


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    :						Optional[int]     =							4
__UpperCamelCase    :						Any     =							3



class 							_UpperCamelCase (						A						):

   '''simple docstring'''







   pass





def   _UpperCAmelCase       (     UpperCAmelCase      :			List[str]      ):
      """simple docstring"""




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





def   _UpperCAmelCase       (     ):
      """simple docstring"""




      __lowerCamelCase     :      List[Any]       						=						int(os.environ["""RANK"""]      )
      __lowerCamelCase     :      Union[str, Any]       						=						int(os.environ["""WORLD_SIZE"""]      )

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

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

      __lowerCamelCase     :      int       						=						split_dataset_by_node(UpperCAmelCase						,	rank=UpperCAmelCase						,	world_size=UpperCAmelCase      )
      __lowerCamelCase     :      Tuple       						=						torch.utils.data.DataLoader(UpperCAmelCase						,	num_workers=UpperCAmelCase      )

      __lowerCamelCase     :      Optional[int]       						=						NUM_SHARDS * NUM_ITEMS_PER_SHARD
      __lowerCamelCase     :      List[str]       						=						full_size // world_size
      expected_local_size += int(rank < (full_size % world_size)      )

      __lowerCamelCase     :      Optional[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_torch_available


__UpperCamelCase    :						Optional[Any]     =							{
    'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCamelCase    :						List[Any]     =							[
        'SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST',
        'Swinv2ForImageClassification',
        'Swinv2ForMaskedImageModeling',
        'Swinv2Model',
        'Swinv2PreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_swinva import (
            SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
            SwinvaForImageClassification,
            SwinvaForMaskedImageModeling,
            SwinvaModel,
            SwinvaPreTrainedModel,
        )


else:
    import sys

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



