






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


__a      :       List[str]				  =   logging.get_logger(__name__)

__a      :       str				  =   {
    "abeja/gpt-neox-japanese-2.7b": "https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json",
}


class       __lowercase      (	lowercase_  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				"gpt_neox_japanese"




     def __init__(				self       :			str ,	UpperCamelCase_       :			Tuple=32_000 ,	UpperCamelCase_       :			List[str]=2_560 ,	UpperCamelCase_       :			List[str]=32 ,	UpperCamelCase_       :			Any=32 ,	UpperCamelCase_       :			Optional[Any]=4 ,	UpperCamelCase_       :			Union[str, Any]="gelu" ,	UpperCamelCase_       :			Tuple=1.00 ,	UpperCamelCase_       :			Union[str, Any]=10_000 ,	UpperCamelCase_       :			Dict=2_048 ,	UpperCamelCase_       :			Tuple=0.02 ,	UpperCamelCase_       :			List[str]=1e-5 ,	UpperCamelCase_       :			Union[str, Any]=True ,	UpperCamelCase_       :			int=31_996 ,	UpperCamelCase_       :			Union[str, Any]=31_999 ,	UpperCamelCase_       :			Union[str, Any]=0.1 ,	UpperCamelCase_       :			Optional[int]=0.0 ,	**UpperCamelCase_       :			Any ,	):


         """simple docstring"""



         super().__init__(bos_token_id=UpperCamelCase_ ,	eos_token_id=UpperCamelCase_ ,	**UpperCamelCase_				)
         __A         =	vocab_size
         __A         =	max_position_embeddings
         __A         =	hidden_size
         __A         =	num_hidden_layers
         __A         =	num_attention_heads
         __A         =	intermediate_multiple_size
         __A         =	hidden_act
         __A         =	rotary_pct
         __A         =	rotary_emb_base
         __A         =	initializer_range
         __A         =	layer_norm_eps
         __A         =	use_cache
         __A         =	attention_dropout
         __A         =	hidden_dropout












def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			str = "The quick brown fox jumps over the lazy dog"			,				)						->      bool:





    """simple docstring"""

    __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(__lowercase    ) == 2_6



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			str = "The quick brown fox jumps over the lazy dog"			,				)						->      bool:





    """simple docstring"""

    __A         =	[False] * 2_6
    for char in input_str:
        if char.islower():
            __A         =	True
        elif char.isupper():
            __A         =	True
    return all(__lowercase    )



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			str = "The quick brown fox jumps over the lazy dog"			,				)						->      bool:





    """simple docstring"""

    return len({char for char in input_str.lower() if char.isalpha()}    ) == 2_6



def 			_SCREAMING_SNAKE_CASE						( )						->      None:





    """simple docstring"""

    from timeit import timeit

    __A         =	"""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()




