




'''simple docstring'''


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_      :  Optional[int]				     =  4
lowerCAmelCase_      :  Optional[Any]				     =  3







class      SCREAMING_SNAKE_CASE    (						snake_case_				):
      pass



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					int				):




     """simple docstring"""


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



def  _SCREAMING_SNAKE_CASE     (      ):




     """simple docstring"""


     a_     :							str     						=  int(os.environ["""RANK"""]				)
     a_     :							Dict     						=  int(os.environ["""WORLD_SIZE"""]				)

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

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

     a_     :							Dict     						=  split_dataset_by_node(__UpperCamelCase ,    rank=__UpperCamelCase ,    world_size=__UpperCamelCase				)
     a_     :							Tuple     						=  torch.utils.data.DataLoader(__UpperCamelCase ,    num_workers=__UpperCamelCase				)

     a_     :							Optional[Any]     						=  NUM_SHARDS * NUM_ITEMS_PER_SHARD
     a_     :							Dict     						=  full_size // world_size
     expected_local_size += int(rank < (full_size % world_size)				)

     a_     :							List[Any]     						=  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()





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


lowerCamelCase		       =      {}


try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase		       =      ["""MLukeTokenizer"""]

if TYPE_CHECKING:
						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_mluke import MLukeTokenizer


else:
						import sys

						lowerCamelCase		       =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


