code: 
  
  
  
  
  
  import warnings
  from contextlib import contextmanager
  
  from ...processing_utils import ProcessorMixin
  
  class 				A    (lowerCamelCase_				):
        _SCREAMING_SNAKE_CASE					=							["""image_processor""", """tokenizer"""]
        _SCREAMING_SNAKE_CASE					=							"""AutoImageProcessor"""
        _SCREAMING_SNAKE_CASE					=							"""AutoTokenizer"""
  
  
  
        def __init__(					self     ,  lowercase_=None     ,  lowercase_=None     ,  **lowercase_					)      ->   str:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Tuple     				=						None
               if "feature_extractor" in kwargs:
                      warnings.warn(
                          '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                          ''' instead.'''     ,  lowercase_     ,  )
                      _snake_case      :							Optional[int]     				=						kwargs.pop('''feature_extractor'''					)
  
               _snake_case      :							int     				=						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__(lowercase_     ,  lowercase_					)
               _snake_case      :							Tuple     				=						self.image_processor
               _snake_case      :							List[Any]     				=						False
  
  
  
        def __call__(					self     ,  *lowercase_     ,  **lowercase_					)      ->   Tuple:
  
  
  
               '''simple docstring'''
  
               if self._in_target_context_manager:
                      return self.current_processor(*lowercase_     ,  **lowercase_					)
  
               _snake_case      :							List[Any]     				=						kwargs.pop('''images'''     ,  lowercase_					)
               _snake_case      :							List[Any]     				=						kwargs.pop('''text'''     ,  lowercase_					)
               if len(lowercase_					) > 0:
                      _snake_case      :							str     				=						args[0]
                      _snake_case      :							List[Any]     				=						args[1:]
  
               if images is None and text is None:
                      raise ValueError('''You need to specify either an `images` or `text` input to process.'''					)
  
               if images is not None:
                      _snake_case      :							Any     				=						self.image_processor(lowercase_     ,  *lowercase_     ,  **lowercase_					)
               if text is not None:
                      _snake_case      :							Union[str, Any]     				=						self.tokenizer(lowercase_     ,  **lowercase_					)
  
               if text is None:
                      return inputs
               elif images is None:
                      return encodings
               else:
                      _snake_case      :							str     				=						encodings["input_ids"]
                      return inputs
  
  
  
        def    __a (					self     ,  *lowercase_     ,  **lowercase_					)      ->   List[str]:
  
  
  
               '''simple docstring'''
  
               return self.tokenizer.batch_decode(*lowercase_     ,  **lowercase_					)
  
  
  
        def    __a (					self     ,  *lowercase_     ,  **lowercase_					)      ->   Dict:
  
  
  
               '''simple docstring'''
  
               return self.tokenizer.decode(*lowercase_     ,  **lowercase_					)
  
  
  
        @contextmanager
        def    __a (					self					)      ->   Tuple:
  
  
  
               '''simple docstring'''
  
               warnings.warn(
                   '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
                   '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
                   '''your images inputs, or in a separate call.'''					)
               _snake_case      :							Any     				=						True
               _snake_case      :							Dict     				=						self.tokenizer
               yield
               _snake_case      :							int     				=						self.image_processor
               _snake_case      :							str     				=						False
  
  
  
        @property
        def    __a (					self					)      ->   Any:
  
  
  
               '''simple docstring'''
  
               warnings.warn(
                   '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''     ,  lowercase_     ,  )
               return self.image_processor_class
  
  
  
        @property
        def    __a (					self					)      ->   int:
  
  
  
               '''simple docstring'''
  
               warnings.warn(
                   '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''     ,  lowercase_     ,  )
               return self.image_processor
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  import cva
  import numpy as np
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_  )			->						int:
  
         try:
                return int(image[x_coordinate][y_coordinate] >= center  )
         except (IndexError, TypeError):
                return 0
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						int:
         _snake_case      :							List[str]     				=						image[x_coordinate][y_coordinate]
         _snake_case      :							List[str]     				=						[1, 2, 4, 8, 16, 32, 64, 128]
  
         # skip get_neighbors_pixel if center is null
         if center is None:
                return 0
  
         # Starting from the top right, assigning value to pixels clockwise
         _snake_case      :							Any     				=						[
             get_neighbors_pixel(lowercase_      ,		x_coordinate - 1      ,		y_coordinate + 1      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		lowercase_      ,		y_coordinate + 1      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		x_coordinate - 1      ,		lowercase_      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		x_coordinate + 1      ,		y_coordinate + 1      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		x_coordinate + 1      ,		lowercase_      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		x_coordinate + 1      ,		y_coordinate - 1      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		lowercase_      ,		y_coordinate - 1      ,		lowercase_  ),
             get_neighbors_pixel(lowercase_      ,		x_coordinate - 1      ,		y_coordinate - 1      ,		lowercase_  ),
         ]
  
         # Converting the binary value to decimal.
         return sum(
             binary_value * power for binary_value, power in zip(lowercase_      ,		lowercase_  )  )
  
  
  if __name__ == "__main__":
     # Reading the image and converting it to grayscale.
     lowerCAmelCase_   =   cva.imread(
         "digital_image_processing/image_data/lena.jpg", cva.IMREAD_GRAYSCALE
     )
  
     # Create a numpy array as the same height and width of read image
     lowerCAmelCase_   =   np.zeros((image.shape[0], image.shape[1]))
  
     # Iterating through the image and calculating the
     # local binary pattern value for each pixel.
     for i in range(0, image.shape[0]):
        for j in range(0, image.shape[1]):
           lowerCAmelCase_   =   local_binary_value(image, i, j)
  
     cva.imshow("local binary pattern", lbp_image)
     cva.waitKey(0)
     cva.destroyAllWindows()
  
  
  
  
  
  
  