






'''simple docstring'''





import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		snake_case    =   ["image_processor", "tokenizer"]
		snake_case    =   "CLIPImageProcessor"
		snake_case    =   ("CLIPTokenizer", "CLIPTokenizerFast")
		def __init__(  self		:       str						,_SCREAMING_SNAKE_CASE		:       Any=None						,_SCREAMING_SNAKE_CASE		:       List[Any]=None						,**_SCREAMING_SNAKE_CASE		:       List[Any]		)    ->   Any:


						'''simple docstring'''
						A	 =      None
						if "feature_extractor" in kwargs:
										warnings.warn(
										    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
										    ' instead.'						,_SCREAMING_SNAKE_CASE						,)
										A	 =      kwargs.pop('feature_extractor'		)

						A	 =      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__(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)
		def __call__(  self		:       List[Any]						,_SCREAMING_SNAKE_CASE		:       Optional[Any]=None						,_SCREAMING_SNAKE_CASE		:       int=None						,_SCREAMING_SNAKE_CASE		:       Any=None						,**_SCREAMING_SNAKE_CASE		:       int		)    ->   Any:


						'''simple docstring'''

						if text is None and images is None:
										raise ValueError('You have to specify either text or images. Both cannot be none.'		)

						if text is not None:
										A	 =      self.tokenizer(_SCREAMING_SNAKE_CASE						,return_tensors=_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE		)

						if images is not None:
										A	 =      self.image_processor(_SCREAMING_SNAKE_CASE						,return_tensors=_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE		)

						if text is not None and images is not None:
										A	 =      image_features.pixel_values
										return encoding
						elif text is not None:
										return encoding
						else:
										return BatchEncoding(data=dict(**_SCREAMING_SNAKE_CASE		)						,tensor_type=_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       List[str]						,*_SCREAMING_SNAKE_CASE		:       Tuple						,**_SCREAMING_SNAKE_CASE		:       List[str]		)    ->   Dict:


						'''simple docstring'''
						return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE		)
		def       A(  self		:       Optional[Any]						,*_SCREAMING_SNAKE_CASE		:       Dict						,**_SCREAMING_SNAKE_CASE		:       Optional[Any]		)    ->   Optional[Any]:


						'''simple docstring'''
						return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE		)
		@property
		def       A(  self		:       List[str]		)    ->   Union[str, Any]:


						'''simple docstring'''
						A	 =      self.tokenizer.model_input_names
						A	 =      self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)
		@property
		def       A(  self		:       Tuple		)    ->   Union[str, Any]:


						'''simple docstring'''
						warnings.warn(
						    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'						,_SCREAMING_SNAKE_CASE						,)
						return self.image_processor_class







		@property
		def       A(  self		:       Optional[int]		)    ->   Tuple:


						'''simple docstring'''
						warnings.warn(
						    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'						,_SCREAMING_SNAKE_CASE						,)
						return self.image_processor













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
lowerCAmelCase_           = '.'


if __name__ == "__main__":
					lowerCAmelCase_           = os.path.join(REPO_PATH, 'utils/documentation_tests.txt')
					lowerCAmelCase_           = []
					lowerCAmelCase_           = []
					with open(doctest_file_path) as fp:
										for line in fp:
															lowerCAmelCase_           = line.strip()
															lowerCAmelCase_           = 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:
										lowerCAmelCase_           = '\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.')





