from __future__ import annotations

import torch
from torch import nn


def patient_burden(lesion_probs: torch.Tensor, patient_index: torch.Tensor | None = None) -> torch.Tensor:
    """Compute malignant burden as 1 - prod_l(1 - p_l).

    If ``patient_index`` is omitted, each row is treated as an independent
    patient. Otherwise rows with the same index are aggregated.
    """

    lesion_probs = lesion_probs.view(-1).clamp(0.0, 1.0)
    if patient_index is None:
        return lesion_probs
    burdens = []
    for idx in torch.unique(patient_index):
        probs = lesion_probs[patient_index == idx]
        burdens.append(1.0 - torch.prod(1.0 - probs))
    return torch.stack(burdens)


class DiscreteTimeSurvivalHead(nn.Module):
    def __init__(self, in_dim: int, num_bins: int = 12) -> None:
        super().__init__()
        self.num_bins = num_bins
        self.net = nn.Sequential(
            nn.Linear(in_dim, in_dim),
            nn.SiLU(inplace=True),
            nn.Linear(in_dim, num_bins),
        )

    def forward(self, features: torch.Tensor) -> torch.Tensor:
        if self.num_bins <= 0:
            raise ValueError("num_bins must be positive for survival prediction")
        return self.net(features)