


'''simple docstring'''







import cva
import numpy as np


def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     np.ndarray   ,_UpperCAmelCase     :     int   ,_UpperCAmelCase     :     int   ,_UpperCAmelCase     :     int  )  ->      int:

       try:
              return int(image[x_coordinate][y_coordinate] >= center  )
       except (IndexError, TypeError):
              return 0


def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     np.ndarray   ,_UpperCAmelCase     :     int   ,_UpperCAmelCase     :     int  )  ->      int:
       _a   :			Optional[Any]					 =image[x_coordinate][y_coordinate]
       _a   :			Optional[int]					 =[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
       _a   :			List[str]					 =[
           get_neighbors_pixel(_UpperCAmelCase   ,x_coordinate - 1   ,y_coordinate + 1   ,_UpperCAmelCase  ),
           get_neighbors_pixel(_UpperCAmelCase   ,_UpperCAmelCase   ,y_coordinate + 1   ,_UpperCAmelCase  ),
           get_neighbors_pixel(_UpperCAmelCase   ,x_coordinate - 1   ,_UpperCAmelCase   ,_UpperCAmelCase  ),
           get_neighbors_pixel(_UpperCAmelCase   ,x_coordinate + 1   ,y_coordinate + 1   ,_UpperCAmelCase  ),
           get_neighbors_pixel(_UpperCAmelCase   ,x_coordinate + 1   ,_UpperCAmelCase   ,_UpperCAmelCase  ),
           get_neighbors_pixel(_UpperCAmelCase   ,x_coordinate + 1   ,y_coordinate - 1   ,_UpperCAmelCase  ),
           get_neighbors_pixel(_UpperCAmelCase   ,_UpperCAmelCase   ,y_coordinate - 1   ,_UpperCAmelCase  ),
           get_neighbors_pixel(_UpperCAmelCase   ,x_coordinate - 1   ,y_coordinate - 1   ,_UpperCAmelCase  ),
       ]

       # Converting the binary value to decimal.
       return sum(
           binary_value * power for binary_value, power in zip(_UpperCAmelCase   ,_UpperCAmelCase  )  )


if __name__ == "__main__":
   # Reading the image and converting it to grayscale.
   A__:   Any    =	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
   A__:   Tuple    =	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]):
         A__:   Any    =	local_binary_value(image, i, j)

   cva.imshow('''local binary pattern''', lbp_image)
   cva.waitKey(0)
   cva.destroyAllWindows()






'''simple docstring'''







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







class A__	(						UpperCAmelCase__				):
      __UpperCamelCase	:  Optional[Any]     	=							["image_processor", "tokenizer"]
      __UpperCamelCase	:  Optional[Any]     	=							"EfficientNetImageProcessor"
      __UpperCamelCase	:  Union[str, Any]     	=							("BertTokenizer", "BertTokenizerFast")






      def __init__(			self      :Union[str, Any]					,						SCREAMING_SNAKE_CASE      :int					,						SCREAMING_SNAKE_CASE      :Optional[int]     )	-> List[str]:




             '''simple docstring'''
             super().__init__(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE     )






      def __call__(			self      :str					,						SCREAMING_SNAKE_CASE      :List[Any]=None					,						SCREAMING_SNAKE_CASE      :Dict=None					,						SCREAMING_SNAKE_CASE      :Union[str, Any]="max_length"					,						SCREAMING_SNAKE_CASE      :Any=6_4					,						SCREAMING_SNAKE_CASE      :List[str]=None					,						**SCREAMING_SNAKE_CASE      :Union[str, Any]     )	-> Optional[int]:




             '''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   :			Dict					 =self.tokenizer(
                        SCREAMING_SNAKE_CASE					,						padding=SCREAMING_SNAKE_CASE					,						max_length=SCREAMING_SNAKE_CASE					,						return_tensors=SCREAMING_SNAKE_CASE					,						**SCREAMING_SNAKE_CASE     )

             if images is not None:
                    _a   :			Any					 =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   :			Dict					 =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 __UpperCAmelCase	(			self      :Any					,						*SCREAMING_SNAKE_CASE      :Optional[int]					,						**SCREAMING_SNAKE_CASE      :str     )	-> Dict:




             '''simple docstring'''
             return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE					,						**SCREAMING_SNAKE_CASE     )






      def __UpperCAmelCase	(			self      :int					,						*SCREAMING_SNAKE_CASE      :List[str]					,						**SCREAMING_SNAKE_CASE      :Union[str, Any]     )	-> Union[str, Any]:




             '''simple docstring'''
             return self.tokenizer.decode(*SCREAMING_SNAKE_CASE					,						**SCREAMING_SNAKE_CASE     )






      @property
      def __UpperCAmelCase	(			self      :Tuple     )	-> Tuple:




             '''simple docstring'''
             _a   :			int					 =self.tokenizer.model_input_names
             _a   :			Union[str, Any]					 =self.image_processor.model_input_names
             return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )


