"""simple docstring"""



import functools
import operator

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


__lowercase		:		Optional[Any]														=							logging.get_logger(__name__)

__lowercase		:		List[str]														=							{
    "microsoft/wavlm-base": "https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json",
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}



class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					List[Any]							=					'''wavlm'''
    def __init__(		self							:     Any	,      A_							:     str=32	,      A_							:     Union[str, Any]=768	,      A_							:     str=12	,      A_							:     Dict=12	,      A_							:     List[str]=3_072	,      A_							:     Dict="gelu"	,      A_							:     int=0.1	,      A_							:     List[str]=0.1	,      A_							:     Union[str, Any]=0.1	,      A_							:     Dict=0.0	,      A_							:     Optional[Any]=0.1	,      A_							:     Tuple=0.1	,      A_							:     List[str]=0.02	,      A_							:     List[Any]=1E-5	,      A_							:     Tuple="group"	,      A_							:     Optional[int]="gelu"	,      A_							:     List[Any]=(512, 512, 512, 512, 512, 512, 512)	,      A_							:     Union[str, Any]=(5, 2, 2, 2, 2, 2, 2)	,      A_							:     str=(10, 3, 3, 3, 3, 2, 2)	,      A_							:     Optional[int]=False	,      A_							:     int=128	,      A_							:     int=16	,      A_							:     List[Any]=320	,      A_							:     Union[str, Any]=800	,      A_							:     Union[str, Any]=False	,      A_							:     Tuple=True	,      A_							:     List[str]=0.05	,      A_							:     Any=10	,      A_							:     List[Any]=2	,      A_							:     str=0.0	,      A_							:     Tuple=10	,      A_							:     int=320	,      A_							:     Union[str, Any]=2	,      A_							:     str=0.1	,      A_							:     List[Any]=100	,      A_							:     Union[str, Any]=256	,      A_							:     Dict=256	,      A_							:     Tuple=0.1	,      A_							:     List[Any]="mean"	,      A_							:     Union[str, Any]=False	,      A_							:     Any=False	,      A_							:     Union[str, Any]=256	,      A_							:     str=(512, 512, 512, 512, 1_500)	,      A_							:     Any=(5, 3, 3, 1, 1)	,      A_							:     Dict=(1, 2, 3, 1, 1)	,      A_							:     Union[str, Any]=512	,      A_							:     str=80	,      A_							:     int=0	,      A_							:     Optional[Any]=1	,      A_							:     Dict=2	,      A_							:     Union[str, Any]=False	,      A_							:     List[str]=3	,      A_							:     Tuple=2	,      A_							:     int=3	,      A_							:     List[str]=None	,      **A_							:     Dict	,      )    ->       int:
         super().__init__(**A_	,      pad_token_id=A_	,      bos_token_id=A_	,      eos_token_id=A_    )
         __snake_case				=							hidden_size
         __snake_case				=							feat_extract_norm
         __snake_case				=							feat_extract_activation
         __snake_case				=							list(A_    )
         __snake_case				=							list(A_    )
         __snake_case				=							list(A_    )
         __snake_case				=							conv_bias
         __snake_case				=							num_buckets
         __snake_case				=							max_bucket_distance
         __snake_case				=							num_conv_pos_embeddings
         __snake_case				=							num_conv_pos_embedding_groups
         __snake_case				=							len(self.conv_dim    )
         __snake_case				=							num_hidden_layers
         __snake_case				=							intermediate_size
         __snake_case				=							hidden_act
         __snake_case				=							num_attention_heads
         __snake_case				=							hidden_dropout
         __snake_case				=							attention_dropout
         __snake_case				=							activation_dropout
         __snake_case				=							feat_proj_dropout
         __snake_case				=							final_dropout
         __snake_case				=							layerdrop
         __snake_case				=							layer_norm_eps
         __snake_case				=							initializer_range
         __snake_case				=							num_ctc_classes
         __snake_case				=							vocab_size
         __snake_case				=							do_stable_layer_norm
         __snake_case				=							use_weighted_layer_sum
         __snake_case				=							classifier_proj_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)`, but is `len(config.conv_dim) ='''
                  f" {len(self.conv_dim    )}`, `len(config.conv_stride) = {len(self.conv_stride    )}`,"
                  f" `len(config.conv_kernel) = {len(self.conv_kernel    )}`."    )

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

         # parameters for pretraining with codevector quantized representations
         __snake_case				=							num_codevectors_per_group
         __snake_case				=							num_codevector_groups
         __snake_case				=							contrastive_logits_temperature
         __snake_case				=							num_negatives
         __snake_case				=							codevector_dim
         __snake_case				=							proj_codevector_dim
         __snake_case				=							diversity_loss_weight

         # ctc loss
         __snake_case				=							ctc_loss_reduction
         __snake_case				=							ctc_zero_infinity

         # adapter
         __snake_case				=							add_adapter
         __snake_case				=							adapter_kernel_size
         __snake_case				=							adapter_stride
         __snake_case				=							num_adapter_layers
         __snake_case				=							output_hidden_size or hidden_size

         # SequenceClassification-specific parameter. Feel free to ignore for other classes.
         __snake_case				=							classifier_proj_size

         # XVector-specific parameters. Feel free to ignore for other classes.
         __snake_case				=							list(A_    )
         __snake_case				=							list(A_    )
         __snake_case				=							list(A_    )
         __snake_case				=							xvector_output_dim







    @property
    def  lowercase					(		self							:     List[str]    )    ->       int:
         return functools.reduce(operator.mul	,      self.conv_stride	,      1    )
"""simple docstring"""



import re





def    SCREAMING_SNAKE_CASE       (     snake_case):
     return [char.split() for char in re.split(R'''[^ a-z A-Z 0-9 \s]''',      str_)]





def    SCREAMING_SNAKE_CASE       (     snake_case):
     __snake_case				=							split_input(str_)
     return "".join(
         [''''''.join([char.capitalize() for char in sub_str]) for sub_str in string_split])





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      snake_case):
     try:
          __snake_case				=							split_input(snake_case)
          if upper:
               __snake_case				=							''''''.join(
                   [
                       separator.join([char.upper() for char in sub_str])
                       for sub_str in string_split
                   ])
          else:
               __snake_case				=							''''''.join(
                   [
                       separator.join([char.lower() for char in sub_str])
                       for sub_str in string_split
                   ])
          return res_str
     except IndexError:
          return "not valid string"





def    SCREAMING_SNAKE_CASE       (     snake_case):
     return to_simple_case(snake_case)





def    SCREAMING_SNAKE_CASE       (     snake_case):
     try:
          __snake_case				=							to_simple_case(snake_case)
          return res_str[0].lower() + res_str[1:]
     except IndexError:
          return "not valid string"





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
     return to_complex_case(snake_case,      snake_case,      '''_''')





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
     return to_complex_case(snake_case,      snake_case,      '''-''')


if __name__ == "__main__":
       __import__("doctest").testmod()