
"""simple docstring"""


_lowercase				 =					'''Tobias Carryer'''

from time import time







class 					lowerCAmelCase_					:



						'''simple docstring'''


						def __init__(			self  :   List[Any]	,A_  :   str	,A_  :   Optional[Any]	,A_  :   List[str]	,A_  :   List[Any]=int(time()   )   )  -> Union[str, Any]:  # noqa: B008
							A     = multiplier
							A     = increment
							A     = modulo
							A     = seed




						def     _SCREAMING_SNAKE_CASE   (			self  :   Any   )  -> int:
							A     = (self.multiplier * self.seed + self.increment) % self.modulo
							return self.seed


if __name__ == "__main__":
		# Show the LCG in action.
		_lowercase				 =					LinearCongruentialGenerator(1_66_45_25, 10_13_90_42_23, 2 << 31)
		while True:
				print(lcg.next_number())


'''simple docstring'''







from ...configuration_utils import PretrainedConfig


class        A__   (   _snake_case      ):
    lowercase						       =		"bert-generation"



    def __init__(       self		,					UpperCamelCase__=50358		,					UpperCamelCase__=1024		,					UpperCamelCase__=24		,					UpperCamelCase__=16		,					UpperCamelCase__=4096		,					UpperCamelCase__="gelu"		,					UpperCamelCase__=0.1		,					UpperCamelCase__=0.1		,					UpperCamelCase__=512		,					UpperCamelCase__=0.02		,					UpperCamelCase__=1e-1_2		,					UpperCamelCase__=0		,					UpperCamelCase__=2		,					UpperCamelCase__=1		,					UpperCamelCase__="absolute"		,					UpperCamelCase__=True		,					**UpperCamelCase__		,					)    ->   Any:





           '''simple docstring'''
           super().__init__(pad_token_id=UpperCamelCase__		,					bos_token_id=UpperCamelCase__		,					eos_token_id=UpperCamelCase__		,					**UpperCamelCase__							)

           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_						       =   initializer_range
           A_						       =   layer_norm_eps
           A_						       =   position_embedding_type
           A_						       =   use_cache
