


'''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


snake_case_						:			Any													=							4
snake_case_						:			List[Any]													=							3

class 						A__							(		UpperCamelCase__  ):
						pass







def 						lowerCamelCase(   a__):
				for shard in shards:
								for i in range(a__):
												yield {"i": i, "shard": shard}







def 						lowerCamelCase(   ):
				_SCREAMING_SNAKE_CASE			  =int(os.environ['''RANK'''])
				_SCREAMING_SNAKE_CASE			  =int(os.environ['''WORLD_SIZE'''])

				_SCREAMING_SNAKE_CASE			  =ArgumentParser()
				parser.add_argument('''--streaming'''	,type=a__)
				parser.add_argument('''--local_rank'''	,type=a__)
				parser.add_argument('''--num_workers'''	,type=a__	,default=0)
				_SCREAMING_SNAKE_CASE			  =parser.parse_args()
				_SCREAMING_SNAKE_CASE			  =args.streaming
				_SCREAMING_SNAKE_CASE			  =args.num_workers

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

				_SCREAMING_SNAKE_CASE			  =split_dataset_by_node(a__	,rank=a__	,world_size=a__)
				_SCREAMING_SNAKE_CASE			  =torch.utils.data.DataLoader(a__	,num_workers=a__)

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

				_SCREAMING_SNAKE_CASE			  =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_speech_available, is_torch_available


snake_case_						:			Optional[Any]													=							{
    '''configuration_audio_spectrogram_transformer''': [
        '''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ASTConfig''',
    ]
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							snake_case_						:			Optional[Any]													=							[
							    '''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''ASTForAudioClassification''',
							    '''ASTModel''',
							    '''ASTPreTrainedModel''',
							]

try:
							if not is_speech_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							snake_case_						:			List[Any]													=							['''ASTFeatureExtractor''']

if TYPE_CHECKING:
							from .configuration_audio_spectrogram_transformer import (
							    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    ASTConfig,
							)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_audio_spectrogram_transformer import (
														    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ASTForAudioClassification,
														    ASTModel,
														    ASTPreTrainedModel,
														)

							try:
														if not is_speech_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
							import sys

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