"""Parts of the U-Net model"""

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


class DoubleConv(nn.Module):
    """(Convolution => [N] => SiLU) * 2"""

    def __init__(
        self,
        in_channels,
        out_channels,
        kernel_size: int = 3,
        norm_module: nn.Module = nn.BatchNorm2d,
        mid_channels=None,
    ):
        super().__init__()
        if not mid_channels:
            mid_channels = out_channels
        self.double_conv = nn.Sequential(
            nn.Conv2d(
                in_channels,
                mid_channels,
                kernel_size=kernel_size,
                padding="same",
                bias=False,
            ),
            norm_module(mid_channels),
            nn.SiLU(inplace=True),
            nn.Conv2d(
                mid_channels,
                out_channels,
                kernel_size=kernel_size,
                padding="same",
                bias=False,
            ),
            norm_module(out_channels),
            nn.SiLU(inplace=True),
        )

    def forward(self, x):
        return self.double_conv(x)


class Down(nn.Module):
    """Downscaling with maxpool then double conv"""

    def __init__(
        self,
        in_channels,
        out_channels,
        kernel_size: int = 3,
        norm_module: nn.Module = nn.BatchNorm2d,
    ):
        super().__init__()
        self.maxpool_conv = nn.Sequential(
            nn.MaxPool2d(2),
            DoubleConv(
                in_channels,
                out_channels,
                kernel_size=kernel_size,
                norm_module=norm_module,
            ),
        )

    def forward(self, x):
        return self.maxpool_conv(x)


class Up(nn.Module):
    """Upscaling then double conv"""

    def __init__(
        self,
        in_channels,
        out_channels,
        kernel_size: int = 3,
        norm_module: nn.Module = nn.BatchNorm2d,
        bilinear=True,
    ):
        super().__init__()

        # if bilinear, use the normal convolutions to reduce the number of channels
        if bilinear:
            self.up = nn.Upsample(scale_factor=2, mode="bilinear", align_corners=True)
            self.conv = DoubleConv(
                in_channels,
                out_channels,
                in_channels // 2,
                kernel_size=kernel_size,
                norm_module=norm_module,
            )
        else:
            self.up = nn.ConvTranspose2d(
                in_channels, in_channels // 2, kernel_size=2, stride=2
            )
            self.conv = DoubleConv(
                in_channels,
                out_channels,
                kernel_size=kernel_size,
                norm_module=norm_module,
            )

    def forward(self, x1, x2):
        x1 = self.up(x1)
        # input is CHW
        diffY = x2.size()[2] - x1.size()[2]
        diffX = x2.size()[3] - x1.size()[3]

        x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2, diffY // 2, diffY - diffY // 2])
        # if you have padding issues, see
        # https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a
        # https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd
        x = torch.cat([x2, x1], dim=1)
        return self.conv(x)


class OutConv(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(OutConv, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)

    def forward(self, x):
        return self.conv(x)
