import torch.nn as nn

class SISUGenerator(nn.Module):
    def __init__(self, upscale_scale=4):  # No noise_dim parameter
        super(SISUGenerator, self).__init__()
        layer = 32
        # Convolution layers
        self.conv1 = nn.Sequential(
            nn.Conv1d(1, layer * 2, kernel_size=7, padding=1),
            nn.PReLU(),
            nn.Conv1d(layer * 2, layer * 5, kernel_size=5, padding=1),
            nn.PReLU(),
            nn.Conv1d(layer * 5, layer * 5, kernel_size=3, padding=1),
            nn.PReLU()
        )

        # Transposed convolution for upsampling
        self.upsample = nn.ConvTranspose1d(layer * 5, layer * 5, kernel_size=upscale_scale,  stride=upscale_scale)

        self.conv2 = nn.Sequential(
            nn.Conv1d(layer * 5, layer * 5, kernel_size=3, padding=1),
            nn.PReLU(),
            nn.Conv1d(layer * 5, layer * 2, kernel_size=5, padding=1),
            nn.PReLU(),
            nn.Conv1d(layer * 2, 1, kernel_size=7, padding=1)
        )

    def forward(self, x, upscale_scale=4):
        x = self.conv1(x)
        x = self.upsample(x)
        x = self.conv2(x)
        return x