





'''simple docstring'''




import functools
import operator

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


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

__SCREAMING_SNAKE_CASE     :Tuple       =				{
    '''asapp/sew-d-tiny-100k''': '''https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json''',
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : List[str]           =							"""sew-d"""




 def __init__(					self       :    Optional[Any]	,			snake_case_       :    Tuple=3_2	,			snake_case_       :    Optional[Any]=7_6_8	,			snake_case_       :    Tuple=1_2	,			snake_case_       :    Union[str, Any]=1_2	,			snake_case_       :    Tuple=3_0_7_2	,			snake_case_       :    Tuple=2	,			snake_case_       :    int=5_1_2	,			snake_case_       :    Optional[int]=2_5_6	,			snake_case_       :    Union[str, Any]=True	,			snake_case_       :    Any=True	,			snake_case_       :    str=("p2c", "c2p")	,			snake_case_       :    Dict="layer_norm"	,			snake_case_       :    str="gelu_python"	,			snake_case_       :    Dict=0.1	,			snake_case_       :    List[str]=0.1	,			snake_case_       :    Tuple=0.1	,			snake_case_       :    Any=0.0	,			snake_case_       :    Tuple=0.1	,			snake_case_       :    Union[str, Any]=0.0_2	,			snake_case_       :    str=1e-7	,			snake_case_       :    Optional[Any]=1e-5	,			snake_case_       :    Optional[Any]="group"	,			snake_case_       :    Tuple="gelu"	,			snake_case_       :    Tuple=(6_4, 1_2_8, 1_2_8, 1_2_8, 1_2_8, 2_5_6, 2_5_6, 2_5_6, 2_5_6, 5_1_2, 5_1_2, 5_1_2, 5_1_2)	,			snake_case_       :    Dict=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)	,			snake_case_       :    Dict=(1_0, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)	,			snake_case_       :    int=False	,			snake_case_       :    Union[str, Any]=1_2_8	,			snake_case_       :    int=1_6	,			snake_case_       :    Any=True	,			snake_case_       :    Tuple=0.0_5	,			snake_case_       :    Tuple=1_0	,			snake_case_       :    Dict=2	,			snake_case_       :    Tuple=0.0	,			snake_case_       :    List[Any]=1_0	,			snake_case_       :    Union[str, Any]=0	,			snake_case_       :    Any="mean"	,			snake_case_       :    Optional[Any]=False	,			snake_case_       :    Any=False	,			snake_case_       :    Tuple=2_5_6	,			snake_case_       :    int=0	,			snake_case_       :    Optional[Any]=1	,			snake_case_       :    List[str]=2	,			**snake_case_       :    List[str]	,			):
      super().__init__(**snake_case_	,			pad_token_id=snake_case_	,			bos_token_id=snake_case_	,			eos_token_id=snake_case_    )
      _UpperCAmelCase     							=   hidden_size
      _UpperCAmelCase     							=   feat_extract_norm
      _UpperCAmelCase     							=   feat_extract_activation
      _UpperCAmelCase     							=   list(snake_case_    )
      _UpperCAmelCase     							=   list(snake_case_    )
      _UpperCAmelCase     							=   list(snake_case_    )
      _UpperCAmelCase     							=   conv_bias
      _UpperCAmelCase     							=   num_conv_pos_embeddings
      _UpperCAmelCase     							=   num_conv_pos_embedding_groups
      _UpperCAmelCase     							=   len(self.conv_dim    )
      _UpperCAmelCase     							=   num_hidden_layers
      _UpperCAmelCase     							=   intermediate_size
      _UpperCAmelCase     							=   squeeze_factor
      _UpperCAmelCase     							=   max_position_embeddings
      _UpperCAmelCase     							=   position_buckets
      _UpperCAmelCase     							=   share_att_key
      _UpperCAmelCase     							=   relative_attention
      _UpperCAmelCase     							=   norm_rel_ebd
      _UpperCAmelCase     							=   list(snake_case_    )
      _UpperCAmelCase     							=   hidden_act
      _UpperCAmelCase     							=   num_attention_heads
      _UpperCAmelCase     							=   hidden_dropout
      _UpperCAmelCase     							=   attention_dropout
      _UpperCAmelCase     							=   activation_dropout
      _UpperCAmelCase     							=   feat_proj_dropout
      _UpperCAmelCase     							=   final_dropout
      _UpperCAmelCase     							=   layer_norm_eps
      _UpperCAmelCase     							=   feature_layer_norm_eps
      _UpperCAmelCase     							=   initializer_range
      _UpperCAmelCase     							=   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
      _UpperCAmelCase     							=   apply_spec_augment
      _UpperCAmelCase     							=   mask_time_prob
      _UpperCAmelCase     							=   mask_time_length
      _UpperCAmelCase     							=   mask_time_min_masks
      _UpperCAmelCase     							=   mask_feature_prob
      _UpperCAmelCase     							=   mask_feature_length
      _UpperCAmelCase     							=   mask_feature_min_masks

      # ctc loss
      _UpperCAmelCase     							=   ctc_loss_reduction
      _UpperCAmelCase     							=   ctc_zero_infinity

      # sequence classification
      _UpperCAmelCase     							=   use_weighted_layer_sum
      _UpperCAmelCase     							=   classifier_proj_size




 @property
 def      lowercase	(					self       :    Any    ):
      return functools.reduce(operator.mul	,			self.conv_stride	,			1    )





from maths.prime_check import is_prime





def    __lowercase     (						lowerCamelCase					:     int	):
 if not isinstance(lowerCamelCase  ,  lowerCamelCase	):
  UpperCamelCase_					:						List[str] =					F"Input value of [number={number}] must be an integer"
  raise TypeError(lowerCamelCase	)
 if is_prime(lowerCamelCase	) and is_prime(number + 2	):
  return number + 2
 else:
  return -1


if __name__ == "__main__":
   import doctest

   doctest.testmod()



