




import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


lowerCamelCase				:		int     =   4
lowerCamelCase				:		int     =   3







class   A(						UpperCamelCase   ):



 '''simple docstring'''


 pass

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       List[str]     ):


      '''simple docstring'''



      for shard in shards:
            for i in range(lowercase     ):
                  yield {"i": i, "shard": shard}

def 			_SCREAMING_SNAKE_CASE			(							):


      '''simple docstring'''



      lowerCamelCase_		     =    int(os.environ['RANK']     )
      lowerCamelCase_		     =    int(os.environ['WORLD_SIZE']     )

      lowerCamelCase_		     =    ArgumentParser()
      parser.add_argument('--streaming'	,							type=lowercase     )
      parser.add_argument('--local_rank'	,							type=lowercase     )
      parser.add_argument('--num_workers'	,							type=lowercase	,							default=0     )
      lowerCamelCase_		     =    parser.parse_args()
      lowerCamelCase_		     =    args.streaming
      lowerCamelCase_		     =    args.num_workers

      lowerCamelCase_		     =    {'shards': [f"""shard_{shard_idx}""" for shard_idx in range(lowercase     )]}
      lowerCamelCase_		     =    IterableDataset.from_generator(lowercase	,							gen_kwargs=lowercase     )
      if not streaming:
            lowerCamelCase_		     =    Dataset.from_list(list(lowercase     )     )

      lowerCamelCase_		     =    split_dataset_by_node(lowercase	,							rank=lowercase	,							world_size=lowercase     )
      lowerCamelCase_		     =    torch.utils.data.DataLoader(lowercase	,							num_workers=lowercase     )

      lowerCamelCase_		     =    NUM_SHARDS * NUM_ITEMS_PER_SHARD
      lowerCamelCase_		     =    full_size // world_size
      expected_local_size += int(rank < (full_size % world_size)     )

      lowerCamelCase_		     =    sum(1 for _ in dataloader     )
      if local_size != expected_local_size:
            raise FailedTestError(f"""local_size {local_size} != expected_local_size {expected_local_size}"""     )


if __name__ == "__main__":
 main()









import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


lowerCamelCase				:		Union[str, Any]     =   logging.get_logger(__name__)







class   A(						UpperCamelCase   ):



 '''simple docstring'''


 def __init__(				self					:  int						,							*A_					:  str						,							**A_					:  Optional[int]  )   ->	None:

       """simple docstring"""






       warnings.warn(
           'The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
           ' use GLPNImageProcessor instead.'						,							A_						,							)
       super().__init__(*A_						,							**A_  )



