import torch

class _InfiniteSampler(torch.utils.data.Sampler):
    """Wraps another Sampler to yield an infinite stream."""
    def __init__(self, sampler):
        self.sampler = sampler

    def __iter__(self):
        while True:
            for batch in self.sampler:
                yield batch

class InfiniteDataLoader:
    def __init__(self, dataset, weights, batch_size, num_workers):
        super().__init__()
        if weights == None:
            weights = torch.ones(len(dataset))

        batch_sampler = torch.utils.data.BatchSampler(
            torch.utils.data.WeightedRandomSampler(weights,
                replacement=True,
                num_samples=batch_size),
            batch_size=batch_size,
            drop_last=True)

        self._infinite_iterator = iter(torch.utils.data.DataLoader(
            dataset,
            num_workers=num_workers,
            batch_sampler=_InfiniteSampler(batch_sampler)
        ))

    def __iter__(self):
        while True:
            yield next(self._infinite_iterator)

    def __len__(self):
        raise ValueError

class FastDataLoader:
    """DataLoader wrapper with slightly improved speed by not respawning worker
    processes at every epoch."""
    def __init__(self, dataset, batch_size, num_workers):
        super().__init__()

        batch_sampler = torch.utils.data.BatchSampler(
            torch.utils.data.SequentialSampler(dataset),
            batch_size=batch_size,
            drop_last=False
        )

        self._infinite_iterator = iter(torch.utils.data.DataLoader(
            dataset,
            num_workers=num_workers,
            batch_sampler=_InfiniteSampler(batch_sampler)
        ))

        self._length = len(batch_sampler)

    def __iter__(self):
        for _ in range(len(self)):
            yield next(self._infinite_iterator)

    def __len__(self):
        return self._length