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


UpperCamelCase_  =	logging.get_logger(__name__)

UpperCamelCase_  =	{
    "microsoft/mpnet-base": "https://huggingface.co/microsoft/mpnet-base/resolve/main/config.json",
}

class     _SCREAMING_SNAKE_CASE							(		snake_case							):
				lowerCamelCase_       =     'mpnet'





				def __init__(		self :		Dict					,      snake_case_ :		Dict=3_0527					,      snake_case_ :		int=768					,      snake_case_ :		List[Any]=12					,      snake_case_ :		int=12					,      snake_case_ :		List[str]=3072					,      snake_case_ :		Optional[Any]="gelu"					,      snake_case_ :		int=0.1					,      snake_case_ :		Any=0.1					,      snake_case_ :		List[Any]=512					,      snake_case_ :		int=0.02					,      snake_case_ :		Dict=1E-12					,      snake_case_ :		str=32					,      snake_case_ :		List[str]=1					,      snake_case_ :		int=0					,      snake_case_ :		Optional[int]=2					,      **snake_case_ :		Optional[int]					,      ):


								"""simple docstring"""

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

								A     :  str   =				vocab_size
								A     :  List[str]   =				hidden_size
								A     :  List[str]   =				num_hidden_layers
								A     :  List[str]   =				num_attention_heads
								A     :  str   =				hidden_act
								A     :  str   =				intermediate_size
								A     :  Dict   =				hidden_dropout_prob
								A     :  List[str]   =				attention_probs_dropout_prob
								A     :  List[Any]   =				max_position_embeddings
								A     :  List[Any]   =				initializer_range
								A     :  str   =				layer_norm_eps
								A     :  List[str]   =				relative_attention_num_buckets

UpperCamelCase_  =	10_00





def 							_lowerCamelCase			(					lowerCamelCase_:  float		):



				'''simple docstring'''







				return x * x





def 							_lowerCamelCase			(					lowerCamelCase_:  Tuple				,	lowerCamelCase_:  float				,	lowerCamelCase_:  float				,	lowerCamelCase_:  int = 4		):



				'''simple docstring'''







				assert callable(
				    lowerCamelCase_		), f"""the function(object) passed should be callable your input : {function}"""
				assert isinstance(lowerCamelCase_				,	(float, int)		), f"""a should be float or integer your input : {a}"""
				assert isinstance(function(lowerCamelCase_		)				,	(float, int)		), (
				    "the function should return integer or float return type of your function, "
				    f"""{type(lowerCamelCase_		)}"""
				)
				assert isinstance(lowerCamelCase_				,	(float, int)		), f"""b should be float or integer your input : {b}"""
				assert (
				    isinstance(lowerCamelCase_				,	lowerCamelCase_		) and precision > 0
				), f"""precision should be positive integer your input : {precision}"""

				# just applying the formula of simpson for approximate integration written in
				# mentioned article in first comment of this file and above this function

				A     :  Tuple   =				(b - a) / N_STEPS
				A     :  List[str]   =				function(lowerCamelCase_		) + function(lowerCamelCase_		)

				for i in range(1				,	lowerCamelCase_		):
								A     :  int   =				a + h * i
								result += function(lowerCamelCase_		) * (4 if i % 2 else 2)

				result *= h / 3
				return round(lowerCamelCase_				,	lowerCamelCase_		)


if __name__ == "__main__":
					import doctest

					doctest.testmod()