import torch
import torch.nn as nn
from models.Layers import CNNBlock


class Discriminator(nn.Module):
    def __init__(self, in_channels=3, features=[64, 128, 256, 512]):
        super().__init__()
        self.initial = nn.Sequential(
            nn.Conv2d(in_channels * 2, features[0], kernel_size=4, stride=2, padding=1, padding_mode="reflect"),
            nn.LeakyReLU(0.2),
        )

        layers = []
        in_channels = features[0]
        for feature in features[1:]:
            layers.append(
                CNNBlock(in_channels, feature, stride=1 if feature == features[-1] else 2),
            )
            in_channels = feature

        layers.append(
            nn.Conv2d(
                in_channels, 1, kernel_size=4, stride=1, padding=1, padding_mode="reflect"
            )
        )

        self.model = nn.Sequential(*layers)

    def forward(self, x, y):
        x = torch.cat([x, y], dim=1)
        x = self.initial(x)
        return self.model(x)
