from __future__ import annotations

import torch
from torch import nn

from .blocks import MLP


class ClinicalEncoder(nn.Module):
    """Clinical MLP that explicitly receives missingness indicators."""

    def __init__(self, clinical_dim: int, feature_dim: int = 256, use_missingness: bool = True) -> None:
        super().__init__()
        self.clinical_dim = clinical_dim
        self.feature_dim = feature_dim
        self.use_missingness = use_missingness
        self.net = MLP(clinical_dim * 2, max(feature_dim // 2, 16), feature_dim)

    def forward(self, values: torch.Tensor | None, missing: torch.Tensor | None) -> torch.Tensor:
        if self.clinical_dim == 0:
            batch = 1 if values is None else values.shape[0]
            device = None if values is None else values.device
            return torch.zeros(batch, self.feature_dim, device=device)
        if values is None or missing is None:
            raise ValueError("Clinical values and missingness mask are required when clinical_dim > 0")
        if not self.use_missingness:
            missing = torch.zeros_like(missing)
        return self.net(torch.cat([values.float(), missing.float()], dim=-1))