

from ...processing_utils import ProcessorMixin

class    _lowercase				(					__lowerCamelCase			):
   _lowercase     :       Any  					=    ['image_processor', 'feature_extractor']
   _lowercase     :       List[Any]  					=    'TvltImageProcessor'
   _lowercase     :       Tuple  					=    'TvltFeatureExtractor'

   def __init__(				self	:    Union[str, Any]			,						lowerCamelCase__	:    Any			,						lowerCamelCase__	:    List[str]       ) ->			str:
         """simple docstring"""





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

         A_							       =		image_processor
         A_							       =		feature_extractor

   def __call__(				self	:    int			,						lowerCamelCase__	:    List[Any]=None			,						lowerCamelCase__	:    Dict=None			,						lowerCamelCase__	:    Optional[Any]=None			,						lowerCamelCase__	:    Tuple=None			,						lowerCamelCase__	:    Union[str, Any]=False			,						lowerCamelCase__	:    Optional[Any]=False			,						*lowerCamelCase__	:    Optional[Any]			,						**lowerCamelCase__	:    List[str]			,						) ->			Optional[int]:
         """simple docstring"""





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

         A_							       =		None
         if images is not None:
               A_							       =		self.image_processor(lowerCamelCase__			,						mask_pixel=lowerCamelCase__			,						*lowerCamelCase__			,						**lowerCamelCase__       )
         if images_mixed is not None:
               A_							       =		self.image_processor(lowerCamelCase__			,						is_mixed=lowerCamelCase__			,						*lowerCamelCase__			,						**lowerCamelCase__       )
         if audio is not None:
               A_							       =		self.feature_extractor(
                   lowerCamelCase__			,						*lowerCamelCase__			,						sampling_rate=lowerCamelCase__			,						mask_audio=lowerCamelCase__			,						**lowerCamelCase__       )

         A_							       =		{}
         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	:    List[str]       ) ->			Optional[int]:
         """simple docstring"""





         A_							       =		self.image_processor.model_input_names
         A_							       =		self.feature_extractor.model_input_names
         return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names       )       )








__lowercase				      =						[4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
__lowercase				      =						[3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
__lowercase				      =						{
    0: """Sunday""",
    1: """Monday""",
    2: """Tuesday""",
    3: """Wednesday""",
    4: """Thursday""",
    5: """Friday""",
    6: """Saturday""",
}







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







      assert len(str(SCREAMING_SNAKE_CASE   )   ) > 2, "year should be in YYYY format"
      assert 1 <= month <= 12, "month should be between 1 to 12"
      assert 1 <= day <= 31, "day should be between 1 to 31"

      # Doomsday algorithm:
      A_							       =		year // 100
      A_							       =		(5 * (century % 4) + 2) % 7
      A_							       =		year % 100
      A_							       =		centurian % 12
      A_							       =		(
          (centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
      ) % 7
      A_							       =		(
          DOOMSDAY_NOT_LEAP[month - 1]
          if (year % 4 != 0) or (centurian == 0 and (year % 400) == 0)
          else DOOMSDAY_LEAP[month - 1]
      )
      A_							       =		(dooms_day + day - day_anchor) % 7
      return WEEK_DAY_NAMES[week_day]


if __name__ == "__main__":
      import doctest

      doctest.testmod()





