





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













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()






