import torch.nn as nn

class SISUDiscriminator(nn.Module):
    def __init__(self):
        super(SISUDiscriminator, self).__init__()
        self.model = nn.Sequential(
            nn.Conv1d(2, 64, kernel_size=4, stride=2, padding=1),  # Now accepts 2 input channels
            nn.LeakyReLU(0.2),
            nn.Conv1d(64, 128, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm1d(128),
            nn.LeakyReLU(0.2),
            nn.Conv1d(128, 256, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm1d(256),
            nn.LeakyReLU(0.2),
            nn.Conv1d(256, 512, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm1d(512),
            nn.LeakyReLU(0.2),
            nn.Conv1d(512, 1, kernel_size=4, stride=1, padding=0),
            nn.Sigmoid()
        )

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