import torch
import torch.nn as nn
import torchvision.models as models

class ResNetLSTM(nn.Module):
    def __init__(self, num_classes=1, hidden_size=256, num_layers=2):
        super(ResNetLSTM, self).__init__()
        
        # Load Pretrained ResNet
        resnet = models.resnet18(pretrained=True)
        
        # Remove last fully connected layer
        # ResNet18 fc input size is 512
        modules = list(resnet.children())[:-1] 
        self.resnet = nn.Sequential(*modules)
        
        # Freeze ResNet params (Optional: unfreeze later or partial unfreeze)
        # For now, let's fine-tune all or freeze? 
        # Fine-tuning is usually better if we have enough data. 
        # User has ~4k videos, which is decent. Let's NOT freeze.
        
        self.lstm = nn.LSTM(
            input_size=512, 
            hidden_size=hidden_size, 
            num_layers=num_layers, 
            batch_first=True,
            dropout=0.5
        )
        
        self.fc = nn.Linear(hidden_size, num_classes)
        self.sigmoid = nn.Sigmoid() # For binary classification logic if needed manually, but we use BCEWithLogitsLoss
        
    def forward(self, x):
        # x shape: (Batch, Frames, Channels, Height, Width)
        # Example: (B, 30, 3, 224, 224)
        
        b, t, c, h, w = x.size()
        
        # Flatten batch and frames for CNN processing
        # (B*T, 3, 224, 224)
        c_in = x.view(b * t, c, h, w)
        
        # CNN Feature Extraction
        # Output: (B*T, 512, 1, 1) -> squeeze -> (B*T, 512)
        features = self.resnet(c_in)
        features = features.view(features.size(0), -1) 
        
        # Reshape back to (B, T, Features)
        r_in = features.view(b, t, -1)
        
        # LSTM Temporal processing
        # Output: (B, T, Hidden)
        # hidden/cell: (Layers, B, Hidden)
        lstm_out, _ = self.lstm(r_in)
        
        # Take the output from the last time step
        # (B, Hidden)
        last_out = lstm_out[:, -1, :]
        
        # Classification
        out = self.fc(last_out)
        
        return out