



'''simple docstring'''




from ...processing_utils import ProcessorMixin






class 						_UpperCamelCase  (							__UpperCamelCase    ):


			'''simple docstring'''

			_A      :		Dict			    =		['image_processor', 'feature_extractor']
			_A      :		List[str]			    =		'TvltImageProcessor'
			_A      :		Dict			    =		'TvltFeatureExtractor'

			def __init__(							self     : List[Any]							,     lowerCAmelCase__     : Optional[Any]							,     lowerCAmelCase__     : Optional[int]						):





					"""simple docstring"""






					super().__init__(image_processor=lowerCAmelCase__							,     feature_extractor=lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		image_processor
					__SCREAMING_SNAKE_CASE       :		int           =		feature_extractor

			def __call__(							self     : int							,     lowerCAmelCase__     : Optional[int]=None							,     lowerCAmelCase__     : List[Any]=None							,     lowerCAmelCase__     : Union[str, Any]=None							,     lowerCAmelCase__     : str=None							,     lowerCAmelCase__     : Dict=False							,     lowerCAmelCase__     : str=False							,     *lowerCAmelCase__     : int							,     **lowerCAmelCase__     : Any							,     ):





					"""simple docstring"""






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

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

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



			@property
			def UpperCamelCase__					(							self     : Optional[int]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		self.image_processor.model_input_names
					__SCREAMING_SNAKE_CASE       :		Dict           =		self.feature_extractor.model_input_names
					return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names						)						)



import math




def    snake_case_     (  snake_case			,							snake_case     )  ->				float:
       return math.pow(snake_case			,							2     ) - a




def    snake_case_     (  snake_case     )  ->				float:
       return 2 * x




def    snake_case_     (  snake_case     )  ->				float:
       lowercase__:   Dict  			=   2.0

       while start <= a:
              lowercase__:   str  			=   math.pow(snake_case			,							2     )

       return start




def    snake_case_     (  snake_case			,							snake_case = 99_99			,							snake_case = 0.0_0_0_0_0_0_0_0_0_0_0_0_0_1     )  ->				float:

       if a < 0:
              raise ValueError('math domain error'     )

       lowercase__:   Tuple  			=   get_initial_point(snake_case     )

       for _ in range(snake_case     ):
              lowercase__:   List[Any]  			=   value
              lowercase__:   Any  			=   value - fx(snake_case			,							snake_case     ) / fx_derivative(snake_case     )
              if abs(prev_value - value     ) < tolerance:
                     return value

       return value


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

 testmod()


