


'''simple docstring'''

from math import factorial






def __UpperCamelCase   (			__lowerCamelCase		: int      ,  __lowerCamelCase		: int      ,  __lowerCamelCase		: float				)      ->     float:







     '''simple docstring'''






     if successes > trials:
          raise ValueError("successes must be lower or equal to trials"				)
     if trials < 0 or successes < 0:
          raise ValueError("the function is defined for non-negative integers"				)
     if not isinstance(__lowerCamelCase      ,  __lowerCamelCase				) or not isinstance(__lowerCamelCase      ,  __lowerCamelCase				):
          raise ValueError("the function is defined for non-negative integers"				)
     if not 0 < prob < 1:
          raise ValueError("prob has to be in range of 1 - 0"				)
     _a						=		(prob**successes) * ((1 - prob) ** (trials - successes))
     # Calculate the binomial coefficient: n! / k!(n-k)!
     _a						=		float(factorial(__lowerCamelCase				)				)
     coefficient /= factorial(__lowerCamelCase				) * factorial(trials - successes				)
     return probability * coefficient


if __name__ == "__main__":
   from doctest import testmod

   testmod()
   print("Probability of 2 successes out of 4 trails")
   print("with probability of 0.75 is:", end=" ")
   print(binomial_distribution(2, 4, 0.75))





'''simple docstring'''

import copy

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


lowercase__				     =		logging.get_logger(__name__)





class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):
     UpperCAmelCase 					=				'''encoder-decoder'''
     UpperCAmelCase 					=				True






     def __init__(			self     ,							**__UpperCamelCase		)       ->    List[Any]:
          super().__init__(**__UpperCamelCase		)
          assert (
              "encoder" in kwargs and "decoder" in kwargs
          ), "Config has to be initialized with encoder and decoder config"
          _a						=		kwargs.pop("encoder"		)
          _a						=		encoder_config.pop("model_type"		)
          _a						=		kwargs.pop("decoder"		)
          _a						=		decoder_config.pop("model_type"		)

          from ..auto.configuration_auto import AutoConfig

          _a						=		AutoConfig.for_model(__UpperCamelCase     ,							**__UpperCamelCase		)
          _a						=		AutoConfig.for_model(__UpperCamelCase     ,							**__UpperCamelCase		)
          _a						=		True






     @classmethod
     def  a_					(			cls     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							**__UpperCamelCase		)       ->    PretrainedConfig:
          logger.info("Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"		)
          _a						=		True
          _a						=		True

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






     def  a_					(			self		)       ->    Any:
          _a						=		copy.deepcopy(self.__dict__		)
          _a						=		self.encoder.to_dict()
          _a						=		self.decoder.to_dict()
          _a						=		self.__class__.model_type
          return output

