





'''simple docstring'''

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


lowercase_					= logging.get_logger(__name__)

lowercase_					= {
    """weiweishi/roc-bert-base-zh""": """https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json""",
}


class 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = '''roc_bert'''


    def __init__(		self ,		A=3_0522 ,		A=768 ,		A=12 ,		A=12 ,		A=3072 ,		A="gelu" ,		A=0.1 ,		A=0.1 ,		A=512 ,		A=2 ,		A=0.02 ,		A=1e-12 ,		A=True ,		A=0 ,		A="absolute" ,		A=None ,		A=True ,		A=True ,		A=768 ,		A=910 ,		A=512 ,		A=2_4858 ,		A=True ,		**A ,		)		->   Optional[Any]:
        _SCREAMING_SNAKE_CASE									=			vocab_size
        _SCREAMING_SNAKE_CASE									=			max_position_embeddings
        _SCREAMING_SNAKE_CASE									=			hidden_size
        _SCREAMING_SNAKE_CASE									=			num_hidden_layers
        _SCREAMING_SNAKE_CASE									=			num_attention_heads
        _SCREAMING_SNAKE_CASE									=			intermediate_size
        _SCREAMING_SNAKE_CASE									=			hidden_act
        _SCREAMING_SNAKE_CASE									=			hidden_dropout_prob
        _SCREAMING_SNAKE_CASE									=			attention_probs_dropout_prob
        _SCREAMING_SNAKE_CASE									=			initializer_range
        _SCREAMING_SNAKE_CASE									=			type_vocab_size
        _SCREAMING_SNAKE_CASE									=			layer_norm_eps
        _SCREAMING_SNAKE_CASE									=			use_cache
        _SCREAMING_SNAKE_CASE									=			enable_pronunciation
        _SCREAMING_SNAKE_CASE									=			enable_shape
        _SCREAMING_SNAKE_CASE									=			pronunciation_embed_dim
        _SCREAMING_SNAKE_CASE									=			pronunciation_vocab_size
        _SCREAMING_SNAKE_CASE									=			shape_embed_dim
        _SCREAMING_SNAKE_CASE									=			shape_vocab_size
        _SCREAMING_SNAKE_CASE									=			concat_input
        _SCREAMING_SNAKE_CASE									=			position_embedding_type
        _SCREAMING_SNAKE_CASE									=			classifier_dropout
        super().__init__(pad_token_id=A ,		**A       )




def  snake_case  (       snake_case__			:str = "The quick brown fox jumps over the lazy dog"   ,	)						->   bool:
       _A        =  set()

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






def  snake_case  (       snake_case__			:str = "The quick brown fox jumps over the lazy dog"   ,	)						->   bool:
       _A        =  [False] * 26
       for char in input_str:
              if char.islower():
                     _A        =  True
              elif char.isupper():
                     _A        =  True
       return all(snake_case__)






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






def  snake_case  (       )						->   None:
       from timeit import timeit

       _A        =  """from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"""
       print(timeit("""is_pangram()"""   ,	setup=snake_case__))
       print(timeit("""is_pangram_faster()"""   ,	setup=snake_case__))
       print(timeit("""is_pangram_fastest()"""   ,	setup=snake_case__))
       # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
       # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    benchmark()




