"""Clinical-guided gated fusion of MRI and tabular embeddings.

Instead of plain concatenation [z_mri; z_tab], learn a gate g = sigma(Wg[z_mri;z_tab])
that scales the tabular contribution before residual-adding it into the MRI stream:

    z = LayerNorm(z_mri + g * (Wt z_tab))

Rationale: when demographics (esp. age) are informative the gate opens; when they are
unreliable or missing the MRI branch stays dominant, preventing age from swamping the
imaging signal. This is the mechanism ablation A4 (concat) tests against.
"""
from __future__ import annotations

import torch
import torch.nn as nn


class GatedFusion(nn.Module):
    def __init__(self, mri_dim: int, tab_dim: int, dropout: float = 0.1):
        super().__init__()
        self.tab_proj = nn.Linear(tab_dim, mri_dim)
        self.gate = nn.Sequential(
            nn.Linear(mri_dim + tab_dim, mri_dim),
            nn.Sigmoid(),
        )
        self.norm = nn.LayerNorm(mri_dim)
        self.drop = nn.Dropout(dropout)
        self.out_dim = mri_dim

    def forward(self, z_mri: torch.Tensor, z_tab: torch.Tensor) -> torch.Tensor:
        g = self.gate(torch.cat([z_mri, z_tab], dim=-1))   # (B, mri_dim)
        t = self.tab_proj(z_tab)                            # (B, mri_dim)
        z = self.norm(z_mri + g * t)
        return self.drop(z)


class ConcatFusion(nn.Module):
    """Ablation A4: plain concatenation baseline (no gate)."""

    def __init__(self, mri_dim: int, tab_dim: int, dropout: float = 0.1):
        super().__init__()
        self.proj = nn.Sequential(
            nn.Linear(mri_dim + tab_dim, mri_dim),
            nn.GELU(),
            nn.Dropout(dropout),
        )
        self.out_dim = mri_dim

    def forward(self, z_mri: torch.Tensor, z_tab: torch.Tensor) -> torch.Tensor:
        return self.proj(torch.cat([z_mri, z_tab], dim=-1))