



'''simple docstring'''




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


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

a     :    Tuple      						=					{
    """SCUT-DLVCLab/lilt-roberta-en-base""": (
        """https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"""
    ),
}

class     UpperCamelCase_				(							__A    ):
 lowercase    =				'lilt'





 def __init__(						self	,	A=30522	,	A=768	,	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=0	,	A="absolute"	,	A=None	,	A=4	,	A=1024	,	**A	,	)    ->    Optional[int]:
        super().__init__(pad_token_id=A	,	**A					)

        UpperCAmelCase  :	List[Any]							  =	vocab_size
        UpperCAmelCase  :	Dict							  =	hidden_size
        UpperCAmelCase  :	Optional[Any]							  =	num_hidden_layers
        UpperCAmelCase  :	int							  =	num_attention_heads
        UpperCAmelCase  :	str							  =	hidden_act
        UpperCAmelCase  :	Optional[Any]							  =	intermediate_size
        UpperCAmelCase  :	Optional[int]							  =	hidden_dropout_prob
        UpperCAmelCase  :	List[Any]							  =	attention_probs_dropout_prob
        UpperCAmelCase  :	Union[str, Any]							  =	max_position_embeddings
        UpperCAmelCase  :	Optional[Any]							  =	type_vocab_size
        UpperCAmelCase  :	int							  =	initializer_range
        UpperCAmelCase  :	int							  =	layer_norm_eps
        UpperCAmelCase  :	Union[str, Any]							  =	position_embedding_type
        UpperCAmelCase  :	Dict							  =	classifier_dropout
        UpperCAmelCase  :	str							  =	channel_shrink_ratio
        UpperCAmelCase  :	Any							  =	max_ad_position_embeddings






'''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()

