import torch
import torch.nn as nn

class ConvBlock(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(ConvBlock, self).__init__()
        self.conv = 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 forward(self, x):
        return self.conv(x)
    
class UpConv(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(UpConv, self).__init__()
        self.up = nn.ConvTranspose2d(in_channels, out_channels, kernel_size=2, stride=2)

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

class UNet(nn.Module):
    def __init__(self, in_channels=3, out_channels=1):
        super(UNet, self).__init__()

        self.encoder1 = ConvBlock(in_channels, 64)
        self.encoder2 = ConvBlock(64, 128)
        self.encoder3 = ConvBlock(128, 256)
        self.encoder4 = ConvBlock(256, 512)

        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)

        self.bottleneck = ConvBlock(512, 1024)

        self.upconv4 = UpConv(1024, 512)
        self.decoder4 = ConvBlock(1024, 512)
        self.upconv3 = UpConv(512, 256)
        self.decoder3 = ConvBlock(512, 256)
        self.upconv2 = UpConv(256, 128)
        self.decoder2 = ConvBlock(256, 128)
        self.upconv1 = UpConv(128, 64)
        self.decoder1 = ConvBlock(128, 64)

        self.final_conv = nn.Conv2d(64, out_channels, kernel_size=1)

    def forward(self, x):
        enc1 = self.encoder1(x)
        enc2 = self.encoder2(self.pool(enc1))
        enc3 = self.encoder3(self.pool(enc2))
        enc4 = self.encoder4(self.pool(enc3))

        bottleneck = self.bottleneck(self.pool(enc4))

        dec4 = self.upconv4(bottleneck)
        dec4 = torch.cat((enc4, dec4), dim=1)
        dec4 = self.decoder4(dec4)
        
        dec3 = self.upconv3(dec4)
        dec3 = torch.cat((enc3, dec3), dim=1)
        dec3 = self.decoder3(dec3)

        dec2 = self.upconv2(dec3)
        dec2 = torch.cat((enc2, dec2), dim=1)
        dec2 = self.decoder2(dec2)

        dec1 = self.upconv1(dec2)
        dec1 = torch.cat((enc1, dec1), dim=1)
        dec1 = self.decoder1(dec1)

        return self.final_conv(dec1)