import platform
import random
from copy import deepcopy
from functools import partial

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

from .samplers import DistributedSampler

if platform.system() != 'Windows':
    # https://github.com/pytorch/pytorch/issues/973
    import resource
    rlimit = resource.getrlimit(resource.RLIMIT_NOFILE)
    hard_limit = rlimit[1]
    soft_limit = min(4096, hard_limit)
    resource.setrlimit(resource.RLIMIT_NOFILE, (soft_limit, hard_limit))

DATASETS = Registry('dataset')
PIPELINES = Registry('pipeline')


def build_dataset(cfg, default_args=None):
    """Build dataset.

    Args:
        cfg (dict): Config for the dataset.
        default_args (dict | None, optional): Default arguments.
            Defaults to None.

    Returns:
        Objet: Dataset for sampling data batch.
    """
    from .dataset_wrappers import RepeatDataset
    if isinstance(cfg, (list, tuple)):
        raise NotImplementedError('Currently, we do NOT support ConcatDataset')
        # dataset = ConcatDataset(
        #   [build_dataset(c, default_args) for c in cfg])
    if cfg['type'] == 'RepeatDataset':
        dataset = RepeatDataset(
            build_dataset(cfg['dataset'], default_args), cfg['times'])
    # add support for using datasets from `MMClassification`
    elif cfg['type'].startswith('mmcls.'):
        try:
            from mmcls.datasets import build_dataset as build_dataset_mmcls
        except ImportError:
            raise ImportError(
                f'Please install mmcls to use {cfg["type"]} dataset.')
        _cfg = deepcopy(cfg)
        _cfg['type'] = _cfg['type'][6:]
        dataset = build_dataset_mmcls(_cfg, default_args)
    else:
        dataset = build_from_cfg(cfg, DATASETS, default_args)

    return dataset


def build_dataloader(dataset,
                     samples_per_gpu,
                     workers_per_gpu,
                     num_gpus=1,
                     dist=True,
                     shuffle=True,
                     seed=None,
                     persistent_workers=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 (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): Number of GPUs. Only used in non-distributed training.
        dist (bool): Distributed training/test or not. Default: True.
        shuffle (bool): Whether to shuffle the data at every epoch.
            Default: True.
        persistent_workers (bool, optional): 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: False.
        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,
            samples_per_gpu=samples_per_gpu)
        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

    if torch.__version__ >= '1.7.0':
        data_loader = DataLoader(
            dataset,
            batch_size=batch_size,
            sampler=sampler,
            num_workers=num_workers,
            collate_fn=partial(collate, samples_per_gpu=samples_per_gpu),
            shuffle=shuffle,
            worker_init_fn=init_fn,
            persistent_workers=persistent_workers,
            **kwargs)
    else:
        data_loader = DataLoader(
            dataset,
            batch_size=batch_size,
            sampler=sampler,
            num_workers=num_workers,
            collate_fn=partial(collate, samples_per_gpu=samples_per_gpu),
            shuffle=shuffle,
            worker_init_fn=init_fn,
            **kwargs)

    return data_loader


def worker_init_fn(worker_id, num_workers, rank, seed):
    # 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)
