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


class UNet2(nn.Module):
    def __init__(self, in_channels=3, out_channels=3, num_layers=4, base_filters=64):
        """
        Args:
            in_channels (int): Number of input channels (e.g., 3 for RGB).
            out_channels (int): Number of output channels (e.g., 3 for RGB).
            num_layers (int): Number of encoder/decoder layers.
            base_filters (int): Number of filters in the first layer. Subsequent layers double this.
        """
        super(UNet2, self).__init__()

        self.num_layers = num_layers

        # Encoder
        self.encoders = nn.ModuleList()
        self.pools = nn.ModuleList()
        filters = base_filters
        for i in range(num_layers):
            self.encoders.append(self.conv_block(in_channels if i == 0 else filters // 2, filters))
            self.pools.append(nn.MaxPool2d(kernel_size=2, stride=2))
            filters *= 2

        # Bottleneck
        self.bottleneck = self.conv_block(filters // 2, filters)

        # Decoder
        self.ups = nn.ModuleList()
        self.decoders = nn.ModuleList()
        for i in range(num_layers):
            self.ups.append(self.upconv(filters, filters // 2))
            self.decoders.append(self.conv_block(filters, filters // 2))
            filters //= 2

        # Output layer
        self.output_layer = nn.Conv2d(base_filters, out_channels, kernel_size=1)

    def conv_block(self, in_channels, out_channels):
        """
        Creates a convolutional block with two Conv2D layers followed by ReLU activations.
        """
        return nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
        )

    def upconv(self, in_channels, out_channels):
        """
        Creates an up-convolution (transposed convolution) layer.
        """
        return nn.ConvTranspose2d(in_channels, out_channels, kernel_size=2, stride=2)

    def forward(self, x):
        # Encoder path
        enc_features = []
        for i in range(self.num_layers):
            x = self.encoders[i](x)
            enc_features.append(x)
            x = self.pools[i](x)

        # Bottleneck
        x = self.bottleneck(x)

        # Decoder path
        for i in range(self.num_layers):
            x = self.ups[i](x)
            skip_connection = enc_features[self.num_layers - 1 - i]
            x = torch.cat((x, skip_connection), dim=1)  # Concatenate along channel dimension
            x = self.decoders[i](x)

        # Output layer
        return self.output_layer(x)


class DoubleConv(nn.Module):
    """(convolution => [BN] => ReLU) * 2"""

    def __init__(self, in_channels, out_channels, 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=3, padding=1, bias=False),
            nn.BatchNorm2d(mid_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(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):
        super().__init__()
        self.maxpool_conv = nn.Sequential(nn.MaxPool2d(2), DoubleConv(in_channels, out_channels))

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


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

    def __init__(self, in_channels, out_channels, 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)
        else:
            self.up = nn.ConvTranspose2d(in_channels, in_channels // 2, kernel_size=2, stride=2)
            self.conv = DoubleConv(in_channels, out_channels)

    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)


class UNet(nn.Module):
    def __init__(self, n_channels, n_classes, bilinear=False):
        super(UNet, self).__init__()
        self.n_channels = n_channels
        self.n_classes = n_classes
        self.bilinear = bilinear

        self.inc = DoubleConv(n_channels, 64)
        self.down1 = Down(64, 128)
        self.down2 = Down(128, 256)
        self.down3 = Down(256, 512)
        factor = 2 if bilinear else 1
        self.down4 = Down(512, 1024 // factor)
        self.up1 = Up(1024, 512 // factor, bilinear)
        self.up2 = Up(512, 256 // factor, bilinear)
        self.up3 = Up(256, 128 // factor, bilinear)
        self.up4 = Up(128, 64, bilinear)
        self.outc = OutConv(64, n_classes)

    def forward(self, x):
        x1 = self.inc(x)
        x2 = self.down1(x1)
        x3 = self.down2(x2)
        x4 = self.down3(x3)
        x5 = self.down4(x4)
        x = self.up1(x5, x4)
        x = self.up2(x, x3)
        x = self.up3(x, x2)
        x = self.up4(x, x1)
        logits = self.outc(x)
        return logits

    def use_checkpointing(self):
        self.inc = torch.utils.checkpoint(self.inc)
        self.down1 = torch.utils.checkpoint(self.down1)
        self.down2 = torch.utils.checkpoint(self.down2)
        self.down3 = torch.utils.checkpoint(self.down3)
        self.down4 = torch.utils.checkpoint(self.down4)
        self.up1 = torch.utils.checkpoint(self.up1)
        self.up2 = torch.utils.checkpoint(self.up2)
        self.up3 = torch.utils.checkpoint(self.up3)
        self.up4 = torch.utils.checkpoint(self.up4)
        self.outc = torch.utils.checkpoint(self.outc)
