import torch
from torch import nn

class VisualProcessingAGI(nn.Module):
    def __init__(self):
        super(VisualProcessingAGI, self).__init__()
        
        # Backbone network (Feature Extractor)
        self.feature_extractor = nn.Sequential(
            nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )
        
        # Attention Mechanism
        self.attention_layer = nn.Sequential(
            nn.Conv2d(in_channels=128, out_channels=64, kernel_size=3, padding=1),
            nn.Softmax(dim=-1)
        )
        
        # Decoder (Reconstructing the image with attention weights)
        self.decoder = nn.Sequential(
            nn.Upsample(scale_factor=2, mode='nearest'),
            nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.Upsample(scale_factor=2, mode='nearest'),
            nn.Conv2d(in_channels=128, out_channels=3, kernel_size=3, padding=1)
        )
        
    def forward(self, x):
        # Forward pass through the feature extractor
        features = self.feature_extractor(x)
        
        # Apply attention mechanism to focus on important regions
        att_map = self.attention_layer(features)
        
        # Multiply each region by its attention weight and sum up
        attended_features = (features * att_map).sum(dim=1, keepdim=True)
        
        # Pass the attended features through the decoder for reconstruction
        reconstructed_x = self.decoder(attended_features)
        
        return reconstructed_x

# Example usage
visual_processing_agi = VisualProcessingAGI()

# Assuming we have an input image tensor of shape [batch_size, 3, height, width]
input_image = torch.randn(1, 3, 256, 256)

# Forward pass to get the reconstructed image
output_image = visual_processing_agi(input_image)
print(output_image.shape)  # Should be [1, 3, 256, 256]