





'''simple docstring'''

def 						__magic_name__       (							__UpperCAmelCase = 1							,	__UpperCAmelCase = 1000      )						->					int:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       1
   __SCREAMING_SNAKE_CASE      							=       0

   for divide_by_number in range(__UpperCAmelCase							,	digit + 1      ):
      __SCREAMING_SNAKE_CASE      							=       []
      __SCREAMING_SNAKE_CASE      							=       numerator
      for _ in range(1							,	digit + 1      ):
         if now_divide in has_been_divided:
            if longest_list_length < len(__UpperCAmelCase      ):
               __SCREAMING_SNAKE_CASE      							=       len(__UpperCAmelCase      )
               __SCREAMING_SNAKE_CASE      							=       divide_by_number
         else:
            has_been_divided.append(__UpperCAmelCase      )
            __SCREAMING_SNAKE_CASE      							=       now_divide * 10 % divide_by_number

   return the_digit


# Tests
if __name__ == "__main__":
    import doctest

    doctest.testmod()












'''simple docstring'''

import functools
import operator

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


a        =						logging.get_logger(__name__)

a        =						{
    "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 __a (   _snake_case			):
   __UpperCamelCase   : Tuple	      =	'sew'





   def __init__(					self				:			str				,lowerCamelCase				:			Any=32				,lowerCamelCase				:			str=768				,lowerCamelCase				:			str=12				,lowerCamelCase				:			Union[str, Any]=12				,lowerCamelCase				:			Union[str, Any]=3072				,lowerCamelCase				:			int=2				,lowerCamelCase				:			Union[str, Any]="gelu"				,lowerCamelCase				:			Tuple=0.1				,lowerCamelCase				:			Union[str, Any]=0.1				,lowerCamelCase				:			Union[str, Any]=0.1				,lowerCamelCase				:			Any=0.0				,lowerCamelCase				:			Optional[Any]=0.1				,lowerCamelCase				:			Union[str, Any]=0.1				,lowerCamelCase				:			Optional[Any]=0.02				,lowerCamelCase				:			List[str]=1E-5				,lowerCamelCase				:			Tuple="group"				,lowerCamelCase				:			Optional[Any]="gelu"				,lowerCamelCase				:			List[str]=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)				,lowerCamelCase				:			Any=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)				,lowerCamelCase				:			Dict=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)				,lowerCamelCase				:			Optional[int]=False				,lowerCamelCase				:			Dict=128				,lowerCamelCase				:			Union[str, Any]=16				,lowerCamelCase				:			List[Any]=True				,lowerCamelCase				:			List[Any]=0.05				,lowerCamelCase				:			Optional[int]=10				,lowerCamelCase				:			Any=2				,lowerCamelCase				:			Any=0.0				,lowerCamelCase				:			Tuple=10				,lowerCamelCase				:			str=0				,lowerCamelCase				:			Tuple="mean"				,lowerCamelCase				:			int=False				,lowerCamelCase				:			Dict=False				,lowerCamelCase				:			Optional[int]=256				,lowerCamelCase				:			str=0				,lowerCamelCase				:			Tuple=1				,lowerCamelCase				:			Tuple=2				,**lowerCamelCase				:			Union[str, Any]				,):

      '''simple docstring'''

      super().__init__(**lowerCamelCase				,pad_token_id=lowerCamelCase				,bos_token_id=lowerCamelCase				,eos_token_id=lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       hidden_size
      __SCREAMING_SNAKE_CASE      							=       feat_extract_norm
      __SCREAMING_SNAKE_CASE      							=       feat_extract_activation
      __SCREAMING_SNAKE_CASE      							=       list(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       list(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       list(lowerCamelCase    )
      __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      UpperCAmelCase__     (					self				:			List[Any]    ):

      '''simple docstring'''

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





