"""
LiquidFlow Block — Hybrid CfC + Mamba-2 SSD architecture.
CORRECTED VERSION: proper dimensions, no sequential loops.

Architecture per block:
    Input → Mamba-2 SSD (bidirectional) → CfC adaptive gate → Output
    
The CfC provides adaptive gating that modulates the SSM output
based on input-dependent "liquid" time constants.
"""

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

from .cfc_cell import CfCCell, CfCBlock
from .mamba2_ssd import Mamba2SSD, Mamba2Block


class LiquidMambaBlock(nn.Module):
    """
    LiquidMamba: CfC-gated Mamba-2 SSD block.
    
    Flow:
    1. Input → LayerNorm → Mamba-2 SSD (bidirectional scan)
    2. SSM output → CfC adaptive gate (parallel over all positions)
    3. Gated output → residual + feed-forward
    
    The CfC gate learns WHEN to trust the SSM output vs the raw input,
    creating content-aware adaptive processing.
    """
    
    def __init__(self, dim, d_state=16, d_conv=4, expand=2, dropout=0.0):
        super().__init__()
        self.dim = dim
        
        # LayerNorms
        self.norm_ssm = nn.LayerNorm(dim)
        self.norm_gate = nn.LayerNorm(dim)
        self.norm_ff = nn.LayerNorm(dim)
        
        # Mamba-2 SSD: bidirectional scan
        self.ssd_fwd = Mamba2SSD(dim=dim, d_state=d_state, d_conv=d_conv, expand=expand)
        self.ssd_bwd = Mamba2SSD(dim=dim, d_state=d_state, d_conv=d_conv, expand=expand)
        self.merge = nn.Linear(dim * 2, dim, bias=False)
        
        # CfC gate: parallel adaptive gating
        self.cfc_gate = CfCCell(dim=dim, dropout=dropout)
        
        # Gate projection (learnable mixing)
        self.gate_proj = nn.Linear(dim, dim)
        
        # 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),
        )
    
    def forward(self, x):
        """
        Args:
            x: [B, C, H, W] or [B, L, C]
        Returns:
            Same shape as input
        """
        is_2d = x.dim() == 4
        if is_2d:
            B, C, H, W = x.shape
            x = x.flatten(2).transpose(1, 2)  # [B, HW, C]
        
        # === SSM branch ===
        residual = x
        x_norm = self.norm_ssm(x)
        
        # Bidirectional Mamba-2 scan
        fwd_out = self.ssd_fwd(x_norm)
        bwd_out = torch.flip(self.ssd_bwd(torch.flip(x_norm, [1])), [1])
        ssm_out = self.merge(torch.cat([fwd_out, bwd_out], dim=-1))
        
        # === CfC gate ===
        # CfC processes the SSM output and produces adaptive gate
        gate_input = self.norm_gate(ssm_out)
        cfc_out = self.cfc_gate(gate_input)  # [B, L, D] — parallel!
        
        # Sigmoid gate: how much SSM output to use
        gate = torch.sigmoid(self.gate_proj(cfc_out))
        
        # Gated residual: blend SSM output with residual
        x = residual + gate * ssm_out
        
        # === Feed-forward ===
        x = x + self.ff(self.norm_ff(x))
        
        if is_2d:
            x = x.transpose(1, 2).reshape(B, C, H, W)
        return x


class LiquidFlowStage(nn.Module):
    """Stack of LiquidMamba blocks at the same resolution."""
    
    def __init__(self, dim, num_blocks=4, d_state=16, expand=2, dropout=0.0):
        super().__init__()
        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 — DiT-style noise predictor.
    
    FIXED: Output shape == Input shape (no patch_size confusion).
    
    Architecture:
        Input [B, in_ch, H, W] 
        → Conv2d projection to hidden_dim
        → + sinusoidal timestep embedding (AdaLN-style)
        → + learnable positional encoding
        → N × LiquidMamba Stages
        → Conv2d projection back to in_ch
        → Output [B, in_ch, H, W]
    """
    
    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
        
        # Input projection (pointwise conv)
        self.in_proj = nn.Conv2d(in_channels, hidden_dim, kernel_size=1)
        
        # Timestep embedding
        self.time_embed = nn.Sequential(
            nn.Linear(hidden_dim, hidden_dim * 4),
            nn.SiLU(),
            nn.Linear(hidden_dim * 4, hidden_dim),
        )
        
        # AdaLN-style conditioning: scale and shift
        self.t_cond = nn.Sequential(
            nn.SiLU(),
            nn.Linear(hidden_dim, hidden_dim * 2),
        )
        
        # Positional encoding (learnable, supports up to 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 projection
        self.out_norm = nn.LayerNorm(hidden_dim)
        self.out_proj = nn.Linear(hidden_dim, in_channels)
        
        self._init_weights()
    
    def _init_weights(self):
        # Zero-init output projection for residual learning
        nn.init.zeros_(self.out_proj.weight)
        nn.init.zeros_(self.out_proj.bias)
    
    def _sinusoidal_embedding(self, timesteps, dim):
        """Sinusoidal positional embedding for diffusion timesteps."""
        half = dim // 2
        freqs = torch.exp(
            -math.log(10000.0) * torch.arange(half, device=timesteps.device).float() / half
        )
        args = timesteps.float().unsqueeze(-1) * freqs.unsqueeze(0)
        emb = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
        if dim % 2:
            emb = F.pad(emb, (0, 1))
        return emb
    
    def forward(self, x, t):
        """
        Args:
            x: [B, in_channels, H, W] — noisy latent
            t: [B] — diffusion timesteps (integers 0..T-1)
        Returns:
            [B, in_channels, H, W] — predicted noise (same shape as input!)
        """
        B, C, H, W = x.shape
        L = H * W
        
        # Project to hidden dim
        x = self.in_proj(x)  # [B, hidden_dim, H, W]
        x = x.flatten(2).transpose(1, 2)  # [B, HW, hidden_dim]
        
        # Timestep conditioning (AdaLN)
        t_emb = self._sinusoidal_embedding(t, self.hidden_dim)  # [B, hidden_dim]
        t_emb = self.time_embed(t_emb)  # [B, hidden_dim]
        t_cond = self.t_cond(t_emb)  # [B, hidden_dim*2]
        scale, shift = t_cond.chunk(2, dim=-1)  # each [B, hidden_dim]
        
        # Apply conditioning + positional encoding
        x = x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
        x = x + self.pos_embed[:, :L, :]
        
        # Reshape to 2D for processing
        x = x.transpose(1, 2).reshape(B, self.hidden_dim, H, W)
        
        # Process through all stages
        for stage in self.stages:
            x = stage(x)
        
        # Output head
        x = x.flatten(2).transpose(1, 2)  # [B, HW, hidden_dim]
        x = self.out_norm(x)
        x = self.out_proj(x)  # [B, HW, in_channels]
        
        # Reshape back to image: [B, in_channels, H, W]
        x = x.transpose(1, 2).reshape(B, self.in_channels, H, W)
        
        return x