import torch
import torch.nn as nn

class UNetBlock(nn.Module):
    def __init__(self, in_channels, out_channels, down=True, use_dropout=False):
        super().__init__()
        if down:
            self.block = nn.Sequential(
                nn.Conv2d(in_channels, out_channels, 4, 2, 1, bias=False),
                nn.BatchNorm2d(out_channels),
                nn.LeakyReLU(0.2, inplace=True)
            )
        else:
            self.block = nn.Sequential(
                nn.ConvTranspose2d(in_channels, out_channels, 4, 2, 1, bias=False),
                nn.BatchNorm2d(out_channels),
                nn.ReLU(inplace=True)
            )
        self.use_dropout = use_dropout
        if use_dropout:
            self.dropout = nn.Dropout(0.5)

    def forward(self, x):
        x = self.block(x)
        return self.dropout(x) if self.use_dropout else x


class UNetGenerator(nn.Module):
    def __init__(self, in_channels=3, out_channels=3):
        super().__init__()
        # Encoder
        self.down1 = UNetBlock(in_channels, 64, down=True, use_dropout=False)
        self.down2 = UNetBlock(64, 128)
        self.down3 = UNetBlock(128, 256)
        self.down4 = UNetBlock(256, 512)
        self.down5 = UNetBlock(512, 512)
        self.down6 = UNetBlock(512, 512)
        self.down7 = UNetBlock(512, 512)
        self.down8 = nn.Sequential(
            nn.Conv2d(512, 512, 4, 2, 1), nn.ReLU()
        )

        # Decoder
        self.up1 = UNetBlock(512, 512, down=False, use_dropout=True)
        self.up2 = UNetBlock(1024, 512, down=False, use_dropout=True)
        self.up3 = UNetBlock(1024, 512, down=False, use_dropout=True)
        self.up4 = UNetBlock(1024, 512, down=False)
        self.up5 = UNetBlock(1024, 256, down=False)
        self.up6 = UNetBlock(512, 128, down=False)
        self.up7 = UNetBlock(256, 64, down=False)

        self.final = nn.Sequential(
            nn.ConvTranspose2d(128, out_channels, 4, 2, 1),
            nn.Tanh()
        )

    def forward(self, x):
        # Encoder
        d1 = self.down1(x)
        d2 = self.down2(d1)
        d3 = self.down3(d2)
        d4 = self.down4(d3)
        d5 = self.down5(d4)
        d6 = self.down6(d5)
        d7 = self.down7(d6)
        bottleneck = self.down8(d7)

        # Decoder with skip connections
        u1 = self.up1(bottleneck)
        u2 = self.up2(torch.cat([u1, d7], dim=1))
        u3 = self.up3(torch.cat([u2, d6], dim=1))
        u4 = self.up4(torch.cat([u3, d5], dim=1))
        u5 = self.up5(torch.cat([u4, d4], dim=1))
        u6 = self.up6(torch.cat([u5, d3], dim=1))
        u7 = self.up7(torch.cat([u6, d2], dim=1))
        out = self.final(torch.cat([u7, d1], dim=1))
        return out