
"""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     :			str  						=			4
_A     :			List[Any]  						=			3

class 						a__		( a_		):
   pass


def     __magic_name__      ( __snake_case							:			List[str]    )					->							Dict:
       for shard in shards:
              for i in range(__snake_case    ):
                     yield {"i": i, "shard": shard}


def     __magic_name__      ( )					->							Dict:
       lowercase       :							Tuple									=				int(os.environ["RANK"]    )
       lowercase       :							Dict									=				int(os.environ["WORLD_SIZE"]    )

       lowercase       :							Any									=				ArgumentParser()
       parser.add_argument("--streaming"	,					type=__snake_case    )
       parser.add_argument("--local_rank"	,					type=__snake_case    )
       parser.add_argument("--num_workers"	,					type=__snake_case	,					default=0    )
       lowercase       :							Dict									=				parser.parse_args()
       lowercase       :							Union[str, Any]									=				args.streaming
       lowercase       :							List[Any]									=				args.num_workers

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

       lowercase       :							List[str]									=				split_dataset_by_node(__snake_case	,					rank=__snake_case	,					world_size=__snake_case    )
       lowercase       :							int									=				torch.utils.data.DataLoader(__snake_case	,					num_workers=__snake_case    )

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

       lowercase       :							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()




"""simple docstring"""




import warnings

from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor


_A     :			Optional[int]  						=			logging.get_logger(__name__)

class 						a__		( a_		):
   def __init__(     self      ,       *_a      ,       **_a							):
          warnings.warn(
              "The class OwlViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
              " use OwlViTImageProcessor instead."      ,       _a      ,       )
          super().__init__(*_a      ,       **_a							)


