import functools
import operator

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


__lowerCamelCase      :			Tuple							       =		logging.get_logger(__name__)

__lowerCamelCase      :			Optional[int]							       =		{
    '''asapp/sew-tiny-100k''': '''https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json''',
    # See all SEW models at https://huggingface.co/models?filter=sew
}







class __snake_case    (						_UpperCamelCase		):
   lowerCAmelCase_														=							'sew'




   def __init__(   self      :    int	,     _lowercase      :    List[str]=32	,     _lowercase      :    Tuple=7_68	,     _lowercase      :    List[str]=12	,     _lowercase      :    str=12	,     _lowercase      :    List[str]=30_72	,     _lowercase      :    List[str]=2	,     _lowercase      :    str="gelu"	,     _lowercase      :    int=0.1	,     _lowercase      :    Dict=0.1	,     _lowercase      :    Optional[Any]=0.1	,     _lowercase      :    Dict=0.0	,     _lowercase      :    Any=0.1	,     _lowercase      :    str=0.1	,     _lowercase      :    Any=0.02	,     _lowercase      :    Optional[Any]=1E-5	,     _lowercase      :    Optional[Any]="group"	,     _lowercase      :    List[Any]="gelu"	,     _lowercase      :    List[Any]=(64, 1_28, 1_28, 1_28, 1_28, 2_56, 2_56, 2_56, 2_56, 5_12, 5_12, 5_12, 5_12)	,     _lowercase      :    Any=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)	,     _lowercase      :    List[str]=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)	,     _lowercase      :    str=False	,     _lowercase      :    str=1_28	,     _lowercase      :    List[Any]=16	,     _lowercase      :    str=True	,     _lowercase      :    Optional[int]=0.05	,     _lowercase      :    List[Any]=10	,     _lowercase      :    List[str]=2	,     _lowercase      :    str=0.0	,     _lowercase      :    Optional[Any]=10	,     _lowercase      :    str=0	,     _lowercase      :    str="mean"	,     _lowercase      :    Union[str, Any]=False	,     _lowercase      :    Tuple=False	,     _lowercase      :    List[Any]=2_56	,     _lowercase      :    List[str]=0	,     _lowercase      :    Optional[int]=1	,     _lowercase      :    Union[str, Any]=2	,     **_lowercase      :    Union[str, Any]	,     ):



     """simple docstring"""






     super().__init__(**_UpperCAmelCase	,     pad_token_id=_UpperCAmelCase	,     bos_token_id=_UpperCAmelCase	,     eos_token_id=_UpperCAmelCase				)
     SCREAMING_SNAKE_CASE__      							=				hidden_size
     SCREAMING_SNAKE_CASE__      							=				feat_extract_norm
     SCREAMING_SNAKE_CASE__      							=				feat_extract_activation
     SCREAMING_SNAKE_CASE__      							=				list(_UpperCAmelCase				)
     SCREAMING_SNAKE_CASE__      							=				list(_UpperCAmelCase				)
     SCREAMING_SNAKE_CASE__      							=				list(_UpperCAmelCase				)
     SCREAMING_SNAKE_CASE__      							=				conv_bias
     SCREAMING_SNAKE_CASE__      							=				num_conv_pos_embeddings
     SCREAMING_SNAKE_CASE__      							=				num_conv_pos_embedding_groups
     SCREAMING_SNAKE_CASE__      							=				len(self.conv_dim				)
     SCREAMING_SNAKE_CASE__      							=				num_hidden_layers
     SCREAMING_SNAKE_CASE__      							=				intermediate_size
     SCREAMING_SNAKE_CASE__      							=				squeeze_factor
     SCREAMING_SNAKE_CASE__      							=				hidden_act
     SCREAMING_SNAKE_CASE__      							=				num_attention_heads
     SCREAMING_SNAKE_CASE__      							=				hidden_dropout
     SCREAMING_SNAKE_CASE__      							=				attention_dropout
     SCREAMING_SNAKE_CASE__      							=				activation_dropout
     SCREAMING_SNAKE_CASE__      							=				feat_proj_dropout
     SCREAMING_SNAKE_CASE__      							=				final_dropout
     SCREAMING_SNAKE_CASE__      							=				layerdrop
     SCREAMING_SNAKE_CASE__      							=				layer_norm_eps
     SCREAMING_SNAKE_CASE__      							=				initializer_range
     SCREAMING_SNAKE_CASE__      							=				vocab_size

     if (
         (len(self.conv_stride				) != self.num_feat_extract_layers)
         or (len(self.conv_kernel				) != self.num_feat_extract_layers)
         or (len(self.conv_dim				) != self.num_feat_extract_layers)
     ):
       raise ValueError(
           """Configuration for convolutional layers is incorrect."""
           """It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"""
           f"""but is `len(config.conv_dim) = {len(self.conv_dim				)}`, `len(config.conv_stride)"""
           f"""= {len(self.conv_stride				)}`, `len(config.conv_kernel) = {len(self.conv_kernel				)}`."""				)

     # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
     SCREAMING_SNAKE_CASE__      							=				apply_spec_augment
     SCREAMING_SNAKE_CASE__      							=				mask_time_prob
     SCREAMING_SNAKE_CASE__      							=				mask_time_length
     SCREAMING_SNAKE_CASE__      							=				mask_time_min_masks
     SCREAMING_SNAKE_CASE__      							=				mask_feature_prob
     SCREAMING_SNAKE_CASE__      							=				mask_feature_length
     SCREAMING_SNAKE_CASE__      							=				mask_feature_min_masks

     # ctc loss
     SCREAMING_SNAKE_CASE__      							=				ctc_loss_reduction
     SCREAMING_SNAKE_CASE__      							=				ctc_zero_infinity

     # sequence classification
     SCREAMING_SNAKE_CASE__      							=				use_weighted_layer_sum
     SCREAMING_SNAKE_CASE__      							=				classifier_proj_size




   @property
   def      __a						(   self      :    Optional[int]				):



     """simple docstring"""






     return functools.reduce(operator.mul	,     self.conv_stride	,     1				)

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      :			int							       =		4
__lowerCamelCase      :			Dict							       =		3







class __snake_case    (						lowerCamelCase_		):
   pass





def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	List[str]     )							-> List[str]:







  """simple docstring"""





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



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







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				int(os.environ["""RANK"""]     )
  SCREAMING_SNAKE_CASE__      							=				int(os.environ["""WORLD_SIZE"""]     )

  SCREAMING_SNAKE_CASE__      							=				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__      							=				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(__UpperCamelCase     )]}
  SCREAMING_SNAKE_CASE__      							=				IterableDataset.from_generator(__UpperCamelCase			,					gen_kwargs=__UpperCamelCase     )
  if not streaming:
    SCREAMING_SNAKE_CASE__      							=				Dataset.from_list(list(__UpperCamelCase     )     )

  SCREAMING_SNAKE_CASE__      							=				split_dataset_by_node(__UpperCamelCase			,					rank=__UpperCamelCase			,					world_size=__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				torch.utils.data.DataLoader(__UpperCamelCase			,					num_workers=__UpperCamelCase     )

  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()
