


'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowercase	:						Union[str, Any]       =	logging.get_logger(__name__)

__lowercase	:						Any       =	{
    'MIT/ast-finetuned-audioset-10-10-0.4593': (
        'https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json'
    ),
}





class 							__UpperCamelCase				(		lowerCAmelCase_					):
	A_				=    "audio-spectrogram-transformer"




	def __init__(   self						,				__a=768						,				__a=12						,				__a=12						,				__a=3072						,				__a="gelu"						,				__a=0.0						,				__a=0.0						,				__a=0.02						,				__a=1E-1_2						,				__a=16						,				__a=True						,				__a=10						,				__a=10						,				__a=1024						,				__a=128						,				**__a						,				):

						'''simple docstring'''





						super().__init__(**__a							)

						__a			:       Union[str, Any]					    =  hidden_size
						__a			:       str					    =  num_hidden_layers
						__a			:       List[str]					    =  num_attention_heads
						__a			:       int					    =  intermediate_size
						__a			:       str					    =  hidden_act
						__a			:       List[Any]					    =  hidden_dropout_prob
						__a			:       Union[str, Any]					    =  attention_probs_dropout_prob
						__a			:       int					    =  initializer_range
						__a			:       str					    =  layer_norm_eps
						__a			:       Tuple					    =  patch_size
						__a			:       List[str]					    =  qkv_bias
						__a			:       List[str]					    =  frequency_stride
						__a			:       List[Any]					    =  time_stride
						__a			:       Optional[int]					    =  max_length
						__a			:       Any					    =  num_mel_bins








"""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


lowerCAmelCase_   						=   4
lowerCAmelCase_   						=   3
class        __A       (	A_    ):







		'''simple docstring'''





		pass





def      __UpperCAmelCase      (    __lowerCamelCase	)							->      Dict:
		for shard in shards:
				for i in range(__lowerCamelCase	):
						yield {"i": i, "shard": shard}



def      __UpperCAmelCase      (    )							->      Tuple:
		lowercase__   :    int				=			int(os.environ['''RANK''']	)
		lowercase__   :    str				=			int(os.environ['''WORLD_SIZE''']	)

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

		lowercase__   :    Optional[Any]				=			{'''shards''': [f"""shard_{shard_idx}""" for shard_idx in range(__lowerCamelCase	)]}
		lowercase__   :    Dict				=			IterableDataset.from_generator(__lowerCamelCase						,     gen_kwargs=__lowerCamelCase	)
		if not streaming:
				lowercase__   :    int				=			Dataset.from_list(list(__lowerCamelCase	)	)

		lowercase__   :    int				=			split_dataset_by_node(__lowerCamelCase						,     rank=__lowerCamelCase						,     world_size=__lowerCamelCase	)
		lowercase__   :    Optional[Any]				=			torch.utils.data.DataLoader(__lowerCamelCase						,     num_workers=__lowerCamelCase	)

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

		lowercase__   :    str				=			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()
