import torch

class IdentitySubset(torch.utils.data.Dataset):
    def __init__(self, dataset, indices, id_mapping, transform=None):
        """
        Args:
            dataset: The base dataset (CelebA or ImageFolder).
            indices: List of indices belonging to the selected identities.
            id_mapping: Dictionary mapping {old_label: new_label_0_to_N}.
            transform: Transformations to apply to the images.
        """
        self.dataset = dataset
        self.indices = indices
        self.id_mapping = id_mapping
        self.transform = transform

    def __getitem__(self, idx):
        # Access the base dataset using the stored index
        img, old_id = self.dataset[self.indices[idx]]

        # Apply transform if provided
        if self.transform:
            img = self.transform(img)

        # Handle Label Logic:
        # CelebA returns a Tensor, ImageFolder returns an int.
        # We convert to a standard Python int for the dictionary lookup.
        clean_id = old_id.item() if torch.is_tensor(old_id) else old_id

        # Map the original identity to our new 0 -> N-1 range
        return img, self.id_mapping[clean_id]
    
    def __len__(self):
        return len(self.indices)