


'''simple docstring'''


def      lowerCamelCase					(      lowerCamelCase  :      str = "The quick brown fox jumps over the lazy dog"							,						):
      A_						:  str          =				set()

      # Replace all the whitespace in our sentence
      A_						:  str          =				input_str.replace(""" """							,						"""""")
      for alpha in input_str:
            if "a" <= alpha.lower() <= "z":
                  frequency.add(alpha.lower())
      return len(_lowercase) == 26




def      lowerCamelCase					(      lowerCamelCase  :      str = "The quick brown fox jumps over the lazy dog"							,						):
      A_						:  Optional[Any]          =				[False] * 26
      for char in input_str:
            if char.islower():
                  A_						:  Tuple          =				True
            elif char.isupper():
                  A_						:  Tuple          =				True
      return all(_lowercase)




def      lowerCamelCase					(      lowerCamelCase  :      str = "The quick brown fox jumps over the lazy dog"							,						):
      return len({char for char in input_str.lower() if char.isalpha()}) == 26




def      lowerCamelCase					(      ):
      from timeit import timeit

      A_						:  Dict          =				"from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"
      print(timeit("""is_pangram()"""							,						setup=_lowercase))
      print(timeit("""is_pangram_faster()"""							,						setup=_lowercase))
      print(timeit("""is_pangram_fastest()"""							,						setup=_lowercase))
      # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
      # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     benchmark()




'''simple docstring'''


from ... import PretrainedConfig


__magic_name__			=      {
    'sijunhe/nezha-cn-base': 'https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json',
}



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
  a_  					=							"""nezha"""






  def __init__(							self :       int	,_a :       Union[str, Any]=21128	,_a :       int=768	,_a :       Any=12	,_a :       List[str]=12	,_a :       str=3072	,_a :       int="gelu"	,_a :       int=0.1	,_a :       str=0.1	,_a :       Tuple=512	,_a :       List[Any]=64	,_a :       Dict=2	,_a :       List[Any]=0.02	,_a :       Optional[Any]=1e-12	,_a :       List[Any]=0.1	,_a :       Union[str, Any]=0	,_a :       Any=2	,_a :       Union[str, Any]=3	,_a :       int=True	,**_a :       int	,):

        '''simple docstring'''




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

        A_						:  Tuple          =				vocab_size
        A_						:  int          =				hidden_size
        A_						:  Any          =				num_hidden_layers
        A_						:  List[Any]          =				num_attention_heads
        A_						:  Tuple          =				hidden_act
        A_						:  List[Any]          =				intermediate_size
        A_						:  List[str]          =				hidden_dropout_prob
        A_						:  Tuple          =				attention_probs_dropout_prob
        A_						:  Dict          =				max_position_embeddings
        A_						:  Optional[Any]          =				max_relative_position
        A_						:  List[Any]          =				type_vocab_size
        A_						:  int          =				initializer_range
        A_						:  Tuple          =				layer_norm_eps
        A_						:  Dict          =				classifier_dropout
        A_						:  int          =				use_cache
