import torch
import torch.nn as nn
import torch.nn.functional as F

class VQModelInterface(nn.Module):
    def __init__(self):
        super().__init__()
        # Placeholder for VQ model interface
        pass

    def encode(self, x):
        # Placeholder for encoding method
        return x

    def decode(self, z):
        # Placeholder for decoding method
        return z

    def quantize(self, z):
        # Placeholder for quantization method
        return z, None, [None, None, None]

class IdentityFirstStage(nn.Module):
    def __init__(self):
        super().__init__()

    def encode(self, x):
        return x

    def decode(self, x):
        return x

class AutoencoderKL(nn.Module):
    def __init__(self):
        super().__init__()
        # Placeholder for AutoencoderKL
        self.encoder = nn.Identity()
        self.decoder = nn.Identity()

    def encode(self, x):
        return self.encoder(x)

    def decode(self, z):
        return self.decoder(z)

    def forward(self, x):
        z = self.encode(x)
        return self.decode(z)
