from typing import Iterator, Optional

import torch
from torch.utils.data import Dataset, IterableDataset


class ValidationWrapper(Dataset):
    """Wraps a dataset so that PyTorch Lightning's validation step can be turned into a
    visualization step.
    """

    dataset: Dataset
    dataset_iterator: Optional[Iterator]
    length: int

    def __init__(self, dataset: Dataset, length: int) -> None:
        super().__init__()
        self.dataset = dataset
        self.length = length
        self.dataset_iterator = None

    def __len__(self):
        return self.length
        
    def __getitem__(self, index: tuple):
        if isinstance(self.dataset, IterableDataset):
            if self.dataset_iterator is None:
                self.dataset_iterator = iter(self.dataset)
            return next(self.dataset_iterator)
        
        random_index = torch.randint(0, len(self.dataset), tuple())
        random_context_num = torch.randint(2, self.dataset.view_sampler.num_context_views + 1, tuple())
        # breakpoint()
        return self.dataset[random_index.item(), random_context_num.item()]