



'''simple docstring'''




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


a     :    Any      						=					logging.get_logger(__name__)

a     :    Optional[Any]      						=					{
    """studio-ousia/luke-base""": """https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json""",
    """studio-ousia/luke-large""": """https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json""",
}

class     UpperCamelCase_				(							__magic_name__    ):
	lowercase    =				'luke'





	def __init__(						self	,	A=50267	,	A=500000	,	A=768	,	A=256	,	A=12	,	A=12	,	A=3072	,	A="gelu"	,	A=0.1	,	A=0.1	,	A=512	,	A=2	,	A=0.0_2	,	A=1e-12	,	A=True	,	A=None	,	A=1	,	A=0	,	A=2	,	**A	,	)    ->    Union[str, Any]:
								super().__init__(pad_token_id=A	,	bos_token_id=A	,	eos_token_id=A	,	**A					)

								UpperCAmelCase  :	Dict							  =	vocab_size
								UpperCAmelCase  :	Optional[Any]							  =	entity_vocab_size
								UpperCAmelCase  :	Dict							  =	hidden_size
								UpperCAmelCase  :	Union[str, Any]							  =	entity_emb_size
								UpperCAmelCase  :	Any							  =	num_hidden_layers
								UpperCAmelCase  :	Dict							  =	num_attention_heads
								UpperCAmelCase  :	Optional[int]							  =	hidden_act
								UpperCAmelCase  :	List[str]							  =	intermediate_size
								UpperCAmelCase  :	str							  =	hidden_dropout_prob
								UpperCAmelCase  :	Any							  =	attention_probs_dropout_prob
								UpperCAmelCase  :	Tuple							  =	max_position_embeddings
								UpperCAmelCase  :	Optional[Any]							  =	type_vocab_size
								UpperCAmelCase  :	Dict							  =	initializer_range
								UpperCAmelCase  :	List[Any]							  =	layer_norm_eps
								UpperCAmelCase  :	Union[str, Any]							  =	use_entity_aware_attention
								UpperCAmelCase  :	Union[str, Any]							  =	classifier_dropout






'''simple docstring'''




from collections.abc import Callable

import numpy as np



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase	)   ->    np.array:
							UpperCAmelCase  :	Optional[Any]							  =	int(np.ceil((x_end - xa) / step_size	)	)
							UpperCAmelCase  :	str							  =	np.zeros((n + 1,)	)
							UpperCAmelCase  :	Optional[Any]							  =	ya
							UpperCAmelCase  :	Union[str, Any]							  =	xa

							for k in range(_lowercase	):
														UpperCAmelCase  :	Dict							  =	y[k] + step_size * ode_func(_lowercase			,       y[k]	)
														UpperCAmelCase  :	Optional[int]							  =	y[k] + (
														    (step_size / 2) * (ode_func(_lowercase			,       y[k]	) + ode_func(x + step_size			,       _lowercase	))
														)
														x += step_size

							return y


if __name__ == "__main__":
							import doctest

							doctest.testmod()

