import torch
from torch import nn
from torch.nn import functional as F
from nanograd.models.stable_diffusion.decoder import VAE_AttentionBlock, VAE_ResidualBlock

class VAE_Encoder(nn.Sequential):
    def __init__(self):
        super().__init__(
            nn.Conv2d(3, 128, kernel_size=3, padding=1),
    
            VAE_ResidualBlock(128, 128),
            VAE_ResidualBlock(128, 128),
            
            nn.Conv2d(128, 128, kernel_size=3, stride=2, padding=0),
            
            VAE_ResidualBlock(128, 256), 
            VAE_ResidualBlock(256, 256), 
            
            nn.Conv2d(256, 256, kernel_size=3, stride=2, padding=0), 
            
            VAE_ResidualBlock(256, 512), 
            VAE_ResidualBlock(512, 512), 
            
            nn.Conv2d(512, 512, kernel_size=3, stride=2, padding=0), 
            
            VAE_ResidualBlock(512, 512), 
            VAE_ResidualBlock(512, 512), 
            VAE_ResidualBlock(512, 512), 
            VAE_AttentionBlock(512), 
            VAE_ResidualBlock(512, 512), 
            
            nn.GroupNorm(32, 512), 
            
            nn.SiLU(), 

            nn.Conv2d(512, 8, kernel_size=3, padding=1), 

            nn.Conv2d(8, 8, kernel_size=1, padding=0), 
        )

    def forward(self, x, noise):
        for module in self:

            if getattr(module, 'stride', None) == (2, 2):  
                x = F.pad(x, (0, 1, 0, 1))
            
            x = module(x)
        mean, log_variance = torch.chunk(x, 2, dim=1)
        log_variance = torch.clamp(log_variance, -30, 20)
        variance = log_variance.exp()
        stdev = variance.sqrt()
        x = mean + stdev * noise
        # Constant taken from: https://github.com/CompVis/stable-diffusion/blob/21f890f9da3cfbeaba8e2ac3c425ee9e998d5229/configs/stable-diffusion/v1-inference.yaml#L17C1-L17C1
        x *= 0.18215
        
        return x