import torch
import torch.nn as nn
import torch.optim as optim
class UNet(nn.Module):
    def __init__(self):
        super(UNet, self).__init__()

        # Encoder
        self.encoder = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=4, stride=2, padding=1),  # 256 -> 128
            nn.ReLU(inplace=True),
            nn.Conv2d(64, 128, kernel_size=4, stride=2, padding=1),  # 128 -> 64
            nn.ReLU(inplace=True),
            nn.Conv2d(128, 256, kernel_size=4, stride=2, padding=1),  # 64 -> 32
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 512, kernel_size=4, stride=2, padding=1),  # 32 -> 16
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 1024, kernel_size=4, stride=2, padding=1),  # 16 -> 8
            nn.ReLU(inplace=True)
        )

        # Decoder
        self.decoder = nn.Sequential(
            nn.ConvTranspose2d(1024, 512, kernel_size=4, stride=2, padding=1),  # 8 -> 16
            nn.ReLU(inplace=True),
            nn.ConvTranspose2d(512, 256, kernel_size=4, stride=2, padding=1),  # 16 -> 32
            nn.ReLU(inplace=True),
            nn.ConvTranspose2d(256, 128, kernel_size=4, stride=2, padding=1),  # 32 -> 64
            nn.ReLU(inplace=True),
            nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1),  # 64 -> 128
            nn.ReLU(inplace=True),
            nn.ConvTranspose2d(64, 3, kernel_size=4, stride=2, padding=1),  # 128 -> 256
            nn.Tanh()  # Output range [-1, 1]
        )

    def forward(self, x):
        enc = self.encoder(x)
        dec = self.decoder(enc)
        return dec