


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


UpperCAmelCase_   :				Any						      = 4
UpperCAmelCase_   :				Dict						      = 3
class 				SCREAMING_SNAKE_CASE__						(       lowercase__    ):
      pass







def  SCREAMING_SNAKE_CASE_		(    __A				:    List[str]       )			->	List[Any]:

       """simple docstring"""

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







def  SCREAMING_SNAKE_CASE_		(    )			->	Dict:

       """simple docstring"""

       a_      :	List[str]							=						int(os.environ['RANK']       )
       a_      :	int							=						int(os.environ['WORLD_SIZE']       )

       a_      :	Optional[int]							=						ArgumentParser()
       parser.add_argument('--streaming'						,					type=__A       )
       parser.add_argument('--local_rank'						,					type=__A       )
       parser.add_argument('--num_workers'						,					type=__A						,					default=0       )
       a_      :	Tuple							=						parser.parse_args()
       a_      :	List[Any]							=						args.streaming
       a_      :	Tuple							=						args.num_workers

       a_      :	Any							=						{'shards': [F"""shard_{shard_idx}""" for shard_idx in range(__A       )]}
       a_      :	Optional[Any]							=						IterableDataset.from_generator(__A						,					gen_kwargs=__A       )
       if not streaming:
              a_      :	str							=						Dataset.from_list(list(__A       )       )

       a_      :	List[str]							=						split_dataset_by_node(__A						,					rank=__A						,					world_size=__A       )
       a_      :	List[Any]							=						torch.utils.data.DataLoader(__A						,					num_workers=__A       )

       a_      :	int							=						NUM_SHARDS * NUM_ITEMS_PER_SHARD
       a_      :	Tuple							=						full_size // world_size
       expected_local_size += int(rank < (full_size % world_size)       )

       a_      :	List[str]							=						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()






UpperCAmelCase_   :				Optional[int]						      = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

UpperCAmelCase_   :				List[str]						      = [{'type': 'code', 'content': INSTALL_CONTENT}]
UpperCAmelCase_   :				int						      = {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}


