





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> str:




    """simple docstring"""




    _A     =							int(_SCREAMING_SNAKE_CASE				)
    if decimal in (0, 1):  # Exit cases for the recursion
        return str(_SCREAMING_SNAKE_CASE				)
    _A,			_A     =							divmod(_SCREAMING_SNAKE_CASE							,     2				)
    return binary_recursive(_SCREAMING_SNAKE_CASE				) + str(_SCREAMING_SNAKE_CASE				)





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> str:




    """simple docstring"""




    _A     =							str(_SCREAMING_SNAKE_CASE				).strip()
    if not number:
        raise ValueError('No input value was provided'				)
    _A     =							'-' if number.startswith('-'				) else ''
    _A     =							number.lstrip('-'				)
    if not number.isnumeric():
        raise ValueError('Input value is not an integer'				)
    return F"{negative}0b{binary_recursive(int(_SCREAMING_SNAKE_CASE				)				)}"


if __name__ == "__main__":
 from doctest import testmod

 testmod()












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





