import torch
import torch.nn as nn

class Autoencoder(nn.Module):
    def __init__(self):
        super(Autoencoder, self).__init__()
        self.encoder = nn.Sequential(
            nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(2, stride=2),
            nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(2, stride=2)
        )
        
        self.decoder = nn.Sequential(
            nn.Conv2d(64, 32, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.UpsamplingNearest2d(scale_factor=2),
            nn.Conv2d(32, 32, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.UpsamplingNearest2d(scale_factor=2),
            nn.Conv2d(32, 1, kernel_size=3, stride=1, padding=1),
            nn.Sigmoid()
        )

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