


import logging

from transformers.configuration_utils import PretrainedConfig


A							:					Dict     						=     logging.getLogger(__name__)






class     UpperCamelCase(  _a   ):
					snake_case_		:				str										=	"""masked_bert"""


					def __init__(						self	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		List[Any]=3_0_5_2_2     ,    SCREAMING_SNAKE_CASE	:		int=7_6_8     ,    SCREAMING_SNAKE_CASE	:		List[Any]=1_2     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=1_2     ,    SCREAMING_SNAKE_CASE	:		List[Any]=3_0_7_2     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]="gelu"     ,    SCREAMING_SNAKE_CASE	:		List[Any]=0.1     ,    SCREAMING_SNAKE_CASE	:		Tuple=0.1     ,    SCREAMING_SNAKE_CASE	:		int=5_1_2     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=2     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=0.02     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=1e-1_2     ,    SCREAMING_SNAKE_CASE	:		Any=0     ,    SCREAMING_SNAKE_CASE	:		str="topK"     ,    SCREAMING_SNAKE_CASE	:		List[str]="constant"     ,    SCREAMING_SNAKE_CASE	:		List[Any]=0.0     ,    **SCREAMING_SNAKE_CASE	:		List[str]     ,    )			->	Optional[Any]:




											'''simple docstring'''

											super().__init__(pad_token_id=SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE						)

											__snake_case				=		vocab_size
											__snake_case				=		hidden_size
											__snake_case				=		num_hidden_layers
											__snake_case				=		num_attention_heads
											__snake_case				=		hidden_act
											__snake_case				=		intermediate_size
											__snake_case				=		hidden_dropout_prob
											__snake_case				=		attention_probs_dropout_prob
											__snake_case				=		max_position_embeddings
											__snake_case				=		type_vocab_size
											__snake_case				=		initializer_range
											__snake_case				=		layer_norm_eps
											__snake_case				=		pruning_method
											__snake_case				=		mask_init
											__snake_case				=		mask_scale









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


						'''simple docstring'''






						__snake_case				=		set()

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





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


						'''simple docstring'''






						__snake_case				=		[False] * 26
						for char in input_str:
												if char.islower():
																		__snake_case				=		True
												elif char.isupper():
																		__snake_case				=		True
						return all(_lowerCAmelCase )





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


						'''simple docstring'''






						return len({char for char in input_str.lower() if char.isalpha()} ) == 26





def 	_lowerCAmelCase       (    )						->	None:


						'''simple docstring'''






						from timeit import timeit

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


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	benchmark()





