1" == "" goto help + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% + +:end +popd diff --git a/transfer/code/scGPT/docs/references.rst b/transfer/code/scGPT/docs/references.rst new file mode 100644 index 0000000000000000000000000000000000000000..f97cb5b2acabe4fcb830ebfe442a5927a69a0c9d --- /dev/null +++ b/transfer/code/scGPT/docs/references.rst @@ -0,0 +1,2 @@ +References +========== \ No newline at end of file diff --git a/transfer/code/scGPT/docs/requirements.txt b/transfer/code/scGPT/docs/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..7f837be4c1b9b3161b9a484937488cdab2b6720d --- /dev/null +++ b/transfer/code/scGPT/docs/requirements.txt @@ -0,0 +1,21 @@ +anndata==0.8.0 ; python_full_version >= "3.7.13" and python_version < "3.11" +datasets==2.3.2 ; python_full_version >= "3.7.13" and python_version < "3.11" +leidenalg==0.8.10 ; python_full_version >= "3.7.13" and python_version < "3.11" +llvmlite==0.38.1 ; python_full_version >= "3.7.13" and python_version < "3.11" +networkx==2.6.3 ; python_full_version >= "3.7.13" and python_version < "3.11" +numba==0.55.2 ; python_full_version >= "3.7.13" and python_version < "3.11" +numpy==1.21.6 ; python_full_version >= "3.7.13" and python_version < "3.11" +pandas==1.3.5 ; python_full_version >= "3.7.13" and python_version < "3.11" +scanpy==1.9.1 ; python_full_version >= "3.7.13" and python_version < "3.11" +scikit-learn==1.0.2 ; python_full_version >= "3.7.13" and python_version < "3.11" +scikit-misc==0.1.4 ; python_full_version >= "3.7.13" and python_version < "3.11" +scipy==1.7.3 ; python_full_version >= "3.7.13" and python_version < "3.11" +scvi-tools==0.16.4 ; python_full_version >= "3.7.13" and python_version < "3.11" +setuptools==59.5.0 ; python_full_version >= "3.7.13" and python_version < "3.11" +scib +torch==1.13.0 ; python_full_version >= "3.7.13" and python_version < "3.11" +torchtext==0.14.0 ; python_full_version >= "3.7.13" and python_version < "3.11" +tqdm==4.64.0 ; python_full_version >= "3.7.13" and python_version < "3.11" +transformers==4.20.1 ; python_full_version >= "3.7.13" and python_version < "3.11" +typing-extensions==4.2.0 ; python_full_version >= "3.7.13" and python_version < "3.11" +umap-learn==0.5.3 ; python_full_version >= "3.7.13" and python_version < "3.11" diff --git a/transfer/code/scGPT/docs/scgpt.model.rst b/transfer/code/scGPT/docs/scgpt.model.rst new file mode 100644 index 0000000000000000000000000000000000000000..39852478a2d81733697b8892f93d95b284e5d1ff --- /dev/null +++ b/transfer/code/scGPT/docs/scgpt.model.rst @@ -0,0 +1,45 @@ +scgpt.model package +=================== + +Submodules +---------- + +scgpt.model.dsbn module +----------------------- + +.. automodule:: scgpt.model.dsbn + :members: + :undoc-members: + :show-inheritance: + +scgpt.model.generation\_model module +------------------------------------ + +.. automodule:: scgpt.model.generation_model + :members: + :undoc-members: + :show-inheritance: + +scgpt.model.grad\_reverse module +-------------------------------- + +.. automodule:: scgpt.model.grad_reverse + :members: + :undoc-members: + :show-inheritance: + +scgpt.model.model module +------------------------ + +.. automodule:: scgpt.model.model + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: scgpt.model + :members: + :undoc-members: + :show-inheritance: diff --git a/transfer/code/scGPT/docs/scgpt.rst b/transfer/code/scGPT/docs/scgpt.rst new file mode 100644 index 0000000000000000000000000000000000000000..bfe057d7e4d9586e3dee69df4a2e2591e8b4cc60 --- /dev/null +++ b/transfer/code/scGPT/docs/scgpt.rst @@ -0,0 +1,54 @@ +scgpt package +============= + +Modules +----------- + +.. toctree:: + :maxdepth: 4 + + scgpt.model + scgpt.scbank + scgpt.tasks + scgpt.tokenizer + scgpt.utils + +scgpt.data\_collator +--------------------------- + +.. automodule:: scgpt.data_collator + :members: + :undoc-members: + :show-inheritance: + +scgpt.data\_sampler +-------------------------- + +.. automodule:: scgpt.data_sampler + :members: + :undoc-members: + :show-inheritance: + +scgpt.loss +----------------- + +.. automodule:: scgpt.loss + :members: + :undoc-members: + :show-inheritance: + +scgpt.preprocess +----------------------- + +.. automodule:: scgpt.preprocess + :members: + :undoc-members: + :show-inheritance: + +scgpt.trainer +-------------------- + +.. automodule:: scgpt.trainer + :members: + :undoc-members: + :show-inheritance: diff --git a/transfer/code/scGPT/docs/scgpt.scbank.rst b/transfer/code/scGPT/docs/scgpt.scbank.rst new file mode 100644 index 0000000000000000000000000000000000000000..710c2f0777c81ff04a405cade1113c60d1762f81 --- /dev/null +++ b/transfer/code/scGPT/docs/scgpt.scbank.rst @@ -0,0 +1,45 @@ +scgpt.scbank package +==================== + +Submodules +---------- + +scgpt.scbank.data module +------------------------ + +.. automodule:: scgpt.scbank.data + :members: + :undoc-members: + :show-inheritance: + +scgpt.scbank.databank module +---------------------------- + +.. automodule:: scgpt.scbank.databank + :members: + :undoc-members: + :show-inheritance: + +scgpt.scbank.monitor module +--------------------------- + +.. automodule:: scgpt.scbank.monitor + :members: + :undoc-members: + :show-inheritance: + +scgpt.scbank.setting module +--------------------------- + +.. automodule:: scgpt.scbank.setting + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: scgpt.scbank + :members: + :undoc-members: + :show-inheritance: diff --git a/transfer/code/scGPT/docs/scgpt.tasks.rst b/transfer/code/scGPT/docs/scgpt.tasks.rst new file mode 100644 index 0000000000000000000000000000000000000000..0a41109960a30394f7117f08eaaa40f69917cf3f --- /dev/null +++ b/transfer/code/scGPT/docs/scgpt.tasks.rst @@ -0,0 +1,29 @@ +scgpt.tasks package +=================== + +Submodules +---------- + +scgpt.tasks.cell\_emb module +---------------------------- + +.. automodule:: scgpt.tasks.cell_emb + :members: + :undoc-members: + :show-inheritance: + +scgpt.tasks.grn module +---------------------- + +.. automodule:: scgpt.tasks.grn + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: scgpt.tasks + :members: + :undoc-members: + :show-inheritance: diff --git a/transfer/code/scGPT/docs/scgpt.tokenizer.rst b/transfer/code/scGPT/docs/scgpt.tokenizer.rst new file mode 100644 index 0000000000000000000000000000000000000000..98f40cfd82e784646398f32a88d4d9482b381d6c --- /dev/null +++ b/transfer/code/scGPT/docs/scgpt.tokenizer.rst @@ -0,0 +1,21 @@ +scgpt.tokenizer package +======================= + +Submodules +---------- + +scgpt.tokenizer.gene\_tokenizer module +-------------------------------------- + +.. automodule:: scgpt.tokenizer.gene_tokenizer + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: scgpt.tokenizer + :members: + :undoc-members: + :show-inheritance: diff --git a/transfer/code/scGPT/docs/scgpt.utils.rst b/transfer/code/scGPT/docs/scgpt.utils.rst new file mode 100644 index 0000000000000000000000000000000000000000..cbef25bc18c9726ad962cf70706f0a3ef1798b57 --- /dev/null +++ b/transfer/code/scGPT/docs/scgpt.utils.rst @@ -0,0 +1,21 @@ +scgpt.utils package +=================== + +Submodules +---------- + +scgpt.utils.util module +----------------------- + +.. automodule:: scgpt.utils.util + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: scgpt.utils + :members: + :undoc-members: + :show-inheritance: diff --git a/transfer/code/scGPT/docs/tutorial_annotation.nblink b/transfer/code/scGPT/docs/tutorial_annotation.nblink new file mode 100644 index 0000000000000000000000000000000000000000..549d6a045c08ed7cc16a1b599d270530c3208d62 --- /dev/null +++ b/transfer/code/scGPT/docs/tutorial_annotation.nblink @@ -0,0 +1,3 @@ +{ + "path": "../tutorials/Tutorial_Annotation.ipynb" +} \ No newline at end of file diff --git a/transfer/code/scGPT/docs/tutorial_grn.nblink b/transfer/code/scGPT/docs/tutorial_grn.nblink new file mode 100644 index 0000000000000000000000000000000000000000..7d57d3c35f413af58f2dea89307a87a2991214e7 --- /dev/null +++ b/transfer/code/scGPT/docs/tutorial_grn.nblink @@ -0,0 +1,3 @@ +{ + "path": "../tutorials/Tutorial_GRN.ipynb" +} \ No newline at end of file diff --git a/transfer/code/scGPT/docs/tutorial_integraion.nblink b/transfer/code/scGPT/docs/tutorial_integraion.nblink new file mode 100644 index 0000000000000000000000000000000000000000..06229f136f40352bf670c2dbbb5453ee66e15e4f --- /dev/null +++ b/transfer/code/scGPT/docs/tutorial_integraion.nblink @@ -0,0 +1,3 @@ +{ + "path": "../tutorials/Tutorial_Integration.ipynb" +} \ No newline at end of file diff --git a/transfer/code/scGPT/docs/tutorial_perturbation.nblink b/transfer/code/scGPT/docs/tutorial_perturbation.nblink new file mode 100644 index 0000000000000000000000000000000000000000..b824e9cc1b683f85df2dd8035dcbfaf80330c13f --- /dev/null +++ b/transfer/code/scGPT/docs/tutorial_perturbation.nblink @@ -0,0 +1,3 @@ +{ + "path": "../tutorials/Tutorial_Perturbation.ipynb" +} \ No newline at end of file diff --git a/transfer/code/scGPT/docs/tutorial_reference_mapping.nblink b/transfer/code/scGPT/docs/tutorial_reference_mapping.nblink new file mode 100644 index 0000000000000000000000000000000000000000..e6f199891f3961b28efe69e0716af33883da1305 --- /dev/null +++ b/transfer/code/scGPT/docs/tutorial_reference_mapping.nblink @@ -0,0 +1,3 @@ +{ + "path": "../tutorials/Tutorial_Reference_Mapping.ipynb" +} \ No newline at end of file diff --git a/transfer/code/scGPT/examples/finetune_integration.py b/transfer/code/scGPT/examples/finetune_integration.py new file mode 100644 index 0000000000000000000000000000000000000000..ac44a8d0c469e853d2af2daa661404a8e16678d9 --- /dev/null +++ b/transfer/code/scGPT/examples/finetune_integration.py @@ -0,0 +1,798 @@ +# %% +import copy +import gc +import json +import os +from pathlib import Path +import sys +import time +import traceback +from typing import List, Tuple, Dict, Union, Optional +import warnings + +import torch +from anndata import AnnData +import scanpy as sc +import scvi +import numpy as np +import wandb +from scipy.sparse import issparse +import matplotlib.pyplot as plt +from torch import nn +from torch.nn import functional as F +from torch.utils.data import Dataset, DataLoader +from sklearn.model_selection import train_test_split +from torchtext.vocab import Vocab +from torchtext._torchtext import ( + Vocab as VocabPybind, +) + +from scgpt.tokenizer.gene_tokenizer import GeneVocab + +sys.path.append("../") +import scgpt as scg +from scgpt.model import TransformerModel, AdversarialDiscriminator +from scgpt.tokenizer import tokenize_and_pad_batch, random_mask_value +from scgpt.loss import ( + masked_mse_loss, + masked_relative_error, + criterion_neg_log_bernoulli, +) +from scgpt.preprocess import Preprocessor +from scgpt import SubsetsBatchSampler +from scgpt.utils import set_seed, category_str2int, eval_scib_metrics + +sc.set_figure_params(figsize=(4, 4)) +os.environ["KMP_WARNINGS"] = "off" +# os.environ["WANDB_MODE"] = "offline" + +hyperparameter_defaults = dict( + seed=42, + dataset_name="PBMC_10K", + do_train=True, + load_model="save/scGPT_bc", + mask_ratio=0.4, + epochs=30, + n_bins=51, + GEPC=True, # Masked value prediction for cell embedding + ecs_thres=0.8, # Elastic cell similarity objective, 0.0 to 1.0, 0.0 to disable + dab_weight=1.0, + lr=1e-4, + batch_size=64, + layer_size=128, + nlayers=4, + nhead=4, + # if load model, batch_size, layer_size, nlayers, nhead will be ignored + dropout=0.2, + schedule_ratio=0.9, # ratio of epochs for learning rate schedule + save_eval_interval=5, + log_interval=100, + fast_transformer=True, + pre_norm=False, + amp=True, # Automatic Mixed Precision +) +run = wandb.init( + config=hyperparameter_defaults, + project="scGPT", + reinit=True, + settings=wandb.Settings(start_method="fork"), +) +config = wandb.config +print(config) + +set_seed(config.seed) + +# %% +# settings for input and preprocessing +pad_token = "" +special_tokens = [pad_token, "", ""] +mask_ratio = config.mask_ratio +mask_value = -1 +pad_value = -2 +n_input_bins = config.n_bins + +n_hvg = 1200 # number of highly variable genes +max_seq_len = n_hvg + 1 +per_seq_batch_sample = True +DSBN = True # Domain-spec batchnorm +explicit_zero_prob = True # whether explicit bernoulli for zeros + +# %% +dataset_name = config.dataset_name +save_dir = Path(f"./save/dev_{dataset_name}-{time.strftime('%b%d-%H-%M')}/") +save_dir.mkdir(parents=True, exist_ok=True) +print(f"save to {save_dir}") +# save the whole script to the dir +os.system(f"cp {__file__} {save_dir}") + +logger = scg.logger +scg.utils.add_file_handler(logger, save_dir / "run.log") + + +# %% [markdown] +# ## Loading and preparing data +if dataset_name == "PBMC_10K": + adata = scvi.data.pbmc_dataset() # 11990 × 3346 + ori_batch_col = "batch" + adata.obs["celltype"] = adata.obs["str_labels"].astype("category") + adata.var = adata.var.set_index("gene_symbols") + data_is_raw = True + + +# %% +# make the batch category column +adata.obs["str_batch"] = adata.obs[ori_batch_col].astype(str) +batch_id_labels = adata.obs["str_batch"].astype("category").cat.codes.values +adata.obs["batch_id"] = batch_id_labels + +adata.var["gene_name"] = adata.var.index.tolist() + +if config.load_model is not None: + model_dir = Path(config.load_model) + model_config_file = model_dir / "args.json" + model_file = model_dir / "best_model.pt" + vocab_file = model_dir / "vocab.json" + + vocab = GeneVocab.from_file(vocab_file) + for s in special_tokens: + if s not in vocab: + vocab.append_token(s) + + adata.var["id_in_vocab"] = [ + 1 if gene in vocab else -1 for gene in adata.var["gene_name"] + ] + gene_ids_in_vocab = np.array(adata.var["id_in_vocab"]) + logger.info( + f"match {np.sum(gene_ids_in_vocab >= 0)}/{len(gene_ids_in_vocab)} genes " + f"in vocabulary of size {len(vocab)}." + ) + adata = adata[:, adata.var["id_in_vocab"] >= 0] + + # model + with open(model_config_file, "r") as f: + model_configs = json.load(f) + logger.info( + f"Resume model from {model_file}, the model args will be overriden by the " + f"config {model_config_file}." + ) + embsize = model_configs["embsize"] + nhead = model_configs["nheads"] + d_hid = model_configs["d_hid"] + nlayers = model_configs["nlayers"] + n_layers_cls = model_configs["n_layers_cls"] +else: + embsize = config.layer_size + nhead = config.nhead + nlayers = config.nlayers + d_hid = config.layer_size + + +# %% +# set up the preprocessor, use the args to config the workflow +preprocessor = Preprocessor( + use_key="X", # the key in adata.layers to use as raw data + filter_gene_by_counts=3, # step 1 + filter_cell_by_counts=False, # step 2 + normalize_total=1e4, # 3. whether to normalize the raw data and to what sum + result_normed_key="X_normed", # the key in adata.layers to store the normalized data + log1p=data_is_raw, # 4. whether to log1p the normalized data + result_log1p_key="X_log1p", + subset_hvg=n_hvg, # 5. whether to subset the raw data to highly variable genes + hvg_flavor="seurat_v3" if data_is_raw else "cell_ranger", + binning=config.n_bins, # 6. whether to bin the raw data and to what number of bins + result_binned_key="X_binned", # the key in adata.layers to store the binned data +) +preprocessor(adata, batch_key="str_batch" if dataset_name != "heart_cell" else None) + +# %% +if per_seq_batch_sample: + # sort the adata by batch_id in advance + adata_sorted = adata[adata.obs["batch_id"].argsort()].copy() + +# %% [markdown] +# ## Tokenize input + +# %% +input_layer_key = "X_binned" +all_counts = ( + adata.layers[input_layer_key].toarray() + if issparse(adata.layers[input_layer_key]) + else adata.layers[input_layer_key] +) +genes = adata.var["gene_name"].tolist() + +celltypes_labels = adata.obs["celltype"].tolist() # make sure count from 0 +num_types = len(set(celltypes_labels)) +celltypes_labels = np.array(celltypes_labels) + +batch_ids = adata.obs["batch_id"].tolist() +num_batch_types = len(set(batch_ids)) +batch_ids = np.array(batch_ids) + +( + train_data, + valid_data, + train_celltype_labels, + valid_celltype_labels, + train_batch_labels, + valid_batch_labels, +) = train_test_split( + all_counts, celltypes_labels, batch_ids, test_size=0.1, shuffle=True +) + +# %% +if config.load_model is None: + vocab = Vocab( + VocabPybind(genes + special_tokens, None) + ) # bidirectional lookup [gene <-> int] +vocab.set_default_index(vocab[""]) +gene_ids = np.array(vocab(genes), dtype=int) + +# %% +tokenized_train = tokenize_and_pad_batch( + train_data, + gene_ids, + max_len=max_seq_len, + vocab=vocab, + pad_token=pad_token, + pad_value=pad_value, + append_cls=True, # append token at the beginning + include_zero_gene=True, +) +tokenized_valid = tokenize_and_pad_batch( + valid_data, + gene_ids, + max_len=max_seq_len, + vocab=vocab, + pad_token=pad_token, + pad_value=pad_value, + append_cls=True, + include_zero_gene=True, +) +logger.info( + f"train set number of samples: {tokenized_train['genes'].shape[0]}, " + f"\n\t feature length: {tokenized_train['genes'].shape[1]}" +) +logger.info( + f"valid set number of samples: {tokenized_valid['genes'].shape[0]}, " + f"\n\t feature length: {tokenized_valid['genes'].shape[1]}" +) + + +# %% +def prepare_data(sort_seq_batch=False) -> Tuple[Dict[str, torch.Tensor]]: + masked_values_train = random_mask_value( + tokenized_train["values"], + mask_ratio=mask_ratio, + mask_value=mask_value, + pad_value=pad_value, + ) + masked_values_valid = random_mask_value( + tokenized_valid["values"], + mask_ratio=mask_ratio, + mask_value=mask_value, + pad_value=pad_value, + ) + print( + f"random masking at epoch {epoch:3d}, ratio of masked values in train: ", + f"{(masked_values_train == mask_value).sum() / (masked_values_train - pad_value).count_nonzero():.4f}", + ) + + input_gene_ids_train, input_gene_ids_valid = ( + tokenized_train["genes"], + tokenized_valid["genes"], + ) + input_values_train, input_values_valid = masked_values_train, masked_values_valid + target_values_train, target_values_valid = ( + tokenized_train["values"], + tokenized_valid["values"], + ) + + tensor_batch_labels_train = torch.from_numpy(train_batch_labels).long() + tensor_batch_labels_valid = torch.from_numpy(valid_batch_labels).long() + + if sort_seq_batch: + train_sort_ids = np.argsort(train_batch_labels) + input_gene_ids_train = input_gene_ids_train[train_sort_ids] + input_values_train = input_values_train[train_sort_ids] + target_values_train = target_values_train[train_sort_ids] + tensor_batch_labels_train = tensor_batch_labels_train[train_sort_ids] + + valid_sort_ids = np.argsort(valid_batch_labels) + input_gene_ids_valid = input_gene_ids_valid[valid_sort_ids] + input_values_valid = input_values_valid[valid_sort_ids] + target_values_valid = target_values_valid[valid_sort_ids] + tensor_batch_labels_valid = tensor_batch_labels_valid[valid_sort_ids] + + train_data_pt = { + "gene_ids": input_gene_ids_train, + "values": input_values_train, + "target_values": target_values_train, + "batch_labels": tensor_batch_labels_train, + } + valid_data_pt = { + "gene_ids": input_gene_ids_valid, + "values": input_values_valid, + "target_values": target_values_valid, + "batch_labels": tensor_batch_labels_valid, + } + + return train_data_pt, valid_data_pt + + +# dataset +class SeqDataset(Dataset): + def __init__(self, data: Dict[str, torch.Tensor]): + self.data = data + + def __len__(self): + return self.data["gene_ids"].shape[0] + + def __getitem__(self, idx): + return {k: v[idx] for k, v in self.data.items()} + + +# data_loader +def prepare_dataloader( + data_pt: Dict[str, torch.Tensor], + batch_size: int, + shuffle: bool = False, + intra_domain_shuffle: bool = False, + drop_last: bool = False, + num_workers: int = 0, +) -> DataLoader: + dataset = SeqDataset(data_pt) + + if per_seq_batch_sample: + # find the indices of samples in each seq batch + subsets = [] + batch_labels_array = data_pt["batch_labels"].numpy() + for batch_label in np.unique(batch_labels_array): + batch_indices = np.where(batch_labels_array == batch_label)[0].tolist() + subsets.append(batch_indices) + data_loader = DataLoader( + dataset=dataset, + batch_sampler=SubsetsBatchSampler( + subsets, + batch_size, + intra_subset_shuffle=intra_domain_shuffle, + inter_subset_shuffle=shuffle, + drop_last=drop_last, + ), + num_workers=num_workers, + pin_memory=True, + ) + return data_loader + + data_loader = DataLoader( + dataset=dataset, + batch_size=batch_size, + shuffle=shuffle, + drop_last=drop_last, + num_workers=num_workers, + pin_memory=True, + ) + return data_loader + + +# %% [markdown] +# # Create and finetune scGPT + +# %% +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + +ntokens = len(vocab) # size of vocabulary +model = TransformerModel( + ntokens, + embsize, + nhead, + d_hid, + nlayers, + vocab=vocab, + dropout=config.dropout, + pad_token=pad_token, + pad_value=pad_value, + do_mvc=config.GEPC, + do_dab=True, + use_batch_labels=True, + num_batch_labels=num_batch_types, + domain_spec_batchnorm=DSBN, + n_input_bins=n_input_bins, + ecs_threshold=config.ecs_thres, + explicit_zero_prob=explicit_zero_prob, + use_fast_transformer=config.fast_transformer, + pre_norm=config.pre_norm, +) +if config.load_model is not None: + try: + model.load_state_dict(torch.load(model_file)) + logger.info(f"Loading all model params from {model_file}") + except: + # only load params that are in the model and match the size + model_dict = model.state_dict() + pretrained_dict = torch.load(model_file) + pretrained_dict = { + k: v + for k, v in pretrained_dict.items() + if k in model_dict and v.shape == model_dict[k].shape + } + for k, v in pretrained_dict.items(): + logger.info(f"Loading params {k} with shape {v.shape}") + model_dict.update(pretrained_dict) + model.load_state_dict(model_dict) + +model.to(device) +wandb.watch(model) + + +criterion = masked_mse_loss +criterion_dab = nn.CrossEntropyLoss() +optimizer = torch.optim.Adam( + model.parameters(), lr=config.lr, eps=1e-4 if config.amp else 1e-8 +) +scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1, gamma=config.schedule_ratio) + +scaler = torch.cuda.amp.GradScaler(enabled=config.amp) + + +def train(model: nn.Module, loader: DataLoader) -> None: + """ + Train the model for one epoch. + """ + model.train() + total_loss, total_mse, total_gepc = 0.0, 0.0, 0.0 + total_error = 0.0 + log_interval = config.log_interval + start_time = time.time() + + num_batches = len(loader) + for batch, batch_data in enumerate(loader): + input_gene_ids = batch_data["gene_ids"].to(device) + input_values = batch_data["values"].to(device) + target_values = batch_data["target_values"].to(device) + batch_labels = batch_data["batch_labels"].to(device) + + src_key_padding_mask = input_gene_ids.eq(vocab[pad_token]) + with torch.cuda.amp.autocast(enabled=config.amp): + output_dict = model( + input_gene_ids, + input_values, + src_key_padding_mask=src_key_padding_mask, + batch_labels=batch_labels if DSBN else None, + MVC=config.GEPC, + ECS=config.ecs_thres > 0, + ) + + masked_positions = input_values.eq(mask_value) # the postions to predict + loss = loss_mse = criterion( + output_dict["mlm_output"], target_values, masked_positions + ) + metrics_to_log = {"train/mse": loss_mse.item()} + if explicit_zero_prob: + loss_zero_log_prob = criterion_neg_log_bernoulli( + output_dict["mlm_zero_probs"], target_values, masked_positions + ) + loss = loss + loss_zero_log_prob + metrics_to_log.update({"train/nzlp": loss_zero_log_prob.item()}) + if config.GEPC: + loss_gepc = criterion( + output_dict["mvc_output"], target_values, masked_positions + ) + loss = loss + loss_gepc + metrics_to_log.update({"train/mvc": loss_gepc.item()}) + if config.GEPC and explicit_zero_prob: + loss_gepc_zero_log_prob = criterion_neg_log_bernoulli( + output_dict["mvc_zero_probs"], target_values, masked_positions + ) + loss = loss + loss_gepc_zero_log_prob + metrics_to_log.update( + {"train/mvc_nzlp": loss_gepc_zero_log_prob.item()} + ) + if config.ecs_thres > 0: + loss_ecs = 10 * output_dict["loss_ecs"] + loss = loss + loss_ecs + metrics_to_log.update({"train/ecs": loss_ecs.item()}) + loss_dab = criterion_dab(output_dict["dab_output"], batch_labels) + loss = loss + config.dab_weight * loss_dab + metrics_to_log.update({"train/dab": loss_dab.item()}) + + model.zero_grad() + scaler.scale(loss).backward() + scaler.unscale_(optimizer) + with warnings.catch_warnings(record=True) as w: + warnings.filterwarnings("always") + torch.nn.utils.clip_grad_norm_( + model.parameters(), + 1.0, + error_if_nonfinite=False if scaler.is_enabled() else True, + ) + if len(w) > 0: + logger.warning( + f"Found infinite gradient. This may be caused by the gradient " + f"scaler. The current scale is {scaler.get_scale()}. This warning " + "can be ignored if no longer occurs after autoscaling of the scaler." + ) + scaler.step(optimizer) + scaler.update() + + wandb.log(metrics_to_log) + + with torch.no_grad(): + mre = masked_relative_error( + output_dict["mlm_output"], target_values, masked_positions + ) + + total_loss += loss.item() + total_mse += loss_mse.item() + total_gepc += loss_gepc.item() if config.GEPC else 0.0 + total_error += mre.item() + if batch % log_interval == 0 and batch > 0: + lr = scheduler.get_last_lr()[0] + ms_per_batch = (time.time() - start_time) * 1000 / log_interval + cur_loss = total_loss / log_interval + cur_mse = total_mse / log_interval + cur_gepc = total_gepc / log_interval if config.GEPC else 0.0 + cur_error = total_error / log_interval + # ppl = math.exp(cur_loss) + logger.info( + f"| epoch {epoch:3d} | {batch:3d}/{num_batches:3d} batches | " + f"lr {lr:05.4f} | ms/batch {ms_per_batch:5.2f} | " + f"loss {cur_loss:5.2f} | mse {cur_mse:5.2f} | mre {cur_error:5.2f} |" + + (f"gepc {cur_gepc:5.2f} |" if config.GEPC else "") + ) + total_loss = 0 + total_mse = 0 + total_gepc = 0 + total_error = 0 + start_time = time.time() + + +def define_wandb_metrcis(): + wandb.define_metric("valid/mse", summary="min", step_metric="epoch") + wandb.define_metric("valid/mre", summary="min", step_metric="epoch") + wandb.define_metric("valid/dab", summary="min", step_metric="epoch") + wandb.define_metric("valid/sum_mse_dab", summary="min", step_metric="epoch") + wandb.define_metric("test/avg_bio", summary="max") + + +def evaluate(model: nn.Module, loader: DataLoader) -> float: + """ + Evaluate the model on the evaluation data. + """ + model.eval() + total_loss = 0.0 + total_error = 0.0 + total_dab = 0.0 + total_num = 0 + with torch.no_grad(): + for batch_data in loader: + input_gene_ids = batch_data["gene_ids"].to(device) + input_values = batch_data["values"].to(device) + target_values = batch_data["target_values"].to(device) + batch_labels = batch_data["batch_labels"].to(device) + + src_key_padding_mask = input_gene_ids.eq(vocab[pad_token]) + with torch.cuda.amp.autocast(enabled=config.amp): + output_dict = model( + input_gene_ids, + input_values, + src_key_padding_mask=src_key_padding_mask, + batch_labels=batch_labels if DSBN else None, + ) + output_values = output_dict["mlm_output"] + + masked_positions = input_values.eq(mask_value) + loss = criterion(output_values, target_values, masked_positions) + loss_dab = criterion_dab(output_dict["dab_output"], batch_labels) + + total_loss += loss.item() * len(input_gene_ids) + total_error += masked_relative_error( + output_values, target_values, masked_positions + ).item() * len(input_gene_ids) + total_dab += loss_dab.item() * len(input_gene_ids) + total_num += len(input_gene_ids) + + wandb.log( + { + "valid/mse": total_loss / total_num, + "valid/mre": total_error / total_num, + "valid/dab": total_dab / total_num, + "valid/sum_mse_dab": (total_loss + config.dab_weight * total_dab) + / total_num, + "epoch": epoch, + }, + ) + + return total_loss / total_num, total_error / total_num + + +def eval_testdata( + model: nn.Module, + adata_t: AnnData, + include_types: List[str] = ["cls"], +) -> Optional[Dict]: + """evaluate the model on test dataset of adata_t""" + model.eval() + + # copy adata_t to avoid reuse previously computed results stored in adata_t + adata_t = adata_t.copy() + + all_counts = ( + adata_t.layers[input_layer_key].toarray() + if issparse(adata_t.layers[input_layer_key]) + else adata_t.layers[input_layer_key] + ) + + celltypes_labels = adata_t.obs["celltype"].tolist() + celltypes_labels = np.array(celltypes_labels) + + batch_ids = adata_t.obs["batch_id"].tolist() + batch_ids = np.array(batch_ids) + + # Evaluate cls cell embeddings + if "cls" in include_types: + logger.info("Evaluating cls cell embeddings") + tokenized_all = tokenize_and_pad_batch( + all_counts, + gene_ids, + max_len=max_seq_len, + vocab=vocab, + pad_token=pad_token, + pad_value=pad_value, + append_cls=True, # append token at the beginning + include_zero_gene=True, + ) + all_gene_ids, all_values = tokenized_all["genes"], tokenized_all["values"] + src_key_padding_mask = all_gene_ids.eq(vocab[pad_token]) + with torch.no_grad(), torch.cuda.amp.autocast(enabled=config.amp): + cell_embeddings = model.encode_batch( + all_gene_ids, + all_values.float(), + src_key_padding_mask=src_key_padding_mask, + batch_size=config.batch_size, + batch_labels=torch.from_numpy(batch_ids).long() if DSBN else None, + time_step=0, + return_np=True, + ) + cell_embeddings = cell_embeddings / np.linalg.norm( + cell_embeddings, axis=1, keepdims=True + ) + + adata_t.obsm["X_scGPT"] = cell_embeddings + + results = {} + try: + results = eval_scib_metrics(adata_t) + except Exception as e: + traceback.print_exc() + logger.error(e) + + sc.pp.neighbors(adata_t, use_rep="X_scGPT") + sc.tl.umap(adata_t, min_dist=0.3) + fig = sc.pl.umap( + adata_t, + color=["str_batch"], + title=[f"batch, avg_bio = {results.get('avg_bio', 0.0):.4f}"], + frameon=False, + return_fig=True, + show=False, + ) + + results["batch_umap"] = fig + + sc.pp.neighbors(adata_t, use_rep="X_scGPT") + sc.tl.umap(adata_t, min_dist=0.3) + fig = sc.pl.umap( + adata_t, + color=["celltype"], + title=[ + f"celltype, avg_bio = {results.get('avg_bio', 0.0):.4f}", + ], + frameon=False, + return_fig=True, + show=False, + ) + + results["celltype_umap"] = fig + + if len(include_types) == 1: + return results + + +# %% +best_val_loss = float("inf") +best_avg_bio = 0.0 +best_model = None +define_wandb_metrcis() + +for epoch in range(1, config.epochs + 1): + epoch_start_time = time.time() + train_data_pt, valid_data_pt = prepare_data(sort_seq_batch=per_seq_batch_sample) + train_loader = prepare_dataloader( + train_data_pt, + batch_size=config.batch_size, + shuffle=False, + intra_domain_shuffle=True, + drop_last=False, + ) + valid_loader = prepare_dataloader( + valid_data_pt, + batch_size=config.batch_size, + shuffle=False, + intra_domain_shuffle=False, + drop_last=False, + ) + + if config.do_train: + train( + model, + loader=train_loader, + ) + val_loss, val_mre = evaluate( + model, + loader=valid_loader, + ) + elapsed = time.time() - epoch_start_time + logger.info("-" * 89) + logger.info( + f"| end of epoch {epoch:3d} | time: {elapsed:5.2f}s | " + f"valid loss/mse {val_loss:5.4f} | mre {val_mre:5.4f}" + ) + logger.info("-" * 89) + + if val_loss < best_val_loss: + best_val_loss = val_loss + best_model = copy.deepcopy(model) + best_model_epoch = epoch + logger.info(f"Best model with score {best_val_loss:5.4f}") + + if epoch % config.save_eval_interval == 0 or epoch == config.epochs: + logger.info(f"Saving model to {save_dir}") + torch.save(best_model.state_dict(), save_dir / f"model_e{best_model_epoch}.pt") + + # eval on testdata + results = eval_testdata( + best_model, + adata_t=adata_sorted if per_seq_batch_sample else adata, + include_types=["cls"], + ) + results["batch_umap"].savefig( + save_dir / f"embeddings_batch_umap[cls]_e{best_model_epoch}.png", dpi=300 + ) + + results["celltype_umap"].savefig( + save_dir / f"embeddings_celltype_umap[cls]_e{best_model_epoch}.png", dpi=300 + ) + metrics_to_log = {"test/" + k: v for k, v in results.items()} + metrics_to_log["test/batch_umap"] = wandb.Image( + str(save_dir / f"embeddings_batch_umap[cls]_e{best_model_epoch}.png"), + caption=f"celltype avg_bio epoch {best_model_epoch}", + ) + + metrics_to_log["test/celltype_umap"] = wandb.Image( + str(save_dir / f"embeddings_celltype_umap[cls]_e{best_model_epoch}.png"), + caption=f"celltype avg_bio epoch {best_model_epoch}", + ) + metrics_to_log["test/best_model_epoch"] = best_model_epoch + wandb.log(metrics_to_log) + wandb.log({"avg_bio": results.get("avg_bio", 0.0)}) + + scheduler.step() + + +# %% +# save the best model +torch.save(best_model.state_dict(), save_dir / "best_model.pt") + +# %% [markdown] +# ## Gene embeddings + +# %% +artifact = wandb.Artifact(f"best_model", type="model") +glob_str = os.path.join(save_dir, "best_model.pt") +artifact.add_file(glob_str) +run.log_artifact(artifact) + +run.finish() +wandb.finish() +gc.collect() diff --git a/transfer/code/scGPT/poetry.lock b/transfer/code/scGPT/poetry.lock new file mode 100644 index 0000000000000000000000000000000000000000..d0fedd424852af4d5267bfd43b2185a5b5e74c17 --- /dev/null +++ b/transfer/code/scGPT/poetry.lock @@ -0,0 +1,5437 @@ +[[package]] +name = "absl-py" +version = "0.15.0" +description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py." +category = "main" +optional = false +python-versions = "*" + +[package.dependencies] +six = "*" + +[[package]] +name = "aiohttp" +version = "3.8.1" +description = "Async http client/server framework (asyncio)" +category = "main" +optional = false +python-versions = ">=3.6" + +[package.dependencies] +aiosignal = ">=1.1.2" +async-timeout = ">=4.0.0a3,<5.0" +asynctest = {version = "0.13.0", markers = "python_version < \"3.8\""} 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"<0.1.8" + +[tool.poetry.dev-dependencies] +pytest = "^5.2" +black = "^22.3.0" +tensorflow = "^2.8.0" +flash-attn = "^1.0.1" +torch-geometric = "^2.3.0" +dcor = "~0.5.3" +wandb = "^0.12.3" +plotly = "^5.3.1" + +[build-system] +requires = ["poetry-core>=1.0.0"] +build-backend = "poetry.core.masonry.api" diff --git a/transfer/code/scGPT/scgpt/__init__.py b/transfer/code/scGPT/scgpt/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..570e5084e3ab31cd52af078597b164c0482c4e7b --- /dev/null +++ b/transfer/code/scGPT/scgpt/__init__.py @@ -0,0 +1,29 @@ +__version__ = "0.2.4" +import logging +import sys + +logger = logging.getLogger("scGPT") +# check if logger has been initialized +if not logger.hasHandlers() or len(logger.handlers) == 0: + logger.propagate = False + logger.setLevel(logging.INFO) + handler = logging.StreamHandler(sys.stdout) + handler.setLevel(logging.INFO) + formatter = logging.Formatter( + "%(name)s - %(levelname)s - %(message)s", datefmt="%H:%M:%S" + ) + handler.setFormatter(formatter) + logger.addHandler(handler) + +from . import model, tokenizer, scbank, utils, tasks +from .data_collator import DataCollator +from .data_sampler import SubsetsBatchSampler +from .trainer import ( + prepare_data, + prepare_dataloader, + train, + define_wandb_metrcis, + evaluate, + eval_testdata, + test, +) diff --git a/transfer/code/scGPT/scgpt/data_collator.py b/transfer/code/scGPT/scgpt/data_collator.py new file mode 100644 index 0000000000000000000000000000000000000000..9769eba2e0acebcf9aad77f08d0dbc6714dc2b07 --- /dev/null +++ b/transfer/code/scGPT/scgpt/data_collator.py @@ -0,0 +1,202 @@ +from dataclasses import dataclass +from typing import Any, Dict, List, Mapping, Optional, Tuple, Union + +import torch +import numpy as np + +from .preprocess import binning + + +@dataclass +class DataCollator: + """ + Data collator for the mask value learning task. It pads the sequences to + the maximum length in the batch and masks the gene expression values. + + Args: + do_padding (:obj:`bool`): whether to pad the sequences to the max length. + pad_token_id (:obj:`int`, optional): the token id to use for padding. + This is required if do_padding is True. + pad_value (:obj:`int`): the value to use for padding the expression + values to the max length. + do_mlm (:obj:`bool`): whether to do masking with MLM. + do_binning (:obj:`bool`): whether to bin the expression values. + mlm_probability (:obj:`float`): the probability of masking with MLM. + mask_value (:obj:`int`): the value to fill at the expression postions + that are masked. + max_length (:obj:`int`, optional): the maximum length of the sequences. + This is required if do_padding is True. + sampling (:obj:`bool`): whether to do sampling instead of truncation if + length > max_length. + keep_first_n_tokens (:obj:`int`): the number of tokens in the beginning + of the sequence to keep unchanged from sampling. This is useful when + special tokens have been added to the beginning of the sequence. + Default to 1. + """ + + do_padding: bool = True + pad_token_id: Optional[int] = None + pad_value: int = 0 + do_mlm: bool = True + do_binning: bool = True + mlm_probability: float = 0.15 + mask_value: int = -1 + max_length: Optional[int] = None + sampling: bool = True + keep_first_n_tokens: int = 1 + + def __post_init__(self): + if self.do_padding: + if self.pad_token_id is None: + raise ValueError("`pad_token_id` is required if `do_padding`.") + if self.max_length is None: + raise ValueError("`max_length` is required if `do_padding`.") + + if self.mlm_probability <= 0 or self.mlm_probability >= 1: + raise ValueError("`mlm_probability` must be between 0 and 1.") + + if self.keep_first_n_tokens < 0 or self.keep_first_n_tokens > self.max_length: + raise ValueError( + "`keep_first_n_tokens` must be between 0 and `max_length` " + f"({self.max_length})." + ) + + def __call__( + self, examples: List[Dict[str, torch.Tensor]] + ) -> Dict[str, torch.Tensor]: + """ + Each example is like: + {'id': tensor(184117), + 'genes': tensor([36572, 17868, ..., 17072]), + 'expressions': tensor([ 0., 2., ..., 18.])} + """ + if not isinstance(examples[0], Mapping): + return NotImplementedError + + device = examples[0]["genes"].device + + max_ori_len = max(len(example["genes"]) for example in examples) + _max_length = self.max_length if max_ori_len >= self.max_length else max_ori_len + + # pad and truncate + padded_genes = [] + padded_expressions = [] + for i in range(len(examples)): + genes = examples[i]["genes"] + expressions = examples[i]["expressions"] + if self.do_binning: + expressions[self.keep_first_n_tokens :] = binning( + row=expressions[self.keep_first_n_tokens :], + n_bins=51, + ) + genes, expressions = self._sample_or_truncate_plus_pad( + genes, expressions, _max_length + ) # torch tensors of length _max_length + padded_genes.append(genes) + padded_expressions.append(expressions) + + padded_genes = torch.stack(padded_genes, dim=0).to(device) + padded_expressions = torch.stack(padded_expressions, dim=0).to(device) + + data_dict = { + "gene": padded_genes, + "expr": padded_expressions, + } + + # mask + if self.do_mlm: + masked_expressions = self._mask(padded_expressions) + else: + masked_expressions = padded_expressions + data_dict["masked_expr"] = masked_expressions + + return data_dict + + def _mask(self, expressions: torch.Tensor) -> torch.Tensor: + """ + Mask the expression values with MLM. + """ + device = expressions.device + shape = expressions.shape + + probability_matrix = torch.full(shape, self.mlm_probability) + # set padded postion probability to 0 + probability_matrix[expressions.eq(self.pad_value)] = 0 + if self.keep_first_n_tokens > 0: + probability_matrix[:, : self.keep_first_n_tokens] = 0 + + mask = torch.bernoulli(probability_matrix).bool() + mask = mask.to(device) + + masked_expressions = expressions.masked_fill(mask, self.mask_value) + return masked_expressions + + def _sample_or_truncate_plus_pad( + self, + genes: torch.LongTensor, + expressions: torch.Tensor, + max_length: int, + ) -> Tuple[torch.LongTensor, torch.Tensor]: + assert len(genes) == len(expressions) + if len(genes) == max_length: + return genes, expressions + if len(genes) > max_length: # sample or truncate + if self.sampling: + return self._sample(genes, expressions, max_length) + else: + return genes[:max_length], expressions[:max_length] + else: # pad + return self._pad(genes, expressions, max_length) + + def _sample( + self, + genes: torch.LongTensor, + expressions: torch.Tensor, + max_length: int, + ) -> Tuple[torch.LongTensor, torch.Tensor]: + # NOTE: the fastest way to sample in torch has been benchmarked here + # https://discuss.pytorch.org/t/torch-equivalent-of-numpy-random-choice/16146/19 + # it shows the randperm on gpu is the fastest. + # NOTE: also, the current implementation permute the orders of the genes + # and expressions, although it is probably a nice argmentation. + device = genes.device + if self.keep_first_n_tokens == 0: + indices = torch.randperm(len(genes), device=device)[:max_length] + return genes[indices], expressions[indices] + + # keep the first n tokens unchanged + _n = self.keep_first_n_tokens + indices = torch.randperm(len(genes) - _n, device=device)[: max_length - _n] + indices = torch.cat([torch.arange(_n), indices + _n], dim=0) + return genes[indices], expressions[indices] + + def _pad( + self, + genes: torch.LongTensor, + expressions: torch.Tensor, + max_length: int, + ): + device = genes.device + genes = torch.cat( + [ + genes, + torch.full( + (max_length - len(genes),), + self.pad_token_id, + dtype=genes.dtype, + device=device, + ), + ] + ) + expressions = torch.cat( + [ + expressions, + torch.full( + (max_length - len(expressions),), + self.pad_value, + dtype=expressions.dtype, + device=device, + ), + ] + ) + return genes, expressions diff --git a/transfer/code/scGPT/scgpt/data_sampler.py b/transfer/code/scGPT/scgpt/data_sampler.py new file mode 100644 index 0000000000000000000000000000000000000000..439beda2a08025fbde15762a6318762b8b35cd94 --- /dev/null +++ b/transfer/code/scGPT/scgpt/data_sampler.py @@ -0,0 +1,95 @@ +from typing import Iterable, List, Sequence + +import numpy as np +import torch +from torch.utils.data import Sampler, SubsetRandomSampler, BatchSampler + + +class SubsetSequentialSampler(Sampler): + """Samples elements sequentially from a given list of indices, without replacement. + + Arguments: + indices (sequence): a sequence of indices + """ + + def __init__(self, indices: Sequence[int]): + self.indices = indices + + def __iter__(self) -> Iterable[int]: + return iter(self.indices) + + def __len__(self) -> int: + return len(self.indices) + + +class SubsetsBatchSampler(Sampler[List[int]]): + r"""Samples batches of indices from a list of subsets of indices. Each subset + of indices represents a data subset and is sampled without replacement randomly + or sequentially. Specially, each batch only contains indices from a single subset. + This sampler is for the scenario where samples need to be drawn from multiple + subsets separately. + + Arguments: + subsets (List[Sequence[int]]): A list of subsets of indices. + batch_size (int): Size of mini-batch. + intra_subset_shuffle (bool): If ``True``, the sampler will shuffle the indices + within each subset. + inter_subset_shuffle (bool): If ``True``, the sampler will shuffle the order + of subsets. + drop_last (bool): If ``True``, the sampler will drop the last batch if + its size would be less than ``batch_size``. + """ + + def __init__( + self, + subsets: List[Sequence[int]], + batch_size: int, + intra_subset_shuffle: bool = True, + inter_subset_shuffle: bool = True, + drop_last: bool = False, + ): + self.subsets = subsets + self.batch_size = batch_size + self.intra_subset_shuffle = intra_subset_shuffle + self.inter_subset_shuffle = inter_subset_shuffle + self.drop_last = drop_last + + if intra_subset_shuffle: + self.subset_samplers = [SubsetRandomSampler(subset) for subset in subsets] + else: + self.subset_samplers = [ + SubsetSequentialSampler(subset) for subset in subsets + ] + + self.batch_samplers = [ + BatchSampler(sampler, batch_size, drop_last) + for sampler in self.subset_samplers + ] + + if inter_subset_shuffle: + # maintain a mapping from sample batch index to batch sampler + _id_to_batch_sampler = [] + for i, batch_sampler in enumerate(self.batch_samplers): + _id_to_batch_sampler.extend([i] * len(batch_sampler)) + self._id_to_batch_sampler = np.array(_id_to_batch_sampler) + + assert len(self._id_to_batch_sampler) == len(self) + + self.batch_sampler_iterrators = [ + batch_sampler.__iter__() for batch_sampler in self.batch_samplers + ] + + def __iter__(self) -> Iterable[List[int]]: + if self.inter_subset_shuffle: + # randomly sample from batch samplers + random_idx = torch.randperm(len(self._id_to_batch_sampler)) + batch_sampler_ids = self._id_to_batch_sampler[random_idx] + for batch_sampler_id in batch_sampler_ids: + batch_sampler_iter = self.batch_sampler_iterrators[batch_sampler_id] + yield next(batch_sampler_iter) + else: + for batch_sampler in self.batch_samplers: + yield from batch_sampler + + def __len__(self) -> int: + return sum(len(batch_sampler) for batch_sampler in self.batch_samplers) diff --git a/transfer/code/scGPT/scgpt/loss.py b/transfer/code/scGPT/scgpt/loss.py new file mode 100644 index 0000000000000000000000000000000000000000..ce1812aa8a33d816724804637329733e6b4f695e --- /dev/null +++ b/transfer/code/scGPT/scgpt/loss.py @@ -0,0 +1,36 @@ +import torch +import torch.nn.functional as F + + +def masked_mse_loss( + input: torch.Tensor, target: torch.Tensor, mask: torch.Tensor +) -> torch.Tensor: + """ + Compute the masked MSE loss between input and target. + """ + mask = mask.float() + loss = F.mse_loss(input * mask, target * mask, reduction="sum") + return loss / mask.sum() + + +def criterion_neg_log_bernoulli( + input: torch.Tensor, target: torch.Tensor, mask: torch.Tensor +) -> torch.Tensor: + """ + Compute the negative log-likelihood of Bernoulli distribution + """ + mask = mask.float() + bernoulli = torch.distributions.Bernoulli(probs=input) + masked_log_probs = bernoulli.log_prob((target > 0).float()) * mask + return -masked_log_probs.sum() / mask.sum() + + +def masked_relative_error( + input: torch.Tensor, target: torch.Tensor, mask: torch.LongTensor +) -> torch.Tensor: + """ + Compute the masked relative error between input and target. + """ + assert mask.any() + loss = torch.abs(input[mask] - target[mask]) / (target[mask] + 1e-6) + return loss.mean() diff --git a/transfer/code/scGPT/scgpt/model/__init__.py b/transfer/code/scGPT/scgpt/model/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4411db4709c1c7fc43b8a484603d3a3c68894b27 --- /dev/null +++ b/transfer/code/scGPT/scgpt/model/__init__.py @@ -0,0 +1,11 @@ +from .model import ( + TransformerModel, + FlashTransformerEncoderLayer, + GeneEncoder, + AdversarialDiscriminator, + MVCDecoder, +) +from .generation_model import * +from .multiomic_model import MultiOmicTransformerModel +from .dsbn import * +from .grad_reverse import * diff --git a/transfer/code/scGPT/scgpt/model/dsbn.py b/transfer/code/scGPT/scgpt/model/dsbn.py new file mode 100644 index 0000000000000000000000000000000000000000..324b128e6c0dc23cf4d6bebc3819109df9901c5f --- /dev/null +++ b/transfer/code/scGPT/scgpt/model/dsbn.py @@ -0,0 +1,82 @@ +from typing import Optional, Tuple + +import torch +from torch import nn + +# The code is modified from https://github.com/wgchang/DSBN/blob/master/model/dsbn.py +class _DomainSpecificBatchNorm(nn.Module): + _version = 2 + + def __init__( + self, + num_features: int, + num_domains: int, + eps: float = 1e-5, + momentum: float = 0.1, + affine: bool = True, + track_running_stats: bool = True, + ): + super(_DomainSpecificBatchNorm, self).__init__() + self._cur_domain = None + self.num_domains = num_domains + self.bns = nn.ModuleList( + [ + self.bn_handle(num_features, eps, momentum, affine, track_running_stats) + for _ in range(num_domains) + ] + ) + + @property + def bn_handle(self) -> nn.Module: + raise NotImplementedError + + @property + def cur_domain(self) -> Optional[int]: + return self._cur_domain + + @cur_domain.setter + def cur_domain(self, domain_label: int): + self._cur_domain = domain_label + + def reset_running_stats(self): + for bn in self.bns: + bn.reset_running_stats() + + def reset_parameters(self): + for bn in self.bns: + bn.reset_parameters() + + def _check_input_dim(self, input: torch.Tensor): + raise NotImplementedError + + def forward(self, x: torch.Tensor, domain_label: int) -> torch.Tensor: + self._check_input_dim(x) + if domain_label >= self.num_domains: + raise ValueError( + f"Domain label {domain_label} exceeds the number of domains {self.num_domains}" + ) + bn = self.bns[domain_label] + self.cur_domain = domain_label + return bn(x) + + +class DomainSpecificBatchNorm1d(_DomainSpecificBatchNorm): + @property + def bn_handle(self) -> nn.Module: + return nn.BatchNorm1d + + def _check_input_dim(self, input: torch.Tensor): + if input.dim() > 3: + raise ValueError( + "expected at most 3D input (got {}D input)".format(input.dim()) + ) + + +class DomainSpecificBatchNorm2d(_DomainSpecificBatchNorm): + @property + def bn_handle(self) -> nn.Module: + return nn.BatchNorm2d + + def _check_input_dim(self, input: torch.Tensor): + if input.dim() != 4: + raise ValueError("expected 4D input (got {}D input)".format(input.dim())) diff --git a/transfer/code/scGPT/scgpt/model/generation_model.py b/transfer/code/scGPT/scgpt/model/generation_model.py new file mode 100644 index 0000000000000000000000000000000000000000..7c65d9aebf34ea42bde10f6aeffc53305b9dfb4d --- /dev/null +++ b/transfer/code/scGPT/scgpt/model/generation_model.py @@ -0,0 +1,549 @@ +import os +import math +from typing import Mapping, Optional, Tuple, Any, Union + +import torch +from torch import nn, Tensor +import torch.distributed as dist +import torch.nn.functional as F +from torch.nn import TransformerEncoder, TransformerEncoderLayer +from torch.distributions import Bernoulli +from torch.utils.data import dataset +from tqdm import trange + +from .model import ( + ExprDecoder, + MVCDecoder, + ContinuousValueEncoder, + FastTransformerEncoderWrapper, + FlashTransformerEncoderLayer, +) +from ..utils import map_raw_id_to_vocab_id +from .. import logger + + +class TransformerGenerator(nn.Module): + def __init__( + self, + ntoken: int, + d_model: int, + nhead: int, + d_hid: int, + nlayers: int, + nlayers_cls: int, + n_cls: int, + vocab: Any, + dropout: float = 0.5, + pad_token: str = "", + pad_value: int = 0, + pert_pad_id: int = 2, + do_mvc: bool = False, + domain_spec_batchnorm: Union[bool, str] = False, + n_input_bins: Optional[int] = 0, + cell_emb_style: str = "cls", + mvc_decoder_style: str = "inner product", + decoder_activation: Optional[str] = None, + decoder_adaptive_bias: bool = False, + ecs_threshold: float = 0.3, + explicit_zero_prob: bool = False, + use_fast_transformer: bool = False, + fast_transformer_backend: str = "flash", + pre_norm: bool = False, + ): + super().__init__() + self.model_type = "Transformer" + self.d_model = d_model + self.pad_token_id = vocab[pad_token] + self.pad_value = pad_value + self.pert_pad_id = pert_pad_id + self.ecs_threshold = ecs_threshold + self.domain_spec_batchnorm = domain_spec_batchnorm + self.n_input_bins = n_input_bins + self.cell_emb_style = cell_emb_style + self.explicit_zero_prob = explicit_zero_prob + self.norm_scheme = "pre" if pre_norm else "post" + if cell_emb_style not in ["cls", "avg-pool", "w-pool"]: + raise ValueError(f"Unknown cell_emb_style: {cell_emb_style}") + if use_fast_transformer: + try: + from flash_attn.flash_attention import FlashMHA + except ImportError: + import warnings + + warnings.warn( + "flash-attn is not installed, using pytorch transformer instead. " + "Set use_fast_transformer=False to avoid this warning. " + "Installing flash-attn is highly recommended." + ) + use_fast_transformer = False + self.use_fast_transformer = use_fast_transformer + + self.encoder = GeneEncoder(ntoken, d_model, padding_idx=vocab[pad_token]) + self.value_encoder = ContinuousValueEncoder(d_model, dropout) + self.pert_encoder = nn.Embedding(3, d_model, padding_idx=pert_pad_id) + + # print("Using simple batchnorm instead of domain specific batchnorm") + # self.bn = nn.BatchNorm1d(d_model, eps=6.1e-5) + + if use_fast_transformer: + if fast_transformer_backend == "linear": + self.transformer_encoder = FastTransformerEncoderWrapper( + d_model, nhead, d_hid, nlayers, dropout + ) + elif fast_transformer_backend == "flash": + encoder_layers = FlashTransformerEncoderLayer( + d_model, + nhead, + d_hid, + dropout, + batch_first=True, + norm_scheme=self.norm_scheme, + ) + self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers) + else: + encoder_layers = TransformerEncoderLayer( + d_model, nhead, d_hid, dropout, batch_first=True + ) + self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers) + + # self.decoder = nn.Linear(d_model, 1) + self.decoder = AffineExprDecoder( + d_model, + explicit_zero_prob=explicit_zero_prob, + activation=decoder_activation, + adaptive_bias=decoder_adaptive_bias, + ) + self.cls_decoder = ClsDecoder(d_model, n_cls, nlayers=nlayers_cls) + if do_mvc: + self.mvc_decoder = MVCDecoder( + d_model, + arch_style=mvc_decoder_style, + explicit_zero_prob=explicit_zero_prob, + ) + + self.init_weights() + + def init_weights(self) -> None: + initrange = 0.1 + self.encoder.embedding.weight.data.uniform_(-initrange, initrange) + + def _encode( + self, + src: Tensor, + values: Tensor, + input_pert_flags, + src_key_padding_mask: Tensor, + ) -> Tensor: + src = self.encoder(src) # (batch, seq_len, embsize) + self.cur_gene_token_embs = src + values = self.value_encoder(values) # (batch, seq_len, embsize) + perts = self.pert_encoder(input_pert_flags) # (batch, seq_len, embsize) + total_embs = src + values + perts + + # total_embs = self.bn(total_embs.permute(0, 2, 1)).permute(0, 2, 1) + output = self.transformer_encoder( + total_embs, src_key_padding_mask=src_key_padding_mask + ) + return output # (batch, seq_len, embsize) + + def _get_cell_emb_from_layer( + self, layer_output: Tensor, weights: Tensor = None + ) -> Tensor: + """ + Args: + layer_output(:obj:`Tensor`): shape (batch, seq_len, embsize) + weights(:obj:`Tensor`): shape (batch, seq_len), optional and only used + when :attr:`self.cell_emb_style` is "w-pool". + + Returns: + :obj:`Tensor`: shape (batch, embsize) + """ + if self.cell_emb_style == "cls": + cell_emb = layer_output[:, 0, :] # (batch, embsize) + elif self.cell_emb_style == "avg-pool": + cell_emb = torch.mean(layer_output, dim=1) + elif self.cell_emb_style == "w-pool": + if weights is None: + raise ValueError("weights is required when cell_emb_style is w-pool") + if weights.dim() != 2: + raise ValueError("weights should be 2D") + cell_emb = torch.sum(layer_output * weights.unsqueeze(2), dim=1) + cell_emb = F.normalize(cell_emb, p=2, dim=1) # (batch, embsize) + + return cell_emb + + def forward( + self, + src: Tensor, + values: Tensor, + input_pert_flags: Tensor, + src_key_padding_mask: Tensor, + CLS: bool = False, + CCE: bool = False, + MVC: bool = False, + ECS: bool = False, + do_sample: bool = False, + ) -> Mapping[str, Tensor]: + """ + Args: + src (:obj:`Tensor`): token ids, shape [batch_size, seq_len] + values (:obj:`Tensor`): token values, shape [batch_size, seq_len] + src_key_padding_mask (:obj:`Tensor`): mask for src, shape [batch_size, + seq_len] + CLS (:obj:`bool`): if True, return the celltype classification objective + (CLS) output + CCE (:obj:`bool`): if True, return the contrastive cell embedding objective + (CCE) output + MVC (:obj:`bool`): if True, return the masked value prediction for cell + embedding MVC output + ECS (:obj:`bool`): if True, return the elastic cell similarity objective + (ECS) output. + + Returns: + dict of output Tensors. + """ + if self.explicit_zero_prob and not do_sample and not self.training: + do_sample = True + logger.warning("Auto set do_sample to True when model is in eval mode.") + + # binning input gene values + if self.n_input_bins > 0: + from ..preprocess import binning + + processed_values = torch.stack( + [binning(row, n_bins=self.n_input_bins) for row in values], dim=0 + ).to(values.device) + else: + processed_values = values + + transformer_output = self._encode( + src, processed_values, input_pert_flags, src_key_padding_mask + ) + output = {} + mlm_output = self.decoder(transformer_output, values) + if self.explicit_zero_prob and do_sample: + bernoulli = Bernoulli(probs=mlm_output["zero_probs"]) + output["mlm_output"] = bernoulli.sample() * mlm_output["pred"] + else: + output["mlm_output"] = mlm_output["pred"] # (batch, seq_len) + if self.explicit_zero_prob: + output["mlm_zero_probs"] = mlm_output["zero_probs"] + + cell_emb = self._get_cell_emb_from_layer(transformer_output, values) + if CLS: + output["cls_output"] = self.cls_decoder(cell_emb) # (batch, n_cls) + if MVC: + mvc_output = self.mvc_decoder( + cell_emb, + self.cur_gene_token_embs, + ) # (batch, seq_len) + if self.explicit_zero_prob and do_sample: + bernoulli = Bernoulli(probs=mvc_output["zero_probs"]) + output["mvc_output"] = bernoulli.sample() * mvc_output["pred"] + else: + output["mvc_output"] = mvc_output["pred"] # (batch, seq_len) + if self.explicit_zero_prob: + output["mvc_zero_probs"] = mvc_output["zero_probs"] + if ECS: + # Here using customized cosine similarity instead of F.cosine_similarity + # to avoid the pytorch issue of similarity larger than 1.0, pytorch # 78064 + # normalize the embedding + cell_emb_normed = F.normalize(cell_emb, p=2, dim=1) + cos_sim = torch.mm(cell_emb_normed, cell_emb_normed.t()) # (batch, batch) + + # mask out diagnal elements + mask = torch.eye(cos_sim.size(0)).bool().to(cos_sim.device) + cos_sim = cos_sim.masked_fill(mask, 0.0) + # only optimize positive similarities + cos_sim = F.relu(cos_sim) + + output["loss_ecs"] = torch.mean(1 - (cos_sim - self.ecs_threshold) ** 2) + + return output + + def encode_batch( + self, + src: Tensor, + values: Tensor, + src_key_padding_mask: Tensor, + batch_size: int, + output_to_cpu: bool = True, + ) -> Tensor: + """ + Args: + src: Tensor, shape [N, seq_len] + values: Tensor, shape [N, seq_len] + src_key_padding_mask: Tensor, shape [N, seq_len] + + Returns: + output Tensor of shape [N, seq_len, embsize] + """ + outputs = [] + N = src.size(0) + device = next(self.parameters()).device + for i in trange(0, N, batch_size): + output = self._encode( + src[i : i + batch_size].to(device), + values[i : i + batch_size].to(device), + src_key_padding_mask[i : i + batch_size].to(device), + ) + if output_to_cpu: + output = output.cpu() + outputs.append(output) + return torch.cat(outputs, dim=0) + + def pred_perturb( + self, + batch_data, + include_zero_gene="batch-wise", + gene_ids=None, + amp=True, + ) -> Tensor: + """ + Args: + batch_data: a dictionary of input data with keys. + + Returns: + output Tensor of shape [N, seq_len] + """ + self.eval() + device = next(self.parameters()).device + batch_data.to(device) + batch_size = len(batch_data.pert) + x: torch.Tensor = batch_data.x + ori_gene_values = x[:, 0].view(batch_size, -1) # (batch_size, n_genes) + pert_flags = x[:, 1].long().view(batch_size, -1) + + if include_zero_gene in ["all", "batch-wise"]: + assert gene_ids is not None + if include_zero_gene == "all": + input_gene_ids = torch.arange(ori_gene_values.size(1), device=device) + else: # batch-wise + input_gene_ids = ( + ori_gene_values.nonzero()[:, 1].flatten().unique().sort()[0] + ) + input_values = ori_gene_values[:, input_gene_ids] + input_pert_flags = pert_flags[:, input_gene_ids] + + mapped_input_gene_ids = map_raw_id_to_vocab_id(input_gene_ids, gene_ids) + mapped_input_gene_ids = mapped_input_gene_ids.repeat(batch_size, 1) + + src_key_padding_mask = torch.zeros_like( + input_values, dtype=torch.bool, device=device + ) + with torch.cuda.amp.autocast(enabled=amp): + output_dict = self( + mapped_input_gene_ids, + input_values, + input_pert_flags, + src_key_padding_mask=src_key_padding_mask, + CLS=False, + CCE=False, + MVC=False, + ECS=False, + do_sample=True, + ) + output_values = output_dict["mlm_output"].float() + pred_gene_values = torch.zeros_like(ori_gene_values) + pred_gene_values[:, input_gene_ids] = output_values + return pred_gene_values + + +def generate_square_subsequent_mask(sz: int) -> Tensor: + """Generates an upper-triangular matrix of -inf, with zeros on diag.""" + return torch.triu(torch.ones(sz, sz) * float("-inf"), diagonal=1) + + +class GeneEncoder(nn.Module): + def __init__( + self, + num_embeddings: int, + embedding_dim: int, + padding_idx: Optional[int] = None, + ): + super().__init__() + self.embedding = nn.Embedding( + num_embeddings, embedding_dim, padding_idx=padding_idx + ) + self.enc_norm = nn.LayerNorm(embedding_dim) + + def forward(self, x: Tensor) -> Tensor: + x = self.embedding(x) # (batch, seq_len, embsize) + x = self.enc_norm(x) + return x + + +class AffineExprDecoder(nn.Module): + def __init__( + self, + d_model: int, + explicit_zero_prob: bool = False, + activation: Optional[str] = None, + tanh_coeff: bool = False, + adaptive_bias: bool = False, + ): + """ + Predict the expression value of each gene in an affine like form of Ax + b. + This decoder takes two ExprDecoder intrinsically to genrate the coefficient A and bias b. + + Args: + d_model: The embedding dimension. + explicit_zero_prob: If True, predict the probability of each gene being + zero. + activation: The activation function for the coefficient A and bias b. + tanh_coeff: If True, use tanh activation for the coefficient A. + adaptive_bias: If True, use a learnable bias for the bias b. + """ + super().__init__() + self.explicit_zero_prob = explicit_zero_prob + self.tanh_coeff = tanh_coeff + self.adaptive_bias = adaptive_bias + self.coeff_decoder = ExprDecoder(d_model, explicit_zero_prob=explicit_zero_prob) + self.bias_decoder = ExprDecoder(d_model, explicit_zero_prob=explicit_zero_prob) + + self.activation = activation + if activation is not None: + assert hasattr(nn, activation), f"Unknown activation: {activation}" + self.activation = getattr(nn, activation)() + + def forward(self, x: Tensor, values: Tensor) -> Tensor: + """ + Args: + x: Tensor, shape [batch_size, seq_len, embsize] + values: Tensor, shape [batch_size, seq_len] + + Returns: + output Tensor of shape [batch_size, seq_len] + """ + coeff = self.coeff_decoder(x) + bias = self.bias_decoder(x) + + if self.activation is not None: + coeff["pred"] = self.activation(coeff["pred"]) + bias["pred"] = self.activation(bias["pred"]) + + # if self.tanh_coeff: + # coeff["pred"] = 1 + torch.tanh(coeff["pred"]) + + if self.adaptive_bias: + # bias["pred"] = bias["pred"] * values.mean(dim=1, keepdim=True) + non_zero_value_mean = values.sum(dim=1, keepdim=True) / (values != 0).sum( + dim=1, keepdim=True + ) + bias["pred"] = bias["pred"] * non_zero_value_mean + + if self.explicit_zero_prob: + return { + "pred": coeff["pred"] * values + bias["pred"], + "zero_probs": coeff["zero_probs"], + } + + return dict(pred=coeff["pred"] * values + bias["pred"]) + + +class TokenEmbedding(nn.Module): + def __init__( + self, + num_embeddings: int, + embedding_dim: int, + padding_idx: Optional[int] = None, + zero_out_idx: Optional[int] = None, + ): + """ + Generic token embedding module. + + Args: + num_embeddings: The number of tokens. + embedding_dim: The embedding dimension. + padding_idx: The index of the padding token. + zero_out_idx: Indicate if any idx embedding should be zero vector. + """ + super().__init__() + self.embedding = nn.Embedding( + num_embeddings, embedding_dim, padding_idx=padding_idx + ) + self.enc_norm = nn.LayerNorm(embedding_dim) + + self.zero_out_idx = zero_out_idx + if zero_out_idx is not None: + self._fill_idx_with_zero(zero_out_idx) + zero_vector = self(zero_out_idx) + assert torch.all(zero_vector == 0.0) + assert not zero_vector.requires_grad + + def _fill_idx_with_zero(self, idx) -> None: + with torch.no_grad(): + self.embedding.weight[idx].fill_(0) + + def forward(self, x: Tensor) -> Tensor: + x = self.embedding(x) # (batch, seq_len, embsize) + x = self.enc_norm(x) + return x + + +class PositionalEncoding(nn.Module): + def __init__(self, d_model: int, dropout: float = 0.1, max_len: int = 5000): + super().__init__() + self.dropout = nn.Dropout(p=dropout) + + position = torch.arange(max_len).unsqueeze(1) + div_term = torch.exp( + torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model) + ) + pe = torch.zeros(max_len, 1, d_model) + pe[:, 0, 0::2] = torch.sin(position * div_term) + pe[:, 0, 1::2] = torch.cos(position * div_term) + self.register_buffer("pe", pe) + + def forward(self, x: Tensor) -> Tensor: + """ + Args: + x: Tensor, shape [seq_len, batch_size, embedding_dim] + """ + x = x + self.pe[: x.size(0)] + return self.dropout(x) + + +class Similarity(nn.Module): + """ + Dot product or cosine similarity + """ + + def __init__(self, temp): + super().__init__() + self.temp = temp + self.cos = nn.CosineSimilarity(dim=-1) + + def forward(self, x, y): + return self.cos(x, y) / self.temp + + +class ClsDecoder(nn.Module): + """ + Decoder for classification task. + """ + + def __init__( + self, + d_model: int, + n_cls: int, + nlayers: int = 3, + activation: callable = nn.ReLU, + ): + super().__init__() + # module list + self._decoder = nn.ModuleList() + for i in range(nlayers - 1): + self._decoder.append(nn.Linear(d_model, d_model)) + self._decoder.append(activation()) + self._decoder.append(nn.LayerNorm(d_model)) + self.out_layer = nn.Linear(d_model, n_cls) + + def forward(self, x: Tensor) -> Tensor: + """ + Args: + x: Tensor, shape [batch_size, embsize] + """ + for layer in self._decoder: + x = layer(x) + return self.out_layer(x) diff --git a/transfer/code/scGPT/scgpt/model/grad_reverse.py b/transfer/code/scGPT/scgpt/model/grad_reverse.py new file mode 100644 index 0000000000000000000000000000000000000000..7dc7e6127717d1682253fb6d278d2f389ba974c0 --- /dev/null +++ b/transfer/code/scGPT/scgpt/model/grad_reverse.py @@ -0,0 +1,17 @@ +import torch +from torch.autograd import Function + + +class GradReverse(Function): + @staticmethod + def forward(ctx, x: torch.Tensor, lambd: float) -> torch.Tensor: + ctx.lambd = lambd + return x.view_as(x) + + @staticmethod + def backward(ctx, grad_output: torch.Tensor) -> torch.Tensor: + return grad_output.neg() * ctx.lambd, None + + +def grad_reverse(x: torch.Tensor, lambd: float = 1.0) -> torch.Tensor: + return GradReverse.apply(x, lambd) diff --git a/transfer/code/scGPT/scgpt/model/model.py b/transfer/code/scGPT/scgpt/model/model.py new file mode 100644 index 0000000000000000000000000000000000000000..ad0639bee3ca200966291dfed65b557c648c86ee --- /dev/null +++ b/transfer/code/scGPT/scgpt/model/model.py @@ -0,0 +1,1045 @@ +import gc +import math +from typing import Dict, Mapping, Optional, Tuple, Any, Union + +import torch +import numpy as np +from torch import nn, Tensor +import torch.distributed as dist +import torch.nn.functional as F +from torch.nn import TransformerEncoder, TransformerEncoderLayer +from torch.distributions import Bernoulli +from tqdm import trange + +try: + from flash_attn.flash_attention import FlashMHA + + flash_attn_available = True +except ImportError: + import warnings + + warnings.warn("flash_attn is not installed") + flash_attn_available = False + +from .dsbn import DomainSpecificBatchNorm1d +from .grad_reverse import grad_reverse + + +class TransformerModel(nn.Module): + def __init__( + self, + ntoken: int, + d_model: int, + nhead: int, + d_hid: int, + nlayers: int, + nlayers_cls: int = 3, + n_cls: int = 1, + vocab: Any = None, + dropout: float = 0.5, + pad_token: str = "", + pad_value: int = 0, + do_mvc: bool = False, + do_dab: bool = False, + use_batch_labels: bool = False, + num_batch_labels: Optional[int] = None, + domain_spec_batchnorm: Union[bool, str] = False, + input_emb_style: str = "continuous", + n_input_bins: Optional[int] = None, + cell_emb_style: str = "cls", + mvc_decoder_style: str = "inner product", + ecs_threshold: float = 0.3, + explicit_zero_prob: bool = False, + use_fast_transformer: bool = False, + fast_transformer_backend: str = "flash", + pre_norm: bool = False, + ): + super().__init__() + self.model_type = "Transformer" + self.d_model = d_model + self.do_dab = do_dab + self.ecs_threshold = ecs_threshold + self.use_batch_labels = use_batch_labels + self.domain_spec_batchnorm = domain_spec_batchnorm + self.input_emb_style = input_emb_style + self.cell_emb_style = cell_emb_style + self.explicit_zero_prob = explicit_zero_prob + self.norm_scheme = "pre" if pre_norm else "post" + if self.input_emb_style not in ["category", "continuous", "scaling"]: + raise ValueError( + f"input_emb_style should be one of category, continuous, scaling, " + f"got {input_emb_style}" + ) + if cell_emb_style not in ["cls", "avg-pool", "w-pool"]: + raise ValueError(f"Unknown cell_emb_style: {cell_emb_style}") + if use_fast_transformer: + if not flash_attn_available: + warnings.warn( + "flash-attn is not installed, using pytorch transformer instead. " + "Set use_fast_transformer=False to avoid this warning. " + "Installing flash-attn is highly recommended." + ) + use_fast_transformer = False + self.use_fast_transformer = use_fast_transformer + + # TODO: add dropout in the GeneEncoder + self.encoder = GeneEncoder(ntoken, d_model, padding_idx=vocab[pad_token]) + + # Value Encoder, NOTE: the scaling style is also handled in _encode method + if input_emb_style == "continuous": + self.value_encoder = ContinuousValueEncoder(d_model, dropout) + elif input_emb_style == "category": + assert n_input_bins > 0 + self.value_encoder = CategoryValueEncoder( + n_input_bins, d_model, padding_idx=pad_value + ) + else: + self.value_encoder = nn.Identity() # nn.Softmax(dim=1) + # TODO: consider row-wise normalization or softmax + # TODO: Correct handle the mask_value when using scaling + + # Batch Encoder + if use_batch_labels: + self.batch_encoder = BatchLabelEncoder(num_batch_labels, d_model) + + if domain_spec_batchnorm is True or domain_spec_batchnorm == "dsbn": + use_affine = True if domain_spec_batchnorm == "do_affine" else False + print(f"Use domain specific batchnorm with affine={use_affine}") + self.dsbn = DomainSpecificBatchNorm1d( + d_model, num_batch_labels, eps=6.1e-5, affine=use_affine + ) + elif domain_spec_batchnorm == "batchnorm": + print("Using simple batchnorm instead of domain specific batchnorm") + self.bn = nn.BatchNorm1d(d_model, eps=6.1e-5) + + if use_fast_transformer: + if fast_transformer_backend == "linear": + self.transformer_encoder = FastTransformerEncoderWrapper( + d_model, nhead, d_hid, nlayers, dropout + ) + elif fast_transformer_backend == "flash": + encoder_layers = FlashTransformerEncoderLayer( + d_model, + nhead, + d_hid, + dropout, + batch_first=True, + norm_scheme=self.norm_scheme, + ) + self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers) + else: + encoder_layers = TransformerEncoderLayer( + d_model, nhead, d_hid, dropout, batch_first=True + ) + self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers) + + self.decoder = ExprDecoder( + d_model, + explicit_zero_prob=explicit_zero_prob, + use_batch_labels=use_batch_labels, + ) + self.cls_decoder = ClsDecoder(d_model, n_cls, nlayers=nlayers_cls) + if do_mvc: + self.mvc_decoder = MVCDecoder( + d_model, + arch_style=mvc_decoder_style, + explicit_zero_prob=explicit_zero_prob, + use_batch_labels=use_batch_labels, + ) + + if do_dab: + self.grad_reverse_discriminator = AdversarialDiscriminator( + d_model, + n_cls=num_batch_labels, + reverse_grad=True, + ) + + self.sim = Similarity(temp=0.5) # TODO: auto set temp + self.creterion_cce = nn.CrossEntropyLoss() + + self.init_weights() + + def init_weights(self) -> None: + initrange = 0.1 + # TODO: check if this initialization is helpful and shall we apply to all? + self.encoder.embedding.weight.data.uniform_(-initrange, initrange) + + def _encode( + self, + src: Tensor, + values: Tensor, + src_key_padding_mask: Tensor, + batch_labels: Optional[Tensor] = None, # (batch,) + ) -> Tensor: + self._check_batch_labels(batch_labels) + + src = self.encoder(src) # (batch, seq_len, embsize) + self.cur_gene_token_embs = src + + values = self.value_encoder(values) # (batch, seq_len, embsize) + if self.input_emb_style == "scaling": + values = values.unsqueeze(2) + total_embs = src * values + else: + total_embs = src + values + + if getattr(self, "dsbn", None) is not None: + batch_label = int(batch_labels[0].item()) + total_embs = self.dsbn(total_embs.permute(0, 2, 1), batch_label).permute( + 0, 2, 1 + ) # the batch norm always works on dim 1 + elif getattr(self, "bn", None) is not None: + total_embs = self.bn(total_embs.permute(0, 2, 1)).permute(0, 2, 1) + + output = self.transformer_encoder( + total_embs, src_key_padding_mask=src_key_padding_mask + ) + return output # (batch, seq_len, embsize) + + def _get_cell_emb_from_layer( + self, layer_output: Tensor, weights: Tensor = None + ) -> Tensor: + """ + Args: + layer_output(:obj:`Tensor`): shape (batch, seq_len, embsize) + weights(:obj:`Tensor`): shape (batch, seq_len), optional and only used + when :attr:`self.cell_emb_style` is "w-pool". + + Returns: + :obj:`Tensor`: shape (batch, embsize) + """ + if self.cell_emb_style == "cls": + cell_emb = layer_output[:, 0, :] # (batch, embsize) + elif self.cell_emb_style == "avg-pool": + cell_emb = torch.mean(layer_output, dim=1) + elif self.cell_emb_style == "w-pool": + if weights is None: + raise ValueError("weights is required when cell_emb_style is w-pool") + if weights.dim() != 2: + raise ValueError("weights should be 2D") + cell_emb = torch.sum(layer_output * weights.unsqueeze(2), dim=1) + cell_emb = F.normalize(cell_emb, p=2, dim=1) # (batch, embsize) + + return cell_emb + + def _check_batch_labels(self, batch_labels: Tensor) -> None: + if self.use_batch_labels or self.domain_spec_batchnorm: + assert batch_labels is not None + elif batch_labels is not None: + raise ValueError( + "batch_labels should only be provided when `self.use_batch_labels`" + " or `self.domain_spec_batchnorm` is True" + ) + + def generate( + self, + cell_emb: Tensor, + src: Tensor, + values: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + gen_iters: int = 1, + batch_labels: Optional[Tensor] = None, # (batch,) + ) -> Tensor: + """ + Args: + cell_emb(:obj:`Tensor`): shape (batch, embsize) + src(:obj:`Tensor`): shape (batch, seq_len) + values(:obj:`Tensor`): shape (batch, seq_len), optional + src_key_padding_mask(:obj:`Tensor`): shape (batch, seq_len), optional + gen_iters(:obj:`int`): number of generation iterations + batch_labels(:obj:`Tensor`): shape (batch,), optional + """ + # TODO: should have a tag indicate the generation mode + # TODO: if gen_iters > 1, should have a tag indicate the current iteration + try: + self._check_batch_labels(batch_labels) + except: + import warnings + + warnings.warn( + "batch_labels is required but not provided, using zeros instead" + ) + batch_labels = torch.zeros( + cell_emb.shape[0], dtype=torch.long, device=cell_emb.device + ) + + src = self.encoder(src) # (batch, seq_len, embsize) + + if values is not None: + values = self.value_encoder(values) # (batch, seq_len, embsize) + if self.input_emb_style == "scaling": + values = values.unsqueeze(2) + total_embs = src * values + else: + total_embs = src + values + else: + total_embs = src + + if getattr(self, "dsbn", None) is not None: + batch_label = int(batch_labels[0].item()) + total_embs = self.dsbn(total_embs.permute(0, 2, 1), batch_label).permute( + 0, 2, 1 + ) # the batch norm always works on dim 1 + elif getattr(self, "bn", None) is not None: + total_embs = self.bn(total_embs.permute(0, 2, 1)).permute(0, 2, 1) + + total_embs[:, 0, :] = cell_emb + + if src_key_padding_mask is None: + src_key_padding_mask = torch.zeros( + total_embs.shape[:2], dtype=torch.bool, device=total_embs.device + ) + transformer_output = self.transformer_encoder( + total_embs, src_key_padding_mask=src_key_padding_mask + ) + + if self.use_batch_labels: + batch_emb = self.batch_encoder(batch_labels) # (batch, embsize) + mlm_output = self.decoder( + transformer_output + if not self.use_batch_labels + else torch.cat( + [ + transformer_output, + batch_emb.unsqueeze(1).repeat(1, transformer_output.shape[1], 1), + ], + dim=2, + ), + # else transformer_output + batch_emb.unsqueeze(1), + ) + output = mlm_output["pred"] # (batch, seq_len) + + return output # (batch, seq_len) + + def forward( + self, + src: Tensor, + values: Tensor, + src_key_padding_mask: Tensor, + batch_labels: Optional[Tensor] = None, + CLS: bool = False, + CCE: bool = False, + MVC: bool = False, + ECS: bool = False, + do_sample: bool = False, + ) -> Mapping[str, Tensor]: + """ + Args: + src (:obj:`Tensor`): token ids, shape [batch_size, seq_len] + values (:obj:`Tensor`): token values, shape [batch_size, seq_len] + src_key_padding_mask (:obj:`Tensor`): mask for src, shape [batch_size, + seq_len] + batch_labels (:obj:`Tensor`): batch labels, shape [batch_size] + CLS (:obj:`bool`): if True, return the celltype classification objective + (CLS) output + CCE (:obj:`bool`): if True, return the contrastive cell embedding objective + (CCE) output + MVC (:obj:`bool`): if True, return the masked value prediction for cell + embedding MVC output + ECS (:obj:`bool`): if True, return the elastic cell similarity objective + (ECS) output. + + Returns: + dict of output Tensors. + """ + transformer_output = self._encode( + src, values, src_key_padding_mask, batch_labels + ) + if self.use_batch_labels: + batch_emb = self.batch_encoder(batch_labels) # (batch, embsize) + + output = {} + mlm_output = self.decoder( + transformer_output + if not self.use_batch_labels + else torch.cat( + [ + transformer_output, + batch_emb.unsqueeze(1).repeat(1, transformer_output.shape[1], 1), + ], + dim=2, + ), + # else transformer_output + batch_emb.unsqueeze(1), + ) + if self.explicit_zero_prob and do_sample: + bernoulli = Bernoulli(probs=mlm_output["zero_probs"]) + output["mlm_output"] = bernoulli.sample() * mlm_output["pred"] + else: + output["mlm_output"] = mlm_output["pred"] # (batch, seq_len) + if self.explicit_zero_prob: + output["mlm_zero_probs"] = mlm_output["zero_probs"] + + cell_emb = self._get_cell_emb_from_layer(transformer_output, values) + output["cell_emb"] = cell_emb + + if CLS: + output["cls_output"] = self.cls_decoder(cell_emb) # (batch, n_cls) + if CCE: + cell1 = cell_emb + transformer_output2 = self._encode( + src, values, src_key_padding_mask, batch_labels + ) + cell2 = self._get_cell_emb_from_layer(transformer_output2) + + # Gather embeddings from all devices if distributed training + if dist.is_initialized() and self.training: + cls1_list = [ + torch.zeros_like(cell1) for _ in range(dist.get_world_size()) + ] + cls2_list = [ + torch.zeros_like(cell2) for _ in range(dist.get_world_size()) + ] + dist.all_gather(tensor_list=cls1_list, tensor=cell1.contiguous()) + dist.all_gather(tensor_list=cls2_list, tensor=cell2.contiguous()) + + # NOTE: all_gather results have no gradients, so replace the item + # of the current rank with the original tensor to keep gradients. + # See https://github.com/princeton-nlp/SimCSE/blob/main/simcse/models.py#L186 + cls1_list[dist.get_rank()] = cell1 + cls2_list[dist.get_rank()] = cell2 + + cell1 = torch.cat(cls1_list, dim=0) + cell2 = torch.cat(cls2_list, dim=0) + # TODO: should detach the second run cls2? Can have a try + cos_sim = self.sim(cell1.unsqueeze(1), cell2.unsqueeze(0)) # (batch, batch) + labels = torch.arange(cos_sim.size(0)).long().to(cell1.device) + output["loss_cce"] = self.creterion_cce(cos_sim, labels) + if MVC: + mvc_output = self.mvc_decoder( + cell_emb + if not self.use_batch_labels + else torch.cat([cell_emb, batch_emb], dim=1), + # else cell_emb + batch_emb, + self.cur_gene_token_embs, + ) + if self.explicit_zero_prob and do_sample: + bernoulli = Bernoulli(probs=mvc_output["zero_probs"]) + output["mvc_output"] = bernoulli.sample() * mvc_output["pred"] + else: + output["mvc_output"] = mvc_output["pred"] # (batch, seq_len) + if self.explicit_zero_prob: + output["mvc_zero_probs"] = mvc_output["zero_probs"] + if ECS: + # Here using customized cosine similarity instead of F.cosine_similarity + # to avoid the pytorch issue of similarity larger than 1.0, pytorch # 78064 + # normalize the embedding + cell_emb_normed = F.normalize(cell_emb, p=2, dim=1) + cos_sim = torch.mm(cell_emb_normed, cell_emb_normed.t()) # (batch, batch) + + # mask out diagnal elements + mask = torch.eye(cos_sim.size(0)).bool().to(cos_sim.device) + cos_sim = cos_sim.masked_fill(mask, 0.0) + # only optimize positive similarities + cos_sim = F.relu(cos_sim) + + output["loss_ecs"] = torch.mean(1 - (cos_sim - self.ecs_threshold) ** 2) + + if self.do_dab: + output["dab_output"] = self.grad_reverse_discriminator(cell_emb) + + return output + + def encode_batch( + self, + src: Tensor, + values: Tensor, + src_key_padding_mask: Tensor, + batch_size: int, + batch_labels: Optional[Tensor] = None, + output_to_cpu: bool = True, + time_step: Optional[int] = None, + return_np: bool = False, + ) -> Tensor: + """ + Args: + src (Tensor): shape [N, seq_len] + values (Tensor): shape [N, seq_len] + src_key_padding_mask (Tensor): shape [N, seq_len] + batch_size (int): batch size for encoding + batch_labels (Tensor): shape [N, n_batch_labels] + output_to_cpu (bool): whether to move the output to cpu + time_step (int): the time step index in the transformer output to return. + The time step is along the second dimenstion. If None, return all. + return_np (bool): whether to return numpy array + + Returns: + output Tensor of shape [N, seq_len, embsize] + """ + N = src.size(0) + device = next(self.parameters()).device + + # initialize the output tensor + array_func = np.zeros if return_np else torch.zeros + float32_ = np.float32 if return_np else torch.float32 + shape = ( + (N, self.d_model) + if time_step is not None + else (N, src.size(1), self.d_model) + ) + outputs = array_func(shape, dtype=float32_) + + for i in trange(0, N, batch_size): + raw_output = self._encode( + src[i : i + batch_size].to(device), + values[i : i + batch_size].to(device), + src_key_padding_mask[i : i + batch_size].to(device), + batch_labels[i : i + batch_size].to(device) + if batch_labels is not None + else None, + ) + output = raw_output.detach() + if output_to_cpu: + output = output.cpu() + if return_np: + output = output.numpy() + if time_step is not None: + output = output[:, time_step, :] + outputs[i : i + batch_size] = output + + return outputs + + +def generate_square_subsequent_mask(sz: int) -> Tensor: + """Generates an upper-triangular matrix of -inf, with zeros on diag.""" + return torch.triu(torch.ones(sz, sz) * float("-inf"), diagonal=1) + + +class FastTransformerEncoderWrapper(nn.Module): + def __init__( + self, + d_model: int, + nhead: int, + d_hid: int, + nlayers: int, + dropout: float = 0.5, + ): + super().__init__() + self.fast_transformer_encoder = self.build_fast_transformer_encoder( + d_model, nhead, d_hid, nlayers, dropout + ) + + @staticmethod + def build_fast_transformer_encoder( + d_model: int, nhead: int, d_hid: int, nlayers: int, dropout: float + ) -> nn.Module: + from fast_transformers.builders import TransformerEncoderBuilder + + if d_model % nhead != 0: + raise ValueError( + f"d_model must be divisible by nhead, " + f"got d_model={d_model} and nhead={nhead}" + ) + builder = TransformerEncoderBuilder.from_kwargs( + n_layers=nlayers, + n_heads=nhead, + query_dimensions=d_model // nhead, + value_dimensions=d_model // nhead, + feed_forward_dimensions=d_hid, + attention_type="linear", + attention_dropout=dropout, + dropout=dropout, + activation="gelu", + ) + assert builder.attention_type == "linear" + return builder.get() + + @staticmethod + def build_length_mask( + src: Tensor, + src_key_padding_mask: torch.BoolTensor, + ) -> "LengthMask": + from fast_transformers.masking import LengthMask + + seq_len = src.shape[1] + num_paddings = src_key_padding_mask.sum(dim=1) + actual_seq_len = seq_len - num_paddings # (N,) + length_mask = LengthMask(actual_seq_len, max_len=seq_len, device=src.device) + + if src_key_padding_mask[length_mask.bool_matrix].sum() != 0: + raise ValueError( + "Found padding tokens in the middle of the sequence. " + "src_key_padding_mask and length_mask are not compatible." + ) + return length_mask + + def forward( + self, + src: Tensor, + src_key_padding_mask: torch.BoolTensor, + ) -> Tensor: + """ + Args: + src: Tensor, shape [N, seq_len, embsize] + src_key_padding_mask: Tensor, shape [N, seq_len] + + Returns: + output Tensor of shape [N, seq_len, embsize] + """ + if src_key_padding_mask.shape != src.shape[:2]: + raise ValueError( + f"src_key_padding_mask shape {src_key_padding_mask.shape} " + f"does not match first two dims of src shape {src.shape[:2]}" + ) + + if src_key_padding_mask.dtype != torch.bool: + raise ValueError( + f"src_key_padding_mask needs to be of type torch.bool, " + f"got {src_key_padding_mask.dtype}" + ) + + length_mask = self.build_length_mask(src, src_key_padding_mask) + output = self.fast_transformer_encoder(src, length_mask=length_mask) + return output + + +class FlashTransformerEncoderLayer(nn.Module): + r"""TransformerEncoderLayer is made up of self-attn and feedforward network. + The class is modified from torch.nn.TransformerEncoderLayer to support the + FlashAttention. + + Args: + d_model: the number of expected features in the input (required). + nhead: the number of heads in the multiheadattention models (required). + dim_feedforward: the dimension of the feedforward network model (default=2048). + dropout: the dropout value (default=0.1). + activation: the activation function of intermediate layer, relu or gelu (default=relu). + layer_norm_eps: the eps value in layer normalization components (default=1e-5). + batch_first: If ``True``, then the input and output tensors are provided + as (batch, seq, feature). Default: ``False``. + + Examples:: + >>> encoder_layer = nn.TransformerEncoderLayer(d_model=512, nhead=8) + >>> src = torch.rand(10, 32, 512) + >>> out = encoder_layer(src) + + Alternatively, when ``batch_first`` is ``True``: + >>> encoder_layer = nn.TransformerEncoderLayer(d_model=512, nhead=8, batch_first=True) + >>> src = torch.rand(32, 10, 512) + >>> out = encoder_layer(src) + """ + __constants__ = ["batch_first"] + + def __init__( + self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + activation="relu", + layer_norm_eps=1e-5, + batch_first=True, + device=None, + dtype=None, + norm_scheme="post", # "pre" or "post" + ) -> None: + factory_kwargs = {"device": device, "dtype": dtype} + super().__init__() + self.self_attn = FlashMHA( + embed_dim=d_model, + num_heads=nhead, + batch_first=batch_first, + attention_dropout=dropout, + **factory_kwargs, + ) + # Version compatibility workaround + if not hasattr(self.self_attn, "batch_first"): + self.self_attn.batch_first = batch_first + # Implementation of Feedforward model + self.linear1 = nn.Linear(d_model, dim_feedforward, **factory_kwargs) + self.dropout = nn.Dropout(dropout) + self.linear2 = nn.Linear(dim_feedforward, d_model, **factory_kwargs) + + self.norm1 = nn.LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs) + self.norm2 = nn.LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.activation = self._get_activation_fn(activation) + self.norm_scheme = norm_scheme + if self.norm_scheme not in ["pre", "post"]: + raise ValueError(f"norm_scheme should be pre or post, not {norm_scheme}") + + @staticmethod + def _get_activation_fn(activation): + if activation == "relu": + return F.relu + elif activation == "gelu": + return F.gelu + + raise RuntimeError("activation should be relu/gelu, not {}".format(activation)) + + def __setstate__(self, state): + if "activation" not in state: + state["activation"] = F.relu + super().__setstate__(state) + + def forward( + self, + src: Tensor, + src_mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + **kwargs, + ) -> Tensor: + r"""Pass the input through the encoder layer. + + Args: + src: the sequence to the encoder layer (required). + src_mask: the mask for the src sequence (optional). + src_key_padding_mask: the mask for the src keys per batch (optional). + + Shape: + see the docs in Transformer class. + """ + if src_mask is not None: + raise ValueError("FlashTransformerEncoderLayer does not support src_mask") + + if not src_key_padding_mask.any().item(): + # no padding tokens in src + src_key_padding_mask_ = None + else: + if src_key_padding_mask.dtype != torch.bool: + src_key_padding_mask = src_key_padding_mask.bool() + # NOTE: the FlashMHA uses mask 0 for padding tokens, which is the opposite + src_key_padding_mask_ = ~src_key_padding_mask + + if self.norm_scheme == "pre": + src = self.norm1(src) + src2 = self.self_attn(src, key_padding_mask=src_key_padding_mask_)[0] + src = src + self.dropout1(src2) + src = self.norm2(src) + src2 = self.linear2(self.dropout(self.activation(self.linear1(src)))) + src = src + self.dropout2(src2) + else: + src2 = self.self_attn(src, key_padding_mask=src_key_padding_mask_)[0] + src = src + self.dropout1(src2) + src = self.norm1(src) + src2 = self.linear2(self.dropout(self.activation(self.linear1(src)))) + src = src + self.dropout2(src2) + src = self.norm2(src) + + return src + + +class GeneEncoder(nn.Module): + def __init__( + self, + num_embeddings: int, + embedding_dim: int, + padding_idx: Optional[int] = None, + ): + super().__init__() + self.embedding = nn.Embedding( + num_embeddings, embedding_dim, padding_idx=padding_idx + ) + self.enc_norm = nn.LayerNorm(embedding_dim) + + def forward(self, x: Tensor) -> Tensor: + x = self.embedding(x) # (batch, seq_len, embsize) + x = self.enc_norm(x) + return x + + +class PositionalEncoding(nn.Module): + def __init__(self, d_model: int, dropout: float = 0.1, max_len: int = 5000): + super().__init__() + self.dropout = nn.Dropout(p=dropout) + + position = torch.arange(max_len).unsqueeze(1) + div_term = torch.exp( + torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model) + ) + pe = torch.zeros(max_len, 1, d_model) + pe[:, 0, 0::2] = torch.sin(position * div_term) + pe[:, 0, 1::2] = torch.cos(position * div_term) + self.register_buffer("pe", pe) + + def forward(self, x: Tensor) -> Tensor: + """ + Args: + x: Tensor, shape [seq_len, batch_size, embedding_dim] + """ + x = x + self.pe[: x.size(0)] + return self.dropout(x) + + +class ContinuousValueEncoder(nn.Module): + """ + Encode real number values to a vector using neural nets projection. + """ + + def __init__(self, d_model: int, dropout: float = 0.1, max_value: int = 512): + super().__init__() + self.dropout = nn.Dropout(p=dropout) + self.linear1 = nn.Linear(1, d_model) + self.activation = nn.ReLU() + self.linear2 = nn.Linear(d_model, d_model) + self.norm = nn.LayerNorm(d_model) + self.max_value = max_value + + def forward(self, x: Tensor) -> Tensor: + """ + Args: + x: Tensor, shape [batch_size, seq_len] + """ + # TODO: test using actual embedding layer if input is categorical + # expand last dimension + x = x.unsqueeze(-1) + # clip x to [-inf, max_value] + x = torch.clamp(x, max=self.max_value) + x = self.activation(self.linear1(x)) + x = self.linear2(x) + x = self.norm(x) + return self.dropout(x) + + +class CategoryValueEncoder(nn.Module): + def __init__( + self, + num_embeddings: int, + embedding_dim: int, + padding_idx: Optional[int] = None, + ): + super().__init__() + self.embedding = nn.Embedding( + num_embeddings, embedding_dim, padding_idx=padding_idx + ) + self.enc_norm = nn.LayerNorm(embedding_dim) + + def forward(self, x: Tensor) -> Tensor: + x = x.long() + x = self.embedding(x) # (batch, seq_len, embsize) + x = self.enc_norm(x) + return x + + +class BatchLabelEncoder(nn.Module): + def __init__( + self, + num_embeddings: int, + embedding_dim: int, + padding_idx: Optional[int] = None, + ): + super().__init__() + self.embedding = nn.Embedding( + num_embeddings, embedding_dim, padding_idx=padding_idx + ) + self.enc_norm = nn.LayerNorm(embedding_dim) + + def forward(self, x: Tensor) -> Tensor: + x = self.embedding(x) # (batch, embsize) + x = self.enc_norm(x) + return x + + +class Similarity(nn.Module): + """ + Dot product or cosine similarity + """ + + def __init__(self, temp): + super().__init__() + self.temp = temp + self.cos = nn.CosineSimilarity(dim=-1) + + def forward(self, x, y): + return self.cos(x, y) / self.temp + + +class ExprDecoder(nn.Module): + def __init__( + self, + d_model: int, + explicit_zero_prob: bool = False, + use_batch_labels: bool = False, + ): + super().__init__() + d_in = d_model * 2 if use_batch_labels else d_model + self.fc = nn.Sequential( + nn.Linear(d_in, d_model), + nn.LeakyReLU(), + nn.Linear(d_model, d_model), + nn.LeakyReLU(), + nn.Linear(d_model, 1), + ) + self.explicit_zero_prob = explicit_zero_prob + if explicit_zero_prob: + self.zero_logit = nn.Sequential( + nn.Linear(d_in, d_model), + nn.LeakyReLU(), + nn.Linear(d_model, d_model), + nn.LeakyReLU(), + nn.Linear(d_model, 1), + ) + + def forward(self, x: Tensor) -> Dict[str, Tensor]: + """x is the output of the transformer, (batch, seq_len, d_model)""" + pred_value = self.fc(x).squeeze(-1) # (batch, seq_len) + + if not self.explicit_zero_prob: + return dict(pred=pred_value) + zero_logits = self.zero_logit(x).squeeze(-1) # (batch, seq_len) + zero_probs = torch.sigmoid(zero_logits) + return dict(pred=pred_value, zero_probs=zero_probs) + # TODO: note that the return currently is only for training. Since decoder + # is not used in the test setting for the integration task, the eval/inference + # logic is not implemented yet. However, remember to implement it when + # the decoder is used in any test setting. The inference logic will need + # to sample from the bernoulli distribution with the zero_probs. + + +class ClsDecoder(nn.Module): + """ + Decoder for classification task. + """ + + def __init__( + self, + d_model: int, + n_cls: int, + nlayers: int = 3, + activation: callable = nn.ReLU, + ): + super().__init__() + # module list + self._decoder = nn.ModuleList() + for i in range(nlayers - 1): + self._decoder.append(nn.Linear(d_model, d_model)) + self._decoder.append(activation()) + self._decoder.append(nn.LayerNorm(d_model)) + self.out_layer = nn.Linear(d_model, n_cls) + + def forward(self, x: Tensor) -> Tensor: + """ + Args: + x: Tensor, shape [batch_size, embsize] + """ + for layer in self._decoder: + x = layer(x) + return self.out_layer(x) + + +class MVCDecoder(nn.Module): + """ + Decoder for the masked value prediction for cell embeddings. + """ + + def __init__( + self, + d_model: int, + arch_style: str = "inner product", + query_activation: nn.Module = nn.Sigmoid, + hidden_activation: nn.Module = nn.PReLU, + explicit_zero_prob: bool = False, + use_batch_labels: bool = False, + ) -> None: + """ + Args: + d_model (:obj:`int`): dimension of the gene embedding. + arch_style (:obj:`str`): architecture style of the decoder, choice from + 1. "inner product" or 2. "concat query" or 3. "sum query". + query_activation (:obj:`nn.Module`): activation function for the query + vectors. + hidden_activation (:obj:`nn.Module`): activation function for the hidden + layers. + """ + super().__init__() + d_in = d_model * 2 if use_batch_labels else d_model + if arch_style in ["inner product", "inner product, detach"]: + self.gene2query = nn.Linear(d_model, d_model) + self.query_activation = query_activation() + self.W = nn.Linear(d_model, d_in, bias=False) + if explicit_zero_prob: # by default, gene-wise prob rate + self.W_zero_logit = nn.Linear(d_model, d_in) + elif arch_style == "concat query": + self.gene2query = nn.Linear(d_model, 64) + self.query_activation = query_activation() + self.fc1 = nn.Linear(d_model + 64, 64) + self.hidden_activation = hidden_activation() + self.fc2 = nn.Linear(64, 1) + elif arch_style == "sum query": + self.gene2query = nn.Linear(d_model, d_model) + self.query_activation = query_activation() + self.fc1 = nn.Linear(d_model, 64) + self.hidden_activation = hidden_activation() + self.fc2 = nn.Linear(64, 1) + else: + raise ValueError(f"Unknown arch_style: {arch_style}") + + self.arch_style = arch_style + self.do_detach = arch_style.endswith("detach") + self.explicit_zero_prob = explicit_zero_prob + + def forward( + self, cell_emb: Tensor, gene_embs: Tensor + ) -> Union[Tensor, Dict[str, Tensor]]: + """ + Args: + cell_emb: Tensor, shape (batch, embsize=d_model) + gene_embs: Tensor, shape (batch, seq_len, embsize=d_model) + """ + gene_embs = gene_embs.detach() if self.do_detach else gene_embs + if self.arch_style in ["inner product", "inner product, detach"]: + query_vecs = self.query_activation(self.gene2query(gene_embs)) + cell_emb = cell_emb.unsqueeze(2) # (batch, embsize, 1) + # the pred gene expr values, # (batch, seq_len) + pred_value = torch.bmm(self.W(query_vecs), cell_emb).squeeze(2) + if not self.explicit_zero_prob: + return dict(pred=pred_value) + # zero logits need to based on the cell_emb, because of input exprs + zero_logits = torch.bmm(self.W_zero_logit(query_vecs), cell_emb).squeeze(2) + zero_probs = torch.sigmoid(zero_logits) + return dict(pred=pred_value, zero_probs=zero_probs) + elif self.arch_style == "concat query": + query_vecs = self.query_activation(self.gene2query(gene_embs)) + # expand cell_emb to (batch, seq_len, embsize) + cell_emb = cell_emb.unsqueeze(1).expand(-1, gene_embs.shape[1], -1) + + h = self.hidden_activation( + self.fc1(torch.cat([cell_emb, query_vecs], dim=2)) + ) + if self.explicit_zero_prob: + raise NotImplementedError + return self.fc2(h).squeeze(2) # (batch, seq_len) + elif self.arch_style == "sum query": + query_vecs = self.query_activation(self.gene2query(gene_embs)) + cell_emb = cell_emb.unsqueeze(1) + + h = self.hidden_activation(self.fc1(cell_emb + query_vecs)) + if self.explicit_zero_prob: + raise NotImplementedError + return self.fc2(h).squeeze(2) # (batch, seq_len) + + +class AdversarialDiscriminator(nn.Module): + """ + Discriminator for the adversarial training for batch correction. + """ + + def __init__( + self, + d_model: int, + n_cls: int, + nlayers: int = 3, + activation: callable = nn.LeakyReLU, + reverse_grad: bool = False, + ): + super().__init__() + # module list + self._decoder = nn.ModuleList() + for i in range(nlayers - 1): + self._decoder.append(nn.Linear(d_model, d_model)) + self._decoder.append(activation()) + self._decoder.append(nn.LayerNorm(d_model)) + self.out_layer = nn.Linear(d_model, n_cls) + self.reverse_grad = reverse_grad + + def forward(self, x: Tensor) -> Tensor: + """ + Args: + x: Tensor, shape [batch_size, embsize] + """ + if self.reverse_grad: + x = grad_reverse(x, lambd=1.0) + for layer in self._decoder: + x = layer(x) + return self.out_layer(x) diff --git a/transfer/code/scGPT/scgpt/model/multiomic_model.py b/transfer/code/scGPT/scgpt/model/multiomic_model.py new file mode 100644 index 0000000000000000000000000000000000000000..5128fac74e1604df8ae75251aab4ff86a2bb93ed --- /dev/null +++ b/transfer/code/scGPT/scgpt/model/multiomic_model.py @@ -0,0 +1,1079 @@ +import gc +import math +from typing import Dict, Mapping, Optional, Tuple, Any, Union + +import torch +import numpy as np +from torch import nn, Tensor +import torch.distributed as dist +import torch.nn.functional as F +from torch.nn import TransformerEncoder, TransformerEncoderLayer +from torch.distributions import Bernoulli +from tqdm import trange + +try: + from flash_attn.flash_attention import FlashMHA +except ImportError: + import warnings + + warnings.warn("flash_attn is not installed") + +from .dsbn import DomainSpecificBatchNorm1d +from .grad_reverse import grad_reverse + + +class MultiOmicTransformerModel(nn.Module): + def __init__( + self, + ntoken: int, + d_model: int, + nhead: int, + d_hid: int, + nlayers: int, + nlayers_cls: int = 3, + n_cls: int = 1, + vocab: Any = None, + dropout: float = 0.5, + pad_token: str = "", + pad_value: int = 0, + do_mvc: bool = False, + do_dab: bool = False, + use_batch_labels: bool = False, + num_batch_labels: Optional[int] = None, + domain_spec_batchnorm: Union[bool, str] = False, + input_emb_style: str = "continuous", + n_input_bins: Optional[int] = None, + cell_emb_style: str = "cls", + mvc_decoder_style: str = "inner product", + ecs_threshold: float = 0.3, + explicit_zero_prob: bool = False, + use_fast_transformer: bool = False, + fast_transformer_backend: str = "flash", + pre_norm: bool = False, + use_mod: bool = False, + ntokens_mod: Optional[int] = None, + vocab_mod: Optional[Any] = None, + ): + super().__init__() + self.model_type = "Transformer" + self.d_model = d_model + self.do_dab = do_dab + self.ecs_threshold = ecs_threshold + self.use_batch_labels = use_batch_labels + self.domain_spec_batchnorm = domain_spec_batchnorm + self.input_emb_style = input_emb_style + self.cell_emb_style = cell_emb_style + self.explicit_zero_prob = explicit_zero_prob + self.norm_scheme = "pre" if pre_norm else "post" + self.use_mod = use_mod + + if self.input_emb_style not in ["category", "continuous", "scaling"]: + raise ValueError( + f"input_emb_style should be one of category, continuous, scaling, " + f"got {input_emb_style}" + ) + if cell_emb_style not in ["cls", "avg-pool", "w-pool"]: + raise ValueError(f"Unknown cell_emb_style: {cell_emb_style}") + + # TODO: add dropout in the GeneEncoder + self.encoder = GeneEncoder(ntoken, d_model, padding_idx=vocab[pad_token]) + + # Value Encoder, NOTE: the scaling style is also handled in _encode method + if input_emb_style == "continuous": + self.value_encoder = ContinuousValueEncoder(d_model, dropout) + elif input_emb_style == "category": + assert n_input_bins > 0 + self.value_encoder = CategoryValueEncoder( + n_input_bins, d_model, padding_idx=pad_value + ) + else: + self.value_encoder = nn.Identity() # nn.Softmax(dim=1) + # TODO: consider row-wise normalization or softmax + # TODO: Correct handle the mask_value when using scaling + + # Batch Encoder + if use_batch_labels: + self.batch_encoder = BatchLabelEncoder(num_batch_labels, d_model) + + if use_mod: + self.mod_encoder = BatchLabelEncoder( + ntokens_mod, d_model, padding_idx=vocab_mod[pad_token] + ) + + if domain_spec_batchnorm is True or domain_spec_batchnorm == "dsbn": + use_affine = True if domain_spec_batchnorm == "do_affine" else False + print(f"Use domain specific batchnorm with affine={use_affine}") + self.dsbn = DomainSpecificBatchNorm1d( + d_model, num_batch_labels, eps=6.1e-5, affine=use_affine + ) + elif domain_spec_batchnorm == "batchnorm": + print("Using simple batchnorm instead of domain specific batchnorm") + self.bn = nn.BatchNorm1d(d_model, eps=6.1e-5) + + if use_fast_transformer: + if fast_transformer_backend == "linear": + self.transformer_encoder = FastTransformerEncoderWrapper( + d_model, nhead, d_hid, nlayers, dropout + ) + elif fast_transformer_backend == "flash": + encoder_layers = FlashTransformerEncoderLayer( + d_model, + nhead, + d_hid, + dropout, + batch_first=True, + norm_scheme=self.norm_scheme, + ) + self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers) + else: + encoder_layers = TransformerEncoderLayer( + d_model, nhead, d_hid, dropout, batch_first=True + ) + self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers) + + self.decoder = ExprDecoder( + d_model, + explicit_zero_prob=explicit_zero_prob, + use_batch_labels=use_batch_labels, + use_mod=use_mod, + ) + self.cls_decoder = ClsDecoder(d_model, n_cls, nlayers=nlayers_cls) + if do_mvc: + self.mvc_decoder = MVCDecoder( + d_model, + arch_style=mvc_decoder_style, + explicit_zero_prob=explicit_zero_prob, + use_batch_labels=use_batch_labels, + use_mod=use_mod, + ) + + if do_dab: + self.grad_reverse_discriminator = AdversarialDiscriminator( + d_model, + n_cls=num_batch_labels, + reverse_grad=True, + ) + + self.sim = Similarity(temp=0.5) # TODO: auto set temp + self.creterion_cce = nn.CrossEntropyLoss() + + self.init_weights() + + def init_weights(self) -> None: + initrange = 0.1 + # TODO: check if this initialization is helpful and shall we apply to all? + self.encoder.embedding.weight.data.uniform_(-initrange, initrange) + + def _encode( + self, + src: Tensor, + values: Tensor, + src_key_padding_mask: Tensor, + batch_labels: Optional[Tensor] = None, # (batch,) + ) -> Tensor: + self._check_batch_labels(batch_labels) + + src = self.encoder(src) # (batch, seq_len, embsize) + self.cur_gene_token_embs = src + + values = self.value_encoder(values) # (batch, seq_len, embsize) + if self.input_emb_style == "scaling": + values = values.unsqueeze(2) + total_embs = src * values + else: + total_embs = src + values + + if getattr(self, "dsbn", None) is not None: + batch_label = int(batch_labels[0].item()) + total_embs = self.dsbn(total_embs.permute(0, 2, 1), batch_label).permute( + 0, 2, 1 + ) # the batch norm always works on dim 1 + elif getattr(self, "bn", None) is not None: + total_embs = self.bn(total_embs.permute(0, 2, 1)).permute(0, 2, 1) + + output = self.transformer_encoder( + total_embs, src_key_padding_mask=src_key_padding_mask + ) + return output # (batch, seq_len, embsize) + + def _get_cell_emb_from_layer( + self, layer_output: Tensor, weights: Tensor = None + ) -> Tensor: + """ + Args: + layer_output(:obj:`Tensor`): shape (batch, seq_len, embsize) + weights(:obj:`Tensor`): shape (batch, seq_len), optional and only used + when :attr:`self.cell_emb_style` is "w-pool". + + Returns: + :obj:`Tensor`: shape (batch, embsize) + """ + if self.cell_emb_style == "cls": + cell_emb = layer_output[:, 0, :] # (batch, embsize) + elif self.cell_emb_style == "avg-pool": + cell_emb = torch.mean(layer_output, dim=1) + elif self.cell_emb_style == "w-pool": + if weights is None: + raise ValueError("weights is required when cell_emb_style is w-pool") + if weights.dim() != 2: + raise ValueError("weights should be 2D") + cell_emb = torch.sum(layer_output * weights.unsqueeze(2), dim=1) + cell_emb = F.normalize(cell_emb, p=2, dim=1) # (batch, embsize) + + return cell_emb + + def _check_batch_labels(self, batch_labels: Tensor) -> None: + if self.use_batch_labels or self.domain_spec_batchnorm: + assert batch_labels is not None + elif batch_labels is not None: + raise ValueError( + "batch_labels should only be provided when `self.use_batch_labels`" + " or `self.domain_spec_batchnorm` is True" + ) + + def generate( + self, + cell_emb: Tensor, + src: Tensor, + values: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + gen_iters: int = 1, + batch_labels: Optional[Tensor] = None, # (batch,) + ) -> Tensor: + """ + Args: + cell_emb(:obj:`Tensor`): shape (batch, embsize) + src(:obj:`Tensor`): shape (batch, seq_len) + values(:obj:`Tensor`): shape (batch, seq_len), optional + src_key_padding_mask(:obj:`Tensor`): shape (batch, seq_len), optional + gen_iters(:obj:`int`): number of generation iterations + batch_labels(:obj:`Tensor`): shape (batch,), optional + """ + # TODO: should have a tag indicate the generation mode + # TODO: if gen_iters > 1, should have a tag indicate the current iteration + try: + self._check_batch_labels(batch_labels) + except: + import warnings + + warnings.warn( + "batch_labels is required but not provided, using zeros instead" + ) + batch_labels = torch.zeros( + cell_emb.shape[0], dtype=torch.long, device=cell_emb.device + ) + + src = self.encoder(src) # (batch, seq_len, embsize) + + if values is not None: + values = self.value_encoder(values) # (batch, seq_len, embsize) + if self.input_emb_style == "scaling": + values = values.unsqueeze(2) + total_embs = src * values + else: + total_embs = src + values + else: + total_embs = src + + if getattr(self, "dsbn", None) is not None: + batch_label = int(batch_labels[0].item()) + total_embs = self.dsbn(total_embs.permute(0, 2, 1), batch_label).permute( + 0, 2, 1 + ) # the batch norm always works on dim 1 + elif getattr(self, "bn", None) is not None: + total_embs = self.bn(total_embs.permute(0, 2, 1)).permute(0, 2, 1) + + total_embs[:, 0, :] = cell_emb + + if src_key_padding_mask is None: + src_key_padding_mask = torch.zeros( + total_embs.shape[:2], dtype=torch.bool, device=total_embs.device + ) + transformer_output = self.transformer_encoder( + total_embs, src_key_padding_mask=src_key_padding_mask + ) + + if self.use_batch_labels: + batch_emb = self.batch_encoder(batch_labels) # (batch, embsize) + mlm_output = self.decoder( + transformer_output + if not self.use_batch_labels + else torch.cat( + [ + transformer_output, + batch_emb.unsqueeze(1).repeat(1, transformer_output.shape[1], 1), + ], + dim=2, + ), + # else transformer_output + batch_emb.unsqueeze(1), + ) + output = mlm_output["pred"] # (batch, seq_len) + + return output # (batch, seq_len) + + def forward( + self, + src: Tensor, + values: Tensor, + src_key_padding_mask: Tensor, + batch_labels: Optional[Tensor] = None, + CLS: bool = False, + CCE: bool = False, + MVC: bool = False, + ECS: bool = False, + do_sample: bool = False, + mod_types: Optional[Tensor] = None, + ) -> Mapping[str, Tensor]: + """ + Args: + src (:obj:`Tensor`): token ids, shape [batch_size, seq_len] + values (:obj:`Tensor`): token values, shape [batch_size, seq_len] + src_key_padding_mask (:obj:`Tensor`): mask for src, shape [batch_size, + seq_len] + batch_labels (:obj:`Tensor`): batch labels, shape [batch_size] + CLS (:obj:`bool`): if True, return the celltype classification objective + (CLS) output + CCE (:obj:`bool`): if True, return the contrastive cell embedding objective + (CCE) output + MVC (:obj:`bool`): if True, return the masked value prediction for cell + embedding MVC output + ECS (:obj:`bool`): if True, return the elastic cell similarity objective + (ECS) output. + do_sample (:obj:`bool`): if True, sample from the output distribution + and apply to the output. + mod_types (:obj:`Tensor`): shape [batch_size, seq_len], optional, only + used when `self.use_mod` is True. The token types for the tokens. + + Returns: + dict of output Tensors. + """ + transformer_output = self._encode( + src, values, src_key_padding_mask, batch_labels + ) + if self.use_batch_labels: + batch_emb = self.batch_encoder(batch_labels) # (batch, embsize) + + if self.use_mod: + mod_emb = self.mod_encoder(mod_types) + + output = {} + + if self.use_batch_labels and self.use_mod: + cat_0 = ( + batch_emb.unsqueeze(1).repeat(1, transformer_output.shape[1], 1) + + mod_emb + ) + elif self.use_batch_labels and not self.use_mod: + cat_0 = batch_emb.unsqueeze(1).repeat(1, transformer_output.shape[1], 1) + elif self.use_mod and not self.use_batch_labels: + cat_0 = mod_emb + else: + cat_0 = None + + mlm_output = self.decoder( + transformer_output + if cat_0 is None + else torch.cat( + [transformer_output, cat_0], + dim=2, + ), + ) + if self.explicit_zero_prob and do_sample: + bernoulli = Bernoulli(probs=mlm_output["zero_probs"]) + output["mlm_output"] = bernoulli.sample() * mlm_output["pred"] + else: + output["mlm_output"] = mlm_output["pred"] # (batch, seq_len) + if self.explicit_zero_prob: + output["mlm_zero_probs"] = mlm_output["zero_probs"] + + cell_emb = self._get_cell_emb_from_layer(transformer_output, values) + output["cell_emb"] = cell_emb + + if CLS: + output["cls_output"] = self.cls_decoder(cell_emb) # (batch, n_cls) + if CCE: + cell1 = cell_emb + transformer_output2 = self._encode( + src, values, src_key_padding_mask, batch_labels + ) + cell2 = self._get_cell_emb_from_layer(transformer_output2) + + # Gather embeddings from all devices if distributed training + if dist.is_initialized() and self.training: + cls1_list = [ + torch.zeros_like(cell1) for _ in range(dist.get_world_size()) + ] + cls2_list = [ + torch.zeros_like(cell2) for _ in range(dist.get_world_size()) + ] + dist.all_gather(tensor_list=cls1_list, tensor=cell1.contiguous()) + dist.all_gather(tensor_list=cls2_list, tensor=cell2.contiguous()) + + # NOTE: all_gather results have no gradients, so replace the item + # of the current rank with the original tensor to keep gradients. + # See https://github.com/princeton-nlp/SimCSE/blob/main/simcse/models.py#L186 + cls1_list[dist.get_rank()] = cell1 + cls2_list[dist.get_rank()] = cell2 + + cell1 = torch.cat(cls1_list, dim=0) + cell2 = torch.cat(cls2_list, dim=0) + # TODO: should detach the second run cls2? Can have a try + cos_sim = self.sim(cell1.unsqueeze(1), cell2.unsqueeze(0)) # (batch, batch) + labels = torch.arange(cos_sim.size(0)).long().to(cell1.device) + output["loss_cce"] = self.creterion_cce(cos_sim, labels) + + if MVC: + if self.use_batch_labels and self.use_mod: + cat_1 = batch_emb + self._get_cell_emb_from_layer(mod_emb) + cat_2 = ( + batch_emb.unsqueeze(1).repeat(1, transformer_output.shape[1], 1) + + mod_emb + ) + elif self.use_batch_labels and not self.use_mod: + cat_1 = batch_emb + cat_2 = batch_emb.unsqueeze(1).repeat(1, transformer_output.shape[1], 1) + elif self.use_mod and not self.use_batch_labels: + cat_1 = self._get_cell_emb_from_layer(mod_emb) + cat_2 = mod_emb + else: + cat_1 = None + cat_2 = None + + mvc_output = self.mvc_decoder( + cell_emb if cat_1 is None else torch.cat([cell_emb, cat_1], dim=1), + self.cur_gene_token_embs + if cat_2 is None + else torch.cat([self.cur_gene_token_embs, cat_2], dim=2), + ) + + if self.explicit_zero_prob and do_sample: + bernoulli = Bernoulli(probs=mvc_output["zero_probs"]) + output["mvc_output"] = bernoulli.sample() * mvc_output["pred"] + else: + output["mvc_output"] = mvc_output["pred"] # (batch, seq_len) + if self.explicit_zero_prob: + output["mvc_zero_probs"] = mvc_output["zero_probs"] + if ECS: + # Here using customized cosine similarity instead of F.cosine_similarity + # to avoid the pytorch issue of similarity larger than 1.0, pytorch # 78064 + # normalize the embedding + cell_emb_normed = F.normalize(cell_emb, p=2, dim=1) + cos_sim = torch.mm(cell_emb_normed, cell_emb_normed.t()) # (batch, batch) + + # mask out diagnal elements + mask = torch.eye(cos_sim.size(0)).bool().to(cos_sim.device) + cos_sim = cos_sim.masked_fill(mask, 0.0) + # only optimize positive similarities + cos_sim = F.relu(cos_sim) + + output["loss_ecs"] = torch.mean(1 - (cos_sim - self.ecs_threshold) ** 2) + + if self.do_dab: + output["dab_output"] = self.grad_reverse_discriminator(cell_emb) + + return output + + def encode_batch( + self, + src: Tensor, + values: Tensor, + src_key_padding_mask: Tensor, + batch_size: int, + batch_labels: Optional[Tensor] = None, + output_to_cpu: bool = True, + time_step: Optional[int] = None, + return_np: bool = False, + ) -> Tensor: + """ + Args: + src (Tensor): shape [N, seq_len] + values (Tensor): shape [N, seq_len] + src_key_padding_mask (Tensor): shape [N, seq_len] + batch_size (int): batch size for encoding + batch_labels (Tensor): shape [N, n_batch_labels] + output_to_cpu (bool): whether to move the output to cpu + time_step (int): the time step index in the transformer output to return. + The time step is along the second dimenstion. If None, return all. + return_np (bool): whether to return numpy array + + Returns: + output Tensor of shape [N, seq_len, embsize] + """ + N = src.size(0) + device = next(self.parameters()).device + + # initialize the output tensor + array_func = np.zeros if return_np else torch.zeros + float32_ = np.float32 if return_np else torch.float32 + shape = ( + (N, self.d_model) + if time_step is not None + else (N, src.size(1), self.d_model) + ) + outputs = array_func(shape, dtype=float32_) + + for i in trange(0, N, batch_size): + raw_output = self._encode( + src[i : i + batch_size].to(device), + values[i : i + batch_size].to(device), + src_key_padding_mask[i : i + batch_size].to(device), + batch_labels[i : i + batch_size].to(device) + if batch_labels is not None + else None, + ) + output = raw_output.detach() + if output_to_cpu: + output = output.cpu() + if return_np: + output = output.numpy() + if time_step is not None: + output = output[:, time_step, :] + outputs[i : i + batch_size] = output + + return outputs + + +def generate_square_subsequent_mask(sz: int) -> Tensor: + """Generates an upper-triangular matrix of -inf, with zeros on diag.""" + return torch.triu(torch.ones(sz, sz) * float("-inf"), diagonal=1) + + +class FastTransformerEncoderWrapper(nn.Module): + def __init__( + self, + d_model: int, + nhead: int, + d_hid: int, + nlayers: int, + dropout: float = 0.5, + ): + super().__init__() + self.fast_transformer_encoder = self.build_fast_transformer_encoder( + d_model, nhead, d_hid, nlayers, dropout + ) + + @staticmethod + def build_fast_transformer_encoder( + d_model: int, nhead: int, d_hid: int, nlayers: int, dropout: float + ) -> nn.Module: + from fast_transformers.builders import TransformerEncoderBuilder + + if d_model % nhead != 0: + raise ValueError( + f"d_model must be divisible by nhead, " + f"got d_model={d_model} and nhead={nhead}" + ) + builder = TransformerEncoderBuilder.from_kwargs( + n_layers=nlayers, + n_heads=nhead, + query_dimensions=d_model // nhead, + value_dimensions=d_model // nhead, + feed_forward_dimensions=d_hid, + attention_type="linear", + attention_dropout=dropout, + dropout=dropout, + activation="gelu", + ) + assert builder.attention_type == "linear" + return builder.get() + + @staticmethod + def build_length_mask( + src: Tensor, + src_key_padding_mask: torch.BoolTensor, + ) -> "LengthMask": + from fast_transformers.masking import LengthMask + + seq_len = src.shape[1] + num_paddings = src_key_padding_mask.sum(dim=1) + actual_seq_len = seq_len - num_paddings # (N,) + length_mask = LengthMask(actual_seq_len, max_len=seq_len, device=src.device) + + if src_key_padding_mask[length_mask.bool_matrix].sum() != 0: + raise ValueError( + "Found padding tokens in the middle of the sequence. " + "src_key_padding_mask and length_mask are not compatible." + ) + return length_mask + + def forward( + self, + src: Tensor, + src_key_padding_mask: torch.BoolTensor, + ) -> Tensor: + """ + Args: + src: Tensor, shape [N, seq_len, embsize] + src_key_padding_mask: Tensor, shape [N, seq_len] + + Returns: + output Tensor of shape [N, seq_len, embsize] + """ + if src_key_padding_mask.shape != src.shape[:2]: + raise ValueError( + f"src_key_padding_mask shape {src_key_padding_mask.shape} " + f"does not match first two dims of src shape {src.shape[:2]}" + ) + + if src_key_padding_mask.dtype != torch.bool: + raise ValueError( + f"src_key_padding_mask needs to be of type torch.bool, " + f"got {src_key_padding_mask.dtype}" + ) + + length_mask = self.build_length_mask(src, src_key_padding_mask) + output = self.fast_transformer_encoder(src, length_mask=length_mask) + return output + + +class FlashTransformerEncoderLayer(nn.Module): + r"""TransformerEncoderLayer is made up of self-attn and feedforward network. + The class is modified from torch.nn.TransformerEncoderLayer to support the + FlashAttention. + + Args: + d_model: the number of expected features in the input (required). + nhead: the number of heads in the multiheadattention models (required). + dim_feedforward: the dimension of the feedforward network model (default=2048). + dropout: the dropout value (default=0.1). + activation: the activation function of intermediate layer, relu or gelu (default=relu). + layer_norm_eps: the eps value in layer normalization components (default=1e-5). + batch_first: If ``True``, then the input and output tensors are provided + as (batch, seq, feature). Default: ``False``. + + Examples:: + >>> encoder_layer = nn.TransformerEncoderLayer(d_model=512, nhead=8) + >>> src = torch.rand(10, 32, 512) + >>> out = encoder_layer(src) + + Alternatively, when ``batch_first`` is ``True``: + >>> encoder_layer = nn.TransformerEncoderLayer(d_model=512, nhead=8, batch_first=True) + >>> src = torch.rand(32, 10, 512) + >>> out = encoder_layer(src) + """ + __constants__ = ["batch_first"] + + def __init__( + self, + d_model, + nhead, + dim_feedforward=2048, + dropout=0.1, + activation="relu", + layer_norm_eps=1e-5, + batch_first=True, + device=None, + dtype=None, + norm_scheme="post", # "pre" or "post" + ) -> None: + factory_kwargs = {"device": device, "dtype": dtype} + super().__init__() + self.self_attn = FlashMHA( + embed_dim=d_model, + num_heads=nhead, + batch_first=batch_first, + attention_dropout=dropout, + **factory_kwargs, + ) + # Implementation of Feedforward model + self.linear1 = nn.Linear(d_model, dim_feedforward, **factory_kwargs) + self.dropout = nn.Dropout(dropout) + self.linear2 = nn.Linear(dim_feedforward, d_model, **factory_kwargs) + + self.norm1 = nn.LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs) + self.norm2 = nn.LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.activation = self._get_activation_fn(activation) + self.norm_scheme = norm_scheme + if self.norm_scheme not in ["pre", "post"]: + raise ValueError(f"norm_scheme should be pre or post, not {norm_scheme}") + + @staticmethod + def _get_activation_fn(activation): + if activation == "relu": + return F.relu + elif activation == "gelu": + return F.gelu + + raise RuntimeError("activation should be relu/gelu, not {}".format(activation)) + + def __setstate__(self, state): + if "activation" not in state: + state["activation"] = F.relu + super().__setstate__(state) + + def forward( + self, + src: Tensor, + src_mask: Optional[Tensor] = None, + src_key_padding_mask: Optional[Tensor] = None, + **kwargs, + ) -> Tensor: + r"""Pass the input through the encoder layer. + + Args: + src: the sequence to the encoder layer (required). + src_mask: the mask for the src sequence (optional). + src_key_padding_mask: the mask for the src keys per batch (optional). + + Shape: + see the docs in Transformer class. + """ + if src_mask is not None: + raise ValueError("FlashTransformerEncoderLayer does not support src_mask") + + if not src_key_padding_mask.any().item(): + # no padding tokens in src + src_key_padding_mask_ = None + else: + if src_key_padding_mask.dtype != torch.bool: + src_key_padding_mask = src_key_padding_mask.bool() + # NOTE: the FlashMHA uses mask 0 for padding tokens, which is the opposite + src_key_padding_mask_ = ~src_key_padding_mask + + if self.norm_scheme == "pre": + src = self.norm1(src) + src2 = self.self_attn(src, key_padding_mask=src_key_padding_mask_)[0] + src = src + self.dropout1(src2) + src = self.norm2(src) + src2 = self.linear2(self.dropout(self.activation(self.linear1(src)))) + src = src + self.dropout2(src2) + else: + src2 = self.self_attn(src, key_padding_mask=src_key_padding_mask_)[0] + src = src + self.dropout1(src2) + src = self.norm1(src) + src2 = self.linear2(self.dropout(self.activation(self.linear1(src)))) + src = src + self.dropout2(src2) + src = self.norm2(src) + + return src + + +class GeneEncoder(nn.Module): + def __init__( + self, + num_embeddings: int, + embedding_dim: int, + padding_idx: Optional[int] = None, + ): + super().__init__() + self.embedding = nn.Embedding( + num_embeddings, embedding_dim, padding_idx=padding_idx + ) + self.enc_norm = nn.LayerNorm(embedding_dim) + + def forward(self, x: Tensor) -> Tensor: + x = self.embedding(x) # (batch, seq_len, embsize) + x = self.enc_norm(x) + return x + + +class PositionalEncoding(nn.Module): + def __init__(self, d_model: int, dropout: float = 0.1, max_len: int = 5000): + super().__init__() + self.dropout = nn.Dropout(p=dropout) + + position = torch.arange(max_len).unsqueeze(1) + div_term = torch.exp( + torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model) + ) + pe = torch.zeros(max_len, 1, d_model) + pe[:, 0, 0::2] = torch.sin(position * div_term) + pe[:, 0, 1::2] = torch.cos(position * div_term) + self.register_buffer("pe", pe) + + def forward(self, x: Tensor) -> Tensor: + """ + Args: + x: Tensor, shape [seq_len, batch_size, embedding_dim] + """ + x = x + self.pe[: x.size(0)] + return self.dropout(x) + + +class ContinuousValueEncoder(nn.Module): + """ + Encode real number values to a vector using neural nets projection. + """ + + def __init__(self, d_model: int, dropout: float = 0.1, max_value: int = 512): + super().__init__() + self.dropout = nn.Dropout(p=dropout) + self.linear1 = nn.Linear(1, d_model) + self.activation = nn.ReLU() + self.linear2 = nn.Linear(d_model, d_model) + self.norm = nn.LayerNorm(d_model) + self.max_value = max_value + + def forward(self, x: Tensor) -> Tensor: + """ + Args: + x: Tensor, shape [batch_size, seq_len] + """ + # TODO: test using actual embedding layer if input is categorical + # expand last dimension + x = x.unsqueeze(-1) + # clip x to [-inf, max_value] + x = torch.clamp(x, max=self.max_value) + x = self.activation(self.linear1(x)) + x = self.linear2(x) + x = self.norm(x) + return self.dropout(x) + + +class CategoryValueEncoder(nn.Module): + def __init__( + self, + num_embeddings: int, + embedding_dim: int, + padding_idx: Optional[int] = None, + ): + super().__init__() + self.embedding = nn.Embedding( + num_embeddings, embedding_dim, padding_idx=padding_idx + ) + self.enc_norm = nn.LayerNorm(embedding_dim) + + def forward(self, x: Tensor) -> Tensor: + x = x.long() + x = self.embedding(x) # (batch, seq_len, embsize) + x = self.enc_norm(x) + return x + + +class BatchLabelEncoder(nn.Module): + def __init__( + self, + num_embeddings: int, + embedding_dim: int, + padding_idx: Optional[int] = None, + ): + super().__init__() + self.embedding = nn.Embedding( + num_embeddings, embedding_dim, padding_idx=padding_idx + ) + self.enc_norm = nn.LayerNorm(embedding_dim) + + def forward(self, x: Tensor) -> Tensor: + x = self.embedding(x) # (batch, embsize) + x = self.enc_norm(x) + return x + + +class Similarity(nn.Module): + """ + Dot product or cosine similarity + """ + + def __init__(self, temp): + super().__init__() + self.temp = temp + self.cos = nn.CosineSimilarity(dim=-1) + + def forward(self, x, y): + return self.cos(x, y) / self.temp + + +class ExprDecoder(nn.Module): + def __init__( + self, + d_model: int, + explicit_zero_prob: bool = False, + use_batch_labels: bool = False, + use_mod: bool = False, + ): + super().__init__() + d_in = d_model * 2 if use_batch_labels or use_mod else d_model + self.fc = nn.Sequential( + nn.Linear(d_in, d_model), + nn.LeakyReLU(), + nn.Linear(d_model, d_model), + nn.LeakyReLU(), + nn.Linear(d_model, 1), + ) + self.explicit_zero_prob = explicit_zero_prob + if explicit_zero_prob: + self.zero_logit = nn.Sequential( + nn.Linear(d_in, d_model), + nn.LeakyReLU(), + nn.Linear(d_model, d_model), + nn.LeakyReLU(), + nn.Linear(d_model, 1), + ) + + def forward(self, x: Tensor) -> Dict[str, Tensor]: + """x is the output of the transformer, (batch, seq_len, d_model)""" + pred_value = self.fc(x).squeeze(-1) # (batch, seq_len) + + if not self.explicit_zero_prob: + return dict(pred=pred_value) + zero_logits = self.zero_logit(x).squeeze(-1) # (batch, seq_len) + zero_probs = torch.sigmoid(zero_logits) + return dict(pred=pred_value, zero_probs=zero_probs) + # TODO: note that the return currently is only for training. Since decoder + # is not used in the test setting for the integration task, the eval/inference + # logic is not implemented yet. However, remember to implement it when + # the decoder is used in any test setting. The inference logic will need + # to sample from the bernoulli distribution with the zero_probs. + + +class ClsDecoder(nn.Module): + """ + Decoder for classification task. + """ + + def __init__( + self, + d_model: int, + n_cls: int, + nlayers: int = 3, + activation: callable = nn.ReLU, + ): + super().__init__() + # module list + self._decoder = nn.ModuleList() + for i in range(nlayers - 1): + self._decoder.append(nn.Linear(d_model, d_model)) + self._decoder.append(activation()) + self._decoder.append(nn.LayerNorm(d_model)) + self.out_layer = nn.Linear(d_model, n_cls) + + def forward(self, x: Tensor) -> Tensor: + """ + Args: + x: Tensor, shape [batch_size, embsize] + """ + for layer in self._decoder: + x = layer(x) + return self.out_layer(x) + + +class MVCDecoder(nn.Module): + """ + Decoder for the masked value prediction for cell embeddings. + """ + + def __init__( + self, + d_model: int, + arch_style: str = "inner product", + query_activation: nn.Module = nn.Sigmoid, + hidden_activation: nn.Module = nn.PReLU, + explicit_zero_prob: bool = False, + use_batch_labels: bool = False, + use_mod: bool = False, + ) -> None: + """ + Args: + d_model (:obj:`int`): dimension of the gene embedding. + arch_style (:obj:`str`): architecture style of the decoder, choice from + 1. "inner product" or 2. "concat query" or 3. "sum query". + query_activation (:obj:`nn.Module`): activation function for the query + vectors. + hidden_activation (:obj:`nn.Module`): activation function for the hidden + layers. + """ + super().__init__() + d_in = d_model * 2 if use_batch_labels or use_mod else d_model + d_model = d_model * 2 if use_batch_labels or use_mod else d_model + if arch_style in ["inner product", "inner product, detach"]: + self.gene2query = nn.Linear(d_model, d_model) + self.query_activation = query_activation() + self.W = nn.Linear(d_model, d_in, bias=False) + if explicit_zero_prob: # by default, gene-wise prob rate + self.W_zero_logit = nn.Linear(d_model, d_in) + elif arch_style == "concat query": + self.gene2query = nn.Linear(d_model, 64) + self.query_activation = query_activation() + self.fc1 = nn.Linear(d_model + 64, 64) + self.hidden_activation = hidden_activation() + self.fc2 = nn.Linear(64, 1) + elif arch_style == "sum query": + self.gene2query = nn.Linear(d_model, d_model) + self.query_activation = query_activation() + self.fc1 = nn.Linear(d_model, 64) + self.hidden_activation = hidden_activation() + self.fc2 = nn.Linear(64, 1) + else: + raise ValueError(f"Unknown arch_style: {arch_style}") + + self.arch_style = arch_style + self.do_detach = arch_style.endswith("detach") + self.explicit_zero_prob = explicit_zero_prob + + def forward( + self, cell_emb: Tensor, gene_embs: Tensor + ) -> Union[Tensor, Dict[str, Tensor]]: + """ + Args: + cell_emb: Tensor, shape (batch, embsize=d_model) + gene_embs: Tensor, shape (batch, seq_len, embsize=d_model) + """ + gene_embs = gene_embs.detach() if self.do_detach else gene_embs + if self.arch_style in ["inner product", "inner product, detach"]: + query_vecs = self.query_activation(self.gene2query(gene_embs)) + cell_emb = cell_emb.unsqueeze(2) # (batch, embsize, 1) + # the pred gene expr values, # (batch, seq_len) + pred_value = torch.bmm(self.W(query_vecs), cell_emb).squeeze(2) + if not self.explicit_zero_prob: + return dict(pred=pred_value) + # zero logits need to based on the cell_emb, because of input exprs + zero_logits = torch.bmm(self.W_zero_logit(query_vecs), cell_emb).squeeze(2) + zero_probs = torch.sigmoid(zero_logits) + return dict(pred=pred_value, zero_probs=zero_probs) + elif self.arch_style == "concat query": + query_vecs = self.query_activation(self.gene2query(gene_embs)) + # expand cell_emb to (batch, seq_len, embsize) + cell_emb = cell_emb.unsqueeze(1).expand(-1, gene_embs.shape[1], -1) + + h = self.hidden_activation( + self.fc1(torch.cat([cell_emb, query_vecs], dim=2)) + ) + if self.explicit_zero_prob: + raise NotImplementedError + return self.fc2(h).squeeze(2) # (batch, seq_len) + elif self.arch_style == "sum query": + query_vecs = self.query_activation(self.gene2query(gene_embs)) + cell_emb = cell_emb.unsqueeze(1) + + h = self.hidden_activation(self.fc1(cell_emb + query_vecs)) + if self.explicit_zero_prob: + raise NotImplementedError + return self.fc2(h).squeeze(2) # (batch, seq_len) + + +class AdversarialDiscriminator(nn.Module): + """ + Discriminator for the adversarial training for batch correction. + """ + + def __init__( + self, + d_model: int, + n_cls: int, + nlayers: int = 3, + activation: callable = nn.LeakyReLU, + reverse_grad: bool = False, + ): + super().__init__() + # module list + self._decoder = nn.ModuleList() + for i in range(nlayers - 1): + self._decoder.append(nn.Linear(d_model, d_model)) + self._decoder.append(activation()) + self._decoder.append(nn.LayerNorm(d_model)) + self.out_layer = nn.Linear(d_model, n_cls) + self.reverse_grad = reverse_grad + + def forward(self, x: Tensor) -> Tensor: + """ + Args: + x: Tensor, shape [batch_size, embsize] + """ + if self.reverse_grad: + x = grad_reverse(x, lambd=1.0) + for layer in self._decoder: + x = layer(x) + return self.out_layer(x) diff --git a/transfer/code/scGPT/scgpt/preprocess.py b/transfer/code/scGPT/scgpt/preprocess.py new file mode 100644 index 0000000000000000000000000000000000000000..0f6603b5be72939cbb7746f2ea1bd692f2d3f405 --- /dev/null +++ b/transfer/code/scGPT/scgpt/preprocess.py @@ -0,0 +1,303 @@ +from typing import Dict, Optional, Union + +import numpy as np +import torch +from scipy.sparse import issparse +import scanpy as sc +from scanpy.get import _get_obs_rep, _set_obs_rep +from anndata import AnnData + +from scgpt import logger + + +class Preprocessor: + """ + Prepare data into training, valid and test split. Normalize raw expression + values, binning or using other transform into the preset model input format. + """ + + def __init__( + self, + use_key: Optional[str] = None, + filter_gene_by_counts: Union[int, bool] = False, + filter_cell_by_counts: Union[int, bool] = False, + normalize_total: Union[float, bool] = 1e4, + result_normed_key: Optional[str] = "X_normed", + log1p: bool = False, + result_log1p_key: str = "X_log1p", + subset_hvg: Union[int, bool] = False, + hvg_use_key: Optional[str] = None, + hvg_flavor: str = "seurat_v3", + binning: Optional[int] = None, + result_binned_key: str = "X_binned", + ): + r""" + Set up the preprocessor, use the args to config the workflow steps. + + Args: + + use_key (:class:`str`, optional): + The key of :class:`~anndata.AnnData` to use for preprocessing. + filter_gene_by_counts (:class:`int` or :class:`bool`, default: ``False``): + Whther to filter genes by counts, if :class:`int`, filter genes with counts + filter_cell_by_counts (:class:`int` or :class:`bool`, default: ``False``): + Whther to filter cells by counts, if :class:`int`, filter cells with counts + normalize_total (:class:`float` or :class:`bool`, default: ``1e4``): + Whether to normalize the total counts of each cell to a specific value. + result_normed_key (:class:`str`, default: ``"X_normed"``): + The key of :class:`~anndata.AnnData` to store the normalized data. If + :class:`None`, will use normed data to replce the :attr:`use_key`. + log1p (:class:`bool`, default: ``True``): + Whether to apply log1p transform to the normalized data. + result_log1p_key (:class:`str`, default: ``"X_log1p"``): + The key of :class:`~anndata.AnnData` to store the log1p transformed data. + subset_hvg (:class:`int` or :class:`bool`, default: ``False``): + Whether to subset highly variable genes. + hvg_use_key (:class:`str`, optional): + The key of :class:`~anndata.AnnData` to use for calculating highly variable + genes. If :class:`None`, will use :attr:`adata.X`. + hvg_flavor (:class:`str`, default: ``"seurat_v3"``): + The flavor of highly variable genes selection. See + :func:`scanpy.pp.highly_variable_genes` for more details. + binning (:class:`int`, optional): + Whether to bin the data into discrete values of number of bins provided. + result_binned_key (:class:`str`, default: ``"X_binned"``): + The key of :class:`~anndata.AnnData` to store the binned data. + """ + self.use_key = use_key + self.filter_gene_by_counts = filter_gene_by_counts + self.filter_cell_by_counts = filter_cell_by_counts + self.normalize_total = normalize_total + self.result_normed_key = result_normed_key + self.log1p = log1p + self.result_log1p_key = result_log1p_key + self.subset_hvg = subset_hvg + self.hvg_use_key = hvg_use_key + self.hvg_flavor = hvg_flavor + self.binning = binning + self.result_binned_key = result_binned_key + + def __call__(self, adata: AnnData, batch_key: Optional[str] = None) -> Dict: + """ + format controls the different input value wrapping, including categorical + binned style, fixed-sum normalized counts, log1p fixed-sum normalized counts, etc. + + Args: + + adata (:class:`AnnData`): + The :class:`AnnData` object to preprocess. + batch_key (:class:`str`, optional): + The key of :class:`AnnData.obs` to use for batch information. This arg + is used in the highly variable gene selection step. + """ + key_to_process = self.use_key + # preliminary checks, will use later + if key_to_process == "X": + key_to_process = None # the following scanpy apis use arg None to use X + is_logged = self.check_logged(adata, obs_key=key_to_process) + + # step 1: filter genes + if self.filter_gene_by_counts: + logger.info("Filtering genes by counts ...") + sc.pp.filter_genes( + adata, + min_counts=self.filter_gene_by_counts + if isinstance(self.filter_gene_by_counts, int) + else None, + ) + + # step 2: filter cells + if ( + isinstance(self.filter_cell_by_counts, int) + and self.filter_cell_by_counts > 0 + ): + logger.info("Filtering cells by counts ...") + sc.pp.filter_cells( + adata, + min_counts=self.filter_cell_by_counts + if isinstance(self.filter_cell_by_counts, int) + else None, + ) + + # step 3: normalize total + if self.normalize_total: + logger.info("Normalizing total counts ...") + normed_ = sc.pp.normalize_total( + adata, + target_sum=self.normalize_total + if isinstance(self.normalize_total, float) + else None, + layer=key_to_process, + inplace=False, + )["X"] + key_to_process = self.result_normed_key or key_to_process + _set_obs_rep(adata, normed_, layer=key_to_process) + + # step 4: log1p + if self.log1p: + logger.info("Log1p transforming ...") + if is_logged: + logger.warning( + "The input data seems to be already log1p transformed. " + "Set `log1p=False` to avoid double log1p transform." + ) + if self.result_log1p_key: + _set_obs_rep( + adata, + _get_obs_rep(adata, layer=key_to_process), + layer=self.result_log1p_key, + ) + key_to_process = self.result_log1p_key + sc.pp.log1p(adata, layer=key_to_process) + + # step 5: subset hvg + if self.subset_hvg: + logger.info("Subsetting highly variable genes ...") + if batch_key is None: + logger.warning( + "No batch_key is provided, will use all cells for HVG selection." + ) + sc.pp.highly_variable_genes( + adata, + layer=self.hvg_use_key, + n_top_genes=self.subset_hvg + if isinstance(self.subset_hvg, int) + else None, + batch_key=batch_key, + flavor=self.hvg_flavor, + subset=True, + ) + + # step 6: binning + if self.binning: + logger.info("Binning data ...") + if not isinstance(self.binning, int): + raise ValueError( + "Binning arg must be an integer, but got {}.".format(self.binning) + ) + n_bins = self.binning # NOTE: the first bin is always a spectial for zero + binned_rows = [] + bin_edges = [] + layer_data = _get_obs_rep(adata, layer=key_to_process) + layer_data = layer_data.toarray() if issparse(layer_data) else layer_data + if layer_data.min() < 0: + raise ValueError( + f"Assuming non-negative data, but got min value {layer_data.min()}." + ) + for row in layer_data: + if row.max() == 0: + logger.warning( + "The input data contains all zero rows. Please make sure " + "this is expected. You can use the `filter_cell_by_counts` " + "arg to filter out all zero rows." + ) + binned_rows.append(np.zeros_like(row, dtype=np.int64)) + bin_edges.append(np.array([0] * n_bins)) + continue + non_zero_ids = row.nonzero() + non_zero_row = row[non_zero_ids] + bins = np.quantile(non_zero_row, np.linspace(0, 1, n_bins - 1)) + # bins = np.sort(np.unique(bins)) + # NOTE: comment this line for now, since this will make the each category + # has different relative meaning across datasets + non_zero_digits = _digitize(non_zero_row, bins) + assert non_zero_digits.min() >= 1 + assert non_zero_digits.max() <= n_bins - 1 + binned_row = np.zeros_like(row, dtype=np.int64) + binned_row[non_zero_ids] = non_zero_digits + binned_rows.append(binned_row) + bin_edges.append(np.concatenate([[0], bins])) + adata.layers[self.result_binned_key] = np.stack(binned_rows) + adata.obsm["bin_edges"] = np.stack(bin_edges) + + def check_logged(self, adata: AnnData, obs_key: Optional[str] = None) -> bool: + """ + Check if the data is already log1p transformed. + + Args: + + adata (:class:`AnnData`): + The :class:`AnnData` object to preprocess. + obs_key (:class:`str`, optional): + The key of :class:`AnnData.obs` to use for batch information. This arg + is used in the highly variable gene selection step. + """ + data = _get_obs_rep(adata, layer=obs_key) + max_, min_ = data.max(), data.min() + if max_ > 30: + return False + if min_ < 0: + return False + + non_zero_min = data[data > 0].min() + if non_zero_min >= 1: + return False + + return True + + +def _digitize(x: np.ndarray, bins: np.ndarray, side="both") -> np.ndarray: + """ + Digitize the data into bins. This method spreads data uniformly when bins + have same values. + + Args: + + x (:class:`np.ndarray`): + The data to digitize. + bins (:class:`np.ndarray`): + The bins to use for digitization, in increasing order. + side (:class:`str`, optional): + The side to use for digitization. If "one", the left side is used. If + "both", the left and right side are used. Default to "one". + + Returns: + + :class:`np.ndarray`: + The digitized data. + """ + assert x.ndim == 1 and bins.ndim == 1 + + left_digits = np.digitize(x, bins) + if side == "one": + return left_digits + + right_difits = np.digitize(x, bins, right=True) + + rands = np.random.rand(len(x)) # uniform random numbers + + digits = rands * (right_difits - left_digits) + left_digits + digits = np.ceil(digits).astype(np.int64) + return digits + + +def binning( + row: Union[np.ndarray, torch.Tensor], n_bins: int +) -> Union[np.ndarray, torch.Tensor]: + """Binning the row into n_bins.""" + dtype = row.dtype + return_np = False if isinstance(row, torch.Tensor) else True + row = row.cpu().numpy() if isinstance(row, torch.Tensor) else row + # TODO: use torch.quantile and torch.bucketize + + if row.max() == 0: + logger.warning( + "The input data contains row of zeros. Please make sure this is expected." + ) + return ( + np.zeros_like(row, dtype=dtype) + if return_np + else torch.zeros_like(row, dtype=dtype) + ) + + if row.min() <= 0: + non_zero_ids = row.nonzero() + non_zero_row = row[non_zero_ids] + bins = np.quantile(non_zero_row, np.linspace(0, 1, n_bins - 1)) + non_zero_digits = _digitize(non_zero_row, bins) + binned_row = np.zeros_like(row, dtype=np.int64) + binned_row[non_zero_ids] = non_zero_digits + else: + bins = np.quantile(row, np.linspace(0, 1, n_bins - 1)) + binned_row = _digitize(row, bins) + return torch.from_numpy(binned_row) if not return_np else binned_row.astype(dtype) diff --git a/transfer/code/scGPT/scgpt/scbank/__init__.py b/transfer/code/scGPT/scgpt/scbank/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..0cfe600458117234a4daf6c9ac822d52501edaeb --- /dev/null +++ b/transfer/code/scGPT/scgpt/scbank/__init__.py @@ -0,0 +1,19 @@ +# scBank is the single cell data bank toolbox for building up large-scale single +# cell dataset to allow flexible cell data access and manipulation across studies, +# and to support large-scale computing. +import logging +import sys + +logger = logging.getLogger("scBank") +logger.setLevel(logging.INFO) +handler = logging.StreamHandler(sys.stdout) +handler.setLevel(logging.INFO) +formatter = logging.Formatter( + "%(name)s - %(levelname)s - %(message)s", datefmt="%H:%M:%S" +) +handler.setFormatter(formatter) +logger.addHandler(handler) + +from .databank import DataBank +from .data import * +from .setting import Setting diff --git a/transfer/code/scGPT/scgpt/scbank/data.py b/transfer/code/scGPT/scgpt/scbank/data.py new file mode 100644 index 0000000000000000000000000000000000000000..af952aee1aeb68cef416fea6ed97d01f6069c32c --- /dev/null +++ b/transfer/code/scGPT/scgpt/scbank/data.py @@ -0,0 +1,135 @@ +import json +from dataclasses import dataclass, field, asdict +from pathlib import Path +from typing import List, Optional, Union +from typing_extensions import Self, Literal + +from datasets import Dataset + + +@dataclass +class DataTable: + """ + The data structure for a single-cell data table. + """ + + name: str + data: Optional[Dataset] = None + + @property + def is_loaded(self) -> bool: + return self.data is not None and isinstance(self.data, Dataset) + + def save( + self, + path: Union[Path, str], + format: Literal["json", "parquet"] = "json", + ) -> None: + if not self.is_loaded: + raise ValueError("DataTable is not loaded.") + + if isinstance(path, str): + path = Path(path) + + if format == "json": + self.data.to_json(path) + elif format == "parquet": + self.data.to_parquet(path) + else: + raise ValueError(f"Unknown format: {format}") + + +@dataclass +class MetaInfo: + """ + The data structure for meta info of a scBank data directory. + """ + + on_disk_path: Union[Path, str, None] = None + on_disk_format: Literal["json", "parquet"] = "json" + main_table_key: Optional[str] = None + # TODO: use md5 to check the vocab file name on disk + gene_vocab_md5: Optional[str] = None + study_ids: Optional[List[int]] = field( + default=None, + metadata={"help": "List of study IDs"}, + ) + cell_ids: Optional[List[int]] = field( + default=None, + metadata={"help": "List of cell IDs"}, + ) + # md5: Optional[str] = field( + # default=None, + # metadata={"help": "MD5 hash of the gene vocabulary"}, + # ) + + def __post_init__(self): + if self.on_disk_path is not None: + self.on_disk_path: Path = Path(self.on_disk_path) + + def save(self, path: Union[Path, str, None] = None) -> None: + """ + Save meta info to path. If path is None, will save to the same path at + :attr:`on_disk_path`. + """ + if path is None: + path = self.on_disk_path + + if isinstance(path, str): + path = Path(path) + + manifests = { + "on_disk_format": self.on_disk_format, + "main_data": self.main_table_key, + "gene_vocab_md5": self.gene_vocab_md5, + } + with open(path / "manifest.json", "w") as f: + json.dump(manifests, f, indent=2) + + # TODO: currently only save study table, add saving other tables + with open(path / "studytable.json", "w") as f: + json.dump({"study_ids": self.study_ids}, f, indent=2) + + def load(self, path: Union[Path, str, None] = None) -> None: + """ + Load meta info from path. If path is None, will load from the same path + at :attr:`on_disk_path`. + """ + if path is None: + path = self.on_disk_path + + if isinstance(path, str): + path = Path(path) + + with open(path / "manifest.json") as f: + manifests = json.load(f) + self.on_disk_format = manifests["on_disk_format"] + self.main_table_key = manifests["main_data"] + self.gene_vocab_md5 = manifests["gene_vocab_md5"] + + if (path / "studytable.json").exists(): + with open(path / "studytable.json") as f: + study_ids = json.load(f) + self.study_ids = study_ids["study_ids"] + + @classmethod + def from_path(cls, path: Union[Path, str]) -> Self: + """ + Create a MetaInfo object from a path. + """ + if isinstance(path, str): + path = Path(path) + + if not path.exists(): + raise ValueError(f"Path {path} does not exist.") + + if not path.is_dir(): + raise ValueError(f"Path {path} is not a directory.") + + if not (path / "manifest.json").exists(): + raise ValueError(f"Path {path} does not contain manifest.json.") + + meta_info = cls() + meta_info.on_disk_path = path + meta_info.load(path) + return meta_info diff --git a/transfer/code/scGPT/scgpt/scbank/databank.py b/transfer/code/scGPT/scgpt/scbank/databank.py new file mode 100644 index 0000000000000000000000000000000000000000..a938db2452df6a2e6eec292d09db54c1b981fb97 --- /dev/null +++ b/transfer/code/scGPT/scgpt/scbank/databank.py @@ -0,0 +1,802 @@ +import json +from pathlib import Path +from dataclasses import InitVar, dataclass, field +from typing import Any, Dict, List, Mapping, Optional, Tuple, Union +from typing_extensions import Self, Literal + +import numpy as np +from scipy.sparse import spmatrix, csr_matrix +from anndata import AnnData +from datasets import Dataset, load_dataset + +from scgpt.tokenizer import GeneVocab +from .data import DataTable, MetaInfo +from .setting import Setting +from . import logger + + +@dataclass +class DataBank: + """ + The data structure for large-scale single cell data containing multiple studies. + See https://github.com/subercui/scGPT-release#the-data-structure-for-large-scale-computing. + """ + + meta_info: MetaInfo = None + data_tables: Dict[str, DataTable] = field( + default_factory=dict, + metadata={"help": "Data tables in the DataBank."}, + ) + gene_vocab: InitVar[GeneVocab] = field( + default=None, + # Because of the issue https://github.com/python/cpython/issues/94067, the + # attribute can not be correctly set with default value, if using a getter + # and setter, i.e. @property. So just explicityly set it in the __post_init__. + metadata={"help": "Gene vocabulary mapping gene tokens to integer ids."}, + ) + settings: Setting = field( + default_factory=Setting, + metadata={"help": "The settings for scBank, use default if not set."}, + ) + + def __post_init__(self, gene_vocab) -> None: + """ + Initialize a DataBank. If initialize a non-empty DataBank, will check the + meta info and import the data tables. The main data table idicator will be + required, if at least one data table is provided. + """ + # primarily like the DataSets here, should support slicing, indexing, map, etc. + + # also, because DataBank is desined to work with large-scale data. The class + # instance itself works like a scheme framework that stores the metadata of + # complete data to easily show or print the info. In the meantime, the actual + # data chunks are kept synchronized in disk, and the DataBank will efficiently + # load and return the partion in memory when queried, in the memory and + # computational effective format of Datasets or Apache Arrow. + + # DataBank always tracks the current accessed and queried table/partition, + # and the changes made. When trying to close the current table or switching + # to another one, DataBank will explicitly ask whether to save the changes + # to disk, and show the info of the updated table fields. + + if isinstance(gene_vocab, property): + # walkaround for the issue https://github.com/python/cpython/issues/94067 + self._gene_vocab = None + else: + self.gene_vocab = gene_vocab + + # empty initialization: + if self.meta_info is None: + if len(self.data_tables) > 0: + raise ValueError("Need to provide meta info if non-empty data tables.") + if self.gene_vocab is not None: + raise ValueError("Need to provide meta info if non-empty gene vocab.") + logger.debug("Initialize an empty DataBank.") + return + + # only-meta-info initializtion: + if len(self.data_tables) == 0 and self.gene_vocab is None: + logger.debug("DataBank initialized with meta info only.") + self.sync() if self.settings.immediate_save else self.track() + return + + # full initialization: + if self.gene_vocab is None: + raise ValueError("Need to provide gene vocab if non-empty data tables.") + # validate the meta info, and the consistency between meta and data tables + # we assume the input meta_info is complete and correct. Usually this should + # be handled by factory constructors. + self._validate_data() + self.sync() if self.settings.immediate_save else self.track() + + @property + def gene_vocab(self) -> Optional[GeneVocab]: + """ + The gene vocabulary mapping gene tokens to integer ids. + """ + # if self._gene_vocab is None: + # self._gene_vocab = GeneVocab.load(self.meta_info.gene_vocab_path) + return self._gene_vocab + + @gene_vocab.setter + def gene_vocab(self, gene_vocab: Union[GeneVocab, Path, str]) -> None: + """ + Set the gene vocabulary from an :obj:`GeneVocab` object or a file path. + """ + if isinstance(gene_vocab, (Path, str)): + gene_vocab = GeneVocab.from_file(gene_vocab) + elif not isinstance(gene_vocab, GeneVocab): + raise ValueError("gene_vocab must be a GeneVocab object or a path.") + + self._validate_vocab(gene_vocab) # e.g. check md5 every time calling setter + self._gene_vocab = gene_vocab + self.sync( # sync to disk, update md5 in meta info + attr_keys=["gene_vocab"] + ) if self.settings.immediate_save else self.track(["gene_vocab"]) + + @property + def main_table_key(self) -> Optional[str]: + """ + The main data table key. + """ + if self.meta_info is None: + return None + return self.meta_info.main_table_key + + @main_table_key.setter + def main_table_key(self, table_key: str) -> None: + """Set the main data table key.""" + if self.meta_info is None: + raise ValueError("Need to have self.meta_info if setting main table key.") + self.meta_info.main_table_key = table_key + self.sync(["meta_info"]) if self.settings.immediate_save else self.track( + ["meta_info"] + ) + + @property + def main_data(self) -> DataTable: + """The main data table.""" + return self.data_tables[self.main_table_key] + + @classmethod + def from_anndata( + cls, + adata: Union[AnnData, Path, str], + vocab: Union[GeneVocab, Mapping[str, int]], + to: Union[Path, str], + main_table_key: str = "X", + token_col: str = "gene name", + immediate_save: bool = True, + ) -> Self: + """ + Create a DataBank from an AnnData object. + + Args: + adata (AnnData): Annotated data or path to anndata file. + vocab (GeneVocab or Mapping[str, int]): Gene vocabulary maps gene + token to index. + to (Path or str): Data directory. + main_table_key (str): This layer/obsm in anndata will be used as the + main data table. + token_col (str): Column name of the gene token. + immediate_save (bool): Whether to save the data immediately after creation. + + Returns: + DataBank: DataBank instance. + """ + + if isinstance(adata, str) or isinstance(adata, Path): + import scanpy as sc + + adata = sc.read(adata, cache=True) + elif not isinstance(adata, AnnData): + raise ValueError("adata must points to an AnnData object.") + + if isinstance(vocab, (Path, str)): + vocab = GeneVocab.from_file(vocab) + elif isinstance(vocab, Mapping) and not isinstance(vocab, GeneVocab): + vocab = GeneVocab.from_dict(vocab) + elif not isinstance(vocab, GeneVocab): + raise ValueError("vocab must be a GeneVocab object or a mapping.") + + if isinstance(to, str): + to = Path(to) + to.mkdir(parents=True, exist_ok=True) + db = cls( + meta_info=MetaInfo(on_disk_path=to), + gene_vocab=vocab, + settings=Setting(immediate_save=immediate_save), + ) + + # TODO: Add other data tables, currently only read the main data + data_table = db.load_anndata( + adata, + data_keys=[main_table_key], + token_col=token_col, + )[0] + # update and immediate save + db.main_table_key = main_table_key + db.update_datatables(new_tables=[data_table], immediate_save=immediate_save) + + return db + + @classmethod + def batch_from_anndata(cls, adata: List[AnnData], to: Union[Path, str]) -> Self: + raise NotImplementedError + + @classmethod + def from_path(cls, path: Union[Path, str]) -> Self: + """ + Create a DataBank from a directory containing scBank data. **NOTE**: this + method will automatically check whether md5sum record in the :file:`manifest.json` + matches the md5sum of the loaded gene vocabulary. + + Args: + path (Path or str): Directory path. + + Returns: + DataBank: DataBank instance. + """ + if isinstance(path, str): + path = Path(path) + if not path.exists(): + raise ValueError(f"Path {path} does not exist.") + if not path.is_dir(): + raise ValueError(f"Path {path} is not a directory.") + + if not path.joinpath("gene_vocab.json").exists(): + logger.warning(f"DataBank at {path} does not contain gene_vocab.json.") + data_table_files = [f for f in path.glob("*.datatable.*") if f.is_file()] + if len(data_table_files) == 0: + logger.warning(f"Loading empty DataBank at {path} without datatables.") + + db = cls(meta_info=MetaInfo.from_path(path)) + db.gene_vocab = GeneVocab.from_file(path / "gene_vocab.json") + data_format = db.meta_info.on_disk_format + for data_table_file in data_table_files: + logger.info(f"Loading datatable {data_table_file}.") + data_table = DataTable( + name=data_table_file.name.split(".")[0], + data=load_dataset( + data_format, + data_files=str(data_table_file), + cache_dir=str(path), + split="train", + ), + ) + db.update_datatables(new_tables=[data_table]) + return db + + def __len__(self) -> int: + """Return the number of cells in DataBank.""" + raise NotImplementedError + + def _load_anndata_layer( + self, + adata: AnnData, + data_key: Optional[str] = "X", + index_map: Optional[Mapping[int, int]] = None, + ) -> Optional[Dataset]: + """ + Load anndata layer as a :class:Dataset object. + + Args: + adata (:class:`AnnData`): Annotated data object to load. + data_key (:obj:`str`, optional): Data key to load, default to "X". The data + key must be in :attr:`adata.X`, :attr:`adata.layers` or :attr:`adata.obsm`. + index_map (:obj:`Mapping[int, int]`, optional): A mapping from old index + to new index. If None, meaning the index is unchanged and the converted + data rows do not have explicit keys. + + Returns: + :class:`Dataset`: Dataset object loaded. + """ + if not isinstance(adata, AnnData): + raise ValueError("adata must be an AnnData object.") + + if index_map is None: + # TODO: implement loading data w/ index unchanged, i.e. w/o explicit keys + raise NotImplementedError + + if data_key == "X": + data = adata.X + elif data_key in adata.layers: + data = adata.layers[data_key] + elif data_key in adata.obsm: + data = adata.obsm[data_key] + else: + logger.warning(f"Data key {data_key} not found, skip loading.") + return None + + tokenized_data = self._tokenize(data, index_map) + + return Dataset.from_dict(tokenized_data) + + def _tokenize( + self, + data: Union[np.ndarray, csr_matrix], + ind2ind: Mapping[str, int], + new_indices: Optional[List[int]] = None, + ) -> Dict[str, List]: + """ + Tokenize the data with the given vocabulary. + + TODO: currently by default uses the key-value datatable scheme. Add + other support. + + TODO: currently just counting from zero for the cell id. + + Args: + data (np.ndarray or spmatrix): Data to be tokenized. + tokens (List[str]): List of gene tokens. + ind2ind (Mapping[str, int]): Old index to new index mapping. + new_indices (List[int]): New indices to be used, will ignore ind2ind. + **NOTE**: Usually this should be None. Should only be used in the + spawned recursive calls if any. + + Returns: + Dict[str, List]: Tokenized data. + """ + if not isinstance(data, (np.ndarray, csr_matrix)): + raise ValueError("data must be a numpy array or sparse matrix.") + + if isinstance(data, np.ndarray): + zero_ratio = np.sum(data == 0) / data.size + if zero_ratio > 0.85: + logger.debug( + f"{zero_ratio*100:.0f}% of the data is zero, " + "auto convert to sparse matrix before tokenizing." + ) + data = csr_matrix(data) # process sparse matrix actually faster + + # remove zero rows + if self.settings.remove_zero_rows: + if isinstance(data, np.ndarray) and data.size > 1e9: + logger.warning( + "Going to remove zero rows from a large ndarray data. This " + "may take long time. If you want to disable this, set " + f"`remove_zero_rows` to False in {self.__name__}.settings." + ) + if isinstance(data, csr_matrix): + data = data[data.getnnz(axis=1) > 0] + else: + data = data[~np.all(data == 0, axis=1)] + + n_rows, n_cols = data.shape # usually means n_cells, n_genes + if new_indices is None: + new_indices = np.array( + [ind2ind.get(i, -100) for i in range(n_cols)], int + ) # TODO: can be accelerated + else: + assert len(new_indices) == n_cols + + if isinstance(data, csr_matrix): + indptr = data.indptr + indices = data.indices + non_zero_data = data.data + + tokenized_data = {"id": [], "genes": [], "expressions": []} + tokenized_data["id"] = list(range(n_rows)) + for i in range(n_rows): # ~2s/100k cells + row_indices = indices[indptr[i] : indptr[i + 1]] + row_new_indices = new_indices[row_indices] + row_non_zero_data = non_zero_data[indptr[i] : indptr[i + 1]] + + match_mask = row_new_indices != -100 + row_new_indices = row_new_indices[match_mask] + row_non_zero_data = row_non_zero_data[match_mask] + + tokenized_data["genes"].append(row_new_indices) + tokenized_data["expressions"].append(row_non_zero_data) + else: + # remove -100 cols in data and new_indices, i.e. genes not in vocab + mask = new_indices != -100 + new_indices = new_indices[mask] + data = data[:, mask] + tokenized_data = _nparray2mapped_values( + data, new_indices, "numba" + ) # ~7s/100k cells + + return tokenized_data + + def _validate_vocab(self, vocab: Optional[GeneVocab] = None) -> None: + """ + Validate the vocabulary. Check :attr:`self.vocab` if no vocab is given. + """ + if vocab is None: + vocab = self.gene_vocab + # TODO: implement validation + + def _validate_data(self) -> None: + """ + Validate the current DataBank, including checking md5sum, table names, etc. + """ + # first validate vocabulary + self._validate_vocab() + + if len(self.data_tables) == 0 and self.main_table_key is not None: + raise ValueError( + "No data tables found, but main table key is set. " + "Please set main table key to None or add data tables." + ) + + if len(self.data_tables) > 0: + if self.main_table_key is None: + raise ValueError( + "Main table key can not be empty if non-empty data tables." + ) + if self.main_table_key not in self.data_tables: + raise ValueError( + "Main table key {self.main_table_key} not found in data tables." + ) + + def update_datatables( + self, + new_tables: List[DataTable], + use_names: List[str] = None, + overwrite: bool = False, + immediate_save: Optional[bool] = None, + ) -> None: + """ + Update the data tables in the DataBank with new data tables. + + Args: + new_tables (list of :class:`DataTable`): New data tables to update. + use_names (list of :obj:`str`): Names of the new data tables to use. + If not provided, will use the names of the new data tables. + overwrite (:obj:`bool`): Whether to overwrite the existing data tables. + immediate_save (:obj:`bool`): Whether to save the data immediately after + updating. Will save to :attr:`self.meta_info.on_disk_path`. If not + provided, will follow :attr:`self.settings.immediate_save` instead. + Default to None. + """ + if not isinstance(new_tables, list) or not all( + isinstance(t, DataTable) for t in new_tables + ): + raise ValueError("new_tables must be a list of DataTable.") + + if use_names is None: + use_names = [t.name for t in new_tables] + else: + if len(use_names) != len(new_tables): + raise ValueError("use_names must have the same length as new_tables.") + + if not overwrite: + overlaps = set(use_names) & set(self.data_tables.keys()) + if len(overlaps) > 0: + raise ValueError( + f"Data table names {overlaps} already exist in the DataBank. " + "Please set overwrite=True if replacing the existing data table." + ) + + if immediate_save is None: + immediate_save = self.settings.immediate_save + + for dt, name in zip(new_tables, use_names): + self.data_tables[name] = dt + + self._validate_data() + self.sync("data_tables") if immediate_save else self.track("data_tables") + + def load_anndata( + self, + adata: AnnData, + data_keys: Optional[List[str]] = None, + token_col: str = "gene name", + ) -> List[DataTable]: + """ + Load anndata into datatables. + + Args: + adata (:class:`AnnData`): Annotated data object to load. + data_keys (list of :obj:`str`): List of data keys to load. If None, + all data keys in :attr:`adata.X`, :attr:`adata.layers` and + :attr:`adata.obsm` will be loaded. + token_col (:obj:`str`): Column name of the gene token. Tokens will be + converted to indices by :attr:`self.gene_vocab`. + + Returns: + list of :class:`DataTable`: List of data tables loaded. + """ + if not isinstance(adata, AnnData): + raise ValueError("adata must be an AnnData object.") + + if data_keys is None: + data_keys = ["X"] + list(adata.layers.keys()) + list(adata.obsm.keys()) + + if token_col not in adata.var: + raise ValueError(f"token_col {token_col} not found in adata.var.") + if not isinstance(adata.var[token_col][0], str): + raise ValueError(f"token_col {token_col} must be of type str.") + + # validate matching between tokens and vocab + tokens = adata.var[token_col].tolist() + match_ratio = sum([1 for t in tokens if t in self.gene_vocab]) / len(tokens) + if match_ratio < 0.9: + raise ValueError( + f"{match_ratio*100:.0f}% of the tokens in adata.var[{token_col}] are not in " + "vocab. Please check if using the correct vocab and token_col." + ) + + # build mapping to scBank datatable keys + _ind2ind = _map_ind(tokens, self.gene_vocab) # old index to new index + + data_tables = [] + for key in data_keys: + data = self._load_anndata_layer(adata, key, _ind2ind) + data_table = DataTable( + name=key, + data=data, + ) + data_tables.append(data_table) + + return data_tables + + def append_study( + self, + study_id: int, + study_data: Union[AnnData, "DataBank"], + ) -> None: + """ + Append a study to the current DataBank. + + Args: + study_id (str): Study ID. + study_data (AnnData or DataBank): Study data. + """ + raise NotImplementedError + + def delete_study(self, study_id: int) -> None: + """ + Delete a study from the current DataBank. + """ + raise NotImplementedError + + def filter( + self, + study_ids: Optional[List[int]] = None, + cell_ids: Optional[List[int]] = None, + inplace: bool = True, + ) -> Self: + """ + Filter the current DataBank by study ID and cell ID. + + Args: + study_ids (list): Study IDs to filter. + cell_ids (list): Cell IDs to filter. + inplace (bool): Whether to also filter inplace. + + Returns: + DataBank: Filtered DataBank. + """ + raise NotImplementedError + + def custom_filter( + self, + field: str, + filter_func: callable, + inplace: bool = True, + ) -> Self: + """ + Filter the current DataBank by applying a custom filter function to a field. + + Args: + field (str): Field to filter. + filter_func (callable): Filter function. + inplace (bool): Whether to also filter inplace. + + Returns: + DataBank: Filtered DataBank. + """ + raise NotImplementedError + + def link(self, data_path: Union[Path, str]) -> None: + """ + Link to a scBank data directory. This will only load the meta info + and perform validation check, but not load the data tables. Usually, can + use the `.load_table` method to load a data table later. + """ + if isinstance(path, str): + path = Path(path) + assert path.is_dir(), f"Data path {path} should be a directory." + raise NotImplementedError + + def load_table(self, table_name: str) -> Dataset: + """ + Load a data table from the current DataBank. + """ + raise NotImplementedError + + def load(self, path: Union[Path, str]) -> Dataset: + """ + Load scBank data from a data directory. Since DataBank is designed to work + with large-scale data, this only loads the main data table to memory by + default. This does as well load the meta info and perform validation check. + """ + if isinstance(path, str): + path = Path(path) + assert path.is_dir(), f"Data path {path} should be a directory." + raise NotImplementedError + + def load_all(self, path: Union[Path, str]) -> Dict[str, Dataset]: + """ + Load scBank data from a data directory. This will load all the data tables + to memory. + """ + if isinstance(path, str): + path = Path(path) + assert path.is_dir(), f"Data path {path} should be a directory." + raise NotImplementedError + + def track(self, attr_keys: Union[List[str], str, None] = None) -> List: + """ + Track all the changes made to the current DataBank and that have not been + synced to disk. This will return a list of changes. + + Args: + attr_keys (list of :obj:`str`): List of attribute keys to look for + changes. If None, all attributes will be checked. + """ + if attr_keys is None: + attr_keys = [ + "meta_info", + "data_tables", + "gene_vocab", + ] + elif isinstance(attr_keys, str): + attr_keys = [attr_keys] + + changed_attrs = [] + # TODO: implement the checking of the changes, currently just return all + changed_attrs.extend(attr_keys) + return changed_attrs + + def sync(self, attr_keys: Union[List[str], str, None] = None) -> None: + """ + Sync the current DataBank to a data directory, including, save the updated + data/vocab to files, update the meta info and save to files. + **NOTE**: This will overwrite the existing data directory. + + Args: + attr_keys (list of :obj:`str`): List of attribute keys to sync. If None, will + sync all the attributes with tracked changes. + """ + if attr_keys is None: + attr_keys = self.track() + elif isinstance(attr_keys, str): + attr_keys = [attr_keys] + + # TODO: implement. Remeber particularly to update md5 in metainfo when + # updating the gene vocabulary. + + on_disk_path = self.meta_info.on_disk_path + data_format = self.meta_info.on_disk_format + if "meta_info" in attr_keys: + self.meta_info.save(on_disk_path) + if "data_tables" in attr_keys: + for data_table in self.data_tables.values(): + save_to = on_disk_path / f"{data_table.name}.datatable.{data_format}" + logger.info(f"Saving data table {data_table.name} to {save_to}.") + data_table.save( + path=save_to, + format=data_format, + ) + if "gene_vocab" in attr_keys: + if self.gene_vocab is not None: + self._gene_vocab.save_json(on_disk_path / "gene_vocab.json") + + def save(self, path: Union[Path, str, None], replace: bool = False) -> None: + """ + Save scBank data to a data directory. + + Args: + path (Path): Path to save scBank data. If None, will save to the + directory at :attr:`self.meta_info.on_disk_path`. + replace (bool): Whether to replace existing data in the directory. + """ + if path is None: + path = self.meta_info.on_disk_path + elif isinstance(path, str): + path = Path(path) + + path.mkdir(parents=True, exist_ok=True) + raise NotImplementedError + + +def _map_ind(tokens: List[str], vocab: Mapping[str, int]) -> Mapping[int, int]: + """ + Create a mapping from old index to new index, for a list of tokens. + + Args: + tokens (list): List of tokens, in the order of old indeces. + vocab (Mapping[str, int]): Vocabulary mapping from token to new index. + """ + ind2ind = {} + unmatched_tokens = [] + for i, t in enumerate(tokens): + if t in vocab: + ind2ind[i] = vocab[t] + else: + unmatched_tokens.append(t) + if len(unmatched_tokens) > 0: + logger.warning( + f"{len(unmatched_tokens)}/{len(tokens)} tokens/genes unmatched " + "during vocabulary conversion." + ) + + return ind2ind + + +def _nparray2mapped_values( + data: np.ndarray, + new_indices: np.ndarray, + mode: Literal["plain", "numba"] = "plain", +) -> Dict[str, List]: + """ + Convert a numpy array to mapped values. Only include the non-zero values. + + Args: + data (np.ndarray): Data matrix. + new_indices (np.ndarray): New indices. + + Returns: + Dict[str, List]: Mapping from column name to list of values. + """ + if mode == "plain": + convert_func = _nparray2indexed_values + elif mode == "numba": + convert_func = _nparray2indexed_values_numba + else: + raise ValueError(f"Unknown mode {mode}.") + tokenized_data = {} + row_ids, col_inds, values = convert_func(data, new_indices) + + tokenized_data["id"] = row_ids + tokenized_data["genes"] = col_inds + tokenized_data["expressions"] = values + return tokenized_data + + +def _nparray2indexed_values( + data: np.ndarray, + new_indices: np.ndarray, +) -> Tuple[List, List, List]: + """ + Convert a numpy array to indexed values. Only include the non-zero values. + + Args: + data (np.ndarray): Data matrix. + new_indices (np.ndarray): New indices. + + Returns: + Tuple[List, List, List]: Row IDs, column indices, and values. + """ + row_ids, col_inds, values = [], [], [] + for i in range(len(data)): # TODO: accelerate with numba? joblib? + row = data[i] + idx = np.nonzero(row)[0] + expressions = row[idx] + genes = new_indices[idx] + + row_ids.append(i) + col_inds.append(genes) + values.append(expressions) + + return row_ids, col_inds, values + + +from numba import jit, njit, prange + + +@njit(parallel=True) +def _nparray2indexed_values_numba( + data: np.ndarray, + new_indices: np.ndarray, +) -> Tuple[List, List, List]: + """ + Convert a numpy array to indexed values. Only include the non-zero values. + Using numba to accelerate. + + Args: + data (np.ndarray): Data matrix. + new_indices (np.ndarray): New indices. + + Returns: + Tuple[List, List, List]: Row IDs, column indices, and values. + """ + row_ids, col_inds, values = ( + [1] * len(data), + [np.empty(0, dtype=np.int64)] * len(data), + [np.empty(0, dtype=data.dtype)] * len(data), + ) # placeholders + for i in prange(len(data)): + row = data[i] + idx = np.nonzero(row)[0] + expressions = row[idx] + genes = new_indices[idx] + + row_ids[i] = i + col_inds[i] = genes + values[i] = expressions + + return row_ids, col_inds, values diff --git a/transfer/code/scGPT/scgpt/scbank/monitor.py b/transfer/code/scGPT/scgpt/scbank/monitor.py new file mode 100644 index 0000000000000000000000000000000000000000..db779de30b8782a26d6ea128247d65441b2660e1 --- /dev/null +++ b/transfer/code/scGPT/scgpt/scbank/monitor.py @@ -0,0 +1,2 @@ +# A potential monitor helper class/mixin to track the syncronization between +# in-memory objects and on-disk files for DataBank. diff --git a/transfer/code/scGPT/scgpt/scbank/setting.py b/transfer/code/scGPT/scgpt/scbank/setting.py new file mode 100644 index 0000000000000000000000000000000000000000..0486590bc5883a43cb151061ab7166ccae8d21c1 --- /dev/null +++ b/transfer/code/scGPT/scgpt/scbank/setting.py @@ -0,0 +1,31 @@ +from dataclasses import dataclass, field +from pathlib import Path +from typing import List, Optional, Union + + +@dataclass +class Setting: + """ + The configuration for scBank :class:`DataBank`. + """ + + remove_zero_rows: bool = field( + default=True, + metadata={ + "help": "When load data from numpy or sparse matrix, " + "whether to remove rows with zero values." + }, + ) + max_tokenize_batch_size: int = field( + default=1e6, + metadata={ + "help": "Maximum number of cells to tokenize in a batch. " + "May be useful for processing numpy arrays, currently not used." + }, + ) + immediate_save: bool = field( + default=False, + metadata={ + "help": "Whether to save DataBank whenever it is initiated or updated." + }, + ) diff --git a/transfer/code/scGPT/scgpt/tasks/__init__.py b/transfer/code/scGPT/scgpt/tasks/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..3f642058942fe49ea2b1a0336db56b3f1d9d4d5f --- /dev/null +++ b/transfer/code/scGPT/scgpt/tasks/__init__.py @@ -0,0 +1,2 @@ +from .grn import GeneEmbedding +from .cell_emb import get_batch_cell_embeddings, embed_data diff --git a/transfer/code/scGPT/scgpt/tasks/cell_emb.py b/transfer/code/scGPT/scgpt/tasks/cell_emb.py new file mode 100644 index 0000000000000000000000000000000000000000..d3837213a35f0e3ea5062b8e561f2f2b20f6ff56 --- /dev/null +++ b/transfer/code/scGPT/scgpt/tasks/cell_emb.py @@ -0,0 +1,280 @@ +import json +import os +from pathlib import Path +from typing import Optional, Union + +import numpy as np +import scanpy as sc +import torch +from anndata import AnnData +from torch.utils.data import DataLoader, SequentialSampler +from tqdm import tqdm + +from .. import logger +from ..data_collator import DataCollator +from ..model import TransformerModel +from ..tokenizer import GeneVocab +from ..utils import load_pretrained + +PathLike = Union[str, os.PathLike] + + +def get_batch_cell_embeddings( + adata, + cell_embedding_mode: str = "cls", + model=None, + vocab=None, + max_length=1200, + batch_size=64, + model_configs=None, + gene_ids=None, + use_batch_labels=False, +) -> np.ndarray: + """ + Get the cell embeddings for a batch of cells. + + Args: + adata (AnnData): The AnnData object. + cell_embedding_mode (str): The mode to get the cell embeddings. Defaults to "cls". + model (TransformerModel, optional): The model. Defaults to None. + vocab (GeneVocab, optional): The vocabulary. Defaults to None. + max_length (int): The maximum length of the input sequence. Defaults to 1200. + batch_size (int): The batch size for inference. Defaults to 64. + model_configs (dict, optional): The model configurations. Defaults to None. + gene_ids (np.ndarray, optional): The gene vocabulary ids. Defaults to None. + use_batch_labels (bool): Whether to use batch labels. Defaults to False. + + Returns: + np.ndarray: The cell embeddings. + """ + + count_matrix = adata.X + count_matrix = ( + count_matrix if isinstance(count_matrix, np.ndarray) else count_matrix.toarray() + ) + + # gene vocabulary ids + if gene_ids is None: + gene_ids = np.array(adata.var["id_in_vocab"]) + assert np.all(gene_ids >= 0) + + if use_batch_labels: + batch_ids = np.array(adata.obs["batch_id"].tolist()) + + class Dataset(torch.utils.data.Dataset): + def __init__(self, count_matrix, gene_ids, batch_ids=None): + self.count_matrix = count_matrix + self.gene_ids = gene_ids + self.batch_ids = batch_ids + + def __len__(self): + return len(self.count_matrix) + + def __getitem__(self, idx): + row = self.count_matrix[idx] + nonzero_idx = np.nonzero(row)[0] + values = row[nonzero_idx] + genes = self.gene_ids[nonzero_idx] + # append token at the beginning + genes = np.insert(genes, 0, vocab[""]) + values = np.insert(values, 0, model_configs["pad_value"]) + genes = torch.from_numpy(genes).long() + values = torch.from_numpy(values).float() + output = { + "id": idx, + "genes": genes, + "expressions": values, + } + if self.batch_ids is not None: + output["batch_labels"] = self.batch_ids[idx] + return output + + if cell_embedding_mode == "cls": + dataset = Dataset( + count_matrix, gene_ids, batch_ids if use_batch_labels else None + ) + collator = DataCollator( + do_padding=True, + pad_token_id=vocab[model_configs["pad_token"]], + pad_value=model_configs["pad_value"], + do_mlm=False, + do_binning=True, + max_length=max_length, + sampling=True, + keep_first_n_tokens=1, + ) + data_loader = DataLoader( + dataset, + batch_size=batch_size, + sampler=SequentialSampler(dataset), + collate_fn=collator, + drop_last=False, + num_workers=min(len(os.sched_getaffinity(0)), batch_size), + pin_memory=True, + ) + + device = next(model.parameters()).device + cell_embeddings = np.zeros( + (len(dataset), model_configs["embsize"]), dtype=np.float32 + ) + with torch.no_grad(), torch.cuda.amp.autocast(enabled=True): + count = 0 + for data_dict in tqdm(data_loader, desc="Embedding cells"): + input_gene_ids = data_dict["gene"].to(device) + src_key_padding_mask = input_gene_ids.eq( + vocab[model_configs["pad_token"]] + ) + embeddings = model._encode( + input_gene_ids, + data_dict["expr"].to(device), + src_key_padding_mask=src_key_padding_mask, + batch_labels=data_dict["batch_labels"].to(device) + if use_batch_labels + else None, + ) + + embeddings = embeddings[:, 0, :] # get the position embedding + embeddings = embeddings.cpu().numpy() + cell_embeddings[count : count + len(embeddings)] = embeddings + count += len(embeddings) + cell_embeddings = cell_embeddings / np.linalg.norm( + cell_embeddings, axis=1, keepdims=True + ) + else: + raise ValueError(f"Unknown cell embedding mode: {cell_embedding_mode}") + return cell_embeddings + + +def embed_data( + adata_or_file: Union[AnnData, PathLike], + model_dir: PathLike, + gene_col: str = "feature_name", + max_length=1200, + batch_size=64, + obs_to_save: Optional[list] = None, + device: Union[str, torch.device] = "cuda", + use_fast_transformer: bool = True, + return_new_adata: bool = False, +) -> AnnData: + """ + Preprocess anndata and embed the data using the model. + + Args: + adata_or_file (Union[AnnData, PathLike]): The AnnData object or the path to the + AnnData object. + model_dir (PathLike): The path to the model directory. + gene_col (str): The column in adata.var that contains the gene names. + max_length (int): The maximum length of the input sequence. Defaults to 1200. + batch_size (int): The batch size for inference. Defaults to 64. + obs_to_save (Optional[list]): The list of obs columns to save in the output adata. + Useful for retaining meta data to output. Defaults to None. + device (Union[str, torch.device]): The device to use. Defaults to "cuda". + use_fast_transformer (bool): Whether to use flash-attn. Defaults to True. + return_new_adata (bool): Whether to return a new AnnData object. If False, will + add the cell embeddings to a new :attr:`adata.obsm` with key "X_scGPT". + + Returns: + AnnData: The AnnData object with the cell embeddings. + """ + if isinstance(adata_or_file, AnnData): + adata = adata_or_file + else: + adata = sc.read_h5ad(adata_or_file) + + if isinstance(obs_to_save, str): + assert obs_to_save in adata.obs, f"obs_to_save {obs_to_save} not in adata.obs" + obs_to_save = [obs_to_save] + + # verify gene col + if gene_col == "index": + adata.var["index"] = adata.var.index + else: + assert gene_col in adata.var + + if device == "cuda": + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + if not torch.cuda.is_available(): + print("WARNING: CUDA is not available. Using CPU instead.") + + # LOAD MODEL + model_dir = Path(model_dir) + vocab_file = model_dir / "vocab.json" + model_config_file = model_dir / "args.json" + model_file = model_dir / "best_model.pt" + pad_token = "" + special_tokens = [pad_token, "", ""] + + # vocabulary + vocab = GeneVocab.from_file(vocab_file) + for s in special_tokens: + if s not in vocab: + vocab.append_token(s) + adata.var["id_in_vocab"] = [ + vocab[gene] if gene in vocab else -1 for gene in adata.var[gene_col] + ] + gene_ids_in_vocab = np.array(adata.var["id_in_vocab"]) + logger.info( + f"match {np.sum(gene_ids_in_vocab >= 0)}/{len(gene_ids_in_vocab)} genes " + f"in vocabulary of size {len(vocab)}." + ) + adata = adata[:, adata.var["id_in_vocab"] >= 0] + + with open(model_config_file, "r") as f: + model_configs = json.load(f) + + # Binning will be applied after tokenization. A possible way to do is to use the unified way of binning in the data collator. + + vocab.set_default_index(vocab[""]) + genes = adata.var[gene_col].tolist() + gene_ids = np.array(vocab(genes), dtype=int) + + # all_counts = adata.layers["counts"] + # num_of_non_zero_genes = [ + # np.count_nonzero(all_counts[i]) for i in range(all_counts.shape[0]) + # ] + # max_length = min(max_length, np.max(num_of_non_zero_genes) + 1) + + model = TransformerModel( + ntoken=len(vocab), + d_model=model_configs["embsize"], + nhead=model_configs["nheads"], + d_hid=model_configs["d_hid"], + nlayers=model_configs["nlayers"], + nlayers_cls=model_configs["n_layers_cls"], + n_cls=1, + vocab=vocab, + dropout=model_configs["dropout"], + pad_token=model_configs["pad_token"], + pad_value=model_configs["pad_value"], + do_mvc=True, + do_dab=False, + use_batch_labels=False, + domain_spec_batchnorm=False, + explicit_zero_prob=False, + use_fast_transformer=use_fast_transformer, + fast_transformer_backend="flash", + pre_norm=False, + ) + load_pretrained(model, torch.load(model_file, map_location=device), verbose=False) + model.to(device) + model.eval() + + # get cell embeddings + cell_embeddings = get_batch_cell_embeddings( + adata, + cell_embedding_mode="cls", + model=model, + vocab=vocab, + max_length=max_length, + batch_size=batch_size, + model_configs=model_configs, + gene_ids=gene_ids, + use_batch_labels=False, + ) + + if return_new_adata: + obs_df = adata.obs[obs_to_save] if obs_to_save is not None else None + return sc.AnnData(X=cell_embeddings, obs=obs_df, dtype="float32") + + adata.obsm["X_scGPT"] = cell_embeddings + return adata diff --git a/transfer/code/scGPT/scgpt/tasks/grn.py b/transfer/code/scGPT/scgpt/tasks/grn.py new file mode 100644 index 0000000000000000000000000000000000000000..3f5daa6b26fca6d585fd8f3a3e0f51ea4965b3f2 --- /dev/null +++ b/transfer/code/scGPT/scgpt/tasks/grn.py @@ -0,0 +1,297 @@ +import collections +import operator +import os +from typing import Mapping + +import matplotlib.pyplot as plt +import networkx as nx +import numpy as np +import pandas as pd +import scanpy as sc +import seaborn as sns +import tqdm +from sklearn.metrics.pairwise import cosine_similarity +from sklearn.preprocessing import MinMaxScaler + + +# The class is modified from https://github.com/nceglia/genevector +class GeneEmbedding(object): + def __init__(self, embeddings: Mapping): + self.embeddings = embeddings + # self.context = dataset.data + self.vector = [] + self.genes = [] + for gene in tqdm.tqdm(self.embeddings.keys()): + self.vector.append(self.embeddings[gene]) + self.genes.append(gene) + + def read_embedding(self, filename): + embedding = dict() + lines = open(filename, "r").read().splitlines()[1:] + for line in lines: + vector = line.split() + gene = vector.pop(0) + embedding[gene] = np.array([float(x) for x in vector]) + return embedding + + def get_adata(self, resolution=20): + mat = np.array(self.vector) + np.savetxt(".tmp.txt", mat) + gdata = sc.read_text(".tmp.txt") + os.remove(".tmp.txt") + gdata.obs.index = self.genes + sc.pp.neighbors(gdata, use_rep="X") + sc.tl.leiden(gdata, resolution=resolution) + sc.tl.umap(gdata) + return gdata + + def plot_similarities(self, gene, n_genes=10, save=None): + df = self.compute_similarities(gene).head(n_genes) + _, ax = plt.subplots(1, 1, figsize=(3, 6)) + sns.barplot(data=df, y="Gene", x="Similarity", palette="magma_r", ax=ax) + ax.set_title("{} Similarity".format(gene)) + if save != None: + plt.savefig(save) + + def plot_metagene(self, gdata, mg=None, title="Gene Embedding"): + highlight = [] + labels = [] + clusters = collections.defaultdict(list) + for x, y in zip(gdata.obs["leiden"], gdata.obs.index): + clusters[x].append(y) + if x == mg: + highlight.append(str(x)) + labels.append(y) + else: + highlight.append("_Other") + _labels = [] + for gene in labels: + _labels.append(gene) + gdata.obs["Metagene {}".format(mg)] = highlight + _, ax = plt.subplots(1, 1, figsize=(8, 6)) + sc.pl.umap(gdata, alpha=0.5, show=False, size=100, ax=ax) + sub = gdata[gdata.obs["Metagene {}".format(mg)] != "_Other"] + sc.pl.umap( + sub, + color="Metagene {}".format(mg), + title=title, + size=200, + show=False, + add_outline=False, + ax=ax, + ) + for gene, pos in zip(gdata.obs.index, gdata.obsm["X_umap"].tolist()): + if gene in _labels: + ax.text( + pos[0] + 0.04, + pos[1], + str(gene), + fontsize=6, + alpha=0.9, + fontweight="bold", + ) + plt.tight_layout() + + def plot_metagenes_scores(self, adata, metagenes, column, plot=None): + plt.figure(figsize=(5, 13)) + matrix = [] + meta_genes = [] + cfnum = 1 + cfams = dict() + for cluster, vector in metagenes.items(): + row = [] + cts = [] + for ct in set(adata.obs[column]): + sub = adata[adata.obs[column] == ct] + val = np.mean(sub.obs[str(cluster) + "_SCORE"].tolist()) + row.append(val) + cts.append(ct) + matrix.append(row) + label = str(cluster) + "_SCORE: " + ", ".join(vector[:10]) + if len(set(vector)) > 10: + label += "*" + meta_genes.append(label) + cfams[cluster] = label + cfnum += 1 + matrix = np.array(matrix) + df = pd.DataFrame(matrix, index=meta_genes, columns=cts) + plt.figure() + sns.clustermap( + df, + figsize=(5, 9), + dendrogram_ratio=0.1, + cmap="mako", + yticklabels=True, + standard_scale=0, + ) + plt.tight_layout() + if plot: + plt.savefig(plot) + + def score_metagenes(self, adata, metagenes): + for p, genes in metagenes.items(): + try: + sc.tl.score_genes(adata, score_name=str(p) + "_SCORE", gene_list=genes) + scores = np.array(adata.obs[str(p) + "_SCORE"].tolist()).reshape(-1, 1) + scaler = MinMaxScaler() + scores = scaler.fit_transform(scores) + scores = list(scores.reshape(1, -1))[0] + adata.obs[str(p) + "_SCORE"] = scores + except Exception as e: + adata.obs[str(p) + "_SCORE"] = 0.0 + + def get_metagenes(self, gdata): + metagenes = collections.defaultdict(list) + for x, y in zip(gdata.obs["leiden"], gdata.obs.index): + metagenes[x].append(y) + return metagenes + + def compute_similarities(self, gene, subset=None, feature_type=None): + if gene not in self.embeddings: + return None + # if feature_type: + # subset = [] + # for gene in list(self.embeddings.keys()): + # if feature_type == self.context.feature_types[gene]: + # subset.append(gene) + embedding = self.embeddings[gene] + distances = dict() + if subset: + targets = set(list(self.embeddings.keys())).intersection(set(subset)) + else: + targets = list(self.embeddings.keys()) + for target in targets: + if target not in self.embeddings: + continue + v = self.embeddings[target] + distance = float( + cosine_similarity( + np.array(embedding).reshape(1, -1), np.array(v).reshape(1, -1) + )[0] + ) + distances[target] = distance + sorted_distances = list( + reversed(sorted(distances.items(), key=operator.itemgetter(1))) + ) + genes = [x[0] for x in sorted_distances] + distance = [x[1] for x in sorted_distances] + df = pd.DataFrame.from_dict({"Gene": genes, "Similarity": distance}) + return df + + # def clusters(self, clusters): + # average_vector = dict() + # gene_to_cluster = collections.defaultdict(list) + # matrix = collections.defaultdict(list) + # total_average_vector = [] + # for gene, cluster in zip(self.context.expressed_genes, clusters): + # if gene in self.embeddings: + # matrix[cluster].append(self.embeddings[gene]) + # gene_to_cluster[cluster].append(gene) + # total_average_vector.append(self.embeddings[gene]) + # self.total_average_vector = list(np.average(total_average_vector, axis=0)) + # for cluster, vectors in matrix.items(): + # xvec = list(np.average(vectors, axis=0)) + # average_vector[cluster] = np.subtract(xvec, self.total_average_vector) + # return average_vector, gene_to_cluster + + def generate_weighted_vector(self, genes, markers, weights): + vector = [] + for gene, vec in zip(self.genes, self.vector): + if gene in genes and gene in weights: + vector.append(weights[gene] * np.array(vec)) + if gene not in genes and gene in markers and gene in weights: + vector.append(list(weights[gene] * np.negative(np.array(vec)))) + return list(np.sum(vector, axis=0)) + + def generate_vector(self, genes): + vector = [] + for gene, vec in zip(self.genes, self.vector): + if gene in genes: + vector.append(vec) + assert len(vector) != 0, genes + return list(np.average(vector, axis=0)) + + def generate_weighted_vector(self, genes, weights): + vector = [] + weight = [] + for gene, vec in zip(self.genes, self.vector): + if gene in genes and gene in weights: + vector.append(vec) + weight.append(weights[gene]) + assert len(vector) != 0, genes + return list(np.average(vector, axis=0, weights=weight)) + + def cluster_definitions_as_df(self, top_n=20): + similarities = self.cluster_definitions + clusters = [] + symbols = [] + for key, genes in similarities.items(): + clusters.append(key) + symbols.append(", ".join(genes[:top_n])) + df = pd.DataFrame.from_dict({"Cluster Name": clusters, "Top Genes": symbols}) + return df + + @staticmethod + def read_vector(vec): + lines = open(vec, "r").read().splitlines() + dims = lines.pop(0) + vecs = dict() + for line in lines: + try: + line = line.split() + gene = line.pop(0) + vecs[gene] = list(map(float, line)) + except Exception as e: + continue + return vecs, dims + + def get_similar_genes(self, vector): + distances = dict() + targets = list(self.embeddings.keys()) + for target in targets: + if target not in self.embeddings: + continue + v = self.embeddings[target] + distance = float( + cosine_similarity( + np.array(vector).reshape(1, -1), np.array(v).reshape(1, -1) + )[0] + ) + distances[target] = distance + sorted_distances = list( + reversed(sorted(distances.items(), key=operator.itemgetter(1))) + ) + genes = [x[0] for x in sorted_distances] + distance = [x[1] for x in sorted_distances] + df = pd.DataFrame.from_dict({"Gene": genes, "Similarity": distance}) + return df + + def generate_network(self, threshold=0.5): + G = nx.Graph() + a = pd.DataFrame.from_dict(self.embeddings).to_numpy() + similarities = cosine_similarity(a.T) + genes = list(self.embeddings.keys()) + similarities[similarities < threshold] = 0 + edges = [] + nz = list(zip(*similarities.nonzero())) + for n in tqdm.tqdm(nz): + edges.append((genes[n[0]], genes[n[1]])) + G.add_nodes_from(genes) + G.add_edges_from(edges) + return G + + @staticmethod + def average_vector_results(vec1, vec2, fname): + output = open(fname, "w") + vec1, dims = GeneEmbedding.read_vector(vec1) + vec2, _ = GeneEmbedding.read_vector(vec2) + genes = list(vec1.keys()) + output.write(dims + "\n") + for gene in genes: + v1 = vec1[gene] + v2 = vec2[gene] + meanv = [] + for x, y in zip(v1, v2): + meanv.append(str((x + y) / 2)) + output.write("{} {}\n".format(gene, " ".join(meanv))) + output.close() diff --git a/transfer/code/scGPT/scgpt/tokenizer/__init__.py b/transfer/code/scGPT/scgpt/tokenizer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..241812fafa8553e37b170870071bf3dba7745404 --- /dev/null +++ b/transfer/code/scGPT/scgpt/tokenizer/__init__.py @@ -0,0 +1 @@ +from .gene_tokenizer import * diff --git a/transfer/code/scGPT/scgpt/tokenizer/default_census_vocab.json b/transfer/code/scGPT/scgpt/tokenizer/default_census_vocab.json new file mode 100644 index 0000000000000000000000000000000000000000..9154a27009483a048915a83868d6c225030b569a --- /dev/null +++ b/transfer/code/scGPT/scgpt/tokenizer/default_census_vocab.json @@ -0,0 +1,60696 @@ +{ + "bP-2171C21.3": 36567, + "bP-21264C1.2": 36566, + "ZZZ3": 36565, + "ZZEF1": 36564, + "ZYG11B": 36562, + "ZYG11A": 36561, + "ZXDB": 36559, + "ZXDA": 36558, + "ZWINT": 36557, + "ZWILCH": 36556, + "ZW10": 36555, + "ZUP1": 36554, + "ZSWIM9": 36553, + "ZSWIM8-AS1": 36552, + "ZSWIM7": 36550, + "ZSWIM5": 36548, + "ZSWIM4": 36547, + "ZSCAN5A": 36539, + "ZSCAN4": 36538, + "ZSCAN32": 36537, + "ZSCAN30": 36535, + "ZSCAN29": 36534, + "ZSCAN26": 36533, + "ZSCAN23": 36531, + "ZSCAN20": 36528, + "ZSCAN16-AS1": 36525, + "ZSCAN16": 36524, + "ZSCAN12": 36523, + 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\ No newline at end of file diff --git a/transfer/code/scGPT/scgpt/tokenizer/default_gene_vocab.json b/transfer/code/scGPT/scgpt/tokenizer/default_gene_vocab.json new file mode 100644 index 0000000000000000000000000000000000000000..fbe11216e91c12b30c4f0d3ee007959e0101cc4c --- /dev/null +++ b/transfer/code/scGPT/scgpt/tokenizer/default_gene_vocab.json @@ -0,0 +1,48294 @@ +{ + "ZZZ3": 48291, + "ZYG11A": 48285, + "ZXDC": 48284, + "ZXDB": 48283, + "ZWS1": 48281, + "ZWINT": 48279, + "ZWILCH": 48278, + "ZUP1": 48276, + "ZSWIM9": 48275, + "ZSWIM7": 48272, + "ZSWIM5P2": 48269, + "ZSWIM5P1": 48268, + "ZSWIM4": 48266, + "ZSWIM3": 48265, + "ZSWIM1": 48263, + "ZSCAN9": 48262, + "ZSCAN5DP": 48261, + "ZSCAN5A": 48257, + "ZSCAN4": 48256, + "ZSCAN29": 48252, + "ZSCAN23": 48249, + "ZSCAN2": 48245, + "ZSCAN16": 48242, + "ZSCAN12": 48240, + "ZRSR2": 48236, + "ZRK": 48235, + "ZPR1P1": 48229, + "ZPLD2P": 48227, + "ZPBP2": 48224, + "ZPAXP": 48222, + "ZP4": 48221, + "ZP3B": 48218, + "ZNT3": 48213, + "ZNT2": 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26202, + "ZNF225": 47489, + "ODF2L": 26203, + "ODF3L1": 26206, + "ODF3L2": 26207, + "TRE-TTC14-1": 44306, + "ODG1": 26209, + "ODT1": 26211, + "TGIF1": 42864, + "OFC3": 26216, + "OFCC1": 26217, + "OR5AN1": 27036, + "OFD1P14Y": 26223, + "TBX2-AS1": 42497, + "RAD9B": 31026, + "OFD1P15Y": 26224, + "HHLA3": 13316, + "NOP16": 25577, + "OFD1P19Y": 26228, + "LINC01842": 18281, + "OFD1P5Y": 26233, + "OFD1P8Y": 26236, + "RPS15AP34": 37428, + "RBMXL2": 31321, + "OGDHL": 26240, + "SENCR": 38844, + "RAPGEF4-AS1": 31115, + "BMIQ1": 2721, + "HEATR4": 13203, + "OGFOD3": 26244, + "OGFR": 26245, + "PAN2": 27824, + "OGFR-AS1": 26246, + "FOXD3-AS1": 10903, + "OGFRL1": 26247, + "MIR4800": 22097, + "OGFRP1": 26248, + "TRW-CCA4-1": 45053, + "OGT": 26251 +} \ No newline at end of file diff --git a/transfer/code/scGPT/scgpt/tokenizer/gene_tokenizer.py b/transfer/code/scGPT/scgpt/tokenizer/gene_tokenizer.py new file mode 100644 index 0000000000000000000000000000000000000000..e79c0886b9a15bfe0a64a81c3e1696241b62ce99 --- /dev/null +++ b/transfer/code/scGPT/scgpt/tokenizer/gene_tokenizer.py @@ -0,0 +1,473 @@ +import json +import pickle +from pathlib import Path +from collections import Counter, OrderedDict +from typing import Dict, Iterable, List, Optional, Tuple, Union +from typing_extensions import Self + +import numpy as np +import pandas as pd +import torch +import torchtext.vocab as torch_vocab +from torchtext.vocab import Vocab + +# from transformers.tokenization_utils import PreTrainedTokenizer +# from transformers import AutoTokenizer, BertTokenizer + +from .. import logger + + +class GeneVocab(Vocab): + """ + Vocabulary for genes. + """ + + def __init__( + self, + gene_list_or_vocab: Union[List[str], Vocab], + specials: Optional[List[str]] = None, + special_first: bool = True, + default_token: Optional[str] = "", + ) -> None: + """ + Initialize the vocabulary. + Note: add specials only works when init from a gene list. + + Args: + gene_list_or_vocab (List[str] or Vocab): List of gene names or a + Vocab object. + specials (List[str]): List of special tokens. + special_first (bool): Whether to add special tokens to the beginning + of the vocabulary. + default_token (str): Default token, by default will set to "", + if "" is in the vocabulary. + """ + if isinstance(gene_list_or_vocab, Vocab): + _vocab = gene_list_or_vocab + if specials is not None: + raise ValueError( + "receive non-empty specials when init from a Vocab object." + ) + elif isinstance(gene_list_or_vocab, list): + _vocab = self._build_vocab_from_iterator( + gene_list_or_vocab, + specials=specials, + special_first=special_first, + ) + else: + raise ValueError( + "gene_list_or_vocab must be a list of gene names or a Vocab object." + ) + super().__init__(_vocab.vocab) + if default_token is not None and default_token in self: + self.set_default_token(default_token) + + @classmethod + def from_file(cls, file_path: Union[Path, str]) -> Self: + """ + Load the vocabulary from a file. The file should be either a pickle or a + json file of token to index mapping. + """ + if isinstance(file_path, str): + file_path = Path(file_path) + if file_path.suffix == ".pkl": + with file_path.open("rb") as f: + vocab = pickle.load(f) + return cls(vocab) + elif file_path.suffix == ".json": + with file_path.open("r") as f: + token2idx = json.load(f) + return cls.from_dict(token2idx) + else: + raise ValueError( + f"{file_path} is not a valid file type. " + "Only .pkl and .json are supported." + ) + + @classmethod + def from_dict( + cls, + token2idx: Dict[str, int], + default_token: Optional[str] = "", + ) -> Self: + """ + Load the vocabulary from a dictionary. + + Args: + token2idx (Dict[str, int]): Dictionary mapping tokens to indices. + """ + # initiate an empty vocabulary first + _vocab = cls([]) + + # add the tokens to the vocabulary, GeneVocab requires consecutive indices + for t, i in sorted(token2idx.items(), key=lambda x: x[1]): + _vocab.insert_token(t, i) + + if default_token is not None and default_token in _vocab: + _vocab.set_default_token(default_token) + + return _vocab + + def _build_vocab_from_iterator( + self, + iterator: Iterable, + min_freq: int = 1, + specials: Optional[List[str]] = None, + special_first: bool = True, + ) -> Vocab: + """ + Build a Vocab from an iterator. This function is modified from + torchtext.vocab.build_vocab_from_iterator. The original function always + splits tokens into characters, which is not what we want. + + Args: + iterator (Iterable): Iterator used to build Vocab. Must yield list + or iterator of tokens. + min_freq (int): The minimum frequency needed to include a token in + the vocabulary. + specials (List[str]): Special symbols to add. The order of supplied + tokens will be preserved. + special_first (bool): Whether to add special tokens to the beginning + + Returns: + torchtext.vocab.Vocab: A `Vocab` object + """ + + counter = Counter() + counter.update(iterator) + + if specials is not None: + for tok in specials: + del counter[tok] + + sorted_by_freq_tuples = sorted(counter.items(), key=lambda x: x[0]) + sorted_by_freq_tuples.sort(key=lambda x: x[1], reverse=True) + ordered_dict = OrderedDict(sorted_by_freq_tuples) + + if specials is not None: + if special_first: + specials = specials[::-1] + for symbol in specials: + ordered_dict.update({symbol: min_freq}) + ordered_dict.move_to_end(symbol, last=not special_first) + + word_vocab = torch_vocab.vocab(ordered_dict, min_freq=min_freq) + return word_vocab + + @property + def pad_token(self) -> Optional[str]: + """ + Get the pad token. + """ + if getattr(self, "_pad_token", None) is None: + self._pad_token = None + return self._pad_token + + @pad_token.setter + def pad_token(self, pad_token: str) -> None: + """ + Set the pad token. Will not add the pad token to the vocabulary. + + Args: + pad_token (str): Pad token, should be in the vocabulary. + """ + if pad_token not in self: + raise ValueError(f"{pad_token} is not in the vocabulary.") + self._pad_token = pad_token + + def save_json(self, file_path: Union[Path, str]) -> None: + """ + Save the vocabulary to a json file. + """ + if isinstance(file_path, str): + file_path = Path(file_path) + with file_path.open("w") as f: + json.dump(self.get_stoi(), f, indent=2) + + def set_default_token(self, default_token: str) -> None: + """ + Set the default token. + + Args: + default_token (str): Default token. + """ + if default_token not in self: + raise ValueError(f"{default_token} is not in the vocabulary.") + self.set_default_index(self[default_token]) + + +def get_default_gene_vocab() -> GeneVocab: + """ + Get the default gene vocabulary, consisting of gene symbols and ids. + """ + vocab_file = Path(__file__).parent / "default_gene_vocab.json" + if not vocab_file.exists(): + logger.info( + f"No existing default vocab, will build one and save to {vocab_file}" + ) + return _build_default_gene_vocab(save_vocab_to=vocab_file) + logger.info(f"Loading gene vocabulary from {vocab_file}") + return GeneVocab.from_file(vocab_file) + + +def _build_default_gene_vocab( + download_source_to: str = "/tmp", + save_vocab_to: Union[Path, str, None] = None, +) -> GeneVocab: + """ + Build the default gene vocabulary from HGNC gene symbols. + + Args: + download_source_to (str): Directory to download the source data. + save_vocab_to (Path or str): Path to save the vocabulary. If None, + the vocabulary will not be saved. Default to None. + """ + gene_collection_file = ( + Path(download_source_to) / "human.gene_name_symbol.from_genenames.org.tsv" + ) + if not gene_collection_file.exists(): + # download and save file from url + url = ( + "https://www.genenames.org/cgi-bin/download/custom?col=gd_app_sym&" + "col=md_ensembl_id&status=Approved&status=Entry%20Withdrawn&hgnc_dbtag" + "=on&order_by=gd_app_sym_sort&format=text&submit=submit" + ) + import requests + + r = requests.get(url) + gene_collection_file.write_text(r.text) + + logger.info(f"Building gene vocabulary from {gene_collection_file}") + df = pd.read_csv(gene_collection_file, sep="\t") + gene_list = df["Approved symbol"].dropna().unique().tolist() + gene_vocab = GeneVocab(gene_list) # no special tokens set in default vocab + if save_vocab_to is not None: + gene_vocab.save_json(Path(save_vocab_to)) + return gene_vocab + + +def tokenize_batch( + data: np.ndarray, + gene_ids: np.ndarray, + return_pt: bool = True, + append_cls: bool = True, + include_zero_gene: bool = False, + cls_id: int = "", + mod_type: np.ndarray = None, + cls_id_mod_type: int = None, +) -> List[Tuple[Union[torch.Tensor, np.ndarray]]]: + """ + Tokenize a batch of data. Returns a list of tuple (gene_id, count). + + Args: + data (array-like): A batch of data, with shape (batch_size, n_features). + n_features equals the number of all genes. + gene_ids (array-like): A batch of gene ids, with shape (n_features,). + return_pt (bool): Whether to return torch tensors of gene_ids and counts, + default to True. + + Returns: + list: A list of tuple (gene_id, count) of non zero gene expressions. + """ + if data.shape[1] != len(gene_ids): + raise ValueError( + f"Number of features in data ({data.shape[1]}) does not match " + f"number of gene_ids ({len(gene_ids)})." + ) + if mod_type is not None and data.shape[1] != len(mod_type): + raise ValueError( + f"Number of features in data ({data.shape[1]}) does not match " + f"number of mod_type ({len(mod_type)})." + ) + + tokenized_data = [] + for i in range(len(data)): + row = data[i] + mod_types = None + if include_zero_gene: + values = row + genes = gene_ids + if mod_type is not None: + mod_types = mod_type + else: + idx = np.nonzero(row)[0] + values = row[idx] + genes = gene_ids[idx] + if mod_type is not None: + mod_types = mod_type[idx] + if append_cls: + genes = np.insert(genes, 0, cls_id) + values = np.insert(values, 0, 0) + if mod_type is not None: + mod_types = np.insert(mod_types, 0, cls_id_mod_type) + if return_pt: + genes = torch.from_numpy(genes).long() + values = torch.from_numpy(values).float() + if mod_type is not None: + mod_types = torch.from_numpy(mod_types).long() + tokenized_data.append((genes, values, mod_types)) + return tokenized_data + + +def pad_batch( + batch: List[Tuple], + max_len: int, + vocab: Vocab, + pad_token: str = "", + pad_value: int = 0, + cls_appended: bool = True, + vocab_mod: Vocab = None, +) -> Dict[str, torch.Tensor]: + """ + Pad a batch of data. Returns a list of Dict[gene_id, count]. + + Args: + batch (list): A list of tuple (gene_id, count). + max_len (int): The maximum length of the batch. + vocab (Vocab): The vocabulary containing the pad token. + pad_token (str): The token to pad with. + + Returns: + Dict[str, torch.Tensor]: A dictionary of gene_id and count. + """ + max_ori_len = max(len(batch[i][0]) for i in range(len(batch))) + max_len = min(max_ori_len, max_len) + + pad_id = vocab[pad_token] + if vocab_mod is not None: + mod_pad_id = vocab_mod[pad_token] + gene_ids_list = [] + values_list = [] + mod_types_list = [] + + for i in range(len(batch)): + gene_ids, values, mod_types = batch[i] + + if len(gene_ids) > max_len: + # sample max_len genes + if not cls_appended: + idx = np.random.choice(len(gene_ids), max_len, replace=False) + else: + idx = np.random.choice(len(gene_ids) - 1, max_len - 1, replace=False) + idx = idx + 1 + idx = np.insert(idx, 0, 0) + gene_ids = gene_ids[idx] + values = values[idx] + if mod_types is not None: + mod_types = mod_types[idx] + if len(gene_ids) < max_len: + gene_ids = torch.cat( + [ + gene_ids, + torch.full( + (max_len - len(gene_ids),), pad_id, dtype=gene_ids.dtype + ), + ] + ) + values = torch.cat( + [ + values, + torch.full((max_len - len(values),), pad_value, dtype=values.dtype), + ] + ) + if mod_types is not None: + mod_types = torch.cat( + [ + mod_types, + torch.full( + (max_len - len(mod_types),), + mod_pad_id, + dtype=mod_types.dtype, + ), + ] + ) + + gene_ids_list.append(gene_ids) + values_list.append(values) + if mod_types is not None: + mod_types_list.append(mod_types) + + batch_padded = { + "genes": torch.stack(gene_ids_list, dim=0), + "values": torch.stack(values_list, dim=0), + } + if mod_types is not None: + batch_padded["mod_types"] = torch.stack(mod_types_list, dim=0) + return batch_padded + + +def tokenize_and_pad_batch( + data: np.ndarray, + gene_ids: np.ndarray, + max_len: int, + vocab: Vocab, + pad_token: str, + pad_value: int, + append_cls: bool = True, + include_zero_gene: bool = False, + cls_token: str = "", + return_pt: bool = True, + mod_type: np.ndarray = None, + vocab_mod: Vocab = None, +) -> Dict[str, torch.Tensor]: + """ + Tokenize and pad a batch of data. Returns a list of tuple (gene_id, count). + """ + cls_id = vocab[cls_token] + if mod_type is not None: + cls_id_mod_type = vocab_mod[cls_token] + tokenized_data = tokenize_batch( + data, + gene_ids, + return_pt=return_pt, + append_cls=append_cls, + include_zero_gene=include_zero_gene, + cls_id=cls_id, + mod_type=mod_type, + cls_id_mod_type=cls_id_mod_type if mod_type is not None else None, + ) + + batch_padded = pad_batch( + tokenized_data, + max_len, + vocab, + pad_token, + pad_value, + cls_appended=append_cls, + vocab_mod=vocab_mod, + ) + return batch_padded + + +def random_mask_value( + values: Union[torch.Tensor, np.ndarray], + mask_ratio: float = 0.15, + mask_value: int = -1, + pad_value: int = 0, +) -> torch.Tensor: + """ + Randomly mask a batch of data. + + Args: + values (array-like): + A batch of tokenized data, with shape (batch_size, n_features). + mask_ratio (float): The ratio of genes to mask, default to 0.15. + mask_value (int): The value to mask with, default to -1. + pad_value (int): The value of padding in the values, will be kept unchanged. + + Returns: + torch.Tensor: A tensor of masked data. + """ + if isinstance(values, torch.Tensor): + # it is crutial to clone the tensor, otherwise it changes the original tensor + values = values.clone().detach().numpy() + else: + values = values.copy() + + for i in range(len(values)): + row = values[i] + non_padding_idx = np.nonzero(row - pad_value)[0] + n_mask = int(len(non_padding_idx) * mask_ratio) + mask_idx = np.random.choice(non_padding_idx, n_mask, replace=False) + row[mask_idx] = mask_value + return torch.from_numpy(values).float() diff --git a/transfer/code/scGPT/scgpt/trainer.py b/transfer/code/scGPT/scgpt/trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..d3961d7379f12d4b02676f6d9a1902ac3720758e --- /dev/null +++ b/transfer/code/scGPT/scgpt/trainer.py @@ -0,0 +1,685 @@ +import os +import torch +from torch import nn +from torch.utils.data import Dataset, DataLoader +import time +import traceback +import numpy as np + +from anndata import AnnData +import scanpy as sc +from typing import List, Tuple, Dict, Optional +from scgpt.tokenizer import tokenize_and_pad_batch, random_mask_value +from scgpt import SubsetsBatchSampler +from scgpt.loss import ( + masked_relative_error, + criterion_neg_log_bernoulli, +) +from scgpt.utils import eval_scib_metrics +import warnings +from scipy.sparse import issparse +from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score + + +def prepare_data( + tokenized_train, + tokenized_valid, + train_batch_labels, + valid_batch_labels, + config, + epoch, + train_celltype_labels=None, + valid_celltype_labels=None, + sort_seq_batch=False, +) -> Tuple[Dict[str, torch.Tensor]]: + assert config.task in ["annotation", "integration", "perturb", "multiomic"] + masked_values_train = random_mask_value( + tokenized_train["values"], + mask_ratio=config.mask_ratio, + mask_value=config.mask_value, + pad_value=config.pad_value, + ) + masked_values_valid = random_mask_value( + tokenized_valid["values"], + mask_ratio=config.mask_ratio, + mask_value=config.mask_value, + pad_value=config.pad_value, + ) + print( + f"random masking at epoch {epoch:3d}, ratio of masked values in train: ", + f"{(masked_values_train == config.mask_value).sum() / (masked_values_train - config.pad_value).count_nonzero():.4f}", + ) + + input_gene_ids_train, input_gene_ids_valid = ( + tokenized_train["genes"], + tokenized_valid["genes"], + ) + input_values_train, input_values_valid = masked_values_train, masked_values_valid + + target_values_train, target_values_valid = ( + tokenized_train["values"], + tokenized_valid["values"], + ) + + tensor_batch_labels_train = torch.from_numpy(train_batch_labels).long() + tensor_batch_labels_valid = torch.from_numpy(valid_batch_labels).long() + + if config.task == "annotation": + tensor_celltype_labels_train = torch.from_numpy(train_celltype_labels).long() + tensor_celltype_labels_valid = torch.from_numpy(valid_celltype_labels).long() + + if config.task == "multiomic": + tensor_mod_types_train, tensor_mod_types_valid = ( + tokenized_train["mod_types"].long(), + tokenized_valid["mod_types"].long(), + ) + + if sort_seq_batch: + train_sort_ids = np.argsort(train_batch_labels) + input_gene_ids_train = input_gene_ids_train[train_sort_ids] + input_values_train = input_values_train[train_sort_ids] + target_values_train = target_values_train[train_sort_ids] + tensor_batch_labels_train = tensor_batch_labels_train[train_sort_ids] + if config.task == "annotation": + tensor_celltype_labels_train = tensor_celltype_labels_train[train_sort_ids] + if config.task == "multiomic": + tensor_mod_types_train = tensor_mod_types_train[train_sort_ids] + valid_sort_ids = np.argsort(valid_batch_labels) + input_gene_ids_valid = input_gene_ids_valid[valid_sort_ids] + input_values_valid = input_values_valid[valid_sort_ids] + target_values_valid = target_values_valid[valid_sort_ids] + tensor_batch_labels_valid = tensor_batch_labels_valid[valid_sort_ids] + if config.task == "annotation": + tensor_celltype_labels_valid = tensor_celltype_labels_valid[valid_sort_ids] + if config.task == "multiomic": + tensor_mod_types_valid = tensor_mod_types_valid[valid_sort_ids] + + train_data_pt = { + "gene_ids": input_gene_ids_train, + "values": input_values_train, + "target_values": target_values_train, + "batch_labels": tensor_batch_labels_train, + } + valid_data_pt = { + "gene_ids": input_gene_ids_valid, + "values": input_values_valid, + "target_values": target_values_valid, + "batch_labels": tensor_batch_labels_valid, + } + if config.task == "annotation": + train_data_pt["celltype_labels"] = tensor_celltype_labels_train + valid_data_pt["celltype_labels"] = tensor_celltype_labels_valid + if config.task == "multiomic": + train_data_pt["mod_types"] = tensor_mod_types_train + valid_data_pt["mod_types"] = tensor_mod_types_valid + return train_data_pt, valid_data_pt + + +class SeqDataset(Dataset): + def __init__(self, data: Dict[str, torch.Tensor]): + self.data = data + + def __len__(self): + return self.data["gene_ids"].shape[0] + + def __getitem__(self, idx): + return {k: v[idx] for k, v in self.data.items()} + + +# data_loader +def prepare_dataloader( + data_pt: Dict[str, torch.Tensor], + batch_size: int, + shuffle: bool = False, + intra_domain_shuffle: bool = False, + drop_last: bool = False, + num_workers: int = 0, + per_seq_batch_sample: bool = False, +) -> DataLoader: + dataset = SeqDataset(data_pt) + + if per_seq_batch_sample: + # find the indices of samples in each seq batch + subsets = [] + batch_labels_array = data_pt["batch_labels"].numpy() + for batch_label in np.unique(batch_labels_array): + batch_indices = np.where(batch_labels_array == batch_label)[0].tolist() + subsets.append(batch_indices) + data_loader = DataLoader( + dataset=dataset, + batch_sampler=SubsetsBatchSampler( + subsets, + batch_size, + intra_subset_shuffle=intra_domain_shuffle, + inter_subset_shuffle=shuffle, + drop_last=drop_last, + ), + num_workers=num_workers, + pin_memory=True, + ) + return data_loader + + data_loader = DataLoader( + dataset=dataset, + batch_size=batch_size, + shuffle=shuffle, + drop_last=drop_last, + num_workers=num_workers, + pin_memory=True, + ) + return data_loader + + +def train( + model: nn.Module, + loader: DataLoader, + vocab, + criterion_gep_gepc, + criterion_dab, + criterion_cls, + scaler, + optimizer, + scheduler, + device, + config, + logger, + epoch, +) -> None: + """ + Train the model for one epoch. + """ + import wandb + + model.train() + total_loss, total_gep, total_cls, total_gepc, total_ecs, total_dab = ( + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + ) + total_zero_log_prob, total_gepc_zero_log_prob = 0.0, 0.0 + # total_error = 0.0 + log_interval = config.log_interval + start_time = time.time() + + num_batches = len(loader) + for batch, batch_data in enumerate(loader): + input_gene_ids = batch_data["gene_ids"].to(device) + input_values = batch_data["values"].to(device) + target_values = batch_data["target_values"].to(device) + batch_labels = batch_data["batch_labels"].to(device) + if config.task == "annotation": + celltype_labels = batch_data["celltype_labels"].to(device) + if config.task == "multiomic": + mod_types = batch_data["mod_types"].to(device) + + src_key_padding_mask = input_gene_ids.eq(vocab[config.pad_token]) + + with torch.cuda.amp.autocast(enabled=config.amp): + output_dict = model( + input_gene_ids, + input_values, + src_key_padding_mask=src_key_padding_mask, + batch_labels=batch_labels + if config.use_batch_labels or config.DSBN + else None, + CLS=config.CLS, + MVC=config.GEPC, + ECS=config.ESC, + mod_types=mod_types if config.use_mod else None, + # do_sample=do_sample_in_train, + # generative_training=False + ) + + masked_positions = input_values.eq( + config.mask_value + ) # the postions to predict + loss = 0.0 + metrics_to_log = {} + if config.GEP: + loss_gep = criterion_gep_gepc( + output_dict["mlm_output"], target_values, masked_positions + ) + loss = loss + loss_gep + metrics_to_log = {"train/gep": loss_gep.item()} + if config.GEP and config.explicit_zero_prob: + loss_zero_log_prob = criterion_neg_log_bernoulli( + output_dict["mlm_zero_probs"], target_values, masked_positions + ) + loss = loss + loss_zero_log_prob + metrics_to_log.update({"train/nzlp": loss_zero_log_prob.item()}) + + if config.GEPC: + loss_gepc = criterion_gep_gepc( + output_dict["mvc_output"], target_values, masked_positions + ) + loss = loss + loss_gepc + metrics_to_log.update({"train/mvc": loss_gepc.item()}) + + if config.GEPC and config.explicit_zero_prob: + loss_gepc_zero_log_prob = criterion_neg_log_bernoulli( + output_dict["mvc_zero_probs"], target_values, masked_positions + ) + loss = loss + loss_gepc_zero_log_prob + metrics_to_log.update( + {"train/mvc_nzlp": loss_gepc_zero_log_prob.item()} + ) + + if config.CLS: + loss_cls = criterion_cls(output_dict["cls_output"], celltype_labels) + loss = loss + loss_cls + metrics_to_log.update({"train/cls": loss_cls.item()}) + + error_rate = 1 - ( + (output_dict["cls_output"].argmax(1) == celltype_labels) + .sum() + .item() + ) / celltype_labels.size(0) + + if config.ESC: + loss_ecs = 10 * output_dict["loss_ecs"] + loss = loss + loss_ecs + metrics_to_log.update({"train/ecs": loss_ecs.item()}) + + if config.DAR: + # try weighting and separate optimizer + loss_dab = criterion_dab(output_dict["dab_output"], batch_labels) + loss = loss + config.dab_weight * loss_dab + metrics_to_log.update({"train/dab": loss_dab.item()}) + + model.zero_grad() + scaler.scale(loss).backward() + scaler.unscale_(optimizer) + + with warnings.catch_warnings(record=True) as w: + warnings.filterwarnings("always") + torch.nn.utils.clip_grad_norm_( + model.parameters(), + 1.0, + error_if_nonfinite=False if scaler.is_enabled() else True, + ) + if len(w) > 0: + logger.warning( + f"Found infinite gradient. This may be caused by the gradient " + f"scaler. The current scale is {scaler.get_scale()}. This warning " + "can be ignored if no longer occurs after autoscaling of the scaler." + ) + scaler.step(optimizer) + scaler.update() + + wandb.log(metrics_to_log) + + total_loss += loss.item() + total_gep += loss_gep.item() if config.GEP else 0.0 + total_cls += loss_cls.item() if config.CLS else 0.0 + total_gepc += loss_gepc.item() if config.GEPC else 0.0 + total_ecs += loss_ecs.item() if config.ESC else 0.0 + total_dab += loss_dab.item() if config.DAR else 0.0 + total_zero_log_prob += ( + loss_zero_log_prob.item() + if config.GEP and config.explicit_zero_prob + else 0.0 + ) + total_gepc_zero_log_prob += ( + loss_gepc_zero_log_prob.item() + if config.GEPC and config.explicit_zero_prob + else 0.0 + ) + if batch % log_interval == 0 and batch > 0: + lr = scheduler.get_last_lr()[0] + ms_per_batch = (time.time() - start_time) * 1000 / log_interval + cur_loss = total_loss / log_interval + cur_gep = total_gep / log_interval if config.GEP else 0.0 + cur_cls = total_cls / log_interval if config.CLS else 0.0 + cur_gepc = total_gepc / log_interval if config.GEPC else 0.0 + cur_ecs = total_ecs / log_interval if config.ESC else 0.0 + cur_dab = total_dab / log_interval if config.DAR else 0.0 + cur_zero_log_prob = ( + total_zero_log_prob / log_interval if config.explicit_zero_prob else 0.0 + ) + cur_gepc_zero_log_prob = ( + total_gepc_zero_log_prob / log_interval + if config.GEPC and config.explicit_zero_prob + else 0.0 + ) + # cur_error = total_error / log_interval + + logger.info( + f"| epoch {epoch:3d} | {batch:3d}/{num_batches:3d} batches | " + f"lr {lr:05.5f} | ms/batch {ms_per_batch:5.2f} | " + f"loss {cur_loss:5.2f} | " + + (f"gep {cur_gep:5.2f} |" if config.GEP else "") + + (f"cls {cur_cls:5.2f} | " if config.CLS else "") + # + (f"err {cur_error:5.2f} | " if config.CLS else "") + + (f"gepc {cur_gepc:5.2f} |" if config.GEPC else "") + + (f"ecs {cur_ecs:5.2f} |" if config.ESC else "") + + (f"dar {cur_dab:5.2f} |" if config.DAR else "") + ) + total_loss = 0 + total_gep = 0 + total_cls = 0 + total_gepc = 0 + total_ecs = 0 + total_dab = 0 + total_zero_log_prob = 0 + total_gepc_zero_log_prob = 0 + # total_error = 0 + start_time = time.time() + + +def define_wandb_metrcis(): + import wandb + + wandb.define_metric("valid/loss", summary="min", step_metric="epoch") + wandb.define_metric("test/avg_bio", summary="max") + + +def evaluate( + model: nn.Module, + loader: DataLoader, + vocab, + criterion_gep_gepc, + criterion_dab, + criterion_cls, + device, + config, + epoch, +) -> float: + """ + Evaluate the model on the evaluation data. + """ + import wandb + + model.eval() + total_loss = 0.0 + # total_error = 0.0 + total_dab = 0.0 + total_num = 0 + with torch.no_grad(): + for batch_data in loader: + input_gene_ids = batch_data["gene_ids"].to(device) + input_values = batch_data["values"].to(device) + target_values = batch_data["target_values"].to(device) + batch_labels = batch_data["batch_labels"].to(device) + if config.task == "annotation": + celltype_labels = batch_data["celltype_labels"].to(device) + if config.task == "multiomic": + mod_types = batch_data["mod_types"].to(device) + + src_key_padding_mask = input_gene_ids.eq(vocab[config.pad_token]) + + with torch.cuda.amp.autocast(enabled=config.amp): + output_dict = model( + input_gene_ids, + input_values, + src_key_padding_mask=src_key_padding_mask, + batch_labels=batch_labels + if config.use_batch_labels or config.DSBN + else None, + CLS=config.CLS, # evaluation does not need CLS or CCE + MVC=False, + ECS=False, + mod_types=mod_types if config.use_mod else None, + # do_sample=do_sample_in_train, + # generative_training = False, + ) + if config.task == "annotation": + output_values = output_dict["cls_output"] + loss = criterion_cls(output_values, celltype_labels) + + elif config.task in ["integration", "multiomic"]: + output_values = output_dict["mlm_output"] + masked_positions = input_values.eq(config.mask_value) + loss = criterion_gep_gepc( + output_values, target_values, masked_positions + ) + + if config.DAR: + loss_dab = criterion_dab(output_dict["dab_output"], batch_labels) + + total_loss += loss.item() * len(input_gene_ids) + + if config.DAR: + total_dab += ( + loss_dab.item() * len(input_gene_ids) if config.DAR else 0.0 + ) + else: + total_dab = 0 + + total_num += len(input_gene_ids) + + wandb.log( + { + "valid/loss": (total_loss + config.dab_weight * total_dab) / total_num, + "epoch": epoch, + }, + ) + + return total_loss / total_num + + +def predict( + model: nn.Module, + loader: DataLoader, + vocab, + config, + device, +) -> float: + """ + Evaluate the model on the evaluation data. + """ + model.eval() + + predictions = [] + with torch.no_grad(): + for batch_data in loader: + input_gene_ids = batch_data["gene_ids"].to(device) + input_values = batch_data["values"].to(device) + target_values = batch_data["target_values"].to(device) + batch_labels = batch_data["batch_labels"].to(device) + celltype_labels = batch_data["celltype_labels"].to(device) + + src_key_padding_mask = input_gene_ids.eq(vocab[config.pad_token]) + + with torch.cuda.amp.autocast(enabled=config.amp): + output_dict = model( + input_gene_ids, + input_values, + src_key_padding_mask=src_key_padding_mask, + batch_labels=batch_labels + if config.use_batch_labels or config.DSBN + else None, + CLS=config.CLS, + MVC=config.GEPC, + ECS=config.ESC, + ) + + output_values = output_dict["cls_output"] + preds = output_values.argmax(1).cpu().numpy() + predictions.append(preds) + + return np.concatenate(predictions, axis=0) + + +# %% inference +def test( + model: nn.Module, adata: DataLoader, gene_ids, vocab, config, device, logger +) -> float: + all_counts = ( + adata.layers[config.input_layer_key].toarray() + if issparse(adata.layers[config.input_layer_key]) + else adata.layers[config.input_layer_key] + ) + + celltypes_labels = adata.obs["celltype_id"].tolist() # make sure count from 0 + celltypes_labels = np.array(celltypes_labels) + + batch_ids = adata.obs["batch_id"].tolist() + batch_ids = np.array(batch_ids) + + tokenized_test = tokenize_and_pad_batch( + all_counts, + gene_ids, + max_len=config.max_seq_len, + vocab=vocab, + pad_token=config.pad_token, + pad_value=config.pad_value, + append_cls=True, # append token at the beginning + include_zero_gene=config.include_zero_gene, + ) + + input_values_test = random_mask_value( + tokenized_test["values"], + mask_ratio=config.mask_ratio, + mask_value=config.mask_value, + pad_value=config.pad_value, + ) + + test_data_pt = { + "gene_ids": tokenized_test["genes"], + "values": input_values_test, + "target_values": tokenized_test["values"], + "batch_labels": torch.from_numpy(batch_ids).long(), + "celltype_labels": torch.from_numpy(celltypes_labels).long(), + } + + test_loader = DataLoader( + dataset=SeqDataset(test_data_pt), + batch_size=config.batch_size, + shuffle=False, + drop_last=False, + num_workers=min(len(os.sched_getaffinity(0)), config.batch_size // 2), + pin_memory=True, + ) + + model.eval() + predictions = predict( + model, + test_loader, + vocab, + config, + device, + ) + + # compute accuracy, precision, recall, f1 + accuracy = accuracy_score(celltypes_labels, predictions) + precision = precision_score(celltypes_labels, predictions, average="macro") + recall = recall_score(celltypes_labels, predictions, average="macro") + macro_f1 = f1_score(celltypes_labels, predictions, average="macro") + micro_f1 = f1_score(celltypes_labels, predictions, average="micro") + + logger.info( + f"Accuracy: {accuracy:.3f}, Precision: {precision:.3f}, Recall: {recall:.3f}, " + f"Macro F1: {macro_f1:.3f}, Micro F1: {micro_f1:.3f}" + ) + + results = { + "test/accuracy": accuracy, + "test/precision": precision, + "test/recall": recall, + "test/macro_f1": macro_f1, + "test/micro_f1": micro_f1, + } + + return predictions, celltypes_labels, results + + +def eval_testdata( + model: nn.Module, + adata_t: AnnData, + gene_ids, + vocab, + config, + logger, + include_types: List[str] = ["cls"], +) -> Optional[Dict]: + """evaluate the model on test dataset of adata_t""" + model.eval() + + # copy adata_t to avoid reuse previously computed results stored in adata_t + adata_t = adata_t.copy() + + all_counts = ( + adata_t.layers[config.input_layer_key].toarray() + if issparse(adata_t.layers[config.input_layer_key]) + else adata_t.layers[config.input_layer_key] + ) + + celltypes_labels = adata_t.obs["celltype"].tolist() + celltypes_labels = np.array(celltypes_labels) + + batch_ids = adata_t.obs["batch_id"].tolist() + batch_ids = np.array(batch_ids) + + # Evaluate cls cell embeddings + if "cls" in include_types: + logger.info("Evaluating cls cell embeddings") + tokenized_all = tokenize_and_pad_batch( + all_counts, + gene_ids, + max_len=config.max_seq_len, + vocab=vocab, + pad_token=config.pad_token, + pad_value=config.pad_value, + append_cls=True, # append token at the beginning + include_zero_gene=config.include_zero_gene, + ) + all_gene_ids, all_values = tokenized_all["genes"], tokenized_all["values"] + src_key_padding_mask = all_gene_ids.eq(vocab[config.pad_token]) + with torch.no_grad(), torch.cuda.amp.autocast(enabled=config.amp): + cell_embeddings = model.encode_batch( + all_gene_ids, + all_values.float(), + src_key_padding_mask=src_key_padding_mask, + batch_size=config.batch_size, + batch_labels=torch.from_numpy(batch_ids).long() + if config.DSBN or config.DAR or config.use_batch_labels + else None, + time_step=0, + return_np=True, + ) + cell_embeddings = cell_embeddings / np.linalg.norm( + cell_embeddings, axis=1, keepdims=True + ) + + adata_t.obsm["X_scGPT"] = cell_embeddings + + results = {} + try: + results = eval_scib_metrics(adata_t) + except Exception as e: + traceback.print_exc() + logger.error(e) + + sc.pp.neighbors(adata_t, use_rep="X_scGPT") + sc.tl.umap(adata_t, min_dist=0.3) + fig = sc.pl.umap( + adata_t, + color=["str_batch"], + title=[f"batch, avg_bio = {results.get('avg_bio', 0.0):.4f}"], + frameon=False, + return_fig=True, + show=False, + ) + + results["batch_umap"] = fig + + sc.pp.neighbors(adata_t, use_rep="X_scGPT") + sc.tl.umap(adata_t, min_dist=0.3) + fig = sc.pl.umap( + adata_t, + color=["celltype"], + title=[ + f"celltype, avg_bio = {results.get('avg_bio', 0.0):.4f}", + ], + frameon=False, + return_fig=True, + show=False, + ) + + results["celltype_umap"] = fig + + if len(include_types) == 1: + return results diff --git a/transfer/code/scGPT/scgpt/utils/__init__.py b/transfer/code/scGPT/scgpt/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..46d3a156a78c6ef994a0ba7e92a334a4a6b16b8b --- /dev/null +++ b/transfer/code/scGPT/scgpt/utils/__init__.py @@ -0,0 +1 @@ +from .util import * diff --git a/transfer/code/scGPT/scgpt/utils/util.py b/transfer/code/scGPT/scgpt/utils/util.py new file mode 100644 index 0000000000000000000000000000000000000000..692fc7461c854c6d92bb3afd2275d614697c4820 --- /dev/null +++ b/transfer/code/scGPT/scgpt/utils/util.py @@ -0,0 +1,619 @@ +import functools +import json +import logging +import os +from pathlib import Path +import random +import subprocess +from typing import Dict, List, Mapping, Optional, Tuple, Union + +import numpy as np +import torch +import pandas as pd +from anndata import AnnData +from matplotlib import pyplot as plt +from matplotlib import axes +from IPython import get_ipython + +from .. import logger + + +def gene_vocabulary(): + """ + Generate the gene name2id and id2name dictionaries. + """ + pass + + +def set_seed(seed): + """set random seed.""" + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + # if n_gpu > 0: + # torch.cuda.manual_seed_all(seed) + + +def add_file_handler(logger: logging.Logger, log_file_path: Path): + """ + Add a file handler to the logger. + """ + h = logging.FileHandler(log_file_path) + + # format showing time, name, function, and message + formatter = logging.Formatter( + "%(asctime)s-%(name)s-%(levelname)s-%(funcName)s: %(message)s", + datefmt="%H:%M:%S", + ) + h.setFormatter(formatter) + h.setLevel(logger.level) + logger.addHandler(h) + + +def category_str2int(category_strs: List[str]) -> List[int]: + set_category_strs = set(category_strs) + name2id = {name: i for i, name in enumerate(set_category_strs)} + return [name2id[name] for name in category_strs] + + +def isnotebook() -> bool: + """check whether excuting in jupyter notebook.""" + try: + shell = get_ipython().__class__.__name__ + if shell == "ZMQInteractiveShell": + return True # Jupyter notebook or qtconsole + elif shell == "TerminalInteractiveShell": + return True # Terminal running IPython + else: + return False # Other type (?) + except NameError: + return False # Probably standard Python interpreter + + +def get_free_gpu(): + import subprocess + import sys + from io import StringIO + import pandas as pd + + gpu_stats = subprocess.check_output( + [ + "nvidia-smi", + "--format=csv", + "--query-gpu=memory.used,memory.free", + ] + ).decode("utf-8") + gpu_df = pd.read_csv( + StringIO(gpu_stats), names=["memory.used", "memory.free"], skiprows=1 + ) + print("GPU usage:\n{}".format(gpu_df)) + gpu_df["memory.free"] = gpu_df["memory.free"].map(lambda x: int(x.rstrip(" [MiB]"))) + idx = gpu_df["memory.free"].idxmax() + print( + "Find free GPU{} with {} free MiB".format(idx, gpu_df.iloc[idx]["memory.free"]) + ) + + return idx + + +def get_git_commit(): + return subprocess.check_output(["git", "rev-parse", "HEAD"]).decode("utf-8").strip() + + +def get_git_diff(): + commit = get_git_commit() + return subprocess.check_output(["git", "diff", commit]).decode("utf-8").strip() + + +def histogram( + *data: List[np.ndarray], + label: List[str] = ["train", "valid"], + color: List[str] = ["blue", "red"], + figsize: Tuple[int, int] = (9, 4), + title: Optional[str] = None, + show: bool = False, + save: Optional[str] = None, +) -> axes.Axes: + """ + Plot histogram of the data. + + Args: + data (List[np.ndarray]): The data to plot. + label (List[str]): The label of the data. + color (List[str]): The color of the data. + figsize (Tuple[int, int]): The size of the figure. + title (Optional[str]): The title of the figure. + show (bool): Whether to show the figure. + save (Optional[str]): The path to save the figure. + + Returns: + axes.Axes: The axes of the figure. + """ + # show histogram of the clipped values + assert len(data) == len(label), "The number of data and labels must be equal." + + fig, ax = plt.subplots(1, 1, figsize=figsize, dpi=150) + max_value = max(np.max(data) for data in data) + ax.hist( + [d.flatten() for d in data], + bins=np.arange(0, max_value + 1, 1) + 0.5 if max_value < 60 else 60, + label=label, + density=True, + histtype="bar", + linewidth=2, + rwidth=0.85, + color=color, + ) + ax.legend() + ax.set_xlabel("counts") + ax.set_ylabel("density") + + if title is not None: + ax.set_title(title) + + if show: + plt.show() + + if save is not None: + fig.savefig(save, bbox_inches="tight") + + return ax + + +def _indicate_col_name(adata: AnnData, promt_str: str) -> Optional[str]: + """ + Indicate the column name of the data. + + Args: + adata (AnnData): The AnnData object. + promt_str (str): The prompt string. + + Returns: + Optional[str]: The column name. + """ + while True: + col_name = input(promt_str) + if col_name == "": + col_name = None + break + elif col_name in adata.var.columns: + break + elif col_name in adata.obs.columns: + break + else: + print(f"The column {col_name} is not in the data. " f"Please input again.") + + return col_name + + +def find_required_colums( + adata: AnnData, + id: str, + configs_dir: Union[str, Path], + update: bool = False, +) -> List[Optional[str]]: + """ + Find the required columns in AnnData, including celltype column, str_celltype + column, the gene name column, and the experimental batch key. + + This function asks the user to input the required column names if the first + time loading the data. The names are saved in the config file and will be + automatically loaded next time. + + Args: + adata (AnnData): The AnnData object. + id (str): The id of the AnnData object, will be used as the file name for + saving the config file. + configs_dir (Union[str, Path]): The directory of saved config files. + update (bool): Whether to update the config file. + + Returns: + List[Optional[str]]: The required columns, including celltype_col, str_celltype_col, + gene_col, and batch_col. + """ + if isinstance(configs_dir, str): + configs_dir = Path(configs_dir) + + if not configs_dir.exists(): + configs_dir.mkdir() + + config_file = configs_dir / f"{id}.json" + + if not config_file.exists() or update: + print( + "The config file does not exist, this may be the first time " + "loading the data. \nPlease input the required column names." + ) + print(adata) + celltype_col = _indicate_col_name( + adata, + "Please input the celltype column name (skip if not applicable): ", + ) + str_celltype_col = _indicate_col_name( + adata, "Please input the str_celltype column name: " + ) + gene_col = _indicate_col_name(adata, "Please input the gene column name: ") + batch_col = _indicate_col_name(adata, "Please input the batch column name: ") + + config = { + "celltype_col": celltype_col, + "str_celltype_col": str_celltype_col, + "gene_col": gene_col, + "batch_col": batch_col, + } + + with open(config_file, "w") as f: + json.dump(config, f) + + else: + with open(config_file, "r") as f: + config = json.load(f) + + return [ + config["celltype_col"], + config["str_celltype_col"], + config["gene_col"], + config["batch_col"], + ] + + +def tensorlist2tensor(tensorlist, pad_value): + max_len = max(len(t) for t in tensorlist) + dtype = tensorlist[0].dtype + device = tensorlist[0].device + tensor = torch.zeros(len(tensorlist), max_len, dtype=dtype, device=device) + tensor.fill_(pad_value) + for i, t in enumerate(tensorlist): + tensor[i, : len(t)] = t + return tensor + + +def map_raw_id_to_vocab_id( + raw_ids: Union[np.ndarray, torch.Tensor], + gene_ids: np.ndarray, +) -> Union[np.ndarray, torch.Tensor]: + """ + Map some raw ids which are indices of the raw gene names to the indices of the + + Args: + raw_ids: the raw ids to map + gene_ids: the gene ids to map to + """ + if isinstance(raw_ids, torch.Tensor): + device = raw_ids.device + dtype = raw_ids.dtype + return_pt = True + raw_ids = raw_ids.cpu().numpy() + elif isinstance(raw_ids, np.ndarray): + return_pt = False + dtype = raw_ids.dtype + else: + raise ValueError(f"raw_ids must be either torch.Tensor or np.ndarray.") + + if raw_ids.ndim != 1: + raise ValueError(f"raw_ids must be 1d, got {raw_ids.ndim}d.") + + if gene_ids.ndim != 1: + raise ValueError(f"gene_ids must be 1d, got {gene_ids.ndim}d.") + + mapped_ids: np.ndarray = gene_ids[raw_ids] + assert mapped_ids.shape == raw_ids.shape + if return_pt: + return torch.from_numpy(mapped_ids).type(dtype).to(device) + return mapped_ids.astype(dtype) + + +def load_pretrained( + model: torch.nn.Module, + pretrained_params: Mapping[str, torch.Tensor], + strict: bool = False, + prefix: Optional[List[str]] = None, + verbose: bool = True, +) -> torch.nn.Module: + """ + Load pretrained weights to the model. + + Args: + model (torch.nn.Module): The model to load weights to. + pretrained_params (Mapping[str, torch.Tensor]): The pretrained parameters. + strict (bool): Whether to strictly enforce that the keys in :attr:`pretrained_params` + match the keys returned by this module's :meth:`Module.state_dict`. Default to False. + prefix (List[str]): The list of prefix strings to match with the keys in + :attr:`pretrained_params`. The matched keys will be loaded. Default to None. + + Returns: + torch.nn.Module: The model with pretrained weights. + """ + + use_flash_attn = getattr(model, "use_fast_transformer", True) + if not use_flash_attn: + pretrained_params = { + k.replace("Wqkv.", "in_proj_"): v for k, v in pretrained_params.items() + } + + if prefix is not None and len(prefix) > 0: + if isinstance(prefix, str): + prefix = [prefix] + pretrained_params = { + k: v + for k, v in pretrained_params.items() + if any(k.startswith(p) for p in prefix) + } + + model_dict = model.state_dict() + if strict: + if verbose: + for k, v in pretrained_params.items(): + logger.info(f"Loading parameter {k} with shape {v.shape}") + model_dict.update(pretrained_params) + model.load_state_dict(model_dict) + else: + if verbose: + for k, v in pretrained_params.items(): + if k in model_dict and v.shape == model_dict[k].shape: + logger.info(f"Loading parameter {k} with shape {v.shape}") + pretrained_params = { + k: v + for k, v in pretrained_params.items() + if k in model_dict and v.shape == model_dict[k].shape + } + model_dict.update(pretrained_params) + model.load_state_dict(model_dict) + + return model + + +# Wrapper for all scib metrics, we leave out some metrics like hvg_score, cell_cyvle, +# trajectory_conservation, because we only evaluate the latent embeddings here and +# these metrics are evaluating the reconstructed gene expressions or pseudotimes. +def eval_scib_metrics( + adata: AnnData, + batch_key: str = "str_batch", + label_key: str = "celltype", + notes: Optional[str] = None, +) -> Dict: + import scib + + results = scib.metrics.metrics( + adata, + adata_int=adata, + batch_key=batch_key, + label_key=label_key, + embed="X_scGPT", + isolated_labels_asw_=False, + silhouette_=True, + hvg_score_=False, + graph_conn_=True, + pcr_=True, + isolated_labels_f1_=False, + trajectory_=False, + nmi_=True, # use the clustering, bias to the best matching + ari_=True, # use the clustering, bias to the best matching + cell_cycle_=False, + kBET_=False, # kBET return nan sometimes, need to examine + ilisi_=False, + clisi_=False, + ) + + if notes is not None: + logger.info(f"{notes}") + + logger.info(f"{results}") + + result_dict = results[0].to_dict() + logger.info( + "Biological Conservation Metrics: \n" + f"ASW (cell-type): {result_dict['ASW_label']:.4f}, graph cLISI: {result_dict['cLISI']:.4f}, " + f"isolated label silhouette: {result_dict['isolated_label_silhouette']:.4f}, \n" + "Batch Effect Removal Metrics: \n" + f"PCR_batch: {result_dict['PCR_batch']:.4f}, ASW (batch): {result_dict['ASW_label/batch']:.4f}, " + f"graph connectivity: {result_dict['graph_conn']:.4f}, graph iLISI: {result_dict['iLISI']:.4f}" + ) + + result_dict["avg_bio"] = np.mean( + [ + result_dict["NMI_cluster/label"], + result_dict["ARI_cluster/label"], + result_dict["ASW_label"], + ] + ) + + # remove nan value in result_dict + result_dict = {k: v for k, v in result_dict.items() if not np.isnan(v)} + + return result_dict + + +def compute_perturbation_metrics( + results: Dict, + ctrl_adata: AnnData, + non_zero_genes: bool = False, + return_raw: bool = False, +) -> Dict: + """ + Given results from a model run and the ground truth, compute metrics + + Args: + results (:obj:`Dict`): The results from a model run + ctrl_adata (:obj:`AnnData`): The adata of the control condtion + non_zero_genes (:obj:`bool`, optional): Whether to only consider non-zero + genes in the ground truth when computing metrics + return_raw (:obj:`bool`, optional): Whether to return the raw metrics or + the mean of the metrics. Default is False. + + Returns: + :obj:`Dict`: The metrics computed + """ + from scipy.stats import pearsonr + + # metrics: + # Pearson correlation of expression on all genes, on DE genes, + # Pearson correlation of expression change on all genes, on DE genes, + + metrics_across_genes = { + "pearson": [], + "pearson_de": [], + "pearson_delta": [], + "pearson_de_delta": [], + } + + metrics_across_conditions = { + "pearson": [], + "pearson_delta": [], + } + + conditions = np.unique(results["pert_cat"]) + assert not "ctrl" in conditions, "ctrl should not be in test conditions" + condition2idx = {c: np.where(results["pert_cat"] == c)[0] for c in conditions} + + mean_ctrl = np.array(ctrl_adata.X.mean(0)).flatten() # (n_genes,) + assert ctrl_adata.X.max() <= 1000, "gene expression should be log transformed" + + true_perturbed = results["truth"] # (n_cells, n_genes) + assert true_perturbed.max() <= 1000, "gene expression should be log transformed" + true_mean_perturbed_by_condition = np.array( + [true_perturbed[condition2idx[c]].mean(0) for c in conditions] + ) # (n_conditions, n_genes) + true_mean_delta_by_condition = true_mean_perturbed_by_condition - mean_ctrl + zero_rows = np.where(np.all(true_mean_perturbed_by_condition == 0, axis=1))[ + 0 + ].tolist() + zero_cols = np.where(np.all(true_mean_perturbed_by_condition == 0, axis=0))[ + 0 + ].tolist() + + pred_perturbed = results["pred"] # (n_cells, n_genes) + pred_mean_perturbed_by_condition = np.array( + [pred_perturbed[condition2idx[c]].mean(0) for c in conditions] + ) # (n_conditions, n_genes) + pred_mean_delta_by_condition = pred_mean_perturbed_by_condition - mean_ctrl + + def corr_over_genes(x, y, conditions, res_list, skip_rows=[], non_zero_mask=None): + """compute pearson correlation over genes for each condition""" + for i, c in enumerate(conditions): + if i in skip_rows: + continue + x_, y_ = x[i], y[i] + if non_zero_mask is not None: + x_ = x_[non_zero_mask[i]] + y_ = y_[non_zero_mask[i]] + res_list.append(pearsonr(x_, y_)[0]) + + corr_over_genes( + true_mean_perturbed_by_condition, + pred_mean_perturbed_by_condition, + conditions, + metrics_across_genes["pearson"], + zero_rows, + non_zero_mask=true_mean_perturbed_by_condition != 0 if non_zero_genes else None, + ) + corr_over_genes( + true_mean_delta_by_condition, + pred_mean_delta_by_condition, + conditions, + metrics_across_genes["pearson_delta"], + zero_rows, + non_zero_mask=true_mean_perturbed_by_condition != 0 if non_zero_genes else None, + ) + + def find_DE_genes(adata, condition, geneid2idx, non_zero_genes=False, top_n=20): + """ + Find the DE genes for a condition + """ + key_components = next( + iter(adata.uns["rank_genes_groups_cov_all"].keys()) + ).split("_") + assert len(key_components) == 3, "rank_genes_groups_cov_all key is not valid" + + condition_key = "_".join([key_components[0], condition, key_components[2]]) + + de_genes = adata.uns["rank_genes_groups_cov_all"][condition_key] + if non_zero_genes: + de_genes = adata.uns["top_non_dropout_de_20"][condition_key] + # de_genes = adata.uns["rank_genes_groups_cov_all"][condition_key] + # de_genes = de_genes[adata.uns["non_zeros_gene_idx"][condition_key]] + # assert len(de_genes) > top_n + + de_genes = de_genes[:top_n] + + de_idx = [geneid2idx[i] for i in de_genes] + + return de_idx, de_genes + + geneid2idx = dict(zip(ctrl_adata.var.index.values, range(len(ctrl_adata.var)))) + de_idx = { + c: find_DE_genes(ctrl_adata, c, geneid2idx, non_zero_genes)[0] + for c in conditions + } + mean_ctrl_de = np.array( + [mean_ctrl[de_idx[c]] for c in conditions] + ) # (n_conditions, n_diff_genes) + + true_mean_perturbed_by_condition_de = np.array( + [ + true_mean_perturbed_by_condition[i, de_idx[c]] + for i, c in enumerate(conditions) + ] + ) # (n_conditions, n_diff_genes) + zero_rows_de = np.where(np.all(true_mean_perturbed_by_condition_de == 0, axis=1))[ + 0 + ].tolist() + true_mean_delta_by_condition_de = true_mean_perturbed_by_condition_de - mean_ctrl_de + + pred_mean_perturbed_by_condition_de = np.array( + [ + pred_mean_perturbed_by_condition[i, de_idx[c]] + for i, c in enumerate(conditions) + ] + ) # (n_conditions, n_diff_genes) + pred_mean_delta_by_condition_de = pred_mean_perturbed_by_condition_de - mean_ctrl_de + + corr_over_genes( + true_mean_perturbed_by_condition_de, + pred_mean_perturbed_by_condition_de, + conditions, + metrics_across_genes["pearson_de"], + zero_rows_de, + ) + corr_over_genes( + true_mean_delta_by_condition_de, + pred_mean_delta_by_condition_de, + conditions, + metrics_across_genes["pearson_de_delta"], + zero_rows_de, + ) + + if not return_raw: + for k, v in metrics_across_genes.items(): + metrics_across_genes[k] = np.mean(v) + for k, v in metrics_across_conditions.items(): + metrics_across_conditions[k] = np.mean(v) + metrics = metrics_across_genes + + return metrics + + +# wrapper to make sure all methods are called only on the main process +def main_process_only(func): + @functools.wraps(func) + def wrapper(*args, **kwargs): + if os.environ.get("LOCAL_RANK", "0") == "0": + return func(*args, **kwargs) + + return wrapper + + +# class wrapper to make sure all methods are called only on the main process +class MainProcessOnly: + def __init__(self, obj): + self.obj = obj + + def __getattr__(self, name): + attr = getattr(self.obj, name) + + if callable(attr): + attr = main_process_only(attr) + + return attr diff --git a/transfer/code/scGPT/tests/__init__.py b/transfer/code/scGPT/tests/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/transfer/code/scGPT/tests/test_sampler.py b/transfer/code/scGPT/tests/test_sampler.py new file mode 100644 index 0000000000000000000000000000000000000000..d6f4afd4f552404901a646a67f76f9c2c231ab0f --- /dev/null +++ b/transfer/code/scGPT/tests/test_sampler.py @@ -0,0 +1,104 @@ +from typing import Sequence + +import numpy as np +import torch + +from scgpt.data_sampler import SubsetSequentialSampler, SubsetsBatchSampler +from scgpt.utils import set_seed + +set_seed(1) + + +def _check_reorder(a: Sequence[int], b: Sequence[int]) -> bool: + """Check if a is a shuffled version of b.""" + if len(a) != len(b): + return False + if len(a) == 0: + return True + if len(a) == 1: + return a[0] == b[0] + + # Find the first element in a that is also in b + first = a[0] + for i in range(len(b)): + if b[i] == first: + break + else: + return False + + # Check if the rest of a is a shuffled version of b[:i] + b[i+1:] + return _check_reorder(a[1:], b[:i] + b[i + 1 :]) + + +def test_subset_sequential_sampler() -> None: + """Test SubsetSequentialSampler.""" + # Test with a list of indices + sampler = SubsetSequentialSampler([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) + assert list(sampler) == list(range(10)) + + +def test_subsets_batch_sampler() -> None: + """Test SubsetsBatchSampler.""" + # Test with a list of subsets of indices + subsets = [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]] + + # Test sequential sampling + sampler = SubsetsBatchSampler( + subsets, + batch_size=3, + intra_subset_shuffle=False, + inter_subset_shuffle=False, + ) + assert list(sampler) == [[0, 1, 2], [3, 4], [5, 6, 7], [8, 9]] + + # Test sequential sampling with drop_last + sampler = SubsetsBatchSampler( + subsets, + batch_size=3, + intra_subset_shuffle=False, + inter_subset_shuffle=False, + drop_last=True, + ) + assert list(sampler) == [[0, 1, 2], [5, 6, 7]] + + # Test sampling with inter subset shuffle + sampler = SubsetsBatchSampler( + subsets, + batch_size=3, + intra_subset_shuffle=False, + inter_subset_shuffle=True, + ) + sampled_idx = list(sampler) + assert len(sampled_idx) == 4 + print(sampled_idx) + assert sampled_idx != [[0, 1, 2], [3, 4], [5, 6, 7], [8, 9]] + assert _check_reorder(sampled_idx, [[0, 1, 2], [3, 4], [5, 6, 7], [8, 9]]) + + # Test sampling with intra subset shuffle + sampler = SubsetsBatchSampler( + subsets, + batch_size=3, + intra_subset_shuffle=True, + inter_subset_shuffle=False, + ) + sampled_idx = list(sampler) + assert len(sampled_idx) == 4 + sampled_idx_subset0 = sampled_idx[0] + sampled_idx[1] + sampled_idx_subset1 = sampled_idx[2] + sampled_idx[3] + assert sampled_idx_subset0 != [0, 1, 2, 3, 4] + assert sampled_idx_subset1 != [5, 6, 7, 8, 9] + assert _check_reorder(sampled_idx_subset0, [0, 1, 2, 3, 4]) + assert _check_reorder(sampled_idx_subset1, [5, 6, 7, 8, 9]) + + # Test sampling with both intra and inter subset shuffle + sampler = SubsetsBatchSampler( + subsets, + batch_size=3, + intra_subset_shuffle=True, + inter_subset_shuffle=True, + ) + sampled_idx = list(sampler) + assert len(sampled_idx) == 4 + sampled_idx_flatten = [item for sublist in sampled_idx for item in sublist] + assert sampled_idx_flatten != [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] + assert _check_reorder(sampled_idx_flatten, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) diff --git a/transfer/code/scGPT/tests/test_scbank.py b/transfer/code/scGPT/tests/test_scbank.py new file mode 100644 index 0000000000000000000000000000000000000000..8704828dd217a593caaf08a6f78517f085c6ac3f --- /dev/null +++ b/transfer/code/scGPT/tests/test_scbank.py @@ -0,0 +1,228 @@ +from pathlib import Path +import tempfile + +import numpy as np +import pytest +from scipy.sparse import csr_matrix +from datasets import Dataset +from anndata import AnnData + +from scgpt.tokenizer import GeneVocab +from scgpt.scbank import DataBank, DataTable, MetaInfo, Setting + +tmp_dir = tempfile.gettempdir() +save_path = Path(tmp_dir) / "test_scGPT" +save_path.mkdir(parents=True, exist_ok=True) + + +def clear_files(directory: Path): + """helper function to clear files in a dir""" + for f in directory.iterdir(): + f.unlink() + + +def test_empty_databank(): + db = DataBank() + assert db.data_tables == {} + assert db.settings == Setting() + assert db.gene_vocab == None + + db = DataBank(meta_info=MetaInfo()) + assert db.data_tables == {} + assert db.gene_vocab == None + + db = DataBank( + meta_info=MetaInfo(on_disk_path=save_path), + settings=Setting(immediate_save=True), + ) + assert (save_path / "studytable.json").is_file() + assert (save_path / "manifest.json").is_file() + clear_files(save_path) + + +def test_empty_datatable(): + dt = DataTable(name="test") + assert dt.name == "test" + assert dt.data is None + assert not dt.is_loaded + + +def test_empty_metainfo(): + mi = MetaInfo() + assert mi.study_ids is None + assert mi.cell_ids is None + + +def test_save_load_metainfo(): + mi = MetaInfo(save_path) + mi.save() + assert (save_path / "studytable.json").is_file() + assert (save_path / "manifest.json").is_file() + assert MetaInfo.from_path(save_path) == mi + + clear_files(save_path) + + +def test_datatable_save(): + dt = DataTable(name="test") + + file_path = save_path / "test.json" + # make sure the path does not exist originally + assert not file_path.exists() + file_path.parent.mkdir(parents=True, exist_ok=True) + + # catch the exception if the data is not loaded + with pytest.raises(ValueError): + dt.save(file_path) + + # actually load some example data and test saving + dt.data = Dataset.from_dict({"a": [1]}) + dt.save(file_path) + assert file_path.is_file() + + # delete the file + file_path.unlink() + assert not file_path.exists() + + +def test_meta_info_on_disk_path(): + mi = MetaInfo(on_disk_path=tmp_dir) + assert mi.on_disk_path == Path(tmp_dir) + assert mi.on_disk_format == "json" + + +def test_add_gene_vocab(): + db = DataBank() + db.gene_vocab = GeneVocab.from_dict({"a": 0, "b": 1, "c": 2}) + assert len(db.gene_vocab) == 3 + assert db.gene_vocab["a"] == 0 + assert "c" in db.gene_vocab + + with pytest.raises(ValueError): + db.gene_vocab = ["a", "b", "c"] + + +def test_databank_tokenize(): + indptr = np.array([0, 2, 3, 6]) + indices = np.array([0, 2, 2, 0, 1, 2]) + data = np.array([1, 2, 3, 4, 5, 6]) + data = csr_matrix((data, indices, indptr), shape=(3, 3)) + # data is like: + # [[1, 0, 2], + # [0, 0, 3], + # [4, 5, 6]] + + ind2ind = {0: 4, 2: 6} + + tokenized = DataBank()._tokenize(data, ind2ind) + # tokenized is like: + # {'id': [0, 1, 2], + # 'genes': [[4, 6], [6], [4, 6]], + # 'expressions': [[1, 2], [3], [4, 6]]} + + assert tokenized["id"] == [0, 1, 2] + assert [d.tolist() for d in tokenized["genes"]] == [[4, 6], [6], [4, 6]] + assert [d.tolist() for d in tokenized["expressions"]] == [[1, 2], [3], [4, 6]] + + # tokenize numpy array + data = data.toarray() + tokenized = DataBank()._tokenize(data, ind2ind) + assert tokenized["id"] == [0, 1, 2] + assert [d.tolist() for d in tokenized["genes"]] == [[4, 6], [6], [4, 6]] + assert [d.tolist() for d in tokenized["expressions"]] == [[1, 2], [3], [4, 6]] + + # test array with rows and cols of only zeros + data[:, 2] = 0 + tokenized = DataBank()._tokenize(data, ind2ind) + assert tokenized["id"] == [0, 1] + assert [d.tolist() for d in tokenized["genes"]] == [[4], [4]] + assert [d.tolist() for d in tokenized["expressions"]] == [[1], [4]] + + # test array w/ rare non-zero values (_tokenize will auto convert it to sparse) + data = np.zeros((3, 3)) + data[0, 0] = 1.0 + tokenized = DataBank()._tokenize(data, ind2ind) + assert tokenized["id"] == [0] + assert [d.tolist() for d in tokenized["genes"]] == [[4]] + assert [d.tolist() for d in tokenized["expressions"]] == [[1.0]] + + # add the test for recursive batch calls + + +def test_databank_load_anndata(): + adata = AnnData( + X=np.array([[1.0, 2.0, 3.0], [4.0, 0.0, 6.0]]), + obs={"cell": ["cell1", "cell2"], "study": ["study1", "study2"]}, + var={"gene": ["gene_a", "gene_b", "gene_c"]}, + ) + gene_vocab = {"gene_a": 1, "gene_b": 0, "gene_c": 2} + + # test factory initialization from data + db = DataBank.from_anndata( + adata, + gene_vocab, + to=save_path, + main_table_key="X", + token_col="gene", + ) + assert db.main_table_key == "X" + + converted_dataset = db.data_tables["X"].data + assert converted_dataset["id"] == [0, 1] + assert converted_dataset["genes"] == [[1, 0, 2], [1, 2]] + assert converted_dataset["expressions"] == [[1.0, 2.0, 3.0], [4.0, 6.0]] + + assert (save_path / "X.datatable.json").is_file() + assert (save_path / "studytable.json").is_file() + assert (save_path / "manifest.json").is_file() + assert (save_path / "gene_vocab.json").is_file() + + # test factory initialization from path + db = DataBank.from_path(save_path) + assert db.main_table_key == "X" + + main_dataset = db.data_tables["X"].data + assert main_dataset["id"] == [0, 1] + assert main_dataset["genes"] == [[1, 0, 2], [1, 2]] + assert main_dataset["expressions"] == [[1.0, 2.0, 3.0], [4.0, 6.0]] + + clear_files(save_path) + + # test loading from anndata + db = DataBank( + meta_info=MetaInfo(on_disk_format=save_path), + gene_vocab=GeneVocab.from_dict(gene_vocab), + ) + data_tables = db.load_anndata(adata, data_keys=["X"], token_col="gene") + assert len(data_tables) == 1 + + converted_dataset = data_tables[0].data + assert converted_dataset["id"] == [0, 1] + assert converted_dataset["genes"] == [[1, 0, 2], [1, 2]] + assert converted_dataset["expressions"] == [[1.0, 2.0, 3.0], [4.0, 6.0]] + + +def test_databank_load_multiple_anndata_layers(): + adata = AnnData( + X=np.array([[1.0, 2.0, 3.0], [4.0, 0.0, 6.0]]), + obs={"cell": ["cell1", "cell2"], "study": ["study1", "study2"]}, + var={"gene": ["gene_a", "gene_b", "gene_c"]}, + layers={"layer1": np.array([[1.0, 2.0, 3.0], [4.0, 0.0, 6.0]])}, + ) + gene_vocab = {"gene_a": 1, "gene_b": 0, "gene_c": 2} + + db = DataBank(meta_info=MetaInfo(), gene_vocab=GeneVocab.from_dict(gene_vocab)) + data_tables = db.load_anndata(adata, token_col="gene") + assert len(data_tables) == 2 + assert data_tables[0].name == "X" + assert data_tables[1].name == "layer1" + + converted_dataset = data_tables[0].data + assert converted_dataset["id"] == [0, 1] + assert converted_dataset["genes"] == [[1, 0, 2], [1, 2]] + assert converted_dataset["expressions"] == [[1.0, 2.0, 3.0], [4.0, 6.0]] + + converted_dataset = data_tables[1].data + assert converted_dataset["id"] == [0, 1] + assert converted_dataset["genes"] == [[1, 0, 2], [1, 2]] + assert converted_dataset["expressions"] == [[1.0, 2.0, 3.0], [4.0, 6.0]] diff --git a/transfer/code/scGPT/tests/test_scformer.py b/transfer/code/scGPT/tests/test_scformer.py new file mode 100644 index 0000000000000000000000000000000000000000..617a276134a9da62595d2cc9c7cbe9a058d1d231 --- /dev/null +++ b/transfer/code/scGPT/tests/test_scformer.py @@ -0,0 +1,5 @@ +from scgpt import __version__ + + +def test_version(): + assert __version__ == "0.1.0" diff --git a/transfer/code/scGPT/tests/test_tokenizer.py b/transfer/code/scGPT/tests/test_tokenizer.py new file mode 100644 index 0000000000000000000000000000000000000000..b019fad43937972ffc8d2a7c807873a4d59f8195 --- /dev/null +++ b/transfer/code/scGPT/tests/test_tokenizer.py @@ -0,0 +1,48 @@ +from pathlib import Path +from scgpt.tokenizer import GeneVocab, get_default_gene_vocab + + +def test_gene_vocab(): + gene_vocab = GeneVocab([]) + assert len(gene_vocab) == 0 + + gene_vocab.append_token("abc") + assert len(gene_vocab) == 1 + + gene_vocab = GeneVocab(gene_vocab) + assert gene_vocab["abc"] == 0 + + gene_vocab = GeneVocab(["def", "g", "h"], specials=["a", "b", "c"]) + assert gene_vocab["a"] == 0 + assert gene_vocab["def"] == 3 + assert len(gene_vocab) == 6 + + gene_vocab = GeneVocab(["a"], specials=[""], special_first=False) + assert gene_vocab[""] == 1 + assert gene_vocab.get_default_index() == 1 + + +def test_gene_vocab_from_dict(): + gene_vocab = GeneVocab.from_dict({"a": 0, "b": 1, "c": 2}) + assert len(gene_vocab) == 3 + assert gene_vocab["a"] == 0 + assert gene_vocab["c"] == 2 + + +def test_gene_vocab_from_file(): + # current file path + test_file = Path(__file__).parent / "vocab.json" + gene_vocab = GeneVocab.from_file(test_file) + assert len(gene_vocab) == 3 + + +def test_gene_vocab_pad_token(): + gene_vocab = GeneVocab(["a", "b", "c"], specials=[""]) + assert gene_vocab.pad_token is None + gene_vocab.pad_token = "" + assert gene_vocab.pad_token == "" + + +def test_get_default_gene_vocab(): + gene_vocab = get_default_gene_vocab() + assert gene_vocab["A12M1"] == 0 diff --git a/transfer/code/scGPT/tests/vocab.json b/transfer/code/scGPT/tests/vocab.json new file mode 100644 index 0000000000000000000000000000000000000000..c5623ffb3d789144a16cb260f8c9e1e2d15ac548 --- /dev/null +++ b/transfer/code/scGPT/tests/vocab.json @@ -0,0 +1,5 @@ +{ + "": 0, + "DIP2A": 1, + "CSTB": 2 +} diff --git a/transfer/code/scGPT/tutorials/Tutorial_Annotation.ipynb b/transfer/code/scGPT/tutorials/Tutorial_Annotation.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..3b5e43ccebb924e124aaa61eee93606deb98e1ba --- /dev/null +++ b/transfer/code/scGPT/tutorials/Tutorial_Annotation.ipynb @@ -0,0 +1,2030 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c8a4fbd7", + "metadata": {}, + "source": [ + "# Fine-tuning on Pre-trained Model for Cell-type Annotation\n", + "In this tutorial, we demonstrate how to fine-tune a pre-trained model on a new dataset for the cell type annotation task. We use the Multiple Sclerosis dataset as an example and fine-tune on the pre-trained whole-body model. Please download the dataset folder from https://drive.google.com/drive/folders/1Qd42YNabzyr2pWt9xoY4cVMTAxsNBt4v?usp=sharing\n", + "\n", + "We summarize the fine-tuning pipeline in the following steps, which can be used as a general recipe for finetuning on cell-type annotation tasks and beyond: \n", + "\n", + " 1. Specify hyper-parameter setup for integration task\n", + " \n", + " 2. Load and pre-process data\n", + " \n", + " 3. Load the pre-trained scGPT model\n", + " \n", + " 4. Finetune scGPT with task-specific objectives\n", + " \n", + " 5. Evaluate fine-tuned scGPT" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "9406b4da", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Global seed set to 0\n", + "/h/haotian/.cache/pypoetry/virtualenvs/scgpt-kdjsbK9l-py3.7/lib/python3.7/site-packages/scanpy/_settings.py:447: DeprecationWarning: `set_matplotlib_formats` is deprecated since IPython 7.23, directly use `matplotlib_inline.backend_inline.set_matplotlib_formats()`\n", + " IPython.display.set_matplotlib_formats(*ipython_format)\n" + ] + } + ], + "source": [ + "# %%\n", + "import copy\n", + "import gc\n", + "import json\n", + "import os\n", + "from pathlib import Path\n", + "import shutil\n", + "import sys\n", + "import time\n", + "import traceback\n", + "from typing import List, Tuple, Dict, Union, Optional\n", + "import warnings\n", + "import pandas as pd\n", + "# from . import asyn\n", + "import pickle\n", + "import torch\n", + "from anndata import AnnData\n", + "import scanpy as sc\n", + "import scvi\n", + "import seaborn as sns\n", + "import numpy as np\n", + "import wandb\n", + "from scipy.sparse import issparse\n", + "import matplotlib.pyplot as plt\n", + "from torch import nn\n", + "from torch.nn import functional as F\n", + "from torch.utils.data import Dataset, DataLoader\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import adjusted_rand_score, normalized_mutual_info_score\n", + "from torchtext.vocab import Vocab\n", + "from torchtext._torchtext import (\n", + " Vocab as VocabPybind,\n", + ")\n", + "from sklearn.metrics import confusion_matrix\n", + "\n", + "sys.path.insert(0, \"../\")\n", + "import scgpt as scg\n", + "from scgpt.model import TransformerModel, AdversarialDiscriminator\n", + "from scgpt.tokenizer import tokenize_and_pad_batch, random_mask_value\n", + "from scgpt.loss import (\n", + " masked_mse_loss,\n", + " masked_relative_error,\n", + " criterion_neg_log_bernoulli,\n", + ")\n", + "from scgpt.tokenizer.gene_tokenizer import GeneVocab\n", + "from scgpt.preprocess import Preprocessor\n", + "from scgpt import SubsetsBatchSampler\n", + "from scgpt.utils import set_seed, category_str2int, eval_scib_metrics\n", + "\n", + "sc.set_figure_params(figsize=(6, 6))\n", + "os.environ[\"KMP_WARNINGS\"] = \"off\"\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "markdown", + "id": "5f3b5d67", + "metadata": {}, + "source": [ + "## Step1: Specify hyper-parameter setup for cell-type annotation task\n", + "Listed below are some hyper-parameter recommendations for the cell-type task. Note that the CLS objective is on to facilitate cell-type classification." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "d07b5257", + "metadata": {}, + "outputs": [], + "source": [ + "hyperparameter_defaults = dict(\n", + " seed=0,\n", + " dataset_name=\"ms\",\n", + " do_train=True,\n", + " load_model=\"../save/scGPT_human\",\n", + " mask_ratio=0.0,\n", + " epochs=10,\n", + " n_bins=51,\n", + " MVC=False, # Masked value prediction for cell embedding\n", + " ecs_thres=0.0, # Elastic cell similarity objective, 0.0 to 1.0, 0.0 to disable\n", + " dab_weight=0.0,\n", + " lr=1e-4,\n", + " batch_size=32,\n", + " layer_size=128,\n", + " nlayers=4, # number of nn.TransformerEncoderLayer in nn.TransformerEncoder\n", + " nhead=4, # number of heads in nn.MultiheadAttention\n", + " dropout=0.2, # dropout probability\n", + " schedule_ratio=0.9, # ratio of epochs for learning rate schedule\n", + " save_eval_interval=5,\n", + " fast_transformer=True,\n", + " pre_norm=False,\n", + " amp=True, # Automatic Mixed Precision\n", + " include_zero_gene = False,\n", + " freeze = False, #freeze\n", + " DSBN = False, # Domain-spec batchnorm\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "94c08ee0", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mhaotian\u001b[0m (\u001b[33mscformer-team\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n" + ] + }, + { + "data": { + "text/html": [ + "wandb version 0.15.5 is available! To upgrade, please run:\n", + " $ pip install wandb --upgrade" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Tracking run with wandb version 0.12.21" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Run data is saved locally in /ssd005/home/haotian/scGPT/tutorials/wandb/run-20230707_102433-2c4retx6" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Syncing run cerulean-cosmos-32 to Weights & Biases (docs)
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'seed': 0, 'dataset_name': 'ms', 'do_train': True, 'load_model': '../save/scGPT_human', 'mask_ratio': 0.0, 'epochs': 10, 'n_bins': 51, 'MVC': False, 'ecs_thres': 0.0, 'dab_weight': 0.0, 'lr': 0.0001, 'batch_size': 32, 'layer_size': 128, 'nlayers': 4, 'nhead': 4, 'dropout': 0.2, 'schedule_ratio': 0.9, 'save_eval_interval': 5, 'fast_transformer': True, 'pre_norm': False, 'amp': True, 'include_zero_gene': False, 'freeze': False, 'DSBN': False}\n" + ] + } + ], + "source": [ + "run = wandb.init(\n", + " config=hyperparameter_defaults,\n", + " project=\"scGPT\",\n", + " reinit=True,\n", + " settings=wandb.Settings(start_method=\"fork\"),\n", + ")\n", + "config = wandb.config\n", + "print(config)\n", + "\n", + "set_seed(config.seed)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "7d7890b7", + "metadata": {}, + "outputs": [], + "source": [ + "# settings for input and preprocessing\n", + "pad_token = \"\"\n", + "special_tokens = [pad_token, \"\", \"\"]\n", + "mask_ratio = config.mask_ratio\n", + "mask_value = \"auto\" # for masked values, now it should always be auto\n", + "\n", + "include_zero_gene = config.include_zero_gene # if True, include zero genes among hvgs in the training\n", + "max_seq_len = 3001\n", + "n_bins = config.n_bins\n", + "\n", + "# input/output representation\n", + "input_style = \"binned\" # \"normed_raw\", \"log1p\", or \"binned\"\n", + "output_style = \"binned\" # \"normed_raw\", \"log1p\", or \"binned\"\n", + "\n", + "# settings for training\n", + "MLM = False # whether to use masked language modeling, currently it is always on.\n", + "CLS = True # celltype classification objective\n", + "ADV = False # Adversarial training for batch correction\n", + "CCE = False # Contrastive cell embedding objective\n", + "MVC = config.MVC # Masked value prediction for cell embedding\n", + "ECS = config.ecs_thres > 0 # Elastic cell similarity objective\n", + "DAB = False # Domain adaptation by reverse backpropagation, set to 2 for separate optimizer\n", + "INPUT_BATCH_LABELS = False # TODO: have these help MLM and MVC, while not to classifier\n", + "input_emb_style = \"continuous\" # \"category\" or \"continuous\" or \"scaling\"\n", + "cell_emb_style = \"cls\" # \"avg-pool\" or \"w-pool\" or \"cls\"\n", + "adv_E_delay_epochs = 0 # delay adversarial training on encoder for a few epochs\n", + "adv_D_delay_epochs = 0\n", + "mvc_decoder_style = \"inner product\"\n", + "ecs_threshold = config.ecs_thres\n", + "dab_weight = config.dab_weight\n", + "\n", + "explicit_zero_prob = MLM and include_zero_gene # whether explicit bernoulli for zeros\n", + "do_sample_in_train = False and explicit_zero_prob # sample the bernoulli in training\n", + "\n", + "per_seq_batch_sample = False\n", + "\n", + "# settings for optimizer\n", + "lr = config.lr # TODO: test learning rate ratio between two tasks\n", + "lr_ADV = 1e-3 # learning rate for discriminator, used when ADV is True\n", + "batch_size = config.batch_size\n", + "eval_batch_size = config.batch_size\n", + "epochs = config.epochs\n", + "schedule_interval = 1\n", + "\n", + "# settings for the model\n", + "fast_transformer = config.fast_transformer\n", + "fast_transformer_backend = \"flash\" # \"linear\" or \"flash\"\n", + "embsize = config.layer_size # embedding dimension\n", + "d_hid = config.layer_size # dimension of the feedforward network in TransformerEncoder\n", + "nlayers = config.nlayers # number of TransformerEncoderLayer in TransformerEncoder\n", + "nhead = config.nhead # number of heads in nn.MultiheadAttention\n", + "dropout = config.dropout # dropout probability\n", + "\n", + "# logging\n", + "log_interval = 100 # iterations\n", + "save_eval_interval = config.save_eval_interval # epochs\n", + "do_eval_scib_metrics = True" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "17ff2309", + "metadata": {}, + "outputs": [], + "source": [ + "# %% validate settings\n", + "assert input_style in [\"normed_raw\", \"log1p\", \"binned\"]\n", + "assert output_style in [\"normed_raw\", \"log1p\", \"binned\"]\n", + "assert input_emb_style in [\"category\", \"continuous\", \"scaling\"]\n", + "if input_style == \"binned\":\n", + " if input_emb_style == \"scaling\":\n", + " raise ValueError(\"input_emb_style `scaling` is not supported for binned input.\")\n", + "elif input_style == \"log1p\" or input_style == \"normed_raw\":\n", + " if input_emb_style == \"category\":\n", + " raise ValueError(\n", + " \"input_emb_style `category` is not supported for log1p or normed_raw input.\"\n", + " )\n", + "\n", + "if input_emb_style == \"category\":\n", + " mask_value = n_bins + 1\n", + " pad_value = n_bins # for padding gene expr values\n", + " n_input_bins = n_bins + 2\n", + "else:\n", + " mask_value = -1\n", + " pad_value = -2\n", + " n_input_bins = n_bins\n", + "\n", + "if ADV and DAB:\n", + " raise ValueError(\"ADV and DAB cannot be both True.\")\n", + "DAB_separate_optim = True if DAB > 1 else False" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "cf7112d8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "save to save/dev_ms-Jul07-10-24\n" + ] + } + ], + "source": [ + "dataset_name = config.dataset_name\n", + "save_dir = Path(f\"./save/dev_{dataset_name}-{time.strftime('%b%d-%H-%M')}/\")\n", + "save_dir.mkdir(parents=True, exist_ok=True)\n", + "print(f\"save to {save_dir}\")\n", + "logger = scg.logger\n", + "scg.utils.add_file_handler(logger, save_dir / \"run.log\")" + ] + }, + { + "cell_type": "markdown", + "id": "37fc7002", + "metadata": {}, + "source": [ + "## Step 2: Load and pre-process data\n", + "We follow the standard scGPT data pre-processing pipelines for the cell-type annotation task. Note that since now we have two datasets at hand (i.e., reference and query data), the same pre-prpocessing steps need to be applied to both of them." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "95b50200", + "metadata": {}, + "outputs": [], + "source": [ + "if dataset_name == \"ms\":\n", + " data_dir = Path(\"../data/ms\")\n", + " adata = sc.read(data_dir / \"c_data.h5ad\")\n", + " adata_test = sc.read(data_dir / \"filtered_ms_adata.h5ad\")\n", + " adata.obs[\"celltype\"] = adata.obs[\"Factor Value[inferred cell type - authors labels]\"].astype(\"category\")\n", + " adata_test.obs[\"celltype\"] = adata_test.obs[\"Factor Value[inferred cell type - authors labels]\"].astype(\"category\")\n", + " adata.obs[\"batch_id\"] = adata.obs[\"str_batch\"] = \"0\"\n", + " adata_test.obs[\"batch_id\"] = adata_test.obs[\"str_batch\"] = \"1\" \n", + " adata.var.set_index(adata.var[\"gene_name\"], inplace=True)\n", + " adata_test.var.set_index(adata.var[\"gene_name\"], inplace=True)\n", + " data_is_raw = False\n", + " filter_gene_by_counts = False\n", + " adata_test_raw = adata_test.copy()\n", + " adata = adata.concatenate(adata_test, batch_key=\"str_batch\")\n", + " \n", + "# make the batch category column\n", + "batch_id_labels = adata.obs[\"str_batch\"].astype(\"category\").cat.codes.values\n", + "adata.obs[\"batch_id\"] = batch_id_labels\n", + "celltype_id_labels = adata.obs[\"celltype\"].astype(\"category\").cat.codes.values\n", + "celltypes = adata.obs[\"celltype\"].unique()\n", + "num_types = len(np.unique(celltype_id_labels))\n", + "id2type = dict(enumerate(adata.obs[\"celltype\"].astype(\"category\").cat.categories))\n", + "adata.obs[\"celltype_id\"] = celltype_id_labels\n", + "adata.var[\"gene_name\"] = adata.var.index.tolist()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "0dc5a6ed", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "scGPT - INFO - match 2808/3000 genes in vocabulary of size 60697.\n", + "scGPT - INFO - Resume model from ../save/scGPT_human/best_model.pt, the model args will override the config ../save/scGPT_human/args.json.\n" + ] + } + ], + "source": [ + "if config.load_model is not None:\n", + " model_dir = Path(config.load_model)\n", + " model_config_file = model_dir / \"args.json\"\n", + " model_file = model_dir / \"best_model.pt\"\n", + " vocab_file = model_dir / \"vocab.json\"\n", + "\n", + " vocab = GeneVocab.from_file(vocab_file)\n", + " shutil.copy(vocab_file, save_dir / \"vocab.json\")\n", + " for s in special_tokens:\n", + " if s not in vocab:\n", + " vocab.append_token(s)\n", + "\n", + " adata.var[\"id_in_vocab\"] = [\n", + " 1 if gene in vocab else -1 for gene in adata.var[\"gene_name\"]\n", + " ]\n", + " gene_ids_in_vocab = np.array(adata.var[\"id_in_vocab\"])\n", + " logger.info(\n", + " f\"match {np.sum(gene_ids_in_vocab >= 0)}/{len(gene_ids_in_vocab)} genes \"\n", + " f\"in vocabulary of size {len(vocab)}.\"\n", + " )\n", + " adata = adata[:, adata.var[\"id_in_vocab\"] >= 0]\n", + "\n", + " # model\n", + " with open(model_config_file, \"r\") as f:\n", + " model_configs = json.load(f)\n", + " logger.info(\n", + " f\"Resume model from {model_file}, the model args will override the \"\n", + " f\"config {model_config_file}.\"\n", + " )\n", + " embsize = model_configs[\"embsize\"]\n", + " nhead = model_configs[\"nheads\"]\n", + " d_hid = model_configs[\"d_hid\"]\n", + " nlayers = model_configs[\"nlayers\"]\n", + " n_layers_cls = model_configs[\"n_layers_cls\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "d08757ed", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "scGPT - INFO - Filtering cells by counts ...\n", + "scGPT - INFO - Normalizing total counts ...\n", + "scGPT - INFO - Binning data ...\n", + "scGPT - INFO - Filtering cells by counts ...\n", + "scGPT - INFO - Normalizing total counts ...\n", + "scGPT - INFO - Binning data ...\n" + ] + } + ], + "source": [ + "# set up the preprocessor, use the args to config the workflow\n", + "preprocessor = Preprocessor(\n", + " use_key=\"X\", # the key in adata.layers to use as raw data\n", + " filter_gene_by_counts=filter_gene_by_counts, # step 1\n", + " filter_cell_by_counts=False, # step 2\n", + " normalize_total=1e4, # 3. whether to normalize the raw data and to what sum\n", + " result_normed_key=\"X_normed\", # the key in adata.layers to store the normalized data\n", + " log1p=data_is_raw, # 4. whether to log1p the normalized data\n", + " result_log1p_key=\"X_log1p\",\n", + " subset_hvg=False, # 5. whether to subset the raw data to highly variable genes\n", + " hvg_flavor=\"seurat_v3\" if data_is_raw else \"cell_ranger\",\n", + " binning=n_bins, # 6. whether to bin the raw data and to what number of bins\n", + " result_binned_key=\"X_binned\", # the key in adata.layers to store the binned data\n", + ")\n", + "\n", + "\n", + "adata_test = adata[adata.obs[\"str_batch\"] == \"1\"]\n", + "adata = adata[adata.obs[\"str_batch\"] == \"0\"]\n", + "\n", + "preprocessor(adata, batch_key=None)\n", + "preprocessor(adata_test, batch_key=None)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cbc1b20f", + "metadata": {}, + "outputs": [], + "source": [ + "input_layer_key = { # the values of this map coorespond to the keys in preprocessing\n", + " \"normed_raw\": \"X_normed\",\n", + " \"log1p\": \"X_normed\",\n", + " \"binned\": \"X_binned\",\n", + "}[input_style]\n", + "all_counts = (\n", + " adata.layers[input_layer_key].toarray()\n", + " if issparse(adata.layers[input_layer_key])\n", + " else adata.layers[input_layer_key]\n", + ")\n", + "genes = adata.var[\"gene_name\"].tolist()\n", + "\n", + "celltypes_labels = adata.obs[\"celltype_id\"].tolist() # make sure count from 0\n", + "celltypes_labels = np.array(celltypes_labels)\n", + "\n", + "batch_ids = adata.obs[\"batch_id\"].tolist()\n", + "num_batch_types = len(set(batch_ids))\n", + "batch_ids = np.array(batch_ids)\n", + "\n", + "(\n", + " train_data,\n", + " valid_data,\n", + " train_celltype_labels,\n", + " valid_celltype_labels,\n", + " train_batch_labels,\n", + " valid_batch_labels,\n", + ") = train_test_split(\n", + " all_counts, celltypes_labels, batch_ids, test_size=0.1, shuffle=True\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "4cd701b1", + "metadata": {}, + "outputs": [], + "source": [ + "if config.load_model is None:\n", + " vocab = Vocab(\n", + " VocabPybind(genes + special_tokens, None)\n", + " ) # bidirectional lookup [gene <-> int]\n", + "vocab.set_default_index(vocab[\"\"])\n", + "gene_ids = np.array(vocab(genes), dtype=int)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "818bfcc0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "scGPT - INFO - train set number of samples: 7059, \n", + "\t feature length: 3001\n", + "scGPT - INFO - valid set number of samples: 785, \n", + "\t feature length: 3001\n" + ] + } + ], + "source": [ + "tokenized_train = tokenize_and_pad_batch(\n", + " train_data,\n", + " gene_ids,\n", + " max_len=max_seq_len,\n", + " vocab=vocab,\n", + " pad_token=pad_token,\n", + " pad_value=pad_value,\n", + " append_cls=True, # append token at the beginning\n", + " include_zero_gene=include_zero_gene,\n", + ")\n", + "tokenized_valid = tokenize_and_pad_batch(\n", + " valid_data,\n", + " gene_ids,\n", + " max_len=max_seq_len,\n", + " vocab=vocab,\n", + " pad_token=pad_token,\n", + " pad_value=pad_value,\n", + " append_cls=True,\n", + " include_zero_gene=include_zero_gene,\n", + ")\n", + "logger.info(\n", + " f\"train set number of samples: {tokenized_train['genes'].shape[0]}, \"\n", + " f\"\\n\\t feature length: {tokenized_train['genes'].shape[1]}\"\n", + ")\n", + "logger.info(\n", + " f\"valid set number of samples: {tokenized_valid['genes'].shape[0]}, \"\n", + " f\"\\n\\t feature length: {tokenized_valid['genes'].shape[1]}\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "37a80818", + "metadata": {}, + "outputs": [], + "source": [ + "def prepare_data(sort_seq_batch=False) -> Tuple[Dict[str, torch.Tensor]]:\n", + " masked_values_train = random_mask_value(\n", + " tokenized_train[\"values\"],\n", + " mask_ratio=mask_ratio,\n", + " mask_value=mask_value,\n", + " pad_value=pad_value,\n", + " )\n", + " masked_values_valid = random_mask_value(\n", + " tokenized_valid[\"values\"],\n", + " mask_ratio=mask_ratio,\n", + " mask_value=mask_value,\n", + " pad_value=pad_value,\n", + " )\n", + " print(\n", + " f\"random masking at epoch {epoch:3d}, ratio of masked values in train: \",\n", + " f\"{(masked_values_train == mask_value).sum() / (masked_values_train - pad_value).count_nonzero():.4f}\",\n", + " )\n", + "\n", + " input_gene_ids_train, input_gene_ids_valid = (\n", + " tokenized_train[\"genes\"],\n", + " tokenized_valid[\"genes\"],\n", + " )\n", + " input_values_train, input_values_valid = masked_values_train, masked_values_valid\n", + " target_values_train, target_values_valid = (\n", + " tokenized_train[\"values\"],\n", + " tokenized_valid[\"values\"],\n", + " )\n", + "\n", + " tensor_batch_labels_train = torch.from_numpy(train_batch_labels).long()\n", + " tensor_batch_labels_valid = torch.from_numpy(valid_batch_labels).long()\n", + "\n", + " tensor_celltype_labels_train = torch.from_numpy(train_celltype_labels).long()\n", + " tensor_celltype_labels_valid = torch.from_numpy(valid_celltype_labels).long()\n", + "\n", + " if sort_seq_batch: # TODO: update to random pick seq source in each traning batch\n", + " train_sort_ids = np.argsort(train_batch_labels)\n", + " input_gene_ids_train = input_gene_ids_train[train_sort_ids]\n", + " input_values_train = input_values_train[train_sort_ids]\n", + " target_values_train = target_values_train[train_sort_ids]\n", + " tensor_batch_labels_train = tensor_batch_labels_train[train_sort_ids]\n", + " tensor_celltype_labels_train = tensor_celltype_labels_train[train_sort_ids]\n", + "\n", + " valid_sort_ids = np.argsort(valid_batch_labels)\n", + " input_gene_ids_valid = input_gene_ids_valid[valid_sort_ids]\n", + " input_values_valid = input_values_valid[valid_sort_ids]\n", + " target_values_valid = target_values_valid[valid_sort_ids]\n", + " tensor_batch_labels_valid = tensor_batch_labels_valid[valid_sort_ids]\n", + " tensor_celltype_labels_valid = tensor_celltype_labels_valid[valid_sort_ids]\n", + "\n", + " train_data_pt = {\n", + " \"gene_ids\": input_gene_ids_train,\n", + " \"values\": input_values_train,\n", + " \"target_values\": target_values_train,\n", + " \"batch_labels\": tensor_batch_labels_train,\n", + " \"celltype_labels\": tensor_celltype_labels_train,\n", + " }\n", + " valid_data_pt = {\n", + " \"gene_ids\": input_gene_ids_valid,\n", + " \"values\": input_values_valid,\n", + " \"target_values\": target_values_valid,\n", + " \"batch_labels\": tensor_batch_labels_valid,\n", + " \"celltype_labels\": tensor_celltype_labels_valid,\n", + " }\n", + "\n", + " return train_data_pt, valid_data_pt\n", + "\n", + "\n", + "# dataset\n", + "class SeqDataset(Dataset):\n", + " def __init__(self, data: Dict[str, torch.Tensor]):\n", + " self.data = data\n", + "\n", + " def __len__(self):\n", + " return self.data[\"gene_ids\"].shape[0]\n", + "\n", + " def __getitem__(self, idx):\n", + " return {k: v[idx] for k, v in self.data.items()}\n", + "\n", + "\n", + "# data_loader\n", + "def prepare_dataloader(\n", + " data_pt: Dict[str, torch.Tensor],\n", + " batch_size: int,\n", + " shuffle: bool = False,\n", + " intra_domain_shuffle: bool = False,\n", + " drop_last: bool = False,\n", + " num_workers: int = 0,\n", + ") -> DataLoader:\n", + " if num_workers == 0:\n", + " num_workers = min(len(os.sched_getaffinity(0)), batch_size // 2)\n", + "\n", + " dataset = SeqDataset(data_pt)\n", + "\n", + " if per_seq_batch_sample:\n", + " # find the indices of samples in each seq batch\n", + " subsets = []\n", + " batch_labels_array = data_pt[\"batch_labels\"].numpy()\n", + " for batch_label in np.unique(batch_labels_array):\n", + " batch_indices = np.where(batch_labels_array == batch_label)[0].tolist()\n", + " subsets.append(batch_indices)\n", + " data_loader = DataLoader(\n", + " dataset=dataset,\n", + " batch_sampler=SubsetsBatchSampler(\n", + " subsets,\n", + " batch_size,\n", + " intra_subset_shuffle=intra_domain_shuffle,\n", + " inter_subset_shuffle=shuffle,\n", + " drop_last=drop_last,\n", + " ),\n", + " num_workers=num_workers,\n", + " pin_memory=True,\n", + " )\n", + " return data_loader\n", + "\n", + " data_loader = DataLoader(\n", + " dataset=dataset,\n", + " batch_size=batch_size,\n", + " shuffle=shuffle,\n", + " drop_last=drop_last,\n", + " num_workers=num_workers,\n", + " pin_memory=True,\n", + " )\n", + " return data_loader\n" + ] + }, + { + "cell_type": "markdown", + "id": "77105fda", + "metadata": {}, + "source": [ + "## Step 3: Load the pre-trained scGPT model" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "219bb9db", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using simple batchnorm instead of domain specific batchnorm\n", + "scGPT - INFO - Loading params encoder.embedding.weight with shape torch.Size([60697, 512])\n", + "scGPT - INFO - Loading params encoder.enc_norm.weight with shape torch.Size([512])\n", + "scGPT - INFO - Loading params encoder.enc_norm.bias with shape torch.Size([512])\n", + "scGPT - INFO - Loading params value_encoder.linear1.weight with shape torch.Size([512, 1])\n", + "scGPT - INFO - Loading params value_encoder.linear1.bias with shape torch.Size([512])\n", + "scGPT - INFO - Loading params value_encoder.linear2.weight with shape torch.Size([512, 512])\n", + "scGPT - INFO - Loading params value_encoder.linear2.bias with shape torch.Size([512])\n", + "scGPT - INFO - Loading params value_encoder.norm.weight with shape torch.Size([512])\n", + "scGPT - INFO - Loading params value_encoder.norm.bias with shape torch.Size([512])\n", + "scGPT - INFO - Loading params transformer_encoder.layers.0.self_attn.Wqkv.weight with shape torch.Size([1536, 512])\n", + "scGPT - INFO - Loading params transformer_encoder.layers.0.self_attn.Wqkv.bias with shape torch.Size([1536])\n", + "scGPT - INFO - Loading params transformer_encoder.layers.0.self_attn.out_proj.weight with shape torch.Size([512, 512])\n", + "scGPT - INFO - Loading params transformer_encoder.layers.0.self_attn.out_proj.bias with shape torch.Size([512])\n", + "scGPT - INFO - Loading params transformer_encoder.layers.0.linear1.weight with shape torch.Size([512, 512])\n", + "scGPT - INFO - Loading params transformer_encoder.layers.0.linear1.bias with shape torch.Size([512])\n", + "scGPT - INFO - Loading params transformer_encoder.layers.0.linear2.weight with shape torch.Size([512, 512])\n", + "scGPT - INFO - Loading 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torch.Size([512])\n", + "scGPT - INFO - Loading params transformer_encoder.layers.2.norm2.weight with shape torch.Size([512])\n", + "scGPT - INFO - Loading params transformer_encoder.layers.2.norm2.bias with shape torch.Size([512])\n", + "scGPT - INFO - Loading params transformer_encoder.layers.3.self_attn.Wqkv.weight with shape torch.Size([1536, 512])\n", + "scGPT - INFO - Loading params transformer_encoder.layers.3.self_attn.Wqkv.bias with shape torch.Size([1536])\n", + "scGPT - INFO - Loading params transformer_encoder.layers.3.self_attn.out_proj.weight with shape torch.Size([512, 512])\n", + "scGPT - INFO - Loading params transformer_encoder.layers.3.self_attn.out_proj.bias with shape torch.Size([512])\n", + "scGPT - INFO - Loading params transformer_encoder.layers.3.linear1.weight with shape torch.Size([512, 512])\n", + "scGPT - INFO - Loading params transformer_encoder.layers.3.linear1.bias with shape torch.Size([512])\n", + "scGPT - INFO - Loading params 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transformer_encoder.layers.11.linear2.weight\n", + "--------------------\n", + "name: transformer_encoder.layers.11.linear2.bias\n", + "--------------------\n", + "name: transformer_encoder.layers.11.norm1.weight\n", + "--------------------\n", + "name: transformer_encoder.layers.11.norm1.bias\n", + "--------------------\n", + "name: transformer_encoder.layers.11.norm2.weight\n", + "--------------------\n", + "name: transformer_encoder.layers.11.norm2.bias\n", + "--------------------\n", + "name: decoder.fc.0.weight\n", + "--------------------\n", + "name: decoder.fc.0.bias\n", + "--------------------\n", + "name: decoder.fc.2.weight\n", + "--------------------\n", + "name: decoder.fc.2.bias\n", + "--------------------\n", + "name: decoder.fc.4.weight\n", + "--------------------\n", + "name: decoder.fc.4.bias\n", + "--------------------\n", + "name: cls_decoder._decoder.0.weight\n", + "--------------------\n", + "name: cls_decoder._decoder.0.bias\n", + "--------------------\n", + "name: cls_decoder._decoder.2.weight\n", + "--------------------\n", + "name: cls_decoder._decoder.2.bias\n", + "--------------------\n", + "name: cls_decoder._decoder.3.weight\n", + "--------------------\n", + "name: cls_decoder._decoder.3.bias\n", + "--------------------\n", + "name: cls_decoder._decoder.5.weight\n", + "--------------------\n", + "name: cls_decoder._decoder.5.bias\n", + "--------------------\n", + "name: cls_decoder.out_layer.weight\n", + "--------------------\n", + "name: cls_decoder.out_layer.bias\n", + "scGPT - INFO - Total Pre freeze Params 51341843\n", + "scGPT - INFO - Total Post freeze Params 51341843\n" + ] + } + ], + "source": [ + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "\n", + "ntokens = len(vocab) # size of vocabulary\n", + "model = TransformerModel(\n", + " ntokens,\n", + " embsize,\n", + " nhead,\n", + " d_hid,\n", + " nlayers,\n", + " nlayers_cls=3,\n", + " n_cls=num_types if CLS else 1,\n", + " vocab=vocab,\n", + " dropout=dropout,\n", + " pad_token=pad_token,\n", + " pad_value=pad_value,\n", + " do_mvc=MVC,\n", + " do_dab=DAB,\n", + " use_batch_labels=INPUT_BATCH_LABELS,\n", + " num_batch_labels=num_batch_types,\n", + " domain_spec_batchnorm=config.DSBN,\n", + " input_emb_style=input_emb_style,\n", + " n_input_bins=n_input_bins,\n", + " cell_emb_style=cell_emb_style,\n", + " mvc_decoder_style=mvc_decoder_style,\n", + " ecs_threshold=ecs_threshold,\n", + " explicit_zero_prob=explicit_zero_prob,\n", + " use_fast_transformer=fast_transformer,\n", + " fast_transformer_backend=fast_transformer_backend,\n", + " pre_norm=config.pre_norm,\n", + ")\n", + "if config.load_model is not None:\n", + " try:\n", + " model.load_state_dict(torch.load(model_file))\n", + " logger.info(f\"Loading all model params from {model_file}\")\n", + " except:\n", + " # only load params that are in the model and match the size\n", + " model_dict = model.state_dict()\n", + " pretrained_dict = torch.load(model_file)\n", + " pretrained_dict = {\n", + " k: v\n", + " for k, v in pretrained_dict.items()\n", + " if k in model_dict and v.shape == model_dict[k].shape\n", + " }\n", + " for k, v in pretrained_dict.items():\n", + " logger.info(f\"Loading params {k} with shape {v.shape}\")\n", + " model_dict.update(pretrained_dict)\n", + " model.load_state_dict(model_dict)\n", + "\n", + "pre_freeze_param_count = sum(dict((p.data_ptr(), p.numel()) for p in model.parameters() if p.requires_grad).values())\n", + "\n", + "# Freeze all pre-decoder weights\n", + "for name, para in model.named_parameters():\n", + " print(\"-\"*20)\n", + " print(f\"name: {name}\")\n", + " if config.freeze and \"encoder\" in name and \"transformer_encoder\" not in name:\n", + " # if config.freeze and \"encoder\" in name:\n", + " print(f\"freezing weights for: {name}\")\n", + " para.requires_grad = False\n", + "\n", + "post_freeze_param_count = sum(dict((p.data_ptr(), p.numel()) for p in model.parameters() if p.requires_grad).values())\n", + "\n", + "logger.info(f\"Total Pre freeze Params {(pre_freeze_param_count )}\")\n", + "logger.info(f\"Total Post freeze Params {(post_freeze_param_count )}\")\n", + "wandb.log(\n", + " {\n", + " \"info/pre_freeze_param_count\": pre_freeze_param_count,\n", + " \"info/post_freeze_param_count\": post_freeze_param_count,\n", + " },\n", + ")\n", + "\n", + "model.to(device)\n", + "wandb.watch(model)\n", + "\n", + "if ADV:\n", + " discriminator = AdversarialDiscriminator(\n", + " d_model=embsize,\n", + " n_cls=num_batch_types,\n", + " ).to(device)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "1e4ea79e", + "metadata": {}, + "outputs": [], + "source": [ + "criterion = masked_mse_loss\n", + "criterion_cls = nn.CrossEntropyLoss()\n", + "criterion_dab = nn.CrossEntropyLoss()\n", + "optimizer = torch.optim.Adam(\n", + " model.parameters(), lr=lr, eps=1e-4 if config.amp else 1e-8\n", + ")\n", + "scheduler = torch.optim.lr_scheduler.StepLR(\n", + " optimizer, schedule_interval, gamma=config.schedule_ratio\n", + ")\n", + "if DAB_separate_optim:\n", + " optimizer_dab = torch.optim.Adam(model.parameters(), lr=lr)\n", + " scheduler_dab = torch.optim.lr_scheduler.StepLR(\n", + " optimizer_dab, schedule_interval, gamma=config.schedule_ratio\n", + " )\n", + "if ADV:\n", + " criterion_adv = nn.CrossEntropyLoss() # consider using label smoothing\n", + " optimizer_E = torch.optim.Adam(model.parameters(), lr=lr_ADV)\n", + " scheduler_E = torch.optim.lr_scheduler.StepLR(\n", + " optimizer_E, schedule_interval, gamma=config.schedule_ratio\n", + " )\n", + " optimizer_D = torch.optim.Adam(discriminator.parameters(), lr=lr_ADV)\n", + " scheduler_D = torch.optim.lr_scheduler.StepLR(\n", + " optimizer_D, schedule_interval, gamma=config.schedule_ratio\n", + " )\n", + "\n", + "scaler = torch.cuda.amp.GradScaler(enabled=config.amp)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "b734269a", + "metadata": {}, + "outputs": [], + "source": [ + "def train(model: nn.Module, loader: DataLoader) -> None:\n", + " \"\"\"\n", + " Train the model for one epoch.\n", + " \"\"\"\n", + " model.train()\n", + " (\n", + " total_loss,\n", + " total_mse,\n", + " total_cls,\n", + " total_cce,\n", + " total_mvc,\n", + " total_ecs,\n", + " total_dab,\n", + " total_adv_E,\n", + " total_adv_D,\n", + " total_zero_log_prob,\n", + " total_mvc_zero_log_prob,\n", + " ) = (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0)\n", + " total_error = 0.0\n", + " start_time = time.time()\n", + "\n", + " num_batches = len(loader)\n", + " for batch, batch_data in enumerate(loader):\n", + " input_gene_ids = batch_data[\"gene_ids\"].to(device)\n", + " input_values = batch_data[\"values\"].to(device)\n", + " target_values = batch_data[\"target_values\"].to(device)\n", + " batch_labels = batch_data[\"batch_labels\"].to(device)\n", + " celltype_labels = batch_data[\"celltype_labels\"].to(device)\n", + "\n", + " src_key_padding_mask = input_gene_ids.eq(vocab[pad_token])\n", + " with torch.cuda.amp.autocast(enabled=config.amp):\n", + " output_dict = model(\n", + " input_gene_ids,\n", + " input_values,\n", + " src_key_padding_mask=src_key_padding_mask,\n", + " batch_labels=batch_labels if INPUT_BATCH_LABELS or config.DSBN else None,\n", + " CLS=CLS,\n", + " CCE=CCE,\n", + " MVC=MVC,\n", + " ECS=ECS,\n", + " do_sample=do_sample_in_train,\n", + " #generative_training=False\n", + " )\n", + "\n", + " masked_positions = input_values.eq(mask_value) # the postions to predict\n", + " loss = 0.0\n", + " metrics_to_log = {}\n", + " if MLM:\n", + " loss_mse = criterion(\n", + " output_dict[\"mlm_output\"], target_values, masked_positions\n", + " )\n", + " loss = loss + loss_mse\n", + " metrics_to_log = {\"train/mse\": loss_mse.item()}\n", + " if explicit_zero_prob:\n", + " loss_zero_log_prob = criterion_neg_log_bernoulli(\n", + " output_dict[\"mlm_zero_probs\"], target_values, masked_positions\n", + " )\n", + " loss = loss + loss_zero_log_prob\n", + " metrics_to_log.update({\"train/nzlp\": loss_zero_log_prob.item()})\n", + " if CLS:\n", + " loss_cls = criterion_cls(output_dict[\"cls_output\"], celltype_labels)\n", + " loss = loss + loss_cls\n", + " metrics_to_log.update({\"train/cls\": loss_cls.item()})\n", + "\n", + " error_rate = 1 - (\n", + " (output_dict[\"cls_output\"].argmax(1) == celltype_labels)\n", + " .sum()\n", + " .item()\n", + " ) / celltype_labels.size(0)\n", + " if CCE:\n", + " loss_cce = 10 * output_dict[\"loss_cce\"]\n", + " loss = loss + loss_cce\n", + " metrics_to_log.update({\"train/cce\": loss_cce.item()})\n", + " if MVC:\n", + " loss_mvc = criterion(\n", + " output_dict[\"mvc_output\"], target_values, masked_positions\n", + " )\n", + " loss = loss + loss_mvc\n", + " metrics_to_log.update({\"train/mvc\": loss_mvc.item()})\n", + " if MVC and explicit_zero_prob:\n", + " loss_mvc_zero_log_prob = criterion_neg_log_bernoulli(\n", + " output_dict[\"mvc_zero_probs\"], target_values, masked_positions\n", + " )\n", + " loss = loss + loss_mvc_zero_log_prob\n", + " metrics_to_log.update({\"train/mvc_nzlp\": loss_mvc_zero_log_prob.item()})\n", + " if ECS:\n", + " loss_ecs = 10 * output_dict[\"loss_ecs\"]\n", + " loss = loss + loss_ecs\n", + " metrics_to_log.update({\"train/ecs\": loss_ecs.item()})\n", + " if DAB:\n", + " # try weighting and separate optimizer\n", + " loss_dab = criterion_dab(output_dict[\"dab_output\"], batch_labels)\n", + " loss = loss + dab_weight * loss_dab\n", + " metrics_to_log.update({\"train/dab\": loss_dab.item()})\n", + "\n", + " model.zero_grad()\n", + " scaler.scale(loss).backward()\n", + " scaler.unscale_(optimizer)\n", + " with warnings.catch_warnings(record=True) as w:\n", + " warnings.filterwarnings(\"always\")\n", + " torch.nn.utils.clip_grad_norm_(\n", + " model.parameters(),\n", + " 1.0,\n", + " error_if_nonfinite=False if scaler.is_enabled() else True,\n", + " )\n", + " if len(w) > 0:\n", + " logger.warning(\n", + " f\"Found infinite gradient. This may be caused by the gradient \"\n", + " f\"scaler. The current scale is {scaler.get_scale()}. This warning \"\n", + " \"can be ignored if no longer occurs after autoscaling of the scaler.\"\n", + " )\n", + " scaler.step(optimizer)\n", + " scaler.update()\n", + "\n", + " if ADV:\n", + " # rerun the model for adversarial training\n", + " output_dict = model(\n", + " input_gene_ids,\n", + " input_values,\n", + " src_key_padding_mask=src_key_padding_mask,\n", + " batch_labels=batch_labels if INPUT_BATCH_LABELS or config.DSBN else None,\n", + " CLS=CLS,\n", + " CCE=CCE,\n", + " MVC=MVC,\n", + " ECS=ECS,\n", + " do_sample=do_sample_in_train,\n", + " #generative_training=False\n", + " )\n", + "\n", + " # TRAINING DISCRIMINATOR\n", + " loss_adv_D = criterion_adv(\n", + " discriminator(output_dict[\"cell_emb\"].detach()), batch_labels\n", + " )\n", + " if epoch > adv_D_delay_epochs:\n", + " discriminator.zero_grad()\n", + " loss_adv_D.backward()\n", + " optimizer_D.step()\n", + "\n", + " # TRAINING ENCODER\n", + " loss_adv_E = -criterion_adv(\n", + " discriminator(output_dict[\"cell_emb\"]), batch_labels\n", + " )\n", + " # NOTE: the loss is negative here because we want to maximize\n", + " # the cross_entropy_loss, in other words, disguise against the discriminator\n", + " if epoch > adv_E_delay_epochs:\n", + " model.zero_grad()\n", + " discriminator.zero_grad()\n", + " loss_adv_E.backward()\n", + " optimizer_E.step()\n", + "\n", + " wandb.log(metrics_to_log)\n", + "\n", + " total_loss += loss.item()\n", + " total_mse += loss_mse.item() if MLM else 0.0\n", + " total_cls += loss_cls.item() if CLS else 0.0\n", + " total_cce += loss_cce.item() if CCE else 0.0\n", + " total_mvc += loss_mvc.item() if MVC else 0.0\n", + " total_ecs += loss_ecs.item() if ECS else 0.0\n", + " total_dab += loss_dab.item() if DAB else 0.0\n", + " total_adv_E += loss_adv_E.item() if ADV else 0.0\n", + " total_adv_D += loss_adv_D.item() if ADV else 0.0\n", + " total_zero_log_prob += loss_zero_log_prob.item() if explicit_zero_prob else 0.0\n", + " total_mvc_zero_log_prob += (\n", + " loss_mvc_zero_log_prob.item() if MVC and explicit_zero_prob else 0.0\n", + " )\n", + " total_error += error_rate\n", + " if batch % log_interval == 0 and batch > 0:\n", + " lr = scheduler.get_last_lr()[0]\n", + " ms_per_batch = (time.time() - start_time) * 1000 / log_interval\n", + " cur_loss = total_loss / log_interval\n", + " cur_mse = total_mse / log_interval\n", + " cur_cls = total_cls / log_interval if CLS else 0.0\n", + " cur_cce = total_cce / log_interval if CCE else 0.0\n", + " cur_mvc = total_mvc / log_interval if MVC else 0.0\n", + " cur_ecs = total_ecs / log_interval if ECS else 0.0\n", + " cur_dab = total_dab / log_interval if DAB else 0.0\n", + " cur_adv_E = total_adv_E / log_interval if ADV else 0.0\n", + " cur_adv_D = total_adv_D / log_interval if ADV else 0.0\n", + " cur_zero_log_prob = (\n", + " total_zero_log_prob / log_interval if explicit_zero_prob else 0.0\n", + " )\n", + " cur_mvc_zero_log_prob = (\n", + " total_mvc_zero_log_prob / log_interval\n", + " if MVC and explicit_zero_prob\n", + " else 0.0\n", + " )\n", + " cur_error = total_error / log_interval\n", + " # ppl = math.exp(cur_loss)\n", + " logger.info(\n", + " f\"| epoch {epoch:3d} | {batch:3d}/{num_batches:3d} batches | \"\n", + " f\"lr {lr:05.4f} | ms/batch {ms_per_batch:5.2f} | \"\n", + " f\"loss {cur_loss:5.2f} | \"\n", + " + (f\"mse {cur_mse:5.2f} | mre {cur_error:5.2f} |\" if MLM else \"\")\n", + " + (f\"cls {cur_cls:5.2f} | \" if CLS else \"\")\n", + " + (f\"err {cur_error:5.2f} | \" if CLS else \"\")\n", + " + (f\"cce {cur_cce:5.2f} |\" if CCE else \"\")\n", + " + (f\"mvc {cur_mvc:5.2f} |\" if MVC else \"\")\n", + " + (f\"ecs {cur_ecs:5.2f} |\" if ECS else \"\")\n", + " + (f\"dab {cur_dab:5.2f} |\" if DAB else \"\")\n", + " + (f\"adv_E {cur_adv_E:5.2f} |\" if ADV else \"\")\n", + " + (f\"adv_D {cur_adv_D:5.2f} |\" if ADV else \"\")\n", + " + (f\"nzlp {cur_zero_log_prob:5.2f} |\" if explicit_zero_prob else \"\")\n", + " + (\n", + " f\"mvc_nzlp {cur_mvc_zero_log_prob:5.2f} |\"\n", + " if MVC and explicit_zero_prob\n", + " else \"\"\n", + " )\n", + " )\n", + " total_loss = 0\n", + " total_mse = 0\n", + " total_cls = 0\n", + " total_cce = 0\n", + " total_mvc = 0\n", + " total_ecs = 0\n", + " total_dab = 0\n", + " total_adv_E = 0\n", + " total_adv_D = 0\n", + " total_zero_log_prob = 0\n", + " total_mvc_zero_log_prob = 0\n", + " total_error = 0\n", + " start_time = time.time()\n", + "\n", + "\n", + "def define_wandb_metrcis():\n", + " wandb.define_metric(\"valid/mse\", summary=\"min\", step_metric=\"epoch\")\n", + " wandb.define_metric(\"valid/mre\", summary=\"min\", step_metric=\"epoch\")\n", + " wandb.define_metric(\"valid/dab\", summary=\"min\", step_metric=\"epoch\")\n", + " wandb.define_metric(\"valid/sum_mse_dab\", summary=\"min\", step_metric=\"epoch\")\n", + " wandb.define_metric(\"test/avg_bio\", summary=\"max\")\n", + "\n", + "\n", + "def evaluate(model: nn.Module, loader: DataLoader, return_raw: bool = False) -> float:\n", + " \"\"\"\n", + " Evaluate the model on the evaluation data.\n", + " \"\"\"\n", + " model.eval()\n", + " total_loss = 0.0\n", + " total_error = 0.0\n", + " total_dab = 0.0\n", + " total_num = 0\n", + " predictions = []\n", + " with torch.no_grad():\n", + " for batch_data in loader:\n", + " input_gene_ids = batch_data[\"gene_ids\"].to(device)\n", + " input_values = batch_data[\"values\"].to(device)\n", + " target_values = batch_data[\"target_values\"].to(device)\n", + " batch_labels = batch_data[\"batch_labels\"].to(device)\n", + " celltype_labels = batch_data[\"celltype_labels\"].to(device)\n", + "\n", + " src_key_padding_mask = input_gene_ids.eq(vocab[pad_token])\n", + " with torch.cuda.amp.autocast(enabled=config.amp):\n", + " output_dict = model(\n", + " input_gene_ids,\n", + " input_values,\n", + " src_key_padding_mask=src_key_padding_mask,\n", + " batch_labels=batch_labels if INPUT_BATCH_LABELS or config.DSBN else None,\n", + " CLS=CLS, # evaluation does not need CLS or CCE\n", + " CCE=False,\n", + " MVC=False,\n", + " ECS=False,\n", + " do_sample=do_sample_in_train,\n", + " #generative_training = False,\n", + " )\n", + " output_values = output_dict[\"cls_output\"]\n", + " loss = criterion_cls(output_values, celltype_labels)\n", + "\n", + " if DAB:\n", + " loss_dab = criterion_dab(output_dict[\"dab_output\"], batch_labels)\n", + "\n", + " total_loss += loss.item() * len(input_gene_ids)\n", + " accuracy = (output_values.argmax(1) == celltype_labels).sum().item()\n", + " total_error += (1 - accuracy / len(input_gene_ids)) * len(input_gene_ids)\n", + " total_dab += loss_dab.item() * len(input_gene_ids) if DAB else 0.0\n", + " total_num += len(input_gene_ids)\n", + " preds = output_values.argmax(1).cpu().numpy()\n", + " predictions.append(preds)\n", + "\n", + " wandb.log(\n", + " {\n", + " \"valid/mse\": total_loss / total_num,\n", + " \"valid/err\": total_error / total_num,\n", + " \"valid/dab\": total_dab / total_num,\n", + " \"valid/sum_mse_dab\": (total_loss + dab_weight * total_dab) / total_num,\n", + " \"epoch\": epoch,\n", + " },\n", + " )\n", + "\n", + " if return_raw:\n", + " return np.concatenate(predictions, axis=0)\n", + "\n", + " return total_loss / total_num, total_error / total_num\n" + ] + }, + { + "cell_type": "markdown", + "id": "734a503c", + "metadata": {}, + "source": [ + "## Step 4: Finetune scGPT with task-specific objectives" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "8e48b893", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "random masking at epoch 1, ratio of masked values in train: 0.0000\n", + "scGPT - INFO - | epoch 1 | 100/221 batches | lr 0.0001 | ms/batch 231.48 | loss 2.16 | cls 2.16 | err 0.74 | \n", + "scGPT - INFO - | epoch 1 | 200/221 batches | lr 0.0001 | ms/batch 207.85 | loss 1.27 | cls 1.27 | err 0.46 | \n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 1 | time: 50.44s | valid loss/mse 0.7017 | err 0.2293\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - Best model with score 0.7017\n", + "random masking at epoch 2, ratio of masked values in train: 0.0000\n", + "scGPT - INFO - | epoch 2 | 100/221 batches | lr 0.0001 | ms/batch 214.15 | loss 0.63 | cls 0.63 | err 0.20 | \n", + "scGPT - INFO - | epoch 2 | 200/221 batches | lr 0.0001 | ms/batch 207.92 | loss 0.53 | cls 0.53 | err 0.17 | \n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 2 | time: 48.58s | valid loss/mse 0.5118 | err 0.1618\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - Best model with score 0.5118\n", + "random masking at epoch 3, ratio of masked values in train: 0.0000\n", + "scGPT - INFO - | epoch 3 | 100/221 batches | lr 0.0001 | ms/batch 213.87 | loss 0.44 | cls 0.44 | err 0.14 | \n", + "scGPT - INFO - | epoch 3 | 200/221 batches | lr 0.0001 | ms/batch 207.42 | loss 0.42 | cls 0.42 | err 0.13 | \n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 3 | time: 48.63s | valid loss/mse 0.4593 | err 0.1490\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - Best model with score 0.4593\n", + "random masking at epoch 4, ratio of masked values in train: 0.0000\n", + "scGPT - INFO - | epoch 4 | 100/221 batches | lr 0.0001 | ms/batch 214.81 | loss 0.36 | cls 0.36 | err 0.11 | \n", + "scGPT - INFO - | epoch 4 | 200/221 batches | lr 0.0001 | ms/batch 207.25 | loss 0.34 | cls 0.34 | err 0.12 | \n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 4 | time: 48.64s | valid loss/mse 0.5050 | err 0.1580\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "random masking at epoch 5, ratio of masked values in train: 0.0000\n", + "scGPT - INFO - | epoch 5 | 100/221 batches | lr 0.0001 | ms/batch 214.85 | loss 0.29 | cls 0.29 | err 0.09 | \n", + "scGPT - INFO - | epoch 5 | 200/221 batches | lr 0.0001 | ms/batch 209.23 | loss 0.28 | cls 0.28 | err 0.09 | \n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 5 | time: 48.96s | valid loss/mse 0.4364 | err 0.1210\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - Best model with score 0.4364\n", + "random masking at epoch 6, ratio of masked values in train: 0.0000\n", + "scGPT - INFO - | epoch 6 | 100/221 batches | lr 0.0001 | ms/batch 212.97 | loss 0.25 | cls 0.25 | err 0.08 | \n", + "scGPT - INFO - | epoch 6 | 200/221 batches | lr 0.0001 | ms/batch 208.92 | loss 0.24 | cls 0.24 | err 0.07 | \n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 6 | time: 48.62s | valid loss/mse 0.5546 | err 0.1376\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "random masking at epoch 7, ratio of masked values in train: 0.0000\n", + "scGPT - INFO - | epoch 7 | 100/221 batches | lr 0.0001 | ms/batch 215.25 | loss 0.20 | cls 0.20 | err 0.06 | \n", + "scGPT - INFO - | epoch 7 | 200/221 batches | lr 0.0001 | ms/batch 209.19 | loss 0.22 | cls 0.22 | err 0.07 | \n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 7 | time: 48.95s | valid loss/mse 0.4456 | err 0.1274\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "random masking at epoch 8, ratio of masked values in train: 0.0000\n", + "scGPT - INFO - | epoch 8 | 100/221 batches | lr 0.0000 | ms/batch 214.98 | loss 0.20 | cls 0.20 | err 0.06 | \n", + "scGPT - INFO - | epoch 8 | 200/221 batches | lr 0.0000 | ms/batch 207.85 | loss 0.18 | cls 0.18 | err 0.06 | \n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 8 | time: 48.78s | valid loss/mse 0.5445 | err 0.1299\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "random masking at epoch 9, ratio of masked values in train: 0.0000\n", + "scGPT - INFO - | epoch 9 | 100/221 batches | lr 0.0000 | ms/batch 214.92 | loss 0.15 | cls 0.15 | err 0.05 | \n", + "scGPT - INFO - | epoch 9 | 200/221 batches | lr 0.0000 | ms/batch 207.42 | loss 0.15 | cls 0.15 | err 0.05 | \n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 9 | time: 48.76s | valid loss/mse 0.5371 | err 0.1210\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "random masking at epoch 10, ratio of masked values in train: 0.0000\n", + "scGPT - INFO - | epoch 10 | 100/221 batches | lr 0.0000 | ms/batch 215.45 | loss 0.12 | cls 0.12 | err 0.03 | \n", + "scGPT - INFO - | epoch 10 | 200/221 batches | lr 0.0000 | ms/batch 207.70 | loss 0.10 | cls 0.10 | err 0.03 | \n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 10 | time: 48.86s | valid loss/mse 0.6581 | err 0.1274\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "best_val_loss = float(\"inf\")\n", + "best_avg_bio = 0.0\n", + "best_model = None\n", + "define_wandb_metrcis()\n", + "\n", + "for epoch in range(1, epochs + 1):\n", + " epoch_start_time = time.time()\n", + " train_data_pt, valid_data_pt = prepare_data(sort_seq_batch=per_seq_batch_sample)\n", + " train_loader = prepare_dataloader(\n", + " train_data_pt,\n", + " batch_size=batch_size,\n", + " shuffle=False,\n", + " intra_domain_shuffle=True,\n", + " drop_last=False,\n", + " )\n", + " valid_loader = prepare_dataloader(\n", + " valid_data_pt,\n", + " batch_size=eval_batch_size,\n", + " shuffle=False,\n", + " intra_domain_shuffle=False,\n", + " drop_last=False,\n", + " )\n", + "\n", + " if config.do_train:\n", + " train(\n", + " model,\n", + " loader=train_loader,\n", + " )\n", + " val_loss, val_err = evaluate(\n", + " model,\n", + " loader=valid_loader,\n", + " )\n", + " elapsed = time.time() - epoch_start_time\n", + " logger.info(\"-\" * 89)\n", + " logger.info(\n", + " f\"| end of epoch {epoch:3d} | time: {elapsed:5.2f}s | \"\n", + " f\"valid loss/mse {val_loss:5.4f} | err {val_err:5.4f}\"\n", + " )\n", + " logger.info(\"-\" * 89)\n", + "\n", + " if val_loss < best_val_loss:\n", + " best_val_loss = val_loss\n", + " best_model = copy.deepcopy(model)\n", + " best_model_epoch = epoch\n", + " logger.info(f\"Best model with score {best_val_loss:5.4f}\")\n", + "\n", + " scheduler.step()\n", + " if DAB_separate_optim:\n", + " scheduler_dab.step()\n", + " if ADV:\n", + " scheduler_D.step()\n", + " scheduler_E.step()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4a6ce176", + "metadata": {}, + "outputs": [], + "source": [ + "# %% inference\n", + "def test(model: nn.Module, adata: DataLoader) -> float:\n", + " all_counts = (\n", + " adata.layers[input_layer_key].toarray()\n", + " if issparse(adata.layers[input_layer_key])\n", + " else adata.layers[input_layer_key]\n", + " )\n", + "\n", + " celltypes_labels = adata.obs[\"celltype_id\"].tolist() # make sure count from 0\n", + " celltypes_labels = np.array(celltypes_labels)\n", + "\n", + " batch_ids = adata.obs[\"batch_id\"].tolist()\n", + " batch_ids = np.array(batch_ids)\n", + "\n", + " tokenized_test = tokenize_and_pad_batch(\n", + " all_counts,\n", + " gene_ids,\n", + " max_len=max_seq_len,\n", + " vocab=vocab,\n", + " pad_token=pad_token,\n", + " pad_value=pad_value,\n", + " append_cls=True, # append token at the beginning\n", + " include_zero_gene=include_zero_gene,\n", + " )\n", + "\n", + " input_values_test = random_mask_value(\n", + " tokenized_test[\"values\"],\n", + " mask_ratio=mask_ratio,\n", + " mask_value=mask_value,\n", + " pad_value=pad_value,\n", + " )\n", + "\n", + " test_data_pt = {\n", + " \"gene_ids\": tokenized_test[\"genes\"],\n", + " \"values\": input_values_test,\n", + " \"target_values\": tokenized_test[\"values\"],\n", + " \"batch_labels\": torch.from_numpy(batch_ids).long(),\n", + " \"celltype_labels\": torch.from_numpy(celltypes_labels).long(),\n", + " }\n", + "\n", + " test_loader = DataLoader(\n", + " dataset=SeqDataset(test_data_pt),\n", + " batch_size=eval_batch_size,\n", + " shuffle=False,\n", + " drop_last=False,\n", + " num_workers=min(len(os.sched_getaffinity(0)), eval_batch_size // 2),\n", + " pin_memory=True,\n", + " )\n", + "\n", + " model.eval()\n", + " predictions = evaluate(\n", + " model,\n", + " loader=test_loader,\n", + " return_raw=True,\n", + " )\n", + "\n", + " # compute accuracy, precision, recall, f1\n", + " from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n", + "\n", + " accuracy = accuracy_score(celltypes_labels, predictions)\n", + " precision = precision_score(celltypes_labels, predictions, average=\"macro\")\n", + " recall = recall_score(celltypes_labels, predictions, average=\"macro\")\n", + " macro_f1 = f1_score(celltypes_labels, predictions, average=\"macro\")\n", + "\n", + " logger.info(\n", + " f\"Accuracy: {accuracy:.3f}, Precision: {precision:.3f}, Recall: {recall:.3f}, \"\n", + " f\"Macro F1: {macro_f1:.3f}\"\n", + " )\n", + "\n", + " results = {\n", + " \"test/accuracy\": accuracy,\n", + " \"test/precision\": precision,\n", + " \"test/recall\": recall,\n", + " \"test/macro_f1\": macro_f1,\n", + " }\n", + "\n", + " return predictions, celltypes_labels, results" + ] + }, + { + "cell_type": "markdown", + "id": "16236bf2", + "metadata": {}, + "source": [ + "## Step 5: Inference with fine-tuned scGPT model\n", + "In the cell-type annotation task, the fine-tuned scGPT predicts cell-type labels for query set as inference. The model performance is evaluated on standard classificaton metrics. Here we visualize the predicted labels over the scGPT cell embeddings, and present the confusion matrix for detailed classification performance on the cell-group level." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "79730e19", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "scGPT - INFO - Accuracy: 0.875, Precision: 0.775, Recall: 0.737, Macro F1: 0.732\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 1165, + "width": 5399 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "predictions, labels, results = test(best_model, adata_test)\n", + "adata_test_raw.obs[\"predictions\"] = [id2type[p] for p in predictions]\n", + "\n", + "# plot\n", + "palette_ = plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"] \n", + "palette_ = plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"] + plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"] + plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]\n", + "palette_ = {c: palette_[i] for i, c in enumerate(celltypes)}\n", + "\n", + "with plt.rc_context({\"figure.figsize\": (6, 4), \"figure.dpi\": (300)}):\n", + " sc.pl.umap(\n", + " adata_test_raw,\n", + " color=[\"celltype\", \"predictions\"],\n", + " palette=palette_,\n", + " show=False,\n", + " )\n", + " plt.savefig(save_dir / \"results.png\", dpi=300)\n", + "\n", + "save_dict = {\n", + " \"predictions\": predictions,\n", + " \"labels\": labels,\n", + " \"results\": results,\n", + " \"id_maps\": id2type\n", + "}\n", + "with open(save_dir / \"results.pkl\", \"wb\") as f:\n", + " pickle.dump(save_dict, f)\n", + "\n", + "results[\"test/cell_umap\"] = wandb.Image(\n", + " str(save_dir / \"results.png\"),\n", + " caption=f\"predictions macro f1 {results['test/macro_f1']:.3f}\",\n", + ")\n", + "wandb.log(results)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "b72676ab", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 927, + "width": 919 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.metrics import confusion_matrix\n", + "celltypes = list(celltypes)\n", + "for i in set([id2type[p] for p in predictions]):\n", + " if i not in celltypes:\n", + " celltypes.remove(i)\n", + "cm = confusion_matrix(labels, predictions)\n", + "cm = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis]\n", + "cm = pd.DataFrame(cm, index=celltypes[:cm.shape[0]], columns=celltypes[:cm.shape[1]])\n", + "plt.figure(figsize=(10, 10))\n", + "sns.heatmap(cm, annot=True, fmt=\".1f\", cmap=\"Blues\")\n", + "plt.savefig(save_dir / \"confusion_matrix.png\", dpi=300)\n", + "\n", + "results[\"test/confusion_matrix\"] = wandb.Image(\n", + " str(save_dir / \"confusion_matrix.png\"),\n", + " caption=f\"confusion matrix\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "62ebbbb9", + "metadata": {}, + "outputs": [], + "source": [ + "# save the model into the save_dir\n", + "torch.save(best_model.state_dict(), save_dir / \"model.pt\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/transfer/code/scGPT/tutorials/Tutorial_Attention_GRN.ipynb b/transfer/code/scGPT/tutorials/Tutorial_Attention_GRN.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..2705144e4473d62c5c85d071ae7e66f75668269a --- /dev/null +++ b/transfer/code/scGPT/tutorials/Tutorial_Attention_GRN.ipynb @@ -0,0 +1,1637 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "3bbebfa3", + "metadata": {}, + "source": [ + "# Attention-based GRN Inference on Fine-tuned Model\n", + "Here we use the fine-tuned blood model on the Adamson perturbation dataset as an example of the cell-state specific GRN inference via attention weights. scGPT outputs attention weights on the individual cell level, which can be further aggregated by cell states. In this particular example, we compare the most influenced genes between a transcription factor repression condition (perturbed) and the control. However, this attention-based GRN inference is not restricted to perturbation-based discoveries. It can also be used to compare between cell states in general, such as healthy v.s. diseased, undifferentiated v.s. differentiated, as a broader application.\n", + "\n", + "Users may perform scGPT's attention-based GRN inference in the following steps:\n", + "\n", + " 1. Load fine-tuned scGPT model and data\n", + " \n", + " 2. Retrieve scGPT's attention weights by condition (i.e., cell states)\n", + " \n", + " 3. Perform scGPT's rank-based most influenced gene selection\n", + " \n", + " 4. Validate the most influenced gene list against existing databases" + ] + }, + { + "cell_type": "markdown", + "id": "36b674f4", + "metadata": {}, + "source": [ + "NOTE in advance: to run this tutorial notebook, you may need to download the fine-tuned model from [link](https://drive.google.com/drive/folders/1HsPrwYGPXm867_u_Ye0W4Ch8AFSneXAn) and the list of targets of BHLHE40 from CHIP-Atlas for evaluation from [link](https://drive.google.com/drive/folders/1nc1LywRHlzt4Z17McfXiqBWgoGbRNyc0)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "8a59dea6", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Global seed set to 0\n", + "/h/chloexq/.cache/pypoetry/virtualenvs/scgpt--qSLVbd1-py3.9/lib/python3.9/site-packages/flatbuffers/compat.py:19: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses\n", + " import imp\n" + ] + } + ], + "source": [ + "import copy\n", + "import json\n", + "import os\n", + "from pathlib import Path\n", + "import sys\n", + "import warnings\n", + "\n", + "import torch\n", + "from anndata import AnnData\n", + "import scanpy as sc\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import networkx as nx\n", + "import pandas as pd\n", + "import tqdm\n", + "import gseapy as gp\n", + "from gears import PertData, GEARS\n", + "\n", + "from scipy.sparse import issparse\n", + "import scipy as sp\n", + "from einops import rearrange\n", + "from torch.nn.functional import softmax\n", + "from tqdm import tqdm\n", + "import pandas as pd\n", + "\n", + "from torchtext.vocab import Vocab\n", + "from torchtext._torchtext import (\n", + " Vocab as VocabPybind,\n", + ")\n", + "\n", + "sys.path.insert(0, \"../\")\n", + "\n", + "import scgpt as scg\n", + "from scgpt.tasks import GeneEmbedding\n", + "from scgpt.tokenizer.gene_tokenizer import GeneVocab\n", + "from scgpt.model import TransformerModel\n", + "from scgpt.utils import set_seed \n", + "from scgpt.tokenizer import tokenize_and_pad_batch\n", + "from scgpt.preprocess import Preprocessor\n", + "\n", + "os.environ[\"KMP_WARNINGS\"] = \"off\"\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "1d846b2e", + "metadata": {}, + "outputs": [], + "source": [ + "set_seed(42)\n", + "pad_token = \"\"\n", + "special_tokens = [pad_token, \"\", \"\"]\n", + "n_hvg = 1200\n", + "n_bins = 51\n", + "mask_value = -1\n", + "pad_value = -2\n", + "n_input_bins = n_bins" + ] + }, + { + "cell_type": "markdown", + "id": "2c246caf", + "metadata": {}, + "source": [ + "## Step 1: Load fine-tuned model and dataset" + ] + }, + { + "cell_type": "markdown", + "id": "b8e73800", + "metadata": {}, + "source": [ + "### 1.1 Load fine-tuned model\n", + "\n", + "We are going to load a fine-tuned model for the gene interaction analysis on Adamson dataset. The fine-tuned model can be downloaded via this [link](https://drive.google.com/drive/folders/1HsPrwYGPXm867_u_Ye0W4Ch8AFSneXAn). The dataset will be loaded in the next step 1.2.\n", + "\n", + "To reproduce the provided fine-tuned model. Please followw the integration fin-tuning pipeline to fine-tune the pre-trained blood model on the Adamson perturbation dataset. Note that in the fine-tuning stage, we did not perform highly vairable gene selection but trained on the 5000+ genes present in the Adamson dataset. This is to provide flexbility in the inference stage to investigate changes in attention maps across different perturbation conditions. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "7a45d1c9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Resume model from ../save/finetuned_scGPT_adamson/best_model.pt, the model args will override the config ../save/finetuned_scGPT_adamson/args.json.\n" + ] + } + ], + "source": [ + "# Specify model path; here we load the scGPT blood model fine-tuned on adamson\n", + "model_dir = Path(\"../save/finetuned_scGPT_adamson\")\n", + "model_config_file = model_dir / \"args.json\"\n", + "model_file = model_dir / \"best_model.pt\"\n", + "vocab_file = model_dir / \"vocab.json\"\n", + "\n", + "vocab = GeneVocab.from_file(vocab_file)\n", + "for s in special_tokens:\n", + " if s not in vocab:\n", + " vocab.append_token(s)\n", + "\n", + "# Retrieve model parameters from config files\n", + "with open(model_config_file, \"r\") as f:\n", + " model_configs = json.load(f)\n", + "print(\n", + " f\"Resume model from {model_file}, the model args will override the \"\n", + " f\"config {model_config_file}.\"\n", + ")\n", + "embsize = model_configs[\"embsize\"]\n", + "nhead = model_configs[\"nheads\"]\n", + "d_hid = model_configs[\"d_hid\"]\n", + "nlayers = model_configs[\"nlayers\"]\n", + "n_layers_cls = model_configs[\"n_layers_cls\"]\n", + "\n", + "gene2idx = vocab.get_stoi()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "fed785b4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using simple batchnorm instead of domain specific batchnorm\n", + "Loading params encoder.embedding.weight with shape torch.Size([36574, 512])\n", + "Loading params encoder.enc_norm.weight with shape torch.Size([512])\n", + "Loading params encoder.enc_norm.bias with shape torch.Size([512])\n", + "Loading params value_encoder.linear1.weight with shape torch.Size([512, 1])\n", + "Loading params value_encoder.linear1.bias with shape torch.Size([512])\n", + "Loading 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track_running_stats=True)\n", + " (transformer_encoder): TransformerEncoder(\n", + " (layers): ModuleList(\n", + " (0): FlashTransformerEncoderLayer(\n", + " (self_attn): FlashMHA(\n", + " (Wqkv): Linear(in_features=512, out_features=1536, bias=True)\n", + " (inner_attn): FlashAttention()\n", + " (out_proj): Linear(in_features=512, out_features=512, bias=True)\n", + " )\n", + " (linear1): Linear(in_features=512, out_features=512, bias=True)\n", + " (dropout): Dropout(p=0.5, inplace=False)\n", + " (linear2): Linear(in_features=512, out_features=512, bias=True)\n", + " (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (dropout1): Dropout(p=0.5, inplace=False)\n", + " (dropout2): Dropout(p=0.5, inplace=False)\n", + " )\n", + " (1): FlashTransformerEncoderLayer(\n", + " (self_attn): FlashMHA(\n", + " (Wqkv): Linear(in_features=512, out_features=1536, bias=True)\n", + " (inner_attn): FlashAttention()\n", + " (out_proj): Linear(in_features=512, out_features=512, bias=True)\n", + " )\n", + " (linear1): Linear(in_features=512, out_features=512, bias=True)\n", + " (dropout): Dropout(p=0.5, inplace=False)\n", + " (linear2): Linear(in_features=512, out_features=512, bias=True)\n", + " (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (dropout1): Dropout(p=0.5, inplace=False)\n", + " (dropout2): Dropout(p=0.5, inplace=False)\n", + " )\n", + " (2): FlashTransformerEncoderLayer(\n", + " (self_attn): FlashMHA(\n", + " (Wqkv): Linear(in_features=512, out_features=1536, bias=True)\n", + " (inner_attn): FlashAttention()\n", + " (out_proj): Linear(in_features=512, out_features=512, bias=True)\n", + " )\n", + " (linear1): Linear(in_features=512, out_features=512, bias=True)\n", + " (dropout): Dropout(p=0.5, inplace=False)\n", + " (linear2): Linear(in_features=512, out_features=512, bias=True)\n", + " (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (dropout1): Dropout(p=0.5, inplace=False)\n", + " (dropout2): Dropout(p=0.5, inplace=False)\n", + " )\n", + " (3): FlashTransformerEncoderLayer(\n", + " (self_attn): FlashMHA(\n", + " (Wqkv): Linear(in_features=512, out_features=1536, bias=True)\n", + " (inner_attn): FlashAttention()\n", + " (out_proj): Linear(in_features=512, out_features=512, bias=True)\n", + " )\n", + " (linear1): Linear(in_features=512, out_features=512, bias=True)\n", + " (dropout): Dropout(p=0.5, inplace=False)\n", + " (linear2): Linear(in_features=512, out_features=512, bias=True)\n", + " (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (dropout1): Dropout(p=0.5, inplace=False)\n", + " (dropout2): Dropout(p=0.5, inplace=False)\n", + " )\n", + " (4): FlashTransformerEncoderLayer(\n", + " (self_attn): FlashMHA(\n", + " (Wqkv): Linear(in_features=512, out_features=1536, bias=True)\n", + " (inner_attn): FlashAttention()\n", + " (out_proj): Linear(in_features=512, out_features=512, bias=True)\n", + " )\n", + " (linear1): Linear(in_features=512, out_features=512, bias=True)\n", + " (dropout): Dropout(p=0.5, inplace=False)\n", + " (linear2): Linear(in_features=512, out_features=512, bias=True)\n", + " (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (dropout1): Dropout(p=0.5, inplace=False)\n", + " (dropout2): Dropout(p=0.5, inplace=False)\n", + " )\n", + " (5): FlashTransformerEncoderLayer(\n", + " (self_attn): FlashMHA(\n", + " (Wqkv): Linear(in_features=512, out_features=1536, bias=True)\n", + " (inner_attn): FlashAttention()\n", + " (out_proj): Linear(in_features=512, out_features=512, bias=True)\n", + " )\n", + " (linear1): Linear(in_features=512, out_features=512, bias=True)\n", + " (dropout): Dropout(p=0.5, inplace=False)\n", + " (linear2): Linear(in_features=512, out_features=512, bias=True)\n", + " (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (dropout1): Dropout(p=0.5, inplace=False)\n", + " (dropout2): Dropout(p=0.5, inplace=False)\n", + " )\n", + " (6): FlashTransformerEncoderLayer(\n", + " (self_attn): FlashMHA(\n", + " (Wqkv): Linear(in_features=512, out_features=1536, bias=True)\n", + " (inner_attn): FlashAttention()\n", + " (out_proj): Linear(in_features=512, out_features=512, bias=True)\n", + " )\n", + " (linear1): Linear(in_features=512, out_features=512, bias=True)\n", + " (dropout): Dropout(p=0.5, inplace=False)\n", + " (linear2): Linear(in_features=512, out_features=512, bias=True)\n", + " (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (dropout1): Dropout(p=0.5, inplace=False)\n", + " (dropout2): Dropout(p=0.5, inplace=False)\n", + " )\n", + " (7): FlashTransformerEncoderLayer(\n", + " (self_attn): FlashMHA(\n", + " (Wqkv): Linear(in_features=512, out_features=1536, bias=True)\n", + " (inner_attn): FlashAttention()\n", + " (out_proj): Linear(in_features=512, out_features=512, bias=True)\n", + " )\n", + " (linear1): Linear(in_features=512, out_features=512, bias=True)\n", + " (dropout): Dropout(p=0.5, inplace=False)\n", + " (linear2): Linear(in_features=512, out_features=512, bias=True)\n", + " (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (dropout1): Dropout(p=0.5, inplace=False)\n", + " (dropout2): Dropout(p=0.5, inplace=False)\n", + " )\n", + " (8): FlashTransformerEncoderLayer(\n", + " (self_attn): FlashMHA(\n", + " (Wqkv): Linear(in_features=512, out_features=1536, bias=True)\n", + " (inner_attn): FlashAttention()\n", + " (out_proj): Linear(in_features=512, out_features=512, bias=True)\n", + " )\n", + " (linear1): Linear(in_features=512, out_features=512, bias=True)\n", + " (dropout): Dropout(p=0.5, inplace=False)\n", + " (linear2): Linear(in_features=512, out_features=512, bias=True)\n", + " (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (dropout1): Dropout(p=0.5, inplace=False)\n", + " (dropout2): Dropout(p=0.5, inplace=False)\n", + " )\n", + " (9): FlashTransformerEncoderLayer(\n", + " (self_attn): FlashMHA(\n", + " (Wqkv): Linear(in_features=512, out_features=1536, bias=True)\n", + " (inner_attn): FlashAttention()\n", + " (out_proj): Linear(in_features=512, out_features=512, bias=True)\n", + " )\n", + " (linear1): Linear(in_features=512, out_features=512, bias=True)\n", + " (dropout): Dropout(p=0.5, inplace=False)\n", + " (linear2): Linear(in_features=512, out_features=512, bias=True)\n", + " (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (dropout1): Dropout(p=0.5, inplace=False)\n", + " (dropout2): Dropout(p=0.5, inplace=False)\n", + " )\n", + " (10): FlashTransformerEncoderLayer(\n", + " (self_attn): FlashMHA(\n", + " (Wqkv): Linear(in_features=512, out_features=1536, bias=True)\n", + " (inner_attn): FlashAttention()\n", + " (out_proj): Linear(in_features=512, out_features=512, bias=True)\n", + " )\n", + " (linear1): Linear(in_features=512, out_features=512, bias=True)\n", + " (dropout): Dropout(p=0.5, inplace=False)\n", + " (linear2): Linear(in_features=512, out_features=512, bias=True)\n", + " (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (dropout1): Dropout(p=0.5, inplace=False)\n", + " (dropout2): Dropout(p=0.5, inplace=False)\n", + " )\n", + " (11): FlashTransformerEncoderLayer(\n", + " (self_attn): FlashMHA(\n", + " (Wqkv): Linear(in_features=512, out_features=1536, bias=True)\n", + " (inner_attn): FlashAttention()\n", + " (out_proj): Linear(in_features=512, out_features=512, bias=True)\n", + " )\n", + " (linear1): Linear(in_features=512, out_features=512, bias=True)\n", + " (dropout): Dropout(p=0.5, inplace=False)\n", + " (linear2): Linear(in_features=512, out_features=512, bias=True)\n", + " (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (dropout1): Dropout(p=0.5, inplace=False)\n", + " (dropout2): Dropout(p=0.5, inplace=False)\n", + " )\n", + " )\n", + " )\n", + " (decoder): ExprDecoder(\n", + " (fc): Sequential(\n", + " (0): Linear(in_features=512, out_features=512, bias=True)\n", + " (1): LeakyReLU(negative_slope=0.01)\n", + " (2): Linear(in_features=512, out_features=512, bias=True)\n", + " (3): LeakyReLU(negative_slope=0.01)\n", + " (4): Linear(in_features=512, out_features=1, bias=True)\n", + " )\n", + " )\n", + " (cls_decoder): ClsDecoder(\n", + " (_decoder): ModuleList(\n", + " (0): Linear(in_features=512, out_features=512, bias=True)\n", + " (1): ReLU()\n", + " (2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (3): Linear(in_features=512, out_features=512, bias=True)\n", + " (4): ReLU()\n", + " (5): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " )\n", + " (out_layer): Linear(in_features=512, out_features=1, bias=True)\n", + " )\n", + " (sim): Similarity(\n", + " (cos): CosineSimilarity()\n", + " )\n", + " (creterion_cce): CrossEntropyLoss()\n", + ")" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "\n", + "ntokens = len(vocab) # size of vocabulary\n", + "model = TransformerModel(\n", + " ntokens,\n", + " embsize,\n", + " nhead,\n", + " d_hid,\n", + " nlayers,\n", + " vocab=vocab,\n", + " pad_value=pad_value,\n", + " n_input_bins=n_input_bins,\n", + " use_fast_transformer=True,\n", + ")\n", + "\n", + "try:\n", + " model.load_state_dict(torch.load(model_file))\n", + " print(f\"Loading all model params from {model_file}\")\n", + "except:\n", + " # only load params that are in the model and match the size\n", + " model_dict = model.state_dict()\n", + " pretrained_dict = torch.load(model_file)\n", + " pretrained_dict = {\n", + " k: v\n", + " for k, v in pretrained_dict.items()\n", + " if k in model_dict and v.shape == model_dict[k].shape\n", + " }\n", + " for k, v in pretrained_dict.items():\n", + " print(f\"Loading params {k} with shape {v.shape}\")\n", + " model_dict.update(pretrained_dict)\n", + " model.load_state_dict(model_dict)\n", + "\n", + "model.to(device)" + ] + }, + { + "cell_type": "markdown", + "id": "ebdda8f4", + "metadata": {}, + "source": [ + "### 1.2 Load dataset of interest\n", + "The Adamson perturbation dataset is retrieved from the GEARS package with the following code." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "de3c1d15", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Found local copy...\n", + "Local copy of pyg dataset is detected. Loading...\n", + "Done!\n" + ] + } + ], + "source": [ + "data_dir = Path(\"../data\")\n", + "pert_data = PertData(data_dir)\n", + "pert_data.load(data_name=\"adamson\")\n", + "adata = sc.read(data_dir / \"adamson/perturb_processed.h5ad\")\n", + "ori_batch_col = \"control\"\n", + "adata.obs[\"celltype\"] = adata.obs[\"condition\"].astype(\"category\")\n", + "adata.obs[\"str_batch\"] = adata.obs[\"control\"].astype(str)\n", + "data_is_raw = False" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b0feb668", + "metadata": {}, + "outputs": [], + "source": [ + "adata.var[\"id_in_vocab\"] = [1 if gene in vocab else -1 for gene in adata.var[\"gene_name\"]]\n", + "gene_ids_in_vocab = np.array(adata.var[\"id_in_vocab\"])\n", + "adata = adata[:, adata.var[\"id_in_vocab\"] >= 0]" + ] + }, + { + "cell_type": "markdown", + "id": "eda94662", + "metadata": {}, + "source": [ + "In the scGPT workflow, we compare each TF perturbation condition with control one at a time. In each run, the data is subsetted to contain one TF and control only. In this example, we use the TF BHLHE40." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d99c7d82", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['BHLHE40+ctrl', 'ctrl'], dtype=object)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "TF_name = 'BHLHE40'\n", + "adata = adata[adata.obs.condition.isin(['{}+ctrl'.format(TF_name), 'ctrl'])].copy()\n", + "np.unique(adata.obs.condition)" + ] + }, + { + "cell_type": "markdown", + "id": "e5a8d08c", + "metadata": {}, + "source": [ + "We further pre-process the subsetted data following the scGPT pipeline." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "b81799b8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "scGPT - INFO - Filtering genes by counts ...\n", + "scGPT - INFO - Filtering cells by counts ...\n", + "scGPT - INFO - Normalizing total counts ...\n", + "scGPT - INFO - Binning data ...\n" + ] + } + ], + "source": [ + "preprocessor = Preprocessor(\n", + " use_key=\"X\", # the key in adata.layers to use as raw data\n", + " filter_gene_by_counts=3, # step 1\n", + " filter_cell_by_counts=False, # step 2\n", + " normalize_total=1e4, # 3. whether to normalize the raw data and to what sum\n", + " result_normed_key=\"X_normed\", # the key in adata.layers to store the normalized data\n", + " log1p=data_is_raw, # 4. whether to log1p the normalized data\n", + " result_log1p_key=\"X_log1p\",\n", + " subset_hvg= False, # 5. whether to subset the raw data to highly variable genes\n", + " hvg_flavor=\"seurat_v3\" if data_is_raw else \"cell_ranger\",\n", + " binning=n_bins, # 6. whether to bin the raw data and to what number of bins\n", + " result_binned_key=\"X_binned\", # the key in adata.layers to store the binned data\n", + ")\n", + "preprocessor(adata, batch_key=\"str_batch\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "da616114", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AnnData object with n_obs × n_vars = 24767 × 1200\n", + " obs: 'condition', 'cell_type', 'dose_val', 'control', 'condition_name', 'celltype', 'str_batch', 'n_counts'\n", + " var: 'gene_name', 'id_in_vocab', 'n_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'highly_variable_nbatches', 'highly_variable_intersection'\n", + " uns: 'non_dropout_gene_idx', 'non_zeros_gene_idx', 'rank_genes_groups_cov_all', 'top_non_dropout_de_20', 'top_non_zero_de_20', 'hvg'\n", + " obsm: 'bin_edges'\n", + " layers: 'X_normed', 'X_binned'\n" + ] + } + ], + "source": [ + "sc.pp.highly_variable_genes(\n", + " adata,\n", + " layer=None,\n", + " n_top_genes=1200,\n", + " batch_key=\"str_batch\",\n", + " flavor=\"seurat_v3\" if data_is_raw else \"cell_ranger\",\n", + " subset=False,\n", + ")\n", + "adata.var.loc[adata.var[adata.var.gene_name==TF_name].index, 'highly_variable'] = True\n", + "adata = adata[:, adata.var[\"highly_variable\"]].copy()\n", + "print(adata)" + ] + }, + { + "cell_type": "markdown", + "id": "2bd3ffd5", + "metadata": {}, + "source": [ + "## Step 2: Retrieve scGPT's attention weights" + ] + }, + { + "cell_type": "markdown", + "id": "36cc26b6", + "metadata": {}, + "source": [ + "### 2.1 Prepare model input" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c5639266", + "metadata": {}, + "outputs": [], + "source": [ + "input_layer_key = \"X_binned\"\n", + "all_counts = (\n", + " adata.layers[input_layer_key].toarray()\n", + " if issparse(adata.layers[input_layer_key])\n", + " else adata.layers[input_layer_key]\n", + ")\n", + "genes = adata.var[\"gene_name\"].tolist()\n", + "gene_ids = np.array(vocab(genes), dtype=int)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "83bc0468", + "metadata": {}, + "outputs": [], + "source": [ + "batch_size = 16\n", + "tokenized_all = tokenize_and_pad_batch(\n", + " all_counts,\n", + " gene_ids,\n", + " max_len=len(genes)+1,\n", + " vocab=vocab,\n", + " pad_token=pad_token,\n", + " pad_value=pad_value,\n", + " append_cls=True, # append token at the beginning\n", + " include_zero_gene=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "5b4bea9d", + "metadata": {}, + "outputs": [], + "source": [ + "all_gene_ids, all_values = tokenized_all[\"genes\"], tokenized_all[\"values\"]\n", + "src_key_padding_mask = all_gene_ids.eq(vocab[pad_token])\n", + "condition_ids = np.array(adata.obs[\"condition\"].tolist())" + ] + }, + { + "cell_type": "markdown", + "id": "f6661cd8", + "metadata": {}, + "source": [ + "### 2.1 Retrieve attention weights\n", + "Note that since the flash-attn package does not output attention scores, we manually calculate q @ k.T to extract the attention weights. Users may specify which layer to extract the attention weights from. In the manuscript, we used the attention weights from the last (12th) layer." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "bb6993a7", + "metadata": {}, + "outputs": [], + "source": [ + "torch.cuda.empty_cache()\n", + "dict_sum_condition = {}" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "91bd96f8", + "metadata": {}, + "outputs": [], + "source": [ + "# Use this argument to specify which layer to extract the attention weights from\n", + "# Default to 11, extraction from the last (12th) layer. Note that index starts from 0\n", + "num_attn_layers = 11 " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "53301b87", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1548/1548 [08:56<00:00, 2.88it/s]\n" + ] + } + ], + "source": [ + "model.eval()\n", + "with torch.no_grad(), torch.cuda.amp.autocast(enabled=True):\n", + " M = all_gene_ids.size(1)\n", + " N = all_gene_ids.size(0)\n", + " device = next(model.parameters()).device\n", + " for i in tqdm(range(0, N, batch_size)):\n", + " batch_size = all_gene_ids[i : i + batch_size].size(0)\n", + " outputs = np.zeros((batch_size, M, M), dtype=np.float32)\n", + " # Replicate the operations in model forward pass\n", + " src_embs = model.encoder(torch.tensor(all_gene_ids[i : i + batch_size], dtype=torch.long).to(device))\n", + " val_embs = model.value_encoder(torch.tensor(all_values[i : i + batch_size], dtype=torch.float).to(device))\n", + " total_embs = src_embs + val_embs\n", + " total_embs = model.bn(total_embs.permute(0, 2, 1)).permute(0, 2, 1)\n", + " # Send total_embs to attention layers for attention operations\n", + " # Retrieve the output from second to last layer\n", + " for layer in model.transformer_encoder.layers[:num_attn_layers]:\n", + " total_embs = layer(total_embs, src_key_padding_mask=src_key_padding_mask[i : i + batch_size].to(device))\n", + " # Send total_embs to the last layer in flash-attn\n", + " # https://github.com/HazyResearch/flash-attention/blob/1b18f1b7a133c20904c096b8b222a0916e1b3d37/flash_attn/flash_attention.py#L90\n", + " qkv = model.transformer_encoder.layers[num_attn_layers].self_attn.Wqkv(total_embs)\n", + " # Retrieve q, k, and v from flast-attn wrapper\n", + " qkv = rearrange(qkv, 'b s (three h d) -> b s three h d', three=3, h=8)\n", + " q = qkv[:, :, 0, :, :]\n", + " k = qkv[:, :, 1, :, :]\n", + " v = qkv[:, :, 2, :, :]\n", + " # https://towardsdatascience.com/illustrated-self-attention-2d627e33b20a\n", + " # q = [batch, gene, n_heads, n_hid]\n", + " # k = [batch, gene, n_heads, n_hid]\n", + " # attn_scores = [batch, n_heads, gene, gene]\n", + " attn_scores = q.permute(0, 2, 1, 3) @ k.permute(0, 2, 3, 1)\n", + " # Rank normalization by row\n", + " attn_scores = attn_scores.reshape((-1, M))\n", + " order = torch.argsort(attn_scores, dim=1)\n", + " rank = torch.argsort(order, dim=1)\n", + " attn_scores = rank.reshape((-1, 8, M, M))/M\n", + " # Rank normalization by column\n", + " attn_scores = attn_scores.permute(0, 1, 3, 2).reshape((-1, M))\n", + " order = torch.argsort(attn_scores, dim=1)\n", + " rank = torch.argsort(order, dim=1)\n", + " attn_scores = (rank.reshape((-1, 8, M, M))/M).permute(0, 1, 3, 2)\n", + " # Average 8 attention heads\n", + " attn_scores = attn_scores.mean(1)\n", + " \n", + " outputs = attn_scores.detach().cpu().numpy()\n", + " \n", + " for index in range(batch_size):\n", + " # Keep track of sum per condition\n", + " c = condition_ids[i : i + batch_size][index]\n", + " if c not in dict_sum_condition:\n", + " dict_sum_condition[c] = np.zeros((M, M), dtype=np.float32)\n", + " else:\n", + " dict_sum_condition[c] += outputs[index, :, :]" + ] + }, + { + "cell_type": "markdown", + "id": "571d16b4", + "metadata": {}, + "source": [ + "### 2.2 Average rank-normed attention weights by condition\n", + "In the previous step, we retrieve the attention weights for all cells and keep the running sum by condition (i.e., control, perturbed). We further calculate the mean here by dividing the number of cells per condition to obtain a gene * gene attention matrix for each condition." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "2544f5dc", + "metadata": {}, + "outputs": [], + "source": [ + "groups = adata.obs.groupby('condition').groups" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "8485f055", + "metadata": {}, + "outputs": [], + "source": [ + "dict_sum_condition_mean = dict_sum_condition.copy()\n", + "for i in groups.keys():\n", + " dict_sum_condition_mean[i] = dict_sum_condition_mean[i]/len(groups[i])\n", + "gene_vocab_idx = all_gene_ids[0].clone().detach().cpu().numpy()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "7b23f521", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'ctrl': array([[0.7667165 , 0.5071312 , 0.363181 , ..., 0.6558552 , 0.6433118 ,\n", + " 0.5029277 ],\n", + " [0.35063276, 0.90670127, 0.32875505, ..., 0.67563516, 0.4646933 ,\n", + " 0.4744476 ],\n", + " [0.5349599 , 0.30492574, 0.63999563, ..., 0.37222496, 0.45482123,\n", + " 0.4780819 ],\n", + " ...,\n", + " [0.5522125 , 0.72981244, 0.347227 , ..., 0.7278823 , 0.5751889 ,\n", + " 0.48467204],\n", + " [0.5728605 , 0.54362035, 0.49283794, ..., 0.5536558 , 0.71726304,\n", + " 0.59353757],\n", + " [0.646479 , 0.52209556, 0.410678 , ..., 0.5902157 , 0.6444179 ,\n", + " 0.54989314]], dtype=float32),\n", + " 'BHLHE40+ctrl': array([[0.6798215 , 0.43131036, 0.39218244, ..., 0.67530143, 0.6967861 ,\n", + " 0.5009627 ],\n", + " [0.28934428, 0.809951 , 0.30970335, ..., 0.61639196, 0.26737106,\n", + " 0.2312922 ],\n", + " [0.5355754 , 0.28648922, 0.6779555 , ..., 0.3633715 , 0.51926744,\n", + " 0.59302956],\n", + " ...,\n", + " [0.5082808 , 0.69850725, 0.34680215, ..., 0.75059116, 0.61094743,\n", + " 0.42005002],\n", + " [0.60800856, 0.47265798, 0.58815396, ..., 0.54617417, 0.76434106,\n", + " 0.635293 ],\n", + " [0.67291903, 0.40639865, 0.5096317 , ..., 0.52571416, 0.6641971 ,\n", + " 0.5768942 ]], dtype=float32)}" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dict_sum_condition_mean" + ] + }, + { + "cell_type": "markdown", + "id": "066e8d9a", + "metadata": {}, + "source": [ + "## Step 3: Perform most influenced gene selection" + ] + }, + { + "cell_type": "markdown", + "id": "5b89dbcf", + "metadata": {}, + "source": [ + "In the manuscript, we proposed 3 settings for the most influenced gene selection, namely *Control*, *Perturb*, and *Difference*. In this example, we focus on the *Difference* setting to explore how the gene-gene network changes after perturbation compared to control." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "c9c4abeb", + "metadata": {}, + "outputs": [], + "source": [ + "def get_topk_most_influenced_genes(topk, setting):\n", + " attn_top_gene_dict = {}\n", + " attn_top_scores_dict = {}\n", + " for i in groups.keys():\n", + " if i != 'ctrl':\n", + " knockout_gene = i.split('+')[0]\n", + " knockout_gene_idx = np.where(gene_vocab_idx==vocab([knockout_gene])[0])[0][0]\n", + " control = dict_sum_condition_mean['ctrl'][:, knockout_gene_idx]\n", + " exp = dict_sum_condition_mean[i][:, knockout_gene_idx]\n", + " # Chnage this line to exp, control, exp-control for three different settings\n", + " if setting == 'difference':\n", + " a = exp-control\n", + " elif setting == 'control':\n", + " a = control\n", + " elif setting == 'experiment':\n", + " a = exp\n", + " diff_idx = np.argpartition(a, -topk)[-topk:]\n", + " scores = (a)[diff_idx]\n", + " attn_top_genes = vocab.lookup_tokens(gene_vocab_idx[diff_idx]) + [TF_name]\n", + " attn_top_gene_dict[i] = list(attn_top_genes)\n", + " attn_top_scores_dict[i] = list(scores)\n", + " return attn_top_gene_dict, attn_top_scores_dict" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "cceab4f4", + "metadata": {}, + "outputs": [], + "source": [ + "gene_vocab_idx = all_gene_ids[0].clone().detach().cpu().numpy()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "cd2a086b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.27003407, 0.27020293, 0.27515042, 0.28356528, 0.2868172, 0.2719887, 0.27392942, 0.28411555, 0.27914363, 0.27320516, 0.27022785, 0.2799158, 0.27595383, 0.27598792, 0.27211004, 0.28957933, 0.29275084, 0.2942825, 0.30242187, 0.29776084]\n", + "['SH3BGRL3', 'HBA1', 'PSMD4', 'FKBP2', 'HSPA8', 'HNRNPD', 'NDUFB6', 'RTN4', 'DNAJA1', 'SSR2', 'NENF', 'UQCRB', 'HSP90B1', 'PRRC2C', 'ADRM1', 'DBI', 'HSPB1', 'HMGN3', 'TMBIM6', 'ACAT2', 'BHLHE40']\n" + ] + } + ], + "source": [ + "# Specify top k number of genes to be selected, and the selection setting\n", + "# Here calculate top 20 most influenced genes for CHIP-Atlas validation\n", + "topk = 20\n", + "setting = 'difference' # \"control\", \"perturbed\"\n", + "assert setting in [\"difference\", \"control\", \"perturbed\"]\n", + "attn_top_gene_dict_20, attn_top_scores_dict_20 = get_topk_most_influenced_genes(topk, setting)\n", + "print(attn_top_scores_dict_20[TF_name + '+ctrl'])\n", + "print(attn_top_gene_dict_20[TF_name + '+ctrl'])" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "802bcd2c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "if setting == 'difference':\n", + " for i in attn_top_gene_dict_20.keys():\n", + " example_genes = attn_top_gene_dict_20[i]\n", + " gene_idx = [np.where(gene_vocab_idx==vocab([g])[0])[0][0] for g in example_genes]\n", + " scores = dict_sum_condition_mean[i][gene_idx, :][:, gene_idx]-dict_sum_condition_mean['ctrl'][gene_idx, :][:, gene_idx]\n", + " df_scores = pd.DataFrame(data = scores, columns = example_genes, index = example_genes)\n", + " plt.figure(figsize=(6, 6), dpi=300)\n", + " ax = sns.clustermap(df_scores, annot=False, cmap=sns.diverging_palette(145, 300, s=60, as_cmap=True), fmt='.2f', vmin=-0.3, vmax=0.3) \n", + " plt.show()\n", + " plt.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "9b0693fa", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.13435426, 0.13445804, 0.13521338, 0.13658851, 0.13494265, 0.1378941, 0.25253183, 0.27598792, 0.27320516, 0.19422996, 0.1749267, 0.14947963, 0.28356528, 0.19787788, 0.21807176, 0.27211004, 0.19592029, 0.192339, 0.26201934, 0.27022785, 0.18904561, 0.2630661, 0.17665666, 0.25517708, 0.1592493, 0.28411555, 0.15643954, 0.17658818, 0.28957933, 0.23216242, 0.27515042, 0.17963398, 0.17776287, 0.21403193, 0.1494323, 0.26097095, 0.2147252, 0.2868172, 0.1864397, 0.19282275, 0.17112768, 0.18815774, 0.19837373, 0.14006501, 0.15750575, 0.22382861, 0.2719887, 0.25040454, 0.25988054, 0.26159328, 0.14806324, 0.20562506, 0.26052994, 0.20960271, 0.17409635, 0.22525501, 0.26490605, 0.20550716, 0.15476418, 0.14542323, 0.30242187, 0.16769892, 0.1891504, 0.16443193, 0.2682315, 0.25884175, 0.25024498, 0.26073533, 0.15007758, 0.27020293, 0.27914363, 0.24032599, 0.16723317, 0.18915558, 0.27392942, 0.15589827, 0.1956358, 0.15076661, 0.14257717, 0.2255286, 0.18222451, 0.2942825, 0.18665028, 0.29776084, 0.29275084, 0.27003407, 0.14769578, 0.15558112, 0.24545509, 0.23430616, 0.23824137, 0.2799158, 0.27595383, 0.23613232, 0.17815387, 0.25799888, 0.2626958, 0.23995376, 0.19605911, 0.22703129]\n", + "['IL1RN', 'ARHGDIB', 'COL2A1', 'TRAPPC6A', 'PCNA', 'IFITM1', 'MS4A4A', 'PRRC2C', 'SSR2', 'HAX1', 'ASRGL1', 'SLC3A2', 'FKBP2', 'CFD', 'PRSS57', 'ADRM1', 'NEAT1', 'GMFG', 'CCDC85B', 'NENF', 'BLVRB', 'PARP1', 'SMYD3', 'DNAJC1', 'PTPRCAP', 'RTN4', 'RP11-660L16.2', 'MTHFD2', 'DBI', 'VIM', 'PSMD4', 'MLLT11', 'C2orf88', 'IDI1', 'PSMD12', 'DDX5', 'HMBS', 'HSPA8', 'HOXB2', 'MANF', 'CHI3L2', 'HSPA5', 'LMO2', 'GATA2', 'PNMT', 'TMEM97', 'HNRNPD', 'SNHG8', 'GYPB', 'GYPA', 'MVD', 'UBE2S', 'PITX1', 'IFITM2', 'CLCA1', 'PTTG1', 'FUS', 'CKS2', 'SPN', 'SSR1', 'TMBIM6', 'ATF7IP2', 'CARHSP1', 'XBP1', 'SRRM2', 'GSTO1', 'NPW', 'SFPQ', 'TMEM54', 'HBA1', 'DNAJA1', 'LAPTM5', 'AIF1', 'CTSH', 'NDUFB6', 'BRD2', 'LGALS1', 'DDX17', 'MCM3', 'EEF1A1', 'TSPO', 'HMGN3', 'CITED2', 'ACAT2', 'HSPB1', 'SH3BGRL3', 'PHLDA2', 'STXBP6', 'MYC', 'BTG1', 'RGS10', 'UQCRB', 'HSP90B1', 'TIMP1', 'LCP1', 'EXOSC8', 'UBC', 'BEX4', 'RPL39', 'MYL4', 'BHLHE40']\n" + ] + } + ], + "source": [ + "# Specify top k number of genes to be selected, and the selection setting\n", + "# # Here calculate top 100 most influenced genes for pathway validation\n", + "topk = 100\n", + "setting = 'difference' # \"control\", \"perturbed\"\n", + "assert setting in [\"difference\", \"control\", \"perturbed\"]\n", + "attn_top_gene_dict_100, attn_top_scores_dict_100 = get_topk_most_influenced_genes(topk, setting)\n", + "print(attn_top_scores_dict_100[TF_name + '+ctrl'])\n", + "print(attn_top_gene_dict_100[TF_name + '+ctrl'])" + ] + }, + { + "cell_type": "markdown", + "id": "db5083ac", + "metadata": {}, + "source": [ + "## Step 4: Validate most influenced genes" + ] + }, + { + "cell_type": "markdown", + "id": "025cde91", + "metadata": {}, + "source": [ + "### Step 4.1: Validate against CHIP-Atlas" + ] + }, + { + "cell_type": "markdown", + "id": "c10e8242", + "metadata": {}, + "source": [ + "First load the tsv file from CHIP-Atlas containing targets of BHLHE40. The tsv file for BHLHE40 can be downloaded via this [link](https://drive.google.com/drive/folders/1nc1LywRHlzt4Z17McfXiqBWgoGbRNyc0). This tsv file was originally retrieved from the [CHIP-Atlas](https://chip-atlas.org/target_genes) website." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "3089cb62", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('./reference/BHLHE40.10.tsv', delimiter='\\t')" + ] + }, + { + "cell_type": "markdown", + "id": "8fa4ada4", + "metadata": {}, + "source": [ + "Examine the overalp between the selected genes (top 20) and known target genes from the database." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "dcd587ec", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "({'ACAT2',\n", + " 'ADRM1',\n", + " 'DBI',\n", + " 'DNAJA1',\n", + " 'FKBP2',\n", + " 'HBA1',\n", + " 'HMGN3',\n", + " 'HNRNPD',\n", + " 'HSP90B1',\n", + " 'HSPA8',\n", + " 'HSPB1',\n", + " 'NDUFB6',\n", + " 'PRRC2C',\n", + " 'PSMD4',\n", + " 'RTN4',\n", + " 'SH3BGRL3',\n", + " 'SSR2',\n", + " 'TMBIM6',\n", + " 'UQCRB'},\n", + " 19)" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gene_list = attn_top_gene_dict_20[TF_name + '+ctrl'][:-1]\n", + "set(gene_list).intersection(set(df['Target_genes'].values)), len(set(gene_list).intersection(set(df['Target_genes'].values)))" + ] + }, + { + "cell_type": "markdown", + "id": "50001e4f", + "metadata": {}, + "source": [ + "Visualize the network and strength of the edges (annotated with rank-normalized attention scores)." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "9752a485", + "metadata": {}, + "outputs": [], + "source": [ + "score_list = attn_top_scores_dict_20[TF_name + '+ctrl']" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "30807b1b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "hits = set(gene_list).intersection(set(df['Target_genes'].values))\n", + "\n", + "G = nx.DiGraph()\n", + "edge_list = [(TF_name, gene_list[i], round(score_list[i], 2)) for i in range(len(gene_list))]\n", + "G.add_weighted_edges_from(edge_list)\n", + "\n", + "plt.figure(figsize=(20, 20))\n", + "edges = list(G.edges)\n", + "elarge = [(u, v) for (u, v, d) in G.edges(data=True) if v in hits]\n", + "esmall = [(u, v) for (u, v, d) in G.edges(data=True) if v not in hits]\n", + "pos = nx.shell_layout(G)\n", + "width_large = {}\n", + "width_small = {}\n", + "for i, v in enumerate(edges):\n", + " if v[1] in hits:\n", + " width_large[edges[i]] = G.get_edge_data(v[0], v[1])['weight']*30\n", + " else:\n", + " width_small[edges[i]] = max(G.get_edge_data(v[0], v[1])['weight'], 0)*30\n", + "nx.draw_networkx_edges(G, pos,\n", + " edgelist = width_small.keys(),\n", + " width=list(width_small.values()),\n", + " edge_color='grey',\n", + " alpha=0.8)\n", + "nx.draw_networkx_edges(G, pos, \n", + " edgelist = width_large.keys(), \n", + " width = list(width_large.values()), \n", + " alpha = 0.6, \n", + " edge_color = \"slateblue\",\n", + " )\n", + "labels = {}\n", + "for i in pos.keys():\n", + " if i == TF_name:\n", + " labels[i] = ''\n", + " else:\n", + " labels[i] = i\n", + " \n", + "labels1 = {}\n", + "for i in pos.keys():\n", + " if i != TF_name:\n", + " labels1[i] = ''\n", + " else:\n", + " labels1[i] = i\n", + "nx.draw_networkx_labels(G, pos, labels, font_size=30, font_family=\"sans-serif\", horizontalalignment='right')\n", + "nx.draw_networkx_labels(G, pos, labels1, font_size=30, font_family=\"sans-serif\", font_weight='bold', horizontalalignment='right')\n", + "\n", + "d = nx.get_edge_attributes(G, \"weight\")\n", + "edge_labels = {k: d[k] for k in elarge}\n", + "nx.draw_networkx_edge_labels(G, pos, edge_labels, font_size=20)\n", + "ax = plt.gca()\n", + "ax.margins(0.08)\n", + "plt.axis(\"off\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "1c006c98", + "metadata": {}, + "source": [ + "### Step 4.2: Validate against the Reactome database" + ] + }, + { + "cell_type": "markdown", + "id": "4686dcf3", + "metadata": {}, + "source": [ + "We perform pathway analysis on the top 100 most influenced genes by checking against the Reactome database. This replicates the reported pathways found in the *Difference* setting in the manuscript for the select TF." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "05ded107", + "metadata": {}, + "outputs": [], + "source": [ + "# Validate with Reactome \n", + "df_database = pd.DataFrame(\n", + "data = [['GO_Biological_Process_2021', 6036],\n", + "['GO_Molecular_Function_2021', 1274],\n", + "['Reactome_2022', 1818]],\n", + "columns = ['dataset', 'term'])" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "a01e550d", + "metadata": {}, + "outputs": [], + "source": [ + "databases = ['Reactome_2022']\n", + "m = df_database[df_database['dataset'].isin(databases)]['term'].sum() #df_database['term'].sum()\n", + "p_thresh = 0.05/((len(groups.keys())-1)*m)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "8de3374a", + "metadata": {}, + "outputs": [], + "source": [ + "gene_list = attn_top_gene_dict_100[TF_name + '+ctrl']" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "acd997e1", + "metadata": {}, + "outputs": [], + "source": [ + "df_attn = pd.DataFrame()\n", + "enr_Reactome = gp.enrichr(gene_list=gene_list,\n", + " gene_sets=databases,\n", + " organism='Human', \n", + " outdir='test',\n", + " cutoff=0.5)\n", + "out = enr_Reactome.results\n", + "out['Gene List'] = str(gene_list)\n", + "out = out[out['P-value'] < p_thresh]\n", + "df_attn = df_attn.append(out, ignore_index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "4b556f31", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(df_attn)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "1ea5a0ca", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Gene_setTermOverlapP-valueAdjusted P-valueOld P-valueOld Adjusted P-valueOdds RatioCombined ScoreGenesGene List
0Reactome_2022AUF1 (hnRNP D0) Binds And Destabilizes mRNA R-...6/543.029798e-070.0000870026.119737392.046796HSPA8;PSMD12;PSMD4;UBC;HNRNPD;HSPB1['IL1RN', 'ARHGDIB', 'COL2A1', 'TRAPPC6A', 'PC...
1Reactome_2022Regulation Of mRNA Stability By Proteins That ...7/883.082629e-070.0000870018.219858273.157814HSPA8;PSMD12;PSMD4;EXOSC8;UBC;HNRNPD;HSPB1['IL1RN', 'ARHGDIB', 'COL2A1', 'TRAPPC6A', 'PC...
2Reactome_2022Cellular Responses To Stress R-HSA-226275216/7226.458517e-070.000117005.11728072.935042HSPA8;PSMD12;HSPA5;CITED2;HBA1;HSP90B1;EEF1A1;...['IL1RN', 'ARHGDIB', 'COL2A1', 'TRAPPC6A', 'PC...
3Reactome_2022Cellular Responses To Stimuli R-HSA-895389716/7368.318572e-070.000117005.01411870.195667HSPA8;PSMD12;HSPA5;CITED2;HBA1;HSP90B1;EEF1A1;...['IL1RN', 'ARHGDIB', 'COL2A1', 'TRAPPC6A', 'PC...
4Reactome_2022S Phase R-HSA-692427/1611.753446e-050.001971009.547872104.562021PSMD12;PCNA;PSMD4;UBE2S;MYC;UBC;MCM3['IL1RN', 'ARHGDIB', 'COL2A1', 'TRAPPC6A', 'PC...
5Reactome_2022APC/C:Cdc20 Mediated Degradation Of Securin R-...5/672.239572e-050.0020980016.664147178.417027PSMD12;PSMD4;PTTG1;UBE2S;UBC['IL1RN', 'ARHGDIB', 'COL2A1', 'TRAPPC6A', 'PC...
\n", + "
" + ], + "text/plain": [ + " Gene_set Term Overlap \\\n", + "0 Reactome_2022 AUF1 (hnRNP D0) Binds And Destabilizes mRNA R-... 6/54 \n", + "1 Reactome_2022 Regulation Of mRNA Stability By Proteins That ... 7/88 \n", + "2 Reactome_2022 Cellular Responses To Stress R-HSA-2262752 16/722 \n", + "3 Reactome_2022 Cellular Responses To Stimuli R-HSA-8953897 16/736 \n", + "4 Reactome_2022 S Phase R-HSA-69242 7/161 \n", + "5 Reactome_2022 APC/C:Cdc20 Mediated Degradation Of Securin R-... 5/67 \n", + "\n", + " P-value Adjusted P-value Old P-value Old Adjusted P-value \\\n", + "0 3.029798e-07 0.000087 0 0 \n", + "1 3.082629e-07 0.000087 0 0 \n", + "2 6.458517e-07 0.000117 0 0 \n", + "3 8.318572e-07 0.000117 0 0 \n", + "4 1.753446e-05 0.001971 0 0 \n", + "5 2.239572e-05 0.002098 0 0 \n", + "\n", + " Odds Ratio Combined Score \\\n", + "0 26.119737 392.046796 \n", + "1 18.219858 273.157814 \n", + "2 5.117280 72.935042 \n", + "3 5.014118 70.195667 \n", + "4 9.547872 104.562021 \n", + "5 16.664147 178.417027 \n", + "\n", + " Genes \\\n", + "0 HSPA8;PSMD12;PSMD4;UBC;HNRNPD;HSPB1 \n", + "1 HSPA8;PSMD12;PSMD4;EXOSC8;UBC;HNRNPD;HSPB1 \n", + "2 HSPA8;PSMD12;HSPA5;CITED2;HBA1;HSP90B1;EEF1A1;... \n", + "3 HSPA8;PSMD12;HSPA5;CITED2;HBA1;HSP90B1;EEF1A1;... \n", + "4 PSMD12;PCNA;PSMD4;UBE2S;MYC;UBC;MCM3 \n", + "5 PSMD12;PSMD4;PTTG1;UBE2S;UBC \n", + "\n", + " Gene List \n", + "0 ['IL1RN', 'ARHGDIB', 'COL2A1', 'TRAPPC6A', 'PC... \n", + "1 ['IL1RN', 'ARHGDIB', 'COL2A1', 'TRAPPC6A', 'PC... \n", + "2 ['IL1RN', 'ARHGDIB', 'COL2A1', 'TRAPPC6A', 'PC... \n", + "3 ['IL1RN', 'ARHGDIB', 'COL2A1', 'TRAPPC6A', 'PC... \n", + "4 ['IL1RN', 'ARHGDIB', 'COL2A1', 'TRAPPC6A', 'PC... \n", + "5 ['IL1RN', 'ARHGDIB', 'COL2A1', 'TRAPPC6A', 'PC... " + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_attn" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/transfer/code/scGPT/tutorials/Tutorial_GRN.ipynb b/transfer/code/scGPT/tutorials/Tutorial_GRN.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..61b4c69e62f24362c1a297a65e830663fec792b6 --- /dev/null +++ b/transfer/code/scGPT/tutorials/Tutorial_GRN.ipynb @@ -0,0 +1,1327 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "id": "7a1bd7ef", + "metadata": {}, + "source": [ + "# GRN Inference on Pre-trained Model\n", + "Here we use the pre-trained blood model as an example for GRN inference, particularly regarding gene program extraction and network visualization. We also present the cell-type specific activations within these gene programs on the Immune Human dataset, as a soft validation for the zero-shot performance. \n", + "\n", + "Note that GRN inference can be performed on pre-trained and finetuned models as showcased in our manuscript.\n", + "\n", + "Users may perform scGPT's gene-embedding-based GRN inference in the following steps:\n", + "\n", + " 1. Load optimized scGPT model (pre-trained or fine-tuned) and data\n", + " \n", + " 2. Retrieve scGPT's gene embeddings\n", + " \n", + " 3. Extract gene programs from scGPT's gene embedding network\n", + " \n", + " 4. Visualize gene program activations on dataset of interest\n", + " \n", + " 5. Visualize the interconnectivity of genes within select gene programs\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "18b08bed", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Global seed set to 0\n", + "/h/chloexq/.cache/pypoetry/virtualenvs/scgpt--qSLVbd1-py3.9/lib/python3.7/site-packages/flatbuffers/compat.py:19: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses\n", + " import imp\n" + ] + } + ], + "source": [ + "import copy\n", + "import json\n", + "import os\n", + "from pathlib import Path\n", + "import sys\n", + "import warnings\n", + "\n", + "import torch\n", + "from anndata import AnnData\n", + "import scanpy as sc\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import networkx as nx\n", + "import pandas as pd\n", + "import tqdm\n", + "import gseapy as gp\n", + "\n", + "from torchtext.vocab import Vocab\n", + "from torchtext._torchtext import (\n", + " Vocab as VocabPybind,\n", + ")\n", + "\n", + "sys.path.insert(0, \"../\")\n", + "import scgpt as scg\n", + "from scgpt.tasks import GeneEmbedding\n", + "from scgpt.tokenizer.gene_tokenizer import GeneVocab\n", + "from scgpt.model import TransformerModel\n", + "from scgpt.preprocess import Preprocessor\n", + "from scgpt.utils import set_seed \n", + "\n", + "os.environ[\"KMP_WARNINGS\"] = \"off\"\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "948bbc49", + "metadata": {}, + "outputs": [], + "source": [ + "set_seed(42)\n", + "pad_token = \"\"\n", + "special_tokens = [pad_token, \"\", \"\"]\n", + "n_hvg = 1200\n", + "n_bins = 51\n", + "mask_value = -1\n", + "pad_value = -2\n", + "n_input_bins = n_bins" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "9565a71c", + "metadata": {}, + "source": [ + "## Step 1: Load pre-trained model and dataset" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "83ad0952", + "metadata": {}, + "source": [ + "### 1.1 Load pre-trained model\n", + "The blood pre-trained model can be downloaded via this [link](https://drive.google.com/drive/folders/1kkug5C7NjvXIwQGGaGoqXTk_Lb_pDrBU)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "86247948", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Resume model from ../save/scGPT_bc/best_model.pt, the model args will override the config ../save/scGPT_bc/args.json.\n" + ] + } + ], + "source": [ + "# Specify model path; here we load the pre-trained scGPT blood model\n", + "model_dir = Path(\"../save/scGPT_bc\")\n", + "model_config_file = model_dir / \"args.json\"\n", + "model_file = model_dir / \"best_model.pt\"\n", + "vocab_file = model_dir / \"vocab.json\"\n", + "\n", + "vocab = GeneVocab.from_file(vocab_file)\n", + "for s in special_tokens:\n", + " if s not in vocab:\n", + " vocab.append_token(s)\n", + "\n", + "# Retrieve model parameters from config files\n", + "with open(model_config_file, \"r\") as f:\n", + " model_configs = json.load(f)\n", + "print(\n", + " f\"Resume model from {model_file}, the model args will override the \"\n", + " f\"config {model_config_file}.\"\n", + ")\n", + "embsize = model_configs[\"embsize\"]\n", + "nhead = model_configs[\"nheads\"]\n", + "d_hid = model_configs[\"d_hid\"]\n", + "nlayers = model_configs[\"nlayers\"]\n", + "n_layers_cls = model_configs[\"n_layers_cls\"]\n", + "\n", + "gene2idx = vocab.get_stoi()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "19de9b6c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using simple batchnorm instead of domain specific batchnorm\n", + "Loading params encoder.embedding.weight with shape torch.Size([36574, 512])\n", + "Loading params encoder.enc_norm.weight with shape 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"data": { + "text/plain": [ + "TransformerModel(\n", + " (encoder): GeneEncoder(\n", + " (embedding): Embedding(36574, 512, padding_idx=36571)\n", + " (enc_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " )\n", + " (value_encoder): ContinuousValueEncoder(\n", + " (dropout): Dropout(p=0.5, inplace=False)\n", + " (linear1): Linear(in_features=1, out_features=512, bias=True)\n", + " (activation): ReLU()\n", + " (linear2): Linear(in_features=512, out_features=512, bias=True)\n", + " (norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " )\n", + " (bn): BatchNorm1d(512, eps=6.1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (transformer_encoder): TransformerEncoder(\n", + " (layers): ModuleList(\n", + " (0): TransformerEncoderLayer(\n", + " (self_attn): MultiheadAttention(\n", + " (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)\n", + " )\n", + " (linear1): Linear(in_features=512, 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elementwise_affine=True)\n", + " (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (dropout1): Dropout(p=0.5, inplace=False)\n", + " (dropout2): Dropout(p=0.5, inplace=False)\n", + " )\n", + " (7): TransformerEncoderLayer(\n", + " (self_attn): MultiheadAttention(\n", + " (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)\n", + " )\n", + " (linear1): Linear(in_features=512, out_features=512, bias=True)\n", + " (dropout): Dropout(p=0.5, inplace=False)\n", + " (linear2): Linear(in_features=512, out_features=512, bias=True)\n", + " (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (dropout1): Dropout(p=0.5, inplace=False)\n", + " (dropout2): Dropout(p=0.5, inplace=False)\n", + " )\n", + " (8): TransformerEncoderLayer(\n", + " (self_attn): MultiheadAttention(\n", + " (out_proj): NonDynamicallyQuantizableLinear(in_features=512, 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elementwise_affine=True)\n", + " (dropout1): Dropout(p=0.5, inplace=False)\n", + " (dropout2): Dropout(p=0.5, inplace=False)\n", + " )\n", + " (10): TransformerEncoderLayer(\n", + " (self_attn): MultiheadAttention(\n", + " (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)\n", + " )\n", + " (linear1): Linear(in_features=512, out_features=512, bias=True)\n", + " (dropout): Dropout(p=0.5, inplace=False)\n", + " (linear2): Linear(in_features=512, out_features=512, bias=True)\n", + " (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (dropout1): Dropout(p=0.5, inplace=False)\n", + " (dropout2): Dropout(p=0.5, inplace=False)\n", + " )\n", + " (11): TransformerEncoderLayer(\n", + " (self_attn): MultiheadAttention(\n", + " (out_proj): NonDynamicallyQuantizableLinear(in_features=512, out_features=512, bias=True)\n", + " )\n", + " (linear1): Linear(in_features=512, out_features=512, bias=True)\n", + " (dropout): Dropout(p=0.5, inplace=False)\n", + " (linear2): Linear(in_features=512, out_features=512, bias=True)\n", + " (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (dropout1): Dropout(p=0.5, inplace=False)\n", + " (dropout2): Dropout(p=0.5, inplace=False)\n", + " )\n", + " )\n", + " )\n", + " (decoder): ExprDecoder(\n", + " (fc): Sequential(\n", + " (0): Linear(in_features=512, out_features=512, bias=True)\n", + " (1): LeakyReLU(negative_slope=0.01)\n", + " (2): Linear(in_features=512, out_features=512, bias=True)\n", + " (3): LeakyReLU(negative_slope=0.01)\n", + " (4): Linear(in_features=512, out_features=1, bias=True)\n", + " )\n", + " )\n", + " (cls_decoder): ClsDecoder(\n", + " (_decoder): ModuleList(\n", + " (0): Linear(in_features=512, out_features=512, bias=True)\n", + " (1): ReLU()\n", + " (2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (3): Linear(in_features=512, out_features=512, bias=True)\n", + " (4): ReLU()\n", + " (5): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " )\n", + " (out_layer): Linear(in_features=512, out_features=1, bias=True)\n", + " )\n", + " (sim): Similarity(\n", + " (cos): CosineSimilarity()\n", + " )\n", + " (creterion_cce): CrossEntropyLoss()\n", + ")" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "\n", + "ntokens = len(vocab) # size of vocabulary\n", + "model = TransformerModel(\n", + " ntokens,\n", + " embsize,\n", + " nhead,\n", + " d_hid,\n", + " nlayers,\n", + " vocab=vocab,\n", + " pad_value=pad_value,\n", + " n_input_bins=n_input_bins,\n", + ")\n", + "\n", + "try:\n", + " model.load_state_dict(torch.load(model_file))\n", + " print(f\"Loading all model params from {model_file}\")\n", + "except:\n", + " # only load params that are in the model and match the size\n", + " model_dict = model.state_dict()\n", + " pretrained_dict = torch.load(model_file)\n", + " pretrained_dict = {\n", + " k: v\n", + " for k, v in pretrained_dict.items()\n", + " if k in model_dict and v.shape == model_dict[k].shape\n", + " }\n", + " for k, v in pretrained_dict.items():\n", + " print(f\"Loading params {k} with shape {v.shape}\")\n", + " model_dict.update(pretrained_dict)\n", + " model.load_state_dict(model_dict)\n", + "\n", + "model.to(device)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "f5e23d36", + "metadata": {}, + "source": [ + "### 1.2 Load dataset of interest\n", + "The Immune Human dataset can be downloaded via this [link](https://figshare.com/ndownloader/files/25717328)." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "cec95b62", + "metadata": {}, + "outputs": [], + "source": [ + "# Specify data path; here we load the Immune Human dataset\n", + "data_dir = Path(\"../data\")\n", + "adata = sc.read(\n", + " str(data_dir / \"Immune_ALL_human.h5ad\"), cache=True\n", + ") # 33506 × 12303\n", + "ori_batch_col = \"batch\"\n", + "adata.obs[\"celltype\"] = adata.obs[\"final_annotation\"].astype(str)\n", + "data_is_raw = False" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "8f1a521e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "scGPT - INFO - Filtering genes by counts ...\n", + "scGPT - INFO - Filtering cells by counts ...\n", + "scGPT - INFO - Normalizing total counts ...\n", + "scGPT - INFO - Subsetting highly variable genes ...\n", + "scGPT - INFO - Binning data ...\n" + ] + } + ], + "source": [ + "# Preprocess the data following the scGPT data pre-processing pipeline\n", + "preprocessor = Preprocessor(\n", + " use_key=\"X\", # the key in adata.layers to use as raw data\n", + " filter_gene_by_counts=3, # step 1\n", + " filter_cell_by_counts=False, # step 2\n", + " normalize_total=1e4, # 3. whether to normalize the raw data and to what sum\n", + " result_normed_key=\"X_normed\", # the key in adata.layers to store the normalized data\n", + " log1p=data_is_raw, # 4. whether to log1p the normalized data\n", + " result_log1p_key=\"X_log1p\",\n", + " subset_hvg=n_hvg, # 5. whether to subset the raw data to highly variable genes\n", + " hvg_flavor=\"seurat_v3\" if data_is_raw else \"cell_ranger\",\n", + " binning=n_bins, # 6. whether to bin the raw data and to what number of bins\n", + " result_binned_key=\"X_binned\", # the key in adata.layers to store the binned data\n", + ")\n", + "preprocessor(adata, batch_key=\"batch\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "f752d133", + "metadata": {}, + "source": [ + "## Step 2: Retrieve scGPT's gene embeddings\n", + "\n", + "Note that technically scGPT's gene embeddings are data independent. Overall, the pre-trained foundation model contains 30+K genes. Here for simplicity, we focus on a subset of HVGs specific to the data at hand." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "6f190778", + "metadata": {}, + "outputs": [], + "source": [ + "# Retrieve the data-independent gene embeddings from scGPT\n", + "gene_ids = np.array([id for id in gene2idx.values()])\n", + "gene_embeddings = model.encoder(torch.tensor(gene_ids, dtype=torch.long).to(device))\n", + "gene_embeddings = gene_embeddings.detach().cpu().numpy()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "5efa8903", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Retrieved gene embeddings for 1173 genes.\n" + ] + } + ], + "source": [ + "# Filter on the intersection between the Immune Human HVGs found in step 1.2 and scGPT's 30+K foundation model vocab\n", + "gene_embeddings = {gene: gene_embeddings[i] for i, gene in enumerate(gene2idx.keys()) if gene in adata.var.index.tolist()}\n", + "print('Retrieved gene embeddings for {} genes.'.format(len(gene_embeddings)))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "1181bcd5", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 1173/1173 [00:00<00:00, 1365127.25it/s]\n" + ] + } + ], + "source": [ + "# Construct gene embedding network\n", + "embed = GeneEmbedding(gene_embeddings)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "71b9e16d", + "metadata": {}, + "source": [ + "## Step 3: Extract gene programs from gene embedding network" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "0bc58b46", + "metadata": {}, + "source": [ + "### 3.1 Perform Louvain clustering on the gene embedding network" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "569ea02a", + "metadata": {}, + "outputs": [], + "source": [ + "# Perform Louvain clustering with desired resolution; here we specify resolution=40\n", + "gdata = embed.get_adata(resolution=40)\n", + "# Retrieve the gene clusters\n", + "metagenes = embed.get_metagenes(gdata)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "38dad229", + "metadata": {}, + "source": [ + "### 3.2 Filter on clusters with 5 or more genes" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "c763acf3", + "metadata": {}, + "outputs": [], + "source": [ + "# Obtain the set of gene programs from clusters with #genes >= 5\n", + "mgs = dict()\n", + "for mg, genes in metagenes.items():\n", + " if len(genes) > 4:\n", + " mgs[mg] = genes" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "3671c64b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'46': ['ZNF683', 'JAKMIP1', 'LAG3', 'FKBP11', 'ZBP1'],\n", + " '7': ['ZFP36', 'TNFAIP3', 'DUSP1', 'FOS', 'CD69', 'KLF6', 'PPP1R15A', 'JUN'],\n", + " '34': ['YPEL4', 'TMEM86B', 'TMEM63B', 'CYB5R1', 'RUNDC3A', 'DNAJB2'],\n", + " '25': ['YPEL3', 'TLE1', 'CXXC5', 'RBM38', 'ODC1', 'TFDP2'],\n", + " '43': ['YOD1', 'FLCN', 'CPEB4', 'FBXO30', 'MTMR3'],\n", + " '14': ['VSIG4', 'C1QA', 'C1QC', 'C1QB', 'ACP5', 'CD163', 'ABCG2'],\n", + " '71': ['VPREB3', 'VPREB1', 'SOCS2', 'IGLL1', 'DNTT'],\n", + " '17': ['UROD', 'CMAS', 'CDC27', 'MINPP1', 'CPOX', 'RFESD'],\n", + " '58': ['UBE2O', 'FN3K', 'PIM1', 'PNP', 'TRIM58'],\n", + " '47': ['TXK', 'SPOCK2', 'SKAP1', 'CD96', 'RNF125'],\n", + " '22': ['TTN', 'ICA1L', 'KCNQ1OT1', 'BCL9L', 'ABCA5', 'GPR82'],\n", + " '27': ['TSC22D1', 'MEG3', 'PTCRA', 'PGRMC1', 'MLH3', 'GRAP2'],\n", + " '21': ['TRAT1', 'LEF1', 'RCAN3', 'BCL11B', 'TCF7', 'OXNAD1'],\n", + " '0': ['TPX2',\n", + " 'STMN1',\n", + " 'UBE2C',\n", + " 'MKI67',\n", + " 'TOP2A',\n", + " 'CCNB1',\n", + " 'CDK1',\n", + " 'TYMS',\n", + " 'TK1',\n", + " 'NUSAP1',\n", + " 'BIRC5',\n", + " 'PTTG1',\n", + " 'CENPF',\n", + " 'RRM2'],\n", + " '8': ['TPM2', 'TPM1', 'TGFB1I1', 'CALD1', 'MYL9', 'MYH11', 'MYLK'],\n", + " '15': ['TNRC6C', 'DHRS3', 'FBXO32', 'APBA2', 'CEP290', 'PLEKHA1'],\n", + " '42': ['TNFSF10', 'DPYSL2', 'AP1S2', 'GLIPR1', 'FGL2'],\n", + " '68': ['TNFRSF17', 'MZB1', 'DERL3', 'CD38', 'POU2AF1'],\n", + " '4': ['TNFRSF13B',\n", + " 'SNRPN',\n", + " 'SLC4A10',\n", + " 'FCRL2',\n", + " 'SPINK2',\n", + " 'CAPN12',\n", + " 'CELA1',\n", + " 'CPA5',\n", + " 'LILRA4'],\n", + " '12': ['TMOD1', 'SOX6', 'ART4', 'SPTB', 'AK1', 'OSBP2', 'KANK2'],\n", + " '37': ['TMEM154', 'HVCN1', 'NCF1', 'CKAP4', 'POU2F2'],\n", + " '29': ['TLR10', 'FGD2', 'HLA-DOB', 'FCRL5', 'CD180', 'MACROD2'],\n", + " '26': ['TIMP1', 'S100A11', 'LGALS1', 'ANXA2', 'LGALS3', 'IGFBP7'],\n", + " '45': ['TCL1A', 'FCRLA', 'CD22', 'CD72', 'CD19'],\n", + " '67': ['TAGAP', 'PHACTR1', 'PMAIP1', 'NFKBID', 'CD83'],\n", + " '59': ['SYTL2', 'SMAGP', 'LAIR2', 'DTHD1', 'NCALD'],\n", + " '13': ['SYNE2', 'SYNE1', 'PIK3R1', 'OPTN', 'CBLB', 'RORA', 'PARP8'],\n", + " '54': ['STYK1', 'SP140', 'LPAR5', 'FCRL3', 'PRKD2'],\n", + " '11': ['STXBP2', 'SPI1', 'TKT', 'AIF1', 'PYCARD', 'COTL1', 'LST1'],\n", + " '9': ['STRBP', 'TRAF5', 'GNB5', 'BACH2', 'CDCA7L', 'CHD7', 'AFF3'],\n", + " '57': ['STRADB', 'ISCA1', 'CCS', 'NPRL3', 'PITHD1'],\n", + " '1': ['STAT1',\n", + " 'ISG15',\n", + " 'CMPK2',\n", + " 'IFIT2',\n", + " 'IFIT1',\n", + " 'IFI44L',\n", + " 'RSAD2',\n", + " 'IFI6',\n", + " 'SAMD9',\n", + " 'IFIT3',\n", + " 'MX1',\n", + " 'IRF7'],\n", + " '55': ['SPON2', 'MYOM2', 'CLIC3', 'CHST2', 'SH2D1B'],\n", + " '16': ['SPEF2', 'ECHDC2', 'DUOX1', 'CLUAP1', 'CCDC146', 'CLIC6'],\n", + " '52': ['SLC38A5', 'KCNH2', 'POLE2', 'CERS3', 'DHRS13'],\n", + " '51': ['SLC38A11', 'IL6', 'CA3', 'FAM177B', 'PTPRS'],\n", + " '49': ['SH3BGRL2', 'PDZK1IP1', 'CTDSPL', 'PDGFA', 'CTTN'],\n", + " '32': ['SEMA4F', 'CARNS1', 'ARHGAP12', 'XYLT2', 'RECK', 'CBFA2T2'],\n", + " '61': ['FCGR3A', 'DOK2', 'CX3CR1', 'ABI3', 'RHOC'],\n", + " '19': ['CCR10', 'SLAMF1', 'IL2RA', 'CCR4', 'CCR6', 'CTLA4'],\n", + " '72': ['ZFP36L1', 'EGR1', 'NFKBIZ', 'RHOB', 'NR4A1'],\n", + " '50': ['VMP1', 'GPCPD1', 'NAMPT', 'CD55', 'SOD2'],\n", + " '5': ['PRSS57',\n", + " 'AZU1',\n", + " 'CLEC11A',\n", + " 'CTSG',\n", + " 'ELANE',\n", + " 'MS4A3',\n", + " 'MPO',\n", + " 'RETN',\n", + " 'RNASE2'],\n", + " '65': ['LILRA5', 'LILRB2', 'GTPBP2', 'HCK', 'LRRC25'],\n", + " '3': ['LCK', 'CD2', 'LTB', 'CD3E', 'CD3G', 'CD8B', 'CD8A', 'IL7R', 'CD3D'],\n", + " '70': ['LBH', 'MGAT4A', 'RHOH', 'SLC38A1', 'STK17A'],\n", + " '36': ['PICALM', 'FOXO3', 'QKI', 'CTNNB1', 'RAPGEF2'],\n", + " '41': ['IL6ST', 'ZNF37A', 'ITGA6', 'ACTN1', 'PDK1'],\n", + " '35': ['P2RX5', 'PNOC', 'PKIG', 'BLK', 'OSBPL10', 'RRAS2'],\n", + " '30': ['ID3', 'GPM6B', 'BEX5', 'CRIP2', 'APOLD1', 'TSHZ2'],\n", + " '2': ['HLA-DRB5',\n", + " 'HLA-DQA1',\n", + " 'HLA-DPB1',\n", + " 'HLA-DRA',\n", + " 'HLA-DMA',\n", + " 'HLA-DQB1',\n", + " 'CD74',\n", + " 'HLA-DQA2',\n", + " 'HLA-DRB1',\n", + " 'HLA-DPA1'],\n", + " '24': ['CLEC4A', 'NRG1', 'TMEM107', 'CLEC4D', 'PTCH2', 'S1PR3'],\n", + " '10': ['GNG11', 'CLEC1B', 'DNASE1L3', 'ESAM', 'CLDN5', 'MYCT1', 'EGFL7'],\n", + " '18': ['GLUL', 'SLC40A1', 'APOE', 'FABP5', 'CTSB', 'CTSD'],\n", + " '31': ['GIMAP4', 'GIMAP7', 'ETS1', 'GIMAP6', 'SLFN5', 'DENND2D'],\n", + " '40': ['FGR', 'TNFRSF1B', 'AOAH', 'AGTRAP', 'MYO1F'],\n", + " '56': ['ZSCAN18', 'MAP9', 'ZCWPW1', 'PLXNA3', 'ZNF274'],\n", + " '6': ['VMO1', 'LYPD2', 'LGALS3BP', 'AQP3', 'HES4', 'SCGB3A1', 'IRF9', 'CD59'],\n", + " '64': ['PASK', 'GATA3', 'CD40LG', 'AP3M2', 'P2RY10'],\n", + " '44': ['CYP4F3', 'ARG1', 'LCN2', 'HP', 'S100P'],\n", + " '28': ['PTPN7', 'LYAR', 'TCOF1', 'GTF3C1', 'CMC1', 'MATK'],\n", + " '33': ['HDC', 'CYTL1', 'CPA3', 'CLC', 'LGALS4', 'GATA2'],\n", + " '66': ['DAB2', 'F13A1', 'MAF', 'PRDM1', 'LPAR6'],\n", + " '39': ['IGSF6', 'CPVL', 'CFP', 'LY86', 'RAB32'],\n", + " '69': ['APOBEC3A', 'IFITM3', 'MT1F', 'IFI27', 'MT2A'],\n", + " '48': ['NELL2', 'CAMK4', 'ABLIM1', 'PDE3B', 'SATB1'],\n", + " '62': ['PLBD1', 'GCA', 'NUP214', 'PGD', 'LTA4H'],\n", + " '38': ['GP1BA', 'ARHGAP6', 'ITGA2B', 'SELP', 'RAB27B'],\n", + " '23': ['CCL20', 'CCL8', 'CXCL1', 'CXCL3', 'OSM', 'CCL2'],\n", + " '53': ['BTN3A1', 'CALCOCO1', 'RAB2B', 'OPA1', 'ATG4A'],\n", + " '20': ['NFIL3', 'EGR3', 'NLRP3', 'OTUD1', 'PTGS2', 'TRIB1'],\n", + " '60': ['RAB3IP', 'MXI1', 'PGM2L1', 'FHIT', 'UBE2H'],\n", + " '63': ['MAL', 'MYC', 'PIM2', 'CISH', 'S1PR1']}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Here are the gene programs identified\n", + "mgs" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "dd6066cd", + "metadata": {}, + "source": [ + "## Step 4: Visualize gene program activation on the Immune Human dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "078a3233", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.set(font_scale=0.35)\n", + "embed.score_metagenes(adata, metagenes)\n", + "embed.plot_metagenes_scores(adata, mgs, \"celltype\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "5b3d918c", + "metadata": {}, + "source": [ + "## Step 5: Visualize network connectivity within desired gene program\n", + "We can further visualize the connectivity between genes within any gene program of interest from Step 4. Here is an example of gene program 3 consisting of the CD3 cluster, CD8 cluster and other genes. In the visualization, we see strong connections highlighted in blue (by cosine similarity) between CD3D, E, and G, as well as CD8A and B." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "5d1a61b1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['LCK', 'CD2', 'LTB', 'CD3E', 'CD3G', 'CD8B', 'CD8A', 'IL7R', 'CD3D']\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 9/9 [00:00<00:00, 254.81it/s]\n" + ] + } + ], + "source": [ + "# Retrieve gene program 3 which contains the CD3 gene set\n", + "CD_genes = mgs['3']\n", + "print(CD_genes)\n", + "# Compute cosine similarities among genes in this gene program\n", + "df_CD = pd.DataFrame(columns=['Gene', 'Similarity', 'Gene1'])\n", + "for i in tqdm.tqdm(CD_genes):\n", + " df = embed.compute_similarities(i, CD_genes)\n", + " df['Gene1'] = i\n", + " df_CD = df_CD.append(df)\n", + "df_CD_sub = df_CD[df_CD['Similarity']<0.99].sort_values(by='Gene') # Filter out edges from each gene to itself" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "f7060ce1", + "metadata": {}, + "outputs": [], + "source": [ + "# Creates a graph from the cosine similarity network\n", + "input_node_weights = [(row['Gene'], row['Gene1'], round(row['Similarity'], 2)) for i, row in df_CD_sub.iterrows()]\n", + "G = nx.Graph()\n", + "G.add_weighted_edges_from(input_node_weights)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "ff192716", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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3IiIiIiKjgwI1IjJiPgtnAJ3OGtfPcw4DXnXWnJd7zcrp8K+dNYf7LIwFrgMWA3brLTuml/N2EMd775lbvgE4Kd9kuOY1KxIDNkvklmcANwPnOWteH+i6RVPgRkRERESkPSlQIyIj4rMwK7E58HXOmlP7ed6uwHzOmmPTYwccmg6/B6ztrJnqs7AGcAax78z6fQVbejn/msBPiQ2JIY7lPjjfZLjm+WOA7wC7A/PnDr0PXApc5ayZPJhrl4ECNyIiIiIi7UGBGhEZEZ+FtYDjgZ2dNY/187xNgJWdNfunx/MCv6U6antvZ839KUPmImBpYv+Y64awlyWBU6hOenoB2NdZ80I/r5mVOPZ7a6qTqABeBs4CflOWhsNDocCNiIiIiEhrUqBGREbEZ+EoYCJxLPf0fp63FrCts2br3NoFwBfTw5tz2TbLEbNa3gG+56x5cwj7mQ84Cfh8WnqHOG2q34bBPgsLALsC36UaPAJ4Cji1vyBUK1DgRkRERESkNShQIyLDlsqH7gTud9YcMcBzlwV+7qxZL7e2ObB/evgGsE4le8Vn4SRgReII7Z8PcV8TgMOBb6WlGcDxzpobB/HaJYn9a75ac+iPwOn9Zee0EgVuRERERETKSYEaERm2lPlyEXCIs+Z3Azx3IcADq+SCMQb4Ze5pH5VPpQlN1wEfADs4a54Z4t46gO2IPWgqric2Ge4z8yf32lWBfYBFc4emExsVX+CseWso+ym7PgI3j6b/HlPgRkRERESkORSoEZFh81nYG9gC+Lqz5t0BnjsTcQz22vlSJp+FK4Cl0sNrnDUn5Y4dDqwN/BPY1Vkz5DesVHJ1NNUmw38hNhl+ZxCvHQtsCOwCzJM79A4xQHW9s2bKUPfUCnwW5iGWpX2ZGLgZjwI3IiIiIiINp0CNiAybz8INxJHbuw/45Pj8PxKzY/6dW9ueatbLS8B3KwGZ1DfmFuBN4ERnzV3D3KclNhleIC09D+w32DImn4XZgW2BzYmZJhVdxAlVfxhOEKmVKHAjIiIiItIcCtSIyLD4LCwM3EwMoFw7yNfcCJzgrPlLbu0zQL53zFbOmn/kju8JfB94Hdh0uCOzfRbmB06k2mT4bWKT4YeHcA4D7AGsW3Pob8QJVU8OZ2+tSIEbEREREZHGUKBGRIYl1wj4e86aFwf5mvOAW501d9Ss30C1F8zFzpqzc8fmAG4FXiOOyr54BHueCTgCWCctTQd+4azxQzzPMsB+wPI1h+4Ezhzsx6OdKHAjIiIiIlIfCtSIyLD4LJwLzOOs+f4QXnMc8E9nzeU167sD26eHzzlrNq45vgUxk+UtYJuR3PSnRsHbA7vllq8lZsT022S4l/OsCewFLJw7NBW4BrhkMH1w2pUCNyIiIiIiw6NAjYgMmc/CnMDvgUvz2S+DeN3+AM6ak2vWlwKuyC1t4qz5T+74BOAm4gjv5501h49g+5VzrgX8FJgpLT0AHDrU4IrPwnhgE2BHYM7cobeA8wDvrJk20v22OgVuREREREQGR4EaERkyn4V1gGOB7YbSl8VnYWvAOmt+XLPeQRzTvWBaOru2xMlnYT3gSOBF4Ehnzd9G8E+onPNzwMlUmww/R2wy/N9hnGtOYrBmU2Bc7tDzwGnAve3ecHgoFLgREREREemdAjUiMmSphOnLwLrOmhlDeN16wPrOml16ObY/caoSxPKoLWuOjwGuAj4gBkK2Hcq1+9nT/MRgzdJp6W3gIGfNI8M838LEcqi1ag49Siyv+udw99rOBgjcPOqsebXA7YmIiIiINI0CNSIyJD4L44hlT3c5a44e4mtXBA6s7UGTjn0RuCC39LEmxT4LqwCnA48Btztrbhvq/vvY10zEbJ1vpqVhNRmuOefyxIbDy+SWu4E7gLOUMdI/BW5EREREZLRSoEZEhsRn4SvAOcAPnTV/GuJrPwtc5KxZo5djY4DfAPOmpZOdNVfXPKcjXXtWYG7gB86a94b6b+hjbx3E0qV8ts81wKlDaTJcc84xwDeAPQGTOzQZuBK4zFnz/vB2PLoocCMiIiIio4UCNSIyJKlEaWPg686aD4b42rmJ2TirOmsm93L8UMClh487a3bs5TlLA5cDfwC6nDWnD+1fMOAe1wZ+QrXJ8P3EJsPvjuCcE4DNgB2A2XKHXicGnm4bbjBotFLgRkRERETalQI1IjJoKevkFuA/zpp9h/H6McTpShvWljWl4ysDZ6SH3cQeOJN6ed7PgMWIwZS9nTUvDHUvA+xzKWLfmk+kpf8Qmwz/b4TnnQfYGdgIGJM79G9i5s4DIzn/aNZL4GYcPZsTK3AjIiIiIi1BgRoRGTSfhcWA64Hjhtu/xWfhDuDg3qY2pVHXvwNmT0u9Xic17L0RuA2Yz1mz/3D2MsA+PwGcRM8mwwc4ax6rw7kXJTYcnlhz6AHgNGfNv0Z6jdHOZ2FeYuDmS8TgzVgUuBERERGRFqBAjYgMms/CtsR+K+sNtxmuz8LlwCXOmrv7OH40sF56+KCzZs8+nncQMdDxCnBBI7JRfBZmJjYZ/kZamk4MHt1ap/N/hdhweMnc8gxiAOqc3rKJZHgUuBERERGRVqFAjYgMms/CxcB4Z81WIzjHycADzpob+ji+JnBCejgd+Iaz5u1enjcvsQzrNuCrwObOmmnD3Vc/++0AdiKWLFVcTSxVqsd48DHEwNTuwAK5Qx8AlwJXOWs+HOl1pCcFbkRERESkrBSoEZFBSTe2vwXOd9ZcMNDz+znPj4FJzppz+zg+M7FRcKWZ7xHOmjv6eO7OxCa99wH/cNZcM9x9DcRn4RvEJsMT0tKfiU2G6zV1amZgS2AbYJbcoVeAs4E76hEYkt4pcCMiIiIiZaFAjYgMis/Cd4llQFs4a7IRnGcX4BPOmmP6ec7xwFrp4d3OmgP7eN6sxKya3wOrAVs7a94Y7t4GkiZOnQzMn5aeJTYZ7qrjNeYHdgW+R8+GwxlwirPmkXpdS/qmwI2IiIiIFEWBGhEZFJ+FE4BlgG87a4b9xuGzsDFxPPd+/TznW8BP08PJwNp9jQL3WdiE2OflCmAeZ81xw93bYPgsLEAM1nwuLb0FHFiPJsM111kC2AdYqebQPcDpzprn6nk96Z8CNyIiIiLSLArUiMiAfBYmEMuRfuWs+fkIz7UmsJ2zZut+njMHcfrTuLR0kLPmrj6eOw64AXgc+DzwY2fN0yPZ40BSmdJPgK+npWnEJsO3NeBaqwD7EseRV8wgTr26oJEZRNI3BW5EREREpFEUqBGRAaVgwenA3s6a+0d4ri8Av3DWrDfA884AVk4Pf+2sObyf564NHEdsQvwNYJeRZP0MRmoCvFP6r+JKYrZLXXvJ+CyMBdYnlkTNmzv0HnARcK2zZko9rylDo8CNiIiIiNSLAjUiMiCfhYOBbwNfH2lAwGdhIeBmYOX+Aho+Cw44ND18j1j+NLWP53YAlwGvAuOBW501fxjJPgfLZ+GbwFFUmwzfR8zqqUuT4ZprzQpsS2w6PCF36EXgTOB3jQ5QyeAocCMiIiIiw6VAjYj0KwVBfgX8X19NfYd4vgnA/cSx232W7eSmTHWkpX6zeXwWvgycCxwG7AJs5qyZPNL9DobPwjLEvjXzpaVngX2dNS826HqfBPYgjvXO+ztwsrPmb424rgzfAIGbR501rxW4PREREREpEQVqRKRfPgsWuAr4ibPml3U6593ATs6afw3wvAuIN7cANztrjh3g+acDswNPAu84ay6sx34HIzUZPgWwaelN4ABnzeMNvOZSxEbKK9Qc+gNwhrPmf426toxML4GbMcBjwCPEjBsFbkRERERGKQVqRKRfPgs7E/uwrOOseb1O57wBOMlZ8+AAz9sc2D89fCPtob9yqSWAq4EjgN2IwaCX67HnwfBZmIXYZLgyWnwacGy9Alx9XLMDmEicELVI7tA04DrgImfN2426vtRHCtysQAzcfIlqxo0CNyIiIiKjjAI1ItIvn4UrgKnOmu3reM5zgV86a341wPMMkA9y7DzQGGyfhZ8Qx4hfDXzJWfPjke53KFKT4V2AHXLLlwNn1rvJcM11xwEbATsDc+UOvQ1cANzYV48fKR8FbkRERERGLwVqRKRPqZznDmKQ4dI6nvdYIHPWXD6I514BLJUeXuOsOWmA5xvAEydAOeDERpYf9bOPdYmZPZWmv/cSmwy/3+DrzgFsD2xGbKxc8V/i5K4/quFw61HgRkRERGT0UKBGRPqUm7y0qbPm2Tqedz+gw1lz8iCeuz2we3r4MvCdgQINPgv7AusCRwJ7Ats0Mpuln318nthkuDJS+9/Afo1qMlxz7YWAvYjjyvP+CpzirHmq0XuQxlHgRkRERKR9KVAjIn3yWTgVWBTYoJ5ZGD4LWwOfc9YcOojnfga4Mbe09UBBBp+FuYBbiSO7lwAedNbcNvwdD1+a0HQKsGRaeoPYZPiJJl1/WWBfYNmaQ78GznLWvNSMfUhjKXAjIiIi0j4UqBGRXqXGuH8g9jYZMPNliOdejxj82XmQz7+BGDACuMRZc9YgXrMtsB2xEfKJwObOmneHt+ORSR/Lo4E109JUYpPh25t0/Q7g68DewEK5Q1OIE70udda814y9SHMocCMiIiLSuhSoEZFe+SysDpwE7OasebjO5/4q8CNnzUaDfP7uxL4rAC8AGw2i/Gkm4Gbg98C7wCzOmtOGv+uRSU2Gd6X674AmNBmu2cME4PvERsez5w69AZwL3OKsmd6MvUhzKXAjIiIi0joUqBGRXvksHEEcM722s2Zanc+9GDEzZvVBPv9zwJW5pUH1zPFZWB84GNic2CtmH2fNC8PYct34LHwLOJxqk+F7gMMa3WS4Zg9zAzsCmxBv2Cv+A5wG/FkNh9ubAjciIiIi5aVAjYh8TMr++A3wyGD6yAzj/HMRy6q+5qz5cBDP7wBuA0xaOttZc/EgXjcWuAb4J3Hq0redNfsOd9/1kvrGnEi1yfAzwP7OmtDkfSxCLIdao+bQQ8Cpzpqnm7kfKY4CNyIiIiLloUCNiHyMz8IXgEuI46R/24DzdwAPAG6wE5B8FvYnZsYA/NNZs+UgXzeRWMK1BXAgMZPn/qHvur58FhYkZvlUmgy/Tmwy/LcC9rICsB/VMegA3cAvgXOcNa82e09SLAVuRERERIqjQI2IfEzqCbMtsezp7QZd41fAIYMNTPgsLA9cmFv63mCCPCkodAHwAXAm8FNiY+G6lnMNh8/CrMQmw2ukpanAT501dxSwlzHEkeZ7AgvkDn1I7KVzhbPmg2bvS8pBgRsRERGR5lGgRkQ+xmfhOuANZ82uDbzG5cRpQ3cN8vmVcqxKudDJzpqrB/naZYGLgd2BtYHnBvvaRkv/rt2IE6oqLiWWdzWlyXDNfmYiZh9tC8yaO/QacDZwexH7knLpJXAzhhi4eRR41FkzqcDtiYiIiLQ0BWpEpAefhYWI/WAGHQgZ5nVOBh501lw/hNccCrj08HFnzY5DeO0JwILAPsBlwDbOmteHsOWGSiPLDwfGp6U/Akc0s8lwzX7mJU6p2oB4E17xNLF/zUNF7EvKKX2+fCn3XwcK3IiIiIgMiwI1ItKDz8JmwAHABs6a/zXwOocSs3bOGcJrVgbOSA+7gXUHewPos/AZ4HrgMGB+4DPOmuOGtOkG66XJ8NPEJsMvFbinxYB9gVVqDv0ZOG0w07dk9FHgRkRERGT4FKgRkR58Fs4GPuGs2aTB19kZ+KSz5qdDeM144HfA7GnpOGeNH8Lrfwx8FdiMmFVzWNkmG/ksGGKT4SXSUmFNhvN8FlYkNhxePLc8A7gZOK9M2UlSPgrciIiIiAyeAjUi8hGfhdmJY7OvdNacMdDzR3itjYDVhjou22fhaGC99PBBZ82eQ3jtJ4BbgNOA/xH7wuzsrCnVG2FqMnwMMDEtTSE2Gf51cbv6qJ/Od4m9fubLHXqfOCXsamfN5CL2Jq1FgRsRERGRvilQIyIf8Vn4BvAzYAdnzRMNvtYa6TpbDfF1awInpIfTgW8MZTJVmmi1IbH3yrHAr5w1vxvKHpohBUX2ALbJLV8MnFt0M98USNoK2BqYKXfoJeAs4LdF71FaiwI3IiIiIlUK1IjIR3wWfgqsDHyz0TfaPgufB05w1nxriK+bmZj1UwkQHDGUcdYpa+hWYr+aXxOza37grPlwKPtoFp+F7wA/ptpk+G7iv7nwUdk+CwsQJ1Z9h3hjXfEUcIqz5q+FbExangI3IiIiMpopUCMiAPgsjCX2f7nHWXNUE65niAGTlYYaFPJZOB5YKz38o7PmgCG+fnNigGEDYEvgA2fN+UM5RzP5LCxHbDI8T1p6GtjPWfNycbuq8llYkti/5is1h+4GznDWvND8XUk7UeBGRERERhMFakQEAJ+FFYDzgYOcNXc14XoTgPuJ2TtDakTrs/AtoNKEeArw9aFkmKRr35iufyZwNbFXTWHTlQaSxqafTLWZ7+vEiVB/L25XVT4LHcDXiOPPP5M7NJ2YvXTBUErURPqjwI2IiIi0MwVqRAQAn4V9ge8Tgx7vN+madwG7OGueGeLr5iBm/4xLS0MOLqVgz0+AjYEvAis6aw4dyjmaLfWGORZYLS1NAY521vymuF315LMwjtgDaBdg7tyhd4ALgRucNVMK2Jq0MQVuREREpJ0oUCMiAPgseKDLWbNXE695PbGXyQPDeO3pwCrp4W+cNYcN8fVjgCuB/wKHEMd1n1z2vipp33sSG/lWXEQckV2aBr6pF9B2wA+ACblDXcDpwF1lm7Yl7cNnYT5gBRS4ERERkRakQI2I4LPwaeAm4BfOmhuaeN1ziFOXbh/GazckNtkFeA9Y21kzdYjnWIlY+rQtMBY4ENi6TAGPvvgsfJf4769kFd0FHFmGJsN5qWRrD2CdmkNPEIN0pSjdkvamwI2IiIi0EgVqRASfha2BvYHvNLNPi8/CMcAzzprLhvHaeYHfUp02tLez5v4hnqODOE56HLFU5xjgYWfNLUPdTxF8FpYnNhmeOy1lxCbDrxS1p774LCwD7A8sV3PoTuBMZ82Lzd+VjFa9BG6gZ+BmSH2zREREROpJgRoRwWfhAmA2Z83mTb7uvsBYZ81Jw3z9+cSbLYCbnTXHDuMcSwFXEJvg/gu4ANjcWfPucPbUbClj5VRgsbQ0idhk+P8K21QfUmBsTWJQ8FO5Q1OAa4GLW+XjLu0lBW7yPW66UeBGRERECqJAjcgo57MwF7Ex78XOmnObfO2tgKWG28Q3jdnePz18A1hnOGVLPgvHEQMdmwPbA3M4a04Zzp6K4LMwG7HJ8NfS0hTgKGfNncXtqm8+C+OBTYAdgTlzh94kTh7zzpppBWxNBFDgRkRERIqlQI3IKOezsB5wNLE3y1NNvva3gA2dNTsP8/UG+GVuaWdnzWPDOE8nsUfPMcSg1bXAvs6a54ezryKkJsN7A1vmli8gjsUuZc8dn4U5icGaTan22gF4HjgNuFcNh6UMFLgRERGRZlKgRmSU81n4ObA8sF6zb+h9Fr4KHOyscSM4xxXAUunhNSMoozoQWIM4Wnoi8F1nzT7D3VdRfBa+BxxKNfDxB2KT4Q+L21X/fBYWBvYC1qo59Aix4XDW/F2J9E2BGxEREWkkBWpERrFUgvIH4njr4wq4/mLApc6aiSM4x/bA7unhy8SGyEN+Y0vNiW8hZqFcCZwHXOas+fNw91YUn4UVgBOAudLSP4AflrHJcF5qjrwfsExuuRv4FXB22fcvo5cCNyIiIlJPCtSIjGI+CysSpx7t56y5t4Drz0kcK73acMdK+yx8BrgxtzTsEi6fhR2JfWo2ABYk9n3ZfKhjv8sglXOdQrXJ8GvEJsNNLW8bqlTC9Q1ihs2CuUOTiQG0y5w17xexN5HBUuBGRERERkKBGpFRzGfhIGB9YC1nzeQCrt8B3A9s7KzpGsF5bgAWTQ8vcdacNczzzErMqrndWXO6z8KhwPPOmquGu7cipSbDPwNWSUtTiGVQvytuV4PjszAB+AGxufNsuUOvA+cAt5a1945ILQVuREREZCgUqBEZpVKQ5DbgGWfN/gM9v4H7uB34sbPmiRGcY3fiDT3AC8BGw21C67OwEfBDYq+aKcDlwDateiOVMlT2JWYKVZxPbDJc+m8APgvzADsDGwFjcof+DZzqrHmgkI2JjIACNyIiItIfBWpERimfhSWAa4CfOmtuLXAflxHLWe4awTk+RyyLqdjUWfPsMM81DrgeeNxZc7TPwg+Azzprjhnu/srAZ2F9YpPhsWnpd8BPytxkOM9nYVHiVKvVag49QAzY/Lv5uxKpDwVuREREJE+BGpFRKteEdx1nzaQC93ES8BdnzfUjOEclO8ikpXOcNReN4HxrAT8HNiNm6FwNHOGs+edwz1kGqcnwicCcaekp4IBWatLrs/AVYsPhJXPLM4BbgXOL/FwWqRcFbkREREY3BWpERqmUydLtrNm24H0cCrzprDl7hOfZn2p5T+as2WIE5+oALgUmOWv291lYGdgB2KkVyoX647PwKWKT4UpPn1eJTYb/UdyuhiaVc32bGGj8RO7Q+8BlwFWtkikkMhgK3IiIiIwuCtSIjELph/7fEkceX1zwXnYGFnTWHD3C8ywPXJhb+p6z5sURnG8FYi+XnZw1f/VZOJk4xvzOkeyzDHwWZgeOo9pkeDKxyfDvi9vV0PkszAJsCWwNzJI79Apxmtmv1XBY2lEvgZsZVAM3jylwIyIi0toUqBEZhXwWNgAOAzZz1vyr4L04YHVnzT4jPM8Y4DfAvGnplJFOa/JZOBWYi9ioeGHgdOLHrOWzNXwWxgL70LPJ8HnAha2WNeSzMD+wG/A9oCN36J/Ez4NHC9mYSJOkr4EVgC8TAzfTqWPgprOzcyyxofd3gJWABYBZgTeBp4F7gau6urr+nnvNZ4D/9HK6ycDbwGvA34CHgOu6urr6nfzX2dm5OHFK4RrAssCCwDSgK13/7K6uLn2ti4hIW1CgRmQUStkhSxCzTgp9E/BZWJ2YtbJlHc51KODSwyecNTuM8HyLExsu/8hZc5fPwt7Ah86a80e41dLwWdgQOJhqk+E7iU2Gmz6ufaRSg+x9gRVrDt0DnOaseb7pmxIpQD0DN52dnSsRSwrzfaGmAu8Ac9NzGpsHftDV1TWlJlDzNvBB+vtYYgB8fO5104HrgH26urpe62UPqwL31Sy/A8wETEiPZwDHdnV1HTHYf5uIiEhZKVAjMsr4LMwE3AXc4qw5oQT7WQY4yVmzbh3OtRJwZnrYDaw70uayPgtHAssBmwAzEwM3uzhrwkjOWyY+C18CTqBnk+H9nTUfu2Equ9RfaGViwGax3KHpwE3A+c6aN5u/M5HiDDdw09nZ+V3gBmJAZBKxGflNXV1dz6TjY4EvErNtdie+h8zT1dX1Zk2gZruurq5La85tiF+rOwLfSssvAqt2dXU9V/PcNYDfA78ErgLu7urqmpSuvwJwMvC19PQdu7q6ht1MXkREpAwUqBEZZXwWViM2k93DWfOXEuxnQeLEppVG2k/EZ2E8MSNkjrR0nLPG12F/HjjRWeN9Fr4HrOysOWQk5y0bn4WFiZ8Xn0lLrxCDNS056SqVdq0P7Eq1HA7gXeBi4FpnzZQi9iZStMEEbjo7O5cAHiEGX54C1unq6vpfX+fs7Oycl/i1te1gAjU1r90UuJKYZfMUsFxXV9e03PFPAbNUAkS9vH4C8DCxJOrfXV1diw/4QRARESmxMQM/RUTazETidJzHit5I8jrxvWjukZ7IWTOV2KugYq06nPMl4Fpgl9S89nagMzUbbhvOmv8C2wEPpKUFgIvSqPKW46yZnoJ0GxJvHitBmdmBvYEbfRa+mTJwREYVZ81rzpo7nTXHOWsq2TB/Bb4KXOCzcP2nFrc3EoM0HwIb9hekAejq6nq9q6trA+Ctoe6nq6vreuDQ9HBpYJua4//rK0iTjk8hBnoAPtvZ2TnPUPcgIiJSJgrUiIwiqeHuasD9KahRuJTV8DYwf51OeXfu71/xWZizz2cO3qXEPgg/SFk/JwI/TB/PtuGseYdYMnRtWpoJON5nYYdWDWg4a95Lo983BO7IHVqIOPnqYp+FZQvZnEhJ1AZubjrr5CP/96/s8wCfX3m1l86465FjfBZ+5LOwts/CvP2dq6ura7ip2mcSGwxDTaBmkPJN3sf2+SwREZEW0FY3GSIyoM8RAyL3FL2RGq9Rv0DNA8SpIhB/WP9aP88dFGfN28AlwLY+C/M4a/4GPAtsMNJzl03KRDmRGMSYnpZ3A45J/Y1akrPmZWfNEcRR3vlssi8QgzU/91noLGZ3IuXyx5uuXo70M+LfH7h3T3rJuBls4Gawurq6PiT2TwNYsbOzc+YhnmKN9Gcg9tMRERFpWQrUiIwuE4mTMf5c9EZqvAbMV48TpdHZ+X9fvUp3riP2N9k+PT6DGLiZo++XtK5UNrQnMdsJYB3g/NTbomU5a54CdgF+CLyQO7Q2sRxq3zplYYm0smVyf//rYEql6hS4eSL9OQH41GBf1NnZuTLVwPmFI8jqERERKQUFakRGl4nEsdVD7iHQYJOoU6AmyZc/rZJ6y4xIGld9LrCJz0Kns+YV4FZgp5Geu6ycNQ8D2wKVsdbLAJf7LNjCNlUHzppuZ82fgO8Ty9gqwajxwJbALT4LP0jNqUVGo/z78ccmQg0QuDnfZ+H6fU+7cNdhXDd/rUEFfDo7Oz9BnMY3BngGOH4Y1xURESkVBWpERok0vWhJylf2BPUtfQK4D6hMDJlAHAFbD7cTszB2S4+vBL7ms7Bonc5fOs6aF4jBmsqEsJZuMpznrJnqrLmW+Jv4K4BK36Y5iRk31/ksrNGq/XlEmqUmcLMxsPubr778f5Xj391hj91zGTdb+yx0+Cys5bMw20iu29nZOTtxauCngXeATbq6ut4d0T9GRESkBBSoERk9JqY/yxioqWtGTWqK+1BuqS5BhdRI+AxgXZ+Fz6UsmzOB/dv5Zj59PPcGrk9LMxObDG/fDv9uZ83bzprTgI2B3+UOLULMuDnPZ2HpQjYnUox8j5chlzI5a1679JgffzSB797bbrwceOo//xh37jN/G3/ps/837vUXnhl37cv/Hfsrn4X8hKb8tfrtM9PZ2Tkb8CtgJWJZ6npdXV1P9PcaERGRVqFAjcjosRrwgrPm+QGf2Xz1zqiBnuVPq/ksTKjTee8jNqPdK3edCcCqdTp/KaUmw8cDPyf2OYJY7nB0HT+2hXLWdDlrDiH2IXoyd2gFYsnXT1Nmmki7+7/c37840pPNPtfcswGHPPfP8Z9+7E8zdTx818xz3/+bmef/x2MTliVm6FW+rpZLf04Guvo6Xy5IMxF4D/h2V1fXfSPdp4iISFkoUCMyCvgszAp8mXJm00D9e9QA/AmoNJScjfjvHzFnTTcxq2ZFn4UV0+MTgX1HQ08TZ82NxCbD76SlbxEzTur9/68waarX9sAhwIu5Q98CvM/CHiMt2RApubupBmQ3HOnJVl5v/b2B5aZNpZqB1033hJm6xxMz1y4+9ba7LNXsxwfTFKiPyQVpVgfeJwZpyvq9TUREZFgUqBEZHVYiNkot6w+zdc+ocda8TmxuWVG3nirOmieJY2T39lkY46x5BngE2Kxe1ygzZ81DwDZUpyZ9gZhxsmRxu6qv1HD4d8RyqNOIpRUQs6e2A272WdjIZ2FsUXsUaZSurq6XgZvSw807OzsH/bXd2dnZAbDwEp+bqbI2fsJM8wFjpk3p6OjuZkblvwkzdY8DOoAFHr/nD7dT/T5waR/nng24gxikeY9Y7vSnof3rREREyk+BGpHRYSJxsk1Z6/cnAbOmzJ96ypc/reGzUM/3vLOAJYBvpMfnApuOcDRty8g1Ga70AvokcLHPwhpF7akRnDVTnDVXEBsOXwdMT4fmJWbcXOOzsGo79OoRqXEYMUA5C+A7Ozs7+3tyZ2fnPJ2dnTcBc/ksLLrFj448KXe4A5gx+cOOydOndXzQ3c30GdM7Jo8d1z0ZmHD/HbfM84frr/gswISZZ/43sVF77fkrQZpKuZOCNCIi0rYUqBFpcyk48TXgPmfN9IGeX5B3gCkMo2nlAPKBmrmB5et14tTr5xZgD5+FCc6aN4GrgD3qdY2yc9a8TWwyfENamhk40Wdh23YLXDhr3nTWnABsQiyrq1iMmHFzVjtlFIl0dXU9DWxFfG9eBni8s7PzR52dnYtXntPZ2Tm2s7Pzi52dnUcDzwLuR+df9Q3givETZvro62H69GnvAlPee2fMmx9+0DGpu5vpUya/Mubff7v93ZP23O4T15x4zGIzpk/vmGPueacedN5Vb59x1yOr5/fS2dk5K3Hq3kRi8OhbKncSEZF21tHd3T3ws0SkZfksLAdcBBySSjlKyWfhl8DhzprH63zey4HKxJ5rnDUn9ff8IZ57fmKw5kxnzbU+C+OIwZqjnDX/qNd1WoHPwqbAAVR/AXAHcIyzZkpxu2ocn4UvAfsBn8stdwO/BM5x1rxayMZE6qyzs3NVYinS4rnlKcSAydxUv+a7F7FLP7nf6RdNGzd+/ITXQteiP9li/dkAZppl1hnjxo+fMfmDsWOmT586DT6c0D1j6kcn6+jomPGFVVd/Y7P9Dv3vHPPMO534tfQzZ41Pe9gauCw9/UPgrQG27bq6uu4fyb9bRESkSMqoEWl/qwPTgAeK3sgAGtFQGHpm1axVz0wPZ81rxMDMjj4LszlrpgEnAwe2W0bJQJw11xMnYVWaDK8HnNuupWDOmkeBrYEjgFfScgfwPWL/mp19FmYpan8i9dLV1fVnYkDyB8T3u38RgyVzAK8D98057/xn/PDMS3514DmXTx03fvx44NN0d0+unGPyB++Pee/tt8ZNm/r6mBnT3x87duxs3bPMvhifWWb1t1d3mz04dvz4hXc6+sSfpiANxK+lQ30WtkvvpfmfV2cmllr2919bTKITEZHRSxk1Im3OZ+EG4FVnze5F76U/PgsnAg87a66r83k/TbUpJsDWzpqn6nj+2YBbgZucNeektZOB3zprfluv67SK9PE+hTjJBeAlYH9nzdPF7aqxfBZmBjYn9uzJ91l6FTgHuN1ZM6OXl4q0PJ+FbwCHEz/3xwOfAd4kfv6/A/wa2PS9dzrG/GzXeZ7+x6MT/rn+Du/tNX5C9xwrffPDZxZdatokYA1nzQyfhW2IAd+8q4FT9TUkIiKjiTJqRNqYz8LCwKL07KlRVg3JqEm9ZP6TW1qzzud/D7gQ2CKVQkEMVOw2GjMq0sd7W+DhtLQgcJHPwup9vqjFOWs+dNZcTBxj7KmONf4EMePmSp+Frxa1P5FG8FmY4LNwEPAzYpBmHLAQcaT9q8AHwP6k99zJH3S89fQTE56BDqZ82PEWwPiZusem13YCOGsuA46l+jUEMQh6ZCotFRERGRUUqBFpb6ulP+8tdBeDU/cR3Tn58qevN6AsyRNLAHYGcNb8F/gDcYT1qJOaDO9FNZNpFmKT4W3auSTMWTPJWXMcsUQk3x9jSeBsn4VTfRYWLWZ3IvXjs7AQsffZpmlpLLFfTRdxItMUYF9i4OYTAK92jf3V9Gkd0wE+eK/jdYAJM330c6itnNtZczNwMFBtYgPfBo73WZgJERGRUUCBGpH2tjrwL2fNi0VvZBAm0bhAzV25vy9CzDKqm9Qw9yxgg1T6A/EmZr10QzPqpH49PweOJ/52vIMYvDnKZ6Gt+0c4a/7trNkb2JPYz6Pia8B1PgsHt2vvHml/PgtrEMuRlkpLY4hf468R+6FNAw4EHgO2TM+Z4s+f/c7KOd55c8yrABNm6q78HNpjYpqz5i7iRLn3c8sTgTN8Fmav4z9HRESklBSoEWlTPgtzAl8EWmWE6Ws0ppkwQAaE3OO6lj8lvwOeJo3ndta8D1wA7NOAa7UEZ013ajK8N3FCDMTfjJ8zGgIVzpoHiWUbxxADkRC/724M3JLGmCtDQFqCz8I4n4X9gBOBSrDkPeBl4pQmiAGbHztr/gx8FVgird/2yN0zf9Rc+I1Xxr4EMH5C99i0lJ+eBoCz5mFgV2K/m4oVgPNHw/uHiIiMbgrUiLSvVYhf460UqGlIRo2zppua6U8NuMYM4HTiZKkvpOVfASaNch61UsBiW+C/aWk54DKfhSX6fFGbcNbMcNbcQuxfcyFQuVmdlZhxc5PPwro+C/p+LKXls7AgMfC8RW75n8ALpNKm5GhnzR/S37dKf3YTM3A+Ckq+9MLYl+GjHjWQK33KS43fdyQGgyqWJPa9GpXZiiIiMjroB0OR9jWR2DelbhOOGmwSME8Db1jz5U+2ET/kO2seAv4C7O2z0JGCNycCPxztN+LOmueIPXseSUuGeLM1sbBNNZGz5n1nzbnEgM0vqWYgLEjMuLnUZ2H5grYn0iefhVWJgZYv5JZvIGa6LJVb+4Wz5vb0miWAldL6n5w1L5Abmf3Bex2TZ8zomDp+ApUGwfP5LPSaUZneO7YHnsstLwxc7LPw2WH+s0REREptVN84iLSrNB1jFeDeFhpp+jrxPWmeBp3/b+kaFY0of4KYVfNFYj8SnDV/I/Yp2aBB12sZqcnwnsTmyxCzSk7yWdi6nZsM5zlrXnHW/ITYu+Ph3KGlgQt9Fo5P09pECuWzMNZnYU/gNGDOtPw+cCgwL9VADMAZzpobco/zmTdXpD8/yqjp7u7o7qD7/Y4O8pOces2qAXDWvEzMrMn/4mF+4tfMsoP8J4mIiLQMBWpE2tMXiT0EWqXsCWfNVOAtGtSnJgWs/phbqnv5U7pOBvwG2CuXRXMmsG3qGzSqpSbDPyNmGlWaDO8NHNHuTYbz0ufJ7sTxxc/nDq0F3OCzsL8+X6QoPgufAM4llixWPE0sZ1oF+Hpu/aI0Vrvy2gWAddPDJ4lBcsgFagC66XiLOC2qos9ADYCz5k1iz5qHcstzECeqrdLvP0hERKTFKFAj0p4mEsejPjTQE0umkSO6oWf507I+C4261tnE6VLfgZhFAdwC7NSg67WU1GT4WuL43vfS8neJN1yNyqgqnfRxuAf4PvALqk1TxxGbEN/is7C5z8L4grYoo5DPworEUqcv5peB7YDNSO9ryTXEgE7e9+GjTJkrUo8wyJU+AcyYxqTc82CAQA181KR9X3q+l88MnOKzsM5ArxcREWkVCtSItJlUQrI68JCz5oOi9zNEjRzRDfAo8E76e+XjVHdpHPoNwK65qT5XAqv6LCzWiGu2ImfN/cTf2HelpeWJTYZHVd8JZ820VDayAXAZMcgKsdxkf2KGzVqjpTxMiuGzMMZnYRdiBmAlYPoBcDgxC25nYJPcS24FTs4FYvBZmBVw6WEXPbMYe2TUTJnc8TI9M2p6jOjui7NmCnAwcHNueSxwjM/CpoM5h4iISNkpUCPSfhYFFqKFyp5yGjmiu1JedW9uqSHlT8nFxPKz76drTyHeAO2vG+4qZ81/gK2Bx9LSQsAlPgtfK25XxXDWvOusOYM4vvu3uUOfAo4n9uP4fCGbk7aWxl2fScz6q7w/PQts5az5NTGbZpvcS+4Ejs0HaZL1ieVIAFfV9EjrEaiZNo03iEHJSlbNIinQM6B03uOAS3LLHcBBPgs76z1WRERanQI1Iu2nMkXnvkJ3MTyNLn2CninzX25UHxBnzRvApcB2uWvcTfzN72qNuGarcta8BexB9TfksxJLGbYcjTdczpoXnTU/JmYbPZE7tBxxOtRxGk0s9eKz8CViCdNXc8u/BLZx1jzns7A5sZ9SxT3AEbWN6n0WxhJL9gDeTufI61H6NH1axxvAhwwjqwY+Kh08Czi15tDOwIGjfdKeiIi0Nn0TE2k/E4F/pL4orWYSDcyoSR4k3hxAvEFoZObGNcBk4m+jSb99PgnYZzQ1zh2MlO10HHAy1SbD+wKHj9YeLc6avxMn3RwE/C936JvAjT4Le/sszF7I5qTlpVKn7YFzqL7vTgaOdtb8xFnzgc/C+sTyu4qHgINTU/BaXwdM+vuNvZTe9ix9+rDjdeJ7cb5PzaADNRXOmiuBo4jvGxWbAkenCYgiIiItR4EakTaS0te/QGuWPUETMmqcNR8C9+eWGlb+lG5Uzge+77OwYFr7F/FmZ7NGXbdVpd+QX03PJsPfY5Q1Gc5LH5O7iDeeJxMzFSBmJ2xNbDi8qW5IZSh8FuYmZqLsTvVnweeJWTS3peesAxyWe9kTwA9TGWft+TqII+cBpgLX93LZHoGaD9/vNVDzuSH+UwBw1twOHEC1vxPEyVMn+yzMPJxzioiIFEmBGpH2sioxE6FVAzXNyKiBWIJUsYrPwiwNvNatQAB2ya2dC2zis9CMf2vLSU2Gt6PaZPiLxCbDo7YRs7NmSgpibUCcyFPJaJibmHFzrc/CxNFYKiZD47OwHPFzKD/S+rfEfjT/Ss+ZCPyUar+afwL79NOg/ovA0unvv3bWvNbLc3pkEb79+phX+XigZsDJT31JE9T2BN7PLa9CDPRq1L2IiLQUBWpE2stE4BXg6aI3MkzN6FEDsX9P5UZ3ArByoy7krJlObNL5HZ+FxdPaW8AVxL4s0gtnzbPE5qX5JsOX+iysWtyuiuesedtZczJx+k6+39JniBk35/gsDPtmV9qXz0KHz8JWwAXAAml5CrHk8LA0+hqfha8Sx8VXfkZ8FtjTWfNuP6ffMvf3K/t4To+Mmle6xk4ilivlgz+fHUmpo7PmMWJD5Ndzy8sCF/gsNON7i4iISF0oUCPSJlLPk5WAe3qZxNEqJgGzDHbyx3A5a94hlh9VNHL6E8QRtX8n/ra34iZgaZ+FpXt9heCseZMYzLo1LVWaDG8+2jNHnDX/ddYcRLwpfSp36MvAlT4LR/ksLND7q2W0SRklJwH7UP3Z73/Ads4aX/me4bOwPDHgNz73nN3T12Jf5/401Sb296cgaw8dHXRQE6h549Wx7xCzX/IBoHHEyYXD5qzJiL2dQm75s8DFPgufGsm5RUREmkWBGpH28WVgFlq37AniD+xTaH7502qNbO6bboJOB77ms7BCWptOvCE6YLQHHfqTmgwfQ7XJ8Bhic9PDRmuT4TxnzV+J06EOA15Kyx3Ad4CbfRZ2bXTgU8rNZ2EZ4CqqwRSI2VhbpqBG5XlLAacBlZ4urwC79VHGlLdF7u9X9PGccXz8Z87JxCzKyTXrI84Ic9a8AOxAzAaqWIgYrBlyw2IREZFmU6BGpH1MJKaQP1L0RoYrBTSaVf70J6CSeTQbMdDVMOmG+h7ixKeOtPYQMUV/3UZeu9XlmgzvT7X/xPrAWakp6qjmrJnhrPkNsBGxzK7yMZqJmFlws8/CBhpXPLqkUqfNgIuoTmOaBpwA/ChfypT6P51JfC+E+L60q7Mmn5XS2zXmJQYFATL6/v7TWyB8CjGLsjYDtC6le2ny4Y7Ak7nleYHzfRa+WI9riIiINIp+aBNpA+nGfyLwQG8TOVpMUxoKO2teB/6aW2p0+RPEG6Glaq51KqCsh0Fw1txHzB55MS2twChvMpznrJnsrLmU2HD4RqrjiucjZtxc7bOwUjG7k2ZKY9t/QZyEVGnWG4AdnDXX5ctjfRYWJo7onistvU0sd3phEJfahGoQ5op+ym5n6mVtMvH9vjYzrm4ZL86at4mTrR7ILc9ODPKuVq/riIiI1JsCNSLtYUlic8h7i95IHTQrowZ6lj+t0eiMg9S74XZgj8o4ZWfN/4DfEQMQMoBck+FKkK0TuMRnYZW+XzW6OGted9b8HPg+Pd8TFgfO9Fk4XcGt9pWaSV9Jz4DwPcAWzpr/q3nuJ4lBmkpw/H1gr8r0pwGuMzMxUAOxTOr3/Tx9oEBNvk+Nred7cZpUtT9wZ255AnCSz8J69bqOiIhIPSlQI9IeViemj99X9EbqoFkjuqFnoGZuYPkmXPNcYEFi6U7FxcC6PgsLNeH6Lc9Z8wbxt+S3paXZgFPVZLgnZ81/nDX7ET9W+UlwqxDHeR+aSlekDaRSJwdcAlSa5s4g9p35YcouyT9/PmKQZsG0NBnYtzaY049vE983Aa521kzr57n9lT4BPJ9bn43YT6ZuUq+rw4AbcstjgKN9Fn5Qz2uJiIjUgwI1Iu1hNeDJdAPb6pqWUeOseYmeE3MaXv6U+iZcA+xSKXdKY3HPA/Zt9PXbRbrx+imxdKybapPhQ9VkuKfUC2lL4Gji1xfEj5cDbvFZ2D5lR0iLSu8lPwUOpRoUeQXYyVnzsZKkNAXqLGCRtDQNOCCNtx7M9cZQbSL8HnDzAC/pK6Om8vn4Us2xuo+Yd9bMAI4njifP+6HPwm4K8oqISJkoUCPS4tII3qWIzXHbQTMzaqBnVs1aTfph/TJi34jNc2u/BhbwWWhoU+N2kpoMX0nPJsMbEst75ur7laNPajh8G/Hjcx7wYTo0KzHjxvssrKeGw63HZ2Fx4rSlfFPyB4ilTk/08vzZiP2yFk9LM4BDnDUP1D63HxOpBnludta8N8Dz+yt9Aqj9JUPdAzXw0XvGecCJNYd2AA7R57+IiJSFviGJtL6vpT/boT8NxB/cm9WjBuKY2opK0KuhUgnCJcDWldKT9NveE4H9fRbGNnoP7cRZcy+wPdUmw18iNhletLhdlZOz5gNnzQXEhsO3Up24swAx4+ayygh5KT+fhe8RA7+fTkszgLOBfXrLsEyZU6cAS6elbuAoZ83dtc8dwJbpz+nEDMGB9FX6VMmoeafmWEMCNRXOmmuBw4n7/2gZOM5nobe9ioiINJUCNSKtb3Xgf8B/it5InTSzmTDOmueBZ3NLzZj+BHAd8eZkh9xe/g48Q8x6kCFIzU+3BSoZBJ8CLvVZWLmwTZWYs+Y1Z81PieUrD+UOLUUcX3ySz8Iivb9aiuazMLPPwpHAEVSzVSYBuzlrLk6B39rXTCCO5s4H4n7mrLljiNf+AtV+Xnc6a14exMtqM2qmdnczg2pGzQxi4KaioYEaAGfNr4nZeJNzy2sDp2gKn4iIFE2BGpEW5rMwC/AV4J5+xqK2mteAuZucgt708qc0Rv0cYCOfhU/lDp0JbJN6SMgQpJHruwG/TEuzAaf5LGym/hO9c9Y8DewB7EPPgOXqwA0+Cwf6LMxdxN6kdz4LnyFm0Xw3t/ww8ANnzaN9vGYscCyQD1ye6qzxw9jClrm/XznI19QGairBkTeIWT3zEYPUFfM3o9G1s+bPxNK/fEbPisDZKp8UEZEiKVAj0tpWJKaU31P0RurodeJ7UzOn0eTLnxYBmlUycwfwHPFGAQBnzauAB3Zp0h7aSgqAHQ2cTrXJ8AHE/hPjitxbWaW+HX8GfgAcR/waBBhLHPF9i8/C1ioJKZ7PwrrEfjSfTUvdxOa4e6RAZW+vGQMcCayZWz4/9Xca6vU7c+d52FmTDfKltZ87UwCcNdOJwZr5gNpzNTyrJu3hCWBnqtk9AJ8HLkw94ERERJpOgRqR1jYReBd4vOB91E0a8fomzW0o/DTV/ibQ84amYVJ5whnAN30W8r1xrgJW9llYrBn7aDcp8HA58EOqTYYdcIYylfrmrJmeMiw2JI6Mr5SizA7sDdzos/ANZSc1n8/CBJ+FQ4FjgFnS8hvAXs6a83ordUqv6wB+BKyXW76Kj08+GqzNqf7seMUQXtdXRg1UG8j/s+Y5TQnUADhrniGWoXbllhcFLlYJoIiIFEGBGpEWlX5L+jXgzym40U6a3aemG/hjbqlZfWoA7gceBfaq3ACnrJDTiWNjdVM8TM6ae4g3X5XRv18hNsv9TGGbagHOmvecNWcTg1v5/iULAT8j3rwuW8jmRiGfhYWBS4n/PyoeJ051erCf13UQS9o2yi8TS56GXCqbgpzrp4fPEidLDdZAgZr5iQHzvKYFagCcNf8jvl/kS7AWBC6qCaSLiIg0nAI1Iq1rGWJ5UDuVPVU0e0Q39Cx/sj4LCzXjoumG6XTgq8RStoo/AR3ErCkZpvSb8q2Bv6WlhYlNhlcqbletwVnzkrPmCOLH77HcoS8QgzU/T6Uw0iA+C18nZsAsmVu+DNjFWfPKAC/fiZ79ZH4N/HwE/cw2BmZOf79yiOfptfQpmUT8XvZvYlPhiqYGaiA22SaWQT2eW54HOM9n4UvN3o+IiIxeCtSItK6JxB9qh/JbzVbR7BHdEG/k8z0emlL+BOCs+T/gD8DelSbK6SboJGAf9QYZmdS7Y1fgV2lpduB0n4VNi9tV63DWPEXsmXQA8ELu0NrEcqh9fRbmKGRzbSqVOh0I/AKoTCB6G9jXWXNG6u3S3+u3IAYcKv5IHMPda4nUYPZD7FcE8f35N0M8xUAZNeOIwZzncusLFzF9yVnzDrAncF9ueVZi6eQazd6PiIiMTgrUiLSuicBjzpq3i95IAzS19Ak+6hfzx9xSM8ufAM4CFgfWye3p38CDxCavMgKpnOwoejYZPshn4WA1GR5Y6vvzR+LN+onEoAHAeGLWxq1pupY+liOUsvkupBoYAXgS2NxZc1/vr+rxegfsl1t6EDh0oODOANalmuV4Xfp6Gor+AjWvpT/no2f5UwewxBCvUxfOmg+Jgcl86d8E4Hifhe8VsScRERldFKgRaUHpB/nP0p5lTxB/cG926RP0LH9a1mehmX1yXiD2j9itJoPmXGDjZu6lXeWaDB8AfJCWNyZm16jJ8CA4a6Y6a64FNiCOZp6aDs1J/Lhe77OwhnorDY/PwkRiqdPSueWrgZ2dNS/1/qoer18POCS39FfggGEEVvLn7KBaQvUhcOMwTjNQ6RPE4Hwhk596k3q/HQVcm1seAxzhs7B1IZsSEZFRQ4EakdZU6VvSzoGaIgITjwLvpL93AKs3+foXEPshbFxZSBlTlxNT8aUOnDV/omeT4a8S+9Z8urhdtRZnzdvOmlOJn6u/zx1ahJhxc57PwtK9vVY+zmdhnM/CvsDJQKWM7F3gQGfNyc6aqX2+uHqONYmBhUqQ7CliqdSHI9zeykBlAt1tw8ziHGxGTWkCNfBRpuVJwDk1h/b2WdhbAUkREWkUBWpEWtNE4D9pSkU7KqKZMOlm6N7cUlPLn5w1k4hZCjv4LMyeO+SJDY6XaeZ+2pmz5ml6NhlehBisWbHvV0ktZ02Xs+ZgYHtieU7FCsDlPgtH+yx8spjdtYb08Tmfno1//0mc6nT3IM+xMnAc1Z/r/k0c3f1eHbZY2dcMYnbPcAzUowZKGKiBjzLxLgJ+TiybrNgaOMxnYWwxOxMRkXamQI1Ii0k38F+ifbNpIGXUFPTbynz505cLKIm5gnhD9FFqfeotcQpwQKXZsIxcrslwpQ/FHMSGoZsUt6vW5Kz5GzFYcyjwYu7QesDNPgu7F9EYtux8FlYhBj/y485vALZ31nQN8hwrELOYxqelF4DdnTVv1WF/lphxBnD3cH450NHBWKA2mNFr6VPa88u5Y58tS98jZ82NxM/vabnl9YGfq+G7iIjUm37gF2k9KxN/6G3nQM0k4hjYWQq49oPEPgwQP86rNfPizpr3iSVQW/gsfCK3/hAxgLVuM/fT7lLvjiOJzZwhfl/8kc/CQfpN+dCkzIM7SX1/iKU7EPuTbA/c4rPg9HEFn4WxPgu7Ez9Oc6Xl94lNf38x2J4yqbzsVKoZKy8RgzST+nzR0OSzfK4Y5jlqs2mgZ0bNu8TATSWLMp9VMx5YdJjXrTtnze+IjZrz5WRrEvtczVbMrkREpB0pUCPSeiYCb9KzzKDdvEf8wb3pfWpSP4f7c0vNnv4EcDPwKj3H60K8IdtVmQn1lQIMlwAHUr0B25SYXaMmw0PkrJmSmjZvAFwPVKYNzUvMSLjGZ2GV0drfIwVgzyEGryqeAbZMga7Bnmdx4Eyq47snAbsNpunwIM//SeCb6eETzpq/D/NU/QZqnDXd9Cx3/WfNcwsvf8pz1jwA7EZ18hnAl4FzfRbmKWZXIiLSbhSoEWkh6TfRqwL3pSaHbSn94F5UQ2HoWf60crMDI6lXztnA+j4Ln8mtdwG/AbZt5n5Gi9QPZHuqpReVJsOLFLer1uWsedNZczwx6JXPAFyMmElyps9CIeOXi+Kz8FXiVKcVcsu3ANumyW+DPc8ixPeISiDxbWImzX/rtFWAzaiWLA03mwY+PvEJepY+QQzUVN7vn645VqpADYCz5klgR2JAvWIp4CKfhQWL2ZWIiLQTBWpEWstyxB/M27nsqaKoEd0A91HtQzCBWG7WbL8nlgDUTnu6FFjHZ6Gz6TsaBXJNhivZA4sAl6UbbBkGZ83zzpr9gV3omS2xInC1z8Lh7T5+3mdhjM/CzsQSu3nT8ofAEc6aY5w1k/t+9cfOZYgZOZXzvAfs4az5dx33OzuwUXr4AiP7njNQ6RP0zKgpXUPh3jhrniVOj8sH2BYBLvZZKE25loiItCYFakRay0RgKrGPSrsrLKPGWfMu8FBuqenlTylj6nRgDZ+FZXPr7wPnAfs2e0+jRervsTMxewlik+EzfRY26vtVMhBnzaPEINiRwCtpuYPYkPVmn4WdfBaK6EvVUD4L8xJLlHamOjr7P8DWzpo7+nxh7+eanxikqUzSmgzs46z5R522W7EB1ZKqq0aYwTnYQM1cPgvjiX128mVFtqxlcs6aF4mZNfng0gLAhZrSJyIiI6FAjUhrmQg8nG7W210hI7pz8uVPXytiqoez5mHgAWDvmhuV3xCnYinLo0FSM9fDieUlEL9fHqImwyPjrJnhrPkV4Igf2w/SoVmIGTfeZ+G77TLdLE1kuprq5CSIU8a2ThkZQznX3MSP2afS0lRgf2fN4yPfaY/rjAN+kB6+BfxqhKccTOnTa+nPeVLpaz7wMRuw0Aj30DBpetzOwGO55bmIPWv0Hi0iIsPSFj8IiYwGPgufJqZV31v0XpqkyB41EFP9K79Fno3YLLIIZxBL3j6aPpV+u30isJ+CBo2TmgxfDBxEzybDp/kszFHczlqfs+bD9LHdAPBUv9Y+Qcy4udJn4SsFbW/EUqnTtsC5VN/HpgA/BY501nzQ12v7ON/sxPeCxdLSDOBgZ81f6rPjHtammrFzfWqwPhKDzaiBFupTk+eseY9Ypvqn3PIsxGlQXy9mVyIi0soUqBFpHaunP0dDfxooOKMm/Zb08dxSEdOfKj1Tfg3smQ/KOGv+j9jvQ+U4DeasuYvYi6JSrrMSajJcF86aSc6a44gZHPlpa0sC5/gsnNJq/T58FuYCTiHeuFd+znoB2MZZc2vKGBnK+WYBTiM2qwXoBg531vyp71cNT8rc2yo9nALcUIfT1gZqpnV3fzQJrKKSUdNSfWryUhbeQcAvc8vjgJ/7LLhidiUiIq1KgRqR1rEa8LSz5uUBn9keii59Arg79/c1CizHOBdYGPhOzfpZwFbpxlAayFmTEfur/F9a+jQxWFNUplVbcdb821mzNzG4kW+Kuxpwnc/CwanXS6mlflJXE6fzVdwJbOWseWYY55tAzJ5bLrd8nLPmtyPaaN++TDUo8qsUsB6p2tKn3honVzJqWmJEd1+cNdOBo+k5JasDONRnYbuy9toREZHyUaBGpAWkG/Hl6JlW3e6KLn2CnoGauYHli9hEalh5A7CLz8LMufXXgBuJvT2kwdLHe2fijTfECWxn6bfl9eOseZCYXXMM1Zv3McDGwC0+C9sU0S9qID4LHT4LWwAXUC0bmgr8AvhxKo0Z6jnHAT8jTseqONlZc/NI99uPLXN/v6pO56zNqKntTwMfL316vuZ5LRGogY9KJk8jlqrl7UEsV9XP3iIiMiB9sxBpDasSv15HS38aiD+4z1NkDxZnzUvAU7mlQsqfkouJvXI2q1m/BljRZ+Gzzd/S6JPGKP+YOHkHYCzxt+UHqF9QfaSGw7cAGwIXUs3AmBXYi9hweN2y3PD6LMxJ6hlF/HwAeBHY3llzw1BLndI5xwA/oVryCnCus+bqke63n2suRjUT6B5nzXN1OnVtoGbAjJqUmfKv3PFPtEJGVZ6z5jLgWKr9lwA2B45MQTgREZE+leKHHBEZ0OrAq3w8HbydVVLui/7hPJ9Vs1ZRqevOmjeBS4Ft86VOqS/CacABSqtvjvQb84uI/SgqN52bAaeqyXD9OGved9acSwzY3E7szQKwIDHj5lKfheUL2h4APgtLA1fSM6ByN7DFcEdmp6/jQ4F1csuXAxcNd5+DlM+mubKO5x2w9Cm9j71Dz3LX2u93S9ZxT02Rsp8OJmZXVXwbON5nobcmyyIiIoACNSKll9L8VwbuTdN+RgVnzTTgTYrvU5Mf070A1YaeRbiGOM54u5r1e4m/tV39Y6+QhumlyfDKwCU+CwsXt6v246x5xVlzFDGQ8Eju0NLAhT4Lxzf7Y55KnTYlBk8qo6OnAycDBzlr3hnueYmZORvklm8AzhhOZs4Qrjsf8K308Cngr3U8/WBKnyBm1eTLXVuuoXBv0vvE3sD7ueWJwJlpmpeIiMjHKFAjUn4rEFP+R8u0p7zC+9Q4a54Hns0tFVb+lMbkngd832fB5Na7gZOAvcvYv6OdOWv+CWxDtUTuM8BlPgtfKmxTbSo1dN4N2J/Yw6RiLeAGn4X9UxlSQ/kszEbsHXMQMD4tvwTs6Ky5eoQBlV2I5TEVvwJOaGSQJvk+1X/LlXW+3mBKn+DjDeRbakR3f5w1DwO7En/5UPFF4PxWK+kSEZHmUKBGpPwmEn+wfbjojRSgDJOfoCTlT8kvgS7iD/0fcdY8SxxvvEURmxrNnDWvAjvRs8nw2T4LGxa3q/aUys7uIQYWjqd64zuOGOC4xWdhc5+F8X2cYkR8FpYklgWtnVu+j1jq9OQIz701sGNu6S7g6EZnUqbx3xunhwH4Q50vMZipT5De73Pvr8/Qs79LywZqAJw1TxH//+YnNy4JXOSzsFDvrxIRkdFKgRqREks/sE4E/pKamI42ZQnU5MufFgEWLWojqcnmmcB66aYx73zA+Sx8ovk7G91yTYbPS0tjgR+nLA81Ga4zZ800Z831xBKhy6n2AJmTmHFzg89C3YKqqdRpQ2KfqEqZ1QzgdGB/Z81bIzz/JsTymIr7idOipo/kvIP0XeLHDeDqBlxzsKVPrwEzA7PARxmE+cypRXwWZq3z3poqNWjeHngut7wwcLEawouISJ4CNSLltjixceZoGsudV3jpU/I0cYpLRZHTnyB+PjwJ7JlfdNa8DVxWuy7NkbI9LiA2D60EVjcHTlEvisZw1rzrrDkd2IhqRhPAp4gZNxf4LCwzkmuk4MBPiIG4SnbIq8DOzprLR5rx4rPwbeBHuaXHgAOdNVP7eEndpOlSlSy8d4FbG3CZoZQ+Qd99ajqI3xNbmrPmZWJmTX6i4PzEfkvLFrMrEREpGwVqRMptYvrzvkJ3UZza5pKFSP0aepQ/FbUX+Gg/pwGr+Cx8uebwzcASPgufb/7OBMBZ83tiKdSraWkVYpPhTxW3q/bmrHnRWXMosC3wt9yh5Yk9g47N93UarDSy+nJgvdzyg8RSp8eHveHq+dcCjswt/R3Yt4kZlGsAnenvNzlr3u/nucM12NKn19Kf/fWp+VxddlSwNMVvV+Ch3PIcxJLJVQrZlIiIlIoCNSLlNhH4u7Nm0oDPbE9lyaiBnuVPS/osdPb5zCZw1jxBzKzZO1/ekcoWTgYOTL8tlwKkfhT5JsOLEgMGKxS3q/bnrPk7cRLXj4i9nCrWAW7yWdhrsNlNPgvfAa4gNoiGWOp0LrC3s+b1ke7VZ2FV4DiqP4s9k87diGBJb9fvALZOD6cB19X7Gh0djKHapLiiv6lP0GYjuvuS/j/vS8/vLTMTM/DW6fVFIiIyauiHeJGS8lmYH1iG0TntqeI1ytGjBmKpUT5gtmZRG8k5i/gb5q/nF501jxAbVn6rtxdJczhrXgF2Bn6XluYCzvFZWL+4XbW/VIL2B2AT4BSgMip7AjF4dovPwiZ99Q7yWZjZZ+EI4CiqZTuvA3s4ay6sR3PfNBXsBGITZIAX0vnfHum5h2BZoJJ599v0+VpvtWVPMLTSp7aZ/NQbZ80UYqnkzbnlscAxafy7iIiMUgrUiJTX19Kfoz1QM3/BU5YASDdnf8wtFR6oSZOebgP28FkYV3P4VGCXVm++2epSQ9RDiY2eId6EHZ6aDOt7cAM5a6Y4a64iNhy+Bqg0yZ2bmHFznc/Cavn3F5+FTxMbBn8vd6pHgR+kEcsjlsoST6FaEvQisGs9snSGaMvc369s0DVqy55g4EDNR8H5VCKUDyAt3st7XUtL31uOAy7JLXcAB/ks7FyG738iItJ8+iFRpLwmEkel/rvojRRoEvE3smUJNuT71Cybsp6Kdj7wSaDHKGhnzYvAr4kTRqRAKcPjfGLAplL2UWkyPFtxOxsdnDVvOWtOImbY5L+GP0MMmJzts7BkKje5gmrD2m7gImD3epWfpkltZ1B9T3stnb8R2Sz97WMRYn8agAedNc806FK9ZdT0Vfr0JrG8rDaLMl/+NJ5qKVrbSO8RZxED7Hk7ozJWEZFRSW/8IiXkszATsCJwT2ocO1q9T/zta1nKnx6lWkbRAaxe4F6Aj8prrgZ26iV75lLgG2piWw7OmjuJ014qTVNXJTYZLrTf0WjhrHnBWXMg8eY3P3Hnq8Q+IRdTHVP9JrFfzDn1GledsnXOIjaNrVxjd2fN/+px/iHanPgeBjE41SiDLn1KmSWv8/G+ZFnN47Yqf8pz1lxJLLnLl9dtChzdbplEIiLSPwVqRMrpq8QfcEdz2VNlulFpGgqncbn35paKHtNdcRmx10W+lAFnzQfE5qf7FrAn6UVqMrw11SyBxYhNhr9Y3K5GF2fNY8TpUIcDbxEzNOYhlkR9FvgQ2MFZ80C9rumzsBBwTroOxFHYe6TyxabyWZibamnXM/ScPFRvQyl9gphFWRuYHzWBGgBnze3AAfTMPFoXONlnYeZidiUiIs2mQI1IOU0kZpM8VvRGSqAUI7pz8hM6vuyzMGefz2wSZ807xBKNrXwW5q05/BtgXp+FrzZ/Z9KblAW1I/CHtDQ3scnw9/p8kdRVyt6YTCyleZtqBsMbxMk75/ssrF+PkhOfhU8QgzQLpKUPiNk6tQGIZtmYagDlygZnbQ6l9Al6D9S05Yju/jhr7gH2BN7LLa9CLNMr/HuOiIg0ngI1IiWTbgxWA+5PGRyjXWkyapIHib9xh9gYdrUC95J3A7GUYsf8YroJOxH4YV9TbqT5UpPhQ4AL0tI44AifhX3Uj6KxfBbG+yz8EDie2CtmEvBXYgnQS+lp8xEzbq7yWVhxBNeaBzgbqJS3TQF+6Kz523DPORI+CxOA76eHrwJ3NviSQ5n6BPH9ft6ar4FADKZVLDkaGuymzK+dieVgFcsCF6Tgn4iItDH9MChSPp8jBiZGddlTTplGdFdusO/PLZWi/CmNeT0H2MhnYeGaY08B/wdsVMTepHfOmhnOmvPo2WR4K+AkNRluDJ8FA1wI/CC3/H/A9501uwKbAfflji0BnOWzcLrPwmJDvNYcwJnAomlpOvAjZ00jS40G8m2q5VfXNOGXAbWlT9OpTt/qzSTiz6ZzVxZSsDmfVTM7YOq0v1JLWVc7EoNVFZ8FLqp9nxcRkfaiQI1I+UwkpuH/ueiNlETZMmqgZ/nTyiUagf0b4pSw3Xs5dhaxNGqu5m5JBpKaDO9EdTzxasDFqa+J1InPwmrAVcAyueVrgB2dNQHiyHtnzb7Er6F8cGAV4FqfhUN7KS/s7VqzAqdR7acyAzjcWXNv369qrJSlskV6+D5wcxMuW5tRM6W7m/5KrSqNtkd9+VOFs+YFYAcg389oIWKwZslidiUiIo2mQI1I+UwEHnfWvFX0Rkqit54FRbsPmJb+PgFYucC9fCT13TiTOOlp6Zpjk4jlUbsWsTfpn7Pm/4jZNJW+JZ8FLvdZWL6wTbUJn4VxPgt7E0dxV/p7vAcc5Kw5qbeskpT1siVwNNXgwRjAAbf4LGyfpvP1dr0JwEnEMpWKY1JArkirUh1tfUvqbdVotR+j/sqeoBqs7G9EN7R5Q+Faub5WT+aW5yX2UlIjchGRNqRAjUiJ+CwsCCxJz8lCo13pAjXOmnfpOSmlFOVPyQPAI8DevfRxuBb4qs/C4s3flgwkdzNWydiaGzjXZ+G7hW2qxfksLACcR5y0VZEBWzhr7ur9VVEqTbsN2BA4n2pvqlmJGTc3+yysl++nkkYo/wL4Su5UJ6TzFG2r9OcM4ntBMww3UDPQiO5Rl0nirHmb+HmXn0Y2O7E0ryy90kREpE4UqBEpl4npzz8VuotyKWPpE/Qsf1ot/Ra9cKmfw+nAl6nJ9El9bE4DDhgNzThbURqpfjBxihfEJsNH+izsrSbDQ+OzsDJwNbBcbvlGYDtnzf8Gex5nzQfOmvOJAZvb4KPSnQWIGTeX+SyskP7/HE3PBuNnO2uuG8E/oy5Sht0K6eHvnTUvNunSte+L/U18gr4zap6ree2oyqipSO8P+9OzCfQEYl+r9YrZlYiINIJ+6BMpl9WAF1JNukSTgLnTb6rL5B6qI31npedv0AuVmgf/Dtirl5v7e4GpwJpN35gMSsrkOAc4jOrN6dbAiSXqh1RaPgtjfBZ2IwYs507L7wM/dtb8PAUsh8xZ86qz5mhin5d8Rt1SxIybe4Hv5NYvcdZcPJxrNcCWub9f2cTrDjWjptceNc6a6cC/cksLpIlao04q1TuMWMpaMQY42mfhB72/SkREWo0CNSIlkW7AvoyyaWpVRpMO2MCzmZw1rwOP55bKVP4EcSTwYsC6+cWUcXMysGdZsoCkd86a39BzPO9E1GS4Xz4L8xE/93cAKllj/wK2ctb8th7XcNY8DewB7AP8Jy1/Elie+DX3SeDWtI/Cpc+XtdPDx1Igt1mGGqj5gFhi1lsW5agvf6pI/ciOBy6oOfRDn4XdlDEpItL6FKgRKY+VgPGoP00P6Tepb1CyPjVJvvxpdZ+FsYXtpIaz5r/ATcDutQEZZ81/iFPFtuzttVIezpq/E3uLVKbeLE4stVmu71eNTj4LXyZOcfpybvk2YFtnzfP1vJazpttZ82fiOO/HqDYp7iD+bPV1YMuSBEM3o/rz3hVNvvaQSp9SILmvvmS1gZpRWf5UkT4HzwNOrDm0A3CISiVFRFqb3sRFymN14G3giaI3UkJl7VNzd+7vc9OzF0YZXEi8gdykl2MXABukZqtSYs6al4k3X5XPt3mITYa/XdyuyiOVOu1IzGCpZN5NBo5y1hztrPmw71eP2NbE3i//Jr5PvQEEYpPXfYAbfRa+UVSGg8/CHMAG6eHzxABtMw01owb6DtSM2hHd/XHWXAscDkzPLwPHlSRQKCIiw6BAjUgJpN98fQ24L2WQSE+lm/wEH91A58sISlX+lMqzrgR2SDds+WNvA5cCexawNRmi1ET0R0Cl58l44Cc+C731IRo1Up+S04lj5ysfh+eArZ01tzf42t8nlkBB7Fd1MzEz8te5py0E/Ay4yGdhWZpvQ2IPLYArU8lMMw0nUPMavb/fP0O1kTOM8oyaPGfNr4lNhvMf37WBU9TXSkSkNY3aH+5ESmZZYC5ig1r5uFIGapJ8Vs1aJewNcCUwDdiml2O3AIsXdAMpQ5SaDJ8NHEG1hGQb4ITReDPms7A8cBUxOFLxa2KQ5t8Nvvb3gANzSw8DP3LW/M9Zczgx0+avuePLEvsL/axZPYZ8FsYDleayrwO/asZ1awx16hPE9/s5einZ/ICYFVSxiM/CLCPcX9tIpXi7A+/kllcEzvZZmKuYXYmIyHApUCNSDhOJN9MPFL2Rkipr6RP07FOzAHECTGk4a94nljltXlvmlH67fiJxXLe+H7QIZ80dxAySSpPh1YkZG6a4XTVPKnXamjhpqfI5PQU4Fjgifc438vrfIE7dqfgb8ENnzUfZDKlh787EYM5/c8/9BnCTz8I+tVluDfBN4BPp79cPd9rVcHV0MIaPB2oGm1EDA/ep6QCWGMbW2paz5gni592k3PLngQtV5ioi0lr0g7lIOUwkTuN4r+iNlFRpM2pSk9Jnc0ulKn9KbgZeBnapPeCseQx4EVC/kxbirPkbMWuj0rdjCWKT4bbOjkqZAScDe1P9Gea/xIbBN6dmtI28/mrAMblrPw3s01twKDV7vRvYFDiJ2IMMYtnaVsAtPgvf91kY14B9dlBtFj4ZuLHe1xiE3vqjDLZHDaih8LA4a54Btge6csuLEjO6FilmVyIiMlQK1IgULP3g9Bk0lrs/Zc6ogZKXPzlrpgFnAd/1WVisl6ecDuzkszBbc3cmI+GseQnYkep7x7zAeT4L6xW3q8bxWfg8sdTpa7nl3wNbppHZjb7+V4BfAJXpbv8B9nDWvNP3q8BZM9VZcw2xqe9VxOxJiOWuBwLX+yysXuf3ja9SzTb5pbPmzTqee7B6C9QMtvQJBheoGbUjuvvjrOkiNiB/Jre8IDHzrlRZnyIi0jsFakSKt1r6U2O5+zaJcgdq8uVPixB/e1k2fwD+SS/Ng501LwJ3EH8LKy0kZXIcSGwMDTFb42ifhT3apZzNZ6HDZ2Fz4hSzBdPyNOB44JBmZCKmTKWTqQYfXiQGad4Y7DmcNW87a04BNiZ+PVYsQsy4Oa+ON9FbpT+7gavrdM6hqm0kDEPLqOntPb82IKeMmj44a14jlkE9nlueh/h59qVCNiUiIoPWFj/EibS4icC/0s2y9O41YP6yZarkPE28casoXflTKgk5DZiYmrDWugz4us/Cwk3dmIxYajJ8JrHJ8NS0vB1wfKs3GU59XI4nTrSplAi9CGzvrLm+0aVOaQ+WmHVWaVz7CrCrs+aV4ZwvNRz+ETHj4e+5QysAV/gsHO2z8MkR7HcJqg2W/+SseWG45xqh4QZq+uxRkwJj+Y/74o0oHWsXKdtrT+C+3PKswBk+C2sUsikRERkUBWpECuSzMCfwRTTtaSCvEX+TXcrSnHSz2KP8qai99MdZ8yhwP7B3bdArTVQ5B9iviL3JyKUmw7tQbTK8BrGJ6IJ9vqjEfBY+R5xatmZu+U/EUqenmrSHRYllg7OnpTeA3esRWE+NX7cDDqVnoHc94Gafhd2HGWjbIvf3K0awxZEabulTJUupryzKfPnTBGLpsPTBWfMhcAAxa7JiAjGQ+71idiUiIgNRoEakWKsQvw4VqOnfB8CHlLShcJIvf1rSZ6GzsJ307wzgC8RJQbXuBObyWVipl2PSAlKT4W2Af6WlJYHLfRa+UNyuhiaVOm0MXAJUvo6mA6cABzhr3u7zxfXdRydwNjB3WnqHWO70XL2ukRoO30kshzodqJRxTSCWIt7is+B8Fsb2dY6aPS8ArJsePkmcSFWUYWXUOGumAm/R9/u9+tQMUepTdhRwbW55DHBEmqAmIiIlo0CNSLEmEn/73ZTfDreqlLFS9obCT9JzJOqafT2xSGkiyB3AnrU3f+njfCKwn8oJWpezJhBv8isB4EqT4XX7flU5pAySY4GDif12IJa67OSsuaoZpU5pHwsQM8wq460/APZuVNNiZ80UZ83lxIbD1wMz0qF5iRk31/gsrDKIU32faonYFc36ePVhuKVP0P+kv9pAzecGvaNRzFkzg9gL6ZyaQ3v7LHwsy1JERIqlQI1IQdKN8CrAvekHKOlfaUd0w0c/BP8xt1TK8qfkXGKmwsfS3p01/yD2zdi42ZuS+klNhg8ALk9LE4BjUjlNKb/3p94qVwLfzC3fD2yeMoWatY95iTezC6WlKcC+zponG31tZ80bzprjgU3omWm5GHC6z8KZPguL9/baFOTaKD3souf7URFqS5+6qU68GshrDD5Qo4bCg5QyuC4Cfk78/1GxNXDYYDO3RESk8Ur5w5rIKPFFYt8DlT0NTtkzaqBn+dOyPgul3G/KuLgO2MVnYeZennIWsIXPwtxN3ZjUVWoyfDqx5KHSZHh74Bc+C7P0+cImS6VO6xMbWi+SlmcAZxIDJG82cS9zput+Oi1NAw5M/Z2axlnzvLNmf2BXegYmVgKu9lk4rJf3l/Wp9tK5qgS/AKjNqJnc3c1gM3wmAfP1keURiGVoFUsqG2RonDU3EjO18oGz9YGf+yz01ltIRESaTIEakeJMJP6m9qGiN9Iiyj6iG+BRet5ArFHQPgbjEmBmYPPaA86a14nlF7s1e1NSf86a24k3/JUmrWsCF41kslC9pIDRUcDhVDMwXiNOVbq0mcGGlJFyOtWeJzOAHztr/tysPdRy1jxCHLV9JNVpR2OIJVI3+yzs5LMwS8qEqHwtvw38stl77cXHAjVDeG2lgfzstQdSOVe+BG0OwAx5d6Ocs+Z3xObxH+aW1yRmbpWycb+IyGiiQI1IAdJv/1YHHkrTdmRgpc+oSQ0b8xlSpS1/cta8RQzWbNNH5sy1wAo+C2rU2QbShKFtgH+npUqT4c8XtSefhcWIWTTfzi0/RCx1eqzJe5mJ2Kw4//E42lnzh2buozcpM+pXgCOWZFW+Z8xCnPLlidkRlWDFjSX5vlKbmTGYiU8VlX5fKn9qIGfNA8SAfL5B95eBc30W5ilmVyIiAgrUiBRlUWL/A5U9DV5/PQvKJF/+9KVUSlFW1xKnzGxfeyBNXjkN+KHKCtpDGim9HXBvWpoPON9nYZ1m78VnYT1i/5zF0lI3cD6wZ8roauZexgMnAF/KLf8iZSKVhrPmw9RfZAPgZqoNhz8B7EP8vjKBWNZYBiPJqFGgpklS76UdgVdzy0sRs+4WLGZXIiKiQI1IMSamP+/t91mSV+pmwjkPUk0lHwusVuBe+uWsmUxsLLyJz8JCvTzlz8Sbq1JOsJKhS02Gf0jPJsPH+izs1owmwz4LM/ksHAYcTSy9gzj5bg9nzfnN7quSSoaOITZ2rzjdWXNDM/cxFM6aSc6aY4mlTg8AsxKza2Ymvkce5rPwmeJ2+JF6BGr6yqLUiO46ctY8C+wAvJBbXgS42Gdh0WJ2JSIyuilQI1KMicBTzppXB3ymVJS+9Ak+Cn7cn1sqbflT8ivgv8QeJj2kXhAnE0d59zZqV1pQrsnwT6g2E92B2Ei0YU2GfRYWAS4lZoRUPAZs4axpeq+uFJg6Avh6bvmiNCa79Jw1/3LW7AU8QTUIMokYHL7eZ+FHBZevjKT06bX0Z1/B+edqzqcR3SOUMu52pGcQbAHgQp+FZYrZlYjI6KVAjUiTpdGvX0DZNEM1CZg7jTUvu3z508qpSWkpOWumE6fcfKu3fjTOmueA+4Atm7w1aTBnzS+JAbo309JawAU+CwvU+1o+C98gjt5eIrd8MbBbEQHrVM53ID3741xDzDBrGSlzZgngWeBu4KV0aAxxxPctPgvbFDTJp2EZNakf2L9zSwtoSt3IpbLDnYkB1Iq5iD1rvlrMrkRERicFakSa72tAB/CnojfSYl4n9mSYt+iNDMJ9VDMVJgArF7iXwbiX+Fv5vfo4fgGwfiNu4KVYzprHga2JN/oQMxMur9dv0H0WJvgsHAT8jFiiA/AWsLez5uwUKGyqFKTZixjIqLgFODllkbWS/NS2nxCzlS6iGhSZjfhvvclnYZ0m95saSaDmHeJ7aH/lrip/agBnzXvAnvT8GWUW4jSor/f+KhERqTcFakSabzXgZeCZojfSSlLvijdpjfKnd+k5dr3U5U/p5vR0YvbPx35r6qx5h1iysneTtyZNkGsyfF9amp+YWfPNkZzXZ6GTGDTYNLf8N+JUp/t7f1VTbE8MTlX8Fjiu1YI0KTvzO+lhBjzirHnfWXMOsCGxrLHCAMcCl/osLNekLQ679Cm937/O0AI1Kn+qE2fNFOAgeo55H0csj3TF7EpEZHRRoEakiVL6+crAPa12U1ASLdGnJsmXP61WUOnBoDlr/kYsndirj6aytwCLNvEmT5oo/RZ9f2J5EsSb7ON8FnYZTpNhn4U1gKuI02MqrgR2dta8PMLtDpvPwubEccQV9wBHNruJcZ1sQjUYckX+e4qz5hVnzZHEksVHc69ZhjjN53ifhU81eH8jyaiBgSf9afJTA6Vst6OBK3LLHcChPgvbaRqgiEhjKVAj0lxfJk7mUH+a4WmVEd0QbwArN3+zAl8pcC+DdRbxZmft2gPpRvYk4rhufe9oQ6nJ8KnEm7NK6d5OxIDNzH2+MMdnYbzPwv7AicDsafkd4IfOmlNTb5FC+CxsQAxGVTwEHFzknoYr/f+oZCq9Avy+t+c5a/5J7EO0Pz0n+qwF3OizsL/Pwpz13l9HBx18PKNmqIGagSb9/Ys41r1CgZo6c9Z0O2tOI2Zc5u0B7KfvBSIijaM3WJHmWh14H3ik6I20qFYZ0V1pyvh4bqnU5U/wUePgW4A9fBbG93L8MaCLarmFtCFnzW3A7sReMhADdwM2GfZZWBA4n559U54iTnUqtCeXz8I6wI9zS08Qg0dDmURUJt8mNnkFuLq/YFO62b6HGNg5nur/13HE/1e3+Cxs3tvX/AiMJ2Zf5A31Y/0aME8aof4xadR8Pvj06UZOLRvN0iS0Y6j+8gHi586RLdLgX0Sk5ShQI9IkKU14NeDBFr45KForlT5Bz/Kn1fu64SiZC4gf4w37OH4asKPPwmzN25I0WwrK5ZsML0VsMrx0b8/3WfgacDVxol3FdcCOqQdOYXwWJgI/pRo4+Cewj7Pmg+J2NXwpi2GL9PA94ObBvM5ZM81Zcz2wPnA5MDUdmpOYcXO9z8JadSppqS17guFl1HQA/Y0Yz5c/dQCLD/EaMkjOmluAg6l+3kAMGB7vs9Db/28RERkBBWpEmscCC6Cyp5FomYya5O7c3+cGli9mG4OXRiVfBezUWzDGWfMScDuwQ7P3Js3lrOkiNhmuNP6dH7gwjdoGwGdhrM/CnsCpxBt+iFmDBztrTig6KJ2aY/+C6s87zwJ7pobfrWoisEj6u0/9hQbNWfOus+Z0YCPgztyhhYkZN+f3FZAbgnoFakB9akrDWXMXsan8+7nlicCZPguz9/4qEREZDgVqRJpnIrGe/r6Bnih9aqmMmtQ09ancUunLn5LLid8ftuzn+Fo+C4v0cVzaRAoC7EvMloHYd+RnPgs7p1Koc4Ftcy95GtjSWdNrz5Rm8llYHjiZWIYD8D9gd2fNm0XtqU4qX5fTgWuHexJnzYvOmkOJwbi/5Q59kZg9dYzPghnm6Xtrnj7cQE1/7/kK1DSZs+ZhYt+jN3PLXyQG+OYtZFMiIm1IgRqR5pkIPOmseaPojbSwVsuogZ7lT2u2QvPFlG1wEbClz8LHPt7Omg+Bs4H9mr03ab7UZPhkYo+K6Wl5P+AvwAq5p3pgO2fNCxTMZ2EpYplepQnyy8BuzprXitvVyPksfIFqZt6d9Zig5ax5kpgh9yNiD6qKdYGbfBb2HEa2RG8ZNcPpUQPKqCkdZ81TwI7Er6uKJYkTxRYqZlciIu2l9DcMIu0g/eb5c0ChDTXbwGvA/C02FjQfqFmAnuOKy+xG4A3i1J/e/A6Yw2dhleZtSYqUelTsQZzmtAjx8/nTxAlRhztrjnPWDDVrou58FhYDzgQqpXuvE4M0obhd1U0+y+3KPp81RKnh8B+II79PIU7qgpgZsy1ws8/CxkPos1XP0qc+M2rSLz5eyS0t3iK9wFpeaj6/PfBcbnlh4GKfhc8WsScRkXaiQI1Ic6yW/lR/mpGZRLxxaJla+JRd8GxuqSXKn1JvkXMA11uJk7OmmziCeV9N/RgdUlnDDsQb7spN9xjgQ3rerBXGZ2Fh4udtZSLS28Ryp8KzfEbKZ6ETWDM9fNhZU5tNMmLOminOmquADYBrqGZQzUNsJHutz8LXBhEsr0fp0+vpz4GyKJ+uue5nhngdGaaU0bUjPUt8K72sli1mVyIi7UGBGpHmmEjsj/CfojfSytI41g9o/fKnVskI+i3wL2IWxcc4a/5J7G2xaTM3Jc3ns/Al4o37V4hTX54DniS+p81OvDFbu7AN8tF48HOovj+8T2wc/K/idlVXm1P9ue2KRl7IWfOWs+YkYoZNvin6osTG0Wf5LCzZzylqM2q66TktaDB7+ID4/3Cg93uVPxUo9XzaFXgotzwHcI4yLkVEhk+BGpEG81mYhXhzc0/KQpCRaamGwkn+RmcR4s1O6TlrZgCnA1/3Wfh8H087G/iBz0J/I3SlRfksjPFZ2J6eAZDJwFHAqsQJYRBvzH/us7BjEYHI1EvpbGDB3B73Tb00Wp7PwpzEsdoQM/QeaMZ1nTUvOGsOBHamZ9bEV4GrfBaOTKW9tWoDNVO6uxnO97/XUKCm9NIvUfal5y8lZgJO8VlYp5BNiYi0OAVqRBpvRWI69j1Fb6RNtGJD4aeBF3OPW6L8KfkL8Tele/d2A+6seZ04eWa3Zm9MGstnYW5i9sTuVH9eeB7Yxllzm7NmemoyfBzVEpldgWN9FnrrUdKofc4JnEV1ZPU04ABnzWPN2kMTbEy1MfKVzQ76p4/ltsDhVBvIdgDfBbzPwi4+C7PmXlJb+jTc3kWDeb9XoKYEUrnswcDNueWxwDE+C8q6FBEZIgVqRBpvIvAu8HjB+2gXLZdRk26q8lk1LROoSXs/gzjdp6809uuB5QcohZAW4rOwHHEkd/7/+W+BrWpLiZw1nlge93Za+iZwgc9Cw79OfRZmIzYOXjwtzQAOcdY0JeOkGXwWJgDfTw8nAb8pYh9p+tevAUcMjL2fDs1MbDrufRbWT5PtPpZRM8zLDub9/kXi99gK20LlpW0lZWEeB1ySW+4ADvJZ2Fn/X0REBk+BGpEGSj+wfg34s7NmWtH7aROtmFEDPVPCl0yNQVuCs+YfwJ3ErJqPfd9w1kwlZl4coB/EW5vPQofPwlbABcSpThBvso8DDkslDh/jrHkE2IZqU+Glgct9Fj7XwL3OTPy8WzotdQNHOWvu7vNFrWldqu9516XMhcI4ayY7ay4hNhy+iRgcgxhQORy4asVvfFBbKjmSjJpZUwlxX/vppmdD4TmolsBJk6UJYmcRvzbzdgYO7O17iIiIfJzeLEUaaxlgXlT2VE8tl1GTPEl13CxUp7e0irOIo5jX6+2gs+Z+YqPnrzdzU1I/qYToJGAfqj8f/A/YzlnjByq3cdb8F9iOav+UBYCLfBbqnkGWskxOAL6YW/6Zs+aOel+rSCnwWRnJ/SFwY4Hb6cFZ87qz5mfAZsCfc4eWWGH1ybtO/N7735rfTK/0rhpJoAbi99H+/LPmscqfCuasuZLYy2pGbnlT4GhNChQRGZgCNSKNNZHYu6Ft0vBLYBItGKhJKeF/zC21TPkTgLOmi/jb893STXJvTgb2aGZ/EqkPn4VliI2BJ+aW7wK2HMoYaGfNO8SmotempZmA430Wtq9XtpXPwljgWGDl3PKpqQSr3awMLJb+fquz5u3+nlwEZ82zzpp9iL2MngGYOrmjY855Ziy80jc/2HjFb3yw2vxm+vhhnr4SqBnoPf/pmscK1JSAs+Z24AB6lr6tC5ycMuJERKQPCtSINNZE4K9l/OG6hQ1mCkhZ5cuflm1GD486u5BYVvD93g46a54nZo9t3cxNyfClUqfNgIsAk5anEbNVfuSsebfPF/chNRk+EfgZ1d+m7w78dKRBvFQ2cSQ9M9LOT7+9b0eVbJoZxJ5BpeWseQjYAjj6g/c6KiVyHZ9YaPpS39/rnf1TsG6o//9fS3+qoXCLctbcA+wJvJdbXgU4O2XxiYhILxSoEWkQn4WFgM+isqd6a9XSJ4BHgXdyj9coaB/D4qx5A7gc2K6fH7AvAL7rs/DJ5u1MhsNnYXbgF8TfeFdKEQKwg7PmupFOFnLW3ETPJsPrAuelUdrD2W8H8CN6lt9dSfycazs+C5Y4Bhvg7pTVVmqp4fBt/rzZL+h6dtyj3TM6pgHMNEv3BGKwzvssrDeEPiWDzaj5DzA191iBmhJJU8N2Bl7PLS9LbDr+iWJ2JSJSbgrUiDROpYRAgZr6mgTM5bMw3FT6wqSG0vnPh5Yqf0quJmZcbNvbwZSBcTGwdxP3JEOUggBX0vNz8B5gC2fN/9XrOs6ah4mfK8+npc8DV6TrD1oK0uwDbJRfBk5r9qjqJtoy9/crCtvFMHzw3pgxf7135kfvuX2W6ya9NDYbN767kln1SeBo4FKfhRUGcapKoKbf4F56b/13bumTPgtzDXnj0jCphHJHYjC44rPEPlYLF7MrEZHyUqBGpHEmAs86a/5X9EbazBvEMoCBmkuWVb786cutlvqdpv6cB2zWT9bMbcCnfRaWb9rGZFBSqdNGxPG5n0rLM4DTgB82okzTWfMCMVjzl7RUaTI8lIbaO9EzcPFr4OftGqRJX1vrpIdPOGv+XuR+hmEmgHfeGPPeA7+d5U9/unWWo4CHcseXBs73WTjRZ2GRfs7zBnGa12CysFT+VHLpvWAH4Nnc8kLE94Mli9mViEg5KVAj0gCppOBLwL1F76XdpKa8b9C6fWoeJE5vgfgePLGf55bVrcBLwC69HUz/j04kjuvW95mS8FmYFTgGOASoNIR+BdjJWXNFI4Meqcnw3sD1aWlm4ITBNBn2WdiSWDZRcTdxDPeMPl7SDjaj+jNaS2XTdHTQQfXzC4C//G6WfxPL4PYllilVrAHc4LNwQG8ZMM6a6Qz+/V6BmhbgrHmFmFnzZG55XmLg7ou9v0pEZPTRD9AijbEKMBb4U9EbaVMt26fGWTOZnqNsW678KZUZnAl8x2fhs30853HgBeC7Tdya9MFnYXHiDf86ueUHiKVOTzRjD6nJ8PHAz+nZZPjoviaJ+Sw44s19xQPAj9MNfFtKgf5KidcLtF757Dg+/vPlFGdNt7PmPmIQ6mfEAAzE75WbAbf4LGzZy+fCJAYXqNGI7haRMvd2p+dEzNmBs3wWVitmVyIi5aJAjUhjrAa8CbRaunqraMkR3Tn58qeVUqZDq7kbeIr4W/K+nA7skG48pSA+C98DLgM+nZZmAGcD+6QG0U3lrLmROAWm0lj7W/TSZNhnYT1i9k/FX4EDnTX5Ub/taAOg8p5wVQtmDvU22Wly5S8pYHcT8d95KdXRzXMQg3I3+Cysncu0Gmxg/l/EMqkKBWpKzFnzAbA/cGdueQJwks/Ct4vZlYhIeShQI1JnPgtjgVWBe1vwB+xW0cojugHuozqhZAIxA6ulpDKZ04GJfaWrO2teAm4nprlLk/kszOyzcCRwBNWb50nAbs6ai4t8f0qjnLchZowAfAG4vNKnIvWvOQqo3Kw/BezrrPmQNuazMA7YPD18C/hVgdsZrn4DNRXOmvecNWcSs4d+nTvUScy6ushn4QvEz9l5ByqjTP2z/ptb+rTPwsxD3bw0j7NmKnAYcENueQzwE5+FzXt/lYjI6KBAjUj9LQfMifrTNFLLlj5BvEEBHs4tDaWpammkkav3AXv302fkcmB1n4VP93FcGsBnYVHixz5fevYw8ANnzaPF7KqnXJPhSpPZTxJvzncnlsZUfkb5N7BX+rppd2sTmy0DXN+iganeytj6zIJy1gRnzeHA1sSsqYpliU2vVwZmIWbcDCRf/jQGWHwQr5ECpYDx8cAFNYf291nYbaAeViIi7UqBGpH6W52YLfFg0RtpY4PtWVBm+fKn1frq0dECziSOXO412JRuNM8G9mvmpkYzn4VvEYM0i6WlbuJN0B7OmtcL21gvUq+Kvan+Rn0+4BfEoA3EjJvdnTVvFbC9pko3pFulh1PomWXQSgaVUVPLWfMUsWn0gfTMjFmCOMZ5f5+FgYI1aijcglL/ovOITejzdgAOUVN6ERmN9MYnUkfpB+2JwMMpDVsao6UzapJ7qDZUnRX4SoF7GTZnzb+I5U17prK/3vwemNVnoeVKvFqJz8IEn4VDgZ8SMxAgNmzd01lzXllLMZ0105w1vwCuIo4MH0PMKpmT2EdnUpH7a6IvUw0s3F62oNoQDCtQAx/dsN8NbAqcBLwNTCOWwG1CbDi8aSoR683TNY8VqGkhzpprgcOBfLNwBxzXwr/MEBEZFgVqROrr08DCqOyp0Vo+oybdhD2eW2q56U855wELAuv3djD1szkR2M9nYXwzNzZa+CwsTGzM6nLLfyVOdfpLIZsagjSV6rtAF/EmbRrwAbFXxbxF7q2Jtsz9/erCdjFyQyp96o2zZqqz5hpiw+FfErPCxgNzAQcB1/ksTOylLEYZNS3OWfNrYpPhfHBvbeCUFm28LyIyLArUiNTXxPRnq41TbTWvAfO3Qe16vvxp9X4yUkotNQ2+FtjFZ2GWPp7zNDFwsEkz9zYa+CysTcxGWTK3fCmwq7PmlUI2NQQ+C4sQy+PmBN4DniSWjk4l9im53GdhieJ22Hg+C4sRm9AD3OOsea7A7YxUbxk1w5rUlUrjziD2KcoHYT4NnAyc67PwudzzXwdezT1viVZ9Xx3NnDV/Jo7vfie3vCJwts/CXMXsSkSkuRSoEamvicDTzpqXi95Im3ud+NvVwTSXLLO7c3+fG1i+mG3UxaXE36T/oJ/nnAP8YBRlSDRUKnU6iDghp/Kb5reJ05HOdNZM7/vV5eCzYIifF5XPifeIU8I2odpwe0HgYp+FiR8/Q9vIZ9NcWdgu6qM2UDOlu5uRlN1NIgbtfk/sWfL33LEvAVf6LBzts1Dpa5Qvf5pAdSy9tBBnzRPEnkX50sfPAxf6LCzQ+6tERNqHAjUidZJ+y7Ms8Kei99LuUv+f92n98qeXiWOHK1q2/Cn95vsSYFufhXn6eM4bwDXAbs3cWzvyWVgIuJDYy6PiSWBzZ819xexqaHwW5icGaSo32JOJPWn+kT6f9gJuSsdmAU7yWdi6DTLpevBZmA/4Vnr4FD0nH7Wi2tKnYWXT5LybzjFfunnfDjgUCLnnrAfcnCaGPVvzepU/tShnzTPA9sSSyIpFiYHbRYrZlYhIcyhQI1I/qxK/ptSfpjnaoaEw9Cx/WrPFp1tcR7yp2r6f51wPLOezoJunYfJZWJ1Y6rR0bvlqYOdUhlZ6PgtzE8udPpWWpgL7O2serzzHWTONmC10PLHxdgdxQtRRbdZY9PvEDEGAK1NPp1ZWm1EzqEbCfUkfj4/6kqWGw3cCGwGnE7OwIAaItidOzpo7dwq917QwZ00XMZPqmdzygsBFPgtLFbMrEZHGa+UbApGyWZ1YG//PojcySrR8Q+EkH6hZAGjZHzydNZOBc4FNfBY6+3jONOBU4IB2y4xoNJ+FcT4L+xKn4VTK/t4FDnTWnOysmVrY5obAZ2F24lj3yvjwGcDBvTU9Tjfl1xMDNO+m5W8D57RDCV3q6bRxehiAPxS4nXqpa6Am+Vhg3lkzxVlzObHh8PVUp+iNBwzx82s2FKhpec6a14hlUI/nlucBzvNZ+FIhmxIRaTAFakTqIP12d2Xg3rKOwG1DbZFR46x5gZ6p+i1b/pTcDrxAP+VNzpr7ib8F/0azNtXqUv+N8+nZy+QfxKlOd/f+qvJJgYnTgEoD2G7gcGdNvyWjzpoHgW2B/6Wl5YDL0rSoVvZdYhNlgKtboa/QINS79An6Ccw7a95w1hxPLAO8h5idNYMYMFoE+G4bfJ6Mes6ad4A9gXxp56zAGT4LaxSyKRGRBlKgRqQ+ViD+wKBpT83TLhk10DOrZq1WzjRJgcozgHXz01h6cTKwu8/CzM3ZWevyWViFWNq0bG75emCHVBbQElJA+0RikKXiWGfNbwfz+jQJaRvg0bRkaOEmw6nMcYv08F3g1gK3U0+NyKiZBMzlszC+ryc4a55z1uwP7ArkSwDnAW7wWTgs9QOSFuWs+RA4ALgjtzwBON5n4XvF7EpEpDEUqBGpj4nEH0YfHuiJUjdtkVGT5AM1C1MtCWlV9wGPEZvB9iplEt0NbN2sTbUan4WxPgt7EPtwVEbSvg8c6qw53llTj0yFpvBZGEfsN7NibvlkZ80tQzmPs+YtYA/Ap6VZad0mw2sAlRLBm1KT9HbQqEANVKeD9clZ8whwHvAiMbsGYjPqDYBb/p+9845zqkr/8JPJNOoASrlEARW99t4LinV3dV29dqzYK3bX3nXtDQUrKiL2I7o/y+oq9t7rHhEbhiNIr9OS/P44J5M7YUpmJj3n+ZDPTM69N3kzJDf3fM/7fl8h1bFWIC5cTPns5Whj+jhlwKVCKvt9YrFYigYr1FgsXcRMDkYAHxiPDkt2KKaMmmnoSUWcgi5/MuafY4GthFRbtbHrA8BeQqpB2YmscBBS9Ud3RBrtG54GHGaMVAsGkzlyBfo8Geduz3Umd+bxzETtX+jsHL/J8KWFYjJsvjfik8pGtBF3sZCJ0qc55meq53wJLASmo73jgma8GzrjRgip9ipw8/aSxWRu3oI+R/oZI6QaU4CircVisayA/YKyWLrOcHQHAlv2lF3mUiQZNUbYaNb9KVexpAvPdb5Gv6YxrU2GPNdZgm4xPSabseU7Qqot0V2dNvUNTwGOMplIBYOZMF0I7OEbnogW6TqNMRl+HDiDRNefvwPjWmsPn2dsCKxvfn/Zc53ZuQwmzWQyoybVc740P2NokecV9Gco7iE3AJ2V8YiQavM0xGfJMuYc8AA6U8/fKe0I4GIhVbDlIy0Wi6UwsEKNxdJ1RqAvEmxb7uxSTKVP0FyoWau1rkkFxl3AmrRtGvx/wKpCqk2yE1L+IqQqE1Idj/67xUs8aoFLPde5utAy9oxIcya65CTOU8DYdLWgNsbURwFxr56N0SbDa6Tj8TOI3xT60ZxFkRkyKdSkmlHzE4myJ4ChnutcDYwCPvCNu8DdQqpbhFTDuhylJet4rvM0Wgxu9A3/A7iuUDLsLBaLpSWsUGOxdJ0RwLee68zLdSAlxhygdxFdiH1DYjICxZFV8yt6FfuU1v6fTAr7TcDZpVyGYFpN34luQRtP2/8JOMJznRdbPTC/OQE9MY7zAnBjukSaOJ7r/IxeRf/MDA0GHhRSbZ/O50kXQqohaH8a0CWz03IYTibIVNcnSFGoMeVx031Dg4RUNZ7r/Oi5zqnoLD5/t70RwJNCqvMKJCPL4sNznVfRonCtb3gkcIeQqkduorJYLJauUbIXxRZLOhBSrQyshy17ygUL0Gns7ZpLFgJGsHjDN1TQPjU+7kX/H3mt7eC5zpfAr+hV0JJDSLUpuqvTlr7hF4EjPdf5qeWj8htj6nmsb+h14ErzPk87PpPhZ81Qd+BWIdWheehXMYqEGPdILgNJN4EA5ST8YOLkIqMG4Iek+278F5OJdQhwLRBfZClDt/ieYsypi2URoCTwXOd94CRgkW94c3TGlBXfLBZLwWGFGoula8RXbK1Qk2XMhG8exWMoDM3LnzY0QmBB47nOHHRpx7HtrGzeDowWUvXKTmS5x5Q6HQXcTaKMrx64CrjMc53luYqtKwipDqC579B7wEWe60Qy+bye6zSgJ963kDAZPhO4pK22ztlESNUHiLcRngZ8lLtoMkJy2ROkQagxHc4W07Hz/f+S7q+V9JgRz3UEujRvAonMnx7o9+8zQqrd81Dos7SC8UY7Fm0gHWcd4AFrWm+xWAoNK9RYLF1jBLpbz/T2drRkhGLzqfkUPRmJs1OO4kg3E83PVlunGjPV52mehVG0CKlqgFuBU0l8F/+GzqJ5Lt3lQdlCSLUn8E/f0KfAuUZEyTjGYHQy2mQ43u56b+AuI5Lkmv1JlAZNKtT/5zZoKQslXW3kO3q+l0n33ZZ28lxnmec649CCzQu+TQ5a+HtQSLVRB57XkkNMFuIx6PNpnCHABCHVarmJymKxWDqOFWoslk4ipKoGtgLeLsKL7UKhmFp0x30V/NlZRVH+5LnOUnR3p0PbyRJ6BBhR7KaeQqoN0aVO2/mGX0G33i5YvxIh1c7AZb6hb4Azc2GC7DMZjre93xSYKKRaPduxxDGlNAeZu3+i/8+LjYxk1Bg6er6fRvNuQC0KNXE815ntuc5laKPnT32b1kdnZFwvpFqlA89vyRGe68xEi/5+sW4AcL+Qar3cRGWxWCwdwwo1Fkvn2RJ9UfpmrgMpYYqmRbcPf/nT5kKq3jmLJL08gy5VO761HcyE/k7grGwFlU2EVAEh1aHAfcBAM9yAbi97kec6y1o9OM8RUm2Hzj6IX1dMA8bk8jWZlfVkk+GHTKy5YE8g7pXxWLayjLJMxoWaVEuRzHtvhm9omFlgae+4/wEnos9D/qyMXYCnhVRnFtF5uWgxDR6OJ/H5B6hBe9Zs2fJRFovFkj9YocZi6Tw7AEuBz3MdSAlTbKVPoFvHxjtXlKHL6woeMym9C9hHSDW0jV1fB6pzOJnOCGZidxPaMyVuthoGjvZc5+lCzsoTUm0G3AiUm6FfgVM811nU+lHZwXOdBWiT4efMUNxkeFQ2vUdMR7N4S+5lJEyPi41Mlj7NBarR/4ep4s+oKANSattuSujeQpsL3wAsNJvKgUPRhsOH5Iv3kaVlTDbnqTRfUOuG7ga1S26islgsltSwQo3F0gnMRfcI4L0iXRUtFOZQRKVP0JRV8q5vqCjKnwyvojuxnNLaDkawuAk4o1gmQUKqdYFJwI6+4anoUqfvcxNVehBSrY/22olP0GcCJ5nV7LzAnKOvRpsMx9DXPmcBF2XxPbYdEBcop3ius7itnQuYTGbUzDE/O3LOT8mnpjU812n0XOdJtH/NRHQGHEBv4Gx0S++R1nA4fzFG1OcB//YNlwPXCala7UZosVgsucYKNRZL51gbfbH4dq4DKXGKMaMGmpc/bS2k6sgKct5iOnXdAewspNqgjf1+QKerH9TaPoWAKXU6EHgAXXYDEEELBucV+mRdSLUWMJZEhsMc4GRjDJ1X+EyGzyRhMrwP2TMZPtz8jAKPZ+H5ckWmS5+gY+f85Bbda3fmiT3XWey5zh3AfjT3FloVnU12rxFkLXmI6Th3JdoHLU4AuFBINdoKbRaLJR+xQo3F0jl2RF9wv9vejpaMUlRmwj7eIbFyWwlsm8NY0ornOh8BHwJj2rk4vhs4SEjVLzuRpRfTivxf6JXceNbGH8CxnutMLuRSJwBj+HwXEG+nvgAt0vyeq5hSwXOdd1jRZPjhTJoMmwn8pubuf43RabGSXPrUGIsRTdNjx4WatLXo7iie68z0XOdCYDTwlW/TJmiz6qttG+j8xIi1t6MXC/ycApxpMqUtFoslb7AnJYulc+wAfOG5zsJ297Rkkjl0wFyyUDB19R/5hoqp/An0hfImwPat7eC5znx0Z6STsxVUujCZJpOAXX3DbwOjPNf5OjdRpQ8h1WBgHAlj3CVoT5qfchdV6pg4jyThLxZCt2DOlCB6mO/3SRl6jnwhOaMmnR2/OizUmBK8Ob6hNdMxITef42OA80mIfgB/AYSQ6lQj1lryDM91JqJLIf0C4ijgMiFVectHWSwWS/axQo3F0kGEVA56Vc6WPeWeeehshV7t7ViA+MuftjetfYsCz3Uk8DJwWjuTpqeADYVU62Qnsq5hSp32BR5Cl0SAngzcAZydD+a6XUVI1R8Yj251C7Ac3d0p2QskrzFC4CnA82aoB3CbMYhNm/BrRK24YPeZ5zrfpeux85S8EmoM/vKnKmBYOoIxGRr/BfYHbkMLlqCzio5CGw7vL6QKtvwIllzhuc4UtMjm9xjcE7hBSNVS+Z7FYrFkHSvUWCwdZwfz07blzjGe6yxH+00Uo0/NWyRW/Lqj28EXE+OAIcBere3guU4j2qj2nHzPmjI+QlcAF5Eo//gTON5znYnGn6egEVL1Rf+/hcxQPVqA+qr1o/IXYzJ6FXA7CZPhs4EL0riyfjCJa61H2tqxSEgWlNPV8Ql0eV2Ujgs1yeVPHTIUbg/Pdeo915kE/APtPxQxm/qixYDHhVTb5/s5rNTwXOd1YAwJzyrQTSLuFFL1zE1UFovFksAKNRZLxxkB/Oa5zm+5DsQCFKmhsFnx97d+L6ryJ+PT8RRwYlsrmJ7rvI9ujbtbtmLrKMbfZCLwN9/wB+hSpy9yElSaEVL1QnvSrGaGIsA/jedQwWKyIh5Bd4GKT9g8tMlwTVce2/zN9jF3f6U0PM0yllFjxM65dPx8n5zt1SWfmtbwXGeh5zo3AQcAb/g2rYbOuLnLlEVa8gTPdT4GTkSLgHE2QZtDF6Q/msViKR6sUGOxdACzar4ZNpsmnyi6Ft0+/OVPI4owhf4BoCftd3e6DThZSNUt4xF1ECHVXuhMiWFmKIouDRpjxLaCx5z3bicxwY0Cl3iuUzTln+a1HE3Cb2QztMnwsC487L4kOmJNKoasqhTIZOkTdM5AvkstujuK5zq/ea5zDnA88L1v05bAo0KqS00JoSUPMOWIxwKzfMNrAQ+Y0kWLxWLJCVaosVg6xtZoT5SimaAUAUWZUWN4w/d7H2DjnESRITzXWYD2cxktpOrdxn6/oUWrI7ITWfsIqaqFVJcCl5OYnM5Dm+o+UCyTcuONdDOwoW/4as91XmnlkILFc50f0d4iX5qhVYCHhFRbd/SxhFQVwCHm7jzghXTEWAAklz7lg1Azk+blLWtnowzJc53P0KbVl5IQAQLA3sCzQqrj81F8LkU81/kFLdT+4hteFZggpFojFzFZLBaLFWoslo6xI7CIxIW8JfcUa4tuPNeZBfjNR4uq/MnwGHoyN7qd/R4A9jRm3jlFSDUULTDt7Rv+FDjEpNIXBcan5XpgC9/wjZ7rPN/KIQWP6RJ0EvB/ZqgncIeQ6qAOTu53B+JZE08aP5xSIDmjJt2vey7QryOdm4xo6s+q6U3CDDujeK4T9VznRXQ53TgSglE1OuPmWSHV3rY1dO4x37fH0vw7d2XgfiHVhi0fZbFYLJnDfjFYLCliLqS2B97xXCfS3v6WrFHMGTXQvPxpZLFd0BtD6HuBg4RUg9rYbylwP3B6tmJrCSHVHuhSp+FmKIYWkU7yXGduqwcWGOZ9dhUJ83SAuzzXeSJHIWUNI6pcge7WFTcZPpcUTYaNoHO4uVsHPJ2hUPORTJc+zUH/f/Tp4HFZLX9KxnOdOs91JqA9iwQJo/iV0Rk3jwqpis0wvuAwWZ4nAn7vrV7AeCHVtjkJymKxlCxFdcFvsWSYDYEadDceS/5QtBk1Br9QMwAoiFbVHeQ5QAEntLPf/wGDhVSbZj6k5gipKoVUFwDXkPAdWYD2ohlfLKVO0CTSXExzA+cHPdd5MEchZR1jMjwR3QXKbzI8tq0yPcOWJIS8f5vJX6mQjdIn6Lqh8NppiKXDeK4zz3Oda9HdwN7zbVoTGCekut2Yk1tyhOc6y4AzaP7dWwXcaoR6i8ViyQpWqLFYUmcE0Ai8n+tALM0oaqHG+LP85BsquvInk6F2J7CXkGp4G/tFgZvQ7bqz9v0lpFoFeBDYzzf8JbqrU1GdD0w2yNk0L+t6Al22UXJ4rvMWcAzwhxnaAm0yPLSNw+LZNDFgcgbDy0eyUfoEeW4o3B6e6/zkuc4Y4BRgmm/Tduh23hfYrkO5w2TVnQ886xsOAlcLqQ7MTVQWi6XUsEKNxZI6I4BPTQmGJX8o9tInaL6yt3M2jDBzwBvA18Cpbe3kuc5XwHQSbY8zipBqZ+BRmk/sJgIneK4zOxsxZJmTaN6F63ngZs91YjmKJ+d4rjMNbWT9lRlaFS3WbJW8r5BqTbTpPMAbRmgtCQIBgkByaVimMmo6KtT8jF5oiZMXbbI91/kQOBS4Ev1dBvrafD9gipBqtJAqWfyyZAGzMHAtWqSPEwDOM0bQxfg9bLFY8ggr1FgsKSCkGoJuv2vLnvKPuUBv052mWPELNasCRZcab4SAscD2KZQ2jQWOSqEEpdMIqSqEVGcDNwA9zPAi4EzPde7wXKex9aMLEyHVaHTnkzivojs8FU1ZV2cxJsMnAi+aoZ7oMqjk1fVDfb9PykZseURL5+C8EGo812lAC7xxnEyePzqCMRx+Hl1adx+Jv1l3dMbNM0KqvxabP1khYEog7wJuS9p0PHCu/T+xWCyZxJ5gLJbUiBtq2rbc+ccCtDFj0ZY/oVPjZ/ruF135E4DnOp+jxdDT21qtNJksU9AdOtKO6Sx1P4n2ygDfAod6rlOU5wAh1UHoSWGcd4BLrUiTwJRDXIYWCuMmw+cJqc4XUpULqQYAfzG7f00iA6dUaCnzI92lT/Gsk86c7/Oq/CkZz3WWea5zD7AvOpMtnsU2CG3s/ZCQapNcxVfKeK4zCbichAk0wIHAVakYjFssFktnsEKNxZIaI4AfPdeZ2e6elqxiJpLF7lMTI6n7U65iyQJ3og2T2xOjHkVn36yWzicXUo0wj72eb3gycKznOiqdz5UvCKn2Rnc1ivMx8E+ThWDxYVbYHwbOAZab4f3RHaKOJFH680gJlou1JNSkNaPGGL0up3PlrslCTV6UPyXjuc5sz3WuRGdn+bsPrQvcJ6S60WT5WrKI5zr/h/7c+8XHPYBbhFTVuYnKYrEUM1aosVjawaRHb4Ite8pnSs2nZi1jcFt0eK7zE7q70yltrVR6rlOHFnXOSodXgMmIGAPcAsRLIpYC53muc0uxihZCqt3QHZ7ifAWcbf6+llbwXOdNmpsMbw2chy7/CaM9l0qNbJQ+QeeF+bzOqEnGc50f0FluZwC/+DaNBJ4SUp0tpKrJQWglizEXPxX93RBnW3THrrwopbNYLMWDFWoslvbZFv1ZsUJN/lLUGTWGb0j4M0BxZ9XcjU7336ed/aYCFcD2XXkyU7JyD9owNo5Elzq93vJRhY+QagfgahLXAj8Ap5usBUs7mIl03GS4D9pTZBjwQYmWjGWj9Ak6f77/Iel+Tlp0dwSTwfUOup33dcB8symILs2cIqQ6tMg92vIKz3U+Q3vUzPMNb4jOduqfm6gsFksxYoUai6V9RqC/kL/LdSCWVil6ocZM/Kb6hopWqDEeNI8BxwupurexXwy4Ge1pU9GZ5xJSbYMubdrIN/w0MNpznd8785iFgJBqC+B69IQPdFecUzzXWZy7qAoPYzJ8Ms07CnlCqgNyFFIuyXjpk2EuncigNALkDN/QsELpqOS5TqPnOk+j/WseIiGA9QLORGfY7Go7EWUHz3Uk2iPNXw67BvCAkGrV3ERlsViKDSvUWCxtYCZ/2wJvl+gKaaFQCqVP0Lz8acMiX717GO33MaqtnUzr5E/QK84pI6QqE1KdhPYW6WOGlwEXeq5znTGOLUqEVBuiS7ziq/Az0SLN/NaPsrTBCHRHsNnojIcA8E8h1XlCqmCbRxYXLWV1ZOJzNAfo2UmRxV/+VIaeXBcMnuss8VznTnT77pd9m0LojJsHhFQb5CS4EsNznd/Q5Y8/+YYHo/8P8tL/yGKxFBZWqLFY2mYTdBvWN3MdiKVNOrXCWoB8hp4QxtkpR3FkHM91FgETgCOEVP3a2f1u4EAhVUpZVUKqlYHx6Ivs+Ar0j8Dhnuu80smQCwIhlYsWp7qZodnAiSaLydJBTAbDYebuH8AYoNbcPxDdwrtUvCuShZNILEYm2th3qkW3IdmnJu/Ln1rCcx3luc7FaAPrL3ybNgQeFFJdK6QanJPgSghz3jwW3eUtTj/gXtuhy2KxdBUr1FgsbbMDekXwo/Z2tOSUksio8VynkeYt4ou2/MnwJLAYLai0iuc6C4BJNG8v3SKm5GcysJlv+HngKM91fu10pAWAkGp14C60+Aw6++Nk282uS2yC7sYD8JLnOs8BR6MFMIAt0W2VS6FLT7JQkylD6nQKNXltKNwenut8CxyHNrL2l2ruDjwjpBojpOqVk+BKBLOocDLwvm+4J3CX8QGzWCyWTmGFGoulFcxK6Y7Ah57r1La3vyWnzKHIPWp8+MufNi/m1XpTfjQe2C+FLlfPAOsJqdZtaaMpdToWLVTEM3TqgMs917my2D/jQqoQ+rX3MUOL0eVOv+QqpiLhMN/vk6DJZPhwtAE4wBC0WLNFlmPLNsmlT4Ug1BR8iYoxHH4dOABd0hjPuqxAm11PEVId2FYXPUvX8FxnOXAW4M/IrARuFlLtmZuoLBZLoWOFGouldVZD1xu/3d6OlpwzB1ipRIwUPyBRWlGG9scoZl5Et6Y9ua2dTLbRLcA5ye8DIVVfdLnPiSS+934BjvBc5//SHG/eYbpajQfinkbLgDFGULB0EiHVMBKfv3dNa3kAPNeZi+4ME/cR6Y1eYd8vq0Fml+SMmkz5PHVaqDH/L/7ueWsKqYriWthznQbPdSaju+VNJmFwXYPOuHlCSDWiRL4ns47nOg3AxcBTvuEy4AohVZteaxaLxdISRfHlZLFkiB3NTyvU5D/z0MazRZtdEsdznTrgXd/QzrmKJRsYE++xwO6tZcv49v0QXc6zR3zM+ARMBrb27foSWqSZnv6I8wvj7zMeLTqDnjyf6bnO160fZUkR/+RrUvJGkxF2CTDODJUBFwipzi1Sk+FslT7NMT87m0Xpz6qpBoZ2LZz8wnOdRZ7r3ILOsPFnYA5Fi9l3C6kK0psn3zHfVzcA9yVtOktIdZIVySwWS0ewQo3F0jo7AN95rvNnrgOxtI0pW1lKaZY/bd1WC+si4T3gU+C0FC50bwNOElJ1F1IdCdxDIpOkHrgGuNS06i1qTFncnSQmoo3AuZ7rfJq7qIoDI4DtZe5KdOexFTBlKRPQGQ3xTLiDgNuL0DskW6VP8e5knfUlKyqfmtbwXGeG5zrnoc1uv/Vt2gx4REh1hcm2s6QR85m/B7gxadMxaKHWzr0sFktK2JOFxdIC5iJ8A2w2TSFREobChneABvN7JbqFfNHiuU4MXbq0BbBVO/vOQP99/g2cRuJ7bgbaMPhZ83hFjRHv7iDhwREFLvJc593Wj7J0gANICBOPtPeeMh4ix5AwGd4a7VuzauZCzDpZKX0yJSYLSE9GDRSBT01beK7zBTAauAhQZjgA7Ak8azI9il3szzqe6zyBzqiL+IeBa4VULbWyt1gslmZYocZiaZnt0Rcyti134VAqLbrxXGcpzTuRFXX5EzR1N/kvMKatFUkh1Qbobliboc00MccdViqeLEKqKuBWYH3f8JWe67yWo5CKCiFVNbr1Nmjh5b+pHOe5jkSbu8azG4YCDwupNk97kLkhW6VPoM/36RJqir4MyHOdqOc6/wH2Q5eSLjWbqtAC4hQh1b422yO9eK7zEtpk2P9Z2BW41YpjFoulPewJ2WJpmR2AWcC0XAdiSZlSyqiB5uVP25fICt04YDg+D5o4QqqAMWy8HxgA/Il+P9wAXGDEraJHSFWBTrn3tx+/vhRMk7PInmiDVoDJxsg6JTzXmYM2GY53h4mbDHvpDTEnZFuo6ez5Pow21I7jlop3iOc69Z7rPIw2HH4KnWkHuhPeRcBjQqptchReUWKyGE9Gd9qLsxUwXkhV0/JRFovFYoUai2UFzIR3G+CtUiiRKCJKqUU3wFskLrK7A1vmMJas4LnOb4BAe9A0CVPG6+MG9Mpl3KT1e7RvyPRS+Rwbg9qraV4Kd4fnOk+1coilg5iMg0PN3aXAsx19DGMIfhHa5Bn0e/ZCIdXZBW4ynCwWZ6rrE3Sh058xfPVn19Wgxd2SwXOd+Z7rXI/2S3rLt2kNYKyQaqyQao3cRFd8eK7zJVqg9XccWw+43/oEWSyW1rBCjcWyIpujO0FYf5rCoqQyajzXmQ987hsq+vInw31AX2B/ACHVOsCj6HKnOG8Ch6FbpRb65DcljIBwKbCLb/gBz3Um5iikYmUEMMT8LjqbqWUMRx8A/kki8+QQ4DYhVc+uh5kTsp1RUwF01pC5JAyF28NznZ891zkLOInm4tU26Oyai4RUpbQAkjE815kGHI3O6IqzGjBBSDWk5aMsFkspY4Uai2VFdkSnRbfYxcOSt3TFs6BQ8Zc/7VgKgoTnOnPRrZCPEVIdDkwg0Xo6gvZmOce0qP0a+BGd5l+0mKyCc9ElOXEmA3fnJqKi5nDzMwI83tUHM75Bx5IwGd4GeFBItUpXHzubBAKUkfCEipNpoQbS51NTkkJNHM91PkaL21egy0ZBzxH2RRsOH2O8mSxdwHOdMNoTyF9WPwh4wCw6WCwWSxNWqLFYfJgJzw7AB57rZDJt25J+SlGoecP3ew2wSY7iyDZPA6ujy3zik8PZwHGe6zyaVOp0J3CUaVVddJhz1mnoLkRxpgC3lkrJV7YwRtUbmbuveK4zKx2P67nO98CRwHdmaDVgopBqs9aPyjta8sjK5HeoFWrSjDEc/je6M9HdwHKzqTs640YIqfa0hsNdw+dT9YVvuC9wT4F95i0WS4axJ1uLpTkuulb9rfZ2tOQdJVX6BGAmit/6hoq+/ElItSa6/CmKnqSVA+8BozzX+Sp5f891ZqN9RI7PZpxZ5Gh0J6E4/wGutSJNRjjM9/ukdD6w5zp/AsfR3GR4nJBqn3Q+TwZJLnuC/M6o+Qnwm0AXdYvujuC5znLPde5HZ9NMIeGFNgCdcVNoImLe4bnOYuBU4B3fcHe0P9BOOQnKYrHkHVaosViaMwKIAe/mOhBLh5kL9CqR7kd+/OVPOxXraqfp6vQP4GG0R8gCoAFd2nSG5zoL2jh8ErCtkGr1TMeZTUyXq5N8Q28BlxmzVEsaEVKFSPggfWRabacVn8nwPWYoCFwspDqrAD7X2RZq5pifnRJqPNdpQIs1cQYXa9ZdZ/FcZ47nOlejzbM/8G1aG539cYuQamhuoit8PNepBc4BXvQNVwI3CKn2zk1UFosln8j3L36LJduMAL4yRq2WwmIh2jeipLJqgKm+3wcA6+YqkEwhpOqGXsm9hESJxZ/oSe0QEuauLWLKGMeijYWLog2vybQ4yzf0EXB+R1pFWzrEKBLXTGnNpvFjTIbvA84nIXSMIv9NhnNV+tSV832y2GazalrAc51pnuucCoyhubg1AnhSSHWekKpPToIrcMz5+nLgMd9wGXCpkOqIFg+yWCwlgxVqLBaDaZG4NrbsqSAxWQQl51NjWlb7L55HtrZvIWKyYB4G/uYb/gg9eb0bPdk6NYWHegP9nbdDmkPMOkKqPdAiVZwvgbOtr1ZmMJkW/zB3fwLez/Rzeq7zX3QpVNzYdVvy22Q42xk1i9ClS10531ufmg7guc576M5k1wLzzHAQOBCYIqQ6ogQzWruMuXa5BRiftGmMkGpMsSwuWCyWjmOFGoslQXwCZ4WawqXkfGoM/vKnnYvlwk5I9TdgIto4GHRZ4r3AqZ7rzDMXuHegS742bOuxjGfLzcDphTyZEFLtCFwFxP+P/wec7rnO8taPsnSR/YF4x5tJ2fL/8VznO1Y0GX5YSLVpNp6/g2RVqDH/B10V5q1Q00E814l4riPQnfQmkMia6onOuHlaSLV7sXwHZQuTSfcAcB36ey7OEejyx6Lv6GixWFbECjUWS4IRwO/ALzmOw9J5Si6jxuAXalYlIWwUJEKqKiHVxcCVJCbI84BTPNe51+/BYtrKvo8WYNqcHHiu8yPwIXBwZiLPLEKqrdAX8vHv7p/QotWS3EVV3BhR7yBzdy7wcjaf35hhHw+8aoZq0CbD/2j9qJyQ7dIn0P8fXRHmf0i6b4WaFPFcZ5nnOuPQhsN+j5XB6IybCe2J55YV8VznaeBCmhtd/wO4rpAXGCwWS+ewQo3FQpMHxhbAW7ZbSkFTqkLNNGCm737Bdn8SUg0BHkKv2Mb5DDjUc52PWjlsLLAhqZU13QMcIKQqqPeJkGpjdEZQvB3578DJ7ZgoW7rOX0icU57IRXmZMR29EJ1NBrrT2SVCqjPzyGQ4OaMmSvPJZiaYQxfO957rLEV/juKsJqRqKTPI0gqe68zyXOdS4HD0eTrOBmix5ro8LtfLSzzXeRU4A6j1DY8E7hBS9chJUBaLJSfkyxe8xZJrtkKvCNqyp8KmJEufjLjoz6opSJ8aIdXuaKPWNX3DE4CTTPviFvFc5wfgJeDU9lLEPddZCDxCar42eYGQah3gdhLZRbPQf5M5rR9l6SomQyvekrsWeDpXsZjSiHvRgk1cLDoUuDVPJm/JAkddLEamFz3mAn2EVOVdeAx/+VMZsEbXQipNPNf5HjgBOBv4zbdpV3Q51Jm2q1bqeK7zAbqj3yLf8ObA3UKqvrmJymKxZBsr1FgsmhHAYuCLHMdh6RqlmlEDzYWatQppFVNIVSmk+ic6Zb67GV4IjPFcZ5znOpEUHuZudNnXXins+wywjpAq7ztkGTPlO4H4ZHweWqRRuYuqZNiGRBnhc57rLGpr52zguc4rwLEk2lNvhzYZDuUuKmDF0qdsZB7NRXs1dWXian1q0oQRE99EmwvfiD6Hg84AOxRtOHyIkKqitcewJPBc52v0Z92/SLEO8ICQalBuorJYLNnECjWWksekju8AvGdb2xY8JZlRY/iGRMtaKJCsGjPBfAA4wDf8FTDKdBlJCc91ZgJPAScIqarb2TeC7rJxTj6bXgqpVkV3AqkxQ4vQ5U6/tX6UJY0cbn5Ggcm5DMSPMRk+Am0kDVpMelhItUnuoloxoyYLzxk/33VFnP9f0n0r1HQRz3UaPdd5Al2++gjQYDb1RmfcPCmkGpnP5958wXOdn4BjaJ6lNARdVrZabqKyWCzZwgo1Fgusj16RezPXgVi6TFfNJQsWY7A71TeU90KNkGok8Ch6lTDOJOB4z3VmdeIhJ6AzT9o1CzZ+N/OAv3bieTKOWTEdT2ISugxtHPxj7qIqHYRULtq3DGCq5zrhXMaTjDEZPhZ4zQz1AcYLqfbOUUiFKtRYQ+EM4bnOYs91bgf2I2GGDTrz8UbgnkLIasw1ZhHiWJpnfw0A7hdSrZebqCwWSzawQo3ForNpIujOMZbCZg6wUh4ZbGYbf/nThkKq/jmLpA2EVBVCqrPQF+s9zfBi4GzPdW7rbGabMdZ9CDhKSFXT9t4A3IrOwOne7p5ZxBgdjwPi6e11wBkmk8KSHQ7z/f5IzqJoA2MyfAFwvxkqBy4VUp2eg3NgLkqf4uVfXTEUnoMWbOOsWcLfHxnBc52ZnutcABwNfO3btCkwUUh1lS3laRvPdeahu7/5DZtr0J41W+YmKovFkmnsl5HFAjsCn3uuszjXgVi6zFwgiE6xLkU+o7n54E45iqNVzAX5fcAo3/B36K5O6chqewxYjp4UtInJkngFOCoNz5sWjMA0Dp3eDrpzzjme63zW+lGWdCKkGgjsYe5+4bnON7mMpy0814l6rnM3cBEJceRw4OYsmwznMqOmq1mU/vKnahKfPUsa8VznK/R5+QKadyn8KyCEVKfkiTF2XmK6lJ1K8+zvbuhuULvkJiqLxZJJrFBjKWmMP8bq2G5PRYHnOnXAEkrUUNhkovjfy3lV/iSk2h7t9bG+b/gJ4FiT3t1lTJbBPcCBQionhUMeBP4ipBqcjufvCmaSMpZE55kocIHnOjbbL7scTOL6aFIuA0kVz3X+AxxHQrzYAe1jka33daGWPoEtf8oaxnD4VWB/dCe7JWZTJTAaeFZItV973ftKFc916oHzgH/7hsuB64RUXm6islgsmcIKNZZSZ4T5aYWa4qGUDYWhefnT5imWAGUUIVVQSHUacBuJbKdlwPme69xoLj7Tyb+BMHBiezt6rrMMLeyckeYYOoQxQL4NiHs2xIDLPdeZ2upBlrQjpOqJ9tQAbeBZMN8Nnut8i86miXtZrIE2Gd44C0+f9dInI8oupetCje38lGU816n3XOcRtOHw4+jyc4B+6Iybx4RU21nD4RUxZvhX0rwkMwBcKKQ62v7NLJbiwQo1llJnB+Anz3V+z3UglrRRyi26AT5El/6APsePaGPfjCOkGoBunX2kb/gHdKnTfzPxnOZCdizwNyHVWikc8hIwQEi1Rbt7ZgAhVSXar8fftedfnuu8mIt4Spx9SLSIf9SYdBcMPpPhuGDbF20ynErb+q6Qi4waSI+BvBVqcoTnOgs817kJ3fXvDd+m1dEZN3eleA4vKUxm0u3AHUmbTgbOtD5LFktxYD/IlpLFrJxuBryd61gsaaWkM2pM+de7vqGclT8JqbZGd3XyCxACOMpznRkZfvq30MaVp7a3o5mM3wScle2Ue/N81wDb+IZv81xHZDMOCwipykl4Jy0EXshhOJ3Gc53lwPnoLmgAFcDlQqoxGZzA5UqomUPXhfnf0Rl+cVyblZBdPNf5zXOdc9CGuX7PoC2BR4VUl+arOX4u8VxnInA1ukw2zijgMnM+s1gsBYwVaiylzLZo41nblru4KNkW3T785TJbZ7urkZCqTEh1Ajqrpa8ZXg5c4rnOtRkodVoBz3Vi6BXZbYVUm6ew/zfoTJ+s1fmbSfNlNBfT7vVcpyB8UYqQXdFtbwGeNKU1BYkxGR4HXEyiDOkI4KZ0nw8CAQLkpusTpCGD0gi103xDfQArCuQAY5p+BHApMNsMB4C90f41xwupuuUqvnzEc50paGG2wTe8J3CDkCpZQLVYLAWEFWospcwOwAIgbzt6WDpFSWfUGN4hcdFWiRYls4JpLX0X2tg0vir9E3C45zovZSsOAM91vkQLsWNSXCEfCxwhpMp41zATz/nA33zDk9AdsSxZxvx/HG7u1gNP5TCctOG5zsvoLIV4C+oRaJPhVIy2UyVZpIHslj51T4P4ZMuf8gQjMr4I7IvugBfPdqpGv5eFkGpvW96TwHOd14ExNM8MGwHcabLHLRZLAWJPcpaSxJQbbAe8XWgeBJZ2SUcqfEFj2nh+5BvaORvPK6TaDN3Vye/18m/gCM91fslGDC1wF7A2OluiTTzXmQM8A5yQyYCMKHA6zbN3BHC7yQSyZJ/NSUzO/89znXlt7VxImGyxw0l0NxqONhneME1P0dKqfTaFGtAmtF3hf0n3rVCTYzzXqfNcZwLaN0qQKO/pj864mSSk2jJH4eUdnut8jDbQX+Ab3gS4V0jV1c+HxWLJAVaosZQqG6G7z1h/muKj1M2E4/i7P21vDGszgil1OhoYT+JvXwdc6bnOFbksIfFc5yfgeeCUFGv2JwPbCKnWaHfPznMccJjv/ovAdVakySmH+36fnLMoMoTnOrPQJsNvmKF+wD1Cqj3T8PAtnVuyWfoEtkV30eK5zjzPda4FDgbe821aCxgnpLpNSLVabqLLLzzX+Q79OZ/lG14LeEBINTg3UVksls5ihRpLqbIjujTkg1wHYkk7tvRJ8xaJFcjuaFPGtCOk6oP2gjmZxHfKr8CRnus8n4nn7AT3oldh921vR+OfcwdwdiYMRYVUh6HT9+NMBa6wmX25Q0i1OonywLdymP2VUUwr+vNobjJ8hZDqtC6WkeQyo2aO+dlVoeYnEi2iwQo1eYfnOj95rjMGOAX40bdpe+AJIdX5NnMEzPnraOAX3/Cq6JLHTC5AWCyWNGOFGkvJYSZfI4CPzYWrpbiYC/QsdRM9z3XmA5/7htJe/iSk2giTgeIbfhntR/Njy0dlH9OyeDJwXIpeFnGD8bS2NhdSecAZvqH3gYtMO3FL7vBnNxW1kbPPZPhSElkvRwI3dsHnJR9Kn7okzhuBdrpvaLCQqldXHtOSGTzX+RDd2egqEv//ZcD+wBQh1Wj7/d+UQfedb3hl4P40ljxaLJYMY4UaSykyFL268FauA7FkhEVAI7b8CZqXP+2YrtbTQqqAkOpwtPFtvEtOPXAturNTPgqgE4Fymk/KW8SUIN0MnJ6ukjEh1d+AC3xDnwPnZqMDlqV1jPn1X83d72gubhYtxqz1RBImwzuiJ3GDOvFwuTQTTldGDaxoKLxWGh7TkgGM4PgcOkvyPhLvt+7ojJtnhFR/LWXDYc91FqA/436/ul7AeCFV1hoMWCyWzlOyJzBLSRNfJbf+NEWIKSGZhy1/goQfBUAN2liwS5iOSDejzXDj3yG/A6M91xH56rPiuc5i4H7g8FTS4z3XmY4ujRzV1ecWUo0ELifRBes74IxCbv9cRByELgECmJSv799M4LnOV+hWyHF/lrWAiZ1YcW8peyFbAuR8dIlnOoQa61NTYHius8xznXvQgs2/gfjndxA64+YhIVWXv/cKFbNocgbNF22qgFuFVHvkJCiLxZIyVqixlCIjAGlSQy3FifWpoSn9+VvfUJfKn4RU6wGP0rwk6HXgMM91klej85Gn0R0xjk1x/7uB/YRU/Tv7hEKqbYB/kfi+nQ6cZjpzWXKIkKobulwCQAGv5TCcnOC5zh/oz0O83C9uMvy31o9agWShJob2gMs4RphfQHrO97ZFd4Hiuc5sz3WuAA4FPvZtWhe4T0h1o5BqSG6iyy0ma/N8dOesOEHgaiHVgbmJymKxpIIVaiwlhTE+3RBb9lTslHyLbh/+lbSdOpMKbkqdDgYeABwz3AjcCPzTc50lXQ8z85gL1vFo8aXdi3bPdRahS6ZO6czzCak2BW5Cl1wB/Aac7LnOws48niXt/B3d/Q9gcql6BZlV93OBh8xQBXClkOqUFM8XyaVPdbEY2cxMStf53mbUFDie6/yANrY/k+ZmuiOBJ4VUZ5us0JLCCJr/Ah70DQeA84RUx2fCON9isXQdK9RYSo3t0O97K9QUN7ZFd4Kpvt8HoFcYU0ZI1RO4HjiHhOCggGM813miAEtFXkZntZyU4v4CcIVU63fkSYRU6wK3kcg2+AMt0sxt9SBL1jACxKHm7mLguRyGk3OM58edaJPheDbMaOD6FEyGkzNqsu27lJbzvRGcw76h1dPlUWXJHp7rxDzXeRvdzvt6dMYV6O+vQ4DnhFSHltr/rfm73IX+XvJzPHBuKfv5WCz5iv1QWkqNEcCfwP9yHYglo9jSJ4PnOr/RvJtJyuVPQioX3QXHf8xbwKGe63zb8lH5jVlZvBPYzYgp7e0fAW4Bzkn1QlZINdw8R3yCOxc4yZSZWPKDnYCQ+V3kqQF21jEmwyegvV9AZyLcL6Qa2MZhyUJNtoyE48wFVkrTRNNf/lQGDE/DY1pygOc6jZ7rPAXsAzxMQkDshc64eUpItUupZZN4rjMJ7ZkW9Q0fCFwlpCpv8SCLxZITrFBjKRnM6sk2wNsFmAVg6Rg2o6Y5/vKnke1dmJpSp/3QadKrmOEoeiXubFMSVMi8j/YxGJPKRbrnOh8Ds4G/tLevKakaR6KkZiE6k2ZG58O1pBPzf36EudsIPJHDcPIOn8nwj2ZoLeARIdUGrRyyQulTpmJrhbloz410lLQkL+LYzk8Fjuc6SzzXGYv2o/qPb1MInXFzf0czJgsdz3X+D50l689+2wO4RUhVnZuoLBZLMlaosZQSm6JXuG3ZU/FjM2qa4y9/WhVYo7UdTZnD1ehW0vEJ2GzgOM91iqIrjnkNY4HN0eJtKtwOnNhWGYiQykF74MS7Si0FTjUdpCz5w4ZAfGL2suc6s3MZTD7iuY4CjibxfRk3GW5JrMyH0ifITItu61NTJHiuM9NznYuAI4EvfZs2QneHulZINTg30WUfz3XeAk5Ff0/F2RYYV4o+PhZLPmKFGkspMQKopXlHAEtxYjNqmjON5t4LLZY/mZKdR9Ara3HeA0Z5rvNlS8cUKp7rfAe8ApyWSsmE5zphtL/N6Ja2C6lWRos08RKRWuB0z3W+T0/EljRyuO/3R3MWRZ5jysHOQRtqgxZurxZSnZz0mcl16dMc89MaClvaxZTtHgucB/zu27Q78LSQaozxZit6PNf5DO1RM883vCG6U1anux1aLJb0YIUaS0lgUt1HAB96rpPti0hL9plD+jwLCh6TQeLPqtkpeR8h1d7oOv6hZiiKLuE5w3OdBRkOMVeMB1YnhZImw0PA7kKqkH/QdJMbR6JMrAE4y3OdL9ISpSVtmNK0Hc3dDzzXmZbLePIdYzJ8B9rTIm4yfDRwnWlvDvlR+gTpyaKcQ/NJ65r2e6T4MMa6rwMHoD3I4uW8leiyvylCqgNLwbPFcx2JFq6Ub3gN4AEh1aq5icpisYAVaiylw5rAIODNXAdiyQpxz4KaXAeSR/h9atYSUq0CIKSqFlJdhu72El8Zj5vfTjDmu0WJ8Y15Bjg5lQ4gJsPgHuCM+JhZeb0TLfiAFrjO91zno7QHbEkHo9BtaUFnj1lSwHhanEjCZHhnEibDuS59SltGjRG1/eVP3dDlopYixHOdBs91JqMNhyejPasA+qAzbp4QUo0odsNh03TgGOAn3/BgtFhjfZoslhxhhRpLqbADEAPeyXUglszjuU49uuWuLX9K8A2JlWfQpsKrocsa/u4b/xg4xHOdT7MZXA65H21CekCK+7+Mztba0mQU3A6sbbbFgEs817GCcB5iMp/2NnenAVZM6wCm/PFIEl3kXGDikLUahibtmquMmnSd7235U4nhuc4iz3VuQX8P+Bc1hqIzbsabLohFi/HqOhb42jfcD7hXSLVJbqKyWEobK9RYSoUdgW8915nX7p6WYsEaCvswmTH+8qfD0CJNPBMkBtwLnFJKnxPzWicBxwipeqWwfxS4Ce3dcTPaiDLONZ7r/KfFAy35wP4kynSKwhg723iuMxPt0/S2GVpp0xF1B625Yb3foDzbQs1SdBZPus73yYbCa7e4l6Xo8Fxnhuc656EFi299mzYHJgmpLhdSDchNdJnHdHQ8Gd0ZMU5P4C4h1Q65icpiKV2sUGMpeozJ57rYbk+lhjUUXpHX0WUfg4DtgbgwMR/dnejeYi51aoNJ6JT3I1Pc/we0H43fdPkWz3WmpDkuS5owpW0Hmbt/oo2kLZ3AlACeDUyMRSHSSLm7Sf0um4yo3TwQiAXIcumTEdzmkL7zvW3RXeIYf7HRwEXAH2Y4AOwFPCukarMDYCHjuc5y4CyanyMrgZuFVHvmJiqLpTSxQo2lFIivAlihprSwGTUrMhsIAX3N/V7A5+iuTh/mLKocYyae9wGj2lstNcaiVwDd0RPDIDDe+BxY8pc9SbzvH/Ncp6GtnS1tEzcZXrIocG00GogBhFZr3HS7v9XuOnDVxvYOzwTpFOZ/B5b77rvF7lFiWRHzHv8P4AFjSbSxrkJn3EwRUu1TjGbT5vx4MfCUb7gMuEJINSo3UVkspUfRnVwslhbYAZhJoq7eUhrYjBofQqpd0aVO/oyZMHCi5zp/5iaqvOJZYBZwQms7mMnahehMmkZ0JtJvwIRsBGjpHGYidZi5uwz9f21JA0dtPeiVL96p/L9IY6AWoM/KkdVOv2HBxTkoD5lLmoR5k1Xo7wbWF7CtiksUz3XqPdd5GG04/BSJ79B+aDFjspBq6xyFlzHM5+AG9CKGn7OEVCdZ8dJiyTxWqLEUNUKqamAr4G3rR1By2IwadMmHkOo84Dp0FsgiIIIWGKrR9eclj+c6jcBdwN+FVKsnbzcXpWeiL9bj3IcWbNZI3t+SV2xHou38FM91FucymCKjKvxTxR/vvVz9bN3ysvkAPXrHhgEThVTrZjGOuUDvVLq3pYgtf7I0w3Od+Z7rXI8uoXzbt2k4cKeQ6g4hVVF9F5g25vcANyZtOga4oBiziSyWfMJ+wCzFzpboNFXbhaX0SNsKa6EipBqM7mp0oG/4Q+A7dBp3GTAiB6HlK6+hJ2intrDtBHRr5zgvoMWv24Gz7epiXnO4+RkFHs9lIEVIFcDCucHFbz1fPWXRvOBvFVWxGPrce7+QavcsxRHv/NS3zb1SJ9lQuKg7/lhSx3Odnz3XORNtuuvvELYt8JiQ6iIhVVFl83qu8wRwCXqRp2kYuDaN4qjFYknCCjWWYmcH9IT081wHYsk66TSXLDiEVDsCj6KNtOM8iq6tf8M3NjKLYeU1JuvudmCEkGrj+LiQ6gj03y3O68CVJjX8bbQAsFP2IrWkisnq2NTc/a/pWmRJH02TtLrasoa3/6/6P/W1gSd9264VUh2fhZX3OeZnus75VqixtInnOh+hSyqvQBuUg55X7Ys2HD7GZHUXBZ7rvIQ2GfZ3ddsVuLVYjZUtllxjhRpL0WIuDEcA71njyJKkJEufhFTlQqoz0K2j412dlgDneK5zq/ksvO47ZGt7kZXAc51PgfeAMUKqgJDqAGCMb5f3gIs814mY/WPov/VpdmUxLznM9/uknEVRvFT578RigdgZe/UfC1yJLgsEOB4t2GRy0hrPqEnXOf8nmmcP2BbdlhUwhsP/RmeX3EPChLo7cBIghFR7FkuJkOc676Izifzlo1sB44VUNbmJymIpXorixGGxtMLa6NW1t9vb0VKUzAV6FNOKVnsIqQYC99J8cvo9cKjnOm/4xt4F4uJlJdrDw5JgLLABcD7wT9/4p8C5ycKv5zo/oQWcQ7MWoaVdTOnfrubuZ57rfJfLeIqUqhbG6jzXeR49oVtoxnYF7sugyXBaM2o816lHizVxBguperW2v6W08Vxnuec696GzaZ4D4p6IA9AZNxOFVJvlKr504rnOl2jxda5veD10qWO2TcQtlqLGCjWWYmZHdEnCO7kOxJITFqFXdEui/ElItS0wGdjQN/wkcIznOmH/vp7rLEV71cSx5U8+PNeZhha4zvUNfwOc6blOXctHcS/g2QvVvOJgEtc5j+QykCKmJaGmHsBznc+AI0gIHuuQOZPh+KQxnef75PKnNdP42JYixHOdOZ7rXIX2M/N/x64N3COkukVINbTlowsH8x15NLpzZJzVgAlCqiG5icpiKT6sUGMpZnYAvvBcZ1GuA7FkH1OSUvQtuoVUQSHVKcAdQDz1eBlwgec6N5iV4Zbwlz9tb8t2EgiptkOvEFYCfdCGkWM811nW2jHmPPMQLRsRW7KMyX7Yx9z9BZ1FZkk/yeeN+lisKZsAIxKPRmecgS5Nuk9ItVua45jne/x08UPSfVv+ZEkJI2Scii6b9WdmjQCeFFKdJ6Tqk4vY0oX5bB9N81b2g4AHhFTr5CYqi6W4sEKNpSgRUjnodppv5ToWS04pap8aIVV/YDx6IhRnGnCY5zqvtnP4W+iMM9D19FulP8LCw6Sn34hOXZ+Hbl9+doqC77PAcCHVBhkM0ZIa+6Lf1wCPGuNnS/pJzqhZIePMZPCdgc74ix/zL2MynJZuaUaQXkR6hXnbotvSaUxr6/eAQ4BrSYiJQXQnxilCqiMKeZHEc5256DKoL3zDfdHZQ5vnJCiLpYiwQo2lWNnB/LRCTWlTtC26hVRbors4beobfhY4ynOd39o73nOdBTTvhlby5U9CqvWBW0lkCXwDTAf+msrxRgy4BTinWMwjCxEhVQV6cgR6cvRCDsMpdtoVaqDJdPUW4GoSJr3HA9cIqVoqn+oM6c6gTM6osZ2fLB3Gc52I5zoCLR5PwJQGohcBxgBPC6l2T5domW0811mMzh7y2wx0B8YKqXbKSVAWS5FgLyQtxcoI4NdUJqyWoqboWnQLqcqEVMcDdwH9zHAtcKnnOte04aHSEv7ypx2FVMF0xVloCKnWQpsIx7Mw/kRPJO8Fjkw1Td1znU+AWcDfMhCmJTV2B/qb359so/zP0nVWKH1qa2fPdaagTYbjGWq7o0uh+rd6UOqkVZj3XGcJzT04Vi/k7AdLbvFcZ6nnOuPQgs2Lvk2D0Rk3E4RUG7Z4cJ7juU4tcA7NX1cFcIOQau/cRGWxFD5WqLEUHUKqHsDm2GwaS5GVPgmp+gF3ogWE+OrbT8ARnuu82OqBrfOG7/caYJMuBVigCKmGoYWveFeXBcDJnuv8DjwOLEXX4qfKbcDxtu159jGr0oebu3XA0zkMpxRIKaPGj+c6n6JNhn82Q+sCDwupuuoBMxdYKc2ZCX5D4SCwRhof21KCeK4zy3OdS9Gfgc98mzZAizXXCalCuYmu83iu0whcDjzmGy4DLhVSHZGToCyWAscKNZZiZGugHCvUWIrITFhItSna42FL3/ALwJGmPXSH8VxnFvCtb2jnzkdYmJgWzuPQdfUAS4BTPNf5GcBkKN0NHGD2bRfPdWYCL9ExcceSHrYEhpvf/21K/CyZo8NCDYARQUcD75uhAWgT0l26EMtcE086BdLkzk/Wp8aSFjzX+Q44AZ2J4s/+3hVdDnWGkKp3ToLrJL7y3/FJm8YIqcYUanmXxZIrrFBjKUZGoNOqv8p1IJacU/AZNabUaTRaLIi/lnrgSuByz3WWd/Ep/OVPO5WSt4pppT0ePUkEWI7u7pQ8OXsBmAGc2IGHfwjYTUi1alfjtHSIeDZNjIR5rSVzdKj0yY8pLTqDxAp8FXC9kOrYTk7o5pifmWzRbX1qLGnDGA6/ARwE3ESiJLACOAxtOHywkKo8RyF2GPOaHgCug0QHOHQG0cWlXGJtsXSUkrkgt5QGZpK5PfCO5zqR9va3FD0FnVEjpKpBl9GcQuJ8/Rs6i+Z504K8q0z1/T4AXYZQ9Aip+qIzaeIp5vXAWZ7rrCDwmnPJncBfjZdNuxgBbTx6ImrJAkKqNdEZlQBvWI+yrNCpjJo4xmj1ZrRHR/w7+0Q6ZzI81/y0LbotBYXnOg2e6zwO7AM8AjSYTb3RGTdPCql2KqSMFM91ngYuBBp9w/8ArrNeTxZLalihxlJsbIj22rBlTxYwZsKFmCViTAUfA7b1Db+Cbr09LV3PYyaz031DRV/+JKTqhfakGWaGIsA/Pdf5uI3D3ga+BE7rwFP9B+grpLKtz7PDYb7fJ+UsitKiS0JNHNMV5xSamwzfK6TqiOiSiYyaP4H5vvtrFuL3iaUw8Fxnkec6twP7A6/6Ng1BZ9zcI6QqmMUUz3VeRS9W1PqGRwJ3GD9Ji8XSBvbLxlJsjECr9++3t6OlJJiHPs/1yXEcKSOkCgipDgXuI1GS04BOI77Ic51lGXhaf/nTyEJatesoxuD3dhJeE1HgEs913m7rOJO9dAewjWmN3i7mmJuAswopdb0QMWVse5i7X2NLX7NFp0ufkjEd044EfjFD6wEThVSplhvFM2rSJtSYz7C//KkbsEq6Ht9iaQnPdcKe61yA9jn72rdpU/Rn4koh1cDcRNcxPNf5ADiJhAgLuuHH3Saz1WKxtIIVaizFxgjgU891luY6EEvuMW15F1Eg5U/GOPAm4Ex0hxHQ7WGP9lzn6TSVOrWEv/xpVYq0s4lJt74ZnXkX52rPdV5J5XhTFjUVOC3VVXVjGPktsF8Hw7V0jIPQJvIAj2Tws2IxBAIESFNGTRzPdWagTYY/MENxk+FUMv0yUfoEK/rU2PInS1Yw3zlHAxcAM32b/gY8K6Q6uRC6C3qu8zVwLDpDLc466M/2oNxEZbHkP1aosRQNQqoh6FIGW/Zk8VMQhsImnXkSsKNveCq61On7DD/9NLQgFKfoyp9MRsv1wBa+4Rs913m+gw91F9pQdNcOHnOYkKpPB5/LkgJmohIXwsI0bztvyRzlrHgd2SWhBsBzncXA6cATZqgauEFIdXQ72X4L0Rly6RbmraGwJWcYc95X0eVQt6M7E4LOZjsabTjs5btJr+lOeQzNO1wNQbckXy03UVks+Y0VaizFxA7mZ5slDJaSI68NhU2p04HAA0C8/XME3eLyPDNpySgm+6BZ+VOmnzObmOyXq0icIwDu8lzniVYOaRXPdX4BpgCnCKkqUjxmLvAUHesaZUmdfwA9ze+PmhaxlszTkiFop0uf/BiT4RuBf6HFF4CTgataMyI1/++ZON/bFt2WnOO5Tr3nOo+gDYefIGG+3Q9t2vuYkGrbfC5d9lxnJjqzxv+ZGgDcL6RaLzdRWSz5ixVqLMXEjsCP5ovAYomTtxk1xkzvX8B56HacAH8Ax3quMznL5Rv+8qc1i6WttBFpLgZ28w0/6LnOg1142PvQ76l9O3DM48DmpjORJU2YVeRR5u4i4N85DKfUaKkrU5czavx4rvMMzU2G/4I2GW5NjJlL+s/3M4DlvvtuPk+GLcWN5zoLjIh5APCmb9PqaB+1u/L5e8ZznXnA8cBnvuEatGdNSv5vFkupYIUaS1FgvD02xpY9WVYkLzNqTJvnSTQvoXkbGGXqubPNNyS6pkARZNWYydTZwN6+4SfQbbk7jec6fwKPAsel2rnC+CXdDpxtJ3lpZRfAMb8/bdqiW7JDxoUaANON7SgSJRProw1VW8psSfv53mTq+Dvt9SNPxX9L6eC5zm+e65wNnAD8z7dpS2CykOoSIVX/3ETXNsZH8lSaC03d0N2gdslNVBZL/mGFGkuxsB36/WyFGksyeZVRY0qd9gUeQhv3gk7tvwM423OdRa0dm0nMZMSfVVPwQg2608RBvvvPAzenKVNpIvqcc1h7O/p4B93Bqxj+tjnHCF7xv38DCU8TS3bIWOlTMp7r/IYWaz4yQwPR3hbJn6W5QN8MtNC2PjWWvMRznU+BI4BLgdlmOIAuCX1WSHW8kKpbruJrDbN4cR7NsyDLgeuEVF5uorJY8gsr1FiKhR3QrZi/y3UglrwjbzJqjOnplcBFJCY5fwLHe64zMQ+8NfxCzQam5XFBIqQajTZajPMqusNTWv7GnussQfsKHdZGGUbyMTG099CpQqqWshEsHWMTYF3z+0vGC8iSPirRJcVnAeeihc/D0BlqIx9+mM1GjmSlDTag54ABVAZ0nljaM2riGBH7NOBJM1QN3CikGu3LUpuDvrbtk+antz41lrzFc52o5zovAh46Y3SZ2VSNLjMSQqq9MyBgdgnPdSLoa6JHfMMB4MIUzMMtlqKnvP1dLJb8xhh6bgu8lgcTXUv+kQnPgg4jpFoduAHdmSzOB8AlnuvMz0lQK/IZ2guit7m/I9oEt6AQUh2E9rWI8zZwaQbOD08DBwPHAdelcoDnOj8Lqd4BDgUmpDmeUsOfzTQpZ1EUJ1sA/wGC6MleAGg0tyjAoYcSPOggKsvKKIvfgH+i/VyWmtti9Dn4K+BhdKv6TmMmdjcIqX5Cr8aXoT/rqwmprqZ5i+55XXmuJGyLbkve47lOLTrT7Dl0SdQ+6M9If3TGzcFCqts81/mo9UfJLmYB43Yh1XxgjG/TyUAfE6+9treUJHmlrFosnWQTdMePN9vb0VKS5Lz0SUi1F3rFaJgZigLjgTF5JNLguU4jzcsHC65ER0i1N3r1P87HwD8912lI93OZ1O3xgCekGtKBQ+8H9i3kjKVcI6QaBowwd981rV8t6WEwugtcX7RoW4k2O+8G9EIbf9YEg/SsqqKyooLyYJCyQAACAarNcaugy4M2B/YAzkB/Fh8nDdeenus8jfa4iHfF+xtwD4mMnnRnUU4n0X0KbOmTJY/xXGeu5zrXAocA7/k2rQWME1Ldlm8tsT3XmQhcTfPP2SjgMiGVTSywlCRWqLEUAzug6+LzZoXAklfMAbrnokZbSFUtpLoUuJyE8eY84BTPdR7I01Uif5vuzYVUNTmLpIMIqXZDd3iK8xXa9ycjvhmG/wA/0jyDp01MCceD6DIOS+c41Pe7zaZJL8eQ/ozruNCzF3BJOh7QZAUcScJkeAO0eXgV6TcUrgf8YmBISNWztf0tlnzAc53pnuuMQYuaP/o2bQ88IaQ6X0jVLzfRrYjnOlOA89GeY3H2RGfR2XJhS8lhhRpLQWPqV3cEPjQpnxZLMovRX/pZ9akRUg1Fp/r7Ow59ChxiupjkKx+SaEVbRiJrIa8RUu2AXo2Lf6/9AJzuuc6y1o/qOkZsuwPYRUi1fgcOnQKsIaTaMCOBFTFmYrGnuSuBT3IYTjFyALrcKRP0QJcKpoUWTIZ7ozMXt0/Xc/iwPjWWgsRznQ/Q2Sn+8sAyYH9gipDqKCFVS+bgWcdzndfRJVD+7+4RwJ1WHLWUGlaosRQ6q6HTtN/OdSCW/MTUP2fVUFhItQe61GkNMxRDG8+elO+Gp57r1AHv+obyvvxJSLUlcD3aTwPgZ3TW0uLWj0of5iL4I2BMquaHRuC5CTgn3wweC4ADSJhxP5KmLl6WBIMy/Pj9SeP52GSojUH7aUXQ17ajhFRHptmM1HZ+shQsxnB4CrAvuvw2XibYHZ1xI4RUf8mH7yOzmHUisMA3vAlwbz5lAFksmSbnH0aLpYvsaH5aocbSFlnxqRFSVQqpLgCuQV/8gL7QGOO5zvg8LXVqCX/509amW1VeYjJSbiYxcZ+JFmmy7f0zFtgUbWyeEp7rfIaOd69MBVVsCKmqgQPN3VnAf3MYTrGS6c97I2nO2PFcp9FznevRpt5RdKnVacDlacwUsEKNpeDxXGeZ5zp3owWb/0MvJIEWaK8GHhJSbZyj8JrwXOc74Fj0eT7OWsADQqrBuYnKYskuVqixFDo7AN95rvNnrgOx5DUZz6gRUq2C9h3Zzzf8JTDKc533M/ncGeBdEjXilcB2OYylVYRULrrsKO4/NBs40XOd2dmOxXOd74FX0Fk1HfluvR04VkjVIzORFR17os1sAR4zBtiW9JKpsqc45cCSTDyw5zpPorvXxSefewJ3p2kV/oek+7b0yVKweK4z23Ody9Hd8/zlo+sC9wupbhBSrZqT4Aye6/wCHA384hteFd3Zao2WjrFYigkr1FgKFnPhtQHNu9RYLC2R0RbdQqqdgUdpvsI6ETghF6JBV/FcZynaqyZO3pU/mXbnd6E7vgHMB072XGdm7qLiLmAougNNSniuo4AX0AauljYwAljcRHgp8GwOwylWqtCtuJsRiUSIRCJEo9GmWywWa9rW0NChpmoVZEioMfyIFslnmPsbAhOFVGt25UFNKaX//LJGvvh6WCydxXMdCZwEnAX86tu0M/CUkOosIVXvnAQHeK4zC51Z851veGW0mGQ93ixFjRVqLIXM9ugLSivUWNojI6VPQqoKIdXZwA1ok0yARcCZnuvcUeCr/f7yp+3zaUIipAqhRZE+Zmgxutzpl1zFBOC5Thh4Bjipg3+vicDOHWzxXYqMAOJ/I2EERUt66YXuotiMYDBIMBikrKys6RYIaD3nm2++4cknn2wSblKgEe0lkynmorPsjkS3BAdd1jFBSNVVc3R/+VMQWL2Lj2ex5BzPdWKe67wFHIT2e1tgNpWjTYinCKlGCakqchTfArRnjb+7ay9gvJAq5XJji6XQsEKNpZAZga5dnZbrQCx5zxzSXPpkaqTvBw7xDX8LHOq5TjF4Jr2F9noA7VmxVQ5jaUJINQAYjzYkBd0Z4jTPdZLLEnLF/egLyINSPcBzneXo13RGhmIqFg43PyPA47kMpIjpTZKI8vXXX3Prrbfy4IMPMnnyZP7973/z+uuv88EHH/Dbb78xadIk7rrrLmbOnEl9fX0qgs3y9nboInPQmXZ1aJ+aZ8x4N+BmIdURXTAZtp2fLEWL8Xp6CtgH3bUyLtr2RmfcPCWk2jnNJt2pxrYM/R3pX0SqAm41DRwslqLDCjWWgsSsVm8NvGU7flhSIK0ZNWZVdhKwnm94MnCsKWUpeMwK1me+oZyXP5lyx/HoTm+gLyLP9Fznm9xF1RxjYjwRGN3BdPFXgBoh1TaZiaywEVJtAGxk7r5i0uEt6acXSULNSy+9xM0338yTTz7JpEmTuOaaa7jwwgs588wzOeSQQ3jxxReZM2cORxxxBNtssw0TJkxo7zmWtbdDF4l31lvJZDVeh856jKKzcMcAl3UySzBZqFm701FaLHmK5zpLPNcZi27f/Ypv0yroz9J9Qqr1Wjw4s3HVA+cDwjccBK4WUh3Y8lEWS+FihRpLobIF2vDQlj1ZUiEtZsJCqnIh1RjgFvQKE2ivjPM817nFc50OGTUUAP6Vq52EVMFW98wwRvSIe8CALp8413OdT3MVUxs8io7vqFQPMILzTcCZQqryDMVVyBzm+31SzqIofnqRMOIFYOutt2bw4MEsXLiQffbZhxtuuIHzzz+f448/npNOOolVVlmF/v37M2LECLbddluGDRvW3nNkumRtjvm5EjSVdTyJFmji3jh7ocsmOmoybDs/WUoGz3Vmeq5zIfq77Evfpo2Bh4VU1wipnCzHFAX+hW7eECcAnCekOj4X2T4WS6awQo2lUBmBXpXLx0maJf+YA/TrYDeeZpiSm3uBI3zDEl3q9HrLRxU8b/h+741uP511THvwsUDcDDQKXOi5zru5iKc9TCnTPcDBQqqBHTjue+Br4IBMxVaImI5q8Yyuj4z5pSUzrJAFtsMOO3D33Xezww478PHHH7No0SL22WcfRo8ezWGHHcaQIUPYZJNNuOyyyxg7diy77LJLe8+xKDOhN9GUUeMf9FznA/SE83cztBF6sjm8A4/9Jwn/DoA1u/K9YrEUAiZr9VjgPBKfH4A9gGeEVKcJqXq2eHBm4ol5rnMXcFvSpuOBc+1n0lIs2DeypeAwavkOwAcmDdJiaY/56PNdn84cbMzqJqO7h8R5Ghjtuc7vLR9V+JiOVf6yoqyXPwmpqoBbaV5mdmUBiGPPAX8AJ3TwuLuAUUKqvukPqWAZReJ6xWbTZJZeJHV9CgQCbLrpplx44YWsueaajBs3jgsvvJAvvvgCgCVLllBdXU1jY8re6YvTGvGKtCjUQFO73yNJLPI4aJPhHVJ5YJP55hcKu6PLQSyWosaII68DB6KziuOCayX6MzVFSHVANjNCPdeZBFxOwk8PE99VNjPVUgxYocZSiLjAAGzZkyVFjKC3iA761AipgkKqk4HbSYg8y9DZHNeViFA41ff7yGyuVJkOEzcCm/mGr/dc5/+yFUNnMd4YdwJ7CanW6MBx84An0B0uSh5T8ra3ufsT8H4OwykFeqE9H5oRjUapqanhvPPO48orr+Snn37i4osv5o033mDmzJn07NmTYDDlysgF6Qy4BeJCTYvne891FgKnkPC56A7cIqQ6PMWyCVv+ZClZPNep91xnMtpweDK6zBf0NdI/gceFVDtkqwTJXA+cQ/NudXugP9PV2YjBYskUVqixFCIj0DX0eVn2YMlbOmQoLKRaGRgHHE1ihflH4HDPdV5p9cDiwy/U9AfWzcaTGj+cawB/6807TEeKQmEq8B1wagePewLYVEhlO8poM8v4xfYkax6fcXoDK7TgLSvTl4uRSITNN9+cxx9/nF133ZUrrriC6dOns8oqq3SkPff89IXbIvPMz1Z9yYyQ+i+0L1TcZPh04JIUTIatUGMpeTzXWeS5zi3oUl1/huswdBbsOCFVVj4bprX4qTT3v9rWxNARU3+LJa+wQo2lEBkBfGW6q1gsqZJyi24h1RbolSJ/JsdzwFGe6/yagdjyFs91fgOm+4Z2zvRzmqydS5Oe6wHPdSZm+rnTiREV7gB2EFKl7O9jTKlvB84pZWNEM2GOtzmfC7ycw3BKhV60INTECQaDRCIRYrEYZ5xxBuPGjeNf//oX2223XZOYkwLz2t+l8xgRZgHtCPOmlONxdMvf+ARvb/Tkrq3SQ9ui22IxeK4zw3Od84Dj0AsTcbYAJgmpLjcef5mO4zO0R43//LIhukNV/0w/v8WSCaxQYykozMl+bWzZk6XjtJtRI6QqE1Idi/YJiXcDqQMu91znKs91ajMcY77iXy0bmUnxwDz2ucCevuHJwN2Zes5MYi4e3wFO68jfzXOdd4BasiCM5TF/ISGuPlEipYa5pi9JHjXJBINBAoEAsViMddZZh7/+9a8MH56yH2+MzGfUQAc6/Xmu8x7aZDhshjZGmwy3VrL4G/qzGce26LaUPJ7rfI7+HF2M9mcDfS7ZC3hWSHWiaQyQyRgk2vRY+YbXAB4QUq2ayee2WDKBNVqyFBpxwz8r1Fg6SpsX7qZN61XAVr7hn4F/eq7zU4Zjy3emolfLAFZFX/j8mO4nMULGaTTvejQFuLXAS17GAo+jzZg7YoJ8C3CrkOodz3XqMhJZnmLeC/GW3LVo825L5lnhHDlr1iwCgQCVlZWUl5cTDAabsmeCwSA77rgj119/PXvttVcqjx/3C8sYoVAouPdxp/X58avP1jvtw3d/QHvadUdn2fwAvA08Gg6Hm4zST9t58xgJLyTQ2ZR/HxMI1MdisYVoof8r4CPgibGvf/IDCXP5fkKqlT3XibcFJxQK/R3Y0TzOEHTZaLV5nC+BJ4FHwuFwiw7MoVDoF2Boii/54XA4fFSK+1osGcO0zn5ZSDUVOARdOt4dqEILKPsKqcYDz5t9MxHDb0KqY9Aecaub4cFoseZUz3V+yMTzWiyZwAo1lkJjR3RrwF9yHIel8JgDbNDSBiHVJsC16IvpOC8C13musywLseU709CrzSFzf2cyINSgL+r87c//A1xb4CINnutMF1L9H3CqkOpNz3UiKR73i5DqbeBw4P6MBpl/bEPiIvs5z3Uy3dLZolmh5OfUU0+lvLyc+vp6ysvL6d27Nz169KBnz55069aNJUuW8M4777B06VKqq6v5+9//3lYZVCMZ7PoUCoW2Bh5+/r6x/nKkBvOcKwHbmdv5oVBIAIeEw+FmmVrlFRV1Vd26BwGi0WhZ7dIl/WKxWH9gHXQp3g03nnT418dffWugZqWV40KLi/6OifMvmneqWwxE0F2mHHS22KmhUOhv4XB4VhsvqRZY2M7Lbm+7xZJVzMLCQ0Kq59HlSB66imMldMbNwUKq2zzX+SBDzz/bZEffTuK6rx9wr5DqTJP9Y7HkPYEOmL9ZLDlFSNUNeA142hiYWSwpI6T6C7Cf5zrH+cbK0JPgU0iUgtYDN6Anh/YEaRBSnY7+WwFM81znkDQ//ijgLN/Qm+hsppR7/uYzQqpB6C4zN3muI9rb33dcL+BR4DjPddqa0BUVZtV1C7TR676e64TbOaSrBNBlOaXO28D2/oFVVtHdp/fcc09qamr4/fffWbRoEbW1tSxZsoRwOEy3bt2IRCLU19czY8aMth5/EXplPe2m4CaL5SmgqqKyaulO+x+y4KdvvtzrrZf+7wuzPQhsAuwHnIw2Tu4bDocXhEKhYegMSggERo997eN6tHdNGUB4+rTXbz519McNdbWjgb8C9OzTt+GM2+/738BVh9UD4zzXmeCL5XK0uP0u8HM4HF5uxgeb13+Zeez/hMPhv7TwWn5BZ9TYbBlLwSOkWg1t1r190qb3gNsylbVs5g03oIX/OPXoa4u3M/GcFks6sUKNpWAQUu2E7tBwouc6n+Q4HEuBYQyCL/BcxzP3a4AraH7hMAP9BW5TY5MQUm0ITPAN7eu5Tpszsg489j7oVbY4HwFnFJsniZBqDNp7Zx/PdZZ34Lh9gc0817m43Z2LACGVG4BHy8sCZVXBsnd3X33AzUC3Fm7dWxnrhZ6E9zC37kn7VqNT8auASrR5bhk6e+Ed9PfMf7LyYvOPL0mU9ADwxhtv8Mwzz9DY2Mjhhx/OttsmGrHNmjWLzTbbjKeffpr11luPuro6Vl65TSuwheislLT+fUOh0JrAJ+j/9+9Ou3n83WttssWRwAGe6/zcwv790Oezo1YQamB0OBx+SEi1LXAd+r0D8Blw3mk7b747MAmoWHnwKrUXP/T0t8Hy8tc81/lnB+K9FrjA3F01HA7/nrT9F6xQYykyhFRbogVQf8ZbFF3ifLfnOmk3GhdSVaCv9XZPes4rPNd5Id3PZ7GkE2smbCkkRqDTh7/IcRyWwqTJTFhItQHaoNYv0vwXOMyKNK3yDc1T+0em40GFVHsAF/mGvgTOLjaRxvAQWhgY1cHjngNWE1JtlPaIcsfawM1o348ZwJ/obIvafdca9P0+aw3abM/hAzfebbX+x6Hfex+i/X3+D5018QhwL9r/52Z0qcmVwIVon6Mj0a29/4oumd0CWB/trxRCnwt6ocWa+LVQNbAr8CzaFLMU6Zk8sNNOO3H66afTr18/brrpJm666aamrJnly5cTCATo3bs3vXr1ak+kAZ21lInSp6vRIk0tsO9am2wxzYy36EsWDofnhcPhfWijbMhnMjzTDG0KPDz29U8+Ka+ouARgzszfq9+a8sTKdLxFt7/kI9TqXhZLEeG5zkdo77ErSVxPlKFLo6YIqY4WUlWl+Tkb0AtB/iy+MuAKk8lrseQtVqixFASmRGUH4L1iKYWwZJ25QHch1VFov4+BZrwBnRp7gec6S1s5tuQxxn9TfUNdFmqEVDuiDZzjXWa+B07vSLZJIWF8Vh4Ejmyn/W/ycVF0lsc55lxY6ByBzk44De0fsAo+4SSgoUz/rEJnxfQCaoA+5laDnpj3RGfJVJK+a5pu6M5v3dL0eIVEj+SBWCzG8OHDueaaazj00EN54403uOKKK/jggw/4888/qaiooLKyMtXHLyPNQk0oFBqIFuVAmwT/gD7fQzudn8LhcJtp5aYkI/5+BS2qPHjxw898WN29Rx3Ax/99eSVgFSHVCn+7Nog3RogBpW5WbykhPNeJeq7zPLAvcA+JDmrd0SWJzwqp/pbO7zrzHXoDcF/SprOEVCdnspOlxdIViuGCz1IarI82OXwz14FYCpYY2sTxTCBoxmYCx3iu86T1o0kJv1CzgZBqQGcfSEi1FbqsIP499BNwmuc6S7oQXyHwBLAEbZycMsb8cAa61Wkh4wLj0SJIRY5jaYsIzVvElworiA3xVtwA++23HxMnTqSmpoZLLrmEcePGEQwGKS9PuTdFkPR3fRpJ4jzyrPkZF2raTfFpD891FqB9zJ43Qz1WGjT4hiHuOnMAZv40rUd9bW2A5uUcKxAKhXqGQqH1Q6HQDcDZZviRcDj8Z1djtFgKDc91lnuucx+wDzprNH4NNgCdcfOwkGrTND5fzHOde4AbkzYdDVxQJIsgliLDvikthcIO6Avn93MdiKXwEFKtgzZk7UGi292b6FKn73IWWOHxGc0nWTt15kGEVBujy1XiE/XfgZPNhKioMd0wxgMHCKk6WvJwB3BsB1fu8419KYxrjx7oLj+lRnVLg4GAXnCORCL069ePm2++mXPPPZcZM2aw9dZbU1WVcrVCOekvffJ3V4p3c4mXVbSZUZMqpnziKuBW9ISybLX1NuoOEGlsDMxR4UpaKH8KhUJbh0KhWCgUipd8fQ2ci/bImACc2M5THxQKhf5o57ZtO49hseQtnuvM8VznKuBQtD9dnHXQXZpuFlINSePzPQFcgp5TNA0D1wqpUk4NtFiygW3PbSkUdgQ+81wnY209LcWHSWfdH91NqALdGrYMuAV4zGbRdAzPdRqFVG8CfzdDI4EnO/IYRjS7ncSEcBZwkuc6c1o/quh4AV2nfxLNTZTbxHOdP0yb73jb0UJkJK2IAV0lFos1CQppoAzdzrWUqCCRbdgiwWCwKbtm9913Z/fdd29r95aoJP1CTZMYU1e3w4JAgOqeNQOjd7z4Z2zxwsCQwNoMRr+28lZ+VnTrdmeob99TAVi+/B+bBgIsjW9L7OeUAxVrblT/0Tqb1f9t9q9O0zX0ey/V9Vu8sNfB+z1MPdr3ZgGwYKWVtupeWfnhLPO27GteP+iSj+vj3aDaoJr2Py92cmkpeDzX+UFIdQqwLbpD1Opm047A9kKqp4H70rGg47nOS0KqRehyqLjKvCvQS0h1ruc6y7r6HBZLOrBCjSXvMavOq6Nd4S2WlBBSdUdPgv0ziUXABM91JucmqqJgKgmhZjMhVZ9UL5yEVKsDd5Ior5iHFmlU2qPMYzzXiQqpxgK3Cakmea7zvw4cPhF4XEj1rOc6v2UoxEzS5qQzGo2yaNEili5dSkVFBSuvvDJlZe0n4ESjUcrKyvjmm2+4/vrrWX/99TnppJPo3bt307Zff/2VG264gddee41gMMg//vEPTjnlFEKhVhObenXi9RUyvdCeXW1O/ONiWCwWIxqNEgwGm1pzV1dXtymWxWJEysroT5JQQhsiShvbyoGKlVbaeuOqKu3NO2/evRcCjUsWlvHEnT1rapcFRqBLDduksXFIn/jvkYgzhOZZOgAEy2NlA1aJrNyte2zQ4vll88uCZU1vnKWLKmpql5atQVLHrLlzBcDdOoaGRdXV/6nq1eumXcvLp50IgSMHDNjppD//fOOJWIzWzNNt1ydLyWAWz94VUn0A/AOdcdYPLSAfBOwppHoAeKKrDQc813lXSHUycBuJc/1WwHgh1RjPdVo1GrdYsoUVaiyFwAjz862cRmEpGIRUawLXA/502XeB+YA1DO4aHwDL0R4jZejP5/NtHgEIqVZFl/zUmKFF6HKnQhQb0sG7JAx1T0n1IM91aoVU49BeS2dmKLZM0upKZX19PRMmTGDcuHEsW7aMoUOHcvzxx3PQQQe1+6BlZWVEo1Gef/55pk6dytKlSxk9ejS9e/emrKyMWbNmcdFFF/Hbb79x8803s2TJEm655RZ++eUX7r33Xnr2XKHZEbTg11LIBAIE0BOeFsWQSy9l6GWXEUlBFzOPF2gSZd5//32uvvpqRo8e3eb/V309UeD4rryOZKLRvg3x34PBGd0aG9dbDNBYH1heUdnUWrvDVFVHKwYNiQxcaVBkUO9+0UE9esUGBMpiTdfNC2YtbMrI/G3aSouregQayitj5Y31gZYaHvSEip61tXtRW7vX5336nFXdvfsTu5eX/3J/efn3GwUC6/yJycIBFjhOZVUgUE802rNbIEA3oDYWw2aAWkoCz3UigBBS/Qfdee1QtIDcE51tc4BZ7PhvVzKjPdf5Ukh1PHoBKZ6Ztx5wv5DqFM91ZnfhZVgsXcYKNZZCYAfgJ891fs91IJb8xpQ67Q38k8SqcBQYh85EOAZtKFy0hEKhILAf2nR2a7QxX3f0BOAH4G10Z5RvfMcMA35u4eHq0ILKHHQb5Y+AJ8a+/sm76DRh0D41zYSaUCj0d3S68mbAEAKBAWVlZT269+zVOHDIass33H6nP7bY9a+jj9x6vR9beQ2/AEOThhtNLAvQ3aE+A0Q4HP6izT9InuK5TsxcaD4opNrKc50PO3D4q8CBQqptTQvhQqJVoXTy5MnceOONnH/++eyxxx6MHz+ea665hqqqKvbZZ5+mzJhk4uOvvvoqX3zxBdtuuy1LlzZ/mrfffpsvv/yS2267jV122QWA/v37c8455/D4449z7LHHtvT4Gc2o8QknXcoo6eC2VtNdPvmEAfX1lFW3U2gTL33ymwwvWLCAH374gfnz57d5bEMDDW3u0AkaG9f6E14CoKrq7UFxoaa+PrCsZ+9ohw3PB6/ed/gWw5et3q1HdCVa+XuVV9Bt6cIfjMdWJcuWrjqgrq6sZ2VVrKIVoaYZCxde/nG3bk/vEgg0VPbsed8GCxbc8j5a/HYAYrFu3QKBehob13bR32d1gQAL8JVVmVv8/jIr5FiKDdOJ8y4h1TPojlB/M5sGA/8CDhFS3ea5zlddeI5pQqqj0deJ8Sy51YAJQqpSXkyy5AFWqLHkNUKqnujJ3iO5jsWS3wipugEXkPgiBy0wXOi5zmdmn7nodsBFSSgU2hp4mObdRxrQnhArAduZ2/mhUEgAh4TD4eT04UXojBnQk8g+QH+0sd9BwA2Xj9r7zTPHTiivWWnlRmBrIVX3pJruf+ErHaiorGyMRaMsWbigYsnXn1dM//rz3s+Ov/WhC+Fv4XB4VhsvqRY9EQE9YeqNToNeHd2R55JQKPQBcEI4HO70hVqu8FznayHV68AYIdXhpoVoKsfFhFQ3AlcJqT7yXKfdiWEe0aJQs2zZMp5//nn23HNPjjvuOAAuvvhi/vjjDyZNmsQ+++zTaglUWVkZy5Yt4+WXX2b48OGstdZajB8/vklEAPj111/p06cPm2++edPYmmuuycCBA3nnnXc49thjV3jc+npqqqoYQBrKc9rYljdtYVdemarWJvuRSIRgUNvX+Eub4mN77bUXe+3VfkOyurpWS3w6zfLl+/zcs+cdsUAgFqiufmWdpUtPnAbQUMeyYHmsuiwYK4tGAkmfrRj9BkZrBoQig/r2jwyqrK4eMu1jvaVmpW6hbj2irXrHBMupqKyq7bFs0QcBgPKqTejWo7KxZ010ufolVrYsBQeeWKx3YyzWbXkgsKRnMPh7Kl5IVcBAc2uJhkCgmYiTLOgssUKOpVDxXOcP4FIh1ePAGUC8G9SGaEHlVWCs5zozO/n4YSPW3AmsaYYHAQ+YMqjvu/QCLJZOYoUaS76zLXqyaMueLK1ivE+uI2E+Bzr742LPdeb5xuaQhnat+YjJYnkKfUE/F7gJeCYcDk8z24PAJuhsm5PRXQ66wwoTp9PD4fBDSY/tANugTWz/OvePmTtfd+wh9WfccZ8cuOow0OLPq75DngZuX229Db88+fqx51V37zEMYK6ayX2XnvNZePoPx6AvtB4G/tLGy3oi2Z8hFAr1Rou3hwBHoLOGPg6FQvuFw+H/a/OPlJ/chf5/2w34T6oHea4jhVRfAgcCheS51KJfSG1tLX/++Sf77rsvAA0NDfTo0YORI0dy66238uOPPzJ8+PAVjotnwbz++ut88cUXPP7447z00ks0NjbS2JjQr+JGw/420pWVlXTr1o3Zs2fT0NBARUXzbuEzZ7Ia+rNSEvTpQyVJwlH87xYXZJRS1NXVEQwG6datGz169KBbt27N9m2LTAg1jY1rLWloWO+byspvNqis/HT9qqrX/l1Xt4vq0TsWHrhqZPWVnUh4/uzgwrU3re+/2roNQwaEIsP6rBxdvbI61iMYjMbKKwKxJQtqI3Ghxt142ZItdls2v7yCWLA8FiuvIFZeHouWBZleURkLB4PsNuXeexujkfmDAIau/ff6PgMb5+283/I5J14ZuefwzQf9iRa4+6BLPfsk3Q+Wlc2qDASWddd/t+51afgzVKC/21r7fov4hJyWsnIWxWKkJBSXKAHgOLRnytrozl9zgV8BaW6/mtsvwGywwli68VznOyHVCeiM3TEkytt3A3YyQs6EzjQe8VxnrimDuhXY2Az3Be4RUp3luc4nXX4BFksHsUKNJd8ZgfYV+aa9HS2liZDqb8CFJExKY8C9wAMtZCjMIU3tWvOJUCi0JjAJLdJ8B+wRDoeblQqGw+EI8AnwSSgUuhHdGjYlwuGwAgQgQqHQgcCkJQvnV959wRlrXvzQ098Gy8tH4hNqwuHw5aaF9HhgmBmOruQM/udHb02dGgqF5qCzn/YIhUKrJMfaTiyL0IbGU0Oh0J3A/wGrAo+HQqFN4sJUoeC5zq9CqmeBU4RUUztokDgeeFhI9ZLnOm3XnKQBU6pTThcySj78kIFbbrniY8diMebPn8/gwYPNc+kJ/9ChQ6mtrWXGjBkMHz58BTGgrKyM+fPn89xzz7H11lszcOBAYrEYjY2NRCKJ7qurrbYaSil+/PFHNtpoIwAWLVrEjz/+yIABA1i4cCErr9x8jltZWVrXSH36UOXXWeJ/65kzZ/L000/z6aefMnfuXACqqqro3r07Q4YMYccdd2TLLbekT58+qTzNInQJZgO6nLGlnx3dFq2s/OYx4NNAoLHnSisdsd/wjTb9x8lX39sHGPG3w5btiD5HdIsHsXjB/LKJ114y8MiLrv6lZ02fyKwZ9U0GyjUrNTYOHhZZij6XfoFu+f0V2rPo4fdenNLtjacnDALo1nN4tKb/vvPLK4gMHtZYV15B1eDBoaXhcHghetLeDPMZ6tm//1+uCgSiZQCNjau8CvxIQsypSD4uDQTR2YitZe9EAwEWQatZOYtiMQopcy/dPIAWxf2+Vd3R76vt0e/D5ejrjyr0eW82MAOYhi7XjYs4vwKK5u2hLSliPGneEFK9i154Oh6dbVsBHA7sLaS6F3imo9mmnussFlKdil74294MdwfGCqku8FznjTS9DIslJUrqIsRSWAipytEr9W+kWhJgKR2EVFXAucA+vuF56Cyaj1o5bA7QV0hVVmTvqavRFyq1wL7tCR/hcHgesE8oFOpw2UU4HH4yFAoNAW6cM/P36remPLHyyP0P3V5IVRkXGYRU1ehOCuuaw2LA5Z7rTDX3P/A9ZAjolP9UOBz+KhQK7W8erwdwKfpCrdC4D13K5QGPp3rQfms78x/55I+nIo2cFghwG9nxOukSP/1Ev803h+Qqpnj5UrUxSImLMT179qSyspKFC1tvwDF16lS+/vprXnjhBQC6d+9OQ0MD9fUJzWuTTTZh8ODBXHXVVYwbN45YLMaDDz7IvHnz6N+/f4uPW1GRkQlzLoiSguCx7rqsVF6eyAIIBAJMnz6dCy+8kJ9//pn11luP9dZbj7KysqYMqHfffZdHH32UDTfckBtuuIG11167zUAGD+aHWCz9GWBCfjJn4rWX3PDJ6/+5LBaNrheePu1/Lzx494ItdvtbjwGrDNkEWBqJRPj1+2+6ff7Gq33ff/n5AXXLljW1Io9FY02lm1+8+dqDO3kHX+cXTf9y4KjV195860c+f+PV9X788rMagPLK/pHQWuOnEyjv5WiRBvSCwaGhUGg/4CHgnXA4PBsgFAqVDR7M+sCp6OwMgHd79nzwyoULr45Bk5DTPRBYcgxQU1amwsCHNM/KibcTTidlvsdP9gcDiAUCLKFlEWchsCAWS7//UJ6wMXAwPqGvBeLnSD+DzW0r9GdsOfqzWEEi8/V3tEj3PVrE+QUt5PwORfv3TAue6zSgOyC+CByN/j8qR2etnQscJKS6A3izI4bDxrD/HPT1RLyUvgK4QUh1tec67TZPsFjShRVqLPnMRmgzR1v2ZGmGkGoIuqvTmr7hz9B+NHPaOHQ++oK0L/oiqeAJhUIDgf3N3UfD4fAPqR4bDoc7m5p9J4HA+cRiK33835dXGrn/oXPQF6NvC6kqgRvRZVZx/uW5zou++zuYnzHgp07GAEA4HP4oFAq9iBY6DgiFQseHw+FW/SUyiS/jpIMiiFM+6sxFn/QfHPnn0dtFIgvnBqOpHn/U1gPLdvaW7zcg1Nh/drg879/TtbU0RqNEysoI+scrKiqIRqM0NDSfm1RVVdHY2NjMb8bPzJkzefbZZznhhBNYaSWdLDdgwAAikQg9eiQWv1dffXWuvfZazjjjDDbddFMCgQBbbrklo0eP5qWXXlohm0bHlLFrJL9w0tnMkpSPj8VSXrkfTFLp07hx41i6dCk33XQTI0aMaPGgb775hjPOOIMrrriC22+/nQED2vTv7XLmlzGNXwU9gd7E/BxyxIVXscXue0577KarV5s/+4+qlx+5f+WXH7mfYHn52hVV1ZG6ZUuDTe+jQIBh627wXbC8/Abgk5tOPrIRcy6a/vXnZ5+28+Ynn6b3LENP/Cq/fvdNfWhZGX3677FsydJLBWUrrwXRXqsOb6w14XVD/w3/bm6EQqGl6El6b5q3Pn8dOMB/HjY+MktDIZ0OVl4e3nnw4NBm/tevX0JZGQQCECiLxXrM+eOP7y4lUWbV6U5XbRBAX4/1QmeRrLhDgKW0IuKghZx0lHjlgjG007I+BcpZ0Zx8gLltij4nLENn2ZSj30cLgDAwHS3k/ExCyPkNvTBT8niuswi4TUj1FLqLYrzRwRB0CfhnQqpbO+Iz47lOo5DqcvT79xAzXIb2yenjuc7EtL0Ai6UNrFBjyWdGoC80O9INxVLkCKl2By6m+cXoBOAe09KxVTzXaRBSLUSXP+X9pDZFRqIvIACezcYThsPh2lAo9Bpw4MyfpvWor60NVFZXjxRSvQdcg/aziXOb5zoiFAr1RJdBHQGcbbY9Eg6H/0xDSC+ghZoqtGfNVFhBOEl395zWfnaKZ8b3XLrzfstr1liv4cjP3gqmXAsfaQxEf/iy4oN1t6zfdvazwX/nkTdtiyxbRkNLpqbl5eV0796dOXOa66zxTJtevRJznGg02rTtjz/+YOrUqUSjUaZOnUptbS2zZs1i+fLl7L333my33XbcdtttxGIxtttuO1577TVmzpxJZWUloVCISy65hFVWWaXpcf2GxZWVBNCT97SKKR0QTrJNL5KyAj744AOOO+64JpGmrq5uBVPn9ddfn9tuu43DDjuMWbNmpV2oEVIF0aJ8XJTZmFZKWNfZfOullz363DcfvPR83+8+fLffzJ9/rFk8fx71y5cHKiqrllVWV/9MIPDf5UsWj3/31Zdl/LjTli8b5nuY3uYGUF9ZVd3Yq99K0UFDVls+ZO11lwwc6i2f8sBaX/76Q9X0VddcvnpZGbEhazYJNdXo89Fx6I54G6MNgPuixZqfgI+Bx8PhsF+8bo1qEmW9gJZn8NnJBAILF8diPOHbXsmK/jj+31vsRZ8GephbqKWNgQC1tNyxKv778jw1PN4GmgvLGaCMFf9f4qVqGwD/QAs5jSaWbmi/LwncDTzKin5zJYXnOmHgfCHVhsCZJBpHbAo8YjJv7vJcp60GBv7HiwqpbkG/P0/ybRojpOqDNi/Ox/erpYiwQo0lLzErZiOAj5O6yVhKFJOpcSZwgG94IXBJB1sUxw2FU848yXPW8/3+eRaf90vgwEhjY2COClcOXm2NndCrjiPjO7z93FOvPnn79Y+cltS1TU9UqyfPn3/7hYEAg0gSQxynoiIQaKCxcXD/QIDtaUco6dHjqqE1NZcAsHTpEScFAmxFwkulIKirLWuY8WP5Z0PXatyyZ030uyULy1I+7/0qK2YMdRvWG75Bw+o/fl3ZpQylTLN8OQ3RaMtCTSgU4ptvvmH//fdvEgPC4TAArusCuhzH71EzcOBA/vrXv1JRUUEwGGTQoEHU1NTw888/M3LkSLbffvum4wB69+5N7956Dv7zzz/z5ptvctZZZwGsIEB07040FqOUVk77kRB9Ad3C/LPPPuOoo44CdIZTayxfvrzJWLgV4gasbWJKJ9cnkTGzAR3IEgkGg43b7bXvW9vtte/XaP+KyZ7rXNnWMeFw+BdaUDmFVEehS5WaePSWXl9/93HVrwDRCI39Q5H66u5NKV/VRny+39w6RTgcHtbZY2Mx6tH+KLNb2h4INJWH9KFlQac3mVF8q9GddAa1sr3etCBfQMtZOUtzJOS0KDxlmQDN/XFA/19tiS4xvgL9eZlHieO5zlemg9Nu6M/uYLPpb8CuQqpJwEOpzC2MEPOAWeD7J4nPxRFAjZDq2vYWCC2WrlAwF7GWkmMoOr320VwHYsk9QqoQ2txtHd/wV8AFqa6O+JhLcXV+8q8sZ/Mirem5li6cH/zszaoN/lRBt6Ge+khDIPD79PKP338pVF5T030xEAgE6roFApEgQH399p8tWnT+3IaGTY5r6YFjse49AoGFRKODQyTSmFulsXF400pkILCsL0kr0IXC959Wfjd4tcYNaLpRhAABAABJREFU1tuybrMPX+32dkeO/ebDqvc336n2L7/Kit8a6gPpNv2MkqYynW22YWF5ObuQlLkRDAbZbrvtePHFF7nwwguprKxkwYIFPPPMM6yxxhqsuqqutmhoaGDq1Kk4jsMGG2zA4MGDuf322ykvLycYDBIMBvn44495/fXXOfjgg9l0U93Ftb6+npdeeonq6moGDRrEN998w/jx4xk+fDgHHXRQa6+7q+UOhUZN8sDpp5/OKaecwpgxY9hrr70YNmwYNTU1TR2y6urqqK2t5aqrrmLddddtL5umAVihG4tZnd6IRMbMOnQsg2EZWjj+Ai1Wf+e5Tq157H+w4gQ3JUz2ZjORprGBN569r8c8zHsjEqFh1TUa/SUobSpV+YAxBZ5LK6JZIEAQLda01LEq/rOspWO7SCWJcqCWaExqQb6A5oLOkgx0rupGOyLhJ598wquvvkpNTQ3bbrstG2ywQVOXtCzRE30+fQzYI5tPnK8YgeUVIdUbaO+ao9F/p0rz+z5CqruB51IRWjzXedqINVeRmDv/A+gtpLqog00ALJaUsUKNJV+JF8N3aLJiKT6EVCOBy2ieFvwIOoW1MxPSom3RnSuCFRXOgrllvaZ/Ux6IRonW9IstreoW23TEPn/ZKBr5y+/RaKAx0hhprF06PThP3TuAec9vMWDA/pv2GXTF19W99v8jGqExEgk0RhppjDQGGhfNCpYTg6rqqu6DhzUObGyksbEh0NhYT2NDfaCxoT7Q2NhAJN/LfDpKpDEQ/fm7io/cTep37jcw8tW8WcHWHHQjJIkg82YFVaCMQSO9Zc4rj/f4Lx0XU1rdlubJz/BYLLbC/10gEOC4445jypQpHHHEERx88MF89tlnvPPOO9x4441N+y1btoyjjz6avfbai7vvvptAIED37s3nUZWVldTV1bF8ecKqKBgMMnv2bO677z7mzJlD37592XffffnnP//ZVqylJtTEu9Y0/eeMHDmSK664goceeoirr76a6upqevToQY8ePQgGgyxZsoTvvvuOfv36MWHChKZspVZoiMVii5/94Y/BNC9jWq2Dcc5FCzKfo8WZH9uYbM2lE53+hFQbAZcnDX939j4rPxCLBZqUvUgk0DjEbSbUFKRI7MeU5s2nlTK1QKCpTKcPrWflZGJ+UY7+v2zt/zOaQgvyjmY/DEULgcn+MgDcfffdPPzww6y88spEo1GefPJJLr74YnbdddeU2tWnsk+KVKGzWaugYL2A0o4RUCYKqZ5HZ9ftjxYZ+6G7hR4spLoNeL+9MibPdV4VUi1G+97EP+cjgTuEVGd7rrM0Qy/DUsJYocaSr4wAZCeyJSxFgpCqAm0MN8o3vBi4zHOdrhhMF1uLbv+qaD9gZqafUEgVCK2x1obh6bp6rFefft3/nFkWU7+WB6ORQKS+trGhb/9ot8Saop779awZzsqDb4j98fMai2b+eEvvhbMv3yg0fGunsnpQMyHg6/mV1Q110GdA31VW26D2H63FEYsGGqNRGhfNbSj/+Ss9Nmwdd62aAcv6RKOBxmiERr8IlCwIRRppjEQCkUgDjY2NgcbGBiMINfgEoXoisVigvXR7v3DSJQPY36aVNx5/+cJeO/5jefW5Xv8bWjquNeFEyEWPApNPuHzRVM91/mgn5lyxPAblLU1NQqEQTz/9NFdccQWXXXYZoVCIq6++mr/+9a9N+/To0YNjjz2WTTbZpIVH0AwfPpzJkyezwQYbNI0Fg0EOP/xwDjroICoqKqiqqlqh1KkFguZWKqntb6K7pjVTWw444AB22203fv/9d2bOnMmsWbOYP38+dXV1dO/enZNOOolddtml3QlnJBor/3zWwhOAEzoY168kRJkvgHAHvCHmAC239WoFIdWqwC00F+oUcMbv0yu29u/bs3d0Xk2/qF/4L3ihpj3M+WeRuf2WvN14g/VgRQHHfz8TImi8UUDfVrbHWmhBvsB3f2ELLciHQsvn2++//5677rqLUaNGccIJJ1BZWclJJ53EDTfcgOu6TVmArRGJRAgGg7z22mu88MIL7LXXXuy4445dycapBVx0trHFh+c6C9Bdm54ATiexGLw6cAfwoZDqNs91prXzOB8IqU4CbidxntwcuFtINcZznS6bpVssfqxQY8k7TBr0hsADOQ7FkiOEVIPQpU7r+4a/A873XKerQsQcdJp9sfCt7/dNyKBQI6QqQxtkHlWzcv+9wtN/oLyigtnhUNlvP1SUAwQCsbJZM8qroZG+/aMtthcdMOTIpWr6nb2jkeWBeeq5boNWO6FTK1GBslh5sIzyhtr/NaVU9F5pjR41K0UHl5cTKwvGYuUV5mc5sWB5LBYsJ1ZeEYsFgxCsMPfNtvIKYkFzTLDCHKP3rQ+Wx2qDQWqDFbHl5RXUlpfHlldUxpaVV7AsWE4tehUzhhZVImhRpdZ3q0u6n3yr01kBZQjZeC0w7pn/qZU91/k61b+H5zpLhFQT0F1KLuzM3zTTRGOx5dFYrKKslUn98OHDeeSRZpZGzVady8vLufzyy9t8jh49erTYoai6urqp/XeKNKIn3qWyUvoqtOwB0qdPH/r06cP666/f0uZUqVRLatsTvqLoDjdfxG9dnPzMBdruGe5DSFWDnoT5y8CWAKfvt7YzHz0RbsIZFpkBrOUbyvvSp0xjfGSWmNvvyduNkNON1kWcPmRG8AqY56hBdwRacYekFuRvvcWI7bajsiVNd8qUKay66qqceOKJ9O2rtaHzzz+fvfbaixkzZrQp1MRiMYLBID/++CN33nknH3/8Md27d2ebbbZZwefpo48+4pVXXiEYDLLOOuuwxRZbEAq1aptjW3q3gec6vwJnCak2Q3sexs8NWwGPCqn+DYxvq3uo5zpfC6mOBe4iIQKvg/ayOTmPF0ksBYgVaiz5yHbolRHblrsEEVJtD1xJ81XdJ4Db01QH3KlU+DxmKnpyUwbsi+44klaMkfNfgSOBIfW1tYGfv/2qF8Dg1ddr/PrD3oGyMmLEIBYNRAkQmDGtItJQF1nWp38kUl5BsKIyVlZRGSsvr4CyYGWsoqpXpL52brAs8DOrrNG43C+cfP9BNNZQBzUr1TdsvlPdwmB5c+Gk3PxervfnoateWQ0gWF4RO+GKtX6t7rE4E4aTVea2go9HuhBSNZIQbvoBU4RU79AxwWcxsJmQahTat2OF4zpZMpgWfl6wbJWhNd06tGScptKAzhCgldX0ImUBsDvwmrmftu5AkWgsNmtp7byGaCxZpFkOfE3CX+Ybz3WWJx/fBeYA3YVU3dszDzXnuZtoPomPAOd5rvMT4JD0+Xc3rv8l6WGKPqOmqxghZ5m5qZb2CQSoprknTh+aCzqd8h1KgZ7mtgrA/PnsGAi0LL699NJL7Lzzzk0d6SKRCOussw7l5eX89ttvbLvttq0+SfycdsYZZ7DJJpvw1Vdfsdpqq1FZqRON4uL0xRdfzNSpU1l99dVpaGjghRdeYJddduHMM8+kT58+yQ/bDd3O29IOnut8KqQ6An1dcwraF6kM7Tuzu5BqIjCptXOR5zo/CamOAe4kcb4YAkwQUp3iuc7PGX8RlpLACjWWfGQE8Cfwv1wHYskepgXryWgxIM4y4ArPdV5r+ahOUVRCTTgcnhUKhZ5Bd8MaFQqFbgiHwyl1tAqFQoFwONyqqCGk6g54wKH4ygdefeyhAcuXLC4H2GK3XWcMGlZXX7s0MKzvgAj1dYEFZWXEfpXl7330WvW3wNOxWCIVW0hV/ty9Y1eur507A2Dx/Hce3+3A5XegJzhVQLUYu+gxYEDdsulfbLBN/cMkWtQ27RO/ff3+W6Gfvvl0I4B1tthaVfeoXmz2aWZWWyCUk5goLEWnZW+FXpnuCN2AsUCLF4tCqgjtCz6tiULJY23t05BcohJeXLvr0JpuhWIuNAf9OkqJj9BdUg4B/o7ueNML03o5Fot1AyoiEWKRCERjOgWnoiIWJQABkzBRFqAsEAgQicZiQGzu8vrFH6kFv6JNyL/w3WSGu6bES0P7ob9PWsR0mrwUnZXo5xrPdT4yv7tJ2xavu0V9stBghZo0EIs1nUtaLH83Lcjj2TF9WFHQadFTpqMMHcrKLenEsViMn3/+mbXWWovycj2VCgaDNDY2EggEWLx4Bc/sFbjxxhuJRqMceeSRTJ48mX79+jWVPQUCAe677z6ef/55/vWvf7HFFlvQo0cPfvzxR2bMmNFa97V6dDmaJQU814kCLwipXkOX2I9Gf3d2Q5dnekKqccALZt/k42eazJqxJM4NA4D7TRnUt8nHWCwdxQo1lrzCrGhtA7zcgRp0S4EjpBoAXEPzi+QfgH96rjMjzU9XjGbCF6NXhnoCIhQK7RGO9zVugVAo1BfdOvYY9Cp6M4RU/dCdEg4g6YL3vRen9H1l8oMm7zrw7Q5777coWN5QGY0S/vyt6oHEGqKBsnLWWL9xyzkq+PscVb5vIEAkFtMlWp7rNJ72+MMnY75/Fs75c0ryBc1pdbXLAdTP03/2XGdCG69jAxIZREu/ef/tEZ7rTDevIUiSqEMbgk8r+6ayT6YMZ+vQ3gkD6LhQsxx90d6HFv5/0b4r3elAy+NOEhVS+YWbQDAQ2LosEMhEx5h0Uwc8TSulQEXOYuBeIdX9aLFwYxLmvwPffbG6Jjytomf3boFA926BsoFOLLLrPnWLKsrKguVlgWBFWaCsvKwsGCwLlDVGo7PnLKt/689l9f+NaWFmRpa/2+NCzUq0UIbj43jgL0ljEzzXed53P7mESgaDK5SaWKEmC5gW5H+a2wqYFuS9aVnE6UOKLcgHDqRfS+O1tVq/7d9fr19Eo1HKysr4448/qK6ubuqK1hpvvfUW999/P88/r99edXV1zTqmNTQ0MG7cOEaNGsWee+7ZNL7RRhux0UatVm7bkptOYDrETRBSPQeciM6qKUMvTl0GHCKkutVznY9bOHaekOp44FZgUzNcg/asOdsn8losncIKNZZ8Y1P05MGWPZUIQqqt0S0P/QaAArgpQy0P5wLdUkmFLxTC4fAPoVDocHSJ2HrAF6FQ6CbgmXA4/CNAKBQKor2f9kWbNPdBCzXN2NE7+K/oSUuTADF/9h/lP3z+Sc+3pjxZ/Zv8Ll4cH95guxGHBMsrHgRYd/OG+bXLyn5/7YlnN1ww+9Vu/Qbvu2y9Lbbc/d2Xhj3d2BDYr6xsacxx1ipHt7uNt+V+F3i5I681FAr1AjZDr/gfgZ4Y1QMHhMPh6fH9zCp9PL0+YxjfHr+A01nBp6VjFgBboM+JHX0ds4Bh6BXWXJXvlNFcEBoUicW6/bmsPjKwR1VW+9d2hFgsVheNEf5Izb9bLakbRCJLqK6lldViwSyUrENClNmIFjIT6usCgaXLiC1dFotBLLo0Eq1XS+oWo99nkkQZ05ee68zNcf5iXKhpVZwXUu1F4pwU5z/A+PidQIA+wKCkff6HzjjyY4WaPMCYAs8ztxUwnaviQk5LWTk1QLCmpuXMnEWLFtGnT5+mzJa4UBMOh4nFYk2eNS11dfr222+56qqrGDNmDK7r8sorr1BVVdWslOmDDz5gwYIFbLTRRlx66aX8+eefbLnlluy+++5t+dP82t7fxdI6nuvMBa4RUj2ONhyO166tBYwXUr2NLsH/Jem4pUKqU4F/ATua4W7oblAXpTkj3FJiWKHGkm+MQF8Ur6BcW4oLM8E9DjiWxMrWcnSqeYcm7x1kCXpivzItdKwoVMLh8JRQKLQz8BAwHG3GfF0oFKpHv+Y+6Ikz6CyBxzAmqX854rihL0+8D4D3X3xuv0/++1IM9EVm3fJlwUhjo/9KMwJMBs44/qqbm01+Nh1RJz57fV6P375/e7NFc9+uAvoOGFB1QTRSVhcI1F+KzuSI8zpaXGlrdf2gUCgUX+UOoDOGkrNA3gNOCIfD37T198kUZuK+3NzSjpDqbGAXtMAGHRN8dkWXrHzQzjHJY5koTQqi34N8N2fxkpW6VfQqT6HtUmeIxmI0/QNi8awY804z8ybjaUogENBDMYgtb4zU/7Zw+ezpC5Yuqo/EHkp+bCFVPZkrFWu6ZUMQElL1Qou3G6PFmXVJIUOsoV5nRMWigcaliwOzGxsCXwL3Al/nofgdNwVtUS4SUm2Ozkj08wW65NZ/bloraZ964JcWHtcKNQWA6Vy1gJYzDgkECPz97/Srrl7hvQHA4sWL6du3L3V1uhN2JBKhvLyc77//noqKCoYOHZr0fFqwmTFjBnfddRdDhgzhlFNOAWDGjBn06tWLHj16ND3WtGnTaGho4Oabb2bVVVeloqKCe+65h88//5ybbrqpycvGz5Il/NyrF8FOtCC3+DBZuWPMIuKZwBpm0w7AdkKqZ4B7/SbnnuvUC6nOQ59L/m6Gy4HrhFT/8lxHZO8VWIoJK9RY8gZTIz4C+NBznbpcx2PJHEKqlYCr0dkCcX5Cmzb+ksnn9lwnJqSKt+guGqEGIBwOvxsKhdZGlyzthfY3GYBeFZ+HXgF+E3gkHA5LIdXGQqqjNt9lj53jQk197fJgva48oqysLIK+kP0D3fLzQ+CJcDg8E0BIldwCRjlDlxxeuds5L0376psByxf/UNHYMK+M2KJusViwIRLpMx+i7wSD8+8Lh8MvpvCS4sIB6A48i00s3wOfoTOGvuz4X6qgeACdin2Q5zoTSZREtYuQ6km0IPek6XaRyjEBtL9PukvF1kH/Hwbm1zb8/s6MeWw8sGZY76ry7tGYFlQCBOLqSVw4IRYjFo3FolHMz1gsGo0Si8Ri0UgsFo1EY9FILBZpjMaijVH9MxqLRRujZntin5j5qbdFY9FGs60xGotFYrFUhZFKc+vd3o5dQUjVXtewlASfpFsPtIi7Nvr/YzU6JsotAL6Qn1f0+t/nlY2zfw/OjUYCUeDblx7t8WFXXm8GaTWjRki1Gto82H8tPAM4p4VszuSypx9jMRqFXEGgLfmuT8WAMTzuQ+Jz04yqqirq6upYuHBh032AqVOnsvbaa7PGGmuYx4kRiWjdpLy8nMmTJ/Pll18yduzYpsf6888/GTBgQFOmTDQaZeZM3bxxxIgRnHfeeVRVVfHoo49y7bXX8uSTT3LYYYc1y9ZpbCTyxBOEgIsDARbTvO24//eFsZjtDJUKphX3IcDewEnoa8Yy9PXV30yHxcfi5wrPdSJCqivRf+fDzcMEgAtNN9sHraWDpaNYocaST6yJTi2+N9eBWDKHaYt4Lc1XIp8HbjC1wtmgGH1qAAiHwxHgcXNbAZPJtJ1Z/dkIYOCqw+rHvv7Jp2aXBuDf6I4H7QlZA5Luz37guiu+F1Id+embVfd89V5lb4BIY6D2rX93e2rporLl6Mm6bOc1DGvneUsGz3UWCKkeAkYLqaZ4rpOyWaTnOrXGDPEsdCp3KsfE0NkCaTOmNCU1LwA/oj0l9p5X29D4+q9zytdZqWe/mqqKYfWRaOWShsbYkvrG2OL6CEsbGgPRWJPQk2qpWGtjhXitU2FuXTFFrSRRetadlg22Y+YWNbeY7+didFee39FlFX8AtbEYW/V3Ij379Y+sHGkMNA4a2hgRcuH2tC0aNeZokjIf/XqaZb4YH67bad7ZaiFwuuc6C/z7mg5Ew5Ie93/mZ/J3ls2oKR6Gor+vVmDVVVdl1VVXZeLEiWyxxRasssoq3HfffXz66adcccUV9OrVq6kcyp84GI1G+fXXX/E8j4qKClZaaSWWLl1KVVUVN998M4cddhirrLIKs2bNYuDAgfzjH/+gulq/pfbdd18ef/xxPvnkk5aEmsaffmIBWhjoTRtCciDAUhICzgKSBJ1YDLtQajCZjVOEVK+gS60PR3+v9ECXkO8vpLoTeMVznZg5x90upJoPjPE91MlAHyHVbcVcPmtJP4V48WIpXnZAXxy+k+tALOnHCARHoc3a4lcudcD1SYaN2aCoOj+lgpCqHN169yi0QWgyy9DmqZM915nTwvaWGJh0fxaA5zpvgnooFmX01x9U9gqWx6o3H1k78q3nu70UiwXKgUMCAR6NxVruSmRZgceAA9FdKW7v4LH/BQ4QUm3nuc67aY8sNfYk4UH1mOc68RXdBvR7psXOLunCvPfj7dW7Ivi0t0+mjKVTIWDi6I7O6uhO81LDto4LoM/JtejzwHLzsxH9utYgkf7P4GGRAb36RJuuH9fepGELdNZXW8SNpTNSKua7Nes05rlOVEi1AN/5XkhVBdyC7m4VpwE4uxVxejiJ7yzQws8087sVaoqXobQxT7ryyis577zzGD16NIMHD+a7775j1KhR7LbbbgCUlZVx8803U1lZyVFHHUWvXr049thj2WeffVi0aBF//PEHSinGjRtHWVkZr7zyCttvvz2rrLIKS5cupX///k2+NfHSqmAw2CTcJFeOSplapiWmgxsr+isBEAiwnBUFHP/9WpNxVDKYks67hVQCLbrsiT5vOuhGGKOEVLd4rvOl2X+ikGoRcCGJc8cooEZIdZXnOi0KgBZLMlaoseQTOwLfeK7TovmbpXAxaZ9XoTt6xfkVXeo0vcWDMkvJCDVCqmr0JOpwVjTDBF0S9RjwtOc67fcUbU6LQo3htk13rNs0EmGj7z6u7NWrT3SV9beqX//rD6q+Rn/3jAoEmBSLWQPE9vBcZ7mQ6l7gHCHVE57r/NGBY2NCqpvQJokf+USSrGAE2sPM3WXAs9l8ftCdxtCiw9JMPo95rekqFWtLSKpCX/zHBZm4ONORMqYYCUEmLs6ktNLbUN/8eSqqYqlM2pKNpTNFLKnTWC06W3d1IVWFub8tMITmGURPAmsJqYaQJAqts9lKO876PVjTWE9jY0MgEo0y/dHPZtXpOdoKQo0tfSoeVqON/891112Xm2++mbfffpuZM2dy0EEH8fe/a3uSeDbNpEmT6NWrF4cfrith+vfv39QpCnR3p/vvv59Ro0YxevRoevbUCV5rrrkmUkq6ddNPHwwGUUoxffp09tlnnxViKS+n/LPPWvbb6QTxFtVOK9vrAoFWRZwFwLJiFXI815kNXG4Mh88ANjeb1gMeEFK9Dtzhuc7vnutMMWLNNSSyGfcEegmpLrAWD5ZUsEKNJS8QUq2MNjIcl+tYLOlFSLUR2g3fXybzMnBtDo0n59DKalKxIKTqjc7COBhj4prETGAi8O8uXDD4hZoGfMaMpuzmoi13rpsYiwYC339a0XPIWo1bzfo9OHP27+Vz0RcuhwYCPBKLke4W7MXIc8Ch6Iy0yztyoOc6PwipPke/Hx5Nf2htsh16ZRpgSifEwILBpLRnpNOY+Y6Md2PaGG1uGxckFpLIiinz/e4fq0N/5mejhepF6Ayg9oSjFcSfxobmbWzKK1L298kGARITzTjd0a8l7tmVLNLPBnY2t2ZEGmHtTesHrbF+IqNm853qFgInCKnq0Oe94SQEn5ApN0xb5pAtlcgZa9I8k2oFXNfFdd2m+/FypHi2y8MPP0xdXV2zjk5+Fi9ezB9//EH//v2pqalpGj/00EOZMGECd911F6eccgoNDQ1ce+21BINBRo4cucLjlJUR/e03XmLFFuSZMGyvQn+Okkuf4zT4hJyWBJ3FhS7keK7zPyHVSehKgNNJfMftDIww/nD3e67zupBqDHAzCZF6BHCnkOpMz3WWZDt2S2FhhRpLvrCD+flmTqOwpA1jSnoYuo43frFQjzZvfDbHpmpz0d1Oig4h1QD0hN6j5dXAH4CHgf+aFtZdwX+h9mfyhMJznWlCqju22rX27GgE5BcVPTfatm6XN54Lioa6QCN6oniYEWt+72IsRY0xKrwTuEFINclznR87+BDjgYlCqpeynLUYN1WM0opvkqU55tw5lIQoswktC8ttfX4VukX2F+b2S0cn/C0ZSy9fGuj20X+rzyiviJUHKygPBmPBvv2jn2+8ff3vpJ4p1FLmUEY6gBka0aUefVhRpFlAwnB4BWb+Ul7VUN88tiFuYzyL5v/ZO8/wNqq0Dd8juaY3kkxMCXVCD+xSQgm9LGUJs0tvYeFj6b33tsDSOyxlaQmdoSw1QGghvfdJLziT5sSx425rvh/nKBrJkq1mS7bPfV2+bI1Go2NbGs15zvM+b/D38Gb/5AP7pzbccDydxlqiVGzLPmn4PGhv5Cf6gMhW3Hvt1fRlRqdOnXjuuefYZ599wrZvu+22PP744/z73//mhx9+oEuXLmzatIkHHniAgQMHNjqOz8fq+np+Dh8LPkTGVQ/CW5AHb3enZeaBuYgMwFg5gA1SyIkWdlwKlMmuXFmNvIb91bKdsYjrrUsQf9scRJnTSZbtvAZ8hFhgeZbQotk+wCuW7VypqggUTaGEGkW2cChitW9JpgeiSB3p5rgXsXIQ5A/gFtPQmwySbSXaXZiwZTsDEWF3JxD93D4V0bp7XDpEMjmJ8zpqYpXjvK/5GDLkuOqDAi7awhm5PfYdWj1kwveFv8n78xFizduuy6pUx9XO+RmYBVyJsF3HjWnoGy3beQ/RveJfaR9ZFCzb2Q3YV978wTR09f+NgszQMQiJMoOJ7oKLhYsIap4e/DINPeXcn2jB0jJYN+zYi2bl/fD2Y91mJfs88lySQ8uVinVC/D0jSzkqEIJWTFYszAnLnOneO1DXvVfAK2hEnktborV9sNNYi2LZTj1pEHyae0wbyueYgChVabG/fUFBAaeeemrU+04++WR23nlnZs2aRXl5Ofvttx977713rEM1KiGWYkdQEGmE7LDXhXARJ1LQiRZAnip+oJf8ikbA07mqlMaunDLXjR7ynAmuOvLPdwL3ADw3evI1wFmIv1u3q47880hg5La77Pr2TS+/czHwAqHrpl0Q5VJXeD8bi4qK7g0eL04OLy4u3rLIXVRUNBCi5v8FEAHxi4AfgReKi4vbVefT9ogSahQZR2ZoHAhYqnVd28eynd2BRwi/KP4ReCCLbJ4ltBOhRv69hwOHE32S8AvwlmnoM9P81N0Jv4BdG20nmZFyr+bj/YOPr0ZOcXbdcffalYvn5C2TuxUA50mxpsmJU0dG/i2fA161bGdf09CnJniID4F3LdsZZBr6/BYYYiTnen4e0QrP1yawbKcTsAchUWZPEguhrQPmIESZacCsRLqBpUi0SWtkK+uEkJ/7dfIr7aVxlu1cB9wPLEMIBz5gBXAzwpUUVfAJNFAw9puCk2trtG4+P36/380ZMLChGCFKe4WibRETs2C5WQPxBTlnGzmIiXuX5nZMBct2AiQm9iTrHKpL8ZryG+BuMhQS7vP52G233dhtt922bPN2eoog4QUwWX5ULr8aOVqlkFNIdBEn+HMi56148RFy/GwXbYdmWpCXZqoFuWnoz1q28zFiMeXY4PYd99rnGMT57RFEqdRAedc2wH+lWBPMa9xM8wH7PRGvyxrE4k0syhD5YyDOUb2AP8mvy4uKik4vLi7+Jr7fTpEJlFCjyAb2R5xwfs30QBTJI1dFz0Cs9AfPLfXAU8CHWSbCrQd6Wrbjb4t2b/m3PgAh0Pw5yi4NiIvMt01DbymXWmSQcFShBsA09A1SrHn20BOrN7kBTWto0A5buypnXflGXzDgtRAh1rzlui3bBagtYxr6NMt2fgWusWxneCLvK9PQ6y3beQoRSvx/LfmetGxnAHC0vDnVNPS5LfVc2Y5sBT2YkGPGILFSn83ADEKlTHNNQ09JHEmBaOUgWRuKKUtBz0L8vXMQY10PXN6cw0vT0BGr3luY8H3hq/+5t3txxHOMItwdcCKiLXg8rqB4nUKx3EOZ7DSWLK0dLJ204HPw1r3e6Nsp72IgR9O0jItvMUSaOpIQappDCjnBzK2o7xXpsOtBbFdOS/2Pu8qvbWKMq4LYpVWbXLdRCHjakOeV2y3beZdwEWWw/PoJ4cTbXm7vA7xm2c41pqHPLC4ufhwRERCVoqKiPMT/ozfwaXFxcVOlU9cUFxe/6XlsJ0RW3VOI/897RUVFOzRzDEUGUUKNIhsYirAgT8v0QBTJYdlOF8TKkzeM0QFuNQ19TmZG1SQbECufPREX7W0C2VXmKIRAY0TZpRqwEC22Y5UipYtIoabJ5zMNfaxlO+9qPs4+9KSq0kCAHlWbtSN++bzwK9fVgoJBJ+B8TeNN12Vdi4y6ffA8Iu/lSIRbLW5MQx9n2c7pwDHAqBYYW5AzCYkR77Tg82QVUkTdhtBF+WCE4yIR1hISZaYBS7IoULbNCDXSufQUIYdIUKi5Ns4yvMhzbDnRJ6xVEbcL5AJAiwRLe5GfCUERJ5lsoESEpLZGtGDphPj9jw30yM9ZtGufrnqvgtyueX5fbsAlEMB1NdD8muaLIZ60JlUIt1irIwWP1cT4/Nc08ogt4nRHiC0tQbAF+YAY46omhogjv1elGnhsGvrsq+TPdTU1XsfjEYhFzCo5xgDi7/CSZTs3mYY+tplDn0ooa+u1RMZUXFxcCbxZVFTUgGgm0R34O/BKIsdRtB5KqFFkFHmRcSgwtrXbxirSg2U7gxB2zq09m38F7m1FO35CSGdBKeLDLuuFGst28oCTEMGs0VaQyhAT9w9MQ49aj94CxO2o8fA8sJ/Pz86H/bWq1A3Qc+Na/94zxuZP9+zTGbhAijVZ/7/JBKahL7Fs50vgCst2fkki8+Ep4FnLdn41DT3tK4syoyoYvLAM+D3dz5EtWLbjR7guvB2ZYmUvxGIJIVFmOrA6yxyIXtJe+tQSyP/LQwixJfj+8AN3JuDuihRq7BiTt8j3UKu16JYCXhWNxaK0IgXIYJewVAWf5oSjjCsfXkpr6qvHFW9cCmJgnXL9eV1yc/I65/nzOuX687vm5hR0yvPnF/j9ebl+Ldd1cQOuG9A0NJ/W8kpOg+t2/m1FyakbqusOIk0h0+kShl2XWmCd/GqEppFDSLyJJuh0o2VeDwVAf/kVjdpmWpBXJCLkjPvm84/OuO62+cDFCFEmh1ApUgMhB95Tlu3cbRr6d00c7iL5fQkwOt4xRPCt5+fdkzyGohVQQo0i0+yKmCyrsqc2hrxwM4EbCF28BxDJ9iOzeKIRJBgonA3hxlGxbKcz8DdEF6fIbiUgxJF3EG2PW/RCPQqRrTmbLVcyDb3Wsp07gBE+P3mHnVK1MRBg9zXF/uLVy3O8F3JdEGLNG66LsuRG52XgM2AY8HEiDzQNfYVlOz8hwqdbYiXtVEKT1ZFZ5AZJGct2ChH5MoMR4syeJDYxrwfmEh78m5WCdgyy3lEjP5tuAA6Rm4JCze+mof8UzzE0je40Dh+O9VkRKdS0RfdJk8jP8xpa+H8dpdNYukvFvF8JdxpzgYq6htqKuobaWF6pwhxfbpe8nLzOuf68Trk5+V3z/AWdc3PyC3J8+Xl+Xw5AwBUt7X2a5ktVyNlcW1+9obpuFyLK9FLBsp060txVLNpt19XrEZmBUTuvaRp+hFgTy5XTjZbJg8oDtpJf0agPCjm9e/95l/z8ycHxbkuoBfmWz72G+voG09BHygWWixHlR37EdWh/YAfE9dxm4EHLdrqbhv5h5JMWFRVth3BVA/y3uLg42ets72su4yV9itgooUaRaYYiJvftdsW1PSIt5XcAx3k2r0WUOqU7tLalKCG6+JFxZKbFWcBpRA92XIposf1dBp1okY6auHJlpBvkCeA2fw4ccWrVpupKbf+RT3UbVVuteX+XrsBwKdZsTNeg2wumoa+VXZwusWzna9PQEy2xeA14z7KdL9JZJmfZTi6i7AlEieFX6Tp2JrBspyewNyHHzCASu7CtQOTLTJdfc0xDzyphI0GyXqhBnDtP99wOIFb0G3XGaYJIN00t0TupQAcQalqLaJ3GWgrZbS1VwafR/VX1gfyq+tqCdaFtYXOtAr8vp7MUcjrn+vO75PnzO+fmFBTm+PPz/L5cTYOA67qui+vTNM3v06IKSgHXJeASmLm2bGWa/zQgxLJcWq40CQDLdhpoQvD5ZH6Tgs8fdbXUrlqaoy2bn5tTvMSft7Y4J2/DWl/BphJfYUWZr7CqUutUV6NpDfU0pNmYk4O4fuwdCPT3llf9Q34PaBqbBsh7amv3HKhpDAZ9E/Df18es+aRHn8AViDKo1QhBaBvE58Ua4GbLdnoAr0Ysev4DITA2AG+kMP7jPT+rbrtZjBJqFJlmKG1vRbFDY9nOTsCjhOcujAXuNg29NCODSo6sa9Ft2c7WiPKmk4leYjAb8eH8Wxa4FLxCTR3CDhwvFnAQcJg/B44/u1LbVOIfZL3SJbJ7QTdCYk0ix+8ovIVwr5xNgrXqpqFXWLbzGqIDxW1pHNOxhFYhP8xg6G3CyNX8AYSXMQ1M8DDrESVMwTKmRVnwXk0nkeclF7KnVa5lO4cD10Vs/h0R3JmIMB8p1CxqoiVwpJux1UqfFMkjS0Y3y68WQ5bhbRF0qhsCBdVVtQUlVdEFny65/p49C3IHdMnL6V+Y6+9bmOPvV5jj65vn9/XI9fm6+DRyGly3rrymvnTa2rJ5pdV1DbTdYGk/oTyZhMnNg+2MerYzor813QBUVmi+zaU+rWyjr6Fsgy9QttEXKC/1UbbRp20u9flqqwkEAlpDQwP1gQbqGxo0+Z36QL1W31BPfUO9Vt/QQEN9vVbfUEd9fZ1WXy+/+/F7Wpi7SEHIh8hABKChYbuBCPcrABcd0s8FnF3/VPvlQcdXHd5nQEPX7r0CG7v1CvTq3C3QuVNXtzQnh8uBHpbtPG4aeqCoqMiHyCcE+Ka4uDienK0wZJjwacDTclMN8F6ix1G0HkqoUWQMy3Z0YGdCJwxFlmPZzl+BWwitqgYQJRhvtsHJSNa06LZsZxfEB/DRRLdjjwXeBKZlUUmZt/RpXSL/f9lm+gFgN2CrnFw446ry7VYt9S8a/31h5KSnO6HMmtbK32kTmIZeZtnOfxGuGss09ETLxL4E/p5kq+9GSKHjPHmzhgRLslobmZG2M+EdmRI9JywjJMpMB1Zl0Xu0JYh01NSkGrqZLizb2Q34F+FL5wsQQuQLxCnUyE42AyM2N1Uiqxw1ipi0QLC0lqNpbs/CPI7cLnS6igiWbolSMW9odZtA80Hnrm6gc9cG+m3TAOL6ass1lhuAmmotp7xUKygv9fk3l/r8FWU+/+YyzV9R5supKPf5a6ubLpErXtiv65pl4ueTLqj4PzegNQQC1AcC1M/6RWwfsP1Wxl5DKzsFGrT6QICGoCDUUE/91F/zy7t0d/N69GnolpNHgz/HLQD6FhS6W+V3cm8v7OQO4VTnNMT1YXCB9PU4/wTPFBUVPSJ/DmbiBKkDLiguLnbiPJYiAyihRpFJDpXfVT5NliNzGW5BBNoGKQFuNw19SmZGlTLrEZOzjCAntfsgBJqDouwSAL4H3jINfUErDq1Z5Ni9jpqES2dMQy+1bOcexARKy8mFa58oPfC6v+Z+4izLiQxj7UlIrFHuu3A+RJQaXQQ8lsgDTUMPWLbzOHCLZTvnpUFs3R/YSf78v2xz2Fm2k48IThyMeO/tTWLtYxuAeYREmRmmoXe0srxGQk1GRhGBXPh5mvDxrUV0eKq0bGc9sF2ch9uJ8PK2AELwiYUSahStSVRhtBWDpX0I905aSsWibPPebtEgZs0HBZ1ct6CTW7fVgEDUMvLaarSyUp9/8yYh5Gze5MvZXKb5K8t8/oryRi3bNc3n5vh95HjvyCvI7dK1R2BrmsAF1w1QL37Av2JRDlXlWvdDT64+AeGCMeWuqxGLLPHQTX5FsgI4rri4eH6cx1FkCCXUKDLJUGC5aegrMj0QRWws29ke+Dci7CzIJOCOJFbws4n1ZCCjxtPpbDgiiDSSWuALYIRp6H+04tASoTvhNut4Oj41wjT0iZbtvIMItSU3j85Pfr5u8Ll/6v9bQ70WOanqRah1d4ta1dsSMqD5JeAuy3beS/Q1Yxr6TMt2lgKnAJ+mOJygm8YF3k3xWClj2U53hBgzWH7tRmLXPZXALEIdmeZkILQ724gsr8i4UGPZThfgGcJXiysRIk3w3FQCdLNsJy+OcrxBEbdXuG6Tk99G7bmbG7NC0VaRglAwJ6bFXK5RgqVTEXyaE5JiumbyCnD79A/U9+kfiFpf9fFzm/1rlokswQOOrindXKb5N2/y+SvKfHF91mgamqbh13xiDJtKfNVri/2d6mpFNlFpiebeeurRPyJc1QBvFRcXx1tuemFxcfGbAEVFRd2AfYEHgYOBN4qKio4pLi5W11NZjBJqFBlBdrP5M6o2MquxbOcvwO2Eau5d4FXgtTZY6hRJq4YJy5DV4xGixPZRdqlAuCPeawMCWFJBwjF4CdgP0QGOvHz2enPcmknn7dd/BeE5SCDKUoLOmooUnrO98Q1CJLkc8X5NlGeB1yzb+cE09PJkBmDZzs7AgfLmz60twMuLep2QKDOYcHE5HjYQXsa0QJYtKEJEOmoymkEkA2EfJfx/HUAE23tdMMGuMj1p4nwlu8zsFLG5uc6AylGjUKSZ1gqWlp8d3mDphASf0vXOicgOcbvtV/uDd59RbwsX3057VG4+dFhlZWW5r7CiXMurKPflVpZrOVWbfTmVFZrPDUBdLaxekaNtKvH5NA0NcNHQihfn5nTqOuDWik2lQZE83rKnMIqLi8uAn4uKio4FJiI+r58nlHujyEKUUKPIFAciXn+q7CkLkSUCNxCyWgJsBO40DX1CZkaVdtYDfSzb0VoyU0J2yBoGnEvjltYgJhAjAcs09LayspE2ocY09DrZsnskUhDs1NW98MXv115++TF9fUCkXXgrhLPmLddNW81/m0aWMD0HPGPZzgjT0Ocm+Pi1lu18Afwf8GSSwzjX8/M7SR4jbqQzbUdCZUyDif7+aooVhESZacAf7TxfJh1kTemTnGDdhii58/KoaehjI7YFhZo+NH2+2o7GQktz5QFKqFEo2ijynF8nvxJaqLBsJ3fpnJlFwJFy02xEu20dT8lRXkHALdqhoRQaViHOP+sR56SNtdVssl7pUrRyUe6RnboEBvXsGyisq0VzA1pBXS2+ynJtydqVi06WTdx/KS4uXpjK71tcXFxZVFR0FTAauKCoqOiV4uLiyPOlIktQQo0iUwxFKORtpZVzh8GynW2BR4BdPJunIfJo1mVmVC1CCeKCupD0hfxtQbZWPB2RHxKtRvgP4G3gy7bUGUeSTkcNpqGvsGznMeBuucnXb5uGey65Z9PwV+7r/jdEJ57I5w+KNR29FCXIWGAKcJVlO5cnITi8A7wvQ4mXJfJAy3b6AsfJm7NMQ0/7ed2ynTxE6VJQlNmb6K3rYxFATLinB7/agHMtG8mm0qfhiJI9LyNMQ48WYr1efm/ORRlZ9rTWdWkuh0h1fVIo2iEyn1EnJL54v/oDW/3pyOOKfvp4S6XvaYCDyJFZgDw/zRzz8yfm5dffAZR7P5s1jX7b7lw3fIfd604eMLB+ACIHbbPPR47PT8NWAxpWjf3y8js0rdaSD0nKTRNJcXHxT0VFRb8AhyGu94em47iK9KOEGkWrI1dCDwHGKFt5dmHZztGIybI3YPNN4KV2+L+qQEwy+iBW1tOCZTv9gXMQbZOjrazaiL/pj224fCzSuZBURk0E/wOGAMfI2/pxZ1Xe8OELXR4oXe8/H2kt9tAfOE/TeNt1G61odzhkJ61nES27DwDGJ/j4Gst2ngeut2znmgSFnjMIXU+kxU1j2U5XQvky+yBEmtymHhNBNSJfJljKNNs0dOXASp2sKH2ybOdY4IqIzaMRZXzR8DpqoiLLDSLbcscTtqkcNQpFG0M68roSXYAJ/tzD85AAsA4hxBQDk4HV9tRJJyDC/DEN/RjP/lwlHaolq1eVmIa+pXxL08jr0afh2P2OrLus3zYNe4O7JSOnvNT3R69+gW4HHV9VP9Cof+ir134+Ud5VSno7Kf4LIdQcKrNqvk/jsRVpQgk1ikywFyKMVJU9ZQlytfpahAMkSBlwl2nov2dkUC2MnNgGW3SnLNRYtrMDIn/mL4R3DAkyCSHQTGwH5RVpddTAlv/Hw4jzQ/D4x74+Zu3vfxukvwNcEOV5BwDnahrvuG7mQ00zjWnocyzb+QG42rKdiUkIgaMRosvBwJh4HiBL+/4mbxYDPyf4nMHj9CO8jGlHEuv4sZHwMqYFpqHHG7ioiJ+Mlz5ZtrM3cG/E5jnA3U285oNCTVOOmn6IaxMvzeXTgBJqFIqsQy4K96ZpR4x3UbIO4YRxgIWIOYrj+Vob7TPlqiUL94pjOJ2Kior6AGza9MyOg/YdfMmA7XMPz82jc52UugOBbmVL5nT+ffDBNev+ftnmo/IKWPbirVd9DfxHHuPd4uLitDmIi4uLvy8qKpqEyAi8H9FlVJFlKKFGkQmGAvXAuEwPRAGW7QxAWB9382yeBdxmGnrCbZfbGOtpYoU1Hizb2RNhwT8syt0u8BOixfacVJ4ny/AKJnWIlZ6UMQ29zLKdOxEXJsEVpls+me/M/Nsg/W2EWBPp5tkaIdaMUGINAC8CHyFKkb5J5IFSLHsceMiynQmmoUdtVxrBMEIlSCPjEYfkBfRAQqLMYBo7pprjD8KFmRXtQABtC2S09MmynW0Qq9TecawCrjMNvSlnXbDMrSmhJrLsqVweuzkin1eVPikULYwMEu9LbEdMf8JdmJWERJcphEqUgts2tKDL+Sb5Rffu11C2Gsoirq6rqq/8/E9HXfP02ddt/g/i2vGJeRPH/Y3Q5+trLTCuhxDdHg8sKio6sbi4+KsWeA5FCiihRpEJhgJTTENXXVsyjGU7hyFWJrt6No8Eno9zktbWSapFt7TMDkEINPtG2aUe+Bp4O9G8jzaCV6hZl86LG9PQp1m281/gYrmpE/CvT+Y7/5BizXAai2vbAGdrGiNdN7NdaDKNzPuxgMss2/kx0fwj09AXWLYzBeGsGdHUvpbt+IGz5M0yRPlatP3yEJPgwZ6vaLlNsQggVjiDZUwz2lleVlsiY6VPst36M4S7XjYj2nA3mTck29iX0fT5PrLsyXZd4hH/lKNGoUgzlu0UEL0cKfjVh/C22qWERJffCHfDOETkw2QbBfkvfXvZA8P/jrjm+d009LFXwePy7qnFxcXTWuBpP0e4EXdHuGqUUJNlKKFG0arIoNqBiDbEigwhVyKuQmSpBNkM3Gsa+s8ZGVRmSKhFt5yYHo1wduwSZZcqwEI4C9KR25J1SJHK62ppCdfVq4hOLkFL8W7AP12XFzSNtxBiTeT/bTvgLE3jXdelI4iMTfEacBLwd+DdZvaNxkvAO5btfN3MBPgoQk6Yj01DrwKwbKcL4n83WH7tQWMnRlPUIlx90xHizCwl7GcemeGSEUeNFPseB7b1bG4AbjINfUmch4l5vtc0utPY1RVP2RMooUahSAhPPkyskqQBhOfDuIh8mNUIl9tUwh0xq4OfP61NcXHxvTQuxQRg1arioj2H1Fw/YLv6k3Pz3S0LovV1WsXi2bnjF83K/Z/rat+6LmWW7ewK3I44rz0pj31QEuNZRpxlw8XFxS7i81mRpSihRtHaHCq//5bRUXRgZBZEMAskyDzgVtPQizMzqoyxHuHGaBLZrvxkRAZNZAciECs57wMfegPj2ik9CJ+spV2QMg29QZZAvU+ohny4ZTvjXVefIsWaC4GeEQ/dHjhT03jPdemw+SSmoZdYtjMCuMiynS8SbftuGnqpZTsjEWGtD0TbR15oB1tyu8Aay3ZuRggzOxG+0tkcZYREmenA/A7i6Gtr5NJ4AtDiQo18rd2NKJXz8qBp6JMSONR6oCjGfZFumlpgaZzHVV2fFAoPsry1F007Yrz5MPWERJdFiIy0yHyYNvOZoGnk77Rn7WlD/1p/Rbeege08d7mrV+TMmj0h78fqSt9nrstC2HKOuxFxfn3fNPTlmRi3IvtQQo2itTkMWGgaupPpgXRELNs5CDHx8lrHPwSeboMtotNBCWJiGRXZeebviPKOXlF2WY1osf1FM/kI7YnIjJiUg4SjYRr6Kst2HgIelJs04AHLds50Xb3M46zpEfHQHYEzNI0POrJYg+i+9HeEuPhiEo//GHjXsp1dTUOfF9woLygHAibCXdYJ0UHttgSOvQpPm2xgWRvugNaRiCx7gtYpffoncHzEttdNQ49aatcEJcBelu1oUUogIoWaRQmcP5SjRtGh8OTDRBNgdER5tHdBp4qQ6DINURrudcSUtIfPAE1D69WvYa8Dj629o3f/wBBNc7c0lthc5nPmTMj7dd2qnK+AXyKcv8cguhxupGWyaBRtFCXUKFoNy3a6ISbFb2Z2JB0PWbJzKcKFEKQSeMA09I6c9B41TNiynT7A2YiJbqfI+4EliNfxqA7YWSbtHZ9iYRr6t1JcPEFu6gvcadnOLa6rl3rEmshOLTsDp2kaH7ou7a2tfFyYhl5p2c6rwDWW7XyUaKaLaej1lu08Bdxk2c6ThHdk6o4IcQ5auUuiHUPiIlZIg26Z6e21LLADEE2oaVFHjWU7JxPKqwryLfByEocrQfwOQXERAE2jACE+eom37AmUUKNoZ0gXcayW1TqwFeGuyU2E58N4Q3odoCyb82HSgabRY9+h1df2GdBwZl6+uyWDrb5Oq1oyN3f8whm5o11X+9J1w6+ZZBbPtfLmC6ahl7fisBVZjhJqFK3JwYgT+y+ZHkhHwrKdrYB/ER56uxC4xTT0lNtSt3HCMgtkhtJ5iHyP3Cj7zwTeQAS9tfnVnyRpNaFG8m+EOBAsOTsS0WnoU9dlo6cMqmvE4wzg75rGxx1VrEF0czgbuARxDmgW2W57T0KizHHAIYjypCB5hP7emwl3VdQiwgmnE8qXURee7YNoOUMtJtRYtrMfcGfE5unA/UlO+oKCYh88Qg2iVM/vuR0Mr44XVfqkaFNIt3CskiSd8LJiF7GoFRRdpnt+Xo3Ih6lsrbFnG5qGf9c/1Z5w2Cn1N3ftEdjRc5e79o+cObPG542pqvB9BUyNEU5+PmIRyga+aI0xK9oOSqhRtCaHIi6U5jW3oyI9WLazP2KC5v3Q/RR43DR01cpYXHz0tGxnd4RAcxTRQ9jGAG+ahj69FceWrUSWPrWoO8I09ArLdu4AXie0gnejZTvTTENf5rps0DTeRIg1XSIevitgahqfuC4dTlgzDb3Osp0XgQct2xkZrQOZZTu9CYX+7oMIyfaulK5HBLiWw5aLTG8Z4ErE+yPomJnXQcsoOwKtVvpk2c72wGOECygrgBtSeH2tl997A94MiMiypxWuSyITT+WoUWQNsjzVmw8TTZDp7HlIPWLBxQEWI87nXkfMmraUD9Oa9NumfvtDT6q7r1vPwOGaz90yp64s962ZPSFvzNrinJ+A71yXqGH4lu30R7iCQVyXd7jrFEXTKKFG0SpYtpOLcNR8r05ELY8McrsY+D9CwkM18JBp6F9nbGBZhLyY2RExCR0JjfIIAsB3iBbbiayutnda21GDaeizLNt5GbhcbsoHHrJsZ7hp6LWuS4mnDKpzxMN3BwKaxqcdUawBfkCE/l5p2c5NiPDswYQcM82FadchRJo+iK4bGxCvgdXAbOBUdU7vMLRK6ZNlO70Qbbi9wusm4BrT0DelcGivowYQq+GIUkkviZQ9QRShJkYOjkKRMrKUvS+xHTH9CXe/VRMSXWYgSge9jpj16hyeGJpG4YHHVV2298GBi/ML3B7B7Q31WvXSebkT7Om549yA9qXr0lxHuqsR/6tRpqG3RPttRRtHCTWK1mIfxATq10wPpL0jL3IfRLQ3DrIE0dUp3jam7RYpYh2OmNTvhlj9zCEk1NQCnwEjTENf1fojzHq8Qk0douNVa/AmcCChEr5dEMLN0wCuyzqPWBOZK7Qn0KBpfB7DetwukRf0BrAAuBI4iMTaZINYYf0C+Asi5+pAhAAM8JK6wO9QtHjpk8xreIrw7np1wPWmoa9M8fBBocbbons7GjtgEhJqZJ5TPaFrah+idFY5yxQJI/NhIgUY7+2+hLseywgJL78THtLrAJuUaJgeNA1t8CHVhx35t8DdnboEBnnuctet8s+bOTZ/fFWF70fgt+bCyC3b2Qc4FnGeeLYFh61owyihRtFaDEWcjCZmeiDtGct29gUeIjwg9yvgEdPQI+voOxSW7eQhOodcgLg4DxK8wC5HdMD6wDT0Da0/wjaDV6hZ21oTddPQA5bt3IVo2R3MRznXsp3xpqGPB3Bd1moabyP+x5E5EYMRzpr/tVexxrKdQoQoNVh+7Uno76AhnANNtf2sB+YSKmOaEWw3b9nONIQzJyiUrUG4dRQdh0hHjQukrSRCiuj3I1xwXu4xDX1GGp4imlATWfa01nVJ5vxfTbgDqAAl1CiiYNlOF5puWx3ZYdKbDzOT8JDeDp0P05pss1Nd3yPNuvsLOrt/8fncLRmGVZt96+ZMyhuzekXOJOBL191SYhkTea67Ud580zT01S00bEUbRwk1ihZHlpgMBSZ0oBbGrYo86V8AXEZopaUWeAT4X0deTZHhqKcC59A4XwVEGOrXwJ3qgqdp5HvZ+zds8bInL6ahr7Fs50FEwHCQ+y3bOTMorrkuqzWNdxABfZEr5fsixJqv2oNYI91zexMqYxpE+Eqrl7XADojJ5Ga5rQJhhZ8uv+Y0kV31FXAD4v9fCbzXATuedXQihZraNL+PrkKEhXt50TT0UWk6/iZESWtvEKvjiPeMl0TLnoJEE2rKYuyraKfIz8ieRC9HCv7sfZ00EMqHWQqMJdwRs0ZlfmUWTSPn8GGVw3ff370mN8/dIvI2NGg1y+bnTJw/JW+q62rfAjMTOB/+FSESrwXeboFhK9oJSqhRtAY7IGzMb2R6IO0Ry3Z6IFYhD/JsXoHo6tRhs1Us2+kJnAmcTuOOQCD+Rm8BfwaWKZEmLnoQXv7QqkINgGnoP1q28zlwitzUC7jbsp3rgoKk67LKI9ZETi7/jBBrvmlLYo2cABQREmUGE+4Ma44axIW/iwhpnQYsStARVY9wVM1GhJIrOhaR76W0lT1ZtvM3RKC7ly9I43WDdOV5O/31Q7Sa9zI/ycNHOlZVoHA7RJaTbkXTjhjvZ2TwvOsAs4BRhDtiVD5MFnPICVWDjz0z8HBevru3d3vJar89Y2z+hMpy31jgh0TCx6Wj6gp582m1gK1oCiXUKFqDofL7bxkdRTvEsp29EK4Zr8thFPBgRxUeLNsZgCjROIXo4ZdzEXknP8sL920Jt8IrYtOqHZ+a4AmEYLGtvH0IcBqidA0A16VY0xiBmPxFZmvsj8isGZWtYo10ye1CSJQZTHhJYzwsJdQme7rc9glQbRr6ggSPNRQh1NUgBJ6oXSwU7ZrI91FaVvot2zkIuCVi80RE+H2635/rCb2PIsueyoFkc8lU56d2gCyRjhXQqyPEvch8mKD7ZRzhIb0OUNqRHc1tlZ32rO26y+C6e7v35m8+v7vlvFdd4SuZOzlvzKplObMRZU5NlRLH4mKE62oG8H2ahqxopyihRtEaDAXmmobebN2mIj7k6vo5CKt4sH1pHWIC+0lHvDCwbGcnhIPieKKXf0xECDSTIv4+6xGTfkXztHrHp2iYhl4pW3a/Qehz7FrLdqaahr4ouJ/rslLTGIkQ7nIjDjME4az5IRvEGhmiujuhjkx70TgUuSkaECLkdEL5MqVRnucj4J+W7XyX4Epe0O3gAFtbttMtmF+j6DCk3VFj2c4uiMUG7zl7CcIR2hKldSXArvLnyLKnBSmcC5RQ0wawbKczTbetjly0KUGc81YhnIReN4zTURfE2iuahvaXcypO32Uwt/r97pbrnUCDVrt8Qc6keZPzZgYC2s/AWNelIdHjW7azHXAWwtn6eEe8VlckhhJqFC2KzFDYA/hPpsfSXrBspxtwD3CYZ3Mx4sI2Wdt2m8WyncGIfJ5Do9ztAj8Cb5mGPi/GIUpI3KnQUckKoQbANPR5lu28AFwjN+UB/7Js53xvzorrslyKNefQWKw5GCFwjG6NMXuxbKc74fkyu5LYZ3IlIlhyOsIxMydO4eV1hNvsTIRwGc9Y95RjBZHntBTR+emJBMaraPukVaixbKcvomubV5DcgGjDXZ7KsZugBOjZR2/oAX494r5UPj8jS58iw8wVLYwnH6YpR4y3DDqAcL6sRoSsjyfcEbNa5cN0HE44r2Knk4e7j7kuB3i3b1jjXzhjbP74ijLfLOAr12VjCk9zHWJx9fMmrkkVii0ooUbR0hyC6Dai2nKnAct2dkOsPnpbl/4E3N+CF7ZZh7wgOwTRinnvKLvUAV8C75iGvqKZw61HlT7FS6RQk6nSpyAjEc6YYCv6HRHCzaPenVyXZZrGe8DZNP7cG6ppNLguv7TUIOXrVSdUwrQPsH2ChykhvIxpoWnoCa/omYa+ybKdN4Hhlu18ahr6pjgedq7n5xHAMuA9+fgliY5B0WZJW+mTDHl/mvByyhrgWtPQnWSPGwclgK93v4Y/l6z2e7fXIgTIZFGOmhZGloN682GiOWK8YmItIeFlDqJLndcRs07lwyj2ObQ6b8fd6+7w+XznuW7ofVtT5ds4b0rumD8W5y4EvgHmpuK+lSWehyAWWV5IeeCKDoESahQtzVDEqnuHDbVNB3KidxpwPaH3bQPiQvf9jmKftGwnBzgW4aDZMcoulcDHiI406+I87Hqgj2U7Wkf5O6ZAZEZNxhw1sCUc9B5Ey+5gKOjplu2MMw09LBPLdVmiabyPsB37Iw51hKYRcN305GjJCcVOhOfLROs41hQrCIky04DiNL4+30OEbP8DeKqpHS3b2Ro4Qt6caBq6Lbc/D1xv2c5V6n3TYUiLo0YGsj6MyGAK4gJ3mIY+N8mxxct6gIaGRuWui12XVEqtlFCTIp58mFiOmH6En7vLCblfJhDRthrYqM5NiqY446ry47fdWbu/plrbWtPESyUQ0OpWLsyZPGdS3uxAgzYBGO26jd7fCSGvXW+QN18NdqlUKJpDCTWKFkN+6B5IB28PnSoyIf5O4GjP5tXAraahz87MqFoXmd/xV0RORqRdHWAjYvL5cRK5GSWICUgnRLtiRWz6e36uA0ozNI4tmIa+zrKd+4AnPZvvkS27w3KxXJdFmsYHwBk0FmuOks6asYmOQZ7rdidUxrQX4S1YmyOAKLuYjhBlZrTkhZxp6NWW7fwHuMWynfebcTCcTSg/ZIRn+08IsedQlGOyo5CyUCMXHW5AlB16eco09J+THFcilNRUoQUatJ0JDw5OtWw4ciKnSp8ikC6qWCVJA2jsbN1AuCMmMh9GfV4rkuKc68qK6mq1R6sqtMMRrn8ANq7zL545Ln98+UbfYuB/rktxmp7ydESXxhWIhSWFIi6UUKNoSfZDrCqpi/gkkUGL/wa28Wz+DbinIwR5yjye0xAuiB5RdlkFvI0QA5PNS6hEXGT3QQk1zeF1hazNFtu4aei/ypDc0+SmHsB90u0RNkbXZYGm8RHiwikydPpY6awZ39Tzydfl3oTKmHalcf5NU1QTypeZDszOQCjl/xAlTZcBd0fbQf6ef5U3lyC6mgBgGrpr2c4TwCOW7YxXWQ4dgnSUPp2FeO95+RAhtLcGJSsX5ebnF7heIdUldddvh3bUSAGuB7FbVvcHunkeEkA4Mh1gJSLsP8wRo84pinRzxpXl/vxO7rWbN/kuCwRC2Vg1VVqpPS3v9xULc5ciFiEmuC5pub6xbKcncIm8+ZRp6HXpOK6iY6CEGkVLMhQxCZ6S6YG0NeRFz6nAjYQujgPA88CIbJkgtxQyZPJswCR655uFiCDUH5LJ6fAiJ5wliNW8ZFotdgjka9Ir1GS07CkKTwN/AnaQtw9ATApHRu7ouszXND4G/k5jseZ4KdZMDG6wbKc/IVFmMNHL7ppiI6EypunAghbqaBM3pqE3WLbzHPC4ZTsjYrTr/juhCeeISGekaegLLduZiAgmfrtlR6zIAlJy1Fi2cwQiTNPL78ATrei6LVmxMKcgv9D1fq6scF1SFUrbtVAjyzn7ENsRoxP+O9cSalE9F5EPE7wdzIdJ6bNboUiES+/bdHAg4Ht041rflnw4N6DVr1ycM2XOxLxZDfXaXOAb1yWe3LZEuAzhsB0HjEnzsRXtHCXUKFoEOak7FFArrQki7cG3AX/xbF4H3GYa+vSMDKqVkK0LzwdOJPr5aSpCoBmX5gv79ajOT83Rg/AV9awSakxDr5Etu98iNM6rLNuZHMxV8eK6zNU0LOBveKzPmuZqvfs3nHPRHRWHn3h+ZTeEMNM/8vHNsJLw4N+VWVr++SswC7gSuNp7hyznOlPeLAG+jXGMl4ERlu18ZRp6SUsNVJFZNA2NFIQaGYT/IJ73GrAA8bnWahP2eVNyNzrLcgryClxvaVI6uiW26a5P8v3ej9iOmMh8mM2ERJdJRJQlofJhFFnCzc9t7LVmpf/Rjev8x7tuaGGmdL1/6cxxeePKNviLEQJN2rumSlf8qYiF1ifVe0KRKEqoUbQUBmL1XZU9JYBlOzsiSp0GejaPB+4yDT2VloBZjbyIH44ILdWi7PIr8KZp6DNbaAiqRXfzRIbhZrrjUyOkw+NZhBMNxGfcQ5btnGsaeuRECtdlds+tGvI6d3Uv7q036D16B/p37h7o7/e7+WuL/cyfmrtx0L51jR4XQQAx4QyKMjMis3GyFekmewZ43bKdP5uGPtlz9/FAL/nzB7EEd9lFagRwBXB/y45YkUFyaHxujmsRxrKdAQjHm1foWYvo8NSqJX93ntOn7wnnVTTkFYQ5ahoJuUmQ1Y4auQDUVNvqPoT/fzcQcsDMQ5QZb3HEmIa+udUGr1AkgWU72g8fF16yZmXudbXVobK72hqtzJ6W9/tyO3c5IoT6J9dNvoNdU8+PuBbREE0/Uukqp+igKKFG0VIMRUxgfs/0QNoKlu2chHDSBC9mA8B/gDfaY6mT/BDbHyHQ7BdllwbEKv5brdACWLXobp7I1txZ5ajx8AFwkPwCEeB3PfAvAMt2ugJ7IsuYXh/D7vOn5nYf911Bz8gDjR9V0EPzgTE4TKypQbhQpsuvmRnIl0kbpqHPsGznF+Bqy3YukOKNRqgldzWik1pTfAKMtGxnt1bo2qPIDJFuGojDUSPfb08TEv1AlERfaxp6JsReo76Wytz8LULNWtclHcHdGRNq5Pu1O7FbVus0zodZixBd/gAm0zgfJtnMN4Ui49z64obBxUs6P1G63rdrcJvrag3FS/zTZo3Pn9FQr61AhAWvbsFhHAXsC2wCXm3B51G0Y5RQo2gphgKz2rMLJF3IjkY3EwrtBLGadXvECne7QNa6H4kQaAZF2aUa+AwY2Uw3mnSyHjGhV8SmTQg1Umi4F9FZoRfic+4Cy3a2BboCOxPhDBi0b11VIIA24fuCHt7tros25svCwspybfI+h9b+gHDN2O0wDPB5hMB1NPA9MIRQ1s/nzQWXm4ZeL4OFb7Rs5yJl726XJCzUyJa0/yb0WgIhEtwaIxOpRZHlW0ZdrVaVm7el9CkdbhpoQaHGkw/TlCPGW2oVzIdZjSjr+olwIWatyodRtEee+2Ztl7mT8x5cvSLvb/V1oVK9sg3+5TPG5o3dVOJfj8hLmpKusOBoWLaTD1wrb77YEZp/KFoGJdQo0o4Mgh0EPJvpsWQ7MpPlUcLDSacgRJp2lfcga+BPRGTQbBNllzLE5PpD09BLW3FoIEqf9m3l52xrZL1QI1eWt0fkyvyBcGwFuzGdhehcFDXEd7c/11UGGjR+/6bAv7nUt3rjOp+ztjhndclqX+nXIzoHgKmuy5yW/y1aH9PQl1q28wVwhWU7PwHnybsCwLtxHmOiZTtnIEqmvmmZkSoySGTHJ2ii9Em+F29HvAe9/Ns09LHpHFgC9AN61NVqlYWd3aDDJ11CTdIZNZbt5BLKh4nmiOlH+PV6BSHRZTLhIb3BfJh258JVKGJh2Y42aXT+mcvmF965eZO2xR1bW6OVL5yZN3bp3NzlwGzgO9elvBWGdB7ifbwQ+LQVnk/RTlFCjaIlOFR+/y2jo8hyLNs5DriDUFcjF3gdeKU9XWRZttMZEdh6NtFzYNYCI4DPMlhCosKEmyfrMmrkBGcQoY5MeyNKAIKUEyq58ANFhHf2CrblnQ5M3+OA2un3XNB7B4TY4MUH/E3TaGiJwMEs4RVEgPkVhEoRfzINvTiBYzwFvGDZzi9tuRxMEZVEHTUXEu4SBXjHNPRP0jekhDEAamu0Kp/fzcvNdyvrarREXt9NEdNRY9lOIU23rd6KcJffRmQJEkJIcvBkxJiG3hoTTYWiTfDYJ+uNhTM7Pbl6hX+w64r3ketqDauW+WfMHp8/va5WWwd85bosao3xWLbTD3H+A9HRrt1czytaHyXUKFqCwxBdT5ZleBxZiXSW3IAQL4KUIgKDx2VkUC2AZTu9EC6G0xCtCSNZhujQ820WlJIE23MrYuPtfFSHmEy0KlL025IvA+xB9AlkkLUIITQ4aSpEdCv5kFC+TFgopuuyVtPwAcdGHMsHnKZpfOC6tHrZRktjGvpay3beReT5bEK4ad5J8Bh/WLbzPXAB8FL6R6nIIHELNXIR4vKIzaOB59I9qAQxAGqqtEqArXeoX7Nkbm5KZXrSOdQN4XrpinDw5QKHyZBtnXDxOIDo4ugAxTR2xKw2DT1S9FEoFBG89tuaQnta3l1L5+WfU1OlBZ2zlG/0rZw1Pn/shrX+jYiczF9dl9a8xrwKcb4c3R7jCxStixJqFGlFdhb4M/CxyilojGU7WyNq9g3P5umIUqeMOxTSgWU7RQjb51+JbpefA7wB/JpFKw3rgZ6W7eSYhh61NEYR5qhZ2xrvb8t2+iAEmcEIcWZnCLXXjINNiLyjQxHiUjVCuPnVNPTZsR7kuozVNPyIMEAvfuAMTeO91lqda2W+AR5G/J4/NPU3aoL/Au9btvO5aeir0jo6RSaJq/TJsp3BwL0Rm+cAd2fyfK9pdAMGQEio2f/o6g2hysjoyHyY3jTtiAmKwVsjXHp1iMDkBcAvCDdM0CGzVn3GKBTJY9mOtmBG7sn2tML71ju+LSXZdbVaxaJZuWMXz85dCtoK4EvXbV3nr2U7eyMcubWIEHWFIiWUUKNINwcgLuh+yfRAsg3Ldo4E7gE6eza/jQgaa/MXbpbt7IJYST+G6JPpccCbwNQsFPE2Ii6we5EFJT3Zhlw19go1ac+nkc+xLSFRZjBi4pMIqwm1yZ4GLDMNPWDZjonIywDx2vyXZTtnNVWe47r8Jp01R0Tc5QfO1DTedV1auhtZa/NXhGi5FULgShjT0Cst23kFEaR4c9pGpsg00Rw1YUKNZTvbAE8Qrn6sAq7LApfIlsWR6iqtKieXwInnV1Ra9uYcwvNhIgWZfoT/PpWE3C9TCAkwfoTIGfwsX2IaumpXr1Ckkf/+vmbgzHEFjy+bnzukoT5YLqgFnOX+mTPH5U+tq9HKEYH401yXVr3OlKLujfLmCLVQoUgHSqhRpJtDEbkQMzI9kGxB5mhcjSgDClIG3GMaepvO8ZGT630QHZwOirJLAJGw/5Zp6OkKbUw7pqE3WLazEbFyqoSaxvQgfEU9ZaFGdoTZhZAoMxho1CK7CVxgMSFRZoZp6LFabX6KeH0eLm8XAbcghNPYT+Dyi3TWDI24Kwc4S9MY6brto8TTsp0ugIkQLQuB3YEvkzzc18Bplu38WVm/2w2RQk2tt2uKZTvdgWcIL/PZDFxjGno62l8nheyqqO9zaM+Tqqu0QYWd3S7degb6brtLXbfOXd3HgAbC82FKCQkxCwkP6XWA8mgLDdJJ6l1wabX23ApFe8eynYI5k/Kunzel08XlG7Ut763Nm3zFs8bn/16y2l+KmHeMcl0qMjTMk4BdEaWNb2ZoDIp2hhJqFGlDqsmHAr+3B4dIOrBsZwBilW13z+Y5iPakrdV6Ou3I//UhCIFmryi71AL/Q4RH/tGKQ0sFFSgcm8iOTwmLWbIscg9Cjpk9SWwyUwfMJeSYmRlvy0vZsvsBxPtwK7n5RMt2xpqG/l0zD/8J4cI5JGJ7LnCOpjHCdcMCitsqwxAlHC6ifMm0bOc909BXJHog6WJ6HLjNsp3zVCvgdkFk6dMWN43MXXsC4YgL0gDcZBr60pYakFwo6ErsltU60KO2Gm2bnev7BxqgrlarrK/Tqoq2b6hGtK7+mFBGzGrT0CO7N8VL0l2fFApFbF74bu0xcycXPrhiYc6WbqH1dVrl4tm54xbOzF0MWgmizKnFzjXNIfPzrpQ3n1Nh+op0oYQaRTrZA7Ei/mumB5INWLYzFFGr382z+V3ESTzT4blJIV0QxyNabO8QZZcK4CPgvTbYXlwFCscmsuNTs44aGSY92PM1iMTyZTYjVsimy685pqHHbAfcHKahb7Js527gRUIr6LdbtjOrKYuy6+JqGj8iShuGRNwdFGvecV1WJju2TCPf12fLm6XAg4g255cBtyVzTNPQZ1m2swg4FTEZVrRtIh01NbBFLLkb8R738oBp6JNSeUK5INCL2G2rdUJdE0E4WoKiy0JE50nn09e6FP7yeeEB5aW+ikCDFvD5Xe3C28pOAmabhm6lMkYPMbs+KRSKxHlvujNgzqT8f8+fWnh4TZXml5vd1StzZs0amzelptpXhXiP/+66ZHpx+CLEuWoW8G2Gx6JoRyihRpFOhiJW0dpN56JkkJOeyxFiRpAK4D7T0EdnZlSpIduLDgPOpbG7AmADMBL4JLKLThtCOWpiE/k/DxNq5GRta8LzZbYlMdYSKmOaDixOd/ioaeiTLNt5C+EEA5EX9aBlO//XlOtDijWjEELTARF35wHnahpvuy7pavXb2hxDSIz7yDT0Kst2ngeetWznHdPQ5yZ53OeA/1q2Mype95Mia4kq1ACX0rid/WumoTdbNic/K/sS2xHTn8b5MEEhZhqixM7xbCuJds742/P8DVGSDUCgQVtW2NndQHrP942EGst2tCzMY1MoshrLdvKW2zmXz57Q6Yq1xf4tmY4VZT5n9oS839etytkALEG03M74gqBlO9sSWuh4PIuaZCjaAUqoUaSToYig2PJm92ynWLbTF3iE8HKg+YhSp7ZSArQFmTtwhvzqHmWXYkSL7S9TcTtkCcpRE5tIoWadZTuDCM+XSfRvt4SQW2Y64LTSpOY/wP7AbvL2XojVsFeaepAUa75FOGv+HHF3PnCeFGvaVICgFNnOlTdrEY44EIL7JOAay3YuTeZ/Yxr6Ost2LOAS4PF0jFeRMRqVPlm281fEe8fLt4j3WDAfxuuCiXTEbEW4y24ToSyY3wgJMMGuSVHzYZpCZkztHLF5Pmk+38tyv1pCfydN/hy1hblCoWjM2xNXHzpzXMFDC2fm7hBoEM7X+jqtasnc3PELpucuBK0C+A6Y1dphwU1wLWI+/aVp6HMyPBZFO0MJNYq0IIP0diDJTiHtAct2DgLuRwSvBvkYeLKtiRiW7fQDzkGEi0azcNsIgebHdpQ/sR7YLtODyFKCJQbBr9eI3q43FvWIfJnp8muGaeib0jvE+DANvc6ynTsRDrBgjsTFlu1MNA19elOPlWLNV4jJ5b4RdxcgxJq3XJdYocbZyJ8JdcT5Mhj8KnN9nkN0phsCjE3y+CMR7bp3MA29vXXJ6kiEOWoGH1JTBFwht+fKr7WI98Fblu3ohIeDu4iQzaD4Mp2QKLMakQ/TErkO29L4M8xGCDXpPt9XE35eLEAJNQpFs1i209+ennvfrHGd/1JeqgXnpu7a4py5M8fmTaqu9NUiuqz94LqN8qAyhmU7ByIWqSuB5zM8HEU7RAk1inQR7IrS4fJpLNvxA/8ELiSUfVEJPGga+qiMDSwJLNvZHlGydQLCORDJZESa/YR2aOlWjhqJZTs9CM+XOYFQW3mX5kWaSkL5MtOAuVnQnncLpqGvsGznUUJdn3yIEqizmnMESrHmf/IxgyPuLgTO1zTedN020z3sPM/PI713mIY+17KdUcBVlu2MT8bSbRp6rRR8brBs58p2eN5ol0in1ZZ8mMNO6X54VYVvu4JCt0th50CvPQ6o3TriITUIp0oXRDe2MYQ7YtZmKJttUMTtda7LBsumBNg3zeVJ1YRn0hUgXEIKhSIKlu3klqz2XTjj98Lrli/I2eLariz3rZk9MW/M2j9yShAC8P+yLQdOlm5eL2/+1zT09Zkcj6J9ooQaRboYCixpi+U9qWDZTh/gX8CfPJsXAbeYht5mOsFYtrMncAGh9sWR/IRosT271QbV+pTQATNq5IRMJ1TGtA8wMGI3b05EtMlWCUKQCebLLGwDddpfIpwix8rb/RFdiu5obuImxZovEGJNZNezTsAFUqxZl+5BpxPLdnZAtC0H+DXGOeslhDPweEQmSDL8DJyG6ArY4cT8bEQuMPQjekBvf/m1RZAt2r6+x7pVOTV1tVTld3J7aT7KgTLE+aAEODeZDmEtiaahEXKLBZkvv69HnNe6In6PdBApRqvOTwpFDD6c4+w/Z0Lew7Mn5Bu1NaIEsqFeq146L3eCPS13getqtYjPjvGuSzY6t/+GqCQoRjQKUSjSjhJqFClj2U5XRBnAO5keS2ti2c5+CJGml2fz58Bj2eQeiIWcoA9BBKtGlnGACIb+CtFiO2NtD1uR9UCf9h4AKTup7ER48O9WTTwEwoWaemA5IVFmGrCqrf3NZGnPwwihpb/cfCyixKfZIFTXJaBpfIZwnu0ecXdnQmJNNq+ynev5eUS0HUxDX2nZzifA5Zbt/JBMGaf8Wz8BPCqdOW2qFLQtYtlOPo1FGO/tvoTnw5QRKkX6nfCQXufdZ7oOz82lz6EnV50UaKDO53M3y8fUAldlm0gj6Ut4KTKIsidgSwhpH1pOqFGdnxSKCCzb6fvHYv/t037rPGy94wuKwe66Vf75M8bmT6yu8NUAC4CvXZfSzI00NtJ1fKm8+ZT6TFO0FEqoUaSDIYjJSodYKZUT3X8gAjKDF7rVwMOmoX+VsYHFiVxJPQoh0OwSZZcq4FNgpGnozbZhbkesR6wgd0a0hm4XyAnb7oTKmPYmVMYUL9WIcqYq4AvT0G9K4xAzhmno5TKv5hVC7+VbLNuZYRp6szZrKdZY8rG7RtzdBSHWvOG6bEjrwNOAdAP+Rd6cixDcYvEacBLCFTOyif1iYhr6Yst2JgBnIfKtFCkgF0hitazuT/gCgos4vwXFl5mERBmHOPJhNNyCA46pPqJTl0BfgNx8go65e01Dn5Gu3yvNRJY9bYYtndmCQk1vRLB5OlBCjUIRA8t2cio3a2fPHFtwoz0tt3cgIKICqip86+ZMzBuzekXOOkR3tm+AeVkUFhyNfyLceBOBXzI8FkU7Rgk1inQwFNgItOeyGAAs2+kFPEB4i96liFKnrA7KtGwnDzgZkUFTFGWXTcD7wIeZCnrNJKahV1q2U4VYYW2zQo1lO90Iz5fZjcTO9VXALEKOmVrgdc/9baakLx5MQ59u2c7rwP/JTYXAvyzbuSieTA3XpUHT+Bg4ncZlFl0JOWs2pnXgqXM6IafUO005okxD32DZzgjgIst2vkihs99/gBGW7Xyl6vljI92OPYndtlpHCIFBGoA1CNFlCY0dMWtSWfHVNLT9j6oZ2qNPw/bBbbm5rgu8kOU5bJHvR9sz+fMKNekiMuRUlT4pFIBlO/sumpX7wNRf8/esKBNhwQ0NWs3y+TkT503Jm++6WgAheox23ewO4LZsZydE2VMAeKKtuYkVbQsl1ChSQoZpHQz83AYyKVLCsp19gIcILxP5GnikhbpVpAXLdroAfwfOJnyVNchqRNnD56ahZ02afoYIBgovy/A44kZ2VxlMqJRphwQPsYFQN6ZpwAJvJy/LdoZG7N9WQnIT4TWE+BrMm9kNsWIWVxcHKdZ8hGhjH9kKuDshsaY0PcNNDct2ChHnBBAT+dFxPGyEfMwFJNndwjT0TZbtvIPoFnRfMsdoD0hXY1+adsR4A7urCblfZgGjCHfErG/Jz9+RU1ef9t4zXQZ7t20u8/2ECJbPSjSNbsCAiM225+eWEGqUo0ah8GDZTu+N63w3Tfml8LSVC3O2CJclq/32jLH5EyrLfcFz2/9cl1WZG2l8SBH9RoSL9kPT0BdneEiKdo4SahSpsjdi1bjdlj3JUqfzEJOLYHlELfAoQtzISjVdljachZhcRSt1WYK40B5lGnp9Kw4tm1lPFgcKy9fiDoSyZQYjAkETYQUhYWY6sLKZ13D/iNttqfV0XJiG3iBLoN4j9F65QOapTI7nGK5LvabxAeI9t2PE3T0IlUGlKw8jFU4m1J3mXa8wFwvpOHsVuM6ynQ9NQ09WsPsEGGnZzu6moc9J8hhZjXQvxhJgdMR7NjIfJuh+GUt42+pVwKZMfc5YtnNwIKDd7N1WXuorfvnu7i89dnXPrPzsk0S6aeoQ7tcgQUeXEmoUijRj2Y6/vo7T5kzKu3nWuPy+dbXifFdd4SuZOzlvzKplOWsQ19GjgYmuS1tZ6D0c+DPinP1yZoei6AgooUaRKkMRF0ATMj2QlsCyne6Ild9DPJtXALeahr4gM6NqGst2tkEISycTHgIbZCZCoBnT3l1QSZBVLbrlhG83wvNluiZwiABiFTlYxjTDNPSSJh/RmL4Rt9ujowbT0FdZtvMQIiAcQAPuly274yoFlGLN+wj32vYRd/cEhkuxJtnSoZSRYl8wRLgcEYAeL58ifrd/IkpAE0aKYk8CN8rysjZ3DpIuxWjlSMGvSOdiMB/GQZQIe90wTrY6Mi3b2QV4uK5W8we31VT5No4fVfB9TZUvK8fsIVKoWeS6YR3ryhHXLukU5lXpk6LDY9nOXs5y/70TfyjYd8NaERYcaNBqVyzMmTx3Ut6cQEBzgXnAN1mycBEX8nrsOnnzZdPQ28zYFW0XJdQokkZaAIcCk7L1QjMVZMvqRwh3LHwPPGgaekVmRhUby3YGIcoSjiJ8tTbI7wiBZnq2uoCygIy26JYBoXsRKmPajfASiOaoRpRGTJdfs9Lw3ox07LRLoQbANPTvLNs5CDhRbuoL3GnZzs3xvmdclzpN4z3gHGC7iLt7ESqDylQO0hGESkKsRF4fpqHXW7bzAvCQZTsjk83lMg19omU7p5Fay+8WwZMP05QjxiuWBvNhViNKJscR7ohZ3RY7gli20xd4GuhUVyNCP+vrtKqJP+Z/U1Plq0WshmclmkY+jYVSb9lTsBNZuoV55ahRdFgs2+lZuVm7dsaYgrPs6bmdXVecNzas8S+cMTZ/fEWZrwqRhfiV65KVC53NcA7is3MJwhmqULQ4SqhRpMJ2wDYk2QUkW5EX6mcB1yC6WYFYeXsS+DibRA451j8hOjgdGGWXACLP4O1sdQBlGeuBga31ZHIyNJhQKdNOCCdHvJQSni9jt0AZm1eoqYOsC8VNN/9G/C+CgdtHAKcCVrwHcF1qNY2RCOfKthF39yEk1rSq4CvPF+fJm/XAB0kc5kfEauiVwPUpDOdp4CXLdn5uTaFfOoq2QlxwR3PE9AfyPQ+pIdwN8wPhjph1bdEV1BSW7XRC/H/6AtTWaD43oNVPH5P/7aYSf1BgzObAz50IfXaD6HoV7fNPCTUKRYpYtuNraGDY0rm5N0/5Ob+ocrNw4NVU+TbOm5I75o/FuQ7iWnQc8IvrZq/IGwvLdrZCdHsFeDyecmGFIh0ooUaRCsGQ0XaTTyMdDXcjJmdBViFKneZmZlSNkZONwxACze5RdqkFPgNGmIae9QFtWcR6hPCVduT/bCDhwb96gocpJlTGNB1Y3grCoVeoWZtNQmVLILNY7gD+S8iZdoNlO9NMQ1/axEPD8Ig15wFbR9y9FXC+pvGW69KabsS9gD3kz98mkzMjnQjPAv+xbGewaejTkxmIaejFlu18hziHvZjMMaLhyYeJ5YjpR/gkvpyQ+2U8EW2rgY3t/TXvRQYdPwzsEtxWV6sxb0rej7J9bpBsFmoiy55WxHiflRAKEE8HkaVPSqhRtGss29lt4zrfXRN/LDhg1VJ/AUAgoNWtXJQzZc7EvNmBBi0A/IEIC16T2dGmxJWIUsafTUOfmOnBKDoOSqhRpMJQxAp+uyiFsGxnV8RqurdTxM/AfSm0o00rlu3kAn9BlDhFllWAaCv9IfC+aegbWnNs7YS0hQnL/9WuhLfK7hb7EY0IAAvxBP+ahr6uqQekG+nA8GbUtOULrbgxDX22ZTsvA5fLTfmIlt3DEyljcV1qNI0RwPk07kDTDzhP03jbdRtN8FqK8zw/j0j2IKahT7FsZyxwtcyZSVbIeAN437Kdz01DL47nAZbtdCZ2y2qdxg6JEsIdMasJz4fJujLWTOHpaHKwd/uSObkjlszN7RSxe1auimsafjwik8SOti/ifN/Dsp2cNDkRlaNG0SGwbKdbXS1XzZmYd+6scfld6+vFosbGdf7FM8fljy/f6KtAvB9+AKa4Lm1W7LZsZw9EOXQdwmmoULQaSqhRJIVlOz0QK1GvZXgoKSMvTv+OsPEHw3cbgGeA97JhNVVa0U9F1MhGhruCuOAcicicUBOP5EnaCi8nkHsRKmPag8TyZWoRE8npCNfMLNPQM5VjEqQH4YHUHUKokbyJKCfcV97eBbGq9mQiB3FdqjWNdxDiamQHLZ2QWBM5yUsrlu1si3DhAYw3DX1Riod8DnhXHvPnZA4g3UsvI8pMb5bn4h403bbaK3YGEK9JBxHyPoEIR0xbzIfJIGcDp0Vs+/DFO7v/iAin95KtjpptaSyQxBJqgsHqvUhP9pYSahTtGukMPslZ7r9p/KiCbUvX+3IBaqq1Untq3u8rFuYGBfdZwHcZzGJLC/L3vVHeHGka+h+ZHI+i46GEGkWyHIwoC/gt0wNJBTm5vgM41rN5DaLUaVZmRhXCsp2ewBnA6UR3Y6wA3ga+VhOStBBcYc01Db2uqR1lzfJgz9fORA9xjkUZ4W2y5zX3nBkgMki4wwg1pqEHLNu5C9GyO/jeO9uynXGmoY9L5FiuS5Wm8TZCrIn8mw4AztU03nHdFp38nk0o/+idVA9mGvpCy3a+Bq60bOe3eGr2PfkwkQLMyZbt/AnhXPJObmsJtaieg1id9Tpi1qmsgPRg2c4RwLURm8cATwQatP0jttdlcTvdyLKnda5LrE53we29SY9Qo7o+Kdotlu3sUlWh3THl5/yDFs3K7eS6aG5Aq/9jSc7U2RPyZjXUaw3ABkRY8OJMjzdNHI9YdCtBlEMrFK2KEmoUyXIYsA6Yn+mBJItsPfoI4WGfvwN3x9uOt6WwbEdHBJEOIzzYMsg8xIr/T+0tyDLDbESs0vfEc+EuV/q3Izz4t6jxw5vEITxfZlkb+N91iNbcsTANfY1lOw8Cj3o232fZzpmJlha6LpVSrBmOECu8bA2co2mMaImgRSn4/lXeXAikq8b+JUTI8l+BT2U+TD9iO2Ii82E2I94XExGt559EiDJBIaZD5cNkCst2dgceJDzI3AZul0JY5GdQVi4KaBoaMChicyw3DYQLNelAOWoU7Q7LdroEAly2eHbu+ZN/yu9RXSnCgktL/Etnjs0bV7bBvxnhQh8DjHFdsm3BKSmkk/1qefO59tjdVpH9KKFGkTDyYvxARBhlm7uIlpPuU4CbCZWmBIAXgHcyOXm2bGdHxKr78UR3Z0xECDST2uLfPtuRLopSoJ90zARFmcGIkox4cYFFhMqYZpiG3hbdKJGlOqszMooMYhr6aMt2PkOIpiDKJO6xbOfaRN+DrkuFpvEWQqyJzELaFiHWjGwBsebvhM517yR77pAXrpHiSwXwnGU7lyEETu9kfwMh0WUuoYDeYD7MZs+x7wWqTEP/IZmxKZLDsp0BwFOEizFrgWs9E5NIoSZby5760vg83dRikhJqFIoYyGvlv2xc57tx/KiCgatXiLDg2hqtzJ6W9/tyO3el3HUZwkXTqhl6rcBwxOf0XODrzA5F0VFRQo0iGf4EdAJ+yfRAEsWynULgNuAEz+b1iJXDqZkZFVi2szdCoBka5W4XGA28lU2dp9oTcgK6J0KQ2R6R95PIZLkOUZoRdMzMMg29LL2jzAgd2lHj4QlEVk3QfXcwohwx4fbWrstmKdZciBB9vGwHnKVpvJuuVUkprJ8ub64Dvo+xnwZ0J3ZIr07jfJi1iE5kOyPK4l4iPB8mkQn988Ablu18107eO1mP7HL4NOGvw0qESOOddEVmbWWrUBPpptmMeH3GYr383lJCjSp9UrRJLNvZsa6WW2eNzz909oS8rg31aK6rNRQv9U+bNS5/hixzqgRGATPaclhwNCzbKUK42kG0485297OinaKEGkUyHIq4IJmU6YEkgmU7OyBKnXbwbJ4I3JmJDklyYnQwQrUfHGWXeuBL4G3T0Fe03sjaP5bt9CK8TbZByMHUDeEKaEqoKQdmECpjmttOM4I6bEaNF9PQqyzbuR3hZgt+bl5j2c5U09AXJno816Xc46zpGXH39sCZmsZ7rks6OtGc6HmOL4HdZGllNEEmWj6Mgyi1HE24I2ZtMB/Gsp0LgH8gSjFLkxmkaejrLdv5BLiU8FIzRQsgu9L9m/DPwwAin21BxO5tovSJxvk0C5qZQAYdNWnp9Idy1CjaOHLR6p/FS/wXjB9V0LNsowgLLtvgXzFzXN7vpev9wQ6o04DvY7S9bw9cgxCovzYNfWamB6PouCihRpEQUlw4DNE1JFsv1hph2c4JwO2ELpxc4BXg9dZWyi3b8SPCiy8AdoqySyXwCaLjVEd1MKQN+ZrdhvDg321jP4J6Gp8b1yIuTIKOmSUdZIXFK9TUAqUZGkfGMQ19vmU7zxMKXM1DtOw+L0HnCACuyyaPWNMj4u4dgTM0jQ/iFWvkxDuYDxMUYAYgWoP3RGTDXIRw8oAoWQq6XyYR0bYakQ8T72v8fUTo+T9IsCtWBO8C71m2s6Np6O0ljDLrkOfE24DIkOBHTEMfG+UhWV/6pGl0Q7zevTSZoWcaeo1lO5tRpU+KDo48JxxTuVm7YdKPBTsumZvTCaCuRitfMDNv7NK5ucvlruuAL12X5TEP1saxbGc/4EjE+/n5DA9H0cFRQo0iUXZGTAb+k+mBxINlO/mILJpTPJs3IFw06QrUjHcsBYjQzXNpfEEJIsj2PeBjZf1PHimE7UJ4vkxkiUlT1CM6Mn2CdMyYhu6kdZBtB69Qs1blIvEuMAQ4QN7eAbHylpQDxHUp9ZRBRXZ12xk4TdP40HVpkCudsVpW6whXgDcfZiMi4LEQ4QD7GXgNKciYhl5OmpAT3peB2y3bed809FVJHqfWsp1ngRss27lCvd5ajAsJhUsHecc0dCvG/m2h9CnSTVMHLI3jcSWkT6hRXZ8UbQ7LdgYGAty8YHruYVN/ze9WU6X5XVdrcJb5Z8wanz+9rlarR1wX/Qr87rq020578vrxBnnzv2qxVJFplFCjSJShCDfKmEwPpDks29kWYe3e2bN5KiKPZn30R7XIOLohwjzPonGZA4guJyOAL0xDj1yRUzSDzB3ag1AZ054kdoFcjwiLmy6/dgD2MA394XSOs60hV9i8GTUd/oJFhk3fg3CQ9JCbT7dsZ7xp6L8mc0zXZWNh58Cb/hwu69I9oHfpFujSqavbtaCT2yW/k3vMjrvXXf7RnIpafw7dPQ8LIFY2HUQGx2TCHTGrTUOvtmznFYSAEyxpSUpAiZOvECL0pcDdKRznV4Q75zCEuKRII5btHAdcHrF5NPBcEw9rC6VPkULNojhznkponMWVLMpRo2gzyGunizas8Q0f+11B73XF/nyA8lLfH7PG5f++Ya0/2P10MSIsuNUjAjLAqQin+ypEVqFCkVGUUKNIlKHA7ExkuiSCZTvHAnciQo+D/Bf4TzBXoRXGsBVwNvC3iHEEWYTIvPi+tcbUHpCthvcm5JgZRHjL3+aoIDxfZo63dEWGrx6WntG2aXoCuZ7bHa7jUzRklsp9iE45Qe6RLbujdr2wbMeHWLWPdMPoQP+RU9E3rvN1/fbdTr2rK8VqZl0Nm2uqtc32tNzyktVdF5x7Q/lXuXmsQvwf1pqG3mRJlGU7uyECkAF+aGGRBtPQG2Rp2BOW7YyIknMS73Fcy3YeBx63bGdsWyqxzXYs2xkM3BuxeTZwVzNlblld+qRp5COynbw01Zbby3pg1zQNRQk1iqxHLsIcUVfLjdPH5O84d3Jel0ADWl2tVrFoVu7YxbNzl0pz5mbgW2BOewsLjoZcVL1M3nw6mZJmhSLdKKFGETdSeNgNeDHTY4mFnGRfB5zm2bwJcSEarfa+JcawHSIX4gTCJ7pBpiEEmrHK2t808oJiACFRZh9EZ5xEWE94vsyiZiYl6bTCt2VUx6cYmIb+m2U7HxLqptQDISw8T6gUyVum1I/wc0ElIffLFMDpuVVg9ba71Fe/9kC3I8o3+nyuq4WdG758q0seMN11iTc35lzPzyMS+gWT5zeECHqV/EoK09CXWLYzDiF0v5meoXVspMP0ScJfh6uA6+OYkGR76dNOhIv1LhCvUFgCFFq208nTjjxZIv8u+Zbt+DpInpmiDSDPAzeuXJRzxPjvC7pvLtVyQAs4y/0zZ43Pn1pbrdUh3j+TgR9dt5H42J65BNH5cArwU4bHolAASqhRJMYh8ntWtuWW7fT+TXiLzpnAbaaht3i3GrmCPRw4gvCsiCC/Ilpsz2jpsbRVpPNgZ0KizGAS78ixjJAoMw2Rx5GIILYe6GPZjtbBhbTIjk8dVqiRFvHIfJheCEEv6DwahOiItwERuhwUYhYQHtK7GiiP9toyDXjyOuYjziORLry9gICm8Xlzq5uW7QwAjpY3p5qGPjex3zg5pBvmWeC/lu3sn2IO2H+AkZbtfBXLqaSID8t2ugPPEJ6DVA5cE6c7NttLnyLLnlYk0I0m2PmpN6TWwUaWRtYQ/vfKp3F2jULRqsi8xgsryrULJ4wq6LN8QU4hwOZNvlWzxuePKVntL5W7rgH+57r8kamxZgLZFfZ0RJnwEx382k+RRSihRpEIQxErcEsyPZBILNs5ArgH6OLZ/A7wQnMlAik+rwbsh5hYRXbQAHHS/xYh0KguJhHIi4fdCYkyexO9TCwWDYjWwdMRosxM09A3pjisEsQKchfEZKajEinUtMvSJ/ke7krskF6d8K5MAYSY5wBfA3+R2+oQpQ8XpSLGui5rNY23EV3hIrOWBiPEmv81I9acSajd/DvJjiUZTEOfadnOT8BVlu1ckKybwDT0Mst23gauRJzbFUkgXaZPIDrfBakHbjINvdmwXU3DT+Nrxaxx1Mjx7RKxOd6yJwgXalamYUjVhAs1BSihRpFBLNs51A1w09zJeTtNH5PfrbYGX32dVrl4du64hTNzF8t1xTqEi2RCew4Ljoa8BrgB8Zn5SbJluwpFS6CEGkVcyI5FBwBWNinNsiXtVQiLfJBy4J5kwz3jfF4fwjkznOj17TXAZ8CIDtwxqBFyZXdvQt2YdiOx81AlMIuQY2Z2ugOYTUOvtGynEuHkUUJNiDbpqJHv1V6IErpYXZO84mAdYlXRARYinHBeR0xYPoxlO6cjOssFuceynXNMQ096cua6rNY03kGUUEbmXOwLNGgaX0cTa2Sd/any5jLg92THkQIvAB8iXD2jUjiOBZiW7exhGvrstIysAyFf+/cgzrVeHjQNfXKch4kse4IsEmqAbWn8HklWqEkH1RAW/K1yahQZQTorb1zv+I4c+21hj5LVvjzAXbMyZ9bMsXmTa6p9wbBtG/jGdSnN2GAzy6GI+U058HKGx6JQhKGEGkW87I+4YGsx8SNRLNvpDzyC6PgTZC4t2N1Erk6egJhAbRtllzLgA+DDNDg72jRylUInJMoMRnRUSoQNhJcxLWyl4OVgTk087V3bK5EZNS1ePpgMlu3kIMYayxHTn8b5MMEOSVMJL0lygJIEXSAfAQcRKg3dFrE692Byv5HAdVmlaYwAzqNx6cl+CGfNt1HEmlMJOXFGZCIfwzT0ZZbtfAZcYdnOT6ahx9N9J9pxGizbeRK4ybKdC1XWR8JcChwXse0109C/TOAYka89yK7Sp8iyp3Wuu0V8iYdgB0jVolvRLpDXqefV1nDx1F8KetvTcjsHAmgVZb7VsyfmjVlXnBMsdywDvgHmd4Sw4GjIv9X18uYrHf26XZF9KKFGES9DEd1ypmV6IACW7RwC3E94zf0HwDMt0SXEsp3OiO5NZxM9M2UtIrDzszQEErZJ5OrtjoTKmAaTeNvTFYREmenAHxlycK0n8Wyc9obXUVMLmVltk26+aOVIwa8+hMp8QISHB8WX3wh3wzjEyIdJFpnLcj+iZXcvuXmYZTvjTEP/MZVjuy5/eMSaSGfDAQixZlTwIls6DM+U929AlGZlileBExHC0YfJHsQ09EmW7axBlJh9laaxtXss2/kr8I+Izd8gsn8SIZpQkxWOGk1Do7FQk4ibBkJCTbrO96rzkyJjWLYzBLh52fycnSb8UNC9slzLqa/TqpbOzR1vT89dKMucXGAC8JPrZsd7OYOcCWyNcJ9+lNmhKBSNUUKNolnkBPxQRJeipFZG0zgWP3A5Ir8hSCVwX6qTohjP1wtxIj+d8PybIMuBt4BvMv23aW3kSsTuhESZvYn+N4pFAJhPqE329Cxq+646P4ULNWtbQjDz5MPEKknSEWG9QVxgHSH3yzTCHTGrMyGUmoa+wbKde4DnPJvvtGxndqpB5q7LSk1jJKKLU2QXuSGIMqgfpVhzLLCVvO/DTLa2Ng19nWU7I4H/k4HAFSkc7mngZenO6ZBCeCJYtrM/cEfE5qnAA0m8j7NWqEEsBPSM2JaoUFOK+CxKZ+mTFyXUKFocy3b6ATds3qQdPX5UQfeVi3IKAXdtcc6cmWPzJlVX+oKfBasQYcEdviTfsp3ewMXy5hMtmWepUCSLEmoU8bAr4iImo2VPlu30BR4ivN5+AXCLaejpCAH0PtcAxCr2KUSv0Z+DaBv7S0ex48vsi70IdWTajejtx2NRjejCNZ1Qvky2Tro6tFAjBRSvGyqpfBpPPkxTjhhvPkw9oXyYxcAYGufDZKUgahr6OClMnCM3dQUesGzn0lTPEa7Lck3jXYSjL/I9dwjQYNnOz4hzFoiJ9MepPGeaeBvhRDyXxJ0cWzANfZVlO98CFyLybxQxkN1LHiW8XfUK4MYkhbton3+tIQAOAraXP89ATDAjiXTTbIbEutXITk0babnSJyXUKFoM6aI8O9DA/82ZmNd7+u/5Xevr8FVu9q2dMzFvzJqVOUHHWA3wIzDZdekQ16xxcAXi+uM309DHZXowCkU0lFCjiIehiBWnTIRSAmDZzoGIzIce3s3A4+lcNbZsZ2eEW+dYwsspgoxHCDRTsilUuSWQKzSDCZUy7Uj0tuOx2EhIlJkGLGhDKxbrCU0SOiLBltNBonZ8kg63fsR2xPQnfKJXRcj9Mh1RiuF1xKxv48LnC4j8mGAXmn0RgeP/TfXArstSTeM9hFgT+dl92Mt3dxt06f1lO8nb/zMNvTTV50wV09A3W7bzGnC5ZTsfm4aeSHZIJG8C71m287lp6B2qdWy8SAfoM4Q7G0sRbbjLkjxsaztquiLKiI8hJAjlA8XAe4jP/ekId92giMcuSDJrowRV+qRoY1i2sx9w85o//LuM+66g+8a1vryGeq162fzcifOn5tquqwXfC3OAb123QzdHCMOynd2AvyIWh57K8HAUipgooUYRD0MRZSnJXugljVyRvwS4iJBIUAX8yzT0b9P0HBpCiBgOHBxllwBiJeIt09Dnp+M5sw35dx5ISJTZBzHJToQ/CGXLTAdWtGExqwQx4e6oBN00GkKw6WTZjkljQWYrwgXNMkLCy+80zocpa8OviWYxDb3Wsp3bgZGEJriXWrYzMR1di1yXJZrG+8BZhDsmqK3RLpw2Jq/LPofUlgPvpvpcaeQTxHj/DxH+nhSyG9t/gGuBG9MztPaDzHJ6GvG+DFILXJ+i47S1hZr/IBZKCggP4t0R0V3tWqCmqoovr7wS3xtvsLyiYks74UTLnoKsB3ZO8rGRKKFG0aJYtrMVcF1NFcdN/rmg28IZuZ1cF9at8s+bOTZ/YlWFL/j+LAW+cl0WZm602Ye85g9+hrxnGvqKTI5HoWgKJdQomsSyHR1xAfN0Bp67N/Av4M+ezUuAm01DX5aG4/sQZQPDESU9kdQB/wPeSXdpVaaR+TKDCIkyexMezNwcAUTZ2XRC+TLrm3pAG6NDhAlbttOV6CVJgxHv++BnxKnAYYi/S9D9MoNwESYj+TDZhux49DihjBAf8C/Lds5OMacFANdlkabxISI3yw+wVVF9r649AltP/y2fzaW+KU/d0DNrLjylePUScJ9lO++meFH8DfB3y3b2Nw19YpqG2OaRn2UPIMpRvdxrGvrMFA8fWfrU4Lq0VOe9IYhV7ljiRp786pKfz3mPPUbdk0/inz+fJV9+yfTDDkuo25OXEuBAy3Z8aXD0qa5PihZBdjg8ww3wzyVzc3tNGp3fvapC81dV+NbNmZQ3ZvXynHVy1wAwFvjFdcnKUuEMcxzimn8D8HqGx6JQNIkSahTNcaj83qr5NJbt/Bkh0njrxr8AHjUNPXLFKtFj5yBO1BcQvV10JSL9/b32Ij5YttOFUL7MYERL82jZA7GoBWYRKmOalY5JZxaznjaeUSNXjbz5MNEEmc6ehzQQyocpQ5Su1cmvuxDlNNnUljeb+QzRsvsIebsIuAW4Ox0Hd11sTeNj4DTAt9OedVuE5g+f71r29I0c6LqMT8dzpYnvEPk5VyD+DkkhO2w9Dtxt2c45pqG3lGDQ1riG0GstyAumoY9Kw7EjHTUt6aa5hDiFDZ8PX0GBGNuee7LLoEFsk5vLMISA/A7iemF5nM9bghBUuyPOe6mgHDWKtGPZzj7ALZs2+Izx3xV0X7XMX9DQoNUst3PGzZucN89T5rQSERacVK5ce8eynULgannzBdPQN2dyPApFcyihRtEcQ4HlrWUNlCuDw4FLCZVU1AD/Ng39ixSPXYgIBz6X6GU9GxAlA5+Yht6ma3mlNdbbJnsnomfuxKKM8DbZ87M1xLWFWA90t2wnN1t/b5kP05fYIb2R+TDVhNwvMxGTZ68jZks+jGU7VxGe0TNbiTTxIwWFBxFd0YJlZCfIlt3fpOM5XJd5msYn3Xo2nN+7X2AngMpy35pVy3LWAMdrGg2uy6R0PFeqyMDWZ4HnLdvZI5UyMNPQ51q2Mw8RUpx02+/2gmU7pxEKsA7yOSLTJx20llDjA4aR2OfUFnJztwg8+yHed48icm3eAF5CfL7HIujE6Y0SahRZhMyduqahnhNnjs/rMntcfpf6enwlq/32zHH5EyvKfEEHVxXwAzA1yZymjsIFiM/k+QjHvEKR1SihRhETy3Y6I8qOWiXvwLKdnsD9CPtzkOWIUqfFKRy3O6JM4EzEilkkxYjuJF+ahp4tbUfjRjonBhIe/DsgwcOsIjz4d3kbD3VNlVKEfbgXwmXS6sjytP6I/2U0R0xfGufDBNtWj6VxPsymBPJh+kbcVqtzCWIa+ibLdu5GTBKD+Vq3WbYzwzT0aB1sEsZ1mXPj02UNC2bkaq4LyxfkeMtcTtQ0Aq7LlHQ8V6qYhj7esp2JwNWW7fwzxayi54E3Ldv5zjT0TWkaYpvDsp2DgZsiNk8AHk5jFlSk87KlBNs/kb5r0mAnuR2BO4HrEe6z0TH29wo1i1J87kihRpU+KRJGLlr+HbjcWe7vNe67gu6bSny51ZW+krmT88asWprjvS6ZCXznurRnl3PKyG6u58ubj3fwa1xFG0EJNYqmOBDxGvmtpZ/Isp29gYcJnyB+CzyUbO6FbOd9LiJfI9rF0gLEquOPbclCL9sxGoQ7ZqIJULFwgYWE58uoibiHiJatLSLUyHK0ptpW94p4SDAfxkGUoYUJMWnOh+nn+bkWIVwpEsQ09MmW7byJaCsNYgL5oGU7/5eOc45lO50O+gsH9dYbSn/5vNC/ZE7usohdTpZizbQ4DjcEUZbUB3FuvB+IPF6qPIcoSzmIFLoImoZeYtnORwjn5b/TNLY2hWU7uyA+M71i7RLgljR312stR40Z5bnSQYH8+h9CuInWwS5Y4pyOclflqFGkhGU7ewK3VldqgyaNzu+2aFZu50BAq1uxIGfs3El5cwKBLWVOJYiw4CUZHG5b4mqE8DzKNPTpGR6LQhEXSqhRNMVhwCaEWt8iyFWDc4ErCV1w1gKPA58msypo2c72iDyEE4j+Gp8CvAWMawsdaCzb6UR4vsyeJHZBW4tozxgsY5rV1ku7WomkA4Wly6kn0cuRgj97W+gG82FWIybH4xAup6BDZk0rlx55hZq1beF9ksX8BziAUNDrXsDFcnuqDAO6GIPrqpbOzf3QdbVOUfb5qyyDinUez0eIJyciBG0N2B/RqekO4Mk0jBMA09DnWbbzHcJVMy7FFc33Ee26dzINPVUXRJtCLkI8Q8g5AmLSdk0LZC60llBzFqLDXEuRA9xAYwcShISadATIK6FGkRSW7fQArnIDnLJwVm7hlJ/zu1VXav4Na/0LZ47Nn7B5ky+4GNOAWEAd47qkU5Rtt1i2sy9wNOL89WyGh6NQxI0SahRRkfkXBwO/t5TbxLKdbsC9iBycIH8gVgQTbrNp2c4eiHybw2Ps8jOixfasRI/dmshuV4MJlTLtQmJ1++WElzHNV/kiSVFCjAt3+f7YitiOmP6ET3BqCHfDfE+4I2ZdtthwpXjqdbYpt1UKmIZeb9nOHYiW3cGJ9UWyZXc8TpeoyNfgWfJm2fFnVz736v3ddwNOjthVA06VzprIbJgCRCel/Qmf9OfKrwfk9geTHWcUXkS07D4B+DLZg8huUs8AN1q2c1lHEROlcP804vwTpBq4zjR0pwWesjVKn3agcblluskDto1xn7f0KVVU1ydFQsjP3GHAlaXrfT3HfVvQY/VKf35NlW/jvCm5Y/5YnOt9Xy9FuGjaRaOL1kD+fYPtuN8yDT2aq06hyEqUUKOIxV6IcppfWuLgUlR5GDGpDfIj8EAiK4LSuXAgQqD5U5RdGoCvgbdNQ1+a9IBbCDn+bQjPl9kmwcOsJjz4d2m2TPrbKjIfpg7Yz7KdAI0FmX6Ei2flhNpWj5c/ex0xpW1oItmD8JVtdVGTIqahr7Rs51GEMA3itfOgZTtnmYZeluRhjyJ0/vzYNPQq12WKpuFHiCBeNMCUYs1cua0TMArYl9iTyU7AbYjJ+aNJjjMM09CLLdv5BLjMsp1RKYrIvyHyxw4HfkrH+LIZKc49ghDvg7jAHaahz43+qJRpDUfNX2PdsWTJEmbPnk1ZWRkHHHAAO++8M67romlarIc0RSz3QSXi91KlT4pWxbKd3YBb6+vYbfrv+V3nTMzrUl+n1f+xKGf87Il5swMNWvBarhLRAGCmCgtOmGGIc+ZqRB6lQtFmUEKNIhaHIi5q0triVQoTZwDXEnr91QNPAR/GO5mVF6xHIQSaXaLsUg1YwEjT0DMSBhsNOW6DkGNmMI2zSJpjMSFRZrpaHUgcGZTdVNvq3ogVaz9wDGLFNeh+mU1IgAnmw7SnEL9+EbeVoyY9fIXIZjlW3u6HCBe+PVERT55Hz5M364APgve5LhM1DR9wfMTDfMDfNY0PXZeVCGF8b5qfSHYC7kGcp9NVBvUawvlzBqLsKilkd60ngcct2/m9PTsH5f/8JsRryMtTpqG3yIKKpDWEmvOIIhZ++umn3HHHHdTW1tKvXz/ef/997rrrLg444IBknqMC+DXaHfJ1FNNBmSBKqFE0i3SUXwGYfyzx54//rrBHeamWs3Gdf/HMcfnjyzf6vNcUU4HvXbeRW0vRDJbtdAUulzefMQ098v2pUGQ1SqhRxOIwYHI6J6AyPPVu4EjPZge41TT0OXEeIw84CZHcvnWUXcoQ2QUfZEM3EGlT34PwfJlErNB1wFxCpUwzUliB7xDICU0PGufDeAWZrp6HBBD5MA6wAtE1ZRUiU2R74Mr2PAGMQmQJQtYInW0ZORl8GHEOCDphjkHkEX2R4OH2AXaVP39jGnqJ907XZbx01hwT8TifrnNOZSXDOnViJ+KfRHZClEHVIjoupYRp6Bst23kbuNCync9TOaeZhr7Esp2xiKyz/6Y6tizmbEQXGC8fAu+18PO2dOlTL8RnZBgzZ87koYce4rTTTuPCCy9kxowZvPbaawwfPpy3336b/fbbLzSg2lo2b95Mr15Nrnn4EWJpLNajSp8ULYwswzkJuLpys9Zr4g8F3ZbOy+lUW61tmj8tb8yKBbnFnt3XAl+6LisyM9p2wf8hrgenIdqXKxRtCiXUKBph2c62wHZ4VmnTcMxBiO4cRZ7NvwL3xnORLkWevwHnEN2BsgYYAXxmGnrGVh0s2+mFWKUOljENIrF8mQpgBqFSprltsWV4SyIvdLz5MNEcMd5V4FpC7pc5iA9rryNmXbQcJst2NgNGBxNpoLGjRgk1acI09HLLdu4EXiV0XrjJsp3ppqEncjF+rufnEdF2cF1+l86ao4LbBgwgf+JEhufm0ofEP/87Ic7h9cDLCT42GiMRZUvDST3c8RVgpGU7X7bHDnaW7RyBcKF6+Q3RYralyyBa2lFzgjxmmCD00UcfMWjQIK677jp69OjBwIEDOfbYYzn77LN5+OGHGTFiBJ06iViliRMncumllzJt2jRyc2PmEa9EZODFogQYmPJvoxw1ihjITm23ugH2mj8tt9PUXwu61VRqgT+W5EyaPSFvZkO9FrwOqUfEDox1XdpMR9Jsw7KdgQjXpkvrnCsVirSjhBpFNILhvim35ZbuBhMR5BW8ggogLsxHNnfilMG6wZXEzlF2WYLo4PRdmluSNov83YoIiTL7EDusMBbr8JQxAYs6er6MdE31J3rL6v4IIcHvechmQqLLRM/PwYyYjUl+QJeQnhXWtoYSaloQ09BnWLbzGnCJ3FSIyKu5yDT0uuYeLy8+g+fo301Dj9ma1XX5TTprDt92WwrGj+fCPn3olZsb+7O/qqqKxYsXM2vWLPr168fQoUPJydmyeyfgCYT4mZJ7xTT0Kst2/gPcYNnOB6mUqJqGXibboF+JcG22Gyzb2R0R5uwNZbERuTQt+lkhhb5I5SPdQs3ZhDscAZg3bx5DhgyhSxfRHK+2tpbCwkLuueceLrroIkaOHMn//d//UVdXx+jRo9l6662bEmlqEcJgU5QA3SzbyUtRnFdCjSIMudB4KXB6yRpf3rhvC7qvW+XP31TiXzZzXN7YTSV+by7jIkRY8MbMjLZdcT3iWvHTZBqUKBTZgBJqFNEYCixMtYOELPu5AzjOs3ktotSpyZbflu1sg6hbP4nG1msQnXPeAMa0lrAhnRy7EB78m+hEfgkhUWY6It+kQ6n88nXRVNvqyJyADYQ7YoKhvcF8mHS3ow2yHuhj2Y7Wwf5HkaVP7c6hkAW8jmjZvbe8vRviQv65OB57jufnePJdfhkyhO6ffsprvXvTMycnTOQMY+XKldx6661MmDCBgoICOnfuTJ8+ffj8888jxZrnEWWZSefLSD5H/D7/BO5P8VifAn+zbGev5j5f2gqW7QxA5Ld5XS1rgWtNQ6+M/qi0Eu2zN50Ow3zgiMiNlZWVFBYWsnjx4i2vu7w8MZTBgwfz97//nRdeeIGTTjqJrbbailGjRnHuuedGHsZLHeL10RTB8sFepBagHunoVUJNB0Uu5v0FuLault5Tf83vMn9KXpfqKq18wfS80cvm56707L4Z0YFvrgoLTh3Ldg5G5HlVAC9leDgKRdIooUYRhgw4Gwy8meJxdkJ0CfE6TMYCd5uGXtrE4wzgAuBoopcMjZVjm9bSk2fLdgqA3QmJMnsR3sK2ORoI5ctMA2Y29bu3ByLyYWI5Yrp5HhJATDwchDV9EiE3jAOsyWDpVwliNbkrIvuoo+B11NQCpRkaR7vFNPQGy3buQuSLBJ2C51u2M8E09ImxHidLK0+UN21gSnPP5br0dl0eDQTo7vfHFmnmz5/PqaeeyoEHHsiTTz7JiSeeyLx587j11lu55ZZbeOKJJ7y7FwL/QUyA329uDLGQrcufBx6xbGekaeiLUzhWwLKdJxDtuoe3dWeiDMF8hvBS30rgGtPQ17XSMCLLniC9jpojEeeYMDEjLy+PY489ltmzRTf5QCCAzxe6HLjsssv44osveOCBB7j66qtZvnw5p556alPPU4lY3GkKb4vuVIQa5ahRYNnODsCtwL4rFubkT/i+oHt5qU9btdQ/dea4/OmeMicXcd0z2nUbvXYUSWDZTi5wg7z5imnoGzI5HoUiFZRQo4jkYIRAknQXCct2/or4gAquxgUQivZb0S6e5eR+X0RWwZAohwwA38vHL0h2XM1h2U53wrsx7Upi75FKYCahUqY57S1hXrqK+tC0I8Z7YVpLyP0yF9FpxuuIWRstHyYbkKUZlYjft6MKNWs7mJuo1TANfZVlO/8CHpKbNOA+2bK7NMbDTiN0Xn0njv9NX2CsprGN39+ohGULs2fP5vjjj2f48OGcf/757LKLaKS3xx57cMABBzB16lTq6uoiS0sKEeVP9cDHzYyjKX5CnBuuBK5L4TiYhj7Fsh0HIWb9L5VjZRI50XgUEWYeJADcYhr6wlYcSksLNacDXSI35uTkcN5552257RVpAHr06MEdd9zB9ddfT1lZGdtuuy39+kVWbG6hAfgEmnUprJffUy13VUJNB0Y6hi8Bzqoo03LHf1/QfcWCnMKyjb4Vs8blj924zu+9lnAQYcHF0Y+mSJLTEYvEKxCB6wpFm0UJNYpIhiJWluYl+kDLdgqBWxDlSkFKgNtNQ2+08isn/UMRAk2jrg+ISf4XiAlJWj/IpDikExJl9iH8ojgeSgiJMtMQ+TJZKTrEi5wg9AMGEN0RE5kPU0GoLGkSIQEm6IhJNh8mWwh2AomZA9KekO9Jb+mTyqdpQUxDH2XZzkGEzplbAXdZtnNj5PtGOvxOlzfX0HwHi/6IjlJFNM4Z2YJt25imyUUXXcQll1xCUZHIe6+trSUvL4+Kigr8fj8NDQ3k5OSgad6oFAoRGWF1iDKmhJHdsJ4FXrFsZ1/T0KcmcxwPzwAvW7YzOp1dC1sL+dl0O7BfxF2PmIY+rpWH05KlTz5gGImF7QNQV1fHSSedxBdffMHXX3/NDTfc0NTuFcBHcRw26KhJtUV3LUIUCr5R8izb8bV1h5eiaeT79mjg+kADW82dnNdp+pj8bpXlWsXCmXnfLZmbs9wTM1ULjAYmui7qdZFGpOs0mP/2RDy5bwpFNqOEGsUW5CT9IOD7RC8qLNvZHtERZAfP5onAnZG2Q/k8f0G02B4Y5XCbESr4++myLMoJ6E6EO2YisziaYzkhUWY6UNzWRAi52hOrJCmYD+OdiW0gJL7MIyTKrEbkw5S32uAzw3pSv3BvS/QgfFKvhJqW51HE+Whrefsw4FTAitjvJKC7/PndZsLTt0K0mddpQqTZsGEDN954I8cddxzDhw/fItLU1dWRl5dHcXExn3/+Oeeffz4FBTGNAZ2AdxHdNb5sYkwxMQ19qmU7Y4CrLNv5RyrnVelU+gb4B/Fl/mQbFwInR2x72zT0yNdDa9CSjpo/EeUadN26dVRWVtK3b18KC6N3tg46u66++mrq6+sZNmxYU8+TQ3yNEdLiqJHCYzXhbbkLEI5bRTvEsp3tEIuU+69b5csd921h9/Wr/TnOcv+MWePyp9XVat5z9XzgG9dlU2ZG2+65DFFOPNY09N8zPRiFIlWUUKPwsg/iBPdrIg+ybOcE4DZCFyYuov3sa17BR4oEwxCtZaOJJCWIzgyfpLoSKjsH7UFIlNmLKBbrJgggPlCnI4SZGdle5ypXdLohJmexHDGR+TDrEMJLMTCZ8LbVq9tb6VYSdLTOT6rjUytjGnqlZTt3IMqIgm61G2TL7iWwRWg+W95XAXzWzGEtxPs/pkgDMH78eBYtWsStt97K9tsLQ2GwxKmiooILLriA7bbbjssuuwwA13UjHTVBOgEfAH8Dvm1mbLF4DpF3cwRitTkV3gTet2znM9PQVza3c7Zg2c5xwOURm0cjwpszQUsKNadGO/4dd9zBsmXLGDp0KPvvvz+GYbDVVluRn5+P3+/Htm18Ph8777wze+yxB//9b7PNx0YhHF/NEeyyk47zvRJq2ga9gf0R+XhzITF3i3SR/wM4r7aG3Ck/F3S1p+V2LtvoK541Lv/3DWv9XjFmE/C166K6D7UQMuNyGKLc8cnMjkahSA9KqFF4GYqwZMYMs/Ri2U4+ou22N8VvI8JFM8GzXw/EausZhAsFQVYCbwNfJdsWU4Yg702ojGlXmpmkRFCFCBucLr9mmYYe2b0ho3jyYZpyxHgvDoP5MKsRotNPhDti1rZ2S/M2SEcTalTHpwxgGvocy3ZeBq6Qm/IRLbuHy3PiUELB7FYzQvZhiHNgtLKVMMaMGcPAgQM5+OCDgVC5U1lZGf/4xz+oqanhxhtvpHt3YeSJIdIE6YTIAjmF5suyGmEa+mLLdr4ErrRs55dUykhlvtRLiMyb65M9Tmti2c4+wL0Rm2cDd2WwbKYlS5/OIspn9KxZs1i5ciUbN27k5Zdfpm/fvgwZMoShQ4dy6KGHcuutt7L11lvz9NNP4/fHzMYOUo4I7G4W09BrLdspIz0Oyiqgp+d2dGuQIlP8CXgRcb1YhRDIS4FjENdKTSIXxQ5HBNb2Xzovp2DiDwXdN23wVS+alfvD4tm5Sz3G5AAwHvjZddPaMU3hQf5PbkT84T8wDX1ZZkekUKQHJdQogC0nuaHAhHhcFJbtbAs8gmhXHWQaIo9mndxHR7RejbpyhvhAfBMYnUSpVX/C22TvmMjjESU90z1fCzItWsiSsL4IN0y0rkn9CH/PVhISXqYQHtK7CpEPo+qfU2M9ib+22jLKUZM53gIOREwiQJxbr0SsDAaTVRtovsvSkYQ6STXJgAEDmDlzJhs2bKBXr15byp0uuOAC6uvrufzyyznqqKOaE2i8dEJk1ZwI/Bzvgzy8jGijfAqNS78S5TvgdMt2DjQNfXyKx2pR5OfpE4QLF6uA6zPY9Q4af24HEOHRqbI9jc81ALz66qs88cQTHHfccRxwwAF89tlnjBo1ii+//JLCwkKqqqrYeuutmTVrFltvvTW9e/du6vWZT2IOr2AmWaqoQOHspBui1PR8xP9EIyRGdkYsUh6IcNdExbKdrYGbgYPKSzX/uO8KuxcvyclzlvtnzRqfP7W2WvO6t/5AhAWn0kVMER9HI+YDpQhHv0LRLlBCjSLIDgiB4I3mdrRs52jgbsJbVb8BvCzbzu6AaLF9PERtBzsJIdBMjCeLQDpJtickyuxDjIu8JlhJKFtmOrCytfNlpE22qbbVWxGeD7ORkPhiExJlgtvK21pGThukBGGN7igooSZDyPbSdyGEmKDz8GzLdtYi3IIAo0xDb+5/ohEeZhqTo48+mueee47HHnuMbbbZhqqqKl577TUGDhzIddddx3HHHUdOTk5TJU/R6ITIqjkeGBPvgwBMQ19j2c77wD8t2/kmFVejzAp5DLjXsp1zMi3Ex0I6Tp8h3G1ajmjDnely20ihpsZ1m+2eFA+nxLpjjz324LjjjuOJJ57gjjvu4LrrruO6666jtLSURx99lHfffZfvvvuOTz/9lAMPPJCPP26y4dgUEuvYV4II304VJdRkFxpgAq8gzk/R/h8aQqz5FNidCEFSOsiHAxcEGsibNT6v88xx+V1L1/tWz56Q//t6x7/Rs3s1osPlFBUW3PLIoP1r5M0XO0B2oqIDoYQaRZCh8nvM0D2Z+3Itoc4jIC6C7jIN/XfLdvaybGe451heXETpzZumocdcrfA8z66ERJm9ga5x/RaCALAAjzBjGnpJk49IEelI6krIDRNNkOnueUgAsXoXdL94HTGrUPkw2UJHCxOOFGpU6VMrYhr6Wst2HkSs+gZ5ALFK2ACMiOMwnyEs+Z2a2Y9BgwYxYsQIHn30UX799Vc6derEOeecw4UXXsiAAQPQNC0ukSYQCES2UO6McDIcg+g8lQhvIlyYZwOvJ/jYMExDn2fZzhzg7zTvRGp15GfdE8A2ns31wI2moS/NzKjCiCx9Slfpxrk0UQ505plnkp+fz913301paSkXXHABPXr0oKysjCFDhvDkk08yadIkNmxoUseqIL73i5cSYG/LdrQUF0EiP7tV6VPmGIjI/9qf5p2GPoRQdw3ifQmAZTuHIFw0A1av9OeO/66gx7pV/rrFc3J/Xjgjd1GEJj4b+NZ12ZzG30HRNOchrrcX0Hx+m0LRplBCTRuhqKjoXuAegOLi4riXNj2P9yOCHk9CWDv7Ii7kS4EFfz7q+G4HnXTqsltOP2l9xOMGAksBTrzw0rHHn3exd4VtFiJEeMdnvvz5vVfuvP7U0nVr8vMKChv+cffDi3Y/8JDNiIvOrxBdK5ZHG5tlO12BPQk5ZnYnjnwFDzWE8mWmIfJl0hrcJ109vWnaEeOdGNURKkNaAPxCuCNG5cO0DUroWEKNN6OmFnF+ULQipqGPtmzHQqwAB8shOyFC1uMJopyKENwPJ3rJaRj77bcfr7/+On6/n+rqanr3DlV+RBFgwrYHRZxo+yAmRaOAo4gz9wzANPQyy3beAC62bMcyDX1jsw9qmheAtyzb+dY09NIUj5U25GfKvYTcUkEeNA19SuuPKCqNHDVpOGYvxOd9k5x66qk0NDTw9ttvs9NOO3HIIYfw008/ce2116LrOn/961+bO4Qf+CLBsZUgrj06Q0oTbeWoyTy5iMySOxH/03jnO52B+4EPLdtpQIjeh9VUoU36qaDbwpm5hWtW5MyZOTZvck21z1vmtBH4ynVZlMbfQdEMlu30QzidAB5X5f6K9oYSajoARUVFByLyD7x5MnUIe3Vv4ODJP37L5B+/3fPZ6/gEOKu4uLjRyllB5y4DEBcyINqx2sBTK+y5e7502zW7bC7dmNupa7f6Sx58cuGOew5ej8gYeNc09LBVect2+hLqxrQPom12IuLTJsLbZNumocfT1SEmlu3kEJ4PEynI9CM8P6CSkBAzlYi21UCJ+sBoF6wHulm2k5ds0HUbo7/n57WqtC5jPAXsCxwgb3eBhHIOzkCIIwOJQ/Tu1k1U3US24Pb5fAQCgS0/e7eDaO89efJk/vjjD3744Qe23XZb/v3vf3sP0QURLHw44jwZLx8AZyI6qjzRzL5NYhp6iWU7HwKXInLVsoVLgWMjtr1qGnpSLc5biJYQak6Qx2nydem6LsOGDWPevHnce++9nHnmmdTU1HDQQQfF+zwrERkhiRC8vumNEmraMkMQbqp+xOEsjMR13dzKuoZvgE1ugPzFc3ILJo3O716yxr9uzoS8b9cW53gd2g3A78BvrhtXdzFFerkacZ76wTT0RD5jFIo2gRJq2jlFRUUnAx8hTmQlwOPAJ8XFxQvl/f5h/7zmmpLVxTeN+eKTLq7rmogPtlrLdnKueeqVfzxz3SXeQ1Yg2oUeDpy9cPrkTq/cdeMu1RWb/d169a699KGnp2yzy66vAx/JlVFNZtYM9nwNSPDXKCaULTMNWJ7oBFLWsMYqSdIRrgnvsnApIfFlIeFuGAeVD9NRKEWUqfUisYlym0Ou8G/l2aTyaTKE7Fr0EKFA3hrgL5btvGca+sI4DrEJOBjRbWRb4uyAF+yi8/HHH7Ny5Uquu+66MIGmuLiYBQsWMHPmTL7//ntyc3P5448/OO644/jtN1E1e8YZZ7Dvvvt6D9tV/h6HAjPiGYdp6DWya9Mdlu28bxp6cTyPa4L3gPcs29k5zr9fi2LZzl8RIpSXrxEZGtlEpJiSDqHmbOIoZdY0jZycHO666y7q6uq499572WOPPRgwIK7Lh1ri7PYUQdBR3BuI6gCOEyXUZIaewNOIUseEBZogmqbl5uf4du3t67Rs5LtofyzOcZfMzf1twfTcha6rea/7liPCgtelNmxFMli2Mxg4DvF+fzazo1EoWgYl1LRjioqKdkasKuQjUuyPKy4uDlthKi4ubrBsZwAwdeKor8+rqar8L2yxEz7ctVevPwf3dQOBeoTz5WSAWWN/7frmA7fvVFtT7evZt//ms2644+Ztdtn1HUTw7zB5Et2b8GyW5nARwsh0hCgzI9KRE4knHyZWy2od6BHxHOsIuV+mEeGIybbW3IrMIANeNyAu3Nu1UIN4j3gn9EqoySyDERPHfgiRPRf4l2U758eZX7UeOAjhrBlAHGKN67ps2LCB++67j27dujFs2DC23357XNfl4osvZu3ataxYsYKjjz6agw46iLPOOovCwkKee+45evTowT333MPOO+8c7dBdEOWfhyAyHOLhK0SWyWWI8oWkMQ29zrKdZ4AbLdu5NJMiu2U7+wN3RGyeiih5yjbxP9JRk6qrMB84It6dg6V1999/P3/5y18A6No1rri6OpLrGhZ0SqRa7hp5/aAyaloWDdHu/UXEa6xJYayiooLOnZuOq8nx+Xz76t0G3j+j5pexP+RNqK7weUXKKkRZ5/Q0hWsrEkQuLN0ob75tGvqqTI5HoWgplFDTvnkQ0UmiGjg1UqSBLWGGBwJfLFm0cAMw7KM5Kw9CBFh219C2vEZ8Pl8ucpVi4qiverz7+IM7NNTXad1691l52cPPPK9vv+NgxIV1s7kIHmoRF+7T5ddM09DDLMfyhNyLph0x3tWTekIizEJEXkNkPoyyqCripaMECquOT1mCPC+fCWxAfE4HO9fsgAh0j7eEZy2iDGAi4rzZ5Ge+pmn07t2bkSNHUlpayvbbbw/AxIkT+e677xg2bBjvv/9+2CTn2muv5YsvvuCRRx7hpJNOorBQzEkjQog1xGfRbwjxaF5zA5ci6XPA05btjDANfX6cv3MsxiCC8I9AuEJbHekufYzwbogrEOHB2Vhame7SpyMQn/lxOUy8YdZDhgzZsj2OgOsqYGYS4/M6alJBOWpaj52At4G9aCYsOBAIcN9997Fx40ZuueUWioqKmnwt5ecTuOXygs7HfR72up8OfO+6VKRp/IrkOAkYhPiMeyvDY1EoWgwl1LRTioqK+iHsnwAji4uLF8TYdT/ERcRvlu34gUv9OTkXIizPvX05OVtahrrCiZLz2+cf9fvoucf6u4EA2w3avfzyR59f36lL19OjH74RZYREmenAfERpSV/EROJwy3YiBZn+hK8GVxFyv0wHviHcEbNe5cMo0kgJqV+4twX6RtxWHZ8yx/EIcRpEB6hTELZ+gL9btjPWNPRf4zyWgxDjJyL+x81+7u+1115ht/fcc08eeugh7r77bvbYYw8uvfRSampquOaaaxg9ejSPPfYYJ554YljGTZTJj4ZwV45BiEexPpO8/I5wm1wFXBHH/jGR7bqfBJ6wbOd309DTUcYTN5bt9Ea04fZOJkuBq01DT6SFdGuS7tKn0xHuqrjxijUVFRVomkanTk1WtQSAjyEpp4M3oyYVlFDT8uQDtwM3IV6n/qZ2XrJkCffccw8//fQTvXv3ZsiQIZx11llNCn55uVrOEUew7ymnMPXzz5mDKHNalsbfQZEElu10Bq6UN59VDnhFe0YJNe2XIwhlrnzaxH6HIYJxlwMvIVY7eyOdOIGGhjXIC6u8/IK+P7z/Vp/PX32+C67LLvvsV/rPfz21JK+goKkLIgfRkWkFbKnj7QfsjGjjrSNyMbz5MJsICS+/EXLHBL/KstAirmi/KEeNotWQpZznypvViNXi6YjshSD3WLZzpmno8WYjFCPEmkmI13KTk5pIOnXqxBlnnEFNTQ333XcfrusyZ84cfvrpJx599FGOP/74MJHm888/Z8yYMXTp0oW9996bYcOGBe/SECV2YxFByYubel4prjwLvGnZzgGmoU9IZNxRjrfUsp0xiL9vSq2/E0FmpD2F+LwLUgtcbxp6ooG3rUk6S580RNv1qC3CILzLmOuKj3hN0wgEAvj9fl555RV++OEHHnjggcgcJC+bEUJNMpQhwmFV6VN2czjwDkLMbvZvW1NTw6hRo5g9ezYPPPAAlmXx7rvvsvvuuzcSpSPx+/G/8w7H3Xkn1zzzTNra0ytS4yLE/34m8F2Gx6JQtChKqGm/7O75eVq0HeSE4FBgGULM2R4hyvgQNd65Pr9/S3Lfz5+863OWLekEsPchR5RcePfDy/w5Ocj9cxGvp40Iu36wPXZPwjtbuIiJb9D9MoNwEWZ1ultrKxQp0lFadCuhJjsYgihxAvhcui3GWLbzAaKbEwhnyn2W7VyZgHtwJSFnTW+amDBHIz8/n3/84x/U1NTw0EMPkZ+fz6uvvsqQIUPCRJo33niD++67D8Mw6Nq1K5999hlz587l9ttvD+7iQ4g144D9oekVatPQZ1u2Mxq42rKd89LglnwVeNeynf81l3+WDmTp7oPAbhF33WMaejLlOa1JOkuf/kQz15w+n4/NmzdTWFi4JdgaQiHXGzduZNGiRfTq1SvWIZDPEa/bLAxZbpcOB6Vy1LQMfYAXEGUvcYcF5+fn069fP+666y5M0+Sggw7ilFNO4YsvvmD77bena9euMUugfD60rl3p+8wzXIzIwFFkEMt2tkUEkoNox60WbRXtGiXUtF+8FxobYuyzL+JCuRAhtAQv3Ovkz64bCJTK+3GWLSkA6NKjZ+DcW+8N+HNyBsp9axErSJWIldqAfM7IltXBfBi1KqFoS6xH1MG3d5RQkx2cJ78HgHc9258B/gzsKG/vj3CGvJ3AsZchxJoJiBXJhMSauro6Jk+eTH5+PtXV1SxbtowjjhDZsK7rUl1dzRdffMFpp53GzTffTNeuXRk9ejS33XYbgwYNwjTN4KH88vl/Anal8cQ2khcQ3QuPIcUVVNPQyy3beQNRTnVXKseKk2sQDgAvz5uG/n0rPHfSaBoa6S19MomSX7du3Tq++OILvvjiC2pqathmm23Yaaed2GWXXdhjjz3YbrvtyMsTw7j//vu59957wzqRReF7SKlNshJqsg8N0SXtKcRrsskcxClTprBo0SJOOumkLXlap5566pb7DcPgzDPP5LPPPmPfffflhBNOaC7zqDOiBPVTxHWsInNci5i7fmEa+twMj0WhaHGUUNOx+R/iYtklVM9dL78HgAbN59vSsWm7XfcILJ8327e5dKPvyav+4V752IsvdevVewnhjhiVD6Nob6yn42XU1CJKEBWtiGU7BiI3DOAnb1tq09BrLdu5HWH5D06gr7BsZ3KCF6xLECWu4xHOlrjEmrq6Os4991xmzJjBU089xaJFi1i9OtQIbdWqVRQUFNCtWzd23XVXttpKdHo/6qij2G233Zg8ebJXqAEh1myFsLG/0NRzm4a+3LKdTxG/709pEPs/Q2T97NWSrhbLdk4Dzony3G0h/DJSpIHUSp/OIkrnsRtvvJEpU6ZwzDHH0KVLF2bOnMnkyZMBKCoq4sQTT2T48OEUFhaGlUbFoIzk2nJ7KaGx+ylRIkuflFCTPLshznkGzYQFA8yYMYNTTjmFHj16sP3227P//vuH3d/Q0IDf7+eOO+7g+++/56OPPmLXXXdl++23b+71lQu8jMgLU2QAy3aGICITKmnmM0OhaC8ooab9UuL5uRcQrXXdQkTNvCa/QFw81yIEm/W11VVLEauYbFyz+irEBccTztLFfe/4+3GHA/8uLi7eHHlghaId0VHChPt7fl6jLMUZ4VzPz+9E3mka+mLLdp4Gbpab/MCDlu2cm2DJ6EKEWDMOIdY0uZwMkJuby7nnnsvFF1/MMcccI57c72fz5s3ccsstTJo0iYaGBnw+H0VFRVseV1FRwcaNG+nbNzKrGhATr2uI76L7VeBEhDPj/Tj2j4kscXkc0a57eEssLli2cwgi6NTLBOCRNvLeiuZaSNZRsz3h5xcAvv32W2bOnMnIkSPZe++9t2yvrq7m119/5eOPP+axxx5jxIgRPP7442Gdn2JQgGgukAolQA/LdnwpvC6UoyZ1CoH7EKGx+cQpKPfu3ZuBAweydu1a/vOf/7DjjjvSu3fo49vv99PQ0EBubi633347119/Pd9++y0XXXTRFudWDPKAoxFB798m+0spksOynRzgennzNdPQS5raX6FoLyRke1a0KeZ4ft4n8k7ZWcl74eQiQvQqEaVKK4BNmubbEtS2VdE2fZ4bPfkF4Dq56RDg26Kioq5pHrtCkU2sB3rLTKd2iczR2MqzSXV8amUs2+kHHCdvTjcNfXaMXT9ChKwH2Ra4IYmntBHn8DLi7JAzbNgwjj32WPx+/5bckJtuuomff/6ZW2+9lYcffpgrrriC9957j7vvvpu33nqLp59+mgULFnDggQfGOmyTs6MgpqGvB0YAF8uuHylhGvpUxALGSakeKxLLdnYBHib8GmsxcItp6PXRH5V1RPu/JCvUHI9w6YYxceJEdthhB3baSVSW1tTUUF9fT0FBAcceeyyvvPIKo0ePZuutt+app56ipqbZp59KqJV9spQg/m89m9uxCZRQkxrHIZx/VyAEm7jnKitXrqRHjx48++yzfPfdd3z33XfU14e/5YLnrhNPPJFDDjmEjz76iOnTp295fF1dzMq5TsArqEXuTHAaQvD9gxSFeoWiLaGEmvbLT4QujE6Ncv9GxEVXPUKgceXPfkQJRE/A5/OHSp8GH3bUhcCvz42evM/+x574mdx8MEqsUbRvShC25/b8Gu9BeFmCyqdpfc4i9JncyE0TRLox7ifcNXmKZTtHJ/GccxFW8nLiFGu8pQG//fYb3377LQ899BCmaXLsscdy3nnncdttt/HNN9/wzTffMHv2bG688UbOOuusaIerJ0bYfQyCf5fzE3hMUzxDmoSfIJbt9JXH9XajKQGuMQ29LblPozlqki19+hNRwl+HDBnC0qVLWb58uXjC/HxycnJwXZe6ujpqa2sZOHAgl1xyCStWrOCnn35q6jkqaeJ9kwDr5fdUXJSq61Ny6MDngIVYSIw7MDiIYRiUlpay6667cvrpp/Pss8+yZMmSRvsFxZuHHnqIdevW8e677/Lmm29y1lln8cwzzzT1FD0QbkRFK2HZTk/gn/LmkyrnUtGRUEJNO6W4uHgN8Im8eXZRUdEu3vtNQ69GdGMaj7gwqUYIO6Wu625GdBXZyefzdyccP7D7ebfet81JF12+Qm47qHf/AfNe/nHChZbt7CYtigpFu0C+Vypo352fVJBwBrFspwuipAeEm/G3JnbHNPSNwN0Rm++wbKdReUkczESE3SYsIhQUFJCXl0f37qGPCb/fT9++fXFdl5deeomRI0dy+eWXA6GWyx7qgFvjfT7T0CsQJVDnWLaT8vvRNHQH+AqRk5Mylu10QrRR97rTqoFrTUNfHfVB2Us6S58aom3cb7/92GabbTj33HN5/fXXWb58OQ0NDWiaRm5u7pZSlD/96U80NDQQCDRZieRD5O6lSlAATeX1pRw1ieFHuGcWItxXCQs0QVauXLnlHHTjjTdSXl7O66+/3siNlSM6ltKvXz9OPfVUPv74Y+6880723HNPrrjiiqaeogDRLVXRelyK6Eg7gWY+GxWK9oYSato3dyIuvgsBq8gbHADIC8drgOXA8oqyTdWv3HVD34qyTesQH5hLXdf1XhmFXWUfd84/1p188ZUr0DRKVq8qev3eW56rKNs0EvjFsp1XLNu50rKdoZbt9GjB31GhaA3ae4vuSKFGlT61LsMITU5GxpONYRr6BEQpUJCuwAOyjC1RpgFHkqBY06lTJ3RdZ9WqVWHlBRs2bMDv91NdXR3Wujuis0olcCOiBCsRLERXwUsSfFws3gaOlG1fk8ayHT/wCOBdFHGBO0xDn5fKsTNEOkufpiLE7jB69OjBm2++ycEHH8xrr73GjTfeyAsvvMDo0aOZM2cOGzZsoKamhueff56KigqOPPLIpp7jD0QL+lRJh6NGCTXxMxCYAvwbkVkVtRQyisgblR133JHq6mrmzZvHgAEDuOmmm/jggw/4/vvvuf3225kyZcqWfUtLS3nxxRexLItBgwbx6aef8tJLL1FY2KQBqoHGjilFCyHLSE9FLCQ/0UbyvRSKtKGcD22QoqKi5iaM9cXFxaXFxcULioqKzgM+AHYHphcVFT0OfFJcXLwI4Koj/zzjtKtv+XjdH8uvG/ftF91qKiv92k1390Z0cKpz3UB58KBV5WUrgecRZVF7AXsee/ZwfD6f+/mrz223asnCzs9e/8+dr37yPws7d+u+L6L9NwCW7awAZiBWb2cAy1R3KEUbYj3tW6iJTHpVjppWQjoQz5Y3SxEOj3h5EdElypC39wEuBF5PYiiTEWGZPxJHdxWA3XffnWHDhvHAAw9QW1tL3759WbNmDf/61784++yz0XU91kMrgdvl+BPCNPQ6y3ZeQIhSI01DX57oMSKOV2XZzouItq/XN7N7VGR+1c00Lol40jT0X1IZXwZJZ+nTG4hW6HlEdH7q2bMnzz77LCtWrODDDz/kxRdfJBAIsMMOO1BXV8eyZcsYMGAAt912W5joF2Vc78a6M0GCjpp0CjWq9Ck6vRGB5lshXDUx8Yq8ruvGbKe9evVqcnJy6N9fmAuHDx/Of//7X6666ir69OnDsGHDtuy7du1aRo0axemnn87dd0caFGMSAEbHu7MieeR59QaEqeAD09Ab17ApFO0cJdS0TdY1c/8MYDBAcXHxZ0VFRUcCbwI7IVb8HikqKqpFrJ72+OjZf4sVWE1jjyGHbsgv7BRstd0Nz+pGYddu2yIuRr9CZCSsB3Y4+szz9ypdv3b4L59+cPKqJYs6P3PtJbtc/dR/FnTp3sNrd95Wfp0sb2+2bGcWIeFmdoJdSxSK1qS9t+hWpU+Z4xhCQtmHstQuLjwtu0cSWrX/p2U7k5JsOz0BEeQ5ijjLD6699lp8Ph8jRoxgxYoVNDT8P3tnHSZHkf7xz2xciWCVxQ4OOnBcOLg7ODjgOOQOOA6p4O7uboe7uzsJnsLdnR9OsBRuSyFJgLjsbv/+eGsyPb2zycrsjmx9nmeebHdP99RuZnq6v/W+328D2223HUcckQ48msN0JA2p1SJNgieBHZB2iSPn8dyWHm8rY92qOlKvtmH/7YCRqXV36ki1Nyq6lKSFmhhpVWsLs4C/IJNGKyHvrTmVX5lMhsUXX5wjjjiCI444gp9//pk333yTL774gkUWWYRll12WZZZZpplDzzn+vW0cW5qJ/t/Q+tTxXIekkjYr0lhrufvuuxkyZAgjRoxg9dVXb1akAYl0r6mpYdKkSdTX13PYYYfxzTff0NjYiNaaESNGzHnu7373O0aPHk2/fi22qJqK+E+929IdAu3in4i/1STg6hKPJRAoCUGo6QLU1dW9XFtbOxxxTd8IWAW5MRiAXJSM69a9+4sHXnj1Mkv+YYUl/G4LA9dP/e3X3wF/TBwu44+xHmLcd4uO1Gf60vNNrbljN+Ba99Xnfc/cbauhR1x167OD5l9waQqXsvYHVvUPgEZj3WeIcJN91IUyx0CZUO0R3UGoKQF+xjAbyT0LuKe1x9CR+tpHTR/vV9Ugkd3beE+X1vIysAHwCC2srDnwwAPZcMMNmTFjBo2NjXk3Q0nimOmZDAchPjNtxsdrXwJcYaz7o47U++08Xuz/hid7kavFyUzGurWRFuIkLwLnt2dMZUD6e3tmHLfMcLoZfkC8kP4O7IS0M/Tyr5P3WgsssAAbbLBBa449A5nwaTc6UtOMddMIrU8dTQap4Gs29e2aa67hrLPOYpVVVuGzzz5jxowZbLbZZhx77LHNVlf9+OOPNDY2cuGFF/L444/z5z//GWMMjzzyCLfccgvrrrsuf/3rXwHo0aMHPXr0KHicFDHyf3oxUhkW6GCMdb2QKkeAK3Wk2pvmFghUJEGoqRDq6upOAk5qx/4NSKRds7F2xrqhiPiSnd3dZcnl/3TQpc+8eRVywkyWdfcCdgc2M9ZdCTxQV1d3PamSe2NdD2BpYAWkXWoFmrZZgNxcLOMfm/t1E411WdHmPeDj4PYeKBHjkfdxtZL8TM4CfivVQLoYfyHXtvSQjtTEuT15LtyPnJ+zJh7DgKNoajjcUl4ANkbMWVtUWZONWG6OWbOoN4brt9mG61toNzFXdKReN9a9BhxorNuzvaK+jtTHvspzC6BFlTDGuuWB05CbziwW8aWp9NbedEVNMb57Y+Al/9gTqfzVwDb19SzW2Ag9e9KiO+cEDYjAWcxJnfYK87ORFpls1VB3Y133Copm7wy6Ubi9DoApU6Zw//33s99++3HggQcyceJEnnvuOQ477DD69u3LnnvuyZAhQ5q0QS2xxBLU1tby0ksvcfLJJ7PxxhszePBgoijCGMPnn38+R6hpIVOBrxBB/d02/aaBtrAd8j32OeJLFgh0SYKZcGAOOlITEHPH7AVZDXAGMFtH6kCkzPyz1G5DkZnc24x1fytwzNk6Uh/pSN2uI3WMjtSGwH8Qf4LbkXjYgokQSEnsWsCBiAD0grHuJmPdoca6dYx1CzSzXyBQbKq9oiaZFvRjqGTrNHZI/Dy6rQfx/1+nkW8CvaGxbsO2HhPxYdgM8ZNpF7NnU3/++Ty4zTZMADbOZGi+d6F1XIr48qxepONdjiRKDZ7XE411w4ALyK8I+BFJeKqGNt70TXRbjYSbI0ZMrP+XybDcGmtw8Y038tTXX1NXX09DQ0NT8+FmmEn72ugK0S7zeP95DFU1c6ce+KC5jR9//DHvvfcem2yyCT169GChhRZiq6224vDDD+fWW2/l4YfFyisp0mQNza+66iqMMWy33XYMHiwf5X79+vHcc8+x9dZbt3R8sxCR5ihkgvHd1v6CgbZhrFsQ2NUvnqcj1dw9QiBQ9QShJpCHjtRHwOmJVQOA8411fX3KyLbAqeQM97L8HrjMWHeJsW7JebzGjzpST+hIna8jtSPwD2R27TJkJvfXZnbtDizvx3A28Kix7kFj3enGuq2MdcuGaPBAB1G1ZsI+JSgpeobEp07AnyezVYovFMEUdxJSlp8U2Y421tU2s0tLeAKpMGlzysns2dSfeSb3HXssWc+cPwEbFUOs0ZGywKPAAW1Mu0ofbyJSdbrP3J5nrBuItEEMSayehog08/KQqxSatD514Gst9dprTN17b15fYgmuW2opzv3lF/YHHkLee5OQCpU004BLgHa1vhWgGMJ8EGrmzWU0kzQ3dOhQBg0axKeffgrA7Nlij3TwwQez3HLLMWbMGMaOlVNKHMfEcTwncnvIkCEsvfTSc5azDBw4sKXjmgY8hlTRXk7zk4mBjmF/5PPyjI7UG6UeTCBQSoJQE2iCjtTD5CcoLImPfdWRatSRuh+Zab2WphdvqwF3GOuONdYNoQXoSM3QkXpbR+omHalDEf8bjbR6GaT0sbkZfoWYXx6BtG09Z6y72li3r7FudWPdfC0ZQyAwD6q5omYQ+UkswZ+mc9g+8fOoZp/VCnSk3kISdrL0Rfxq2iNgPwJsRRvEmsZGZpx5JneeeCIfpjb9GdiwSJU1VyJG9RsV4Vgghrcr+ljYJvh23nOA3yVWNwJH6Uh9WqQxlAMd0frUHMOTC998w2fzz89NSPjAIKQd+gYkSGEWuWqH0+kYz5AJQD9jXXvElfTnJSQ/NeVmoI4C13dKKWpra3n99deZMWMGPXr0mCPWHHvssXz00Ue88MILzJ49m0wmQyaTYdy4cdxzj9h81dTUtDjSO8E0xEtJA5sgoRqBTsRYNwLYEPmMX1zi4QQCJScINYHmuBh4PbH8D2CP7IKO1DQdqasRweZB8r9oa5AvuvuMdbt6U7AWoyMV60h9oyP1kI7UGTpSWyHeCwcA1yDJJM2VlvdGbgJ2BS4CnjbWjTHWnWCs29RYt2QxZl4DXY7xwEBjXbPGhxVMMBLuZIx18yOGvSDtn+8U8fDXQJ4w8kfET6w9PIgIS60Ra6bX1LD1iSdyNIWrMf4KrN9esUZH6nvgbmDv1n7XNHO82ch3x+He7HkOfvlYxFsoyVltTIsqZzq69QmATGaOP12ScYmfZyGpXHsgXloL+8cQpDW7I3xfxvt/WzTZ1AyhombeNCDnlSZJd3369GG77bbjqaee4v33pWCqR48eNDY2suKKK/Kvf/2L++67j0wmM0eQOeSQQzjqqKN45x05nc4tHSpFI3JuuxyZmHy8nb9XoA34a/PD/eIoHam6Uo4nECgHwg1roCC+J/QY4PvE6j18wkXyeT/pSJ2MfNmmSxT7AvsC9xrrNmyPQKIjNVlH6lUdqWt0pPZDvGu2Ac5EZny/ncvuiyPGmMcDdyHizSXGut2NdSsb61pklhno0vyGXMxVY1VNEGo6ny3JVTHdWkxPIG9Yehz5YvauxrqV2nloA+xMy8SaaUjL1P1xzHeI/06hioxVgPWKUFlzPZIkuFU7jwOAjtTLyM3jOqlNuyJVHklu0ZGqRrPLThFqkGqodLWJncvzf/GPjqzwybZ2h4jujufNmQ2N9zY2xk3ai7bffnsmT56MMYYJE+S/JOtDs/POOzNu3DistXMEmeOPP54DDzyQFVdcsTWvPxXxn/kLcCTtaPMMtJsNgeUQofSm0g4lECgPglATaBYdqd+AQ8n/4jrZWLdUgedaRJQ5FEh7LSwInALcXISbhezrNepIfaojNUZH6gQdqc2AfwGHAbcgX7zNXcgNQFq09kZMCJ8z1t1mrDvKWLeBsW5YeiY10LXxCS7V2v6UTmELHjUdiLGuD7lkO4eY9hYVHanvgLMSq2qQ9tUWmzQ0w11IZcPcbmamIRWVD2dXxDHfIGLN7ALPXw1Ypz1ijY7Ur8iF/S5F+B2zXADsl63SMdatT1PvmqcRn41qJF092FHCSJRanhDHcypaSkX29dtzvg+tT/PAWNfbWLfvE1/8tExDgTalmpoaRo0axZgxY3j44YdpbGykZ095W9bX1zN48GB++eWXOc//+9//zkEHHdTSl58JTAYOQqr7PmrnrxNoB37C9AC/eEmVGLIHAu0mCDWBuaIj9RlwYmJVH+CCQhfDvmXpBWRW82yamgIvC1xjrDvPWLdYB4x1oo7U8zpSl+hI7Y60a+2EXHA/hfS3FyJber0FYpT8APCYse5cY90OxroRVdryEmgd1WoovHBq+YeSjKLr8F8ge/68rQMTLR4lv4R/IeC4IojQo4FdEEEm2XaSvfH5DwVaB+KYrxHvs0KtKqsjVZLt4XY/hl3aeRwAdKS+QsztdzDWrUj+9yCIge0JVRDD3RwdXlHjxbm0UDOu0HM7mWxFTXuEmlBR0wzGuoyxbi0kVn3X2Y1x5rUvJn87c1bc5LO0/PLLs8022zBq1Chuu02sExsaGvj8888ZPHgwSy+9dFuGMA24H1gKqcar1s9wJbEr8nn7ADFyDgQCBKEm0AJ0pJ5Bvsyy1AJnGuu6NfP8eh2pu4FNEbO49EzcWsDdxrrDO9Ls10eDf6gjdZuO1NFIWeVGiMfAncgMSnNf0EOBfyKzLTcAzxvrbjDWHWysW9t7TAS6FqGiJtAufPtn1kR4MnKz0CH4dqozyW9fXYemrTtt4U4kvekBpA1lInAdYgr7XHM7xTFfIoJKIbHmH5kMa7Z1QDpS0xF/nq2MdWnxsa1ch0w8XEK+4fb3wGE6Uh2ZhFRqOqP1aQGa+sDMre2pswitTx2EsW4RxAPqPGDh+tnw1nO9+h6xX6+Pvvmabxsaml6THXfccYwYMYJjjz2WzTffnP32248TTzyRDTbYgPnnn781psHTgO8QMXkrmp+8C3Qi/j2xnV88r4rF70Cg1YQo40BLuRqJKsxeSK+ClCle1NwOOlJTgEuNdWOQuL1/JTZ3A7YG/mOsux64S0eqI3vOszcuP/jHEzCnDWFZYAVghP+3UOl8D799RHaFse57YKx/vAd81oGz44HSU60VNUmPmlmIH0+gY/gnMMz/PKajy7t1pKYY645HBIfsxMyRxrp3daS+aefhPwVGtnanOObzTIY7kfN/WuxfO5OhMY55qY1juh+54N8bSQ1sL92QVtnFkXQaEIHtQB/lXZX4SpfOaH1KV9NMRW6kS80vSEBCMVufurRQ41sId0J8rnoC1H3ZredTd/Wd/MpjfV4e77r9suOOfPr88+zbrVv+JHLPnj05++yzWWONNXj33Xepq6vjqquuYt11123pyzcg79/zEAPqahZYK5GDkWvsh3WkPijxWAKBsiLThvi6QBfFWNcPqZBZIrH6BB2pR1q4//KIh82IApvrgEuBp4tprNlafFvAYohg80f/75It3H06kraSFW7e15Ga1BHjDHQ+xrp9gME6UmeUeizFxFj3ADnx4Fvv9xQoMv7cciOwPFJR8l8dqU6Z0TXW7QHslVj1MbCrTzgqCZkMETKrXaiy9/E4pk0pSsa6fyLx2dv41t024dtdr0K+r36HCPyTgf18DHrVksnQE6k8TXJvHPNekV9nd2CRxKp34rjjqsxag7HuSeQ7/NA27n8U0k6d5RwdqbuKMrgKw1j3d8SotxZg+tRMzSuP9s48dXffDz95r8dnJOypRo1ijS22YPWePZsIhW1lKtKmuCMiLgfKCGPdyohX5HRAd9Z3YiBQKYSKmkCL0ZGaaqw7FDHr7e9XH2+s+0pHap5GbDpSHxjrdkOitg/Ef2l7ahHzy7HGugt1pN4v8vBbhBeJvvaPBwC8H8/y5CpqlkcSrdL0QZID5kS3Guu+JFd1Mxb4OpR1VizjkaqyqsG34iRbn0LbU8eRPXcAPNbJF6Q3IFWQf/LLyyLGuJd04hjyiGNsJsPdyM1sWqz5t6+s+b82HPo55MbsAKR1tdX4z8VJ5CYVfkC8nI6qdpHGUyjmvKhVCJkMA8gXaaA82p6yTCC0PrULY51C4pb/ARA3gn23R++Hb+331VvP9Ro7c3pNukqr/qCDOHXbbbkBEUfbwwzkPbsf4o0VZqXLDG+fkI3jviGINIFAU4JQE2gVOlLfGOuOQ1qesuXR5xnrtm9JKbgXQp421r2IXKDvjpSWZxkB3GisewK4TEfq+wKH6VR8Vcwr/pG9iP89OeFmBfJFpyS/849N/PIkY937SMXNWOCj4G5fMbT3wr0cGUT+90CI5u44dkj8PKozX1hHqsFY9z/EHyYrsu9orHtNR+r1zhxLkjjm40yGMUgKVtrkeAMv1rzRmmPqSMXGukuBa411f26jsLI3+a2604H/o2mrVrVSqJqh2K1Py6SWZwOfF/k12sME8quHW0uXTX3y1WjbA7vhRb+JP9V0f+quvpOfvbfPqz/VdZ9QYLfPgYfHj2ei3/dJCk+ItYTpwN1IS80vc39qoISMRCrWv0dM6gOBQIrQ+hRoE8a6nchF6YEID3u3tpTemwnvDmxJ04vg2ciNxY06UpPbMdwOx1g3hHzhZlkKX+ymaUTKcbPCzVjAlbL9K1AYY90I4Ewdqf+UeizFwli3LHBrYtWNOlKXl2o81YpPuRuDiBGv6UjtX6Jx/AvxaMgyHtjax1uXjEyGPyKR3oUSqR6IY95u7TGNdRcgHiM7t+Z8aqzbGDghtfoRpDz/GmBb779WtWQyDAP2TK2+No7n+PQU4zW2JV+ssXHM7cU6fnsx1p2CiHWrtuX72Fi3I1I5nOVWHamLizW+csVYtwpwFNJCzuxZZN58tjcP39Lv43Fv9/g8jjPpv+UUJOXnwzjOq3q5GbkubE0l0lSkKnR7/MRaoDzx1/73Ip6QR+hIPVviIQUCZUmoqAm0lVsQI8DsrOMKwBHk3wTMEx2p34DzjXV3I8LPPxObeyB9xZsY664GjI5UobSQkuOriZ7zj+yMUkROuBlB4WqMGv+8CLkoARhvrEu2S43raKPlQIsYDww11mWqSEhbKLUcKmo6hm3JiRC3zu2JHYmO1BPGulXJJT/ND5xgrDuslO/pOOb9TIYaJCkwLdb811fWvNvKw14G3IG02j7dkh28X8JxqdVvA6fpSM0y1j2EVAlU+w13h7Y+eQ+ctPdbObU9gZzvuyMVv23xmutSrU/GugURD8I5Dr/ffNq95wM39vvq1cd7fzhjak36/RMDbwJPx3GTvxVINcwmtOzvVo9UfJ2OGAaXzHsr0GL2QkSaN5hLUmAg0NUJQk2gTfjy8lOQ0uDsrJg21o3TkTJtON43wBHGupWQL+jlEpvnQ4zotjLWXQy8WO43yl5Yed8/Rnsj0YXJF26WobCR5vzIzcXafnm2se5jElU3OlKFSocDHcsE5Jw5kOpJRgrR3B2MsW4wsLFf/BQoWauR51zEq2ZRv7wmUoJ+T6kGBBDHvJfJ0I3c3ypLBtjEizVjW3o8HakvvLCyn7Hu+XmJ/Ma6JZG/TbKy82vg8IRQfgtwh7Hu3iKkZpUzHd36tBT5158x8EkRj18MkhHdxRBqqrL1yVjXHRGi98D/jlMnZ2qeNX0mPTKq3wc/ftt9fIHdfgAeiuO5Jnz9gvjLXA30m8vzpiLn1F2Br1r/GwQ6G2PdUki7ayNwfrlfzwcCpSQINYE2oyM1w1h3GDJDPMivPspY94WO1LttPObbxrqdgX8jkd7JGf/FgQuAN411F+lIjWvr2Dsb/0Xk/ONxmBMNvhw54WYErYsGzwo37wGfh2jwjkVHaqaxbgpy4V4tQs3CqeUfSjKK6mZzcje+t5b6olRHapr3GbuRnChxqLHubR2pL0o4NOKYt31lzUapTRlgMy/WtCa+9SrgPqRSp1khylg3FDFWTt4Q/goclEzu8995VyCVAwe3YhyVRkebCQ9PLX8Xx5RbO1lWYBgKtOVzUfUVNca6PyNtTkuCmAW//38943uv6f/h+6/1/KJAm9Ns4FngtTimJaEKtyGVxuvS1K9mun/sCRiCWXBF4CctD0MmKe9uTzJfINAVKDSbHwi0GB0ph3xRZ790uwHnGOvSLRWtOWajjtSjiGfB5UDabPcvwK3GupN9uW1FoiM1XUfqLR2pG3SkDkYuRjYHTkFuLuZ2cTgM2AD5298GPGusu8JYt7exbjWfVBUoPhOQC/dqIVTUdCDGul7kWhp/RgwyS45P6bsisaoncLpv2SwpccybiCdMmgygM5m8asu5oiP1E+JztqexrqAxqRfMLyRftJwFHKojVWjG/0mgn7FutZaOowIpJNQUpaLGC3Hp9Lxya3uCXEVNW8/3VSvUGOvmN9adhlS7LAkw3tV0v+GMgd+ee8Dgp8e+2quQF40FLotjXmmhSAMivoxEjGZnAJOR6qaZwHVIUMMYgkhTSawJrIz8P15V4rEEAmVPqKgJtBsdqbeMdech7UkAQ5AkqN11pNo8C+f3vdFYdz/Sz7oZOXExA/wHWNdYNwq4udLTk3xs91f+kY4Gz1bdLE/hEuq+yJffytkVxrovyPncvAd8U+rZ/CpgPNWV/JQUVGdRPZVC5cKGwGD/8+2tNVvvYG4F/gb81S8vjfiEnV+yEXnimNd9G9S/U5tqgM0zGe6KY1paUXkz8t2xHXBtcoNP8DsNmog/J+hIFWyz8m2/5wGnGuveKLP/02KRFuxmpYxe28OiNK2OKMfq2CDUpPBxylsiqWj9AGbNJPPKo31+HXN1/4+//7Jgm9Mk4JFWfF7T1CNVM6cBqyGTCf+HtDwFKgg/EXCoX7zae1QGAoG5EISaQLG4GylnzvoLLAscb6w7ob3igDfqPdNYdydwEPD3xOZeiLnjZr4k/QEveFQFBaLBu9E0GnxYM7sv6R+b+uVJCZPi95Bo8HSEaGDuVFtEd1Ko+TEIecXDiwDb+8VpSMJF2aAj1WisOxEx3M1W4G3jI7tfLuHQAIhjXvXVF+ulNtUAW2Qy3BnH8/Y10ZGaZKy7AamqGeO/T7IcDPwjtculOlJPzeOY1lj3HrAFUtFYbaQrajqy7WlCHFPoBr/UJD1q2kJVxXMb61YAjiZRDfXFR90b7rhkwLi3n+/1VYEKmhh4DXgujovy/vnGPwKVyzZALVItPqbEYwkEKoIg1ASKgp9lPAspRf2jX70BUu46qkiv8QVwkE/mOIT88ukhwPHA1t6/5rVivGa54X1orH/cDVKGTE64GYGIZD0K7D4QWN0/ABqNdRYxPM763fwQbtbnyniqpPXJCwnJ1qeQ+FRcVkd8tQDu05GaXMrBFEJH6idj3amIiW6WE411W6cEjZIQx7zsK2vWTm3qBmyVyXB7HNMSj4O7gK0RUf9cAGPdlogJapL7EMPglnAlcLOx7rFy+FsVmQ4RajIZMkjCYZJyrKYBabOZTRevqDHWDUFixuf4Rk3+NdPtoZv7ff7o6H6fT51UU2iypw4xC3adNc5AeeOvU3fzi+eXa4JrIFBuBI+aQNHwyRhHIF4MWQ401v2tyK/zOlLGfgo0mYn7PXCZse4Sn+JR9ehIjdeRekZH6iIdqV2RGeJdgYuAZ8jNDKapQUSdLZFYyweBR4x1ZxvrtjXWLW+sKyT4dGWqqfVpEPliffCnKS7ZappGxCelLNGRehYx48wyBDjJC3klJ455gcLxrd2ArTOZJjHPTfDfTVcCI411ixjrVgcOTz3tNeCslgrVXpy5HWkDqTaatD4V6bjzI++vJOXoT5MNAGhPBWVFCzXGuhpj3RZI5cNGAI0N8MYzvX45YYehL91z5YAPCog0MxF/qeuDSBNIsR/S8viCjtT/lXowgUClECpqAkVFR2q8se4IxAugByIGnGGs27EZY8a2vk4j8ICx7ilgB2BH8mcBVwP+Zqy7D7iqCmc8m8XflGRbnLIu+4r8dqmlKSzULgCs4x8AswpEg3eZv2UBqslMOCQ+dRDGuuWAlfziU950vZy5ABnvEn55NWArykdgeh4RZtZIre8ObJPJMDqO5xnN+yjyXXE88Afyz3+fA0e3YZb3bmC0sS7SkSpLwaGNdFTrU7rtaSrMNaK51LTnfF+xrU/GuuWRNqc5/18/fNOtfvSFAz587YnedY0NmULt5R8Cj8UxZVc5GCgt/vvwv4jf0IUlHk4gUFEEoSZQdHSkPjDWnQGc6FcNBC4w1u1cbMNff7yrjXX3AvsgMz8Zv7kGSY5a31h3E3CbjlR6lqvq8TOD3/vHYwA+AeUP5LdMDSiwe09E2Fkhu8JY9x05IWgs8Fk1+QLNg2qqqAmJTx3H9omfi9L62ZH42OljEePdbBXdgca6t3Sk5ukD09HEMXEmwzOIWJNOW+oBbJvJMCqOm/ew8J48tyKeMt+Sq3iYgMRwtzoeWkdqtrHuIuBwY92eVdQ22lFCTbrt6ZNWJACVggnId2NbqLiKGmPdfMD+iK9dBmDmdDJP3tX303uuHPD15F8Ltjn9AjzcwhbEQBfDTxQe4Rdv05H6tpTjCQQqjbIobQ5UHzpSDyImlVmWBE7uqHJ6HamfdKRORlqi3kht7gvsCxhj3YblUtJfSnSkpulIvaEjdb2O1EFIBc0WwKlI4tRXc9l9ESTN5mjkpuc5Hw2+l7FuVWNdIcGnWqimipqFUstBqCkCxrphwLp+8W0fhV32eEHm0sSqHkhkd1ncYPrUoSeRFqU0PYHtMxkWaW5/Y10/RECbRU6knAEcrCPV5moyHalXkMqQdef13Aqi6K1PmQz9ocn/T7lXIY0HBhnr2jKp2USo8TetZYdvc9oUaYHcDMjEjTDu7R4T/7f90BdvPHO+cQVEmkbgReCKINIE5sL6iG/lROD6Eo8lEKg4QkVNoCO5CPGM+Ytf/idiJnZtczu0Fx2pT4x1+yJGngeRK+UHuTg/BUk2uVBH6u2OGkel4StivvSP+2FONPgfyY8GL3TTlo4Gj411X5Jrl3oP+LZKZpvHAwOMdb3aEz1fJqSFmtD6VBy2JjcJcmspB9IG7gBW9Q8Qc/hDgDNLNqIEvrLmceTvu3Jqc09gh0yGW+KYuuQGn5Z3JtLy+SPye/UFDtWR+rgIQ7sAuMRY92KVVG12REVNupqmHkl/KWey/m5DaKWQrSNVb6yrJ3edXYOIn8Xy+ykKxrrhyKTL8tl1v02omX3bRQPee/bePj811Bdsc/oGMQsO4n6gWXzl9oF+8TIdqRCpHgi0kiDUBDoMf6FyNJKikY2Q3stY96mO1HMd+Lox8KKx7lVkdmgvxDg1y7LANca654GLdaRC5GMBfDT4y/6RvdlZmvx2qULR4Bly0eCb+XW/+TjbbMLURxV6QzMJaECqar4v8VjaS6ioKTJe3My+57/Cf3YqBd8edBIi2Az2q0ca617tyHN2a/BizaNIG9SfU5t7IWLNzVkzU1/FcCS5lqkZyOd4IvBSMcakI/WNse45xCvtmmIcs8R0hlDzeRyXl2hRgGxYwVDadn6cAfRPLPemTIQaf67aB9gc3+bUUA8vPtTH3nLOgG9+m9it0P/5dKSq7R1f4RYIzI2dEN/Dj4GHSjyWQKAiCUJNoEPRkfrVWHc4cAO5aoxTvF9Nh86meWPIu411jwK7ANuQX9L9D2B1Y909wDU6Ur915HgqHR8NPs4/7gIw1i1AftXNcApHg88HrOkfAA0+Gjzrc/OejlTZx0P7G9ls+1OlCzVJj5pZQHj/t5/NyJmGjqpE7yYdqQlerLk4sfoEY91HOlJlIeZ5seYhpEphxdTm3sCOmQw3xTE/Iu1OI1PPuQFpVfo3YjJcDK4DbjPWPdCeVqoyoaitT5kMPaFJOle5tz1BrqKmPRHdaaFmUrtG1E68cPkfpOI4K8by7Wfdf776xPk++Pitns1VPbwHPBHHhKqIwDzxLcA7+MVzK/G7MBAoB7q8V0eg4/HeByclVvUFzvczOp3x+lN0pC5FZo4eT23uhqSb3G+s28FYl75ADcwFHamffTT4hTpSu5CLBr8YeBaZtS5EN2A5pE3kDOBhY90jxrqzfDT4H8o4GrxaDIWTFTU/VklrWsnw79et/eJEJKa2ItGRepl8j7GBdKDHWFvwM/oPIjeQafoAO5144wSN3JAmeQH4H+LHsU+xzvnejPh6cqX+FUkmQ4biV9QsRf7EYAyU3KS6BbRXqEn7upTU78lYtzTSen4SXqSZMS1TP+r8Aa8ctun8bzYj0kwAbo5j7g0iTaAVHIQIvo/pSI0t9WACgUolVNQEOgUdqaeMdTcgN/EAiyKx3Qd2ltKuI/U9cJyx7nbEd2GFxOb+yBfLFsa6S5FI3XDj2kqaiQYfRq7iZgTiW1Tohm9BZJY7a8o5y1j3Ibl2qffLJBq84g2F/Q13sqKm7KuZKoB/IWXeAHf5z0IlcwniL/Z7v/xXpDrllpKNKEUc05jJcD9yPvljcptavH6JD1/vue9iS9f/OniBxmzs9jjgeF8Zdy2SErg5YopeDB5EvkNW1JF6p0jH7Gy60/T83F6hJt329F0c0+qUrRKQFWraKsyXRfKTN9Lei4R/VtwI77zY66Mr/zffNxN/6lYolr4BMQt+KY5pbWx9oAtjrPszElAxA/keCQQCbSQINYHO5CpgGcToF+BvSBRkp57IfXz47oi58YHkJ1EMQ0wntzHWXRRmAtqHF7vq/OMRmGMwtzz5Xjf9C+zeE2lrmNPaYKz7lpxB8fvA5yUoqa2GippB5J//y6KlpVLxgmS2zHsmcE8Jh1MUdKRm+cjuUeRaYfYz1r1ZTklWXqy5l1yVHvMNbRjwp9Vn/nvm9EzPx2/vO3T9baeNHzR/4/fAITpS0wB0pCb6uO7dfLtSu4UDLwCdh8R171ih5f6FKozaLDpmMtQg3/tJKqHtCYrT+pSkU4Uaf176NzIxNed3mPBDzfgrT5jvrXde6D27mV2/RMyCJzSzPRAoiJ8EOtwv3lQu7bKBQKVSNmXMgerHX7QeD3ydWL2jsW79Eowl1pF6BtgSSexI942PAG4w1p3pe20DRcJHg7+uI3WdjtSBwNrI/8NpSDT413PZfVGkv/5Y4HYkGvxyY92exrq/GesKCT7FpuIraoCFU8uV7qlRalYhV3nygI7UryUcS9HwPmIXJlZ1QyK7+5ZoSAWJYxqBMcC43v0ae628zowNunWP+wBMn5rp9uiovgNffKj3CTpSP6d2HYVUDuxYrLHoSL2LnMM2LtYxO5l02xO0r6JmUaTdOcm4dhyv0/DJflNouzCfbn3qU/BZHYCxbkngSuR7dShA/Wxm3X9Dv+f3/deCrzUj0kxFWgJvCSJNoI1sioRO/EDlpR4GAmVHqKgJdCo6UlOMdYci5fP9/OoTjHVfFykmtbXjmYUYQD4E7I4IBsnPxXrAWr5d6kYdqcmdPcZqxwt4X/jHfQDGukFIK0O24mZ5Ct9A9EVuklfxy7Gx7nNy7VdjKX40+HialvJXGgumlsOsV/vY3v8bU7w2mnLhHiSuO2sEvigyY3pKyUZUgDimYf8zf733l59qdp/0S82g5KY3nun97D1X9VjzosP5PI75NbtBR2qase464CBj3T1FnP29BLjWWPd0BX5nFFuoSZ8rJ0BFiQDtEeY7vaLGi6i7A9shwioAn73f44PzDxn0+U/fde/WzK5vAU/FcRNxKRBoEd53cj+/eJEXOgOBQDsIFTWBTkdH6mvgOJgT79gTOM9YN6SEY5qkI3UBsAXwTGpzD2TG9T5j3VbGuiBwdjA6Ur/qSL2oI3W5jtReiEnxDsC5wBM0XwGSQSobNGKYaIAnjXUXGOt2MtataKwrdCPSGqqhoiYdzR08atqIN+j8m198Tkfq21KOp9h4kfMUclHFABsb69Yr0ZAKYqzLrK2nH7PJblMHqcUb5twgf/5Bjxe//qTHd0jy3M6ZDPOldr0X+BnYo1hj8Ql2DyI3zJVGodanNt1weWPi4anVtsKinStCqDHWZYx16yDC6o54kWbq5Mz4Cw8b9MBRWwz9qhmR5ifghjjmwSDSBNrJHsh59m3g6RKPJRCoCoJQEygJOlIvIWW5WRYCzi510o+O1Lc6UkciXzhpH4b5gCOAu4x1//D934FOQEeqXkfqYx2pO3WkjtWR2gjYEDgKqWD4AJo1PByEVAMcgCRevGCsu8VYd5ixbj1jXbq6ZF5MoPI9aoJQUzy2T/xclaXevpXrhNTq44x1qgTDaY7dgI2694B1Np/2y8KLNsx0X3d/7+O3eiXbbAYBO2UyzEkc1JGaDVwBbGKsW6KI47kFWNNYt3gRj9kZFBKy2+pRMz+QnoCpiLanBOMpnlDTIa1PxrrFgEuBs/HVknEj9c/e2+ep3ddY6MWXHu5TI3MYecwGngKujmO+6YhxBboOvtVuK2QC9rwQxhEIFIcg1ARKyY3IhUKWFYHDSjSWPHxix85IjGv6JnYx4HzgKmNderYw0EnoSP2kI/W0jtQFOlI7I1U3uyNtB88x72jwbRDj6EeMdQ8b684w1m1jrFtuHlVT44Gh5RRV3AaCUFMEvMiX9dh6v5rNx3WkXic/8ak/cGo5fA6MdRsAe2eXe/QkXnvzaXe/80KvOws8fQgi1gxIrHsKERD2L9aYfNn/5ZTJd1orKGbrU7rtaRrwXRuPVSomAH3a6MvUoRU1xrrexrp9gTvJVfXxwzfd3j9k4/nHXHbMoBmzZmQKTX59ClwRx7wUxzQUc0yBroeftDwUuae8V0fqkxIPKRCoGkILR6Bk6EjFxrqTEeEjmwqxubHO6kjdW8KhAXO8Ux411j0DbAvsQr4p4p+BW411jwCXB3f70uJvjN71j+zFQy3icZONB/89BaYWEeHiX/4BMNNHg8/xukkYxI5HxJ6BkPO7qDCSVUSzaGqmHWgZW5HzgajKapoUVwIrk2tn+ROwK3BdqQZkrFuRptU+Y3v15n+NjZkY8epIV7UMBXbMZLg5jpni05ouBa401o0oouD2NLClse7vOlIvF+mYHU0xU5/SExmfeOPnSiLb8jcUEZpaQ4cINf67bU3EK2pOVdvsWYy/5ZyBzz4yqm8vyPQrsOtk4FHg4wprPwuUN6sjQuEU8ivlA4FAOyn5TFiga6MjNR252PgtsfooY90KJRpSE3SkZupI3Yi42d8DeReaGSSF6F5j3T7llobSlfHJXt/pSD2iI3WmjtQ2wFrAvsDVwKtIykUhegErIVVVFwBPGeuMse4kYCOkbHyBjv0NOpRkRc0PoUy59fjP+ki/WIdUcVU1vk3oOPJvQPc01o0oxXh8y8f5iI9YljrgMB2pmXHMLKQ1spBv0AKIWNMXQEfqDeAVxFi4KG2t/nN1HnBwqdt6W0G6omZ2W8SVTIb+iFCepNLanqB9Ed1FT30y1tUiSWznkxNpGt95sedjO/9toacfGdWvD2TS1/Yx8DpweRzzURBpAsXCn9cO9YvX6Ej9UsrxBALVRhBqAiVHR+p7xGskezHYHTi3Dd4hHYqO1EQdqbOArYGXUpt7IR4J9xnrNiuHdoBAU3Skpvpo8Gt1pA4A/olURZwBPARz7dVfDBFpjkVK+u8w1l3mo8FXMdYVmsEsO/x7M/nZCpVgbWNTpP0HYLSvwKt6vBn8uYlVNcBpxrr+zezSIRjrBiNtjgMTqycBByVvFuKYmcBoRMBJsyAi1mRvoC9DKu/WKNY4fRvAO8h5phJICzVtbXtahvzqxXok2a/SaI9QU7SKGmNdT2PdnsDdSAUDAL9NrHn/qC2G3nraHkPrZ0yrKfQd5IDr4phH4rjJeAKB9rI1kgT4FXBXaYcSCFQfofUpUBboSL1prLsAqa4B8RE4z1i3u4/QLht0pL5AZkhXBg4Blk5sHoLMOG9trLtIR+rVUowx0DL8zfXn/mFgzg1gOho83Q5Qj9wg/o2cN0BsrPsMaZV6D3gf+K4Mq1UGk3/uD0JNKzHWdUM8jkDEgQdLOJxS8ACwGrCOXx6GiO3/64wXN9b1RCoKFkmsrgcO15H6Kv38OGZGJsOtwE4kWkU8CyNizS1xrD4x1j0KHGCse1lHqlj+HVfi22R1pJrzzioX0ue6tgo16banL3yFU6WRFWraYiBfFKHGWLcacCSJ93tjAxPNtf0fuf2i/n0gU0hEmoUkWL5ege1mgQrAJ7Vm0/Iu0JFqLtAhEAi0kSDUBMqJO5FKhf/65eWA4411J5bhzS46Uq8b67ZDqiz2Jf9CbingUmPdq8BFOlKfl2KMgdbjZ+Nf8A+8sfAy5ISbFZCS/vT5M4OIdkuTa4n5xViXFW7GAh97L51SkjYSbi7qPNA865C74b/Ht3B2Gby/2OmIiJl9P21grHtVR+qRjnxtXxF2EvJZTHKKjtTbze3nxZpbELFm4dRmBWyfyXDrmHFcBYxBzuv3F2PMOlK/GOtuQ74nTivGMTuQdEVNq8WVTIaewJKp1ZXY9gT5HjWtpV2tTz5V7TCkZTdL4xcfdX/05F2Gjp/yW01zY/oYeDSOg/dYoEPZD/FtfElH6pVSDyYQqEaCUBMoG/zF/5nIBd4f/OoNkQu820o2sLngKzIeMNY9hcT07kj+rNmqwCrGuvuBq3SkJhQ4TKCM8bNEH/nHHQDGuhOQKoJPkRvG4eRMZZMMRtKo/uGX6411lpxwM7YEJtRpoSZU1LQC71+yg1+cjQjMXQ4dqUnGuv8hfk/ZFpejjXVjdaQ6MtlnH3Km31muaYlAFMdM92LNzuS3/4GIr9uNHK5GjRnn7gb2MtY9riNVrHaRu4DbjHXDdaTKWbQoRuvTUuRfX8ZApSbB/Iq0ZXdaRY33/dgeSTGc8/8xY1rmg3MOGPziey/3WoTCwtFvwCNxjG3DWAOBFmOsWxbYGGhAfPwCgUAHEHw0AmWFb3M6nNwsFuTajMoWHalpOlLXABppC0hWANUAmyGGw7sZ64oa0RkoCV8BE3WkzteR2glJ4NgDuBSpxPm1mf26IyLktsBZSDT4Qz4afCtj3bLziAYvBukb1BDN3TpWBJb1Pz/SlcVXX8FyY2JVX8SvpkPew8a6TZD0vSSPANe29BhxzDTgZuDnApsXBbZ96/letwD9EP+FouAF3wuBI4plVtxBFKP1KR3LXRfHTGnjeEqKn4z5hU7yqPHXOncg1Qq9AOJGfn36nj43bP+Xhd5/7+Vei9I0ubARMcK+PIg0gY7Gn78OR96Ht+tIzc3bLxAItINQURMoO3SkfjbWHQFcg6R51ABnGet20JEqZAhZNvjqiFOMdXcAByNRtln6IrPBI411lwOPdhUD0ipkPIkLd9/O9I5/ZC9kFiE/GnwpCkeDL+wf6WjwZNXNbwX2ayvpipog1LSO7RM/jy7ZKMqHa5Dz3PJ+eXlgT+CKYr6Iv4E9NrX6beC01rbGxjFTE5U16Rvwxc/Ya8hGo9764dY+/eKdjXX3FuvzpyP1qrFuS2A94IliHLMDaFfrUyZDDdIqmqScK4hawgSK0/rUrFDjwxMOQd4bWeKf6ro9csxWQ7/+dXy3xZrZ9TvgoTgOLayBTmM95LrmF+C6Eo8lEKhqQkVNoCzRkXofODOxaiBwQaXEX/ukj/0QsebL1OYFgZOBW4x1f+7koQWKw3jmUgrvo8G/1ZF6WEfqDB2prZGEqf2RG9vXmHc0+C7IDPzTxroxxroTfaLYku1MFQtCTRsx1i2BVE8BvOyNxbs0vlLkOGBaYvUuxrqVivUaxrqlkKSpZHvh14h5cJsMauOYyUhlTSFz3yV2X3PBePYsZgC7tuX4c+FCYN8yrqxsb+vTosikRJJKr/LIE+ZbwTwraox13Y11OyC+SHNEmvp6Pr74iEEX7rPOgrOaEWlmIEmF1weRJtBZ+PPWwX7xch2piqyUCwQqhSDUBMoWHakHyI/7Wwo4qczLxufgb9ZfQsrnz0JmH5IMB6421l1grFu80wcYaA+tnmHVkZqiI/WajtQ1OlL7I8LN1kg0+MPAt3PZfXHEZPs45DPxjLHuEmPd7sa6lVspYCZbn2ZBMJxsBdslfr61ZKMoM3yl41mJVRmkBWpgM7u0GGPd/MDFSCtSll+RGO52vXe92erNFGhVnDG1ZtHLjxv0cf1stvSmrkXBtwk8g5galyPtbX1Ktz1NIL+VuRKZAAxtg0A+V6HGi5m3AQeRMxqe9O5LPa/eesTCr77wYJ9lkKriNO8Dl8Uxb8YxZRe0EKhqdkSuISzS5h8IBDqQ0PoUKHcuQASabOXJ2sgM5/UlG1Er8RGv9xjrHkNK7bcl/2J4TeDvxroxiCnmr50+yEBrGQ/0N9b1amuKk297+8w/stHgQ5DWkWy71B9oeuME0B+JR17NLzcmosGzKVPfN9MSkqyo+aEcE9XKEf9/8x+/OA54q4TDKTt0pB4x1q0KbOBXLQgcZ6w7uq3vMWNdH+Q7IJnSNAs4tFiGxXHMb5kMNyEVbPMlt738cO9JSy7XL7Ph9lP3pbjR49cDtxvrHtCRckU8bjFoc+tTJkOGprHctgrEhPHIxOZ8NJ1wmRsFU5+MdUORqoQNkhunTso8fMTI+T/68dvuwyncJvsL0uYUUiQDnY6xbmHkGhbgvNC6Hwh0PEGoCZQ1OlL1xrqjgVvIxeHuY6z7VEfqhRIOrdX4EtHLvCCzH7B+YnM3YEtgQ2PdDcAdbS3pD3QKk4F6pKrm+2IdVEdqIvnR4D1oGg2eNgMG5vhCLANs7tdN9NHgWeHmYz/m5P4h8anlbEFONBsVBK6CnI28R4f55XWQZJBWx1z76oXTgOVSm07QkRrbnkGmiWN+zWS4GbkJmVMF1NiYiV98sM/Hffo1bjt96g+jd1pl4aJ4rehITTXWXQccCBxTjGMWkfa0Ps0PDEmtq/S2J5CKGpDzfWuEmiYVNca6bYC9SVSINTbyyajzBtxz/w39lyRnVJ6kAXgZeDGOmd2K1w8EismByHfgEzpS75R6MIFAVyC0PgXKHh2pX4DDyL9gPM1Y97sSDald6Eg5HanjkdL391Kb+yNfhvcY69arlDavroafSZpI2yJbW/M6s3WkPtSRul1H6hgdqQ2Rqo5jkWSQj5DEj0IMAdZC3k/XI+LPKOQmegAi1Ad/mhbg+/K39Is/Ak+VcDhlixejjyf/PXlEG1s7DyYXa5/lUh2pDvnbxzETkTaoycn1X3zU/etP3u056cm7+l7gjXKLxUNAbTG9fNpLJkN38n2AoHVCTbrtaRpzb+msFJJCTWtICjV9EBHzMHIizZSvxnW/fOsRCz92/w39RyDf/2m+Bq6KY54JIk2gVBjrVkQCD2YBl5R4OIFAlyEINYGKwJvznpxY1Rc431g3oERDajc6Uh8CuwNHIskNSYYhZsrXG+tGdPbYAi1irobCHYWO1I86Uk/oSJ2nI7Uj0jq3J3AZIsY0l1DTHanKGYokUi0NbGasO91Yt6WxbrixLn2TFhA2ItcWc5s30A0UwFe7XJNY1Rs43VeHtQifjLRtavW9SGVlhxHHTEDEmoRBZoZx7/R87bcJNX/681ozDi+WWOPF3vOAw9tpDl5MCrVZtqayMy3UfBLHzQrJlURWqGnt+X4GInwpYAkSVTT1s3n00I3nP/ewTReobajPLFlg32lIJdpNcVwwSj4Q6BT8+elwv3iTjlQwrw4EOonQ+hSoGHSknjDWLUOuR3Yx5Abg4ErtlfXtE88Y615EWit2J1F6j9xY32CsexKZTS5am02g3bQ1CaSo6EjNQGKK34Y50eCLIrO3f/T/Lol4HqRvlvsC//YPgBmpaPD3ixwNXnH4i9SsifBU4L7SjaZiuAFYBVjRLw8H9qEFM7HGujXI3RRkeQ04uzPazeKY8Yno7r4A33/Z/cel/tDtq0FDG7fLZOJPM5nM/cUQIHSkxhrrvgA2xftUlZh02xO0sKImk6E/IgAnqYa2J8iZIbf4fO/PGxo59yavtb987La+N157yny/A/7azO7vAk/EcV6SWiBQKjZGRNif6GCxPBAI5BOEmkClcQXiw5E1UV0N8Xu5tGQjKgI6UrOB24x1DyFizZbkfz7XA9Yy1t0B3KAjNbnAYQKdS0kqauaFv5n9xj8eBPCVZ8sjhqmbImX4NYhnTZLeiHH3nNh4Y91X5HxuxgJfV6ow2kbWRIQvAKMj1VysesCjI9VorPsf0p6XbefY0Vj3mo7U683tZ6wbjqSgJStMPgOO6swqpjjmJy/W7IQ3gP3ozZ6vr/rv6VsstfzszT57v2djJsMDRTLJvQS4zlj3ZBmc1wtV1LS09WkZ8g1w66FqTG9bVVFjrFsOOBrxV8r+TRpnTMuMP3ST+e/78dvuKzez63jELPir9gw2ECgWxrr+yDU2wEV+YigQCHQS5VJuGwi0CH+DeBxyE5plJ2Pdv0o0pKKiIzVJR+oCpLrmmdTmHsAOwH3Guq2MdUFoLS2tjuguFTpSk3WkXgU+QD47FvgCaZd6hKatd0mWQGbU/gfcDTzto8F3M9b9tZXR4JXIDv7fBkR4CLQAXx5/emr1Kca6wYWeb6xbCLiIXEwxyGfs4FKIY3HMD8js8QyACT90+3W862aX/MPslWu6xX8GNvIpR+1CR+onpMVlj/YeqwgUqqhpaetTOu3pizhuVdtUOTMNEazmer431s1nrDsWaZ/LmmA3xjGT7Ls9vjfX9Os+fUrmTwV2rUe+768KIk2gzNgdGIxM1DxZ4rEEAl2OINQEKg4/63gY5JUFn2isS/fHVyw6Ut/qSB2JXLx/lNo8H3AEcJex7h/BcLhklGVFzTxIRnPPBG7VkTpBR2pTxCjwcOTm9F2av0EbgFSy7QNcCTxnrBttrDvKWLeBsW5YtbwnjXXZ1jGAx3WkgvlyK9CRehJ4ILFqfuB/6feHsa4fItIkP08zEJGmZH4IcYwDbsVXlXz4Rq+3evSg77J/nrUsUnW2YTHEGsTke40yMMhvU+tTJkNPpMUnSbW0PWWrFJsV5o11Nca6TZH2NU2uiuariT/WfPDIqL4zXnm094CZ0zPdevRq0n76OXBFHPNCHDepcAwESoY3gd8GiJE47pB0GAh0MkGoCVQkOlJfIuki2S+OXoi5cMHZ2krFRyDujPyu6RuWxYDzgauNdYUiPQMdS8VU1CRICjWzgEnZBR2piTpSz+lIXaIjtTuSuLMzcAHwNDRraFmD9K9vAZyK3Jg/Zqw7x1i3vbFuhLGuUEtFJbB94udRJRtFZXMe+RWQa5KLkMcbWJ+JmFtniYFjdaQ+7pQRzoU4pg4v1kz+pWaq+6bb+4v+vn6lnr3jHojHyL/bK9boSM1EqtsOLbHI2VaPmrQPS0wVCTWegp5kfoLoeuQ7Oms4PmP6lMwV2/95oavvuqL/gJ++6zbn79q9R5z9O00FxgCjfOJYIFBuHIKYYT9QDufiQKArElonAhWLjtQLxrqrkJl9gIWBs411+1ZTKotv93rMWPcsMruxK97k0rMScKux7hHg8jDr32lUolCzYOLnH+Y2Q+Z9kz7wj9v8DeRCiMH1Cv7fiMKC/1Bgbf8AmG2s+xjxuBkLjNWRGl9gv7LBWLcI8E+/+LpPngu0Eh2paca644EbyUU/H2Ksewv4EjiKnOdYlvN1pF7oxGHOlTjmu0yG0cD2H73R872FFp2+3B/+OnPEOy/2fgv4G9CYyfBkOz1rnkG8yVYHXizCsNtCW1Of0m1PdXGcTM6qCiYgbaDAHN+vfRDRMXkOfObSo+cb89x9fVcB/t4wO9NI79zbonsPugNvAk/HMdM7Y+CBQGsx1q2GnIumAZeXeDiBQJclCDWBSucGxMRwHb+8EnAocE7JRtRB+FnXm4x1DyBxzJr8C8QNgXWNdaOQCMWQGNGxjAeGGutqKsFc16eQJIWan1qzvxd1fvCPJ/wx+yBeDCPICTgDC+zeI/Gc7Hi+JyfcvAd8piPV0JoxdTDbkvt8hWqadqAj9ZGx7nLgQL+qJ2Ia/BhyHktyh45U2XkBxTHfZDLcNmNazXbffdb97UWXrv/LJ+81fjR1Us10RGhqyGR4pq1ijY5UbKw7HzjDmy7PLuov0DLSFTX1ccxcP5M+rnyZ1OpxRR1VeTAeGOirA9cDDgKGJLZ/+7XtftmhmywwCNggu7KhgTn/j4Pmb5yt95ry6CVHDnqok8YcCLQa7394mF+8VkcqVHwFAiUiCDWBisZf3J4MLA783q/e0lhndaTuL+HQOgz/pXmWse5O5GJx9cTmnkjFzaa+2uj+Mrv5rSYmIBUC8wG/lHgsLWEw+ef8dlde6UhNB97yj2w0+GLkKm5G0NS7Issw/1jfL0831n1ATrx5X0dqUjP7dijGuoGIgTKIh8SrpRhHlTEKWJVcJPGKwN/Jfx++gLTalSVxzFeZDLd//FbP7sOWqF/+DyvPWun1p3q/7DevgRhOP9fW4+tIfeIrjbZG2q06m7RQ05K2p0XIr/CE6mt7Ajnf90Iqw5J+eLMaG7hhtzUW/GDSxG5rIcl5c2hsyNR3707jH1edOfmPf5s1tVt3JsGgTht0INAGtkSuqb8hGOgHAiUlCDWBiseX1h+K3AhkZ/OPMdZ9qSM1toRD61C8T8/BxrqVgYPJn9UcAhwLbG2su0hH6pUSDLGq0ZGaZaybhBigVoJQs1Bquegtcr7q5mv/eADmiB7LkxNulqfpjR1I2s9fyd3IY6z7kvxo8G86qXppc3I3XKODiWL78ZHdJwK3I22qtYjp6lRgClKFcVy5V6fFMV9kMpnRX3/SY6nf/3H22kMWbPhg4k/dfvOb18pkaIxj2tO2dSXSyvpwCWay061PbWl7mohUn1QN3ux6NUR0zuCTwIAX7rq8/013XjpgZXKCcx4LLVr/0392nNpn4OA4O2HSu9DzAoFywPs87ukXLyxRZV8gEPAEoSZQFehIfW+sOxoxZKxB3tvnGut28PGnVYuO1OvGuu2BjYB9yU9OWRK4xFj3GnCRjtRnpRhjFZP1qfm01ANpAR0u1BTCV8W84h9Z89ilkKqbbKpSbTO7/84/NvHLk4x175MTbj70VT1Fw7c2bO0XJyDtOYEioCP1k2+BupFcMo4C/g9JeKoIz4445rPefbuftujSs1dY9i+z/vLyI32eTmxeO5OhIY55udkDzAUdqV+NdaOB/YFTijLgltOqihpvopxOWxzXTq+essFXCP4LMVVdwq/uDnz/6/iai3ZbfaHuwKYU9umaBDy61QFT6mtqWCSxvk+B5wYC5cI+QH+kivSlEo8lEOjyBKEmUDV4weIixKMG5Ab6XGPdHjpSLZkZrFj8LPQDxrongR2AHcmfufsbYgj7AHCljtSEEgyzGqmkiO4FU8slETB9K94n/nE3gLFuKPk+N8MpbGw6EGmX+btfbjTWfUKu6uZ9wLWzAmZ9ct4Td1T7uaMz8dVV2wKTkVY8kJvcH6Gykm9mTKv5ZOejG8745af4goUXqx/7wzfdk6lo6/nKmra2zN2DnK+X05H6qAjDbSmtbX0aSlND9apoe/JR6UeSq/CrR9Ksntv/3wtc7r7uvi65lKckMSI8PhvHzDR2TvVNllBREyhLjHXLAJsBjcAFoZI0ECg9QagJVBu3Iy1AG/nlPwDHGutO7gpfOn5G+hpj3b3IzMh/yc1c1yCzf/821t2EtHSkLyIDraOSkp9KUlHTErxw+Kx/ZKtaInLCzQoU/jvXIKLOcKSvHmC8sS7ZLmVbKrb4GfRsJPcMJD43UASMdT0Qk/clkPdeX0QY+A5YFqliuq1U42sLG+8y9a4PXu+52eTfalb+4ZtuD5Of0v1vL9b8X2uPqyNVb6y7ADjcWLdbJ353tbb1Kd32NA34tnjD6XyMdX2B3YHtyKWUAbw25bcMd10+oMZ93X1kM7t/DzwUx3yfWBeEmkDZ47/7DkdOYnf41vpAIFBiglATqCq8ufAZSMvPcn71Rsgs3+0lG1gnoyP1M3CKse4OxL9m5cTmPoiIM9K3ITxa7r4QZUwQajoAL6y87x+j/UWkIld1MwIRZAu1HMxPfjT4rALR4M1VlK1Kzvz4/lKZGVcb/v/veOAvflUM1CHCwFS/7gBj3ZuVFIMuAoo7pXef+K4vP+qxyFfjenyXesoGvg3qzTYc+zVj3ZbAv+m89rvWVtSk254+iWMq8rvEv0f/idysJqsPf5w1kwv2/MdCM9fZfNr6413NkpAnxID8nZ4B3ijw+6fb+YJQEyhH1kZSU38Dri3xWAKBgCcINYGqw5u8Ho6kZmRvog8x1n2uI/V6CYfW6fgUkf0QI8SDEb+PLAsCJwPbGOsu1JF6qwRDrHTGI9UAlUBSqJmJeChUBL6i4Hv/eAzmzHxno8GzfjeFosF7kqvKwe9bR67iZiwSDd6ItA2ClH5XVHVHmbMb8J/UumuQz082BrYHEk29fSVV+ulIfWRwD+xyzKQ/nbTzkO/iOJN+yka+subtNhz+QuAyY93zneTf02KhJpOhP+R5r0CFtj0Z6xYDjkCE2iwNwKjjtxv68Mdv9VwPUDNnZGb37NXECP0j4LE4bvZ8GipqAmWNsa4Xcn0IcEWYoAgEyocg1ASqEm9aeQRyM9AdmXk/y98EpGfDqhp/k/uyNxTeFNibnD8ESPn61ca6F4CLdaS+7vxRViwTqEyPmh8rvRVQR2oa8KZ/YKyrQaLBs8LNCPKFySS1/rGhX55mrPsO8b6ZhlSZ1XXc6LsOxroNkHNOkieBy5HKmlURIRmkLepQ4IzOGl+RuHL5VWbds9NRk8ffdNbAoaR6oID/erHm3dYcVEfqW2Pd08BOwFVFGuvcaE3r0zLk/571SJR9xWCs6w3sgni69UhseuNr2/2iQzdZYElEvM0A1M/KTOvRM86aAf8KPBLHzKsCLAg1gXJne6Ri9VPg3hKPJRAIJAhCTaBq0ZEaa6w7Cym5B5ltP99Yt2ulpIsUE2/iOrT3r3UAANHISURBVMZY9xhycbot+RfmawJ/N9aNAa7Rkfq180dZcVSEmbAXMfKEmlKNpaPwFTFf+Uc6Gjwr3CxP4dSVvsBa5MxB/2Wsu4tcxc17SDR4RYtbnY2xbiXghNTqscBJ2XZLY91JwB3kDJy1se5VHalnO22g7cQLKmP+u/PU9e+9tt8pv03otmHqKRlgE98G9X4rD38dcIex7oFOmGRoTetTuu3pizhuUZx3WWCsWxNpcxqWWD2+oZ4LtvnTwt811Gc2AgYk95k9i2m9+8YDgJeB51v4+6avNULqU6BsMNYtiFwPApwf2uADgfKiUH9/IFA16Ejdh0+W8SwNnOj70bskOlJTdaQuA0YCj6Y2d0NMWe8z1u3oTV0DzTOeyvCoGUy+MF/VkfVZdKQm6Ui9oiN1pY7UPogYsx1iavsYOa+J7uREmmnILPiSSAXaCYip8FPGuguNdbsY6/5srAs3XHPBWLc4cB75lQp1wGE6UnMEAB2piUgLZpL/+RuISuI6YOANL/+0NPBwge0ZQGcy/KE1B/WVY9cCB7V/iPOkRUJNJkNPYKnU6opoezLWDTPWXQhcQE6kaQRGX3H8fLtuubxaoKE+syUpkQZgwKDGHzbbY8rkMePcU60QpUJFTaCcOQB5Tz6jI9VqL61AINCxhIqaQFfgfOSiciW/vC4SDXxDyUZUBuhIOeSG6HbgEGDFxOb+wIHA5sa6y4AnQzVBQcYD/Yx1vcvcV6NijIQ7El9VZv3jLgBj3fyIUDAEqaxpTsSaD1jDP0CiwS25ipuxVEFLWTEw1g0GLibfM2gScKCO1C/p5+tIvezPQ9v4VQMRM/R9K2WGV0dqorHuVmC3MePcJiOHqxpgg9TTMsBI3wb1cSsO/zCwhbHuzx3lJZbJUEPTa8LmxIglCzy3rIUaP+mwE1I9kJyAeGfSL5lzd1l14WxlQY8Cu88AntzxyMm/1tSwPdAPmNLClw5CTaAsMdaNQM5Rs4CLSjuaQCBQiFBRE6h6dKTqgaPJvzndx1i3eomGVFboSH0E7AkcSdNo1WGIX8QN/ks9kM9kxJuh3KtqglDTPDMQI+KfgBcQQXdX5ML1GWBiM/vVIEbSWyGfkYeAR4x1ZxvrtjXWLe8jqbsU/ob4fPKNZuuBw+fhf3Up4pGQ5S/kzJ0rhdHI77qTj+V+vMBzaoAtMpkmrUPN4sWq84DDfBtjR5CupoHmW5/SY/8ujlssXHQ6xrrVEGF2L3IizUTghK1HLHzKLqsuvC4ygVPo8zoWuCyOeaumhmxaXGvO92mhJlTiBUqOP48c7hdHdTXvxkCgUghCTaBL4MvrDyN34ZkBTjfWLVGyQZUROlKxjtQzSNvTBTRNBPojItacZayr7fQBlim+eqISIrrTQk2XaH1qIZvCnCSX0TpSM3WkxupIjdKROhKJR94Y+B/SRvkJNBtBvACwDmKIexPwvLHuOmPdgca6fxjrhjSzX1XgL/5PRvyAkpyiIzXX1CMfyX4c+VUc+xrrlivuKDuORJvStsa6BeOYV4GnCjy1Btgyk2HpVhx7LPAZsFlRBtuUFgk1vvJmmdTqsqymMdYtbKw7B7iEnHDYCNz16Oi+240crrrNnpXZjXz/riwTgVviGJMQobJCTWt8yUJFTaAc+Q+Smvgz8l0VCATKkND6FOgy6EiNM9adApzuV/UDLjDW7aQjNbmEQysbdKRmA7cZ6x4CdkeEm+R5Yl3gH8a6O4EbQowjUBmGwukbkVBRAxjruiOm2iApLk28RVLR4I/6/foCfyA/GryJpwUye/8n/8i+5nfkt0t9XintPS1gH2C91LprdKQeacnOOlJfGOvOB47xq7ohgvp2XgSpBO5F3lN7AafGMS95cWPt1PO6AVtlMtwexy1OS7oMuN5Y92QHnHsL+ZEVan1aBPnuTDKuyGNpF76SbTvkOywpjLw/exZnbT1C9QJ2punvARLL/RLwYhxTn9o23v/bnoqaINQESoqxrh/iTQNwaQWdWwOBLkeoqAl0KXSkHgduSaxaDDitA8vJKxJvwnoBsAXS/pGkBxLneJ+xbmt/s9uVqYSI7tD6VJj1yIlYd7XUZ0hHapqO1Bs6UtfrSB2IVNFsCZyKJE59NZfdF0FiwY8BbgeeM9ZdYazby1i3qrGukOBT9hjrNiWXHpLlYaTCpFWHAp5PLC8KHNH2kXUuvtX2cuC/xrolAeKYF8j/nbJ0B7bJZJqNkU8f+ydECNqjSMNN0tLWp3Tb00RyAkbJMdatjKSI7U9OFPkNOHXXvy94xNYj1CqAprBI8xVwZRzzbAGRBtpWURNSnwLlxq6IJ9v7iKl+IBAoU7r6DVaga3IZ8HtgNb/8d2Qm+PKSjahM0ZH6FjjSWPcnxHA4mVgyEOlx3tJYdwnwfBc1Uq2EipqkUDOTpq1tXQ6f/Jb1QJkF3NPWY/mKmC/8435//PmQSpts1c0fKDyb3hdY2T8AYmPdF+QqbsYC35bzZ8tYtwpwbGr1W8BprR23jlRsrDsV+XtlP1f/9ZHdT7R/tJ3C08DHiFhwqF/3HFJFk/ZG6w5sm8kwKo6Zm4dPllFIXPe9OlJfFGm80HKhZnhq2cYxJX9v+pSwg4F/JVbHwL32nR5XHrvN/COAvSl83TsN8RMaO4/fpRgeNaGiJlAyjHWLkqsiPbeKqjkDgaokVBEEuhz+i+l48o1zdzHWpUv2Ax4dqXeR2fLjAJfavBhidHm1sW7ZTh5aOVAJEd3J1qeQTCT8hZzXxkPex6po6Ej9piP1ko7UFTpSewH/QCrRzkFuCtOfoywZJKVOAychFSZPGusuMNbtbKxbyVhXNjd7xrqlkN8peT3xNXCEb6VsNTpSvyKx6Mn36bHGOtXWcXYm/vN1CbCmF7nxAsDTwCsFdukBbJfJsFgLjj0LmWw4zIuNxWKerU+ZDPPT9FxX0rYnY113Y932iNCaFGk+AnYaOVzdfuw2828H/JPCIs3biFnwey0QnCYhrVFBqAlUKocg55sHfZBEIBAoY4JQE+iS+P7+w5CZtCwnGuvSJokBj45Uo28dG4kktExNPWUl4FZj3SnGunSrTTVT1hU1vq0vT6gp1VjKjGSi0OiOfjEdqQYdqXE6UnfpSB2nI/VfJBr1SP/670PBdguAQcCaSIXGNYhJ8S3GusONdf8q1efNR5tfTH4byS9IDHe7qrZ0pF4Hbk2s6o+0qXZrz3E7Cx+j/TJwYFZQ8ULAk8BrBXbpiYg1ixTYluZZpDpnjXk9sRW0pKImXU0zjaZJgZ2GsW4l4DakkiZrCD4JOHP/9RfYd+RwFSGR3IXOzz8DN8YxD8QxLfLo8JM8rTWPT/8Ne4VW60ApMNb9DfkemUaoIA8EKoLQ+hTosnjjyv8hUbIgM13nG+t28DO6gQL4Gd2bjXUPILHeI8kXfTcE1jXWjQJu6gJGdeWe+jSY/HN9l0988t4h2dbHF+YRG91h6Ej9jHhAPePH1ROJ/M4aFK+AeAmk6YYkdiwHbO33/Qlpl3rf//tJWytaWoKxrg9wIbBwYvUs4FAdqboivcyVwF+RvwnI32NXWu97UyouQ4SEfyCtT8QxcSbD48j/4V9Tz+8F7JDJcHMc02xcrm8POw8421j3mj8nt5e0UNNQwKcl7U/zaRw3m4DWYRjrhgIHId81SR6YNjlz6Q5/Xfh3wH4U9oOpR/yCXoljGtrw8q063+tINRrrZpL/9+1FU++aQKDD8F6C2TbM63WkysZXKhAINE9Q9QNdGh2p54GrE6sUcFYwyJ03OlK/6EidDWwFvJja3BO5obrPWKcrZRa8jZS7UBOMhJuyfeLnUSUbRQodqVk6Uu/pSN2iI3UEEg2+CdIGdA9zjwZfEDFHPhS4Gam6udZYd4Cxbk1j3eBijdNXBJxOTkDJcoKO1PvFeh0vNB1H/k3tHsa6FYr1Gh2JjtSnwCPA/slzoK+seQTx8UnTC9gxk2GubV46Up8Br+OFuiKQbn1Ktz31hybVPp3a9mSs62as2wppB0yKNJ8Au44crq7a4a8LbwZsTGGR5jPg8jjmxTaKNNC2CsrQ/hQoNSOBJYHvEBP7QCBQAQShJhCA65FS8ix/Qfp4Ay1AR+pLHalDgH2RC+YkQxCT0duNdas12bk6GA8MLeNy9iDUJPDtOhv4xY+Ad0o4nLmiIxXrSNXpSD2iI3WWjtS2iNfGvsBViN/JlGZ27wmsiLR+XID43NxrrDvZi6e/b8d79mCkhD7JJTpST7XxeM2iI/UNcG5iVQ3SAtW/2K/VQVwJDEPEgzl4seYhCr//eiOVNfNqabsK2MJXmLSXdEVNumVnGcQ/KUs9tDhWvN0Y60YgrXBHkGu1mwqcd+nR8+0ycrhaADELLuTzMwW4Gxgdx/zSzqFMAAa18rMTkp8CJcNYNwj5bABcWKQKvEAg0AmEqoFAl8eXJp+IXOAt5VdvZayzOlIPlHBoFYWO1Ove1PE/yI3kAonNSwKXGOteAy7ys8HVwkTk5nGQ/7ncCEJNPlsiZooAt1aasbKO1FSkkuJ1mFPdsgTSFjTCPxZvZvdF/eM/fnmqse59JFnqPeADf/xmMdZtSS41ZM5q8v1kis2DSKvaun5ZAccY644v9/8/HakfjHV3AXsZ6x5NRsD7NqgHkTaoEald+yKVNTfFMT83c+zfjHW3Im0+p7RzqPMSatJtT1/EMR1+w+crwQ4gJXQhFUkXjxyuBiM3oYUqxmLgDeCZOG5S1dJWJiDn+8HkUqDmRaioCZSSvYAByHfGCyUeSyAQaAVBqAkEAB2paca6w4BbkNhpkBuBL4tZyl/teLPFB411TyHtJTuRf1H6N+A2729zpY5USy90yxYdqVnGuklIOXw5CjULppa7rEeN91XZ3C9+j/eGqWRS0eD3wpwZ1HQ0eCGz2H7IZ/Jvfjk21n1OTrgZC3yXFUOMdWsAh6eO8SpwdkcKJt6T5Qzkd8oKj/9GzHof6ajXLSI3AJsiAtcNyQ1xTGMmw33Izf/yqf36ATt5saY5T4kxwGhj3XLtTHFptvUpk6EHIrYnse14rXniBcjNEBFqYGLTF8BZI4erT4H1kfd2IX4AHoxjiuWXlCX7nTU/QagJlDnGut8jbU+NwPnlLmwHAoF8yrVUPxDodHSkvgOOJucB0QM411i3QPN7BQqhIzVdR+pa5ObkfvJjdmv8+vuMdbuXU9RwOyjniO6FU8s/lGQU5cF/yd303a4j1VafirJGR+pXHakXdaQu15HaEzGz3RFpIXqC5t8DGeD3SDT4yYjw84SPBj8aiZxO+k19BhzdGX9HnyJ1PPkePUcb61qSklRS/NhvBHbyIloe3pDXIK14afojYk3B84v/218AHNHOuO65VdQsRa4KLUu6zbVoGOuWA24CjiH3eZ0GXPTKo723GzlcdUcS0AqJNLOAx4FrOkCkAeYIZq0536dbn6rhOy9Q5vjzweHINdc9OlKd1qoYCASKQxBqAoEEPhL24sSq+YFzfBpLoJXoSI3XkToV2A7fqpGgD1Kyfq+xbqMy9nhpCeUc0Z2sqJkJTC7VQEqJf39lTYQnIwJil0BHql5H6iMdqTt1pI7VkdoIMWM9Gkkl+oDmo8EHA2sjN83LIC0wSyACwhjyo7k7FB2pd8ivSOkLnF4h5u93IJ4quxba6MWaMRQ26B2AiDUFDaH999ZEpMKkrcxNqEm3PX0Xx8U/jxjrBhrrjkXMsJdLbHoC2HzkcPXU+YcM3hl57xaqEBuHmAW/2oFpVMmKmpYSKmoCpWAtxHNxEuJnFQgEKoxKvjEKBDqK28gvp/8jMnPbntnKLo2O1CdICftBwJepzQsAJwG3Guv+0slDKxblnPyU9Kj5sQuXPv8TMXUFGNMFYuPnio7UTzpST+lIXaAjtTNSdbM7UjXzPMwxXa1B/LuyYkgGuUmuB44CHjHWPWysO8NYt7WxbrkOFk6uRVqysvwB2LMDX68o6EjNRG6WtjTWDSv0HJ9ElE33SjMQ2DmTYVAzL3EhsLexrm8bh1iw9SmToQYR6JIUte3JWFdjrNsEqeDS5EyLvwb2HTlcnTRyuFoR+X+uLXCI34A74pg74pjfijm2AmSFmtac74NQE+hU/ORiNhTjKl/VFwgEKowg1AQCKfyN7Onkl6FvDGxRmhFVBz7B5mUkTvZMaJK+EQFX+TaLJTp7fO2kLIUaX0WSrKjpkkbCXmTdwS/WA3eWcDhliY7UTB2pd300+GHAvxA/n++QtpPkzWZdankh//zDEZ+v54111xjr9vfR4IOKOM4GpAUqKbTtYqz7c7FeowN5GPgG2Ke5J8Qx9cBdSFtZmvmQypr50ht86+6TwM5tHFtzFTWL0LRqqmhCjbFuGeA64H8w5/eaAVwGbD1yuJqEiPyr0vSatRHxSLo8jjstKjzrQ9ae1qeQ+hToaLZDJia+QCr1AoFABRKEmkCgAH728wjyzWEPr+CKj7JBR6pBR2oM4lNzIzRJDlkTuMtYd6RP/KgEyrX1aQj5niJd1Uh4BDmj1sd0pAqm6ASasC1ysf8DUglngTMQT5RXkVaeQvQCVkJEgwuAp4x1xlh3oo8GX6o9rY46Ut8jYm+WDHCqsW5gM7uUBV5kugzYwFiXbieagxdr7kRustIMRsSaQr/rDcD6zVXszIPmhJr0OCdC4RSq1mCsG2CsOwIYRX7i1bNIm9O9I4erkYiw30SYQsTCa+KYxzsjfSqLr8SbRqioCZQp3lcx22J5XrV6sQUCXYEg1AQCzaAj9SNwJDnvhhrg7DZeBAdS6EhN1ZG6HCl1fzS1uQaJUb7PWLdjBXgElWVFDU0Tn7pkRQ25ahqQG8PAvNkB+WwmGa0jdbqO1DU6Ugcg7WRbIeLNQ0i1SHMshpg5H4uIEM8Y6y411u1hrFvZWNcqrxsdqUfJb1FdEDi+AlpUXwTeRcxwmyWOmQ3cDnxVYPMQRKzpn1zpRYSrgYPbMK70ObY5ocbGMW1unzTWZYx1GyKz/FuRuw79Djhw5HB11Mjh6ndIFc3wAoeYiVQmXR/HJTNGb+35Pgg1gc5kf6Rq6znvXxUIBCqUSjDgCwRKho7Uu8a6c5CbC5CZvfOMdbvpSKXLmQNtQEfqB+B/xrrbkZ7qFROb+wEHApsb6y4DnixTj5VyrahZKLXc5YQaY91iiP8KwKs6UoVaSgIJjHXrIJ+7JC8gPihz8NHgn/uH8fsOJhcNnq1kKiS09kfaWVb1y7Gx7jPyo8Hr5vF5Pxv4EznvobWBTYD75vErlgwfNX4JcIOxbuW53UjFMbMzGW5DTLAXS20eSi66O1nZ9Cjig/MXHak3WzIm70PTxKMmk2F+mp7X2tz2ZKxbCjGwTp7jZyGVlTePHK4WQHxo0kl1WT4AHu8II+NW0trzfRBqAp2CsW554D/AbOCi0o4mEAi0l1BREwjMAx0pQ36P7zKIsFDuM7cVhY7UR8hF+hHAt6nNw5BZ+xuNdSPS+5YBEwhCTbmyLTlz0lBNMw+MdX8ETk2t/hg4zgszc0VH6hcdqRd0pC5LRYOfj3ioNPcezABLAyOBUxCx5XFj3Xm+qu5Pxrq89hwdqanAceRHdh9e7h5XOlJjkRafA+bVAubbekYjFSdpFgB2zGSYYyDs/4/OAw4z1nUrsE8hCglpM2laTTOduVdNFcRY19dYdwhSIZQUaV4Cthg5XN06crhaDzGzLiTS/AKMimPuKQORBtpfURM8agJFx59LDveLo71vVSAQqGBCRU0g0DLOA5ZCZm9BjDM/AW4q0XiqEj97/qyx7iXEyHQPyPNiWB6ZiX4auLSMLkTGA32NdX3KrNIq3frUpTxqfHXHxn7xU5pGxAcSGOtqEU+Z5I37D8AhbX1f60jNRozZP0Ju1DHWLUSu4mYFRBAoJCoMQSJm1/LL9ca6cSSqbnSk3jfWXU3OoLc3Etm9i45Up3mXtIHLgLuBdZH46WaJY2ZmMoxC2tHSqUcLIWLNzXEsprX+b/IpsBmSIjUvmhNq0q1Hn7Qm9tpPZqyHVEoukNjkgHNHDlcvIqld20B+G5enEXgZeMG3gpULE4B+xrreOlJpEaYQoaIm0Bmsj1wjTUD8qgKBQIUTKmoCgRbgbzaOJP9Gdz9j3d9LNKSqRkdqto7U7Yjh8GhyPkFZ1gHuMdYdUiYGopORUuNy86lJz06XytOhVGxO7ib01jJtmysL/OfoYsSsNstU4CAdqfHFfC0dqR91pJ7UkTpfR2pHpOpmD+BSpMXq12Z27Y7ciGyLtD09aqx7EBHRfyN3AxwB+xZzzMVGR+prpGpoP2Ndj3k9P46ZgVSEuQKbFwZ2yGTyBIBLgZ1aeH5MGwmDCGeLpNa1uO3JVzVdgVRCZkWa2cD1SBXN+0gyzeYUFmm+Aa6KY54uM5EGWh/RHYSaQIdirOtLrl31Uu9XFQgEKpwg1AQCLURHaiJwGLmUogwyc5v2DggUCR2pSTpSFyIX80+nNndHLvTvM9Zt05KbnY7CCwDlaCicrKiZCWXRNtAp+DaZLf3iT0jbTaAA/rNzLrBEYnUjcJSO1Ocd/fo6UjN0pN7RkbpZR+pQpApDAych3jefQbMGtgqpcByK+LhEwOLAIca6vYx1hRKDyoVrkZbJtGlzQXzFzK0UFlyHkRBrfLKZAfZqwaELCTWLkGsZBBHL5+nvZKzrY6zbH7gD+Gti02vAViOHq2tGDld/RcyCf1/gENOBB4Ab47hsKwCzwmVLz/chnjvQ0eyMnEs+It9kPRAIVDCh9SkQaAU6Uh8b604l5+HQH7jAWLezjtSUEg6tqvEtTkcZ61ZAyuiXT2weiAhoW3iTzudLVDlRjobCSY+aH7tYRcmG5KpD7vBVcYEUvj3leODPqU1n6Ei9VoIhZYXPb/zjIQBjXX/kc59tl/oj5LxZECHBIQJDX/84B9jbWPcFOYPiscBXLfHb6Wh0pH421o0G9jDWPeQ9d+ZKHDMtk+FWYCeatjbWAttlMoyKY2Yi1Yh3GOuW1JEqFPWdpVDr0+Kp5S/nFoPt30f/RM7FyfPOT0jr8LMjh6tFgb3Jb4NK8h7wRMocuRzJVtS09HwfKmoCHYZvWd3eL55XDue2QCBQHIJQEwi0Eh2pR411y5CL/F0CONVYd1j4guxYdKTeM9btgsy4H4DMpmdZDLkheNtYd5E3J+5MyqqixhsLJm/kuoyRsP/dsxeu04B7Sziccmd3JCUkyU06UveVYCzN4oXw1/wj+3+8JDnhZgSwKNI2Ncjv1h2pNGlAhIesX9FkY9375ISbD0rYKnALYqC8PRKtPU/imKmZDLeQm0VPsiiwbSbD6DhWs7x4fbixbr+5CLXpipoaf5zk88c1Nx5fVXoEuQQvkL/5aOC6kcMVSDT7Ss0cYgLwUBzzZXOvUWaE1qdAOXEQIrY+4o3KA4FAlRCEmkCgbVyKJJT8zS+vgcwUXlGyEXUR/M3GE8a654Ctgd2QGO8sKwG3GOseBS738d+dQblV1Awh36C1ywg1wOrkKgLu05HqMi1frcFYtyFNW2OepALOY14U/8w/stHgQ4C/IG1cw5AWk/5IZdUvid0HAKv5B0CjjwZ/D3jf//t9Z1Sg6UhNMdZdB+xrrLtHR2rCPHcC4pgpmQw3I2JNWjBYHNgmk+G2MeN4HmkBXBN4vpnDpYWa7N8r+ft/kt7JtxfuglT3JFtP3wTOHjlcfYUIaP8mv/opSz3wIvByHDfxIStnWivUhNanQIdgrPsrsDYiBl5W4uEEAoEiEzxqAoE24G8SjiU/MnVXY906JRpSl0NHapaO1C2I4fDd0CSNZAPAGOv29UZ7HU25RXR35cSnbDVNIz5pKJCPsW4l4ITU6rHASZVaGagjNVFH6glEwP0cMb/9EqnueJPmPwM1wDLAFkg0+P1INPi5xrodjHUjjHWF2oOKxRhEGNmjNTv5qOqbyRehsvwO2HrkcNUNiUY/aC6/Q3r9UPLPp3XpWGxj3ZrIeXd3ciLNeCQufZ+Rw9VkRMDZjMIizRfAlXHM8xUm0kBOxAoVNYGSYazrhrQaAtygI9WVvuMDgS5BEGoCgTaiIzUJOBRprchysrFu6RINqUuiI/WLjtTZwFZIYkySnsCuiOGw9hc2HUW5VdR0ycQnY91y5FosntKRKpSS06Ux1i2OtAkmq2rrgMN0pGaWZlTFQ0fqY6QqKEZukichVSKbIW1exyJmtx/RVODNMgTxXDkIibp93lh3o0+aW9tYV7TPuo8RvwLQrTWnj2MmATdROClrKUSs+RppGdummcMkK2oy5FrHssxpezLWDTPWXYDEuA/zqxuB24CRI4erp0cOV2shcelLFHitqUgF1K1xTIuqh8oNHal65O8dhJpAKdkMMeT+HmkzDAQCVUZofQoE2oGO1BfGuhOQmx6QC7DzjXU76Ej9VsKhdTl0pL4EDvWlwIcgM+RZhiA3Z1sb6y4CXu2AtobxlJFHDV23omb7xM+jSjaKMsVYNxi4BDHhzjIJOFBHqlBlRqUyCmlNXdkvL4X8jucCT/gHxro+wLLkfG5GAIWSonogBsZ/RNLmMNZ9T87nZizwqY5UQxvH+wTie7YfcFRrdoxjfstkuAlpQ0qP/ffAll/b7tcsHtXfaqx7uEDcelKoGUDTSTzrq3F2RITvZAXOu8DZOlKfZjIsCWyEnG8L8RbwlE+vqnRaI8ynf98g1ATahbFuICKGAlxUDQJ7IBBoSqioCQTaiY7Uc8A1iVXDgLM6uHoj0Aw6Um8gN+snAz+nNi+J3KRe1gGVT2VlJkx+8gp0AaHGWDcMWNcvvlUCQ+myxnuKnI+kA2WpBw7Xkfq6NKPqGHz71olAUjDfyli3eup503Wk3taRulFH6hDk/aOR88e9SItOcwwD1geORISh54x1V/l2y7/7m6nWjPcSYB1j3fLzen6aOOZXpA1qUoHNyxy6yQL/njWDUcD+BbYnhZf5kVaxLL/c9YFbCrgT8WHLPnci8vfdY+Rw5TIZNCLkFBJpfgJuiGMerBKRBlp3vg8VNYFisyciyr4FPFvisQQCgQ4iE8ddKa01EOgYfALJOcBaidW36UhdUJoRBWDObPn2yA1E2sCxEXgQuLLADHNbXmshf7y/lYPHh7HuDOBfiVVr+3a9qsVYdyiwrV88REfqxVKOp5zw56jTkcS0JCfoSD1SgiF1Ct5LJXke/hXYujWfeWPdAPKjwZensO9KIb5Cqm2y8eBfz+38YKy7Aql23qstVX+ZDEMRg+EB6W3duscf3/7eD5t168apOlIfJvbZDPm9QCqQ6oG3Bw5u6LfVAVMWX3/baUsmDtMI3ANcOXK4moK0Ga5HYfFhNmJg/Goc09ZKo7LEWHcyYpK86rz+n7xA+nJi1SwdqdWae34gMDeMdUsirZsA2+tINTH6DgQC1UEQagKBIuENa29CqjaynKQj9VBpRhTI4v0k9kHieTOpzdORiNxROlJtnu011vUAXgX+pSM1sa3HKRbGuuvJ3XzNBFbvjBSbUuGrFx5GBLmvgC3LQTArF4x1+yM38Emu1pG6tgTD6VSMdUchRsFZXkPaoNr0/vCi1+/JtUqNABZp4e6TyCVLvQ98mIwGv/WNcX9476VnH3jslus++eWnH5ZCWhj7IgLTJ0hK0ui6uroPsvvU1tYuAU2jreO4W0Mc95oZx32nNTTU/lhfP7xusWW3efP46xap7daNXbO/fybD1sBw/zor9+17e++Fal8a2r37N4vWdKvLzJz2W/f62bMzvfr0nRHHjW/OmDr1xh9/fOHxhoalNkBivAvxKfDIsGG13RBT4XWAPyBVKD0QQ95PgNeB+4CX6+rqKub8ZKw7EJkAWGdebc7Gugzyeya/e1YO56dAa/HvpcuAVYAxOlJnlnhIgUCgAwlCTSBQRIx1iyI3/dnZzFnAHsnZy0Dp8O1OByMXOWl+Rgw9H27HDdzTwD7lMMNlrHsQUH7xGx0pXcrxdDTGup2AA/ziaTpS95VwOGWFsW4zJI0nycOIkFz1FwG+ouFW8kX0C3WkimbA6aPBs6LNCojvTUuSohoRUeO9R26+Ztbjo67fv7GhYanE9tnAZMTgN9muboBt6urqZqWEmknA9DiuqYnjXn0zmZl9MpnGOfvFcSYeqlar2/7I3Y48aqt/3w6QybAj8rdZtO+Axj8NGfyPEY31X8zxMOzZq3fD7NmzGuLGxjm/T0PDwm7ChNtH19cvMzX1+0wGHh0w4PyPBwy44GjkfdcvsX0GIo7Pl/p9xgI719XVvdOCv1nJMdZti4QJbKkjNbf2uOzzXyS/qnPNpEAXCLSERIXgZEBXma9YIBBIEYSaQKDIGOv+hngNZC9CfwZ2KEZ7TaD9+BmpVRHBZskCT/kEuYl7ow3Hvgsx9nulXYNsJ37G/1Ug65P0ho7UPnPZpaLx1UwPAAsg3hkb+SSdLo+xbhXgUvJvit8C9teRml2aUXU+XqS9mZx4Ug/spCNlO+j1eiKG5lnhZgXmYj775tOPzTfq7JOXaqifnendr3/92ptvO32Zlf5611J/XPESYNwBa/+lAVgRGAnsi5hBD66rq/s1JdTsUldXdxNAJsPCwE7du3+8QO/ejy/ap8/9K/Xo8cnvAXr2HjJ7wUWGjfi/5x8fl8mwx4BBjUsPXbhh027dY1UTn9t98IJDZ0Yr/em39bYadH2/gfNdrCP1W//+163es+crx/bu/cT6mUycmT176c9//vm5rGF3jFSNPDNsWO0s4HYkiQ/g/4ALgWfq6up+Bqitra0BlkM8gfYGImCfurq6q9r1h+8kjHX/RloJ99WRer0Fz38SSR7LUhaVl4HKwZ9T7kSq2M7Xkbq9xEMKBAIdTEh9CgSKjI7Ua8a6S5FYV5Cbx3OMdXuHm8fS4ysIXjHW/R+wCXKTkDTAXAa40s+AXqwj9VUrDl8uEd1DyIk0AD+WaiCdxL+QzxnAXeFzJhjrlkK8s5IizVfAEV1JpAHQkfrUWHcJcLhf1R043Sf0Fd3g1r8HP/CP27xAvDD57VIRUPP9F5/1uv3803/XUD87M/+wRWbsd85ln8w/bJEhwOaIODPr0mfe/BipOnny4Zuuvu6xW649f15jiGN+yGS4pb5+2Z2mTFl2ypQpB388YMB5f+jf/2I9a8bEHj991/0Vvdsp6m//OuLvA4c0rv3Fhz0GxTHMt+Bh0wYMbfxu0q+Nx+/w1/4PZjIMBLaE3ZebOnX31wcP3nVAnz6Pr96jx6dL9egxduDs2SMs8GAc8z1AbS3HkxNpzgWOSrc11dXVNWb/PrW1tRcjaVJT2vVH71yy0eItNRSeTr5Qk/ZMCwTmxdaISPMVcHdphxIIBDqDINQEAh3DKOSGfwO/PAI40lh3eldoNagEfIyuMdY9jngobE9+q8IawN+NdWOAa1pYYlwuEd1dJprb3wDv4BdnIkanXR7vy3QJ+W0nvwAHVbup9Fy4E1jNPwCWAA4BzujoF/bnfecfj8Mcs/Plbjz1mCtnzZjerVuPHo27n3zuZ/MPW2Q2ci4ZhNzcTyQn7vCfnffiPzvv9X1jY+NhxrqxG+681/hHbrq64OvGMS6T4VbET6XX5MmHf9itW918ffvetd6sGT8NnjZl4EeLLFXf84dvuvWKYzKNjdT/8HW39996rtczcZx5InM9qwBrk4jwnj37z9/16fM4AH36mA9mzx5xUxzTCFBbW7sgcIx/6qN1dXVHzutv40Wc61vz9ywDWivUhOSnQJsx1g0FdveL5+tI1ZdyPIFAoHMI8dyBQAfgL8pPA8YlVm+KzJAGyggdqak6Ulcgkbzp9JsaxIT0PmPdTr70eG6US0T3wqnlH0oyis5hFcTYFeABHalfSziWssALABeSH9E+CzhUR6quNKMqPf68fBIifMxZbaxbu0TjmX7A2n/57oevv/wzQMPs2TfWLrX0RsApwBjk+2N+Cl+rDaupqdkAOGqlf653cXbl3/+r/2msWy0ZDR7H1CGTB7MAZjeeNLamZsBsgK8+fHqJ7z7v3nvyrzXdp06qGf/NJz0+/O6LHl/EcWYCImBvQEKkAejZ8/8W88eN+/e/9sGsSOPZhVwi1slt/uOUP0GoCXQm+yGfqxd1pF4t9WACgUDnEISaQKCD0JGaiZTZJysxDjfWrVSiIQXmgo7UDzpSJyAzz2+nNvdDjGrHGOv+7as4ClEurU9dpqKGXDVNDNxWyoGUA4kY7mVTm/6nI/V+CYZUVnhfkJNSq4831qU/M53FP8ldi92rI/W1jtQDOlKnAWsCbyAR19chHjBzbdNaePEl10QqqZ4x1t1lrDveWLfxmHGu28DBDbeNWG1m9I+Nu28+YOgqDQBTfnu/xn0zu9vHb/X88Ydvun/dUJ9pINeiNSx73Jqan3v26vXUgkOGbLNmr15PrwqQyXBr1nMmwTr+3x/r6ur+rx1/l3JnMiJ8tVWoCa1PgRZhrFsO+C/iq3VhiYcTCAQ6kSDUBAIdiI7UD8CRQINf1Q3xq1HN7xUoJTpSHwF7ISLbt6nNCrkJvslYt0J6X8qnomah1HJVetR4g9hsgtdzOlLp/6+uyCHIDX6SS3Skni7FYMoRb/adFPUGAqd6kauz+UPi57zEI++dcwXyHr9XR2pfYC1gW+Bs4FEQX5hmWBKp5DwBePjGV396ba+Tflu3T//Ggf3mG94IEDfOZtY0l5k1I9MHOW/UIn5PE/r0GbPIsGG1Jw4bVnviwgv/6ZihQ3fap3fvF/6ZydAI3ID4e6VZzv/7Xiv+BhWHr86aQMuF+VBRE2g1flLocCTa/TYdqW9KPKRAINCJBI+aQKCD0ZF6x1h3Drm+/UHAeca63XSk0hdvgTLAX4Q/Z6x7GUlZ2RO5mcvyB+B6H8d9qY7Ud359uVTUdAmhBvEVynJryUZRJhjrtgK2Sa8m/G0KcRnwF8RLDODPSKvPjZ08jqSwWygF6H5gO0Q8PsV7a33iH3cD/PeUY/6EF3mmT5n8LdDDP0AmBxZEvndYeLEG1tbTM7d9OXhOVWBD/a89e/WOF5g5g9mQmQZ8A8yK494NjY39pkJDQyYzo3cmM8fD62rg7Lq6ukLVPdnfp9lEo9ra2uOQCsU039bV1f21uf3KkNYI80GoCbSFfyHVbROpPB+nQCDQTkJFTSDQCehIjUFumLJEwAlzaaEJlAE6UrN1pO5AZqVHIaXHSdYB7jHWHeI9IcqxomYGUqZfVfhWlfX94lgdqbGlHE+pMdatCRyWWv0qcHYwMG+KT2U6HjGgzrK3se4PzexSErxp6GXARj7FqwlvP/fkr9mfH7np6huAfwC7AS8hCXD9E0+fufBiDV8ttsyUnJCSqenVq0/cp6aGTKYmbsD7r8yY8Z8vf/jhk7V++OHzQZkMfYClgcuRSpoPa2trN27jrzUAOUelHwvMbacypDXn+7SoFVqfAnPFe41l00Mv05GaWsrxBAKBzicINYFA53Eu+eXg/yLnrxEoY3SkJulIXYRU1zyV2twdmfG+D2lL6Ges60tpSfpt/FSlN+pbkYsgH1XKgZQaY91wJLko+Z3+GXC0r8AIFEBH6gsgGXPdDYns7szP74TEz0Oaec6zwEfA/i085u8R0W514GfgUyQK+3Ikrv2dHj0mzDknLLtiv3jldaY3LL3CrBnDlqjvn8nEDngfuCyOeTOOievq6hrr6uo+q6ur2x84AhF/RtfW1qbbeLO/T3O/C3V1dUfX1dVlsg8q13R4AjDIWNdjns8MFTWB1rMj8l3+MfBQiccSCARKQBBqAoFOQkdqNuJXkzR2PcBYt1ozuwTKDB2pOh2po5HZ6g9SmwciyQxLILPfJamW8j4bSaGm6hKf/I30SL9YBzxXutGUFmPdQsBF5N/4jUdiuMMM7Ly5l/z3zyLIebqz+DDx84qFnuCF1kuANeZlRr/WyG3+A9xMzisG4EngvzpSRyLeNld/88nHGYBu3Xuw1Arzz9r1uMnsdfIk1vjPjMn7nPrbG3HMmDhmSjMvcwVSidSfpq12H/l/C3l4VRvj/b+DW/DcINQEWoz3MdzJL56nI9U4t+cHAoHqJAg1gUAnoiM1ATGGm+VXZYAzjHWLlW5UgdaiI/UeEkN7LOBSm7sBJwLX+LSGzmYIuUoTqM7Ep03JtXOM7qoXsca6fsDF5PsiTQcO1pGqVl+iouJFkFORypMsGxnr/t1JQ3gW5sRbb9bck3Sk3gZeRMT9PBG4V5++c5aHqtq/IN8rIF4z++lIHaMj9ZOxbgngklkzZlz47SfjFgSoXWrpKRtu1/Bxrz58vdjv67/fbM8pP62z+fS15jbgurq6GeQ8aH6f2pw1rV6otrZ2FaqbbPVQS3zJQutToDUcCPQEHvfXG4FAoAsShJpAoJPxqUKnJVb1B873N12BCkFHKtaRegKp7LgEyFYv1CPtUCsCtxjrTjXWLdyJQ0sbCVeVUGOs60ZuFn8S8GAJh1MyjHXdkeqI5I1yI3CsjtS40oyqMtGR+g1JRkq2CB5rrBvWzC5Fo66u7kdgjF/ctra2dpm5PP0yYHkk0pva2tqMsW6Zgy++9uzU82YibU5b60j9n7Guv7HuEOBOYNUnb79pwelTJncHWH7VNd7J1LAjcF6mhobu0sSzgrEuHe8+h9ra2gHk/GTS/lc3khMlTpzL71INZIWalvjUhIqaQIvwVXPrIZ/jS0o8nEAgUEKCUBMIlAAdqUeA0YlVv6N08bCBdqAjNUtH6hakyuMuYDb5iXobAMZYt18niXFpoabaWp/WQWLSAe7xEcZdCl9RcSTwt9Sm83SkXizBkCoeHak3gFsSq/oBp3lhsKM5HpiCVFmY2tra2kJP0pH6HHho0sTxhyy2+BL3nzT6geOBUT169Rqefc7kiRPGAZvrSN0I1BvrNkGM7LcDur3yyH2Dn7jtxlqA3n37ffX+y8+v7ScP7gHqG+rn+KWnW5qSHEHuHPdcckNdXd1PiF8SwAa1tbVn19bWVqtpfhBqAkXFXwMe7hdvCpWRgUDXJsRzBwKl4xJkNjxbHr4mEgN9VclGFGgzOlK/AOcY64Yisb/zJTb3RFqlNjHWXQ3c14Emr1VbUeMFiqwB92ykQqArsgOgU+tu05G6qxSDqSKuAv5Kzt9lBOJHdU1HvmhdXd0ntbW1OyDv5z8A79bW1p4HjKmrq/sMoLa2thswot98gwY2zJ69YUP97JpeffsuBuSdR5647cY7bzz3NGesG4EIKsv+8tMP3T95583+rz32wPyfvfd29rz0/YxpU9d67bmn6wF0pMYb65586cEx24996dnBf13vP1vde9Xnt9164dnj/OvXINU8+wN7+GO8DDxW4Fc63T93K0RQ/Edtbe2FwDN1dXVzWsxqa2sXBzYEdm7XH7B0tEaoSQvKQagJFGJTYBlkguXW0g4lEAiUmiDUBAIlQkeqwVh3DPJlnJ1B3d1Y94mO1DMlHFqgfXyGtEE9AhyCRLFnGQIcA2xlrLsYeKUDEpkWTC1X04zcikC2JeMR7/nUpTDWrYP4FyR5HjEUDrQDHanZxrrjkWrHrIfI7sa6/+ton4i6urr7amtr1wZuQgT8s4CzamtrZyHVNoOAmqm//Uomk2HFtdad3btvv0Zg1q8//TQG+KMcKXPJkr9f+rLuPXr0iuOYmdOndWuor09WtDQAtwEH19XVTSSf2+M43v6Td96c75N33pwP+Lj2rlFTEZFhICI4Z3kG2KKurq7J+auuri6ura3dBhgLHIdMRtwBUFtbOx05P/YnX6wYS66SoFLImgmHippAuzHWDQD29YsX60il3zOBQKCLEdosAoESoiM1CTiU/Nm2k411aYPGQOUwHphfR+pNpPLhJJpWtSyJmMBebqybmydFW0hX1FSTUJOMsx/d7LOqFGPdHxHj2yQfAcd3VUPlYqMj9Q0SYZ2lBmmBGtDRr11XV/cyMBxpOxqNiL4zgAE9evWavujSw6esudlW7qhrRn+06wlnzejeo8dEYIvLjtj39txR4gEzp0/rN3XSb91nTp/WrWfvPg2DF1xo8nxD538K+a5ZrK6ubscCIg06Uh/9cdU1X91sn0O+/uNq/5g4dOFhU5DvpsGIAf44ZGLhP3V1desUOkbid4nr6urOABZHKnseA75FfIAGAL8BryAC4+p1dXUr1NXVPdmev19noyM1C/HoaYmZcBBqAvNiD0SQfQd4qrRDCQQC5UAmjos9mRsIBFqLsW5t8m8O6oAdvJATqCCMdWsCe+tIbZtY1xvYHonbTKd9xIgh7pU6Uj/TTox115OLxp0BrNEBVTudjk+succvvqwjdVAJh9PpGOtqkWqLZBTwD8BOXbGyqCPxLXZnIIaeWZ4AjuvMz5Ifx3pIZd4CiU0OqUBZA9gEqaY5jFxlZpYpSNvWXTpS9bQAY916wJmJVSfpSD3Upl+gC2Csuwf4TUdqt3k8b2Uk1jzLGzpS+3To4AIVg/9+uxMRhrfXkbKlHVEgECgHQkVNIFAG+Fan6xKraoGzOsnIMlBcxpMqhdeRmqEjdR0Sv3sfuThekCjdjYF7jXV7GuvaG9uabH36qRpEGs92iZ+7VO++sW4g4mmVFGmmAgcFkab4+M/MmeQbcf8L+E9njcHfuF2OCEZZkWY2cAOwBXAykjD3IHAB+SJNDNwLaB2p21oq0nieJb8CcNt0HHggjwmE1qdA+zkU6Ib41wWRJhAIAEGoCQTKiWuAFxLLK9PUiyJQ/owHBhdK8NKRGq8jdRoiOryW2twbMZM2xrqN25IA5vdJCjVVkfhkrBtC7iZ5HPBWCYfTqRjregDnIS0kWRqAI30KUKAD8NWM/yNfVD3SWLdoR76usa6PsW5/xNNl5cSm/wO20pG6AuiBmPrWAn/yy1nGAjvqSJ2uI9Vsa1JzeFEnaUq9DOINFSjMBGD+FohZQagJFMRY93dgNUR8v7LEwwkEAmVEEGoCgTLBe0z8D/gysXo7Y92GJRpSoG38gpxbBzf3BB2pT3Wk9keEuC9SmxcATgBGGev+2srXHoLMymWplsSnLcgZmY6qoiqhueJv/v4HrJTadIaO1P+VYEhdCh2pd4DrE6v6Aqcb64oexGCsyxjr/gncjaQgZV/jJ+AoRJj5zli3KRK3vS3ijzILOWf8jMR876Yj9XE7h3MvMDOxvHU7j1fNjEdEl3lVQqaFmvZWTgaqAC/EH+YXr2mLuBoIBKqXINQEAmWEjtRU5Et7cmL18ca65ZrZJVBm6EjNBn6lBQaTOlKvIMahZwDpC7RlgCuNdRca637XwpevOiNh7++zpV/8ka5lsrgHEl+c5EYdqftLMZguynVIlUqW5YC9ivkCvkrnYuBcYGG/ugG4BdhcR+ppJCr8FkSMSYrA3yMtUcfoSD1WDBFTR+o34OHEqrWMdcPae9wqJdt6OK/zfaioCRRiS2Ax4BvyK9kCgUAgCDWBQLnhU0eOI1dy3xM4z1jXkj74QHnQUt8CdKQadKQMsCniPzEr9ZQ1gDuNdUf7FqC5UXVCDbARMJ//ubV+GxWLr6TbM7X6CUJpfKeiI9WAiCNTE6t3Ntb9pb3HNtb1MtbthZiIrpbY9BawjY7UJUB/Y91pSGXP8NQhngP+DTyOiHrF5I7EzzXkxNJAPlmhZl7n+yDUBPLw3+fZc/z5fpInEAgE5hCEmkCgDPGVFpclVi0InOPLZAPlz3haFtk6Bx2pad5/YjPgkdTmGmBz4D5j3U7Gup5NDiBUlVDjPXeyJsJTESPmqsdYtxLS/pZkLJLAE2K4Oxkdqe/JT0LKAKcY6+ZrZpd5YqxbA2lz2oNcW98ERBTaG2lz2hUYA6yf2v0LYF8dqcN1pOoQo+lVjHWrtHU8aXSkvgBeT6za1FjXt1jHryKCUBNoK/sA/YBXdKReLvVgAoFA+RGEmkCgfLkVmSnNsgJwRInGEmgdE2ilUJNFR+pHHakTgB2Bt1Ob+wIHAGOMdf8uYGC5YGq50j1q1gSy5q3GtwZWNca6xRHz4KQPynfAYTpS6WqrQCehI/UY+QLqgkhbaqsSkYx1w4x1FwAXAtl2okbgNkAj5/x/ICLOvuR7mUwBzge21ZGaI6LoSH0APAMc2BYT8rlwe+Ln/nRi6lUF0VKhZjb5xtTdO8LrKFAZGOsipIq2AUltCwQCgSYEoSYQKFO818CpQDKqURvrRpZoSIGW0ySiu7XoSH2EeGEcjvSvJ1HA6cBNxroVEuurqqIG2MH/20B+K0ZVYqwbjFRHDEysnoTEcP9SmlEFEpwN1CWW/4ncbM0TY11PY91uwD2IAJnlPWA7HakLkM/vZYhQl47bNsBmOlK3N9P+dxmwNBIjXixeBr5NLG9dZCGoGhjv/53r+d5/n4eqmkDWJP5wpDLvTh2pr0o7okAgUK6EL9xAoIzRkZqBfKH/mlh9pLEuxKWWN61ufSqEjlSsI/UcsBVy8zYp9ZQ/ANcb684x1i1CvlAzg3xT6orCWDcCqSIDeFxHqtJFp7lirOuFVEskb9BnA4frSH1dmlEFkviKrqR/GMDhxrol5rafse5viNC4D7k2p1+Ak5DWpx+MdYf556Tbl94DdtCROmNuYp33NrsX2HcurZGtwrfZJQXSxYG/FePYVcSvyPuhJef7INQEANZFIu9/Ba4t7VACgUA5E4SaQKDM0ZFywJFIVQFI/PI5xrqFm98rUGJabCbcEnSkZutI3YHM3o9CbuCTrI3M1P+N3Hn9pwqPsd4+8fOoko2iE/BVCicjyT5JTtGRSre/BUqIbzO6KrGqF3BGIXHEWLeQse4cpNplMb+6EUl3GYm0Um2KCCzbkH9N9hMiCu2uIzWuhcO7FhiCtFAViwfJN1LepojHrni8mDWRlp3vp6eWQ0R3F8OnGB7kF6/QkarYyZRAINDxBKEmEKgA/M3aeYlVg5EkqDAjV54UpaImjY7UJB2pixBj4XRMdXdkxvv3yM1axfrT+Oqgtfzi6zpSn5RwOJ3Bfsgsa5KrdKQeLcVgAvPkJvL9o5ZB/g8BMNb1MNbtiIinayee9yGwo47UOcCSiA/ZscCgxHNmIQlPI3WkHm+N2KojNR4RNXc31vVrzS80l2NOA5Jx8KvOq4KoC9JSYT5U1AR2ABYGPqGLmOMHAoG2E4SaQKByuIf8L/bhtMHMMtApFLWiJo2OVJ2O1NHArsD7fnV3pOe9G9IC9Q9j3doV+v7Yltz3U7VX02wG7JRa/RBysx4oQ3wVxf/Ib0Xczli3qo/tvg04kFzFxCTEU2oXYKKx7nTgOiBKHfpZYHMdqSt1pNLVFy3lVv/vjm3cvxB3Ij45WbYu4rGrgZaaxwehpgtjrFsI2NkvnhcS/AKBwLwIQk0gUCH4mdVzkJjeLOuT3yISKA/GA306Os5WR2osItYci3heJOmLvF+uMdYt15HjKCbGuoHAxn7xc+DVEg6nQ/HeJcekVr8JnF7hbWtVj/dMOj2xqjswGmk/+p1fFyNtTZsBDyM3aQb4d+pw2bjtI3wUeHvGNdWPYTtjXVGq+nwE+AuJVRv5z2lAGA8MaYHRcmh96tociLRKPhVaWgOBQEsIQk0gUEH4eN4jgZ8Tqw8w1q1aoiEFCjMVaWEoevtTGm84/ARwJtLulJ2ly/rYrAjcYqw7rUJ8jTYnN9M8uloFC2Pd7xEhLfk9/BVwpI5U2oMoUIboSD2NtAUNAZZCKtmykdvjkAqaM4CVkIrIfcmvopgMnAtsk4zbLgIGqfLYs4jHTEZ196aFaVddhAnI53jQPJ4XKmq6KMa6PyEC7Swk2S8QCATmSRBqAoEKw/sQHI584YN8js8w1i1aulEFknhxod0R3a1kKHLD8BlibjkrtX19wBjr9i+Wf0Wx8Yas2baKCcBjJRxOh2GsWwC4GKl6yvILEsOdTvYKlCn+5msE4hmWvZ7qg1Sf7AhMAy5HxJhhiV2zcdtaR+pOHakGiogX+i4HNjXWLV6kw74FfJpY3tJY161Ix650Jvh/53W+D0JNF8RXWh3uF29pb9VcIBDoOgShJhCoQHSkPkRmarMMAM7v6FabQKvoEEPhubCg/7cB+BGJAn4h9ZyeSPvFfca6zcvwRmt9pDoB4A5fQVZVGOv6ABeSH6U+CzjEt5gEyhxj3RBj3cmIz8zvgDpEfPkVadf7O9IWdQewcmr3d4Dt5hW3XQSeQgxL95vXE1uCF5+TVTULA/8oxrGrgJYKNenWpyDUdA02QjwFfwJuLvFYAoFABRGEmkCgQtGReoj8C+clgVNa0Ccf6Bw61FC4AAullt/RkToU2BuwqW2DgaOBO4x1q5eD4bAfQ9ZvaTowpoTD6RD8Z/N05KI9Swwc72OfA2WMsa7GWLclUg3zn8Sm9xHh3CGi+bLAXoixd5afEC+pPTsjxcwblV4CrG2s+2ORDvsYIkZlCVHdQqioCRTEV6/u7xcvaYdJeCAQ6IKEG7pAoLK5CHgjsbwWsHtJRhJI09kVNUmhZgbif4GO1JtIJOiJNI3s/h3yHrrcWLdMJ4xxbqyKiI0AD1RpC9ChwJqpdZfoSD1TisEEWo4XO25FPML6+9XTgPMRgfExYCCgEIGmF1LlNgupvBmpI/VEZ3oued+b14ADiyHG+go3k1i1orFueHPP70KM9/8GoSaQZjekSnQs8HiJxxIIBCqMINQEAhWM9zY4Gkj2PO9prPtniYYUyFHKipofkzeEOlKNOlIPAxq4kqYl+CsDo411J3j/lFKwg/+3EYk3riqMdVvTNNbYUOXx45WOsW6Qse544Eby47QfQz5PzwCnAtcgwk3Sb6YHcK6O1FUlnEm/FDEUX71Ix7ub/N8xVNXkhJp5CfMh9akLYaxbDNjWL55Xrcb4gUCg4whCTSBQ4ehI/QYcRv5s3cnGuiWb2SXQOXRaRY1vqUkKLD8Wep6O1AwdqeuRxJZ7ySVEAWSQWOx7jXV7ei+VTsHPyv/VLz5TbV4txro1kWqaJK8AZ4eL9/LEtzlpREzbNLHpS6Sd8FS/fgzwL7+tHhHNZwJfA98B+xUrJrst6EhZ4FEkHbDd13w6Uj8DTyZW/ctYN6S553cRpiPfv6GiJpDkYKA7UiH6UYnHEggEKpAg1AQCVYCO1KdIa0uWvsAFxrqBJRpSoHMraoaS74dRUKjJoiM1QUfqdGS279XU5t5IrK8x1m3cSZ5H2yd+rqoKE2Pdsoh/SfLv+ClwTLHTfgLFwf+f3Yh4ymTPodMRz5dt/bq7EcEmebM9CTgBOBuprgGJbD65xN5hVwKLIqamxSDpjdYD2LxIx61IvNjakvN9EGq6CMa6VZE212zyWyAQCLSaINQEAlWCjtTTwA2JVYsgsd3lluzTVZhA53nULJhaTnvRFERH6jMdqQOAA4EvUpsXQG46Rxnr0sk1RcNYtxC5ioR3q8lU11i3MOIBlLwh+xk4WEdqakkGFWgWY91AY93RwC3AHxKbngJGAi8jYs055MdtNwL3IHHbdyH/58nP0yrkWiA6HR8HfDewt7GuVxGO9yFioJxlc2Ndz/Yet8Jpyfk+LdSE1qcqxFjXnVwF5XU6UhPm9vxAIBBojiDUBALVxVXAi4nlv5FLHAh0LuOBwZ0klKUTn+ZaUZNGR+oVxGviDGBiavMywBXGuouMdb9r+xCbZRty30W3dsDxS4JP+7iY/Fn26YhI06r/n0DH4tucNkbanDZH2gABvkHOn2cgsfa3k2vRy/I2sL2O1Fk6Ur8C6EjNRKpxkvHy+5fYePcGxAR5qyIdL1lVMwRYr0jHrVTGEypqAsLmiFH/t8AdJR5LIBCoYIJQEwhUET6S9X+IP0KWHYx1G5RoSF2ZrOAxuBNeq11CDYgxtY5U1o/jBvJvMkHMSO801h1dLE8KY11/xJAV5Kb4xbk8vWLwM6pnA0slVjci7U7pqPRACfFpZ9ci1WOD/OqZwBWIiFiL+DltSf4104/AMcBeheK2daQ+Q4S6LN2RCseSVFF4EekmYJcitcQ+Q37l3jbFSJaqYCYAA+ZRWRSEmirHWDcI2MsvXuiT0gKBQKBNBKEmEKgydKSmIGW3UxKr/+d9FwKdhI5UPfArndP+lBZqWtT6VAgdqWk6UlcAmwGPpDbXILOF9xnrdi5Cu8OmiJ8SwGgvNFY0/mb1KKSaLcl5OlIvlWBIgQIY6/ob6w5DPJFWSGx6DtgCeBfxqTkGmC+xfRYi7IzUkXpyHmbQdyHtUlkWo6mpdGdyOyJC7dLeA/nz292JVcPJ/zt2NbLtLXOrqgmpT9XP3sAA4DWqZOIhEAiUjiDUBAJViI7U18BxQPYmoidwfkjn6HQ6y1A47VHT7tYaHakfdaROQGKz305t7ou0hBhj3fptMUr1VSdZ345fgYfbMdxyYgdE5Epym/cuCZQYY13GVxiOIb/trg5JaTkPOACJ214mtfvTwOY6UlfrSKWrI5rgRZyTyW8n3MxYt3a7fok24iPCrwG28v5J7cWQX3nXlaO6WyLUhIqaKsZYtzRSIdoIXBAS/QKBQHsJQk0gUKXoSL1MftrAgsA5xroeJRpSV6SzIrqTFTUzgMnFOrCO1MdIKfdhSHtSkoWB04CbjHV/auWh1yMnMN3VkhvfcsdYty5izJzkecRcNlBijHVLAlcj0drZG+pZiHixPbAsIuCk/VY+B/bWkTrKG/O2GB2pieQn8gEc7020S8H9gENm/tuFjtRv5Ffd/dNYp9p73AolCDVdGF9JeRhyX3WXjlTanD8QCARaTRBqAoHq5mbgicTyn4DDSzOULklnVdQkb/p+LPZMno5UrCP1POLTcS7wW+opywHXGevOMdYtOq/j+YvaHfziLCQxp6Ix1o0ATkmt/gg4vhpauioZY11fY91BSOvPSolNLyPv6c+B2xBBMpmKNAlJeNpWR+rNtr6+jtSrwOjEqoHAqaWI7PaR8JcB/zHW/b4Ih0yapdYgbWNdkfH+39D61DX5J/AX5LvxmhKPJRAIVAlBqAkEqhh/w34qkDS7HGms083sEiguHR7R7W/2Fkis6rBEIR2peh2pOxFvmVuB2amnrA3cbaw7dB6GpX8h11bykK86qFiMdYsAFyAthll+AA7x7SaBEuDbnNZDhMAdgGwC2w+IYH0pcDxi/JysBGlE/Fc205G6y4sb7eVy8s/DKyFJUqXgOSRe+4D2HsibJr+eWLVZqQyTS0xWqJnb+T5U1FQh3qvtYL94pY7UpBIOJxAIVBFBqAkEqhx/o3g44gOS5UhjXVc2fuwsOqP1aSi5G1DoQKEmi47UZB2pixFj4SdTm7PeM/cZ67ZtptVuh8TPowtsrxi8IHUxucQggKnAgTpSEwruFOhwjHWLI5UjZ5JrsatHEs12BVZGqmj+ktr1bWA7HamzfWtPUfDpL8chZr5Z9jbWLV+s12jFWGJEpPq7se7PRThksqpmALBhEY5Zafzi/w2tT12P7YFhwGdIQlwgEAgUhSDUBAJdAO+rcBQyUwxyM32usS5tQhsoLuPp+Nan9P9hmxOfWouOVJ2O1DHIje/7qc0DkYSbu411a2eje411SwGr+ee84I2vKxI/k3oesHhidQNwRPAoKA3Guj7Guv2AO4FVEpteR8xuf0aEhXTc9g/A0Ujc9qcdMTYdqS+B8xOraoDTjXV9m9mlw9CRegd4ATiwCLHaLwHfJZa3KUVbVynRkZqNtL20Sqjp4pHmFY+/hsqmqJ1fpOq7QCAQAIJQEwh0GXSk3kJuKrMMAc4z1vVqZpdA++mMipq0KWmHV9Sk0ZEai4g1xwBps9VFEJ+Pa411ywHbJbaN6pwRFh9/g3U8+Z4nAGfoSL1eYJdAB+LbnNZCWpZ2QcRoEOHyaOB64AxEsE625WXNhDfXkXqqE5Ja7gWeTSzX+jGVgssQA+V2pVB5D6Y7E6uWIF8k6yrMtdXVR5rXJ1bVAMHcv7LZH/EaekZH6o1SDyYQCFQXQagJBLoWdwMPJJaXA44Ls3odxgRgaAf/fUsu1MAcw+EnkXaoi4Epqaf8CWk12RO5OfkIeKczx1hk9qBpi8eNOlL3l2IwXRnvEXQRIkRnY6cbEB+l/YF1kbSnpVO7PgWM1JG6prNSx7wQdBr5lW//Mdat3xmvnxrLF8BDwH7Guu7zev48eACYlljuilHdLamgDO1PVYKx7o/Id8As5DsvEAgEikoQagKBLoS/STiL/DaVDRFPkUDxGY9ciHdka0NZCDVZdKRm6UjdihgO34ncMGcZ7B9LIW0C/Tp9gEXAWLchIjgleQK4sgTD6bIY63oZ6/YE7gL+ntj0NrATkrIzChFqknyKtDgdrSPlOmWwCbz3zQlAsnrnGGPdsM4eC3AVIm5t2p6D6EhNRaK/s6zmfYK6Ei0R5tPm4kGoqUB8a182QXOUjlRdKccT+P/27jpOjvr+4/hr7+JGQkLIZIsFGQgUChRooYVSpMVhcHdaXItTpFhxt+KuQwpFyg8rLVKsQIEyuG0GiSux298fn+/mZvf2fO3u3s/HI4/czO7OfM/2dj77EZHuSYEakR7GNbU8nsYpFQBHhlHcE1PVy20W1jy0nH1qqtajpiWB700JfO9CrBfIC0AKC9KApf//HHg4jOIdwiiub+YwNcc1X/1jwe53gDM0hrtywij+BRagOYjGaVuTgNPc/osKbgMbt30+sIcrBa0aN+77tsSugcDZlf5dCHzvO2xs+UEl6JVzH/nBp507ebyuZiL28zaohfsoo6Z72BxYGXsddWt1lyIi3ZUCNSI9UOB732PvBuXGK9cB57kSAikRl8FU7hHdoxIfzwaml/Fc7Rb43heB7x2DXTznft5y47iHYf1D7g2j+Be1XoIXRvHSWAAgWSbyNXCMC4BKmYVRPDqM4ouxUqe0292ABRv+gGWGnE/+70UD9vO3beB7D9ZQw8/rsBLAnFWBA6qwjluxn+ndW7lfiwLf+xr4Z2LXVmEUD+7MMbuY3JQ3TX7qxlxAMzfa/orA92a1dH8RkY5SoEakhwp8711sdG3OEODiakwg6ebK3VA4mVHzXQWaobabSxNfD/gM+AT4sOAuy2AX3leHUbxCZVfXNmEUDwOuwMYP50zDxnBPqcqiepAwivuEUbwf1mdrg8RN7wC/xwINfwEKx02/DuwW+N4Fge9Nq8hi28g1lz2F/HKY/cMo/kmF1zEdG1u+ZxjFi3bycPckPu4PbNPJ43UlbQnUFJY+9S/TWqR89sO+x+8CT1Z5LSLSjSlQI9KDBb73CPnTOpYFzuhpo1XLrGwjut33KRmoqWp/mhZsCOT6b9yAZT1cQ9OLlrWBu8Io/mMYxYtVbnktc5PRLqHxcwDLDjo28L0vq7OqnsOVZd4LHALkptRNBv4E/B3LctqR/Nc0MVbieXDgex9XbrXtE/jeV8CfE7vqsBKoSmei3I8FHvfv5HFex4KxOTv1oL8nuXJiZdR0Uy7rOJd5dpHKXUWknHrKH08Rad6l2IvrnF9j7xhJaZQzo2Y4+c/jNReoceVMe7rN+cB9ge/9EPjezVjA5mGsNCUnBWyN9a85KIziqr7j7C4yzwJ+XHDTmYHvdeWpVTUvjOKRYRSfD1wNLOl2Z4EHsclJu2LlTslx23OwCU87BL73bC1mmBXxGNaMOmcUcHIlSwFd6d61wPadKYF1X+9kVs1o8jOgurNcoKal53sFarq2o7CphY+5rGQRkbJRoEakh3Pp9ycC4xO7fx9GcU95cV1uEylfM+HCiU810Ui4wKrAKu7jJ11/JAAC35sY+N452AX3ywWP64c1g304jOKtq/iu/KHARgX7rgt8TynvZRJGce8wivcCHiJ/YtN7wDFYb6OLgeUKHvoUNm77L4HvzanIYkvABTfOw7KAcjYBtqzwUp4APscylzp7nKmJ7Z4yqls9arqxMIrXBn6FZYJeVd3ViEhPoECNiOB6bBxH/ovIP4VRPKY6K+pWyplRUzjxqeYyamjMpgEbldxE4HufBL53ONag8ZOCm0dgU5buci+UKyaM4u2wMc9JjwI3VXIdPYmbqnU3cASN/TumARdiwbw/0zRw9iFwUOB7Jwe+902l1lpKrk/MqeRnlx0fRvGSzTykHGtowPowbRpG8dhOHGcOECZ2rVGrvadKbDqWNdieQI161HQBbhpbbhz3zck3HEREykWBGhEBIPC9D4EzE7sGYM2FhzTzEGmbSmbU1FSgxl1k5jKzXm6tV0jgey8DuwHn0DgZKmd54Jowii+vRAAxjOKfAycV7H4dOLeLlNN0KWEUjwij+GysbGmZxE3jsODBXthEpMJx2+dh47bfrNBSyybwvbeBGxO7+mP9anpXcBkvA28Ah3ey9OoBIDldq9tn1bhA1yRU+tQdbQ+MwTKP76ryWkSkh1CgRkQWCnzv/4BbEruWwC4U9FzRcT02UIM1Xcxd7BXNpikU+N6CwPcexvrX3IT1HElaDxvnfVIJJtQUFUbx8ljmRvLn/nPg+MD35hV9kHRIGMX1YRTvhmVg/DZxUwScgfWmOZX8n/XkuO2HullDz5uwSVY5Y4HfVerkLgh5ObAW8LNOHOc74JnErt+W6/e1xrTWPF6Bmi4mjOJFsMlyAJe6fk4iImWniy8RKXQt8GJie13gsCqtpTuYAAxzqdOlVlj6VDM9atw4663c5kfAq+15fOB7swLfuxbYDmu2mlSHvcM5Lozifd1UppJw06YuxzLKciZhY7hrarxzVxdG8WpYAO8YGr/eM7D+D+9hJW9rFDzsNWDXWhy3XQqB7y3AAlMzE7v3DqN4rQqu4X3gaSyrpjOvE5NNhXtjv7PdXWuBeY3n7np+hzUsfw14vrpLEZGeRIEaEcnj3p0+BUiOHd4rjOLfVGlJXV2uhKcc7yaPSnw8G+uRUCt2oLFM5Y6OlgsFvvdd4HunA3tgJRlJA7Bmvw+FUbxZZzO/wigegE1BSwbA5gLHBL43vvijpL3CKF40jOIzsOyR5RM3PQbchpU5BeS/RhmPjds+JPC9wj5G3Yr7WTsnsSsFnOXe2a+Ua4BlgQ4/7we+918s4JazQ4XLuKphIi0H5pVR04WEUbws9resAbhYZa8iUkkK1IhIE4HvzcDe5U6+q/vHMIpXrNKSuiz3DvlkytNQOBlQ+K5WXkS6DJed3OZ3wP919piB732ApZ8fQ34QESxg9Sfg1jCKV+/I8V2Q52wg+TOeBU7VGNbSCKO4LoziHbFpTsmJRh9jAbIVscBb4bjt64Adu9C47U4LfO8p4G+JXYsBp1VqZHfge19i5WgHh1Hcp7X7t+DuxMfDsWlW3dlELLA2rJnbFajpItzv2rHYtdJDrfVYExEpNQVqRKSowPc+xzJrchdGfbHmwj2hz0CplbxPjQssJAM1tdSfZnMaL1TuLVVfl8D3soHvvYAFgS4gfwQwWD+Pv4RRfGEHpuUcA6xfsO+KwPee7dhqJSmM4lWA24ETgMFu9yzgZuAr4GgsgyPpKSAIfO/GrjRuu4QuAL5ObP8KKwWslL9gv8c7dOIYzwDJCTm7VCrYVCWtjehW6VPXsT6wNta0/Loqr0VEeiAFakSkWYHv/QvrWZOzOHB+GMW9qrSkrqocI7qHk/8cXhOBGhdA2sNtzgIeLvU5At+bH/je/VjD4TuAwkDQhsD9YRQf05apZWEU7wLsUrD7IdrYAFmaF0bxImEUn4I1KU9mKz0DPIL9rGxY8LAPgQPduO2a+LmuhsD3ZmHB8uT0pGPDKF6mmYeU+vyTsN+v/cMoHtTBY8zHJkDljAVWLcHyatUE939zgRpl1HQBLovsGLd5feB7hW8KiIiUnQI1ItKaW7DGkjlrYOnA0nblmPxUqxOffgEs5T4eF/he2frmBL43PfC9y7EmpU8V3NwLG/U9Lozi3ZrrjRFG8fo0/Xl+Cbigp5TZlIMrc9oOC9RtR+P0r8+w55QfY8GxZFnNVOBcbNz2fyq43JoV+N575L+b3xc4p5PlSO1xJxYo2qsTx3gY6/WU051HdecCNc0F5hWo6Rp2BdLAp1jQXkSk4hSoEZEWuYvVM7HJPTk7hlG8bXVW1CWVI1BTqxOfctk0DeRPfSmbwPfGB753MrAv+aONwfqdHAM8EEbxr5NlF2EUr4QFBpKlGB8BJ7neQtIB7ut6C5YNksto+gEbqT0V+z4lf34bgHuB7QLfC7vZuO1SuI38RtorUKFJfC6r50Zg9zCKC59z2nqMycATiV2/DqO4MNDcXbS39EmBmhoTRvEIYH+3ebHLChMRqTgFakSkVYHvzcayDpLpvyeGUdydU9hLqRylT6MKtr8p8fHbLYzisTSOU3468L24kud3U2b2B07EpgQl/Qjr+fGXMIpXDqN4FHAZ+RdK3wNHBr43E2m3MIqHhFF8AtaLZuXETf8CXsB6nfyk4GGvArsEvndRdxy3XQoucHUa1isjZ7cwitet0BIexn43DuzEMZJB2zoam413N7kpfyp96roOxSYKvhD43r+rvRgR6bkUqBGRNnEjY0/E3v0GKy25sKPvsvYwEyl9oKYWM2r2SHxclf4uruHw01hQ4HJgRsFdfoL13XiWpuPNjwp8rxa+jl2KK3PaCisR2JHGDKUvgXHAasCmNB23/Qfg0MD3Pq3carsm93N5dsHuMyrR3N01A78a2Kaj/XHcxJzXE7u2C6O42wUp3Jsas1DpU5fk3mzYCpiPTaITEakaBWpEpM0C33sNuCSxazhwUQX7JXRV5cioKSwdqGqAIYzi0cDGbvONwPfer+Z6At+bG/jeHVjD4fvIb8j6I6yPzrI0BrxOCnwvqugiu4EwipfHpgOdTuOkr7lYqUsD9vUfnHjID8A1wA6B7z2nPkBt5yaQJZtzLwqcXqEpSs8AH2DZBh2VzKoZgk2H644moKlPXY77PfqD27w78L2vqrkeEREFakSkve4DHk1sjwVO7uYjVztrAjC8xF+jZKBmNlC2pr1ttCuNf1NqZlpS4HtTAt+7EMv0+AfgAQPdzSnsgqoP4IVRXF+dVXY9YRQPDKP4GOAuLGMm5w3gLWAzYOmChz2Jjdu+OfC9uUhHXAJ8kdhejwqUEbnyqyuBX3Wi5PWf5JckdtdR3S31JFNGTe36LdbkfBJwU5XXIiKiQI2ItI97B/w84L3E7i3p3pM8OmsCNq1lYGt3bIdkoOa7amYmuBHY27rNz4EXq7WW5gS+9yXWaHgq+RdLk7AMkBOA+8Io/mU3vXgsiTCKU2EU/xYIsalaudcR3wDPYxc6axc8LAIOCHzvVJWWdY4rrTkZK83IOdJlNpX73K9hE9GO7MjviAv23JvYNYamPyvdQUulrgrU1KAwigcAR7jNq9SnTERqgQI1ItJu7t3w42iccAFwVBjF3fFFdynMxl6gl2TyUxjFdcBiiV3VHs29HY0p/HfW4tSeMIo3AQ7H+kd8hr2z/y35X7ulsb4E14RRvEKl11jrwigeg42KPpvGn+W5WGCuF/Ar8sdtTwHOAfYMfO+tSq2zu3MlelcmdvXBRnb3rcDprwJWBX7Zwcc/gv0O5nTHAP9EYEAYxcXKmpoEahQYrgl7Y39T/wf8rcprEREBFKgRkQ4KfO97rJ57nttVB5wfRnG6equqTS7bpZQjuoeT//xdtUBNGMW9abzYmgQ8Xq21NMeVapxZsPtlbELVNeRfOAKsBdwVRvHpapZt7zaHUXwE1mNkzcRN72NlOOuRn0GQG80eBL73cC0G7rqBe4BXEttjgKPKfdLA9z7E+g8d3pFSwcD3ZpBfOvuLMIqXLNX6akTuDYwmjZ7d70Ky7C9FfnBTKsz1V9vTbV6o5ysRqRUK1IhIhwW+9w5wfmLXEOBil0Ys+UrZULiwkXA1M2p+Q+PndX+t9R4Jo/hHWF+P5MVQDBzt+tfcjGUEhTRONAO7gNoKCMMo/l1P/Jl2ZU4bAw8CewG5C/OJWB+alYDCkpvcuO2LNW67fNzF5BlY1lLOjmEUr1+B01+HNeTesoOPvw9Ilmru0ukV1ZYJ7n/1qekajsT+PjzpXtOIiNQEBWpEpFMC3/srcH9i13JUbhJJV1LKEd01MZrbfY9zI7nnYBf0NcP1zrkcGJrYPQM4MvC9hWV7ge9NDHzvXCwz6KWCw/QDDsQCNtu4srNuL4zipbAyl/Np/Hmbj/X56YeNOU/+jo/HyiE1brtCAt+bQNNMsdPDKF6s2P1LeN7x2HP+7zsyYtv1i/pXYteWYRQPKtX6akAuUNPWPjWa/FQlYRSvCWyEfU+uqPJyRETy9IgXnCJSdpcAbya2NwL2rdJaalUpM2pGFWx/U6Ljttc6WGAO4JHA96ZUaR1NuJHxF2FjuHMWAMc3F0gIfO+TwPeOAA4DPi64eQRwGlYStU4ZllwTwijuF0bxIVjWQ/Lz/Bz7GV6V/KbYyXHbz2vcdmUFvvdP8gPliwBnViCgeDMwgI5nwyRHdQ8Atu70impHLgisjJoa5n5HjnObt6jRuYjUGgVqRKTTAt+bj03NSQYMDq5QGn5XMYHS9aipiYwaGuv6s8DdVVpDEy7T51SsB03SOYHvvdra4wPfewWbaHQ2+Q2zwUp9rg6j+HLXXLdbcGVOGwAPAPthzYEBpmHNl5emaYDwCTRuuxZcDnyS2F4b2L2cJwx8bypwK7BPGMWLdOAQrwHJgOku3ShbTYGarmFb7Pk8Bu6s7lJERJrqLn8URaTKAt+bDByDlcCAlUWcHUbxMtVbVU3pVj1q3DjgXMbF84HvfVXpNbTgQGDzgn03B773SFsPEPheQ+B747D+NTfS+HOdsx5wbxjFJ4VR3KRpaFfi+vhcBlwMeImbPsOyHQp/hz8A9g987zS9C119ge/NwUZ2J4Nlh4ZRvFKZT30PNtFuv/Y+0GVeJUd1jwa6S2B/Mha8bu75fnbBtkqfKsyVxR7qNi9zv0MiIjVFgRoRKRk3ESTZM2EA1lx4cJWWVEtKOfUpGaiZjfVdqbQ9Eh/fUYXzFxVG8RbAQQW7n8IaoLZb4HuzAt+7DgvY/I38Jqh1wPbAuDCK963QeOSSCaO4TxjFB2GlM+slbvoO+5lahsbMGrAL0LOBvQLfe7tiC5VWBb73CRZsy+mFjewuWxPswPd+AK4Hdgqj2Gvt/kU8jmVs5XSLUd2B7y3AfleUUVO7DsDKBN8Enq3yWkREilKgRkRKKvC9p4DbEruWxC4YevrzTbkCNd9Wui+IG1n9W7f5Tq1MynCNIU8r2P02cEZnR64Gvvdd4HtnYOVebxTcPAB7dzYMo3jzrvCzHkbxeliZ00E0TsT6Afs5HQkkm7suwErbgsD3xml8bc16APhnYntJ4Ngyn/NR4Gvg4PY+0AV6wsSuNV2mXnfQ0vO9AjVV5LJ8d8am/F2kvloiUqtq/sWkiHRJV5M/PWdd4JAqraVWTACGhVHcq9V7tsAFAZJTXapRerILjaOaa6K2P4zipbHmwcmv71fAsaXsnxL43gfA77Eyvy8Lbl4cOAu4NYzi1Ut1zlIKo9gLo/girK9J2u1OAd9jJRiFF5evYOO2Lwl8b3rlVirt5S44zyK/r9I2YRRvVMZzLsCmg20WRvEKHTjEA9gFc063yKqh5Sl/Kn2qEte/7Bjs79c4lwUsIlKTFKgRkZJz77ifQv6F7D5hFG9apSXVglzfgs72MxlB/nN3RSc+uVKKwG1mgOcref5iXI+YK4Bkid00bAz3lFKfL/C9bOB7LwA7ARcAUwvuMhb4SxjFF4ZRvGSpz98RrsxpP2yE+q8SN83Axm4vRn5ZVwa7oDk88L3PKrVO6RzXK+z0gt2nhlFc2NeqlF7AxrYf1t4HBr73LfBMYtdvwygeVqqFVdFEYNFmsuuUUVM96wE/x573rq3yWkREWqRAjYiUhXv3/VhgVmL36R1817XLS/Qt6GxD4WpPfNqWxrKYu6pdBuP6wlyMNSPNmYdl0hRmvJRU4HvzA9+7H9gGuN2dN2lD4IEwio91zSurIozitbHGrYcAuT46DdgF4yAas6PA3u2/Gtgx8L0XVBbQ9bipZclMt8HAn8pVkud+Rq4A1g2jeK0OHCI5qrsPjYHgrmwilt1XrD+bAjVVEEZxbyz4DHCDC2qKiNQsBWpEpGzcO/Gn0vhOfV+suXB3eMe0I0oxorvwnfGKBWrCKK7HxlaDZaw8WqlzF+MuPM8Cflxw05mB7/2nUusIfG9G4HtXYI2Fnyq4uR4r5/hrGMW7h1Hcp8kByiSM4pFhFJ8PXIP1KwG7eJzt/i+8QHwc2D7wvVs0brvLuwaIEttrAPuU62SuufQ/gMNdeUl7/Bd4P7G9o7uo7somuP+LPd8Xlj4pUFMZO2PPg59jDdRFRGqaAjUiUlauRCQ5cccD/tzZXi1dVClGdBcGaipZ+rQRMMp9/GDge4UXHJV2GLampGsD33uyGosJfG984HsnA/tipSBJg4GjsQybjTpwMdtmYRT3CqN4L+AhYGO3uw5rCgzWEyOZCfU/YL/A9/6ocdvdgwu0nUx+9sbvwyguDGqW0lXAijT+zLWJy8hJZtWMoOnvdVeTC9QUe75XRk2FufLYA93mJYHvza/mekRE2kKBGhGphJvJH4G5Bo0pyD1JKSY/VSWjxgUW9nSb84D7KnHe5oRRHAB7Fex+FPtZq6rA9/4L7A+cCIwvuDkN/Bm4MYziVUp9bjf56m7gCBqblOayE+qxfjQ5k7CMpL1rZXKXlE7ge19gZYE5ddgEvoFlOt9nwCPAoR0IxD9NY3ADYLdyBjMrINfQudjzvQI1lXcIMBD4V+B7L7V2ZxGRWqBAjYiUnXvH9Azg48TuncIo3ro6K6qaUgRqCnvUfNvJ47XV6sBK7uPHA9+b2NKdyymM4nWxIEjSa8C5tdJTxTUcfhrYAbgMa16ZtBo2HercMIpHFz6+vcIoHhFG8Z+A64Exbnc/bKJTA/kZNAuAu7Bx249Uu8+QlNU48oPko4ETyni+G7DG1O3qMxP43jys0XXOWJqWNHYlLQVqNPWpgsIoXhHrI7YAuKTKyxERaTMFakSkIgLfm4U1F56W2H1SmVPxa02pS59m0zQAUC57Jj6+q0LnbCKM4uWB88n/+/UZcLy72Kspge/NDXzvTuxC4V4ay49yNgUeDKP4iDCKBzU5QCvCKK4Po3hXrMxpM7e7nsYsmmzBOV8Gdg5879LA9yr1syNV4gKXZ5Ofebd5GMWbNfOQzp7vOyyj6wA3Ia49QiDZG2mXki2s8pRRUwNcVtZxWMD6nnI3mBcRKSUFakSkYgLfy2CZELl38HsDF4ZRvFj1VlVRpS59+rYSGSRhFC8N/NJtvhj43qflPmcz61gMuBxIXgBOwsZwT6/Gmtoq8L2pge9dBOxI05HmfbAyrnFhFO/U1rKRMIpXw6b7HIul9YN9bfpi5WnJC8KvsXLDIwLf+7yDn4Z0QYHvTQNOI3/8+klhFKfLdMrbsGbVe7TnQYHvTQKS/aU2DqO4MIOwq5iBBZ3Uo6a6NgF+gk1cvLG6SxERaR8FakSkogLfexUrBckZgQVrKjYNp4o6lVHjphwlg1qVavy6e+LjOyp0zjzu3fnLyC/9mgscHfheYR+YmhX43peB7x0HHIQ18U0aChwP3BtG8S+b69ERRvGiYRSfDtwELO92D8SCNHOAWYm7zwKuBHbSuO2eK/C9N4BbErsGAGe7SW6lPtcM7KJ4T9fEtT3uTXxcB+xUsoVVkPs9ay4wr0BNBYRR3A840m1erQxCEelqFKgRkWq4B3gssb0KcGIXbx7ZFhOBEZ34PEeQ/7xd9olP7kJrC7f5AfBGuc9ZZA11wDmAn9idBU4NfO+9Sq+nFALfexPYG/gjTQNuSwOXAteEUbzwcw6juC6M4h2xMqet3O7e2ESpLBaUSZY5PYaN275N47YF6x+T/H35MY2TcErtQWAKcEB7HhT43ofAm8ld7oK7K5pI2zJq1KOmPPbCMlAjrMm1iEiXokCNiFSce7fxHOD9xO6t6aLvnrbDBKzMpd29SJzCMoBKZNTshK0Z4M4qZWQcS2PpVc7lge89W+zOXUXgew2B7z0ObAdcQ34mDMBawJ1hFJ8RRvEvsZKSE7DATB2WgTMAmF7w2PeBfQPfOz3wve/L+1lIV+FGEp9C/s/KfmEUr16Gc80FrgW2D6N4yXY+/O7Ex0No7L3U1SijpkrCKB4F7OM2L1LDdBHpihSoEZGqcC/kj8N6jOQcG0bxWlVaUtm5hsqz6XifmsLR3GWd+OTeyd4xca6ny3m+ZtawK7Bzwe4HqWJD41ILfG9O4Hs3A9tiDVWTFxW9sKyEx4H1sb/bi7h/M4Cpifvmxm3v40aEi+QJfO9rbDx8Th1WAjWkDKd7EvgEOLidj3uB/LH2u3bRbMuJwCJhFPcu2K9ATfkdgb3B8FTge/+p9mJERDpCgRoRqRo3IeQ4YL7bVQecX4pxxTWsMw2FKxqoAbbEAgIAd7t35CsmjOINsAa4SS8BF3bHXiuB700KfO9cbNrNS1jGzLLAMOx3YyQ2Ir0v1hwz9/1YgDUV1rhtaYvHgb8nthfHmguXNBjifg6vBDYJo3hsOx93X2LXGCy7rKuZ4P4fVrBf47nLyGWIbYr1MLuiyssREekwBWpEpKoC33uH/Hd4FwEuDqO4u7547UxD4YoFalxfmFwT4ZnAuHKdq5nzj8XK45IXjx8CJwa+Vzjiurvpg5V8zMOCMSkss6YOy7YZDiyDNRB+CWsUfJmaZUpbuCDneeRnrWxCY9+jUnoFeA04sp2BoEfID2jsWtJVVUYuUFP4fK+MmjJxf7eOc5u3Br5X9j5uIiLlokCNiFRd4HsPY+UsOcsDp3fRdPfWdCajppI9ajYAlnAfh4HvzSzjufKEUexhzXSTFzDfA0e58rFuKYziIWEUHw/cjjXYnoU1ZJ2JTXNKZjTV0RjEKvnkHuneXFDvVPLL7P7QgX4yrZ0ni2XVrAn8vB2Pmw48mtj1izCKl2ju/jVqovu/8PlegZry2RprOv8d9jwqItJlKVAjIrXiYiBZS74xjc0Au5NSZdTMxnqUlMse7v8F5I/MLaswigcBl5N/cTMbONKVynU7YRSnwijeEpvmtBP2t3kgMAoL0HwBfIz97MzHLkI+xb7/62LjvE/uwChk6cFcNuNfErv6A+cU6anS2fO8DzwFHOEyHtoq+byTommvqlrXXKBmTsF233Z+XaQI97fjULd5WeB7hQExEZEuRX8YRKQmBL43D5tokyznOSSM4l9UaUnl0tzI1rZIBmq+LVefljCKVwVWc5t/D3yv3L1wcufthZXBjUnsbsDKnT6sxBoqLYzi5bCL5TOwXhZ9gNHYRfM3WDYN2NfhFmAdbPpTMhOiDgiAcWEU7xdGcd+KLF66g5uBtxPbK9H+5r9tcS1Wrvfbtj4g8L0vgRcTu7YOo3hgqRdWRkUDNa4HT5NgTUVW1L0dgD2Hvg38X5XXIiLSaQrUiEjNCHxvEjaKOfciNoW9w7tU9VZVch3KqHHvuC6W2FXO7JI9Eh/fWcbzLOTK3E7EAhFJFwa+92KRh3RpYRQPDKP4GGwU8U9obBa8GFbmNQHIBeLexyY5nRH43geB752BfY9eLzjsAOAQIAyjeHO9Sy+tcf2eTiU/O2/PMIrXLvF5vsIyxg4Jo7hPOx56T+LjAcA2pVxXmeUCNcWe71X+VELuNcKu2HPmRd2x2byI9Dx6ESciNSXwvQ+wEcM5A4FLXFpzdzCBjvWoGUH+c3ZZmiSGUfwj4Fdu89UKZrLsjY2nTror8L0HKnT+inBlTr/BLlp3o3Hc9o+wnjQZrIkw2LjtM7EgzbvJ4wS+F2GZD8dgpVFJi2O/Q7eGUbxGmT4V6SYC34uBcxO7UsCZYRQPLfGpbsSaZO/Yjsf8G/gssb1zVwlABr43F5hO8ed7TX4qraOxXl2PBL73v2ovRkSkFLrEHzsR6VkC3/s7+Y0AlwLO7iov0FvR0WbClWoknAseQOWyaTYFDivY/RzWq6bbCKN4DFYCcg4WeOsHLImVO31FY1bDfOznf7vA9x5tbtx24HvZwPdewHp3XIA1Hk4aC9wQRvFFpW4SK91L4HtPkd+8dzHgtFI2dHcZk3cA+4dRPLiNj8mSn1WTBrpSOWxzpa7KqCmRMIrXxX4mZgFXV3k5IiIl0x0uekSke7oKeDmx/Qvg91VaSylNAIZ2oGFn2Udzh1G8CDY1A+AT8r/+ZeH64ZxRsPt94LTmAhRdTRjFA8IoPgK74Pwp9s6vh10Mx1ipUy5V/0Vs3PYVbZ20Ffje/MD37scykm6nMSMn51fAA2EUH+e+xyLFXIgFDHM2wHofldJdWCBy73Y85nFgWmK7K43qbi4wr0BNCbi+Zse6zb+4YKCISLegQI2I1CR3kX4K+RcO+4VRvHGVllQqk7FGsMPa+biyB2qA7Wm8YLir3HX+rszqEiyjJCfGxnB3+YkdrsxpI2z0/F5AL2BRLItmBvaznQuqfIlNtjrSNVFtt8D3ZgS+dwX2fXyq4OZ6YBes4fAe7ewTIj1A4HuzsOfcBYndx7pMsFKe4wZgtzCKC7MEm3vMD8C4xK61wihevlRrKrOJwPAimUmFpU8K1HTMTljG7ZdUcDqhiEglKFAjIjUr8L1p2LtlsxK7zwijeIUqLanTXABqMu1vKFzWQI27cN/FbU4Enizl8YucbxHgCmBoYvcMLFjR5d8VdaVGV2JTrEZivZaWwQImn2O9K8B+ti8Hdi5V0+TA98YHvncyNt7+nYKbBwNHYRk2G5eytEW6PjdK+9rErj5YQ/dSBvbGYc9fv2vHY+4nf9LZLs3dscZMwIIwhT1olFHTSWEUDwMOcpuXusmRIiLdhgI1IlLTAt/7FDgtsasfcFEZGl1WUkdGdJe7R81vsWwPgHtdI8yycBd9F2KZJTkLgOPd97vLCqO4XxjFhwD3AT/DLnSXwMofvia/zOlRrA/NHeW4yHANiPfHxt5nCm5OA+cDN4VR/ONSn1u6tNvJnyi2PE17SHVY4HvzsV4iW7U1WyfwvW+AZxO7NusifwOam/ykQE3nHQwMwkp0/1XltYiIlJwCNSJS8wLf+wdwfWLXaOB8V5/eFXVkRHcyo2YW+eN0O8U1ad7Tbc7GJhKVhcvgOBUonEZ0TuB7r5brvJUQRvH6wAPAfkBfLLi2JDAVS83PBb/eBfYOfO/MwPcmFjtWqbiGw89gk3YupTGTJ2dV4JYwis8No3h0OdciXYPL+vsj+X1hdnNNW0vlGeB/tC8AlGwq3IfS988phwnu/8I+NZr61Akuq3Y7LMvqEo3jFpHuSIEaEekqbsImAeX8FCvh6Io6MqI7Gaj5rsQvTH+OleWAjTed1tKdO+kgYPOCfTcHvvdIGc9ZVmEUp8MovhTrt+Nh47aXdTd/SuMF70TgdGC/wPfeq+QaA9+bG/jeXVjD4XvI70MCsCnwYBjFR4RRPKiSa5PaE/jed8CfCnafEUbxosXu34HjZ7HSx/XDKP5JGx/2DhbcydmxCwTrc4HYwud7ZdR0kAv2H4eNkb8v8L3PWnmIiEiXpECNiHQJ7l3e07EL35xdwijeupmH1LJ2jeh2GS+LJXaVupHwHu7/BuDuEh97oTCKtwQOLNj9FHBduc5ZTmEU9wmj+EAsi+aX2MXW0ljfnS+x8rQG8sdtP1bNaVaB700NfO9iLMPmuYKb+2BNj8eFUbxTF7gIljIKfO85IEzsWhQL1pTktWPge29gU86OaEuvpCKjuhcDNirFWspIgZrS+zWWkTkV+EuV1yIiUjYK1IhIl+EmhhxDfkr+SWEUr1KlJXVUe0ufRpD/fF2yQE0YxSsCa7nNZwPfK+xlUqrz/BQreUp6GzijK47hdmUg92MNUQdg5XhLYI2ivwDmuLv+k8Zx27OKHasaAt/7MvC9P2AZTv8ruHkocDxwXxjF66vhcI92Cdb8OmddYOcSHv8q4MfYKPC2eJrG4AfU/qjutvaoUelTG4RR3JfGTNprypz9KSJSVQrUiEiXEvje18BJNE4A6Q1cGEZxe3u+VFO7Mmoo78SnPRIf31nC4y4URvEyWPPgZIbGV8Cx5WxaXA5hFI8Ko/hCrGwj1yR4DFZK9An2Li9YRs0Rge8d3dFx25UQ+N6bwN5YT5LCn6ulsAv1a8Mo9iu9Nqk+Nxr7FBrHyINlwJRkPHbgex8BjwGHhVFc34b7z8XG3eesUuOB+inY3ypl1JTGHlh56UfAw1Vei4hIWSlQIyJdTuB7/8YulHMWw4I1pRwhW07tzagpy8SnMIoXx3qTALzlpgSVlOtpcTk2FjpnGjaGe0qpz1cuYRT3DqN4X6zR8obYtJEx2Of1BRbkaMAaPV+Gjdt+qTqrbZ/A9xoC33sca856DfY5JP0UuDOM4jPCKC78WZRuLvC9CBs1n9MbG9ldquDCdVhGWlvLWB8iP3BUs1k1LltwEpr61GnuuWdft3lRV8zEFBFpDwVqRKSrugt4PLH9Y+CELlKmMREY3o61liujZlca/w7cUaJjLuTS1C/BLsJy5gHH1HKWSaEwitcG7gUOxQIzS2Cf0wSsLCRX5vQI1ofmznKM2y63wPfmBL53M9ZwOKQxaw2sceeWwMNhFP8+jOIBVViiVM+92BjknDGUqJm7G719P/C7tgR/At+bBPw9sWvjGg8gFsug1NSn9jscC2g94/obiYh0awrUiEiX5BpLngO8n9i9DdYktdZNxBq3Dm7tjk7JAzVusk9uvO2XWC+VknENR/8EFJYlnBH43lulPFe5hFE8Mozi87Ask2WwzKYxWLDpYxrLnP4L7BX43lnlHrddCYHvTQp871xgF6AwK6gvcAAWsNm2VI1lpba57IUzsB5MOTuEUdzW3jKtuRm7CN+tjfdPNhWuB3Yo0TrKoVigRhk17RBG8arAZsBcLENTRKTb0wssEemyAt+bA/wBSy3POTaM4jWrtKQ2cU1lZ9H2PjWFgZpSlD5thzXBBbirDGnkh2HTOZKuCXzv78XuXEvCKO4VRvEeWC+MTWgctz0Ay6D5Bss2mYD1dtk/8L33ix+t6wp879PA947AMok+Lrh5ONYc+u4win9W8cVJxbkg5BkFu08vRTaLawp7C7B3GMVD23D/CHgzsWt7l8FXiyYAixYENRWoaSP3dTvObd4R+N74aq5HRKRSFKgRkS4t8L1vsQk1892ueuDPYRR71VtVm7SnoXDyQmgWMKMzJ3Zjl3N9HaZgzTxLJoziABvznPQodiFW08IoXgMbUX4U9v1ZGguUfY8FaX7AMmpuA4LA9x7v7r0SXE+o3bAMqcKMoeWAq8IoviKM4jEVX5xUVOB7LwL3JXYNoXQju+8FZgL7teP+OYsAvy3BGsphIvZ6e2hin0qf2m4LYCz2BsWt1V2KiEjlKFAjIl2eK6W5ILFrKHBxCZtdlkN7GgonM2q+c2VfnbEJjcGf+91kl5JwY6tPLNj9GnBuCdZdNmEUDw+j+CzgBmAFrAfN0lj/mU+wgBbAC9i47Stradx2ubmGw3/FMrH+QmNfnpx1gXvDKD7ZNZCW7uty7HciZ23yp8d1iMuQvA7YKYzi0a3dH3geiBPbu9Voj7IJ7v9kYF4ZNW0QRvFArDcNwJWB7xUGuEREui0FakSkWwh8L8QaoOasAPyxRl+4Qxszatw71YsldnWqP437euzpNgtH3XaKG9l7Pvl/Wz4Fjq/V5rphFNeHUbwL9rOzBfY9WRbrxfI5diG4AJvsdETge8cEvvdVlZZbdYHvzQp873osYPMokAy+1WF9j8aFUbxfDZeiSCe4EdknY88fOYeGUTy2BIf/G9Yz6+A2rKOB/OyeZbEJZbUml4WmQE377QcsCrwDPFnltYiIVJQCNSLSnVwIvJXY3pSmJTi1oq0ZNSPIf67ubCPhtbAgFsDf3ASVTgujeDHsnfbkNKBJwFGB700vxTlKzTWovAPrf7A41ih4BJZi/xlWnjATuBTYpauM266EwPe+C3zvTGB3LGMqaQBwCNZweHM1HO5+At/7BPu9yKkHzu7sNDAXfLkK2CyMYr8ND/kr+WVEtTiqu1igpjAzRIGaAmEUL0Fjc+mLajkjU0SkHPTiSUS6DZe1cTz5zXYPc+U4taatPWpKPfEpWaJwVyePBYC7OLuM/F46c4Cja7HxYxjFw8Io/iM2aWYVbNz2EtjF08fYZJssdhG4XeB7d9VqRlC1Bb73IRaUORrLQEoaCZwF3OZ6/0j38iBWCpizJI1NXzvjn8B/sIbkLXJB4L8ldv0yjOIflWANpZQL1CQD88qoad1RQG/g0e7YrF1EpDUK1IhIt+IyRI6lMS0/BZwbRvGS1VtVUW3NqCmcqNLhiU9hFC+L9RIBeCHwvS86eqzEMeuwMenJd7+zwKmB773X2eOXUhjFdWEU74CVOW1L47jt3lhp03iszOkdYO/A9/5Uqoyj7izwvWzge//Exnn/mcZ+PjkrATeEUXxRDf4eSge5DIezaOzBArB1GMWblOC4VwI/D6N47TY8JNlUOAXs3Jnzl4F61LRTGMXrABtgzfOvrvJyRESqQoEaEel2At/7HzahJmcQcIlrTFgrJtK2QE0pM2p2T3x8RyeOAyzsd3Mc8MuCmy4LfO+5zh6/lFz/jFuxRsc/wvpZDMMCX59iFwTdetx2uQW+Nz/wvQewINht5PcwAfgV8EAYxX8Io3iRCi9PyiDwvSnA6QW7TwmjeFQnj/sO8BxweGulcy7gnCxL3KbGnutnY4GZEQX7kjT1yQmjuB57swXgpsD3JrR0fxGR7kqBGhHplgLfewK4M7FraeBPNdQvYwIVLH0Ko3gEsJnbfJ/8Xj4dtSuwU8G+B7Hx1jUhjOJFwig+GQscrIn9HIzGAjOfYH105mGjw3PjttULoRMC35sR+N6VwA7AUwU312MZD38No3jPMIr7VHyBUlJufHsy8DuI0jzXXoVl6m3chvvek/h4ALBVJ89dMu75pLDUdR75jbh7uwCFwPZYpuPX5H9fRUR6lFq5YBERKYcrgX8nttcHfleltRSaACwSRnHvVu5XqoyanbASH4A7OhuMCKN4A6wvSdKLwIW1EOhwZU7bYGVOOwFpLEhTj5U5ZYD5WI+NHQPfu7onjduuhMD3xge+dzKwD/B2wc2DgCOBB8Mo3riGp7NJ21wDfJDYXh3YtzMHdJky47CJUq09T75Cfo+knWsoKA8FgRr3HFlY/tTjp6S5TLvfu81L3YQxEZEeqZb+iImIlFTgewuAk7CL8pz9wyj+dZWWlDQFaMBGj7Yk2aNmFjaFqF1cs98d3OZ44Nn2HqPgeGOxvjTJi+sPgZPc17yq3LSYm7AypjFYmdNg8sucPgcOc+O2v67SUnuEwPfeBQ7AGn0Xfq1HYyPdbwqj+MeVXpuUhmu2fQr5wYffuclqnXEDFuAIWjl/lvxeNUsA63Xy3KVULINSfWqa+h0wBHiV/EbVIiI9jgI1ItKtBb43DTiG/J4AZ4ZRvFyVlgQsHEM7idb71CR7PXzbwWyVrbAXvwD3dCaYEkaxh014Sl5UfI+N4a5qRkoYxYPDKD4eK8P4ORakGYkFtz7B3tWeAVyCjdt+pVpr7Wlcw+FnseymS4DCke2rAreEUXxuGMWjK75A6TSXAXNhYlcdNrJ7UCeOOQErpTywDX1nHiP/56qWRnVPBAYXlPqpT02C+5u8A/YGxsW1kJkpIlJNCtSISLcX+N4nWHZFTn+suXC1G5q2OKLb9SxIBnLaPfHJpf/nmghPx0ZOd4i74Lqc/CygWcCRge91eBpVZ4VRnAqjeAvgIWz8+NLYO+oAX2JZHPOwMortAt+7O/C9+VVYao8X+N7cwPfuxhoO34NN2UraFHgojOIjwigeXOn1Sac9AjyT2B4NnNDJY96GZe/t0dKdAt+bDTyc2LW2m3RXCzT5qQWu9PFY7LrkQfc3W0SkR1OgRkR6BDeF6IbErtHAeVVu4NjaiO7h5D9Pd6Q/zYbY5wrwUEezXsIo7gVcgGWp5DRg5U4fduSYpeDehb0Bm/K1Era+AViWz6dYNs07wF6B752tcdu1IfC9qYHvXQzsiE33SeoN7AWMC6N4J/ezJ12Ay4I4h/yg8mZhFG/eiWPOBG4E9gijuLUG7A9gz0s5u3T0vCU20f2vQE1xGwBrAdOA66q8FhGRmqBAjYj0JDcCzye218YamlZLayO6O9VI2L1LuZfbnA/c157HFxznJOzrlXRB4HsvduSYnRVG8cAwio/GyiI2xPrQLEpjmdME7GLxNGzc9v+qsU5pWeB7Xwa+9wfgIGwaWdIiWF+b+8IoXl8Nh7sGV256KvlTjU4MozjdicM+BEwGDmzl3DH5z/Fb1EDmJLQtUNMjS59cOViuMf117udHRKTHU6BGRHoM1xfmj1imRc5urmymGlob0V0YqGlvedFqwMru4ycD3/u+nY/P2QfYpmDfXYHvPdjB43WYK3PaFLtwOwAL0HjYReFX7t8s4GZg+8D3nlCvg9oX+N6b2M/ZaTQNSC6F9bW5LoziFSu8NOkA9/28JbFrAHBOR7Oj3PSfa4AgjOIlW7l7cqRzH1ppRFwhuUBNMjCvjBqzGzaV71PseV1ERFCgRkR6GFf6cyz5TSdPCaN45WYeUk6tlT6NLNhub+lTsqfDne18LAAuKHJowe7nsF41FRVG8TLYxdoFWPPZpbGRthOwLJoZ2LvpOwS+d021mxtL+wS+1xD43hPYhfXVWMAtaU3gjjCKzwyjuPB3Q2rPDcC7ie1VaCUjphVPAR/R9Pmo0FvkjwrfsQbK59SjpogwihcD9nebF9XC1EARkVqhQI2I9DiB730FnExjL4M+wIVt6H9Qai02E6YTpU/uXecN3ObLge993M61EUbxasAZBbvfB05z2UkVEUZx/zCKD8fG7/4Wy6JZBAvMfEpjP5pDA987LvC9TLMHk5oX+N6cwPduwRoOP0R+z5EUsAXwcBjFB7vR81KDXMPuU8gPuO0XRvEaHTxeA3AFsFEYxau0cL8s+Vk1I4Ffd+ScJTTZ/Z8MzGvqExyGfd7PB773arUXIyJSSxSoEZEeKfC9l4GrErtGYsGaPs08pBxay6jpTI+a3bGLWrBx1e0SRvESWLlJ8usRY2O4C98JLgtX5vRr4EHsXfQVsO/TAmyS01fYiPOLgV0D3/t3JdYllRH43qTA987DGsIW9kLqi70TPy6M4u3cdDOpMS5oen5iVwob2T2kg8f7N/AqcEQrPYv+D3tuyKlqU+HA9+YBU1FGzUIu2LYFNpHvsuquRkSk9uiFjYj0ZHcAf09srwr8oYJNSycCw1s4X7K8YxbWKLdVYRQPA7Zymx8Br7VnUa755uVY1krODGwMd0WmJrmMoCuwF/BrYeO2e2Nfs0+x6SAPY+O279G47e4r8L1PA987EjgE+3lOWhTL2rgnjOKfV3xx0hZPAE8mtkdi5aYdfZ69AlgDWLe5O7ieNskeWqtWqbw1qTCDsscGalxg9Ti3eVfge19Xcz0iIrVIgRoR6bFcivyfyO9nsB2wfYWWMBELPgxu5vZRiY+/bUdT3B1ozIS5oz3NdF1G0UVAsmHnAuD4wPc+Lf6o0gmjuF8YxQdjY3a3xcZtD8SCVJ9iDZX/A+wZ+N45ge9Nbu5Y0r240ojdgbNo7PmRsyxwZRjFV4ZRvGzFFyfNcs8/5wPjE7s3ojGY3N7jfYAF2I9oJZPqISxbI2fXjpyvhAqbx/fk0qffYj2LJmKN30VEpIACNSLSo7kynuNo7CEAllXToT4K7Tz3bCxTpkn5UxjF9QX72zTxKYzivsBOicf8X1vX497h/iOwesFN51Sif0AYxesD92PNnlfEsiUWABngS6zc6RTgAHexJj2Mazj8CNZw+AaaZiX8HMuuOaUKPaekGYHvzcBGdif7DR0fRvFSHTzkNdg0sM1bOOdErAFxziaueW21FGZQ9siMGtdX6gi3eaWavouIFKdAjYj0eIHvfQMcjwUFAOqBP4dRPKr5R5VMcyO6h5P/HN3W/jRbAMPcx/e63ght9Tvsnc6km92FcdmEUTw6jOJLgWuB9bBx272wHhOfYF+j3Ljtv2vctgS+NyvwvRuwgM0j2Hj2nDosM+7hMIr3C6O4R1wA17rA997Bgms5/bCR3b07cKwMVtp0cCt9xZJNheuxbMNqmYBlOg5y2z0yUAPsg70J8T7weHWXIiJSuxSoEREBAt/7D3BhYtcw4OIKXOQ111C43Y2EXRnA7m5zFtbDpU3CKN4SOKBg99+x4ElZhFHcJ4ziA4BxwM7YuO1+2No/xT7nZ4Ad3bjtwlIB6eEC3/su8L2zsJ/7wqyvAVhfmzCM4s3VcLgm3IyVLuasCBzcwWPdhJWN7tzcHVzm3VuJXdtXuGF80kT3fy4wX/h81u0DNWEUp4E93OZFlZweKCLS1ehFi4hIo4fID274wGllbi7c3Ijujkx8+gVWDgAwLvC96W1ZQBjFP8XKEpLeAs4sV/ZKGMXrYmVOpwArYY2L52N9LL4A/gccEvjeHzRuW1oT+N6H2GSwo4DPC24eifW1uT2M4jUruzJJchfmp2HNyXP2CqN47Q4cazJwG7BvK1Okklk1Q2maNVgphYGanphRcySWVfS4y7ASEZFmKFAjIuK4oMQFQPIF5G+APct42uYyakYWbLelR03uncoG8i9OmhVG8TJY8+Beid1fAce5ySklFUbxqDCKLwBuAX6FfZ51NJY5fY1lNu1aib440n0EvpcNfO9f2CjmPwNTCu6yInB9GMUXu6liUgWu1PScgt1nhVE8tAOHuxtrGLxPC/d5Hvgmsb1rBSf7JeUCNbnn+x4VqAmjeC3g19jnfVWVlyMiUvMUqBERSXA9Xf5AfmDksDKO/m0uo6awP843Re6zUBjFY7GRtQBPB74Xt3biMIoXxcZwD0rsnoqN4Z7S2uPbI4zi3mEU7wM8il1U/QibeDUb+Az7/B4EgsD37gt8b0EzhxJpUeB78wPfy00Nuw0oDDhuADwQRvEfOhgckE4KfO//sN5COSOAP7Y3gOLKIa8HdgmjuDALMXefBVj2Xs7yND5XVlJuUllzpU/dduqTa45/rNu8OfC9NjXHFxHpyRSoEREp4KaFHEfjBV4dcG4YxUuU4XSlyqjZI/Hxna2d1E2HugQYndg9Dzg28L0vW3t8e7iyhgexUehjsXHbC4AYK1N5Bdgj8L1zNW5bSiXwvRmB712JNZD9e8HN9Vhvk3FhFO9Zxb4lPdlF2DS3nPWB7TtwnEewQO/vWrjPOPIzWKoxqrsnZ9RsByyHlbbeVeW1iIh0CQrUiIgUEfje+8DZiV2DsebCA0p8qrb0qJkFzGzuAGEUjwY2dptvuLU3yzVV/ROwSsFNZwS+91ZrC26rMIpHhlF8LlaesCE2bhtsFPonwIfAycCBge9FpTqvSFLge+MD3zsF2Bt4u+DmQVjfjAfDKN6kSiUxPZIby3wqjdP2AI4Jo3hMO48zHyul2TKM4mWbuc804LHErg1cY9tKmo714epRPWpc/6Bcw+jLAt+bU831iIh0FQrUiIg0I/C9x7EgQ84Y4E8lnh4zkdanPn3bSlPfXWl8Pr+jDec8HOsVkHRN4HuFWQcdEkZxrzCKdweewN7l9rAMhh+wMqcvsXKF7QPfe0rjtqUSAt97D5tsdjzWCylpNHAecFMYxatWem09lQsqX53Y1QfLXmxvhtNz2Ljnw1q4z72Jj1O0MC2qHFwj5Uk0H6jprqVPB2HN4t/Avk8iItIGCtSIiLTscvLH/m4AHFjC408AhiQvTFw9fzJ402zZk3u3clu3+TnwUksnC6N4e5o2R34Ea+7baWEUr4GVOV2ENW/th71j/g0WpHkc2CHwves0blsqzTUcfhbYESv9m1Zwl1WBm8MoPs9lqkn53Qm8ltheDjiiPQdwwd4rgF+656Bi9/kMeDmxa5syZEi2ZgI9KKPGZUfthDW4v0hBeRGRtlOgRkSkBa4R5UlYbX3OgWEUF2akdNQU7EXsool9w8l/fm5pNPd2NL4Te6d717YoNxL7hILdrwLndvYFdBjFw8MoPhsIgY2A3LjcKViZ0xvAwYHvHR/43vjiRxGpjMD35gW+dzcW5LwbK0lJ2gR4KIziI8MoHlzp9fUk7jnrdPKDZruEUbxeO4/zJvBP4PAWStiS0/AGAlu25xwlkCx17daBGvc9OBb7WxYGvvdRlZckItKlKFAjItKKwPemAseQP6XjzOb6IbTz2A00LX8qnF5SNFATRnFvGptiTsKyVYoKo3gF4Hzyn/c/BU5wPR46JIzi+jCKdwaexcoOFnPn+AHL8PkQKynZNfC915o7jkg1BL43LfC9S7AMm2cLbu6NZZ+NC6N45zCKezU5gJSEmwJ0VsHu091kuva4ClgZ64lVzCvkNzDepcSlrK2ZAAxzWZPdferTL4F1sN4811V5LSIiXY4CNSIibRD43sfYu745/YFLXOlRZxU2FG5ToAb4DY0BnvsD3yscQwxYU1/gMiCZ5j8JOCrwventXm3jcVcFHgauxMoVemPZQd9gWTR3AtsFvne/xm1LLQt876vA947HyhoLm3EvAvwBuD+M4g3UcLg8At97HngosWtR4Iz2BFIC3/sE+BtwWLHAmguMJ7NqlgTW7dCCO2Yi1h9nGJbFlcyArO8uwUBXynu027w+8L0pVVyOiEiXpECNiEgbud4WNyZ2pYHz3LujnVE4orvV0dzuYjE3knsO1hemCdeD4dKCY87BgjQdKkEKo3hYGMXnYFNUNsJKCACmYgGaZ7Bx2+fpBbp0JYHv/QfYB5tG9E3BzUsCFwPXhVG8YoWX1lNcivWyylkX2KWdx7geGAVs3cztjwEzEtuVHNWdG9E93JWbdtfyp12AJbDvZdG/TSIi0jIFakRE2ucG4IXE9jrYFKXOKMyoGVVwe+EFY+68y7mPHykWEHEBpHMBP7E7C5za2gjvYsIornPNiF/APudcWcIc4AvgP9hEnd8Fvvdhe48vUgsC32sIfO9JYHuslGZWwV3WBO4Mo/isMIoLs9+kEwLf+wE4BZiX2H24K91s6zG+xSY8/S6M4iblRG4s+LjErnXaOxK8E3KBmlxgvtuVP4VRPBybrgZwSWdKa0VEejIFakRE2sGlzp9G/ru+e4RRvHknDlvYo6bVjBoaJzdlyR8hDuQ1cvxFwU2XBb7X7hGpYRSPBf6KBarGYOO2G7CyrA+w6Vgaty3dRuB7cwLfuxVrOPwg+WUqAJsDD4dRfEgVpgd1Wy7Ie2ViV2/gnDCK25Ntcis26nu3Zm6/n/zvZ3uzdjpqgvu/OzcUPhQrs30h8L2XW7uziIgUp0CNiEg7Bb43EwuCJNPnT3XBjI4oLH1Kvks/K/C95HlyjYHXcZvPB773VZFj7oqNRU16gCJBnZaEUTwkjOJzgaeBX9N4ITENK3N6ABu3fb17N1ykWwl8b1Lge+djF/P/Kri5D7Af1nA4KEEZpJh7gZcS28vQ2POkVYHvTQNuAfYOo3hYkdvHA88ndm1Ron5jrSnMqOlWgRr3N3ArrP/OZdVdjYhI16ZAjYhIBwS+9yVwMpbRAnbBdlEHppSABWqaayZcrJHw7omP7yi8MYziX9H0ouZF4KK2Zru4MqcAm5JyBNZQFWAuVub0AnBA4HsnaNy29ASB730a+N5RwCFA4ajhRbHng7vDKP55pdfW3bjMxTOwpuc527vntra6F5s4tH8Lt+f0BbZrx7E7Kvf55J7vu03pk8viPA5rlny3+xspIiIdpECNiEgHBb73EtbDImckcKEbm90eCzNq3DvyyeyavECNm+D0W7f5TuB77xTcPhY4B3uxnPMhcFJbJy+5jJ3HgJuBpdyxslgJ1lvYBdRuge+93pbjiXQnge+9igVLz6KxlCVnWeDKMIqvDKN42YovrhsJfG8ScGbB7j+658C2PH4uNhZ6hzCK00Xu8h/suTFnp3JnRAW+NxvredQdS582BVbFglE3VXktIiJdngI1IiKdczvwVGJ7NWyUb3tMBIa7dySHk//cXNifZhesPwzY+OuFwij2sHTzvgWPP8o10GxRGMWDwig+HyvvWB/rDQH2rvRH2DSVbTVuW3o613D4ESwL4waaXnD/HLgnjOJTXHNV6YDA914kf5z2EOCsdozsfgzLADykyLGzBcdeHNiwg0ttj2QGZbcI1LimzUe6zatcebCIiHSCAjUiIp3gXuyfBUTJ3a5sqK0mAr2wi5BmJz65hqW5435NosdCGMWDsIa+ydKrWViQplgz4oXCKE65aU5vYtOccuO25wJfAo8AOwW+d37ge1Pb8XmJdGuB780OfO8GLGDzCI2lkGCvsbbDGg7v385muNLoSvJLzX5KYzP1FrkSqiuB3zQzUv3vwOTEdnPNh0spOeWvsPSpq/6M7IVllP4P+FuV1yIi0i0oUCMi0kmuie5xwJTE7hPCKP5JOx4/E3vx3tLEp22BQe7ju91FCGEU9wIuwKYx5TQAJ7Y2JjuM4uWwjKBbgFx5QBb4HmvmeTg2bruwJ4eIOIHvfR/43llYSdSrBTcPAA4GwjCKt2hHNoiwsITpFCxwnHNIO5q3v0hjELrYsR9M7Fq1E03h2yrZPL7LZ9S4TM693eZFub9LIiLSOXqxICJSAoHvxcDxQK4kqB64IIzixZt/VJ7ciO7C+38HC3vX5N7tnQY86vansCamaxc87gLXQ6eoMIoHhFF8EfBvYF0ay6lmYOO2z8XGbT+tcdsibeMCo4cCRwGfFdw8Euu5cnsYxWtWeGldWuB7nwKXJHbVYyO7Wx2L7p6/rgDWCaN4nSJ3eRCbUpRT7lHdE4EBrlyoywdqsGbzfYC/B773drUXIyLSXShQIyJSIoHvvQlcnNi1KHBxGMV9m3lIUu5d1sJATa70aSMay6IedE0pAfYBti54zJ2B7z1IEa7MaWfgv9i7/LkLg3nAV1hmzVaB792gcdsi7Rf4XjbwvX9hF/znk19aA7AicH0YxZeEUbxUxRfYdT2ETZvLWQLLZGxV4HvvAs8CRxRmNAW+N5H8PmObhlGcbOhearkR3YvSxQM1YRSvAWwCzMGCYSIiUiIK1IiIlNYDwF8T2ysCp7rMl5bkGkw2yahxj831ZJgH3AcQRvGm2Lv3Sc/SzAtmN83pOWwiRy7ok3XnfgrYPfC9E112kIh0QuB7C1zAdFvgVvJLd8Aadt8fRvHxYRQPrezqup5EP7DkpK2t3fNgW1wFLI9NJyqUHNXdC9ihQ4tsm1ygZgRdeDy3C3jlAmW3Br73bUv3FxGR9lGgRkSkhNzFxJ+B5NjszbDeFS3JZdQke9TMCnxvBrA6sJLb93jgexPDKF4NG5Od9B7wx8IeAWEU9w+j+AqszGktGkd3zwTexl5s7xr43hutf4Yi0h6B780MfO8qYHvgyYKb64GdgHFhFO8VRnGfii+wCwl8bwrwR/KbNp/s+qS09tgvgYex/jZ9Cm57H3suzNm+jN+LXKBpOF07o2YbYAUs6/OOKq9FRKTbUaBGRKTEXIPK47GGvDlHhFH8sxYelpsEksyoyb1DmZxwclcYxUtg/RqSFxLjgaOT5UquzGkPrOfM/on758qcLgI2C3zvAY3bFimvwPfiwPdOxRqvvlVw8yCs18eDYRRv2oYMvB4r8L1XyQ8MDAL+5Pp4teYGrORo+yK3JUd1DwN+0+FFtiyZUdMlAzVhFA+mceT55SqTFREpPQVqRETKIPC9CVimSq7coQ44N4ziHzXzkAnAYjROAwH4NoziZYBfuu0XsRf5lwOLJO43AxvDPSm3w00u+SdwfeKYWff4h7A+NBcEvjetY5+hiHRE4HvvAQdiwdyvC24ejTXyvjmM4lUrvbYu5FosAJ3zE2Df1h7k+tHcCRwQRvGggpufozE4DrBrmQJmuUBNsYyarlL6dAAWzPoP8HSV1yIi0i0pUCMiUibuguzcxK4hwCXNTCqZCHjkPy9/R+OkJ7B3fC8ClkzsWwD8wU1FyZU53QC8jJVM5czCxgbvCxygcdsi1eMaDj8L7IhlxxUGTH+MBWvOD6M4XfEF1rjA9+Zh0+6SgY6D2hjcugMLWiczFXFZhfcndq1A/nNoqUx25++SpU+uAfYu2OdwkaYCioiUhwI1IiJlFPje38hPqR8DnFk4eQQL1BQ2Ep4GbOE+/gDYkqYXDmcHvvcaQBjF+wMfY/1wernb5wOfYxc1mwW+94xeWIvUhsD35gW+dzfWcPhu8sdEA2yMlUMd5cpNxHE9Zy5I7KoDzi6SKVP4uJnAX4Ddi0x3ehibYJSzaynWWnD+BViwpksGaoBjsN5K4wLfi6q9GBGR7qpX63cREemZ0ul0PdbLYEvgZ1ij3wHAFOBDrLTorkwm827iMUsDnxUeq66+fkG/AQOz/QcNnj9qyWXG9O7bd6nDX3jm6Ewmk3F3mQAMxcqYcoEUH+izYP58Hrv1ulEfv/3mVt9++fmAH2bOrO/Vu3e2d79+38ycOmXjp393RHbjnfc6HRhbcNpJ2ISoiwLf+8atbzPgcXf7V8DSmUymARGpGleCeEkYxQ8AhwO/TtzcG9gD2Mplyz0U+F5hQKenehRYFwtogZWOnRRG8amtBKRDLFvxIBJZj4HvTQuj+DEgcLs2CKN4dOB740u87lxPsi419SmM4vWA9bBG9NdWeTkiIt1aKpvVG6siIoXS6fTPgNuw9PececB0LKCSzIgJgV0zmczcgkDNNHIvxFOpXnV1dYs2LFiQ7HnQgI2FPfLKZ1+fhAV/ZrrzpICG6VMm97rquEOWH//pRwNzD+rTr1/DvDlzyWYb6gB69e7DHieeMWfNDTfNNQSeBbwCnB743psFn9eD5DfS/G0mk/l7O740IlJmYRT/BDgaWLnIzV9ifapeUHYchFE8BMtaTGYk/jHwvcebeUjucZsCZwM7Br73RWL/GPJLoO4MfO+y0q0Ywii+ElgWOAG4JXHTe4Hv7V3Kc5VKGMW9sb9XSwGXBr53V5WXJCLSran0SUSkQDqd3gp4HgvSTAROAlbIZDJ9MpnMcGx60lrA+VgwJsAybQodmclkRmUymVGZr78ecfn//ds/4+5HXt3jhDM+WXbV1adiz8G7AW8f/uufLok1Hs5lOg4F+t5/2fnL5II0G+6w+/hzHvz7Wxc//q9bLnniX8cdeuHVk0YttUx2/ry53HXBmX0nf/ftAqz06ffANkWCNIsBW2N9bXLvhu7f+a+YiJRS4HtvYf2kTsHGHyctCVwMXB9G8UoVXlrNcdlIp2GB75wTW2jcnvM0EAGHFhzvU+DfiV3bNtNXrDMmYtOn5hTsr+XSpx2xIM2X5AeyRESkDBSoERFJSKfTy2NTQfoC7wM/yWQy52cymYXNdzOZzIJMJvN6JpM5CVgG+Gtbjh343kfDR40+ZZ3fbDnlqMv+8vGux536aV19fRZL139swfz5DTQGahadN2dO/bsvv7AIwGq/3HBicMjR8ZBFh08B1unVp8/ZK665Tr+Dzr7kB4B5c+bwf/fc+hzwy8D3Hgp8r1g5055YGcX/Yb0dssA26XR6eDu/TCJSZoHvNQS+93csA+4qLFMuaQ3gjjCKzwqjuLC/VY/igtLJzJQBWL+aZkv83XPklcCvizQhvjvx8SCs/LWUJmLP9YUjxWuy9CmM4kWxMjGAi10zZxERKSMFakRE8p2NTWf6Adguk8kUjs/Nk8lkJmUymW2BqW05eOB7TwM3A6y7+baTN91t31yPmrH/fOTBYdiL98FAr1kzpv1o/jx7PbzkCmNnA0tgmTxjcsdbLL3ErL79B8wF+OdfH3ihlXHbueyZ2zKZzOfAC1h20B5tWbuIVF7ge3MC37sVazj8IPmZIwCbAw+HUXxIGTI/upK/AP9NbK9CY3ChqMD3XsXKRA8vGMX9MpY5krNLkQbwnZEb0T2wYH+tZtT8HgtYvRT43ovVXoyISE+gQI2IiJNOpxcHdnCbd2UymQ/b+thMJtOeXhHXYY2I2WTXfb7rP2jwfIDXn35iGPYO66JAnyGLjqjv3bdvA8C3X32exgJIuYuJBcD7rzz56FFzZs/q4/a93twJXc+dsVip1ji3+zb3v8qfRGpc4HuTAt87H9gZ9/yR0AfYDxgXRnEQRnFhpka35xosn0p+5tG+YRSv0cpDr8Sm6f0icaxc/7CcJbGG8qUywf1f84GaMIp9YDvsb84lVV6OiEiPoUCNiEijDWl8Xny4XCdxFwGnAV/06dcvu8zKq04H+PrjD/vPnTOnP7AIUJdKpX5Yb8ugAeDVpx5LPXbLdfUzpk4BmDB/7txTT9x2o5PuuuDME91hH8hkMv9o4bT7J+6XGwn7IHZR8+N0Or1WST9JESmLwPc+C3zvaOAQrAF50qLAycA9YRSvW/HFVVngexngvMSuFFYCNaSFx0TAE1hWTfJ18d+w5u45pRzVncuoKRy53q8gs6eq3FqOw76O9wW+93l1VyQi0nMoUCMi0ig5YeU/5TxR4HszgGOAmaOXWW4WwIL585jy/be5rJp6YNjWBxxWt9YmmzcAPHnHjb1P2m7jAYdvtNawo3+77sUzp039P6ynwQm0cBGRTqcHYu/CQ2MWDZlMZjqNAan9SvsZikg5ubKdPYCzaMzQyBkDXBFG8VVhFC9X8cVVUeB7TwDJiU8jgVNbCYBci5WWLuxFE/jeLPL7j/08jOJlSrTMXKBmGJActV6H9RGrFRtj2UZTsNIyERGpEAVqREQaJZvqTir3ydxI2JMHDB6y8IX6Vx/P7P232wfUvfBov9R//tmHLz4a1LDxrmfM2nDH/T4n94I+mx1MY9PhAdi76H1bONVO2Du3nwH/KrgtF7jZNZ1O12QjSxEpzjUcfgQrTbkB662V9DPg7jCKTw2juCc1Df8zMD6x/Wts4l1Rge+NBx4Afh9GcfK59F7yewLtUqL15QI1w2n6PauJ8qcwivsBR7rNawLfm17N9YiI9DQK1IiIVFHgey+O//SjhSVLM6elmDm1jvjzXkT/6cNzD303688HBQ3PPXDz0nPn/vi9KVMuPPvbb1/cdfLkq7afO3e1k7LZuhSWUfNCOp0e1MxpctkydxTppfMMkMHKrXZARLqcwPdmB753AxaweQSb6JZThzUiHhdG8QHuArxbC3xvJtavJhlk+UMYxUu18LCbsZ4xuezDXADnhcR9tmipjKodZgBzsUDN7ILbauX7sycwCiuvG1fdpYiI9DwK1IiINJqY+HjRSp309WeejHIfz583bC5ANkvDt19n33jruT3mNMz/esjcuT99e8KEJ8fNmrXbggULll5x9uztVp0w4fF+kybd/lA2Wz8fWHPOnJ/fnkqxSSrF6qkUS6RS9E+n0z6NTTJvLzx3JpNpAO5wmyp/EunCAt/7PvC9s4DdgVcLbu6PTe8JwyjessRTjGpO4HvvANcndvUDzg2juE8z95+CZRjuWxCMuafgGNuWYG1Z7O9NTWbUuHHv+7jNi1xfNRERqaBu/UdaRKSd3kt8vHoFz7saAKnU3Puv/tEJM6fVffXas/1ufeOZez+sq/t2MYDp0w97qdgD58zZ8Pt581b6CKBXr49+CawHbIM1Dz7hhx82vC5x94/T6XS28B+Qa0i8QTqdXrY8n6KIVErgex8Ch2KlK58V3DwSOAO4I4zin1Z4aZV2C/n9xnzg4Bbufw8wB9g3se9N4KPE9s4lmqo1ARhBDQZqgCOwctqnA997s9qLERHpiRSoERFp9ByNqfLbVeKE6XS6H9Y/AbLZl78fP2LBY3cMvPnzD3p/1bv3B4vl7jd37pqTmztGQ8PoSQB1dVOG5t/yQ12fPq+250Islc32PrAd9xeRGhX4XjbwvRexvirnAYXPIT5wXRjFl7RSEtRlJSbsJfur7BlG8TrN3H82loWzcxjFo9y+LPlZNYsDvyrB8nIZNYWlT1XtFRZG8U+A32ClWVdUcy0iIj1Zr9bvIiLSM2QymW/T6fRDwI7Abul0+oJMJlM4/raodDqdKtL/pS0Ow95VBbgVS70fBIzo1euDIcCmAH36vF43Z86mRQ+QSk0eCJDN9pmb3D9o0F+Wr6ubOSibrWv4/vsnLmtoWHxOc4sYMuS8NQcMuG/TbLbfIanU3AnQ53vsHd8J2AXFBGBqNktHPkcRqZLA9xYAD4VR/CRWzrI7kCz/WR/4RRjFDwJ/CXyv2aBwVxT43jdhFJ+NNRjOOSuM4l2a+VwfwaZp/R7LPAJ4Ejgcm9IENmXvmU4ubSLWG2xuwf6qZdS4crjj3ObtrkePiIhUgTJqRETynYo1euwPhOl0Ot3SndPp9DAX3FmkvSdKp9M7Aee6zXeBO7NZstks07NZPuvT5+2Fo2GHD993IHAOcB3wIPA88G7v3m/P7NPnnRUBFixY6qvk8fv3/+saAPPnL/fp/PmrTG9oWGxuc/9mzNj/3Ww2la2rmz544MBbVgeWAdYCNsMuWo4CTk6l+H0qxQ6pFL9KpVgllcJLpSja80FEakfgezMD37saCIAnCm6uw6bDjQujeK/m+rh0VYHvPYMFYHKGA6cVG9ntAltXYY2Dl3P75gJh4m4/CaN4pU4ua2Iz+6tZ+rQlsCLwHY0TAUVEpAoUqBERSXAZNHti73KuDLyVTqdPSKfTy+Xuk06n69Pp9OrpdPos4FPswqdN0un0qHQ6HaTT6ceA+4De2NSlLTOZzPyCu/8TeNt9fNjo0ek/jx6drstmeXf06PQro0enZyy22OYHplJz+mWzZBcsGH0mdgH2Wu/er0/q1euD5QDmzNnwPVoxf/7K0xcsWPIrgP79w+b68/TGpoCsgqX+7wD8DgvgHJ1KsWcqxWapFGulUiyTSjE4laLJhZCIVE/ge98EvncasBf5/VvAph4dATwYRvGmxQIZXdhFwJeJ7fVpftLd88B/sSyanAeBBYntXTu5ngnu/8LX4lUpfQqjeCCW4QlwuSsDExGRKklls8piFxEplE6n18NKkZZL7J6LZdsMpfHFdRa4F9g7k8nMS6fTS9PYvHMajf0H6rCsm+Q71QuAu4GjMpnMpGbWsSzwFDAmsXsGMCCxhgXAMZlM5orE404EzstmmTd16rmrzZq1d2+sxCr3bziQ1xBzkUVOWHvgwDs3y2brGr777vmLFyxYdlbxr067zKGxdCr5b1I2S2FgSkQqyAViNsQCEksUuct/gUvdBKUuz2XB3EJj6f9cYM/A9z4pct+fADcCvwt87w23709YliHAfGCLwPeay4xpbS0bABdjwfjVEjedHfjeuI4cszPCKD4CC969DRzgevOIiEiVKFAjItKMdDpdj/Wr2RJYB5uW0h+YCnwA/AO4I5PJRInHLE3TKStgFwTTgO+Bd4B/A/dlMplWewCk0+mBwIHYNKdVsEDRXOzd4X8A12QymXcKHvMhsDzwRCaT2bzwmKnUwsDRwuBN795vLDtixDaXpFLZ1KxZOz81ZcolL7e2tk7IAlNoGsCZAMxSLxyRygmjuDeWXXIgMKTIXZ4Grgx8L1PRhZVBGMV7YVlDOZ8AewW+16SHVxjFl2BB7X0C38uGUTwWuD1xlxsC37uhg+tYBXsz4D0sezPnosD37u3IMTsqjOIlgfux4P1ege/9r5LnFxGRphSoERGRPKkU/bGLkxEF/xalMiWzsymehTM5m80rPRCREgqjeAiwP7AzTQdOzMPKNW8OfG9apddWKq5h7lXA2ond9wW+d2GR+47BMiZPcn1uCKP4ZmBVd5dJwJauh0171+EBjwIfAiskbro68L1b2nu8znABqfWBRwLfO6uS5xYRkeIUqBERkTZJpajHpp4UC+JUoq9CA3ZhlJxENQGYkM02GXErIh0URvGPsKyTXxe5eRpwA/BQ4HvzKrqwEgmjeDEsAJNsAn9U4Hv/KnLf04DVgZ0C35sfRvGmNDaBBzg98L3HOrCGPsBLwBdAcjz6TYHvXdve43VUGMU/B64EZgHbdbSUS0RESkuBGhER6RTXMHgA+f1vch8Pg4o0FJ5J03HiE4Ap2SwNFTi/SLfj+rQcTX5pTs6XwBXAP7piP5NEj5icycAuhYGKMIpHAuOASwLfezCM4l7YBKmR7i4RsEdHvgZhFD+HPXeNSuy+K/C9S9t7rI5wn8s92JS/KwLfu72Vh4iISIUoUCMiImWTStELK5kqFsTpW4ElLCA/cLPw42yWJj0pRCSfKxXaBJsI5BW5y5vAZYHvvV/RhZVAGMUnkj/56RXgiMD3GgrudziwFbBt4Huzwijeh8YJSQAHBr5XOEGrLed/AHtOTPYFCgPfO7eZh5RUGMW7AMcBXwE7d6SES0REyqOw/lhERKRk3GSn79y/hVwWziCallANx5oll0o99s73yMIbUimm07QPzkRgqpoZixgXtPi7y/7YFdgPG+OdswZwexjFj2P9Vb6twjI76jJs/bmpej8DdsGm8SXdCmwH7A78BXgYa7ycCzbvStNR520xkabTtvp14DjtFkbxUOB3bvNSBWlERGqLMmpERKSmpFL0pngfnOFA7wosYR7FmxlPzGbpkj05REoljOJhwEHA9jRtLj4XuBO4NfC9WZVeW0eEUbw8cBvQx+2aD+wd+F5UcL89sM9728D3JoVRfAoWvAHrn7VN4HtxO899DrA15GX3PRv43vHt/0zaJ5FN9ApweFcsXxMR6c4UqBERkS7BZeEMoWkAZwQwuELLmErxkeIzlIUjPUkYxctgDYd/WeTmScB1wF8D36v5SW2JEqCcz4E9A9+bnbhPHyAEXgh874IwipfFpmDl3BH43uXtPO8xWGbObFjYS+ulwPeOaP5RneeCU3e5zV0C3/u0nOcTEZH2U6BGRES6vFSKvjSfhVNfgSXMoXgWziRX/tXdpYBtgPWA5YGvgfeAD9y/b0CBrO4ojOK1sIbDKxS5+VOsvOjlWs7YCKM4BVwOrJvcXdgrJoziLYA/AtsHvvd1GMXX0DjmezqweTK404bz7gWciGXx5EqP3gx876COfSZtOmcKuBb4KXBv4HsXletcIiLScepRIyIiXZ5rDDze/VsolaIOG8FbLAtnIKXTFxjt/uUtLZViMsWDOLO6SRbOAGwqzs+xr2luytds7OKzD1Yi8wXwLtZ8NhfA+RjU1LkrC3zvNVcWtAVwCLBY4uYx2GSof4dRfFngex9VY42tCXwvG0bxGdjI7kVzu8MofjnwvecSd30C2AP7PE/GJiblAjWDsa/Bg+049UQsk6YXjYGacveo2RAL0kzFxqyLiEgNUkaNiIj0SKkU/Wk6iWoEdqFW2HujHGbTdJz4BGByNkvNl4sknIpdtPZv4/3nA7Ng4Vj3CcCHWAAnmYUzAWXhdClhFPfHAhl70zTg0AA8Clwb+N6ESq+tLcIoXhcLLOVMw0qDvityn72wn9OHaGwI/DmwU+HUqBbO9zOsOXFfdy6ATwPf26kTn0ZL5+uDBZJGA+cHvteeoJKIiFSQAjUiIiIJqRT1wDCKB3HaGozojAasx0eTIE42S5vLKiqkD7bWUmQnZbEAznzsIn8BduH7XyyIE2EXxp+CmjrXsjCKRwAHY41yUwU3zwZuB+5sT5lQpbi+Mbsldr0OHJILvrjSoeuw39NDgJ3J729zROB7L7XxXMtjgZNBwGS3Ow58b6tOfRLNn28/bM0fA7t3hf5BIiI9lQI1IiIibeCaGQ8gv/9N7uNhNL0gLYeZNB0nPgGYks3SpnfxS+ynwDNYk+dymocFceqxIM53WODmDSwLJxfEmdzcAaTyXCDiKGCdIjd/D1wNPN7WDJRKcFknt5Lfc+eqwPduTdxnLBZsOhx4GyuJygUr29wMOIziRbHfn0WxrwfA5MD3NunEp9DcuUZi2T/9gYMD33ut1OcQEZHSUaBGRESkk1IpemEXW4WNjEdgZQ3ltoCmJVQTsSyccvaA2Ru4CssIqIYsFrxagAXRfgA+A94BXgOexfriSJW4DJSfYwGbMUXuEgGXBr73eiXX1ZIwiscAd9D4u7sA2DfwvfcT9zkfWBIr9TqK/CycHQLf+7wN56kDXgU8IDfae3bge8UmaXVKGMVnAZtTofHfIiLSOQrUiIiIlInLwhlE8WlUQyu0jOk0bWQ8EZhagmbGF2BlH0WziWbPns0zzzxDnz59WHnllUmn0508Xbv8gJWnvAjsh02ikioJo7gemwz2exob9ia9AFzRlgBHJYRRvD1wUmLXV1i50Cx3+5LAA8CZWFbNOBp/Dx4IfO/PbTzPE8Aa7vhgwce1SzklK4ziHwO3YA2Ldwx8L1OqY4uISHkoUCMiIlIFqRS9aX6keO8KLGEexadRTcxm29wD5mlgo2I3fP7555x88sl88sknNDRYZcthhx3G3nvv3emFt9MCrCxqddTbpurCKB4I7APsjvU4SmrAynNuCHyvqmVsLhPoImCDxO5HA987M3GfE4BfANsD5wPru5t+wEZ1T6MVYRTfCfwGm4qWs17geyXJhHNZO7cAKwM3B753TSmOKyIi5aXx3CIiIlXggiHfuH8LuSycIRQfKT64hEvoDYxy//KkUkylaQBnAjCjIAvHL3bg2bNnc9555/H5559z5ZVXsuKKK3LVVVdx8cUXs9JKK7H22msXe1i51ANLAdsB91fyxNJU4HszgavDKH4IOBTYLHFzHbAjsHkYxTcB9wa+N7fIYcrOjez+EzCWxpHjW7mR3U+57b8AWwI7AHfTGKjph2UP3dGGU02k6ZS5fpRubP1mWJBmAtZ7R0REuoBKjB8VERGRNspmyWazTM1m+SSb5d/ZLI9ls9yWzXIxcB5wAxBipSLvY411Sz29ZRFgWawJ7BZYL5pjgRNTKQ5KpQhGjGDDbBav2IM/+ugj3nvvPY477jjWXntthgwZwgEHHMDIkSN58sknAViwoPUlv/zyy6ywwgrsueeefPvtt535fAYAq3XmAFJage99E/jeadiY6/8U3DwQOAJ4KIziTV12S8UFvjcFOJ38MfEnh1E82t0+CQvG7I9lbX2UuN9OrtyrNROxYGJSSabLhVE8AGt4DFZWNqsUxxURkfJToEZERKSLyGaZk80yPpvlnWyWZ7NZ7s9muQY4B7gCuAv4OzYN6Qus0W4p9QVGA6uuuirB3LnFA0QvvvginuexxhprADB37lxGjhzJ0KFDmThxIgD19cWvYXMBnFdeeYWbb76ZWbNmMW9e04qlDz/8kBNPPJEtt9ySY445htdea3GITR2NWRFSQ1yD3oOwXkdfFdzsAecCt4RRvGql1wYQ+N6r2ISnnEHAnxJBmDuxQOlewL2J+3nkl001ZwIWCEr+QvTr8ILz7Ytl4r0LPFmiY4qISAUoUCMiItLFZbM0ZLNMymb5KJvl5WyWR7NZbslmuRD4M3AT1uz0X9g7/xOgc+O8f/pThjc0FG9G/Pzzz7Pccsux+OKLA1BXZy83ZsyYQTabZebM4vGjbDZLfX09U6dO5fzzz2fMmDGsttpqLL744gwe3Fj19cILL3DAAQfw5ptvst566/Hll19y+umn85//FCZmLDQP+KTDn6yUVeB72cD3ngd2Ai4GCnu7rALcHEbxn8Mo/lGl1wdch2Wv5ayGZdHgslRuxHruvAFMTdxvlzYceyL2u5hsR9DpQE0YxWlsIhXARbU0Al1ERFqnQI2IiEg3ls0yO5vlq2yWt7JZns5muTeb5SosC+cqLAvg/7Dyk6+A2W057tixjOjdu3ivuzfffJOll16a/v2tgqNXL7vbzJkzGTx4ML17F++VnBtwcOSRR+J5HnvttRfTp0/H8zz69GnsO3vhhRcyevRorrnmGk466SSuvfZapkyZwu233878+fOLHXoWNgZaaljge/MC37sH2BbLDiv8Zm4EPBhG8dFhFA+p5LqAU8n/3TggjOJcOd3DwPdYieBDifusEUZx0T5OCcUCNaUofToK60P1WOB7GlEvItLFKFAjIiLSA2WzLMhmmZDN8kE2y4vZLH/NZrkJG7l9ITYp5lHgZeBDYBKJXh1jx+L16tWktwbz588nlUoxdOhQdx57yPTp05k+fTojR47MC7ok1dXVcc011/D+++9z+umnM2DAAL7++muWXXbZhcGe8ePH88EHH7DDDjuw3HLLAbDYYouxzTbb8K9//Ytvvvmm2KHrsUwi6QIC35sW+N6lWJPeZwpu7oVlr4wLo3jXMIorMSGNwPe+xH4vcuqAs8MoHuQCOVdjDYRfJb9n1K6tHHoi9ntVsoyaMIrXBjbEAktXdeZYIiJSHQrUiIiIyEKumfHMbJYvslneyGb5ezbL3dksV2BZONcA96+0EgOLPX7mzJkstthiC3vN5DJcPv74Y6ZPn86YMWPceZpWTT399NNcffXVXHLJJYwaNYqvv/6auXPnkk6nF95n6tSpzJ49m2WXXXbhvoaGBoYOHUocxwwYMKDYsvqh0qcuJ/C9rwPfOwErMyrMChmCNbi+P4ziDSvUcPhRLPssx8OaC6ewgNIHWLnT04n7/CaM4kVbOGZJS59c75xj3ebNge9939FjiYhI9ShQIyIiIm2SzTI/m+W7bJb3Bw9mZLH7NDQ00K9fv4VNg3NlTk8//TQjR45klVVWcceyiFBDg7XOePXVV7nqqqs4/vjj+cUvfgHAZ599xoABA1h00cbr3Lq6OlKpFN9//33evvfff58BAwYwZ07RqcbfY31qpAsKfO9trDHuyUBccPMSWKbL9WEUjy3zOrLY5LVk2tamwOauB8wVwK+wXjU5vYHtWzhsrl9UqUqfAmxi23isfExERLogBWpERESkvUZgF6BNDBs2jKWWWoq//e1vjB8/HoBnn32WMAzZbLPNWGaZZWhoaFgYcMk1Gv7HP/7B66+/zptvvsmtt97KG2+8wZtvvsnIkSNZcsklFx5/6NChrLHGGtx0000Ls3L++te/8tBDD+F5HqlU08SKL75geirFDqkUv0qlWCWVYlQqVXz9Uptcw+GnsKDHFTSdaLYGcHsYxX8Ko3hUGdcxDTiN/GbcJ4RRvETge68DL2Ej7ZMZQDuGUVy03s81I55LCTJqXN+eg93mpYHvze3IcUREpPqKNgEUERERacGKWP+Lohefxx57LMcffzyHHnooY8eO5cknn2SVVVZhn332ASwD5o033mDQoEEst9xy1NfXs9566zFp0iQ+//xzrr32WiZMmMDcuXOpr69nk0024bzzzmP99ddn8cUX5+CDD+aYY45hlVVWYdSoUSyyyCKkUimWXnrpvOlQANksvPceE7HJQXlSKaZiGQ2F/2Zks8UnWkl1ueDD7WEUPwociPWxSb7xuBmwURjFdwK3ukBIqdfwnzCKb3LnBxgAnBNG8X5YT5i7sCbduZ+5RYGNgcebOeQMyCsl7Gjp0++wkrDXgOc7eAwREakBCtSIiIhIe61IC68hVl55Zc466yweeeQRPv30U/bbbz/22WcfBg4cuDCb5tBDD6VXr16EYcjIkSP52c9+xtprr828efOYPXs2M2bMYOedd2bppZdm1VVXZdQoS5LIZrP89re/ZY011uC5555jxowZjBw5khkzZrDssssycGB+65x585j3wQc016djEfdv2YL9c1IpJmD9Q5IBnEnZbJNJRFIFge9NBi4Io/h+4Ahg/cTNfYD9gG3DKL4eGBf43oIih+mMG4F1gFXd9ljgoMD3rgmj+Ang58B3sLBEcNcwip9w5VOFpgGjE9vtDtSEUTwG2BHL9Lm4mfOIiEgXoUCNiIiItNeKWBZBs9Zcc03WXHPNJvtzpU7bbbcdqVRqYf+Zuro66urq6NWrF/3796d///5MmTKFTTfdlL322ov6ehswNWPGDPr378/IkSPZeeedAbjjjjuYNm0am2++eZPzNTTQ8PbbTGjn59cXSLt/SdlUislQNIgzS1k4lRf43ufAMWEUrwUcDayQuHlR4CRg5zCKLwdeKlUAI/C9BWEUn4plzuR+F/YNo/jfwHXYmO7XaAzUrIQFdd4ucrip5JcStitQ45oZH4dlFj0Q+N7H7Xm8iIjUHgVqREREpL1GAZ2asnPCCSe0ePukSZOYPn06o0aNWhikAXj00UeZOXMm66yzDqlUiqeffpobb7yRo446ih//+MdNjtOrF71efLHdgZrmpLCL/2JTfGa7LJzCIM7kbJZSZ3NIgcD3XgujeA+sP8whwGKJm8cAlwOvhlF8aeB7H5XonOPDKD4XONvtSgF/wiY/3Q9sjjWxzgVhdqN4oGYKFmRJYaO625tRsz6wNpaZc107HysiIjUoVWw8poiIiEgLTgVOp8xv+GSzWebNm0efPtYKp6Ghgb/97W9cdNFFfP311wwbNgzP89h888055JBDmjkGM+rqWAZrgDwCGJ74uDPTddqqAZgETYM42SyzK3D+HieM4v7A7sDeNP0eZ4FHgGsD3ytJAC+M4rOwoEzOs9go+79i3++l3P4GYOvA974pePzVwD7Ax1hg5+HA985p47n7AA9g2V8XBr53X8c/ExERqRXKqBEREZH2eoGmk2pKLpVKLQzSgJVHbb311my99dZMnDiRr7/+miFDhuRNkmp6DD7LZhcGSZL7U1jJygiaBnGG0cmMoYS6xPEL1zaTpo2MJwJTstm8qULSDoHvzQZuDKN4HPB7YGsaGw6ngG2ATcMovh24092/M/4MrEZjqdyvgZeBW7EpTPXAAreGnbCpVUkT3f+9sEBNezJqdnXn/RQrtxIRkW5AGTUiIiLSETdiJR4DW7tjuWWz2aJjuZ27gD3ac7xUil5YedMImgZy+nZ8pW02n/wsnIVBnGyWORU4f7cSRvHywFFY899C3wPXAI8Fvtfh4FgYxasAN9MYEJqDNTS+DJvElPu5mQZskQwOhVF8JHAu8DUwHXgu8L0/tOGcI4AQCzgeGvjevzu6fhERqS3KqBEREZGO+B02Avg8LIgxB7sY7eho4Q5rIUgzF3irvcdzk52+c/8S5yEFDKJpAGcENj2qVHphTWhHFt6QSjGd4iPFp6mZcXGB730URvFh2CSmo7CeNTmLYWV8u7r+Na918BzvhlF8HdYfB+x34Y/ATcAZWEbNPCxosxkWYMnJTSXLNWNqa0neoViQ5h8K0oiIdC/KqBEREZHOSmPjiX3gJ1gZyLJYUGM2Vm4ykMZsg0qZimXT/K3cJ0ql6EPzWTi9W3hoqcyj6SSqXBbOvAqcv0sIo7geK336PcWbQv8TuNxNk2rvseuwZr5rJHbfBawL/BiY4fZ9BuyUm0AVRvFGWN+c77Hv2VuB7x3QyrnGArdj3/edAt/7qr3rFRGR2qVAjYiIiJTLYGxc8opYIGcNbExxGsvAmY9l4JSrnOgHd/6qXcS6LJxFyG9inPs3uELLmELxIM6MnpqFE0bxQKzZ8B5An4KbG7B+LzcEvje5ncddHBvZnfze3oSNz54FC0vXDgt87xX3mB8DrwCTgW+ADwLfa7Zcz43jvhkL/twW+N6V7VmjiIjUPgVqREREpNLqsUk4PhbEWR1YFVgGK/uY5e4zkM419f0ACwzVpFSKvjSdRJXbrm/hoaUyh6bjxCcAk1z5V7cXRvEorFxp8yI3z8QCIvcEvje3HcfcCGswnDMJ+xqvD8Ru34uB7x3p7r8MNrZ7Ftan5ovA97Zv4fibYWPAJwLbBb43q61rExGRrkGBGhEREaklw2gM4IwF1nTbi2MZMguwvhytlRPNArYHnizbSssklaIOGErxIE4lmjdnseyOYkGcWd0xC8eVEh1FftlSTgxcBTyVK1dqw/FOBbZN7Pof8CusWXAusBIEvvdlGMUe8B6WyfM58G3ge1s0c9wBWLbPYsCZge892pb1iIhI16JmwiIiIlJLJmNlIK8U7O+NZdys6P6tAazi9tVjQRywDJx64AC6YJAGwI3mnuT+5Uml6E/TceIjsH4rpeoBlHLHK9bDZXYq1WSc+ARgcjbLghKdv+IC33s/jOLfARsARwBLJm72gHOA3cIoviTwvbfbcMiLsZ/R3HFWwjK8VseCMWBT0y7Afnbn01iC1VJD7r2xIM37wGNtWIeIiHRByqgRERGRriyFBSpWcP9/hWUvzG7pQd1NKkU9lo1UWEK1GJWZxJULLjUJ4mSzXet7EUZxbywb6yBsSlOhZ4ErAt/7upXjrATcQuMboykscDMZG9M9Gyu5mgd86s71ATA38L11ixxvNPAgFtDZL/C9d9r9yYmISJegQI2IiIhIN+WaGQ+g+DSqYXSuB1BbzaT4SPGpLnuoJoVRPATYD8t8KcxCnw/cD9wY+N60Fo6xJ3BkYtdQ7Ov/sdu+FLgHy7IZDkRY0GvtwPfyvjZhFP8Z2Ah4IvC90zr0SYmISJegQI2IiIhID5RK0YvmR4qXaxJX0nyaZuHkRorPaemBlRRGcRo4HNi4yM3TgBuBBwLfazIG3Y3svgpY2+3qBSyPTXeaDIzHetl8gE1D+wSYC/wy8L3ZieOsCVyPlUkFge99V4rPTUREapMCNSIiIiKykMvCGUTTAM4IbNR4JUyneBbOtGo1Mw6jeFXgaGwsdqGvgCuA5wsbDodRPAIb2T3U7VoKy3LKZc/8AbgEK9/7Ams2vGnge5Pc4+uAu7AAz7WB791U0k9MRERqjgI1IiIiItImqRR9aD4Lp7VJXKUwj6aTqHJZOE0yWkotjOIUsAlwGDC6yF3+A1wa+N77BY9bHwvGgDW7XgXLqvkWeBObcLY6kMGydLYOfG+8e2wAnIxNn9oh8L2ayTYSEZHyUKBGRERERDrFZeEsQtNx4iOAwRVaxhSKjxSf0ZEsnHQ6XY81Fd4S+BkwEsuEmZJKpT5adtXVJ21z4OGjlh7744V9fr796vM+Z++9Q7GMmzl9+vabP3jR4fWjllxm9rKr/qTPWhtv3mfoYiPfw0bOzwR+igVuJgE7Bb73aRjFQ+bPnzfun+PuH/PU3bdOnDFl8hJYb6EfsL42zwJXZDKZT5r5HJ7HJlkVygIzsCbGTwNXZjKZL9r7NRIRkfJQoEZEREREyiaVoi9Nx4nntusrsIQ5NB0nPgGYlM0yv9gD0un0z4DbsHKknHlYSdZQEqPQR49Z7t3jrrnth959+qaSgZo+/fovIJudNXfOD7Pd/RchkXVUV1fPautvOHf7Q4/73yLDR9QDY9z6vgP2Cnzv/eufffWUG08//qTxn340MLGO6UB/GhsczwH2zGQyDxT5PJ7HAjXzyB/33pv88euzge0zmcwTxb4eIiJSWYUd7EVERERESsY1Bs64fwulUtTROAWpMIgzkNLpizXqTRcuLZViMgVBnMUXX22tujrudAGmicBFwEOZTOYjWJhpszqWbXPI+E8/XuXtfz6/2k83+s0+JBoOb3XAoV/9KthlItY0+Dpg3D/Ce9fpO2DAza88+eioT975zyL/ef7pPh+//ebKx1x585QRo3+UpfG1eb8wipcZd/3lRyWCNGcAV2UymYluDb8ArgZWBm5Lp9MvZTKZvK9xwkuZTOZXyR3pdLofsI07xnDgznQ6vWQmk5nZxq+riIiUiTJqRERERKSmpFL0p2n2zQgsC6SuhYd2Sp8+Ly06fPjuB6VSc/s2NAwdP3nylafNmfPriMZMnMnZLAty90+n04sCNwP7ZDKZKUdfcMVW91/+50cAtj/suM9doCbnU+By4EfAH1558pHF773k3B8tmD+fkUssteDkm+6bW9+r1wzgS+CIObNn7X7CNhsdvGD+vBRwWyaT2adwvel0elkaR33/PpPJXF9w+/NYRs0/CgM1ifvsAdzhNjdXVo2ISPUpo0ZEREREako2y2xsktJXyf2pFPVYj5bCIM5iQL/OnnfIkD/9OpWa2zeb7TV/0qTrw7lzf7EksGTiLg2pVHKkeGYCcFhd3bdzAO6//M//zd1x/ry50woOPwYL1LwKvPez327N9EmTRjxy41X9vvvqi/p///3RPutusV3utflPZ02ftp4L0gC8Xmy9mUzmk3Q6PQkLYA3q4Kf9VuLjjh5DRERKSIEaEREREekSXDZLrlRpIdfMeADFp1ENA1K0olevDwf27v3fsQBz567537lzfzGxyN3qEsdeqKFhcVIpZvbte3d2+PDdAPjrDf+8b6W19p3hLbVgx7p6+ifuvrY7zpANtt8lfuaBO5eZOXUKrz71WP26W2zX1611l6GLLT6/rr5+TsOCBX2xRsNNuIyaXK+ZosGcNlgt8XHUwWOIiEgJKVAjIiIiIl2am+o00/3Lm16UStGL4iPFRwB9cvfr33/cMqlUNgXwww+b/q8DyxjY0DBoaG5jxvR11j5i85FvDR7a8M+f/2b2KsuuMm/sIsMbFiwyvGH+0BEN84eOWDC/T99+P1r+J2sueOsfz9R//sF7zJszp2/vvn2HAQ2pVOqtbDb7AnA0sHc6nf6M4j1qAB7IZDL/aM9i0+l0X2Ar4FK366lMJvNOBz5vEREpMQVqRERERKTbcpOdvnP/FnJZOINwQZu+ff+xcMLTnDm//KZU558+pW7uU/cNfHOxf87/fOxac382eGjDj3K3DRnWMH/ODyun4BkWzJvHx/+dULfST9OjgG+AS7INDW9iWUF7Yc2Ez0in09Ow7KFeWN+bE4CLW1nGuul0Ovk5Jac+fQVc4I4vIiI1oGzN2EREREREalU2SzabZXo2y2fZLK/16fPWlNxtAwfefQY2qelB4HngXSx4Mq+j5/t+fK9J//jrgMf/+0rfJ+bMrpsMMG1K3awfZg1d2Jz47Rdnp7ApVZ8EvvdSJpP5ATgA+EPi3ENofLN1ABZw6dvK6XsDiyf+JUdzL+K2F+no5yYiIqWljBoRERERkYSBA2+dN2XKOd9gwZmFXBbOIjQdJz4CGNyWY38R9f7qq496fe2vMXfFpVaY/9P58xb8gAVSGDgkC9aj5l8A6XR6GeCvwI+Be7FR4RHWd+fXwHlYRs3G6XT6V5lMZkYzp20y9SmdTg8B1sIyaQ4AfpNOpzfKjSEXEZHqUaBGRERERMRGcOcsCowvvIPrhTPF/fskeVsqRd/+/R/+CXAkQF3ddxms3Go4UJ+8b0NDKvu/1/v+75N3+3y85JjM9oAP0H/ggM+AK4FbXR+aXJDm9kwms3fiEDOA29Lp9GvAm8CaWMDmtLZ+splMZhrwTDqdfhX4L7AUcD0WABIRkSpS6ZOIiIiICLyX+Hj19j44m2XOoEG3fJvbHjAgfC2b5RrgHOAK4G7gKeANrOHxzLk/pOZN+e7N+faI3g23XTB0l8D3Lg18bzKwKRakAcukaSKTybwPPOY2t2/vmt0xpgP3uc0N0+m015HjiIhI6SijRkREREQEngMasDcyt6MxANIp2SwNwCT378PkbYMHXzG0d+93DwdYsGDwu7NmLJfM0hmb+Dgve6dArlRpmU4sMzkpa2kg7sSxRESkk5RRIyIiIiI9XiaT+RZ4yG3ulk6nV2jp/knpdDrVkXMOGfLnA1KphkUB6usnXZrN5pVfNSQ+XqqFwyzu/p/ekTU4P0p8PLMTxxERkRJQoEZERERExJyK9X/pD4TpdDrd0p3T6fSwdDr9EB2YmJROp3cCznWb7wJ3FtzlzcTHBzdzjFFY9g/Ay+1dgztGPyBwmzOwZsUiIlJFCtSIiIiIiACZTOZDYE9gLrAy8FY6nT4hnU4vl7tPOp2uT6fTq6fT6bOAT2kMcrQqnU6PSqfTQTqdfgzrC9MbyABbZjKZ+QV3/yfwtvv4sHQ6fUk6nR7tjtMvnU7/FngBCxJlgUva+/mm0+mx2DQp3+26OpPJzGnvcUREpLRS2Wy22msQEREREakZ6XR6PeBWYLnE7rlYxslQGt/szGKBjr0zmcy8dDq9NPCZu20aMNt9XIcFVPokjrcAazB8VCaTmdTMOpbFGhCPSeyeAQxIrGEBcEwmk7miyOOfBzYA5mE9cpIGu+Pk3Afsmclk5hVbi4iIVI4CNSIiIiIiBdx47B2BLYF1gJFYSdRU4APgH8AdmUwmSjxmaRoDNUlzscDN98A7wL+B+zKZTJMR4EXWMRA4ENgGWAULFM0FvnRruCaTybzTzGOfxwI1xfyANQ3+N3BbJpN5srW1iIhIZShQIyIiIiIiIiJSI9SjRkRERERERESkRihQIyIiIiIiIiJSIxSoERERERERERGpEQrUiIiIiIiIiIjUCAVqRERERERERERqhAI1IiIiIiIiIiI1QoEaEREREREREZEaoUCNiIiIiIiIiEiNUKBGRERERERERKRGKFAjIiIiIiIiIlIjFKgREREREREREakRCtSIiIiIiIiIiNQIBWpERERERERERGqEAjUiIiIiIiIiIjVCgRoRERERERERkRqhQI2IiIiIiIiISI1QoEZEREREREREpEY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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the cosine similarity network; strong edges (> select threshold) are highlighted\n", + "thresh = 0.4\n", + "plt.figure(figsize=(20, 20))\n", + "widths = nx.get_edge_attributes(G, 'weight')\n", + "\n", + "elarge = [(u, v) for (u, v, d) in G.edges(data=True) if d[\"weight\"] > thresh]\n", + "esmall = [(u, v) for (u, v, d) in G.edges(data=True) if d[\"weight\"] <= thresh]\n", + "\n", + "pos = nx.spring_layout(G, k=0.4, iterations=15, seed=3)\n", + "\n", + "width_large = {}\n", + "width_small = {}\n", + "for i, v in enumerate(list(widths.values())):\n", + " if v > thresh:\n", + " width_large[list(widths.keys())[i]] = v*10\n", + " else:\n", + " width_small[list(widths.keys())[i]] = max(v, 0)*10\n", + "\n", + "nx.draw_networkx_edges(G, pos,\n", + " edgelist = width_small.keys(),\n", + " width=list(width_small.values()),\n", + " edge_color='lightblue',\n", + " alpha=0.8)\n", + "nx.draw_networkx_edges(G, pos, \n", + " edgelist = width_large.keys(), \n", + " width = list(width_large.values()), \n", + " alpha = 0.5, \n", + " edge_color = \"blue\", \n", + " )\n", + "# node labels\n", + "nx.draw_networkx_labels(G, pos, font_size=25, font_family=\"sans-serif\")\n", + "# edge weight labels\n", + "d = nx.get_edge_attributes(G, \"weight\")\n", + "edge_labels = {k: d[k] for k in elarge}\n", + "nx.draw_networkx_edge_labels(G, pos, edge_labels, font_size=15)\n", + "\n", + "ax = plt.gca()\n", + "ax.margins(0.08)\n", + "plt.axis(\"off\")\n", + "plt.show()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "ac302953", + "metadata": {}, + "source": [ + "## Step 6: Reactome pathway analysis\n", + "Again with gene program 3 as an example, users may perform pathway enrichment analysis to identify related pathways. In the paper, we used the Bonferroni correction to adjust the p-value threshold by accounting for the total number of tests performed." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "f3a07188", + "metadata": {}, + "outputs": [], + "source": [ + "# Meta info about the number of terms (tests) in the databases\n", + "df_database = pd.DataFrame(\n", + "data = [['GO_Biological_Process_2021', 6036],\n", + "['GO_Molecular_Function_2021', 1274],\n", + "['Reactome_2022', 1818]],\n", + "columns = ['dataset', 'term'])" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "1a3e18c9", + "metadata": {}, + "outputs": [], + "source": [ + "# Select desired database for query; here use Reactome as an example\n", + "databases = ['Reactome_2022']\n", + "m = df_database[df_database['dataset'].isin(databases)]['term'].sum()\n", + "# p-value correction for total number of tests done\n", + "p_thresh = 0.05/m" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "7a1da728", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Gene_setTermOverlapP-valueAdjusted P-valueOld P-valueOld Adjusted P-valueOdds RatioCombined ScoreGenes
0Reactome_2022Translocation Of ZAP-70 To Immunological Synap...3/174.270574e-080.00000200713.46428612106.727222LCK;CD3E;CD3D
1Reactome_2022Phosphorylation Of CD3 And TCR Zeta Chains R-H...3/207.154717e-080.00000200587.4705889665.600052LCK;CD3E;CD3D
2Reactome_2022PD-1 Signaling R-HSA-3899483/218.345311e-080.00000200554.8055569042.765180LCK;CD3E;CD3D
3Reactome_2022Immunoregulatory Interactions Between A Lympho...4/1231.675722e-070.00000300133.5932772084.302453CD8B;CD8A;CD3E;CD3D
4Reactome_2022Generation Of Second Messenger Molecules R-HSA...3/323.104597e-070.00000400344.1724145157.496482LCK;CD3E;CD3D
5Reactome_2022Immune System R-HSA-1682567/19432.439382e-060.0000240032.640754421.841460CD8B;LCK;CD8A;LTB;CD3E;IL7R;CD3D
6Reactome_2022Costimulation By CD28 Family R-HSA-3888413/683.111647e-060.00002700153.2769231943.606325LCK;CD3E;CD3D
7Reactome_2022Adaptive Immune System R-HSA-12802185/7337.270042e-060.0000550033.075206391.337520CD8B;LCK;CD8A;CD3E;CD3D
8Reactome_2022Downstream TCR Signaling R-HSA-2024243/948.274085e-060.00005500109.3406591279.546191LCK;CD3E;CD3D
9Reactome_2022TCR Signaling R-HSA-2024033/1161.556787e-050.0000930087.955752973.696687LCK;CD3E;CD3D
\n", + "
" + ], + "text/plain": [ + " Gene_set Term Overlap \\\n", + "0 Reactome_2022 Translocation Of ZAP-70 To Immunological Synap... 3/17 \n", + "1 Reactome_2022 Phosphorylation Of CD3 And TCR Zeta Chains R-H... 3/20 \n", + "2 Reactome_2022 PD-1 Signaling R-HSA-389948 3/21 \n", + "3 Reactome_2022 Immunoregulatory Interactions Between A Lympho... 4/123 \n", + "4 Reactome_2022 Generation Of Second Messenger Molecules R-HSA... 3/32 \n", + "5 Reactome_2022 Immune System R-HSA-168256 7/1943 \n", + "6 Reactome_2022 Costimulation By CD28 Family R-HSA-388841 3/68 \n", + "7 Reactome_2022 Adaptive Immune System R-HSA-1280218 5/733 \n", + "8 Reactome_2022 Downstream TCR Signaling R-HSA-202424 3/94 \n", + "9 Reactome_2022 TCR Signaling R-HSA-202403 3/116 \n", + "\n", + " P-value Adjusted P-value Old P-value Old Adjusted P-value \\\n", + "0 4.270574e-08 0.000002 0 0 \n", + "1 7.154717e-08 0.000002 0 0 \n", + "2 8.345311e-08 0.000002 0 0 \n", + "3 1.675722e-07 0.000003 0 0 \n", + "4 3.104597e-07 0.000004 0 0 \n", + "5 2.439382e-06 0.000024 0 0 \n", + "6 3.111647e-06 0.000027 0 0 \n", + "7 7.270042e-06 0.000055 0 0 \n", + "8 8.274085e-06 0.000055 0 0 \n", + "9 1.556787e-05 0.000093 0 0 \n", + "\n", + " Odds Ratio Combined Score Genes \n", + "0 713.464286 12106.727222 LCK;CD3E;CD3D \n", + "1 587.470588 9665.600052 LCK;CD3E;CD3D \n", + "2 554.805556 9042.765180 LCK;CD3E;CD3D \n", + "3 133.593277 2084.302453 CD8B;CD8A;CD3E;CD3D \n", + "4 344.172414 5157.496482 LCK;CD3E;CD3D \n", + "5 32.640754 421.841460 CD8B;LCK;CD8A;LTB;CD3E;IL7R;CD3D \n", + "6 153.276923 1943.606325 LCK;CD3E;CD3D \n", + "7 33.075206 391.337520 CD8B;LCK;CD8A;CD3E;CD3D \n", + "8 109.340659 1279.546191 LCK;CD3E;CD3D \n", + "9 87.955752 973.696687 LCK;CD3E;CD3D " + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Perform pathway enrichment analysis using the gseapy package in the Reactome database\n", + "df = pd.DataFrame()\n", + "enr_Reactome = gp.enrichr(gene_list=CD_genes,\n", + " gene_sets=databases,\n", + " organism='Human', \n", + " outdir='test/enr_Reactome',\n", + " cutoff=0.5)\n", + "out = enr_Reactome.results\n", + "out = out[out['P-value'] < p_thresh]\n", + "df = df.append(out, ignore_index=True)\n", + "df" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/transfer/code/scGPT/tutorials/Tutorial_Integration.ipynb b/transfer/code/scGPT/tutorials/Tutorial_Integration.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..4e44aa727996569bb7efb08b29496dd8b9b8c8b4 --- /dev/null +++ b/transfer/code/scGPT/tutorials/Tutorial_Integration.ipynb @@ -0,0 +1,1498 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fine-tuning on Pre-trained Model with Batch Integration\n", + "In this tutorial, we demonstrate how to fine-tune a pre-trained model on a new dataset for the batch integration task. We use the PBMC 10K dataset as an example and fine-tune on the pre-trained whole-body model. \n", + "\n", + "We summarize the fine-tuning pipeline in the following steps, which can be used as a general recipe for finetuning on integration tasks and beyond: \n", + "\n", + " 1. Specify hyper-parameter setup for integration task\n", + " \n", + " 2. Load and pre-process data\n", + " \n", + " 3. Load the pre-trained scGPT model\n", + " \n", + " 4. Finetune scGPT with task-specific objectives\n", + " \n", + " 5. Evaluate fine-tuned scGPT\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Global seed set to 0\n", + "/h/chloexq/.cache/pypoetry/virtualenvs/scgpt--qSLVbd1-py3.9/lib/python3.7/site-packages/scanpy/_settings.py:447: DeprecationWarning: `set_matplotlib_formats` is deprecated since IPython 7.23, directly use `matplotlib_inline.backend_inline.set_matplotlib_formats()`\n", + " IPython.display.set_matplotlib_formats(*ipython_format)\n" + ] + } + ], + "source": [ + "import copy\n", + "import gc\n", + "import json\n", + "import os\n", + "from pathlib import Path\n", + "import sys\n", + "import time\n", + "import traceback\n", + "from typing import List, Tuple, Dict, Union, Optional\n", + "import warnings\n", + "\n", + "import torch\n", + "from anndata import AnnData\n", + "import scanpy as sc\n", + "import scvi\n", + "import numpy as np\n", + "import wandb\n", + "from scipy.sparse import issparse\n", + "import matplotlib.pyplot as plt\n", + "from torch import nn\n", + "from torch.nn import functional as F\n", + "from torch.utils.data import Dataset, DataLoader\n", + "from sklearn.model_selection import train_test_split\n", + "from torchtext.vocab import Vocab\n", + "from torchtext._torchtext import (\n", + " Vocab as VocabPybind,\n", + ")\n", + "\n", + "\n", + "sys.path.insert(0, \"../\")\n", + "import scgpt as scg\n", + "from scgpt.model import TransformerModel, AdversarialDiscriminator\n", + "from scgpt.tokenizer import tokenize_and_pad_batch, random_mask_value\n", + "from scgpt.tokenizer.gene_tokenizer import GeneVocab\n", + "from scgpt.loss import (\n", + " masked_mse_loss,\n", + " masked_relative_error,\n", + " criterion_neg_log_bernoulli,\n", + ")\n", + "from scgpt.preprocess import Preprocessor\n", + "from scgpt import SubsetsBatchSampler\n", + "from scgpt.utils import set_seed, eval_scib_metrics, load_pretrained\n", + "\n", + "sc.set_figure_params(figsize=(4, 4))\n", + "os.environ[\"KMP_WARNINGS\"] = \"off\"\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step1: Specify hyper-parameter setup for integration task\n", + "Here we provide some hyper-parameter recommendations here for the integration task. Note that the PBMC 10K dataset contains multiple batches to be integrated. Therefore, in addition to the default gene modelling objectives, we also turn on ESC, DAR and DSBN objectives specifically to faciliate batch integration." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mchloewxq\u001b[0m (\u001b[33mscformer\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n" + ] + }, + { + "data": { + "text/html": [ + "wandb version 0.15.4 is available! To upgrade, please run:\n", + " $ pip install wandb --upgrade" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Tracking run with wandb version 0.12.21" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Run data is saved locally in /ssd003/home/chloexq/scGPT/tutorials/wandb/run-20230703_155312-vlmktqyd" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Syncing run charmed-blaze-8 to Weights & Biases (docs)
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'seed': 42, 'dataset_name': 'PBMC_10K', 'do_train': True, 'load_model': '../save/scGPT_human', 'GEPC': True, 'ecs_thres': 0.8, 'dab_weight': 1.0, 'mask_ratio': 0.4, 'epochs': 15, 'n_bins': 51, 'lr': 0.0001, 'batch_size': 64, 'layer_size': 128, 'nlayers': 4, 'nhead': 4, 'dropout': 0.2, 'schedule_ratio': 0.9, 'save_eval_interval': 5, 'log_interval': 100, 'fast_transformer': True, 'pre_norm': False, 'amp': True}\n" + ] + } + ], + "source": [ + "hyperparameter_defaults = dict(\n", + " seed=42,\n", + " dataset_name=\"PBMC_10K\", # Dataset name\n", + " do_train=True, # Flag to indicate whether to do update model parameters during training\n", + " load_model=\"../save/scGPT_human\", # Path to pre-trained model\n", + " GEPC=True, # Gene expression modelling for cell objective\n", + " ecs_thres=0.8, # Elastic cell similarity objective, 0.0 to 1.0, 0.0 to disable\n", + " dab_weight=1.0, # DAR objective weight for batch correction\n", + " mask_ratio=0.4, # Default mask ratio\n", + " epochs=15, # Default number of epochs for fine-tuning\n", + " n_bins=51, # Default number of bins for value binning in data pre-processing\n", + " lr=1e-4, # Default learning rate for fine-tuning\n", + " batch_size=64, # Default batch size for fine-tuning\n", + " layer_size=128,\n", + " nlayers=4,\n", + " nhead=4, # if load model, batch_size, layer_size, nlayers, nhead will be ignored\n", + " dropout=0.2, # Default dropout rate during model fine-tuning\n", + " schedule_ratio=0.9, # Default rate for learning rate decay\n", + " save_eval_interval=5, # Default model evaluation interval\n", + " log_interval=100, # Default log interval\n", + " fast_transformer=True, # Default setting\n", + " pre_norm=False, # Default setting\n", + " amp=True, # # Default setting: Automatic Mixed Precision\n", + ")\n", + "run = wandb.init(\n", + " config=hyperparameter_defaults,\n", + " project=\"scGPT\",\n", + " reinit=True,\n", + " settings=wandb.Settings(start_method=\"fork\"),\n", + ")\n", + "config = wandb.config\n", + "print(config)\n", + "\n", + "set_seed(config.seed)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# settings for input and preprocessing\n", + "pad_token = \"\"\n", + "special_tokens = [pad_token, \"\", \"\"]\n", + "mask_ratio = config.mask_ratio\n", + "mask_value = -1\n", + "pad_value = -2\n", + "n_input_bins = config.n_bins\n", + "\n", + "n_hvg = 1200 # number of highly variable genes\n", + "max_seq_len = n_hvg + 1\n", + "per_seq_batch_sample = True\n", + "DSBN = True # Domain-spec batchnorm\n", + "explicit_zero_prob = True # whether explicit bernoulli for zeros" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "save to save/dev_PBMC_10K-Jul03-15-53\n" + ] + } + ], + "source": [ + "dataset_name = config.dataset_name\n", + "save_dir = Path(f\"./save/dev_{dataset_name}-{time.strftime('%b%d-%H-%M')}/\")\n", + "save_dir.mkdir(parents=True, exist_ok=True)\n", + "print(f\"save to {save_dir}\")\n", + "logger = scg.logger\n", + "scg.utils.add_file_handler(logger, save_dir / \"run.log\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 2: Load and pre-process data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 Load the PBMC 10K data" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[34mINFO \u001b[0m File data/gene_info_pbmc.csv already downloaded \n", + "\u001b[34mINFO \u001b[0m File data/pbmc_metadata.pickle already downloaded \n", + "\u001b[34mINFO \u001b[0m File data/pbmc8k/filtered_gene_bc_matrices.tar.gz already downloaded \n", + "\u001b[34mINFO \u001b[0m Extracting tar file \n", + "\u001b[34mINFO \u001b[0m Removing extracted data at data/pbmc8k/filtered_gene_bc_matrices \n", + "\u001b[34mINFO \u001b[0m File data/pbmc4k/filtered_gene_bc_matrices.tar.gz already downloaded \n", + "\u001b[34mINFO \u001b[0m Extracting tar file \n", + "\u001b[34mINFO \u001b[0m Removing extracted data at data/pbmc4k/filtered_gene_bc_matrices \n" + ] + } + ], + "source": [ + "if dataset_name == \"PBMC_10K\":\n", + " adata = scvi.data.pbmc_dataset() # 11990 × 3346\n", + " ori_batch_col = \"batch\"\n", + " adata.obs[\"celltype\"] = adata.obs[\"str_labels\"].astype(\"category\")\n", + " adata.var = adata.var.set_index(\"gene_symbols\")\n", + " data_is_raw = True\n", + "\n", + "# make the batch category column\n", + "adata.obs[\"str_batch\"] = adata.obs[ori_batch_col].astype(str)\n", + "batch_id_labels = adata.obs[\"str_batch\"].astype(\"category\").cat.codes.values\n", + "adata.obs[\"batch_id\"] = batch_id_labels\n", + "adata.var[\"gene_name\"] = adata.var.index.tolist()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2 Cross-check gene set with the pre-trained model \n", + "Note that we retain the common gene set between the data and the pre-trained model for further fine-tuning." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "scGPT - INFO - match 3256/3346 genes in vocabulary of size 60697.\n", + "scGPT - INFO - Resume model from ../save/scGPT_human/best_model.pt, the model args will be overriden by the config ../save/scGPT_human/args.json.\n" + ] + } + ], + "source": [ + "if config.load_model is not None:\n", + " model_dir = Path(config.load_model)\n", + " model_config_file = model_dir / \"args.json\"\n", + " model_file = model_dir / \"best_model.pt\"\n", + " vocab_file = model_dir / \"vocab.json\"\n", + "\n", + " vocab = GeneVocab.from_file(vocab_file)\n", + " for s in special_tokens:\n", + " if s not in vocab:\n", + " vocab.append_token(s)\n", + "\n", + " adata.var[\"id_in_vocab\"] = [\n", + " 1 if gene in vocab else -1 for gene in adata.var[\"gene_name\"]\n", + " ]\n", + " gene_ids_in_vocab = np.array(adata.var[\"id_in_vocab\"])\n", + " logger.info(\n", + " f\"match {np.sum(gene_ids_in_vocab >= 0)}/{len(gene_ids_in_vocab)} genes \"\n", + " f\"in vocabulary of size {len(vocab)}.\"\n", + " )\n", + " adata = adata[:, adata.var[\"id_in_vocab\"] >= 0]\n", + " \n", + " # model\n", + " with open(model_config_file, \"r\") as f:\n", + " model_configs = json.load(f)\n", + " logger.info(\n", + " f\"Resume model from {model_file}, the model args will be overriden by the \"\n", + " f\"config {model_config_file}.\"\n", + " )\n", + " embsize = model_configs[\"embsize\"]\n", + " nhead = model_configs[\"nheads\"]\n", + " d_hid = model_configs[\"d_hid\"]\n", + " nlayers = model_configs[\"nlayers\"]\n", + " n_layers_cls = model_configs[\"n_layers_cls\"]\n", + "else:\n", + " embsize = config.layer_size \n", + " nhead = config.nhead\n", + " nlayers = config.nlayers \n", + " d_hid = config.layer_size" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.3 Pre-process the data\n", + "We follow the standardized pipline of depth normalization, log normalization, and highly vairable gene (HVG) selection for data pre-processing. We further introduced value binning to obtain the relative expressions of each HVG." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "scGPT - INFO - Filtering genes by counts ...\n", + "scGPT - INFO - Filtering cells by counts ...\n", + "scGPT - INFO - Normalizing total counts ...\n", + "scGPT - INFO - Log1p transforming ...\n", + "scGPT - INFO - Subsetting highly variable genes ...\n", + "scGPT - INFO - Binning data ...\n" + ] + } + ], + "source": [ + "# set up the preprocessor, use the args to config the workflow\n", + "preprocessor = Preprocessor(\n", + " use_key=\"X\", # the key in adata.layers to use as raw data\n", + " filter_gene_by_counts=3, # step 1\n", + " filter_cell_by_counts=False, # step 2\n", + " normalize_total=1e4, # 3. whether to normalize the raw data and to what sum\n", + " result_normed_key=\"X_normed\", # the key in adata.layers to store the normalized data\n", + " log1p=data_is_raw, # 4. whether to log1p the normalized data\n", + " result_log1p_key=\"X_log1p\",\n", + " subset_hvg=n_hvg, # 5. whether to subset the raw data to highly variable genes\n", + " hvg_flavor=\"seurat_v3\" if data_is_raw else \"cell_ranger\",\n", + " binning=config.n_bins, # 6. whether to bin the raw data and to what number of bins\n", + " result_binned_key=\"X_binned\", # the key in adata.layers to store the binned data\n", + ")\n", + "preprocessor(adata, batch_key=\"str_batch\" if dataset_name != \"heart_cell\" else None)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "if per_seq_batch_sample:\n", + " # sort the adata by batch_id in advance\n", + " adata_sorted = adata[adata.obs[\"batch_id\"].argsort()].copy()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.4 Tokenize the input data for model fine-tuning" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "input_layer_key = \"X_binned\"\n", + "all_counts = (\n", + " adata.layers[input_layer_key].toarray()\n", + " if issparse(adata.layers[input_layer_key])\n", + " else adata.layers[input_layer_key]\n", + ")\n", + "genes = adata.var[\"gene_name\"].tolist()\n", + "\n", + "celltypes_labels = adata.obs[\"celltype\"].tolist() # make sure count from 0\n", + "num_types = len(set(celltypes_labels))\n", + "celltypes_labels = np.array(celltypes_labels)\n", + "\n", + "batch_ids = adata.obs[\"batch_id\"].tolist()\n", + "num_batch_types = len(set(batch_ids))\n", + "batch_ids = np.array(batch_ids)\n", + "\n", + "(\n", + " train_data,\n", + " valid_data,\n", + " train_celltype_labels,\n", + " valid_celltype_labels,\n", + " train_batch_labels,\n", + " valid_batch_labels,\n", + ") = train_test_split(\n", + " all_counts, celltypes_labels, batch_ids, test_size=0.1, shuffle=True\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "if config.load_model is None:\n", + " vocab = Vocab(\n", + " VocabPybind(genes + special_tokens, None)\n", + " ) # bidirectional lookup [gene <-> int]\n", + "vocab.set_default_index(vocab[\"\"])\n", + "gene_ids = np.array(vocab(genes), dtype=int)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "scGPT - INFO - train set number of samples: 10791, \n", + "\t feature length: 1201\n", + "scGPT - INFO - valid set number of samples: 1199, \n", + "\t feature length: 1201\n" + ] + } + ], + "source": [ + "tokenized_train = tokenize_and_pad_batch(\n", + " train_data,\n", + " gene_ids,\n", + " max_len=max_seq_len,\n", + " vocab=vocab,\n", + " pad_token=pad_token,\n", + " pad_value=pad_value,\n", + " append_cls=True, # append token at the beginning\n", + " include_zero_gene=True,\n", + ")\n", + "tokenized_valid = tokenize_and_pad_batch(\n", + " valid_data,\n", + " gene_ids,\n", + " max_len=max_seq_len,\n", + " vocab=vocab,\n", + " pad_token=pad_token,\n", + " pad_value=pad_value,\n", + " append_cls=True,\n", + " include_zero_gene=True,\n", + ")\n", + "logger.info(\n", + " f\"train set number of samples: {tokenized_train['genes'].shape[0]}, \"\n", + " f\"\\n\\t feature length: {tokenized_train['genes'].shape[1]}\"\n", + ")\n", + "logger.info(\n", + " f\"valid set number of samples: {tokenized_valid['genes'].shape[0]}, \"\n", + " f\"\\n\\t feature length: {tokenized_valid['genes'].shape[1]}\"\n", + ")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def prepare_data(sort_seq_batch=False) -> Tuple[Dict[str, torch.Tensor]]:\n", + " masked_values_train = random_mask_value(\n", + " tokenized_train[\"values\"],\n", + " mask_ratio=mask_ratio,\n", + " mask_value=mask_value,\n", + " pad_value=pad_value,\n", + " )\n", + " masked_values_valid = random_mask_value(\n", + " tokenized_valid[\"values\"],\n", + " mask_ratio=mask_ratio,\n", + " mask_value=mask_value,\n", + " pad_value=pad_value,\n", + " )\n", + " print(\n", + " f\"random masking at epoch {epoch:3d}, ratio of masked values in train: \",\n", + " f\"{(masked_values_train == mask_value).sum() / (masked_values_train - pad_value).count_nonzero():.4f}\",\n", + " )\n", + "\n", + " input_gene_ids_train, input_gene_ids_valid = (\n", + " tokenized_train[\"genes\"],\n", + " tokenized_valid[\"genes\"],\n", + " )\n", + " input_values_train, input_values_valid = masked_values_train, masked_values_valid\n", + " target_values_train, target_values_valid = (\n", + " tokenized_train[\"values\"],\n", + " tokenized_valid[\"values\"],\n", + " )\n", + "\n", + " tensor_batch_labels_train = torch.from_numpy(train_batch_labels).long()\n", + " tensor_batch_labels_valid = torch.from_numpy(valid_batch_labels).long()\n", + "\n", + " if sort_seq_batch:\n", + " train_sort_ids = np.argsort(train_batch_labels)\n", + " input_gene_ids_train = input_gene_ids_train[train_sort_ids]\n", + " input_values_train = input_values_train[train_sort_ids]\n", + " target_values_train = target_values_train[train_sort_ids]\n", + " tensor_batch_labels_train = tensor_batch_labels_train[train_sort_ids]\n", + "\n", + " valid_sort_ids = np.argsort(valid_batch_labels)\n", + " input_gene_ids_valid = input_gene_ids_valid[valid_sort_ids]\n", + " input_values_valid = input_values_valid[valid_sort_ids]\n", + " target_values_valid = target_values_valid[valid_sort_ids]\n", + " tensor_batch_labels_valid = tensor_batch_labels_valid[valid_sort_ids]\n", + "\n", + " train_data_pt = {\n", + " \"gene_ids\": input_gene_ids_train,\n", + " \"values\": input_values_train,\n", + " \"target_values\": target_values_train,\n", + " \"batch_labels\": tensor_batch_labels_train,\n", + " }\n", + " valid_data_pt = {\n", + " \"gene_ids\": input_gene_ids_valid,\n", + " \"values\": input_values_valid,\n", + " \"target_values\": target_values_valid,\n", + " \"batch_labels\": tensor_batch_labels_valid,\n", + " }\n", + "\n", + " return train_data_pt, valid_data_pt\n", + "\n", + "\n", + "# dataset\n", + "class SeqDataset(Dataset):\n", + " def __init__(self, data: Dict[str, torch.Tensor]):\n", + " self.data = data\n", + "\n", + " def __len__(self):\n", + " return self.data[\"gene_ids\"].shape[0]\n", + "\n", + " def __getitem__(self, idx):\n", + " return {k: v[idx] for k, v in self.data.items()}\n", + "\n", + "\n", + "# data_loader\n", + "def prepare_dataloader(\n", + " data_pt: Dict[str, torch.Tensor],\n", + " batch_size: int,\n", + " shuffle: bool = False,\n", + " intra_domain_shuffle: bool = False,\n", + " drop_last: bool = False,\n", + " num_workers: int = 0,\n", + ") -> DataLoader:\n", + " dataset = SeqDataset(data_pt)\n", + "\n", + " if per_seq_batch_sample:\n", + " # find the indices of samples in each seq batch\n", + " subsets = []\n", + " batch_labels_array = data_pt[\"batch_labels\"].numpy()\n", + " for batch_label in np.unique(batch_labels_array):\n", + " batch_indices = np.where(batch_labels_array == batch_label)[0].tolist()\n", + " subsets.append(batch_indices)\n", + " data_loader = DataLoader(\n", + " dataset=dataset,\n", + " batch_sampler=SubsetsBatchSampler(\n", + " subsets,\n", + " batch_size,\n", + " intra_subset_shuffle=intra_domain_shuffle,\n", + " inter_subset_shuffle=shuffle,\n", + " drop_last=drop_last,\n", + " ),\n", + " num_workers=num_workers,\n", + " pin_memory=True,\n", + " )\n", + " return data_loader\n", + "\n", + " data_loader = DataLoader(\n", + " dataset=dataset,\n", + " batch_size=batch_size,\n", + " shuffle=shuffle,\n", + " drop_last=drop_last,\n", + " num_workers=num_workers,\n", + " pin_memory=True,\n", + " )\n", + " return data_loader" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " ## Step 3: Load the pre-trained scGPT model" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Use domain specific batchnorm with affine=False\n" + ] + }, + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "\n", + "ntokens = len(vocab) # size of vocabulary\n", + "model = TransformerModel(\n", + " ntokens,\n", + " embsize,\n", + " nhead,\n", + " d_hid,\n", + " nlayers,\n", + " vocab=vocab,\n", + " dropout=config.dropout,\n", + " pad_token=pad_token,\n", + " pad_value=pad_value,\n", + " do_mvc=config.GEPC,\n", + " do_dab=True,\n", + " use_batch_labels=True,\n", + " num_batch_labels=num_batch_types,\n", + " domain_spec_batchnorm=DSBN,\n", + " n_input_bins=n_input_bins,\n", + " ecs_threshold=config.ecs_thres,\n", + " explicit_zero_prob=explicit_zero_prob,\n", + " use_fast_transformer=config.fast_transformer,\n", + " pre_norm=config.pre_norm,\n", + ")\n", + "if config.load_model is not None:\n", + " load_pretrained(model, torch.load(model_file), verbose=False)\n", + "\n", + "model.to(device)\n", + "wandb.watch(model)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "criterion = masked_mse_loss\n", + "criterion_dab = nn.CrossEntropyLoss()\n", + "optimizer = torch.optim.Adam(\n", + " model.parameters(), lr=config.lr, eps=1e-4 if config.amp else 1e-8\n", + ")\n", + "scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1, gamma=config.schedule_ratio)\n", + "scaler = torch.cuda.amp.GradScaler(enabled=config.amp)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def train(model: nn.Module, loader: DataLoader) -> None:\n", + " \"\"\"\n", + " Train the model for one epoch.\n", + " \"\"\"\n", + " model.train()\n", + " total_loss, total_mse, total_gepc = 0.0, 0.0, 0.0\n", + " total_error = 0.0\n", + " log_interval = config.log_interval\n", + " start_time = time.time()\n", + "\n", + " num_batches = len(loader)\n", + " for batch, batch_data in enumerate(loader):\n", + " input_gene_ids = batch_data[\"gene_ids\"].to(device)\n", + " input_values = batch_data[\"values\"].to(device)\n", + " target_values = batch_data[\"target_values\"].to(device)\n", + " batch_labels = batch_data[\"batch_labels\"].to(device)\n", + "\n", + " src_key_padding_mask = input_gene_ids.eq(vocab[pad_token])\n", + " with torch.cuda.amp.autocast(enabled=config.amp):\n", + " output_dict = model(\n", + " input_gene_ids,\n", + " input_values,\n", + " src_key_padding_mask=src_key_padding_mask,\n", + " batch_labels=batch_labels if DSBN else None,\n", + " MVC=config.GEPC,\n", + " ECS=config.ecs_thres > 0,\n", + " )\n", + "\n", + " masked_positions = input_values.eq(mask_value) # the postions to predict\n", + " loss = loss_mse = criterion(\n", + " output_dict[\"mlm_output\"], target_values, masked_positions\n", + " )\n", + " metrics_to_log = {\"train/mse\": loss_mse.item()}\n", + " if explicit_zero_prob:\n", + " loss_zero_log_prob = criterion_neg_log_bernoulli(\n", + " output_dict[\"mlm_zero_probs\"], target_values, masked_positions\n", + " )\n", + " loss = loss + loss_zero_log_prob\n", + " metrics_to_log.update({\"train/nzlp\": loss_zero_log_prob.item()})\n", + " if config.GEPC:\n", + " loss_gepc = criterion(\n", + " output_dict[\"mvc_output\"], target_values, masked_positions\n", + " )\n", + " loss = loss + loss_gepc\n", + " metrics_to_log.update({\"train/mvc\": loss_gepc.item()})\n", + " if config.GEPC and explicit_zero_prob:\n", + " loss_gepc_zero_log_prob = criterion_neg_log_bernoulli(\n", + " output_dict[\"mvc_zero_probs\"], target_values, masked_positions\n", + " )\n", + " loss = loss + loss_gepc_zero_log_prob\n", + " metrics_to_log.update(\n", + " {\"train/mvc_nzlp\": loss_gepc_zero_log_prob.item()}\n", + " )\n", + " if config.ecs_thres > 0:\n", + " loss_ecs = 10 * output_dict[\"loss_ecs\"]\n", + " loss = loss + loss_ecs\n", + " metrics_to_log.update({\"train/ecs\": loss_ecs.item()})\n", + " loss_dab = criterion_dab(output_dict[\"dab_output\"], batch_labels)\n", + " loss = loss + config.dab_weight * loss_dab\n", + " metrics_to_log.update({\"train/dab\": loss_dab.item()})\n", + "\n", + " model.zero_grad()\n", + " scaler.scale(loss).backward()\n", + " scaler.unscale_(optimizer)\n", + " with warnings.catch_warnings(record=True) as w:\n", + " warnings.filterwarnings(\"always\")\n", + " torch.nn.utils.clip_grad_norm_(\n", + " model.parameters(),\n", + " 1.0,\n", + " error_if_nonfinite=False if scaler.is_enabled() else True,\n", + " )\n", + " if len(w) > 0:\n", + " logger.warning(\n", + " f\"Found infinite gradient. This may be caused by the gradient \"\n", + " f\"scaler. The current scale is {scaler.get_scale()}. This warning \"\n", + " \"can be ignored if no longer occurs after autoscaling of the scaler.\"\n", + " )\n", + " scaler.step(optimizer)\n", + " scaler.update()\n", + "\n", + " wandb.log(metrics_to_log)\n", + "\n", + " with torch.no_grad():\n", + " mre = masked_relative_error(\n", + " output_dict[\"mlm_output\"], target_values, masked_positions\n", + " )\n", + "\n", + " total_loss += loss.item()\n", + " total_mse += loss_mse.item()\n", + " total_gepc += loss_gepc.item() if config.GEPC else 0.0\n", + " total_error += mre.item()\n", + " if batch % log_interval == 0 and batch > 0:\n", + " lr = scheduler.get_last_lr()[0]\n", + " ms_per_batch = (time.time() - start_time) * 1000 / log_interval\n", + " cur_loss = total_loss / log_interval\n", + " cur_mse = total_mse / log_interval\n", + " cur_gepc = total_gepc / log_interval if config.GEPC else 0.0\n", + " cur_error = total_error / log_interval\n", + " # ppl = math.exp(cur_loss)\n", + " logger.info(\n", + " f\"| epoch {epoch:3d} | {batch:3d}/{num_batches:3d} batches | \"\n", + " f\"lr {lr:05.4f} | ms/batch {ms_per_batch:5.2f} | \"\n", + " f\"loss {cur_loss:5.2f} | mse {cur_mse:5.2f} | mre {cur_error:5.2f} |\"\n", + " + (f\"gepc {cur_gepc:5.2f} |\" if config.GEPC else \"\")\n", + " )\n", + " total_loss = 0\n", + " total_mse = 0\n", + " total_gepc = 0\n", + " total_error = 0\n", + " start_time = time.time()\n", + "\n", + "\n", + "def define_wandb_metrcis():\n", + " wandb.define_metric(\"valid/mse\", summary=\"min\", step_metric=\"epoch\")\n", + " wandb.define_metric(\"valid/mre\", summary=\"min\", step_metric=\"epoch\")\n", + " wandb.define_metric(\"valid/dab\", summary=\"min\", step_metric=\"epoch\")\n", + " wandb.define_metric(\"valid/sum_mse_dab\", summary=\"min\", step_metric=\"epoch\")\n", + " wandb.define_metric(\"test/avg_bio\", summary=\"max\")\n", + "\n", + "\n", + "def evaluate(model: nn.Module, loader: DataLoader) -> float:\n", + " \"\"\"\n", + " Evaluate the model on the evaluation data.\n", + " \"\"\"\n", + " model.eval()\n", + " total_loss = 0.0\n", + " total_error = 0.0\n", + " total_dab = 0.0\n", + " total_num = 0\n", + " with torch.no_grad():\n", + " for batch_data in loader:\n", + " input_gene_ids = batch_data[\"gene_ids\"].to(device)\n", + " input_values = batch_data[\"values\"].to(device)\n", + " target_values = batch_data[\"target_values\"].to(device)\n", + " batch_labels = batch_data[\"batch_labels\"].to(device)\n", + "\n", + " src_key_padding_mask = input_gene_ids.eq(vocab[pad_token])\n", + " with torch.cuda.amp.autocast(enabled=config.amp):\n", + " output_dict = model(\n", + " input_gene_ids,\n", + " input_values,\n", + " src_key_padding_mask=src_key_padding_mask,\n", + " batch_labels=batch_labels if DSBN else None,\n", + " )\n", + " output_values = output_dict[\"mlm_output\"]\n", + "\n", + " masked_positions = input_values.eq(mask_value)\n", + " loss = criterion(output_values, target_values, masked_positions)\n", + " loss_dab = criterion_dab(output_dict[\"dab_output\"], batch_labels)\n", + "\n", + " total_loss += loss.item() * len(input_gene_ids)\n", + " total_error += masked_relative_error(\n", + " output_values, target_values, masked_positions\n", + " ).item() * len(input_gene_ids)\n", + " total_dab += loss_dab.item() * len(input_gene_ids)\n", + " total_num += len(input_gene_ids)\n", + "\n", + " wandb.log(\n", + " {\n", + " \"valid/mse\": total_loss / total_num,\n", + " \"valid/mre\": total_error / total_num,\n", + " \"valid/dab\": total_dab / total_num,\n", + " \"valid/sum_mse_dab\": (total_loss + config.dab_weight * total_dab)\n", + " / total_num,\n", + " \"epoch\": epoch,\n", + " },\n", + " )\n", + "\n", + " return total_loss / total_num, total_error / total_num\n", + "\n", + "\n", + "def eval_testdata(\n", + " model: nn.Module,\n", + " adata_t: AnnData,\n", + " include_types: List[str] = [\"cls\"],\n", + ") -> Optional[Dict]:\n", + " \"\"\"evaluate the model on test dataset of adata_t\"\"\"\n", + " model.eval()\n", + "\n", + " # copy adata_t to avoid reuse previously computed results stored in adata_t\n", + " adata_t = adata_t.copy()\n", + "\n", + " all_counts = (\n", + " adata_t.layers[input_layer_key].toarray()\n", + " if issparse(adata_t.layers[input_layer_key])\n", + " else adata_t.layers[input_layer_key]\n", + " )\n", + "\n", + " celltypes_labels = adata_t.obs[\"celltype\"].tolist()\n", + " celltypes_labels = np.array(celltypes_labels)\n", + "\n", + " batch_ids = adata_t.obs[\"batch_id\"].tolist()\n", + " batch_ids = np.array(batch_ids)\n", + "\n", + " # Evaluate cls cell embeddings\n", + " if \"cls\" in include_types:\n", + " logger.info(\"Evaluating cls cell embeddings\")\n", + " tokenized_all = tokenize_and_pad_batch(\n", + " all_counts,\n", + " gene_ids,\n", + " max_len=max_seq_len,\n", + " vocab=vocab,\n", + " pad_token=pad_token,\n", + " pad_value=pad_value,\n", + " append_cls=True, # append token at the beginning\n", + " include_zero_gene=True,\n", + " )\n", + " all_gene_ids, all_values = tokenized_all[\"genes\"], tokenized_all[\"values\"]\n", + " src_key_padding_mask = all_gene_ids.eq(vocab[pad_token])\n", + " with torch.no_grad(), torch.cuda.amp.autocast(enabled=config.amp):\n", + " cell_embeddings = model.encode_batch(\n", + " all_gene_ids,\n", + " all_values.float(),\n", + " src_key_padding_mask=src_key_padding_mask,\n", + " batch_size=config.batch_size,\n", + " batch_labels=torch.from_numpy(batch_ids).long() if DSBN else None,\n", + " time_step=0,\n", + " return_np=True,\n", + " )\n", + " cell_embeddings = cell_embeddings / np.linalg.norm(\n", + " cell_embeddings, axis=1, keepdims=True\n", + " )\n", + "\n", + " adata_t.obsm[\"X_scGPT\"] = cell_embeddings\n", + "\n", + " results = {}\n", + " try:\n", + " results = eval_scib_metrics(adata_t)\n", + " except Exception as e:\n", + " traceback.print_exc()\n", + " logger.error(e)\n", + "\n", + " sc.pp.neighbors(adata_t, use_rep=\"X_scGPT\")\n", + " sc.tl.umap(adata_t, min_dist=0.3)\n", + " fig = sc.pl.umap(\n", + " adata_t,\n", + " color=[\"str_batch\"],\n", + " title=[f\"batch, avg_bio = {results.get('avg_bio', 0.0):.4f}\"],\n", + " frameon=False,\n", + " return_fig=True,\n", + " show=False,\n", + " )\n", + "\n", + " results[\"batch_umap\"] = fig\n", + "\n", + " sc.pp.neighbors(adata_t, use_rep=\"X_scGPT\")\n", + " sc.tl.umap(adata_t, min_dist=0.3)\n", + " fig = sc.pl.umap(\n", + " adata_t,\n", + " color=[\"celltype\"],\n", + " title=[\n", + " f\"celltype, avg_bio = {results.get('avg_bio', 0.0):.4f}\",\n", + " ],\n", + " frameon=False,\n", + " return_fig=True,\n", + " show=False,\n", + " )\n", + "\n", + " results[\"celltype_umap\"] = fig\n", + "\n", + " if len(include_types) == 1:\n", + " return results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step 4: Finetune scGPT with task-specific objectives" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "random masking at epoch 1, ratio of masked values in train: 0.3997\n", + "scGPT - INFO - | epoch 1 | 100/170 batches | lr 0.0001 | ms/batch 757.40 | loss 173.33 | mse 89.35 | mre 2663176.96 |gepc 73.21 |\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 1 | time: 130.90s | valid loss/mse 75.9452 | mre 4443419.7672\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - Best model with score 75.9452\n", + "random masking at epoch 2, ratio of masked values in train: 0.3997\n", + "scGPT - INFO - | epoch 2 | 100/170 batches | lr 0.0001 | ms/batch 747.07 | loss 116.37 | mse 51.26 | mre 1768156.19 |gepc 53.94 |\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 2 | time: 129.46s | valid loss/mse 50.5663 | mre 1523694.5974\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - Best model with score 50.5663\n", + "random masking at epoch 3, ratio of masked values in train: 0.3997\n", + "scGPT - INFO - | epoch 3 | 100/170 batches | lr 0.0001 | ms/batch 742.33 | loss 110.88 | mse 48.25 | mre 1595595.39 |gepc 50.59 |\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 3 | time: 128.22s | valid loss/mse 47.3394 | mre 1329758.4306\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - Best model with score 47.3394\n", + "random masking at epoch 4, ratio of masked values in train: 0.3997\n", + "scGPT - INFO - | epoch 4 | 100/170 batches | lr 0.0001 | ms/batch 745.60 | loss 109.07 | mse 47.46 | mre 1555602.24 |gepc 50.33 |\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 4 | time: 129.36s | valid loss/mse 45.9131 | mre 1213080.1915\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - Best model with score 45.9131\n", + "random masking at epoch 5, ratio of masked values in train: 0.3997\n", + "scGPT - INFO - | epoch 5 | 100/170 batches | lr 0.0001 | ms/batch 748.42 | loss 105.71 | mse 47.35 | mre 1546922.04 |gepc 49.55 |\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 5 | time: 129.56s | valid loss/mse 46.9950 | mre 1717378.3617\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - Saving model to save/dev_PBMC_10K-Jul03-15-53\n", + "scGPT - INFO - Evaluating cls cell embeddings\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 188/188 [00:54<00:00, 3.43it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NMI...\n", + "ARI...\n", + "Silhouette score...\n", + "PC regression...\n", + "Graph connectivity...\n", + "scGPT - INFO - 0\n", + "NMI_cluster/label 0.814257\n", + "ARI_cluster/label 0.843118\n", + "ASW_label 0.699138\n", + "ASW_label/batch 0.955014\n", + "PCR_batch 0.825616\n", + "cell_cycle_conservation NaN\n", + "isolated_label_F1 NaN\n", + "isolated_label_silhouette NaN\n", + "graph_conn 0.873379\n", + "kBET NaN\n", + "iLISI NaN\n", + "cLISI NaN\n", + "hvg_overlap NaN\n", + "trajectory NaN\n", + "scGPT - INFO - Biological Conservation Metrics: \n", + "ASW (cell-type): 0.6991, graph cLISI: nan, isolated label silhouette: nan, \n", + "Batch Effect Removal Metrics: \n", + "PCR_batch: 0.8256, ASW (batch): 0.9550, graph connectivity: 0.8734, graph iLISI: nan\n", + "random masking at epoch 6, ratio of masked values in train: 0.3997\n", + "scGPT - INFO - | epoch 6 | 100/170 batches | lr 0.0001 | ms/batch 766.20 | loss 103.49 | mse 47.02 | mre 1527726.00 |gepc 48.65 |\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 6 | time: 132.67s | valid loss/mse 45.6797 | mre 1421589.9451\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - Best model with score 45.6797\n", + "random masking at epoch 7, ratio of masked values in train: 0.3997\n", + "scGPT - INFO - | epoch 7 | 100/170 batches | lr 0.0001 | ms/batch 758.39 | loss 102.51 | mse 46.71 | mre 1518484.73 |gepc 48.05 |\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 7 | time: 132.07s | valid loss/mse 46.0452 | mre 1774720.6303\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "random masking at epoch 8, ratio of masked values in train: 0.3997\n", + "scGPT - INFO - | epoch 8 | 100/170 batches | lr 0.0000 | ms/batch 756.79 | loss 102.26 | mse 46.71 | mre 1507389.60 |gepc 47.85 |\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 8 | time: 131.08s | valid loss/mse 46.2436 | mre 1736326.6960\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "random masking at epoch 9, ratio of masked values in train: 0.3997\n", + "scGPT - INFO - | epoch 9 | 100/170 batches | lr 0.0000 | ms/batch 755.87 | loss 101.76 | mse 46.54 | mre 1504583.20 |gepc 47.90 |\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 9 | time: 131.23s | valid loss/mse 46.6282 | mre 1929727.1859\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "random masking at epoch 10, ratio of masked values in train: 0.3997\n", + "scGPT - INFO - | epoch 10 | 100/170 batches | lr 0.0000 | ms/batch 750.91 | loss 101.39 | mse 46.51 | mre 1526292.84 |gepc 47.80 |\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 10 | time: 130.18s | valid loss/mse 45.2987 | mre 1240932.0140\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - Best model with score 45.2987\n", + "scGPT - INFO - Saving model to save/dev_PBMC_10K-Jul03-15-53\n", + "scGPT - INFO - Evaluating cls cell embeddings\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 188/188 [00:53<00:00, 3.49it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NMI...\n", + "ARI...\n", + "Silhouette score...\n", + "PC regression...\n", + "Graph connectivity...\n", + "scGPT - INFO - 0\n", + "NMI_cluster/label 0.858342\n", + "ARI_cluster/label 0.892640\n", + "ASW_label 0.745845\n", + "ASW_label/batch 0.947944\n", + "PCR_batch 0.636703\n", + "cell_cycle_conservation NaN\n", + "isolated_label_F1 NaN\n", + "isolated_label_silhouette NaN\n", + "graph_conn 0.895191\n", + "kBET NaN\n", + "iLISI NaN\n", + "cLISI NaN\n", + "hvg_overlap NaN\n", + "trajectory NaN\n", + "scGPT - INFO - Biological Conservation Metrics: \n", + "ASW (cell-type): 0.7458, graph cLISI: nan, isolated label silhouette: nan, \n", + "Batch Effect Removal Metrics: \n", + "PCR_batch: 0.6367, ASW (batch): 0.9479, graph connectivity: 0.8952, graph iLISI: nan\n", + "random masking at epoch 11, ratio of masked values in train: 0.3997\n", + "scGPT - INFO - | epoch 11 | 100/170 batches | lr 0.0000 | ms/batch 755.50 | loss 100.61 | mse 46.27 | mre 1480725.00 |gepc 47.29 |\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 11 | time: 130.97s | valid loss/mse 45.3457 | mre 1603972.0365\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "random masking at epoch 12, ratio of masked values in train: 0.3997\n", + "scGPT - INFO - | epoch 12 | 100/170 batches | lr 0.0000 | ms/batch 746.86 | loss 100.49 | mse 46.08 | mre 1485683.62 |gepc 47.37 |\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 12 | time: 130.04s | valid loss/mse 46.0835 | mre 1862575.2738\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "random masking at epoch 13, ratio of masked values in train: 0.3997\n", + "scGPT - INFO - | epoch 13 | 100/170 batches | lr 0.0000 | ms/batch 754.29 | loss 100.68 | mse 46.31 | mre 1510157.08 |gepc 47.34 |\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 13 | time: 131.26s | valid loss/mse 45.9947 | mre 1604693.7741\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "random masking at epoch 14, ratio of masked values in train: 0.3997\n", + "scGPT - INFO - | epoch 14 | 100/170 batches | lr 0.0000 | ms/batch 752.90 | loss 100.04 | mse 46.10 | mre 1473999.56 |gepc 47.03 |\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 14 | time: 130.39s | valid loss/mse 45.3608 | mre 1330498.1018\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "random masking at epoch 15, ratio of masked values in train: 0.3997\n", + "scGPT - INFO - | epoch 15 | 100/170 batches | lr 0.0000 | ms/batch 747.97 | loss 100.17 | mse 46.15 | mre 1473452.98 |gepc 47.09 |\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 15 | time: 129.55s | valid loss/mse 44.9956 | mre 1290994.5793\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - Best model with score 44.9956\n", + "scGPT - INFO - Saving model to save/dev_PBMC_10K-Jul03-15-53\n", + "scGPT - INFO - Evaluating cls cell embeddings\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 188/188 [00:54<00:00, 3.48it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NMI...\n", + "ARI...\n", + "Silhouette score...\n", + "PC regression...\n", + "Graph connectivity...\n", + "scGPT - INFO - 0\n", + "NMI_cluster/label 0.855717\n", + "ARI_cluster/label 0.884772\n", + "ASW_label 0.726386\n", + "ASW_label/batch 0.949340\n", + "PCR_batch 0.525635\n", + "cell_cycle_conservation NaN\n", + "isolated_label_F1 NaN\n", + "isolated_label_silhouette NaN\n", + "graph_conn 0.939571\n", + "kBET NaN\n", + "iLISI NaN\n", + "cLISI NaN\n", + "hvg_overlap NaN\n", + "trajectory NaN\n", + "scGPT - INFO - Biological Conservation Metrics: \n", + "ASW (cell-type): 0.7264, graph cLISI: nan, isolated label silhouette: nan, \n", + "Batch Effect Removal Metrics: \n", + "PCR_batch: 0.5256, ASW (batch): 0.9493, graph connectivity: 0.9396, graph iLISI: nan\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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t9Wy8yybgJddMfmH0SSn2+jY28WfK/WN2YJc9tl+kzLRUSm2vn1Zq/58/tlpq61LXOX4Hyp+wi/vbE6W2nnbb1tQ/iZbX5DRLmq23vO6I1KCj/g6Ub5Byf3v9Q7dJKVWJzZdek6kmtla2JvADm4Ox7/Faxnbn5fvU0aQ7nWhWiJ0lAdmuu207z9+ecv/DEs+lfrg03c527mH7/cZSm/F3ZN65N1PuX6+UqrfVknunsj6/qVL7h12slGqMl10f4B+t9cRtrJvaUnepUmqrv8aVUicDB/2Leu0qAy+ZZ6kSGdxvSFlU8j29I4al3L9RKZWxjf2dyuYv5QUUD2b/lT1plKXWej6bg/pMNmexL81VeDN+APy0i+lpUlvXtnmJPpHW5dyURTt97hOuZPP/2OitdZFQStXCC8ZaJBZ9B5y8vcubu0IplU3x87HFRO9C7HY7k7xsf75Reqb+W0spp/D6LyXLbJGpH/Dh9YnReCOejihlOwZef6WS+wxupX5rEs9vZBsJN1PKf5CyzclsO1N/kFISRVI8MWyp9Uope2RK2fBufq3K/PymrJeFFwBrvMtoD6Sse90O1G1HM/UvLVGvPrvhPB1ZYh82pcy/mjifb6eUG8vOZ+ofnVLmU0qf2eEQis+puUuZ+ve0G97MFcljK6CURMl4LWMFKeW2mkyZ4tnrX99KmdS5LDVeyovSXkM/8FZKuQ1ArVLKhYBO2zlOhReMxVO2d8FWytbC6yOXLPc120g0vZ39tsX7cVFhO+Xq443aTO5zCv/RXLtyk1vqTfqQ7ZpxeP0qHlVKnYYX3CzDG9Z/PtAxUc4BLtMlOqJqreNKqf8B/fH6841USr2DN+3OWrz+RefhjS5ajJcF+zi27Vu8hI4ZeDl13sCbpih5OXOW1jo1jcLleL9Ek7fJSqmP8S5nrsBLTdEY78vjcOBWvC/i3SaR8+iH5GO9k60Ru+n8Jre9QSn1Ed78jrXxzgtAjNJzj5V0FV6m9CqJfU5VSr2O92UQwPsi7o33xfg+m1sqdvco1h/w+re9p5S6EPgSL8hviNdfLtkpvgAv2Npm2pdtuAAvKKuE19E8efzT8N5zR+DNiZpMvvsF3tyK+wyt9Y9KqafxWhzTge+VUm/hdVwH7/8t+R4Abz7JX3dxnyOVUm/jnVvw5mc9RSn1Pl4KFgPvc+ACivepvF1rvbSUTZ4APKiUmo13WXES3qXIGF4fsRZ4Ob5SLwe+qbV+p+SGlFJZeO+/ZJ/BJXhB5gk7kIXiF71liplKeKluHlRK/YA3wnoO3mVbP97ndGe8FCDJQSprgQv11vt/CrH7lHdEuLfdKDE9EV5n+DkU/9WZetsInLeN7fnw5nrb2voa74OyLcV/AQe3sr0WbG4VKu0WLmWdSniJYbdVh+TthlLWH7W9eqWUPXJbdSmljN7F16tMz2+JbR9VyrY+/Bd1a4t3GW5r9YriBW6XpyzruRve06nn+3W8YGjFNuq1CjhqG9vrk1K2zzbKtcTLMba999yr/AfznpbHDa/16HE2z/VY2s3Bm3Nxi5asEttKff++vo1yFt40Qe52zrvGaxnru41tjdqBbaRuK7S146D0qw87ejtyO+/rHbn9ArQo7/eE3Pbfm7SQ7SKt9WSl1CF405CciferMh0vI/3XeL9q87axflwpdTpe+oRL8C7TVMCbW3ImXqD0ktZ6/Y7kKtRaT1FKtQFuwgsYgnh9VLbaX1BrvRboqZQ6DK/F5wi8X48V8ALKPLz+I5+zax2Kd1Rqn6Jd6lxb1ue3hFF45yaYsuz1f1G3cUqpg4Dr8d47jfF+uS/CG132rNZ6olIqlLLaqi23VLa01j8l3kPX4bVuNMDr+5OH9x54XGu9y52eE/87LfFagc7Ae22q4wWii/BG1b2itf5rV/e1p9Jaa+AWpdRQvMD7KDYnzl2E1+r0stb6nzLcpw3coJQagtfq2Q2vj2U2XmCyGq/7wnd4gd2ybWzuLLyBKV3xfmAciJezzIcXgC0DJuK9n4fp4jn7dref8AZdHZ342xzv3GbhXT7Nx0u6/BfenMTSb0yUK+V9Hgix51BKPczmwRDXaq2fK8/6lDel1CfA6XhfllUSAbQQQoh9iARkYo+jlBqH149pGtBK78f9ORKjN6fiXWb6R2vdoZyrJIQQYjeQtBdij5IY8p7Myn3HvhyMKaW6JVILbO35Rnj935JdC579TyomhBDiPyd9yMSe5li8js4/aK2/KO/K7GaPAo2VUsOBf/BGerpATbw+OT3ZPMLuO/5F/zQhhBB7F7lkKUQ5UUr9ARy6A0U/BXrrEmlTEoMVdsUIvRuTbQohhNhxEpAJUU4SIxnPBLrgJaesijcd1wa81rJfgbe01j9vZf1d/ec9cFsjgIUQQvx3JCATYi8lAZkQQuw7JCATQgghhChnMspSCCGEEKKcSUAmhBBCCFHOJCATQgghhChnEpAJIYQQQpQzCciEEEIIIcqZBGRCCCGEEOVMAjIhhBBCiHImAZkQQgghRDmTgEwIIYQQopxJQCaEEEIIUc4kIBNCCCGEKGcSkAkhhBBClDMJyIQQQgghypkEZEIIIYQQ5UwCMiGEEEKIciYBmRBCCCFEOZOATAghhBCinElAJoQQQghRziQgE0IIIYQoZxKQCSGEEEKUMwnIhBBCCCHKmQRkQgghhBDlTAIyIYQQQohyJgGZEEIIIUQ5k4BMiP1UMBQxgqGIKu96CCGEAKW1Lu86CCH+Q8sHNej8mH32O/PdGsENpMUm6kbH5uXm/FyyXDAU6QS0AT7Jy81Z+d/XVAgh9h9WeVdACPEfCmcfUw014iHfS8mWscA98d4fQU6N1GLBUKRTgNhvLVWemadr3Qo0/+8rK4QQ+w8JyITYv1xmKF3sMuXxxl8VAYKhSDowBDjZxDG/8A8wmxqLcLRqNmbgo5e0u++f18qjwkIIsT+QPmRC7A/C2XcSzp4LnJe6WGtYpKu9mnh4K3AxULUuKyo1NRYBYCpNXbXilUcHXP5HMBQx/9N6CyHEfkJayITY14WzDwJySy6OaXPmo/Y5w190Tjnv1lCkBfB78rnLrK+Kla1p5KvbjA8OBUZDTvvdXWUhhNjfSKd+IfZ14ewGWjNLKSytQSUuWEa1OaVZ9M2DQKkmaiE25s9zde1GDdXimt/6bzdNteVng6MVNubGgLKPJJz/9398JEIIsc+SgEyI/cAd/W+d0s/37kGV1cZiyx+Mn08aMW72fURMm1wRv42KbOR//meKlUsN5ADmudWXdo899QnwB/BXXm7O1N1/FEIIse+SgEyIfVgwFOmmcC+91Pj6koH+d7Z4fpWuwBpdgcbGYgCG2kcRVEvpYm47vvrC7sQqKtHOmMkb9vHuR+4Rp+Xl5ny5Ww5CCCH2AxKQCbGPCoYiFfzEl/wQuCWzrlpVapnlbjZLqaJbG3OVoxVjnMZ0tGYWPV+yZWy9TuN7p62epeuqW30fAWBrgw7R539eS9YDebk53+zWgxJCiH2UjLIUYt/V6RA1q9RgzE38Dqth5NPamKtW6wzbVJr25sxi5ZLBmNYw363OdbEbaGfMLgrGAOJYFOI7HPg6GIrcv7sORggh9mUSkAmx7+o4R9dik/YVWzjOORCjxIRJVVSBBWAoL/gqSSm4In4rWikOMFYUey5AjPbGjOTDngCEs2sQzq5dJkchhBD7AQnIhNh3DV9BZf50Dyq28C3nWMa7BwKlB18aeCB2Hn1id6wa7rQvmOQ24KH4eUzX9ZngNmSdTt+QWt5QcJhR1OfsdcLZPYGFwELC2X3K+qCEEGJfJAGZEPuovNycscB0H06x5YaCihQAXsuXWyIoMxRc4fuaUW7bqlfGb218cuzBQ4c4py4AVq2lwrAzo+HZcW0UW6u3OaIgneghwNfjnIavAj68z5dLdtfxCSHEvkQCMiH2bUf85TZ7OXXBVLc+6SpW9NhQ4JQIypbqKgAxIB0Ym5ebUx+YGiB2Tl8r0ma5rlTsomclVZAxNe2SmY9Yz0famnMqpTw1siwPRggh9lUSkAmxD8vLzVl+k+/jXFejAbRGN1YLVytcN7WcmQivNmkr9p1zyLo+sTsA/MBsYGEwFKkDdDvf/J6zrZ+oa6ymUPtYr9OJaxMgTDh/4zHmmOrJba7TaRuBGoSzQ4Szs/6L4xVCiL2VBGRC7OOChUO7j3abKwAbpR73PV+lpso3Sus/lq5s/2R9oH8V2amLawCnAO9sIlC0cA2ZLNZV8CmHv90mt7UNvXcoUDT8sqIqzASuBR5co7PeCYYiXXbH8QkhxL5AAjIh9mXh7MzXfQ/1a2946Sx8SheNsEzNLxbVm6e1XakrppWypUuBRV86hzUd7TYdOsOtS03yaZaYgLyDMTOrqlr3xyHRF87doNPWllx5ow6cUpGNvwZDkafK6tCEEGJfIpOLC7Fvu/lIc3zj5IMCbWECAWWzSZvMdWuBMhhmd6eJsYQFujrvOMekrj8JaAp0AjptJP2fTsaMgpI7WelWYJmuBCjrmviNS+63Xq1UT60oCvrqqlVqXKAvIfvy8yHnxt13uEIIsXeSFjIh9m1FQyzzdQZj3KYElA1AunJoYS6ihbGAsP9tprj1ed45FXfzx8JU4FggnlzwuW9Ab625vOROqhnrGWS9SQaF/OS2+efw2FOXPmSfOyH5vFJgKM3t1rCijQdDEfn8EUKIBPlAFGIfdmPs2le+djpO3agDOlsV0M2cstWyfazhZLMhDpwHnA/kAIXAVcCXF5rfvtHanHtystXL0RTlMwM40/yF8YEr6G0OvwAoCPnevxUoNnigusofARAMRcJALBiKjAmGIlXL7oiFEGLvJAGZEPuwMbrx47+7LQ4CinqMOVqVmhC2ibGYx31DfEAIeBeYAiwA3gLGD/a99kNq+Z/c1phoNmkfrvZawHzKYZD1ljpYzTkrrs3uJD5jXI0DPANcEAxF/MDdgAkcApxb9kcuhBB7FwnIhNiHvecffNq9vjdIJ8pancl4tyFj3cZFfbviuvgcSs3VfIC2iYdpQDJdxeVtC188YKTTVke1xSbto7sxgYONPNJVnE+czQMoLeVyvPFX/YvioSartbf6C84pbrBw6I2E83Vebk4MSM61pIHJu+PYhRBibyKd+oXYV4Wz0+oqL6AyFFRiI5XUHCa5DYqK/O62pIsxBUt5VxZrqdV86evn/ua2NHpav7JKV+Ty+K0s1DVGriXr3ohzmOphjgOKT7s0wu1AF3cqtY01OFpxve+zTq2duZ26Rp+mIgUso8p6IAPYEAxFgniXQtcAD+bl5vyYqG8GUJVw/oLdfWqEEGJPo3Rp1y6EEPuGcPYDrlb9CvGRkcjO/63Tjjxdi0L8PGufxvHGXzzlf67YalpvTouxTFf65wOn+2uP2uc+09mYzLv++4uVWeJWxq9sqqr1iUuXm7fTqvAlqqv89aeav2VNcBtFv3cPOQE4B7gmUWQS0L+xWvTIR/5BwWxVEACeIZx//e48LUIIsaeRgEyIfVgwFFEW9rqe5i9ZudZL2JhcHb+J7912RWUuM7/iLuvtYnnJStIaPcJtr56In8mBxjJ6myPoYk7d+gp4c2S2jr7MD4FbdXWVr1yt6Bu/5bs/3eZN15NRP9Gt7c90Cjs+4nvBONn8M7mqDfgJ58uHkxBivyEBmRD7uNsH3H7VNebnj6erqDUo1ts3UnfATumtMDpwNTVU/g5v71OnizvEPtX4xh8qCuJ+clpxiDGLP9zmrNcZdDBm2F87Ha13nB78lHZr0bpT3forDzLmV8tza2DisIk0V+EaTYwlqbsYSTi/WDI0IYTY10lAJsS+Lpz9NnABeJcZ15LFJbHbOckcvbqhWlzlaGNsscuMO+KJ+Bks0tW41vqc8fpA7oxfSRT/6cApDdXif77133GXqdw6C3U1PrO70tsawWQ3SOdSWtVSL48mXE44/5WdPFohhNgrSad+IfZ985N3lILKbKC/NZRO5vQqW1vB1kZRR3+AhW5V6qpVRYHTzb6PaV84hCXxqtzre51xgb5Ouoo1IZx/OeHsLkAdgHpqJd+67Xkkei4WNqONa9ZUURsqO1phKl1UpxJ8ZXPYQgix95C0F0Ls+8LLdKXwNLdeUdb+dTpjq4Xnu9XYQKBY4/nbzjHODfFrcBPLbG3wqG8Ib/kfpJGxhHQVM4GHCGf7gHGJG65mXJ6ueScwwcb6pED7b9qgA+5KXYG1bnrR9le7mSx0q7FUV/4EeLXMjlwIIfYScslSiP1E59AbLQ83Jo7ubo7PCBCnuzEOX6KVaqMOENU+PUvXUR2NGVu0Wv3pNvt1pNOuS3/fu9u6uDmXcH5DAMLZAaARMJtwfrSoRDh7FVAFvPkvqxnrAe+yZSGWm67smwjn/6+sjlkIIfYW0kImxH5iCdU2ZKnCjBxzNMeYY1mjKxQ9l6mi9I3fon5xDy51tOVHzhFda6k1WzyzRmcCMMENbgRuJZydDUA4P0o4f0qJYKx2ofalJR9upOguSkG6sg1H89Qpoafb7PrRCiHE3kUCMiH2H/MPUMvHJB/ElY+Zbl0AZru1KcRPX+urosIr3AoUaovFugqj3ea8ZOcwxm3MGp1Z8I/bxH3ePoVO0efoUPgsldmogY+BMYSzKxXbazg7g3B2f2Bemopn5OsM8tyaXBTrxydO52JJYE2F+iwwcNyCuxstXnB34/a77UwIIcQeRi5ZCrE/CWenr9fpL01yg+c9ap9jTtUNaKYWMEPX06eYv6tc38tFRaPaJKC8bmfv2D0YYF8GMA0YAHyULNdS5REJ9E/dy9GE839I7M8Cfgc6JJ+c4B7IqbH7ARa1VrPrfh4YWGpVN+qAkxN7MGPUg5fFyuLQhRBiTyYtZELsT8L5myrcs/TC8+MDO/6jm+kzzJ+5yfqIk8w/1S9uK1boioDXpysZjAHM0zUAJgB/4H1u/H2AWkZP42fW6zTW6MyFiaKFwFDC2eclHtciJRgD2KDTvwDeAOou0NUp0P5Sq5pOzFyiq3xaRkcuhBB7NEl7IcT+yXeQmqcG+14D4HBjAj+6bXgofh7T9QG847+fihQCXnB2tvkjXzqdWy6mWmugd29zxDF3WW9HAsrOWKszNy3RVbpUVhs/BdrhBWFPA+8Bi4EfgKOAZUDfXvG7fgVWAKyhIqWNErC1wRP2mUTxd9qtZ0EIIfYQ0kImxP5pmoW9OPlAAUeZ48n1vcwaKnBH7MqigkpBE2MxfazhZmKR2VwtaBdQdgZAJbUx/SBjQVO8YCypMuHswYTzXeA84HtgAbAKKEjcAJioDyxWsaluPZpHX+dZpyfAZ2V3yEIIseeSgEyI/VBebs66ibpR62+ddi9v1P7xyZGVlnKpRv4X3+hDGWofVZR3DGCBrp68O+s88/txeHNOAowG/gHWpOzCAgYQzm4B3AwcjXfp8uW83JxNwNfJgpfGbqdf/DKujt3AVbGb5l0c6/eIjfUNcEVebs5lZX7wQgixB5JLlkLsvw66In7b+UDmNeanbg9zrPGLe/CscbrJpcCK/vYVPGyfy92+t3/NIPrPW86xl+L1EbtwE4H7syi0AFbqima1exasJZx9NHAVcAXej70osBZYn7LPdYm/DwE9gEobyPjzXafHrcD0vNycVf/FgQshxJ5GRlkKsZ8KhiJfAjmlPFURL5BKtqA7eEHVycBvebk5+ouBx489xfyjLcAwu/uKO+wrT8ILshb/Grj+s7pq1ZHAx4Tzv08kiR2IN53SYML5cxL7DwD+vNyc1IBNCCH2SxKQCbGfCoYiE4BWJRb/lJeb0z0YitiAWeK5eXm5OUGAJqHPBvY0f77XxOUj54hIDN+BQItEufvycnPu3q2VF0KIfYxcshRi//UJxQOyfCAQDEVa4fUHq1aifNHjONaDw5yjNuJNg/Q4MDKlnOQNE0KIf0layITYjwVDkduAI/AuU3ZPLP4OGAwMAzITN4BJebk5JVvUkttpDtyDl+aiX15uTuHurLcQQuxrJCATQhAMRd4Dzk08HJGXm3N8YnlV4AWgBnBLXm7O38FQ5GrgeuBn4Jq83ByntG0KIYTYcXLJUggBkJVyv6jvWGLU41kAwVAkPRiKXAk8g9fh/yDgG7xLn0IIIXaB5CETYn8Xzm54v/VKlQ5qGvXVMnqbI2zC2ZmllPwYeJ7inxur/5tKCiHEvk0uWQqxPwtnpwGzgTquVtrBcHzKsaa6BxScGHuoU15aryl4oyeXBguHzgayE2uOBZ7Oy815vXwqLoQQ+xZpIRNi/1YJLz8YhtLKpxwL4CBjQUY2Gx4BXgImAbOPNsa8nVhnCXCeBGNCCFF2JCATYn8Wzl8KvF5y8ZfOYVRV6xpozUWJRdmv+h9dhJfmIpiXmzPjP6ylEELs86RTvxD7udvjfT98wHqlj085OFrRJ34HNibf+O9skZzjEtDAyLzcnDVb35IQQoidJQGZEPu5D5wjj1qtK3CSOZrvnUP42W3DAOtt/KpYNguFd+lSCCHEbiABmRBiwUi3PSPd9kULvnPac5E5goCyk4veJZxfUC61E0KI/YD0IRNCrCq54E99EEdGn+Dc6F3LgcbABf99tYQQYv8hLWRCiD9LPF4FVF1CVZboqvcTzp9dHpUSQoj9ibSQCbGfy8vNmQn8lXgYBZanPB38zyskhBD7IQnIhBDk5eZ0Alri5SRbmfLUhvKpkRBC7F8kU78QophgKBIEBgErgEF5uTmbyrdGQgix75OATAghhBCinMklSyGEEEKIciYBmRBCCCFEOZO0F0IIsYuCocilwI3AH8A1ebk5znZWEUKIYqSFTAghdkEwFKkIvAi0rsK6vo9Zzz1BOLtxeddLCLF3kYBMCCF2TQzYeABL+TtwNWdav1yvNTO/HtjjtvKumBBi7yEBmRBC7IK83JxC4KQXfY8vNpQ3al0paKtm5QZDEVW+tRNC7C0kIBNCiF2Ul5vzaxNj0eTUZbXUWrOv8emLhLMPIZwtgZkQYpskIBNCiDJgKX2TrSlK7KgU9PMNuxwYg9fHTAghtkoCMiGEKAvh/CkOVnC1m1m0SCXaxWxt9CqnWgkh9hISkAkhRBkJ3LNq/jXxG38suXyE2y4tGIpklUedhBB7BwnIhBCiDP2hDz5phlO72Jx0xxhjjKZqwRHlVSchxJ5PAjIhhChDebk5BU86Z61OnSbYr1xut95/oPxqJYTY00lAJoQQZSgYimR95XZeUqiLT4SSzUZfOVVJCLEXkIBMCCHK1pCuxsSDA8ouWrBWZ/Kec+QV5VgnIcQeTuayFEKIsnVgczUfIzHCMq4N2keH4GBFHy/fegkh9mDSQiaEEGVrQGO1qCCuDbSGp+0zcLA0sLK8KyaE2HNJC5kQQpShvLRecSAj+XiM2xhAAQvKq05CiD2ftJAJIUTZqpH64DHf8wBuXm6OWz7VEULsDSQgE0KIsjUipo2ipBe1jLVYxDfJRONCiG2RgEwIIcpSOL9goa5ebJGFmwl0L58KCSH2BhKQCSFEGbqw/4ODK1BQ1BqmNRTi18CKcqyWEGIPJ536hRCijARDkVPe8306oLqxvmjZN057QF2Zl5szufxqJoTY00kLmRBClJ0tJhCfqBsB/PXfV0UIsTeRgEwIIcrIYcaUYXPdWitT57FcpSsC5JdXnYQQewe5ZCmEEGXkPf/gMFAtddliXXUFsLhcKiSE2GtIC5kQQpSdjqkPXA1TdYNOebk50fKqkBBi7yABmRBClJEv7UMnpV6uXEMFVlJpXvnVSAixt5CATAghysgw98i+KiX96+2xKwByyqs+Qoi9hwRkQghRRv5wW6jfnBYARJxO/KDbATQt10oJIfYK0qlfCCHKQDAUqQy+rF7xuwjEY0TxJ59qV571EkLsHaSFTAghykY+MBkgit9JWf5L+VRHCLE3UTq1B6oQQoid5rWScRIwHmgErM/Lzfm+fGslhNgbSEAmhBBCCFHOpA+ZEELsokTL2N2AA9ybl5uzrpyrJITYy0hAJoQQu+5p4MLE/YpA33KsixBiLySd+oUQYtdV2cp9IYTYIdJCJoQQu+52oDLeJct+5VwXIcReSDr1CyGEEDshGIpYebk5dnnXQ+wbJCATQggh/qVgKJIL3IGX4uTovNycNeVcJbGXk4BMCCGE+BeCoUgmsCFl0RPAAuDdvNycpeVTK7G3k4BMCCHEXi8YijQDzgR+zkvr9QfeHKJzCecX7MS22gO3AVOBwXm5OS4A4ezKwPta0/im+LXZn7ldtxjA0VAtvuP7wG2jgbGE8yX9idhhEpAJIYTYqyVarOYC1U0ce1zginEVVGEHYB5wDzCFcP6fAISz7wQGAi5wH+H8R4KhSHOg3Ru+3DndzQmzg4VDx2VRUOcR3wt0NKbPq6bWXU44/7spd7d6toUx/xoAWxtaodVCXZ2zYmFWUIlsNvBVoB911SqAaUAHwvkb//MTIvZKkvZCCCHE3q4KUB3gYd8LViIYA2gAvAr8QTj7yp79nrwKyAUygQrAwwMG3NQF+Ocu6613upsTfteaOYeomRUuNkdwovkX1dS6Blrz6YMDrnr9VefEa5I7tJSrTKVpYCznVut9AFoY85LBGEDzW2JX3RkMRRr/FydA7P0kIBNCCLFXy8vNWXCH9e7UMYG+nGmWPpf7Wp15xgxd77mNOpC6eOOvbqtmQEbPxHpKkXWy+buN17qWXJZ5mzXs4j/dZtwVv4QxTqNi2zbQsyqyQbdX01muswFYobP5wW07EJgZDEVGBUORYjsWoiTJQyaEEGKvEwxFsoGHgIKIv9+6q815Bym1+XlHK0zldcmxtWK023z4RtKPOz92FzdbH9LOmPlrtiq4PU/XmgbcPsptc9CZ5i/Etcl3bvvKpnYfAE4GugP4lENFNvG2cywbdDrtzOcS+4EfnNYjfwzcnF5Zbayb3H91lc/t1vsMsi/hInNE9x7GmCGEe11LOH9Tov4nAD7gy7zcHOk7JKQPmRBCiL1LMBTxPWC9vOYEc3TmWLcxPcxxxZ5/zz6SD5zuVGY9F1rfMVvX0WeaP511ffyGw352Wx8BfJCXm/NYyvasNKJt2xkzf1uiq/qW6sru+EDflX5l14hrk8W6Cl+6nXnEPg8Ahctv/uupbXiZLlyNNhSKEr512jFX16avFQGgQAcK83RNOxS/YsQE3eiMRLEH8nJzBuyG0yT2MhKQCSGE2HuEs31f2x1OPdH6+8PSnra1onH0HYD4J/67zUOMWUVdcxyt4i87J1155eChr5VcLxiKPII3spL6apn7U+DmovUujd3G9267xCPtdjEmT3vF90jldBWvXVodYtokn0yeiJ8V7+d716qgNhUL1r5yOvKOcwxz3DosoeqUvNyclv/2NIh9jwRkQggh9g7h7IOAH7SmRhxT+ZVDVFuMdpuv6mJMrmoqzQ9OG26KX0s+Wfzkv7GwvrEiLXUT98d78ZJz8kd5uTlnJbZpjnEb/2+Ort37a6dTVl8rQjqFrNcZdDWnMMZt7PaKDTAKCWhg+k/+G6fUN1acAcTxpspKs7XhWsrdok92VFsE1JaJ/F0NhoINOo3TY/fpWbru43m5ObfthjMm9iISkAkhhNgjDRpw/Z3N1YLwZB1c9rZz7OGf+Ac+cYgx+0yATdrPV05HRrrtx37lHnZQLValXW19zsXWt2zSfi6J30FdVnC99SnpKmrXVGutQu3jN7clA+OX2L/m9vERzq4BjANqAxRqH2kqDoDWcHu8b+xD90hfZdb/tIYKDwOFcwK93jUUNRJVHAW0AqoCrNUZVFL/Lu3ZQ/FzWENFGqrFJ/Yd/O43u37WxN5KAjIhhBB7nnB22jqdsbGiKjAAboxd+1Uh/hNe8D9RrCUqqn22jXLGu40DtdVqfaCxVAGMclpxuDEJU2m+cA7b4GBknG7+ZgD87Bzs9I73//Jz/11jWxtzwsltpQ4EAPjK6UAFCjncnMQfbvPohbH+gUHWm9He1ncBrXGUwkyty9Px01lINU43frO7mFO2GDSnNaQOPNio/cSxqKQK0JqNStGCcP78sjh9Yu8jaS+EEEKUv3D2GYSz3yWcfUFiSSCLTUXfUfXVMme429G4L3ZBsdUCKm5lqligizmFOmrlnwCuVriYRcFVD2NMrFAH3OQ6LYx5ZjqFp71mH3+tq9EAcW26D9q9Zi1wq80D0Bo9za3P4eYkAA4zpgVaqbkMtC8NnB69Z/4sXSee3N5yna3ftnvoZ53TGeYcTa/4XXG81jOievPXbDIY0xpWuRXIVLGiFjWlyNykfWNv6t/vumAossUAAbHvk4BMCCFE+QpnNwCGAee5Wr19fL/nC4KFQ1uvpOIda3TWqqnuAb8dZ/59BvDkW+5xM1fqiqVeFxyrG7/+pXOoBk19tYy4NgsB1pH55sP2uZadCI6qqvU8aT3L/b5XqxsKla8zOCL6uHrZyZl6eOzp2idFH7Cvid9414fOkZH1Oj0OsEZnMU/XJECMmbpe/fvtC9PW6CwW6mpcFOun7rIvU1H8pFNY+LX/zlWu5nCAgHJZ6FYtVk8NFLBlWrJ0Fa9ygfnd/xTu3WV2bsVeQy5ZCiGE2H3C2c2Ag4ERhPPXJxcHQxHffdarH7YxZnf+y20Wvcz6pl7yuRU6m3SizNW1rmh17/iXS9nmQcB3QM0ZTp1PhutOZ81w6/GD23b2pLTLi7K2btK+m9NV/HPg0CtjNz/0uO+5AzJVFIClbiVqGWuLNhnVFnfGr+BT9/DkoqVArQPUMnoYYxePdA+pU411vOF/iEwKySeDAHFuj1/JV+5hRdu5xPx65iDfW01Sq/uT0yp6hDkxAN5l0becYznKGEcDY/kWlzEBvnI6zTvpvm+DO36Sxb5AAjIhhBC7Rzi7NfAnkLZSVyx81z5KfeZ2/d93D155+yX9Bz/6mv+RW5NFh8RPprM5BROXVkYeAHPdWhsPvHd61la2rQAzWDj0cmAIgMLVcwIXzlGKRnhzVXYBLk/c+MVpSTdzMgAv2yfQxphDR2NG0SYLtY/m0TeSD9cAlS1szjO+d7sZk4y6xsqiuiXNc2tEu8eeTDZ3TexsTKr9ju+BaobyLk3GMZceF3u4ZhO1SPUyR3KUOX6LQ1nvBqhgRFPrsS7tnpXZ2z2/Yp8imfqFEELsLj2BNIBqal3a9b7P6K2/u+2Zu34dcqk5u0dq69CHbncecnpxuRkpCnpm6rrugUAwFDHy0no1SWzPAp4mnL8OsAlF3gMuzjH+aNfVmPS1UtyCl2F/DOH8Pwlnv5isTDdzMsOd9rF0Yjc/YF+w0MW89FPfXd3amnOqAgSI81vgWi6J3cl0Xb8ywKO+50kOBtiKsT2Nn/tr1LM/uq1bNWc+RuKYXFTBQdHXL3AwR+bp2lxpfVm00nqdRgVVCECWihbb4DR9wJ9t//25Fns5CciEEELsLseWXFBJbeRC87sG2WxskwzGPnc669m6rgJ42TkJG3NqBtHsT52uffuGIiNutYYV205cm+2bhCI3AMvycnPWEs5+AzgMOA1wCeefkVL8MeA1En2mjzP+sZRiwonO6GjE7XxK73g/4yM1iEZqCabS1GENl5rfcKfdF4AOKS1oXl0P41unQ7yCKljSWs2ZONZt/OI9vte/rKg2ZazUFVmlKxaVNZUOPO17ZtS18RufBI6LOIfVaadmVnIw+Mg+nD6+b4HilywfjZ+14RnnjPmEIt/iZfH/4d+edLF3kk79Qgghyk44O8sdVOkHe1DlgkLtq5NcnOwdk6/TF3WLPnXRQl19A3gjIt+yj02OPySD6Jrw/f9rccf9L9ZdTDXnHPOHY68yPy+2i5m67knAfGB8MBSpgtdHLalV8frkv/l4/MxHnMRoSqXYBPz8pO+5SHXWGOvJ5LjYo/zmbk6WP0MXTUnJa/bxuBocbTDLrbX07vglzhduF99Q55j6IbtvzgJqDKuoNmUAVFPraKIWpu59Yc59I9y83JybgW/+cZtUmqdrkqdrkqZiuKX0GNqg08e96Xvwsl8D1x9ztjnqi+2fcLGvkIBMCCFEWTrbUPpIS7npaSp+4Ein7bx37aNYrKuiNWQSrdPZmNLn1Nh9Fe6K9+Gx+Fk0NhZxAEuZELiCKWmXVl49qO4IgPZq+oIHrFfwKS9jhdawUldgsH2hP7Gv5sBReB38k+FNset/wVCk69POmXf2jN2nHoyfHwMyAXzKqX6h9d2bPuJrTzF+K0pv8bfThFeck5Kr61ecnAntoi+4LaOvxo+JPV5rLRWK5R4b5zYOTHEbFD1O9h1LaEA4OzlK4Kg7rfc40FhKE2Mx51k/Fl3aBPjVacEz8VPpZk7qeoQ5kbpqFWHrjcxgKPLETr0KYq8jlyyFEEKUpVnJO0t0Fe6M913dxxreoK6xCgALV+X6XmK9zmCurslR5gQACq3NWfIrs+HYVYPq3HOQcY5/PRlUZgMA98QvdMbppubTvv+hgeviN9oTdcMeUe2bEVDxZHjTknC2j3B+nHD2ob8Fqj7+jdMBF4M0Yv6YNsb5ldsWWHyj9cmGG61PKkX15q/CmmotbJ4nXAGt11IBEg0YFjb25q/ODQWkjT0tdm+TZ31PrT3OHNN8jc6gAoVYFKU9S955aRmVnyt5sta4maSrGIcYs+hkTMOn3KKdL9VVAG4MhiJ35OXmxEuuK/YtMspSCCFEmRo44MZPO6mpp3U1J1FFbeQvpykdzRlblCuZGT8p2dm/UPu4Ld6XB32vUkFtYo5bi3ydwSHmHAAmuQ04OfYgLVXeV2/4c0+qptYx0jlkZY/7RlUHiA+qstSnnJolNv858ICtjZil3DEl93uv3ZvXnBN/wGt5K2aQ9QaXWMNZristeNU+odvzzqlfAS0DxIjiPzUvrdc/Bxe+/El3Y0Kns80fHR/2KxfEB1yLN9rz1iwKTr3CilCdtXQ2plBAGo3VolLnu/zcOYyH7fNYqGtMy8vNOWjHzrzYm0kLmRBCiDKVY/6pDzOmFj1O7Rj/k92SIywv9UQUk3Rto9TmCbdhcyf3NBWnvlpBBbUJgIbGUpyU+O0AtQKAJbpKdo/oo1RT+czWdSxCkXTg1DGBjCpVWE+qqLa6Hx/LnTXMf+8tNVhX7LlXneN5zTkRYDolArJsNnCJNRyAGmrtASHfe92fd05tfLv1Htdan5Pn1vzoqOhjz28g4+iIe9hJEfew+3uZI/v+5L/xoj/cFoF+9uVqAxk8YZ+dslXNtECfLc7fPLe6viV+tbLxxYEzt36mxb5E+pAJ8R8JhiJNg6FIg5THzYKhyFfBUGRYMBSpsa11hdibHKzmHpL62N18CZCvdOfCu+KXsEJXJEPZRcFXnq7J704LXrJPZKZb9wetcVbrLG61hhXbtpnS7+oP9yAGWW+4Y9Ku6vqlf4CTRQEXmCMrNVfzpgLvXRm72feT04qRdtuidQLKzj5MTb2lhtocjM11a/KFcxhP2WeBl3/sLSgWrbkbSGe+W917oJX7YPz8in7iI682vX73QWOZ73BjwvXAKUCsAgVN7rNepb6xIu0c60d1gvFXKWdKcWX8ZhztHZTWMNutSUW1SU0NXMoHvrCvszH5yWAo0igYilTe7okXezUJyIT4DwRDkZvxfnXPCYYityUWDwFOBM4G7iuvuglRloKhSPYsXbcoVYOj4X77fBa61ZYDL3zjdOz+tnNsQRqxYuutoSLnx+/ifrs3x8Yeuft958h3q6gNxQKwVPPd6twav5pLrOEGwAHGCvMd/4Pc73uVD/33NGivptHT/Jm/3Wa6v30ZG3QaAHFtxn51Dy4KrgCW6cr6+vgNrCPzW+BbwAROAF4GLgWudDBn9Inf+emD8fNXnBkLGy84pzxztfl5j3hifnFHK6a4DTjbHPX21ECfT7/wD9AFXgo2AFZ7/dDW4QV8Sb+OdZsUXbZVCipQSGW1AZ9y6GjO4GHrxWPx+uXlBUORdv/y5RB7EblkKcR/4/zEXwN4OBiK1Gmu5jW83vqElTqbR+1z0oOhyHAgADwPrAK+y8vNkU6eYq8RDEUygD9Pj93XrKfxc+woY5wTcQ9Nv9r6gnrGyhrA0ePSruwXLBz63VS3wamdzOkAbNBpDI5f+BmwDPgDOPRP96Aa53nzcxejNQyI9+ET9wg2kcYUt4FuYcxTroasokSrhbztf5B0b5CA2kgaZ8cG0d0Yz1i38e8LqNn9K/dQrjK8RK1tjdnxauTfvpLspxK7ORGolpeb83vRjsPZk+a4tTKPjj1+OkBjtZCbfR8VTUj5kH3OnFutYXUPM6YFlIKgWqYmO/VZoqvwjT6U3720GscAIwCuML/kRHN0xTft456b71a7qL6xMktrr9XvVOOPot0GVFFf/orAWUCxfm9i3yEBmRBlKBiKmPdbL99ynjnqzo2k6YtioXnjdOMKQK2UYgq4+WHfi7Q25gIQwG4Xsq9IJkLqnvgbbR16f9kZ5i8jpun6n773wO2R/+5IhNgpjYFmAJ+4h/s/cQ+nuZq3qq0xOzm7dpMFbvXuwLNDnR4ndTBmWIbSvO0cw1jd5DRgXCO1qPkqXfGIT9zDKYgFaGfMoCPTaWfNBrxWpMutbxgWO5p2agZVVb7Sif5nqYME0jcHMlRW65iqGzDVaQBeAtln3rKPvfhs88cKVdV60lTc/7b/gZ794pfTyZjGt277tDm6TlF6C2dQpZtMxRMNjaXcab076yH7/MYFOo2YNvErB0crYvizO5vTis0Y3tKcTws9n/fiRwMsBOYCD59k/PHAAN9QgFaH+GbVbRx9a3k3Y2LWYl2VmbrevJ/dVjXONX9MR2n9kn3yGKA94ADfl/HrJfYgEpAJUbaGtjTyzjGVS0UK6G2NqDYu3rjUginD4mltzM7ubExO/orGwuZu683AWeZP9TNU7PJC7bt80ID5q39y2wzUqJdHPXhZrNSNClG+pgN/N1ELO/QyRzJFN2C40+El4FSgha2NZafH7n2pEuur9TR/0Qt1Nd6xe7gvOKcaANeYn7a9wzeMAh3g4tidDHc7MU/X4srAV8V20tBYSgc1neutTxNpKjyljdiMaouX7JNSF60EZvya26fiykEDvwN6ADRTC9I/8N/jWso1btEfFAaUXWP+3TVuedU54YLuRquqyfQcZ5s/rn/IPt9eTDXrhvh1HGOM4Vu3PUt0laqlTRSuFBxr/KO/czvUA97Ny805dvWgvmuBZAqMDKDKj25bAKqwrsEUHeR1J41LrG/UrdYH836Itb0rhm9BXm7O5H/9ioi9hvQhE6Js5eTpzY1hea53P0AMP8XTCN0Wv5KfnFas0+m0MObXe8d3P4cbExYBnGf+wEXWd2QoL+5KU3HC1ptVfgjc+uyLvscWEs5OQ4g9TF5aL3dGoPc33/hDsUus4Tzie5FvA3f8DXQGepwYy31yFdnVLrGG092coOobK7jYGmEACxUul1tfrQfIUFHONH8uAH5KI/Z7aRntH/cP0Qt19aJnXI2rNRtsvflrbZWuQE7sQSefigCFicV1gaeDociyG+PXrNeaPGCRUnxoKdcACCi7gq2NMfWN5TeEfW9WXaUrYmsDRyt+clo5Zxo/WWcbo/if7xmON/9mta7IBN2Y1+zj3Zg2vUnFtZmsl37LPiYZpnUCqKI2vAy8vEn7J73mHP+Sg/kuQEO1mB8CtxAJDOBp3/9ob8ykqbHwjBlpF1eUYGzfJy1kQpStz/rFL+812Q1iY7JaV+BZ6wlOMr0RVst1NpfG72SyDjJN1+dTpwvdjImAd8mlgVpW9+etbDj5y7upsaj6CKf9ucfBG//B8Qjxb9zgV85dqQtqqrWVEhOBfz8zFFkNPLgyZb7HlTobH/YtkwKXXhlQdo/EYn2e9cOF5w3+9BOAV+76/ZODVN7pCk1ncxoAWRSqQfbFrKbCnKutL54xFEcBp1i4RcHQ/fEL7Fm6rgVQi1VpQWMZ/7hNiWPxgPVSjXPNUaenVDU6xm288mA1t9pIpx0nWn9VST7R2ZjC6dF76WxMtvv73u1whvUrMW3hUw4+NnGB9R1/xZtzr3Mx43QTnvY/gw8HYMJg+8K+k2j0EV4g+HBik8+6mt4/ua2t++0LDwYvN8cRxgSyVQFAsSz+gPwA2w9IQCZE2fphE2m9XnJO5gt/f1oZeaRexqip8hlgvc10fQD11XJ6mGMBLwfTWrIWfekcVhfgPecou42avaS6WntAvs6kjTGLoOHlXNIaBsb7vNo3FFmbl5vzWfkcphApwtnV8EYjdkhZagOfAZ8Szn4FqJOXRr9g4dCv3naOOSlAnLpqJa/Zx3ORMcKfEowB3EY4/5Pkg/vs3uOA0wPEeIQX7NZqzvr77AsrFxLgIfv8UVcPfvsJwtlFKWVWUvGjk6IPDl9DxRcBmqiFfOK/myxVyE9OKwbal9DLKj5n9wy3zqDxbqOqlYwNVFAFxf5v6xqrecT/QmFFClYpRV0Af0oy10Ltpy4rWER1o4mxeS7LmLbqv+qcNBRYAhybl5szlXB2C+AKQ8Hx5t90cqbxu9uyAsCv7sFs0AGyVLHZn+gfv/S1oaHIq8DbwCUy2GffJJn6hSgr4exaG3XgozU6q/Mwp7u6xfdxqcUK9eYpYlJpzfKc2AMjpujggUAYqAq8B3C8MZoX/E8CsMCtyg9uO44z/16j0I8NsU/9NOx7cwrhfPlnFmUnnN0V+Biva8tZhPN/3EbZ3/E6y7PErTy/uspf9Ypz0tAH7V43DbDe9l1hfZXMszcxWDi0HXA0ULm7Me7sF32PHx9QdgbeHJTpeKkhWhPOn5fcfDAUqd9ILfq5Muvr/a2bGYmpjWzASiM660Lzu+fv8r3zJtAbaAn8eEp0cGSibrgE8PU2R3Cf7/Wi6h5X+MCiEWn9N88gDqzQFaiuNieRXeFW0NWN9Wrz89m8ax+lb/B9WqztKhm45esMTozmYijNm74HqatWMdi+YMNbznFZiaJxYOMhambfTwKDXgKyY9rSPWKPqAV682QCg8w3uMQ3vOhxVPvoGbuHKTqYXNQqLzdn0lZfC7HXkj5kQuyiYChSOaff/15fr9PmZKpol3rGKnWL72O+cA4DYLFblRH2IfzlNGGBW5VASl+y1Dn0lKJGK2POOUBXoBvwdwO1lHut16inVnJL7Cqet0/hafsMLrK+pZZaU7kGaweHfW9OGu02i7cJvX/5f3vkYh/XD6gBVAMGpD4RDEXM1L94k3wD8K7To37j6NsrHrR79QbqbtDpqUmP3bzcHDsvN2dEXm7O+2/4H14WUHYW3ndReqLMn6nBGEBeWq8mIwO31/0wcK/xuDUkudiysHnPP7jxXb53HnW1moaXNuZS4I0vAnc9UE8tn/aW7wHON0eSrzMA+NrpyCJqfhHXxlrw8qTdGLuKqiUy+qcGY6t1BZ6yz2CWrqvuiV3ICrdCUblkK1q2KuBgYy4LdXV9dOxxmkVf5y3nOF/KJn1ApbG6yc3A4cAAB6PbAl0zMXJSc5/1Chdbw4t+WK3VmdGTYg+kBmPxlPMk9jESkAmxi04xfvv4Y/+giyuowqIPSlsr/WC818QOhUO4OH4HDY1ltDTmcYCxqugD3NGKttHnGe02A2CT9tFMLfBX9hKE9wcmvep7RF9kfctA39uYyiXXPp/ZevMP++S2OhnTzcZq0UvBUGRztkshdlY4uyGkDAOGqQDBUEQFQ5GhQbU4/vvAQ6PTAhfb3w/sviyqrZeSHdmnuPUBmmdRMP9R3/McYY5nhltnqq2NL750Dr0lucHL+t/71I9OqytL7voPt3n7YChSGAxFwsllBdp/F16yVk4xf+O3wHVE/P3ooKbR1vDSYRhKVwGuTdlUp3d8D8w93JxEC2MBCpdH42dxTfxGNpJ+wmdu19Ofss+wL4734zP3iNg6MlOPt5j/xU/nNmsYT/uf5QrfV1wfu57UwQNJs9w64LX0kWjFK61L6GS8PmO90lXsq1/813/1pb+/nh64mN7WSAy1eVqDN+1jAp2NKdEjjPHJRT7g22AoUquU7Yq9nARkQuyCYCiS0dGYflhAOUXL4toAlHrL/2ArC5tLzOE0NhYXjZhMWkk2j1sv0E7NZK3OIF3FudQazrO+p8H74E2rnHIJpWpiJpep+oCiqVaS1ugsEqM770lptRDiXwmGIlk5/Z55e5P2T8ebAigpGei3bKdmnD/Cf6fqbE7zp6k4R5vjaqwnI0cp8CmHS6zhDtD/18ANf55l/kR7YxZNjcUHfeN2POm6+A0jg6HIsweHPrjqId9LN3Q3J5oAEafTZGDYHLf2mNviV1fBa+kaFAxFKgRDkcNGOB0OT1bEQFNHraalMY+brY9Yp9NTA6mNQD7e5czHGhjLiybUrKgKudn6iFpeovy3zrrvyx+fsM866he31SPA8ffbF7wz0Q3yo9OKPNdr1FunM/jQOXzjbGpTSW0EoI5aTcj/PpZyWeJWYXWi5W2M24g8akPxDvj/4OUPiwFP4gWM1yX+tgSys9XGhw428lRA2aT2IFroVqW9MZPBvtcCb/ofYqjvPs4xvwd0NtDwX7ysYi8hnfqF2DUDRrjt03rrb4taq3zK+35opJZwl+9tNurNn8/z3Wp6sa4e30ja2ulunRrX+Lxcr5UoKCqTnDAZ4P74hVOvsz5tOkvXthqopXzjv4OKbMBUGq1hOZWowEY0UF8tZ5XOvhovz9Ldu/vAxT7prrpq5QXpaos0d+cCvYBF3YxJtl85Rd8dBTqAjTkFaAHQ1Zw8NM/s1RhvVGGRk80/zcfspczVta/yYf+QOun3GLdJy2vjNz0LPKVwf8FrXpoPFAAXPmSfbx5kzKeWWsVCXYOWyruieag5nd+d5gWtjTlZPmy+czsMO8kcfR9gBQuH1q9Kfub3gVuHZauCcwAMtHOx+U3OVb7ILXoQy/PSGEw4/w6AYIiqHzhH9gbwE6eGWssiXRWNMae3Obw23qVb1ujMola52sZqrotdzzJdmQm6IS4GPmxe8D3OEcYE/ZV76GU3xK838Vr3agKPAD/dHe/T9N5En7b5bg2zpTkfgIW6OrPd2u7hxiSjnrGKSnpD0TnqYk6lizmVA9TKaY/Z55Q2MabYy0kLmRC7Jv0XtzVLKBohX+xX7gadRlAtK3ocw6cuiPf3Xxa/vUbyF3dJP7sHgzd33SMfuUd0Oyr2+Lqfndacb42iubGQOl7XF5Ty8ixlqDhV1Ab6Wl8mN3F7MBQ5oCwPUuyjwtkG4exzCGef3C70bq2jjLHd5ri1mevWLFlyKXh5xsbqRpFYIq3ECrcC58YGcnT0se+BG4Cb8fpAhoErVrtZo5KX9pbpSizTlTFxnLt8bx9WyObuVccYf9HX/Pzp6YGLvp8TuFD9Ebh2UYDYEXm5OQ4wfwlV6Be/nJNjD3BhrP/Kqe7mt3e6imW1ib5Mq+irXBO/qWawcOhhwcKhHwPjV5H9fJvoy93W6fTbga+V4uKLrW8bAccpRXVXqycJZ/sB8nJzPsKbMmlyDB8LdXW09xXZqqFaWjSxdwGBonkwV+ss/nSb09GYzu+B63jF9wjdjXEcbY7DUq461fy9WlAtSa56EHA90OlN57hKd8avWPWaffz48+N38XT8NH5yWnFV7EZWUdEwEz/q1pNJyRxs11ufjszLzdlyVJDY60lAJsSuub8uKxYZie42fzrNuS1+FdPcuqx0K3KMOZZGxqKiwo2NJdxqfQDAe06PeIG2KDnQ+VBj2rKbrQ/qNVELbwfmAP3bGTOXlrbzSmz+BT1zc9+yNOCQMjo+sQ8r1L5ngPeBL4b575n/mv+Rrl8GBmBQ7E35F9CGcPa3wNrHrSFH+ROX6Ksb67nE/JqLzBF9xrmNOr5vd/8OKEo/8b17SKXTYvcxIH4pZ0TviReQ5l5rfvbdmeYvmamX8FsZ8+nve88KKDugFNRSa+pOT+uT3M6zj/mGrPw4EOYH/63u34GrznvQ7vXFMl2J9Tqdp+0zsLGI4gfvO+1z4HjwFgB1WkdfWUs4/yTC+e88bfdskdzvEqqoLoVPt/z77o4/rhhUf+JzvieDeJcSixnq9DCX60rY2mCIfZp9Q/w6fnJa8ZZ9LIX4ucP3PlXUBnqYYwmyFFsbNkBcG4UxXRR4tgXWJh+87xw11Y99xRXmVzNv8H3GEeZE3g8MpjIbcBNdEoY53ekfv/wxKGpCX4V36VPsgyTthRC7Ipxdc5pbb35zY2Hyw59zondxpvkz51pelgBHg5nS5esbpyNXxW8GGH22MWrNI/4Xj08+l/x3VMrri9Y5+gwrqfRPKzVHf+C/p0OairNKV6Bqom+Z1rCJgPOlc+g//ewr4g5mV2Aa0CUvN2fN7j58sXdbeHfD1fWMVZWBYnm3VussqqiiYD8MjKT0DurF0rhMcoOxALHHgmrpDSYuhiJzstuAM2NhCgl8CVzd0/j5gyf8Qw5L3cZGHSCzRO6tqLYaNIu+2eI049esx33PvWsqb0jyJu1/+qDo68uA+0EDiiONcXQ3xjPc7cgfbgtvYXEbgOy8tF46WPjO0hON0TVaGnl85Byx4URj9Pw7fO+3AIhpq7Bp9I18UDUVbv7rvof8XY3J6auoyDmxgSzVVXV9tXzEp/6BxyfrG4pfxq3Wh1RX+QDcGLuGi83hD7czZ98OqAnuge6psfsNvGmlWgH3AW2uMCO/9LfeuUupLZO+vmsfxfvOUYzTjQHCeWm9ngIqE86fu5WXUuwDpIVMiF3zQGowlufWZFhgcFEwBrCabKa7dYlpi+U6m7/dJk4tVs0ELn7E/+JJw+zuOjkkXymK9UU7xfwdoN1E3bDD4dGn6Bu9CZVojUt+gWaoqFlAWicHs4mPeNNpgYtfykvrFSacfeB/dRLE3icYimRO0wcURUGpczAOtY9mlNOG9+wjaV84RAN3lbIJAOamTBVWW63yHxt79LJzY3dnGopMgJbGPI4x/hkFfAR8uYqKRcGY1l5S5FedE8hza7JJWyxyqxYC5zeLvnkt8HVnY/IHyWDM1Yrr49cdBpwAjAfFgWoJL/ke4xJrOG/5HqQeyxWQB/yRUk01O3DBcUD+2MCV1Zfpyjxqn8tcXXvqKioUXf+M4lOgXgPGXGZ+/UJ3c2K6pVxqqrUMtN4mil/dYb1/fGrwWIFNPBffPP7hId+L+mAj7yISQWFLNU8ZuFcA3W61hj39lu+BO2+1hp2QTuHg0oIxrx5WMhgDODBYOPSuYOHQzlt7DcS+QQIyIXbBYrdK0+T9RW5VRrtFD7ETlx2qq3zecE6gXfR5Yvi4yzfU/CPt+gp5ab1WEs5377CvXB2KX7HFtrWGamqtbqQWKYAVVCLbKNBVEn3PUr9AjzP+poOaVuOPwHUnpqn4Y3j9eT7dDYcs9nLBUCQ9GIr8lEHhhnbGzMoln5/u1uVZ53T6xO8kZPdlFdkDtObo5PMlR/i+Zh+/fLzb0F2lK/Cwfd4qoMYsXZd1enO6rP6+oS7wGtCmcvF8X0736ONLv3EOpZ5aQbqyqaLWx7oWPrUWuAVgDZtzfv3iHsx3bodOeHm8WgI3VmVdzJe4hOpTDrm+lwGCeJN2A1CRjfNMpZ8GKlRWG9R9vtcK8JrXOg5zjkx/2j7dHe500BfH7gwAIaDdt277G1P7by3TXj/R7JRuAovcqgx1emCrzePj0pSt/MqplTxP/3NOVy7Gw5lsGtrXjFx1uDmJ661PaaYWENVbDoiOaYv/2T2TDxcDxwG3Au8EQ5ETtlhB7DMkIBNiZ4WzW1RXa7omH9ZRq6hGPrY20BostfnT/HbrPX4LXE89tTK5qNYQ++S+N/Tv39vEMb52OzHEPoW5bk3iWsWAOXHMTddaX6gR/jsZG7icJ33P8LfbVG3SPkqqY6zmCd+QTVXV+mNTFkuuIlGaI4DDa6tVVFEbA8mFtla/9Y9dQk7sQTYVb7gZ/5F7RAwgpk0W6yrFOppXUgV5p8UGZ7SPvnDI+85RzYDR68hkgntgfrKMiZecDOAr9zC+cTqsXaGz7Y0EeN7/VK1D1AysREf2dBWr6GI8RCILwJP2me4wu/v49+3uSwbYl76aUi8LuO5v3eymaW69og5pjYzFybv1vX07fOYf2ABoknyimVrgkGjB0hjW4/Y5j18Zv0WN0Zt/UM3TtczJOthnqVs5b7jTfuF99oWkU0hLI6+ozL12bzaSzgdOd/5ymm50NUXzKWk0g+MX8IJ9MkBlB3Wsweb0ODnWaPwpjwEmuAe6/7NPo5Zam1z0AcX/j+sh9lkSkAmx81r41ObmAqXgaGsClnKLtV5pDZXVRiqqTUXLlujKK7obE+5/2v/smx/670n34fCQfT6nxO5nJZVXAA39ykkHMJVLZVXA6eZv3G29Fb8sdtsr/ziNKameWuEDTra1wXKdXRDT1lW779DFXmwasGmtzmKjDmgAV7PeUrrLA/7XuNr8HLx8Xpfi5cw6O+Icmulo8CuHA4xVGArmu9X5yOnGq84JhwA/4uUtqwLUrssKp4MxM/nLofAN+9j3gWUAcSx9VfyWSut0hpWlomZLYx5NjEX84TYvjGuDv90mxLCKgqcofuMO+8o2d9pX1l6gax7a3RifW521yUimCXDTDfHr/at1luNqxYt2TnLVb4Bza7Jm8oHG0szUE2BjjGmoFkcDeHFcJda3rsGa+S1UHjeYH9NBTQP4qtW94984LPZstyvjt1YsJIAPmzS2SAlCO2Mm7YyZGYbCcjWJH2Rwl+8dPvSHGRPoy3f+291kSpzSxLWiAgXGrb6P+NQ/kEPUTPDylSU/TVYBQ7f5yoq9muQhE2LnfbNUV86rpdYEUztEJ+XrDPJ0Dd3GyCv2zPN2zpAJbqPDn/M/XR3gEGNWWj/rHfdZ+3SjmzGR2mp1XbbiKHO8r5ZavenE2EOFjzEk7Qxjcz9rpbz/Z0u5XB29KeMf3WxRXtkdq9gHBEOR8xVuq0d8LwRONn4nTdkKwFCbrwt2MSbb/3POODgvNycvuez7gQ//YSqK+n7FtcmF8f7M9+Zg9AGHJm5NgQMONaaSpuLJS4ZpDdTyAYOsN3jCPot1ZCqARboajfBSQmSxKfqL0ypwmG8aHdRMnvU/nX5ebGCxzvkmDsP9d7ZsbCxu6WjFhfF+/O6liGk6Qx9Ap+hzph+bgs2te+cAvy8nu8F4pyFtzDmsdCvwidPNOd36tfv3gdtY5FYlFL+cIf6njsugkBjeAIXrtckpscHVgqFIGjAMqFiRjWwgg4H2JdHrrU/Xf+8cUu1btwNA/hHGREylsxPnspgWaj5KQRW1oagBRGtYRzrDnCOdjmp6rIUxL92vHA5MpMjxKYfWxhzGOk1Sv6N/zMvNKUDssyQgE2InBQuHDgaCAWKxI4wJ/p7mzwRZFm9hzvcBLNFVuTV+jfrCP4D0xCi0jdrPEPvUNg3V0mdX6QpDkqMlL7WGG4ca05joHojWaOVNnzIX+A04Xmuykh2Av3IOvS6TTeuqkp9WMggE+N5pyzjdOB+Yt+WzYn8VDEUGAINbqbmcZRYfMLlBB1b7cKoYaD51uy3IS+u1mDA3LdfZ2adE7/+nr1X7pQ162mFZqpDJbgOetU9fOV/XzLOwO9RSa1isq+J6F1wmA4zWzVmv0+MV1CbfJu3fcK71YxZANZXP8/Yp3GJ9yGJdlaHxozjR+oszrV8C8ZT+VHVZCSVGSnYyptE4cTnSVJpLzOEbf3cPXoJ3paehjYVd/CvNAAY873sq1sacA8C9dm/3YJVnVlferBd1jVVcan1DlioEIC0xz6xPOQyy3px4fnzgp0CXB6yX6GX9wGy3NmfHBvmHOsesB6opXF71PTz/QLXk4JIjRW3tJQ+JY67PIL65IxwQcTpynX0zgNlRTYt/ELi32PyUC9zqjHA6rAEeAirjzV85eFuvr9j7SUAmxM67BCCK3/+j24bLra9oYcz3ac3aObp25jCnu6+n+Quf2V2c83w/mgCZKsYPgVu7XBa7ff08t8Y3Vc31RZ10m6sFtLTmAajVOmteFbWhG+H8xYSzmylFk/U6vd9XzqFdXnZO4irry4pHmhO2qNCD8XN5wTktH+icl5uzbIsCYr8UDEWq4aWvYJmuTIEOkKGiRLXFs/ZpG6+yvmh+Yiz34hhWcKGu8fBDvpdygZtrqHyutz7hLvsynrJ76opqk1qoq7tAjy7GJGuQ9eboZsZC8x+3ycbesdAtU3LPejEYioxbqGs0+MltPTzH/LOuj/iLJLL4H2v847b2zXEaGMu36AjpUw7rdRobyOA++8ItjmGBroGtDSzlojX84zTOTKew8SbSfsCb+/WpA9WSmleYEXeermm86OSgMWocZGz+XdLFmGx03zwvJK5GT3QPVEeZ45OPi1q4AiqW0VLNPX6prkwv6wcAGhlLONP8aepLzsmfA6HjjH/so81xrcBLb/NErCdX+SJY2EUzdvhw3wJWfe+0ddborAE9zV99J5j/cKr7K5+7XRmrGy8Y5bRueKQ5If0HpzV3xPuyhorYWJWBr/Jycybu/Csv9iYSkAmx8wqBigCt1WwONaYBoBSVnoif6Tzpew5LucS1UWwoVRW1gSOMCcdP0UHa4U3B4mpFIRaZif4pcawGwcKhd74+8IiKR5r0cbTSd8aviH7lHuYAppMysbGjFRrNR073lYt0jU1P+p5593TztxnelH5CwMXmN28GiFvvO0exjCqcF7uLHuZY+3unrTVeN37+Ft9HNb4P3HaFq1WFx+yzC1fqih2qJVqR6qvlAKwjS63TWaOBB4BqLdW885sZC02A9sbMzClpl/0KZ5GXm/ONt9ccCGcvtpSeTyIgCxA36qkVayFlaosUC3V1Tow9tEUesXpqBWt0Fj1j93KeOZK2xhz6+d+ntx7JGdF76i6n8jKg4yf+u/+spDbW9uqbwbtOD96wj6O/713W6kwONyZSOzHThaMVptLqGutzlrhVWERVJrgNudQajqvRLVVez0hgABPcIDPdOjQxFhPTJr+6B7dII7rExuywnvRRQBZ4zXE3+z/hyfjpXG99llr9s4Dg0ff9uMkZVOlwQ+keBpqzzJ+cz92uP9lYR10Wv503yOUocwJvqoc4OzaIDd7Xs8xLux+RTv1C7IRgKGKwecJl5urarNZZAES1tdbBMJOjxkp25HU1ZKpNtDDmMd+tzlT3AC6M95szxm26LlnmJ6c1wA2V1fo+AKbS6njz7zQSH9AvOjl87XSc/p3TLnZCLJfm0TcJqPgvz/j/d8Dp5m93AE/vxsMXe5Nw9pn3+N48sb/vXX4LXE8FCpigG8WfsM/qcZH17Zl5ab2ewUv10NRQuvYx5j+3XhK7o+t8tzoLdTWetM9M3dpgoBMw8lOn6+XJ9/wCt5rGy/2Vul8/8BNezrAiptJVXI1eqzNc8PpT5et0NugAz9inr+1qTFSXml9TNfGD4iHrxfxfAjfyW+B6HvC9TC21hpaJVq96aiU9zZ+bGrjDAV8ltbHoO61yIj3FGiowzD6Cy2O3bEjOMAAUzUZgKZfaxmo6GDM5MDHNkaFQAeVkAbQ28rgzfgW3xa/k9Nh9dDamMDlwaY+/Alf/sEBXz3o8fgZL3UpFfUgvtEZipoywBmoAbwVDkYrPOqcVDeP80W2jgL8Bgmop3czJABxkLOBoY+x44Oa83JxxW39hxb5GMvULsROCoYgfrwmqqAdxA7V05VHGuNWj3Lb583X1Fo/7hmQeYUygEhu26OibemnkjGiYMbppXl5ar4O/cToOm61rH/SUfWb9GD7zevNjbvV9iKsVD9nncpz5j45pU/3itKKysWH1d277CX+4LSoCE2cGeqf7lHNOYhejgZxg4VADuAA4Gi+AzM3Lzfl0954dsSeZdXfzbxobS4pmg7g1dhUfuUdsfMr3zNLTzN8aaU1cKd4DeoM3Q8RF8X787habQWg8cH1eWq8Zz9injR7jNqm/Qleij/lNfDUVrPecoweNfLDvfQDBUCQHqPpn4JoJNdXasckNbNQ+naniW/R6nOnWLlhPZkYrNYdFupobNJYbANPdehwfe5jZgQvsZGLYpBW6ItXVuqL/o2+d9lwRv7VbXlqvbCB3oa628ZTo4CWdjSmNnvM/3RogX2c4d8SvMJ7wDVEGumh2gVR5bk2CRvEr/Rt0Wrx99Hkril8BzvjA5Wa28vrWf2Afzmnmb/iVU5So2dXwhduZU43fi4K0Qm392Dz65krgzHZqBhrFWN0EvGmQ2qdT2OQbf6hWA2M5G3RaPEsVHkg4fxFivyIBmRA7IZGg8evtlfNhMyFwWVGn/qTUUZl/Os2xlL22vTHrB+BqwvnLgqHIg8BtAK3VbKuAALm+lws6GDMyUrezXqfRI/rosiusr77rY34zyafcfq4mY5WuqKob68wVuqJzc/xa8xe3VXKVTUCFxKTNYh8WDEWqAkMesYacdbb1s5dzS8OpscFM1A35NXA9ddUqAH5yWv3Z2ZhSy6ecBgB/O43IVpuoq1byjnMM99sXPpyXm3Mn4ezxQGugqB9aQjfC+b8GQ5ErgecBKrBx6MS0K04DL2N/gfaTnL+yUFukKS9l12K3cmEdY80WGesdrWgRfe28CYErLg+o+DGpP2L+cpoOQ1G9ozHjqGT55bpSRo175m1K3cbqQXUvq6I2vOxtz6BD9LnpH/sHZR5oLKtX8n/wR7cN1dUaLrFGFKvHP26TWWfG7knmmVk4JdCnUoaKZQHMcmtvamwsKdYhH+Cc6MBNBxtz0u+03me9zuD8+IBpM/UBzc8zv6eVmss7Tg+m6CDnmj/oS8xv1AS3of2cc+rbPYyxtRuqJf0vGPzxmG2+uGKfJH3IhNg5c4AY3gTGpc2dNw+of5DKUyWDMfC+CJJfCIea0wAqAT2BlYSz773POqbPFN3Aetc5mgm6EQArdXZGye1UUIUMst6smWONvgDgQ7vbF7N13eZ3+t5vAlBdrTOH+J6gdfRltNdDoQDYejIksS95ATjzT92CsxPTUP7mtmSibkg18pnq1qeuuYqotpjgNjz0YGOOroI3C0QHc3bRRq6wvmKo0+PyYCjSeW6A5skgJp1o6vs+mby0KMX8ejK7DI73GnuH9V43Hy6pk4m7GIuBOlrDl85haX2Nzb9t1uu0lZlEq9iYD01P67O8ZzRMFbWemLa4wffJ+I7GjG8ujocq11GrTv3CP8BOVzEL+K7GPfM2BUORQ4EngOXAZQGeOTHX9xJt1WxecE6muZrf7MBEC1jqCOURbntecXLwE2eJrkpN1tDenEEUv+4XvzyK9z9jAF+sISsng9VZADN0vfRMHaW2Ws0ctxY2JhPdA2ltzE772DmCV52inGjBo40xyVkEOM/8gT/cZs6hxnTTUi7NjQXWYqpmPWmf9QPQfkAoMi4vN0f+T/cz0kK2BwiGIl2BTXm5OfKraC8SDEV+xMt6nrQayAtbr7c8w/wl8It7MB873XjZ//i/2ez/gG7AIQC3xa/kQ6f7P0D7quRzq/UB55ij8i3lZrta8ZHTjebGAlolsoePcxuSRaFuqBarZGvCOp1Om+hLMY2xAbghLzfnnV08dLEXCIYio4GOAN2N8XQxJg1vruY39GE3aWosoppaxxqdyY3Ra3gj8MgWefSSNmkfnztdqa1WsUBXX3+B9b0fmIo3N+WlwC/ApcHCoTcAjyVWKwrWWqtZfB64u2h7s91a8TpqtZmuYgbAs/aprNOZ+ibro1npKvYu4fxBhLNfSWzby3kW68+f+iCAF4EI8BlATVZzmfX1bX2tyP8I58eCoUhRCx7wKF5i1aIWrI/8A2lvzE4cl5/VugIzdD2uj1/PhsRMS52NyXQ1JjHc6chE3ZCgWkI7NZNf3YNjy6jy2SFq5tkP+V6kgDRuiF/Hy75HdFNjsQKI2J04yRyNUhDVFo/a5/CKcxIuxuSzzB9bPup7odi5jWofgcQPtgti/fh1c0t2/7zcnAe3+yKLfYq0kJWzYCgyiMRw9GAocllebs6r215D7EGalHhcsb5a1q5P4pLHSeZohjvtiWvwJb7skr9/lPKGyZvKuzQzS9ee1MxYHACuhs3zqdxtvUmmLmh/oLEsOtztGOhvXz73LefY8yycX2bqur5CApxq/Kof9T2vovhoa8yBxBfhXLdGtJZabRpot4cx1v+d274KXqoOCcj2D7cB33U3xvv6W+8UNFSL6/mUW+w9W1lt5EbfJ8m8d8W4WrGKChsizmH+PtYIP8AanVUByCacnxyAsjk3VijSKWX1ou1N0I34zOnCaeZvaA2NjKVFKS/W6ky+dDozVTdQLzinNASy88LZ1UgEY+Clw+hmTuRP+yAXeA74LvncMqroB+wLjn3AvuDLPJgOKRNNelMnJf+X1gPvNVcLLyAxx+U7ztEMti8C0KcZv6hOxnT+cA/icd8QfMrhYnMEZ0fv5gP/vVRQm1isq/iPjT5yzFjdhOfs07jYGsGF5nc0VEuLjvUoc1xRYBtQNgN8Q7FwGeKcOvVzp3PF7saE2scaf1vJ/mtvOsfqihSosbpxajAGW362iP2AjLIsJ8FQpHMwFHmRREfahLuCoUjbcqqS+PdeKvHYCugYqY3OcSz+dFoUPVZq86USU8GfTtOC0W6zUDNjcR+8D2ELCGhNFKCi2kTY9xZ9rBGBd3wPxJ/xPX36VN2g1kTd0FeINw3h527XTzpGh9x+cvSBGbFEck2t4UBjeSBd2VaWKvT3t4pisDVlfRLEnikvN+cnoN7/fP+LNTMWZvjU5l76hdrSAOPdhqSpWFFAkfreHRC/9IXq9yyocKBa+kZyWWW1AeAnwtned0c4uxrh7OR8mM/hBT4lLrsoboxfx5Wxm4q1wi10qyw4IZobmqobzE0sMoEbj4o+5gdmJMsVaB/djfGM8t9s3GoNuxyoVmzjcHxjtfBdwtkPfeO/82O8Scxzm6oFx1VmXVZK2aOm6gYPaM261TprXsTpfCfQ5Vzz+2ef8j/HBdZIHvK9SHKi8gpqE22M2VRITHlWR62mkVo8vhLrZzzie4FDjFn0tSLM0bWLzl0asS0uOdVVKwDOiuE/4Pr49VaP6KPua/bxDI5fwAN2L3Wn3Zf3nKK52wuAmcAjJbcj9n0SkJWRYCjSMhiKPBMMRfqWWiCcfTHh7EcIZzduF3o38zjjr5FHGWOvABqllDqwGmu/XT+oVp47qFJcD8r+OjF0XOyB8nJzBgEvpy7bQAZuSmPDIl2dZ5wznK31DDjUnJFxmDHtHrz/xXUAG3WAoc7RRa3XyS8xQ2nfyeYffuCH3uaI2J+Ba3jd9xAV2ZieT9Yr86j1ztP2Ga9pzTcF+It1XFtHJo3UIgd4atePXOwt8nJzlq/RWUUtUrPc2nNX66yRd8f73Ne18CnOjIV5wT4FWytsXbyRLEsV1A+GIiMvj9969s/OwanvpzZAJcLZYWBFVPuWHdvvhVV4c0f6fMTfaqgW68Q8kcmOY/p7tx3j3IZo7SVktbEmtTLmNgXeA7SJw6u+hwu/9986D/gHuHSKW/+yD5wjC1sbcwkay7ja/Pxitgj44BXfo4cAdzQ3Fjw6JtB3Yl5ar8wRgTsr/RS4iQuMb6mnllfIoqCxhrCN6c8g+tInD970cF5uzu/rdeZ5ye2kEWeE065wtc7iZftEPne7MM5tCMA8t8b8t/wPHPhL4Ma6Bm7R+bjfvmDjY/EzP1xP+iojpaXR1TDJDfKCc3Kxui6iuvuF0xkbkxqsTT1H4PVJvTYvN2fqNl5WsY+SPmRlIBiKKGARUDux6Iy83JxPEs9lzghceIlfuf8DKNCBJXHModmq4FaAh+Pn8JxzOkcbY3jMN4R0YsWGY89xa9kvOydNGuocMxcYlZebI/ml9iCJ1/5n4DC8jvzGWeaPNc81R8W+djplv+qcSA9jzJLnfY9/5FPudVB8hGXSNPeAEZ84Xd9NI/7od277qjXVKh62XqSasb6ozDy3xqbusSevyEvr9b6rVaGhvOawAfFL9TvOMZOBg2+z3uc66zO0xlUKw9XoWW5t1dRcgq0NXneOf+Tywe/d8d+cHVFegqFIOl7/xmMPVnNO6mONOGieW8N+xjl9k8aoAHyFlx/MqMI6HvU9T0O1uKCOWpWRzNX1pH0GT9pnAVCNfD4NDHTqqZVmgQ581iL6WnBC4PI2FRPpH56yz+AJ+ywMXN72PUAXcwobdWDxbF33kFNjgw1gEHAVEDvOGH3hEN9TL5hKVy7UPo6NPcwCXZPOxmTe9d9fdAybtL/1QdHXF00JXHJbhor2Sy4fFL94wBS3wU1VVH71663PKNQ+2hozsRL/U2t15toKbDJN5RZNV1SofbzsnMh11udA0f9gR+CVaW69VnN1bdVULeQd5xjnVefEYcD5AAqXE43RHGZMprc5suj/drnO1qPd5vztNuN154RPD1DLev4cuLmo7rY2GGhfwrtOj5IvzeqLzBGPhq3XBxsKY4muEu8WfdKXTQGrqUDiSu8zebk51+/kSy/2YtKHbCcFQxEfUCUvrVejuQGGztc1al8Tv4nJOkgtVh1AOPvZ6W7dLs9atdv6UxKDWti1l+pKpyfz2LQ3ZoID91qvUVlt3GI/DY2l1mD1WtsA8bavOSf2DIYis/NycyL/1XGK7coGuibuNwR6PXr/w+8GQ5FZQPZlZoSBvndqA9clV1hHBpm6ECvlffG90/a4F5xTjjvW+IfX/A9RQ3lJMVODNxfSTZy3goVDb5oZ6L3cwKkNsEhXVUBLgBON0QAo5bV+GwoVNJY7gGkplwvMkZV225kQ5SucXQfouUxXngDPPkViYMgk3ZDb4lc9gXc5bECi9Al4Qcc7N1sfWkeb4wAykr/PXQ0T3WDRpleSzdHRx2Iz0i5u3jL6Si/gtL/dphxtjsPVMCbRNa2eWkEXcwoAmSpaR2mdA+TgtWqdCkx+0f+kH29+RtJUnKBaxgJdk4CO4WrlGkobWrP28OhT7wItz4oN+iLi71/Uz+1m68OGldTGoqTMABt0Gll481Eu05UrjtJtjNMTfdaU8vZTJaV7WeJ/6g2gRXNjIYuc6vSIPQZgVmTD+U3UIibrIHdY73Op9c0Wp3qhW11dF78x+bBnL/P7lOeqkhN7kHyySq5WANx1vPFXdSPx/1lbrfZ95A/H2xpzfL86Lbk4fic2iahR7HfkkuW/EAxF/MFQpGMwFOmGN6Ju6SS3wcdK0aCBsZzXfbn2e777Fv0UuKklcE0zY1HbHOvvYttYrKvysH3+AVFtUah9vO0cA4BF8bRQS9zKRfcNpRnke4sOaioVKKiF2JNsAlakPJ4fDEXuInEpupMxfYsV/Ni87hwHQEybRLXJNb4veNL3LA/4Xi4KxqB4S9qBxnK6GZMU0KFn7J4Jo5w2o26IXcso9xCbxE/rr9xDAe+SUNH+lJN8c0XTVey9XT9ksccJZ1t4ox2fqcGa71uqvENKlCgEPsQLCsAbqegCVjzld/nmy+NwrvnjEqCouT6GLxosHJqmMW4H+NrpxDJdKbaaij//muietlRXYYZbF4ANOi16Y/zaM4Ez8aYP6pWXmzMHmB7X5lBXw69Oy/h4t+HHPY2flz3j/59rKG9OsE+crm+vJLslwBQdPGW6PuBa4GPg0jhWUe6xpI2k8YfbnI06QFRbRm78fIbaRxcdT0ybvOacEP8tpT8nXgZ9ADbhXwIsrcw6vg7046PAPYwK3ELHxHRoJU3RDYqlpDjb/LHo/lfuoSWDsUIgitd/87kr4zeH57k1lmvvfzTS1pjjA+hqTqa3+W3/vNycb0vdqdjnSUC2gxbc3bjiR/67F/8RuHb0LeawUWcZo7J+9t9AU7WwZrJMdWOddZg5ta5fOedubTtzdW2+cTvF20VfcNtFX+AH9xAOUvPuq6nWFn2Bfmx35Zz43bwZ39zcrTV8GLiPHwM3PUE4u+5uO1Dxr+Tl5kSBo4CHgJ55uTm/kkjoCvC502V6ofYR1wZz3Fqs0Vk8ZffkVfukeLfCJ3nMPptA4hLRqcbvrNIVt7m/eKLT/iTdMO9Ic/yx55qjbnjMN6R3yBrqfOu/jfU6nZOj9xHHLArlCrVvONDiZfvETsHCoQ8HQ5G1wVDksrI+F3ujYChSPRiK9AuGIlv9n91LZAMHAiiF1dqYvTix3AH+AJ5OTMNzEN779Xy82Rvcx+2zGJ/oJxVLmSP1OPOfiQ3V4ofus17lF/8NTApcmv6k9cwLB6vZVQLEuN/3CjXVWn81te7wM8xf1gHE8OkzY/fQJ3Y7x0YfWT1H1+meWsdgKJIVLBz6R5Pom73ec44e09Wc/NmH/nC3J/xDamZSWLTzBsbyOXg/dgDmnBB7iGDh0B7BwqFXp1FoJ8u5WlGofQyKX0xbNYtMFaW1mce7/sGolK5mc3UtZup6vqFOD9xEX7lfnZZ/TXHrv/y03TN6e/zKmsDo1sYcJ5kst5ZaQ0U24Ojigx0AbIy4D5vbrfcYG+iLRhWCd6nyZ7c1JaThtUzWBdhAhuoee7JG8+hrT8e02TFZKF9n8JVzaMeSK4v9h1yy3JpwtomXgqAG8FS2Sn/sALWiKsANvk+LTfiamkEaIK6NdJ9yHRLzDm5yrWWWcqqZaLOhWsJBKu/7qTo4DLgYGPF1oN+LwC0kMlpvJI2fAzezSft50T5JH25MUAcZCwGoojZUwPvFKX3J9hB5uTmT8eYCTPoTOA7QEfewZj9GW9NGzeYt/4MYCm61PtQh3/u+2W5tXrZPZJP2k65ijHLbELYvXnul+aX/KGPM33WMNUfA5suWG3Ua470ksW8A92/Qae92NSeflVqXfupdHrXPxMEkOeL/5vg1J33tHpoOVDlIzWvX3pjBKLfN48Aru/3k7Pm+AjrUZDU39w+d2MBYdsVNg1/bMpPvni6cv4pw9nfAMcDkhmrJ8UBjYEJebk7RyNq83Jz5eK2499dXy67ubExhtNt82WmxwTWrs5YaajXP+55acYCxYsmz9mlv3mG9/9YJ5l/J1QOnmb8debr1G6t0FlH8rp9NBsDZ5qhbhjlHVgQeX08Go9xDwPvsTP2stIDjgU4nGX/Sy/q+HdCuifJiR6WgUFtumrI/aG/MfBYYjpdH7Ru8kYcVLjW/7lhh8+wAzHZrcU58EFF8WCn5jmup1SzRVZjt1qKS2kiu3QuAL93OdHYmcbz5N2t15gkXxvv/plEBr3L2qacbvyYnHQegmlrnmKr4BN9aQx/r20AjtYTDzUnJxWnv2915yclhlq5X2is0A1gM1EkucDGvNXE3D95BU13llzrputg/SKf+pHB2Jby+QGMJ5y8mnH0biaHHcW06K3TFBXWMNcHSVo1rs2iotKMVL9g59tXWl0ayHw/wF4kEjQAxba33K7sS4Xw3MXz8KqADYIx1G02oo1aFa6q1FQC+sjvFTjD/8hspk9W6GttQHE04/+eyPQliVyUmHR+I98UYx2uN4Azj58WP+4fUKW2dWW5t7rN786t7MDbWa3m5OZcSzs4E3gVaAR9FnEO7vWyfVGWsbjLZT/z0883v47dYH1jZqmAr6Txhqa7EP05TupqTma3rcE+8N8P895Gm4qzSFeJV1frKhPO37Li4vwhnm4/Ez4l2MyaYnYzpmErzvdMmfmn8zrS9JUt6MBSpD6zPS+vVFfgisXgxUI9w/uYPDe9z5ly89+RHR0SfeP0z/8CLKqsNrNMZzlHRxwpXkZ2ZKP0c3mffie/573vuMKP0AX+v2ceRRSH11Aq3kzFNm0pPblf4/NTVVDwXb8Rwyebe5/ASH0/pafysnvAPAUod5HJRsHDoB3m5OYWJY2yAFzi3eN9/L4eWchlxlluHN+1jud03jDRiRZ/HL9kncb99YVG5g9Q8vg4UjQ/g/lgvXnK9UZCnG7/wpP+5ojrFUczVdd9oqhZepBRKa3DZHKyt12m4GCT7AwM8b59Crn0+AB3VtG86G1PyP3C6f7qEqlPw5gMtcrrxC73NERxizCr6QT/ZbfBmy3snXFzqCRf7PLlkCRDODjha/QF8uVZnLji139P3fuZ0KRrl4lOOWVOtDS5xs3G1F3TFU5r2Vcovs1FuWxxlWclgzNWqYIFb7cvU3fmVXcHW6kHC2YcBNwLP4iXsrNMzdl/wK+fQCt66UE+tMFKDMQBDYRXogOSp2TNdipfotxvQFq/Pjv7G7fgaXh+eZet1WuowdxobS1ilK2J7DdbnBkORQwjnbyScfyrQdJIb7NBS5TU7y/zxS6D1fdZr3ON7w1daMJb6vqzIJo41x1BJbaS9MZPe5rdFI3irqvU+Nk93s7+6/HbfMLOzOa3oS/YoY7zvMDXpgnKu1w4JhiID8Eb2zv/M6VI0ZZHW1Lg4dmelEsUfBoYCHwA/PWi9/GkipxgVVYH5mv/hnsD9wCTgGmAKMOPeeG93nNuQGW5dRjjt3OTvd63hEmsEXc0JHGZMNUxvxG/rMWlXTQZOZMtgbDUwIC83Zxqw8nO3C6/bx/GX25Tv3eLd3f5xGj8AbAqGIj8HQ5GBeNOUtQC+yteZr5V2Lhobi9lAOq2ir/Cre3DR8iaq+PzcRomMGVXUOhLnpGAdm2cmczDW9YwNbtDs3sl9lOJM4NnH7LMHHh/LZbX2+oe94xzDebGBeqp7QNF6hxsTACafYvz21jD/vT1u8X147u9p118NzAUWJMsdYYznSf9ztDdnFbu68oHTvSZivyUBmecAU+lmAJXURqOGWnvXw/Fz689xN39fbSCd2kY+hgJTaXzKLepXYKUETG2NWXzntGOD9ubK/drtuPjw2NMvPxnv6RZoP64GWysspe8Avsf7oEnqcYzxz4X32L35xO6CoaC1Odda7lakQPtxUvIE2RgBxH8rnG0Rzv6QcPY6wtlPpSxXhLOvIJw9qBarz05ZozJeVnBVQFooWDj0NcL5tUa5bRveF79g4mK3igbvskvY9yZ/B67iHPOHDOC3YCjyeTAU+eUTp9tTBxt53YPGskpnmT/dbGEHmyQuX8Pmvi2OVjwSP/ud82IDWeBWQ2vIUFH8qqi7DT+7re1VOmtqcr1/3CYfNg59tt/2WZnq1m9ZcplScJL511PBUKR6aevsYS7vboznKGNs1vdO26LM9lN1fetHt830C/vntiWcfTPh7AuA9inrdWtuzGsMJDuPf9vamDsKLzVF8pykA62n6ODvp8cGc1zsEfrGbzNOid7Hy/aJxBNfHXXU2pKtW4NG+G8/7irzc1qpOYlFmi7GpCqt1OzJwVBkEZDhYJJrn8/D8fO4NX6Vk68z8pIbqKg2Ja/5dQPuZfP3VNu+8Vt/vz12BTFtFOvXFdU+JukDAXjOPpW1OpNYYuBUFhtprWY7FdnAQp2aU5ZkUtdvgYe+d9txV/wSxriNx1vKvTQSGOD9o4XzPyGcf90zTs9DZukDeCDei6fjp/OcfZo7VTe4O9fupaPaS/Vm4PK874n6nYxphyiFD0BruuWl9doIdMJLBD7uLuvtYvWIapNzowN53TlhAmK/JZcsAcLZgdU6q6CK2mCs1ZkcEX2CdYlRMqcZv9DBmM56ncE1vi+2syHPd84h3BG/kqpqHfN0zT9npF389jdOx3Y3xK/r3krNrvFR4N6iIThv2T3uO9n8c2Dy1+pct8aq5VSpmtosH9MGqakzEjYBDwJZwEOE81fvwhkQOyKcfTxefxYAnrZPP/Fx+5wnLzcjte/yvVMRYLpbb9nt8StrTtP1ieEruYUY3hfjJUC7SqxPa6QWH3a4MWHjTb5PMgEKdIAW0c2NACcZfxY+538qDWCNzqJd9Hm6GxN4yvcMhfh5LX4cTc1FvGkft/LTB2+q3rv/g18Mtl49ub5aXvRF+avTgvG6EY/Y5/KRPzytnTGreXL7R0cfLZyj63QDJicvEe0vOoXevmdU4Oa7Uye9BhjjNMJQetMk98C7Lxz88aPlVL3teueuM/66wBrZAWC8eyBtDC/h/QadxsHRVxnhv31yU2NRMsAaCRSNEhocvyD+spOTE1RL6+XpWhuAD/Nyc3QwFHkbuKCNmpX/uG/IxM+crh2ednr6QBXrRzXMH9adjBnFMvynBGYuYMS05Z4Ye3DixeaINhdZ3+JoxTXxmxjudvw/e+cd50S1vvHvOTOTbF8Wlt5C7yhIB8GGogG7iNhFsSA2LLGvBYwdG9gbgg0LSlBUpIigUqT3Enov2zeZcn5/TJLNAnq93uv9Wfb5fPiQTc7UTGbe877P+zwYWHzsuY92MswuVYWJVm97mPGZBjDZ7ha93rzBA1hVKdDby/UsdppwwE26rQZaeInSWGyjtdg820b2Wq4asTaJuxXy3EkbuQmALU516ss97FbZnBl5kPc8D9NA7sFSkoHR+zY8Zrw8oJHYWXWzqv6mT+xqAu6xbFPVfuwZee6ScNC/xhcInQu8O1Cbrj9muAYdBSp1TvvIa9cAS6qRzxhjNF01t6t6ktWdbtpKaoqDbHJqfNLwwbVnx/etXeCDh5emXHU3SfjaPoarzBGLgC7hoP+vx2GsxH8FlaR+F6dUFUUSoIooJo0IDpL2cgM1OMD52kw8wqIwSetGCNd0d4rdhW5yBXVluSPNSdrPdLOXUUQ6D+uve4Dn+mnzWCSHjmkdef26963jOFX7iUVOkxX3WZfX2amqcZvxAQBbVY2cJKIoAAZHpLPouLNHcMm75/x3T0kljoBNMf6enq/SeNPqdxfQooY4mBjQXGzN/cx7Lwudps6d5hCVKwq0OU4blDvJ9wAfAK2yKKa7XMEK1ZDVqkGcu4OXKJ3EKuYrN2aa4nRNedwcuKe13JT2pnXKAYWsN8M5mqMirxb1kYtTr9U/01Y6DViqGmu+QOjpcEowkRIrUilsV1XpLlfQU6xwdZgUiWAsqnR2qyopwHxgmy8Q6hkO+jf9safwz4Pd5Ey/wbz+nrHGaKnjUKhS+Fk1pY/7+0v1iV2P+QKhp8NBv/2v1vX/gcHatESNrbqr+A5Ahijjau1zp77YnRBGVYo+8YBpnt2ccXZfAxgdVrXiGfo84AHgBuCr8Z5RL2SIsl63yIlsUdX5xDl2N1DjeLGQh403qCX2iajSiIvIFpBGdkJRI3axC0vWEAeP6u2W8dCEck27nc52PbE7v50MVwWoKQ5ynjZDW+w0cibb3eU4u68H+KgW+/Z/6r1vSC1xQG5VuZwaCVJIWglABA8rVSN7pWp0Ne7E9PTkc+NNEr+vL11Vmhoin3c9I/nS7sI2O5clTmPSRFnjBmLXck04NBK7KpzfumJf1xwKl/gCoZ7AXYDeWOxIfJ4lSuvjlnhn7yO7V9zKDKCQNNU7MlrkUFjWQOy+/bR7BjbPsy49GVgL6TnbnGrUlftQChTkvW73ezEc9FfcgUr84/DPDsjysrOBs3EzFw4gHSUOWMhdn3rubdlU7qgw88sUFRMIqcKkl7acqXYnLpHfVPjsSeMlUoWJo0gQJFKJXFSL/WvusIY2v8MaCm65cukY+3RrD9l6Q7Fr5XjrxFYheXfcMw44XNUd2EkS/0cpagn3eDTc1vct5OVHDluqEv8Z8vJXXX/Xvd82E9tOnup0Zj9ZTQDesPpxgvy5qK7Ya6SKqBego1wnv/TcaQsBE+3efGF3podcwed291bpopTR+gtUlwUUKy/9o6PY4NSksdyFJhSX6VOZb8bjJoWDrB5FJ4puViN/NVC2n8yjnjeeJVOU0k2upL1cl9NabLmpTOlbU2Jlyh2qKs3l9sTud5PLWaUa4ChQCHWfealZRFrcmqsuMJB/kIdeOOif4QtwTIfIyy382o+h9+3jO12jffZCH21Za4DI4RnOPw/ysi8QwqU7KAXrVN2f6nAgYe49WJt2MFWYebhWWbv3ktWwuuvMxVv2yQzRvqCDXFv/Xet4NlGLq7XJFzn3Xzh8rNEpbZh5Q6pKsv+qJvKZ7rmFemK3bQgnkSmTqjxOnW2341Onp1NFFMq+cuHe4+Si3OlOB35yWvKB3Yfb5QfYSpQtcppMAt6Y7r3123yVtjhblLRSCmrIAmpQIN+2TiEW2JxVT+yRtYQ70a0n9tJYbGexapqFKzIrcDs4T8DlZvYgyePyG7sjTaWrn518D28odzNUhjgz8iCLVFPu0sYngspDmwuWOw04SLoXd7KbDvCO3Ze+cgH1xB4UvA9cCcwCet1tDeEuxuMgi56wBmZE8FBP7Jk7wTPyJ104OQdJZ7R1LgL1Rfy4hAABJe+Our0yGKvEP7xkmZe9EBIB0/PAwVvNq4sOqIzga54nf/Nq5tit6CZXViBn/hI2OjU3Hx99ugCIM09n4ZI9PbhSFrPqiT3iNDGX7tpKGomd631yV8Lv0lHYUjAVOA0gonQcZCRVRGfi3px0YB3Qibz8fCrxX4UvELoFiF8c43DLQHUAVZ0DIuS9mxriILtVdkLgNV+lWRmU6ppQRJSOgVXhWllt1yFMLU7RFgLwqDmIsbY74T9DzuaZWOdXgUojjTI0HOta80btEeM1kRy4xzHDbm/OddoYp2lzOUqGE+9/aB3LebrbmBtVGh0iL1NMSmF3uSLzoMogrGo6x8tFD4zxPDvKVzYhC0gLB/1bD9vA3xij77ncaCx2zMyhsMlcp83Dt498+bn/7306DHnZvXCJ83fF3ikZYw0oPkd+V726OIgUCW5h2c3mdfM+dXodbCK2DbhU+4odqiq1xH4u1V362JFsvC6N3oGGzQ36J4RVzaJ0yj7vqy284Jd2p1QZZtfImP0FpB+RkD5AzrHvMsZvqC0OvEte/v0AvkDoUg/mm63EJl73PE414VqEXRK9g1nOUQCkUsY33tuoK/ZRpnROi45ig1uWXAW0xNV1GQncd+g2jxLrmOjJw4hRPfapTHIoQgqFowSnRh9htWpAJ7GK8Z5ReIVFRGkJTcDVdh3OMh+mhBRwRV29N2kfcro2hyxKEpZmY8wBPGZfAK7WW33cALgpMHSgNp1H9FcTDSMHVAYdIi8D8KLxNDE5kRKgF3n5P//S+a3EPwf/3IDMbQNPKJwrxU7xQH5tXyB06ieee6Z0kBt+fflfwaE3ueS/Nzo1OT769GzcGZ0k9mOPDZ2HS6bNbSU21coz3qSNCFsZIlIhk3lApT232ql/fgOxq0YdefCXduM08vK/+N0HUYlfRIxPkgVsppwYDUAOBTQV26kr9vCUMRYpYJNTw24od2tHXFkSlGLpHdZVKd/aHZs5CPaTxWBtGqMMVy7MUjJhtxQPzsKqJsDMJmJHoiQ1wTqBu6wr6Sd/YrTxAqUYjLdOBATXG64rS6ny0DHyIrfqHzJE/wJHCfardHJlEbYSZU9a52lj7DMN4NZw0P/bZyeV+GORlx0E7gDUTpWzvlR56mxV1Rcfqy3rDu69poiURDZ/kt2DG03XtSudUr71jqBmUok9DkcJpFBYSnJa9BHWqETnoHOD9tGLtxgfXQcVJH7KcAVPKVIeVjiNlFeY4g2rH586vRLrrcn+8A/e6+uLci2v48jLn+kLhF4ChgK0EpusgdoMfZnTiI+c3on1AvzovVbVFPkCYKw1gEetC/bgSggd75dzd8x22vXKJ+OeQ4/HwGKKJxBtJrd7kt93FDxpnccLdqIplSmeAK3lZgBm2u0YZ5/MN05HYo+GdUDTO/QJXKtXaJYHYJeqQtfIGIhVWHA5blcDk2Z7b8iuJ/Ymxn5lH8NQcwTgkv/7yMU/v+F5/Azy8rcctuJK/CPxzy1Z5uU7xffXmJcuIl0Aikip+dLdVxZ2ky0yDg3G4gHVkWaTR0J8jK3ARCdeQipVBnnWZeB2D8WR3C3ZGeACbRr36O+Q7gogHvYdHVSZV3WSa1L0inIY8RsCQDGw6F/vaSV+D8JB/0RwVd6BQiATwINJJ7mGParK8gZit08Kt8xRS+zXPrR6c642q9yXUoGNTMzgAYSg3XFi8dujvK81cxAMN4cz0e7NADmHtjLMJqcGbTWX4hU3dW4qduAousWvzwLSeDtmy/Sl04U2kWN4zniW643PE11pJcrDzea13KOP50xttgKEFIrcWLZNEyrlduMDyvDwvn38UMozgpX4/8dpsf/Fd3a7potVE27UPuoe/1AIyKScWvGD0yrx+hT5U4VgbJtTlVriAJpQfGZ3Zxu5zHXaJAdjAPJj59grsqxiokpnnH0y+WTsXpFyxUzgPIAMEaWLtloAPG68xDeRjhS5EhIvve0JPuggt2oxHuxrVr+jhsBM4HXgIiBtpWqoP2Bdugk3mLkWN9tUHWCNU1/U1NxMczbFrwMPh4P+TeRltwTesJTc1y8a3L9O1cvGtTCbB3S7SPt6TzO5vYJPEhAXZiaXfApEOmudemxQdWiNG5DNd1rwjZPckIoDHOghl+ccui6AhU7TxKpj/7fAvSfUzlfpW+qJvdUA3rL6VtBDc5BMdzpMrwzGKpGMf4TshS8QOscXCN3uC4Qq9Dx3iLy87TXzFBwFmaJM3Gp8mNGQnYctHw97fkswlowtqmYiGAN4xBrMzFg6PgkOMAP4GVC55POw/no8GCsflBR7NZK7Dg3Gpj5unvfSW9bJ6xY6TacCLcjL30El/lCEg/49gOYlSi75jDVG84rnKT723N+6iJQ0K6YJ9oPTmtusa7CTfm5SUEE6BWC3k12WIwov0YWDR9icp80kkxI6yTVkilLaapt42Txt8f3RSxL2L/ucDCWFG9QLAddHr2eVapBYp41GnxipOn79pokoncRadaE+jXQREeDKBpSo8mSCUnCf8Q7vex76K8g//GNgK/EOuFSFb50OPKC/SXVZcMSxm5zqvGu7jZV95GIejXUHgns/uSB6D7tUFQDySXcetwYx22lXgeaQSz6fe+5JuVL/kvP07ygmhaH65Dc3O9VbFqqUuducqhU6jiIYWG4yzAEebfHg8u23mNcWz7CPYpR5AQ9Zl9QECAf9P1Ixu7w1HPSfEvO6TNThrzNv4kOr95S7zCGL77KuPAvXExOlGA6gC6daPzmvKi6fLBvXyLz6JlWzYXwdSrH/A6t3uFR5FLjewJfpX3Gj/gnPe57jC7szT5nnlt1rXsYL9pmHnsbmQNlr1mmJ3+puJ4u7opdzt3k5N5rDDx1vAb5wyuCTWopN1QBm222437p8XhRjKe69HtzM25+2g7cS/z/422fIfIHQQFzyJcBZQHdfIJQCTGkjth3fS1tKcpy1VtXjdvOqyDDtU2/DWHeO/IXy45H+juMDqw+bVA1ukx8CsE9l2W/bp8wDuuKazMYtMjbgEkOXAsJEs010qWFWWKuNhuTwZq9SZTAwct/IpTSZFXurMaCH/9WJqcR/BF8g1Apo2kDssj/wPEgtcSAhyiqFEsP0z9CFw1anavRGc5jRX841C1SaJ5sSCkglR7gC+TYyYfnykXNsSrFKpbvmKqPPc1qWZIjS9yTOZcQmT984xxz1k2oVLTZTPB3lGpbajcQj3nKZjD1UnMh3EGsPC+4n290oIiVxfe1yqnBa9BE0HK7WP+MCbTpxKYhmYmuOLxC6Phz0P/9fPH2V+J1oEhk/vaHYSanykk86RaQmuhuHREdwvf4J8Qx/BJ3h2sdEMegg11bMxgJfegOJ7/lS/Wv5iDX4zTK8FURxe8mlH+WIonMAqot8JnoeONBA7L4tRbj3p/H2ifn9xY/ZhrJYpny8YvePk/JfCwf9GwEmOT1fm+T0vBE30PokafUfAANwr+0PfYHQGFwqxyRcr02zkLSnbrOu8QDB2DKPtgh8sukTT4PU1mIzlpIsUM3j60ttJTbRQy5jhnN0+gan1u3VRX6bmXb744+Ra32LncZ001w5oeR79glyESOsa39N1zH7M6cnS6JNigbJb1+vL3YNvNN4r5bEYaeqyjQ3oxa3ytOBUcAXcTfZZnKbDVwaDvpXAvgCoTSgNBz0/0P5QpX4JfytAzJfIJSBK8YXRzdfIDQbt5Ps+Dv18bRI6kIrUKn01paY6506E7+yO11wlfzisAyiEHBApVOFYqJo7FHZ1BMVJcAsJVimGh7oI5fkLHF8eLEIWZ0Lr5WfdjtDn+OU4dFXOfX3feV0rvat07EpMBuXO0Y+GdqV5q2cq81SHcUaGsg9AkA/QjAGbqfnw543zjgj+nBiF4FVvkDIbCk23bRL5aw7QNb8cNBfcsQVVOLfhi8QuhP3pks/+RPxjql4YKUUCZJyPbnfM9t7405QVTNigdEM66iCAdoPpQphG8J+qUwZ94ZVLf0t6xR2Uo0Fqrl9hvz+6juNd9+4k3dHRZW2dInta/uzaqrdYbxLI7HTU4UipIBibW5iv4pUCmdo3xMQ7/Gl05n37BMqGCwDlCnNCphX6k8aYxLv5Yp8LDT2kc1uVZVkXa5X7dPAzTpUBmR/DmzZpGqVAGkCh2ujN3KR/s1+EJkCjCuitzvTvLd9v09lHbVN5WaNMCYCsNOpUmElQkAa0cSEcqnTiDK8fki0llrATIW4DqiNGyjRXG7LcZIEqq/RQ9nf2B3UUHOEcCoWXN6LvwgH/TfFOGN7wkH/3qT3J/gCoZ9i2zwVuPYkuYCRxmtHeTE3VBHFJ5CXv8kXCH0IcLr8nsZyB+3F+rday80CYIfK4SfH7UiuxT4mevJIFxFuVJ/QPzqySSrR7lO9dzQAaMIOlti+krYynOYAeuwwytxDThxUHfbOBjpsJzcuR5MGEFa10vtqC65rIncknpvX65OYFj0GYCXljVobP7D6zDtdm2OmCNOoKQ4+HA/GYsddeS+uxBHxtw3IfIFQa9wOxmq4nSxxzZ6euDM1uwxPBaJ1JqXcpH9sRJRx4TTn6F9c93LHVxK0BhmTPPcZ9eR+lHJno/HypC4UD+hv5yTPwlp4tlaJvZRA1tFyAwPVLAZG72W+almLGHkUYLuqxhjr9KISUprN9t54m61EL02orr+0P5/b3S8ROPemEB1YSko7IOUefVzKlfoXr21VuQyM3HfQFwjVrbwR/NeQ8Jpb6DRLkO3j3/ehGdMMUVZrq5NLPCA7Q5uTIYWrcnmnOeTu/SqrsK3cmJMuykDBXKdNyVynzffd5Yp3GsrdF3iETVO5jSxVQhNZsRKdRiR5O1yjTUYI15plntOCTFHKUsdHE7EdUHiwWey9Su1SVRJ7KSgvy8eUywFY6dTjcWsQwEm+QCg1HPSX/ofnrRL/IcJB/05fIHR2P/nj53fpE4wGcg+OQkqBcZr2E7eaVy/tGHlpEzB9gvFwovuwVlLzj6PKs/5CwCtmv5+esc/tQoy3FcMT4aD/TvBD3gvHRpQ+xiusqwHWqTpEHIN2WhiAk7SfxXnOjMD79gln4sruPALs8QVC3cJB/w/kZR8XTuE9IEoeZ/nKJuwC7u4pllRf6n3mTANLPGGd9+2rdn/u0sfHeW6N81XaXdkuQX7VDdrH3OIGl8JWIkH6ry/30UpsorrIVzU4MDddRHqAy7HMpPTq3SqbiDLwChNbQV25L00K2OLUoD67MdEx0RE4exXSM1CbsfIR/dUeEkc8YF1S9KbdLyHiDYi6Ym+FZ+Yqpz46FhZaQxCP4d7f999uXf3mw9aFRpYoZauqvjb8u77tSvzT8HfmkA3EDcZoLjYZP3quY433Em7V36erWHnKffpb2nFyEeBmtKD8QeoVJhFluKJ9SQmGDXYNPrO7E7CGvt9TLtfj7cxCgIZVgef1W/hmUigaiN0AaNhNh2qfq7eMIN94buUr7x2Zs703nucrm/C4QiSkB6JKd5L3yVGwwalV/WvP7Q+sTLmiXVB/md5iEUM0t8GynthLP21eFWBtrFRbif8cG+Mv5quWjLP7sldlstvJPuLgMmVwozmMYuVWRWS56Tyny+89Y43ROcP1T/nIk0c/+QMXaV8vzOXA8jRKE1ID6USodoTuuO+cdon1Qvl1JwU0ZhuvGE/QTobxYDHLOQpdOLomlFBIbCWwleBp6+yEM8XXTicC0SHWO9YJ4YujCTFxHZefU4k/AcIpg9u+6HnGaFBOqagS/6w6+UfhkuVbP2ufHd7o1MA+pDC2xGlYgff1jH1ul6JyH0cL+BSXdO8iL9+51rxp7HjrRCbbXbnKHMFV5q1moUqNj9jzqPHqm+GUwTeHUwZ3xCXXLwbm+gKh79Y4daYBNYH6+1VG0IP5zWnyh2te9Tx5TqYo1VKEKW/RJ54E7N2qymPCXSpnry8Q6gQEztVmJt4XSXflLU610jc8j0ff8DwubjI+7vG93brEVoLP7O6sUA3YQw5XmiPMSXb3fVOdzj/HM9cN5W62qVy8wuIy/SsGadNzgbd6yyVdNeFIIRB36RPWAskzoCWPWIP371fpbHJqmA+aF1lFpKi13ktY570483Ltiza4jQ6jAKOADGLHc+qvfZ+VqEQcf8sMmS8Q6oAr+AooPvPca8SzV9frk7hen1RhfDx2UspNX3/ldKa/9sNhQVVjbTeN2c0ap97lx8qlFT7T4DfpkAEccNLRhM1yp3HZFKdrCsAV2hfcZbxbYQ0rnAb3Z1Ly3EKnmdNJrkbgql/vVRnkxnivUsBAfSZN5XYJMEifwfnajMS+K+WSUHG1sgbgGulW4j/DLUO1yScM1Sd71jh16RHjfCHgC6uzai3DZTmiKCVLlAqAFGFynjaTCAbpVORz5QhXGwlcl4gXPc8C9LlB/4TqsYcHuMF7FSomqH60W3CpeQeP6y+Fz9W/8yVnPgAayZ3KI2y35C0cNji1iYsP1BH7EtfIVlUjscxgbRoP66/rAlX3B6fNrslO96rAK+Gg//Bul0r8f+GMQ/7eB6RvdqqXfWz3rBJ7z/jBaf24PxocvSLligoKt5rALFWGN1WYfGe3iXdEbsctTerVyD/zGv3zY8kbfBfwCnn56lun49JvnY6f4PJwAYwzog+Rp7/1RG9tqQGJbqji7nL5zLlOGwEgcHo1FuWXzmKn8QnPGs/LmAbXoVgz3Byee6nzFXvI5lO753tA32u0z/R48AmgEN8UK+O1NKLDaosDji6cPgC1xX6GWTekLVONTo1ifI77fNv9ndO+8XdO+/RGYvurneWaDtVFPpucGtuzRXF1YiXaLJeHN3yW047+2g8AvG6f2rKnXJp6tTYZDyYFpE8cao549i37lGa4nF97rfeiiBAIHYcb9Y+OecM+tRYVYeFqk1WiEv8Sf6uAzBcItQXeEzhtTpQ/U0wKq5z6FTodk7Wc4vyJ5EzXuZEHWK58DPCWc3McBdtULvWlS38YrE2jjiznjZU4nkiajCbSFPtUBlUoTqx3v5NKVVn+MB1hXRv51uk4GHiVmOZOrihvborv1wf2cbmXalPpoq2ukMlMwUyUHQpUCiG7KyfJhYnjSg4khYBWcguTbRRQaVz7X0A4ZfAmpTCEgFytgDJlkOK6MjDWPr14idUk4yixjkeNl2kZMwIfpM/AOgKFd5njo0il0klbW+H9HAoPH3wIumqr6WEt3X+u/p0PDp8QeDGVo9zLYbOT62ygjvzRbkFXbXWFa2SY/inHOKtJI0oXuSoeIBrPe5777vm8h8+jEn82TAOOBVAKtdzxndj2ocWLU+9vMHqud/iN21T1woB15SvfO+0+KyFFfm13oK/m6o46Cnapqt52MZ/HrnIVUz23c6M5rGCValgHYIznGbrKVdWAl3BD+LHhoN8BzvYFQutxG4fYoOpwhXnbe+u0S35M2rf0C7Vp9k9OS8tG05uKrcUaTsIabK7TWo7QJx52QGucugBb8sngWTth+9gYuO0irbwZ86BK/3mh08xzgrboPXB5m0UqxckQZXKp42OlargqitGE8mfbc0BD4KeNqk5638hjHCuXvjffafHGmdrsc/zaj0PDqibj7L4AfGAfr1Y5DUQqUVap+qk/ea/DG3t+KMWDV2hTil63T3slHPRHyMvOVIrtQD2ABU7zXCCf8mzyC8Dd4aC/UqC7Er8Jf5+SZV621k0un1GbfW0e0N/kVc+TvOsZyRna92x0XAFpWwkeMC9ij8rCUYJ1qk6FVZQqD3tUNsC01apewt1bChCoRAkzM9YhB/Cj3aLkAJmJYEwpyKEoEYxZSqAL5cQ/m2AezxDti7IH9Tfe8xLNOVebyXLv5ZylzS742Wlir3dqc4d5FadEgrxp9yOfxL0sgVSiXG3exNd2R7JEGTfpH3N59Da10G56RIPxrS41ZEw46F/9O89uJZLQpuy19rsp52CNsgYzw26/4DrzJmeJapIBsFg15U7zSqLKTUltcmokSMTJ+NrpxN6kamDsGnM+tHurvSqTIuV1NcvU4QsXqlQe97xSNXnZ5NdX659LKdygvKG2Vz5hvES6KIseup6mcgcX6d9ytj6berJcyHKh03TFv3dmKvE/wkMHVMa4lU59+zbratHfHPVGq8BHN1ej4EYpoL7ck5mnv92JWPL/KvM2vrKPeWiMOaD00ugdNEzybPQImxZyK7fpHyR8L+smiZlaSo62789ZG9P9Aki+x6xdFzxjAUkTvYgyVG32DlifcrH+reeW7981Rn6VHPyfrC1IBDjJaCa37wcuw+XSxvFCQH+3Vj25D3CDycesQbKDXNcnedkMUSZn2e1+HBi976lSvMOpWCL043q1pgMcJJPPnR7n7KDa1LH2GUP7R0dxvXljXJEfYMMS1YQfVSs8mHgo31choEilPgVMV/dn3wcUAPW2OlV5xzqBoeaIQ0v7WZXBWCX+HfxtMmSO4r33PCOrRZWsYMZ9l/4uGjZvWiczxjqD3eSodyMniRSiZFLKKONVaoiD0S1O9S0z1NGTdpNTGxjkjz4injWeo7/mTv6qiOJyT8tY2SmqpPOodUHaM0Z5A1rMmywBXSiyKJPxz87Q55AuItk9WU4UjYu1b/AKi3QiWQdUJidGnyC2hg1ZFDde4jRitHkWR8t1xcdpS9PBzeg9bzyXsPloIneQKwrEheZdaqV2hUXse40qjVHWhXxpd+JH77Au5A1eBNxMXv70//oX8A9CMalqUPReztFmscxp5HzpdGn3tn3KNmB7FsVpefqbHC8XkS5K8QiHZU5DXrT685zxghKi/PJ4z+rDVKcL1+mfJdYdwaBr5IWifDKy7rKuAqAq+cz3XpsYU6RS+MzuRi0OcoK+KPG+iXA8qMS1lsFhsReNxc5Nb1gn171c/yoNXINxzyEPyCLl5T7zcj52em8M/xfOVyX+u/CVTWgArGwodmo95XJ8YkeHsKrdYb2qQzOxDaVQzeS26bhlxEFAyVBzRHvg6888d5/eLNZZnq9SyRZu5l7i/IzrZ8qz1llLRuqv1zLRaqSJqAdoaitxuwZXANuPEus4T5vJQqdZfV8ATziFk4BBT5tnX7lW1eswxi2701ju7LnYaTQpN5btLVEeoqqiP2hsEuG8a5+wEpgOrCHW3ATUvVSbmhg73j6RCfaJkR5yGf21HytIDjWXW48pxdsVuBm3ROjg3kiXAN2SNwkYDcVO7tHHU4yXPPNSDrraznNx3QNGAW32kDM95HRt4Zc/9ooLLy9QTXnSGNNJCDqBu/16Yj+vmP2xXT5ABFfWqAB49N/7ZivxT8ffJiATcCaAJ0lrx1EkHjZ9tQVxlfxVFnpJEXq9ItJqXm7eAa6P5HZcLsbN7cQGcZ0+iTKlE1UaHmFTqjxkHGIu7hGOrCkOqHusIeJB/XUaiD0VlNiPxClLSXpIXql/WeGzFnIrTcR21qu61GFv1c+9d1NNFKIUjLQGp2eLEjrI9QB4hc12pxp15D7KlMEy1YhqorAqSfHgJlWLN+1+3K6/R01xoDPAQZX+fhWoQSV+N8JB/4++QGjEE9b5/YDx4aB/hS8QagS8PMp47aY4ByWOtnITneXaiIgJuCoFs5z23G9dTg+5jEainDe8QdUin4wsgJ5yKT6xi01OjQpB/u3Rq2gg9zDYmFFhOx6hEhlvU1V0AVAKZaJxh3lVs8lON2e3qkprGS6qJ/Zc04bwiHyV3iFDlJIqTDJEhFO0+Xzs9O4LvPnfOWuV+G/AFwgdB0ytSoFnkudeqohiClWqc0LkSTkoeg/9tR9oLzdcdc5Dk2eFYZYvENoOxCP+kixKEiW1j+xjSSNKJ7n6w22qWu/j5c/WdKfDqg/s44//wD5+5AvG6Gv82k8AvGz3P/HRQCiQSXHROM8jZIlSLmJaSpoZedJXNqEjro3YnrrsoUR5SRMRokoruDp6S+bF+te0EFt40jqPMrxOHm9FIxgpq516bFY1Z85y2r+3i6pjY/tYobD/k9OSPtoSLCWZ67TBx/Yu71gn7tvk1Kh2gT6dqjEubS1xQJ/luYnzo/eJHVTLAN4BNmdS8uSl2tTzG8kd2fPslvTSlolFThOOl4voqS0HoFClca91BUB3YA5uJ/5w4KrrzRuZqc2gn5zHV84xtkLTztFmV/hOTKVxQGWC60k8KBz0z/mvfNmV+MfhbxOQzVfNV3cWa9okv7dO1aW52AZANvEyo6oC4izg06Zia82Lta9pLraynWrHBs0Ljt1DjnrJ8xR1DtEWSxFRtjmuRlM2xQkidjOxlbftk/nM6Ul/OZccisikJFGyBPcB7ACagD0qa6uOysoVBVmHHkOp0ukmVlBTHKAmB6rEO4KEgGv0yfSMPMM4T5AucjVLHR9Bc9DKU7T5jSfZPbxrXdPdbR/bPWuerX1v7FZZLHIa87LxJFuSOpc2qtrVzwqEGscUsSvxG+ALhAwg+xANpaeAp2KfXwS8DYjqR+iEXO3UO7hDVb0zX6WNzRYlPGOfzWjrXIC9j+svRbJEaV2AdU5troreCsBJcgGvxgzu1zp1KnC+5qo2tBOhX91nQzjYSrBV5aq6Yu97N5vXffKF0/UDy/3Jy7H26WCT8prxWJEQKjdDlOJJ0rrb6MpfHPj3zlQl/gc4DfDUE3uoEqNOZIpSeZv+HvNUS96yT4liEzqnfHyyl6PnCeu81IeMNylSKZwoF9FQ7sZW4rym+g4Gq2+50xzS9n3nxBHANbeZ17DEaUI+6bxvH9cAeKSrWBHMTGou2a8yr8YlxvcAnt1GdS6M3lX9XmPc9EfMwZk7qXZVTDolDnmpGfDidiovBG54SH/9di8mU5yuzHCOrjCNHWrewiDnW+dC7RvneeNZXQBCUO1IgtwN5B7O0L63X7RPPxFXfoMh+pQ2N+kfZwOcJWcjBfTXfmC5Xe5m4VSY7pAJ3AgMAUghwvlyOh3lOjrJ1dqV0RHsVVnkigKUggOkc330BgrcimhlMFaJ/wh/6YDMFwh5gNOB7YL7o6fIedytjydHFPKGdQoFpHG3fBcAE42PPPfRQayvVUjqokHRe1Le9jxK9SQyfTqlPGmdL1KIxFWXE2nxTFFGIWl0iLzExdpX3K+Po5BUOog15i0pHyVuenGT3jhWOA2YZbfjGsN9gNYU+fV+dFo4udrhdicGNiM9rup6nnkxxcqbUFlf5DRREbzie7stmZQw027PWM+zLbNEiThHm7372Mjo5/aTNeAWc1i9O8yruUL7gjsN99g3OjV4wjyPWmI/Y63TTaDssI1X4ojwBUJ1cYV7fb5A6IVw0H99hQF52am95R0jlzs+sY9s3rBOUY2N7cKLSUTpbFI1S+arFl1etE+fNc4+iUeNV7hE+4quYiXj7RN35or8ZvFVTXa6sy0mBdVGhBObqCkOsMBpSgexjvWqzpZiUhe8Yvn7nqnNTq8dcwgwDmlUAdcF4Lxo3nu7yVlXk/0jXzceJ1cUkGdewnpVhzI8emOxY7QhnPrJZf4HzIucWAl25B9yUivxn+AzYPhy5UvZpzILqonCLKVwBuqz5EBm4Sjp+cjpPQNoSV62XOPVF3aIvPRkMamdgAbX6p83yhFF5IiEO1Fi8igFPGK8RqmZ0r6J3M7l2lTmOS241rwJFaMbD9G/LE4OhH5yWiTXIAuBM35WzSJnRx/UJM5KjgyBGzA1bCE297tI+yZVCDhDfU/vyGh2UTXhVhfBs/ceffxGQ9idK6wgaR/iVQxHQa7It48Ra2rG1fuVEonIKznsmuj0Zi+LD5TiyXzWOlMHqMEBqouD9nLVKMFRO0f7LtFwk00JV+lTCgdF790zUJuxZ4HTbPFUp8upQH1gFZX+wZX4D/GXDsiACcA5AArBl04XtpjVaS628o1zjG0jtaPkRhqJHRxQmfTSlgGIbEpSLtOmUo2KQVEfuZR+3gUUqNRiS4msvaoKhaTSTLici5rsx4OFhkIXDjkUc7y+NHkGioXke7stfeQSpjqdyDMv4ekkVXQh4Bi57ojNFMn+lHXEfo6LPE1/bS7FyktfbWHxau8lGXFCbCu5BWL3mHRRVmNhyjX737OOP6qnXMZspy1nJqXVdRTP22dtBb4D3gsH/dupxG/FWYAv9nqYLxC6JRz0R32B0FkGVoelXmPA255HG9hKUIwHDUS6iKAUZEmoTmHaBqvui0Ct9nJjgpPYXVtJN7mybfKD5X2rT8Ig/iPnWPscZ5ZWV+zlZ6cZLeUmpIBmYnv9cZ5HUm+MDksfEHmY87UZhZfqU1flqoJOUiDKS+aCB6xL1t2if9jmKLnhgiKVQmdtDQAveJ6huiigQKXiHFJY36aq8YZ9qgRRH+gPvEIl/jQIB/2zfYFQs+ocrF1NFP4EIJJ07erL3eDQfNBdj3vf8zDRI6z+y1OGrAW6+8omjBKooYeu84DKoApFCOEGZXcZ45fWEgf7A5yo/cxJ9gLqiz2lXeSq4o/tYy/vINdGU4TlWeQ0YR/ZpFJGKSnxiL4E2PK+58H5x4g1TVeohgyN3sJAbSZRdF63TyWaMANAplOWXq7/aJEmIqDYihvkAOQWkF546L3aVPLgATJSxlsnpbxmn2b3135Ql2hf6VfqX3iu0L70XGTeyRyn7eZvnQ4Dm9lbVvfXftKTf2vfOe04XZub3UculQ3Fbj62e3Gb/iEeYWkfWcc2HWG5nM29qqL8Xnu5Yew6VXfTKOvCF3Bt8L4BLgZ+rhTersR/ir96QHYiQD2xh6u1zwHFBdq36EKxVeXu7RV5tub15g3UYi+zvDdXWDBdlC15zDq/7uX6l9UA9qgs2srNAGSJ0iyAWuIAqapcN2qe05IoBrXFvigVSwE4Cn5SLXnNOo2vnU50l8u4Q3+f2d6bMERF26NSPBiHaEodmoIvUR6G6FNY7DRhl8rhMe3VZMVoLCVVESmFVURJVmz50YP06QbABbKcsx9VGiYa0z03pzWSu/bcbV7xky/AQGBpsp1HJcrhC4ROAIbhdmdNw9US0oH5sWDsZODjHApJESbgZhmykjTGkr/LlapBV8DcqaoaydksIcq5XmuduuwhJ/Fg3apqFPSOPpPTVazgfe/DJKOrXJX7rXcEZRhUFcWZQIXsAYCFFl3u+JqOTPK5jCOLEgWILFHKUrtBzSpaedfwFKszSbmE9b/xlFXif4hw0L8V2Ere8CVA+/j7thKMt04EmPCe5+EauAE1QLMPrd63Aed9bnUrbO3ZnOiovDF6HV87x5RcrU9OG659ylpVl5l2+4lDjSlXAtVtJThFzuMMfW4qkNpNrsiNdx6udurxojGaU7T5bFW5606IPHljFEN4iTboKlc1AGgvNjLGM1p1lOsFQA1xsOxB65JES+NC1Zw3rFMO9JDLcz61e8VL5SFcgdVqABdHA43GGqPXFJBW9W2r767Gcuez1+qfj7sheoO8RPvqvZfkU/2+cY7Rm8QmzlIo2osNzKFtrW2q2md3m0N0t8mmnL+bQYnqINdLcCe38UoKwNnad8x0juIzpwdTnc7cbV5OfzkXEG9311beCTyZ9HU0Dgf9M6lEJf4L+KsHZNuBKmONp2knwxU+qMveGoO0aYzQJ4JSeA4Jir6wu7QOOd31F+3TARgg5/CE8SJeYSVS4ADLnYb00FZSpgw2qNpkUMKrlt9ztFxHZ7EaGeu+udu8gnedkxLrf814ooInIMBr5imsVA15wvPyYQdyKB/icn0q2cKdcF0fvd60lFS6cDy2EvzktIi+YZ/qcRBZr3qeii+vjtRI4BE2MWHGqsANDcXOgUAtIOILhLqEg/5KbbIk+AKh9sAUwIsrLnwW0AloA0yODesFsIdsVtn1VEtt6xElgTc51bDwcKE2LX27ymWq07nwMjOQ8rrxmBHPdD5tncMGVZc5TmssNLu+2KXtVjlE8FQBKCKhhl4haE8TUdIO6aJUCnap7IJi0vY+al1QkE/G0bYSaELF7L00VjkNrZ2qqt5Pnw9Apiir4N4QV+wHXg8H/d/+jlNYif8djse1KRoKcIBM9lDFxC017wRWAK2BghftAad3FGtybjVcXegyZfCYdT6Tne4M1z+xq1BEn8iTbKEWwLvfq7YT3vI8ll9Eyqln6HMTAX8K0cQ95hxtViKrX0/sbd5Nrjwwy2lPBIOtKtesJ/YaSqFaii0rY/tBDXEw+XorBNIfsC7NOeS4riTp2bRC+egTHX0QOAWoj8O4R60LbhtnjLSP1Za3AOipreAzuzuna3PZrqqqyU53MUQLee7Wx7cpwcto82xuMz7AIxx+dFoUb1G1Xt/sVB/eQO45bDIsBNymv89n0R4AarzdV4y3+5YCt4cf8jsEQs8CJ+MK6QZ+x/dWiUocEX/1gOwdYFQyH8JRlEhB2hSn69r79XHNU0W0AnlAKZjptONrp5MOikZiJ1Gl8ZjxMl7h2h9dbw6nldjC0XIdx2uLAVdtfbA+nUxRynDzBm6ODmNOyg2A+wM+QVvEvcZ45jitecc+6bBgbI+TyTjn5If6yvl3csh5PxJBNZPy7Hee8fawz+3uZ56lf3+aJhRtZdhzKVOZ5RzFFLvL1NO0nxzgJRs5UeL86ncaxYgrSXtxMyuVAVkMvkCoOq7/qTfpbW846F+MawUTR9uWYjPt5AauMG8TE8SotT65qyZQoVHDwpPwnnzUeJmpkc6Z3ztt6Rd9lCHaFDao2vbr9qla7ALd/a7xkN1dW1m7UKXysd1LPGpdoJarRuIO80r7Cu0Lu4XcViEreyiEgOuiN2ctVM2zcJsMjhpm3ihGGq9RTRSSgs3R2gZ9jyrXmfLJ3YmOYEfBJLtH/KP2vkDICAf95u85l5X44+Erm1AmcNa8YDz7SWe5Sn/IvHgziHfdzHc+5GX3BI67MHpni/Wq7uUDtekJvliKMHnPPoHLtS+5Sf84E6Cd3MjZ0QcBms90jm7uK5twVjhl8OD49vaqLL6wOnOxMQ1wKRYFKo0sUUJUaWV7VPb7wDUgOCeS59xlTPjke6dt2bFyyccDtB9GAZHXrX57geP6y7lmC7kl8337uApOEYBJucG5AkQbEaat3Njpa/uYjfvJ2o7rOkK9JL00gK/sTtxtDnFK8XxnofcZqk9GCsggwsX6NPpERpvHyLUzv3fajjxAZstrzZvMkPdu40id8fXlHq7VJr081j4jAmQAb4aD/l0A4aB/I+4ErRKV+K/iLxuQxcjWO4H77zavuC2gv5uRK/IPVBcFOQC7VE7zbSqXpqIiXaqAVC4z7wTgGeN5ztDmsFdlkRoLoKSA54wXuMO8kr5iwWHbrSNckcKdVOUHpxXd5Ep2qir01RYCcJL2M2bcn4byYKuKKIk0EdunbFXV701e32Y7lwZaxRsLQBR9h1dZ1YVgQ64oGKcL++j4ZxmU0VNbQU9tBW9ZfTN8ZRPS0iltvjxliM0vfKe2EkxxuvCSNSD+1kbc0kAlytGAisKO73MEq6nWItzrE8/9eIVJVNcwsJsBOIqDUlBFKdivMvnM7sHN8iMAilV5YmCjqs091hCg/EKpQmGN7jELpkxRyqX61/i1H8V6VYev7GO0RmJnYmyp8iSu1zhMJZ2Jdp/IQtUsnlLLAIZ+6XR5pZe9lIv0aYmx1UVFPk78ISQF7nrdZ3YnXP2m7/7VSavE/xvGKOSl15k3ATwcDvrv/SwQquYLhLoBP4eD+Qd9gVAqEASYanfec5v+fnEuBQ32UOXFElJ29td+aA+cC1CNAgf3249fax7gcWCMrUTBpdE70teo+p6jtPW0l2E2O9XNR63znz5ZW1j9Feu01itVwyvjO7aLqt4bzevPAvjAPu7Y4eYNDcJBv1oYCGnPG89e31/7YTTAhdo3DDNvZK7TRuHysT4Hno7tg2grNvCZ516kUPJ2/T06R8bWcWKUubftk7lXjEMhmOp0YorTFQcpgT4ABSqdmrGmrbpiLzvINSY7uSd1FqtOesx4iTI8FCkvGSKCpQSbVY3CxmJXopz7s2p6Ia6g7A5cfbNKVOIPxV9Gqd8XCGX7AqEW5v1VW+XfX/u6+mL3PFwD3LtnOkdnnBp9lAVO80Tqu40Mc2n0Dp63zmCu3SqxnpDtagR6iXKG5nYo54oCFtpNEkrnXmHylDGW1iLs+lsqg7BTk01ODYKm6/fsILkoeienRh5hQGQk+1U5xetUbT7T7aNY4DRLZL4MYXufN5796hptkkpWVPeIigkIpSCqpO3Bqi0EOtAc6DZA++GWtU6dWftURoXo7VL9657d5fIOxaQ+9qh5/ufAnKiS7+CW3WbFx2lC8ah1AYWub50NtKz0JzwMPwMf4z6UJgMXxSxjEvAFQqnNxNbq3tj35hF2sqF3FXAD8GqykPftPgtt5brH1xT7aS8qUrJyKOADzwPM817LCfJnljm+Cp/nigK6ylXcrY+vUHJPFVFKDhHY3K5yp9xpXdUfRNz072zcTMKS+63LuMe8jJ3K/Xk4Sdff8+bpfGD12QKsO6jSn1qtGsSlLgqBSmmUPzeaJb1u6guE6gMrzpSz5670Xlqi7s9e200ubxsfkE9GaufIi8PaRV5tVuOBTcPCQf9DR8v11wJfAksbyt19cZukZgBPABPJy38ZyG4deeOd5aqRx0TnnOiDnBIJcnp05OsvjBp5x43m9bWWqcZdqTgZTOaI1CaW9QoH/fZ+ldkv/kFVUcR4YyTDtY/vCwf9J+M2kSTKA2drsxNd69VEIT52kMvBSc8Yz28/US40f3aaqjfsfgwzb8Q55HGW7MRysLwUz13GBBrJXbSSW/jRac2LVn+uNW/eXU/sTcyaIkrf+4PTJm6TUpvyJoNKVOIPw18iQ+YLhJoCc+qyp7qNtLJFif6RJ4/rojdym/G+56DK4E7zSt6wTqWHXI6BzSuWn21U5wnrfDuFyM4hzpSqUYzUt+xTAMigNOFrqRRsUdXpmPTATOZibXaqs59s0imlLMbl7ypWcpP+EVtULtVEATayQumxDIMh0ft5yhhb1l/OLdSFUz1VmJlHaZsqHFuWKOUF83TayY1kUkx7uYEIHg3MhC7UHdEh57/vnOgJB/19yMu+FXfWmkATsZ25tGGsfcbRJXjPecB4exJuZ+CPwBe2kn3fs4+zt6oa8VLcrnDQf7iMeyXuotx25Ztw0H+Yx0s46C/tGtj/9iqn/iUt3U7XX8S7nocXaEJ1BLe801WuDG+ya9Z+yfO0t4nYxny7OV2k62Z1v/E2PSLPcY42i0HadGqLveTEOIRHEhhOSwrki5WHa82bTgEm4VrPVL9XH8fZ2neBSXaPlDzrMix0PJRngR0FD1iX8JbdDyDtduvq88JB/48EQi/h8mOmh4P+bb/91FXi/wH3AeO9REtu1T/4+HXr1JN2UK3GrcYHpApTAk3HGqNzOkRemQq0xFWRDxWRVhLjjy4nL38vSVZDYfe/SfG/XX/gCSfh6nMBYKI7q1WDH4DJvkBIAokAC4gC1wHJRNnPgGqxZhkzg/P7NRNbiWeEpYARxsQ98Bq4GanEtr62O3K59iVCQETp7KIqjxsvHX2a9lMi2jqGdRSoNJ61E+pr77YSm9qWqJR28TcWOM0TO7NdVaVD7PV6VftA0Bo8GRi4XtUxWgn3N61hL8OlLnTHrSRUNkBV4g+HqJCu+RPCFwj1AW4CzuwtF/O2p9yNYq1Th7gNSMjqyjDrRjRsBAodmzO0OexQVZnlHHXYetuIjYS8dyf+Djs18MndR9yH5M/mO805N5rHT97rVA1x8IhkboC1dh0W05SXLT8WWtE0z60Z8WAtzruI4+TIo9yiT6SfNi/x3nd2W6qJAlrLzRxU6VwYvVstV74+4ZTBNYCJ4GbTlitfZHD0bk8B6QLgRLnQfs3zhJa0K1cBT0eV5oy2zkk5XltsFKnUecdri7qTl18h+/NPhi8QGgyMT3prczjob/hL4zsE3qs92zv8RB11z36V3jxXFIpDu2kBdqssaogC1ju1GRi9b+8V+hcHhumfNYOKtkV7VdbWTpEX68WXu0Mbz7VGeUV5p5ONhnKqy4LDstqXRm9jptOBVmLTriZi++2bVM1nP/fekyi93hS9hqeNFw/jKb5gDeBx64L4n0vCQf/hP5RK/LmRl+3BLSt3MZW28ZjIWN9zxvOij+ZSQ+83L93zln2KXY383YO06e13U4UP7T6AuAbYhWszVAU3S3YMsBwoxc2UfYOrWp8Zey+Vw/EJbnNBldjfq3GDmO1AMoE/vryJW5nRbtE/cIZrnyohWAyc5CubUBvXcaVhbPu7gcV5+hvhHnJ589vNa3ovUk3tZ4znss/Q5lZoRlnh1OdHpzX1xW4WOM133ap/UEMTSiyymzBXteYlq3/cHoksiotv0D8uKcNT+r59fN8tqkZ1YPad2jtcbUyJr3Kdr2xCcyAnHPTv9wVCObji0OHf+M1UohL/Nv7UGTJfINQd+JZYafVHpxWLnMbRo+UGz5d2Z9rLch/abtoKsMBGo7tYxkPGGzSNEaonWCdQgpdxdl+2q1yai62EVU0+tXvQX/7AAqcZXbXDfbcdBQ9bF1JbHOAq6f5QYxYZ2EhFRa3BChmyZtp2mrEdv/yBKU7XjI/sXitO0n5uvVnV4IrobTxpvMixcimTnB6A4iixroKUxhdOV27QPwZcH80ztdliueW7xFc2Yf23nluu3amq9r3FvPbsnVTzAgeBrUDbJU5jLV+ll2WL4hRcIvrlQIZH2IzQP4yTeruE7K4P+KECn+2fDIHTYKA2k2rk847dlwLSf/i18T8HB+2AQe8A73QPhLb3lEtrP6C/SSOxs4JLQ1WKaF32OiV4AZG7WdVMlJw3qxqMMU/nDG3OF/dZl6UC9QDSKGOoXh6MRZSORBEPxpSCOU4rvnE6sVrVZ47Tluu0SdxuvF8TN83wIm4g7rWVsK/WJ2uHBmMAtUUFIf72vkBoeDjof+7fPHWV+P9FM6ALgCHsRl3lqs3DzBsanO9MZ5eqymSne3WAZ4zna/WKWQVlUZL/mn3a3VkU179J/wiBYrR1zsB8t6zXJGndyZmvVNwgqw4xsn3s/bMO2Z8PwkH/AV8gdCZwJzE+F+XBnAGMAYynrIGf3aB/GiIvX/kCoWzcwLIqbpatfTjoj92U/bx3zxk/jjJey51qd+JR84LdGZSlHC3XEXczKVKpXK67vpdd5Kqa8d9gLbnfeTRyQRA3+w1AAellD1sXx+1LxuCW998sJvX8pP1U4aBfAft9gVBnXPmbTF8g9FA46L/vF7+NSlTiP8CfOiDDbZWWTcQ2xhjPkC7KeMo8d9IlzjENztNmNu4kV1cjFqxtcPVreNt4hN7a0gorGay73ft95QJ2U4XOcg1bVS6nRx7mJobxpnFkD1gp4HunHWXKIJ1SpwyvfN46E4CrorfI4fqn9gZVWwurmjQX2+gtF9EsyZcQXL7POdp3vGqd2vo+8zIu06dyjf45w8zh+MQuesmlfOUNoBQeR8FBlc5Y+/SyY+TqFFtJEK7I52KnST5uOzgnRJ8qxS1HxuHBJZ+33UMVNSh6z61feO9chquj9TiurUkFLHV8nfz/xhfxd8cP3uv1mjHbo9O0nzb1j466+EjjfIGQDzDDQf828rIHLHIav36L3j73Pet4BkbvP3ivMa7KWdr3ifG6cBA4tBRb2KxqOO/bx7csUwZd5OqNH9u93lmgWtT42Ol9CuXis3gwv5GQ0FDxCosaSUT8YlK40LyHXAq4xxjHRdo3Zje5Yh+unIkOXA/sBV4/SMZnDcSezyjvXANgn8okdi0nXCmAtlTir4YNlMtbbF+j6oVs5GWv26d6FFLLpIQJnocryAI1EjtkfbGr/qvGk7SQWwE4Wc7nE6cXT1nn4SAjuOW6UlwF+niFzwFUGxEWHeUaptkd2U5u8r6UAo+Sl50bTmGxr2zCubj3oORMswKex72X9fSVTZgRhiKgOm4wBu79rDGw2hcI1ektF4962zOjC0BruYkfnFabbjeHnvWS56kZ1UShsUtV4QenVWEXbU0mQBkeDGWTKqLUEgcew7033pW0D8uIBYrd5IoTXzKe2p9FyX4h6I9rxt4QuCdp/PmUl1GH4JaKK1GJ/zr+1CVLXyBUBZj/lDGmydkx5fm1Tt19zeS2E0iSIbAUnBV5yFlKE7nWe3EFIdbkrFWyvhjAVdFb+NrpxELvUKomSWfEMc9uxrP22bxqPIlXWDxnncmT1sDDxmVTRJ7xFj3FMlVD5idyEcnbnmm3o4dckdi33U42NWQ+JcpTQSJjv8qwdqkcPabEzyKnCd/aR33k1348M0OUaU+YA/nEOfbQXfgwHPQPjHVXlaz0XtYxVUT7Au8CU4FLtjtV69SR+x+MLzDbbmN1lqsbex/Y9+tEqH8AfIHQcQ/pr0+7WP9GAphKW98sMu4LXCXup8Mpgz8E7lniNOp7o3l9942qtl2bfRfPTRn+BkllmTJlMMy8YVMjsSN1hP5hjVRhMtduhS5sOss1bFdVzdMijxgHyeQcOWv6Y8ZLx+wlO2tI9FaWqcbx1ViAb4l3yIYsUXpEmYstTi7HRp/lSWMs52iJJsiVQKvkcT84Lc8YFL3v88nGnWvaapuaJn/2otXfDlqD2+CWqV7F9a3sFw76K85mKvHnR152FtBpp6rSc7fKebC93EjYqcnA6H30lMt42jO2wvBnzTM5TfuJpvJww477zUsLxtknaSP0iYVnaN//cE30Jn2ZahwXmKWx2K6+8NwpvMJkp8rhuMhTlCWpxMzw3DzYJ3e9iTsxGOIrm/AxcA0Qn/UuANbiBj4ACTsyXyD0Am7A8xXwHG727fj6YlfL6Z4R6DF/1hOjT9g6tvaN9/bEdodER9BHLqGB2M1o6xy2qlzrRc/T3To9OH+BLxASwAfEukmzKZo51ni6dwu5VZQpD3XlvvhqJpOXn2hDj8MXCJ2G2/0pgbfDQf+l/+orqUQlfg/+7F2WA4FGW1X5LGydqpt+ZuTBd5MH6QLGeJ6RtdjH+qTOGqUqtvosc3xrljvuZG2PyqaV2MRXntvY52Qmr459KpP5dhOmOx04Wc4nLuJ5kfw6PqQCWegm/SPO0r4nORjb5WRjJp3eifaxRJMSkjWk2459qF5ZVVGUCMYAqlJg36h/cloLuU2rK/bxhPEiR4s11KSC+flHAOGg/4eT5PwbvJhvAIOV4iOg7iS7+yVfOJ0f3KPKZbJ6acv1KMbH/MPhC4SygC9esf1ylVOfvSqLW81rJHB9FkWdO4nVbxcp7+3A/e3lxh5jjGcEoB8gY6ytRIXfT4ow6SsXNHzV7l/jpMgT9hvWKZaJRmfpWhbVEfuNPnLJWi+R8CPGKz01obJqioN86rmP5d7LOUnOB/dBtuIxc2DFNsokTHM6AlBf7E4QjW0lDgA99qqsbQCbnBpMtHo/CgyuK/cmgrG9TiZvWX2dd+yTLg8H/avDQf8EICMc9NetDMb+osjLLwCyaomDD7aXGwHwyV30135gjaqPpWSFWXeaiFAjlg0GsJVI/jzrQm1a+jB9Uq16Yu+ZTxtjk8uWdJMr5sY7jGuJA1yqTWWcMYrLtC8BSjapmmNxM1wSGBIO+gvCQf9juJywO3HN0aslrTLxOhz0DwsH/SkD5Jybrtc+mXKZ9uW1GnbLbao6j1sDCdldGGbeSFjV1raq6oSdmoB7v17iNOY+63IuM+9gkWr6816qDOj04PwFsfUqIEFBuFmf2KeHtlJUE4XJwRj8AnE/HPRPwc0SngpccaQxlajEfwN/2pKlLxDKAF4CeNY6m4Mqkz5yEamUpVQXB1uvd2rRRJarNtSXexmozWRAdCSrvJehCQchYLXdgDaaa4lUQ+Y37xt5rOwouSHFUoIPPQ8lMlgfWz3ZoapypT6FaqKQalohnbT1TLR6JbaRI4tpKzZQRKqWSpSVyg3u7KTAq0R5Nr1un5qVQWnOZfpXAOxxsvjc6cVeM4cLtWmscupxs/5RBa7RkcRh9zmZNJB7NJLItALFp948TKWpV6zTXjGEHb5Kn/Il5OMLhIxOIuOKeJu4EHiAO87Q5vYG2Ksy2ejUpJHcBcABldG0XSA0Frg/HPQfuaPh74+6QMpmVZN+0UTpulE18pnivZOa4qCuFMPjH2TGmjHK8FYZao4ofdF4+qvtqlr7hnJ3LUcJZjtuY1euyNfinJb4d7tHZWMhGv3svUpPztTqwkEnwsPGG3wT6QSQ9Y5zCifaP3O8VlG396BK4w37FEboH/Cx1athU2MbVUUREtVjvtO88eDo3fMe1l+rO1CfxROel1ueaP98ZgQP4NojPW6fz/v2CfOAY32B0P5w0B86VNqjEn9J1Ez+QyloIbbwujqVi8w7V5wmf2xzovyZXeTwiuVnkdOU2/X32KDqsF9lBPprP57+mdP9qHftEzKGapMT68kQpTquafZyYHknuWYMrqROjy/sztyhv48UimO1ZcxzWqZ943Qk3lCwXVVbGJ8eh1MGf4sbjL050e79zq3mNVVwuWIP+gKhesC2WODEg8abz+SIIh0gRxTRUmyOnKLN9xaqVJ61zgZQZXjF2dEHdl+mT/3kfeu4E/eQkw3k4GbgTgkH/fmJg8jLrneFdvGK1+1Ti4F0QcWq0MumnwZy93P9tHl3/tLJjTmaVIpoV+IPxZ82ICOmet5ObCBdlLFa1eM+bRwAPeQKSmL892R15dWqHiY6U51OnKb9RKnysENVow1uQCaUopSUlB+c1lwqv6gQANUUB3jaPpdh4vMKO7FHZWMriSYcHCUYIOdwpT4FTcBsuw2XmgGesc4hjTIaiF2sVfU//8juffS12qREJLfJtSNhrtOGuU4bNGyG65+gJSXahICNTq2yhmKnJoXL91mn6lCN1RWOM36shrDFdcbng3EFQC8lL7tjOJhf4guw4jnrzDZ95GLK8DyTQrRrfNacKwrJFYWUKMPRUHKdqlNF4lzjIBvj2pL8E7EalxtTIdt1u/7uwZriYBUAIag1z26e7xFW9lPWeQml8GlOx73GA/tPOTnwSYPOcvWaAyrDu1w1AiDu9weu8f3w6DD2q0ye8bygp4nD1DQAqEqBc732ifzW6cAK5aOhODxGLiGF9zwjqS32A6RFlUv/EgKmWR3ejGLoTeSOBOk6RxR1uzE6bNto44W6teUBbtYnmt/Z7Y/ZTm5X4HJfINQ8pjxeib823l7pNBhWg/3tqknXKPw8bSb3WFfwg9OmzY9OKxbI5hjCZh9ZqyY73VtOjnaPL7v+FmvYg7j0Bt6yT6GtDNNA7OYRtwt3eTjov8gd6gfo2Tzw6Z6aYn/uyZ75iR1QoMbZJ4uVTgM8wmKO03YogdBrI/QPMq7U9FCqsKoAnKvNOvFcbVZdX9kEgO9xtRYP+gKhj4G713jLasfX2U0uX9VFrG4OrmDyyXLB/tV2g9OAFvvJmn7Dw29uueHXzkpe9jnAe/cZ47QectnYK83brnvSGkhzsZWmchsT7d48ag/eis3D4Yf8h7dJV+JvByFEVVyv4rOARrgZ3a3A18ALSqn/N4mTP23JMhz0bx+kfVvwufce3vM8zCCt3DBb4lAlSaPpZes0rojeylSnCwBPWeeyV2WiYdFYlpPsN8csOrxEGWFMrLC9laohW1RNHjUHUajKu7vryX1cbw5nnt0cBVxtuMEYQC9tOZdoX1uFpEULVRq9tBVcrk+9/ktPoMdAfRb7VSafW12oRgHfeG7lTDmbccZIHtZf527zCjY4NYk/UDc4tTgtOiplgWqRKFVliyIm212ZY7dmPxXLqrZCQULtsAUQL0sd96Q1cNjp0ZG9uzz4003DzeFNljiNSKYKpglTeoXFCdpi3jUehn+w6GEsO3QogdBqLTdVif9RpoySweY92WdEH2amc1QdXKmANwC/LxA6vZHYsSqXfGu7yp1CTOd+nmrJh1bvYrccpMgUpeQZb1dQyd+jsvjaLlea8Ahb3mp8yETPA7QW4efG2ydZjoIyVT5vWmE3UDUp746cHjNw2Ozk0kDs1l8xnuAbu6NwlLCKlZcXrNPrDdU/r1tbusvUEgeN07U58RXqlMsVVOKvjLz80lOjwV73WEPmxt8Kq5qYsTn3TfrHPOMZwxPGS9ypv9vykKUfBU7H5RFSSBrXmDdzWvQRvnPaT8bVFeO8O5/ssfN+30vkZT+WRfGULaomt5tXM8tuZ75vHffRCuXrD3w7X7VkjtMWXH7lhGPl0lnxYCwGz9mRvFHAhbjBGLjX4RXApkvMQMdClXIAWNlVrj5bCD4GsJUoPk374dRw0P9jOOh/Oxz0/xb+6wW417noIVd0AyggnQvMe+kceZFHrcFPAu3+wRWCfxSEED1ws70P4pahqwBpuNfhMOBnIcQ1/1/796cNyHyB0HHnarMSwpRtxAYOOmmYSpKcct7pVGGsdToLnXLR6kHadHJFIR7hkCvKM9fdtZW8ZYxCwyGNSOL9eXZzPre7MUz7lAVOM76JcXTAtUr6wunKJmolSozJwU06pXoHsdZzVZJMgdcVZaSqKKSqKKSR3ElTuZ3HjRc5VlvOBfp0zta+Y52qm2gyaCx3cqn2FVdGR7DJcQPHlnIbIBhs3sNb1ilElUap8rDIbsy10Rs3KEVcq2M+bqaHcNC/Nxz0jwkH/d+Rl+29V38n4iD40O6NrcRh+99VW0UOBa/6AqEK7VL/MFwJlCX9rW9J8tebYJ/wnVmeTFbAQ+Gg/4pw0L+0i1g58jPPPamjPWPSP/bc3x032wZAiogWaEIJKeAW7UPSkzbxudWNLpExXGeWf99xpIkIr3seu+4e/R1diooq47myQIy0BrNPZbLSqb/pJmsYLcre5HX7VC7Qp9NXW8iF+jTOj95z8lf2MbxpPMaJ2uKK66dsEu71Mioc9P/8H525SvxpEA76C75wuva8x7z8w6A5yBoUvReJQ0Oxk45iTWLS0VWsAFdAeA/A0WJdk1Zi0zBc3bFDsSgc9Bf4AqF7z9a++76WODAUuG2Od7gAXvzI6f35Jeadnc5/eNK5Ma7VPYcsn5NNUUIXUSnU61Y/FqrmVwGjjrA9zw9Oa9Eu8vpO8vJbk5e/ErcBoJsmVLNWDy776d88LV/GX3xudztSarooHPQf/DfXWYm/IIQQTXEdWGrh3qefB07AVSG4A3dC4gXGCCHO+aX1/JH4U5YsfYFQC+Cr560zjZeNp9CxaCR2H8axAqgpDvKDdxgairusK6Mf2Md5VqkGic93qSpkUJoo9fXRlrFQXk0EHalMNqhaRNCZ4BlFuohgKo3rzBtpLzaQLko5oDJ4zniWOU5rzpSz0YXDQdJJV2VEMNiicmkjwxXU1HerbFVD5Ivv7LaQFDwaopyq00NbxaENrkfLdVysfZ3wywQwMOkqljNc/yQWvNkcrW2gHwtqtYq84W0ithduVjXfLyRNhd1zVws4EA76I8AVJ2kL6wG0FRtLdqmcmabSO31nt/UO0mdk6MKRPztNCw6Q9SRwhy8QOiYc9G/9Pd/ZXxnhoP9DXyB0lIF1d0uxmT0qmxNleZxSV+z7AjgK94f8dDjoT3xBvbUlpfGgupHcmeMlakVi5fRZTvvaAzSXS1xdupmxVU49FjrNeNC6BIXERHJB9B7O0WaqznKV6CmXs1VVp6HLHQSgljhIgUrFg8VY63QWO03wYrJB1ckoJeVx4Ki6Yl813I5JJA4DtB++OEufc8TjvcH4dOgNI9+qzAj8PdH3HbvveQAChzeMxzlOW8wuVSU9TntoIzfxvvGAPsi8b+Yw7dOzbjU+1ABm2u3XXWoGvsft1s0hJhDrC4SWAOdWjWl+AXiEXTUc9F9yhO2vwX2w5eDe/PY8YF3i3KG/V6+A9IJrozdp+8mKWxKlUZEusA2X0wkwM7HGvHybijI/R4TrKoAZ1y/zBUIemPDjt55bejaWO9Ud1tXjkobvA+biZruPtK7qsXUd/FfbrcRfBqNxr0uAIUqpN5M+myuEmIJ7naUBzwshvlRKFf8vd/BPGZDhCmQas5z2jLUGcLH+9RFlKcDlznhjXKwLtG89H9jHMdHuw0GVwUlyPudrMw8L5FKSbGeasCMhIAtgCBuF4MTok4vu098++grdnWBVpYD37OOtgdpMPUe435EHm9HGWJ63zmCDU4vGcifvW73JFQWiu1zJCuVjsFZu6lysvKSL8sxc8n6FnepmP23+YZ11NdjP+96Rhx13oUpNL8PLctUoE1drrLcvENqCW17Y4guEeoZTylMrulCyjth/KkBD+e1i4LKfnBZNL4zeHTfProGryv3+EU/03xw5FL79kuepu7vI1VhKVqD9vmsfPxo3C3l0OOjflbxcifJutJXorAnFNPtoM4JnE7Hy8Yf2cVSjgNv19xIBewuxlUbaTr5xjuFbpyOplPGm51Gay20iTv5vwJ7kTRBVOn0iT0cLSVug4XSf7r2FOi6HrNpku+vG/g99dfsZd65v5MXc4BM7CNnduMWY6OXIiJCUxavE3w6JG2Vt9nNcLDtaM8lVRAjoqq32dbNX+E7W5keI6dD10ZY0Cz/k7wXgC4SuAcbiBmfjgOBj1vntc0U+TcW2ot2qSvfv7jn/pSsefv/q+HpjNkovANm4ivwGcNQMp8O+GdEOdbvL5bNzRGGj/Und3rgyLw/jlo9+AG4DcnF19P4lYgr61XHJbU8ByhcIXYor+fMt0POE6FObcCcrrwMjcb0yzw4H/bN+YZ1DY8ce8QVCp4eD/iNlDivxF4IQoi0xAiQw45BgDACl1DIhxOPA/biT78twr+f/Gf6sJcsZwIQh2hTrRuOTXwzGwO06i+NHx6VGtBCbech4g3O1746YVfslRJWkRHkYpn/KWP2JRoO18t9hXbGXi/RpuucQQrYQMNyYxCXmHbQpe41pzjGcqC0iTUS4Wp+MiZbQp5hlt+NIUAp8co9x6HsAHbTD+da2EkyOmaQnoQ8xrgcuJ2z253bXvPiHZcpIjjGO2q8yv/nU7vWpiT479t5OXKXsfyQu1afmxH0lXTFXmGG35wnzPGY4HQA6AZ0PXe5CfVrLeCk7XZQVAb2BEonDIO1bLtamVsieCuEKvQ7XPiGFCG8ZQZrLbYnP4v/Hv39HwePmQA6Q5XGQZbO9w3fGgjEAsim+B2CxarL9Puvy4m2qOo96XqU6BxNjSpwK8y4vv5AVqMRfH+Ggfw6uS8OEQlLP3qaq5QPkqzQ+tnomritLCfaobD62j41nYm0qTsaSZwVFwIPrVd0O65y6I7JFSUYzub3qxdpXQ4fc9VCfpHFX4oqoSioKEVcJpwy++V3PyEZTPXfQV85P+oi1sf09C5fLFqdO3AzgC4Q0XyB0Siz7VQG+QKgdsBG3/H5b7G0BjMDlCfWMvdcQuD4c9I/CJXE3+KVgLIZrYseQClRqjv3B8AVCR/sCoRd8gdAcXyC0JPb/C75A6Oj/4mbOTXr96q+Me+0Xlvmf4E+ZIQsH/TZw4eL77vEQOyk7nRzetk/ieLmYbFFMc7kNU2k8YQ6kljjAStVAhZxuAuBy7ct4F9phchKlyiA1KUMmBNgKXrf7cbxcQlO5nQ5iPUgSXoCOgnqifMJWpjQM7AS5v1R5cJTkdc/jdBRrEh2RjqLsyuiInadq86puVLXZrGpwKuU3I1MJNqnaFQQaC1QqWaL0sEAy+Tg0oWgod1PspFJNFDDbaYtCjsUNyrqdr01XA+ScBirJ2WmNqpPakq2WR9g6QFVRWG2+0/x0XEX4o4C14aD/AP9E5GWnKnX2Z3tVJrmxskw+aVxm3kGSO1YxsNwXCB2L+7D4GXg4nLJvJtAeoLXY/G446N/hC4RKb9E/TLten3TYpuJoJTfzvPEsXbQ1R/w8/l2HVS2WqCbUZD8plB2sLgprJY87VltWh7zsC8LB/HebBD6bepY2+2yoaEieKix2OdnUjGnfKUXGvzFPqcRfDOGg/1Xysj/BzQZ9DHwzwrzm5hWOr9ME+0TO0WaV7FdZaQrBm3Y//aDKuGK0Z8w08vI3J63jI18gdAtwNK5Iqxe4Y5/KTDhI6Dj0l3OO8gVCvXAbi/I5MuQ+lXlZNVGILhz6afMKvnY6ZeFavl0EJPPC4h3C8Xr7m7Exti8QOi0c9H+VNHYYJO7TybIfR/JkbR07rvAv7GMyZlLuTvBrgVsl/gP4AqFOuJPDw5xkcP1Qr/MFQnOAG8NB//wjjPl30Dvp9YxfGqSU2iKEWI9rIdZDCKErpY7cFv8H4E8ZkMVxlNwwbI/KblyivK1/clqm3G58SEQZDDFvpUwZpQ3ErvVB49W2HmGzzqktatn7Weg0Z40qbxpMDmyUokIwFocmoJXYwi6VQ1MOV6+WwuXlAOxXGexXmTSVO4goXX1tHyPGOycxSJuuuspV4pDlUl42nm46IDqSvWRzqTaVz6xunK67vCJDKF40+3OP/g5VZAmOgrHRAdzh/eCwfTj0OPrL73lMfwkpXK/Ou6wrHwPe7iJWDntEf+W6+AP5DbMvttC5VPsKIxaMAbxmncoaVb8oxjX7d4myfxv4AqHaY43OC242Pq4BsNhuxDpVh9dsP7FgbAeu7coPwGbcQCwbd0a/BrgV117GyRIl8ZnXda3E5jdwuQhH1JgrxUszsY1DEVFSeYWTGJ1GiXrf+5CIKBn9zO75tlKcdei65tqt+lwQCN11ppzbJl6Oj2dChHD/1RT5HFDpLHMa8aXT5Z3Di+CV+JthNG4gA9DuVc9TnRwl1Av2GW+eos0/tqooanql+oIzow+qT51euz4t63U2gdDKcNDviuflZQ8Ip7ADeJa8fNsXCN13gZw26DqjXBZICKgr951P+QN1K650Rm/c7FLclkuMs/vm3uR680abim3X4Ip+27hcrmtwy0RLgMdwKSufxtZ5cux/DVfHLDkg05JeK6CAmFzSEfDvlB1vwZVAKAwH/f/YqsEfCV8g5AcmUtGA/kjoAXznC4TODQf9oX8x9tfQJvZ/gVLq8BtvRazADcg8uPSTVf/Bdv8t/KkDMvLyd3d2+QDvTfOMaAPgFSbPGs9xZfTW1NZyyyaPsNsCNJU7uFeOJ6o0LonewRdWJ/poSyoo4UfREnyzZP0ycFu97zWv4EoVolR5uEafRKo4XJZmm1OVdjGhWa+wxDSnIz86rXjN+0RibZYS6LEyVq4s8Fyvf3LQwN4/UJ/ZGGCrU416ch8bnZocVJk4onx/hnqmMNduQbdY+exIJVch4DhtWeLvXnKZOkN+/3wVUXTBcfJnK/m4ztDnUIVi7KR715XRW0q/cTqtAKZTiduqUJzQPdpGdUZYw+J/fgFcEg769wL4AiGdiqUYL3n5UVyD4gTCQf8H5A0Olynji2JSqn5mdaezXM1BMjhIBl5MXrb6U1/sJmi8gkJgYNk7qKbVFfvKU3JKo5YsEABe4Xh2qSpj7rKGcIP2MemiDC+WcuD7q8wRV3gwjRv1jyoc2KHXTo4o5grzdkz0DiNdf79K/H2RlvS6CYAUSgzXP80kxnH0CpPj5c8rV9oNEw86XyB0+SLvVbWriEQH5PHA1cDVF8Q8gZMxzurbNenPrHDQ3y+2nqa4maY6AKOtcw/epH/cEyg4I/rwY8CZ8YXCQf+5uPyuI2EVLr8VyrNWcbyEy/PRcQMy2U5soKtcyXdOuxWrVYPWsXFlwHu/sP7DEBOonfJbx1fi30PMrP23BGNxpAATfYHQsb8nUyaE8FJ+Df0WqZTkMQ2oDMhcxDoG5wCZ3zodaBIj31cVRTxgvMkV0dvaXq59sV4Tqkl8GY+wGe8ZlSgn2kpQSCrznBY8b53FcXLx9p5yaZ3kUtFGpyYTrWMZqE3nJLmAKDreX+A9t5WJrD6mksxx2nCo8vMb9imcq31HnPy/XtWtcqY2OxGVb6UG55XlsY8s61XjCb1qTFMNXGXq7tpqfglHyrYsVo22PWWMuUATCkuJxHdapLxmVVFsAEhsLCXNF+0B+jdOp1Rckuu5uOTXfzJ2P2Wdy7PyeUyl8ULMPB6XN3N5PBgDCAf9li8QOt8vfxjVVGzb/Zp9an1fIDQMeCkc9CfS2r5AKBsmrMElhN4LHGK25WKeasmnkV44SOUlemEaZW/P917rKeekVVwogienVHk5SAa1xQEAsdqp5y0izbhQ+ybhwAAVJx+4RH7v53Y3TPQyKh82/wTcRrlO4XLgZqXgCWtgm7O02dObyu3H20pumWj3aZ28UA4Fb2xVuXYVkWguG1p0f81Gklfr/OS0JC4ybSqJIRwaiZ2axCGDEgrIeClpVQ1w+Th34XZrnk1e/goAAqHkG+a/ajBZRXm56dDsV3tAv0j7mmZiq/61c0zGa8aTxKydahwbefrmLapmZ+C1cNBfSCX+LBjNbw/G4kjBLW/2/FcDj4BkEc9fJqQfeUzmL476A/CnDshwyZiZACOti8imiIG6W9JvK8JM8d7Z8AentXrWcs1yL9K+IZ8MktuzNaGoQgkKQSle5ZM7ay53fHSQazGEYq/KZJrTkde8T1UIdmwFB1QGy5yGHC3XkRnrjkwOhj6xe7KLqgAX3W1ecf252qxuK1RDHrEGM8Y6k8v0qexV2cx3miOx6tQW+xEo9jsZNJHbzR1ONWO9qkNvftlCcLuTQzVRyJd2R7rJNdSUByt8PtHqZWeKsomaUDcBicwcQBqRCt+vLhyjRFX4HVQwxPyH4ol5qmW/7pHn+xzy/u5DOyoBwimDqwHtAJrI7SfeYA4Ht8P1zXDQ/4UvEBqAm33SOPLvy4q/31ps5D59HFVFgdig6gyqJQ58qQl1+pF2MqJ0p7HYsWyE8VGn5PfzVXrHK7WQU6hSKzToRDF4zjybHnJ5Xg9txTOm0urebF6XDuwMB/2bqcTfG3n5G4i7b+Rlt7GVuF4Tyuiv/dCqb/Sx5o8bL7e9x7x8ZBneZFFoZ4JnlGwlN2vJ98IMUda3sdhO0LqALaoGfeRiTtAWATBM/5T+2lyay21sV9Vagh9fIPQIEIitcyhuQJQceI2I/S+TXv8SHsVtpsnBpQfEji/7sjVe7cX9KotaMdHj09RP8WAMIPc7781TyMsf/dtOWCX+F/AFQh04Mmfst6CHLxA6Ohz0L/o3l0tNeh39xVHliCS9Tv3FUX8AhDpUDOtPBF8gpOFyBk4AqMYBvvXeprJFSYUc0RqnLidHH+cosZbHjFdoIV0preSypKkk21R1fLLiM9ZRsFbVSyyTjKPLXuIgmeoqbbK4S59QIRhzFHxpd+ZZ+xxWqQYLgBeyKHr9Ov1zSpWHF+0BRPDwovE0/bR5iWAv/n9UaapPZHR0D9ne87UZ6ixtlugk1xFRGl5hU6RSuDl6DcONSbSXG9ni5FJfljcW7HUyyJVFWErysHXhiku0b+r7xI5MCy0hNusogUwK0BwluNgM8L3rtxgBjgkH/ct/37fz94EvEJqEq1R+KGTcXy+BvOznccnEbHRqcnz06eRPbwFOpLy9OoH6YhdZlLJc+d4Hzu8mljHBMypxfcavi3jmwVRgxD6baPYqeN0+beCUlLsuxVUeZ6/KYr1Tm66xbOrXdkei6Pg1lw640GnK2dEHtwCNYk0ylfinIi/7MlxnCQDal71MARnjgIsBGottnCp/WirAuln/sINWfp8rA1IOqvSyXpFnUopiVdAT5EJeM55ACNjtZFNDVuDy5/rKJnyJ25UM8G446B/8W3fVFwhl4ZZYl4eD/l9+eOZl7wAqNLiUKA/znJb0kkuZaPfmDuvq1uGg/1dtcHyBkFb5+/jfwRcIvUC5GsDvwZhw0D/sXw8rhxCiOhDXXfxRKXWYRMEh4x8Fbo/9ea5S6qNfG//fxJ9V9iKuaTOSWAcbQGe5lkODMXDNvWuyn3GeYCKw+sjqVYEjtl9lki7KDl0UgWsxcigOqHQKSKeh2CkKSePm6NUVhFylgNP0eUzxBBgg5xwDvDzKeI1r9M+52fiIZd4hakwsGIOKkgYAHmGLLFHstdBLx9snbfjWdt0BvLFgKkOU0VjuTJQIKgRjKqvMFi4nTBcO3eXKBidEnxzdJvI6O1XVpH1UvGf14abotYyIXsPA6H2FsWAM3K6pQ7NC+AIhGdP2+UcgNps/YlYKN8A6FK/YSuy3lVCv2P0PLbd05whdWcfLn/nWcysh713cpr9/LsBV+heHyWGA27m2zcnBEO4DZo/KfudcY3bulOCwqcBTB1V6tEilcLs5NGFuD9BY7GCYeRMjotfwgnV60Z3mlQ8CPSsfNpW4LHr7wW2qmgnwqd2DAreS2Q7gTn0833pv4zbjw3a3Gh92WK4a4bj3uZU/203Outu8fN+pkWAiGANUc7F1dfx6rSYKnKiS8TvjatxOyzdwOV1R4J3fup++QKgGsAxYCEyLcTYPHSN9gdDVe1VmopRUpFLY4NTiDnMol5oBmkTe4Q7rajhytyXkZXsDd4/wxQRvo75A6P7fuo+V+I9xKA/w38XRv2OZ5HJ1xi+OOvKY/2mp+89csrwQ184ggWXKh61EwsIojvl2c77y3l6WJUoT9bhNqgZlykiIwK5W9ZlmdeRuYwLJWmLj7RMZqM2osD5LSS6L3k4OhUzy3EsVUcxBlXZEgr0U0Fsu4XOnh57cNWcIW5ymzWOzU/1AA7mnQoBjKcnbdl8GadMpItX4wOrTaKg+ucJ6bSVYreoTUTpeYSVlUAQ/201K++o/p4CbWSnD8yLwaCux+fIGck+9+DrWOHW5z7qCaDkP/RxcbklfXJ2hL5O36QuEMnFbgjv6AqEPgEGHZYj+frj8Vz7zA08mv+Erm2BVoVCMNUaLEfoHQsNmnB1vBGN8OOif5AuEqgKDcQUr1/eV89sYsUD7Wm2S5hM7WOg05UTtcNciIaCuyw8jTUR50Ty927P22RcRCDWACVOBOaCOA8FGVbt0kDZ9r0DVeNI6zwDkR05vcHgpHPRXPmQqAcAM5+jgcZGnjaoUsIuqFrCA2IPtHK1iE6GJTvPI27aF7sXNZFQ7ZHUiSxTPwJW5QBNqt4Y6BegIhMjLt8IwxhcIfQZE/02PyF6U++r2wu22DB8y5lbg0a/sTgzW3Z6knSqHk6LJP1MBbgf0lxyKvOxOwFej9FerCJR41z4R4D5fIDQymQdaiT8MvyUg+jX825wupVSZEGIP7v243r8aT0Vv5/8pveNPmyGDmP9MEraqGoy2znYclSycKbjYmEa2KEkEY8udhrxrn8hwcxg7nSoA9NaWcb/xNocKu25V1ZeX4UkQD2wlrCHmrSxWzWggdhMnt1YRJbxjnRCfPSbgKME0pyMGFj5RsRwaURq3mNfMWmA3TURqSsEV0dtoIHZzuT6V4fqn+kzvLTKbktj64Ae7JTeYw5nhdODs6APcbV7Bd7HMlo1OqogmArxlqlHpjeb19wJXr1H16m1Vrq7iXpXJ4OhdycFY2MA8e6rn9hZrvRcfWOS9anQ46N/gC4RO9AVCZ/gCIYFbbosbeQ7E5fD93bHhVz47LiY+mYxnB2hzc7prK6kmCrlPHxeXRFkPTPYFQhfhTiTq4z74Tv/a6YQT8xGVAvzaTzhJGnGWEthJ11X82jaVxiynfdP2Yv1ro/RX887Xps8C0uPaaBtV7TEpD+xt4H1gX8oUp5sXt3uuazjoL+faVKISEDXR43xXHeiKmyFnpuMWIJSCMmUw1jodC13zEm38hvHogMXeK5lgPMQC79U8bzyDhs1j1gVzgGuBF4GTyctfQl7+m+TlJ8Rkw0H/1t9h2P0j5aWlhbhWSgn4AqEMoBnAU9ZAtjtuNaCp3MEZ8vv4L2gOrmF0y1+wPboSyJECcaWW6G3ZGFumEn88fgup/tfwezNWcWpOthCi7q+OjGnW4WZ41/3O7f0u/DkzZHnZAzd6GTbbabviCvN23URvQkxz5jn7nPUhp/uCa7TP6CDXnbFLVUntpa1ILKoUfGN35HvvcEAk+FRQzieLc8uiSuM7p10bx1Tcabgi1ZpQ+qPGK5wfvZelqhGz7Hb01pYyy27HM9Y5XHRI67cUikZiBwryBSo7+bMFTgvmq1ZnjLQu4mMtD3DJ1ktUY1VVFIrkdQCsdeoQMK9igWpOVixAW64asdxuxHD9E8C1fdqmcpnnNMfAImAOTW0pNt1+nz4ur6Y4wCPmBTjI/AVO84y95GiUW5j47tPfviZe0q1C8cgBdz7rQJNH2oiNPGO8sLW6yF/aJ/JUyUEy04BNwGGk9r8h7sBtzz+SVqoAHvEFQpuBu2I3+IKtqnpiwEEyih3kY8DYcNBv+wKh5knLNwd2KkS+FOXXRkTpdJDlv3MNRRFeHKVhI3nWPIsDZLJW1WOdqss877UiW5QwmG+NrSq38/dOOxtXrPPu+Dpis/v1sX+VqEQyzgcWU1GyBYBbzWuYaPdhgJxDT7nMOUnOl9tVVVqJzRwfs13qobk0rP7aj3xjd1SfOsfOi5l+/1cRDvq3xRT52wDzwkG/CRCbLL6Dm3WOAOwlm0jS4XSWq8Qkpye4Hc+/JFALbtB3NYCBNRU3a9IRWOsLhHr8K85ZJf5j/IxL7fi9WPQ7l5sJHBd7fRww/kiDhBD1icnEAHP/l6Kw8GcMyPKyDeBtIfAeqy1jurhlbq/os02SRjQaIOdOOEauvauJ3GE0O0TIVQHD9U8q8HOgolxE/LMClc6rnieJKL1CObKWOMCZ8nuesc/hEvNO0s1SiklFwz7MjxLgZv0jrtM/zT40+9ZDW4HXjLJQNecucwg95HIm2sdykMz8B81LvA8br3nTiMjGcicAn9jHslQ15n3PQ3SVq5hhH8UQ81ZstK2OElURLpFjgDaXUrwErQtYpRpwpz4+r4e2QgDcb7zNCZEndxaRdj9uBvQ1YjfiOpSblisFn3juG3mLeR1X6SGayu31gHrveEZ93T/6yARgajjoL/1N39lfGOGg/ztfIPQ2R7ZIOUhFgv51wDXTnQ4nDIvekNVSbmaq3fmzcND/YNKYl4DzJY7vOm3Shvpiz6tfOx33ElMUVwq+tjuUTnG6pZ6sLQTc6zKTCJucGvSJjq6wA5kUL86gtD2xgLGqO0HUgKUxUd9KVOJfoZgjBGO12IeNRkQZ8fKfbChnMkifyXYnx3KU0OUh9JDqIl8AjYE/JHAJB/17OFxJ3QcM1rA5W/vOC/CR3Ztx9sncI96hmBQ+to+Nj23CryEv/41F93UsnmZ3uOwV2z8HiP92qwD9+IOOqxIJvMJ/Rup/5Xcu9xGu+DC4WdIjBmTAkKTXE3/ntn43/nwBmatLU0IspV5P7u3+iP4KY+zT6SpXMVSbrDcT2+5PDqDivDJLwV5VhVpJ0hCfW12xhM4SpxEB/T28SUFTriwAOERFzC1DLlZNyKaQB423KFMGj1oXsJ8sFjjN6J0kygrgERYeDg+kNzk1otUo0F71PKE1F1tZpJowz/XbrLJINaV/9BGAPUeLddV1LOarlt93ESt7dpWuDt1x2mJaWltYrnzvZ4iSy4iJPaaJKGlECeqvcJUWYpHTJHE2aoh8PvA82OKAynyuh1z+xTGRF7fsJ6s5wBj7TNVdWy5SMRECdJS8Vv/cKSYlUbpuIzb1/dC4/6fOD81/8zd8V38XvI7Lr/PgBjtxFd3ksD7Oas4AMkNONzapGqRR1s8XCG0HrgwH/VOAqkDtm/WJnuH6p12BrlXtfNY7tWgidyIE9NfnpT5Xdg7f263pmZTd3axqcAh2FJI+YJlqdEW6Ksv72WnKFKcruIronx86uBKV+AXswuVUNScmuzJI+5ZR+mtYSEZZFx6mb1hHHtCD5iDSRRlnytmqvtwrClUqXzudpuGq2P8h8AVCV+JqJL6UJG+wC9ga0N+td5Xulhkbix08al3AJLsHZXgoJjWKmwUc+6+2MSR667AaYn/v6uLgqVtUzZW4BurFuGbklfgDEQ76F8XskH6P9MWc3yF5AYBSaqkQYgqu28NxQojLDjUYF0K0odwTdReubdf/FH9O2Yu87L64hMxEoLBbZVONfJJaslEKHAQSdUTC/VSrI/vIZr2qy2v2qdQXuxkoZzBUD2Gik3GErstldkPyrMvsbFGojTGeTQRw0+yjrSHm7aqTWGVM9D542HLJiCi9bLuqNv2SaODUL713kJ7kFnCbOVR9aB+XvLf7cYmGZVUpKLlG+yzlYv0bUkWUvSqLi6MBO42I1lGu4W7jyBqu+1UGC51mZSdpPx8mtheyu4aHmTc2xK2HH0ghUusV40mOjQWV39gdtzxoXVx/qucOUmP7uVdlqU6RF+uGg/4dv3qgfxP4AqGPgbM0bC7RvtpeTGqNiXZv5SDLcEmkB4CjwkH/lpj/2rzztek8ariTtSfM83jePmsZLuF4MqC/ZDzFKZorKh0vkSdLn9hInrHOnpMtius0ELsarHIayNftUzlIJilE1M36RJFNMWPt0xduUrW+A26M7W4x0DQc9O/8n56kSvyl4QuEqgGDgKcAz4eePDpLVxx7i5NLKqXkyoQYLGXKUN0jz4kDZFGN/EcWpFw7DVhGXv4fRmPwBUKnAXHXgCJg8njj4ZwGYs+JU5wuTle5Kv9oub46wLf20VxhxpUJiAADw0H/Z7+48rxsDVcypoutxHCJQiF4xjr7q2fscx4GNoaD/sO1jyrxX0fsHvod/544bCnQ+z/xtBRCNMctWVfBTfy8gOv1WgYcC9yJq3engEFKqcM9DP9g/DlJ/Xn5X+OqyC+Mv1VD5CMOSWYJ4Qq/HikYyzMvpo22mcH6dO413uFM+T1bVE2etM+ndeQN2kVeUS9a/Q9bzhGSdaqO9orxdIVsWh2xTweMRaopmx2XQ5Qcyya/3qaqGY3krlM/8uRVCMaUgg1O7cTeDtFCfOS5v2pQf3kKcPELxjPGUGMKqSLKVPuYA+OtE8ZP8twrP/I+QCu5ucI2DjqpiZ0rVGmRx63zJ2xTbkNUsfImxu1XmT7cmv3HTcS25Z967k0EY7YS3G1eXn+zqrljg0q4B5FJiXjXeOjhw8/q3xYbAO7R3+F+Y1ydx4yX9fHGyC8p7+hJNFGEg/75feSi1+/UJyTIiX1crs1G3PKmDvCS1d/OV6moJC08IaBQpbjZSeHQS1vW4WHrYt9Q81b5lD2Qg+7m3h9lvLb8aj3EIH0GTxgvtaZi+/6symCsEv8uwkH/Plxulgdgtl3eq1Jf7q0QjAF4MaMHyLoG6LogOPgu8vKn/TeDMV8gpPkCoRG+QOgJXyBUGyCT4lqDtWmcp81A4GS0ERsH9dRWnFJf7tGv1kOekN01UqBSnYMqnbftk9fi6qhtxq2mTPIFQmf+yiYfA8YBw+PPDCkUZ2jf9wYWVgZj/zvEgqpzcQOh34JS4Lz/1GBcKbUGGICb/ZLAcFz7wLm410cObuJi+P9HMAZ/1oAMIC//k7etk16OByGxB5v4pYRemdIpUG5V6Xu7DePtvmRTfpOpIoo4Xv7Mq8YT6h1jFNM9twoDu2yGlZA5Qyl43epHfbH7EEFV18D7Pv1t/PIHTos+wnXR4c4ku5ybmNw111ju0gBqyPzk7rnog+ZFsxaoFgA0FVu51xjPMXItg/QZx7USm95qJHcmgqwOYt2iG41Pz/EIWwD0lQsqHG+qiEaeMM87MMduzSSrh/eAykw9IfKkdVv0qgTHzVGCF63+Ctce6YIh2pQTWyYJ4Fpo8S7MqteYN68LOzXLALzCoru28gryshsD+AKhHF8g9LgvEBrpC4TSj/wN/KVxF3DLcXJRgml/lFzvhQRB8fuk17zleaxFFVGsgXvNLHd8k4FLcA2RIwALVfM9M5wOh00WVjv1E7y8n52mFbrILtK+tpd5rzi/j1ycsLNpJrZuwpXeKMXtMHr8Pz3YSvxjMTP+4ln7rESX4pEQwSgOB/0vhYP+n/6gfbkZeAJXqf8nXyA041HjlftGGa/xuPEyN+kfkU86UeWyB2INWPN7RZ7J6RQZ23WGc/RR4aD/HSrKILzgC4SO2GG8W2WfdKT380lL4Qh6jJX4YxEzCj8Wtyv21zAHNzP2nxiLJ6CUmo07McnDbRDIx723rsMtd3dQSr3w39jW78GfkUMGgC8Q6vOUXjr2UEHVQx9w+50MHrYuIuR0o5tcyTFiJQfJornYwmPmQO403qUUL0sdHxM8j+AVZnmGSn6REjQH0lMtxxA2EXSu1L/gRvM61th1aK5tx1aCG8xrecR4gyzhPktftk5jnaor28qNlCiDFMwK2mgHVTpVRDEFKpXbzKupKQ6UZlPcpViktjlJzjenOR2npBHpZStxliYUjhJE0XncPF88ZryEJhS5Ir8HMR4dwAqnYUKVHcAr7JRTtJ9oJzfRQ1vBAOeHC06IPjE+inEBsUDbQbCfLAE0AthHeRPobpXN3eYQDrj2cN4tqkbTZ6yzH3zSePE+KRRRJR0dZ6zMy07pJu8p/sFpfSrASXLB1Svuu2PXDlVNvmn3e3bcqDv/JWfjz46YIvjT5A1egkvktNNE9CHcVummwJJDBFYTkf679nHkWZf1wX3AHIP7m5oM9F7oNOMMzb3f7HCqMl81525zSOrR9rpljhINZqv2TXG7Ir8CZt6pT3gvXUTIoEwWK+/mUjzpk+3u24FvcJsCVKVWUiV+L8JB//u+QMgCTj9dzh1UR+5PSAsll9PDqgbXmrdUXRUIDQ4H/RP+oN1JVtmvB9SrJgoSbzQXW9mqajDUHBG9Rf/wu0/tXtu9mDU+89y7vp7Ys+t9+7j7fQHOBObjyvVIXCPzx32B0JRw0J8gZ/oCoeHHyaHtXzRG443xZ8F1xXjMugCgvDxQif8ZYhmvnr5A6GjgKlxtvEzciefPwCvhoH/xf3u7Sql9wAOxf38q/GkDMuCYDaq8vBdVGutVXQpUaoXApIQU+mnzVK7IF3clcayiSmO+0yJBgL9en4Q4go/ttfpkBkfv4lHjZRrLXbQVYa7TP2cTtWnOdjShuE4PJYIxgKH6lAq2TMk4qNIZEh1BrihkufIRk0hI7Sd/euNZ47lOHmHzqd3jqDO1OWcUqLTTdWVpY6zTWa/qqpriQMJYWoryYAxggdOcRnIHNUQBlpLYyHvayU2PxD9vJHdSnYMX1hQHtxITv9OFg5OUBH3OOouo0skUpbxoDWB/Ra9e9Ylz7H7DtDhXmwkgu2hrTga4Rx9X2D/6CJ3EKl42nqwmBdVas4W6Yt8YX6B903DQ/6/86P708AVCNWHCUIGaqRA3h4P+jWH3o3lHGD4EePAd68RzRlkXZuHeRO4DqEoBI/QP+3tFdM8j5gWEVU3SiKgvnc5CITGwGKqFWvfSlsnZdhuetc5qspuqN4VVLbVTVS1qInZkAKxR9bwd5Ppql+lfHb9HVVl328hXfougYSUq8YtwTe+pBnSPYFTQeUye8H5td2aVagBuJuGP2A8vLoeoB67khBfgeetMWhnPkEHpnvft41cANWc4R9/75oN3Tzw9EJr6vPHMyTHru9xmcts4yn0GF1OxrH9od/ixM5wO7FHZFRxPHrAuYa7TBlyHgUr8PyFG1P+37JD+rvjzlizh/ddsf3iy1ZkDKp2FTnMuid7BNso1oPJVGvXkXk7WFogR+ocVFvYIm7SkIGo/mdxsDmOO3Zr5drPE+9mihANkVrCh2aly2KnKxfVzyT9MXiA5GFvu1CeqNKbZR9E58gILVEtsJRitv8Dt2ruAooNc2ymuidZdrsjdqXLGZokSLU1EE/ZIy5xGv1hG6KEtp09kNL0jT9M0Mo7HrfOLF9hNKojshbx3sd6pXdtWwgFX6LGGOGgD04C1UQz1tdOJdmIjjxov27mUy/U0EdtGjNRfbXmdPoku2ho6xQi/AFVEcRjcoC/5uGuLfQBX+wKhw1rq/4IIAgMV4gzg+V8dmZe/lbz8K+6xhkwpqchLVQ/+H3vnHeZE1bbx35mZJLvsstmld0Lv0hERQUQphmIDFVTsvYEtVoKCRrF3RFRQUVQsaFQERAVFBKT3FnqHXdiWZGbO98dJstkF2/t+Kvrmvi4uslPOzKTMPOd57ue+Ha8z1JjFefqcyj+5buBZx/ORLbJqgY4NSNqK9XTVV2gAXfWVvOcazQTH2A9DAW+0gjjSaI/MDkSkNsSBlVBIb6Jtq0YKKfz3+AIly9LoS7sjj0cHsc2udNRG+6TbBtbzn0sM/CJimmIzUOX9dqiJzApg+xz7hO9PCE/opY3Kq/LGw/eeGgp4m4UC3rj0gL5PZifG2SErJ5s+uwCyyOcifcaawdrXzw2/x7e5+90TroutnwDI/CSf6CnmqbylHDbuCgW8c/+/rzOFFP4THLcZsphIYIMsUXwwRxS4O+uruYfJLLXr00tbSDnCZCRNhPRjZL8aC8WX2mVXYFT0Eg6TSdDuzO3GFDqwHlAinRtkTe6OXsUGWZNCmcZrVl8qkMdg/RtcwqSqlusqmxFbZ9dirV2D0/SltNC2MdE8g5GmcuGpwX5ecT6FJiQd9HV015cyyexFkXSSLiLMstphI064yJgFwBblpSkOk8FVkRF86rrvqOzbersmRaSxVaYx3PiAW4wPnzWlxpjoBdzreBeAyuIwLbRQwRHSM7IpJE1EuVifoT9sDk0HGrUUm3jJ8Yysre0TgL5bTuV+8/JoJkWvfO68+y6XMBOmnpqATXa1jfvIzh9vescCTT+3TrzmQn2Wu7XY5CwkjfujlwFkADs8vmCfUMD7M78Ajy94K0rd+xvgulDAe/QH9vfC+oXXv4ZLUBk0P0oOY3ozsdUDNAXVcOKm0Hm/8aazo7aWfbiZa7V8Pl+m3Zjc4Zst8s/w+ILvOXjh0DvOMWdX1XIrI1VJpRgXU61T1v2S2WYKKfwBtCt5KY48b51dNMk6o8oi1zU4kjmzig97dijgDf03B/P4gr1R2l4fhQLeuMdrBRR3CFQgdT+Q/Ts8V697whz0jIto7Rpi/877o5f2SlrXVMNkivMhmmnbmkpJUyFgjV37xUF3574TCtw23eML/nB99NaTr9c/YQeVeM48GxSZ+6n/5hpTSOH/E8dzhoxQwGs7MBNR18liRYHf8SaZohhNSAwhMaVASlWeK0v4LxfzsayuHURD0kms5gvnXZyuLUpsuVg2AsTmw2TwlDmIcVZ/emsL+M41vFSXZXKANNXsyiEy6K3/nCDQX6DPTqyvKfaXagpooW1ltON1RkYuoVg6uECfzWZZnYeiQxkVvRgLjcWuq/nOeQtvOB87Zik0OTPYR1M8W0PYdBGrWWLVB1TzwXBjala2KIzraBHrnkyvJ3Yx1eknFowBUKQmlo5q4mC35GBMjSUYFrmr3vmRB1rPtNtPAjIKSB9xTuSh8g3Cb+9rFZ7ANKWMDUrt+oGjz1pBlQN5EqWDdDVKC+Z4gw+YBEzhd6bPY0rin6FKJwLoM9E6I2DJ0srBzbStGMKmujjEIGPOjU6ivBPtTrFUicWpVjdna7Fh0HuOUVd30NZVBmiphRgauYcO4Zf41m7j+/+7zBT+h/Fm7P8oMDAU8FY9TGb/bbJKqRLfYP0bbbxj7Gv43aPwu/9wE4/HF+zn8QWnA58DtwLT452UKJmfVUmbuyjR/ftFhALe9SsCg84cMvqjVpdGfa/nUy5xc37YGM961yU007YBJeXXpto2dKx9Hl+wGJgjkAUttc2cq82ho7YW4PG4G0AKKRwPOG4zZHF8bp2onxSz7qgk8o66OViIg4aQFaA04X+xVZ862j4qiiOssWuhYfOg4w2aqh+t+MpqxzK7PsU4zRZic72Vsp4NaK3FBkYZb5QKxpJRKJ0010I0i3Urxsmw8+1mAJykraSl2MxSy8MJWihxTg5h0ctYlDA7H6x/Q+/IYztfNp509DEWVgbIEflHSWlIYCeV+MDqllj+hd2JJtp2LCnoYSzFkoJvrVZ2d315qQA7EL3Aftc6bQ8QGarNKHVN75unWE+b5+oesYs3nIFWZTOAmpAM0Wdpj1pDQAXuw2OrJgKvA/1QfJT4d+jXPCGLUET4uLHs/l/Z9m9BKODdz7HV+n8LL5D0O/reblWsixLdkQlmH+qKPZyeZCTuFBb5ohynhJ8hRxxhnazNN87hxPgxABRKV2QfOePDOOf/qr5SCin8Ajy+oKgt9g69z3jrlNO1RdPg7ZaxVQ4U+f2xUMD7Hv4hp1hSTNKFbA7QRNtBE3Z0AjqhlO8v+gPHrIhSRU/mqKWhMmO7QgGv9PiCHYH3UYr/Y2JNNb9n7JNRHdFnEPvN1RJ7Eybjx8KVRtD4MdrCAO66Tp8m4kGbz3iHsyIP/ZanYQop/KU4rgMyjy/YpZtWNR+VgaEYJ4UyjRyOJLoaNfVDPwrvm91oom1jsPEdTbXtfOJ8gFxZLrF+hV2PC4xvqCX2G2Fp4I08rGVQLKc6/cIQpatpO+0K1NAOAkolv5lIlo7QeMc8jcfM8/nAMdJqr63XhYDNdlVmWO3oZagqXqF08rl1YuLBrGN/PNqYcF8fY2Fp2X9UqcrE4Pbo1cywOxLBIFk0/mnzvMUHZdbEa4xPn67JAXQhCeMsFYxZUlhvWmcEUH6H1U/QS+KlXXYF+w7zOh1gqD6TWooLRoF0kCFKJozXOT4jaHfmbH2ulSsz9XFWfyI4NOBSSs9qF6MyTMdEKOA97PEF+6B0g74NBbw//tK2xzX8boHi4JwLvIc/7zrAJbC5Rv+MRtqOwhfMgW++YfayLjW+0nfL7Mgu8pBvGwAA2e9JREFUWdH5nHk2A+253G+8ncjk/mw3WrqP7NZxXoyVlKxebDXYkk/6oNmPXHmshoIUUvi9GD3aeO2e7voygKv7az/wqZ0QSG8PTPH4gjmhQN443e/udUSmby8vSvPhLSl6/mb6qjQMSj9X9gGvhwLeuLkzoYC3kNKWZL8XH0FSqQAIS4cpJUZ84vup2Yn+RolSx4ESC1mxkxK+3A5ZSQL/zPtQCv9aHJcBWazE9T3Q4Du7NbdHr6GjWMv7VjcWyca873yQDkKRzh3iaCqSJQUPu94otay2to+asexTvkzjG6u1HOGYKkDpbjUR2ykvCo8Kxnbb2fSIPMHjxjj6G0f/fh3CZqg+i1V2XTro6xP3rnraHr6wOtErpm27VVbhQ7sbW8JVi8uLwrRv7dYdMige1UdfQCVxOJFpU8FcNXpFHk8+TDFKB6t+7O+2k6xenhyOMNwxlWLp4DWrL0GrE/c73iadMFOsU98rIP3u+AA/2C3pFCPqj7P6aQD1xC7O179JHKQcUYqkY2+6iFYBCEvdesbx/J4G2u4aAIaweMocBKrkkXyftn9rlhsKeL9Hfab/SHh8wRqnaL4P33QGTowtuha/+1WYfMMD+pvTLnNM9wDlWouNXB69g3IUMdiY47zP8TbttPVcH72VeXaLg584H/gwXUQ++MLunGz38PO39gljGmi7LgK2tNU3+vAf3UiSQgq/BzHi/Bjgumba1sTyZtrWeEBWREmHotK88+ftGHXPHY8/4nj19uR76g9WM5FwifwdCAW8ezy+4BUov8DZoYD3/v/mWsog+Xm1Cfg4guMGE81wxDjEX9onUs3O5QSxkYV2E3uUOSwx03nOPIs8mUFzbctPr5l9Hg4FvKkJTwrHFY5L6ySPL3gXqustGbIqB0U7bT0uIox2vE4Ug8+tTvTRF1CBI8dU7I9jt13a43KKeepOCTUuML5hqV2fIZF7aSZCvOJ4igpaSfPizZHrOVFbSyWRy3a7MtXEQTzaHqJSp00s63RQlufm6A285VSnLKUSXsyVGYx0vAWAKTUahSchy9D26oud9NV+ooG2g3P0knjlZ6shmpB8anVmguXdTExLrJHYThttgzXbaqvvx829xpt01lYzzerCeCvhPHAI5XLQM/lYXbQVmFKP/CSbOQBxq/EBtxofHuvtmoMy2W0E9IgvXGvX+rJ35LGPY+tGoVrNo8AloYB3+i+/+/8ceHxBD5AbCnhzk5YZwKQa7L/wG9cIYibyNkpQ8hKUhg4AB2QG5Skm3lELsMmuxmmRJzlLm8tIx8SVOaLg03rFb1WWaFcAnK3Nefkp50vFwNf481IelSn8IXh8wRpAfijgPbz1gUYX5JLxxEyrfY1nrXO4SJ/BSGMSe8hhSORetqoGok9QZb89QJ9QwJtoqV72QOtPT9BCiRtJVGq8YvZ7c6x1wY2hgPdw2WP/lfD4gj1QGf+VwO2hgDfq8QXvPUub++BFxkxtod2EgHnBLhAmUDu223KUVU787whQJxTw/mkWUCmk8J/ieA3IzkKlp+OQlcjd84XLV62yOMwuWYHe4QCHyQAEbvJZ4LoWZ2xmF5ZGgi9lS3gzenruQMf32W6KEkHbdKvD59dER5x5hraAmuynujjAJTEPybA0cGDyvd3SLCTtp976wi4AS+wGnBV5KHFS52rf0UrbxHvWqeYq6TH6afPorS9Y8bLZv9xKWa/+YP1rHnO8CsAauzZ9Io+GUbM83cCks7aa7bISe2QOQ/RZXKZPD9fS9pfSHwO4JXI9n9hdaSB2EHTeQ5qIErKrMiJ6HR+6/IntnosO5AnrfFCzx3g2jWv0T3cN0H8oP9NuZz5lDpoMXA9wqraE1xzHbCL4AEVsT75prQF64c/blvQ5VUZxqLqglI4vDwW8v8YlO67h8QUfRvmZHQYuqSt231NDHGiz0G5sRXGkA3zquIdWeii+yzSUFYcAKJZG8U92M1c3fXniHQ1Lg7uiVzPbbsMi17XEM7AF0jW4e/ipqk85XhzcRVvZRRdSRwV57fHnLflrrjiFfzo8vuA9qGzY4eYiNOBj5/1fO4WlgZpMBm3lJmKVJLQLgaqhgDf/WOM18n2yfpg+veFtxnukx+gLRdLJieEXNhwmo+0v7fd3wOMLdkW5D2goxXX3MTbzhQLeRz2+4AWoCeqroYB3/l94mimk8LtxXJYsQwHvxx5fcCVJwoT1xa5qlWNKztXFQeqIvayQKuYYpn+VCMZmWW0YbV7M+04/lcQRNAF9HQuM7DLciCzyu1+oz+IRx4Sjju8SJuOiXjnM+GptWhKnSi+jhjDV7ian2t1CxLJXn9kn8Zl9UjVgbBux4dExxuuAUsUfFrlrFfHyABx+wfFsVm99IWFp8LPdiFjjgisqNRwxnlE8eHzUMZ4fwi3tC/Svd6SJaG0Aj7YHvzGx1HbDjOnhJ6zzBUnBWF2x27zb8U51gBbaFlbZdStOtzsB8I3dhquit3/7mOPlvVnkn+EQZANIyblCsB3VDdVcSgpvid4wfpp9ci98wUlJnUlPAINir2uibH3OPeoN/QcglpW9I/ZnViOxfepHzgf0TFHMd1YrLonezZnajzTRttvEupN/thrUb6dvTARfn1pdnOtkLdFNXw6AlBS1Dr8yppi00eUpxETHiJVWIhhvP+l4ac0p+opWSaehUVrBPIUUfgtxra2skKw2QyISKfhnnS8ywp4qz4v4xf6SWOWtXwuqohiFFcQR0ii576WLCFXFoYaHZUZfFBn/eEE7SpQCslATGg0wgRuBHaGA9zOAUMD7LvDu33GSKaTwe3E8y14ki/WJpbKBvcRWccZCuzFrZe3Eyi5agi9KXbGH95wP4ordUKJSR0g7kzKoJg5ltBKbj3ngb6wT7GscQZEmoi1Q2R+AhFdm8nkRC8aSUAl4pIu2HEesbFWZPJ5wvNjcq80D4ESx+mBPTXHLXMKkpbY5wReabzdjSOQe+zlzYGLANBGlv/6DdqE+u3bScThB38y+EtIqRaQ5a4l9ie6m7tpSXnY8ZdhJSdD9Muu8lmITztj7M8tuF2kffuXsBXbz7MRFCQRw6zvmqbcC518S9b04zT75CeBVVEYscWplrr2AfyA8vmAzVIk8MUHprK3S41ph3fTlOInygONNnMLUAEJ21W2vWme2XGnVSYzTT/9Re9vqyTjTyzq75rqzI6MOF5N2C2AfoRy3R69JkHNyRIGjCocSwVhY6hySmR+gbJRSSAGPL1jR4wv29PiC2WWW53h8QZ/HFzw1myM7JzjGMst5G1215Y6bojeZP1pNE3xOj7ZHXGtM+wzVQdybWAAXG/srjy+4zuMLJkqUJ4kVl15vTCtF/1ht12ajrAGxzPpxhPdRmXlQk8ELUNI158Z8OD/7284shRT+AxyXGbIYDiX/0UdbsCFflsuYY7Wofoq+UpvrvJm5diu+s1uxO0nB2SP2JMpCc63mfGp14VHnq4n1u203DmETMC9ku6xCX30+2RQghLJbGhr2caq+TDtZW3FUw0BjbYcERE32cYa+iMV2Q8qJMLtkBUKylB2aVk0csogR34WArvoquuqraGeuNy/Tv6wTLxPaEt4xe7iuMr5ACOisrcZvDtOetAcXd9FXuTpo6wXA/cZbx+TIbacyhrSpII5QVeSKgPEKF0XvBeB5x7PEu6YKpMscaw7Wn3G+qNcS+1li12elXZcMEe46JjrU8JvDmKqNpHwsCCmUTsaYF02727y6O3BW0iGTLUpuR81Mm6F4Z8P5Z6IQNas2UEoj4jv7BA7KTCqIfL6wOhLBwXZZmWpCfS13yQr2i87njhrIQucRc+jrjzC0FxDXdtsEbJxud3p9i13lubra3opHZBp1tb2J/d62Tl/2oDlscCjgPf44BCn85fD4ghVQ3cu1gQ0eX7BtKODN9/iCGUAI9bsj4BhPz1jndsAxnnbhV4xv7DYN1+nDPgROACJXGl88cOXodxeXOcTNKB4ZwMv43fWAj95x0UdKjghBeVvCC2aCBgFJk9PjAaGAd5fHF2wMlAsFvPHJ4PGUwUshhT+E4zkgS2SDqnCIJxwvNTKEnQhJqmp5nKvN5VzmMt1qn9hJo+R5VlEcoZnYUso41xe9mm9kW1DkTmf78DhOEz8XP+d8Pi1dRHjV9RRuUQhQyq9SSnjF9Mo0ivdPd91ZOVOEE+NGpM7QyL0skE0BpUV2iTFTj++XHEgN07/SNVGiYaEJ6KktSWxjCJte2kLcdkHaW+YZdHAqR4HkMUJ2Fb6zW5EtCm2QWjttQ2KdQ1icpv3Mpfp0nJQ0PqYTNnbKStQSSgKsjbaJNtomgPQq5PKy1Z8B4Yfkp677RaYoZrbdhnzKpaF4F88AvVCdWQ8nziPg3YpS4v6nwwIuQ11jb6DKFlmNHuEnqSH2m3tkjn6ytlzcHrmGYcZXOy8zpo9srm05E6gLsEdms9auzetWH8I4qSP2DLzPeKtCFB1/dBj7yKmdw+H6bbUN7k+sk07ebNZ4a5HdqOWXrrsTGcbLjemBy0e/lwrG/ofg8QVvQf2+GsQWXRlKG7IYf14xKpiK3wMbAtd6fMHPUUKqWQDtxDr66AsT4x2UqhAQwfHTa2aftpcbX54CLMOft/wYh0/mh+5GNah4AYSg/Fq7Fg3EThpqO7iZqejYfGW31/G7Nfx5x43LRmwC84/MzKeQQlkczwHZV8BQAKeIkhyMlUVFDrNfZlGRwwmF/ALpopm2jWbaNmwJSEUwGON8jYsi97BZVnM+aoznbH0OO2TltHShgpcsChPjJpPdXzb7Md7qpw3SZpMZE2KPB0lOYdFVX8ECsylAVMdOeDuWzWoZwhZlg7S62m7Gm30Zpn9VdICsyJ2O99wAs60TgNJBXURq3BS9meWKP6fdYZTQIgqls8AfvSTjI+dIkrlvAD/azVhsN2KvzKaKyKVYOuw0EdUATtJW0UVfxSq7Tn6mKC4P4NV/4tZotCCK47NQwLs8RuDXfofFyT8KsYfi0yhpkX5AK6AKQB6ZVJGHjO9dN5Muomy3K9E78mi5UeawUChtyGNS0kOCOyp1OdNuF5ltt3UC2lOOF4vbayqQjuCQj0YvcHzqupfK4nAnU2pvGcI+CX+eiX9EH+A2VEfslL/h8lP4O+B3i5HRYde6OfnpPDJpJrbQRVvBEP3rGUAGfvfGdS59Ve/IYxs2y+oNUQHHWBR5/1SUBE6N9CRDCClhTHRo/M8KD5qXpF8+esrbv3IWYVQ22IqNvRn1G0grlka4ibbdBdBXX0jfWNB3Cx/p+2XW7EoqeEshhRT+n3E8c8jeQsVQbJdV8EcvYaldn3Gml3Gml7A0Eqr27bQNVBKHSwU5ydkhLabvpQuoKQ5wjf4pDcUOzje+wSks6mm7MaXaOZ5JSxaRtSV8ZHcFYL2sdZQQ7RGZzgyrLadqixmozXV0E0s4ZGdgy2N7IgoBSywPebKcBTDZOp0x5sWbG4ffzCpP0Zb4didpqxPbx+HApl1MT8xFhKocYqesYB+SmdO3yqqNN8vqOyRHx65z7VZ5+8imdzjAarsW8WBsv10+MX5tsTczKlU31jK7HlEce0MB73JQM9HfCsY8vqCzLN/lH4ArY/+noXg2JySvvM/xVqLbrJa2H4/Ykw2MxZ/34xpZ61pNIGppBzS/MSmtCods4P22YkNCmbKDWPdlB21dON6QYgi7A6Dq2/68L/HnnYE/767jKeuQwn8Gjy9oeHzBVz2+4HKPL6iMbf3uBvjdF+N310/a9O1RjokvfuMawcnacj50juR+x9s00HbFnUgaOIXVf4bzjo0oAdcMgGv0T53fOW+ZPN7x+DSA7+1WRGK/VyGgubaNDmINdxrvHljvuihZKf9YuB7FgTWA/p7iye7B4fu3Pxy9kMsjd7jCUs3VLVn6XlJJHO7W9e7X7+jim9g8JpScQgop/D/huM2QhQJe2+MLfktMB+sNqw9vWCXVsXGml4WuGxDIUr6RC6zGzLDacY/zlxtqGoid7JPZ5MoMskUBxdLBiMi1vOhSnCAhIEuWZMoW2E1ZJxV5e4lspI+KXpx/uf5F5iHK84552jdf2R0aDjc+qBU3C0/CL4pcf2V34rVoXz2HfHZRsRClYl/zGfMcl894BxstkcWypAom4+c2TP+Kwfo3NBNb41k8DTg1R+RvW+m6YspUq+uSgfq8M1wiWgNoXiid0el2RzcgD5El6ooS7tJSu4E8TSwRQkB5USw22VV4OjqIr+22AFs8vmA64ErW5ToWPL5gE5RxeDWPLyiBI0Dv40mVPxYsNgGWhgLeYo8veAPQqJnYwmnaYo5QbtMkq9d+YhkygL1J/MTDshwbZA1u0D+su+aBO3e5KUh0REbQCeMwgEGXRe8YNtH5WBEQra3tu32ZrD9pj8zuU1XkssL2HGmphXb+RZecwv83/O40oNvH5knR7vryO3NEfiXgXvx5XzkwB3XQ1l6xX7pZL2uNzxtZPcMtiBMNC/G7T0Dxvy4EZZV2gTZ7X7qIVD7WoQxhO0JpQxZ7iifvbSU2Vbnb8Q6Ap7bcd81pYiFt9I3sl25qCOUiEpHG/inO0RV0YVcEvsPvro8/79CxxkZxPtslvZ70k2zW8CerGY8Z4xKyQV9bbWio7aCO2IcuJIvsRvJ0bdFj9xtvPWZJsU33u0/Fn7cpxnm7GFgXCni/+K/e4xRS+B/FcRuQxdATRbiOc20OAM8DIx90TEwEYpZEfm53Fqvtuoyz+tFX+3WZmSWyPnlkcl5kJD21xcyzm7NMNmCiuYaL9Zksk/VpJLaREcuyFchS0mDRxXajLfPE1hbrZC2m2D3eX++6eEUE/Wt+h0luHJ/aXSLFuJy7lMF3OWJdpeOtfpFC6WSM8w2yRaEGsENWIkOGqagdAaC8KKS+2F12SBeALuwL37F6trzTvPasUNqQ0Gq7dtehkXu/PahoJwJ48RDlD6fJg3dtk5XFWllL9BRLEoPUE3sx0Q7nU+4jwINqrnB6fMGbQgHvCwAeX/A24ETgpVDAGzeSuyid4monaatYL2uKbbJq1lOOF949MvLytCOUe7dL+Pk9wGDgo1DAm6xS/5cg5v6wEKgFMv/We3wbf3C917pYOqgqDsVN4u9aYXue2UvOLb21BWk/240ImEMWnqvPWWEjKnvDY3oP1OYadzg+yCk7/tvW6VhoPGBMorHYNhzogz9vD8BWX/DJXuHHetYVe4z1sub7qwPn/qvKvv9jmAN0GKjPS85cT8HvLlzl0twOYWNKjWuiw4vLU3h10n7lgHb48zbid3+J4l4W9jd+7LfRrv5hA21XzTlWS07RSzmpnQb85CZ/mYl+etJy8ZrryVIntV9msYnqlfSSRqRsVMf3LwVkI4BZwJFQwPuNxxdMdFDWSWo2CQsnPSJPU4P9kfraLucCu4mY57oxbl1XG8W9vB9lJH4igMcXHJjyX00hhT+O4zogixnR/gTEnbWfDAW8D3t8wTPrid0d49vtk9lrb4re3DSDIpqKrSyxG3BQlqeCOMIOuyJVxUF0ZOIGepXxJRYOAuaFbLBqAVCegvAMu4P9jHlO2kHc4lr9E3yOKUSkwUS7D8D2VmJTnhOz0fPOZ1tUj81Ke2iLb9OQ9dN/wYy85ByzyKSIdBElIo2C24wpGe9aPdkuK3Gr8SHpFLParsMHVndnsqMAQB2txIv7nshlDDK+I14CAzhsp1NOhKOGsB15shybZbVWwKP48wb19QXnoDhK7VB8kTYnh5+7J4Oi3ALSA1U4xJXGFwlleSHgGccLX3wZPrHzbcZ7TVqILYy3zuR7u9UtwAseX9ALxH2d+np8wcqhgLdYw5o/2fkwbbUNFEoX90Qv52z9+7rqvS2+pYHYwS5ZkcH6N22+vT8QHhb1vYJ6SB1C3dT3hQLe937rO3EsxLJz7wHlgUtDAe93x9jsZKBWOsW84xyd2UbbpLpFSxdd0tII3zXNeS8VRD6m1OQCu+kIbVTenMjISm3COLucr39zVDCmrrGAO4wpDDNmALQBXgLOia3el0emY5kiXV/m8QUfDgW8G/+Ta03hb4TfXQ7oAEdxQ8sB2fGubEPY3Gu8/akm2IniJIL6nq+NvR4IdAU24s/b0tMXvNFJdKKNyFqqXRWfHMTRYZD+7bRXLS93Ra9iiD6L1trR2suVxGHW2zX5wWq+r4u+qhzwBv689b90KaGA1waSXSGGAA8CuT/azTacKFZfV0Ca42WzP8DanVRqtdOuVMGBSW6s+xjg0egFrV7yBd+ldKm/JUo0OYUUUvgDOK4DshgmUxKQ3ebxBR8B+keksZgYF8chzIxsjkQ/dd7nqK3tI1dm8LrZix/sVlxiTKf/MTJmV+hBFtqNyBH5fGW15yPnSFd9bTd5slzeKeGns162Boqg3ZmwNOivz5djnBN21BD7T9RiXZVxtBKb6h9LjsKSgq2yMvVis83K4jAPRYdSj11c5Pg64yx9Hr20ReyT2Qn5A6/+E8ONqTxpnsdmuyp1xF4OyCwqiTw0ocb8QbZkfrQ5I42JnKBtstbbNfUronfs+MR5/wuGtHwfmKdk5VIeYKDHF2wWCnhXxyxHxgJXo1rXPygg/T6AveRwTXQErzvHJs59lazT/XHjpWrnGMrKqYO2ltbh8XGxt2QxNhexrODF+swVbWPdnuVEmNpiH7YUaEIiJTQW27jbmBw3Vw8MtWeMets6wwUsI3Yz9/iClUIB74u/9mWItbkPRJmUx7la91DyQHgUOOkYuzYFJSvSpswDzZRa2BC267BMX3OW/n3T+MPGELY4SV9VHb/7hTTB9fNcNxaHZOW9xEqayc0W3fXlzLebJXsElv+160jhHwmbmCxKfEHsOzCP0kT3gvra7ueBH4CvUV3KDYC5+N3tY4HS15CwPXqridiW8ZTjhbLBGEXSgUfsHvC641Fetc7k1ugNBJ33UE6EMaVGGIMMEeFTq7O1larbh0TvGxJ6yPvDH72wmPl3TNTZC7z+SCYQF/Ly+IJ3Aw/fYUyhvqay8z9YzcMvWQPigom7AAdKF+yNP3r8FFJI4Z8RkCW3C+qgDGxn3//oBuIBGVbN4cYHT9bW9t0OkC0KGO74iAWRZmywa4GuArLkB+hOWYlXnSrtf6tekZraAQDcotDdVGxjs6zOK44naSx2oKsOz7ipNHtlDodlButkTU7Tysr7qCaA/TIrv562NyFImysz+MI6keedzya2SydCnSQ+F4AuJLcYU2kSnpQv0TIdmLzgeIZmYisTzD4JvbOv7XZ2N32F3kFbH1nkunaBQ9gPAwx3fsS6cG2+kJ0dQ/SZdxWNHHb6BpdVcFv02nc/iTUmoB4sCZLxbLstN0Vu5FJ9+tJaYm+T1lqoWmstVOq8nESnxl5ORT1gOgHPxPV/Jlq9P+ioraGfPp9dsgIbZY1ESVkIeMrxEholvPWGYme8DpxwYwC6e3zBAmBaKOA9qtTi8QWzgHlABcDy+IKzULpoq5M221p2vxhMgPWyFqbUki2MNmaIcAOATIpqdtTWFRILOm3JPk0QBF6OfTZpu+1KRXXlfkCyWValUax0nE5kQw5H+sXem3Tgltg5D0FlAD+JLZ+Syo79w+B3V0B9zyIkBWOmFAWGkJcAM1DK8Idjrw/iz9sf23c+JbIW5VGTheTMVR0g4yHHazTUdiUWFkud961TkQguMWYC0FFbywnhV/FGxpgtxBZjjt1K5pEhMikin3KvhgLea/+U61f4FqCW2JdY4BRmcuk9H6gVy7ylkEIK/wH+CQHZJFTavxUQiAtnLrCbrOyhLz0FIEsUaYvthrdfLGeUkqqoyx5yyWCR3YjFdgM+tzpxirYCgeRCfXZiu5raAYqkg3QRZb1dk5XSw1B9Js20bcnnkQjo3rJO52VrAMOND8ikxJKpWBo4sSjCSQWRnwjG5lrNuT16nbmbik9X4Eh3oCPAAbJ2VBKHa5a94J2yMm4KzHpid3iA9r2rl74IgEHGt7wR6QvAefp3GoAQOHVpD0ze/0R9NV+YnRmmT784PdZN+ZTjpdvmh5s9t5uK9YFHUDpE1wJZWeRzkraKquKgVkXkJbSxfrKaUISLSdYZ24tIi1uQ2MCtx/icatwYvZmx5h72ymyrjbaxFJ8uLsMhJWyXlZhk9Yqvim9nAmejeGbLPb5g62SR1JggZhVUMBbfLz5IVWAk6mH59DHODeBFYPh2WbnaftxUi1FrVtj1Kp2or4ltIjL3yay368o9F0XRrB2y8mX1R60twO+eBgwLSwcVxJGceDA33TyRbXILTqLMsDvcO2rMs2tJ0mXz+ILVUd3C8W9lt1DAO+cXzi+F4xF+d0VKBFrXooKpRsB2Q8g2+PMOxLZ85Fi7e4onRza4LvrMEHY/YMdks0f/cXdP8G+R1b7trS24f5lr3C1rZS2ZQVGpPPtUsxtDjVml7mcZFNNTW8QMu6OxWdZ4B/ADbfIpl4H6nv0p8PiCacA24KY3zN7ntNPWt3MRlXky43JUWb4OyjMyFYylkMJ/geM+IAsFvCbHUIA/V5/jPyQzL80R+Wkb7epUEoePMskeasyiZSzT00js4FHzwuKfrabWbOfwjKpleFrpIspe6aZ/5CGKSWObrLID5c8IqNLBXdGrWCHrs0nWwMDkJv2jxDHnW00oLwporm0ng9Jlh7l2K3ZT0QBuvyTqW3Gn8S4WOqOjF+X01efTR1vAbLu1rCP2CQcm71o9+Mp1V3YVkUtUliiTtNC2cr4+mylWD76zT0hcWwFpojxKYd+W8HqsGzVPZiZ21oTMqK/tGrLbrjgJmB8KeE2PL3gAyPIZ7zLE+BpK+C4USBc3RG/5eh/ZXYBawD0eX/BJlG7WHuCpMjIYt4OYtEVW0wExz25xaIVdd11zseVEiYiTgDlIebpFnkYerbiS/F1shcpSFcQCsQUoN4B3gIOUBGVx5KHkAfIS4/jdOiqDugt/noUK2qoBPGWexxhjAkdIL3zEvDD9Y31k/D0SH9tdB4yKXsK1js/07trSt/G7fwQuuSByX//tslKF1x0lpd2G2na+sjvwkdVVhnEuHlX2ilT2L/lbeQKKFJ7CPwF+d2VUmT8u0NoExf1yAUuTgrFjwuMLXg281Cg86cgd+pRh1xifBYYYs8/rq//EuWF/vZpi3yVZolDrKNYRkToRqeMUFgXSJYc6Zie+N3F/WyFgoP4DM+yOAOeEAt4hwLo/49KTrqEaqvRaD3jnvUduP00ZdKiOq5DfvRDFl/Tj5wb8eRt+cbAUUvibIITIRj0jOqJ4oB2ICXsDW6SUnr/nzErjeNYh+1U0eHDNntPDY3OHRO7hrMhDzLFbUSidpZTO41o6AOkUoyHTgIy4CGxZVBF5xJ6f476wT6y3wGqUSH+liyjz7WZ0EGt4xfE41+nT4v5u2FLwpDkYgUhoZST7R16of42bfDRsdOzmU61u3BG9hn1kl5tk9WZI9D4G6vPExcZMBunfcpY+V1YRuQBH2Tf5jMlytDHBfso8jwsi9zEg/FDRoPAD7LczOSxd1l6ZPXiLrB4AHrsjes1P+2UWAJvsaiyz61dEBbeXenxBDcjRscgSJULXcW23d63T2Ee2k5IO1wtRmYLbUXy0VR5fsFF8v1DAOxlVkluF+l7l9Is84mgSnkST8ERGRi8pXmfXnP2F1enAufocG2QE1TG7PTbEHkio8u4G6np8wf6o7q1mSedwGfAdyiLltdjy8sAAVNv9QzFpgrmoWf03Pe5+9QTACewDmGL1oEl44p624VfKLZGNnN9ZKg49KDOZa7eSzzmfp7/+I1miyI1S7r/tR7t5wXZZhXujl7PSrmvvt7N299EXEXC8yhdO3/xQwHssAvVmIPk7OTz2vqdwvMPvboj6/EZDQk/wZ2AR/ryvfysYi+FWQJNo7u9lywG6UHyDHFHATNcddNTWJr4LTmElGmsyRLjU1FJPKvV/b7eMn8uM/+i6/jj6UuLXe+ExdAYDKNJZL345O51CCn83FgMzUZnscykJxo4r/KMfDgdwb/vBbskRyhHFkFdEbhf3RC4LbbKrvTvJPCNydXQE75tdKZIOHMLGb0ykJvuiL0f7sVe6sSXky4SoPnOslhTjWg3cFwp4o1EcCXHFXFmOL11385jzVXrpP3Ob4wO+sU5gkd2IBXYT9pDN4+bgIinVA3i1LDGdrqvt4xvXCNa6hvGV8w7tdedYXnU8nljvwExku3QhGaR/K8ykzFhxkghujigQFxmztPP12fyo5DpmXGJ8ZVbS8skSYb2alvtkKOC9+1b9vQkPOt6olEWB/MFqzsDIQ+SX8PEFcLWb/OwvnT766fMplkYBEN0pK0Suj9zE+1Y35jhvbvGl8y6aiq2gHkYJ3S2gMfBU8ucRCnijwE9Jiz6LYuw3MZho9Xm6sbZj70XGrIqPO8ZpnzjvfwVlBlwDKEJlsOInWA1YhOrU6pY0no3isH0dCngHA21jy8t+jzsAnWOvu2ZRsBRYAdyH0oHCQq8aT15dHr2DgeEH6Rl+nG2yyro6Ym+pKPgV09s/dm7LfpLNxnkjjwSWy3oJ8l99bXdFjg0XpTNkDYCjjO5TOM7gd3dFTSziQq06cClwcszW6Nf2bY7ffe4H93kfeNsxpuYC17VMdASQknnEiPygxKrbauvZbWcfNcQ+W02i7JgoqybgE+skzgo/yDtWz6dRAdC5/9U1/n78BAlexmIUTy4Zxb/wOoUUjick34cPoJyA8v+mc/lFHPcly1+CxxfsRayz7jr9k913OaZUi3G8PAXS9cYD5mXZwNi59gk3DDLmAnChMZvB+jeOePlssVWfxtr2uKk0h2U5BmrfN51ht5/g8QU3DtB66CdqqxHADKs9g4zS1aaT9JW01BSH/FExnvMjD1S8JOrbWFvsrfGF1Sn9M+e91NQOYEslApmMLtpK4qy0WmIfP1jN6KIrbnpGmQzedKsjA415pZa1FeuZRhcOkzGgpUiI+2NKrbrhdw+/2eDJeDm1i76KllbILCeLjTHO14oqcPi5e80rXGHppJG2A4A0YWYA1NQO0kVbtfU67VNHbW1/dYDHHS/la8h+RyjHqMhFHBEZ7JduikgrlZH0+IJuVCYq3omWhpqJZIcC3p34SXRA5Mv001A6SVDSmRiHRUlmLg4bFXhpwEiPLxhA6SjFg7KfgZUoTSQB7AWqHJDlo5tlNUdsPy/H0IozMVgqG4KyqLnbEHZDlGdn0Vyrxc+Pmhf0j22aA0xvLTbcM8U6lbbahnC2KNBQljal4PEFL0FlD5LxfSjgLftAS+H4w4uojsE4DgPv/45gLG7DxXmxew5Ad30Z3fVlY4EvgetQJT5s9LlZoqAtscBPSthLNk+Y57FdVpaXGdM3naH/3CAsDXuS2UtbIhtGgQ9DgT/eRfmfIhTwrvT4gm1QNIIZcZ5YrNu5/XDjnIdvMT6M2hJnz8gTYrMvuBx4PhTwjvurzjGFFH4Hnge2AAullJsBhBAhjrMJspBS/vZWxyE8vuBE4BKAT533FrXSNice6lKyu1548tnA5U3E1qs+c95zVOnv1zDDas9V0dsArFpir65j4yaf952jcMXKCvvtTLJEUaLM8JPdhMGRkQDhyhxymejWj64bNJewSpUfItLAKUzeNU+1fObVHV5xPHFnL33RhZYU9hq7lllFy3Puk9k0jwV6ptRYateX7fUNR4lrbLUr0zcS4Fz9Ox50TASUsnZzbUukmjiUyO5J5eP5hC64Lb5sv8zi/Mj9fOa8l3QRwZRa1BAJD87hqIAkHeCALE9FcaTUsffL8kwyex0Y4Zja1FM8+UBMM+5LVIkvjuWhgLdEn8jv7o9q0si9NXL9yx/bXeMBy89ARVQWaxaKPD0OxV0DRfg3KR2k+VG6SWcAh49yBPC7awInnxJ+6qZtsmq8vXQBKnv2S5YvH4YC3lKZB48veDol5aGdDxuv7hhifN0RYFT04k0jHW82xZ8XLbNPDWATMbFeVIAaApqGAt5j18tT+Hvhd7tQcg0dpaRYCNX9G5vknYQ/77cdJ/zuuSi9u19D3yLpKHrF6nfrIrtx9iTno6fGVyy0GoZaa5tqO4Stf281t8uLIhqKnfsttOtbhSeEgY2hgHf1Lw/918DjCzYElqACyXWoYK0v8HFsExuoEgp4jyrrenzBckD6sdal8D8Kv7sNcBVqcp2JylwtBsbjz1vyZx02FpDV5TjikP1jM2Soh+QlAKvsuj+10jZ3j3dBfmF3qtZXmz/vVmMqW2TVosfN89LvdhytOWpLjmoEADhFWxZ/qW2XykVnkuORRDD2hnkGXn1+IhgrlgZF0sUl+nTqid2uy4zp7JHZR2ViiqSD08Nj0YVkq6yqA0166YsaA+hCam5R6HzZ7M8Kuz4TnQHSlKk6xwrGAOpo+7jbmExf/adtG6zqtQ+QxX3Ry7jd8b7zHL1khi4ECCl6JtOZdshK4Y2ypqtfZIzdUazRLjBmO5qyzQ5L40B5UXSaJkqyVpvsalTUSwdklcQRRjimVtxkV98NssDjC56PIj0n481Sf/nzPkVlmXga+NgXXIQqT74fCngTnRAeX7AfKkCLw+Do72rPUMA7CpV6js/YzwK+CwW8P+LP2wG8t80XTO5A1VHE/+zY3/tQQWd8ljTQ4wuK5O7OUMA784J7xl77pOPFUVU5lGkjEjY3nbQ1Hk/x5B6h2DnEzuMCVMdb8ud/bijg/YgUjmcMRpXQkSAP2FlE0bnPvJxZdvuiUHwrv7sZkIc/b6fHF7wcFeCPDgW8O4EPgZOlVL+0bbKyVVfbl/geSEl4RPS6Oh/Zp9QCzmootidr9ckO+oYPiDHmu2irtJhETxXgjlDAGy/BHw9oR0k5tzFQmdKOAIWAx+MLPoi6R89BdWO2QPF43B5fcEQo4H36LzvjFI4/+N0dUDJBXY6x9iTgevzuH4Bb8Oct/EvP7W/CP5ZDFgp430LdDHueb3xzrik1O64x9obZiyccL9NE204vfVF6G23zG/OsppsPy3T2xvgZRdLJFOvUlVKyq+zYaSJKNQ4cISmTUiEpQySQVEqiUrgw6a4v40HHRIbp6tlcVeQy127FXKsFX5gdecvsyZDIfeygCltlVYCCchR/HZbGflAz8VragZjJ8A4Ev53RkxIuMmZRURyp3VDfxYn6Wu53vMUd0WuKb41cS17MIP2QzOBZ82ynKTUsCUXS8fHLZv8eQIuNsuaiCiKfNtom0kRUc2tFlTVB/6jUcwEW2I25MXozIbvK/mMlU+tru/RyhLNQpcK7UeWd9cCpoYB37NF7lCAU8M4MBbxvJQdjMdxLUhnTTf6hNxyBjS85nrLriD02YGdQNDFG3sfjC5ZHdYI9Cnzn8QWbJo31GCSc5puhzMTjxOjKZdbvAc4HwO+ui9996c6R9Rq/4HimcQ1xsKouZJZD2JVAZS4/tU7SgLc8vuDnHl8w3ok3jNLB2CKUBlkKxzf2xF8IEJW0w1TXDpFBES85njofv3s4fvfDwCop2TTq3pteBSYA112iT99ojcyeDuQCrffInNPOjjw06ubozfdImWhaWXtldMQXH9mnjEP9Vtgga3GXeRVfWe0ZbV60GNWkEobSTgArbI/7z7/8P4QZKI4dwAfAzpg7xhUoIe+1KJuy61GTnb5APxQPLxt1X735Lz3jFI4v+N1eVKB+rGAsGV2AObHt//X4xwZkAKGAd1Eo4P0acCR3ItUXu0ijpDL0hdXp0gujDxzuHH7+nDiXK11EyCPTJUbl1QBOQRG+E2inrSultD4yOoy1di3rB6s5r5j9CCdRTGRSBWyTrB4FFSz11JfQUNtJV3057bX1+ZtltWTR0ozJztEjXMLsrcYowTX6p4ls3K9BiJKuyMSgFNsW+uMf290+6Rx+ngsj93J6+HGets5r3jX8DP7opeyVOb3O1uf+kEZ4OdBxp6x4VKj1ud1xT4/wE/aFkfvYQ0XLQs8+liPBT1YTClUlcSXqJjwD1QW55Dcv4JexPOn1ZzNcd352qr6sQV99gTbDecfcdxwPnbUy7YongMP43UNQZaJ4Rs2B0omKoyGK1wYqyDMo6eaMn3e8w6IG8M75d4/tgypvvp5J8epno+eMSNp+2Xnhka90Cz/N53ZnUEFdX1QwCEnZshjaA83/0NWn8NfDn/fVO2aP12ZZbdaT9HMc5ZhY3FdfcDfwZFgadwAIgauztrI/QBOxlQcdE9N0IXtJyYQHoxc17hx54eKlssHIA2Q9+rA5ZA5KVLbFLLtDq7KHfd86laujtzHBOrOdp3jyDqCUMff7ZjeGRu5pGhNFPi4QE21ujZKVGRzPKIcC3tdQv4f2ZXaxUR3P3yct+54U/jfhd3dEBfJlecK/hDTgg1hG7V+Nf3RAloA/b7cQXGNJ8dNsq/XMiuJIQiU+KjWm2SfjJNp6kvPRD+NcskLp4GurbUOPL5jhKZ6cc0PkpnrJQ/bUfi4VES2STezekcesT+yT+cZ1GwW4WGLVY6J5BndFr2KpXZ+XzP70i4xxPBcdmJjhVhOHKC+KaaZtzXzK8eJXlNzsDzQUO+Npf/LItOMm5nW1feTHXofsKkRk6Y8pLqmRJ9PkO9apfGW1Z7nl4XurhfSbwzRUcHmgiLSd8+wWHMANYO8lh/scb1FX21uul76Iac77NA2bj+2u4nOzo5kv09hru/nI6sL90SuabJbVXzIx5gD6XLvFMcvbS2TDZ4AbUITJr1HdX1egOF7/KW5E6T8NBQZUEbmJ9KRLmNZJ+uoHADcq+LqNkrb8OL4D8PiC96N++HGsRgVM/WPL7w4FvB9SIjALQEst1Ar1YCFLFGorZV1ujtzA51anLweEH3ploWzSYyeVkoM6iGXdQgHvU6jyTLwzbR8l0h4pHKfw+IJd7zavuvyK6J2NZtjtEssdWIkfn4VI/AZO1lZkdhCr7VO1paXGyZWZbwLNW4gQM5x3cK9j8oVAIKaFNz62WQQomxXeGGv4uCwqtdXF0iHfM7tzh3kteWRuRTWcHDcIBbxmKODdnVzej+FYcgIaihu6D6Xjdh5KviaF/008ze8PxuJIQ5U3/9X4J3PISsOf92oDX3AykN9WrCcsHbhElBm2Cqqbiy100Eo0FC0E77keYoXteaZf5OErWohQYp2UcK7xvW5icJd5TXyxBsibjQ9xCIuK5PORPIXR5kW86HiG1tomnER5kYFMsM7kbON7aon9hKWBK2Y8Ps9qduKp2mIxz25BGIfjuehZQ65zTMt1U1gctDq/VU/suv1kXVlGZsY87TxaaWslKOG9ladYDDG+YbrVgf7RhwFEH+0n+mvz+MDqNnAHlauiZqbVAc1G7DTRq7gwDYDG2g5qin1EpbHhTGNBQ3XcYoKRkzhMBmmE1w7VZ2bUFXtOyZelfz8xvt6Eq0dPvvVhX/D+chQ/OMaYQDd9GVtlFfzRYcc04f49iBHfxycW+HkAVeooD0yhtCr5clRWYTeKj/ZOKODNi607I2m7L4EBMWmOb2P/4ggSM40G5tUQ+5+L7XvGfLupXCYbiIWyKdPsk/uggreyExkTVWaNn/9HHl+wI0qyY3oo4M39Q29ACn8HyoPqXnZgiU12NXLJ5E2zp3zQ8QYZhCknSno3MkW43AeuhwBFCcgRBQgBDzjeTPso3DV4irasRbqIxLmJfd6675xPHzPMMyZavVgp6zmTjmuhCMzKgsOfl+uA5o18QQdKW68B8G4ZEebjGbcB73F053QGcGMo4D37rz+lFI4b+N1t+e0y5S+hC353mz+T6P93498TkCmUA8Ri2YjTI49RS+ynidjKItc1bLMrlvKyLB+Tlmgstg0B4kr1QMk2g/VvaaetY4p1Gm5RwBTrVHOV7XHV1FWD0Cq7LjU4wJm6kt5qpm3jZG2F/NLuJPqGAzQUOzggyzNAn0cxjqK7HFOaOYTFUrseVTmUVS3mFrDDrrjuPvPyUTXYP+xG+XHlMA4u0GeTLiJYUiCQaALyZDnKU5gIyOL/n6ytAKCntoiXnU8DcKHxtbNz+AVBjM80UJvLIP3bGnkygzQi6EKy0a7OXpkz38Bqny9diSDwYv2rrTPt9j++4Hj2mZ764iTFcP2wQ1hZSe9Rw9iqTjcbH3J2zIy8ojjC+85RDWJ2jv89lAjnUPXafXrSGgk8Ggp4QzFSf3VK+wS+jipnWsArsWDsKIQCXn/MF7M4FPAuUOoY9MLvzrkock/1KMb3lDQCJAdji1FZt9dCAe/WMmOuRJVDU/hn4Gfg23HOp7rHRZnnWi24Wv/cVV6UTWaVRk6SsLJQii8n+hzvdrSk9qMubPcyy5N3kTGrH0BP/Wc6hF9KdqrQgU2hgHd/8pix7+pU/gHw+IKdUR2qRajGiExUN/UQlDZZPDj76Vj7p/A/hSv/y/2vQlVk/pX4VwVkoYB3v8cXfA24fJusyk5ZiTddj2AI+6guwTjWy1oHgYobZfW09qK064cQ0Ejs4j7tbQDO1ufqp4fHcrY9l92yArPttjiJypBdVXi0PRRJp71R1lgLNDtCORbLRhHA+bx1NsP06ZpDWAZAa21zqeNUFYfqA0N2UqnSPeaVNhD+0W62oIu26uRv7RO0DIqmpBMZFLQ761cbn624xfioEeCSkrAQuN6xTgOgbpJReVUOpWnYZ9toVRqLbTzleLFUR+n7ZjdGmxdFwzhPDAPTrC4MMZS/Z3d9eU56tNidHIwBOISVtcSqL9vomwTACtuzuKVaVS+njF6kU1jtIeFBWT4U8O7+xQ/uj8CfNxO/ewMqGIyTg68LBbxHgFIfcijgfd3jC34FmKGAd8/Rg5Xa9ihLI0/x5MMowdrlKJ5hMg4Dp4cC3oP/8bWk8LfD4wsKVEbnPOCwTkkiqqv+++PpeVYzIjh42eqPROApnnxrBQ7vrSTy3K21jdlj9VcAyKaADIoxsMglQVPt7fEFjZhN3D8RT1PSYf0Aisw/NPZ3OvACSsNMNbf43Q2AEShpmKfw56U8MP930Pa3N/lVtPn/OInjFf8ODlkSQgHvFcTU3S10dsrSIupx/tUyux7DI9dwTmRUTSDt/Mj9zLTaYkmOIrjHUZlcVzHObe9YPZlttwX4OIuCzpdG7pS3RK7HG3lYWy9rNUvaJaGf8aPdfDaqrMaeMurcEmY/63hu9FrXJWK84wnNSfST6XannSPNS3ULXYx1vDI44Hg1epfxrv8pc1B7VGnu7CJcj10c8THGvAiAD6xT+MluQlgaRe9aPabbaO8BNBQ7jpL3qCTyyKTo8/jfn9pdiLsD/Gg3ixaR1vsbq7U6v6R3JCIMMTh8H89Gz+Lh6JA6Hl/wTKD+Flmt1PgHZbmoxxc8AdgK7PL4gj94fMH1Hl9w5C+9v38Am5Je/+qkIhTw7vitYOxYiD2oP0VlwDwoy41XkjbJosRZIIV/LmqhgjGArBfMgXKPnY0pj+5g+dpqzUVh31GcToDyoohhUR/z7Ba0F+vO+Mx5zzVvO8c0eth4lbZifeI3pAubea4bWZJ2DSP0KfHd3cBBjy+4xOMLnvgnXOOfhpjxeHKzQjFKRyo+67RRnLmE1ZOUfIQK2h5/3ew9zeMLnrF3ZJ2W+N2ev+asU/gb8d8KsZb/7U3+ufjXBWSQyHbcBVhDoveywG4sQQUWd0evoF3xywyIjOYjuzthnDQTW6guDnJl9A6GRe563ZYlLZum1PjRasZ+mcW71mnTQExAlcoKXnY8uewn1w0/fpN2m2iphdikvC23xXY9DCRKZGtl7Vyg8Q2Rm4afFRkVnWqdwqdmJ26OXL9vSOTe/QP0eZVdwuQMfRFX65/tItatd5n+JWkiqmlCpl1izOgSCngjU61T6hZLx4flRPj+FxzPEs9OHSaTwZGRNAlPmni3eVV2/Ni1xP6jCME99KV84fI1QKWQl8yzWyy5NHrXtLfN0wKXRHwVAK6M3sY5YT8jotdgxwLVRmJHZJzzKW52fMzbzofP6af98A6QvtBukrB6OSzLcXHkXgeqZBE3Aj8JldXyl5Gl+E9wA/AZKrNx729s+5+iKnFejzKXXo4SzM1N2ua6P+nYKfwFiAXdHmJ2WlkUFNxjTI5U1XIxRMksJB5MddVWYAuNXWUmeQAHZWYURbx/2+94w9lSC9FM20YHfT1DjNmlRGzKCyX4f5PxCedq3yUWozoX/2nE5YqUnpjsj/HdTgGmo54xw4m5EwBEMOKCzxyQWd6r9M++qiLylgMb8LtTHLN/N/5bu6Jjl7r+JfhXlSzLYC+gb5dVmGOdIDpq6xACTtFXMsXumdjoIWMCFxuzsCWMiF7Hx/Ypl98SuZFHna/gxORu80ret069DaUldRngz6To8Knaksv66AufJaZVNkz/ijHmRVtQKdlTUbIP9VB+j8XASPx5R16Apz2+4Ljbotc9AZwI1M4i/8JcmUG2KMCUGptkdfN7102hfJn+cp5M70uscylkV/F4gNlWm8fO1ecIgCxRSDVxiEMy0RVvowy5f0Z1NDHHbpVRLB2kidIUqnKEW6A0keoAYq7dqs1cu9WA+PoWIsSTjpfQsNGEus4cUZAgJAsB5+lzsj6zu/CTbMbgyP001bbxmdWZXMp/jdKZuRMQGjYniE3slBXNveRc5vEFHwwFvP9Z55g/bwOqU/IP7OM+AZVZnIk/z44ZozcDZoYC3rIdk6A6whajPs+46/z0NMJmE7GNjbIG+ZT7o51CKRxfeBW4HPX7fMir//iFIeyj5BjinFKnsJjkCLBXZgOlhaWriDwH6n76uYY8DcVnTGCjrEljseOocc/R5zDVTrZrZd//w3X9ZQgFvDs8vuCEYfr0K2433mO9rHlnG9+RG6C8n9KTl4QUzXS74+imYusTW2VlXES41vg0vkpHZStTIsr/XixGTc7/Uyz5fzqP4xL/ygxZDN+hHqTUSepUrC1Kdy1eqCvelCbgdkOp+X8qu9A8/AYNw2/yvnUqKPHQM4A7moitzHHdkvW887mpRdKR6CTKJbMA6BPT6JmFUu2ehbIW6RwKeBMtnqGAtygU8F6Pmp1XPkwmE8w+ABjC5iJ95h1VyJ3ZRNt+bWttc6KNPF2EPQDT7Y4HplsdMKXGh1bXmJG53N9OrPviNO3njqGA99NQwDseWJBJIVcYX3BYHrO6JgR2XX7BSsjvmHjAo+2hjraPaKxME5VaQlxXSvjMOjGRSlgom/KWdQa5lLeBy0MBbxAVdJ472znix49dDzDLdbvRRGy9E5hY9ngeX7Cvxxec7vEFn/D4gn08vmDlstv8Gjy+oPD4gqM8vmDQ4wuWdFj63f1QN4LpwHiPL9gaVU7+BFjo8QVf9PiCPZPHis3yuwFnokoyT7iIdJ3q9Ff6xPUA0113RW81Pnj+j5xfCscd4jZZacAd71g9c1GSK8uAZ1faddZCCc0B1O+zhqZog8k0gCOqC1kAb10Ruc011eoqD8pMCqWD76yWNNZKgrG9donOazttHWdq84tRPKtnUJIxfzlipcf/CC3FpmvvMd6mvCiinbaBc/U5GcBYoCaqdLmHmBguwICHvnyyV2TsRYvsJj/f5PiETFHKInQAfvfn+N0vxSZRKfy7MP63N/lT9z+u8Y/1svw9iHkKTm8gdrQc53iKTFHEfLspJ2pr+MZqzQrp2fmg8UaN+I11i13F7B55Op41nAl0pqTmfQjIGWG8x83Gx4DqepxltcWJyQSrb8+PHhn+tccXHA5cQ2kboXqhgDcEgN99GUoo9CVP8eSZxDS07jPe5EpDaULulW6qiDzKYpp1EjdHb7oaVWbsVOLhDa87Ht3VQ19a3ZYc1gQd8OetH3ffkG5txIb3TtTXVk0eR0ooxMVY83zesPocdZxzte+4yghSWeTuryiOVCq7/hPrJL6zTkDDfvF9u0dVSh5shagZfiAU8L6c2MHv1lDSEALgoehQJlheG2gdCnhXxD4rJ2pGndwuvyO2zYFYcPYFyn7lnlDA+1RZm6Nkf1NU+SgnFPBG8bufAm6NLd/mKZ78CMpAOhlhoH7MAucoeHzBzU3EVs90ly+x7LHo4GfuHDP+1mNtn8Lxj7fuO2ePV59fpTyF/Gg34+robXMKSXsiFPB+4vEF0wX2pgZiZ7UztIXcYbyHoESMOVmUWQjYI7PpGn6WNmIDrzofV8T9mOZh3B4JwJKCQZGRXKEH8RoLANhoV6dn5InpoYD36B/jnwyPL2igPCi9qMz6wJZikzdTFL+01K5vFZH2UCjgfeVXBwEOjKy1p6I4UgXgssgdcY5tfijgVZwfFVzl489L8D8n3XfuV5cYM8841ngx7AWqp0j//zL43d/zn0lf/IA/77e8Yn83jkcvy39zhozYw7XNRlmz3umRx3sPjdyTN1CfRzVxiAuMbxjtKAnGLCn4zm51N6qEMQA1U30uNpRNLDD7yW6GFeNKzbNbMMK8gRvNW1gsG5seX7A58CSlg7EQMTI/fvdglD3K7ShdrMHEREPftk5nm12ZYungsegFCYI9wHdWK26I3MyI6HUAdxD73ASSDIrQseihL60OoAmytthVZpx29/iVQ/VZ35QNxkA9QMoR5kbjYzxiF6iSze3AG2mECTjG01TbRkVxpNIRmTZ/n8wqNYUtkGlMtbvzvt1jL9AxaZUTlRXs7fEFr/T4ggM9vqAzdkP9EKBQOqkt9tJYbNMoTQYGjmqoqAnEGjm5GKUAngY86vEF5wERjy/4ECQCuovLnEs8hzGFEu7CBJTuWNnAy0WJtMVReNnx5NhXHY/nx7Ml22UlPrFOTpUs/6nwu0++yJhVJUfkYwibrvpKumtLTwE+9viCvYGTJFq1DbIWL1lncUL41ciV0duiY6ODeTx6rh0PyuLlzKoil3IUc5nxJW5RmAjGgEQwJiU8bp7Pz7IxC2RJ788mWR1UOf3vQHtiOi/AmY3E9hHjnE99/K5zdPUPnKNqOYmO8/iCLX5rkIriSLsfrObvXh0ZHg/GIC5B43ffDywF1uF3J7xlHzUvjM6zmmFLjtlEYUsqUNqGLIV/B25BPXP+CIr4f9NROn7xb+aQAYnSUwgIdfK9ta1AutwZIlyK/xGR+sEIjgnn6HP7r5Z1K062Tq+LCsC+RskqPEssCJprt6J/ZAw1xAG+sVvHD/NiKOD9zuMLNkQFbxoquLgbmBgKeONfvppJp1YL5XtYC2CzrM4pkWcAaRlY+uNiXGLDCuIwBaRhqo+rEbClMrl73nU+VLWBtovltoeDdiYVNBVz1BT7675oPF2qFLDAamRXE4esmmK/Q4s9SCpxmAv02QTMIWlAFQPzwoH690QwcMTa/zMp7lReFItiaXBQZhGS1TY/YQ7egSpFvEJpRX4DFdACnJVJIdcZ01biH/IgyiPyXBfmO5caM7Sz9R/oHn7yVo8vuCyUNmRHKI1Po1KXL1v91zxhDs5C2RitQHlBAqxJOs4hVPYS4D6PLxhAZee2oMrAoKyMsoG9+PN+xO+ui+qM3BYKeK1Y8LwPEh5Yn4YC3lUeX7ASqruyKeALpQ0ZD8zpo5ee0Q0KP2DvolJ7jy9Y45eyaikc18iVElsI9bu2pEaopFO4EWoCUUiMsJ5POecsuz2zaD/jW+ctYSHoF984LA1etc4kj0xW2XUTuoRlIUQi+OINqzf5pBfXZH/+61afIygZiL8Dm1GZ6Wwgr4HYeUNNoXQWW2hbqC32slHW/O2Juz9vxxBltt4VdU+LAjd4fMG3vnNW7l9H2wcquDqbmLdrAenPXxi9/8xyFJNOMS86nqGBtou9djYuEeVz+8RZN42eeEztQPxuJ2Djz/unSoX878KftxC/+zx+v31SETDovzEYF0K04WjJjHj1K1MIcWmZdUuklEv+0+P9p/hXZ8jKYi85N10RvWPru2aPXXdHr9y/0q7LIZlJ0Oq8PlMU35Ehwt1GGRNbVCQv/kGdBknCRDGskp78tbLWwjuNKYfvNiZPC6UN+WHHyPozz9R+/BHYj5oNHkR16SXfMF5DdU9uA65FdfKVgZhqYnSUUnna2RJaalt4w/kYPzmv41XHWDIoqnutMe3nBpryRW+lhXCLfBmRKr42hE1TfTv7pOKqzLDaMTg6Ujsl8uys16y+pY622443QdJmtPGa61HHq2SIMIvtBkw2T0PEyPxpwuSsyENbh0TvO2NRYMgpoYD3klDAuwsYlXSNpd6r5x3PcYMxrQUqQ3UZcEQXtgbgFgVkicJOzUVoHMqMuKtDWBk3GR839Go/NkCVdTuGAt58gFDA+zmKyP8wSiYgjq2oH+yFqKBtKvAEqvtyj8cXjPO8KqN4hYXRkRUuQ3V/HkoaZ3yMR/M2KtjLRmU7u1Imvb5HZlt7ydFQ6v43lf0EU/gHwJ+3UgiGxTuqbaQ8KDOPAHOBt2JB9gCOnsm/V0fsixtrUyidZpPwRMaaFwDwvHWWymZHrmWyeepRfJCO2prEeHtkjjXcMbXnssDg+jFP3r8coYB374fOB0ZNdoyeeaP+0ZWL7EbbtkvFUlhue9gjc+4NBbzLf2OY+FhFqN/O1ajM28XA0A/tU+IdRxaxYCy2/RfApELSOEA250dHyg7hl784MxqgZ+QJ8wlz8NPHPJB6mB8B9uN3J0pYHl+wq8cXPP2Y+6RwfMGfF0R14v7wG1v+AHSLbf/f4CyUUHjyv3i7dMVjrDvrvzzef4R/fYYsGaGA9xvw1oU78PmCU6dGup0zVJ/FWdrcUl12ScnzKPBMT20RdxpT2CKrMjx6PQWkP/Odc/iZQpCFumn3rykOiGccL7A43IhdVKyImg12R0k0jALAn5cH9E6M7gvOR2V0nCj+xiGU4nWzR83zJ99uvN83ufRRRcvjdBZzo/x4yVn696MtSR89FjDpAjHF7MoQ45vE9oHo+cy1T7D3kKOB2AP0HG1ezFyrpX2/402tgbabex1vsyeaQ2dt1bJO2poz4pe/SVZnlHkJNcV+OmpredM6w9xLTrNQwFvo8QVdQHYo4N0TCnj9KCmLjrHzvzZ23Y1P0DYmGyI3QxH5P4lI3TvZ6mlslVV5yfFUNaBKfKN90l0QtDuXDwW8q4/x+X3m8QUPAvcAuMlnmvM+WVPs33eveUXOFKuHQGUmNyd9jFehiNrXouQrOEDWS6gS5f7YOc0LBbyfenzBsZT2tdwAbItKXTqEJaSETbLahsujd9Ww0ONdElvKnmcK/ww8EB1284OOiRqAQ0jxgdOv1dYO3Ic/LxcgFPDOikm0dEMFGOuBCSeFn3tvjOM1qov91ptWr0kgLi8ZVRBUpvOss2uZA/V5joyY0n+eLCenWx3nAT3GGBMYanydASzB774Nf95Tf9mFJ8Pv7tVO4ymARtqOni9YA0W/8MNLe2qLvuumL7t3RWDQH5UZKIcqgXYj5tf5ojmQDFlkVRWH5g8wfiz7YL0ZxRttiOp6XY8ibq8KBbxrf+EYd6DumU7U/fV7jy94PaoxAo8vOCYU8N73B887hb8aKuN1Mn53G9R9ug1K/uUIqglrPP68pb+4/78Q/1MBWRlcPNERyDhZX9Ub6AEgJfJLq8Oi/bhXoTIkAwAec7xCRXGEJmznInsm46z+3YtwNS9X4g8sABzCIk1EQJbiPfyisXTshtPJ4wtWR6ldGyjCfuNV0mMnB2OWRMaDr5W2p017c1z/cY4n+zcVW0ZXEXkNPrdPLF+ujF9xfW03U+1TNYAWYnPlXvpCbYHVhGedz2tZQnlfO4TFc45nLUPI20F1URaSxiSzF2GcDIsmSOwWIDy+YCvgR6Ccxxd8CxV49kDNZIaFAt7bAQ6MrDWsgsh/I7ZvFMXJuw4Y3jj85sWoINXVXlu3C3gIYJNdlWERnxt1g23xC1ZH81EZxtoX6rOoq+2tCzDSmMQUqweAaCa27BjrGFc/kyLuMa/I9fi46ip9SJ97HZMBWG7Xd8XGqgSUCwW88fpwsj5aCOjjKZ7s7CDWyu76EjHXasl82aIO6kFADoe5z/F2PfxDGsakOACINXY8gMqUDoiZRqdwHMHjCxqVOLHjKGNiggdWWzuQEZX6TIffXRF/3mGAUMC7BXgz9g+PL6jvpuK5V0TvAPXdX4HKoGYAF6EU6o3KHGKi69FEMAaQSaG42vhs74Jo00n99XkXEOM5Fkvjsaa+YDEqOKmOMr1P6Hb9mVhu12vfKuYcUoHDwolJLpnNn3j4sTb/4ZDjiN1PUQHWV/cbb/a62JipA112y5yXqsU6ST2+oAf4PofDNW40Ptm/TVZ+YZLVCxvtCNAev7sZ6iH9eWwyG8cioBPAl1ZHI9YJcVrS+h6k8M+B8qb8U+2QpJR+StNrjkv8zwZkoYC3cMfIm0qpBguBOFlf2QSTC0lKpebKTCoKNVE8pMrOO0eZlxy53XjPtVlWY41de/95+py9z5pnezbL6vHMyTzgAL/Pz/BJFMEfgFpiH+vsmtpn1ol005bzk92E0zRlY7TJrsYsRZq97proiGuAHBQHxOwoVhsD9XmJB8xBqRqcsjnCO87RWpYowtJBL8OfNYRMBJASDbco5CJ9JkvNuFUl+cAVoYC3wOMLPkuJEORFScOcA8zx+IIvAlU3uY5USVqnU1JmfCYWACnOjD9PTDR7taou9g8+XVvMJ677uShyT6NV0jPB4wtuBsbEzMbB764RSuPeL62OG2+O3li7l7YocYBCXMUoRfCsa41p7VpqIQDGOZ7a3yo8Ydx4q5/YIqtSReQy1SrlgnS2xxdchSpznhlbdhAYHAp4d3l8wY8WyibaQrMJ7cXawvGOx51rZB2eNwcy1emnvrb7TuB6/O7W+PM2eZQp9OMoOkB3lDjuy6RwvEHuJ1t+Yp8kztLnJRYaWMZ14Ztf+sIXvASoPMJ4T9xsfLwvzlWK8Q+/RNERCoHZoYB3SWz3mR5fcEw1Dnz/pOOlyvF7Rhy6gB7akvMFcuEuWWF7lthRH8CBZaRT/GJRCZ3mSZKEVP9MXBG5fesYx2u00dbzttmTsJpr/DecyOQybTFwq0fsXkls0ppNfo2k9QOAGs84XqCbvrwSgAOT8Va/8oP12UNRtAMDsPC7u+LP+zG23833RC/vf0C6a023Ow7CFxz6iDF+1R5yBn5gddceNF7fjX/Iz1GpZVron6WJ6G348/69cgIp/GvwP8UhK4sbIzfX3GpXTmhsAUhJeYG9FsU5ArDvil79xmTzNHt0dCjvW90B+k6xerzUMfwSgyMjecC8vFLz8OviZWvAScA7qDJYZ6AfMM/jC14I4PEFu3h8wQs8vuAWjy+4z+MLnho/bPz4l+tfFM113cJ3ruF8ZHXlhPCrco+skBtvQKiv7eZNZwBUgJMT2y0buGKBbPbjLZEbWGw34JXomTFJC0l3bSnxjFjZYCwZUoJTKDrYefp3dBSrqCt2gyI/HowFY6dU4DBPOl7kBcczsgalPJHDqIzB1k7hl7pKySwp2WFKLbn0EI29F8apd0+oZEuGDzO+2naKtmyfJiQVRD4X6l8XofgnDwC3eXxBzeMLvjbPah4Cru+jLzj1dcdjHzXRtiVmzV9aHdJQpH1CslpGycGMhADUV3ZH3rLOoKg0j9QAmrUUmx7wGe9op6sgT6AylwaUiKy/7nxMP0P/2bjJ+Jir9OCB+lrCnjOTGGE0ltVLLmNu/OV3PIW/C7Fmnytujd6464HoJYkvsRBQJNKGAFtv1D/adbPx8c6INHbG/BfjGAj0BJonBWNx5I52vFaxi56gmVEkHYnXG2RNJKLD/dHL6seX6ULSTJTypi9tqvsnYi85dZbZ9agsDnOj8Ql9tJ9sFN/zP8XVqIaIicADoYB39XJZf/hhWe5IWBq700T0gaRtfwDM6qLEDraG2A9w5DJ9ukZJwkBHWS0p+PPMydbpmdNt1eB9uf7FaRcas++/1fjQ+NZ564HT9CXnAG0dwm6UJqLDr4vcstvjC3b6L64phRT+EvzPZsgAFstGG7pFnvHUZC/vOR+khjhIRS2fJx0vaQ9GL+Zpx4tUEwe1SdYZznvMKx9FdU2CqnN/jipTxLuj6qHS9U+gSnDJoU8vjy9YBwiUOYVZMZHS22Lba3ca7zYDWjmFRcDxKsvtWdHT9CUXotSr0wDaifXoWFglldF9KNuVe6fJk2dNi5RItYwxXmWoUZovvEe6sdDIlEVkaSWc5VyZZueIYiWpIeB912ikhElmz+KR1hXvoEp83GO8zTn6XABRkdzI+1YPx3ZZqXi+bF6dmCL3ftwD6oUn1wXeSqf4lA+cow41F1v2CcGcr+/vPsXFreedpi3W4oFmGmYiKO2p/2xUEbncFb2KXMrnAF3d5F9WUeQmzrWFFmq/S1a06rOLQ2TyptULlMdl/efMs8mX6bhFQeQVs9/pZT6LeBesSez77yLCJGegTgWRjy0F50b8OYtlo+dRQfktKOeFChIRL3WSS2bmbKtNcQ99SRqwCtWRG0cP4FJUyTLP4wsOAIKxICCF4wShgFcReP1DxRyr5dqTtZWNvrdb8IPdAqDGNcZnADiFWRlVinwwtl+U0p93Mt6rL3aWmuimEeWuyJVkiUL5kXWKAFglPWyxq1BX28tWuzLrZS1QE7PPUTqGfxV6nR8TxzaEzQD9hyMvPzxq9n86WCjg3UiJLiEA149+8xmObQnVADAeNwfxnOM5nMKihdgiO4sV5T+yutxQR+wmQ0Ti25ZtXb0B1Um9/jpjWqIKoYujfa0OU64KamJ8xt/VPJFCCr8H/2ph2N9CTOLgVmDPO47R3U7SV50HcESm84HVbe9lxvQqAFvsKnSPPN0ARTiN32w7xP5+HWUFEbdKKUAFagBU4ZDdUts8/Gu73dmoB3tZfAmcnZDG8LsfAsoSUscD76N4LFW/tVrJ1dIjPrS6sk7WLlGHhWmorFzigfCT61qqiNIUpkHhB+iirWS4Y2qp5XvsrEgVcdgpymTRIlKnaXgizzqe4zRtCWEMcoRyPTKlJg1hC4CbIzesn2afXAtF0l01QPthdCGuyT/azcinHD+6bvi0mjjUH2CdXZNldn3OM+aUOpYlBXpCRBOpQaQIZzEIdzkR5oAsjyk1WVXLEwBfWB24JXpTbgTHMyh18IWU5oIB0EZswC0KmGO3PMtG34oi+l8OqqS7JK3kGXhD5OY4MfstVMC9F+BkbTk36p+wTtbkIfNiTHSqkBu9y/Fu+XMf+ixc9pgeX/AcVGu3ACaFAt5hZbdJ4fhBQ98nb5gYF6N+P0VvOcakd9VXIiVSCLz48774tf09vmD5FmLzwaDr3qMmun3Cj+xfI+s+guoUbgu4syigtbbx0G5Zwbde1toGLIl1Lv9l+OL+nvl99YUZoDLkN0VvOvD8w6OPEoP+M+DxBccA93TXljLR+ehR6wuki/HRM9lIzTXPPTym2dEjqKpDNkee/8F1c6tyImwAxcXSkW+iVQrZ1fjQ7kZSZ/moWBNSCikcl/ifzpCFAt79xIKfx+/9yG6nrTvLJUyjvCiirVgfLweST3pRKODdFCs9DkGRcSejUum1UST3eEAWIRaQna4tKnzR8XQ5p7Ce+cDq9umPdjPZSOzgLet0tskq8bCnD0qoNG7d8yhwAarrCIBdds7A6tqhq6Sk+FWz75arHF/U7c5yztO/pUv4uUgEhwugvVjbW8fSfpLNE9f4qdWFK4wvE38flulUErn2Gfqio8rVblEkLWCfnUMlkYcj1lRQQJrVWVsV7afPTwMlKhuHoETRsYG2s1G8wNdZW/X5047nX9EErLVrcX/0UtNNQcLPrrG2g6riIAvsxlYLETrsIpKjCzhMBjkxDddYE4OrHJFEZiqdsNyHO3HMumIPERxuYGAnsZoe2uImLbXNLJJNeNo8FxAM1ObyjFMJ8++UFQZ3CT//NopAXQvolEv5mWOjg8+51Jiu7ZY5a76y22eheHMvojKfnwNnfm+32vG93SpKidYZe8nZGQ/GPL5gFZTe2nLV0Ut3SoLl7mXf7xSOL2wIDLzU4wveC5wM/HC3eVXnAONbnqyvnIE/7yiPy2OgYhEuIz6psCVSEywGJn35yPXxDNGTHl/QC3xymAx9jn3Cu79HCf/PQndtWcIZw0LwmX1Sxb/QD2wCMGSNXbt2rszQs0UBUakl7jsZIsxeUYFPrS4vPUdiAv0CiqoxPBTwrgRethGtypU0T6SliWgawH6yo69ZfeP1Ygs1OUohheMW/9MZsjg8isA78Xztax51vppY/nx0IJmiiG/t1te8/vB9r8S2bUeJWCl1xW4OyiyOUO5ZlLbVsyj+U86TjhcvipX2sCW2FhOh3GRX57TIE8mnIAFnKOA18btPB2YkryySDtJjxuDfWy04WS/pE2hX/PLSg2SFX3WMbXK6vtgNMNE8g5FmCQ1kqPYVY5xvlLrmuVYLYrN/hABbEtWEEkmVEvaSTVWRS1TqfGx1Oe8dq+fp7zjHXOsSUfJlGovsRmSIMCssD5c6vmK3zOGCyH2EYsKXA7Tv5bPOF0rl2vJkeqFbFCVMNaWEE8MvTNtLzoBmYgsesXvjdllp7bvOMQcyRHF3lOl5YlshYI1V65BTmDlx/lbQ6sQE80xO1xYxxJhJdowrB3BT5EY+tbvwqPEK58fkQNbategdeQzgo1DAe05825g1kzMU8O6I/V0blW2rAnyLypQVoqQyTkWJ/NYAxocC3g2xfVagrJ0kquurAGXBlQX4QgHv0WmAFP5V8PiCTw3Uvr/uPP3b/e209bdnjNr77i9s1wz13fou2f7rr8aGB5oWNtR2pQMckhm0DY+3QgHvXzpR9/iCemex4oOL9Fm9T9aWyxytMHGPWGvX9Dd5cNWo2HbPoTLboOgJfVHVg25vOB7lVL20QsInVhduiarNXURkHbG3aIesuK+Q9D6hgHcNKaRwnOF/OkOWhA4Ai2UjIlLHKSx2ygo8Z50d7zr6LmnbXNRsSx9tTOAiYxaHZCZXRG5v1EZsuPwB51tPAJU22tXHPG+edeFZ2ve6JiTxYAygekwJOwkTQwFvXFx1eaF0hsuJkqzQHlkBj9iDLQXvWj3IJ5222gZeM/twkKzWwIEe2pLy8e17awsZqXi5h4CcT+yuXGsHqa3UshO4LHI7rzsfByAejIEKfKqSCyhZjNO1nwccIivvwsi9nKytYKbdntUy4XnOE9agNYWkNU3itPG93VLslBWiNcTBxLixYCxKTB1fCOYcJqMVwGpZl9Wy7o9AizMjD3f52jkiM9aAEDWlWGYI2R6gibbd3iSrz7MlnY9QTnxideEd5xhc4miFjKqo9/kjuyv95TzSiPCuleiI7wmqzITiis0Ganl8wTtQOkrJbfTdUVnMasBroYD3KAPoWANAvKwigJahgPd5jy9YC8gIBby7y+6Twr8PoYB3OHiH/47tVgNHae391bg8cof9jOMF0ohwm3kdqM7GvxShgNfCPySN0j62ADTRdiQ3BCXTAuqjTOBvBFZdHR2+f412WQ9NyJMBVtp18EcvQWAz3vEEp+uLBVBuh12h7gXRB+7iv2tcSCGFPwWpDBng8QVPQGUyKp8gNtJW28AMqz07FYd9PqpEOSkU8D4d234fUGmt6xJcsa7EsdHBXGZ8cbCSOFIBICr1bY3Cb9ZuLkI84XiJZto2QBmSh2RVqopc+xPr5GWPmEMuDQW8S2Pj1rhU//LaB4xJ92kCcVim87HVpegp87z0ptp2cmVmqUAoGV847qKZro7xlnka95lX7kYFBhlAZg6HGaZ/xXXGNADuiF4daSB2Oy8wvqaqIssf2WXnLKokDp/qECXcc1sKqQlVlnw8OsjWsbX1suaCz+3OBqqRgNhxCiixogAgg6KPTtMW7/M5Jl9dUxxko11dNtB2lcqafWl1/OLa6PA+qBb5dICOYg3vux5M3uxBVMdlwvJKSlhgNwagk77umO/JOrsmvSJjAcginwzCO3ZRsRyq5BGNnXM2sKOHtrjmYP0bFthNi16z+pZ9MOwn1tAQw+YqHHrzp7QbqqFUnu/Bn7fO4wv6gftRJP8esZJ4Cikcn/C7O+bLtPmZolh8aJ6M37zEnO+68fF0Ec0GxuDP+0UNxf/n86iFykZXBVhmeSgnwuhY794Wvf6yn2XjuiixZxeKc5vsnCGB20IB71P43TUA/ya7ema/yJhehaRV7KH9nJh0xnFH9OqNY8eMbUgKKRxnSAVkMXh8QR1lm3P+r2zWLhTwLvb4gtuAWq86xnK6vpiwNLg1egMvOUsaiQpkWl6L8GtugAoc5iJ9JjtkRbu5tmXiFcaX8dmZBHLw5+V5fMEMYPUg/ZvaYx0llJJnzbOrPWkOuhClkXVGmfPZhJopkkERF+qz2CMr8KndhV/CzfoH3GB8whEyqBQj+yfzNp6NDtx7o/FJlXj342GZbmaJIgOgSDpJFxEsKRgQGc1KWQ9QumnX65+wl2zrefMsYWIIQNRk35E62t7ya+xa1BH72CorM9d1K8limavt2m/1jTw6AqXMXFPHIot8HnW8uqeXvsiBkhG5+YhMLyovipy/8tlQKJ3YSDKTsmVti19efYgsE2VSLlDcvzNJMhLP5gjzXTckguvB4futn2SzZHHfx1HNH4mMss94h2uNTwEIS8dC16j9HUF9j47VTenxBeui9KWiwC2hgHfPr11LCin86fC7nyDJQzNPphe4RVG8Ielb/Hmn/gXnUBml1VhZSuRGWV1cGr2LHbISF+szXp9ntxi8XtbKQNFEzgXWERNmToLtINp0fdowz1a78sNFuNq/aA6IfmJ3nfKk8cJ55xjfl/DkJHQLP8MOKrcOBbzL/vTrSyGFP4D/aR2yZMQeomP4FWV9wOHxBU8jli25NjqcYZG7ODvy4LzZdpv5m2xlTiwl5mtW7wnxnQ6SxbPWOUy1u2vf2G16J413AOXDCIqPVPtLqxNL7PrEPPYeuXn0G3tCAe/ToYC3F8r+J/lhfxnKw3FfAem8avU7VjAW7SRW84XTx/fOmxjh+BCXsBLBGIBeIrVFbe3A1LHm+YdzZQY/2w2PvGb2WRKWBnulm/RYC7ouJHXE3sQ+zzueZYjxNbcaH+qX619ogGgqtvKV687y7zjH8KLjWZbKhlQUh0sFY/vt8jxjnnNGKODdB8yvzCFmO0ewOO06umgrg57iydU9xZNvx59n69iJ72p8DmGXmUuUExEeMYcSienc/mw3tA9RvhmK1xXPzJ1CUqnIRYRxjictZ5Ll6Hn6tz5gDkrc91pUk4WhYduVOGS7iKAlvWdrZO22Mc4ZxwrGYngJJZ57PjGLlxRS+JvxbfJ8PCkYgxKNwz8bTYhpPgqBeMy84N10ItunOe/b/aBj4mWfO+/O6KotB2Vd9S5lgrGr9U+Z4bxNW+W67Fvgqzravg5NtO3iMccrTgPz4rWyTinRwS2yGjvU4VLPvhSOO6QyZMdArAvqPVQGS0OV0g6iOu4+4OhMVTFQ5CY/p7O2mi2y6tQ1ss5KlPxCMXA2SrsMgBnO269ppO1sCryBP29Z7Jgayhj7LGCRR+zu8c0jVxzlI+fxBe9BPdhnATtQ3KaRqEaCOA6j9NAqA02+dN51RtNYyTQZm+2qRHDYc+yWWl99AT9bjRhuXm+bGMk3q7CGVXyZ/qX7fsfbAOy3s3jcGsQsqz37yLZnO0do9WIk+xfMAYw1L+AhYwIXG7MAFUB9a5/AKdoK9CQ7KIAi6ZQXR3xnLpRNZz/nePbl/vqPl8bXdSx+IX+I/nV6U23rhGV2/Rrn6nP6bZLVecU8k0H6nNmLZKMe52pzqKftoorII1+mzTwh/OrplcmlgbaTRXbjOAcQFP/E1VVb/kEbsaF10O6cv1lWf+1+480WVxhfXJt0St+iRH0PAr09xZN3Avsrk0vQdTdVRB5SwkZZjSgO9sgKjDGHsl7WGhoKeCcf4/PSUEKZA8useiAU8D501IeSQgp/IT6/r2femcbCLEg0zqxFeQnejD9v3q/v/Z/D4ws2Ano1FDt+mOm640XUb24O8KCUfC6SOK0vmf151LywGBKKzvlAZi2xl7muW485/n6ZRcfwiwjgHcdD8ztpazsKgTYmeiGNxY5od31pOI3oo1mjdo3+s64xhRT+KFKzhGMgFPAGUZ1xblRABaqD8lqObYWUBmTmkcl0uyNrZJ2zUZynwcB0FP8hjqWNHlz9Cv68EfFgDCCUNuSsUNqQ3G+cw4cAnX4hGDsDlcVrjzLYfQaVxXkCYix8JRLbIhTwPoiSZzgjl4yyQyEl4ZujN7V6yRwweKj+NTXFAU7UV+MiqlXmEI8Z43jIeI0sClw2urtZUkBXQRwh4JjAh86ROInm2whvVOrLvrVOKHrF7EcvbWFRPBgD1SRwqr7sqGBMvXERMVSf9UVN9u3+2Do5W0rlWlAkHTzleDFzuGOq3ldfcLVLRNpcEb2dW6M3sFA24y7zas9BmSXrabukQJozrTZ3tQy/1stGm3GALOqKPXuH6jM/A3ajsp6uumK3HO94/LzbHe83muJ8qG15CgYdkFlLKMk6hlEEfhdQ/SPr5G+BVsBbvfUFsorIS1xPQ203zbRtBO0T46KeJ8ceMmXRmKODMYhpoKWQwt+J3VTsbceUa2L6g02ACX9yMFYRJRX0/AZZ87tOxS8MRknQ9ABOTQ7G8mUaP9lNv0JJz8SRBtBSbMaUpR9hUiK/tlozLOJDomGjbT8/OvJ8IfAstz1ttsqqewcZ3zmqiLzMLFH4UIx3lkIKxwVSAdkvIBTwWrHy096kxXtQgdBTx9jFkfQ6+X2thypTvYcqW3U+ak+/uwlK+PVSj7ZnUihtSG2PL9g+JrGRjOZJr+MluMqoQKIi6uFfPxTwxsuu9RuIHWRJJeJaaCefIs5PXfetv9n48LR4KbKKyKOV2DT3MccrBYONb7nYmMkDjjfzgU2vm705KDOtiNTRYsKttbV9vOB4JtJA21XTMepg62FRX9U8Ml9vIra6KIM8qTrZ4wnZQzKD/bI8QsDZxveMdYzL3i0rniViBurpIko7rYSs31/7sda3rhF84xpBbbGnGKj3nOM5UUXkicrisHG6vuShyhxqAnR90HidRxwTqjzgeKvfpfqXVVHlYBqInSIuH1JF5FJBHOm2WDZ8MSL13aiMWKnz/tlumAM8B/RZbtcTZUukACeJVaDU/68HfvT4ghXKbLId2KFh84TjRea7rme48QEo4n8KKfytuHz0ez9qQk5PXjbHavGwxxf8Vb7mf4k6qAkuQOZecurhz9uBqiK4TaluSNvsivxgt1i7Q1a6FbgJ1TUOYJysLV/9ouNZjNgkT0oIS6Potui1L1wevYuV0gNKpqZtKODdgj9vW6sHly49JMvnxQPQiNTDqMaeFFI4LpAKyH4b/YDnURY6k0IBrxkKeEcAp1M685Ush28Ceajs1SuhgHdNKOA9PxTwXp9Q5C+NLEo+C+OayK3XoLqOFnl8wZuStnsbWBIbP+53NxlYGwp47VDAuz4U8OZ7fEGXxxd8bID2vfjKeRfNY92X5bQSsvteskXz4tfqT7NOGpp8Iu+6xuT00JcmZscCOxOov1LW290+PG5qh/BLrLA9AISlwRn6z5WAV/C7+6LkQS6banXTQnZVLCnYZVeYdGV4hDXWPJ/pVruE8XmOKGC6VWIvV14Usl9mYSXNeNOJckhmsMquQwNNCZhXEbn4jHfl5867S/G4AOdrzscmAemttU2Jha20zSL+3v5gt9izT7pXA3xsdWGLrMqDxhuaU1g1KXlAAGBKjcnW6VTnwIH7jDezTtJWMShyPwfszFIcw2baFpI+uwqomX4CoYA3H+jcU/t5z7n6XKqKXG4xPqSTWL2YFFI4PlDqntROW5/jIPrtn3i8pagyPsBXqPskwBTgRkNI5wE7k9raAXrpi5qcqi1ZhaKKfBIfoInY7opPDCHmQYrj+42yxjU5JbfiSWU7nX+SzW4dEb0ub4rZ/cgMu/0l+PPySCGF4wQpHbLfQEz086ZjLJ/l8QVPQflQbkfxv95FZcAMYFMo4P3ldsdk+PMW4HePQZW2pky3O52StNaLytLEnQXaenxBEQp4pccXdKAyZHd6fMHloYA3nta/BbijvbauVJnwoMxkqnkK2aKAl63+FJJW7w2rT/kz9Z9orCU8uFsAfkD+ZDdp81j0grjJejWJGHyYTPpFxlCRw/zoujEx9u3Raz4mRrjdSSVOjTwlPezq5XNM/uBl59O6IWx22BUTshW2FHxudaCe2EW6CDPN6mJWEbnGjdEbecbxAk5hIQSstOpxUfQepjgf5ERtDcXSoK/2U3r8ZlwsjWiaMB0A1cWhDgK51EmkNahZc5bM5yPnA7iIrCkg/dTKo7buaet756pDZL0CkEdGhDJE4ULp5J7olVjoR551Pte2o7bOCTDDbLvcKazhKIkUAA7LcqRTHDcsjxAzT09GKODdftk9iz+zpLhCF5J8mSabaNsn/ep3IoUU/iLYEj3eVQ2QISKcJFZ29vgc/UMB76f/38cLBbw2cK7HF3SGAt6EWaWUNIxP2Iyk+5ZDMQpOQnU6zwEGfmB1a36mPp+2YgO6sLGlkIUyvd0nrgccEalzU+Qm5skWHvxuA39eSccO3PSx3dX9sd0V4FKvqlykkMJxgRSp//8RHl/wPpSxOMDHoYD37P9wnEuB11BNBZejPCyHoyQu9qNKk+NDAe/XHl9wDdDEwOQ950NvtNPWFw0MP1i8VDYc3kVbweuOsbhElGlWZ3uvzNauNL7EloI7otccnmp3ywIOlacgbYbrTlFNHErbJ92HR0Sv6/bmw3cv9fiCLYG5KC4dbvKZ7BxNU7GNOXZLPrdP5Gr9c5bLetwZvYZomfj+Bv2jvDsc77t/6ToLpIsMEWaXXWFfde1gZSlhrazNbLsN18UkJWwpuDV6A9PtDnTRVtJBrOEGR8kz4gPzlHd66wsvLC+KeNEcwJvmGe/OS7vpgvj6uMI/wDK73ncnaJvXAOsbFU/aHsWo/YTjpc/O1edckyszLs4WBRUAXjX78oI5kPHOJ2kj1mOUzMTNQPSCvpVE3vtebX52de0gAN9ZrbgkGvedZx/QBtWJmw6E4hIXU+4b4OuuLetRTTv0LfA2/rwtv+PrkEIKfyqKR1aq4SK6I9nDdnDxPfxEy3NCAe9Hf8U5eHxB7QL965U+452me2QOT0bP41JjurWbCpF7olekF5G2FcXnHI6aMILK7Nmtxcb1UbTWn7tKNG0LpZNyqvK5CzgVf9662HHeAwbFNpPAuX/VNaaQwm8hFZD9PyKm1n49qmX8mVDAm/tfjFUPkKGAN+TxBa8GxpXZpABF2t8LiKv1T7nH8Q4AptR+ahh+az3QqhoH6pcXRZnrZS2CzrtlC22LAPjWakk7bT3liPCl1RGfebXVXGzRV8m6HKEcwNWoBoY5xEpyF+tf8ZDjjcQJjI4O4VWrX+LvtmI91cUBZtgdqEgeX7tuk+VEJHGbn2u1iKySdZ0D9Hnsl25aaiGAuHmzKBl3KJfqX1JLU0r7+TKNxXZDnjLP42HHqzTVElXD7T/ZTZbMttr0+9zuxBZZHZB87rib5vpWgITzAsARmRYuL4rjPLEr8eclpEnwu93zrGaTFsompz5vnpV1mf4lPsfRrjeXRO4yQ7KaMVSfyTVGEIBi6WC85WWC2Zdc1Uy7gRIv0nygG7D0dUdg2anashZJD77n8eclsq997n6xRRtt48HAmCf+UoPpFP7H4XefHpb6NJew0gFMCc3Dr78cwXX9X2XrFPOBLavNNwMYBnQCfkA130wGkqSDJPcab9NVW0FlcYhKQvVC2VKQVNIcgz/vvthxKqN+n1mxdS+GAt4b/oxrSiGFP4pUyfL/ETH7o2f/n8ZK5qdVPsYmLlRpUgBUFIcTpTdD2DmhgPciAI8vuHa3pDHAh1ZX0ULbgpREu2irHHExWK/xE9tkZT1glaKT3YDSy9JAMkD7gVqitPVStiigtdhAK20zRdLFWMc4NCH53mpB0DpxZzkRSXQwjYpctGcttd8zpXHTw+ZQTtJWMckRwKFKk6XU+3fKihRQIh+UKYo5RV9BK20z2SLBwbWB/E7a2n6dtLUUR528blVfA6LpwOhorrI/o4I4wgyrffQcfY5jP27rCv1zM/a+EZVa5Ua+YHVgd+yhU7+NtrFJPXbnfmR1zdoqqySOnyfTcYsifrSa0VmsMCY5H6VYOjggM2VFkS/SRJSbjI9pLrZwRfQOSDKGR7kX3NRQbF/STVueHIyB0pG7CeD2e+/8ZKrzjQFOTMIj39isIZc4hHUF/rxDpJDCnwW/Ow34JB6MLbfqcrd51Wd/ZTAWw36UfVkPFP/2qe+dN62oqR1ohz/vEwCPL7gA6NBLW8BIx6RoyK7mmGSdzlWGYmrYEmaZbagmDpIhivGUaCWOwO9ehD/vo1DAu8/jCz4GjM6gqGiw/s2P+Ido+POObgFPIYW/GKkM2T8AHl8wG1W2bIBSqq6OajRoher6pBK5axamXb8JZXp9K6psNnqbXSm3X+ThmnkxV6O6YjcX6TN2XWV8UT35GDtlBbqEn09eNCs2frlHjPEZFxqzBcA6qwY5WgFhaVBVHERHognVRekWhYmdC6TTPijdWm1tHyvtunsfMS+s8rpjLA5hMcU8lbvMq2kotvOI41U6xropD8rM8MPmUFcbsYGLjFmlyo0AUamTZOs0LiwdZ7pEtDbAy2Y/AuYQgLGx96kjUDv5gnpqi3jM8crO8hQuah9+2XNE+Wh+Bgz8ynnH5sbajjoAn1udrOujt+oDtB+or+3kHfM0GottvOYci4GdOKdtdqUpP9gtBp1vfKsBrLLrcmbkEVBNF/HJjg1ofbX51kvOZ5LV/wG+wZ/Xw+MLZo5zPJnXW19YqslmmnXSzwMe+rI9KaTwZ8HvLkdSh/EemX1x1VFb3vo7TiXW2dka6DzO8cSo3vqiuDjtRPx5l3p8wTDgnO+6Pm73xvtmt+JBxneKwCkN+znzrOLbHB+UO8bw5mGZdnLQOumT7bJStVyZYd/i+FCrIg5jSfGpPip3wF9wiSmk8KtIZcj+AYiVPvuXXe7xBSuhdIOq7Sf71oR2kN+todL9Rm1tf8Y1xmfLHjMvOKGW2EtvbQE77UrVy6T0mW83A8Wp2Ao8BgSIidl69R8LQdUxq2qH6Bp+juVpV5Y6F7co5JDMICeWwcoQES1KAfdFLmWy3dN9pzElEUydpi8GEzbIWkw0e9PWsQFD2DxtnuuabzXh8TRVnS2TTULDRkrYS3b03Ih/SCOxI3OU8QZ7yGGC2ZdTtGX2KOONg2tl7Ueuiw4/WPb9mmW3p3143A5gPDAttrgfcNlGWaNOY1Rjwx6ZowNMs7uADQLbnu68U3MkEY0tCZ/ZJ+1+xzotr4m2LSebAkabQ21UefdJFPHfheqMzVou6+uHZTpZooh8mcYau/b4Dvr66z2+YE9g2hK7geitLyx1vmvs2u1u9gXdoYA31QmWwp8Df14hfvcQVLZ9flWR+/bfdSqhgDfi8QXXAfM6a6sTk5dC6TyvuS/oA0YBD+2T2Vo8INtBpRW3Ra5pfYXxuagpDvw8wvjgEKVKmgkY5Smef6ExO/53YvKjC9kfvzsnlY1O4e9GKiD7ByPWdXm06Kg/z8bv3oByCmCB3aSZkyjvOx+kujiIKTV80StxCIttdiXSRYSZdntQ5c+6qOxSCJUh4zvrBFc/Yz6gpChqin3sl1lFlcTh9HgW6w2zF4+YQ3jW8Sy99Z8ByBaF3Oj4hLfCvVxfWR24RJ9BORHmY+vkxKl+Zp/EkkhDHJhsltUZpCdumAkUS0NqSBHngu233Y7tsopju6zC7EhbAHprPzHO+bQGPFJJ5g1AmcKfeIy3bTZw14X6TO433iKfdHuh3bhrT20RAN9arXjJ7M+zjudkDkfEw+ZQs5o4aGQnZf8Abo3eWPSp3WUAkHNWZDTATuBSYF0o4N0CCR6gAbBdVqZ/ZMyWjtrawnlW88++D1x6JwC+4Hig3EvWQHbLnJ2jHG9UzhLFjojU+dLuCCoQTgVkKfx58Od9SIkMxd+NCFD4o92sfG9d/SY/t07MAGaG0oa0WmF72kTRB82w2jHHbsWbVq/oGtewK13CnIjimh1C8TbjWbJE4FV2ghfHMrte9ARtc+6fdkUppPA7kQrI/r04HRj2mXVi5Yv1r269Vp9GdaGSRoawqaPtZb7djDnyBJBQg/286HyaSuIwr5l9tNesM89EdYxWvtW8wdtdX0p5UYxTmJyl/8A5kVG7B2rf5yyQTbN/thsRwZELZF8XHcFd9jtc41CE9z1SVR1+lo3pFn6abHGEDTIh1XUY+L/27jvKiept4Pj3zkySrezSO4TeRRCUpoLYIBaw944FbKho7AtYYsfexd4QxbL2riCiCIL0FjpLX7YmmZn7/pFNyAL66k/KCs/nHM7JzNy5U3RzntzyXL1C184BMHEYZEzarqsyTdmVvkp/cdskPw8zJ9DXmE7LrWk7cDB6VnzUgDrG+IkORjg6xW178zdul97Xmm/2vtz6AKUgg6gxwPjlsMT16qlN7n2ep9ccas5sAPCQesK+JXZ+8u9Ea/jS3X/xh26v5hmUN7vZeo3qqoj77FMbFOv0ca9473qg9PbzvmgfefF84mPEPAC1KGS9zmn6jnNo9cRkj4oBxs0Sdf+uW66vpsobAHiVQ0u1isW6YWKtUyH2Sv5g/lCgHfBoOBSY7w/mD7w0Nvyi851PW6/QtXuWqjS6qXltXa0GdzTCJ0N80P4mnUkmZa0+d7sdcqw5GYCINrN8ygl96+yXfmFsRKdx3pH+rsbCNqnX26wzyVUlrHOr8ZXbhTec/o++f/dVMnZH7HEyhmwv596eO8tQuj2wXaAD8EDsJB51TnAetB43T7AmUlFufrPI6wPaq/Dclmql5yu3a+RxzyNOX/P3DIDh0ct4zz14mytRAszzEe0aweIu64WPyvAd8aw90LeGmiUQX7+pOlsoJiORIiNKfA3OK4A+fYyZvOq9u1Klqfcc0wbDY8PIdw9CY9DXmM6L3nu3Kz/aPpsXnAEAHG1MKXjKO6ZuxeElF0avW/W89/7eVBbWGr9SlWdlAqzVOQsOjDzZLGQ9owebPxb6lP2Wv/z1ZsDA6603GWrFez5/c1uQQyktjNU4WulB0dFqpm4OwEVmPrd4XqNIp2Pi9MpQ0cXA41rT4377FO/jzqDaQEzhPvOE5+FhA8xfmO025bzo9Q9MCZ113Z/+xxXiP84fzD+d+MxJiOdubEFeTjZw/vP2gLbleC4bZiVGF/Au8XV8kx63j+Nh+8S1o62xddoYy8mlyPUbaw2Aq6JDed/tzVfe65KJpTe6mVxuX8lYz714cRLfLXPIK0xdBUWIPUJayPZyhtI1E5931GR/oDEXHMye5uzk4r0ORvYJxg/X3+N5xuNRDtPclr7Tozd/cKd+/rjB5o/c5XmeNbEa/OR2ADQ+YviIZT7jfaBLD2Muy9xanBu70bdE10+kmMgEuMF6g8usD1mta3By9DbW6VzvU56H3jjImKvGOkcxzjmUiLbwKTuZQFYp+MNtio3FBKcXM3RzXvLcA8AHbs/tnudLt6v7gjMg0U1R3tv44zdgAICraVaHDbmp5W2tHEtpf+LdpAZj892G4dbGysAzngc4wpg6RylqAcNusl695C77rAN8RLOp6BqxcJJf+qbSqq2xjJlOPCA72/wCgGxVRrn2vEG8Wxil4Drrbf2B22vocl1nosbYclls+DE1YluaFJI5zcEc///+Bxbiv61uyufE1OY3gYEXWp+w1K2TWrZSMAZgoIniyQ3aF3G08Qv3e55OdlE2VWsBxadud4YZ8aDuVfcIzjG/wJfydw60Iy8njbzCHa2iIsRuI0sn7f0uAcqIz/ybmNipNUS1wRvOYQDrSrUvmWfiE7d73Wqq5JLEIPz91UJON78+rpOxWBsK0lWUe6xnONn4hl99lzHHdx7vem+jhzFXATQx1nOM8VP3lHsoA7jA/ASA+mojxxiTOdz4lX7m70aGiqhh1geU6HTOjN5Ulm8fGE69TwOHDipMnucV3vDcwSHmTA4xZ9LPmM5DsROZ5jRPrpHZy/jDSFk6Je0e+7QBRTpNQzzAG2J9Ur2iXj3dbbZlrc7dduZj8rrHR0f7/eWvT3jAPnkEW9cO5ULz0z+Ak6a47XoBL7taffq12/W0Ip3+NMActzGfO90SxTdVTJiI35CKNU29jlJs+MF39fPE1yM9Chi9kWrKwewKfLKL1xQUYo/xB/N9xIOwRcSXoUvMFOqQKLNU18HRim07ckq1zx1nH8Lj9vEA3pus1/WT3ofJVOWUaS/T3eZMdttFu6r5PGoP4lX7MB6NDXIetE9mg67GNt6TYExUBdJCtreL5/CJD3DNyzkL6A2gUfqc6I1XTtYd3GqU5DZTa+5MnJJDifGp050hVr5uqDYopeB2zysscepoKgKTJsY67vE8l5yp2YLKuUzL8G540vNQTi9jltqss2bWUlsO0CgT4vmCLrE+IouyZJfkel2NItKLftVtCxu56/0BawoQP9ZebV0+smFFsliAtmo5uUYJ+U5Pupjx9SszVZQsVUYLVnGi+QM/u+1Youur/VQ8rVtzY02iXvWqfaQ3aL2RrK/AzY3VUZs9SsGPbsfEckjt5+km7a+LXVo+0vPi1CxVPqFF5NWLgPM/c7tH/eWvHxEOBb6/GvAH85flUDy/kKzDibfKfQUMGG2f3WmlrnV5c2PV18ebPz1PRb44rVlXSGbmArfhlsvMD8ynnYDVQG0oXqdzicSLZBL/G00uLyPEXuQH4hOIIJ4exgswyWn/Qldjwci1Opfb7PNxtcFXvmvxpKxd+5rT37jTPiu5vb+xKPnd9J3bmS+crrzpHe01FITduviNAgBzrm7CGPskjjR/pbZK/nAbTF7OMPIKH9/VDyzEX5EWsn3L28Tzmc0xlB7y5t3XPwa8F8W6XafkZt2oc/QaanJT7MJZqb9Mm5lrjZje2qCk2HrQxkj+itUaOqilTQaYv6gcVUpTY+2BmarcTFMxpjnNtUZRXRUnksJSqDM4LXpLSTm+bKDRF+4BLHFTezK2KtE+t1R7WeTWo4Wxmt7mLC7xfMAEp9cmrVk6223yQkR75rzhvZPTrW8Y432Cl+wj+MHpyA9OxxVz3CYa4tn/F7gNv1qtayQf4hfd5m2lOH69zi6przbQSS2mi1rAAONnPnB7pXWMvNAQeABI5CzyUjHF3h/MPwD4oZCsB4jPbk0PhwKHAx2KyBj+sHPigk5qyQSt44P8AZSidq4qyehuzvfd4HnT+sI7gh99V2d95bvOrsvGmcB54VAgPr0zL6cxeTn3kJcz5O//5xaiSjsANMcakzjL/MLwEX3aH8xvfkbslq/bRl50Dok+TFjXd0pIw0wJxu6PncSd9lm2hc0Z5lecan7DWPsoI6ItSrSPl50juMHzJon1ORtvTRBLR2MJ68ilX+RB1ulqq1Lupe/ueWQh/py0kO1L8gqjwDnb7M0ox+cdEbuEa61xlJCGoWx1jDFpbTdjXsdtx515lMP3Tic3W5UaE52ODLHy8Skbj3JZ4tYtbqYKspSCI8yprtY4SmGmDpTvbCxWRWSQw9Y0EpPcDs5C3egd4sukUEI6v7itaGZUXkkloq1Ipor4APwpq6w4WPrq2OXVxxl9f+puzF3xhveOlikJZAlabzJHN+Vxe1CjmbpZrIcx2zNfN2KFrjPzqtjlDc4zP+uyStfkGeeYM+uqTQd3N+Zn1lJF3ON5hrZqOYbSfO90pK2x3B/RVuwU85u5bzv9agKlbM1n1gFIRKvNiI/HKyeeeLYhwPDY0O7v+277k8n30KKi9a6RWm895HnizV6jf3rNH8wPAKFvvXUb+42C+LqgeTkR8gplcXLxX1d8oflxtVs98dRn3Yz5aVfHhi0CHFD3EV+h5KejzV+emeK2NRoZ6/nJac8TziCA/Jut144/3/oMgKfsY+kceRYfUfyqgDRiyYss03VpQgHrydEfOQcVHmdMyg3rurxoH71shOftusSHc4zdrU8uxA5IQLYP8gfzWwJfE0/JMAC47l33kHNmxfz7feIN0tpYybHGz3XK2Dp8aZOb6VY3Soz1uhq32ucbS3U96rOBU8xvqFPR9N9ArZ8LdANwUc6bTr8vNpIdmOE0Uw95nyJDReK/WrVmvN1nfQHVa853G6uP3YNMoBHxcVS+dmopJ5oT2ZZP2YlJApgVXaVRbdo3Ri+0HrYe5Vjzp4GGYmBUVx4WVtvYQm1m0sVYSKfI856v3a6JQ6cu1g2a3WafD0A7tZTNOqtJ4qAH2zaUtgC6G/NJjy9WbJ5rft7hbaffZcQXkF9TUfx94FfgAGBMyjqmWYn6Zujmg1e4NZNrdAKsd7OpZcTX39uos6ihiinRPjZQLZGQ7XmgbrWtS0YBNEGI/75n26gV1yY2WqsViR8rJtATOGyW7/xzMlXEAFjoNmCEfSm12cSr3ru7NlZrnYqytFIrUGje9o6mjbGCtW4OaVis1bmcGb2JDVQjiqVe99yZ1dOcg6MVQ2LXNQIaADZ5hdslkhZid5OAbN80GUjMvpwYDgUy/cH8jzOIzDRU/AuuIkdX8oSb7QuMebpJudY67XDzN2qxmbOtL8hSkWSZxW69WDszng+smirLON365pj5bkN9jvk5GSnlclQZTY2CkddGhz4MKBObVz139m+q1nKffQo9jDnJgOv/s5GszY95H62VmVJ/KWl4dDyASW3hy2C7cbvJHb2NmbzkuQdLuSxwG7ifu92KvnS65rzgvZ/qqpjJbjv6mb8DMFc3BqiREowRDgUK/cH8V4HZxLPzJ5wJ5AHVNEbrObopjYgHZFFtcVz0TgaYU3S2Kl3wsn1ETk9jtldj3PzEXaN+qjh/PVD3ttj53OR5HYWeU19tfPJvvRwhqrBwKHDdQzePn2Fr4wkg83Ona+rhQ4GXf3Q7ehMJYuuoeCL9odYHtDFWNAawtcFmsnjCPo5WaiVtjPh40zpGPJeyjxiGcono+I/L/Y1FFsR/0O1vLMwkr3AtQlQREpDtm9JTPidmV74wTbcyX7UP40zza5TamgNsjtuYH9zOlJCW9p33ahob63dYaS1jy3a/MlsbK7froivVPm6IXXwLFWMYx1iP08ecBcBDnicYnTJY968UuNlhr7L9qcEYxLsvPnYPWtCIdT8cbMy44DLPRwAs0fVcwKjOFhqqDRtnaf8cjWoLqAONuVgVSyPVUZuN++zTcgB6RB6jFoWvr6bm+8c5k8ZmqvKMd5xDANr5g/l3A1+GQ4Gv/MH8E4ExFbcwwB/MrxsOBXQ4FMgH8v3B/M7A9EftwXQ0lpBOlGDsosJV1Lr9eWfgD+FQ4Lerd/yYg4DPPnJ7Nv8o0hNgUzgU2LDjokL8twz3jF9NRVqci62PecY5ltLkVxLH3G+felkasZObqdXca5/qAsZmnZUcwL9AN2SMfRJTdWsyiLDIrZ9MPwNQWxXS1/idV50jAHjeGcDl1vus0dWZ4PSOXLkbn1WI/48kht0H+YP5FxJfzzERLN0KXEBF1vinrAc52oqvq7jErUv/6AO4KJ7yPMTR26y3mKpQZ3ybo0r7Ask8YvPcRrqZWqW8FcGO1nBsdBR/6Jb41WrSiXKn53nd1VioABytOCjyGL+mDUvWmwgMHa0qtZzZ2tCWcisFfGvdHD50e5JOZOZc3WTTK84Rh5xnfkZ7tXTlC86ADxyMy97xjqSaKmW804drY0MBaKOW8ab3DqqrYp63j2a0nRxqp4kvi3QxcGLFe3Iq9lvEx590BPoAz1WcEwGywqGAvc17P414GpIfiC+CXh4OBWL8P/zB/CuBhys2Q+FQ4Mb/7xwh/hPyco4HJgDEtMkBkafYEo/PEt4lnq7neuJLIk3xEjv9MvMDDjOnuZ2NxQbAF3YXvMpmpa7Fh04vrvGMo7sxnzLtZXB0FHN1E4j/zUZzKfKVkE4Ma0o4FNjR8mpC7BESkO2j/MH8z4AjKzZXAx/61eqLm6i1sc5q0cprPe/4AT5xunNZbLhuogrU977hyfMj2sSDk5zJBFCmPboMn4poD9+6nelpzHZX6trTWqllRh2jqEui3NmR68lUUR73PIypNCXaS2Z8fBavxPpzh3N20a++SzOzVfl2s4DLtMdNV7Ht9hfqDDbq7O0mAtwQG7Jkqtva84jn0UXVVOnvb9uHnH+N591siHd3tIy8mix7n/UUJ1vfU6J9keOid/gW6YapVS0kvpD7ocQzi9dLOdYf+BF4BtgfuDscCry1o/f+v/IH8/sCaeFQ4NOdWa8Qe1RejgLuX6lrnnx/7JTGFSuA/AJ0H2ZOoKuxgLecfps/d7vlVpyRbB17xXN35GBzpm/bKu+InckrzhH0MOawSNfH0SaN1Vqm6xYvRfGexdbJN8PDocCYXf2IQvxd0mW573oKOIL4l1v9LmrBkDe9o/Ep2zPZbZd1efQK2qswuaqYM80vNo93Dqm+SteggdpIqfZyt30Goz0vVqowXcVUOjFQcIr6DlNpw8/aA7RmZWq5B71PMtHtpE2lFZAMxgB+pyURvAU3xi76MGi9MbyRsYH1uhq1KiYOpKuYUaTTyKQ8GQw+Yw/kXvs0Qp7naEblgKyFWpVxnuezuu2MZY2AQ7NUOTFtxlNuoOlnTFv/jdtlI7DqBPPHQwAjU0V8RxhTWeRUCshWhEMB1x/Mn0Tl7OI/Ad+GQwGX+OLiu0Q4FPh2V9UtxO7iD+afRXxM5SzgzHCosBi4tiFc+14wvzmQDRQfbkydOcLzdjpAb+OP3GGxKzHQ+IiqDkZYz3Gb8KPbcXov448DzIqJN1tpolh873biOnMcQ6yP8CqH5W7t/Q6OPryR+OxNgDm76bGF+FskINtHhUOB9/zB/N+Jt+jQ05itfCrew3aAml/jTPcmRvpepKYqAqi+XNfhhMhIDjVnMNtt4p5ufuO6Giu1hSzRtehqiGFhVkw9V4pKkU1tVcR3Tmf3GOMn01KaZU4t6hqb+cP1k+8cBNByrm7ynUc5ZwJ11ujcdQvchjk9zTlegD/c5oywL2aY+T5rqc5j9iBsLB6xB9NSraQ+G8hRJSzR9XjBPto70Pdz8tolOp3luhbNVQGm0gTMydW+cbvUAloUkf5DLiWHulrFJrvtPQBXmO9ytPnLlhxV8gEECIcCEX8w/ybi3by/AYGKYEwI8Rf8wXxFfKhEGtACOB94NHE8HAosJi/HDzy6wq21kYp0MSYOz3sfSK1KARTp9IMujF7LGO+T5FbMQtYabrDe5DprHBHtIcfYml6nkVq3fzYlXYvIvACYGg4FPtuFjyvEPyZdlvswfzC/LXA3MKilWsE73pHkqhJczTMHRp74+WffsOcSrViv2odRShpvOf04x/ycc60vKtX1ldOFGa6fc60vqKGKKdVefnHbFi/QDb89z/yszFLuyYmyq3QNekUe4w3PaHqaW3+kFul0rotdzGfugVxvvhke6vnQnzh2f+wkuhnzyVGlXBe7hEW6YbLrgvjSTKkTFRI+Byb2NGaNvNF6Xa/StdRL9hEMMKdwjvUlrlZcGbucjyrWxMyk7NxZaReu+clpt/b02K2/dldzzXG+UYm6XKApeYUrdnAdIcT/wx/Mt4CNxFvBGGz8MPwh75NrgE/JK9wMQF7OBOB4iI8pW6Ors0bXoLs5f4d13hi7kM+dbnzvu6pSS/uO/O42n9V51LSOO+lxhNjpJCDbx/mD+bnAJoBqlHCT9Ronm99HTeVu+dVpuaa6KulYoKvTy5wNgKsVC3X9Na2NValjqPje6cTlsSuZkbY1kfyI2MWMc/qi0F+2VCtig8yJA2pQxGP2IFZRkx98V9NIbT9jc4LTi+OMn5LLMiUkBvWPtY9ipH3uCuK/oBXwKfGM+dvO6OxEfAD+D0DNlz13c4g5k406i7zYuSzUDZmt/anlI8CAcCjwjT+Yf3hvY+a9r3nvTox9s4HG5BWuQQjxj/iD+dVutl7+7mTzh/3nuE24wz6LD723RAylfcBM4i316cCLwEmJ87SGR2KDuMIzodJ4VQBHw0S3I1Pd1gz3vPuX1y/VHjpGXnhycei4oTv3yYTYeSQg28dV/GotBnyN1Fp+9F2dPOZqOCT6ML2MmdzreS65P6JNZms/NeLpIwBYpWsyQ7cgjQiHm9Mo0LkMioxmNTXppBaziWxW6PjQjT7GTJ70PISFg5cYpto6KxMg7NbBb8TTAyW6QVMV6FwOijzxO9A5dX8ztZocSligG3KFNYHzzU8+WKurd/nG3b/xQ/aJTEu7NFn2+tgQ3nb6rQVygdQFvJ8KhwKXJbfycoaWa09gia7/3oBoaHw4FNhU8d6OpmLGZDgUePAfvnYh9mr+YL6HeALjWsC6DMp/neW7oHrib/lrpzOHVeT1qzASuA0oIN7i3eyv6k/9vvg7lrh1db/oQ43CocCq/7+0EHuGjCET2YAPoD6V01u5KGLa5AOnN3nWy2RUdAl4cLggOoJNVKMGW/jcdx1NjHU0YR13xU4jZJ+uV+uaqoR0brFe4SLrE6La5PzY9Ux0O3Gp+SHZqnKSVkNBIsP+0/axXGh9QnO1mqluKzobi5JLLwF86+xfTDwNxAuJff2NqTzteQhLucxyG9PBWA5wXGO1jnOML5il/Ux0Ouje5iy1WWeyyG3wNDAM6Ep8fcqDiXdLfg5AXk4d4OBBkVF1p+uWA4CBwOP+YP5JwHfAe8THwgzyB/NnhkOByn24Quyj/MH8DOB7hXtAP2Ma7dRSJrkd0ajk+rcxrFhUm6u8ymkKvApcSbyFux7wBtsEZFFt4VV2ckb2jmKxYu3jIftErrDeX+iiGlen2JcIABupdXdKMCaqOllcfB9X0eLzLcBGquGkLBD+hdOVAmpQjo9DImP43W3ubtKZ5XfYZ7OJauRQzP7GQkp0RrK+IdYnjPOOKu2gwgAcbf4CgFc5nGt+xmnm18xPSSexWcdzDkW0h5Oit9M18jRvuP05PHo/R0fvZj9jCV7lENUGy90a3Bi9gBvsiycDXxFvoXIA+hvTkold26vlW6O3ClFtcX5sxKTToresOjxy35apus0lwDPhUOAX4qks+gKdw6HAe+TlZBOfev/OWO+9t9Rmc+L73wtcRPzvJvXHTGoLmxD7ui6gD/jUG+QF7wOM8LzD295RfF6xZFm59ui6atORXuW0AhqQV3g28b+3hDeJ/1j6gniaC75yuzjHlI9OjhNLbTV34sNcyVIRYnj1/pFnD6wxcmWaUgwG5gIfepR7/y5+ZiH+NemyFPiD+X2AbwDrHusp51TrexPiQdnQ2JV84vaoVL6dWkoXYz7DzPdpaGykRHv5yelAQ2M97eItU0xx23BK9HZutl5liPVxpS6Gr5wufON2tgcbEy0UTHQ78LHTg7m6SQR4F/TpPY3ZHGtM4gzrm+R1Y9rgrOjN/Kzb2cRzCSni3RvqUON361nPfVYiAW3CMrd28XjnkKxHnMGOxjiBeDD1Xurjh0OBpann2LdX389SbrI/ZZrbgo+cnjzvDIT4skg/A1uAC4nnH7s5HArIH5IQgD+YX6MeGxZOTruieur+IdFr3I06+9W7PM9f12bUrHWVTsrLyQBOARaTV/h9yv5uQONjI3f8NEc3uflL73Vn+Y21uRD/flpLDgpFHbUZgHH2IYtOvuPDlrvy+YTYVSQgEwBULO3T527rmbzTrW9rJfYXax+3x85jvHsordVyehqzuNl6lW0Dny06ndluE3qY8wD4yDmIy2NXkUMxv/ouxZNSfrWuwdvOoVxlxeOisFs31jf60AVHGr9Mesb70Iuupo+h4r0S244he9YeyJ07XlrJvcd62jjV+i65I6ote0D0bislwes9xAcNTyfeTbsKaBUOBUor3oEFvGNhH/uh9+b17YzldVKvf1LkdudX3SaRVPLdcChw4t98vULsU1oHJ9z3s2/oddUr0lGscGvGRtiXtnvjrusX/Zt6H7nlvOqmdqbWVpubfeT25Hu3M4cbUxntGatd1PKI9hzcfNS8ZTvlIYTYzSQgE0n+YP7zWZRc8LNvWKUp5I6GL5xuHGn++rcH0pZqb/lSXde9OXp+xhu+u/GpWEVdiny3B32N6VRTZQBs1hn6gMjToTzrpeyzrS8v/7M6tYYLYiP4xu2yw+N9jJm84LkXr3L4yunCaPvsaFjXS+1OjIRDgTR/ML8r0Av4MLV1rKKl8AcAA5fDjN/GP+t5cJCqWHD95Mht/KLbJorPC4cCbRFCbMcfzG+Wy5bvLjU/arRWV5v6gntMz22XEvuX9dcGJgPNgVKgRzgUmLmz6hdiT5CATCT5g/l3ADenEWGO7/xky9COZjpu61enFd3MBdvtj2qTQjKwtYexzpHu687hxu++IZXWpAQ4K3ojG3Q1+wPvLZZHObganWglq3QdtxUv2kdxsfURFi7fOvtxmvUt63QOz9kBZuqmtFUr+MQ9iAje+cS/tBMLU34fDgUO3cFzXwCcQTwYu4GUnGbHGz9yqvlt7Ht3P89TznGJ3THgrHAo8PZfvxUh9l3+YL4B+MKhQNkuvEYaEJXkzGJvIAGZSPIH873AdcCZv/gubV+7YrmibbkazoveQCGZmLj8oZsRwyRkPcvx5qSIBl+62n7N7C+crizQDTnJ/J46qjC539GKgdG7maeb2F3VfKuLsYB+xrQ3+pizT4ftA8JybZFWsarAtsdKtI9MFWGB2zAWjF3Ucfzd18z3B/MPBFoD74VDgZJtnrkZsAhQHVSYrmoeWik92W2vFupG2z6CA4wHzgmHApG/806FEEKIv0MCMrEdfzC/dme18KmB5s8nzHWbcLH1UXKwfsJL9hEcaMxlptucm+wLsbdOOixuxNqsCb7bqKW2YGuFtU1r2Md2d1ZSmyxKo3XUZuMN5zDrS7dbapF3H/Y8ln28OemIHd1fakD2V8q095h0Ff2cvMLtokN/MP9g4BpgKXDFVeY7xlXWu8ku2RLt46jovcncaRW+DocC/f/fCwshhBD/kOQhE9sJhwLrgBP9wfwrgQf6u79Z2wZkZ5lfYipNO2M5U9y2vOMmewKzVlCHHpHHaKzW0Vkt4EHP05Wy7nuUw0SnIy947vUYCpVGbM2Xbjcjl6I6Ic9z1FcbAh/YPXzHGJMwt+m03Kwz3GB0iHGtZxytjFXMdxtSj3VUMyovmxLRFukq+hGwgLycbuQVJpv7KtbUe/9wY2r1OmozW3T65uGed3NTz89UEVqqlYmA7FigMfF8SUIIIcROJwGZ+CsLAesh5yQON38jraIbslR7cTDJJj405F7P0wzT7zHOOZQOahkvOEcxVbdlia5PBxWuFIz94TbhXvs0Bps/khgjdpAxxwfkXmp9mMhb5mvvWYqpoFBnkKO2LhD8mdPN+FQfxKfRg/AR5XhjIvd6n00eL9U+d5x9sHGu58vErlbr3GqLa9yeWxrBe0XGyLXvA+oM88v0uzzxvLKz3Sa52z54oc5gstsO4ONwKPDRznmdQgghxI5JYljxVxyARbohh0UeiJ0TvYFDyh+kR+QxzozexAd2D3RFfrFmxlqu94wjYP3MOO9oOqglAHzpdmW+23BlVJs8Ywc4JhpigW7Eh05PyrWnDOB5Z4ADqFKdlrywpyIzf2owBjDXbUIOxQBE8HKC+WPyWLm2OCly28bFNNyY2Kc11Da21DSVblxC2vgRN49oEw4F3Fus1zYlyrRQqyhOuXai7nJ8IeKtY2IX8gfz21WkXRFCiH2WjCETf8kfzA8CfYBngPOAwYljXdV83vXl7fC8jTqbaW5LroxdTgnp84HWJg4Pep5wuhvz+MDpNftxe1AsnUjXtcTzR/qIcrU1nias4XBzGj5lbzdo39EGLorhsaF85PbkJ+8w6hvx2OobZz9m6JZMctrfOdrzwjG5lOw3XTdXR5rTkuev19UiAyN3b/nWd3WtDBWr1CGamBCgNZSQ9lLWyILz/v0bFH/FH8y/MJvS53JUCSt07aVAM0myK4TYF0lAJv4RfzD/VOAlwGfiRMd67vUeYs5khtuMRmotNVRJpSBqZOxsxjoDABhg/MyT3ocB0Bp9QOTJNQbU30g27tbG2ijg/cp7DS2MNX96H785LZinm3B6Sib/hLluY46O3gOw3sD99mpr/IArrfcyE8cviV5Nd2Neco3NxDqZEW3h2zpZ4G7yCm/6X9+T+HsCNz625jXvXXVzVQlP2wHuts+cAVwRDgW+/39PFkKIvYgEZOIf8wfzOwJHAKOALC8xHcWjAWOUNZZzrK3rbF8evZwuxiK6GfOY7LYtvsT6OAugWHtZq2vMaG6s2W+O24RTordRxNY1MYdb73CV9W5ye9uWsk06i+qqOLkd0Sa+isCqWKfRMZJcd/xZE2fIu97b6WwsZp3OIRC5i7VUp5Fai6kdrvRMcF2tDI+y5wwyJ7UDNgJ9ySuURJO72GO3nFN+ufW+D8DWBu84h3Cbfb6O4jlOxu4JIfYlMoZM/GPhUOAP4ukisgCieBRwMPBHa2NFstxSt86WCF4utD6hs7GYC81Ps8bGjsDWiiwVpbmxZj+AdsYyehmzEqfFAB6yT+L8yHVMd5sDW4OxmU5T5/zodTGTrXkgbU0yGNManrMHJhYX/wBo6GByavRWTo7cxpGRe1hL9S3AByt0HZZSf+O1scuMEfalXB27fDVQH2giwdjuMd1tuTCm46tRWcrlNOtbfvFequb5zv7wnpuHRPzB/FZ7+BaFEGK3kIBM/K++BhJR1PvAT+FQoNNvbqvnotoioi3nLvvMNSVsHSwfxeI067vt8pLFtFm+QDecX7HpATYB9je6KxdGR1CqfYlyvOseet4SXd/1EU9zEdEWVkrLmVLwi26zCagZDgWOB+4FNpXjK/9Ft2UT1QAerjiWDTyVciv7kVe4hrzCSsljxa7zpXvA6UNjV212Uv6XyDHK8CmH6623vWlEZvmD+c333B0KIcTuIQGZ+J+EQ4HNwP5AA2BwYiD20DteGeJVdqZP2Vmfud2fneR25LbYue7nzgGFwdgQ0lWlfGG/A2d6lLPfYt3g9ZT9pUBvYM0GcjgreiNP2wFut899d6xz9Put1YpYYqyXT9lMcHrpiLaim3Wm+4J9dOlEt9Ol4VBgY8V9fgfUBDKAQcQnJdxecawYeA5YAtiJ/WL3CYcCM2/3vPzRtvnmIB5cd1ELPcT/PxNCiL2ajCETu5Q/mN8BKCPexdlriu+yM+uowlOBacAA8gojFeWqE0+82gy4LhwKfOwP5h8PvAEo4hMJrgQOS6f8k9e8d9HVWMhKXfPrwyIPXDAvNHjpDi7/T+7TDIcCzv9fUuxs5bfXuiRNxZItlYnxguXaQ4/IY+5msmsnAmwhhNhbSUAmqjR/MN8EdGLxYH8wvz4wE6iZSdmSEtI7hkOB0r+sRFR5kdurP+ZT7rDE9ujoWXzg9mQd1QuBTOCacCjw6J67QyGE2LUkIBP/Of5gfmOgG/BDOBRYv6fvR+wEeTneb53OG7sYCzJfcw7nXvu09cAnwNkVJTaEQ4Fae/AOhRBil5Klk8R/TjgUWA4s/38Liv8Mf/nrMUjJewK1oCJjcNyM3XtHQgixe8mgfiHEHlcxKeSFbXbXBQLAMOITMoQQYq8lXZZCiCrDH8w/jXhg5gUuCocCL+7ZOxJCiN1DWsiEEFXJ18Br9dnwkoX9mz+Yf7Q/mO/b0zclhBC7mowhE0JUGR7sJ5/1PHBCX/N3fnHbXHBW9EYieL8kvlQX/mB+XWBzOBSI7Nk7FUKInUtayIQQVUYXtaBhX/N3ALob8+hiLATo7w/md/cH858G1gCL/MF8/567SyGE2PkkIBNCVBlZqmz4Jp0VAyjUGSxy6wM4wBTg4opiDYET9swdCiHEriEBmRCiynjhrlt/qq6KW77n9H782OidrItnvrDaqaU87hlD0HoDC9sBJu/hWxVCiJ1KZlkKIaocfzC/D/BDYvsb73CaGQUAjIqd5bzgDDwTyAHGh0OBDXvmLoUQYueRQf1CiKqo0i9Fb8Vi8gAeHBN4s2LzcmC/3XdbQgixa0iXpRCiKvqN+KL0ANwYvYhinQbAZdYHjPXcQy5FAJ38wfxn/cH87nvmNoUQYueQLkshRJVTkd5iTWL7OGMSj3gfq1TmC6crETx86PTkM/fAYuBXIB24JBwK/L5bb1gIIf4laSETQlQ54VCgAPg4sT1fN8Ld5rfj4cZvHGP+zFOeMXRkcZaB2xc4CBizG29VCCF2CgnIhBBV1VBgI8Bc3YSxztHJA384TVAq/lkpeM13F/N853Ku+RlAbPffqhBC/DvSZSmEqJrycs4s1mnnT3bbz7kodt1G4LaDjRn4iLlful2NKb5h1FGb0ZpkcLZRZ9E18swaoEk4FJDATAjxnyEtZEKIqicvx681L2ep8v6Hm79dfrr5VSnQ8wd3vy++dA8wQHFE5F5ujl3Ag/ZJydPmuk0A6gG37aE7F0KI/4mkvRBCVEWmBqOi4YtFboOzgcepWNMSoJCs+a85h7cGmKmb0UitZ4LTO3H4SODW3XnDQgjxb0iXpRCiSrr/5oum9zJmdf7a7cJzTuBR4GpgFtAWsIGewM3AoB2cHgXah0OBRbvpdoUQ4l+RLkshRJXjD+bXf8wZ3PmM2C085wQAvgyHAi5wCPE1LXuFQ4FfgUjinKZqDa3UisSmlx0HakIIUSVJl6UQoioqBYqBLOJZ+zcBhEOBdcCzKeWuBeodY/x06MOexzCV5s7YGTzrHAPxoEwIIf4TpMtSCFEl+YP5hwHjgBrATKBHOBQo3VHZWbft93sHY+l+AGt0dbIpZZ3Onec3CrqQV1i2o3OEEKIqkS5LIURVFSEejAF0Ajr+WcEOxtJ7tI6vf1lPbSJTRfAbBW2A43f9bQohxL8nAZkQoqr6A1hS8XkRMOcvyrZUClWkfZENOhsArXErzhNCiCpPuiyFEFWWP5hfHegC/BYOBTb/acG8nM1ADkDYrf22T9lL66tNn5NX+OXuuE8hhPi3JCATQvxn+YP5PqBmOO2M54GjXQ1POsfdN+yOV67f0/cmhBD/hARkQogqyx/M9wAnAgXhUOCbbY41ACYBTTMpe/twY+qJy3Rdc5pupYEu4VDg9z1wy0II8T+RtBdCiCrrLPOLL84zPzt0ia5H7+C6ayeGznsw5fBxQFOAEtJPed/tk9ivgMzdfKtCCPGvSEAmhKiyrrPePjhXldCSVfzmtr6vQzBr/hOehy9vpNZV72WcXzjJ7egSn5z0K/Ae8da0/HAoMGmP3rgQQvxDMstSCFFlRfFsSXxeTzXjVPOblw41ZxzVwlh94D3Ws0cQ/w6bRDw9xp3Az8CoPXO3Qgjxv5OATAhRZV0fu/iBWW4TbG1wmfk+jdS6tMQxn4olPvYCmld8vgwo8wfzg7v5VoUQ4l+RQf1CiCrry1v7tTjc/G1hYrtMW26BrqGyVLm+I3bmggnuwU2A9B2cqoF24VBg3m67WSGE+BdkDJkQosq6NnbpBd8Zw8lVJQCkK9vwq7UAqq/5e7MJ7sHRPzlVAYOBUGKHP5ivgNOANODVcCgQ+5NzhRBit5MuSyFElVVI1rUnRm9jgdsAR8NqXSN5zMLxEl98fIc8xPJ+ua37On17Tjl5OY8DNwGvAy8AY3bxrQshxD8iLWRCiKopLyftPW+Lsk5qie8HtxPHRUdTS23hDmssLoqQfUai5HogFwgDzU4zvzZbqxXMcxv5uhvzfRVlhrZX4S9ma3/inM6JD/5gfj/gJaAYODEcCvzVEk1CCLFLSEAmhKiqjuxiLMoF6Gf+zng1koHREOfGgtOAusTHjqUDtTzYPOF5uOXBxgzSKgb7r9I1iGoTr3Io0b7IMl3nCMAFyknpygTuBhpXfL4ROGe3PJ0QQqSQgEwIUVUtBBzABGhvLCOL0tXFZAwGTgLuTxQ8xPidI8yplU7OpYTTo7dwkDGXz9xuvmIyID5M4+pwKPBRStElwEEpn4UQYreTgEwIUfXk5bQjHnD9SHxx8WrfOfutKyZjErAZWN1IrWWAMYVVbo0tg82JZa5WdQ2lsbVBMeklebFzM6bqNmqq0wbisy4VEAOmbnO1IcBMoAh4cvc8oBBCVCZpL4QQVU9ezo9Ab4Albt0JF8ZGHFSgc+tH8RLDmpNOZP0Pvqt61lJbLK1Bqfhpm90Mco1SCnUGx0bvZJmuu23NC4HqwCzglHAoULD7HkoIIf6czLIUQlRFduLDV27XQX2N6fVn+Ibws28oHVS4bRZlB9dSWyzYGowB5BqlAOSoUg5Q8zjP/JTbrZdorJJxV0ugJnAIIMljhRBVhgRkQoiq6ILFbr2fXrcPw0OM2zyvYipNDVXMKeY37jpyGWsfha0N5rmNCLt1+d7pyESnPQAFOpeaagt5npc53/qM5zwP7Oga6f5g/lEV+cmEEGKPkjFkQogqx1/++lrgtwPV7G5v++7wpB5rpNbfDTQYaZ9bY6R9znGgkj8sTRxa2StZqWtxtvl58pxaqjC1ijXAMuCSin+PA5fvuqcRQoj/n7SQCSGqolA/Y9qwN713VgrGHE15f3PaG+FQ4EIgmhKMLQVwMJmrm1BEBuOdQ5jhNmOZW4cbYxclqpgJvAscmNgxwPj5JPJyJpCXM3zXP5YQQuyYtJAJIaqimt2NeRgqPukoMXDfVKQ5Wr2ubs99z+KliF3xFZZFqepkLGGe25iNVNMKVz3nvV93MsKqSKczTyfSjNGp4h8A1SjhYc9jtYHjgeNH3Dyizzin7xrgiXAoMGt3PrAQYt8mAZkQoiq65QvngFZnmF/tn6tKzFXULG3IhgytwVS6M9D5He/IkXPdJr/2NGa1zVJlTWqqIgp0LgMjd6tMyuhkhBVAtiqjq1rAUl1vu4sYuNqD41LRW7BWVz+h4tAl/mB+33Ao8OPuemAhxL5N0l4IIaquvJwMIGv/8qc7djKWfPqo51FPYqHx75xO3xxqzuy37SlRbWLhotAoBbY2ODYymjk0KwDq9jWmcZ/naYp0hhuMDXn5DOvr9X2MmUe/5fRrc599amoX6a3E17ysHQ4FJGGsEGKXkoBMCFF15eX4gBFfOF2PvSF28YEdjDDXWO+wWNfnaTtQkO+9qa5HuaTmItuREu27oENkrAd4+jPv9bQxVgDwht2PG+0hqUWTKwMAU4B2QDbwZDgUGLrzH1AIIeJkUL8Qoiq7BRh9hPnbgRO8t9JMrWZwdCR5sXM50phad7LTFrfiN2WR69thBVpTdlvsPA0cnU75ikZqXfLYOp2zbfF1KZ8PJB6MAVy8U55GCCH+hIwhE0JUZclU+02MdYwyXsKv1nCq+S2ZKlKpYLaKsNCtx2du9+JLzQ8zTBX/wfmRc1D5ePfQsQCN1PpK503VrSvXQWnNU8xvWalr8al7YOqhzTv3sYQQojIJyIQQVdkdQDut6alUvCvxJPP77YKxhA3kcJ99elY1yjjb+hJbG4x3D62eOL5E1+Nnty0HGXOZ5zZiqtuaDmoJN1mv0dxYQy7FnnQVBWB49DLecw9OnGr5g/n9wqHAN7v0aYUQ+ywZQyaEqNL8wfzHjjJ+GXaj9TrLdW2aqxU0NDZvV65Ip3Ns9A7Cun4MtKe7msdGslmkG1YqZ+LQSK1jta5pR/FYn3pvoK2xfLv6nrSP5R779G139wmHAhN33tMJIUSctJAJIaocfzDfAIYDzYFzPnO781m0G6OsFzjY/GOH57zr9CGs6wN4QPGLbutmUG4857k/2kQVeB+wT+Ezt/saB3PtUl0vg3jrW/s6atP1qfW4WrGKmmWvOYen7+AygwEJyIQQO50EZEKIqugi4P6Kzy7Aw57HOd6c9KcnHG9MpK5nE3XVJhxMboxdtHas995nG6n1twLc73mSzyLd6wG3AS8C9snmtzfUUMXJOj5zDuBO+yzqs6H8cc/D6S6KtTqXiW5HXnaO0sAHu+RphRD7PAnIhBBVUa2Uz0YmZX8ZjAHkGqUcza/J7RusN2s3UusXJrYziNBUrWGprncl8CSwtkSnnVmuPXaailnl2sNo+xxW6Nq85r2remNj64TLo8ypLNQN1SS3Yyvg+530jEIIkSRpL4QQVdHjwIfAeoAS0pnutgDA0fGvLVfHl1T6Mxt1tjkgcldy21SaW6xX3eps6Ug811j95mr1U15ilqMVf7j+ko+9N/KpdwSpwVhCBhGAR/zBfC+AP5iv/MH8wf5g/iB/MP8vsqAJIcT/T1rIhBBVTjgUKASO8wfzawIv9zFmdFiha1WbYTez3nL6Zh9izCSTMi73bO1BnOH4WUUt1ulcqqsiCnR1Skl7abLbdksPY241gCPM34zHeJQzYzcT357a2lAAmm7mgkyAaqo0WedGncEyXZ8pblu+dLsCZACtyMuZa/LK7Q7mrRVFRwJ5u/i1CCH2YjLLUghRteXl5AArgUyApW5t3dRYp1a5NWhgbATA0XBdLJ6m4iA1ize9d6IUbNTZ9I48zJ3W85xgxcfil2ofHSLPozG43nrTGWp9YAIUax9Z26TTeNfpXXpNbFg6kGgBi/7iu+zk2qrwtTLtTb8oeq1ZSho+FVsy2W1/bTgUeG+3vBMhxF5HAjIhRJXlD+b3rc+Geyf6ruhu7KBT8DunE4eaMwGIaA/tImOZ4r2MWkZRskzX8qfopubyjG9Mct+o6FmzX3SPjrkYL/cy/hhVqtPMcjzRgebkascak2lmFFCuPQyOjtoSwbNigDGl/UzdjO/dzrzjzSvtZszPAIhqC6+yARgdO5PnncB54VDgpV34SoQQeykZQyaEqMpeWU3N7qPsc9igs9cCzxXonEKAVbpG7GPnwE2JgjFMsiiuFIzZWhHFYrpuRZGOZ7HQGm7zvtp+qveSjjUoPHmS2zFzum6ZFsOqdpr5HU1VAe/YfRgQDTFPN672ivfu9iM8b/Oi514OUPNY4DbISNSfCMYADjOmAZy/q1+IEGLvJAGZEKIqKwX40e3IG85htS6ODj8mGLu47wpd68gGamPtj9xeX0922gKQpcoZbr3HEje52hKW0kz2Xa7rqw2cEB3Jt06n5CLk1Y0S80PvLT3GWI/ylfca3vSMoo7ajKGgrzmDJbo+6URoqDYAYChNM2MNefZ5rHez3W1vdHE8B1rRtvuFEOLvkEH9Qoiq7MRMym552zvqlBqq2HA19R6yT7qj0chFx5CX88istAtPjOitX2NNVQGXxIbzlvcOcilGKchS5ep6661Ye2OpZ7POrFR5A7WBQdZP2110htscoLirsSBrjZtLHbUZF0UWZXRWi8hWZQZAkU4jW5UT04psVcZp5tez/EH6EF+D8/1wKGBvV7kQQuyAjCETQlRteTm1gbWJTVfjGooBwGeJfeXawyay7dGxM626ajNT3LZcbOVzvDkJrcHGxKMcANa7WdQyire7TMIitz4Do3cTwctU3yXUVFsbvUq1l3HOoZxrfQHAOp3DO84hkcusD32J490iT1FKGsAr4VDgnJ35KoQQey/pshRCVG15hetsbXyX+O1oKIyX7CMeKdG+ZJGZbjMGRu6O3Ot5Nna75xXe9o7kLfsQFrn1UIpkMAbwhds9+TmiTSA+rkzreG6zr5wuRPACUMbWawBkqCjHmJOT533qdGe629JMPd7XmJ7YHOAP5psIIcTfIAGZEKJqy8sxLOX2SYz9KtUeinRGm0+cA/V6N5sVbk3y7HOppzZmZqlyD0CmivK6L0R9lRzzzx9OU5a7tehnTkvu0yiujA5Fo1AKDAUXez6mq5oPwAOxk7dLPltTFfGsPdAdFBnFrfYFfOl2tZyUMgcZcxhqTqCFWlkLuGnXvBQhxN5GAjIhRNWWV+gCf0C8Betj5yAu97zPSdYP6nfdkj7RR5mlm1GOpV1NpfApQ0VY4tZlmVsb0DQ21lNPbU5m+09TNv2M3zFU5airptqChU0PY3ZyEkCqi6xPjZm6GQC9jFm4mGgN691sTje/4nrP23zgvYVGFHTaBW9ECLEXkoBMCPFfcDhw5VWxy78tT+lG7GtMp4uaz2DjB/dz7w3aUFQKn0q1t6iZUUATYx3tjBXJ/abaOknSYLsJk5xpfsVI6wVOtb4DwNWq0uzNNBWjNpunGbgMNd/HoxyUgnXk4q2oO1NFeMN7Zyt/MH+/5Il5ObLEkhBihyQgE0JUfXmF68krfPRDt1er8c7Byd2m0tzleZ4rrXcNj3INiAdP79q9uSl2wYcP24OTUygNXNxtuh8Xug141glg68pfhX3N3xlgbl2ofJmuHTsjehOFOp6CrFR7v5kcOqfrt95rpvY05yTL/eB20svdreuiR/HsD0w/46Z7HiQvZwlQSl7Ouf/6fQgh9joSkAkh/kuii3QDYnrrWPkCncs83SS5bSiNofSm153Dj33aOe7IJ+1jKdC5yTFiqVqoVXRQYS6OXcNUp9VMoATiMy036GrJcuX4Vq+mNt0iT3JUJDQ2Q0X7D7jxiSOaGGsPSJT5yu4cSyfC724L1ukcItrDo/ZgANXPmD4c8ANpwCjycmSwvxCiEkl7IYT4z/AH87sAn/ZWM+uM8LzFal2TEbFLcDB4zPMo/SsG7Jdry/7e7WT1NX7nQ7cXU9y23ON5DoiPQ0sNzKLapGvk6dnFZJzbTc096TTr22g1inPqsqlfB2NpbUu5G9fo6md86nS/7izzy1NN3BVKcaS//PVDx3nznutuxCcAOBrMinpnuH5OiI7iAvMTrrXGsU7n0MiIJ5jV8XFu65XicPIKZ+yudyeEqNokIBNC/Kf4g/lHAu8C3rZqmQdgrm5CF7WAcd6RWMolpk3Ho5xkK9TAyF341RqnoVpXfJ01LsenKuVrLStwczv0iD72u8bIPsT4nZe99wBQqDNKu0SeCbsYPy70nRWwlNsQYJPOdH50O6knY8dEP067JW1H9/mwPZih5gfJlBvj7EPob0zVNYwSBfCJ031+A7WhX2dj8X7Az+QVbtpRPUKIfYN0WQoh/lPCocDnQO7Lnruf/NQX5FNfkLPNz1ms62NVDKhPDcbKtUWBrs7Hbg/zWefYnPecPsm6bG1Qqn3L6xqbF7/tHZXtI0o9tTF5PIuyDA92e+DiebpxclR/dVViHmtONsZ4n0zbep3KvZB+VcBanZvcLiKdbFWebJtbq3Nat1bLVwKf2Nr4jbycrJ3weoQQ/1ESkAkh/nPCoYB9iDmzTWL7OPOn4kzKnijTnkpN/q5W3By7iA3kJPeNtM9d8p7ThxluMx6xB6/MUJHWAN2N+eynFvO+09sp0b7Py7R3w+32eckkse86fbZbaq61sTL5uYTkmuMU6TQ2uNk0rOimnOq04GTzu2Rr2Xt2T04zvyVdxQCwlOsv0Wmt/+17EUL8d0lAJoT4r3ob0IDubswbPintqvrpKpZsgdrgZpWPss8O1labl91ivbK0BoVOTQrXl+EbPDw29JkzojdThq9hoc7QABt1Fst0ndkRvP0zR649Kn3kulqvOkeUJOqb5rZc4mhVmDrKI1bxWWsqTQK42z6TusbWHsgDzEXolK/bBW4jzJR0G4vdelwdG7o1L4cQYp8jY8iEEP9deTmtAEVe4XzycmYD7RKHZrtNXmpvLJsN3APgaCKmwudq9UTzyGt93vDcsV9PczYA4+0+K8c4J2Us13WqA8uBbuFQYK0/mF9GfGYkb3lG6YPMuX+aR2yhW59VuiafO9340j2AVmqF+6L3XsdU2gMQ02b0E6e7FTB/Nkyled/uSVNjLfPcRuTZ575SRto6oD/wWjgUuI+8nDrARvIKZYFyIfYBEpAJIfYOeTknAS8Am4GrgY+B6UCb1GJaw732qVxifUSuijeArdC1Hu0TeeSKlGLHh0OBD/zB/DlA2+OMSYzxPLZd2owd2awz3VxVYmgNSmEDZUB2mfZEXG34Mo0IANPdFgyKji4EcoAY4Km4Q9703hHuYczxb9TZa2KYHeqOXLpxx1cTQuwtpMtSCLF3yCt8h7zCakD/DTq7Tqn23so2wRiAUnCD5y0yKEusU7m6Pus7v+kdXdiA9QArgckVxWdZ2Ay3xiWDMUcr5rkN+dVpBcQDvGlO82T9OZQYiesAFrAaiKSrmC/TiCTXxlzu1gbIyaQMLzFP4vw6bKaHMccPUEMV1XvL6feUP5j/jD+Yf50/mC+Z/oXYS203SFUIIf6z8nIaxbT5e01VlL5Fp293WGu0qlheKbHE0QadnVtTFR3SQ83hXd/tX/qI/lhdlcwhj/QPvX5zpa5FM6MgWYepNG3USvYrf0Y3t1epNGIs0A15Xt1Pe7UUC6fS+k0Fbo6uaxQm13tKrI15rDUZpTQDjCkUkcHw2NAPvnG7HLuJbLXYraebG2tUmfbyvtPrCCC34vQNwNid+cqEEFWDdFkKIfYeeTlHAJ8nNn90OizrZcxq8lddjQvdBrQ0VgFQrH3TslSkS+rxbRPJJoyIDuFM62v2NxYRdutuXK1rZPY05yQDL0crPnO6cb9zyoavvCNqJAJBW4NVUd8WnU41VZY45Sl/+evPAm0aq4I+B6gFl/6h/eULdSMbSMwYeAC4OxwKbPgnr0UIUfVJl6UQYq8xMnb2tD9cfxnABp1NH3NWck2liDZJ/f35h9uU+2KncFn0Sop1PJ1Ylop02fY3aiIY23Z/PbWJ/Y1FAPiNghqtjJXh1OPfuvvzmns4vY1ZNa+PDYkl9lsKotpija7ODLd5Ucopk4F0YP1yXff4CW4fY6FulMHW72kNXAss9Afz2yGE2KtIC5kQYq/hD+YHQd/dVc3nXd/IHZZZ41a3v3H3t+6wz6SEDA42ZvCKN1SpzG9OS7e6KvqlmVHwg9Zcp7ZpIVvh1qSIdJqo9TpTlStHqzUnRvPWXmm9t19btYw5ukn0Hedg60nvowbAVLcV1SmiubEGVytOi95MKWkMt96x+5vTP//O6TT+3NiNvYELALzEyKa0Uv60bRQA84GLwqHA/H/xyoQQVYSMIRNC7E1KQTFXN2WjzqKGKt6ugE9F3UW6PtN8lwDwh/azVufYdVShBbBW5zDcHmos1fUygbwlvjPqA2dWzJoEoKHakPisgFsOj97XeIlucEl9tZEGxkYasNGbRrQEyARop5bRN/Igx5qTmK39TNHt+d57FU2MdRYwcLWuOfBCM593nEPJVmWM846kvtrI2/ahK663L/kFGLzNY9St+DfdH8yvHw4FCnf2ixRC7F4SkAkh9iZPALVLSWt+SvTWjMc9jwxqk5JNH6C6KvFea72DtyJrfle1iNluE2t07Kz5m3T2uN9185uziAB0ONiYscXGNDw4pLaSpX4u0Lm3DDZ//PZB+xQyKE/un+02zWygNtBErWWMfQJrqc7zTgCAdmopGWpr2dOsbwE41/yMapSSq0oBOMH8oeEqajYZY5/0FdAv9TkaqXU867k/vZlas4K82HnkFY7/d69OCLEnSZelEGKvNOnWnoMaqXXjMykzMlQkuUwRQKn2kqGilcp/4nRnqVtn08XWx9UNpXnP7s16chhiffz/XsvWhm4XeVEdpn7jBs+bLNYNuDo2lGIysLCxK377tlbLOcH8novMT7CUywadzQq3Fp3NJUB8nFpqsFesfWVZKnJX6/IXJ0bxjieeqywMdLzVeoULrU8SRReTV9jif35ZQog9Tgb1CyH2Sp2NRbc3MdYZNY1i0ohVOjbPbRzZtnxdtTE2pCIYAxhsTeRrZ3/KdHwtS63jMy6dHfyGLccbyaSUu73P09xYw0HGbCziLXCJYKy5WsUH3lu41MpPLoKeRpRLYtfoBW5DotqsFIy5GrJUJB0Yfa017nLiMy0XAJsAwjq51jnT3RbZ/mB+vf/pRQkhqgQJyIQQe6Xpbsvaic+pgc48p6Fepuv4HL11Z7n2MMdt+m2Brp7ct8itz0+6I4dExrBZZ2ql4jMuP3R6VrrOSrfm/DJ8B7dSKzfUUPFJk9mqnNPNryA+M3IhUNBarSBNVQ4MX3UOZw011bWxS7k8doX9rdNpc0ybLlROtbFM1x0MmEAXYBnAK84RjIhdzH2xUzgnekNt4DV/MN/0B/PP8AfzT5UkskL8t8gYMiHEXunq2LDPH+ax83tVrFcJ8VauNuZK1YaV/OK0Zr5uxOHG1Pl1jcLWZ1pfH/GNs9/yb+zOOelEV9xiX9j+YvNDzrG+oBxPMrgpJoP5bkNaGyvRmi0NjQ0D/eWvh9MoT3O1ItnCZk5kPbnqE+fATX5V0GKK24Y/XD8djTCfOQcUj4qds34ltfWV5vhmw63xKIXlanJX6lp2Y7XeAHC14l2nD287fVODqzOB8S3UqhN/dduwRNdP7O8DPA5cUrHdHrh9V71fIcTOJWPIhBB7JX8wPwu48QIzf+ixxuTcDkY4OZAfYK7bmKOj93wXTjujGxWzIYkvc9T/1th5pR87B82amnZZYj8f2Qcx0PzZNRTGQrcBIfs0Vug6H8zVTRYAVwwyfvReaY2nptpCTkWyV1crlutaNDXWMdttyuBoHh4cislwgLbHGz+2v8fzzIQ0ZVdqzVru1ma6bsE7ziE6rOup1bpmYRSPBnIbso5jzZ+Kgp43s12tuMEewjinb+LUlDUx+SgcChy7s9+rEGLXkIBMCLH3ysvpoDUzlUI5WmFWtF65Gqa6rTGUfvkAY0F/oGHFGSsrPhdOdNoPPMBY8FWaiqUBPGEfy1Drw2TV7cpfoIw0DahrzbcYZr2/XUb/bbP8L3XrMMVtyw32EF7z3PVtD2NOX6jcpQowzW3JydHbeM17FwcZc5nvNuDU6K28583DbxRUqnetzuHAyBOAogZb8GA7BdRQwDPhUOCynfEahRC7ngRkQoi9V17OYcBXic0tbhrVjPJtSz0F1CeebPXilP0/AMNX6ZpXPWsPbPK12+XX5zwPXNvKWMlY+yhC9un0NaZTSxVyp2f75SUdrbjLPp3zrc9ppNZXmkG51q1GdVWMp2JwP0C5Ngm79SmgOqPsczDQfOG7Pnl8ntuQbVN4JJRqL8Wkk0sxFi432xfwhtMfYGQ4FMj72+9LCLHHSEAmhNh75eWo5W7t95TSx+dSTJbaLhgDWAo0BcZrTV+lqAlQpj1z2kVe+gT4LRwKvOYP5ntBl2QQsUpJ4w3PHfQ0ZxPTRjKwcjSYFUFXsU7jltj5XGFNwNGKVsaq7VrCtnVp9Gr9qXugAuijZvCE9+HUtS6xtYGlXLSGKKb2KUcl9qWa7rZgUHQ0QAlwSDgU+O0fvzshxG4lAZkQYq836MYx503w3b5tM1YBsIr4zEUA7oyd8cdQ6/2OmZTr951e5TfaQ9LTiPKQ58lJDdX65RfHrvlpha59L2jvIt9ZyS7Qb539MNC86vSnrVpBJ2MJLzpH8Lznge1mVsL2+cYAotoqOC4yOvtc6/OMumojh5m/J8pqpVAFOodRsbPoYczlB6cTs3VTPvTdQnVVsl39j9vHcZ99WmIzBrwMXBEOBcq2KyyEqBIkIBNC7P3ycjzEA7BEXovHgasnu+2adlfzZprKTXe12nJA5Mlqd3peYKA5BYCH7ROox0ZOrcik/5bdV99gX6wAHrQe5wRrImXaw0nR25mlm6decRNQfbJvGPXUpu1uZ57biGZqNV7lJIOzUbGz6GbMdwaaU8zUssXaywadQ1NjHQBjYifoMc5Jqi4b+Tnt8kr1lmuLB+yTeNY5bkdv4bpwKPDAP3pvQojdRvKQCSH2fnmFMaADcDVwEHmFl2/WmR3SiUz71W2V/khsUMn19sXBTVSb10BtSJ7mV2tKa6nNdmK7oVqvuqm5gKa2ii8fma5inG5+A2gCxuQtt1ivPG3gTgU4Jxpkvtvwe0dv7SuNapM2xorkjM9ES9kRxm+kEa0UjAF4cZLBGMAF1qex6b4hvOkdTZFOr1Q2Tdlcb42jiSrY0VvYLhmuEKLqkDxkQoh9Q17hauDhxKaP2JOdjcXZAEt1vcwH7VOeAGJvOX2f7aiWHGkpN3q8OWneardGq6VunTZRPBxkzOEd3yjetg9NBmQAGsUV5ntc63mnGnCJjUnIPuPX+brx8NbGSgP4LlE2NfVGqi/drnztdom2Vcu89dXGZKC2bflMyrym0uSqEmJ6u/gNj3KozSbOtz6lj/EHbzl9ec4JuIDXH8w3w6HAjm9ACLFHSQuZEGKfZGMkx1OVkJb46HnD6V9iKbc9UBc4pr6xsU1TYy1hXRdPRXB0vDmRtsZyAFa5NbjfPoX9jCXJuvdTiwG6AhOBKUBp6rW3HSmy3s3meWcgxTrd08DYuO16lpVKJ8atASzTdVjq1uFrZ39etg9nrc7ldfsw0lSM863PaGWs5BbPazRgvQE8AAT/8YsSQuwWEpAJIfZJQ2NXv/CG3Y8X7KO53z4lsdsFPgB8WpOVWr5ce5KffSrZi0kEL4Vk8ZJzJBFtxSLaYqxzNMAEwCSvsBw4x9aUFWufU+DmRm1MCtycZB21jCLSKaeYNLVeZwMQ0wa/Oq2imURUYh1NqBzMzdeNODQ6hgti13ObfQEHRp7gJvsiNuss3IqlobSGj3030tOYBXCNP5g/yx/M7/7v36AQYmeSgEwIsU/6wd1vwo32kN9H2edQQnIslgJWkVe46V23z2ur3RqUai+TnHY85wxMBkWOVqx0a1CifTzlBJYC/Oh20p0jz350XuwG/xdut1+AE4BJ/mB+BnmF41tGXj/6xOjI+XWNzV6PcqhrbO3yLNA5nG5+zYuee6ilinC14ienPd3MBV6l4uPMEolglYqn19Aaehqzecszihc99+jGKePGZulmG6+KDWOFWwulIFeVkGe95Bq4NYgvqfTirn27Qoh/SmZZCiH2Wf5gfn1g1fHGj9zleZ51OpcLY9ctW6QbDgDaAO+mlh9k/MiR5q+U6LSHRtiXLn7TO7pHdzX3zEW6AWdEb2E9OQAXAc+lnNYPmAWsrEaJ53vf1eSqEiLaIs8+lxZq1cbB5o81aqqiSukwotqsNH4sNd/ZjnzpdOGi2AiIt/LVAd4fYb3Ze5j1QbLMG3Y/brSHQHzR89uBt8OhwLz/7e0JIXYmaSETQuyzwqHAamDVtdY4MlUEv1HAtdbbTYCHgB7bln/f7akXufU/P9n6fkY47YynDlJzTjOVprWxkuPNiQBRYCrxJZgA1gNziAd3ni1kcnL0du6Nncrp0Vt4w+nP607/4pqqCKicmyyaMueq0E3j6thQZruN//RZosklLFkAXAX0fsA+hbluo2SZA425iY8KGAX87A/m1/1bL0sIsUvJLEshxL6uSxm+qUAjgCONqTRWa49YruscCdiA5SXGWM899DJmK6U4EjiyUGfckaNKlwAtAUpIexf4GjCB6cSDseHhUKDAH8wvJJ4Hre4C3YgFTjxIyqaU7mpuE1fjGAqzQOdG0ij3bdTVMNBksg4N5BjlPO59bLsbX+3m8p3bGUtpXrEP51HPIxFTu23GOQffuoraHGX8QltjBRAfgzbeOWTbKnKAZhX3JoTYgyQgE0Ls08KhwNqHbh7/WBtjRQjAUi61KFTLqQPx78hNhxgzqvc2Z1c6L0eVNnS0KjWVvhaY/qZz2DBg26jpduCbcChQ7g/mnwT88LR1P/3NaazXOaSpGLnxTPsmwAZdzddWFZKt1ibHjMW0qb04lfL6l2tP5Gk74HvROZpCshhi5tsveO+jpiryAQy04oltS7Q3eY6h4DJrAj+7bZmq26RW1xOY/L++PyHEziFdlkKIfZo/mH/+w86JoRfso1mvqzmv2IczTbdMHF4IXJhB+W2OVtsNuDXQ6cDxwACFe0xiv4nDTdZrjPfe3oG8nEMBwqHAj197r/ngKOs3LKWpZ2xOBGNJDViPoXQyGANQ6DWpZbSGa2KXnv6EM6i8mAwuND/mRs8bVk1VtN0P7EwVrbSdrSKM847iKc+DXGjmU41igL5//20JIXYVGdQvhNin+YP5DwFX12UjAXNyBLRvntuYI82prNO5POMcUx7Fs+RANafdY56HqWNsSZ6bOgj/udiA2D3O6Z4YFqeZX7shz3OJH7wbySusSV6OKtOegnQVq73t+Uvcum5TVWCkBmK2NlhLLlOc1nnHm5PzEtcp0mncYZ89a7T1QrsoHuNbtzPHmD//T8/uarjevoR3nEOfAUaFQ4GV/+9JQohdQgIyIcQ+zR/Mb+sh9sU3vmsbNVLrgcqB1hK3LmdGb2YVtXjK8xBHm7/8aV2bdCYL3EY0VOtoaGxM7N5IXmHN+bd1eK+1sWJQat3rdDVecw4fs0ZXvzrkeT5ZT2oZV/OroegGsFFncmb0FkZ6XuRAIz458itnf7JUObUopLbaTDW1df1wR4Optv+caobbjOOidwKUA4eHQ4GJ/+T9CSF2DumyFELs08KhwNwFaed2SwRjUHm2YzOjgDGexwG4ITaEYteXPKY1RLWZXCOyuirhQHMeDY2NuBoH+BS4nbycUTmq+Nht667FFs4xP3/tY6fHnF/d1kS1xUpd4zGlSG2p2j/xYaLbkUzKaKOWJQ/+6rbh1Oht9I8+wFS3daVnSw3AtpDJH65/u+dfp3MTH9OAwTt8SUKIXU4CMiGEyCssWK+rvf5nh7sYCwBNIVmMts9O7lcKvMrx7egcQ2EARwKPALdWp8gAKNcWBRVBkFKQS8nXD3meOPOkaN5JrSMvt+kdeey9dbraVyn1JMeGjbMPXZnnebkkJ6UVLCvlc8qKA9uZ6HTkhtjFTHNbstKtmdzf2/gDLzEGGz/wlOfBi5+65cyH/MH8HbSlCSF2JZllKYQQQLfIUyXplHOB+Ql12aTTVUSdbP0AgIXLZN/lfG4fwGnWN3/a/Wdrg8+cbtGB5hRLxQOyZCmvctRl0StX/uK2a3ii+T03et4AwFA6u5la/TVQExh3svntCbXV1nFqjjYwlUtMm4VztH/eZp3ZBshMHO+glpJJ2frRnrG12qswrlYYSuNohak0ZdrLK/ZhzhTd3jRxGBwdxVHGFJ72jonXj6HbqGXqIe+TANlRbV79sXPQeuDOnfuGhRB/RQIyIYSIyygjjcedwQAKtN3FWGi1NFajFNRjE2dbX1bqctzWHN2EZ5xjlgesKS1S9zta8b7Tq/hIc2rOMeZkxtgnMsidSDsj3vVooHOJJ48d2Nv4I3neQrc+V8au4Ejz11mfOd03rSP3sDH2ifQ0ZmlToVyteM4ZwOOeh3/sa84YlHrNpbou98dOYYrbjje9o3+62Pi0j6sVV8Qux8bi+djRNDQ2MM1tsfIF733J7LFe5eCi9vu3L1MI8c9IQCaEEHEPAKcRzwm2CNTJTdS672HrIuPleEvTiWa4Gia77Vmla1JDbYn1NGZvmes2rXmFfQUrdK0Wb9j9ONX8Jpm+4m3nUEpJz7rQ/ASAGhRxavQW7vM8Q321gbvsM23i2f1/m+D07jXAmIIHm5eco5it/TNm2/6uwHsAfqMAU8Vb3gylcTEO6WvOeDJxj4kJAdN1y7kfuz18wLM5qmRkovw9nmeLslR5dkR7OCE6kuHWuEaJFrkinc6D9kn8oZvX2bWvWgixLRlDJoQQQDgUmEZ8uaRLgd7hUGCaV9nPpBT5LV1FW//qtHprma7j7mcsZppuxQWxG4rbRV66fnBsFCt0bUBxoz2EGbp58sT5ujEGW9ehPMiYw0OeJ4svi13NGdGb6WHMnjHNN6QXcFAEL25Fb6dCAzwfDgWcoPX6qAHGz5GpTkvmuw0AiGkz+rr3rl7E164E4sGYq+FI49fscNoZRwNfjrWP8gBs0RlkqfJsAJ+K0cVYQFjXS97Xe04fxjoDALr7g/nyg12I3UjSXgghxF/Jy+kA5AKT/OWvq2HmhMgIz9sWQLH20TEyFmATUD3lrIJ+amqdc6wv1S9uW55yjiWbUsZ4Hqef+Xuy0AmRPEZYb9OzYhWAVW51p57abBoVOWg368zNuaqkPfEFw2cCtVfpGtwTO4WHvU8l61nj5oyvZxSeuO2ta81PZfjaT3Q7ZI+MnWMMMH8uv8F6K81SLsU6jXtjp/KG258zzK8wcHnNOTx1Tcxm4VAgvFPeoRDi/yW/gIQQ4q/kFc5Kfg7mY+Caic2U4CU1GOMU85vZd1vP1TWVphwvrmNQSNZbN8UubPO5ccP+2aqMEu1jpa5FB2NJ8rwGxiYztZ5cVZK7SWeu+MHpdMdx1uTaAA3URq63xhVoTR2lUFpj1zMKTyzXJmnK2fbue2SoiDrC/I1PnAMZbr2bZql4S12WKmeU9yU2RHN4yTmqCMhOOW8aIElihdiNpMtSCCH+pnAooKup0uR6RwY77mE41vgpYla0ch1ozAXQwKmrqbX/ydHbuSd2mn1y9HYKqMGj9mBcXXmmgNZb+zerqxLjO7fzucCixL6Gxoa6d8TO3Pyq3R9VkRYjNRjb7GbYm3TWVVvISK6dtJqaLNO1tvabVjhQzeEz74jsH71XcrH5AQbuRODgcCgQ+0cvRwjxr0hAJoQQ/0AuxZdGteUAfOj0TO6vy0bOMr+ghzH71yft4w4o1BkuwCvOEZCS/mKubsKTznHWbO3XAM86x2xqG3mRMfZgO6qN1cCvStFvqVsnDLDYrce37v6rr4wOfUprygHW62rlr7v9q99qn4+tt/8aX04dq0vkmfqnRW9d/7Qd0MOiV9o/uR3yTbg3tZyjFWdbX9DGWEkjYz03ed7kc+/1i8KhQMl2lQohdinpshRCiH/g5Ds+fO3jW4/45XF70NxZ2q/6GdO40MznIGMeHuXgatXNUJplbi3G2keR7/bYtgoNPApMBa4EPozimdhYrVde5b4JdAOOf8/t03aG3Txvitu2YTEZp59qftdDKSjTHi6LXpVWRpoGlIOBVdGgFtUGCohh0U4tHWbiZHdSS2hvLi143PvIWUAbV3ODUTFL09x+vXQaq7WnXnDT6NEv3HXrwl30CoUQOyCD+oUQ4h/yB/OzgE3N1Srrc+/1JMZl7YitDW6IXcSJ5o/aVO7ycc6hJ73jHDoTKGLrj+Krw2ln5AJ5Fduxq6ND+09w+zwN+NOIpM9NOz9Z52Ox47nfORXAPt38yhphvYWBJldtbdia4zYGVDLXWVSb97WOvPLkAWr+wkc8jxoNjQ3Jsq6G1IXNb4xdtPkN57D64VCg/H98RUKIf0i6LIUQ4h8KhwLFwCXVKdLbBmMRXbnjwVIud3jG0sucrQ4y5ja5z3r6LOAcKvdQHDrJaT9X6+SgNM+p5revA+2A9HJ8lOvkBAJ6GX9EmqnVANYbTn+6Rp4hrOtWum42ZSi2jitbT05/oMNU3doYFruC0or6yrVFCd5kOa1hgdsgF7jdH8yvNMlACLHrSEAmhBD/m+VTdRs11j4yOY7L1XBL7Hyujl7GOnfrpMXUfogXnKMvAJ5ObGdSRi/jj8GXx6584xu3c7KdKkeV2KkX+97tGK9LQ1dzke99760EjJ94yPM4p5lf80DsFFI7PGqrTfzhbs2FZuHsB7wPcKU1wc1Q8TH7C3QjslVy7D8fu935VbcFCAKX/W+vRgjxT8kYMiGE+N/MANbNcv21LSveSmYoON38mp/ddrq2UZQMrj50erpe5RjFOj06wemddZL5Hd85nSkkk/HePNoay1mrc9UFkevIUJGiWhRufNs59EagL3AKUN1HPD5LLN1UTZXyoOcJfMphsDmR+6MnVbo5F5Nf3VacRHw9Ti+25cEmhoWNkfwxvlbnJrP7A8xzm6ZWU2Mnvi8hxF+QgEwIIf4H4VCgwB/M73K0+cttwMWJ/bkUcZb1RaU8Fh7couGxYc+0V+G+47153dNVlGVmbe62z7ivrbF8BEAdtZnaRiFnRW/KtLGygdcPUrN/CnrefOiC6HWjylO6FQHWuNWpZ2xKbh9u/ZYMqsq1hwtj1+JlayNbrirhQc/jONogdexw2K1HnnM2p1jfMdNtxjNOIHHoA+CRnfCqhBB/gwzqF0KIfyMvJ7NUe4syVPRPlx0/NjJ6yUzdotnRxhSe8o5JPdS8SKdPyFZl+xXrtI2leLNqUOR90zmMz51uPOe9H69yWOTW14OjeepN753UUxu5N3YKt3leI0NFAPjdbc6XTleu9bwDwOt2v+Ix9klZDgYf+W6mvtrIFp1GNRUfo+9olZxh6WqFi+K62KV6gtsn8QxLwqFAc4QQu42MIRNCiH/BX/56jxedo7cLxqI6Ph7+E6e7M0s3a5ZBOT+6HZm1tUvwzTH2CeEDIk95L4heyyy3aY06aovXUpqzrK940jsGb0Wy1xbGajXeOzL6g9uJrpFnmKebJoMxgOlOc8Y7hzAkeg0vxI7iNPObrJ99w7jL8xwDIndxUuQ2llSsfwnxdBdl2kNUWxhKYymXU8xvU9P8L0IIsVtJl6UQQvw7B9xrn8ZStw73eJ9L7tyi06ilSjjMmGpO8Q2lltrCw/YJHBO9k2xKt2wh6wfgMB/RtiHP89RRmytVmqkirHRr0NDYiNbQyljlbWWsYqLbsXSNrpGRSFWhNZzr+ZLjrMk8Zw/gXOvzZAqLo8ypfOwcyPvuwdxkX8jbxigyVYTVugYDI3dxovkDt3heA2CxrmcC3wE+4Jrd8eKEEFtJl6UQQvwL/mB+E+B7oOk91jOre5t/1F/s1uMQ84/tyka0h/aRF7jbeo7uxlxedo50PnJ6mL+kDUuWSYwNW+LWZXB0JJmU85VvBGkVsyIHRUbp6bqlOs6YxHHmRA43pyXPtbXC2ibZ6wadrU+L3vryAt3olRpsadxIrRu7WNenmAwAPvHeQDtjOa5WXBq7esnnbvfDw6HA4p3/poQQf0W6LIUQ4l8IhwLLgBZAtVPveL9Bn8gjz4bsM3BS1qdM/O4t1mnrDzd+4xTrO5oZBdxmvWKauDxrDySiLX50OtAv+gADI3dNGRgNLd5MNVZSh2tjl25eo6tPCsVO+326bqkAPnB7cUnsGiY6HYB4yg3F9sPYaqoi9YXv+sxw2hlDbrDezJihWySDMYBmag0AhtK0UKubIa1jQuwR0kImhBA7mT+Y/3x7teSCc8zPnaPMX2dXVyWdKg4tGRU7663bPK8GE2Uft4/jPvu0HVUzGejRXK3iHW8eNVQxnzgHFlwWu7pSBlgTmym+YdRURQAsdWvbq3Qtq6laQwNjE442tKncRKRW0rZ8bEkMq87p5tf4iFKNUn2V5z21QtfilMhtrKLWteFQ4MGd/1aEEH9FWsiEEGInC4cCF87WzboE7UuaVlclT6QcmttAbTAL3JzkjqOMX9+hcu7YhB4A/Y2p1FDFABxp/FIXdBQYAkQAnvKMSQZjALVVYV4zY41uYGxikVufu+1Tk81mrlZp1Sm+5yprPHd4xnKr5zXOsr5QR5ffxcGRMayiFsDynfUehBB/nwzqF0KIXSAcCkyPfyp8irycIqA+0PEi65MRAEvcuizTdaMTnN4PAyf9WT12yte0QlONUu8WMtsDnmoU09/4LXm8WPso0hnUNzYpgBbGaj6O9qC+2kRXYyFj7aPM1dTcuL9atAaoB1BTFTPON4rekUfYQhZAV2DcTnwVQoi/QVrIhBBiV8srfI28wvuB3oldy3Td6efGgt0fuiv0IzAUWLajUxfqhsnPMTwo9O/AiYBhY1HO1jUus1SE6mrLHYntsFuHV713cbb5OR87B/KB2xugawTPOVFtJlvlslU5HYylAKuBF3fKMwsh/hEJyIQQYvdJdF8WHWrOGBoOBWYAhEOBJwE/8cAsllr+B3e/rJ/dts994XTVl8auppCsmVTkCSsljQftUx4q0b4liRPS1NZ0Yn5jLc2NArzK5UbrdbxEAV46YvQ3X3iV01JrNgAU6oz13dT8ZkDjcCgwb5c9vRDiT8mgfiGE2J3ycuoBpeQVbtnRYX8w/3LgIeItZv3DoUDYH8wPATdUFFkLtCe+8PdK4MULzY97DjR/nthBhZPpMVKz8Sf84HT85ODREwem3EsG0BGYQ15hEUKIPUYCMiGEqGL8wXwzHAo4Kdvdga+AbOCecCgQ3ME5791ovTboEisfgOluM/Y3lmxbzAXakVc4f5fdvBDifyIBmRBC/Af4g/k1gBrhUGDhnxyvZeB+e5wxqb2Js+EY86da/cwZOyrambzCHR4QQuw5EpAJIcRexB/M9wBtx3gemzHInATAUrfOmqbG2jDwLnmF9+3J+xNC7JgEZEIIsRe65Ka87meZXzzQVK1d1MRYe9WfjVkTQlQNEpAJIYQQQuxhkvZCCCGEEGIPk4BMCCGEEGIPk4BMCCGEEGIPk4BMCCGEEGIPk4BMCCGEEGIPk4BMCCGEEGIPk4BMCCGEEGIPk4BMCCGEEGIPk4BMCCGEEGIPk4BMCCGEEGIPk4BMCCGEEGIPk4BMCCGEEGIPk4BMCCGEEGIPk4BMCCGEEGIPk4BMCCGEEGIPk4BMCCGEEGIPk4BMCCGEEGIPk4BMCCGEEGIPk4BMCCGEEGIPk4BMCCGEEGIPk4BMCCGEEGIPk4BMCCGEEGIPk4BMCCGEEGIPk4BMCCGEEGIPk4BMCCGEEGIPk4BMCCGEEGIPk4BMCCGEEGIPk4BMCCGEEGIPk4BMCCGEEGIPk4BMCCGEEGIPk4BMCCGEEGIPk4BMCCGEEGIPk4BMCCGEEGIPk4BMCCGEEGIPk4BMCCGEEGIP+z+BKS00tOsm2QAAAABJRU5ErkJggg==", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 277, + "width": 448 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "best_val_loss = float(\"inf\")\n", + "best_avg_bio = 0.0\n", + "best_model = None\n", + "define_wandb_metrcis()\n", + "\n", + "for epoch in range(1, config.epochs + 1):\n", + " epoch_start_time = time.time()\n", + " train_data_pt, valid_data_pt = prepare_data(sort_seq_batch=per_seq_batch_sample)\n", + " train_loader = prepare_dataloader(\n", + " train_data_pt,\n", + " batch_size=config.batch_size,\n", + " shuffle=False,\n", + " intra_domain_shuffle=True,\n", + " drop_last=False,\n", + " )\n", + " valid_loader = prepare_dataloader(\n", + " valid_data_pt,\n", + " batch_size=config.batch_size,\n", + " shuffle=False,\n", + " intra_domain_shuffle=False,\n", + " drop_last=False,\n", + " )\n", + "\n", + " if config.do_train:\n", + " train(\n", + " model,\n", + " loader=train_loader,\n", + " )\n", + " val_loss, val_mre = evaluate(\n", + " model,\n", + " loader=valid_loader,\n", + " )\n", + " elapsed = time.time() - epoch_start_time\n", + " logger.info(\"-\" * 89)\n", + " logger.info(\n", + " f\"| end of epoch {epoch:3d} | time: {elapsed:5.2f}s | \"\n", + " f\"valid loss/mse {val_loss:5.4f} | mre {val_mre:5.4f}\"\n", + " )\n", + " logger.info(\"-\" * 89)\n", + "\n", + " if val_loss < best_val_loss:\n", + " best_val_loss = val_loss\n", + " best_model = copy.deepcopy(model)\n", + " best_model_epoch = epoch\n", + " logger.info(f\"Best model with score {best_val_loss:5.4f}\")\n", + "\n", + " if epoch % config.save_eval_interval == 0 or epoch == config.epochs:\n", + " logger.info(f\"Saving model to {save_dir}\")\n", + " torch.save(best_model.state_dict(), save_dir / f\"model_e{best_model_epoch}.pt\")\n", + "\n", + " # eval on testdata\n", + " results = eval_testdata(\n", + " best_model,\n", + " adata_t=adata_sorted if per_seq_batch_sample else adata,\n", + " include_types=[\"cls\"],\n", + " )\n", + " results[\"batch_umap\"].savefig(\n", + " save_dir / f\"embeddings_batch_umap[cls]_e{best_model_epoch}.png\", dpi=300\n", + " )\n", + "\n", + " results[\"celltype_umap\"].savefig(\n", + " save_dir / f\"embeddings_celltype_umap[cls]_e{best_model_epoch}.png\", dpi=300\n", + " )\n", + " metrics_to_log = {\"test/\" + k: v for k, v in results.items()}\n", + " metrics_to_log[\"test/batch_umap\"] = wandb.Image(\n", + " str(save_dir / f\"embeddings_batch_umap[cls]_e{best_model_epoch}.png\"),\n", + " caption=f\"celltype avg_bio epoch {best_model_epoch}\",\n", + " )\n", + "\n", + " metrics_to_log[\"test/celltype_umap\"] = wandb.Image(\n", + " str(save_dir / f\"embeddings_celltype_umap[cls]_e{best_model_epoch}.png\"),\n", + " caption=f\"celltype avg_bio epoch {best_model_epoch}\",\n", + " )\n", + " metrics_to_log[\"test/best_model_epoch\"] = best_model_epoch\n", + " wandb.log(metrics_to_log)\n", + " wandb.log({\"avg_bio\": results.get(\"avg_bio\", 0.0)})\n", + "\n", + " scheduler.step()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# save the best model\n", + "torch.save(best_model.state_dict(), save_dir / \"best_model.pt\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "Waiting for W&B process to finish... (success)." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(Label(value='208.240 MB of 208.240 MB uploaded (0.000 MB deduped)\\r'), FloatProgress(value=1.0,…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "

Run history:


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test/ARI_cluster/label▁█▇
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test/PCR_batch█▄▁
test/avg_bio▁█▇
test/best_model_epoch▁▅█
test/graph_conn▁▃█
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valid/sum_mse_dab█▂▂▁▁▁▁▁▁▁▁▁▁▁▁

Run summary:


avg_bio0.82229
epoch15
test/ARI_cluster/label0.88477
test/ASW_label0.72639
test/ASW_label/batch0.94934
test/NMI_cluster/label0.85572
test/PCR_batch0.52563
test/best_model_epoch15
test/graph_conn0.93957
train/dab0.72778
train/ecs4.44531
train/mse43.72255
train/mvc44.7052
train/mvc_nzlp0.32442
train/nzlp0.2438

" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Synced charmed-blaze-8: https://wandb.ai/scformer/scGPT/runs/vlmktqyd
Synced 5 W&B file(s), 6 media file(s), 1 artifact file(s) and 0 other file(s)" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Find logs at: ./wandb/run-20230703_155312-vlmktqyd/logs" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "92911" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "artifact = wandb.Artifact(f\"best_model\", type=\"model\")\n", + "glob_str = os.path.join(save_dir, \"best_model.pt\")\n", + "artifact.add_file(glob_str)\n", + "run.log_artifact(artifact)\n", + "\n", + "run.finish()\n", + "wandb.finish()\n", + "gc.collect()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/transfer/code/scGPT/tutorials/Tutorial_Multiomics.ipynb b/transfer/code/scGPT/tutorials/Tutorial_Multiomics.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..aac1fbdc468ac4ccea717ad1a57027ebb3f9c543 --- /dev/null +++ b/transfer/code/scGPT/tutorials/Tutorial_Multiomics.ipynb @@ -0,0 +1,1832 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "944c58ab", + "metadata": {}, + "source": [ + "# Fine-tuning on Pre-trained Model for Multiomic Integration\n", + "In this tutorial, we demonstrate how to fine-tune a pre-trained model for multiomic integration. We use the BMMC dataset as an example for RNA and protein abundance data integration from CITE-seq. The BMMC dataset consists of multiple batches by donor origin. In the paired integration setting, each cell contains both RNA and protein measurements. We fine-tune on the pre-trained whole-body model.\n", + "\n", + "We summarize the fine-tuning pipeline in the following steps, which can be used as a general recipe for finetuning on integration tasks and beyond: \n", + "\n", + " 1. Specify hyper-parameter setup for integration task\n", + " \n", + " 2. Load and pre-process data\n", + " \n", + " 3. Load the pre-trained scGPT model\n", + " \n", + " 4. Finetune scGPT with task-specific objectives\n", + " \n", + " 5. Evaluate fine-tuned scGPT" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "190fb7c3", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Global seed set to 0\n", + "/ssd003/home/chloexq/.cache/pypoetry/virtualenvs/scgpt--qSLVbd1-py3.9/lib/python3.9/site-packages/scanpy/_settings.py:450: DeprecationWarning: `set_matplotlib_formats` is deprecated since IPython 7.23, directly use `matplotlib_inline.backend_inline.set_matplotlib_formats()`\n", + " IPython.display.set_matplotlib_formats(*ipython_format)\n" + ] + } + ], + "source": [ + "import copy\n", + "import gc\n", + "import json\n", + "import os\n", + "from pathlib import Path\n", + "import sys\n", + "import time\n", + "import traceback\n", + "#from typing import List, Tuple, Dict, Union, Optional\n", + "import warnings\n", + "\n", + "import torch\n", + "from anndata import AnnData\n", + "import scanpy as sc\n", + "import scvi\n", + "import numpy as np\n", + "import wandb\n", + "from scipy.sparse import issparse\n", + "import matplotlib.pyplot as plt\n", + "from torch import nn\n", + "from torch.nn import functional as F\n", + "from torch.utils.data import Dataset, DataLoader\n", + "from sklearn.model_selection import train_test_split\n", + "from torchtext.vocab import Vocab\n", + "from torchtext._torchtext import (\n", + " Vocab as VocabPybind,\n", + ")\n", + "import pandas as pd\n", + "import scipy.sparse as sp\n", + "from sklearn import preprocessing\n", + "\n", + "sys.path.insert(0, \"../\")\n", + "from scgpt import prepare_data, prepare_dataloader, define_wandb_metrcis, evaluate, eval_testdata, train\n", + "from scgpt.tokenizer import tokenize_and_pad_batch\n", + "from scgpt.model import MultiOmicTransformerModel\n", + "\n", + "import scgpt as scg\n", + "from scgpt.tokenizer.gene_tokenizer import GeneVocab\n", + "from scgpt.tokenizer import random_mask_value\n", + "from scgpt.loss import (\n", + " masked_mse_loss,\n", + " masked_relative_error,\n", + " criterion_neg_log_bernoulli,\n", + ")\n", + "from scgpt.preprocess import Preprocessor\n", + "from scgpt.utils import set_seed, category_str2int, eval_scib_metrics\n", + "\n", + "sc.set_figure_params(figsize=(4, 4))\n", + "os.environ[\"KMP_WARNINGS\"] = \"off\"\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "cell_type": "markdown", + "id": "c3931b62", + "metadata": {}, + "source": [ + "## Step1: Specify hyper-parameter setup for integration task\n", + "Here we provide some hyper-parameter recommendations for the multiomic integration task. Note that the BMMC dataset contains multiple batches to be integrated. Therefore, in addition to the default gene modelling objectives, we also turn on DAR objectives specifically to faciliate batch integration. We also turn on the use_mod argument as default to ensure that the model is modality-aware during training." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "79e9da7c", + "metadata": {}, + "outputs": [], + "source": [ + "hyperparameter_defaults = dict(\n", + " task = 'multiomic',\n", + " seed=42,\n", + " dataset_name=\"BMMC\", # Dataset name\n", + " do_train=True, # Flag to indicate whether to do update model parameters during training\n", + " load_model=\"../save/scGPT_human\", # Path to pre-trained model\n", + " freeze = False, #freeze\n", + " GEP=True, # Gene expression modelling\n", + " GEPC=True, # Gene expression modelling for cell objective\n", + " CLS=False,\n", + " ESC=False,\n", + " DAR = True, # DAR objective weight for batch correction\n", + " DSBN = False, # Domain-spec batchnorm,\n", + " mask_ratio=0.4, # Default mask ratio\n", + " explicit_zero_prob = False, # whether explicit bernoulli for zeros\n", + " ecs_thres=0, # Elastic cell similarity objective, 0.0 to 1.0, 0.0 to disable\n", + " dab_weight=1.0,\n", + " use_batch_labels = True,\n", + " use_mod = True,\n", + " per_seq_batch_sample = False,\n", + " epochs=25, # Default number of epochs for fine-tuning\n", + " input_layer_key = \"X_binned\", # Default expression value binning in data pre-processing\n", + " n_bins=51, # Default number of bins for value binning in data pre-processing\n", + " n_hvg = 1200, # Default number of highly variable genes\n", + " n_hvp = 4000,\n", + " max_seq_len = 4001, # # Default n_hvg+1\n", + " lr=1e-3, # Default learning rate for fine-tuning\n", + " batch_size=16, # Default batch size for fine-tuning\n", + " layer_size=512,\n", + " nlayers=4,\n", + " nhead=8, # if load model, batch_size, layer_size, nlayers, nhead will be ignored\n", + " dropout=0.2, # Default dropout rate during model fine-tuning\n", + " schedule_ratio=0.95, # Default rate for learning rate decay\n", + " save_eval_interval=5, # Default model evaluation interval\n", + " log_interval=100, # Default log interval\n", + " fast_transformer=True, # Default setting\n", + " pre_norm=False, # Default setting\n", + " amp=True, # Default setting: Automatic Mixed Precision\n", + " pad_token = \"\",\n", + " mask_value = -1,\n", + " pad_value = -2,\n", + " include_zero_gene = False,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d9e7e294", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mchloewxq\u001b[0m (\u001b[33mscformer\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n" + ] + }, + { + "data": { + "text/html": [ + "Tracking run with wandb version 0.15.12" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Run data is saved locally in /fs01/home/chloexq/scGPT/tutorials/wandb/run-20231019_144009-g5f0ui2j" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Syncing run frosty-bush-454 to Weights & Biases (docs)
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + " View project at https://wandb.ai/scformer/scGPT" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + " View run at https://wandb.ai/scformer/scGPT/runs/g5f0ui2j" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'task': 'multiomic', 'seed': 42, 'dataset_name': 'BMMC', 'do_train': True, 'load_model': '../save/scGPT_human', 'freeze': False, 'GEP': True, 'GEPC': True, 'CLS': False, 'ESC': False, 'DAR': True, 'DSBN': False, 'mask_ratio': 0.4, 'explicit_zero_prob': False, 'ecs_thres': 0, 'dab_weight': 1.0, 'use_batch_labels': True, 'use_mod': True, 'per_seq_batch_sample': False, 'epochs': 25, 'input_layer_key': 'X_binned', 'n_bins': 51, 'n_hvg': 1200, 'n_hvp': 4000, 'max_seq_len': 4001, 'lr': 0.001, 'batch_size': 16, 'layer_size': 512, 'nlayers': 4, 'nhead': 8, 'dropout': 0.2, 'schedule_ratio': 0.95, 'save_eval_interval': 5, 'log_interval': 100, 'fast_transformer': True, 'pre_norm': False, 'amp': True, 'pad_token': '', 'mask_value': -1, 'pad_value': -2, 'include_zero_gene': False}\n" + ] + } + ], + "source": [ + "run = wandb.init(\n", + " config=hyperparameter_defaults,\n", + " project=\"scGPT\",\n", + " reinit=True,\n", + " settings=wandb.Settings(start_method=\"fork\"),\n", + ")\n", + "config = wandb.config\n", + "print(config)\n", + "\n", + "set_seed(config.seed)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "327a9ffd", + "metadata": {}, + "outputs": [], + "source": [ + "# settings for input and preprocessing\n", + "special_tokens = [config.pad_token, \"\", \"\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "2bf30635", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "save to save/dev_BMMC-Oct19-14-40\n" + ] + } + ], + "source": [ + "dataset_name = config.dataset_name\n", + "save_dir = Path(f\"./save/dev_{dataset_name}-{time.strftime('%b%d-%H-%M')}/\")\n", + "save_dir.mkdir(parents=True, exist_ok=True)\n", + "print(f\"save to {save_dir}\")\n", + "logger = scg.logger\n", + "scg.utils.add_file_handler(logger, save_dir / \"run.log\")" + ] + }, + { + "cell_type": "markdown", + "id": "b26f7516", + "metadata": {}, + "source": [ + "## Step 2: Load and pre-process data" + ] + }, + { + "cell_type": "markdown", + "id": "b653e6b7", + "metadata": {}, + "source": [ + "### 2.1 Load the BMMC data\n", + "Please download the data from https://drive.google.com/file/d/10RxboePS5p2Jj2Sfq1Ghzgqnl6nqPv5V/view?usp=sharing\n", + "\n", + "For the purpose of this tutorial, we selected a 12K+ cell subset from the BMMC dataset (originally 90K+ cells) to speed up training. More specifically, we filtered on the first 3 donors (batches) with B, Mono and T cell subtypes as shown in the data loading step below." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "14f8edf6-4c5d-4056-8954-c11c737a50a3", + "metadata": {}, + "outputs": [], + "source": [ + "if dataset_name == 'BMMC':\n", + " adata = sc.read('BMMC_processed.h5ad')\n", + " # subset to first 3 donors with B, Mono and T cell subtypes\n", + " adata = adata[adata.obs.DonorID.isin([10886, 11466, 12710]) & adata.obs.cell_type.isin(np.unique(adata.obs.cell_type.values)[:17])]\n", + " adata.obs[\"celltype\"] = adata.obs[\"cell_type\"].astype(str).astype('category')\n", + " adata.var[\"gene_name\"] = adata.var.index.tolist()\n", + " le = preprocessing.LabelEncoder()\n", + " encoded_batch = le.fit_transform(adata.obs['batch'].values)\n", + " adata.obs[\"batch_id\"] = encoded_batch\n", + " adata.obs[\"str_batch\"] = adata.obs[\"batch_id\"].astype('category')\n", + " adata_protein = adata[:, adata.var.feature_types.isin(['ADT'])].copy()\n", + " adata_protein.var.index = ['p_' + i for i in adata_protein.var.index]\n", + " adata = adata[:, adata.var.feature_types.isin(['GEX'])].copy()\n", + " data_is_raw = False" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "1bfe331a", + "metadata": {}, + "outputs": [], + "source": [ + "if config.use_mod:\n", + " gene_rna_df = pd.DataFrame(index = adata.var.index.tolist())\n", + " gene_rna_df['mod'] = 'RNA'\n", + " gene_protein_df = pd.DataFrame(index = adata_protein.var.index.tolist())\n", + " gene_protein_df['mod'] = 'Protein'\n", + " gene_loc_df = pd.concat([gene_rna_df, gene_protein_df])\n", + " gene_loc_df['mod'] = gene_loc_df['mod'].astype('category')" + ] + }, + { + "cell_type": "markdown", + "id": "a7371ebc", + "metadata": {}, + "source": [ + "### 2.2 Cross-check gene set with the pre-trained model \n", + "Note that we retain the common gene set between the data and the pre-trained model for further fine-tuning." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "5cc7d1a3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "scGPT - INFO - match 12587/13953 genes in vocabulary of size 60697.\n" + ] + } + ], + "source": [ + "if config.load_model is not None:\n", + " model_dir = Path(config.load_model)\n", + " model_config_file = model_dir / \"args.json\"\n", + " model_file = model_dir / \"best_model.pt\"\n", + " vocab_file = model_dir / \"vocab.json\"\n", + "\n", + " vocab = GeneVocab.from_file(vocab_file)\n", + " for s in special_tokens:\n", + " if s not in vocab:\n", + " vocab.append_token(s)\n", + "\n", + " adata.var[\"id_in_vocab\"] = [\n", + " 1 if gene in vocab else -1 for gene in adata.var[\"gene_name\"]\n", + " ]\n", + " gene_ids_in_vocab = np.array(adata.var[\"id_in_vocab\"])\n", + " logger.info(\n", + " f\"match {np.sum(gene_ids_in_vocab >= 0)}/{len(gene_ids_in_vocab)} genes \"\n", + " f\"in vocabulary of size {len(vocab)}.\"\n", + " )\n", + " old_vocab = vocab\n", + "embsize = config.layer_size\n", + "nhead = config.nhead\n", + "nlayers = config.nlayers\n", + "d_hid = config.layer_size" + ] + }, + { + "cell_type": "markdown", + "id": "b52f414d", + "metadata": {}, + "source": [ + "### 2.3 Pre-process the data\n", + "We follow the standardized pipline of depth normalization, log normalization, and highly vairable gene (HVG) selection for data pre-processing. We further introduce value binning to obtain the relative expressions of each HVG. Given multiple sequencing modalities, we perform the pre-processing steps on each individual modality first, and then combine them into multi-modal sequences as model input." + ] + }, + { + "cell_type": "markdown", + "id": "32228422", + "metadata": {}, + "source": [ + "#### 2.3.1 Pre-process the RNA data" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "69be564d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "scGPT - INFO - Filtering genes by counts ...\n", + "scGPT - INFO - Filtering cells by counts ...\n", + "scGPT - INFO - Normalizing total counts ...\n", + "scGPT - INFO - Subsetting highly variable genes ...\n", + "scGPT - WARNING - No batch_key is provided, will use all cells for HVG selection.\n", + "scGPT - INFO - Binning data ...\n" + ] + } + ], + "source": [ + "preprocessor = Preprocessor(\n", + " use_key=\"X\", # the key in adata.layers to use as raw data\n", + " filter_gene_by_counts=1, # step 1\n", + " filter_cell_by_counts=1, # step 2\n", + " normalize_total=1e4, # 3. whether to normalize the raw data and to what sum\n", + " result_normed_key=\"X_normed\", # the key in adata.layers to store the normalized data\n", + " log1p=data_is_raw, # 4. whether to log1p the normalized data\n", + " result_log1p_key=\"X_log1p\",\n", + " subset_hvg=config.n_hvg, # 5. whether to subset the raw data to highly variable genes\n", + " hvg_flavor=\"seurat_v3\" if data_is_raw else \"cell_ranger\",\n", + " binning=config.n_bins, # 6. whether to bin the raw data and to what number of bins\n", + " result_binned_key=\"X_binned\", # the key in adata.layers to store the binned data\n", + ")\n", + "preprocessor(adata, batch_key=None)" + ] + }, + { + "cell_type": "markdown", + "id": "12efe68e", + "metadata": {}, + "source": [ + "#### 2.3.2 Pre-process the Protein data" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "a72dc9b9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "scGPT - INFO - Filtering cells by counts ...\n", + "scGPT - INFO - Binning data ...\n" + ] + } + ], + "source": [ + "preprocessor_protein = Preprocessor(\n", + " use_key=\"X\", # the key in adata.layers to use as raw data\n", + " filter_gene_by_counts=0, # step 1\n", + " filter_cell_by_counts=False, # step 2\n", + " normalize_total=False, # 3. whether to normalize the raw data and to what sum\n", + " result_normed_key=\"X_normed\", # the key in adata.layers to store the normalized data\n", + " log1p=False, # 4. whether to log1p the normalized data\n", + " result_log1p_key=\"X_log1p\",\n", + " subset_hvg=False, # 5. whether to subset the raw data to highly variable genes\n", + " hvg_flavor=None,\n", + " binning=config.n_bins, # 6. whether to bin the raw data and to what number of bins\n", + " result_binned_key=\"X_binned\", # the key in adata.layers to store the binned data\n", + ")\n", + "preprocessor_protein(adata_protein, batch_key=None)" + ] + }, + { + "cell_type": "markdown", + "id": "58e11257", + "metadata": {}, + "source": [ + "#### 2.3.3 Combine RNA, and Protein data" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "7ab9f01f", + "metadata": {}, + "outputs": [], + "source": [ + "data_combined = np.concatenate([adata.layers[\"X_binned\"], adata_protein.layers[\"X_binned\"]], axis=1)\n", + "adata = AnnData(\n", + " X=data_combined,\n", + " obs=adata.obs,\n", + " var=pd.DataFrame(index=adata.var_names.tolist() + adata_protein.var_names.tolist()),\n", + " layers={\"X_binned\": data_combined,}\n", + ")\n", + "adata.var[\"gene_name\"] = adata.var.index.tolist()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "7f717788", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AnnData object with n_obs × n_vars = 12578 × 1334\n", + " obs: 'GEX_n_genes_by_counts', 'GEX_pct_counts_mt', 'GEX_size_factors', 'GEX_phase', 'ADT_n_antibodies_by_counts', 'ADT_total_counts', 'ADT_iso_count', 'cell_type', 'batch', 'ADT_pseudotime_order', 'GEX_pseudotime_order', 'Samplename', 'Site', 'DonorNumber', 'Modality', 'VendorLot', 'DonorID', 'DonorAge', 'DonorBMI', 'DonorBloodType', 'DonorRace', 'Ethnicity', 'DonorGender', 'QCMeds', 'DonorSmoker', 'is_train', 'celltype', 'batch_id', 'str_batch', 'n_counts'\n", + " var: 'gene_name'\n", + " layers: 'X_binned'" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "adata" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "372f68a7", + "metadata": {}, + "outputs": [], + "source": [ + "if config.per_seq_batch_sample:\n", + " # sort the adata by batch_id in advance\n", + " adata_sorted = adata[adata.obs[\"batch_id\"].argsort()].copy()" + ] + }, + { + "cell_type": "markdown", + "id": "c68c2a1c", + "metadata": {}, + "source": [ + "### 2.4 Tokenize the input data for model fine-tuning" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "116c8c69", + "metadata": {}, + "outputs": [], + "source": [ + "all_counts = (\n", + " adata.layers[config.input_layer_key].toarray()\n", + " if issparse(adata.layers[config.input_layer_key])\n", + " else adata.layers[config.input_layer_key]\n", + ")\n", + "genes = adata.var[\"gene_name\"].tolist()\n", + "\n", + "celltypes_labels = adata.obs[\"celltype\"].tolist() # make sure count from 0\n", + "num_types = len(set(celltypes_labels))\n", + "celltypes_labels = np.array(celltypes_labels)\n", + "\n", + "batch_ids = adata.obs[\"batch_id\"].tolist()\n", + "num_batch_types = len(set(batch_ids))\n", + "batch_ids = np.array(batch_ids)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "a9ac9a87", + "metadata": {}, + "outputs": [], + "source": [ + "if config.use_mod:\n", + " mod_type = np.array([gene_loc_df.loc[g, 'mod'] for g in genes])\n", + " vocab_mod = Vocab(VocabPybind(np.unique(gene_loc_df['mod']).tolist() + special_tokens, None))\n", + " vocab_mod.set_default_index(vocab_mod[\"\"])\n", + " mod_type = np.array(vocab_mod(list(mod_type)), dtype=int)\n", + " ntokens_mod = len(vocab_mod)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "753b6a35", + "metadata": {}, + "outputs": [], + "source": [ + "(\n", + " train_data,\n", + " valid_data,\n", + " train_celltype_labels,\n", + " valid_celltype_labels,\n", + " train_batch_labels,\n", + " valid_batch_labels,\n", + ") = train_test_split(\n", + " all_counts, celltypes_labels, batch_ids, test_size=0.1, shuffle=True\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "a942b56b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "max num of non_zero genes: 687\n", + "min num of non_zero genes: 206\n", + "average num of non_zero genes: 341.5295053003534\n", + "99% quantile num of non_zero genes: 510.0\n", + "max original values: 50\n", + "average original non_zero values: 25.3482856428962\n", + "99% quantile original non_zero values: 49.0\n", + "num of celltypes: 17\n" + ] + } + ], + "source": [ + "num_of_non_zero_genes = [\n", + " np.count_nonzero(train_data[i]) for i in range(train_data.shape[0])\n", + "]\n", + "print(f\"max num of non_zero genes: {np.max(num_of_non_zero_genes)}\")\n", + "print(f\"min num of non_zero genes: {np.min(num_of_non_zero_genes)}\")\n", + "print(f\"average num of non_zero genes: {np.mean(num_of_non_zero_genes)}\")\n", + "print(\n", + " f\"99% quantile num of non_zero genes: {np.quantile(num_of_non_zero_genes, 0.99)}\"\n", + ")\n", + "print(f\"max original values: {np.max(train_data)}\")\n", + "print(\n", + " f\"average original non_zero values: {np.mean(train_data[np.nonzero(train_data)])}\"\n", + ")\n", + "print(\n", + " f\"99% quantile original non_zero values: {np.quantile(train_data[np.nonzero(train_data)], 0.99)}\"\n", + ")\n", + "print(f\"num of celltypes: {num_types}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "0fd4b316", + "metadata": {}, + "outputs": [], + "source": [ + "if config.load_model is None:\n", + " vocab = Vocab(VocabPybind(genes + special_tokens, None))\n", + " vocab.set_default_index(vocab[\"\"])\n", + " gene_ids = np.array(vocab(genes), dtype=int)\n", + "else:\n", + " pretrained_genes = [g for g in genes + special_tokens if g in old_vocab]\n", + " new_genes = [g for g in genes + special_tokens if g not in old_vocab]\n", + " gene_ids_pretrained = np.array(old_vocab(pretrained_genes), dtype=int)\n", + " # https://discuss.pytorch.org/t/expand-an-existing-embedding-and-linear-layer-nan-loss-value/55670/2\n", + " # Retrieve pretrained weights\n", + " vocab = Vocab(VocabPybind(pretrained_genes + new_genes, None))\n", + " vocab.set_default_index(vocab[\"\"])\n", + " gene_ids = np.array(vocab(genes), dtype=int)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "b578ef0c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "scGPT - INFO - train set number of samples: 11320, \n", + "\t feature length: 688\n", + "scGPT - INFO - valid set number of samples: 1258, \n", + "\t feature length: 663\n" + ] + } + ], + "source": [ + "tokenized_train = tokenize_and_pad_batch(\n", + " train_data,\n", + " gene_ids,\n", + " max_len=config.max_seq_len,\n", + " vocab=vocab,\n", + " pad_token=config.pad_token,\n", + " pad_value=config.pad_value,\n", + " append_cls=True, # append token at the beginning\n", + " include_zero_gene=config.include_zero_gene,\n", + " mod_type=mod_type if config.use_mod else None,\n", + " vocab_mod=vocab_mod if config.use_mod else None,\n", + ")\n", + "tokenized_valid = tokenize_and_pad_batch(\n", + " valid_data,\n", + " gene_ids,\n", + " max_len=config.max_seq_len,\n", + " vocab=vocab,\n", + " pad_token=config.pad_token,\n", + " pad_value=config.pad_value,\n", + " append_cls=True,\n", + " include_zero_gene=config.include_zero_gene,\n", + " mod_type=mod_type if config.use_mod else None,\n", + " vocab_mod=vocab_mod if config.use_mod else None,\n", + ")\n", + "logger.info(\n", + " f\"train set number of samples: {tokenized_train['genes'].shape[0]}, \"\n", + " f\"\\n\\t feature length: {tokenized_train['genes'].shape[1]}\"\n", + ")\n", + "logger.info(\n", + " f\"valid set number of samples: {tokenized_valid['genes'].shape[0]}, \"\n", + " f\"\\n\\t feature length: {tokenized_valid['genes'].shape[1]}\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "716f78ec", + "metadata": {}, + "source": [ + "## Step 3: Load the pre-trained scGPT model\n", + "Note that for multiomic integration, since the pre-trained model does not include the ATAC and protein tokens, we expand the embedding layer by adding these new tokens. We inherit only the gene embedding layer from the pre-trained model, and train rest of the model from scratch." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "7d57aaf6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using simple batchnorm instead of domain specific batchnorm\n", + "TransformerModel(\n", + " (encoder): GeneEncoder(\n", + " (embedding): Embedding(1337, 512, padding_idx=1089)\n", + " (enc_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " )\n", + " (value_encoder): ContinuousValueEncoder(\n", + " (dropout): Dropout(p=0.2, inplace=False)\n", + " (linear1): Linear(in_features=1, out_features=512, bias=True)\n", + " (activation): ReLU()\n", + " (linear2): Linear(in_features=512, out_features=512, bias=True)\n", + " (norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " )\n", + " (batch_encoder): BatchLabelEncoder(\n", + " (embedding): Embedding(3, 512)\n", + " (enc_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " )\n", + " (mod_encoder): BatchLabelEncoder(\n", + " (embedding): Embedding(5, 512, padding_idx=2)\n", + " (enc_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " )\n", + " (bn): BatchNorm1d(512, eps=6.1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (transformer_encoder): TransformerEncoder(\n", + " (layers): ModuleList(\n", + " (0): FlashTransformerEncoderLayer(\n", + " (self_attn): FlashMHA(\n", + " (Wqkv): Linear(in_features=512, out_features=1536, bias=True)\n", + " (inner_attn): FlashAttention()\n", + " (out_proj): Linear(in_features=512, out_features=512, bias=True)\n", + " )\n", + " (linear1): Linear(in_features=512, out_features=512, bias=True)\n", + " (dropout): Dropout(p=0.2, inplace=False)\n", + " (linear2): Linear(in_features=512, out_features=512, bias=True)\n", + " (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (dropout1): Dropout(p=0.2, inplace=False)\n", + " (dropout2): Dropout(p=0.2, inplace=False)\n", + " )\n", + " (1): FlashTransformerEncoderLayer(\n", + " (self_attn): FlashMHA(\n", + " (Wqkv): Linear(in_features=512, out_features=1536, bias=True)\n", + " (inner_attn): FlashAttention()\n", + " (out_proj): Linear(in_features=512, out_features=512, bias=True)\n", + " )\n", + " (linear1): Linear(in_features=512, out_features=512, bias=True)\n", + " (dropout): Dropout(p=0.2, inplace=False)\n", + " (linear2): Linear(in_features=512, out_features=512, bias=True)\n", + " (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (dropout1): Dropout(p=0.2, inplace=False)\n", + " (dropout2): Dropout(p=0.2, inplace=False)\n", + " )\n", + " (2): FlashTransformerEncoderLayer(\n", + " (self_attn): FlashMHA(\n", + " (Wqkv): Linear(in_features=512, out_features=1536, bias=True)\n", + " (inner_attn): FlashAttention()\n", + " (out_proj): Linear(in_features=512, out_features=512, bias=True)\n", + " )\n", + " (linear1): Linear(in_features=512, out_features=512, bias=True)\n", + " (dropout): Dropout(p=0.2, inplace=False)\n", + " (linear2): Linear(in_features=512, out_features=512, bias=True)\n", + " (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (dropout1): Dropout(p=0.2, inplace=False)\n", + " (dropout2): Dropout(p=0.2, inplace=False)\n", + " )\n", + " (3): FlashTransformerEncoderLayer(\n", + " (self_attn): FlashMHA(\n", + " (Wqkv): Linear(in_features=512, out_features=1536, bias=True)\n", + " (inner_attn): FlashAttention()\n", + " (out_proj): Linear(in_features=512, out_features=512, bias=True)\n", + " )\n", + " (linear1): Linear(in_features=512, out_features=512, bias=True)\n", + " (dropout): Dropout(p=0.2, inplace=False)\n", + " (linear2): Linear(in_features=512, out_features=512, bias=True)\n", + " (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (dropout1): Dropout(p=0.2, inplace=False)\n", + " (dropout2): Dropout(p=0.2, inplace=False)\n", + " )\n", + " )\n", + " )\n", + " (decoder): ExprDecoder(\n", + " (fc): Sequential(\n", + " (0): Linear(in_features=1024, out_features=512, bias=True)\n", + " (1): LeakyReLU(negative_slope=0.01)\n", + " (2): Linear(in_features=512, out_features=512, bias=True)\n", + " (3): LeakyReLU(negative_slope=0.01)\n", + " (4): Linear(in_features=512, out_features=1, bias=True)\n", + " )\n", + " )\n", + " (cls_decoder): ClsDecoder(\n", + " (_decoder): ModuleList(\n", + " (0): Linear(in_features=512, out_features=512, bias=True)\n", + " (1): ReLU()\n", + " (2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (3): Linear(in_features=512, out_features=512, bias=True)\n", + " (4): ReLU()\n", + " (5): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " )\n", + " (out_layer): Linear(in_features=512, out_features=1, bias=True)\n", + " )\n", + " (mvc_decoder): MVCDecoder(\n", + " (gene2query): Linear(in_features=1024, out_features=1024, bias=True)\n", + " (query_activation): Sigmoid()\n", + " (W): Linear(in_features=1024, out_features=1024, bias=False)\n", + " )\n", + " (grad_reverse_discriminator): AdversarialDiscriminator(\n", + " (_decoder): ModuleList(\n", + " (0): Linear(in_features=512, out_features=512, bias=True)\n", + " (1): LeakyReLU(negative_slope=0.01)\n", + " (2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (3): Linear(in_features=512, out_features=512, bias=True)\n", + " (4): LeakyReLU(negative_slope=0.01)\n", + " (5): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " )\n", + " (out_layer): Linear(in_features=512, out_features=3, bias=True)\n", + " )\n", + " (sim): Similarity(\n", + " (cos): CosineSimilarity()\n", + " )\n", + " (creterion_cce): CrossEntropyLoss()\n", + ")\n" + ] + } + ], + "source": [ + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "model_dict = torch.load(model_file)\n", + "ntokens = len(vocab) # size of vocabulary\n", + "model = MultiOmicTransformerModel(\n", + " ntokens,\n", + " embsize,\n", + " nhead,\n", + " d_hid, \n", + " nlayers,\n", + " vocab=vocab,\n", + " dropout=config.dropout,\n", + " pad_token=config.pad_token,\n", + " pad_value=config.pad_value,\n", + " do_mvc=config.GEPC,\n", + " do_dab=config.DAR,\n", + " use_batch_labels=config.use_batch_labels,\n", + " num_batch_labels=num_batch_types,\n", + " domain_spec_batchnorm=config.DSBN,\n", + " n_input_bins=config.n_bins,\n", + " ecs_threshold=config.ecs_thres,\n", + " explicit_zero_prob=config.explicit_zero_prob,\n", + " use_fast_transformer=config.fast_transformer,\n", + " pre_norm=config.pre_norm,\n", + " use_mod=config.use_mod,\n", + " ntokens_mod=ntokens_mod if config.use_mod else None,\n", + " vocab_mod=vocab_mod if config.use_mod else None,\n", + ")\n", + "\n", + "with torch.no_grad():\n", + " pretrained_emb_weights = model_dict['encoder.embedding.weight'][gene_ids_pretrained, :]\n", + " model.encoder.embedding.weight.data[:len(pretrained_genes), :] = pretrained_emb_weights\n", + " model.encoder.enc_norm.weight.data = model_dict['encoder.enc_norm.weight']\n", + "ntokens = len(vocab)\n", + "\n", + "model.to(device)\n", + "wandb.watch(model)\n", + "print(model)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "ecff528e", + "metadata": {}, + "outputs": [], + "source": [ + "if config.GEP and config.GEPC:\n", + " criterion_gep_gepc = masked_mse_loss\n", + "if config.CLS:\n", + " criterion_cls = nn.CrossEntropyLoss()\n", + "if config.DAR:\n", + " criterion_dab = nn.CrossEntropyLoss()\n", + "optimizer = torch.optim.Adam(\n", + " model.parameters(), lr=config.lr, eps=1e-4 if config.amp else 1e-8\n", + ")\n", + "scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1, gamma=config.schedule_ratio)\n", + "scaler = torch.cuda.amp.GradScaler(enabled=config.amp)" + ] + }, + { + "cell_type": "markdown", + "id": "cf4d9e08", + "metadata": {}, + "source": [ + "## Step 4: Finetune scGPT with task-specific objectives" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "62c9cc1b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "random masking at epoch 1, ratio of masked values in train: 0.3988\n", + "scGPT - INFO - | epoch 1 | 100/708 batches | lr 0.00100 | ms/batch 68.39 | loss 7695.31 | gep 380.20 |gepc 7314.00 |dar 1.11 |\n", + "scGPT - INFO - | epoch 1 | 200/708 batches | lr 0.00100 | ms/batch 46.00 | loss 242.67 | gep 163.71 |gepc 77.97 |dar 1.00 |\n", + "scGPT - INFO - | epoch 1 | 300/708 batches | lr 0.00100 | ms/batch 46.34 | loss 144.25 | gep 73.30 |gepc 70.11 |dar 0.83 |\n", + "scGPT - INFO - | epoch 1 | 400/708 batches | lr 0.00100 | ms/batch 46.23 | loss 125.95 | gep 63.33 |gepc 61.88 |dar 0.74 |\n", + "scGPT - INFO - | epoch 1 | 500/708 batches | lr 0.00100 | ms/batch 46.02 | loss 123.15 | gep 62.42 |gepc 59.90 |dar 0.83 |\n", + "scGPT - INFO - | epoch 1 | 600/708 batches | lr 0.00100 | ms/batch 46.72 | loss 123.12 | gep 61.53 |gepc 60.91 |dar 0.67 |\n", + "scGPT - INFO - | epoch 1 | 700/708 batches | lr 0.00100 | ms/batch 46.08 | loss 118.63 | gep 58.42 |gepc 59.49 |dar 0.72 |\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 1 | time: 36.81s | valid loss 57.1919 | \n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - Best model with score 57.1919\n", + "random masking at epoch 2, ratio of masked values in train: 0.3988\n", + "scGPT - INFO - | epoch 2 | 100/708 batches | lr 0.00095 | ms/batch 52.34 | loss 118.76 | gep 58.58 |gepc 59.47 |dar 0.71 |\n", + "scGPT - INFO - | epoch 2 | 200/708 batches | lr 0.00095 | ms/batch 46.62 | loss 116.45 | gep 57.26 |gepc 58.47 |dar 0.72 |\n", + "scGPT - INFO - | epoch 2 | 300/708 batches | lr 0.00095 | ms/batch 46.74 | loss 115.72 | gep 57.05 |gepc 57.97 |dar 0.70 |\n", + "scGPT - INFO - | epoch 2 | 400/708 batches | lr 0.00095 | ms/batch 46.27 | loss 113.91 | gep 56.33 |gepc 56.89 |dar 0.68 |\n", + "scGPT - INFO - | epoch 2 | 500/708 batches | lr 0.00095 | ms/batch 45.93 | loss 113.27 | gep 55.73 |gepc 56.82 |dar 0.72 |\n", + "scGPT - INFO - | epoch 2 | 600/708 batches | lr 0.00095 | ms/batch 45.97 | loss 111.45 | gep 54.83 |gepc 55.92 |dar 0.70 |\n", + "scGPT - INFO - | epoch 2 | 700/708 batches | lr 0.00095 | ms/batch 45.99 | loss 111.84 | gep 55.05 |gepc 56.07 |dar 0.72 |\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 2 | time: 34.99s | valid loss 55.5510 | \n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - Best model with score 55.5510\n", + "random masking at epoch 3, ratio of masked values in train: 0.3988\n", + "scGPT - INFO - | epoch 3 | 100/708 batches | lr 0.00090 | ms/batch 46.50 | loss 112.09 | gep 55.07 |gepc 56.32 |dar 0.71 |\n", + "scGPT - INFO - | epoch 3 | 200/708 batches | lr 0.00090 | ms/batch 46.05 | loss 108.88 | gep 53.38 |gepc 54.78 |dar 0.71 |\n", + "scGPT - INFO - | epoch 3 | 300/708 batches | lr 0.00090 | ms/batch 46.11 | loss 109.68 | gep 53.93 |gepc 55.09 |dar 0.66 |\n", + "scGPT - INFO - | epoch 3 | 400/708 batches | lr 0.00090 | ms/batch 46.23 | loss 108.77 | gep 53.34 |gepc 54.76 |dar 0.68 |\n", + "scGPT - INFO - | epoch 3 | 500/708 batches | lr 0.00090 | ms/batch 46.14 | loss 109.98 | gep 54.16 |gepc 55.13 |dar 0.69 |\n", + "scGPT - INFO - | epoch 3 | 600/708 batches | lr 0.00090 | ms/batch 46.82 | loss 110.03 | gep 54.07 |gepc 55.23 |dar 0.73 |\n", + "scGPT - INFO - | epoch 3 | 700/708 batches | lr 0.00090 | ms/batch 45.92 | loss 110.50 | gep 54.46 |gepc 55.30 |dar 0.74 |\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 3 | time: 34.38s | valid loss 55.1387 | \n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - Best model with score 55.1387\n", + "random masking at epoch 4, ratio of masked values in train: 0.3988\n", + "scGPT - INFO - | epoch 4 | 100/708 batches | lr 0.00086 | ms/batch 64.29 | loss 108.20 | gep 53.18 |gepc 54.32 |dar 0.71 |\n", + "scGPT - INFO - | epoch 4 | 200/708 batches | lr 0.00086 | ms/batch 64.46 | loss 108.02 | gep 53.07 |gepc 54.25 |dar 0.70 |\n", + "scGPT - INFO - | epoch 4 | 300/708 batches | lr 0.00086 | ms/batch 61.71 | loss 107.58 | gep 52.96 |gepc 53.92 |dar 0.70 |\n", + "scGPT - INFO - | epoch 4 | 400/708 batches | lr 0.00086 | ms/batch 50.92 | loss 105.60 | gep 51.80 |gepc 53.14 |dar 0.65 |\n", + "scGPT - INFO - | epoch 4 | 500/708 batches | lr 0.00086 | ms/batch 46.14 | loss 108.32 | gep 53.05 |gepc 54.54 |dar 0.74 |\n", + "scGPT - INFO - | epoch 4 | 600/708 batches | lr 0.00086 | ms/batch 47.72 | loss 108.86 | gep 53.42 |gepc 54.74 |dar 0.70 |\n", + "scGPT - INFO - | epoch 4 | 700/708 batches | lr 0.00086 | ms/batch 46.15 | loss 107.06 | gep 52.54 |gepc 53.81 |dar 0.70 |\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 4 | time: 40.14s | valid loss 51.8802 | \n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - Best model with score 51.8802\n", + "random masking at epoch 5, ratio of masked values in train: 0.3988\n", + "scGPT - INFO - | epoch 5 | 100/708 batches | lr 0.00081 | ms/batch 46.85 | loss 108.80 | gep 53.28 |gepc 54.76 |dar 0.75 |\n", + "scGPT - INFO - | epoch 5 | 200/708 batches | lr 0.00081 | ms/batch 46.90 | loss 104.90 | gep 51.64 |gepc 52.58 |dar 0.68 |\n", + "scGPT - INFO - | epoch 5 | 300/708 batches | lr 0.00081 | ms/batch 46.03 | loss 106.43 | gep 52.42 |gepc 53.36 |dar 0.65 |\n", + "scGPT - INFO - | epoch 5 | 400/708 batches | lr 0.00081 | ms/batch 46.02 | loss 105.34 | gep 51.62 |gepc 53.01 |dar 0.71 |\n", + "scGPT - INFO - | epoch 5 | 500/708 batches | lr 0.00081 | ms/batch 46.52 | loss 106.13 | gep 52.03 |gepc 53.37 |dar 0.73 |\n", + "scGPT - INFO - | epoch 5 | 600/708 batches | lr 0.00081 | ms/batch 46.30 | loss 105.66 | gep 51.81 |gepc 53.20 |dar 0.65 |\n", + "scGPT - INFO - | epoch 5 | 700/708 batches | lr 0.00081 | ms/batch 46.05 | loss 106.61 | gep 52.47 |gepc 53.41 |dar 0.72 |\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 5 | time: 34.49s | valid loss 52.1031 | \n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - Saving model to save/dev_BMMC-Oct18-21-39\n", + "scGPT - INFO - Evaluating cls cell embeddings\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 787/787 [00:11<00:00, 69.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NMI...\n", + "ARI...\n", + "Silhouette score...\n", + "PC regression...\n", + "Graph connectivity...\n", + "scGPT - INFO - 0\n", + "NMI_cluster/label 0.666764\n", + "ARI_cluster/label 0.710007\n", + "ASW_label 0.663498\n", + "ASW_label/batch 0.824583\n", + "PCR_batch 0.328881\n", + "cell_cycle_conservation NaN\n", + "isolated_label_F1 NaN\n", + "isolated_label_silhouette NaN\n", + "graph_conn 0.873434\n", + "kBET NaN\n", + "iLISI NaN\n", + "cLISI NaN\n", + "hvg_overlap NaN\n", + "trajectory NaN\n", + "scGPT - INFO - Biological Conservation Metrics: \n", + "ASW (cell-type): 0.6635, graph cLISI: nan, isolated label silhouette: nan, \n", + "Batch Effect Removal Metrics: \n", + "PCR_batch: 0.3289, ASW (batch): 0.8246, graph connectivity: 0.8734, graph iLISI: nan\n", + "random masking at epoch 6, ratio of masked values in train: 0.3988\n", + "scGPT - INFO - | epoch 6 | 100/708 batches | lr 0.00077 | ms/batch 46.96 | loss 106.66 | gep 52.42 |gepc 53.52 |dar 0.72 |\n", + "scGPT - INFO - | epoch 6 | 200/708 batches | lr 0.00077 | ms/batch 46.16 | loss 104.88 | gep 51.41 |gepc 52.76 |dar 0.71 |\n", + "scGPT - INFO - | epoch 6 | 300/708 batches | lr 0.00077 | ms/batch 46.11 | loss 104.46 | gep 51.22 |gepc 52.56 |dar 0.69 |\n", + "scGPT - INFO - | epoch 6 | 400/708 batches | lr 0.00077 | ms/batch 46.15 | loss 105.72 | gep 51.97 |gepc 53.06 |dar 0.69 |\n", + "scGPT - INFO - | epoch 6 | 500/708 batches | lr 0.00077 | ms/batch 46.88 | loss 104.28 | gep 51.17 |gepc 52.41 |dar 0.70 |\n", + "scGPT - INFO - | epoch 6 | 600/708 batches | lr 0.00077 | ms/batch 46.06 | loss 104.40 | gep 51.29 |gepc 52.41 |dar 0.70 |\n", + "scGPT - INFO - | epoch 6 | 700/708 batches | lr 0.00077 | ms/batch 46.09 | loss 103.24 | gep 50.83 |gepc 51.74 |dar 0.67 |\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 6 | time: 34.45s | valid loss 52.4797 | \n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "random masking at epoch 7, ratio of masked values in train: 0.3988\n", + "scGPT - INFO - | epoch 7 | 100/708 batches | lr 0.00074 | ms/batch 65.00 | loss 105.44 | gep 51.72 |gepc 53.01 |dar 0.71 |\n", + "scGPT - INFO - | epoch 7 | 200/708 batches | lr 0.00074 | ms/batch 61.32 | loss 102.60 | gep 50.29 |gepc 51.61 |dar 0.70 |\n", + "scGPT - INFO - | epoch 7 | 300/708 batches | lr 0.00074 | ms/batch 46.25 | loss 105.84 | gep 51.90 |gepc 53.25 |dar 0.69 |\n", + "scGPT - INFO - | epoch 7 | 400/708 batches | lr 0.00074 | ms/batch 46.09 | loss 102.99 | gep 50.55 |gepc 51.73 |dar 0.72 |\n", + "scGPT - INFO - | epoch 7 | 500/708 batches | lr 0.00074 | ms/batch 46.00 | loss 104.17 | gep 51.25 |gepc 52.20 |dar 0.73 |\n", + "scGPT - INFO - | epoch 7 | 600/708 batches | lr 0.00074 | ms/batch 46.04 | loss 103.58 | gep 50.91 |gepc 51.96 |dar 0.71 |\n", + "scGPT - INFO - | epoch 7 | 700/708 batches | lr 0.00074 | ms/batch 46.21 | loss 104.74 | gep 51.40 |gepc 52.61 |dar 0.73 |\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 7 | time: 37.67s | valid loss 51.1444 | \n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - Best model with score 51.1444\n", + "random masking at epoch 8, ratio of masked values in train: 0.3988\n", + "scGPT - INFO - | epoch 8 | 100/708 batches | lr 0.00070 | ms/batch 63.71 | loss 105.25 | gep 51.72 |gepc 52.84 |dar 0.69 |\n", + "scGPT - INFO - | epoch 8 | 200/708 batches | lr 0.00070 | ms/batch 65.70 | loss 103.17 | gep 50.69 |gepc 51.77 |dar 0.71 |\n", + "scGPT - INFO - | epoch 8 | 300/708 batches | lr 0.00070 | ms/batch 49.78 | loss 102.37 | gep 50.20 |gepc 51.47 |dar 0.70 |\n", + "scGPT - INFO - | epoch 8 | 400/708 batches | lr 0.00070 | ms/batch 46.30 | loss 102.78 | gep 50.40 |gepc 51.69 |dar 0.70 |\n", + "scGPT - INFO - | epoch 8 | 500/708 batches | lr 0.00070 | ms/batch 46.38 | loss 103.08 | gep 50.60 |gepc 51.79 |dar 0.69 |\n", + "scGPT - INFO - | epoch 8 | 600/708 batches | lr 0.00070 | ms/batch 46.13 | loss 103.78 | gep 50.88 |gepc 52.19 |dar 0.71 |\n", + "scGPT - INFO - | epoch 8 | 700/708 batches | lr 0.00070 | ms/batch 46.55 | loss 101.13 | gep 49.69 |gepc 50.71 |dar 0.73 |\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 8 | time: 38.45s | valid loss 53.1357 | \n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "random masking at epoch 9, ratio of masked values in train: 0.3988\n", + "scGPT - INFO - | epoch 9 | 100/708 batches | lr 0.00066 | ms/batch 46.94 | loss 101.78 | gep 49.98 |gepc 51.08 |dar 0.72 |\n", + "scGPT - INFO - | epoch 9 | 200/708 batches | lr 0.00066 | ms/batch 46.37 | loss 101.57 | gep 49.85 |gepc 50.99 |dar 0.74 |\n", + "scGPT - INFO - | epoch 9 | 300/708 batches | lr 0.00066 | ms/batch 46.12 | loss 101.28 | gep 49.64 |gepc 50.93 |dar 0.71 |\n", + "scGPT - INFO - | epoch 9 | 400/708 batches | lr 0.00066 | ms/batch 47.16 | loss 102.49 | gep 50.39 |gepc 51.41 |dar 0.69 |\n", + "scGPT - INFO - | epoch 9 | 500/708 batches | lr 0.00066 | ms/batch 46.10 | loss 103.11 | gep 50.50 |gepc 51.90 |dar 0.71 |\n", + "scGPT - INFO - | epoch 9 | 600/708 batches | lr 0.00066 | ms/batch 46.41 | loss 101.75 | gep 50.09 |gepc 51.00 |dar 0.66 |\n", + "scGPT - INFO - | epoch 9 | 700/708 batches | lr 0.00066 | ms/batch 46.92 | loss 101.69 | gep 49.91 |gepc 51.11 |dar 0.67 |\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 9 | time: 34.59s | valid loss 50.0050 | \n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - Best model with score 50.0050\n", + "random masking at epoch 10, ratio of masked values in train: 0.3988\n", + "scGPT - INFO - | epoch 10 | 100/708 batches | lr 0.00063 | ms/batch 46.76 | loss 102.78 | gep 50.62 |gepc 51.49 |dar 0.68 |\n", + "scGPT - INFO - | epoch 10 | 200/708 batches | lr 0.00063 | ms/batch 46.33 | loss 100.66 | gep 49.47 |gepc 50.47 |dar 0.71 |\n", + "scGPT - INFO - | epoch 10 | 300/708 batches | lr 0.00063 | ms/batch 45.98 | loss 99.95 | gep 49.05 |gepc 50.18 |dar 0.72 |\n", + "scGPT - INFO - | epoch 10 | 400/708 batches | lr 0.00063 | ms/batch 46.31 | loss 100.58 | gep 49.39 |gepc 50.48 |dar 0.70 |\n", + "scGPT - INFO - | epoch 10 | 500/708 batches | lr 0.00063 | ms/batch 46.12 | loss 102.02 | gep 50.15 |gepc 51.17 |dar 0.70 |\n", + "scGPT - INFO - | epoch 10 | 600/708 batches | lr 0.00063 | ms/batch 46.21 | loss 100.71 | gep 49.55 |gepc 50.49 |dar 0.67 |\n", + "scGPT - INFO - | epoch 10 | 700/708 batches | lr 0.00063 | ms/batch 46.86 | loss 101.20 | gep 49.70 |gepc 50.77 |dar 0.73 |\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 10 | time: 34.50s | valid loss 50.7140 | \n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - Saving model to save/dev_BMMC-Oct18-21-39\n", + "scGPT - INFO - Evaluating cls cell embeddings\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 787/787 [00:11<00:00, 66.25it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NMI...\n", + "ARI...\n", + "Silhouette score...\n", + "PC regression...\n", + "Graph connectivity...\n", + "scGPT - INFO - 0\n", + "NMI_cluster/label 0.790962\n", + "ARI_cluster/label 0.940832\n", + "ASW_label 0.649485\n", + "ASW_label/batch 0.829177\n", + "PCR_batch 0.399370\n", + "cell_cycle_conservation NaN\n", + "isolated_label_F1 NaN\n", + "isolated_label_silhouette NaN\n", + "graph_conn 0.902561\n", + "kBET NaN\n", + "iLISI NaN\n", + "cLISI NaN\n", + "hvg_overlap NaN\n", + "trajectory NaN\n", + "scGPT - INFO - Biological Conservation Metrics: \n", + "ASW (cell-type): 0.6495, graph cLISI: nan, isolated label silhouette: nan, \n", + "Batch Effect Removal Metrics: \n", + "PCR_batch: 0.3994, ASW (batch): 0.8292, graph connectivity: 0.9026, graph iLISI: nan\n", + "random masking at epoch 11, ratio of masked values in train: 0.3988\n", + "scGPT - INFO - | epoch 11 | 100/708 batches | lr 0.00060 | ms/batch 46.96 | loss 101.77 | gep 50.03 |gepc 51.07 |dar 0.67 |\n", + "scGPT - INFO - | epoch 11 | 200/708 batches | lr 0.00060 | ms/batch 46.38 | loss 99.63 | gep 48.97 |gepc 49.97 |dar 0.69 |\n", + "scGPT - INFO - | epoch 11 | 300/708 batches | lr 0.00060 | ms/batch 46.35 | loss 99.46 | gep 48.77 |gepc 50.01 |dar 0.68 |\n", + "scGPT - INFO - | epoch 11 | 400/708 batches | lr 0.00060 | ms/batch 46.35 | loss 99.55 | gep 48.82 |gepc 50.02 |dar 0.71 |\n", + "scGPT - INFO - | epoch 11 | 500/708 batches | lr 0.00060 | ms/batch 46.34 | loss 100.69 | gep 49.44 |gepc 50.53 |dar 0.71 |\n", + "scGPT - INFO - | epoch 11 | 600/708 batches | lr 0.00060 | ms/batch 46.12 | loss 100.68 | gep 49.51 |gepc 50.49 |dar 0.68 |\n", + "scGPT - INFO - | epoch 11 | 700/708 batches | lr 0.00060 | ms/batch 46.34 | loss 100.52 | gep 49.29 |gepc 50.52 |dar 0.71 |\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 11 | time: 34.52s | valid loss 50.1476 | \n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "random masking at epoch 12, ratio of masked values in train: 0.3988\n", + "scGPT - INFO - | epoch 12 | 100/708 batches | lr 0.00057 | ms/batch 47.18 | loss 101.08 | gep 49.68 |gepc 50.72 |dar 0.68 |\n", + "scGPT - INFO - | epoch 12 | 200/708 batches | lr 0.00057 | ms/batch 46.35 | loss 99.00 | gep 48.61 |gepc 49.70 |dar 0.68 |\n", + "scGPT - INFO - | epoch 12 | 300/708 batches | lr 0.00057 | ms/batch 58.38 | loss 98.55 | gep 48.36 |gepc 49.48 |dar 0.72 |\n", + "scGPT - INFO - | epoch 12 | 400/708 batches | lr 0.00057 | ms/batch 59.40 | loss 98.76 | gep 48.55 |gepc 49.52 |dar 0.69 |\n", + "scGPT - INFO - | epoch 12 | 500/708 batches | lr 0.00057 | ms/batch 58.59 | loss 99.43 | gep 48.84 |gepc 49.88 |dar 0.71 |\n", + "scGPT - INFO - | epoch 12 | 600/708 batches | lr 0.00057 | ms/batch 58.63 | loss 99.51 | gep 48.89 |gepc 49.94 |dar 0.69 |\n", + "scGPT - INFO - | epoch 12 | 700/708 batches | lr 0.00057 | ms/batch 58.37 | loss 99.76 | gep 49.03 |gepc 50.05 |dar 0.68 |\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 12 | time: 40.80s | valid loss 51.3144 | \n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "random masking at epoch 13, ratio of masked values in train: 0.3988\n", + "scGPT - INFO - | epoch 13 | 100/708 batches | lr 0.00054 | ms/batch 64.15 | loss 100.37 | gep 49.33 |gepc 50.35 |dar 0.69 |\n", + "scGPT - INFO - | epoch 13 | 200/708 batches | lr 0.00054 | ms/batch 64.09 | loss 97.38 | gep 47.75 |gepc 48.93 |dar 0.70 |\n", + "scGPT - INFO - | epoch 13 | 300/708 batches | lr 0.00054 | ms/batch 59.97 | loss 98.72 | gep 48.53 |gepc 49.50 |dar 0.68 |\n", + "scGPT - INFO - | epoch 13 | 400/708 batches | lr 0.00054 | ms/batch 46.42 | loss 98.35 | gep 48.25 |gepc 49.42 |dar 0.68 |\n", + "scGPT - INFO - | epoch 13 | 500/708 batches | lr 0.00054 | ms/batch 46.41 | loss 97.75 | gep 48.01 |gepc 49.08 |dar 0.65 |\n", + "scGPT - INFO - | epoch 13 | 600/708 batches | lr 0.00054 | ms/batch 47.05 | loss 98.20 | gep 48.29 |gepc 49.22 |dar 0.69 |\n", + "scGPT - INFO - | epoch 13 | 700/708 batches | lr 0.00054 | ms/batch 46.40 | loss 99.69 | gep 49.02 |gepc 49.97 |dar 0.70 |\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 13 | time: 39.55s | valid loss 49.9826 | \n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - Best model with score 49.9826\n", + "random masking at epoch 14, ratio of masked values in train: 0.3988\n", + "scGPT - INFO - | epoch 14 | 100/708 batches | lr 0.00051 | ms/batch 46.97 | loss 99.85 | gep 49.11 |gepc 50.05 |dar 0.69 |\n", + "scGPT - INFO - | epoch 14 | 200/708 batches | lr 0.00051 | ms/batch 46.30 | loss 97.34 | gep 47.76 |gepc 48.88 |dar 0.70 |\n", + "scGPT - INFO - | epoch 14 | 300/708 batches | lr 0.00051 | ms/batch 46.16 | loss 98.84 | gep 48.59 |gepc 49.57 |dar 0.68 |\n", + "scGPT - INFO - | epoch 14 | 400/708 batches | lr 0.00051 | ms/batch 46.45 | loss 97.67 | gep 47.88 |gepc 49.07 |dar 0.72 |\n", + "scGPT - INFO - | epoch 14 | 500/708 batches | lr 0.00051 | ms/batch 46.65 | loss 97.45 | gep 47.77 |gepc 48.99 |dar 0.69 |\n", + "scGPT - INFO - | epoch 14 | 600/708 batches | lr 0.00051 | ms/batch 46.49 | loss 99.40 | gep 48.81 |gepc 49.87 |dar 0.71 |\n", + "scGPT - INFO - | epoch 14 | 700/708 batches | lr 0.00051 | ms/batch 47.50 | loss 97.40 | gep 47.83 |gepc 48.86 |dar 0.71 |\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 14 | time: 34.65s | valid loss 48.7023 | \n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - Best model with score 48.7023\n", + "random masking at epoch 15, ratio of masked values in train: 0.3988\n", + "scGPT - INFO - | epoch 15 | 100/708 batches | lr 0.00049 | ms/batch 47.40 | loss 99.37 | gep 48.78 |gepc 49.88 |dar 0.71 |\n", + "scGPT - INFO - | epoch 15 | 200/708 batches | lr 0.00049 | ms/batch 46.38 | loss 96.97 | gep 47.53 |gepc 48.73 |dar 0.71 |\n", + "scGPT - INFO - | epoch 15 | 300/708 batches | lr 0.00049 | ms/batch 46.20 | loss 97.53 | gep 47.94 |gepc 48.91 |dar 0.68 |\n", + "scGPT - INFO - | epoch 15 | 400/708 batches | lr 0.00049 | ms/batch 46.34 | loss 96.34 | gep 47.32 |gepc 48.35 |dar 0.66 |\n", + "scGPT - INFO - | epoch 15 | 500/708 batches | lr 0.00049 | ms/batch 46.26 | loss 98.50 | gep 48.37 |gepc 49.42 |dar 0.71 |\n", + "scGPT - INFO - | epoch 15 | 600/708 batches | lr 0.00049 | ms/batch 46.29 | loss 97.37 | gep 47.86 |gepc 48.86 |dar 0.64 |\n", + "scGPT - INFO - | epoch 15 | 700/708 batches | lr 0.00049 | ms/batch 46.19 | loss 96.73 | gep 47.50 |gepc 48.54 |dar 0.69 |\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 15 | time: 34.55s | valid loss 49.1982 | \n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - Saving model to save/dev_BMMC-Oct18-21-39\n", + "scGPT - INFO - Evaluating cls cell embeddings\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 787/787 [00:10<00:00, 71.90it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NMI...\n", + "ARI...\n", + "Silhouette score...\n", + "PC regression...\n", + "Graph connectivity...\n", + "scGPT - INFO - 0\n", + "NMI_cluster/label 0.772104\n", + "ARI_cluster/label 0.919801\n", + "ASW_label 0.637356\n", + "ASW_label/batch 0.848759\n", + "PCR_batch 0.384912\n", + "cell_cycle_conservation NaN\n", + "isolated_label_F1 NaN\n", + "isolated_label_silhouette NaN\n", + "graph_conn 0.923960\n", + "kBET NaN\n", + "iLISI NaN\n", + "cLISI NaN\n", + "hvg_overlap NaN\n", + "trajectory NaN\n", + "scGPT - INFO - Biological Conservation Metrics: \n", + "ASW (cell-type): 0.6374, graph cLISI: nan, isolated label silhouette: nan, \n", + "Batch Effect Removal Metrics: \n", + "PCR_batch: 0.3849, ASW (batch): 0.8488, graph connectivity: 0.9240, graph iLISI: nan\n", + "random masking at epoch 16, ratio of masked values in train: 0.3988\n", + "scGPT - INFO - | epoch 16 | 100/708 batches | lr 0.00046 | ms/batch 54.54 | loss 99.08 | gep 48.64 |gepc 49.72 |dar 0.71 |\n", + "scGPT - INFO - | epoch 16 | 200/708 batches | lr 0.00046 | ms/batch 46.91 | loss 98.03 | gep 48.10 |gepc 49.28 |dar 0.66 |\n", + "scGPT - INFO - | epoch 16 | 300/708 batches | lr 0.00046 | ms/batch 46.74 | loss 95.73 | gep 46.96 |gepc 48.03 |dar 0.75 |\n", + "scGPT - INFO - | epoch 16 | 400/708 batches | lr 0.00046 | ms/batch 47.17 | loss 96.59 | gep 47.34 |gepc 48.57 |dar 0.69 |\n", + "scGPT - INFO - | epoch 16 | 500/708 batches | lr 0.00046 | ms/batch 46.69 | loss 96.63 | gep 47.41 |gepc 48.51 |dar 0.72 |\n", + "scGPT - INFO - | epoch 16 | 600/708 batches | lr 0.00046 | ms/batch 46.40 | loss 97.38 | gep 47.85 |gepc 48.84 |dar 0.69 |\n", + "scGPT - INFO - | epoch 16 | 700/708 batches | lr 0.00046 | ms/batch 46.33 | loss 97.70 | gep 47.97 |gepc 49.08 |dar 0.65 |\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 16 | time: 35.49s | valid loss 49.1442 | \n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "random masking at epoch 17, ratio of masked values in train: 0.3988\n", + "scGPT - INFO - | epoch 17 | 100/708 batches | lr 0.00044 | ms/batch 47.15 | loss 97.73 | gep 48.01 |gepc 49.05 |dar 0.67 |\n", + "scGPT - INFO - | epoch 17 | 200/708 batches | lr 0.00044 | ms/batch 46.56 | loss 97.46 | gep 47.82 |gepc 48.98 |dar 0.66 |\n", + "scGPT - INFO - | epoch 17 | 300/708 batches | lr 0.00044 | ms/batch 46.76 | loss 96.47 | gep 47.31 |gepc 48.45 |dar 0.71 |\n", + "scGPT - INFO - | epoch 17 | 400/708 batches | lr 0.00044 | ms/batch 46.23 | loss 96.14 | gep 47.24 |gepc 48.21 |dar 0.69 |\n", + "scGPT - INFO - | epoch 17 | 500/708 batches | lr 0.00044 | ms/batch 46.30 | loss 97.58 | gep 47.97 |gepc 48.92 |dar 0.69 |\n", + "scGPT - INFO - | epoch 17 | 600/708 batches | lr 0.00044 | ms/batch 46.19 | loss 96.13 | gep 47.21 |gepc 48.21 |dar 0.72 |\n", + "scGPT - INFO - | epoch 17 | 700/708 batches | lr 0.00044 | ms/batch 46.73 | loss 97.47 | gep 47.93 |gepc 48.86 |dar 0.68 |\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 17 | time: 34.65s | valid loss 49.0224 | \n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "random masking at epoch 18, ratio of masked values in train: 0.3988\n", + "scGPT - INFO - | epoch 18 | 100/708 batches | lr 0.00042 | ms/batch 46.59 | loss 96.78 | gep 47.49 |gepc 48.60 |dar 0.69 |\n", + "scGPT - INFO - | epoch 18 | 200/708 batches | lr 0.00042 | ms/batch 46.25 | loss 95.55 | gep 46.87 |gepc 48.00 |dar 0.67 |\n", + "scGPT - INFO - | epoch 18 | 300/708 batches | lr 0.00042 | ms/batch 46.48 | loss 97.20 | gep 47.68 |gepc 48.82 |dar 0.71 |\n", + "scGPT - INFO - | epoch 18 | 400/708 batches | lr 0.00042 | ms/batch 46.40 | loss 95.88 | gep 47.09 |gepc 48.12 |dar 0.67 |\n", + "scGPT - INFO - | epoch 18 | 500/708 batches | lr 0.00042 | ms/batch 46.51 | loss 96.11 | gep 47.21 |gepc 48.19 |dar 0.71 |\n", + "scGPT - INFO - | epoch 18 | 600/708 batches | lr 0.00042 | ms/batch 46.37 | loss 95.02 | gep 46.68 |gepc 47.63 |dar 0.70 |\n", + "scGPT - INFO - | epoch 18 | 700/708 batches | lr 0.00042 | ms/batch 46.44 | loss 96.24 | gep 47.30 |gepc 48.28 |dar 0.65 |\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 18 | time: 34.53s | valid loss 49.1206 | \n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "random masking at epoch 19, ratio of masked values in train: 0.3988\n", + "scGPT - INFO - | epoch 19 | 100/708 batches | lr 0.00040 | ms/batch 46.88 | loss 96.95 | gep 47.54 |gepc 48.74 |dar 0.67 |\n", + "scGPT - INFO - | epoch 19 | 200/708 batches | lr 0.00040 | ms/batch 46.08 | loss 95.77 | gep 47.02 |gepc 48.05 |dar 0.70 |\n", + "scGPT - INFO - | epoch 19 | 300/708 batches | lr 0.00040 | ms/batch 47.05 | loss 96.86 | gep 47.49 |gepc 48.65 |dar 0.72 |\n", + "scGPT - INFO - | epoch 19 | 400/708 batches | lr 0.00040 | ms/batch 46.57 | loss 95.52 | gep 46.84 |gepc 47.97 |dar 0.72 |\n", + "scGPT - INFO - | epoch 19 | 500/708 batches | lr 0.00040 | ms/batch 46.25 | loss 96.09 | gep 47.14 |gepc 48.23 |dar 0.72 |\n", + "scGPT - INFO - | epoch 19 | 600/708 batches | lr 0.00040 | ms/batch 46.21 | loss 95.53 | gep 46.93 |gepc 47.95 |dar 0.65 |\n", + "scGPT - INFO - | epoch 19 | 700/708 batches | lr 0.00040 | ms/batch 46.30 | loss 96.47 | gep 47.35 |gepc 48.45 |dar 0.66 |\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 19 | time: 34.57s | valid loss 49.3791 | \n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "random masking at epoch 20, ratio of masked values in train: 0.3988\n", + "scGPT - INFO - | epoch 20 | 100/708 batches | lr 0.00038 | ms/batch 63.17 | loss 97.28 | gep 47.70 |gepc 48.84 |dar 0.73 |\n", + "scGPT - INFO - | epoch 20 | 200/708 batches | lr 0.00038 | ms/batch 63.83 | loss 94.76 | gep 46.45 |gepc 47.62 |dar 0.69 |\n", + "scGPT - INFO - | epoch 20 | 300/708 batches | lr 0.00038 | ms/batch 48.43 | loss 96.20 | gep 47.23 |gepc 48.26 |dar 0.71 |\n", + "scGPT - INFO - | epoch 20 | 400/708 batches | lr 0.00038 | ms/batch 46.25 | loss 96.23 | gep 47.35 |gepc 48.23 |dar 0.65 |\n", + "scGPT - INFO - | epoch 20 | 500/708 batches | lr 0.00038 | ms/batch 46.22 | loss 94.84 | gep 46.62 |gepc 47.53 |dar 0.70 |\n", + "scGPT - INFO - | epoch 20 | 600/708 batches | lr 0.00038 | ms/batch 46.92 | loss 96.31 | gep 47.26 |gepc 48.35 |dar 0.69 |\n", + "scGPT - INFO - | epoch 20 | 700/708 batches | lr 0.00038 | ms/batch 46.18 | loss 95.12 | gep 46.72 |gepc 47.72 |dar 0.68 |\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 20 | time: 38.11s | valid loss 47.8895 | \n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - Best model with score 47.8895\n", + "scGPT - INFO - Saving model to save/dev_BMMC-Oct18-21-39\n", + "scGPT - INFO - Evaluating cls cell embeddings\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 787/787 [00:12<00:00, 62.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NMI...\n", + "ARI...\n", + "Silhouette score...\n", + "PC regression...\n", + "Graph connectivity...\n", + "scGPT - INFO - 0\n", + "NMI_cluster/label 0.735496\n", + "ARI_cluster/label 0.853957\n", + "ASW_label 0.643390\n", + "ASW_label/batch 0.871946\n", + "PCR_batch 0.410969\n", + "cell_cycle_conservation NaN\n", + "isolated_label_F1 NaN\n", + "isolated_label_silhouette NaN\n", + "graph_conn 0.945513\n", + "kBET NaN\n", + "iLISI NaN\n", + "cLISI NaN\n", + "hvg_overlap NaN\n", + "trajectory NaN\n", + "scGPT - INFO - Biological Conservation Metrics: \n", + "ASW (cell-type): 0.6434, graph cLISI: nan, isolated label silhouette: nan, \n", + "Batch Effect Removal Metrics: \n", + "PCR_batch: 0.4110, ASW (batch): 0.8719, graph connectivity: 0.9455, graph iLISI: nan\n", + "random masking at epoch 21, ratio of masked values in train: 0.3988\n", + "scGPT - INFO - | epoch 21 | 100/708 batches | lr 0.00036 | ms/batch 61.85 | loss 94.97 | gep 46.55 |gepc 47.68 |dar 0.75 |\n", + "scGPT - INFO - | epoch 21 | 200/708 batches | lr 0.00036 | ms/batch 55.51 | loss 94.77 | gep 46.51 |gepc 47.60 |dar 0.67 |\n", + "scGPT - INFO - | epoch 21 | 300/708 batches | lr 0.00036 | ms/batch 46.19 | loss 95.06 | gep 46.56 |gepc 47.81 |dar 0.69 |\n", + "scGPT - INFO - | epoch 21 | 400/708 batches | lr 0.00036 | ms/batch 46.25 | loss 94.84 | gep 46.54 |gepc 47.59 |dar 0.70 |\n", + "scGPT - INFO - | epoch 21 | 500/708 batches | lr 0.00036 | ms/batch 46.37 | loss 96.40 | gep 47.32 |gepc 48.40 |dar 0.68 |\n", + "scGPT - INFO - | epoch 21 | 600/708 batches | lr 0.00036 | ms/batch 46.35 | loss 94.51 | gep 46.44 |gepc 47.36 |dar 0.71 |\n", + "scGPT - INFO - | epoch 21 | 700/708 batches | lr 0.00036 | ms/batch 46.64 | loss 96.00 | gep 47.13 |gepc 48.18 |dar 0.68 |\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 21 | time: 37.03s | valid loss 47.6024 | \n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - Best model with score 47.6024\n", + "random masking at epoch 22, ratio of masked values in train: 0.3988\n", + "scGPT - INFO - | epoch 22 | 100/708 batches | lr 0.00034 | ms/batch 47.01 | loss 95.59 | gep 46.94 |gepc 47.95 |dar 0.70 |\n", + "scGPT - INFO - | epoch 22 | 200/708 batches | lr 0.00034 | ms/batch 47.10 | loss 95.14 | gep 46.74 |gepc 47.75 |dar 0.66 |\n", + "scGPT - INFO - | epoch 22 | 300/708 batches | lr 0.00034 | ms/batch 46.59 | loss 93.76 | gep 46.02 |gepc 47.03 |dar 0.72 |\n", + "scGPT - INFO - | epoch 22 | 400/708 batches | lr 0.00034 | ms/batch 46.49 | loss 94.30 | gep 46.30 |gepc 47.33 |dar 0.67 |\n", + "scGPT - INFO - | epoch 22 | 500/708 batches | lr 0.00034 | ms/batch 46.50 | loss 95.00 | gep 46.65 |gepc 47.64 |dar 0.71 |\n", + "scGPT - INFO - | epoch 22 | 600/708 batches | lr 0.00034 | ms/batch 46.56 | loss 94.69 | gep 46.49 |gepc 47.52 |dar 0.68 |\n", + "scGPT - INFO - | epoch 22 | 700/708 batches | lr 0.00034 | ms/batch 46.53 | loss 95.94 | gep 47.07 |gepc 48.18 |dar 0.68 |\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 22 | time: 34.68s | valid loss 47.3449 | \n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - Best model with score 47.3449\n", + "random masking at epoch 23, ratio of masked values in train: 0.3988\n", + "scGPT - INFO - | epoch 23 | 100/708 batches | lr 0.00032 | ms/batch 47.84 | loss 95.72 | gep 46.96 |gepc 48.07 |dar 0.69 |\n", + "scGPT - INFO - | epoch 23 | 200/708 batches | lr 0.00032 | ms/batch 46.49 | loss 94.48 | gep 46.33 |gepc 47.44 |dar 0.70 |\n", + "scGPT - INFO - | epoch 23 | 300/708 batches | lr 0.00032 | ms/batch 46.25 | loss 93.34 | gep 45.82 |gepc 46.83 |dar 0.68 |\n", + "scGPT - INFO - | epoch 23 | 400/708 batches | lr 0.00032 | ms/batch 46.44 | loss 95.59 | gep 46.92 |gepc 47.98 |dar 0.68 |\n", + "scGPT - INFO - | epoch 23 | 500/708 batches | lr 0.00032 | ms/batch 47.01 | loss 94.66 | gep 46.52 |gepc 47.45 |dar 0.70 |\n", + "scGPT - INFO - | epoch 23 | 600/708 batches | lr 0.00032 | ms/batch 46.31 | loss 93.95 | gep 46.13 |gepc 47.11 |dar 0.71 |\n", + "scGPT - INFO - | epoch 23 | 700/708 batches | lr 0.00032 | ms/batch 46.57 | loss 95.87 | gep 47.06 |gepc 48.12 |dar 0.69 |\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 23 | time: 34.72s | valid loss 47.7477 | \n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "random masking at epoch 24, ratio of masked values in train: 0.3988\n", + "scGPT - INFO - | epoch 24 | 100/708 batches | lr 0.00031 | ms/batch 46.92 | loss 95.19 | gep 46.74 |gepc 47.76 |dar 0.68 |\n", + "scGPT - INFO - | epoch 24 | 200/708 batches | lr 0.00031 | ms/batch 46.53 | loss 94.81 | gep 46.55 |gepc 47.58 |dar 0.67 |\n", + "scGPT - INFO - | epoch 24 | 300/708 batches | lr 0.00031 | ms/batch 46.21 | loss 95.61 | gep 46.83 |gepc 48.07 |dar 0.72 |\n", + "scGPT - INFO - | epoch 24 | 400/708 batches | lr 0.00031 | ms/batch 47.76 | loss 93.62 | gep 46.05 |gepc 46.91 |dar 0.66 |\n", + "scGPT - INFO - | epoch 24 | 500/708 batches | lr 0.00031 | ms/batch 46.71 | loss 94.28 | gep 46.31 |gepc 47.29 |dar 0.67 |\n", + "scGPT - INFO - | epoch 24 | 600/708 batches | lr 0.00031 | ms/batch 46.40 | loss 93.55 | gep 45.99 |gepc 46.89 |dar 0.68 |\n", + "scGPT - INFO - | epoch 24 | 700/708 batches | lr 0.00031 | ms/batch 46.36 | loss 94.57 | gep 46.40 |gepc 47.48 |dar 0.69 |\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 24 | time: 34.73s | valid loss 47.0392 | \n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - Best model with score 47.0392\n", + "random masking at epoch 25, ratio of masked values in train: 0.3988\n", + "scGPT - INFO - | epoch 25 | 100/708 batches | lr 0.00029 | ms/batch 47.33 | loss 94.85 | gep 46.55 |gepc 47.63 |dar 0.67 |\n", + "scGPT - INFO - | epoch 25 | 200/708 batches | lr 0.00029 | ms/batch 46.37 | loss 93.23 | gep 45.80 |gepc 46.74 |dar 0.70 |\n", + "scGPT - INFO - | epoch 25 | 300/708 batches | lr 0.00029 | ms/batch 46.28 | loss 94.40 | gep 46.35 |gepc 47.39 |dar 0.66 |\n", + "scGPT - INFO - | epoch 25 | 400/708 batches | lr 0.00029 | ms/batch 46.22 | loss 93.60 | gep 45.94 |gepc 46.97 |dar 0.69 |\n", + "scGPT - INFO - | epoch 25 | 500/708 batches | lr 0.00029 | ms/batch 46.25 | loss 94.76 | gep 46.53 |gepc 47.57 |dar 0.67 |\n", + "scGPT - INFO - | epoch 25 | 600/708 batches | lr 0.00029 | ms/batch 46.23 | loss 94.25 | gep 46.29 |gepc 47.28 |dar 0.68 |\n", + "scGPT - INFO - | epoch 25 | 700/708 batches | lr 0.00029 | ms/batch 46.20 | loss 94.89 | gep 46.62 |gepc 47.58 |dar 0.69 |\n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - | end of epoch 25 | time: 34.54s | valid loss 47.4910 | \n", + "scGPT - INFO - -----------------------------------------------------------------------------------------\n", + "scGPT - INFO - Saving model to save/dev_BMMC-Oct18-21-39\n", + "scGPT - INFO - Evaluating cls cell embeddings\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 787/787 [00:11<00:00, 71.31it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NMI...\n", + "ARI...\n", + "Silhouette score...\n", + "PC regression...\n", + "Graph connectivity...\n", + "scGPT - INFO - 0\n", + "NMI_cluster/label 0.776199\n", + "ARI_cluster/label 0.921540\n", + "ASW_label 0.632463\n", + "ASW_label/batch 0.871329\n", + "PCR_batch 0.421968\n", + "cell_cycle_conservation NaN\n", + "isolated_label_F1 NaN\n", + "isolated_label_silhouette NaN\n", + "graph_conn 0.934274\n", + "kBET NaN\n", + "iLISI NaN\n", + "cLISI NaN\n", + "hvg_overlap NaN\n", + "trajectory NaN\n", + "scGPT - INFO - Biological Conservation Metrics: \n", + "ASW (cell-type): 0.6325, graph cLISI: nan, isolated label silhouette: nan, \n", + "Batch Effect Removal Metrics: \n", + "PCR_batch: 0.4220, ASW (batch): 0.8713, graph connectivity: 0.9343, graph iLISI: nan\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", 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", 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W7wI/aWpdjeXuSwn/y9lOAxdYGUM6RUREREREREREGqsrjdiJ905v7EXkrPiF4XfcfeOiJTs4d19uZtcDZxBSCY0G/hHdnU2J5MDVZVbZu6ECZlYJZOfCyQ+uxZ/bn7l7W8yJURYzW48wUgfgI+Br7l407ZuZNThKwd3fNrPHgT2Abc1sq1jKqn0IrwnAve7+WYEq4s9Xg899I8u1hHKOFS/T2GBrvHy5jyseDGvyaBUz253ygijl+NTdn2jKjtHIrzfIpXjckBDMKGXD2HJrpC48Ibb8tyjY0ZAfRH/fAA42s4OLlNsgtvy96HWAkO4tm+ZxF2CraPl/wBnlxFbM7LzY6u1NTQXn7u9GKQ83J4wK24w2SBEpIiIiIiIiIiJdS1cK7MwmBCoMSJmZNTYdm7svMrPFhMDQ2mZW5e41rdDWtnIFcDrhOTkJ+IeZjSDMvQPwgLu/U2ZdmzSyTP58PLNjy+uXecy2Morc6LYpDQR1+lF+WrgbCIEdCKnXzootx8sUspAwUqU3MMTMepaRjq2c16illHOsTWPLDc3PlC9efliZ+2wWW27s8eIuIjfXVnM9CuzdjP1fJhfY2Ykwf1NBUWB127x9W0w0j9kRsU1/amQVWwDlBnSPjS1fTm7+rngU58DoVo74cV+l/PR+hcSDjqs1ox4REREREREREZGCukwqNnefD2RHRKxG0y+mPhL97dmMOlpafXahMal/3P1tQkoxgP3NbENgfKzIlY1owxZm1tA8KfvGlmfk3fcYucexX15quPY2OLbc0AXfr1P+/9UdwLJo+btmVhkFhsZE2+YC/yq0YxSUfDZaraCB92L02qTKbFdL2LfhIiXfDyW5+zzg7Wh1kJkNL1XezAaRC1guo4WDGu3ovtjyNxoouxe50U1vuns58yA1xlFAj2j5BXd/oYXr7/CiNIrxkZzzipUVERERERERERFpqi4T2IncGFv+ZdSDvbGujy3/PJrTob3F03L1KVqqsD9GfxPABOCb0fr7wLRG1JMgjP4pyMy6Az+Obbozfr+7fwrcG62uD/ywEcdubfF0UkVHh5hZD+Cn5Vbq7ovJPQ+Dgf2BsUCvaNvN0bxExdwdWz6zgcOdQek5f1pa0sxGF7vTzJLAQdHqclYNUJTr9tjyuQ2UPYfcCMW/N2eknbvv7e7WQre9m9qOyD/IvT/3MLOdSpSNv0f+1szjFhJPw9ZgOsKscp8rwuimrJGx+06L1fVII+or1oZ7mvj4Ifz/ZtP0fQSUO+JRRERERERERESkbF0tsHM18F60PAK4NRohUZCZ7Wpm8dEauPtU4PFodWfgbjMrOd+GmQ03s8amJWqM+MXD7Ru577/IzctxIrke939qwiTmp5vZkfkbzawK+DO59FwzgQcK7P8TYEW0fImZnVJqBJKZdTezI82stYNAz8SWzzazgQXa0pcQaNiikXXfEFv+PmHy9UL3FXIj8Em0vI+Z/bzQ82Vm3wJOa2S7WsKfzexLz0c0euYOoCradI27f96E+n9Pbq6csWY2oVAhMzsGODVarQV+1YRjdUjuvhC4OLbppkIj58zsTMJoMoBPgd8Wq9PMZpmZR7djymmHme1Mbm6bpcBfy9mvMzCz8WY2qqHRkGZ2KHBtbNMf3b2+SHEREREREREREZEm60pz7ODuS83sMEI6tb7A4cDeZnYb8DzhInF/wpwVB0R/twU+zqvqCOApQsqd0cAsM7ubkE5qHuGC9ZrAlsBIQkCjjhA4aQ0PAodEy382s8sJwZ7saI/P3f2/hXZ093ozuxL4dWxzDSEQ0xgPA9sQgmVHEUbfzAc2IgQsklG5ZcAxheY3cvdXzez7hNEElcAfgFPN7B7CROhLCCOS1ge2A/YhvI4F22pmk4Dzo9VHmzo6wt2fMbOngF2BdYHXzeyaqE110eP+HjCIMNdHKipXjkeAWcBQ4FBywY7n3f3VBtq1yMxOJIzcSQDnEVLq3Qp8QHgPHkhI0TUPeIkwXxDE0ve1krsJKeWeM7ObgCcJ78dtgOPJzUP0BiGg12ju/pGZnQDcSphbZbKZHQLcBnwIrEX4v9gvttsEd3+pKcfrwH5N+LwaQZhHaGb0/nwVyKb22z8qWwecEAWEWlJ8tM5t0Wi0r4qdCfORzTGz6YQ0fnMJI836EkbxfZ1cqj+Ah1j1M1VERERERERERKTFdKnADoC7v2BmI8iNrlgDOLnELl+6AO7un5jZjoR0Q4cT5q34XnQrZnaTG92wG4CTCD3mNyJMJh7X0ATtfwYuIMwbBDDV3fODWQ15H5gE3EXxScs/B44odWHd3W83s48Jj2lDYFPg7BLHdcJF/NZ2JOFi7TBgQJE2PRSVe7HcSt3do8DHz8gFdaDh0TrZ/f8ejaq4mvD67Rjd4uYCh7Hq+7y1L7z/k/C+u5QQ0CwU1HwVOMDdlxS4ryzR+6WeMFKiPyG4MaJA0RXA2e7+h6Yeq6Ny9+VR2rtbCfMWrUXh1HSLgJOamWrsS6LRat+KbWrN0YntaQhwdANlaoDfAT9pTro/ERERERERERGRUrpcYAfA3V8zs60JQZnDCD2yBwLdgIWEUQRPAHe6e8FJ1t19PnCEmWVHa+xJGHUxgDAy4dOonmcII2oeL1RPCz2eZWa2K/AjQkAlSeipX9br6+6fm9nzwO7Rpiub2I7Houfjh4T5UzYgzOsyizAXyG+iSe/LqWcYYWTUgcBOhNEwvQlpnmYTRss8Btzr7rOKVNUrtvxJkTJlcffZZrY94Tk+jPAcV0X1zgRuAW6NAjWNrf4Gwtw82R2racQcKO7+FzN7nDDH0QHAelEd7wH3EFJCfWJmP4t2qSWXwqzVuPvvzOwJ4BRCYHEIYZRDmvB8Xd0SF7/d/U4z+w8huPl1wqiVAYTg1TuE/78r3b01g6vtKkplt5+ZjQG+SwjuDSL8v7xHGEF3tbu3RhD024T/TYBX3P2ZUoU7oR8T/o/2IIzg3JgQPOtOeH4/JQQpHyV8BrRFoFlERERERERERLowK5ARS7oYM1uHcPG3Aki7++Zl7rc3IQUbwI3ufkxrtK+pzOy/hAvc9cBwd3+lnZvUbsysgjByZw1gprtv285NEhEREREREREREZEmSLR3A6RDOIkQ1IEwl0SnZ2arA9tHq9d35aBO5ChCUAdCyjgRERERERERERER6YQU2OnizGwwIb0YwHzKnNulE9iX8P5eSkhz9pVkZhVmtmcDZfYDfh+t1gFXtXrDRERERERERERERKRVdMk5dro6MzuAMJ/LUMK8LP2ju37VnInsO5j9or8Xu/tH7dqS1lUFPGpmbxPmknmFMOdHFbA+sD9hfpusC9z9rbZupIiIiIiIiIiIiIi0DM2x0wWZWaEX/Ulgb3evbUQ9e9OB59jpCsysB7C8jKJ1wIXufmHe/msCuzejCcvc/cFm7C8iIiIiIiIiIiIijaARO13bcuAd4Fbgt40J6kjH4O4rzOzrwGhgB2AwYS6dHsBC4G3gEeBP7v5OgSq2BKY2ownvEUZ+iYiIiIiIiIiIiEgb0IgdkS4sb9RVU7zn7kNbpDEiIiIiIiIiIiIi0iAFdkRERERERERERERERDqJRHs3QERERERERERERERERMqjwI6IiIiIiIiIiIiIiEgnocCOiIiIiIiIiIiIiIhIJ6HAjoiIiIiIiIiIiIiISCehwI6IiIiIiIiIiIiIiEgnocCOiIiIiIiIiIiIiIhIJ6HAjoiIiIiIiIiIiIiISCehwI6IiIiIiIiIiIiIiEgnocCOiIiIiIiIiIiIiIhIJ6HAjoiIiIiIiIiIiIiISCehwI6IiIiIiIiIiIiIiEgnocCOiIiIiIiIiIiIiIhIJ6HAjoiIiIiIiIiIiIiISCehwI6IiIiIiIiIiIiIiEgnocCOiIiIiIiIiIiIiIhIJ6HAjoiIiIiIiIiIiIiISCehwI6IiIiIiIiIiIiIiEgnocCOiIiIiIiIiIiIiIhIJ6HAjoiIiIiIiIiIiIiISCehwI6IiIiIiIiIiIiIiEgnocCOiIiIiIiIiIiIiIhIJ6HAjoiIiIiIiIiIiIiISCehwI6IiIiIiIiIiIiIiEgnocCOiIiIiIiIiIiIiIhIJ6HAjoiIiIiIiIiIiIiISCdR2d4NkI4nnUwNBsYAz6Qy6RfSyZQB1wPfBh4GrgCeBI4EXkll0o+3W2NFRERERERERERERLoQc/f2boN0IFEQ5w1gE6AaGA4Y8L+8otVAN6Ae2COVST/Vhs0UEREREREREREREemSlIpN8vUkBHUgBG6GAR8Cn+SV6xb9TQCptmmaiIiIiIiIiIiIiEjXpsCOrCKVSS8DzgeWAP8C7k9l0guBXQsUd+BF4M62a6G0haETpm09dMK0/YZOmKbPCBEREREREREREZEORHPsyBfSydSawLbAlcBLwOupTHplOplahzDHTj6Lyj+cTqa2T2XSyuvXUUzqvxewC3ArkxbOym4eOmHaDkDVrCmjny6y39Ef+2qnjK0Yu+MddXsbcBUwvg1aLCIiIiIiIiIiIiJl0Bw7QjqZqgSOAi4B1iCMxDGgBtgZuBHYqoFqvpPKpG9pzXZKmSb13xp4AagA3geGAgOGrvjrWWDnRqXOmjVl9KXx3eacv9FG99ft8OYi75X4bsVDHF5zAe/7oMysKaOVak9ERERERERERESkg9CIHQG4GTgytm7R3yrga8CQMuoYlU6mbtWonQ5hKCGoA7BerdvUSvNDdk+8yhP1X8TnDgZCYGdS//WAs06svmjsq75RAuCuuj25r/u5XFM7+iEY3batFxEREREREREREZGiNH+GABxW4r6HgaVl1HEscELLNEea6V/AbcCHc+sHPFlpfgjASRX/JEE9azGfW6su3P30c89Zss2E2z58qX6jN4Afv+eD1s5WMJu16MlKzqi6c3j7PAQRERERERERERERKUSBnS4snUwNTidT61P4fVBPCNSsBNYvs8qdWqpt0gyTFtYwaeG3gBMGJRbsnt28hb3LrvYKI+wV/l2/fWJq/Z69F9JnCF7fY3b9mhxbcR8AhnNCxb1UWj3AzHZ5DCIiIiIiIiIiIiJSkFKxdVHpZOpg4E5Cyq7ZfDl4kwD+A8wlBHmywZ+XgG2KVHtUOpk6I5VJL2r5FksTbJZd+Mz7Mrr6l3zMGqzGIibaX+nLUg6xJ/iINdkmMYvTE1MZlXiOD33gRQdUPv8UsBph5I+IiIiIiIiIiIiIdBAK7HRd3yTMoQOwdmx7LeF9MROYncqkq9PJ1EHAGcD9qUz6knQydSIwhXDhP6478GPgotZsuBQxqX8PoPKq2gP3mVq3+yH7J45YfEbVnQC8Ur8hH7MGAPPpxx72CqMSz/MuQzgzcdcXVVSYTTvg5//5aXs0X0REREREREREREQapsBO1/UP4DuAkQvwAFQDRxNG6qTTydRSYGwqkx4FEAV1ri5Rr7dOc6WQoROmDQWWzOrxnQOAP9XV0+2G2v0rPmYN3qhbj30Sz7NNxbtsm3iLjWwO7/gQhtub9eNqzn7+HdbZsRcrebl+I/ZMvMxsX2v5/9Wc+P6rE6bdAFwwa8rod9v1wYmIiIiIiIiIiIjIl2iOnS4qlUnfDrxW4K5eQB0wEdgI2Ao4O3b/yBLVzgH+0FJtlNKGTph2BvDu2nw6250bgZ7z6VsxNxpI5Rhjan7O5iuu46aaUZyfuI4teIeXfcPE62ywRTXdWEBfflc7BjM4oebMf7/qG40Hvg/c0H6PTERERERERERERESKUWCna3s1tjw/+jsTeBl4L3bf6wDpZKoPsHe0rdDInCHApelk6g/pZOqjdDJ1RYu2VnIm9U+tyydnA3zEmt0vrzssAXBx3bfw2L91PQmW0YNL67/F9+vO439sRD2VOIle2TIDbT4/r/kuGV/v4NgR4qO4RERERERERERERKSDUCq2LiqdTP0UODK26RNgW+AxIA2sjLbXAu9Hy5sAg6NlK1L1cbHl8elk6tpUJv1CizS6K5vUP/GRr/7LU6tP+d77PtB/322ztXdOpBOz6wcCML1uu2WnVd7d0x0qqLM/VP2OkYmZPFC3A9tXvEkldZxcfSov+LAvVf2v+p2IxXhvBXoA57bRIxMRERERERERERGRRlBgp+s6PLa8HHgI+AGwfrSte/S3ErglnUy9DNwc2+dTYM0iddcTIgVLgY9aqsFd2X/rNzvz2frkOf/1FADn1Yzj7MrbuKt+T8DY0TLdAZtYdQuDaz+v/3rFswmAQyqfZpl358H6HTi+Ylr1j2o3freOis3idR+UeJpF9KYXK6+78pcXHvelg4uIiIiIiIiIiIhIh6HATtf1V2AboAZ4Czg52l7Dl9NwGXAasEVs2wvAvnx55M5S4EeEoM/0VCatwE4zDZ0wbYdDEqMm75L4HwD9WMplVX9kjq/BABazgL4AFQCr2RKOrvx3YrH3oK+tYJH35Ac1Z/B0/RZUUNftksqrPjy99pTFwA6hdmfLxKzLf1A5bR5Qw6TL12HSwg/b5YGKiIiIiIiIiIiISIMU2OmC0slUFTCAkG6tO7Bl7O7fAscAA2Pb/geMyqtmvyLV/ziVSV/fIg2VrB3/Ub9LRT+WkKCO9fmYMdUXsiYLuKbbb+jHMs6tOZY6NyrMWdMW8fOa77KMHjxStw2f0w+AOiqY5/22BB7li8CO8Vz9Zuv/gGk/IoyyOg7YrHAzRERERERERERERKS9JRouIl8V6WRqg3QydT8hUHMuuXRr2VE3tcDfgT2BeUAd4UL/RcDQMg/z53Qy9bd0MlVsDh5pvLudxMK76vekngpeZWNW0J3jKu9nx8QbbJaYzdlVdzDLw/RH7vBW/RBm1KWYy2ocXfEAldSSslmsbZ8tBMbG6q7bv+K5t8l9FmzIpP4Vbfz4RERERERERERERKRMGrHTtfwa2L/A9mVAL8L74ReAAycC/0pl0ivTydQLNC4I+G3gJ8C7zWuuAMyaMnru0AnTZg+1j/vP9z58xFoAfOi5KY62sFk8U79Z/SD/PNHHVvLbqitYPbGUTP16JKjj3MpbMIMn6rbYNFezs7fNPOyIisceADYBdgIuYtLCujZ9gCIiIiIiIiIiIiJSNgV2uoh0MvUdVh2pUQ10i5Y/IYzIqQH2jrbtAlEOr5CyrRxOGP3zAfBx01srBaz+mm8IwGosYj79eKJ+CxbV96CPrfC+ttxGVcxMEEZarbV6Yun7S7zH+lfVHsQ99buzb+JZrqi6nI0TuSmPulPN4ZWPP8ekhSuBMe3yqERERERERERERESkURTY6TouJ5dy7S3gDOAf0fpQQtCnArg12rYcqE8nU8MIIznKka2/P0rz12KGTpg2glzaPE6ruIsdKt5gE/uQE2rO+Gxy1Z/XWIfPcAczDiKMwHq5j63gsm5X8Fl1P/5dvyOfeX+urzugrjvVn63FgrqdE5nJB/38gTnt9sBEREREREREREREpNF08b0LSCdT3wTWjG16HngaWBStfwwsZtU0bZ8Qgj3dafz7pF/e8aSJhk6YNgR4BFi9glpOqbiH71VO5/36NTiqeiJP1G/9+berz+Py2sM4v/b7U5m08BngTaA+W8cGNheoX75P9SXz/1R3YMVKug2czcCBd9XvWTd0wrSvDZ0wTXPqiIiIiIiIiIiIiHQSCux0DV+LLdcB7xBG1fSNtg0G7gfGxcptBtwCHNmI49QRRov8NpVJv9fk1krcmkSjdeqoZGzFo9RQwV/qD+BZT1FPYuH7PujR39YecetNdfufAMCkhSuAk6u9YtFTdZtzd90eQKJnd6q/Gau3Avgj8BBwQ5s+IhERERERERERERFpMnP39m6DtKJ0MpUkzJfzO6AXuWDeTGALoCpvl+w8OVl/Ak4s83CfpDLpQU1urORM6j/87frBf3nPB9ecXHOqr6D7dgDfTPznuR7ULLupfr9dwYwQoFkCbDFryuj341X8/CenHPCXun3/VU0V69on3N9twi5brrxuPcJrOiBW9ONZU0avzaT+Buy2/8opX/vQ1zy2grplK+j+g9enjHm8bR60iIiIiIiIiIiIiDREgZ2vsHQydQxwHSFQ48CHwLrR3bXAFcCPS1TxCnABcGcjDtstlUnXNLqxkjOpv33mfZatYUt6ADxRt3n1UTXnHQmsmDVl9P0AQydMW4uQLi9r/1lTRj+YV0+PG2r3e+uF+k3XGV/5jw9SiQ92AsbcULPfhEl1318/Fr/7OdDj6qrfjF7X5m1+ee1h/LHqd1RSxzK61/e2lbszaeHTrf2wRURERERERERERKRhle3dAGlVB5O7em+EVGnZETmPAb8BTubL74M64GLgZ4R0XVlLCSN93gC+BfQscMzalml6l1ZJlH4NYLYP7LZv4rml1/zy/H/HyvyK8FrSg5VP3N3t/FlDJ7AFMJEwx87P4W/bgq8Nxn3VO895q8fRVwMHH1P1IE/7Fkyv3w6of6uOqhrgp5vabJbRg29VPEyV1QHQm5WJN+rX+fV+E6Y9Clw1a8ro2W3yDIiIiIiIiIiIiIhIQQrsfMWkk6ktgWuBFcCthOBORXT368ABwNrA44S5dfYH/gH0jlVTAZwN/Aj4W2z7XwmjRA6kcFBnJXByOpnaGrgylUnPbJEH1YUMnTDNzq0cfcu2vL4oY937r6SK39SOpYbK9WNlNiaaD2lD5nBv95/s0dtWvr4GCz/5jP4Do2LvA5VgCYBaKrckzNcDwKY2mwfYEcM2AU4HmFq7G2d2u4vn6oatko7vL3X77g7sDnwD2K5VnwARERERERERERERKUmBna+eycDO0fJbhNRrxxPSbe0HXApcRBiN81NgMbmgTvyCfkW0fU3gVOA04CBCUChuEdAvWu4O/CFaPoQQOJJGOK3yzgknVk47HODimrF+Zd0h3oPqV5fR446hE6YdRRiR8wIhrd46Z1fdVt/bViYAaqhcK1ZVFXB7gvpj60lsA5wLfARc8Xb92qvfVrd3AsDDlEurATzvwwDYoeINc49m8AH+Vz80W+eQVnvgIiIiIiIiIiIiIlIWBXa+ej6KLdcQgjE7xbZ9nTDyoj5a7xu7rx54Ndpvh2jbQcChxEZwRFYQAgwXEAJHhwNPAntE9/dJJ1OJVCZdj5Rl6IRpyf4ccMrDdcP5TdWV/Lhyqh1S8VT3YRe+VjN0wrQEYSRWd2Bz4AdA9c6J178LjHqqbnMW0Tv7Gvn1VVNeGFnx8kvAQOA4Ji38Gyxk6IRpc/qyZMoKuu+ef/z5sbeCxV7tvRIv1b5QNywD/KSVHrqIiIiIiIiIiIiIlMncvb3bIC0onUz1Ioyw6UVIsRVPsVZLw8G85wiBny2Au4lGc+TV8SwhCLQhYb6dnwBPE4I/vyIEki5OZdL3Rm3aGOiTyqRfavID+wqK0ubdSHhOv/f1Qy+5CBgLsLu9vPKoyocmHfDz6VOy5YdOmDYL2CBa3X3WlNFPMql/N+AnD9TtMOQHNacfF4VkFrzR/XvXdrO6s6KyrzBp4dZRHZ8Ca0Tb7wXe7c7Kk2uoqFiPeVRR57tXvGyTqm6ON3Ulkxb2aJUnQUREREREREREREQaRSN2vmJSmfQyYHI6mdoHOC/v7oWEQE2iRBU7AH9KZdJj0snUbHKBnWyatkpCqrdsHUOAe1KZ9MCozNnxytLJ1EHAVKAinUxNTGXSU+iConmHLgPmAT9IZdILgAvJzVnzM+CDbPknfOvf3fzzifnP1SjgWODZWVNGPwnApIXVwPn7A0yYdj+wF3BTN6vbILbffwGGTpi2PzAgu3E9m/vXa6suHfdg/Q4VCa/n4rpvAdg7dUM4u/IOettKZtUPZGjik3+1wFMgIiIiIiIiIiIiIi1AgZ2vmHQy1QdIAs8ALwNbx+5eo+BOXzYsnUxVENKq/QWoI4zEeYovp2SDENAp5uuE+XoADgZWCVakk6mewIpUJt0pho6lk6mzCPMNPQJ8P5VJ15W56xXAbtHyW4RRTu/H7n+PEOiZTXg+r8ivYNaU0W8R5sopaNaU0XcBd4W1hc8xqf8ehHmO7omK/Inca3Hfusyb/I3qXw6toxKox6jHSdCXZXSjFoBr6kbXf58HvzmszAcpIiIiIiIiIiIiIq1LgZ2vkHQy1ZeQSm0Y8AqwXhOr2hy4I5VJH0YIxmTrP5oQ4BmSV/7iEnXdDhwD9CCkHYu395fAROC1dDK1VyqT/rRQBelk6ljCXD9/TWXSdzbuobSc6Pn9NSG49V3gZuD+MndfUGD5HEJApxa4ataU0TXAb1uirQBMWvhE3paPgfWj5ZFP+5Y9c3clgHqOSDzCHolXGFdzNiu8G8958pVf/OKymhZrk4iIiIiIiIiIiIg0iwI7XxHpZKoSGE0I6gBsVcZu9eRSqsWXAQ6ORtOcAiwlpGe7OZ1MncqXAztFU7ulMulH0snUukCPVCY9J+/ubNq2zYG708nUzsBnwDapTHpe9LhSwLWEYMqB6WTq8VQmPbeMx9YalgMfER5/LSEoU65jCUGsT4iCN6lMeiUtGchp2GHAdMKIrp75dzoJHq3fhsMrHqMHK2tf8o3PIYzYEhEREREREREREZEOwtw7RQYsKSGdTCUIF+xHEgIOxQJ2SwkjZ7LpuP5CGIGzL/AwYX6dKcCawPWEYM9xUdlzU5n05Cj4cjlhbpiq6L5XUpl0POVbue2eFx0r36fAiYRRR1XA/wiBnVpg3XYM7JBOpjYjBEgeT2XS+SNiOryhE6ZNACYD9GPJ54vos3p+mS3sXS6ruoJNEx8eyKSF09q8kSIiIiIiIiIiIiJSlEbsfDWsTQjqQPHXdCHQP2/b94DuqUz6yGj9pXQydSuwdiqTfiudTD0YK7sBQCqTfgYYkU6m3gY2iu4rmEKtDO9TOLCzJnB3tPw2IbiUTcXWnkGd/YCphIDXwQ0U75BmTRk9ZeiEaa8B9nj30z/7Q/WBj//d92ABfaimGwDr2jwG2vw6whxNIiIiIiIiIiIiItKBKLDTyaWTqS0II2g+JQRElgB9YkWmAycALxap4ptRqrTtCWnQjk9l0v+K7psADCSM9JmSt98fgEsJI2meamLzx0X79o7W68iNJsraGLg4lUmvCZBOplYDLiAEV36WyqQXNfHYTXEK0CtaHk8Y5dTpzJoy+h8ATFpaSSLxyW0VFw4cxHz+WjeK13woByWepL8tq6BAujYRERERERERERERaV8K7HR+fwD2jpZvJAR4zozdvwfwJ0JgZ4to20DACUGZNLBrtH0I8EeikTipTPoFYHj+AdPJ1EDgF9H+AN8Azmtsw1OZ9MtAn3QydQxwPtAXWKNA0TXSydRlqUz6NOASwnw1EN6/P2zscZvhcXIjdTpdGrYvmbSw9poJ07Z8pj71uzVZOPh533TbCypv6v+1ypep9UR1pdXPb+8mioiIiIiIiIiIiMiqFNjp/BbGlt8GfgsMBQ6PtnUnzKGTdSlwHzCXEOg5GUjF7v+gjGOmWHU0x7B0MrV+KpN+v1EtzzklanMp30gnUzOBbWPb2nRESSqTviSdTD0P1KUy6cfa8titZdaU0fOAbwMMnTCt26u+4f9tVj9741Ti/SuYtHBeOzdPRERERERERERERPIosNP5nQC8AwwmjJo5mzCCpmf0N9+2qUz6LIB0MvUXcoGSZ4D/EAJDDXmGMAIou29vwhw/NzbtIfAesEO0vJIQjMr3GnB9tPwBIYXbuU08XpOlMulOmX6tHLOmjK6G0Re1dztEREREREREREREpLhEezdAmi6dTFUCVxCCO7sB3QjpzKYRJr6fAjxGGJ2T9bV0MvV4Opnaidx8MQD3pjLpc1OZdDmjNKqBtfO2DWtC+0ekk6nLgdsI8/kcCwwAXo+KOPArYEvgzdiuc1KZ9LdSmXT8cYmIiKzCzPY2My9xW2Fmc8zsATM72cx6l1HnxmZ2pJldbGaPmNmiWH2TWrDtxzTQ9mVm9p6Z/cPMjjazqhY45g2x+vcuc5/tzexCM3s0as+S6Hmda2b/NbOrzeyIhp7b2HFnlXHMMWa2MipfY2ZHFSk3xMxOM7N7zeyd6LVaGbXtaTO7zMz2MDMrtL+IiHQNZra7mf3azJ4xsw+j77El0ffaP83sbDNbt8i+xc416s1soZl9YGbPm9mfzWy8mQ1uQvtGm9mt0XfZsui77CMzu9/MTjSzHs1/FlpedJ5U6lymnNusZrbBzOwAM/udmb1oZh+bWXV0TvC2md1pZqeY2ZpF9i92PlZnZvOj98gMM7vSzMaZ2YBGtK0xz0PBes1sUls/p22puedyZjaryHOwPHovZMzsHjP7mZnt0gLtrTCzZ+PHaqB8Q79V4reZJeop9bthRfR58R8zO9fMBjXhcY3Lq3OHhvcSkbagETud26HAEdFyn9j2foRAyV8Jc+6cQhhhk329dwceBF4FNou2rdeI41YQ5umJ26gR+5NOpvpGbegbtW/LVCadie4bTnhs76Qy6f9G234L7AysSRiV1Okkrx8/ADgMeDkz7srn2rk5IiISRoiuHd32A84xs0PcfWahwma2F/BIm7WutJ7A+tHtIOAsMzvQ3ZuaFrVRzCxFGOW7f5EiA6PbjsCJwFIzuwqY7O6fNeO4RwE3EM5FVgLfcvd78sr0JMwFOB4odLEr27YRwKnA/8zs/9z9vqa2S0REOh8z2xr4HbBXgbu7EzJTrA8cCEwxs1uACe4+u5zqCb/L+wHrAtsROjL+zsz+CZzl7u800L4+wO3A1wvcPTi67U84BzjY3TNltKvLiM7bLqPAvMFAFeFaxEaENPa/NbOrgZ+5ezlzzSYInVIHEN4jOwMnAX80s1sJ75NPmvcIWk2Hf5+0wblcj+g2iHBN7BDgAjN7DbjQ3W9rYtPPJpeNpqPoTu7zYiTwf2Z2rLvf3Yg6xuWtHwPompZIB6DATuf2tbz13wE/IHxw1wHfjbZvB7zPqsGX/oQLIlk7ppOpPwATU5n04kIHSydT6wAXAQuAmVG9WQ80su09yAWjKggnRACkMukVwK3xwqlMeg6wZyOP0dE8SLjAVZu8fvzOmXFXvtDeDRIR6UL+TkhZGtebMG/cicAuhB/mD5hZssiP+nhvQCeMJv2IwheEWtKVhBG6cf2AbYAfApsDWwH3mdm27l7Tmo0xs4MInUf6RptmA3cCTxKej+WEjhgbA6OAAwjP9ZmEH4G30gRmdhLheTBgGXCou/87r8zawD9YNcXr7cB0YBawhHAhYAtCQGyvaPmXhDkIRUSkCzCzQ4GbCd9PENKb305I+T2X8Bt1bcJv0EOBDQi/r58jBAsKyT/X6EH4nbspsCswJjreGGBfMzvO3W8v0cy/kgvqzCbMl/sy4Xt2GOEcYIeo/gfNbAt3L/hbvp2MI/f85juEcG0BCp/nZFU35cBmNp5wfSR7zesVwrnKs8A8wmuzDuGayqGEc4MfAg8B9xSpNr+dvQnXVTYndJ49mNDxZhxwoJl9090fKaO5hc5R8y0qsv0KwuNqyG/Izb18famC7a0Vz+W2ii1nr0ENAnaK6hlGeC1vNbODgWMac05tZpsB5xN+I3wKrFXuvpHzCO+FYpaXWU/++3QAuc+LbQnv2VvNbBd3f76hysxsE2CPaHUx4fz/O2Z2pruvLL6niLQFBXY6t5diy06Yq+YNwhdWRey+ocAKwhw4RxMuiNQRTmI+Bz4jfMBvS/iyKDYi5ipCb6VChjem4alMel46mRoPHA/cn8qkZzRm/05qePS3kpBeToEdEZG2s8DdXy2w/Rkzuwm4l3DxZCDhu+niAmU/BP6PcFHneXdfZCFlWWvPv/ZJkbY/ZWY3EC5CDSf8qD0UuKO1GmJmO0b1dwfqgZ8Bl5T4YXelhfQmp9KMEbdmdjbw62h1ITDa3Z/MK9ON8IM4eyHgEeBod/+gQJX3A5ea2faEix39m9CmWYQLfRe4+6TG7i8iIu3DzHYmpAPvRvgd/RPCd1mhi7h3m9lZhB7qUxqouti5xnTC9+EAwsXn8YROjjeb2Tx3/9J5hJltSwgUAKSBnfOCNk9H5y83A98hZOA4juJBp6Ja6/vM3d8tccz4qIZi5zlNYmZjyF3YXgmcDFzv7oXSYt1mZqcRLnpf0EDVxdr5IHBZFJD4AyFLx1rAPWa2m7v/r4F6i71vGhSNCio5MsjM+hFS9wPMp3jgqqS2OO9pzXO5Es/x7dF55ljgakIg5DuETkQnlNnuBHAdIWB4FaHjWGM7fn3YQv8Hhd6nT0S/GW4iBKirCIGkMWXUd0z0t47wf3IjsBohOFsqMF2WWLq6ce5+Q3PrE+lqNMdOJ5FOptZPJ1Oz0slUdTqZujnafA8hKAMhWHMpqw5TfSq23IMQ7PkTIQVbNvCzOqGHT1apL8NSgcBGR+pTmfTVqUx6x1Qm/dPG7puVTqa2TydTv0snU4c1tY42dB4hwPYUTTyZEhGRlufu9cAlsU0jipR7090vdveH3b1Yz8k25e7Lgd/HNhVse0uIfmzfTgjqAJzk7r9oqLeeu3/q7j8FtgfeasJxLyQX1PkU+Fp+UCcyiTAyFsIcg/sXuRAQb9vzhI4uv2lsu0REpPOJvstuJQR1AMa7++RSPfPdvdbdryVkrHi5qcd29wXufjKhVz+Ei6s3WOF58naPLf+m0EicKFARD0bsnl+mqzGztQgpWyEE7ca4+3VFgjqhkPtyd7+YcA5V8ryhFHf/yN0PJzcipj9wTVPra0FHkptf+W8dfJTFJNrhXM6D2wkBsKXR5uPNrFjK4Xw/IozKmwOc09R2tKbo985PYptGRQGpoqL7j45WHyQEkj+M1vPTs4lIO9CInc7jJELvCIDvppOpUYThuGvklbuAkOpkc8KokAcJ8wYAxCcXzg6hrGXV98HeJdowHribMLIHwomSAR8TeiW0qXQy1Z3Q+2kA8MN0MrV9KpN+sa3bUa7MuCt/Te7ClIhIlzd0wrThhJ5w2xJ6ri4hzAl3zawpo2e2cXPiPxo75CTEJbRV279PGAUMcJ+7N+piRRk9VlcRTYT7G+C0aNMcYJS7pwuUXY3QixBC2paj3b2s9C3uXkfowSgiIu1s6oy5wylybjBmxKCZLXCIo8l9lz3o7leXu2M0j11LzGV3ESH10w6ENLBHA3/OK9M3tvxeibpmxZa7FyvUhZxKSFcLcI27/6vcHd29yUG7PKcQrsGsA+xiZvu4+0MtVHdTHBNb7rBp2DrCuZy7v2ZmEwlp/CB0zi057YCZbUSYDwjglGhEf3Ob0irc/T0z+5SQMrkPoaP3pyV2GUVuPu6b3L3ezP5CmNN7PzNbx90/LL67iLQ2jdjpPPKHUg4ijLSpj9Yd+Hsqk/4rYah2d8IJTRVhCCmsmpf1IyAJ5PdMWi+dTBX8Fkpl0rMI8xBkv1zvJgwxHprKpIsOs25F3cid8BphOKiIiHRwQydM22HohGlPEi7UnEyY32ar6O/JwItDJ0x7cuiEaW05+ei6seVSF1A6orZq+3Gx5Utb8TjZHoLXkAvqvAvsXiioEzmS3DnBXe7e2V5DEZEubeqMuTtMnTG35LnB1Blzn5w6Y25zzw2Ojy236ndZMVHP+ctjmw4vUOyN2PIGBe4vdN/rzWnXV0T8XKVdRuNGo6njHV8Lvb6rMLNKM1vfzDY2s0anhy1R7zDCSBKAV8qZU6UddZRzuevIXTvb3cwGFSsYdUK6hjDf0t3ufk9zDmxmvaL3wPpm1rM5dZVQV2S5kOyonIXkss5kA2jx0Twi0k4U2OkkUpn038j1Goj7J2GoZ+9UJn1otO2/sfsXEr7If0yYFO4a4D+EXrc7ESb3y3oXOCmVSRcdppzKpJ8jjAQ6APhWKpP+NJVJt8tQ3lQmvZhw4vYs8OtUJv2f9miHiIiUb+iEaaOBx8n9yCxmV+DxqHyrioIIZ8Q23d3ax2wpZtaDXO/GOkpPutqc4/QlpFKDkKLi0dY4TnSsSsKE0dmLM2lgj1K5+oGRseV/tlbbRESk5U2dMbdR5wZR+UbL+y5bRvhd3F7iowB2M7OKvPunkUt5dHrU9lVEF5UnRau1dIy0X+3GzFLA4Gj1DXdvz0BX/PVtaK6VQwiBhPcIKWsXmNlbZnaxmQ1pZjuOiS132NE6kQ5xLufuS4EnYpv2LFH8REIauIXkzsebagrhffAW4b2w2MyeM7Mfm1mv0ruWx8zWIMwnCrDQ3eeXKLsaYe5OgDvcfQVA1Mnq2Wj7MS3RLhFpOqVi60RSmfSp6WRqA8IXf9YhhEkVPyHKJZvKpC9PJ1M/AjYmfBDfmMqks/n3T8zumE6mZhNG7qwN3JLKpL9TZjveBN5s1oNpIalM+kbC5G0iItLBDZ0wbUfgTspPF9YDuHPohGl7zJoy+rlmHn6AmW2Zt60XYXLTE8hNKnudu09v5rFa2sACbe8LbE1I97FVtO3CVryIsSW588aZUcqL1lBFCKwdFK2/COzn7qXSRAAMjy135N6oIiISM3XG3CadG0ydMXePMSMGNfbcIP5d9pK71zZy/xbj7vPMbDZh1G0/QkDiw9j9y83sEMIF7s2B18zsEsIcPysI2Tt+SJiPpJYw711XH7EzPLbc3ucCLxGyqyRYdU7jQgYU2LYxcBZwgpkd7e7/aGwD8uZHqSHMj9KRDY8tt/fr9wLwjWh5WKECZrYuuVT7/+fuHzXzmPkjgyoIgejtgfFmNsbdM808xpmEbDcAtzVQ9tvkPpfzr7ndSPjsGWZmuxWZ+1JE2oBG7HQ+ZwP5I2SM6AslnUz1TCdTvyDXUwXg6+lk6uF0MnV+fKdUJj2bkI5tS+DDdDL173QytU/rNV1ERLq4y2j8HDA9WDVdSVMdArySd3uG0CliN2AGcKS7H1esgnY0ni+3/SlCmo+tgPsJk8te2IptWDO2PK9UQTMbaGZbFrkV/HEcM4RcUGcmMLKMoE5+++aWUV5ERDqGy2i7c4OO9l3xWWx59fw7o7RZ2xLm0V2N8Fz9h3AOkL2weiuws7vnz9HTFXWY1zeaG2ZxtFpVaMQV8DZwPrA7Ye7kKsJoioOA7Jw8/YE7zayhUT+F7EuY5wdgmruXPH/rADrM60cD/5uRqwlB2cdo+mg5J4wO+hEhsNWXMOXABoSR69kO1UngwVJp4YoxswFmtqOZ3UyYGwfCvJW/KLEb5NKwvePuT+Tddwu5KRrGISLtRoGdzuctwkncq4QLUlmJdDKVIJwYnEvI8Zl1HLA3MCmdTO2d3ZhOpg4kpGjbg9AbZBRwe+z+gelkql+0fFU6mVqQTqauLTYHj4iISDFDJ0zbloZTrBSz69AJ04a3YHMK2Qk43sy2beXjtIZ9gJPMbGgrHqNfbHlJA2VP5suBqOztwQb2jaeDHcaqvTdbqn0iItIBTJ0xt1nnBlNnzB3eyH062ndFvA39ipT5JvBdVv19H/cNwjlAsYvPXUlne303dfcL3f1Jd//c3WvdfZ673+vuo4BfReWqgKsKpOtrSPyCe0dPwwYd6/Ur+dqZ2dGE/72VwInuXnQ6gwY85u57uPsf3P0ld1/i7jXu/r67X0cI7D4clV0PuKiMOs83M8/egPmE6Rq+S+gU/iCwl7u/X6yCKFNAdj6zm/Lvd/fPgXuj1W+2VKo4EWk8BXY6kXQydRKh58aJhFE2O8fuXoOQu3VggV3rY8vnpJOp09LJ1B8Iw7p/TugBlLU8nUxtk06mTgY+Bj58dputjnpw68E/uHun9fov61ZxHCH1i4iISGMc33CRkk5o5v43urvFb+R6xB0LzCb0bHzczA5o5rFa2gUF2t6DkNrjNEI+7jHAM2a2XSu1YXFsuU8rHQPgfWBytNwLmGZmpXKbZ7V4+8zshvgP4/wbuQmrzy9Vzsz2bon2iIh8BbX1uUFbfZeVKz6KY1H8DjNLmNmthHl2NwHuI8xB0g/oDmxG+B3fnfA8PGdmmxQ6SBf6Pus0ry9AGcGAiYSUYBBGbJRzPgSEURrkUvjPJbx/Gtqnvd8nHen1K/W/ORj4bbR6UXNSIDb0Hojm+/kusDzadJSZ9SyxS0M+An7r7m81UC4bFHTgL0XKZNOz9QWOKFaRmT3SwPsq6/oG3ldDy3h8Il2OAjudRJQi7UpWnVAurg4YwarBniWEL/GTgD8TvigPIHwJnRgrtxZh5M5fo3pmEnqHGNAns06/i/d7+WNGv/Ah/9xh3RpiuX9FRETK1NyRMMNbohFxsR5x1wO7EHq09Qb+ambFes52CO6+0t3fcvfLCZO21hI6d9wS5VRvafF0aIU6kcTbNqlAIOq9cg/k7ueS66XamxDc2b0R7Wt0mgoREWkXbX1u0NG+K+Kppz7Pu288cGS0fLO7j3b3R9x9sbtXu/sb7j6J3OTmG1KgZ30X02FeXzPrTi44UOPui0uVLyS66B9/TfdoxO7fIpfi8Ob2nE+qETrM60fp/80/EtKzvUrufLXVRHP3PBCt9iCkYCzlSkKq5q2AbYCvA5cQrg+uDdxrZocW29nMKoGjotUn3f2dIkX/RS49s9KxibSTyoaLSAdRKu/w54Qvll/mbf9hKpO+ESCdTF3PqlH0z8l9Wc5OZdK/TydTrxJ6A0Csh8SGnywZDNC9tp5BC1d8ksqky8l132Ulrx9fAXTLjLtyeYOFRUS6jub2vCuUm7zFuPscM/sLoaPD6oTvzOta85gtxd1fNrP7gIMJ6cv2JuTgb0mvEjp/VADbmFmFu9e18DG+4O4TopQjZxHeO/8ys/3d/akiu8wkNznx9sAbLdCMnxB+CBfzAGFOoCuBK0qUe7cF2iIi8lXU1ucGr9CG32WlRHNlDIlWFxJ60sfF5/ybWKwed7/fzB4ipGXdxcy2cvdX8op1le+zmbHl7durEZHh5Capb/KIDlY9nxlctNSXNSUNW3u/T2bS8udyTRUfAZ/JLphZCjgsWn0QONys4EwFA2P7fCu2/S53r2lCexrzPvjE3V+Nrb8M3G9mtxDmA+oNXGdmz7p7oU7bo8m1f/e8UTXF7GVmG7p7odd+HMVTSUL4XAY4D/h7iXLqYC5SgAI7ncd9hMnNDiEMxc4Gej6m8Af7S8Ad2ZVUJu3pZOpw4EzgWcKQ7g8JQ7fXi9K8vUv4whhG+MD/FDhswNIwJ1pNwqiqrT+vxR/ZV0jy+vGbE3KgrpG8fvwPMuOu1CSWIiJBc3NlN7qnYxOkY8vD2+B4LSlNCOxAaHuLBnbcfZGZvUDoJdibEDx6qOROzT/m2dHoozMIF//uj4I7Txco/jAwNlo+mDCpa3OP/yElfkSaWfaHef4PaBERKU+bnhu4+2Ize54wr14vwhyzD5Teq9XsH1t+0t3r8+7fPPo7191nN1DX84TATna/VQI7XeX7zN1fM7O5hA6sw8ws6e6ZhvZrJfHX97Fm1BOfV6esUTdR8GGnaPVZd/9fOft1gPdJi5/LNYWZ9QF2i22Kv37dY8tnlFll/HGsBixoQrMa/T7I5+4vmNm5wOVRO34BHFOg6LFNqN6ius4vcNySgb5YYOzDzvz5I9JelIqtk0hl0k7oQbwlq47eKRatfyKVSS/Lq+Mh4CDgQkK6mXhPgSuB+4G/EebQGQVsAVARxeer6v2B4//+2A3NeyRfeUcTejdUAKe3c1tERDqSF5u5/8yWaEQD4h1eqtrgeC2pLdoeH8FU7o/ZZnH3M4HLotW+hODOzgWK3kbuAuFhysMtItIptMe5wbWx5Tb5LssXdVo4NbbprgLFshdvy/lOj5dpymiAr5J4x8r2en17Aj+IbSr0+pYrPr9xuSMWmjJapyPoKOdyx5EbDfi4u88rVbiNNOV9UMgVQHZ+ne+Z2RbxO6ORhN+IVh8Fvl3GLdsx7vtWZPiSiLQeBXY6lxEFtuX37AFYSV6uz3QytUM6mbqYkIJtPmGunrGE3rYvx4qeAXyfcCIZ7xH7IoWj+bKqJwgTzAE83p4NERHpYK5p5/3LEc9Z/X4bHK8ltUXbrwc+iJa/YWaNnbS6Sdz9dMJIYwiTRj9gZjvmlfkc+EO02g240cy6lVO/mVWY2fdaqr0iIlK29jg3uJHcvG/7mdlJ5e5oZuub2deacMx8PyWX6uk9Ck9Onp3XYnUz27KB+vYusF9XdRm5ye6PN7Ovl7ujmW1lZi2Rwu2P5NLsPe3uTRpFHc3Tc3xsU4P1RGlss/OjrKCdRr00RUc4lzOzzQkjWbIuymvjzPx5JAvdCEGR7D7x+xY0oU0pwnyaENI2Pt/oB5ZrS23sMSWAn+UVOYpcZ7Gr3P3Whm7kPr82iLVTRNqIAjudy8WsOuzyRQq/hrOAT7Ir6WRqa+ApQp76foTeBz9OZdL3pzLpUcCPyPWM6EdI17YvcBKht8IhqUx6u1Qm/XGLPpqvoMy4K+8FdiD0cjilnZsjItJhzJoyeibhu6gpnor2bzVmtgNhotmse1vzeC3JzA4G9oxWVwL/bo3juPtK4JtAdbTpKjP7SXThoVT7erFq6oqmHPtUcj/2+wMPRq9Z3CRCulkIz8f9ZrZuA23bltDJ5czmtE9ERBpvzIhBM2nGuUG0f6O4ezVwJLnvsj+a2QQzKzoyJrpofCzwAqv2nG8UMxtgZn8kfF9BGF0zrsicG/G5Jv4QjQIpVOePgG2j1fcIKdm7rGh0RXbEigFTzWxcqZEEZtbDzM4EZgDrNfXYZra2md0VO/5CoGAnGDM7tIH3XA/CBfMNo01PufuzxcrHHACsHS3f05RAQjubRDucy1kwFniS3Hww17r7g02ts4xjrmZmoxoosx5wJ7lUbFdEwZnmuJncqJ0j8kbtZN+7y4B/llnfHbHlcUVLiUir0Bw7nct+hNfsE8IEiqPIncTFbQbcl06mfpDKpN8Cknx5CPcXo3FSmfRj6WRqCGFOnWyPiJWpTHolnWTi6I4kM+7KF9q7DSIiHdSphNGMPRoqGLOcVdOVNNWAAj1eKwk/frOdGbLfgX8uMPEwAGZ2BKtO9pyMLQ83s2PydrnT3Zs7h8DAAm3vRrj4cBCrjqi9yN0/bebxinL3GWZ2JOFiQx9Cr7/xZnY74cfwx4TOIr0JFyN2I4wQXjOqYtmXKi3/2D+K0tecDAwgBHdGufsL0f0rzewQ4B+EThYjgTejtv2bcMFrCbAWId3saELPQqOLXwgTEWlHbX5u4O7PRN9lNxO+ryYDJ5jZbYTvsk8IHSjXBnYHxgAblVF1/rlGD8L31SbAroRJ17MXjZcCx7n7w0Xq+g3hIuk6wF7Ai2b2e0Jv/ZWE79jvAIfH9jnL3cuZ6Pwrzd3vNrOTCaN9uxOuaZxuZncQggbzou3rEM4VxtDwhPTw5fOxXoTXNwXsQTgny55LzgO+WWJ+m8sIHWTuJlybeYfwnugP7ExI5ZZ9zy2gSICogGNiy50pDRvQuudyea9dBeG5HkSYj+hgwlzTWbcA41vgIZXSH/i3mb0G3AM8B8whBJ0HE+bNOj4qByGw/MvmHtTd68zsIuAGwufcT4FvRaPhs0Ge+9x9aZn1vWVmLxKuTR5mZv3cfVFD+4lIy1Bgp5NIJ1MGZIeWDiQM7+1BGF57POGkb5/YLl8DHksnU0MJvY7/QzhBuA24PZVJP5BOpiqA3qlMehHhBCQ+zLVX6z0aERHpimZNGf3c0AnTjiD0PCvnAs5yYOysKaOfa4HDHxLdGnItpX/IXUJINVDuMR6h+ZNDj2+gTRBG9E5x94saKNds7n6PmY0gTL66D+HCyOmUnltuKSFlzs+befgfEn6M/4Aw8eu/o+DOi1HbPjKzPQlpNMYT3mdHR7diXgLObma7RESkCcaMGPTc1BlzG31uMGbEoGadG0TfZbsCvyeMDNiI0HmymDpCp4bbSpQp51yjlvD7/Ex3L5o2zd3nR7357wI2J3Te/EOR4suB0939zgaO3WW4+5XRBfPLgW2AraJbMSsJ848UC7RBeedjK4BbgXPc/ZMGyg4qo87XgO+4+2sN1IWZrU4IUADMBqY3tE9H1IrncgU7beV5DbjQ3Uv9n7e0zaNbKX8nBIKb+5si66+EgM7GwFgzu5BVR9vc3sj67iAEdnoSMiD8qSUaKSINU2Cng4uCL+cTeg88TBils4iQMg3Cl9xWhAsmNaw6MmdtQuBmPqsGfUgnU+sQeoasl06m6gi9H54HtieM3FHPVRERaXGzpoyeNnTCtD0IP7R3LVH0KeDUFgrqFFNH+E59l9BD94bs6I9OoIbQ9jcJebyvc/c32urgUQ/UUVHvvkMIvYmHAmsQev8tJEzu+gIhuDW13J5/DRzXzWx8dIwTgNWB6Wb2NXd/KSqzHDjDzC4hpNvZh9ADcU1CD90FhBQUzxBGVD3Z3HaJiEjTjRkxaNrUGXPLPjdoblAny91fBvYys93JfZetS/guqyX8Ln6Z8D12i7vPaUz1hN/oCwkjgGYSeuTf7e5lpTh394yZbUdIg3oY4cLpQMJ1nIXA64QUVNe6+wdFK+qi3P3RKE3XAYRU6bsT5r5ZjRDI+YSQ3n46cHs0x0u56gkddxYRRlm8CPyX8PouKGP/7xNG+exMGNG1RtSu5cBcwsiiu4C/NyL11nfIdda90d0LzcfcKbTBudxKwmu3AEgTzlcfdPenS+3UwuYARxDeAzuR++zpAywmzJn5FPCXlm6Xu9dGo3auJzfXzv7R3UuBaY2s8nZyo4nGocCOSJsxjdTt2NLJ1LHAn7OrhKG16wB3x4rlB3SccDHl96lM+tdF6j2dMLw7bgzhQ/wVzacjIiKtbeiEacMJF+eHE+Z/W0z4YXzNrCmj1cFARESki5k6Y+5wipwbjBkxSOcGIiIiIhEFdjq4dDL1U+DCaLU6lUl3TydT/QnR+35FdnszlUkPK3Jftt6DCHlL43ZPZdLqtdpIyevH70L48fF4ZtyVnS6PrYiIiIiIiIiIiIh0Hon2boA0aH5suVs6mTqLkDYtf0TNCsIka0spkqM1nUwdnk6mJqeTqU1SmfQ/WXV4ZTXQZilcviqS14/vRkiRNw64Lnn9+P3auUkiIiIiIiIiIiIi8hWmOXY6sHQy1QN4kJBazaLNFwC9ChTvAVwGnJPKpKtjdaSAG4DehJykEHKUbpTKpA9MJ1OHAgcBt6Uy6Xkt/yi+mpLXj08Q8hxfTMjxmnUk4TUTEREREREREREREWlxCux0AOlkagvCxHjdgG8D7wFTgRGxYh8BvwJOBzYoUtWQbFAnnUytDSwDfk6YiC1ucDqZslQm7alM+h7gnpZ5JF3H+jWL/vl+Zd9vYJZ/18Pt0R4RERERERERERER6Ro0x04HkE6mbgK+F61+DqxepOirwAJgN3IjeLJWAlsBw4CxwNHAcmA6cHBsfwN+kcqkb2mh5nc9k/pv+JPVd3z7rr6b5r8GSzLjruzbLm0SERERERERERERkS5BI3Y6hnRsuVhQB2DLBur5P+D42HovQpq1xcBjwBrAEGBhE9ookek91/n6okT3Lw3VAX7T5o0RERERERERERERkS5FI3Y6gGguncmE0TTHE+bDaYp6IFHkvvg8Pa+mMumtmniMLi15/fiNcJ+JWd8Kr6fOvni6a4DumXFX6h9KRERERERERERERFqNRux0DPcA+0fLtYT5diqAQ/hyyrVSlhNStfUC3gB2jt33OWHEDsBbTW9qlzccs75APKgD8C8FdURERERERERERESktSmw0zHsHluuBNYBzgaGAsMbUU9voCdwC/DdaNvVwAfRtu2iuv/crNZ2bdOBF4FtY9vmkHu+RURERERERERERERajVKxtbN0MrUa8DKwbmzzp0B3oG8Tq42nXbs1lUl/u+ktlHzJ68cbcDBwGyH13bcy4668u31bJSIiIiIiIiIiIiJdgUbstL/prBrUAViTEJxpKgNWEuZ9uboZ9UhhBvyQEHx7DXiwfZsjIiIiIiIiIiIiIl1FouEi0lrSyZQB2xS5uzFz68wkBBqWResLgYHA6qlM+pGmtk+KOg0YFS1vDmzffk0RERERERERERERka5EgZ32tRfwXgvU81Iqk/4jYaTPXsDAVCa9KJVJ17RA3RKTvH58P+DXsU0fE1LpiYiIiIiIiIiIiIi0OqViayfpZGobQhq2CqAu+luuaqBbtFwH/AIglUkvBx5rwWbKl1UDi4EBhHR5R2TGXTm/XVskIiIiIiIiIiIiIl2GRuy0nw3IBXMaE9Rx4MXY+qepTPrNFmuVlJQZd+UKQhq2y4HDM+OufLKdmyQiIiIiIiIiIiIiXYi5e3u3oUtKJ1NVwE3A/sBqJYo6pefbuT6VSR/bkm0TEREREREREREREZGOSYGddpROproDi8ilVYsHcZYDPUvsXk8YcfUCMELz6YiIiIiIiIiIiIiIfPUpFVs7SSdTawOnE+bIycoGdRYTAjfF1JJ77bYDhrZ0+0REREREREREREREpOOpbO8GdGEPAlsWuW8ZMChadkLwp4JVAz9zgSTwPPBe6zVTREREREREREREREQ6Co3YaT8bF9m+AJgYWzdCAC4+z04VsEN02z2VSVe3RgNFRERERERERERERKRj0Yid9vNDYBKwNqu+Dv2AvcnNoVPI/alMeilhtI6IiIiIiIiIiIiIiHQRGrHTTlKZ9HWpTHp9QnDHY3clgKMp/dp80IpNExER+Uows73NzEvcVpjZHDN7wMxONrPeZdS5sZkdaWYXm9kjZrYoVt+kFmz7MQ20fZmZvWdm/zCzo82sqgWOeUOs/r3L3Gd7M7vQzB6N2rMkel7nmtl/zexqMzuioec2dtxZZRxzjJmtjMrXmNlRRcoNMbPTzOxeM3sneq1WRm172swuM7M9zMwK7S8iIl2Dme1uZr82s2fM7MPoe2xJ9L32TzM728zWLbJvsXONejNbaGYfmNnzZvZnMxtvZoOb0L7RZnZr9F22LPou+8jM7jezE82sR/OfhZYXnSeVOpcp5zarmW0wMzvAzH5nZi+a2cdmVh2dE7xtZnea2SlmtmaR/Yudj9WZ2fzoPTLDzK40s3FmNqARbWvM81CwXjOb1NbPaVtq7rmcmc0q8hwsj94LGTO7x8x+Zma7tEB7K8zs2fixGijf0G+V+G1miXpK/W5YEX1e/MfMzjWzQcXqKVF/HzM7wczujv5vFlv4LJpl4XPoNDNbq7H1ikjzmXvJzxlpZelk6gbg+3mbS43WWQpsCxwIHAL8JZVJ/7nVGigiItJJWQhOPNyIXd4HDnH3mUXq2wt4pMT+F7j7pEYcrygzOwa4vhG7vAIc6O7vN+OYN5A7Jxnp7o+UKJsCfgvsX2b1S4GrgMnu/lmB+rInpO+5+9ASxz0KuIEw9+BK4Fvufk9emZ7AL4DxQDkXu/4H/J+731dGWRER+Yows62B3wF7lVG8HrgFmODus2N17E3jzjVqgX8CZ7n7Ow20rw9wO/D1Bup8EzjY3TONaEerM7NHKO+5LaXkeUEDx98LuAwYXkbxGuBq4GfuPj9WxzE07nxsOXAr4X3ySQPta8zFuNXcfUGBOiYB5zeiHoAH3P2ARu7TplrqXC4KYm3QiEO/Blzo7rc1Yp/48SYAk+Pb3L1oB6JGfn685O7Di9RzDOW/TxcCx7r73eUUNrNjCY9pYANFF0flprguNIu0GaVia3/vFtiWDeosAfrk3dcb2Br4TbS+RzqZ+lcqk57TSu0TERH5Kvg7cF7ett5ACjgR2AVYH3jAzJLxH/Ux8R9mTriQ8hHNv2jRkCuBK/K29QO2IaR23RzYCrjPzLZ195rWbIyZHQT8FegbbZoN3Ak8SXg+lgNrEuYTHAUcQHiuzwSeI1zwaMpxTyI8DwYsAw5193/nlVkb+AdhHkIIwZ/bgenALMK51UBgC+Agwmu3BfBLQIEdEZEuwswOBW4mfD8BvEP4vngKmEvoQLA2sCdwKOHi8HcJ32OXFak2/1yjBzAA2BTYFRgTHW8MsK+ZHefut5do5l/JBXVmA5cCLxO+Z4cRzgF2iOp/0My2cPfFDTz0tjSO3POb7xDgomi50HlOVpPmEzaz8YSgXfaa1yuEc5VngXmE12Yd4GuE13cg4fl8CLinSLX57ewN9Cech+0OHAz0JDzuA83sm6U6ycQUOkfNt6jI9isIj6shvwH2jZYbE6hqc614LrdVbLmC8L85CNgpqmcY4bW81cwOBo5pzDm1mW1GCLI58CnQ2BEs5xHeC8UsL7Oe/PfpAHKfF9sS3rO3mtku7l50eodoFNRvgVOjTU54Xe4G3iIEqTcgdDr/NuF3wS+Bbc3se+6+ssz2ikgzKLDT/i4ChgLHFLgvP6gDUAd8Hv2tIPQsadLJjoiISBeywN1fLbD9GTO7CbiXcPFkIHA8cHGBsh8C/0e4qPO8uy9qQk/dpvikSNufikbZPEXojboF4eLEHa3VEDPbMaq/O6H38s+AS0r8eLvSQnqTU4Gzm3Hcs4FfR6sLgdHu/mRemW6EH8TZCwGPAEe7e6EUtvcDl5rZ9oSLHf2b2jYREelczGxn4DagG+Fi5U8I32WFLuLebWZnEX6vT2mg6mLnGtMJ34cDCBc+xxN+699sZvPc/UvnEWa2LSFQAJAGds4L2jwdnb/cDHwHWA84juJBp6JioxpabOQxgLsX6sSaPeYOsdVi5zlNYmZjyF3YXgmcDFxfZBTBbWZ2GuGi9wUNVF2snQ8Cl0UBiT8AhxEu6t9jZru5+/8aqLfY+6ZB0aighkYG9QN2i1bnUzxwVVJrvU/yjtFq53IlnuPbo/PMsYRRWwMI/1PLgBPKbHcCuI4QMLyK0HGssR2/Pmyh/4NC79Mnot8MNxEC1FWEQNKYEvX8H7mgzmfAYe7+WF6Z/wJ3mNmvCK/bJoTncR5wSlMa3xbvM5GvEgV22t8mhDl1suJp2JxVewcD3JTKpB9OJ1NjgdHAbalM+tPWb6aIiEgLm9R/OOEH07aECxxLgBeBa5i0cGZbNcPd683sEnK9YkcUKfcmhQM+7cbdl5vZ74FsWtYRtFJgJ/qxfTshqANwkrtfU0YbPwV+ama3EnqyNva4FwI/jVY/BfZ39xcKFJ0E7BgtPxaVK9n5xd2fN7OvEX7kiohIe5s+cTjFzg1GTZ7Z3Oqj77JbCUEdgPHufnWpfdy9FrjWzB4k/H5vkiiV1slm9jEhiFAF3GBmmxQIKu0eW/5NoZE47u5mdgHhInR2n8ua2r6vgmiejxuiVQfGuPu/Su3j7suBi83sAcJr0iTu/hFwuJldRxi10x+4hjBaqz0dCfSKlv/WwUdSTKIdzuWioN/tZvYqIVjRGzjezO509wfKqOJHhNd5DnAOYWRLhxL93vkJuedplJkl3L0+v6yZbUVuRN1KYF93f7FE3a+Z2UjCZ/WahM+5f7r7/S37KEQknwI77W8bVp1PJ75shC+zPaP1GuBXAKlMeiowtS0aKCIi0qIm9d8BuJzCP3R3AU5mUv+ngFOZtPC5NmpVvCdgh5yEuIS2avv3CaOMAe4rJ6gTV0aP1VVEKSB+A5wWbZoDjHL3dIGyqxF620IYyXx0QxcCYu2qI/RgFBGR9jJ9YsPnBtMnhnODUZObc25wNLnvsgcbCurERfPYNXkuu5iLCKmfdiCkgT2aXAeNrL6x5fdK1DUrtty9WKEu5FRCulqAaxoK6sS5+8st1IZTgP0Iqd52MbN93P2hFqq7KY6JLXfYNGwd4VwuClBMJKTxgzCqpWRgx8w2IswHBHBKNKK/uU1pFe7+npl9Sgi+9AFWJ3SayjeB3PXiKaWCOrG6Z5vZmcCN0abzCKOqRKQVJRouIq3s8BL31RN6eNwNPE44oX0rnUydmE6mTkonU03uTSIiItIuJvUfTfhOa6j34q7A41H5trBubLnUBZSOqK3aflxs+dJWPE42pcU15II67wK7FwrqRI4kdxHsLnfvbK+hiEjXNX1i484NQvmmOj623KrfZcVEPeQvj20qdE3gjdhyqcnf4/e93px2fUXEz1V+U7RUK4pGAF0V21Tqmg8AZlZpZuub2cZm1mLpYc1sGLn/q1dKzanSAXSUc7nryM1ptLuZDSpWMOqEdA1hhM/d7n5Pcw5sZr2i98D6ZtboUe5lqiuynG1DX+Cb0epKQnrBcv0N+Dha3s3Mkk1qoYiUTYGddpROpvYF9i9RJAH8BRgC7JPKpJ8nDNm+mjAh2i9K7CsiItKxTOq/I2GC13JHlfQA7oxG+LSaKIhwRmzT3a15vJZkZj3I9W6so/Skq805Tl9g+2h1KfBoaxwnOlYlYcLo7MWZNLBHqVz9wMjY8j9bq20iItLCpk9s2rlBGOHTKHnfZcuA/zS2jhYUHwWwm5lV5N0/jTC3H8DpUdtXEV1UnhSt1hIuMHdZZpYCBkerb7h7ewa64q9vQ3OtHEIIJLxHmJR+gZm9ZWYXm9mQZrbjmNhyhx2tE+kQ53LuvhR4IrZpz2JlgROBrxHmf/xhiXLlmEJ4H7xFeC8sNrPnzOzHZtar9K7lMbM1CPOJAix09/kFiu1ObrTOE1FK5bJEaSunxTY1dp4hEWkkBXba1zXkJnirLVFuBGGINsDGse1Nzu8rItJipk9cg+kT+zVcUITLaHyqsB6s2qu1qQaY2ZZ5t53M7PuEtKcHRuWuc/fpLXC8ljSwQNt3MbMfEPKAbxeVu7AVL2JsSe5H3swo5UVrqCIE1r4Vrb8I7OnuHxbfBYDhseWO3BtVRERWdRltd24Q/y57KboI2S7cfR4wO1rtRy4gkb1/OeGC/0fA5sBrZnaqmY2MzgGOBp4hzK9TS5j3rquP2BkeW27vc4GXCBlYADZtoOwAvjwH4cbAWYTX/eCmNCDquJSdz7kGuLkp9bSh4bHl9n794nM5DitUwMzWBX4drf5fNMdScwwC4gHeCkIg+nLg+RYa/XImuXm8bytSZtvYclPSXsZfu22LlhKRFqE5dtrXPHJDp/N76CwBVhByX2aAc9PJ1N2EfLxbROUvaKN2iogUNn3i9wk5wVcwfeJ9hDzfE6f2OW03woXyv48ZMejJdmyhdBST+m9L0yeP3ZVJ/YczaeHMZrTgkOhWzAzgt+5+ezOO0VrGR7di7ie0/cFWbMOaseV5pQqa2UByvQHzVbv7G0XugzBKOds7dSYw0t0XNrJ9c8soLyIi7W36xOadG0yfOJxRk2c2Yp+O9l3xGbl0qquTG6EDfDEp/LaEc4CzCEGwfLcCF7v7CwXu62o6zOvr7tVmtpjQkbfKzPq6++K8Ym8T5oV5iDA6eRGwGrAzIRXtPtH+d5rZvu7e2NHS+xLm+QGYFgUTO7IO8/oR/jezVi9S5mpCUPYxmj5azgmjg24jpKN8m5D+bG1gFGGum02BJPCgme3o7o16bsxsQFTHqYRAMIR5K4tlAIq/Dh8XKVNKfJ81i5YSkRahwE77OozwIb4+IWq+nFxPjV6EvJbZoYxJ4HvARqlMeuu2b6qIdFnTJw4AljFq8pcmr3Q4zkKguTcwFmAl3Y9Yre7jdeZXDO6G+4+mzpi7P7A18K8xIwa93ZZNlw7l+IaLlHQCYTLa1rITcLyZvVnOBKEdzD7AcjN7w91ntdIx4qPyljRQ9mTg/CL3vUdu0upCnFxPwmGE3pvlXMhoTPtERKRjaOtzg472XRFvQ7HR798Evks41y7kG4SUTRPc/fOWbFwn1BFf32yGln5AfmBnU3f3vG3zgHuBe81sCnAOYTTzVWa2ZSNHTI+LLXf0NGzQsV6/kv+b0Yi5bxCCMCcWeB3L9Zi771Fg+/vAdWZ2GyEt3UhgPUJH7xMaqPN8Myt2Hg7wIHCKu79f5P542sf892w54vu02HxRIlKYUrG1o1Qm/QHhgzn7JRAffpsAriUMl62KtlURDVNPJ1O90snUvulkau02aq6IdEXTJ54DzAdmMX3iJgBTZ8y1qTPmnjl1xtw37un9492f6f4NamNfJ91ZueGey2/rtlrdR2DWw7z2EeD3QGbqjLk3T50xt7HpNuSroblD8Yc3c/8b3d3iN6AbYeTssYR0KPsCj5vZAc08Vku7oEDbexB6351G6OE5BnjGzLYrUU9zxH+k9WmlY0D4ITs5Wu4FTDOzUrnNs1qsfWbWu0Dqu/it2MU1ERFpnLY+N2ir77JyxS+gLorfYWYJM7sV+B0hBft9hIu7/YDuwGaEDB7dCRd6nzOzgqnazewGM/NiN3JZRM4vVc7M9m65h94qOs3rC1BGMGAiuZRgSUrP9bKKaJRGdqT6XML7p6F92vt90pFev1L/m4OB30arFzUnBWJD74Fovp/vEjqBAxxlZvlp+xrjI8Io/7dKlGnu6xDf50uj7jvA+0zkK0WBnfZ3L3Blge1OSEWSTWWyDPhhKpPOfgA/RIi0v5JOptYtsL+ISEvITgK5NnBEtPw94BJgUyxhc6qG8VrVbl8kkQZI4AyqncXQ6pdxq8z2vq8knJge0xYNlw6nuT/QvjRpcHO5e427v+/u1wO7EIKYvYG/mlmHnjfK3Ve6+1vufjlh0tZawjnDLVFO9ZYWnzi1WJq1bNsmFQhEvVfugdz9XOBX0WpvQnBn90a0b1C5xypiR+CVErcdm1m/iIgEbX1u0JLfFS0hnqYof7TNeODIaPlmdx/t7o+4+2J3r3b3N9x9EnBoVGZDQlqvrqzDvL5m1p3c+7OmQBq2BkUX/eOvaaGRHcV8i9zcVTe353xSjdBhXj9K/2/+kZCe7VVy56utJpq754FotQcNn4deCWwV3bYBvk747b6E8Jv+XjM7tMT+8ddhcNFSxcX3+axoKRFpEUrF1o7SydSJwFXkUo7E5W97PZVJ/zHary8wItq+BjAjnUwZ8N1UJv1IKzVXRLqYqTPmbjis266z16tJr9vDl9W81H2kz54xdx2ilGtZq9V9xFY1j2OAR2mU6qhko5oXq7tR3W1BxUAWVKxyTqgTvK6puSkVmpIKoGzuPsfM/gL8mPBj7QjgutY8Zktx95fN7D7gYEL6sr2B/7TwYV4F6gipF7cxs4pGpgNpFHefYGYVhDkF+gD/MrP93f2pIrvMJDc58fZAqXl8RESkY2jrc4NXaMPvslLMbBC5OeUWEnrSxx0XW55YrB53v9/MHiKkZd3FzLZy91fyiv2EcGG3mAeitlwJXFGi3Lsl7usIZsaWt2+vRkSGk7um0+QRHax6PtOYi+xNScPW3u+TmXScc7n4CPhMdsHMUoQpFSB0tD7crNDlvFwnKDP7Vmz7Xe5e04T2NOZ98Im7vxpbfxm438xuIcwH1JuQ5u1Zd/+wwP4zY8s7NKGt8f+9Qumt2/t9JvKVosBO+/oBhYM6+ZaS661DKpNenE6mbgaOIvQuzk6I9zPgkRZuo4h0NdMnbl9D1T+69Ro3+I1uOyXeqNqxFnwllpiC+9mY9YqVvn9g7aztLDp5tegzrZJaZldszpC6t2b2q//svQUVg/9DmFcjM2bEoDuyO0+dMfcYQhqOK8aMGNScHz3S8b1IGBXTVDNbqB2lpGPLw9vgeC0pTQjsQGh7iwZ23H2Rmb1A6CXYmxA8eqglj1HgmGdHo4/OIAR37o+CO08XKP4wuaDzwcAtzTjuI5R3fiYiIs3TpucG7r7YzJ4nzKvXizA5+QOl92o1+8eWn3T3+rz7N4/+znX32Q3U9TwhsJPdb5XATnTxttAFXADMLHuhOf+CcKfi7q+Z2VzCaI9hZpZ090xD+7WS+Ov7WDPqqYgtlzXqJgo+7BStPuvu/ytnvw7wPmmxc7nmMLM+wG6xTfHXr3ts+Ywyq4w/jtWABU1oVqPfB/nc/QUzOxe4PGrHLyicSeMJcgHwPcxsDXcvq2OmmVUCB8Y2fWmezA7wPhP5SlEqtvZV7tDz/6Yy6TfjG1KZ9PcIkfqfxzaX9YUtItKAM6ut55DqRK/wHWFWuV5tps/mK5+gT/3nayTqa97pUb+YCl9Jr/qFu25Q+78vUlZlkwRX04Ohda9160b18G1XPjSqV/3Cq8eMGHQGsM7UGXOfnTpj7g+mzph7AKEH2Y8pI++zdHrXtPP+5Yh3eKkqWqpjaou2x0cwlftjtlnc/Uzgsmi1LyG4s3OBoreR6/l9mJkNbf3WiYhIM7XHucG1seU2+S7LF3VaODW26a4CxbIXb8v5To+XacpogK+SP8eW2+v17UnoxJtV6PUt19ax5aIXw/M0ZbROR9BRzuWOI5dG73F3n9dO7YhryvugkCuA7PQO3zOzLfILuPsi4M5otTtwSiPq/xa5EUVPu3u6VGERaT4FdtpXOUNLnyY3r8UqUpn03FQm/VtCr4ZxwOkt2DYR6bpe6u2L2LD6JRJey8DaWWy/8kE2q3mOfZf/heHPP7rFJvNfYPW6uSxL9O+X7rZLNn8zjvFG1Q683D03t2eNde+9LNF/6tQZc3cmjCzcgTC0esPYMddoqwcn7WTSwplAsTRaDXkq2r+1xXNWv98Gx2tJbdH264EPouVvmNkJrXScVbj76YTJoyFMGv2Ame2YV+Zz4A/RajfgRjPrVk79ZlZhZt9rqfaKiEiZRk2eSXPODcL+jXUjuXnf9jOzk8rd0czWN7OvNeGY+X5KLtXTe8BfCpR5J/q7uplt2UB9exfYr6u6jNxk98eb2dfL3dHMtjKzlkjh9kdyafaedvcmjaKO5uk5PrapwXqiNLZHRasraKdRL03REc7lzGxzwkiWrIvy2jgzfx7JQjdiI1Xy7lvQhDalCPNpQkjb+HyjH1iuLbWxx5Qg/DYvZDJh1A7AuWa2TRntHAJcGtt0UbGyItJyFNhpXycDLzRQZt1UJp0/WdsX0snU/oSo+4TnNlp9y+T143u2ZANFpAsaNflXz3Xf/71BdbM4ZOkf2G3FPavkJFpnkwybdn+R3VfczeDad6izKupI8GHFxsyq3JJNap5n+MqH+Swx+JH3K5M83eMQKn3l3htXv9iN3AniEuBW4E+bVj8764Cl1yxg+sT5TJ/49/uP+HizO0bWjbtjZN36bf7YpbWdSviR2RjLWbVXa6swsx0Ivcyy7m3tY7YUMzsYyEZTVwL/bo3juPtK4JtAdbTpKjP7SXThoVT7erFq6oqmHPtUcj/2+wMPRq9Z3CTg2Wh5T8LonnUbaNu2hJRyZzanfSIi0mRtem7g7tWENOfZ77I/mtkEMys6Mia6aHws4bf71sXKNcTMBpjZHwnfVxBG14wrMufG32PLf4hGgRSq80eEtMYQgkQvNbV9XwXR6IrsiBUDpprZOCsyEQqAmfUwszOBGcB6TT22ma1tZnfFjr8QKNgJxswObeA914MQ8Mt2hHvK3Z8tVj7mAGDtaPmepgQS2tkk2uFczoKxwJOElMMA17r7g02ts4xjrmZmoxoosx5h9Ew2FdsVUXCmOW4mN2rniCKjdl4iF/TpDvzbzHYv0c4UIZVedm6hq91dGTlE2oDm2Glfkwk9dZziudwbStc2CVjrP1sOWusPX0++ACxKXj9+v8y4K//bcs0Uka7mg6rUz9ao+/AGCnw21VZWsbxuAB/UbE1VTT2Du73+nxe7j0p9ULX52smVT7Pc+tLbF9WtsD4/e77HAdcPqJu78TeW/ilRQd1jg+veuf7Jnocvsfr62wfOm39pn8Snu23Z88mhAO4wd/lGB/v6idF87hW4zb1jZN0I4NyKyupEXW23c8Y+XFFWfl/poCYtfI5J/Y8g/EDp0VBxwoWbsUxa+FwLHH1AgR6vlYQfv/sCJxF6BwL8ucDEwwCY2RGs+t2cjC0PN7Nj8na5092bOzn0wAJt70a4+HAQq+bHvsjdP23m8Ypy9xlmdiThYkMfQm+88WZ2O+HH8MeEwG1vwsWI3Qgji9eMqljWjGP/KEpfczIwgBDcGeXuL0T3rzSzQ4B/EEYGjgTejNr2b8IFryXAWsAWwGhCD0iji18IExFpN6MmP8f0iY0/Nxg1ucnnBu7+TPRddjPh+2oycIKZ3Ub4LvuE0Al2bWB3YAywURlV559r9CB8X20C7EqYdD170XgpcJy7P1ykrt8QAgTrAHsBL5rZ7wm99VcSvmO/Axwe2+csd/f8iroad7/bzE4mjPbtTkgle7qZ3UEIGsyLtq9DOFcYQ8MT0sOXz8d6EV7fFLAH4Zwsey45D/hmifltLiN0kLmbkKXlHcJ7oj+wMyGVW/Y9t4AiAaICjoktd6Y0bEDrnsvlvXYVhOd6EGE+ooOBYbH7bwHGt8BDKqU/IWDyGnAP8BwwhxB0HkyYN+v4qByEwPIvm3tQd68zs4uAGwifcz9l1c5tWZMJI89OITzfj5nZ3wmpBd8mdNhcn/AafIfce/9u4LTmtlNEyqPATvvKTnBowIOEVET5Q3+7p5OpHwA3pDLplQXqSAMj7t92HTxhRvjQPwpQYEdEmmzMiEE3/ffx9S6s8uUbrFP39irRnU8r1+XjxVtS7X2gDlb4mis+6rnx82vVvn9gquaZbLH6dereehbYbPfld15cQd3pAAPrPhg9Zsll1z679IjhNfQcV13fg1rvRqVVYwar9ZjL6nvNqUj0rmfRE2sNquy+4rd7HHnToWuu9z5z3thsVzhm8/y2SiczaeE0JvXfgzBx564lSj4FnNpCQR2AQ6JbQ66l9A+5S4ANGnGMR8jlC2+q8Q20CUIu/inu3uppD9z9HjMbQXgN9yFcGDmd0ilhlxLmQvh5iTLl+CHhx/gPCBO//jsK7rwYte0jM9uTkEZjPOGi2tHRrZiXgLOb2S4REWmqUZOnMX1i+ecGzQjqZEXfZbsCvyeMDNgImFhilzpCp4bbSpQp51yjljAq+Ex3L5o2zd3nR7357wI2BzYjN3I133LgdHe/s8j9XY67XxldML8c2AbYKroVs5KQCaVYoA3KOx9bQchKcI67f9JA2UFl1Pka8B13f62BujCz1QkBCoDZwPSG9umIWvFcrmCnrTyvARe6e6n/85a2eXQr5e+EQHBzf1Nk/ZUQ0NkYGGtmF+a/x6Ig8Q/N7GVCR661gEOjWyFLgF8Bv3T3+hZqp4g0QIGd9vV7wpfVHCBDmED8C9UJY1mPyl4DltVcBezHqr1x4nWM2/q9+bw5pB+4O2aPtHK7RaQL+KxinWc+rNpsgzk1r7NF9ZP09pCuekD9J7xH7y+CPXX1Vd/Ydfk9/32nbodPSazSK79uzIhBdYseWO2pysS8H4NXvF+9zcBF9YPOraGqDqCaPryw7OC6HXvdWWEG3RNL6ZVYwJIBq9N72Lw5q+05a9s1B4XpQoYMez3F9IkDGDV5Qds+E9LiQrBmNyb1H07ogTicMEnpYuBF4BomLWyLERR1hDzs7xJ66N6QHf3RCdQQ2v4mIY/3de5eztx9LSLqgToqmuvmEEJv4qGETioJQvqRDwm9Cx8Bprr70hY4rpvZ+OgYJwCrA9PN7GtR2gjcfTlwhpldQki3sw+hV+eahB66CwgpKJ4hjKh6srntEhGRZgrBmt2YPnE4xc4NRk1u0XMDd38Z2CtKMZT9LluX8F1WC3wKvEz4HrvF3ec0pnpCp4aFhBFAMwk98u9294/LbF/GzLYjpEE9jJBybSDhOs5C4HVCCqpr3f2DohV1Ue7+aJSm6wDgG4TRV0MIHUNWEl6XFwkBkNujOV7KVU+4kL2IcD3nRULn2rvLTH/2fcIon50JI7rWiNq1HJhLGFl0F/D3RqTeio+auLEzX1xvg3O5lYTXbgGhs/QLwIPu/nRLtL9Mcwjzae9MGDWU/ezpQ/jce58QzP5LS7fL3WujUTvXk5trp9CoHdz9T2Z2C/Btwv/R1oTPoQrCyLQMcD9wcxnBTBFpYaaRuu0rnUytRjjhe5vwQQ7AygrjlBN2Zn7f7px2b5o90p/MTmXS6+XtezDwa0KUvfLZjdd46YoDhh0340fXNXkyNRGRrKkz5g7oXb/gvr51n+5Sk+jBrsvvoYJaDHhl+b4srg8ZC1ZPzGKJr0G192W1ig9YWr9aba13e6ui75K/b8bT335r6e5ruVf0hHpWer+Cx9qp120rKq26x5L61ZlZ/XXm9xtAbbcqetYvYp9lf6GKGurrEq9Pvfin4+vrqsYA/xj7cEWn7IUmIiIiIiIiIiLSHIn2bkBXl8qk56cy6WpgWnz7wt7d+Kx/D+oTxsNbDnLCyJ4vpJOptQk9ODYjGnm149uffUdBHRFpKWNGDFqwNDFg3w1q/vfputUZZlds8sUonfW7vQxWT8Kq6dltPtXeF4D5detR7X0q6+mW7LV82Tn96j9fP0Fdz5XeJy+ok+tU0DfxyeOVVr1FLZVTn++1z5wFa/RjkIXMFMsT/Xi413eWAEe+9NDXD6mvq7oP+BEw7Y6RdeveMbJu8B0j63ojIiIiIiIiIiLSRSgVWztJJ1P9CLlXhwFnEXKHZtOKMGBpdTatmr00dLUfpTLpq/KqqGDVwNwSwpBdEZFmmzpj7raE3Lu1z/Yc/WQ9FYdgxsqVfdio5iU+7rEeq3V/kyH1b/N+RYrq2kq61dbiUG/RZ1Nf+wx3Y+Oqp+iZWMz8unVYUr8G81Zs+Oga3d/ba25dmHN+Sf3qHzFq8juVcNheANMn/rEeG2f48g8rN31vaWK1SYya/I+3flG3MblJdbsRPjtPBT67Y2TdnmMfrmgw97SIiIiIiIiIiEhnp1Rs7SSdTP2YMJEfwEfAhPoov2Ui5M0/Y8w5ez8EVGXGXflykTq+D5xICOicn8qky5kMTkSkQVNnzL2HQpO/urPr8rv5X/c9+Nryv32x+f6e40jUsnRFos+1Wy16cs8eiSXbDq58EzPn6aXfBiBBPXV0o4/N81qvshUMyFbKVj3+9fW++591/5eON31iFfA9YDGjJt9xx8i6Mwj5fe8ETgbWj0r+dOzDFa0+abyIiIiIiIiIiEh7U2CnHaSTqbXr4bnaChvSrS48/w51rw/pV5Gcsyhb7M5UJj223RopIl3a1BlzLwHOBMAdzMCdhNdSQR2VVLPfshtIUE89Vr/E+if6+gLeqdyGBHUPb1j76kiAj6uH8U7Nzl+qf1DF68yt2wyABNXs1OsOElb/C0ZNPi9ermb6z/5cRc2xALVUPl1J7YGMmvw5wB0j664mBLergZFjH6546o6RdSMJQfLPgcPGPlwxq1WeIBERERERERERkXaiOXbawezVe56zvHvFkG51Tk0izFhhUNGzuvaLMg6ftFf7RESAiYQUkRMA1qj9AID6RBU1iR4stz51L3Yf+dq8xJDZhi/r5wswYKPal1hsq63lUO8OS+Pz6kQdCfon5rBR9/+yXuXzHw9IvP/GNj3/RcLqAUblN+IzG7D/QwzhI3pSSe0uwB9id58E7AGkxj5c8VS07RfABsC2wIw7RtZNacHnREREREREREREpN1pjp12kF63/7b7vvwxAFX1X4zY8ak7rmdrLq1m7fnL6vd5Ze7Z7dlGEenyHNgJ2Bcztqp+jGd6HMRy64d5PavXfVix5conNu/OSupI1FRQD4ABq9d/vKVFK4Mq32bR8vXoV72QtZZ/vLyi19KePfrMrV1Wu8Zt643+3lFMn7gz8CDQG5jD9Ik3AVMYNfk1gCNt5wGvJ3ozwFfy7/oHWZvqIdkvrrEPVzjwRF673wB2iZYHAefcMbLugbEPVzzcek+ViIiIiIiIiIhI21Fgpx0MXLCiem6/HgxatIL/bDmIe3Zan54ra55f1KvbFjVzl3Q75L8fnJPKpJe1dztFpEs7BBiXXZlbMZQ9l9/BB5XDqKEHW9Q8iUX3VVBfBSES9HFiAxbWDPF17C0zc+bXrcfyqj64Jdh48Vs96fsxVNX+sff+558GwKjJzzB94trA94Eroiq3A7Yc/PDM3ljv3gALrDuvsBorqgbds2mh1k6fmAA23X709mc+P23MC8AvCcEiB+a37FMjIiIiIiIiIiLSfhTYaQfbvL/g4Q8H9Bh16YFJnthicNjovgNmfLx6L55KDXw8075NFBGZk11IeA1vdNuJBTaQ5YnerF+b+SKoAyFyYkA9zvL611has4Y9XfsdKqihju4AmDv0Www9qgH2WeVIoyYvY/rE2tiW7gAfjxy+9PD/PPDakzZo8019MetUrP7xq933vKJgYAduA47YaPjzH2w0/Pmd7vjFRU8D3wMeGftwxcwvSk2fuAdwO7Ac2IdRk99typMjIiIiIiIiIiLSXhTYaR9T1lmwYt1jH377mO513u2dgX3u61FTN2pxz6qes9fsXQcsaO8GikjXNmbEoKenzph76MDa98Zvs/Lh/Sup5qkeY1ia6E+vmkU19STqDe/+cre9WKvuA4bUvU3alvA+y+hfm8RIfBHUAapX6/HecQyY/3ugP3B1gUPeAGwODAMmZTfeVf/Ilk+x1uGbsfDdNUZd8Pw2hRo7fWI34IhobT1gr7EPV9x2x8i6D4FP80rfQUjRxrJFfWdOG1k3A/gt8NTYhysWRfUNBoYALzJqsjfqiRMREREREREREWll5q5rVm0lnUwNrTNOqq5MnLmyqqKyR3UtPWod4B1go3rD79tunfPP/uv0n7dzU0VEvjDj8SenrEj0Oqebr2Czlc8yu2rYdUb9Ju9XbbFnjfWgd/VCBs7/uP7T7k8nqru9Tt+lx1BRvyZAXU1FxZ8XDuizaV1l5Ss7Lb/3p+vUvVXFqMmflXXgEGBZH3iOUZPrGyh7LzAamAvscMcvLroAOBZ4G9hl7MMV8+4YWWdjf3Lep8DqAF5v/P0351KzsieEETy/2/O7f7ZBQ989HagC/sSoyT9o/DMmIiIiIiIiIiLSehLt3YCuIp1MrQU8v7BXt3N61tRXDlhWw2vr9s/e3Rsg4diBz3/Yt90aKSJSwEdVmxwzv2IIcys34tHeR/DSWp9vuVn1M7usXjeHPvWfs/riz1nGmoleKw+i5/L9WN79UepZkgb2+nzNASPrKitHAj/+b88Dv92IoM4WwBvAM8CNZexxKLADsPkdv7joY0JQB2DjwRu9ceIDYz+cA17/0PXHD8juUFdXifsXSeV6du+15Jy11nvv/whBHWpr67996ZEH9mb6xGFMn/gPpk+8nukT+5XVfhERERERERERkVaiVGxtZ2Ng9QU9K1l9aTUAW723AOAp4G/A7wkp2G5tn+aJiBT1ITBoQN1ctl9+HysX9t3JE86uK/4BwBN854teAt1rt6Z77dYQPsve6bdw8Rr1iQRL+vQCs5WNOObeQDbQfUiDpUdNrgWeBxg7Cu4YWfcQsM9qa89evNs3b570z8vPqQTj8zlDE//952EM3vAtZr28LbXVPb6oIlFRiyVyA4Myby/oC/wbWAGMjDbPAX7SiMchIiIiIiIiIiLSojRip+08B/x7/c+WfbGhKmTBqyCkBTJgMTCvHdomIlLKnsA9yeVPf/z6slG8uWIkM1YczP8SKV7s/jXm912NeFLPCqrnjeh1833dE4sf77mievXey1bQf+GSh4GbGnHMfwGfRMs3NKHNo4GR23/jnstn/vsbq3RieO/l7Xjm799k7rubrrLD8sUDal59dNTrS5bWvfXMzE944LEPAUa4+4p4MQCmT9yN6RNXrUBERERERERERKQNKLDTRlKZdC3wfuWXpzSqJdf7e33gpHQytUcbNk1EpKQxIwYt3WrlI0/MY+jgGu8FQLf6XpzKdrxbuRW13apY3LvnF+W7J5a8YsaMnrZo4+y2Hiurnx0zYlDpeXLiRk1+B9gE2JhRk3/c2DaPfbhi5diHKx554rajB7z9/Aiql/emstvyhnabn3ly75/ffvVuX3/i2bmvRNt+Y2bHA1cDvwQuZvrES4EngNeYPnGfxrZNREREROT/2TvveCmq8w8/7+6t9F4tgKB7URCsCKigWFAsqEjUWFaiuJLEQjQhzZhfEkwIalRcUclirIiKKIoFBQuIHaXcBemi9A63757fH+8sO3fZ3dspch4+fO7szJkzZ3Z2Zs457/t+X4vFYrFYLJaaYKXY9i3lZIgM8Ni5R/eZe0wLTl+0gRvfXxrxGH4L/D7fl3d/Xjh/5P5ppsVisShT5q5vBvRv6j369q1ZbWlQtJH6ES+fZkRZQBkF3u1I9jrKPG2IZDbEE42Wnlz23ETBnNUp6zNWlpzIOm+n77JKyv5R5YMPGL0TjWSsNkW7Gm2PLZeV5KYrCtAKeKZh4aDvGhYOOnZ1q0syRk6aFrMG3bKn1IxR5ztLGcAA4L2atNFisVgsFovFYrFYLBaLxWKpCtawsw/I9+WdisoCvQScA3QBWNmyPu/0bAfAGycdxuedmsk/n/3a06SgFODc/dRci8ViAWDK3PWZaB6wY7Z525QiwqZmzbmvaAsLKeOsrAwKWryF8ZQg0SwyNlxU1iSy65+ZkchogCxPAdGGxSWH89UbR2V+cwKMnrkfTuM+oDEqeTkDOAz4E5CZZp8uQNORk6bFpOCY3D/SCNg1ZKY3CjwBPIDmRXsxWQVjhw5q3fHwhrd1PKzBpp7HtXiQAaMrjFYaO3SQZ+SkaZWParJYLBaLxWKxWCwWi8VisRySiDF7a4NZao98X15rYDlQD03AnY3m0ynZVi8za8TNp1KQnYFEDUYoCz0yZ3eTgtJGwK/ywvnj9mPTLRbLIc6UuevbAj/GPjeMbGKntwUAxSaKN3M7W1q8vqd8s40Xk1e4dMFxJR8fB2CgLELGVxmUnaIf6c+A0R/s05NIYHL/yP3AHWmKGGD8kJnegGufB4DbgUXAGUNmejczY1QroIABo3e5dx47dNDhQIcOhzUIXXpeh6O8HmHbjpLXmlw29pJUBxw7dFAu8BlwLPDYyEnTbq3u+VksFovFYrFYLBaLxWKxWH76WMNOHZPvy+sKLExcb2C5wKQlbRsOfPrMTrsXHNl0B/DYlH/OmgHUywvnb9rnjbVYLJYEpsxd/xRwHSa6sl/Bsx1KpB6f5F6CkQyMiSINX2djvd1kF3Wk0Y7TaFW2Ylafoqk90SiZH1B5s1h0zHAGjH58X7Z/cv/Ir9AIyAnAWcCvACJiWNhlp2m2PVOMwMcnbgF45qo32t8wZKY34trfC5SiBnlQo9C7Q2Z6FzrbjwYebtxynel+0SOrZn2x7Noff5TcXic0Mb1PbiYAxSWRDdlZXj9wGTCFAaPfiNU/duigi4C/Aj1czT4OaAh8DvQEjgReHzlpWkntfjsWi8VisVgsFovFYrFYLJaDEWvY2Qfk+/IWAXlAQVGmp8gbMc0eP/doM+P4tm+9OOaDLzKj5m5gLirX9mvgXmAtcHZeOH/p/mu5xWKxwJS563Mv3BVs9YO388oG0S18Um8wEckC4Pii99nlacyyrBMBOKb404u7ln7yBfACcAaAgSLRZ9wlDBi9Y1+1e3L/yMloJAxABPDGtoUuWx356rjtXqKQW+yhMHePAtpZ6/r3iEvGzRjV8rUHfze1eHeD09Coy0ynnjuGzPQ+OLl/5A2RyAUX/HIs9RrtIBKBd54IAMKZ1z1Kbq5h4ZKt8449uulxIpJhjCl76uXvXti8tfjxHl2bDT68XYM71m8s5LNvNsaOWAYUAI3Q6KGYQenFkZOmDa2L78lisVgsFovFYrFYLBaLxXJw4dnfDfip4+TXyXM+1nu+b8etPxt5BjOObysZkejAjKj5IyrPdiZwK/B3dOLwCMC/XxptsVgsLgb3al2YNeAvqzpGFj7Ywqzd1b5sCQAZppj60S0sz+yhBY2J5ka3b5uRfcZzKzyNOsf2X5bZ0zOlwe2/2suoM2OUMGPUEOe/UPtEXMte17JZcViBfvZASVY8rc0V09uOcrXvamDtRbfdd8LxA964B3jdVc/dk/tHBgLbvZll1Gukp+b1QoMmu9m1uR2vPHYhjz6dz9yvN/YQkQwAEcmIRMzPgbf6nNT6tqM7Nub0U9rQ6YiGsaNmoEYdiBt1AC4bO3RQ2xp8FxaLxWKxWCwWi8VisVgslp8I1rBT9/SJLRjgy6Oa5UQ9OldX5vVQmOVd5WwuA/5G+Ym8fvuojRaLxVIxA0bf8WqD208pNZlFmChlZLDRc3j02OKP2Fyyii0m6lntiXxQXPpSv3y+a/elp0F0m6cFOdFdWc0jP/wsSY1/Al50/v+xtps7ZKb3K+Am4KvYupKMKICc/UlzJGpotHMXl7/ZMnrUqnqcPacFZ3zefMDk/pEsp/j1gFeE7KNP/aQzKudW7GxrC7wK/F9ZSfa/V37b4x1j+PG7xTlRE4VzfvEwfS55n2jUsH1nCZ9/s7HEGFNaXBKhfm4GQD1jqvQOzkCl3Cw1RET6isi/RORTEflBRIpEZJeIrBKR10XkLhE5LMW+/UTEJPkfFZHtIvK9iHwpIhNEJCAibarQriYicraI/E5EXhKRla76V9bCeR8uIjtcdc6qaZ11TcJ3YETknkru1yXJNepV1+21WCwWy8HJgdo3cB3jQhF5QUSWi0iBiBSLyFoReUtEbhaRnJp/C7WPiMxK8d1U5f/KGrZBROR8EXlIRL4WkXUiUuL0iZY5fa4RItIixf43pGhXRES2Or+RuSISFBG/iDSpZjtbisjdIvKxc21LRGS90+ZHReSiCvbPFJHrReQNZ/9iEdkoInNEZKSI1K9Ou/Y3InK8iNwrIh8591Khcw+sEZG3ReQeETkmxb4d0vyudjr3+jci8rSI3CEinWqhvVXqb4vIX6pwLzyYpp6JafbbJdqnnioiN1bneSEiT7nq2ywiWRXvZbFY9gXWsFP3vAysB7XY9F+wvj3wLhACrqpXEjkJGAbMK8r0JD4ce+f78hrsy8ZaLBZLBbywLrNLDsAZBS9xbNknngcj2fwxksW9xRtYa7aLoBKfO9gSbRLdxGGR7zi++P2rpsxdnxiVc3yK5VpjyEzvk0CzKIYnhqxi5O8X8tC1y4tPnL+ak7/5CE+0mHWtwp7bnzqKS2e0RZAtQ2Z6Y7ls3nT+GmD6kJnetylvcM8ECobM9N7V4c6h58k5o9u/9uHnT3e/cCJNWq+n41GFdPM1A6Bhg0xEJDM7y0uvE1oVAUx9ZxVLVmzno8/WsXz1zsqczq/GDh10eC18LYckItLdGVx9BNwFnAK0Q6Nm66ORsoOAfwGrROQZSTGJk6x6NNLqMOAE4EbgUeB7EXmlkgPFr4EZwGjgcjS3Um3yOJq76WDmOhGpTHTfDXXdEIvFYrEc/BzofQMRaSAibwLTgKFARyAXyALaAOcB44FvRcRXyXYdMojImaiD13Q0z2YPoDXah28IdEL7XI8AP4rIwyLStJLVe4Am6G/kVOAW4L9OPf8VkVZVaOd1wGLgn6hjcBunja2cNgeAh9Ps3xGVn54IXODsnwW0AE4D/g3MF5E6GW/VBSJypIi8CswD/gz0Re+lHPQeaI/mUf0LEHYMWnlJK0tOA/Re7w78HLgfWCoi74pIzxo0/UDsb9dHxxUXo46K31TluxKRhsAVrlXNnLosFssBQMb+bsBPnbxw/qp8X944NDk2/eav5ZkzO30Y9gf/BoAfzhx7/Rvb62eNL8rO4KTvNnHn64t25pZGGwIz88L5u/Zj8y0WiyWRDCMe6ke20tysBeA9UYWwEiDsOZZTowvAROhgMgpxOraF0qATcAczRj0L9AQ+QQfJpzn1jqnDNs/a2Lzkhm/zVC7tu467sxcdVZ/Pep4BwIenQp8vC2i3qZ5BB07KgNH/YcaoD4BiBozOB/jVn+evbLElK5xRJu3Pmtvi4fsfaLkq4Vj+3FxzHHAiwO6CUgA2bCrM8h3VRJc3F0UBflhfwA/rV1flPI5x2vf7quxkARG5FHgGHdgALEcjxeagzhdeNArrDOBSdPBzDfAF8GCKaqdSPtIsBx3gdwF6A4Od4w0GzhGRYcaYF9M107W8GfjSqafGDh4icgNwPnqurWta336gEB3Ed0Kv0QepCoqIB7guYT+LxWKxWMpxkPQNngUGOstrgLHAt+j77Wjgl8BJTv3viMixxphKeQvtI/zEv99ELkEVSwCCqNErGSUp1qdFRALAQ8TnvOYDLwGfAxvRa9MeOAu9vq3Q7/M9NCo/GYntrA80BrqihoeL0X6HHxgkIlcaY2ZV0M5fA/9xPq5BDQOfAJvQPqAPNdYkNdyJSDPUcfgoZ9XHwDhgCToOOw/N49wReEtETjHGfJ+uTfsbEemNXoOWzqr1aP7WD9Fc1GWo8eo09Nrlod/Rd8DtKar9gvKpDrKApqhh7jTUwNcMGAB8KiJ3G2MerGK7b6Bm/e3zgB/TbN9UyXpuRH/nMdoB3VDjdWv02fGWiHQ1xuyuRH1XAvWc5Z3o78qP3k8Wi2U/Yw07+4ZvYxmwmxSUlf7+pfkP4oeej93UpOv32xdEWtRrX5Stl+KLLi247ld95ky+/6N7gS99oYAXfTBnAU+E/cESXyhQD2gR9gerNCNosVgstcDPgb+cXfj0eSVkZWdRwtVmBY+Kj0Z4OF6acHq0PVkUEqGBZ6OnGUVSn4XZfciN7jgJ7VC2ARYtntvn5G/fG/gROmA+fcgAPq2jNg/LLvF81nBXxv07G5TlNNqZQYcfO5JVsoSSLPBGTElZJiOBeUNmej8ut+eA0fMS6rprU7MSH8BzF//Q9/494w1l5KRphhmjLgRGrPphp4SXbb8QaPL5t5s6bt1RQqvmOZM++WoDqNdlKbAM6IAOLitDfpXO3IKInApMQt+jBvgD8G9jTGmS4q+IyG/QiI/7Kqh6mzFmQZL1M4CgqBTHP1BjXAPgGRHZaIyZmaK+R4BVwBfGmBVO21dSQ8OOiLRFvRABbkMHxjXGaduRwL3GmL/URp1pWA0UoEbh60lj2EEH5DFv6inA1XXbNIvFYrEcbBwMfQMnaiDmFZ8PnJpgtPlERP6HGqeuBg5HlUAerKCNe1FX7/RYfybFMU9yfdyQ4nurFiIymLgBphjNZRwyxpgkxSeJyO2oUefeCqpO1c53gAedPtcjqHxyS+BVEeljjFmYop29gQecj1OAa4wxhQnFPgaeTCN99SfiRp1ngeuMMVHX9g9EZApqFGmDRu8MTXeSKdraAYhdz/4VGayqixN9NA01uoAa536fwgDxuoj8ATXK/LuCqnen+Y2FnN/AXWh0UCbwgIhsMsY8U8l210Z/e4kxZmU19ktkRcK5LkANv08Cc1Ej4RHAL4gbFdMRM4gtRI3XvwfOE5G2xjientVERPoBsedfx1o6f4vlkMJKse0btsXccL3GZJ68bHM3gCM27X76y87N229qUt6ZtCzTe97g3/ZrmxfOL0E9fh7vk7/hkd+/NP/zn/156BvAVmCVLxSozEPYYrFYao3BvVp/PbhX60u8RDO2eNrwfu7V3Ci7+TryGl9GXubS4pdpQDGZeMiloH7L6Bo2ZhxOoafRxhOL33kFHVAAdN24quNQ1AOoDfCvyf0jdaX9nNlkZ+YFox7rkjPsxSO4+4nOtNqazW1PHcXAD1px3ZQjXhj5XMNH9jLqJGedazl5R3bA6PUMGP3nl95cWYJORDcBfly6cseGOV9ueM0Yfo56ZP0S7VhXZNSJoIOMS0dOmvZ0JdpocXAGwS+gEzcAAWPM6BQTNwAYY8qMMU+isinfVvfYxphtxphbgVhemExgoohkpij/b2PM5HSTINXkUXRwPM0YM6mW696XTHT+XiEi9dKUiw0+56CemxaLxWKx7OEg6hv0dS3fnywSxzFUuI0RfRPLHGqISEvifQYDDDbG/DeFUUcLGVNojBkD9AKqHc1ijFlrjLkcld0HjeZ5IkU7BY3O8aCGu6uSGHXcde8VuSQiXtThBdQB5lcJRp3Yvp+jBieAK0Xk6Mqd0X7hWeJGnfuMMbeliyoxykvovZnKeapCjDEFxph7KR/VExSR5pWs4oDvbxtjtlPeOH1eRfuISBfiecP/BzzlLHuJR8hbLJb9iI3Y2Td8iL60r0DDO2fn+/Ku9V7dI5aEm7abd7OtfjaFOXsuSexldlSXH3fwm9cWAXQ/5sft3f2/7E3U4wGN5LltX52ExWKxuJjd1Gw8Y5enCcWe+hwVLT8OcWtK9Sye8ecTTj/7/5ixJhf4FDjVwNaTL3rlZ2+OG1lWVpKdgQ6iUg5mqsyMUeOAwdGI51n4ax/gtIYFGfQIN95T5Ii19ThibT1w8qBVkvuBIvQZXZFx/Sbnb1Piz/Snrrig44KWzXIunb94S6uPP6/Uob3AxJGTph3QsgnVwRcK9EC/p56o5+ouNNfME2F/cF4tHOI6NCIK4B1jzPjK7miMWY1GitSUvwEXoVIpRzhtmlAL9VaIiAxFJSp2od6qBzPPod6YMZ3v/yUWcDyhL3U+TkQlViqFiAxCr00vVI6lCL3+7wAPG2MSZRfd+84CzgRWGWM6OFrktwFDUPk4UO3854BHkk3OJNSXhXpdX45KZzQFtgNh4DUgaIyxUr0Wi+UnyfiVY3qQom8wvMNd82rhEAdL38CdpyPlOwhY6VrOroW2HezchuY2AnjCGDO9sjsaY6pttEtgBJr/pT1wmoicbYx5L6HMWcCxzvJ9xphiqk4X4mOMOcaYrWnKvgn8xlkeAvy9GserU0RkAHGZ7oVoNFKlMMZsQaNJaoQx5n8icgkaddUAlXZL246DrL/9hWv5iEqUjxm6osAzxpgfRWQu2l++Ac0LZbFY9iM2YmcfkBfON3nh/OFo2CPonOegRYc3ubbvovXfnr5wfXH3VVtLCnMytqGD9ieAmFf2P5rvLF4Wqyu3OII3usfZJJbY22KxWOqMOVMXNZ8zddE7c6YuWjRn6qIBzuqB2abwuhOL3jp/UdZpfyui3i5QtzjnCfUp8A3whsQ8xAaMLpz20F0DC0obFgo0za5XcG7vnz/7DBr2fsaQmd69PMyqxYxRPdFOddvignq/wRkgbG5cwo76ezljLqC8Fnpa1vXvEVnXv8fD6/r3+Ou6/j22pyo3duigIcQHlW68jRtmflEvNyNwao9Wl3c6ouHmShy2BLht7NBBa8YOHfRgZdt6IOMLBU7yhQKz0YmaW9Fr1M35eyvwtS8UmO0LBU5KU01l+IVreWwN66oWjuek2wh4+b44roi0IJ5o9/cHup56RRhjNqHSHBD3Tk3kZ2gEXCEqsVMhTmLqacDr6ETH4ejEWGP0NzkSWCwiwypZXxc0UfP/oQl5Gzj/T0R/g++ISMqJN8eLdgHq+Xk2amTKRBMg90Vzky0RkZMr0x6LxWI5WBi/csxJ41eOSds3GL9yzOzxK8ccKn2DJa7lI9NU5962uCbt+ongfl/fn7JUHeJE3jzmWpXs+v4sVhyXQUJEGolIFxFp5UT1pKOZa7kibzG36kC/CsruL9z35kPGmLL91I4HXMtp++213d8WkaYicrSItBORunDEj6RYTtYWd97KGcaYWP6fmHOVT0RO23tPi8WyL7ERO/uWZ9BwxwjwXNgfLASOB/CFAq3RsG4f6nnxEfB02B8M5/vyuqCJxfu+1bNdfmmG14sadSbvh3OwWCyHHrcC5zjL/5kyd/0wGtx+Oxpl0xZYNqveVRfkmF3vFEjDnFLJuf+S09qPTFZR4c7G/ynIbJRbD1WT2HVEw8whM70VaSJXlbXAbqB+Tv3d28QTmT/rxK2nvzRwLRllwi3Pd+CYFQ0Apg2Z6b2olo8dYzw6MQwasv4zdLJYcrIz9sht1MvNqIxnZRY6uQxq4Hly5KRptaZDvq/xhQIXosk2K5Kg6w185AsFrgj7g29U9ThO1MSJzscC4P2q1lGLvO1a7iMiXmNM2sFULfAwqu8+F02i+1NgIppwur+IHOF4Tru5wfk7xRizo6L5EGfC5BXiz7dF6CTQt2iS2PNRT80cVN++wBjzfJoq66HGp7ao1MU7aKRNV9Tb82g0smcU8Jck7WkJzHL2B5gOPIl6Y7dFk3Zf5Sy/JyInGmOs3JzFYjnoGb9yTJX6BuNXjrlieIe7fup9gzeAH9CojztEZFKiHJvzHvuL87GMFLJfhwoikkdc9nmJMWZ/GrreRp08QN/9ifRy/q40xmwXkcuA3wFux431IvIy8HfXpLobd/Rukwra495+bKpC+5l+ruXX91cjUDnfnWjUXJ6ItDTGbExRtjb727OBdq7PBSIyG3jUGPNqDeuO0dW1XJH883nEo9/dkfIvoMavbDSi55NaapvFYqkG1rCzD8j35WXkhfPL8sL5z+f78mYCkbxw/kZfKHAY+kL/AH2JtXJ28aIemU+DRvzghM3mAXfv4/ZbLJZDHvfk6SpUCqilu0Chp+GaQhp2AloN7tX6mzR1Nf1000V0afsFu6URKzO7LTwqTeFqMWD0OmaMOv37H3cN/eKbHSuj0egdXx6nwTVlGYZ5edvNMSsaLCZuLKkLVhOXRngO9aK7G+CdD9cU9DqhVb2Nm4tY9N22Bk4ZQ3kFu1TsBDbUdmP3Fb5Q4GQqN3ETIwd4yRcKnB72B7+osHR5jiPez/lmP3r9YYzZKCJrgMPQSK426GRNnSAiF6PGxFLgpmR66wcpb6K//1Zo1E5swiQ2mXOq83FiJeu7kbhR5wPgfGNMkWv7ByLyKjrxVw/VWn/T0ShPRkt0kNs7Qc7lKxF5CzUctQRuFZH/S2Lcu5+4UeefxpjfJWx/Q0TmoJMIDdEJvH6VO1WLxWI5MBm/cky1+gbjV445fXiHu36yfQNjTKEjCfU6Ohm7SET+jTofFKHOoL9EDQFlwC372ZBxINDDtfzl/mqEwzeofJUHvVZ7cAxyec7HTSIyhrhMmpvWqIPdlSJysTEmcQJ9KRrZnwX0EpHsNJJubuNSWxHJTJdXal8jIm3R8wVYa4xJnst0H2CMiYrIN8RzVh0N7GXYqYP+druEz/XQfuo5IvIa8PNkubYqi5OT6Q7Xqoqi22MybDuBKbGVxpitIvI6Ko08VERuS5cfymKx1C1Wiq0OyfflSb4v7zmgNN+X90a+Ly8zL5y/DrhmzondF9YrKl3cJ3/D5JOWbvoWld1w09IXCuTtXavFYrHsW3pf0vUpdBL1D6jHuDdJsYzBvVqvrcCoAzCyaFuj7xdm92VlVncQGVzb7QUY+8T80hffWPHrz3Z7gu/0fv7oY5bXB8ATJbq2ZdGNQ2Z684bM9C6poJqacOHalu0feLP/ZQ+OueVvC9DcILOBH79buaPe068sjbz1wZrSaFxaszJGnW+AM0ZOmnbQGnaAB6n8xE2MHCrOZ5SMFq7lquRRqivcsnvNUpaqIU6emaDz8T5jzEEb3ZWIMwH3rPMxMWHrDc7f74FEHftUxPIUlgDXJhh1Ysf8FM2FABqF508sk8Cfk2n0O1JysWTKLSnvMYmItAaGOh8XoM/bvTDGPIJGAgGcKSI9KmiPxWKxHOg8iO0bQJK+gTHmSzTX0L2ow9CDqLPBHDQi/GTUe/5UY8w+yd93gHPAXF8nn15sEj7TiRaL0Zj4eKobatTZDNyMGjdy0Ov+olOmBfCqY/xwH6OAeJ+gORrxsxci0o7yE/pQPofTgcABc+0c0t6btdjfLkEN29cAnYFc1KBzHCoXHnMmuhh4sRLyfHshIm1F5DzgY+LGqlnEf1/J9mnmHBPgJee35uYp528j9pHMtMViSY6N2KllfKHA2ehL8rXbjm19Ur+F669yNl0AnJDvy/seeGB941x+/uEKBn79I0CbVS3rD7z9xpN/B4xGJ/i8qLZ6/r4/C4vFYilP70u67gm/njJ3/WWorNDZriKfVaaeITO9S6bMLfmT15RMjEgWWdHCvFde3VQv8p+mlzlFnquNXDubmrYasLVR09w3BlxJaWY2zbdu484Jnb588YIfr5p9w9F1Ll005pa/FQI/RydwhxRl5SzMKSnq4yrixW0gM16ySrtQ4l0D3pQ50aMjJ02bV0dNrnN8oUBPVEKlOvT2hQI9wv7gvCrs485xdCAkmne3IVn+pdriftTjbzEHYGLcWmAiOjnRWUT6GmM+djwQr3W2P10Zj0kRaYNOpgBMr0ATfTwaHeRFkyE/mKbss2m2fe5a7gTMd33uj+bSAZhQgVTfo047cP7OS1PWYrFYDljGrxxTo77B+JVjegzvcNe8KuxzMPYNrkQnfeun2H4BsFNEfuckkD+UORCvb0yauRFxQ08DV5kcoBg42xjjdpCbh0ZDFKIOdq1QKddfJxzjz2hfIAv4i9O/eQTN0dTQ2Xafs38sugfUeHAg/V4OxGsXI9m9WVv97dHGGJNk/UJgoYi8gKZpaItKBF9N+r4mwMw09p9i1ChzRwV9zavRKHSIG3HcvEU8it6Ppp2wWCz7ARuxU4v4QoFfADPQMMXnHjvv6A82NNqTPmEjsAzN+7DzqHU7OW7Vtj37Ntpd0g0N43Q/ge/0hQK5+6DpFovFUmkG92r9AepZHhucFGaawnUl0+59YFMouOED/5Klk/tHkmlJA+A1pV9GRMcUJZ7cBi0X/zAJlZ58Grinpu1rM3Pe4ROv/OUfXx14LaWZ+gze3LSUGX02Df7tE1021bT+StKRuFxd+4LcBuemK5xRdgRlGWmNOgWk8MI7iPhFxUXSclMVy7ulChqkLLXvcHtG7qiLA4jIuejgyqCSEKnkOCpT10QRMan+E0/WfE+6ciLSr8Yn5sKJhvna+XiD8/c84hJmEytZVTfXclptcGeiLCZv0z1N0U1OZE4q3BMoiZMElW4P6qkdI117LBaL5UDH9g3ilOsbiIjHmdR9CPXkfxN1AmiETrgeg0byZKPfwxci0jnZQQ7Ud3odcLBc38QI4YkJRh03d6EGGYCrEiM2jDFfo85kMSmsW9DI3xI06uR59PqOQ6P/Y+wl6SUiK9P8Rtz5WGZW8HuqDgfLtavV/nYKo457+zL0msa4ubrHcpgHjE0SgZNILEJ9JfBhkna5o+j7i0iHVBVV8FuZ6Sq6Ik3ZlZU7PYvl0MMadmqXU1zLA4ozvTm/vfYEHh54zDrgpLxw/qa8cP72cecffde9Q49f8HzfDtsLsrxsaJTNgxd1LaH8oB50YvCf+6z1FovFUkkG92q9Gc3t8Beg/6Dd4/tl5RTd3uLw1S1Pvujlo9BBaFJaR1auaBTZaAAyTBElm3N1ECpR+p4x/XrGB1cwPnhbqv0rwWlGPM1dn0tabs56/qYXj1wAbJrcPzKsBnVXil9P+D9Pw53b5mIMvqXf0nR7entSWcYKop60zmkrRk6a9k66AgcBPWu4f48qlnd/6a1Tltp3uCUmat1DUkQaAI87Hx83xnxU28c4gJjo/B0iIrnEB59zjDGVjchzPyPWVaJ8TOu9eZoyuyuowx1JlChpWen2OAl8Y3kh0rXHYrFYDnRs3yBOYt8gQFyi8xljzIXGmFnGmJ3GmBJjzBJjzF+AS50yHSmf4PxQ5IC5viKSTdw4UJqQGyXRqDI9VT3OOz8W8dsCvc6JZSYDJ6DXP/F3NA+42hjzS1d7ItSRk1ENOGCunUPSe3M/9benudpwmhOpno4b0bnFbsBJwGA0n45B81F+JCKdUu0sIt3R3xPosyeV8SkWySNoVJnFYtkPWCm22iWI6lA2wNEJ3tYgm/e7t23zfve2Z4ch5AsF6nN8u7E4odSf5LWK7fs26gVxD/HwWIgn9bVYLJYDgilz12cO7tW6dPCuB3egYfwd0KSRAJioB1zJX2dPGHc54AOe7DNsxPpT+/bdPfej2Y/s8Lb8VWlpDlvmtzsaoE2bNbRt933MY/ABxgcnMDywC6DNzHm5wGuoXMc/1/Xv8Vd3m958/8Yj0OSii5Bfvw2sQr3TXlzXv8fQyf0jDxH3kB8O1JkO+dihgy7MhtdvefbfEhXBk94RK47xgOyZ9406/2Pv6aNqvaH7npp631VVC3w+OnD1AseLiLcCyYE6w8mfEkuIup24kaA2uRb9zUeAr0XkZxWUb+Uqs9UY83bC9j+guaFS8TZ6TkFUGiwVK9Jsqy7PoW1rhHp7X+Ssn1gHx7JYLBZL3WH7BkqyvoHbEWlUqnqMMW+JyHuoRPJpItLNGDM/odiB/E6vTea5lk/cX41w6EFcjWWxe4MxplhEYjJWoPkB0+He3gpYnljAGBMGrncietqg98Z6Y8x2ABHJIG4UWpRisj4m6ZaMdujvBNRw8HmKctXCGPOj6ztpKyJtjTF10V+uEMdwcnysaZS/frXd364QY0xURJaijuSZqFNPupyrKxJy/nyJ5miaCTyGfsfPikifFPLF7nySfxSRP1aimdeLyF9T/K4SHdjdnAz811k+D/gxRbmSFOstlkMea9ipRcL+4NdAG18o4EEfnj1cm09Bk+ZmoROhMUYDb4f9wQ8AfKHAJOA74p2AGU599cL+4IGgNWqxWA5B5kxd5Fnfunk9VG7y1Clz1/9nsE6mxrx9zjKGUTs2tr50wYdnf42T+Hv2hHEXoQkhAQYBpwGszew8D6Bp4Xo2FbXwABQUNMAYIiJ40cGtW6ZgIDDAWf5Lm5nz/rmufw932PtrOB3w/5qHLrlRft0VHYAsc7a/B4xAI1Vn1PwbSUtvnGe4x5hStAO+NcfbYHpRZNfVSfcQoHy/ehJq2LnG+ZwzduigNiMnTatMZMGBSk3fYXtJRqTDGLNTRL5E37/10N9PlQdTtcR5ruXZlckBUw1i2q9edNBWEXmoPAeoNEe578YY8wMuA20iIhIz5m6oQcLYamGM2SQi01APxH+h516I3jeVxZ0Ut00lysfKbE5bqvoktmdVqoIi0oJ4H76u2mOxWCz7Ats3UJL1Dbo6f9cbY9ZUUNeXxHNfdqV8DrcD+p1emxhjFonIejTi42gR8TkGj/2B+/ruJWWFyqWd5SxXFIHh3l6ashR7pL3WsrehsDvxvuLcFPsuSVWviLjv1UTDQW0xC80pBeowPb4OjlEZ+hDPaZWfILNbq/3tKuD+DZSlLJUGY8x4ETkP7T/3Aq4jwSlKRDJRab+q0hFV85iZuCHdb8Xp08ZYYoxZWY1jWyyHNFaKrQ4I+4NR4ELgBXRich3q/QKQS/k8OhtiRh2HG9FcCltRzcoV6KB9py8U+KsvFBjqCwWm+kKBG+v4NCwWi4U5Uxe1mDN10TdAcZOtOx5Gw7cBbovgOcxV1CfnjL6v8dW39+rzWLfAkJneWLj4ka4yHVzL/zut8NVv+3ufp9tZb1G/6WYiHjMZ5FzgT0A/hgfcndYwcU+dxW6jTpuZ8zJL8LqP02Zd/x4F6/r3WLqufw8DMGSmdypq+Dl9yEzv76v3bVSap9HBVATNi/Pz01vc8G3/NkOvLv/438O/gKuQcl6QIfR7iA3e1nNgJTitDl9XXCQt86qxz5Ou5TtrePxqISIewC0t+PL+aMdPkInO3xzn7xRjTFVkRb51LfdKV1BEmqFRh1Ben742qXR7KJ9ovK7aY7FYLPsC2zdQkvUNYv3gzEpU6S6TduL/EMAdlb+/rm8uqhAQI9n1dU+AVxSZ786dlNJAVwFDXcvPpyy1f3Hfm792ooz2B3e4lvd7v11Esoj3Q2NzhdXlbuLPlr86dbu5iLgMXRC4qoL/17rq82OxWPY51rBTd6wHJgPnTPnnrL9N+eesYfm+vA6okeddV7kHfKHAC75QoL0vFGgJ/B71DmgKXA2MBZo4ZUehxp6LgQm+UGDwPjkTi8VyKHMF6uGVkVVS6h4Q/Ogh6ja8pBp0PgW8gUbOjIitHNyrdVmbyEoPgO+0j+l/6/hdJ/9qckBuueV9hgf+xvBAuTwZ6/r3WIROdt4MnJl4jEe5oMlqWrCRRjNJoS8+ZKZ3wZCZ3o8rPOMaMnLStDBwGFB/5KRp97fa+veCL7dOOXP6DxPIjPedo8A7wG9GTpr225GTpr2AhqLfCQwcOWnauyMnTVsBHIcmiO8xctK0gz0E/Yn9sP9TxCMfzhWRW9IVdiMiR4jIWRWXrJA/EdepXoUa/modY8yDxhip6L9rlw9c63vURZvqmDeBNUCx8z9UlZ2NMeuJG1MGishhaYrfRNxTsq5yXc0kPhk3zJn0S0XAtXyw596yWCyHNrZvkLpvEJPbaiYix1VQX78k+x2qPEg8f8wvRGRgZXcUkW4iUhsSbuOIy+x9Yox5P0mZl1zLV6RpU2fiSjCLjTFVjt53+jixcdgCY8xeURUHAsaYd4lHE3UF/lbZfUWkmYhcXNM2iMj1xPNW7QL+k9DG/dHfvp54BNGsNDlvKsQYsxR4xvl4OOUlHyFunIkCfzPGvFDB/2eA2O/7chGpqjymxWKpIVaKre4IohMBzDmmJb0XbwToG/YHT/SFAo8AXYh7rw9FJ/SORfV1GxPX/nWTeL3+AEypi8b/lPGFAhlhf7DMFwocBjQK+4OL9nebLJYDmK9xnkdlGd6taNQhQKloRGJMWlJl02aMug74J7AQuKzPsNE7UAm2ZDxm4OEysvgk99IG272tXhicIq/Y5P6Rmx6m25+BL4Cn6V9u82nfSke+Vdno99b171GUpIp9yshJ06LoZDPFmYsuKYhsB6DUFFM/o8nfd5dte2vkpGkfJ+yzGXggYd0SIKUswsFE2B+c5wsF5lA+2qCyzAn7g/OqupMxpkREhqISGFnAOBFpAow1KpO3F46u9vVoJNXfiA9WqoRznL+juZ9AJ+39qY5rqRrGmDJ0QFoT/oN69mYDT4nIBcYYt8QjInISOgEHsI06yuNjjNkgIi+gno/dgL8Ce2maOxOQ5zsfZxljbMSOxWI5aBne4a5541eOqXbfYHiHu+ZVdaeDqG8wlXhuikdEZKAxpjBJnb8CejofV3GIR3IaYzaKiB+NtBBgiogEgImpJsRFJAc1fPwVlUH+sjrHFpG2wCPAZc6q7ThzQknaGRaRl4HLgctE5CJjzOsJ9WWjcmQxQ8G4FMdt78jtJdvWBnWyq49O1t9cpZPa98S+/ybAb51r83tjTEGqHURkMHA/es+8Vp2DOlFWdwN/dq0OGGPqTPJWRLoDxcaYxWnKnEn58eF/UpWtAn9D5dYygFEiMsF5LrYh3sf82BiTKt9NIpPR/Ez10LnNJ9MXt1gstYk17NQdMbkiPj+qOb0Xb6Qwy3u4LxQ4B33hgCZii72kO6GhjI2dz4K+eAWdMI1Nprqp7IP2kMQXCjRDO1KCvqA2odqfJ/hCgU9RL6lMXyjwx7A/+Pf911KL5cBkztRFJwA3RDzy4K4G9a4vyslu59rc3sAulzvSSGaMGoEarBsBbUrKPP7PJ4x7Hwj3GTZi7wHrgNHjZs3+ZvtOT/OnW0dWctru1wbsep/lDaK7TmLA6D2yY5P7RwR4ZOapm7K+9e04rNHOjEeG0PEXrpr+iQ6i1qBRjQcYngc9kSbXRL3bMjDe4t1l245DJwYORW4DPiIun1UZCikvV1IljDGfOhM4z6CD2tHATSIyCZiNJh/1AG2BvqjudKcU1blpkuBBm4MOQjujE1SXEfeu2w0MS+chKSI9KJ+bD+JJpRuIyA0J2+YZY+ZVop2W1ISAn6EG5bOAL0XkfjSSpx6qj38H8T5YIJaEuI4YieZ7aAv8wflNTEAn6lqjkx2xvFs7OfAnZywWi6Uy2L5Bcu5Hvefbo9HqX4vIw+ikdzE6rr0aNQzE+E1NvPl/KhhjXhGRW4GHUOeN/wJ3iMhk4HNgo7O+PdAfvb6VybfXKuH61kOvbx5wOipjFQvP3whcaYxZmKa+kc5+rYCXReRRdK5oh1PnncSNdrNJndNljogsBl5H+zA7gJbOud1CXAHmDmPMJ5U4z/2GMWa5iAxCHZhbovf5z0TkedQYG5O7bo3OuV2KKhxURP2Ea5eFfi9HoqoQlwPNnW2lwN1ONEpdcgIwQUQ+BKaj124jOn/VET23ocQdvh83xtQ4UtsYs0xEnkFVIQ5H00E8hjoXxeaIX6xClVNQx/YM9JllDTsWyz7EGnZqgC8UyATuBY4A/hr2B91e1c/gTNzNPqYl/eev5aU+HXIo3yHchr6YuqK5I9zaqjEJjq1AqnDGA/qlfADwEiT49cc51bV8Oeo5ZbFYHOZMXeRFJX6ae6OGiNcLUi4/zArRZ9wYdGB6NEAkSsTrgbKo8OWPbX6DyiF8OHvCuLP6DBsRSTzONm+bt6JSVNat4MOMXLMLDB1Lybo10wm9n9w/0hm47od2O7a8ct5aHXAZc2ObmfP+uq5/j9UA6/r3eKzNzHkT1vXvcUBGQvzijaHzxg59uiWGb5DIEcAlqO72mP3ctH1O2B/8whcKXIE+nyszgVMIDAn7g1/U5LjGmFdFpDfwMHAG+i4elWaXCCqLMilNmUuc/+koA6YBI40xFUmjXArck2Jbc/aWGruX6uUWsDgYY4yIXIbmRLwQjZyekKRoMTDCGPNCHbdno4j0Q38zXZw2XZik6FrgEmPMd0m2WSwWy0HF8A53fTF+5Zgq9w2Gd7jrJ903MMZsFZEBaORJV+AY1JEpGYXopP1LKbYfchhjgiKyCI1wOB6NfuqWZpdi4FGSJH93EaC8HGoyitB+xW+NMRsqaOMqJ5n9FNQ57jaSGyzfB4akifr2ok4qSZUP0Dml240xSeWqDzSMMbNF5GTUMHcxasS53fmfiqmkiGhyOAmYX9Gh0e/6bmPMV5Vtbw3xoFKK/dKUiQD3kXqcUB3+jhpyvGjUzn8pL8NW6WeJMWaziLyPRu30FpGjjTE/CcUJi+VgwBp2asYtxDt/XShvLPg8tlCa5eWeq3sCLEat78XoA/zPxJPqZaFybC+g3qMxmqIvmGQ0TrHeonSsZLnn6rQVFsvBiYd4tACiskcZwBzUo+h/DBi9GniSGaNuQw04bCvM8e4ozqawLOPFqJErW9XfDXDG1qKc9sDqxIMM7tV6Uyj/hTd2FTS5pF5kJwBRvIthT6TO+8Dh9Y+Yb7ymPhHxkkFEysgoJ5d0oBp1YoycNG3b2KGDVqOOAKATs4ckYX/wDV8ocDo60E4nvTIHuK2mRp0YxphvgTNFpC866XImmgupOTrJsgn1lJsFPF8F+QHQ9/RuVHJjA2pw+QJ4pTpa6JZ9hzFmFzBIRC4CrkO9NluifbVVaF7Eh4wxq1LXUqvtWeJ4lP4C9ezujnqU7gDC6MRF0Gm3xWKx/CQY3uGuN8avHFPpvkFNjToxDvS+gSPXdQJwJfpO6IlGd2Q49S4G3gOeNMZ8X4W2HRIYYz4QkZ6oescFaPRVO3SOpRi9Ll8DM4AXjTFbUtWVhCiag2UHqqTyNfAZen23VaGN85z3/i2ow+nRqPrBJnRO6Wng5QoisUYAA9E+TDu037AVWIpG8Uwwxmyswrntd5x+1yVO9PJlqMNuR+JRNZtR6e+PgeeMMcuqeIjd6LWL3eNfAlMr4YhVm7yBRsucit7bbdDzy0SdwBcDH6DXb2VtHtgYs9SJ2rkeHZ+OQqPEAD50clFWhRdRww6ogSidkdxisdQiYiN1q48vFLgDDZEG+CrsD57o2nY9cfmOd1CJoJnAp64qXkYH6X8gLstWir6gerjKxSYA2yY0YT3qYbQLCIb9wYM9sXat4gsFLkYnm7cST06ZyHbgxrA/+IovFGgFnAjMDfuDW/dNKy2WA5c5UxddbuD2kqzMsm1NGi5G5D3g1cG9Wpc3oswY5QF+8f22hn/fXpTd4qjmWwG+31KQ26h9412NAYzhH3LO6D8kO874lWO8rTb3fPiIwtLzsqORV447Y9BdAJP7RzKAAiDT22kB4WteYx4d6c7KVX88648d6u7Ma5exQwdlAW+jnlj5wL9HTpr23/3aqAMEXyjQA5XM7IFGp+5EB8ZPhP3BQ1oj3mKxWCyWQ5HxK8f0IEXfYHiHu2zfwGKxWCwWi8XBGnZqgC8UyEZzOxwJ/CnsDy5w1p+JevWAhkVPQo03F6PakzGeR5OWnYB6d5TTOUL1NVtWsjn/CvuDv636Wfx0cXLsjEK9c44mdYRaEfEkby2B74Djw/7gXskpLZaDDUdSrQvwfe9Luu5OW3h8sAFQwvBACcCUueuzUc3xO5wS0wf3an1Bqt1nTxh3bI+269+tn1XaFiBqWOkROjibX2bA6CtS7Ttl7vq/A7/HmOipRa9vXxfZtuxqzxlvnTu9ozn9i+ZXAnPrjQq8LRllxwGPX3DWf/eJ93xtMHbooN6oLnaMTiMnTVuxv9pjsVgsFovFYrFYLBaLxWI5uLGGnTrAiRSZmrD6O9SI8wGqHVzk/N2Ghpb2RY0/2RVU/wwaIn54wvrnw/7g1TVq+E8Ix+j2LU7ej2rQBZW6+w8aGfULG8VjORiZM3XR68AgYAVwSu9Lum5KWnB88GbU8LxlUbv6N33euf4ET6RBs/q7um31lhQ19ZbsorRei/WDe7dLn1h0xqhHcbSnv6bptI7sOrkRpTs9cCUDRn+darcpc9d/hZMctG3x1/gjTdkq2aDG8WPW9e9x0MpLjB06qAUaidkKWAYcN3LStKL92yqLxWKxWCwWi8VisVgsFsvBimd/N+AnyuvAWOAH17ouwB/RpIcXEU8M2QTVdB0HvJWkru0Jn3+OGnVK0ORxX6DRPvfWSst/OrSmYqPOjhTrY5J244A+qOHtjhRlLZYDljlTFzVGjTqgmsTpNMt/jb4TWhjP7seLcpc3K2jwLUXZXzdptuwdmq76iKYrZpZVdMzPvm/70vfbGn4xq6DtJwM95wzyeS9r3c47dG6iUWf2hHGZCbs+ARivKY00jKyIGXVA5SzbMWNUd2aMuo4Zo5pUfOYHFiMnTduERmZeBvSyRh2LxWKxWCwWi8VisVgsFktNSCVNZakBYX/QAL/p+dhNnQuzM9q7NjUHjkLlwWKJyEEnU31AvYSqNgPeFIfJAi5Ec/M8HfYHF9dS8w9KfKGAB+iATgKPQWXspgCD0+zWKMX6LGAJquccY3PNW2mx7Ft6X9J1+5ypi94DzkYNzZ/OmbpIUMnBTb0v6Rp1FZ8FHAtE1zYt2e6UQcq2bvFEI80BMoq2t58yd32Twb1ab0t2PDXWeF9dvb1xw0yga8YOFuU2BjVox8rUR43ZvWZPGPdwn2Ejfg0wuFfr4JS561/ylczNPjq6+ooTPF1O+UpanA68/n3kxd1oQsssVGM9Vc6sA5aRk6b9gD6TLBaLxWKxWCwWi8VisVgslhphpdjqCF8ocBnw8p4VURPFIxcDjwJHJBSPArvR5JDVwQBHhP3BNdXc/6DGMeosAPISNq0AfgM8DLSr4WEeBB4K+4M2L4bloGDJCzObtCxc33Zlo6OWFmbUPx5ogcoK3gecC3wInNP7kq4aoTY+KMAAYMP483aVSjQz6InWi3b8zOvNLPOe7i3dTUGzzuxq2+OCwb1aT092TCcKZztqYOW+1r7vPq/fPBe4cV3/Hu86ZRKlKhv2GTZiV9qTmTHqKuA5AGOIipDFgNGR6n0zFovFYrFYLBaLxWKxWCwWy8GNjdipA3yhQH3g2XIrPeJBJdhKXWsL0AlQD9U36oDmoCiswf4HOwPZ26gDKj31MjCH5IadUmCNUw5gJCqhl4zbgZ/7QoFjwv7glhq11mKpIU7UzUBgZ+9Lun6UuP3byXMv6bJr1ZTcSKHkbl3w+cqGnQq25LQ8E2MMIuIUOwPohkbCwPCAAd4FGK7bzwT4+INxpds6nEFZdmOMx7sd+CpZm8avHNOYs3mDKKblyowFrVdkPfv6oLPvS1J0AWrIrg/Md5bTsujjfrNad1gWbdr2B0/+7H6y6KOzfEMGsLCi/SwWi8VisVgsFovFYrFYLJafItawUzc0IJ5Dx83whM9zUA/5mvAkKsVWTirMFwoMBNoAz4X9weIaHuOAxRcKNADGV1CsCfA+mri8LbAFzXmUCQiwDjWufYHKUZ0BFONEHbhoAazwhQK3olEP2cCVYX9wVs3PxGKBOVMXnYkaKZ/vfUnXxPxabu4D7nb28fe+pOtE98ZGJdvuy40UCkBOpOjkSCydmhp1tgONgVXA0j07jQ8eC5wFvMXwwHex1QKzm6z88MxIdsNtxQ3a9j330nPWp2jTlUAfPLCxU1mHy866I5lRhz7DRiyfPWFcT6An8E6fYSMqDBtd+MGAkoUfDDAanChCahlFi8VisVgsFovFYrFYLBaL5SePNezUDXdXslzrWjjWfCDLFwrcBjwT9gc3+0KBq4lHDJ0N/LwWjnOg8hDQvoIyXZ3/MZq7lo+A2Kw3H7jWp5J5agQ84/r8O9QYZLHUiDlTF/VDDZACXI0aGGF88ALUSPsswwMxI+1prl1PAybu+TQ+2KhBdovdJZJBlimj2JNlWhZukO3ZzQATQTyvAS8Bs/cYj8YHWwOfoJGDmxgfPIrhgR2zJ4w7FjhTgIzinQ0yinduhHNSncJCVFbSI9HM5VPmrh8HfDS4V+sXEgv2GTbiO+C7xPWpGDLTu3ly/8gvQG4C3h4y0/tJZfe1WCwWi8VisVgsFovFYrFYfmpYw04t4wsFWrB3ZE4iO4F5wOmudRHAW41D3ko8Mfk9vlCgHSqvFKPb3rsc/PhCgWbATcCgalaxCVgNdACaJdnewPm7GDUEtUhRT2unPaejk+LTw/6gTVxlqQ5dUaMOwHGMDx6B5sE50ll345ypi55Eo2reAU5GZcweB2B8sD7wGnBmi+JN3jLxlu721CvJiRb+rUnptlubF22stzm3VXPgWqCo9yVdX3Md+3DicpAt0Oi2HaihxjjtMoBhxqjBTh1vMGD0hFgFwzvcNWf8yjFnYDzdm2+87B/os+nWKXPXLxvcq/XnNf1yhsz0TsRtwLJYLBaLxWKxWCwWi8VisVgOUaxhp/Z5Bc0d4WYXOinaEPgMuAz4OKHMdKA30BTNc9EK6FGJ45W5lpsCl6LSZBejXv73uAv7QoEOaDRPfeBGVIqpJOwPFlXiWPsUx3hzNvA58ANqyBHUWHUtUK8G1TdHv+cP0fw5qfgDGqKQyljXwxcKzAeOcz5/5wsF/GF/cHYN2mY5NHkeuAY4FvgtMIK4UYedGQ36AH2cj7vRZ8Tu3pd0jTrrrkCNPgBkmEjmoqbHPLYrq9GRqOHGTauEz1+iRpNLgGcYHlgK0KfMs+ELb/TWYmEA8EyfI9cATEJlDAczY9RcBozek+tmeIe7Zk+Zu/4r4D+uuptUdOKT+0e6A7uGzPQur6isxWKxWCwWi8VisVgsFovFcqhjDTu1T2Jkx0rKy32dAqxFc+P8DfWK/z/gYXcuHF8o0AlYQsVRPEHgX8SNHC3C/uBKdHK4HL5QQIBXgeOdVW+gEScFvlDgjLA/mDQp+v7AFwpkojmIjgG2AVMAfw2rjRK/DgJcBWxMU/5lYBRwomtdKZrfZBTx++c41/YuTlsTJ84tlrT0vqTrVuKGGxj/Qbnfe3akiFPWf8z2rCZ819hXv/3u77etrdd+7Zypi57sfUnXv6DPC1PiyZSsaCnbMxuzK6PBRWXe4u0ZkWwADNH1gmcxajiKMzxgSLy/xgevA/57UsRTtLpBhzvXNDhyMTwN8aiipAzu1bpwytz11wO/Rg2nM9KVn9w/Mgr4B1A2uX9k8JCZ3mnpylssFovFYrFYLBaLxWKxWCyHOmKMVY2qKr5QwItO5q8M+4PbnXXHAXc5RYaik59ZSXaPAplhfzDqCwXaAjvD/uAuV91/Qo0/r6ORN+lYDhwNdAK+BXLQCJ6uYX9wr/wVvlDgLOC9FHVtRQ1ON4b9wU8rOG6d4wsFWgHuJO2FQG41qtpN+QiqmKxUZfZrjxqVEvkAODNhndtoVASsAO4N+4OTqtJYi4XxwSbAnWj0TZsIdPAmGHi3ZzRkYYsT3KuO731J128ZH+wXbtn4d7vpdF6xpyFNijfTsmgD4WZHU5y9ix/bL3z2umNvqlzOrfHBD4Az1tQ/nNUNO4H+xq/sXf9pg0bMTXNLsVWXyf0jnwC9nI/BITO9t9a0TovFYrFYLBaLxWKxWCwWi+WnjKfiIpYkTEVz5CxyjDOgEmzXOf/TGQ5iUmKE/cG1CUadi4C/onljxgAbKmjHm8BzwALUqAMaRZIqH8zWNHU1RXN8POy0paUvFGiYpnydEvYHNwCxSeM1lDfqbAAK0OiZiliZ8DmKGsF2V7BfPvBL1+eYBTQKbE8oayh/L+UAecTbb7FUhaeAP6HRO0clGnUAGpftpPXuHxETpXnhhmjzkq1zN7321eaiI/oe9sEJP2za2mQ9jUq20apoAx4TJac0yqoOX1JYf9ucKrTjbYCt2XtSUHmA8xgw+hUGjB5cG0Ydh/+h91Ah8GIt1WmxWCwWi8VisVgsFovFYrH8ZLGGnSriCwVygQudj+3QvDgQN6yARurEonWilEeA2b5QILYfvlCgmS8U+AY1GMWIoMaBt9nbOBGjOXCl61hLgDFhf/CTZIXD/uDXqOEosU1uTvCFAstQ48laXyhwWpqylcYXCjT2hQK/84UCNziScLH1fX2hwFJfKPClLxTomLDbvWhkVIlrXTGaOL4dmvvmLSBdPptIwv5eNBrieMpHBMWIAnPRa+yOypmGRin0QnMTbXNtS2XI+zFNuyyWVCTmw0nKEbtW0Hb39+HNua082zPq564olWaU7H4IeCYi2eRtW0iLoo00L95MhtmCkbJJwzvc9Whs//Erx/Qdv3LMS+NXjvld0gMMD/wD6GXw/AG9LwpQQ3KtMmSmN4ie8+FDZnpn1Xb9FovFYrFYLBaLxWKxWCwWy08Na9ipImF/sBB4zfm4hrhR4ZYUu6wGEmXR6gMf+EKB4U4unX8B3VEDgUG91geF/cEtYX/wfFRubXWSuvsCsYifdcCJYX/w7graf49T3+WoZFgiXlTaLdbOG9PVlwpfKJDhCwUe9YUCc3yhwJVoYvjRQAi4yVX078BRwAnASNf+f0bPeQEaTRQjG/g58MewP/gB8CjQCM3l8TZ6TT50le9OeUm8GU4dS9H8Qsm4yIkYet617iynrp+hk9zXp9jXoPmT5qORFxZLVbkdWARsMGqYBMAYo/8Bg8FrSmbuzvqhpHXBj5y84RN6bv6czHXzmpyyOOsPwuqCeJAZZEay6bj8tMFzpi5y3wtT0efA6PErx1xIMoYHPu0+pNc/gLZAZ+DYOVMXXV7bJzxkpveHITO9m2u7XovFYrFYLBaLxWKxWCwWi+WniM2xUw2cHDt5wKqwP7jTtf4+VL4riua6WYHKft3gFFlPeWPCDtR4kii1tARohhptngc+As4ArkAnV90sAB4E3g37g8mMP+nOowca9fJXykccuVkHvIRmTf887A8aXyhwBDAANaC0d+p4G40eApWRugB4NkWd/wj7g39w2jCBuPHo9rA/+B9n/VqgjbN+K7DYOVYsmuHpsD94XUK5UcAfgAYpjjsVlbnzUD7KZglqzMpAZ8NPQRO/98CRzUvgc+BU4H6gP9AFqOdsWwa0AmIydv0cA5TFUmXmvfTZsYdt/WJBZNsO1u/cQff2+vM3GPN9vW1rDitocniZJ5usaDworSgjyuymZWQ1as6xa7LYkdWYH+vrfutbL7lgcK9LpwOMXzlmM/qcAbhoeIe7pqVry5ypi15A84cB+Htf0nUiwJS56xsADQf3ar221k7cYrFYLBaLxWKxWCwWi8VisaTEGnZqEUdK7CngdGfV95SXVVqDRuf8BzUsrEU94SsiihojSigffQKwOewPlsup4wsFmqKSYV86kScVtbspasTIxMmrkYIfgK+B81EjyA40901mQtteBzYB/oT9S9AIp6vC/uB6x0CWCfwClTZ7D+gIfAZsIW4ceQeNtDmTuAzerrA/2NAXCnwF9HTW/R74h+t4se8N1ADWBY3WSWQTKmsXM/YsBI5N8z2AGu6+B4YB/wROdtYbyhuNBob9wbcqqMtiScrsCeOymrbscNvKzybd14qWnpMOT7TrxjEYCj1CuNkR7Mhpytp2y8gszaXlhqPIjGSzu94WlneeC5gNCJcDguE3wGfDO97194ra8vFr+cs9xnQEKMrO/Pis87ucPmXu+u7ALDSq7neDe7X+Z22ct8VisVgsFovFYrFYLBaLxWJJjTXs1BBfKNABNUp0B95lb8OLm0VopMtRqJGhPvBwQpm3UMNJKhINB1+H/cETXO3JAb5B5dZ+BI4L+4NbK3Eqsf1HoRE3uZXdJwWzgH7OcqzND6MGp65oBE5P4BU00qczGgnTCI2sucRV11L2jlQyYX/Q40jZ3YxKn01yyh6ZUPZBYDLpc/EAlALb0SgGt0zhGqAFyaOaIiRJbu9Q7NR5WdgffLeCY1ss5Zg9YdxvUfnCJS3qFQw8puGuTH5oM81guojzCDCO3NoWr7CqcU8KMxsgoj/dqERY2O0txHjIKsklY1cRu1vsUXZbddx79UahhujtLaJMzIHztwiTC4RVgA94tM+wEd/Hdnjr/VWv199dOCjqEbY3bvjVxX3bnThl7vp7gT87RZYO7tW6S51/MRaLxWKxWCwWi8VisVgsFsshjs2xUwN8ocCfULm1VWjel3RGHVC5rx9Rw8n/gN1JyjQD/ks8d04ibqPOVuD3vlDgVl8ocLSz7gzUqAPQDjimgnO42RcKTPeFAtc6q15HI3NqyploRE+szfehxpyTUYPWCc76y4HjUJm5Rk75gcAXrrqSSast8IUCA1CDzq+BTWF/sAx4P0nZHmiklJvtScplogYc931RjBqFViUpD3sbddyW0myn7bel2NdiScco9B45ZlNBvYEMuncJwwNHbxROLcUUA4jzb02DYyjKarTHqAMQ8ZbScnkGPRdnMOhTQ88lrseToR1wB5CZa2jRJSq/OTLqOe6YiNyLYSLwO2CKuzGF9XLu29Sy6a4dTRtFTy7YsaVo+vwHGpWVzkUj8QDerLuvwmKxWCwWi8VisVgsFovFYrHEsIadmnGd87cRKvu1zflc5vzdhUaJbHE+xyI+zkWNPO+7tsU4Bc05k1nBsdei+V+eBcYBc32hwHA04ifGx6h0WlJ8oYAPGI9GCE30hQKHOZ9j0TGJbYuRzCCViBA31ADchebCiRk+YpPB61Cjya9cZd9HjSnL0O8wlkOnGHgAlVvrBwTQ3Da5wC1OmX9T3riCU/aUhHWpcgolko1OgKcykC1DpeNAr8mLScp08IUCVyZZb7Gk4xPnbxkuQ2erm2/9LHP4rTnA/QZTsBsT3VUWt1Oa4g3szlzF5npf0Gqjl5NXZ9GiyEPXnR4aFKpd2GMoyzEsBcg14HHsxfUQ90uhDS4G92o9u9l3b112wtpFO5tGSgcAt/fZuXUQmm+s9+Bera0B02KxWCwWi8VisVgsFovFYtkHWMNOFfGFAuf5QoE/+kKBI1HJMFCDw3uoBNhhaI6dIBqt0QfNP+FmMSoPthyN0Cl1/rtJlgsGVPoLNDfP34gnP2+KypfFInreA84I+4PFaU6nFDVIxeqNUN5o8y/gqyT7fexaXoVOOu8GioCJxA1c21zt9QJD0Iiim1DpumvQCJ5dlP+Ozkfz1hxF+WidmJHlbjTi5wPXtg8Bwv7gIuDLJG1ONJRlu9qWyOoU693sAK4M+4OdgTfQvDxvo99n1FUuiubred4XCvSrRL0WS4zB6D1zQp9hIz7ba+vwwEgZfmv9eRnmXIp/LDDbviayfR6rjvyS749cSKvlJWxtGyEqauc0QL1d8lqL7Z7NA7/Kye1e/8ifdW3WbUlDbw67MUQwbBHzTlT4EDXqPpl4yIySXX8F09i1Kndwr9bLB/dq/Uli2UORounzGxVNn58oBWk5wBCRviLyLxH5VER+EJEiEdklIqtE5HURuUtEDkuxbz8RMUn+R0Vku4h8LyJfisgEEQmISJtk9aSou4mInC0ivxORl0Rkpav+ldU810wRucE5r1XOuW4RkXwReU5EbhKRyjo67HOSfM/XV3K/AUn2rfS1sFgsFsuhxYHaN3Ad40IReUFElotIgYgUi8haEXlLRG4+UN/lIjIrxXdTlf8ra9gGEZHzReQhEflaRNaJSImI7BCRZU6fa4SItEix/w0p2hURka3Ob2SuiARFxC8iTarZzpYicreIfOxc2xIRWe+0+VERuaiC/TNF5HoRecPZv1hENorIHBEZKSL1q9Ou/Y2IHC8i94rIR869VOjcA2tE5G0RuUdEkjrhikiHNL+rnc69/o2IPC0id4hIp1po7+HObyt2nFkVlP9LFe6FB9PUMzHNfrtExxVTReTG6jwvROQIEfmtiMwUkdXOddguIktExxQ/F5GKFIwsFksdYHPsVAFfKHAKMBc1niwP+4NH+UKBf6CSSVHgqrA/+KJTdgCacycZPwDtE9ZNQ/PP1HOO0QqVKCtCc+acmqSej9AJ2LPRCJeXUPkkD3Bt2B98oRLnNBS4GHgx7A9O9YUC7YHfA+vR/B6/BO5P2O1C4FbUiHVb2B90G1jwhQId0SiZ94G+wDPOpufC/uA1KdpxtXOc1gmbosQNkKXEDTT/dNp5Hmqg6YgaZA5Ho44qYjXwPfqdJ8qpjQbCQIjkxs+tQMewP7jdFwoch8rBJWMN+h3FKHb2W1uJ9lksccYHj0CNwcXArxke2OjePHvCuHbF9aI3r8krubWwSbSFp5RXPVEGl2XDsasyX+uyNqN5dqk82OTaES8xPri+rH7rVmXNNR2OlOwie9081on55zKveQhYid5nZahxtRcq7yjAvZmeLDm88VFkZ+R+VS+zwYU5A7ut21dfw/6maPr8DKADjvE3Z2C3kqLp89ught6OwBw0UnGFs/xWzsBuzySvzbKvEZHuwEOoVGhFRIHngd8ZY9a46ugHzKzCYctQidPfGGOWV9C+FejvKxmrjDGptqWq72RU9tVXQdGOxpiVVal7XyEiiZ3UWcaY/pXY71ng6oTVbY0xh8zzymKxWCwVcxD0DRqgihADK6jzO+BiY0y4Cu2oc5xJ7cp8t+moch/Idfwz0TFUj0oUL0XnEf5sjNmTo1hEbkDnBSpLIfAC+jvZUMl2Xue0M9Eh2E3K70FEOqK5i3uk2X8FMNgY801l2rS/EZEjgf9QPvdyOt5E76l8Vx0d0POuLAZ1kL7bGJNS+SYdIjKd8jmzPzDG9EtT/i/APZWs/j/GmNtT1DMRqJQDFLAEuNT9XaVpXzbwd3ReMJXzeYyVwG3GmNcq2Q6LxVILWMNOFfCFAkOIS22VAk3Q6JWezrpn0EmUICq1tgtoWMnq/xf2B/c8iH2hgBc1WvwWjQCSVDsC14X9waed/ZoD3rA/WKlORGVwjBetgD+g0mk3h/3BysixxfY/AzVwvBT2B0tc6wWNSBC0I7IWaO5sLkQnT2PJ2VcC7wBj0fw4l6ISdMeiL+uOzn4folFByShCo2qmoHmMPOjL26AGshbARuBE1HizDs0HBNqZ87vq+jXwGHAR2sGvrHdCGWpQOifsDy6r5D6WQ53xwcloHiqAxxgeCCQttnKMF8gZ3uGu3eNXjukJZAzvcNfnse2zJ4zzNozyfv0Gbc9o38Qx7JQWkL32K748vCSydZfnodydntsFI9kZZRSVZSwDOcrZfY9htTSn6UdbO521GpE3Bvdq/TwzRt2EyiDmAxdMaXD78eh9sRu4eHCv1gtr/TvZBxRNn+9BJTfro8+AGcBpqIEtG31O5TrF3YZnN6vQ+34X8Kecgd1er+NmW5IgIpei7+jYM305+j6fgzoyeNFI2DPQ90ss8uoOY8yDrnr6EZ+8mQr80XWYHLRf0AXojUbdxY63CxhmjEkm1xmre6XruJvR6NPeaORqlSY1RKQPMB3tgxSg77x3UceSDPSd2d8511MPAsNO7F4zQKd07RWRxmh/Ipfy96g17FgsFotlDwdJ32Aq6oQJ6jQ4FvgWfb8djU62nuRs/x441hizs4JT32c4BodUkSKXoAokoPMnj6YoV2KMWVKNYwdQo12Gs2o+6gj7OTrmz0Gdbc9Cr28rp9xgY8yrrnpuIG7YSWxnfaAx0BV1aL2Y+HhgI3ClMWZWBe38NWrAAL3Gj6OS3JvQPqAPuADwGWO6Jtm/GSoNHxuzfYzOkyxB+4HnoXMX9dH5jVOMMd+na9P+RkR6A68CLZ1V61Fj2YdoH68MlQ0/Db12eU65coaPBMPOF5Sfz8lCDWlHOPVcTlwNpxQ17jxYxXbfgP5W1hN3WK6KYec8NCd3Kjal6ssmGHZuRH/nMdqhaRzucrVrNdDVGJNyXs+JPptKfH6tAHgaTf/wA3oPHQMMBQY4ZQxwlzFmbJrzsFgstUhGxUUsAPm+PM8LGZ7zHrnAx+J2jSLZpZE/r2lRfypxo04Z+nJ4x7Vb7l4VpWaWc5xrgGunwGuDf9vvNrTDlgy3MeMM9AEL2mE82xcKPBv2Bz9Pumcl8IUCWeiLbyc6QXs12ukB7YQOr2xdYX/wwxSb/gD8n7O8lfIeKpvD/mDMy/h5V7ueRV8oF6BGHYh/D6DeSqcQz6HzNtrJqg98GfYHL/WFAnnEI3HE+f8datDZFPYHC5xjDUKNSseiHYcf0ZciaCfxfuL30Eq0sx8h/X0Vm1AbhkYcWSyVoci1XJiq0PAOd+2RUxze4a5kXkZjdno4Y1fBOjIycmnhzYWCVaxvUca3XUq89Td47zhiSebu7m021K+XWcbWwuyjFm1ogd4iBhAMUNywbW9ETgeunjJ3/deDNcqtERpZeA06UIt1Gu8AflHD89+nFE2ffw4qK9mY+GD0TtRgD3FvJfczPlVeNLc022tF0+cvAs7JGdgtXafdUouIyKnAJPQdbdB3z7+NMYkSqACviMhvgBuA+yqoepsxZkGS9TOAoDMY+geaD64B8IyIbDTGpPLqfQRH3tQYs8Jp+0rKS5JWiIg0RaN4G6LRp+cbY1YlFPsUeMGZ9EjnOJLuODGji98YM7E6dVSBOeiAtBVqbP1rmrJDiRt13kIn0SwWi8Vi2cPB0DcQkZ7EjTr5qCOG22jziYj8DzVOXY0qVwxDIz+qhMu55F5jzF+qun8qYv2ZFMc8yfVxQ4rvrVqIyGDiBphiVG0kZJJ7NU8SkdtRI9m9FVSdqp3vAA+KSFu0P3cZapR4VUT6GGOSOrk5BowHnI9TgGuMMYljvY+BJ9PIXP2JuFHnWeA6Y4xbGv4DEZmCGkXaoM54Q9OdZIq2diBuJOlfkcGqujjGwGnE54YeAn6fwgDxuoj8ATXK/LuCqnen+Y2FnN/AXej8TybwgIhsMsZUSn3BufYxpZvbUENUVVlSS85WKxLOdQHwjog8iaoD+VCD1i+IGxXLISKC/p5iRp0vgCuSjCk+Qn+flzrl6wH/FpHVxpjJVW34vvqdWSw/JWyOnUqQ78u7EtidXRa9ceRri3j8sbnehyd8vpnykSG/Qa3Vbr4FvkY7i26KUEOQm8J8X14b4CnUUj/uqHU7U+WAAZ1ILUGjVyYA+EKBY1HPhtuAd32hQL1KnmIyHkIjUp516nNrAdeWTn0313Ji2HFSo1jYH9wQ9gd3oZ7MsY5tLApoM9qxOdX5OwF9KccmZvs4EnkfOPu4J1ZXhP3B1UCmLxRo5RxrFup90woNf38KfSnGcBtwOqCd6pdSnm15evhCgQNSC9lyQHI7OkgYi3Y2q8tAAINhzY7l7Ni2kM867WBWd0NJJnhLhYZZJfXrZerjqWluMZmeKGDIbh0p030hc8sy7yc71xAs2iRTSnf4cOQIDZir5fSr/lO8ufM7pbuI6tgplVThAUHR9PkNi6bPH1M0ff4DRdPnNymaPv8Y4GXUiOv2MKyx5rJDV1RK0rIPcAbBLxCPqgwYY0anmLgBwBhTZox5Es3l9m11j22M2WaMuZW4F14mMFFEkhoBjTH/NsZMTjcJUkn+hnoYFwGXJBmAuY8ZNcak62scKJSh/RGA65zBZipi3phTUJlEi8VisVj2cBD1Dfq6lu9PFonjGCrcxoi+iWUONUSkJeqgBTp0GWyM+W8Ko44WMqbQGDMGlaCudjSLMWatMeZy4hE+jYEnUrRT0OgcD2q4uyqJUcddd0niOhHxEo/SKAB+lWDUie37OTqWBLhSRFI5Dx8IPEt8bug+Y8xt6aJKjPISem9WRRIxsZ4CY8y9lI/qCYpI81T7JPAo2u5pxphJ1W1HXWKM2U554/R5aYrfgjpTgzpBn1PBmOJV1MAWu8+eFJFWqcpbLJbaw0bsVI7fEo8AwcC23157wgVolElrNMz1ed1UzuL9RdgfHO4LBT5Fo0gAlgI/QyM7XiXuzX0SqucZQcO+o6cvWv/rZW0aPkPc870UvWavhP3BJ3yhwDNAxCVv1oy4sa6h0+YCXygQBK5E8+gklXBKgttI5UM7jEeinuq/rWQdFTEGjTBqhlr2YxjgM18o4Av7g+V0gn2hQENU1m0xahi6mXjkS3M0UiBI/IXinvwpBN6gvGzaDagE22RfKHAdagzy+kKBx9EXk9tT+nLiUjDJJpV+hn5H55FeHxd0gv1TXyhwWixCyGJJyfDAZuBXtVDTt4DPa6BlFMpym3DK9mPptd3DF40XsXvb+rJdJd6M4jIv2RkRdhRnRUqjHi8IJVu8udsbHUvbrOYcnWnoXSbM3/49VzRted82Mk/ZQnbgTs/Jf58rrU4jWkI4WsKyaMl/3zkzL6kX0P7GyY0TQAdw5zqre6A64KkmjVPd+1XlmqLp858CugOLgHdyBnbbaxB2KJDvy+sB3IRGvzZAI0K/Bp7IC+fPq4VDXEc8b807xpjK5GADwBizGiefUg35GyrbeRLqHRd719Q6ItKI+AD/+epIlxzATEQjAI9CJWr3igYWTZ7by1U+aV6/JPt5gKvQ9/iJaH9iN7AM1Wx/2BizKc3+K9H3/wfGmH4i0tpp66XoNS9BHUP+C0xMNumSUF9D9Pl0Mdofa4xKxs5HDc8T0k1AWiwWy0HN+GAPUvQNGB6YVwtHOFj6Bm5J95QTqqhyRIyKcmAcCtyGKgkAPGGMmV7ZHY0x1TbaJTACHV+0B04TkbONMe8llDmLuALJfcaY4mocpwvxeYc57txASXgTdUYGlcP/ezWOV6eIyABUFg1gIeqsWymMMVtQ2bAaYYz5n4hcgkZdNUAdLNO2Q0SGon2+XWh02IHMF67lI5IVEJEM4HeuVTcbY7ZVVLEx5i1HEs6P3oO/prw0pcViqQNsxE7l+Cy28OzpHcpuHHFa0XftGl2KGlw2A9vQwXYr4tJiZcBUXyjQifKRKRvQh+lXlO94jRz8237RXdkZQ4DngJ9PPKvzdjQcdBKqkZmJTioO9IUCzcP+YKE7Z03YH/wItcB/CdwS9ge3OFE8t6DGk1ucz5Xhz2gHcj4wNuwPrgr7g2eH/cG+icaW6hL2B79ADTuJkUWCGj5m+0KBRgC+UKCxLxS4BTXoLAImh/3BVaiXyzZnv3nEvQpiEmtucilv1FmHeoRkoB3ip5xlQcNSW6DGsZj39NFoiLsk1OFut4fyxqB0uY66A0/5QoG2acpYLLXJPQANDazzArkt8eJFEI4oacvq7iUZBVnyzdc/tubbdS3XLVrf4i+xn7uJ1qdJViuaegWv4yzfOasJx5TtOObMrIF/6u298IW50qrcPfdttLjPvj29KvEK+pw717UunVGHCGVlO2XD7jKqM+4qh6ASUWPRPChlRdPnP180fX5tGI0OCvJ9eSfl+/JmoxM1t6KDuG7O31uBr/N9ebPzfXknpammMrhlAPeL1rMzie82cF5eh4e7iHik2auxlSKSLSKdROQwZ7B20OFMtsQkJlMlh415WX6POstUiGOE+QSNuh2ERjvFdNdPQp8Ty0TkgpSVlK+vF/AN6gRzDNr3aAz0QSft0kp6OPt/h0b29UH7Iplon3MA6rwyX2RP/jOLxWL5aTA+eBLjg2n7BowPzmZ88FDpG7idM45Msj3ZtsU1addPhGGu5ftTlqpDnMibx1yrkl3fn8WK4zJIiEgjEekiIq0qiFCGeE4Y0Lwu6XDPW/SroOz+wn1vPmSMSVS52Vc84FpO228XkRbAw87H39c0f5GINBWRo0WkXR312SMplt2cQ9zo81UV5dDc99ywlKUsFkutcVAO7vcDvwRm/PGqHs8uPKJJNntLkcWicf6AekEMRKNIbkXDod2yYr2dv5KknpXX3t5XUD34bFQDX4DX0EmKk51y9VBv0s2JDQ37g6OAUa5V61DDRxPn7zoAXyggaARAJ/Th2935/1nYH5zhGIk6JP02ahdDPLlxlPLGxmbAyb5Q4An0xeJ1bbvMFwrkhP3BVb5QoDeqW9zDqSfi1FNG8rwXRehvvzEawZCXpIybdNJzjV3LGehE2jbiif7c4affO5/dBr0rgL6+UKB92B88JD32LfuOPsNGhGdPGPePbR5+C3i3F2+hRb02iAjfN9yI8UBRw+j/sooy3thZnL0GOH/PztESIqYsuiPq9bTyRPGIsK5kC/fsWIG3hN80i9LwjKP63IB6IMY6gjW2gNQF695666Ms6p2WSxNE7Tixe8/9/ImihvITgVMNpomXjMyGplXmNxlT6VZ2EZ6a+Ua4n2eCDu7uRXOi/KTJ9+VdiMpWViRH2Rv4KN+Xd0VeOP+Nqh7HiXo40flYALxf1Tpqkbddy31ExFtHEmi9XMvzRKQ7ek8OJN7n2y0i7wJ/M8Z8WQdtqEsmoh7cQ0TkV8aYPRGvjhzJtc7Hp40x0YrmQ0QkF+0/HOes+gSVKlmMGnYuQyODGwFTRaS/MebjNFW2RftsgvYJP0T7OCejBqK2wFUi8k6yvEQi4nPaEzPOPef8X4v2yW5Cn8vHoJr5xxtj9uoLWiwWy0HH+GCV+gaMD17B8MBPvW/wBpqgvD1wh4hMSpRjcyb+/+J8LCOF7NehgojkER+7LzHG7E9D19vEnX7PTLI91mdbaYzZLiKXoVESJ7vKrBeRl4G/G2OS5efc5VpuUkF73Nsr6+y7r+nnWn59fzUCze24E42ayxORlsaYjSnKPozO/cwFxtXwuLOJ53QGKBCR2cCjjtRZbdDVtZxK/rmfa3lKVSo3xiwQkWVohH0bETlmP9+HFstPHhuxUwnywvmRvHD+ywuPaOI2hL2Devg8lFC8CxohczpwIeUn/tOxG430qI8aWv5D3HP8YtSLNMYPYX+wUvIqYX9wM9oBvh3o7XwGDff+Dxqq/An64vw7mpvnpkq2uUr4QoHTfKHAMl8oMN8XChzjCwXaoC+vXHQS5QK00/MiaoC6D51U6Uj5SVCAN8P+YCyhfGviL8BjnLJCasNljrPtKJIbdTYT18WFFPl+HFYQnxQG9e5tmaTck+jE7QQ0Omita1sb1ChlsVTI2KGDrhk7dNDzY4cOuqiaVfwZlYRke9FmFm74nDmeT1mesYo2SzJptDHj/D7DRizuM2zEbjQf1XIATNk7nmjZ8RsK16yZt2kRxWu/IGfHCiiGiIGNMPw3674bkGGib6D6xvOAt9rMnPdqm5nz6jJCodIUTZ/ffck7T3201PtR36+yJvNB1sNs8CwBfRe634cxg9RyNLnonoGFwVAoW9ko37HcO4fdsiXV4VLqeKcgCnxRNH3+xRWWPIjJ9+WdTOUmbmLkAC9VM3LnOOLvgW/2o9cfzmBwjfOxEbWXqy4R90D9dDTa9yLKvw/ro3IRn4rILXXUjrriOVSWtiF7e1CeS7wvMLGS9f2euFHneaCvMeY5Y8yXxpgZTh6Ey9D7MwPNg5DYH3FzNOo8coIx5h/GmI+duh5DB8mxZ8uvU+z/BHGjTsAYc40x5g1jzFfGmFeMMQOJJwduz37yNLdYLJZaZXywWn2DakbuHDR9Ayfq4xJ03NgVWCQit4lIfxE5TUSuQx1Br0aNOrfYCdRyY+r97bzyDfF5gi7uDY5BLjYPsUlExqBSq26jDug8x63ANyJyGnuzlHi+4V4ikk6Kz21capsq5+P+QkTaEk9BsNYYszZd+brEiaj7xrUqaU4iEbkYneMpBW6qSGq3ErRL+FwPjZ6ZIiJTHcN0tXH6sHe4VqXKBdTTtfxFijLpcN97PVOWslgstYI17FSNp5y/29FJy3dRL4y/ApvQh/8Y4tJgicQG9Mm8dBe6lo9j70TdbgmxjyrbYICwP5gf9gf/E/YH812rm6ZYhngEUo3whQIdfKHAjb5QoLuz6l70vI5DJeb6EffsPwaYGfYHPwz7g0PD/mBbJ/rom4Rqv0SjoC5xrfuKeNi522ulMrJGidciikbV3IwanWJ8m6Qt89BOdmXuo5NRg9WtqAEvMUT333vtYbEkMHbooGOAp9EO5Mtjhw6qTkLClrhyaBVHijZlrynmqC9yaPF9Jrg6b32GjdiE3q9dgAvY+slvW+9c2rhX4ZZI49IiyI7bLtob+M2OTdd9tfzLwNGlu48e0fi7e4G70Xv1+TYz57WoRltrjaLp828G5u2WzX3LpIgS2U1UIqzy7jnd2DN4CxpV50E997cB10aJsEVWk5/xNjmmEQszp7My41O+zpxMlKR9+KrIqm12jlef8nrGP0UepPITNzFyKC9XUlncv7mK5Cn2Be7IimYpS9UMd71Poo4O/0AdGbLRd/Bf0QkgLzBORM6po7bUOk6em2nOx0Q5thucv3OMMd9VVJczoRHLO7gBGJ4i4fBr6HcJ+j1WZFT/VTIZDiffUUxqpYeTD8ndnhOIJ71+yzEGJWMUKkkLcLVNTGuxWH4CPIjtG0CSvoETWdsTHUc3Rb+r99GIgqfQMeYLwKnGmDrJ33eQccBcX2NMCRr1AZCZMCnfmLjjajdU9WUzOgfRGv1990TnD0DP61XH+OE+RgHqcAyq6JJ0HCEi7Sg/oQ/lczgdCBww184h7b0pIk1QeVzQHEkLqnmcEtSwfQ3QGXUqroeOwf+Izj2COnu/WAl5vr0QkbYich7wMfG+5iziv69E3NdiXYoy6XDvs1/nACyWQwFr2KkkvlDg56j39mloSPTdaG6EvmF/8J6wP9gy7A/2CPuDy9AomIlJqol5UCR6e5aiBqJUg3jQSZjvUCPGX6t7Hi4eB/6HGi/+D32hGPQF9mhNK/eFAh3QidIJwDe+UOBWyusEL0E7pO5kxJ/4QoFEL5NG6LlHUOPK5WF/cHbYH9zjXRX2B3egIfU/Uj6/ze4UzXN70idei+lhfzAa9gcNOmk0E+08X4p2qGOTPhuJv2Qrw/Gody3oyy3ReNbHiWCyWNIRywEF+vxO5zmeivWoVBDoPdUJ7UD+Gk3sPX72hHFZsyeMywToM2xEYZ9hI5aicgE/3yE0/NJrvOvEDPqxiK5f5DYt3ejNol1Um9UqUoa/dGX7xutKpty1PZ9GkZLE4+8v7gPk6LJ+RIg7ZzYwLXah0YrdgBMo33HfM8ngJYOm5nCOKjvTFMo2EH2MlFCISWKrN1UL2GnuWk6XHPegJt+X15O4HGlV6Z3vy+tRxX3cE+e7Upbad7jb0ChlqZrhfgfmAL80xvzBGLPcGFNijFlhjLkHlfQCfY4cbI4FE52//UXkcFA9cuIOHxOT7JOME4nfe88nytsk4O4XnZuyFOwgvXTI585fQaOR3bjrTZnI2/Euf9z5mMmBq5NvsVgsFTM+WKO+AeODPaq4z8HYN7gSnfStn2L7BcAtIlJXTiMHEwfL9U3srxUDZxtjnjDGbDDGFBtj5hljhhJ3Lm5Fecn9GH8mHrXzFxEJisixIpIpIs1E5GfovEsrVznYO8/x/uZguXYx7kcjbBajY8nqMtoYM8SJGF9mjCkyxhQaYxYaY/6O9ldj0UvnoxF6FTFTREzsPzpP9hY6ni9G+5EXppGFdhv90vWPU+Hep7IKRhaLpZpYw04lcIw6T6M69W+gHpugA/PEyBrC/uB2NFHYJEjuyp3A5ahxZ3iK7e8Ax4b9waPD/uCJCZE31SLsDxaE/cHrw/5g37A/OBrtKGaG/cEWYX/w64r2rwTHU76zcDVwJ3qO1wFjwv7garSjGuME4KzYB18o0BRNHpyBTmB3B37nCwVO9IUC9Vzl2qH5A8p5sKBGsI8pb8gpJLknfalTx6WxFY6Rbgz6Ur8EfWn/FTUYtSS5Vm4iha7lWGiAW581lh8gC1joGMQslqSMnDRtIephPh24buSkaVUOUe8zbIRBf+eHAyf1GTZip7P8b+BG9He6Ddg8e8K4obMnjLt99oRxJwKrcX6vJcL6ZV4zs8+wX+aPbtv16lCLTmzO0tssIoYm7dbRY+NWem3ewqi1i9YDV67r32MT+xcPgJdMTiv1073kEtpFuv1rZmjmr8dNHNV43MRReZTPCbSXZUYQIpQUFnriNt1Okd67vWTuZb2qyLCzfefmVNIf3Yumz/+pJkX/RcVF0lJVmVD3oKJBylL7DvcgaUcdHaPItbw4VdSHk98l9q7vLiLHJZYRkVnuQWHif1fRULpyItKhtk7O4U00wsZDPGrnKtR5ppDUshKJdHMtf1JB2W+JD/C7pym3pAIZDrd2Y+IkQVXaM8e1nK49FovFcqBj+wZxyvUNRMQjIi+g0u+d0fdff/T9kY1G4N/rLN8EfCEinZMdREQmVvBOP9Ipek8F7/R+tXfqdcLBcn2LEspNNMYkqoPEuIu4QeaqxIgNY8zXwM+JzzvcAixw9tmMSs0eieZ/cR9jrwl7EVmZ5jfizscys4LfU3U4WK4dInIu4EfHizcZY6qdV9YYk/b7MsYsQ69pjJureyyHecBYd57KJNT0Wrj32csZej//ziyWnxypcpBYAF8o8Hs0LNYdStgMnfTciE6KJA1fDPuDUV8o8AbQBzisgkPFImeSGRx+BK4J+4N1OinqjoCpKb5Q4FhU2/9H4jqhm4HBYX/w8YTiiXIl7gmR3uxtfLwRfbEt8YUCPVCD1NeoB8omNPIg5s3UCDUwuYnlyzGolMmnaAc5AjRwfw+O8egV1IvmYqdtFRlDDeWv41I0UeHraJ6jeqim/yXoS2s4cQ/9ZsCpaA4eiyUpIydNe4z00X0V4hh31rhWdSQu9+gOlw6h90wRmrujN+odPq3PsBEFAOv693ipzcx5g69p2mXgXRkLbt5Z37A929AwK8r3J5eQQUmLx817UcZ/0hlYxvDA/uqEvY1jSPbgLWphOj3/4oQnZxjDO00bt+Tic4cFUMPVBNSLvx3xyLolqEE/WkrhQ16T+VsQaRY9kiMjJ89CZTH+ixrI0GOkflREo9GXnnl57BVXXvRLWjZPlFKmK/Bm0fT5vpyB3X5qHdaaaiz3qGJ593uzdcpS+w73vZUyOVMNcQ/EpldQ9k3i1+QUdBLggMcYUyYiz6KSItehTjd+Z/MUY0xljWbuSLm0MhPGGCMi69GBavM0RVNFCsdw93ESIy6bu8psqKAet1E/XXssFovlQMf2DeIk9g0CaK5HgGeMMdcmbF+CRmjMRd/5HdF5hepGQP0UOGCur5PvJmYcKE2IDE40qqTssxljNorI5+i8Ugv0Oi9PKDNZROajET2DKK9AMA/4lzHmeRGJOQhHqDsno+pywFw7h6T3pog0IB45/bgxpkppEqrJNKcNzYDTRMSbJtoGdM4sFiWejY5Rf4aOhU8FPhKR04wxy1Ps774Wbdg7JUFFuJVoNqcsZbFYagVr2EmBLxSoTzyksin64ot5V64P+4O+Cvb3oElwY9Jia0ht4GlCcgPHJlQy6ULiIbiVwhcK/B8aCbQU+F/YH3ypKvtXF18okIXKl7V0Vr2Jfn8XAxf7QoFo2B+c7NrF7aW1AHjPqech4FeubQbtlMQ6/0ejL7gs1KgD+vL9O/AH53M6I4ygL8c30RcfwLNO9M/4sD9YjEqcuCdWE+sroXzuo3z0unUlbtyJeeDWC/uDpb5Q4L9oImaAf4T9wdW+UOB/6OTUMlT2zWLZ17wPTEYj5r4GBjjrY4bQHKBDn2Ej3idJx25d/x6vAq8+8V3+F7N3tXl8YP421hy7k15bu9Nxa3Nvxvqvp2pQHC/iDFCXLr3/JOAkNErmZ8CMzp3vHFNnZ1h+8DJtTZd3/+o7p1Mw/51lHHvMqTSo39iLDr6GoQbfX6PfSVP0vvYC3sa06d2zZMijDWgxwqPzsmfnDOw2qGj6/HeJP0vS4vF4Lu5xXF+ys1LKyR8NPFk0ff4vfmLGnZp631VVC3w+OnD1AsdXYhBUZ4hIa+KODtspPzFfm6xGZRZg735FIu7tyfK0+Ekt+wL6/YLqf09NU+6HCtpRHSaihp0uInIz+iyJrbdYLBbLwYPtGyjJ+gbDXMvJJLgAMMa8JSLvAWejk77djDHzE4r9gfTSq287bQmSXpZ9RZptBwLzXMsn7q9GOPQgPiew2L3BGFMsIhuI97+q2mfba0LeGBMGrncietqg98Z6Y8x2ABHJIC4DuyhFpMi5lJ/fcNMO/Z1AecNBrWCM+dH1nbQVkbbGmLrqL6dFRLzEHYQN5a/ftWgEVAT42pG6S0crV5mtxpi305ZOgjEmKiJLUUesTNSpJ50T0IqEnD9fojmaZqIOoq2AZ0WkT4pI83lALAfnScSve2Vx33vJ1ID22+/MYvkpYg07qSlErcsxT8iYUScCDK5oZydiZy3QwVm1idSGndVoFMdmyhslstDolFt8oUBHZ/sJwENuuTRfKDAMNZw8F/YHJ/lCgePRiRaAPOAiR07ufdQoFfWFAq2ARmF/cGlF51JFsinvPXoBsNX1uUNC+RNcy1+6ImZuSCj3CTCXuGGniLhs20bUkLQIDVWO0Q19kXQneS6So9HJ2xhZaBLOrr5Q4A40oVwu+jIvI57fJBaVk0X5CJ28JMeIkelqU4zzUKPb9b5Q4A/ARsegZLHsU/oMG1EGXDl7wrgs4F1n9Teo8XQ4MAP4oKJ6/rTmzA8AVjTYwTC+peOOtniKN5FRVhorciXjg9fln1rU3eNltjd3z30BcO7Spfd/3LnznRVJEFWXPZ5xJdnbPcCCo/t0rL99VQHLf5xPh8PzaNxwj3Pbqajhdjfq4eQ25Gc1onUH1+ecGe/9anAffnG67G1L3knyCYesPiddUFF7bwRaFE2fPzhnYLfKSHoeDNRUL7tKGs/GmJ0i8iU6CKqHGiyrPJiqJc5zLc+uQK6rJiwgLnFaUQ4u9/bSxI3GmLSTNy4lkB9qkDC2WhhjvhWRr9E+QSx59vc4ziGVxO1BmDbHnTNJEvMerSvPw1i9HnTAnS5xsPWEtFgsPxVs30BJ1jfo6vxdb4xZQ3q+RA07sf3KGXaMMT+QxtFCRGL9gA37+p1emxhjFjkRtq2Bo0XE5xg89gfu6/thku0LiM9n1KjP5sYx2Kxlb0Nhd+JOx3NT7Lsk2XoAEXHfq4mGg9piFvF+7MWkyTlYx/Qh7tyUb4xxR7C482ZXRkUjD5XDAx1fV/d54/4NVEttxxgzXkTOQ+cze6HOxROTFJ1FfF5tMFXIISQix6LSkaDPrr3uvwPgd2ax/KSwOXZSEPYHo6iUWCLrqpDjZgD6EBzH3klyY5SiL93rw/7gHynvVe5ONPYzVF/3BnSyFQBfKJAHPIm++J71hQJtUUNKooFgtHM+032hQF/U2+Y7XyjwR2oJXyhwMRql9D/KJ+ZriMqLveu01c2/nPauRc8vRiz58G40N895qNHrT065kKvsVPT76crelv/laLTBWewtkdIaOMNZdr9AjkKl5Ho4nwU1zIjrc0Ukdsyv8oUCf6e80etqXygwHCDsD66xRh3LAUAe8XvieODFPsNGtOwzbMRVfYaNqIw348pmZcVrFuc04pmtx7A9ayfRrIZEPepD8Fn9+mZDm+5XlCxo8e6mz7Izd3y31ximbOnS+x9fuvT+r5cuvX9IbZxQ0fT5zYqmz38f9Qx6HnhgU7tv3pCop37mt51Yt2Q9P65ZxUvTxhVFo3tu20I02tFteIp5tfXC9e4spTBaIrtfWZIxq6nBRCh/7+eS/D1SWS6mYinPg4ma5m+bV4193O+cO2t4/GohIh7gNteql+vwcDNdyxXlanLr8NdFVE1dM9H5Gwt9e7qKBrNvXcu9UpZSuhH3Kq+qHEVlqUp73DI7ddUei8Vi2RfYvoGSrG8Qm7zNTLItEXeZtBP/hwATXMv76/rmUj5/crLru6/7bENdy8+nLLV/cd+bv3aijPYHd7iW67LfXilEJIu4o2EB5R2nq8rdxJ8tf3XqTuRd4lFiJ4rIGUnKpML93f23Gu2zWCxVxBp2kuALBfr7QoGRlPf8LAOeRicH0+17ry8U+NYXCtwd9geXoQaIWylvpHGTiXqHP+ALBc5HvU9jg/SYAcEA7V37tPGFAjHP0SjxCUcDRMP+4GrgCtR4UwgsJJ774Vw0fLSe8/nn6c6nsvhCgZbAS2iH4XpUEzhGBnBx2B88N+wPlnsJhf3BGWF/sFnYH2wX9ge/cm26FjgNGIhK0T0DNA77g38L+4O3oS+kfwJj0TxIqby1LgeuRvVI0yWI+xL4Cg2zfRB4ocKTVpIZeULAL1GZtxgeNCqrWULZU7BYDhyWAksFoVF20zVHNe1aJe3ldf17lPxp7cJB/1wzb8fYH3fQqKQBpRkZ5u2uzfgyD3YcuUtWNfzgmR/bfti4rM2PFKz1gA6+nkZlnzqgyV97ABOXLr3/2KVL779l6dL7D1+69P6jli69v/fSpfcnJhyviCloHq1jUY+j30Qzil/NLG64vHB7MVFnHriouCCnpLRoCuqhdBoaTRhLJDuPeELSCCpTsSZKdOeCjDc9AD94v2kxN3PiCeiEbMwIlkFcYqM6lFJB7o+DjCf2w/5PEc9jdq6I3JKusBsROUJEzqq4ZIX8iXh06ir0915XzCFuTLxYRJJOBjkSE7HoY0NyT9IDnWfRd38xGsU7sYr7f0VcQ/wqRzM9Fe4+zTtVPE5lcdebMjGuc+1iycJL0GeWxWKxHKzYvkHqvkFMbquZiBxXQX39kux3qPIgcWfZX4jIwMruKCLdRKQ2JNzGER8DfGKMSSa37pbKvyJNmzoTdzhdbIyp8thARA4DRjgfFxhjZqYrv78wxrxLPJqoK5pHsVKISDMRubimbRCR64FLnY+7iEeGx9r4oDFGKvrv2uUD1/oe1WzW9cQjiGalkNGrFMaYpejcGugc4bAkZUpRB+wYj4tIqvnMPYjIOcSlyXeS8N1ZLJa6wRp2EvCFAr1Qg86/UV3JWORJBvBB2B9clGbfbsCfUc/Ofzq5U6B8nhaIdyQTyQ37g8vRjoAbobz+sAe4zRcKNAr7g4vRKJ6XgKFhfzAm3TEQjRLKRSXKYhb3JcAk4p48R/tCgdrwjPcQ/z0JmqPiOVQi5NGwP5io85uWsD8YDfuDc1FjyNnAJcRl6gj7g7vC/uDvwv7gb8L+4PawP/gm6pGT6PW1De28v0Q8788SdELIHdYZM5AtQ6ObmqZomkn4m+gR9U+0M/kwKkOXKsrBOPv28YUCC32hwMe+UKBLirIWyz6hz7ARu73iPbl7m14LujTvdliT3BafFk2fXyXDxFXX3fhN55LdR3ZqdFRYEDLxSqvCppEdSw1ZKwrZyFKMRNidu5YlDeXzzp3v/EXnznde17nznRMprxW8FX2OBNEJ2DAwG9i2dOn9gaVL7x++bMmDf9j9zpeji6bPv69o+vzGb75/YzJDq/vZ6QWkc+c7tzTZ6DvusHZHfdWl0/E0qNeYPidfQE52vcU5A7v1zxnY7Rs0yi/mJdYQfaYGgctyBnZ7PWdgt8NnZf+n+Vbv6pgDwJuFnm0LcwZ2+5yKZQOKcBmjC1OriGRSOS/Ng4K8cP481PBQHeY4+1cJY0wJ6nAQe5ePE5HfpTJ4gE6ci8iN6O+ue3Ua69TTRETGAX9xVpUCfmewVCc4ESujnY9tXcuJ/JW4NOoUY0xNIsv2C8aYzcaYRsaYHGNMrjHmuyruX4re06ARvI+KS18uhohcSNzQsgxX1HRtYoz5Gogl4L1ARH6RoujfgdgE33PGmI110R6LxWLZJwwPzKMGfQNn/ypxEPUN3PnrHnGiQJLV+SvicuWrOMQjOZ33ot/5KMAUEfEne8fHEJEcERmJGhUOT1WuIkSkrYi87Dr+duLOGIntDBOPBrlMRC5KUl82Oq6ItT1xnihWrn2y9c62NsAbqGEgShrnkQOEa9A5HIDfisiDIlIvTXlEZDDqqFtto6uI5IrIPZSPMgkYY+pM8lZEuovIMRWUORN4wLWqNowlfyMetTMqRdTOOOKycccA74hIynvDMapNIf5bvdkYk05W2GKx1BI2x87edCb+MOqCRoSMQj3ZKxrMb0M9R2O6m9eiobKfAy3QScuXUM/xZEm2b/OFAp8C01EDRc8kZWLcCYzyhQKzAH/YH/xfmrJuDGroyUc7pYLqmE4G8IUCOYAn7A+Wi27xhQIZQCTsDyb1Dgj7g+t9ocA16Dm/jhpGrnY2V5hMIg1bXMtpQ07D/uADvlDgE3RiJAP9Dg3l8/iAyqG9QnnPpjySa+yXod9VLDdOohxb4gDgMvR6xzSmvOgkbmKW9Ji8m/tF/nfimrIWy36hR9s+hvikYav54bm3nDyw25+rUkefYSO2rZsya3uTnOYYY1iyu4gjjKEokkVZJIsMbwlFJot/ND+h2+9nzjtyXf8eqxgfzKrX3eMtaBmNPUMX43TOM0ojLeoXFFOYk0VJdoagBvQ2xhNle4slNFzfma8yX/wVhty5c0ctb9W0XbPcna1fbHvSlbegUYmvo1GKv8wZ2C0C0OisPoXLww//sm+jfrPr7WojAAbzu9VvT7nlnYw5l/+c6yajnm3NgfE5A7t9SEJUwwVn/bcUGPDm+zc2ueCs/25buvT+9kuX3n+CdMr4ffvl/beg745kmtk5OEbfHaxnk2cFnaIplZcu5sCVS6gOt6HP6MRnYjoKKS9XUiWMMZ+KyFDUO60+auy4SUQmocbCDahjQlugLxrJ0qkSVTdJ8KDNAZqg/Yje6Psg5l23GxiWzkNSRHoQ98iMETNMNhCRGxK2zTPGzEtS1WPohFVfYKSIdEEj475Ho3/9TttAI1buSFLHocI/0Ot9HNp/OUpEHkEdQJo4225Bfx9lwA11nGT7JnRioj7whDOYfx6N3DvS2R7zPP4BjVi2WCyWgx3bN0jO/eg7uz1wJpqo/WH0PVGMucR4cgABAABJREFUOnFejSpUxPhNTbz5fyoYY14RkVtRCfdsdLL+DhGZjI7VNzrr26OR/YOpIN+eQ6uE61sPvb55qJT7RcSl4TcCVxpjFqapb6SzXyvgZRF5FDXo7XDqvJP4nNBsUud0mSMii9Exz7fO/i2dc7vFaSPAHcaYuspnWisYY5aLyCDUSNASvc9/JiLPo2Oxteg4qjWqfHMp8bFrOuonXLss9Hs5ElVcuJy4bH4pcLcx5hnqlhOACSLyIToH+C36uxH0/r4U7dPHxpOPG2NqHDlujFkmIs+gDuKHo3OTjyWUMSJyFfqb6oMqzeSLyP9Qg88P6PPtGKeN58R2BUYZYyqrgGOxWGqI2Pd+eXyhQC6aKLw78EDYH/yzLxRoCOx28u5UtP9ZxCcRjfM/FslSCPwC+AyV3EiWd6fMKX8j+hIfRnmP82QsBz5GjQ+Po9qkDYH70BfWi5SXBdtCXBIsCvw87A8+7wsFzkCNV9nAz8L+4BTnnK5BO0ObgLPD/mCFCQh9ocCVaGQQaMezXmW+vyT1NAV+i4bBjqkoD41jmPKhL5jX0E7aU5Q3Ym4j3rmJ4Ta+LCKerBL0e/xdQvkS9s7nE+MX7J1LqDK8HvYHaxw+bLHUlLUvvD+3aeOWp27ZtoFX3nxsxy+fnlxh6HUisyeMu6dBVuO/RKJlzMnI5LDSAh5qdTTrc4TjzSr6byzkT61P4YZNy8devn3tzcdHJCsLyd56ZCmbO5eRVdB0i/GUbSjL3HFkhx82izdqcqICq9s3oyzD+y2Ot2RWQVMia+uRn6kORYLQ+fCueKKZtF/efyNwc87Abq8CLF16f0e003wYcEtpWWmOR7xPNN7egSab4zbWs7MiLPPwu5VF3oeB+jkDu1XoEb906f1t0US1zYEFbZefcbk3kp1P6sjYMiDDEOU77yyOjqR0MCsBGuYM7FaSqsDBRr4v70LUyaEyEziFwJC8cP4bNT2uiHRHoykroxMdQWVRfm+M2ZN8VkT6UV4TvSLK0PfqSGNMWmkUEfkLcE8V6r7XGPOXFHU1Rb1A+6fZfxVwiTGmWp69IhLrQPqNMROrU0cVjvG2Meb8auw/EZWvAGibTL5ERFqj/YV00qg7gKuMMW8m2ygiK9GJgQ+MMf3StOcG4jkC+xtjZiUp0wt4FZ2wSMVi4EJjzLI0ZSwWi+XgYXywyn0DhgcOhb6BD32fd01XDv1O7jDGVCvZvOs9lrJvUdskvBPr5LiOg8R/0NyhFVEMPOq0Zburjhson9+3IopQWfffGmM2VFTYceyZQjySOhnvA0OMMVuSbRSRNZSX7k9kK3C7MaayzsDJjtEBlfqHFH2Y2kREjkQNc5WdH5kK3OWO4E5oc2Uw6Hd9tzHmq4oKp8PVh03ZN6zCbyuCzkndk8rBKKHPW+H1cST+wqjRaDXQxYlmTCyXgxq+byX1/FeMVehzaEoF5dK1qwP78HdmsfwUsBE7Dr5QQFB99stR+TIPMMIXCvwr7A/udJW7APWofSrsD+6VuC7sD77vCwVOQT1sBlB+Ui8XlQDbjYaYbkeNSDEL/HriA/krUSmzZJ5I36MeJbFokU7EPYiCqNfpNcCvUC+f0ajmfkxSyZ3n5eawPxjzBh+GGoRA9eRjD+S70Yd4O9TgdHeSNiUyBfgfcDLwj+oYdQCcnDyJRpW98IUC2ajnwJloOH9n1MOlFA2p7usq3iRJFTloh+dPqMHtGOLX5UT0Og1wPhegHrMXoIOA0xLqOhF9+Sbz1E/HQSeFY/lp8vyrD9xTP7fRWwVFu4hGI1vGDh00CFgwctK0lVWo5l+7SrY3AE7uWcaZ3zXOJT+3EQbhfelGs5zVAB9fuGPtsGaGhllOEFzjNRn8u1lHFu84vKk/4ml2drRgpzdzakMAj4Em27jHs+u0Plvbzj/MSLReg61H5JR4ihDjwUiU+jRHRMgoywH18noIeHXp0vs7oblyGgLsLtx13w8bVzYGTNumu4saSiev12RmrcewTptyZ87Abv8smj4/t2j6/FuBb3IGdpvtPsGi6fNPRju5c+nCcol4mzfdkIc3kn2cMdxK+ef/b1AjdUwSMgMgSpQOkV5skx/JMQ3J2fMI3sNOUks6HpTkhfPfyPflnY4OtHunKToHuC0vnP9FbRzXGPMtcKaI9EXlPc9EjXzN0UmWTain3Czg+SrKkxn03b4d9fKdB3wBvFIdLfSaYozZKiJnAz9Do9Z6oL+9nWjevSnAeGNMutxzhwTGmPUichrq9fwz1HuyBXo9l6LG4IeMMZtS11Kr7ZnrRFndik5mHAM0Qvso36ITfP9NNvi2WCyWg5bhgTcYH6x034DhgUOib2CMCYvICejcwGWo42crtB+5HTX0vwc8aYz5PmVFhyjGmA9EpCdwPjp274vOaTRFDTkbUJWPGcCLqQwnKYiizqc70HH816gD7yvGmG1VaOM8J5LkFnQu6mj0vb8JjS56Gni5gkisEej8RC/n/Jqg/YalqNPxhINNutUYswq4xDF8XYY6K3UkHlWzGe3TfoxK01bV2WU3eu1i9/iXwNSKjK21zBvo/Nqp6L3dBj2/TNQZeTHwAXr9VtbmgY0xS52oneuBI0gSteOUK0Kj3R5E+8rnoXNtLVAHxPXod/cGeg+ldcS2WCy1j43YcfCFAj1RzdxE2qNGkxedz22dvwvD/mDSkE8nwmcTcYt2skn+Ymf992gH4H208zfR2f6c055/O59L0Qf8KuAkNCoklXZ+DENcMgzgEeCXrs+fAOeE/cHdjjHqXbQTAfDHsD/4d+d8ngKuc9ZfEfYHX3bVgS8UaI9OPswJ+4P7RdPXFwr0o2qeUjHc31EUOIq9vTq+RyWhPkJftu7vJgc1ogWJG9qeAtYQzwkU6/Cly1VShF6Lj6txDhZLrTN26KDr0cngM9GO5k6g58hJ06rUaZ49YZwHuFe8ptsLnY4//8VIw+wOZWX0NxDKzPjjy8tnn1/P0Pd4I3iiwvfNPJzc4lQAcgzML/aQ6/kGr2cZUdN0ZWmk3yzw3OBUv7yw/sZOEvVQWhRlt2yiQUazjyINd3ZvsO2wxt5IDsAHOQO79Vu69P7fAGNi7fph46qVuwt3dnA+zupX/Cv/h941D/0+o/2FP4rHc1TmrvlPtfns+PbfDfhSkJ7o86E3arj9dxE7i7KpX1/wZAFEPaXDdjf88a8Ntx/ZHqDMW0RGpJzT6TZ0kBVBO8G5u2ULX2W+SKkU0qmsNx0ip7rLG7Rz/WTOwG418hg7kMn35fVA5aV6oEa3nejA+Im8cP4hrRFvsVgsFsshyfhgD1L0DRgesH0Di8VisVgsFgcbsRNnDfGJtyLUA6YZKq2zmLhBJ0YyGTXyfXknZN15+iMlmV53mKIX1e+9hrgRIctZPgZYE/YHJzryZTGuRL01e6PGht8A3wHrw/5gkZPzpiISEwTegOrGlwD3hv3Bl1zbbidu1JnrMly0BM511i9D5Ur24AsFMlEviQ5AkS8UOC7sD+4PaZDFqFdKU3QCNpX8USLu78gDPEj5PEmg12epLxToADQI+4N7Eug51+IZNNLnSNSz67GwPzjXFwr8iOqRfod+P7nO9nfZO+T2j9aoYzmQGDlp2lNjhw56AX0egg6sT0CfA5Wmz7ARUfT+oOf0b6b/ATm/AVl85GFDiOh44JE5jQ+798OGmeeenb0pb0VuPRWTADLAgPmhLNrjsLJoD0qydyz3UNLQZTDZkbu7JRFPSSTHZHkbmpZQQj6bW/8ONcIeBxQUTZ/fjy58RFxC8ZPCot2PoYZ0Ad6rN7DnyvPpeXF0wRPLNpqsTqfmbOn2w/eb/teWSE+vvio9QN5K72cP/+D9xlPM7twzSm4lw7Hfe6KZExpsP2JbrGFRbzEmko3oIyZKPFKwEJXbGrvRs5RS0ZP9wftNtEPk1I9QQ1oU+E3OwG7uRJk/SfLC+fNQL0OLxWKxWCwWGB6Yh+0bWCwWi8VisVRIZSe/f/KE/cGNqKTWCHQycAEagdEMymnjLEAjOgKJdeT78u4HvswujSRKc4EmxfsdGnmzm/KyW2f7QoFeaK6cmGTZirA/uD3sD16Ohp4eh+aOiUX+vICGDoMaIipDAzS096IEow5o+GSMd13LxxJPJHgUGvrtpiFxPdgcNCxznxP2B9eiUQXLqPh3/RoaBj85Yf1qNAQ/Gw0pLUST893hHKPYbdRxcRhq1AE1lopT/lE0rPseNMR1MHBX2B+ciCZ/X+z8fxyNprJYDihGTppWTHlDTjrt5rS0mTmv8UbkP42RHV5kS7+oDO4epdfsw/vefnWDjrcNN4fnfV/cg//t6kzv0lIDcG4EycRzGECZt6h4Y7uvzkJMLEFsEZpUFG80y4tGM34D3AzMjlDmK5Atw4ExBvP+Yd+dUw/NQ3YOcPr5/Z/8H2qo6nvBWf/9W6ydXYql5ZmeIjLEUFBYcMUy70eUUsRu2bL6m4xXP16eMdtTLLtoZo6gQLZRxA4MGvkqSJOIp2T6zsarI5vaz2PDYZ+vQJPW30r82f4p8ABwdYbJug9DCUApxc/mDOzWD33WdzoUjDoWi8VisVgsFovFYrFYLJbqYSN2XIT9wTCaQAxfKPAJcLaz6RFUTqs4iUHEzaUAP/9wBY+de3TUeMRtYDgSNeb8G43c+Zryk6RB1LA0AJVae95px7Go5mesrm98ocApTgRJR6euYtQL/W/snWx3I5qE9yrUsLMV6OcLBd4HMmPRNWF/cKwvFFiMGrNede3/GartejKqyV9OVzjsD27xhQKj0Xw+76GScvuFsD+4yhcKbHWt2g4kJn3fFfYHLwHwhQJe9Dp0RD33h6HaohD/HnNIE6HgCwVORSXXVjj1fI7qJ8dwG/kyUSPYj2F/8P+A/6vcmVks+5VtruV0ybxT0mbmvADwyCk50a3ZhjMWF3u/neiNPv1KieeaCJDtBM4dAXzkhUaeTAHoEo0H1EWyCrM9xktGWW5sVQ76fDoFNfJchOb/Oh6gSHZcl2XqtQMQRICunTvfORONWgTggrP+O8/dzqLp8wccZk6tLwI/bNtF6ZbM9Wvyvj5yTcY8gxr914vx7DJEGxxXeiEZTmDfTjbQUG3eW7zRrBHbW/0/e+cdJkWR/vHPOxtYclSCJBV1RFEUs6KCEVxFz7BmGdTTMZxh9c6cPfnpreEMY2SMpysmdAUjqChmRVAcEQUk57jLsql+f7zddM/szGwk1+d59tkO1dU1PT3V1fV9w28tgL2qMsveyRnUZ1npmMn3AoEqKgiQ2TpnUB8DvNyLPi9PHTvuaaBrlVSMB8gZ1OeX+lxji8VisVgsFovFYrFYLBbL1oMVdlIQC0VudsSdEnRy/m7gi2A0PCoWipQHo+HWQEUsFCn2HfY8cOvRP81bcfRP8w4/6V+HGzQB5GFoXoW9gKudst1RMWGos94X9czJQkOi/R6Mhmeh3iV+gWhPNFfLDDRfxCXO9j+A16jutr4NGgrsHVQw2gkNA1YFBILR8JWxUOQh5zMXJbkOJcFo+EA0iduiWChSLSlTLBS5AbghcftG4lzgNtR6/070s/sno5sHo+EAOincLhaKrMtTFIyGY+gEcWs0gXIGGtO5lNS8in6XoMLaZPQ7dIJJUeDU1RJN9vl1/T+axbJRuAbN9bIQ7c/qw6VoP9Ze4Bxgj/5VcmQmQiZQgSET4Z2Adi8tK2HvxU04t3WFGyxx+bJWv79WIWWhsuxVJVllLVoIMgbIAw4Hfhvb5IHWHSp32Hu3iuMqA2RULgz8/mWprNylV0V/SmXlypZm25dq0c7jRbS/bW3aLVv7ztDDMnf9sXef8uMPb27aX4aRNV81iR4CnBQg82bnM5FBk1WGqiFC4OecQX0W9aIPaF8AQBUV037OfJfFGX/SuqpLpxVjHwgMHjiiCmDwwBF/UMfwdhaLxWKxWCwWi8VisVgslq0bMabaPL3FRzAa7oSG43K5Ep3gfB6d8B8cC0XGuzt/De7aE1i+a+zX5c7xNwN3JKm6Cg3h9QkaHq0C+BIN2ebnAeAfeCHYPgAGxUKRqmA0/AvQ21fWnxvG4OWPcfNKJOPLWChyUIp9my3BaFiAbqgA5s+jMx4NizYBDTF3fywUyU9y/HGo99QrsVAkpRgTjIb/AHZwVt3r/2MsFNk7sT3JRDGLZWug07iJD6H9WOWlFXL/tRWBa58KVPFJhuGIKiGjyrArcFoT6DP7TwaXVTCsze40aVa5ro4vsp8qL6c00Lf8bxltzHZVwKE5g/p84e4fPXbYl8ABAZNJJtn/KZOSG4Hb0X729sEDR0yrqZ2lYyYfCryH5sO6OmdQnwfmvTf62Lam2xiACsqqMsi8WQj0qKTiuAwy/V6XT+UM6vP3ZPWOHjusL+od6LL74IEjrGeOxWKxWCwWi8VisVgsFoulXliPHR+OJ8fZqBX2C7FQpBINr+VnT6Afeu1aAK8Fo+EX0NwpZtfYrzOcujLR0GW5KU4XAE4DOqOizo04uVwSiOF57FQAQx1RZ380DJFf2Ekm6kC8qFPirLvf/YIU7dvceRU4JWFbFSrWfImXN+gCIE7YCUbDHVHR7sZYKFJSw3lORsM/ZaDfJ8BewWi4aSwUcb12sKKOZSvnSjTE48JrKwLNFmDy78kygSqBLzIMh1VCVps/6PvVYo4a/w5VwPK8G+jopDcrNavK17I6C4F5GVNoU7FdAO37vvCdYyFAlVRQRsXswQNHlAHX16WROYP6fFY6ZnIPoHnOoD4zAFYFFu3etrIbAAEyAkLgboBK1q5ZJrPoYNY9ItqkqXoGaiDQGZiD5vOyWCwWi8VisVgsFovFYrFY6kVNSea3Nu4EnkNDlbnJtL/HmTBEhYHOwB6+Y7ZFhYFB7oZgNNwOeIrqok55wvqDaIiuTOBo57x+8oBPfevi1N8PndAcioYK83MJ6qGTjPeBXVCvFZcewWg4qTdPMBpuGoyGDw1Gwx0Stu8bjIaHBqPhVinOs1Fx2pUo6oBe/+5ownSXrxKO7YTmNPoI+MQR+1ISC0UmouHWjvFt/q9f1LFYtnbmD+hr5g/oO27+gL6/5Azq8+3UAMcHtD8F4NMM+NFk0XPBzHXHFE76mBULmzBvaks+/25+pStVt6nabg3weUVm6chp0+7ff9q0+zs7hwwD/o2KSI/Ut605g/osckUdgGmZnz4+K/DjymXyF8tlzrpyAbJWxjI/ZFHgD1bIvGXAv1LVOXjgiOVoKMzTgH0GDxyR2G9bLBaLxWKxWCwWi8VisVgstcZ67DgEo+FLic8TsytALBQpcyb7B6L5WvxhudagIXsgPsF4FDjBt74cDe9zAzAX+AXYMaEJA9HcLiVAM1SwGR8LReYFo+HzgSHAb8BZQDFeaLZmOPlygGVomDHXc+cTNL+P673z71goMjsYDd8OHOSU2wu4As3X478eAWAscACwIBgN942FIvOD0fB+aBizDNTb5RA2PVYBP6HeVX7vpZ+AJQnbHks4di9UrAPYF2gHLK7hfHnodwfwSywUuaLeLbdYtgKOOmaP0b0+njhsGjxbIYAxjFzdnS69oduMqQSqKvmx6y5s/6zbTXYLNN1z/JGBliuWbX9M3g8A06bd/xJwJrBq2rT7Dxg8cMQU1POxURk8cMTqkjE/XltB2RMZZFPSfL4JVGXJwpY/dyhfuea0yYG3s4E3Bw8ckda7b/DAEXOBkY3dPovFYrFYLBaLxWKxWCwWy9aH9djxuCxhfaq74ITR+gV4G53od/kDeBQN39Y3GA3fHIyGW+OF+QL1uDkciAAHAj2Bu9GwauV43kCCWnQ3c9ZbomG+iIUiUdQD6J+oAHM3mq+hHJiI9z22JV4w2oX4kGzHBqPhQjQXzFTf9qagIciC0fDAYDTczKnrAGd/R1TwAPVWckWluDwymxCnAbs5y4J6Xd2NetWUOH8uixKO/QIv6fnIWChSk6gD6t1T4SyPrk+DLZatidIxk5u/V5ZxyNdrAxOGVPBp0Mh/wJi5nbrz5plX0mPAWWzXY2fTdv9FhRIwnwJn/fa/3+XXJxbuWpCXm+VUc7zzvyXwxLRp9zdJfraGEyDz6Wya3VDWfMlbS7tMlsVdfyDQuiyjWZNmOw0eOOKlmkQdi8VisVgsFovFYrFYLBaLpTGxHjsOe05f+tNZn00PlmcGeDA3yKLWTY8JRsM/Am/GQpFS4BzUA8TPbqjgcSkaVg1U+PgH8DDq+XEY6uXjn3S8F82PcxCed0giBvjWt76Tb7ktkANkofl+/Lhh094ETkrY5+abOME5HmAF0D0YDV+JJhpvheb16Q28AfwNmIJ66QDMxPMQ+iBF2zc2txN/b38QC0VucleC0fBxwN+Bz2KhyAT/gbFQZGUwGt4b2AaYX5uTxUKRD4LRcG+gQywU+bLBrbdYtnyuAC5oj/BQRQZA/7OyKsfsO2vi4P5VxVRUlNPsz1LZ47BAXqvDFr3y6LPXZwEfAkhAjp427f4HgJeAi536DimvKLt49NhhzVCxfMTggSMaLa9VzqA+VcA9s6d92KqisnxWZkZWq7VlpRXlFeWvNtY5LBaLxWKxWCwWi8VisVgsltpihR2HW1+d1MJ1bTl1wkweGxTsA/wPGAWciOZdSWRmLBSpCEbD2/m2bRcLRb4G9gtGw/OcbYmW5PlAZcK2uajI434nNwErg9HwcFRUcfP+NEeFlaakZzrqRZLsO87xLbcGzk/YH0SFrFOArsCCWCji5u05Fs9DqEcwGm4eC0WKa2jLhmYS6q3kcmEwGn4kForMBYiFIp8Sn7sojlgoUoEmOq81sVDkdzRUn8ViSUGncRPbAC/8I1P2vroizmE0MLRC+jRbuxyyssjMyqKivIRW6nB4BPCXW7B5+6anAueigvNvOL/12QtnnAns5xTLRr0kG5Veva5eOW3a/d1Wlaw4es3aks+OPDSysOajLBaLxWKxWCwWi8VisVgslsbFCjsOAuuyYjcpr5qAetOA5lkB9bC5EM2Fc4azrVUwGs5CJx0nol42Vwaj4e2BNsALwLVozpdmeCHMMnzLLm1QseZMYBrwFdqmTk69+wG9gDCaR6cjsBL13nFDExWjItAa4OoUH7UcmAXs4KxXJmkLwPBYKPK8U9bPe8DlzjkDwOpgNPw5cHQsFFmT4pwbmnPR/EKPOusd0LBxczdWgyyWrZHSMZObAeU5g/qUO5vCQO5jGYYWpmrx3ysDU9A8XSsqhDm/tOrUrd+aJayRAG9228U0CVTJSxnmj6+G3tD7zLee+qv9ikXtdz1qh+ZOXb3RcJVHAr+VV5Sd7Tt19/X1mXr1unol8Nr6qt9isVgsFovFYrFYLBaLxWKpCZtjx+Mq4DrgilcP7nEmGo6sDLgjGA13Qa3Dn0IFlWmo2PJ/wH/RsGt9gcdRD5jfgB9QgWUnZ9+jpOdr56/KCQ+WhRdWTYBOsVBkPur90wP1ummCJ+qAevPkUz1vjJ8sVNT5NzohOsq3z+9FVA4QjIZbBKPhdXl6YqHIh6iF/DFo2DnQidkDa/h8GwwndN549PsDDc00fuO1yGLZwHx0fYCPrm/PR9dLzYXXD6VjJodQ8Xlu6ZjJbl8x94BKGFQl3JdpJuUM6nMYmresS9XCic1+btqSq7fry2Xd+vFxs9ZyYbbhkwwOKM1pljvi9Cv+GnLXwHZddtv2a6euycD7vXpdfUmvXlc/hPbhvwDj8EJjWiwWi8VisVgsFovFYrFYLFscYkyjpSHY4ghGw4FYKFIVjIZPAUb6djUFTCwUWRuMhr/A8+55FFiL5y0zEbgNeJ3kXjEua/BCq1WiuSOeBtxcMG2AqagXz2TgP05Zg4o+LvOAU4FX0BBqLhNRccmPG6ZtAZp8fDLwGephBCqKPAlc5uw7NBaKLHcPdjyVJqFh25YCu8dCkTqFL1ufBKPhW9BcO6DCTp9YyIZNsmwFfHR9EzQ/Vx80bOMdHHnP7ekPanxKx0yeiJeXrCBnUJ9rSsdMPhV4FaAY83L7QXucCXDpG+/06llW/HvPtcX83LQ1X7TszM85zckuK2XvyV9RmtOUn3bd9/N5R+zdf9q0+7NRwfyPXr2uLt3Qn8tisVgsFovFYrFYLBaLxWLZ2NhQbD6C0fDDaBivUcDQWChS5ewaj4Yk6wa84XiEuNwBvIhapm+DCjjFqPfM/4B/kl7Ugfh8ORlOG85KOG4X58/1qqki3uPqdVR8yide1MFpt18EqsT77js6/41TR75T7wxU1AGdID4Kn7gVC0XKg9HwwcBhwHebkqjj8BFwA+rVtC3wZTAa7h0LRdZu3GZZLOudv6O/WdDf8m18dP1YjrxnQ3utfYoKOwbPY66fu7M5sjtAp3ETs2jTtQgRmldWcN7i6ZXzJSOj2do1DP5iDNtP/YF522xHhyULfuWIvenV6+oy1DPHYrFYLBaLxWKxWCwWi8Vi2SqxodgcgtHwjqiQ0Qo4B9jH3RcLRRag+Rx2RT1i8O17PxaKbANMB05Dcz68goopg/C8eUAnOP2MwpfbB5jtW04lBrnbA6jXDWjoNfe8yWiPJ+qYFHV3RkPIvQx8DLTw7VsD/Jh4QCwUWRoLRd6MhSKzgtHwNk5uoU0CJ5zdub5NO6DCm8WyRbNWOC3J5lGOJ8+G5Eo0ZOM+OYP6uCEfn0b7yjXA3c62dojsAlCckUm0TdfA4iY5lDRpytLWbVd9t8dBvHhymI8OPeHCTuMmnuc/wZtfLfCHorRYLBaLxWKxWCwWi8VisVi2Cqyw47EQLzfNKuCvYDScHYyGnwxGw58B+8ZCkZjPiyeRA3zLu8RCkdlo7hmXu4B7fOsVwH6od08F6vFzJpob4mNgvq+sQcMX/erbVo7nddPSt/174BLUi8gvJLnLqTxWejjHnAUcgQo9LtfGQpFpwWh4n2A0vHPigY7nzgzgz2A0fE2K+jcGb6GeOwDPOt+JxbJFs6Bp1pIkm1uh3msbjJxBfUzOoD4f5Azq84Nv27ScQX12AJrnDOpTCDB/QN8FwAinyMQ2yxevCy+5sGnz3zPLyyt3nTrR3XQewJtfLch486sFbwJlb3614I03v1pQk1ekxWKxWCwWi8VisVgsFovFssVghR2HWCiyCjgQ9do5IBaKzAeGAhcC/YHnaqhisW/5+WA0vB2eOFOOijz7+MpkouLJzsDnqPX6d7FQ5KpYKHIkKrTcB3wJXBgLRfKA3VAvlKuAsb66PgOWAz8AzzhtOY34/DuCet0cBvwElCS0/0/ivYsEzZ3zMPC4k7PmW2BKMBo+PuHYvwHNnOWz2USIhSJlsVDkKCArFoqENnZ7LJYNwZguLW8Y3bmFebdLSya2yaFCmAxcyJH3rGzsc3UaNzGj07iJIzuNm1jcadzEh5Ls/1encRPHdxo38Xz/9pxBfeK8F+cP6Hs+kAP8q/3Shez3/Sdkry1l1z9/2bvvr99m5I59jd5TJzKsgrbLPvng4L2zRn7bunLhic7hJ6EelRaLxWKxWCwWi8VisVgsFstWgRiTGB3M4hKMhk8DCp3VybFQZI+E/dlALzSfzje+XWcBjxPvSVMbnoyFIhfV0KamwBV43j8Lge1ioUiFr8xsYLsUVTwDXI7mn7kpyX5/Lh6AdrFQZFkwGv4K2N/Z9gVwNBAGBgITgFuAbOAd4KRYKFKJxWLZ4Dwx475OaIhHV7jf66Ke105cH+fqNG7iIXj5cwC6zR/Qd7azby9UbAbNCbbd/AF955OGu8895ZYqCdw+p2N33jn6dIa++gjtVqhm3rVfH47pk0uLjF8qMwPTMwzChJwhLMzsOQ/Y9aQDOq5o9A9osVgsFovFYrFYLBaLxWKxbIJk1lxky8cRS0JoDpZ3YqHIDwCxUOTVYDS8LRBEQ6T5j8kCPkG9fBYmVHkv8aJOoljiUoaKIS77BKPhn4BdgKtjochjCedsiXrb+HPZlMZCkYpgNNwaeA3YHWiT5uOejwo0/jqW+475C+gCZAF/oPl5lgFRNHScAAejgleuc8wRQBHquXM88LATvu6NWChSlqYtFoulkbmo57Xzn5hx30Wox2HR+hJ1HKYDq9GcXHMBfxi4YlTQCaAhIFOFgQSgIC83JwduA9hpZowd/prKx4ccx1Gfv0OnlsU0230en2e/wuCqThkAgmEH+XrOQnruZ0Udi8VisVgsFovFYrFYLBbL1oQVdpQngHOc5duC0fCLsVDkHIBYKPJIimO2R0UdgG3xxJvlVPeWWYyKRn4M8DsaXs1lPjDYbQfwWMIxexEvyIBOquK0/8gk7fwSDffW3rdtN+KFpjbO/5/QidoezvqOwKRgNLxfLBR5IhgNDwDynH1dfcdXAu1862HnrxA4PUmbLBbLeuSintc+DTy9vs8zf0DfOZ3GTdwfOBQYPX9A3zW+fVM7jZt4MjAIeGX+gL7LaqhuLRoSckcDlctbtata1L6TfJJ3avn18mZTLVLMx1/N+3350kU77b5rh7Luu2WffNIBHeeulw9nsVgsFovFYrFYLBaLxWKxbKLYHDvKLgnreUlLxTMD+N5ZjqETmyeheWkSaYeGRvIjxIs6FXiiDsCPwWh412A0/FAwGj7X2fYTagHvp3cwGr4ImJbkvKVo/p72Sfa5lPuW5+CFW3NpCuwXjIbvJP66LAcuBl4BLgVeBSYBM31l9k1zXovF0oiUjpm8W+mYyTeUjpm834Y87/wBfafMH9D38fkD+v6VZN9b8wf0vWj+gL7jaqonv7DIoP3oFcXNWg5c1L7Th4HKisxen3zT9OfXVrN0ejmfz+tWMennGTv9NbeY98bNzl4567Cd1sdnsmzZiMghInKviHwtInNEpFREVovITBF5R0SuFZGuKY49XERMkr8qEVkhIrNE5HsReUZEwiLSqRHa209EKnznerahda4PRGRGimtTl79PGtiGTBE5RUSeEpHJIrJQRMpEZLmIxETkfyIyVESShsoVkdtStKtCRJaKyHQRGS8iD4rI6SLSLFk9Sert7Jz3ERH5QkT+FJGVTtsWisinInJjbe4XEdlWRAaLyC3O/Tqvsa6fU/9mcb+lQ0R6icj1IvKRc1+udn7n80TkExEZLiJ7pTk+1f1ZLCJzReQXEXlVRP4lIn0aob0tReQv33lm1FB+aB1+U2+lqSfV/W5EpEREZovIeyLyDxFpXY/PdbuvvvLa3N8Wy8ZCNvGxgYgcJyKvOM+PEhFZ6/Rp74nI30Ukp+FXofFx+tyGjg1m1OI8qb6Duv4N9dVZ6/M75bNE5DQReVJEJonIfNHn/CrnPnpPRO4UkbRzJAl9/G2+7Rt9nLW+EZGmInKuiLwgOm5b4jw/loqO66Ki47wmKY5/NsXnLhORxSLyh+jY4F4RGSIiWY3Q5gcTznV4DeXr8n21SVFHzzTHlDvX7VunbXUep4jIDqL9l1vnNXWtw2KxbDhsjh0gGA0fCbyJhhMC+DAWihzt298JDXE2IRaKlPi2N0WTdsdioUixs+1M4CWniBuG6HNgCHCVU8+JqHW6+0CqBDJ8TboFuB+YAnR3th0ZC0U+DkbDxwE3o+Hh3JesZ2KhyAXOvt7APsBpvvreBY5L8fHL0bBricwDOjv/v0JFJ/8D9JdYKLJ7MBo+DPgI9f56BrjTWe8OfAuMA2735wCyWCyNx+ixw9q1qNpm137lee8Yqtpm0qTsK6k6+vQm5mo0rOI/5g/o++X6OHdBXm4OsAMwLb+wqFHCLnYaN1GA74C9e0/9kePGvg7A2uwmFB4/jHNfjwCQmZnN3/524UuT245cjPaztw4eOGKZ067tgYz8wqJkgrdlK0VE9gD+CxxWi+JVwMvAdcaY2b46Dkefa7WlAs09d40x5s86HOeeLws1IvG/lD1njBla17rWN87ER4+aytXAp8aYw+t5/r+hoXB3rEXxYnScdY8xZp2noTOBcmsdTrsCGAHcYoxZnaqQiFwJPFCL+lYCFxtjXk5TV7qBe72vn1P3ZnO/JUNE2qHf69nEj6tT8SWQb4yJe0bWcI2T8TVwozHm4zoe557vccCfY3OmMaZnmvJD0RDJtWGUMebEFPXcRu3v93nAacaYz2tTWESE+CgAANcaY/5Ty/NZLBuETX1sICItUAPKQTXU+TtwgjEmVod2rHccIaE21zYdaftE5zyHU7fvIBUhY8yzTp3us6A25z8PuANv7qYmfgPuNsa8kKSuoXh9/O3GmNuc7TPYiOOs9Y2IXIRGramN8LkY+DfwkDFmneGzqDHKeXU47ULgEWC4Maa8psKJiMhBaL5Zv8H8AGPMJ2mOqcsYo60xZnmSOnqiz9jaUIV+vhtre1IRuQOdc3T5xRize22Pt1gsGxYr7PgIRsP7opOU+6G5Iu5Dw6xNBDqgL24HxkIR45RvB5TFQpHVCfUcAbyIPpQq0BBrI9AcFL8APzv7fk3RlLmoN88sPLHp9FgoUug7x2DUW6YEOAsNt/ads/tDPNEHNN/PC6igVFsWO583lSD0HpqX6Cf0GgFMj4UiOzjt+xq9jgCXxUKRR+twbovFUgtGjx22A/o77SAmwF5lp9CG7SjFlD2RUZHdg5W8kFE+9d0jjkr0SmwwBXm5zdBJsT2c/4flFxbVeUDsp9O4iZehk5+ZAD1n/c6p7z4HwNLW7XnmjKu45rvPvgkuW7RfmzbbsHr7GVVV7Ze7A+mnBw8ccWFBXm4e8D/UK/KC/MKiEQ1pk2XLQERORJ/LzZ1Nf6ITJROABegkcGfUa+xEvBfnq4wxD/rqORxv4mAUcJPvNDloaNOdgINQL173fKuB840xr9ax3behE68LgI7O5k1yol1EdiY+b6CfS9AQraDXbFSKcsXGmNq+qPrPfTdwg2/Tl8Bb6PhtMToO6g4cjeYCdMdIexljJvrquQ1vojuxnS3R73cP4HCnLrf/+RMYYoz5OUX7rkCvwWdOm+agE+UZqAA/BDgVNaCpAgYZYz5IUZd/4D4PHfsd76w3VNi5jc3kfktERIJovkdX2FuJTsCOA2ajXuwdgX7ACagRFCQRPnzXeC5wjG9XJtAW/c72R3NLuuGXDVBgjLm2ju0eAHzstK8Cvc/qIuwMQw2pUrHSGFPNo9ap5zZS3+/bALsCV+Nd0xXA7v4J7TRtPAI19AJYhX6uKcaY3VIfZbFsWDaHsYGIjEL7LNC+rACNkrEGff+/DK8/mwXsZoxZVcNH32CIyPZ4nzeRIcBdznKE6mHoXcqMMVNrOE9zqoes9/M+2ndDvPFCIrPdifTaCDsikom2/QLf5s+A0ejzfgn67NgWjWZyNN4cyVpjTDVPqzTCzkYbZ61PHKOSJ9H8sC4foM/0KWhEnLbo9zsInZ9yr1uc8JEg7PifjwK0QiPp7IWmMOjvO9+PqDBa4/PNd64c57gg8eOm2go7iX1FMqb4hStfHT3xhJ3v0Hk5l6bonOapwMm+7f8wxjxcw/kQkYBTd3e85zfAfsaYdOMNi8WykbA5dnzEQpFvg9HwjeggA9ST5hdU1AF9iTsHeD4YDYfQHBbFwWj42FgoMsFX1UI8S4NMNB/NLc56FTo4bIc+pPy5aVy6oBMS1wH3oIOhW4LR8PhYKDLXaevoYDTcOhaKmGA0PAV9+apAXw79os5qtKM/HbXk6VbDZViChm7rQGpRpwoVqk7HE3UAvg5Gw88ATxE/6EjqKptIMBrORgd3XYFbY6HI77U5zmLZijkcp38KkEEbZ34pB8m+ojILaM8hVTN3Hj12WN/BA0dMbMwTi8juxpg9nNUD0cF22peuGus03GbEey7N6LoTVYFL+X73n8zE3fYVgFf22Oe+az99deRv3/9I55YtA23aN3WLuwP8k/AmW09G+yrLVoyI7I/mfMtGJ19vBP6TwjLvDSfcwFBgeA1VL08xkf8REHHCJ/wbfdFuAbwoIouMMbWyKHVCJ9yAPnOvQgXLBuN7oVxnldoYpJt0EZGFvtU5qQSQ+iAiV+GJOsuBc40x76Qo/oKItAX+BdQUViJVO8cA/ycivdD+pT/6Aj1aRPY3xsxLcsyjxpiH0pzrNRGJAJ+gXtT/Ric1knEn+hL/nTFmLtTLw6Qam9v9lnCOtuj30tPZ9CpwiTFmSZLio4E7ReRI1LsnHeVp7tWXRCQfuBCdaM0BrhGRFcaYu1Ick9juZui7hAC3o31F0jCBaZjeSL+nZPf7OBF5Gr0XD0PfL/LR+6Mm3EmmZeg70MNAbxHZzxjzTUMamjChlXYCzWJJxeYwNhANGemKOr8C+yeINl+KyPOoOHUm+p5/PvBgDW2shs8bZJ2Q0BikExFEZB/f6sKG9GXGmGLUeDbVucp9ZRttDIJ6CruizmzgTGPM+BRl3wZuFpH9UO+ew+tyog09zvKJJOvby+c/eKLObNQ7NFW0iWdEpAv6zLwgRRmXVM/Hd4A7RGRv4HnUoHov4B0R6Z/OAzuB21BRZxIqQt2QtnR1UvUVdaU4ST3fAoWOF9TjzrZbROQxY0xlDfUNxPM8uwp9fjdFn+sNFnZ8XnybheGQxbI5YIWd6rTxLf8dFV9m4L0sRoPR8B2oJVsAfQE7G7XsIRgN90JfGlMRQK0978ebiDToS91CPKHkMNRCyH3B6w18E4yG94+FInMAHFEnCy9HUCbVQ5C0AB5CJ107kp65eJYsLm7HvxZohhdC7lpUeHLDzYEKPaAWjAPRB+4CoFcwGr4fFWvSWRBd7tQLer0PqqG9FsvWzlg0pFDzSimnnFKyiDf8aqLre6NWY3WmdMzkS1GrpqdzBvV5191+8nHhjI/Hj2TZikVs0347jjnsjCyAgrzcTFQE/yO/sGh+Xc7VaVGTlfO2XbsuJ5hUQdvVA9hu0T6/fNJ6ziLgi+8y3nkjsmDePKDzwq+LyW6Z8WezLtnfoxO1oBN6J6P9UmG1k1g2KUYOqOyLTozuhT6vVqPWb0+dOi5jYkPrF5Fs1LvVNTYIG2OeSHeMMaYCeFpEPgB61ffcjgXhJSIyH30eZgHPikivmsI9iEgGKhpkoSEi1ks4xc0dEdkNnVQB9XgY4PfASYYxZhlwnYi8h3og1AtjzDQRGYiO+Y5CJ9TuB85IUrbGcLTGmAkiMhb1EOknIi2STS4YY26pfnTD2ALut0fxxukvA2eZGkISGGM+ciZ2h6QrV0Md5cBjIhJDxY8M4HYRed0Yk8or38/dqCg4ERWHwmlLbwSMMWUicisqOkK8B1NSRKQV+i4A2v++gEZByEEnhhok7Fi2fCaMmtKXFGODg4b0ntjQ+jejscEhvuX7k3niGGOMiNyOCjvuMQ/Wt32W2iMiR+MJ3QuBA4wxiXmVq+GI28c64du2apxr+A9ndRFwYE1eM45Ry4XOb7He0SKMMT8444Cv0HQJfVGB9/patLsfaiBUhfZVg9MfsdF4En1H3h41Bu1Hzc9gv2HGi8AR6Nj2DBG52hhTup7aarFY6kmg5iJbHZejrtRT0ZetfdBQaq4LZAC1ZnET5lbhhRoA9ehJmlgR9aiZgE48rvVtF+f/h+iDxSXRu2Y7El76YqFIOfoAWuUcnyzPhaCDjlSuuy7tk2zLQF/OWqATF673zb6o4JXMaq85MCUWipzglA875e6o4fz++zHoePBYLJYUDB44Ygbwurv+a+YHVFBGCctYxUJWyFymZXw+i9Tu+ACUjpl8bemYyT+Wjpl8U8L2/dBJvhOB10rHTHZDQ9KxQ7c/Ts29dNVpx1/G3wZdtKx1q/bui8wbaF6xXwvycmuT62Idlz+/w2NDPvDCKpsMiP5tVknHxTnXzh/Qd+D8AX1vLqHz+z0H9+lMhlC51pTPfGf55F+fWHjX4IEj5gLkFxa90b5tp51Pzb30okuH3jOxLue3bDhGDqjcZ+SAyi/QiZpLUK+vPs7/S4AfRw6o/GLkgMp90lRTG87Fm/D9oKaJGz/GmL+MMWMbeH5QT1Q3VGp3p001cQ06/phN3S0AtyauxzNSuqMmUcePMeaThoYjcSb6zkFD4gCc5oRKqS/+SbtaeTs3Epvt/eZc7zxndRE6QVsrDyZjzBpjzCsNbYPTT7geWQFqcQ1F5EB0MqsSuLA24t9G5Dvfcm3yR5yOWvcCPG+MWYFGIgA4XTbRJO+Wjc+EUVP2mTBqStqxwYRRU76YMGrK1jI28HvwzUxT1wzf8oZ8dmzt+HOUXVYbUcePMea5Rm7P5ojfWOWKuoRCM8aMdDy16o1z/Fm+TZc6XsApcULHjUDnyR5uqBfq+sQZD33v25T2GS4irdHoFwCvGmPWAu592oa6pXawWCwbCCvsJBALRSbHQpGBaGgEl27ED5hcHgH2jIUib/i2fY164IDmn/GTCRwTC0WWoKp+4oPob3gxckEteKo1MUmbh8dCkVaxUORo1PooHWPRuK+JGFIPBG9FQ8LNQEPTgU7c/oWKVAucba74lYUn4vhf3pqSnud9y21Rrx+LxZKC0WOHHYxnHb56WWD2r581eZQfs18nm+a0NB3Zq+Lk1YMHjlhSkJfb7+GLhuw8euywHqPHDlvX95eOmbwDavHeF7izdMzkWsW/zxnUZ2FWVpODtmm/3dWZmVkH5Qzqs7wgL1fwQji2AQ6uy+e5+crYtz/tumJ81lrWYLQ/+aNncbP7z/9jf6etVwTKBxw5oNkQ9rw6jzb5+2Sh1tZj/PWcPuSK/2zboetTwA+lYyYfUu1Elo3KyAGVx6GJRmvyyjwIGO+Ury/+MA0FDain3jixsf1huE5OVRZARHZBwzuAhpPaZGLlb0qISEu8Cf0SNMb9BscYswAvbFmAer70ikhH1CoSYFGKMGKNzhZwvw3De5952hERNgYP4Y2DhzheUEkRkSbopFAATfz8XaqytUFEWorITiLS1fFEaGwqUyynwrX2nWqMcQ3W3DF+G7xJI4tlHRNGTanT2MApX182l7GBP/RWjyT7k+37rSHtstQOJ6+be6/OxGdoZ6kdzjV03xVno/NKGxxjzCQ0nQGomHpUDYfcgEbg+Yuac+SkRUQyRaS7iOzoiCrrg7o8w/2GGa6g8xGa0xHic/lYLJZNBCvs+AhGw92D0XAsGA2vRsOSXYWG6dgO9d7xcy/wj1goEhfPMhaKjEaTuo1HB2N+q8E5wBVObpwJJExGop2oPzzeIlT8+RxNXnpSLBR5MaHNA4PRcFEwGr43GA2fiIY7OJF4C/25vnbsDhyLJnCs7cBvb9SF8zlUeOqNhmYysVBkPpqYDeLvJ1eUyUcHOq8A/63hPEvwBLRSNCeQxWJJzdF4AvCKSlm7+27lg/P3Kzvn8yY0J0AGQLOCvNx7Alny3XZHtf4NmLF6dtmqgjNy+zrHrcazNl+LLzRRzqA+3wCXol44J+cM6hMXFihnUJ+fcwb1eSBnUJ8YQH5hkcFL9jmLeG/GtHQaN3H3ykzz0Yxua/qXZ5umSFx/stj5vy4ETJe121Cxap2DYseCvFz/ZJb7opWBhoWzbCKMHFC5L/AaUFuL7Rzgtfp47jgT//2c1RLUsGFj8b5v+eBUk75OwtJn0M/9appcMRYNN+OOmT73J8/dCPi/38Nqe5CINBORXiJyKeqx7VqJbpCJxi3kfhvgW95o7TfG/IVnfNUSHTun4jY0Lv904OYGnvp/6HN7KvrcXSUin4vIeU5S78agt285rZebiOwKHOCs+g22PkAjIICdGLIkMGHUlHqNDerjubOZjQ3eRecPAK5y2h6HiAieOF+B5rq1rH/8z54xyRLcW2rEfw1H1yL3y/qkVuM4Edkdzyv34jrk40nGEGAlKgxOA5aLyDQRuc/JI9RY1PoZjhrLAPzu5jlyvhd3DvJIEUkVnchisWwkrLATzzA0X01z4JZYKPIgmog3Ge/EQhEDmlcnGA2Pd/72RL19+qOCx0Q0tMYMVCC6C3g/GA03Rd0+LyO1a/W2QMtYKNI/Foo8EAtF3vLvDEbDgk64HofmpnkT+BMYiQo5zzpFu+CFe9sWaBMLRabhJVIDzSWUyM+o2LKuPbFQpNSpbzGwNBgNn0x164pK4DFneTYqSvUHfglGwykTTsZCkQp0oiYMHBgLRf5IVdZi2VoZPXbYqaPHDntj9Nhh16O/LZe5gweOqOpxzCn3Z9FkICqkfgic1a7Ntid36NmOJm0yWPpzCWsWlDfLbCp3gnreoILJA8DgnEF94lzgcwb1eSxnUJ+Tcwb1SZc7bB35hUUXoOEtdskvLJpbh4/Wo83yxZkXvvQfrnjmDnaZNhnUeupKPEv8Z4DyKqoqFjB/9OoXprjHZgB7+uq6D+0Dp7ORrL8sKXmQ2k/cuOQQb9VaW3bHm/j/aWOGOjLGLEKfhwCtgE4pil6GWi8uw4s5bklOX9/y96kKbSB+8C2nDcUmIteIiBERgxrv/I56gPd0ijyN5urZEGwJ91tf538VGr5pY1LjfeAka77GWb3YGFPSwHN2xhvjg4ZdPhh9B/hURDo3sH7w2gs1565zRRuDGpvpSvzE0BEikhhu2rJ18yB2bFBtbGCMWYNO/s5DJ2eniMgVIjJARA4UkXPRaCFnoqLOxcYY67GzYejrW97YY5DNlb6+5Y19DWvz/M5ADRizgZeNMYlG2nWlDdUj2uyIPnOniMgJDazfzWHUx1mNUT2ikL9sb2A/Z/X5hN2u904A2OpzQ1ksmxqNZcm1pTDRt7wkGA33Bk4BrkNFjmao6/Zo4AdHxPkN9UZxLX9GEJ9j59tYKLJ3MBq+FxVfQC3IR6LeLPc69a5FO8olxA/qtkGT8SXjJLxcPy6CWvCfSLy3UAUqUj0RC0VcV8qH0UFkT+DfQEdU7NkdKAJOQEWuZ9CHjNv+a9CcO6AJazv6zlOOPgi2D0bDi1Hr0/GoqAUq2lyX4vMQC0XmEC841UgwGm6PPoC/j4UiyXIMWSxbBKPHDuuCJobOwAllUl5cyZoF5TTdNisHoHTM5OZoDqw7cwb1WVw6ZvK5pw+5sldp1Sre/v6/LP5BI0AGJOOAgrzcZvmFRSU5g/qMR3+njUJ+YVG6OOCpeP+g78f92mbV8l0Bjvz8nVn/2P2oJwMi/wR2efOrBZefNKjP61/c+/BOP/3y+Z1rSoubooJ5T9SacZ2HX86gPveXjpn8KFCWM6hPrXItWNY/IwdU7kXNIVZScdDIAZV9Tx2XMbEOx3TwLS9IWWrDsQRvfNAOzwoXABHZHn0WA1zjhPiypGZT+n79RjDt6lnHZDRGf7JwuY3OlnC/Odbrrrfmyk0goW/a+8AXlz8TeMEY80E9z1MFvIe+S3yJGohVoqGjB6Pj7M5ofztaRA6uq4AkIh2AXdE8VoOczb+g4/5Ux2SgOacAPnG8mPw8h75DuBNDd9WlTZYtkwmjpjRobDBh1JS+Bw3pPbEOx2xKzw6oYWxgjPleRPZC36GvQUWwRF4B7jPG/JBkn2X94L+PFqUr6OSCSxUmc6ExJtVcz5bOpvRbrM04Lh+NXrMEuKIB5/oDnS/7GPgV9dppi84RXomG5W0NvCYiRxljPq1L5U4eux1QI/N/OpvLgatqyEGY1DADwBjzi4h8j855DgXurkubLBbL+sUKOz5iochbwWh4IGrldiBqObBXLBQ5xVfsn8FouDlqHbM7Kuzs4ts/L2H5X87y/aiHjutWeSiat8YVZtz8Nn5RJwON8flSYluD0XB31BI9ZQxvVOT5DR0sfgScHAtFKoPRcGegF/BVLBS5w6kvgIZX+xA4H/guFoqYYDR8OhpS4Xv05RHiw0skuh1nOcf78buz1mrCIhgNt0Jjl/4UC0VSxnsPRsNdUAuLjsA4bF4ey1ZE5doqZry5jIriKgJZ0v2lDy5pc/Jx4Y/QQdfc0jGT+wIniohkVDVhyU++tF6GDsDygrzcc/ILi2qywF3vzB/Qt6Lg8Ztuw7EGblZa8mFA5DnUIrPf/5Uu7hQeN2/H0+f+trRbafHhzmGjUcupcSS8MOUM6rN2gzXeUlsuqLlIWi5EQwPWlla+5YaESmgs/G1olWT/U6gxxThjzIgN06TNmk3p+63pu/UTRSflQS01t0eF+jwgKiI3GGM2RJ+8Jdxvm9I9ADXfB9ej3qWLife4rSsvGGMSrWlBQ7n8V0ReRp+Lu6EW0VdR8yRMVESiKfZVohECLjXGrExTxyC895hqScGNMT+LyA/oe8RQrLBjUezYwCPV8+M0dB6heYr9g9EwjNcZY5JF4bA0PnW5jz4gdY6k2/FC6W1tbEq/xbS/Q0ecu91ZzXe87erLTkkElkWoYXWRiAxH5xCzgMdFZPcawtQd5niBp+IXp83vpyrghG51DTM+NcYkM9J8Dp1j6CUi/Y0xjWYUarFYGoYNxVadCUB3ZzkHx3UxGA2fGoyG/wpGwx+hSv3uThm/qFOOFx+9GDg1FoosB3By0XzjK9uUmpOHlqAx15ORSfz3V+78r0rY1h4dBA4BhgWj4V6oZcBnwBvBaHibYDQ82SkbQV/+Ho2FIlVOqLdH0OtxEnCyIwC19Z3jQeCTGj6H/0FTU1kc4exb1IPgm2A0nOiV5Gc/PI+hAcFouEWqgsFoeK9gNBx2xCCLZbNj8MARc4EzcKyayldXUlGsP/mqctO6WdOWffG8B7ugE0gvA5XZWU0qt2nebV2YtZZZ7UAHjJeVjpmcVTpm8nGlYyb7Y/BucPILi15F4y2fAVy0oHLFqj/XfsrUshhdqiaddJUZtUfmNk0O9x2yBzrI/gxYUJCX+/CGb7WlDuzVwOP71rG83ygg5bNhA+KPjR83OSoif0ct9EqBvzfkJCLyiRvqK9mfr2g0XTkR6dmQdmwANqXvN+V3m4gxZokx5mfn71tjzKvGmDPQsLpdgVdEPRXXG1vQ/bYp3QOQ/je+O3Cjs3qVMWYx9aQGi1s3vNPZeOPvBn3HqGD0n1pMZLnWvsWkTiTuCj47isihqSoSkRlp7il/joBxtbz/LJsudmzgkdhvBETEzVPbCzVoGoBOPDdB5yFud5YvBL4TkV7JTiIiz9bwW3GFh1tr6KsPb7yPvlmzqd1HdUZEhtZwT7ghtw6r4Z64rZ5N2JSuYbrfoeDlI/zIGFPNcKEu1PQMR41AXO+7IGoQXl/KgWg6UcdhEN6cWjLDEdA5BXfOMWWuPBG5rYb7yjX6Pq+G+2po7T6ixWKxwg4QjIb7BqPhr4PR8IeoEPJ/6MvQ13hWlQ+iIQ6OQMMcTXa2f4e+qBWhniquK3lznNwOwWhYgtFwBprgtNKpuyZvqZXAwFR5ZmKhyJ+o+OHiJlBPTDjud3HNdtrX2lkfhIaa2z3huB2D0fC2Tg6hGc42A0yPhSJVqPizEHUjvRk4HBWhaqIK+DYYDR8ajIa/DEbDL6YQbXbCi20aRMPAxRGMhncNRsP3OZ/FvUavx0KR1cFoOCMYDQ8LRsN/D0bDmU75nVCPo8eAz53vw2LZ7Bg8cMRI9Dc7rUmbTFp0V0eVlt2aVkjfxd/hJY+eBHydM6jPSFSc7TGg/Zk7H7DN8f/rmNNz3orydfMzOy1dvvANtA+bWDpmcn3DYTQK+YVFn+QXFr1y38V3ta5aO6LdrpXvsVvFc5zJp/RhJof1W4K0yHoFDWezTcLh4YK8XElSbY0U5OXuV5CX+2hBXu4pNZe21JOGvrRVSxpcA/6J044pS204/M/jdRa1oklI73NWbzfGTNugrdp82ZS+36TfbV1wYrX/11n9t4js1OBWJWFLut8c7xE3BG8rJ/zIxiTVbzwDfWZlA+8bY15MPLCxMcZMxIul311EuqcpDnATaszWB51ozwWeRCdxdgHGisj+qQ4WDd12vLP6ZpqE0v+jFhNDlq0KOzbwSHx+hFFvToAXjTHHGWM+McasMsaUGWOmGmNuQ0Owg3qAppqUtTQu/vto23QFjTE9jTHi/qHinGXT+i2m+x1ehuaBXgNctL4b4gg//t9x/xoO+Q7v+d0Hvb/ygb/QOcL/iMh/aqjDfR6XAK+laNdi4F1n9VQRSeVBaLFYNjA2FJvyCF6isOdiochRwWj4xlgo4nd5/AMvjFoMjYHZC5gaC0XWAjgiwo14Xjwdg9HwAeiEaQu0g+6LPhTuI95jZzmaQA1UAPmVGuK1ktod28WfMHU6GnajPdrJd0dfMn9FxaYMNA9PJuqRMz8YDY9CQyrMBFYA1wSj4b+jgtUexFsANkNDKP0NfYAYvGSuVc5fAHU17o6KNQegD6IHE9r9KyrCHAh8gYaTW4fjSfSx7/MdDszCs+C7GbjVWd4pGA3fhSacdMPdbY9eu7RWtRbLpsrggSMWjx47bHw7evSSQX9RVW4IZEkmmJ1Yy4loTqv5OYP6lAPkDOozF6DrIOhK/7O+ysstQq3DATp+8OnLx54+5ArQ3+6BqOfiBuXNrxYchVoYv3fSAR1fBrbrxLIM0I6jnEAVEAhkBwie1fbLwQNH/LcgL/cEvN81wMf5hUV1ttAtyMvNQcMktEbFob3zC4smNvAjWarT0DALKcNypmAy3vNtTxHJqCGUwXpDRDrijSFWEB+29XLU+nYlMFtETk9ShV/E3N5XZrYx5vOEsiHSjw9cw5SbgFFpys1Js29TYKJvuV+qQhsIf4jaWAPqeRPNoZCBGt7c05BGpWBLu99+Qg2uAqgg8WX64uuVVPfB0WgbAT5Jcc3Bu47NfWXKjDFv1LM9U/G8GTqh4/9UzDHG/Oxbnwi8KyKjgLedtr0sIn2MMcVJjj8Lz8jsbBE5uxbtO1VELk8hAh1N6pwUXQDX+ngY8YZuls0POzZQEscGEB/e/PpU9Rhj3hORj1ED1AOd3+nkhGI3Aukmd9932hJBDSFTMT3Nvq0JfxL6fug8y+bGW+hcTCruQqO+fEd6Ib6+OYIm+pY35XHcTc7/T4H9RGQ/qrO7b3mgiLhhSccbY+ozvpnqW+6UspRSnPD8Bh1rRNG5tF2BfBEZa4wZnXiwiGyDGnOAzumtVCeltLRAc/g8m2TfY6QQhxyiaASkUXjXNhmz0+yzWCw+rLCj+C19Dg1Gw61ioUjipP9JwB3A7FgoUuRsixswxUKRimA0vA8ah3pX9IX8QlRMwVneIRaKHBmMhsPAMWjnWYrnRQP6cro/8EAwGh4H/BYLRcb4z+WEE9vNt6mM6uHZXMajrrSt0WRvQfSlfS76QHW9V/wTooJn/eO2bQ/gT1S4yqU6J6Exs08hXrTK8J1jAPEPqiXO5zkTHUw+GQtFVgaj4UPRkCSzY6FIRcJ5MvGuKahos8LxMALP2wd0gHs1Olgej770P5jk+7VYNjfeLwksH9q8qoMUZy0my+RUlktpLGdQnypU6EzHSDxhh5Wrl61GheVZpB+IrRfe/GpBW3TiKAc4582vFvwETP6YPSYczs8HLaf5vO1Y+hNwrDPMPAu1bL8WtShegL7wvlrPJmThWYwK8f2xpfH4ERUO68vEuhQ2xqwSTfS5H/qsPRJvMnBDc4xv+QtjjD9sqitOtiIhWWkKDsULyzAKiJtoN8aknXTxvawlTuRubozHM0jpLyJtjDHLN1Jb/N9vrXIJpsBvvdqzAfWkY0u738bhiSYnsJGEHRHpgWfYtZL4/spvgFAbsa4DGvIEdPxaX2HH752eOJauFcaY0SLyX9Rbf3v0uXtbkqL18b5pjk4MVcvtY4yZWr24IiJ+IWD6Zt6PWezYwCVxbAD6jguwwBhT0yTn9+h7r3tc3DyFM7mccoJZRFxPuoX2N1UrxvmWjxWRQJLvb5PGGTMtT7VfRNx9yYSDxsB/DQdvTJGV9OM49xl+rPNXEzf7lk+ifoYrjfH8XiYiZ6HCXAB4UEQ+NMaUJxQ9G88woy6ESCLsGGMWkkbsExHXOGS57WsslsbBCjvKnehEJ6h12KVUf/HKAy4BCEbDFbFQ5L6E/a4nyZpYKHK0b9vfUWsyl50BYqHIgmA0fIRT79F4A7cyPAu1E5w/gtHwLWinOxM4HfhHwunvQBX5C539rsDzGyrC3IAmYVsJnBYLRd4PRsPbES8OJXboS1ABxe99szzhmNmoAIPT7pOJd0d2LaL86yuAF9GX7xeD0fBQvJe6/sAQR8yZQRJioUi5E4bNtVx6FJBgNPxP1NJpIGr9NQ/NZeTmBVoC/Ax0CEbDmUkEI4tls2HwwBGFo8cOW7AWnsZIl3IpvWPwwBFranNsfmHRcwV5ud2AK4Di8vK1F6D5vNbkDOqzMQbUmXj9jwBN5g/oa6DvwQALXrlyeEZ263/MXlPC3M7lrFjMsk7jJj7DxXcVb7Nk/jaTTzl2BcDIAZW9Rw6ovBj47tRxGbUKRVGQl9sCjfk/D7X6fDO/sOjTxv6AFkCtGS9p4PF15Wk8j9yr2QiTNyISQH9rLqlyT1jqgDM59ypwJvqsvwT494Zuh2OVeYazWoVawNaX7XzLGzuR8ObCCNTLKQCcLyLDjTErNkI7rsQbK4/aiJNTfvbwLTfEA+8OdAKnDXCNiDzizxEkInujOf1AjUNq08c9jApYIZIIO5atCjs2UJL9birQSeXaTLr6yyRO3FoaGWPMFBH5GjXG7YHOgYxMf5TFjzHmVxGZgKYK6IrOi/1vQ7dDRPbEC4+3CvhwQ7chCY3y/DbG/CgiL6BG3jsBF6BeeX5cw4xFVJ9jTMYp6P3eX0R2NMYkTR1hsVg2HDbHDhALRV5DrVxckinMh/mWqyUwC0bDe6FW46uD0fA6b5VYKPIk6vnyM+ohc3UwGm4XjIZ7o+r5cXiiDmh+n/lJzn85Kgodhcb2PM63ryoWitwdC0U+QfMC+b/XOWgouGuc9VbAG8FouKnTnnd9Zf2Twrc57brNOfd9wN2oK+j2TpmleKJOFfA7GortYF8949FBzmRU0MlALSuPRl1eT0ZjgbrskOSzJ6Or8zkDeC/Sx6Nh3Tqi1vf3O5/dbd9RaPi3i4HTankei2WTZfDAEZ8gXIaYTOCe0WOHXVHjQQ75hUV35RcWbZNfWNQzv7Doo5xBfVZvJFGHkw7ouAgdcH4M5J90QMcfATqNm5hx4tgPbujYoem/OrTKadq3YztWfljM3NcXHrPPT18MAy5f1L6T3yrqfbS/em7kgMojqp0ogYK83Aw0NMVJaJ/SIr+w6Mb0R1nqy6njMiZS/zB/E5zj68pzqEEEwNEicnFtDxSR7iIysB7nTORmvBAPM0nwkjDGXOmPvZ7sD++5C/Ccb9+JjdC+zZl78CwZbxaRvrU9UEQOE5Htay6Zto5MNMyum9ul0BjzewOqzPMtT2pAPSnZ0u43Y8xveN6a2wARqUUMEQARaZomLFqtcfqJy53VKhIERmPMWzVdc+e6u33VTN/2NvVs01HoJA7AFGPMgvrU47R/OV7Y5Oao144fv7dOgTHmlZr+8LyQ+qdK9m7ZOjhoSO+JNGBs4BxfVzb5sYHDn87/diKye5L9fg5Pcpxl/XK7b/kREdkuZUlLKu7wLT/k5AGsFSLS4DwvzvH+vHePJHp/G2Pa1OL57b8XBvj2vVWPNjVBBRiXsXWtI4E78cbK14vIujCnItIPby7ujVo+vwvcw9FoPRaLZSNjhR2P44DhqMXQiCT7nwCKUfEjUeUGVbe3QV/u8/07YqHIqFgo0icWimwH/ILm6/nFqcefHLcIDcuWGEdzGrDMtz6TeCHqm2A0vF0wGu5P9WRvA1EvGL/XTFOgmRO67HjUyvAuYDAaDuke9AGwFxr38hHUSu9q1JvJxR8yJEDypHeHoWEW+hAf3mhbNG7wSOBc1FtgMdArGA2PD0bDrZLU5ecD4kPHgcbY9icBnua0/Xw0xrhfsFsSjIbPD0bDPwej4ceC0fCTwWh4bTAa/swR6SyWzYUT8az0Tt2I7WgQJx3Q8aU/Hzh/8p8PnH9JQV6uG2/3X7NLMu4ur9DIBpWVVaxaqHmyt12yLgx5JsDIAZWCeua5+MM1rqMgLzdQkJfbvyAvtzvaj1zn221j+a5/rkCfc3VhDfFWrbXGGFOGTpa7CdYfFZHrRCSl9auIZIjIMOAH4i3m6oSItBGRR/HCFpUDoSQhECz1xAnh8E9nNQcYJyLHpTkEEWktIv9GheR6h110JqPHokYjoKEs85OUayYi5znW2enqOxvvRX4Z6fPRWOK5FM/L+ww0F0y7dAeIyOHo2LPewo6IZIpIGDWScsfZtxhjGpJnqaZzbi8i+9ZQZnfiEy8/1AinfhAvZM+lItLBOVcT1GsOVJD6qpb1+S3bhzZC+yybN3ZskHxs4H8OPCIiTVPUeTn63g46T/BTsnKWxsUYMwb1PgSd2/hKRGpKdA+Q9vm0NWGMeR8Nrw3qxTlBRA5Id4yIdBKRJ1CjjvqEEHPr2Rs1inZF04msZ89vETmxhn4mBxV5XQObCcaYBuWRczxqXPGqG/G5u/yGGYW1rPIrvLDvNY5vLRbL+seGYnOIhSJujgYAgtHwrmhIjLGxUKQK7cA+RwdN3f3HBqPhK1Frc5ffE/Z3QwWjGDrx2MbZdRLwOOpBAhoyzW8BVA68AwxCxZhVzvrfifcgygB+RXMF/ZLw0Vbj5Y4ADTf0OHBUMBoei1oXuOfvFQtFhvjafQzePXK20wZQxX8SEEZfFt2HbzIxpjZWkx3QgflzzvkOQT1/ng1GwxmxUKSaF0EsFPlfMBr+BU3OdpCzeU/UM2gSmoAY9LvIAm5Bhauwc47TUUEpQHxouf7Ad8FoODcxr5HFsonyJjooy6b+OWYAKMjLHYD+Lj687+K72qF9wwfzB/S9usGtrPnc+6IiM8CdBXm5z3HxXd3bzJrB67Hp7Lx9a/6ctYqVq8upEmFB+06r0c97G8Cp4zLMyAGVZ6FhJ78jdSiY59D+bA1en+aywd3/tzZOHZfx3cgBlaeg4XpyaiqPfk+nnjouI11y17QYY74WkTz0paY5Tv47ESlEQ5guRJ8FndHnz0nUznu0TYIFbQ76fO+FPpf+hpcMvRg43xgzDkujYox5wJlkvgG9/kVOaI830Zf0Jeg4qDuaS+FEvHFYOrZL+H5boGO4Pqhl9DF4BlLTgROMMYmJr0H75meBu0XkNXQS4S90fNYSHYOcipcboQq4NFW+IBE5BL3HktFJRIYmbPvcGDMtWeEtBWPMUhEZhBpI7YhO2B4rIi+jMfxno5PGHdFx/PF4Y9d0OYKyEu6BTPTe2Q4Nv+N6e4IaG91vjLm7MT5TGnqgAua36DvBj2jEgEp0wmYQ+k7i9q+jgWcaelJjzAoReRB95jZHIwFch4aMdicp6xKGaBwa9mUb4FwRuWVzy09haTwOGtL7uwmjptR5bHDQkN5b+tjgfnSMvx367v+jiDyMGniuRSd/z0QjYLhcY4xJNH60rD+uRp/lQ9HnwWci8hkq+E9EjW6r0PFDb6rnaSnZgG3dVLkGNbQ5D32OfSki76PP9CmosUtr9H4/Bn2GN6tFvduLiGuILOj31A4dBxxJfBSeiUCuMWZ9h8F9EHhcRN5A0xL8ifYDrdFxxUV4/cxyNM1CY3AXcA46b3i9iDyDXhPXMGMBUKtQ5MYY44xnr0K/ryPYNMLXWSxbLVbYSSAYDZ+LetK4D4un0Q71FLykag+i4ojLRcQLGOcFo+GPY6GIq4y/hedqfQUakqw1+gL0T3Qi1T2f+z/i7DsFHQCCPozcztdPN2cfxIsUvzvnu8s5fyn6QnYP+lCbTXw89z7BaPhAtMMXdNL0QrzBp8v3sVDkAIBgNHwKDbdyr3Ta6N6PVYBxhJtdgtHwtbFQ5IHEg2KhyE/BaPg1PGHnMPTBUoZ6HF2NZ8VxYiwUuTMYDT+OhsXzWxZUoRM/2zjrATTOqhV2LJs8gweOeP/B845/urLUnAsc8usTuY/mFxbVOaRaQV5uS3QA3czA0G0WzzOLOnQWYLdO4ya+OX9A3/GN3fYEFqAvqU3QgewKYPjC9p33n7G4fM858+dJ005ZBJoAa40Z+OV7+e9eedmT/gpOHZcxCp91o5NHKB+15H4ov7DIoJNeoKJOKfETCMehXn4AzL5u/C3oIDvSdXj/IiyNwqnjMt4dOaCyP2oYcFCaohOAKxoi6rgYY94SkYNQy8pD0Zem69McUolazKWzXhvi/KWjAv1d5RtjbGiU9YQx5kYnGfa96MT+QaS/t1aiIWZ/TVPmLucvHStRL++bazEZsB01W5cvAC4xxryRpswFxBsT+dmF6vlKQsR7M2+RGGNiIrIfOhF6NjrOvhjPeCkZn5H+O+5CQgLyFHwLXG+M+biWzW0M9nX+0vE0cEUj5vt5CJ3IaY167fyHeGvfWhuXGGMqReRN1FitGzrJ9kEjtdOyGXLQkN7vThg1pdZjg4aIOi6b+tjASX5+JGqs1Bvt4x9JUXwNcJUx5rUazm1pRIwxFUBIRD5HDWa3Q++laqH7E/jdKb/VG5U53mpDReRLdK6qEzrvdkyawxag8z0r05RJFoUnkYVovuZ7NqBHfUfU2DicpswU4ExjzJTGOKEx5g8ReRFPPBuGio5utIvX62hcMRIdD4COA6ywY7FsRKyw4xCMhjsB/0I7O78FwJBgNNwRtYwxqOAxNeHw94Ggb78AZwej4eNRd21/aLUK1GKncywUmeyc+3jnvINQcaEcHWC2xnPvdetOpILqodtcdkIt9Vxy0Aega1mXGMN0N+JjHF+NdvzN8OIQg4ZrIxgN/wNNPJmsbVUkD/X3J9WtnTKIj+ceQAetrqfRTUA1YQcgFoo8EIyGV6Kh8tz7Ods59k+8iVtXZNvV165K5zwfoRabPye002LZ5CnIy+2El3T21O7HtRk8euywkwcPHFHXRLCZqKiCAHtP/lLGH3A0JU1bVKJWteuV/MKivwryco9Awxq9CVx/7eM3HQ1Eep7YtmdVhbm+SfsMZo5aXlK2trIZ6t34ZLo60RfvA53lFeiE55N4L+2JVqHrRKHZ141/GLgMoNJUHvvG+dfv8Ldn7pmJpVFwxJqDRw6o7IsaEPRFDRRWoRboT506LqNRQ4kYYyYBhzkeD0NQY4CuaNi+CjQc6CTgE+BlY8zculSPWtytQF8SJ6KeY28YY5LlzbM0MsaYN0TkbdQj51jUI6MjaildjOYv/B54D/1eiutQfSV6b65EvW1+RC0tRxlj0lrbGmOWi8j+aG7B/dHxzrboy/Qa9H75CR2vvWqMWVWHdll8GGOWopNDd6G5FAeiY+EO6FhzGfAbOtZ9xekT6sIa9B5YinrIfw+8a4ypjfjTWHwPnIXeS/ug3gQdUGOFFWi458+BEcaYRC/+BuHcyw8Ct6Lj7H+j9zXA9HqEi3kVFXZAJ4assLOV44g1B08YNaUvKcYGBw3pvVWNDRzRem+0T/sb6m2wLTpuX4H2aR8DTxtjZqWsyLJeMcY84ySp/xsqVB+A96wvRZ8/U1FDgCJjzBcbq62bKsaYJ0TkedSL+Wj0GbctXh8wB+f6odewLFVdSahAn98rUIO/79Fn5egNHCL5PDRCzP7onGB7vPHgAvTzvY6OLytSVVJP7kINXzLQd2G/aFTXqB9foePh7sBJItImlae5xWJZ/4j11FWC0fBHeGEwXNyLI6hQ8Q4qcjRFw6pdHgtFSp3j+wP9UAvQSrRzbuMcfzs6UIyhycZOAj6NhSIfBKPhHDSc2G6oF1BL4KtYKPJNMBo+BGhsK/mfUI+WRCu/31ArID9vxUKRk4LR8AXAU842g068NsMReNABa4nT9tq4zydi0EFuKsvD92KhyKAU+1yByY0fXoEmQ2+JCkIGuDQWikSc8Ho/OG0sR8M+LcXzTvoOfYFYhVp47Il+p2+hE83vx0KREuecZ6LC1+fA1U64Potlg1OQl5uDWmNv137vZmzTrzkSkEXAdoMHjqjTQLUgL/efwP+568tbtFn01NnXnD1/QN8NOtlSkJfr7/uqsloGOvY6s8PpSyeV7LPgy9V+S/Vt8wuLUopOBXm5U1AxFyA/v7Do/ilXv7XbrJLffv55WdKu9ZX8wqIzAGZfN3460BOgylTy9l+P3XHZy6/e2sCPZrFYLBaLxWKxWCwWi8VisTQYm+jKwx/CrBJNxLoXnrgTQMWZo9A4uxfgWJkFo+GmsVBkfCwUeRAN29AFFRhcfoqFInuh4sNYNA786GA03Bv1GAmhni9PA6/HQpFvnOO+RL2BGpM9UQHlRjzPo7Wo1V2J0+61qGXJjGA0PJ/4pKuLUVdLvwt6FWoBkSjquJZINVnFPkV1UcllDenDaIBaK7p8EAtF7sXLVSRoPHzQcHRuG7PQWLgvoy7Qt6BWDP9FLaCuQGPot0Tjkb4BvBuMhpsEo+Hz0Dwd/Zxy/ji5FssGJb+wqBQ4uMM+zSZtu28LJCCgnn9Hpz8yaV33or85ANqsXr7NtY/fdFJBXu6Gfla48agBVpWvqjrw1ycWrlzw5eqb0T4IVKReWkM9F6Di6ws44TObZDSd0TGnBzmBFsnKn16Ql9veWX4VwJgqJi37jLVVJb/V/+NYLBaLxWKxWCwWi8VisVgsjYcNxebxG15Iswzg2Fgo8lgwGj4bdZlMFuNzdTAaHgWcEIyGp6Hu1f+IhSKLgtHw31BBZFtgv2A0vAoVadwJ0gznfF189QWAJk7ot3PRuN43Jzl3qrBsoJ4obl6Zu1DB6qQk5fo7f+egHjxvo+LF3mhosizgcqedLgtRLyRBvZbmoKLNtmhM+0TcePNujp4K5zMmThKfS7woVOk7b1PUU2qPJPW77OpbdkOiPIaKZcXA88FoOOCco9jXnta+44ag4k46DgZeIj5BJdQueZ/Fst7ILyyaOXrssERxdKfRY4edD7w1eOCIJXWo7kXiEzVejIZ2+U8Dm1lr8guLphTk5Q5BvSgX43kHvoD2BXsCXyTLJVSQl3sgsDPwWn5h0QS0n1vHjvcdUxw568zPO+Rsd8ickt8xxDnb/YCGSqDr8P7/mnTVyDE/Lvn46MVrZ/+UX1i0Tsyefd34pqjr+bSuw/s3Vu4Ci8VisVgsFovFYrFYLBaLpVZs9aHYgtFwNnARXrxql/djocixTpmeaKijDNSK/CU01u57VE+qOi8WinQJRsOtUGtyV6CIoXl4QIWZJ9GEaX/HsSQH5sdCkc7BaPgnPCHjbLz8MOkoRL1PKoDhqMgUQuMBH+yUWY6KeVmop8muaA6JDKd9e6ITp6elOMftaFxtl3QCUzKmoaGNahIUE/PzrImFIuvEk2A03BnPi+gK1NvnLee4E2KhyOdOuZZAeSwUKQ1Gw0+h1vug392HaD6NF5zP8CfqoQPxwhKoWNQMzfPzN6CHs93/+f/tHNcE+HcsFFlRw2e0WBqN0WOHHUh8fizwRN5fBg8csXtt6yrIyx0GPJOweQXQNr+wyDhluqFJyouBa/ILi5bXs+m1ac9wNP8ZwKT8wqI905Q9HPWKFOCD/MKiY7545tEs9LcbnP7rL28umPXXbahAnYmXgwvUU/EQNK56LL+wKJbsHLOvG98G+AbN2/A+MKjr8P5b94PUYrFYLBaLxWKxWCwWi8WyQbEeO2qFfnmS7W5OGWKhyIxgNDwEyAVei4UiHwMEo+FuwDziQ4G5YXzK0EnPVs560FdGUCvySag3ikuHYDTcBujt23YyMBudbEzHMWiCua6ouLA76nnzG56w0wYVkfLwxCSXIJpkeDSesLMceNCpaxKae+MwPAGkLqIOwCw0SRzoJGoTZ3kVOgHtTrD6RR2Dl+jc5R40qR5AaSwUOTcYDV+Bhsnze/7sCjwejIaXEn9N26PeOfsC/WKhyJJgNOxP+OoXdUqB09HQa1cBM9HvdgUa7srlBt/ysGA0vHssFFmAxbJheMVdWLOgnAVfrSarRSCr86EtCWQFeo8eOyx78MARtU0w+RWemOnSGriqIC/3YbSvGA4MdvatRPNNrS+eQD3qOgO3jR47LBtoO3jgiGS/r754/dJezv/z0NCalJYUH4R6Abr4+4smaDjMPYG1BXm5B+QXFk30Vz77uvH90Nw/TQGWrV1wzDeLR5+yPO//XkP79MvQBJj/zC8s+rFen9ZisVgsFovFYrFYLBaLxWKpASvswPYptv/qX4mFIu8C77rrwWh4f1Q4aQGMAw5EJwbvdYocihfyKxmu0NATz0MkEw2b9gdezpm/0LBiY6j+fc1DvVS2Q0WbNgn7D6K6983ZTpuT0SIWijwXjIZXomLQ26gX0VQnlNnPxIc9S0WixwvATcBI4DM0dNuNwNfo9f/NWU6GAPnBaDgfWIJ6zJT69q8JRsN9gWed9VOC0fAsNH9QF3SiF+AXPAFuvNOOfsBc53g3H1AiWXhhoEC9dX5Bv6dviQ/n5tIB9Q66O0WdFkujMXrssGZoWDAASuaX0emQlqz+ay1Lf15T2WGv5nfUQdRxw6ANAj5N2NUe+AAVdv05xFLlx2oU8guLpuP0O6PHDuuM9hc9R48d9vDggSP+kVD8f2jurCCOd6ExpnhN8Wqym+TQpGnzbO1G1rEK/T3vj3o89XO2NwH2KcjLLQGuQT0aH8jb/l+PGmOafr2oiL+KY24Yt1eJD4EJahiwT6NcAIvFYrFYLBaLxWKxWCwWiyUBK+xoDpseqBixCs3N8GIsFJlSw3FX4AkkA4A7gNtjoYibsCGX6uKGi1/4aIFayB/gbG8DdEMFmxnOeU4Fzke9Rvx0JjWLUVEnUcRpQXyeGT/jgtGwoN4oa4F8wDh5hi4muaizBs1JsdKp+3dneYivTAnwf7FQpCIYDe/hfJaOQCAWirwAEIyG/w1cS/zkqEs33/8f0AlTV9y5jfgcRE1Qr6BeqGeNyyzgPqeNeXgTuF1QC/2/o/fCaXheV5D8O+yFhrgLOOfITlLm1yTbLJZGZ/DAESWjxw5bF76w/Z76085pn8ny39b8MXjgiDvqWmd+YdFnBXm5k4jPbfUDnmea/9mRGI5yfTIIFcNB+6Q4YSe/sGghnphLQV5uM+BMgEBGBpmZWe7vOYyGZ2uJ9r3H5hcWvV+Ql3s76s33GyrojkXzlIEK6VOXlc3ff2ZxtcdDXL+VQaYNzWaxWCwWi8VisVgsFovFYllvJCax3+qIhSITY6HIHqio0h9oh+bOWUcwGg4Eo+GrgtHwA8FoeDtnc7OEqk5zRZ1gNHwAajXusiqhrF8s+AA4FrUaz0C9PJqh340raHRBPVLK6/DROuCFgTPOH6hgMxENOZRIR9SbZic0/Bqox8yLJCQg9/EMmq8miIaBGwCc4OxbBSwEvsebjP0czUdzLfBJMBp2RZmRxE+O/gkkC7XUBjgyFopc6fwtB0akaJtfcOkSC0WeQ72O/J9ljnPuMagY4xd1UtEE/U5aklzUKQW+rEU9FktjMd1dcPOmmSpD651zWo0eO+ze0WOHpRKZ03ECKtwCLEJFjs+c9floiMiv0HCNG4oJwGpn+UM+un4gH11/Gh9dX81IoSAvt4lTPhegqrKSsrWloMJ1IfHC7xqA/MKiW1HRe1dHJFongHdv3vsI4LysQM4IIaMyXSP3aHdYr9nXjf9i9nXj186+bvyN9fysFovFYrFYLBaLxWKxWCwWS1LEnQTcmnG8VMrxBJcq4IxYKPKqs/8ivJw0n8ZCkcOD0fAJwJt44tgdsVDk1mA03BK19vZ707hePFOJz7UDcAoq7qxM0rRf8KzFQS3Id0AnG5OFkCtBrcp39G3ze5RU4FnaL0bFn8bgaTT0WDo+QieKSxK2V6F5a85Cr2dHYC56nbqhk8kdE8rvh173oUARGiLPzasxBw1NlxgaCTTPRjvgduf4+ei1aVfjJ9Tz1kUIPSwWinxWczGLpeGMHjssiOa5CZJchB06eOCIRI+/GinIy+2G5tWaBgxERdr5wB/5hUWr0x27vhg9dlg3YKdjqrbtmoG4n+lJjrznIn+5grzcINU9514ECvILiyYW5OX2By5CBeh+wI9oH9EW9ZScBfzSvkmXMW2zO7bo2/4IMiTjhK7D+7/zSujKD+aUTDsKoFfLvSirKmXx2rmUVKwAoGfz3dl/2+Pcc1YC2V2H96/CYrFYLBaLxWKxWCwWi8ViaQS2eo8dgFgoYtB8DO7EWwA43VfE78Wxl5Nv5nSn3J/APrFQ5FZn/+3EizprnHIBdKLwd9R75i9n/zOoEPOR75hVwDuod8oqp3wl6llyPanzAj0fC0V6AaN82/weJX6r9nbATynq8eMKTqkUQIMX1iwde6DC0tqE7QH083yBJjK/ARW7PkS9exJFnTNRj5ivgUuA0U5Zg1riHwKcgyf0+HkYuBPvvu9IclGnEngeiPq2Tce7P/5I90FRMcp67Fg2GIMHjogNHjji74MHjjiU5B40tc6x4ye/sGhWfmHRi2jumrvR31vmxhJ1AAYPHDFr8MARYzOQvsYY1pRWQPJ8Nn+gHjt+5juizsPAQ2ifeiawLxqO8dRtc7oXdW66442CPA7sdGSXc4r7dTiaDMkA9dRjr/ZHDu/ZYnfTNrsjxeUrivdqfwT7tj+GDNEudtHa2eD1F9OB0bOvG39LI14Gi8VisVgsFovFYrFYLBbLVozNseMQC0XuDkbDs/Em80cBBKPhNoA/oUIr1GPkDGd9B+LFB78INBsNT+ayt2+/G+KnNfBP4GBfuZbA8QlNzAAup3pYNz8XB6PhQuBKNLxbkzRlA2humZpohX7+DmiYtkQE2KsW9bRHr9UrwHkp6mntnOdiNJl5sjKjUTHML9zMREO+dQQuRPNu+O/tMqd8ogdPMvEH9FpfD5wMhJxtOU49OagQV0L1cHwuA1Gh8KYU+y2W9cm/UOF4O1Rk/hn93dWLgrzcLKC7sxoAvi3Iy/0WOCq/sGhlQV7ujmjoxo8bS/ApyMvdHg2h9ml+YdGkZGXmLSx57u0PZ16+uqQiM6dJRuvSp3Ilv7BonQCdX1hUXpCXez4+r53OTXc8pyAvdzZwmbPpDfRatQLYudU+O/ZtN3A/EeGPlRP5bsn7XdA+5W6nnucBdvlP7tjm17XeAw0x+R7QcvLy8dMrTUVrgJyM5jjHzAAeRfNyHTP7uvFfdx3e//3GuEYWi8VisVgsFovFYrFYLJatFxuKLYFgNLwzQCwUmRqMhs8BnkIFAb93UylQjIoVi4E9Y6HI3GA0vCMaPs0VVFahIk0yfgV2RcWCn1HRpzF4GU0w3iZhu8ETMirxws4Vo2HLEsunw19XXfg3Krq0cNbXoh46eahYVgWc6PwNS1HHePS6DnbWK9Dvw61zBSoQubyKegDVxTtteSwUaRuMhrOBe9DJ2xMT6vgaFaF2rHa0UhwLRVqk2GexbFYU5OVej/5+/ZwNfIOGMGsOfJ1fWHRAQV7u0ag3zEzgjPzComV1PFdT1NumM9o/7plfWBRLUu584nOF7eu0JQjMzC8sWl2Ql7tNdqDp/LKqNQGAHVv2ZU7J7w+WVhZf6TvuQLQP+jFv+38FUVGX4ooVq4pmPd4d9c7sATyUX1g0P1mbZ183/r4vF75zzV/FagOwS6v91nRs2qNN52Y7dEDDQ7oc33V4/6K6XA+LxWKxWCwWi8VisVgsFoslEeuxk0AsFJkKEIyGzwKewxMw/DlWcpy/B4D7YqHIPGf7ScR7yaQSdUCTkQ9FPU0uwRN2Pketx3fE8+pJJqT48+X4GUJyTxL/8f5cQhOAo1BxZ6pz7m6+sv4cPcnqqomVTp1rgXPxBBjQa1XgWy8FhpPcM8ilPyqeuZQn1OkXdZ4CfgBOq0N7AdoEo+EwcAsq3J2DCjt+HkYt/T9Ghb/fUKHPzVuUdALYYtlMmZawXo72A1fg9VP7F+TlZgP/BXZBBZYw1QWhmmiHF84yG81vdri70znHs6hg62LQ39wbGDkBMX8V5OXuC1wckECgVVZ7sgJN2KX1vn8sL1t4fWll8RFoH/t0fmHRVwV5uX8A97w6/d6TswPNKts36RzYtc0BtwP343jtCXLAd1c8P+3PVT8dUVpZ8sKi0lnPAi8BOQdve9JX/dofRbPMlpRXlZmZq39p+tvKbx7P2/5fDyd8tt/qeC0sFovFYrFYLBaLxWKxWCyWatgcO0kIRsM7oCHZ/AJGAA2/5WdsLBSZF4yG9wpGw7cDy9EJT1DBJhG/e9R+wAXAZ2heGJdD0Hw0zX3bEoUUQ2pRLlV4sFQc7vzPQoWso1CBZzGa98Z/7nLqTivn/yKc/BRpaAb0xhNHUrGbbzkxvJqLQXNnRFBxaZrzv7KGukE/52NAJzTE1LXEf3flwJhYKDIe/a4+QCey/e2uq5hksWzKfI+Xp6cYFXSeBy7Fy8P13/zCojK8/GGgXjt1Ir+waA7xOaoS82CF0FCY7m+/DDgVWNmy+G8ndFryCE1LD+6OySgAFpRWFmMwtMrq8PNvy785acnauQcD++QXFjUHri/Iy+2BevqcbzBt1lYVZ8xdM00+nvfiwXihGOnRfLfgt4vGXDCr+LftF5XOuiVTsp9DvX32mrDwre2zM3Lu7tR0+69mrY5JaVUxwBmF0/+vBBWmFgKfVpmq46ZeM/qD2deN/3X2deMH1fXaWCwWi8VisVgsFovFYrFYLGCFnVTsQHLB4Hg098u9wLmxUKTIycHzCerd8TCaX+VINL/FXcQLCX6RJAfN3dANnRx0SRYbzxVUqtBJwg9StNt/rsXO/2nopOwiYElC/QG8z7na+WzdYqHILrFQZBtUfPJfh8RrshgNjVYbtmP93G8ZVL9mrjeBK461AobGQpHW6KR0XSnDya3hkAXMC0bD4VgoEkNDtfmpwhd+KRgNSzAa7hKMhq2HnGWzJL+w6E9UTFmL/q7+i9efrQJa5BcWXeGsnwHcDJybX1j0Uj1PefKOPVr+stdu7Ze0bZ19S8K+xDxjb+cXFr0uJnvkquZvML/9lazJ+QKk8mzUg++iVeVLY9NXT9r9j9U/TQQ+At4qyMvdD+3Dpu/You922YGmiW3Y179iMK3KqtauW9+2aY/9fPtmdh3e/6ZP5r9yXplZ4/bXOcDTXYf3D6O5wQ4LSOCBqSu/O+rLhW8HgUfqcV0sFovFYrFYLBaLxWKxWCwWG4otBZ8ArwNHoKG8BjrbHwMmA/+MhSLTnW2t8bxScoA1wHfASFQoqcILfVYbBA1JlpNkXwC1IA8CN6Gh3k7Fm2DNAF5EPYdaoCHEevmOL0OFmENR8cMv1LQATgZOCkbDe6K5aVJ5w4B+rg5oaDQXN2TcAudc/pBuNYVvW0zNnjqp2vE3NAm6K/IkC1v3WjAa3hZYin6HX6PJzV8D+iWUT/zc5cCshG3ZwK2oR9AbqFeP+z0/HgtFFgAEo+EA8DZwHPB9MBo+NBaKJHp+WSybAz3wQk36nx3P5xcWrRNM8wuLlqCidr3Jv7DPHnieeRcX5OXuCFwHfIX2eVeioSz7AoML8nIPQTgaAIlzLGwPFAJPOOuuuHwU6oHZFGBZ+QKO7nIePy79eMWckt9znM/Z1V/RzOJfmvVrf3TFjNU/Z7bN7sSe7QY0GTP7qRtKKld2AQIFebm5+YVFRQV5uX6h2Q0r2cPd0DyzFb+t+Ia+FQPmYbFYLBaLxWKxWCwWi8VisdQD67GTnG6oMDEfOMy3fRc0r0PE3RALRWai3jp/APfHQpHvnTInonki0okjoALSKajQAPAT1UWdNb7lLFSouAgYQLyIsQINdxZGJz0TBaVsVNRx60lGABV1dk3YnhjCLNm947bla+CFFPWnoj6iDmj4uOPxPqtQXbDMRMOqBZzzfA7cGQtFZgCD0FxJZSSnChXv3kQ9nvyUBKPhb9EJZ4Ne931jocilvjJdUVEHVEDapw6fzWLZlPiQ5L+Tiwvycqu5uzSQPu5CZaXpg3pJdgBygdz8wqKHAlnS3inSrFnnrOvQvhRM1gox2T8Bo5zj5gMxtzrn/+OoFyMA7bI70TyrNZ2a7rCG+Dxp6xACrCxbnHlE57O/7dfhqJLMQOZTJZUrH0U9Ly9BvYDOaZfdxd//7FyQlzvt9RkPdFy4ZtZfi0tnE1uuXf24+a88Wt+LY7FYLBaLxWKxWCwWi8Vi2brZaoWdYDSclbC+QzAa3t1ZfRINJxTEEwwqfMXjTMJjocidsVCkVywUyQ9Gw/sC3fHCgyULreav73jgXHTCcjmwZ5KyrXzLAeAGVKRJzD3RGvUu8gs69cmLsyvxnxeSex0lCh0uxwPXk/qz14UfE9b9IlcZMAzNVZSMVOcfBswMRsM3ot42V6HXsypJ2QCaL+dzVAxb6tu3PZ5Qkwn0iIUi3yUcPw+Y5CzPAX5O0SaLZZMmv7DoZ7Rv+ythVxscz5dG5DnU83FFRobcgArnoMLM7wAtt2+yAkAyoN0ezbZBf6e9kfKuV7/6Rt/8wqITnfJ9UM/C49DfbOf8wqLL8XkUrixfOrOkYtXTPy0d2ylZYzIkC0MVs4p/Y9KyT/eduXpKM+D0DMnMwxOCMjIl+5pdW+8f6Nx0R//hO1aYst7j5v8v6+N5L1FSqZHkVpUvbY7FYrFYLBaLxWKxWCwWi8VSD8SYxph737wIRsP5qCX3NNQjZz+8UF7XAMeieXJcFgN7OPsGAs3QicY5qFfOTmhYn0w0fJA46xnA7ahXiD9kWTK+Ag7wrVeRWni7FRgBnAn8A81fk4zH0fw9ycSidFSh4syTaeoGeAi9Hn3SlPkCOIiaQ7El4y48b6jtnW0TUeEpi9TXpxK9/lPQcHipqCDeu+dJ1AuqJ6k9mhY4522Gl8MHVJQbGAtFEoUogtFwC/QemxQLRRYn7rdYNicK8nIfAVyvtOVoqLMvgZfzC4tSeb419Jxd0BBs3+YXFk0AGD122GmlSypezmgigawWGRcPHjjiCV/5s9BQix1RD8gYXt6rLsDTwO5oaEuAO52/z9HfqotpGmg1dU3Vqp5g4jx5+rU/ykxeNv6qsqrSB51NJUJGVZOMnBY7tNiTX1d8hfFpxQEyVlRR2dpZrUCF+NbAwvzCokSPSIvFYrFYLBaLxWKxWCwWiyUlW6uwswTP2+Uf6ATf3531z9FJy/Gop8xcYFAsFJkUjIZ3o+4eF2tR8edFYHCact8Sn6w7Wa4YgGXAfXg5LKpQcWIuXrgx0EnMdKJMKipRQaoSFZCS5cpYCvyK5p351SmXSmSZ6rSjvtbpnYDZ1D4f1A9o/oxS4H/AkFoeNxX1DirBE3fc7TPAyd+h/Er1UHXPAxOAqbFQZBxAMBruCewFjI2FIitq2Q6LZZOmIC83gPaPByXseim/sOjsDXD+Ds75t83ZNuPm7U9qP3bwwBGx2deNb4X23QsLp//fA2gOsnT0QvOQrUHDMVYA56MCr5+7W/TIvrFdn2asWVDOom81ndCOLfdcuk1Otz2nr/r5yzWVq9quLF+SieO90za7I22zO//55+qJO7iVHNhhSNlvK7/OXl2+ku2a9VqzunLZ3EWls3bMCuTEyqtK984vLFqDxWKxWCwWi8VisVgsFovFUgtqO1m+pfEVKrJUouF+fgXOQ8NxveDsc8OfdQHuDEbDTwPfo4JBYg4cP6vRsERu6LImqHdN7zTHfO+c/xughbMtlYdLW+DfvnVXUClDJyhdAWUB8cLOIjRHRW09ZzLQEGWJ/BcYh+acqQ3bk9z7JdFbJhUfpylXheYVaoYXDmlvYDiaJ2hOiuOSsROwc5LtbVGPhErU8wqqh8ADDd13LkAwGj4G9RCYiFrkTw5Gw31joUiyUG8Wy2ZFfmFRVUFe7g5Jdp0AUJCXexzq9ViYX1j0+XpowitomExKF1beM3jgCNcLJrq6fNnf1latIVOaLKwwa/3CTqJQXooKU8PzC4tKnXbfCtyWcK4PJJM12x3RmkCW0Hy7bErml1M8q4ziipXn7tNit8t6tNitK8DrMx6oqDDqsLSsbCHLyhbEXaMW2W2zB3Q+k3dnP8n04klNA5K5I0B5VWmwQ5OuRwLvNPzSWCyWTR0RcS2qnjPGDN2YbdlcEZGhQNRZHWCM+WTjtcZisVgsFovFYrFYNg5ba46dk9F8DPvEQpEvgV9Qj5oH0BfFFgnlTwDeRkWKo4AIMBP1WPF78CxEw6k1wxMVSoET8TxAktEP6OqUrS+difeKmeFbrgK2IX5iM1XIJH8uHTc5+VrftiGox1BtSRXSzBVrkokd/m27panbAEVUT3Z+IZro/YzaNNAhleC1DXAzcAwqCP4JXITeQ/4cQ/5QSruh4encCWf/ssWyJZCsr6oqyMvdCRgFXAa8V5CX22Y9nNsvrK/rm+YU/77bmNlP89HcF2iTve08XxtdUcffrzRBveymFuTlXl2Ql7sb8eE3Qb317jeVnFNV6Xm2VlXo8vw105//a/Wvmd8v/oBvF42hS9OdprOuH6juCVtWWUJJxcq1pZWrtR5TwZ5tB9A0oxW9Wu01q64XwVJ/ROQQEblXRL4WkTkiUioiq0Vkpoi8IyLXikjXFMceLiImyV+ViKwQkVki8r2IPCMiYRFJmrepju3tJyIVvnM929A61wciMiPFtanL3ycb+3NYLBaLZetjUx8biMhxIvKKiPwpIiUislZE5onIeyLydxFJZ3i60RCRTxphbDCjFudJ9R3U9W+or85an98pnyUip4nIkyIySUTmi0iZiKxy7qP3ROROEdm3hnqG+s59m2/7Fj/OEpGmInKuiLwgIjERWSIi5SKyVEQmi0hURE4RkcT5H/f4Z1N87jIRWSwif4jIR85vfYiIpJqrqkubH0w41+E1lK/L99UmRR090xxT7ly3b522pUubkKqNAef6PC0iU5z61orIXBH5UkTuEJF083QWi2UDsrV67AxEvV5mBqPhPOAZPG+M89C8EcnoFwtF/huMhm8EejjbVvr2d0CFnhV4E/mPA1dQs6fMDs7xtSFZ/p3Eh9si33Ji2XLUOykdbkg20NBrnZ3lHsmLr8Od0aytZ1AZGvoojCcC1TY+YAYaTikVbWtZT20IAPcA81BRcBGe8AX6ve8OTEK9vtbghdd7JhaKLGvEtlgsjcLs68ZvA5wC/Nh1eP+v6nDoHagnImh/Ugrko56Obr/RDPVeXF7XdhXk5R4FPIWK5cOBK1Gh5TI03Nq7aB92snvMpGWffVdF1S4AS9bOyUG9977E64sDaN9S5WtjN6DA+UvMc9MSeBFDh1mjl9O2d1PWLChnzbxyd3+7Lxe9fYKv/E7+g7fN6UGLzDbMLplKWdUaWmdvQ0Yzsrq23In5q2fSpdmO7Nx6H2aVxKq+WvTO0Qdz0cS6XidL3RCRPVCv08OS7G6CGkd0B3KB4SLyMnCdMWZ2bapH7/9WqKHG3sAw4L8i8g5wjTHmz3q0OQs1OMmoqaxl4+C8wI9zVkPGmGc3WmMaEWcy6VZndXtjzIyN1xqLxWJZP2zqYwMRaYFGohiUZHcn5+8Y4BoROcEYE6tFuyyNjIich74fdU+yOws1HO6Oflc3ichvwN3GmBc2XCs3bUTkIjR6QjLhs63ztzswFFgsIv8GHjLG1CYqShY6d9MenXc7ArgWWCgijwDDjTHlaY5P1eaDgMvretx6JhONMNMO2Ae4XESGG2NurM3BInIw8BiaYzyRzs7fAcCNIvI8cJUxZnljNNxisdSPrVXYeRAVBHqhuXX8IXvcDj+ZeOJ6uezp27YbGlYsw1fe752RiydyGOfPX69Bc7s8iQ7Y/DlhnkC9QxKpydNqNnBomv2/kz40XDnx98YUPGGnJp5HxbHa0gTNc+T3IKrtBFYlycOn1YYKNF/RNs56qpxGfn5Gv6tWzvH+Y/Zx/kdjochiZ3m/YDTcPBaKFNezjRbL+uZj1KOscvZ14/frOrz/D7U87k20T8gG3kdfej/JLyyqKMjLvQXt957NLyyaV892DUdF5B6ogNQa6A/8mF9Y9Cj6chzHyvLFDwLHA60MVU8BJ1E9z85LQKo8QBlCoMJQ5fZ9nYG/AEoXVbDos1IyJIudW+3DrOLfWFO5CjQnV1L26XC0aZnVTvblWL5b9D7Luv7Ooj4TAy0qYfvPdqRveS4CHNn5nMCPSz/6d0Fe7n35hUVbX9K7DYSInIh65rqerX+iEyUT0NClGeh3fijqZdsDOAsN1/pgimpHATf51nOANqjIdxB6DzZ3/h8lIucbY16tY9NvRH+jC4COdTx2Q3M0qY1GLkENOECv2agU5bb456UxpraGLxaLxWJZj2wmY4OX8ESd2agx0iTUiHBn1OhpH6f+D0RkN2PMqho++oYkROpcu0Pw8vlG0MnkZKSKNOLnW3S8lIr30RD71FCuNoLdOkQkE237Bb7NnwGj0bDsS9B5lW1Rg8+jgf2AXVAjtroIO1vkOMsxYnoSFWxcPkAjs0xBjYzbotFzBgHHoQbR96PGT8tTVD0MvS/AE1nboTmQj0TfLbdFBbmTHGG01t+/qJfcM+jcXH3G6Yl9RTJW1rAftD8K+dabouLVqagRZAC4QUTmG2MeTleRiJwOPId3n32H9pGTnLZ0RPNRD0W/g6HAviIyyBhjI1BYLBuJrULYCUbDgnY8S5w8J9PxrKtnAB8BhyQc9gJwJvGhxFxBZW1CWUGtL0ahHamfzgnlJhOvfgtqvfMt+mBZ49RRiYYUqw/upOcqdGJzLTqBO9jZnk7UAR3E+iceEi3Z03FSwnF+kSZZXh33PNloeLtuxAtXc1Ch7XfiB2EvAacT7zUzCx08b0NqXMEuEx2QTwIuRgcIiRSj30UAmOtscyeKU/129vavWFHHsqky+7rxAWBXZzUDzVtTW2FnFJ54fD36+30NODW/sOhO4M4GNm8q3m/J7wG5HKAgL7cF+vLXDfhnfmHRt/mFRd8V5OV2Qy3i2qAhNhPx5x2b65R186lV+kQdUOH2XZyXpApTToUpZ+rK79z9FU07ZnZr0aMJq2eV+T15EAJkSrYATF72GX+snkh5hzY0l2zIhFWdZ/PXlCn0bLk7ADu32rd87/ZH7T/7uvHnA591Hd7fWu81IiKyP5orLRv9Xm8E/pPCMu8NEbkGfVEZXkPVy40xPyfZ/hEQccIn/Bu9h1oAL4rIImPMuCTHJGt3H+AG9Ll1FfC/2hxXi3pdAbFRvUuMMVPTnHOhb3VOiutmsVgsFssGYXMYG4jIXjg5LNGcwPsniDZfOhbzL6LzFt2A80ktOqVENNxYD+B2Y8xtdT0+FcaY6WnOuY9vdWFDxgbGmGLiQ+QnnqvcV7YxxyD34ok6s4EzjTHjU5R9G7hZRPZDxYTD63KiDT3OEg39ex7wqTHm8IbWl4b/4Ik6s4HTjDFfpij7jIh0AW4nXkxLxvQU1+Ed4A4R2Rs1St4NFXveEZH+xpjVtWz3bej78yRUhLqhlse5pOor6kpxknq+BQodL6jHnW23iMhjxpikc3uOp84L6DxXFRp16FFjTKLh4WgRuQcVgHLR6zdKRA40xiTOk9bIBrzPLJYtli0+x44j6ryBhvSZEIyGm6IT+TPQCUMhuYByHhBDBQXQzu0bZ/kuVKRw3T4LYqHIh6jVzEFocm/Xkru5c25Qi41U8W/7oYOxpugDze8BVBf8nWkAHTC0RT1Naov/vGVo6KP7SJ4Px8UdLLmTpKupbvFSk5DYI+Hca1BLlxxU1PE/VHanumdPN5KLOn6vAbf+KvR7aYe6kiayEhX/9kcn1AahA3K/4FWR5LjHk2yzWDY5ug7vXwVch97rHwNv1eHwXX3LrkXP0Y3TMkBfSi8H8lCroEfRcGzuxPalwDlo//ake1B+YdHK/MKiuXgidiL7oILtAtSyaR90QP/fHVv0/bdbKCvQBCEwhzQvDIEmktltcJt2HfZqzrYHtCDQRNijzWEM6X45+3UYTEnFan5b8R3TVkwEYPUM7ZpNlWH1zLVUVJVjTBVrK9fww5IP53696N1PSytLLgCen33d+P3qcK0saRCRbPSZ7N6nYWPMPenCLRhjKowxT6Pi4qT6ntsYs9wYcwleOKss4NnaxPMWkQzUWy0LFTFTveBaLBaLxWKpA5vR2MBveHp/Mk8cZ9L19hTHWNYjInI0Ok8ATq7lNKLOOowx3xhjjiV5ZJatCuca/sNZXQQcmEbUAcAYM9cYcyEaHr/O4dN89fyAzvW4okhfVOCtERHpB1yDzild2JB2rGeeRI3aQQ3d+yUr5OQs+h/efN3lxphHkog6ABhjlqLeQJ85m/ZChS6LxbIR2Bo8drqhrtOgHfe+aIfW09n2Askn6EHFhD/QzvCfwMHBaPhfzrGVeNfv7WA0/ADqtRNFLdd3xrM4j6CWMz1QocJPGdVdav1hhlwPk3ShwvyeMVPQUHEBVFSKxEKR3sFouKev/Fqq5+RJ5C/0c5+CCmHnk15oShyMCtXz8SQLb5fo1ePnR6p7N7l8jxcSL10doC6j5U4bi9GHbyt0siwLFfgSaQWMQT/7yajFQs+EMv7fz1Tg6lgoUluPB4tlo9N1eH83v0xduRYVW/yhFSY3SqOA/MKiEuAR36bLEoqsTLHs0t+37O8fXI+75sCU/MKi2QV5uT8A9/2xeuKve7c76uNKU75npamY8vPyz12PJAOYAIFAq6wOLC9XnT4jS8jIDlC6pIK/Ri3DVMFsM5Vd2x5A+5zOfDDnOSqMFzli6eQ1FM8uo6rCUL6qilad2i8sq1q77U9LP2Hemj93cMvtv81xoBacWwUFebl90T55L/Rzr0b7/qfyC4smNsIpzsXruz8wxjxR2wONMX/hhONrIHehYQL3QeOrn4uGbkjHNU752agFYPv0xbdeHIvLIehkVhB9ca1EJ1m+AZ43xhTVsq4mqGHP8egL/jZoHzAHHXcUAa8ZY9aISE+8l2WXqIhEE7Y9Z4wZ6juHSbH9XVSULgE61mQxKiIP4/WNvY0xv/r2tUKtvI9Cf1s90LxnK4FpaIiTR4wxC5LUOxQdy/qZLlJtCJrU40tEdkAt0Y90ztscWIxaj74IvJ5qoiChjn+i4+rOqLfmT8ATxpg30h1rsVg2f7545tG+pBgbHHz+pRMb4RSby9jAH1J4Zpq6ZviWa3rHtzQet/qWLzPGzKnLwcaY5xq5PZsjt/iWr6hLKDRjzMiGntwYUywiZ6FjDIBLReReY0zK/MiOCDsCfb98yBjzjYikMircqBhjjIh8j4axA+1rvklS9Fy8/FCfGGNShUX0110mIsNQb8Is4DIR+T+bb8di2fBsDcLOfDQkz27O8hTiJyMDpBcsdnT+30LqeKz/wfP6uDfJ/oGo23bfJPtSxUlNRFArhmQeKT+jSea2RQfAfuYGo+FD0MHgLPQFeQFex51K5Onu/N2BF681HauJn4xMFks32XVOFGT84dr2R72mSpxy/nb+F33R74qKT+nwf8duTOMT8MSo1skOQu+ZZBYjrkjkpz06qfRuDW2xWLYEXgKeTdj26AY8/5Nof9ON5H3u/9DwigY4A/Wk3AftU7LRvsMVhG5Df897/LD0w8vyC4uOLMjLvR0v1FwVkFdF1X3Lyxe2wplgL19dxcKvVxPIFtyUncvK5mOMYV7Jn3GijsvaZZ7n+yfzX2ljMDQJNFu3LUOyFqN9XqtqB29hFOTl7gM8hH43iRwIXFKQlzsBuCK/sOi7JGVqi9/rqj4iZoMxxlSJyEN4cdRPJo2wIyK74Fm9XWKMWSUiVthJgpMf4c0Uu908XaeKyFtoeJQ1aeraHxiJ9iuJ7Oj8nYaOR56td6NT8wIq7DRD75GUEz7OpMLpzup3flHH4TPi80G6tENj++8H/ENETjPGvN/QhvvadR06bkwcI3VBxbchwMcicmqqSRMRGQK8THxo446oV+jRIvIUmv/CYrFsYXzxzKM1jg2+eObRCcAVB59/6dYwNvCH3ko0mCTFvt8aoXmWGhCRIN59OhN4fSM2Z7PEuYYHO6uz0fxWGxxjzCQR+Rg4AhVTj6qhLTegxsd/UXOOnLQ4OZq6oOOmxcaYZAbHDaUyxbIff594f20rNsb8ISJvo/1XC3RsaiPYWCwbmC0+FFssFClDHxiDgL6xUGRxLBQZQ/xLeSqBy9/xpUuylyyUl5/+aNifWlsDOaxCww/9hr7kpsodMxkdWPjNKWeik68vAePRB1A39LN2R72IdqLm2K5J3TWT0IL0odoSSfVQ8X8XGai4kkN8vgw3RNsV6IO3rgnHB1OzNVMV1T0QJgH/QsWxxId4e+C6YDR8Rh3bYrFsjnRJWK8CjinIy32yIC/3fwV5ud2THdRY5BcWVaIv/veilvSJ+wvR/q5rfmHRa/mFRVfnFxYdig7Y7wAG5BcWrSzIy90JL1RjFZ5H5Xu+6irQhKtXkSD0LplYwqJvihHnUdouu8uksqq1lZ2abm8CqZ0IVwMYTDbA2qoSOuVsT7fmu6zo1/7IMlQgfnP2deMPrP0V2bwoyMs9Dn0uJZu48XMQMN4pX2dEpCXeM6wEGFufehoJ/+T5wU6otWqISACd2MkBXjXGvLMhGrcZk4mOlV5BPWwHoN7SR6PhHF3B40TS5BxwRJ1P8USdIuBsVAzeD01A+zBqIOQyBx0bDvNtu8nZ5v+rVVgPNBymKzifU0PZY1HPJNBwkolkov3ZXagxy/7O36nA06i3eCvgNRHZKeHYt5x2R3zbjqH653rLf5CI3Ancg05O/IJe/2PQ3+Df8Ca9jgBeT/YbcPJJvIqXazKCev7sg1qT/oha8dfG4MhisWxGfPHMo3UaGzjl68xmNjZ4F2+ce5XT9jhE3Slvc1YrgKcau5GWpAzwLY8xxtRlHsSi+K/h6FS5XzYQ/t/iYakKicjueLl0Lq5DPp5kDEHHfTNRb+rlIjJNRO5z8gg1Fv782tVyXolIC7w+sZT49+Da8JZvOeW1s1gs64+twWOHWCiyAqeDCkbDAdRbI4p6rlyOWkcmYzjqAVNb18qZqDdHryT7Urkzpgsj9jP6UjwH9UpJFsoMNAFcF+K9XdqiCc5TWdE3j4Ui04BpwWj4DzzPpET2RV1Tk1l+JlIXoTBd6LRk9a5AH0R/opMCbh1t0xyX6notI7lHkX//nugg2fWAqgLOjoUirthzdzAa/g19sPu9pE5CRTiLZUtmLjoQdT1LAmj4IpdWaDLF9UJBXm5bVGTZBf29nZlYJr+waF6SbZ8Dnzt1bIu6ordB+5cT8guLvnDKfVmQl3tNpmTn9m03oEmXZr2eenf2k6dVmvIAXr9SFiAj22DYtkl3VpYvZknZnOYfzn3uVWMqT6miMlUelWph1sqryiaurlzxuEjgv77NnWt9QTYjCvJy90XDlabKN5dIDvBaQV5u/3p47uyO90z8yRiTKuzqescYs0hEZqNepq1QL9tkITsuQ41RluHFHLek5nNgu2R5B4APReQxNFzGecAFToiIP/2FnFwLhajBRxVwjjHmfwl1fYuKINfgjDucXAw/i0gHX7l6Jyw2xpSKyEg09O0AEemaJiTJuc7/cpKPOU4yxvyeZPs3zud4GPV6aYGOY0K+dixHJxf8iZinGmNmpGq7iPTHE7DuAG5LCLf2A/CmiFyGCmQDgLOoLkpF8DzZTzfGvObb972IvIJOdB6Vqi0Wi2Xz44tnHq3X2OCLZx7tXw/Pnc1mbOCE/RyCvuv3BqaIyH9QY8NS1EjzMvR9vQKdaLYeOxuGvr7l7zdWIzZz+vqWN/Y19IfT3zlZAUd4jaLjlJeNMWMaeM42SbbtiIZjvlBEzjXGvN2QEzg5jFwD9RheyDk/e+DNzf2ULtdYCvzfXWL0IIvFsgHY4j12/ASj4d6oJdCbqGXmZXiiTmlC8Ur0JbUmUacc7SA/RL2CahMeYhXqxdMLHcAl67DXoC7nx6Gh3LJI/X3loS/2fqGuFWr16J9wcF+yK4C9gtFwv2A0fCqQ7OXfRUgu6vxF9WuWjJRhT+rIADQ26Azg61oek+p6dUixHTSn0v6xUGQW8Z5YL/hEHQBiochrsVBkb+ItHw4NRsOdgtHwIcFo+PNgNPxiMBpOJyJZLJsjnYmP+51IFUBBXu6ZBXm53xfk5T5ckJcb93ssyMvtW5CXm84TMh2HoqIOwBkFebkp21KQl3txQV7uBwV5ucMSdvXAG1C3JmGSPb+wqODknlfJjq36Htg0s8Xpwdb7ue0P9Dy57YuSyYVVVGKoYsHaGaypWg2wY3HF8jNKKlelEnVc/FZ9a5aUzem7unzp4+/NHrFwVdmy6ehEdIMG8pswD1L7iRuXHNRDq674+/pquUQ2Akt8y+0Sd4rI9sC/ndVrkuU/scRjjJmfQtRx91cBV6NjugBqHZnI2XhhbB5MIur46ytbz9+LK3QEUOGjGiLSBs3LAGolvDixTApRx79/EhrSEuBESZJAp47ciI4XvzTG3Jom2e4jgDsJ6w/74SYi3t9ZfS1B1HGPL0c9pDbVJMUWi6V+PIgdG0CSsYEx5nt0svR21LDgQdTLaAIasnNf1Gt1f2NMTfn7LI2H/z5alK6giOwsIrun+Nt2PbdzU2ZT+i2m/R065KMexEvQyDH15Q80P9MhaNSXLDSlwvHAx06Z1qghTp09YEQkR0R6i8iteOGKy4GrUozP/N/D/CT7a8J/TLp5NovFsp7YaoSdYDR8MCrA+N27/R3bv1CLzRdRF+aSJNWsRUURP1mo8LEEfQi4L+KpHvBr0RAYZbFQ5A80xMQJScrV5SX7b+jDwMX/uSTJciaq3H+Bij/H+sr4XWAXkTq8Wnc0VFni/irUq8ZtxxLqRk1JB89Hw6y8ioZGqvbiXwtSvThUoMmWXYHsLef/WpxEwsFouHUwGk6ctN3dt9wRFaGeQq2uz0LDw1gsWxJuP5aM94EpBXm5N6G/m71REX1dyIyCvNxL0JA6kwryckNJa0nPd2gyboAv8guLkk7sFuTl7oJagB8FPF2Ql+uPQf492o+sBZ7KLyz6I0kV6/r7llntPszpkFna6dCWNO2QdXaP49t2Ir6vjRsoN8to6R7n32wyJJM+bQ4NdG0WpEVmu+JsyVnnvbiifFHX0XOe7FE4/f8onP5/bxfk5S4tyMsdnvZKbEYU5OXuRc0hVlJxUEFebt86HuPPVdSQUAmNhb8NyfIoPYV6k44zxozYME3asnBeZruJyK7upAnq0eyORZJZErpjMMNGyrXgYzxeEuxU4dhOxQsnmywMWzVEpIOI9BKR3XzXxY3j3gYvqW6dcUIDuZ7UtYmP/6nzf/+EsEN+L5xoqoMdL6YP6tRIi8WyyfLFM482aGzwxTOP9q3jMZvb2AA0v9tZpI44MRi4WERSTUhbGp+63EcfoPMmyf4uWS+t2zzYlH6LaX+HIrIzKq4C5Btj0op5NbCTMeYOY8wXxpilxpgKY8wiY0yRMeZI4P+cclnA46nCN/s4TESM+4caVv+CRp9p5iwfb4xJFWLNbyCZ0lgqDf5jUuWutlgs65GtRthBvV8SQ8/9gKri56EP1lNRy80QXgfnFy6aoNa05xMfhxM0fns/PDfGJmgen1W+Osqc7e2BK4LR8EGkDrVSV6ulbN9ybV2wk+WZcdu/BI3Dns56Yl+q30MBYAdnWVDX8trwJnpt90OtjpLFWHUnkrPQAe5wPKv92pBooWCIT0qZid4nTwPEQpEb0VjuvwIXBqPhG9Ck6zOD0fC6cHuxUKQEcF1xl6AWVEt99e4fjIYPxWLZAijIy22FhoRw+5y1vt2lqAfbv4A7iRfC/QPgQSmWa0V+YdEcVFAdgIrjqSjD60sq8Fl55xcWVeUXFuXlFxbl5G3/r+tnXzc+Ovu68S/Nvm68P6bxMNQa8sEeLXoP3f7kdr+33VXzeTfdNmsh8RZK60T0nIzmlFTqGHdVub8rQLo3703vtgfSLLMlqyuWNi8zpf6+G7QPHYZel7bAvwrycreUsGwX1FwkLRfWsbz/RaNaCLyNgP/FaaV/h4j8HZ0cLwX+3pCTiMgn/he8xD9f0Wi6ciLSsyHt2FCISBsRuVVEJqMv5n8BU4ifOHGNX5JZEu7t/J9mjJm7vtubDseS8kVndTcn50wiruCzDO2LkyIiR4rI6yKyFO1/f0dD/LrX5DZf8YZYWO6FN75+oIZ7yqAWr6DPEP8kpN+D85sazlnTfovFsvlgxwYeiWODgBOC8r9opI/R6Ni3Ffoevws62dwEvQ7fiUiykPCIyLM19M2u8dOtNfTjhzfeR9+s2dTuozojIkNruCfcMNuH1XBP3FbPJmxK1zDd71Dw8l9+ZIx5riEnSuXV7ON6vNBwQTRSRX0pB6LGmMS5Sz8N/R78x6xI3LkJ3GcWyxbPFp9jJxgNH4KG3jgeneRzJ9EMXie5ALUed0UK/3VJFC6uRK0KX0VflF0BJgcNmebSEn2BznWWy4CReB49P6HJef05Ygze5KA/f0U6lhL/YmzQsCPNgfvxEgEnI11+n/boQwVUmFpB+nw2daUYjVm/AxrW6UR00uFGNATRmair+V5o2DqIF69AB7HJQjmtxnvAGKeeK6nuBSUkj6Ha28mfcwsae357NAbscmd/ZzTnkd+SfgjqmjstFoosCkbDecB9aJi8U4FTg9HwAmCfWCiSKma+xbI5cBwqwLr4BeIcVPh2+QQVej7NLyz6yrf9WTR/mCGJxbmTQ+dBtO/Mzy8sml6Ql9sFyMwvLPoLIL+waAE1uO07x52G9i8j8wuLUk3a3gUMLa0spspUNUW9IOk6vP9fs68b/zhqafePnb484dbfD3z7OzTB5fPEJxd3+51lLTLbflllKgeXVVWPVvlX8RSWly2kvGpttX3EPwMAyJRsju06bG80r8TmTkPjLvetY3l/iKqODTx3Y+CfPF+n+IlIV/R5AXC7MWbaBm3VZoyI7IEa2nSq5SFNk2zbxvm/UUUdH88DNznL56LjU2BduL5DnNVCY0w1z0lnAuJh4NI6nDPZdaktDQkj489x2d75X5UsvFwCGztki8ViaTzs2MBjacK+MPouCfCiMSbRk3MqcJuIfIUaGW6PPkPq6wFlqT3++yjtc9AY09O/7ohj4xq9RZsfm9JvMd3v8DJ07LUGuGh9N8QYY0TkeTzDo/6kv1++w5crEf0se6Ph4roD/xGRzsaYa1Ic7/8eajue9uM/pq7ReiwWSyOwRQs7wWj4SuCBhM1V6MSZf/LsTNS6cyoqNHyPdobJ8iS0QkNrvU51r5q9fcsC/Me3nk38IGsQ1a+/v03p8leACkXzUHHJL+yUoPGGOxAvxFT4zlfhnKsmt06XAI0r6oAKT32AiegDB+Cfzv9LUE+mZsBX6IC/LtYDy3zl/4f3eSHJxGkCJWhYkjao59BM374YmnenHCcB+7odoUg5mszdXZ8djIY/Ak73FeuIfrYb6vBZLJaNxuzrxgsqild0Hd5/tLO5uIbD3H6zHE3GOBjoWJCX+2t+YZGbVHwRKoq8m19YlCykzk14ycGzC/Jyn0D73MyCvNwL8guL1oWpKsjLPQi1PI+hIlBc7oX8wqI3gDeSNbR0zOS/A0dmNAs0+WPhZL5Z9C4gQ0zeY4PyC4tcL7wzcfrjpqvaDxk8cMS+7vG/PpG7AM/CsQU6mN3lvOcfXzLhsifPnb5q8vULSmdub6haJ35VmgqWlaUMXyxNAs2oMOVUOnkrOzfdgeaZrfdnyxB2GmqNV9NzMZHJeAYMe4pIhjEmmTfoekdEOqIhwUANJeb5dl+Oji1WArNF5HSqs41veXtfmdnGmM8TyoZIHa4F9LqA/s5GpSlXU2jUjYqIZKEGM+4L5XNovsFf0bCqa12rSBH5CzV0aWgumfWOMeZ3Z5LuAOAMEbnGd9+ejfcZUoVhOxdP1JmOhpf7HB3PFLtJcUVkGGqBCg27Lv6xrGucU1s2FTHNYrFsPOzYQEkcG0C8sdT1pMAY856IfIx6/h4oIn2MMZMTit1I/NxEIu87bYkAj6UpNz3Nvq0JfxL6fmg43c2Nt/Dy3iXjLtR4NVE4SGRhPc8/0bfcr551NBb+ebxYwj7X2OZTYD8R2Y/q+EPzDxQRd2w63hhTn/G0P6pMTWJLsTHm54Rtn4hIFE29sCuQLyJjjTGjqx/Oz8T3iZnGmMT0E+nwf3c/Jtn/Fhv3PrNYtni2aGEHODzJtlTh50LAh0D/WCiyMBgNTya+g/azHWoBnkiXJNv8+GNOdgdOAq5BJz87kzwfTirK0QnFHqjIU45O5DQHdkpSPhMVKk53llPlzvHj93Dy8wdqpd8VDcM0AXULryvt8bxxEjnA+b8H1UOoQXqBxvVSWoOGLNkTHRi3TXJMotC3ingL0h54D7r9URf44bFQJHEiLRmvoZMrfku0xIGCxbIpc6vzx+zrxl/TdXj/AjQHhN+jcBXJX6oDeKEYTwGGOCLMfNQDpglwQUFebo/8wqJE6+y1Ccun4glGpwP+/CMvAT1RT8pJOKEUa6J0zOR+xSWrnpj4y3iaN2lVOWX5V8ZgBIybtNwVdj4CLp2/Znpg4pJxbVbk/d9N+YVFdzn7vsUTdkD7tH4Febnt0H4o6J4O7UOSGQvEYTB0yO7CioolZEgms0piLCrt93ZtY1pu4jQ0fnad4j4bY1aJyPeoh1kzNGxfulAE65NjfMtfGGMSw7yC/qZeqEVdh+J5HI8iwdDAGJN20kUdOgCYk+RFcHNiAJ7X7T3GmHRGE+mMUxahv9eaxnAbkhfQcVBH4Gi8/uhs5//vxpgvkx2IZ026HDjQGJPKu6WxDHb8/Xd5A+4p18ozICIdavDa2diWvRaLpfGwYwMlcWwA0Nv5v8DJL5aO7/HynfXGM+IAwJlcTjnBLCKuYdTCzXxssKHwe1AcKyKBJN/fJo0xZjleRJJqiIi7L5lw0Bj4r+HgjSmyEv9b/CxhnztOP5b43NSpuNm3fBL1M5TyG2DXRWRZhzFmmYichQomAeBBEfnQNfDxlVspIj+gaRaaop+xqA6nOtG3/Gnizk3gPrNYtni2uBw7wWh4h2A0/HIwGv4vmnx1DTqpVoh65bjMQ8OplaKd5Y6oJ87DwWhYiA9LUQU84Vs/Es8t2k86D5g1qNpf4fxdg4Zu64m+FH9Yw0dLfMj5rXGzSW+d63K8bzmAfq45aEI1t/4VaJikD6gu6rhldkSFKNAHXaKoU1PcUD/JhKNEEsWYtaiyn8gSwJ8UbkEsFDGxUGQiOjGcTAgKJGwvp/rD1/1eBfU+eC4YDcd918FouF0wGi4KRsPfBaPhAwFiociKWCiyNyoE3gycFAtFng9Gw4FgNJwbjIb3xWLZtNkncTm/sGgZ8Z6QGST/zScOQrNQi562eAPkZiQPOXkXalX4JOrlNgqv/0n0vlnjWy5J9iFSEPh/9s47TG7q6sPv3e6+7h2MsWFoxmA6GExvQy8DoYShhAwlkCwkpoQACWDyZRISytCHmjD0MmB6M9U0YxuQMeDee9u+e78/jmRptJqytgGvue/z7LNTrqQrjXR1dX6nvDYuxYSvxvH+5y8Wr2pY4h0HdoxHwkUAA8aMfL6+qWbYu/OfqF7RsGgI8Nd4JOxM6rfzrXMWYpD9H5ljVAXZRZ2MY1ffXMPi+jmEB55PeOD5DO08ou7NeY9+nmXZtkaQF1drmLAOy3iFvj+s5/bXCaVUEZIOweGpn6MfmyDeVKyPZWuklNqa3B7hn9n/hyil1kXcac2cp1Aew60reCaAUmoPXCErlwDoHJe3cog6kDm+B1Hofn2B6yy0T66GefAaIYO8YWnF9waDoe1g5gZC0NzAmUvndQzytWnI2sqwQdBafw18bL/dHDjhZ+xOm0Rr/Q3iIAziDBhkX/vRUUrtiGvPWkV+u9xPwTDP63WOoNdaf4E7ZxxK9ppmXqfJgsdEpdRgJNoGJKtHqrV9NBgM688mJ+wghsBTkNQmOyJpyrpa0cQpiIGwBjEQVlnRRASJnPmfZ/mTkXBBf66cM3ENi0W0Pux7ohVNJBADXy8kHPpKxKN7GOKRmSvFUTbjaWvwTwqLENFhO1zxoguSyiybB763P0F8RUsBJV+u9NZSS3A0VncyvSj6hJKxG0LJWAOF36C/RApXX4YbNbDc12YL4JlQMnaa57NLkNojI4DbQslYeSgZ+08oGXsO6GlFE3+zooln7bZ3IAWPPw4lY0cX2C+D4ecgjpz/i5GaNw6v4o6H7wP7IZNA7xjlrb2jkfRIT1el0pOA65Gx4gXglngknHEdVKXS1VWp9OVVqfT5Van0gqpU+ilkMrp1VSp9p6+PxyOT0XeB38Qj4cML2bGKw3f4ZOmyBdkmytsDW8Qj4b/HI+Hxz8z8z1GajFoWzfFI+AUktH0tXct6dy0tKvfm2a7GV0Sya1kfDux7Ovv2PonKst41BAjOxaqUYiUBtZt12KakKpVuUx6AOVjfNBXrsvyDuCk1D1FK/bbQBZVSmymlDsjfMi9/xk3xMAOfUV5rfanWWuX6Q+47Dg96vjt2A/SvreKNOm+ftZXM/XLhpKNTQNU69MMrLpdnbdUKtNZLcdMvHqOU6oREIIOMp7mEHee4ZD0mtoB1bJ5uFLRfWuslSCQnwJFKqVC2tnnwztOypuKwa1Idso7bMBgMGx9mbhAwN7D5wf7fTSmVLZOIw6iA5Qw/Ltd5Xt+mlOr/s/Wk7XK95/W/7Xt8QSilTlJKFeLcnGsdHYBHPB/dZkeYrEVrXVnAPN17Luzv+e7ZdehTOZkCzJutXYePv+KKxFcopYIcqx8AnKjA/ZVSeWsJ2SmR78O1Md5hz18NBsNPzKYo7HgpBsLAY6Fk7FwrmngHyVPf3Yom/hdKxvZBJj6/QnJLOhwN7O15X4RE8OSKyGlRwNbH16Fk7E5EOPo1wXlEG3AHVGhZHNZvAHSMqI2Ip/j3efpQSHRMDfB7YE8y0yE5331EboGpyff9V8ANbFjPoS5ICrd83gsViHhWQua5Xkt2Ea0PYkz5B2LIWI2kePJzFPBQKBlzjG1eIXAekgrlYuRcetC3rCMIKYxxwrARM2DMyLeAbgPGjOw5YMxIxyuNqlT6A8Q7ZwoigrwL/JPs6REVct1V28v/BTgbuY6OAh6PR8JBkTsAxCPhAUjKtRfjkfBB3u+qUmkLSZG4LyIwpeKRcEH1ImpqV5+AeIo9TqYA/QNSn+tyJCz9JqTu12PApcDcvu22DG/WYRt755TuWNKVDiWVHXftfrg3Uun/kAilXyGCbuO2lXvQo6I/fdsPZkD7oSWIZ9jaMVOh6FTajSY7E8G8mh/ypd5oM1Sl0hNwPfNaywf28q3CLi4fwb1H366UGm0/jASilCq26498TqbHXKtQSlUqpW5HakCB3Aej/hQIhnXGm3/8rKAGSqnjkKK3uXgUt2bApUqpX2VrqJQqs2siePHWRBiSZ1utwamh0w4ZQxxP1nFa6+k5lnOOyz5KqRb9UUp1RMYyf51IP63Zr2uRcawYeEoptVmuxkqpXZVSR3g/01p/huv9fKJSqoX3s33d3k9h3usGg6ENsPc5F05gPeYG9vKtog3NDbx18G5TSrULaINS6mLc1N8zyKz/YviR0FqPBW613/YCPlJKZUsz76Vb/ia/DLTWrwD/sd/2AD6wI5SzopTqo5S6C3l+W+f5gFJqZ2Te4YimE4Ab13V9BW7z2DzjTAUi8jo2pg+01p+szza11t/jilcDyazd5bSpReaajuPm7UqpmPLkb/b1sytSdmCU/dGXwDXr00+DwbDubFI1duz0WF5RYVskMqYYOCaUjH1oRRNfeb4/HTc9Ryli2OuBpJRwhIDFSPSK4604EUmF4X0g/hY3PYaX8YjR7kBc78PzEWNfEO2BSs/7fDnEnYG2BBmknfzAy5H8lruTKVBlw1uvxntOrCbTS/Mc4A3kGPiLuC1H+u6d6DYg+/AvWqaSy0YDhd+gewT04QsklDeozlAz8nsci+znA0jEkhf/RL0jIiKNs/97+1eEmwLvLuQY9kUiG072rKMBIJSMdUQeIrwpYb4L2jGDYWNhwJiR2YTc44CtW7GqnYCdZ48edxCwz45dR7345bK3ne80uQXjSxCxGeDVeCR8QlUq/Yzne29+9lVVqXRB0Y1VqfTHwN7xSLgSiVQEoJRy3UCdN9VPEzCuKpV+ePbocb+qb6o9TKGaS4rKirap3INJy95rmls9tWR14zKUDKV7I/cdp/bJa8i95D+NujEF7Li6YQWTl79Xim+802iW1M3hxVl3UaJKalc1LjuuoBCktsMlyHiaz6jspYbMdCWtQmv9sVIqgjzUdECEuvOUUikk4mwhMp73RVJJHQcMLmDVlT4P2grkPjgE2AuJJnPuEWuAc7TWb2HYULyCOFX0Ac63HzIfBuYiv+VJSJTLd4jA2jNoJVrrBqXUKYhAXQ48ar9/DJiKjE0DEfE4gjiMPOBZfpZSagaSiuVcpdRXiOHPMRiu0Fr7C2IXwktIitnuwBjc+eFD2RaweQAR2jsA7yilbkbSzTUhQvWlyPn9HrlTp73veX2j/Ww/HXc+N09rvQJAa/22Uuo6xFC5LTBZKXU/UqNsLjLO9UGcmo5GIupvsPfRSwxxICoDUrbh5ikk8nEbJDJ0J2R+bdKxGQybDmZuEMw/ERtCf8R56Qul1K3ImF6HGH9/RWYasMu01j9GilBDMH9AbEVnITaId5VS7yKOohOApYgNoityf/TXaWlNGulNlcsQx91fI/OtD5VSryB1Xr5GntG6IOf7oYhTYK5IbYctlFKO455CfqduyDziINx6lSC/VVhrvb41v/JxC3CnUupp4EPEmXANsn+7I7ZCZ5xZDpy3gbb7N2ROXIxE7dxnC9xr0VqPU0qdhetAcwcQVUo9jNj/ViG2vVHIuOTMq78BjrHFIYPB8DOwSQk7yEDo9ebencwom7WTnFAytgtSmNbBMVA6BnyQwXQ3JA3btfZnwxBDvYVbGLsXYlj053AvomX9GRAR6FYkosNLIRE1uTgQ+U0/B0Zb0URDKBk7A4nA2c5ef5P9592WV4l3jIyNSNjnSfb7GUiB4dG0FHWaySzY6FCEK74U29stJlM4a7K3X2Rvs4nChR1/qqdOyPF+DKkDpHz7VgT8wYom3gYIJWO3A1fYfWrvWY+fGGII2YaWYtLewGQrmmhGbn7Y674HOU6DEOMFiNeVN3R/JplpAA2GNkE8Eh5C5mQY3IjDm3vu3iG2+LM1O+pGKC4qpam5AWDmwf3O7FXbtOaaKSvGU1HcYaSi+EJN0yjg/qpUOlfxW29KCYWMnV5hxxsB+UaWPu+ATJTHVqXS/gjLOiS6cDuAyvJeWy6qm+X9/vqqVHr67NHjLgBuLyuuQGvdrJSisqwXCjUfeZhjVcPS96tS6Q/ikfB+yMMcyLjWHVj4+eJX39uswzY7zljt9TFoSU3TKjqVdL24KpVe39zzGxVVqfSn8Uj4RMTLqxADTg1wUlUq/en6bFdr/axSai/k3rsvci5ckWORJkQkyJUr+hjcvNLZaEQeTKu01iY1ygZEa11tF4V9HjGSnUymUwXI2HEMmfX3gtY13k6t8wTQDzeasFD+itRsqETqO3p5kCwRRXn6VG8bGC/AFXVq7D7m4lZkrhFG9uXf/lUjQtEUcgg7Wusf7If5M5C5r7+QbpRMges6pdQixHmpE2JwzWV0XRmwzS+UUicjc6N2yL77U+ndjRhDjLBjMGwi7H3OhZ++f9/trZ4b7H3OhZv03MAufn4QInBvi9grbsvSvAb4vdb6yTzbNmxAtNaNiPH7PSQdV3/kXPI/J/mZarf/74/bw40fO1rtLKXUh4jNrQ8i4ByaY7EFyNyrxVzCw/05vnNYCNwO3PQTRtT3RuxLsRxtvgZ+ZddyWm+01t8rpR7BFc/OBvzpzdFaP2I7K92BRDLtav8F0YxEvV+itV6WpY3BYPgJ2NRSsXlrGzSQmV7NnyLsAcRjECRFkMNiXO/vB6xoYhpyU5jraVOKiDrLEe/FTmSKOs2ICLIdwcf4cNY9BDebB47GFep2BvqGkjGFFBqfiKRqAxEx/AJSUBq5EmS/nLzpmyPGE78SPxYYjkxgr0e8Mx38dSGcfO2rcNO8FeMeo5UUNpkP8gZQuCLekfY6g0JHLwYIJWPDEcGlE66o0wQEPSAMRbxuKgO+C4y4saKJRiua+Atynj0fSsbeJtP4fD1wKnBNKBk71btsKBnrGErGYqFkzOvNYzBsFNhp0b4hMyruZWRc3AL416Lxa7bXdkKypuYGRvY6cSrwx7fnpf7ywsw7sVaMZ8LSt0qhmapU+uSqVDqn0RWZeHpzqL/n+94bOeQXX4lHwnsjgvezwCvxSPj9eCT8v3gk3DEeCZ+HjEn9gamdSrt9v0uPQ+lQUuldxR/ikfBO3n1WSv2AiLNv1jfXjEDE7eXLGxb2iEfCnyGh/JX2X3d7sV4Nuv74jxa9QKPHSUoFDFUVRR05fMB5Y2aPHldwrum2QlUq/SLiQJEv9coHwL52+/VGaz1Ra72fve1/AJ8g6abqEY/JmYih5TJgM611tBWRFhqZO8xBIkeTwIXAQK31cUbU+XHQWr+JzHnuR36/BkRQ/Qy4GthJa20VuK4PEI/qi5Eou/n2+moQA8z/kPt2C4cMrfV9iJCSRs6pDWUc8EfnPKe1zmXEcIxMxyDOTh/izrlmIo4vB2itcxkuvUSRNJTvI8c1Z/S11voOxKHlaiRyfAHuMZyJHNdrgGFa679nWcdziDHhbsSpqB4xvLwKnKC1zpv33WAwtD32PufCVs0N7PbrzcY+N7DvYTsjjqbPIuNiDTK2LkbG5+uBrbXWdxXYL8MGxp4HDEbmCfchDmOLEBFvNWKLeQNxrNhHa72V1vpRE13lYp+/gxHx4VHEAWUZcgyXIba9JBKhtpnW+natdaF1SBuRecw04C3kWj8WGKC1vv4nFHV+jdTXSiOO4s45sgqxK/0POBHYUWu9oVMq/g13Hpet1g5a63GInfR45HhbyPFvQObGHyN2tB211mcaUcdg+PlRm8q9JJSMbYkMMt1xI0O+QEQHx2p2lRVN3Gi3/5LMtFvNiLBwOOIpcAiSYu0pZLJUjAzA/gicR+xluns+Wwn460V4051By5RjNYh3Yrblc+FdFuTmcA1uMbr1iczy9zuIZxBv9cHIcVhk9ylXjvU4Ekn0Y4iLUwlOxQbyezo5RIP2yx955Y0u+hJXDPwOuNiKJtYapEPJWDmgrGii1vPZ57g5j19FQle/RjxQvsY9b/a0oomP7GVeBJzc88dZ0cSz2XfVYPhpiUfCh+CmfQS5Rl7GPWdbsEv3Qyd8uuSVbWkpKq8ADq9KpT8scNt7Ax2qUulXPZ8di4i5+yPX9K+RSee0qlS6yW5ThUzgwb0/gEQgnoUdfVmqymnQdSt27xGes7huzoBpq75saqa5q932hsgWf7obGUO6A2cPGDPybXv9OyIh/P5965JjdxpAPd6rYuABC2tn9fVr9l1Ke3LYgLMBjh0wZuRzgWvYBIhHwsORNAPDEaF9FXLvvqcqlTY54g0Gg8Fg+IXx/n23DyfL3GDvcy40cwODwWAwGAwGm00pFdsBuEZyx2i3k+f7JiS1GKFkrAiJwPEKO0WIx4vCDfvcGsmlXoqkswgSC1YhnpFeD/KFtBRm/CJCKa7nQC9EmHFEBO+yteSPYvEXUixBDJaF/L7/RdT4bNsopAj5cb73gXnsfVQV0MZLI4XtzyLkd3IKmHuXaUY8aPw53b3CWEck9HQQIuztgYg53yBp6f6IRARcYUUTUwFCyVgZ4p1zG1ASSsZOsqIJJ2XJDNzz8AkrmjjUs0wnTx8qPa+39bzeBvHOMhg2Fr5EvL/LEYH6eOT6GIVEv7UQpr9Y+sY0YIeAdXVBvJayikJeqlJpb80H4pHw7khUokLG9BDiybk98Fo8Ej6sKpVuRoprXoREHs7ELUi5pFiVft6kG0IADboOoMvHi9Od7XWusdvVA68PGDNyJsHh6EtoOVZPRcbCze33fgG+FPRJC2tnLgna10ZxHPsG8XrfZKlKpScg3qsGg8FgMBgM7H3OhRMwcwODwWAwGAyGvGxKws4bSDiyNw3PIlyR4W5gcigZiyKRJEFpro7ANcJ5OR6p2RJEPRIp5PXOfgQRAbbL0+cSMtPHBUWvfIxEwgzMsy4v3xK8HyBpMbries5vh4QGZ4twyUchET0g0Snb5m2VifeYZjtX/dt3fv+g9kW0TI/SAPwJ+I/9frXdz1FIJJbyLDvfiiZ+6104lIyVAm/jFnYHiIWSsdesaKIOiR74rb3s2pQqVjRRH0rGzkRq97xHZgTEnxCRyEkDaDBsTJyNW9/qeyT9YlcklVl/REQ/Fomi2QfQTbrxcaT+jr+uGEiqgnWlN+412hOpueAUrD0YEeRnV6XSs+KR8JZ2v7siwvIMoHe5andKdcvoe2ed7ZBIoJlVqfTadBmzR48rRsblWQPGjKxFwtN/hYg+Tg2zW+3tXYkcM78Aj92ub8Dnuq5pzdPWivGnH5SoMoUoDQaDwWAwGAwGg8FgMBgMGWwyqdgAQsnY/thROTYjkRyca5A8kM8hxj4vhUTETCLY2xwkguPfSA7QQfZnzyHGQH/UzkIkqqiY/GhEoDkfMf4fW8AyheAXQhzPey8/IOLUZbj1Z9aXJYjxsw+Zqc4cpuMeP4dJSMRKCYULSLnSH2nkeN7t+/wZxOi8td3HSJbl/4CkUyuxookvAULJ2O7AR75205F9udeKJs5zPgwlYwOBCifSx2Boi8Qj4Siu4PiBRm2h0H2RiMc+uCL4ckTUnIyIlF8h14UGbkGEkb0RYfmSqlTaqefVmr4UIxF2eyGp1p5HUnVsjuRI36sqlW4MWO44JGdwZ0CVFpXT0FznbbIaN7f6zVWp9G3xSHhbJMXl9BMHVe1crEoOBr55c95/f7WodtYriEh/c1UqPdrexnV2+zmI4LUunF+VSvvHK4PBYDAYDAaDwWAwGAwGwy+cTSZiJ5SMdQXOQYyJlYiR8UukmG0PxNi/U8Cic+3vc9W0GYgYI+uRY+YIM88jXtgLcFNpzUEif0ppSS/EkHmF3S+HBsSA6KT/wf4/GUkh5I3qec1ex7oKLvW4Qk4zLUUdEIPrrkiBtRvXcTt+utt/2QrcdUeO59GezxwxbSVyPDr5FwpgGfL7OOKRRiJqtgQSiBDnZx9gHFJXKVdR4q2R2kAqlIzdhRirL/B8PxsxJjup6c4NJWMvI1FXwxEBqSSUjF1sRRO35duRUDLWAYn4mW1FE8/nadsfiU741IomWqi1oWRsL+S3/B64yIomavJt32DIwgPIOLj5nF4Du/dfOGsv+/O9fe0qgf2rUumUHS0zyP5cIWnJjrHf90CEolZHDdo1dNYW0o5Hwh2QMbM3cFlVKt0Yj4Q7V6XS/uv6GhwBuBRKejTSkFkG1xk/ugG3xiPhFUhNsJ0Aflg1kaGddwYRnn+DO0afi6TBBIm8AxF1xgL7Ah2y7MoCZPzoQ6YIlLNQucFgMBgMBoPBYDAYDAaD4ZfJJiPsAFcBp9mv30Eic67GNa71sd//CzGidUQMioOBh5G0W04ar5lIujaHSvu/I4I8iYhGvyVTiACJyunn+2wNYtB7CjHwdUYEhxhSD+Jr4AzEg9zLCQH7+SrwIuLx3loWI0bIS4D9aJn6bQZuCredkbpFhfAhIrpsn69hwDYdOiHHcj5iJPW260BhUU4gIs7pnvcKaLKiic1Dydi5wJCAZboh6fZAfptmxKD6MWK4bYeITqfhCm/n05KbgNs976uRc2UFkmrNud5ODCVjdyCRQ4cCz1jRxO8C1vcwtkgUSsZOtaKJx4J2OJSM7YwIU+2Bu5Dz0k8S2Ar53b/ETT1HKBnri6Se+xC5boYDt1vRxNdB2zP8sqlKpTVwTzwS3n1On81u7Ll0PmWNLVKZOZxm18Fph6TLPBARp2f72jXFI+HrgUerUukp69G9G5DxDaAmHgn3BI6PR8JvA4dWpdL19ndfIuc5NEDNvBb6SQOZ4vxDSAQlAPXNtdMRoWpmbWP1a7jp6V4EiEfC+3mWb7KXPzxbp0tU2fwjBpz3UkNz3cSxc+4tRsYjCxHRDAaDwWAwGAwGg8FgMBgMhgyyGdnbDKFkbPNQMnYhmem3llrRRAOZKdbaAQ8ihrdBZNbiGUqmoTHTd7slRwF/JTi9zg5IVIzD9/b2bkTEJScySAGDrWhikhVNNJG/eLhGhJlnkLRm65JDrweSXi1byqMVSGq2JkRo8UbzZNueRurLaHv9VyGpkLJF5jj8gNRA8tOHludla2pMnEVLwfKgUDJ2BbAqyzL+7RUhRtldkUii9kgqvKAUctXIvr5KZqq4Jtyoqi52nxzrd8ru5znAAODiUDIW9Ptv43kdytJ3gIM82/ILjQ4rPK/XRi+EkrGtkN/iv0gE0t1IFNLLObZn+IUTj4RPBT7abeL7B3w9ZFjzm3seTm1pOcg4UO1p2hERULa237evSqUPAe5BRO55yLUzFPgz8JadXm1dqfS8HoAr2I4CRni++y0+calTaTf26XU821butQQZw/wsRoTaf7Qr7rADsMdLs+8ZvapxyVPIWFmHK86fgD2uFFE8e+duB/UIWJ/lvGjUDTtWN638c+ey7k9EtvjT8qpUOlKVSv/FjkgyGAwGg8FgMBgMBoPBYDAYMmjTETuhZKwCSbnWHzEm3mV/da39/yYkJU8lImj8GTgxYFV72P/rgZdwUwRlIyh9mUMxmcd1S8RwGZQG7nuAUDI2EjgpzzYVIpw8g0TGFFJvJoh2iKgQxDDPa3+qtxm0rIHj9AtE0JqK1KHZkfyi4UO4v1M+sqUvag27INFaT+KeAyuQWhrZ6l+UZ3ntUANE7XU+CtwGNCLnUXtgAmLUbkYMzJOBY6xo4odQMuYvIl9HS0Yj6eNmAxNDydhE+/XpVjSxFCCUjA1HUmBV29t8MMu+nIxcA9/72uxDcI2pXgGfGQzEI+Ft8ESsbffdxAlLKntuW9FQV4GMB87YoRGB0xkPF1Wl0k4KwAtwI1i+xx0vegNl8Uh4e+A+RIQ8tSqVnlVg965GxslioAoRlIYgkYDfxiPhPwBj7L6VeRfcrcfh9KgYQP8OQ7tr3Zz8ZsVHu9rtnL7thUT3VX+y+OXnP1n88v72/jpjYDnwfDwS7oNEPALQq93mmw3tMuLWRt0wfuKyd0qRsXL14I7Drpq2evIjmuZ2oPl40Usc3PdMSovLtkYi/AwGg8FgMBgMBoPBYDAYDIZA2rSwgwg2jlG+PfCYFU287XxpRRPLgbNCydhQJO3ZDuSmDKmDkEs0WYEY8IKM4WRZdtssbe8KJWN/xU7fUyD59iEXzUh9n3WJxhiAGDlzHZuuwK3kF3U0UjB9Q7EAMQjn4mjE497rif+l/ecVWaYiv8elns+WkxkJ4NAOEXMm46atK8G9rnZAhB/Hi397xMAMErGwPyIqPmJFE2/4V25FE88Bz9nn7+dI9MMOwIVIxBjAs7jp8y6wookEQCgZ62nv12zgBeB3wClIOiwVSsZGINdOUBQSSLosgyGDeCTcF0lR2Am5jhtKGxv+3mfx3BMQcdo7RnyJiLwO98Qj4QrkvPOmutwNiRjbBfhXVSpdE4+Eb8Yd66bGI+HfV6XSiTx9KwGORCKB7kHSDq5C0mPORNJebpVtee0JShzWbb/0Nys+uh25drwpJmdXlvV6d3n9wkOyrKYjMBLPGNitrLcC6FWx2ZaIMKSAyu4V/f9XVty+zFrxEQCrGpawpH4uvSoG7o0nVaLBYDAYDAaDwWAwGAwGg8Hgp00LO1Y0MT+UjP0f4j3+CtlTjL2M1NIB+AgRgYZlaetPOdaIHKenEHFoDTDJs758aHJH+PTCNcyvD96aEM0EiytFwHWIsbNTnvWNQwyUDoWeK9mEAi/zkN/LqT2UjXxCksP7SGRMrt/EEVx293xWgRifneM1ARgPXISIQO3tNu2QiBrnd/Qe35729ivt9wvs1+WIIfkpe30jgDusaKIawIomanHTROXj32Qe1ytCyVi5FU1cTWbUwZahZOwepHj85Ui9HIA7cY/jyfZxOI7cx/awUDJ2thVN3F9gHw2/DAbhjh0KEQ77Iuf4ODIFiSVIJFk58ATwGSKshpB6Tg5lwFNVqfRpns9mel6XA39HotdycQ0SlQkSqXMyruCbKwKtGSh6c95/GdJp5ykjehx8x4AxI98hcnOYlnXD2ncp7XHY8vqF/uU1EiWkkAi+J4BdQS3dstNO9UD/OdVTX8atA8eS2rllI3oczMw1X1PduJLSonI6l3anSTcNz7OfBoPBYDAYDAaDwWAwGAyGXzhtWtgBsKKJPwJ/9H4WSsauRiJvbrOiiefJjOYYhERRjM2yygeA83Br4TwNnGZFE42e9X9C4cJOkPHcKxJApve6F40YDQupOVGPK+zkipjZPcd3DquBb8gUdjYk/RCxwitKaCRaxvGon44cl3zCTjX5RQovi+ztg4g4d+Mer07Ab+zX3poY5UgESwckYutTXAMyuKJOs93nQcB2iHi1PVKTqQyJHvsHcL8VTXwdSsb6Ap2taGJKKBkbhkRFvWLXXPLirw3UDrgqlIzdgkRJXIkYwi9FzpUzkLo5Dv5jMzLgMz8lwPWAEXYMXsYD7yEp/EDGwX8B/wSW+druj3ttDUXGHqdW1J6edhoRR71cYC97hv3+K+eLeCSsqlLpoJpf3jH5QrKPg/7x9xXstHDfrfp8wTH3Xu+IUzORVHLFuAI/M9d8419fEVKfagv7fUfkHgPoXunZiUjn0h7fr2hYdBwyTpcBawZ33nFVkSrus1/vyMtvznt0h917Htm/RJU0NzTXXpOl3waDwWAwGAwGg8FgMBgMBgOQP2VWmyOUjB2ApKk6GEjZdXhu8TTpg0RROPgNhBcihkqHk5EIie3s9e9My3o4S1rZzSrf+yFZ2v0FiQ4qhHx1aP5Oy33NxlVISqMfkxNwhSgQoeFpz/tBBa6nPS1FimwFx69E0jPdjdSbKSLzGrgdEZSC+MKKJva2ookjgNeRKJi7kd/SqRvyqRVN1FvRxLdWNPEMkqbtY+BbJGrsWrv9xFAydgkivlihZOw+JNXai8C9Adu+ADknbwNq7c9mASutaOJ9RKQ8FFcALEOiFyZg13Gyj4nz+7+UZR/91OZvYviFcQCwa8DnCkkz5g1l8V5b2yDn+Gz7/RfAUvv1E1Wp9ALf+iqAw+zXK4FIPBL+v3gkPA2oi0fC38cjYb8g/jdgbsC2F3ler0DSw6203y9ArqsaRJhNAsQj4U7AP+z2T7JWqMlM2eZhC4LTSzZrdP8VDYs+QsbVMoBuZX3+1728b5/Jy95jwtI3DgEuenfBE/s/M/M/h7wwK2Hq6xgMBoPBYDAYDAaDwWAwGHKitC7U1t82CCVje+F6f68CuiMp1G5EIh2mIml6HJYTXD/FTyNwECIa+SNZ/B7guXgaiczYLU+71xHDfHu7/dEFrj8bUxGv+XysQtJ43en5bBHwHHDuOm67ATlG+dK0XY9EuBSaoqw1LAG2saKJtUbeUDI2ChE5KoDLrWgiHkrGhiNijDea6HvkHFqORDNNRLz35wNb4qbmW4pE6lyIpFzqg5xz4HrqO3jPGW9KunmI4LUGOBGJYPrOiibG233eCSni/oIVTcwMJWNdkEgJR9yaCNxuRRN3h5Kx/kique5IxMN1dj/eRwzafmHXn/ouDRwbEEFk+IUSj4STwFn2W/85DSKOfIWcR940Zq9VpdKHxCPhrsj4OwE55wcCEyNb/KkjEtk2acCYkRPjkfBOiBDk8FsyxySAp6tS6RO8H9xy1lELm2p0T1+7q5FIut7ApVWp9PR4JHwvcA5yze6FCFJljsAUj4R/A9xlL79i717Hpt5f+OxvyEJZUTtd31wTJDAX29vo5v2iQ3GXOT3aDew/Y/Vk56MVyBi9CzAD2KkqlfZHQBkMBoPBYDAYDAaDwWAwGAzAJpCKzY8VTXwQSsYuQIx2y5BIld/hGrG3JrNGyhoKE3ZKEG/1rgHf+Y2budgOEQPycZD95+V7JOXPgQRHqeRK2VaIqAOSjuxG32c9kaiT1ghYXkrJjM7JxhaIqHQMhaWf85KvHk934LxQMrYYqQlyDHIeHADMtqIJJ5JgAC1/zwHAq/brf+BeN72RyBgnBVQ35De72rf8aqT2yGWedXuPYwe7/xqJJNgaSQX3FGKQbg4lYwch596VSK0Sx+hci9QD6omcA7+yogknbdWO9n6DnHePe7YZpOj6j18YEduuCmhr+GXivWfMQqIKRyBpABUyru7gW+Y/wH/jkfD2iDjTCTinKpX+FDvacfbocWMRgaVh9uhxuyGC5NO4Iu8faXmNb+fvXGWoXcWSL6pBQVEJurmBJ4B/V6XSq31NHaG8G3KeP41cm07k0FSnYXlR+6Xzq2dkE3WaR/Y+YfWK+sWdJy57B4ASVcaePY9Z1bvdwE7frvyMicve6Wavt5dCKY1mTdOK/mtWr8hYDyLqgNRc2xF4O8s2DQaDwWAwGAwGg8FgMBgMv3DapLATSsbaI5EzlcA1VjQxx9dkGGJshJbiCGQaB/sjNVN28bUJEgr60LKY9kok4qNQcWco654CbzPg9wTv08fATrgRIg41AZ/lo1vAZzcg0Tyl5O9/LpEl13dhpA5Ha0SdZiQVWSlwiOfz1bSMELohYPk/ANNCyVgZImL40+xBpgjTG0nXdCRSkP1PvrZLkePUyfNZR6R2zyjgWURs8e+jQiKitseNKKu0/xchKeSuQM6zEcCbQMqKJupCydj+wCnAu1Y08VUoGSu3ook6JOJhGcFiZKE1iUL5mxg2deKR8O5IHZpRno+7ItdCKSKEvI2cw95zqwE5hz5C6mG1tz+/GRGoHZz0bqXA8KpUekI8Er4WV9gZjIx9o3FrpvX39O84YIutz+n5TJch5Weu+K5Wr/yh/v3mFU2/CxB1AJ5HxP9qZAyZChTHI+ELq1LpO5CUcVcAQ+qba86YseargFXAXr2OvbF3xaA/Vpb2ZtqqiaxuXMGgjtvRr8PgTgChLruzon7J07v2PLRbk27qDbr5pVn3FNU1V3tX8yZSs+taJIXoZES8NRgMBoPBYDAYDAaDwWAwGAJpqzV2RiMG+bNpmZ4HxOM5G4uRyAnHUvcQsAdwh69dkOE7yGu7M66o462xkC3Hnf+Yr8ra05aUAkdk+W4vggWcVYjQ1ej7vBaJ8PB/nmvb3SjsnMkmGjTh1nwJoiuSeiwbNQGfFSEiywtIRJFDRyQS5r+4tTyCGIREA1yK1LFZlKVdIxLddT9SJ+eLLO0UYvxu8H3eA7gEEYOKkfMwba8TJBXU60jE0q+QiKIr7PVYSPorr3jo7edS4CYrmngtlIxdBdSEkrEpwEUEizqF0kSwGGb4BRGPhLcE3kGicwZ4vuqGG4k3FPg3bu2aGuAxpEaZI7i29yw707eZ65Hz7XNE/ASJEvzYfj0eeIVM0Vnb/TsRibiJT7l/UZ/5H6y+fckXNaphRdM+yPju3Zf+8Ui4AjgPGfcjwKm4Quvx8Uh4F0RcuQk4WqPLGnU93cr7ze/bbss/DO92wKw+FYPYo8dRDOyw9bkoZn+14n1WNS6jSBWzpG4eTopTpRR79DryiGJVMqqsqByFKqprrmn2dGkscHBVKv0BIpwNAXapSqVbc18wGAwGg8FgMBgMBoPBYDD8wmiTETtkGriDImWuQiI/uiHGwq9x0wP9B7jFfj0MuNKKJppCydjlSJH6daUJEQeeQMQGhaQZegkxHmaL6OmEpOnpneV7P3uRO+rFHy1SiQgf/t+61F5P0DlQb39faFRHoRQjhsts5KsDlC3yqBa41fdZkxVNVIeSsbOR8+BVJHWTd5+ayUzntCViTHaYiURJLUXOn2VIpNibvvU0IcfyaWCsFU00h5Kx3wP/RASh9kgEgHMugAhDIxHxaR4ShbPErmfzP2fFoWTMES69QlGjFU28aX9/IyIArQ4lY7sjEUQKEcjWN4WaAvzRcIZfHgeQPwVjM7A30MV+3w44DKkltgQ3JeALwHvA7d6FB4wZ+bfZo8fdmJp2swJCRG5uqkqlV8Uj4ZFI2rRPkBo83pSOneKR8J/xXhuaUdVzGiZ42vSyhZoJyLh/ITDX7sdiREgtW7u0jCVneT5z+s3Surl/Bw4c1HG7gcvqF7BoyRyUUnUDOmz9zuw1UwYDNOkGltcvZMqKTxjSeSdATy8pKhvkrKOI4upiVVLWpBscgXw3ZOy6sCqVzid8GwwGg8FgMBgMBoPBYDAYDAAox7O4rRBKxkqBcYhwswLYy4omvvZ8vx1i1B6LGPQdceUAu8ld9mdX2u9XIjVX6mkZZQGZ6YP8NJBpaAyqc3MYYkw8KsdunWf36Snyiym1SOq3IMYjUSFPI/sE8AMiRJybZ71eFiE1W9aHfDVv/CxBjMFBx9qpPxMULfQhEpXiTxnWCNwDxJA0UAch0VVPIAboIN5E6vxsEfDdTYhQ0kDmb1wPhK1o4rWgFYaSMYUIRvOsaGKNXStnMFIr6VVP0/9DIsm+AUZZ0cQi33reRYQgh8eAXyPXgHM+rEJS0PWl9Tjncj2ZIuRQK5r4bh3WZ9gEiEfCFyB1pQpN5zgFOb+946JXbL6pKpW+ssVSsq1OiAAzGDkP/4UI3mch5/UlyPjtFaNnA8MRscY5b5ci13xPREjuioyDg8mNU0Ms29hVBcTLi9rjpFIrViUfnjio6ojxi16aNW31pI7FqrShSTeUjux9Iv3ary2l9i6SXvFV4NkltXOfmrZ6Mr0qBvLF0jeobVoDkn7uyzz9MxgMBoPBYDAYDAaDwWAwGIC2GbEzCBF1QLzD10anhJKxXYH3EaPiS1Y0caT9+Z+RyIvViAHdG93RGYl42CbL9hbbywaxBKm74xBUG+YFe7tBxsIlwL3AA4jRrxAhJEjUWYOIOvsiQoeXwdvPWNb+4pcsXVtWrK781U6saVfacg2ZdM/XoAA+RKKLCiXbNj9CUiYFHZt6JMrqaOA633cliKiDvfz9VjQRCSVjJyPe/31969TA/gGfOe9PtqKJK0PJ2Exc4ecp4JKAGk8AhJKxYjsCZ60wYkUTr9vflSKp2A5H0rtdaG9rW+A44O5QMtYRqfkzBYm6egu3dtQpuCmrHDrZf94IiUKpQ64br6hTTcuUWYZfCPFIeEd8kTUFUIrUivGm8HsfiRxcCMRzLPtXXPGlDIk+c9IvdgTupuUYuxy5LubgXpfdkJSVjlADmaKO97qea/f5C9yUcUFjzXJEnKmva65ee4006ca3B4wZuZzRdG1ort9tdvWULsBL7Yrd8l5rGlZ8mZ59Zwfg6B7lA74/sN9p1d0r+rWvaVyt65trFXJ/mJ/1qBgMBoPBYDAYDAaDwWAwGAw+2mKNnRm4haWn4NbKAUlr46gWewOEkrE+wD7AxVY0sRNiIC9DomscrgZO8G1nNSKWXI9EyQRRSPq0UsRjXNGy7k53xCA5iszaEUFMQ6I5vExE9r8DIkoECUuc+OGMPr1W1qnNFlfTc2VdUBM/rTkvmgM+08CJwMOIYbYpoE02piE1Zebbr/fI0bYMEcT+hqRJy8XJoWSsyoom5lrRRH/gWN/3ipYG3RWe11uGkrEqpPbN9cB5VjRxYg5R52yk1s20UDLWIgLIiiYagIuBHaxo4jeIgAVy7A4NJWPlwHOI6PMesLMVTeyCm6pJI6JLUFX3dRHmOgZ81p71q9FjaNvstA7L9ELO5b8j5+3fgBOrUuk9gZOqUuklAPFIeNd4JPxsPBIeE4+ES+KR8P6I+OPHGykUNL5tj6RJ3IKWEZfe9HHOGFQDfOD9vIiiIlxRJ4jvu5T0rCxWJb/BFT7nAc9gpzscMGZkYyQZ/wARQyd+sfSNxjWNK5fUN9W+9+rcB89DBNkui+tmXwHsCVw8ZcX4Q5p10x+BfapS6QU5tm8wGAwGg8FgMBgMBoPBYDBk0OZSsQGEkrEKpDbKFERAOAAxgFtI2pshwJ+taOKGUDI2AdjRXvRCXA/0ZloKGF5P7km4dXmcqJENhTfd1UPAGZ7tPooYHv2e8m8ABwasaxyZKbpacMq4adULKtu177e0mpk9OjBuu7V6lEbEq/sQcStbZFIuvMfM+zqNCCAlSDTNwZ42zQQbaRcgESo/IKn0dgho46cZqYvjT8UWlEJvEXA2EkEzB4kq2o5gmu2+evupEQPxG8BA4P+saOKBoIVDydi3uPWCrrOiiWt93x+FpMwrAf6ApJj6HjcC7CLk+DmC38VWNHFbKBnbHEnB9qkVTbwUSsZ2Aj7FPZeDzut64Bzk/G/NedxgRRPZakMZNlHikbBCxqfTgDvJTKtWCC9XpdKH2+sajqQi3Bv4PfA5IkJ/gRtF8779fQMwGUmjOA0R2wPFahuNXOcd7GX+QmakkJ+lwOn2ti5D9jFvlGQJZbOP3uyCAS/MTtDQ3EIYP7AqlX7T3tc97H0psvdzN6TO1u887b+oSqV3zrdNg8Gw6aKUcibeD2qtz/o5+9JWUUqdBSTtt/trrd9eh3WMQiI+AaJa6wc2QNd+0ZhjajAYDC1RSj2APL+jtd7QNYwNBoPB8AunLaZiw4omaoHPQsnYLrgPdscj3uIXA4cCL4eSsWIkfY5Df8/rZiTKowg3KmEVkpoNMlOz7UHra8as9KzLT4m9/RnAa8CZnu+OQIyB/u0tp2WKrYXkNtTPBKKP7TPodpQS4SNTyFNIWrskLUWdx4CTyR29460p9IPdv13t92H7z49CRLMdcfdvNWI87o0Ic/5999cVarbX0QkxDocQw67/fB6LnAvOPigkNZ4GZiGG3XlILQ7/skVIGr4evr6X2OsEuD+UjH1qRROTA/ZzPCLsOOKZn6M82/wdctyW4wo7qxCxLY5Eaj0GYEUTMxDBB/v9F6Fk7HfAbZ5+v4IYlZ3zugwRPltroDdRBL8w4pHwAOAdJAJmXR88Vtjr2gd4m0xxZmdkjK70fOaIq6VIlNBLiEBycp7tKOB5RKStQ8YgZ6xwhNmFuDWnuiFRcKXAtCKK+jfTnFe4VEr1q21ewz69jmfKivHMrfne2YZGxjzikfD2SGpEZ6wZhog7wzyrWoYIuNx26kkHtivp9GLHksry7buOvG+7fx7XmhpohvVEKbUPksJzP2AAcl9tRH7Pich96H9a69kBy47CNVx60ci4vRI57yYgovszWuv1SrWnlBoBfIx7LW2UgoBSajqw+Xqu5h2t9aj1743BYNhUUUoNQhxAstGA3HO/Rp4F7tNaL8mxvum0fuy6Tmt9bSuXMWzEbOxzA6XUkYgz6G7I82Ix4rT0JeIs+JDWOluWkZ8NpdTbyDFdH2ZorQetf28MBoPBYNj0aIup2Lx4jf2liIH/RcQz/A2k4L3TZjpi1P8rIniUIBM2y7OOlZ7X/lCmfEbORt/7esQz49qAtkX2X3/gUt93XYGXEY9yLzshaYnqgAcRg39PXGO9E03iUIdEypyFUq7ApNSXiGDh7W89klbISy5RpxoxpnoNtoOAy5HJZT6Gk5nC7X0y0yb5j/XzvvdFiDDk/f39wkx75Hg5+7DU00YhQlYfxOhbQzA9kBocXrz9VsCkUDJ2HUAoGWsfSsaG2xFlUST6aE8rmngpYN1PIL+RRo7dqXZ/70R++4etaCJhRRPtrWhihBVNLM7SR6xo4nYya+H0oWUKtdaKOiARcYZfFichYunaa3B5p0pWdOwSmHPRxjuWNAB/i0fCf0TGEGeM8I6nf8ONRJuDjD9ejgD+XWB/eyMC+lXA/5CIn3eQ674YV9RxcK6DLRxRp2tZbwa035psNOi6opdm30N148oF/ToMfcTuWwI4viqV/nL26HEVm3XY5p/lRe29qTlLyBR1QK7J2+OR8Oka/eTy+oXls6u/Zcbqr86ZPXqcSXn4E6CUGmYbGMYh96vdgH7I/acDcl8II6kEZyilHlFKDSh09ci5OAARMM8G7gBmKaWeVkoNzrVwjj6XIs4XuaLXDD8jSqlRSilt/531c/dnQ6GUutazX4N+7v60VTbV82MjpxRx9hsF3AxMUUodnHOJ1mPlb2JoC2zscwOlVEel1EtINowI4nzVDnHc64M4HN4FTFSOI6fBYDAYDIZfDG0yYsfBiibeCyVjVyCp2O5CJjmO8aMzmem5NkdSbzWRaQTfEzHWKzINJ7kM4f50V5OQovdeeiBpgXYC/kywUaYMqb3gpyfi3fMpsIv9mXfiVwIcTqYA0ox4Gb1ovy9HUqw53zkMRoSDm3CNq7GAfuQS/drTMs1ZEfAUMuG8HBFe+pAd7/HYLUc7cD331yATbId+eZbzCl3dgEeQVEx+OuVYRz/ctG4PIufLVr4214SSsfeBfyHnwWRghBVNPJltpVY08ZqdVu1Ueznn81iOvuTiAOABpJ7UjrmbZuA/pl52yfK5YdNlPJmReFSuWp5vmWrc6MT/QyJVShHBx4mkc2qM+UXb/qw7jcjD8RWez1p9zoa67M53q77I2+7jxS92QNLTKeCzqlT6AoD6ptqP9+x19LC6pmpenp2ktnl1rtUUAw836ca1YpaWKKNVre23oXUopY5F7gHOePcD8DiS+nQBrhC4L1KDbXPk9/4UuCXLap9DIisdKpBotKHAXsBx9vaOAw5WSp2jtX68lV2/CklLuoDC6vr9nByCm2bWzwXIXAPkmD2Xpd2aDd2pjQ2ThmXjwE7fZn6LDcjPdEw/RZypvLRD0sD+GjgMceR7Tim1g9b6e1qSa+xy6IREbJQg0UDPrkefDRsJbWRu8Cjy3A8wG3HunIg4Jm6FpO/exV7/q0qp7bTWG9O8Mkr2Z81jEIcvEKepO7K08zuBtSnsKOuzfuZuGAwGg2ETpU1H7ISSsT7AP6xo4hArmnjKiibeB/6BGNanIJMgx1PcedAoxi1A71Bkf+94d7+eZ9Njfe87ESzcdLcjLX4FzEcMkf6JSVDIdANwN2LMaQr4/kNgqu+zv9qRIUsD2nt/507Af5CoHYcRbJhzoTsiLr2LiDq5CjjVIRFSzRRuyKlGRJ7PA9a9smVzdiLz+GVL7eQcs/kB600jE02QlHk/ZFnHK7ji3vZkn5iuxYomFgC3It6EzyOi2DphRRPfI5FQQeT6HTqQKXR66RJKxtq0+GtoHVWptFPvZi5AUWFxXp2Rse08xBPRWaqETOeBDW3sKUbqZd2IjCetpR7gw0XPs6h2ViHtO+Luw4h4JHzM7NHjOpYVVwwDKC9uT/cKf4AQ73lerx3n+rffqmRA+61X9W03eG6RKtptwJiR/ohPwwZEKbU7kELGOw1cCYS01ldorV/QWo/XWn+otX5aa30pUqfvPOxUezlYrrWe7Pn7VGv9utY6obU+A/HQde4fHYFHlFL7t6LfO9h9bUYikTcIP1b0gNb6W9/xWPuHpKBxmJOtndY6V3olg8Fg8LMmYBz5RGv9mNb6cOR5CkTs+UPQCnKNXZ4xbDvcOc3/1iXllS96a9A67KthA9IW5gZKqZ2QZ2uQ1Nzbaq1v0Vq/afftQcRB8r92m4FIXdVWo5Sabp+b167L8tnQWk/LcV3N8TRdmOMa/HZD9slgMBgMhk2JNivshJKxh5H6KF+GkrFK53MrmrgcqScSwvUUB/Gu0oh3yw1I5MbLSFSLP8vQLYgX+Iosm5+A1GhxmBjQRuPWQnkRmXA5Rckd4ogI4PXSqcU1jJaTKRg5OX2PIzN1WjPwx1AyVkPL1GFBLAFOsf83se7nQZDoNAG3ZlAuQ+5+iPjTg8LThF2O1OPpFLDuMlr+jmt87coINgB/jKSt25lMQ+ybSC7jve33ChF2jkJqEwV5/TmcHkrGDsnxPQBWNNFkRROjrWjiGCua+Dhfez+hZOy0UDJ2VygZ2w1JIQBisHaEu3nkN6h3wj2HLVyx8SMrmjAG518YVan0x4hIeUNzQ8GLlSBjlbcGGKoCN3xFkTWCbR1RiBdgyt6+X+zOR9D47h1DNJIa8QWCBfinUtNu3mVF/eKPANY0rGRl/RL/vaCXZ51rvRUbm+uLtuqyS6d9+5xUcvhdV05qZb8NrUApVYbUKHPuvTGt9U1a66xnt9a6UWt9L3JPCLq/F4TWernW+gLgL/ZHpcADdnq1fP0uxq2Ndgfi0GEwGAyG1nGz53WuuqT58EYFJbO2MrQJ2tDcYB/P638GReJorTXyHBu0jMFgMBgMhk2cNinshJKxjrgptbalZUG+Hr73ByIpTPoDA6xo4m0rmnjUiiYOt6KJc4GtkTDsr4ExwLlImPWygM1rpFC9t0DiQcBnvnazgM9s0ekVxEvMK9K8j4Rm32e/dgyH/jw+3gmmk8/3QMTg41CEpAqrQISibDjr6oekRBmG6zHUWmYhQoI/2qaelsdCB/y/B0kf9iKZ9XW+8rz2iwr7IIXVhwb0p4KW5/NA32dBk/WZSGTXXxDhbJ7nuwOQyK+nERFrJfCIFU2krWhiPJLe4QvcY+DtbznwSigZOyBgmxsEW8x5BPgNco6dhKQJuBT3GmgRQhBAJW49kBAioJ0AbOh85IY2QlUqvQKJ7AuKhHNoRoTAFUjU2534RFpdS8eu27djq1/3oKgk59jkpxBJ6TUNF2KLSlrGhefJXSvNS8+A7ycjdcImIuP6De2KO35VWlTxaMDyxcC1r85JTlhRt2heh9LOHDHwvC5kjolbEXCfnVvzHW/Oe5TvVnzeZfbocb/oVECzR48bPnv0uNtnjx73wezR4yba/2+fPXrc8A20iTOROmYAr2qt7yp0Qa31TK31mxugD39D0raA5Oo/M0dbh8uQ1CqzES9iQxaUUjsrpa5TSr2hlJqjlKpTSlXb3sePK6XCrVhXuVLqN0qpF5RSs5RStUqpGqXUd0qplFLqDKVUO7vtIKWUJnM+mPR45Dt/D/i2ke3zF+3P1yilOhbQ11s969rG911npdTpSqkHlVITlVIrlFINSqklSqmPlVJ/VUoFpvZTSp1l79dfPB9PC9ivs7IsP1gp9X9KqS+UUkvt32OOUupZpdSJSqm8Y569jjuVUtPs32C+UuoVpdTx+ZYtlHy1b5zjYP+Nsj87Rik11u5PnX2O3RNUJ2Ndzw/P8p2VUpcrpd5WSi1QStUrpRYppd5RSv1eKeVPiZxtHdcrpSbb59VSpdR4pdQlSqkyp4/ZvPSDjpFS6mil1HNKqdn2OTX9pzqm64jXEa8ia6scKKWcFFoAk7XWn+Zqb1h/asdOGl47dtLttWMnfVA7dtJE+//ttWMnDd9Am2grcwNvuvAZOdY13fO6PFujXwrKVyNOCb+2x8/F9n1lqlLqX0qpXnnWNdAeM59VMhdYY4/Hzn0pppTKObYopR5w+uP7/CRPP88uYL9GeNrfnKPdUUqp/9r30Gql1Cql1NdKqduVUtkLixoMBoOhTdJm0iyFkrF+iHgxB7eODEh6rqpQMrYFcI8VTawhc0Iz3YomnMmX12jv5QJcoagJSYGWjUV2H7zGuvaI946XzYBX7bb+WjMfIwbEC+33++KKAstxjfJrkPQrEUQs+aNnHesiynmNrk7apL8GtNPIcV2BeJwHnScD7T+nvfOgfi+Zwk4j8jD0OO4EWiHH+BlaRuusRmrFHIUcV2fb08j9uxTC67g5ikH28RPktwI5poPt/r6BpG3ohXjpFSPCn7cYx21Iurc6pE5OUKqc3ZDInw1CKBk72d7Op2TWKGgPNFvRxHP2tZI3FZwHv5HlICuaOHa9OmrYFLgHt3ZOEEVk1jELZMW3tbTrVbKkuSGjbVC9HS+lyBjpRPUFcbB3BfZrJ13FPcj1PYXMa+E1YCRi3HkByavv3C80rsAZrkql3/zPqSfMrWla3RegVJV/36DrtvSsqwHYr5nm/cYvGcvB/c4E2LxYlcxr0o3ZcolnMGnZew2jEpfkEp82WWaPHrcL8G9cY5mXPYELZo8e9wFwyYAxI9fHiHau53V8Pdazzmitm5VS/wYetj86AbcGXgvsh+5r7bcXaK1XKaW6Z2v/S0ZJfYRnsny9uf13klLqWeBXWuuaHOvaHXgCd27jZUv772RkPvDAOnc6Ow8DRyD38xOQun6BKPHsPsV++6nW+htfk3cJrrfXDZmX7Ab8Til1stb6lfXtuKdfo5Fodf/crh9SS+EY4A2l1Ela6yDnKZRSxwD/Q+ZgDr2R8foQpdQ9SP2Ln5IipdT9tKzlsjkyxpyklDpEaz1+Q2xMKXUoUlfDf933QJ4Z9kV+v6O11oFRn0rEvtfIrGXXHtjV/jsVOL913Qo8BuvKT3VMvUXucxnGc2GidX4iasdOyjs3qB076QPgkorDd/glzA28Kcg2z7E673dTNkD3NiXaIY6hh/k+H4I4Q56klNovqP6WUqobMm4EPbOsvS8BFyulwlrrbCnbs/ECYvupRLKE3J+rsd3G4aGA/vZAbC5Bqf22sf/OV0pdrrX+V0Abg8FgMLRB2kTETigZOxQx7E8F/ok80Di0Rwx1/wI+DyVjFYD3IefLPOu+hUyDfD6v8l4EH7dsRsr+SE5cL7vjPpA7yzoPwVt4Pi9FckMfSHC+XEcMaiK3Z3o2ipHIJH8KM4WkDepDYeKfd98rkNRlTn8WIg/og3zLNBGcgu0FZH+7k/lQ/woiqgXxPi1TrPnTMr2PPBB4KbX75Q1rf9eKJj4EnrLfr0ImQSApHFaHkrFXQ8nYENzzsBzXIOxnrcgYSsZ2CSVjw7O0y4t9bj9q9+MiRPC7EYmcOsOKJhyD1a8pLCVftnPmo3Xto2GTwntOt6aGzTQ80WvN9Zp541Z5DZT1wDV51qERT8ZxedplLOBhy6pU+tKqVDqBRD58jhSyfQXXY7ccEbinIVE+3nGsMh4Jq8bmhrUe7X0qN89IPdmtvN/ah7fapjVrltUtYFndwslNurGQKDm7z82/yHois0ePOxL5bYMMN172AsbZ7VuNUqoTUkMOROjbYCL7OuA1nu+tJNVaC5RSRYhhpwJ4XGv9wk/RuTZMCXKffgz4LWLM2Bnb0II7/zqW7IWuHVHnHVxRJ404/OyOiCAnITXx5nsWm4M4nHi9bK+2P/P+XVXgvjyLG3F4Ro52IAYqR/RuYdxBjssEZBw9DtmP3ZH9uBcZhzsDT9rRCP5+7EBmRPehtNyvZ70LKaX+CtyEzK2+Qo7/ocg1eDzuvOpA4Kmga0BJPYnHkflfk92Hg5DotTMR55rzkKjzn5LrEeP+i8CJyD4dglvXogvwqFLKO2dep/PDFnXSyDx4ESLyHuPZ5r+QKP9BwGtKqX7+ziqluiDOZY6oMxY5D0YgjlNPIOfDna04Bpcix+BD5PzcFRhl92ddWJdjui5c7nn9dGsXtsdk53psRCLlDT8CtWMntWpuYLdvNW1sbvAibh2a39t9z0AppXCdQRoR5yaDyz3IvehRxAFsZyCMe+z7k93Zpgixk7yGjCWHIefOvsg96TW73TbAs0pS/BWMXavrCfvtfkqpzbK1tcfCU+23n2utv/J93wlx6tjf7vNjiJ1nL8QGchFiHykG/llIhJDBYDAY2gZtJWLnWNwcuLsiD/GdcD0cHLZChBFvZE5GegqAUDK2LXJzL8IteO+gkJuhV7ypJ7M2TmvZHngbmUg4HvBdkIiYDkgB8O5IaqAliHiEb5vdaenl7vx+xUi9IL8nShBNuCnhmnHTmgV50DuTmXURAJ11+R84v0EmHWORCYc3dPkTJK1YkLfsb8lMddaM/C6OkFTta+83VDSRea6AGB9GIEaCmxFP1h1DyZhCoqOGI7/JatyUZsVIirJvEQ+eQYjn/m4BfQY7tVwoGbsE26gUSsbOtqKJdfH4a0KOgfO7H2RFE2uFwFAytjMi+txYwLrq7b51xRXdvkYigf5vHfpm2ISIR8JFiCB7MVJLqo7s57h3jJiG1Cd7zPN9vW7ISJdZg1xvoxADXxCN9jqyetc7OAOXZwBrxuPpXpVKx7G9MeOR8LWeRYch6eYqfav8EkhVpdL6sZvO+W7Bd4u2atezlMo9GlYuTLVbVddc0wmgTJVXIp67g6sbVy5+de4DJxCcJtKhHnhTUbS5pnmbIoqXN+i6QsbsTYrZo8ftCjxJ4SlxKoAnZ48eN3IdIne2xx0vv9Ra/2w1w7TWi5RSsxHv8c6I48ScgKYXIfe0ZcDvfroetlneA/oH1R1AjN53IB6wvwbOVUrd7PeotQ0xKUTsbQbO0Fr/17euTxAR5DLkvoldi2Gy7SHrMMcuCN1qtNa1SqknEEee/ZVSA7TWs7M0d1L2NCBjtZ/jtNZBdcfG2/txKxL10hFJ9bc2IkFrvRxYrpRa6FnuW6319Gx9V0qNxBUorgeutes+OHwOPKOUuggRyPZHDE5+USqBO/c9RWvtrc32mVLqMcTQ+VOnit0buE5rfa3v89eUUnXI8RuCRFw9D+t2ftiCzCPIuPUycKLW2p/y+DX7OLyFeIz/jUzxCMR5wolU+bdd+N3hcyCtlLqCwuaLDsOQc+10rbXXIeydVqzDS6uPaRY6KKX8Tnnl9rJnAI7x/xVyRMHl4GDcY/mi1nphrsaGdaN27KR1mhvUjp00ch0id9rM3EBrXWNHMb6A2Cy+Vkr9A0kZXIvMOy9C7CONwG+11iZiJ5O9gajW+gHPZ18opcYiAviBiKgyTGvtr520EthSax0U7TcOeNgWSO5DhPpTaf048xBii1CIQ0m2cflQXBtRkEPHPxG71zLgkICUkR8ppZLIvWUkEFdKPam1zpV222AwGAxtgDYRsYN4Bdbbrx9GBJJTkRooc3GdtSchuZS9qc+2CiVjfo+emxCj/TDcaI16z/d+L3F/REsQ3jYLkQgRL6NomdZoMjKxDCE36EuRtEFBkwfHbllvb2spYoh3cAyEuSanyxFPlfvt7XijgBSZBcKbkWNb6DnSlOM7bwTVNkjqh/to6eVXgZsWzeE7z2uvF1MRmZP/XMWo3yYzystLM1K7p5v9/kD79bnIxL83LVNhgByvQZ5tez2ovMLiCfb/Qz2fHZKjr1mxookGJLrMOdced74LJWMXICnwbg9YNOj8LUPSyM0DzkIeprZFjEVV69I/wybF/YhAU46kjPwqR9smpGjrLci19BFufbKpyLXnTaPYBfGe+zzHOp3ruZ3v8xZRZo6CbA9Uq4EFiGieQTwSVoiI44yRfWgp6sxA0lMujkfC37fbtvHdoaf1YMAhXWjsvGZScVHpWsFqef3Cb5ExaQ/EGx1a5jVfhZtioww4WNN8OFD6+9RzXatS6UIi6zY1bqH1dQ4qkNQsrcVrUF2wDstvaJZ4Xnfzf6mU2gL3gf4yrfXG0OeNGq31/CyijvN9M1LjsAkZJo4JaHY6bhqbWwJEHe/66n/k38Ux1hQhwkcLlFKVSNQFwFit9WJ/myyijvf7iUhEOMCxtsf3+nAVMhx/qLX+i0/U8W73NtyaEt5USCilRiBRJABP+kQdZ/kGRMQopA7bhuQLMouTe/E6w/hrfraWGDJurUEEFL+oA4CdnsxJM3qasus+gdSJwhXqZgJ/yrKtMeTJauBjJWI0LuSZqBA21DHdBXnO8P59ijiHHIk4lJ0LHGmfP63lLM9rk4btx+MWzNwAAuYGWuvPkGe26xDHgluQKKMPEBFhV+R8311rnTXN6y+YZ32iDrB2fuBNw9dirLHv+TlTOGqt78dN1d7qOnBa6/cAx+EkV7SuN3IwY56ilBqAO1Zdka0OmNa6GjfitRKJljQYDAZDG6dNCDtWNOEUhj/Riib+bUUT31nRxGNWNPGFFU30R7yyViDGQycSxJsn9cpQMjYxlIxZoWRsBBKF4dAeuRF6ixPvjjwUOEJHIRPNImAxEgq9P+Idkou37H47nIVM1K5FPCj9Qonz0F2GGDe7EewdXoKkcHgY8ULx0hmZcPRFhAj/w57XKFlES6OqFyfnr0YMwNkeDichD11+z/vuuF50Dl7jr0Y8ZJ39bkYeQl9DRC1vu4XIsfMKUw4XInV1sqUde5VMUa8WOZe83k6tjWzzGpm62v/vQwwRtaxDbv5QMnZuKBn7FDE+bQnsakUT3t/P6605BdfosRD5LecR/EC6J+LJ7H2QuCqUjGWra2L4ZeBE0pQiYvCxOdqWAourUunfV6XSc6tS6RmISHgg8hB6BJlFXUGE7l097ws1tuQzPnYE+taVlt3353PPuNCOPHI4CLiEzOt5Apli+CPIuNgFGPzDk0tr65Y13r1qRt07M15Ydm9148rfYhsBapvXjESE0HJPv1Yi0UgvAn9G7jVew1wx0KUqlf7ZvEN/TmaPHrcT+VOsZGOv2aPHDW/lMl5nitXruN0NibcPQfWr7kGieN+yDQWGVqKUqrALHW+jlNre9uTvh2s42ylgMac2l+ZnqrXgYRzueJnNwHMS7nwtyGu3BUqpHkqpIUqp7TzHxRHAK8lMA9wq7PQvzj3j8VxtbZwIj919aYe8UThZDeh2FJN/fvtj82gOseob3Gt78Hpu5zj7/+ta6yU5W7rHsQyZZzuMwJ17Pqa1Dkylau9PQeePzfMb2LP7pzqm2yCObKNau6Atoh5rv11IZo1Xwwaiduyk9Zob1I6dNLyVy7S1uQFIfbfTkDlCEEcAv7VrwhgyyZU+8RPP67xjjVKqSCnVTym1tXMvte+njqNW0ByjNX0MKaV28X+plOqMO1d5WWvtT09/FO7zTc77sJ3Czbm/5LNXGQwGg6EN0CaEnVAy9nukUPyToWTsNwFNdkIMcSAe48VkenuFENFgayS/stebpRyZ8L+Pa/yvQB6SWus51B15QJ5JpljgpwaJOPIKEd19r4uR0Oq/Iw9vb9vfadzIlVIkRZL/wegSK5o4E9ej06EI2dfDEYOBv/C5/+GvK+LJei6ZUSggae+eRyag3yFRVEFsj4gGQYbMbHVpQAylKUTEcPr+rhVNHIL8lhOQh6y/IBEwlxD8e01DRDKvB/9SRISbjUQ6nYd46tfY63gbiRI7BklFdQmZYs0ackdxPW4v/yZwaSgZ64VEJ/QG+thCZcGEkrFK4C7kYX00MNCKJj71fH8Gbq5oEM/ZfkjqLCdkuy9yTPfBjaZw2J/MSW9n1uEB2LBpEI+E98Adj1YiaRC7Zl8CgCvjkfCb8Uj4yngk/BYSOXY2cl5dg4wXjUhEZT0iUI6yl/2O7BF3jeQeS1tQU1ZBeUN99wU9+t720fCR99v71BGJWHRoQnJa74g8CDlC+m+BTiWqlJ4VA2nf3GXOD48vPW32yyv2q57b8BLiIdfbsx7/ONAREYamV6XSf6tKpeuQwu5Ou/eqUml/modfEufmb5KT81rZ3jtud1zPbW8IvJGdGQZSpdRvEON4LZKSdJ1RSr2tlNLZ/jxNk7naKaUGrU8/fiqUUpVKqb8opSYhBrKZSESz14PfuRcGOS0485fvtNY/axSdbeh27sfb2TVn/DiCzzIkPU8gSqmDlFJPKaWWIrVapiKR4s4xudbTfH2cOXbCNSj9K885pXGjgsvIdCrxOveMz7PNfN9vaKw83zvzqmxG2bzYIpdzLh5TwHH0/vbeTAXetGReo2UQ+b730pronkLYUMf0Ha218v4h52M/pBblZMSJ6WWlVDTXigI4BffZ4pFCUnbl+c3e8jSdlqPt9Fb2s61j5gYu/rlBkZ168T+IQ+hLyHNbZ8SGsTXiROXUjfxUKeV1HPWu64E856cTufqXPOPPqA236z8JucYar7No4FijlCpWSp2jlHoHmWPMsdfpnWM4zqrrei/1iuxBTh0n4jrcBgnyXme5pQXcP5znvD4B6zIYDAZDG6NNCDtk1mE4IOD793AjSB6yi8gncY113gfHzRDvBO8DUT8rmviI1hUId2hGBAIQw/mlSMSPIywFpSgbi9SRaZE+w8eBiGfORCSC6BYyPdbHIDf5Ibhpjd4AZth1hB4gM5VZvvQJfmFkJSJStEeiQL72fX80MiE+juwoJOS3tekP5iNiklNc/BvgvFAy9hqyr8MRQ831iMgTVANpuhVNjEUmwxp5uHsQOR964BaVBRHknAnT3vbfWCuauM2KJm4DXve0/R0y+fsCEXn+huSq/RMSVfZnK5o4zoomDkSMwDOQEOuoFU20SBFVAPW4DyHNtBRmtva9vwmJGpiMG9oNEhX2MpkGbpBzdA/cFIRL+OmNJoaNgHgkXIZ4pDrXwhpaPug8iptKx6Ev8qB5AyLYHE9LYbkEOcf816r/vTctRknA97m4sV296OVbT/ua+b0GHBCPhP8PuX4eQFLMgYjjW+KOp45Y3l2hQgf2O50D+v6KA/qedm1pUbnjHdkFMRI5TETGaK9Q69xT1xrmq1LpVxERfa+qVHpkK/ZlU2RdPRkdhreyvfce2ztrq58O7wP/WmOCnULDmTNcp7X+DkNBKKWGIfODaxGDtr/wtJ+gSGQnintjSY3oNdqc6f3CTte3j/02pbVuIXwr4TYkwvl48gvzkDtCOx+98jfJSnvPa8fQ1ByUXs7HT50+KTAlmgdnfp3v/MtFV9a99qn3OHqfefLVg2lNvRj/3HN9+dGOqda6SWs9T2v9OBIJ8h1ybO9USrUmAugsz2uThu3Hw8wNXJb6vovhzj0f0VofqbV+W2u9yk4T9q1dp+pYu80WtC4S75dA1rHGl1qyxVijpO7Zu8C9SGrpfPfKdbqXaq2/R1LrAZyqlPLfC5y5wHKCa46t6324ff4mBoPBYNjYWdcHiJ+au5E0Ok1kRtsQSsb2Q0SaUsTD4clQMpZGvLNqCQ5Z3h2JAHIMj463+A9ICqHW8B3ivTHA89lYxCvrB1zvFxBxY569vUK85PyCiTc92EokfdyXiJejhRg1LCRNh7eY7idIKgKvV1KD3feeZPcuuRnxvM/mCTUPmYD660r4KUeEtz3ytAMR1+5B9m0+7oRjG/uvEOYinnoDQ8nYnxAvfJCHZu8EziuSvY4cv62R6J43ASuUjO2FePUf62nreLf0Rc6vS4A7rGjiPadBKBkrRc7Vo3AFs/OQwoatwoomqkPJ2MFICP6bVjThL7z7H8QD1tnOUPtvGC3DyjvS8vcqRgzRNwNXA1OsaGJjyDlt+OnpRqZRaKL9dyhiSPtzVSp9TzwSjuKKJNmYg9xjFO6DzlphXhVD1+3bsfTLGq+3mEau89uRB9lCnA+acB/GDmooLqG0qZElXbozcsJHC4DL7O+2JVOcHogIvRE8ona74k5UlsnzUbuSjuU9ywe8M7fm+y0RQet83Lo8c6tS6bfjkfC7yJh1FCL+lCEC6lqqUumpiLf8L5319YztlL9JBpNwz48dlVLFWutc9eB+NJRSvZH7Esg9xhsFezEioK4EZiulTglYhTeF7BaeNrPt/OxeomRP1wJu3burkblQNubk+O5nRylVikTeOWPIg8jc5xvEYF3npHpSSs1Ervn1rSXzo6O1nqqU+giZM52qlLrMc96ejrsP2Yx3ZyJpaEGcY+KIA9QMYI1TZ0S5BZ9h/Y6L91niKnIXuvezsYhpGwPe4/gsks6zUGbnb7Le/Cxj5/qitV6llLoDmX+XIWLNNfmWU0ptg1vz6VOttX/unY0dcny3K+7c6VCyn/+tilTeBDBzA8E/N4DMerhXZFuP1vplpdQbiMPRnkqpHbTWk3zNrgL+kaM7r9h9SeDW8ApiWo7vNjVuwU0TOA45Lp8hv1O1IwwppR4id32cQnjI3lZPJKNI2l735ri1gh/Pkl7TuX9oxG6RLQW9n3wCu8FgMBjaAG1C2LGiieftmh/aiib8N6Df4E7oTgOqccNhvTc1jaT1KUUMfMvt9yXAZ6FkbAxu5MM72BEWBXRvC1rWdilF0oP5KUEihjbzfT4dMQjm86ocavd5DZJrN477ABFCvDOrkXRHXnah5UN7KWJArfZ93ogYcP9tRRP/F0rGPg7ox/eIIfRdu0/5vD32p/C0duWI8fToVizjx5kgF5OZagRE7HB+99nIuVOEHMuvEEOK44Efwi1O6D1+ByAp/RxjUifg9VAy1g4xzD2DGJb8E7x1TtFhRROf4BMDQ8lYMRIhkWGY9uCP5HHwpr36AtdTrsmKJt5d1z4a2gbxSLgC6FaVSrcwKFSl0vPjkbDzcPIVcFxVKl0Tj4Q7IGPFa/FI+JZlnbrOXtalO8sqe7CmfSf2Hf+af1WrgDDy8Pc0rjA6FhhaVEao1x6d5i/7unoKcIJnuWlVqfSyeCS8GZmiTrP9fjJyXU5FvCynAh8jUXQAu5U2NbKsczc61KxprFixZG2eaoWq0+g4YljZHHlwGobv2qlpWsW86mn0bb8FS+vmsaB2xtbI2PovJFrwEeR+cqd9zJqxH7zjkfAAJBLoAwxBrG8u+1X5m7jYBr3PkLSU7REHkValwtyAHOp5/b7PS9QR2zsj9fHysS/uQ/5ziNF+LVrrnEYXpdbezua0wli5MbI/kuYR4Cat9ZU52uaaXy1CRJ9+Odr81DyMzEd6I/PJsfbnp9v/p2qtP8yy7Pn2/+XAnlrrbI4ahUTyFILX+71hPc4pJ+d/kVKqR56onY3By35DswS5tyigbD2Oo9fjP58X9/pEW7UlvvG8Hl7gMmd5XhccrZPrd1NKeR3pvtVaTy90vZs4Zm4g+OcG4DqcLrDri+XiM9wsJ9viOnEAoLWeQw6HDaXU2vqsbXxusEFQUj/uV/bb94FRAb+Pw4a4nz4O/BuZE56BLewg9q18Dh3O/VIB8wNq8BgMBoNhE6atpGLDiiZWB4g64BYPBREavsOt5/I9EvnxPhIt4X3oH4rr5X0EkkbLed8J8VJ/gODaMF6WkF+AWINMrrKtaxCuF3guihBBogsSJXKE7/u+iFHRW7vGeUjMhl+U0cDJVjThpIX5KzJhbsCdOG+JpDE7FTG0HpZj/RqZOPv7lItSRPwKOj9XkD+lnJcKJH2Ed5tfI4bdOcix7ISk8FtJy1yzh9Ey6uVAJCLMO8HeFhH5dkTEpMqAvkxoRb8L4TTkvB3k+/wbRPDM5YntYCGRCLcjKdwMmzDxSLg/Mi7OiUfC4+KRcJHv+x5I5IlCjJyd45HwFch19z1y7rfvumrZVoNnf8eIyR9B8PXYCXgMGZOO9Xy+Gti7uZ4e899dVVu3uMkrdq4BKuKR8DNkFvNMIBE/2yHjTgmwvCqV7o5ERv6ODBRdVy6lor52bYqZfu22ZLvKvd9BxCmv6NIiD7lGv/XegidPfn7mHbw+92GadGMf5AHrWSQVw0Rgm6pUusX1VZVKz65Kpd+pSqVbm3ryl8IX67n8hHVY5l7P6z+s5/bXCaVUEXLPdnjq5+jHJojXM/6xbI2UUluT2yP8M/v/EKXUuog7hXrGtobHcL32zwRQSu2BK2TlEgCd4/JWDlEHxOknF4Xu1xe494F9cjXMg9cIuVuetvm+35go6DjakVTOMdg9IBVPoXgNsvl+43zfbyp4j2W2mn5rsesdOc5ZdWRmQTBseMzcQAiaGzi2g7znra+NmYeuP1vhpoN+PJuoo8RbZkTQd61Ba70MV8w52k4DB+5Y9L3W+v0si3/meb0+92GDwWAwtEHaRMQOrE1tdR7iEfEfK5pYBWBFE3eHkrHJQLkVTbwVSsa2RIzUdcAtVjQxD9tgHUrG+iIeWEMQr+vf26v3p6baDvHOXoI8PHsjd15EvEQdQWQm+QvPdcB90K4mOMKltWkwjiAzLYI38iKoVk4Dbv5yL0vJTL1UCrwfSsbiVjRxmRVNpEPJWFdE9Lqd4AKX/r473vWvIh6n/kiVXPua7fg4dMnxXTa6Iun6jkIesF9BUsOV+9oAXB6wvFP43Xu99EMEowEB7ZsRD+pjkeiAGmRSdkQoGRsLHGVFEzkFw1Ay9nukhtJE4FArmtto+rgAAME3SURBVPDnXIaWQuES4Bgrmng/lIz9Ddez12E58vt6U/RsZ0UTO+bqi2HjxK6H8wTiad6EROo9g4xv4xEBc2VVKr3EjtJpAA7H9UrfB4k0ucez2n/hXgvdkKivvyLX/xZB/eg/f8YnuOlKvOyKiKc1iDBTjUQZONf/LmRGO3aw/471fKaB26tS6fp4JLy1p297xiPh9rjpEdYytPPODOq4ffMXi19/aGXj0sMqy3pu3q/9kPZfLH3jEEQwOgMRpSE4fcfCZpqfqWlaNQs5tg7DkbF1BBKN9J+AZQ25uQe4YD2Xby0PIulHNgcOUUr9Vmt9ZyELKqU2A4Zord9ch+16+TNuUfQZ+IzyWutLkfp8ufoyCDf9yYNa67PWs0+bAt57cq55Q75z7jlk3FFIWtOqVvajxvM6X2ragtBaL1VKvYik5D3G9hx2jDua3MKOc1yyHhNbwDo2TzcK2i+t9RKl1DhgP+BIpVRIa52rWHU2XsN1MIkiRcJbYNekCoqK31hpzfnxDOII1R15Xrk3Z+tgPkPme5XAKUqpa7LVYmL9Uwe1FbyFxWcW0P5QxDEF4Fnb4Gr48TBzg4C5gc0PiA2hm1Jq+zyRNKN8yxnWj0LnGMfijhfry0NIJoMK4CSl1JdIpgLIfd9/HqnVWAz8Xin1rJOK1mAwGAybPm0mYgeps3M7Uqj+m1AyVg4QSsY6Ad/aok57JDrnEuAi4P5QMjY3lIzdAGCLPLshhb8vRQzw3yP5U71GcufBqzvykHkKYmS/DInGcG7uTQR7aOQKf11By8LjXlqTV7kYeXi7H3lo+TWSOsy7LwoRVvyizlxkQtuNYM4JJWPvhZKxL5Dw3lXIJHE5+b0Pi5AUSYfQUtRZmWfZ9ojo8j1yrDYUTnSTQo6T/+H673Y9ne2zLO8Xo/5mt12OezyaEa++ExGD8RwrmrgaeVB30qIdRu782w5/RbyEdrHXF8T/kHRQnyO1EgZa0YTjydPC4I085PvrLvziQ+3bMKuRlIU7IefJccgDwTWIx9cPwLx4JNyIiCqz7T+vx9mvyMR7T5iPpDkLRMNXjcUlN24589tzyJ2juR0yfu5W3r3E75WZL9pxZVUq/ZX9+m3cdCpPIp7NTjoqFEVsV7k3O3YdRbfyPkXFRaWj65tr/rmwduaBny55pbxJN4Kc//miJQ5Drt99kWP5LFKHwkmFqDHXzToxYMzICax7mroP7OVbhW3QjODeW29XSo2267MEopQqtuuPfE5mtGmrUEpVKqVux00J2gBEnRonhvXmW8/rs4IaKKWOQ+aDuXgUVzS7VCnlHxe96yuzayJ48dZEaBEFuB44KVfaIWO1U0B7XJ4UTs5x2Ucp1aI/SqmOSERQvvG3Nft1LTI2FgNP2YbPrCildlVKZUSda60/w73nnKiUOiFguVJkzluI9/rGQmuO479xU6n9Sym1f67GSqkBSilvDQ7s+gtOHZfNyR6NfTnrX7R+o0cptQVuzSkQR698eB36Ck7DZlg3Kg7fYQLrMTewl28VbWhu4I0Ov00p1S6gDUqpi3Gv5xlIDV7D+vEd7jPTKUqpFsK8Umorctcjai1jcdOqnUGBDh1a6+89348EbrEjDwNRSpUqpaIB8xmDwWAwtEHaTMQOmekC+gP9Q8nYAMSjr0MoGatHHl6cG1R73BRhV4aSsQ+taCINHIOb5kch4kEfWh6LZcADVjThFL1OAYSSse08bbLdMIMiYxz6IlEnEaSuizfiw/FsB4k4moak2arAFTn8ESuVwNmIcHEvLQ212eiHTCjnIMdzGWLIHWT3oZLMdEggXuo9keP1NZne7pOQdGTOMRmaZbuF1JnZyvdeIxP+WchvdjTucfKjkX1pT6bRwvtbTUairhQyYXsIOJjMukt+gn5rhZtybTFwmhVNvBpKxi5B6ooQSsZ+gwgwM5H0cnMprOjk58jErAmJ2llLKBnbFjnXd8CNotgZ2DGUjG2FpCf8t728sy9rcEWdBchvNwMR9wxtjHgkfBCtT8vQB7mm7wJi9mej4pHwdh7xZDSSskgBsapUui4eCb+CjC/1SLTbKOCjy1LpQ+KR8J3kFzmqgVuqUukFTz9xZu/5762kZn6+DJdrWR2PhJNItN33yHXagKQhzBA8Nc2sqF9MbfMaFq6ZyYLa6SD3DX/kWr7oyC6It+4sZJwbighlbyAi/5SqVPpdWJu6Tlel0kuyrMvQkkuQArStqaFWQ2a6klahtf5YKRVBInU7IOPneUqpFOIMshARNfsikWzH0TIFZxCVSimvM0AFck8Ygojrx+OOu2uAc7TWb63rfhha8Aoyb+kDnK+U6ooYNuYiv+VJiFHkOyTar2fQSrTWDUqpU5B0vuXAo/b7x5D5n0ai9/ZF5m5XIql6neVnKaVmIEb0c5VSXyH3cMdguEJr7S+IXQgvIZG43RFxvNL+PFuOfYcHkELxHYB3lFI3I1EcTYgT0KXI+f0euVO2eFO+3GjXZpqOGy0+T2u9AkBr/bZS6jpkXrktMFkpdT/wOvJ7lCK/0whkDrcjUiPQH5UTAz5CHFtSSqm7EDF+BVIX8g+I8XI8bSQdW2vOD631Mnusegm5F7+ulHoScTD4Hjn23RGj8iFIzcePEOcDL9cjtTgHAH+w0xHeg9zX+iLXRcRedg+nqxt2z38yOvjGYZA5e0/kmr0IN9r3da11mhwopbohcw4QZ5gWRQQNPwpmbhDMPxGhsT8SFfmFUupWZEyvQ54Df0VmvcrLTLTG+mNHo6Zx71nvKaX+hcwLOiDPJBcjz9qf40Zfrc82G5RSjyHj1kh7uyD1l/JFYf0OucfuYL8+UCl1H3KurESe/Z1z8DjkXjIUsQsYDAaDoQ3TJoSdUDJ2KDKxDiF9bkAM5yHcyIsy4ArgZiRd2GRkAuSwuf3/W9xUYSBp17YJ2Ox0YI9QMnawFU14J/VfI57ju5IZ/bAQtwhpEfLAVub53lvrpj3yYHovrrcOZIoVS3FDb0HEgE5kT0XWB4na8FONeLkHRRaVIqnlTkYEjYHknlB3AJqtaGJ2KBl7lswUDk/Z60niPiSCHOsXkYdPf7RIoXwJnGNFE1Ng7fnwcpa2NchxzrYf0xAj9XbI8R+C6+m7NyJe7Y9E2fyHwg0HC4BJoWTsfTILsx5oRRP3hJKxkUhxzpesaGJ5KBkrQY7ZEcB9VjTxW9/6wsjE/ysrmnAiBQglYx2Qh43KgD6ciOz7jsgxX4kbkfVHJJXhcFzxcxs2UNoYw09Ovmg27xjn/ewD3MLaIEaiaDwSHgb8uyqVfhERv708hYiS05HzUgEHxyPhI5BaTgAUq9LmElX6cl1z9R+RFBfOmJOsSqUX2Ns4yLfuOuQcDOrvTGRMPct+3x0R2P8IjEeh0ajyHsXULRY74+zqKcyunuLs1xLEsFtIegTv+FyHRMJ5x9pTgcFVqfTadB/xSPj4IlX82I5d9y+2ql74uGNp5bkDxoz8uoBt/aIZMGbkp7NHjzsRiboqxIBTA5w0YMzIXJGuedFaP6uU2gu4FTH2DUbmDNloQkSCVI42x9DyevHTiAiDVQU8lBtagda6Wil1GpKCpAMyBznZ1+wH5DfKNmdw1jVeKXUAkt6yH2LYPSrXMj7+iswpKmnp4f8gWSKK8vSp3jYwXoB7z6+x+5iLW5E5VxjZl3/7V40IRVPIIexorX9QSj2MzPWG4eb+d4iSKXBdp5RahKSD6YQYXHMZXVtEcGutv1BKnYw4xLRD9t2foulu4EPaiLBjU/D5obV+XSl1IBKBP4Dg89pLi/mA1nqFUuoQ5NmpP3Ck/eflY+TYfm6/r6Vtsgu+IvFZeAFxCsnHr3Dnxg/lKJZu2IBUHL7Dp7VjJ7V6blBx+A6b9NzAFnsPQubi2yIZIG7L0rwG+L3W+sk82zYUTgzJ0DEYGWse9X2/BhlXjmEDCDs2DyHCjsJ9Fsnn0IHWepVSal/kvnwMYuv4Z45F6uw/g8FgMLRxNvpUbKFk7DTkgfz3uGl4ShHjtd8o/bUVTYy2ookeVjQxCpmkLUMebB4EsKKJzxBjn+NxWE7L6I9GRHjZE3g5lIwNCSVjHULJ2IGIB8QoWooU/gfU3yHRMA5fkFnIcBm5i/n66/YMB7a0X2typ2z7Anno/RgYaUUTuyBerV5WIR4cv0EeNHvieupnY0cgHkrGwmQaPCba/RmICApevkK839dF1HG8jYYDVigZ+yaUjCnEsJuN9rieeQ7LkeiUlcg5UY+ETe9EZtRRKTDbPoc+Rjx0Fnu+fxI3jVMtmQ/TbyFeO3vZfdDIBDsZSsYGIb/Hfbh1nfaw118CnB9Kxry1PLCiiZVWNPGAV9Sx6UywqANuCj6NpAP0Rmtsjh1F5COcZV2GjZiqVPoTJBJlKTKWNSPjzSjEUNcfOb93Ra7xC4BdqlLpsch57FxbTyD1JA4GnohHwhlRQPFIuCfi5bs9rqjjkALmFKsSQNGsG4vqmquPwPWi1/afU9DzBlpGGY1Bxp4gY0uQ0Lw34tE5sMvW5bUo1oo6PhQiCmUTdTQi0s9FrpV5ns9foaWAXoxHqI9Hwv8AHuhW1rd0qy4jijqWVu6Jx8BpyM2AMSNfRDwR86Ve+QDY126/3mitJ2qt97O3/Q8kvd485J5QjYiJaSTt6mZa62grIi00kh5xDnIPTiLpfwZqrY8zos6Pg13jYGck9dRMZJ61FBl3rgZ2KrTei9b6A8TZ42Jk3jjfXl8N4qH7P0TkbVFIXWt9HzJGppFzakOl2/Mbc57TWudMaau1bkSMOucjc49ViPFmJhKFdIDWOpfh0ksUmc++j3u/ybXtO5DI76uR6OEFuMdwJnJcrwGGaa3/nmUdzyH3nLuRuVs94jz1KnCC1vr8oOU2Zlp7fmitxyHn4rlIKqZZyLyzATkWHyA18Q4iiwCptf4GMez9Dbnf1SDz4U+Quei+vkU2ZPrjn5tmZM4/CRHU9tdaH621XlXAsmd5Xj+w4btmyEbF4Tu0am5gt19vNva5gX0P2xk4E4nem4Fczw3Ic+r7SJTe1lrruwrsl6EAtNZzEUe1G5BxtBb5PacgDqDDtdbPbOBtfoJr88Le5uMFLrtca30sYo+4C7HDrEDu3Svt9/9Daqz21VrP2nA9NxgMBsPPhdqYI3XtmjmP4T60rCa3GHITIjL0Be61oonACXwoGTsV8YRz0EgkxyxEvKj0LbIv8gA1Arm5Fhomvgzx5HDSrWlEYHI8ghbgRk800TLdVzNyM/YLFQ71SOoQvxf8y8hD/MvI5Pc7RMj4tf39CkTMKmPDMAW3how3nZxGvJ7GeNo63zfa/ehGsJg0B4lmeYfM3+M3yL440WYrkOPmPy808nv+C/FwOs/+vAE59tkmMtVWNNEBIJSMjSez4OpiROzrgZwnTpHNZsSYvi1uodvngNOtaGJ1KBm7GVeMWmFFE5WhZKwvMmnrYvdlayua8BbYzUooGbsamcAHHbc7gFetaOK5UDL2JG5o/ieIkPQqmTV+7raiiTZnJDGsH/FIeGvcVCVv2x8vB3pUpdJNnnaViPgRmPqww8DSF0Jl+2yxZuma7a3l49fKRcUViqbatfeW56pS6WPjkfB83PFuMnBRVSr9jr2dI2gpCgdF8Tg0IGOAcw009ygfUDSg/dasbFjMD6tzphZ/GxGIHJFpV+Sh2Dse+lMyjq5KpW+2+/oIHiFq/z6n0rmsBxXF7T8aMGbknrk2bGjJ7NHjhuNGE3ZCjNBfAPcMGDPS5Ig3GAyGHwml1Bm44uGBG6AYvMGwQagdO2k4WeYGFYfvYOYGBoPBYDAYDDYbu7DzOnCg/bYQQeUBXE+rp61ookXhVXu9fYAJiJGxFjjLiiZSoWTsLjyphTzbfQGJ8tkQBAk4ubgIMeJ3y/L9zohxdhTZQ8edOjrgpj7KhVec8eI1dn6FGwVyMsG/zXNWNHFsKBn7muB0dyDeLsNwU9s5ecenIvtzA64gVYcIWQfb7z9EPDrvJ1jkuNmKJkaHkrEluMdPA28Cu5NdJLwKqX+0kpbi15PIedOMm+pvhhVNDAolYzHEC3IKcKot6vRGREPneD5jRRPHA4SSsaGIgfk1K5rwRnflJZSMdUM8h/xFD6+wookxdpsIIoyC5KPfGsnRvi3u7zXTiiY2x/CLJR4Jn48IlndVpdIfBny/P+LdlXGulXRUNK7WFJUpBh3TleKKIma8sIzOW1ZQ2rmIeW+tArnezkFEoyeQsc9JezYDiSJaHI+Ed0DG5CLEO7J96/dE4SsR4E2vthLJoT4DiTSagjsO7ooYDu7AFXucuh0gItJsJILoTWT8cdbbBBQXUdzUv8OQU0++P54vRZLBYDAYDBsFSqlXkFo99UDPfNFgBoPBYDAYDAaDYeNiY0/F5s2fHZS2wJsSogZJW+AwhABCydhxiHf2d4hQUIGEVkNwUfsKRNTx5yBtQISFXPhTVtTTUtSZiIgEQSxG8ug6ooS/3VIrmvjCiiZepWUBWi/9Pa8LqakyJcvnf0KMrouQmjedgLNpKao0IobRa+33L+TYVi/k2Dqp2nZA0pSdgRhiJyIRLV8i+cHf9iz7MBKRki193Kv2f++DqkLEwgeQCLBmJK2FlxsISLVicxQSqr8f4jn2HXCtnZ7udqQIYRg43G7fk0yR7CrnhRVNTLXTrbVK1LGXXUpwvt1TPW1SiMH+dORa6oIIgd5zIF8hecMmTlUqfVdVKn1WkKhjf/8WATmYG6tFRGmu18z/YBXTn11KcUUR7fuVUrlVOwZHurH5MZVPIeOFI+qAe85tDuwaj4QHIVFBJyFRhuuSDmZVQN1nBbB5h+3YvXu4CBmrbkbGkN8iYlMzcERVKn0vIn46LESiLUHEni0QUfsQ3HtRA3YKt2aaimetmRJUx8xgMBgMhp8cpdRgpVTW5zyl1EXIPQ3gaSPqGAwGg8FgMBgMbY+NPWLnj0h6tWm49WW8pJBoEa9xuhkx2J1iRROvhZKxI5HaJvORVFTv46ZGc9BAmRVNNOaJLgERCcYhRvUisgsAhZIr3VC+1HMg+cqPsdczFfEqX4OkMAshacemIenpCkkhNw6J6Oju6Z+y/95GwuFHIILGrsix8uZCdup9nI7kuJ+JRIrcjAg0uyNpzLy/WT1uZMwK3PoWQVFaMXs/661oYlwoGTsdSW/n1JXpg4Tr34Wk25tg93Wwbz2PkruIqtfbXyO5ld9BzkfHq/8Au49v0VIwm4REy0xAPPxPB563oolzcmyzVYSSsROQCLMp9v9y4Bormvirr10XJC1gkIjzdyua+NOG6pNh0yQeCf+JzJSKa1GloD2ye/v+pQw8vBI0DbWLGo5fMbX2qYZVTWVrZq9t5EQENiM1G3qQeb05rEbGE3+9mwxKOxfRsDJ7beOtu+zG4I47MHbOfd6PH8CN7tTIOPNrJAIQ8kdW/gNI2Ov4MzIOHFKVSo/L1VeDwWAwGH4KlFK3IU5GKWSePheZv24FnIKkOwaZMw/TWk//GbppMBgMBoPBYDAY1oONWtgBCCVjxUgh1imIoa0JiQRpAv4PKSJ4pG+xeiuaKLeXn4AICSCGyYOAXez3qxDh5GakNsq9iNHxEHIb9X4P/JX8oks2ChFsAJ5CIlJWIYbQkiztzgMsxNu9PfBnK5q4IZSMbYscm8eQFET5ojNeQaJyJng+CzK4OnyICB432O+9qdqcdErjkSK4haZWmomkYzsA6Bzw/VgrmjgCIJSMXY8U9R2PpKv7NxKR4vS5CUmh5E019iByzvRBDLNeVtjtm5D0cF6C0kPdhJwv1+fZp68RsawRGGlFEx/laZ+XUDIWQtLhFdl9GAFgRRPfZGl/A3Cl56MmZP9/Z0UTG/cgYPjZiUfCv6Xl9QJAUQU01+PEEzYDn6PojmZmvwM6T+8ytOLXAIu/WMOi8WueAY4rYJONiGh8PDI+rxOdS7vr/fueqpp1M+lZd6LdoMdpSBSOQwKJbnNE5zXA60iEnkLGhkq7bTUwvCqVngoQj4S3A5ZVpdJz17WfBoPBYDBsSGxh58I8zRYDx2mt3/sJumQwGAwGg8FgMBg2MBu9sAMQSsYqEIN7d99XzyHRKn7mWNHEADui4WHcVFhOKjRHtKkDpiO1IzrjRs4sA7oS7LX9HeJlvhst8demmYZE0AQJMqsR46G3bsUC3/ujkMiPgxDRKRuzkSiW/e334xFvvImIgDQDiVLKJlY1IMeyFIny6YqkSKtHPNMvx41S8bMYMXx2R+rtHJ+jn4Xwjr3tbFFTp1vRxKOhZKwr8ju0hjeRNGmdEKGw0vf9o4i41Q34F/mFsOXAvkjdn0rkeG+DGIF7eNp5I5IutqKJ21rZ7xaEkrGdkYgosNNJWdHEK3mWOQhJc3csthAEXGJFE/9Z3/4YNm3ikfBw4COgvKRD0dLGNc3dAEo7FdVtcWK30voVTUXLv6mZuvyb2lNVMTfpJqmD1a5vycouQ9t17rpNO2qXNqya9sSylWSmhvTjFZK/RureONfLGvu79kWl0NyEpjnvNVrTv93Q8qX184pqmlav/bDDgNK6+uW6uGF1Y0m5ak+drg5a9u9VqfTaaLZ4JLwjItB+WJVKT8+zXYPBYDAYfjaUUoOROfnBSNaDHoiD0grk/voScKfWel3SnxoMBoPBYDAYDIaNgLYi7AxA0ngVymormugUSsbmAP1ytFtJcFRINv4NXJKnzZ5IWp4i+++THG0XI4KIxk3J5k3LVo88lH2IRNEMzLEuZx3FwF+QqJdctW0gdxo4DTxkRRNnhZKxa+11ZqMRqY1zNBJN1S5H22w4UUyPIxFTlfbna3Dr7zxjRRPHh5KxMuBWJHWSNwWaP7rIK7QtsKKJPgChZGw34GPf9jUi0OyXpX9Bx6rB7lsEWG5FE2l7/SOR+j4V9n59jaStmwrsZ0UT87Nso1XYqQovxk0teLIVTeQs3h5KxpwIH0doWhsBZTBkIx4JdwU+BQb33rvjxIZVTcMaa5rpvVfHJSUVxY7g3nDEAfeX3X7xsTNrFzZmjFV99+s4e+nkmuq6JU1b5diMVwB1OAEYhUTwbef5fClwD/AnFLVFJdQ2N7QQakGiHTvZrz8B7u26XbtLeu7aYdsec7ah09dD+HLp28yvCSqvxmIksu9oYFFVKm08mg0Gg8FgMBgMBoPBYDAYDBsFWYtqbkxY0cRs4EnPRzVZmjp5dqaFkrEZZBd1vkFSl7VG1IH80Sj3W9HER1Y0McGKJj5HUmV9YX/3HRIh5ChpDYj3nEJ+hxJa/h5lSNowgAORFEB+nPXVA3sDu1rRxPVIdMr4gPZecv3+Cvh1KBk7F7gOKWx+uv3/H4hg4V1PI5LqLp+ok+23c1LTnYzUx5mM1C8aj+zjd8Cv7DZn4taUacAtcu7tUzNSAN1hYSgZS4WSsTuQ3/9Fz3fLkYiofbL0bRyZx6rJ3u7VwP1IVNjzoWTsCAArmhiHCF3/sPdrN+R4TtpQoo69nb9jF2+3ydZ/AELJWAkS/eXdl9c2VH8MmzT7Ydepqv68eNiIFSc27tV8anPXpYPvQCIGAe6PR8KdUS1qmDHvndV984g60FLU0Ug6yiORSELv5ztXpdKj+7TbYouRvU78exZRpwn40vO+HXBvefeSj5vqNbU9FtOlvEfjdpV7o1DOvaPOt45bkUjEcfFIOJKn/waDwWAwGAwGg8FgMBgMBsNPQpsQdgCsaOIkYChwKCKI3G9/1eRp5kRr7ABsFrCaZkQoKSHTUFgo2SJmpiEp06pDydjaVGpWNFGD5Le+CrgRESKU3Y9s6biagG8970uQmhS5UrE9ZbepQIqjYkUT1eSOsimU/ZH0YmfZf98hYksFIuZMBB5BUnwFpUWq972vBu7EFaT8aKTOz3fA7fb2FTAEN02dV8ApRdKqLUc88531FpFZW6cjIhrFkNRyXoNvJVKnyJuqrtHu+21IejaHBuACu29/QIQu7D5eG0rGbgolY0cjot5lvn0bkmWf14db7D4tAZJ52j6G7Eszco4lkCg0gyEfnyLXGFtUDNfdivqVdGnuXdT+ky0vQgoxb37EAff/FqitXdDY4Fu2kcxraxpwLhJVmEvodMaTwb7lFSJQsl+fk2/s3W7QNe2Lu8g3RTThjgHFuGJnHRJNmJz/7qro9/9bUj97+qw/afTQiuIO57Qv7uzcC8uB75FIn8uB3T3b9b42GAwGg8FgMBgMBoPBYDAYfjbaRCo2L6FkrAPwAVLcfjzihb1DAYs2IQbCuYiRb0OJWkuRmiwOk5EaPa8Db9t/isw0YY53eBPBtWs+BXaxX9cBbwGH5ejD/ogYchiSO/tIJH2bkz5pUMAy/rRljQTXAtoSeALY2X5fD7yHpEYCN4Waf33VSPq4vkiRcm0v9yfgmjz743AdcCnQBUnFdxJSC6izvd2eWfbFzzJEyBllv78WGEvLdGx+moHtkRRqF9rbvQU4HEjlWO4r3LRRzm+9EPi1FU28mmebrcauQdVoRRONedrVIIIcQBo43oom/EZ4g6EF8Ui4JyK2di5WJRy92UWUFZUzdeXnfL7ktZLIFn9SQHFq2s1FSDSkI6zXItfDCM/qmpDr4klkHN4JEYcKpn1xl6nVTStmDOu679AeFQM3f3fB4zTRQO+Rnahb1Niw7OuaUjJTbb5clUofHo+EF+HWv7p6cMdhR/VqN2j3jxY9D8AWHYfpET0O/rxYlRw7YMzI2XaUzoPIOH9QVSr9dWv6aTAYDAaDwWAwGAwGg8FgMPwYtEVhZx8kNZbDA0gkSSHcghj1Ts/y/VLgP4jh30+2ejReEcbPJAoTnXJRR2YdGT8LEaPoct/nrwJHIIVS9wfuxo14WYiIPkGikpdqJErqJWBHz+eF1BpqwvWy/xj4PbAIqWPTN8+yDp8j9Wv2QfbnL0gKtlwE/U4LgWORaJ0lwFVWNFEdSsaeAE709dXPblY0kVEnKZSMHY9ESWXjbWAvJLXUO8BFiOA2GxHEjkGEqv/a/WlvRRNL8+zXehFKxrZEDPNeLreiiX/8mNs1bBrEI+GdkOsRgEEdtqeipD1TV35Oz4qBL+zb+8SRoDpOXv7e379e/sGVAat4FamdFcRi5Ho63/PZc4gYNAAZRztgp4IDUCi0HZjTq2LzmoW1M9oBdBhYxmZHVC7/5q6FLyO1cYqRiLYT7PXdaK9iCTCriKLhHUoqWdUol1+38r4c3O9MgH8OGDOyyt73YqC5KpVuWzdLg8FgMBgMBoPBYDAYDAbDJktQhMZGRygZ2wGJyvgGqZvjRMl8hhgDX0SiMq5EDOfZIjguzbIJjaTzOh04iuAi3hqYA/T3fZ5N1IH8ok62KBkv2USdZqS+yv7A/wV8fwjwNWIM9W7jn4hxc3Ge7YKIQo8gkTM3IZE3zwNViBd7I1J36DKkltBQRDxSSJSLk+6uHEmT9gSFiTrO7/eMFU18hy1IhJKx8eQXdoLEt15IdMqZ3g+taOKkUDK2BZIWKsgY/ZVX1AklY0ci9XIeRNI1belrXwt8BEQRYedRpDbJZ8j59CkwHPf3+KO9rz1DydgfrWgi6HfMIJSMlQF3IBFr11vRRDrfMjZ1tDzfOmZpazD4mYCk+jseeHv6msnbY5//qxqWHlXbVMOsNd+wqn7p5YiQ2gtXlG5G0lHWImKLnx5Irac9EAFZI2OJU6tnhy1P6XZ4u4bKe3u8t2t/VBHvzn8cbWfhrOk3fzI/sCtAu96lLP+2pj1wimf9NyCCvXfseQr4TTPNa0UdhWJo57WBRT84L6pSaW+6T4PBYDAYDAaDwWAwGAwGg+FnZ6OP2AklY+cC99hv1yCe2wAzgZBdx4ZQMrYj8AYSifIsYoB0CBJkgviWVqYEKpAgoelT4HHg777P/aKSk+psFSKOeIki0SAjKJwGJHrnwlYsswLx1reAS7Kl77JFktcR8e0iJOVaUK2jQlkKHIh41w9Hol92QUST/ezvcqVg8/ItIoQ8avf1CETEq0QErJHIuQNyzGuR+h8fIsdqF+B9e3sLkBRP7Tzrfwe4HhHC6pCaSH9rxb5Ot6KJLfI1CiVjv0ai1ACWWtFE90I3EErGwsDZSGq+ycBFVjSxshV9NBgAiEfCmyG1cooA+rbbknk13ztff4TUoDofEZYnINfY3ciYdbDdrtr+HCQq52mC64J9sM35vcqAXTot6k+XGUO++ejLN7asaVpdtvl2m+luoQ5qQd0MdJNuXjWtrmj5N7XeZRsRYck7ptYhEXwPIekca0F9V0Tx9lt1GbFqcKdhV3Uq7XbbgDEjN+6bo8FgMBgMBoPBYDAYDAaD4RdLWxB2ngaOy/L17Ugx+O2BgUiqLZA0VwPt1zMRI3zlOnYhV1SNV2jKxd1INFB7z2caiYS5Ns86pyMe7f7oiiZETDiAluRL37YItz6Nw/OIcHG9vWxQG5CUb5dY0cRDzgehZKwr8A9gb2Br++MFSD2jnXzLB6VKy1UjJ4n8/pWIsPQ3YJwVTcwMJWOWZ3uFoJFzpQYRery/63y7D7XI79QNNz3bRUgUwuNZ1vsYcAYSBWVXcecNRJjaFehOpihXg4hCHyGiXClwjxVN5ItGcgSpF+2331rRRGv232DYYMQj4bew61aVqHIadZ3361rcek4OdcC2SH2q3vb/y3xt/GPBaqDPNuf3uhc4ZcXUWhZ8tHpaU3XzpMP7nTe4c3m37VfUL+brijdoHrJs4pzXVgzTzRnrm2Vvq8z32e7INXgQUsNsFhKN901VKr2oFYfBYDAYDAaDwWAwGAwGg8Fg+MlpC8LOcUgKr2w1UBx+QCIRihAP8eGe7xrIX08mqM1CxPjuTx/0AzAWSck1Ps96AX4FfIAIAHvYn41HxIMhvrb1dj/yRaLkEkMgs26MPwqoEbgN2AaJqFkJHG1FEwvtiJA7keP4pb2NHcgUimqRdGyv2n+bE5wC7XDgZiRN3gBEKGrCjYwphKAoqjokZVMEEcf85BLcGoAXyIzoAhGyRiJe/Lv5vvsM2BNJN7ed77t3gD6I8NYP9zd5A/mtO9jLt0eON4jgMw64FTEo9wfesaKJTJN0FkLJ2FlIKrY77FR1BsNPRjwSvhnYv0f5gGc6l3a/rrSorPTblZ+urXmTg5VA96pUuvGRW0/ZZ9mkmn/Wr2gaiFw/2agBjtzm/F7jG2ubY1MfXDwGe1zbtsuezdtW7lX0wqw7qGuuAWikiM9pZjdknPkYie4DGbM3xx0TH6hKpaPrsPsGg8FgMBgMBoPBYDAYDAbDz06QMX6jwoomnkFEhCC8KcE2Q6JboKVYkk3U8ebsCfLS7gV8gkRjxBFBYQFwsBVNXISkAsvHQnv56YihfyEiyowgs0bLbMTgv9juy1NILZyl9vcNiLFypd3WL+rU2+t18Aph/n0rQeoNHYqIDbsDv7a/OxnxtC9Dok0G0jL6pwKJlnoUqbuT7Ty6BNjPiiZGWdHEECua6IJEVQWmcvPsh5eg1HjlwBi7D28i0TZeUWQ5cKZvO3Pt/6W4os4SpLZHIyI6vY/UBvIz0E4/97Hv83FIHaOtEXFmut3/KYhA5IhLI8isg9QNOAZJGTgNeJuWafayYkUTD1jRxB+MqGP4qYlHwgcgtaF2Hdp55xt27XlY6Zadd8ol6jQikXYg4uYeL7159qDaBY1v1a9o2pWcoo6iU3G3dsBr39y1cO53jy5WSFpIAObV/FBU27TGEXUASmhee80XI2KswzuISORwVjwSbk0KS4PBYDAYDAaDwWAwGAwGg2GjIVuKsY2NmVk+9wo2JUg9Byi8KPx0xFBYhIgYQVyBCEBXW9HE2tRCdk2fmwrYRi8kcsX7HoIjkF7FLfrdD4nw6Wa/d/a1s/3npQn4BolicfBG9HxKnhpDPYvalz/21OOPd1UVRyzTGTUqeudYbKBnO/NpaaQ9DBgfSsbeRlKpLbL3K1f01IeI0OQca+96vWncZlrRxBJscS2UjF2GK55Ms/s0FUn9BCLAdCdTpGoHXImId9jf3wac5uvTW6Fk7BCkztFy3LR+k+31OowDQshx6Y+bzu7fwIyAfVX2fr4K7BFKxhYj5/FlVjRxX0B7QsmYQmqX9AX+Yx+DnISSsRKk3tEWwA1WNGHlWcRgyMYy7Gu+STfWA+UdSrrQqaTbnFWNS/uXdyum4xZljUs+q3HuLSXIdeW8Tk9/Zmm6okdp3ntPj/L+LK9fBDJWdtaN/L24g7q9aY3eAxixrH4BL86+y2muEaF5NZISEjLF7/60vC8MRKLpDAaDwWAwGAwGg8FgMBgMhjbFRi/shJKxCuCkAps76ce8osYy3NRfDUj0TAi43P6fj/b2362hZOxZJMpiC/t/LoJqyeRiAG5RcRDBIJ9H+XIkxdDOZIo6IOLGYESMmO75vB45Jl7BRh9bEfrd0qbqnh5Rx5/qLajW0CdIVNFmSCTSm7Tc5yHA0Dz7ASKwFQM3AI/gCjtPIjVuQCJmlgLfA1cBhJKxXkiqvoH2cmsQsedBxEPfqbd0kL2OV5C6P72Q+j3DcSOdUkiKvPeAfZDjG0eEpVc8fZ2EHN+uSCTQSUhtnc2RSCGQWkBFyHG7HYnEmo6kC5yFGJRvQ9K+Oen5etj//w4ECjuIqJOwX48AwlnaebkBibIASas3vIBlDIYWVKXSX8Qj4ROB/X5Y9eXTW3Ta4agiVdQwrNt+Y5bWzTu8T7tB966ZvrLDEsZiX1aLyYyU6VKzsPG03nt1/EgVs/vSr2qaabZF7iIy4u66lvVmcd3sjO2XdSoZdsH9z+wSj4S3Aj7X6A7IuH9MVSr9YjwSbo84AgxBohAdAf0Q4H7gbPv9CkRQNRgMBoPBYDAYDAaDwWAwGNocG72wgxj1Ty2gnbdYt1eQ+BdibB+CRE28A1xL9hos2VgJnIVbf+WcPO2LEONhl1ZsozswD9mXLckuHi1HBIyvyRSDHOpwo5c6IZEpDmVICrIeuEZPdU/15z1969CIgDHMs07v+dIMXGhFE7NDydjnyHFJASfiRuTkqwPk8CpwJHA18Ft7PX2QlHM3IgLS2cB+dvtG4CVEbPktsK/9eS0S7bPSft8OEXW87IYIeu2taGJ6KBn7xtPH1ci5Ngm4B0hb0cTSUDI23beOGty6SycDQ6xoYlooGTvF08YRuEqAv1jRxOmhZGw4kvruSyuaqAEIJWN9EON3D8+ykwOPktDX87pfjnZevBFIueqZGAx5qUqlnwaett++AzDxzeRZQ98/5tZ2a7p1oAxWNS7jmxUfgowTU1FshpJ3qpi66c8u/09Jh6KxlVtX/GH1zLo/l3YoprRzsV75Xd3a8WL66skr8UQnFrdTukO/0n/Yffg2HgkfAhwHjK1Kpd+0P6/GjqSMR8K3IeNEH+At4Fyk5teewOt2W4PBYDAYDAaDwWAwGAwGg6HN0RaEnUIKys9Gohc+RQz5TrTMKiQNluOlPQSJkigkUsdPJzIN5E40RjFuujC/iOFNmbbabtuO3DiG+y1wIznwbEvZfSlDarv4aQRuBS5AIo2+RyJAwrhCwDa44g6IUOHvVxGuqAMthbD5VjThuNOPpWW9oWr78xN8n/sjma4DTkd+qwpacjEQpWXavcdDydhBiCDk0A+JdPkvktKuLmC/uiL1hb4JJWN7khm51BeJ+gGIAD1Dydj7uEIRyPEt87wvct5b0cRjoWSsGjkHD/f07bRQMvYpksJtKbBLKBnrDqSsaMKyBZ8RyLEZaPc/G/+x2/YFfpejnZcpuKn47ilwGYOhIF568+z+wH0NFdVF7dZI5shGvbZU1njgxpL2RdtW9CjpW9qpiOVTastp4r+Na5ono9lp6Ok9uwB7rZpe+/DK7+quQMbS5m57lU5qWF68d9G0Sj2k7w6qb9mQOWXLOr7rrLgqlf4A+MDuw6lIOs27jzjgfsv+/ot4JLwNEjH4ZVUqrYFv7T+DwWAwGAwGg8FgMBgMBoOhzaK0zlr0eqMglIyVIWmtzsOtbeLlLfu7nrj1T24CRiEpel4HDvW0r8c1zAelF9tYeRqJWOnu+zwoKsb72UtISqJDkBoUQSzAFYzyCU8OU5Bj/SKucObwhv3ZLkiatFwp6QqN6rkP2Q/v7+Wk3vMTlAYvW2q8H3Cjm8YAo33f+8+RGJIObbj9XlvRRMZ6Q8nY37BTxeVhITDQiibq87ZcD0LJWG/gD4gAersVTRQilhoMBfHSm2f3BmaW1FWU9Zy2gy6eW3n/W989cX+jrh8IPGf/HQLQaXA5q36o8y7esyqVXgwQj4TvQaJq6Fk+kIqBRayoW8SujcfTo6I/RWWKyl06TSwqLaoAzqs4fId37e2PQu4DALOOOOD+zX6C3TYYDIYWKKXeRuZqM7TWg37e3hgMBoPBYPi5UUo9gNgx0FoXYvcwGAwGg6FgNnpRw4om6kPJ2JVI/ZKTA5rsD3wFlCNRMWcD29rfFWMbFG0m2utxhJ18+78UifD4qW/AY4G9cNO4LUTqpBwf0DaobzVItA7AEUgEyGHASOR4DPa1742IYt6IGe1bD2SKHE600NnAOHvd1YgH/bn298OQqJXHEdEoiEKPbX8kwuhTJPIK5PcNEmyCBJxsCuYkZL/7AdshNXX2QVK6Qctz5EN7XXfa799xvgglYx2QqAGv0LUGERePCdh2T+DJUDI2E7jcSc+2obGiiQXAn36MdRsMiNC5orG8dtn0Ph/e8MM7S19yxBqAeCTc6LxWJVDWtXh5/bKmSuABj6jTAan/JO2UYtZ3MwAYV/Iku3c/kiFDtltQVFrkRBH+HTei0ZtGssdLb56tjjjg/o3bY8Hws6KU2gdJp7kfUt+uO3J/W4LME94F/qe1nh2w7ChcIdGLRiJPVyL37AnI/eoZrfX89ezvCKTGnOPI8KDW+qz1WeePgVJqOjLHWh/e0VqPWv/eGAwGg8FQOBv73EApdSRSK3g3JLq9GLFVfIk4gD6ktVssd2PB4/CwPhhniY0YpVQ/xE53EGKH64HY5pYjTrQfA08B7+kAr/Ic88dapLTBcsACPgde01p/WGC/SoDtEWfjXRA7zQ64mWD211q/XcB6Kux9O9Bex9aIg3M1Utt1HHCP1vqLPOuZTuvnyddpra9t5TI/CxvrGKqUGopk09kXsU/2R2x8S+31PQc8rLVeU8j67HUqxDZ7MnJO9EHquS9Eyiq8CTyitV5W6Do3BPY5fywS2LAHYuetRM7V+cg19CrwlNZ6VcDy1wJ/CVh1E/IbrECcxT8DPgKe11oXlN5eKTUA9zp0rslu9tetfrZVSg1EyrYcidR8B7keXwRu01rPyrN8B6RWvXd8GIJtn25rIvxGHbETSsY6I0bxXZCB3RtNMhkZqIN4DRl4F5GZauto5MbwFJnGwCA0crE+09p+BzANOdmCokvATYU2B6kDdC+wI3CJ/f1NwJ+B95ALtAkZGPdCbpqFcBuSumszJBVRWe7mgdFMUxGB5ivkIuiKiGlbI2nnZlnRxJpQMrYjMmgUIb9BZ7ufS+y/zXP021tXYy5yQygCTrOiiSfsCK7rgD8iN/grkKgg7/7U2utPI/VrjqdlraNZyOB7E5npyUZY0cTnoWTsETJT71UD/7SiiT8DhJKxk5EBfDekZkccEbR2IjMK6S/I7zTU3q/unu+8KfCutKKJm7IcE4Nho+SlN8/uh4xbVM+tZ0Z6eQ2acuAh5Nr/D3L+p5xlSrsWTW1Y1nxAVSo9Ox4JKyS68mSkXhbtijvVDem805RJy951RBwNqBHD9p+6x86HDLU/S1UcvsMpdh+KkfFtV2DMEQfc/+SPu9eGtopSahhyThZiYGgG/geM9j6A5HjwyEYj8AJwmdb6h1Ys52yvFJk87+D52Ag7GykmYsdgMBjaFhv73EAp1RFxkjw8zzqnAkdrra1W9ONHxwg7m27EjlKqHeJ8HCM4pb6fr4A/aq1f8q1nOq2bP34NXK+1TuVqpJT6C1JbOxt5hR17fHiP7E7KDhq4A7hEa92UZV3Taf08+VSt9WOtXOYnZWMeQ73XXh6mA6dorT/O11ApNQR4mMyyGUEUJBxuKJRSxyPOr1sW0HwN8E/gJq31WufyHMJONlYA9wPXaK1X5+jb5sgxzkarnm2VUscCD5JZ+sTfr19rrZ/LsY63EDtUIG1trN7YhZ0IEDSQ/Rsxyl+WZdE5SKqw/XGN6MsRdfFt5OZTjGuAX42IJV7j/yLEcP8OYpiE3CnFsvENUi8n283uW6TmzYlIqjRHTLkDqaPyCfB/VjTRGErGipC0c30QxTXh2T/In9bsLCuaeDCUjO0NnIoIXQMD2vlTnNXYf97opVp7n6YAe1jRxHKncSgZOwJRSqFlRE1QH5cCtyOF0BuBXkik0PmIh0c10ODbRol9THZHooS822iw9+0xWgo6AMutaKKrvZ4eiKeTU39oCZJm7Xqkto/DSkT5HgWchQhkXyP1boL202EG7g3894jHwb0B3/3Riib+L2B5g2Gj5aU3z+6I3KS7z3tnZdNyq9YvXn8FXIl4wnj5FrnO3unRre+u++91Ao2N9bw+7nFWrVkOMr69htRD29VepumCX994jlKqB3BXxeE7ZJ08GAx+7AngI7j14n5ADCUfIOlIi5F77r6Ip9PacVtrfYtnPaNwHzyeA672bKYC8YoaiswbjvNsbzVwjtb68Vb2+1pkgr0A11FlYxV2tiK708gFyNwL5Jhlm2iv0VpP29B9MxgMBoPBT1uYGyilnkOea0FsGXHE+70G2ArxWN7F/n4WsF2QJ/bPhVJqC1rW6nU4Bkm5D2LXuCNLu3qttamRuRGhlOoLPI977tUh187ryLPhasSmsx1wFK7R/0ut9XDfuqbjXlteR6Zi5NrpjTjTHoWc8w7/Bc7SWjdk6eO1uEbqeiRTS5lnG4UIO/sgETkgjlYvIDVkFyBG5QMR52nHwHyv1vq8LOvKNU926IQ4cJcAy4B+G2MknsPGPoYqpV5HfqOVwLNIJM33iH1xK8Sx1Dk3VwJ7aq2/zrG/W9v97IvYLR9B7J4z7CYDkUxGxyPCwtvZ1rUhUUrdgNhcHD5E9ncC4ujeCbFfHoJcR46NdCet9QTPeq7FvWb8z2udkN9hGGITPQTX/vkDcIzWenKW/g1Cgh0cZiB2ZCe7VsHPtkqpvZDfsRyxSf8DiULCXt/l9nd1yDUeGOHnczpYiQQmhBBbe5sTdjb2VGzZLqplwDU5luuPWyzeodL+80b5OD9WRyQ6ZgxuBMWbSPqqfQrop1cImYsrEoBEjfiL3Ncgg3oxMqB8idz0vCfPBfb/4VY0cVMoGStG1NAz7c+XkCmyXIAIWWfk6OdOwINWNPF+KBkbTbCoAy0ji9qRGS2lcYWqrREB5mbP968ADyCeReORQWBP5MIvJZMaRBzZG/e3WYgIWP9FBJM6oHMoGatFbsoXWNHEo6FkbATiQeEXVL5BorLaE8yCUDJWYUUTtchx/Ar3N+uOGJyXkynWdEYGSC8jPK/nIRFEB3o+ex0555wbWI0VTdwXSsa+Ro7ndMSLZB7QIZSMzUdugnHgw3y1cELJWHtkUP3KiiZm5GprMPwYHHHA/atfevPsfYHjGmubm5GHM+/1uMVmR1W+uNyqsVZNr9taN6wds7ZCPEp23WPEYfTqIcP1zsNG8c6Hz4LcUM9AbrBTkHHmlnZHDHvwp9gvw6aFUmp3JGqsDLl/XQX8I8uD4NNKqcsQAX9MnlUvzzKBfR1IKKUqgRsRQaMj8IhSapHWuiCPNKXUDsgkvRlxDPhvIcsVsF7HoyeqtX5gQ6wTIJfRRSm10PN2TraJv8FgMBgMPwVtYW6glNoJV9T5BtjdJ9r8f3v3HSdXWf1x/PNssukNQkKA0ANcwEDoAtJWmgvSEQQLd0HxYqWoqD8VFAVEEFEYFd2hiBCkCWGlLk2K0kK/QCCUAAnpPdns7vP749ybuTs7sy1tN3zfr1demZlb5k6/+5znnPOkc+46bHDxJOxv+1OBy9s5xlYyg+srtfRTW5M1nHO7Zq5+rHODnsE51wcb8E1fv4eBr5QpfXQPcGlSVvgySk+6Xa6N98DNzrnvA8cDf8bGWE7CBuhLBlKwsZsIK931ove+IRm4Hltm/VKasXGlX3rvXyix/GHn3N+xMan1gNOcc3/z3j9VvGJHgpPOudMojNHe2JWgTlGQZHPv/Tud3UcH76fbf4diwfBvAteUKBn2nHNuPJY08G1svO8yrIVFK0kVhX9iQZ2pwOeyQZHE01hpzDOT9Vc559yZFII6c7DP4l1lVr/eObcONs5dLkkiVe7vtX8DFyeZS7VYIGsLoM45t4f3/qMS28zHKlA9AzzjvZ9RItjTLudcBTYJoC/22Tzce/9gZpXHnHOPYOPRfbH3y87e+1Ljqv/AKjc9A7zhvfdJsGdUZ46pu+hKBsrqFGNf1sXOYuUce9q0fgE242APLNtnGfAgheBKe7IBmY+LlvXDfoDqsBTpRdhAfjZV6lNF+8jaNMhHh2MDnF/J3D40Oc70Pi7CMoN88nhKeStzeVqZddqyGEt5u6zo9hZ1MuMw14TNbFgfiwinQalSX279sTTG7Ou8EPvSr8Q+kOkMiH7J5V8m13eidXDyo+R4sqXeGorW2QaYHuSjccm6B2WWvYnNdvoenXuPnYk91vRkuwmbQXUSNrPjIgqZOs9h6YHT4zD3Fezk5OfY83U0dmJwXQfu835shsBLQT4a097KIqtCdVXtq9VVtb86/eLbLsROlP+aWfz6+3Vz3q+orAg2P3Zd5wohYz+y36anASxcOHf5jQsylxM7k8wqOXv8hPZOPkRaSf74vInCDLnIe39hudl9AN77Ru/9X7H334tdvW/v/Rzv/RkUZj5VAtd05ETf2acl/d28itYTC0RERKQLetC5QXaC6WWlMnGSniXnl9lGZFU4j0JFhUeBQ9rrZ+G9fxaoovU4Uod5czM2ITjth3Kac+6QMuvf673/k/f+Ge998XhQR+/zCe/9cWWCOuk6r1MYnwI4oSv3lchWjMmvwH5WqZ7yHeq9P8V7f1W5PjDJ9+c5WKAG4CDn3Lql1sXGB8di460nlgjqFO+77HOxsjjntscmy4JN9j+gjaBOelyzvffnYn2jWg2+dJT3fhL2mb4/uWljyny+vfczvfcXeO/v8d7PKLVOB1VjGUNgfeUeLF7Be/8AVioPrL1JyTKm3vu/eO9v8N6/7rtzGbMO6taBnTjMLcNS9oqbTpWrpZe1AMv0SM0rsc6tWB+VL2BfwKdjmT6VwB+wpl8dkf1AjCtaVgPcE4e5w7Av5wFYRHMWlr4Hltb5PhaAyGZeeCyaejOFN3CqNy1n44zETuQcFrkuNh1oCPLRRUE++gz2xXRHZnlaWzFtcFbKUuA7cZg7B/sQ54GvxmHuBoAgH1UG+WiLIB/1pmVtx32wiHq5D8ymcZi7PTmmv2GvxaOZ5cXbpT+st2MZUlkbYB/457HHvBSLnBcbhL3G2eyqZmyW0/cpBNqaaRkYasSyaoqPaQ4WoErrr/bCZkr8CxuY/jbwfpCPpmGv/TPAC0E+OhRLCy52eInblgvyUSWFEoGDsR9Ignz0hSAfXR3ko33b2l5kVTh7/IRXga9jn+FvADv5JjaY8+piPnxwHhWVLp0t4Zqal/XrRSWP/feupf994b4lz7z/b557+RGoYAGF2RtPAe+cPX5Cqe9vWQvU1deMq6uvubKuvuaJuvqaF5P/r6yrrxm3ku7iK8BmyeX7vPd/7uiG3vv3vPf1K+EYLsC+88HS4L/Sxrqpc7CZkFNomVovGc65a5xzPs1Ccs5VOue+45z7n3NutnNuoXPuZefc+c65NuujO+e2cc6d65yrc86965xb7Jxb4pz7wDl3p3PuZJcNT5fex8PJ8bxTdPv30+N0zlV14HEdm1k/KrNORXJMtzvn3k+Oda5zbqJz7jfOueLMdRGRHmHSpMvGTZp02ZWTJl32xKRJl72Y/H/lpEmXjVtJd9FTzg2yv1ttVWd4J3O5o/1311rOufMyv6GbOfNV59wjzrkZye/lm8653znnRrazr42dc991zt3hnJuUnFc0OOemOufudc5Fzrk2+8sUn6tkbj8+c5w1HXhcu2TWv7iN9T7vnPuHc26yc26Rc26+c+5V59yVzkpJdVky2/9bydUGLDugQ0ET732T974jk1fb28+rWJ/l1P+VW3c1yn4nbFV2rTY459JSYwAve++faWv9NaynfId25HgagMeTqxXYWG0LSabIN5OrD3vvH+nKfa0CP6Iw0f0X7QWbsrz3D7eVUdnBfTRiVVbSseQvOCs5uKocl7n817Jr2ZhyqW3WWt06sJNowHq7dIbHBu6zJzbvlVjvKSyT5gYso+JUCgP2pepfPoZ96IvTB6/CgjOlTAIWB/noXSxIlRpJ4UO4ExYAGoilW6eBoinYm7JcSa5HseyPjugP/AkLFN0PDI7D3NFY9tNHFEqt9aZl2bWFmcvDsLqDxGHuIWzg9oYgH41O+urMxrKCHsW+XIs5LDWx2LeCfDQ2ud99sb5GFyX/dsQyt1I3A18L8tHpWDDjijKPdztgBPYe2LvMOsUzmiqwFM+Ti27LasbeB2ngZxlWMvAntPxBBwsybZtcHphcT/sHgWVYnZa5PhUL+oDN1C4rCXqmdYhfBe5PnsObkn3WBfmoVIBPZJU6e/wEf/b4CTdjjRGXz45ZMr2RpiV++edpZsOHNLGMxqZlvD/g6Reff/ZxcM3QzCBsBs5YYL+zx09Y5bNdZPWrq6/Zta6+5nEsCH8GVq5zbPL/GcDzdfU1j9fV1+zaxm464rTM5UtXcF9dkqR//z5z07FtrZ/80X1ecvWMUjN0pTXn3Ahs4sXvsZmkw7Df1+2x8r1POueGl9l2HHaucSE2s2sTLEu4LzYB5PNYqZtHnPX56qwbKJzLtVUyN5Wu00CJXpPOehY8nxzTUdhEpDTDeUdsgsqbzrkVmTUqIrJaTZp02a6TJl3W5rnBpEmXPT5p0mWflHODbOmmtpquZ5e9viLHtRbqj433XIONMwzHfi/HYJNKn3POlWw2nszcfxebTHskNnF1ADYJeH2sn8NVyT5aDQZ3wF3Y5FDo3LkBlKju4ZxbzzlXj1Wi+SI28N4fGxfbFvsMvZKUbuqqEygEHG/13q+pcvC1FCZuf8Y5t35bK68G2bHDpi7uo0dk6yR6yndoR7X3+u1J4Xv2jvTGZELZZs65TZxzqzWonkxYS8/zF2ElylY77/00CuXCK2g55r2ypZPXF2MtP8p5ikKwab821ltrdMvATpCPzgjy0bQgH92NBWTmdHIXpcqaZetTvon90Xsl9uOeBo76YQGWkyhEbbNqscyac4tufwWLlpbKSNkMy97YBGv6VkoF9sf5aGxQIq09ujHWi+IILECUvb+Tk9uuptDQrZw00JXqBwwL8tE6WG+cDTLL3qCQTtdM6+fyVbDMEKzs2DzsObmTQhOzPUkCQJn7T2WDNKnRWFbNhdgMh/7J/R4Wh7kXaZkRVYGdyPwJa5L1JlbbM5X+wGf762SDU+njyh5bdtCkVEBveuZyZbL+7GTbSiyIVPyF0Vh0vdR7Yz6WJpgOXF+LZepsHoe5s0qs30Ic5r6JvXfHxmFuNvb8p69XP0qXvhNZXX5OiT5Xw/qs/+hmg7bPloLsu3R24y5bHL8uW315PfqN7M3AjSr/uO3pI9c7e/yELqXNS/dWV19zGPa7tVc7q+4FPJas32nJCW/aC20RrYPvq9O9mct7l8v8SGaE/Q37Dr+5vXR6aeF2LKhxFXAINvnjOAon/ttT/o/P3liG77+wvogHJttXYX+8pvvYGwvSdIr3/kOsxC/Asc65/uXWTYJP1cnVCd772UXLN8JK8+2ABX7+jP1htweWIf1DChN2/uGcO7CzxysisrpNmnRZp84NkvU7rYedG9yNlYkH69nQKvPUOecoTAZpxMYGpOBq7JzgBmxMZWfs7+30ud+IlrO7syqwcYP7sbGjQ7H3zr5YdkA6ZrItcIez8lQdlvRP+WdydT/nXKmJsQA453pjwRqA57z3rxQtH4xNrj0gOeabsPGivbCxmW9h4ya9gMs6kiFUxgGZy2vsHNV7vxArX59a09VKsvdfrk94Wcn5fxq4a8TGBrulHvYd2i5nJdz2TK4uo+W4a+rTmcsTnXObOuduwMYpJ2MB4HnOufudc58tsf2q8BkKiQL/8d7PWU33W0r2dVglgRTn3EAKWWJvtlP2L/s6btrW311ri24X2AnykQN+h2U2VGM/du1lHiyi5cB5Y9H/s7AZCv/EMj52jsPcb7EPwo+xcl4zgaviMHdNHOZuxJrSf5WWQYMrgBOL7vtl4L/YH9OlAkppkKa9un3rYwMSAzO3LcWCLr+hZWmz7bDg00tYZHYHCsGBcrLLF8dh7jUKM06y1sEGZK/B3h8DsLJ2b2Oly84L8lGA9ZTpQ2HgNvsl+iEW6Epn6zvsBOMJWpeqS5WKcN+X/J82UQcbpNk/s862cZg7ERts+Q7WLycdDJ4D/ADLxMrKvu8dpWtLppH62dj7ZiH2Gs7AovUbYVk6qezruwiLEmf9nta9fgYDv8VKy30VCyo+AVxS4njKmUfy3MVh7qnkmB7Fgo+/DvLRt4o3CPJRVZCPwiAfrfVfcLJGbVfitnfmNEw75J0Fr4zGgsEArLNt/16uwtGrbwVDt+rHkDH9hmDfN7KWqauv2Q24BQtcdEQ/4JYuZu58isIJ7wtJuvga4b2fTqEf3RDKN2b8FvZ7Nhv7TZOO2wOo9t5/03t/n/f+ee/9rdg5Q/pH9kllMm4mARt774/y3v/Be/9gsv1D3vu/ee/3oFA//WDnXFf+aEln1w7G+umVcyKFc7NSJUuuwc4Z3wW2895/w3t/s/f+f977/3jvf4OdF8bY+c5VXflDV0RkdZk06bIunRt0MXOnx5wbeO8XY5kiH2Hn1a8mZcEOcM7t6Zz7CjYOcRI27vGNpN+HFOwN1Hjvv+S9vyv5bb8bG2dKJ1zs55wrLnsP9nf2lt77g733v016tjznvX/Me3+99/5gCuMMYykEXjoj/Z13wJfaWO8QbHwsu03WZViAaTawh/f+i977f3jvn/TeP+W9vxIbh0knBF/qnOtIe4Ni4zKXy1WsWV2ylWtWZfmnNiUDzt9LrhZPGu6ogyi0gbjbe1/ct7s76THfoR30dSD92+CuMpUSts9cHoONw55Ey0pHfbCJYQ84537VhePorHGZy5+Ez+JoCuPtbfb0KlqnAkuYWKt1u8BOHOY8NpsALBiwC4UvjnL6URS4wH6sj8JKg61LIRviC8Cfg3x0JRY4qMHKbCwF3gry0ZVBPtoc+1KeQ2EmBrSsc5v6VHIfP83clh3AX4iV2HqOlpkfxd7CgjTZ7JK+2AzUXWgZNHLAYRSel6EUBgHm07rsnKNlXd6KIB9tHYe5D4Bf0TIoMSI51uwX2gtY4OvbwNnY81ac0ZSWC7kUCOIw9wAts18qsIBINeU1YEGkB4F9k14+xGFuEoX6mdDyCzRNsXszuT3Egio3Yq/5OtgXXbpecVm7pcltxYG3PbEv+AnYD3WaDTMCe012Sh7rQ8n62ddnAK3LvNXQegaVx34Ydk32NSK5/ZggH7X72Qzy0SbY+2ZhkI/OBojD3K/jMLcfdpL5DeAPQT46NrPNEdjzW0sXZh2LdMSlJxxejc3o8iSfuYGj+yzd8ovDX9z29JH/2fb0kR8FXx+xzvp7D2LdHfpTOcTGHL33DNq0D/1HVfL2zTNHX3rC4f+59ITDS5ZHkB7rcjo+cJPqR8tU+47KDuBPK7vW6pPtX9eqMWdSXuvXydVzktR26bg/Jg0zW0gGxv6YXM3OysuuMyf547At51M4jzumC8d3G3aOA22XXEmXzcDKxyznnPs09kcjwOne+7dK7SBpTHpOcnUrPiFlCESkx7ocnRtAiXMDb03nd8J+g9bBnqt6bDLgtVjp0ZuwwfxymSefZHd4768pvjEp45TN4m31O+m9b2iv1Jj3vhYrHQhdODfw3v8Hm0ALHTs3aKRQ9ggA59xo4JTk6o/K9WZJGsinffuG0bXeE93p89PmZ2c1uoxCma4bvPcvtLVyGadkLnf3Mmzd6T0AK/A+SMpfp397NVC+X1N2v1di48J/wQI+fbFJ32dSGMv9sXOueHL5ytadXofV8VnMjsUvKLtW6XXa7LO6NmgvYLKmHIhFQKdiMxMrsTfLxxR6lmRV0DKIMBj7Yc3Wfsxu90VaZ9dkf9x3x2ZDbpzcfieWuttR2WNpwCLH7UWPs4OXvsTxddQgSr9xx2Dl6NKa8b/H6sjvWuK+KrDn7uzk8rdoWbt3BBal/h+WAfUY9tocgDUbG4gFht5J7hdsNmxbtYHBnrePsRJsS4N8dAr2xTkXy6z6eolt0hS7O2mZItlI4f29CfZ+yv7oNmMNt7aicIKTmo3NhGksc8wOez+8gs0QLmcqhdd9SIn7yT7v2S/m8XGYawYI8tH3gNOx1/WmOMx9P8hHA5PHdDiFdMRzaHlymu1LtXmQj36BnXRmZxuUmpkksjKcQZJJVtGH+3v1rdijcVHT0KWzGo/okwRxnHP7rPup5ZXamoDj+i4c+vOK/gt3/ODeeY1LZzcNw2bZnU/bM9ikh6irr9mJ9kuslLNXXX3NuOqq2omd2CY7E7EjJ4CrWvYYSs2SvBr7/XwoGSiQzmmrbMXTmcvt1sFPyjKsj51PZbNdPsDOgXbq7MF57xc5527Dyrcc5Jwb5b2fWnS/W1E4r7ipRJmBNNNnDoWs5nKyjV33Zs2WyhARKWnSpMtW6Nxg0qTLxo0Zc9bETmzT084NwCamnkzLyh5Z1cB859y53vtZZdb5pFqZ5wYV2N/2g2lZ9eRD7Lyg0+cGib9jvQAD59yuxYGZJLMmHYu6p8RElM9TGPe4ua078t6/4pybibUj2Jt2+vqW0J0+Px357KxSzrmIwhjVO9hE6M7uYxiFviQfYyUYu7Pu9B6ALr4Pkuf9X5ltfui9f63M6sVtLS723mfbc3wIXO6cexHr2e2AXzvnbkhKLq4K3el1WB2fxezk/o6U619aZtu1UrcM7MRhbipwWZCPzqTwozkAG5y/BhuQLhX4WIid8EyldK+U9Ecsu+3TWK3yOdgPKtigeJquVYFFQN/EggCdNSxz+TVKB6aKlQvqvENhIB9aBoBmYdHR4m2z6/ybwqDAvCAfbYBFl0vpn6x7IvC1omWPYK8D2CyhMViGSnpisB9WZ/RT2EDV0Vgg5XkKfYb+m/xfHBgZjfXneQHr/zMg+XcFVrvxEGxWy93YF8iJQT76PK1T/rLv7QOS65tnbkv7GPyX1tahZWCkWGOy/cm07CHyEfa+S5szF5cQ7EiG3IvJfklK3v0us+ycIB+9jZWX2wzLmkoDWI8H+egP2PvrUQrZP29gg+zpYz8+uY/NKLzfRVa2R7GsQioH995r6czGQQBT7p3LyE8PZPiOrf4uvRyY3DBo3jiAimHNlcuTqu1ESdYOp7W/Spu+BnyzE+tnM0/bK+m6OmQnXczLLnDOfR34LDYBo9Qkhg5zzj1MxzI08s65tmYFbu69f2dFjmU1K9XDL5Ud6Cr5B4dzrh82AeMkrDRuW33qSpVz64jrsMBOL2yS0e+KlrfZGBk75wI7t2y21god0pXSFCIiq4PODQqKzw0qsOyMtEF2HVay+1ls0Goz7DfrXOx5ONA5d7D3vlWPCOfcNVjp7/b83Dn38zaWH+C9f7gD++kuVvTcoBeWTfEV7De4rQHCFTk3SP8u/zItK5WAZdb0z6xbbLfM5Vmr+NxgPoVxkkGULmm/upT97KwOzrmjKJQOnwsc3cU+JydSyFj8e0dKmznn2mvzkJrcxvvhXe/9Zh3cT1aP+Q4tJ+m5ciewTXLTtd77y9vYJBucmYNNPG3Fe1/vnLsDG/8ciSUsTEjucyAtxyOLTU56R3VUd3odVsdnMfsadKSfWbbVx+Kya3VREhgc3cYqr7fVB2hl65aBnYwPMpf7Y/Xm98ICNMXp2nOwQfZFWNmrqdjshewA/bexchxpethzwJlxmHs8yEf9sBdmS6yh/V8z2+WxwM5vuvAY0m/S2VgdxL/QsZOqUjbLXF6ARZhPxgJabaW8vYuV9LgUy7QZggUhPqDlB6TYZ7CalcUR4GGZyw7LlMn2ckmDLEOwE41ByW03YMGtJiw4MQI7We2PlcMbgpUWS+shvk+hluxgbKDq1GSbEVi2Tvqhnot9iZQ6KRtc4nF+gKWtN1NojPgmhS/3Ut5O1t0Ma6AIhYDaNKwfEtjzPR8LbLXnUSwwtmFyfXtgyyAfDceep2zmEdj7f7Pk8t7J9fOw52a9zDppEKk44NWXJJgWh7lVNXtAPuHOHj/hN3/96XEzh27T77fz3146bOnMwvnprJcXM3zHgb/E3tfHYbPIfzj51lmHVQ7s7fuP6uU22Gk4TG64evbCj99AvXbWJl2dyZga18n1Z2Qur7+C970yZP/gXz6YkJTQSHurnV9qQEba184fQ9kyrK36zTjnNsZK77Z1DpDV1ZlfD2HnNhtjg0TLAztJA+w0O/E17/3TrTdffk7UWQPaX0VEZI3QuUFBcbZNRCGo83fvfXGprjeA85xzT2ETODfHBv67mgG1Nip7buC9z06QKHVuMBQLpnX0+ezSuYH3/i3n3BPJ/XzROXd20eD+V5L/55DpUZqxOs8NZlAYX1ufNRvYaeuzs0o55w7GxpJ6Ye+xau/9xC7u7pTM5e5ehg161ndoK865PlhrhX2Sm26hdU/uYtkgysNJmedy6ihMpt+dJLCDjYs+VHILcwDwcDvHkdWdXofV8VnsbCAru06pvkkr6ija/rxujiVmrBbdOrATh7mbg3x0BoWZpwE2qP0GNmiezYAYlvzfB6uNWIcNmKdZFGB1D38L/Bwb4N4ZeCTIR3/D/ph+Aaux+OPMfpdhP3o/zNzmgcmUT9ktHowH+wEcjH1pHEv7b8Y/YT/SR9L6RGMWFhj5bXK9ePp7HVZmzSX/Nk3+PYoNXHwB+1Jx2AlIjA1mlAvnFx9rNsvmY6wcyMWZ2x4N8tFYrNdN9oThZOAPcZhLG0LPxF4Dgnw0EhiPNTmOg3y0L/bl9m/sS7APFsw7DAvoXEPLSO0QrPzeedhM26z/AGdhZefSUmTZTKWF2CBLA5ZVVC4za7MSt6Wv87DMbRvR8c/WnrScFdwLKz93MBaougqbxbEe9r7/Cfa8bISleW6TrJtVqkb1Muy5nEvyZRvkoygOc9d28DhFOmXDA4bsBgzrv34ly+Y3s+A9y4bt3bfXe9VVtemstB8DXHrC4RXAdUtmNLr570L/Dze4uKb2T+eW3rP0YCs6m6iz9XFfwgLkvYAdnXO9vPdNK3gMXeKcW59CAH8udm6S+jb2GzYPmOKcO7HELkZkLm+eWWdKUp89K6R8uRaw5wXsXOlfbaz3QRvL1jbXUQjq3IllIL+ITdpYktTixzn3KPaHYJfK5SaDSDdgs6vHOec+5b1/OVn8GQoz+a4vs4v03OID4NBO3PXsTh+siMjqoXMDU3xuAC0HG39Ubj/e+3uccw9imb97OufGeu9fKlrtJxTGDkq5NzmWHPb3ZzmT21i2trmcQlDnMex5eRZ7nRZlzg2uo+3+OB2RBuRGYL/v6Sz/TbFKKAA3e++Xltg2PTfw2DhIR7M5OpMdkJpIYaxkF2x8Yk3ZOXO5rcyslco5tx8WGOiLZQJ83nv/RBf3tS2FsbVnMueE7RnbxrLdKFTSOYTy1S86Us6qlJ70HVq8fiVWrvCQ5Ka7gJM6cPzZHubvl12r9fKuBl07YmLm8i6r8H46YnV8Ft+nUI1q43bWJbOOh0wtmLVUtw7sJI7APnD7YoPg9bQfkdwr+bcnFp1LS2PtRstUVbAvpFOT//fEPujpbIvp2B/Pd9IyQOEoH9RppvQf/Auw8mG/wAIYxVHhBRRObKcl/75R5j7ewGb3DiuzvD8W+f8m9uOepqFVYF9iUXIsO2NBKLBo4kjKDwY1YtlQxRkxH2LBkg0ytz2a3E+pWSBhkI/yWGbUx9gPyjNYQCX9Uh4BHBOHuV8H+eh9LDMlNQA7KS1OY1yG/cB+nNz/vpllr8ZhbhbwqSAf/ZPWjQIb4zA3GyDIR7+mZeR1PoU/GIpLqTVSeD6yqX5tfa6K+ycVl3p5Fgtukew7DYLVx2Hus8kxfgoLWl5K6Shxun+f/KtI7udAWqaQXxPko/XiMHdp612IrLAngdObGzzNDX5p7769+47stRm7DT109JRzH/vM6Iv2KR6MXu7d9yc9vPoOU1ajFa3/26nZNt77+c65Z7HZUgOw78B7V/AYuuqQzOXH08GARPr7MYTyA/pZ+1L4jfsXNnlhOe99m4MumdmpH3TiD8i1lnNuawq/uzd6709qY/W2yrR21PVYYAdsIOiHmctg55HlegLMwAJQQ4FXvPcdHbwREemudG5gis8NALZL/p/mvW9vYOpZLLCTbtcisOO9/4A2Jmw459KSMR/r3ACcc4OxMndg5c/3L/H6pFbGucHNWA/kvtj5QDrL/2QKf9uXKsMGhZn7DphaogfPyvQQVtodbJzuxlV4X2U55wbRcozo0dV0v3tjr80ArBzi0d77trIw2nNK5nKHs3Xa+ow657LZE2+s7LLKPew7dLmkrOI/sInzYMd8fAfLZWWf71bZfUWyy5fvOylh2dU+6qU8RiGhYB/n3LAulgJcGbKvwyr5LHrvFzrn3sUm22/lnKss99olAby01/s73vtFq+B4rqHQnmSN60jPjzUqDnPzsFmTqbaint/AerqkPoelrLYn/cO4icyHDwswjKNzaaoVtP6wz8eCNntgQapfYVknqXnAQVjpuL9gpeTKHfd0rMdQW7ObPPBiHOb2wIJIxQ7A3oT7YNH8AAuUlAvq5LEgwlO0ngEyDksZTB+zB/6HDepmS32llx124vlPrFfPk9gMog0z6zZjmVR9sGyVrBexUmfF0uydkcm+X8G+6CYD7wb56GdJT6GrS2w7OMhHOySXr8NqZj4G3Eb5WWAfYJlRH5dY1oQ93tdLLEu/zBdSekbBzpROaV7emykOc3OwwN++JdbLuoSWJ1sDaBmAg0J6v8hKVV1Vey2w33t3z/nXoqkNfRuXNtLQtIQXP36k4smP7/zzpSccvrxcwdnjJzRjJ1m3YYOcz116wuHtnTRJz/P8Cm4/sQvbZMuqnrWC998lSY3872ZuunVNHIeUlZ31eFO5lZJBhGBF78x7/yp2HgRwsnOuIunvkw6UPOy9LzcbMN1uEJ0vPyQi0h3p3MCUOjdIJ2C21fMtlV1ntdX1X4ttTWF84eZyA8ZJGdUVni3vvZ9NIZhzRFIGDgqTPt7y3j9eZvNnM5c/s6LH0o7xFIKxxzjnNlvF91fOqRTGaR5bxcEsAJxze2DjP4OwycnHeu+7HNBIAg3p67uUNRQk66Ke8h2aXfd6ChO8HwSOKpMBV8ojmctbtrPumMzlVVb9wHs/HwsIg03cPmNV3VdbnHOjsL6hYOO4d6zCu0tfh/4UereX8mkKk9lXS9B3Tev2gZ3ERVimzoe0jHJeQsu66e9hL2KqAvvg3UPrP9azL/DLWOPGn2A/VB/R8gcy1ZWZkVNpGUyowAIr6Qe+ASsr9yb2wfw69mG4m9ID/yOwAfpKLDj0NoUgUJpOWwU8FeSjTYGfYiW4sg7BetncRMsAUymTsR+ZH2Ilv9pr5vY17DW5BysJ9g9ssDY1kNaR6vMovI4eqInD3ONxmGtI9gP2Y3cdcAEW+PoB9ly913JXvIml3W2fHPvPgCuwYM2/sOfudlqmHlcATwb56GVgkzjMnReHuX1pOxq/ITZLpVSg8e04zPXHAnl/SI49fWypAVjtzXOLjqWJwmwcj6UyTgfOAQjy0YAgH92GBdPKzdzywFfjMPdD7KSnBgsElXJzmdtFVlh1Ve2jS2c2Lv8uXdA4m8kLXuK9ha9tR9GP/tnjJzxy9vgJx2K11qdhwZ2SjUylxyoVWF/V219LYTLAwc65cpmwrTjnNnHOVXXhPov9lEKK+rsUZeV477/nvXdt/aNlluq1mWVHrYTj+6TLZtm2NZHna6y8TPf0d34j7Jzt8xSysMvNyAU7f0mdvZKORURkTdK5QYlzg8Tbyf/rOufa6926f4ntpOs6em5wFK0nTnZV+vvfDzjeObcbhQklbWV034mNIQCc6TKp2Sub934W1rMaLPB1bdKzpF3OuV7OuRUtWYdzbjtsonTqghXdZwfuc2dsXGoIFjj9gvf+7hXc7SEU3jt3JMG9nqKnfIemwddaCsGHR4AjvPcd7jftvX8Pm+QOsF9RVlSxbHWghzt6H110IYXx2Z8658Z1dEPn3H7OueIqSJ3inOuNfW+lrSDGe+/fXJF9tiMbvDutjfWy1bFuWUXH0q30iMBOHOY+SMpQfY3Cj1ZtHOZ+ADyHDWTPxAbbS9WbPBTL/EiDAMuwgMaXsC+lr8dh7m9YxHdD7At2FyzY0ETX6o+mRmEZLxOxAf7baTkzqRnL0BhAIapYib1pH6B1ICWbRjYGKwk3DAu6ZANBfbGslXexsh2lskc2wlJ3TwKKm/R64AmsH0925kcTVl/221h9/tkUUuCfwj7YR1LIKNob631Tqu9LVgUWVHPAaUE+Stc/AgvW7Yf9kP4GaIrD3CVxmLualv0BpmOv/7eT61thWVupMdjzfzSts5MGYMGgKwCCfPRDCmmaHjtBvgfLxJlC22mUdUE++h8WcNuKQpmd7DYOex77Y5lTt2HBy+PjMHcOVhZwBywgdRcwLchH52BfTEdjr/voEvf9MdaM+fggHz0I7BmHuTytI+dLsHJ3bdVaFlkZLgZ+0nd47xt7D2sRiD/40hMO3y+74qUnHD6cQpbeDsDUS084/GhkrVBdVTsR+13piieS7TvFe9+AZSamdaSvdM6dm6Rol5T84VmDnV/sUG699jjnhjnnrsQmL4Cde4QdTPmX1SdbH/6rpQZEnHN7snIHDm6kcH73ZQozNhfSxozDpIzDw8nVk51z57R1J865gc65M5OZiiIi3c6YMWdNZAXODZLtO6UHnRtk/879o3Ouf4l1cM59G5sYBfa3/wtdPT5ZbhKFiacnOuf6Fq+QlHJtqx9RZ/2bQlm17LmBp43Ajvf+rczyfYDLk2yQkpxzlc65MOlP0hXnURg72he4xzlXalwie587YVkSXZ6U4szxWGm8dCznr977+7q6zw7e76ewntLDsHO3L3rv2+pR2VFh5nKHy7B1Bz3oOxSsb9hXk8v/AQ7rYmmuNJjYD3u8rc6tnXNfwz6DAM95758qXmdlSkry/SBzXA855w5raxvn3FDn3K+xz+PQttZtZz9jsPHLg5Kb3mfVTzq7m0KZ0a+UChA65z5L4bvzJSzLbq3XE3rsABDko5HY7IBe2Bvo60E+Wh/YNVklHRBsoJA2W+x6rFzbcCyLYResB81Xgnz0BK1796Qf1vSHo73ZD/OxYMDbWDbRkcD1cZh7mcLJVvp4TsOCMf/Avtj2x+o8borNzBhL6yBVA3Al9uEZV7SsNxZ9zqb+pce9BaVn7jQAj8VhblKQj+7HAhbZIMReWMm27Yu2Oz8Oc2kJsl8F+ag/lhk1GQs23ItlmPTDTkhPpe1Gzqk0GPQZ7LV5PA5zy4D/BvnoMQoBpkOCfDQBS/vMNoMbgWXAPIY9tw3Ye2YYFtx6HPhKsm5xr5tUesJ8QOY2B2yCBammUTjhKjYFS08eRKGXU1vNjXdO/i2Ow9yx2QVxmHsqKQ/3j+T+wzLHmzUPyyDKpsNWBfnoixRmKIAF0HaIw9wnqfmlrCFnj5/QAPy6rr6m95KZy45YcOvCgZncteITsNlYz630e70/lkl5O7K2+C72Hd1esD9rMS1T7TvFe/9f59wJWN+Sgdjspq8558ZjvwsfY7/3G2C/M2nwvD3DimbQ9sN+b8Zgv5/HUPjtWwicuoJ1uGXVmIgNgu2I/Wbf55z7E9Z7cF0sm+Z0bBLOFKw8ywrx3k93zt0DHA4cS+G89XbvfXv9Jk7GSt5uBFzinDsGO799EXufDcXOI/fFJscMwrKHy9YdFxFZw3RuUNpl2N+AG2GTHJ93zv0BqyyyFMvmPQn7HUmdo/5rK857P9M5NwH7Hd0R+I9z7ndYdZCB2HjMt7ExmOdo2Ty8q/e5zDl3E/AtbHB4x2TR49779rKwvoONn4xNLn/WOfc37L0yDytblr4Hj8bGw7aifFWPto5zqXPuSCxTaFds3ORN59zNwP1YcHEBNjazPXAYlp3saCfoWPTZ6YWd06yPlV06gpbnYDdivaPL7WsQrXsrj8tcPrSolNwC732L2f3OuS2xydZpz+4LgdfbyaBr8N6/0cZynHPp+SXYueX9ba3fHfWE71Dn3G+xc3iw8/rvA5u3k9Q2pVSvGu/9hOSxnYBNft8g+T6eBKyX3F6TrL4ES0pY5bz3v0syiH6MPU8TnHNPYOMnE7EEiEHYeOaBWJbhsA7seqOi12EQ1k9sLDZ2fQiF8fLJWBZUqYpTADjnDsWSHlLZrKcxzrlTija5x3s/NXuD977ZOXcGFpTqA9ztnLsEC7yCjQF/PzmuBuCMNspojqF16cpRmeXFxzPJe1+2R/Sa1iMCO0E+2hGrPZ6milVhWS/FwYIDsUBCA4Va5akZWC3I7AcsDZw4WjZfS5tQFWumfJbT29gHfEMsw+TEOMy1ehMF+Wj75Pj/jWULPUlhEBParjP8JvZGLTYdGxw4FRsofZ/WX5obYxHOkRSCDtOBU4N8dDL2gb8YS2nMftNtR0v9sB/Xj4N8NBAYEoe5j4J89G7yWMZigZ2tgHXjMPdisuzSZPtygbeHsD4yo7Dnf36QjzbGAmRLaFnGbHPsROq/WC3aCzPL/g87cTkEy1LaEntfVFCow9qEBZ6GYqXasq/pzOT/O2nZBKw39gU4jfLvgdHANynMXMiajj2vHjvJydoHe+5J+gr9HhuUeYTCa5F9Tcq9D8uVrfpZ0bIXaJn5JbLK7XBfuOGiITNuHbbbi7u8/sHE5mFb93916Nb9WpxVnT1+QvOlJxy+P/Y5TpuLr2jtdelGqqtqn6mrrzkOyz7syADOYuD46qraZ1bkfr33dzjn9sIGuPfFfiN/1MYmTdhgeak+dakjKWR2ltOI/U6d3YE/ymUN8N5759yXsPOQ9bBzhgOLVpuO/UH6K1ZCYCdxHXbuNrDotjZ57z90zn0aK6e7N5bhu2cbm8yna6WERURWizFjznpm0qTLOn1uMGbMWWv1uYH3frZz7kAsk3M7bKLiH8usvhg4s3hgWlZIhJXM3wIbr7mhaPlCbLLFkayEwE7iOiyw4yjMpu/IucF859y+2KTcI7GAymVtbLKUQrn4TvPef5Tc36+w56kfNoH2K21s9gKlx7KyXmpnOcCrwC+89219DsHO6drKhPlh0fV3aV22aR9aTv7+afKvLWmD97acRGFC9XXlBp+7u+7+HUrLwN5m2Hhle0Lsc1TKV7HP5hew98Y+JdaZBRzvvX+uA/e1Unjvf+KcexarbrQlFgDbq41N5mHjrK+1sc4FtF+tYB5W5u6nHZiYdi42QaGUvWk5Hg8WMJ5avKL3/j/OuZOwz/ZgSn8m5wOntBOI+Qxtfz8UL7sWy/jqlrp9YCfIR4dhg+wV2I9P+gVYquQaWNBiHPZDuzsW2X0DOCgOc1ODfHQ7hajtQgqD/dkMjlLPy4XYF8FXsR/2TYuWbwH8mULzvF0p+uFKMoyewAbZm7HZleOK9rME+zJYNzm+7B/82YDAQuwH9EMsMpmmmFVis0aKAzuVWNbQ3tjshl2w2T/p4OnGmWN5G/tRPYLWWSLL4jD3UpCPtsHe2OsF+eg8rGdR+pocAvg4zL2YXP99sv+dksdV/Nql/XjSx9cby77aBovIQyG9eE8Kr88CrNzcVArR1Rj7cumNZa58iUIgKe2H0wuLNn8KC6h8l0Lt3PT9NazoGBcB/8Sex2FYAC1NNX2XwvuhH6178yzDTszKZZIdFOSje7H36wcUMqS2onQQ5zZazz75M9ZzqNT0gwFF+9kbeDbIR98HborDnAZ8ZHW4Z8C89bbdmioaDv3wrebey04Ajq+rr/l0dVXt8lKQZ4+fsBD40aUnHP4g9plVH6i1THVV7d119TX7YL8NbZ10PgF8d0WDOinv/YtYXeTPYH8w7IcF5IdjfyDMwH6XHwZu9N5/WGZXJXeP/S7PxWaoTcSyz24rnm0k3Y/3/mXn3I7YH/nV2Ky2xVgJ3wnAFd77qSu5bP2dWMnWYcn1D7AZaB053inAZ5LZbydin6NR2O/9fGxW4vPYDLY7vfdNZXYlItItjBlz1t2TJl3W4XODFQ3qpLr7uYH3Pk76e3wB+7t4J+z8uHey39ex346/eu/f78SxSTuSiRS7YBNC02yDRuz3+l7gD977SUn2ysq6z6edc69hE17BxoY69LdQkmVwVFI+9qvYwOVobKb9Qmzy74tYBsrtK9rTxXu/GDgryYo4AfgsNo6xHjamMgfLZvgvcIv3/vFO3sVSbOB4DjYA/Rxwn/e+I4Pz3d0pmcvXrKFjWCm6+3foyuS9Xwqc4Jy7FgsA7YEF/hZhY853Y98Lq71fkvf+NufcnVhGzqFYssH62N8ZC7Ex02exsdfbvPedaTfShP19MQ/72+h5bGz8X10sa7dCvPe3Oueexib7H4aNNZMcWx32GhT3Yl+rue6eqRvko4sp1A0sVz6r2LtYethY4JU4zC2P4gb5aAT25bkPhaDOv7D6qEuxH8BykckHsZTJ2yiU2MoeU/ZH+IE4zB2U3TgpifWPon2+QyGivzQ5riDZ53bYmzXbsM9jX2jfjcPcY5l9/wj4NVbKqG9mm+zxvRmHua2DfDQUCx6VyzyZGYe59YJ89DKty7A1xWGud5CPzqWQKfMR9iV+K/ahegbr7dKiP1BSWux3WMZSi33SOhjyJHYSkgaBssETgPfjMLdJst/NsddwNBbYSWeujsc+2Nckz8EUSvelSTUAh8Vh7oEgH30aC1ZVUj5Dph57Hn+EvU7fKfG4fooFXR6mZUBrPoX3XzkvYZkLx2Ep9gOS+xuDDTo9igUJPRaE+yGt0wnbc2Yc5i7v5DYiHZb0zfnb8L4bfn6X4QdXDOs7kpcOuqYJt/wz/5Xqqtq2GoLKWqyuvmYclkk7DvtOnI+dLF5dXVWrGvEiIiKfMJMmXTaOMucGY8acpXMDERERkUS3z9ih0HcFOhbUAQsAPI3NFlgY5KOd4jD3JkAc5qYDhwX56HUKA+tPxmHuPljey6ecz2LpptlAy9VYBHkRVl7tASzLZqMgH/WNw1w2xfWrtPYvCvWB+2JN7jds4xgcFgRZ3sAvyEffTvZ9DRYNz6bdTsRm9iymUIP0UMoHdTyF9MnjsDru2QBEGkx6iEIG1chkvfOx+qDPlQjq9MMCIcOT+1hCoZ9NqeZ+2bIir9I6nTWb2rgzhaBJdrslcZi7LshHzyb38W3gtBL3lbogDnMPwPIeN9tggaorKNS4zbofS40+E5uN8k9spm+aZdULmBCHuVlBPjoYK4mWvgZtBXWex57PS5P37Z1JgPMg4J44zM0GZgf5qAqb7TEEm/nblRTe4lJ7IivbOcCRM5d+yNMz/k0wdA/mxkvvG7pt3z2xWSO3pSteesLhWwA/x4Kw5509foKazK/lqqtqJ2IlLEVEREQYM+asiejcQERERKRd3TqwE+Sj7YAzim72wMtYzbuzKGRgeKxu57jketqMaSCwc5CPZmEZDbOwfi/fxgIg7wA3BvnoJlr2ukl9SMtAy5lFyw+Mw9yWQT7qj9U0TANR2wKX07KhW3EzqSW0DliUCuq8iwVE0n3vANwX5KO9sPTr32MBn21oXZd0p+T//sA6QT7altKPcxYWpPl5HOauTm5bj5YBiF8BFwf5aEAc5v4b5KMAK2X2hWT55+Mwd16Jfaf3v25y2WGBqH/Q+j1YKjvmXSwF+TwshTCPPdeprYrWfw17Xc8GiMPcKwBBPvo9lpq4Hq3NoaiOYhzmJgOTg3x0Eva8Fh/XL7A6smmWVg3wZSz4tVty3B8E+Wj35HhyWD3VoVgq9MaU1gB8Pw5z8zPH8grwSpCPNg/y0bpxmJsVh7lng3z0QbKvNODXWW92YRuRzpiRXpjdMI0np98J03l06LYjj8MyK9fD0oPBAqV7Z7b73eo8UBERERERERERkZ6gXNZGdzGP1o3om7HaultgA+kph5W9Km4u9wpWY/xvWM+bC4Gz4jB3XxzmPgW8laxzAtZoastkOw/cRcseN6X0DfLRd7FmSt8qWvb5ZFnq/2iZVdEPy/Ao53qsBuJNWN+aL2F1KsEe7xZYJs6szDZp5koTVhotvb+52GP9LzaDvti62GP9WZCP9k3Kxr1GoUHUbVhg5WYsC+oOrIbhnyg03lveBC3IRzsH+egbQT4aBZBkmfwEmIZlKd2C1X38N9aDJvXnouP6HxbkOB/LlNowDnM/jsPcnOR+xmC9crK2xfoOFWfFvJrc9zysXNwVwO1Yo7dDgIuCfFQcuCMOc69S6GEE8FTyfyUWTEuNoDBQDZY59hz2nL+d3OdQLHNpHK2b86X2wEqvtRDko/9L9vNukI92S27+HqUDtFOx90B7JnZgHZEVcTn2nZPDvst+gwVs7sfq0L5UV18zJlm3MrNd9rKIiIiIiIiIiIgkekKPnQOx4EapbIS7sJ43qR9gZcF2xQb1z8HKWj1EIXMF4JI4zP0gyEd7UBikLzYF63eTlnSDQumxUuZQaIBb7P+AG7FZ6R+UeCzTsaDLZKys1t5Y1scdWHbRsMy2W2PBmvux7JMNkmMcXuJ+l1EYHH0LeBzLMGlPut0TWM+WoXGYmxPko12w4ERqO6zm8ZexoEkzVq7uceDvQB8sI2SbOMz5IB/9D8tkAbgEy7iagwUn9k8e8xjs+UqtmwSFAAjy0TgsE+clLJByDuV7Lz0N/CAOcw8n2+6D9aVJbRCHuanJsqlYczGwPjvLAzlBPhqEZXulx1Wu5851wMm0fH1LHZun0GOnXHnBm+Mwd0L2hiAfxRQCSR9hfXVuohCMLHYtcDwWEFtEyxKCi4AfxWHuijLbiqwydfU1fYClIyaPZeDs9Vk6cO5FO0XRjy494fDtsF5hU4Dvnz1+wuI1e6QiIiIiIiIiIiLdT7cuxZZ4EMtKGVRi2f6Zy1MplOd6Pg5zOwME+ehEWgZ17sDKh4EFSbKWYFk0YCXe7qQQ1JkFVGFBplHJbU3YIH4z5YM6ABck/67HeqL8Ctgks9xj2Ss7YkGoYVjAKhu0Gpg53grgBqxs2yGUDupAyxnvIygEdZZhmTEfYUGSdTPrvZc5tt3iMOex4AtYcOgjLJj0XnJ5IpaZsjS5vwqsj02fZJutsDJsiyiUyQMLBvVKjn1cHOZOAwjy0fcy6zTEYW52kI+GYjP8t07+jSh6nI7WgQuwINIdQT66H3utPkch0DIdy9whyEcb0jJgl+1fNAIr8bdBZnm5TLcv0zJQUy7g5LAAXlu+EOSj0+IwNz/IR6OxYN/LFAI7G2ClBMsFGsFe73OxrLedaBnUG0DLbCORVaKuvmYE9r0ZAGdVV9X+ubqqtuGJv//qjg3e3PUoAD9j9PemnPvY+WePn/AqFrAWERERERERERGRMrp7KTaSwMJhWAmvtprDv5a5vAlA0rB+JywwBPC/OMwdHYe5mUE+ctis8KzJRdezgZFZWGBmVOa2B7BSZNMyt7XV7PvLWJZLGjhZggVo0vJq/YE9S2y3GMu++Edy/a3k+iQsw6UjsmXJ3gbewAJM6xat9wqW5dSMBaOWS8qf7YKVCdsNC1BsmizuS+H91B8LyC0Ezo/D3KISxxNnLmdLyT2CPS9gZdrAMmVCLLhRHNRJ3VPm9iHAcVipvSHJMU/A+hKdG+Sj07FeOEOwAM4FWPAvtTctgzptKQ7ilArqzMVKsxVbhvX4WZBcfw14IMhH07DspJuwDJ2sGRR6TEHrz4fDgpiXUjpTa2GJ20RWtpOAnbFg4i954EcDeOBHAzZ5ed9L0xUcrhELhIqIiIiIiIiIiEg7un1gByAOc4/GYW4UVr4rKw1WNCWX5yXXhwT56JvAvVh5tkasFNqBQT46N+kP8wbW62R2Zn+b0tLvscF+sADK2MyyZixj5QlaBoTa6gvxES17p/SOw9yXgNrk+tzMY8g6JQ5zp8Rh7mSsR8tkbNDeAd/AynqVku2xkg0ybAPsnrm+AMvqeB/4cZLt1CcOc78o3mEc5j6Kw9xtcZj7OCmR9qvkmB+k0A/pA6wc3qPARZnN88n/U2nZFP3HQT7qn+z/eex5/hxWRgxaB8vSQeBJybFfAJwBzCxaXqqMk8eCfRdgPZn+hGWuVQDvxWHup8DgIB/dE+SjyVjGVmOJ/XyIvafeLrGslEasn9KVWEBlatHySuBoLIPqIixgsztWWnBYZp2szYquZz/PzWVuT83AHr/IqvYiSdBmA9/3fey9N3P0gXWDsc/tjcDnR1+0z9LyuxAREREREREREZFUt++xkxXko02w7IqOKNUH5WFalm9ra12wUmxDSmyTDkCmZbCuBb7axrF44CdYJshLFHqwTMYG8T/Cerh8CgvcFLsKC0AcAdwHHA78LLN8VywgsBT4IuXLc03BAgZzsd45F2LZKD8B7kuyo1oI8tERWN+YCXGYu77cAwzy0beAP5RYdGwc5m7LrLclcCQ2oJv2hmnAeumUzCAJ8tFA4Dbg4KJFC+IwNziz3iAseHRcmcP8XvL/5SWWeeC0OMzVBvnoO1hQD2wQ+kjgb1gpqVRDHOb6BvnoeVqWmCvlBex5703p13dVa6R12cV/AmcCi7I9jERWhbr6mr2BrQ9tHnlkBe5IgHkLGp64+sbXh2LB0S+dPX5CqaC2iIiIiIiIiIiIFOkRGTupOMy9hw24txeNKheo2T9zObuP4nUXYMGPI5JtikuJVdIyeBJjgZV6Spdic8CJWIAmOyt9c6zXzZ1Yya9Sg/4ey3x5FssyeQLLRErLv70BvJ7cvi9t91y5DStJtnMc5l7GAhZTsDJmjwb5qF925aS/zC3AF4Brg3wUBPlo0yAfXRLko1OL9l2XOaY5yf/LkuPL2h8rDZYGdf4LnFguqAOQLPtxiUVPZY51MPAM5YM6TwBXYOX00hJ86XugEQtApZlTwzLbDY7D3BNYf6X6zO1zg3x0LO0HdcDeT8NZOUGdtsoRllMc1PFYMG0K8H6Qj0qV/xNZaaqrah+vrqrNV+DuT2+b+OrMbYDtsV5iX19jByciIiIiIiIiItLD9KjADkAc5r6HDVSfgwVfoHXJreJgQrEJWIDjqTLLB9FyEL5f0fLi560flk1yAPBqmX3ugAU1BmABiPa8A/wVeBP4PoUyXL2xAM36yfWtsUDKH4At2tnnHnGYuzkOc2n5sM2w4A5YBs/ORetXUHisDgsaPY09939NAhsAJPscgwWrtgW+CeydBJAACPLRACCX2X8T8NU4zN3eznETh7lngcuw/kJXYYGyIzOrjMVKzJWyCCt/tkkc5mYBO2KvxcbAacBeRceQff8sDfJRb+A/WHAnLcs2Aji/6H5KlWx7GevFVMxjr3F2myYKZeTKyb73PsIyidorYbUMC/6BPbbPYL2eAAZSKHknsmodeOGVwF7A3k+/MOOVzJLifmciIiIiIiIiIiJSRo8qxZYV5KMzsYF+sF4rYzKLH8KCLKnrscHrftgA9/ZAH+BxrN8KWOZIb2CXzHYeC2g8gA2A75Jsly57K7m+SdHhvYQFGpqwoNOgEg9hNpYZ4mk5WD+HlhkjxRZjQZ7iLIyOmAeEaWm0JENnIhYQ+QgYG4e5mdkNgnx0AvBl4K7kpj9lFp8LXJ0ES9oV5KM+wMcUgmbficNcqfJtHRbko52xMnZvAsdgQaVy/h6HuS93YJ+9scywnbAeQg1YCbxiM4F1KfQvuhI4BXuvpHJYJlVFO8eWtQwL1mTfN28nx/InCkG+m+Iw98UgH32X0uXlynkJC27+CAskHRaHuVKPT2SVmHLuY5u9MOvh/vHc/54AvH32+AnXreljEhERERERERER6Sl6cmAnwjI3wIIyrwJhcn0iLUtkHYr15tkNuCcOc9ODfHQ7cFRmnaux7Jf9kutLsIH7v2OD7D9L/k9Lnb2EZaU8WnRoy4BvJdv+DziMllkqK+rHWFDop1h5r+LSa9kydI3Ad4DPYeWO0uMbF4e5VwGCfLQONoP+mTjMTaMNQT76KnBNcnUR0B97nqrjMPdwifWHA+vFYe71zG2fBmqAR+Mw9/f2H27bgnz0NpYlBNakfYfM4jrsOTgiuX5FHOa+W2Y/G2Nl59bDeu08lFn2H6xUXmp6sp6jtTSoBxaE619inTlY1tUpFF6rZRQCNmCv41+wTKNRwGVxmHsuyEeTKJSx+yH2vv19sk4Vhf5NHwAblXqsie8BzwNTMhlcIqvclHMfS4Pyi4FDR1+0T/F3qIiIiIiIiIiIiLShK1kf3cVfsHJYm2KZDAdSCOxsjQVTarBsm4fiMNeA9cJJZcuOvQ9cBHyDQmCnHzYwfhaWeQMWRJmFZWmMpWXPFbDsm7lY35x3knVeZ8W8hPVo2RMLKDyGBXR2AAKsBNyRWHkxsECGw4IEFVipsexjrQS+DUQAcZibDdzdwWO5HgsgbI0FznbGAhcnAg9nVwzy0VisfNmQIB99DAwGzo7DXI7yJfC6IpvttF3m8mXAD7DX8YdYoOQ3beznu8DuyeWLgnx0FDAtDnPNFEr+pUYUXc8G/MYCT2KvV3/svdOraP3z4jD3+yAf3QfclNxWWbROBXA6heDRMUE+2hYLpKW2xbLO0kyghRSyhTZKrvemdN+ly7EMrq1KLBNZZTz+S87e1v2xLDsFdkRERERERERERDqhxwZ24jDXBPwivR7ko9uw7JRtgV/FYe7XQT66C8uq+R6tB/V/jmX8TAYOjsPcB0E++j+sJNmmyb4c8GFyPbVu5nL2+ZuYbPu55PpmwKcpZLh01GLgWiwg47FAQRpceBQroTWUQobHLKzs3AvJ9n0y+6qgdd8csMyWdgX5aAusqfnEOMzdlAQ5Lk6WnZvsuwnLjClWDQxJLo9M/v8FKzd7CeALWEmxERSyat4DLkneIwuxbKv2TMpcHoO97j7IR/OwAFVbsoGTu4ENM9en0jJzpgkLyBGHufFBPvoOljFVSjYjqH+yXvpcvoVl/GQ1FV0fiGXl7ERpQ7Dygv8us1xkpaqrrxm1/hY7bbH+2+PweO9wu7//o0fPWHrqMzeMGXNWcQBVRERERERERERESuixpdjKCfJR7zjMNQb5qC9WsiwthXUOln0zB/gDNgj+ABa0eSIOcw1BPhqMDcq/ARyClW67HrgO2IeWmRmp9LZsCbTU3VgptqwZWBmvYg9hQZu7aZlZ1BGvJvvdt531rgAeiMPcXe2sB0CQj96gkNFxUBzmHihavhMWYNofCwAtBr4Uh7k3g3w0DssuyvaJeSAOcwd15L47K8hHaXk0gO9jpc42Ax5JAjwd2cdJWKbNtzp4ty/QMlNqbyyodDwWOPwAy4r5TIlt94vD3KNBPkoDNpthgbITsIysrGXYczuEts3DgjnFGUJtrb9ZkrUlssrV1decBNzQf/YItnz6c1Qkb9WlR72CX2/RVWPGnPVNgEtPOPyL2Hf2k8B3zh4/oXmNHbSIiIiIiIiIiEg302MzdsqJw1xjctHTMoPhFCxosyGFrJG0TNY9QT46HevVMwq4Ng5zpwD3AAT56ELgVizT5X5aDpyngZ7ioA7AFrQO+NyGBUGKfTEOc9OCfDQUG3AvNYi/EJgCbFN0+3bY4P8PsLJo22ElyLKmAxfFYe6jEvstZ1Tm8nFBPvoLFkS6CHgyDnPPB/noQay3S+pnwJfjMDcxyEdbAutjwZ1tsB42HRLko2EAcZib08FN/g18GQuAfJQcZ1/gRuCkDu7j7WTdRcCAomV3YEHCz2Kfm0VYoLAuuZ+FWHm6xyl8roYnx/M+sHFmXx44IMhHg+MwdzfwICzvd1Qc1HkV6yFULqgzDwtE7trGOlm3Ab8DRgP/isPc4g5sI9Jll55w+Chg5tnjJyyb9sT8Kb0HVCwaNIoBOJrxyXej8zQ1NZ7x7/pT32lsaHoFy3TsgwU77wf+taaOX0REREREREREpLspFYxYKyQ9dY4AxmPl2Er1dUkDNAdjA/ZpIGN5ICDIR2dig/eXYz1YojJ3WSr16TVa9p5ZCLxZZvsLgnzUB3iC0gP052EZIdsCv072Mz2zvBK4EMtGKg7q/BTYMQ3qBPloeJCPBtG+EAtmXQd8CQswHIZl4tyarFOcjfJ+eiEOcx/HYe6lOMw9GYe5a+Iwt6AD90mQjz4PTAOmBfnoiI5sgwXu9sWen5EUAm4HdnB7sMd0GK2DOs3AP+Mwdyg20Px/wP5xmKun0PNmKPbeKA6W9qdlUKcZe6+cB0wI8tHDmWUflDim7ShknaV85t+5WNCplHSdhuT/J7Cg5vyktJ6COrJKXXrC4ddjgdZ5l55w+AVz4iU3U+EGzJ+6sOnFJfUX+D6N9yzb+50lfvhi3v948gKP/01FpbvbVZD9rpheZvciIiIiIiIiIiKfSGtdKbZykqDJl7EAyO9oGfz4E3AJ8DTWQ2ci1nvnSqyB/fHJev+Kw9xRQT7aChvE7w98NVn2HoVMGg/chwUVeiXXXWZZtndK6masH8qFJZbdGoe540o8puHJcQ7O3HwJcCaFAMP8OMwNyWxzGvBnLMh0cBzmSgW8Wgny0cvA9pmbmrHn8pfAj4GPgd9jvW2WdWSfbdzXbcDRydXb4zB3TGbZ57Ag22TgxDjMzQny0QbA1DjM+WSdzbHMmQ2AX8Rh7udBPjoD6390bRzmbkneD/XYa/bzOMxdFeSjDyj0xykuu7cIGFJc1i3IRzdh5dMWYZlMv6BtaZZY1hZxmJuc7C/E3lMjsKBOOe9hAaMG4FIsOLkVVrrqEOx99hEt+/sswd73HvhCHOY6nEEl0lmXnnD4IGB+9rb19xrEgA378O6ds2lu8AAXH/mrqj81NjTt+NaHr17nKtwQgHlvL7ngg/vnDQCeOnv8hH+u/qMXERERERERERHpvtbajJ1icZhriMPc3+Iw9yfgKKyk2bPArnGYi+Iw9zY2yP9HrKTW0Vj5nxwwF1iAZcOAZZNch2Ww9E/+zcvcncMG5XtlrlPi8izgGaxc1zGUDuoAbJ4EK4o1YcGVVDOWMZN9XZuCfJQNCn09WT4YOLnM/ZVyCHAxMDO5fkMc5prjMPcTrNTaqDjM/bqrQZ0gH40K8tGNQT66EQtOpO4tWvUPwNbJ8URBPnof+BCYm5QyIwmSbA6MTII647Ag3eHAP5KA2F+wDKj1gD8G+ag39prfgmU4rYP1Y0oNAGpKHPrJWI+hbYF3sNdyJnAD8BYWeMn6gJYlAucB+wX5qC7IR88CLg5z+2Pl7e4EXi5xn2BBHYcFn36cHO/6WCm+JdhrvFHRNmkw0wEHlNmvyEpxwuY/HFFZ0ff1zE1LKwdXNC14b2ka1AHHV9947+Wd/v3rR7/14cPzhzQuam5ubvT/GbJFv0vOHj/hbAV1REREREREREREWvvEZOx0RFL2K9vLYQlWFi1t3P0brDxZf2yQPM2AAMsQ2bsTdzcBywY6HzitA+u/ivUA+nMc5v6bHO9wLMiUzQB5DwsWfCpzmweq4zB3T5CPfomVEmsGjo7D3J2dOGaSEm4bAJPSDJmVIchH1wJfSa5ei2X/EIe554vWy/b0mUnLnjTnxGHu0hL7HouVlAPLxNkQuB6oTm5bFIe5gUXbHELSYynjfeAbcZirK/MYJgFbJldLZeYAnA38E9gB2A84A8jedzOFXlA3Jtd/DFxAy4ypUm7Deu1sUnqxT47LLQQO6Wi2lkhnTTn3sXHAE43Ny/q/Me+ZeVN6vzS1adiizRdOaahc51MDmPncQnwTrLN9/6Z1dxzQa8mMZXxw3/LY+PFnj5+gbDIREREREREREZEyPjEZO+0J8tHu2EB6Vj8sINIEfA04C8uM6JdZDtbjZM8O3tVUrBfMEXGY+xAIyqxXnOmxHRZUeizIR/0B4jA3EwuGvJRZbwqWQXIOhcwQB9wS5KPN4zD3UywANbazQZ3kPhfEYe7NlRnUSSzNXo7D3PNpUCfIR5VBPkrLyX0DmJRczgZ10vJ3pY75Jey5uxk4Lg5zs4CfYYGhRcmyYqeXuG1j4M5s9lSQjw4M8tF/g3x0P/BGZt1sUKeOQq+b3bH3zWlYP58WAaVknQYsa2gbLBPob7Qf1AHL+ioT1AFwHD6s6XtYdpWCOrJKTDn3sUObXdOjQP/eFZVsN2zPIZ8ddsrWFb1cZdNiz4ynF9JvZGXT6IOH/rn3gIpeb/1jJh89tLxi2zvAo2vs4EVEPkGccxXOuW865550zs11zjU757xzbuKaPjYRERERERFpW3Gj90+y07FyW8W2Tv6P2th2dyyIkg2U3YaVA9spc9vb2ED+w8CvsOBCudJlfTKXs9kflUAY5KO/xGGuMQ5z/0h60vwCK8v1ayxjZyRWam73ZLuBybFMjsPcE208ljXlh1hwx2PPCwBBPtoCG+jdKMhH5wKbAWOKtp0NfC4J4JQUh7lrgGsyN70I7Aa8H4e5xsz9DQO+T+F5K9YLK5l3YnL9Buy5BsvgKnYzsAsWXOuD9eMJgB2T5Q3J7UuBN4HvxGFudpCPskGidcs9rkQz7QZpPXsPamqoGtp012+PvnpxO/sT6bLGysUXvLPjQ4M3eWk/+ixN45a+eb2dB1Y0zG3yDXObptDMFwdv3vf1aU/OD4E+zcs862+/7qsbVQ2du2TZ4r2B29fgQ5C1nHPuM8ARWNbkaGySQCMW7H8R+8250Xs/pcS2+wMPlditx3pKzcN6zk3ESq3e7r2f2snjOwzrCbg7MAr73ZkFvICdW1znvS/1e7NGOecexp7TFfGu936zFTgGh5VqrQb2wTKM18V+n6djvQwfAsZ772eU2P4UIF9i183YazsP61/3PPA/7PWd09XjTe7zSGyCUOp87/157WzjgCMpvE9GYBNVJmNlXK/y3k/vwN3fQOF8RkRERERERHoQZewUZPu63Ir1bXkUuCPIRxdjgz6pS7A/nlMLsQyLrGOwAE5W2gelAvhpUtbshxSCO09jgw/zsT/MU8UlvS7Besp8Lrl+FxaMOA0boIqAH2B/7Kd/2L8M1NMBQT7aO8hHU4N89EGQj3bryDYrwW5YoPHuOMzNydx+HIVeMd+ldHmzPdLydB0R5KO+wGNYoO3hIB9l+xRdhZU+y/anmUahHB/AQZnL2SBJP1rbOLmvrHUyl/+JHX+/OMyNjcNcOmD4f8A3KfQ0gvJBQFd0vdV6m1S6j48d3rRtdVXtu2X2IbJSzB353qxF607j9X1u4ZX9bmTxwNn3Pzn7X398+5+zZi+d3fhE46Lm7c644vbHgWMGbtTnIwDXyzUNGMN2ixsW7ul986119TXb1dXXbFBXX3NMXX3NyHbuUqRDnHM7JMGHxygE8DfEJkUMxDIeD8fKrr7rnPu7c250R3ePlW4dDeyM9WS7CnjfOXebc26LDhzfIOdcHVaq9QQKffz6YAGeQ4A/Ay8658pl+35iOef2A54D/g18G+uXuD42IWYwsAVwLNZL8UPn3B+cc+uU3lsrFcAw7D2yB5Y9XJvsp9Y516XvqeT+c53cZj3gASwAfgz2nuuLnVvsDJwHvOKc+2w7+9mTQlDnKey9vyMwFnueREREREREpBtTxk4iDnN/DfLR68DAOMwt760S5KN7gYMzqz4dh7kfBPloP2zQBeAVLMvEUxhk/xgLrszABhjWpXVQYizWN+cObHbp/7AZpj7Z9xFlDjfNLPoONoCxf+b23WkZsHsPK+k1Nw5z2eBEW36ADYaA9YRZpbM5g3y0ERac6gPUBPloizjMfZAs/g8WqKjEZtl+H3te1seCbcfEYe7NTtxXb+An2MAMWFm644J89EWslNqIzOpTsfJ8P8MyrL6T3J7tvfO55Pom2GDcaKx/TvoaNGEBmj2x1wFgQfK/x95bJwf56OI4zJ2bHOPWyT5HJ487lb2cVRzYKazX7Nm8soKbRw5Zd3Cze6fM9iIrrK6+5nDgq27r3ve6xt67+d6Nw5r6Lln05t53HPRR7fQDmpf53ixjb+D0uvqa24A/bbDfELfO9v3ZYNToibMbp+2S7Mphn6cHsNn279bV12xfXVW7cM08MlkbOOeOAv5Oofzl21hG5RMUetVtgJVKPQrYFDgZy7q5vMxu/4UF4VP9sMH/rYC9gKOT+zsaOMg5d6r3/uY2DvMG7DcFrKzqpVgG0WIse/hbWB+1rYD7nHPbe+/nl9rRGhLSurxo6kisVxxYIOOqMusVl6HtEOdcBFxB4bz2JeAWbMLMdOy12Qjr0XcUlmn7LeBBWmbLZBUf50BgKFYaN8366o897sOdc1/w3j/cyUP/Hfa+m0bhvKss51wf7Hzp08lNLyX7eAk7h9oPO28bAdzhnNvXe/98qX3RcpLKad77Vzp57CIiIiIiIrIGfWIDO0E+GoD1LgmAH8Vh7p44zD1WtM41tAzqAOyWBAeyPU+OpPXg+sCkxNevg3y0ATaA0IdC8GcONkNyN+D4ZJtvApfFYe5t4PUgH/0f8AXgXiyo0LfoPmYn//8e+0P+TayfyxwsU2RT4GdxmJtN57xAIaj0QlsrBvmoMg5z5TJJOiqdkUzy//KSeHGYeyLIRztgj+XB5DkdFeSjdYE5nQhWpS7AsqSybqDw+mXLuY0ChsRhbgHw3SAf/RF7Xh/OHN9rQT76AXAtFiT6OTYId3WyymewIF8/7HWZQSGzx1EIJJ0BnJtc/jqFoOEK6b+siXcqHNfPXzrzjGN/19nnSqRD6upr1sEyHfv4ysbjsPf9WJKZ6L0GVPRunpu2/OIb2GB1I1DZb73Kxl6D+OF6fv3c3IWzN2hqany92Tdvhw12gn32d6N1BqRIhzjn9gDGU/gN/gnwW+99qd+u25xz5wCnABe1s+s53vuXS9z+AJBzzg3DyqNGwCDg78656d77VqXcnHM7UfjdfQ3Yoyho86Rz7josOHUS9lt0KuWDTmU5597BPlftlvzqDO/95HLLnHO7Zq5+XOZ56xLn3NEUAjBLsd/TvPe+VC/A8c6572HnZOe3s+tyx3kfcLlzbgMs++cYCoGUvTsaIHHOHQp8FSuh9iMsA6g9EYWgzsPA54rK8j3hnLsJmzQ0AnteyvWAzGYmv1FmnXY559LnOfTeX9PV/YiIiIiIiEjnfJJLsYVYJso44K/FC4N8NBj7g7vY9Ulw4ZdYNsZkWgd1APoH+ejhIB8diZVWSz0GxNjszOOwjJp0+Qxs8Ickg+TzWFDnwqJ9pGKAOMydg5Xg2DYOcx9jf/gfjw1wdOWP9Z9jAaVj4zB3YakVgnxUGeSje4CGIB/dEOSjUs9Bh8RhbhLwPawc3pnFGThxmIvjMHdvthdOHOZmtRfUCfLRukE+2i/IR9kZxFuXWDV77JsWLTs5yEdxkI/GYRk3k0vc79kUyqBcQeuyKgOx2eDDsP5AM7DBxVkU+vI8kFm/Vd3/Dlg+ak5mMGtx39544Mp5S9RXR1YlR8vP0UIscL0IYMgWfd5LF1T0cVtgA6OvYn2vjtt95wsfnDF3WrCssWFZs2/eBfgtNrid+uOqPXxZWyUZDjdRmDwQee8vLBPUAcB73+i9/ytW1urFrt63936O9/4M7DcVLJvyGudcqezLz2QuX1YqEycJVGSDEZ8pXueTxjk3gkL/PA8c7b2vLRPUsZW8X+y9vwQLkLzf1fv23n/kvT+WQk+eoRQmdbR33IOxsnpgWcFlg2JFajKXv1Gq11ISYEvfJ592zh1UvE5i+WShtj4PIiIiIiIi0j19YjN2sDJbrS4H+ehkbBDmOaw57k7YYMF7wB+Ay5JVG7DA2KZYhkZ/bHA9LbdWgZXE2APr2fIsVo5tHwoDoOOxwc8QK4lxKjAxyEffSe6nMtl+CrBeicfw8yAfPR6HuQfTvjRJJtLvkvs4CbgtyEfLgNfiMPdmsnxYHOY+LPfExGHOY71f2rILVu+f5H5+ipW26ZI4zP0eyzxaKYJ8NBxrXj0aeD7IR7sngaFfYGVUmrGBlGrstd0EyyAYkuxiUeb6NlhptPWBBUE+2rko+PQEllGQau9zNRILAi3GZsxuSctshJM68VBThTJ/1lS5hSYbSBdZ6erqay4GzsICMe8Bd1dX1U6sq6/5F0n23fBxA6cs+qhxYMPcpoHr7zUozVjbEaiqrqqdlVzfh6T/1Jx4sZv+9MJt+67bm9EHD6GismIjZK006qGJ44CvYb+1g7BSlc8DV089YNzElXAXXwE2Sy7f573/cxvrtuC9fw97T6+oC7CJGrtivzVfwTKGswZnLrfVC+2dzOXiLN5Pou9S+N2+2nv/745u6L3vctCuyDex7O6NgD2dc5/13j/Yzja/wd4Lz2FZV/u0dyfOuf5YqVeASd7719tYvS5z+QvA/ck+NqNEECmTdZPa3Hv/TnvHJCIiIiIiImvOJzawE4e5W4N8FGKl2LKzwf+CDUZuhQ3YgwVJNsVmkffHBmnOpTBLfSbW+2U3bIAzq4JCMKiYw0pknIWVUUtfj2OwDJ11k+vPAPVYbfg52CBGRfJvd6xGfOqQ5HjWwwJNP8EGzJqSLKArsFJmy3u6dNEkLLNkPWyg6aOu7ijIRxVYeaZRwOVxmJvVziYdMRYL6oA9/lHAlDjMTcRe8/S++8ZhbmlyDDH2uoMFgDbGBmygUPt+EHBpkI9eAS6Iw9xC7PXzWNZROU9RKJ9yRRzm5iaXJwOTg3x0XpCPzsEyuppK7SCR7ePUlkbs/TQXy+Aa34FtRDqlrr5mMNaTC+wz9yaFGevz0vV69anYa7Mjl/coT9+b91EoJwmZ/lbTnlxAc4OncVEDs19b8uHwHQZ8B1mrjHpo4q5YMH+vEov3BM4Y9dDEJ4DvTj1g3DMrcFenZS5fugL76TLvfbNz7vfA9clNx9I6sJPNri3OHKXMsrYG9j8pTs1cLneutUp57xc75/6EZXKDvb5lAzvOuf2B07HvwtO8900l5mOUsk7m8rR21s1OXtq/IzsXERERERGRnuWTXIqNOMxdE4e5c+MwNyVzc7YsR6lm3V9ON8/c9jwWfGnCMnNewGrzz6JQ/qUtM4DbsD/yPTYIfxAWcDoRyzDZBStdNIzC6/YO1h8GgCAf/STZz3rAo1jm0bhkcS9s0GFUcv3rHTiusuIwNyM5phOBT8dhbkVKfZ0BXIll/VyzIseV8QyWsQM2c7VkhlIS1KnGAjkXAq8Ad2MlUs7Eyp4cTyH4NwebeX0uySBOUpqtfzvHMw0LNN0AfC7IRzunC5Isqp9jJdsOxYKLf6dQ3gVgGfAypYM62VIsU4G3sP4QBwPbxWHuxi70IhLpiIW0LB14DIXP8HewgfSbirbx2PfSYdVVtb6uvmZwXX3Nf7DPxnPAlD5DCwloHz+54BvVVbW3rpKjlzVi1EMTD8OC2KWCOll7AY8l63daUu5ql+TqImyCxJpyb+by3s65XkXL7wY+SC6fmRx7C85G/89LrjbSwbJfayvn3LYUzmneaCeDZVXLvr77lVvJOTcAK//rsJJ7z3fiPhZkLg9rZ93s8i2SbB+w99jY5N+/MuuMLfr3ASIiIiIiItKtfWIzdtpwMFYabSKWZXE/9kduausgHy0A/o3NDl2MDcq/gw3ceyyLZgY2+J/1AVaDfVBy/VGsr8xv4zA3I8hHm2OZIQ4LxMzDMmH+mGw3NLOvBqyU0Y+DfLQlFpjYObN83+Rf1vNYFsqA5PgJ8tFWWOmvx+Mw9wCdEIe5lVWmZlTm8gZl1+qEOMwtCPLR7sn+ppQLbAT5aBdgAvacfwhsXLRuGly5JQmc/Q3rjQQwJMhH22KDa5+mbUdiQaGTk+vrUXi9lmCZDlthr+sBWFbRhdhg0Z7AcOBLZfY9FXtdG4EwDnP3tHMsIitFdVVtc119TQ4Lyqb2T5bNBs4BqKuveRObWe+Bn1VX1b6QWf/zwN7J5XFA5fp7Djp+5guLTljwbsNY4M5LTzj8H2ePn3Ay0uONemjibsAtQL/21k30A24Z9dDEfbqQufMpCuc5L3jvG9taeVXy3k93zk3BzhOGYL97H2SWL3bOHQnchU3meNU591usx88S7PfhW1hmcCPWX+WTnrEzLnP52TV1EIkXsBKvFRQyf0u5ACu/+haFIF2HeO/nOec+wEq+Bc659b335TJ3ssGlCux992bSS+dlAOfcnMy+X+7MsYiIiIiIiMiap8BOkSRYsbw5cZCPPqRlYAcss+I44Ng4zN0W5KM+FMp+OWB7LOPifizzJgZuzOy3EfvD+gtxmMv+UV6JDeQPp9Cv5xwsU2c9Ctk8e2Kzey/HglBgfWC+ARxIod586h0sk+fnwA+xUi6PB/moF9bbZUOsVNsucZh7gdXvcgrl0lZayaU4zC2j/cDThhSyYNbHXoOl6cIgH+0G+DjMPROHuXlBPvo+MAZ7D/wVy07YPbO/j7FeCcUZPB4bwE59KshHz2MlW0Ynx/kWNqP8N8k6eSwI909aZjLNwEq4pX191kv20ZhkUomsTtOLrl9evEJ1Ve3PsAbhLdTV1xyGZb6lJQZfqK6qbQbGX3rn4XNJAtDASZeecPh3zh4/YebKPHBZIy6n40GdVD+sbNve7a1YJNubrr3SVavDTArnCutSlBXhvX/WObcTVj7zHEp8lrAMuEu898+twuPsKbrN6+u9b3DOzccm4FQ65wZ77+dn13HOfRrrCQRwuve+K5nOt2MBvl7Y5I+a4hWcc4Ow872sVhlgIiIiIiIi0rMpsNO+S7FASnGwBCy4c1uJZUviMNcU5KPPYTNvd8JmDqd6Y8GiV5LgypPAQ9gA6fBknbRMSx8Ksz/nAVtjgZkziu7Tx2HuiSAfBVg5uXT7Bck+9wcGZbNskhJgabZML2zAabUHdpJgRJdK7awEdUAtNmB4aRzmskGdb5KUYAvyURSHuT9hAbRxySrXYgN1WSOT/+fR8n0xkULTY7AA0jisP89xFHr4PJVZZ3Zyf/WZ/YK9Vp/F3ns7AD+Ow1y2nr7I6nQTNri4HfDj6qrakn1M6uprelVX1Rb3j7oKayCe7if7vfYi9vkajn1+5qy8Q5Y1YdRDE3ei/fJr5ew16qGJ46YeMG5iJ7bJfgcvKLvW6pM9hlLnFGCN7k/GJg+UUg3Md86d671fGf3oerLu+PqmmdVDsF6JADjn+mLnGhXANd77sj142nEx9v5YBwiTkn0XAy9h54v7Ar/Gsn4bKJQDHtDF+xMREREREZFu6hPdY6cj4jB3P1arPA14ZMt0pdkRxQM0c5Jtm7A/5K/FMlEeyqzTCxuwHAZ8DsvS6IuVXiuWDvAMpWXZslQjSYmvOMx9hAWNfokNtM7EZmruTCG7J31si7DZo28nx/iJK+EVh7mmOMydGoe5IA5zxf0KDspcPjD5Pxtg2RrYDLiTlgEZaD0jfSyFYFu2Yfzr2OBL6g0g7ScyCsvWGkXLz+rzcZibH4e5g+Iwt34c5oqbcIuscnX1Nb3q6muuxwIwt1RX1fZtI6jzXWBpXX1NXFdfky23mO1pdktSvg2As8dP+BALXFYD+549fkJxUEh6ntNWcPuvdXL9bMbEoLJrrT7ZrIl52QXOuQrn3E3AFVhWaB1WlnMIdm6wDZb12xd7Hp5xzo0pdSfOuWucc77cP2xyCMDP21rPObf/ynvoq0SPeX2xDJptsazes7t6B977KcBRWA9HsIkhT2Pl+uZhpWV3wPrnTMhs2iJ7qDOccw+3835K5dt5P23W1WMQERERERGR1pSx0wFxmPNBPjoAK6U1Gfgi1sw+7SvxHtYIeh+sxNp/AJJsnLSPSl+spNYBbdzVAmzw5iQsMDOcwoD+Yqyx+GgsWPNG8m8scEEc5pbXR4/D3GskZY+CfPQShUGcVjXU4zD3R5KslE+qIB8NwwaFfogF0dIMmL8Bh2JlomqT1ccDVcAJWLm1DbBSe3/ASu+l+tDSJGwGLVgW2CxgThzmbgzy0RPYIN5IbOZt8YBQ6g6s/8LNXXiYIitNXX3NUGxwMe37dEFdfc2fqqtqZ9bV12wFXId9d51SXVX7GvATLLC5DXAi8Ltku2OwEpKTqqtqb6XI2eMnfFhXX7Mr8Me6+prbqqtq/1W8jvQoO63g9uM6uX62NOX6ZddafbKlw4qzbSLsdwXg7977LxctfwM4zzn3FFaicHPsc9bVDKi1Qbd5fZOMnDSwsyxbhi0pr/f95Op3VzTTynv/qHNuR+yc5ThaTvh5AzsfuRI7r0h90rO7RERERERE1jrOe9/+WtKuIB9VYg1x34nD3JLM7ecD/4c19j0I+CZwOvAMFjQYALyJDdr/KMnySbe9BKuz/wFwBK2bA9fFYa7NEmZBPhqIZfO8HYe5B1bgIa6Vgnx0BBZw60Uho+aWOMwdnywfgpW5m1+03dUUZp8fH4e5W4J8dClWWg0sENcf+C8WCJyDvZazgcvjMNeY7Kc3FhCsb+dQTwf+Goe55nbWE1kl6uprtseyB6Zj5SPXo9As/CMsI7ASe7+flGx2K5Zl8DQW3G4G9q+uqn2srr5mY6BvdVXtpLr6mn7AetVVtVOy9/nS76856v3tH7udCg+eRhxbVFfVZrN8pAcZ9dDEF2nds64zXpp6wLgd2l/NJGWqZmPf7YuAId77Fcr8SrJY0uzba733p3Rwu/WBtGTmXGBd731zZvlzFAJfGyeZGeX29QBWjhNgB+/9S0XLN8JKdZVzL9ZfLoeVQyxnsvd+YRvLO8w5dwrWNw7gfO/9eSthn9sBryRX3/Deb7MS9nkKXThO59weFDJ3X/bej80sG4+V2HsPC8aUsh2FyUK3Arckl1/33j/fzn2PxLK/Z3nvZ2RufwvYAnu/reOLTvidc9cAXwXw3jvKcM5tTvnSgGBl4MDOddsKvr/uvV/WxnIRERERERHpBGXsrCRxmFuGZW4U3/7zIB+dnxmQ/3XyjyAfrQMMjcPcO2X2+f0kWDAbywBpohB8ANi9A8e1EPhLJx5KtxPko6FYJks/4NwV7ScT5KMK4EisNEkNrbNrlj/HcZgrlz0TYYNjU4HRQT56BQu8/RzryXQRNtgxOw5z6WDKT4qOw2EzarMl30p5Kw5zPfo1lJ6trr7mGCwAWgHcTSHzwANLsc9BWsrxrcymU4CvY0EdgFeSoM4R2OBl7/oHv3f+rsu+eO4Av07f1++rfXSbg2v2SzfuP2/dH1Co9NN72lPz775z8SmP9O5fcXV1Ve2Lq+Chyqq1on1QOlVOyns/3zn3LPZbOQArqXnvCh5DVx2Sufx4NqiT2C75f1pbQZ3EsxQCO9tRGFgHwHv/ATYhpCTnXDq4/rH3vlUmb0/hvX/VOTcNy9bZ2jkXeO9bnYetJtnX99GiZen33ybAjR3Y17HJP4DfA20Gdrz3H2Ml3pZzzq2HlYoF+F9xUKczvPeT21ru3PKY0Ac9+f0kIiIiIiLS0yiwsxqUy7KIw9xsWvZbKbVOGsRYGuSjhyj0emnGyrUtl5R+Oye5eslalN1xPpaxApYFc0Ib63bExRSepxsyt/8n+f+ApIRdNZahsC8WgNkFm7X6D2w29OtxmHspyEcLsUHD7bBMrF2xbKzfxGGuxezcIB99Fyvl9hdsdm+poI4H0pGSlykM4ImsKQdRKAu5ETYDfCiFTLdsia0tk//fx2anZzMFn0v+/xHJ78+IpjGnD/Gj+gJs1LTDvgB19TXHAQdv03zszI1e3Yu5oyaz6ONljNzDj3XOjQW+WFdfs1F1Ve3Slf1AZZV6Hgt8d9XELmzzVwqTIM5iDQR2nHMVWD+7VKuyg1ivvL5Y1lt7sut80jMg/gb8OLl8FhZIXq2cc/0pnKNA6dd3dTuewnd2R4JJIiIiIiIi0sNUtL+KdCPHYgMYXwcq4zB3RbogyEfbAB9imSIXAY8lmSlrA1fmclftlrm8CAvE7ByHuX2wvkbDgE9hDY6fwkrVvIgN1lyH9duZBLwY5KOrsDJUqV0zl38Q5KO/BfmoL0CQj/YELsfK6t2CNTsuLsG2EGucfQY2ELhLHOY+RmTN+juWbdGEzSDfCst6S8tOlspsG52sn/28PVFXX3M88On0hka3dILHJpMvcfNnJyXfbga+9sbet1cNnbbZOYuf6Pdc01bTszPD16EwC156jqvXwPbXAu8mlw92zn2joxs65zZxzlV14T6L/ZRCv713getLrPN28v+6zrlPtbO//Uts90l1OYXvn9Occ5/r6IbOubHOuV1WwjFciZW2A3jSe9/id917f5T33rX1j5b9F8/PLPteZw/GOTeIQobwdOCmzj8kERERERER6e6UsdODJGXBLiyz+ApgZOb6XsD7QT7aNO3n0oP9HCvD1p9MffogHw0ArgEC4CdxmLsrs2xLbCbzp4A34jA3KbO/57BZ47OxAMvXgY+TcmoDMusdBwxKLg/J3P7pzPUTsIHrU7HyOGfQMsOmJrn9KiBbCsUn/w7FMn2GYLPK74rD3NtY7wORNa6uvmZDLCtnY6Ciuqo2bcJ9Z119zZ7YZ+kuLJNnXSyTcGfgN9VVtY119TU3YX0clmDNzodndj/1o16vfLufH/LmYD9y40rf73ysJ4QD8BXNfV+t+se1jUuae/XuXbFzZrsXgAfq6mvOra6qba8/lXQTUw8YN3HUQxOfwH6fOuuJqQeMm9jZjbz3Dc65E7DyWH2AK51zw4BLy/X7cM71wt6zvwEuoP0eaCUl9/Mr7HcB7DcpLHO//6LQf+iPzrnPee8Xl9jntylkyL2LfRY+sbz3051zITbxwgG3O+ci4Jpy5cecc/2wfoe/wHoQFvcv7BDn3AbAH4FjkpvmAl/ryr46eb8bJeX2Si0bgj0XGyU3favU+0hERERERER6PgV2erggH7mkh0txnxiwGaRfptAIuEeKw9wcMmVOgnz0RSzwsYDC4MVfgA2S5WcBl1Jo7N4c5KN7gNOAzwFnJts8g5VkS8uhLaBlAGejzOX/YM/nAOz5TANsfYD34jD3k+S+7wBmYWWqUvOSx/FUkI/OwGbm/jUOc4uS5enA3GMdeDpEVrm6+ppNscHsTZObegF1O9wX/nrKfY99Ebhv9EX73FldVTuRQnmstC/Iv+vqayqrq2rTwetLsO+hfthn57fYZ68/NrD6yOTeT+4OvIMFWzfAPr+7A3+rrqqdkRxPVjqwfTWF0m/SM3wX+67r14ltFtOylFmneO//mwR3/o6V07wQ+FrS1P5xrD9JBfbe+wxwNBZgbM+wouyafljG5xgseHUMhabzC4FTvfcPldnXZUCI/e7sBzzvnPsDFnRYCmwOnESh9wrAOSvSO2Vt4b2/zTl3BjbBpS+WVXumc+6fwNNY1kpf7Lk9AHt9R3Vg1yOLXt8B2Ou7LbAP8HkK517TgS94719Z4QfUvpuS8n53YBNVZmFZjHsD36CQPXSZ9/7m1XA8IiIiIiIisgYosNODBfkoAn4f5KM082T/EqutjWW8fokFTrLBkw8zl7+Y/F+R+b8aG9TLBk82pTDoBjaY9hcsK2hh0bKfxmHuYYAgH2VLvQzCBnvS4E1zkI+aMsvvItPHJw5zOZSNI91fDUUD2xWNldUev7/DDQC+MeXcx7YffdE+r5faOBPUAfucpp/FwVgwJ3UVhQyezbCSVQAvVVfV7lBXX7NZXX3NBAoDlcVG1NXXPAh8H3i+uqr2Ez/I3d1NPWDcM6Memngc9pvVkeDOYuD4qQeMe2ZF7td7f4dzbi/gD1jftC2wXk/lNGEl08a3sc6Ryb+2NAITgLO992XLpnnvZzvnDsSyLbYDtsGyQUpZDJzpvb+lnfv+xPDe55xzr2KlInfEsp/GtrHJUuz7p1ygDSBK/rVlCVbq7Ife+9V1vuWwwGG5zLfFwHne+9+spuMRERERERGRNUCBnZ7tZ1gT5ZHY4MMCCqXDwDJJ7l4TB7aKvUhhlv5l2CDGnzLLb6Flr5vUcGygbG+s/8d3gf/DZkIDLI7D3LlBProCK5nzW2AH4Io0qJO4HxvI2R/4fVIiL+sSbEb4u8A3k4wqkW6vrr5mOJYd12o2e7NrYtJudQOa+ixh9Gt79ho0a8MhrfcAdfU1hwGHA/+srqqtr66qfaKuvubfwIHYd9Q6mdWHA89jGTgzgPWS28fW1dek2RWHpYdAIUD0ATACCxRVYVkNd9XV15wKLKiuqlXpoW5s6gHj7h710MR9sEH4tsqyPQF8d0WDOinv/YvAfs65z2ABmf2w34LhWABmBvb78jBwo/f+wzK7Krl7bELAXGxCxUQsM+027/3UDh5f7JzbGfgClu2zE/b73jvZ7+vAg8Bfvffvd+LYPhG8948453bCSpxWY9lXG2LfOUux1+V54AHgZu/9rHL7KqEZ+/6ah00keR74H/b6zllZj6GDfoq9f9NzmeHJcb0D1AF/896/W3ZrERERERERWSs4VfHouYJ8NJ3CQCjA+SPmLq6a369ynyV9l8fsNo7D3JTWW/dcQT7qj2XlvBuHuQfLrLMdFlz5HDbg9iFwbBzmnita71BshnQzcEQc5tqavduZYxyIBYqaV8b+RFa1uvqaXsBTFIKi6Xu3onjd3ksGfHRw9R9bZdHU1ddsgQ0+98Zmsm+MlVdbQmEiQYxl56TZGt/EBlovxz6vWWnQp9g8WpZNzJoB7FtdVftameXSjYx6aOI4rC/JOCxQNx973a+eesC4T3T/GBERERERERGRchTY6aGSwMGC7G3HPT6ZQyd+xOnf+DRNvZaPxR6+lmbtrDRBPuoD+DjMlWykLbK2q6uv6YvN9u5I3wmA26urao8pvrGuvmYslvEAFlDdpLqqdkpdfc0bwFZYsOjTwL0UMnfy1VW1NXX1Nb8Fzi7a5S+AI7BBfw/cg83GdyWOaSnWRwPgZ9VVtb/s4GMRERERERERERHpUVSKrec6ufiGA175mKYKR3NFizHPN1bfIfVMcZhrWNPHILKGPUbbQZ1sGTQPnF1XX7Mr8GXg4eqq2tuTZUuAJ7HgzTLg2rr6mnuT/d8B3FtdVft0XX3N4HTHmzfu2Xfhv5+fvYf7StPzlbe+2uAWbg5MxTLpLgbuw/phxcCMxsXN73z0yDyaFjez/mcG039EJcAiLFNop+T4Xl7B50NERERERERERKTbUsZODxXko4ewHi+p5i898nbFsU+9x7932pAb9t181sJ+lT+Jw9yfyuxCRD7h6uprtgP+gTUb74yLgG8Aw5LrJwJTsEbklWW2ea26qna7uvqafsAsoD/ALktPaJzRa3LvAX4dFrlZvNv76XT9/YHfYcGaJqzfypc+fGjeA3PfWLIVQP9RlWx25DrF9wNwT3VVbXFZNxERERERERERkbWCAjs9VJCPvg1cgc1O/ydwHrDbEf97v882H8x99NQ7H1OmjoiUVFdf0we4EjgeGNrF3XgKJdGagOm0nfWzGOuNsxTYZPG0ZXgPw0eOYHHFHAAGN41ifq+pYNk+EfDXon1cM+O5hUdNf3rhMIDBm/Vl9CElD38RluVzOdbL56zqqtr3Ovn4REREREREREREuiUFdnqwIB8FwJI4zL2zpo9FRHqOuvqafwBfXMHdNNLFcp4zX1w0/eMnF4wA2KZmBBWVy8tHzgcGA/Ra2q9p0xcO6DV1zHMsWndaurzBN/s+s15eTNOSZobvMIBe/Spa7R+Yi2UQbZ9cr6uuqj2sK8cqIiIiIiIiIiLS3SiwIyLyCVFXX7MFsBfwZ2BAJzZdCnwbywzcMLntfuCgrhzHlPvmNs+fvLQCYIOx6zFip8E091r2UVOfhg3SdfrNW4etnzqKpl7LeGPP22cvG7CwZM21DmoEdq+uqn1+BfYhIiIiIiIiIiLSLZSc6iwiImuXuvqaHYCXgOvpXFDnleqq2n7AIxSCOmClICdg5dXasiz5f0Z6w7Bt+1W43oCD6S/P48N7FtLYe+kGWHk3AIa/tx0AvZoq6btw2ANF+5yTrPtuBx9Db7oYhBIREREREREREeluFNgREVnL1dXXDALq6FxAJ3VB8v8k4K7k8kPAZcDhwBCsX8+iMtvPBD4AKtMbBm3cly1PHA4eGn0DMz7+mIVTGqDQs4dpW06csazP4uuB7ywY8cFXgKuAx4DvY718TgXWxwJGDR14HL+qq6+5sAPriYiIiIiIiIiIdGtd6o8gIiI9SgBs1M46b2D9bdJyaE3AJcAFdfU11wHXVFfVHlFXXzMU+ALwl8y26wL9Mtt9BIxOro8qup9GoHfv/hX0HlRB44JmXAVUDumVXWdBY/+Fn9r8FwdPA3ixPv8n4Mv9+vSftPH6W2zjnNvgjfde/mlyn/2AnwKXAhOBrZN9LAX6ZvbZGzi3rr5mfeAEYBZQVV1V+2Y7z4uIiIiIiIiIiEi3oowdEZG13ytA3M46I7Csm3nAfKAW+BawJZZt87W6+prK6qrauUD/zHazsWBN+nvSCyvRVlw+LdUbwFU4NjtiHdbfaxCbHrkOfYf1BngRy/75CBhfV1+zYV19zRjgdGDAkobFOyxtWHwa8Hfg2WR/Hniuuqp2MXBn5n4+An5F61JxIZa5NBq4ta6+Zo92nhcREREREREREZFuRYEdEZG1XBL02BG4kNIl0zywDhbEGYJl7nwNGFS03u7J//2w3jnzsOyXW4HmZFkzcDdwUTuHNa1ycC/WHTuA/iOXV2n7FDAO2ArYDzgbmAZ8DOBweA/vTX1rB2AT4Fxgn+qq2rq6+po9gSjzeH4L/Bx4NXOfS4uOYSzwcF19zWbtHKuIiIiIiIiIiEi3ocCOiMgnQHVVbUN1Ve2PgXtKLJ5Z4ramErf9sq6+5kjgYiyLZwiwE/Ak8G0sG2ZMdVXtBCxQ1JYRyxY2LVy2oCnbH6cB2Cxz/ePqqtr5wDHAQo/nwxnvLlnSsHgwFmQ6oLqq9vG6+prjsb4/A5PtXq6uqr0S+HTyL+VorR/tl6kTERERERERERHpNtRjR0TkE6KuvqYCGFZi0UxgveTyMuA64DbgCKwMWmpX4PCibXcCzgLWT/azXXL7k20dS+OiporJt8waCLDBfkMYtGkfnHPFQZZ08sG2JEGbpuamfpnlU5P/f0HLfjpb1dXXDAEmY2XlBmOZRH1KHQrwelvHKiIiIiIiIiIi0p0oY0dEZC1XV19TUVdf8wCWhbMtMKNolW0yl3sDu1A62HEfcBOwBCt3tgA4EQvqAAzHyqiBZd58SKFEWwtLZzXRtMTTtMQz5d65NC0uuVpDXX3N1cAfM7e9mLncWFdf82laB2z6AcOqq2o/xDJ7LsSCP1NK3Edv4MG6+prKEstERERERERERES6HQV2RETWYnX1NXsDc4DPJjdtgJVQK8dhfW7+Bnwmc/uNwInVVbUPAqOBQ2ndg+dh4KS6+pra5PKGlPmd6b9+Jf1GWNLo4C360qt/q9V+B9QAp9EyGycbhBoLPAFsgQWa3sEyju6rrqp9L1mnD/B94Dysv9DlwP+K7muHov2KiIiIiIiIiIh0WyrFJiKydvsRVoosqyPf/fsBDwIBluFzQXVVbWOybBRwV9H672MZMWd05KAqKh2bHb0OzUs9vfq1Cur4ZF/blti0L/Axln3Ul0LfHEehP8/BdfU1pwDXYgGb9PGOAX6AZRH9A/hCZr/fBb7WkWMXERERERERERFZkxTYERFZu/UqcVtHszWbsaycZdVVtU2Z27endfmzjbGePG3vsNHzYf08lsxqZMSuAxk6pl+p1RxwCYWgDUBD5j6nYsGlHdu4qzxwJnAscC/waeDC6qraZXX1NdsCxxStf1pdfc0PsOydk4EBwI+qq2rfb+8xiYiIiIiIiIiIrE4K7IiIrN2WdmGbZqARuBqguqq2qa6+ZihwEdZD54PMuvMpZAQNAaYDrwH7ltrxvElLmD/ZDmnqo/PLBXagdfCpD1ZmrRILvmRljyFrB+CK6qraQ9Mb6upr+mBl4op//5qwgFE2YDUU+Hy5AxQREREREREREVkTFNgREVm7vQIcWWbZEiyQURxEqUhuvxnwdfU1XwN2A04vsY/JtAy0jMCCSZ6WGTcAVA4qJBBVDra7rfC96OeHNC92c2d41zwyWfwxMLJ48zKP4x2gf3LcvwcuxDJuADasq6/5MfBXrNfQ0BL7BctsKpXdJCIiIiIiIiIi0q0osCMisnZ7q41lfYGngd3bWMcBPwOeLbp9Ilam7Xlgi+Qyvskz86VFo/0yz7o7DqBXn+UxIw/MGji6z7obHTzULZ3ZyLCgHxW+N7su+yKD/HoV89zUgc9W3oR3vik5rsPaOK57gT2AYcDYzH38EngX6/kzNFlnR+C8ZJ2vAi/Qdhk3gP/QwX5BIiIiIiIiIiIiq5MCOyIia7dX2lj2Jq2DOjEQFN32Aha8SS0G7gN+AIzJrrhsQRPT/7swudzMhgcMSRc5YDjAkM37zmfzvhXAQLxnoZvJIL8eQ/yogX0ZwhLm9qJ8UOd1YBvgkBLLHFYObgiwLRboSaXZPtcAj5TZd9bH6q8jIiIiIiIiIiLdUUcbaIuISA9UXVX7X+C3ZRbPyFz2wO+AusxtTcn/WwDZZjh9aRnoWa73gF6NlUN6UVHpGLRpn1KrgPXDyQM0uybi3vd7gHluKkuZX2r9RuABrMSaZQZ5X2q9Yo6WwR2wEnMHdWDbBztyByIiIiIiIiIiIqubAjsiImu/+4quvwL8BLg/c9uvq6tqzwJ+ATyX3Jb2nNke+Fdm3QqgHivD1pzdcUWl673lCeuy5cnDGbJFNhZEYxpiGdC8DkOaNojSBU0sWzqx9+33Pld5M94127ot9cZKqv0I2GhOvLjoXtvUqs9POx4Gjqiuqr2qk9uJiIiIiIiIiIisFq6Ds55FRKQHq6uv+TVwNHBtdVXtRZnbDweWVlfV3p+57VFgnxK7eQfYDJgG7ARMB/4EnNqBQ/jOmGX7fWmQH777ED+KlyvvZlbFu8XrPIP1xvk8bZQKnfLAXEYfOLQDd9nCs8BdwNeBDZPbmigEr1JXVFfVfrezOxcREREREREREVldFNgREZEW6uprTsZ60SzGgjfZsmtNwNeAb2HBnQuBzYEvtrHL14Evj2za+pFPNR7WH+C13vfzUa+XS607BRjd1vEt/rhh8tI5TZsP3aofznUoIecxYC8s02g2Vs6tN6WzVi+prqr9QUd2KiIiIiIiIiIisiYosCMiIq3U1dcMBZZh/WhqgQEU+uzcCxySXG7Geua8RJm+O4kmoNfQptEMYjgfVbxCsyuuuFbS7cDOwEigf3pj87LmqU1L/ajKQcUJN8stBn4NXAr8FTipnfvxwBvAgdVVtVM6cmAiIiIiIiIiIiJrggI7IiLSrrr6mm8AVwELgNOBv2MZL43AKODfwG5Fm3k63+MmaxZWmu07wJ7ARZllpfbdAPRJLr9dXVW7ZXLsRwH/xNN7w6axrOe34M1ej7C4Yg7A74CJwN3VVbUzV+BYRUREREREREREVgsFdkREpEPq6mtGAkuA9bHsltRlwB7A3sn1RqzUWTOWOTOwE3fjgXnAXGCT5LbZyW2bZtZrpnUptfOx3kAVQFhdVftO5thH79hw9O+G+82OA1joZr7x3z7X7VJdVbugE8cmIiIiIiIiIiKyximwIyIiHVZXX9Mb+CZW4iytg3YC8AzwMyzgEmY28VimzTlAZTu7X4j19vlmBw5lFhYw6pu57b7qqtq0RBx19TX9gB2xgNOFmzfu2Wfzpk+ni//Z73Njv9CB+xEREREREREREelWeq/pAxARkR7lPOAnyeXXgIurq2pvBqirr/kx8GzR+g74PO0HdcACNcdkrk/FeusUZ+aA9ezpW3Tba+mFuvqaXsDDWCaRB9y7vf6Hw7FO88YPDPUbdiR4JCIiIiIiIiIi0u2UGiwTEREpZ4vM5bi6qvbazPXPYP12igXt7LM5c3mD9MKg5pGvbdq4WwOlE0tHQKsld2cuj8KCOpD04ml2Tbzd+4n5z/YZH/X73Njp7RyTiIiIiIiIiIhIt6SMHRER6YwLgG2xMmznFS17DPgA2Ah4HfgfMAQ4Ml3Be49zbvn/iYnAOIomGyx2s/efXtHocJSTXbIMmJS5/hFwP3BQcix/ARYAd1RX1X7c7qMUERERERERERHpptRjR0REVpq6+prBwCbAa9VVtc119TUhUAuwbFHTh+9NmLN+vxGVvfoOrWD4TgOzwZ33gY2L9ze4eX3mV0xLr3ooG+b5S3VV7elFx9ILyzB6v7qqdsmKPjYREREREREREZHuQIEdERFZZerqaxzwPWD7xR8vmzV/8tLvj9xjEABNS5uX9Opb0a/EZhbA8TC4eQMa3PxHl1Ys2ANoAAZn1mvCMocAplZX1W5QvCMREREREREREZG1jQI7IiKyStXV1/QGXgW28s2+2VW4tOTaG1hGTVoW9N9YGbfewHbJbbOBYbTO1FkEvJNZ747qqtqjV8Xxi4iIiIiIiIiIdCcV7a8iIiKyQoYAWwEkQZ0GoBnYmpa93q6vrqrdEfhzct0D69AyqPMX4HfAXkANcAvwY+CYVXj8IiIiIiIiIiIi3YYCOyIiskpVV9XOAn6PBXPqgc+ny3o39wNPM/A48Giy/hXAm7TO0nkWuKm6qvYsoE+y/nHAHtVVtUo/FRERERERERGRTwSVYhMRkdWirr7GVVfV+rr6mjOAKwF6+75s3rjngjcrH+6HBX5OAF4D4qLNGylk9xwLjMaCRQCzq6tq113lD0BERERERERERKQbUMaOiIisFpmsmlt7+T4f4WGd5k14s/LhQVjQpg8QAn2LNp0LfJS5vhNwG9ZjB6w0m4iIiIiIiIiIyCeCAjsiIrJaVVfVTtuv4Zub7tfwrRsH+HVmFi2+B1gIvJu5bShWwm0h8Abwt+qq2inAGGBIdVXtL1fHcYuIiIiIiIiIiHQHKsUmIiJrTF19zTrADcA2wG+qq2r/XFdfUwd8rmjVh4GBwF+qq2r/unqPUkREREREREREpPtQYEdERLqVuvqa24GjyixuBkZUV9XOWn1HJCIiIiIiIiIi0n30bn8VERGR1SoCZgN7ANslt3nAAQuApWvouERERERERERERNY4ZeyIiEi3VVdfswswCBgGfBa4obqq9r9r9KBERERERERERETWIAV2REREREREREREREREeoiKNX0AIiIiIiIiIiIiIiIi0jEK7IiIiIiIiIiIiIiIiPQQCuyIiIiIiIiIiIiIiIj0EArsiIiIiIiIiIiIiIiI9BAK7IiIiIiIiIiIiIiIiPQQCuyIiIiIiIiIiIiIiIj0EArsiIiIiIiIiIiIiIiI9BAK7IiIiIiIiIiIiIiIiPQQCuyIiIiIiIiIiIiIiIj0EArsiIiIiIiIiIiIiIiI9BAK7IiIiIiIiIiIiIiIiPQQCuyIiIiIiIiIiIiIiIj0EArsiIiIiIiIiIiIiIiI9BAK7IiIiIiIiIiIiIiIiPQQCuyIiIiIiIiIiIiIiIj0EArsiIiIiIiIiIiIiIiI9BAK7IiIiIiIiIiIiIiIiPQQCuyIiIiIiIiIiIiIiIj0EArsiIiIiIiIiIiIiIiI9BAK7IiIiIiIiIiIiIiIiPQQCuyIiIiIiIiIiIiIiIj0EArsiIiIiIiIiIiIiIiI9BAK7IiIiIiIiIiIiIiIiPQQCuyIiIiIiIiIiIiIiIj0EArsiIiIiIiIiIiIiIiI9BAK7IiIiIiIiIiIiIiIiPQQCuyIiIiIiIiIiIiIiIj0EArsiIiIiIiIiIiIiIiI9BAK7IiIiIiIiIiIiIiIiPQQCuyIiIiIiIiIiIiIiIj0EArsiIiIiIiIiIiIiIiI9BAK7IiIiIiIiIiIiIiIiPQQCuyIiIiIiIiIiIiIiIj0EArsiIiIiIiIiIiIiIiI9BAK7IiIiIiIiIiIiIiIiPQQ/w8acRFQFv/8mwAAAABJRU5ErkJggg==", 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R3WSop3Ni2G7H5ZA9yCEb2sr/qy1+fR3tslEKsERNo93iaPvLPoytr5n613QZ13lb4Tod1OUYSbqpv2pfz/865L5k0zP1f+qQeY7uKzvsSueampuVqX97W1CVK9Ln1kI3iZJRlrEWh1yPyZRRVtm03IN9HIMPlRQ63e6QzTynaY6+moCcTejjeed5AGY3Mqeg/L/Scgf3sW/n7+6P/f0d0ItenIv2Ids8ZqMcYf8qhDgepdysRYX1nwHsaculgAuklE4LFlLKhBDiLuA6lD/fG0KIR1Bld+pQ/kWnozK7r0JFEB1B77yGSpqYiao3+B9UmaL0dOZCKaUzjcLPURmr08s3QohnUNOZVagb9njUw2N/4NeoB/FWQwhxEPBW+rPcRGvEVrq+6b6bhBBPo+o7DkVdF1BTJ93lHuvKxcAslA/fEcA8IcSDqPQjXtSD+GxU4MDjdORI29rRYG+h/NseE0L8DDWFVIsqm3UuHU7xLShlq9e0L71wFkopy0MlD02ffxT1nTsANfWVTr77Ap3THHzvkVK+I4T4O8rimAG8KYR4GFWnEdTvLf0dALhNSvnBFh7GKXTkA1yI43e3iXStg7kpQR+XotwHRqK+c3vagTRLUN+XY+iccPY2KeUGxy2EGEXH79tClV3SaLYf+lsj/L4tdClPhHKGX+zY1nVpBk7vpT8365cC6bosQt2gHnRsK+qhvyl0WIW6W0LdtMlDJYbtbQzp5fJu2r+9oXE5ZA/qbSzdyMjN/H9t0evbpe+Du+nrqY0Y2y6oabiexhVDKW4/d2w7cSt8p53X+0GUMlTVy7iq6cUiQR8sZLbcDqgcYxv6zv2bbVD3tD8WlCX9Njpbe7ouKeBWurFGdunL+f19sI/Hf8fRZr2avBt5Ljl0WI0lMG0z+pqIevHr7XuRRLka9HpdHH3e6Gj7Un//7/Wil66L9iHbTKSU36CmVq5HWTxqUVmuF6Ki73aQUvaYg0eqVBMnoKwBb6CmDxIoS9v7KMvLLlLK2X0cz7coC8ffgbmoqM5erSpSyjop5YnA3igrxDcoC1IKpdzNQfmCHAf8oy/j2EycPkVVm9PRlr6+XXgbVf/SyYMbMbbZKP+w61C1DetRJXIWooqd7yGl/Cedc1BVs5WRUr6L+g79GWU1bERZxL5FTcMHZB8sEn04zjcopex8lCVuJcrC2IiylN0L7CWlPF9KGd/c422PSMVVqKm+e1HBJ8328h3wT9Q1+K2UUm7JYwshJtJRrSPBRnx3e+AMOlJQzJVSbo6/33eoGYbzgHJUhY846n40F7gDmCylvKEv18WuoHG+Y5N25tdsd4gt/BvXaDYbIcRf6AiGuFRKeU9/jqe/EUI8i1IqJTBASlnXrwPSaDQazRZHK2Sa7Q4hxGyUhSYK7CR7zuf1g8eO3pyH8oH7QkrZayoAjUaj0Xw/0VOWmu0KIcQQYKr98eofsjImhNjPzovU0/5xKP+3dPDNtpgu1mg0Gk0/oKMsNdsbh6Mcnd+SUr7Q34PZyvwVGC+EeAXlQ7YK5e83GJXT7EQ6IuxeZ/N9fDQajUaznaKnLDWafkII8TEdGdJ74zngbNklbYoQ4oTNHMKrsm/JNDUajUazldEKmUbTTwghdgZOBvYBRgEFqHJcTShr2QfAw7KHQs9CiM398Y6RUlZsZh8ajUaj2QJohUyj+Z6iFTKNRqP54aAVMo1Go9FoNJp+RkdZajQajUaj0fQzWiHTaDQajUaj6We0QqbRaDQajUbTz2iFTKPRaDQajaaf0QqZRqPRaDQaTT+jFTKNRqPRaDSafkYrZBqNRqPRaDT9jFbINBqNRqPRaPoZrZBpNBqNRqPR9DNaIdNoNBqNRqPpZ7RCptFoNBqNRtPPaIVMo9FoNBqNpp/RCplGo9FoNBpNP6MVMo1Go9FoNJp+RitkGo1Go9FoNP2MVsg0Go1Go9Fo+hmtkGk0Go1Go9H0M1oh02g0Go1Go+lntEKm0Wg0Go1G089ohUyj0Wg0Go2mn9EKmUaj0Wg0Gk0/oxUyjUaj0Wg0mn5GK2QajUaj0Wg0/YxWyDQajUaj0Wj6Ga2QaTQajUaj0fQzrv4egEaj0Wg0PRLy3wX8AngZOJlQfbKfR6TRbBW0hUyj0Wg02ych/3Dgl4AH+Amwb/8OSKPZemgLmUaj0Wi2V+qBFiATaPvWnX/AWQ9c+NdWw1UABKMl4Vf6d3gazZZDK2QajUaj2S75b87ECxe7cjMD8Vp2ilf7Xs0c+cdWo/2x9VdAK2SaHwxaIdNoNBrNdkcgEjTM/N1+nxIdnjVeq5P72MJtPiiNZiuifcg0Go1Gs10RiAQPBZ5KCSPHuT1muBgZb6g3pfV74Jz+GZ1Gs3UQUsr+HoNGo9FoNAAEIsFfAXc4Ni0ERqEc+wHigAl8ZkqrKd+KXfX+zx+cu21HqdFsebRCptFoNJp+JxAJpiMpH0MpXGm+Aj4DpgMZwDhnu51i6xJPrnl1V0L132yrsWo0WwPtQ6bRaDSa7YH/AUd1s31ne+mW5a5sN3AWcN1WGpdGs03QPmQajUaj6Vds69iRfRSPAQ3pD8OTTQDvbYVhaTTbFD1lqdFoNJptTiASzAZ+jVKw9gRO6lFYShCi210uK7UyaZhJ4M5oSfj2rTBUjWaboBUyjUaj0WxxApGgC3BFS8Jtjm2FwC0ox/ws4Fx7Vzr56+ZybrQk/NAW6Eej2eZohUyj0Wg0W5RAJLgb8BqQA1yI8ldeCpyH8vcCWAGMsNe/ohc/MaBXK5mDVdGS8PBNGrRG089op36NRqPRbGnOBwbY67c51t93yAyy/1qoacve6UkZ66yoJTZumBrN9oN26tdoNBrNFiMQCQ4BahybnM+Zj4AZwJN05BUzgL026iDOqZ3OitobtnVOo/neoacsNRqNRrNFCESCD6CsY6CsXo+i6k2GUVOWxUAuUAQchJrCzEHlFwP4AkgCu+OYwZGW0SQMK9s2hlUhZS1CTAQoSLZa1a6MDqVPyhRCHBwtCevIS833Cm0h02g0Gs1mE4gEM+hQxgC8KIXrw2hJOD9aEt4FlXH/K+BF4GrUtGWGo83uwDTgufQGaYl4YtUu2dBuDJMI0Z4Ettb0ru40ECFM4Odb5KQ0mm2ItpBpNBqNZrMJRIJ5QG2XzXHga+CvwCLgDygr2Yb4JzABZUUzrbgXw9PuZtaCsqLl2p8tpFTGhY7pyxSq/NLV0ZKwtbHnotH0B9qpX6PRaDRbglFdPleiLGC7Af9BOe977b/dzc5YKAUuDvyMjpqVGJ4YVtKTEGYcIdZLj2F04/BvonKcfQo8saknpNFsS7SFTKPRaDSbTCASzESlsliBsoBNByTg1JJiKGUszTvAgY7PtShr2GqUItd30lGWUpJjxWk0PE5LWRNwFfBstCS8bqP61Wi2MVoh02g0Gs1GE4gEAyhF62/AifbmOB3Rk06c218CylDO/j572zxU3rJpqIjLHhOOmSli/mTcm2u1MjFVS54V583Mkal1rgwTwJTW0pQw/ECeo1klsGu0JLxq489Uo9k26ClLjUaj0WwUgUjwUuBulK+W02+sO2Ws6/ajgYXY05E2k+2ld6QkZeCt8XqowUMFfgAyUon2vlKI0d20HIRS9J7b4DE0mn5CR1lqNBqNZmNJ1500gYH2eryvjTOtxGWAe6OPKkS3CWJbTbeauuxCbiqWAnBJaym6ALlmO0dbyDQajUbTK4FIcChwF+ol/nJgNnBIFzGngjUL+BC4BOeLv5QWQhh7ta3lE99gWo2N18l6RAimtaxJ7RqvMp/ImYAF3FX1njk01UJ+KvZB1g3V1VvuYBrNlkcrZBqNRqPZELcAJ9vrCZS/V1fSpqsq4NJoSfjjQCT4DHATasryU4R44B+V7zz5nds/7u3MEV3bpwBTSKtZQJYhJUnD7CoDQFYqTrPh7mQtG5hoS91f+dbXHiELr6ifO6xLk5Ubca4aTb+gFTKNRqPRbIhmx/oJwKm9yF4aLQl/DBAtCb8F7Ndpb8g//Y68nV8Fdu3SzgSQwsi6uuaLmv8bsLuqf6miKGVesk3UuVQMQFdlDGD/tpXmEo9/53wrtnBQqvVqVFSnAbQBd27EuWo0/YJWyDQajUazIa5DJWO9ENbLAwbwBrATqlbl8911EIgEi4BLGX3mSuD3KAd79QzqXCB8/jrT9zpwKUCmTMoW4T5ECl4iHZXpVMakxECyxJ3L8cOOIctKjJ+x5tW/TErUtwF7E6qfHYgEs4gExwDf6USxmu0VnfZCo9FoNBskEAnuAXzWw+4joiXh17rIHwLscFndnJcurf/64mkjTjqv3vQV2LtVnrLOilgHUsbyrFiszvDm2vvr6JzGoh1TWqSE0Umpu7pmFuc3RgEuCYw+8ylUgtgi4PloSfj4jThtjWaboS1kGo1G8yMnEAmawBHA6mhJeLZjux9oipaEU8C32H5e3XSxqEt/BwGvA+LFzNE1l9Z/PaBVdHrcKM3JTui6nlImhLfO9HUkkpUyFyG6JpsFqEgJo6i9LyVLIF4DUIGy1u2DUsYAfhKIBHOiJeHGbi+ERtOP6LQXGo1Go/knquD3F4FI8CiAQCT4F5Rlal4gEhyE8vnq3sseDu/yeRK28lThzslf7MrlvIZot6kpuqFdSEhpDU404bfiBt0niy0aFW9Y0qlfIbhg8CHLbsnfbTKh+pXAJ0A6IexrWhnTbK9ohUyj0Wg0B9h/DWBfe/0K++8EVEHw3XtpXwgQiASnBiLBP6GiGqMAKWGI4KADGnNl4tNupye7bPNYidb0uhTCaDXc1JuOqksO5UtIi2We3DFdu5TCGPVQbmAAQLQkvAbYAdiDvhU212j6Be1DptFoND9iApGgG7gAuAdlhZoL7A08i7J8taCy3I9ElT3qjJR4ZTIZE655CDEFZUVLAk8Dp9lS3wKPA0GU8vY2cGhPYxqeaGxc6c7JyUu1LW80PCNTonvbgSlTpITDaNcxqfkoqr7mbqjams+myyYFIkEXcA6q7NOMaElYPwQ12wXaQqbRaDQ/QgKR4PmBSLAapZj8hY4pwZ1Qflc/QVmUpkZLwt+g6k3G2jtIv8wLQcxwuxBiJzqmNF3AaaZMyT1a16Ymx6qXAzcCQ4BvUL5diZ7GNjFR5wKy31j5/G7HNlc8aEqrCkclALeV4qZ1H3NtzawWVFHzmdNq2h4/clXq9hdXzDw7unTG31HTph+iSjy9H4gE08+7vwAPAP8FrtnoC6fRbCW0U79Go9H8yAhEgj9FKSVpcoAaYACwDvg6WhJuQ/mVARAtCS8NRIIHA+cBq4W0pMQIdTsNaZMSpljryjQHp1r2cWyeSue8YJ0DBaTkyOblsXDVexL45P+qPx47KtnUeFfeVA+AS6aaflk3J/umgj3xyFQGcGp06Yxi4HpHn/LAlhX3vJM5Il1DcwwqXUcTEHDIbbh+pkazjdAWMo1Go/nxsXM3224AjgN2iZaEVwMEIsGMQCQ4Mi0QLQl/FC0JXwR8Jg3zV2llzJdKfg38im7qRa50ZbFDrObrXsbyFPCyz0qobPpCMCN3YiswvNL0jV1n+PjcW5iTFk4KM3aff4e6uDBpMjwC+Plj2eNPfiA3QKNQpZg+9Q4S72UMD9pN4sAN0ZJwk/35j8ASlI/b//V6lTSabYj2IdNoNJofEYFIMBPlV3U3Kh3Emygfr6uASuDgaEl4fiASHI5K9DoSuDdaEr7Ybj8Q5bTvcXR7Eaq+5dXAiXR92ZdyLUIUoGZlWoGM9B5UPrIEQpjt7aSMG8jfgLjTxBL+VLxtnSvD18Mp3Y9KWMuBLStT91a9I2/Pm9p0r3/HPHt/RbQkvJ7jv0azvaEtZBqNRvMjIRAJ5qN8uN4AlgNZ0ZLwsSh/MRMYCvzMFj8CpYyBcvonEAnuDnxNZ2VsJmAh5avAyUi5/nNFiMF0uMi0OvfY+904n0dCePJSsT9aQoiEMFnnypiDChRIOtq+irJwpS1fvJMxbP7j2eMz7vXv+H9AI0rhGxCIBL8LRIJ6elKzXaMVMo1Go/nx8CgdSVKPAAba6+/bfyXKKgbwAVBvr6d9ya4ABtvrVSin+WNRVqpcoPvM+52pTK/4U7F1LplCdDNTMy22Nk+NSErgEcBCKXXJwkTLR5lW4lDgGqT8mT3WxQjx6xsK9roK+DPKL67aHtcE4MoNDUyj6U+0QqbRaDQ/AgKRoAAOc2yaj1JYQE05/gTYM1oSfhHgvIZ59Q+vefX/Lq2bew1wsi0329H+PmBY+oNIh1327AbTiEqh0YZKhzHtkxVPH/71ssebd4xVrycc9eQzc9XM1AUN3x6flYrfRIdVzpUUxrQWw60CAYQoBM6LloTHRUvCL9O5mPkAx/o3PQ1Mo9ke0D5kGo1G8yMhEAk+C5yAlA1X1n11wUUN375OqL6uG7nLhZR3FlhtPLTmdcYmG08nVP+4ve8nKOXoaVTKiWFd22em4l+2mJ45wLmOzcuAUfb63+z230aXzsgOjDpjGaJzsrGBqdb4+yuevWitmfHbA0ecOKW783HLFEWJxldeuOjRoxxj/znKYgcqTcfFQGO0JPz0hq+QRtN/aIVMo9FofkAEIsFs4C5gHGrKcTZwY7QknAxEgqYhrT3fXPm/24akWveuNTwrThh6zN/WujI/jJaEP3X0sQgYC/DLujn8sv7rEKH6Gx37s1DWrm9YP3VEOj1rHR0FwVfY8hPtz2nlbA0qQKDQ/myhEtNWAn+MloTft0L++qOGHZu7zJ2brnvZAmQKKeWfqj8pu37g9JnAf1AWuJ8Ci1G+ZXsDt0VLws9uxuXUaLYZWiHTaDSaHwiBSLAQ+K29OAlGS8L/BCDkH4jy/+KUIUfytbcAVC6w6dGS8Od2Pw8A55vS4u9V781/OXPUBS9kj3kYGGBaqTdShnkSUlbY04VZfRjaZ8CeG5CpB/wAoepPn54cry26pPAAl4k1ttKVlQOQYSWsVsM9CRV48HG0JPxyIBJ8EzjY7iMClAOjUUrZOuCQaEl4Xh/GqNH0K9qHTKPZBIpKy8cWlZb/pqi0fFqX7fo3pdn2hPxFob8f+g9DyjVI2VUZg85JwKuxSyDN9+Sl38hNVL3HNBcOTTaf+PDa1989tHWl5wtv4Z2o5Kr+lDBOAkCIIrpXxtIZ+NNZ/SVKSdoQ7vRKjpU4+a/5u+xe7crYOa2MKUQzyqL2HvAVgD8VW+3o41hUXrO/2uc8hM7TphrNdovO1K/RbARFpeUCFWl2E+phZBWVlq8EVqEeaFlFpeVLgUOws59XlBWv6Kfhan4MKIvX5+WZRQWWHeE4MNmaXOfKeA71HfwW5YAPIf9I4DVg9NupqY8masbvKwdUDBVCfoqqXQlAtCRsEfIPBw5oESZthjl6I0bUAIRR2fj3RSlOPdattPkclbD1ujGJ+sp9W1f/5B5/Z7cxQ1qLWg3XTfb49wVqApHgrrNXPjf56ayxJIXgzwP2KLTF06GeEhUJqtFs92iFTKPpI4FI8HhXwYQTk9UTzwWBa+A8jKx1Rqp29MhUw6iRPmIExHIWyOFFzWR8h/p9xYpKy/8EPFBRVrx6A4fQaDaFoiSioMnouJ3vFVvbetuFL/20G9lzUTUeGSdWnzGqJUV1rkXcEKMTJoNQyhSBSHCPwuEnnn9w6wrqDC81ZoYzR1hHb8qnq+sxCoBSYBEqe/+wnFRsZaPp7e0csoCWaEl470AkuNf0kScfh+jUsbSEMRZ40LFtALCHy5KDz2pawD9zp2BKK5kShvO59nS0JPx8bwfWaLYX9PSKRrMBikrLj5pw1w1PA8+5Bi441yxYgMioxlWwCMNXj2vIXNxGG095buSfmX9ml5FP4Bv5vku4mwC8KGva50Wl5UcWlZbv2K8no/lhEfILINOFfPXKujnSYyWtMYn66ovrvzmyhxazUFYjlsghycLCL2h2CxImI9wy5ZzqjFS5MnZ7ImcCH2QMbXBs/68hrVmAyg/WU84xKV0of65dgEFNhmcX00qCtLrKpdNkTAZeCESCOwEPdFHGQFm8um6zgD/8Ul5Y8SHjuSN/FxzK2GqUT9pOgUjw2h6uhUazXaGd+jWaXigqLT8NeMw97AvMHGXgSjUOJlk9Cc/odxECZMrNoMVTec/za34/YC+ezBkPgEy5sOLZJFfviky0u9pYwMkVZcXP9cPpaH5ohPx3Ab9E+W0dTqj+nT602Q8Y/WRy/9zHRmTe841y6ifLis9oNjxZqMjIfFQRcIA7gJOAtaj6jz9zW8lvE4Zrh65dbyxTY1Vr5ngLhwAgpdU19UV7TrNeks1asaxKb8Xug8SEt4gbZke7zm12iS6d8Q1wClBHqP7lzR27RrOl0RYyjaYbikrLXUWl5acDx+XSzMjaDGTSg0x6SNaOQ8ZySazag2TNGOLLp7HKGsQnVoB8K9behzCTmBl1uIbMdnZtAEdv27PR/IBJ599ys2E/LUWo/n1C9Y/MGRS7f62Z0W79ajY8ewHHAweiyhHdD1yHKpU0AggAZwMiYbh22KW1Eq+VlEjZst4x+viiPz5en/fb2llMa1vD4GTLes+jqbF1G8z8LzzNhfv5Pnk57tTlOrdJolJi3IGqVPASIf/5fRqgRrMN0QqZRtM9dwOPDqH6rNe9v+VN+SfuWjaX2KIjkK0q+bfVNIRk1Q7IWB4pTM6MX8//Kn+GVTUBaTkfDlbXvt/dVieh+cFzn/23BhVd2DdC/kHzXAU161yZufaWGB1JWwEyoiXhX0RLwn9GZfE3gBxPSilahrSYnTGImOESCJHZqe/u/crS+yTKtwyA8qwiebd/Kq2Y5FjxdjEhZaq05gtiwuy2G6+VaGuXFcwflj/ncoNulUAJXBUtCS8GdnNs37X7AWo0/YdWyDSa7tkdYE9jPoNEHQBHmF/g6lTbuDMpTJZaw4nXTCJZNUW5x6RckPTgHvoFuFpBTS313IlGszGE6m9FpXYYRah+DkAgEvQFIsF7ApFgeSAS3KW7ZgutYXd948tzpJPAS2cfrcEARaXlXqtlwBpQetaRtVUc27gYS/Ty6OiijLllKpVe3y1WtQ6lRM4CPosZroxWw8UcXyELvflpsWSe1TZ/WttahqU6G98MaTEhXseIZIsvvW1kovGRmVmjX+9hTIIO5esWVNDCUuCenk9Ao+kfdJSlRtM9NwMPf2JNzloj88UQUctHqcmrk7iGsL5z8Xqk6opI1Y3GNXgurrxl9lZBYvVubpST/+NbceyaHxOh+rVdtlwCBO314SjH+k5USX9bhkzSItzOzU4Tk9/++5/48n2OExk1GL4aXigExCCElEileDWOi9c1LPLkDXd25E+1MTLRxNfeAhLCNL1WknMbopjIp2b5Bv1fN8dz4qo1M6b8bPBhdZkyZZAuWg6MSjbxwuoXOWT4T9qFq03fmBbDPaq7jmwWAhCqn+k4L41mu0MrZBpNN9hO9zlFpeW7HRe7+dy9jW/WvmhNq0T5f53Ut14ESMdbe8f6nC05Vs2PhJA/gJpWfIO0RStU39yNZFsP6+3cMmTSFS2G+5wum+cDO9nrWYFIMA+O3UV46zEyajBzVtYgVLFuWxm7HvjHE2teveP6gmnnvZzVkaqs3vAk630D258vMeHi05ojH5w9duUax/F6fbFpNtx5CWm8g/JpA8BvxReF/Tt84papfGxfzBbDfXKv06Sw13pbQn4TNRWbB9xFqL6xt7FoNNsCrZBpNL1QUVY8C5hVVFp+M3C/lzjHmR/VLbaG5s2SEzfUnGT1eITZhrRcJKumAFwG/Gvrjlrzg0NFRr6Jct5PJ0Y1CflPJVTfNc/WvcBAVPmgm7vrbkFmZgOwBJV9P01+F7GbRUb1nZ4Rn94jjBQeK5kfl2Za8VkHhKMl4XpCM357TFXDvm/6rAlx037p6JwLDAR8mlGwj4eVNX0+ZyGIC9eBSJlAiCeB1q+8A0/8yjvwTJTPXBq/CneWCEhKIVyoagQF9n4VuBDy3wKchUqAuxL4i71/CqoUk0bTr+i0FxpNHygqLX8KOPmf7ts5yvwMSwr+mzrs+j8kS7p94CkknlEfYmTUIi2T+Io9Vi254bzhPctrND0Q8l+L8oECaAUy7PVyQvXH9tLOCxwHLCJU/2V6cyASHAa8LiXjkbiE0aO1KojKut+BlC0I8TrKH/LKaEl4+dib7/vEPXT2XkL0/DyREkuIDr/libFaDmxdxX9zJsRaTU+vWWOBE1HFyt9ybFsOjEx/MKXV/Myql55/KmfckodzA23AT1BVAn4bXTpjwBxPwcJXM0eyX9tqpretnQGcaTd9h1D9QRs4vkaz1dFO/RpN3/gTULWjsQQAQ0jiuM8DPuixhZHAyKgFQBgpPMM/H7L1h6n5gfIkyioF4Jz2e3MD7R4DnpTwWTKUd4Bj+3nAZCFwg9Hb1OH6SVVVVOVPgJORclEgEjzVPWT2kG6UMYkjgMWpjAEc0bqcNa5MHMpY99qcMhocCnyMXb/SZgRwNbDQlNZnT61+xZUjE2dkWKlrUWWY9gCmRkvCtXfkTU2dN/gQ/uWfwgWDDiaSE3gMpdzNZv1C7BpNv6AtZBpNHykqLc+4yvXkyxeZMw9YJgfxs/i1rGWARCXmPB3Yv0PawsiowTX0Swy3yk1mu7kMi5aEdQklzcYT8s+hw8frDuBJQvW912kM+VfP8g4cEiw8kBbD1ZYQZnG0JPxmIBIsBmYCWK1+hK8+PesnheiwlpnSiqeE4QEYlWhYssKVM8gSogJHIfKcVMxqNL0berlPoQqYAyCk1Xx+Q7S+PGt03hpXVmYv7ZwMRE1Dfmv31XBuQ3T8tbWzioB137n9C08fcoTRYnQKVFiAsoTtD9zm2H5ftCR8UR+Pq9FsE7SFTKPpIxVlxa23JX/620mxBzk8fitrlX+zAK6hkzJG0j3iUzyjPkaYjkSx6jHXU0kbjWZDtDrWo2llLBAJjg5EgjPsVBfZXdqUPZwzSdabXhLC9AFXAERLwuXAwdIyfo0Un1pNgxviq3YlsWaqlLLDUnVp3VzPyERjJXDfMnfuxG/P/2c2sLcVz2yf/uyDMgYOZQyQUhhZD/inDEPK7pUxKclLtiJsg4EprSVAfbQk/B1wLHDbwFTr0dfWznoN+BR48V+5Ux5zKGNJVL6/v6Asa7cBzhwaXa+TRtPvaIXsR0QgEhwaiATPCkSCRf09lu8xy8BOTNaB0//lLbD+a2ap2SVhgEypZ5GUtAK9WzQ0mp45AxUQ8js6B4aE7X1BVFFvApGgCESCfw6MPvPYlzNHPpIW9Kdic5+7dZe9ApGgN1oSflsY1t5GZt1eZs7aXDN3OcIVM5LrxpORTDEhXsdPmxbx2qoXZkdLwhdFS8JJgGhJuDG+5BBrdEucTaTdAteojG/dSAjqXBlIIchLtfHKyhdmA8cGIsGFwOXAje+veLYW2NluEdgjVjkDWGV/vjNaEk5HZ6aVQQNV+ulregh20Gj6Ex1l+SMhEAlmovwlBgHVgUhwMjAW5RT7IsqfYk9Uxm4L+B/ws2hJeL008z82ikrLBcqHbDowDBUq30THW/ZnKH+WBuAcMNpSjUOOMnPWDLHimcSX7Y3ha1jsGvTNofMv+lvFtj8DzfeNotJyUVFW3NmfJFS/GLhwA03TbU7AVs6kMCSAx0otPbPxu1+XDtz7uiwr0RyIBHdG5SkDwMxeh5m9DkC0YrKAPO7x72jF6kc3PfqHxx6W8ZxQRVlxOsv+W0vMAbsbNHU5ulRTk0KQasv8xvS1tE9t5qbaaDDb87kyoa0BRIoFZg9KmY1XWoxINbehqmcMB8YBJShF9GPU7/LLU5sWvfmHgmnjgAHRknBaMXs600pcGRdm4MSmxZ/cVPPp4YTqExu4hhpNv6AVsh8Pf0MpY6D8MN5Dykm240gSFSoOkL5jngHcCXyyjce5PXIcqqafk2xU1FcVcG1FWfHXzp1FpYxLuFqXkPIMQpr1VnPGAd9ddP7KbTNczfeVotLysai0FsOKSst/XlFW/MiG2gAXoyw+tUCZvc05vSkA4oY5+gm78H2z4c5CJSje25aRdJMX7OnssUY813WSx3qPxOpdphWVll8CeICrXSljgAXnu2SKZLrEkVAr+clYZWXT2B1M3+L2vhoMhyHZQkbXHSjMEV/SFdNKrU0JY3B6jj+FaAauBJ6hQ4GsIFQfJ+Q/EPViuYRQfSwKBCLB1YFIcFeUteyYaW1rR5zRuMA4oG31gcD5hPxfADmE6t/qemyNpj/RCtmPgEAk6MeRXNFmUnsixQ5lrCu5PWz/sbF+8WTFqoqy4h2621FRVtxSVFr+R5TP2JN0jozTaHrifJSCAWpqcoMKWbQkvAxV9Nu57eVAJPhLlMI1DRg/MNX66t6taw55IXuMy5RWKiWMwY4maWWsXkqyBdJECOJ2OjFhWJh5S0fJRPZrnuGfgRlvOaSt4qJrVs46P9NKUjz0GOpcGe2d1bq8g2TSF0cpb/YROvQ9CfUpM/WZCYd3PZ+UYTrHRY3pfZRQ/VoiwZuBfyLlCoRQylSoPo6ahiQQCQ5H5Wm7DmVJbAay3socwQcZQ3l/+TNkyeRu1xdM++d7GUPZ/e7i9//+y3Kn76dG069oH7IfIIFI0AxEgn/b4YEL37z87mOWCGnVAZM3oatfbuGhfV+ZDTyKusE7WWtPZ65HUWn5T1FTLMcBD7ExhZ81P2Y+72F9gwQiwb0CkeBVaR/RaEn4HyiL2anA0HVmxlFX1X01+pjmip+nhDEJNaWZ6tLN7UIwEHina/9mVrXXVfgNwt2KMFKZr7smXtccH9A2wIqRa3XuxmsllwjD+kWqftjH3QTyp/5c/eHT13gfnrKhc8pMJeJvrHy+LBAJZgopnwBGIsTe+7euus8pF4gEjwMqgMUo6z5AVnp/HIN3fUPvP3PI4Yc/lz2WajODV7NG7WfnY9Notgu0hewHQFFp+WWom+vHb3muusY9+sD/JQxzSspw8WrmqPVKilxaN4f5nnxZafjEHO/A3kqODA1EgqcB/4uWhLstwfIj4Q1gquNz+s3/YOA3wK3dtGlPWIlIYuauOn7Sv4PXCEE28GC0JLyomzaaHzkVZcXPFZWW74+amnumr+0CkeBE4D3U9/KqQCQ4FpXu4d8o61cwWhL+J4RX3QYP2PkfFgUiwSxUKo3zUVOe/wechxBdLeoAGJnV7et7tFSPPiF+06fH+p8+YJlHuVNKicQy5sVM1xT3oOi9wJHJulH7mzkrb8BImcDMG2o+u/+klorn7/bvuMHzEkjPArf/kVTjkLfNnDXtCtb+rasP6iJ6CvbzTCbdQrgSoKZhW4FMhOA3g/bbFUdlAkNacUsYG0pIq9FsM7RC9j2nqLTcDdwOmD8xPjipSKw50RIODauLsuW1EszMLGKlK0ue0fjdwjnegRN66X5PVGLJmShLz4+OotJyA0fOJZsWOqZiJvXQ9F/AUcDB7uGfucysakGHf8/ZQNEWHqrmB0JFWfH7zs+20jQGiKYjHbuhiI7v5HCUdegYOqYijwH+GYgE3SjLtwf4e7Qk3IqyxH1uH2s3oLinsQlBe0K9Zl9r5jTj6532cX3NTNsVzU1qddJkhC3uBd505S3bN7p0xj/aEMf9acCep32QMeTWfVtXW2c3zjfmegbyjnsMuGPdHq/Z9PD7gmk7JVeOnWpmrwEBhckWftY4v2tC3Gel5GwhEMl1gSxX4bdtwkz5gPa0GoOSLZmVro4sG5bKr/Z3fqT3Ns32h56y/J5TUVacACowY/zU8xpCIPZqXdujvFdacqknl6RhGg/nBibQU3bszkzfUuP9vlFRVmyhspU3owo116KygC8G5tJRD69ru4aKsuIjgKFGZnVXH7ShgUjQ7K6dRuMkEAnmo6bM5wKvBCLBnu7Zb6JeniqB30dLwrXAwyhrbtJeB7gBlZOrDPhruvHZ4ZP2veXOg5aY0voClefLye9R0dc4i3jP8xVQkeGqObxpFSX189i/ZRU+mXoHFeySxgB+Abz9Wuao/zyVM/6YNzJHTrpk0AHykryjmz82RpPXkPXFhHhdRX6qrT2PRmGyuTa93mB4Vsi2AYn48ukMrSzggtWVdxiq0kA70ZLwc/Gl+z82eMlkDmusREjRPofqkqnVppWi0syYkpWKS5+VWO5omoVGs52gM/X/AJgYvuZi4W0Mu0nx96r3+NI7kPv97UadKPAP4K4+dea44TqYh/KBuiFaEv7Rf2GKSsuPAKyKsuLXNyB3DxB0D51VZeauKrQ3J4CfR0vCD23tcWq+/wQiwcOBVx2bhkRLwmvtfV4gL/25h/YFgBEtCVfZnyN0KDPPR0vCxwciwR1Mac1OdS4IbqGm+14DfiET5j1Suo8SrrYsZ91LKbGGNLiXWhnNQ1tNljWZnoldx5BpJU6YtfzJZ2/N21k8YN+X8pJt1Ll87Z28uvJ5lrpzeTp7LGMTDfy84dvmR3MmZC115XBs89I7T62/4xmwLgVDYsT3Ft6mr2XrgBPsF1IAPvnDtP12EwvedouUWVJ48LqPMocOBMiwEtWthjtdaJxDW5bXvJE58kVUtPlV0ZJwtPf/gkazbdBTlj8AhLfxOCEkSQxeyCpiXKK+fZ8Vz2g2PK0PjE403LnUnWsMTTSTI+MsceeSEN0Yabr3J5uMekuew4/cOb2otLy9yHNRafmVFWXFd/Qgl4NK1EmyZnwhnuZnTF99LXB7tCT8zbYar+Z7zxfAEtSU5TuoNCsEIsGRqDqqIwOR4F+jJeGe6jEmgXNsi+wHKMvubFQKnHcI+QeOGnbcEcvcOepZICUmUqaEYaCsRycA+wl3aqBYz/9f1adc60+MsWdLuypjKWBZi+G+5f/ydpn5WubI9qlBj7MvIXg2eyz3+nfAEgYDUm0E67/2XNCg9KQm4frMN2nmtVJypNU0BCOjWghXYpQVy345EJn5GTAACEWN6ETsJLA31H6We1TmTz4AjCTGWvs8ABgQQ0YvDZ9t+909FIgEBXCuVsw0/Y1WyL7nFJWWTzLzdjjUPfhrkLB/yyqKW5bShJf/cAjxdZN3J+UL/d13pVHrg8nxGuqTeXKgUS0ez5nAnXk702a4VEJH0Z2G1onD+JErZHSevt0bVVOwO5qAOaZ/2VT3kDkAJ0kpLo19V1xaVFp+KHB3RVnxLVt3qJrvO9GScE0gEpyKSob6bbQkbNkKxAV0BI5cGYgEz0ZZsO+1pzVPAw6y5dK/6yTqnp/++7e3M4aVDUk2u5e5cwAYm2j4+qL6r//5u4HT704IE5+VpM1wDXSOSVoiIQwpcaa06B4T24k+4p8yBSnrAT9ApcsxUygl33ryGyxh5ALUGR5iwrTcMvkrYPkeo05dBxwlBJg5HdljhKdpf+AQ++NeO406rfm8+mjlYKt10DJX9qsoK9+OCcPcGyAnJti11uKZ+nMGPFxa7vJNIoRKCQLKDeHUDZyPRrNV0QrZ95/fpuqKvFbLAC42n+ckuQSAK+rmMCN2Ea0qz+sxl8Wu4rzky8yURfw3dbj4yvtzdo+tU8oY0AdlDJRC9mPnLtRDIIWaCu6WirJiWVRafoA5YOFLpJNvJr1nAfvYIjcXlZaHK8qKa3vqQ6MBiJaEm4CvHJv+hYqKTGMCg1EvB/ei8pfd2E1Xri5/qTQz3CvdHWUdF3v8O147cO/QTdWfxBZ48rxrzMymlzNHZSJEu++aMGS6YGSnguFATy4P9i7TJyQSw+osIATvZI7InRKrJi5Mftb4HdkyORflf1aDKhBeC+RLKdYg8QtDZgjBt6RLJ0m5c0KY3J/X7qrR1Q+OesNjvVp/uAF8UFFWnAxEZq5w7F7eVV6j2dZop/7vP18BDIonOSP1WftGn0gwWlTiGvoF3gkvTl4yYnnquuTP+W/qcPJoJJNWJsVr2a2tUjXoyZdQyvZ9QlqJQCT4Ssk9Jzy07E/D/4+Qf0T3jX642H5jecCAirLidzcgW294Wm5BOVYnZCLzESNrTdJVMB/haapk/bxmGk1fOMexXk1HYM4C+293yYrXKzw5LNn02U+aK1KlNbPIshJ1aRlLGAMfyJ0SPrVx4a9ezhqd7VTGutCMlHKntiryk62yF2UsDpCqHetN1owTAEJa681/fust4JzG+ZzetLANFR15O/BmdOmM2x9c88adQsozhZC7CEOOnN665sA3Vjz31JhEw+tF8fp5XmlXeOvFJ9pwxwzPmDfeypgwsyEQCa5CKaZXAb+eGlt3PSH/rwj5byXkH9pjJxrNVkQ79f8AKCot/3iG+0/TWrPr+F3BdLKtBLesmcVH3uHcM2h8u1xybYCJ9S6apY/nfddw3tBDmecZwEmNC/kkYwgrXdnqngoxREfB7OmtqxmWapHPZI9rv9tOiNfxwuoXvyZUv9M2Pt3vFUWl5Tdixn4izMQj3jFvvy8lHwqBkJJV888PD99wDxpNB4FI8CBU3dk0S1F1HXdBOeDf7ZGp8T4rMbzB8IIQ5KZi/20wvXvSOUXLS0BxdOmMcUAGofq5gUhwGfY06JBE82Nr3Fm/AFYCOY52a1FRmoeTtpj3oIjJpCslXMlbgBZpiZGxxYf9lJS38Gfe55One15xXTz4INaZGXisVCpumCbAIS0r2Ldtdfz+3CmeXWPr+L91H+GhvZzuYqAcuAJ4n46yT29+7Bt8SHnmaEAuejJnQgbKYrhBq39s6b7lsi3/3ArfmSejrIsAbxGqP6S3dhrN1kBPWf4wWJMtWrlk4H60GS5qTR+3F05mrregk9A15pNc6J1Lo8zgV57TUvM8lgnwTM54Lqv9irvyd07fVzslS4wJF7mp2EKgPWfZAk8ebcIY6UOTpqi0PA94EFVo/EpUmow/mP7lmFmVu8iU+YQwU8pCIBgWiATPipaE+1KrUKNJs6tjPQlcEy0JvwW8FYgEy4AD48JkYrJO3ln1kljozmvxydQVL2WNPulj3+CbDGktWezJuwSYoyKmwwvbe5MyL30D2CFRfUqb4WqqM71OZQxUPjMPHTn11lPG0vqZcCVN1PSpEIbEO+61uNU8+M1zq95dPiFRe+6bK/5Ho+H++Etv4R5XFu5LEoP3fEN5K2O4RwrBS64sjmpZxpEty0kicCHHvpYx4rKyAbvtdX79vClnNaUNgnw+vW3t5Olta73AhU/mTPgMaFzvynVRHGXKhYxnFdtjrHJIFnRtqtFsC/SU5Q+DG3+fKKmMO2YWEhjtUZRSQkG9jyqPmwdyJ5Mp2sho8xpGyr75xqoZnWzqsfMvfYWsM3wdCWTtacxDh59QGYgEs3ts+COiqLR8BPAAcDywO3APUC98dSl3YRQjswaM1E+7NOvRB02j6YEZwDeoaUoX8EAgEkz/NpemhSrNjPcuGHzososGH5x53uBDX7mmdtZfXl41c/CLq1+cHl06IzO6dMYwQv6uFqT26ZLVZrYrJozurERfSMlbdCSctYYkm+90W8l2b/su+plwbPeY2WsPeaBw1O3ANx6s1dcOnD7r3YyhjX+teh8pBAnDRNodCClZ4Mph3xEnyh1HnU5owB5cVbgvK13Z024q2DPnG0/+GlRKkJsJ1Q8jVF9gFwxvBuavN3IhQEoKki3s3bSuNb5sHxASV8H8oodzJn6DeoECZYXTaLY52kL2A6CirPjLotLyy41Vvsdcg+ZB0hfbY23jU/VD2k5Z7snwemtGMCxrLg9lBABwWymub3hWXLsmxnx3HtPa1pIlkyyr8XO3cThx01qUlbWyxkTu2WK4GZpsptl0BFTZN8xq0zcBNW3x3DY/6e2AQCR4EXC+1ZoXhf3OxmwTWC6QLlBv3H/DMlqlJFtlODeElDIpDJn+3a3n16PR9Ea0JLy28qYhVx4w4sR0brIslGP7AuCfqNxhgypdma8BswCkELvPc+cnRicbeTVzJOMT9Q/vGatSaTRC/sMJ1atcXkI8hF2/9ltPvkSIsXSDEHQyjFsQH5lsGrTYk9dZUEoEWBJmIcRu2E76F9V/c/A3nvwdvvHk827G8EsAnskehyktUsIgJxXDb8VZ4c7h7rydwa488ljORBUNbk9Fnjz06COiJeG5nY4Z8g+NQk1g9Jn3AJcg5US6VC6pdmXyUVamKSvFA96id04SrvgJf7V2OfqIluXewalWUC9V1/XtP6LRbDm0QvY9oqi0PAOYAkQryoq7OoTvYjWNIN40AkA+SuPI51f/rn6UUTXoS2scN+d0+JLNZA/OMWZAEkYkVTfLrYEYtSMxk5NJkjn2D+7Pg9O8s179wlvI9La1NBgeZnsHLqo1fWtRJv1JqDfRr7fBqW93BCLBwUAYEEZG3V6uQXNw5S9DptzEl+2DjOccBAgZzyWxejeMzCqshuHIpNflHvGJJVyxWmHIrhYzjaZ3Qv7Bg+D5Mxq+49HcibiktSApjNcA7KTND0J70tj52H5jZw453MiUyWSL4Xa5pDXm6dUvMSbReGBw0IG/+CASfMPOwfU3VGHuAoR4H9gf6DVyEjAqzczfVrpEh0N9WlYIJBi7tlWmvvQNmg9MthK+zy8fcPDvFmRkk2V1vI8kbev+/i0r5c8avxMXDT64c1+Ax0quiRuuSiAAPA80BCLB24HvoiXhMCH/3cClH/kGrwaGApjI5vPqvs2akTuRuDBJtc8iSHFPzs1LrnLtlw8QM1zexe5cOTjVKlD+aRrNNkdPWX5PKCot96ISO34OfFZUWt615Md9wHeoUHTfvsY3B4wyqgYB7CwWEar5lD3a1nJ483KG12VQJf0AfJCawm2JkxlprONiVzm3ucNgxMVsRp0yItksj2+uYHCqlQmJ+u8+WvHM1GhJeF+UM+0ZwG7REocPyo+LmJS0gnoOmbkqgl6YCYyc1ZCeqhEpZJu/Ibl2apPVOhCZyCG+5DAjtqA4r23+sd/11+A12zeBSNATiAT93ezKBnzfegcAkEKMxFY+nERLwjGcZb2EMFsMtwuU8vNQziR+Meigpg8yht4NfBWIBPeOloQrUMrOXrdXvX/E2Q1RO7y6R2WMTvuF4KCW5bOAeufuLCsZQCWXxnC3HbEgI7sAoNnwsEfbWnJTHXUsFzbuLOZ4BmJIy9kF4+J1/KruqyHAVMAjJaegEuVeAdwz+d8Xnw5cDJDEaL8eKWGYEf8Uq9VwkxIGhzeusg5rWc5t6z5oOaZl2Z9+2rgQISU7x9Y17tZWtRK4Gjuhs0azrdEK2feHMXQ49E62l3YqyoqXVJQVTwKeBIjKkSSlkQD4Wo7h65ad+e/aN7hr3Xv8znyCw2K3UpwIUTL4MB4b7eaVzJHUGl6eHpiLd/yrPDXG+sXduTs478QTgLrb7zhg+aBky8qTmhbd9+3SGacQ8k/d+qe+fVFUWp7RNv/YF+PL98lM1atASWGqB4i0BFaznUfTjOEd8zbesW/luod+2dXXzgS0Q79mPWyfsKVAbSAS/HWnnaH6ReHcHR74yqP8zqUQPlSEZXe8lG3F13duB57NHtv6sW9w+qMHeCcQCR4TLQmvAz4/+uq5bcc0L3vKLdfPzp+mMLF+1pa3MkfntS065I1frlq8YlyslhGJRo5trmjOtNSs6MRYHUc0LwNgaLKZv637cHXZ8m8evbh6vrxw7VLW+Nv4R/5OSAR7t6xiTLx+3hW1s/nf6pcYl+g4FVsHHJ3+LIUYiF1iav+21Y3Dk03/QNX3PD1luVcrIbigaa5xd9V7HNOyzA9wU82nzF322KrH17ya48UaARxHqL6zNqjRbCO0Qvb9YTHwib3+Fcqxtzt+AVy/SA4PxnHtDJxenpr2uz2M+e2JJStlvlVPNtEcN0bWOhrcBqEBe3Lg8ON5O3eo8n0Vgn/kTaW5I3JcVBte973+HUZUujIznskelzPLW3gzMJuQ//StdM7bK6cBe8vWASRrx3YyICSrAsi2ARhZlbgHf4VwtwJg5q7CyFyLmbcYjPapmn3sEksajZPTgCEoK+tlzh2BSPCaO/N3vsDxpVuKKq9EF7lBSHnLAc0rs3PSU4OOFEcp6Z6frJ7ovP+7URn/nwKSgUjwyWyZKPll3dyWngZZZWbIqW3ramy/Lhs51j1o7kl3Dxk/YpE3nxXuHP4vf/eM51a/xLn1UTJTKWtSW6OcubLcenb1S08MTrXOG+VZNewF/3Dx70EjEV6ldEkhiBuuG+pN7+/ezxjWeo9/Rw5oXc2AxgykBCvhiTmGIoH/AifEMA47bNhxZStd2dV5qbarp7euuXqQbPAhkZ6Yd8VOseo2R7smYJ4L+bhj24JAJOgORIJXByLBm3qwUmo0WwXtQ/Y9oaKsOF5UWn4AqoTK4oqy4lgPco3YtRahCkL++de6H1uFyssDELkhcW7hWebrx36WzGeZvTFTJqk1OyexyLQSeLCotrJTBUaTmSmT5KXaqDN9uGWKQuUAK1A5iR7bwqe8PVORXpExP9IyLWGk1MNNYAlvg+Ee/mk6qAshwGr14xmpEveauauJL9sXVHqR3wHXbPMz0GzPvEdHeaO3uuzby7EeQ1mJXg9EglOiJWGnyeo2hDjrxZwxHVuEQEospPlIbNGhR2N5MiUp3AMXpyUOpSMq8pSThx5VFqr+9G1Uqov1fckMQ8zxDbzVbneYfQjMHGcGCaiWub5Ls49jYS5IIYzZmQVUN7hP/UPt508CvJ4xkpUuZUD2p9pkg+GtlULM+rj2sCddQ+Z+VePzuT/3DeJfrSe/37Bq4n4isxLPiE+dqXkqgHhg9JknoaY0rwVoEe6rP84Y0n5Ti/tiI/6av+sR19R9+WuUMeK3hOq/2utf5+cc2Loqe2q8uunsxu+uQ/0eb7KbjQXOQqPZBmiF7HtERVlxHJi3kc2MBCJvRs5E4sKguHlp+cOeP//OK5JYCcFLlaPaVrqzfDu1reOiwQcTN0yElExvW8PVtV/iRpInms2yxGnNV7me+uSU1U0DX8somHp02wKGJ1qwIGYIZm6N891eqSgrfnvMjQ8/Yngaz7RaBtbLWO4tIqP2amBBqnHYMsPTdJrzuRVbPi1leJpMI0O51ghPpxQjV9u1LQ/XZZQ0ANGS8Dt2/coRwBtddt+JKt1l0pGwdRTwcCASHIDKS/aJs0FmQja1uEQ2QhUDtxK+sViegWDhynNWD+pIUeG1Uk1vr3z2w2bhWt2RWKxbX7Icei+pJoUZdy/wi3TSaQB5VMuyM9MCO8ark4a0XJYw2Ldtjbht3YcnE6p/u6h0ZoNr0Dw3pprubM2uv5cGclz5C8cJgdMF4C3gf/Y42s2AKSHcdCYZ8U8W11zx9lG25StFJHgzpveaF7LHmC8wRt48YI8XgEJHm0G9nJtGs0XRmfp/BJwdPumpz3yDTwZA8q/oshlnARld5X6TOo9zfM8zKVGHR1qsdGVxZeF+NBhuhq8dzJvNhzaj7qmZJinuzL6ZQWYNe8Uqq4CdCNWv3aYn1k8EIsHBMuVaJcykIZNeYhX731px88lXAxSVlrvBOskz5u1TDU9LALirbf6xszDjv3cP/eJYw9tIomoyVsPIrt1eVlFWfPc2PxnN9w67uLiJijQ8GuXCsLO9+9toSXgHOwr4XmBXK+kuJOnLSE8HArFUzTgrWT0xwzvhpU56liktDm1evmJYqqVwcqLWu1NbNceMOK67YVjAv1HW+K9RpY4AKEi2ptwyZa4zfSTTJXLtg0iJJQQvPrLmtb12j1WllZ3/fOwdPPnRnPF7zfEOTNQZnrtrF5z4MliviMwqzNzVII33XXnLyoEVQIQOY4KFUsReoiOhdT2waECq7b8FqbbrKk1fbr3pS+ftiQMh4GYcCqiDK4DHUTkFc4FgtCT8o4wk12x7tEL2IyAQCT6AXYw41VQY/1/Vc7dOMZZeBnz1RPa4xtvzdj4m00qRiPmpzE6yf+sq/rr2o9TfB+xoPpKrqq0MaU2lKpYd3+5Qlpc7n7ahKlP2r+q+4ruq/d951jrgpxVlxVXrj2Crntv/ARcClcDZ0ZLwZxtosiWOuSfwafpzfPUu5y++7qJIb22KSsuHox4mPXFIRVlx1+kpjaZX7PQW56CirAE+jC6d8QtgtymjzrjREqJ9zjLVPKDZzKppj86Or9r1VPfQL38tBNMAMlNxSuu+5IYBe7UnZx2SaKbKleFIF9GJWpRSOBGYhsMn2Z+KUW96u2sDgCmt1Aurys2xycZ64IC9R5yUqDV936b3x5bvtdY9eO5gw9NKsna0ZeYtqxRCDumhu0+ABLCf/fmj6NIZpwAvAIFLB+5f90bWyGEO+eXYJaK68DVwsB3YoNFsc/SU5Q+ZkH9n4ML7fEPn/jz3xFYgI1k12XNMfFqqoqzYD3DffT9bWWv6qDUBdxKA9zKGMc/rbx2SakmiCmkzNlXbXKHeGEEkcWevak9r/Yl3MOeZ7x/4rHXAjcAl2+LUApFgBqp23772pnzgk0AkOD1aEv6055ZbhC+k5FHgJJK+FzakjNlc3su+C7UyptkYApHgHijfpgAQBUqBEb+p/fIZ4PPfD9jLt16ooOXqZBHyDPtyMg6ftBbTw1NZY9uVMYA17q7ZdTqRB5zbdWNGKt5Sb3oz1xfvICUM8+YBe/z8gcq3niVUX1MbCQ5EOdlnI2WL4atLGB47ICZvqSEEPSljSMkOQjAReBjIP7Bl5U2oJLm7Ady27oPMw7w/WVrlyhyNepGahZ0iw2MlrfMao5Wtwn3p9b9685n1Og/5BwC/R/mmzQJuIVSvXQs0WwWtkP1QCfkNlMJSeEDbap5veX7F2fFrR1SRmzSy1nwdiAQvP6ch2ry3cA170q1cUfypNupNH/5UjKJkQ/aA1hiLXbmpJe5c4aEtIjJqviLpOd8z6qP9Gl0xXNJCIDm9aQF7G6tBZQnfVlxHhzKWRgAfByLBKuCsaEn49a1x4GhJ2ALO3KBgZ5x+Ke2JK1E+L+s/CDSaHrCnI99GZekHOAq4PloSvoyQ/+TFrlzfk45E0MDXVizrf8macdebOZXO7SWo7196PlHKRNY8fEzZ1LEJadFqetZTxrxW0ooZrrQFrRF46YOMof8hVJ8EiC6dUfdo9vhbF7rzSo5rWjJ6Qeq7/N+lpiDMFFbLAIzMmvZZT9Q0pbPsU7aUfCQEh0WXzpBxjFnv+Ibljko2MsBq4/xBh7DOzBgB/D5aEv7TS3/ZyXgoZ9LsXWJVwbOaFuw8Itk8BHiAkP+VwOgz26IlYWeuj3tQUa+gfPeGs/G/fY2mT2iF7IeLQdqiBXg89SMOHzBj0ZNi+u/N3NV/BsY9nDMp9fTql9g1VkWeFWdJ67i37s7ZY/UjLQ+cflXhfsYs3yDouPH9ypP5cUK2+R8Wrth+AEkEo5LNjE000CAz24AbtuH5je9hu0A54j4BDNh2w9kgN6Kcr92owuOPo6ZN7q8oK67pz4Fpvj/Y7gfnsX7KovTnl/Otto/zU23T7ajpWuDgeMXB1cD4VOOg0xxKWZH9VwLvIURoVtuuV2b4FkxJutvtax0K2/qst112P7VJTJjtO0bGG9yhms8n/7n+0mBRaflnwLfzva6/nNG08KK0zM5UZ5W17kpzdgvCTJBqGI7LvzJ9zE41OB05yd4ArruicL/cNzNH4JEpftq4cOk33oLRgImUvyPk/+Zo+PXRLcv2ta8NABbkHTz8hOeBQwKR4HPAKbZiltflVAYEIsEsYBiw0K6OoNFsEXQesh8q6s3zZ8CaNmHysyGH84x/9Dgzd/WDqFBupBDmcndO20nNSzikdeWKC3j3qC9/fcNZq+WAOlsZ64QwLHeqZeBpMmUHLwnBMncOD+RO5sHUka3QucbdVmZDvmr5gUjwmC190EAkeG4gEpwRiASP2ph2FWXFSyvKig+rKCs+EOXrMh7lhHzSlh6j5odJIBIcjfIFTd+31wE1qASofwMgVN+c/4eqvZsM9zjUtNy0aEl4nW/SzJ18k2ZeafgaAsBCVFqNNI1AMTDFXTj/OIcyBj0rYwA3ZVqJb0cmGhkTr+9FjE4Rmss9ub5fDdpvp9u8d/8d+BComSPH/dwpvsLIoTlbpUAzfI0YGdXr+XVJidVlQ+FyV9bcj32DLYC4MHkkd9Lj6XM1kO4jhh33zCJX7r5vZQzj/tzJ+VWGrxpggTtv1VpXZrqY+gmoYAmAX6EqAqwG3nsqe+xNwLeoqiiP9n7SGs3GoRWyHzKh+qeAwDrD96+ajhxjHuB+1I38hVGJxqnAqcAuhOrjAANSsct3ilVLAENajdIymwFk0kuqflRWbMnBJO0M9QBVseHcmzouH+XYu5XPyZ9HyP/HUxsXrFcuphv+uiUPHYgEd0LVCjwDeC4QCeZtTPui0vKxRaXl19ORrgAcOc00mq4EIsH8QCS4ZyASdKMCV9KpA+uANSgr8CGkc4XZzD3/vsXRkvC9QHUgEpyLisRcLNxtflu2fXbEkFb972o+vzQ7Fe/4/UrZKZFsD5zeYrinv7bqhTOvXfvth1l2Nv5MK4HLSrZJiYVFCinX66pZuPnj4B0x8xcIwLw9ebK5TvisuZ4BJBHkpuIvypRrUVrc8LQN7HpwIdTzq71vITJezRg5e++2NelUIRXA31F+clFLGMYydw5/yd+V4KCD+Fv+rhw64vgVwH7vZQydBqxynPsLuzxw4cWE6ucTqj+IUP0wQvUH/K5g+nCUpRvgtCPu+5mHkH8wIX8nq51GsynoKcsfOqH6+hFwoXXvbxqEGTsPKV4XrkQwWhK+yCG1wNlkhxvnPVJ011HPrnJl7vDU6ld2Eklv5d7iTyfJePZOWO49AJJrdoFkxisy6Rv6Zt3oqSAq6KgksDUJA6ffWPMZGTL11L/ixx7lLliY3cN7fEEgEiyMloS3VOSnM6+RyUa80BSVlpuoN+0RqHqjv0ZZFO/fQmPT/MAIRILDUFn4h8ik55P5F945PRAJTgeOBN61lzRHBCLB14A/ol66/mBHC/4R2NGW8QEn2ks7ljBG3pE3tSyOkBvIORa3+04zATiIUP2j55SWL3h/1YWfvZI5kodzJlJr+syk4HEEZ4Do9PMcnGiUla5s8XnGYNwZ89lTfMdJLd9x+PDjE62m6d2ndXXbvyvful6YyYWourl/RwUvtCMlUoj1f/WvZY4Uj619bVRg9Jl+oNmedlwZiARfT/fhllYLdpqOBMYo4JqfN8zzCTj15cyRb8z1DvQC5KQSVwciwae6RF1+iLLOFyLlS6+ueuFZlIL7ESH/IYTqnZUANJqNQlvIfgQUlZYPji85+ILYwqMGxBYdeXDb/GN7DZ0CuPWyl1s+WPFscGiq5YEhovaFJVz6Gpb7hQ4JsXzhb359VKquaHcQOwE7VZQVb/Vw8Xd8QyfMd/sRwFU1c1aPqcnOHpRcv6aezSDgri117GhJeBZqCuMl4MxoSXhjfL+8KIdgUMrcgoqy4lu2dZoQzfZJIBKcGIgEfx+IBA9ybN4bVUIJ4YpPGxP67y2oeo3/Bs5GOeWnA2l+ATyE+n4Ggdvt5KfO3BMplFI2qevxm4WbuOEW3Spitglqcqz67S6mrhQQDUSCF/gmzRxWlxg46/XMkaxxZxMzXG6UJblrX6kDWtcIZyTn1wVJ1ua20GqqPBkfZgz1BUafGfVaqX+7ZOohxzHbpyiF4HHHejsL3H4eyZ4w35bdOxAJpv1orwTO8Vqpk1/PGjkY9Rte/qu6r2YBxzUI9+HnNkR/e3bjd2tc0gIpqXJljAG+DUSC7SkyoiXhFag6wtPeWfncFXRYJvemo9awRrNJ6DxkPwKKSsv3AOszM3cl0nJhNQ2dUFFWvLAX+ULguZc91+wVMJanrai3FrXNWIhKNgnwekVZ8eFbe+xOApFgCLhBSMnvaz778j7/Do8PbPJdf2nbOznBwQf39Fb/JfAxcF+0JDx7Gw53PYpKyy9HWcbeA86rKCtObqCJZjsgEAl6gANRTtxLtkL/btRU5BCkTCLELtGS8DeBSHCQTHoWC1c8K9U4BKs1f5570LzJdrMY4Ada6Hix/hiYbq+/jnIhyGm3ekkpESJOh5ImkVLkWPFYi+H2dso1JpEgxUlNi7+y4NpdW+qvvHfAxMPXuDJxS9kYM8xFqDqbF6LyoAGcj5THIcSJ7b304IM2MNHCOndHMKYpU+SnYqxzZSIlTT4r8XHMdPdUAWAOcMXBLSv+9IFvyD5xY72JnuVAA7ADyl9ut2hJuNsi64T8ZX8csMc1M3ImMiTZXHd17ZeHDEs2X3H60CPPcUidFi0JP9FNWx8qFcZkVI7BqTolhmZz0BayHwez3MO/WOAe+hWe4V/gmzSzt1InAD8H9rk9ebKrWXolsAiVePL+U8x3bn3V89uKud4L1hLyr5ftfytzOKjCw38s2Kt2jSur7Os8MyectzPn189bz+clw4qvQb21BoFX7Azn/UIgEpzkmzTzl75JMzN8k2Y+pJWx7xUfoCxT3wQiwUM2JLwJZAhpJz0VwmVI63+BSDA7WhKujC/b55TYkoNaEqt2T2C5IqhpQ1DKVx6d7+GLUApaBcoNoWvheklni5lACBpN7xedlDELYqt2nScRY5/JGTfvuZxxL95UuPPhq9zZWMIgJUROdOmM/OjSGQehlB4ARsfrrxiYatsDac2XspfoTCmpdnW+daSEyV5tlfYlIDtmug/ryYdNSqYCb37uHbTHHrFuDczVjnGNpxuLIEAgEjxlyqgz9p2RMxGANa6svKsK9xu/y3WLz6WjNm8FKsXI+qjpyb1RU8i7amVMs7loH7IfARVlxVYgMrMS5fMBaZ+SkL8AuAM1jXEVofrl9v75AK9Ye7FDbK+XK8qKjwHb+zx05tGocPki1NvhbdvgFNLch3rrb0HV9DwEYI53IHN8BesJtwq3M5lkLurhlVpPcNtwGR3X/w+oB7xmOyYQCY5HTTPvYW/KQBXyvjhaEr6v55Z97t8AJl5eN6cGKfl7vqp+ZAljHLAP8OqSm057uai0fBBgLrr2koZnbt016cL63cEtK40cmSi5dOD+89/IHDEGIRbSUQS7CFhkW9tcJpKU0m0k8Ex+qu3AXWLrCt7KHJEeyqfAe0h5DUKAAQUFX01ptMyLMVOnAySNDp91l0xhweg1ZuZNIxMNVyx3547MT7a5lrpzp9qWuO6N1VKmAHNYopFVnlzndoYmW5iQWD9SMzMVp8X0gJTW6EQTNS6v0WgoN7ZG0+M5o2E+EoGwDPJazaqBnqr4ga2r7rtg8CGnAwfY59Ze+igQCe6Himx+DXjEEsLpE9eGuqeByjV2PbA6WhLuOb9iqL4e/VvWbCG0QtYbIf++qGmBl1BJGHdqEq5x2TKZBfzn++LAWVRa7jey9yh0D54LUC1csb/bu25EpcYA9TZ7CkBFWfEzRaXlP0Hd2P/TpbvmHta3OtGS8H8CkeDzQPzmVV9N+cOAfQ5MuuKDhCvRfQFgIfBZiXVthvs74G9dEj5uVYpKy/0oS+OyirLiJ4G5jt1zu2+l6XdC/l2AyTuNOu1ohHk2ytriRKD8tzZbIQOeBk74e97UpW+seO7TV7NG7RX15GNIWWMJ0f4dqSgrbg5EgkMCkZlvZQ/Ybdcbaj4XOTKxf6Nw7z/fk5+eqncmc00CLyDEacCeDuuXCcTDle8kMmSKMxoX8KW38LFdY1V/vbRw/6uSwmyvzN3ksxBwdWYq0dpiujOc0563Vn2YvGjQQe73MoYB3CGlWLl3bUvBi4V2JHf3rgMgVGHLVZ5ckJJsmSCFoNVws9qdxXPJ/UlVDlltFH6XKQR+hKDFcEuknFWYaiv8Y80nowqTrRwz/Nj0WFoz2jIbJ7qbB81PjeYvLU8WupolwB23V73vv7JwvyGoqUQjEAkegXrxfBIVmHApSgFzKmReYN0nf57g9w46KBgzXNmoSO1tmfBa8yNGK2Q9EfKfjXKSRUrWVpGb/d/8MVn3505hcryWSOWb1/pD/oeBG9PZprdjjrKahkyMNQ3hStdTBZebzzxNaMYjdEx/gPJJaaeirPgFulBUWj4kaJ76u2vcT/wUZTD711YddTdES8K1hPx/By47aM0yzklexYox8xMx03CDyhTuTE45KtZa+fwl/+qa0X+LUlRaPgzlS/N1RVnx0/bmR7FzGRWVlp9RURa+NxAJrkZNM+n8RdsjIf9ewAcSXClEWjcpQPkkZaBSTBjA/zb3UOfec2IuGUNOsD+OPmHYMdfcWP2JvLZg74tjhqsG2q05D6L8oV4HdmsyPPxuwF5MjNfSItyscGd31/1j0ZLwd3ady65MPH3okUMALq+ZnfhV/Zy6f+ZOWZGw/bAGJFuoNb1Iuyh4SoiMW9Z9tDZDJgdH3fmc3LQo9rW34IL3MoY9ku5QCDn8xUKVg1lIiSmtWNKwC1laxKXAvV5EpBCMjjfK5a5skdZ2FiSLpCtv+ftCMJR0XUohBLB7lSuDqwfuwwNr38BAYiEoTLV6rx+686drPd5BANNTJ/PUmlcoSjbWH92yLH50SbjCvo7PA10rpHvsZZ6UTLJTaFR/tPyp4seyJzwUM1xpk+A4ugtO0Gi2AlohcxLyj0dF5bUB7fZzIRhcQCP3505BCsG33gG85xs6+tiWpb8DVqLqpm3PfAW0ThTLM37legbUlOWfUfXZDNSb4R9666CotHxv15DZ7/471+16IHbxfMPXcOm2tDh14RyAAtHIs64/8e66Ie5fDjoAhMAjLWK2Qiak5Lu1h00pKi0/p6Ks+KGtOJ6Z2BFWRaXlR1WUFb+CupGnGVdUWr4THHscMLuirDixFcei2VhC/kyAL7yFBwuka5fYOg5qXWW9mTnCREXrpaPszgFmR0vCm23hjFS++ddpI06m0dZbGg3P5KsK9x8AHGyLpIDBdHyPVqXbxgwX5w0+NPryqheeG5NouGSJO9cx/8dC4OpAJGgCa7s5dLsG9/f8nd1vZI64wOPIr1pjdvbtcmNxXHOF8fNBB/NJxhBeyC7y/qRpyfuoF7Kirp0LJEnD9CKlNSjVwj+q3vP8K3fKG69kjTq0q+wKM4sG+/z3blnNikzX6uUZyZ92EpLSQqgfdJPhXvrf3Em5ljDyAapcmYYhrf3Tok2mh7vydvr0b+s+/AWhemfS2PWO3dG9GJ1YuWfMyKg2Zcpzdb4V/0W16XPmFBsUiAQPAB5A5W78abQkvKz73jSazePHrZCF/HnATQCnxX739GHZ01/K91b6jm5exn3+KbNWmVmpU5sWmm9lDKfO9DIpXkvUOwCvlWRyvN1/c7u/hhVlxdGi0vLdGmXGJzHpzvWKBElpxFzCWk2o/oq+9CF8tWe5/Ctcar1hEqq+2yO9t9pqPAecm5QGHpHisLaVPLbmVf7nmlrxnj932AqPywOwd+tqzMQyXmPQIdjWzq3EqG7WrwLuBpaiIlNnYT/Yi0rLl1aUFc/ciuPR9IWQ/0LUi8jQee68+DmDDxUpYXBs0xJ5R9X7v5s6+vS3UA7dPoChyeZ/vrby+aWEZlxCqP7tvh7GDib5GcrCdj9wc96Ik0vSypjNNFT29zQS5Zh/oP35LdT020UAtabPNW3kT1ejlLZ5dChHd0dLwqsDkeB52EEwDhbizKUnBN/4Bro7STimG6fG1i25pP7rhuOHHbNosdt/EsAqV7ZY7cp66JyG6DkP5QaeBgqlpFEIFUBgpa3TQhjnNn7HTvEaTm5aNOXlzFGq6/TUJ1Dv8rUf7DtvHtUZiWFd4wDGJhrWLvb4/wxMfnDtGyNSUhz3ePaE9gLoljAMx3QqX3kKPguMPnMykeC5wCPRkvAXqJfsa1AuFj4cpZdkm7/Bah40xGoeRAaxY/FR/a0nX52ClCkpxJ9RL6/pUm1X2otGs8XZ7pWJrcwD2KVrds7+9JLbho4xpBjLI9kTmOct2A3g1cyRxGxz/t6tq7m96n0mxesYm2xYiapHuCV8SbY6tlLWena8NPcQczb1MuvGa26+t+95wyz3k9IyLhVG+4vnP+g/heznbdL9xZfW+Jv2Nuf5AQa0GQTNj4tivqms8CijwhzvQPxFCziyZvkyVRlmy1BUWu5F+ZaMRj3QLwZuQTkPPwJQUVZcDpQXlZa7gf3pHPHW7TyTZutTVFo+AbjTS/zAb70iwxRSAHzuG5SR9rWamVVU/9fLXi6LAoFIMIz9AF7tysqsMjMmD0213AnsvBGHvQSlnINSkorrnMqYUijCqMTBXnu5FpWNvxn1/foj8FOUpexslIJwJ8qi9jUdCtngQCT4HiooII0FJIWU10h4sDsfL6+VVPc5h8I0xzuw8uJBB/0O5QDfzgJ33oHjEvXPoZRG8qyYp95cf3b0c8/A1BjfMPN3+fsWth+yhzxn1Wb3AduL3blDd4hVX//omte866y82mFGNTNXlXN5wb5Vi3z5hc4+DSQr3DmXovzDAC4LRIISda8qQJWISqJ8O48FPowvny5QChdPeUJjWoS5a7psnFQ+b7mo6NX09ewxXZBGs7n8qBSyQCS4N8qR1toxVl06oWDaiS4pyUvFWOVqM9JvXd+5O2pSx4XTei04umWZisYWXECo/pVtOf4twImfysm/+TQ5eTZQds1GNFzyx9PemXj/BwuFYY0HkJKcQCRoREvC1obabjFC/t0T0rw3gWdCpojn7mIs4v7E0SxiON9mZLBkWCUpBIc3L+PtjGE0mR6aTA+rB8lrApHgHRuZyLU3LsZs+6Vr4HcgxTRX/tLR0ZLwUz3IPoGqjdeGivj6AOVYrNmGFJWWH2xkr/mDObB2HzOzymMlMnkgviNHxCooSjZyUOtK/mbt0tBmuHIR4gFH0/YaYbmpGINSraCsnhuDc/p6MCpKOBMpmxHiHwjxqCNH3sXOhna2/rS2cx1KOXCWDTsBcOZG2520/5XiPeCKt1Y8lz001fLsDfl7eB/PmZBWYlKj4g1r9o6t9b+SOTIZw2UihPPFYRpSvmYg45ZQoY2mTPGtdwDfege03yQbDHd3vmq8lTXSfCtzJIguzxkpyZDJuCktT5Pp7VDSukuUIQTfeAsGl2eNwm8l8h5oPJVGXKnFHr4EjnCKWusXNk8f9wrgT9GScNpV4H4cFTKKSss/B+QOxtK7kXBU81JezhoN6lq/C7yMqphQTf+9hGp+BPzgE8MGIsGhwPOokjUV2IkTc1KxtkbT26kYtkumSGGSbB6Ima2MRyPjDUyPVTLLO5Dx8Xp2SNQwKNnK8S0V6wQcTqh+9jY9oX6k6PePf+gZ8cnewoyTqh/1vmvAkgOBC1DWn3Cv4eFbgpD/faCTg/7vE+fxcOoIhg59i9pcFfQ5JNnMGtd6xQiWoxSi30RtZ99Npai0/J/u4Z9eZGarvEnJ6vHNyXWBb4DLK8qKP+ki24xdpgU1bblXRVlxf/ne/SgpKi33mgMWNLgL53u67vNalnXVihWvnCvf/2Ng9JlzgQHRknA6/QuBSLD9O+exkvUPVr55326xdbcQqq/r7Zi2D9eOKOUtF6WYF6B+L/sB5wFPR0vC126gn8tQpYNg/dJFad5ARYM3o4qM/48O1cYCfhJe+/ZlTYb7yMNaV/BmxvBPfl24Xxsd06HNqCjyDWJIaz3FJ8NK0KpiapwGtg2yU6yqdq5nYL5LWqSEQAoDU1oMTTazwu3QC6XExCIlTKQUCFXhaY0QDOm5906sQVmlG4CjoiXhuYT8BqF6y049Mh1YHl06Y1/gfgtW3Jq/64WfeAd/8cyal+9CXac7CdXf3fMhNJrN5wdpISsqLfegcvLU+SaxM3YeIWmJLGEoBbSrMgaQtK1hOakErQkPw2UNf6r5lPmePJ7MGc8iTx6vMBqAtbWZAy9q+PZcYPa2OKftgkT2l/Elh+5tf3rCNWDJ1djmflTY/c+35uFbpKcuU3QEhq6SA3gjtRuATDUPRuQsElIIdojXdKeQjbSXUahiw5vMYGqyTKMGh7kty+7zATrqBqZ5GjXFBLAbavry7c05vmbjMHOXXe4auL4yBhAzDCMkzzz6hrZL/l1RUvwxdiqXQCQ4HGXN+hJbIUsI03/mkCN+C9RGO773PfE0cDxKGdgzWhKebvdbCLyJ8mMqDUSCj0VLwl/10s9SlCJfSWefsBVAIcqu9CVQCxyFutc5VSIDmBkcdCAIwT6tq/l35Vsv/bojoz90r4x1Uv4GJltxYdWsMTMHdBVMK2PQjTLWmslYWUO2FW/7mlFeK7uuXWKutzC/MNlSW+XKzE9vSwmD1a4s8pJtDXUuXzpYQaaEKVT/Mn2cIaMTDe+vcGXv157WQ8oEQri6nP+7MmUOF2ZqCEopu+SY+84MNA4/4cBTb9+vaWDO+DfWmRknAC2B0WfuFy0J5xgohzNC/qNQCjRzPAV3XvyvkoYa0/d4tCTcKSJdo9lS/GAUsqLScgMV7XhALs3VZ+c+us+qTJjVPPSztVm2FVDIzpmrpWSX1jq+8g6IScPyph1DW/wqwHL/hlVMi1XyiW/wesdb4s4FNf30vSIQCY5GKQifRUvCrxDy74qqx/YSofpZvbfmCuBzoLGirPipQGRm2LGvaGuMt73z0vKT8rhrjytdT1Ens3g4dQT1ZJNQX2FR2TDlnclt8sDz3C9yaupLns0aw1ueUbxkTJWu7GrnDXr3QCS4AijewIOwR17z/nbvmloXZezGJ4mp1Na0z0gN7UbcadFrAxZvyjE1m06m2Xx8Ij0rJkEmfLMMT1uTlOIAq3EIss0PDr++QCS4F0pp7nBssqtZAwxKttxIyP8/QvXfdnc8uxTS8fbHISiLWDrzewrlx2RKCYlVe5yKioImEAkOQkXrfhQtCTfY/TyOHVRgU42ygh1Ex5Tdbxz79wOa6OqnaI/9U9/gZkL1N/oeuPCUNmEe1YM5K4GKwG4ekmx+dkyicdff1M5ae8rQo/O7yL+PesFJB7KkUOlzMpGSQ1tWUG1msNiTy8/q5vvua3rlmWlZPx1sYO0jhSEAakxvuzKWNq+lhEGTlUOqMa/VzKnKQAjhlimZsJUygGHJJl5ZNXN8YJQjI4UQnQMUFAfQ4ffKXs3rdv00a+A0gH/k7ZSTaSWOsndlovzxvnS0XQmk3vUNNX8x6CADIf6D8ifUNSs1W4UfUumkI1G5oCad5i3f5z9DBvGSfwhrM6090wLd3Xuq4oULLEO62nc6hJ7KGced/h35d25AbUhP70p4IWNcPDD6zDjfP95ARZa+dEb4lCNRPhJ/At4h5O8+wapNRVlxoqKsOFJRVpz2l/o/VAmXr1FOyFuT+w4xvhx8tvk6v3T9j0DWl4gx7+EZ+SHCW38jcMy8+A733t58fuXX1pjYfk21bbVr9yG5crpI1hTNQznjJlHf+eGozPkbT8jvzRWt44qSjfyz6h2mVxoWVvtzoFOdw6LSchfYJlXF9RVlxTpkflsS8puvtt45bK+2tXitFN51I2V8yWEXREvCB8a+Kz4osXr3t0DcDfzX0eoInMoYdLovVLoy3VNHnvZVIBK8qrtD2r5K6Xx0a1C/sfS+mlRLwSWphuEk1uyC1TTkuqLS8gcm/D00EvUdfRlYGogE/4LyJ+vqBuADZtDZf8rpxylQzv8NXfa1AamkMP5AyO+evfwJ14hE9+UdgTuiJeH50ZLwircvfGjPyCXPuk4edsyTUohO1yQnFR9N5yz1TagyRQe/tPL5xkpXBrN9A2kwPdyavyu35+9ygBDsm1bGQJVMSjOtbXX7etKTyDWzq9qPV5BqEzdWf/KCx0quGh+v4++V73H80KOH9GV+NG1VsxoLubBxzp6GVJckOxVndKLxQ1us6o6q9yxC/gsJ+T2E/IOBG4DKGTkTqh3H2cWe5tRotjg/GAsZsBqwhlBtHOH5iIeFnXqmtx+sEKzMS05YT8J+U0sIk3De1E7y6i+kTOkB/oLyT/teYN9I0m+zwkBOoeNNOhsYiJoa6RO2L9beG5LbQmQdaX6OISRLXdksHFKDYXrB04y36D1vtCTcgnqAXQznUFRa/idU6ROSVVOaXAMqfHT+vvfZUhWIBM9GvRX/Mxqq/46Q/y/Ab4FPRoqqk1ERlkPpbKmgwndmwdnx0j++Z029EPiEjkg7zbZjwFAaxjy09g0AHk8e9JvTyq6bDVBRVvwOdvmtQCQ4JhCZeQbwEaoyxw2AC2mH8HS5j8QN0wX8NRAJ3hstCXdXseIRlJWsAJXS4tn0jsTyvV9BWaHcwleL6V92PiK+Cx2RuHmo7xeo781udKSrqENZyZbTkR/NqSAsBF5BfV+PQr0wgUqNEYuWhK3Km56Z+buC6cUJwwXSwi0lCds/K1k9sS1VM+7h9t5C/ux57rz/jh2476GLPf5OJ2hgjUTK8x3X5ttoSXgFsOKc8Nq2ud6B7TMShakWns0em0sPuK0Uv66bw4xkK8/l2BZnu1vTSrJjrIZMK+mbs/yJkcAjvx64zynzPfntv+ecZFsybrhcsfULjbfjyqxq+0X2wb60pbPJ9DDPHDAdoCDZ6j6sZcXttuhuwKB1hu+kp7PG4rGSTue4tds0kEnzo+IHo+lXlBXPBor3Mb5p2zOxlrMa5ncuNi2BHiIYhiUaU0iJkJbcsW0d09u6y6e4Pnmp2JrNH/m2w76RXIIKbnjiC9+gfwC3AsuAv/Y0BdOVotLy4UWl5Yfa6R+2FWvLU9OxJFw86ECcaQNSzYXd3YXvAF4aI1bPvT8rVAHs5NjXjEoZsEHskisPoaYqXgIgVH8N4CZUv/d1N4dXAb9G+RR1KHkh/wFAxcOeshsrfGfeWVFWfEpFWfH30aL6vSYw+szm2/Omfv63vJ3ZbeRPrdDYoZcGIsEJnWQiQUNK5gA3oyzIvyKtvAuxnjLmYCXK8tQdZ9t9uOnwIQSgoqx4JXA8WMs8Iz7FlbccM7t2tx76mYYzd5iy7i5CBah0x8JoSVhGS8KLoyXhe4BvUC5Rc9PjeCx7wmHvZgxjrSsTF5KEYdrKj0GqYbjPM+adZwKR4FvX33XEfvPceRWnDTny+MUef/rFbUX6QKZSUpzPkDsDkeC/A5Hgz2d5Cz9Kb5wQr12+wpWdihvmen58QlpJgIRhcl3BdPZrXd1VBCkMXs8ayW8H7nNQYPSZRYTqzyjPKnq6Q0DS6PK5YoaLTKtLzmXHLd9H0iM7/y8ldsBNtSsj71+5k9PbJ0fdeQWHDf8Jtw/YhVezi/DJZB1QiopihZDfRcg/ipDfGYav0WwWPxgLWSqUV3zZ4J2vq5dZvuomL5MTtZ2tYwKQHc6e7Xl3gFrDayIEEiG+9hZwS/XHjG5O8PhA+wVUSvZtXc0KVxb1ppfpbWvZPVbFfq2rnT5U3wuiS2c8AjxDqN72SQ9fDVzd1/ZFpeXjUCHgflSyykO2/Ci75dTnrX3ePd76wFtndChjybpRJNdOXdFV2DdpZjXw33NrvrjVk2j6aZfdxdGScLMdCXc5ys/nb9GScHfWwYHdrofqUwBFpeUFYH1lZK4bLlO+JUWl5VXAuq+8mcv9osUHUGENvu6g0vLbKsqK9Zv1tuff9/p3TBcHN4CxKEvqr9MCbUv2LfWN+SCtcKRrVaZJ0FkhSiOB56Ml4VRRafkuF5rlxVe5nszKEPFnCNV/DrwInGjLvtS1cUVZ8Utjbnj0cIzkPDb+xXgQjnQcXWgJRIIDHCleTgZOtdf/FYgEn9rfPeAr7MCWpDOtj2XgHvR10vA0jwfGf+0ZEL6icL+CuNFJ53gsP9V23PBk82ghraYaV4bz9/EvlKW95P/Wfbj2tcyRsTmegg8XePJ/AkS7jnlQorm10p3VPi250O1nQDy1Zte2Ss+XXlWLaWiymdXp8lDKR6wI9eLze1Nap6WE0ek+PzZeb33tLTCcLijj43U0Gh4m1gnj3QEehLDkkFTzUo+07lnmzvkTdvDCKrM9tuGGO/OmXtjmsLYlhJF7cMuKE97JGP4lIf861L1vb+A9Qv5DCdXr6huazeYHYSFruHHg6DMHH/bC/XlT9nkifzS/HbgPMbq8uDjisV0yhVt2PBtbnS9uQvBA5q71T4uOmTg3Fh9kDmOpx0+d6eOVzFEc2rLiyTHJxo63tO8DIf+OqKmOdYT8v9rEXvZCKWMAB9t+UludirLiT4G51yZ+zrGVDQxriyPqhpJcu6NF97UhrwIeKRuw+7D/ZY3F7Ph/VwEf2dO3DwO3oRTSx7rpA1S6gvuAz7BLNjkR7uY93ENnD/eM/BTP6PfGCF/tXsAx1yYu3Dkm1XP8idRB+axfS0+zbQh0s83puI178LzfdiMDyufw9h72CeCSsTf9+6jB1Hx2levJP5kiee13bv87+9x/3jWo6etngd2iJeH7u+tgyY1nfCeEPFVI2e5XWJhowWMl19rHTtN1StRCuUo4ZRqQ0gJOKorXrznk/rM/2+dfJZ+U1H1zhUPGtUPbutnvZQ7rLsq4LdU8+Bwju7LdittguGuWubrkMJbyN7Wmb9LX3gJfq+Guz7QScQBDWs04XvBbhHvwBxlDvavc2QejErP+BqXcgp1Qdp0rYz0/vXL/8CGPrn19wLxlj/KrRY2pY5uWdT33lwKR4L5X1s4+3lE0HUNKfFaSTsoYIKQV/+/a1/9xztK2efU1O/DQ8g95Z8UzvL3yf0W7xqp+j5TtyvZ8T17slCFHvnvA8OMfiLrzzuo0NInxVuaI6RJeDPt3OJoOV439gYndXE+NZqP5QVjI3s8YevNXvsL2X+EqM5OfNi1isTuXhW4/c7wFnUKzk8KkyejB0iwlh8QX+hOpSvkgowRAhpUkYXbIW9JoO2TECedFS8Lbe1HxrpyG8mkBCNLHabsuvIYq6TIBuL+irHibXIOi0vJs4LFK8ne4v+H8jHaXZcUDRaXlJ/smzXSjknDOw/Egrnb5rHDlO81/GrDHjGXunLuiJeF4IBL8J52LBh8UiASnRkvCc5wd2//ji7qOxy6H87B3LGdJKSQghJCY2aswhszmDU/z9J2t0xi0bALLUhNABVJsdlFqzUZzO/BvOl4+W1AZ8dMRx0ebmeR1aTMfNY3/z2hJ+Jlvby464695O4/8MHMY2alYbZPpzUZZzVZYrQMm+M0VLkMkOXvIYXzlHZiJlGV2P5NQLwtfFpWWTznW+Oi8P7ofnDJANH4K/IlQvfXQmtfnP5U9buBidy6nNi7g1ObF/Dx+5Vfvja2eIszkCLufGJ1TUzyNml51/vZy04pIhTvXjRB7AET8Uzqd2DfegvE9TMGaLv+K4XTkzGO1K3saXZ08HG0XevLGHde4mKNblxOI1zYcMuKEi4HLD2hdmUgI46hGo/1F92RUqYz0TViASuTqkimSdFi5nssey26xdZzcvJjjPG+ZJ+Ue1TUlhyc3FTvvnMb5576UNYqoR2XhsISgTaz/OBuZbPLkWfFLS8xX6ovNTxqHUJuDhVjqyuZ/2WM7Rd1/5Sv0Age0XxArRcp+TiRt5U8KYT6TNbYpWP9N+h4YRUdOa7YQ338LWch/5NRYdSDtO2BKi+tqZ+HC4gtvIZ/4BndSxtrpek+SksLWBHlWjPvzduSdfL/415o3+GvVB1y1eiEuh0UtWTs2ZTuRf994m46b+Oub0kFFWfE6YAdgSEVZ8S+20Lj6wnuockVda6wYwLHCW38cKn3AV6hz+xvwTUGqteVXdXOMA9pW57y66oW8aEn4G7vd/l36EcDHgUjwlD6OZwQq1x3CLr8jk65W078cw9usni+mxeqC9hr1erqyf7iczve5TCAQiATzUVbPMLblRkqqUT6Wu0ZLwkdES8LPAExJ1N5157oPCNbNbRmVbL4TZSW+DNjPO+71vZeN/4aDh/6Ur7z27F1nhWdNUWn5QODDX7ue+O0A0Vj8VPbYG3ceeWpNIBL89oLBB3/+fPaYzK+9BTyRM0GWJn6eet3Y4QiHMgaqBmYaCxVFKVAKTtrhtd2nrNPRuypfzs9SOhU6N51zq8UB73r3SQDHvfDVrFEc0rqSfCs2dGCq9Xhg+bsZwy/Ms2IXeWQqHcY5m44XQYC4kFZzcdOSusGp1kReKpYcZkd8poTBP/JUKr/vfHntxcc7jo0cXlUwcZWRaSYxOvsJd8OAlEoZZgjpHyJq2y2RBam2JrdMOX061+so5XhpH5Rqrc9Pta0Ebn3tF/99HdgTlTB2GqH6rZsQW/Oj4fttIQv5jwZeXObOJttK4LfiXF75HbQV8GBeLlHvejkMMS1LpgxDSIllIA1TWiQNE4Rgx3gtb2WozA9L3H7GJRsZGlvLh6nMDwZXDvJW5TXs0RorIFU9/uNAJHgQsCZaEo5u03PeHEL1bxDy74TymXpnU7upKCtO0PEg6EQgEtwB9bD6KloS3pI+djt1+uRqRZjxpIz5XUCbqzCaQUeJmgOAhmhJeEdC/r+hpi+hc8mbv6GmIiUdv4MMlCWrpzJIAAQiwUw60hqkeU24kod3fT5YKsfVx6iULJptSCASHIwjZ5SUIOPZxJftM9k34dVKVGJVUMrI5ULwJrA4WhJuDUSCO6J8nl5n9JmPu6zUmUnD3AUIoZL/TgD2FUbqdIAajwkqwjFdhBqUr+Uy1O/Nb9o6+S35uxMzXH7An3D4cDUIT91jqUPywEIm3QhXZ7ck2+vCqVxaKIvfP1BT7u8BGFgxpJiTEsaedIPHSnJJ/dfP3JG/y2XAHDorS+1i7QdJuhs9MmEk3cpK55ZSJoRS1QosFdNwr3+H5Doz43y7SeExV8899p/3nt60zsy4tdbw7tnFmd4jheEpzx7TXiypTnYoXs3CtQwYlQT8qTZZb/rE1Ni6pt3XyYrT+WjHYYnWA44ecnzzCo9PWc+kRbsaah9HWFbryc2LxSX1X6dzuCVQVvM4sDxbJtfu3lZ178cZQ+63z7W3/BnvVboyT0j75QUiQQ+jzxTRkvC7vbTRaDaa713pJNsR+3SgObp0xlTgxpOHHMk3XnVPOat+QfKBNVe+fWHe/Qc9PHhou8K5b8sKftK8lD8P2J060wdSNv6t6v33H8mddPQs3yDyUm3suSKLtwozSWY2ItqyCVbP5/Kk0luWW4UcHv8LbXgXeSe8+KYwrAtR1qYjoyXhN7f5hdjOsBWx61FJZtM+ZsdGS8LlW6L/otLy+di+GiKjGs+ITxCGRap54IuJFdOv802auRRl8RgPvJNcN+HQZPWk//MQ3/MB91+j+5tffwuECdW3vxUHIkEf6kZ9JSraFOCJaEn4tA2c62l09jnrVHrGinstDKvWahr0bXLd5LKKm09+0T6HUlResj/ZkXaarUggEsxDRUK2T8PJpJvYoiO/8k2auRvKP/AkOj+MG1ApJ+5BZdP/L3AY9FimJ52EtQ4VDPAESrFP2osPeKJt/rE1u4oFZ9zg/k/NlSN3yljtylq/Pym/bfvuuFuB0zyj38k3fI3T1Gak1TRIJBuH4C5c0Gi4W3OAb1HVMdLchPIPPRz1AlCJSsWyL6qQdicfjZ1i686Z6x04ly7+dGlXW5lyt1pt/ovNrHULoiXhj+wp+k9QlqH2c9+rdc1/Y4brsNVm5ppKV2a6huYrwE89VqIubrg3ZRbmf3u0VbZ97ht0GsDgRPOjxS1Lg6fUrFkw1lhTKIEpI0+X0rDzmTn8g3NSscZG05sDMDZen1rmzjGLmysoq/64/Z9cZ3i4rHB/FrtzrWozYxXK2t2OaaVkymhPQpsCTGmZ3wkjtTvqe/CufW2viZaE/7IJ56fRdMv3USF7CuWTwPh4nXxq9cvi8sIDeDdzGAAn1y17O4XpbzXZdYknk+/sRNDOqMo0hmVJy/5RF9RlLVsTH+1zFSwYAJYpzJQQUvLomlfZJV4NwEXxK3nF2rPZN2nmKtQbMl4rectXF9x//bY5++2L135/8N4Vcsjdr6V2r/pq/JIxwrC6OreeFS0Jz9gSxyoqLb8O5TeDa+A8XAWLAJCSb+efH94BIBAJZmH7kLXNP/YndFi6GgF/RVlxj192O73FCGBGtCTcUyqDtOwuKOXPhXoQXyEl9wuhfGSk5A4haAB+D8RSrXknJ5bt9zc6/NpaUUrgv3Rdyy1LIBL0J9aNPZNk5nz3kK93QllCDdJ+S615xJft95fZmedFzhp82JuLPHndVVf4ENjHXl+B44FtJd11hiuR102bCEoZSstWoqIhQVmyzkdZs15BKXt7AgOQsgChtInRifp1S93+UbaFbjgqb112fOXuy62moSUAwtX8sXfcW0WsryAuRfkonk9HbsEmlEJ5MevzEMoxfULXHfE1OyKExPSvqDd89X9GuTo8hVJCnIrdYlTUY1rpWo66Xr9ByscRopOi0xseK0lcmHikRbK26BdWwfLr6Kj+IQH8qfi6uyrfKdw9Vi13Gn2qZYn1HYG7lmJK8+LKFxiWauG/ORP50juQNzJHdhVBSIs7qt5vvmbgPvPbDNd6aUhSscwvTW9LHSqjP6jcl/8G3ouWhF/p67lqND3xvVLI7Dfeahw+ISMTDSx35yITXumqGVZhFi5Kxg1jAqhoyk5h3b0gJSmBtLqW37hp3ScyV8ZFeeZoGhtH81bT4XjHvn6fEKkL862YiKx9syGQqDvwx1RkHOCm6y8dc5nr2UV5olk0SR97DTujxfLE0paIBaiM41dGS8KbrHAUlZYXGjmrfoOrdSchjSGphhG7YrkRvlo8Iz9GGClSzYUzFvzyj2cVlZZPR5WTiQLXoZSl9HTV6oqy4mF9Pa6jZM1BwO3RkvBNjn1/QEWMzUFZQx5D1bA8Ni0jJZZzaslqy07Flx5kIpK4h32B4W0kURXAahzxdEVZcV991jQbIBAJ7iklHwmhUmQBUogO65eUYmVi5R4XLf79+eXvlQUWXDj44PHddGOhAl7+n73zDo+jutr4785sVVtJVrFcZeOyNAMGYsD0FkAmEHpoiWhBlEAIHyiEspBARAohEBChiSSE3kH0julgwGCz7nKVbdVV3d0p9/vjbtdKLkDAoPd5/Hg1c+fOndnZO+ee8573BFBGz99RJcPUznD+fDtSsI2etxY0K7X/PlI8cSgjIr4vAsRjci+jPG4JFFlhtoqGmO8uok9zRVHFxKcBIeAG23TsK3Tzj4CUhvt+zRU5McuYB/NEpe6TqLqYQWL8x0y4DL1j2qriog8rWxJ0MykJCZHweoOUYQ2p2UJzZelCohIpslYxGNg66d06pHc5da3vsn/4xkvap753JEmjOAG3bcpfd3wq6kbskrZdSNuSQqv+Uf/aXT/wlJ+fypXzWRH72TXP/LO6fP/Kha6iQ4cazuE9y54+vmfxvOry/UcZQt+WuO5Y6rcppR3TXwujPKAWinf4+UZd8zCGMQi2NA7ZAHb+SlVTEuGMiEhO3wSHlpyXTKGn/eAHIGWfEOilZr/e4siJxQgcUkZ84h+hU0XbxC8xhYbIsSksed4Ka9pZEyPd0UfWPufKwSpAZet9+nVf7NcNf0PNfiji7mLgzGB1/aaTUQO+HOCEHbTdti4UvQIgT4QZ31zevWz8ihdQopWXxcrHfCXohcs+dJbPS5Qe0n2riC7fKyLDRT2RJQeMQDNtzJwTY3VMf4p66aVOnV3AYyiR2CERI/PfghLNvZGkhtQ1/oaav8dqC+YCV8e2zwT+FKyuX+9vqFmZ2lcGzwfN06M7SoJIw4ue1wKAs3weke4xsxjG14ljhVAenPjPGkg8EbtE1vXlFD9h+Rs+vLvaU14U95o7bavP0PTjUPPLomB1/Tx/Q83dwNSxRld9fI4B0Dzd21gd42+mYPV5KcbYGiDT4Beo0GEHinMWd8kM4Gt1ay7ypUGfsm9cKI20eN9Hag4zvqoUIt0Ykyjv1Y7AAK9QfMjjw32thTKM1Ow/P3T2w3/yN9SMQsoT4565ODxhZ/SdtfcVmTrMlEdjxDILU42xydHO+zo097GtDm82bTYAMS7atc0KV1ZRfouM0Gnq3Lxaz2fHcccj5HunTe9fd+Ycb/mzKCqAiN8PQ+hisTO9YgDAVkbo3sWuovs/8I68PnNfSHffNeLK9ecsbKhJzXJ+GUWBGJfattTqP3yXSMvh76189N6dxx23c3x76uLebZvRiO48Hikfj41fn2iEpqDEd7PC31BzOeo98XCwuj6QZf/WwDUoL2NtsLp+WET6B4gtyiALVte3+Btqfg7cDANS1dFzU3Q948ZWpjGWYoQVmVGrW3fqJjpokhZHDj/qX8cpXYuaH+w6tuJle2cijlVp6Tdhh3KTL3UXuHo1Jzm2ZaFEArcE3AVMQKl/v4+6jxuHgM8FHIBa+R74E/09XrN26N9eW+Z905rGl+Z25SyYdkNTXdVsAH9Dzc4oja81wC+D1fVdg3c+EJW1jR5nRXuakKTmCQG2GzQ3tut9bNeM2K6fkPRApH7hBcBrTXVVn8XGNA7IT8m0TMX1qBBTGSqcFEW9HJeT1IHqR9WrnIDyPFzib6hZAVxk9xdawhM6L143D9LXArpvOUZzMgoio7mgNNGG8fXhRSm5ONUrZkdd7NvZ3b6g2Cr+2FM2GSmfAyUHcVDPyrWL3D653JE/D0Xm/zJ+3HOrn5504siDPl7pLMisRiGdFV+UEHvOpIToyj1yHCXz39FzOndHGR0OlPekFmXAx42xbuAM4EqULp0AhCk05rhKXkd5ZCH9GR7M8ImgOGo3xfp9FnBlW4CucbtLXljxBGHpvHTRteOuOT9n7JpnxLTFKwusySCQSCzb3T+pL+pd6C5g62gHf13/nnl+6d4OoScTMccaPTzV/OyJe48+MuVuJM5nowySV67q+HDM6WX7Z1sIDxqu2Ktv9X/e9I4+UQh0KfAXSOOtlN1LUYK+CGTfIwWTc5zSsgw06ZKW6UB2LHYVbYv6fWY7x5n+hprbURzXmSiOaztwDFCPlDvHx5oby9YP295dpOlaLxzRshw7Sl9K4MRUFQd2OqZnSc8cT2nBj8Lrubz9o0To199Q8xdUtYcVKG/6FyiOH8BV/oaah4LV9fNjKv//AvYrH32kvc6REw/xrmRw/bthfI+xRche+Btqxvobap7wN9Q8BryKlFm9L0JLvgzHxtKonTIjYpYySbRaI4xwxyRkykt0hTOPp/PGVZyT/y+O0t6kmjfYv28VHttk1/B6pkQ7ANi/bxUlKsPoUgKh57+eK/3G0TbI543Bw6hJ/4D4hh20JTc1yXL7KMds7nDegIZdV1nbeHps9/2obMcTUJPTBuFvqMnxN9SMrqxtPARot7rGSmlrKjtOgtm2FSmPbBnK+xA/192ol1QqbGBkZW3jgf6Gmh+jPINf+BtqLs1y+i9TPr8F7Bkb954pYddxJGU3NNTk/iJgaN7OX4FMrGpTlAEUhI2j/HOM9VMxWvxEV+8K6XIGw/iKCFbXv2ys3f6CVBaG0CSfFjqL1zli0cSU3/8L7skjm5wFFVKIA1HPDwR8Iwj4Lsm3jfs6dE+6Maaew1+Q8pzZfSOQ4YJCzdW/B8qQcqAykCcGq+vfIF001IsKQx5CCq8N6A85PJkyLBuCG+U9qg1W17+CWmS9mxpnjEtC7BRpZYHTx29Ldi0+ZuSP3TcXTpuwKo+tTmmKyFuXzWXnZWVIIb1fFAtOqPgx+445kroRO64RupmmKuGWJqeX7UdfmoyQXAzcV2SFT39l1ZNvBpffJ3Js6/mpRueQXBiplGLQbcmM3nWv3nHuU6cKwfsALmlhpyc9zidWosoSWg6AIXQdIRxRzeHp05wVwC6oey9EdhrOw6hQ9IhYu+OAV4GdU5+Jf/q2ZbannCsK9vILR7QMCeOM7nBqG0toGhB4JH9SwW/b5xBo/xCHug9xWs1vYueYiMrGnowKaRO7jvi8dQAqdDyq3OpL5dsNS+T8QLFFGGSoMNsRwE8ru+3g6aFgKajVzNRIO247RU5HSpzS4sr2j3ll1RP8ru0jZvatYUbfGpmpWaN7ejyOEUuTvzUpWevI5cXccZxTujeH6e/PLnS2dL+UO46w5uADbznXtL7P66se55aWNxHASrvkmlsvP2VntgwcjfJa1WwG2X7f2P8CNanMLRY9b090rNBOKT+AP40dTUnuopnAHVPuuOA+0snCHWwA/oaaiaiV8CrnyE9vBrzCEXbaPeWLf7tu/m0fLn/UfKbrnvZ8euOE+4dj55iGkpQoRb2kUrWVFqA8Xy/Z/YUXk/Q2HJVlCCcA59rRnOPCC2YVhxfMEsHq+ptixZLjOJyBZOpSlOCuTsrvye5LOFGUE0G30Fx96L6VWO2TwHIDeCprGwfzgAxjM2B3jzlJGsqOkhKEw6DLpVFgRfoBhLS7AaSloznTcjcMgBWOvGeqy/a//pyyvXfapX8dWuqCToAdKTgA9SLvkhK0nDb0EYsQjrS1wPpgdX28KOOnKdv7Udm8qYbeFyg+Yppnxw7nz7VN1wqyI3VBukNM0PhTUtXwU6ID07p7zJ+NPJjn8ioTiU2GjvZP597iVPO3kbf0rRFa8jo7dA9rnDnj4t0AIGGps4B3vBX0phpkQpsELOrQ3HcfPGrW+b8o2+/XP6s4+O4FzkILQEg7lEUrbGl09U57W93lL5tCtL+fU7Zf9S1HPPXyqidvP7h3xVWPNT9n/KbjM8YY3a0oztvzKK7WRkFmp6jkonTDUpGX2SiqOTijbH9eKyqMXR8E3SOyedQBuKNg67eBYwmEUhOIFqU0EbFzH4Saiw5KeTZWEPu+rm17f70m5YOo52N9jF4yjB8YtghSf0xV/ZcAJ3Yt5MqOj1ine3HZ1k3PRPd07Oz95Jzn8sZRHJG0uATz3CM4qncpL3vG8FJejCIwFJcsC/KtKDlL9lh0sPfVKx4b7f1vr+bUhS14bvXTVNrp0beXrZ0+PfD3r+80SFffDwR8V6NCLU3AngRCqwn4im8snNZ0m2+7fAAZySWy6ke2e+JrWmKhbrp6I0sOvB+0hqa6qnfi3VXWNu6B0nl6wjP1GSklXwjBVABpa/1G845e1+g5APgjHTyxVpUD/Itx7Of/sI44q6nu8PdS+hoBtKaMdiWKy5ZPjJSrFax83Dly7iwhpNNs22qu2br1rtmKfU+8+l+fS9OznTTyLGCPWMkmYh62I4FfoF4O8ZAmqNWvZfWWvCD0yDFoFnb3SOz+IlsvXqahRdE9PYlzGOu2m2t1Vo4HrmuqqxpOm/8aMfkfV3yu57Zul2XXH1AZsetsw3OW0My9UsJxNjAdOHe00VO92pmnrJYsc4axbjvpKJvXK4RMvMztaM4q4ezvE0JOQWXe7RGsrm8C8DfUXExSUuVd1CLhFyldfoSqfvHb+AYpkWbLVIGQOEsXZrvMz1ALgThv7bpgdf3vAKbeXVMjBLemNp7VvZRn8icmN0iQUkNoNtJ0t+Uvm/Zmztj3dlzv0SfE9+uWjuVIjy7otm1bmpa+iJdSIsR6oDzbQAH+vP4t/lo0nbXOhDLMQpTcRlJeRkrOCM1jx2j7n+7Ln7zkPc/Im22IIMTVqPs3YPIuNMOyU3f3IERccX91bBzZqDghVAh5c7QR16E8qBNQi9rURVQUmBKsrl8OiQoepcB1qOSN2UD1oJzagG8mytP+KIHQEn9DTT2xrFira9RfjebptzbVVQ1XAviBYEsxyHw/7l3x5hizZ9qpXUEez9sKh7T4wllizs4ZqffoLvHLzs950zuaL+NisBtJ5s+EkDZFdoQjulZSv/4iAEMvXlyj567bx2qb7P/Y+t2OhaLXmdrFE9bMp478/bNHfO0X/l1DwOcDegmETH9DTb6U/Eez9AOkw8oD2KWvjSvaP+CIMbFEJinZbl0OH4UOAOjFET7BNfrDpdHlMyeD9kSs18eFp3Nv9/jZCbKzHfUaVs9Ip7N4GaAKDL+2+klWOXI5ovQIehwaVuf4+xb/5pKTAGKk/jdRE9saYKemuqr1lbWNPwH+i3oRz0KPzBHC0qSZA3BUU13V46mX52+oCQBXSSkwVu9CRa92btvU958A3kDKSQiBz4oY0yMtv3gtZ8xnqBDIDFS4BCl5UkZ8lZontAOANDWEw47fikRGntk+cYXZss30prqqTQ0bD2MDmHp3zXtCMCPLrhVkELgz8BDJItzpiP3YpQ3RlTNwjflovdCtssRu0xUSjugoaTn8xvptm5Zedna8sDf+hhoH8Bsh5c+lEFsjZR9C6CS9ZEtQhlVmBQqs3lLsnvKnneVfPICS1ciW1WgBhwar61+qrG2cITwdL7rGvlOQoG9IGT2gb5X+Su7YQflbNze/2zmKzsKjK9Tv1mmb3L/2JZ7RpsmGsjERoUlPbPvK3cLrSmd7R3niXiinbbYamiNRYFxIGyk0ROyBT71/+Xa0v1tzORicF4cubdsSWifJcP5nwA6JBikT7+Rop73IVaiRnsgzD8X7jIeF4zBQC6pdUVSEwZIgBkMb2QV0Ad4pM/vMPcLNE57MnThGwvsIcVCwur5nkPaDwt9Q8yGx+cRsn4CnZULPi+5L/ztKtLUAfyQQ2hIrxAxjI/HdN8gCvp+jBBf3BfSLSvbg2dzKAc3iE8FQ2KG/hc+8pSBtdoi00SILWONV8+IIs1/qyIXrHTlTARzSpmfxoWA7QU3mnq3E6pKXXf+X8P48bO6NhXbfWopPvfAPDT8oPSl/Q82FxIin0nSya2eE+r5HyZcGd+Rtx5u55VT1Lufg7rVMj9wOWhTX+Nlorj7ssG9BdPleU2NdRdEMl7vyDYQznDBcpC2we8uZ5FzC5aH3mRley02+7bm1UAn229Ecc+Ev/5qY2CtrGz0oL0ewqa6qvbK2MW4gP9VUVyUraxsFaqIei5rA92qqq3o745oSk+FeoY7+Ozqf815dvMvq+/OnJJILhJR8vPKh+3Kuaj8pdsydwOkA0vA029H8Aj23JbP+noramF6k4SW6Zmew3FeiXsrtwE3/q5qg32f4G2p2QWmIOTfRIW6gEl6y6XWlISGcKtMlNexw/qXG2h1+I01vqaMk+LCjcOWfgtX1HwMQ8OVPH3tsZ98miqRKW7Miiw6b4J7yzOFCcEuWJn3ALvFkhMrLH/yve8IbJ6ZyaQGc0mo3hJ6VrygiHg4xgixwFREVgtWOPJljm6JXdzHO6MbfLnixPCWyJ+VDJVZ4XLfmnC6hP6o5Pgb2B9KMsLNC8z57JG+rHdp1T+qxcbmILBeb/QubFm65b66ndDeUoHIzcZ03adsIbbD7GUIZuJkGbKrhtvHIPu45qFCkJza2zGu4MFhd//fBuvQ31ExBZd++EKyuTxRQP/3WI+s/cpeelWNJrWXVvlxjP8KJjkTO2A0EQr/Z5PEPY4vBd5tDFvD9CLgHRX7UAdbrOVmbDmWMjY92ccfa17qa9BGKkyE0WsinpeVHTGouknu19ZG3yi9Wtez+bvwYCy2VWDkOKDtWfzNhjIVkDm/a27ed4Hh94oWOx26MZcz8kJBUmhfwaaGDh/KUrNNOXRH+s+4VTuhZzEpZZgArtZx2NJda3Gme0FQ0YxVqgnRiOzHatsKO5IXjLzmhScz2STyw9iVmhtcCsF004XhAGjmp3C6a6qrCTXVV78SMscuAJ2L/LovtlySLAAsUHyyBybVP/G63NmMkUiKk7K3p/9ALUGhF0jI9D+5bSY60lqdsWpsYk+2oMNdtnWt1lyPNDCeArRNZegDRlXvE+WOXorK+/ooSiR3GV8fexLwvm2CMSZSY6o9Q32VCCibXirZmEsTj/aYaY0Cf5um+3jX+7TJ35RvCUbjyOOB9f0PNtv6GGu2IikNvKrbCAyYos3N05qb0c2n2VU11VSsxvWmVI1KG9Exw+X0mAd8xBHz5rtFzxmYaYwAxYyzT4J8bXbXLE5bt4rm8Spa6fKxyFiCFJnp1ZcescObjc2bQP4U4rtXh3e2A/lWu+pY37wHujo/Hk1IaM8+OWr3CsSjj2FiFbmmTLL0WGWN0157WNT8tESGObaPtJ97Q8tYtqN/J4pSbM9S7y0d2b2K2p6IVJZLbCbyElN0Amm2HD+luYnr/Ov7SMnsVSUpEI+pZeRoVak6S8VMeOo9tDBDcjcPfUONHcQsfjfUR337L296Ks93S0u5b/zzPch1TxcrelEM31as3jC0M31mDrLK2seD06G/uAmjWc1juUKu0SzvmMDXaYe3cv44f9y5n375ViBTflJbxg54WaeWF5mdYLksLOjV3ImSxxuPBGPcZCwtl+DXXRJZXLsdZNv9nwCXAg1JQhe28K7WvoJ1Ud37F3onrnXcWA7sB55HUrfpBIFhd/7CU4hd2f+GXQjcwHTZ/LtqJ840aTor+jnOi5y8FPtpBW9r3mfuMF+3+Qluaao6Utv4BtvM+Ymn/wtGHs3wemrvHIyXYES9m+0RkuBBLJh/RSb1Rxq4cs9ZonfKUsXrX+SkZnZnYeZDPoZTPCRZ2ZW1jVbX+/B/u6Xl4zD3rXmGE1W9dVDLT+NxVzFO5ExIP1DaRto//2vr2hcBV8W1jje778+1otyal1Bz9uCa8id1XQmTVLolMSynBWDstc4ypXrSyzJ3D2CyMYaDhsSGsQCWFTEclbBjAK8CjvbrLNQhBPA6JCnVGAISQoCdoiTpwvybt0AJX0S9WOfMpssL8rKOp3+73YazfBnPdjlh9g75jbw5W119LwPfzk3vmjkrV3okN6W8To6El3cL5KSrB5RXN07UsObIBhlkmr+pL3bfqVs07uBqNLi3aeyautsMF3VJiou4VAM/ljOf08v0vkLb4S0LaxdIS2e0P502aHhH6EyndGapPm6ean+3RpR13nblH2JGK80JfdP68OzjAkr6/YCoXlez5V9S8vC+ZRcAHj/D0onQIN4QS4OJgdX0R8GOEeADA1jTP2zkVzPGWc1XJbmNQz8j9KI/226g54DxSkzFiY3FIi/37V78xxDmnkwxR7xYrBwhwIkCX7ma2t4LJYjXTtcU5AJ+6RnRfWzT9to24nmFswfgu65Cd91rOqO2OzTua+QVOLKFxZduHnNiziCfWPKfHf7fP5oxb/3rOmDJQLvNzO+ey3FlAiRGhxyiWv+l/sx/I+W/J2AHFegE0b6c3/soXuuFGqXKXoEinv0d558YBzz5h7/lUKJp7e6no5AlrT3Z3fylyku/1dn5gWHDarf+actvF86UUHwghkaabp609JYjAWY5n30aJL+ITfaePdK+kQ7OQUmD3FT9OCgk/c0qNrv4RGIqn+6Ucz0xUktN15kksiu7YTR9VqInw0MraxtlNdVULMrq4AeUtAbWyjuNs1Mq8E6hL2S4rhPr6HsmfRKsjpwDgl/lH0CkRcdttK6PrHUegMxmGCPgufAn+r1s48n8y6jCaHXkIQC9qQvdpxJ22xqqdsfsqOET7gDLRwcPWPvSrpDGJkhL5M8P4SvA31Exj4zyNLSSLiktUuClVcqAAZbiPiH0eCgJ4BmUEniBtWhCUkfTEbG+neO47NXfk/vVnfhAV+j5afjOab/lSq6ckpHk7dspi931IwDdluZbT8N/CSSKLb+fXS10+Li7Zg3+2vEFY6LsIKe2YAbn0jy3vzvlt6W7HMEjkYL++VVPO7Xn+rmPyD0ndvDZ2b3SAbSIdNOePaNI8xszY/oUu23RpMDIcL0MnZGIxkWMIu93j1gBWOvMBFClNSkPHti2hAxKPtAp2jLR+8bGnzOexTVbpORdMH3ucnBlufl9Ie0cptHS5kfSbI7LuSw0Xqs+5Qtp2PHISU/LvR4UYU997L8b0LfcDniIl87JbV8PoVaHm60mXMIkjUQlh3/7VnN71pZUjzYe2vazp4Sxt43gOpdm2PfDXYHW95W+ocaGMyEJN2nzsLmVFUR5TzJBY4izgnoKt84E3/9NQc2Cwun72EH0PYwvGd5JDVlnbOEsrWPWkq+LTtNlkRngt/1r3KobUudk8gj30Lz+9MufAthXlbQltLIe0uWvtK1Qa3XRbRS2TtGYf4DqjbF9me1VS0u59zSxzFbDWkRtX6VZx0FSugJT41xW882lonyLglqa6qltiPKQ7UJw212SxihrHU81H6bN/SyD0r//FvfkuYuqd534gdHtXKcHqrDxj8UWX3kXANxKVTZW/WIxgVsVPwKkMG2m6P4osOeh5VGmUfQHpnvS8LXQV5zPWbYfVWYmbKEdqs/Fry3lT+HnN2C0M4ijUxOlATYb+prqqRVmGNShiSQDXobxnzwNzy2l/8UbnrbxQVMQDRYoSYrZtxV496zHLF5NnG/y19e2enKvalaUY8I1BZXPyaO5EfleyW6J/s3Mcen4zQlcLgPFri3ksfAe5Ql3/M9YMzjMusFFG6Q2ompbDBP+vAH9DzXiU3IATxa36D7HM7DisnpIeY/WMLlfl6x9o7t4jM7rIprY/JOyou1VzRUoApKRDCApRBoOFeummVWKQlkPakQIh9LChufs2JHdSdfjivLLQqAUNb+YMHtqsMHsjr61+0v2bEXu805hXmSg15LQtw9D0bOcwdwi3vG4LcWCznkurY0AuQQJHdi9hsVYa/SK3IB7+S3KwVHZl0jiKGURO28LQhq6Qcm7nXLu6K3jT+57y0z91leTfUbhtfFcvKjHnl6jF04YDz1IikFIKbdO5YUCJ1W/8vWX2EyeVH7gPQpSlXksGEkkhmrSJGdrNwMn79q668/XcMRMAju9e9OTVv3r5yMyDK2sbT0F5+d4Dzm6qq7L8DTU/QWWub4eigEzMPA7AZ4UJJbl4vw9W11+5Odc6jO8+vqsG2V2OEQtOc5TE3rNSIoCr2j/kuO7F/Nk8jnrrSEoIvX+J44EZT5V5+Sg/Pbsyz47ywNoXmWR0EZZOe62Wp72YX8Eos5dZfcvpku7oowUT9l7uzLvtgbzJOwIJtm78x/jT7qU8veYMesixUfXfilET/XrgLFS1gJOb6qpe/N/dnf8N/A01FahU73yUbtlQZUFaUF5FSCWzBnzb3mwe+UBDceV2fUXJKgpGi19pcSVxhavyjSs1d7cTILpmJ2R3Bfc6r2OG/iVnle3LO94K7Eheu+buGRVeMKsKlTL/VFNd1X83dC2VtY1nA5ejCoKfAByGKqmUAtmqFzaVCFePoeWuq8NyR6IrZu69c+HrI+eV900D2Lt/Te/t5zyZF7u2IpRBljvHVcJJIw9CCoEdySXatC9afjOOkgVokRzebr+dEUlqEl/a4zg0WkcGbm+qq/pl5sZhbDz8DTUHoWpQ9gGXSJvnhKbkGKTENtbsrNnhAtwT3rCFZqcu9h5E8fkWMbQRsCmk8DeRcjeQrsG8VAM6VzVQTZTH5cTwgsP28Ux59twNnLEmz4629GiuR1K2JSRZnLaJW1p9Pbo7Qb512GaPqTkGaHClQkjJP1re5LLiGYQcnsQAN4Wcl3phIInfB03acpzZI5qcBYww+2nTPan9LkYtlFrIzgPLhO2QVocp9BGbM8ZtI23Mc8XeHbHjKoxuDKHT6kjjK1+6W//a61Y48vQKs4cZkZZ/3Fq4/aXAWbq0z7CEti3Aj3tX9P+9dfbOBEIJoenK2kYH6pl0AjhKglc4Riw+HMVFS0VqzdNs6AL2DVbXf7LRFziMLQrfVQ7Zc2bneOz+QqThYcLaYh5d/RIn9CxGE5AnwquBV2523mQf53iDfcwUjnXsR9WjuXjPozQ8PcLQciybs7rmc1jvCv5r7t+6UI47vLo7eMR7npHFqSKKudKMguIBHNy3Ag9RUEKo21zoeGTkR+6z/+9vzlv+rGOFgJlpxljAN4mA7xkCvgcI+ErYsvFbVOr4TNLDftnwJ9TLqgXlGVAIhOZNFqt/NlVbHoxvsqPe4L6dIf7rvJaLHA/FN59urN7FaXZUMnJ9Kb/vf55HnFczU5/PCkce73grANDcPcXAtKa6qsc8U5+52zP1mT39DTUH+xtq8vwNNVl1kGLesJtQmmdHokRhMxX90X0rbWf5PBxFy52aK1y18Jy6a5vqDv/xvPK+xIvuXU95UussEOpAiT3+7eae6vbwypkYa7cnumImILC7RxFdth8TmkemGWO90s315gnZhnpWZW2jb9A7PIyNQQnK63og8JbQeDS+Qwg01+iPcY2fTYYxBnC8NN27A+egvLoGyXJZqUi86aWkcwNj2QshNsoYk6YjMUaUEeIAzgPtMatvhB07XzZuXD/wjx7N9WDG9oRnzNAcpBpjANmMsUw+vSZtfGY4aYwBlcYASlY3Uq7X7RRl7ngnaeUSBKn3QZOSplh90DaHN9OAmgS8XdIrPhhwtVkwLtoVqjB6k3IUsb48liHZgLfBbZus071pIrqatGWzI08ZY+mHLy61+j9f48zjY+9IGgr8u5Sa/Y8Bf7OEtm38BpaafV47XWcOlMdUrUj1iNSLF1/OQGMMYsZYodnfNSXakY0f97thY+z7je+qQbYjlodZq2yeWvUip/bN47HwQdhSEJEOztAb3xpJ2+8LRU8bwFE9Sym01DvWY8T0cUzBcrM8/POy/amqqOKRorE0ySK50FVAjtZnVIj2BuC3Pjuapk10atcC/YXVT/Hy6qfYvX+9bKXwFuDflTRzoeMxSkQXP9Xf5kdacBcgqWMV8HlRxamrgOOX22U3VdY2/qaytjHNFfStIOCbScDXRMA3n4Bv6w20dRHw7V1i9acWHg9nNvM31Ah/Q80v/A01F4+Ldq9CvaxK3bZ5e7zNfy//aeHB2kcv/qvzSf/5nXMjRVb41rJu76G3Om+yZ+rz+JXjCfbS5+Aa/1aFa8Jr6Lbgqb7bOcnxCtM15R0dZfUxNqrkfKThJbpyhuZvqBmjSdkInK1J+zlpay3A2kl/+cvcytrGtFV1U12VjZoQ49gWFaZMh2YkeCVSiq1S9twtJXOBkCH02vR7FXqXQOii1+yd8mR/MVZoPNjpi/pFcjRvW9sA8KU1hmmRO3jd3nHA6VFq7d3Zdgxjo5Ea8slF0QveT22gZeGRApgdE24zWycvBnJQHKoBRnsqYuFJpERKy/mKHck17LDK04i9Rzfopik2+5nZ1bZWmgOdIna4YLmWu/5Cu6/kH1ZvyalC8KuU3b0ogWZvbKyZGd4CxYHN8AInuwduRdVljV9Pml1kaTo/rziIPbpbQdJdaXR9+nTzsxzWsyxxgYVWuP62ZfMW/LX1bUdlNERlpCu1kwEGpG7b/cBdZrZU0HRsd3bo86mD7HuFOFlfSpoduUUxvloawrpTIMSQBbojmmOA4WULTaSm0saRb0Xu+9BTtn38b7e0iiqs3h8nGsRu4L0+P7uOPfYUf0NNImknluF9AHC95m0/R4g0L9iAe9GjuRxPNT/H9tE0WnIX2eatYXyv8F0l9YcdmFzvvAOnsJiqreJvxtHcmLcL83Nz2a9v9QmHmB+ecHz0Sl5y/R/ldNK4+mkuLdqH1+3pWB1jsSXcWyk9sZJp3OSaxi2+7YQlNEaYfRV3rlnKaAm/b3uf80v3/jLPjhpXtX84bVq0XQc1Y51ffICY6H34zNND811H9jQlBheRTlbJEoiTfgO+bYE3gBESMBE8Y+92LPAz4JLK2sYJTZ4TtwWkf/yJEsiL1bn7X+Eq4lo5qs7aGUO0fRo4uLb9496LS/eMb/NnaXcO8A8AU4iE58iFPQOgsrZx8miOePskx6ulLiTnhr5wnxv64j5ghYHeR4w8uy7HQvOEYmJwy3B0q3k8Phcucvpo071IW8dYvzVIsZUm7XtsoYq820LThFAZW1reuu1p3fowlNxFKpYD8Qm+vamuyq6sbXwJ7INia5J2q3tkk6P0y2J1XunzN9RsG14wax3Meh7YBriyqa7qgUHu2Q0oFfABMHFwkvE7RhhdtFFAlvd0Fypb66mY8TiMzcdtKJmTHJS3dgnKw3sYKmT9I5QH7P+AvwOVUoLdX4zVOT7XOfLTS0gS/DeqzqgQLA6ecdOBlbWNXc7RHzihd7CIWS/pmbW0O7y8XeAdKWRvaij039LwPC0NzwOuMR+oZzyaexvwK5THugLFQzo/yznmADvFPv8DZbxMQ3mdErAN12vCEZ0lBOMAM8acHSDbYwmNt3NLLSHk503OAv+MMUevjGiOcoRwAfyse9E5Rl5H3l+LdoqT+HHYljQ1Xbil9cRefc3HvJybzEzXkPdZ8GuE+BmQg5Ts0b+GyUaIB71TCbuSQ3iocJzHaxv0awNocD9CJeYUIASGGERtSN3RNEt30GjmRoQ4w5rD3S2S3XVrzonbRdpY6cgjz4oaK535zng/vaq25kUkC4oTSzyq9TfUnIwypivjZ888lym0pcC/71z/6lUvecfkahCa7yre5fILXl2c2XYY3y98Vz1kf7bQ3zZT5oj8vKX27SWTme0dxe9H7MrcQkEXucTb3FI4jdn5pTh8K3EUN3G98w5k6qMuJVbMbd7myOFSx3GslUXruiNlC5ev22trT9S1daueJLi+5RnFKwXlrHF6XdeN2AldSy74LjTOeXylLH8TRe4HOBYYscyRzz6jj5Q7jDtB3pQzI27sls12/+pC4IOnc8Z/iCqV8rq/oSYhnfA/QHCQz+kI+ByoMBxRoae+PAYuQZMTCgLk9pE28uwox3SsWh2rz3jMakpL7zEPJiydEngEeI9AyD7WuMy4nf251DiTRZGtkbEIkh0u4DLjTD60p/CxNakL4N78KfTpOkKzGFU8p9M99r2xttASBuIIsz+xxrR7S23USzgTPwdeR2na3eZvqPG5pzxT4pn6LO6tXqxvqqsaIYR8LT43CgHG2mmHoaRMtkP9Tn436H2Laxhl8SQKZw+Osi/oLOxgEKfJvU11Vf9pqqsKZds5jI1HsLq+DbV4OAnYMVhd3x2srreC1fVPowyzbYGdgtX1TwHbG807zIku2x9j5R5gOyNaTtumhozfJUncP9nuGblYSmwps0ozfJFxbCIkmqFpdg+asaeevz4x+Qk9shuKNvATlNL8AAFbo3WrN8ILqraJLN/zeWk5tgtW198dXjBrdWTJ/ndKW6Q9l0I3DogZYwCOuDEm5MD1gNCkjgoDF/fprrGW0BIu4Hrfdnnnl+3NakdyqjA1fRXqd3PROLO7/cDeFcS13AxNPw3FDzt8rNHdPdrsodiO8qFnZJoxBrAwpyC/X3OiSdtEylRPVz4ZBmZWZPmpDbC7kuHV+IV3oIj396N4eAmMMdJF9y00530FU+nQPax05uvjjK7XhiJk+xtqnP6Gmp2Af5Eyd6ZgZcpAe0Ka6zmfbeQe07uMo3qX+S7v+HhIzt8wvh/4rpL6TwLurdX/y9nORgAezt0qckXJjMQSxdFdbPes2YOL9fvXnux6ftQNhTvwUIHKShbhPD5bdye3F2zDbYXbYgodr2XQrznimUAyYhZ0CT2KsW5bn7PiM6UhJCUXt3/KGT1f8rqj0jx71B4OBBRaYZ5b1UiRimI8QiB0bIzvsx2w80HaR6fc5vzbTjcXba/f5lNl9KThCUWWHhgB7mjynLjdAmfhEaeUH0BXLJXaNryLF551w6DigWkI+CoBD4HQ4MbU4MfOAA6O/bUYeIBAaPAvPeC7CzjNQFu6+9ij3ujRXFsBV4cXzFoI3Iji11zgmfqMA0WGLtkqGroosHrxFcvskbv/yTyBVnyNKF7Zyyg+yz1NdVXVABOu/vc/XKM+Plc4opjtEzFbtkG4uhHuEHZvOdjORcA9W4nV1z7qCtCYrwxwgJrOLyi1+h+/ZsSuCc23kWYvsrviwFWR8UdYofEPZqrvZ4O/oeYE1KQLypxzhRfMqtfy1pyh+1Zj95RhhcabKC7SS7FreK2prmr/ytrGsSghyaJK0fz0rc6/n1cqOgt/b5zKU/YeA87lqnwNza3evdHVO2P3VGQ2uampruqCDY15GF8P/A01BwCeYHV9Y2Vt43xga2U0z/9Iz1tfgeIaKmFfzQJshEY/itulkXzVG6iqAOcGq+vn+RtqJqKkVLpRRsOxGac2gbVIOSarVANQbETWdDhcPhvhFCKN0P4hylg4mEEgpcDuHaEyezVrjbFm504ZzV8BHCJc3ThHf9ginH2lQzmDdNsi3w7LTkfugOzJTYCJirzE/8/ELShB1FezXoctbITUspyyD+X5zA5pK55aqq9RSi5v+4gbi3agR99gfsBqVHj7x2R4MgEmRTue2j28PvxQ3laFEc0xnWQSUwJHdi/hlZwxRrfufh84PFhd3wkw6Ybrd9F9K54Rml1OcoQWqtxbKWohN5XUha+U/w6uuN+FSl56HFXAfNiD/j3Hd9Ug+ytw0TaiiWfdlwEqDHjw6MPfWafn7OGxTa5ZN5fzuq9k1qg7u1/KH5U/wQhhScEKl4oiHtTRzhnhDzm+IhnmLzbDVHcGzQdytp29Ose5L4AdyUNzJ1c/DttkrNWLQ0q6OqbQUdhpR91RTbO0vv8LfXRodXdwdmX4vlxUeCCxUqsUzXLb8uevf6Wg5NJYOvjdwep6JVwa8M36VcmeT76YO06DmFBo80623T3a7Zn6jIYqeLuts7vort+tn7/N7tq858dfs+iFytpGfYn7pMN0IR9DTW4XEggNWo5jAAK+cahixh6UTlolgdCGeUpK0qGFQCjBo7nmd+e9I2H3B6z96MczICOwsrbxfdKJqg+gQqVjUcaMDTDphuvfdBQ17RW/D5Gl+4OZk5hG/+O87t1dtIXbPmztXbDAHotTmLhzmpnh+JL9+lcj4O3XPBUz/1a0I2Hh4P86Poke1L+qIHWsG8KkG+oO0AuXN8a4HB8Eq+tnVNY2Xo9aHQOg5a1B961aZ/eULzy4e23Z750NzSWi68nK8H1TiXkoTtcbucL5Xz5wlzFfLyPQcSnI9JW+e6uXEA41NKN5B6yusWRgAbDNcLjym4W/oWYblOfsstimq832iT3C0X95Tu6avIieHvuKT4sbYYu8F6yu393fUDMPFdpGSPvfUminbuoYx0dDLHcN6qQ7ByWRMHJj+rK6RmE0TwdAL16MXtjUK/TwSgR+IUgztIa0ueKGTqxhrjTo1TYm+XFQnI7S4Noo0v5Xxcye1r6P3SXusHNgSDbjwsOoeXIwnBusrr8VwN9Q8ymp9TVj2D7SysNrX+wnEEoYjpW1jTvoRUs/cJbNj9+0XuBFlKDwI6h7kY0SEgI+yLOip7cuPqoMRbVYlqXdML5H+K5yyI4HWCDH8rq1A/vqn9Fh51sXLV+31SxHkqP7f/TzUv6ofIBlTh/bRpJSTu9qW3F99CXGR7tYHjPS2h0eru28aLW7+LXdRCyqUGxY/CQc5N8+9ZtwxPoCKPd2EHUZGoCt2zlNlP4NgqM/dZ+5cveC8ybZhaux+0ZgrN6VJllhNq2tvsnje+YxlJ7RM4nBBELPvHXXGX8nJlxptU/E7h69pKmuyvQ3PHMGqnQLHm/LjHllvfwpZ58LI3f/koqtotzZuY08q3defNo4CsV92VhUkJxkilEyHekGmSoYfhZKZ+cRAiGLQGhVRpvqK53sDrCLtoBzjQvTQiCxTMadJ4o1nKE3skSO5i7rsOOBQFNd1Ssp7SYJ95gxemFTgkSs+1ZhtU0RwN9+qT/98V76F/cCnOp4OdH/3f2HMFUP002OWSD6rtov3Ny4X3Nz3Ft6x6YYY5W1jdNg+6etjgluLbf1Q2f5FwfGdl0FbAUcLZx9OEfNQQjKtdyW8lrjGUrsrqnAvjPEl5H3pcqLWCpH8Zp3FDVl+wLg9HyCsWaX1NNFjOYdbceIRV47mo/VlVVPairKaF2ebecwvjr8DTWnokJFqZjpKF66O5AbGZx/bzBEIewY4skviS9XCu2gykhoSZPbt9Ugx2TFCmdSh1ZaOmhWqqH0V7IUIEd5jjQyjAkpBQgrLBz9nztLg7uivD7JF39Kx4M47GI7U1gtQiAlHNe9eOFrnoo3Wx05Z2ygkkEm7gD+Fayut3a56/TqKNqVUc0xYaOPzjLADTnw3s4rGaTWnsyUOTJIv4eZEif7+BtqPgb2IYsxBrBasTwy9WwOsPuKXdLWEKoa38sobt8uwMlkN8ZAlX86qFt4Fmh5a7x2X4lRWdt4SFNdVVbP4jC+H/iucsgKACx0fmFcQqfMpVTr0mc53i//whpPWDq52zyk1YUttT7l5XXZkrGt+ehhb9SOepGawUpnHn9oew+vbSgOWWiUJTxdUaHJxA/vgMiiJb/tnMO1re9S0/k5h/cmFyHH2HPYM7wGUCnSv+j/ZDpQjm7sIotXIjQbPa8Fd8GK2aispifCC2aJYHX9k8Hq+rRi4/2a8xLgbKu35C9m29RLgP0ArN6ShJu6QIZ5OH8SfbqqTNDh8PC3kh3E7QXbcHe+n13GHrODv6HmJX9DTdFG3scPUNlUK4CrCYRWZmlzP/AnqTxaJgHfPWl7A74JKO0uAHbSFregCNIJNNVV2eiR4DYVjXxSHuaXnkeYpb0rgGBlbeOHlbWN8Wusl5HCCXaPUqiQEmTYB2ry+/Wj1l6/nucs6v7tiBn8K39qwmO0rdY0b2bk5genRe7cjkDoFRQp20Tx4a7dyHsRx8WAVxp5WJ2VWwer67tj1xBGeb469OJFKZO8xEqZly2Em1jW5mv2TlyrHZHYJ9whtPzV4EgkqH5o95WOjK7c42Fz3fZ2ys/NJlnZ4XOUwOQwvjmclWVbGyk6V5VGF6RwqIRACpFujJUbvX2kZ+y+QlLi4J6U7RXL9JIJgwYfMnbYUfe6Xbs6O9O2phtjkN0YAxXGa0Al4zyKlH8rsKL9Dt9qyiY+3eWe+PpPdWlv7IJlg+GSPs3FQ3lbTW53eE6PG2NC2sQkJuZs4PD9gfem3XXmn95c9fhvdgqv33hjDLJaXkPwwkDKFQMOyDww2UEmT1agyPdxTADeQ8rrB+vSENpHQBEB35EEfNcR8C1403VBpYwUtkWb9sYOFyxG3YN/oJKrqgYdX3wQmu11jZ6Da/zbToR5yobaD2PLxnc1ZDkLtaItFtjMdZ9JvlAvuXbp5XHfeF70jGWK2cn5HV9wln4Gn4Z3BNMb1gpWeVwVnwIwwoiak6x2x/sxPTLZMY5Iqx/vpBeRQokf7rXS+2Z+cXDPxrxKbbzRzb1rX+SVnLF027mc1jeXk8oP5FOPqrRyeftHnNy9EBPBjLHHrO/VnGVe2+Rvqz9e8Yu+38dJsh801VXN2IRrPVz3LX9KuHq4qu8F/j6qkp6MkECl0ZXQ7QHYNtJ286NrXzgENWmcD9Sg3vbnEggNPgllQ8AXrCvaaeq/86cyNdrJf9a9TJ40KwmElsekPJaiwiQ2Ss38dAKhNCHcytrG05wjP6nTfatLAQ7rbaJ2/Tx68PKMvRs3mMf9FsWpma5jcpHjQVblWV0vRPda3t43YR2KqwVA3uSnW0xNlAL8rWV2y6F9KzqBowmE0oVpAz6dQCjN6N0QKmsb3aiQQTx88WhTXdUxKfv/KRx9Z7m3Si5CzdZJ7NZhcKL+Ch/Yfu6xDoHU1bOjH9eYDxDOXpA6QjeQpotI075guX7SVFf1dKxvD8oTNhKVAdiKWml/0FRXNSx38Q0ilkATyLLrPSHt8KGdbdOPDi0vOH38LtnzLqRkt/5m3ssZIOT/BeoFeyQw2wrn36h7uhM8L2kLEHLosKeUbyHEF6jf8OYh6eV5ra713YdrS3a/NWXfsjzbKOrRXYUZbbN3ZSvvmqZnfS8kygQN0s9BQDkqa3i3oU430uhlrXMAVQtdWhFLKKJtrhWld8Pcr0ExJdwZXegpTHSQOhYhJRnevSUob+i4lPYRIehGlZQqJLXEVpYLO7Xry/BlHZ94omjSQbJo1iklh54ddBTv2O2WA5IxBukqKyJNe7287OoTD9pwy2FsqfhOhiyb6qqeqaxtfBr4uUTjbONCTtefQ8Pm8fICXshVv5nPKGUro4szOj/gPHP3e4Hddd/yRJig3eG0NVMuJaZPZJo5YDt5bNXzvJQ/moN7V/Cn6Gl7N+ZVArDcmc8cTxnHdy9GCOiTbvpFcpH8hauYOwq2/uzonqWX3bH+tfHLHAW3Tou2MckKjdWwsdVclZSk3zg8b4XGPwDsdWOR9fz+bZ+eUKK15xpOi8bc8eTYJr0i/Ws6tXvBkaiXO8CTJFf6JUpzbAjyZ8C3C8orZv+zYJuLCvMm9t+T7wch+NJdTGPuePP4niXrYq0LSXJWNOBEAqG3UrurrG0sA+6QtiPh/tGlpEwLUUaIX2lP8Ko1/apP5SSPjx5ecv8fZSIEYQr+yPOdldz3UxSZdhvgbUOIqfG56dele/7q0Or67FITm2iMxWCgvp8KlFH1x4z9p0jbiTSdCIeBtDVphsaL2baX2fb2qe2S06fpJdq0D8LZg3vi62qnI4qjaOnZZqt/bWVt4zKUV+WYprqqT1Fq8HG8wjC+cQSr66/2N9RcycCIwAwptI5nciYc+ljLqTu6ee6WrO9FIXjPOyAZA5SBspSYhIvuSberNyy3BQixl5TM3BwB/NTxxbD3jP61gW0jbcxzj4jvm5BOaE87URtgSUlhPIlAaCCQgxkJD6CyShdsHWn9eKWz4Iw+zelIqdfpDFbX/9ffUPMJqibwkUIk6oamIZsxBiCVB5oxRjersuiLDRjYIAPVbJuoJtKsudRmMwYa2HkoY9KWEi1GqXCjxFpLUN6/MQ5pYyKynvM9z0j3VcW78mjeRFFgGzSse4UXcsZFP8wtui3N+ajGbNr9eV1W19hirbAJ3d0/oD8AabgRzghWTzkykv+9qwgzjHR8V0OWADcD63Us6wT9NXbUFrOrtoDP4hNNDB5p8bE9BZTW0FY+R1JMzyEt1xFdq38M1BotU5dY7VsBglFWOPqr0Of4zRBR6UEa3oWxQ9omGl2Pxn9ra+QIObf5J9h9Ra1CyrefyJvIX4t22mGPsUcfOT3S+ujRvUuXTjZCSLjbRrsAVR/xF5tykU11VUZTXdXPPFOfubG3bMXpT5cW5z4g9px7VFvL/HdWPvbiK2uemrZ3f/McAE2FHn5zRG/TupQuUq213WxJlIAv+2yncDkwaa5rxJSbC6c9ddWI3XZMnVze9ox8nkBIccQCoWZUUd0OlBGXLYMxCkTt1insG2rjmO7F/LYjGbmQEqZoKzwA1zvvUMZYDPflTd7WM/WZRvfk5+5FFdrdXwiOQ2U2/gVFfP1qCPjKCfieJOB7tclz4hRUqPha4PCmuqqPM1o/ju0kunImxvqtia6YKTAHr/WXCmnkYnWpCd7qKcNsm9SFIpBXorhpWXXKhvHNw99QM5X0ue4LXdoGyjop1tw993imPHcXiNcH7SS7xeQnpRh1JqRkoxI1Ygr92fuwsyr0GwAOe8CaxN5v7E+fWK975x/Uszw7aT79MmwgB8QAN1TschtJyjHYwIkoOsmuX7pLzp5shB6yhXYSMB9VoeMVf0PNLFQY/kwgzcqQlr4atSB6FynTarc6bEuq2lHqVmQ1xgCXtNaXm31pA821IgMWwbam0aml0erSbtZ73gqEnWYwxw3HbBmeIOX0H/c0RaaH10cynoUQUhoAC11F4sH8yZhCp1338NfCHd//b/6UTwf0pY5/ULgiTmf5l2iu7MaY3e8jsnR/wosODhurd60G7S9ZGw7je4PvZMgyFb+57NLn/uq67ZD43/fmT+G6oul4pcXxXYu4qPMzjoxe8+4XcqvdAS4Y8Tf+OWISCMEJXQtZtm7/V/913e8OqKxt/DlwFxDeq/Tx292etiNyun39T3Uee69z9Ad/1/PW7/L4mudm+KMd18W5I4bUHp4cufdKYPEOE/7z5QJX0SQAXdrvzzvtn7sR8LmBEv/4E3+B4jXNBo4OVtdvNMk8Dn9DzaMo0j7AY8Hq+qMz9o8FQsHq+i4CvjIUObQc+BvKwEhM6qvs4kVjrlk2JeuJAr4/Af93d76fPxWrLKyUYrkAnwer66dtytgraxsPnyjWPPiq++Ks1ktYOmnFhyl1KjVlS67XPfbeo39Kopi70o36bFPOmzEGF4rrtja1nFXP1SNuzZVmTWwKfZ5A6NAh+tBQHLM4TyST2LsRsAEtiiLrn0kyq++qprqqazatr2F8Vfgbag4BjiZFDNllm6v2CK8d83pOWgTqdrtp5p565VvbZCl3tEFy/xBhJ4uBSvo4TboMh+LKSikkyKQe2cDOMp/DEHCGw7YuNjV9MHrEkGOOFUMfwEXVbEvami5S2tlDGYxIaSISLvxbUQu0C2N/r5KSMSmX0ov6LqYCc4SUb0ixkYU+Y/DYBvXrXm84s3z/n5uargGMMrrtNc78Af04TRGO6tIVG//jSLkvQqRds226cOhhwxYJBdqs39dm4lhUxYeHhLSlAI+dLII+D6WLNyichk506f5Ekvq2C4F9muqq1n5N4xvGdwzfZQ8ZAP24GiNS/d6lhJO7F/LZigf5eOXDXBL6FIeQjBZtBXrxQpxj3+ELczIfr3yYd5c/wv6tEd60p+0N0FRX9S+gzFkxZ/sPi53nzM4ZOfHFcu9Uz9RnHlx0/tX9weX3rfJHO/6USuT9P+Psg4EJBVOe/HXcGPPaBqd1ffkSAIFQxD/+xHbgD6ismCpp64dv5qXegsqA7I59hoBvVwK+3xLwbRusrl8ZrK7vip13PYHQNAKheP3GtO9xtGifTMDXTMCXbpQFfPuiMns4qH8lpbGV5q7h9R0prTaZYN5UV/W0A2u3xfYAjg0AHmEwRrQyWrQwx57El/bY5te8o6enlDaxrK5R3sraxqcqaxtfq6xt3GZTxwDcieIdvlBZ23gCgL+h5spdxh1Xc9ioKto0N62ywKqsbbyksrbx5MraRhFrM8bfULNj7DrsprqqP6Eyqa4FjkBpQG0CNIBfN9VVNQFXoERpT0Y9I8P4H8LfULMn8CzKGEt4SKKaY8zrOWPw2mnan6eYQtvmH6veR0/xPElb67cNVzax4WQbCZHle2G0TP07A79n3TadSCvdWovq4rDo+qnXScsREUIKIYgnfiMGOtYyTT0bmJtpjElJal0op5TpGdXxmpnqD1X6KY7SiBn5U+s7XNT5maiM9CRW6UMaY6pBSskxzino14uR0oz9PTrDSM1FfR9/R8oXNsoYy3AYhDUnlxXte0rcGANY48jL2o/hkB4h0DRp2zm2UQQZBqgE//q8l/JsI9Vw3TRjTKaHImMeji+Ag4PV9Y+gstv/EWmefl541Y8MmVQrXwX0pHYj7fSkCsNpcV7hbejFCxGuboApKC/lML6n+E5yyFLxrL1bNwbc6ropsWh0IjGlIIKLF+xdeaWg2O8sVZqps71QtfzyRX2R0onrKdKB/8b7aqqravc3PJNLknPlIJm9FEFl7jltCfdb+/OkvYcP+H1U6PPjfUyPtNq/6fzs3pQhhqXp7BAOo0hKgbF6l8NR+jKbhGB1/av+hpri2GeTgG8sqhyTF7iYgG8CgdCA6r4otfC0VV3sPo0EPiXgewVVxmMpim9WADDW7OWV1U/RqzkotKNF/8mf0nZd8S63ogpxAxAbzx9RVsZvg9X1yeLaGVgoxwZ/Gr26917ndbk76MuIoHH5iBkschXyq4657B9ezWpZylHRawCKaGWEZ8Qzs1BerWeN5uk/A+LG7N9QAo0bDb1w6X7CEcHsrATTuwOK73IJKBmTK9xVzO44tIpkZtN4f0PNaygel8ffUPPnYHX9JQBNdVVvokQbqaxt3AOljr6xsIlJnsS0xf69KdcxjK8VE0gaM2bsc+LlPTkSWjPXWzoKQAi8rvGzrYvbjtEtbXWiA6HZXkR0MGkCAGS4ACI+rIjviSWXXPi6v6FmK1TZNKQcWD9Tk3K9LZjvKlswDVLK+8TlJ4SushbFoMyyBSipnDTPmTS8DqkbCGEijbzlQo+OxaGMTttwIyO5Us9rF+q60oy8ube0vP7U8/mjLr/bF5dSE+1Ck8nyUYO5AON0sxitaizrT10ifYSFAyEQdrgA4e5KPVTdfyEGyFFIGbNKVduuszs/b7+tcPvKzHYtLj39vbUBAp4tNK1PaPsmBiBtKnuscLB755X9RR8t6tLdBwHk2VHyrSjNzrzs1yslICMILeU7S5EPQXZLoeWjBMMf8DfU1AK3AzhKFoasjokuIRI210yU9llevBuZYXjnWVHqyypxioWIkoWEm/ZERgubhrzYYWzR+M57yID7ltkV/zFl+lA1JHtG/s5FxjmInPY0Y+QW99/z33Gf/+6R2lszlrlP7CHgm0PAdyJAsLp+JSqj6Q3gomB1vTK2AqE1N/mm/e46z/6hM42L+J15BlLdnvnAjULKNS5pdXulWU0gtCB+vmB1vYyu2PM1Y912RFfMxO4r3dTyKwkEq+vNYHV9nDNSQdJYjGuIDUQg9Aawc5NdfsrL1k6ZlZO9KBLu56gfftpNdGFTZEcRwKndCyPB6vorM4yuP6IkA84A/jzU2Js8J3r+4GwI/reoku3HHc+Row7l6bwJBF1FXFq6O38o3GXx4SN/0qXlrwal93NmsLr+lWB1/ZnB6vrHScpAgCIbZ7lW3yQCvumZm/0NNcc7y+ePcYxYgmv0h2Hgdn9DzY/jfUpb47m+g+hKF+A+ElUbME40OYIMVNY2lpMiFrsBvIfi0ZzWVFe1aZmuw/hGEFmx+7vS8LwtJStQ/Mf4898DvB43xuIQQur9RWsTPJ2NFYcVrp4Qij86bdIN198ube1n0tItKVmaeWyBFeGR5ucbwgtmWdGVu50b95jYhjPdGZTdGGtGLRR2Ay512ebS0Ua3GT9Qc/ULTTdBgNAj5ejR5O/d8K6XkcKstRDNzjHdR0Wu+dW/8rdObNOw0klcg9wEmTHSFa48wpoDpETaAuHqzXpcNrikkWqSFJzdNT93crQzOQQbKno0rA18HxuChcbSfKfHWbpw8rLIxMRiq8COcmTP0tjJBhhjKl1WGWNPO20z4X0ssCL4rMgaKbR3U44oRpWQUt05IsLuLUFayhk3wQi9l1ESKnlKKTt2Dq/v6dFd2DG9YinAURaMoHh9w/ie4jvvIWuqqzII+J4kWTcSAE3Ai+5LmRX5Q3fY09oViQkzTox2RreNdoxEMPJGV/1FxERmgQYCvocJhIxgdf0/gX+m9udvqDmaou3+BGAXdT3JMh5Cudj/HeOEJVU9A77DUC/qj4BLpHHf76zO3Imo+3nV13TpH6KEFI8Gnh5SziIQ+qwSPjuo9sk5c7XTP/AKI5PU70ZxFi5DpaZPQxUbfwplcG6PeqFkQhvkczY8foj+3s6XFinJsri4LkC3cHKvb8ok6MXp+ZRIbxnYzjczjv8jivdSQJLDlXKNviqUh08n4LuCQCg1NBQP3aJ5uta5xs3WpeRZIdCkxIyu+hEy4st81i3gSSm5SAhyra5Ra6feef5+MprXE12xZxAl7nvJRlx3HNOByqa6qmFNsW8R/oaaQuAiaTlmaK5t9jLWTvMKV+8XzvJ5qeH7p1BCvPumbIsC64RuRaSkT4ghyvSQ4UARwnaNffsyaTsRzj4ZEwDViWV3p2JqtJMLS/fcw13+3AK7t2yk1TkWGc3BCo1vcY557wE9J5StaHgcFaSU7HEiK/o0pyPTeBACcBjpQrG2XurIaSmzo16pOfvTqmc68lfvYTnCwtKSG/PtqLMrToqXEq8dbe3X3QPKBaWe2tmXS7c3acUqb9AQydAqvCfinRiaI3E+nx1t9EjrP6d0L7jlquIfjZBgmz2jb5zQGv6VHLfSsdaRPsXZUQ/CEcEppWHqQ/P9EtIXzjDC2Tst/l2uceTxn4KpA9pPirS/u9JZsHskFp3dLtJ281izZ9ZzueMByJVmd7Oe8yUqYShC0ut5LyohamuhWVdII2ftvu3dh55pvnFFt+4+8B++7drmJW9psvi8EEVz3CUDyN2aJ/RwU13VJvOTh7Hl4DtvkAFEpeMTE2HmCCNtvKUixHvu83sviO45+kWnksKY1hNOzRj6ScrndlTYgsraxt2Ba1C1HS+MPeSJsj9CWLujiuA+OeAHEPAJVDgsH8U16mjynNiOiu37gLmKc/sVEQhJAr5RqJXWqQR892Xqf2ViUd0R8wmcmvxhp2M0yui5CDXAcwiEnt3AKH6LerHoqKSFQcbqGyMl+7uFzbhoV6J8VeKtpWbAWHhF9gtH/0+WXXPky6ldNNVVRcluFMZxKMmw7OGkc3XuRBVAngJcarZOvcA19n0NQAgcKZmS64Gy2OftwwtmzUOPLBDCmq4XNe0tdPNVPCG03HWW3Vu+qcReJ0OXXhnG/wYPAQcJ3cQ5cm5823Yp+23UAmQlyZdnO9CFyohNFJEfyjOWsq9baFaRyFE0TDuSO2giiMOW1jxX0eo+3bWXwEIvSNruWl5rKaZzoDUwEAljo9CKzI8KLVEaotTso033pCboJJCXs070aw40EKIvF+ntSV6ELoWel3SMbxNpY6TZx6uOsYmL7dfdJUNlLkgJEVdkkBTFQTBYSFYIQrq7yj/+xLuD1fUlV6oQMHrBmtveKcChS28aRUNKLGPNrqtBPiJHzbnO7st92JHfsl9Kj6cDOwLnomQtPheCnaRE6rkdbtIwcEiL3cW7n9QVZIGriEqj+5M/tH/w/pueCvvlnDG6gUazIzcfOCDlkA9R74+/ALsHq+uXA/gbaj58A88ub8uDMIWGJmWqZEAPKfO2jJH/hbQZ2S/714fHdRihyuwSQMP43mBLCFkyJfLvnuropQ4J/KFoZ348ahZ35ytahxCMvLr9A04Lzcff5qSx5Xh+b5zEUrv8UdLVrX+cUlT73ygx0rNRP1aiq3fulKYzLCWm0bJ1GUoE8NzK2sadAQj4Sgj4DkZ5cFKXfb9HZRfNA94NS+e/K2sbi/l6sFPsf8Eg5TqyQHnU1Lh+BmlEXw/KM3gycD+BAV6jNASr61uD1fWnBavrfx6srh9UX63RmlEUpyH/smtecocQjDD7yenyWdLW7gc+EIIjl13zs5ez9bMBPIgqEyNJV0UnWF3fF6yuPyG8YNZh4QWzPrH7SkPG+q2x+wsxWvztMukwTA0R5AB/xHJr0sxBy2mJDVkyM+9NvZhsdL0BmINS3J4NnDdca+47gQ2VK9JQBtmlJD0ZxShjDEjaHJqtCm6nKvhnQVpoTzh7u8y2if+0ozkDFjuGdOp9umtc5nYAPbcVvaA1ISqrS3uAhySGPikxpaRllSNvWosjByRss94tx5s9dqoxpsTqQZPSzrOS60rp7R3S2pzvHsGruWMHXrYQVBg9lsuS3TN61nVn7EJzZFPoyA4psRIU9lR1/RRUtnv/u0ftv65FKebH52wsoeko/lX83Jq78q1x7srZF2iuvv0d+S0Hkixd9y5wn5ByJOq7d5Sb/WgR72NCpFtfQkprsCLkb3pH85GnnMfyJm57d77/16sdOXq50TfYfZweO1dFrm3cCeBvqBmNKpeEGfuO7Jj8BWpeSnj5U++DFBrNHof3gZ5/j/pcXPg0Ad9xWQc4jO8FvvOyF6DkCLyE32so+O2uv6hILkTeXvkoI2JVQaSEX0XP42m5B2PF+r43XL8OayJBSr2bQOh0Ar5JwHnXGCcfdbd1WFxY9ReoEkPzUB4cE0QvytvVB0xu8pzYD8xFGWmLUWRhnSyyCBHp4HLzNOtha9+Tm+qqvtqKJuA7DaXHtgg4hEBo09OdA77LUcYZKO9B/AcdBnwEQtGsx20CKmsbZxylvfHelc7/sN7l5CejFG9ekzazVz3OJbmHMrtYRYCkrd8dWXSojkqsuLiprmpNTG7iTNSkdFNTXVXnINdSBHhi+mip578EFY4tQE1uP0N51IqBu1GFmb3AaygPYWq21T+B03TfcqerfC75tkHD+ldY0T+JGuPXG7r0vzfVVV24EbdoGP8j+BtqjkQRqUtQguxGZgmkOHRpYw30JvUwhLZYCixU6HMiAxdLxSgv9EpglJRgdY9Ez187qB0UE45P7B1ndL+xypG7T6a3y7a0+TKSN1nzdjnT9FFtrXWi2SGXuXxZhVgBvJZBvz7gVlgo42Fo15aUCGzFqxUCIW2c0iaqOTKbZef+Z9ueujH2WUjZ45S2d4zZo1/X9h7v6eO5sdTfxwZCyClYBYx/sPm5a/9YtPOl3cIVXaEXvpUrjW06nc5RsIkVALIQCcvN3o51jty0jE1hW21S09NFMoE8Kyr/3PZORU3Zvn0oT6wj1qdEiNWjje7D1jtyPjZE4ouZDeyZ2U+p2cdDa1+kwur7lEBop8z9w/h+YIvwkDXVVdn9ePbJt6Lr9NiyTUjJes2DIVXaihAwUWu2gXV/ctx+Q4oxZhAInR77/DRwwZXOe8eeqr/4IoojlJEFJ0ARvC9Dab6sQenmxEWLJsUb2RIWOHx0pJQ6cguTPztv14/Q3t5YMvjgCITuJhDKJRDacbOMMYXU4+LGmAT+/HUYYzGsmicnmIWijylGiN+3vc8BvSu5sWU2xXaE5c7cznhDGc09ECUF8TNUwWRQSRa3AVej6vJlRyDUkcUYy0Nxznyo78UNHNlUV3UWqoTL/ajMyv1RhuntqISOOPYGnFZoPGc3dfPm6sfZNtpBXrqmZSp+gwr9/p6kxtgwviMIVtc/gfKOqJBPNmNMSkrMfq5oz6pooqF4PxuChjK4VgPvSFvNQ2bnWMILZm0XrK63gVnS0ufZkVwrizEWd1kZwIq4MSYl2IZbLjPG7mwLLbM2Y1Ro9lQ9J90YAxCaXdLkLCgdZ3SzZ99qkPYAd5XXkgO8UKiFpQA+S2zJbBIfgtAThokUGv5oO5lIIaZv1PbMBlKIvLdXPabvEl7HCSMP5sYyP5tgjCElY6Sk+eflB9V+6ikTS9yFbsPBgZ1O56gSsw9/pD3NGDuiZwnHdi4HmX7VIupuLzX7rN3C6wZYkuv1nLTx6NLGiT0CGXs5SZlwR/boLvGBp3zPWM3cvYFHEOIxhLgSmPTKmqeP/uf6150H967gjND8z1CJQYlbFb9dLY4cZqtqEdkFf4fxvcAWwSEDaKqr6idwoiPXNujS3UghuKNwW25ofSf+a2k92fHyAb++9u65BE6cgApHlgAPp3STCCVe47zngWuu/Xv85f9lZW3jGShu0n+b6qreIP2l/SnwDoqn9Nyn9sRX1suiP/+5eHuxsjBKnh1leriFha5CvLbJpZ2fsKv5ZWqJnG8TA7ISURNw4dd1gqa6qtU//u1tx3bKnHsLRV/usT1LOLZnyfXAe8vskeGV+XIRcB/gMVunzidZLy7+0hid0t0YNg39qOLp8T5tlNYRqDBUQUb7bVAG4M1AJyp0uTXA3dGfMNHZio9erjZPTT3mkdi4Hmmqq7phE8c3jP89Mj0VFqDbhltqzohACFodXm4s3MFEykxSfE7s34YggF8l/pJCWt2l6L5VaO6uf/gbnrkJuF3olqbrvWl2ld1XvFbPbT8KNaek1U8E0JwRgTOivHRCMKN/Le97R4KUrmxKDAknkxCscOZjooHQBszt7S7XAN2shCFkablCV/aEHS7od7o7vJamJ680iwNtqaOom4FFuVV/iOwut9QLyOJKqzB6jHxpOB/On5xddmID22Ify8Ji4Kut1ZFDqyP9q30/Mt1sbd2pSxa8WSxE3J4S2GgftOienXqEM4KUo9L01oRI8s6kxEJgKV1ZDXhVSO1PRWb46Xan21lmhCNveEc9fykQrK5/199Qo6OyL4+Ktc/fI7yOPcLrANbd6dvm5NRrkbYGwkaXdu+2kfb5wMEEfOcQCCXrlQ7je4MtxiCLwXKliCa2a2l8zBmlV69QOcuB0LKYKOpYlEhfHKeivDBfkKJPBtBUV3U3Krw1EIFQmIBvL5SB13Jk9A+7AMKd/wIC6NFcvJmTtCmuKv4Rp3f3XL2Z1/h141aUcZqYtVo1j3w5Z8zHJ3yNJ3nhj2c/QeDSySh3+9sEQmtAxXaVQhwzACprG30oWQsnqowTKN2xnVGE+wviffobai5DZS7VB6vrHxvk1DrKQzYVlfX6QVNdVVyW5AMUR+MIlEeiE/hDU13Vl8T4KLFanK8AI0PkfXa+8asDUC/wRajV6g1NdVWrNvO2DOPbwe4Zf18HtAlH5DpSjK1O3TPU/Gcy+Pw4QM1d6LbQ8xXNUnhD06QlrhZ6UqtHmh5sy4Xm7kLPbR8pJbOzhfYyt2lSrp3jKc0D8rIZMNn6WGlWoDsVB1JazlVCN8ZkO6DACtPliNFsNXuilAJpeBHuLq+9EeT8HoczLe7ntQ36Y4L3aR6xofrK2J8T9jy/wB6zg1Rzd2LHduHWpV94SgZkrSIEmrQQiGzhZ5Bg9RdIzdttCCFdmadsLg45ZO7HxZqWSpiTPZqr/xDQ6Nc3EEQaeG1LbCGfbXO4NSSs0/Lca83S7YH3/A01ZSR1EAHKLhmx+22XdXw8vtCO5v7TnPV3abouF46UwIWq73TG7etft7cxOuIOhJsJ+O5OlLgbxvcGWwSHLIGAb1Z12X73veutyAfYv28Vt7a8iSX5WBfsmkLa/0ZRWdu4HXCOs2LO0XrBmjLSU50pCgual1f1AOc21VV9+8KgAV/YBndjbiUr9Vzu8G1Lv1pAB4LV9d8VwzENk28O/EzPW3df7M8oUBysrk+IGvkbagTwBztccLrZOrXc7i2XwGFNdVXPf5XzVtY27gq0DhP0t1z4G2qWotYCkEzA+VrK4bhsMxLVHO5s++Ivemk5QTMSnDBpacuiK2aWOcd8kKs5s6oWtEtJGKhIHGMLKTT5FmoRMRaV4NMFPI/SIruODO+vlEgsl4i/0KVNV3TlnifJcMHTztEfoeWsT6sK5bEMGdadg1tLiQuSIMQnJJOMNgke28AUGmZMUyvHNvr6hEMD4cl0o9n9hTK6Yk/hmvCyrbnCqdbQYNnjCS5gJg8vjmjz9jjLPkfoIC3NtrorNEdhUvw3lQnstU2EtG/u013p8iMbMixjw0fphKVVa5GStUIk5JdSIy93ojQe24yWKb/H9NxgG15NL1jzqqNw5W4kFw//dkjrkCnRUNmtLW8w0upfCVQSCG1UrdRhbDnYIjhkCQRCz7zrrUh4Sgw1u0hdcP7XbYxV1jbOmFb70F3Tah86JWXbtMraxj+gCP41RvP036MytrYGbpW2/o7ZNpG1q/bjWP31vF/oz9+z128b9vo6x7WZaLixcAf+r2QPbiraIW6MgVLJ/8qorG0cU1nb+LVJPlTWNv7FbJsUN8aQEjO86MdXVNY2HpTSrAq4TPN0lTtHzUFJJ7JLZl+biqa6qg+HjbEtHoejuIgvkZRt2RwMmFMGM8ZAva+9EYHVn0eqYSB0e0JO6Re5hfQNdmixEIxCJt/4QpMCxTn6CcoQagNmBavrjw9W19+C8gin8cTM9q1aU70rQqPAUTZ3b0f55+h56weU6AxrjqEtjIRglwA1z20WHFJSaSSTMmPaacuyxjT1fiFy12KHizPfTenGmLTxxEpfxT1jsXs+IBtc961AxKOvuq3p+Zl0XEmZ2csEI8TN69/ojmiOSUArYKtySDYndC+kwuhhA+hDCXGnRYaFYCSq9nArirQPKsHqsNjnEY7iZdc4K+ZqrrEfYHWN3UGarrg3zAZOMYVeNt9dzKyKqtDH7tJDho2x7ye2LINM4U/AAl3aKw/rXX4rKvvw3Q0dtCmorG10H6HNfu0j99mnveW+4N93Xn7CzytrG89FEV9/R9KVfliwuv6LYHX9smB1/bmRRYfuZbZu89oZvMqfnbcTcP5b/NN5Q+DrHNtm4qLFKUKtQiYS2h/6qh1X1jb+B0VubqusbZz0VfuL4ZcyXITZthXScvbZ3aN0bMelbqLP3Xf5kccQ8OVCyttNRYaWAP/5ms4/jC0Ywer6ecHq+tNQ4fqvAkEWoyz2tl2a7YB+t8SRNzAnwMrrYFZ/siSmbbgG9Cs0ibSR0hbZ1FRHSCnerLzssS8raxsno3QUU0OqEc3TOkCJX/d2/Z+jcOWAznTbSvLHBpD4JR4jed1SEpYy3ZqUMst9yUCJ0Uep2Wf26C4WuwoZG+0GKftRGdZbkyIfFB+D5orgGv0RVl/hDcCaQTsXGmHNlW1PGYol0Q8YdtTTJZzpkT2hZ9xeIWjTPL1FVvTL34zYa54ltENR9BQNIZBC47G8rYiIIe16C5UMEk8meS1jv/O47sUzg8vvC81e+ejNweX3nURSdb8fTQn5CiHRvK0jzLZJVaiFf1oGbI/u8p008qBLhxrIMLZcbHEGWbC6fn6wut4/77R/jvvp/3167obEUjcTPz/F8bLXJSx8oo/9tE+PIj32H8eTqX/EahdePF6sS2yrEG0FlbWNH1fWNrZW1jYe+w2MdYPwjz9x+3w72l5i9lNu9snd+9eeCWwbrK7f7KoC/oaavM+vnbD3KfqLJ5eqpLQc4ImvachhAL1wBUI3cvSCNW5X3hoedQX0Ex2vPRyWzrn5C/bcyY7mXgY0IKwZTXWzJjXVVS3/ms4/jO8BYhmXtZMjHV8c0tu0ud0ooyzFaskxRZ/VW7wxmZiJw7YOh7i4Yw5/bH2X/dcaNpYr6R9K9aZoCKFJ3Y7mIO10A0AIiZbT4gd+DWTW13TruaFM7tzAi5H2IqSMJgj7ZMmAFILRdnuq/8pjdY9Mu9600OAgtJcu3cVYs7cz/vdKZx5bGx2p2pCZ9XcTn90j55cAf0dVEhkKEQYazX7AKyVzNWd/geaMDJ7dGYOl6blzPKVbdzpcu2XbHxU6XYNLZcSrujwe+9tG8ZHjarsmcPd5nXN/+3TO+Ko+zXk+Sm7plyi+41QhqJVSGHa4AEfxEpzl8yuRcnLiDOnjP9XfUDOZYXzvsMUZZP8jHPS+nZzvttKa70fJY1gkf/xPEiscm4qmuqo5D1v7/Gu+PS7aKgvarjFOnYPKdBzB0Er03wj8DTXjgTeezJtY3KZ77HWOHPFOTsUZKD21ze2zSkjZevLIA984oOBFHnddSey2FG3g0A2isrbxeuLlYWSK9o9oa95OawLAI4yJI0X7X6LL9jsvvGBWzYLTbxlOBR9GVgSr669HiHteyhm7oZeyTUYIMAVrUi2GXgc5WO6dh+osU77qS5ePnccdz9tiMrNDB9vRddtYyeGokNkoMyUkZjvAdtjSdM2WdnJcjrIg7imNpwMjUR6kjadqSIlmOaIIkdWySLXAlriT2tZCgObpSuwecBuFyEouz7eN9l0j6/aSUiyOd7TckY/IIrSb5as5FZWss81glxODO2PoyWFJdsoIu24QgzYTQmWvZsdxqCSgAKo03fRgdf29qOovq1GezOpDR80a93+lMzlsVBXPescVo7L+JwDuYHX93yILq4rM1ilvJDx4QnjP75hr/7n1HR5rfs4mGdkwScqmDON7hGGDLDv+82fzhOjJ0d9avzdOriUQeiAm8joKKAWKmuqqjmyqq8o6GX4mJ9UeFq376y6R2373uL3XCym75mVr/w1jNLGSPlIkWCS7ozgom4vTpRDuiObg0dyJjNHacKuCAF9HgkCc/Iqxftu7UTpiv3nLqNsBVZKE+fZ4FsoxoL4PX7ZOhjEMAH9DjWOZM/8iK6ahNUaFzeK/27jYXBT1Us/Mqoy3yyhCDlJY2FHvAgZBnAufgKZkKZ4uHEkXuQ5he/WUWpi0ax6uaPswfGjv8uulpE7zdCEcEQ09uqe0nYlxac4wsWzByajQ3KCWRhbDCcthb3tY9zLctplmyulhr3nt+g/7t44M1BbLtaJtx/d/MSreYRaBVxfwHgMKV8riCzrnHmW2TF0XF9Xq05zIuL5arD9pOeZZnePQpZUuvvVVoWV9v22+9uLQRt1YVIjxF8Dj/oaam1FzUzz9XvTqLh3AFDoXlc7cGcUnuw+Y42+oGddUV9XrGvPRgajFvgHcfm7XFycd3tv0xDZG5y8Q4icobuTRwer6wWsbD2OLxZYme/E/QVNd1VOVtY2jZtvbi3vraltTtg9aPigDD6IIuaBEUA9FFfO+9+sd6UbhXeAOt20e48TO69FczglGl3lTy1tfhYT/MkpLh93Daq410PtRyuVfFQ+h6mb2/Lb/tVfPXP7sl7M9I3fqEs7VXtsUVxin8Zi9txnF2Q/cvAnfyTB+IPA31EwCRLC6fhHQYAo9YVCtcuVD0oiJh89Wk8zKTK2+kfr/K6hi5LqUoOd0dKMZfcD7xCRdMo/P9v62w2r9IKP5GC1TcIxYhNAkpqZTU7ZvV/C022r3/vOON142YjfF8ZcCzWEM7EidZ7+0DXJg6G/gUZIF7iIiGTJlkajPcVHnNQ6PPq8f9zJvSnt5dM+SovsLpmiDGiRCaFKyW+buDt3DyWUHXFng+sLZn6ELVml285uOT6jL24sVdnGnXrjCtIQ+2PsoLYs980KlzYCEhSGwkRL9G0Q0S19jgJNin89DeflaSRaET0qpCNFMUqg7H+UJXBGsrjeBI5Nd1oOqnRyXD2pkGN9bbFmyF99BxMr++IFVTXVVXbFtQZIeqEua6qr+/G2ND4CAbyKw8J78qfpjeRM5tHcFNV3zbiEQOm8z+poMHP+2Z+SsfNuYMS3aRkTqTI38B2BGU13VVw4fPnflvjUvluZc3eLwlnpsM65QzfWt76B3V3Ce8SuAa5vqqi4fsqNh/ODgb6g5mWT1jdNQ1SA2WFvWNp1rhW6MSFH2nx87biTqRWqRahQk8TCK6zQDRQPYHahNbRC3HzRTp2/pgYAwhavH4RixmNSMPymRc1Y+9NNcaT7xnqecd5yj+GfR1oPZQR+gQmJp54h/hnSDLHV/rmXYvbozzYSJrtkBu3ssjpEfS4evecAZBykzNShETINiMEwM94RN6fKs8G6UfTQduAOlVTjQ8tw4SYoNIvEqlFg6tm5rQ15vEPV9z2TDVI24kd4LvIgSsv4dKkGtBngLOCRYXT9oeZBh/DAw7CHbDFTWNhYA/0DVXrSBQ4DWytrGVcAyVHmdq1FFcW/7loaZCvc77nK9rmg6CMFCVxFP5Vb+dFlDzRXB6vqNIicDEPC5UBpII2eG15rAol7pHnuZcboNPIYSZt0g/A0126K8Dc8Fq+tjYr6+H6HqAkb78ife+lze1qpxymTbULA1U0JjQYWahleKw8iGI0l6tk4g28syywtc6MbIjE3beG3j5bM6vzBuKtpxrBRZpN+BI3uWHPaad8whId3tQL1kg2QIx8b7tR0W57nv4/aRlQ7N3Z1NNF8cP/Kgc0qsCCPsMGVG/6B1OFHGWMIbJy19Obo1XgiwusYgbR1nUTLHJfXaDCGkP9ret0bPXdulu0dKiY3lyQPQc1uyWjYWwo4NcqOsMrmB0piLHSPeRbKPRmSD/WnSbrSFVpF2Manf4ddgjKV2I0HfOtzFPG9h1nZS0g9MEgI/irwfQSnvtxOr+pHZdez/XOChYHV9vMbxuf6GmgtiXrFhDGPYQ7Y5qKxtDABDZSheDtyEcmEHB+Oa/S/x09uO/+BLd/GuqdvGGD31L5/1n3M2qoOAb2vUS6CB5ATze+BqAqFsafpZ4W+oGQksRLnp1wOTgsvv2xV4SYJ2cckevc/njMvNthofZ3Q1LFx64l+A9qa6qs2t7TmM7zH8DTXHoniHIuVfAg7bZKTZK1e5fEnNhyFe6HlmmB7HwOi+Hs6xfxadoz2QPzkheIoyxASDcXOl5KiuZTzmm5i6CUjNdEwZcernISAlPUIkC6JH10xHRnNxjX8rKsTAEJ2QNnNWPsxtvu36/unbNgdAWg5ptk8UjqJltnAY8fG3onivo1AE+xbUwmug/oOU5NiG3ac5lVTE0LfVjh2iZWuT1+dc1OOJjkNTJYp0aWENLTmxyZA2JoLPhGA6GXdZ2jo7rMlnzchmDKER0hOO0T6rd8Sxdl/JQ87SBbmxa1gjBBNifUxCeS6VoKuUUkNiCy3e/ypgx2B1fVvq+SprGwuB3qa6qqyx6WH8cDBM6t889KZ8jnOYUsmiUWCBjjV/Z7HgGQK+jS6O+03hS3fxfzO3eaS5cS7ygG9bYA5wD0pzLM6ruwK4cROHMo5k/bsyVJLEzoC2xFlAY25lNmNMAv/34ln/Pa2prmr+sDE2jMEQrK5/GPWMNZPFnDE1B7uGU7xA2SyClEVqj54tSgk/6/tCe8k7LtUYA6WkP/icKkSaMRY/ffoQUjUwQNpat5R0MwRSjTEA4epB6BEg6V2TEuxwnnRHdX7XMQeHtGnTXAltMaGbwlm6EHQjhKoOsATYJVhdv2+wun5KsLr+rmB1/VNAdmkZIahrezdheWRGFKXkI9Qidm/gBRhojI01uox9OzvIsZgcN8YASqxwsqONxRBtpYRpa/McHmkNMMaA2VZozAEf9P/o7RnNTv6x9m3cdmK9mSOEPNbursiVZszOlfrrqPmwEyVHkqonIu5e96o4umeJHGH1/wGozGKMXYHSL1tSWduYVtN0GD88DBtkm4cbUZPLrShjYjpKyboOOBdoFtgV/3H+kUfdVx8mJZ8Q8H2r2YDB6vq/k+TWALDYVbhBbk0MOxLL1ER5/e4HmOMu4fDyw8+d/I8r27a6rn5jRWE/QiU3dAE3x0KW9wELy8x+w2PHcv+TE+pi4KRgdf1fNrL/YfzAEayuXwO8nbH5k/gz9WReZTJEFM/qy5BWR0or8TkDurS5t3gi65wD1lk2SrB4g5IEVn8BVnd52jCynU9aznfM9gmPpg7Pjniw+oqTBcutdNa/o3gResEqM61agADh7hG7NnvYqlPnlJJDQ4/kTy4hA0JQhFKbPxV4curdNS9OuOr+2sraxj+8dcXMvfboXzOoF0cguKB9fo8dzh9wO4FdpOW8yuorHg/oSGlnGk09wun8Vd+7tOal3751eo68Zd3ry6q75lNk9lNs9hte2wgONg6AvXtX9sVEaLOME5aXtRDR9AFf7kizN+ooWn6ze6uXZzSOdfDLir2IpGi22ZF8Uxp5cyNLDyCydL8nhWaBWmB6UJzFhCG5faSNXSMt/K79oyjQAxyTZTgXxP4fiwp7DuMHjOGQ5TeAytrGcRW0zXnXc/6IlM0HEAi9+q0NCvA31FyKMhpTcURs5Ts4Ar5C4FWUYfZHlAH1wqyKqtGLXcrOtLpGvbrogisO2NyxVdY2VmnYF0tPZ9A17p05QtCCmjufHuZYDGNT4W+o+TcQL3v2V+AZSFFP30BMzWWbMjpIaSEhbSXdsJHwWib9uiMtBGkbnk6zdWquY8QiXXP1DdXZXGBa6gajo7JH93TkCU8IQCLpQ5Cb5pWy9deFZu1EhiyMJm0ebn6BX5bvK1t172A34AxUep8TwOopQ/N0Mt5uk6ZAtOg5GMpIWYtKegDg8JUGb9g7tIVGLxohsmeGIi1HSOimD0CzbTKJ8zuFW/jEU5rlQFsKEPH77rENosJh2EKEUBqP6XHSzSD6u2yTqDY0rdoOFxBdMXMVUt/ZM/WZEPAOakEOKdzBcqN36aNrX/hbiR0+44SRB3k+dZfGk7zODFbX3xnvr7K28WGUoRYF9myqq/pwkwY9jO8Vhj1k3wCa6qpWeERkarf0fhrbtBT45FscUhx/zdywbaRt5gaPCoQ6CYSmA24Cod8RCM0jEBqz0B7bmtIqaymZjUEsU/VBG21fGS4+O7Jw1rpgdf0Twer6x4eNsWFsJlLDP25gK1LjgVlDlXbiWYsKXYw1skcKsxhjQ4bQ+3UHO4fXc03Lh31aTNtTd4QLXRWfOYVzcGMstlaelrlNz1udp3lD8XCnEJoyxlLLGQnN+pyM4uMAttC4sGRPTuv6coCHKgV3khLuzPU2IxxRVrjyxRpnftwYA0U3SOCpihwjNHbBAGMs4cmTArATXqtMY6zIDKcZY2nDE5pIve9hzYkthBMplTEGie9Ut+2NN8Zk/J8c1BhLFbLVPF0UjH9ljBDmQ8b6rV8kaYxBSrjytK4vJ5bY4V8DO0TRUzUfMyMJP0PJIm0/bIwNY9gg+4bw2h/PbMsX/buhQpo7EghtfDbjN4SYcZNmlG0Tad97kOYDEQilzbTS8M6wektesXrK79QL1pz1FYYmIa1W3gar+A5jGBvApSjpindQApx3MghFPtc2Ory28VLD2ld7XDG+0Agr3PRE83OLZ/SvXUb2MEIo5fPILPvTsNRZwJWlu+TYMaMiXoQiZkiZef16d2aJRbLUixQCNGd271NKiDKCklbIer0rnXns37vy57mWPbDIZRZMNQaKxcaQzrTXDC0WwhswZmlpbXZv8R+Fbg/Ijha2zc87v+TatnfR48aPlBtnU4mBrawMQ0/KwTuSYKvUjyHaCC3NOIy6o1zo+dc+drhg78FqepbZYYjJrfym81MKrGgHivD/j9R2TXVVZlNd1fNNdVULBx3AMH4wGA5Z/sDgb6hxjza616125quwgbQtW2jOYHX9t/ogVNY27ozS5Hm/qa7qjm9zLMP4fsHfUPMe6eKtiZCW1zZ4a9Xj5EnzbuC0Vs3DCmce20faXljtyHt2tNnz9vbjf/YUGWr9mRhMdyvfipo+O+oICz3a6hhcdGvfvlW8njMmbXh2xIvu2TxpqhFm3xdtuncrhPBm2++yrVYHdkmfNpiqhhrE0T2LqTD7+EfhtK8kLxGPIA4aVZQSj7QIaw5yrCh9g9eN3JxzSynp1rSBHsMhD7IclnBYA9I740ajiYbdX/ymntP+ErCDyzb3HG32RI7sWe7Zr7vVuLroR0v3sxZvVd0V/ERHnkIg1Pl1XdMwvp8YNsh+gHizzv/GL8v22TslBHBJsLr+2xWvHcYwviH4G2o6gMIBO6TELS1+3LuCZa6Clv37VhfUdM1zWwjOKd2bN3JGU2L20er4WpKkBxexkJKRVh9rHbmZm0FqgB0WGjoZWZNZ7KO0c8S5bjIWlhuM9qZJGzvLThnOXTKJteOWuPOcG2uMfU0arUwI97LMk4vLNiNRzZE91fUrYEPjPDkUZIkcxbv5xaCbgx4oJeuFYGyObXzSpzm38dgmN6/5wDgt/1in7lsNgNk6eQ3O/uMcvlWTgBeD1fXNX/f1DOP7geGQ5Q8Qe4ebH967f03ib5dtXf/lteM3VMR3GMPYUrEu61YhiGgOnsqfyOfuktK/F+3g/shdyh+KpvNGjipBmGaMZS5eN20xG3+L96E4Z8mXshBEpBbVMopuCwFCs0Hwe0gTiO0dUCsTwsDfHbaVkOSRKdQqoYG06coc1E96lvLKqqd4dM1zqr5l6vndvVst8eQPZYwNiJ1uhjGWdhXKM5jLYjkSKbGimsOdoaD7tXgQhMg8s4ImbSZEQ9zr8/NuYQEVZi9W90h7sCxYISizDU9nn+bcBiCsOfg4N9+p5SfnV5y9o/T8NS+jZIPe8zfUfJWydcP4HmPYIPshIhD6x27h9QmeVlTTRb/QB+iUDWMYG0TAN5aAbz4BXy8B38nf9nAGwTsZf/dlbQXcVrAN9xdMzb4z09rIFNvaOOSgOGcVqRs7nF5XzEtloPhpCWNHCHYDPga6UfViPVmG4wEuNIXmLTb7DQANmWZhCY1OknqJ9uRwaNn0nu72Y4uPsX+ft2/HkT1L0q9lcOOqH1VOaoh4ZxJSqpDuIEg7ixCguXvRvd0IEeOoxa1P5Z3aoMmX7VTZuF7+SCelRm/aNltoLHMlE1PXulyghzW7b3CFIM0ZToSFdWnzeO6U9UKTibHoBWsQmh03wsaRkQwxjGHEMWyQ/UBxXM/ixvHRJC/5j8U7V357oxnGlohooGjSSkfu1bYqF5PD0NUrvk38EyXcGUFltaUaaP2Q9BzN9o5iTDTFkZTdkFiSut9lW4wZJCNzM+BESVWkCk0fjkoOygd+Qjal/DiE0Nod3ghSRm0RSxuUUurSMk/rnH87cBhwk9lV9tC8jr0qryzevbg119Q+LfAWPZk30Yj1sSH70ssGyu4NkHX7OuKYG2GLDdYsVZPNYVvm5EgHQbePFmdKmDizlhVq3HpOJ3pue+o+iTJIU86qYAmNdS53QgMvRfi3DSWLcXuwun6jkimG8cPDsEH2A0NlbaOorG08Z1p/w5Idwx3Px7cvdhbmEvDlDXXsMIYRR//VxbPOKdt74UGjj6g+tfwAomoq+S5IuwxAsLr+fZRXwherIzgvZfcTqLqzCkLgkwbEw4cD3+4GkCZSfP/aF3ix+Rmub3k7hHrxxpEeg0zFhj1qX4W4locQSVa8EMISuuPF3LF/+GXnF6NybONQR8H6E1wVc4VwJp2FfYZvbfKQjRpjKlpS/8gSUv1q2IAxJqTdQYa3rSiu8J8BUwjHInfR0B7Poc6nvHSOsUa3PLFrAVe3fGjHjTJp6wZw9u/aP2o+tSuY6hmsA5zB6vpfDnkhw/hBY9gg++HhTOAWC/2yB1tOd483unud0uLXnZ85gYu/7cENY8tA0Fl0+mzvKAHwkaecdz0jn0Gpu38nEayuN4PV9XEJ+CNTdv0M+FNaW1eRmhizWxRO4GbifhEhmOce0dWtOWnMrQyS7jnSgC9Su0bKhBdqAxiyweYYO6uc+TyQP+nuPs05OXGS5Fnarc6x11rh3OxabSnVDGzTmc3SGRCG+5pqfqdjkAsfa3QVpe6TEvJN03LILDZxRgKDNgQvzTZ1y+73fWp2VK432ytJlEwCVjrzxX0FU5nrLdL0jlFI0wESDdjhlO6Fz17WMYenmxvlYb1NfwFe/rYz2Yfx3cewQfbDQ7Jei+kd8cKap9+eu+JBju9eBOlhkmEMY1BMMkId4w0V2qswe5kWbcsjEMrukvjuwcz4rEKAUnYWWJEOS2gq63DQIpM4SDGY/ly446/3Gf3Tl9/MGT2DDGV84BZUhYtGwO+R5oZ4VxJF+H9tiDYRIegctIPkSMPAwwVmNJHUENI9Dj2bkQLFjrIFf9U9vSJme0lhZ8QdY/dDaJZnKIPwG0/cz3KCZkcek6Odib+FgBXuPN02NiyfYafw0qQtEkJqdjQHc91OunD276gXLi9zFDdhGx6krXWT8jw8mj8Jq3gNwmEidEsHaoFfAkf8bsRuf302t/Ji4BN/Q031Zl3vML5WCCGKhRBXCCHmCCE6hBC9QogFQoh/CCG2/jbHNmyQ/fBwM/AkiiR8DnDGxSV7LN5h/AkcWXHoNQuuHXfEtzu8YWwJyJfGDQ+tfYE71r3GY83PU2RHbvu2x7QJ+Cnwuss2P54WabmaOIFeiMIu3Z30cMVe/F7biACPDtZZt+6+Oqw5BqvluhWwH7APKIV5rz3kukegCP9jgWsHaeMmm4xHvIOkHemJrt75ufVLq0pSbRgr5iHSLUmGbZYbP77M6muWWnYXl9DsDUT0sm+XEozWiQNrh24KBlZjB8AQOovcRYnzxKE7IjKuxL9R3WsypXCljqNkHsIRRQh1vO7tQhqevYH3geZsulEOaX8GnAD0fOYuSX3B/3ijBjGMbwxCiD1QlIVrgJ1Qv6McYAqqDvUnQoizv7XxDeuQ/bARS8FOqE9e0/b+/OMunrPttzikYWwpCPhOAC4BHiYQ+uO3PZyNRsBXgFLuP6rJka8fMmpWZnju83361/zj7NAX/3wlZww/Cq/jU3fp4bcWbv8wsQxHYBkwIXkIISEGeMdSoTTCpKTICtPhyKrXmn7ARmp6ZYqtorjoAjClrRtWe6XXUbJkQGM7koPm7kvtRx0npXRLU0SGEo1N6WqD45R2WKKFZCQ/Xzj7csSAkgQbASkNr22Ift01dLHJLMixDVIFcAvNvmin7nWl3DRL2g5L6JYLQFqOfjTTKwRIWyej+sBTqEoiJ2ae56Su4MfjzZ7oyd0L/RoUAQSKd7nhgfwp56P4hD8NVtc/t6njH8bXAyHEJFS1hCLU93Er8BjKk7wXyrNZhPqtHiulHHQR9o2Ncdgg++HB31BzMHA5qnDxhSVW/9JW3Ts2xzb497pXXtjusmWHfMtDHMYwvjkEfPXA2et1Dz8rP5jVziy5LFLiwMZEhS6FtBdKoU0BGG30fLzamXcXakKPtQcp6REaG06MyWbBxNVbye4BykCaMbghWL0jLC2nTR+g0jGEWOxg0KVlWULXY9cQARZKyXaJLMYs15ZvRd7v1t0zYruthJzF0BhcSHcT4LRNjPQalXKs0XXSej3nnxHNkZ/tGNtw25ozkqQR2g6EbmIbnn7NGS4EbkRVFUlUaJjZv0b+c/0bjzqQx2R090f/+BP/DNjB6voQw/jWIIR4BqiK/VktpbwnY/92KM9nDkorcJKUMl0X5RvGcMjyh4n/olYE5wIntOre7f7c8nb7Y83Ps120/SACvl2+5fENYxjfJHIBHs+dmN0YAxACU+gJ40IipsR3dWnOnffrW/U8MqnzpUsLp7WRNPZBdBkYyFvLDinnbbhREpqn8xppei6QMskRFQIQGdpcgy3OY9t9VhhL6EltMBU63TZVUiJz/P5Ie/PhvctmpOzWM9Ql0uvjxqOZQxWgHALSSj9s1/61kYwmYoWj4L5+o+AFKcnqqksYY7bA6hiHiCn1x/TGfkpKNrEUgnM7PuPStQuWO5CpIUkbWATcGayu7xg2xr5dxIytuDH2eqYxBiCl/AKIV6wZCfzifzK4FGyy+3cYWzYqaxu9nqmkkq+7gsvv64VEnTcNxXv56H8+uGFsmQj49n3PXX7pazmjV/+rwP+rYHX9oMKr3xH8Fsgvt/pHA7tu6sG9wik/9JTtNNLsW7HWmTsRwNJ00GTuho7dJEiJ2zZlRHem1erRkIfZm+A8Err1E6FbvUAaw10IRFk03HVQeHnB+55yFrsKU0RhB0pAhPSsAvMZlbwBAROjnYw2e0OfuEsrgu50UVUhwOrPR0bz0fObLYR0pu6LfRrscgyGEqSV2odSWrvG+/nYWz6g7JIQgLP/mGy2b6qDT2gS4enGjuZ0a66+uDft/rTBSclO4Tbusarar9PuSvW4nU4gdM+g4xxGApW1jTuisv93AvJQIeFPgDua6qo+/ZpOk+q5vHOIdneR1FM8BpWU8z/DcMjyB4LK2sZC4HUtr3kH56iP45POa8Hq+v0BCPjOQ5GIe4FzCISe+HZGOowtCgGf28NcgwAAsFNJREFU+1PXiPaTRx6UYwqNQiv84XtnNPzo2x5WHP6GmqNQhcXvCVbXf5ll/36oDMgNkbr6kFJpg8V+PIVWuK5Tc9durL6DkPZLUmgHobwnYdK1xjYuRPd1FYuMQTc0djDXyjmeUrGx/Raa/XTqnl6ESDNApaRbQDdi6ELs0taJLDkAR9k8HLF6jxuJMEkO38B+JTKy5MAV7q1eHh+/FIdtYgodBzYTo10scBUNynsbZJuBFAcKTb6ReT6fFaGg08cqezR20UpGmNHga+seCLqxPyIQGiwhYxgxVNY27gL8HdhjiGbvABc01VV9JQeBEOJVVHINwBgp5aAPnhBiMcopEQVypUyvePFNYjhk+T2Hv6FmlL+hpkb3LT8L2EHzdqROOoWxNp47CrZebSIKOjVXxUUlMx/d/u6znvM31Gz1LQ17GFsOxHxXsdOMkZH6NOcOG3ugv6Hmd/6GmnZ/Q83j/oYaFwGfTsD3tc1J/oaa3VHZkZcAL/sbagZYHMHq+tfIUpMxC/oQwp/64+nUPUsfXPviTYd3L01vmZK66LGNPoCxRjdPNj+3EiVCqwE5SDt+XpON5Ut9zeJepsNmjrdsUGMs23pdwAqE2AnSa2MKQf6GjDEAoVk4x7yL5g71Z9ufoNMNPHlWDYt4M7u3VGDr483QyMQ2U3Mkws/dwm0mvF+DRI0zodnCGVlUdYaUGSK/UhLS3awv6kWWLkE4orR78O8w/oS7ho2xDaOytrEKeIuhjTFi+9+Ktf8qiCeqdQ1ljMUwP/a/Cxgse/obwbBB9j2Gv6HGAcwGbnWUf36NcPb0l3e5ybEsW0hpATf6G2rygQ//WrTTY6eX78ctvu14Nne8Zgj9EODub/UChvHdRyAU3qd/zZOTo51o0ub8zrkuAr6fbugwf0NNHvAHVFbTkSd0L6pF1XBsIeCbMeTBG4/ylM8lDF5yaKiwhI0qlTQbmJOx7+Edom23X9HxcbDC7DGRkjwraokUjarjepZ8+emKB3lxzdNMMUKFKDkLBZFI/ctqmGw2NkYkTIK0MyoaZdNQzWKkdOieccBChvBWZTttmhyFtxvN05PVKxnjt8VLOKWOKquAmgqBFkjh7sY96UWchWsHjltCsytnIEUnS8F4LWXbaLMf3dd0ihAZ78rYCSKanrC/pcSKrPzRVZW1jQ9W1jaWZBvrMKCytnFX4BE2/vnxAI/EPGqbDCGEGyiL/bkxZatS24zbnHNuLoYNsu83collYwmB2zn23Qc7xs+jX9O0XcPr9A9WPNyNittvB/C+ZyQtmjd1ub+3v6HmZ//7YQ9jS8Joq9f5dPOzfL7iQc7s+hJiz9MG0A80xT4bx/QsORj1vBaj9PG+DjwN3I7KJj49WF2fNfQQrK6/jFjWXBb8GEUGPpL0EOOXZ4bmeYDG+e5if7Mjz4EQ9OguXQotkXG41FFw4yfuks9OL9uv/eBRh69BFQhPh5RfT8kyKVWpnvR0yo74PiCFIAVCJF15EyJdGO0DEzez2nbJ/l0oI2m1Lq1sLR9KPWRTnXvS1oiu3PVhaYmO2KbBOM+W7u0SmjNMvKj3gL5SP6cah5k1noTgpnVv8svQFxzes4yd12s4ytJyKAZ4U+OZqkKgu0Z9tItwdR3H/5h7tIXhRjbBmI/Bgwpvbg5SuX09G9E+tU3WTNxvCsMcsu85/A01fwAuAF4HdiBlhX5u5+d/uaVw+2tRIrETgQ+B/YFWVAYVwNxgdf1Gh6GG8QNDwDcLZfikYgqwAjgFaCMQejzbof6GmmtRek6PBpff1wdcEdt1DoFQ/Tc04qzwN9TsiQqhpKKHWPo7qRwvNWeagGOs2cPx3Yv4S/H0wbp+EJgJjIn9/QYxkdh4XwK5UgptbJZjB8UmyFY8FBt/2gALzLDscni+ieJGqbCAY/KtyDXdunt7YABRK/b6sVDey/kxLbfRiQ56StDzWjf5xNlyE2JYSaqXMgWatDm2ZwmvecbSonuYsj6feR4fjsJVqf3aA7xlGTBaJ2G1+V9sqqsaFoLNQGVt404M9DRvCnbaVKK/EGIsaj4CeEtKufcG2l9Dci46RUp57yaPcjMx7CH7niNYXX95sLo+P1hdfzjK4AKUntBjeRNvD1bXdxprpx0sLccewN7B6vp4hkscn/2vxzyMLRoh1EvvNuAO4DECvjMSewO+HAI+j7+hZhvgMqAS+I1//Il3A7OAA/7XxhhAsLp+NkrS4FrgbFQm5h9IckjSpR2EcCAEK535/KVox6G6Ph4py1L+ztQ1klJoZ0J2CYbMtslPIrsxlrHAPrxn2Y/GGt3bZDbrxfO/WInrSPlot+baPttOKZHScjwrBEojzdK3JsUYA9ByBxpjG+NDGMIjN3KwY/bsb8YjLdY7PUgNgqX96AXNmf0O+c6UEqTt6ALOGKrdDxhf9b6cuRnHpKoKbLiWVtIZAV83nWADGJa9+IHA31CzLapG3j3AOkvo/33tzH8vqqxtfBDGHWeFxn2G0iYLA4cCfxhvdOXdte61xwjcJwiEhl2pwxiIQOgZAr7LgJ8Aq4E/EQiFP/zj5F1/XTqTKDr/1/nJj49VZP1bUHNO39NrGl88suJQ2xKahnrm+giEGr+9C4Fgdf0TwBPxv/0NNTsBfyQb4T5Npn7odW2BHXV16WqO361/rTXB7Prz/flTxgIlCPEWiqfyY5TQ7JTBe1LjkBIrrcRPckhSiHQz5AtXcWVYDKTOWY6vcTE+VH6oEFm5V0iJEEKgm4fGb6VwWELa2nqh2WVZmpu50njp6PbVlZ96Snyf5+XFkwc2WkA2dh5n7LONRAgteWyH7rYrzN7keDUTu7cUPa8leTzpZzugdwU+Q/KY1w/ufoQAV1nwveClF2wMV+mHiJ2+4vE7bsYxqTSBjaEHpLYZSDH4BjEcsvyBwN9QswZVIw9g32B1/Rsx4mlLSrNDmuqqXgAg4DsSVVZCWIi/64HOC/+Hwx3GFo6Dbj/55ZXO/AMAcmzjzTkrH96RpNYdAA/kTuJO39b9q5z5dwIXAyfH/j0RrK6/yd9QUwxcF2t+WbC6vn3IkwZ8M4FjgecJhJ7/Oq7D31DTSyp3TErTK80V/cJxWYnV94tWR+4hqfsAzS2ttRHNUUGsVNL5nXP5Z+F25NpG10NrXygYa/YCXOAff+IrSPkJQsTJ/V+iwqQ7sHEr+Q1irNHNSuc3R4Px2gYR6cLWN/weKTH7e1sd3iG12qRElll9dq5t6E3OgqQwr6V3urRogS6ldkTvUh7MT9itayB7ZqfTNjFSxH1twy01ZySr8RY3Ct2mjER04SZmaxvrtpnrGjl/Wrzd/j2r+l7NG6OeB1vIUUu3FWusCssoXqk7S4PxZrcGq+vP3eAN+QGisrZxLpDVY7qR+LyprmrahpulQwixHigFQlLKwg20fQo4PPbn1lLK4FDtv04Me8i+B/A31JQBPYMJcvobajTSixGPiP3fjuKP7YwyzFLDk3sB4rmccVxasvuvokrP6eBgdf3/7OEcxhaIgE8Hnj0x37/n9TFeVZ/mfAvl+UkzyO70bcMqZ54XOB9VNSKeX7efv6HmFeBK4LhYcwdDhTsCviLgBVRiwLkEfFMJhBIJKv6GmlLgQFRplKNQoo8PBKvrb9zAFbWTNMhCCOHrxzEReKDVkcs2kTa+dBWjIbGE9jTw34hwlAIq7CoE+4bXcPaK+ffqyDHAvrG+tt2nb7X7jZzRqSKnqYWoNxnSRqZ6fABm9De/km/ISfNzCsZvcn8bIXnWrzmxm7f5QBv5xS4DvGEZiBljq0jy6QZACESLI0dvASqifdJrIpq8HmydQgMdQ8Ar3jHsHF7/6ceesu0ZJAQppEyWTJISq7cMo2WqcFfOHlJ/LOIQiXCVECBcvRN87cU48tZxct98mhz5CePc6q54cqlVeSOwkI7KQmdpcFfU83fXUPfhB46NIdUPhc31WM1D/fZ8QojRG5C+iIf4o8DizTzfZmHYINsSEfC5UA/Xosml51+h51AtpejyN9TsHqyun5/ZPFhdb/sbak5B6TF9QCws01RXZVfWNu4D7A588f/snXd4VFX6xz/n3mmpk06H0AcEBRQLiGIvsa5lFdcyltXY17Ib+7i6a9yiu7bRtYz6U+zd2DuKBaQIwoCUUAKB9Dr13vP748wkkxAQERT1fp6Hh8yt595Mzn3vW75vZXlJdcpujwFn/M89uiAqdIFKhD0PuHJHXprFLxSf2wncARwKDPO2BBkSayYqNA4MVc0Brlmvp/N01jAyzdhH5zUvnteoO0rpzNfQ6MyRMoDL6TTGYPOSFXgCpSdl9z/xGH/NR+m7R2oBbIEsz+jbA6VPoqqzzgWeRuWDxemc9/byBEpfC3r9y3s45jiU0XYj6u9jvl0ap8aEPjn1ib7WlhmbFF6vf5bWV0PloG0i+fGtPXfOVQWTLmzQnDPaNTtnNgfrLmlccOftdZ+f/CexL1+4enXTnwDUw2CrvWRSIkHcBvLa1OXPZ484qKidjZPaq5mZrmwXpxkzI5r9e3OhjLYCbFuRUF+cPXfiGmxCJtxKks1rzMZqh9ba8pf37ZKLZQrooTpyvSNduGwxunclqLWl06I5BtDtOyElcbM91yY0Ey0tpVOREMTW7QF6tEEI1fQ7lS0ZnfbcVVlNAvrHIhzfsoKzex/Yeb5YxnHA+ZXlJRuB9XDsJsLDFpswF/X3tK3M28b9PqbzZWgqqn3gJiQKAJL6m5//lKKwYIUsf5GsunHovPfdBbutsWUaz6btoht2VYktY647l/zxzivwuTOB/0N5Jf68rbk5r9++6yXleRPuqrUpuaC9Q9Vlj1740u3b6TIsfk343Jey+bL0GqDw2D5HsMTR8TwsQeWO/RsVohPAhygpjNeR8m6ESIai5gKHB73+jd0P7AmU7oqapIVNGpEv1rywKFPGX/EMmjYI8CY2ew/lHetOBCgOev2pLyIktPnWAG6UYTQq6PWvKP/v1A8fzR41NXXbMeHaqqAzb1VcaJsKXHZ1Mc2ja/5LdNdIbfmz1e9c9oeig9yz01Il05iB8iQc0cOYU46fSKASIKWYJ4S0kZAc0RLlgEmSTbB/BDEpiQmhvIVb8p7JuIaw9SgZhhHKBUlMT2+wAxTHmsmKwoKM7B633yxSRhDC2WWJqa2KrR83UM9dqenpDSkrILpy6idIbZyeuzLbmVnF+Nj6+Nz0XJuxGadeT9d3Q90sFseH8EJhAabhIrpmb2QsYzZwTmV5yTc/7AJ+myTaJM39vu22wA+usgQQQoxFyd+A6mV5wGa289HZOukSKeU92zLIbcWqsvyFUVxWccbMrNzd/pE3gaeyR+i6SBhjUhCrHZmc1f+J0k0aTTJ08kPxuT3PZQ75T9IYA8gyo+VxX84VP2b8Fr9awltYFwXYqHfRAe0b9Po/CHr941Eh8+OAQ4Orps8Mrpp+/7TW7/IA0s0Y/o0ffRNcNf1NfO4n8LmndhzB504/tnXFriTSrONCt+0x8OQD8TXdTGeZO6gmz8+mfDZQxS1HdTfGEmShjDFQXqpCgLKGuV+8VfUqFzfMJ92MSYDV9qx+ZfWzV6OMO0ithOz6RN+NrsKmjm+cBRcCQ752FXUP40zh+4wxAAHHbmzk1A012E3pJGGM9Yq3c1jbqi7liD0ZY1vTvzvlEJGkMQYgIluQkdJ7NsYA9LQGtLSGjjBtr3g7p4a+JSFUvdmTb0JXY0wt0cxie985mp7e0F1aDFvRwjWOIR9m2/JWsndkHftHV9veXvsKJ7d8R4YZ7TjXgFgLJW2V9G4XT0tJh7/CJk2Wt3t4ovFMQt8dSWTFQchYBsAewE8mi/BLJ2FMzdzG3Wdua29LKeUC4I3Ex6lCiLO6byOE2AW4OvFxA2qO+EmxPGS/IIrLKkYALxyW/9yYjwvUw01E0ghvGAdxFzKWseJh+z8eP0if50vZ7Qt8TT/YRTz/70PmntL70HHdJ/ITWpbVvZA17HmUuJ8BrAh6/VtTsm/xa8bnLkAp2vfk6ogB9vfS+slr8/de26w7ZwJnBb3+TY04n7uRhDG0xO6mwAhTq7v4NK0v+4bWMTLWBCqU+RYq/O75e+6E1f+XNXK1FOJ/Qa///zyB0rEoQ2ofVEj03qDXH/YESk9DSWs8k6io3CyeQOnVwNnAG0Gv/8rE2NLahX7jGb0OPnGhM7+jpUpp44Jn9wutX/5KRvG+62zpf/0kvf9TQAFSlRJ2O3QVXaUdHgSO7LZsq9BNQ36z5lkhgcP7HtW01p7lBtXYe4UjBwxM9J5fup1mnBHNBgtyNum9/b2kmzGyo9Ksdjk0AGlSjSBHiB8s9qmuQ5oMbDeeXZlhP4EthKZ/DFKyTIiubXByjDCntHzH6xmDWWvPxC4Nnl/3FkUxw5wS+Y/W5jCWYYqhIqNWYDgwW/uCknU5G+T+tsLFl2pp9RhNA1Ytv+bC4h0x7l8jCcX9GfwwcdgQsN+P6WkphBiByiHNQb0c3YsqXAujXoKuQXUOkcApUspnez7SjsMyyHZyPIHSM4ArjLaCWHzDLntIwyWRQqT3mothjxKtGY0Md6ZF3GJ7hNNt7yU/bgDG4WvqyQuweXzu3HfT+q+5pGg/VRGV8N+LRNZwgmSj3zdQngbri/Rbxue+HZWjuAkrbVkUGGGyZAzgQ3xNB/a0XeI4X6CagQNQrzk5tN/RtGoOMs0o71a9Rq4ZuR3VEPyTlD0H42uq9ARKzwICiWUXB73+ez2B0uMSyzYCR/aUM7a1JHIxH+++fEykjoXOfBzSMKNCPwJlYM1GPcAfAJLVmBtRBuf3WUImcBfK63UwgC7NkCG0Lm7Gf2+cwZ2541lrT6nU7xpvS7qr6pCyIGkgjo3UcuO6hW2nDZqSEdX0HgVbf0iE88f2PJextMuEPTSNlN89XfP9NotmQjySiZbWusWxJKK2WxxlYZtAVu/G2nh/EAZaxoZGe985OUKAGcp5PLp634sry0taRj588WFCM95KHDcuBNlBr/8n1az6JZPoTbm17ZNCwEmV5SU/WhZHCLFv4ry9NrNJFLhCSvmzdFqwkvp/ZjyB0r2BY4GKhDhl6jonqmLHpmfUog/5GBl3isiqyfH26olrUdb+75PbF4v1nKB3iI3HgfN/kDHmc09BuWzNqaGqjIPb1/BeWv8OvSXZNd8i+Yd0JKpq84fLaVv8muix+umvuXswPXsEOUaYp6vfpTjesgc+90eoSbEVJQ47GzgLX1MUlVt2Gqoa78aNetrgVs2RDdCqOajW09bkmpEHUXlpS1F5kp8ntoeuob4LPYHSW1DzXBbqzfgCOsMS28KGnhYudKrC5ajQNWDfoNd/Y3KdJ1CamvtWT2dfve68gyqKqE4cY7knUJoBnHV5w/z9okI7+b6crooBfymYJOOa3sXIEGDITk+TBqwEWhLyGjkAC5wFhJ3xT6dveOewZzKHs8Yo4oucTufmZo2rzVg7W6pc3OIKKck0Y/SVDUcvJXfPblt3fz51NdASx9IwcDibiSecgd3OGwOlPfZ9xhhATYbE7LMau7kOPXMjZsyVkzyeltY4qbK8pAVAaEZdch8haE6MzWIrqSwvqSguq5iCyjvdUoPxmcBlP8YzloqU8tNEaPJiVJrEYJQ3vQp4F7hHSrlJYdxPhZVD9jPiCZQWaFJ+AJQJKd/zBEr7jXv4vEmeQOknnkDpF6jqsHpIaQVii2DvPb+8srxkMOrBdQOJHJ1dxUrSRTKVBR3lRfghvIjSXznWjpS+ulmdX5DOWU7S+eYPqunylvWhLH4L/JMeujq8llkMQKPuYkZaH1CG0f7A3Siv1XDgVOB5fO6LUN+luShD/3bPdavcKE/RBqT876jrVg3E17TcM2haFNUOaDxwAL6m5APxcVQ+VwwYiQpBpApxzfsxFxn0+t/xNi1+8viWrk62NCMaAdCkDAHSEyh9xBMoTaYKpIb0U+UnUpd/jPKi9UcVGiz3BEonoooeGi9o/jYv2kMCendjDKBfvLV72G8wsKsRyeiyvEXTD7s/e0z0uaxhzM50qyIB9S+y2cDJVrrBUgRduySUjY3ULQXeTF3WqjtY6sg9mC2Fa1SOWfcboHpYaTpxbbOPss+AJVscbLcxCmcLeqayoTV7l6h6x/co6PXPRr0M34OSA9qkx6XFlqksL5ldWV4yGfU3fB/K+FqQ+P9eYFxlecnk7WWMJZFS1kkpb5ZSjpdS5kgp06WUw6WUF/6cxhhYIcufD5+78Du7+72j+3aK3D257t3FNxVMHLXMkdOxmW7wiqnJY2Qi1CClaJFh96To6n0XoR6CJWixkUKPkh2TPO+4meFaFcA9+Jou2cqxjALKgUOALiGR5zOH8FLGEHaJ1mEziT+fNeTYZt15EnBWYpNTgl7/M9t4Fyx+DSgZlinAdLp5f67L34sXMoeSZUZ5ev07DI03d9m1QXOw1pbJ6GgDunq+lgL/Quk5SWCCZ9C0WsCDMv4jifOcgvKQ3Rz0+qcnelGGgl7/155AaS7q4f0RXRudLwN2/dGhJZ9bLLVlR4/tW2KTynuczBWTKG/UkMSWcZRB+D7Q3fsDyoC9eEp7VdqDNR9nAZ/ia9oI4AmU2lDhzVyASxvnX1mlZ9z+Qtaw741qHNi6mg8yB26y3GgtREYzsLlXg2Z8r3G1LWFIIaUhhWoNIE2kELQgyEbKKEJsvdhtt5Nnx8PtzTZX+hb22BwfAGcj5ZXAxR05fQl9fyHBhOVCKKmDZK/KLo0YOofxdtDrP7yHc1hYbBeskOVPSHFZhQ311l75jqPfjSNiVbte1jif19OLObR9DWNidaO6a+7kRsUxtWmJjh1SEl52cBam6yngZuAKYW/FMfAzhC1GW/1gDq25nVFi9Ylv3HbRCz2NISET8GcgCPw96PWbwEPApCgaVbaMYHG8ZaRQCv3YpckfmpdwRGgNgO20xtX7H9jvuN8Je8czbRyqgbLFb5fXUKG2Tbi17ktOa15KL6OdfDPSZd1aPYOT+hxGg+7i4PY13FMzA1Q4MflSID519T7MZcZvCWs2Oypn7CyUMQYqXPmkJ1B6JwlD0BMoPSPo9f9f4ueDUKKz1ye2H4Yy0Dp6um4Tvib5xF2HvCaFSGqOJdv3CDqNMVDzax3Kiz0xsX4Dqp9lP2DZm1WvLR4cb1mA6qKxCp97F3xNbSgPd4eq/V05u5UjZZgeWr90b+nTkzEGoGfWEK0aiOmuNLXuIq49WF9CsAQpi3uoaOyCGbOj2aKqL5E09KiwgYQpoar6TzP65ScOtnljTEoyzSitesppuo2lRXekI2U7QmzZKNu0q/iB0XW73fRo+An3lYNGiJbkOQRIQ5dSM1oFcnBKPySZ3D3WMMC05axJvU9/3eK5LSx+JFbI8ieiuKxCoCrDFgLzHzJKjgQobfqWV9e9wWVN33Bx0X6scLjRpIkwtLgZyWToxkyxT6iadDPGeY1BMJ2gHirjALTMjQib8pY73Kuij9j/+dYbzmu35D5/HhXqvAWYllgWWaFncXTfIzmi39GeSwv2FQD/ytmNvxRM4k9FU/h3zq48kuXhUfvE38dqRmZLsyMckbe975XFLwilzH/I5lYLYHSsgXwzsokrfo6rkAZdpSK+l9afGa7eoAyajnmpWXPcljDGAPZ7f+3LpwopK7sdKtUrd7gnUHqoJ1Can9At+xuQDEMsRbUn+tE8mzXsFhJSF8NiTXILEg0OVChyAio/bhgq/9IJnHBCn8M/PbrPkX1muPpgwqCTeh92iSdQemFi3z+gvIIAdoTIBEwhzS9TT9BhjH1PtENKsPf9Gi218jKxj10am+z8++al76bJ+BaNMSlBGi4zaQBFk+r4Aua4ivIHxJq3sHfyAkRXY6yn8wiN7zPGcowwveLt8e7GnJa1zrve5vrd6FhDl+VCN4QQZCGE1j/WyjPr3mZQbVZIGnaMtkKMmjFafMOuyLhjCXB90OvfVrkGC4utwgpZ/kQUl1UU0ZEQbHJQ76fMdeloBzTXssLoR1VOmJX2bOKJF9d4w6CX4xvHHmcnbi5xnoGWULY+K3o1H5nj20m0cxHOZuUh0wyObFlj3FE/Q5cS2azb99trwElDgMbwkqM+REkFLHEOe/s1ocdyAIzWotu/u+Tmsv0fPPOhDbb0c1LH+9Xq5yjpW0KNTc2BmjQxhQYSjHA2elqXifbAoNf/4Y66dxY7OT73g2yprVHPxDfoabYTex/W8R3Tpcmr697oEtZs0Byc2vtQKu3ZnNCyjF2iDay1ZSx8xD16DnBGD8dNykqsRglBDkC1JHIA1wS9/vIffH2bwRMoHfGP2pnPHN62etxqWwa35e2+ZK0tc2O/eOuUz129U4tgIonzx1CJxPfQ1ZNGbjyME6O12paR9ID5g17/hZ5A6fmo/JoUI8qch9DGgdLHin9Pc/MUemrEbbCp1EQ9kIuUYmS0QYn5bm3sUppdmq3r0sDoobn5D6VPrJX19k2cgx2MitTLxc68zQ4ywzB4tPodvkjvzWJ7Lm8kchuTnNSyjFvqv+K+2NH8wzgVgckVtucZJqp4OH7k0tly5KjK8pLNC6xZWGwHLINsO5EwuIYAsyvLS+Ld1mUDF4J5Fmgjj01/zXx7QOes1U1OAlSuy5TwkqP6H6h9zSOOf88kUSl0YuSmptlypDtl27laes0dDmdj33ntt9zuEurUx/Y+8t0lzpxDAGI1I5cY9cNHAnJgWrAmlremqD7Sh7LGWe96be827TLwlGMMoXWEFCaEN8pHN7wvdh106tZe/uPAc0Gv//Wt3cHiV4bPPRGVs7W1eT4SWLxeTxt1YL/jRFLv7q6aGZ8f2r5mb1IMhxiCZs3B/9y78Fi2J7n4ZuA6uqZdzEKFBjfHl0Gvf++tHN/W4XP/i852YhcD962xZTx5dJ8jTw1rHfqnETplLr4CHDYz3i9NmmktukNZGZuGDN9FFT1M73bGZGUpAMI0DalpyuKRJioxqvM4ZtSJ5oiwBTqqEJFyzS7R+tpKzT26zW774QJlSaQ0SOSRfU8iWhSVJ9fR2zLDiMg2zbFJ7yVNmonMe9Hj8TKMCG3f42Wzm3Emh6tp0JxU2rJosinv7MS2Gh6tfY+wdHBA5N9sJI9T9fc33mZ/uAhglVnE/tH/FFWWl9RszeVbWGwrVg7ZdqC4rGIo6mGQC3xUXFZxKOrN81yU5tBkPW/ZsbaCIDKawan1H2gfyKnEhE62EUEgadJdaNIksnpSvRHOnw+ivrK8pBrftDNITJhSEp8tR6bORjHg4hU3njWzuKxi9PX6OX+/QH9NXyIHNC+x52YnRcO1tIbBhhZllFwvXjH/Xuioi/ONMdjY1bbyEICpoSr5fvqADi/YPFG8eHz4f6ON1m87qo0ApCkQPfScQ3kqzvAESi1P2W8VX9MsfO6ldG0PBMA7af1Z6MznhNblDIp3iNILYHQfI8Q1DV/Hns4ark8OVWsHt6/JQUmoFCY3tCPJNyOssGWZdHqKDgTWAakJU/V0tklaiaouBKXFpQHPbZ+L7UKyP2xbskXZIYHSxo61yiBZjaomBVVRZo9rtg6dkF7xtu9q9PThZuf2K1E9NK/q4XxrSDHIOowxQIflBjIfRE5ymbBHOv1iKrG+mq73LKW/khhwWPvqAXfkjOtcJM3WDBmf16o59t3iXUg1vERXl5jNjDfENVtuD3v5UdXaHRIhbbozdX7r+H2bPXgBU0/5fcYYQEyz8VF6/86dE3wZ2vu9c6Pjnp5hjj3bcLZN0tMqQ4MiK15HcjaAS0TbsGR9LH4CLINs+7AvdDStnYqaNE1Uoi6AtOUtRwgQzjZm2/vy4MaP+MLVi68bJrObvpKarDpWtXn4IlyQB5wArCouq9ij0sUiEmGFNlzfAX8B8zJbrwV9NVdzixnOtkEJla5p9nqZ+fkq2UteFzv77WhV+BZ7nzkSPRbTMzc6HIM/4Mw1S3GIuADwaKs7Jr7/1HwqpmeN4La83QEwne2jD8p5m/dkP4yWXhv1rA0fybi9NlK533H2Xov66hkbDDQz+QZs0pkgfCSqH6HFb5MzgftJaR48y1nIpUX7AVCRMYj3q17dZKczWpbaz2hZmvw4Cvgf8Mfu2/UyQqlP5X1Q81dqGK44sf8IlHF2Lurv8jEgHvT6tyx/sC34mky6tmWCVE02IRajjA5lkHUL6QEUGaEBf26Yy/vp/RkZbXz+ztxx04ADUPNAKhFUuHMq3cKMujTj+4bWDf04vX+XHbo5kxx0Ncag2zPg1fRBZJkxWnSHksEQ2hGtwpGPmuM2z+a8YEIQF5sYY7HEdVyHmis3x+uAiZQlCQ21DramQXoHPXnphGBSaN23M9P6Xr706qveAxj+31uWODKrPxGamXZHdOjZk6uWB4pFTZGbthsry0usUJLFDsdK6t8+VJPaw06pAPdJ+SzMUA6g+sc92f47Obt1bxZvPIgZkSnc034Gz2w4ny9a9089Zm/gWHxNsyvNogNvjp1ePSly1yjgVufQ99605awZpbma9tRcTe8Xl1XkAo/kidZ9x2vLp3htb11otheKSOX+QggcAIYtzut9MogjuMs9lkn9TtKOcU/jG3Nw1I7kmLZK8uIqtDE41sTMQoHIqkHP2lB0V82MMV+sf2ri8AGvPS/08DWPbHz3gf3aq3AbSnopZcybNH+2+A3ha0rmbHVQm9K/slZzsZVPNSdwEl17P7LMnhKplzJpSKQ+aTeiNK4Wo3LIvg56/X8Oev3f7hBjbPN8gKoIfRdlICbDpFEbMhke7Nh4gbPAtdSRgy4lDZpjKbACeDvleBGU4K0HVaWdaoy9Bdw+c+0LoU10ylLOoUlzq/LAljnzyDBjnN20iL7R8EzgmKlta27b7A5bmfIipJn6u7QD3yTkRz7YzC6rUdXfx3Q3xgAiwqZlmNGtOjfJubnb/fhLw7wvg17/ewDFZRUPGc19ZwjNFACao52jRdmsicXHPzGu+CRL9NXiJ8EyyLYPe7FpsmwXYuv2IFo1geiqKawPDRX/iv+et82OdJcFm9mtCmBq9D/tAeOI3s2q4n1Xo6XvsSnb2FChmQ4FwxxavwXAcBoy5uwQ1Zvt6sWrGYO4L2csrXaNpTlwrHZVdKXZ+8qXo1NxrNqdK6u/48n173b5YmSb0dHX5u89cY0z/VItveG2dzIGuuNCo0l3JloqSRP4GtVVwOK3TZdqj4Pb13BM6woK4iEOCFUR6oxmJSuBe+qDeibq76nL/LRbtEMcnV0jdRL1vUtlBHBYYl8XPSf971A8gdJdUJ6d/VCNp1NxxJPXLwROUz3nbdI0H3CP4bXMwTzqHn0NKTlVwAvAccFV013BVdNvzjVCp3c7ZnHQ6y9zm7Hm41pXdi7t5hXqdqMiwIIe7h8AG2xpfOTqu26d0zUJuPqjjAGj1Dh7+FV1M/LsZpyeBF6l0LSx4drENobxh+YlZ+BzjziteckfrmqY8/6JLcvak+sTDAReRcrUYoYu572kYf66HoZfDawiYYQVxttnd2iPCYHDjEuAw9tWzx4Za7wCoLisIh04x2wvVFptpk68YdAy56BPTwWeAmZ7AqXdf5cWFtsdK2T5A0iINU4GlktTk0Iz24JefyPqrfzPKN2gJhLNkbsg9WRz2u40AmN7WpFynG+BOajSeeI1o6YAMWEL2eN1w9cDx5fFzv1Xuf2hI4G1Z9re/dtNce+eIOqEPbLCaciGiC4ybNIgLWKXdlMaMU3YpKmZQo9ccED4jtdQWkk5zzafZA6zRTVv9XqWumH3SA17hzfw99wJHYPKNcKnCCQzE05AKYSmSdO96OwHLMV+i+NQoagzAGFH0sdop9aWxlu2QbjNKDfXzwLV6kijq3I9ABv0NDLM2N2Zsqtj4sqGefSKt6MhmdbynXg4e9SS/+bu5knZpEt/unQz9h4+95HAd/iavtuuV7l5+tM5r3aG6qRkbLROHty+VrySMZhV9iwimg0hTYZEG5uWOvOS26ZaOGFg2pUNc3f7c/4+X27UXaJB26T1n8cTKP1D737H2sdGOg3WTUN0XWxbJzC2J+VTUBITK5w5m0xWcTSQZghEWk+9kqa2r+W8psWc2fugprjQc5LLk+dZ4CpAkyYxTdefyBpxgIb84i8Nc890YB4EcFbh1E2rQFPO86eG+Q135e62UZOyCEHGP/J2T/aRTH2O9U7dPcuM7hE2bCoECxzRvjr+SuaQkXdc8maH9VpZXtJeXFbxJVLfK1a1VyMqD3K1nrM6lrDr7MALnkDpmKDX32OLMAuL7YFVZbkVFJdV7AuMdI6oOF4IWSKlQAiJlCISW7PXajNUUAhygZZZPQXThtle+L3H3AKtKPFHiVL4PqKyvCReXFbhQPW6238z+4VQ4YwJQKiyvOSd5Iph//7HXmmGfFe052Y1h/s97hr5+j+BY4D3gl7/V4lrHIfKWfkGuOIAbW7fgOOfyXwT+WTm8CduydvjUJc08k5pWWr/Q8tSTuhzhPKSAUgZQYg0q8m4BQA+9xxUAnuHWj/Age1rua/mk83u9l/3rvhzxpBrhHmy+l2GxFsA5D9yxok3MwZxRNsq/tw4D4CNuov9+h2/uVDcQ8FV0/ugtL/CwN74mjZp7bS98QRKdVSo7SigILn8wLY13Fc7ow34bJ4j/9BT+hy2pcNIVMiuHAhqpnzR1ERPSfFJOiolXWacsNb1PdsmDeLfJz3xo7uDy6TXLx7RbPcCl6I6Fdh72i7J4GZefqN++jGGENpxfY6go0tJD+PJj4c21tnSjkOFgjPYAulmjHbNTln912SZUW7I3wuJwBOtr1zszN8t6PV38eQWl1VkoLpNLKwsL1lbXFYxUc9f/JW9oEuLrMODXn9qKNnCYrtiGWTfQ3FZxVRUnoNwDn9DJnMMksTrhxCvGU1W4RwZy1snAMxwFghJvGYUZtvmmspvlu5vidUoD9x5wEuoXpPQgydOOFrCMu50IXX03BX32guXlAe9/rWJ68gE+gLfbS5Btbisoj9wNvBNpWvaqyg5i1MB7dL8fde/kzmwIy9u//YqPk7vlxixNBHiSeDcoNe/1YkdFr9SfO6DUQ9NAKr0dK4p2IcoGrfWf8mwLYiFTul3XIcu2dUNczmneTHL7dlmSd+jOlw8/1f9Livt2eQbYS4q2uT9pAHlmVr55ZrnernNWFKG4wJ8TQ9snwv8fjyB0j8BdyQ/D482MCZSv+DlzCFzjmxf9eAMV593m3VnGigNsgbbJp6v7yNZgdhlviiIh2K1ukvLNcIb40LvExI6I2KN6xc587N7x9tChfFQwQJXwk7sQSZjsyQMJJHYR6bsk2lEzFbdmeqCqyVhjDrMeENUs+UKKZFq/0+c0tw7rNkcAPG6oUypDxsXpj+x5PR+B4xOHuDa2lmxe3J33RgReq+IZku1MLvIfvTEkGiT8Wz123pI2Cg0w9yauztPZI9M3eTRoNfv3dIxissq+oJc6hg0I0NzNSMldUIwNuj1r9/yjbKw2HasHLLvZwyJCc9oGqj6SRpqfpBSYLYVMVRUMSb9m44ZSnO1oDlbsfda2NPxUhMlommEu+dxdJ8dewNeoBL11r0GmIHycL2GCmeutxUtxDn4Y5dzyAc4Bn6KvXDJRVKKbwff8Oya4rKKdcC4Ste03Std0y7H5+6isFhcVtGnuKziBNRD9GbgpeLw9CW+2Bl9Ae2pzGGkGmNI2WmMAYkqy9NRjZAtLLqInvYz2nl8w/s8veFdhsWa27a04/4hlRbkNOPsGVY6yrlGRMswVcpZuhnjgqKp62/M34vLC/flvMZvGRxriqFeHv5EZ5hw8Ptp/ZPtw1YAm5Z37lgeQCXcA/CdI5eXsoaOlUKcWZFR/J8Pq15ZOTTaCECbbufihm9kv1jL5nJJO5AmMZT3LDl3J+eLRuDxWluaHSH0Bt3laNEdxDWdRc78PoBZradnLXTmdXbx2FpB2RRv1S7RBnx1X84lJffPbcY6DmSXBiLlJT+q2XJ1aV4ohRgBTJVCOyCs2Z4EwtKwSz1vOZ8NaNZ9sTPeGhhriQFkmLHaQM7owq/OfaT/Gc3Bs7oVDmzahURKld8mJUKa8qrGeXqmjFNohgHiw2JN3ffI39LlegKlN7lGvv6CY+DMf0XX7nmt0Vp0rhCMsYwxix2NZZB9P0+RmATiG8cQXnYwucsnxPuvGkp05QGY7QXsoy3i/OaFuMw4NrMz70XG0no63hfJH/5he8Cx2HW29pLjBtI6c/LnoSqsuhtq/VGTbyEw6z3HVYdUuqYdXOma1nquXjFFz1qnerDpcTSXSnMQQmYLZ0t/oM8F+qv3oUQm70DJAABQXFbhBmajWiql5uMMe8nY94BmmV79csbgzqVSkh8PdfmcwvieLtjiN8eTKK9uT57Yb7a04y31X/HU+nd4c93rjI3Ws8iey6dpfXhgw4dc3jAf/4aPv2sTtt4AMaEzItrAm+sq7g56/WcCd9NZtffNtQX7XAjkACPwNf2kD9Og19+O6o6RbNvUkeAlpByWIeNrV9mzAIgKnXrd9WKVPWuc0ZZ7uIxryW0Nut1DoWFnU+kKgM9RRqkEGBZranclEtgTZCGEUwqtawhxk1ywLtOO1KQZTt1moTOfm/L3Mkmp9KyyZyY8Z+p3IoUoSD2IIbT9UJI4r6HmPy/gEnpMCKFeYL/rv0Ffbc/qg5QvtGn2+ettGY95AqVPPJgz5kOEuCVxXTWo5vNfpN6XTCOmQrJCIIUm3kobwBJ7DqhQ9WmntC5799TmJc+giqc+QvVL7RFPoHQvwAfsraU13OAa9t5D311y88NBr796c/tYWGwvLIPse6gsL6lL/TzStQjnkI9s2b1ncYGuXro/NndlRKSRqNCIJ/I34k39ia7bPXVXE1WFuDtADi2cbPsYgPHach533H4mqu/eSuAgNv+7caGHr3giv9/192ePTosj9rre/uQBSO1pAClpBV4GkHFHtdmuXgZ31Va0phxjaPKHC/VX9h8qqron8JouItxlv4c0IpkuaXQ0gcsyY9GkwrXYtJT+q82M2eK3hK+pDV/TkagHaCoVpHz3ekIA46O19DXaWejI5eQ+h/HngknckTuOC5q/ZbdY3fBcI9Lxpbu6cLL0DJoW9gRKp6D+xg5NnGNi0OtvxdfUhK+pp0rOHU4iAXx3VIjtxeTywbGm2BJ7zqFmwrmlSzP2Rsagi4Jevwna88Jm5oNy+JDwgCX6xnZBSJOieNvbwH9RmmuqyllKM9cID3hsw3tibLim+0tTzx5KKdGlyahol36PwhTapoqrQgxI3Q/o3mmEbusfRnnPs1CdFDa5Fs0WzQBOQYgTUPPfsaieu3cEvf4bgWxUysW/UTIicdT3iVZb177lr2YN4dg+R3BE72McM7TBT19SsO8ha22Zv3+o+oN7gl7/Ad8jgdKcMr52UqrXLSx2NFYO2RYoLqsYg5K0eEg4G7EVLibdVUtEVxNpbjxCTcsI4hvHcJL7WV7r3ZlnGl0/FrO5o4AsmefxKWoysWmYvOEow6OtJSb1thvi3t99bOz2dgtptPbcfUaivHXT7P2/RM9Qz7rzmr6VK+zZ175RfUa9sLfvhx67b8V1580cfNP00suNt686SJub/o055Pkj9a9uyxFtz6L00c7H1/QePvepUvKEEGjXxM7hKeOgjpNdqz/JH+0VAMx35OPtdSDtwsbYSP3CBa78MWpEPSYC/zno9f9zW++5xa8In/sMVPsfDZiLKjyZlLJFFCVW2h0JiKczhuIr2AsAISV/bPqWZ7OGdTQk74EHgl7/Bdtr+NsTT6D0Y5QUhlFeO/NLiZh0TUGHfm4MmCMluwjB5hs2dsMdD5tfVr1YCLRcWTDpuYqM4lQ5HAZFm1jl6EgzjaEMt1K+JyG+B1pRlaMam/6+OnLYdNMIG0JzJecEmzTx1X454/rCfcahjLFUFpjhLFNztewmpZAymnGY5mwtRokCpzI96PWfBuAJlIrEdSS9c535c5spSugda6farubTdDNWX//d8bujXljzAW9lecl73ffxBEqPRRn204Ne/2dbuC8WFtsVyyDbDMVlFaejQnsCwDFoBpprk1wEAGIbRn9oNPc/wDX4A7DFMaMuoisPZEsOyCO1L/DZH6NJZrBR5ph7a4s1XUiaZDq/j95IUHZEJZ5GvbW9h5pI7ncM+uT3mqtZTapSxhHCZobdRFdNAZVjNn6sWLHhNef1OoApkQdE/t26ij4x4OTK8pL3AfC5nwJOAfjEGBs9I3aNA2CkWM0bjmvQRed3o1XYmM9ARou1nN7r4M5qqE1ZFPT6d9nshVv8tvC55wO79rAmgipUebyHdRIQr6cP5KrCzQjESxnrQTC0Muj1D+5x+5+RROVljMRcMjLacPmDGz889NTeh06u0jOyEWIJXdMFtgohZaMUYtSFjQvuqtLTT3olc8iWEvTXouR16uhpYko1aLa94jK1tRW94m1ssG3e9jMjGTK2YayQsQyIp3ldI1//P1TIeb/EJnXA6KDX3yE47QmUXocKKYbY1MgDQJOmaQpNQ4JuaBg2M3ldbeEVBz+mZ1ZfKKOZmO2FX1WWl+y1LRdqYbEjsEKWm+cIUhLsk4n8AFpzIcLoLCOXkez9EfIzKUTCvS0oFPU4esg/TXKd/UmKRBPDtXVM1hdpSePHLdo5VFNartm08qD9X85K17TPKstLnqssL4lVlpecE68fdr8Zym01o+mrEcIGKGNRGDhFKO+Pve742xDnd5gJe0oTiP867ssC8oBXEiXeoIy9GGDmitZrUVpk7bmitcMYkxL5qbFL/Ufx3QnGRkRzzSivrX+Dt9a+Sq4R7qmiMhufOxef+x187mX43Edt5f22+HXSezPLnaj2OT0hQCWQO82uWmQdCeNCrOphv/u3aYQ7mKDXbwAPJj4uX+LIfarohg0l75/3fzkI4UTlNXWnJ9HTLkghcoBH7ssZe9IrWUN7MqLiQBmqZ+T1wC30lBQPIAQuM8bu4Y2MjdZ931v65sbW8TwZHamNbckYA9CcbcLR/wucg2ag5aysS9yn1GbqM4Ne/0ZPoPRYT6D0955AqQh6/X9Dtarq0RgDMIUmAOzSiBl6yhdIiGudgz45xt7rWxwDvkTPXxL3BEoHegKlt3kCpX/4nmu2sNjhWAbZ5vk/UnIdYuvHE28oJlbjoX3jrpgJAWkznI0ZztW0tLrJQo+5ADRHiGczruN9x1UU0tjTsT9fbvYoEktU2vjUVBHBe+13Rw7R5xwPPDbrxok3FJdVLC8uq1hrtvS9Mrp6cmZ01ZSB0tSDAEZLr/lI/ctdBr4QfTxnxPkfDAzrz9o6c+yHiI6c5gxULgb4ml5BJQgPHPvXef+udE2bPsd5/pyz9DfDj8cPZpE50Lg2fo78Q+y6dRfHLrulXbqmgXpaFhutDIw19xRuiqLyWQ5B5fLc9T332eLXzfmoVkbLeliXvaUdB8dbeGzD+4wN15JpRBgdqefBjR+2ndu06C/ANJTBASrJu1fQ6799u458OxL0+s9HpQsoj4/P7cLnLg+umn7fgxs+/BdKTDc1B/MW4EwpeyiMkGbqss5EVVVlmJor2gb8E/gSeBS4GGUI022bBIIhsWaGxpq25B6TpBQooAoP6lCVrB3LFjkLOryXujTM5Pi6HkkiNBC2KI5e316YWPooqqIcYLQnUPodKjLwNKpwAZR3rKdx1aBkggRATGh2uorGfixssQ4dIlv+dyaq0KAM+D9PoPRoLCx+RiyDbDNUlpe8iWqouwRYh+EivnEMRv0wtLQGhE29aGquZhzFH+LoNwcZV/bJ1PYqanUX69IEJ+ofdT+0BMRFsUtDN8dO57zoFZHn4lNktZnDU/H9mRq5gzlyhAG8N1lb0PEQ+9zc5TqUnEA/kiXnpj0a+e6Iw8PLDzw4tm5iCFixwpHpBlXt9KlzIF+Yo2iQmfwtPk0mzv0kqQ9HX1M1vqaqxKc780TLvofoc1yNuo0jo7fpX5qjtEftt4/5c8b9NzyS5XmkRjjrTOCKgsl849xUANeMpc2gc0Kl288WvzV8TS/jaxqNr2k46uEHPSR1b45P0/qwwFVAq+7EbUbZN1z9zFWXf/yPoNc/C1VB2Tvo9e+TGtbaWQl6/dUpOn1XAX8Bzp0SXn93cNX0dxavml54R82n1SVtlaVBr/9+YCaJeyUlYCIzjCj7hNaLhHEjgSI6vYYxKbTVKafMBg5HyYGkkmoZdbxUhTUbz2UNI8eI4kjISGjS7N59o4GunUVeDnr9BShvZ/K4HeEDpxmXb1e9pjnNeJeQ6MMbPsDW9WuQNOiGoZrEg3qhG5ayzV6eQGl64t6l9IlSVw/8Pej199kzvOGjodEmrmmYwyWN3zQ4TWPpAe1r3w2umm6iJIPUDoLnpOzsOWy0Fh2IhcXPiGWQbRkDuBHVK/LR5EIzlI9UzjDMkNvUHCpSKWxRBizzMD7UwOm9D+HM3gezuqABx4CZ2Pt/AbYQqIlj7xYy0gLGEcvfNffIuzpe6ts7el/zNfHz69ZR8DQwvLK85BBNcNlys4+cYYwhED8s9c12OvAvVFjVSTz9/1DFAqf2asg2APrE2hnUKvnK9MzbI+J/8xnjwGmAvbK85A/dhWGLyyqcAN+Z/QpjCM7sdRD3FefhGPA519seR0/fwF39+hHqtTp7//4n2N51DXjmjYxBXcQhpWHDaC0kumrSycXh6XOA3wPXoppEW1gA/A5VJHMZPUtibBFDiBgpLxNBr78t6PVv2H7D+wnwud343F+i9P6SOIFrBQw+sn1173/XzkwaBm4hOhPYb66bdVlxvIXP0/uhq9vX0aNx//YqI9OIPkxX0dSngVeA3bqN4jY67/+3qJysjvLKRt3516jQCxCiz4y1L300IbwRpxlncLRpFXAiKv8vyaueQKkD5eHbxLPWN94WsUuTSEr3AAHhNBmf3617QNKgXoZqEwc9hFdvrfsi+OWa515HzcndafUESvee7Sx0rLZlxh/L9kQGxFuvm7/mmQJ/zSeHAJ+9uq7iTFSv1KOCXv9d8dqRb5nhLIzmvsTW7V7SwzEtLH4yrKT+zVBcVnEISg9MAM8BNyLi3+g5q+3ScGC29ELYw0hTX5U96MNBUZtkUmg9N1V/y6UF+7EkS714psdN2m3K7o03DiC+ocvcaKJyS85PfK4BeleWl3S8OhaXVbyHKgOPoibOtcD0yvISo7isYiBqQu1amSUM/mX7HyfqHS+DZfiauoRzPIHSiVKK/5mhvOGxdRPSMZw3FIvqtMmZH173Qt/O9Iyr1q5iSY7Ja5lq/pMSIssPanEO+ShTaIaQEozWInJkO63ZrSAh3tz378suv+G6bbjtFr8FfO6ZwD7fux0QEjp35uxGve7i8ob59DfaAI7G1/T6Dh3jjkJVniZ1ACXKYPozykhI/s3cjK/JB0qkFLgIKEBKKVC9YwGyjOhMHXPSoe1ruLl+lhwz8JR1htCSis31qM4e56SePtuIxnsZ7f2/c+Q8hmqz9hBKSmIwSsx2DXBy0OuPegKle+Ua4Q9d0ki7q2YGY6P1d+FruswTKN0V+APwftDrf9sTKM1ECdNu0p+pT7ztoXerXp06fuDJw2JJA0wVDcRRDoGkU+A71Fx2G0qrbiTKO/Zsyjb8o3Ymx7RVckavg9q/cvVKR4VcTZSH7RhU2LcjLFkYDz0+o+ql1Cbzu+NrmpO4twPMUM7/jJa+hxsNgwHxbmV5yaHdr8Hil49Q+Za7o2RX9kj8S8ogrJJSFv88I+uK1Vx880yk841vL8egGXsKR6tdaCpaGNN2wWgcDNBnrzWZHJX1Noe1raZO5LKoYTLOjFlIpNmO82uITVSHEStRRt75iWNrwPEp5yxA9X6LACQMrtXAC8CtleUl87qNcSTdjDEbcQplI7uL75rpzNEZwKb8Rwg5Tk+vIzNvIa6aQWWVsnfxurx+f9FotgHIuIN726dxjPYGZKjKqxGxRs7XHsy6o20o67JUFMKWtZFWmahAF5CZtXoacJ0nUDoOcCT7ZVpYJPiSrTTI0qTBtQ1zoGuLoB8q27Az8Q2dvSdn4Ws6Hp87HRWmW4aStvlryvZPol7EQAgh1XxQCHzdojuODK6afjFKs+txQ2gH0zmf5CHlmd0T/cdEam2jYo3XfOfISTbTvAa4CClXIcRYYEJRvP1vKPHUsgbdlQZwe+6EVU9seO9mgKDX/w3KiCTxudUTKD0fuAQl7TMH9WxZv96W8boN+UlF1evDDu2fUOVQY0o+e5KG2fDEv8OAj4HSoNf/gidQOh8Yj5Qc1VaJ24hwY95E/tC8pO0rV68rUQULoLyAM+jmpauxpe09duDv40e1VsZvrp/1fw7MuSmrn9LSGidraY0Ie/uL8Y1jztvkt2Xxa2EunaHwnRbLINs8T6D6Og4E/iYcrX9NGmMAdlcNhUPmY2jS8Zl0MsM2iafSxhCt2oOY7EVs+WFnuYa//TRazCYlV2DaNS299rbK8pJocVmFROkBhYBPUGEAgPsqy0tSwwGPAgckfv4CpeKfyifAF7/XP9z7etsTNMhM+ogabIAQZLdIl2wjbUlv0dBTsnOH8vRlvM7pzqWZd8WPf/B+R1pHObls6svx2ueG0V6gD6gq4NT0CvYKV1NkD3NmtJ7b2KPHG6dHHXmeQOmZifHjCZReEfT679zi3bb4LXElqrIwGyWJ8Qc6qzEjdE08fxOVzL0WJRQ6F+Wx/mXia5qHz70n6rpfSSz9I+raAE7B13Rjyh7NdL0nHwLLgZeU8Kz/NpRXCQKljwA3oZLUyTfCtjpb124hM9P7sktTw2q6GrjZCWMMgH3C1SejDLIFwHEAs11Fsz2Dpt1EoPSBoNe/yBMotaG6exwB/C/o9V9CUph2k2ue7htotD1+WNvq9LczBuakrIkAU+lM1gdIQ+W93Y5Ke1CueSFo1hzPXlS0/8lxofFc5rBcUgsaFANRxUQXobxma4DLYkLnpayhtpeyho4GBgYhWaHb0bDdllv5+bIr/tI9X87i10OqoV4HfI3SRNxq3b+fAitk+T0M++cddlvBd89IyXFCqF+qLWpnj+h6vsjs0iFEhfOWlpD43bcAkyvLSxYUl1UMQ1Vt2oDLgTtRLlNxrvZ6bB990UN/iZ1/3+zyaQtHPnhZGVK7zIxkzYtV7eWm05NwQ2V5ya3dx1dcVlH0tfOP1fmidZP8jai0MSLy+MbK8pJNOpx7AqW5MpZ283nNC86/ovUrhwZUy1z2H3A0QlfFayObIwzX1jEuUsuteXt0JOU6TIOLm74hx4jwkHs0TZqTJr3zGWqEstHs4deFLZqUvHgr6PUfsZW33OK3hs8tUK3BNuJriuBz/x/KSFsKTMDXtMX+l794fO7T6dRj+wJfUxfvoSdQejbKczQPZXA5UTlfgxLdAOi2/Ykntiy7+aKmhaNX2zL5OK3P4kD2qGEpbZPuA15H9ffs8lI+MtrA/zZ+NKvXDdV7egKlyR61J6NaHwGsDXr9AzyB0v1Qnqwk/YNef5UnUJqFMoziQa9/SeIYAeDoxDVGUHNaDaoCdCEqQb/rZKoEYj9EzZs6yjAtJ2l8KlKNSgnckJDFwBModQGXJvZJnRsbgcFBr7/REyjdB2X4VaI8cr/u79lvGCHEVShDfLaUcmViWSUqbLnThCwtg+x7GHHftYdraQ1vJj/3adF4t246j7lH8M/cCQCkmTFCml0lhq6fkLp7cgJZhKqOBDUBjQF40XEDE7TlyW0f9Qyadh2QrHgkWrX7I2ZrnyKUN+vyyvKSHieM+pv6teSJ1k0s/ZnGCM7sdWidnlHfcGnjN19d2LTwKFTl1vH4msLFZRXP32e/84Qj9VkAhKWdPQu9RDMSlfNJgcjNCEXaTANTCMxubVOM1vy4ll7/rtDkoag31WlBr//5nsZuYdEjPrcLX9Nvo22NMkhLUVWF/8HXtCa5yhMovQsVCgwDFwKPpOw5IOj1r93MMY+jM//qdM+gacPoDIU2Bb3+HE+g9EtgTwCkNAqNUOsz1e/M7mu0X4av6dvE+Z8HTkg5chvKg94XJWeShfJEjUDJ3dyFMoBk4vMiunrAkixGhT0/B8YBZ6GM8CS5KJHY7v1x30d51brnq30IjEYZcqMSYxZSgpBItC5G2dig17+whzFZ/Bz43ONQItHjUR6rVpQn/EF8TfN21Gl3RoPMCllugeKyiuHC5XnRMfDzDntkfaaBWQ/nNAfpH28jikYMwa2cSH39WIlKTB2TOMQVqC9Yv5TDrkiu302kSvewF9AuTd0UmqFJKZBxV1zHmGegZwOB4rKKoajy8tcqy0tqkztGpOMVREfIo4MbcyejZ9TnA/n3uMcMW2vL4OqGuYfnmtEj6v5aVO3N3/vgN+IDGROqZICoodmmc2HkU/6TMU4dIHnRm1Htjmub5PAiJYaWVrdOaCQ9Yl8Hvf7nE5VYlwEuVH86623UYvP8VowxAF+TRHmteuKYxP8u1DxyL3AU8MhmjTF1zJfxuQcAGr6m9QRKR6FCxW46+2oehWr0vQIhbqqxpY85sP9xLcCiIJD4m02tQqpCGYdZwP4oA6oQ5QEPewKll9LpjRKJsb+NMuIyUFXryUljFEoGJYQKU95LV4/XAbBp1SbqoZ068SR7WibbRt2QurEQ0D/eItZqHYVKJvCsJ1B6ZkI6xeLnwufeA9XOa1IPa/cBLkwUAF2Gr2n2Tzq2nwnLQ7YFissq/qhlrXvA0XdOx7IBsRbz3XWvaVLC5+Yo+Vp2P/FSQREAUgqMpgFXxjfseqlwtA6Sps4F8t0lYzNmj/xn3m6sN3qFojWjz5HhvIfO0d9Iu8H+RLJxsKzXnWflm5FDZzsKT3g8fVetou3w+f8Kv2werX2+10I5mD9Er6GVdAQmo8TqkJPYaXPl8AWV5SXLjr/mPwf9237fS0O0DR2zTkjauaDwAL7M6BoJOKllmbyl/qu/+vImXv101vB0gOHtbaxxOYkKHVMItLgN0xbHYRpEhUZmTJgRu6HF0bpIXWxCz560BlRfuLtR0hwADwW9fiuB1sLie/AESn2oMGUDsH/Q61/wI45VhAoNzw16/R0T//n3HH3xxxn9707ZtBfKcPkQGCOlMDD1FUKPHxD0+qvGPvLHr2JCn5j4ew+hvHtHoF4qixPHMIHTgl7/055A6VhUyHVLPW6/Q3nFkhPW7Sidtu4sRXk1UvMMk8m9Ol2NPgDy4yEualrAfTlja2v1tOTx3w16/VZF5c+Fz12CykHcbGPaFMLAifiaKrbnEHZGD5llkG2B4rKKwWixrzMGfpxrOELkRCFz7ShOYJYxXlt23inRG+Y6ij/+p+ZsOTi5j9FaFDNDudJeuMQhTY0HNr5vXlq0nxZNeJP0UHq4ffX+ro8cVzJI24gJnFV4SPNX6YXZk0PruKDpW4bFmsg1o+caUjyoCykA5hjDuDR+CVfZnuE4fSYNMpNjorewRvZ6RdjaD3vSdptrIiuwIzEk6ALmOAs4q9dBRNE6DKXft3zHzfWzOKvowPgXab179pCGM/lqQ4BsYtRrdk7tfTir7FnkxttpsPXY+HxTW6xzwSOoHJTUkOrrQa9/+6pi+9wHot7Y38LX9Ox2PbaFxc+IJ1A6EGgMev3N2/3gPrc4qdch4QWuQgdAmhmrDGn29XSrgg0vPyBGPGN4xohXhphoH3R7MdsIFCV+rkFJeMwLev0d7UE8gdKMxLquVQad3I7ytp2Nkvi5IbFMIWULQiRfOJNete6kVpZ34ajWlbF30ge0RjVbMpF/p21E/6vH556IKkjbGmMsSRiYsj09ZTujQWaFLLdAZXnJyuKy1/6aYWu5s1nYaXTCGGclt7edqgN7VZaXBDyB1y8V0pwhhZYP0C8Ut6/LVAWMQjN5NHuUGRVaR5KVkdbuOqD30wxqVDqIq21ZfJVemA3wWVpfPkvrS2E8xN01n2TsEmmcr2OM+8LVi6+caVzR+kjzEfFvswFyRSuHaLOpLGw8dqbbzdlyTzLleB7e8CG7ReswJUyI1PLZmhdZr6fxeLYHAfypcT4mkBZ2rkpzxIdmmjHCmqBFd3UaUa0FvJ0xgPnOAl7MHIqZmHy7G2Oaaaj8MSGU7ZVqlan/v0G9raYaY9/Q85vvtuNzu1FJymnAWfjcC/E1Ldqu57Cw+JkIev2rv3+rbWNy/+OH1WmuDrV+pzTaQ9g3kSRx9J9lF3ro/wy0KT3klbanbFpotPS5/LtLbzys2yHupCcjSpoGQpuPCl19mjwdas54HxUare8Xb7NV2dU0kmFGZZvWMeTFqPAnqNBoGkpSpAtzXIX2FGOsDlVcZfHz8B9+mDFGYvv/ApO3+2h2Iiyl/i1QXFaRBUwLpShKH2//iN3FElATAUGvf/Hi1U8/+lj1ewQ2vM+drW9htKhWkQ5p8EVaL1vHBJZgXbro+GiL62ZS9T9JjS2Nj+1Dfnd+7E/nPK7tLc8tOoD7csZyW5/hrlYcHfHTafqHzMlKSDIJQavm4PnMoQC0Jb7vWTJGbhR6GSGezxzCYX2PZnK/480Pc3OHhnQbNfY0ZYwB7rhBZMV+yNy13FCwN89nDeswxpKkNns2Nb2LW8wWdWIzO9uhyJizCPW2nGQNqqhh5ObuuSdQKjyB0j97AqWPegKlu2xuu27Y6WwBI4Ce3XgWFhZdqNPTxqf+DTfqrtHJn+2m0aGUrzlbETZjSk95pVLSX8btHUaZ2Z5/aHFZRWp7JejeZF5KMo0ICE1HCdTOBGarVSJuRjI/C3r9B+8SqdtNk9JdZc/Mc5lx9m+v4pn176RPCq2fjUr+TkNpt32A8sT/L+Us9ULKpUXxdk5o6ZKv+2nQ6//t5CjuTPjc4+k5Z2xrmJQoAPjVYnnItsx/QJuYX90fCpcxPNbAEeEVjLI/sHTIX5f8N7lRhbFXXknkSwDmk8H5zQs4Mfom37jyKStIfPdSjLLdwzWUFu5PtK03VY0TVkdWFUa1tIYR43I/ZlF6JvnxEFpL/qgPzAm535At4iIIQItmt9v12OjX0or5T86ujIg2MiW0jnczBnYMeGbbpMgxkaOd19ifDK/Oirl0aTIhUovfvQtSaEl5ih4N8WbNSbqtAUOP97QagFwzTFndXC4vnNJluRlz0Fp5MKChpdd8AGIPe++5vbvtPgDVFPpkT6B0cE9JyeMiNX+a5yxMhir2YXPGm8/twNeU7AvYhNJeOg4lnPlD374sLH6rvIuqhBzdfcWwWBMAi515XVdIid5cVGe4a/JQL0B6ZO3EdFv2emTchdE4KI4ylvAESiegWqhVof5O3QB7hjcsnOXqNYrOfK9i3RS5rJlAe9xtwxYu8wRKbyrW7LuZQjhB9dq8ve5zcswotbprOMqLlonyeCUrRlO9cHEpxNhPql6+Fbh8cLx5wXX5ez0U0uyPYfFzce6P3P88lM7crxLLINsyewKsTdex2dOptqfzRNYIRMPAfgeWVXyDSmD9UHDp0PeNGRyhf8Uh+hx201ZQH8/k6LZV3OHejY32Ti/WuWs2EOg/GENokAFm7tJKe6jm1Nj6CR+ssI0ZRXoldbY07isYED5V3rvywoaFbfe1eDJmpPU1282M/7gM8/Kb8/agVXNQZcvkr3Vf8PvmFbFGm83eP97GU63HO1+Ru4UvHDBuesimnZ28ELtpYG4mHz/LiOI2oxzatprp/SMYia+FJqVq/5vyNnxY22oOb1/DuY3f8lBOpwNLs0dxDnkXhCBateeBMpwj443F2AuXIKWqFUg5pU5P3z2f+9iTMob8a15n03JXt/VHo8Qn3YADn7sS9TCZhGo0DcqlfSo+97P4mn7f8xVbWFgABL3+hkTS/X+BixOL48CKs5sXzxscaz75xL5dJQT7Gm3stjEaedPN2cA/haDAOWgmRuNAtIyaVlv+dxuFHh8AJStR0htDU3ZvBrK/SuvtQokDH5RckW+2ul+SPk4wfFT1rtwdyKq0Z9Mr3kaNnoZEyD8WTo2HNV1fb8uoI2HcoQRidweIN/eeb8vu0LwuQlWW+gDtyPbV7iPbV7+Mryk1xGrx09JdxuSHMm57DGJnxQpZbhkbgOboVGh4Ux+DL35mBjAWVf59jETIF839WCOLOraLIdCRjIw1diyThm7+r2D4ojhaR/xSs4em2rOqbnrDUXa+zFnX0Z/PzKw1X8oauOx3/Q/JmOUqotaWpkWFVnpe/DItL9bpweoXa2dEpM1+VPtqxkXruN3xEB85r3AZscyjSCGm6WgyEU7sVsjRojtYa8vgEfcowokmwA7T4MLGBZQ2LWRceCO5hvLwz3cWEkNwZdN8BrfGVnccSoKwxxC2KPY+X4eAvxv1w+PhZYdgtvR9VW1BE6q9yTlBr7+yh/t9xLFtK8WZzUF2Cdc1AYs9gdKHPIHSnMT6W1GJv8nwZDFKsDKn+4Ho2pLKwsJiMwS9fhN4K2WRAC48+s/f/H5MrGG024jcBiwTiT/2Q1urMND+nNinABIt1HJXo2fUZgo9PgS4p7isor80bN2T7JOfh6EMso7OJBpwbdEEzP5zELZwRxVlHA1TaEghxDeuAvtSR67WojmGAO9tcjHRzN2koUdTllyJ8tBdifLObE5exOKn4ccq42d9/ya/XCyDbMssAIjVjiQvFmN0pJ5/tr7NSfrHVd22+xAwH4gfxefGaK5xT2HqoKP4Xe/DOTBUpXQtJKAZQktrHp1U/E/iNsJnnFQ86ZNcrf4ILeqMyrjDQIochBAtuoO1dvUdjNvjaZ/YhrB0zVFk1fbliPURHmw5netjZ7PKLMJQBZn0Fg1Mro0XmdE0ZQYlSAq42qVBF5IJukKgJ4y2qKZzT+6u+HPG4o4bsiGRZzbPVcgivYgGmcXh1fGBV61ZR5YR6aIYJGzhdlRVjA3DSWz9hPGot9nC8JKjbjFa+gz1BEo/8wRK/9btPj5lQ7Zf0zDHDGu2ZahS+XOAmxPrFwHMdhZyVcEkns4ctqXf3Q5LhLaw2BnwBEr7eQKl+ybaGP0ogl5/BZ3K+zpQ4QmULvYMmqZ/ee4j1wa9/uFDYs27XdD47WUXNi4ecNff//4ksHGXcF3n22rKi56UVAOfRdfsU2i0FhlS0trDaf+EkscAoE3Y+Ti9HxvTJUKPdWTt19nSNnmJTBBL/WBGMjBaexOrGR1KLusTb3PRtfdpqlyGxU9PT9+DH8ImnSl+TVgG2WYoLqvwAScByFA+gbWf82L1W/SPt1EsqnNRbTm+BB4EbgRaN5DHqbFreck9ACkEi5x5zIt5pL5yH6JrJ9JhiCUml+Qc06C70g2hUW3P1Ac1a47out11ockctRGkRUU7gC2u1TsGfm46B39MbfsgXmo+gY/M8bxjTuTi6CUIOo+7NjQUGU/rKq2YOGEspUgBKTmkbY3ynkmpQqndmJnWW/SOJnJgIxntg+P15IkWMvODfJmvk3oSKcFsK8x1Dn/jeufQd9DSawAaw0uOWhxeUvKZcNW9o2VWX4cKM17rCZQe2LGzr+ljlPhl3+UOd0cLA8DmCZSW7tP/d4+2CduF5xUdEH89oxhf/p585urdk+G1gR5yYiwsfi14lNDrYpTHeXvJvARTfnYCHlTzcQAqzp/+zeV/+uSu7JvXr/UESk8BPh8VawjZzcQLnhBIw9YoY2n3RFdOnQUMlBE3sao99djavc9G9SVNJQ+VWyaRsj5Nxl5LrjAj2TEpqevYclN9wxqgSyxVc7bhGPQZxDIyfXVfRU9vXsKjG97PQGmXBYAX+BXnH/1CmPv9m2yRedtjEDsrVg7Z5kltF8I/47/nNvtDbJC5TDcOWlxZXnJ3cVnF80AtyohIuOI1hkRbjBXOLN1lxqkNDxBtsQKIwWl1a1iZbmNqexUf2IbHv3C71f1PmWzWRwcgunjciX+67pn0RpuDq/L3zZubVgAYpOcFaavqqACOPeL8V0QTyh38hauI5X2Xo9k7j2MzjR6V9R3S4La6zznKeRTV9oxN1qtLMrmp5nOmZ43gM0bG9x50AoVGmA0JGQyR8vZqNPfF5l6nAU60KLZeC5qjKw/cDcDW65t+tpw13V92m7p88jU1AhAovQxVxh5GhYcvbNBd7D7w5CNQb8Y2gHN6HXhOcNX0Y1HilDrwDr6m7iX3Fha/NvalM3xz5JY23BoSPSd7aic03hMonYrSBjsPpR81HdWX0v581jAKoxFqHGpuMSPZIrZm0smo/K2YcDZFhRb70t57/lwpua8HXWnl5hYib6MtYx9p6vfISFYvo2mAT8+oLUap8IOUHN+0hpeiBz1M0XcGqiF7B51Ofsm52Y+bpzQuq0I1JpfAfHxN/h95iyy2Dw+iWoD9mP1/tVgG2eZ5AtWL0gS098zdeS+ye3Ldg8VlFS+j2nWs2E+b//Zys2+4ikIXIO+vntG6MD3b7Yk28kT82I4Drq8fj7/lPsI4eL7XqBCJCTXHiJhhoc0H8UZjy4jFYI6MNw68RnM12UY3xVdmiPjwDCOOg87csQLnOtnW6ZpaW0BTTnJdadFUTC3axTmWaozlGSHqE8ZUVLPxtbOoizHW0cLSFAhNEhM6l/baL+FZa80GrcMYAxBIZNL5F8kG1nWss0XSs5NmoZ6ldCKFAGnYFwg99veg1/91Tzc/6PVXo0Qi8QRKN6asGoHyXF6MKl9/D/zv4XP/HWUY93g8C4tfGW8Ca1HK+9vjIfUSqtVRjK46XqOAVxI/Z6N6TqaR8uzYqKVHBYZDmIJ4/dD7EcZfbIWL0NIa7Jqr2Q4caIaz5mn2jtBhUk1/LcpwS4YnC4RmfLnkwtue8ARKrwD+0TEKIZjjKsRMXztV61okkFytkJLj2pYnx385yhibuU13xGL742ual2iHtC3SFzN3ZG/LnQHLINsMleUltxeXVTyH8tAciTIAkn3ddiHRO+2P+mtDrrU/VRqVOufGruITczdRFr3QfasZaF4kR2c/ZRyQ2MXkDTmR9yMPYaCFBrbPzCJNCW8f37pi8V8u/6ijK3lxWcWfrqr71naINpsKY8++0oYUAnFgqIov05SSxPB4A/91XP8iiJHzzKGXCUFOq3Q9kSnCrk4lMLp2h0uQa0ZokGlIIcg0onJUtF4Wxdu1jUmPV2L7bCNGi2ZDSLNrmLPbcY9vWU6GNPk8vAcLGoexC2vjruyVttGxBg6qm8vp7I2evRo0ZVBKSavQYycClZ5AqWsrNIH+DPwbFVJ5Iuj115N8c07ia1oPrN90VwuLXx9Br3+tJ1A6AsjfYk/LrcATKHXT2TPTDsxCGU17pyxLzb06k85ZZY6wGRMApCa513ln5NLMUz+y5ayZmnoOzdWS0fGiJ6kQgguARlRY9L7Eudah8nFBVUZ2mXT2ia4xK7PSBqQsakB52IuTC45srWR0rAHgW3xN/8ViZ+QyVKj9h8gThRL7/aqxDLItUFleklQTfKi4rKKEToOsQ07hcF31p3UIg7/ZHmFq9A5mmmM4MPrvjuoi4WzEMeBL0OIyXjvyQ6N+2GMraqc8NiLyNb1FfduK1qmHF5dVHA18VVlesuEYbebAC2wqneJS7ZWOprx/aFlKC04adbtxVkvwrc+ye//uC1dvorQ/XZ4x7en31778ZqbB8b2NdlZridMnpk2baRBPqOr3i0ZY7lArWnWHMHC8cnfVrOOeTh/FSwVFHftMiazluPqVFBkhAlke85XMwZqUuhGvH9qiZ9TkaGmNgPK+XVs3iwpD5yL2Z2Jzm+2G0LsArFVFWIaWVa2naEq+Z7bnjRZp9V8Lgd0TKP190OtPvoVvQtDrfxR49Af98iwsfuUEvf4Qysv0YzkapeWVjzKKjg56/Rs8gdKrUUr5R9JpgL2MSiFI8syYSF2fhc78PvuF1nFo+5qJtkFrXgWmdj+J8owLU+jyGLtpHBzT9HQpiZuhvE9kNGM/W86auUGvP5n0/TUwNemuL4y3c1B988ZmfdXyNzIGTU4zYytCmn1PVB50hwf9rcxBkcubF1w/MN56/3a4LxY7Al/TbHzuE9n6XpYh4KQf0zZJCDGOTSUzkhWfmUKIs7qtmyelnLet59tWrF6WW0lxWcWuIN89T68oKhYbeMA4ylgte+mX6s9Hr7C/2FERtMF0ky6iXBW7gLfNiQDYihZgy10FgIw7lgtb9F0zkrlrbN2EpTKa/RTwAOotrwoYM0hUf/KK89qxVxTtw0JHPn9s/ralyAhlzXD14Zi2SqaE1/NQ9qhX/5U7/pjUMR7Qvjb8t41zXJf12otZab26jP+Z9W/xbmxidG/7Avue0WpxceEUvnYV4W1ezLTGyo3vGxPSn+vnyvzGpXrvDos28nT1O2TKzjBpTGqcErmea+1P8VraUJ7qVYhDGvLxjW+zW6ReXBm7gJfMKWTRzl32uxkkNvAn1+9YlJGGLuJLRObGkTqSE2pql093jh+quzuKVd8Jev1W3peFxQ7AEygVqLyv41FJ1fsFvf5YYl1/YBXKsJHAnkGvf3bKvvmoPNkktXQ2APcHvf4L8bmPaRW25zNlXAPO9Aya9hzKw+Wlu0L/ZoisOKBKxjIGV7qmnQWMn+MseOQkcfljx8Xmjz7ENptoOJcFcc+86+zTx0XR0JHrdhl06tGRVfsMsBcteklLa+qIA8Trh0xeduXVVphyZ8fn3gOlf7el8OVM4LIf28NSCOEDbvoBu9wspfT9mHNuC5aHbCupLC/55p/XnbfiavuzRQC7asv1o6N/5wHjGMfptveMQtGsS0msl9ZkB7jI9jJvRyeCiJOqPSHjzl7CFr1Ac7Zi7zc7LbrywDPorHbtB0xeJwsc09PGMDOtDwB35OyWKRFIIXgnfQAz1r7EF7YB9d3HGBeaK0+08ueahTyf28w6PZ1qWzpTQ+vYLVpPljHPMURuAOCEthWss2VSacuWmaK96ETbJ/zdcVLHsaaE1tNuZPC2NoLMtPXsGdlIjhHlaeet2IXJ7tHvyFtxDHcYJ/9Zd3z+waGxv7zRILN6lWhfMNcchjf2F7CFcPZ+H100gmTkIe2rua5hDr2M0NBnzf2Q7o7TzdievysLC4suTABOSfy8N0rPL9lP1iCRJ4uaqBpSdwx6/XWeQOkSVMcMiZIdSBpkXwHga3o10+ceAOj4mtYlSjWv9QRK46gm4d1aX3ZdICUIR2u/D8X1x5FofTQhUnukER4y6QWGXP+CdtQ+9r6zC3Nd6wv3q+3DlPB6ziucmgd87RjwVXtk5X7CVrQIzdmE0TwQo27EA3T14lnsjCgja3KiHdJ5KA9WFuo7Nhd4EF/T/J9tfD8DlkH2AzhO//RtEnkV6Qk9wwgOjo78TZ+qz6c3dfWX21/qBbDIHGTur81fqPebtevM9N4gIVbjQUtrWoKrZXcAGUsrpqv0iIEWfb2Xe359oVaPLk0ModEv3ibWJLTIYkLjAfNw3lt/0nHOzLcXCyFHIYn3irdp+UZIW6znmpfl76e1OCRTQ1Xct/4T7AmDcIi+ASlhnczjmrx9aNdtLHO4xeTwepqFnZDWmcs7PlLD3+RJfNi/XUb14WJgrIXX1lXgkJ0Zav1FHaANPSb6txOcRHu967iagVoN9TKTQyL/pD41N1jAOxmDuK5hDsvM3oSbiiGSh7BF5664/pxbt/OvysLCopO1dCbSk/I/Qa9/vSdQehIqDeOVoNe/vIf9J6Nani0EqlFtyr5DVVoC4Bk0bSOgB7vut7LjJylApERjUqwzIUDPXTm7uHZDas6Yu7K8ZB1woSdQejFwdzMO/lQ4ufG5VZ98PSO970EAQjPS7b0WxvTMGrs07JgtfUAVOlj8UlCJ+jtUjiTh7fLtyHNsDyyD7AcwXFt32wKzeL92XJPLY6faSeRVVJPP08aBZBDqpQuTFpnOw8aR8r/2e8fc7lBeLrWlRqx612IZd0ihx6KxmlGpibJ1QL5jwJfUuprybpJ74av/imbNwe9aV/B85hDuce9KVNN5sG8vWKnlRJaWIGzt6HkrjA25lbaX7UN5OX2oljTxXssczMhYI+c2L0ZKVRggBDwWP0z2NprFCl25qObaevNszmA1TCnZPVzDh/HdOCLjHd7W9xUAq+1Z1Ohp6DEHRTRSK9344meCqrjau1A0MVCrASBPtDJQrKfOGEZs42jsRYvU/CvhYvvvWdDHxCHeJbZuAmZrb2vytLDYgSTywU5AVS0uB/7Wbf3LqNywzdECDAEOBG4Mev1npq5MtF56F8jzBEqfAi4Oev0tQN/kNkLbYmqM1DNqr6eW91Dhq/HA3z2B0v1RrZY6eiG1CMeyg2zXLdRbKg/SszYg405Dy6hpBAqFHkPLWr/RqMuytMYsfpFYOWTbQHFZxRfAXt2Xv+i4kQnaMiLSzjHRW9hXW8jI3M/5W+7uZMR01q05FIwtC0U7h7+B0JQXKiOi0+Y02Ce0nhaZJhem53S8VkbW7o5s6wNI7AM/RU9r6vF4FzYu4JLGBd8KweNNMuPWT7XB9rfy8smWYdxmhDGRepbq+dyfPwqAQbFm4qaDKqeLPCNEcayFOa4iDm9bxW7h+ghCOk9t/Y4mM6txr8h9/yqgsSRPtOyzVPbjTruf4/XPeE4bzw19xiL1OPENY7EVBBE2JapthtxoibEabQXE1u4dAYoqy0uaf/AvwsLCYruSqLh8DdgVuCbo9fs9gdJLUbk+APODXv+4bvt8DOyXsmgBKic2nW6Vkpvh8R6MvL+iwp1SSubIaOZws60gGyFrbbmrCqSpydiGsdJsL7jaNfT94cAFKN3CfYNe/7wfeNkWFjsFlods2/icTQwyya5CFWU6RYzRYhUPG0eyX30/xtebfGSOj7MV9zteMwpbwRLSoxptaUrB63OVS9ZFvMKOSRTQstZtYowZbQUtwh7KkuFs/q92jzsutV9xdXF4ugCaxg549v7v0pW8xSWN35hHhlZr+4t1rHKls9aWyXlN33JpkZpb6/U0/lnzOeOjNUzPGsHt+eOdAEFHDrV6WnqG69VLL6ubl3t6W5Dn4vtxhTiDN+LjGJH/GdjiCMBWEMRoHIStYBky5sRo7SWTCbgylAuqnN6NehO2sLD4efk9MCXx8z8AP6QIIHb9mYT0RqoxBluZvzUw1nzPnxq/OfzQ9tVj8E0fja9pUcrqZAcPIQS7C2crZns+Wlq96p2pmcKWv+ypaPOAx1HRhQeB9UGv35K+sfjFYrVO2jZeIqUHm0Jwr3EsphR8aw7ifVPJin1i7sZH5nhQxlhjD8fq0o/NaByMe9levLfx6Y6G3qTkbaWbMU5rXkJWWwaAeYT2Vce6RKuR38XW7v1ydOUBxNbvTo3Re4/i8PTvUHpqfaI2s6NMvlakrYshqNed/KNmJs9Vv80hobWMDLWsBeXNOr359nij4X6xytYpHLvAkc9naX0chtCK/p23qx1gSUE7zsEfM2twLXlmuKNNk7DFMNsLCC87lMjKA5HRbCFNDWnYMFp7AzxcWV6yZutuu4WFxXbH594Ln/sP+NzpqH6xybBJUrn/ceB9lBTFmd32DtPNSPsekjrRSwbGW0v+mrfHsH/kTpiwQU/7uydQ+kdPoDT5ontf4rid86Npw2zrVQ8gTc2MVe86DdgQXnLUgUGvf45ljFn80rEMsm1jL7reuzqAO+MnMTzyeGtJ9DaayWhL3SGPZnm3/a7M++130o+a5OIWVFVRqhI9mSJEPmH6xRKSPCn9JU9uWcawmizyRIv5geNP2t2R57iwcQGHtK2meH3Rq0Gv/yXUZFa/r/ZN+AXHTcUTC18f4hj8gc1WuOimpWtLMvu3x9eOCdd/sKxx/KX79zmJQ/odS0m/o4iiUSuzWbv6yP7h5Qc+Hl2971umtD93e/zUi89qCq7fLVLLsGgjZ7UEO9olDYi3GgCvZgwBIKzpPJM1jHSzQwQWaTjBcIDUseUuR2gmQo+j56wGuO7H/jIsLCy2EZ97f5S0wP8BLwS9/k9R2mMX0tmSqRw4CNgduMQTKNU8gdI8T6D0YJRn+zi6zWHSFKliz6kGW1IiSP80re/gBt3FY9keDu971KEo+Z/PPIHSPYJe/3RUr8ti4C5pav+6suXL0lebHz0O8ERWHChkKB/UPHzJ9rgVFhY/N5ZBtm08Q1dBxmRPOQz0pNhcamNIebntBXG0/oXtcH0WPvtjEdQkFkJVlxSlHnyF7Et57BQGRbr0tATgdacnck38j1xie1kbom1AAy5tWsDtNbNoby2OJDbrc7T2WdZj9ttdRY51AxfkgeZox5a3AjQzZ9ma4/rXrT5kw0Wulx+sd6go6mp7Fh+m9aWAZr50XsRJ8qvjQBwOnPqKOfm5dyL75T5Z/S5jovU8nD2KM1qCXFAXpHHtpKfPjl51f33z8I4xrrZnY2vLrZVx+71G/dB/ymhWx4ScE+5MKZkcWQ3wz6264xYWFjuC8XQ+B5K94TYAw4F9Ep9TC2/ORwl6zkYl8s9Hqfy/KCWmNLVYvHb4C2Y0axDwUWIfG8qTlpqwPCy1qW1Is6clftRJCHAnCgNqwkuOmnHuiuilp+gf+9McDZ8EV03fG8O1MuVYz2zbpVtY7FxYOWTbQGV5yerisorTgI8Tixypq9GixY7+X4IWJ75uHMfGF4p+olNbsZU0J/AiWzCI7zeOgWqJM+OdjoR4QFa3jngTOG6V7BR+XWAONsrjp6xeI4s+AuhD3RV32u+z60KSZcbINKO0ag6kqYFhZ6o2lz20pRMGR5vzU/WACowQQoANk5P1j23PGVOTp5h8W3wan6T3ZlamevF93JaFXHoQYdJPWwNQA8LVhJ6u5NHqwgMx1g+5BlhKyvfsjIZKRsW/JsuMkRuy8zElXYxRCwuLHY8nUGoHRv0nfcArh7evOWeeI3+kL3/PVcFA6Y3Atajczj95AqXTUF7sXUg2AlcCs0kGkmj0feXG4JxHW6atXmf0DbgKvhOk5L1KiWuTxuJqQffemQDHeAKlr6LmjhzHgE9be9evdBzU9xjaNTvHty6/gio8ifPOrSwvsURgLX4VWFWW20hxWYUA7gUOAx5DJaa7gcmOgZ8Wa2mNLoD0uMmcqqcB+MLwsEQO4D/xE2igo7NSFGWc7cum+jmrtfSaPva+s02hGy3AO1KKf0eWlvTOoq39AfudR2jC1M6MlmVFcJwPUKJ9fv9t9odOyyKUlZwAF9lzzXPdx7yyvnbPY26Nv6ifZnsfKVU3yoqMQUzPGs5erXXs2mwy3raYiKbzUPj40IPGUYtB9geKQCCcDTgGfZbsR0dk+cFgpHS+0KLouZUQd2I0DQTEOGCOcDRrWkYNZlshk+KrOMX9FLPSCrE39eLhtmmvVZaXdOk4YGFhsePwBEp14ANUMv5yYA+k/A4hCnrYPBBcNf3cWc6iy88rmnpTWLNlo6owc1DJ/x1dbR/e8AH1bcVcUbSXqWfWaFISEoKk5wspmSsE41FhzuQE+A+UQXYxnYbZTJQH7tLkvr2anGxwqwCAw4xXf3POg322y82wsNiJsDxk20hleYlE5VkAUFxW8VdU81M91TEvRGdC/kZywzfFvd17d10DtAOjgGyRVucUtojTbOn9LWgeM5yjR9ftEXQO+HIkMC1Nxn73ScY5B/a6ofpzOPkjgEhZxZcA/UUN5+hvXpAtQkCnGPboWIM2rbrR8a94rr6nI5gYl5pEj2pfxVHtq5hljuAlxy5c0XckIaGTFidt9MZZuz0de0A30Hg0fihRTAKiswG55mzBbHchnE3oWesww7lBo26EJ+XaWrCFvnQMnLmP0ONI4ztmrp3I14WTQIBMt8Fy4+jisooJleUlc37s78TCwmKr6EVnZeRQYHcdmW4kHVpSmgihARHgbuD6iZGNN39c9RKznUVPXFS0/1koQ6wIOHBgrOXfh7av6T0pXM3T+iD0zBoNIGmMSUPDaO6HNB3T7PnLx6HmyWTj8lOAAcAKVG/BGPBnVNeSS9VwCFdnsEqobgFENVuXfDULi18LVg7ZdqC4rCIDpZmjA0TXTcCMpiNjTiasT5Ot0sUGmcOLxpRnUMZXkjtQzVX9wG5a5vps58DPnY6+c3AM/KxFd6/WncPextF3tofEW2hcaK4sM/Y2Prc7cW5tgNj48ZuOMnOG4zIm6Ms6Dp70kJmS5lP0D+1P2W+Jb5A5GFIQll1tcSFNMrNXEdJsIAQhO9T3Wqtni3ZyRSt/sr/In8XL7NGmQpJmJAszlAsivtoxaEaTLX859r6zhwlHazK+2g406VnrHha6SiETehxhD3UGMjQDlMFqfQ8tLH46qulMt4gi5UV7hqo7BRKVMfZf4E+JJcMB3GaMg0JV7qDXbwS9fhNlsA0JCf22KxrnPb7U7D/3bu2wTV6shNS5un3G+l45384BngKGSYmUhgYq5ClQhmFL0OsfEF5y1KzwkqNc8ZZe/waeEIJdhS3yYcohX9mO98LCYqfB8pBtH0LAMpI5Fka6jK48UOTQwn+dl4p0EcEh4wjkl0AhndVLXwEHJw+iuRo7j6iZeztyliMFoBs4w05jhFinn9aylHQZz0IVEjSlE77/Hvtd543SVm92cC2kOwtE86EFeqfUl06ctWY+t8WnUaJ98e6RtlmHhEJ9eCJrBGaiqtMW76rpKIAHat7n9I1XMic6BtBAjw4kkawrBDb0qAQ+Aa4HHjUahh4lHK3oWesxWnpjtvRvjdW2Z+rptcSbBoJpn15ZXvKjGsdaWFhsPUGv3/QESv8MfAk4EOL4z9P7dt/sUtSffPSfOeNOuLpx3jhUruzNKds8BhxdY0tn9KBp7wLHwLKbUb0zO7HFuLdPvz7RztZsBUJAvLkvuns9QjOSy3sD7CUWPTdWW3HMiI1rMTdq951y6yvfeQKlVwCVKCPw3u1xHywsdjYsg2w7UFleYhaXVUwB1qDuqQDIIEy6SOQ9iDjVMu8+VP7Fn4DVqKqmYaiJpthoGoCevQ70iBxcnyYmp3/KdNcIhISWujHRZ2KP/lcgT10kBzaeFL3purayivIz9E+O3E1b0TGW7k18X47vwz76Yqe7i2NO0Vs00EjW7CNtsx4GDpkSXs8rVW/Jv6YfLGbL4dQ2DuBe2c56mce+jtnc02cg62wZlNU9T2V9cVs92RkYjplI7QVp2MqM5n6FMpQnUOGGWcBRCAMtvVbJXLjXorvXNsTW7K1H60Ykc0sO3mRgFhYWO5qNqPzVzoKkrpNH8gfHw+7Rtqsv/7gnsdfUnNdDUNWO83o6WVSzbzI56TlVxNaNj9t7z18kdCMduBGf+9DpDnGMnuh7ucKWdeGUB88ahC1tDnBz0Os3ejq+hcWvAStUtJ2oLC+pRlUjRVEJqeY4bTmmFEgJ7xgTWCwHARwNvAPoAvNaYCrKa4aMZRJZcVA08t1hwpa9hunuEewe3sjfqhZgtvZ5XRNygRAMGq2t3u02+0MXAMuaZfrS5BhqzOz47pH7+MzYBYBPjLEskYO4KXYmS81+xKRGm7R3VJtrmMaTjr/vAZQmjzE43sznNcdg1o7kde1WLrK/yg32J1iRbbLMkUO7ZueBnNE4iGUAZwMHf7DqzcMWrX0s57Wmx6WOAVCAKomvQo+iOVROmxAgBANsRd+miuEuw8LC4icl6PVXAocD/yYZvtykDBIDNVe91X1FQoPsa7rqjxUC146N1L25S7jW7L5P9+MLIbEVLn52ybn37Bb0+ocHvf6ngClJY0wCVxVMpsaWVoJKCfnUEyi1nlkWv1osD9l2pLK85B/AP4rLKuxA2+H6V5qWmFxsncL+NY/ayx0TtSX32DApi53LS+aUGEAW7Rxlm+FY5MxjSYZK6fjaVcQ5VPKe46qp7UJveyZT6X3t0VQFYHvZ3PcAVyy6eKDYOPjJ+EGuenI4LXYdk+ILme78O/vpC1hsDiCEU9qFKeyYhKWt3UX8I010hE73Wqen82zWMIqjzc0iwnlvOv5y6xBtwwgAp4izS7Sx4zrzowZ3Of7F/xmH3D7bHHFBP61uT4BRYg1/1p+uvc04rQBVNfpv4mmDpaEfL3Sj89Xb0f4WykOoo0KbFhYWPzFBr/9D4ENPoDQLqCJFTzHBP4EHgl5/qsgrnkDpBOBt1Av9WuA5VLuk/wL2Bc78y8xQzlEajXds5tTJykxTc4SeTV1xa+7usz5I72c0ak49JHQ02cWI2xslen3BNlyuhcVOj2WQ7RiGA/Y3jL04QvsKgWShHPw28ALw1f7aNycJocRgL7a9LF+KTmkEcq7Nui/8795DXO0iim5oGLqkd7ydvcxKMrR44fW5ex7+fJZKU+sn0jveTZ82DhyF+tRRwbmLVtkxmJFibehNOT68IGtEbq94O4eE1qYDR9BZfv7W8X2OmNCkOwcCbr3wrdZBlTUd4Yh6mRkTht6qSyPXEDrtdpN52Q7sjm8Ka2tHFaZGIkaKNS0oDxkoo2xAbONYYe81D2k6MdsKv7a5155VWV4S2r633MLCYlsIev0tnkDpYaiQ44CUVWXA5Z5A6X5Br39WyvIBdEZX+gKTUGkKfqS8FyE0tEg8OS9IiSmE2j7NiIadmM2NumsecH3Q65+Fz60DZwHyyYGnTpFCdCSvGoIUYQ0Axm3Xi7ew2Imw3L87hkpg+YfmOP4ZP5mrYudzR/ykw4B/AfPKYuf+MbnhIjlQoNqDsCDT5WrVHJhCkB2X/K66iefXv0WGVFWKq3R3hwG9Wnen5lK0oCa0h4C6vtRyie0lAGJS5xHj8DeuLJiSe2veHlxStB+vpQ9CSgSdWkCHy5SJ2Gjpddgd8RPTTClYL3Oa8kRr/3Nzj77ZSMyTy+w53JI/keeyhhHqv7B1vcztGMh4fdmdqDdliWox1dds7k/ku6OILj8kHK8etya85Kgf0vvOwsJiBxP0+j8HHulhlQs4oNuyCuBJ1Dx3O8oYU6gKTTRnyJYSoeyYq8Ka3dWou4qAQ4EPPIFSH3ALau56+NLGb45OPZHDNPhj40LVqk1KEzW3WFj8KrEMsh1AZXlJOzDxbcdfZl9jf5o7HfdzqDYLEgbQOG15YXJbO0ZHPlVl6y5oiQSv5taBRFoGUGCqaMFccygLNh5aLQ17EPhCy6reBSgBalGhhieAKzJpv+hw/atoUovMLgz+Fz+qRbpaO0osF9vzeco4IFUR2PW3ui/F8Ggjo9tbqmPrJ0y61zhOjIw8xmXRS5qBP5ZUh502A1WhkJoLYou0/TV+5r1vGRPl/fGjjAmRB/6BageVGmt4LHkeVMuo7hO8hYXFz89hPSzbCLycuiDo9ceDq6a/H1w1vX6/UFVP+ySFpwEQolOJX3bNI8sEblpjy+woGCht/jb/vzUzOKV+1SL3+oHmC9VvcUXzN8xY+xL/rv2sJJFnZmHxq8QKWe4gKstLGsybTstPfh4h1vIOEzlM+4p9tQUd2x2gzbPn0Uw92cxsOQARaQE9hgzl8QJQFS0gmzbeNXdHoo1hWT8D2LWyvGRJcVmFjc7wYJ7A/O8C57kDhMBhSmKawP6qsU/9RnL/7rSFXpKGbbqMZmbcV3sxGC7RKtM42fZxcxbt2iGhtZmHhNZiSHovdMzr/YRxCJ/FxzHd8bfewC1/F08wKzSN2kzVXzM9Cq3xPMxQ3v/eMj3nvGXuKVA5YV21MmAucB7KCBuI6mlnJfJbWOx8vEunYCtAG/CfoNe/FMATKHUDu/ylfs4yLzwI6LtG6vgkrd8mB0oIT5uol/44oLmN8ILdwzUDPknvmxtHAyEEELqsYPK3L1a/PQzoM9PVy/2dLZt3snv1btJWzaRK6MA+BWZ4Zkn76g83OZGFxa8Iy0O2A9GEvDYs7W1Bs3/kZXPyjN7UGffY72aA1tnX0iniDBCdhUoymhWXobyOz1+Yo3nHnIjs/FXpdDYj72LYuGmbIoSSkdAE9g0y5/Rjbnkr3zXy9UzgETQjo39jGn8TT3K0NpND9NnkiLZsXchkQ3Q+Tu/H6YMm8c7gVnIzVmATph0gTUR5ofEZbHE1jnYHxBuKidd66lB6RqkkQxTzgEMqy0tiwGRUZ4N9KstLVmBhYbFTEfT6b0SFLZMpBRnA3z2B0v8ljLE5wGf/yB3/ThQtBrBneAM202iXkp7SEN5GeddsgBYV+ohx4Zq8OJpIGGMAaYuc+X/xDJrm22PAifef3esg7sndjXrdlYcQ+x7d7wiBylPbD19TZMddvYXFz4/lIduR+JqedsHTHmBtWYXoS21MS1RbSgkmgvfNCSyQQ1L3qgQGsWnDXVCGTjUQTHzWgVWJ7Wkk6+mPjN2un6rPZ41ZyGmxa/f8RIUyLxOCfJDk5n3LadH3OVW+zyZF7sDzmUOJCh0ELHdjVtflPlVEwzRNIHoZIZzS7Jh5NVfTfLO178PA/cAxqNCpRJXRuyvLS+Z2XFR5yVpURwILC4udlKDXf44nUNofleOV5CTgUWAIgBRit7W2jI1zXIWuh7JHE9f09G5ziUrDl/LwcZHaf85zFqjuH5o97T9543qS1wDIj6Ptv8lSIez4mtZvj2uzsNjZsQyyn4jK8hJZXFbxydWx8w84Sv+Ct8yJPGtMJZFqldpsd9jmDoFK/u8HXI6qgHoLZYxFgEuAMefErmJIfD1VsoB2XLvefee+wpE9+oSopiKJg6NNAGg9zImVZi9Wto6GdJVuZo845+wdufeQY7TPxPX2J1hu9iOyYSyy17I2YQ99YMtfftqyq65qTez+3DbfHAsLi52JY5FyKUIkC33morzd81BVjh8+nj3SfDprxEGb2V/NLkKIM1qW/HmlLYsmmyoAT3YB6c4DGz48aEZa372fyB6pFkhJlhlradEdf9geF2Tx20YIMRylLLAfsCvqOWoD6lHf61eA/5NStv1cYwTLIPup+d2L5n5rXjT3y+y2PLuHbWURDeIYfSYL5WD5hTl6NYlqTGB1cVmFDuybTSutpDtNtDGDxfpJk7WFzDJHEsPG6fo7w/dtbHnf7xYd56trL178lVk/ar3M42jt8y6GWVAORDQOjEyP3+mwC0MMi7TaR/Fo0avmZF6NTAZYQ4xmVgw6qbK8ZPH2vDEWFhY7B8FV0+1/LNo/KyU3bDzqwfUGcDoQfDprxG3AQQBuI0KTlhD8T3i/MsxY09RQlXtYtKnDGIOuEhgpC82+8bbjhqdoHSIERUYoa9ba55dYjnWLH4MQ4lHgzM2s7o0SSD4c+IsQ4hQpZfcUnJ8MyyD7CaksL2ksLqtwA/OBMXRvXdIV8ZTjVoZq6zGkEL+L3rzXfDkMVH7HK8CR5bb/GafYPrItMfvzh+g1l053/I0+op526eQ9YwLH2D7vC/S9rt7Ow+5RjAvXmudFl512RPQ/BwAZRzq/PFTD3BfgC8PTeFXs/Lbb7Q8+NSFWc5UaAbsIzPsk2hmJc55RWV6yqQK3hYXFr4nJQ2LNOSkGWU7i/+OB6qDXf6EnUHoHsDdS7h4Ruuu6+tniXcdwvnT1NtGin2XLaMuljd8c8pmrtz3ZMkmaAqO1ULNld+TM1gF5CPHC8X2PPCEutC7tldJltAFfU2o1uIXFtpDU1GxG5TR+ACwH2oERKKHh/VEOj3eEEPtIKRf99MMEIaX1ff+pKS6rcAF7oMKTD9G1MtEHLBeYdy91npljFyo//pLoxbxmTkpu85qT6BFLXGd1GNTPx6dwom1Gx0GWmX0Zpq3r6fR34Gu6Up3J3R+4EVXa/ld8TVF8bjeq2moC4MPXdOuPv2ILC4tfCrded+Fe59pe//yt7N5imd1NUow6wXNBr/9kT6B0InAtSsams54yBV2atQ5pRkOarS9ArG4oRu1Ixrs/aSzMXrLmk/R+Su5CyhBCpMEm+7/y7dkPHLfdL9DiN0XCQ/YV8KiUcpOmzkIVmPwXlfYD8LaU8vCfboQpY7EMsp+X4rKKgaiG47sCLwEnJPLNjjldf+eVS2wvM98cyiWxi5eEcY4E0DLXg2bwTPh/TNQ6Cy3nmMMYLqr42NyVl4zJPGi/o6dcsVPxNT39vQPzuYX1dmph8dujuKzij72pe+AL1yWcX7g/H6f3AykZGW0wJkRqz30qe0Q7Ksm/w4iSpogLTXaPuBjAU8AfpETGqiZGi9sN53uOq+XXrgJxZq+DVE5Zt6bjKZwT9Pp7Equ1sNiuCCEcqAK53qjXi0IpZf1PPg7LIPv5SeSDFQIbKstLZMryCujoNykBU89Zqdt7fauWNAzgsYaX2UtfAsAbxp5cGLu847iTtIXVf7U92tJH1BXbibc6hHEVviZrgrOwsNgsxWUVw4Avb8q6M+/2vsM7lu8VquYbRx4hvecsCxm3f4oeLxKYwxBCK442rX5t/Zuxh7NHzf1P7m7/CC85quqvtsABZ9jefQJggSNP3pw7USx05fd4PODCoNdvJZD9yvEESsehtCrHo8SCW1GFJA8Gvf55P9U4hBDPAyckPk6UUs7+qc7dMQbLINt5KS6rGAN8jcozWwDk2Xp908+WsxoAoz2foWv7cZf9bgRwWeyi8CJZ/DzKiHunsrzk1J9r7BYWFr9chvztf/3Tc5dcFk9rV/mkm/FiCdNEah2xyjk2aQ6JQw5CI8OMUrGugt5GCOB6z6Bp/wEeLYyHppS0VcaeyxqaE0HPjGvdtaQB5V3bM+j1z9kBl2exE+AJlO6BChVO2sJmM4HLgl7/DjeOhBCvAsnWXROklHO3tP0OGYNlkO3cFJdVjEPlc70CTBWOlmft/WZpQjOIrR9vmu0FqZkbd1aWl1zxswzUwsLih+FzH4WqVHwZX9PHP/dwkngCpQL1INwb5a34AikP7jGsuPlwIwAHtK/FX/MJEambo9NvbrUXLUpWlPeQddaF9UGvv++2XoPFzo0nUFoCPI9qp/d9hIETg15/xY4ajxDCDqxDdb6JAflSypYddb7NjsMyyH5ZFJdVjEV9kePADcBpQC7wUGV5yfSfc2wWFhbfj/faWwtO1j569XB91j4JWyYKDMHXVPXzjkyRUOVvTFl0ClI+laKuDxLs7RmRWEabs2ORRCZaJnVwSPsa9m+v4rW0YXwW2xVbzrqUI/SoTZ3kX0Gv/+ofey0WOx+JgpBP2DpjLEkYmLKjPGVCiIuAexIfX5RSnrCl7XcUlkFmYWFh8RMx4/pJH0/Wv92vh2KbMfiavv0ZhtQjnkDp23Sq9b8HqiVbksJ4G1kr9vpo/bC5r8eNtPNCTQNHClcLWkZNh8PMbhqc0bx43cPuXfoiRM/OtNSFUpoI4QPeC3r9n++4q7P4OfEESj9jy2HKzTEz6PVP3t7jEUKMRFVhZqNejsZJKX8WnU3LILOwsLD4Cbjl2lL/9fbpF3Q3StqlY2P6zTW9fp5R9YwnUHo18I/Ex1ZURWVHspfdjOOJNodW2LPS9Ji9qclmdwtbbNMDSSm7eNa2gC5N49uzH7C0MX/FeAKl41E9UbeV8dsz0V8IkQN8ASRaRPAnKeV/ttfxfyhWc3ELCwuLHUxxWYWmCc5JNU0MKaiS+TxuHLoz5n0+B6xBhRYz6aqVSEyzscCVl9am22l24UbvwRgDEAnXWFeM1A+6aYCUjI3U/axtayx+Es79kfuft11GAQilffcqncbYYz+nMQaWQWZhYWHxU6C/aExhnZmHlFBpFsnxkQfmTY7cffQFtz755M89uO4Evf5KYDBw8tZsv0Uf2KYrUz1tSrxMCOa5CrM9gdLDfthILX5hjP+R+4/bHoNI6I69BExJLHoeOGd7HPvHYLmHLSwsLHYwleUlseKyijMmRe+52kn0mwiOc3b2NmRBr98Anh9+102f6pkb901Nwc80o4SETRpCE4n0fINUL9r3VF8miWm27httxtVm8Suhex/nH0rWjx1AoqLyWSBp/L8GTJNSGpvf66fBMsgsLCwsfgIqy0ueBr6/S8ZOxqy6B+9d1+yY/FVaoZht689QWcsFTd+SJo23PYOmlSN4CujTZafNGGP58XYEglrd1X27DcDfgl7/BzvqOix2Clp/5P4/SopCCKED04FjE4veBk6SUu4ULwKWQWZhYWFhsVlyRNtBOfE2MbqlgUlmW6SPqNedGGEEdwKVpBhjNmkQFx2OstXAwNRjtWgOopoNpOTMpsXyMfeoZoRwARcEvf6Xf5orsvgZmQvs8yP2n7etOwohNOD/gBMTi94HjpNSRn7EeLYrVg6ZhYWFhcUmeAKluidQevYFhfvVmxACzBFa1YVZIuTQBG58Te8Evf5VwENIaewbWhc+v3Fh6iEGdj9mVEv4AITgsZzRAiE+A9yWMfab4cGfY/9EA/FHgGT3mo+BY6SU4R85nu2KJXthYWFhYbEJnkBpOfAXALs03vNEG+5/7oLnXkis01EVbxnAfUB4t0htySpb5j8bNefIrZW6SOAIev07RcjIYsfzc+iQCSHuB85PfPwUOFxKudNV9VohSwsLCwuLnkjKARAT+sELnAUHewKlBwHVwFvAgMTqEUGv/3xPoPRBoPcPPEeNZYz95rgMmMEPU+oPJfb7wQgh/kWnMVYJXA0M/p53hrVSysZtOd+PwQpZWlhYWFj0xK3AMlTfySQe4DY6jTGAQYn/f6hhtQQ4YptHZ/GLJNH+6ERUO6StIQSc9CPaJp2Y8nMx8Dmw4Hv+HbeN5/pRWAaZhYWFhQWgGot7AqV2gKDX/3XQ6x+OEvOsRfUffBLVhDnJRuDaxM/H7R7eGDygfW3n2tSUmE3TY7xBr//r7XsFFr8EEo3Cp6Ca2G+JmcB+O7Kx+M6ElUNmYWFhYYEnUOpBVZ4VAucGvf7HN7NdOnApEAHuDXr90Y6VPne/x7NGvPf3vD0833O6jUDfhNaZxW8YT6B0HCofcRxKZ6wFVY35YNDrn//zjeynxzLILCwsLH7DeAKlbuA0lMfilMTiJagWR4XAWUGv/40fcDyhSfNNU2hbUt2/O+j1X7qtY7aw+DViJfVbWFhY/IaRkleFYL9ui3sBOYmf/wpsYpAVl1WIyvIS6QmU/gE4GmjVpbn/mGjD0ic2vHfOuIG/nw/kJ04i06SxOqTZrkUp+j+1gy7HwuIXi2WQWVhYWPyWkfpEhIocntm0mBezhpotmiMnZYs13XcpLqt4EDh78M2PfecYwEghACkxhMZCZ/7Q+Y78+ZqUTaYQyiAT4uq5Zz/47x1/MRYWv1yspH4LCwuL3zDxumELNENjl3AtcaFhILo/Fz5N/VBcVjEAOBekZu89f2R39QCbNOllhPKPaF91GzAH1bj5gR14CRYWvwqsHDILCwuL3zDFZRVDHUQfHjD45X2rHOmq75GUtQhRADQDk4Je/7eg8sPMSKYnumrftxDmQNfwd7ocyy4N438bP9L3CW94FfgdviYrad/CYiuxPGQWFhYWv2Eqy0uWayPfuajKkd75PBBiFlACDE8aYwme0Jyti5zD39ZtvRY82v19Pib0xfuENzjxNR1rGWMWFj8My0NmYWFh8RvGEyjdHfgXMLXbKhM4HTgMmIiSIpiWsn4mnS1w5qHU++9P9Le0sLD4gVgGmYWFhcUvBE+g9GrgSpRI62k/pu2QJ1A6GWVQlbP5aMlXwJ4pnyUgUEKxfwHuT+x7RtDrn76tY7GwsLBClhYWFha/CDyB0gzgdpQkxUkoz9W2HussVLL+P+jpOSClidIhe6/bGgFcBIwKev2PoFooDbCMMQuLH49lkFlYWFj8MgjTKUERB1b+iGPtm/JzZ5hESvYOrefp6renA4ODXv91qLDlR0AV8AgqLFkLEPT6NwS9/vU/YhwWFhYJrJClhYWFxS8ET6B0IMo79nnQ6/++PoCbO4YtcYwA4Oy+/m+1X3BM28o/jh10ahWwweo3aWHx02AZZBYWFha/ETyBUh0Vhpza03q7abSdXb3xX/cXDhkj7KETUN6zY4Je/+s/4TAtLH6TWCFLCwsLi98O/dmMMQYQDhXNvlsefRPCOCGxSACTf4qBWVj81rFaJ1lYWFj8dsjpYZmJMryaZCi3wNFvNgBSghBsAB7/6YZnYfHbxfKQWVhYWPx22NDt89nAEFTlZn/hau7MS5OiHugX9PoX/3TDs7D47WJ5yCwsLCx+OzQBETqT+b9LFXL1BEqvklIUgxwhNHlV0Ou31PYtLH4irKR+CwsLi18gxWUVZwAnAM9Wlpc8ubntPIFSLej1mymfjwTOBz4Oev137PiRWlhYbA2WQWZhYWGxk+MJlN4EHAc8EfT6/11cVjEEWIbK/TKBIZXlJZu0LPIESn8HPAE05MdDFwyMtx5jCFHx7AXPvfzTjd7CwmJrsAwyCwsLi50YT6B0PDAnZdHR4SVHfYsyyDSUov6QyvKS1T3s+yWJ1kcuM26ENZuuS5P9QusO8F/02kc7fPAWFhZbjZXUb2FhYbGT4gmUHg983m3xi66Rr2cBfwCeB07ryRhL8GXyh/9v777j267u/Y+/zleS5ZFYzg4ZjkloowTCXkkgrDAdVnubC7TcH+qgFW25hctwodyqpQUzLmWrBVq1vW0phV5owey9oWGEqQBJnEW2R+Kl9T2/PyQ7iuMEEpLIjt/Px4MH1vkOna/+yOOtc44+p8N4PAAZ4zA8037EjuiviGw7LeoXEem9LmTTavo+YFJ9bfXdwN2buzAYCweAn5Ld9qjhW+s+vLGubNw+k5ONTae1LLx5R3VYRLaNApmISO/1Chv2nbwPOBF4FXhwSxcFY+ETgPsBD/AIcO3FTW/vd3HT2yOB1USa0zuuyyKyLbSGTESklwnGwobsLygzmZZhI2xygDe9es/b6murP9c/2MFY+B5gdl5TG1AZD0XX7oDuish2oBEyEZHe52fAFQBue4WbaZjoABOACz7rwhN/c9aN+AbOwmy0RLgUGAAokIn0UgpkIiK9T+c0Jd6hnziZpvHg+mb2dGIwFh4CHAu8eUzrkusWlo09Je/wo8BI4H/zC8CKSO+jQCYi0vu8DxwFYIzF+NdZ2z4k2v2kYCzsBV4CJmJt6qmysb6ug9nNKP8rHop+sLM6LSLbTmUvRER6gWAsXBaMhXfLvawlu8UR1tLkFDePq6+tvr2HywLARACM8eUf8GDbFMZE+g4FMhGRAgvGwpOARcCnwVj4J/FQdBkwFDjGGEbNv/SHS7pfU1VTV9Qxb9al3qRnfVejtXhtJuN1M29njHPgTnsAEfnCNGUpIlJ4XwGG5P7+DvCLeCjaAjy9uQu8I9793+IBS2anve6GRmOoSCVef/HcP0zbkZ0Vke1PI2QiIoX3NLkpSrJ1w7bo2huPCHkrFm0cxgCsZY2vVBuGi/RBCmQiIgUWD0VfASYBhwPhLZ0bjIUPfrik8tfd28uTSaifOjceit63g7opIjuQCsOKSL8WjIUdskFoaTwUnV/o/mxJMBY+CngKMN2PTVvhvvJ08ymz62url+78nonIF6URMhHp7+4ku9/jB8FYeGqB+/JZTqSHMIa1XOQ+dr7CmEjfpUX9ItKvOdY9yc1WtS8CjiC7f2SvUVVTVwl8w/hay/1VXNj9a7RjXffLqebbJ19eP6cgHRSR7UKBTET6r0ig4puBfQbcVbEngzId7pdSzY/vyLfL7VF5ErAuHoq+EIyFPYAbD0V7XDtSVVM3G9y7MdbBSW86p2Htv1zjHPXAd//auiP7LSI7ntaQiUi/U1VTVwTcNdH//hHNYz+sbPIUE256l282ffzDgaYjRqR5hwScYCx8DXBJ7uWNwLlAK3BCPBR9s9u5v7Ku8wOM9QKklu/D0MD7rCtNMCiT4PTWBU9e8qPnj90R/RSRnU8jZCLSH30V7NnzK1J4PcUA/N+ACXx9/ce3WOs7pzwSOIhI8+f+tvq9207Z6/LGOT8fnWqrqEl/+4n7/Ht9WDT2tQDwWjwUjeedmr9G7d/JbvpdCnyPbDgDIBgLzwZ+ZJwNZS18w99nnTcF1mFik+++S1rnzt6WBxeR3kmBTET6nQrWt7eMnI83sDS35SOMSrdy+JjTGWBTB4xJt8y4H57r6dpgLHwgcBEwl+wWR2FKRt32csksfr3qWb7W9sxRD4zIZjlrDV/+zYXvOkXtx8dD0eXADcBBQBJ4HTg1d9vXur3N5d3f13iSgAEDrw71fsrFnz8wikjvp19Ziki/8vcrZs190/+9+0eWfQJkw5hpHMVqpxRrDOudIiYlG2NEAv8kEhjXwy0eIDu6dRXWJkakW2/AGJLGw/1l4/nFiEk4RW25e1ucovYpB3SsfOqs6Fe/FA9FHwAeBMqBaqAGOA6IAQRj4bODsXAcCG7yrsZgLVhr2oA/b99PRUQKTWvIRKTfaP7pyJ+U036lMfDb8klcN2g/rOtpSS6e9rp/5FtrKG6Z7VjX3rXqGTOtYyVkq+cfQqR5LgCRwPC9K/99QdJ4yjrvWeKmaHd8eK3LHol1TfHiioru71vmpmh1fAB3kQ1inZuIrwcGABngRbJTmv7N9T/dNPYNp7ThyI++e33Ldvg4RKQXUSATkX7hpzfPPHe1p+Q3eyYbOK/5PVzrsNQZ8GTKS3iPy5d8AhCMhafeu/zRy6ckG6rne8u5tWIKKzyl7671FJ/x+KcPzgbO/7eRxw96zz+k677T25dT0/gmJW7afmPkzDtXeMvO3eiNO+dEs1zgUuC6nvpoLW3GUJrf4OByWNvyFc+XjakB/ri5X2SKSN+mQCYi/cLevzt3XdJ4BgLcvOp5e1z70s6U9AyR5qO7TowEJq1xil+eOfrkio7sqBaj0i1tTy/7ZynALYEp3FYxBYDTW+ZzRcMcSm0mAXzz7aIhr98e2Otfz5eMqsgLYTiua13H6Wy4nuwo2A+799GmvNZ4Uyb/2mvWvPzgqa31PyDSvHh7fRYi0vtoUb+I7NKCsfDwgW7yHtd4B3a2ebH51e67TxFescpT0hXGABLG0zVq9cPmd9kvsZoSm+GAxOrO5heINP9l30jgl3esfq5igXcgVww+pH1O8Qi/TQ5w0oky4y1f0Xnuudb1vOomS9Z4/C1D8+vuW7ymPNPKeu+GLl06dNoNp148V2FMZBenRf0isqu7dL1TdGTaOAQyCS5sfIuj25cBrAL+AXy72/mZSalGTm+Zj89mGJZu5/o1L1vgHaAFcA/rWNF+QGL13cDjZBfYnwnwQmavFoDx6fWct3z5msRH1beklu9HcVFD/v3LjZM5rsjTMbTIZroarWswno6NwpixNhEPRZ/dvh+HiPRGGiETkV1d1zBWs8fP2/5hLnz4D+BiIs09bSZ+oYG2q9e+lrp67WsPAv8GzAFuAzy5c6YRaX57o6sigYvGmJFn35I6jaUM44HM9AF42x4tHfvif6Y9Lt1lvGkyxtP12jibLh8Zlmmft7UPKyJ9k9aQicguLRgL+4HFwPC85op4KNr8uW8SCQwBlpGd3rTAnkSaP8w7Pg14CSBpveyX+A2tlNxhilre91c9c1P+mrDPzVocrOsa56u5chkisgvTlKWI7Op2Y+Mw9tZWhTGASPNa4HSy05Nf3yiMZSU7//CRbhtA+3nAt6zruWnj5Wob2E0HzboMTreDMbjGcYDvb1VfRaRP0pSliOx6IoGDgf8E3mDcWTcDLwPTgEXAtu3/GGl+BHhkM8fmEAl8Czg6jef3Kxn8AOBxylb1+LV3dHI9y4oGbnoAKE8nU2NazQsNATp/+fn8NvVXRPoUTVmKyK4nEljJhlGxk4PjznqcbPX7T+KhaNuOfOuqmjoP0AgMLKp8Hqdk3ee+1ma8S4wnvUc8FE0GY+GpQEk8FH16R/VVRHoPBTIR2fVEAg3AoNyrrxBpvn9nvG1VTZ0BTgLGAzP9X36o2pjsDwFmttbzln8Ea70l2flKs8nQ2YvACfFQtHVn9FVEehetIRORXdFXkjiPPF+8268PG336ozvxfW8AHgJuBu4GLrfW2JEdGX6x5l+MTrdmK/ez6bqyv3/6yIr4or+csRP7KiK9iEbIRKTPC8bCXwP2Bu6Mh6KLg7GwF3gWmA68DxwaD0V3+P6PVTV1rwEHA0w29bF7iq48OoMzrib1HYaXxfnbqMEbX5DbVqnIzWANXLfmZU5oWzKDSPMLO7qvItK7aFG/iPRpwVj4WOBvuZenA3uR3Styeq5tT2Af4KVgLHw28A3gwXgoemswFj4IOBd4OR6KxrapA5HAhIdLK3/1z7LdB3jKTv400zoKgHO9dfsMNO3jHi0dy7KBn7AgFdj0WmPYt2M1bxcPA+B35ZM4oW1JyTb1Q0T6NI2QiUiftv9vv/2dNsd3R+5lM9lCrk/knfIxcCBQSraWWOdSjX2AZ4DOYavD46Hoiz29RzAWPgQ4Grg/HorG848t/sXoubNGVe+dNB68rs20zD/eg1vE1z2Pv3OR/+69p435CjZXh8yfSZNwPF2bjY9It/HN5g+4esiBABzW/ulrd533j0O/wMchIn2URshEpO+KBA552TjXnTvsKPuv4mFpa5x5QDrvjFbg4Hgoui5XIDYNFAFu7u/SvHMHAARj4dOBCuAF4CxgCRAlWxT2wmAsXJW/8L7R8Q9M5irupx3jKRr7Ssb42jz3OZkp9R0zrM1bMJbweLl51fMMtCkmJxsoddP4sLR4ii67pWLv918sGfXQ9v6IRKRvUCATkT4nt2Zs2u+KR4ye1rEy8L5/ENY4PrLrt36cd+qSeCjaBBAPRVfnwtaZwEPxUPSDYCx8BvAjsnXKHgvGwk9BV/2vdUA52cr8naFqaK6tlUhgr0bH/8PrBh/lVCbXYwHHkl5U3PXvqnmjZNjGHU/7oHVI0wHmvYoikyFlnQzGnv/9C166XdVfRfo3TVmKSJ9yxw1Tf3zDoH2vAvC7mea3ltxTvv/Y2abDyeagMan17Ut9AzvXYS2Oh6LjgrHw7mQ3Eh8GhOKh6Ca/vAzGwr8ELtvM2yaAJrIjbuuA8+KL/nLvrYG9Rt9asTcAE5ON7JFsom7A7l0XDcp0cEzrUt4oGUZZWwlzVldDxv/UUJqPme68y0oGXfXXqy65/It/KiLS16nshYj0Gf+8bp8HHywbd1Xn64TjKWlwig+dmGy8A2ut301z9dpXS05tWUggk5gHfDN36veBKcBI4MrN3D60hbf2k12LNh7YF2tfeLV4xBBf3v5H84oG4WKYnGiwZZkEM1sXc9/yRzlqTfLFby1Z979vrzglTsb/W+DUNQQu+Id72Ldedfe8Yts/DRHZlWjKUkT6hkjgxOtHnzZrlTe77Ks8k+A/1s/rKHeTjXOLhx0BmAyG+d5ylntLGZde/847Hn9nlfsPOm9jrDssGAvfBFzWuRYsGAvvB2ypgn8GOKzrlTGe/xoyLfnMsn/wUvFIXi8ZCcBzpaN5c8m9BrIVLT62o8495srn7oTsLw3y3LjtH4SI7Io0ZSkivVowFi7zWPcBn3UPcWFg0tnwPbLUTfGHlU+++7XdTpzS2eZY17rGMQDHty7+z8fKKm994NOHq+8on/z3hwdU+fJu/Thwm017puPJXJL74WPnerGudWNFbrot6XjzF/9vuMGyf/JU8WiuGXIAuavt+4vubvMYWwY8S6T5qO33SYjIrkwjZCLSqwRj4WlAQ3zRX8YBB5w4dPqIR8rGzcxsutUQbY6PVuNLGGvXWWPKsRa/m6Hdkz13iafsm4MzHT89bdRJg4vddPfLjwOOM95MV4O1uJn1I5YaX3vSU7KuEWuHJB3vhB47am1HwE2+t2eqcQrZKU0wzPcYOxOYRLYwrYjI56JAJiK9RjAWvhqowdrMIyVjnQo3YU5b/8n6R0ors7W7cpXtAYrctD2mfdmKvZINxz+z9IE77hsw4asHJlbxZOnYpj+VTxwE8IF/8CSMKQJI5EpTbIkxeLzlK8cBONbFQsLmbXNU7Kbe73B8o7F20W9WPfNkwE3udnBi1bnAWOAAIEakeRGwaPt+MiKyq9OUpYj0GsFY+C1gX8gGItc4GGvx2zQdjq/76Y/HQ9HjAYgEngcOB3inaHDD7JHHD8QYn2PdVhdThjHs07GaucUbylDkZbvNGptal17iK/fmXXBkPBR9jkhgNnBP7rRFRJqriAQOB9qINL/xhT4EEemX9CtLEelN7sBai7W4uSlKa8xGYcyxrvW7abci01EcjIUH5Zof7Dw+KdnYUZFJGADXOGUYg9fN8IdVT7V4rPvHz+yBtWAt41LrMJYN85nGPBgPRZ/LvcqfXfASCfwEeB6YQyTwjW16chHp1xTIRKQwIoHBRAJdQ1bBWHgWcC1gNjd09eVE4ydHty01CcfrNHmKZwSTjX/KHbrp0ZKxv//J4IM/vrN88qgmb/FGyzHSjoe3/MPOzBjnKnKV/G3az/QVcETrsmwIyym1aU5orV+41FuWWVxU7s+7zdy8v/8KXA08BMxmQzFZAC3kF5GtpilLEdmpPv7vycPneoc93VjaMfn2QVNMu/FSZN2O4Zn2tUt9A0Zv4dL024v+WnPZ0EOvf7isCoBjWxe3P1FW+Uey1fPPhA1TnRux1mLMKdYajLFdo2lfSjZxWusCrhu0f/fzXcxGN8kAQ+KhaHOPPctOYf4J6ACqiTS/8Dk+ChGRLgpkIrJTvRI5oO2CMXuXNHmKN2rfM7GW9/1Der7IWkrd5HoHXrlpzUutdWXjTvdal7qycZlWp2iT1frGuq41TvcZgLSbGPA+1tnHKV7X1XjWunk8VlbJWk+uuL9lPYZlQJDsnpdzgQayVfov7b65eJdIYACQIdLc/jk+BhGRjWjKUkR2Kq+3dZMwBtDkFHHF6jnLgNespdVN+TaUwTeGNo9/YIvHf9wlFTOOP6NhyfmntSy8odX4NqllAayyxjm21E01DE235X/j9Dr+ln3Sq4P4EhtmIt1EgP0WjmxLrZr0b8CXMVSSLQL7XeBQ4H7gGOAU4E4AIoFTiQQuzZ9yJdLcojAmIttKI2QislPVXn7ui8tHLp/+TOluDM60s8I3oOvYd5Yvef2/ki9klpry9pmVs47u6XpjXXyutUmPxwCvk91QvHNa0gDMXMaTD7XMOrd44kOXAd/uvNZaKGovJVXS2vUTy9TyvcmsqwR4pr62epP3DMbClwDX5F4+FV/0lxuAutzrV4g0T/sin4eICGiETER2sppf3nHYP5Z9b/SsBWWBcMO8t/MX1K8ekJoATC2l42hnM98WrXHIhTGs5cC8QwbAWMsRztyZvrEvv8uGvSyzJxhIlbZ1hTFjLb5E12jd4s10+UbgKuC3ZPe7HJ93bHxPF4iIbC2NkIlIQR1w88XNrQNbygF8bsZiMCPTbSzxDcyesKWCYV0bHGUd3baEw9qX8+fSvZhf0uNuRxsJtrUsf3vJGfcBS4Hb6murWz/zokig/KmS0Y9fM2j//duN9+PV3pKZ8VB0+WdeJyKyBarULyIF1dg8+TVf2ZxjjeOScrIjX11hDLZcvTX/kLWMXe+58YZBB/+opWijZsA84bZXHIT1VLgtw8FYjCdZHx8yf3J9bfXm131FAkXAT4HRwM+JNC8g0rzu+7GwA/iAycCPgEu37qlFRDamQCYiBeW2Dj8juWTqVU5xY5F3+AfHG8MorF13Ytui0k+dUu/c4mGfXVIfwBju8sw43lf0SVdTZv2Itanl+w7B+oYAM4E/AxNzh+d9ctFFm4Sxqpq6M4GvAH+rL2YMcFnu0HhgRu7vtXmX1G/VA4uI9ECBTEQKqr62ugH4HkAwFq4EjvvTyienH5hYfc6M0ad1LdQfmWphhW8AExLrWJCqpNjfxGi3kX0SDTwwYDyp9qFkGiYEnZJGPGVrcTsGrk19esCQ3FLZ/YE5ZMtYdHqne1+qaup2J1tPzAFOX2aHXDPadGWvolwfD2BD8de3gV9v309ERPojBTIR6TXioehiIoHfkgs5len1ZpU3uxbsvxvfYHr7cmZ1XE07Y2kHDOtWfEzxsARdtchMaumhpDyJJBn/wI3nNIFs0PqY7K8zr+6hCw5dF7nm/NR5v/970ZUVLoxO41yWmwk9Heism7F7PBTVQlwR+cK0qF9Eep9I4Cng6JWmLHNmyTmei9w6TuqoZ5WtYEbiV3Tg/6w7uPT8K/IW4Kj62uo5m7uwqqbu/3mGfHShd8hHe5lsgdhLgbtyh08Gbgb2zL2ui4eis7bm0UREeqJAJiK9TyRQDBx1fKL2h/Ns5YmTzCIOcT7kKXc/ltgRm7sqzYZR/znAbmQX43f6EJhRX1u9prMhGAuPAFrjoWhL/o2CsfAioDL3cg0wFMC6ZplxbAVQljt2XjwUjW7rY4qIdFIgE5Feq6qmbgTwc7LbFg0Fzs477JJdUD8+93eI7HZHuwHn5M55BZia+ztaX1t9XufFwVj4QuB/rOskkkumLrcdg9KA4wksvtY38p1bAW+u4sYnwB6wSQWOxcCEeCja024BIiJbRWvIRKTXqq+tXkl2CyMAqmrqPgYiwApg//ra6pVVNXVTgYb62up5uXNuyLuFA5wADALu63b7bwMYx/V7BqysSicG4hlUj1PcdC15/zYmFk3/wLfbm+MdX7vT7cee6xTGRGR7USATkT6jvrb6yqqaul/U11bbvLZXup12C9kSF0OAK+prq5/YzO0eASZZa3DbhuIb8R6ewFKAcjftdR1v2sk07o5NFZ8yMtXOqqJNrv/ldnkoERE0ZSki/VgwFp6aWjFlfKZ53F5Fuz/zNaeodQJApm0QTnETWJMtIuvk/TtpLbNaFn56/fmPjd7MbUVEtpoCmYgI2fpi1nX+aDsqJpvixo1DWJ6TWupX3rD25aOINH+4k7soIrswBTIRkTxVNXX/45/w+AXGm9xoxZi1uAb7PMacEg9F1xeqfyKya1IgExHpJhgLV1rLn8AcCNZvk2W/c/ytYS3iF5EdRYFMRGQLgrGwEw9F3c8+U0Rk2ymQiYiIiBRYT1uLiIiIiMhOpEAmIiIiUmAKZCIiIiIFpkAmIiIiUmAKZCIiIiIFpkAmIiIiUmAKZCIiIiIFpkAmIiIiUmAKZCIiIiIFpkAmIiIiUmAKZCIiIiIFpkAmIiIiUmAKZCIiIiIFpkAmIiIiUmAKZCIiIiIFpkAmIiIiUmAKZCIiIiIFpkAmIiIiUmAKZCIiIiIFpkAmIiIiUmAKZCIiIiIFpkAmIiIiUmAKZCIiIiIFpkAmIiIiUmAKZCIiIiIFpkAmIiIiUmAKZCIiIiIFpkAmIiIiUmAKZCIiIiIFpkAmIiIiUmAKZCIiIiIFpkAmIiIiUmAKZCIiIiIFpkAmIiIiUmAKZCIiIiIFpkAmIiIiUmAKZCIiIiIFpkAmIiIiUmAKZCIiIiIFpkAmIiIiUmAKZCIiIiIFpkAmIiIiUmAKZCIiIiIFpkAmIiIiUmAKZCIiIiIFpkAmIiIiUmDeQndARPqPYCzsB74LJIE746FopsBdEhHpFRTIRGRnuh74Qe7vEcDPCtgXEZFeQ1OWIrJTVNXU7W1dTzC/qVB9ERHpbRTIRGSHq6qp+xkwN7l42hE241kAzAGuKnC3RER6DWOtLXQfRGQXFYyFPcCXOj4+4V5c71655mvra6svLWS/RER6G60hE5Ed6X7gZP+EJ9a77eXYjsGp9JqJDxS6UyIivY0CmYjsEMFYOACcDGCczEBPWSOUNfq8Q+YPzTWLiEiO1pCJyPYXCcyKL/rLm1MSaxtyLW7e0dZCdElEpDfTGjIR2f4igU+ACSkMj5ZW3nLxsOn3A2cDr8ZD0TsK3DsRkV5HU5YisiMsBCb4sJzctujFi5n+XDwUfabQnRIR6a00QiYi21UwFjZTEmuOvnLt60cGU03vBsedlQb+5LOZ1jtWPfvsPu1ND0xOxJ4DDgEW1tdWv1ngLouIFJwCmYhsV8FYOAacA6wHpgF3AocCfH3dPC5veINZyav4wFZBdm3ZzPraao2eiUi/pilLkX4uGAtPBh4CSoD/AObEQ9HGns6tqqn7OrAX8Ov62upFm7nlabn/DwSOBt4hF8gmJxtwDJzteZwfuyFwUg7p0oMBBTIR6dcUyET6sarLHjjIP8E8ZRw7MNf0OEAwFr4deAx4PB6KdgBU1dQ9CMzKnXdKVU3dvmTD2XygiOwelYuLJ/JH4HxgDfAw8JXO91vsy75NQ3EK/25PYpwMNu3fDap37IOKiPRymrIU6aeqauoONyUNz/orX95S+ZsEMKxj3iwXaMlr7wBeAo4BlpANZUfmjp1TPPGhF4A18VB03cTfhVuNoRRg74613LHieaaOPR3r6brXsngoOma7PZiISB+kETKR/mt/myx1rAtm85HMD1ztH//knxP1M1xMximqfBnjay92W4cdk1p2MGDGAvnf7MbEQ9EFXa+sE8e4+wO80XyYPbBk+kKvJz4+7/y3tutTiYj0QQpkIv3XPWSKL0gumTrOP+6VLZ13nvF1fN+/x+MYs6HRM2A16aIWbHJgPXAB8HNgMXB7MBY+EbgQeNUmB56caR/yhk0XDbOZovc8wz/cp9v9e1yvJiLSnyiQifRT9bXVK6pq6o6wyfK4tRTnhy0Aa+kMYAagh+MZ401MLRr7yjjjTZ4H3BsPRa/MbSh+L1AGzHRbh5e6HRUjfaPexBjbPYwBHLa9n01EpK/RGjKRfm7CtTfd5RsW/9bWXOOm/KRXT/x10ah3fgQ0AcW5QzOBZ6xllTEMAXCTftd4U45xsrsndQa9vMBXEw9Fr9k+TyMi0jdpL0uRfs62V7xmXbNpe2ZDW/fvbW7bsPnu+sofk60jlsw7dCRQTsb3h0yiFACnKOFg8raytGBdJxvKUn4Si6d+Y+Jvf3DQ9noeEZG+SCNkIv1cVU2dKRr3dMopbvN89tldLgf+SHZ/yiTw70BnqFoKjMkbAduivPOuioeil29FH0REdhlaQybSzxVPfAhrt/A7y54Z4AkgSPYXlnm/qrSjMWaLYaxr2jLtw3hTnc0nkw16IiL9jqYsRcTZdFIyy9rcf+mitTbtW5VrzpCtQVaVe22w1td1kemKYk2bfUfrkFg4g+SyA7Guk8m1xrb9EURE+jYFMpF+Lh6KZozhte7tNu0jsWBGvbtudDAx/7h7kosPfd1NFi/MtA1OJBYc9QPrOueTLXNx/8Rk0zV+N43ZONcN6On9bNpHcslUbLIc2zHkb5nmsePI1i771Q55QBGRPkBryESEYCx8LPAg4HPT3sfc5srD0027F5Mu+TZQ6R32wc89gxZ0TUOmm0dhOwZf6hvx3k0d82alSug4emLVvf8X9w/q3IKJ7mvIrAU3UUZq8QzyyvQH62ur5+2kxxQR6bU0QiYixEPRJ4AxwNiPvnPLSek1kwOkS8rqa6v/gKfD6x28oFtR2FX4Rrx3DfB20fjHGzJVrzzxoW/owPx7dl9DlmkZQWrRUflhbA3w0Y57KhGRvkMjZCKyRbv/9M8lRWP+tdR4E4OtJeN2DFzqKVk/rvt5btqH8aTTxthF1lJqDLvlH7cZD4n6IyFdkt88qr62evkOfgQRkV5PI2QiskULf/b1duNNTAT+wximpJZOa+npe5zBsYmPqtd0fDLz78bwik0XuW7Sv+G4J4MnsCT/ko+AtTu4+yIifYJGyERkq1TV1L3jGfzRFO/Qj8D1rDKeTId1nUBy6UEB2z4Mz+CP8Q3bdFmYtZBcenDCtg2fCewGPF1fW61AJiKC6pCJyNY7M9Pw5f/ONE5YiPX8pL62Oj3+F3fdY4yZbQHcnuvLZlpGYtuG+4HW+trqe3dmh0VEejsFMhHZKvW11e+TrcwPwB43/vKmotFLZwOk1+5Beu0E6x06zxhPZqPrbNtQyG61tGwndldEpE/QGjIR+UKMkz6t82+npAFPYMkmYcxNe1syTZUPAjPqa6tXISIiG9EImYh8IZn1o552Steeg5Mm3VSFMelNzjGe9O31tSdfWoDuiYj0CVrULyJfSFVNnQH3A4wNZmuMWXyVL+EUN+XXIrPAqfFQ9MGCdVREpBfTlKWIfCH1tdUWnIUbCr4aHP+67oVhDXDLTu+ciEgfoUAmItvDrUA7kAawrq+nc1QAVkRkMxTIROQLq6+tfhgYBJQD5xiTXtntlHeB03d6x0RE+gitIROR7S4YC78ITM+9/GU8FP1JIfsjItLb6VeWIrIjnAFcANSTnc4UEZEt0AiZiIiISIFpDZmIiIhIgSmQiYiIiBSYApmIiIhIgSmQiYiIiBSYApmIiIhIgSmQiYiIiBSYApmIiIhIgSmQiYiIiBSYApmIiIhIgSmQiYiIiBSYApmIiIhIgSmQiYiIiBSYApmIiIhIgSmQiYiIiBSYApmIiIhIgSmQiYiIiBSYApmIiIhIgSmQiYiIiBSYApmIiIhIgSmQiYiIiBSYApmIiIhIgSmQiYiIiBSYApmIiIhIgSmQiYiIiBSYApmIiIhIgSmQiYiIiBSYApmIiIhIgSmQiYiIiBSYApmIiIhIgSmQiYiIiBSYApmIiIhIgSmQiYiIiBSYApmIiIhIgSmQiYiIiBSYApmIiIhIgf1/8x46DzXnM1EAAAAASUVORK5CYII=", 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", 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" + ] + }, + "metadata": { + "image/png": { + "height": 277, + "width": 827 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "best_val_loss = float(\"inf\")\n", + "best_avg_bio = 0.0\n", + "best_model = None\n", + "define_wandb_metrcis()\n", + "\n", + "for epoch in range(1, config.epochs + 1):\n", + " epoch_start_time = time.time()\n", + " train_data_pt, valid_data_pt = prepare_data(\n", + " tokenized_train=tokenized_train, \n", + " tokenized_valid=tokenized_valid, \n", + " train_batch_labels=train_batch_labels,\n", + " valid_batch_labels=valid_batch_labels,\n", + " config=config,\n", + " epoch=epoch,\n", + " sort_seq_batch=config.per_seq_batch_sample)\n", + " \n", + " train_loader = prepare_dataloader(\n", + " train_data_pt,\n", + " batch_size=config.batch_size,\n", + " shuffle=True,\n", + " intra_domain_shuffle=False,\n", + " drop_last=False,\n", + " per_seq_batch_sample=config.per_seq_batch_sample\n", + " )\n", + " valid_loader = prepare_dataloader(\n", + " valid_data_pt,\n", + " batch_size=config.batch_size,\n", + " shuffle=False,\n", + " intra_domain_shuffle=False,\n", + " drop_last=False,\n", + " per_seq_batch_sample=config.per_seq_batch_sample\n", + " )\n", + "\n", + " if config.do_train:\n", + " train(\n", + " model=model,\n", + " loader=train_loader,\n", + " vocab=vocab,\n", + " criterion_gep_gepc=criterion_gep_gepc if config.GEP and config.GEPC else None,\n", + " criterion_dab=criterion_dab if config.DAR else None,\n", + " criterion_cls=criterion_cls if config.CLS else None,\n", + " scaler=scaler,\n", + " optimizer=optimizer,\n", + " scheduler=scheduler,\n", + " device=device,\n", + " config=config,\n", + " logger=logger,\n", + " epoch=epoch,\n", + " )\n", + " val_loss = evaluate(\n", + " model=model,\n", + " loader=valid_loader,\n", + " vocab=vocab,\n", + " criterion_gep_gepc=criterion_gep_gepc if config.GEP and config.GEPC else None,\n", + " criterion_dab=criterion_dab if config.DAR else None,\n", + " criterion_cls=criterion_cls if config.CLS else None,\n", + " device=device,\n", + " config=config,\n", + " epoch=epoch\n", + " )\n", + " elapsed = time.time() - epoch_start_time\n", + " logger.info(\"-\" * 89)\n", + " logger.info(\n", + " f\"| end of epoch {epoch:3d} | time: {elapsed:5.2f}s | \"\n", + " f\"valid loss {val_loss:5.4f} | \"\n", + " )\n", + " logger.info(\"-\" * 89)\n", + "\n", + " if val_loss < best_val_loss:\n", + " best_val_loss = val_loss\n", + " best_model = copy.deepcopy(model)\n", + " best_model_epoch = epoch\n", + " logger.info(f\"Best model with score {best_val_loss:5.4f}\")\n", + "\n", + " if epoch % config.save_eval_interval == 0 or epoch == config.epochs:\n", + " logger.info(f\"Saving model to {save_dir}\")\n", + " torch.save(best_model.state_dict(), save_dir / f\"model_e{best_model_epoch}.pt\")\n", + "\n", + " # eval on testdata\n", + " results = eval_testdata(\n", + " model = best_model,\n", + " adata_t = adata_sorted if config.per_seq_batch_sample else adata,\n", + " gene_ids = gene_ids,\n", + " vocab = vocab,\n", + " config = config,\n", + " logger = logger,\n", + " include_types=[\"cls\"],\n", + " )\n", + " results[\"batch_umap\"].savefig(\n", + " save_dir / f\"embeddings_batch_umap[cls]_e{best_model_epoch}.png\", dpi=300\n", + " )\n", + "\n", + " results[\"celltype_umap\"].savefig(\n", + " save_dir / f\"embeddings_celltype_umap[cls]_e{best_model_epoch}.png\", dpi=300\n", + " )\n", + " metrics_to_log = {\"test/\" + k: v for k, v in results.items()}\n", + " metrics_to_log[\"test/batch_umap\"] = wandb.Image(\n", + " str(save_dir / f\"embeddings_batch_umap[cls]_e{best_model_epoch}.png\"),\n", + " caption=f\"celltype avg_bio epoch {best_model_epoch}\",\n", + " )\n", + "\n", + " metrics_to_log[\"test/celltype_umap\"] = wandb.Image(\n", + " str(save_dir / f\"embeddings_celltype_umap[cls]_e{best_model_epoch}.png\"),\n", + " caption=f\"celltype avg_bio epoch {best_model_epoch}\",\n", + " )\n", + " metrics_to_log[\"test/best_model_epoch\"] = best_model_epoch\n", + " wandb.log(metrics_to_log)\n", + " wandb.log({\"avg_bio\": results.get(\"avg_bio\", 0.0)})\n", + "\n", + " scheduler.step()" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "30430479", + "metadata": {}, + "outputs": [], + "source": [ + "# save the best model\n", + "torch.save(best_model.state_dict(), save_dir / \"best_model.pt\")" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "a7f160cc", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "Waiting for W&B process to finish... (success)." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "wandb: WARNING Source type is set to 'repo' but some required information is missing from the environment. A job will not be created from this run. See https://docs.wandb.ai/guides/launch/create-job\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "

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Run summary:


avg_bio0.77673
epoch25
test/ARI_cluster/label0.92154
test/ASW_label0.63246
test/ASW_label/batch0.87133
test/NMI_cluster/label0.7762
test/PCR_batch0.42197
test/best_model_epoch24
test/graph_conn0.93427
train/dab0.57504
train/gep44.45348
train/mvc45.66718

" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + " View run atomic-jazz-449 at: https://wandb.ai/scformer/scGPT/runs/lrpm65jz
Synced 5 W&B file(s), 10 media file(s), 1 artifact file(s) and 0 other file(s)" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Find logs at: ./wandb/run-20231018_213957-lrpm65jz/logs" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "92987" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "artifact = wandb.Artifact(f\"best_model\", type=\"model\")\n", + "glob_str = os.path.join(save_dir, \"best_model.pt\")\n", + "artifact.add_file(glob_str)\n", + "run.log_artifact(artifact)\n", + "\n", + "run.finish()\n", + "wandb.finish()\n", + "gc.collect()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12c5bf54-0d47-44da-a34b-4df3fac76124", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.10" + }, + "vscode": { + "interpreter": { + "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/transfer/code/scGPT/tutorials/Tutorial_Perturbation.ipynb b/transfer/code/scGPT/tutorials/Tutorial_Perturbation.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..9567fa66f8f6b205be7c454da43e97c4f196efb9 --- /dev/null +++ b/transfer/code/scGPT/tutorials/Tutorial_Perturbation.ipynb @@ -0,0 +1,1343 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Fine-tuning Pre-trained Model for Perturbation Prediction" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import os\n", + "import sys\n", + "import time\n", + "import copy\n", + "from pathlib import Path\n", + "from typing import Iterable, List, Tuple, Dict, Union, Optional\n", + "import warnings\n", + "\n", + "import torch\n", + "import numpy as np\n", + "import matplotlib\n", + "from torch import nn\n", + "from torch.nn import functional as F\n", + "from torchtext.vocab import Vocab\n", + "from torchtext._torchtext import (\n", + " Vocab as VocabPybind,\n", + ")\n", + "from torch_geometric.loader import DataLoader\n", + "from gears import PertData, GEARS\n", + "from gears.inference import compute_metrics, deeper_analysis, non_dropout_analysis\n", + "from gears.utils import create_cell_graph_dataset_for_prediction\n", + "\n", + "sys.path.insert(0, \"../\")\n", + "\n", + "import scgpt as scg\n", + "from scgpt.model import TransformerGenerator\n", + "from scgpt.loss import (\n", + " masked_mse_loss,\n", + " criterion_neg_log_bernoulli,\n", + " masked_relative_error,\n", + ")\n", + "from scgpt.tokenizer import tokenize_batch, pad_batch, tokenize_and_pad_batch\n", + "from scgpt.tokenizer.gene_tokenizer import GeneVocab\n", + "from scgpt.utils import set_seed, map_raw_id_to_vocab_id, compute_perturbation_metrics\n", + "\n", + "matplotlib.rcParams[\"savefig.transparent\"] = False\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "set_seed(42)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " ## Training Settings" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# settings for data prcocessing\n", + "pad_token = \"\"\n", + "special_tokens = [pad_token, \"\", \"\"]\n", + "pad_value = 0 # for padding values\n", + "pert_pad_id = 0\n", + "include_zero_gene = \"all\"\n", + "max_seq_len = 1536\n", + "\n", + "# settings for training\n", + "MLM = True # whether to use masked language modeling, currently it is always on.\n", + "CLS = False # celltype classification objective\n", + "CCE = False # Contrastive cell embedding objective\n", + "MVC = False # Masked value prediction for cell embedding\n", + "ECS = False # Elastic cell similarity objective\n", + "amp = True\n", + "load_model = \"../save/scGPT_human\"\n", + "load_param_prefixs = [\n", + " \"encoder\",\n", + " \"value_encoder\",\n", + " \"transformer_encoder\",\n", + "]\n", + "\n", + "# settings for optimizer\n", + "lr = 1e-4 # or 1e-4\n", + "batch_size = 64\n", + "eval_batch_size = 64\n", + "epochs = 15\n", + "schedule_interval = 1\n", + "early_stop = 10\n", + "\n", + "# settings for the model\n", + "embsize = 512 # embedding dimension\n", + "d_hid = 512 # dimension of the feedforward network model in nn.TransformerEncoder\n", + "nlayers = 12 # number of nn.TransformerEncoderLayer in nn.TransformerEncoder\n", + "nhead = 8 # number of heads in nn.MultiheadAttention\n", + "n_layers_cls = 3\n", + "dropout = 0 # dropout probability\n", + "use_fast_transformer = True # whether to use fast transformer\n", + "\n", + "# logging\n", + "log_interval = 100\n", + "\n", + "# dataset and evaluation choices\n", + "data_name = \"adamson\"\n", + "split = \"simulation\"\n", + "if data_name == \"norman\":\n", + " perts_to_plot = [\"SAMD1+ZBTB1\"]\n", + "elif data_name == \"adamson\":\n", + " perts_to_plot = [\"KCTD16+ctrl\"]\n", + "\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving to save/dev_perturb_adamson-Jun03-12-45\n", + "scGPT - INFO - Running on 2024-06-03 12:45:07\n" + ] + } + ], + "source": [ + "save_dir = Path(f\"./save/dev_perturb_{data_name}-{time.strftime('%b%d-%H-%M')}/\")\n", + "save_dir.mkdir(parents=True, exist_ok=True)\n", + "print(f\"saving to {save_dir}\")\n", + "\n", + "logger = scg.logger\n", + "scg.utils.add_file_handler(logger, save_dir / \"run.log\")\n", + "# log running date and current git commit\n", + "logger.info(f\"Running on {time.strftime('%Y-%m-%d %H:%M:%S')}\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Found local copy...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Local copy of pyg dataset is detected. Loading...\n", + "Done!\n", + "Local copy of split is detected. Loading...\n", + "Simulation split test composition:\n", + "combo_seen0:0\n", + "combo_seen1:0\n", + "combo_seen2:0\n", + "unseen_single:22\n", + "Done!\n", + "Creating dataloaders....\n", + "Done!\n" + ] + } + ], + "source": [ + "pert_data = PertData(\"./data\")\n", + "pert_data.load(data_name=data_name)\n", + "pert_data.prepare_split(split=split, seed=1)\n", + "pert_data.get_dataloader(batch_size=batch_size, test_batch_size=eval_batch_size)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "scGPT - INFO - match 4399/5060 genes in vocabulary of size 60697.\n", + "scGPT - INFO - Resume model from ../save/scGPT_human/best_model.pt, the model args will override the config ../save/scGPT_human/args.json.\n" + ] + } + ], + "source": [ + "if load_model is not None:\n", + " model_dir = Path(load_model)\n", + " model_config_file = model_dir / \"args.json\"\n", + " model_file = model_dir / \"best_model.pt\"\n", + " vocab_file = model_dir / \"vocab.json\"\n", + "\n", + " vocab = GeneVocab.from_file(vocab_file)\n", + " for s in special_tokens:\n", + " if s not in vocab:\n", + " vocab.append_token(s)\n", + "\n", + " pert_data.adata.var[\"id_in_vocab\"] = [\n", + " 1 if gene in vocab else -1 for gene in pert_data.adata.var[\"gene_name\"]\n", + " ]\n", + " gene_ids_in_vocab = np.array(pert_data.adata.var[\"id_in_vocab\"])\n", + " logger.info(\n", + " f\"match {np.sum(gene_ids_in_vocab >= 0)}/{len(gene_ids_in_vocab)} genes \"\n", + " f\"in vocabulary of size {len(vocab)}.\"\n", + " )\n", + " genes = pert_data.adata.var[\"gene_name\"].tolist()\n", + "\n", + " # model\n", + " with open(model_config_file, \"r\") as f:\n", + " model_configs = json.load(f)\n", + " logger.info(\n", + " f\"Resume model from {model_file}, the model args will override the \"\n", + " f\"config {model_config_file}.\"\n", + " )\n", + " embsize = model_configs[\"embsize\"]\n", + " nhead = model_configs[\"nheads\"]\n", + " d_hid = model_configs[\"d_hid\"]\n", + " nlayers = model_configs[\"nlayers\"]\n", + " n_layers_cls = model_configs[\"n_layers_cls\"]\n", + "else:\n", + " genes = pert_data.adata.var[\"gene_name\"].tolist()\n", + " vocab = Vocab(\n", + " VocabPybind(genes + special_tokens, None)\n", + " ) # bidirectional lookup [gene <-> int]\n", + "vocab.set_default_index(vocab[\"\"])\n", + "gene_ids = np.array(\n", + " [vocab[gene] if gene in vocab else vocab[\"\"] for gene in genes], dtype=int\n", + ")\n", + "n_genes = len(genes)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " # Create and train scGpt" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "scGPT - INFO - Loading params encoder.embedding.weight with shape torch.Size([60697, 512])\n", + "scGPT - INFO - Loading params encoder.enc_norm.weight with shape torch.Size([512])\n", + "scGPT - INFO - Loading params encoder.enc_norm.bias with shape torch.Size([512])\n", + "scGPT - INFO - Loading params value_encoder.linear1.weight with shape torch.Size([512, 1])\n", + "scGPT - INFO - Loading params value_encoder.linear1.bias with shape torch.Size([512])\n", + "scGPT - INFO - Loading params value_encoder.linear2.weight with shape torch.Size([512, 512])\n", + "scGPT - INFO - Loading params value_encoder.linear2.bias with shape torch.Size([512])\n", + "scGPT - INFO - Loading params value_encoder.norm.weight with shape torch.Size([512])\n", + "scGPT - INFO - Loading params value_encoder.norm.bias with shape torch.Size([512])\n", + "scGPT - INFO - Loading params transformer_encoder.layers.0.self_attn.Wqkv.weight with shape torch.Size([1536, 512])\n", + "scGPT - INFO - Loading params transformer_encoder.layers.0.self_attn.Wqkv.bias with shape torch.Size([1536])\n", + "scGPT - INFO - Loading params 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params transformer_encoder.layers.11.self_attn.Wqkv.bias with shape torch.Size([1536])\n", + "scGPT - INFO - Loading params transformer_encoder.layers.11.self_attn.out_proj.weight with shape torch.Size([512, 512])\n", + "scGPT - INFO - Loading params transformer_encoder.layers.11.self_attn.out_proj.bias with shape torch.Size([512])\n", + "scGPT - INFO - Loading params transformer_encoder.layers.11.linear1.weight with shape torch.Size([512, 512])\n", + "scGPT - INFO - Loading params transformer_encoder.layers.11.linear1.bias with shape torch.Size([512])\n", + "scGPT - INFO - Loading params transformer_encoder.layers.11.linear2.weight with shape torch.Size([512, 512])\n", + "scGPT - INFO - Loading params transformer_encoder.layers.11.linear2.bias with shape torch.Size([512])\n", + "scGPT - INFO - Loading params transformer_encoder.layers.11.norm1.weight with shape torch.Size([512])\n", + "scGPT - INFO - Loading params transformer_encoder.layers.11.norm1.bias with shape torch.Size([512])\n", + "scGPT - INFO - Loading params transformer_encoder.layers.11.norm2.weight with shape torch.Size([512])\n", + "scGPT - INFO - Loading params transformer_encoder.layers.11.norm2.bias with shape torch.Size([512])\n" + ] + }, + { + "data": { + "text/plain": [ + "TransformerGenerator(\n", + " (encoder): GeneEncoder(\n", + " (embedding): Embedding(60697, 512, padding_idx=60694)\n", + " (enc_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " )\n", + " (value_encoder): ContinuousValueEncoder(\n", + " (dropout): Dropout(p=0, inplace=False)\n", + " (linear1): Linear(in_features=1, out_features=512, bias=True)\n", + " (activation): ReLU()\n", + " (linear2): Linear(in_features=512, out_features=512, bias=True)\n", + " (norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " )\n", + " (pert_encoder): Embedding(3, 512, padding_idx=0)\n", + " (transformer_encoder): TransformerEncoder(\n", + " (layers): ModuleList(\n", + " (0): FlashTransformerEncoderLayer(\n", + " (self_attn): FlashMHA(\n", + " (Wqkv): Linear(in_features=512, out_features=1536, bias=True)\n", + " (inner_attn): FlashAttention()\n", + " (out_proj): Linear(in_features=512, out_features=512, bias=True)\n", + " )\n", + " (linear1): Linear(in_features=512, out_features=512, bias=True)\n", + " (dropout): Dropout(p=0, inplace=False)\n", + " (linear2): Linear(in_features=512, out_features=512, bias=True)\n", + " (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (dropout1): Dropout(p=0, inplace=False)\n", + " (dropout2): Dropout(p=0, inplace=False)\n", + " )\n", + " (1): FlashTransformerEncoderLayer(\n", + " (self_attn): FlashMHA(\n", + " (Wqkv): Linear(in_features=512, out_features=1536, bias=True)\n", + " (inner_attn): FlashAttention()\n", + " (out_proj): Linear(in_features=512, out_features=512, bias=True)\n", + " )\n", + " (linear1): Linear(in_features=512, out_features=512, bias=True)\n", + " (dropout): Dropout(p=0, inplace=False)\n", + " (linear2): Linear(in_features=512, out_features=512, bias=True)\n", + " (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (dropout1): Dropout(p=0, inplace=False)\n", + " (dropout2): Dropout(p=0, inplace=False)\n", + " )\n", + " (2): FlashTransformerEncoderLayer(\n", + " (self_attn): FlashMHA(\n", + " (Wqkv): Linear(in_features=512, out_features=1536, bias=True)\n", + " (inner_attn): FlashAttention()\n", + " (out_proj): Linear(in_features=512, out_features=512, bias=True)\n", + " )\n", + " (linear1): Linear(in_features=512, out_features=512, bias=True)\n", + " (dropout): Dropout(p=0, inplace=False)\n", + " (linear2): Linear(in_features=512, out_features=512, bias=True)\n", + " (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (dropout1): Dropout(p=0, inplace=False)\n", + " (dropout2): Dropout(p=0, inplace=False)\n", + " )\n", + " (3): FlashTransformerEncoderLayer(\n", + " (self_attn): FlashMHA(\n", + " (Wqkv): Linear(in_features=512, out_features=1536, bias=True)\n", + " (inner_attn): FlashAttention()\n", + " (out_proj): Linear(in_features=512, out_features=512, bias=True)\n", + " )\n", + " (linear1): Linear(in_features=512, out_features=512, bias=True)\n", + " (dropout): Dropout(p=0, inplace=False)\n", + " (linear2): Linear(in_features=512, out_features=512, bias=True)\n", + " (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (dropout1): Dropout(p=0, inplace=False)\n", + " (dropout2): Dropout(p=0, inplace=False)\n", + " )\n", + " (4): FlashTransformerEncoderLayer(\n", + " (self_attn): FlashMHA(\n", + " (Wqkv): Linear(in_features=512, out_features=1536, bias=True)\n", + " (inner_attn): FlashAttention()\n", + " (out_proj): Linear(in_features=512, out_features=512, bias=True)\n", + " )\n", + " (linear1): Linear(in_features=512, out_features=512, bias=True)\n", + " (dropout): Dropout(p=0, inplace=False)\n", + " (linear2): Linear(in_features=512, out_features=512, bias=True)\n", + " (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (dropout1): Dropout(p=0, inplace=False)\n", + " (dropout2): Dropout(p=0, inplace=False)\n", + " )\n", + " (5): FlashTransformerEncoderLayer(\n", + " (self_attn): FlashMHA(\n", + " (Wqkv): Linear(in_features=512, out_features=1536, bias=True)\n", + " (inner_attn): FlashAttention()\n", + " (out_proj): Linear(in_features=512, out_features=512, bias=True)\n", + " )\n", + " (linear1): Linear(in_features=512, out_features=512, bias=True)\n", + " (dropout): Dropout(p=0, inplace=False)\n", + " (linear2): Linear(in_features=512, out_features=512, bias=True)\n", + " (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (dropout1): Dropout(p=0, inplace=False)\n", + " (dropout2): Dropout(p=0, inplace=False)\n", + " )\n", + " (6): FlashTransformerEncoderLayer(\n", + " (self_attn): FlashMHA(\n", + " (Wqkv): Linear(in_features=512, out_features=1536, bias=True)\n", + " (inner_attn): FlashAttention()\n", + " (out_proj): Linear(in_features=512, out_features=512, bias=True)\n", + " )\n", + " (linear1): Linear(in_features=512, out_features=512, bias=True)\n", + " (dropout): Dropout(p=0, inplace=False)\n", + " (linear2): Linear(in_features=512, out_features=512, bias=True)\n", + " (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (dropout1): Dropout(p=0, inplace=False)\n", + " (dropout2): Dropout(p=0, inplace=False)\n", + " )\n", + " (7): FlashTransformerEncoderLayer(\n", + " (self_attn): FlashMHA(\n", + " (Wqkv): Linear(in_features=512, out_features=1536, bias=True)\n", + " (inner_attn): FlashAttention()\n", + " (out_proj): Linear(in_features=512, out_features=512, bias=True)\n", + " )\n", + " (linear1): Linear(in_features=512, out_features=512, bias=True)\n", + " (dropout): Dropout(p=0, inplace=False)\n", + " (linear2): Linear(in_features=512, out_features=512, bias=True)\n", + " (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (dropout1): Dropout(p=0, inplace=False)\n", + " (dropout2): Dropout(p=0, inplace=False)\n", + " )\n", + " (8): FlashTransformerEncoderLayer(\n", + " (self_attn): FlashMHA(\n", + " (Wqkv): Linear(in_features=512, out_features=1536, bias=True)\n", + " (inner_attn): FlashAttention()\n", + " (out_proj): Linear(in_features=512, out_features=512, bias=True)\n", + " )\n", + " (linear1): Linear(in_features=512, out_features=512, bias=True)\n", + " (dropout): Dropout(p=0, inplace=False)\n", + " (linear2): Linear(in_features=512, out_features=512, bias=True)\n", + " (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (dropout1): Dropout(p=0, inplace=False)\n", + " (dropout2): Dropout(p=0, inplace=False)\n", + " )\n", + " (9): FlashTransformerEncoderLayer(\n", + " (self_attn): FlashMHA(\n", + " (Wqkv): Linear(in_features=512, out_features=1536, bias=True)\n", + " (inner_attn): FlashAttention()\n", + " (out_proj): Linear(in_features=512, out_features=512, bias=True)\n", + " )\n", + " (linear1): Linear(in_features=512, out_features=512, bias=True)\n", + " (dropout): Dropout(p=0, inplace=False)\n", + " (linear2): Linear(in_features=512, out_features=512, bias=True)\n", + " (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (dropout1): Dropout(p=0, inplace=False)\n", + " (dropout2): Dropout(p=0, inplace=False)\n", + " )\n", + " (10): FlashTransformerEncoderLayer(\n", + " (self_attn): FlashMHA(\n", + " (Wqkv): Linear(in_features=512, out_features=1536, bias=True)\n", + " (inner_attn): FlashAttention()\n", + " (out_proj): Linear(in_features=512, out_features=512, bias=True)\n", + " )\n", + " (linear1): Linear(in_features=512, out_features=512, bias=True)\n", + " (dropout): Dropout(p=0, inplace=False)\n", + " (linear2): Linear(in_features=512, out_features=512, bias=True)\n", + " (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (dropout1): Dropout(p=0, inplace=False)\n", + " (dropout2): Dropout(p=0, inplace=False)\n", + " )\n", + " (11): FlashTransformerEncoderLayer(\n", + " (self_attn): FlashMHA(\n", + " (Wqkv): Linear(in_features=512, out_features=1536, bias=True)\n", + " (inner_attn): FlashAttention()\n", + " (out_proj): Linear(in_features=512, out_features=512, bias=True)\n", + " )\n", + " (linear1): Linear(in_features=512, out_features=512, bias=True)\n", + " (dropout): Dropout(p=0, inplace=False)\n", + " (linear2): Linear(in_features=512, out_features=512, bias=True)\n", + " (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (dropout1): Dropout(p=0, inplace=False)\n", + " (dropout2): Dropout(p=0, inplace=False)\n", + " )\n", + " )\n", + " )\n", + " (decoder): AffineExprDecoder(\n", + " (coeff_decoder): ExprDecoder(\n", + " (fc): Sequential(\n", + " (0): Linear(in_features=512, out_features=512, bias=True)\n", + " (1): LeakyReLU(negative_slope=0.01)\n", + " (2): Linear(in_features=512, out_features=512, bias=True)\n", + " (3): LeakyReLU(negative_slope=0.01)\n", + " (4): Linear(in_features=512, out_features=1, bias=True)\n", + " )\n", + " )\n", + " (bias_decoder): ExprDecoder(\n", + " (fc): Sequential(\n", + " (0): Linear(in_features=512, out_features=512, bias=True)\n", + " (1): LeakyReLU(negative_slope=0.01)\n", + " (2): Linear(in_features=512, out_features=512, bias=True)\n", + " (3): LeakyReLU(negative_slope=0.01)\n", + " (4): Linear(in_features=512, out_features=1, bias=True)\n", + " )\n", + " )\n", + " )\n", + " (cls_decoder): ClsDecoder(\n", + " (_decoder): ModuleList(\n", + " (0): Linear(in_features=512, out_features=512, bias=True)\n", + " (1): ReLU()\n", + " (2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " (3): Linear(in_features=512, out_features=512, bias=True)\n", + " (4): ReLU()\n", + " (5): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", + " )\n", + " (out_layer): Linear(in_features=512, out_features=1, bias=True)\n", + " )\n", + ")" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ntokens = len(vocab) # size of vocabulary\n", + "model = TransformerGenerator(\n", + " ntokens,\n", + " embsize,\n", + " nhead,\n", + " d_hid,\n", + " nlayers,\n", + " nlayers_cls=n_layers_cls,\n", + " n_cls=1,\n", + " vocab=vocab,\n", + " dropout=dropout,\n", + " pad_token=pad_token,\n", + " pad_value=pad_value,\n", + " pert_pad_id=pert_pad_id,\n", + " use_fast_transformer=use_fast_transformer,\n", + ")\n", + "if load_param_prefixs is not None and load_model is not None:\n", + " # only load params that start with the prefix\n", + " model_dict = model.state_dict()\n", + " pretrained_dict = torch.load(model_file)\n", + " pretrained_dict = {\n", + " k: v\n", + " for k, v in pretrained_dict.items()\n", + " if any([k.startswith(prefix) for prefix in load_param_prefixs])\n", + " }\n", + " for k, v in pretrained_dict.items():\n", + " logger.info(f\"Loading params {k} with shape {v.shape}\")\n", + " model_dict.update(pretrained_dict)\n", + " model.load_state_dict(model_dict)\n", + "elif load_model is not None:\n", + " try:\n", + " model.load_state_dict(torch.load(model_file))\n", + " logger.info(f\"Loading all model params from {model_file}\")\n", + " except:\n", + " # only load params that are in the model and match the size\n", + " model_dict = model.state_dict()\n", + " pretrained_dict = torch.load(model_file)\n", + " pretrained_dict = {\n", + " k: v\n", + " for k, v in pretrained_dict.items()\n", + " if k in model_dict and v.shape == model_dict[k].shape\n", + " }\n", + " for k, v in pretrained_dict.items():\n", + " logger.info(f\"Loading params {k} with shape {v.shape}\")\n", + " model_dict.update(pretrained_dict)\n", + " model.load_state_dict(model_dict)\n", + "model.to(device)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "criterion = masked_mse_loss\n", + "criterion_cls = nn.CrossEntropyLoss()\n", + "optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n", + "scheduler = torch.optim.lr_scheduler.StepLR(optimizer, schedule_interval, gamma=0.9)\n", + "scaler = torch.cuda.amp.GradScaler(enabled=amp)\n", + "\n", + "\n", + "def train(model: nn.Module, train_loader: torch.utils.data.DataLoader) -> None:\n", + " \"\"\"\n", + " Train the model for one epoch.\n", + " \"\"\"\n", + " model.train()\n", + " total_loss, total_mse = 0.0, 0.0\n", + " start_time = time.time()\n", + "\n", + " num_batches = len(train_loader)\n", + " for batch, batch_data in enumerate(train_loader):\n", + " batch_size = len(batch_data.y)\n", + " batch_data.to(device)\n", + " x: torch.Tensor = batch_data.x # (batch_size * n_genes, 2)\n", + " ori_gene_values = x[:, 0].view(batch_size, n_genes)\n", + " pert_flags = x[:, 1].long().view(batch_size, n_genes)\n", + " target_gene_values = batch_data.y # (batch_size, n_genes)\n", + "\n", + " if include_zero_gene in [\"all\", \"batch-wise\"]:\n", + " if include_zero_gene == \"all\":\n", + " input_gene_ids = torch.arange(n_genes, device=device, dtype=torch.long)\n", + " else:\n", + " input_gene_ids = (\n", + " ori_gene_values.nonzero()[:, 1].flatten().unique().sort()[0]\n", + " )\n", + " # sample input_gene_id\n", + " if len(input_gene_ids) > max_seq_len:\n", + " input_gene_ids = torch.randperm(len(input_gene_ids), device=device)[\n", + " :max_seq_len\n", + " ]\n", + " input_values = ori_gene_values[:, input_gene_ids]\n", + " input_pert_flags = pert_flags[:, input_gene_ids]\n", + " target_values = target_gene_values[:, input_gene_ids]\n", + "\n", + " mapped_input_gene_ids = map_raw_id_to_vocab_id(input_gene_ids, gene_ids)\n", + " mapped_input_gene_ids = mapped_input_gene_ids.repeat(batch_size, 1)\n", + "\n", + " # src_key_padding_mask = mapped_input_gene_ids.eq(vocab[pad_token])\n", + " src_key_padding_mask = torch.zeros_like(\n", + " input_values, dtype=torch.bool, device=device\n", + " )\n", + "\n", + " with torch.cuda.amp.autocast(enabled=amp):\n", + " output_dict = model(\n", + " mapped_input_gene_ids,\n", + " input_values,\n", + " input_pert_flags,\n", + " src_key_padding_mask=src_key_padding_mask,\n", + " CLS=CLS,\n", + " CCE=CCE,\n", + " MVC=MVC,\n", + " ECS=ECS,\n", + " )\n", + " output_values = output_dict[\"mlm_output\"]\n", + "\n", + " masked_positions = torch.ones_like(\n", + " input_values, dtype=torch.bool\n", + " ) # Use all\n", + " loss = loss_mse = criterion(output_values, target_values, masked_positions)\n", + "\n", + " model.zero_grad()\n", + " scaler.scale(loss).backward()\n", + " scaler.unscale_(optimizer)\n", + " with warnings.catch_warnings(record=True) as w:\n", + " warnings.filterwarnings(\"always\")\n", + " torch.nn.utils.clip_grad_norm_(\n", + " model.parameters(),\n", + " 1.0,\n", + " error_if_nonfinite=False if scaler.is_enabled() else True,\n", + " )\n", + " if len(w) > 0:\n", + " logger.warning(\n", + " f\"Found infinite gradient. This may be caused by the gradient \"\n", + " f\"scaler. The current scale is {scaler.get_scale()}. This warning \"\n", + " \"can be ignored if no longer occurs after autoscaling of the scaler.\"\n", + " )\n", + " scaler.step(optimizer)\n", + " scaler.update()\n", + "\n", + " # torch.cuda.empty_cache()\n", + "\n", + " total_loss += loss.item()\n", + " total_mse += loss_mse.item()\n", + " if batch % log_interval == 0 and batch > 0:\n", + " lr = scheduler.get_last_lr()[0]\n", + " ms_per_batch = (time.time() - start_time) * 1000 / log_interval\n", + " cur_loss = total_loss / log_interval\n", + " cur_mse = total_mse / log_interval\n", + " # ppl = math.exp(cur_loss)\n", + " logger.info(\n", + " f\"| epoch {epoch:3d} | {batch:3d}/{num_batches:3d} batches | \"\n", + " f\"lr {lr:05.4f} | ms/batch {ms_per_batch:5.2f} | \"\n", + " f\"loss {cur_loss:5.2f} | mse {cur_mse:5.2f} |\"\n", + " )\n", + " total_loss = 0\n", + " total_mse = 0\n", + " start_time = time.time()\n", + "\n", + "\n", + "def eval_perturb(\n", + " loader: DataLoader, model: TransformerGenerator, device: torch.device\n", + ") -> Dict:\n", + " \"\"\"\n", + " Run model in inference mode using a given data loader\n", + " \"\"\"\n", + "\n", + " model.eval()\n", + " model.to(device)\n", + " pert_cat = []\n", + " pred = []\n", + " truth = []\n", + " pred_de = []\n", + " truth_de = []\n", + " results = {}\n", + " logvar = []\n", + "\n", + " for itr, batch in enumerate(loader):\n", + " batch.to(device)\n", + " pert_cat.extend(batch.pert)\n", + "\n", + " with torch.no_grad():\n", + " p = model.pred_perturb(\n", + " batch,\n", + " include_zero_gene=include_zero_gene,\n", + " gene_ids=gene_ids,\n", + " )\n", + " t = batch.y\n", + " pred.extend(p.cpu())\n", + " truth.extend(t.cpu())\n", + "\n", + " # Differentially expressed genes\n", + " for itr, de_idx in enumerate(batch.de_idx):\n", + " pred_de.append(p[itr, de_idx])\n", + " truth_de.append(t[itr, de_idx])\n", + "\n", + " # all genes\n", + " results[\"pert_cat\"] = np.array(pert_cat)\n", + " pred = torch.stack(pred)\n", + " truth = torch.stack(truth)\n", + " results[\"pred\"] = pred.detach().cpu().numpy().astype(np.float)\n", + " results[\"truth\"] = truth.detach().cpu().numpy().astype(np.float)\n", + "\n", + " pred_de = torch.stack(pred_de)\n", + " truth_de = torch.stack(truth_de)\n", + " results[\"pred_de\"] = pred_de.detach().cpu().numpy().astype(np.float)\n", + " results[\"truth_de\"] = truth_de.detach().cpu().numpy().astype(np.float)\n", + "\n", + " return results\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "scGPT - INFO - | epoch 1 | 100/849 batches | lr 0.0001 | ms/batch 328.30 | loss 0.10 | mse 0.10 |\n", + "scGPT - INFO - | epoch 1 | 200/849 batches | lr 0.0001 | ms/batch 315.96 | loss 0.09 | mse 0.09 |\n", + "scGPT - INFO - | epoch 1 | 300/849 batches | lr 0.0001 | ms/batch 314.79 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 1 | 400/849 batches | lr 0.0001 | ms/batch 314.88 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 1 | 500/849 batches | lr 0.0001 | ms/batch 315.20 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 1 | 600/849 batches | lr 0.0001 | ms/batch 314.72 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 1 | 700/849 batches | lr 0.0001 | ms/batch 313.97 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 1 | 800/849 batches | lr 0.0001 | ms/batch 316.13 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - val_metrics at epoch 1: \n", + "scGPT - INFO - {'pearson': 0.9892257294575539, 'pearson_de': 0.9536847512476039, 'pearson_delta': 0.6666769004595279, 'pearson_de_delta': 0.8330308997151171}\n", + "scGPT - INFO - | end of epoch 1 | time: 293.53s | \n", + "scGPT - INFO - Best model with score 0.9892\n", + "scGPT - INFO - | epoch 2 | 100/849 batches | lr 0.0001 | ms/batch 318.85 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 2 | 200/849 batches | lr 0.0001 | ms/batch 314.84 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 2 | 300/849 batches | lr 0.0001 | ms/batch 315.07 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 2 | 400/849 batches | lr 0.0001 | ms/batch 315.34 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 2 | 500/849 batches | lr 0.0001 | ms/batch 315.28 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 2 | 600/849 batches | lr 0.0001 | ms/batch 315.23 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 2 | 700/849 batches | lr 0.0001 | ms/batch 315.95 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 2 | 800/849 batches | lr 0.0001 | ms/batch 315.18 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - val_metrics at epoch 2: \n", + "scGPT - INFO - {'pearson': 0.9885074603800036, 'pearson_de': 0.9520515428485282, 'pearson_delta': 0.6710248258328121, 'pearson_de_delta': 0.8388810980745884}\n", + "scGPT - INFO - | end of epoch 2 | time: 292.53s | \n", + "scGPT - INFO - | epoch 3 | 100/849 batches | lr 0.0001 | ms/batch 317.79 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 3 | 200/849 batches | lr 0.0001 | ms/batch 314.78 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 3 | 300/849 batches | lr 0.0001 | ms/batch 315.49 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 3 | 400/849 batches | lr 0.0001 | ms/batch 315.06 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 3 | 500/849 batches | lr 0.0001 | ms/batch 315.57 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 3 | 600/849 batches | lr 0.0001 | ms/batch 315.81 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 3 | 700/849 batches | lr 0.0001 | ms/batch 314.85 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 3 | 800/849 batches | lr 0.0001 | ms/batch 314.68 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - val_metrics at epoch 3: \n", + "scGPT - INFO - {'pearson': 0.9892629962551931, 'pearson_de': 0.9487707137368578, 'pearson_delta': 0.6665948421192589, 'pearson_de_delta': 0.8166225675462415}\n", + "scGPT - INFO - | end of epoch 3 | time: 292.43s | \n", + "scGPT - INFO - Best model with score 0.9893\n", + "scGPT - INFO - | epoch 4 | 100/849 batches | lr 0.0001 | ms/batch 316.41 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 4 | 200/849 batches | lr 0.0001 | ms/batch 314.60 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 4 | 300/849 batches | lr 0.0001 | ms/batch 314.60 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 4 | 400/849 batches | lr 0.0001 | ms/batch 314.01 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 4 | 500/849 batches | lr 0.0001 | ms/batch 314.04 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 4 | 600/849 batches | lr 0.0001 | ms/batch 313.93 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 4 | 700/849 batches | lr 0.0001 | ms/batch 314.04 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 4 | 800/849 batches | lr 0.0001 | ms/batch 313.86 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - val_metrics at epoch 4: \n", + "scGPT - INFO - {'pearson': 0.9883759825701637, 'pearson_de': 0.9614302664487033, 'pearson_delta': 0.6379006685656928, 'pearson_de_delta': 0.8396512058125575}\n", + "scGPT - INFO - | end of epoch 4 | time: 291.50s | \n", + "scGPT - INFO - | epoch 5 | 100/849 batches | lr 0.0001 | ms/batch 317.14 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 5 | 200/849 batches | lr 0.0001 | ms/batch 314.59 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 5 | 300/849 batches | lr 0.0001 | ms/batch 313.70 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 5 | 400/849 batches | lr 0.0001 | ms/batch 314.54 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 5 | 500/849 batches | lr 0.0001 | ms/batch 314.15 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 5 | 600/849 batches | lr 0.0001 | ms/batch 314.47 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 5 | 700/849 batches | lr 0.0001 | ms/batch 314.41 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 5 | 800/849 batches | lr 0.0001 | ms/batch 314.27 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - val_metrics at epoch 5: \n", + "scGPT - INFO - {'pearson': 0.9898445259378498, 'pearson_de': 0.9544624666565514, 'pearson_delta': 0.6800732289350021, 'pearson_de_delta': 0.8533562199091836}\n", + "scGPT - INFO - | end of epoch 5 | time: 291.61s | \n", + "scGPT - INFO - Best model with score 0.9898\n", + "scGPT - INFO - | epoch 6 | 100/849 batches | lr 0.0001 | ms/batch 317.50 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 6 | 200/849 batches | lr 0.0001 | ms/batch 314.75 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 6 | 300/849 batches | lr 0.0001 | ms/batch 315.72 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 6 | 400/849 batches | lr 0.0001 | ms/batch 314.68 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 6 | 500/849 batches | lr 0.0001 | ms/batch 315.27 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 6 | 600/849 batches | lr 0.0001 | ms/batch 315.57 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 6 | 700/849 batches | lr 0.0001 | ms/batch 315.00 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 6 | 800/849 batches | lr 0.0001 | ms/batch 315.90 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - val_metrics at epoch 6: \n", + "scGPT - INFO - {'pearson': 0.9903230508676109, 'pearson_de': 0.946848227544529, 'pearson_delta': 0.6719841295966481, 'pearson_de_delta': 0.8212238190538768}\n", + "scGPT - INFO - | end of epoch 6 | time: 292.30s | \n", + "scGPT - INFO - Best model with score 0.9903\n", + "scGPT - INFO - | epoch 7 | 100/849 batches | lr 0.0001 | ms/batch 318.28 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 7 | 200/849 batches | lr 0.0001 | ms/batch 315.33 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 7 | 300/849 batches | lr 0.0001 | ms/batch 315.81 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 7 | 400/849 batches | lr 0.0001 | ms/batch 314.73 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 7 | 500/849 batches | lr 0.0001 | ms/batch 314.40 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 7 | 600/849 batches | lr 0.0001 | ms/batch 315.21 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 7 | 700/849 batches | lr 0.0001 | ms/batch 314.44 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 7 | 800/849 batches | lr 0.0001 | ms/batch 314.99 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - val_metrics at epoch 7: \n", + "scGPT - INFO - {'pearson': 0.9904062232661642, 'pearson_de': 0.9573263921820816, 'pearson_delta': 0.6969510807994438, 'pearson_de_delta': 0.8658731821343123}\n", + "scGPT - INFO - | end of epoch 7 | time: 292.21s | \n", + "scGPT - INFO - Best model with score 0.9904\n", + "scGPT - INFO - | epoch 8 | 100/849 batches | lr 0.0000 | ms/batch 318.10 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 8 | 200/849 batches | lr 0.0000 | ms/batch 314.11 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 8 | 300/849 batches | lr 0.0000 | ms/batch 315.36 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 8 | 400/849 batches | lr 0.0000 | ms/batch 315.45 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 8 | 500/849 batches | lr 0.0000 | ms/batch 314.32 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 8 | 600/849 batches | lr 0.0000 | ms/batch 314.85 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 8 | 700/849 batches | lr 0.0000 | ms/batch 314.50 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 8 | 800/849 batches | lr 0.0000 | ms/batch 314.87 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - val_metrics at epoch 8: \n", + "scGPT - INFO - {'pearson': 0.9903405889093087, 'pearson_de': 0.9582105391047795, 'pearson_delta': 0.7029724506153079, 'pearson_de_delta': 0.8825178557724992}\n", + "scGPT - INFO - | end of epoch 8 | time: 292.06s | \n", + "scGPT - INFO - | epoch 9 | 100/849 batches | lr 0.0000 | ms/batch 317.55 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 9 | 200/849 batches | lr 0.0000 | ms/batch 314.71 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 9 | 300/849 batches | lr 0.0000 | ms/batch 315.80 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 9 | 400/849 batches | lr 0.0000 | ms/batch 314.30 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 9 | 500/849 batches | lr 0.0000 | ms/batch 314.71 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 9 | 600/849 batches | lr 0.0000 | ms/batch 313.94 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 9 | 700/849 batches | lr 0.0000 | ms/batch 315.55 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 9 | 800/849 batches | lr 0.0000 | ms/batch 315.73 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - val_metrics at epoch 9: \n", + "scGPT - INFO - {'pearson': 0.9898455688560865, 'pearson_de': 0.9516848526507314, 'pearson_delta': 0.6928932881856177, 'pearson_de_delta': 0.8269343878951014}\n", + "scGPT - INFO - | end of epoch 9 | time: 292.14s | \n", + "scGPT - INFO - | epoch 10 | 100/849 batches | lr 0.0000 | ms/batch 317.94 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 10 | 200/849 batches | lr 0.0000 | ms/batch 314.88 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 10 | 300/849 batches | lr 0.0000 | ms/batch 314.65 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 10 | 400/849 batches | lr 0.0000 | ms/batch 315.04 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 10 | 500/849 batches | lr 0.0000 | ms/batch 313.73 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 10 | 600/849 batches | lr 0.0000 | ms/batch 315.28 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 10 | 700/849 batches | lr 0.0000 | ms/batch 315.31 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 10 | 800/849 batches | lr 0.0000 | ms/batch 314.58 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - val_metrics at epoch 10: \n", + "scGPT - INFO - {'pearson': 0.9901226958730327, 'pearson_de': 0.9578416366552508, 'pearson_delta': 0.6865686571929335, 'pearson_de_delta': 0.8495341738808564}\n", + "scGPT - INFO - | end of epoch 10 | time: 291.98s | \n", + "scGPT - INFO - | epoch 11 | 100/849 batches | lr 0.0000 | ms/batch 318.32 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 11 | 200/849 batches | lr 0.0000 | ms/batch 315.35 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 11 | 300/849 batches | lr 0.0000 | ms/batch 315.20 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 11 | 400/849 batches | lr 0.0000 | ms/batch 315.13 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 11 | 500/849 batches | lr 0.0000 | ms/batch 315.23 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 11 | 600/849 batches | lr 0.0000 | ms/batch 315.90 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 11 | 700/849 batches | lr 0.0000 | ms/batch 315.02 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 11 | 800/849 batches | lr 0.0000 | ms/batch 315.15 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - val_metrics at epoch 11: \n", + "scGPT - INFO - {'pearson': 0.9894306105186443, 'pearson_de': 0.9486990088146493, 'pearson_delta': 0.681914471521785, 'pearson_de_delta': 0.8097841618570546}\n", + "scGPT - INFO - | end of epoch 11 | time: 292.41s | \n", + "scGPT - INFO - | epoch 12 | 100/849 batches | lr 0.0000 | ms/batch 318.66 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 12 | 200/849 batches | lr 0.0000 | ms/batch 315.08 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 12 | 300/849 batches | lr 0.0000 | ms/batch 315.56 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 12 | 400/849 batches | lr 0.0000 | ms/batch 314.79 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 12 | 500/849 batches | lr 0.0000 | ms/batch 315.06 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 12 | 600/849 batches | lr 0.0000 | ms/batch 314.74 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 12 | 700/849 batches | lr 0.0000 | ms/batch 315.30 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 12 | 800/849 batches | lr 0.0000 | ms/batch 315.05 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - val_metrics at epoch 12: \n", + "scGPT - INFO - {'pearson': 0.9904401900223683, 'pearson_de': 0.9556375904590835, 'pearson_delta': 0.6961162737147223, 'pearson_de_delta': 0.8505992941836349}\n", + "scGPT - INFO - | end of epoch 12 | time: 292.21s | \n", + "scGPT - INFO - Best model with score 0.9904\n", + "scGPT - INFO - | epoch 13 | 100/849 batches | lr 0.0000 | ms/batch 317.68 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 13 | 200/849 batches | lr 0.0000 | ms/batch 315.37 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 13 | 300/849 batches | lr 0.0000 | ms/batch 313.83 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 13 | 400/849 batches | lr 0.0000 | ms/batch 315.42 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 13 | 500/849 batches | lr 0.0000 | ms/batch 314.69 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 13 | 600/849 batches | lr 0.0000 | ms/batch 315.00 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 13 | 700/849 batches | lr 0.0000 | ms/batch 314.41 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 13 | 800/849 batches | lr 0.0000 | ms/batch 314.50 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - val_metrics at epoch 13: \n", + "scGPT - INFO - {'pearson': 0.9899041085502839, 'pearson_de': 0.9523392789334268, 'pearson_delta': 0.7005061467213647, 'pearson_de_delta': 0.8224662306348456}\n", + "scGPT - INFO - | end of epoch 13 | time: 291.96s | \n", + "scGPT - INFO - | epoch 14 | 100/849 batches | lr 0.0000 | ms/batch 319.04 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 14 | 200/849 batches | lr 0.0000 | ms/batch 314.66 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 14 | 300/849 batches | lr 0.0000 | ms/batch 315.45 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 14 | 400/849 batches | lr 0.0000 | ms/batch 315.56 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 14 | 500/849 batches | lr 0.0000 | ms/batch 315.45 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 14 | 600/849 batches | lr 0.0000 | ms/batch 315.86 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 14 | 700/849 batches | lr 0.0000 | ms/batch 315.05 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 14 | 800/849 batches | lr 0.0000 | ms/batch 315.16 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - val_metrics at epoch 14: \n", + "scGPT - INFO - {'pearson': 0.9907336896814499, 'pearson_de': 0.9544412007321013, 'pearson_delta': 0.7098260097842066, 'pearson_de_delta': 0.8567521357575016}\n", + "scGPT - INFO - | end of epoch 14 | time: 292.49s | \n", + "scGPT - INFO - Best model with score 0.9907\n", + "scGPT - INFO - | epoch 15 | 100/849 batches | lr 0.0000 | ms/batch 319.35 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 15 | 200/849 batches | lr 0.0000 | ms/batch 315.52 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 15 | 300/849 batches | lr 0.0000 | ms/batch 314.74 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 15 | 400/849 batches | lr 0.0000 | ms/batch 314.69 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 15 | 500/849 batches | lr 0.0000 | ms/batch 314.45 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 15 | 600/849 batches | lr 0.0000 | ms/batch 313.89 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 15 | 700/849 batches | lr 0.0000 | ms/batch 315.58 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - | epoch 15 | 800/849 batches | lr 0.0000 | ms/batch 315.39 | loss 0.08 | mse 0.08 |\n", + "scGPT - INFO - val_metrics at epoch 15: \n", + "scGPT - INFO - {'pearson': 0.9899097634137083, 'pearson_de': 0.9554618649590941, 'pearson_delta': 0.6949415398380963, 'pearson_de_delta': 0.8297403169182545}\n", + "scGPT - INFO - | end of epoch 15 | time: 292.19s | \n" + ] + } + ], + "source": [ + "best_val_loss = float(\"inf\")\n", + "best_val_corr = 0\n", + "best_model = None\n", + "patience = 0\n", + "\n", + "for epoch in range(1, epochs + 1):\n", + " epoch_start_time = time.time()\n", + " train_loader = pert_data.dataloader[\"train_loader\"]\n", + " valid_loader = pert_data.dataloader[\"val_loader\"]\n", + "\n", + " train(\n", + " model,\n", + " train_loader,\n", + " )\n", + "\n", + " val_res = eval_perturb(valid_loader, model, device)\n", + " val_metrics = compute_perturbation_metrics(\n", + " val_res, pert_data.adata[pert_data.adata.obs[\"condition\"] == \"ctrl\"]\n", + " )\n", + " logger.info(f\"val_metrics at epoch {epoch}: \")\n", + " logger.info(val_metrics)\n", + "\n", + " elapsed = time.time() - epoch_start_time\n", + " logger.info(f\"| end of epoch {epoch:3d} | time: {elapsed:5.2f}s | \")\n", + "\n", + " val_score = val_metrics[\"pearson\"]\n", + " if val_score > best_val_corr:\n", + " best_val_corr = val_score\n", + " best_model = copy.deepcopy(model)\n", + " logger.info(f\"Best model with score {val_score:5.4f}\")\n", + " patience = 0\n", + " else:\n", + " patience += 1\n", + " if patience >= early_stop:\n", + " logger.info(f\"Early stop at epoch {epoch}\")\n", + " break\n", + "\n", + " # torch.save(\n", + " # model.state_dict(),\n", + " # save_dir / f\"model_{epoch}.pt\",\n", + " # )\n", + "\n", + " scheduler.step()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "torch.save(best_model.state_dict(), save_dir / \"best_model.pt\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " ## Evaluations" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def predict(\n", + " model: TransformerGenerator, pert_list: List[str], pool_size: Optional[int] = None\n", + ") -> Dict:\n", + " \"\"\"\n", + " Predict the gene expression values for the given perturbations.\n", + "\n", + " Args:\n", + " model (:class:`torch.nn.Module`): The model to use for prediction.\n", + " pert_list (:obj:`List[str]`): The list of perturbations to predict.\n", + " pool_size (:obj:`int`, optional): For each perturbation, use this number\n", + " of cells in the control and predict their perturbation results. Report\n", + " the stats of these predictions. If `None`, use all control cells.\n", + " \"\"\"\n", + " adata = pert_data.adata\n", + " ctrl_adata = adata[adata.obs[\"condition\"] == \"ctrl\"]\n", + " if pool_size is None:\n", + " pool_size = len(ctrl_adata.obs)\n", + " gene_list = pert_data.gene_names.values.tolist()\n", + " for pert in pert_list:\n", + " for i in pert:\n", + " if i not in gene_list:\n", + " raise ValueError(\n", + " \"The gene is not in the perturbation graph. Please select from GEARS.gene_list!\"\n", + " )\n", + "\n", + " model.eval()\n", + " device = next(model.parameters()).device\n", + " with torch.no_grad():\n", + " results_pred = {}\n", + " for pert in pert_list:\n", + " cell_graphs = create_cell_graph_dataset_for_prediction(\n", + " pert, ctrl_adata, gene_list, device, num_samples=pool_size\n", + " )\n", + " loader = DataLoader(cell_graphs, batch_size=eval_batch_size, shuffle=False)\n", + " preds = []\n", + " for batch_data in loader:\n", + " pred_gene_values = model.pred_perturb(\n", + " batch_data, include_zero_gene, gene_ids=gene_ids, amp=amp\n", + " )\n", + " preds.append(pred_gene_values)\n", + " preds = torch.cat(preds, dim=0)\n", + " results_pred[\"_\".join(pert)] = np.mean(preds.detach().cpu().numpy(), axis=0)\n", + "\n", + " return results_pred\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_perturbation(\n", + " model: nn.Module, query: str, save_file: str = None, pool_size: int = None\n", + ") -> matplotlib.figure.Figure:\n", + " import matplotlib.pyplot as plt\n", + " import numpy as np\n", + " import seaborn as sns\n", + "\n", + " sns.set_theme(style=\"ticks\", rc={\"axes.facecolor\": (0, 0, 0, 0)}, font_scale=1.5)\n", + "\n", + " adata = pert_data.adata\n", + " gene2idx = pert_data.node_map\n", + " cond2name = dict(adata.obs[[\"condition\", \"condition_name\"]].values)\n", + " gene_raw2id = dict(zip(adata.var.index.values, adata.var.gene_name.values))\n", + "\n", + " de_idx = [\n", + " gene2idx[gene_raw2id[i]]\n", + " for i in adata.uns[\"top_non_dropout_de_20\"][cond2name[query]]\n", + " ]\n", + " genes = [\n", + " gene_raw2id[i] for i in adata.uns[\"top_non_dropout_de_20\"][cond2name[query]]\n", + " ]\n", + " truth = adata[adata.obs.condition == query].X.toarray()[:, de_idx]\n", + " if query.split(\"+\")[1] == \"ctrl\":\n", + " pred = predict(model, [[query.split(\"+\")[0]]], pool_size=pool_size)\n", + " pred = pred[query.split(\"+\")[0]][de_idx]\n", + " else:\n", + " pred = predict(model, [query.split(\"+\")], pool_size=pool_size)\n", + " pred = pred[\"_\".join(query.split(\"+\"))][de_idx]\n", + " ctrl_means = adata[adata.obs[\"condition\"] == \"ctrl\"].to_df().mean()[de_idx].values\n", + "\n", + " pred = pred - ctrl_means\n", + " truth = truth - ctrl_means\n", + "\n", + " fig, ax = plt.subplots(figsize=[16.5, 4.5])\n", + " plt.title(query)\n", + " plt.boxplot(truth, showfliers=False, medianprops=dict(linewidth=0))\n", + "\n", + " for i in range(pred.shape[0]):\n", + " _ = plt.scatter(i + 1, pred[i], color=\"red\")\n", + "\n", + " plt.axhline(0, linestyle=\"dashed\", color=\"green\")\n", + "\n", + " ax.xaxis.set_ticklabels(genes, rotation=90)\n", + "\n", + " plt.ylabel(\"Change in Gene Expression over Control\", labelpad=10)\n", + " plt.tick_params(axis=\"x\", which=\"major\", pad=5)\n", + " plt.tick_params(axis=\"y\", which=\"major\", pad=5)\n", + " sns.despine()\n", + "\n", + " if save_file:\n", + " fig.savefig(save_file, bbox_inches=\"tight\", transparent=False)\n", + "\n", + " return fig" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# predict(best_model, [[\"FEV\"], [\"FEV\", \"SAMD11\"]])\n", + "for p in perts_to_plot:\n", + " plot_perturbation(best_model, p, pool_size=300, save_file=f\"{save_dir}/{p}.png\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'pearson': 0.9904118130127787, 'pearson_de': 0.9794049791961658, 'pearson_delta': 0.6418570216674039, 'pearson_de_delta': 0.7973933739812668}\n", + "scGPT - INFO - test_combo_seen0_pearson_delta: nan\n", + "scGPT - INFO - test_combo_seen0_pearson_delta_de: nan\n", + "scGPT - INFO - test_combo_seen0_pearson_delta_top20_de_non_dropout: nan\n", + "scGPT - INFO - test_combo_seen0_pearson_top20_de_non_dropout: nan\n", + "scGPT - INFO - test_combo_seen1_pearson_delta: nan\n", + "scGPT - INFO - test_combo_seen1_pearson_delta_de: nan\n", + "scGPT - INFO - test_combo_seen1_pearson_delta_top20_de_non_dropout: nan\n", + "scGPT - INFO - test_combo_seen1_pearson_top20_de_non_dropout: nan\n", + "scGPT - INFO - test_combo_seen2_pearson_delta: nan\n", + "scGPT - INFO - test_combo_seen2_pearson_delta_de: nan\n", + "scGPT - INFO - test_combo_seen2_pearson_delta_top20_de_non_dropout: nan\n", + "scGPT - INFO - test_combo_seen2_pearson_top20_de_non_dropout: nan\n", + "scGPT - INFO - test_unseen_single_pearson_delta: 0.6418570216674039\n", + "scGPT - INFO - test_unseen_single_pearson_delta_de: 0.7973933739812668\n", + "scGPT - INFO - test_unseen_single_pearson_delta_top20_de_non_dropout: 0.797422706700435\n", + "scGPT - INFO - test_unseen_single_pearson_top20_de_non_dropout: 0.9769457152068611\n" + ] + } + ], + "source": [ + "test_loader = pert_data.dataloader[\"test_loader\"]\n", + "test_res = eval_perturb(test_loader, best_model, device)\n", + "# test_metrics, test_pert_res = compute_metrics(test_res)\n", + "test_metrics = compute_perturbation_metrics(\n", + " test_res, pert_data.adata[pert_data.adata.obs[\"condition\"] == \"ctrl\"]\n", + ")\n", + "print(test_metrics)\n", + "\n", + "# save the dicts in json\n", + "with open(f\"{save_dir}/test_metrics.json\", \"w\") as f:\n", + " json.dump(test_metrics, f)\n", + "# with open(f\"{save_dir}/test_pert_res.json\", \"w\") as f:\n", + "# json.dump(test_pert_res, f)\n", + "\n", + "deeper_res = deeper_analysis(pert_data.adata, test_res)\n", + "non_dropout_res = non_dropout_analysis(pert_data.adata, test_res)\n", + "\n", + "metrics = [\"pearson_delta\", \"pearson_delta_de\"]\n", + "metrics_non_dropout = [\n", + " \"pearson_delta_top20_de_non_dropout\",\n", + " \"pearson_top20_de_non_dropout\",\n", + "]\n", + "subgroup_analysis = {}\n", + "for name in pert_data.subgroup[\"test_subgroup\"].keys():\n", + " subgroup_analysis[name] = {}\n", + " for m in metrics:\n", + " subgroup_analysis[name][m] = []\n", + "\n", + " for m in metrics_non_dropout:\n", + " subgroup_analysis[name][m] = []\n", + "\n", + "for name, pert_list in pert_data.subgroup[\"test_subgroup\"].items():\n", + " for pert in pert_list:\n", + " for m in metrics:\n", + " subgroup_analysis[name][m].append(deeper_res[pert][m])\n", + "\n", + " for m in metrics_non_dropout:\n", + " subgroup_analysis[name][m].append(non_dropout_res[pert][m])\n", + "\n", + "for name, result in subgroup_analysis.items():\n", + " for m in result.keys():\n", + " mean_value = np.mean(subgroup_analysis[name][m])\n", + " logger.info(\"test_\" + name + \"_\" + m + \": \" + str(mean_value))\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.13" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/transfer/code/scGPT/tutorials/Tutorial_Reference_Mapping.ipynb b/transfer/code/scGPT/tutorials/Tutorial_Reference_Mapping.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..0e0812b4eff0273937ee8ac6843d54bba24537c6 --- /dev/null +++ b/transfer/code/scGPT/tutorials/Tutorial_Reference_Mapping.ipynb @@ -0,0 +1,473 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Reference Mapping Using cell embedding by pretrained scGPT model\n", + "\n", + "In this tutorial, we demonstrate how to use the scGPT model to embed cells and map them to referenced embeddings. Then the meta labels of the reference cells, such as cell type or disease conditions, can be propagated to the query cells. Particularly, we use the `scGPT_human` model to provide embedings out of the box. In this zero-shot settings, no further training is needed. All the workflow can be completed pretty fast and we observed considerable accuracy.\n", + "\n", + "The pancreas dataset is used in this example. Please download it from https://drive.google.com/drive/folders/1s9XjcSiPC-FYV3VeHrEa7SeZetrthQVV?usp=sharing.\n", + "\n", + "We provide two mode of reference mapping in the following:\n", + "\n", + "1. **Using a customized reference dataset with provided annotations**. Mapping unkown cells in a query set to this reference dataset. This illustrated the use case for users who already have annotations for similar samples and want to quickly transfer the annotation to the newly collected samples.\n", + "\n", + "2. **Using our previously collected over 33 million cells from CellXGene as reference**. Mapping unkown cells in a query set to this reference atlas. This illustrates a generic use case for users who want to map their own data to a large reference atlas. For example, this can be a fast first step to understand the cell composition of a newly collected samples.\n", + "\n", + "According to your use case, you may only need **apply one of the two modes**.\n", + "\n", + "**Note**: please note that the reference mapping is a new experimental feature.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Error: package or namespace load failed for ‘stats’ in dyn.load(file, DLLpath = DLLpath, ...):\n", + " unable to load shared object '/scratch/ssd001/pkgs/R-3.6.1/lib/R/library/stats/libs/stats.so':\n", + " libRlapack.so: cannot open shared object file: No such file or directory\n", + "During startup - Warning message:\n", + "package ‘stats’ in options(\"defaultPackages\") was not found \n" + ] + } + ], + "source": [ + "import sys\n", + "from pathlib import Path\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "from scipy.stats import mode\n", + "import scanpy as sc\n", + "import sklearn\n", + "import warnings\n", + "\n", + "sys.path.insert(0, \"../\")\n", + "import scgpt as scg\n", + "\n", + "# extra dependency for similarity search\n", + "try:\n", + " import faiss\n", + "\n", + " faiss_imported = True\n", + "except ImportError:\n", + " faiss_imported = False\n", + " print(\n", + " \"faiss not installed! We highly recommend installing it for fast similarity search.\"\n", + " )\n", + " print(\"To install it, see https://github.com/facebookresearch/faiss/wiki/Installing-Faiss\")\n", + "\n", + "warnings.filterwarnings(\"ignore\", category=ResourceWarning)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Referrence mapping using a customized reference dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "model_dir = Path(\"../save/scGPT_human\")\n", + "adata = sc.read_h5ad(\"../data/annotation_pancreas/demo_train.h5ad\")\n", + "cell_type_key = \"Celltype\"\n", + "gene_col = \"index\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "scGPT - INFO - match 2999/3000 genes in vocabulary of size 60697.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Embedding cells: 100%|██████████| 166/166 [00:07<00:00, 21.48it/s]\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ref_embed_adata = scg.tasks.embed_data(\n", + " adata,\n", + " model_dir,\n", + " gene_col=gene_col,\n", + " obs_to_save=cell_type_key, # optional arg, only for saving metainfo\n", + " batch_size=64,\n", + " return_new_adata=True,\n", + ")\n", + "\n", + "# # running on cpu, not recommended since it is slow\n", + "# ref_embed_adata = scg.tasks.embed_data(\n", + "# adata,\n", + "# model_dir,\n", + "# gene_col=gene_col,\n", + "# obs_to_save=cell_type_key,\n", + "# batch_size=64,\n", + "# device=\"cpu\",\n", + "# use_fast_transformer=False,\n", + "# return_new_adata=True,\n", + "# )\n", + "\n", + "# Optional step to visualize the reference dataset using the embeddings\n", + "sc.pp.neighbors(ref_embed_adata, use_rep=\"X\")\n", + "sc.tl.umap(ref_embed_adata)\n", + "sc.pl.umap(ref_embed_adata, color=cell_type_key, frameon=False, wspace=0.4)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "scGPT - INFO - match 2999/3000 genes in vocabulary of size 60697.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Embedding cells: 100%|██████████| 66/66 [00:03<00:00, 16.74it/s]\n" + ] + } + ], + "source": [ + "test_adata = sc.read_h5ad(\"../data/annotation_pancreas/demo_test.h5ad\")\n", + "test_embed_adata = scg.tasks.embed_data(\n", + " test_adata,\n", + " model_dir,\n", + " gene_col=gene_col,\n", + " obs_to_save=cell_type_key, # optional arg, only for saving metainfo\n", + " batch_size=64,\n", + " return_new_adata=True,\n", + ")\n", + "\n", + "# # running on cpu\n", + "# test_embed_adata = scg.tasks.embed_data(\n", + "# test_adata,\n", + "# model_dir,\n", + "# gene_col=gene_col,\n", + "# obs_to_save=cell_type_key,\n", + "# batch_size=64,\n", + "# device=\"cpu\",\n", + "# use_fast_transformer=False,\n", + "# return_new_adata=True,\n", + "# )\n", + "\n", + "# # optional step to visualize the dataset\n", + "# sc.pp.neighbors(test_embed_adata, use_rep=\"X\")\n", + "# sc.tl.umap(test_embed_adata)\n", + "# sc.pl.umap(test_embed_adata, color=cell_type_key, frameon=False, wspace=0.4)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Those functions are only used when faiss is not installed\n", + "def l2_sim(a, b):\n", + " sims = -np.linalg.norm(a - b, axis=1)\n", + " return sims\n", + "\n", + "def get_similar_vectors(vector, ref, top_k=10):\n", + " # sims = cos_sim(vector, ref)\n", + " sims = l2_sim(vector, ref)\n", + " \n", + " top_k_idx = np.argsort(sims)[::-1][:top_k]\n", + " return top_k_idx, sims[top_k_idx]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.784257942152679" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ref_cell_embeddings = ref_embed_adata.X\n", + "test_emebd = test_embed_adata.X\n", + "\n", + "k = 10 # number of neighbors\n", + "\n", + "if faiss_imported:\n", + " # Declaring index, using most of the default parameters from\n", + " index = faiss.IndexFlatL2(ref_cell_embeddings.shape[1])\n", + " index.add(ref_cell_embeddings)\n", + "\n", + " # Query dataset, k - number of closest elements (returns 2 numpy arrays)\n", + " distances, labels = index.search(test_emebd, k)\n", + "\n", + "idx_list=[i for i in range(test_emebd.shape[0])]\n", + "preds = []\n", + "for k in idx_list:\n", + " if faiss_imported:\n", + " idx = labels[k]\n", + " else:\n", + " idx, sim = get_similar_vectors(test_emebd[k][np.newaxis, ...], ref_cell_embeddings, k)\n", + " pred = ref_embed_adata.obs[cell_type_key][idx].value_counts()\n", + " preds.append(pred.index[0])\n", + "gt = test_adata.obs[cell_type_key].to_numpy()\n", + "\n", + "sklearn.metrics.accuracy_score(gt, preds)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# # optionally, the built index can be saved/loaded for future use\n", + "# faiss.write_index(index, \"index.faiss\")\n", + "# index = faiss.read_index(\"index.faiss\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reference mapping using the CellXGene atlas\n", + "\n", + "We have previously built the index for all the cells in normal or cancer samples, over 33 million cells in total. You can find the code to build the index at [build_atlas_index_faiss.py](build_atlas_index_faiss.py).\n", + "We applied careful tuning to eventually well balance between the accuracy and efficiency. Now the actual building process takes less than 3 minutes and we choose to use only 16 bytes to store the vector per cell, which leads to 808 MB for the whole index of all the millions of cells. Please download the faiss index folder from https://drive.google.com/drive/folders/1q14U50SNg5LMjlZ9KH-n-YsGRi8zkCbe?usp=sharing.\n", + "\n", + "Faiss is required to use the index. Please install it by following the instructions at https://github.com/facebookresearch/faiss/wiki/Installing-Faiss" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "from build_atlas_index_faiss import load_index, vote" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading index and meta from /scratch/hdd001/home/haotian/projects/cellxemb/all ...\n", + "Index loaded, num_embeddings: 40835767\n", + "Set nprobe from 128 to 128 for 16384 clusters\n", + "Loaded index with 40835767 cells\n" + ] + } + ], + "source": [ + "use_gpu = faiss.get_num_gpus() > 0\n", + "index, meta_labels = load_index(\n", + " index_dir=\"path_to_faiss_index_folder\",\n", + " use_config_file=False,\n", + " use_gpu=use_gpu,\n", + ")\n", + "print(f\"Loaded index with {index.ntotal} cells\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The search runs super fast, especially on GPU. Here the similarity search for 4,000 query cells within the whole reference of millions should take around 7 second on CPU and 0.1 second on GPU." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 252 ms, sys: 493 ms, total: 745 ms\n", + "Wall time: 133 ms\n" + ] + } + ], + "source": [ + "%%time\n", + "k = 50\n", + "# test with the first 100 cells\n", + "distances, idx = index.search(test_emebd, k)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, we propagate the previously saved cell type labels, `meta_label`, from CellXGene annotations by majority voting." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4218/4218 [00:00<00:00, 20353.06it/s]\n" + ] + } + ], + "source": [ + "predict_labels = meta_labels[idx]\n", + "# from scipy.stats import mode\n", + "from tqdm import tqdm\n", + "\n", + "voting = []\n", + "for preds in tqdm(predict_labels):\n", + " voting.append(vote(preds, return_prob=False)[0])\n", + "voting = np.array(voting)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['beta' 'beta' 'beta' 'beta' 'PSC' 'beta' 'beta' 'beta' 'ductal' 'alpha']\n", + "['neuroendocrine cell' 'classical monocyte' 'neuroendocrine cell'\n", + " 'neuroendocrine cell' 'mural cell' 'classical monocyte' 'stromal cell'\n", + " 'myeloid cell' 'malignant cell' 'stromal cell']\n" + ] + } + ], + "source": [ + "print(gt[:10]) # Original labels in the query dataset, used for evaluation\n", + "print(voting[:10]) # Propagated CellXGene labels" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Currently, we found the CellXGene labels are scattered in different levels of the cell type hieratchy, with more generic cell types such as stromal cells in great abundance. The difference in the number of cells in each cell type may lead to less useful annotations, even though the similarity search itself may be sufficiently accurate. We are working on a better way to curate the cell type labels in a consistent way. Our current idea is plan to apply hierarchical predictions and provide a spectrum of cell type labels for each cell, from generic to more specific cell types.\n", + "\n", + "You may see the proportion of cell types in the meta labels by running\n", + "```python\n", + "from build_atlas_index_faiss import compute_category_proportion\n", + "compute_category_proportion(meta_labels)\n", + "```\n", + "\n", + "Meanwhile, the propagation of major cell types is usually more accurate. An example with endothelial cells is provided as follows. " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 16 endothelial cells\n", + "Predicted cell types: ['capillary endothelial cell' 'capillary endothelial cell'\n", + " 'endothelial cell of vascular tree' 'endothelial cell' 'endothelial cell'\n", + " 'endothelial cell' 'endothelial cell' 'endothelial cell'\n", + " 'endothelial cell' 'endothelial cell' 'endothelial cell'\n", + " 'endothelial cell' 'endothelial cell of vascular tree' 'endothelial cell'\n", + " 'endothelial cell' 'vein endothelial cell']\n", + "Annotated cell types: ['endothelial' 'endothelial' 'endothelial' 'endothelial' 'endothelial'\n", + " 'endothelial' 'endothelial' 'endothelial' 'endothelial' 'endothelial'\n", + " 'endothelial' 'endothelial' 'endothelial' 'endothelial' 'endothelial'\n", + " 'endothelial']\n" + ] + } + ], + "source": [ + "# find the endothelial cells\n", + "ids_m = np.where(gt == \"endothelial\")[0]\n", + "print(f\"Found {len(ids_m)} endothelial cells\")\n", + "print(f\"Predicted cell types: {voting[ids_m]}\")\n", + "print(f\"Annotated cell types: {gt[ids_m]}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.13" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/transfer/code/scGPT/tutorials/build_atlas_index_faiss.py b/transfer/code/scGPT/tutorials/build_atlas_index_faiss.py new file mode 100644 index 0000000000000000000000000000000000000000..fecbda0f8e7e402763191407704af35306de3813 --- /dev/null +++ b/transfer/code/scGPT/tutorials/build_atlas_index_faiss.py @@ -0,0 +1,378 @@ +# This script is used to build the index for similarity search using faiss. It supports loading embeddings and meta labels from directories and add to faiss index. The index is saved to disk for later use. +# Options can be set to custom the building process, including: +# - embedding_dir: the directory to the embedding files +# - meta_dir: the directory to the meta files +# - embedding_file_suffix: the suffix of the embedding files +# - meta_file_suffix: the suffix of the meta files +# - embedding_key: the key to the embedding in the embedding file +# - meta_key: the key to the meta in the meta file +# - gpu: whether to use gpu for building the index +# - num_workers: the number of threads to use for building the index +# - index_desc: the type of the index to build, different index may suits for fast or memory efficient building +# - output_dir: the directory to save the index + +import json +import os +from pathlib import Path +from typing import Dict, Optional, Tuple, Union + +import faiss +import h5py +import numpy as np +import scanpy as sc +from tqdm import tqdm + +PathLike = Union[str, os.PathLike] + + +class FaissIndexBuilder: + """ + Build index for similarity search using faiss. + """ + + def __init__( + self, + embedding_dir: PathLike, + output_dir: PathLike, + meta_dir: Optional[PathLike] = None, + recursive: bool = True, + embedding_file_suffix: str = ".h5ad", + meta_file_suffix: Optional[str] = None, + embedding_key: Optional[str] = None, + meta_key: Optional[str] = "cell_type", + gpu: bool = False, + num_workers: Optional[int] = None, + index_desc: str = "PCA64,IVF16384_HNSW32,PQ16", + ): + """ + Initialize an AtlasIndexBuilder object. + + Args: + embedding_dir (PathLike): Path to the directory containing the input embeddings. + output_dir (PathLike): Path to the directory where the index files will be saved. + meta_dir (PathLike, optional): Path to the directory containing the metadata files. If None, the metadata will be loaded from the embedding files. Defaults to None. + recursive (bool): Whether to search the embedding and meta directory recursively. Defaults to True. + embedding_file_suffix (str, optional): Suffix of the embedding files. Defaults to ".h5ad" in AnnData format. + meta_file_suffix (str, optional): Suffix of the metadata files. Defaults to None. + embedding_key (str, optional): Key to access the embeddings in the input files. If None, will require the input files to be in AnnData format and use the X field. Defaults to None. + meta_key (str, optional): Key to access the metadata in the input files. Defaults to "cell_type". + gpu (bool): Whether to use GPU acceleration. Defaults to False. + num_workers (int, optional): Number of threads to use for CPU parallelism. If None, will use all available cores. Defaults to None. + index_desc (str, optional): Faiss index factory str, see [here](https://github.com/facebookresearch/faiss/wiki/The-index-factory) and [here](https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index#if-1m---10m-ivf65536_hnsw32). Defaults to "PCA64,IVF16384_HNSW32,PQ16". + """ + self.embedding_dir = embedding_dir + self.output_dir = output_dir + self.meta_dir = meta_dir + self.recursive = recursive + self.embedding_file_suffix = embedding_file_suffix + self.meta_file_suffix = meta_file_suffix + self.embedding_key = embedding_key + self.meta_key = meta_key + self.gpu = gpu + self.num_workers = num_workers + self.index_desc = index_desc + + if self.num_workers is None: + try: + self.num_workers = len(os.sched_getaffinity(0)) + print("Number of available cores: {}".format(self.num_workers)) + except Exception: + self.num_workers = min(10, os.cpu_count()) + + if self.meta_dir is None: # metadata and embeddings are in the same file + self.META_FROM_EMBEDDING = True + + if self.embedding_key is None: + if embedding_file_suffix != ".h5ad": + raise ValueError( + "embedding_key is required when embedding_file_suffix is not .h5ad" + ) + + # See the index factory https://github.com/facebookresearch/faiss/wiki/Lower-memory-footprint#simplifying-index-construction + # Choose index https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index, particularly, see these options: + # - https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index#if-quite-important-then-opqm_dpqmx4fsr + # - https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index#if-quite-important-then-opqm_dpqmx4fsr + # - For the clustering option, see https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index#if-quite-important-then-opqm_dpqmx4fsr and https://gist.github.com/mdouze/46d6bbbaabca0b9778fca37ed2bcccf6 + # May choose the index option based on the benchmark here https://github.com/facebookresearch/faiss/wiki/Indexing-1G-vectors#10m-datasets + + def _load_data(self): + # Load embeddings and meta labels + embeddings = [] + meta_labels = [] + if self.META_FROM_EMBEDDING and self.embedding_file_suffix == ".h5ad": + embedding_files = ( + list(Path(self.embedding_dir).rglob("*" + self.embedding_file_suffix)) + if self.recursive + else list( + Path(self.embedding_dir).glob("*" + self.embedding_file_suffix) + ) + ) + embedding_files = [str(f) for f in embedding_files] + embedding_files = sorted(embedding_files) + + if self.num_workers > 1: + raise NotImplementedError + else: + for file in tqdm( + embedding_files, desc="Loading embeddings and metalabels" + ): + adata = sc.read(file) + # TODO: set the embedding_key according to self.embedding_key + embedding = adata.X.astype(np.float32) + if not isinstance(embedding, np.ndarray): + embedding = embedding.toarray().astype(np.float32) + meta_label = adata.obs[self.meta_key].values + embeddings.append(embedding) + meta_labels.append(meta_label) + del adata + else: + raise NotImplementedError + embeddings = np.concatenate(embeddings, axis=0, dtype=np.float32) + meta_labels = np.concatenate(meta_labels, axis=0) + + assert embeddings.shape[0] == meta_labels.shape[0] + + return embeddings, meta_labels + + def build_index(self) -> Tuple[faiss.Index, np.ndarray]: + # Load embeddings and meta labels + embeddings, meta_labels = self._load_data() + + # Build index + index = faiss.index_factory( + embeddings.shape[1], self.index_desc, faiss.METRIC_L2 + ) + nprobe = _auto_set_nprobe(index) + if self.gpu: + res = faiss.StandardGpuResources() + index = faiss.index_cpu_to_gpu(res, 0, index) + index.verbose = True + print( + f"Training index {self.index_desc} on {embeddings.shape[0]} embeddings ..." + ) + index.train(embeddings) + print("Adding embeddings to index ...") + index.add(embeddings) + + # Save index + os.makedirs(self.output_dir, exist_ok=True) + # create sub folder if the output_dir is not empty, and throw warning + if len(os.listdir(self.output_dir)) > 0: + print( + f"Warning: the output_dir {self.output_dir} is not empty, the index will be saved to a sub folder named index" + ) + self.output_dir = os.path.join(self.output_dir, "index") + assert not os.path.exists(self.output_dir) + os.makedirs(self.output_dir, exist_ok=True) + + # save index file, meta file and a json of index config params + index_file = os.path.join(self.output_dir, "index.faiss") + meta_file = os.path.join(self.output_dir, "meta.h5ad") + index_config_file = os.path.join(self.output_dir, "index_config.json") + faiss.write_index( + faiss.index_gpu_to_cpu(index) if self.gpu else index, index_file + ) + with h5py.File(meta_file, "w") as f: + f.create_dataset( + "meta_labels", data=meta_labels, compression="gzip", chunks=True + ) + with open(index_config_file, "w") as f: + json.dump( + { + "embedding_dir": self.embedding_dir, + "meta_dir": self.meta_dir, + "recursive": self.recursive, + "embedding_file_suffix": self.embedding_file_suffix, + "meta_file_suffix": self.meta_file_suffix, + "embedding_key": self.embedding_key, + "meta_key": self.meta_key, + "gpu": self.gpu, + "num_workers": self.num_workers, + "index_desc": self.index_desc, + "num_embeddings": embeddings.shape[0], + "num_features": embeddings.shape[1], + "nprobe": nprobe, + }, + f, + ) + print(f"All files saved to {self.output_dir}") + print( + f"Index saved to {index_file}, " + f"file size: {os.path.getsize(index_file) / 1024 / 1024} MB" + ) + + return index, meta_labels + + def load_index(self) -> Tuple[faiss.Index, np.ndarray]: + """ + Load the index from self.output_dir. + + Returns: + faiss.Index: The loaded index and meta labels. + """ + return load_index(self.output_dir, use_config_file=False, use_gpu=self.gpu) + + +def load_index( + index_dir: PathLike, + use_config_file=True, + use_gpu=False, + nprobe=None, +) -> Tuple[faiss.Index, np.ndarray]: + """ + Load index from disk. + + Args: + index_dir (PathLike): Path to the directory containing the index files. + use_config_file (bool, optional): Whether to load the index config file. If True, will load the index config file and use the parameters of gpu, nprobe. Defaults to True. + use_gpu (bool, optional): Whether to use GPU acceleration. Only used when use_config_file is False. Defaults to False. + nprobe (int, optional): The nprobe to set if index contains :class:`faiss.IndexIVF`. If None, will set based on the number of clusters. Only used when use_config_file is False. Defaults to None. + + Returns: + faiss.Index: The loaded index and meta labels. + """ + + index_file = os.path.join(index_dir, "index.faiss") + meta_file = os.path.join(index_dir, "meta.h5ad") + index_config_file = os.path.join(index_dir, "index_config.json") + + print(f"Loading index and meta from {index_dir} ...") + index = faiss.read_index(index_file) + print(f"Index loaded, num_embeddings: {index.ntotal}") + with h5py.File(meta_file, "r") as f: + meta_labels = f["meta_labels"].asstr()[:] + if use_config_file: + with open(index_config_file, "r") as f: + config = json.load(f) + use_gpu = config["gpu"] + nprobe = config["nprobe"] + + _auto_set_nprobe(index, nprobe=nprobe) + + if use_gpu: + res = faiss.StandardGpuResources() + index = faiss.index_cpu_to_gpu(res, 0, index) + + return index, meta_labels + + +def _auto_set_nprobe(index: faiss.Index, nprobe: int = None) -> Optional[int]: + """ + Set nprobe for IVF index based on the number of clusters. + + Args: + index (faiss.Index): The index to set nprobe. + nprobe (int, optional): The nprobe to set. If None, will set based on the number of clusters. Defaults to None. + + Returns: + int: The nprobe set. + """ + + # set nprobe if IVF index + index_ivf = faiss.try_extract_index_ivf(index) + if index_ivf: + nlist = index_ivf.nlist + ori_nprobe = index_ivf.nprobe + index_ivf.nprobe = ( + nprobe + if nprobe is not None + else 16 + if nlist <= 1e3 + else 32 + if nlist <= 4e3 + else 64 + if nlist <= 1.6e4 + else 128 + ) + print( + f"Set nprobe from {ori_nprobe} to {index_ivf.nprobe} for {nlist} clusters" + ) + return index_ivf.nprobe + + +def compute_category_proportion(meta_labels) -> Dict[str, float]: + """ + Compute the proportion of each cell type in the meta_labels, which can be used for weighted voting in the search. + + Args: + meta_labels (numpy.ndarray): A 1D array of cell type labels. + + Returns: + dict: A dictionary containing the proportion of each cell type in the input array. + """ + unique_labels, counts = np.unique(meta_labels, return_counts=True) + category_proportion = dict(zip(unique_labels, counts / counts.sum())) + return category_proportion + + +def weighted_vote( + predicts_for_query, cell_type_proportion, return_prob=True +) -> Tuple[np.ndarray, np.ndarray]: + """ + Use the proportion of each cell type as the weight for voting. + + Args: + predicts_for_query (numpy.ndarray): A 1D array of the predicted cell types for an individual query. + cell_type_proportion (dict): A dictionary containing the proportion of each cell type in the meta_labels. + return_prob (bool, optional): Whether to return the probability of each predicted cell type. Defaults to True. + + Returns: + numpy.ndarray: A 1D array of the predicted cell types for the input query, weighted by the proportion of each cell type and sorted by the proportion. + numpy.ndarray: A 1D array of the probability of each predicted cell type. Only returned when return_prob is True. + """ + unique_labels, counts = np.unique(predicts_for_query, return_counts=True) + weighted_counts = ( + np.clip(counts - 0.01 * counts.sum(), 0, None) + * 1e-3 + / np.array([cell_type_proportion[l] for l in unique_labels]) + ) # the -1 is to reduce noise + weighted_counts = weighted_counts / weighted_counts.sum() + sorted_idx = np.argsort(weighted_counts)[::-1] + predicts_for_query = unique_labels[sorted_idx] + + if return_prob: + return predicts_for_query, weighted_counts[sorted_idx] + return predicts_for_query + + +def vote(predicts_for_query, return_prob=True) -> Tuple[np.ndarray, np.ndarray]: + """ + Majority voting for the predicted cell types. + + Args: + predicts_for_query (numpy.ndarray): A 1D array of the predicted cell types for an individual query. + return_prob (bool, optional): Whether to return the probability of each predicted cell type. Defaults to True. + + Returns: + numpy.ndarray: A 1D array of the predicted cell types for the input query, weighted by the proportion of each cell type and sorted by the proportion. + numpy.ndarray: A 1D array of the probability of each predicted cell type. Only returned when return_prob is True. + """ + unique_labels, counts = np.unique(predicts_for_query, return_counts=True) + weighted_counts = counts / counts.sum() + sorted_idx = np.argsort(weighted_counts)[::-1] + predicts_for_query = unique_labels[sorted_idx] + + if return_prob: + return predicts_for_query, weighted_counts[sorted_idx] + return predicts_for_query + + +if __name__ == "__main__": + # Set options + embedding_dir = "/scratch/hdd001/home/haotian/cellxgene_cencus_embed/" + output_dir = "/scratch/hdd001/home/haotian/projects/cellxemb/normal" + embedding_file_suffix = ".h5ad" + gpu = True + index_desc = "PCA64,IVF16384_HNSW32,PQ16" + num_workers = 1 + + # Build index + builder = FaissIndexBuilder( + embedding_dir, + output_dir=output_dir, + embedding_file_suffix=embedding_file_suffix, + gpu=gpu, + num_workers=num_workers, + index_desc=index_desc, + ) + index, meta_labels = builder.build_index() diff --git a/transfer/code/scGPT/tutorials/zero-shot/README.md b/transfer/code/scGPT/tutorials/zero-shot/README.md new file mode 100644 index 0000000000000000000000000000000000000000..62233a6f9b65c659f1ada7ae2f79878420993eb3 --- /dev/null +++ b/transfer/code/scGPT/tutorials/zero-shot/README.md @@ -0,0 +1,28 @@ +# Zero-Shot Applications + +This directory contains tutorials for zero-shot applications of scGPT. We provide instructions, notebooks of best practices, and we discuss notable findings related to the zero-shot application settings. + +## Best practice and results + +We provide three notebooks: +| Description | Notebook | Colab | +| :---------- | :--- | :---- | +| Zero-shot reference mapping | [here](Tutorial_ZeroShot_Reference_Mapping.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/bowang-lab/scGPT/blob/main/tutorials/zero-shot/Tutorial_ZeroShot_Reference_Mapping.ipynb) | +| Zero-shot integration | [here](Tutorial_ZeroShot_Integration.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/bowang-lab/scGPT/blob/main/tutorials/zero-shot/Tutorial_ZeroShot_Integration.ipynb) | +| Zero-shot integration with continual pretrained model | [here](Tutorial_ZeroShot_Integration_Continual_Pretraining.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/bowang-lab/scGPT/blob/main/tutorials/zero-shot/Tutorial_ZeroShot_Integration_Continual_Pretraining.ipynb) | + +In the reference mapping and integration notebooks, we provide a step-by-step guide for running the zero-shot applications. Running with zero-shot manner is super fast and demonstrating great performance on multiple datasets. + +## New continual pretrained model for cell embedding related tasks + +In the third notebook, [Tutorial_ZeroShot_Integration_Continual_Pretraining.ipynb](Tutorial_ZeroShot_Integration_Continual_Pretraining.ipynb), we provide a new continual pretrained model for cell embedding related tasks. The model, [scGPT_CP](https://drive.google.com/drive/folders/1oWh_-ZRdhtoGQ2Fw24HP41FgLoomVo-y), inherits the pre-trained scGPT whole-human model checkpoint, and is further supervised by extra cell type labels (using the [Tabula Sapiens](https://tabula-sapiens-portal.ds.czbiohub.org/) dataset) during the continual pre-training stage. It can achieve comparable or better zero-shot performance on cell embedding related tasks compared to the original checkpoint, especially on datasets with observable technical batch effects. + + + +## Challenges and potential limitations + +Great zero-shot performance for reference mapping and integration were observed in the provided notebooks. In the same time, it is worth mentioning that the current pre-training is a purely data-driven and self-supervised procedure that optimizes the model to reconstruct gene expressions in sequenced samples. This procedure offers the benefits of avoiding potential human bias in the meta-labels and treating all raw signals in the data "equally". However, it inherently omits other requirements, for example it does not inherently include the mitigation of the technical variations introduced by sequencing protocols and devices. For this reason, we first **(1)** provide the newly continual pretrained model, scGPT_CP, which takes cell types and technical batch effects into consideration intrinsically, as demonstrated in [Tutorial_ZeroShot_Integration_Continual_Pretraining.ipynb](Tutorial_ZeroShot_Integration_Continual_Pretraining.ipynb). **(2)** We also provide a discussion on the technical batch effects and their role in the pre-training process as follows. + +In single-cell data analysis, technical batch effects indicate data distribution shifts that are driven by the sequencing procedure, rather than biological variations. The mitigation of such effects is instrumental for more accurate visualization and clustering of cell populations across multiple samples. Numerous elements contribute to the introduction of batch effects in the collation of cell atlases, including but not limited to, capturing times, personnel involved, reagent batches, types of equipment, sequencing protocols, and technological platforms. Considering the pretraining process reconstructs binned gene expressions from other observed genes, we can classify these factors into two broad types based on their influence on pretraining: (1) The type I factors are the ones that may largely have global effects on the capturing of all gene expression, such as capturing times or sequencing depth. The pre-training may mitigate these factors through repeated training on large data examples. (2) The type II factors have gene-specific influences. Some of the factors (i.e. sequencing protocols and machines) can alter the gene expression distributions. Rather than being ameliorated, these effects can be faithfully encapsulated during the pre-training process. From the standpoint of our pre-training objectives, these factors are not considered noise but important signals to the gene expression reconstruction task. + +We would argue this limitation on the mitigating of type II technical effects is a necessary tradeoff for maintaining an "unbiased" pre-training. While it may be plausible to incorporate a batch effect mitigation objective within the pre-training scheme, the added objective could compromise the original pre-training goals aimed at reconstructing biological gene expression because of the complex, often non-linear relationships between technical and biological factors. Consequently, we advocate maintaining an "unbiased" data-driven approach during pre-training, thereby offering the flexibility to prioritize specific analytical objectives in subsequent fine-tuning stages. diff --git a/transfer/code/scGPT/tutorials/zero-shot/Tutorial_ZeroShot_Integration.ipynb b/transfer/code/scGPT/tutorials/zero-shot/Tutorial_ZeroShot_Integration.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..f3075fc1715a6079d6e05834ef6ce6db83b5a360 --- /dev/null +++ b/transfer/code/scGPT/tutorials/zero-shot/Tutorial_ZeroShot_Integration.ipynb @@ -0,0 +1,981 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Zero-shot integration tutorial with scGPT" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "This tutorial covers the zero-shot integration with scGPT. This particular workflow works for scRNA-seq datasets without fine-tuning (or any extensive training) of scGPT.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will use the Lung-Kim dataset as an example. The dataset comprises 14 primary human lung adenocarcinoma samples and 32,493 cells. This dataset is publicly accessible via the [Curated Cancer Cell Atlas](https://www.weizmann.ac.il/sites/3CA/lung), which can be downloaded from [here](https://drive.google.com/file/d/1z_0vWYMhRuRiD1EyhuFtY9ReIR0msWaL/view?usp=sharing). You may place the dataset under `data` directory at the outer level.\n", + "\n", + "For scGPT model weight, we use the `scGPT_human` model to provide embeddings out of the box. You may download it from [here](https://drive.google.com/drive/folders/1oWh_-ZRdhtoGQ2Fw24HP41FgLoomVo-y).\n", + "\n", + "\n", + "The zero-shot integration workflow is as follows:\n", + "\n", + " 1. [Load and pre-process the dataset](#prepare-the-datasets)\n", + " \n", + " 2. [Generate scGPT embeddings for each cell](#generate-the-cell-embeddings)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "At the [appendix](#appendix-zero-shot-integration-on-covid-19-dataset-and-ms-dataset) of this tutorial notebook, we will also showcase the zero-shot integration performance on COVID-19 dataset and Multiple Sclerosis (M.S.) dataset, you can find and download the datasets here: [COVID-19](https://drive.google.com/file/d/1eD9LbxNJ35YUde3VtdVcjkwm-f4iyJ6x/view?usp=drive_link) and [MS](https://drive.google.com/drive/folders/1Qd42YNabzyr2pWt9xoY4cVMTAxsNBt4v?usp=sharing)\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/bowang-lab/scGPT/blob/main/tutorials/zero-shot/Tutorial_ZeroShot_Integration.ipynb)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Specifically for Google Colab, install dependencies and download data\n", + "\n", + "import os\n", + "import sys\n", + "\n", + "if \"google.colab\" in sys.modules:\n", + " print(\"Running on Google Colab\")\n", + " print(\"Installing dependencies...\")\n", + " !pip install -U scgpt \"torch<=2.2.2\" \"numpy<2\" \"umap-learn<0.5.7\"\n", + " # the optional dependency of flash-attion is skipped on colab\n", + " !pip install wandb louvain\n", + "\n", + " # NOTE: MAY NEED TO RESTART RUNTIME AFTER THE INSTALLATION\n", + "\n", + " print(\"Downloading data and model ckpt...\")\n", + " !pip install -q -U gdown\n", + " import gdown\n", + "\n", + " data_dir = \"../../data\"\n", + " if not os.path.exists(data_dir):\n", + " os.mkdir(data_dir)\n", + " if not os.path.exists(os.path.join(data_dir, \"Kim2020_Lung.h5ad\")):\n", + " !gdown https://drive.google.com/uc?id=1z_0vWYMhRuRiD1EyhuFtY9ReIR0msWaL -O $data_dir/\n", + " if not os.path.exists(os.path.join(data_dir, \"covid_subsampled.h5ad\")):\n", + " !gdown https://drive.google.com/uc?id=1eD9LbxNJ35YUde3VtdVcjkwm-f4iyJ6x -O $data_dir/\n", + " if not os.path.exists(os.path.join(data_dir, \"ms\")):\n", + " gdown.download_folder(\n", + " \"https://drive.google.com/drive/folders/1Qd42YNabzyr2pWt9xoY4cVMTAxsNBt4v\",\n", + " output=os.path.join(data_dir, \"ms\"),\n", + " )\n", + "\n", + " print(\"Downloading model ckpt...\")\n", + " model_dir = \"../../save/scGPT_human\"\n", + " if not os.path.exists(model_dir):\n", + " !mkdir -p $model_dir\n", + " gdown.download_folder(\n", + " \"https://drive.google.com/drive/folders/1oWh_-ZRdhtoGQ2Fw24HP41FgLoomVo-y\",\n", + " output=model_dir,\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import scGPT and dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "import warnings\n", + "\n", + "import scanpy as sc\n", + "import scib\n", + "import numpy as np\n", + "import sys\n", + "\n", + "sys.path.insert(0, \"../\")\n", + "\n", + "import scgpt as scg\n", + "import matplotlib.pyplot as plt\n", + "import anndata\n", + "\n", + "plt.style.context('default')\n", + "warnings.simplefilter(\"ignore\", ResourceWarning)\n", + "\n", + "model_dir = Path(\"../../save/scGPT_human\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set up evaluation function" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We set up the evaluation function, we mainly compare the integration performance on avgBIO and avgBATCH. Refer to our manuscript for more details." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "Calculate the metrics for integration results\n", + "\"\"\"\n", + "def scib_eval(adata, batch_key, cell_type_key, embed_key):\n", + " results = scib.metrics.metrics(\n", + " adata,\n", + " adata_int=adata,\n", + " batch_key=batch_key,\n", + " label_key=cell_type_key,\n", + " embed=embed_key,\n", + " isolated_labels_asw_=False,\n", + " silhouette_=True,\n", + " hvg_score_=False,\n", + " graph_conn_=True,\n", + " pcr_=True,\n", + " isolated_labels_f1_=False,\n", + " trajectory_=False,\n", + " nmi_=True, # use the clustering, bias to the best matching\n", + " ari_=True, # use the clustering, bias to the best matching\n", + " cell_cycle_=False,\n", + " kBET_=False, # kBET return nan sometimes, need to examine\n", + " ilisi_=False,\n", + " clisi_=False,\n", + " )\n", + " result_dict = results[0].to_dict()\n", + " \n", + " # compute avgBIO metrics\n", + " result_dict[\"avg_bio\"] = np.mean(\n", + " [\n", + " result_dict[\"NMI_cluster/label\"],\n", + " result_dict[\"ARI_cluster/label\"],\n", + " result_dict[\"ASW_label\"],\n", + " ]\n", + " )\n", + " \n", + " # compute avgBATCH metrics\n", + " result_dict[\"avg_batch\"] = np.mean(\n", + " [\n", + " result_dict[\"graph_conn\"],\n", + " result_dict[\"ASW_label/batch\"],\n", + " ]\n", + " )\n", + " \n", + " result_dict = {k: v for k, v in result_dict.items() if not np.isnan(v)}\n", + " \n", + " return result_dict" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prepare the datasets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load the Lung-kim dataset (download it from [here](https://drive.google.com/file/d/1z_0vWYMhRuRiD1EyhuFtY9ReIR0msWaL/view?usp=sharing)), and we set the columns storing gene name columns, batch key and cell type key (optional, this is for evaluation)." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "smaple_data_path = '../../data/Kim2020_Lung.h5ad'\n", + "adata = sc.read_h5ad(smaple_data_path)\n", + "\n", + "gene_col = \"gene_name\"\n", + "cell_type_key = \"cell_type\"\n", + "batch_key = \"sample\"\n", + "N_HVG = 3000" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Remove unannotated cells:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "celltype_id_labels = adata.obs[cell_type_key].astype(\"category\").cat.codes.values\n", + "adata = adata[celltype_id_labels >= 0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Make a copy of the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "org_adata = adata.copy()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Preprocess the dataset and select `N_HVG` highly variable genes for downstream analysis." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/h/haotian/.conda/envs/scgpt/lib/python3.7/site-packages/anndata/compat/_overloaded_dict.py:106: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.\n", + " self.data[key] = value\n" + ] + } + ], + "source": [ + "# highly variable genes\n", + "sc.pp.highly_variable_genes(adata, n_top_genes=N_HVG, flavor='seurat_v3')\n", + "adata = adata[:, adata.var['highly_variable']]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate the cell embeddings" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we will generate the cell embeddings for the dataset using `embed_data` function. `embed_data` calculates the cell embedding for each cell with the given scGPT model. The extracted embedding is stored in the `X_scGPT` field of `obsm` in AnnData." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/h/haotian/.conda/envs/scgpt/lib/python3.7/site-packages/scgpt/tasks/cell_emb.py:213: ImplicitModificationWarning: Trying to modify attribute `.var` of view, initializing view as actual.\n", + " vocab[gene] if gene in vocab else -1 for gene in adata.var[gene_col]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "scGPT - INFO - match 2978/3000 genes in vocabulary of size 60697.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Embedding cells: 100%|██████████| 477/477 [00:19<00:00, 24.31it/s]\n" + ] + } + ], + "source": [ + "embed_adata = scg.tasks.embed_data(\n", + " adata,\n", + " model_dir,\n", + " gene_col=gene_col,\n", + " batch_size=64,\n", + ")\n", + "# attach the cell embedding to the original adata" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualize the integration performance" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "UMAP of scGPT embedding colored by cell type:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sc.pp.neighbors(embed_adata, use_rep=\"X_scGPT\")\n", + "sc.tl.umap(embed_adata)\n", + "sc.pl.umap(embed_adata, \n", + " color=[cell_type_key, batch_key], \n", + " frameon=False, \n", + " wspace=0.4, \n", + " title=[\"scGPT zero-shot: cell type\", \"scGPT zero-shot: batch label\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Evaluate the integration performance" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NMI...\n", + "ARI...\n", + "Silhouette score...\n", + "PC regression...\n", + "Variance contribution increased after integration!\n", + "Setting PCR comparison score to 0.\n", + "Graph connectivity...\n" + ] + } + ], + "source": [ + "scib_result_dict = scib_eval(\n", + " embed_adata,\n", + " batch_key=batch_key,\n", + " cell_type_key=cell_type_key,\n", + " embed_key=\"X_scGPT\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AvgBIO: 0.7752\n", + "AvgBATCH: 0.9500\n" + ] + } + ], + "source": [ + "print(\"AvgBIO: {:.4f}\".format(scib_result_dict[\"avg_bio\"]))\n", + "print(\"AvgBATCH: {:.4f}\".format(scib_result_dict[\"avg_batch\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Comparison with HVG+PCs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We compute the HVG and PCs based on the dataset, this will serve as a baseline for the integration performance." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "adata = org_adata.copy()\n", + "sc.pp.highly_variable_genes(adata, n_top_genes=N_HVG, flavor='seurat_v3')\n", + "adata = adata[:, adata.var['highly_variable']]\n", + "sc.pp.pca(adata, n_comps=40)\n", + "sc.pp.neighbors(adata, use_rep=\"X_pca\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "UMAP of PCs colored by cell types and batch labels:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sc.pp.neighbors(adata, use_rep=\"X_pca\")\n", + "sc.tl.umap(adata)\n", + "sc.pl.umap(adata, \n", + " color=[cell_type_key, batch_key], \n", + " frameon=False, \n", + " wspace=0.4, \n", + " title=[\"HVG+PCs: cell type\", \"HVG+PCs: batch label\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NMI...\n", + "ARI...\n", + "Silhouette score...\n", + "PC regression...\n", + "Graph connectivity...\n" + ] + } + ], + "source": [ + "scib_result_dict = scib_eval(\n", + " adata,\n", + " batch_key=batch_key,\n", + " cell_type_key=cell_type_key,\n", + " embed_key=\"X_pca\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AvgBIO: 0.6789\n", + "AvgBATCH: 0.8084\n" + ] + } + ], + "source": [ + "print(\"AvgBIO: {:.4f}\".format(scib_result_dict[\"avg_bio\"]))\n", + "print(\"AvgBATCH: {:.4f}\".format(scib_result_dict[\"avg_batch\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Appendix: Zero-shot integration on COVID-19 dataset and MS dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### COVID-19 dataset\n", + "\n", + "The dataset can be accessed form [here](https://drive.google.com/file/d/1eD9LbxNJ35YUde3VtdVcjkwm-f4iyJ6x/view?usp=drive_link)." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "scGPT - INFO - match 1173/1200 genes in vocabulary of size 60697.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Embedding cells: 100%|██████████| 313/313 [00:07<00:00, 43.30it/s]\n" + ] + } + ], + "source": [ + "# load dataset\n", + "adata = sc.read_h5ad('../../data/covid_subsampled.h5ad')\n", + "gene_col = \"gene_name\"\n", + "cell_type_key = \"celltype\"\n", + "batch_key = \"str_batch\"\n", + "embed_adata = scg.tasks.embed_data(\n", + " adata,\n", + " model_dir,\n", + " gene_col=gene_col,\n", + " batch_size=64,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sc.pp.neighbors(embed_adata, use_rep=\"X_scGPT\")\n", + "sc.tl.umap(embed_adata)\n", + "sc.pl.umap(embed_adata, \n", + " color=[cell_type_key], \n", + " frameon=False, \n", + " title=[\"COVID19 - scGPT zero-shot: cell type\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sc.pl.umap(embed_adata, \n", + " color=[batch_key], \n", + " frameon=False, \n", + " title=[\"COVID19 - scGPT zero-shot: batch label\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: cluster key cluster already exists in adata.obs and will be overwritten\n", + "NMI...\n", + "ARI...\n", + "Silhouette score...\n", + "PC regression...\n", + "Variance contribution increased after integration!\n", + "Setting PCR comparison score to 0.\n", + "Graph connectivity...\n", + "AvgBIO: 0.4888\n", + "AvgBATCH: 0.8741\n" + ] + } + ], + "source": [ + "scib_result_dict = scib_eval(\n", + " embed_adata,\n", + " batch_key=batch_key,\n", + " cell_type_key=cell_type_key,\n", + " embed_key=\"X_scGPT\",\n", + ")\n", + "print(\"AvgBIO: {:.4f}\".format(scib_result_dict[\"avg_bio\"]))\n", + "print(\"AvgBATCH: {:.4f}\".format(scib_result_dict[\"avg_batch\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Comparison with HVG+PCs" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "sc.pp.pca(adata, n_comps=40)\n", + "sc.pp.neighbors(adata, use_rep=\"X_pca\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sc.pp.neighbors(adata, use_rep=\"X_pca\")\n", + "sc.tl.umap(adata)\n", + "sc.pl.umap(\n", + " adata,\n", + " color=[cell_type_key, batch_key],\n", + " frameon=False,\n", + " wspace=0.4,\n", + " title=[\"HVG+PCs: cell type\", \"HVG+PCs: batch label\"],\n", + " ncols=1,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: cluster key cluster already exists in adata.obs and will be overwritten\n", + "NMI...\n", + "ARI...\n", + "Silhouette score...\n", + "PC regression...\n", + "Graph connectivity...\n", + "AvgBIO: 0.3804\n", + "AvgBATCH: 0.6743\n" + ] + } + ], + "source": [ + "scib_result_dict = scib_eval(\n", + " adata,\n", + " batch_key=batch_key,\n", + " cell_type_key=cell_type_key,\n", + " embed_key=\"X_pca\",\n", + ")\n", + "print(\"AvgBIO: {:.4f}\".format(scib_result_dict[\"avg_bio\"]))\n", + "print(\"AvgBATCH: {:.4f}\".format(scib_result_dict[\"avg_batch\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### MS dataset\n", + "\n", + "The dataset can be accessed form [here](https://drive.google.com/drive/folders/1Qd42YNabzyr2pWt9xoY4cVMTAxsNBt4v?usp=sharing). As this is a preprocessed dataset, we will need to concatenate the training/test datasets together first." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/h/haotian/.conda/envs/scgpt/lib/python3.7/site-packages/anndata/_core/anndata.py:121: ImplicitModificationWarning: Transforming to str index.\n", + " warnings.warn(\"Transforming to str index.\", ImplicitModificationWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "scGPT - INFO - match 2808/3000 genes in vocabulary of size 60697.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Embedding cells: 100%|██████████| 333/333 [00:12<00:00, 26.59it/s]\n" + ] + } + ], + "source": [ + "gene_col = \"gene_name\"\n", + "cell_type_key = \"celltype\"\n", + "batch_key = \"Sample Characteristic[individual]\"\n", + "\n", + "train_data = sc.read_h5ad('../../data/ms/c_data.h5ad')\n", + "test_data = sc.read_h5ad('../../data/ms/filtered_ms_adata.h5ad')\n", + "test_data.var.index = test_data.var[gene_col]\n", + "adata = anndata.concat([train_data, test_data], join='outer')\n", + "adata.var[gene_col] = adata.var.index\n", + "\n", + "embed_adata = scg.tasks.embed_data(\n", + " adata,\n", + " model_dir,\n", + " gene_col=gene_col,\n", + " batch_size=64,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sc.pp.neighbors(embed_adata, use_rep=\"X_scGPT\")\n", + "sc.tl.umap(embed_adata)\n", + "sc.pl.umap(embed_adata, \n", + " color=[cell_type_key], \n", + " frameon=False, \n", + " title=[\"MS - scGPT zero-shot: cell type\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sc.pp.neighbors(embed_adata, use_rep=\"X_scGPT\")\n", + "sc.tl.umap(embed_adata)\n", + "sc.pl.umap(embed_adata, \n", + " color=[batch_key], \n", + " frameon=False, \n", + " title=[\"MS - scGPT zero-shot: batch label\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NMI...\n", + "ARI...\n", + "Silhouette score...\n", + "PC regression...\n", + "Graph connectivity...\n", + "AvgBIO: 0.4525\n", + "AvgBATCH: 0.9129\n" + ] + } + ], + "source": [ + "scib_result_dict = scib_eval(\n", + " embed_adata,\n", + " batch_key=batch_key,\n", + " cell_type_key=cell_type_key,\n", + " embed_key=\"X_scGPT\",\n", + ")\n", + "print(\"AvgBIO: {:.4f}\".format(scib_result_dict[\"avg_bio\"]))\n", + "print(\"AvgBATCH: {:.4f}\".format(scib_result_dict[\"avg_batch\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Comparison with HVG+PCs" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "sc.pp.pca(adata, n_comps=40)\n", + "sc.pp.neighbors(adata, use_rep=\"X_pca\")" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sc.pp.neighbors(adata, use_rep=\"X_pca\")\n", + "sc.tl.umap(adata)\n", + "sc.pl.umap(\n", + " adata,\n", + " color=[cell_type_key, batch_key],\n", + " frameon=False,\n", + " wspace=0.4,\n", + " title=[\"HVG+PCs: cell type\", \"HVG+PCs: batch label\"],\n", + " ncols=1,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NMI...\n", + "ARI...\n", + "Silhouette score...\n", + "PC regression...\n", + "Graph connectivity...\n", + "AvgBIO: 0.5062\n", + "AvgBATCH: 0.9075\n" + ] + } + ], + "source": [ + "scib_result_dict = scib_eval(\n", + " adata,\n", + " batch_key=batch_key,\n", + " cell_type_key=cell_type_key,\n", + " embed_key=\"X_pca\",\n", + ")\n", + "print(\"AvgBIO: {:.4f}\".format(scib_result_dict[\"avg_bio\"]))\n", + "print(\"AvgBATCH: {:.4f}\".format(scib_result_dict[\"avg_batch\"]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "scgpt-o4JycL9C-py3.9", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/transfer/code/scGPT/tutorials/zero-shot/Tutorial_ZeroShot_Integration_Continual_Pretraining.ipynb b/transfer/code/scGPT/tutorials/zero-shot/Tutorial_ZeroShot_Integration_Continual_Pretraining.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..20b5cb25f79a2c6d854944cea0fc4d3a107dc2f6 --- /dev/null +++ b/transfer/code/scGPT/tutorials/zero-shot/Tutorial_ZeroShot_Integration_Continual_Pretraining.ipynb @@ -0,0 +1,676 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Zero-shot integration tutorial with scGPT" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "This tutorial covers the zero-shot integration with continual pre-trained scGPT. This particular workflow works for scRNA-seq datasets without fine-tuning (or any extensive training) of scGPT.\n", + "\n", + "Continual pre-trained scGPT (scGPT_CP) is a model that inherits the pre-trained scGPT whole-human model checkpoint, and is further supervised by extra cell type labels (using the [Tabula Sapiens](https://tabula-sapiens-portal.ds.czbiohub.org/) dataset) during the continual pre-training stage. We observed that the scGPT_CP model can achieve comparable or better zero-shot performance on cell embedding related tasks compared to the original checkpoint, especially on datasets with observable technical batch effects.\n", + "\n", + "This tutorial will show how to use the latent space of scGPT to integrate scRNA-seq datasets. We use the `scGPT_CP` model to provide embeddings out of the box. You may download it from [here](https://drive.google.com/drive/folders/1_GROJTzXiAV8HB4imruOTk6PEGuNOcgB)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will use the [scIB](https://www.nature.com/articles/s41592-021-01336-8) pancreas dataset as an example. This dataset is publicly accessible via [here](https://figshare.com/ndownloader/files/24539828). You may place the dataset under `data` directory at the outer level.\n", + "\n", + "\n", + "The zero-shot integration workflow is as follows:\n", + "\n", + " 1. [Load and pre-process the dataset](#prepare-the-datasets)\n", + " \n", + " 2. [Generate scGPT embeddings for each cell](#generate-the-cell-embeddings)\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/bowang-lab/scGPT/blob/main/tutorials/zero-shot/Tutorial_ZeroShot_Integration_Continual_Pretraining.ipynb)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Specifically for Google Colab, install dependencies and download data\n", + "\n", + "import os\n", + "import sys\n", + "\n", + "if \"google.colab\" in sys.modules:\n", + " print(\"Running on Google Colab\")\n", + " print(\"Installing dependencies...\")\n", + " !pip install -U scgpt \"torch<=2.2.2\" \"numpy<2\" \"umap-learn<0.5.7\"\n", + " # the optional dependency of flash-attion is skipped on colab\n", + " !pip install wandb louvain\n", + "\n", + " # NOTE: MAY NEED TO RESTART RUNTIME AFTER THE INSTALLATION\n", + "\n", + " print(\"Downloading data and model ckpt...\")\n", + " !pip install -q -U gdown\n", + " import gdown\n", + "\n", + " data_dir = \"../../data\"\n", + " if not os.path.exists(data_dir):\n", + " os.mkdir(data_dir)\n", + " if not os.path.exists(os.path.join(data_dir, \"human_pancreas_norm_complexBatch.h5ad\")):\n", + " !wget --content-disposition https://figshare.com/ndownloader/files/24539828 -O $data_dir/human_pancreas_norm_complexBatch.h5ad\n", + "\n", + " print(\"Downloading model ckpt...\")\n", + " model_dir = \"../../save/scGPT_CP\"\n", + " if not os.path.exists(model_dir):\n", + " !mkdir -p $model_dir\n", + " gdown.download_folder(\n", + " \"https://drive.google.com/drive/folders/1_GROJTzXiAV8HB4imruOTk6PEGuNOcgB?usp=sharing\",\n", + " output=model_dir,\n", + " )\n", + " model_dir = \"../../save/scGPT_human\"\n", + " if not os.path.exists(model_dir):\n", + " !mkdir -p $model_dir\n", + " gdown.download_folder(\n", + " \"https://drive.google.com/drive/folders/1oWh_-ZRdhtoGQ2Fw24HP41FgLoomVo-y\",\n", + " output=model_dir,\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import scGPT and dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "import warnings\n", + "\n", + "import scanpy as sc\n", + "import scib\n", + "import numpy as np\n", + "import sys\n", + "\n", + "sys.path.insert(0, \"../\")\n", + "\n", + "import scgpt as scg\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plt.style.context('default')\n", + "warnings.simplefilter(\"ignore\", ResourceWarning)\n", + "\n", + "model_dir = Path(\"../../save/scGPT_CP\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set up evaluation function" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We set up the evaluation function, we mainly compare the integration performance on avgBIO and avgBATCH. Refer to our manuscript for more details." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "Calculate the metrics for integration results\n", + "\"\"\"\n", + "def scib_eval(adata, batch_key, cell_type_key, embed_key):\n", + " results = scib.metrics.metrics(\n", + " adata,\n", + " adata_int=adata,\n", + " batch_key=batch_key,\n", + " label_key=cell_type_key,\n", + " embed=embed_key,\n", + " isolated_labels_asw_=False,\n", + " silhouette_=True,\n", + " hvg_score_=False,\n", + " graph_conn_=True,\n", + " pcr_=True,\n", + " isolated_labels_f1_=False,\n", + " trajectory_=False,\n", + " nmi_=True, # use the clustering, bias to the best matching\n", + " ari_=True, # use the clustering, bias to the best matching\n", + " cell_cycle_=False,\n", + " kBET_=False, # kBET return nan sometimes, need to examine\n", + " ilisi_=False,\n", + " clisi_=False,\n", + " )\n", + " result_dict = results[0].to_dict()\n", + " \n", + " # compute avgBIO metrics\n", + " result_dict[\"avg_bio\"] = np.mean(\n", + " [\n", + " result_dict[\"NMI_cluster/label\"],\n", + " result_dict[\"ARI_cluster/label\"],\n", + " result_dict[\"ASW_label\"],\n", + " ]\n", + " )\n", + " \n", + " # compute avgBATCH metrics\n", + " result_dict[\"avg_batch\"] = np.mean(\n", + " [\n", + " result_dict[\"graph_conn\"],\n", + " result_dict[\"ASW_label/batch\"],\n", + " ]\n", + " )\n", + " \n", + " result_dict = {k: v for k, v in result_dict.items() if not np.isnan(v)}\n", + " \n", + " return result_dict" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prepare the datasets" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load the Pancreas dataset (download it from [here](https://figshare.com/ndownloader/files/24539828)), and we set the columns storing gene name columns, batch key and cell type key (optional, this is for evaluation)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/h/haotian/.conda/envs/scgpt/lib/python3.7/site-packages/anndata/_io/h5ad.py:238: OldFormatWarning: Element '/X' was written without encoding metadata.\n", + " d[k] = read_elem(f[k])\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/h/haotian/.conda/envs/scgpt/lib/python3.7/site-packages/anndata/_io/h5ad.py:238: OldFormatWarning: Element '/layers' was written without encoding metadata.\n", + " d[k] = read_elem(f[k])\n", + "/h/haotian/.conda/envs/scgpt/lib/python3.7/site-packages/anndata/_io/specs/methods.py:92: OldFormatWarning: Element '/layers/counts' was written without encoding metadata.\n", + " return {k: read_elem(v) for k, v in elem.items()}\n", + "/h/haotian/.conda/envs/scgpt/lib/python3.7/site-packages/anndata/_io/specs/methods.py:584: OldFormatWarning: Element '/obs/__categories/tech' was written without encoding metadata.\n", + " categories = read_elem(categories_dset)\n", + "/h/haotian/.conda/envs/scgpt/lib/python3.7/site-packages/anndata/_io/specs/methods.py:587: OldFormatWarning: Element '/obs/tech' was written without encoding metadata.\n", + " read_elem(dataset), categories, ordered=ordered\n", + "/h/haotian/.conda/envs/scgpt/lib/python3.7/site-packages/anndata/_io/specs/methods.py:584: OldFormatWarning: Element '/obs/__categories/celltype' was written without encoding metadata.\n", + " categories = read_elem(categories_dset)\n", + "/h/haotian/.conda/envs/scgpt/lib/python3.7/site-packages/anndata/_io/specs/methods.py:587: OldFormatWarning: Element '/obs/celltype' was written without encoding metadata.\n", + " read_elem(dataset), categories, ordered=ordered\n", + "/h/haotian/.conda/envs/scgpt/lib/python3.7/site-packages/anndata/_io/specs/methods.py:590: OldFormatWarning: Element '/obs/size_factors' was written without encoding metadata.\n", + " return read_elem(dataset)\n", + "/h/haotian/.conda/envs/scgpt/lib/python3.7/site-packages/anndata/_io/specs/methods.py:590: OldFormatWarning: Element '/obs/_index' was written without encoding metadata.\n", + " return read_elem(dataset)\n", + "/h/haotian/.conda/envs/scgpt/lib/python3.7/site-packages/anndata/_io/specs/methods.py:590: OldFormatWarning: Element '/var/_index' was written without encoding metadata.\n", + " return read_elem(dataset)\n" + ] + } + ], + "source": [ + "smaple_data_path = '../../data/human_pancreas_norm_complexBatch.h5ad'\n", + "adata = sc.read_h5ad(smaple_data_path)\n", + "\n", + "gene_col = \"Gene Symbol\"\n", + "cell_type_key = \"celltype\"\n", + "batch_key = \"tech\"\n", + "N_HVG = 1800" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "adata.var[gene_col] = adata.var.index.values" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Make a copy of the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "org_adata = adata.copy()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Preprocess the dataset and select `N_HVG` highly variable genes for downstream analysis." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/h/haotian/.conda/envs/scgpt/lib/python3.7/site-packages/scanpy/preprocessing/_highly_variable_genes.py:64: UserWarning: `flavor='seurat_v3'` expects raw count data, but non-integers were found.\n", + " UserWarning,\n" + ] + } + ], + "source": [ + "# preprocess\n", + "sc.pp.normalize_total(adata, target_sum=1e4)\n", + "sc.pp.log1p(adata)\n", + "# highly variable genes\n", + "sc.pp.highly_variable_genes(adata, n_top_genes=N_HVG, flavor='seurat_v3')\n", + "adata = adata[:, adata.var['highly_variable']]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generate the cell embeddings" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we will generate the cell embeddings for the dataset using `embed_data` function. `embed_data` calculates the cell embedding for each cell with the given scGPT model. The extracted embedding is stored in the `X_scGPT` field of `obsm` in AnnData." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/h/haotian/.conda/envs/scgpt/lib/python3.7/site-packages/scgpt/tasks/cell_emb.py:213: ImplicitModificationWarning: Trying to modify attribute `.var` of view, initializing view as actual.\n", + " vocab[gene] if gene in vocab else -1 for gene in adata.var[gene_col]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "scGPT - INFO - match 1722/1800 genes in vocabulary of size 60697.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Embedding cells: 100%|██████████| 256/256 [00:06<00:00, 38.58it/s]\n" + ] + } + ], + "source": [ + "embed_adata = scg.tasks.embed_data(\n", + " adata,\n", + " model_dir,\n", + " gene_col=gene_col,\n", + " batch_size=64,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualize the integration performance" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "UMAP of scGPT embedding colored by cell type:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sc.pp.neighbors(embed_adata, use_rep=\"X_scGPT\")\n", + "sc.tl.umap(embed_adata)\n", + "sc.pl.umap(embed_adata, \n", + " color=[cell_type_key, batch_key], \n", + " frameon=False, \n", + " wspace=0.4, \n", + " title=[\"scGPT zero-shot: cell type\", \"scGPT zero-shot: batch label\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Evaluate the integration performance" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NMI...\n", + "ARI...\n", + "Silhouette score...\n", + "PC regression...\n", + "Graph connectivity...\n" + ] + } + ], + "source": [ + "scib_result_dict = scib_eval(\n", + " embed_adata,\n", + " batch_key=batch_key,\n", + " cell_type_key=cell_type_key,\n", + " embed_key=\"X_scGPT\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AvgBIO: 0.6348\n", + "AvgBATCH: 0.9236\n" + ] + } + ], + "source": [ + "print(\"AvgBIO: {:.4f}\".format(scib_result_dict[\"avg_bio\"]))\n", + "print(\"AvgBATCH: {:.4f}\".format(scib_result_dict[\"avg_batch\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Comparison with the original scGPT model checkpoint" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "scGPT - INFO - match 1722/1800 genes in vocabulary of size 60697.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Embedding cells: 100%|██████████| 256/256 [00:05<00:00, 50.57it/s]\n" + ] + } + ], + "source": [ + "embed_adata = scg.tasks.embed_data(\n", + " adata,\n", + " \"../../save/scGPT_human\",\n", + " gene_col=gene_col,\n", + " batch_size=64,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sc.pp.neighbors(embed_adata, use_rep=\"X_scGPT\")\n", + "sc.tl.umap(embed_adata)\n", + "sc.pl.umap(embed_adata, \n", + " color=[cell_type_key, batch_key], \n", + " frameon=False, \n", + " wspace=0.4, \n", + " title=[\"scGPT zero-shot: cell type\", \"scGPT zero-shot: batch label\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NMI...\n", + "ARI...\n", + "Silhouette score...\n", + "PC regression...\n", + "Variance contribution increased after integration!\n", + "Setting PCR comparison score to 0.\n", + "Graph connectivity...\n", + "AvgBIO: 0.5299\n", + "AvgBATCH: 0.9012\n" + ] + } + ], + "source": [ + "scib_result_dict = scib_eval(\n", + " embed_adata,\n", + " batch_key=batch_key,\n", + " cell_type_key=cell_type_key,\n", + " embed_key=\"X_scGPT\",\n", + ")\n", + "print(\"AvgBIO: {:.4f}\".format(scib_result_dict[\"avg_bio\"]))\n", + "print(\"AvgBATCH: {:.4f}\".format(scib_result_dict[\"avg_batch\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Comparison with HVG+PCs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We compute the HVG and PCs based on the dataset, this will serve as a baseline for the integration performance." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/h/haotian/.conda/envs/scgpt/lib/python3.7/site-packages/scanpy/preprocessing/_highly_variable_genes.py:64: UserWarning: `flavor='seurat_v3'` expects raw count data, but non-integers were found.\n", + " UserWarning,\n" + ] + } + ], + "source": [ + "adata = org_adata.copy()\n", + "sc.pp.highly_variable_genes(adata, n_top_genes=N_HVG, flavor='seurat_v3')\n", + "adata = adata[:, adata.var['highly_variable']]\n", + "sc.pp.pca(adata, n_comps=40)\n", + "sc.pp.neighbors(adata, use_rep=\"X_pca\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "UMAP of PCs colored by cell types and batch labels:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sc.pp.neighbors(adata, use_rep=\"X_pca\")\n", + "sc.tl.umap(adata)\n", + "sc.pl.umap(adata, \n", + " color=[cell_type_key, batch_key], \n", + " frameon=False, \n", + " wspace=0.4, \n", + " title=[\"HVG+PCs: cell type\", \"HVG+PCs: batch label\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NMI...\n", + "ARI...\n", + "Silhouette score...\n", + "PC regression...\n", + "Graph connectivity...\n" + ] + } + ], + "source": [ + "scib_result_dict = scib_eval(\n", + " adata,\n", + " batch_key=batch_key,\n", + " cell_type_key=cell_type_key,\n", + " embed_key=\"X_pca\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AvgBIO: 0.5401\n", + "AvgBATCH: 0.8823\n" + ] + } + ], + "source": [ + "print(\"AvgBIO: {:.4f}\".format(scib_result_dict[\"avg_bio\"]))\n", + "print(\"AvgBATCH: {:.4f}\".format(scib_result_dict[\"avg_batch\"]))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "scgpt-o4JycL9C-py3.9", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/transfer/code/scGPT/tutorials/zero-shot/Tutorial_ZeroShot_Reference_Mapping.ipynb b/transfer/code/scGPT/tutorials/zero-shot/Tutorial_ZeroShot_Reference_Mapping.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..2071dbefbcc3f050aa571ac07b7dad7bb3ea11a9 --- /dev/null +++ b/transfer/code/scGPT/tutorials/zero-shot/Tutorial_ZeroShot_Reference_Mapping.ipynb @@ -0,0 +1,705 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Zero-shot reference mapping tutorial with scGPT" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "This tutorial covers the zero-shot reference mapping with scGPT. This workflow achieves accurate and fast reference mapping for scRNA-seq datasets without fine-tuning (or any extensive training) of scGPT. To further boost the performance, we recommend fine-tuning scGPT.\n", + "\n", + "We will use COVID-19 dataset to demonstrate the zero-shot reference mapping. You can download the processed reference and query datasets from [here](https://drive.google.com/drive/folders/1jSPoPunGQOmd71vDsK0FS7UvmDhGdhQS?usp=sharing). The COVID-19 dataset is derived from the work by [Lotfollahi et al](https://www.nature.com/articles/s41587-021-01001-7), which contains 18 distinct batches and diverse samples from lung tissues. The reference dataset consists of 15,997 cells and the query dataset contains 4,003 cells. You may place the dataset under `data` directory in the outer level.\n", + "\n", + "Particularly, we use the `scGPT_human` model to provide embeddings out of the box. You may download it from [here](https://drive.google.com/drive/folders/1oWh_-ZRdhtoGQ2Fw24HP41FgLoomVo-y). \n", + "\n", + "\n", + "The zero-shot reference mapping workflow is as follows:\n", + "\n", + " 1. Load and pre-process the dataset\n", + " \n", + " 2. Generate scGPT embeddings for each cell in reference and query datasets\n", + "\n", + " 3. Transfer the annotations from reference to query dataset\n", + "\n", + "At the [appendix](#appendix-reference-mapping-on-lung-kim-dataset) of this tutorial, we will also showcase the zero-shot reference mapping on Lung dataset. You can find the dataset [here](https://drive.google.com/drive/folders/1gbfO7VqxCOkfzgHAih6hO88zFv6pd8wO?usp=sharing).\n", + "\n", + "\n", + "We use a similarity-based method for transferring the annotation, which involves comparing the similarity between the query cell embedding and the reference cell embeddings. We use [FAISS](https://github.com/facebookresearch/faiss) to perform the similarity search.\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/bowang-lab/scGPT/blob/main/tutorials/zero-shot/Tutorial_ZeroShot_Reference_Mapping.ipynb)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Specifically for Google Colab, install dependencies and download data\n", + "\n", + "import os\n", + "import sys\n", + "\n", + "if \"google.colab\" in sys.modules:\n", + " print(\"Running on Google Colab\")\n", + " print(\"Installing dependencies...\")\n", + " !pip install -U scgpt \"torch<=2.2.2\" \"numpy<2\" \"umap-learn<0.5.7\"\n", + " # the optional dependency of flash-attion is skipped on colab\n", + " !pip install wandb louvain faiss-cpu\n", + "\n", + " # NOTE: MAY NEED TO RESTART RUNTIME AFTER THE INSTALLATION\n", + "\n", + " print(\"Downloading data and model ckpt...\")\n", + " !pip install -q -U gdown\n", + " import gdown\n", + "\n", + " data_dir = \"../../data\"\n", + " if not os.path.exists(data_dir):\n", + " os.mkdir(data_dir)\n", + " if not os.path.exists(os.path.join(data_dir, \"covid\")):\n", + " gdown.download_folder(\n", + " \"https://drive.google.com/drive/folders/1jSPoPunGQOmd71vDsK0FS7UvmDhGdhQS\",\n", + " output=os.path.join(data_dir),\n", + " )\n", + " if not os.path.exists(os.path.join(data_dir, \"lung\")):\n", + " gdown.download_folder(\n", + " \"https://drive.google.com/drive/folders/1gbfO7VqxCOkfzgHAih6hO88zFv6pd8wO\",\n", + " output=os.path.join(data_dir),\n", + " )\n", + "\n", + " print(\"Downloading model ckpt...\")\n", + " model_dir = \"../../save/scGPT_human\"\n", + " if not os.path.exists(model_dir):\n", + " !mkdir -p $model_dir\n", + " gdown.download_folder(\n", + " \"https://drive.google.com/drive/folders/1oWh_-ZRdhtoGQ2Fw24HP41FgLoomVo-y\",\n", + " output=model_dir,\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import scGPT and dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Error: package or namespace load failed for ‘stats’ in dyn.load(file, DLLpath = DLLpath, ...):\n", + " unable to load shared object '/fs01/pkgs/R-3.6.1/lib/R/library/stats/libs/stats.so':\n", + " libRlapack.so: cannot open shared object file: No such file or directory\n", + "During startup - Warning message:\n", + "package ‘stats’ in options(\"defaultPackages\") was not found \n" + ] + } + ], + "source": [ + "from pathlib import Path\n", + "import numpy as np\n", + "from scipy.stats import mode\n", + "import scanpy as sc\n", + "import warnings\n", + "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n", + "from sklearn.metrics import confusion_matrix\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "import sys\n", + "\n", + "sys.path.insert(0, \"../\")\n", + "\n", + "import scgpt as scg\n", + "\n", + "# extra dependency for similarity search\n", + "try:\n", + " import faiss\n", + "\n", + " faiss_imported = True\n", + "except ImportError:\n", + " faiss_imported = False\n", + " print(\n", + " \"faiss not installed! We highly recommend installing it for fast similarity search.\"\n", + " )\n", + " print(\"To install it, see https://github.com/facebookresearch/faiss/wiki/Installing-Faiss\")\n", + "\n", + "warnings.filterwarnings(\"ignore\", category=ResourceWarning)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load the dataset, you may download the dataset from [here](https://drive.google.com/drive/folders/1jSPoPunGQOmd71vDsK0FS7UvmDhGdhQS?usp=sharing). We set the columns storing gene name columns, batch key and cell type key." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "model_dir = Path(\"../../save/scGPT_human\")\n", + "adata = sc.read_h5ad('../../data/covid/batch_covid_subsampled_train.h5ad')\n", + "test_adata = sc.read_h5ad('../../data/covid/batch_covid_subsampled_test.h5ad')\n", + "\n", + "cell_type_key = \"celltype\"\n", + "gene_col = \"gene_name\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Embed the reference dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "scGPT - INFO - match 1173/1200 genes in vocabulary of size 60697.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Embedding cells: 100%|██████████| 250/250 [00:05<00:00, 42.40it/s]\n" + ] + } + ], + "source": [ + "ref_embed_adata = scg.tasks.embed_data(\n", + " adata,\n", + " model_dir,\n", + " gene_col=gene_col,\n", + " batch_size=64,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Embed the query dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "scGPT - INFO - match 1173/1200 genes in vocabulary of size 60697.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Embedding cells: 100%|██████████| 63/63 [00:02<00:00, 31.16it/s]\n" + ] + } + ], + "source": [ + "test_embed_adata = scg.tasks.embed_data(\n", + " test_adata,\n", + " model_dir,\n", + " gene_col=gene_col,\n", + " batch_size=64,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Mark the reference vs. query cells and mask the cell types on query cells" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/h/haotian/.conda/envs/scgpt/lib/python3.7/site-packages/anndata/_core/anndata.py:1785: FutureWarning: X.dtype being converted to np.float32 from float64. In the next version of anndata (0.9) conversion will not be automatic. Pass dtype explicitly to avoid this warning. Pass `AnnData(X, dtype=X.dtype, ...)` to get the future behavour.\n", + " [AnnData(sparse.csr_matrix(a.shape), obs=a.obs) for a in all_adatas],\n" + ] + } + ], + "source": [ + "# concatenate the two datasets\n", + "adata_concat = test_embed_adata.concatenate(ref_embed_adata, batch_key=\"dataset\")\n", + "# mark the reference vs. query dataset\n", + "adata_concat.obs[\"is_ref\"] = [\"Query\"] * len(test_embed_adata) + [\"Reference\"] * len(\n", + " ref_embed_adata\n", + ")\n", + "adata_concat.obs[\"is_ref\"] = adata_concat.obs[\"is_ref\"].astype(\"category\")\n", + "# mask the query dataset cell types\n", + "adata_concat.obs[cell_type_key] = adata_concat.obs[cell_type_key].astype(\"category\")\n", + "adata_concat.obs[cell_type_key] = adata_concat.obs[cell_type_key].cat.add_categories([\"To be predicted\"])\n", + "adata_concat.obs[cell_type_key][: len(test_embed_adata)] = \"To be predicted\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualize the embeddings" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We visualize the embeddings from query and reference datasets using UMAP" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sc.pp.neighbors(adata_concat, use_rep=\"X_scGPT\")\n", + "sc.tl.umap(adata_concat)\n", + "sc.pl.umap(\n", + " adata_concat, color=[\"is_ref\", cell_type_key], wspace=0.4, frameon=False, ncols=1\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reference mapping and transfer the annotations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We run the reference mapping using cell-level majority voting. You may adjust the `k` parameter to control the number of nearest neighbors to consider for voting." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "# Those functions are only used when faiss is not installed\n", + "def l2_sim(a, b):\n", + " sims = -np.linalg.norm(a - b, axis=1)\n", + " return sims\n", + "\n", + "def get_similar_vectors(vector, ref, top_k=10):\n", + " # sims = cos_sim(vector, ref)\n", + " sims = l2_sim(vector, ref)\n", + " \n", + " top_k_idx = np.argsort(sims)[::-1][:top_k]\n", + " return top_k_idx, sims[top_k_idx]" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "ref_cell_embeddings = ref_embed_adata.obsm[\"X_scGPT\"]\n", + "test_emebd = test_embed_adata.obsm[\"X_scGPT\"]\n", + "\n", + "k = 10 # number of neighbors\n", + "\n", + "\n", + "index = faiss.IndexFlatL2(ref_cell_embeddings.shape[1])\n", + "index.add(ref_cell_embeddings)\n", + "\n", + "# Query dataset, k - number of closest elements (returns 2 numpy arrays)\n", + "distances, labels = index.search(test_emebd, k)\n", + "\n", + "idx_list=[i for i in range(test_emebd.shape[0])]\n", + "preds = []\n", + "sim_list = distances\n", + "for k in idx_list:\n", + " if faiss_imported:\n", + " idx = labels[k]\n", + " else:\n", + " idx, sim = get_similar_vectors(test_emebd[k][np.newaxis, ...], ref_cell_embeddings, k)\n", + " pred = ref_embed_adata.obs[cell_type_key][idx].value_counts()\n", + " preds.append(pred.index[0])\n", + "gt = test_adata.obs[cell_type_key].to_numpy()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Evaluate the performance" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/h/haotian/.conda/envs/scgpt/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n", + "/h/haotian/.conda/envs/scgpt/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, msg_start, len(result))\n" + ] + }, + { + "data": { + "text/plain": [ + "{'accuracy': 0.8668498626030477,\n", + " 'precision': 0.5449474091628096,\n", + " 'recall': 0.49698960480359766,\n", + " 'macro_f1': 0.483531805052145}" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "res_dict = {\n", + " \"accuracy\": accuracy_score(gt, preds),\n", + " \"precision\": precision_score(gt, preds, average=\"macro\"),\n", + " \"recall\": recall_score(gt, preds, average=\"macro\"),\n", + " \"macro_f1\": f1_score(gt, preds, average=\"macro\"),\n", + "}\n", + "\n", + "res_dict" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Confusion matrix:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "y_true = gt\n", + "y_pred = preds\n", + "cell_type_list = np.unique(y_true)\n", + "matrix = confusion_matrix(y_true, y_pred, labels=cell_type_list)\n", + "matrix = matrix.astype(\"float\") / matrix.sum(axis=1)[:, np.newaxis]\n", + "\n", + "df = pd.DataFrame(matrix, index=cell_type_list[:matrix.shape[0]], columns=cell_type_list[:matrix.shape[1]])\n", + "\n", + "ax = sns.clustermap(df, \n", + " cmap='Purples',\n", + " annot=True ,fmt=\".2f\", \n", + " annot_kws={'size': 8}, \n", + " vmin=0, \n", + " vmax=1,\n", + " row_cluster=False, \n", + " col_cluster=False, \n", + " figsize=(14, 14))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Appendix: Reference mapping on Lung-Kim dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The dataset can be accessed form [here](https://drive.google.com/drive/folders/1gbfO7VqxCOkfzgHAih6hO88zFv6pd8wO?usp=sharing)." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "adata = sc.read_h5ad('../../data/lung/sample_proc_lung_train.h5ad')\n", + "test_adata = sc.read_h5ad('../../data/lung/sample_proc_lung_test.h5ad')\n", + "\n", + "sc.pp.normalize_total(adata, target_sum=1e4)\n", + "sc.pp.log1p(adata)\n", + "sc.pp.normalize_total(test_adata, target_sum=1e4)\n", + "sc.pp.log1p(test_adata)\n", + "\n", + "gene_col = \"gene_name\"\n", + "cell_type_key = \"cell_type\"" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "scGPT - INFO - match 2978/3000 genes in vocabulary of size 60697.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Embedding cells: 100%|██████████| 363/363 [00:14<00:00, 25.45it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "scGPT - INFO - match 2978/3000 genes in vocabulary of size 60697.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Embedding cells: 100%|██████████| 114/114 [00:04<00:00, 23.30it/s]\n", + "/h/haotian/.conda/envs/scgpt/lib/python3.7/site-packages/anndata/_core/anndata.py:1785: FutureWarning: X.dtype being converted to np.float32 from float64. In the next version of anndata (0.9) conversion will not be automatic. Pass dtype explicitly to avoid this warning. Pass `AnnData(X, dtype=X.dtype, ...)` to get the future behavour.\n", + " [AnnData(sparse.csr_matrix(a.shape), obs=a.obs) for a in all_adatas],\n" + ] + } + ], + "source": [ + "ref_embed_adata = scg.tasks.embed_data(\n", + " adata,\n", + " model_dir,\n", + " gene_col=gene_col,\n", + " batch_size=64,\n", + ")\n", + "test_embed_adata = scg.tasks.embed_data(\n", + " test_adata,\n", + " model_dir,\n", + " gene_col=gene_col,\n", + " batch_size=64,\n", + ")\n", + "# concatenate the two datasets\n", + "adata_concat = test_embed_adata.concatenate(ref_embed_adata, batch_key=\"dataset\")\n", + "# mark the reference vs. query dataset\n", + "adata_concat.obs[\"is_ref\"] = [\"Query\"] * len(test_embed_adata) + [\"Reference\"] * len(\n", + " ref_embed_adata\n", + ")\n", + "adata_concat.obs[\"is_ref\"] = adata_concat.obs[\"is_ref\"].astype(\"category\")\n", + "# mask the query dataset cell types\n", + "adata_concat.obs[cell_type_key] = adata_concat.obs[cell_type_key].cat.add_categories([\"To be predicted\"])\n", + "adata_concat.obs[cell_type_key][: len(test_embed_adata)] = \"To be predicted\"" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sc.pp.neighbors(adata_concat, use_rep=\"X_scGPT\")\n", + "sc.tl.umap(adata_concat)\n", + "sc.pl.umap(\n", + " adata_concat, color=[\"is_ref\", cell_type_key], wspace=0.4, frameon=False, ncols=1\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "ref_cell_embeddings = ref_embed_adata.obsm[\"X_scGPT\"]\n", + "test_emebd = test_embed_adata.obsm[\"X_scGPT\"]\n", + "\n", + "k = 10 # number of neighbors\n", + "\n", + "index = faiss.IndexFlatL2(ref_cell_embeddings.shape[1])\n", + "index.add(ref_cell_embeddings)\n", + "\n", + "# Query dataset, k - number of closest elements (returns 2 numpy arrays)\n", + "distances, labels = index.search(test_emebd, k)\n", + "\n", + "idx_list=[i for i in range(test_emebd.shape[0])]\n", + "preds = []\n", + "sim_list = distances\n", + "for k in idx_list:\n", + " if faiss_imported:\n", + " idx = labels[k]\n", + " else:\n", + " idx, sim = get_similar_vectors(test_emebd[k][np.newaxis, ...], ref_cell_embeddings, k)\n", + " pred = ref_embed_adata.obs[cell_type_key][idx].value_counts()\n", + " preds.append(pred.index[0])\n", + "gt = test_adata.obs[cell_type_key].to_numpy()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'accuracy': 0.9693975572938109,\n", + " 'precision': 0.9632803312169889,\n", + " 'recall': 0.9426045602672533,\n", + " 'macro_f1': 0.9515099156163878}" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "res_dict = {\n", + " \"accuracy\": accuracy_score(gt, preds),\n", + " \"precision\": precision_score(gt, preds, average=\"macro\"),\n", + " \"recall\": recall_score(gt, preds, average=\"macro\"),\n", + " \"macro_f1\": f1_score(gt, preds, average=\"macro\"),\n", + "}\n", + "\n", + "res_dict" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "y_true = gt\n", + "y_pred = preds\n", + "cell_type_list = np.unique(y_true)\n", + "matrix = confusion_matrix(y_true, y_pred, labels=cell_type_list)\n", + "matrix = matrix.astype(\"float\") / matrix.sum(axis=1)[:, np.newaxis]\n", + "\n", + "df = pd.DataFrame(matrix, index=cell_type_list[:matrix.shape[0]], columns=cell_type_list[:matrix.shape[1]])\n", + "\n", + "ax = sns.clustermap(df, \n", + " cmap='Purples',\n", + " annot=True ,\n", + " fmt=\".2f\", \n", + " annot_kws={'size': 16}, \n", + " vmin=0, \n", + " vmax=1,\n", + " row_cluster=False, \n", + " col_cluster=False, \n", + " figsize=(14, 14))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "scgpt_env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/transfer/code/stack/.github/workflows/python-publish.yml b/transfer/code/stack/.github/workflows/python-publish.yml new file mode 100644 index 0000000000000000000000000000000000000000..c4755685e7c4423f094244801ea33b97958c0826 --- /dev/null +++ b/transfer/code/stack/.github/workflows/python-publish.yml @@ -0,0 +1,62 @@ +name: Upload Python Package + +on: + release: + types: [published] + +permissions: + contents: read + +jobs: + release-build: + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v4 + + - uses: actions/setup-python@v5 + with: + python-version: "3.x" + + - name: Build release distributions + run: | + # NOTE: put your own distribution build steps here. + python -m pip install build + python -m build + + - name: Upload distributions + uses: actions/upload-artifact@v4 + with: + name: release-dists + path: dist/ + + pypi-publish: + runs-on: ubuntu-latest + needs: + - release-build + permissions: + # IMPORTANT: this permission is mandatory for trusted publishing + id-token: write + + # Dedicated environments with protections for publishing are strongly recommended. + # For more information, see: https://docs.github.com/en/actions/deployment/targeting-different-environments/using-environments-for-deployment#deployment-protection-rules + environment: + name: pypi + # OPTIONAL: uncomment and update to include your PyPI project URL in the deployment status: + # url: https://pypi.org/p/YOURPROJECT + # + # ALTERNATIVE: if your GitHub Release name is the PyPI project version string + # ALTERNATIVE: exactly, uncomment the following line instead: + # url: https://pypi.org/project/YOURPROJECT/${{ github.event.release.name }} + + steps: + - name: Retrieve release distributions + uses: actions/download-artifact@v4 + with: + name: release-dists + path: dist/ + + - name: Publish release distributions to PyPI + uses: pypa/gh-action-pypi-publish@release/v1 + with: + packages-dir: dist/ diff --git a/transfer/code/stack/.gitignore b/transfer/code/stack/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..07d5edc9688cdf5bcbb50fd373db0217fd7f7042 --- /dev/null +++ b/transfer/code/stack/.gitignore @@ -0,0 +1,63 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +*.manifest +*.spec + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# IDEs +.vscode/ +.idea/ +*.swp +*.swo +*~ + +# OS +.DS_Store +Thumbs.db diff --git a/transfer/code/stack/LICENSE b/transfer/code/stack/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..cbe5ad1670406e4402217edfb82d2c56af7e8631 --- /dev/null +++ b/transfer/code/stack/LICENSE @@ -0,0 +1,437 @@ +Attribution-NonCommercial-ShareAlike 4.0 International + +======================================================================= + +Creative Commons Corporation ("Creative Commons") is not a law firm and +does not provide legal services or legal advice. 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For +the avoidance of doubt, this paragraph does not form part of the +public licenses. + +Creative Commons may be contacted at creativecommons.org. diff --git a/transfer/code/stack/MANIFEST.in b/transfer/code/stack/MANIFEST.in new file mode 100644 index 0000000000000000000000000000000000000000..b8a8b7349e3ca9738af6797f91a0e00a069df58b --- /dev/null +++ b/transfer/code/stack/MANIFEST.in @@ -0,0 +1,4 @@ +include README.md +include LICENSE +recursive-include configs *.yaml +recursive-include src/stack/cli *.py diff --git a/transfer/code/stack/MODEL_ACCEPTABLE_USE_POLICY.md b/transfer/code/stack/MODEL_ACCEPTABLE_USE_POLICY.md new file mode 100644 index 0000000000000000000000000000000000000000..acbcfc8ee95fa8095c0b21af1a033b24d7915434 --- /dev/null +++ b/transfer/code/stack/MODEL_ACCEPTABLE_USE_POLICY.md @@ -0,0 +1,62 @@ + +# Arc Research Institute Stack Model Acceptable Use Policy + +**_Last updated June 23, 2025_** + +Arc Research Institute (the “**Institute**,” “**we**” or “**us**”) makes its Stack Model available and free to use for non-commercial purposes, subject to the terms of the [Arc Research Institute Stack Model Non-Commercial License](MODEL_LICENSE.md) (the “**License**”) and this Acceptable Use Policy (“**Policy**”). The purpose of this Policy is to ensure the Stack Model is used safely, ethically, and in accordance with all applicable laws and regulations. Any defined terms used but not defined in this Policy have the meaning given in the License. + +## Purpose and Permitted Use + +The intent of the Stack Model is to support and enable research that advances knowledge and serves the public interest. This model is made available for use only by non-commercial entities such as government institutions, non-profit organizations, research institutes, and educational institutions. + +Although the Stack Model may only be used for non-commercial purposes, you are free to use any outputs you create for any purpose. + +## Prohibited Uses + +Unless otherwise expressly permitted pursuant to the License, you may not use the Stack Model, or any outputs or derivatives thereof, for any of the following purposes: + +**1) For-Profit Use:** +- Use by or for any for-profit entity, including but not limited to corporations, partnerships, or other commercial organizations. +- Use for any for-profit purpose, including but not limited to commercial research, sponsored research, product or service development, marketing, advertising, or any activity intended to generate revenue or commercial advantage. +- For clarity, Outputs (as defined in the License) are not subject to this Section 1. + +**2) Unlawful, Harmful, or Unethical Activities:** +- Any activity that violates applicable local, national, or international laws, rules, or regulations. +- Any activity that causes or is intended to cause harm, including but not limited to the generation of offensive, abusive, or unlawful content. +- Any use that infringes upon the rights of others, including invasion of privacy or jeopardizing the safety of others. + +**3) Misinforming or Misleading:** +- Generation, dissemination, or promotion of false, incomplete, or otherwise misleading information. +- Any use intended to deceive, misinform, or otherwise mislead individuals or organizations, including but not limited fabrication of data, fabrication or manipulation of outputs, impersonation, or to otherwise misrepresent facts or scientific findings. + +**4) Security and Privacy Violations:** +- Introducing malware, viruses, or other malicious code. +- Circumventing or attempting to circumvent any security or access controls. +- Collecting, storing, or sharing sensitive, health, or personal data without proper authorization or consent. + +**5) Claiming Endorsement:** +- Making any statements or claims or engaging in any other behavior or communication that indicates or suggests that the Institute endorses your use of the Stack Model, including any Derivative Works or Outputs (each as defined in the License). + +## Attribution + +If You redistribute the Stack Model, or any outputs or derivatives thereof, you must also include a prominent and readable citation to the Stack Model research paper: Adduri, A. et al. (2025) Predicting cellular responses to perturbation across diverse contexts with Stack. + +## Disclaimer + +The Stack Model is provided AS IS and is intended for informational, theoretical, and research purposes only and is not intended for use in the diagnosis of disease or other conditions, or in the cure, mitigation, treatment, or prevention of disease. It should not be used or substituted for professional, medical advice. + +## Third Party Integrations + +The Institute is not responsible for the content, security, or privacy practices of any third-party technology, data, materials, or services that you may use in connection with the Stack Model. Use of such integrations is at your own risk and subject to the terms and policies of the respective third parties. The Institute disclaims any liability for damages or losses resulting from third-party integrations. + +## Legal and Regulatory Compliance + +The Stack Model may not be appropriate or available for use in some jurisdictions. Any use of the Stack Model is at your own risk, and you must comply with applicable laws, rules, and regulations in doing so. This includes, but is not limited to, data protection, privacy, and export control laws. + +## Enforcement and Disclaimer + +Violation of this Policy may result in suspension or termination of the License and access to the Stack Model, and may subject you to legal liability. We reserve the right to investigate suspected violations and to cooperate with law enforcement authorities. The Institute disclaims liability for any misuse of or unauthorized training of the Stack Model. + +## Contact + +For questions about this Policy or to report suspected violations, please contact the Stack Model administrator at . \ No newline at end of file diff --git a/transfer/code/stack/MODEL_LICENSE.md b/transfer/code/stack/MODEL_LICENSE.md new file mode 100644 index 0000000000000000000000000000000000000000..25342fc87893a373a0ec91dbf8990cd6dd44dd16 --- /dev/null +++ b/transfer/code/stack/MODEL_LICENSE.md @@ -0,0 +1,73 @@ +# Arc Research Institute Stack Model Non-Commercial License + +**_Last Updated: June 23, 2025_** + +Arc Research Institute (“**Licensor**”) is releasing certain materials collectively defined below as the “**Stack Model**”. This license (the “**License**”) is between Licensor and any individual or legal entity exercising permissions granted under this License (“**You**”). + +By downloading, copying, executing, or otherwise exercising any permission granted by this License or using the Stack Model, You acknowledge that You accept and agree to be bound by the terms and conditions of this License. If use of the Stack Model is within the scope of Your employment, You hereby represent that you are fully authorized to enter into this Agreement on behalf of any such employer. + +Licensor may make changes to this License. The “_Last Updated_” date above indicates when this License was last changed. The amended License will be effective immediately, and Your continued use of the Stack Model after such update will confirm Your acceptance of the changes. If You do not agree to the amended License, You must immediately stop using the Stack Model. + +## 1. Definitions + +1.1 “**Commercial Entity**” means any natural person, legal entity, organization, or institution that is for-profit, or otherwise not a Non-Commercial Entity. + +1.2 “**Derivative Work**” means any modification of, work based upon, or derived from, the Stack Model or any portion thereof, including adaptations, fine-tunings, adjusted model weights, distilled or pruned versions, additional training, or any other alteration, transformation, or integration, including use as a prior, of the Stack Model or any part of it. + +1.3 “**Non-Commercial Entity**” means a (a) government body or (b) a natural person, academic institution, research institute, or other entity registered with relevant tax authorities as a not-for-profit organization that, in each (a) and (b) above, does not own or control or is not owned or controlled by and is not acting with or on behalf of a Commercial Entity. + +1.4 “**Non-Commercial Purpose**” means a purpose that is not undertaken for direct or indirect monetary compensation, financial consideration, or any other commercial undertaking or advantage. For the purposes of this License, research, development, or educational activities engaged in or sponsored by a Commercial Entity (whether or not performed by a Non-Commercial Entity or otherwise undertaken for a purpose not associated with monetary compensation, financial consideration or other commercial undertaking or advantage) are not Non-Commercial Purposes. + +1.5 “**Output**” means any results, data, or other material generated by execution of the unmodified Stack Model or a Derivative Work. + +1.6 “**Stack Model**” includes, individually and collectively, the pretrained model (_i.e._, the model code combined with the trained model weights), model weights, documentation, and any other files released by Licensor under this License (provided that source code and object code are licensed under a separate license made available by Licensor). + +## 2. Grant of Rights + +2.1 **License** Subject to the terms and conditions of this License, Licensor hereby grants You a personal, worldwide, royalty-free, non-exclusive, irrevocable (except as provided in Section 8), non-sublicensable license: (a) to use, copy, modify, and display the Stack Model; (b) to create Derivative Works and Outputs; (c) to reproduce, distribute, and make publicly available (i) the Stack Model and any Derivative Work, each subject to Section 3 (Redistribution Conditions), and (ii) any Output; and (d) under the Licensor’s patent rights covering the Stack Model, to make, use, import, and otherwise exploit the Stack Model and any Derivative Works, each (a) through (c)(i) and (d), solely for Non-Commercial Purposes. + +2.2 **Patent Infringement** If You initiate patent litigation (including a cross-claim or counterclaim in a lawsuit) alleging that the Stack Model or a Derivative Work infringes a patent, this License to You is terminated as of the date such litigation is filed. + +2.3 **Reservation of Rights**. All rights other than those expressly granted to You herein are reserved by Licensor. + +## 3. Redistribution Conditions + +If You redistribute the Stack Model or any Derivative Work, You must (a) include a complete, unaltered copy of this License; (b) keep intact all original copyright, license, and disclaimer notices; and (c) provide prominent notice stating that the redistributed Stack Model is governed by this License. If You redistribute the Stack Model, any Derivative Work or any Output, You must also include a prominent and readable citation to the Stack Model research paper: Adduri, A. et al. (2025) Predicting cellular responses to perturbation across diverse contexts with Stack. You may choose to license a Derivative Work or Output under different terms and conditions, provided that the limitations and restrictions herein (including, in the case of a Derivative Work, the Non-Commercial Purpose) remain in substance and effect. + +## 4. Restrictions + +4.1 You may not use or distribute the Stack Model, any or Derivative Work for any purpose other than Non-Commercial Purposes, unless You first obtain a separate, express, written license from Licensor granting such rights. If you wish to inquire about such separate license, you may contact . + +4.2 You may not remove, obfuscate, or alter any copyright, trademark, citation or other proprietary notice included in the Stack Model. + +4.3 You must comply with all applicable laws and regulations, including export-control and sanctions regimes, in connection with Your use of the Stack Model and any Derivative Work or Output. + +4.4 If You elect to collect or process personal data, health data or any other regulated data using the Stack Model or any Derivative Work, You are solely responsible for compliance with all data-protection, privacy, and related laws. + +## 5. Feedback + +If You provide Licensor with ideas, suggestions, or improvements regarding the Stack Model (“**Feedback**”), Licensor may incorporate such Feedback without obligation or restriction, and You hereby irrevocably assign to Licensor all right, title, and interest in and to such Feedback and all intellectual property rights therein and thereto. To the extent any rights in and to such Feedback cannot be assigned by You, You hereby grants to Licensor a nonexclusive, worldwide, perpetual, irrevocable, transferable, sublicensable, royalty-free, fully paid-up license to use, practice, and otherwise exercise the Feedback without restriction. + +## 6. Trademarks + +This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Stack Model and reproducing the content of the license as required herein. + +## 7. Disclaimer of Warranty; Limitation of Liability + +THE Stack MODEL, ANY DERIVATIVE WORK, AND ANY OUTPUT ARE PROVIDED “AS IS” AND “WITH ALL FAULTS,” WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, NON-INFRINGEMENT, OR THAT OPERATION OF THE Stack MODEL WILL BE UNINTERRUPTED OR ERROR-FREE. TO THE MAXIMUM EXTENT PERMITTED BY APPLICABLE LAW, LICENSOR SHALL NOT BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT (INCLUDING NEGLIGENCE), STRICT LIABILITY, OR OTHERWISE, FOR ANY INDIRECT, SPECIAL, INCIDENTAL, CONSEQUENTIAL, OR EXEMPLARY DAMAGES ARISING OUT OF OR RELATED TO THIS LICENSE OR THE USE OF THE Stack MODEL, DERIVATIVE WORKS, OR OUTPUTS, EVEN IF LICENSOR HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. LICENSOR’S TOTAL AGGREGATE LIABILITY UNDER THIS LICENSE SHALL NOT EXCEED TWENTY U.S. DOLLARS (USD $20). + +## 8. Termination + +8.1 This License and the rights granted herein terminate automatically without notice if You breach Sections 2, 3, or 4. Upon termination, You shall cease all use, reproduction, and distribution of the Stack Model, any Derivative Work, and any Output, and Sections 2.3, 4.4, 5, 7, 8.2, and 9 shall remain in effect. + +8.2 Termination shall not affect any third-party’s legitimate possession, under this License, of copies of the Stack Model, Derivative Works, or Outputs obtained from You prior to termination, provided such third party is itself in full compliance with this License. + +## 9. Miscellaneous + +9.1 **Governing Law**. This License shall be governed by and construed in accordance with the laws of the state of California, excluding its or any other jurisdiction’s conflict-of-laws principles. Venue for any disputes related to this License shall be in the federal courts in Santa Clara County, California and You and Licensor hereby agree to the exclusive jurisdiction of such courts. + +9.2 **Severability**. If any provision of this License is held to be unenforceable, such provision shall be reformed only to the extent necessary to make it enforceable, and the remaining provisions shall remain in full force and effect. + +9.3 **No Waiver**. Failure by Licensor to enforce any provision of this License shall not constitute a waiver of future enforcement of that or any other provision. + +9.4 **Entire Agreement**. This License constitutes the entire agreement between the parties with respect to the Stack Model and supersedes all prior or contemporaneous understandings, whether written or oral, regarding such subject matter. References herein to “including” will be interpreted to mean “including but not limited to” and any examples provided are illustrative and not intended to be the sole examples of a particular concept. diff --git a/transfer/code/stack/README.md b/transfer/code/stack/README.md new file mode 100644 index 0000000000000000000000000000000000000000..7edc16f792750659a07947d03e91be960b1d63af --- /dev/null +++ b/transfer/code/stack/README.md @@ -0,0 +1,174 @@ + +# Stack: In-context learning of single-cell biology + +Stack is a large-scale encoder-decoder foundation model trained on 150 million uniformly-preprocessed single cells. It introduces a novel tabular attention architecture that enables both intra- and inter-cellular information flow, setting cell-by-gene matrix chunks as the basic input data unit. Through in-context learning, Stack offers substantial performance improvements in generalizing biological effects and enables generation of unseen cell profiles in novel contexts. + +## Installation + +### Using pip +```bash +# Install from PyPI +pip install arc-stack + +# Or install from source for development +git clone https://github.com/ArcInstitute/stack.git +cd stack +pip install -e . +``` + +### Using uv +```bash +# Install from PyPI +uv pip install arc-stack + +# Or install from source for development +git clone https://github.com/ArcInstitute/stack.git +cd stack +uv pip install -e . +``` + + +## Quick Start + +- Use Stack to embed your single-cell data: [Notebook](notebooks/tutorial-embed.ipynb) +- Use Stack to zero-shot predict unseen perturbation/observation profiles: [Notebook](notebooks/tutorial-predict.ipynb) + +### Training Stack from Scratch + +```bash +# Once installed, the console entry point becomes available +stack-train \ + --dataset_configs "/path/to/data:false:gene_symbols" \ + --genelist_path "hvg_genes.pkl" \ + --save_dir "./checkpoints" \ + --sample_size 256 \ + --batch_size 32 \ + --n_hidden 100 \ + --token_dim 16 \ + --n_layers 9 \ + --max_epochs 10 + +# Alternatively, invoke the module directly when working from a cloned repo +python -m stack.cli.launch_training [args...] +``` + +### Fine-tuning Stack with Frozen Teacher + +```bash +stack-finetune \ + --checkpoint_path "./checkpoints/pretrained.ckpt" \ + --dataset_configs "human:/path/to/data:donor_id:cell_type:false" \ + --genelist_path "hvg_genes.pkl" \ + --save_dir "./finetuned_checkpoints" \ + --sample_size 512 \ + --batch_size 8 \ + --replacement_ratio 0.75 \ + --max_epochs 8 + +# Or use uv run +uv run stack-finetune [args...] + +# Repository wrapper remains available for local development +python -m stack.cli.launch_finetuning [args...] +``` + +### Running Stack with configuration files + +Both `launch_training.py` and `launch_finetuning.py` accept a `--config` flag that points to a YAML or JSON file. Any command line +arguments omitted after `--config` inherit their values from the file, while flags provided on the command line override the +configuration. Example configs mirroring the provided Slurm scripts live under `configs/`: + +```bash +# Train with the preset configuration +stack-train --config configs/training/bc_large.yaml + +# Override a single hyperparameter without editing the file +stack-train --config configs/training/bc_large.yaml --learning_rate 5e-5 + +# Fine-tune using a config file +stack-finetune --config configs/finetuning/ft_parsecg.yaml + +# Direct module invocation is still supported if you prefer python -m +python -m stack.cli.launch_training --config configs/training/bc_large.yaml +``` + +> **Note:** YAML configs require [`pyyaml`](https://pyyaml.org/). Install it with `pip install pyyaml` or use a JSON config file. + +### Extracting Stack Embeddings + +```bash +stack-embedding \ + --checkpoint "./checkpoints/pretrained.ckpt" \ + --adata "data.h5ad" \ + --genelist "hvg_genes.pkl" \ + --output "embeddings.h5ad" \ + --batch-size 32 + +# Or use uv run +uv run stack-embedding \ + --checkpoint "./checkpoints/pretrained.ckpt" \ + --adata "data.h5ad" \ + --genelist "hvg_genes.pkl" \ + --output "embeddings.h5ad" \ + --batch-size 32 +``` + +### In-Context Generation with Stack + +```bash +stack-generation \ + --checkpoint "./checkpoints/pretrained.ckpt" \ + --base-adata "base_data.h5ad" \ + --test-adata "test_data.h5ad" \ + --genelist "hvg_genes.pkl" \ + --output-dir "./generations" \ + --split-column "donor_id" + +# Or use uv run +uv run stack-generation \ + --checkpoint "./checkpoints/pretrained.ckpt" \ + --base-adata "base_data.h5ad" \ + --test-adata "test_data.h5ad" \ + --genelist "hvg_genes.pkl" \ + --output-dir "./generations" \ + --split-column "donor_id" +``` + +## Model Architecture + +- **Tabular Attention**: Alternating cell-wise and gene-wise attention layers +- **Token Dimension**: Configurable token embedding dimension (default: 16) +- **Hidden Dimension**: Gene dimension reduction (default: 100) +- **Masking Strategy**: Rectangular masking with variable rates (0.1-0.8) + +## Data Preparation + +### Computing Highly Variable Genes (HVGs) +```python +from stack.data.datasets import DatasetConfig, compute_hvg_union + +configs = [DatasetConfig(path="/data/path", filter_organism=True)] +hvg_genes = compute_hvg_union(configs, n_top_genes=1000, output_path="hvg.pkl") +``` + +### Dataset Configuration Format +- **Human datasets**: `human:/path:donor_col:cell_type_col[:filter_organism[:gene_col]]` +- **Drug datasets**: `drug:/path:condition_col:cell_line_col:control_condition[:filter_organism[:gene_col]]` + +## Key Features + +- **In-Context Learning**: Zero-shot generalization to new biological contexts +- **Multi-Dataset Training**: Simultaneous training on multiple single-cell datasets +- **Frozen Teacher Fine-tuning**: Novel fine-tuning procedure with stable teacher targets +- **Efficient Data Loading**: Optimized HDF5 loading with sparse matrix support + +> **Note:** `scShiftAttentionModel` remains available as an alias for backward compatibility. + +## Citation + +If you use Stack in your research, please cite the Stack [paper](https://www.biorxiv.org/content/10.64898/2026.01.09.698608v1). + +## Licenses +Stack code is [licensed](LICENSE) under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0). + +The model weights and output are licensed under the [Arc Research Institute Stack Model Non-Commercial License](MODEL_LICENSE.md) and subject to the [Arc Research Institute Stack Model Acceptable Use Policy](MODEL_ACCEPTABLE_USE_POLICY.md). diff --git a/transfer/code/stack/configs/finetuning/ft_parsecg.yaml b/transfer/code/stack/configs/finetuning/ft_parsecg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7d4593de72c7677f6056bbd2d1cf6e65e511b450 --- /dev/null +++ b/transfer/code/stack/configs/finetuning/ft_parsecg.yaml @@ -0,0 +1,20 @@ +checkpoint_path: "Stack-Large/bc_large.ckpt" +dataset_configs: + - "human:parse_bio:sample:cell_type:false" + - "human:Stack-CellxGene45M:donor_id:author_cell_type:true:feature_name" +genelist_path: "Stack-Large-Aligned/basecount_1000per_15000max.pkl" +save_dir: "stack_checkpoints/bc_large_aligned" +max_epochs: 8 +sample_size: 512 +batch_size: 8 +accumulate_grad_batches: 4 +num_workers: 4 +learning_rate: 0.00002 +weight_decay: 0.003 +scheduler_eta_min: 0.000005 +replacement_ratio: 0.75 +scheduler: cosine +scheduler_T_max: 8 +scheduler_warmup_epochs: 1 +early_stopping_patience: 5 +cache_file: "cache/parse_cgft.pkl" diff --git a/transfer/code/stack/configs/training/bc_large.yaml b/transfer/code/stack/configs/training/bc_large.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5f6911830602c57394c58351d575847b6ee0db22 --- /dev/null +++ b/transfer/code/stack/configs/training/bc_large.yaml @@ -0,0 +1,20 @@ +dataset_configs: + - "scbasecount_fullset:false:gene_symbols" +genelist_path: "Stack-Large/basecount_1000per_15000max.pkl" +save_dir: "stack_checkpoints/bc_large" +max_epochs: 10 +sample_size: 256 +batch_size: 32 +n_hidden: 100 +token_dim: 16 +n_layers: 9 +dropout: 0.0 +num_workers: 4 +learning_rate: 0.0001 +weight_decay: 0.003 +scheduler: cosine +scheduler_T_max: 10 +scheduler_warmup_epochs: 1 +early_stopping_patience: 10 +no_localize_data: true +cache_file: "cache/bc.pkl" diff --git a/transfer/code/stack/notebooks/tutorial-embed.ipynb b/transfer/code/stack/notebooks/tutorial-embed.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..8259dd339e712c61418a26ec4bae990fd70350a5 --- /dev/null +++ b/transfer/code/stack/notebooks/tutorial-embed.ipynb @@ -0,0 +1,347 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1d641bda-8c2a-4efc-8b2a-aafb8294b798", + "metadata": {}, + "source": [ + "# This notebook covers how to use the Stack model to generate embeddings for cells in a user-provided AnnData." + ] + }, + { + "cell_type": "markdown", + "id": "31d3c48a-ba2f-4363-80e5-73c28504a8b9", + "metadata": {}, + "source": [ + "The notebook assumes that the Stack model has been installed, using e.g. pip or uv install. It additionally assumes that relevant packages (e.g. scanpy, anndata, huggingface_hub etc) are already installed." + ] + }, + { + "cell_type": "markdown", + "id": "f6e36fbc-634a-4ca0-9b9b-464ac3d53ac9", + "metadata": {}, + "source": [ + "## Import packages" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "effbcad6-62b1-4563-8915-ccaaa0c53fcc", + "metadata": {}, + "outputs": [], + "source": [ + "import scanpy as sc\n", + "import anndata as ad\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import os, sys" + ] + }, + { + "cell_type": "markdown", + "id": "992dbff5-cb0c-4578-bf4c-7e6372eb9832", + "metadata": {}, + "source": [ + "## Download the example checkpoint from HuggingFace" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7591513f-8c53-4932-9a0e-31fcf40aca01", + "metadata": {}, + "outputs": [], + "source": [ + "from huggingface_hub import snapshot_download\n", + "\n", + "repo_id = \"arcinstitute/Stack-Large\"\n", + "target_dir = \"tutorial-embed-model\"\n", + "\n", + "if os.path.exists(target_dir) and os.listdir(target_dir):\n", + " raise SystemExit(f\"'{target_dir}' already exists and is not empty.\")\n", + "\n", + "snapshot_download(\n", + " repo_id=repo_id,\n", + " repo_type=\"model\",\n", + " revision=\"main\",\n", + " local_dir=target_dir,\n", + " local_dir_use_symlinks=False, \n", + " resume_download=True,\n", + ")\n", + "\n", + "print(f\"Downloaded to ./{target_dir}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c322558a-f267-48e4-b121-1b4e5727db5d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2025-12-12 01:00:03-- https://datasets.cellxgene.cziscience.com/d6761a21-e226-434f-9370-fbcc7e549aa0.h5ad\n", + "Resolving datasets.cellxgene.cziscience.com (datasets.cellxgene.cziscience.com)... 13.225.143.79, 13.225.143.33, 13.225.143.98, ...\n", + "Connecting to datasets.cellxgene.cziscience.com (datasets.cellxgene.cziscience.com)|13.225.143.79|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 3206851888 (3.0G) [binary/octet-stream]\n", + "Saving to: ‘tutorial-embed-data/lung.h5ad’\n", + "\n", + "tutorial-embed-data 51%[=========> ] 1.53G 3.33MB/s in 7m 16s \n", + "\n", + "2025-12-12 01:07:19 (3.60 MB/s) - Read error at byte 1646197474/3206851888 (Connection reset by peer). Retrying.\n", + "\n", + "--2025-12-12 01:07:20-- (try: 2) https://datasets.cellxgene.cziscience.com/d6761a21-e226-434f-9370-fbcc7e549aa0.h5ad\n", + "Connecting to datasets.cellxgene.cziscience.com (datasets.cellxgene.cziscience.com)|13.225.143.79|:443... connected.\n", + "HTTP request sent, awaiting response... 206 Partial Content\n", + "Length: 3206851888 (3.0G), 1560654414 (1.5G) remaining [binary/octet-stream]\n", + "Saving to: ‘tutorial-embed-data/lung.h5ad’\n", + "\n", + "tutorial-embed-data 100%[++++++++++=========>] 2.99G 4.63MB/s in 6m 54s \n", + "\n", + "2025-12-12 01:14:15 (3.59 MB/s) - ‘tutorial-embed-data/lung.h5ad’ saved [3206851888/3206851888]\n", + "\n" + ] + } + ], + "source": [ + "# Download tabula sapiens data\n", + "\n", + "! wget https://datasets.cellxgene.cziscience.com/d6761a21-e226-434f-9370-fbcc7e549aa0.h5ad -O tutorial-embed-data/lung.h5ad" + ] + }, + { + "cell_type": "markdown", + "id": "abb45f1d-2e4a-4b1a-8e79-6be750007204", + "metadata": {}, + "source": [ + "Here for illustration, we simply input the entire data (ordered by donor column) to the model. For more advanced use, we may split the data by sample columns (e.g. donor, condition) then feed data to the model, finally concatenate them." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "20b2bb1e-21de-4f6d-b25d-3e3ab8330b7b", + "metadata": {}, + "outputs": [], + "source": [ + "adata = sc.read_h5ad('tutorial-embed-data/lung.h5ad')\n", + "sorted_indices = np.argsort(adata.obs['donor_id'].values, kind='stable')\n", + "adata = adata[sorted_indices, :].copy()\n", + "adata.write('tutorial-embed-data/lung_ordered.h5ad')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "12da6faf-f226-42e1-b04c-d841d880ab54", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[KeOps] Warning : CUDA was detected, but driver API could not be initialized. Switching to CPU only.\n", + "INFO:stack.model_loading:Loading model from tutorial-embed-model/bc_large.ckpt\n", + "/home/mingze/shiftab/state-ICL/src/stack/model_loading.py:47: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " checkpoint = torch.load(checkpoint_path_obj, map_location=device)\n", + "INFO:stack.model_loading:Model loaded successfully on cpu\n", + "INFO:stack.data.training.datasets:Loading metadata from tutorial-embed-data/lung_ordered.h5ad\n", + "WARNING:stack.data.training.datasets:'organism' column not found, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Loaded file with 65847 human cells, 14710/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 258 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:Last sample will be constructed from 55 unique cells.\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 258 samples from tutorial-embed-data/lung_ordered.h5ad\n", + "INFO:stack.embedding:Saving embeddings array to tutorial-embed-data/lung_ordered.npy\n" + ] + } + ], + "source": [ + "! stack-embedding --checkpoint \"tutorial-embed-model/bc_large.ckpt\" \\\n", + "--adata \"tutorial-embed-data/lung_ordered.h5ad\" \\\n", + "--genelist \"tutorial-embed-model/basecount_1000per_15000max.pkl\" --gene-name-col feature_name --batch-size 16 \\\n", + "--output \"tutorial-embed-data/lung_ordered.npy\"" + ] + }, + { + "cell_type": "markdown", + "id": "fc31518c-3374-4d46-9c4d-4959f2629d39", + "metadata": {}, + "source": [ + "Note: When there are redundant gene names, stack dataloader loads the last shown gene with the same name. If this is not the intended behavior, some preprocessing of the data is needed." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "84a9e6bc-aa16-49fb-99e0-049938e39b27", + "metadata": {}, + "outputs": [], + "source": [ + "adata.obsm['stack-embed'] = np.load('tutorial-embed-data/lung_ordered.npy')" + ] + }, + { + "cell_type": "markdown", + "id": "03a10d72-b07b-45d7-8fe6-ede1fcc9146a", + "metadata": {}, + "source": [ + "Finally, the embedding can be used for downstream analyses just like other embeddings. Our extensive evaluations in the manuscript show the advantage of Stack embeddings over alternative foundation models or models trained from scratch." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "e518c543-c94f-4462-9c5f-7164c4c135f5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sc.pp.neighbors(adata,use_rep='stack-embed')\n", + "sc.tl.umap(adata)\n", + "sc.pl.umap(adata,color=['donor_id','broad_cell_class'])" + ] + }, + { + "cell_type": "markdown", + "id": "372ed0b3-4493-42c7-91ef-9115231f21be", + "metadata": {}, + "source": [ + "Stack reveals fine-grained cell subtypes within data." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "c94bb5a9-7c22-4e45-9bb0-a432da9a2326", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sc.pl.umap(adata,color=['free_annotation'])" + ] + }, + { + "cell_type": "markdown", + "id": "162d3277-0226-4ac7-aec6-dce066cf4f10", + "metadata": {}, + "source": [ + "Alternatively, we could use inline command (instead of CLI) to generate Stack embeddings. " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "46bfb521-2981-45ab-83f9-6ade505fd1ac", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[KeOps] Warning : CUDA was detected, but driver API could not be initialized. Switching to CPU only.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/mingze/shiftab/state-ICL/src/stack/model_loading.py:47: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " checkpoint = torch.load(checkpoint_path_obj, map_location=device)\n", + "'organism' column not found, assuming all cells are valid\n", + "Extracting embeddings: 100%|██████████████████████████████████████████████████████████| 17/17 [01:35<00:00, 5.60s/batch, cells_processed=66,048, embedding_dim=1600]\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[-1.0577067e+00, 1.2824783e+00, -8.7215833e-02, ...,\n", + " 1.6814174e-01, -7.0379108e-01, -7.3187405e-01],\n", + " [-2.3385818e+00, 1.7310480e+00, 8.8880157e-01, ...,\n", + " -2.3923817e-01, -5.3139347e-01, 3.9351451e-01],\n", + " [-1.5694022e+00, 1.7827749e+00, 5.8080828e-01, ...,\n", + " 1.6119337e-03, -5.9142023e-01, 3.1272419e-02],\n", + " ...,\n", + " [-8.1504577e-01, 1.4400125e+00, 1.8184973e-01, ...,\n", + " -5.4747224e-02, 1.6330967e-02, -5.8790338e-01],\n", + " [-7.2312152e-01, 2.6197818e-01, 1.7648489e-03, ...,\n", + " -7.1969134e-01, -3.2975861e-01, -6.9474792e-01],\n", + " [-7.8625768e-01, 4.7500652e-01, -2.0246414e-02, ...,\n", + " -8.5588127e-01, -3.2196596e-01, -4.0951750e-01]],\n", + " shape=(65847, 1600), dtype=float32)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from stack.model_loading import load_model_from_checkpoint\n", + "model = load_model_from_checkpoint(\"tutorial-embed-model/bc_large.ckpt\")\n", + "\n", + "embeddings, _ = model.get_latent_representation(\n", + " adata_path=\"tutorial-embed-data/lung_ordered.h5ad\",\n", + " genelist_path=\"hvg/basecount_1000per_15000max.pkl\",\n", + " gene_name_col='feature_name',\n", + " batch_size=16, \n", + " num_workers=4 \n", + ")\n", + "embeddings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "605aa7a8-edbd-4b34-967c-cf3be7c4d046", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/transfer/code/stack/notebooks/tutorial-predict.ipynb b/transfer/code/stack/notebooks/tutorial-predict.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..ec79f6112388fcb1d711431c66c4b5254edafb88 --- /dev/null +++ b/transfer/code/stack/notebooks/tutorial-predict.ipynb @@ -0,0 +1,1206 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "11cc9769-705a-4c7d-b64a-a7317d7d0bfe", + "metadata": {}, + "source": [ + "# This notebook covers how to use the aligned Stack model to perform in-context drug perturbation prediction tasks." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "79013c5c-fe82-41e2-95e2-ff7b2d2a622f", + "metadata": {}, + "outputs": [], + "source": [ + "import scanpy as sc\n", + "import anndata as ad\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import os, sys" + ] + }, + { + "cell_type": "markdown", + "id": "c14601ae-10d9-4aa3-a5d4-37df8c8bd855", + "metadata": {}, + "source": [ + "## Download the example checkpoint and anndata from HuggingFace" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "66541cb3-67d8-48e9-a9ff-6067e16cc115", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/huggingface_hub/file_download.py:980: UserWarning: `local_dir_use_symlinks` parameter is deprecated and will be ignored. The process to download files to a local folder has been updated and do not rely on symlinks anymore. You only need to pass a destination folder as`local_dir`.\n", + "For more details, check out https://huggingface.co/docs/huggingface_hub/main/en/guides/download#download-files-to-local-folder.\n", + " warnings.warn(\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/huggingface_hub/file_download.py:943: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n", + " warnings.warn(\n", + "Fetching 4 files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:06<00:00, 1.57s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model downloaded to ./tutorial-pred-model\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fetching 5 files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:01<00:00, 3.87it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data downloaded to ./tutorial-pred-data \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "from huggingface_hub import snapshot_download\n", + "\n", + "repo_id = \"arcinstitute/Stack-Large-Aligned\"\n", + "target_dir = \"tutorial-pred-model\"\n", + "\n", + "if os.path.exists(target_dir) and os.listdir(target_dir):\n", + " raise SystemExit(f\"'{target_dir}' already exists and is not empty.\")\n", + "\n", + "snapshot_download(\n", + " repo_id=repo_id,\n", + " repo_type=\"model\",\n", + " revision=\"main\",\n", + " local_dir=target_dir,\n", + " local_dir_use_symlinks=False, \n", + " resume_download=True,\n", + ")\n", + "\n", + "print(f\"Model downloaded to ./{target_dir}\")\n", + "\n", + "repo_id = \"arcinstitute/Stack-DrugPBMC-Example\"\n", + "target_dir = \"tutorial-pred-data\"\n", + "\n", + "if os.path.exists(target_dir) and os.listdir(target_dir):\n", + " raise SystemExit(f\"'{target_dir}' already exists and is not empty.\")\n", + "\n", + "snapshot_download(\n", + " repo_id=repo_id,\n", + " repo_type=\"dataset\",\n", + " revision=\"main\",\n", + " local_dir=target_dir,\n", + " local_dir_use_symlinks=False, \n", + " resume_download=True,\n", + ")\n", + "\n", + "print(f\"Data downloaded to ./{target_dir} \")" + ] + }, + { + "cell_type": "markdown", + "id": "240d9e42-888c-48da-981d-0191312f2b99", + "metadata": {}, + "source": [ + "This example uses Donor 1 data from the 2023 OpenProblems.Bio competition. Mirroring the original competition setup, we hold out myeloid and B cells and predict their expression from T cells." + ] + }, + { + "cell_type": "markdown", + "id": "c10eb8f2-e41b-41e5-ab2b-d85d53f568cf", + "metadata": {}, + "source": [ + "The context AnnData consists of T cells under each drug-perturbation condition, while the test AnnData contains control myeloid and B cells. To remove artifacts from differential expression predictions, we apply a synthetic control approach using control T cells as the reference." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "cb6670c7-4a4f-46d6-af11-330e46a90d8b", + "metadata": {}, + "outputs": [], + "source": [ + "adata = sc.read_h5ad('tutorial-pred-data/openproblems_donor1.h5ad')\n", + "all_context_adata = adata[adata.obs['broad_cell_class'].isin(['t cell'])]\n", + "ctrl_adata = adata[adata.obs['broad_cell_class'].isin(['t cell'])]\n", + "test_adata = adata[(~adata.obs['broad_cell_class'].isin(['t cell'])) & (adata.obs['sm_name']=='Dimethyl Sulfoxide')]\n", + "all_context_adata.write('tutorial-pred-data/openproblems_context.h5ad')\n", + "test_adata.write('tutorial-pred-data/openproblems_test.h5ad')" + ] + }, + { + "cell_type": "markdown", + "id": "3193cdc5-199b-43d3-9140-c512440f3e6f", + "metadata": {}, + "source": [ + "Now we can use Stack CLI command to predict the unseen expressions of perturbed myeloid and B cells without training." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "86ec3b99-879c-4db0-8c32-969f3db7bd18", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[KeOps] Warning : CUDA was detected, but driver API could not be initialized. Switching to CPU only.\n", + "INFO:stack.model_loading:Loading model from tutorial-pred-model/bc_large_aligned.ckpt\n", + "/home/mingze/shiftab/stack/src/stack/model_loading.py:47: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " checkpoint = torch.load(checkpoint_path_obj, map_location=device)\n", + "INFO:stack.model_loading:Model loaded successfully on cpu\n", + "INFO:stack.generation:Loading base AnnData from tutorial-pred-data/openproblems_context.h5ad in backed mode\n", + "INFO:stack.generation:Loading AnnData from tutorial-pred-data/openproblems_test.h5ad\n", + "INFO:stack.generation:Gene alignment: 11018/15012 (73.4%) target genes found in dataset\n", + "INFO:stack.generation:Adding 3994 missing genes with zero values\n", + "INFO:stack.generation:Running in-context generation for 15 donor(s)\n", + "INFO:stack.generation:Processing split 'Belinostat'\n", + "INFO:stack.generation:Gene alignment: 11018/15012 (73.4%) target genes found in dataset\n", + "INFO:stack.generation:Adding 3994 missing genes with zero values\n", + "Masking ratio schedule: [0.8 0.6 0.39999998 0.19999999 0. ]\n", + "Context ratio schedule: [0.2 0.25 0.3 0.35 0.4 ]\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 5632 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 11 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 11 samples from AnnData object\n", + "Generation Step 1\n", + "0.9396067415730337\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 6144 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 12 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 12 samples from AnnData object\n", + "Generation Step 2\n", + "0.9195926966292135\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 6656 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 13 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 13 samples from AnnData object\n", + "Generation Step 3\n", + "0.8728932584269663\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 7168 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 14 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 14 samples from AnnData object\n", + "Generation Step 4\n", + "0.7640449438202247\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 8192 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 16 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 16 samples from AnnData object\n", + "Generation Step 5\n", + "0.5723314606741573\n", + "INFO:stack.generation:Writing generation for split 'Belinostat' to tutorial-pred-output/Belinostat.h5ad\n", + "INFO:stack.generation:Processing split 'CHIR-99021'\n", + "INFO:stack.generation:Gene alignment: 11018/15012 (73.4%) target genes found in dataset\n", + "INFO:stack.generation:Adding 3994 missing genes with zero values\n", + "Masking ratio schedule: [0.8 0.6 0.39999998 0.19999999 0. ]\n", + "Context ratio schedule: [0.2 0.25 0.3 0.35 0.4 ]\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 5632 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 11 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 11 samples from AnnData object\n", + "Generation Step 1\n", + "0.920997191011236\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 6144 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 12 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 12 samples from AnnData object\n", + "Generation Step 2\n", + "0.894311797752809\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 6656 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 13 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 13 samples from AnnData object\n", + "Generation Step 3\n", + "0.8476123595505618\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 7168 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 14 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 14 samples from AnnData object\n", + "Generation Step 4\n", + "0.7601825842696629\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 8192 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 16 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 16 samples from AnnData object\n", + "Generation Step 5\n", + "0.6169241573033708\n", + "INFO:stack.generation:Writing generation for split 'CHIR-99021' to tutorial-pred-output/CHIR-99021.h5ad\n", + "INFO:stack.generation:Processing split 'Crizotinib'\n", + "INFO:stack.generation:Gene alignment: 11018/15012 (73.4%) target genes found in dataset\n", + "INFO:stack.generation:Adding 3994 missing genes with zero values\n", + "Masking ratio schedule: [0.8 0.6 0.39999998 0.19999999 0. ]\n", + "Context ratio schedule: [0.2 0.25 0.3 0.35 0.4 ]\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 5632 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 11 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 11 samples from AnnData object\n", + "Generation Step 1\n", + "0.9308286516853933\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 6144 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 12 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 12 samples from AnnData object\n", + "Generation Step 2\n", + "0.9087078651685393\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 6656 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 13 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 13 samples from AnnData object\n", + "Generation Step 3\n", + "0.8588483146067416\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 7168 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 14 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 14 samples from AnnData object\n", + "Generation Step 4\n", + "0.7591292134831461\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 8192 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 16 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 16 samples from AnnData object\n", + "Generation Step 5\n", + "0.5761938202247191\n", + "INFO:stack.generation:Writing generation for split 'Crizotinib' to tutorial-pred-output/Crizotinib.h5ad\n", + "INFO:stack.generation:Processing split 'Dabrafenib'\n", + "INFO:stack.generation:Gene alignment: 11018/15012 (73.4%) target genes found in dataset\n", + "INFO:stack.generation:Adding 3994 missing genes with zero values\n", + "Masking ratio schedule: [0.8 0.6 0.39999998 0.19999999 0. ]\n", + "Context ratio schedule: [0.2 0.25 0.3 0.35 0.4 ]\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 5632 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 11 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 11 samples from AnnData object\n", + "Generation Step 1\n", + "0.9403089887640449\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 6144 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 12 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 12 samples from AnnData object\n", + "Generation Step 2\n", + "0.9188904494382022\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 6656 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 13 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 13 samples from AnnData object\n", + "Generation Step 3\n", + "0.8655196629213483\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 7168 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 14 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 14 samples from AnnData object\n", + "Generation Step 4\n", + "0.7889747191011236\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 8192 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 16 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 16 samples from AnnData object\n", + "Generation Step 5\n", + "0.6706460674157303\n", + "INFO:stack.generation:Writing generation for split 'Dabrafenib' to tutorial-pred-output/Dabrafenib.h5ad\n", + "INFO:stack.generation:Processing split 'Dactolisib'\n", + "INFO:stack.generation:Gene alignment: 11018/15012 (73.4%) target genes found in dataset\n", + "INFO:stack.generation:Adding 3994 missing genes with zero values\n", + "Masking ratio schedule: [0.8 0.6 0.39999998 0.19999999 0. ]\n", + "Context ratio schedule: [0.2 0.25 0.3 0.35 0.4 ]\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 5632 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 11 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 11 samples from AnnData object\n", + "Generation Step 1\n", + "0.9318820224719101\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 6144 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 12 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 12 samples from AnnData object\n", + "Generation Step 2\n", + "0.9087078651685393\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 6656 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 13 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 13 samples from AnnData object\n", + "Generation Step 3\n", + "0.8588483146067416\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 7168 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 14 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 14 samples from AnnData object\n", + "Generation Step 4\n", + "0.7531601123595506\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 8192 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 16 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 16 samples from AnnData object\n", + "Generation Step 5\n", + "0.5856741573033708\n", + "INFO:stack.generation:Writing generation for split 'Dactolisib' to tutorial-pred-output/Dactolisib.h5ad\n", + "INFO:stack.generation:Processing split 'Dimethyl Sulfoxide'\n", + "INFO:stack.generation:Gene alignment: 11018/15012 (73.4%) target genes found in dataset\n", + "INFO:stack.generation:Adding 3994 missing genes with zero values\n", + "Masking ratio schedule: [0.8 0.6 0.39999998 0.19999999 0. ]\n", + "Context ratio schedule: [0.2 0.25 0.3 0.35 0.4 ]\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 5632 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 11 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 11 samples from AnnData object\n", + "Generation Step 1\n", + "0.9357443820224719\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 6144 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 12 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 12 samples from AnnData object\n", + "Generation Step 2\n", + "0.9213483146067416\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 6656 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 13 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 13 samples from AnnData object\n", + "Generation Step 3\n", + "0.8669241573033708\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 7168 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 14 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 14 samples from AnnData object\n", + "Generation Step 4\n", + "0.7816011235955056\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 8192 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 16 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 16 samples from AnnData object\n", + "Generation Step 5\n", + "0.6400983146067416\n", + "INFO:stack.generation:Writing generation for split 'Dimethyl Sulfoxide' to tutorial-pred-output/Dimethyl_Sulfoxide.h5ad\n", + "INFO:stack.generation:Processing split 'Foretinib'\n", + "INFO:stack.generation:Gene alignment: 11018/15012 (73.4%) target genes found in dataset\n", + "INFO:stack.generation:Adding 3994 missing genes with zero values\n", + "Masking ratio schedule: [0.8 0.6 0.39999998 0.19999999 0. ]\n", + "Context ratio schedule: [0.2 0.25 0.3 0.35 0.4 ]\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 5632 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 11 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 11 samples from AnnData object\n", + "Generation Step 1\n", + "0.9434691011235955\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 6144 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 12 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 12 samples from AnnData object\n", + "Generation Step 2\n", + "0.9224016853932584\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 6656 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 13 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 13 samples from AnnData object\n", + "Generation Step 3\n", + "0.8739466292134831\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 7168 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 14 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 14 samples from AnnData object\n", + "Generation Step 4\n", + "0.7875702247191011\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 8192 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 16 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 16 samples from AnnData object\n", + "Generation Step 5\n", + "0.605688202247191\n", + "INFO:stack.generation:Writing generation for split 'Foretinib' to tutorial-pred-output/Foretinib.h5ad\n", + "INFO:stack.generation:Processing split 'Idelalisib'\n", + "INFO:stack.generation:Gene alignment: 11018/15012 (73.4%) target genes found in dataset\n", + "INFO:stack.generation:Adding 3994 missing genes with zero values\n", + "Masking ratio schedule: [0.8 0.6 0.39999998 0.19999999 0. ]\n", + "Context ratio schedule: [0.2 0.25 0.3 0.35 0.4 ]\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 5632 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 11 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 11 samples from AnnData object\n", + "Generation Step 1\n", + "0.9406601123595506\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 6144 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 12 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 12 samples from AnnData object\n", + "Generation Step 2\n", + "0.9125702247191011\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 6656 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 13 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 13 samples from AnnData object\n", + "Generation Step 3\n", + "0.8718398876404494\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 7168 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 14 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 14 samples from AnnData object\n", + "Generation Step 4\n", + "0.7914325842696629\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 8192 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 16 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 16 samples from AnnData object\n", + "Generation Step 5\n", + "0.6738061797752809\n", + "INFO:stack.generation:Writing generation for split 'Idelalisib' to tutorial-pred-output/Idelalisib.h5ad\n", + "INFO:stack.generation:Processing split 'LDN 193189'\n", + "INFO:stack.generation:Gene alignment: 11018/15012 (73.4%) target genes found in dataset\n", + "INFO:stack.generation:Adding 3994 missing genes with zero values\n", + "Masking ratio schedule: [0.8 0.6 0.39999998 0.19999999 0. ]\n", + "Context ratio schedule: [0.2 0.25 0.3 0.35 0.4 ]\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 5632 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 11 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 11 samples from AnnData object\n", + "Generation Step 1\n", + "0.9322331460674157\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 6144 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 12 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 12 samples from AnnData object\n", + "Generation Step 2\n", + "0.9069522471910112\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 6656 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 13 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 13 samples from AnnData object\n", + "Generation Step 3\n", + "0.8507724719101124\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 7168 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 14 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 14 samples from AnnData object\n", + "Generation Step 4\n", + "0.7510533707865169\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 8192 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 16 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 16 samples from AnnData object\n", + "Generation Step 5\n", + "0.5712780898876404\n", + "INFO:stack.generation:Writing generation for split 'LDN 193189' to tutorial-pred-output/LDN_193189.h5ad\n", + "INFO:stack.generation:Processing split 'Linagliptin'\n", + "INFO:stack.generation:Gene alignment: 11018/15012 (73.4%) target genes found in dataset\n", + "INFO:stack.generation:Adding 3994 missing genes with zero values\n", + "Masking ratio schedule: [0.8 0.6 0.39999998 0.19999999 0. ]\n", + "Context ratio schedule: [0.2 0.25 0.3 0.35 0.4 ]\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 5632 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 11 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 11 samples from AnnData object\n", + "Generation Step 1\n", + "0.9213483146067416\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 6144 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 12 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 12 samples from AnnData object\n", + "Generation Step 2\n", + "0.8900983146067416\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 6656 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 13 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 13 samples from AnnData object\n", + "Generation Step 3\n", + "0.8448033707865169\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 7168 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 14 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 14 samples from AnnData object\n", + "Generation Step 4\n", + "0.7162921348314607\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 8192 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 16 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 16 samples from AnnData object\n", + "Generation Step 5\n", + "0.5452949438202247\n", + "INFO:stack.generation:Writing generation for split 'Linagliptin' to tutorial-pred-output/Linagliptin.h5ad\n", + "INFO:stack.generation:Processing split 'O-Demethylated Adapalene'\n", + "INFO:stack.generation:Gene alignment: 11018/15012 (73.4%) target genes found in dataset\n", + "INFO:stack.generation:Adding 3994 missing genes with zero values\n", + "Masking ratio schedule: [0.8 0.6 0.39999998 0.19999999 0. ]\n", + "Context ratio schedule: [0.2 0.25 0.3 0.35 0.4 ]\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 5632 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 11 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 11 samples from AnnData object\n", + "Generation Step 1\n", + "0.9322331460674157\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 6144 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 12 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 12 samples from AnnData object\n", + "Generation Step 2\n", + "0.9097612359550562\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 6656 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 13 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 13 samples from AnnData object\n", + "Generation Step 3\n", + "0.8644662921348315\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 7168 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 14 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 14 samples from AnnData object\n", + "Generation Step 4\n", + "0.7731741573033708\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 8192 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 16 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 16 samples from AnnData object\n", + "Generation Step 5\n", + "0.6035814606741573\n", + "INFO:stack.generation:Writing generation for split 'O-Demethylated Adapalene' to tutorial-pred-output/O-Demethylated_Adapalene.h5ad\n", + "INFO:stack.generation:Processing split 'Palbociclib'\n", + "INFO:stack.generation:Gene alignment: 11018/15012 (73.4%) target genes found in dataset\n", + "INFO:stack.generation:Adding 3994 missing genes with zero values\n", + "Masking ratio schedule: [0.8 0.6 0.39999998 0.19999999 0. ]\n", + "Context ratio schedule: [0.2 0.25 0.3 0.35 0.4 ]\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 5632 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 11 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 11 samples from AnnData object\n", + "Generation Step 1\n", + "0.9252106741573034\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 6144 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 12 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 12 samples from AnnData object\n", + "Generation Step 2\n", + "0.9030898876404494\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 6656 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 13 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 13 samples from AnnData object\n", + "Generation Step 3\n", + "0.8444522471910112\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 7168 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 14 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 14 samples from AnnData object\n", + "Generation Step 4\n", + "0.7145365168539326\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 8192 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 16 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 16 samples from AnnData object\n", + "Generation Step 5\n", + "0.5150983146067416\n", + "INFO:stack.generation:Writing generation for split 'Palbociclib' to tutorial-pred-output/Palbociclib.h5ad\n", + "INFO:stack.generation:Processing split 'Penfluridol'\n", + "INFO:stack.generation:Gene alignment: 11018/15012 (73.4%) target genes found in dataset\n", + "INFO:stack.generation:Adding 3994 missing genes with zero values\n", + "Masking ratio schedule: [0.8 0.6 0.39999998 0.19999999 0. ]\n", + "Context ratio schedule: [0.2 0.25 0.3 0.35 0.4 ]\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 5632 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 11 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 11 samples from AnnData object\n", + "Generation Step 1\n", + "0.9445224719101124\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 6144 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 12 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 12 samples from AnnData object\n", + "Generation Step 2\n", + "0.9231039325842697\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 6656 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 13 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 13 samples from AnnData object\n", + "Generation Step 3\n", + "0.8771067415730337\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 7168 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 14 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 14 samples from AnnData object\n", + "Generation Step 4\n", + "0.8103932584269663\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 8192 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 16 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 16 samples from AnnData object\n", + "Generation Step 5\n", + "0.6990870786516854\n", + "INFO:stack.generation:Writing generation for split 'Penfluridol' to tutorial-pred-output/Penfluridol.h5ad\n", + "INFO:stack.generation:Processing split 'Porcn Inhibitor III'\n", + "INFO:stack.generation:Gene alignment: 11018/15012 (73.4%) target genes found in dataset\n", + "INFO:stack.generation:Adding 3994 missing genes with zero values\n", + "Masking ratio schedule: [0.8 0.6 0.39999998 0.19999999 0. ]\n", + "Context ratio schedule: [0.2 0.25 0.3 0.35 0.4 ]\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 5632 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 11 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 11 samples from AnnData object\n", + "Generation Step 1\n", + "0.8830758426966292\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 6144 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 12 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 12 samples from AnnData object\n", + "Generation Step 2\n", + "0.807935393258427\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 6656 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 13 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 13 samples from AnnData object\n", + "Generation Step 3\n", + "0.6548455056179775\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 7168 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 14 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 14 samples from AnnData object\n", + "Generation Step 4\n", + "0.4452247191011236\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 8192 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 16 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 16 samples from AnnData object\n", + "Generation Step 5\n", + "0.47366573033707865\n", + "INFO:stack.generation:Writing generation for split 'Porcn Inhibitor III' to tutorial-pred-output/Porcn_Inhibitor_III.h5ad\n", + "INFO:stack.generation:Processing split 'R428'\n", + "INFO:stack.generation:Gene alignment: 11018/15012 (73.4%) target genes found in dataset\n", + "INFO:stack.generation:Adding 3994 missing genes with zero values\n", + "Masking ratio schedule: [0.8 0.6 0.39999998 0.19999999 0. ]\n", + "Context ratio schedule: [0.2 0.25 0.3 0.35 0.4 ]\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 5632 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 11 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 11 samples from AnnData object\n", + "Generation Step 1\n", + "0.9318820224719101\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 6144 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 12 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 12 samples from AnnData object\n", + "Generation Step 2\n", + "0.9027387640449438\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 6656 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 13 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 13 samples from AnnData object\n", + "Generation Step 3\n", + "0.8563904494382022\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 7168 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 14 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 14 samples from AnnData object\n", + "Generation Step 4\n", + "0.7345505617977528\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "/home/mingze/miniconda/envs/gpu_mingze/lib/python3.10/site-packages/anndata/_core/anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.\n", + " utils.warn_names_duplicates(\"obs\")\n", + "INFO:stack.data.training.datasets:Loading metadata from AnnData object\n", + "WARNING:stack.data.training.datasets:'organism' column not found in obs, assuming all cells are valid\n", + "INFO:stack.data.training.datasets:Using main data for gene names\n", + "INFO:stack.data.training.datasets:Loaded AnnData with 8192 human cells, 15012/15012 target genes\n", + "INFO:stack.data.training.datasets:Generating samples...\n", + "INFO:stack.data.training.datasets:Generated 16 samples for eval mode (eval with upsampling)\n", + "INFO:stack.data.training.datasets:TestSamplerDataset initialized with 16 samples from AnnData object\n", + "Generation Step 5\n", + "0.5396769662921348\n", + "INFO:stack.generation:Writing generation for split 'R428' to tutorial-pred-output/R428.h5ad\n" + ] + } + ], + "source": [ + "! stack-generation --checkpoint \"tutorial-pred-model/bc_large_aligned.ckpt\" \\\n", + "--base-adata \"tutorial-pred-data/openproblems_context.h5ad\" --test-adata \"tutorial-pred-data/openproblems_test.h5ad\" \\\n", + "--genelist \"tutorial-pred-model/basecount_1000per_15000max.pkl\" --split-column sm_name --batch-size 16 --num-steps 5 \\\n", + "--output-dir tutorial-pred-output --prompt-ratio 0.25 --context-ratio 0.4" + ] + }, + { + "cell_type": "markdown", + "id": "a3fa340f-fab1-49ad-9941-474cd561d4c5", + "metadata": {}, + "source": [ + "The output folder tutorial-pred-output now contains one AnnData file for each perturbation condition, as well as the control condition (Dimethyl Sulfoxide). We can then perform differential expression analysis on these datasets." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "04aaf5c9-e57d-47b9-a126-2b4559d82983", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/transfer/code/stack/pyproject.toml b/transfer/code/stack/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..5933b396dd491d51514c63c6f213c7e1023e1376 --- /dev/null +++ b/transfer/code/stack/pyproject.toml @@ -0,0 +1,46 @@ +[build-system] +requires = ["setuptools>=68", "wheel"] +build-backend = "setuptools.build_meta" + +[project] +name = "arc-stack" +version = "0.1.2" +description = "Stack is a single-cell foundation model that enables in-context learning at inference time." +readme = "README.md" +license = { file = "LICENSE" } +authors = [ + { name = "Mingze Dong", email = "mingze.dong@arcinstitute.org" }, + { name = "Yusuf Roohani", email = "yusuf.roohani@arcinstitute.org" }, +] +requires-python = ">=3.9" +dependencies = [ + "anndata>=0.9", + "h5py>=3.8", + "geomloss>=0.2.6", + "numpy>=1.22", + "pandas>=1.5", + "psutil>=5.9", + "pytorch-lightning>=2.1", + "PyYAML>=6.0", + "scipy>=1.8", + "scvi-tools>=1.0", + "torch>=2.0", + "wandb>=0.15", +] + +[project.urls] +Homepage = "https://github.com/ArcInstitute/stack" +Source = "https://github.com/ArcInstitute/stack" + +[project.scripts] +stack-train = "stack.cli.launch_training:main" +stack-finetune = "stack.cli.launch_finetuning:main" +stack-embedding = "stack.cli.embedding:main" +stack-generation = "stack.cli.generation:main" + +[tool.setuptools] +package-dir = {"" = "src"} + +[tool.setuptools.packages.find] +where = ["src"] +include = ["stack*"] diff --git a/transfer/code/stack/setup.cfg b/transfer/code/stack/setup.cfg new file mode 100644 index 0000000000000000000000000000000000000000..37acf9bba2a116cea413ab1ba77dcf4af081ceaf --- /dev/null +++ b/transfer/code/stack/setup.cfg @@ -0,0 +1,41 @@ +[metadata] +name = arc-stack +version = 0.1.2 +description = Training and fine-tuning utilities for the Stack model +long_description = file: README.md +long_description_content_type = text/markdown +license = Apache-2.0 +license_files = LICENSE + +[options] +packages = find: +package_dir = + = src +python_requires = >=3.9 +include_package_data = True +install_requires = + anndata>=0.9 + h5py>=3.8 + geomloss>=0.2.6 + numpy>=1.22 + pandas>=1.5 + psutil>=5.9 + pytorch-lightning>=2.1 + PyYAML>=6.0 + scipy>=1.8 + scvi-tools>=1.0 + torch>=2.0 + wandb>=0.15 + +[options.entry_points] +console_scripts = + stack-train = stack.cli.launch_training:main + stack-finetune = stack.cli.launch_finetuning:main + stack-embedding = stack.cli.embedding:main + stack-generation = stack.cli.generation:main + +[options.packages.find] +where = + src +include = + stack* diff --git a/transfer/code/stack/setup.py b/transfer/code/stack/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..7f1a1763ca9cebc7bc16576d353d3284ee5d3c7d --- /dev/null +++ b/transfer/code/stack/setup.py @@ -0,0 +1,4 @@ +from setuptools import setup + +if __name__ == "__main__": + setup() diff --git a/transfer/code/stack/src/arc_stack.egg-info/PKG-INFO b/transfer/code/stack/src/arc_stack.egg-info/PKG-INFO new file mode 100644 index 0000000000000000000000000000000000000000..c8f9526b8f1e51926c20cc87fadef03ab13ee28e --- /dev/null +++ b/transfer/code/stack/src/arc_stack.egg-info/PKG-INFO @@ -0,0 +1,636 @@ +Metadata-Version: 2.4 +Name: arc-stack +Version: 0.1.2 +Summary: Stack is a single-cell foundation model that enables in-context learning at inference time. +Author-email: Mingze Dong , Yusuf Roohani +License: Attribution-NonCommercial-ShareAlike 4.0 International + + ======================================================================= + + Creative Commons Corporation ("Creative Commons") is not a law firm and + does not provide legal services or legal advice. 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Except for the limited purpose of indicating that + material is shared under a Creative Commons public license or as + otherwise permitted by the Creative Commons policies published at + creativecommons.org/policies, Creative Commons does not authorize the + use of the trademark "Creative Commons" or any other trademark or logo + of Creative Commons without its prior written consent including, + without limitation, in connection with any unauthorized modifications + to any of its public licenses or any other arrangements, + understandings, or agreements concerning use of licensed material. For + the avoidance of doubt, this paragraph does not form part of the + public licenses. + + Creative Commons may be contacted at creativecommons.org. + +Project-URL: Homepage, https://github.com/ArcInstitute/stack +Project-URL: Source, https://github.com/ArcInstitute/stack +Requires-Python: >=3.9 +Description-Content-Type: text/markdown +License-File: LICENSE +Requires-Dist: anndata>=0.9 +Requires-Dist: h5py>=3.8 +Requires-Dist: geomloss>=0.2.6 +Requires-Dist: numpy>=1.22 +Requires-Dist: pandas>=1.5 +Requires-Dist: psutil>=5.9 +Requires-Dist: pytorch-lightning>=2.1 +Requires-Dist: PyYAML>=6.0 +Requires-Dist: scipy>=1.8 +Requires-Dist: scvi-tools>=1.0 +Requires-Dist: torch>=2.0 +Requires-Dist: wandb>=0.15 +Dynamic: license-file + + +# Stack: In-context learning of single-cell biology + +Stack is a large-scale encoder-decoder foundation model trained on 150 million uniformly-preprocessed single cells. It introduces a novel tabular attention architecture that enables both intra- and inter-cellular information flow, setting cell-by-gene matrix chunks as the basic input data unit. Through in-context learning, Stack offers substantial performance improvements in generalizing biological effects and enables generation of unseen cell profiles in novel contexts. + +## Installation + +### Using pip +```bash +# Install from PyPI +pip install arc-stack + +# Or install from source for development +git clone https://github.com/ArcInstitute/stack.git +cd stack +pip install -e . +``` + +### Using uv +```bash +# Install from PyPI +uv pip install arc-stack + +# Or install from source for development +git clone https://github.com/ArcInstitute/stack.git +cd stack +uv pip install -e . +``` + + +## Quick Start + +- Use Stack to embed your single-cell data: [Notebook](notebooks/tutorial-embed.ipynb) +- Use Stack to zero-shot predict unseen perturbation/observation profiles: [Notebook](notebooks/tutorial-predict.ipynb) + +### Training Stack from Scratch + +```bash +# Once installed, the console entry point becomes available +stack-train \ + --dataset_configs "/path/to/data:false:gene_symbols" \ + --genelist_path "hvg_genes.pkl" \ + --save_dir "./checkpoints" \ + --sample_size 256 \ + --batch_size 32 \ + --n_hidden 100 \ + --token_dim 16 \ + --n_layers 9 \ + --max_epochs 10 + +# Alternatively, invoke the module directly when working from a cloned repo +python -m stack.cli.launch_training [args...] +``` + +### Fine-tuning Stack with Frozen Teacher + +```bash +stack-finetune \ + --checkpoint_path "./checkpoints/pretrained.ckpt" \ + --dataset_configs "human:/path/to/data:donor_id:cell_type:false" \ + --genelist_path "hvg_genes.pkl" \ + --save_dir "./finetuned_checkpoints" \ + --sample_size 512 \ + --batch_size 8 \ + --replacement_ratio 0.75 \ + --max_epochs 8 + +# Or use uv run +uv run stack-finetune [args...] + +# Repository wrapper remains available for local development +python -m stack.cli.launch_finetuning [args...] +``` + +### Running Stack with configuration files + +Both `launch_training.py` and `launch_finetuning.py` accept a `--config` flag that points to a YAML or JSON file. Any command line +arguments omitted after `--config` inherit their values from the file, while flags provided on the command line override the +configuration. Example configs mirroring the provided Slurm scripts live under `configs/`: + +```bash +# Train with the preset configuration +stack-train --config configs/training/bc_large.yaml + +# Override a single hyperparameter without editing the file +stack-train --config configs/training/bc_large.yaml --learning_rate 5e-5 + +# Fine-tune using a config file +stack-finetune --config configs/finetuning/ft_parsecg.yaml + +# Direct module invocation is still supported if you prefer python -m +python -m stack.cli.launch_training --config configs/training/bc_large.yaml +``` + +> **Note:** YAML configs require [`pyyaml`](https://pyyaml.org/). Install it with `pip install pyyaml` or use a JSON config file. + +### Extracting Stack Embeddings + +```bash +stack-embedding \ + --checkpoint "./checkpoints/pretrained.ckpt" \ + --adata "data.h5ad" \ + --genelist "hvg_genes.pkl" \ + --output "embeddings.h5ad" \ + --batch-size 32 + +# Or use uv run +uv run stack-embedding \ + --checkpoint "./checkpoints/pretrained.ckpt" \ + --adata "data.h5ad" \ + --genelist "hvg_genes.pkl" \ + --output "embeddings.h5ad" \ + --batch-size 32 +``` + +### In-Context Generation with Stack + +```bash +stack-generation \ + --checkpoint "./checkpoints/pretrained.ckpt" \ + --base-adata "base_data.h5ad" \ + --test-adata "test_data.h5ad" \ + --genelist "hvg_genes.pkl" \ + --output-dir "./generations" \ + --split-column "donor_id" + +# Or use uv run +uv run stack-generation \ + --checkpoint "./checkpoints/pretrained.ckpt" \ + --base-adata "base_data.h5ad" \ + --test-adata "test_data.h5ad" \ + --genelist "hvg_genes.pkl" \ + --output-dir "./generations" \ + --split-column "donor_id" +``` + +## Model Architecture + +- **Tabular Attention**: Alternating cell-wise and gene-wise attention layers +- **Token Dimension**: Configurable token embedding dimension (default: 16) +- **Hidden Dimension**: Gene dimension reduction (default: 100) +- **Masking Strategy**: Rectangular masking with variable rates (0.1-0.8) + +## Data Preparation + +### Computing Highly Variable Genes (HVGs) +```python +from stack.data.datasets import DatasetConfig, compute_hvg_union + +configs = [DatasetConfig(path="/data/path", filter_organism=True)] +hvg_genes = compute_hvg_union(configs, n_top_genes=1000, output_path="hvg.pkl") +``` + +### Dataset Configuration Format +- **Human datasets**: `human:/path:donor_col:cell_type_col[:filter_organism[:gene_col]]` +- **Drug datasets**: `drug:/path:condition_col:cell_line_col:control_condition[:filter_organism[:gene_col]]` + +## Key Features + +- **In-Context Learning**: Zero-shot generalization to new biological contexts +- **Multi-Dataset Training**: Simultaneous training on multiple single-cell datasets +- **Frozen Teacher Fine-tuning**: Novel fine-tuning procedure with stable teacher targets +- **Efficient Data Loading**: Optimized HDF5 loading with sparse matrix support + +> **Note:** `scShiftAttentionModel` remains available as an alias for backward compatibility. + +## Citation + +If you use Stack in your research, please cite the Stack [paper](https://www.biorxiv.org/content/10.64898/2026.01.09.698608v1). + +## Licenses +Stack code is [licensed](LICENSE) under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0). + +The model weights and output are licensed under the [Arc Research Institute Stack Model Non-Commercial License](MODEL_LICENSE.md) and subject to the [Arc Research Institute Stack Model Acceptable Use Policy](MODEL_ACCEPTABLE_USE_POLICY.md). diff --git a/transfer/code/stack/src/arc_stack.egg-info/SOURCES.txt b/transfer/code/stack/src/arc_stack.egg-info/SOURCES.txt new file mode 100644 index 0000000000000000000000000000000000000000..647fc88c53dfa58253a851a4d897fd890694d122 --- /dev/null +++ b/transfer/code/stack/src/arc_stack.egg-info/SOURCES.txt @@ -0,0 +1,61 @@ +LICENSE +MANIFEST.in +README.md +pyproject.toml +setup.cfg +setup.py +configs/finetuning/ft_parsecg.yaml +configs/training/bc_large.yaml +src/arc_stack.egg-info/PKG-INFO +src/arc_stack.egg-info/SOURCES.txt +src/arc_stack.egg-info/dependency_links.txt +src/arc_stack.egg-info/entry_points.txt +src/arc_stack.egg-info/requires.txt +src/arc_stack.egg-info/top_level.txt +src/stack/cli_utils.py +src/stack/dataloader.py +src/stack/finetuning_dataloader.py +src/stack/model.py +src/stack/model_finetune.py +src/stack/model_loading.py +src/stack/cli/__init__.py +src/stack/cli/embedding.py +src/stack/cli/generation.py +src/stack/cli/launch_finetuning.py +src/stack/cli/launch_training.py +src/stack/data/__init__.py +src/stack/data/datasets.py +src/stack/data/gene_processing.py +src/stack/data/h5_manager.py +src/stack/data/hvg.py +src/stack/data/finetuning/__init__.py +src/stack/data/finetuning/datasets.py +src/stack/data/training/__init__.py +src/stack/data/training/datasets.py +src/stack/finetune/__init__.py +src/stack/finetune/datamodule.py +src/stack/finetune/datasets.py +src/stack/finetune/lightning.py +src/stack/finetune/utils.py +src/stack/models/__init__.py +src/stack/models/utils.py +src/stack/models/core/__init__.py +src/stack/models/core/base.py +src/stack/models/core/inference.py +src/stack/models/core/losses.py +src/stack/models/finetune/__init__.py +src/stack/models/finetune/mixins.py +src/stack/models/finetune/model.py +src/stack/modules/__init__.py +src/stack/modules/attention.py +src/stack/modules/regularizers.py +src/stack/training/__init__.py +src/stack/training/datamodule.py +src/stack/training/lightning.py +src/stack/training/utils.py +tests/test_cli_help.py +tests/test_cli_smoke_runs.py +tests/test_cli_utils.py +tests/test_finetune_mixins.py +tests/test_model_core.py +tests/test_model_loading_import.py \ No newline at end of file diff --git a/transfer/code/stack/src/arc_stack.egg-info/dependency_links.txt b/transfer/code/stack/src/arc_stack.egg-info/dependency_links.txt new file mode 100644 index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc --- /dev/null +++ b/transfer/code/stack/src/arc_stack.egg-info/dependency_links.txt @@ -0,0 +1 @@ + diff --git a/transfer/code/stack/src/arc_stack.egg-info/entry_points.txt b/transfer/code/stack/src/arc_stack.egg-info/entry_points.txt new file mode 100644 index 0000000000000000000000000000000000000000..9362395af683807c1502aec3067f96550b219fd9 --- /dev/null +++ b/transfer/code/stack/src/arc_stack.egg-info/entry_points.txt @@ -0,0 +1,5 @@ +[console_scripts] +stack-embedding = stack.cli.embedding:main +stack-finetune = stack.cli.launch_finetuning:main +stack-generation = stack.cli.generation:main +stack-train = stack.cli.launch_training:main diff --git a/transfer/code/stack/src/arc_stack.egg-info/requires.txt b/transfer/code/stack/src/arc_stack.egg-info/requires.txt new file mode 100644 index 0000000000000000000000000000000000000000..9569cb64b1e14c5faa09c380307ef75cee6f2e52 --- /dev/null +++ b/transfer/code/stack/src/arc_stack.egg-info/requires.txt @@ -0,0 +1,12 @@ +anndata>=0.9 +h5py>=3.8 +geomloss>=0.2.6 +numpy>=1.22 +pandas>=1.5 +psutil>=5.9 +pytorch-lightning>=2.1 +PyYAML>=6.0 +scipy>=1.8 +scvi-tools>=1.0 +torch>=2.0 +wandb>=0.15 diff --git a/transfer/code/stack/src/arc_stack.egg-info/top_level.txt b/transfer/code/stack/src/arc_stack.egg-info/top_level.txt new file mode 100644 index 0000000000000000000000000000000000000000..b69c73632df93c2645d711ae44d9e1a0a3f462a0 --- /dev/null +++ b/transfer/code/stack/src/arc_stack.egg-info/top_level.txt @@ -0,0 +1 @@ +stack diff --git a/transfer/code/stack/src/stack/cli/__init__.py b/transfer/code/stack/src/stack/cli/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ed98c46a679cdd8fd3ce26fd7a633aea04b68175 --- /dev/null +++ b/transfer/code/stack/src/stack/cli/__init__.py @@ -0,0 +1,3 @@ +"""Command-line helper modules for StateICL.""" + +__all__ = [] diff --git a/transfer/code/stack/src/stack/cli/embedding.py b/transfer/code/stack/src/stack/cli/embedding.py new file mode 100644 index 0000000000000000000000000000000000000000..8df32dc2201080f93b5881060267466a66a2ee0d --- /dev/null +++ b/transfer/code/stack/src/stack/cli/embedding.py @@ -0,0 +1,193 @@ +#!/usr/bin/env python3 +"""Utility script to extract embeddings from a trained StateICL checkpoint.""" +from __future__ import annotations + +import argparse +import logging +from pathlib import Path +from typing import Optional, Tuple, TYPE_CHECKING + +if TYPE_CHECKING: # pragma: no cover - type checking only + import anndata as ad + import numpy as np + import pandas as pd + import torch + + +LOGGER = logging.getLogger("stack.embedding") +_DEPS: dict | None = None + + +def _ensure_deps(): + """Lazy import heavy runtime dependencies to keep ``--help`` fast.""" + + global _DEPS + if _DEPS is not None: + return _DEPS + + import anndata as ad # type: ignore + import numpy as np # type: ignore + import pandas as pd # type: ignore + import torch # type: ignore + + from ..model_loading import load_model_from_checkpoint + + _DEPS = { + "ad": ad, + "np": np, + "pd": pd, + "torch": torch, + "load_model_from_checkpoint": load_model_from_checkpoint, + } + return _DEPS + + +def _resolve_device(device_arg: str) -> torch.device: + deps = _ensure_deps() + torch = deps["torch"] + if device_arg == "auto": + return torch.device("cuda" if torch.cuda.is_available() else "cpu") + return torch.device(device_arg) + + +def _load_model(checkpoint_path: str, device: torch.device) -> torch.nn.Module: + deps = _ensure_deps() + load_model_from_checkpoint = deps["load_model_from_checkpoint"] + return load_model_from_checkpoint(checkpoint_path, device=device) + + +def extract_embeddings( + checkpoint_path: str, + adata_path: str, + genelist_path: str, + *, + gene_name_col: Optional[str] = None, + batch_size: int = 32, + num_workers: int = 4, + random_seed: Optional[int] = None, + max_samples: Optional[int] = None, + device: str = "auto", + show_progress: bool = False, +) -> Tuple[np.ndarray, Optional[np.ndarray]]: + """Extract embeddings for all cells in ``adata_path`` using the trained model.""" + + deps = _ensure_deps() + np = deps["np"] + torch = deps["torch"] + model = _load_model(checkpoint_path, _resolve_device(device)) + + dataloader_kwargs = {} + if max_samples is not None: + dataloader_kwargs["max_samples"] = max_samples + if random_seed is not None: + dataloader_kwargs["random_state"] = random_seed + + embeddings, dataset_embeddings = model.get_latent_representation( + adata_path=adata_path, + genelist_path=genelist_path, + gene_name_col=gene_name_col, + batch_size=batch_size, + show_progress=show_progress, + num_workers=num_workers, + **dataloader_kwargs, + ) + return embeddings, dataset_embeddings + + +def _build_obs_dataframe(obs_source: Optional[str], n_cells: int) -> pd.DataFrame: + deps = _ensure_deps() + ad = deps["ad"] + pd = deps["pd"] + if obs_source is None: + index = [f"cell_{idx}" for idx in range(n_cells)] + return pd.DataFrame(index=index) + + LOGGER.info("Loading observation metadata from %s", obs_source) + source_adata = ad.read_h5ad(obs_source) + if source_adata.n_obs < n_cells: + raise ValueError( + f"Observation source contains {source_adata.n_obs} cells but embeddings cover {n_cells}." + ) + return source_adata.obs.iloc[:n_cells].copy() + + +def save_embeddings( + embeddings: np.ndarray, + output_path: Path, + *, + obs_source: Optional[str] = None, +) -> None: + """Persist embeddings to disk in either NumPy or AnnData format.""" + + deps = _ensure_deps() + ad = deps["ad"] + np = deps["np"] + pd = deps["pd"] + + output_path.parent.mkdir(parents=True, exist_ok=True) + if output_path.suffix == ".h5ad": + obs = _build_obs_dataframe(obs_source, embeddings.shape[0]) + var_index = [f"latent_{idx}" for idx in range(embeddings.shape[1])] + embed_adata = ad.AnnData(X=embeddings, obs=obs, var=pd.DataFrame(index=var_index)) + LOGGER.info("Writing embeddings to %s", output_path) + embed_adata.write_h5ad(output_path) + else: + LOGGER.info("Saving embeddings array to %s", output_path) + np.save(output_path, embeddings) + + +def build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser(description="Extract latent embeddings from a StateICL checkpoint") + parser.add_argument("--checkpoint", required=True, help="Path to the trained Lightning checkpoint (.ckpt)") + parser.add_argument("--adata", required=True, help="Path to the AnnData file (.h5ad) to embed") + parser.add_argument("--genelist", required=True, help="Path to the pickled gene list used during training") + parser.add_argument("--output", required=True, help="Destination file (.npy or .h5ad) for embeddings") + parser.add_argument( + "--gene-name-col", + default=None, + help="Optional column in adata.var/raw.var containing gene symbols for alignment", + ) + parser.add_argument("--batch-size", type=int, default=32, help="Batch size for inference") + parser.add_argument("--num-workers", type=int, default=4, help="Number of DataLoader workers") + parser.add_argument("--random-seed", type=int, default=0, help="Optional seed for deterministic sampling") + parser.add_argument("--max-samples", type=int, default=None, help="Limit the number of sampled batches") + parser.add_argument("--device", default="auto", help="Target device (cuda, cpu, or auto)") + parser.add_argument("--show-progress", action="store_true", help="Display a tqdm progress bar") + parser.add_argument( + "--obs-source", + type=str, + default=None, + help="Optional .h5ad file with observation metadata to attach to the embeddings", + ) + return parser + + +def main(args: Optional[list[str]] = None) -> None: + parser = build_parser() + parsed = parser.parse_args(args=args) + + logging.basicConfig(level=logging.INFO) + + embeddings, dataset_embeddings = extract_embeddings( + checkpoint_path=parsed.checkpoint, + adata_path=parsed.adata, + genelist_path=parsed.genelist, + gene_name_col=parsed.gene_name_col, + batch_size=parsed.batch_size, + num_workers=parsed.num_workers, + random_seed=parsed.random_seed, + max_samples=parsed.max_samples, + device=parsed.device, + show_progress=parsed.show_progress, + ) + + output_path = Path(parsed.output) + save_embeddings( + embeddings, + output_path, + obs_source=parsed.obs_source, + ) + + +if __name__ == "__main__": + main() diff --git a/transfer/code/stack/src/stack/cli/generation.py b/transfer/code/stack/src/stack/cli/generation.py new file mode 100644 index 0000000000000000000000000000000000000000..164c24409faac2078f3b2e25f38b07bc27e8c40d --- /dev/null +++ b/transfer/code/stack/src/stack/cli/generation.py @@ -0,0 +1,536 @@ +#!/usr/bin/env python3 +"""Generate in-context predictions for a test AnnData using donor-specific base data.""" +from __future__ import annotations + +import argparse +import logging +import re +import sys +from pathlib import Path +from typing import Callable, Dict, Iterable, List, Optional, Sequence, Tuple, TYPE_CHECKING + +if TYPE_CHECKING: # pragma: no cover - type checking only + import anndata as ad + import numpy as np + import pandas as pd + import torch + from scipy.sparse import issparse + +REPO_ROOT = Path(__file__).resolve().parents[2] +SRC_PATH = REPO_ROOT / "src" +if str(SRC_PATH) not in sys.path: + sys.path.insert(0, str(SRC_PATH)) + +LOGGER = logging.getLogger("stack.generation") +_DEPS: dict | None = None + + +def _ensure_deps(): + """Lazy import heavy dependencies to keep ``--help`` responsive.""" + + global _DEPS + if _DEPS is not None: + return _DEPS + + import anndata as ad # type: ignore + import numpy as np # type: ignore + import pandas as pd # type: ignore + import torch # type: ignore + from scipy.sparse import issparse # type: ignore + + from stack.data.training.datasets import load_gene_list + from ..model_loading import load_model_from_checkpoint + + _DEPS = { + "ad": ad, + "np": np, + "pd": pd, + "torch": torch, + "issparse": issparse, + "load_gene_list": load_gene_list, + "load_model_from_checkpoint": load_model_from_checkpoint, + } + return _DEPS + + +def _load_adata( + source: str | Path | ad.AnnData, + *, + target_genes: Optional[Sequence[str]] = None, + gene_name_col: Optional[str] = None, + backed: Optional[str] = None, + align_to_target: bool = True, +) -> ad.AnnData: + deps = _ensure_deps() + ad = deps["ad"] + if isinstance(source, ad.AnnData): + adata = source + if adata.isbacked: + adata = adata.to_memory() + else: + adata = adata.copy() + else: + LOGGER.info("Loading AnnData from %s", source) + adata = ad.read_h5ad(str(source), backed=backed) + + if align_to_target and target_genes: + adata = _align_genes_to_target_list(adata, target_genes, gene_name_col) + return adata + + +def _extract_gene_names(adata: ad.AnnData, gene_name_col: Optional[str]) -> np.ndarray: + var_df = adata.raw.var if adata.raw is not None else adata.var + if gene_name_col and gene_name_col in var_df.columns: + names = var_df[gene_name_col].astype(str).str.upper().to_numpy() + else: + names = var_df.index.astype(str).str.upper().to_numpy() + return names + + +def _align_genes_to_target_list( + adata: ad.AnnData, + target_genes: Optional[Sequence[str]], + gene_name_col: Optional[str], +) -> ad.AnnData: + deps = _ensure_deps() + ad = deps["ad"] + pd = deps["pd"] + import scipy.sparse as sp + + if not target_genes: + return adata.copy() + + # Convert target genes to list and create uppercase index for matching + target_genes_list = list(target_genes) + target_genes_upper = pd.Index([str(gene).upper() for gene in target_genes_list]) + + # Extract source gene names using your existing function + source_gene_names = _extract_gene_names(adata, gene_name_col) + source_genes_upper = pd.Index([str(gene).upper() for gene in source_gene_names]) + + # Find intersection and missing genes + intersection = source_genes_upper.intersection(target_genes_upper) + found_genes = len(intersection) + + if found_genes == 0: + raise ValueError(f"No target genes found in the dataset. " + f"Dataset has {len(source_genes_upper)} genes, " + f"target list has {len(target_genes_list)} genes.") + + ratio = found_genes / len(target_genes_list) + LOGGER.info(f"Gene alignment: {found_genes}/{len(target_genes_list)} ({ratio:.1%}) target genes found in dataset") + + # Determine which matrix to use (raw or regular) + if adata.raw is not None: + source_matrix = adata.raw.X + source_var = adata.raw.var.copy() + else: + source_matrix = adata.X + source_var = adata.var.copy() + + # Find genes that need to be added (missing in source data) + genes_to_add = target_genes_upper.difference(source_genes_upper) + needs_padding = len(genes_to_add) > 0 + + if needs_padding: + LOGGER.info(f"Adding {len(genes_to_add)} missing genes with zero values") + + # Create efficient empty sparse matrix for padding + padding_mtx = sp.csr_matrix((adata.n_obs, len(genes_to_add)), + dtype=source_matrix.dtype) + + # Create padding AnnData + adata_padding = ad.AnnData( + X=padding_mtx, + obs=adata.obs.copy(), + var=pd.DataFrame(index=genes_to_add) + ) + # Create temporary adata with uppercase var_names for concat + adata_temp = ad.AnnData( + X=source_matrix, + obs=adata.obs, + var=pd.DataFrame(index=source_genes_upper) + ) + # Concatenate original data with padding + adata_combined = ad.concat( + [adata_temp, adata_padding], + axis=1, # Concatenate along gene axis + join="outer", + merge="unique" + ) + else: + # No padding needed, just create adata with uppercase var_names + adata_combined = ad.AnnData( + X=source_matrix, + obs=adata.obs, + var=pd.DataFrame(index=source_genes_upper) + ) + + # Reorder genes to match target gene order (this also removes extra genes) + if not target_genes_upper.equals(adata_combined.var_names): + adata_combined = adata_combined[:, target_genes_upper].copy() + + # Create final var dataframe with original target gene names (not uppercased) + aligned_var = pd.DataFrame(index=target_genes_list) + aligned_var.index.name = source_var.index.name + + # If gene_name_col exists, add it to the var dataframe + if gene_name_col: + aligned_var[gene_name_col] = target_genes_list + + # Create final aligned adata + aligned_adata = ad.AnnData( + X=adata_combined.X, + obs=adata.obs.copy(), + var=aligned_var + ) + return aligned_adata + + +def _sanitize_split_value(split_value: str) -> str: + safe = re.sub(r"[^0-9A-Za-z_.-]+", "_", str(split_value)) + return safe or "split" + + +def _select_split_values( + base_adata: ad.AnnData, + split_column: str, + requested_splits: Optional[Iterable[str]], +) -> List[str]: + deps = _ensure_deps() + np = deps["np"] + if split_column not in base_adata.obs.columns: + raise ValueError( + f"Split column '{split_column}' not found in base AnnData. Available columns: {list(base_adata.obs.columns)}" + ) + + available = base_adata.obs[split_column].astype(str) + if requested_splits: + splits = [] + for value in requested_splits: + mask = available == str(value) + if not np.any(mask): + LOGGER.warning("Split value '%s' not found in column '%s'", value, split_column) + continue + splits.append(str(value)) + if not splits: + raise ValueError("None of the requested split values were found in the base AnnData.") + return splits + + return sorted(available.unique()) + + +def _prepare_base_subset( + base_adata: ad.AnnData, + split_column: str, + split_value: str, + *, + target_genes: Optional[Sequence[str]], + gene_name_col: Optional[str], +) -> ad.AnnData: + deps = _ensure_deps() + np = deps["np"] + mask = base_adata.obs[split_column].astype(str) == split_value + if not np.any(mask): + raise ValueError(f"No cells found for split value '{split_value}'.") + + subset_view = base_adata[mask] + subset = _load_adata( + subset_view, + target_genes=target_genes, + gene_name_col=gene_name_col, + align_to_target=bool(target_genes), + ) + return subset + + +def _run_incontext_generation( + model: torch.nn.Module, + base_adata: ad.AnnData, + test_adata: ad.AnnData, + *, + genelist_path: str, + gene_name_col: Optional[str], + prompt_ratio: float, + context_ratio: float, + context_ratio_min: float, + mask_rate: float, + mode: str, + num_steps: Optional[int], + batch_size: int, + num_workers: int, + random_seed: Optional[int], + show_progress: bool, +) -> Tuple[np.ndarray, Optional[np.ndarray]]: + outputs = model.get_incontext_generation( + base_adata_or_path=base_adata, + test_adata_or_path=test_adata, + genelist_path=genelist_path, + prompt_ratio=prompt_ratio, + context_ratio=context_ratio, + context_ratio_min=context_ratio_min, + mask_rate=mask_rate, + mode=mode, + num_steps=num_steps, + gene_name_col=gene_name_col, + batch_size=batch_size, + show_progress=show_progress, + num_workers=num_workers, + random_seed=random_seed, + ) + + if isinstance(outputs, tuple): + predictions, test_logit = outputs + else: + predictions, test_logit = outputs, None + return predictions, test_logit + + +def generate( + checkpoint_path: str, + base_adata_path: str, + test_adata_path: str, + genelist_path: str, + *, + split_column: str, + split_values: Optional[Sequence[str]] = None, + gene_name_col: Optional[str] = None, + prompt_ratio: float = 0.25, + context_ratio: float = 0.4, + context_ratio_min: float = 0.2, + mask_rate: float = 1.0, + num_steps: Optional[int] = None, + mode: str = "vanilla", + batch_size: int = 32, + num_workers: int = 4, + random_seed: Optional[int] = None, + device: str = "auto", + show_progress: bool = False, + on_generation: Optional[Callable[[str, ad.AnnData], None]] = None, +) -> Dict[str, ad.AnnData]: + """Run in-context generation for the specified donor splits. + + When ``on_generation`` is provided each split is passed to the callback as soon + as it is computed, avoiding the need to hold all generated AnnData objects in + memory simultaneously. The returned dictionary will remain empty in that + streaming mode. Without a callback the results are accumulated and returned. + """ + + deps = _ensure_deps() + ad = deps["ad"] + np = deps["np"] + load_gene_list = deps["load_gene_list"] + load_model_from_checkpoint = deps["load_model_from_checkpoint"] + + model = load_model_from_checkpoint(checkpoint_path, model_class="ICL_FinetunedModel") + target_genes: Optional[List[str]] = load_gene_list(genelist_path) if genelist_path else None + LOGGER.info("Loading base AnnData from %s in backed mode", base_adata_path) + base_adata = ad.read_h5ad(base_adata_path, backed="r") + test_adata = _load_adata( + test_adata_path, + target_genes=target_genes, + gene_name_col=gene_name_col, + ) + + splits = _select_split_values(base_adata, split_column, split_values) + LOGGER.info("Running in-context generation for %d donor(s)", len(splits)) + + generations: Dict[str, ad.AnnData] = {} + for split_value in splits: + LOGGER.info("Processing split '%s'", split_value) + base_subset = _prepare_base_subset( + base_adata, + split_column, + split_value, + target_genes=target_genes, + gene_name_col=gene_name_col, + ) + + predictions, test_logit = _run_incontext_generation( + model, + base_subset, + test_adata, + genelist_path=genelist_path, + gene_name_col=gene_name_col, + prompt_ratio=prompt_ratio, + context_ratio=context_ratio, + context_ratio_min=context_ratio_min, + mask_rate=mask_rate, + mode=mode, + num_steps=num_steps, + batch_size=batch_size, + num_workers=num_workers, + random_seed=random_seed, + show_progress=show_progress, + ) + + + pred_adata = ad.AnnData( + X=predictions, + obs=test_adata.obs.copy(), + var=test_adata.var.copy(), + ) + if test_logit is not None: + pred_adata.obs["gen_logit"] = np.asarray(test_logit) + + if on_generation is not None: + on_generation(split_value, pred_adata) + else: + generations[split_value] = pred_adata + + return generations + + +def save_generations( + generations: Dict[str, ad.AnnData], + output_dir: Path, + *, + concatenate: bool = False, +) -> None: + deps = _ensure_deps() + ad = deps["ad"] + output_dir.mkdir(parents=True, exist_ok=True) + + if not generations: + LOGGER.warning("No generations to save.") + return + + if concatenate: + annotated_adatas = [] + for split_value, adata in generations.items(): + adata_copy = adata.copy() + adata_copy.obs = adata_copy.obs.copy() + adata_copy.obs["split_value"] = split_value + annotated_adatas.append(adata_copy) + + concatenated = ad.concat(annotated_adatas, axis=0, join="outer") + output_path = output_dir / "concatenated.h5ad" + LOGGER.info("Writing concatenated generations to %s", output_path) + concatenated.write_h5ad(output_path) + return + + for split_value, adata in generations.items(): + _write_generation(output_dir, split_value, adata) + + +def _write_generation(output_dir: Path, split_value: str, adata: ad.AnnData) -> None: + _ensure_deps() + safe_name = _sanitize_split_value(split_value) + output_path = output_dir / f"{safe_name}.h5ad" + LOGGER.info("Writing generation for split '%s' to %s", split_value, output_path) + adata.write_h5ad(output_path) + + +def build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser(description="Run in-context generation using a StateICL checkpoint") + parser.add_argument("--checkpoint", required=True, help="Path to the trained Lightning checkpoint (.ckpt)") + parser.add_argument("--base-adata", required=True, help="Path to the base AnnData (.h5ad) containing donor cells") + parser.add_argument("--test-adata", required=True, help="Path to the test AnnData (.h5ad) to generate for") + parser.add_argument("--genelist", required=True, help="Path to the pickled gene list used during training") + parser.add_argument("--output-dir", required=True, help="Directory where generated AnnData files will be saved") + parser.add_argument("--split-column", required=True, help="Column in base AnnData.obs specifying donor identifiers") + parser.add_argument( + "--split-values", + nargs="*", + default=None, + help="Optional subset of donor identifiers to process (defaults to all unique values)", + ) + parser.add_argument( + "--gene-name-col", + default=None, + help="Optional column in adata.var/raw.var containing gene symbols for alignment", + ) + parser.add_argument("--prompt-ratio", type=float, default=0.25, help="Prompt ratio passed to in-context generation") + parser.add_argument("--context-ratio", type=float, default=0.4, help="Context ratio passed to in-context generation") + parser.add_argument("--context-ratio-min", type=float, default=0.2, help="Min value of context ratio") + parser.add_argument("--mask-rate", type=float, default=1.0, help="Mask rate used during in-context generation") + parser.add_argument( + "--num-steps", + type=int, + default=5, + help="Number of iterative generation steps (defaults to model heuristic if omitted)", + ) + parser.add_argument( + "--mode", + default="mdm", + help="Generation mode to use when calling the model", + ) + parser.add_argument("--batch-size", type=int, default=32, help="Batch size for inference") + parser.add_argument("--num-workers", type=int, default=4, help="Number of DataLoader workers") + parser.add_argument("--random-seed", type=int, default=0, help="Optional seed for reproducible sampling") + parser.add_argument("--device", default="auto", help="Target device (cuda, cpu, or auto)") + parser.add_argument("--show-progress", action="store_true", help="Display a tqdm progress bar") + parser.add_argument( + "--concatenate", + action="store_true", + help="Concatenate generations across splits into a single file", + ) + return parser + + +def main(args: Optional[List[str]] = None) -> None: + parser = build_parser() + parsed = parser.parse_args(args=args) + + _ensure_deps() + logging.basicConfig(level=logging.INFO) + + output_dir = Path(parsed.output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + if parsed.concatenate: + generations = generate( + checkpoint_path=parsed.checkpoint, + base_adata_path=parsed.base_adata, + test_adata_path=parsed.test_adata, + genelist_path=parsed.genelist, + split_column=parsed.split_column, + split_values=parsed.split_values, + gene_name_col=parsed.gene_name_col, + prompt_ratio=parsed.prompt_ratio, + context_ratio=parsed.context_ratio, + context_ratio_min=parsed.context_ratio_min, + mask_rate=parsed.mask_rate, + num_steps=parsed.num_steps, + mode=parsed.mode, + batch_size=parsed.batch_size, + num_workers=parsed.num_workers, + random_seed=parsed.random_seed, + device=parsed.device, + show_progress=parsed.show_progress, + ) + save_generations( + generations, + output_dir, + concatenate=True, + ) + return + + def _stream_save(split_value: str, adata: ad.AnnData) -> None: + _write_generation(output_dir, split_value, adata) + + generate( + checkpoint_path=parsed.checkpoint, + base_adata_path=parsed.base_adata, + test_adata_path=parsed.test_adata, + genelist_path=parsed.genelist, + split_column=parsed.split_column, + split_values=parsed.split_values, + gene_name_col=parsed.gene_name_col, + prompt_ratio=parsed.prompt_ratio, + context_ratio=parsed.context_ratio, + context_ratio_min=parsed.context_ratio_min, + mask_rate=parsed.mask_rate, + num_steps=parsed.num_steps, + mode=parsed.mode, + batch_size=parsed.batch_size, + num_workers=parsed.num_workers, + random_seed=parsed.random_seed, + device=parsed.device, + show_progress=parsed.show_progress, + on_generation=_stream_save, + ) + + +if __name__ == "__main__": + main() diff --git a/transfer/code/stack/src/stack/cli/launch_finetuning.py b/transfer/code/stack/src/stack/cli/launch_finetuning.py new file mode 100644 index 0000000000000000000000000000000000000000..8deba9e6e4bdaeebb78313a79adfdc65de9db577 --- /dev/null +++ b/transfer/code/stack/src/stack/cli/launch_finetuning.py @@ -0,0 +1,379 @@ +#!/usr/bin/env python3 +"""CLI script for fine-tuning the StateICL model with a frozen teacher.""" +from __future__ import annotations + +import argparse +import json +import logging +import os +from typing import Dict + +try: # pragma: no cover - runtime import resolution + from stack.cli_utils import apply_config_from_file, filter_unused_arguments +except ImportError: # pragma: no cover - script execution fallback + from cli_utils import apply_config_from_file, filter_unused_arguments # type: ignore + +# Backwards compatibility alias -------------------------------------------------------------------- +def _override_model_config_n_cells(*args, **kwargs): + """Lazily dispatch to override_model_config_n_cells for callers importing this alias.""" + + deps = _import_training_modules() + return deps["override_model_config_n_cells"](*args, **kwargs) + + +def _import_training_modules(): + """Lazy import heavy dependencies so ``--help`` stays fast.""" + + try: + import torch # type: ignore + import pytorch_lightning as pl # type: ignore + from pytorch_lightning.callbacks import EarlyStopping, LearningRateMonitor, ModelCheckpoint + from pytorch_lightning.loggers import Logger, TensorBoardLogger, WandbLogger + from pytorch_lightning.strategies import DDPStrategy + + from stack.finetune.datamodule import FinetuneDataModule, MultiDatasetDataModule + from stack.finetune.lightning import LightningFinetunedModel + from stack.finetune.utils import ( + build_model_config, + build_scheduler_config, + configure_logger, + override_model_config_n_cells, + parse_dataset_configs, + ) + except ImportError: # pragma: no cover - script execution fallback + import torch # type: ignore + import pytorch_lightning as pl # type: ignore + from pytorch_lightning.callbacks import EarlyStopping, LearningRateMonitor, ModelCheckpoint # type: ignore + from pytorch_lightning.loggers import Logger, TensorBoardLogger, WandbLogger # type: ignore + from pytorch_lightning.strategies import DDPStrategy # type: ignore + + from finetune.datamodule import FinetuneDataModule, MultiDatasetDataModule # type: ignore + from finetune.lightning import LightningFinetunedModel # type: ignore + from finetune.utils import ( # type: ignore + build_model_config, + build_scheduler_config, + configure_logger, + override_model_config_n_cells, + parse_dataset_configs, + ) + + torch.set_float32_matmul_precision("high") + return { + "torch": torch, + "pl": pl, + "EarlyStopping": EarlyStopping, + "LearningRateMonitor": LearningRateMonitor, + "ModelCheckpoint": ModelCheckpoint, + "Logger": Logger, + "TensorBoardLogger": TensorBoardLogger, + "WandbLogger": WandbLogger, + "DDPStrategy": DDPStrategy, + "FinetuneDataModule": FinetuneDataModule, + "MultiDatasetDataModule": MultiDatasetDataModule, + "LightningFinetunedModel": LightningFinetunedModel, + "build_model_config": build_model_config, + "build_scheduler_config": build_scheduler_config, + "configure_logger": configure_logger, + "override_model_config_n_cells": override_model_config_n_cells, + "parse_dataset_configs": parse_dataset_configs, + } + + +def configure_callbacks(args: argparse.Namespace): + """Return the default callback suite used for fine-tuning.""" + return [ + ModelCheckpoint( + dirpath=args.save_dir, + filename="finetuned-{epoch}-{val_loss:.4f}", + monitor="val_loss", + mode="min", + save_top_k=3, + save_last=True, + verbose=True, + ), + EarlyStopping( + monitor="val_loss", + patience=args.early_stopping_patience, + min_delta=args.early_stopping_min_delta, + mode="min", + verbose=True, + ), + LearningRateMonitor(logging_interval="epoch"), + ] + + +def build_parser(parents=None) -> argparse.ArgumentParser: + parent_parsers = list(parents) if parents else [] + parser = argparse.ArgumentParser( + description="Fine-tune the StateICL model with a frozen teacher", + parents=parent_parsers, + ) + + # Checkpoint arguments + parser.add_argument("--checkpoint_path", type=str, default=None, help="Path to pre-trained model checkpoint") + + # Data arguments + parser.add_argument("--dataset_configs", type=str, nargs="+", default=None, help="Dataset configuration strings") + parser.add_argument("--genelist_path", type=str, default=None, help="Path to saved gene list file (.pkl)") + parser.add_argument("--sample_size", type=int, default=128, help="Number of cells per sample") + parser.add_argument( + "--replacement_ratio", + type=float, + default=0.25, + help="Fraction of cells to replace (should match 1 - n_kept_cell/sample_size)", + ) + parser.add_argument( + "--intra_file_replacement_prob", + type=float, + default=1.0, + help="Probability of using file-level replacement first", + ) + parser.add_argument("--min_cells_per_group", type=int, default=128, help="Minimum cells per group") + parser.add_argument("--test_ratio", type=float, default=0.1, help="Test split ratio") + parser.add_argument("--val_ratio", type=float, default=0.1, help="Validation split ratio") + parser.add_argument("--random_seed", type=int, default=42, help="Random seed") + parser.add_argument("--cache_file", type=str, default=None, help="Path to metadata cache file") + parser.add_argument("--max_memory_gb", type=float, default=None, help="Maximum memory usage in GB") + parser.add_argument("--cache_ratio", type=float, default=0.1, help="Fraction of memory to use for caching") + parser.add_argument("--block_size", type=int, default=1000, help="Number of rows per cache block") + + # Model arguments + parser.add_argument("--n_hidden", type=int, default=100, help="Hidden dimension after gene reduction") + parser.add_argument("--token_dim", type=int, default=16, help="Token dimension") + parser.add_argument("--n_layers", type=int, default=6, help="Number of attention layers") + parser.add_argument("--n_heads", type=int, default=8, help="Number of attention heads") + parser.add_argument("--dropout", type=float, default=0.0, help="Dropout rate") + parser.add_argument("--mask_rate_min", type=float, default=0.1, help="Minimum mask rate") + parser.add_argument("--mask_rate_max", type=float, default=0.8, help="Maximum mask rate") + parser.add_argument("--sw_weight", type=float, default=0.1, help="Sliced Wasserstein weight") + + # Training arguments + parser.add_argument("--batch_size", type=int, default=32, help="Batch size") + parser.add_argument("--learning_rate", type=float, default=1e-4, help="Learning rate") + parser.add_argument("--weight_decay", type=float, default=1e-4, help="Weight decay") + parser.add_argument("--max_epochs", type=int, default=20, help="Maximum number of epochs") + parser.add_argument("--num_workers", type=int, default=4, help="Number of dataloader workers") + parser.add_argument("--resample_each_epoch", action="store_true", help="Resample training data each epoch") + + # Scheduler arguments + parser.add_argument( + "--scheduler", + type=str, + choices=["cosine", "reduce_on_plateau"], + default="cosine", + help="Learning rate scheduler", + ) + parser.add_argument("--scheduler_T_max", type=int, default=20, help="T_max for cosine scheduler") + parser.add_argument( + "--scheduler_warmup_epochs", + type=int, + default=0, + help="Warmup epochs for cosine scheduler", + ) + parser.add_argument("--scheduler_eta_min", type=float, default=1e-6, help="Minimum LR for cosine scheduler") + parser.add_argument("--scheduler_patience", type=int, default=10, help="Patience for reduce_on_plateau") + parser.add_argument("--scheduler_factor", type=float, default=0.5, help="Factor for reduce_on_plateau") + + # System arguments + parser.add_argument("--gpus", type=int, default=-1, help="Number of GPUs (-1 for all available)") + parser.add_argument( + "--strategy", + type=str, + default="ddp", + choices=["ddp", "ddp_find_unused_parameters_false"], + ) + parser.add_argument( + "--precision", + type=str, + default="bf16-mixed", + choices=["32", "16-mixed", "bf16-mixed"], + ) + parser.add_argument("--accumulate_grad_batches", type=int, default=1, help="Gradient accumulation steps") + + # Logging and checkpointing + parser.add_argument("--project_name", type=str, default="SHIFTAB Finetuning with Frozen Teacher") + parser.add_argument("--run_name", type=str, default=None, help="Run name for logging") + parser.add_argument("--save_dir", type=str, default="../shiftab_finetuned_checkpoints") + parser.add_argument("--logger", type=str, choices=["wandb", "tensorboard"], default="tensorboard") + parser.add_argument("--log_every_n_steps", type=int, default=50) + + # Early stopping + parser.add_argument("--early_stopping_patience", type=int, default=10) + parser.add_argument("--early_stopping_min_delta", type=float, default=1e-4) + + return parser + + +def log_configuration(args: argparse.Namespace, model_config: Dict[str, any], data_module: FinetuneDataModule, logger: Logger) -> None: + logging.info("=" * 80) + logging.info("FINE-TUNING CONFIGURATION WITH FROZEN TEACHER") + logging.info("=" * 80) + logging.info("Checkpoint: %s", args.checkpoint_path) + logging.info("Model configuration:") + for key, value in model_config.items(): + logging.info(" %s: %s", key, value) + logging.info("Training setup:") + logging.info(" Using Frozen Teacher architecture for stable targets") + logging.info("Dataset configurations:") + for idx, config in enumerate(data_module.dataset_configs): + logging.info(" Dataset %s: %s - %s", idx + 1, config.type, config.path) + if config.type == "human": + logging.info(" Donor col: %s, Cell type col: %s", config.donor_col, config.cell_type_col) + else: + logging.info( + " Condition col: %s, Cell line col: %s, Control condition: %s", + config.condition_col, + config.cell_line_col, + config.control_condition, + ) + logging.info(" Filter organism: %s", config.filter_organism) + logging.info("Gene list: %s (%s genes)", args.genelist_path, data_module.n_genes) + logging.info("Sample size: %s", args.sample_size) + logging.info("N kept cells (Student): %s", int((1.0 - args.replacement_ratio) * args.sample_size)) + logging.info("Batch size: %s", args.batch_size) + logging.info("Learning rate: %s", args.learning_rate) + logging.info("Max epochs: %s", args.max_epochs) + logging.info("Logger: %s", type(logger).__name__) + logging.info("=" * 80) + + +def main() -> None: + config_parser = argparse.ArgumentParser(add_help=False) + config_parser.add_argument("--config", type=str, default=None, help="Path to a YAML/JSON config file") + + parser = build_parser(parents=[config_parser]) + + config_args, remaining_argv = config_parser.parse_known_args() + apply_config_from_file(parser, config_args.config) + args = parser.parse_args(remaining_argv) + filter_unused_arguments(args, ("dataset_configs", "genelist_path"), parser) + + deps = _import_training_modules() + torch = deps["torch"] + pl = deps["pl"] + EarlyStopping = deps["EarlyStopping"] + LearningRateMonitor = deps["LearningRateMonitor"] + ModelCheckpoint = deps["ModelCheckpoint"] + Logger = deps["Logger"] + TensorBoardLogger = deps["TensorBoardLogger"] + WandbLogger = deps["WandbLogger"] + DDPStrategy = deps["DDPStrategy"] + FinetuneDataModule = deps["FinetuneDataModule"] + MultiDatasetDataModule = deps["MultiDatasetDataModule"] + LightningFinetunedModel = deps["LightningFinetunedModel"] + build_model_config = deps["build_model_config"] + build_scheduler_config = deps["build_scheduler_config"] + configure_logger = deps["configure_logger"] + override_model_config_n_cells = deps["override_model_config_n_cells"] + parse_dataset_configs = deps["parse_dataset_configs"] + + logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") + if config_args.config: + logging.info("Loaded fine-tuning defaults from %s", config_args.config) + + dataset_configs = parse_dataset_configs(args.dataset_configs) + scheduler_cfg = build_scheduler_config(vars(args)) + + data_module = MultiDatasetDataModule( + dataset_configs=dataset_configs, + genelist_path=args.genelist_path, + sample_size=args.sample_size, + test_ratio=args.test_ratio, + val_ratio=args.val_ratio, + batch_size=args.batch_size, + num_workers=args.num_workers, + random_state=args.random_seed, + cache_file=args.cache_file, + replacement_ratio=args.replacement_ratio, + intra_file_replacement_prob=args.intra_file_replacement_prob, + min_cells_per_group=args.min_cells_per_group, + max_memory_gb=args.max_memory_gb, + cache_ratio=args.cache_ratio, + block_size=args.block_size, + resample_each_epoch=args.resample_each_epoch, + ) + + # Setup data module to get dataset info + data_module.setup() + + # Create save directory if it doesn't exist + os.makedirs(args.save_dir, exist_ok=True) + + # Save dataset split information + split_info = data_module.get_split_info() + split_info_path = os.path.join(args.save_dir, "dataset_splits.json") + try: + with open(split_info_path, "w") as f: + json.dump(split_info, f, indent=4) + logging.info("Saved dataset split information to %s", split_info_path) + except Exception as exc: + logging.error("Failed to save dataset split information: %s", exc) + + # Calculate n_kept_cell from replacement_ratio + n_kept_cell = int((1.0 - args.replacement_ratio) * args.sample_size) + + # Initialize model - either from checkpoint or from scratch + if args.checkpoint_path: + logging.info("Loading model from checkpoint: %s", args.checkpoint_path) + model_config = override_model_config_n_cells(args.checkpoint_path, args.sample_size) + + student_model = LightningFinetunedModel.load_from_checkpoint( + checkpoint_path=args.checkpoint_path, + strict=False, + model_config=model_config, + learning_rate=args.learning_rate, + weight_decay=args.weight_decay, + scheduler_config=scheduler_cfg, + n_kept_cell=n_kept_cell, + ) + else: + logging.info("Creating model from scratch (no checkpoint provided)") + model_config = build_model_config(vars(args), data_module.n_genes) + + student_model = LightningFinetunedModel( + model_config=model_config, + checkpoint_path=None, + learning_rate=args.learning_rate, + weight_decay=args.weight_decay, + scheduler_config=scheduler_cfg, + n_kept_cell=n_kept_cell, + ) + + logger = configure_logger( + logger_type=args.logger, + project_name=args.project_name, + run_name=args.run_name, + save_dir=args.save_dir, + ) + callbacks = configure_callbacks(args) + + strategy = DDPStrategy(find_unused_parameters=False) if args.strategy == "ddp" else args.strategy + trainer = pl.Trainer( + max_epochs=args.max_epochs, + devices=args.gpus, + accelerator="gpu" if torch.cuda.is_available() else "cpu", + strategy=strategy, + precision=args.precision, + gradient_clip_val=1.0, + accumulate_grad_batches=args.accumulate_grad_batches, + log_every_n_steps=args.log_every_n_steps, + logger=logger, + callbacks=callbacks, + deterministic=False, + benchmark=True, + ) + + log_configuration(args, model_config, data_module, logger) + + trainer.fit(student_model, datamodule=data_module) + + if data_module.test_dataset is not None and len(data_module.test_dataset) > 0: + logging.info("Running test evaluation with frozen teacher...") + test_results = trainer.test(student_model, datamodule=data_module, ckpt_path="best") + logging.info("Test results: %s", test_results) + + logging.info("Fine-tuning pipeline completed successfully!") + + +if __name__ == "__main__": + main() diff --git a/transfer/code/stack/src/stack/cli/launch_training.py b/transfer/code/stack/src/stack/cli/launch_training.py new file mode 100644 index 0000000000000000000000000000000000000000..006b4d3ac047c41850c30a4830dc1fdf9b43c204 --- /dev/null +++ b/transfer/code/stack/src/stack/cli/launch_training.py @@ -0,0 +1,285 @@ +#!/usr/bin/env python3 +"""Entry point script for training the StateICL model.""" +from __future__ import annotations + +import argparse +import json +import logging +import os +from typing import Dict + +try: # pragma: no cover - runtime import resolution + from stack.cli_utils import apply_config_from_file, filter_unused_arguments +except ImportError: # pragma: no cover - script execution fallback + from cli_utils import apply_config_from_file, filter_unused_arguments # type: ignore + + +def _import_training_modules(): + """Lazy import heavy dependencies so ``--help`` stays fast.""" + + try: + import torch # type: ignore + import pytorch_lightning as pl # type: ignore + from pytorch_lightning.callbacks import EarlyStopping, LearningRateMonitor, ModelCheckpoint + from pytorch_lightning.loggers import TensorBoardLogger, WandbLogger + from pytorch_lightning.strategies import DDPStrategy + + from stack.training.datamodule import MultiDatasetDataModule + from stack.training.lightning import LegacyLightningGeneModel + from stack.training.utils import build_scheduler_config, localize_datasets, parse_dataset_configs + except ImportError: # pragma: no cover - script execution fallback + import torch # type: ignore + import pytorch_lightning as pl # type: ignore + from pytorch_lightning.callbacks import EarlyStopping, LearningRateMonitor, ModelCheckpoint # type: ignore + from pytorch_lightning.loggers import TensorBoardLogger, WandbLogger # type: ignore + from pytorch_lightning.strategies import DDPStrategy # type: ignore + + from training.datamodule import MultiDatasetDataModule # type: ignore + from training.lightning import LegacyLightningGeneModel # type: ignore + from training.utils import build_scheduler_config, localize_datasets, parse_dataset_configs # type: ignore + + torch.set_float32_matmul_precision("high") + return { + "torch": torch, + "pl": pl, + "EarlyStopping": EarlyStopping, + "LearningRateMonitor": LearningRateMonitor, + "ModelCheckpoint": ModelCheckpoint, + "TensorBoardLogger": TensorBoardLogger, + "WandbLogger": WandbLogger, + "DDPStrategy": DDPStrategy, + "MultiDatasetDataModule": MultiDatasetDataModule, + "LegacyLightningGeneModel": LegacyLightningGeneModel, + "build_scheduler_config": build_scheduler_config, + "localize_datasets": localize_datasets, + "parse_dataset_configs": parse_dataset_configs, + } + + +def build_model_config(args: argparse.Namespace, n_genes: int) -> Dict[str, int | float]: + return { + "n_genes": n_genes, + "n_hidden": args.n_hidden, + "token_dim": args.token_dim, + "n_cells": args.sample_size, + "n_layers": args.n_layers, + "n_heads": args.n_heads, + "dropout": args.dropout, + "mask_rate_min": args.mask_rate_min, + "mask_rate_max": args.mask_rate_max, + "sw_weight": args.sw_weight, + "n_proj": args.n_proj, + } + + +def configure_logger(args: argparse.Namespace): + if args.logger == "wandb": + return WandbLogger(project=args.project_name, name=args.run_name, save_dir=args.save_dir) + return TensorBoardLogger(save_dir=args.save_dir, name=args.project_name, version=args.run_name) + + +def main() -> None: + config_parser = argparse.ArgumentParser(add_help=False) + config_parser.add_argument("--config", type=str, default=None, help="Path to a YAML/JSON config file") + + parser = argparse.ArgumentParser(description="Train the StateICL model", parents=[config_parser]) + + # Data arguments + parser.add_argument("--dataset_configs", type=str, nargs="+", default=None, help="path[:filter[:gene_column]]") + parser.add_argument("--genelist_path", type=str, default=None, help="Path to saved gene list file (.pkl)") + parser.add_argument("--sample_size", type=int, default=128) + parser.add_argument("--test_ratio", type=float, default=0.1) + parser.add_argument("--val_ratio", type=float, default=0.1) + parser.add_argument("--random_seed", type=int, default=42) + parser.add_argument("--cache_file", type=str, default=None) + + # Model arguments + parser.add_argument("--n_hidden", type=int, default=100) + parser.add_argument("--token_dim", type=int, default=8) + parser.add_argument("--n_layers", type=int, default=6) + parser.add_argument("--n_heads", type=int, default=8) + parser.add_argument("--dropout", type=float, default=0.1) + parser.add_argument("--mask_rate_min", type=float, default=0.1) + parser.add_argument("--mask_rate_max", type=float, default=0.8) + parser.add_argument("--sw_weight", type=float, default=0.01) + parser.add_argument("--n_proj", type=int, default=64) + + # Training arguments + parser.add_argument("--batch_size", type=int, default=32) + parser.add_argument("--learning_rate", type=float, default=1e-4) + parser.add_argument("--weight_decay", type=float, default=3e-3) + parser.add_argument("--max_epochs", type=int, default=20) + parser.add_argument("--num_workers", type=int, default=4) + + # Scheduler arguments + parser.add_argument( + "--scheduler", + type=str, + choices=["cosine", "cosine_restarts", "step", "reduce_on_plateau"], + default="cosine", + ) + parser.add_argument("--scheduler_T_max", type=int, default=20) + parser.add_argument("--scheduler_warmup_epochs", type=int, default=1) + parser.add_argument("--scheduler_T_0", type=int, default=10) + parser.add_argument("--scheduler_T_mult", type=int, default=2) + parser.add_argument("--scheduler_eta_min", type=float, default=1e-6) + parser.add_argument("--scheduler_patience", type=int, default=10) + parser.add_argument("--scheduler_factor", type=float, default=0.5) + + # System arguments + parser.add_argument("--gpus", type=int, default=-1) + parser.add_argument("--strategy", type=str, default="ddp", choices=["ddp", "ddp_find_unused_parameters_false"]) + parser.add_argument("--precision", type=str, default="bf16-mixed", choices=["32", "16-mixed", "bf16-mixed"]) + parser.add_argument("--accumulate_grad_batches", type=int, default=1) + + # Logging and checkpointing + parser.add_argument("--project_name", type=str, default="scShiftAttention") + parser.add_argument("--run_name", type=str, default=None) + parser.add_argument("--save_dir", type=str, default="../checkpoints") + parser.add_argument("--logger", type=str, choices=["wandb", "tensorboard"], default="tensorboard") + parser.add_argument("--log_every_n_steps", type=int, default=10) + + # Early stopping + parser.add_argument("--early_stopping_patience", type=int, default=20) + parser.add_argument("--early_stopping_min_delta", type=float, default=1e-4) + + # Data localization + parser.add_argument("--local_temp_dir", type=str, default="/tmp") + parser.add_argument("--no_localize_data", action="store_true") + + config_args, remaining_argv = config_parser.parse_known_args() + apply_config_from_file(parser, config_args.config) + + args = parser.parse_args(remaining_argv) + filter_unused_arguments(args, ("dataset_configs", "genelist_path"), parser) + + deps = _import_training_modules() + torch = deps["torch"] + pl = deps["pl"] + EarlyStopping = deps["EarlyStopping"] + LearningRateMonitor = deps["LearningRateMonitor"] + ModelCheckpoint = deps["ModelCheckpoint"] + TensorBoardLogger = deps["TensorBoardLogger"] + WandbLogger = deps["WandbLogger"] + DDPStrategy = deps["DDPStrategy"] + MultiDatasetDataModule = deps["MultiDatasetDataModule"] + LegacyLightningGeneModel = deps["LegacyLightningGeneModel"] + build_scheduler_config = deps["build_scheduler_config"] + localize_datasets = deps["localize_datasets"] + parse_dataset_configs = deps["parse_dataset_configs"] + + logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") + if config_args.config: + logging.info("Loaded training defaults from %s", config_args.config) + + localization = localize_datasets(args.dataset_configs, args.cache_file, args.local_temp_dir, args.no_localize_data) + + try: + dataset_configs = parse_dataset_configs(localization.dataset_configs) + scheduler_cfg = build_scheduler_config(vars(args)) + + data_module = MultiDatasetDataModule( + dataset_configs=dataset_configs, + genelist_path=args.genelist_path, + sample_size=args.sample_size, + test_ratio=args.test_ratio, + val_ratio=args.val_ratio, + batch_size=args.batch_size, + num_workers=args.num_workers, + random_state=args.random_seed, + cache_file=localization.cache_file, + ) + + # Setup data module to get dataset info + data_module.setup() + + # Create save directory if it doesn't exist + os.makedirs(args.save_dir, exist_ok=True) + + # Save dataset split information + split_info = data_module.get_split_info() + split_info_path = os.path.join(args.save_dir, "dataset_splits.json") + try: + with open(split_info_path, "w") as f: + json.dump(split_info, f, indent=4) + logging.info("Saved dataset split information to %s", split_info_path) + except Exception as exc: + logging.error("Failed to save dataset split information: %s", exc) + + model_config = build_model_config(args, data_module.n_genes) + model = LegacyLightningGeneModel( + model_config=model_config, + learning_rate=args.learning_rate, + weight_decay=args.weight_decay, + scheduler_config=scheduler_cfg, + ) + + logger = configure_logger(args) + callbacks = [ + ModelCheckpoint( + dirpath=args.save_dir, + filename="{epoch}-{val_loss:.4f}", + monitor="val_loss", + mode="min", + save_top_k=3, + save_last=True, + verbose=True, + ), + EarlyStopping( + monitor="val_loss", + patience=args.early_stopping_patience, + min_delta=args.early_stopping_min_delta, + mode="min", + verbose=True, + ), + LearningRateMonitor(logging_interval="epoch"), + ] + + strategy = DDPStrategy(find_unused_parameters=False) if args.strategy == "ddp" else args.strategy + trainer = pl.Trainer( + max_epochs=args.max_epochs, + devices=args.gpus, + accelerator="gpu" if torch.cuda.is_available() else "cpu", + strategy=strategy, + precision=args.precision, + gradient_clip_val=1.0, + accumulate_grad_batches=args.accumulate_grad_batches, + log_every_n_steps=args.log_every_n_steps, + logger=logger, + callbacks=callbacks, + deterministic=False, + benchmark=True, + ) + + logging.info("=" * 80) + logging.info("TRAINING CONFIGURATION") + logging.info("=" * 80) + for key, value in model_config.items(): + logging.info(" %s: %s", key, value) + for idx, cfg in enumerate(dataset_configs): + logging.info("Dataset %s: %s (filter=%s gene_col=%s)", idx + 1, cfg.path, cfg.filter_organism, cfg.gene_name_col) + logging.info("Gene list: %s (%s genes)", args.genelist_path, data_module.n_genes) + logging.info("Sample size: %s", args.sample_size) + logging.info("Batch size: %s", args.batch_size) + logging.info("Learning rate: %s", args.learning_rate) + logging.info("Max epochs: %s", args.max_epochs) + logging.info("Devices: %s", trainer.num_devices) + logging.info("Strategy: %s", strategy) + logging.info("Precision: %s", args.precision) + logging.info("=" * 80) + + trainer.fit(model, data_module) + + if data_module.test_dataset is not None and len(data_module.test_dataset) > 0: + logging.info("Running test evaluation...") + test_results = trainer.test(model, data_module, ckpt_path="best") + logging.info("Test results: %s", test_results) + + logging.info("Training pipeline completed successfully!") + + finally: + localization.stage_out(args.save_dir, localization.job_id or str(os.getpid())) + + +if __name__ == "__main__": + main() diff --git a/transfer/code/stack/src/stack/cli_utils.py b/transfer/code/stack/src/stack/cli_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..b63dc070a65e69ec8fb1122c619e6720204f704c --- /dev/null +++ b/transfer/code/stack/src/stack/cli_utils.py @@ -0,0 +1,110 @@ +"""Utilities shared by the project command line entry points.""" +from __future__ import annotations + +import json +import logging +from pathlib import Path +from typing import Any, Dict, Iterable, Tuple + +logger = logging.getLogger(__name__) + + +class ConfigFileError(RuntimeError): + """Raised when a configuration file cannot be parsed.""" + + +def _load_yaml(path: Path) -> Dict[str, Any]: + try: + import yaml # type: ignore + except ImportError as exc: # pragma: no cover - optional dependency + raise ConfigFileError( + "PyYAML is required to parse YAML configuration files. " + "Install it with `pip install pyyaml` or use a JSON config instead." + ) from exc + + data = yaml.safe_load(path.read_text()) + if data is None: + return {} + if not isinstance(data, dict): + raise ConfigFileError(f"Expected a mapping at the top level of {path}, got {type(data)!r}.") + return data + + +def load_config_file(path: str | Path) -> Dict[str, Any]: + """Load a JSON or YAML configuration file and return its content.""" + + config_path = Path(path).expanduser().resolve() + if not config_path.exists(): + raise ConfigFileError(f"Configuration file '{config_path}' does not exist.") + + suffix = config_path.suffix.lower() + if suffix in {".yaml", ".yml"}: + return _load_yaml(config_path) + if suffix == ".json": + data = json.loads(config_path.read_text()) + if not isinstance(data, dict): + raise ConfigFileError( + f"Expected a mapping at the top level of {config_path}, got {type(data)!r}." + ) + return data + + raise ConfigFileError( + f"Unsupported configuration extension '{config_path.suffix}'. " + "Only .json, .yaml and .yml files are supported." + ) + + +def apply_config(parser, config: Dict[str, Any]) -> Tuple[Dict[str, Any], Tuple[str, ...]]: + """Apply defaults from ``config`` to ``parser`` and return recognised keys. + + Parameters + ---------- + parser: + The ``argparse.ArgumentParser`` whose defaults should be updated. + config: + A dictionary coming from :func:`load_config_file`. + + Returns + ------- + recognised: + The subset of key/value pairs that were applied to the parser. + unused: + Keys that were not recognised by the parser. + """ + + valid_dests = { + action.dest + for action in parser._actions # type: ignore[attr-defined] + if action.dest not in {"help", "--help", "==SUPPRESS=="} + } + + recognised: Dict[str, Any] = {k: v for k, v in config.items() if k in valid_dests} + if recognised: + parser.set_defaults(**recognised) + + unused = tuple(sorted(k for k in config.keys() if k not in recognised)) + if unused: + logger.warning("Unrecognised configuration keys: %s", ", ".join(unused)) + + return recognised, unused + + +def apply_config_from_file(parser, path: str | Path | None) -> Tuple[Dict[str, Any], Tuple[str, ...]]: + """Convenience wrapper that loads ``path`` and calls :func:`apply_config`.""" + + if not path: + return {}, tuple() + + config = load_config_file(path) + return apply_config(parser, config) + + +def filter_unused_arguments(namespace, required: Iterable[str], parser) -> None: + """Validate required fields after config/CLI merging.""" + + missing = [name for name in required if getattr(namespace, name) in (None, [], "")] + if missing: + parser.error( + "Missing required arguments: " + + ", ".join(f"--{name.replace('_', '-')} (or set via config)" for name in missing) + ) diff --git a/transfer/code/stack/src/stack/data/__init__.py b/transfer/code/stack/src/stack/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b7919dff168014ebd6a8917241ab28b4a654f3cd --- /dev/null +++ b/transfer/code/stack/src/stack/data/__init__.py @@ -0,0 +1,29 @@ +"""Data loading and preprocessing helpers for StateICL.""" +from .datasets import * # noqa: F401,F403 +from .gene_processing import filter_gene_names, get_gene_names_from_h5, safe_decode_array +from .h5_manager import ( + H5HandleManager, + get_h5_handle, + reset_h5_handle_pool, + worker_init_fn, +) +from .hvg import compute_analytic_pearson_residuals, compute_hvg_union +from . import training as training_data +from . import finetuning as finetuning_data + +__all__ = sorted( + { + "filter_gene_names", + "get_gene_names_from_h5", + "safe_decode_array", + "H5HandleManager", + "get_h5_handle", + "reset_h5_handle_pool", + "worker_init_fn", + "compute_analytic_pearson_residuals", + "compute_hvg_union", + "training_data", + "finetuning_data", + } + | {name for name in globals().keys() if not name.startswith("_")} +) diff --git a/transfer/code/stack/src/stack/data/datasets.py b/transfer/code/stack/src/stack/data/datasets.py new file mode 100644 index 0000000000000000000000000000000000000000..adb88c657380c50eaa16217d1df9114d4ac23cc7 --- /dev/null +++ b/transfer/code/stack/src/stack/data/datasets.py @@ -0,0 +1,2 @@ +"""Compatibility re-export of the training dataset utilities.""" +from .training.datasets import * # noqa: F401,F403 diff --git a/transfer/code/stack/src/stack/data/finetuning/__init__.py b/transfer/code/stack/src/stack/data/finetuning/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..029846add68a6f58383289e5b85746c904a9ee3a --- /dev/null +++ b/transfer/code/stack/src/stack/data/finetuning/__init__.py @@ -0,0 +1,4 @@ +"""Fine-tuning dataset utilities for StateICL.""" +from .datasets import * # noqa: F401,F403 + +__all__ = [name for name in globals() if not name.startswith("_")] diff --git a/transfer/code/stack/src/stack/data/finetuning/datasets.py b/transfer/code/stack/src/stack/data/finetuning/datasets.py new file mode 100644 index 0000000000000000000000000000000000000000..dfa632ff2b465574f0338a5c02bf6861f57955db --- /dev/null +++ b/transfer/code/stack/src/stack/data/finetuning/datasets.py @@ -0,0 +1,2284 @@ +import numpy as np +import pandas as pd +import torch +from torch.utils.data import Dataset, DataLoader +from typing import Tuple, Optional, Dict, Any, List, Union +import h5py +import scipy.sparse as sp +import logging +import threading +import time +from collections import OrderedDict +import gc +import psutil +import os +from pathlib import Path +import pickle +import traceback +from dataclasses import dataclass +import random +import torch.distributed as dist +import anndata +import math + +from ..gene_processing import filter_gene_names, get_gene_names_from_h5, safe_decode_array +from ..h5_manager import get_h5_handle, reset_h5_handle_pool, worker_init_fn +from ..hvg import compute_analytic_pearson_residuals, compute_hvg_union + +log = logging.getLogger(__name__) + +_get_h5_handle = get_h5_handle +_reset_h5_handle_pool = reset_h5_handle_pool +_worker_init_fn = worker_init_fn + +@dataclass +class DatasetConfig: + """Configuration for a single dataset""" + path: str + type: str # 'human' or 'drug' + filter_organism: bool = True + gene_name_col: Optional[str] = None + + # For human datasets + donor_col: Optional[str] = None + cell_type_col: Optional[str] = None + + # For drug datasets + condition_col: Optional[str] = None + cell_line_col: Optional[str] = None + control_condition: Optional[str] = None + + def __post_init__(self): + """Validate configuration""" + if self.type == 'human': + if not self.donor_col or not self.cell_type_col: + raise ValueError("Human datasets require donor_col and cell_type_col") + elif self.type == 'drug': + if not self.condition_col or not self.cell_line_col or not self.control_condition: + raise ValueError("Drug datasets require condition_col, cell_line_col, and control_condition") + else: + raise ValueError(f"Unknown dataset type: {self.type}") + + @property + def group_col(self) -> str: + """Column used for grouping (donor_id for human, condition for drug)""" + return self.donor_col if self.type == 'human' else self.condition_col + + @property + def identity_col(self) -> str: + """Column used for cell identity (cell_type for human, cell_line for drug)""" + return self.cell_type_col if self.type == 'human' else self.cell_line_col + + +def load_gene_list(genelist_path: str) -> List[str]: + """Load previously computed gene list from pickle file.""" + with open(genelist_path, 'rb') as f: + return pickle.load(f) + + +class MultiDatasetMetadataCache: + """ + Enhanced metadata cache implementing the two-phase strategy with comprehensive data loading. + + Core features: + - Efficient metadata storage and retrieval + - Pre-computed acceleration dictionaries for fast replacement candidate lookup + - Precise IO that loads only requested cells + - Complete metadata tracking including file indices and config indices + """ + _instances: dict[str, "MultiDatasetMetadataCache"] = {} + + @classmethod + def get_singleton(cls, cache_key: str, **kwargs) -> "MultiDatasetMetadataCache": + """Return one shared instance per cache key.""" + if cache_key not in cls._instances: + cls._instances[cache_key] = cls(**kwargs) + return cls._instances[cache_key] + + def __init__( + self, + dataset_configs: List[DatasetConfig], + target_genes: List[str], + cache_file: Optional[str] = None, + max_memory_gb: Optional[float] = None, + cache_ratio: float = 0.3, + block_size: int = 1000 + ): + """ + Initialize the metadata cache with all necessary tracking information. + + Args: + dataset_configs: List of dataset configurations + target_genes: List of target gene names to extract + cache_file: Optional path to cache metadata + max_memory_gb: Maximum memory usage in GB (for compatibility) + cache_ratio: Cache ratio (for compatibility) + block_size: Block size (for compatibility) + """ + self.dataset_configs = dataset_configs + self.target_genes = target_genes + self.n_genes = len(target_genes) + + # Setup memory management (kept for compatibility) + self._setup_memory_limits(max_memory_gb, cache_ratio) + + log.info("Initializing Enhanced MultiDatasetMetadataCache...") + start_time = time.time() + + # Try to load from cache first + if cache_file and os.path.exists(cache_file): + try: + self._load_from_cache(cache_file) + log.info(f"Loaded metadata from cache: {cache_file}") + except Exception as e: + log.warning(f"Failed to load cache, rebuilding: {e}") + self._rebuild_and_save_cache(cache_file) + else: + # Build and save new cache + self._rebuild_and_save_cache(cache_file) + + # PERFORMANCE FIX: Precompute cell indices for vectorized operations + self.cell_config_indices = np.array([ + self.group_mapping[gid]['config_idx'] + for gid in self.global_group_ids + ]) + + self.cell_file_indices = np.array([ + self.cell_to_file_mapping[i][0] + for i in range(self.n_cells) + ]) + + load_time = time.time() - start_time + log.info(f"Initialization completed in {load_time:.2f}s. Total cells: {self.n_cells}") + log.info(f"Current memory usage: ~{self._get_memory_usage():.2f}GB") + + def _rebuild_and_save_cache(self, cache_file: Optional[str]): + """Helper function that encapsulates the complete build, precompute, and save workflow""" + self._build_all_metadata() + # CORE FIX: Precompute acceleration pools BEFORE saving to cache + self._precompute_acceleration_pools() + if cache_file: + self._save_to_cache(cache_file) + + def _setup_memory_limits(self, max_memory_gb, cache_ratio): + """Setup memory management parameters (kept for compatibility)""" + available_memory_gb = psutil.virtual_memory().available / (1024**3) + + if max_memory_gb is None: + self.max_memory_gb = available_memory_gb * 0.8 + else: + self.max_memory_gb = min(max_memory_gb, available_memory_gb * 0.9) + + self.cache_memory_limit = self.max_memory_gb * cache_ratio + + def _get_memory_usage(self): + """Get current memory usage in GB""" + process = psutil.Process(os.getpid()) + return process.memory_info().rss / (1024**3) + + def _save_to_cache(self, cache_file: str): + """Save metadata to cache file including acceleration pools""" + try: + cache_data = { + 'file_info': self.file_info, + 'group_mapping': self.group_mapping, + 'cell_to_file_mapping': self.cell_to_file_mapping, + 'global_group_ids': self.global_group_ids, + 'cell_identities': self.cell_identities, + 'conditions': self.conditions, + 'dataset_types': self.dataset_types, + 'n_cells': self.n_cells, + 'target_genes': self.target_genes, + 'dataset_configs': self.dataset_configs, + # CORE FIX: Save acceleration pools to cache + 'group_identity_pool': self.group_identity_pool, + 'identity_to_groups_map': self.identity_to_groups_map, + 'file_identity_pool': self.file_identity_pool, + 'config_identity_pool': self.config_identity_pool, + } + with open(cache_file, 'wb') as f: + pickle.dump(cache_data, f) + log.info(f"Saved metadata cache to {cache_file}") + except Exception as e: + log.exception(f"Failed to save cache: {e}") + + def _load_from_cache(self, cache_file: str): + """Load metadata from cache file including acceleration pools""" + with open(cache_file, 'rb') as f: + cache_data = pickle.load(f) + + self.file_info = cache_data['file_info'] + self.group_mapping = cache_data['group_mapping'] + self.cell_to_file_mapping = cache_data['cell_to_file_mapping'] + self.global_group_ids = cache_data['global_group_ids'] + self.cell_identities = cache_data['cell_identities'] + self.conditions = cache_data['conditions'] + self.dataset_types = cache_data['dataset_types'] + self.n_cells = cache_data['n_cells'] + + # Verify target genes match + if cache_data['target_genes'] != self.target_genes: + raise ValueError("Target genes in cache don't match current target genes") + + # Verify dataset configs match (simplified check) + if len(cache_data['dataset_configs']) != len(self.dataset_configs): + raise ValueError("Dataset configurations don't match cache") + + # CORE FIX: Load acceleration pools from cache + self.group_identity_pool = cache_data['group_identity_pool'] + self.identity_to_groups_map = cache_data['identity_to_groups_map'] + self.file_identity_pool = cache_data['file_identity_pool'] + self.config_identity_pool = cache_data['config_identity_pool'] + + log.info(f"Loaded acceleration pools from cache:") + log.info(f" Group-identity combinations: {len(self.group_identity_pool)}") + log.info(f" File-identity combinations: {len(self.file_identity_pool)}") + log.info(f" Config-identity combinations: {len(self.config_identity_pool)}") + log.info(f" Unique identities: {len(self.identity_to_groups_map)}") + + def _build_all_metadata(self): + """Load metadata from all H5AD files using h5py""" + self.file_info = [] + self.group_mapping = {} # Global group ID to original group ID mapping + self.cell_to_file_mapping = [] # Maps global cell index to (file_idx, local_cell_idx) + self.global_group_ids = [] # Global group IDs for all cells + self.cell_identities = [] # Cell identity (type/line) for each cell + self.conditions = [] # Condition for each cell (human: "human", drug: actual condition) + self.dataset_types = [] # Dataset type for each cell ("human" or "drug") + + global_cell_idx = 0 + global_group_counter = 0 + group_to_global = {} # (config_idx, original_group) -> global_group_id + + # Create gene set for efficient lookup + target_gene_set = set(self.target_genes) + + for config_idx, config in enumerate(self.dataset_configs): + log.info(f"Loading metadata from dataset {config_idx + 1}/{len(self.dataset_configs)}: {config.path} ({config.type})") + + h5ad_files = list(Path(config.path).glob("*.h5ad")) + list(Path(config.path).glob("*.h5")) + h5ad_files = sorted(list(set(h5ad_files))) + + for file_idx, h5ad_file in enumerate(h5ad_files): + try: + log.info(f" Processing {h5ad_file.name}...") + + with h5py.File(h5ad_file, "r") as f: + obs = f["obs"] + + # Get organism filter mask + if config.filter_organism: + if "organism" not in obs: + log.warning(f" 'organism' column not found, skipping") + continue + + organism_ds = obs["organism"] + if "categories" in organism_ds: + organism_categories = safe_decode_array(organism_ds["categories"][:]) + organism_codes = organism_ds["codes"][:] + organism_values = organism_categories[organism_codes] + else: + organism_values = safe_decode_array(organism_ds[:]) + + human_mask = organism_values == 'Homo sapiens' + if not human_mask.any(): + log.warning(f" No Homo sapiens cells found, skipping") + continue + else: + # Get total number of cells + if hasattr(f["X"], 'shape'): + n_cells = f["X"].shape[0] + else: + n_cells = f["X"].attrs['shape'][0] + human_mask = np.ones(n_cells, dtype=bool) + + # Check required columns + if config.group_col not in obs: + log.warning(f" '{config.group_col}' column not found, skipping") + continue + + if config.identity_col not in obs: + log.warning(f" '{config.identity_col}' column not found, skipping") + continue + + # Get group information (donor_id for human, condition for drug) + group_ds = obs[config.group_col] + if "categories" in group_ds: + group_categories = safe_decode_array(group_ds["categories"][:]) + group_codes = group_ds["codes"][:] + all_group_values = group_categories[group_codes] + else: + all_group_values = safe_decode_array(group_ds[:]) + + # Get cell identity information (cell_type for human, cell_line for drug) + identity_ds = obs[config.identity_col] + if "categories" in identity_ds: + identity_categories = safe_decode_array(identity_ds["categories"][:]) + identity_codes = identity_ds["codes"][:] + all_identity_values = identity_categories[identity_codes] + else: + all_identity_values = safe_decode_array(identity_ds[:]) + + # Filter all values by organism mask + group_ids = all_group_values[human_mask] + identities = all_identity_values[human_mask] + + # For drug datasets, also get the actual condition column for replacement logic + if config.type == 'drug': + # group_ids already contains the condition values + current_conditions = group_ids.copy() + else: + # For human datasets, condition is just "human" + current_conditions = np.array(["human"] * len(group_ids)) + + # Map to global group IDs + file_global_groups = [] + for group_id in group_ids: + group_key = (config_idx, str(group_id)) + if group_key not in group_to_global: + group_to_global[group_key] = global_group_counter + self.group_mapping[global_group_counter] = { + 'config_idx': config_idx, + 'original_id': str(group_id), + 'dataset_type': config.type, + 'path': config.path, + 'group_col': config.group_col + } + global_group_counter += 1 + file_global_groups.append(group_to_global[group_key]) + + # Get gene information + gene_names = None + use_raw = False + + use_raw = "raw" in f and "var" in f["raw"] + gene_names = get_gene_names_from_h5(f, config.gene_name_col, use_raw=use_raw) + + if gene_names is None: + log.warning(f" Could not find gene names, skipping") + continue + + # Create gene index mapping - only for genes that exist in target_genes + gene_to_idx = {gene: idx for idx, gene in enumerate(gene_names)} + gene_mapping = {} + found_genes = 0 + for target_idx, gene in enumerate(self.target_genes): + if gene in gene_to_idx: + gene_mapping[target_idx] = gene_to_idx[gene] + found_genes += 1 + + if found_genes == 0: + log.warning(f" No target genes found in {h5ad_file.name}, skipping") + continue + + log.info(f" Found {found_genes}/{len(self.target_genes)} target genes") + + # Get matrix info + if use_raw: + X_group = f["raw"]["X"] + else: + X_group = f["X"] + + # Check if sparse + is_sparse = not hasattr(X_group, 'shape') or ('data' in X_group and 'indices' in X_group) + + # Store file information + file_info = { + 'path': str(h5ad_file), + 'config_idx': config_idx, + 'n_cells': len(file_global_groups), # Number of filtered cells + 'n_genes': len(gene_names), + 'use_raw': use_raw, + 'gene_mapping': gene_mapping, + 'start_cell_idx': global_cell_idx, + 'end_cell_idx': global_cell_idx + len(file_global_groups), + 'organism_mask': human_mask, # Store mask for later use + 'is_sparse': is_sparse, + 'found_genes': found_genes + } + self.file_info.append(file_info) + + # Update cell mapping + for local_idx in range(len(file_global_groups)): + self.cell_to_file_mapping.append((len(self.file_info) - 1, local_idx)) + self.global_group_ids.append(file_global_groups[local_idx]) + self.cell_identities.append(identities[local_idx]) + self.conditions.append(current_conditions[local_idx]) + self.dataset_types.append(config.type) + + global_cell_idx += len(file_global_groups) + log.info(f" Added {len(file_global_groups)} cells, total: {global_cell_idx}") + + except Exception as e: + log.exception(f" Error processing {h5ad_file.name}: {e}") + continue + + self.n_cells = global_cell_idx + self.global_group_ids = np.array(self.global_group_ids) + self.cell_identities = np.array(self.cell_identities) + self.conditions = np.array(self.conditions) + self.dataset_types = np.array(self.dataset_types) + + log.info(f"Loaded metadata for {self.n_cells} cells from {len(self.file_info)} files") + log.info(f"Total unique groups: {len(self.group_mapping)}") + + # Log dataset statistics + for config_idx, config in enumerate(self.dataset_configs): + mask = np.array([self.group_mapping[gid]['config_idx'] == config_idx for gid in self.global_group_ids]) + n_cells_in_dataset = mask.sum() + log.info(f"Dataset {config_idx} ({config.type}): {n_cells_in_dataset} cells") + + def _precompute_acceleration_pools(self): + """ + CORE ADDITION: Pre-compute acceleration dictionaries for fast replacement lookup. + + These dictionaries enable lightning-fast lookups during sample generation: + - group_identity_pool: (identity, group_id) -> [cell_indices] + - identity_to_groups_map: identity -> [group_ids] + - file_identity_pool: (identity, file_idx) -> [cell_indices] (for intra-file replacement) + - config_identity_pool: (identity, config_idx) -> [cell_indices] (for intra-dataset replacement) + """ + log.info("Pre-computing acceleration pools...") + start_time = time.time() + + # Dictionary A: (identity, group_id) -> [cell indices list] + self.group_identity_pool = {} + # Dictionary B: identity -> [group IDs that have this identity] + self.identity_to_groups_map = {} + # Dictionary C: (identity, file_idx) -> [cell indices list] (for intra-file replacement) + self.file_identity_pool = {} + # Dictionary D: (identity, config_idx) -> [cell indices list] (for intra-dataset replacement) + self.config_identity_pool = {} + + # Build all dictionaries in a single pass + for cell_idx in range(self.n_cells): + identity = self.cell_identities[cell_idx] + group_id = self.global_group_ids[cell_idx] + file_idx, _ = self.cell_to_file_mapping[cell_idx] + config_idx = self.group_mapping[group_id]['config_idx'] + + # Fill Dictionary A: group-identity pool + key_a = (identity, group_id) + if key_a not in self.group_identity_pool: + self.group_identity_pool[key_a] = [] + self.group_identity_pool[key_a].append(cell_idx) + + # Fill Dictionary B: identity to groups mapping + if identity not in self.identity_to_groups_map: + self.identity_to_groups_map[identity] = set() + self.identity_to_groups_map[identity].add(group_id) + + # Fill Dictionary C: file-identity pool + key_c = (identity, file_idx) + if key_c not in self.file_identity_pool: + self.file_identity_pool[key_c] = [] + self.file_identity_pool[key_c].append(cell_idx) + + # Fill Dictionary D: config-identity pool + key_d = (identity, config_idx) + if key_d not in self.config_identity_pool: + self.config_identity_pool[key_d] = [] + self.config_identity_pool[key_d].append(cell_idx) + + # Convert sets to lists for random selection and pool to numpy arrays + for identity in self.identity_to_groups_map: + self.identity_to_groups_map[identity] = list(self.identity_to_groups_map[identity]) + + # <<< OPTIMIZATION >>> Convert lists in pools to numpy arrays for faster indexing + for pool in [self.group_identity_pool, self.file_identity_pool, self.config_identity_pool]: + for key in pool: + pool[key] = np.array(pool[key]) + + log.info(f"Acceleration pools pre-computed in {time.time() - start_time:.2f}s") + log.info(f" Group-identity combinations: {len(self.group_identity_pool)}") + log.info(f" File-identity combinations: {len(self.file_identity_pool)}") + log.info(f" Config-identity combinations: {len(self.config_identity_pool)}") + log.info(f" Unique identities: {len(self.identity_to_groups_map)}") + + # <<< OPTIMIZATION 1: High-performance sparse HDF5 reading with block merging >>> + def _load_specific_rows_from_file(self, file_idx: int, local_indices: np.ndarray) -> np.ndarray: + """ + OPTIMIZED IO FUNCTION: Efficiently load specified rows from a single file using format-aware reading strategy. + This version supports both CSR and CSC sparse formats with automatic detection. + """ + file_info = self.file_info[file_idx] + + # Sort indices for better h5py reading efficiency + sort_order = np.argsort(local_indices) + sorted_local_indices = local_indices[sort_order] + + try: + f = _get_h5_handle(file_info['path']) + X_group = f["raw"]["X"] if file_info['use_raw'] else f["X"] + + # Get absolute row indices in the original HDF5 file + human_indices = np.where(file_info['organism_mask'])[0] + absolute_indices_to_load = human_indices[sorted_local_indices] + + # Create result matrix mapped to target genes + mapped_matrix = np.zeros((len(local_indices), self.n_genes), dtype=np.float32) + gene_mapping = file_info['gene_mapping'] + target_indices_in_result = np.array(list(gene_mapping.keys())) + source_indices_in_file = np.array(list(gene_mapping.values())) + + # Dynamic format detection based on encoding-type + if file_info['is_sparse']: + # Read encoding-type directly from HDF5 file attributes + attrs = dict(X_group.attrs) + encoding_type = attrs.get("encoding-type", "unknown") + log.debug(f"Processing sparse matrix with encoding: {encoding_type}") + + if encoding_type == "csr_matrix": + # CSR format: efficient row slicing + log.debug(f"Using optimized CSR row reading for {len(absolute_indices_to_load)} rows") + + # Your existing correct CSR reading logic + indptr_h5 = X_group["indptr"] + data_h5 = X_group["data"] + indices_h5 = X_group["indices"] + + # 1) Get all required indptr start and end points + starts = indptr_h5[absolute_indices_to_load] + ends = indptr_h5[absolute_indices_to_load + 1] + + # 2) Detect physical data block boundaries + block_breaks = np.where(ends[:-1] != starts[1:])[0] + 1 + row_blocks = np.split(np.arange(len(absolute_indices_to_load)), block_breaks) + + # 3) Read data block by block to minimize I/O operations + data_slices = [] + indices_slices = [] + + row_order = np.concatenate(row_blocks) if row_blocks else np.array([], dtype=int) + + for block in row_blocks: + if len(block) == 0: + continue + + first_row_in_block_idx = block[0] + last_row_in_block_idx = block[-1] + + data_start = starts[first_row_in_block_idx] + data_end = ends[last_row_in_block_idx] + + # Single I/O operation to read entire contiguous block + data_block = data_h5[data_start:data_end] + indices_block = indices_h5[data_start:data_end] + + # Split large block back into individual rows + row_lengths = ends[block] - starts[block] + cuts = np.cumsum(row_lengths) + + if len(cuts) > 0: + data_slices.extend(np.split(data_block, cuts[:-1])) + indices_slices.extend(np.split(indices_block, cuts[:-1])) + + # 4) Construct sparse matrix from collected slices + if not data_slices: + sparse_subset = sp.csr_matrix((len(local_indices), file_info['n_genes']), dtype=np.float32) + else: + all_data = np.concatenate(data_slices) + all_indices = np.concatenate(indices_slices) + + new_indptr = np.zeros(len(local_indices) + 1, dtype=indptr_h5.dtype) + row_lengths_in_order = ends[row_order] - starts[row_order] + new_indptr[1:] = np.cumsum(row_lengths_in_order) + + sparse_subset = sp.csr_matrix( + (all_data, all_indices, new_indptr), + shape=(len(local_indices), file_info['n_genes']), + dtype=np.float32 + ) + + # 5) Select target gene columns and convert to dense + expr_subset = sparse_subset[:, source_indices_in_file].toarray() + mapped_matrix[:, target_indices_in_result] = expr_subset.astype(np.float32) + + elif encoding_type == "csc_matrix": + # CSC format: column-oriented reading strategy + log.debug(f"Using CSC column-oriented reading for {len(absolute_indices_to_load)} rows") + + data_h5 = X_group["data"] + indices_h5 = X_group["indices"] + indptr_h5 = X_group["indptr"] + + # Get actual matrix shape + # For CSC, indptr length is n_cols + 1, i.e., number of genes + 1 + n_genes_in_file = len(indptr_h5) - 1 + n_cells_in_file = file_info['n_cells'] + + # Adaptive strategy selection + load_threshold = min(1000, n_cells_in_file * 0.1) # 10% of cells or 1000, whichever is smaller + + if len(absolute_indices_to_load) <= load_threshold: + # Strategy A: Row-by-row reading (suitable for few rows) + log.debug(f"Using row-by-row CSC reading for {len(absolute_indices_to_load)} rows") + + # FIXED: Use np.isin for efficient and correct matching + result_matrix = np.zeros((len(absolute_indices_to_load), n_genes_in_file), dtype=np.float32) + + # Create mapping from absolute_index to result_matrix row number + abs_to_res_map = {abs_idx: i for i, abs_idx in enumerate(absolute_indices_to_load)} + + # Process each target gene (column) + for gene_idx in range(n_genes_in_file): + col_start = indptr_h5[gene_idx] + col_end = indptr_h5[gene_idx + 1] + + if col_end > col_start: + # Read all non-zero cell indices and values for this gene (column) + col_cell_indices = indices_h5[col_start:col_end] + col_values = data_h5[col_start:col_end] + + # Use np.isin to find which cells we need are present in this column + # isin_mask has the same length as col_cell_indices + isin_mask = np.isin(col_cell_indices, absolute_indices_to_load, assume_unique=True) + + if np.any(isin_mask): + # Get absolute indices of cells that have values + matched_abs_indices = col_cell_indices[isin_mask] + # Get corresponding values + matched_values = col_values[isin_mask] + + # Fill result matrix using the mapping we created earlier + for abs_idx, value in zip(matched_abs_indices, matched_values): + result_matrix_row = abs_to_res_map[abs_idx] + result_matrix[result_matrix_row, gene_idx] = value + + else: + # Strategy B: Load full matrix then slice (suitable for many rows) + log.debug(f"Using full matrix load for {len(absolute_indices_to_load)} rows (threshold: {load_threshold})") + + # Load entire CSC matrix + data = data_h5[:] + indices = indices_h5[:] + indptr = indptr_h5[:] + + # Construct scipy CSC matrix (Note: this is transposed, genes x cells) + csc_matrix_transposed = sp.csc_matrix( + (data, indices, indptr), + shape=(n_genes_in_file, n_cells_in_file) + ) + + # Transpose to get normal cells x genes CSR matrix + csr_matrix = csc_matrix_transposed.T + + # Now we can efficiently perform row slicing + subset_sparse = csr_matrix[absolute_indices_to_load, :] + result_matrix = subset_sparse.toarray() + + # Map to target genes + mapped_matrix[:, target_indices_in_result] = result_matrix[:, source_indices_in_file].astype(np.float32) + + else: + # Unknown or unsupported sparse format + log.warning(f"Unknown sparse encoding type '{encoding_type}' in {file_info['path']}") + log.warning("Attempting to infer format from indptr length") + + # Try generic sparse matrix reading + try: + if 'data' in X_group and 'indices' in X_group and 'indptr' in X_group: + # Looks like CSR/CSC format, but encoding type is unknown + # Try to infer format based on indptr length + indptr_len = len(X_group['indptr']) + + if indptr_len == file_info['n_cells'] + 1: + # Likely CSR format + log.debug("Guessing CSR format based on indptr length") + encoding_type = "csr_matrix" + # Process as CSR (reuse CSR logic above) + # For simplicity, we'll use the fallback approach + raise NotImplementedError("CSR format inference - please use explicit encoding-type") + + elif indptr_len == file_info['n_genes'] + 1: + # Likely CSC format + log.debug("Guessing CSC format based on indptr length") + encoding_type = "csc_matrix" + # Process as CSC (reuse CSC logic above) + # For simplicity, we'll use the fallback approach + raise NotImplementedError("CSC format inference - please use explicit encoding-type") + else: + raise ValueError(f"Cannot determine sparse matrix format for {file_info['path']}") + else: + raise ValueError(f"Unknown sparse matrix structure in {file_info['path']}") + + except Exception as e: + log.exception(f"Failed to load unknown sparse format: {e}") + raise + else: + # Dense matrix: direct slicing + log.debug(f"Using dense matrix reading for {len(absolute_indices_to_load)} rows") + expr_subset = X_group[np.ix_(absolute_indices_to_load, source_indices_in_file)] + mapped_matrix[:, target_indices_in_result] = expr_subset.astype(np.float32) + + except Exception as e: + log.exception(f"Error loading specific rows from {file_info['path']}: {e}") + raise + + # Restore original request order + reverse_sort_order = np.empty_like(sort_order) + reverse_sort_order[sort_order] = np.arange(len(sort_order)) + + return mapped_matrix[reverse_sort_order] + + def load_expression_data(self, cell_indices: np.ndarray) -> np.ndarray: + """ + Enhanced data loading with precise IO strategy. + + Groups requested cells by file and loads only the exact requested cells. + """ + if len(cell_indices) == 0: + return np.empty((0, self.n_genes), dtype=np.float32) + + result = np.empty((len(cell_indices), self.n_genes), dtype=np.float32) + + # Group cells to load by file + file_groups = {} + for i, global_cell_idx in enumerate(cell_indices): + file_idx, local_row_idx = self.cell_to_file_mapping[global_cell_idx] + if file_idx not in file_groups: + file_groups[file_idx] = [] + file_groups[file_idx].append((i, local_row_idx)) + + # Load data from each file + for file_idx, indices_list in file_groups.items(): + result_positions = np.array([item[0] for item in indices_list]) + local_indices_in_file = np.array([item[1] for item in indices_list]) + + # Call the core IO function + file_data = self._load_specific_rows_from_file(file_idx, local_indices_in_file) + + # Place results in correct positions + result[result_positions] = file_data + + return result + + # <<< OPTIMIZATION 2: Vectorized cell replacement logic using NumPy >>> + def find_replacement_cells( + self, + cell_indices: np.ndarray, + rng: np.random.RandomState, + replacement_ratio: float = 0.25, + use_intra_file_first: bool = True, + ) -> Tuple[np.ndarray, Dict[int, int]]: + """ + OPTIMIZED: Find replacement cells with comprehensive strategy support using NumPy. + This version avoids creating pandas DataFrames inside the performance-critical path. + """ + n_cells = len(cell_indices) + n_replace_target = int(n_cells * replacement_ratio) + + if n_replace_target == 0: + return cell_indices, {} + + # Step 1: Select identities for replacement using efficient NumPy grouping + cell_identities = self.get_cell_identities(cell_indices) + + # Get unique identities, their counts, and inverse mapping + unique_identities, identity_inverse_map, identity_counts = np.unique( + cell_identities, return_inverse=True, return_counts=True + ) + + # Shuffle identity groups to randomize selection + shuffled_identity_indices = rng.permutation(len(unique_identities)) + + # Select identity groups until we reach target replacement count + cells_to_be_replaced = [] + total_selected = 0 + + for identity_idx in shuffled_identity_indices: + count = identity_counts[identity_idx] + + # Get all cells belonging to this identity group + group_mask = identity_inverse_map == identity_idx + cells_in_group = cell_indices[group_mask] + + if total_selected + count <= n_replace_target: + cells_to_be_replaced.extend(cells_in_group) + total_selected += count + else: + remaining_quota = n_replace_target - total_selected + if remaining_quota > 0: + selected_cells = rng.choice( + cells_in_group, + size=remaining_quota, + replace=False + ) + cells_to_be_replaced.extend(selected_cells) + break + + indices_to_replace = np.array(cells_to_be_replaced) + if len(indices_to_replace) == 0: + return cell_indices, {} + indices_to_keep = np.setdiff1d(cell_indices, indices_to_replace, assume_unique=True) + + # Step 2: Group cells to be replaced by identity for strategic replacement + replace_identities = self.get_cell_identities(indices_to_replace) + #unique_replace_identities = rng.permutation(np.unique(replace_identities)) + unique_replace_identities = np.unique(replace_identities) + + # Step 3: Execute strategic replacement for each selected identity + final_replacement_map = {} + unreplaced_cells = [] + + for identity in unique_replace_identities: + # Get all original cells for this identity that need replacement + original_cells_mask = replace_identities == identity + original_cells = indices_to_replace[original_cells_mask] + + # Get sample cell to determine context + sample_cell = original_cells[0] + sample_dataset_type = self.dataset_types[sample_cell] + sample_group = self.global_group_ids[sample_cell] + sample_config_idx = self.cell_config_indices[sample_cell] + sample_file_idx = self.cell_file_indices[sample_cell] + + # Determine replacement rule based on dataset type + if sample_dataset_type == 'human': + replacement_rule = ('DIFFERENT_GROUP',) + elif sample_dataset_type == 'drug': + control_condition = self.dataset_configs[sample_config_idx].control_condition + replacement_rule = ('CONTROL_GROUP', control_condition) + else: + log.warning(f"Unknown dataset type '{sample_dataset_type}' for identity '{identity}'. Skipping.") + unreplaced_cells.extend(original_cells) + continue + + # Determine scope priority + primary_scope = 'file' if use_intra_file_first else 'dataset' + fallback_scope = 'dataset' if use_intra_file_first else 'file' + + # Try to find replacement cells + replacement_cells = self._find_and_sample_replacements( + original_cells, identity, replacement_rule, primary_scope, fallback_scope, + sample_group, sample_config_idx, sample_file_idx, rng + ) + + if replacement_cells is not None: + # Create mapping between original and replacement cells + for original_cell, replacement_cell in zip(original_cells, replacement_cells): + final_replacement_map[original_cell] = replacement_cell + else: + # No replacement found, add to unreplaced list + unreplaced_cells.extend(original_cells) + + # Step 4: Merge unreplaced cells back to keep list + if unreplaced_cells: + indices_to_keep = np.concatenate([indices_to_keep, np.array(unreplaced_cells)]) + + return indices_to_keep, final_replacement_map + + def _find_and_sample_replacements( + self, + original_cells: List[int], + identity: str, + replacement_rule: Tuple, + primary_scope: str, + fallback_scope: str, + sample_group: int, + sample_config_idx: int, + sample_file_idx: int, + rng: np.random.RandomState + ) -> Optional[np.ndarray]: + """ + Core replacement finding logic with comprehensive strategy support. + + Args: + original_cells: List of cells to find replacements for + identity: Cell identity to match + replacement_rule: Tuple describing replacement rule + primary_scope: 'file' or 'dataset' - primary strategy + fallback_scope: 'file' or 'dataset' - fallback strategy + sample_group: Sample group ID for context + sample_config_idx: Sample config index for context + sample_file_idx: Sample file index for context + rng: Random number generator + + Returns: + Array of replacement cell indices or None if no candidates found + """ + n_needed = len(original_cells) + + # Try primary scope first + candidates = self._get_candidate_pool( + identity, replacement_rule, primary_scope, + sample_group, sample_config_idx, sample_file_idx + ) + + used_strategy = primary_scope + + # Sample replacement cells if candidates found + if len(candidates) > 0: + if len(candidates) >= n_needed: + #selected_replacements = rng.choice(candidates, size=n_needed, replace=False) + max_start_index = len(candidates) - n_needed + start_index = rng.randint(0, max_start_index + 1) + selected_replacements = candidates[start_index : start_index + n_needed] + else: + # Use replacement with repetition if not enough candidates + indices = np.arange(n_needed) % len(candidates) + selected_replacements = candidates[indices] + return selected_replacements + else: + log.info(f"No replacement candidates found for identity '{identity}' with any strategy.") + return None + + # <<< OPTIMIZATION 3: Vectorized candidate pool filtering >>> + def _get_candidate_pool( + self, + identity: str, + replacement_rule: Tuple, + scope: str, + sample_group: int, + sample_config_idx: int, + sample_file_idx: int + ) -> np.ndarray: + """ + OPTIMIZED: Get candidate cell pool based on identity, rule, and scope using NumPy. + """ + rule_type = replacement_rule[0] + + # Get base candidates from pre-computed pools + if scope == 'file': + base_candidates = self.file_identity_pool.get((identity, sample_file_idx), np.array([], dtype=np.int64)) + elif scope == 'dataset': + base_candidates = self.config_identity_pool.get((identity, sample_config_idx), np.array([], dtype=np.int64)) + else: + return np.array([], dtype=np.int64) + + # <<< BUG FIX IS HERE >>> + # If the base pool is empty, no candidates can be found. Return early. + if len(base_candidates) == 0: + return np.array([], dtype=np.int64) + + # Apply replacement rule filtering using vectorized operations + if rule_type == 'DIFFERENT_GROUP': + # For human datasets: exclude same group (donor) + candidate_groups = self.global_group_ids[base_candidates] + mask = candidate_groups != sample_group + return base_candidates[mask] + + elif rule_type == 'CONTROL_GROUP': + # For drug datasets: only include control condition + control_condition = replacement_rule[1] + candidate_conditions = self.conditions[base_candidates] + # Case-insensitive comparison + mask = np.core.defchararray.lower(candidate_conditions) == control_condition.lower() + return base_candidates[mask] + + # Fallback if rule_type is unknown, return empty + return np.array([], dtype=np.int64) + + def get_group_names(self, indices: np.ndarray) -> np.ndarray: + """Return group IDs for the provided cell indices.""" + return self.global_group_ids[indices] + + def get_cell_identities(self, indices: np.ndarray) -> np.ndarray: + """Return cell identities for the provided cell indices.""" + return self.cell_identities[indices] + + def get_conditions(self, indices: np.ndarray) -> np.ndarray: + """Return conditions for the provided cell indices.""" + return self.conditions[indices] + + def get_dataset_types(self, indices: np.ndarray) -> np.ndarray: + """Return dataset types for the provided cell indices.""" + return self.dataset_types[indices] + + def get_cache_stats(self) -> Dict[str, Any]: + """Get cache statistics for monitoring""" + return { + 'total_memory_gb': self._get_memory_usage(), + 'n_files': len(self.file_info), + 'n_cells': self.n_cells, + 'n_genes': self.n_genes, + 'n_datasets': len(self.dataset_configs), + 'dataset_types': [config.type for config in self.dataset_configs], + 'acceleration_pools': { + 'group_identity_combinations': len(self.group_identity_pool), + 'file_identity_combinations': len(self.file_identity_pool), + 'config_identity_combinations': len(self.config_identity_pool), + 'unique_identities': len(self.identity_to_groups_map) + } + } + + +class MultiDatasetSplittableDataset(Dataset): + """ + Enhanced dataset class with locality-aware sampling and comprehensive replacement strategy support. + + Key features: + - Physical locality-aware sampling for optimal I/O patterns + - Supports both human and drug dataset replacement strategies + - Configurable intra-file vs intra-dataset replacement priority + - Proper random state management for multi-process environments + - All original parameters restored and enhanced + """ + + def __init__( + self, + dataset_configs: List[DatasetConfig], + target_genes: Optional[List[str]] = None, + genelist_path: Optional[str] = None, + sample_size: int = 128, + replacement_ratio: float = 0.25, + intra_file_replacement_prob: float = 1.0, # RESTORED: Probability of using intra-file first + min_cells_per_group: int = 128, + train_groups: Optional[List[str]] = None, + val_groups: Optional[List[str]] = None, + test_groups: Optional[List[str]] = None, + mode: str = 'train', + random_state: Optional[int] = 42, + resample: bool = False, + cache_file: Optional[str] = None, + max_memory_gb: Optional[float] = None, # RESTORED: For compatibility + cache_ratio: float = 0.3, # RESTORED: For compatibility + block_size: int = 1000 # RESTORED: For compatibility + ): + """ + Initialize the enhanced dataset with full parameter support. + + Args: + dataset_configs: List of dataset configurations + target_genes: List of target gene names, if None will load from genelist_path + genelist_path: Path to saved gene list file + sample_size: Number of cells to sample from each group + replacement_ratio: Fraction of cells to replace (0.0-1.0) + intra_file_replacement_prob: Probability of prioritizing file-level replacement (0.0-1.0) + 1.0 = always try file-level first, then fallback to dataset-level + 0.0 = always try dataset-level first, then fallback to file-level + 0.5 = 50% chance of either strategy as primary + min_cells_per_group: Min cells required per group + train_groups: List of group IDs for training + val_groups: List of group IDs for validation + test_groups: List of group IDs for testing + mode: 'train', 'val', or 'test' + random_state: Random seed + resample: Whether to resample (for training) + cache_file: Optional path to cache metadata + max_memory_gb: Maximum memory usage in GB (for compatibility) + cache_ratio: Fraction of memory to use for caching (for compatibility) + block_size: Number of rows per cache block (for compatibility) + """ + self.dataset_configs = dataset_configs + self.sample_size = sample_size + self.replacement_ratio = replacement_ratio + self.intra_file_replacement_prob = intra_file_replacement_prob # RESTORED + self.min_cells_per_group = min_cells_per_group + self.mode = mode + self.resample = resample + + # Store initial random state for proper multi-process handling + self.initial_random_state = random_state + self.rng = None # Lazy initialization to fix multi-process issues + + # Load target genes + if target_genes is not None: + self.target_genes = target_genes + elif genelist_path is not None: + self.target_genes = load_gene_list(genelist_path) + else: + raise ValueError("Either target_genes or genelist_path must be provided") + + self.n_genes = len(self.target_genes) + + # Create cache key for singleton + config_paths = sorted([config.path for config in dataset_configs]) + cache_key = f"{'_'.join(config_paths)}_{hash(tuple(self.target_genes))}" + + # Load metadata with enhanced cache + self.metadata_cache = MultiDatasetMetadataCache.get_singleton( + cache_key, + dataset_configs=dataset_configs, + target_genes=self.target_genes, + cache_file=cache_file, + max_memory_gb=max_memory_gb, + cache_ratio=cache_ratio, + block_size=block_size + ) + + all_unique_identities = np.unique(self.metadata_cache.cell_identities) + # Create the mapping and store it in the dataset instance itself. + self.identity_to_id_map = { + identity: i for i, identity in enumerate(all_unique_identities) + } + log.info(f"[{self.mode}] Created mapping for {len(self.identity_to_id_map)} unique cell identities.") + + # Use provided group splits or create new ones + if train_groups is not None and val_groups is not None and test_groups is not None: + self.train_groups = train_groups + self.val_groups = val_groups + self.test_groups = test_groups + else: + self._split_groups() + + # Set active groups based on mode + if mode == 'train': + self.active_groups = self.train_groups + elif mode == 'val': + self.active_groups = self.val_groups + elif mode == 'test': + self.active_groups = self.test_groups + else: + raise ValueError(f"Unknown mode: {mode}") + + # Generate samples using locality-aware strategy + self._generate_samples_by_locality() + + def _initialize_rng(self): + """Initialize RNG for current process, fixing multi-process randomness issues.""" + # Use PyTorch's recommended approach for worker processes + worker_info = torch.utils.data.get_worker_info() + seed = self.initial_random_state + + if worker_info is not None: + # We are in a worker process + # Add worker_id to the seed to ensure different workers have different states + if seed is not None: + seed += worker_info.id + else: + seed = int(time.time() * 1000) % (2**32 -1) + worker_info.id + + if seed is not None: + self.rng = np.random.RandomState(seed) + else: + self.rng = np.random.RandomState() + + def _split_groups(self, test_ratio: float = 0.2, val_ratio: float = 0.2): + """Split groups into train/val/test sets""" + if self.rng is None: + self._initialize_rng() + + all_indices = np.arange(self.metadata_cache.n_cells) + group_ids = self.metadata_cache.get_group_names(all_indices) + + # Get unique groups and their cell counts + unique_groups, group_counts = np.unique(group_ids, return_counts=True) + + # Filter groups with sufficient cells + valid_groups = unique_groups[group_counts >= self.min_cells_per_group] + + log.info(f"Found {len(valid_groups)} groups with >= {self.min_cells_per_group} cells") + + # Split by dataset type to ensure balanced representation + human_groups = [] + drug_groups = [] + + for group_id in valid_groups: + group_info = self.metadata_cache.group_mapping[group_id] + config = self.dataset_configs[group_info['config_idx']] + if config.type == 'human': + human_groups.append(group_id) + else: + drug_groups.append(group_id) + + log.info(f"Groups by type: {len(human_groups)} human, {len(drug_groups)} drug") + + # Shuffle and split each type separately + def split_group_list(groups, test_r, val_r): + if len(groups) == 0: + return [], [], [] + shuffled = self.rng.permutation(groups) + n_test = max(1, int(len(shuffled) * test_r)) if len(shuffled) * test_r > 1 else 0 + n_val = max(1, int(len(shuffled) * val_r)) if len(shuffled) * val_r > 1 else 0 + + # Ensure at least one training sample if possible + if len(shuffled) > n_test + n_val: + n_train = len(shuffled) - n_test - n_val + else: + n_train = 0 + + test_groups = shuffled[:n_test].tolist() + val_groups = shuffled[n_test:n_test+n_val].tolist() + train_groups = shuffled[n_test+n_val:].tolist() + return train_groups, val_groups, test_groups + + # Split human groups + human_train, human_val, human_test = split_group_list(human_groups, test_ratio, val_ratio) + + # Split drug groups + drug_train, drug_val, drug_test = split_group_list(drug_groups, test_ratio, val_ratio) + + # Combine splits + self.train_groups = human_train + drug_train + self.val_groups = human_val + drug_val + self.test_groups = human_test + drug_test + + log.info(f"Split groups: {len(self.train_groups)} train, {len(self.val_groups)} val, {len(self.test_groups)} test") + log.info(f" Human - train: {len(human_train)}, val: {len(human_val)}, test: {len(human_test)}") + log.info(f" Drug - train: {len(drug_train)}, val: {len(drug_val)}, test: {len(drug_test)}") + + # <<< CORE OPTIMIZATION: Locality-aware sampling strategy >>> + def _generate_samples_by_locality(self): + """ + OPTIMIZED SAMPLING: Generate samples based on physical locality while preserving group semantics. + + This approach transforms random access patterns into sequential patterns: + 1. Groups cells by (file_idx, group_id) to maintain group semantic consistency + 2. Sorts cells within each file-group by local row index for physical locality + 3. Creates contiguous blocks as samples within each group + 4. Achieves significant I/O performance improvement while preserving replacement logic + """ + if self.rng is None: + self._initialize_rng() + + log.info(f"[{self.mode}] Generating locality-aware samples with group preservation...") + start_time = time.time() + + self.samples = [] + active_groups_set = set(self.active_groups) + + if not active_groups_set: + log.warning(f"No active groups for mode {self.mode}. Dataset will be empty.") + return + + # FIXED: Group cells by (file_idx, group_id) to preserve group semantics + cells_by_file_group = {} + for i in range(self.metadata_cache.n_cells): + group_id = self.metadata_cache.global_group_ids[i] + if group_id in active_groups_set: + file_idx, local_row_idx = self.metadata_cache.cell_to_file_mapping[i] + file_group_key = (file_idx, group_id) + if file_group_key not in cells_by_file_group: + cells_by_file_group[file_group_key] = [] + # Store (global_index, local_row_index) tuples + cells_by_file_group[file_group_key].append((i, local_row_idx)) + + # Process each (file, group) combination to create locality-aware samples + total_samples_created = 0 + skipped_file_groups = 0 + + for (file_idx, group_id), cells in cells_by_file_group.items(): + if len(cells) < self.sample_size: + skipped_file_groups += 1 + continue + + # Sort by local row index to create physical locality within the group + cells.sort(key=lambda x: x[1]) + + # Extract sorted global indices + sorted_global_indices = np.array([cell[0] for cell in cells]) + + # Create contiguous samples (blocks) from sorted indices within this group + n_samples = len(sorted_global_indices) // self.sample_size + for i in range(n_samples): + start = i * self.sample_size + end = start + self.sample_size + sample_indices = sorted_global_indices[start:end] + + # All cells in this sample belong to the same group by construction + group_info = self.metadata_cache.group_mapping[group_id] + + # Create sample tuple + self.samples.append(( + group_id, + group_info['original_id'], + group_info['dataset_type'], + sample_indices + )) + total_samples_created += 1 + + # Shuffle all samples to ensure randomness across file-groups + self.rng.shuffle(self.samples) + + generation_time = time.time() - start_time + log.info(f"Generated {len(self.samples)} locality-aware samples in {generation_time:.2f}s for {self.mode} mode") + log.info(f" Processed {len(cells_by_file_group)} (file, group) combinations") + log.info(f" Skipped {skipped_file_groups} combinations with < {self.sample_size} cells") + + if self.samples: + human_samples = sum(1 for _, _, dtype, _ in self.samples if dtype == 'human') + drug_samples = sum(1 for _, _, dtype, _ in self.samples if dtype == 'drug') + log.info(f" Samples by type: {human_samples} human, {drug_samples} drug") + log.info(f" Average I/O locality: {total_samples_created} contiguous blocks preserving group semantics") + + def __getitem__(self, idx: int) -> Tuple[torch.Tensor, Dict[str, Any]]: + """ + Enhanced __getitem__ with comprehensive replacement strategy support. + + This method now: + 1. Decides replacement strategy based on intra_file_replacement_prob + 2. Uses dataset-type-aware replacement rules (human vs drug) + 3. Performs efficient IO with precise loading + 4. Assembles final tensor with proper alignment + """ + if self.rng is None: + self._initialize_rng() + + # Get basic sample information + group_id, original_group_id, dataset_type, cell_indices = self.samples[idx] + + # RESTORED: Decide replacement strategy based on probability + use_intra_file_first = self.rng.random() < self.intra_file_replacement_prob + + # <<< USE OPTIMIZED VERSION >>> + # Get replacement information using enhanced logic + indices_to_keep, replacement_map = self.metadata_cache.find_replacement_cells( + cell_indices, + self.rng, + self.replacement_ratio, + use_intra_file_first=use_intra_file_first, + ) + + # Assemble loading lists with proper alignment + originals_to_be_replaced = np.array(list(replacement_map.keys()), dtype=np.int64) + replacements = np.array(list(replacement_map.values()), dtype=np.int64) + + # Build input matrices: kept cells + original/replacement cells + left_side_indices = np.concatenate([indices_to_keep, originals_to_be_replaced]) + right_side_indices = np.concatenate([indices_to_keep, replacements]) + + cell_identities_str = self.metadata_cache.get_cell_identities(left_side_indices) + + ## v8 edit + unique_types = self.rng.permutation(np.unique(cell_identities_str)) + + target_per_type = math.ceil(left_side_indices.shape[0] / len(unique_types)) + + balanced_positions = [] + + for t in unique_types: + (positions_of_type,) = np.where(cell_identities_str == t) + if len(positions_of_type) > target_per_type: + subsampled_positions = self.rng.choice(positions_of_type, size=int(target_per_type), replace=False) + balanced_positions.extend(subsampled_positions) + else: + balanced_positions.extend(positions_of_type) + + balanced_positions = np.array(balanced_positions, dtype=np.int64) + + n_needed = left_side_indices.shape[0] - len(balanced_positions) + if n_needed > 0: + reps = np.ones(len(balanced_positions), dtype=np.int64) + extra_idx = self.rng.choice(len(balanced_positions), size=n_needed, replace=True) + np.add.at(reps, extra_idx, 1) + final_positions = np.repeat(balanced_positions, reps) + else: + final_positions = balanced_positions + + left_side_indices = left_side_indices[final_positions] + right_side_indices = right_side_indices[final_positions] + cell_identities_str = cell_identities_str[final_positions] + + _, first_occurrence_indices = np.unique(left_side_indices, return_index=True) + + temp_mask = np.zeros(left_side_indices.shape[0], dtype=bool) + temp_mask[first_occurrence_indices] = True + + position_mask = torch.from_numpy(temp_mask) + + ## v8 edit ends + + # Convert strings to integer IDs using the map we created in __init__ + cell_type_ids = [self.identity_to_id_map[s] for s in cell_identities_str] + cell_type_ids_tensor = torch.LongTensor(cell_type_ids) + + # Efficient IO: Load unique cells only once + unique_indices_to_load, inverse_map = np.unique( + np.concatenate([left_side_indices, right_side_indices]), + return_inverse=True + ) + unique_data = self.metadata_cache.load_expression_data(unique_indices_to_load) + + # Reconstruct full data matrix from unique loaded data + full_data = unique_data[inverse_map] + + # Split into left and right side data + n_left = len(left_side_indices) + left_matrix = full_data[:n_left] + right_matrix = full_data[n_left:] + + # Concatenate to create final features tensor + ground_truth_tensor = torch.from_numpy(left_matrix).float() + observed_tensor = torch.from_numpy(right_matrix).float() + + # ENHANCED: Build comprehensive metadata + metadata = { + 'group_id': str(group_id), + 'original_group_id': str(original_group_id), + 'dataset_type': dataset_type, + 'sampled_cell_count': len(cell_indices), + 'n_kept': len(indices_to_keep), + 'n_replaced': len(replacement_map), + } + + return ground_truth_tensor, observed_tensor, cell_type_ids_tensor, position_mask, metadata + + def resample_training_data(self): + """Resample training data - call this for each epoch""" + # NOTE: Locality-aware sampling makes resample less meaningful + # since samples are now determined by physical file structure + if self.mode == 'train' and self.resample: + log.info(f"Resampling training data for worker...") + self._generate_samples_by_locality() + + def get_cache_stats(self) -> Dict[str, Any]: + """Get cache statistics for monitoring""" + return self.metadata_cache.get_cache_stats() + + def __len__(self) -> int: + return len(self.samples) + + +class TestSamplerDataset(Dataset): + """ + A simplified dataset for model inference (get_prediction, get_latent_representation). + Uses the optimized loading methods from SimplifiedDatasetCache for H5AD files. + + This class takes a base dataset and a test dataset. It processes the test + dataset in chunks of `n_kept_cells` and, for each chunk, samples a + corresponding set of cells from the base dataset. It handles upsampling if + either dataset has insufficient cells and ensures all gene expression + matrices are aligned to a common `target_genes` list. + """ + + def __init__( + self, + base_adata_or_path: Union[str, anndata.AnnData], + test_adata_or_path: Union[str, anndata.AnnData], + genelist_path: str, + n_cells: int, + n_kept_cells: int, + eval_col: str = 'disease', + control_cond: str = 'control', + split_col: str = 'donor_id', + cell_type_col: str = 'cell_type', + mode: str = 'in-context', + setting: str = 'representation', + ): + """ + Initializes the InferenceDataset. + + Args: + base_adata_or_path: The base AnnData object or path to .h5ad file, + used to provide context cells. + test_adata_or_path: The test AnnData object or path to .h5ad file, + containing cells for prediction. + genelist_path: Path to pickle file containing gene list. + n_cells: The number of base cells per sample. This + should match the model's `n_cells` parameter. + n_kept_cells: The number of test cells per sample. This should match + the model's `n_kept_cell` parameter. + cell_type_col: The column name in `base_adata.obs` that + contains cell type annotations. + """ + log.info("Initializing InferenceDataset...") + self.target_genes = load_gene_list(genelist_path) + self.n_genes = len(self.target_genes) + self.n_cells = n_cells + self.n_kept_cells = n_kept_cells + self.eval_col = eval_col + self.control_cond = control_cond + self.split_col = split_col + self.cell_type_col = cell_type_col + self.mode = mode + + # Process base data + log.info("Processing 'base' data...") + if isinstance(base_adata_or_path, str): + self.base_is_h5ad = True + self.base_path = base_adata_or_path + self.base_file_info = self._setup_h5ad_file_info(base_adata_or_path) + # Pre-load cell types once to avoid repeated I/O + self.base_eval_vals = self._load_meta_col_from_h5ad(base_adata_or_path, eval_col) + self.base_split_vals = self._load_meta_col_from_h5ad(base_adata_or_path, split_col) + self.base_cell_types = self._load_meta_col_from_h5ad(base_adata_or_path, cell_type_col) + self.base_expr = None # Will be loaded on demand + else: + self.base_is_h5ad = False + self.base_adata = base_adata_or_path + self.base_expr, self.base_eval_vals, self.base_split_vals, self.base_cell_types, self.base_gene_map = self._load_and_process_adata( + base_adata_or_path, self.target_genes, eval_col, split_col, cell_type_col + ) + + self.n_base_cells = self.base_file_info['n_cells'] if self.base_is_h5ad else self.base_expr.shape[0] + log.info(f"Loaded {self.n_base_cells} base cells.") + + # Process test data + log.info("Processing 'test' data...") + if isinstance(test_adata_or_path, str): + self.test_is_h5ad = True + self.test_path = test_adata_or_path + self.test_file_info = self._setup_h5ad_file_info(test_adata_or_path) + self.test_eval_vals = self._load_meta_col_from_h5ad(test_adata_or_path, eval_col) + self.test_split_vals = self._load_meta_col_from_h5ad(test_adata_or_path, split_col) + self.test_cell_types = self._load_meta_col_from_h5ad(test_adata_or_path, cell_type_col) + self.test_expr = None # Will be loaded on demand + else: + self.test_is_h5ad = False + self.test_adata = test_adata_or_path + self.test_expr, self.test_eval_vals, self.test_split_vals, self.test_cell_types, self.test_gene_map = self._load_and_process_adata( + test_adata_or_path, self.target_genes, eval_col, split_col, cell_type_col + ) + test_control_mask = self.test_eval_vals == self.control_cond + self.test_control_indices = np.where(test_control_mask)[0] + self.test_treat_indices = np.where(~test_control_mask)[0] + self.n_test_control_cells = len(self.test_control_indices) + self.n_test_treat_cells = len(self.test_treat_indices) + + self.n_test_cells = self.test_file_info['n_cells'] if self.test_is_h5ad else self.test_expr.shape[0] + self.n_total_test_cells = self.n_test_cells # Store original number for output shaping + log.info(f"Loaded {self.n_test_cells} test cells.") + + control_mask = self.base_eval_vals == self.control_cond + self.base_control_indices = np.where(control_mask)[0] + self.base_treat_indices = np.where(~control_mask)[0] + self.n_control_cells = len(self.base_control_indices) + self.n_treat_cells = len(self.base_treat_indices) + + # Calculate the total number of samples. In the in-context mode, test data are (n_cells - n_kept_cells) in replacement data. + # In the personalized mode, test data are n_kept_cells in orig data. + if setting == 'representation': + if mode == 'in-context': + self.n_samples = max(math.ceil(self.n_control_cells / self.n_kept_cells),math.ceil(self.n_treat_cells / self.n_kept_cells),math.ceil(self.n_test_cells / (self.n_cells - self.n_kept_cells))) + else: + self.n_samples = max(math.ceil(self.n_control_cells / self.n_kept_cells),math.ceil(self.n_treat_cells / (self.n_cells - self.n_kept_cells)),math.ceil(self.n_test_cells / self.n_kept_cells)) + else: + if mode == 'in-context': + self.n_samples = math.ceil(self.n_test_cells / (self.n_cells - self.n_kept_cells)) + else: + self.n_samples = math.ceil(self.n_test_cells / self.n_kept_cells) + log.info(f"Will generate {self.n_samples} inference samples.") + + def _load_meta_col_from_h5ad(self, h5ad_path: str, meta_col: str) -> np.ndarray: + """Pre-load all cell types from H5AD file to avoid repeated I/O""" + log.info(f"Pre-loading {meta_col} from {h5ad_path}") + + f = _get_h5_handle(h5ad_path) + obs = f["obs"] + n_cells = f["X"].shape[0] if hasattr(f["X"], 'shape') else f["X"].attrs['shape'][0] + + if meta_col is None: + return np.array(["unknown"] * n_cells) + + if meta_col in obs: + meta_ds = obs[meta_col] + if "categories" in meta_ds: + categories = safe_decode_array(meta_ds["categories"][:]) + codes = meta_ds["codes"][:] + all_meta = categories[codes] + else: + all_meta = safe_decode_array(meta_ds[:]) + else: + log.warning(f"Metadata column '{meta_col}' not found") + all_meta = np.array(["unknown"] * n_cells) + + return all_meta + + def _setup_h5ad_file_info(self, h5ad_path: str) -> Dict[str, Any]: + """Setup file info for H5AD file using the same logic as SimplifiedDatasetCache""" + log.info(f"Setting up H5AD file info for {h5ad_path}") + + f = _get_h5_handle(h5ad_path) + + # For inference, we typically don't filter by organism + # Assume all cells are valid + n_cells = f["X"].shape[0] if hasattr(f["X"], 'shape') else f["X"].attrs['shape'][0] + human_mask = np.ones(n_cells, dtype=bool) # Keep all cells for inference + n_human_cells = n_cells + + # Get gene information using the existing helper function + use_raw = "raw" in f and "var" in f["raw"] + gene_names = get_gene_names_from_h5(f, None, use_raw=use_raw) # Use default gene names + + if gene_names is None: + raise ValueError("Could not find gene names in the file") + + # Create gene mapping + gene_to_idx = {gene: idx for idx, gene in enumerate(gene_names)} + gene_mapping = {} + found_genes = 0 + for target_idx, gene in enumerate(self.target_genes): + if gene in gene_to_idx: + gene_mapping[target_idx] = gene_to_idx[gene] + found_genes += 1 + + if found_genes == 0: + raise ValueError("No target genes found in the file") + + # Store file info in the same format as SimplifiedDatasetCache + if use_raw: + X_group = f["raw"]["X"] + else: + X_group = f["X"] + + file_info = { + 'path': h5ad_path, + 'n_cells': n_human_cells, + 'n_genes': len(gene_names), + 'use_raw': use_raw, + 'gene_mapping': gene_mapping, + 'organism_mask': human_mask, + 'is_sparse': not hasattr(X_group, 'shape') or ('data' in X_group and 'indices' in X_group), + 'found_genes': found_genes + } + + log.info(f"H5AD file setup: {n_human_cells} cells, {found_genes}/{len(self.target_genes)} target genes") + return file_info + + def _load_and_process_adata( + self, + adata: anndata.AnnData, + target_genes: List[str], + eval_col: Optional[str], + split_col: Optional[str], + cell_type_col: Optional[str] + ) -> Tuple[np.ndarray, Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray], Dict[int, int]]: + """ + A private helper to load an AnnData object and prepare its contents. + + Args: + adata: AnnData object. + target_genes: The list of genes to align with. + cell_type_col: The column in .obs for cell type annotations. + + Returns: + A tuple containing: + - expression_matrix (np.ndarray): The cells x genes expression matrix. + - cell_types (np.ndarray or None): Array of cell type strings. + - gene_map (Dict[int, int]): A map from target gene index to source gene index. + """ + # Ensure expression matrix is a NumPy array + if hasattr(adata.X, 'toarray'): + expression_matrix = adata.X.toarray() + else: + expression_matrix = np.asarray(adata.X) + + # Extract cell types if a column is provided + eval_vals = None + if eval_col: + if eval_col in adata.obs.columns: + eval_vals = adata.obs[eval_col].values.astype(str) + else: + log.warning(f"Eval column '{eval_col}' not found in .obs. " + f"Behavior may not be expected.") + eval_vals = np.array(["unknown"] * adata.n_obs) + else: + eval_vals = np.array(["unknown"] * adata.n_obs) + + split_vals = None + if split_col: + if split_col in adata.obs.columns: + split_vals = adata.obs[split_col].values.astype(str) + else: + log.warning(f"Split column '{split_col}' not found in .obs. " + f"Fallback to loading in order.") + split_vals = np.array(["unknown"] * adata.n_obs) + else: + split_vals = np.array(["unknown"] * adata.n_obs) + + cell_types = None + if cell_type_col: + if cell_type_col in adata.obs.columns: + cell_types = adata.obs[cell_type_col].values.astype(str) + else: + log.warning(f"Cell type column '{cell_type_col}' not found in .obs. " + f"Cell types will not be available.") + cell_types = np.array(["unknown"] * adata.n_obs) + else: + cell_types = np.array(["unknown"] * adata.n_obs) + + # Create a map to align genes with the target list + source_gene_to_idx = {gene: i for i, gene in enumerate(adata.var_names)} + gene_map = {} + genes_found = 0 + for i, gene in enumerate(target_genes): + if gene in source_gene_to_idx: + gene_map[i] = source_gene_to_idx[gene] + genes_found += 1 + + log.info(f"Found {genes_found}/{len(target_genes)} target genes in the provided AnnData.") + if genes_found == 0: + raise ValueError("No target genes were found in the AnnData object. " + "Please check if gene names (e.g., HUGO symbols) match.") + + return expression_matrix, eval_vals, split_vals, cell_types, gene_map + + def _load_expression_data_from_h5ad(self, file_info: Dict[str, Any], local_indices: np.ndarray) -> np.ndarray: + """ + Load expression data from H5AD file using the optimized method from SimplifiedDatasetCache. + This is essentially a copy of SimplifiedDatasetCache.load_expression_data_from_file + but adapted for single file usage. + """ + # Sort indices for better h5py reading efficiency + sort_order = np.argsort(local_indices) + sorted_local_indices = local_indices[sort_order] + + try: + f = _get_h5_handle(file_info['path']) + X_group = f["raw"]["X"] if file_info['use_raw'] else f["X"] + + # Get absolute row indices (in this case, same as local since we don't filter organisms) + absolute_indices_to_load = sorted_local_indices + + # Create result matrix mapped to target genes + mapped_matrix = np.zeros((len(local_indices), self.n_genes), dtype=np.float32) + gene_mapping = file_info['gene_mapping'] + target_indices_in_result = np.array(list(gene_mapping.keys())) + source_indices_in_file = np.array(list(gene_mapping.values())) + + if file_info['is_sparse']: + # Handle sparse matrix (use the optimized logic from SimplifiedDatasetCache) + attrs = dict(X_group.attrs) + encoding_type = attrs.get("encoding-type", "csr_matrix") + log.debug(f"Processing sparse matrix with encoding: {encoding_type}") + + if encoding_type == "csr_matrix": + # CSR format: efficient row slicing + log.debug(f"Using optimized CSR row reading for {len(absolute_indices_to_load)} rows") + + indptr_h5 = X_group["indptr"] + data_h5 = X_group["data"] + indices_h5 = X_group["indices"] + + # Get all required indptr start and end points + starts = indptr_h5[absolute_indices_to_load] + ends = indptr_h5[absolute_indices_to_load + 1] + + # Detect physical data block boundaries + block_breaks = np.where(ends[:-1] != starts[1:])[0] + 1 + row_blocks = np.split(np.arange(len(absolute_indices_to_load)), block_breaks) + + # Read data block by block to minimize I/O operations + data_slices = [] + indices_slices = [] + + row_order = np.concatenate(row_blocks) if row_blocks else np.array([], dtype=int) + + for block in row_blocks: + if len(block) == 0: + continue + + first_row_in_block_idx = block[0] + last_row_in_block_idx = block[-1] + + data_start = starts[first_row_in_block_idx] + data_end = ends[last_row_in_block_idx] + + # Single I/O operation to read entire contiguous block + data_block = data_h5[data_start:data_end] + indices_block = indices_h5[data_start:data_end] + + # Split large block back into individual rows + row_lengths = ends[block] - starts[block] + cuts = np.cumsum(row_lengths) + + if len(cuts) > 0: + data_slices.extend(np.split(data_block, cuts[:-1])) + indices_slices.extend(np.split(indices_block, cuts[:-1])) + + # Construct sparse matrix from collected slices + if not data_slices: + sparse_subset = sp.csr_matrix((len(local_indices), file_info['n_genes']), dtype=np.float32) + else: + all_data = np.concatenate(data_slices) + all_indices = np.concatenate(indices_slices) + + new_indptr = np.zeros(len(local_indices) + 1, dtype=indptr_h5.dtype) + if row_order.size: # Guard against empty row_order + row_lengths_in_order = ends[row_order] - starts[row_order] + new_indptr[1:] = np.cumsum(row_lengths_in_order) + # If row_order is empty, new_indptr stays all zeros (valid for empty CSR) + + sparse_subset = sp.csr_matrix( + (all_data, all_indices, new_indptr), + shape=(len(local_indices), file_info['n_genes']), + dtype=np.float32 + ) + + # Select target gene columns and convert to dense + expr_subset = sparse_subset[:, source_indices_in_file].toarray() + mapped_matrix[:, target_indices_in_result] = expr_subset.astype(np.float32) + + elif encoding_type == "csc_matrix": + # CSC format: column-oriented reading strategy + log.debug(f"Using CSC column-oriented reading for {len(absolute_indices_to_load)} rows") + + data_h5 = X_group["data"] + indices_h5 = X_group["indices"] + indptr_h5 = X_group["indptr"] + + # Get actual matrix shape + n_genes_in_file = len(indptr_h5) - 1 + n_cells_in_file = file_info['n_cells'] + + # Adaptive strategy selection + load_threshold = min(1000, n_cells_in_file * 0.1) + + if len(absolute_indices_to_load) <= load_threshold: + # Strategy A: Row-by-row reading + log.debug(f"Using row-by-row CSC reading for {len(absolute_indices_to_load)} rows") + + result_matrix = np.zeros((len(absolute_indices_to_load), n_genes_in_file), dtype=np.float32) + + # Create mapping from absolute_index to result_matrix row number + abs_to_res_map = {abs_idx: i for i, abs_idx in enumerate(absolute_indices_to_load)} + + # Process each target gene (column) + for gene_idx in range(n_genes_in_file): + col_start = indptr_h5[gene_idx] + col_end = indptr_h5[gene_idx + 1] + + if col_end > col_start: + col_cell_indices = indices_h5[col_start:col_end] + col_values = data_h5[col_start:col_end] + + isin_mask = np.isin(col_cell_indices, absolute_indices_to_load, assume_unique=True) + + if np.any(isin_mask): + matched_abs_indices = col_cell_indices[isin_mask] + matched_values = col_values[isin_mask] + + for abs_idx, value in zip(matched_abs_indices, matched_values): + result_matrix_row = abs_to_res_map[abs_idx] + result_matrix[result_matrix_row, gene_idx] = value + + else: + # Strategy B: Load full matrix then slice + log.debug(f"Using full matrix load for {len(absolute_indices_to_load)} rows") + + data = data_h5[:] + indices = indices_h5[:] + indptr = indptr_h5[:] + + csc_matrix_transposed = sp.csc_matrix( + (data, indices, indptr), + shape=(n_genes_in_file, n_cells_in_file) + ) + + csr_matrix = csc_matrix_transposed.T + subset_sparse = csr_matrix[absolute_indices_to_load, :] + result_matrix = subset_sparse.toarray() + + # Map to target genes + mapped_matrix[:, target_indices_in_result] = result_matrix[:, source_indices_in_file].astype(np.float32) + + else: + raise ValueError(f"Unknown sparse encoding type '{encoding_type}' in {file_info['path']}") + else: + # Dense matrix: direct slicing + log.debug(f"Using dense matrix reading for {len(absolute_indices_to_load)} rows") + expr_subset = X_group[np.ix_(absolute_indices_to_load, source_indices_in_file)] + mapped_matrix[:, target_indices_in_result] = expr_subset.astype(np.float32) + + except Exception as e: + log.exception(f"Error loading specific rows from {file_info['path']}: {e}") + raise + + # Restore original request order + reverse_sort_order = np.empty_like(sort_order) + reverse_sort_order[sort_order] = np.arange(len(sort_order)) + + return mapped_matrix[reverse_sort_order] + + def __len__(self) -> int: + """Returns the total number of samples in the dataset.""" + return self.n_samples + + def __getitem__(self, idx: int) -> Tuple[torch.Tensor, torch.Tensor, Dict[str, np.ndarray]]: + """ + Retrieves one sample for inference. Different behavior depending on the mode. + + Args: + idx: The index of the sample. + + Returns: + A tuple (replacement_features, orig_features, metadata): + - replacement_features (torch.Tensor): Shape (n_cells, n_genes). + - orig_features (torch.Tensor): Shape (n_kept_cells, n_genes). + - metadata (Dict): A dictionary containing metadata for cells. + """ + if self.mode == 'in-context': + n_test_cells_per_sample = self.n_cells - self.n_kept_cells + n_base_controls_per_sample = self.n_kept_cells + n_base_treats_per_sample = self.n_kept_cells + else: + n_test_cells_per_sample = self.n_kept_cells + n_base_controls_per_sample = self.n_kept_cells + n_base_treats_per_sample = self.n_cells - self.n_kept_cells + + # --- 1. Prepare test data (the cells to be predicted) --- + test_start_idx = idx * n_test_cells_per_sample + test_end_idx = min(test_start_idx + n_test_cells_per_sample, self.n_test_cells) + test_indices = np.arange(test_start_idx, test_end_idx) + + # Upsample if the last batch is smaller than required + if len(test_indices) < n_test_cells_per_sample: + if len(test_indices) == 0: + raise IndexError("Index out of bounds with no cells to sample from.") + + candidates = test_indices + n_needed = n_test_cells_per_sample + + if len(candidates) < n_needed: + # Use replacement with repetition if not enough candidates + indices = np.arange(n_needed) % len(candidates) + test_indices = candidates[indices] + + if self.test_is_h5ad: + test_expr_slice = self._load_expression_data_from_h5ad(self.test_file_info, test_indices) + test_eval_vals_slice = self.test_eval_vals[test_indices] + test_split_vals_slice = self.test_split_vals[test_indices] + test_cell_types_slice = self.test_cell_types[test_indices] + else: + test_expr_slice = self.test_expr[test_indices, :] + test_eval_vals_slice = self.test_eval_vals[test_indices] + test_split_vals_slice = self.test_split_vals[test_indices] + test_cell_types_slice = self.test_cell_types[test_indices] + + # --- 2. Prepare base data (separate controls and treatments) --- + # Find control and non-control indices in base data + control_indices = self.base_control_indices + treat_indices = self.base_treat_indices + + if len(control_indices) > 0: + control_start_idx = (idx * n_base_controls_per_sample) % len(control_indices) + control_end_idx = min(control_start_idx + n_base_controls_per_sample, len(control_indices)) + selected_control_indices = control_indices[control_start_idx:control_end_idx] + + if len(selected_control_indices) < n_base_controls_per_sample: + if len(selected_control_indices) == 0: + raise IndexError("Index out of bounds with no cells to sample from.") + indices = np.arange(n_base_controls_per_sample) % len(control_indices) + selected_control_indices = control_indices[indices] + else: + selected_control_indices = np.array([], dtype=int) + + # Sample treatment cells + if len(treat_indices) > 0: + treat_start_idx = (idx * n_base_treats_per_sample) % len(treat_indices) + treat_end_idx = min(treat_start_idx + n_base_treats_per_sample, len(treat_indices)) + selected_treat_indices = treat_indices[treat_start_idx:treat_end_idx] + + if len(selected_treat_indices) < n_base_treats_per_sample: + if len(selected_control_indices) == 0: + raise IndexError("Index out of bounds with no cells to sample from.") + indices = np.arange(n_base_treats_per_sample) % len(treat_indices) + selected_treat_indices = treat_indices[indices] + else: + selected_treat_indices = np.array([], dtype=int) + + # Combine control and treatment indices + base_indices = np.concatenate([selected_control_indices, selected_treat_indices]) + + if self.base_is_h5ad: + base_expr_slice = self._load_expression_data_from_h5ad(self.base_file_info, base_indices) + base_eval_vals_slice = self.base_eval_vals[base_indices] + base_split_vals_slice = self.base_split_vals[base_indices] + base_cell_types_slice = self.base_cell_types[base_indices] + else: + base_expr_slice = self.base_expr[base_indices, :] + base_eval_vals_slice = self.base_eval_vals[base_indices] + base_split_vals_slice = self.base_split_vals[base_indices] + base_cell_types_slice = self.base_cell_types[base_indices] + + # --- 3. Align genes for both matrices --- + # For H5AD files, genes are already aligned in the loading functions + if self.test_is_h5ad: + final_test_expr = test_expr_slice + else: + # Align test genes + final_test_expr = np.zeros((n_test_cells_per_sample, self.n_genes), dtype=np.float32) + test_target_indices = np.array(list(self.test_gene_map.keys())) + test_source_indices = np.array(list(self.test_gene_map.values())) + final_test_expr[:, test_target_indices] = test_expr_slice[:, test_source_indices] + + final_test_expr = torch.from_numpy(final_test_expr).float() + + if self.base_is_h5ad: + final_base_expr = base_expr_slice + else: + # Align base genes + final_base_expr = np.zeros((len(base_indices), self.n_genes), dtype=np.float32) + base_target_indices = np.array(list(self.base_gene_map.keys())) + base_source_indices = np.array(list(self.base_gene_map.values())) + final_base_expr[:, base_target_indices] = base_expr_slice[:, base_source_indices] + + final_control_expr = torch.from_numpy(final_base_expr[:len(selected_control_indices)]).float() + final_treat_expr = torch.from_numpy(final_base_expr[len(selected_control_indices):]).float() + + # --- 4. Construct final tensors based on mode --- + if self.mode == 'in-context': + replacement_features = torch.cat([final_control_expr, final_test_expr], dim=0) + orig_features = final_treat_expr + + # Combine metadata for replacement_features + replacement_eval_vals = np.concatenate([base_eval_vals_slice[:self.n_kept_cells], test_eval_vals_slice]) + replacement_split_vals = np.concatenate([base_split_vals_slice[:self.n_kept_cells], test_split_vals_slice]) + replacement_cell_types = np.concatenate([base_cell_types_slice[:self.n_kept_cells], test_cell_types_slice]) + + metadata = { + #'eval_vals': replacement_eval_vals.tolist(), + #'split_vals': replacement_split_vals.tolist(), + 'cell_type': replacement_cell_types.tolist(), + #'orig_eval_vals': base_eval_vals_slice[self.n_kept_cells:].tolist(), + #'orig_split_vals': base_split_vals_slice[self.n_kept_cells:].tolist(), + #'orig_cell_type': base_cell_types_slice[self.n_kept_cells:].tolist() + } + else: + replacement_features = torch.from_numpy(final_base_expr).float() + orig_features = final_test_expr + + metadata = { + #'eval_vals': base_eval_vals_slice.tolist(), + #'split_vals': base_split_vals_slice.tolist(), + 'cell_type': base_cell_types_slice.tolist(), + #'orig_eval_vals': test_eval_vals_slice.tolist(), + #'orig_split_vals': test_split_vals_slice.tolist(), + #'orig_cell_type': test_cell_types_slice.tolist() + } + + return ( + replacement_features, + orig_features, + metadata + ) + + +def create_train_val_test_datasets( + dataset_configs: List[DatasetConfig], + target_genes: Optional[List[str]] = None, + genelist_path: Optional[str] = None, + sample_size: int = 128, + replacement_ratio: float = 0.25, + intra_file_replacement_prob: float = 1.0, # RESTORED + min_cells_per_group: int = 128, + test_ratio: float = 0.2, + val_ratio: float = 0.2, + random_state: int = 42, + cache_file: Optional[str] = None, + max_memory_gb: Optional[float] = None, # RESTORED + cache_ratio: float = 0.3, # RESTORED + block_size: int = 1000 # RESTORED +) -> Tuple[MultiDatasetSplittableDataset, MultiDatasetSplittableDataset, MultiDatasetSplittableDataset]: + """ + Create train, validation, and test datasets with comprehensive parameter support. + + Args: + dataset_configs: List of dataset configurations + target_genes: List of target gene names, if None will load from genelist_path + genelist_path: Path to saved gene list file + sample_size: Number of cells to sample from each group + replacement_ratio: Fraction of cells to replace (0.0-1.0) + intra_file_replacement_prob: Probability of using intra-file replacement first (0.0-1.0) + min_cells_per_group: Min cells required per group + test_ratio: Fraction of groups for testing + val_ratio: Fraction of groups for validation + random_state: Random seed + cache_file: Optional path to cache metadata + max_memory_gb: Maximum memory usage in GB (for compatibility) + cache_ratio: Fraction of memory to use for caching (for compatibility) + block_size: Number of rows per cache block (for compatibility) + + Returns: + Tuple of (train_dataset, val_dataset, test_dataset) + """ + + # Create a temporary base dataset instance just to perform the group splitting logic + # This avoids instantiating the full dataset three times + log.info("Creating base dataset for group splitting...") + base_dataset = MultiDatasetSplittableDataset( + dataset_configs=dataset_configs, + target_genes=target_genes, + genelist_path=genelist_path, + sample_size=min_cells_per_group, # Use min_cells_per_group for splitting logic + mode='train', # a placeholder mode + random_state=random_state, + resample=False, + cache_file=cache_file, + max_memory_gb=max_memory_gb, + cache_ratio=cache_ratio, + block_size=block_size + ) + + # Perform group splitting using the base instance + base_dataset._split_groups(test_ratio, val_ratio) + train_groups = base_dataset.train_groups + val_groups = base_dataset.val_groups + test_groups = base_dataset.test_groups + + # Now create the actual datasets using the pre-computed splits + # This ensures they share the same metadata cache instance via the singleton + log.info("Creating training dataset...") + train_dataset = MultiDatasetSplittableDataset( + dataset_configs=dataset_configs, + target_genes=target_genes, + genelist_path=genelist_path, + sample_size=sample_size, + replacement_ratio=replacement_ratio, + intra_file_replacement_prob=intra_file_replacement_prob, + min_cells_per_group=min_cells_per_group, + train_groups=train_groups, + val_groups=val_groups, + test_groups=test_groups, + mode='train', + random_state=random_state, + resample=True, # Enable resampling for training data + cache_file=cache_file, + max_memory_gb=max_memory_gb, + cache_ratio=cache_ratio, + block_size=block_size + ) + + log.info("Creating validation dataset...") + val_dataset = MultiDatasetSplittableDataset( + dataset_configs=dataset_configs, + target_genes=target_genes, + genelist_path=genelist_path, + sample_size=sample_size, + replacement_ratio=replacement_ratio, + intra_file_replacement_prob=intra_file_replacement_prob, + min_cells_per_group=min_cells_per_group, + train_groups=train_groups, + val_groups=val_groups, + test_groups=test_groups, + mode='val', + random_state=random_state, + resample=False, + cache_file=cache_file, + max_memory_gb=max_memory_gb, + cache_ratio=cache_ratio, + block_size=block_size + ) + + log.info("Creating test dataset...") + test_dataset = MultiDatasetSplittableDataset( + dataset_configs=dataset_configs, + target_genes=target_genes, + genelist_path=genelist_path, + sample_size=sample_size, + replacement_ratio=replacement_ratio, + intra_file_replacement_prob=intra_file_replacement_prob, + min_cells_per_group=min_cells_per_group, + train_groups=train_groups, + val_groups=val_groups, + test_groups=test_groups, + mode='test', + random_state=random_state, + resample=False, + cache_file=cache_file, + max_memory_gb=max_memory_gb, + cache_ratio=cache_ratio, + block_size=block_size + ) + + return train_dataset, val_dataset, test_dataset + + +# Example usage functions +def create_example_configs(): + """Example of how to create dataset configurations""" + + # Human dataset configuration + human_config = DatasetConfig( + path="/path/to/human_pbmc_data", + type="human", + donor_col="donor_id", + cell_type_col="cell_type", + filter_organism=True + ) + + # Drug screening dataset configuration + drug_config = DatasetConfig( + path="/path/to/drug_screening_data", + type="drug", + condition_col="drug_condition", + cell_line_col="cell_line", + control_condition="dmso", + filter_organism=False + ) + + return [human_config, drug_config] + + +def compute_and_save_hvg_union( + dataset_configs: List[DatasetConfig], + output_path: str, + n_top_genes: int = 1000, + theta: float = 100.0, + max_cells_per_file: int = 10000, + random_state: int = 42 +) -> List[str]: + """ + Compute HVG union and save to file. This function decouples HVG computation from dataset creation. + + Args: + dataset_configs: List of dataset configurations + output_path: Path to save the gene list + n_top_genes: Number of top HVGs to select per file + theta: Overdispersion parameter for negative binomial + max_cells_per_file: Maximum number of cells to use per file + random_state: Random seed for reproducible subsampling + + Returns: + List of gene names (union of all HVGs) + """ + log.info("Starting HVG computation process...") + + hvg_genes = compute_hvg_union( + dataset_configs=dataset_configs, + n_top_genes=n_top_genes, + theta=theta, + max_cells_per_file=max_cells_per_file, + output_path=output_path, + random_state=random_state + ) + + log.info(f"HVG computation completed. Saved {len(hvg_genes)} genes to {output_path}") + return hvg_genes + + +def create_datasets_from_gene_list( + dataset_configs: List[DatasetConfig], + genelist_path: str, + sample_size: int = 128, + replacement_ratio: float = 0.25, + intra_file_replacement_prob: float = 1.0, # RESTORED + min_cells_per_group: int = 128, + test_ratio: float = 0.2, + val_ratio: float = 0.2, + random_state: int = 42, + cache_file: Optional[str] = None, + max_memory_gb: Optional[float] = None, # RESTORED + cache_ratio: float = 0.3, # RESTORED + block_size: int = 1000 # RESTORED +) -> Tuple[MultiDatasetSplittableDataset, MultiDatasetSplittableDataset, MultiDatasetSplittableDataset]: + """ + Create datasets from a pre-computed gene list file with full parameter support. + + Args: + dataset_configs: List of dataset configurations + genelist_path: Path to saved gene list file + sample_size: Number of cells to sample from each group + replacement_ratio: Fraction of cells to replace (0.0-1.0) + intra_file_replacement_prob: Probability of using intra-file replacement first (0.0-1.0) + min_cells_per_group: Min cells required per group + test_ratio: Fraction of groups for testing + val_ratio: Fraction of groups for validation + random_state: Random seed + cache_file: Optional path to cache metadata + max_memory_gb: Maximum memory usage in GB (for compatibility) + cache_ratio: Fraction of memory to use for caching (for compatibility) + block_size: Number of rows per cache block (for compatibility) + + Returns: + Tuple of (train_dataset, val_dataset, test_dataset) + """ + log.info(f"Creating datasets from gene list: {genelist_path}") + + return create_train_val_test_datasets( + dataset_configs=dataset_configs, + target_genes=None, # Load from file + genelist_path=genelist_path, + sample_size=sample_size, + replacement_ratio=replacement_ratio, + intra_file_replacement_prob=intra_file_replacement_prob, + min_cells_per_group=min_cells_per_group, + test_ratio=test_ratio, + val_ratio=val_ratio, + random_state=random_state, + cache_file=cache_file, + max_memory_gb=max_memory_gb, + cache_ratio=cache_ratio, + block_size=block_size + ) \ No newline at end of file diff --git a/transfer/code/stack/src/stack/data/gene_processing.py b/transfer/code/stack/src/stack/data/gene_processing.py new file mode 100644 index 0000000000000000000000000000000000000000..f688d62b4f090b71cb22a317169c1f27db0fefdd --- /dev/null +++ b/transfer/code/stack/src/stack/data/gene_processing.py @@ -0,0 +1,86 @@ +"""Gene name extraction and filtering utilities.""" +from __future__ import annotations + +from typing import List, Optional + +import h5py +import numpy as np +import pandas as pd + + +__all__ = [ + "safe_decode_array", + "filter_gene_names", + "get_gene_names_from_h5", +] + + +def safe_decode_array(arr) -> np.ndarray: + """Decode byte strings from AnnData tables to standard ``str`` values.""" + decoded = [] + for value in arr: + if isinstance(value, (bytes, bytearray)): + decoded.append(value.decode("utf-8", errors="ignore")) + else: + decoded.append(str(value)) + return np.array(decoded, dtype=str) + + +def filter_gene_names(gene_names: List[str]) -> List[str]: + """Apply heuristic filters to remove non informative gene identifiers.""" + exclude_patterns = [ + r"^ENS[A-Z]*[0-9]+", + r"^LOC[0-9]+", + r"^LINC[0-9]+", + r"^MT-", + r"^RP[SL][0-9]", + r".*P[0-9]*$", + r"^C[0-9XY]+orf[0-9]+", + r"^[A-Z]{2}[0-9]{5,}", + r"\.[0-9]+$", + ] + combined_pattern = "|".join(exclude_patterns) + + series = pd.Series(gene_names) + keep_mask = ~series.str.contains(combined_pattern, regex=True, case=False, na=False) + filtered = series[keep_mask].tolist() + return filtered + + +def get_gene_names_from_h5( + h5_file_group: h5py.Group, + gene_name_col: Optional[str] = None, + use_raw: bool = False, +) -> Optional[np.ndarray]: + """Return gene names from an H5AD structure, handling categorical encoding.""" + var_group_path = "raw/var" if use_raw and "raw" in h5_file_group else "var" + if var_group_path not in h5_file_group: + return None + + var_group = h5_file_group[var_group_path] + gene_names_source = None + + if gene_name_col and gene_name_col in var_group: + gene_names_source = var_group[gene_name_col] + else: + index_key = "_index" if "_index" in var_group else "index" + if index_key in var_group: + gene_names_source = var_group[index_key] + + if gene_names_source is None: + return None + + gene_names = None + if isinstance(gene_names_source, h5py.Dataset): + gene_names = safe_decode_array(gene_names_source[:]) + elif isinstance(gene_names_source, h5py.Group): + if "categories" in gene_names_source and "codes" in gene_names_source: + categories = safe_decode_array(gene_names_source["categories"][:]) + codes = gene_names_source["codes"][:] + valid_mask = codes != -1 + gene_names = np.full(codes.shape, fill_value="", dtype=categories.dtype) + gene_names[valid_mask] = categories[codes[valid_mask]] + + if gene_names is None: + return None + return np.char.upper(gene_names) diff --git a/transfer/code/stack/src/stack/data/h5_manager.py b/transfer/code/stack/src/stack/data/h5_manager.py new file mode 100644 index 0000000000000000000000000000000000000000..c3c36fe6996eeba7002e9b0eb70657001a0b6fbb --- /dev/null +++ b/transfer/code/stack/src/stack/data/h5_manager.py @@ -0,0 +1,93 @@ +"""Utilities for managing shared HDF5 file handles and worker initialization.""" +from __future__ import annotations + +import atexit +import random +from dataclasses import dataclass +from typing import Dict + +import h5py +import numpy as np +import torch + + +@dataclass(slots=True) +class H5HandleManager: + """Central registry that keeps reusable read-only :mod:`h5py` handles.""" + + _handles: Dict[str, h5py.File] = None # type: ignore[assignment] + + def __post_init__(self) -> None: + if self._handles is None: + self._handles = {} + + # ------------------------------------------------------------------ + # Lifecycle helpers + # ------------------------------------------------------------------ + def get(self, path: str) -> h5py.File: + """Return a cached handle for *path* creating it when missing.""" + handle = self._handles.get(path) + if handle is None or not handle.id.valid: + handle = h5py.File( + path, + "r", + libver="latest", + rdcc_nbytes=1024 * 1024 * 1024, + rdcc_nslots=1_000_003, + rdcc_w0=0.75, + ) + self._handles[path] = handle + return handle + + def close_all(self) -> None: + """Close every cached handle and clear the registry.""" + for handle in list(self._handles.values()): + try: + handle.close() + finally: + pass + self._handles.clear() + + # ------------------------------------------------------------------ + # PyTorch DataLoader integration + # ------------------------------------------------------------------ + def worker_init(self, worker_id: int) -> None: + """Reset state and seed RNGs when a new worker process starts.""" + self.close_all() + np.random.seed(worker_id) + random.seed(worker_id) + torch.manual_seed(worker_id) + + +# Singleton exposed for module level functions +_HANDLE_MANAGER = H5HandleManager() + + +def get_h5_handle(path: str) -> h5py.File: + """Public helper mirroring the legacy :func:`_get_h5_handle`.""" + return _HANDLE_MANAGER.get(path) + + +def reset_h5_handle_pool() -> None: + """Explicitly close and clear cached handles.""" + _HANDLE_MANAGER.close_all() + + +def worker_init_fn(worker_id: int) -> None: + """Entry point that matches the signature expected by :class:`DataLoader`.""" + _HANDLE_MANAGER.worker_init(worker_id) + + +def _worker_init_fn(worker_id: int) -> None: + """Legacy alias preserved for backwards compatibility.""" + worker_init_fn(worker_id) + + +# Backwards compatibility aliases --------------------------------------------------------------- +_get_h5_handle = get_h5_handle +_reset_h5_handle_pool = reset_h5_handle_pool + + +@atexit.register +def _close_handles_on_exit() -> None: + _HANDLE_MANAGER.close_all() diff --git a/transfer/code/stack/src/stack/data/hvg.py b/transfer/code/stack/src/stack/data/hvg.py new file mode 100644 index 0000000000000000000000000000000000000000..6da7e452d2ad708d1413d58b36cf6cb3cf24353e --- /dev/null +++ b/transfer/code/stack/src/stack/data/hvg.py @@ -0,0 +1,185 @@ +"""Highly variable gene computation utilities.""" +from __future__ import annotations + +import gc +import logging +import pickle +from dataclasses import dataclass +from pathlib import Path +from typing import List, Optional + +import h5py +import numpy as np +import scipy.sparse as sp + +from .gene_processing import filter_gene_names, get_gene_names_from_h5, safe_decode_array + +log = logging.getLogger(__name__) + + +__all__ = [ + "HVGComputationConfig", + "compute_analytic_pearson_residuals", + "compute_hvg_union", +] + + +@dataclass(slots=True) +class HVGComputationConfig: + dataset_paths: List[str] + gene_name_col: Optional[str] = None + filter_organism: bool = True + + +def compute_analytic_pearson_residuals(X: np.ndarray, theta: float = 100.0) -> np.ndarray: + if X.shape[0] == 0 or X.shape[1] == 0: + return np.empty_like(X, dtype=np.float32) + + X = X.astype(np.float32) + sequencing_depths = np.sum(X, axis=1, keepdims=True).clip(min=1e-6) + total_counts_per_gene = np.sum(X, axis=0, keepdims=True) + total_sequencing_depth = np.sum(sequencing_depths) + gene_abundances = (total_counts_per_gene / total_sequencing_depth).clip(min=1e-6) + expected_counts = sequencing_depths * gene_abundances + variance = (expected_counts + (expected_counts ** 2) / theta).clip(min=1e-6) + + residuals = (X - expected_counts) / np.sqrt(variance) + residuals = np.nan_to_num(residuals, nan=0.0, posinf=0.0, neginf=0.0) + return residuals.astype(np.float32) + + +def compute_hvg_union( + dataset_configs: List["DatasetConfig"], + n_top_genes: int = 1000, + theta: float = 100.0, + max_cells_per_file: int = 10000, + output_path: Optional[str] = None, + random_state: int = 42, + filter_gene_names_flag: bool = True, +) -> List[str]: + from .datasets import DatasetConfig # Circular import guard + + all_hvgs = set() + rng = np.random.RandomState(random_state) + processed_files = 0 + + for config_idx, config in enumerate(dataset_configs): + log.info("Processing dataset %s/%s: %s", config_idx + 1, len(dataset_configs), config.path) + h5ad_files = sorted({*Path(config.path).glob("*.h5ad"), *Path(config.path).glob("*.h5")}) + + for h5ad_file in h5ad_files: + try: + processed_files += 1 + log.info("Processing file %s: %s", processed_files, h5ad_file.name) + + with h5py.File(h5ad_file, "r") as handle: + if config.filter_organism: + obs = handle["obs"] + if "organism" not in obs: + log.warning("'organism' column not found in %s, skipping", h5ad_file.name) + continue + + organism_ds = obs["organism"] + if "categories" in organism_ds: + organism_categories = safe_decode_array(organism_ds["categories"][:]) + organism_codes = organism_ds["codes"][:] + organism_values = organism_categories[organism_codes] + else: + organism_values = safe_decode_array(organism_ds[:]) + + human_mask = organism_values == "Homo sapiens" + if not human_mask.any(): + log.warning("No Homo sapiens cells found in %s, skipping", h5ad_file.name) + continue + else: + X = handle["X"] + n_cells = X.shape[0] if hasattr(X, "shape") else X.attrs["shape"][0] + human_mask = np.ones(n_cells, dtype=bool) + + use_raw = "raw" in handle and "var" in handle["raw"] + gene_names = get_gene_names_from_h5(handle, config.gene_name_col, use_raw=use_raw) + if gene_names is None: + log.warning("Could not find gene names in %s, skipping", h5ad_file.name) + continue + + all_valid_indices = np.where(human_mask)[0] + num_valid_cells = len(all_valid_indices) + if num_valid_cells == 0: + continue + + if num_valid_cells > max_cells_per_file: + indices_to_load = rng.choice(all_valid_indices, size=max_cells_per_file, replace=False) + indices_to_load = np.sort(indices_to_load) + else: + indices_to_load = all_valid_indices + + X_group = handle["raw"]["X"] if use_raw else handle["X"] + if hasattr(X_group, "shape"): + expr_data = X_group[indices_to_load, :] + else: + attrs = dict(X_group.attrs) + if attrs.get("encoding-type") == "csr_matrix": + indptrs = X_group["indptr"][:] + data_h5 = X_group["data"] + indices_h5 = X_group["indices"] + + n_selected = len(indices_to_load) + n_genes = len(gene_names) + expr_data = np.zeros((n_selected, n_genes), dtype=np.float32) + + for i, row_idx in enumerate(indices_to_load): + start_ptr = indptrs[row_idx] + end_ptr = indptrs[row_idx + 1] + if end_ptr > start_ptr: + cell_indices = indices_h5[start_ptr:end_ptr] + cell_values = data_h5[start_ptr:end_ptr].astype(np.float32) + expr_data[i, cell_indices] = cell_values + else: + continue + + if sp.issparse(expr_data): + expr_data = expr_data.toarray() + expr_data = expr_data.astype(np.float32) + + if filter_gene_names_flag: + good_gene_names = filter_gene_names(gene_names.tolist()) + good_gene_set = set(good_gene_names) + good_gene_indices = np.array([i for i, gene in enumerate(gene_names) if gene in good_gene_set]) + if len(good_gene_indices) == 0: + continue + + filtered_gene_names = gene_names[good_gene_indices] + filtered_expr_data = expr_data[:, good_gene_indices] + else: + filtered_gene_names = gene_names + filtered_expr_data = expr_data + + if filtered_expr_data.shape[0] == 0 or filtered_expr_data.shape[1] == 0: + continue + + pearson_residuals = compute_analytic_pearson_residuals(filtered_expr_data, theta=theta) + gene_variances = np.var(pearson_residuals, axis=0) + n_genes_to_select = min(n_top_genes, len(filtered_gene_names)) + top_indices = np.argsort(gene_variances)[-n_genes_to_select:] + hvg_genes = [filtered_gene_names[i] for i in top_indices] + + all_hvgs.update(hvg_genes) + del expr_data, filtered_expr_data, pearson_residuals + gc.collect() + + if len(all_hvgs) > 15000: + break + + except Exception as error: # pragma: no cover - logging + log.exception("Error processing %s: %s", h5ad_file.name, error) + continue + + union_genes = sorted(all_hvgs) + log.info("Final HVG union contains %s genes from %s files", len(union_genes), processed_files) + + if output_path: + with open(output_path, "wb") as handle: + pickle.dump(union_genes, handle) + log.info("Saved gene list to %s", output_path) + + return union_genes diff --git a/transfer/code/stack/src/stack/data/training/__init__.py b/transfer/code/stack/src/stack/data/training/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..180519966f27135870629c74443781ce99ee03ca --- /dev/null +++ b/transfer/code/stack/src/stack/data/training/__init__.py @@ -0,0 +1,4 @@ +"""Training dataset abstractions for StateICL.""" +from .datasets import * # noqa: F401,F403 + +__all__ = [name for name in globals() if not name.startswith("_")] diff --git a/transfer/code/stack/src/stack/data/training/datasets.py b/transfer/code/stack/src/stack/data/training/datasets.py new file mode 100644 index 0000000000000000000000000000000000000000..b498cdfb5d480cacbeb77ee0e16946ef3ad4f2b6 --- /dev/null +++ b/transfer/code/stack/src/stack/data/training/datasets.py @@ -0,0 +1,1368 @@ +from __future__ import annotations + +import gc +import logging +import os +import pickle +import random +import time +from dataclasses import dataclass +from pathlib import Path +from typing import Any, Dict, List, Optional, Tuple + +import h5py +import numpy as np +import psutil +import scipy.sparse as sp +import torch +from torch.utils.data import Dataset + +from ..gene_processing import filter_gene_names, get_gene_names_from_h5, safe_decode_array +from ..h5_manager import get_h5_handle, reset_h5_handle_pool, worker_init_fn +from ..hvg import compute_analytic_pearson_residuals, compute_hvg_union + +log = logging.getLogger(__name__) + +__all__ = [ + "DatasetConfig", + "SimplifiedDatasetCache", + "SimplifiedMultiDataset", + "TestSamplerDataset", + "compute_analytic_pearson_residuals", + "compute_hvg_union", + "create_train_val_test_datasets", + "create_example_configs", + "compute_and_save_hvg_union", + "create_datasets_from_gene_list", + "load_gene_list", + "worker_init_fn", + "reset_h5_handle_pool", +] + + +@dataclass +class DatasetConfig: + """Simplified configuration for a single dataset.""" + + path: str + filter_organism: bool = True + gene_name_col: Optional[str] = None + +def load_gene_list(genelist_path: str) -> List[str]: + """Load previously computed gene list from pickle file.""" + with open(genelist_path, 'rb') as f: + return pickle.load(f) + +class SimplifiedDatasetCache: + """Simplified metadata cache for file-level dataset management.""" + _instances: dict[str, "SimplifiedDatasetCache"] = {} + + @classmethod + def get_singleton(cls, cache_key: str, **kwargs) -> "SimplifiedDatasetCache": + """Return one shared instance per cache key.""" + if cache_key not in cls._instances: + cls._instances[cache_key] = cls(**kwargs) + return cls._instances[cache_key] + + def __init__( + self, + dataset_configs: List[DatasetConfig], + target_genes: List[str], + cache_file: Optional[str] = None + ): + """Initialize the simplified cache.""" + self.dataset_configs = dataset_configs + self.target_genes = target_genes + self.n_genes = len(target_genes) + + log.info("Initializing Simplified Dataset Cache...") + start_time = time.time() + + if cache_file and os.path.exists(cache_file): + try: + self._load_from_cache(cache_file) + log.info(f"Loaded metadata from cache: {cache_file}") + except Exception as e: + log.warning(f"Failed to load cache, rebuilding: {e}") + self._build_metadata() + if cache_file: + self._save_to_cache(cache_file) + else: + self._build_metadata() + if cache_file: + self._save_to_cache(cache_file) + + load_time = time.time() - start_time + log.info(f"Cache initialization completed in {load_time:.2f}s. Total files: {len(self.file_info)}") + + def _get_memory_usage(self): + """Get current memory usage in GB""" + process = psutil.Process(os.getpid()) + return process.memory_info().rss / (1024**3) + + def _save_to_cache(self, cache_file: str): + """Save metadata to cache file""" + try: + cache_data = { + 'file_info': self.file_info, + 'target_genes': self.target_genes, + 'dataset_configs': self.dataset_configs, + } + with open(cache_file, 'wb') as f: + pickle.dump(cache_data, f) + log.info(f"Saved metadata cache to {cache_file}") + except Exception as e: + log.exception(f"Failed to save cache: {e}") + + def _load_from_cache(self, cache_file: str): + """Load metadata from cache file""" + with open(cache_file, 'rb') as f: + cache_data = pickle.load(f) + + self.file_info = cache_data['file_info'] + + if cache_data['target_genes'] != self.target_genes: + raise ValueError("Target genes in cache don't match current target genes") + + if len(cache_data['dataset_configs']) != len(self.dataset_configs): + raise ValueError("Dataset configurations don't match cache") + + def _build_metadata(self): + """Load metadata from all H5AD files""" + self.file_info = [] + target_gene_set = set(self.target_genes) + + for config_idx, config in enumerate(self.dataset_configs): + log.info(f"Loading metadata from dataset {config_idx + 1}/{len(self.dataset_configs)}: {config.path}") + + h5ad_files = list(Path(config.path).glob("*.h5ad")) + list(Path(config.path).glob("*.h5")) + h5ad_files = sorted(list(set(h5ad_files))) + + for h5ad_file in h5ad_files: + try: + log.info(f"Processing {h5ad_file.name}...") + + with h5py.File(h5ad_file, "r") as f: + # Apply organism filtering + if config.filter_organism: + obs = f["obs"] + if "organism" not in obs: + log.warning(f"'organism' column not found, skipping") + continue + + organism_ds = obs["organism"] + if "categories" in organism_ds: + organism_categories = safe_decode_array(organism_ds["categories"][:]) + organism_codes = organism_ds["codes"][:] + organism_values = organism_categories[organism_codes] + else: + organism_values = safe_decode_array(organism_ds[:]) + + human_mask = organism_values == 'Homo sapiens' + if not human_mask.any(): + log.warning(f"No Homo sapiens cells found, skipping") + continue + + n_human_cells = human_mask.sum() + else: + if hasattr(f["X"], 'shape'): + n_cells = f["X"].shape[0] + else: + n_cells = f["X"].attrs['shape'][0] + human_mask = np.ones(n_cells, dtype=bool) + n_human_cells = n_cells + + # Get gene information + use_raw = "raw" in f and "var" in f["raw"] + gene_names = get_gene_names_from_h5(f, config.gene_name_col, use_raw=use_raw) + + if gene_names is None: + log.warning(f"Could not find gene names, skipping") + continue + + # Create gene mapping + gene_to_idx = {gene: idx for idx, gene in enumerate(gene_names)} + gene_mapping = {} + found_genes = 0 + for target_idx, gene in enumerate(self.target_genes): + if gene in gene_to_idx: + gene_mapping[target_idx] = gene_to_idx[gene] + found_genes += 1 + + if found_genes == 0: + log.warning(f"No target genes found in {h5ad_file.name}, skipping") + continue + + # Get matrix info + if use_raw: + X_group = f["raw"]["X"] + else: + X_group = f["X"] + + is_sparse = not hasattr(X_group, 'shape') or ('data' in X_group and 'indices' in X_group) + + # Store file information + file_info = { + 'path': str(h5ad_file), + 'config_idx': config_idx, + 'n_cells': n_human_cells, + 'n_genes': len(gene_names), + 'use_raw': use_raw, + 'gene_mapping': gene_mapping, + 'organism_mask': human_mask, + 'is_sparse': is_sparse, + 'found_genes': found_genes + } + self.file_info.append(file_info) + + log.info(f"Added file with {n_human_cells} cells, {found_genes}/{len(self.target_genes)} target genes") + + except Exception as e: + log.exception(f"Error processing {h5ad_file.name}: {e}") + continue + + log.info(f"Loaded metadata for {len(self.file_info)} files") + + def load_expression_data_from_file(self, file_idx: int, local_indices: np.ndarray) -> np.ndarray: + """ + OPTIMIZED IO FUNCTION: Efficiently load specified rows from a single file using format-aware reading strategy. + This version supports both CSR and CSC sparse formats with automatic detection. + """ + file_info = self.file_info[file_idx] + + # Sort indices for better h5py reading efficiency + sort_order = np.argsort(local_indices) + sorted_local_indices = local_indices[sort_order] + + try: + f = get_h5_handle(file_info['path']) + X_group = f["raw"]["X"] if file_info['use_raw'] else f["X"] + + # Get absolute row indices in the original HDF5 file + human_indices = np.where(file_info['organism_mask'])[0] + absolute_indices_to_load = human_indices[sorted_local_indices] + + # Create result matrix mapped to target genes + mapped_matrix = np.zeros((len(local_indices), self.n_genes), dtype=np.float32) + gene_mapping = file_info['gene_mapping'] + target_indices_in_result = np.array(list(gene_mapping.keys())) + source_indices_in_file = np.array(list(gene_mapping.values())) + + # Dynamic format detection based on encoding-type + if file_info['is_sparse']: + # Read encoding-type directly from HDF5 file attributes + attrs = dict(X_group.attrs) + encoding_type = attrs.get("encoding-type", "unknown") + log.debug(f"Processing sparse matrix with encoding: {encoding_type}") + + if encoding_type == "csr_matrix": + # CSR format: efficient row slicing + log.debug(f"Using optimized CSR row reading for {len(absolute_indices_to_load)} rows") + + # Your existing correct CSR reading logic + indptr_h5 = X_group["indptr"] + data_h5 = X_group["data"] + indices_h5 = X_group["indices"] + + # 1) Get all required indptr start and end points + starts = indptr_h5[absolute_indices_to_load] + ends = indptr_h5[absolute_indices_to_load + 1] + + # 2) Detect physical data block boundaries + block_breaks = np.where(ends[:-1] != starts[1:])[0] + 1 + row_blocks = np.split(np.arange(len(absolute_indices_to_load)), block_breaks) + + # 3) Read data block by block to minimize I/O operations + data_slices = [] + indices_slices = [] + + row_order = np.concatenate(row_blocks) if row_blocks else np.array([], dtype=int) + + for block in row_blocks: + if len(block) == 0: + continue + + first_row_in_block_idx = block[0] + last_row_in_block_idx = block[-1] + + data_start = starts[first_row_in_block_idx] + data_end = ends[last_row_in_block_idx] + + # Single I/O operation to read entire contiguous block + data_block = data_h5[data_start:data_end] + indices_block = indices_h5[data_start:data_end] + + # Split large block back into individual rows + row_lengths = ends[block] - starts[block] + cuts = np.cumsum(row_lengths) + + if len(cuts) > 0: + data_slices.extend(np.split(data_block, cuts[:-1])) + indices_slices.extend(np.split(indices_block, cuts[:-1])) + + # 4) Construct sparse matrix from collected slices + if not data_slices: + sparse_subset = sp.csr_matrix((len(local_indices), file_info['n_genes']), dtype=np.float32) + else: + all_data = np.concatenate(data_slices) + all_indices = np.concatenate(indices_slices) + + new_indptr = np.zeros(len(local_indices) + 1, dtype=indptr_h5.dtype) + row_lengths_in_order = ends[row_order] - starts[row_order] + new_indptr[1:] = np.cumsum(row_lengths_in_order) + + sparse_subset = sp.csr_matrix( + (all_data, all_indices, new_indptr), + shape=(len(local_indices), file_info['n_genes']), + dtype=np.float32 + ) + + # 5) Select target gene columns and convert to dense + expr_subset = sparse_subset[:, source_indices_in_file].toarray() + mapped_matrix[:, target_indices_in_result] = expr_subset.astype(np.float32) + + elif encoding_type == "csc_matrix": + # CSC format: column-oriented reading strategy + log.debug(f"Using CSC column-oriented reading for {len(absolute_indices_to_load)} rows") + + data_h5 = X_group["data"] + indices_h5 = X_group["indices"] + indptr_h5 = X_group["indptr"] + + # Get actual matrix shape + # For CSC, indptr length is n_cols + 1, i.e., number of genes + 1 + n_genes_in_file = len(indptr_h5) - 1 + n_cells_in_file = file_info['n_cells'] + + # Adaptive strategy selection + load_threshold = min(1000, n_cells_in_file * 0.1) # 10% of cells or 1000, whichever is smaller + + if len(absolute_indices_to_load) <= load_threshold: + # Strategy A: Row-by-row reading (suitable for few rows) + log.debug(f"Using row-by-row CSC reading for {len(absolute_indices_to_load)} rows") + + # FIXED: Use np.isin for efficient and correct matching + result_matrix = np.zeros((len(absolute_indices_to_load), n_genes_in_file), dtype=np.float32) + + # Create mapping from absolute_index to result_matrix row number + abs_to_res_map = {abs_idx: i for i, abs_idx in enumerate(absolute_indices_to_load)} + + # Process each target gene (column) + for gene_idx in range(n_genes_in_file): + col_start = indptr_h5[gene_idx] + col_end = indptr_h5[gene_idx + 1] + + if col_end > col_start: + # Read all non-zero cell indices and values for this gene (column) + col_cell_indices = indices_h5[col_start:col_end] + col_values = data_h5[col_start:col_end] + + # Use np.isin to find which cells we need are present in this column + # isin_mask has the same length as col_cell_indices + isin_mask = np.isin(col_cell_indices, absolute_indices_to_load, assume_unique=True) + + if np.any(isin_mask): + # Get absolute indices of cells that have values + matched_abs_indices = col_cell_indices[isin_mask] + # Get corresponding values + matched_values = col_values[isin_mask] + + # Fill result matrix using the mapping we created earlier + for abs_idx, value in zip(matched_abs_indices, matched_values): + result_matrix_row = abs_to_res_map[abs_idx] + result_matrix[result_matrix_row, gene_idx] = value + + else: + # Strategy B: Load full matrix then slice (suitable for many rows) + log.debug(f"Using full matrix load for {len(absolute_indices_to_load)} rows (threshold: {load_threshold})") + + # Load entire CSC matrix + data = data_h5[:] + indices = indices_h5[:] + indptr = indptr_h5[:] + + # Construct scipy CSC matrix (Note: this is transposed, genes x cells) + csc_matrix_transposed = sp.csc_matrix( + (data, indices, indptr), + shape=(n_genes_in_file, n_cells_in_file) + ) + + # Transpose to get normal cells x genes CSR matrix + csr_matrix = csc_matrix_transposed.T + + # Now we can efficiently perform row slicing + subset_sparse = csr_matrix[absolute_indices_to_load, :] + result_matrix = subset_sparse.toarray() + + # Map to target genes + mapped_matrix[:, target_indices_in_result] = result_matrix[:, source_indices_in_file].astype(np.float32) + + else: + # Unknown or unsupported sparse format + log.warning(f"Unknown sparse encoding type '{encoding_type}' in {file_info['path']}") + log.warning("Attempting to infer format from indptr length") + + # Try generic sparse matrix reading + try: + if 'data' in X_group and 'indices' in X_group and 'indptr' in X_group: + # Looks like CSR/CSC format, but encoding type is unknown + # Try to infer format based on indptr length + indptr_len = len(X_group['indptr']) + + if indptr_len == file_info['n_cells'] + 1: + # Likely CSR format + log.debug("Guessing CSR format based on indptr length") + encoding_type = "csr_matrix" + # Process as CSR (reuse CSR logic above) + # For simplicity, we'll use the fallback approach + raise NotImplementedError("CSR format inference - please use explicit encoding-type") + + elif indptr_len == file_info['n_genes'] + 1: + # Likely CSC format + log.debug("Guessing CSC format based on indptr length") + encoding_type = "csc_matrix" + # Process as CSC (reuse CSC logic above) + # For simplicity, we'll use the fallback approach + raise NotImplementedError("CSC format inference - please use explicit encoding-type") + else: + raise ValueError(f"Cannot determine sparse matrix format for {file_info['path']}") + else: + raise ValueError(f"Unknown sparse matrix structure in {file_info['path']}") + + except Exception as e: + log.exception(f"Failed to load unknown sparse format: {e}") + raise + else: + # Dense matrix: direct slicing + log.debug(f"Using dense matrix reading for {len(absolute_indices_to_load)} rows") + expr_subset = X_group[np.ix_(absolute_indices_to_load, source_indices_in_file)] + mapped_matrix[:, target_indices_in_result] = expr_subset.astype(np.float32) + + except Exception as e: + log.exception(f"Error loading specific rows from {file_info['path']}: {e}") + raise + + # Restore original request order + reverse_sort_order = np.empty_like(sort_order) + reverse_sort_order[sort_order] = np.arange(len(sort_order)) + + return mapped_matrix[reverse_sort_order] + + +class SimplifiedMultiDataset(Dataset): + """Simplified dataset class with file-level splits and locality-aware sampling.""" + + def __init__( + self, + dataset_configs: List[DatasetConfig], + target_genes: Optional[List[str]] = None, + genelist_path: Optional[str] = None, + sample_size: int = 128, + mode: str = 'train', + test_ratio: float = 0.2, + val_ratio: float = 0.2, + random_state: Optional[int] = 42, + cache_file: Optional[str] = None, + train_files: Optional[List[str]] = None, + val_files: Optional[List[str]] = None, + test_files: Optional[List[str]] = None + ): + """Initialize the simplified dataset.""" + self.dataset_configs = dataset_configs + self.sample_size = sample_size + self.mode = mode + + self.initial_random_state = random_state + self.rng = None + + # Load target genes + if target_genes is not None: + self.target_genes = target_genes + elif genelist_path is not None: + self.target_genes = load_gene_list(genelist_path) + else: + raise ValueError("Either target_genes or genelist_path must be provided") + + self.n_genes = len(self.target_genes) + + # Create cache key for singleton + config_paths = sorted([config.path for config in dataset_configs]) + cache_key = f"{'_'.join(config_paths)}_{hash(tuple(self.target_genes))}" + + # Load metadata with simplified cache + self.metadata_cache = SimplifiedDatasetCache.get_singleton( + cache_key, + dataset_configs=dataset_configs, + target_genes=self.target_genes, + cache_file=cache_file + ) + + # Use provided file splits or create new ones + if train_files is not None and val_files is not None and test_files is not None: + self.train_files = train_files + self.val_files = val_files + self.test_files = test_files + else: + self._split_files(test_ratio, val_ratio) + + # Set active files based on mode + if mode == 'train': + self.active_files = self.train_files + elif mode == 'val': + self.active_files = self.val_files + elif mode == 'test': + self.active_files = self.test_files + else: + raise ValueError(f"Unknown mode: {mode}") + + # Generate samples using locality-aware strategy + self._generate_locality_samples() + + def _initialize_rng(self): + """Initialize RNG for current process.""" + worker_info = torch.utils.data.get_worker_info() + seed = self.initial_random_state + + if worker_info is not None: + if seed is not None: + seed += worker_info.id + else: + seed = int(time.time() * 1000) % (2**32 -1) + worker_info.id + + if seed is not None: + self.rng = np.random.RandomState(seed) + else: + self.rng = np.random.RandomState() + + def _split_files(self, test_ratio: float = 0.2, val_ratio: float = 0.2): + """Split files into train/val/test sets""" + if self.rng is None: + self._initialize_rng() + + # Get valid files with enough cells + valid_files = [] + for info in self.metadata_cache.file_info: + if info['n_cells'] >= self.sample_size: + valid_files.append(info['path']) + + log.info(f"Found {len(valid_files)} files with >= {self.sample_size} cells") + + if len(valid_files) == 0: + raise ValueError(f"No files found with >= {self.sample_size} cells") + + # Shuffle files + shuffled_files = self.rng.permutation(valid_files) + + # Split files + n_test = max(1, int(len(shuffled_files) * test_ratio)) if len(shuffled_files) * test_ratio > 1 else 0 + n_val = max(1, int(len(shuffled_files) * val_ratio)) if len(shuffled_files) * val_ratio > 1 else 0 + + if len(shuffled_files) > n_test + n_val: + n_train = len(shuffled_files) - n_test - n_val + else: + n_train = 0 + + self.test_files = shuffled_files[:n_test].tolist() + self.val_files = shuffled_files[n_test:n_test+n_val].tolist() + self.train_files = shuffled_files[n_test+n_val:].tolist() + + log.info(f"Split files: {len(self.train_files)} train, {len(self.val_files)} val, {len(self.test_files)} test") + + def _generate_locality_samples(self): + """Generate locality-aware samples from active files.""" + if self.rng is None: + self._initialize_rng() + + log.info(f"[{self.mode}] Generating locality-aware samples...") + start_time = time.time() + + self.samples = [] + active_file_set = set(self.active_files) + + if not active_file_set: + log.warning(f"No active files for mode {self.mode}. Dataset will be empty.") + return + + total_samples_created = 0 + + for file_idx, file_info in enumerate(self.metadata_cache.file_info): + if file_info['path'] not in active_file_set: + continue + + n_cells = file_info['n_cells'] + if n_cells < self.sample_size: + continue + + # Create contiguous samples from this file + n_samples = n_cells // self.sample_size + for i in range(n_samples): + start_idx = i * self.sample_size + end_idx = start_idx + self.sample_size + local_indices = np.arange(start_idx, end_idx) + + # Create sample tuple: (file_idx, local_indices) + self.samples.append((file_idx, local_indices)) + total_samples_created += 1 + + # Shuffle all samples to ensure randomness across files + self.rng.shuffle(self.samples) + + generation_time = time.time() - start_time + log.info(f"Generated {len(self.samples)} locality-aware samples in {generation_time:.2f}s for {self.mode} mode") + log.info(f"From {len(active_file_set)} active files") + + def __getitem__(self, idx: int) -> Tuple[torch.Tensor, Dict[str, Any]]: + """Get a sample by index.""" + if self.rng is None: + self._initialize_rng() + + # Get sample information + file_idx, local_indices = self.samples[idx] + file_info = self.metadata_cache.file_info[file_idx] + + # Load expression data + expression_data = self.metadata_cache.load_expression_data_from_file(file_idx, local_indices) + + # Convert to tensor + features_tensor = torch.from_numpy(expression_data).float() + + # Build metadata + metadata = { + 'file_path': file_info['path'], + 'file_idx': file_idx, + 'config_idx': file_info['config_idx'], + 'sample_size': len(local_indices), + 'n_genes_found': file_info['found_genes'], + 'dataset_path': self.dataset_configs[file_info['config_idx']].path + } + + return features_tensor, metadata + + def __len__(self) -> int: + return len(self.samples) + + def get_cache_stats(self) -> Dict[str, Any]: + """Get cache statistics for monitoring""" + return { + 'total_memory_gb': self.metadata_cache._get_memory_usage(), + 'n_files': len(self.metadata_cache.file_info), + 'n_genes': self.n_genes, + 'n_datasets': len(self.dataset_configs), + 'active_files': len(self.active_files), + 'total_samples': len(self.samples) + } + +class TestSamplerDataset(Dataset): + """ + Simple dataset class for testing/evaluation on a single H5AD file or AnnData object. + Samples cells from the file/object and returns gene expression data. + """ + + def __init__( + self, + adata_or_path, # Can be either str (file path) or AnnData object + genelist_path: str, + sample_size: int = 128, + mode: str = 'eval', + max_samples: Optional[int] = None, + gene_name_col: Optional[str] = None, + filter_organism: bool = True, + random_state: Optional[int] = 42 + ): + """ + Initialize the test dataset. + + Args: + adata_or_path: Either path to H5AD file (str) or AnnData object + genelist_path: Path to pickle file containing gene list + sample_size: Number of cells per sample + mode: Dataset mode (usually 'eval' for testing) + max_samples: Maximum number of samples to generate (None = all possible) + gene_name_col: Column name for gene names in var + filter_organism: Whether to filter for Homo sapiens + random_state: Random seed for reproducibility + """ + self.adata_or_path = adata_or_path + self.genelist_path = genelist_path + self.sample_size = sample_size + self.mode = mode + self.max_samples = max_samples + self.gene_name_col = gene_name_col + self.filter_organism = filter_organism + self.random_state = random_state + + # Type checking + if isinstance(adata_or_path, str): + self.is_adata_object = False + self.adata_path = adata_or_path + self.adata_object = None + else: + # Assume it's an AnnData object + self.is_adata_object = True + self.adata_path = None + self.adata_object = adata_or_path + + # Initialize RNG + self.rng = np.random.RandomState(random_state) + + # Load target genes from pickle file + self.target_genes = load_gene_list(genelist_path) + self.n_genes = len(self.target_genes) + + # Load file metadata and prepare samples + if self.is_adata_object: + self._load_adata_metadata() + else: + self._load_file_metadata() + + self._generate_samples() + + source_info = f"AnnData object" if self.is_adata_object else self.adata_path + log.info(f"TestSamplerDataset initialized with {len(self.samples)} samples from {source_info}") + + def _load_adata_metadata(self): + """Load metadata from AnnData object""" + log.info("Loading metadata from AnnData object") + + adata = self.adata_object + + # Check organism filtering + if self.filter_organism: + if "organism" not in adata.obs.columns: + log.warning("'organism' column not found in obs, assuming all cells are valid") + self.human_mask = np.ones(adata.n_obs, dtype=bool) + else: + organism_values = adata.obs["organism"].values + self.human_mask = organism_values == 'Homo sapiens' + if not self.human_mask.any(): + raise ValueError("No Homo sapiens cells found in the AnnData object") + else: + self.human_mask = np.ones(adata.n_obs, dtype=bool) + + self.n_human_cells = self.human_mask.sum() + + # Get gene information + # Try to use raw data first, then fall back to main data + if adata.raw is not None: + gene_names = adata.raw.var.index.values + use_raw = True + log.info("Using raw data for gene names") + else: + gene_names = adata.var.index.values + use_raw = False + log.info("Using main data for gene names") + + # If gene_name_col is specified, try to use that column + if self.gene_name_col is not None: + var_data = adata.raw.var if use_raw else adata.var + if self.gene_name_col in var_data.columns: + gene_names = var_data[self.gene_name_col].values + log.info(f"Using '{self.gene_name_col}' column for gene names") + else: + log.warning(f"'{self.gene_name_col}' not found in var, using index") + + # Convert to uppercase and handle string types + if gene_names.dtype.kind in ['U', 'S', 'O']: # Unicode, byte string, or object + gene_names = np.char.upper(gene_names.astype(str)) + else: + gene_names = np.array([str(g).upper() for g in gene_names]) + + # Create gene mapping + gene_to_idx = {gene: idx for idx, gene in enumerate(gene_names)} + self.gene_mapping = {} + found_genes = 0 + for target_idx, gene in enumerate(self.target_genes): + if gene in gene_to_idx: + self.gene_mapping[target_idx] = gene_to_idx[gene] + found_genes += 1 + + if found_genes == 0: + raise ValueError("No target genes found in the AnnData object") + + # Store file info + self.file_info = { + 'path': 'AnnData object', + 'n_cells': self.n_human_cells, + 'n_genes': len(gene_names), + 'use_raw': use_raw, + 'gene_mapping': self.gene_mapping, + 'organism_mask': self.human_mask, + 'is_sparse': True, # Assume sparse for AnnData objects + 'found_genes': found_genes + } + + log.info(f"Loaded AnnData with {self.n_human_cells} human cells, {found_genes}/{len(self.target_genes)} target genes") + + def _load_file_metadata(self): + """Load metadata from the H5AD file""" + log.info(f"Loading metadata from {self.adata_path}") + + with h5py.File(self.adata_path, "r") as f: + # Check organism filtering + if self.filter_organism: + obs = f["obs"] + if "organism" not in obs: + log.warning("'organism' column not found, assuming all cells are valid") + n_cells = f["X"].shape[0] if hasattr(f["X"], 'shape') else f["X"].attrs['shape'][0] + self.human_mask = np.ones(n_cells, dtype=bool) + else: + organism_ds = obs["organism"] + if "categories" in organism_ds: + organism_categories = safe_decode_array(organism_ds["categories"][:]) + organism_codes = organism_ds["codes"][:] + organism_values = organism_categories[organism_codes] + else: + organism_values = safe_decode_array(organism_ds[:]) + + self.human_mask = organism_values == 'Homo sapiens' + if not self.human_mask.any(): + raise ValueError("No Homo sapiens cells found in the file") + else: + n_cells = f["X"].shape[0] if hasattr(f["X"], 'shape') else f["X"].attrs['shape'][0] + self.human_mask = np.ones(n_cells, dtype=bool) + + self.n_human_cells = self.human_mask.sum() + + # Get gene information + use_raw = "raw" in f and "var" in f["raw"] + gene_names = get_gene_names_from_h5(f, self.gene_name_col, use_raw=use_raw) + + if gene_names is None: + raise ValueError("Could not find gene names in the file") + + # Create gene mapping + gene_to_idx = {gene: idx for idx, gene in enumerate(gene_names)} + self.gene_mapping = {} + found_genes = 0 + for target_idx, gene in enumerate(self.target_genes): + if gene in gene_to_idx: + self.gene_mapping[target_idx] = gene_to_idx[gene] + found_genes += 1 + + if found_genes == 0: + raise ValueError("No target genes found in the file") + + # Store file info + if use_raw: + X_group = f["raw"]["X"] + else: + X_group = f["X"] + + self.file_info = { + 'path': self.adata_path, + 'n_cells': self.n_human_cells, + 'n_genes': len(gene_names), + 'use_raw': use_raw, + 'gene_mapping': self.gene_mapping, + 'organism_mask': self.human_mask, + 'is_sparse': not hasattr(X_group, 'shape') or ('data' in X_group and 'indices' in X_group), + 'found_genes': found_genes + } + + log.info(f"Loaded file with {self.n_human_cells} human cells, {found_genes}/{len(self.target_genes)} target genes") + + def _generate_samples(self): + """ + Generate samples from the file/object. + For the last incomplete part, it stores instructions for upsampling. + """ + log.info("Generating samples...") + + if self.n_human_cells < 1: + log.warning("No valid human cells found. Dataset will be empty.") + self.samples = [] + return + + # Get all valid human cell indices (these are the 'local' indices) + human_cell_indices_local = np.arange(self.n_human_cells) + + # Limit samples if max_samples is specified + if self.max_samples is not None: + max_cells = self.max_samples * self.sample_size + if len(human_cell_indices_local) > max_cells: + human_cell_indices_local = human_cell_indices_local[:max_cells] + + self.samples = [] + + # Generate full samples + n_full_samples = len(human_cell_indices_local) // self.sample_size + for i in range(n_full_samples): + start_idx = i * self.sample_size + end_idx = start_idx + self.sample_size + # For full batches, the indices to load are the same as the reconstruction map + local_indices_to_load = human_cell_indices_local[start_idx:end_idx] + reindexing_map = np.arange(self.sample_size) + self.samples.append((local_indices_to_load, reindexing_map)) + + # Handle remaining cells with upsampling + remaining_cells_count = len(human_cell_indices_local) - n_full_samples * self.sample_size + if remaining_cells_count > 0: + # These are the actual local indices of the remaining cells + remaining_local_indices = human_cell_indices_local[n_full_samples * self.sample_size:] + + # These are the indices we need to upsample FROM, relative to the `remaining_local_indices` array + indices_to_upsample_from = np.arange(remaining_cells_count) + + need_to_fill = self.sample_size - remaining_cells_count + + # Perform upsampling + upsampled_relative_indices = self.rng.choice( + indices_to_upsample_from, + size=need_to_fill, + replace=True + ) + + # The reindexing map tells us how to construct the final batch from the loaded remaining cells + reindexing_map = np.concatenate([indices_to_upsample_from, upsampled_relative_indices]) + + # The indices to load from the file are just the unique remaining ones + local_indices_to_load = remaining_local_indices + + self.samples.append((local_indices_to_load, reindexing_map)) + + log.info(f"Generated {len(self.samples)} samples for {self.mode} mode (eval with upsampling)") + if remaining_cells_count > 0 and remaining_cells_count < self.sample_size: + log.info(f"Last sample will be constructed from {remaining_cells_count} unique cells.") + + def load_expression_data(self, local_indices: np.ndarray) -> np.ndarray: + """ + Load expression data for specified cell indices. + Routes to appropriate loading method based on data source. + """ + if self.is_adata_object: + return self.load_expression_data_from_adata(local_indices) + else: + return self.load_expression_data_from_file(local_indices) + + def load_expression_data_from_adata(self, local_indices: np.ndarray) -> np.ndarray: + """ + Load expression data from AnnData object for specified cell indices. + """ + # Sort indices for consistency + sort_order = np.argsort(local_indices) + sorted_local_indices = local_indices[sort_order] + + try: + adata = self.adata_object + + # Get absolute row indices in the original AnnData object + human_indices = np.where(self.file_info['organism_mask'])[0] + absolute_indices_to_load = human_indices[sorted_local_indices] + + # Create result matrix mapped to target genes + mapped_matrix = np.zeros((len(local_indices), self.n_genes), dtype=np.float32) + gene_mapping = self.file_info['gene_mapping'] + target_indices_in_result = np.array(list(gene_mapping.keys())) + source_indices_in_file = np.array(list(gene_mapping.values())) + + # Get expression data + if self.file_info['use_raw'] and adata.raw is not None: + # Use raw data + expr_data = adata.raw.X[absolute_indices_to_load, :] + else: + # Use main data + expr_data = adata.X[absolute_indices_to_load, :] + + # Convert sparse to dense if needed + if hasattr(expr_data, 'toarray'): + expr_data = expr_data.toarray() + + # Select target gene columns + expr_subset = expr_data[:, source_indices_in_file] + mapped_matrix[:, target_indices_in_result] = expr_subset.astype(np.float32) + + except Exception as e: + log.exception(f"Error loading expression data from AnnData object: {e}") + raise + + # Restore original request order + reverse_sort_order = np.empty_like(sort_order) + reverse_sort_order[sort_order] = np.arange(len(sort_order)) + + return mapped_matrix[reverse_sort_order] + + def load_expression_data_from_file(self, local_indices: np.ndarray) -> np.ndarray: + """ + Load expression data for specified cell indices from H5AD file. + Reuses the optimized loading logic from SimplifiedDatasetCache. + """ + # Sort indices for better h5py reading efficiency + sort_order = np.argsort(local_indices) + sorted_local_indices = local_indices[sort_order] + + try: + f = get_h5_handle(self.file_info['path']) + X_group = f["raw"]["X"] if self.file_info['use_raw'] else f["X"] + + # Get absolute row indices in the original HDF5 file + human_indices = np.where(self.file_info['organism_mask'])[0] + absolute_indices_to_load = human_indices[sorted_local_indices] + + # Create result matrix mapped to target genes + mapped_matrix = np.zeros((len(local_indices), self.n_genes), dtype=np.float32) + gene_mapping = self.file_info['gene_mapping'] + target_indices_in_result = np.array(list(gene_mapping.keys())) + source_indices_in_file = np.array(list(gene_mapping.values())) + + if self.file_info['is_sparse']: + # Handle sparse matrix (CSR format) + attrs = dict(X_group.attrs) + encoding_type = attrs.get("encoding-type", "csr_matrix") + + if encoding_type == "csr_matrix": + # CSR format: efficient row slicing (using optimized logic from SimplifiedDatasetCache) + log.debug(f"Using optimized CSR row reading for {len(absolute_indices_to_load)} rows") + + indptr_h5 = X_group["indptr"] + data_h5 = X_group["data"] + indices_h5 = X_group["indices"] + + # 1) Get all required indptr start and end points + starts = indptr_h5[absolute_indices_to_load] + ends = indptr_h5[absolute_indices_to_load + 1] + + # 2) Detect physical data block boundaries + block_breaks = np.where(ends[:-1] != starts[1:])[0] + 1 + row_blocks = np.split(np.arange(len(absolute_indices_to_load)), block_breaks) + + # 3) Read data block by block to minimize I/O operations + data_slices = [] + indices_slices = [] + + row_order = np.concatenate(row_blocks) if row_blocks else np.array([], dtype=int) + + for block in row_blocks: + if len(block) == 0: + continue + + first_row_in_block_idx = block[0] + last_row_in_block_idx = block[-1] + + data_start = starts[first_row_in_block_idx] + data_end = ends[last_row_in_block_idx] + + # Single I/O operation to read entire contiguous block + data_block = data_h5[data_start:data_end] + indices_block = indices_h5[data_start:data_end] + + # Split large block back into individual rows + row_lengths = ends[block] - starts[block] + cuts = np.cumsum(row_lengths) + + if len(cuts) > 0: + data_slices.extend(np.split(data_block, cuts[:-1])) + indices_slices.extend(np.split(indices_block, cuts[:-1])) + + # 4) Construct sparse matrix from collected slices + if not data_slices: + sparse_subset = sp.csr_matrix((len(local_indices), self.file_info['n_genes']), dtype=np.float32) + else: + all_data = np.concatenate(data_slices) + all_indices = np.concatenate(indices_slices) + + new_indptr = np.zeros(len(local_indices) + 1, dtype=indptr_h5.dtype) + row_lengths_in_order = ends[row_order] - starts[row_order] + new_indptr[1:] = np.cumsum(row_lengths_in_order) + + sparse_subset = sp.csr_matrix( + (all_data, all_indices, new_indptr), + shape=(len(local_indices), self.file_info['n_genes']), + dtype=np.float32 + ) + + # 5) Select target gene columns and convert to dense + expr_subset = sparse_subset[:, source_indices_in_file].toarray() + mapped_matrix[:, target_indices_in_result] = expr_subset.astype(np.float32) + + elif encoding_type == "csc_matrix": + # CSC format: column-oriented reading strategy + log.debug(f"Using CSC column-oriented reading for {len(absolute_indices_to_load)} rows") + + data_h5 = X_group["data"] + indices_h5 = X_group["indices"] + indptr_h5 = X_group["indptr"] + + # Get actual matrix shape + # For CSC, indptr length is n_cols + 1, i.e., number of genes + 1 + n_genes_in_file = len(indptr_h5) - 1 + n_cells_in_file = self.file_info['n_cells'] + + # Adaptive strategy selection + load_threshold = min(1000, n_cells_in_file * 0.1) # 10% of cells or 1000, whichever is smaller + + if len(absolute_indices_to_load) <= load_threshold: + # Strategy A: Row-by-row reading (suitable for few rows) + log.debug(f"Using row-by-row CSC reading for {len(absolute_indices_to_load)} rows") + + result_matrix = np.zeros((len(absolute_indices_to_load), n_genes_in_file), dtype=np.float32) + + # Create mapping from absolute_index to result_matrix row number + abs_to_res_map = {abs_idx: i for i, abs_idx in enumerate(absolute_indices_to_load)} + + # Process each gene (column) + for gene_idx in range(n_genes_in_file): + col_start = indptr_h5[gene_idx] + col_end = indptr_h5[gene_idx + 1] + + if col_end > col_start: + # Read all non-zero cell indices and values for this gene (column) + col_cell_indices = indices_h5[col_start:col_end] + col_values = data_h5[col_start:col_end] + + # Use np.isin to find which cells we need are present in this column + isin_mask = np.isin(col_cell_indices, absolute_indices_to_load, assume_unique=True) + + if np.any(isin_mask): + # Get absolute indices of cells that have values + matched_abs_indices = col_cell_indices[isin_mask] + # Get corresponding values + matched_values = col_values[isin_mask] + + # Fill result matrix using the mapping we created earlier + for abs_idx, value in zip(matched_abs_indices, matched_values): + result_matrix_row = abs_to_res_map[abs_idx] + result_matrix[result_matrix_row, gene_idx] = value + + else: + # Strategy B: Load full matrix then slice (suitable for many rows) + log.debug(f"Using full matrix load for {len(absolute_indices_to_load)} rows (threshold: {load_threshold})") + + # Load entire CSC matrix + data = data_h5[:] + indices = indices_h5[:] + indptr = indptr_h5[:] + + # Construct scipy CSC matrix (Note: this is transposed, genes x cells) + csc_matrix_transposed = sp.csc_matrix( + (data, indices, indptr), + shape=(n_genes_in_file, n_cells_in_file) + ) + + # Transpose to get normal cells x genes CSR matrix + csr_matrix = csc_matrix_transposed.T + + # Now we can efficiently perform row slicing + subset_sparse = csr_matrix[absolute_indices_to_load, :] + result_matrix = subset_sparse.toarray() + + # Map to target genes + mapped_matrix[:, target_indices_in_result] = result_matrix[:, source_indices_in_file].astype(np.float32) + + else: + # Unknown sparse format - try to infer from indptr length + log.warning(f"Unknown sparse encoding type '{encoding_type}' in {self.file_info['path']}") + log.warning("Attempting to infer format from indptr length") + + indptr_len = len(X_group['indptr']) + + if indptr_len == self.file_info['n_cells'] + 1: + # Likely CSR format + log.debug("Guessing CSR format based on indptr length") + # Fall back to basic CSR logic + raise NotImplementedError("CSR format inference - please add explicit encoding-type to file") + + elif indptr_len == self.file_info['n_genes'] + 1: + # Likely CSC format + log.debug("Guessing CSC format based on indptr length") + # Fall back to basic CSC logic + raise NotImplementedError("CSC format inference - please add explicit encoding-type to file") + else: + raise ValueError(f"Cannot determine sparse matrix format for {self.file_info['path']}") + + else: + # Dense matrix: direct slicing + expr_subset = X_group[np.ix_(absolute_indices_to_load, source_indices_in_file)] + mapped_matrix[:, target_indices_in_result] = expr_subset.astype(np.float32) + + except Exception as e: + log.exception(f"Error loading expression data: {e}") + raise + + # Restore original request order + reverse_sort_order = np.empty_like(sort_order) + reverse_sort_order[sort_order] = np.arange(len(sort_order)) + + return mapped_matrix[reverse_sort_order] + + def __getitem__(self, idx: int) -> Tuple[torch.Tensor, Dict[str, Any]]: + """Get a sample by index using the two-step loading strategy.""" + + # 1. Get the instructions for this sample + local_indices_to_load, reindexing_map = self.samples[idx] + + # 2. Load data for ONLY the unique indices from the file. + # `local_indices_to_load` is now guaranteed to be unique and sorted. + loaded_data = self.load_expression_data(local_indices_to_load) + + # 3. In memory, construct the final batch with duplicates using the reindexing map. + expression_data = loaded_data[reindexing_map] + + # Convert to tensor + features_tensor = torch.from_numpy(expression_data).float() + + # Build metadata + metadata = { + 'file_path': self.file_info['path'], + 'sample_idx': idx, + 'sample_size': len(expression_data), # The final size of the batch + 'n_genes_found': self.file_info['found_genes'], + 'n_unique_cells_loaded': len(local_indices_to_load) # Info for debugging + } + + return features_tensor, metadata + + def __len__(self) -> int: + return len(self.samples) + + def get_stats(self) -> Dict[str, Any]: + """Get dataset statistics""" + source_info = 'AnnData object' if self.is_adata_object else self.adata_path + return { + 'source': source_info, + 'n_human_cells': self.n_human_cells, + 'n_genes': self.n_genes, + 'n_samples': len(self.samples), + 'sample_size': self.sample_size, + 'genes_found': self.file_info['found_genes'], + 'genes_total': len(self.target_genes) + } + +def create_train_val_test_datasets( + dataset_configs: List[DatasetConfig], + target_genes: Optional[List[str]] = None, + genelist_path: Optional[str] = None, + sample_size: int = 128, + test_ratio: float = 0.2, + val_ratio: float = 0.2, + random_state: int = 42, + cache_file: Optional[str] = None +) -> Tuple[SimplifiedMultiDataset, SimplifiedMultiDataset, SimplifiedMultiDataset]: + """Create train, validation, and test datasets with file-level splits.""" + + # Create a temporary base dataset instance just to perform the file splitting + log.info("Creating base dataset for file splitting...") + base_dataset = SimplifiedMultiDataset( + dataset_configs=dataset_configs, + target_genes=target_genes, + genelist_path=genelist_path, + sample_size=sample_size, + mode='train', + random_state=random_state, + cache_file=cache_file + ) + + # Perform file splitting using the base instance + base_dataset._split_files(test_ratio, val_ratio) + train_files = base_dataset.train_files + val_files = base_dataset.val_files + test_files = base_dataset.test_files + + # Now create the actual datasets using the pre-computed splits + log.info("Creating training dataset...") + train_dataset = SimplifiedMultiDataset( + dataset_configs=dataset_configs, + target_genes=target_genes, + genelist_path=genelist_path, + sample_size=sample_size, + mode='train', + random_state=random_state, + cache_file=cache_file, + train_files=train_files, + val_files=val_files, + test_files=test_files + ) + + log.info("Creating validation dataset...") + val_dataset = SimplifiedMultiDataset( + dataset_configs=dataset_configs, + target_genes=target_genes, + genelist_path=genelist_path, + sample_size=sample_size, + mode='val', + random_state=random_state, + cache_file=cache_file, + train_files=train_files, + val_files=val_files, + test_files=test_files + ) + + log.info("Creating test dataset...") + test_dataset = SimplifiedMultiDataset( + dataset_configs=dataset_configs, + target_genes=target_genes, + genelist_path=genelist_path, + sample_size=sample_size, + mode='test', + random_state=random_state, + cache_file=cache_file, + train_files=train_files, + val_files=val_files, + test_files=test_files + ) + + return train_dataset, val_dataset, test_dataset + + +# Example usage functions +def create_example_configs(): + """Example of how to create dataset configurations""" + config1 = DatasetConfig( + path="/path/to/dataset1", + filter_organism=True + ) + + config2 = DatasetConfig( + path="/path/to/dataset2", + filter_organism=False + ) + + return [config1, config2] + + +def compute_and_save_hvg_union( + dataset_configs: List[DatasetConfig], + output_path: str, + n_top_genes: int = 1000, + theta: float = 100.0, + max_cells_per_file: int = 10000, + random_state: int = 42 +) -> List[str]: + """Compute HVG union and save to file.""" + log.info("Starting HVG computation process...") + + hvg_genes = compute_hvg_union( + dataset_configs=dataset_configs, + n_top_genes=n_top_genes, + theta=theta, + max_cells_per_file=max_cells_per_file, + output_path=output_path, + random_state=random_state + ) + + log.info(f"HVG computation completed. Saved {len(hvg_genes)} genes to {output_path}") + return hvg_genes + + +def create_datasets_from_gene_list( + dataset_configs: List[DatasetConfig], + genelist_path: str, + sample_size: int = 128, + test_ratio: float = 0.2, + val_ratio: float = 0.2, + random_state: int = 42, + cache_file: Optional[str] = None +) -> Tuple[SimplifiedMultiDataset, SimplifiedMultiDataset, SimplifiedMultiDataset]: + """Create datasets from a pre-computed gene list file.""" + log.info(f"Creating datasets from gene list: {genelist_path}") + + return create_train_val_test_datasets( + dataset_configs=dataset_configs, + target_genes=None, # Load from file + genelist_path=genelist_path, + sample_size=sample_size, + test_ratio=test_ratio, + val_ratio=val_ratio, + random_state=random_state, + cache_file=cache_file + ) + diff --git a/transfer/code/stack/src/stack/dataloader.py b/transfer/code/stack/src/stack/dataloader.py new file mode 100644 index 0000000000000000000000000000000000000000..cd9278d005dcff4b445e9e18d39c93b48b55a103 --- /dev/null +++ b/transfer/code/stack/src/stack/dataloader.py @@ -0,0 +1,38 @@ +"""Compatibility layer exposing dataset utilities from :mod:`stack.data`.""" +from __future__ import annotations + +from .data.datasets import ( + DatasetConfig, + SimplifiedDatasetCache, + SimplifiedMultiDataset, + TestSamplerDataset, + compute_analytic_pearson_residuals, + compute_and_save_hvg_union, + compute_hvg_union, + create_datasets_from_gene_list, + create_example_configs, + create_train_val_test_datasets, + load_gene_list, + reset_h5_handle_pool, + worker_init_fn, +) +from .data.h5_manager import _get_h5_handle, _reset_h5_handle_pool, _worker_init_fn + +__all__ = [ + "DatasetConfig", + "SimplifiedDatasetCache", + "SimplifiedMultiDataset", + "TestSamplerDataset", + "compute_analytic_pearson_residuals", + "compute_hvg_union", + "compute_and_save_hvg_union", + "create_datasets_from_gene_list", + "create_example_configs", + "create_train_val_test_datasets", + "load_gene_list", + "reset_h5_handle_pool", + "worker_init_fn", + "_worker_init_fn", + "_get_h5_handle", + "_reset_h5_handle_pool", +] diff --git a/transfer/code/stack/src/stack/finetune/__init__.py b/transfer/code/stack/src/stack/finetune/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..dc7cdc853bce56f0c616510e9ae5a5e271fe254a --- /dev/null +++ b/transfer/code/stack/src/stack/finetune/__init__.py @@ -0,0 +1,12 @@ +"""Fine-tuning utilities for StateICL.""" +from . import datamodule, lightning, utils +from .datasets import * # noqa: F401,F403 + +__all__ = sorted( + { + "datamodule", + "lightning", + "utils", + } + | {name for name in globals() if not name.startswith("_")} +) diff --git a/transfer/code/stack/src/stack/finetune/datamodule.py b/transfer/code/stack/src/stack/finetune/datamodule.py new file mode 100644 index 0000000000000000000000000000000000000000..c5846fda415ee67553170ed0329d9ed593c38cf8 --- /dev/null +++ b/transfer/code/stack/src/stack/finetune/datamodule.py @@ -0,0 +1,155 @@ +"""Lightning data module for StateICL fine-tuning.""" +from __future__ import annotations + +import logging +from typing import Dict, List, Optional + +import pytorch_lightning as pl +from torch.utils.data import DataLoader + +from ..data.finetuning import datasets as finetuning_datasets + +log = logging.getLogger(__name__) + + +class FinetuneDataModule(pl.LightningDataModule): + """PyTorch Lightning data module that orchestrates fine-tuning datasets.""" + + def __init__( + self, + dataset_configs: List[finetuning_datasets.DatasetConfig], + genelist_path: str, + sample_size: int = 128, + replacement_ratio: float = 0.25, + intra_file_replacement_prob: float = 1.0, + min_cells_per_group: int = 128, + test_ratio: float = 0.2, + val_ratio: float = 0.2, + batch_size: int = 16, + num_workers: int = 4, + pin_memory: bool = True, + resample_each_epoch: bool = True, + random_state: int = 42, + cache_file: Optional[str] = None, + max_memory_gb: Optional[float] = None, + cache_ratio: float = 0.3, + block_size: int = 1000, + ) -> None: + super().__init__() + self.dataset_configs = dataset_configs + self.genelist_path = genelist_path + self.sample_size = sample_size + self.replacement_ratio = replacement_ratio + self.intra_file_replacement_prob = intra_file_replacement_prob + self.min_cells_per_group = min_cells_per_group + self.test_ratio = test_ratio + self.val_ratio = val_ratio + self.batch_size = batch_size + self.num_workers = num_workers + self.pin_memory = pin_memory + self.resample_each_epoch = resample_each_epoch + self.random_state = random_state + self.cache_file = cache_file + self.max_memory_gb = max_memory_gb + self.cache_ratio = cache_ratio + self.block_size = block_size + + self.train_dataset: Optional[finetuning_datasets.MultiDatasetSplittableDataset] = None + self.val_dataset: Optional[finetuning_datasets.MultiDatasetSplittableDataset] = None + self.test_dataset: Optional[finetuning_datasets.MultiDatasetSplittableDataset] = None + + self.target_genes = finetuning_datasets.load_gene_list(genelist_path) + self.n_genes = len(self.target_genes) + log.info("Loaded %s genes for fine-tuning from %s", self.n_genes, genelist_path) + + def setup(self, stage: Optional[str] = None) -> None: # type: ignore[override] + if stage in ("fit", None): + log.info("Preparing fine-tuning datasets from %s configs", len(self.dataset_configs)) + ( + self.train_dataset, + self.val_dataset, + self.test_dataset, + ) = finetuning_datasets.create_train_val_test_datasets( + dataset_configs=self.dataset_configs, + target_genes=self.target_genes, + genelist_path=self.genelist_path, + sample_size=self.sample_size, + replacement_ratio=self.replacement_ratio, + intra_file_replacement_prob=self.intra_file_replacement_prob, + min_cells_per_group=self.min_cells_per_group, + test_ratio=self.test_ratio, + val_ratio=self.val_ratio, + random_state=self.random_state, + cache_file=self.cache_file, + max_memory_gb=self.max_memory_gb, + cache_ratio=self.cache_ratio, + block_size=self.block_size, + ) + log.info("Fine-tuning splits prepared: train=%s val=%s test=%s", len(self.train_dataset), len(self.val_dataset), len(self.test_dataset)) + + # ------------------------------------------------------------------ + # Dataloaders + # ------------------------------------------------------------------ + def train_dataloader(self) -> DataLoader: # type: ignore[override] + if self.train_dataset is None: + raise RuntimeError("train_dataset is not initialized; call setup('fit') first") + return DataLoader( + self.train_dataset, + batch_size=self.batch_size, + shuffle=True, + num_workers=self.num_workers, + pin_memory=self.pin_memory, + persistent_workers=self.num_workers > 0, + prefetch_factor=2 if self.num_workers > 0 else None, + drop_last=True, + worker_init_fn=finetuning_datasets.worker_init_fn, + ) + + def val_dataloader(self) -> Optional[DataLoader]: # type: ignore[override] + if self.val_dataset is None: + return None + return DataLoader( + self.val_dataset, + batch_size=self.batch_size, + shuffle=False, + num_workers=min(2, self.num_workers), + pin_memory=self.pin_memory, + persistent_workers=self.num_workers > 0, + prefetch_factor=2 if self.num_workers > 0 else None, + drop_last=False, + worker_init_fn=finetuning_datasets.worker_init_fn, + ) + + def test_dataloader(self) -> Optional[DataLoader]: # type: ignore[override] + if self.test_dataset is None: + return None + return DataLoader( + self.test_dataset, + batch_size=self.batch_size, + shuffle=False, + num_workers=min(2, self.num_workers), + pin_memory=self.pin_memory, + persistent_workers=self.num_workers > 0, + prefetch_factor=2 if self.num_workers > 0 else None, + drop_last=False, + worker_init_fn=finetuning_datasets.worker_init_fn, + ) + + # ------------------------------------------------------------------ + # Introspection helpers + # ------------------------------------------------------------------ + def get_split_info(self) -> Dict[str, List[str]]: + if self.train_dataset is None: + raise RuntimeError("Datasets have not been initialized. Call setup('fit') first.") + return { + "train_groups": getattr(self.train_dataset, "train_groups", []), + "validation_groups": getattr(self.train_dataset, "val_groups", []), + "test_groups": getattr(self.train_dataset, "test_groups", []), + } + + +# Backwards compatibility ------------------------------------------------------------------------- +class MultiDatasetDataModule(FinetuneDataModule): + """Legacy alias retained for existing fine-tuning scripts.""" + + pass diff --git a/transfer/code/stack/src/stack/finetune/datasets.py b/transfer/code/stack/src/stack/finetune/datasets.py new file mode 100644 index 0000000000000000000000000000000000000000..eb35e42b09fcbcc8906bec846c628d6dd1701535 --- /dev/null +++ b/transfer/code/stack/src/stack/finetune/datasets.py @@ -0,0 +1,2 @@ +"""Compatibility layer for fine-tuning datasets.""" +from ..data.finetuning.datasets import * # noqa: F401,F403 diff --git a/transfer/code/stack/src/stack/finetune/lightning.py b/transfer/code/stack/src/stack/finetune/lightning.py new file mode 100644 index 0000000000000000000000000000000000000000..f159cf928192fddcb7d9ee850f59d46d2c7aa0a6 --- /dev/null +++ b/transfer/code/stack/src/stack/finetune/lightning.py @@ -0,0 +1,301 @@ +"""Lightning module definitions for the fine-tuning pipeline.""" + +from __future__ import annotations + +import logging +from typing import Any, Dict, Optional + +import pytorch_lightning as pl +import torch + +from ..model_finetune import ICL_FinetunedModel + +log = logging.getLogger(__name__) + + +class LightningFinetunedModel(pl.LightningModule): + """PyTorch Lightning module that trains a student model against a frozen teacher.""" + + def __init__( + self, + model_config: Dict[str, Any], + checkpoint_path: Optional[str] = None, + learning_rate: float = 1e-4, + weight_decay: float = 1e-4, + scheduler_config: Optional[Dict[str, Any]] = None, + n_kept_cell: int = 96, + ) -> None: + super().__init__() + self.save_hyperparameters() + + self.model = ICL_FinetunedModel(**model_config) + self.teacher_model = ICL_FinetunedModel(**model_config) + + log.info("Freezing teacher model parameters for distillation") + self.teacher_model.eval() + for param in self.teacher_model.parameters(): + param.requires_grad = False + + self.learning_rate = learning_rate + self.weight_decay = weight_decay + self.scheduler_config = scheduler_config or {} + self.n_kept_cell = n_kept_cell + self.teacher_ema_decay = 0.95 + self.ema_every_n_steps = 500 + + self.train_metrics = [] + self.val_metrics = [] + + # ------------------------------------------------------------------ + # Lifecycle hooks + # ------------------------------------------------------------------ + def on_fit_start(self) -> None: # type: ignore[override] + log.info("`on_fit_start` hook called. Syncing teacher model weights from student...") + self.teacher_model.load_state_dict(self.model.state_dict()) + + log.info("VERIFYING teacher weights against student weights...") + student_param = self.model.layers[0].cell_attn.qkv.weight + teacher_param = self.teacher_model.layers[0].cell_attn.qkv.weight + + if torch.equal(student_param, teacher_param): + log.info("SUCCESS: Teacher weights match student weights.") + log.info("Sample tensor sum: %s", student_param.sum().item()) + else: + diff = torch.sum(torch.abs(student_param - teacher_param)) + log.error("FAILURE: Teacher and student weights diverged; diff=%s", diff.item()) + raise RuntimeError("Teacher model failed to sync with student model.") + + log.info("Teacher model weights synced successfully.") + try: + model_config = self.hparams.model_config + n_kept_cell = self.hparams.n_kept_cell + + dummy_obs = torch.rand(1, model_config['n_cells'], model_config['n_genes'], device=self.device) + dummy_gt = torch.rand(1, model_config['n_cells'], model_config['n_genes'], device=self.device) + dummy_ct = torch.zeros(1, model_config['n_cells'], dtype=torch.long, device=self.device) + dummy_mask = torch.ones(1, model_config['n_cells'], dtype=torch.bool, device=self.device) + + with torch.no_grad(): + _ = self.model( + observed_features=dummy_obs, + ground_truth_features=dummy_gt, + cell_type_ids=dummy_ct, + position_mask=dummy_mask, + n_kept_cell=n_kept_cell, + return_loss=False, + ) + _ = self.teacher_model( + observed_features=dummy_gt, + ground_truth_features=dummy_gt, + cell_type_ids=dummy_ct, + position_mask=dummy_mask, + mask_genes=False, + n_kept_cell=model_config['n_cells'], + return_loss=False, + ) + log.info("Model architecture verification passed for both student and teacher.") + except Exception as exc: # pragma: no cover - diagnostic logging path + log.error("Model architecture mismatch detected during verification: %s", exc) + raise + + def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None: # type: ignore[override] + state_dict = checkpoint["state_dict"] + checkpoint["state_dict"] = {k: v for k, v in state_dict.items() if k.startswith("model.")} + log.info("Checkpoint pruned to student parameters only (kept 'model.' prefix).") + + # ------------------------------------------------------------------ + # Core training logic + # ------------------------------------------------------------------ + def _forward_pass_with_teacher(self, batch: Any) -> Dict[str, Any]: + ground_truth_features, observed_features, cell_type_ids, position_mask, _ = batch + + with torch.no_grad(): + teacher_output = self.teacher_model( + observed_features=ground_truth_features, + ground_truth_features=ground_truth_features, + cell_type_ids=cell_type_ids, + position_mask=position_mask, + mask_genes=False, + n_kept_cell=self.hparams.model_config['n_cells'], + return_loss=False, + ) + target_embeddings = teacher_output['final_cell_embeddings'].detach() + + result = self.model( + observed_features=observed_features, + ground_truth_features=ground_truth_features, + cell_type_ids=cell_type_ids, + position_mask=position_mask, + t_cell_embeddings=target_embeddings, + n_kept_cell=self.n_kept_cell, + return_loss=True, + ) + return result + + def forward(self, *inputs: Any, **kwargs: Any): # type: ignore[override] + return self.model(*inputs, **kwargs) + + def training_step(self, batch: Any, batch_idx: int): # type: ignore[override] + result = self._forward_pass_with_teacher(batch) + + total_loss = result['loss'] + recon_loss = result['recon_loss'] + mmd_loss = result['mmd_loss'] + sw_loss = result['sw_loss'] + cls_loss = result['cls_loss'] + cls_acc = result['cls_acc'] + + self.log('train/loss', total_loss, on_step=True, on_epoch=True, prog_bar=True, sync_dist=True) + self.log('train/recon_loss', recon_loss, on_step=True, on_epoch=True, prog_bar=True, sync_dist=True) + self.log('train/mmd_loss', mmd_loss, on_step=True, on_epoch=True, prog_bar=True, sync_dist=True) + self.log('train/sw_loss', sw_loss, on_step=True, on_epoch=True, prog_bar=True, sync_dist=True) + self.log('train/cls_loss', cls_loss, on_step=True, on_epoch=True, prog_bar=True, sync_dist=True) + self.log('train/cls_acc', cls_acc, on_step=True, on_epoch=True, prog_bar=True, sync_dist=True) + return total_loss + + def validation_step(self, batch: Any, batch_idx: int): # type: ignore[override] + result = self._forward_pass_with_teacher(batch) + + total_loss = result['loss'] + recon_loss = result['recon_loss'] + mmd_loss = result['mmd_loss'] + sw_loss = result['sw_loss'] + cls_loss = result['cls_loss'] + cls_acc = result['cls_acc'] + + self.log('val_loss', total_loss, on_epoch=True, prog_bar=True, sync_dist=True) + self.log('val/recon_loss', recon_loss, on_epoch=True, sync_dist=True) + self.log('val/mmd_loss', mmd_loss, on_epoch=True, sync_dist=True) + self.log('val/sw_loss', sw_loss, on_epoch=True, sync_dist=True) + self.log('val/cls_loss', cls_loss, on_epoch=True, sync_dist=True) + self.log('val/cls_acc', cls_acc, on_epoch=True, sync_dist=True) + + if 'masked_mae' in result: + self.log('val/masked_mae', result['masked_mae'], on_epoch=True, sync_dist=True) + if 'masked_corr' in result: + self.log('val/masked_corr', result['masked_corr'], on_epoch=True, sync_dist=True) + if 'sw_predict' in result: + self.log('val/sw_predict', result['sw_predict'], on_epoch=True, sync_dist=True) + if 'mask_rate' in result: + self.log('val/mask_rate', result['mask_rate'], on_epoch=True, sync_dist=True) + + return {'val_loss': total_loss} + + def test_step(self, batch: Any, batch_idx: int): # type: ignore[override] + result = self._forward_pass_with_teacher(batch) + + total_loss = result['loss'] + recon_loss = result['recon_loss'] + mmd_loss = result['mmd_loss'] + sw_loss = result['sw_loss'] + cls_loss = result['cls_loss'] + cls_acc = result['cls_acc'] + + self.log('test_loss', total_loss, on_epoch=True, prog_bar=True, sync_dist=True) + self.log('test/recon_loss', recon_loss, on_epoch=True, sync_dist=True) + self.log('test/mmd_loss', mmd_loss, on_epoch=True, sync_dist=True) + self.log('test/sw_loss', sw_loss, on_epoch=True, sync_dist=True) + self.log('test/cls_loss', cls_loss, on_epoch=True, sync_dist=True) + self.log('test/cls_acc', cls_acc, on_epoch=True, sync_dist=True) + + if 'masked_mae' in result: + self.log('test/masked_mae', result['masked_mae'], on_epoch=True, sync_dist=True) + if 'masked_corr' in result: + self.log('test/masked_corr', result['masked_corr'], on_epoch=True, sync_dist=True) + if 'sw_predict' in result: + self.log('test/sw_predict', result['sw_predict'], on_epoch=True, sync_dist=True) + if 'mask_rate' in result: + self.log('test/mask_rate', result['mask_rate'], on_epoch=True, sync_dist=True) + + return {'test_loss': total_loss} + + def configure_optimizers(self): # type: ignore[override] + optimizer = torch.optim.AdamW(self.model.parameters(), lr=self.learning_rate, weight_decay=self.weight_decay) + config: Dict[str, Any] = {'optimizer': optimizer} + + if not self.scheduler_config: + return config + + scheduler_type = self.scheduler_config.get('type', 'cosine') + if scheduler_type == 'cosine': + warmup_epochs = self.scheduler_config.get('warmup_epochs', 0) + T_max = self.scheduler_config.get('T_max', 100) + eta_min = self.scheduler_config.get('eta_min', 1e-6) + + if warmup_epochs > 0: + from torch.optim.lr_scheduler import LinearLR, SequentialLR + + warmup_scheduler = LinearLR( + optimizer, + start_factor=0.01, + end_factor=1.0, + total_iters=warmup_epochs, + ) + cosine_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( + optimizer, + T_max=max(1, T_max - warmup_epochs), + eta_min=eta_min, + ) + scheduler = SequentialLR(optimizer, schedulers=[warmup_scheduler, cosine_scheduler], milestones=[warmup_epochs]) + else: + scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=T_max, eta_min=eta_min) + + config['lr_scheduler'] = {'scheduler': scheduler, 'interval': 'epoch', 'frequency': 1} + return config + + if scheduler_type == 'reduce_on_plateau': + scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( + optimizer, + mode='min', + factor=self.scheduler_config.get('factor', 0.5), + patience=self.scheduler_config.get('patience', 10), + verbose=True, + ) + config['lr_scheduler'] = { + 'scheduler': scheduler, + 'monitor': 'val_loss', + 'interval': 'epoch', + 'frequency': 1, + } + return config + + raise ValueError(f"Unknown scheduler type: {scheduler_type}") + + def on_train_epoch_start(self) -> None: # type: ignore[override] + datamodule = self.trainer.datamodule + if getattr(datamodule, "resample_each_epoch", False): + datamodule.train_dataset.resample_training_data() + if self.global_rank == 0: + log.info( + "[Epoch %s] Resampled training data -> %s samples", + self.current_epoch, + len(datamodule.train_dataset), + ) + + @torch.no_grad() + def _ema_update_teacher(self): + ema = float(self.teacher_ema_decay) + + for t, s in zip(self.teacher_model.parameters(), self.model.parameters()): + t.data.mul_(ema).add_(s.data, alpha=1.0 - ema) + + for t_buf, s_buf in zip(self.teacher_model.buffers(), self.model.buffers()): + t_buf.copy_(s_buf) + + @torch.no_grad() + def on_before_optimizer_step(self, optimizer): + self.teacher_model.eval() + step = int(self.global_step) + if step > 0 and (step % int(self.ema_every_n_steps) == 0): + self._ema_update_teacher() + if getattr(self, "global_rank", 0) == 0: + logging.info( + f"EMA teacher updated @ global_step={step}, ema={self.teacher_ema_decay}" + ) + + +# Backwards compatibility ------------------------------------------------------------------------- +class LegacyLightningFinetunedModel(LightningFinetunedModel): + """Alias retained for legacy import paths.""" + + pass diff --git a/transfer/code/stack/src/stack/finetune/utils.py b/transfer/code/stack/src/stack/finetune/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..05d85c4586b5b94ec1224c17e1ff80046dd0d617 --- /dev/null +++ b/transfer/code/stack/src/stack/finetune/utils.py @@ -0,0 +1,141 @@ +"""Utility helpers for the StateICL fine-tuning CLI.""" +from __future__ import annotations + +import logging +from typing import Dict, Iterable, List, Optional + +import torch +from pytorch_lightning.loggers import TensorBoardLogger, WandbLogger + +from ..data.finetuning.datasets import DatasetConfig, load_gene_list + +log = logging.getLogger(__name__) + + +def parse_dataset_configs(config_strings: Iterable[str]) -> List[DatasetConfig]: + """Parse CLI dataset configuration strings into :class:`DatasetConfig` objects.""" + configs: List[DatasetConfig] = [] + + for config_str in config_strings: + parts = config_str.split(":") + if len(parts) < 4: + raise ValueError(f"Invalid config string, not enough parts: {config_str}") + + dataset_type = parts[0] + path = parts[1] + filter_organism = True + gene_name_col: Optional[str] = None + + if dataset_type == "human": + donor_col = parts[2] + cell_type_col = parts[3] + if len(parts) > 4 and parts[4]: + filter_organism = parts[4].lower() == "true" + if len(parts) > 5 and parts[5]: + gene_name_col = parts[5] + config = DatasetConfig( + path=path, + type="human", + donor_col=donor_col, + cell_type_col=cell_type_col, + filter_organism=filter_organism, + gene_name_col=gene_name_col, + ) + elif dataset_type == "drug": + if len(parts) < 5: + raise ValueError(f"Drug dataset config requires at least 5 parts: {config_str}") + condition_col = parts[2] + cell_line_col = parts[3] + control_condition = parts[4] + if len(parts) > 5 and parts[5]: + filter_organism = parts[5].lower() == "true" + if len(parts) > 6 and parts[6]: + gene_name_col = parts[6] + config = DatasetConfig( + path=path, + type="drug", + condition_col=condition_col, + cell_line_col=cell_line_col, + control_condition=control_condition, + filter_organism=filter_organism, + gene_name_col=gene_name_col, + ) + else: + raise ValueError(f"Unknown dataset type: {dataset_type}") + + configs.append(config) + log.info("Added %s dataset config: path=%s gene_col=%s", dataset_type, path, gene_name_col) + + return configs + + +def build_scheduler_config(args: Dict[str, any]) -> Optional[Dict[str, any]]: + """Extract scheduler configuration from parsed CLI arguments.""" + scheduler_type = args.get("scheduler") + if not scheduler_type: + return None + + config: Dict[str, any] = {"type": scheduler_type} + if scheduler_type == "cosine": + config.update( + { + "T_max": args.get("scheduler_T_max", 20), + "warmup_epochs": args.get("scheduler_warmup_epochs", 0), + "eta_min": args.get("scheduler_eta_min", 1e-6), + } + ) + elif scheduler_type == "reduce_on_plateau": + config.update( + { + "patience": args.get("scheduler_patience", 10), + "factor": args.get("scheduler_factor", 0.5), + "eta_min": args.get("scheduler_eta_min", 1e-6), + } + ) + else: + raise ValueError(f"Unknown scheduler type: {scheduler_type}") + return config + + +def configure_logger(logger_type: str, project_name: str, run_name: Optional[str], save_dir: str): + """Return a configured W&B or TensorBoard logger based on CLI arguments.""" + if logger_type == "wandb": + return WandbLogger(project=project_name, name=run_name, save_dir=save_dir) + return TensorBoardLogger(save_dir=save_dir, name=project_name, version=run_name) + + +def override_model_config_n_cells(ckpt_path: str, new_n_cells: int) -> Dict[str, any]: + """Load a checkpoint and override the stored ``n_cells`` hyperparameter.""" + ckpt = torch.load(ckpt_path, map_location="cpu") + hyper_params = ckpt.get("hyper_parameters", ckpt.get("hparams", {})) + model_config = dict(hyper_params.get("model_config", {})) + old_value = model_config.get("n_cells") + model_config["n_cells"] = int(new_n_cells) + log.info("Override model_config.n_cells: %s -> %s", old_value, model_config["n_cells"]) + return model_config + + +def build_model_config(args: Dict[str, any], n_genes: int) -> Dict[str, any]: + """Construct a model configuration dictionary for fresh fine-tuning runs.""" + return { + "n_cells": args["sample_size"], + "n_genes": n_genes, + "n_hidden": args["n_hidden"], + "token_dim": args["token_dim"], + "n_layers": args["n_layers"], + "n_heads": args["n_heads"], + "dropout": args["dropout"], + "mask_rate_min": args["mask_rate_min"], + "mask_rate_max": args["mask_rate_max"], + "sw_weight": args["sw_weight"], + } + + +__all__ = [ + "build_model_config", + "build_scheduler_config", + "configure_logger", + "override_model_config_n_cells", + "parse_dataset_configs", + "load_gene_list", +] diff --git a/transfer/code/stack/src/stack/finetuning_dataloader.py b/transfer/code/stack/src/stack/finetuning_dataloader.py new file mode 100644 index 0000000000000000000000000000000000000000..87ee3d9fdd94fdca91e7f72ddc46e0f9f64c4aa5 --- /dev/null +++ b/transfer/code/stack/src/stack/finetuning_dataloader.py @@ -0,0 +1,4 @@ +"""Compatibility wrapper around :mod:`stack.finetune.datasets`.""" +from __future__ import annotations + +from .finetune.datasets import * # noqa: F401,F403 diff --git a/transfer/code/stack/src/stack/model.py b/transfer/code/stack/src/stack/model.py new file mode 100644 index 0000000000000000000000000000000000000000..653f5769ffae0555c4090c95c6a909a739cf2be8 --- /dev/null +++ b/transfer/code/stack/src/stack/model.py @@ -0,0 +1,8 @@ +"""Backward compatible entry-point for the core StateICL model.""" + +from __future__ import annotations + +from .models.core import StateICLModel, scShiftAttentionModel +from .model_loading import load_model_from_checkpoint + +__all__ = ["StateICLModel", "scShiftAttentionModel", "load_model_from_checkpoint"] diff --git a/transfer/code/stack/src/stack/model_finetune.py b/transfer/code/stack/src/stack/model_finetune.py new file mode 100644 index 0000000000000000000000000000000000000000..69ab0e26c737c4c48e84837eb18ad1c8c1aa0a18 --- /dev/null +++ b/transfer/code/stack/src/stack/model_finetune.py @@ -0,0 +1,13 @@ +"""Backward compatible entry-point for fine-tuning helpers.""" + +from __future__ import annotations + +from .models.finetune import ICL_FinetunedModel +from .models.utils import ReparamNBLogSampler, ReparamNBLog_Sampler, safe_logit + +__all__ = [ + "ICL_FinetunedModel", + "ReparamNBLogSampler", + "ReparamNBLog_Sampler", + "safe_logit", +] diff --git a/transfer/code/stack/src/stack/model_loading.py b/transfer/code/stack/src/stack/model_loading.py new file mode 100644 index 0000000000000000000000000000000000000000..bfb74547f96447dea92d489bea35ed8b412d24c0 --- /dev/null +++ b/transfer/code/stack/src/stack/model_loading.py @@ -0,0 +1,90 @@ +"""Utilities for loading pretrained StateICL checkpoints.""" +from __future__ import annotations + +import logging +from pathlib import Path +from typing import Optional, TYPE_CHECKING + +if TYPE_CHECKING: # pragma: no cover - imported for type checking only + import torch + +from .models.core import StateICLModel, scShiftAttentionModel +from .model_finetune import ICL_FinetunedModel + +LOGGER = logging.getLogger("stack.model_loading") + + +def load_model_from_checkpoint( + checkpoint_path: str, + model_class: str = "scShiftAttentionModel", + device: Optional["torch.device"] = None, + strict: bool = True, +) -> "torch.nn.Module": + """Load a trained model from a checkpoint into evaluation mode. + + This helper mirrors the logic used by the standalone CLI scripts so that + users can easily reuse their checkpoints directly from Python after + installing the project as a package. + """ + + try: + import torch + except ModuleNotFoundError as exc: # pragma: no cover - informative failure + raise ModuleNotFoundError( + "The 'torch' package is required to load StateICL checkpoints." + ) from exc + + if device is None: + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + checkpoint_path_obj = Path(checkpoint_path) + if not checkpoint_path_obj.exists(): + raise FileNotFoundError(f"Checkpoint not found: {checkpoint_path_obj}") + + LOGGER.info("Loading model from %s", checkpoint_path_obj) + + if checkpoint_path_obj.suffix == ".ckpt": + checkpoint = torch.load(checkpoint_path_obj, map_location=device) + + if "hyper_parameters" not in checkpoint: + raise ValueError("No hyperparameters found in checkpoint") + + model_config = checkpoint["hyper_parameters"].get("model_config", {}) + + if model_class in {"scShiftAttentionModel", "StateICLModel"}: + model = StateICLModel(**model_config) + elif model_class in {"ICLFinetunedModel", "ICL_FinetunedModel"}: + model = ICL_FinetunedModel(**model_config) + else: + raise ValueError(f"Unknown model class: {model_class}") + + if "state_dict" not in checkpoint: + raise ValueError("No state_dict found in checkpoint") + + state_dict = checkpoint["state_dict"] + new_state_dict = {} + for key, value in state_dict.items(): + if key.startswith("model."): + new_state_dict[key[6:]] = value + else: + new_state_dict[key] = value + model.load_state_dict(new_state_dict, strict=strict) + + elif checkpoint_path_obj.suffix in {".pth", ".pt"}: + checkpoint = torch.load(checkpoint_path_obj, map_location=device) + if isinstance(checkpoint, dict) and "model" in checkpoint: + model = checkpoint["model"] + elif hasattr(checkpoint, "eval"): + model = checkpoint + else: + raise ValueError("Cannot load model from checkpoint format") + else: + raise ValueError(f"Unsupported checkpoint format: {checkpoint_path_obj.suffix}") + + model.to(device) + model.eval() + LOGGER.info("Model loaded successfully on %s", device) + return model + + +__all__ = ["load_model_from_checkpoint"] diff --git a/transfer/code/stack/src/stack/models/__init__.py b/transfer/code/stack/src/stack/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e27ad4369af6f6782d9e05e56e3fcbede8c8ee22 --- /dev/null +++ b/transfer/code/stack/src/stack/models/__init__.py @@ -0,0 +1,10 @@ +"""Modularized model package for StateICL.""" + +from .core import StateICLModel, scShiftAttentionModel +from .finetune import ICL_FinetunedModel + +__all__ = [ + "StateICLModel", + "scShiftAttentionModel", + "ICL_FinetunedModel", +] diff --git a/transfer/code/stack/src/stack/models/core/__init__.py b/transfer/code/stack/src/stack/models/core/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d1aae3b088a28db1e47c234ebcca1650e2dcc10e --- /dev/null +++ b/transfer/code/stack/src/stack/models/core/__init__.py @@ -0,0 +1,20 @@ +"""Core StateICL model and the reusable building blocks it is composed of.""" + +from .base import StateICLModelBase +from .inference import InferenceMixin +from .losses import LossComputationMixin + + +class StateICLModel(InferenceMixin, LossComputationMixin, StateICLModelBase): + """Full StateICL model combining architecture, inference, and loss logic.""" + + +scShiftAttentionModel = StateICLModel + +__all__ = [ + "StateICLModelBase", + "InferenceMixin", + "LossComputationMixin", + "StateICLModel", + "scShiftAttentionModel", +] diff --git a/transfer/code/stack/src/stack/models/core/base.py b/transfer/code/stack/src/stack/models/core/base.py new file mode 100644 index 0000000000000000000000000000000000000000..9163fe3f50ae6c14b14a48edccd07281ddc8842f --- /dev/null +++ b/transfer/code/stack/src/stack/models/core/base.py @@ -0,0 +1,207 @@ +"""Core architecture components for the StateICL model.""" + +from __future__ import annotations + +from typing import Dict, List, Optional, Sequence, Tuple, Union + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ...modules import SlicedWassersteinDistance, TabularAttentionLayer + + +class StateICLModelBase(nn.Module): + """Shared architecture and forward pass for the StateICL model.""" + + def __init__( + self, + n_genes: int, + n_hidden: int = 100, + token_dim: int = 8, + n_cells: int = 128, + n_layers: int = 6, + n_heads: int = 8, + mlp_ratio: int = 4, + dropout: float = 0.1, + mask_rate_min: float = 0.2, + mask_rate_max: float = 0.8, + sw_weight: float = 1.0, + n_proj: int = 64, + ) -> None: + super().__init__() + + self.n_genes = n_genes + self.n_hidden = n_hidden + self.token_dim = token_dim + self.n_cells = n_cells + self.n_layers = n_layers + self.mask_rate_min = mask_rate_min + self.mask_rate_max = mask_rate_max + self.sw_weight = sw_weight + self.n_proj = n_proj + self.sw_distance = SlicedWassersteinDistance(n_proj=n_proj) + + self.gene_reduction = nn.Sequential( + nn.Linear(n_genes, n_hidden * token_dim), + nn.GELU(), + nn.Dropout(dropout), + ) + + self.gene_pos_embedding = nn.Parameter(torch.randn(n_hidden, token_dim)) + + self.layers = nn.ModuleList( + [ + TabularAttentionLayer( + token_dim=token_dim, + n_cells=n_cells, + n_hidden=n_hidden, + n_heads=n_heads, + mlp_ratio=mlp_ratio, + dropout=dropout, + ) + for _ in range(n_layers) + ] + ) + + self.output_mlp = nn.Sequential( + nn.Linear(n_hidden * token_dim, n_hidden * token_dim * 2), + nn.GELU(), + nn.Dropout(dropout), + nn.Linear(n_hidden * token_dim * 2, n_genes * 2), + ) + + self.apply(self._init_weights) + + def _init_weights(self, module: nn.Module) -> None: + if isinstance(module, nn.Linear): + nn.init.xavier_uniform_(module.weight) + if module.bias is not None: + nn.init.constant_(module.bias, 0) + elif isinstance(module, nn.LayerNorm): + nn.init.constant_(module.bias, 0) + nn.init.constant_(module.weight, 1.0) + elif isinstance(module, nn.Embedding): + nn.init.normal_(module.weight, mean=0, std=0.2) + + def _reduce_and_tokenize(self, features: torch.Tensor) -> torch.Tensor: + batch_size, n_cells, _ = features.shape + reduced = self.gene_reduction(features) + return reduced.reshape(batch_size, n_cells, self.n_hidden, self.token_dim) + + def _run_attention_layers( + self, + tokens: torch.Tensor, + gene_attn_mask: Optional[torch.Tensor] = None, + return_attn: bool = False, + ) -> Union[torch.Tensor, Tuple[torch.Tensor, Sequence[torch.Tensor]]]: + attn_maps: List[torch.Tensor] = [] + x = tokens + for layer in self.layers: + x, attn = layer(x, self.gene_pos_embedding, gene_attn_mask, return_attn) + if return_attn: + attn_maps.append(attn) + if return_attn: + return x, attn_maps + return x + + def _compute_nb_parameters( + self, + final_cell_embeddings: torch.Tensor, + observed_lib_size: torch.Tensor, + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + batch_size, n_cells, _ = final_cell_embeddings.shape + flat_embeddings = final_cell_embeddings.reshape(batch_size * n_cells, -1) + output = self.output_mlp(flat_embeddings) + output = output.reshape(batch_size, n_cells, self.n_genes, 2) + + px_scale_logits = output[..., 0] + nb_dispersion = F.softplus(output[..., 1]) + px_scale = F.softmax(px_scale_logits, dim=-1) + nb_mean = px_scale * observed_lib_size + return nb_mean, nb_dispersion, px_scale + + def apply_mask(self, features: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + batch_size, n_cells, n_genes = features.shape + device = features.device + + mask_rate = torch.empty(1, device=device).uniform_( + self.mask_rate_min, self.mask_rate_max + ).item() + n_genes_to_mask = int(n_genes * mask_rate) + + mask_indices = torch.randperm(n_genes, device=device)[:n_genes_to_mask] + + mask = torch.zeros(batch_size, n_cells, n_genes, dtype=torch.bool, device=device) + mask[:, :, mask_indices] = True + + masked_features = features.clone() + masked_features[mask] = 0.0 + return masked_features, mask + + def forward( + self, + features: torch.Tensor, + return_loss: bool = True, + ) -> Dict[str, torch.Tensor]: + batch_size, n_cells, _ = features.shape + device = features.device + + original_features = features.clone() + observed_lib_size = original_features.sum(dim=-1, keepdim=True) + + features = torch.log1p(features) + + masked_features, mask = self.apply_mask(features) + + tokens = self._reduce_and_tokenize(masked_features) + x = self._run_attention_layers(tokens) + final_cell_embeddings = x.reshape(batch_size, n_cells, -1) + + nb_mean, nb_dispersion, px_scale = self._compute_nb_parameters( + final_cell_embeddings, observed_lib_size + ) + + result = { + "nb_mean": nb_mean, + "nb_dispersion": nb_dispersion, + "px_scale": px_scale, + "observed_lib_size": observed_lib_size, + "mask": mask, + "cell_embeddings": final_cell_embeddings, + "masked_features": masked_features, + "original_features": original_features, + } + + if return_loss: + recon_loss, _ = self._compute_reconstruction_loss( + nb_mean, nb_dispersion, original_features, mask + ) + sw_loss = self._compute_sw_loss(final_cell_embeddings) + total_loss = recon_loss + self.sw_weight * sw_loss + + result.update( + { + "loss": total_loss, + "recon_loss": recon_loss, + "sw_loss": sw_loss, + } + ) + + if not self.training: + metrics = self._compute_eval_metrics(nb_mean, original_features, mask) + result.update(metrics) + else: + zero = torch.tensor(0.0, device=device, dtype=nb_mean.dtype) + result.update( + { + "masked_mae": zero, + "masked_corr": zero, + "mask_rate": zero, + } + ) + + return result + + +__all__ = ["StateICLModelBase"] diff --git a/transfer/code/stack/src/stack/models/core/inference.py b/transfer/code/stack/src/stack/models/core/inference.py new file mode 100644 index 0000000000000000000000000000000000000000..f372eb6778a399fcaa5927bbe6f2d031e4264ce8 --- /dev/null +++ b/transfer/code/stack/src/stack/models/core/inference.py @@ -0,0 +1,941 @@ +"""Inference and downstream utilities for the StateICL model.""" + +from __future__ import annotations + +import math +from typing import Dict, List, Optional, Tuple, Union + +import anndata as ad +import numpy as np +import torch +import torch.nn.functional as F +from scvi.distributions import NegativeBinomial +from torch.utils.data import DataLoader + +from ...dataloader import TestSamplerDataset +from ..utils import align_result_to_adata_numpy + + +class InferenceMixin: + @torch.no_grad() + def predict( + self, + features: torch.Tensor, + mask: Optional[torch.Tensor] = None + ) -> Dict[str, torch.Tensor]: + """ + Prediction without loss computation. + + Args: + features: (batch_size, n_cells, n_genes) - original features (no log1p needed) + mask: Optional mask to apply, if None, no masking + + Returns: + Predictions dictionary + """ + # Apply mask if provided (before log1p in forward) + if mask is not None: + features = features.clone() + features[mask] = 0.0 + + # Forward pass without loss (forward will apply log1p) + result = self.forward( + features, + return_loss=False + ) + + return result + + @torch.no_grad() + def get_prediction( + self, + adata_path, + genelist_path, + gene_name_col: Optional[str] = None, + mask_rate: float = 0.2, + cell_ratio: float = 0.25, + context_ratio: float = 0.25, + is_masked_list: Optional[list] = None, + batch_size: int = 32, + show_progress: bool = False, + num_workers: int = 4, + random_seed: Optional[int] = None, + return_metrics: bool = False, + **dataloader_kwargs + ) -> Union[Tuple[np.ndarray, np.ndarray], Dict[str, float]]: # Return type depends on return_metrics + """ + Get predictions for masked genes in new AnnData object, or compute evaluation metrics. + + Args: + adata_path: Path to h5ad file + mask_rate: Masking rate to test (default 0.2) + ratio: Cell ratio to mask (default 0.125) + batch_size: Batch size for processing + show_progress: Whether to show progress bar (default False) + num_workers: Number of dataloader workers + random_seed: Random seed for reproducible masking (default None) + return_metrics: If True, returns evaluation metrics instead of predictions + **dataloader_kwargs: Additional arguments for SplittableDatasetSamplerDataset + + Returns: + If return_metrics=False: + Tuple of (mean_predictions, dispersion_predictions): + - mean_predictions: (n_cells, n_genes) - predicted means + - dispersion_predictions: (n_cells, n_genes) - predicted dispersions + If return_metrics=True: + Dict of evaluation metrics from ``_compute_eval_metrics``: + - masked_mae: Masked mean absolute error + - masked_corr: Masked correlation averaged over valid cells + - mask_rate: Fraction of entries evaluated + """ + from tqdm.auto import tqdm + + self.eval() + + dataset = TestSamplerDataset( + adata_path, + genelist_path, + gene_name_col=gene_name_col, + sample_size=self.n_cells, + mode='eval', + **dataloader_kwargs + ) + + dataloader = DataLoader( + dataset, + batch_size=batch_size, + shuffle=False, + num_workers=num_workers + ) + + if show_progress: + pbar = tqdm( + dataloader, + desc="Computing predictions" if not return_metrics else "Computing evaluation metrics", + total=len(dataloader), + unit="batch" + ) + iterator = pbar + else: + iterator = dataloader + + # Initialize storage based on mode + if return_metrics: + helper_masked_mae_sum = 0.0 + helper_masked_corr_sum = 0.0 + helper_masked_entry_count = 0 + helper_corr_cell_count = 0 + helper_total_entries = 0 + else: + # For predictions + mean_predictions = [] + dispersion_predictions = [] + count_predictions = [] + logit_predictions = [] + + # Set random seed if provided for reproducible masking + if random_seed is not None: + torch.manual_seed(random_seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed(random_seed) + + device = next(self.parameters()).device + + # Process batches + for i, batch in enumerate(iterator): + features, metadata = batch + + features = features.to(device) + + batch_size_curr, n_cells_batch, n_genes = features.shape + gene_attn_mask = None + if cell_ratio > 0 and cell_ratio < 1: + gene_attn_mask = torch.zeros(n_cells_batch, n_cells_batch, dtype=torch.bool, device=device) + gene_attn_mask[:int(cell_ratio * n_cells_batch), int(cell_ratio * n_cells_batch):] = True + gene_attn_mask = gene_attn_mask.unsqueeze(0).unsqueeze(0) + + mask = torch.zeros(batch_size_curr, n_cells_batch, n_genes, dtype=torch.bool, device=device) + #n_genes_to_mask = int(n_genes * mask_rate) + n_genes_to_mask = 0 + mask_indices = torch.randperm(n_genes, device=device)[:n_genes_to_mask] + if n_genes_to_mask > 0: + mask[:, :, mask_indices] = True + #mask[:,:int(n_cells_batch*cell_ratio),:] = False + + # Calculate library size before masking + observed_lib_size = features.sum(dim=-1, keepdim=True) # (B, n_cells, 1) + + # Apply log1p transformation + features_log = torch.log1p(features) + + masked_features_log = features_log.clone() + masked_features_log[mask] = 0.0 + + tokens = self._reduce_and_tokenize(masked_features_log) + n_kept_cell = int(cell_ratio * n_cells_batch) + n_context_cell = int((cell_ratio + context_ratio) * n_cells_batch) + + if getattr(self, "query_pos_embedding", None) is not None and n_kept_cell < n_cells_batch: + qpe = self.query_pos_embedding.unsqueeze(0).unsqueeze(0).to(device=tokens.device, dtype=tokens.dtype) + mask_expanded = torch.zeros_like(tokens) + mask_expanded[:, n_context_cell:, :, :] = 1.0 + tokens = tokens + qpe * mask_expanded + + x = self._run_attention_layers(tokens, gene_attn_mask=gene_attn_mask) + + # Final cell embeddings + final_cell_embeddings = x.reshape(batch_size_curr, n_cells_batch, -1) + logits = None + if getattr(self, "cls", None) is not None and n_context_cell < n_cells_batch: + + mean_kept_cell = final_cell_embeddings[:,:n_context_cell].mean(dim=1).detach() + tail_feat = final_cell_embeddings[:, n_context_cell:, :].detach() + ctx_tail = mean_kept_cell.unsqueeze(1).expand(-1, tail_feat.size(1), -1) + inp_tail = torch.cat([ctx_tail, tail_feat], dim=-1) + logit_tail = self.cls(inp_tail).squeeze(-1) # (B, Nt) + + logits = torch.cat([torch.zeros([batch_size_curr,n_context_cell],dtype=logit_tail.dtype,device=logit_tail.device), logit_tail], dim=1) + + + nb_mean, nb_dispersion, px_scale = self._compute_nb_parameters( + final_cell_embeddings, observed_lib_size + ) + + if return_metrics: + if mask_rate > 0: + n_genes_to_evaluate = max(1, int(n_genes * mask_rate)) + eval_indices = torch.randperm(n_genes, device=device)[:n_genes_to_evaluate] + metric_mask = torch.zeros_like(mask, dtype=torch.bool) + metric_mask[:, :, eval_indices] = True + else: + metric_mask = torch.ones_like(mask, dtype=torch.bool) + + helper_metrics = self._compute_eval_metrics(nb_mean, features, metric_mask) + + mask_entries = int(metric_mask.sum().item()) + helper_masked_entry_count += mask_entries + helper_total_entries += metric_mask.numel() + if mask_entries > 0: + helper_masked_mae_sum += helper_metrics["masked_mae"].item() * mask_entries + + mask_f = metric_mask.float() + valid_cells = int((mask_f.sum(dim=-1) > 1).sum().item()) + if valid_cells > 0: + helper_masked_corr_sum += helper_metrics["masked_corr"].item() * valid_cells + helper_corr_cell_count += valid_cells + + else: + # Store predictions + nb_mean_flat = nb_mean.reshape(-1, n_genes) # (B * n_cells, n_genes) + nb_dispersion_median = nb_dispersion[:,:n_context_cell,:].median(dim=1, keepdim=True).values.expand(-1, n_cells_batch, -1) + nb_dispersion_flat = nb_dispersion.reshape(-1, n_genes) # (B * n_cells, n_genes) + nb_dispersion_median_flat = nb_dispersion_median.reshape(-1, n_genes) + nb_dist = NegativeBinomial(mu=nb_mean_flat, theta=nb_dispersion_median_flat) + mean_predictions.append(nb_mean_flat.cpu().numpy()) + dispersion_predictions.append(nb_dispersion_flat.cpu().numpy()) + count_predictions.append(nb_dist.sample().cpu().numpy()) + if logits is not None: + logits_flat = logits.reshape(-1) + logit_predictions.append(logits_flat.cpu().numpy()) + + if show_progress: + if return_metrics: + current_cells = helper_corr_cell_count + pbar.set_postfix({ + 'cells_evaluated': f'{current_cells:,}', + 'mask_rate': f'{mask_rate:.2f}' + }) + else: + current_cells = sum([pred.shape[0] for pred in mean_predictions]) + pbar.set_postfix({ + 'cells_processed': f'{current_cells:,}', + 'mask_rate': f'{mask_rate:.2f}' + }) + + if show_progress: + pbar.close() + + if return_metrics: + # Return evaluation metrics + masked_mae_helper = ( + helper_masked_mae_sum / helper_masked_entry_count + if helper_masked_entry_count > 0 + else float("nan") + ) + masked_corr_helper = ( + helper_masked_corr_sum / helper_corr_cell_count + if helper_corr_cell_count > 0 + else float("nan") + ) + mask_rate_helper = ( + helper_masked_entry_count / helper_total_entries + if helper_total_entries > 0 + else float("nan") + ) + return { + "masked_mae": masked_mae_helper, + "masked_corr": masked_corr_helper, + "mask_rate": mask_rate_helper, + } + else: + # Return predictions (original behavior) + mean_predictions = np.concatenate(mean_predictions, axis=0)[:dataset.n_human_cells] + dispersion_predictions = np.concatenate(dispersion_predictions, axis=0)[:dataset.n_human_cells] + count_predictions = np.concatenate(count_predictions, axis=0)[:dataset.n_human_cells] + if logit_predictions: + logit_predictions = np.concatenate(logit_predictions, axis=0)[:dataset.n_human_cells] + return mean_predictions, dispersion_predictions, count_predictions, logit_predictions + + @torch.no_grad() + def get_attn( + self, + adata_path, + genelist_path, + gene_name_col: Optional[str] = None, + mask_rate: float = 0.2, + cell_ratio: float = 0.25, + batch_size: int = 32, + show_progress: bool = False, + num_workers: int = 4, + random_seed: Optional[int] = None, + **dataloader_kwargs + ) -> Union[Tuple[np.ndarray, np.ndarray], Dict[str, float]]: # Return type depends on return_metrics + """ + Get attns for masked genes in new AnnData object\ + + Args: + adata_path: Path to h5ad file + mask_rate: Masking rate to test (default 0.2) + ratio: Cell ratio to mask (default 0.125) + batch_size: Batch size for processing + show_progress: Whether to show progress bar (default False) + num_workers: Number of dataloader workers + random_seed: Random seed for reproducible masking (default None) + return_metrics: If True, returns evaluation metrics instead of predictions + **dataloader_kwargs: Additional arguments for SplittableDatasetSamplerDataset + """ + from tqdm.auto import tqdm + from scipy.stats import pearsonr, spearmanr + from sklearn.metrics import mean_squared_error, mean_absolute_error + + self.eval() + + dataset = TestSamplerDataset( + adata_path, + genelist_path, + gene_name_col=gene_name_col, + sample_size=self.n_cells, + mode='eval', + **dataloader_kwargs + ) + + dataloader = DataLoader( + dataset, + batch_size=batch_size, + shuffle=False, + num_workers=num_workers + ) + + if show_progress: + pbar = tqdm( + dataloader, + desc="Computing attns", + total=len(dataloader), + unit="batch" + ) + iterator = pbar + else: + iterator = dataloader + + all_layer_attns = [[] for _ in range(self.n_layers)] + + # Set random seed if provided for reproducible masking + if random_seed is not None: + torch.manual_seed(random_seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed(random_seed) + + device = next(self.parameters()).device + + # Process batches + for batch in iterator: + features, metadata = batch + + features = features.to(device) + + batch_size_curr, n_cells_batch, n_genes = features.shape + gene_attn_mask = None + if cell_ratio > 0 and cell_ratio < 1: + gene_attn_mask = torch.zeros(n_cells_batch, n_cells_batch, dtype=torch.bool, device=device) + gene_attn_mask[:int(cell_ratio * n_cells_batch), int(cell_ratio * n_cells_batch):] = True + gene_attn_mask = gene_attn_mask.unsqueeze(0).unsqueeze(0) + + mask = torch.zeros(batch_size_curr, n_cells_batch, n_genes, dtype=torch.bool, device=device) + n_genes_to_mask = int(n_genes * mask_rate) + mask_indices = torch.randperm(n_genes, device=device)[:n_genes_to_mask] + if n_genes_to_mask > 0: + mask[:, :, mask_indices] = True + + # Calculate library size before masking + observed_lib_size = features.sum(dim=-1, keepdim=True) # (B, n_cells, 1) + + # Apply log1p transformation + features_log = torch.log1p(features) + masked_features_log = features_log.clone() + masked_features_log[mask] = 0.0 + + tokens = self._reduce_and_tokenize(masked_features_log) + x, attn_maps = self._run_attention_layers( + tokens, gene_attn_mask=gene_attn_mask, return_attn=True + ) + + for layer_idx, attn in enumerate(attn_maps): + all_layer_attns[layer_idx].append( + attn.mean(dim=1).reshape(-1, attn.shape[-1]).cpu().numpy() + ) + + if show_progress: + pbar.update(1) + + if show_progress: + pbar.close() + + final_attns = [] + for i in range(self.n_layers): + layer_batches = all_layer_attns[i] + concatenated_attn = np.concatenate(layer_batches, axis=0)[:dataset.n_human_cells] + final_attns.append(concatenated_attn) + + return final_attns + + @torch.no_grad() + def get_latent_representation( + self, + adata_path, + genelist_path, + gene_name_col: Optional[str] = None, + batch_size: int = 32, + show_progress: bool = True, + num_workers: int = 4, + **dataloader_kwargs + ) -> np.ndarray: + """ + Get cell embeddings for AnnData object. + + Args: + adata_path: Path to h5ad file + batch_size: Batch size for processing + show_progress: Whether to show progress bar (default True) + **dataloader_kwargs: Additional arguments for SplittableDatasetSamplerDataset + + Returns: + cell_embeddings: (n_cells, n_hidden * token_dim) - cell embeddings as numpy array + """ + from tqdm.auto import tqdm + + self.eval() + dataset = TestSamplerDataset( + adata_path, + genelist_path, + gene_name_col=gene_name_col, + sample_size=self.n_cells, + mode='eval', + **dataloader_kwargs + ) + + dataloader = DataLoader( + dataset, + batch_size=batch_size, + shuffle=False, # Don't shuffle dataloader since we already shuffled data + num_workers=num_workers + ) + + all_embeddings = [] + all_dsembeddings = [] + + if show_progress: + pbar = tqdm( + dataloader, + desc="Extracting embeddings", + total=len(dataloader), + unit="batch" + ) + iterator = pbar + else: + iterator = dataloader + + # Process shuffled data + device = next(self.parameters()).device + for batch in iterator: + features, metadata = batch + + features = features.to(device) + + batch_size_curr, n_cells_batch, n_genes = features.shape + + # Apply log1p transformation + features_log = torch.log1p(features) + + tokens = self._reduce_and_tokenize(features_log) + x = self._run_attention_layers(tokens) + + # Final cell embeddings + batch_embeddings = x.reshape(batch_size_curr, n_cells_batch, -1) + + flat_embeddings = batch_embeddings.reshape(-1, batch_embeddings.shape[-1]) + + all_embeddings.append(flat_embeddings.cpu().numpy()) + all_dsembeddings.append(flat_embeddings.mean(dim=1).cpu().numpy()) + + if show_progress: + current_cells = sum([emb.shape[0] for emb in all_embeddings]) + pbar.set_postfix({ + 'cells_processed': f'{current_cells:,}', + 'embedding_dim': batch_embeddings.shape[-1] + }) + + if show_progress: + pbar.close() + + # Concatenate embeddings + cell_embeddings = np.concatenate(all_embeddings, axis=0)[0:dataset.n_human_cells] + #print(cell_embeddings.shape) + dataset_embeddings = np.concatenate(all_dsembeddings, axis=0) + return cell_embeddings, dataset_embeddings + + def decode(self, embeddings: torch.Tensor, lib_size: torch.Tensor) -> torch.Tensor: + """ + Decode embeddings to gene expression values. + + Args: + embeddings: (n_cells, n_hidden * token_dim) - cell embeddings + lib_size: (n_cells, 1) or (n_cells,) - library sizes for each cell + + Returns: + expressions: (n_cells, n_genes) - decoded gene expressions + """ + # Handle input shapes + if embeddings.ndim == 2: + n_cells = embeddings.shape[0] + embeddings = embeddings.unsqueeze(0) + single_batch = True + else: + single_batch = False + n_cells = embeddings.shape[1] + + batch_size, n_cells, embedding_dim = embeddings.shape + + lib_size_tensor = lib_size + if lib_size_tensor.ndim == 1: + lib_size_tensor = lib_size_tensor.unsqueeze(0) + if lib_size_tensor.ndim == 3 and lib_size_tensor.shape[-1] == 1: + lib_size_tensor = lib_size_tensor.squeeze(-1) + lib_size_tensor = lib_size_tensor.reshape(-1, n_cells) + if lib_size_tensor.shape[0] != batch_size: + if lib_size_tensor.shape[0] == 1: + lib_size_tensor = lib_size_tensor.expand(batch_size, -1) + else: + raise ValueError("lib_size shape does not match embeddings") + + observed_lib_size = lib_size_tensor.unsqueeze(-1) + + nb_mean, _, _ = self._compute_nb_parameters(embeddings, observed_lib_size) + + if single_batch: + expressions = nb_mean.squeeze(0) + else: + expressions = nb_mean + + return expressions + + @torch.no_grad() + def get_incontext_prediction( + self, + base_adata_or_path, + test_adata_or_path, + genelist_path: str, + mask_rate: float = 0.8, + prompt_ratio: float = 0.25, + context_ratio: float = 0.25, + mode: str = 'predict', + gene_name_col: Optional[str] = None, + test_logit: Optional[torch.Tensor] = None, + is_masked: Optional[torch.Tensor] = None, + batch_size: int = 32, + show_progress: bool = True, + num_workers: int = 4, + random_seed: Optional[int] = None, + **dataloader_kwargs + ) -> Union[np.ndarray, Tuple[np.ndarray, np.ndarray]]: + """ + Performs in-context prediction by mixing a base dataset with a test dataset. + A portion of cells from the base dataset provides context for the test cells + within the same attention window (batch). + + Args: + base_adata_path: Path to the base (context) .h5ad file. + test_adata_path: Path to the test (target) .h5ad file. + genelist_path: Path to the list of genes used by the model. + ratio: The ratio of context cells in each mixed sample (e.g., 0.875 for 7/8). + mode: 'latent' to return cell embeddings, 'predict' to return gene expression predictions, 'generate' to return expression prediction and uncertainty. + gene_name_col: The column in .var to use as gene names for alignment. + batch_size: Batch size for processing the mixed data. + show_progress: Whether to show a progress bar. + num_workers: Dataloader workers. + random_seed: Seed for reproducible shuffling. + **dataloader_kwargs: Additional arguments for TestSamplerDataset. + + Returns: + - If mode='latent', returns a numpy array of latent representations for test cells. + - If mode='predict', returns a tuple of (mean, dispersion) predictions for test cells. + """ + ratio = prompt_ratio + context_ratio + assert 0 < ratio < 1, "Ratio must be between 0 and 1." + assert mode in ['latent', 'predict', 'generate'], "Mode must be 'latent', 'predict' or 'generate'." + + self.eval() + + if isinstance(base_adata_or_path, str): + base_adata = ad.read_h5ad(base_adata_or_path) + else: + base_adata = base_adata_or_path.copy() + + if isinstance(test_adata_or_path, str): + test_adata = ad.read_h5ad(test_adata_or_path) + else: + test_adata = test_adata_or_path.copy() + + # --- 2. Prepare for in-context batching --- + if random_seed is not None: + np.random.seed(random_seed) + torch.manual_seed(random_seed) + + base_indices = np.random.permutation(base_adata.n_obs) + test_indices = np.arange(test_adata.n_obs) + + # Calculate number of cells per sample from each dataset + n_test_cells = max(1, int(self.n_cells * (1-ratio))) + n_base_cells = self.n_cells - n_test_cells + + # We create one large AnnData to pass to the dataloader + num_test_samples = math.ceil(len(test_indices) / n_test_cells) + total_cells = num_test_samples * self.n_cells + + mixed_adata_list = [] + # Keep track of which cells are from the test set + is_test_cell_mask = np.zeros(total_cells, dtype=bool) + is_base_cell_mask = np.zeros(total_cells, dtype=bool) + is_masked_list = [] + base_idx_ptr = 0 + for i in range(num_test_samples): + # Get test cells + start, end = i * n_test_cells, (i + 1) * n_test_cells + current_test_indices = test_indices[start:end] + len_test_indices = len(current_test_indices) + if len_test_indices < n_test_cells: + need = n_test_cells - len_test_indices + pad = test_indices[:need] + current_test_indices = np.concatenate([current_test_indices, pad]) + + # Get base cells, looping if we run out + idx_base = np.arange(base_idx_ptr, base_idx_ptr + n_base_cells) % len(base_indices) + current_base_indices = base_indices[idx_base] + base_idx_ptr = (base_idx_ptr + n_base_cells) % len(base_indices) + + # Concatenate AnnData objects for this sample + sample_adata = ad.concat( + [base_adata[current_base_indices], test_adata[current_test_indices]], + join='inner', + axis=0, + label='origin', + keys=['base', 'test'] + ) + mixed_adata_list.append(sample_adata) + if is_masked is not None: + is_masked_list.append(np.asarray(is_masked)[current_test_indices]) + + # Update the mask + mask_start_idx = i * self.n_cells + is_test_cell_mask[mask_start_idx + n_base_cells : mask_start_idx + n_base_cells + len_test_indices] = True + is_base_cell_mask[mask_start_idx : mask_start_idx + n_base_cells] = True + + # Final concatenated AnnData object + full_mixed_adata = ad.concat(mixed_adata_list, axis=0, join='inner') + + if mode == 'latent': + all_embeddings, _ = self.get_latent_representation( + adata_path=full_mixed_adata, + genelist_path=genelist_path, + gene_name_col=gene_name_col, + batch_size=batch_size, + show_progress=show_progress, + num_workers=num_workers, + **dataloader_kwargs + ) + result = all_embeddings[is_test_cell_mask] + + else: + mean_preds, disp_preds, count_preds, logit_preds = self.get_prediction( + adata_path=full_mixed_adata, + genelist_path=genelist_path, + gene_name_col=gene_name_col, + mask_rate=0.2, # No mask for base data + cell_ratio=prompt_ratio, + context_ratio=context_ratio, + is_masked_list=is_masked_list, + batch_size=batch_size, + show_progress=show_progress, + num_workers=num_workers, + **dataloader_kwargs + ) + result = count_preds[is_test_cell_mask] + cell_indices_to_keep = None + + if mode == 'generate': + new_test_logit = logit_preds[is_test_cell_mask] + cell_indices_to_keep = np.zeros(is_test_cell_mask.sum(), dtype=bool) + cell_indices_to_keep[~is_masked] = True + + unmask_rate = (is_masked.sum() / is_masked.shape[0] - mask_rate) * is_masked.shape[0] / is_masked.sum() + unmask_rate = np.clip(unmask_rate, 0.0, 1.0) + cell_indices_to_keep[is_masked] = new_test_logit[is_masked]>np.quantile(new_test_logit[is_masked],unmask_rate) + #cell_indices_to_keep[new_test_logit>np.quantile(new_test_logit,mask_rate)] = True + is_masked[~cell_indices_to_keep] = False + is_masked[new_test_logit>0] = True + result = align_result_to_adata_numpy(result, test_adata, genelist_path, gene_name_col, cell_indices_to_keep=cell_indices_to_keep) + + from scipy.sparse import csr_matrix + # truncate + #result[result < 0.2] = 0 + result = csr_matrix(result) + if mode == 'generate': + return result, new_test_logit, is_masked + return result + + @torch.no_grad() + def get_incontext_attn( + self, + base_adata_or_path, + test_adata_or_path, + genelist_path: str, + mask_rate: float = 0.8, + ratio: float = 0.875, + mode: str = 'predict', + gene_name_col: Optional[str] = None, + batch_size: int = 32, + show_progress: bool = True, + num_workers: int = 4, + random_seed: Optional[int] = None, + **dataloader_kwargs + ) -> Union[np.ndarray, Tuple[np.ndarray, np.ndarray]]: + """ + Performs in-context prediction by mixing a base dataset with a test dataset. + A portion of cells from the base dataset provides context for the test cells + within the same attention window (batch). + + Args: + base_adata_path: Path to the base (context) .h5ad file. + test_adata_path: Path to the test (target) .h5ad file. + genelist_path: Path to the list of genes used by the model. + ratio: The ratio of context cells in each mixed sample (e.g., 0.875 for 7/8). + mode: 'latent' to return cell embeddings, 'predict' to return gene expression predictions, 'generate' to return expression prediction and uncertainty. + gene_name_col: The column in .var to use as gene names for alignment. + batch_size: Batch size for processing the mixed data. + show_progress: Whether to show a progress bar. + num_workers: Dataloader workers. + random_seed: Seed for reproducible shuffling. + **dataloader_kwargs: Additional arguments for TestSamplerDataset. + + Returns: + - If mode='latent', returns a numpy array of latent representations for test cells. + - If mode='predict', returns a tuple of (mean, dispersion) predictions for test cells. + """ + assert 0 < ratio < 1, "Ratio must be between 0 and 1." + assert mode in ['latent', 'predict', 'generate'], "Mode must be 'latent', 'predict' or 'generate'." + + self.eval() + + if isinstance(base_adata_or_path, str): + base_adata = ad.read_h5ad(base_adata_or_path) + else: + base_adata = base_adata_or_path.copy() + + if isinstance(test_adata_or_path, str): + test_adata = ad.read_h5ad(test_adata_or_path) + else: + test_adata = test_adata_or_path.copy() + + # --- 2. Prepare for in-context batching --- + if random_seed is not None: + np.random.seed(random_seed) + torch.manual_seed(random_seed) + + base_indices = np.random.permutation(base_adata.n_obs) + test_indices = np.arange(test_adata.n_obs) + + # Calculate number of cells per sample from each dataset + n_test_cells = max(1, int(self.n_cells * (1-ratio))) + n_base_cells = self.n_cells - n_test_cells + + # We create one large AnnData to pass to the dataloader + num_test_samples = math.ceil(len(test_indices) / n_test_cells) + total_cells = num_test_samples * self.n_cells + + mixed_adata_list = [] + # Keep track of which cells are from the test set + is_test_cell_mask = np.zeros(total_cells, dtype=bool) + is_base_cell_mask = np.zeros(total_cells, dtype=bool) + base_indices_list = [] + + base_idx_ptr = 0 + for i in range(num_test_samples): + # Get test cells + start, end = i * n_test_cells, (i + 1) * n_test_cells + current_test_indices = test_indices[start:end] + len_test_indices = len(current_test_indices) + if len_test_indices < n_test_cells: + need = n_test_cells - len_test_indices + pad = test_indices[:need] + current_test_indices = np.concatenate([current_test_indices, pad]) + + # Get base cells, looping if we run out + idx_base = np.arange(base_idx_ptr, base_idx_ptr + n_base_cells) % len(base_indices) + current_base_indices = base_indices[idx_base] + base_indices_list.append(current_base_indices) + base_idx_ptr = (base_idx_ptr + n_base_cells) % len(base_indices) + + # Concatenate AnnData objects for this sample + sample_adata = ad.concat( + [base_adata[current_base_indices], test_adata[current_test_indices]], + join='inner', + axis=0, + label='origin', + keys=['base', 'test'] + ) + mixed_adata_list.append(sample_adata) + + # Update the mask + mask_start_idx = i * self.n_cells + is_test_cell_mask[mask_start_idx + n_base_cells : mask_start_idx + n_base_cells + len_test_indices] = True + is_base_cell_mask[mask_start_idx : mask_start_idx + n_base_cells] = True + + # Final concatenated AnnData object + full_mixed_adata = ad.concat(mixed_adata_list, axis=0, join='inner') + + final_attns = self.get_attn( + adata_path=full_mixed_adata, + genelist_path=genelist_path, + gene_name_col=gene_name_col, + mask_rate=mask_rate, # No mask for base data + cell_ratio=ratio, + batch_size=batch_size, + show_progress=show_progress, + num_workers=num_workers, + **dataloader_kwargs + ) + processed_attns = [] + for concatenated_attn in final_attns: + processed_attns.append(concatenated_attn[is_test_cell_mask,0:n_base_cells]) + return processed_attns, base_indices_list + + @torch.no_grad() + def get_incontext_generation( + self, + base_adata_or_path, + test_adata_or_path, + genelist_path: str, + num_steps: int = 5, + prompt_ratio: float = 0.25, + context_ratio: float = 0.4, + context_ratio_min: float = 0.2, + mask_rate: float = 1.0, + mode: str = 'vanilla', + gene_name_col: Optional[str] = None, + batch_size: int = 32, + show_progress: bool = True, + num_workers: int = 4, + random_seed: Optional[int] = 42, + **dataloader_kwargs + ) -> Union[np.ndarray, Tuple[np.ndarray, np.ndarray]]: + """ + Performs in-context generation by mixing a base dataset with a test dataset. + Difference to in-context prediction: generation comprises multiple steps and follows the schedule alpha = 1-1/t. + """ + assert 0 < (prompt_ratio+context_ratio) < 1, "Ratio must be between 0 and 1." + assert 0 <= context_ratio_min <= context_ratio < 1, "Min context ratio must be smaller than 1 and context ratio" + from scipy.sparse import csr_matrix + if num_steps is None: + num_steps = math.ceil(1.0/mask_rate) + + t = (np.arange(num_steps, dtype=np.float32)+1) / num_steps + if num_steps == 1: + cr_list = np.array([context_ratio], dtype=np.float32) + else: + cr_list = np.linspace(context_ratio_min, context_ratio, num_steps, dtype=np.float32) + mr_list = (1 - t) + mr_list[-1] = 0.0 + if isinstance(test_adata_or_path, str): + test_adata = ad.read_h5ad(test_adata_or_path) + else: + test_adata = test_adata_or_path.copy() + print('Masking ratio schedule:', mr_list) + print('Context ratio schedule:', cr_list) + i = 1 + result = None + if mode == 'vanilla': + for mr in mr_list: + result = self.get_incontext_prediction( + base_adata_or_path = base_adata_or_path, + test_adata_or_path = test_adata, + genelist_path = genelist_path, + mask_rate = mr, + prompt_ratio = prompt_ratio, + context_ratio = context_ratio, + mode = 'predict', + gene_name_col = gene_name_col, + batch_size = batch_size, + show_progress = show_progress, + num_workers = num_workers, + random_seed = random_seed + i, + **dataloader_kwargs) + i = i + 1 + if test_adata.raw is not None: + test_adata.raw.X = result + else: + test_adata.X = result + return result + else: + g = torch.Generator() + if random_seed is not None: + g.manual_seed(int(random_seed)) + test_logit = None + is_masked = np.ones(test_adata.shape[0],dtype=bool) + for mr, cr in zip(mr_list, cr_list): + result, test_logit, is_masked = self.get_incontext_prediction( + base_adata_or_path = base_adata_or_path, + test_adata_or_path = test_adata, + genelist_path = genelist_path, + mask_rate = mr, + prompt_ratio = prompt_ratio, + context_ratio = cr, + mode = 'generate', + test_logit = test_logit, + is_masked = is_masked, + gene_name_col = gene_name_col, + batch_size = batch_size, + show_progress = show_progress, + num_workers = num_workers, + random_seed = random_seed + i, + **dataloader_kwargs) + print("Generation Step", i) + print((test_logit<0).sum()/test_logit.shape[0]) + i = i + 1 + if test_adata.raw is not None: + test_adata.raw.X = result + else: + test_adata.X = result + + return result, test_logit + + +__all__ = ["InferenceMixin"] diff --git a/transfer/code/stack/src/stack/models/core/losses.py b/transfer/code/stack/src/stack/models/core/losses.py new file mode 100644 index 0000000000000000000000000000000000000000..9156a36f34f6932b243ae8e13f17a339861a51e9 --- /dev/null +++ b/transfer/code/stack/src/stack/models/core/losses.py @@ -0,0 +1,107 @@ +"""Loss and evaluation utilities for the StateICL model.""" + +from __future__ import annotations + +from typing import Dict, Optional, Tuple + +import torch +from scvi.distributions import NegativeBinomial + + +class LossComputationMixin: + """Provides loss and metric helpers for ``StateICLModel``.""" + + def _compute_reconstruction_loss( + self, + nb_mean: torch.Tensor, + nb_dispersion: torch.Tensor, + targets: torch.Tensor, + mask: torch.Tensor, + ) -> Tuple[torch.Tensor, torch.Tensor]: + nb_dist = NegativeBinomial(mu=nb_mean, theta=nb_dispersion) + recon_loss_all = -nb_dist.log_prob(targets) + mask_f = mask.float() + masked_count = mask_f.sum() + + if masked_count > 0: + recon_loss = (recon_loss_all * mask_f).sum() / masked_count + else: + recon_loss = torch.tensor(0.0, device=targets.device, dtype=recon_loss_all.dtype) + + return recon_loss, recon_loss_all + + def _compute_sw_loss( + self, + final_cell_embeddings: torch.Tensor, + *, + subsample_size: Optional[int] = None, + min_size: int = 32, + max_size: int = 128, + n_proj: Optional[int] = None, + ) -> torch.Tensor: + n_cells = final_cell_embeddings.shape[1] + device = final_cell_embeddings.device + + if subsample_size is None: + upper = min(max_size, n_cells) + lower = min(min_size, max(upper, 1)) + if lower == 0: + return torch.tensor(0.0, device=device, dtype=final_cell_embeddings.dtype) + if lower == upper: + k = lower + else: + k = torch.randint(lower, upper + 1, (), device=device).item() + else: + k = max(1, min(subsample_size, n_cells)) + + idx = torch.randperm(n_cells, device=device)[:k] + final_cell_embeddings_subsampled = final_cell_embeddings[:, idx] + prior_samples = torch.randn_like(final_cell_embeddings_subsampled) + centered_embeddings = final_cell_embeddings_subsampled - final_cell_embeddings_subsampled.mean( + dim=1, keepdim=True + ) + + if n_proj is None: + return self.sw_distance(centered_embeddings, prior_samples) + return self.sw_distance(centered_embeddings, prior_samples, n_proj=n_proj) + + def _compute_eval_metrics( + self, + nb_mean: torch.Tensor, + original_features: torch.Tensor, + mask: torch.Tensor, + ) -> Dict[str, torch.Tensor]: + eps = 1e-8 + mask_f = mask.float() + + diff = (nb_mean - original_features).abs() + masked_mae = (diff * mask_f).sum() / mask_f.sum() + + masked_count = mask_f.sum(dim=-1, keepdim=True).clamp_min(1.0) + + pred_masked = nb_mean * mask_f + true_masked = original_features * mask_f + + pred_mean = pred_masked.sum(dim=-1, keepdim=True) / masked_count + true_mean = true_masked.sum(dim=-1, keepdim=True) / masked_count + + pred_centered = (pred_masked - pred_mean) * mask_f + true_centered = (true_masked - true_mean) * mask_f + + cov = (pred_centered * true_centered).sum(dim=-1) + pred_var = (pred_centered**2).sum(dim=-1) + eps + true_var = (true_centered**2).sum(dim=-1) + eps + + corr_cell = cov / (pred_var.sqrt() * true_var.sqrt()) + masked_corr = corr_cell[mask_f.sum(dim=-1) > 1].mean() + + mask_rate = mask_f.mean() + + return { + "masked_mae": masked_mae, + "masked_corr": masked_corr, + "mask_rate": mask_rate, + } + + +__all__ = ["LossComputationMixin"] diff --git a/transfer/code/stack/src/stack/models/finetune/__init__.py b/transfer/code/stack/src/stack/models/finetune/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6251eb0a23f3d53461be607e8b511a8917ed65b7 --- /dev/null +++ b/transfer/code/stack/src/stack/models/finetune/__init__.py @@ -0,0 +1,7 @@ +"""Fine-tuning utilities for StateICL models.""" + +from .model import ICL_FinetunedModel + +__all__ = [ + "ICL_FinetunedModel", +] diff --git a/transfer/code/stack/src/stack/models/finetune/mixins.py b/transfer/code/stack/src/stack/models/finetune/mixins.py new file mode 100644 index 0000000000000000000000000000000000000000..a0aca1aff8e4fc932949cd6d7f6721a2345b6484 --- /dev/null +++ b/transfer/code/stack/src/stack/models/finetune/mixins.py @@ -0,0 +1,219 @@ +"""Reusable mixins for the fine-tuning model.""" + +from __future__ import annotations + +from typing import Optional, Tuple + +import torch +import torch.nn as nn + +from geomloss import SamplesLoss + +from ..utils import ReparamNBLogSampler +from ...modules import SlicedWassersteinDistance + + +class FinetuneInitializationMixin: + """Provide fine-tuning specific modules and parameters.""" + + def __init__(self, *args, **kwargs) -> None: + super().__init__(*args, **kwargs) + + self.mmd_loss = SamplesLoss(loss="energy") + + self.query_pos_embedding = nn.Parameter( + torch.empty(self.n_hidden, self.token_dim) + ) + nn.init.normal_(self.query_pos_embedding, mean=0.0, std=0.02) + + def scale_pos_grad_hook(grad: torch.Tensor) -> torch.Tensor: + return grad * 10.0 + + self.query_pos_embedding.register_hook(scale_pos_grad_hook) + + self.nblog_sampler = ReparamNBLogSampler() + self.cls = nn.Sequential( + nn.Linear(self.n_hidden * self.token_dim * 2, self.n_hidden), + nn.GELU(), + nn.Linear(self.n_hidden, 1), + ) + for parameter in self.cls.parameters(): + parameter.register_hook(scale_pos_grad_hook) + + self.sw_distance = SlicedWassersteinDistance(n_proj=self.n_proj) + + +class FinetuneLossMixin: + """Mixin containing masking utilities and fine-tuning losses.""" + + def apply_finetune_mask(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: + """Apply rectangular masking to an input tensor.""" + + batch_size, n_cells, n_genes = x.shape + device = x.device + + mask_rate = torch.empty(1, device=device).uniform_(0.1, 0.3).item() + n_genes_to_mask = int(n_genes * mask_rate) + mask_indices = torch.randperm(n_genes, device=device)[:n_genes_to_mask] + + mask = torch.zeros(batch_size, n_cells, n_genes, dtype=torch.bool, device=device) + mask[:, :, mask_indices] = True + + masked_x = x.clone() + masked_x[mask] = 0.0 + return masked_x, mask + + def _compute_cls_loss( + self, + mean_kept_cell: torch.Tensor, + mid_feat: torch.Tensor, + tail_feat: torch.Tensor, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Compute classification loss/accuracy for mid & tail cells.""" + + ctx_mid = mean_kept_cell.unsqueeze(1).expand(-1, mid_feat.size(1), -1) + ctx_tail = mean_kept_cell.unsqueeze(1).expand(-1, tail_feat.size(1), -1) + + inp_mid = torch.cat([ctx_mid, mid_feat], dim=-1) + inp_tail = torch.cat([ctx_tail, tail_feat], dim=-1) + + logit_mid = self.cls(inp_mid).squeeze(-1) + logit_tail = self.cls(inp_tail).squeeze(-1) + + logits = torch.cat([logit_mid, logit_tail], dim=1) + labels = torch.cat( + [torch.zeros_like(logit_mid), torch.ones_like(logit_tail)], + dim=1, + ) + + logits_flat = logits.reshape(-1) + labels_flat = labels.reshape(-1) + + with torch.no_grad(): + pos = (labels_flat == 1).float().sum().clamp(min=1.0) + neg = (labels_flat == 0).float().sum().clamp(min=1.0) + pos_weight = neg / pos + + bce = nn.BCEWithLogitsLoss(pos_weight=pos_weight) + cls_loss = bce(logits_flat, labels_flat) + cls_acc = ((logits_flat > 0).float() == labels_flat).float().mean() + return cls_loss, cls_acc + + def _compute_mmd_loss( + self, + nb_mean: torch.Tensor, + nb_dispersion: torch.Tensor, + ground_truth_features: torch.Tensor, + lib_size: torch.Tensor, + final_cell_embeddings: torch.Tensor, + t_cell_embeddings: Optional[torch.Tensor], + position_mask: Optional[torch.Tensor], + cell_type_ids: Optional[torch.Tensor], + n_context_cell: int, + n_cells: int, + n_genes: int, + time: float, + device: torch.device, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]]: + """Compute MMD loss on replaced cells.""" + + if n_context_cell >= n_cells: + zero = torch.tensor(0.0, device=device) + return zero, None, None + + mask_indices = torch.ones(n_genes, dtype=torch.bool, device=device) + rep_lib_size = lib_size[:, n_context_cell:, :] + + if mask_indices.sum().item() > 1000: + theta = 100 + expected_counts = ( + ground_truth_features[:, n_context_cell:, :].sum(dim=1, keepdim=True) + * ground_truth_features[:, n_context_cell:, :].sum(dim=2, keepdim=True) + / rep_lib_size.sum(dim=1, keepdim=True) + ) + variance = (expected_counts + (expected_counts**2) / theta).clamp_min(1e-4) + residuals = ( + ground_truth_features[:, n_context_cell:, :] + - expected_counts + ) / torch.sqrt(variance) + residuals_var = residuals.var(dim=1).mean(dim=0) + + mask_indices &= torch.zeros_like(mask_indices).index_fill_( + 0, + torch.topk(residuals_var, min(1000, residuals_var.numel())).indices, + True, + ) + + pred_mean = nb_mean[:, n_context_cell:, :] + pred_dispersion = ( + nb_dispersion[:, :n_context_cell, :].median(dim=1, keepdim=True).values.detach() + ) + + pred_dist_all = self.nblog_sampler( + mu=pred_mean[:, :, mask_indices], + theta=pred_dispersion[:, :, mask_indices], + N=rep_lib_size / 1e4, + ) + true_dist_all = torch.log1p( + 1e4 * ground_truth_features[:, n_context_cell:, mask_indices] / rep_lib_size + ) + + pred_embed_all = final_cell_embeddings[:, n_context_cell:, :] + true_embed_all = None if t_cell_embeddings is None else t_cell_embeddings[:, n_context_cell:, :] + + if position_mask is not None: + sample_losses = [] + sample_embed_losses = [] + if cell_type_ids is None: + cell_type_ids = torch.ones_like(position_mask, dtype=torch.long) + for i in range(nb_mean.shape[0]): + type_ids = cell_type_ids[i] + pos_mask = position_mask[i] + type_ids = type_ids[n_context_cell:] + pos_mask = pos_mask[n_context_cell:] + + pred_i = pred_dist_all[i] + true_i = true_dist_all[i] + + pred_embed_i = pred_embed_all[i] + true_embed_i = ( + pred_embed_all[i] if true_embed_all is None else true_embed_all[i] + ) + + type_losses = [] + type_embed_losses = [] + for t in torch.unique(type_ids): + tm = (type_ids == t) & pos_mask + if tm.sum() > 1: + m_pred = pred_i[tm].unsqueeze(0) + m_true = true_i[tm].unsqueeze(0) + type_losses.append(self.mmd_loss(m_pred, m_true)) + + m_embed_pred = pred_embed_i[tm].unsqueeze(0) + m_embed_true = true_embed_i[tm].unsqueeze(0) + type_embed_losses.append( + self.mmd_loss(m_embed_pred, m_embed_true) + ) + + if len(type_losses) > 0: + sample_losses.append(torch.stack(type_losses).mean()) + sample_embed_losses.append(torch.stack(type_embed_losses).mean()) + + mmd_loss = ( + torch.stack(sample_losses).mean() + if len(sample_losses) > 0 + else torch.tensor(0.0, device=device) + ) + mmd_embed_loss = ( + torch.stack(sample_embed_losses).mean() + if len(sample_embed_losses) > 0 + else torch.tensor(0.0, device=device) + ) + else: + mmd_loss = self.mmd_loss(pred_dist_all, true_dist_all).mean() + if true_embed_all is None: + true_embed_all = pred_embed_all + mmd_embed_loss = self.mmd_loss(pred_embed_all, true_embed_all).mean() + + mmd_loss = (0.5 * mmd_loss + 0.5 * mmd_embed_loss) / (1 - time) + return mmd_loss, pred_dist_all, true_dist_all diff --git a/transfer/code/stack/src/stack/models/finetune/model.py b/transfer/code/stack/src/stack/models/finetune/model.py new file mode 100644 index 0000000000000000000000000000000000000000..64399cef14c1cccb80c9713a045b14e0c9194e15 --- /dev/null +++ b/transfer/code/stack/src/stack/models/finetune/model.py @@ -0,0 +1,157 @@ +"""Fine-tuning model built on top of the core StateICL architecture.""" + +from __future__ import annotations + +from typing import Dict, Optional + +import torch + +from ..core import StateICLModel +from .mixins import FinetuneInitializationMixin, FinetuneLossMixin + + +class ICL_FinetunedModel( + FinetuneInitializationMixin, + FinetuneLossMixin, + StateICLModel, +): + """Fine-tuning head on top of :class:`StateICLModel`.""" + + def forward( + self, + observed_features: torch.Tensor, + ground_truth_features: torch.Tensor, + cell_type_ids: Optional[torch.Tensor] = None, + position_mask: Optional[torch.Tensor] = None, + t_cell_embeddings: Optional[torch.Tensor] = None, + n_kept_cell: int = 0, + mask_genes: bool = True, + return_loss: bool = True, + ) -> Dict[str, torch.Tensor]: + result: Dict[str, torch.Tensor] = {} + + batch_size, n_cells, n_genes = observed_features.shape + + features_log = torch.log1p(observed_features) + gtfeatures_log = torch.log1p(ground_truth_features) + time = torch.rand(1, device=observed_features.device).item() * 0.9375 + n_context_cell = n_kept_cell + int((n_cells - n_kept_cell) * time) + features_log[:, n_kept_cell:n_context_cell] = gtfeatures_log[:, n_kept_cell:n_context_cell] + mask = torch.zeros( + batch_size, + n_cells, + n_genes, + dtype=torch.bool, + device=observed_features.device, + ) + if mask_genes: + masked_features, mask = self.apply_finetune_mask(features_log) + tokens = self._reduce_and_tokenize(masked_features) + else: + tokens = self._reduce_and_tokenize(features_log) + + x = tokens + + mask_expanded = torch.zeros_like(x) + if n_kept_cell < n_cells: + mask_expanded[:, n_context_cell:, :, :] = 1.0 + query_emb = self.query_pos_embedding.unsqueeze(0).unsqueeze(0) + x = x + query_emb * mask_expanded + + attn_mask = torch.zeros( + n_cells, + n_cells, + dtype=torch.bool, + device=observed_features.device, + ) + if n_kept_cell < n_cells: + attn_mask[:n_kept_cell, n_kept_cell:] = True + attn_mask = attn_mask.unsqueeze(0).unsqueeze(0) + + x = self._run_attention_layers(x, gene_attn_mask=attn_mask) + + final_cell_embeddings = x.reshape(batch_size, n_cells, -1) + lib_size = ground_truth_features.sum(dim=-1, keepdim=True).clamp(min=1.0) + nb_mean, nb_dispersion, px_scale = self._compute_nb_parameters( + final_cell_embeddings, + lib_size, + ) + + result.update( + { + "px_scale": px_scale, + "nb_mean": nb_mean, + "nb_dispersion": nb_dispersion, + "final_cell_embeddings": final_cell_embeddings, + } + ) + + if not return_loss: + return result + + mean_kept_cell = final_cell_embeddings[:, :n_kept_cell].mean(dim=1).detach() + mid_feat = final_cell_embeddings[:, n_kept_cell:n_context_cell, :].detach() + tail_feat = final_cell_embeddings[:, n_context_cell:, :].detach() + cls_loss, cls_acc = self._compute_cls_loss(mean_kept_cell, mid_feat, tail_feat) + + if n_context_cell < n_cells and mask[:, :n_context_cell, :].float().sum() > 0: + recon_loss, _ = self._compute_reconstruction_loss( + nb_mean[:, :n_context_cell, :], + nb_dispersion[:, :n_context_cell, :], + ground_truth_features[:, :n_context_cell, :], + mask[:, :n_context_cell, :], + ) + else: + recon_loss = torch.tensor(0.0, device=observed_features.device) + + mmd_loss, pred_dist_all, true_dist_all = self._compute_mmd_loss( + nb_mean, + nb_dispersion, + ground_truth_features, + lib_size, + final_cell_embeddings, + t_cell_embeddings, + position_mask, + cell_type_ids, + n_context_cell, + n_cells, + n_genes, + time, + observed_features.device, + ) + n_proj = getattr(self, "n_proj", None) + subsample_size = max(32, min(128, n_cells)) + sw_loss = self._compute_sw_loss( + final_cell_embeddings, + subsample_size=subsample_size, + min_size=subsample_size, + max_size=subsample_size, + n_proj=n_proj, + ) + + total_loss = recon_loss + mmd_loss + self.sw_weight * sw_loss + cls_loss + result.update( + { + "loss": total_loss, + "recon_loss": recon_loss, + "mmd_loss": mmd_loss, + "sw_loss": sw_loss, + "cls_loss": cls_loss, + "cls_acc": cls_acc, + } + ) + + if not self.training: + metrics = self._compute_eval_metrics( + nb_mean[:, :n_context_cell, :], + ground_truth_features[:, :n_context_cell, :], + mask[:, :n_context_cell, :], + ) + + if pred_dist_all is not None and true_dist_all is not None: + sw_predict = self.sw_distance(pred_dist_all, true_dist_all, n_proj=256) + else: + sw_predict = torch.tensor(0.0, device=observed_features.device) + + result.update({**metrics, "sw_predict": sw_predict}) + return result diff --git a/transfer/code/stack/src/stack/models/utils.py b/transfer/code/stack/src/stack/models/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..6cf5c1079782a03cdf1c5be502617e546029d705 --- /dev/null +++ b/transfer/code/stack/src/stack/models/utils.py @@ -0,0 +1,175 @@ +"""Model-specific utility helpers for StateICL.""" +from __future__ import annotations + +from typing import Optional, Sequence, Union + +import pickle + +import numpy as np +import torch +import torch.nn as nn +from scipy.sparse import issparse + +from ..modules import SlicedWassersteinDistance + +__all__ = [ + "align_result_to_adata_numpy", + "SlicedWassersteinDistance", + "batch_sliced_wasserstein_1d", + "safe_logit", + "ReparamNBLogSampler", + "ReparamNBLog_Sampler", +] + +_SLICED_WASSERSTEIN = SlicedWassersteinDistance() + + +def align_result_to_adata_numpy( + result: np.ndarray, # shape (n_cells, n_model_genes) + test_adata, # AnnData + genelist_path: str, + gene_name_col: Optional[str] = None, + prefer_raw: bool = True, + fill_missing: float = 0.0, # value for test-only genes + cell_indices_to_keep: Optional[Union[Sequence[int], np.ndarray]] = None, # keep by cells (rows) +) -> np.ndarray: + """Align model outputs to match the ordering in ``test_adata``.""" + + # --- sanity check on cell dimension + if result.shape[0] != test_adata.n_obs: + raise ValueError( + "`result` n_cells (%d) must equal test_adata.n_obs (%d)." % + (result.shape[0], test_adata.n_obs) + ) + + # 1) test gene names (prefer raw.var) + var_df = test_adata.raw.var if (prefer_raw and (test_adata.raw is not None)) else test_adata.var + if gene_name_col is not None and gene_name_col in var_df.columns: + test_genes = var_df[gene_name_col].astype(str).str.upper().to_numpy() + else: + test_genes = var_df.index.astype(str).str.upper().to_numpy() + + # also get the source matrix to "preserve" from + test_mat = test_adata.raw.X if (prefer_raw and (test_adata.raw is not None)) else test_adata.X + + # 2) model gene order from pickle + with open(genelist_path, "rb") as f: + model_genes_raw = pickle.load(f) + model_genes = np.asarray([str(g).upper() for g in model_genes_raw]) + + # 3) map model columns -> test columns (−1 if missing in test) + pos_in_test = {g: i for i, g in enumerate(test_genes)} + dest = np.fromiter((pos_in_test.get(g, -1) for g in model_genes), + dtype=np.int64, count=model_genes.size) + present_mask = dest != -1 # model genes that exist in test + src_idx = np.nonzero(present_mask)[0] # columns in result to copy + dst_idx = dest[present_mask] # corresponding columns in aligned + + # 4) build keep mask on cell axis + n_cells = result.shape[0] + if cell_indices_to_keep is None: + keep_cell_mask = np.zeros(n_cells, dtype=bool) + keep_rows = np.empty(0, dtype=int) + else: + if isinstance(cell_indices_to_keep, np.ndarray) and cell_indices_to_keep.dtype == bool: + if cell_indices_to_keep.size != n_cells: + raise ValueError("Boolean cell_indices_to_keep must have length equal to n_cells.") + keep_cell_mask = cell_indices_to_keep.astype(bool, copy=False) + keep_rows = np.nonzero(keep_cell_mask)[0] + else: + keep_rows = np.asarray(cell_indices_to_keep, dtype=int) + if keep_rows.size and (keep_rows.min() < 0 or keep_rows.max() >= n_cells): + raise ValueError("cell_indices_to_keep has out-of-range indices.") + keep_cell_mask = np.zeros(n_cells, dtype=bool) + keep_cell_mask[keep_rows] = True + + not_keep_rows = np.nonzero(~keep_cell_mask)[0] + + # 5) allocate aligned output with fill_missing everywhere + aligned = np.full((n_cells, test_genes.size), fill_missing, dtype=result.dtype) + + # 6) fill overlapping genes for rows NOT kept -> use model result + if src_idx.size > 0 and not_keep_rows.size > 0: + aligned[np.ix_(not_keep_rows, dst_idx)] = result[np.ix_(not_keep_rows, src_idx)] + + # 7) fill overlapping genes for rows kept -> preserve from test_adata + if src_idx.size > 0 and keep_rows.size > 0: + if issparse(test_mat): + test_block = test_mat[keep_rows[:, None], dst_idx].toarray() + else: + # make sure we have a numpy array view + test_block = np.asarray(test_mat)[np.ix_(keep_rows, dst_idx)] + aligned[np.ix_(keep_rows, dst_idx)] = test_block.astype(aligned.dtype, copy=False) + + return aligned + + +def batch_sliced_wasserstein_1d(x: torch.Tensor, y: torch.Tensor, n_proj: int = 64) -> torch.Tensor: + """Compatibility wrapper around :class:`SlicedWassersteinDistance`.""" + return _SLICED_WASSERSTEIN(x, y, n_proj=n_proj) + + +def safe_logit(q: torch.Tensor, eps: float = 1e-8) -> torch.Tensor: + """Numerically stable logit transform.""" + q = q.clamp(eps, 1 - eps) + return torch.log(q) - torch.log1p(-q) + + +class ReparamNBLogSampler(nn.Module): + """Reparameterizable sampler for ``log(1 + X / N)`` with ``X ~ NB(mu, theta)``.""" + + def forward( + self, + mu: torch.Tensor, + theta: torch.Tensor, + N: torch.Tensor, + tau: float = 0.7, + eps: float = 1e-6, + logistic_noise: Optional[torch.Tensor] = None, + ) -> torch.Tensor: + if N.dim() == 2: + N = N.unsqueeze(-1) # (B, C, 1) + B, C, G = mu.shape + device, dtype = mu.device, mu.dtype + + # NB p0 + v = mu + mu**2 / theta.clamp_min(eps) + log_p0 = theta.clamp_min(eps).log() - (theta + mu.clamp_min(eps)).log() + p0 = torch.exp(theta.clamp_min(eps) * log_p0) # (B, C, G) + q = (1.0 - p0).clamp(eps, 1 - eps) + + # Moments for U = X + N + EU = N + mu + EX2 = v + mu**2 + EU2 = N**2 + 2 * N * mu + EX2 + + # Conditional on X>0 + EU_pos = N + mu / q + EU2_pos = N**2 + (2 * N * mu + mu**2 + v) / q + + ratio = (EU2_pos / (EU_pos.clamp_min(eps) ** 2)).clamp_min(1.0 + eps) + sigma2_plus = torch.log(ratio) + sigma_plus = torch.sqrt(sigma2_plus) + m_plus = torch.log(EU_pos.clamp_min(eps)) - 0.5 * sigma2_plus + + # Reparam sample on log1p(X/N) + logN = N.clamp_min(eps).log() + eps_norm = torch.randn(B, C, G, device=device, dtype=dtype) + Z_pos = (m_plus - logN) + sigma_plus * eps_norm + + z0 = torch.zeros(B, C, 1, device=device, dtype=dtype) # broadcast + + # Concrete gate + if logistic_noise is None: + u = torch.rand(B, C, G, device=device, dtype=dtype).clamp(eps, 1 - eps) + logistic_noise = torch.log(u) - torch.log1p(-u) + logit_pre = safe_logit(q, eps) + logistic_noise + S = torch.sigmoid(logit_pre / tau) + + Z = S * Z_pos + (1.0 - S) * z0 + return Z + + +# Backwards compatibility with the previous camel_case name +class ReparamNBLog_Sampler(ReparamNBLogSampler): + pass diff --git a/transfer/code/stack/src/stack/modules/__init__.py b/transfer/code/stack/src/stack/modules/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a902e6ac0266fd1ebceb98870fda860270dc9196 --- /dev/null +++ b/transfer/code/stack/src/stack/modules/__init__.py @@ -0,0 +1,9 @@ +"""Reusable neural network building blocks for StateICL.""" +from .attention import MultiHeadAttention, TabularAttentionLayer +from .regularizers import SlicedWassersteinDistance + +__all__ = [ + "MultiHeadAttention", + "TabularAttentionLayer", + "SlicedWassersteinDistance", +] diff --git a/transfer/code/stack/src/stack/modules/attention.py b/transfer/code/stack/src/stack/modules/attention.py new file mode 100644 index 0000000000000000000000000000000000000000..91e1fbacd239f85d64e7331622ac04ffa165a40b --- /dev/null +++ b/transfer/code/stack/src/stack/modules/attention.py @@ -0,0 +1,131 @@ +"""Attention building blocks for the StateICL models.""" +from __future__ import annotations + +from typing import Optional, Tuple + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class MultiHeadAttention(nn.Module): + """Multi-head attention implementation used across the project.""" + + def __init__(self, d_model: int, n_heads: int, dropout: float = 0.1) -> None: + super().__init__() + if d_model % n_heads != 0: + raise ValueError("d_model must be divisible by n_heads") + + self.d_model = d_model + self.n_heads = n_heads + self.head_dim = d_model // n_heads + self.scale = self.head_dim**-0.5 + + self.qkv = nn.Linear(d_model, d_model * 3, bias=False) + self.proj = nn.Linear(d_model, d_model) + self.dropout = nn.Dropout(dropout) + + def forward( + self, + x: torch.Tensor, + attn_mask: Optional[torch.Tensor] = None, + return_attn: bool = False, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: + """Apply multi-head self-attention.""" + + batch_size, seq_len, _ = x.shape + + qkv = self.qkv(x).reshape(batch_size, seq_len, 3, self.n_heads, self.head_dim) + q, k, v = qkv.permute(2, 0, 3, 1, 4) + + attn_scores = (q @ k.transpose(-2, -1)) * self.scale + if attn_mask is not None and attn_mask.dtype == torch.bool: + mask = attn_mask + while mask.ndim < 4: + mask = mask.unsqueeze(0) + if mask.shape[0] == 1: + mask = mask.expand(batch_size, -1, -1, -1) + if mask.shape[1] == 1: + mask = mask.expand(-1, self.n_heads, -1, -1) + attn_scores = attn_scores.masked_fill(mask, float("-inf")) + + attn = self.dropout(F.softmax(attn_scores, dim=-1)) + out = (attn @ v).transpose(1, 2).reshape(batch_size, seq_len, self.d_model) + out = self.proj(out) + + if return_attn: + return out, attn + return out, None + + +class TabularAttentionLayer(nn.Module): + """Single layer of tabular attention for gene expression modelling.""" + + def __init__( + self, + token_dim: int, + n_cells: int, + n_hidden: int, + n_heads: int = 8, + mlp_ratio: int = 4, + dropout: float = 0.1, + ) -> None: + super().__init__() + + cell_attn_dim = token_dim + cell_attn_heads = 8 + # The model dimension must be divisible by the number of attention heads. + # If not, find the largest valid number of heads smaller than or equal to n_heads. + if cell_attn_dim % cell_attn_heads != 0: + for h in range(min(n_heads, cell_attn_dim), 0, -1): + if cell_attn_dim % h == 0: + cell_attn_heads = h + break + else: # This fallback should ideally not be reached if cell_attn_dim >= 1 + cell_attn_heads = 1 + self.cell_attn = MultiHeadAttention(cell_attn_dim, cell_attn_heads, dropout) + self.cell_norm = nn.LayerNorm(cell_attn_dim) + + gene_attn_dim = n_hidden * token_dim + self.gene_attn = MultiHeadAttention(gene_attn_dim, n_heads, dropout) + self.gene_norm = nn.LayerNorm(gene_attn_dim) + + hidden_dim = token_dim * mlp_ratio + self.mlp = nn.Sequential( + nn.Linear(token_dim, hidden_dim), + nn.GELU(), + nn.Dropout(dropout), + nn.Linear(hidden_dim, token_dim), + nn.Dropout(dropout), + ) + self.mlp_norm = nn.LayerNorm(token_dim) + + def forward( + self, + x: torch.Tensor, + gene_pos_emb: torch.Tensor, + gene_attn_mask: Optional[torch.Tensor] = None, + return_attn: bool = False, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: + batch_size, n_cells, n_genes, token_dim = x.shape + + x_cell = x.reshape(batch_size * n_cells, n_genes, token_dim) + x_cell_with_pos = x_cell + gene_pos_emb.unsqueeze(0) + cell_attn_out, _ = self.cell_attn(x_cell_with_pos) + x_cell = self.cell_norm(x_cell + cell_attn_out) + + x = x_cell.reshape(batch_size, n_cells, n_genes, token_dim) + x_gene = x.reshape(batch_size, n_cells, n_genes * token_dim) + + if return_attn: + gene_attn_out, attn = self.gene_attn(x_gene, attn_mask=gene_attn_mask, return_attn=True) + else: + gene_attn_out, attn = self.gene_attn(x_gene, attn_mask=gene_attn_mask) + x_gene = self.gene_norm(x_gene + gene_attn_out) + + x = x_gene.reshape(batch_size, n_cells, n_genes, token_dim) + mlp_input = x.reshape(-1, token_dim) + mlp_out = self.mlp(mlp_input) + x = self.mlp_norm(mlp_input + mlp_out).reshape(batch_size, n_cells, n_genes, token_dim) + + return x, attn diff --git a/transfer/code/stack/src/stack/modules/regularizers.py b/transfer/code/stack/src/stack/modules/regularizers.py new file mode 100644 index 0000000000000000000000000000000000000000..4062274067250bdf88c03c32439cef223d36da32 --- /dev/null +++ b/transfer/code/stack/src/stack/modules/regularizers.py @@ -0,0 +1,30 @@ +"""Regularization components for StateICL models.""" +from __future__ import annotations + +from dataclasses import dataclass +from typing import Optional + +import torch + + +@dataclass +class SlicedWassersteinDistance: + """Callable helper that computes the sliced Wasserstein distance.""" + + n_proj: int = 64 + + def __call__(self, x: torch.Tensor, y: torch.Tensor, n_proj: Optional[int] = None) -> torch.Tensor: + if x.shape != y.shape: + raise ValueError("Input tensors must have identical shapes") + + num_proj = n_proj or self.n_proj + batch_size, _, latent_dim = x.shape + projections = torch.randn(num_proj, latent_dim, device=x.device) + projections = projections / projections.norm(dim=1, keepdim=True) + + x_proj = x @ projections.t() + y_proj = y @ projections.t() + + x_sorted, _ = torch.sort(x_proj, dim=1) + y_sorted, _ = torch.sort(y_proj, dim=1) + return ((x_sorted - y_sorted) ** 2).mean() diff --git a/transfer/code/stack/src/stack/training/__init__.py b/transfer/code/stack/src/stack/training/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ed76444b9f2e1b07b2e02a1cb05ab6ba427504b3 --- /dev/null +++ b/transfer/code/stack/src/stack/training/__init__.py @@ -0,0 +1,21 @@ +"""Training utilities for StateICL.""" +from .datamodule import MultiDatasetDataModule +from .lightning import LegacyLightningGeneModel, LightningGeneModel +from .utils import ( + LocalizationContext, + build_scheduler_config, + configure_scheduler, + localize_datasets, + parse_dataset_configs, +) + +__all__ = [ + "MultiDatasetDataModule", + "LightningGeneModel", + "LegacyLightningGeneModel", + "LocalizationContext", + "build_scheduler_config", + "configure_scheduler", + "localize_datasets", + "parse_dataset_configs", +] diff --git a/transfer/code/stack/src/stack/training/datamodule.py b/transfer/code/stack/src/stack/training/datamodule.py new file mode 100644 index 0000000000000000000000000000000000000000..924744b5439f70faade12d9a617dbb9dc024d735 --- /dev/null +++ b/transfer/code/stack/src/stack/training/datamodule.py @@ -0,0 +1,133 @@ +"""Lightning data module backed by the StateICL dataset abstractions.""" +from __future__ import annotations + +import logging +from typing import Dict, List, Optional + +import pytorch_lightning as pl +from torch.utils.data import DataLoader + +from ..data.datasets import ( + DatasetConfig, + SimplifiedMultiDataset, + create_train_val_test_datasets, + load_gene_list, + worker_init_fn, +) + +log = logging.getLogger(__name__) + + +class MultiDatasetDataModule(pl.LightningDataModule): + """PyTorch Lightning wrapper that orchestrates multi-dataset sampling.""" + + def __init__( + self, + dataset_configs: List[DatasetConfig], + genelist_path: str, + sample_size: int = 128, + test_ratio: float = 0.2, + val_ratio: float = 0.2, + batch_size: int = 32, + num_workers: int = 4, + pin_memory: bool = True, + random_state: int = 42, + cache_file: Optional[str] = None, + ) -> None: + super().__init__() + self.dataset_configs = dataset_configs + self.genelist_path = genelist_path + self.sample_size = sample_size + self.test_ratio = test_ratio + self.val_ratio = val_ratio + self.batch_size = batch_size + self.num_workers = num_workers + self.pin_memory = pin_memory + self.random_state = random_state + self.cache_file = cache_file + + self.train_dataset: Optional[SimplifiedMultiDataset] = None + self.val_dataset: Optional[SimplifiedMultiDataset] = None + self.test_dataset: Optional[SimplifiedMultiDataset] = None + + self.target_genes = load_gene_list(genelist_path) + self.n_genes = len(self.target_genes) + log.info("Loaded %s genes from %s", self.n_genes, genelist_path) + + def setup(self, stage: Optional[str] = None) -> None: # type: ignore[override] + if stage in ("fit", None): + log.info("Creating train/val/test datasets from %s configs", len(self.dataset_configs)) + self.train_dataset, self.val_dataset, self.test_dataset = create_train_val_test_datasets( + dataset_configs=self.dataset_configs, + target_genes=self.target_genes, + sample_size=self.sample_size, + test_ratio=self.test_ratio, + val_ratio=self.val_ratio, + random_state=self.random_state, + cache_file=self.cache_file, + ) + log.info("Train dataset: %s samples", len(self.train_dataset)) + log.info("Val dataset: %s samples", len(self.val_dataset)) + log.info("Test dataset: %s samples", len(self.test_dataset)) + if hasattr(self.train_dataset, "get_cache_stats"): + log.info("Cache stats: %s", self.train_dataset.get_cache_stats()) + + # ------------------------------------------------------------------ + # Dataloaders + # ------------------------------------------------------------------ + def train_dataloader(self) -> DataLoader: + if self.train_dataset is None: + raise RuntimeError("train_dataset is not initialized; call setup('fit') first") + return DataLoader( + self.train_dataset, + batch_size=self.batch_size, + shuffle=True, + num_workers=self.num_workers, + pin_memory=self.pin_memory, + persistent_workers=self.num_workers > 0, + prefetch_factor=2 if self.num_workers > 0 else None, + drop_last=True, + worker_init_fn=worker_init_fn, + ) + + def val_dataloader(self) -> Optional[DataLoader]: + if self.val_dataset is None: + return None + return DataLoader( + self.val_dataset, + batch_size=self.batch_size, + shuffle=False, + num_workers=min(2, self.num_workers), + pin_memory=self.pin_memory, + persistent_workers=self.num_workers > 0, + prefetch_factor=2 if self.num_workers > 0 else None, + drop_last=False, + worker_init_fn=worker_init_fn, + ) + + def test_dataloader(self) -> Optional[DataLoader]: + if self.test_dataset is None: + return None + return DataLoader( + self.test_dataset, + batch_size=self.batch_size, + shuffle=False, + num_workers=min(2, self.num_workers), + pin_memory=self.pin_memory, + persistent_workers=self.num_workers > 0, + prefetch_factor=2 if self.num_workers > 0 else None, + drop_last=False, + worker_init_fn=worker_init_fn, + ) + + # ------------------------------------------------------------------ + # Introspection helpers + # ------------------------------------------------------------------ + def get_split_info(self) -> Dict[str, List[str]]: + if self.train_dataset is None: + raise RuntimeError("Datasets have not been initialized. Call setup('fit') first.") + return { + "train_files": self.train_dataset.train_files, + "validation_files": self.train_dataset.val_files, + "test_files": self.train_dataset.test_files, + } diff --git a/transfer/code/stack/src/stack/training/lightning.py b/transfer/code/stack/src/stack/training/lightning.py new file mode 100644 index 0000000000000000000000000000000000000000..4d7b62c01c9041e8f61ead6e3aa9fb00ab031c3d --- /dev/null +++ b/transfer/code/stack/src/stack/training/lightning.py @@ -0,0 +1,83 @@ +"""Lightning module that wraps the :class:`StateICLModel`.""" +from __future__ import annotations + +from typing import Any, Dict, Optional, Type + +import pytorch_lightning as pl +import torch + +from ..model import StateICLModel, scShiftAttentionModel +from .utils import configure_scheduler + + +class LightningGeneModel(pl.LightningModule): + """Thin Lightning wrapper that delegates heavy lifting to :class:`StateICLModel`.""" + + def __init__( + self, + model_config: Dict[str, Any], + learning_rate: float = 1e-3, + weight_decay: float = 1e-4, + scheduler_config: Optional[Dict[str, Any]] = None, + model_class: Type[StateICLModel] = StateICLModel, + ) -> None: + super().__init__() + self.save_hyperparameters(ignore=["model_class"]) + self.model = model_class(**model_config) + self.learning_rate = learning_rate + self.weight_decay = weight_decay + self.scheduler_config = scheduler_config or {} + + def forward(self, features: torch.Tensor, return_loss: bool = True): # type: ignore[override] + return self.model(features, return_loss=return_loss) + + def training_step(self, batch, batch_idx): # type: ignore[override] + features, _ = batch + result = self.model(features, return_loss=True) + total_loss = result["loss"] + self.log("tloss", total_loss, on_step=True, on_epoch=True, prog_bar=True, sync_dist=True) + self.log("recon", result["recon_loss"], on_step=True, on_epoch=True, prog_bar=True, sync_dist=True) + self.log("sw", result["sw_loss"], on_step=True, on_epoch=True, prog_bar=True, sync_dist=True) + self.log("train/masked_mae", result["masked_mae"], on_step=True, on_epoch=True, sync_dist=True) + self.log("train/masked_corr", result["masked_corr"], on_step=True, on_epoch=True, sync_dist=True) + self.log("train/mask_rate", result["mask_rate"], on_step=True, on_epoch=True, sync_dist=True) + return total_loss + + def validation_step(self, batch, batch_idx): # type: ignore[override] + features, _ = batch + result = self.model(features, return_loss=True) + total_loss = result["loss"] + self.log("val_loss", total_loss, on_epoch=True, prog_bar=True, sync_dist=True) + self.log("val/recon_loss", result["recon_loss"], on_epoch=True, sync_dist=True) + self.log("val/sw_loss", result["sw_loss"], on_epoch=True, sync_dist=True) + self.log("val/masked_mae", result["masked_mae"], on_epoch=True, sync_dist=True) + self.log("val/masked_corr", result["masked_corr"], on_epoch=True, sync_dist=True) + self.log("val/mask_rate", result["mask_rate"], on_epoch=True, sync_dist=True) + return {"val_loss": total_loss} + + def test_step(self, batch, batch_idx): # type: ignore[override] + features, _ = batch + result = self.model(features, return_loss=True) + total_loss = result["loss"] + self.log("test_loss", total_loss, on_epoch=True, prog_bar=True, sync_dist=True) + self.log("test/recon_loss", result["recon_loss"], on_epoch=True, sync_dist=True) + self.log("test/sw_loss", result["sw_loss"], on_epoch=True, sync_dist=True) + self.log("test/masked_mae", result["masked_mae"], on_epoch=True, sync_dist=True) + self.log("test/masked_corr", result["masked_corr"], on_epoch=True, sync_dist=True) + self.log("test/mask_rate", result["mask_rate"], on_epoch=True, sync_dist=True) + return {"test_loss": total_loss} + + def configure_optimizers(self): # type: ignore[override] + optimizer = torch.optim.AdamW(self.parameters(), lr=self.learning_rate, weight_decay=self.weight_decay) + scheduler_dict = configure_scheduler(optimizer, self.scheduler_config) + if scheduler_dict: + return {"optimizer": optimizer, "lr_scheduler": scheduler_dict} + return {"optimizer": optimizer} + + +# Backwards compatibility ------------------------------------------------------------------------- +class LegacyLightningGeneModel(LightningGeneModel): + """Variant that instantiates :class:`scShiftAttentionModel` for backwards compatibility.""" + + def __init__(self, model_config: Dict[str, Any], **kwargs: Any) -> None: + super().__init__(model_config=model_config, model_class=scShiftAttentionModel, **kwargs) diff --git a/transfer/code/stack/src/stack/training/utils.py b/transfer/code/stack/src/stack/training/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..6baa11037446e20ac092bd3093a0b76981b1ea58 --- /dev/null +++ b/transfer/code/stack/src/stack/training/utils.py @@ -0,0 +1,232 @@ +"""Shared helpers for the StateICL training pipeline.""" +from __future__ import annotations + +import logging +import os +import shutil +from dataclasses import dataclass +from pathlib import Path +from typing import Dict, Iterable, List, Optional + +import torch + +from ..data.datasets import DatasetConfig + +log = logging.getLogger(__name__) + + +@dataclass +class LocalizationContext: + """Tracks temporary paths created during dataset localization.""" + + dataset_configs: List[str] + cache_file: Optional[str] + local_job_dir: Optional[Path] + job_id: Optional[str] + enabled: bool + + def stage_out(self, save_dir: str, job_id: str) -> None: + """Copy checkpoints back and remove temporary directories.""" + if not self.enabled or self.local_job_dir is None: + return + + log.info("=" * 80) + log.info("JOB FINISHED. Staging out results...") + + local_checkpoints = Path(save_dir) + remote_save_dir = Path("../permanent_storage/checkpoints/run_" + str(job_id)) + remote_save_dir.mkdir(parents=True, exist_ok=True) + + if local_checkpoints.exists(): + log.info("Copying checkpoints from %s to %s", local_checkpoints, remote_save_dir) + shutil.copytree(local_checkpoints, remote_save_dir, dirs_exist_ok=True) + + log.info("Cleaning up local job directory...") + shutil.rmtree(self.local_job_dir, ignore_errors=True) + log.info("Cleanup complete.") + + +def parse_dataset_configs(config_strings: Iterable[str]) -> List[DatasetConfig]: + """Parse CLI dataset configuration strings into :class:`DatasetConfig` objects.""" + configs: List[DatasetConfig] = [] + + for config_str in config_strings: + parts = config_str.split(":") + if not parts or not parts[0]: + raise ValueError(f"Invalid config string: {config_str}") + + path = parts[0] + filter_organism = True + gene_name_col: Optional[str] = None + + if len(parts) > 1 and parts[1]: + filter_organism = parts[1].lower() == "true" + if len(parts) > 2 and parts[2]: + gene_name_col = parts[2] + + config = DatasetConfig(path=path, filter_organism=filter_organism, gene_name_col=gene_name_col) + log.info( + "Added dataset config: path=%s, filter_organism=%s, gene_col=%s", + path, + filter_organism, + gene_name_col, + ) + configs.append(config) + + return configs + + +def localize_datasets( + dataset_configs: List[str], + cache_file: Optional[str], + local_temp_dir: str, + disable_localization: bool, +) -> LocalizationContext: + """Optionally copy datasets to a local scratch directory for faster I/O.""" + if disable_localization: + log.info("Data localization disabled. Using remote paths directly.") + return LocalizationContext(dataset_configs, cache_file, None, None, enabled=False) + + job_id = str(os.environ.get("SLURM_JOB_ID", os.getpid())) + local_job_dir = Path(local_temp_dir) / f"scshift_job_{job_id}" + + log.info("=" * 80) + log.info("DATA LOCALIZATION: Enabled. Using local directory: %s", local_job_dir) + log.info("=" * 80) + + localized_configs: List[str] = [] + + try: + local_job_dir.mkdir(parents=True, exist_ok=True) + + for config_str in dataset_configs: + parts = config_str.split(":") + remote_path = Path(parts[0]) + local_path = local_job_dir / "data" / remote_path.name + + if not local_path.exists(): + log.info("Copying data from %s to %s...", remote_path, local_path) + shutil.copytree(remote_path, local_path, dirs_exist_ok=True) + log.info("Data copy complete.") + else: + log.info("Data already exists at %s. Skipping copy.", local_path) + + parts[0] = str(local_path) + localized_configs.append(":".join(parts)) + + localized_cache = None + if cache_file: + remote_cache_file = Path(cache_file) + localized_cache = local_job_dir / "cache" / remote_cache_file.name + localized_cache.parent.mkdir(exist_ok=True) + log.info("Using localized metadata cache file: %s", localized_cache) + return LocalizationContext( + localized_configs, + str(localized_cache) if cache_file else cache_file, + local_job_dir, + job_id, + True, + ) + except Exception as exc: + log.error("Error during data localization: %s", exc) + shutil.rmtree(local_job_dir, ignore_errors=True) + raise + + +def build_scheduler_config(args: Dict[str, any]) -> Dict[str, any]: + """Extract scheduler configuration from parsed CLI arguments.""" + scheduler_type = args.get("scheduler") + if not scheduler_type: + return {} + + config: Dict[str, any] = {"type": scheduler_type} + if scheduler_type == "cosine": + config.update( + { + "T_max": args.get("scheduler_T_max", 100), + "warmup_epochs": args.get("scheduler_warmup_epochs", 0), + "eta_min": args.get("scheduler_eta_min", 1e-6), + } + ) + elif scheduler_type == "cosine_restarts": + config.update( + { + "T_0": args.get("scheduler_T_0", 10), + "T_mult": args.get("scheduler_T_mult", 2), + "eta_min": args.get("scheduler_eta_min", 1e-6), + } + ) + elif scheduler_type == "step": + config.update( + { + "step_size": args.get("scheduler_step_size", 30), + "gamma": args.get("scheduler_gamma", 0.1), + } + ) + elif scheduler_type == "reduce_on_plateau": + config.update( + { + "patience": args.get("scheduler_patience", 10), + "factor": args.get("scheduler_factor", 0.5), + "eta_min": args.get("scheduler_eta_min", 1e-6), + } + ) + return config + + +def configure_scheduler(optimizer: torch.optim.Optimizer, scheduler_config: Dict[str, any]) -> Optional[Dict[str, any]]: + """Create a PyTorch scheduler configuration dictionary for Lightning.""" + if not scheduler_config: + return None + + scheduler_type = scheduler_config.get("type", "cosine") + + if scheduler_type == "cosine": + warmup_epochs = scheduler_config.get("warmup_epochs", 0) + T_max = scheduler_config.get("T_max", 100) + eta_min = scheduler_config.get("eta_min", 1e-6) + + if warmup_epochs > 0: + from torch.optim.lr_scheduler import LinearLR, SequentialLR + + warmup_scheduler = LinearLR(optimizer, start_factor=1e-6, end_factor=1.0, total_iters=warmup_epochs) + cosine_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=T_max - warmup_epochs, eta_min=eta_min) + scheduler = SequentialLR(optimizer, schedulers=[warmup_scheduler, cosine_scheduler], milestones=[warmup_epochs]) + else: + scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=T_max, eta_min=eta_min) + + return {"scheduler": scheduler, "interval": "epoch", "frequency": 1} + + if scheduler_type == "cosine_restarts": + scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts( + optimizer, + T_0=scheduler_config.get("T_0", 10), + T_mult=scheduler_config.get("T_mult", 2), + eta_min=scheduler_config.get("eta_min", 1e-6), + ) + return {"scheduler": scheduler, "interval": "epoch", "frequency": 1} + + if scheduler_type == "step": + scheduler = torch.optim.lr_scheduler.StepLR( + optimizer, + step_size=scheduler_config.get("step_size", 30), + gamma=scheduler_config.get("gamma", 0.1), + ) + return {"scheduler": scheduler, "interval": "epoch", "frequency": 1} + + if scheduler_type == "reduce_on_plateau": + scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( + optimizer, + mode="min", + factor=scheduler_config.get("factor", 0.5), + patience=scheduler_config.get("patience", 10), + verbose=True, + ) + return { + "scheduler": scheduler, + "monitor": "val_loss", + "interval": "epoch", + "frequency": 1, + } + + raise ValueError(f"Unknown scheduler type: {scheduler_type}") diff --git a/transfer/code/stack/tests/test_cli_help.py b/transfer/code/stack/tests/test_cli_help.py new file mode 100644 index 0000000000000000000000000000000000000000..0c8f0308840e57a14e62b6780906dd301547e61e --- /dev/null +++ b/transfer/code/stack/tests/test_cli_help.py @@ -0,0 +1,38 @@ +import os +import subprocess +import sys +from typing import Iterable + +import pytest + + +CLI_MODULES: Iterable[str] = ( + "stack.cli.launch_training", + "stack.cli.launch_finetuning", + "stack.cli.embedding", + "stack.cli.generation", +) + + +def _run_help(module: str) -> subprocess.CompletedProcess[str]: + env = os.environ.copy() + env["PYTHONPATH"] = os.pathsep.join( + [os.path.abspath("src"), env.get("PYTHONPATH", "")] + ) + return subprocess.run( + [sys.executable, "-m", module, "--help"], + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + text=True, + env=env, + ) + + +@pytest.mark.parametrize("module", CLI_MODULES) +def test_cli_help_exits_cleanly(module: str): + result = _run_help(module) + assert result.returncode == 0, ( + f"{module} --help failed:\nstdout:\n{result.stdout}\nstderr:\n{result.stderr}" + ) + assert "usage:" in result.stdout.lower() + diff --git a/transfer/code/stack/tests/test_cli_smoke_runs.py b/transfer/code/stack/tests/test_cli_smoke_runs.py new file mode 100644 index 0000000000000000000000000000000000000000..0d9e79c20de16e4704b662bcb68090c358bc8ae3 --- /dev/null +++ b/transfer/code/stack/tests/test_cli_smoke_runs.py @@ -0,0 +1,245 @@ +"""Minimal smoke tests for the CLI entry points to ensure they can be invoked.""" + +from __future__ import annotations + +import os +from types import SimpleNamespace +from typing import Any, Dict + +import pytest + + +def _make_dummy_training_deps() -> Dict[str, Any]: + class DummyTorch: + @staticmethod + def set_float32_matmul_precision(_arg: str) -> None: + return None + + class cuda: + @staticmethod + def is_available() -> bool: # pragma: no cover - trivial stub + return False + + class DummyTrainer: + def __init__(self, *_, **__): + self.num_devices = 0 + + def fit(self, *_, **__): + return None + + def test(self, *_, **__): + return [] + + class DummyCallback: + def __init__(self, *_, **__): + return None + + class DummyLogger: + def __init__(self, *_, **__): + return None + + class DummyDDPStrategy: + def __init__(self, *_, **__): + return None + + def build_scheduler_config(_cfg: Dict[str, Any]) -> Dict[str, Any]: + return {} + + def localize_datasets(dataset_configs, *_args, **_kwargs): + return SimpleNamespace( + dataset_configs=dataset_configs, + cache_file=None, + job_id="test-job", + stage_out=lambda *_a, **_kw: None, + ) + + def parse_dataset_configs(dataset_configs): + configs = dataset_configs or [] + if not isinstance(configs, (list, tuple)): + configs = [configs] + return [ + SimpleNamespace( + path=str(cfg), + filter_organism=None, + gene_name_col=None, + type="human", + donor_col="donor", + cell_type_col="celltype", + condition_col="condition", + cell_line_col="cellline", + control_condition="control", + ) + for cfg in configs + ] + + class DummyDataModule: + def __init__(self, *_, **__): + self.test_dataset = [] + self.dataset_configs = parse_dataset_configs(["dummy"]) + + def setup(self, *_args, **_kwargs): + self.n_genes = 4 + return None + + def get_split_info(self): + return {"train": [], "val": [], "test": []} + + class DummyModel: + def __init__(self, *_, **__): + return None + + @classmethod + def load_from_checkpoint(cls, *_, **__): + return cls() + + def build_model_config(args, n_genes): + return {"n_genes": n_genes} + + return { + "torch": DummyTorch, + "pl": SimpleNamespace(Trainer=DummyTrainer), + "EarlyStopping": DummyCallback, + "LearningRateMonitor": DummyCallback, + "ModelCheckpoint": DummyCallback, + "TensorBoardLogger": DummyLogger, + "WandbLogger": DummyLogger, + "Logger": DummyLogger, + "DDPStrategy": DummyDDPStrategy, + "MultiDatasetDataModule": DummyDataModule, + "FinetuneDataModule": DummyDataModule, + "LightningFinetunedModel": DummyModel, + "LegacyLightningGeneModel": DummyModel, + "build_scheduler_config": build_scheduler_config, + "localize_datasets": localize_datasets, + "parse_dataset_configs": parse_dataset_configs, + "configure_logger": DummyLogger, + "build_model_config": build_model_config, + "override_model_config_n_cells": lambda *_a, **_kw: {}, + } + + +def test_stack_train_main_runs(monkeypatch, tmp_path): + from stack.cli import launch_training + import sys + + dummy_deps = _make_dummy_training_deps() + monkeypatch.setattr(launch_training, "_import_training_modules", lambda: dummy_deps) + for name in ("TensorBoardLogger", "WandbLogger", "EarlyStopping", "LearningRateMonitor", "ModelCheckpoint"): + monkeypatch.setattr(launch_training, name, dummy_deps[name], raising=False) + monkeypatch.setattr(launch_training, "configure_logger", lambda *_a, **_kw: dummy_deps["TensorBoardLogger"]()) + + monkeypatch.setattr( + sys, + "argv", + [ + "stack-train", + "--dataset_configs", + "dummy-dataset", + "--genelist_path", + "dummy-genelist.pkl", + "--save_dir", + str(tmp_path), + "--gpus", + "0", + ], + ) + launch_training.main() + assert (tmp_path / "dataset_splits.json").exists() + + +def test_stack_finetune_main_runs(monkeypatch, tmp_path): + from stack.cli import launch_finetuning + import sys + + dummy_deps = _make_dummy_training_deps() + monkeypatch.setattr(launch_finetuning, "_import_training_modules", lambda: dummy_deps) + for name in ("TensorBoardLogger", "WandbLogger", "EarlyStopping", "LearningRateMonitor", "ModelCheckpoint", "Logger"): + monkeypatch.setattr(launch_finetuning, name, dummy_deps.get(name, dummy_deps["TensorBoardLogger"]), raising=False) + monkeypatch.setattr( + launch_finetuning, + "configure_logger", + lambda *_a, **_kw: dummy_deps["TensorBoardLogger"](), + raising=False, + ) + + monkeypatch.setattr( + sys, + "argv", + [ + "stack-finetune", + "--dataset_configs", + "dummy-dataset", + "--genelist_path", + "dummy-genelist.pkl", + "--save_dir", + str(tmp_path), + "--gpus", + "0", + ], + ) + launch_finetuning.main() + assert (tmp_path / "dataset_splits.json").exists() + + +def test_stack_embedding_main_runs(monkeypatch, tmp_path): + from stack.cli import embedding + + monkeypatch.setattr( + embedding, + "extract_embeddings", + lambda **_kw: ([[]], None), + ) + saved = {} + monkeypatch.setattr( + embedding, + "save_embeddings", + lambda embeddings, output_path, **_: saved.update({"path": output_path, "embeddings": embeddings}), + ) + + args = [ + "--checkpoint", + "dummy.ckpt", + "--adata", + "dummy.h5ad", + "--genelist", + "dummy-genelist.pkl", + "--output", + str(tmp_path / "embeddings.npy"), + ] + embedding.main(args) + assert saved["path"].name == "embeddings.npy" + + +def test_stack_generation_main_runs(monkeypatch, tmp_path): + from stack.cli import generation + + monkeypatch.setattr( + generation, + "generate", + lambda **_kw: {"split": "dummy"}, + ) + called = {} + monkeypatch.setattr( + generation, + "save_generations", + lambda generations, output_dir, **_: called.update({"generations": generations, "output_dir": output_dir}), + ) + + args = [ + "--checkpoint", + "dummy.ckpt", + "--base-adata", + "base.h5ad", + "--test-adata", + "test.h5ad", + "--genelist", + "genelist.pkl", + "--output-dir", + str(tmp_path), + "--split-column", + "donor", + "--concatenate", + ] + generation.main(args) + assert called["output_dir"] == tmp_path + assert called["generations"] == {"split": "dummy"} diff --git a/transfer/code/stack/tests/test_cli_utils.py b/transfer/code/stack/tests/test_cli_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..7cb834285b6d0336ebf8c012bdbe306c5b9521e9 --- /dev/null +++ b/transfer/code/stack/tests/test_cli_utils.py @@ -0,0 +1,61 @@ +import argparse +import json +import sys +from pathlib import Path + +import pytest + +sys.path.append(str(Path(__file__).resolve().parents[1] / "src")) + +from stack.cli_utils import ( + ConfigFileError, + apply_config, + filter_unused_arguments, + load_config_file, +) + + +def test_load_config_file_json(tmp_path): + config_path = tmp_path / "config.json" + config_path.write_text(json.dumps({"learning_rate": 1e-4, "batch_size": 64})) + + data = load_config_file(config_path) + + assert data["learning_rate"] == pytest.approx(1e-4) + assert data["batch_size"] == 64 + + +def test_apply_config_updates_defaults(): + parser = argparse.ArgumentParser() + parser.add_argument("--alpha", type=float, default=0.1) + parser.add_argument("--beta", type=int, default=5) + + recognised, unused = apply_config(parser, {"alpha": 0.25, "gamma": "ignored"}) + args = parser.parse_args([]) + + assert recognised == {"alpha": 0.25} + assert unused == ("gamma",) + assert args.alpha == pytest.approx(0.25) + assert args.beta == 5 + + +def test_filter_unused_arguments_detects_missing_required(): + parser = argparse.ArgumentParser() + parser.add_argument("--alpha", type=str) + args = parser.parse_args([]) + + with pytest.raises(SystemExit): + filter_unused_arguments(args, ("alpha",), parser) + + +def test_load_config_file_rejects_unknown_extension(tmp_path): + config_path = tmp_path / "config.txt" + config_path.write_text("alpha=1") + + with pytest.raises(ConfigFileError): + load_config_file(config_path) + + +def test_load_config_file_missing(tmp_path): + with pytest.raises(ConfigFileError): + load_config_file(tmp_path / "missing.yaml") diff --git a/transfer/code/stack/tests/test_finetune_mixins.py b/transfer/code/stack/tests/test_finetune_mixins.py new file mode 100644 index 0000000000000000000000000000000000000000..0fb2a10743ee970ee245cbceb87511cb321ed23c --- /dev/null +++ b/transfer/code/stack/tests/test_finetune_mixins.py @@ -0,0 +1,35 @@ +import pytest + +torch = pytest.importorskip("torch") + +from stack.model_finetune import ICL_FinetunedModel + + +def test_finetuned_model_uses_mixins(): + model = ICL_FinetunedModel( + n_genes=4, + n_cells=3, + n_hidden=2, + token_dim=2, + n_layers=1, + n_heads=1, + mlp_ratio=1, + dropout=0.0, + ) + + ones = torch.ones(1, 3, 4) + masked, mask = model.apply_finetune_mask(ones) + assert masked.shape == ones.shape + assert mask.shape == ones.shape + + observed = torch.rand(1, 3, 4) + with torch.no_grad(): + output = model( + observed, + observed, + mask_genes=False, + return_loss=False, + ) + + assert "nb_mean" in output + assert model.query_pos_embedding.shape == (model.n_hidden, model.token_dim) diff --git a/transfer/code/stack/tests/test_model_core.py b/transfer/code/stack/tests/test_model_core.py new file mode 100644 index 0000000000000000000000000000000000000000..0e8e73a68827fbe3887e9177d99fc77c03b50fd1 --- /dev/null +++ b/transfer/code/stack/tests/test_model_core.py @@ -0,0 +1,49 @@ +import pytest + + +torch = pytest.importorskip("torch") + +from stack.model import StateICLModel, scShiftAttentionModel + + +@pytest.fixture() +def small_stack_model(): + return StateICLModel( + n_genes=6, + n_cells=4, + n_hidden=2, + token_dim=8, + n_layers=1, + n_heads=2, + mlp_ratio=1, + dropout=0.0, + ) + + +def test_stackmodel_forward_shapes(small_stack_model): + model = small_stack_model + model.train() + + features = torch.rand(2, 4, 6) + output = model(features, return_loss=False) + + assert set(output) >= {"nb_mean", "nb_dispersion", "px_scale"} + assert output["nb_mean"].shape == (2, 4, 6) + assert output["nb_dispersion"].shape == (2, 4, 6) + assert output["px_scale"].shape == (2, 4, 6) + + +def test_inference_mixin_predict_uses_forward(small_stack_model): + model = small_stack_model + model.eval() + + features = torch.rand(1, 4, 6) + with torch.no_grad(): + predictions = model.predict(features) + + assert set(predictions) >= {"nb_mean", "nb_dispersion", "px_scale"} + assert predictions["nb_mean"].shape == (1, 4, 6) + + +def test_scshiftattentionmodel_aliases_stackmodel(): + assert scShiftAttentionModel is StateICLModel diff --git a/transfer/code/stack/tests/test_model_loading_import.py b/transfer/code/stack/tests/test_model_loading_import.py new file mode 100644 index 0000000000000000000000000000000000000000..1c5730530d510467e45157e72614176aa2bd4df7 --- /dev/null +++ b/transfer/code/stack/tests/test_model_loading_import.py @@ -0,0 +1,20 @@ +"""Ensure the public checkpoint loader can be imported from multiple entry-points.""" + +import importlib + +import pytest + + +pytest.importorskip("torch", reason="torch is required for checkpoint loading") + + +def test_model_loading_importable_from_package() -> None: + module = importlib.import_module("stack.model") + assert hasattr(module, "load_model_from_checkpoint") + + +def test_model_loading_importable_from_public_module() -> None: + module = importlib.import_module("stack.model_loading") + assert hasattr(module, "load_model_from_checkpoint") + + diff --git a/transfer/code/stack/uv.lock b/transfer/code/stack/uv.lock new file mode 100644 index 0000000000000000000000000000000000000000..4c6314bf7df238668fb86f23ec69d3051fd5073a --- /dev/null +++ b/transfer/code/stack/uv.lock @@ -0,0 +1,2854 @@ +version = 1 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