


'''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


SCREAMING_SNAKE_CASE_      =			4
SCREAMING_SNAKE_CASE_      =			3


class  lowerCAmelCase_						(       snake_case__      ):





  """simple docstring"""



  pass




def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					Optional[int]:
     """simple docstring"""




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




def        __lowercase						(      )				->					Optional[Any]:
     """simple docstring"""




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

     __a       =   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		)
     __a       =   parser.parse_args()
     __a       =   args.streaming
     __a       =   args.num_workers

     __a       =   {"""shards""": [F'''shard_{shard_idx}''' for shard_idx in range(__SCREAMING_SNAKE_CASE		)]}
     __a       =   IterableDataset.from_generator(__SCREAMING_SNAKE_CASE				,  gen_kwargs=__SCREAMING_SNAKE_CASE		)
     if not streaming:
          __a       =   Dataset.from_list(list(__SCREAMING_SNAKE_CASE		)		)

     __a       =   split_dataset_by_node(__SCREAMING_SNAKE_CASE				,  rank=__SCREAMING_SNAKE_CASE				,  world_size=__SCREAMING_SNAKE_CASE		)
     __a       =   torch.utils.data.DataLoader(__SCREAMING_SNAKE_CASE				,  num_workers=__SCREAMING_SNAKE_CASE		)

     __a       =   NUM_SHARDS * NUM_ITEMS_PER_SHARD
     __a       =   full_size // world_size
     expected_local_size += int(rank < (full_size % world_size)		)

     __a       =   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_torch_available, is_vision_available


SCREAMING_SNAKE_CASE_      =			{
    'configuration_bridgetower': [
        'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BridgeTowerConfig',
        'BridgeTowerTextConfig',
        'BridgeTowerVisionConfig',
    ],
    'processing_bridgetower': ['BridgeTowerProcessor'],
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_      =			['BridgeTowerImageProcessor']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_      =			[
     'BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'BridgeTowerForContrastiveLearning',
     'BridgeTowerForImageAndTextRetrieval',
     'BridgeTowerForMaskedLM',
     'BridgeTowerModel',
     'BridgeTowerPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_bridgetower import (
     BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BridgeTowerConfig,
     BridgeTowerTextConfig,
     BridgeTowerVisionConfig,
 )
 from .processing_bridgetower import BridgeTowerProcessor

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_bridgetower import BridgeTowerImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_bridgetower import (
      BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
      BridgeTowerForContrastiveLearning,
      BridgeTowerForImageAndTextRetrieval,
      BridgeTowerForMaskedLM,
      BridgeTowerModel,
      BridgeTowerPreTrainedModel,
  )


else:
 import sys

 SCREAMING_SNAKE_CASE_      =			_LazyModule(__name__, globals()['__file__'], _import_structure)






