



'''simple docstring'''





def lowerCAmelCase    (__A):



     """simple docstring"""



     return 10 - x * x




def lowerCAmelCase    (__A    ,      __A):



     """simple docstring"""



     if equation(__A) * equation(__A) >= 0:
          raise ValueError('''Wrong space!''')

     _a           =    a
     while (b - a) >= 0.01:
          # Find middle point
          _a           =    (a + b) / 2
          # Check if middle point is root
          if equation(__A) == 0.0:
               break
          # Decide the side to repeat the steps
          if equation(__A) * equation(__A) < 0:
               _a           =    c
          else:
               _a           =    c
     return c


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  print(bisection(-2, 5))
  print(bisection(0, 6))










from ... import PretrainedConfig


__A							:			Optional[Any]         =			{
    "sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     __magic_name__   =			NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
     __magic_name__   =			'nezha'




     def __init__(    self				,     snake_case_=2_1128				,     snake_case_=768				,     snake_case_=12				,     snake_case_=12				,     snake_case_=3072				,     snake_case_="gelu"				,     snake_case_=0.1				,     snake_case_=0.1				,     snake_case_=512				,     snake_case_=64				,     snake_case_=2				,     snake_case_=0.02				,     snake_case_=1E-12				,     snake_case_=0.1				,     snake_case_=0				,     snake_case_=2				,     snake_case_=3				,     snake_case_=True				,     **snake_case_				,     ):
         super().__init__(pad_token_id=snake_case_				,     bos_token_id=snake_case_				,     eos_token_id=snake_case_				,     **snake_case_							)

         _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





