import platform
import random
from functools import partial
from typing import Optional, Union

import numpy as np
from mmcv.runner import get_dist_info
from mmcv.utils import Registry, build_from_cfg
from torch.utils.data import DataLoader
from torch.utils.data.dataset import Dataset

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


class DistributedSampler(_DistributedSampler):

    def __init__(self,
                 dataset,
                 num_replicas=None,
                 rank=None,
                 shuffle=True,
                 round_up=True):
        super().__init__(dataset, num_replicas=num_replicas, rank=rank)
        self.shuffle = shuffle
        self.round_up = round_up
        if self.round_up:
            self.total_size = self.num_samples * self.num_replicas
        else:
            self.total_size = len(self.dataset)

    def __iter__(self):
        # deterministically shuffle based on epoch
        if self.shuffle:
            g = torch.Generator()
            g.manual_seed(self.epoch)
            indices = torch.randperm(len(self.dataset), generator=g).tolist()
        else:
            indices = torch.arange(len(self.dataset)).tolist()

        # add extra samples to make it evenly divisible
        if self.round_up:
            indices = (
                indices *
                int(self.total_size / len(indices) + 1))[:self.total_size]
        assert len(indices) == self.total_size

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

        return iter(indices)


def build_dataloader(dataset: Dataset,
                     samples_per_gpu: int,
                     workers_per_gpu: int,
                     num_gpus: Optional[int] = 1,
                     dist: Optional[bool] = True,
                     shuffle: Optional[bool] = True,
                     round_up: Optional[bool] = True,
                     seed: Optional[Union[int, None]] = None,
                     persistent_workers: Optional[bool] = True,
                     **kwargs):
    """Build PyTorch DataLoader.

    In distributed training, each GPU/process has a dataloader.
    In non-distributed training, there is only one dataloader for all GPUs.

    Args:
        dataset (:obj:`Dataset`): A PyTorch dataset.
        samples_per_gpu (int): Number of training samples on each GPU, i.e.,
            batch size of each GPU.
        workers_per_gpu (int): How many subprocesses to use for data loading
            for each GPU.
        num_gpus (int, optional): Number of GPUs. Only used in non-distributed
            training.
        dist (bool, optional): Distributed training/test or not. Default: True.
        shuffle (bool, optional): Whether to shuffle the data at every epoch.
            Default: True.
        round_up (bool, optional): Whether to round up the length of dataset by
            adding extra samples to make it evenly divisible. Default: True.
        persistent_workers (bool): If True, the data loader will not shutdown
            the worker processes after a dataset has been consumed once.
            This allows to maintain the workers Dataset instances alive.
            The argument also has effect in PyTorch>=1.7.0.
            Default: True
        kwargs: any keyword argument to be used to initialize DataLoader

    Returns:
        DataLoader: A PyTorch dataloader.
    """
    rank, world_size = get_dist_info()
    if dist:
        sampler = DistributedSampler(
            dataset, world_size, rank, shuffle=shuffle, round_up=round_up)
        shuffle = False
        batch_size = samples_per_gpu
        num_workers = workers_per_gpu
    else:
        sampler = None
        batch_size = num_gpus * samples_per_gpu
        num_workers = num_gpus * workers_per_gpu

    init_fn = partial(
        worker_init_fn, num_workers=num_workers, rank=rank,
        seed=seed) if seed is not None else None

    data_loader = DataLoader(
        dataset,
        batch_size=batch_size,
        sampler=sampler,
        num_workers=num_workers,
        pin_memory=False,
        shuffle=shuffle,
        worker_init_fn=init_fn,
        persistent_workers=persistent_workers,
        **kwargs)

    return data_loader


def worker_init_fn(worker_id: int, num_workers: int, rank: int, seed: int):
    """Init random seed for each worker."""
    # The seed of each worker equals to
    # num_worker * rank + worker_id + user_seed
    worker_seed = num_workers * rank + worker_id + seed
    np.random.seed(worker_seed)
    random.seed(worker_seed)