

"""simple docstring"""





import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format







class 				SCREAMING_SNAKE_CASE					:






					"""simple docstring"""



					lowercase__				 =       None







					def        __lowerCAmelCase (       self			:				Tuple		):
						lowerCAmelCase__	:						int  				=	self.feature_extraction_class(**self.feat_extract_dict		)
						lowerCAmelCase__	:						Dict  				=	json.loads(feat_extract.to_json_string()		)
						for key, value in self.feat_extract_dict.items():
							self.assertEqual(obj[key]   ,lowercase_		)







					def        __lowerCAmelCase (       self			:				int		):
						lowerCAmelCase__	:						Tuple  				=	self.feature_extraction_class(**self.feat_extract_dict		)

						with tempfile.TemporaryDirectory() as tmpdirname:
							lowerCAmelCase__	:						List[Any]  				=	os.path.join(lowercase_   ,'''feat_extract.json'''		)
							feat_extract_first.to_json_file(lowercase_		)
							lowerCAmelCase__	:						int  				=	self.feature_extraction_class.from_json_file(lowercase_		)

						self.assertEqual(feat_extract_second.to_dict()   ,feat_extract_first.to_dict()		)







					def        __lowerCAmelCase (       self			:				List[Any]		):
						lowerCAmelCase__	:						Optional[Any]  				=	self.feature_extraction_class(**self.feat_extract_dict		)

						with tempfile.TemporaryDirectory() as tmpdirname:
							lowerCAmelCase__	:						Tuple  				=	feat_extract_first.save_pretrained(lowercase_		)[0]
							check_json_file_has_correct_format(lowercase_		)
							lowerCAmelCase__	:						int  				=	self.feature_extraction_class.from_pretrained(lowercase_		)

						self.assertEqual(feat_extract_second.to_dict()   ,feat_extract_first.to_dict()		)







					def        __lowerCAmelCase (       self			:				Any		):
						lowerCAmelCase__	:						Tuple  				=	self.feature_extraction_class()
						self.assertIsNotNone(lowercase_		)





from ...processing_utils import ProcessorMixin


class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







  lowerCamelCase__   =  ['''image_processor''', '''feature_extractor''']
  lowerCamelCase__   =  '''TvltImageProcessor'''
  lowerCamelCase__   =  '''TvltFeatureExtractor'''







  def __init__(							self       :				List[str]     ,   __magic_name__       :				Any     ,   __magic_name__       :				Any      )     ->		int:
        super().__init__(image_processor=__magic_name__     ,   feature_extractor=__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     image_processor
        SCREAMING_SNAKE_CASE_      =     feature_extractor







  def __call__(							self       :				List[str]     ,   __magic_name__       :				Optional[Any]=None     ,   __magic_name__       :				Union[str, Any]=None     ,   __magic_name__       :				int=None     ,   __magic_name__       :				str=None     ,   __magic_name__       :				Any=False     ,   __magic_name__       :				int=False     ,   *__magic_name__       :				int     ,   **__magic_name__       :				Any     ,   )     ->		List[Any]:

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

        SCREAMING_SNAKE_CASE_      =     {}
        if audio is not None:
              output_dict.update(__magic_name__      )
        if images is not None:
              output_dict.update(__magic_name__      )
        if images_mixed_dict is not None:
              output_dict.update(__magic_name__      )
        return output_dict







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


