


import copy

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


UpperCAmelCase_				:			List[str]   =	logging.get_logger(__name__)
class 			SCREAMING_SNAKE_CASE__  (    lowercase__      ):
      snake_case__    :					List[Any]					       =						'''encoder-decoder'''
      snake_case__    :					Any					       =						True
      def __init__(	self    :	Union[str, Any]					,							**SCREAMING_SNAKE_CASE__    :	List[Any]					)		->      Union[str, Any]:
             super().__init__(**SCREAMING_SNAKE_CASE__					)
             assert (
                 "encoder" in kwargs and "decoder" in kwargs
             ), "Config has to be initialized with encoder and decoder config"
             a_ :		Dict							=  kwargs.pop('encoder'					)
             a_ :		Optional[int]							=  encoder_config.pop('model_type'					)
             a_ :		str							=  kwargs.pop('decoder'					)
             a_ :		str							=  decoder_config.pop('model_type'					)

             from ..auto.configuration_auto import AutoConfig

             a_ :		str							=  AutoConfig.for_model(SCREAMING_SNAKE_CASE__					,							**SCREAMING_SNAKE_CASE__					)
             a_ :		Tuple							=  AutoConfig.for_model(SCREAMING_SNAKE_CASE__					,							**SCREAMING_SNAKE_CASE__					)
             a_ :		Any							=  True
      @classmethod
      def 	SCREAMING_SNAKE_CASE							(	cls    :	Union[str, Any]					,							SCREAMING_SNAKE_CASE__    :	PretrainedConfig					,							SCREAMING_SNAKE_CASE__    :	PretrainedConfig					,							**SCREAMING_SNAKE_CASE__    :	Dict					)		->      PretrainedConfig:
             logger.info('Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'					)
             a_ :		str							=  True
             a_ :		int							=  True

             return cls(encoder=encoder_config.to_dict()					,							decoder=decoder_config.to_dict()					,							**SCREAMING_SNAKE_CASE__					)

      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					)		->      int:
             a_ :		Any							=  copy.deepcopy(self.__dict__					)
             a_ :		int							=  self.encoder.to_dict()
             a_ :		Tuple							=  self.decoder.to_dict()
             a_ :		Union[str, Any]							=  self.__class__.model_type
             return output






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
UpperCAmelCase_				:			str   =	1.054571817e-34  # unit of ℏ : J * s

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







def 	SCREAMING_SNAKE_CASE_						(      __A			:      float  ,   __A			:      float  ,   __A			:      float    )     ->		dict[str, float]:

       """simple docstring"""

       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:
              a_ :		Optional[Any]							=  (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
                  2_40 * (distance) ** 4
              )
              return {"force": force}
       elif area == 0:
              a_ :		List[str]							=  (2_40 * force * (distance) ** 4) / (
                  REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
              )
              return {"area": area}
       elif distance == 0:
              a_ :		Tuple							=  (
                  (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (2_40 * 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()


