

'''simple docstring'''





from ...processing_utils import ProcessorMixin


class  UpperCAmelCase (							UpperCAmelCase__    ):

		'''simple docstring'''






		SCREAMING_SNAKE_CASE_           =					['image_processor', 'feature_extractor']
		SCREAMING_SNAKE_CASE_           =					'TvltImageProcessor'
		SCREAMING_SNAKE_CASE_           =					'TvltFeatureExtractor'
		def __init__(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)  ->     str:






							'''simple docstring'''



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

							lowerCamelCase_  				=      image_processor
							lowerCamelCase_  				=      feature_extractor
		def __call__(					self   ,			SCREAMING_SNAKE_CASE_=None   ,			SCREAMING_SNAKE_CASE_=None   ,			SCREAMING_SNAKE_CASE_=None   ,			SCREAMING_SNAKE_CASE_=None   ,			SCREAMING_SNAKE_CASE_=False   ,			SCREAMING_SNAKE_CASE_=False   ,			*SCREAMING_SNAKE_CASE_   ,			**SCREAMING_SNAKE_CASE_   ,			)  ->     List[str]:






							'''simple docstring'''



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

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

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






		@property
		def 			UpperCamelCase(					self		)  ->     List[str]:






							'''simple docstring'''



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





"""simple docstring"""




import argparse
import importlib
from pathlib import Path


# Test all the extensions added in the setup
A__ :       Optional[int]				= [
    'kernels/rwkv/wkv_cuda.cu',
    'kernels/rwkv/wkv_op.cpp',
    'kernels/deformable_detr/ms_deform_attn.h',
    'kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh',
    'models/graphormer/algos_graphormer.pyx',
]




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  for file in FILES_TO_FIND:
    if not (transformers_path / file).exists():
      return False
  return True


if __name__ == "__main__":
  A__ :       List[Any]				= argparse.ArgumentParser()
  parser.add_argument('--check_lib', action='store_true', help='Whether to check the build or the actual package.')
  A__ :       Optional[int]				= parser.parse_args()
  if args.check_lib:
    A__ :       Union[str, Any]				= importlib.import_module('transformers')
    A__ :       Optional[Any]				= Path(transformers_module.__file__).parent
  else:
    A__ :       str				= Path.cwd() / 'build/lib/transformers'
  if not test_custom_files_are_present(transformers_path):
    raise ValueError('The built release does not contain the custom files. Fix this before going further!')
