'''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


UpperCamelCase_      	=	4
UpperCamelCase_      	=	3




class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE					):
						pass


def    lowerCamelCase       (			UpperCAmelCase__				:  List[str]		)			->	List[str]:


							'''simple docstring'''






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


def    lowerCamelCase       (			)			->	Union[str, Any]:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :str						    = int(os.environ['RANK']		)
							SCREAMING_SNAKE_CASE__     :Tuple						    = int(os.environ['WORLD_SIZE']		)

							SCREAMING_SNAKE_CASE__     :Any						    = ArgumentParser()
							parser.add_argument('--streaming'				,						type=UpperCAmelCase__		)
							parser.add_argument('--local_rank'				,						type=UpperCAmelCase__		)
							parser.add_argument('--num_workers'				,						type=UpperCAmelCase__				,						default=0		)
							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = parser.parse_args()
							SCREAMING_SNAKE_CASE__     :Dict						    = args.streaming
							SCREAMING_SNAKE_CASE__     :Optional[int]						    = args.num_workers

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

							SCREAMING_SNAKE_CASE__     :List[Any]						    = split_dataset_by_node(UpperCAmelCase__				,						rank=UpperCAmelCase__				,						world_size=UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = torch.utils.data.DataLoader(UpperCAmelCase__				,						num_workers=UpperCAmelCase__		)

							SCREAMING_SNAKE_CASE__     :Optional[int]						    = NUM_SHARDS * NUM_ITEMS_PER_SHARD
							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = full_size // world_size
							expected_local_size += int(rank < (full_size % world_size)		)

							SCREAMING_SNAKE_CASE__     :int						    = 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_tokenizers_available, is_torch_available


UpperCamelCase_      	=	{
    '''configuration_m2m_100''': ['''M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''M2M100Config''', '''M2M100OnnxConfig'''],
    '''tokenization_m2m_100''': ['''M2M100Tokenizer'''],
}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_      	=	[
		    '''M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''M2M100ForConditionalGeneration''',
		    '''M2M100Model''',
		    '''M2M100PreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
		from .tokenization_mam_aaa import MaMaaaTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mam_aaa import (
				    M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MaMaaaForConditionalGeneration,
				    MaMaaaModel,
				    MaMaaaPreTrainedModel,
				)


else:
		import sys

		UpperCamelCase_      	=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






