




"""simple docstring"""




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


lowerCAmelCase							:    Dict  							=							logging.get_logger(__name__)

lowerCAmelCase							:    Optional[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 					__magic_name__							( UpperCAmelCase__       ):






				'''simple docstring'''
				__UpperCamelCase    						=		"audio-spectrogram-transformer"





				def __init__( self	,				_a=768	,				_a=12	,				_a=12	,				_a=3_072	,				_a="gelu"	,				_a=0.0	,				_a=0.0	,				_a=0.02	,				_a=1e-1_2	,				_a=16	,				_a=True	,				_a=10	,				_a=10	,				_a=1_024	,				_a=128	,				**_a	,				):





					"""simple docstring"""
					super().__init__(**_a				)

					lowerCamelCase       =      hidden_size
					lowerCamelCase       =      num_hidden_layers
					lowerCamelCase       =      num_attention_heads
					lowerCamelCase       =      intermediate_size
					lowerCamelCase       =      hidden_act
					lowerCamelCase       =      hidden_dropout_prob
					lowerCamelCase       =      attention_probs_dropout_prob
					lowerCamelCase       =      initializer_range
					lowerCamelCase       =      layer_norm_eps
					lowerCamelCase       =      patch_size
					lowerCamelCase       =      qkv_bias
					lowerCamelCase       =      frequency_stride
					lowerCamelCase       =      time_stride
					lowerCamelCase       =      max_length
					lowerCamelCase       =      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							:    List[str]  							=							4
lowerCAmelCase							:    List[str]  							=							3






class 					__magic_name__							( UpperCAmelCase__       ):






				'''simple docstring'''
				pass




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



def 					a__				(       )		->      List[Any]:
			lowerCamelCase       =      int(os.environ["""RANK"""]       )
			lowerCamelCase       =      int(os.environ["""WORLD_SIZE"""]       )

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

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

			lowerCamelCase       =      split_dataset_by_node(snake_case__ ,				rank=snake_case__ ,				world_size=snake_case__       )
			lowerCamelCase       =      torch.utils.data.DataLoader(snake_case__ ,				num_workers=snake_case__       )

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

			lowerCamelCase       =      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()



