from __future__ import annotations

import torch
from torch import nn
import torch.nn.functional as F

from .blocks import ConvBlock3d


class SupportNet(nn.Module):
    """Compact 3D U-Net style lesion support map network."""

    def __init__(self, in_channels: int = 1, base_channels: int = 24) -> None:
        super().__init__()
        self.enc1 = ConvBlock3d(in_channels, base_channels)
        self.enc2 = ConvBlock3d(base_channels, base_channels * 2, stride=2)
        self.enc3 = ConvBlock3d(base_channels * 2, base_channels * 4, stride=2)
        self.mid = ConvBlock3d(base_channels * 4, base_channels * 4)
        self.dec2 = ConvBlock3d(base_channels * 6, base_channels * 2)
        self.dec1 = ConvBlock3d(base_channels * 3, base_channels)
        self.out = nn.Conv3d(base_channels, 1, kernel_size=1)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        e1 = self.enc1(x)
        e2 = self.enc2(e1)
        e3 = self.enc3(e2)
        z = self.mid(e3)
        z = F.interpolate(z, size=e2.shape[2:], mode="trilinear", align_corners=False)
        z = self.dec2(torch.cat([z, e2], dim=1))
        z = F.interpolate(z, size=e1.shape[2:], mode="trilinear", align_corners=False)
        z = self.dec1(torch.cat([z, e1], dim=1))
        return self.out(z)