
from ...processing_utils import ProcessorMixin


class 			lowercase_		(      A       ):
       __lowerCamelCase				       =						["image_processor", "feature_extractor"]
       __lowerCamelCase				       =						"TvltImageProcessor"
       __lowerCamelCase				       =						"TvltFeatureExtractor"


       def __init__(    self						,       __A						,       __A				)						->    Dict:
              super().__init__(image_processor=__A						,       feature_extractor=__A				)

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=image_processor
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=feature_extractor


       def __call__(    self						,       __A=None						,       __A=None						,       __A=None						,       __A=None						,       __A=False						,       __A=False						,       *__A						,       **__A						,       )						->    Dict:

              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_							:				Tuple		=None
              if images is not None:
                     SCREAMING_SNAKE_CASE_							:				List[str]		=self.image_processor(__A						,       mask_pixel=__A						,       *__A						,       **__A				)
              if images_mixed is not None:
                     SCREAMING_SNAKE_CASE_							:				Any		=self.image_processor(__A						,       is_mixed=__A						,       *__A						,       **__A				)
              if audio is not None:
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.feature_extractor(
                         __A						,       *__A						,       sampling_rate=__A						,       mask_audio=__A						,       **__A				)

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





       @property
       def 	_snake_case				(    self				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				Tuple		=self.image_processor.model_input_names
              SCREAMING_SNAKE_CASE_							:				Tuple		=self.feature_extractor.model_input_names
              return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names				)				)


from __future__ import annotations
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     list[int | float]			,							UpperCAmelCase_	:     int			,							UpperCAmelCase_	:     int				)			->						int | float:
       if len(UpperCAmelCase_				) == 0:
              raise ValueError('''find_max() arg is an empty sequence'''				)
       if (
           left >= len(UpperCAmelCase_				)
           or left < -len(UpperCAmelCase_				)
           or right >= len(UpperCAmelCase_				)
           or right < -len(UpperCAmelCase_				)
       ):
              raise IndexError('''list index out of range'''				)
       if left == right:
              return nums[left]
       SCREAMING_SNAKE_CASE_							:				Optional[int]		=(left + right) >> 1  # the middle
       SCREAMING_SNAKE_CASE_							:				Dict		=find_max(UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_				)  # find max in range[left, mid]
       SCREAMING_SNAKE_CASE_							:				Dict		=find_max(UpperCAmelCase_			,							mid + 1			,							UpperCAmelCase_				)  # find max in range[mid + 1, right]

       return left_max if left_max >= right_max else right_max


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)
