text: import copy
  import platform
  import random
  from functools import partial
  
  import albumentations as A
  import numpy as np
  from mmcv.parallel import collate
  from mmcv.runner import get_dist_info
  # from mmcv.utils import Registry, build_from_cfg
  from mmcv.utils.parrots_wrapper import DataLoader, PoolDataLoader
  from torch.utils.data import DistributedSampler
  
  from mmseg.utils import Registry, build_from_cfg
  
  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_pipeline(cfgs, default_args=None):
      """Build pipeline."""
      assert isinstance(cfgs,list), \
          f'Configs for pipeline must be a list, but get {type(cfgs)}.'
      transformers = []
      for cfg in cfgs:
          # if PIPELINES.get(cfg['type']):
          #     transformer = build_from_cfg(cfg, PIPELINES, default_args=default_args)
          # else:
          #     transformer = build_from_cfg(cfg, A, default_args=default_args, mode='module')
          try:
              transformer = build_from_cfg(cfg, A, default_args=default_args, mode='module')
          except:
              if PIPELINES.get(cfg['type']):
                  transformer = build_from_cfg(cfg, PIPELINES, default_args=default_args)
              else:
                  raise NotImplementedError 
          transformers.append(transformer)
      pipeline = A.Compose(transformers, additional_targets={'image1': 'image'})
      return pipeline
  
  
  def _concat_dataset(cfg, default_args=None):
      """Build :obj:`ConcatDataset by."""
      from .dataset_wrappers import ConcatDataset
      img_dir = cfg['img_dir']
      ann_dir = cfg.get('ann_dir', None)
      split = cfg.get('split', None)
      num_img_dir = len(img_dir) if isinstance(img_dir, (list, tuple)) else 1
      if ann_dir is not None:
          num_ann_dir = len(ann_dir) if isinstance(ann_dir, (list, tuple)) else 1
      else:
          num_ann_dir = 0
      if split is not None:
          num_split = len(split) if isinstance(split, (list, tuple)) else 1
      else:
          num_split = 0
      if num_img_dir > 1:
          assert num_img_dir == num_ann_dir or num_ann_dir == 0
          assert num_img_dir == num_split or num_split == 0
      else:
          assert num_split == num_ann_dir or num_ann_dir <= 1
      num_dset = max(num_split, num_img_dir)
  
      datasets = []
      for i in range(num_dset):
          data_cfg = copy.deepcopy(cfg)
          if isinstance(img_dir, (list, tuple)):
              data_cfg['img_dir'] = img_dir[i]
          if isinstance(ann_dir, (list, tuple)):
              data_cfg['ann_dir'] = ann_dir[i]
          if isinstance(split, (list, tuple)):
              data_cfg['split'] = split[i]
          datasets.append(build_dataset(data_cfg, default_args))
  
      return ConcatDataset(datasets)
  
  
  def build_dataset(cfg, default_args=None):
      """Build datasets."""
      from .dataset_wrappers import ConcatDataset, RepeatDataset
      if isinstance(cfg, (list, tuple)):
          dataset = ConcatDataset([build_dataset(c, default_args) for c in cfg])
      elif cfg['type'] == 'RepeatDataset':
          dataset = RepeatDataset(
              build_dataset(cfg['dataset'], default_args), cfg['times'])
      elif isinstance(cfg.get('img_dir'), (list, tuple)) or isinstance(
              cfg.get('split', None), (list, tuple)):
          dataset = _concat_dataset(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,
                       drop_last=False,
                       pin_memory=True,
                       dataloader_type='PoolDataLoader',
                       **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.
          seed (int | None): Seed to be used. Default: None.
          drop_last (bool): Whether to drop the last incomplete batch in epoch.
              Default: False
          pin_memory (bool): Whether to use pin_memory in DataLoader.
              Default: True
          dataloader_type (str): Type of dataloader. Default: 'PoolDataLoader'
          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)
          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
  
      assert dataloader_type in (
          'DataLoader',
          'PoolDataLoader'), f'unsupported dataloader {dataloader_type}'
  
      if dataloader_type == 'PoolDataLoader':
          dataloader = PoolDataLoader
      elif dataloader_type == 'DataLoader':
          dataloader = DataLoader
  
      data_loader = dataloader(
          dataset,
          batch_size=batch_size,
          sampler=sampler,
          num_workers=num_workers,
          collate_fn=partial(collate, samples_per_gpu=samples_per_gpu),
          pin_memory=pin_memory,
          shuffle=shuffle,
          worker_init_fn=init_fn,
          drop_last=drop_last,
          **kwargs)
  
      return data_loader
  
  
  def worker_init_fn(worker_id, num_workers, rank, seed):
      """Worker init func for dataloader.
  
      The seed of each worker equals to num_worker * rank + worker_id + user_seed
  
      Args:
          worker_id (int): Worker id.
          num_workers (int): Number of workers.
          rank (int): The rank of current process.
          seed (int): The random seed to use.
      """
  
      worker_seed = num_workers * rank + worker_id + seed
      np.random.seed(worker_seed)
      random.seed(worker_seed)
  