





'''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


A__    :       Union[str, Any]              =4
A__    :       Optional[Any]              =3




class 			UpperCAmelCase					(      snake_case_       ):
      pass




def        UpperCamelCase__				(		lowerCAmelCase  ):





      """simple docstring"""


      for shard in shards:
            for i in range(lowerCAmelCase  ):
                  yield {"i": i, "shard": shard}
def        UpperCamelCase__				(		):





      """simple docstring"""


      _lowerCAmelCase												=							int(os.environ["""RANK"""]  )
      _lowerCAmelCase												=							int(os.environ["""WORLD_SIZE"""]  )

      _lowerCAmelCase												=							ArgumentParser()
      parser.add_argument("""--streaming"""							,     type=lowerCAmelCase  )
      parser.add_argument("""--local_rank"""							,     type=lowerCAmelCase  )
      parser.add_argument("""--num_workers"""							,     type=lowerCAmelCase							,     default=0  )
      _lowerCAmelCase												=							parser.parse_args()
      _lowerCAmelCase												=							args.streaming
      _lowerCAmelCase												=							args.num_workers

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

      _lowerCAmelCase												=							split_dataset_by_node(lowerCAmelCase							,     rank=lowerCAmelCase							,     world_size=lowerCAmelCase  )
      _lowerCAmelCase												=							torch.utils.data.DataLoader(lowerCAmelCase							,     num_workers=lowerCAmelCase  )

      _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()













'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available


A__    :       int              ={'''tokenization_herbert''': ['''HerbertTokenizer''']}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__    :       Dict              =['''HerbertTokenizerFast''']


if TYPE_CHECKING:
      from .tokenization_herbert import HerbertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_herbert_fast import HerbertTokenizerFast

else:
      import sys

      A__    :       List[Any]              =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






