

import torch
from torch import autograd







class 							a__							( autograd.Function							):







  """simple docstring"""




  @staticmethod
  def 			_lowercase   (      UpperCAmelCase__     :			Any					,					UpperCAmelCase__     :			int					,					UpperCAmelCase__     :			Tuple					,					UpperCAmelCase__     :			str		)       ->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = inputs.numel()
        SCREAMING_SNAKE_CASE			:   Optional[Any]	  = int(0.0_05 * nb_elems		) + 1
        if sigmoid:
              SCREAMING_SNAKE_CASE			:   int	  = (torch.sigmoid(lowercase_		) > threshold).type(inputs.type()		)
        else:
              SCREAMING_SNAKE_CASE			:   Any	  = (inputs > threshold).type(inputs.type()		)
        if mask.sum() < nb_min:
              # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
              SCREAMING_SNAKE_CASE			:   Tuple	  = inputs.flatten().kthvalue(max(nb_elems - nb_min					,					1		)		).values
              SCREAMING_SNAKE_CASE			:   Optional[int]	  = (inputs > k_threshold).type(inputs.type()		)
        return mask




  @staticmethod
  def 			_lowercase   (      UpperCAmelCase__     :			Union[str, Any]					,					UpperCAmelCase__     :			int		)       ->Optional[Any]:
        """simple docstring"""







        return gradOutput, None, None







class 							a__							( autograd.Function							):







  """simple docstring"""




  @staticmethod
  def 			_lowercase   (      UpperCAmelCase__     :			Tuple					,					UpperCAmelCase__     :			int					,					UpperCAmelCase__     :			Tuple		)       ->List[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   Dict	  = inputs.clone()
        SCREAMING_SNAKE_CASE			:   Optional[int]	  = inputs.flatten().sort(descending=lowercase_		)
        SCREAMING_SNAKE_CASE			:   Dict	  = int(threshold * inputs.numel()		)

        # flat_out and mask access the same memory.
        SCREAMING_SNAKE_CASE			:   int	  = mask.flatten()
        SCREAMING_SNAKE_CASE			:   Any	  = 0
        SCREAMING_SNAKE_CASE			:   Any	  = 1
        return mask




  @staticmethod
  def 			_lowercase   (      UpperCAmelCase__     :			int					,					UpperCAmelCase__     :			Tuple		)       ->str:
        """simple docstring"""







        return gradOutput, None







class 							a__							( lowercase__							):







  """simple docstring"""




  @staticmethod
  def 			_lowercase   (      UpperCAmelCase__     :			Any					,					UpperCAmelCase__     :			Union[str, Any]		)       ->str:
        """simple docstring"""







        SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = inputs.clone()
        SCREAMING_SNAKE_CASE			:   List[Any]	  = inputs.abs().flatten().sort(descending=lowercase_		)
        SCREAMING_SNAKE_CASE			:   Dict	  = int(threshold * inputs.numel()		)

        # flat_out and mask access the same memory.
        SCREAMING_SNAKE_CASE			:   Tuple	  = mask.flatten()
        SCREAMING_SNAKE_CASE			:   Any	  = 0
        SCREAMING_SNAKE_CASE			:   str	  = 1
        return mask






"""simple docstring"""





import cva
import numpy as np







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):

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







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Optional[int]     	=       image[x_coordinate][y_coordinate]
   UpperCAmelCase_   :   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
   UpperCAmelCase_   :   List[Any]     	=       [
       get_neighbors_pixel(__lowerCamelCase,						x_coordinate - 1,						y_coordinate + 1,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						__lowerCamelCase,						y_coordinate + 1,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						x_coordinate - 1,						__lowerCamelCase,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						x_coordinate + 1,						y_coordinate + 1,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						x_coordinate + 1,						__lowerCamelCase,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						x_coordinate + 1,						y_coordinate - 1,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						__lowerCamelCase,						y_coordinate - 1,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						x_coordinate - 1,						y_coordinate - 1,						__lowerCamelCase    ),
   ]

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


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

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


