'''simple docstring'''

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


lowerCAmelCase_			:			Optional[Any]    	=   logging.get_logger(__name__)





class 				lowercase    (    __A			):
   lowerCamelCase_            ='speech-encoder-decoder'
   lowerCamelCase_            =True


   def __init__( self					:						Optional[Any] ,		**__lowerCAmelCase					:						str)   ->       List[str]:
         super().__init__(**_lowercase)
         if "encoder" not in kwargs or "decoder" not in kwargs:
               raise ValueError(
                   F'A configuraton of type {self.model_type} cannot be instantiated because not both `encoder` and'
                   F' `decoder` sub-configurations are passed, but only {kwargs}')

         lowercase_     			=   kwargs.pop("encoder")
         lowercase_     			=   encoder_config.pop("model_type")
         lowercase_     			=   kwargs.pop("decoder")
         lowercase_     			=   decoder_config.pop("model_type")

         lowercase_     			=   AutoConfig.for_model(_lowercase ,		**_lowercase)
         lowercase_     			=   AutoConfig.for_model(_lowercase ,		**_lowercase)
         lowercase_     			=   True


   @classmethod
   def __UpperCAmelCase							( cls					:						Union[str, Any] ,		__lowerCAmelCase					:						str ,		__lowerCAmelCase					:						int ,		**__lowerCAmelCase					:						str)   ->       Dict:
         logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config")
         lowercase_     			=   True
         lowercase_     			=   True

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


   def __UpperCAmelCase							( self					:						Union[str, Any])   ->       List[str]:
         lowercase_     			=   copy.deepcopy(self.__dict__)
         lowercase_     			=   self.encoder.to_dict()
         lowercase_     			=   self.decoder.to_dict()
         lowercase_     			=   self.__class__.model_type
         return output













import numpy as np






def 				_lowerCAmelCase			(	_a :       Optional[int]       , _a :       str      )						->      Union[str, Any]:
     lowerCAmelCase_       :							int			 =		np.array(_a      )
     lowerCAmelCase_       :							str			 =		np.array(_a      )

     lowerCAmelCase_       :							Optional[Any]			 =		abs(predict - actual      )
     lowerCAmelCase_       :							Dict			 =		difference.mean()

     return score






def 				_lowerCAmelCase			(	_a :       Union[str, Any]       , _a :       int      )						->      Tuple:
     lowerCAmelCase_       :							int			 =		np.array(_a      )
     lowerCAmelCase_       :							int			 =		np.array(_a      )

     lowerCAmelCase_       :							Union[str, Any]			 =		predict - actual
     lowerCAmelCase_       :							Tuple			 =		np.square(_a      )

     lowerCAmelCase_       :							Tuple			 =		square_diff.mean()
     return score






def 				_lowerCAmelCase			(	_a :       Optional[Any]       , _a :       Tuple      )						->      Optional[int]:
     lowerCAmelCase_       :							Optional[Any]			 =		np.array(_a      )
     lowerCAmelCase_       :							Optional[Any]			 =		np.array(_a      )

     lowerCAmelCase_       :							int			 =		predict - actual
     lowerCAmelCase_       :							Any			 =		np.square(_a      )
     lowerCAmelCase_       :							Union[str, Any]			 =		square_diff.mean()
     lowerCAmelCase_       :							Optional[Any]			 =		np.sqrt(_a      )
     return score






def 				_lowerCAmelCase			(	_a :       Optional[Any]       , _a :       Optional[Any]      )						->      List[Any]:
     lowerCAmelCase_       :							Dict			 =		np.array(_a      )
     lowerCAmelCase_       :							List[Any]			 =		np.array(_a      )

     lowerCAmelCase_       :							str			 =		np.log(predict + 1      )
     lowerCAmelCase_       :							Any			 =		np.log(actual + 1      )

     lowerCAmelCase_       :							List[str]			 =		log_predict - log_actual
     lowerCAmelCase_       :							Dict			 =		np.square(_a      )
     lowerCAmelCase_       :							Optional[Any]			 =		square_diff.mean()

     lowerCAmelCase_       :							int			 =		np.sqrt(_a      )

     return score






def 				_lowerCAmelCase			(	_a :       str       , _a :       List[Any]      )						->      int:
     lowerCAmelCase_       :							Any			 =		np.array(_a      )
     lowerCAmelCase_       :							Optional[int]			 =		np.array(_a      )

     lowerCAmelCase_       :							Optional[int]			 =		predict - actual
     lowerCAmelCase_       :							str			 =		np.sum(_a      ) / len(_a      )
     lowerCAmelCase_       :							List[str]			 =		np.sum(_a      ) / len(_a      )
     # print(numerator, denumerator)
     lowerCAmelCase_       :							int			 =		float(_a      ) / denumerator * 1_00

     return score







def 				_lowerCAmelCase			(	_a :       Any       , _a :       Union[str, Any]      )						->      str:
     return np.mean(np.array(_a      ) == np.array(_a      )      )
