import random
import numpy as np
import torch
from functools import partial
from typing import Optional, Union
import torch.distributed
from torch.utils.data import DataLoader
from torch.utils.data.dataset import Dataset


def build_dataloader(dataset: Dataset,
                     samples_per_gpu: int,
                     workers_per_gpu: int,
                     num_gpus: Optional[int] = 1,
                     shuffle: Optional[bool] = True,
                     round_up: Optional[bool] = True,
                     seed: Optional[Union[int, None]] = None,
                     persistent_workers: Optional[bool] = False,
                     **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 = torch.distributed.get_rank() if torch.distributed.is_initialized() else 0
    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)
