# /// script
# dependencies = [
#   "torch>=2.2.0",
#   "transformers>=4.40.0",
#   "trl>=1.2.0",
#   "peft>=0.10.0",
#   "accelerate>=0.27.0",
#   "bitsandbytes>=0.43.0",
#   "datasets>=2.18.0",
#   "huggingface-hub>=0.22.0",
#   "trackio",
#   "pydantic>=2.0",
#   "numpy>=1.24",
#   "pandas>=2.0",
#   "matplotlib>=3.8",
#   "tqdm>=4.60",
#   "networkx>=3.0",
#   "scipy>=1.10",
#   "fastapi>=0.100",
#   "uvicorn>=0.22",
#   "httpx>=0.24",
#   "pyyaml>=6.0",
# ]
# ///
"""
PolyGuard SFT Training Job — runs on Hugging Face Jobs cloud GPU.

This script:
  1. Clones the project from GitHub
  2. Installs the polyguard-rl package
  3. Generates the SFT dataset from the PolyGuard environment
  4. Fine-tunes Qwen2.5-1.5B-Instruct with LoRA via TRL SFTTrainer
  5. Pushes the LoRA adapter + tokenizer to the Hugging Face Hub

Submit via CLI:
  hf jobs uv run \
    --flavor a10g-large \
    --timeout 3h \
    --secrets HF_TOKEN \
    "https://huggingface.co/TheJackBright/polyguard-training-scripts/resolve/main/hf_sft_train.py"

Environment variables (passed as --env or via job secrets):
  HF_TOKEN          : HF write token (required for Hub push)
  GITHUB_REPO       : override GitHub repo URL (optional)
  MODEL_NAME        : base model (default: Qwen/Qwen2.5-1.5B-Instruct)
  HUB_MODEL_ID      : output model repo on Hub (default: TheJackBright/polyguard-qwen-sft)
  N_EPISODES        : SFT dataset episodes (default: 500)
  EPOCHS            : training epochs (default: 3)
  BATCH_SIZE        : per-device batch size (default: 2)
  GRAD_ACCUM        : gradient accumulation steps (default: 8)
  MAX_LENGTH        : max token length (default: 1024)
  LEARNING_RATE     : learning rate (default: 2e-4)
  LORA_RANK         : LoRA rank (default: 16)
"""
from __future__ import annotations

import inspect
import json
import os
import random
import subprocess
import sys
from pathlib import Path
from typing import Any, Dict, List, Optional

# ─── Config from environment ────────────────────────────────────────────────
GITHUB_REPO   = os.environ.get("GITHUB_REPO", "https://github.com/Vishwa-docs/Meta_PyTorch_Scalar_OpenEnv-Hackathon.git")
MODEL_NAME    = os.environ.get("MODEL_NAME", "Qwen/Qwen2.5-1.5B-Instruct")
HUB_MODEL_ID  = os.environ.get("HUB_MODEL_ID", "TheJackBright/polyguard-qwen-sft")
N_EPISODES    = int(os.environ.get("N_EPISODES", "500"))
EPOCHS        = int(os.environ.get("EPOCHS", "3"))
BATCH_SIZE    = int(os.environ.get("BATCH_SIZE", "2"))
GRAD_ACCUM    = int(os.environ.get("GRAD_ACCUM", "8"))
MAX_LENGTH    = int(os.environ.get("MAX_LENGTH", "1024"))
LEARNING_RATE = float(os.environ.get("LEARNING_RATE", "2e-4"))
LORA_RANK     = int(os.environ.get("LORA_RANK", "16"))
SEED          = 42
OUTPUT_DIR    = "/tmp/polyguard_sft_output"
DATA_PATH     = "/tmp/polyguard_sft_data.jsonl"
DATA_FMT_PATH = "/tmp/polyguard_sft_data_formatted.jsonl"

SYSTEM_PROMPT = (
    "You are a clinical pharmacist agent performing polypharmacy medication review. "
    "Analyze drug-drug interactions, Beers criteria violations, and propose safe interventions. "
    "Respond only with a structured <decision>...</decision> XML action."
)

print("=" * 60)
print("PolyGuard SFT Training on HF Jobs")
print(f"  Model:        {MODEL_NAME}")
print(f"  Hub output:   {HUB_MODEL_ID}")
print(f"  Episodes:     {N_EPISODES}")
print(f"  Epochs:       {EPOCHS}")
print(f"  Batch size:   {BATCH_SIZE} x {GRAD_ACCUM} grad accum")
print(f"  Max length:   {MAX_LENGTH}")
print(f"  LoRA rank:    {LORA_RANK}")
print("=" * 60)


# ─── Step 1: Clone repo and install polyguard-rl ────────────────────────────
print("\n[1/5] Cloning project from GitHub...")
clone_dir = Path("/tmp/polyguard_project")
if not clone_dir.exists():
    subprocess.run(
        ["git", "clone", "--depth=1", GITHUB_REPO, str(clone_dir)],
        check=True,
    )
    print(f"  Cloned to {clone_dir}")
else:
    print(f"  Already cloned at {clone_dir}")

polyguard_rl_dir = clone_dir / "polyguard-rl"
print(f"\n[2/5] Installing polyguard-rl from {polyguard_rl_dir}...")
subprocess.run(
    [sys.executable, "-m", "pip", "install", "-e", str(polyguard_rl_dir), "--quiet"],
    check=True,
)
print("  polyguard-rl installed.")

# Add to sys.path so relative imports work during dataset generation
if str(polyguard_rl_dir) not in sys.path:
    sys.path.insert(0, str(polyguard_rl_dir))


# ─── Step 2: Generate SFT dataset ───────────────────────────────────────────
print(f"\n[3/5] Generating SFT dataset ({N_EPISODES} episodes)...")

os.chdir(polyguard_rl_dir)

from app.training.sft_dataset import generate_sft_dataset, build_external_ddi_sft_examples, format_for_training  # noqa: E402

examples = generate_sft_dataset(
    n_episodes=N_EPISODES,
    seed=SEED,
    output_path=DATA_PATH,
)

print(f"  Generated {len(examples)} episodes.")

# Format for TRL (convert to messages format)
formatted = format_for_training(examples, system_prompt=SYSTEM_PROMPT)
print(f"  Formatted {len(formatted)} training rows.")

with open(DATA_FMT_PATH, "w") as f:
    for row in formatted:
        f.write(json.dumps(row) + "\n")

print(f"  Saved formatted dataset to {DATA_FMT_PATH}")


# ─── Step 3: Load model with LoRA ───────────────────────────────────────────
print(f"\n[4/5] Loading model and running SFT training...")

import torch
from datasets import Dataset
from peft import LoraConfig, get_peft_model
from transformers import AutoModelForCausalLM, AutoTokenizer
from trl import SFTConfig, SFTTrainer

device_map = "auto" if torch.cuda.is_available() else "cpu"
dtype = torch.bfloat16 if (torch.cuda.is_available() and torch.cuda.is_bf16_supported()) else torch.float16

print(f"  CUDA available: {torch.cuda.is_available()}")
if torch.cuda.is_available():
    print(f"  GPU: {torch.cuda.get_device_name(0)}")
    print(f"  VRAM: {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f} GB")

print(f"  Loading tokenizer from {MODEL_NAME}...")
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token
    tokenizer.pad_token_id = tokenizer.eos_token_id

print(f"  Loading base model...")
model = AutoModelForCausalLM.from_pretrained(
    MODEL_NAME,
    torch_dtype=dtype,
    device_map=device_map,
    trust_remote_code=True,
)

lora_config = LoraConfig(
    r=LORA_RANK,
    lora_alpha=LORA_RANK * 2,
    target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)
model = get_peft_model(model, lora_config)
model.print_trainable_parameters()


# ─── Step 4: Build dataset and trainer ──────────────────────────────────────
random.seed(SEED)
ds_full = Dataset.from_list(formatted).shuffle(seed=SEED)
split = ds_full.train_test_split(test_size=0.1, seed=SEED)

sft_config_kwargs: Dict[str, Any] = {
    "output_dir": OUTPUT_DIR,
    "num_train_epochs": EPOCHS,
    "per_device_train_batch_size": BATCH_SIZE,
    "gradient_accumulation_steps": GRAD_ACCUM,
    "learning_rate": LEARNING_RATE,
    "warmup_ratio": 0.05,
    "weight_decay": 0.01,
    "bf16": dtype == torch.bfloat16,
    "fp16": dtype == torch.float16,
    "logging_steps": 10,
    "save_steps": 100,
    "save_total_limit": 2,
    "max_grad_norm": 1.0,
    "seed": SEED,
    "report_to": ["trackio"],
    "run_name": "polyguard-sft-qwen",
    "project": "polyguard-training",
    "push_to_hub": True,
    "hub_model_id": HUB_MODEL_ID,
    "hub_strategy": "every_save",
    "eval_strategy": "steps",
    "eval_steps": 50,
}

# Adapt to TRL version
sft_params = set(inspect.signature(SFTConfig).parameters)
if "max_length" in sft_params:
    sft_config_kwargs["max_length"] = MAX_LENGTH
elif "max_seq_length" in sft_params:
    sft_config_kwargs["max_seq_length"] = MAX_LENGTH
if "eos_token" in sft_params:
    sft_config_kwargs["eos_token"] = "<|im_end|>"

sft_config = SFTConfig(**{k: v for k, v in sft_config_kwargs.items() if k in sft_params})

trainer_kwargs: Dict[str, Any] = {
    "model": model,
    "args": sft_config,
    "train_dataset": split["train"],
    "eval_dataset": split["test"],
}
trainer_params = set(inspect.signature(SFTTrainer).parameters)
if "processing_class" in trainer_params:
    trainer_kwargs["processing_class"] = tokenizer
elif "tokenizer" in trainer_params:
    trainer_kwargs["tokenizer"] = tokenizer

trainer = SFTTrainer(**{k: v for k, v in trainer_kwargs.items() if v is not None})

print(f"\n  Training dataset: {len(split['train'])} rows")
print(f"  Eval dataset:     {len(split['test'])} rows")
print("\n  Starting training...\n")


# ─── Step 5: Train and push to Hub ─────────────────────────────────────────
train_result = trainer.train()

print("\n  Training complete.")
print(f"  Train loss:   {train_result.training_loss:.4f}")
print(f"  Train steps:  {train_result.global_step}")

print(f"\n[5/5] Pushing model to Hub: {HUB_MODEL_ID}...")
trainer.push_to_hub()
tokenizer.push_to_hub(HUB_MODEL_ID)

print("\n" + "=" * 60)
print("SFT training complete!")
print(f"Model saved to: https://huggingface.co/{HUB_MODEL_ID}")
print("=" * 60)