






from ...processing_utils import ProcessorMixin
class     a    ( lowercase__   ):



      """simple docstring"""


      a  : Dict							       =	['image_processor', 'feature_extractor']
      a  : Optional[int]							       =	'TvltImageProcessor'
      a  : int							       =	'TvltFeatureExtractor'
      def __init__(      self					:			int		,      __lowercase					:			Optional[Any]		,      __lowercase					:			Tuple					)							->      Tuple:
        super().__init__(image_processor=__lowercase		,      feature_extractor=__lowercase					)

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

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

        __UpperCAmelCase							:	Optional[int]           =  None
        if images is not None:
          __UpperCAmelCase							:	List[Any]           =  self.image_processor(__lowercase		,      mask_pixel=__lowercase		,      *__lowercase		,      **__lowercase					)
        if images_mixed is not None:
          __UpperCAmelCase							:	Tuple           =  self.image_processor(__lowercase		,      is_mixed=__lowercase		,      *__lowercase		,      **__lowercase					)
        if audio is not None:
          __UpperCAmelCase							:	Optional[Any]           =  self.feature_extractor(
              __lowercase		,      *__lowercase		,      sampling_rate=__lowercase		,      mask_audio=__lowercase		,      **__lowercase					)

        __UpperCAmelCase							:	Tuple           =  {}
        if audio is not None:
          output_dict.update(__lowercase					)
        if images is not None:
          output_dict.update(__lowercase					)
        if images_mixed_dict is not None:
          output_dict.update(__lowercase					)
        return output_dict




      @property
      def 	UpperCAmelCase      (      self					:			Tuple					)							->      Any:
        __UpperCAmelCase							:	Dict           =  self.image_processor.model_input_names
        __UpperCAmelCase							:	Tuple           =  self.feature_extractor.model_input_names
        return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names					)					)










from __future__ import annotations

from math import pi






def 				lowerCamelCase__      (    __lowerCamelCase			:     float  ,		__lowerCamelCase			:     float  ,		__lowerCamelCase			:     float		):

  if (inductance, frequency, reactance).count(0		) != 1:
    raise ValueError("""One and only one argument must be 0"""		)
  if inductance < 0:
    raise ValueError("""Inductance cannot be negative"""		)
  if frequency < 0:
    raise ValueError("""Frequency cannot be negative"""		)
  if reactance < 0:
    raise ValueError("""Inductive reactance cannot be negative"""		)
  if inductance == 0:
    return {"inductance": reactance / (2 * pi * frequency)}
  elif frequency == 0:
    return {"frequency": reactance / (2 * pi * inductance)}
  elif reactance == 0:
    return {"reactance": 2 * pi * frequency * inductance}
  else:
    raise ValueError("""Exactly one argument must be 0"""		)


if __name__ == "__main__":
       import doctest

       doctest.testmod()


