




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


__SCREAMING_SNAKE_CASE 						=logging.get_logger(__name__)

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






class  __magic_name__						(  __lowerCamelCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							Tuple	      =					'''lilt'''


				def __init__(							self:   Tuple		,					_lowerCamelCase:   Union[str, Any]=3_05_22		,					_lowerCamelCase:   Optional[Any]=7_68		,					_lowerCamelCase:   Dict=12		,					_lowerCamelCase:   Optional[int]=12		,					_lowerCamelCase:   Dict=30_72		,					_lowerCamelCase:   Optional[int]="gelu"		,					_lowerCamelCase:   Dict=0.1		,					_lowerCamelCase:   int=0.1		,					_lowerCamelCase:   Tuple=5_12		,					_lowerCamelCase:   int=2		,					_lowerCamelCase:   str=0.02		,					_lowerCamelCase:   Tuple=1E-12		,					_lowerCamelCase:   List[Any]=0		,					_lowerCamelCase:   int="absolute"		,					_lowerCamelCase:   Dict=None		,					_lowerCamelCase:   Union[str, Any]=4		,					_lowerCamelCase:   List[Any]=10_24		,					**_lowerCamelCase:   List[str]		,					):
											super().__init__(pad_token_id=a_		,					**a_		)

											SCREAMING_SNAKE_CASE_   	=    vocab_size
											SCREAMING_SNAKE_CASE_   	=    hidden_size
											SCREAMING_SNAKE_CASE_   	=    num_hidden_layers
											SCREAMING_SNAKE_CASE_   	=    num_attention_heads
											SCREAMING_SNAKE_CASE_   	=    hidden_act
											SCREAMING_SNAKE_CASE_   	=    intermediate_size
											SCREAMING_SNAKE_CASE_   	=    hidden_dropout_prob
											SCREAMING_SNAKE_CASE_   	=    attention_probs_dropout_prob
											SCREAMING_SNAKE_CASE_   	=    max_position_embeddings
											SCREAMING_SNAKE_CASE_   	=    type_vocab_size
											SCREAMING_SNAKE_CASE_   	=    initializer_range
											SCREAMING_SNAKE_CASE_   	=    layer_norm_eps
											SCREAMING_SNAKE_CASE_   	=    position_embedding_type
											SCREAMING_SNAKE_CASE_   	=    classifier_dropout
											SCREAMING_SNAKE_CASE_   	=    channel_shrink_ratio
											SCREAMING_SNAKE_CASE_   	=    max_ad_position_embeddings








from __future__ import annotations

from math import pi

# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
__SCREAMING_SNAKE_CASE 						=1.0_5_4_5_7_1_8_1_7e-3_4  # unit of ℏ : J * s

__SCREAMING_SNAKE_CASE 						=3e8  # unit of c : m * s^-1

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):

							if (force, area, distance).count(0     ) != 1:
														raise ValueError('''One and only one argument must be 0'''     )
							if force < 0:
														raise ValueError('''Magnitude of force can not be negative'''     )
							if distance < 0:
														raise ValueError('''Distance can not be negative'''     )
							if area < 0:
														raise ValueError('''Area can not be negative'''     )
							if force == 0:
														SCREAMING_SNAKE_CASE_   	=    (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
														    2_4_0 * (distance) ** 4
														)
														return {"force": force}
							elif area == 0:
														SCREAMING_SNAKE_CASE_   	=    (2_4_0 * force * (distance) ** 4) / (
														    REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
														)
														return {"area": area}
							elif distance == 0:
														SCREAMING_SNAKE_CASE_   	=    (
														    (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (2_4_0 * force)
														) ** (1 / 4)
														return {"distance": distance}
							raise ValueError('''One and only one argument must be 0'''     )


# Run doctest
if __name__ == "__main__":
			import doctest

			doctest.testmod()


