

import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey


class      _a				:




      """simple docstring"""

      def __init__(      self       :							int	, UpperCAmelCase       :							Dict	, UpperCAmelCase       :							Any  ):
            A_	     =							0
            # max greyscale value for #FFFFFF
            A_	     =							int(self.get_greyscale(255	, 255	, 255  )  )

            if not self.min_threshold < threshold < self.max_threshold:
                  A_	     =							f'''Factor value should be from 0 to {self.max_threshold}'''
                  raise ValueError(UpperCAmelCase  )

            A_	     =							input_img
            A_	     =							threshold
            A_     ,    A_	     =							self.input_img.shape[1], self.input_img.shape[0]

            # error table size (+4 columns and +1 row) greater than input image because of
            # lack of if statements
            A_	     =							[
                [0 for _ in range(self.height + 4  )] for __ in range(self.width + 1  )
            ]
            A_	     =							np.ones((self.width, self.height, 3)	, np.uinta  ) * 255





      @classmethod
      def 		__A  (      cls       :							List[Any]	, UpperCAmelCase       :							Any	, UpperCAmelCase       :							str	, UpperCAmelCase       :							Optional[int]  ):
            return 0.114 * blue + 0.587 * green + 0.2_126 * red







      def 		__A  (      self       :							int  ):
            for y in range(self.height  ):
                  for x in range(self.width  ):
                        A_	     =							int(self.get_greyscale(*self.input_img[y][x]  )  )
                        if self.threshold > greyscale + self.error_table[y][x]:
                              A_	     =							(0, 0, 0)
                              A_	     =							greyscale + self.error_table[x][y]
                        else:
                              A_	     =							(255, 255, 255)
                              A_	     =							greyscale + self.error_table[x][y] - 255
                        self.error_table[y][x + 1] += int(8 / 32 * current_error  )
                        self.error_table[y][x + 2] += int(4 / 32 * current_error  )
                        self.error_table[y + 1][x] += int(8 / 32 * current_error  )
                        self.error_table[y + 1][x + 1] += int(4 / 32 * current_error  )
                        self.error_table[y + 1][x + 2] += int(2 / 32 * current_error  )
                        self.error_table[y + 1][x - 1] += int(4 / 32 * current_error  )
                        self.error_table[y + 1][x - 2] += int(2 / 32 * current_error  )


if __name__ == "__main__":
      # create Burke's instances with original images in greyscale
      __a						:Union[str, Any]            = [
          Burkes(imread('image_data/lena.jpg', 1), threshold)
          for threshold in (1, 126, 130, 140)
      ]

      for burkes in burkes_instances:
            burkes.process()

      for burkes in burkes_instances:
            imshow(
                F"Original image with dithering threshold: {burkes.threshold}",
                burkes.output_img,
            )

      waitKey(0)
      destroyAllWindows()


import pytest

from datasets.utils.version import Version

@pytest.mark.parametrize(
    "other, expected_equality"							,[
        (Version("1.0.0"						), True),
        ("1.0.0", True),
        (Version("2.0.0"						), False),
        ("2.0.0", False),
        ("1", False),
        ("a", False),
        (1, False),
        (None, False),
    ]							,)
def 			__snake_case	(   __UpperCamelCase  :   Any							,__UpperCamelCase  :   List[Any]						):



      """simple docstring"""

      A_	     =							Version("1.0.0"						)
      assert (version == other) is expected_equality
      assert (version != other) is not expected_equality
      assert (hash(__UpperCamelCase						) == hash(__UpperCamelCase						)) is expected_equality