



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__    =	4
a__    =	3







class 		snake_case						(	SCREAMING_SNAKE_CASE_  ):



   '''simple docstring'''






   pass



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	List[str]				)  ->     Optional[int]:
 for shard in shards:
  for i in range(SCREAMING_SNAKE_CASE__				):
   yield {"i": i, "shard": shard}



def       lowercase       (   )  ->     List[Any]:
 _snake_case    :  Union[str, Any]					=					int(os.environ["""RANK"""]				)
 _snake_case    :  int					=					int(os.environ["""WORLD_SIZE"""]				)

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

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

 _snake_case    :  Union[str, Any]					=					split_dataset_by_node(SCREAMING_SNAKE_CASE__				,       rank=SCREAMING_SNAKE_CASE__				,       world_size=SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Optional[int]					=					torch.utils.data.DataLoader(SCREAMING_SNAKE_CASE__				,       num_workers=SCREAMING_SNAKE_CASE__				)

 _snake_case    :  Union[str, Any]					=					NUM_SHARDS * NUM_ITEMS_PER_SHARD
 _snake_case    :  str					=					full_size // world_size
 expected_local_size += int(rank < (full_size % world_size)				)

 _snake_case    :  Dict					=					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_tokenizers_available, is_torch_available


a__    =	{
    """configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
    """tokenization_canine""": ["""CanineTokenizer"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a__    =	[
      """CANINE_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """CanineForMultipleChoice""",
      """CanineForQuestionAnswering""",
      """CanineForSequenceClassification""",
      """CanineForTokenClassification""",
      """CanineLayer""",
      """CanineModel""",
      """CaninePreTrainedModel""",
      """load_tf_weights_in_canine""",
  ]


if TYPE_CHECKING:
  from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
  from .tokenization_canine import CanineTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_canine import (
        CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
        CanineForMultipleChoice,
        CanineForQuestionAnswering,
        CanineForSequenceClassification,
        CanineForTokenClassification,
        CanineLayer,
        CanineModel,
        CaninePreTrainedModel,
        load_tf_weights_in_canine,
    )


else:
  import sys

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