"""
Modified from torch.utils.data.distributed.DistributedSampler
Support enlarging the dataset for *iteration-oriented* training,
for saving time when restart the dataloader after each epoch
"""
import math

import torch
import torch.distributed as dist
from torch.utils.data.sampler import Sampler


class DistIterSampler(Sampler):
    """Sampler that restricts data loading to a subset of the dataset.

    It is especially useful in conjunction with
    :class:`torch.nn.parallel.DistributedDataParallel`. In such case, each
    process can pass a DistributedSampler instance as a DataLoader sampler,
    and load a subset of the original dataset that is exclusive to it.

    .. note::
        Dataset is assumed to be of constant size.

    Arguments:
        dataset: Dataset used for sampling.
        num_replicas (optional): Number of processes participating in
            distributed training.
        rank (optional): Rank of the current process within num_replicas.
    """

    def __init__(self, dataset, num_replicas=None, rank=None, ratio=100):
        if num_replicas is None:
            if not dist.is_available():
                raise RuntimeError(
                    "Requires distributed \
                                    package to be available"
                )
            num_replicas = dist.get_world_size()
        if rank is None:
            if not dist.is_available():
                raise RuntimeError(
                    "Requires distributed \
                                    package to be available"
                )
            rank = dist.get_rank()
        self.dataset = dataset
        self.num_replicas = num_replicas
        self.rank = rank
        self.epoch = 0
        self.num_samples = int(math.ceil(len(self.dataset) * ratio / self.num_replicas))
        self.total_size = self.num_samples * self.num_replicas

    def __iter__(self):
        # deterministically shuffle based on epoch
        g = torch.Generator()
        g.manual_seed(self.epoch)
        indices = torch.randperm(self.total_size, generator=g).tolist()

        dsize = len(self.dataset)
        indices = [v % dsize for v in indices]

        # subsample
        indices = indices[self.rank : self.total_size : self.num_replicas]
        assert len(indices) == self.num_samples

        return iter(indices)

    def __len__(self):
        return self.num_samples

    def set_epoch(self, epoch):
        self.epoch = epoch
