



'''simple docstring'''

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class 						a_		(     snake_case    ):
							UpperCAmelCase			:		Tuple        =				["""image_processor""", """tokenizer"""]
							UpperCAmelCase			:		Any        =				"""ViTImageProcessor"""
							UpperCAmelCase			:		Optional[int]        =				("""CLIPTokenizer""", """CLIPTokenizerFast""")


							def __init__(				self   :						Any      ,						a_   :						List[Any]=None      ,						a_   :						Union[str, Any]=None      ,						**a_   :						List[str]			)			->					str:
										snake_case:    Dict      							=None
										if "feature_extractor" in kwargs:
													warnings.warn(
													    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
													    ' instead.'      ,						a_      ,						)
													snake_case:    int      							=kwargs.pop('feature_extractor'			)

										snake_case:    Any      							=image_processor if image_processor is not None else feature_extractor
										if image_processor is None:
													raise ValueError('You need to specify an `image_processor`.'			)
										if tokenizer is None:
													raise ValueError('You need to specify a `tokenizer`.'			)

										super().__init__(a_      ,						a_			)


							def __call__(				self   :						Dict      ,						a_   :						List[str]=None      ,						a_   :						int=None      ,						a_   :						Tuple=None      ,						a_   :						Optional[Any]=None      ,						**a_   :						List[Any]			)			->					Dict:
										if text is None and visual_prompt is None and images is None:
													raise ValueError('You have to specify either text, visual prompt or images.'			)

										if text is not None and visual_prompt is not None:
													raise ValueError('You have to specify exactly one type of prompt. Either text or visual prompt.'			)

										if text is not None:
													snake_case:    List[Any]      							=self.tokenizer(a_      ,						return_tensors=a_      ,						**a_			)

										if visual_prompt is not None:
													snake_case:    Union[str, Any]      							=self.image_processor(a_      ,						return_tensors=a_      ,						**a_			)

										if images is not None:
													snake_case:    Any      							=self.image_processor(a_      ,						return_tensors=a_      ,						**a_			)

										if visual_prompt is not None and images is not None:
													snake_case:    List[Any]      							={
													    'pixel_values': image_features.pixel_values,
													    'conditional_pixel_values': prompt_features.pixel_values,
													}
													return encoding
										elif text is not None and images is not None:
													snake_case:    List[str]      							=image_features.pixel_values
													return encoding
										elif text is not None:
													return encoding
										elif visual_prompt is not None:
													snake_case:    Tuple      							={
													    'conditional_pixel_values': prompt_features.pixel_values,
													}
													return encoding
										else:
													return BatchEncoding(data=dict(**a_			)      ,						tensor_type=a_			)


							def    UpperCamelCase     (				self   :						Optional[Any]      ,						*a_   :						List[Any]      ,						**a_   :						List[Any]			)			->					Union[str, Any]:
										return self.tokenizer.batch_decode(*a_      ,						**a_			)


							def    UpperCamelCase     (				self   :						Any      ,						*a_   :						str      ,						**a_   :						Optional[int]			)			->					int:
										return self.tokenizer.decode(*a_      ,						**a_			)


							@property
							def    UpperCamelCase     (				self   :						Dict			)			->					Optional[Any]:
										warnings.warn(
										    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'      ,						a_      ,						)
										return self.image_processor_class




							@property
							def    UpperCamelCase     (				self   :						str			)			->					str:
										warnings.warn(
										    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'      ,						a_      ,						)
										return self.image_processor









'''simple docstring'''

import os


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
a							   =				'.'


if __name__ == "__main__":
	a							   =				os.path.join(REPO_PATH, 'utils/documentation_tests.txt')
	a							   =				[]
	a							   =				[]
	with open(doctest_file_path) as fp:
		for line in fp:
			a							   =				line.strip()
			a							   =				os.path.join(REPO_PATH, line)
			if not (os.path.isfile(path) or os.path.isdir(path)):
				non_existent_paths.append(line)
			all_paths.append(path)
	if len(non_existent_paths) > 0:
		a							   =				'\n'.join(non_existent_paths)
		raise ValueError(F"""`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}""")
	if all_paths != sorted(all_paths):
		raise ValueError('Files in `utils/documentation_tests.txt` are not in alphabetical order.')




