




"""simple docstring"""




import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor

class 	_lowerCamelCase  (     unittest.TestCase				):

      """simple docstring"""

      @property
      def  snake_case		(       self   :	Optional[int]			):
          __UpperCamelCase											=  1
          __UpperCamelCase											=  3
          __UpperCamelCase											=  8
          __UpperCamelCase											=  8

          __UpperCamelCase											=  torch.rand((batch_size, num_channels, height, width)			)

          return sample







      def  snake_case		(       self   :	List[str]	,    snake_case   :	Optional[int]			):
          if isinstance(image[0]	,    PIL.Image.Image			):
              return np.stack([np.array(snake_case			) for i in image]	,    axis=0			)
          elif isinstance(snake_case	,    torch.Tensor			):
              return image.cpu().numpy().transpose(0	,    2	,    3	,    1			)
          return image







      def  snake_case		(       self   :	Union[str, Any]			):
          __UpperCamelCase											=  VaeImageProcessor(do_resize=snake_case	,    do_normalize=snake_case			)

          __UpperCamelCase											=  self.dummy_sample
          __UpperCamelCase											=  self.to_np(snake_case			)

          for output_type in ["pt", "np", "pil"]:
              __UpperCamelCase											=  image_processor.postprocess(
                  image_processor.preprocess(snake_case			)	,    output_type=snake_case	,    )
              __UpperCamelCase											=  self.to_np(snake_case			)
              __UpperCamelCase											=  (input_np * 255).round() if output_type == '''pil''' else input_np
              assert (
                  np.abs(in_np - out_np			).max() < 1E-6
              ), F"decoded output does not match input for output_type {output_type}"







      def  snake_case		(       self   :	Dict			):
          __UpperCamelCase											=  VaeImageProcessor(do_resize=snake_case	,    do_normalize=snake_case			)
          __UpperCamelCase											=  self.dummy_sample.cpu().numpy().transpose(0	,    2	,    3	,    1			)

          for output_type in ["pt", "np", "pil"]:
              __UpperCamelCase											=  image_processor.postprocess(image_processor.preprocess(snake_case			)	,    output_type=snake_case			)

              __UpperCamelCase											=  self.to_np(snake_case			)
              __UpperCamelCase											=  (input_np * 255).round() if output_type == '''pil''' else input_np
              assert (
                  np.abs(in_np - out_np			).max() < 1E-6
              ), F"decoded output does not match input for output_type {output_type}"







      def  snake_case		(       self   :	Any			):
          __UpperCamelCase											=  VaeImageProcessor(do_resize=snake_case	,    do_normalize=snake_case			)

          __UpperCamelCase											=  self.dummy_sample.cpu().numpy().transpose(0	,    2	,    3	,    1			)
          __UpperCamelCase											=  image_processor.numpy_to_pil(snake_case			)

          for output_type in ["pt", "np", "pil"]:
              __UpperCamelCase											=  image_processor.postprocess(image_processor.preprocess(snake_case			)	,    output_type=snake_case			)
              for i, o in zip(snake_case	,    snake_case			):
                  __UpperCamelCase											=  np.array(snake_case			)
                  __UpperCamelCase											=  self.to_np(snake_case			) if output_type == '''pil''' else (self.to_np(snake_case			) * 255).round()
                  assert (
                      np.abs(in_np - out_np			).max() < 1E-6
                  ), F"decoded output does not match input for output_type {output_type}"







      def  snake_case		(       self   :	Any			):
          __UpperCamelCase											=  VaeImageProcessor(do_resize=snake_case	,    do_normalize=snake_case			)

          __UpperCamelCase											=  self.dummy_sample
          __UpperCamelCase											=  input_pt_ad.squeeze(0			)

          __UpperCamelCase											=  image_processor.postprocess(
              image_processor.preprocess(snake_case			)	,    output_type='''np'''	,    )
          __UpperCamelCase											=  image_processor.postprocess(
              image_processor.preprocess(snake_case			)	,    output_type='''np'''	,    )

          __UpperCamelCase											=  self.to_np(self.dummy_sample			)
          __UpperCamelCase											=  input_np_ad.squeeze(0			)

          __UpperCamelCase											=  image_processor.postprocess(
              image_processor.preprocess(snake_case			)	,    output_type='''np'''	,    )
          __UpperCamelCase											=  image_processor.postprocess(
              image_processor.preprocess(snake_case			)	,    output_type='''np'''	,    )

          assert np.abs(out_pt_ad - out_pt_ad			).max() < 1E-6
          assert np.abs(out_np_ad - out_np_ad			).max() < 1E-6







      def  snake_case		(       self   :	Union[str, Any]			):
          __UpperCamelCase											=  VaeImageProcessor(do_resize=snake_case	,    do_normalize=snake_case			)

          __UpperCamelCase											=  self.dummy_sample
          __UpperCamelCase											=  list(snake_case			)

          __UpperCamelCase											=  image_processor.postprocess(
              image_processor.preprocess(snake_case			)	,    output_type='''np'''	,    )

          __UpperCamelCase											=  image_processor.postprocess(
              image_processor.preprocess(snake_case			)	,    output_type='''np'''	,    )

          __UpperCamelCase											=  self.to_np(self.dummy_sample			)
          list(snake_case			)

          __UpperCamelCase											=  image_processor.postprocess(
              image_processor.preprocess(snake_case			)	,    output_type='''np'''	,    )

          __UpperCamelCase											=  image_processor.postprocess(
              image_processor.preprocess(snake_case			)	,    output_type='''np'''	,    )

          assert np.abs(out_pt_ad - out_pt_list			).max() < 1E-6
          assert np.abs(out_np_ad - out_np_list			).max() < 1E-6












import cva
import numpy as np




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_ )    ->		int:

    """simple docstring"""


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




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_ )    ->		int:

    """simple docstring"""


    __UpperCamelCase											=  image[x_coordinate][y_coordinate]
    __UpperCamelCase											=  [1, 2, 4, 8, 16, 32, 64, 1_28]

    # 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											=  [
        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.
 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()






