




from ... import PretrainedConfig


_lowerCamelCase			:	Tuple							 =     {
    """sijunhe/nezha-cn-base""": """https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json""",
}


class        UpperCamelCase_					(				_a  ):

    '''simple docstring'''






    UpperCAmelCase__													=   NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
    UpperCAmelCase__													=   '''nezha'''


    def __init__(    self   :			Optional[Any]      ,				UpperCAmelCase__   :			Union[str, Any]=21_128      ,				UpperCAmelCase__   :			List[str]=768      ,				UpperCAmelCase__   :			Optional[Any]=12      ,				UpperCAmelCase__   :			Optional[Any]=12      ,				UpperCAmelCase__   :			str=3_072      ,				UpperCAmelCase__   :			List[str]="gelu"      ,				UpperCAmelCase__   :			Union[str, Any]=0.1      ,				UpperCAmelCase__   :			Dict=0.1      ,				UpperCAmelCase__   :			Any=512      ,				UpperCAmelCase__   :			Optional[Any]=64      ,				UpperCAmelCase__   :			List[str]=2      ,				UpperCAmelCase__   :			Optional[int]=0.02      ,				UpperCAmelCase__   :			Optional[Any]=1e-12      ,				UpperCAmelCase__   :			Dict=0.1      ,				UpperCAmelCase__   :			Tuple=0      ,				UpperCAmelCase__   :			Optional[Any]=2      ,				UpperCAmelCase__   :			List[str]=3      ,				UpperCAmelCase__   :			List[Any]=True      ,				**UpperCAmelCase__   :			Tuple      ,				)		->Tuple:





           '''simple docstring'''

           super().__init__(pad_token_id=_A      ,				bos_token_id=_A      ,				eos_token_id=_A      ,				**_A)

           A__   				=					vocab_size
           A__   				=					hidden_size
           A__   				=					num_hidden_layers
           A__   				=					num_attention_heads
           A__   				=					hidden_act
           A__   				=					intermediate_size
           A__   				=					hidden_dropout_prob
           A__   				=					attention_probs_dropout_prob
           A__   				=					max_position_embeddings
           A__   				=					max_relative_position
           A__   				=					type_vocab_size
           A__   				=					initializer_range
           A__   				=					layer_norm_eps
           A__   				=					classifier_dropout
           A__   				=					use_cache








def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> float:



       """simple docstring"""

       def get_matched_characters(lowercase_				,       lowercase_		) -> str:
              A__   				=					[]
              A__   				=					min(len(_stra		)				,       len(_stra		)		) // 2
              for i, l in enumerate(_stra		):
                     A__   				=					int(max(0				,       i - limit		)		)
                     A__   				=					int(min(i + limit + 1				,       len(_stra		)		)		)
                     if l in _stra[left:right]:
                            matched.append(lowercase_		)
                            A__   				=					f"""{_stra[0:_stra.index(lowercase_		)]} {_stra[_stra.index(lowercase_		) + 1:]}"""

              return "".join(lowercase_		)

       # matching characters
       A__   				=					get_matched_characters(lowercase_				,       lowercase_		)
       A__   				=					get_matched_characters(lowercase_				,       lowercase_		)
       A__   				=					len(lowercase_		)

       # transposition
       A__   				=					(
           len([(ca, ca) for ca, ca in zip(lowercase_				,       lowercase_		) if ca != ca]		) // 2
       )

       if not match_count:
              A__   				=					0.0
       else:
              A__   				=					(
                  1
                  / 3
                  * (
                      match_count / len(lowercase_		)
                      + match_count / len(lowercase_		)
                      + (match_count - transpositions) / match_count
                  )
              )

       # common prefix up to 4 characters
       A__   				=					0
       for ca, ca in zip(stra[:4]				,       stra[:4]		):
              if ca == ca:
                     prefix_len += 1
              else:
                     break

       return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(jaro_winkler("""hello""", """world"""))


