





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__					  = 4
UpperCamelCase__					  = 3







class   _UpperCamelCase  (       A_  ):







					'''simple docstring'''

					pass




def    A_(			A   ):
	for shard in shards:
		for i in range(A   ):
			yield {"i": i, "shard": shard}




def    A_(			):
	UpperCAmelCase_								=     int(os.environ["""RANK"""]   )
	UpperCAmelCase_								=     int(os.environ["""WORLD_SIZE"""]   )

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

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

	UpperCAmelCase_								=     split_dataset_by_node(A				,      rank=A				,      world_size=A   )
	UpperCAmelCase_								=     torch.utils.data.DataLoader(A				,      num_workers=A   )

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

	UpperCAmelCase_								=     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__   : List[Any]					  = {"""configuration_sew""": ["""SEW_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWConfig"""]}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase__   : List[str]					  = [
				    """SEW_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """SEWForCTC""",
				    """SEWForSequenceClassification""",
				    """SEWModel""",
				    """SEWPreTrainedModel""",
				]

if TYPE_CHECKING:
				from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_sew import (
								    SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SEWForCTC,
								    SEWForSequenceClassification,
								    SEWModel,
								    SEWPreTrainedModel,
								)


else:
				import sys

				UpperCamelCase__   : str					  = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


