'''simple docstring'''


import functools
import operator

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


__snake_case						  =		logging.get_logger(__name__)

__snake_case						  =		{
    """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    (		__a      ):


  """simple docstring"""

  A_    							=						'''wavlm'''




  def __init__(		self      :       List[str]   ,				lowercase_      :       str=32   ,				lowercase_      :       int=768   ,				lowercase_      :       Any=12   ,				lowercase_      :       str=12   ,				lowercase_      :       Any=3_072   ,				lowercase_      :       Any="gelu"   ,				lowercase_      :       Optional[int]=0.1   ,				lowercase_      :       Any=0.1   ,				lowercase_      :       Optional[Any]=0.1   ,				lowercase_      :       List[Any]=0.0   ,				lowercase_      :       List[str]=0.1   ,				lowercase_      :       Dict=0.1   ,				lowercase_      :       str=0.0_2   ,				lowercase_      :       Optional[int]=1e-5   ,				lowercase_      :       List[str]="group"   ,				lowercase_      :       List[Any]="gelu"   ,				lowercase_      :       Union[str, Any]=(512, 512, 512, 512, 512, 512, 512)   ,				lowercase_      :       int=(5, 2, 2, 2, 2, 2, 2)   ,				lowercase_      :       Any=(10, 3, 3, 3, 3, 2, 2)   ,				lowercase_      :       int=False   ,				lowercase_      :       List[Any]=128   ,				lowercase_      :       Any=16   ,				lowercase_      :       Optional[int]=320   ,				lowercase_      :       Optional[int]=800   ,				lowercase_      :       Tuple=False   ,				lowercase_      :       Optional[Any]=True   ,				lowercase_      :       Tuple=0.0_5   ,				lowercase_      :       List[Any]=10   ,				lowercase_      :       List[Any]=2   ,				lowercase_      :       Any=0.0   ,				lowercase_      :       str=10   ,				lowercase_      :       Optional[int]=320   ,				lowercase_      :       Tuple=2   ,				lowercase_      :       int=0.1   ,				lowercase_      :       int=100   ,				lowercase_      :       Tuple=256   ,				lowercase_      :       Optional[Any]=256   ,				lowercase_      :       Union[str, Any]=0.1   ,				lowercase_      :       Optional[int]="mean"   ,				lowercase_      :       Any=False   ,				lowercase_      :       Optional[int]=False   ,				lowercase_      :       Optional[int]=256   ,				lowercase_      :       Dict=(512, 512, 512, 512, 1_500)   ,				lowercase_      :       List[Any]=(5, 3, 3, 1, 1)   ,				lowercase_      :       Any=(1, 2, 3, 1, 1)   ,				lowercase_      :       Tuple=512   ,				lowercase_      :       int=80   ,				lowercase_      :       List[Any]=0   ,				lowercase_      :       Optional[Any]=1   ,				lowercase_      :       Optional[Any]=2   ,				lowercase_      :       List[Any]=False   ,				lowercase_      :       List[str]=3   ,				lowercase_      :       str=2   ,				lowercase_      :       List[str]=3   ,				lowercase_      :       Tuple=None   ,				**lowercase_      :       Any   ,				):







    '''simple docstring'''


    super().__init__(**lowercase_   ,				pad_token_id=lowercase_   ,				bos_token_id=lowercase_   ,				eos_token_id=lowercase_      )
    lowercase_     =       hidden_size
    lowercase_     =       feat_extract_norm
    lowercase_     =       feat_extract_activation
    lowercase_     =       list(lowercase_      )
    lowercase_     =       list(lowercase_      )
    lowercase_     =       list(lowercase_      )
    lowercase_     =       conv_bias
    lowercase_     =       num_buckets
    lowercase_     =       max_bucket_distance
    lowercase_     =       num_conv_pos_embeddings
    lowercase_     =       num_conv_pos_embedding_groups
    lowercase_     =       len(self.conv_dim      )
    lowercase_     =       num_hidden_layers
    lowercase_     =       intermediate_size
    lowercase_     =       hidden_act
    lowercase_     =       num_attention_heads
    lowercase_     =       hidden_dropout
    lowercase_     =       attention_dropout
    lowercase_     =       activation_dropout
    lowercase_     =       feat_proj_dropout
    lowercase_     =       final_dropout
    lowercase_     =       layerdrop
    lowercase_     =       layer_norm_eps
    lowercase_     =       initializer_range
    lowercase_     =       num_ctc_classes
    lowercase_     =       vocab_size
    lowercase_     =       do_stable_layer_norm
    lowercase_     =       use_weighted_layer_sum
    lowercase_     =       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
    lowercase_     =       apply_spec_augment
    lowercase_     =       mask_time_prob
    lowercase_     =       mask_time_length
    lowercase_     =       mask_time_min_masks
    lowercase_     =       mask_feature_prob
    lowercase_     =       mask_feature_length

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

    # ctc loss
    lowercase_     =       ctc_loss_reduction
    lowercase_     =       ctc_zero_infinity

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

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

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





  @property
  def 		lowerCamelCase__  (		self      :       Any      ):







    '''simple docstring'''


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


'''simple docstring'''


from __future__ import annotations

# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
__snake_case						  =		10

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->int:
  for i in range(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						):
    if array[i] == target:
      return i
  return -1

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->int:
  lowercase_     =       0
  lowercase_     =       len(SCREAMING_SNAKE_CASE_						)
  while left <= right:
    if right - left < precision:
      return lin_search(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)

    lowercase_     =       (left + right) // 3 + 1
    lowercase_     =       2 * (left + right) // 3 + 1

    if array[one_third] == target:
      return one_third
    elif array[two_third] == target:
      return two_third

    elif target < array[one_third]:
      lowercase_     =       one_third - 1
    elif array[two_third] < target:
      lowercase_     =       two_third + 1

    else:
      lowercase_     =       one_third + 1
      lowercase_     =       two_third - 1
  else:
    return -1

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->int:
  if left < right:
    if right - left < precision:
      return lin_search(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
    lowercase_     =       (left + right) // 3 + 1
    lowercase_     =       2 * (left + right) // 3 + 1

    if array[one_third] == target:
      return one_third
    elif array[two_third] == target:
      return two_third

    elif target < array[one_third]:
      return rec_ternary_search(SCREAMING_SNAKE_CASE_ ,	one_third - 1 ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
    elif array[two_third] < target:
      return rec_ternary_search(two_third + 1 ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
    else:
      return rec_ternary_search(one_third + 1 ,	two_third - 1 ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
  else:
    return -1


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       __snake_case						  =		input("""Enter numbers separated by comma:\n""").strip()
       __snake_case						  =		[int(item.strip()) for item in user_input.split(""",""")]
       assert collection == sorted(collection), f"List must be ordered.\n{collection}."
       __snake_case						  =		int(input("""Enter the number to be found in the list:\n""").strip())
       __snake_case						  =		ite_ternary_search(collection, target)
       __snake_case						  =		rec_ternary_search(0, len(collection) - 1, collection, target)
       if resulta != -1:
              print(f'''Iterative search: {target} found at positions: {resulta}''')
              print(f'''Recursive search: {target} found at positions: {resulta}''')
       else:
              print("""Not found""")

