import os
import torch
from torch.utils.data import Dataset, DataLoader
from sklearn.model_selection import train_test_split

class CustomDataset(Dataset):
    def __init__(self, file_paths, transform=None):
        self.file_paths = file_paths
        self.transform = transform
        self.file_names = [os.path.basename(path) for path in file_paths]

    def __len__(self):
        return len(self.file_paths)

    def __getitem__(self, idx):
        data = torch.load(self.file_paths[idx], weights_only=True)
        
        images = data[:6]  
        labels = data[6]           
        
        images = images.float()  
        labels = labels.long()

        if self.transform:
            images = self.transform(images)
            labels = self.transform(labels)
        
        return images, labels, self.file_names[idx]

def generate_file_paths(base_path):
    file_paths = []
    for frame in os.listdir(base_path):
        frame_path = os.path.join(base_path, frame)
        if frame_path.endswith('.mat.pt'):
            file_paths.append(frame_path)
    return [path for path in file_paths if os.path.exists(path)]

def load_data(base_path, batch_size=4, num_workers=2, test_size=0.2):
    file_paths = generate_file_paths(base_path)
    
    train_paths, test_paths = train_test_split(file_paths, test_size=test_size, random_state=42)
    
    train_dataset = CustomDataset(file_paths=train_paths)
    test_dataset = CustomDataset(file_paths=test_paths)
    
    train_loader = DataLoader(
        train_dataset, 
        batch_size=batch_size, 
        shuffle=True, 
        num_workers=num_workers, 
        drop_last=True
    )
    
    test_loader = DataLoader(
        test_dataset, 
        batch_size=batch_size, 
        shuffle=False, 
        num_workers=num_workers, 
        drop_last=True
    )
    
    return train_loader, test_loader