import math

import torch
from torch.utils.data import DistributedSampler as _DistributedSampler
from .sampler import SAMPLER


@SAMPLER.register_module()
class DistributedSampler(_DistributedSampler):
    def __init__(
        self, dataset=None, num_replicas=None, rank=None, shuffle=True, seed=0
    ):
        super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
        # for the compatibility from PyTorch 1.3+
        self.seed = seed if seed is not None else 0

    def __iter__(self):
        # deterministically shuffle based on epoch
        if self.shuffle:
            assert False
        else:
            indices = torch.arange(len(self.dataset)).tolist()

        # add extra samples to make it evenly divisible
        # in case that indices is shorter than half of total_size
        indices = (indices * math.ceil(self.total_size / len(indices)))[
            : self.total_size
        ]
        assert len(indices) == self.total_size

        # subsample
        per_replicas = self.total_size // self.num_replicas
        # indices = indices[self.rank:self.total_size:self.num_replicas]
        indices = indices[self.rank * per_replicas : (self.rank + 1) * per_replicas]
        assert len(indices) == self.num_samples

        # print('\nDistributedSampler')
        # print(indices)
        # print(len(indices))
        # print(self.epoch)

        return iter(indices)