


from ...processing_utils import ProcessorMixin
class 				SCREAMING_SNAKE_CASE__						(       lowercase__    ):
      snake_case__       :  Tuple       =		['''image_processor''', '''feature_extractor''']
      snake_case__       :  Tuple       =		'''TvltImageProcessor'''
      snake_case__       :  List[Any]       =		'''TvltFeatureExtractor'''
      def __init__( self       : str  ,							SCREAMING_SNAKE_CASE__       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]  )	->    Optional[int]:
             super().__init__(image_processor=SCREAMING_SNAKE_CASE__  ,							feature_extractor=SCREAMING_SNAKE_CASE__  )

             a_      :	Any							=						image_processor
             a_      :	str							=						feature_extractor
      def __call__( self       : Any  ,							SCREAMING_SNAKE_CASE__       : List[Any]=None  ,							SCREAMING_SNAKE_CASE__       : Tuple=None  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=None  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=None  ,							SCREAMING_SNAKE_CASE__       : Any=False  ,							SCREAMING_SNAKE_CASE__       : Tuple=False  ,							*SCREAMING_SNAKE_CASE__       : Union[str, Any]  ,							**SCREAMING_SNAKE_CASE__       : int  ,							)	->    Dict:

             if images is None and audio is None:
                    raise ValueError('You need to specify either an `images` or `audio` input to process.'  )

             a_      :	Tuple							=						None
             if images is not None:
                    a_      :	str							=						self.image_processor(SCREAMING_SNAKE_CASE__  ,							mask_pixel=SCREAMING_SNAKE_CASE__  ,							*SCREAMING_SNAKE_CASE__  ,							**SCREAMING_SNAKE_CASE__  )
             if images_mixed is not None:
                    a_      :	List[str]							=						self.image_processor(SCREAMING_SNAKE_CASE__  ,							is_mixed=SCREAMING_SNAKE_CASE__  ,							*SCREAMING_SNAKE_CASE__  ,							**SCREAMING_SNAKE_CASE__  )
             if audio is not None:
                    a_      :	int							=						self.feature_extractor(
                        SCREAMING_SNAKE_CASE__  ,							*SCREAMING_SNAKE_CASE__  ,							sampling_rate=SCREAMING_SNAKE_CASE__  ,							mask_audio=SCREAMING_SNAKE_CASE__  ,							**SCREAMING_SNAKE_CASE__  )

             a_      :	List[str]							=						{}
             if audio is not None:
                    output_dict.update(SCREAMING_SNAKE_CASE__  )
             if images is not None:
                    output_dict.update(SCREAMING_SNAKE_CASE__  )
             if images_mixed_dict is not None:
                    output_dict.update(SCREAMING_SNAKE_CASE__  )
             return output_dict

      @property
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Dict:
             a_      :	Dict							=						self.image_processor.model_input_names
             a_      :	Dict							=						self.feature_extractor.model_input_names
             return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names  )  )






def  SCREAMING_SNAKE_CASE_		(    __A				:    int						,					__A				:    int						,					__A				:    int       )			->	float:

       """simple docstring"""

       a_      :	Optional[int]							=						(num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
       # formula for sum of series
       return total







def  SCREAMING_SNAKE_CASE_		(    )			->	Optional[Any]:

       """simple docstring"""

       print(sum_of_series(1						,					1						,					10       )       )


if __name__ == "__main__":
   import doctest

   doctest.testmod()


