import torch
import torch.nn as nn

class Upsample(nn.Module):
    def __init__(self, in_channels : int, with_conv : bool):
        super().__init__()
        self.with_conv = with_conv
        if self.with_conv:
            self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size = 3, stride = 1, padding = 1)
            
    def forward(self, x):
        x = torch.nn.functional.interpolate(x, scale_factor = 2.0, mode = "nearest")
        if self.with_conv:
            x = self.conv(x)
        return x

class Downsample(nn.Module):
    def __init__(self, in_channels : int, with_conv : bool):
        super().__init__()
        self.with_conv = with_conv
        if self.with_conv:
            self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size = 3, stride = 2, padding = 0)
            
    def forward(self, x):
        if self.with_conv:
            pad = (0, 1, 0, 1)
            x = torch.nn.functional.pad(x, pad, mode = "constant", value = 0)
            x = self.conv(x)
        else:
            x = torch.nn.functional.avg_pool2d(x, kernel_size = 2, stride = 2)
        return x