"""
LiquidFlow Block — Hybrid CfC + Mamba-2 SSD architecture.

The core innovation: combine Liquid Neural Network dynamics (CfC)
with Mamba-2's efficient linear-time state space model.

Architecture per block:
    Input → [CfC Gate → Mamba2 SSD → CfC Gate] → Output
                ↑                        ↑
            Adaptive gating        Gated output

The CfC provides:
    - Time-continuous adaptive gating (what to process/ignore)
    - State initialization for the SSM (the "liquid" memory)
    
The Mamba-2 SSD provides:
    - Efficient O(N) sequence processing
    - Content-aware selection mechanism
    - Parallelizable computation (no sequential bottleneck)

Together they create a "Liquid State Space Model" (LSSM):
    h_t = σ(-f(x_t;θ_f)·t) ⊙ SSM(x_t, h_{t-1}) + (1-σ(...)) ⊙ h(x_t;θ_h)

Where SSM is the Mamba-2 selective state space model and the
CfC time-gates control how much the SSM output influences state.

This is inspired by:
- LNNs: adaptive time constants for state evolution
- Mamba-2: efficient selective state space models
- DiMSUM: multi-scan architecture for 2D images
- Gated SSM: gating mechanism from CfC applied to SSM
"""

import torch
import torch.nn as nn
import torch.nn.functional as F

from .cfc_cell import CfCCell
from .mamba2_ssd import Mamba2SSD


class LiquidMambaBlock(nn.Module):
    """
    LiquidMamba: CfC-gated Mamba-2 SSD block.
    
    The CfC cell acts as a learned gate on the Mamba-2 output,
    creating a liquid time-constant mechanism for the SSM:
    
    1. Input goes through Mamba-2 SSD (multi-directional scan)
    2. CfC cell receives the SSM output + original input
    3. CfC produces a time-gated output: σ(f)·SSM_out + (1-σ(f))·input
    4. The CfC's liquid dynamics adaptively mix SSM features with raw input
    
    This creates a "content-aware gating" that the CfC learns to
    control based on both the input and the SSM's processed features.
    """
    
    def __init__(self, dim, d_state=16, d_conv=4, expand=2, dropout=0.0):
        super().__init__()
        self.dim = dim
        
        # LayerNorms
        self.norm_in = nn.LayerNorm(dim)
        self.norm_mamba = nn.LayerNorm(dim)
        self.norm_out = nn.LayerNorm(dim)
        
        # Mamba-2 SSD for efficient sequence processing
        self.mamba = Mamba2SSD(dim=dim, d_state=d_state, d_conv=d_conv, expand=expand)
        
        # CfC gate: controls the flow between Mamba output and residual
        self.cfc_gate = CfCCell(dim=dim, backbone_dropout=dropout, use_conv=True)
        
        # Feed-forward
        ff_dim = dim * expand
        self.ff = nn.Sequential(
            nn.Linear(dim, ff_dim),
            nn.GELU(),
            nn.Dropout(dropout),
            nn.Linear(ff_dim, dim),
            nn.Dropout(dropout),
        )
        
        # Learnable mixing ratio init
        self.gate_scale = nn.Parameter(torch.ones(1) * 0.5)
    
    def forward(self, x):
        """
        Args:
            x: [B, C, H, W] (2D) or [B, L, C] (1D seq)
        Returns:
            Same shape as input
        """
        is_2d = x.dim() == 4
        
        if is_2d:
            B, C, H, W = x.shape
            L = H * W
            x_flat = x.flatten(2).transpose(1, 2)  # [B, HW, C]
        else:
            B, L, C = x.shape
            x_flat = x
        
        residual = x_flat
        x_norm = self.norm_in(x_flat)
        
        # Mamba-2 SSD processing with multi-directional scan
        if is_2d:
            # Reshape for 2D scanning
            x_2d = x_norm.transpose(1, 2).reshape(B, C, H, W)
            mamba_out = self._mamba_2d_scan(x_2d)
            mamba_out = mamba_out.flatten(2).transpose(1, 2)  # [B, HW, C]
        else:
            mamba_out = self.mamba(x_norm)
        
        # CfC gating: liquid dynamics control the mix
        mamba_norm = self.norm_mamba(mamba_out)
        
        # CfC receives both the Mamba output and the residual
        # This lets it learn when to trust the SSM vs the original signal
        cfc_input = mamba_norm + residual
        cfc_out = self.cfc_gate(cfc_input)
        
        # Gated mix: CfC controls the blend
        gate = torch.sigmoid(self.gate_scale * (cfc_out - mamba_out))
        mixed = gate * mamba_out + (1 - gate) * residual + cfc_out
        
        # Feed-forward + residual
        out_norm = self.norm_out(mixed)
        out = mixed + self.ff(out_norm)
        
        if is_2d:
            out = out.transpose(1, 2).reshape(B, C, H, W)
        
        return out
    
    def _mamba_2d_scan(self, x):
        """
        Multi-directional Mamba-2 scan for 2D images.
        
        Scans in forward and backward raster directions, then merges.
        This preserves 2D spatial structure better than single-direction scan.
        """
        B, C, H, W = x.shape
        device = x.device
        
        # Forward raster: left→right, top→bottom
        fwd = x.flatten(2)  # [B, C, HW]
        fwd_seq = fwd.transpose(1, 2)  # [B, HW, C]
        fwd_out = self.mamba(fwd_seq)
        
        # Backward raster: right→left, bottom→top  
        bwd = torch.flip(x.flatten(2), dims=[-1])  # [B, C, HW]
        bwd_seq = bwd.transpose(1, 2)
        bwd_out = self.mamba(bwd_seq)
        bwd_out = torch.flip(bwd_out, dims=[1])  # Flip back
        
        # Merge both directions
        merged = (fwd_out + bwd_out) / 2
        merged = merged.transpose(1, 2).reshape(B, C, H, W)
        
        return merged


class LiquidFlowStage(nn.Module):
    """
    A stage in LiquidFlow: multiple LiquidMamba blocks at the same resolution.
    
    Architecture:
        [LiquidMamba Block] × num_blocks
        [Optional Downsample/Upsample]
    
    This mirrors the hierarchical design from DiT/DiMSUM but with
    liquid neural network dynamics in every block.
    """
    
    def __init__(self, dim, num_blocks=4, d_state=16, expand=2, dropout=0.0):
        super().__init__()
        self.dim = dim
        
        self.blocks = nn.ModuleList([
            LiquidMambaBlock(dim=dim, d_state=d_state, expand=expand, dropout=dropout)
            for _ in range(num_blocks)
        ])
    
    def forward(self, x):
        for block in self.blocks:
            x = block(x)
        return x


class LiquidFlowBackbone(nn.Module):
    """
    Complete LiquidFlow backbone for image generation.
    
    Architecture:
        Input (noisy latent) [B, C, H, W]
        ↓
        [Patch Embed + Positional Encoding]
        ↓
        [LiquidMamba Stages × N]  (at uniform resolution)
        ↓
        [Output Head] → predicted noise
    
    This is designed as a DiT-style noise predictor for diffusion models.
    
    Args:
        in_channels: Input channels (latent dim from VAE)
        hidden_dim: Hidden dimension
        num_stages: Number of processing stages
        blocks_per_stage: Number of blocks per stage
        d_state: SSM state dimension
        expand: Expansion factor
        dropout: Dropout rate
    """
    
    def __init__(
        self,
        in_channels=4,
        hidden_dim=256,
        num_stages=4,
        blocks_per_stage=4,
        d_state=16,
        expand=2,
        dropout=0.0,
    ):
        super().__init__()
        self.in_channels = in_channels
        self.hidden_dim = hidden_dim
        self.num_stages = num_stages
        
        # Input embedding: patch embedding
        self.patch_size = 2  # Fixed patch size
        self.in_proj = nn.Conv2d(in_channels, hidden_dim, kernel_size=1)
        
        # Time embedding (for diffusion timestep)
        self.time_embed = nn.Sequential(
            nn.Linear(hidden_dim, hidden_dim * 4),
            nn.SiLU(),
            nn.Linear(hidden_dim * 4, hidden_dim),
        )
        
        # Learnable positional encoding
        # For 128×128 with patch_size=2: 64×64 = 4096 positions
        self.pos_embed = nn.Parameter(torch.randn(1, 4096, hidden_dim) * 0.02)
        
        # LiquidFlow stages
        self.stages = nn.ModuleList([
            LiquidFlowStage(
                dim=hidden_dim, 
                num_blocks=blocks_per_stage,
                d_state=d_state,
                expand=expand,
                dropout=dropout,
            )
            for _ in range(num_stages)
        ])
        
        # Output head
        self.out_norm = nn.LayerNorm(hidden_dim)
        self.out_proj = nn.Sequential(
            nn.Linear(hidden_dim, hidden_dim),
            nn.GELU(),
            nn.Linear(hidden_dim, in_channels * self.patch_size * self.patch_size),
        )
        
        # Timestep conditioner (modulated conv trick)
        self.t_conditioner = nn.Sequential(
            nn.SiLU(),
            nn.Linear(hidden_dim, hidden_dim * 2),  # scale, shift
        )
    
    def _get_timestep_embedding(self, timesteps, dim, max_period=10000):
        """Sinusoidal timestep embedding (from DiT)."""
        half = dim // 2
        freqs = torch.exp(
            -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
        ).to(timesteps.device)
        args = timesteps.float().unsqueeze(-1) * freqs.unsqueeze(0)
        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, x, t):
        """
        Args:
            x: Noisy latent [B, C, H, W]
            t: Diffusion timesteps [B]
        
        Returns:
            Predicted noise [B, C, H, W]
        """
        B, C, H, W = x.shape
        device = x.device
        L = (H // self.patch_size) * (W // self.patch_size)
        
        # Input projection
        x = self.in_proj(x)  # [B, hidden_dim, H, W]
        
        # Flatten and add positional encoding
        x_flat = x.flatten(2).transpose(1, 2)  # [B, H*W, hidden_dim]
        
        # Time embedding
        t_emb = self._get_timestep_embedding(t, self.hidden_dim)
        t_emb = self.time_embed(t_emb)  # [B, hidden_dim]
        
        # Add time conditioning as bias to input
        t_cond = self.t_conditioner(t_emb)  # [B, hidden_dim * 2]
        t_scale, t_shift = t_cond.chunk(2, dim=-1)
        x_flat = x_flat * (1 + t_scale.unsqueeze(1)) + t_shift.unsqueeze(1)
        
        # Add positional encoding
        x_flat = x_flat + self.pos_embed[:, :L, :]
        
        # Reshape back to 2D for processing
        x_2d = x_flat.transpose(1, 2).reshape(B, self.hidden_dim, H, W)
        
        # Process through all stages
        for stage in self.stages:
            x_2d = stage(x_2d)
        
        # Output head
        x_out = x_2d.flatten(2).transpose(1, 2)  # [B, H*W, hidden_dim]
        x_out = self.out_norm(x_out)
        x_out = self.out_proj(x_out)  # [B, H*W, C * patch²]
        
        # Reshape to image
        x_out = x_out.reshape(B, H, W, C, self.patch_size, self.patch_size)
        x_out = x_out.permute(0, 3, 1, 4, 2, 5).reshape(B, C, H * self.patch_size, W * self.patch_size)
        
        return x_out


import math