from __future__ import annotations

import torch
from torch import nn


class AlignedFusionModel(nn.Module):
    def __init__(
        self,
        kind: str,
        dims: tuple[int, int, int],
        steps: int = 50,
        hidden: int = 128,
        dropout: float = 0.15,
    ) -> None:
        super().__init__()
        if kind not in {"concat", "gate", "crossattn"}:
            raise ValueError(f"unknown model kind: {kind}")
        self.kind = kind
        self.hidden = hidden
        self.projections = nn.ModuleList(
            nn.Sequential(nn.Linear(size, hidden), nn.GELU(), nn.LayerNorm(hidden))
            for size in dims
        )
        self.position = nn.Parameter(torch.randn(1, steps, hidden) * 0.02)
        self.modality = nn.Parameter(torch.randn(1, 1, 3, hidden) * 0.02)
        self.dropout = nn.Dropout(dropout)

        if kind == "concat":
            self.fusion = nn.Sequential(
                nn.Linear(hidden * 3 + 3, hidden), nn.GELU(), nn.LayerNorm(hidden), nn.Dropout(dropout)
            )
        elif kind == "gate":
            self.gate_score = nn.Sequential(nn.Linear(hidden, hidden // 2), nn.Tanh(), nn.Linear(hidden // 2, 1))
            self.fusion = nn.Sequential(
                nn.Linear(hidden + 3, hidden), nn.GELU(), nn.LayerNorm(hidden), nn.Dropout(dropout)
            )
        else:
            layer = nn.TransformerEncoderLayer(
                d_model=hidden,
                nhead=4,
                dim_feedforward=hidden * 2,
                dropout=dropout,
                activation="gelu",
                batch_first=True,
                norm_first=True,
            )
            self.cross_encoder = nn.TransformerEncoder(layer, num_layers=2, enable_nested_tensor=False)
            self.fusion = nn.Sequential(
                nn.Linear(hidden + 3, hidden), nn.GELU(), nn.LayerNorm(hidden), nn.Dropout(dropout)
            )

        self.temporal = nn.GRU(
            input_size=hidden,
            hidden_size=hidden // 2,
            num_layers=1,
            batch_first=True,
            bidirectional=True,
        )
        self.head = nn.Sequential(nn.Linear(hidden, hidden // 2), nn.GELU(), nn.Dropout(dropout))
        self.classifier = nn.Linear(hidden // 2, 3)
        self.regressor = nn.Linear(hidden // 2, 1)

    def forward(self, xs: tuple[torch.Tensor, torch.Tensor, torch.Tensor], masks: torch.Tensor):
        masks = masks.bool()
        pos = self.position[:, :masks.shape[1]]
        encoded = []
        for modality, (projection, x) in enumerate(zip(self.projections, xs)):
            token = projection(x)
            token = token + pos + self.modality[:, :, modality, :]
            token = token * masks[:, :, modality, None]
            encoded.append(token)
        stack = torch.stack(encoded, dim=2)  # B x T x M x D
        availability = masks.to(stack.dtype)
        gate_weights = None

        if self.kind == "concat":
            fused = self.fusion(torch.cat((stack.flatten(2), availability), dim=-1))
        elif self.kind == "gate":
            scores = self.gate_score(stack).squeeze(-1)
            scores = scores.masked_fill(~masks, -1e4)
            gate_weights = torch.softmax(scores, dim=-1) * availability
            gate_weights = gate_weights / gate_weights.sum(dim=-1, keepdim=True).clamp_min(1e-8)
            weighted = (stack * gate_weights[..., None]).sum(dim=2)
            fused = self.fusion(torch.cat((weighted, availability), dim=-1))
        else:
            batch, steps, modalities, hidden = stack.shape
            flat = stack.reshape(batch, steps * modalities, hidden)
            valid = masks.reshape(batch, steps * modalities).clone()
            empty = ~valid.any(dim=1)
            if empty.any():
                valid[empty, 0] = True
                flat[empty, 0] = 0.0
            attended = self.cross_encoder(flat, src_key_padding_mask=~valid)
            attended = attended.reshape(batch, steps, modalities, hidden)
            observed_count = availability.sum(dim=2, keepdim=True)
            pooled = (attended * availability[..., None]).sum(dim=2) / observed_count.clamp_min(1.0)
            fused = self.fusion(torch.cat((pooled, availability), dim=-1))

        temporal, _ = self.temporal(self.dropout(fused))
        time_weight = masks.any(dim=-1).to(temporal.dtype)
        empty_time = time_weight.sum(dim=1, keepdim=True) <= 0
        if empty_time.any():
            time_weight[empty_time.squeeze(1), 0] = 1.0
        pooled = (temporal * time_weight[..., None]).sum(dim=1) / time_weight.sum(dim=1, keepdim=True).clamp_min(1.0)
        hidden = self.head(pooled)
        logits = self.classifier(hidden)
        intensity = 3.0 * torch.tanh(self.regressor(hidden).squeeze(-1))
        return {"logits": logits, "intensity": intensity, "gate": gate_weights}