"""
ASB layers: AnisotropicSigmaNet and ScoreDecoder.
"""

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

from .._scdfm_imports import TimestepEmbedder


class AnisotropicSigmaNet(nn.Module):
    """
    Predicts per-gene anisotropic diffusion coefficient σ_g(perturbation, t).

    Input:  pert_emb (B, d_model), t (B,), gene_emb (B, G, d_model)
    Output: sigma_g (B, G) in [sigma_min, sigma_max]

    Architecture: condition c = pert_emb + t_emb → c + gene_emb → MLP → sigmoid → [min, max]
    Does NOT depend on x_t — can be called before bridge sampling.
    """

    def __init__(
        self,
        d_model: int = 128,
        hidden_dim: int = 256,
        num_layers: int = 2,
        sigma_min: float = 0.01,
        sigma_max: float = 2.0,
        sigma_init: float = 0.5,
    ):
        super().__init__()
        self.sigma_min = sigma_min
        self.sigma_max = sigma_max

        self.t_embedder = TimestepEmbedder(d_model)

        layers = []
        in_dim = d_model
        for i in range(num_layers):
            layers.append(nn.Linear(in_dim if i == 0 else hidden_dim, hidden_dim))
            layers.append(nn.SiLU())
        layers.append(nn.Linear(hidden_dim, 1))
        self.mlp = nn.Sequential(*layers)

        self._init_bias(sigma_init)

    def _init_bias(self, sigma_init):
        """Initialize final bias so sigmoid output maps to sigma_init."""
        target = (sigma_init - self.sigma_min) / (self.sigma_max - self.sigma_min)
        target = max(min(target, 0.999), 0.001)
        bias_val = math.log(target / (1 - target))  # logit
        nn.init.constant_(self.mlp[-1].bias, bias_val)
        nn.init.zeros_(self.mlp[-1].weight)

    def forward(self, pert_emb: torch.Tensor, t: torch.Tensor,
                gene_emb: torch.Tensor) -> torch.Tensor:
        """
        Args:
            pert_emb: (B, d_model) perturbation embedding
            t: (B,) timestep
            gene_emb: (B, G, d_model) gene embeddings

        Returns:
            sigma_g: (B, G) in [sigma_min, sigma_max]
        """
        t_emb = self.t_embedder(t)                         # (B, d_model)
        c = pert_emb + t_emb                                # (B, d_model)
        c_exp = c.unsqueeze(1).expand_as(gene_emb)          # (B, G, d_model)
        h = gene_emb + c_exp                                # (B, G, d_model)
        raw = self.mlp(h).squeeze(-1)                       # (B, G)
        sigma = self.sigma_min + (self.sigma_max - self.sigma_min) * torch.sigmoid(raw)
        return sigma


class ScoreDecoder(nn.Module):
    """
    Decodes backbone hidden states to score function prediction.

    Input:  backbone output (B, G, d_model), pert_emb (B, d_model)
    Output: score prediction (B, G)
    """

    def __init__(self, d_model: int = 128, depth: int = 2):
        super().__init__()
        self.proj = nn.Linear(d_model * 2, d_model)  # concat with pert_emb
        blocks = []
        for _ in range(depth):
            blocks.extend([
                nn.LayerNorm(d_model),
                nn.Linear(d_model, d_model),
                nn.SiLU(),
            ])
        blocks.append(nn.Linear(d_model, 1))
        self.mlp = nn.Sequential(*blocks)

    def forward(self, x: torch.Tensor, pert_emb: torch.Tensor) -> torch.Tensor:
        """
        Args:
            x: (B, G, d_model) backbone output
            pert_emb: (B, d_model) perturbation embedding

        Returns:
            score: (B, G)
        """
        x_with_pert = torch.cat(
            [x, pert_emb[:, None, :].expand(-1, x.size(1), -1)], dim=-1
        )
        h = self.proj(x_with_pert)
        return self.mlp(h).squeeze(-1)