



'''simple docstring'''
from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

_UpperCamelCase											=			TypeVar('''T''', bound=bool)



class _A  (    Generic[T]    ):





		def __init__(     self ,       __UpperCAmelCase				)       ->     None:

					'''simple docstring'''
					__UpperCAmelCase   :      T										=     value
					__UpperCAmelCase   :      RandomizedHeapNode[T] | None										=     None
					__UpperCAmelCase   :      RandomizedHeapNode[T] | None										=     None





		@property
		def 							__A       (     self				)       ->     T:

					'''simple docstring'''
					return self._value





		@staticmethod
		def 							__A       (     __UpperCAmelCase ,       __UpperCAmelCase				)       ->     RandomizedHeapNode[T] | None:

					'''simple docstring'''
					if not roota:
								return roota

					if not roota:
								return roota

					if roota.value > roota.value:
								__UpperCAmelCase   :      Optional[Any]										=     roota, roota

					if random.choice([True, False]				):
								__UpperCAmelCase   :      Optional[int]										=     roota.right, roota.left

					__UpperCAmelCase   :      List[Any]										=     RandomizedHeapNode.merge(roota.left ,       __UpperCAmelCase				)

					return roota



class _A  (    Generic[T]    ):





		def __init__(     self ,       __UpperCAmelCase = ()				)       ->     None:

					'''simple docstring'''
					__UpperCAmelCase   :      RandomizedHeapNode[T] | None										=     None

					if data:
								for item in data:
											self.insert(__UpperCAmelCase				)





		def 							__A       (     self ,       __UpperCAmelCase				)       ->     None:

					'''simple docstring'''
					__UpperCAmelCase   :      str										=     RandomizedHeapNode.merge(self._root ,       RandomizedHeapNode(__UpperCAmelCase				)				)





		def 							__A       (     self				)       ->     T | None:

					'''simple docstring'''
					__UpperCAmelCase   :      Union[str, Any]										=     self.top()

					if self._root is None:
								return None

					__UpperCAmelCase   :      Optional[Any]										=     RandomizedHeapNode.merge(self._root.left ,       self._root.right				)

					return result





		def 							__A       (     self				)       ->     T:

					'''simple docstring'''
					if not self._root:
								raise IndexError("""Can't get top element for the empty heap."""				)
					return self._root.value





		def 							__A       (     self				)       ->     None:

					'''simple docstring'''
					__UpperCAmelCase   :      int										=     None





		def 							__A       (     self				)       ->     list[Any]:

					'''simple docstring'''
					__UpperCAmelCase   :      int										=     []
					while self:
								result.append(self.pop()				)

					return result





		def __bool__(     self				)       ->     bool:

					'''simple docstring'''
					return self._root is not None


if __name__ == "__main__":
						import doctest

						doctest.testmod()









'''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey



class _A  :





		def __init__(     self ,       __UpperCAmelCase ,       __UpperCAmelCase				)       ->     Union[str, Any]:

					'''simple docstring'''
					__UpperCAmelCase   :      Optional[int]										=     0
					# max greyscale value for #FFFFFF
					__UpperCAmelCase   :      List[Any]										=     int(self.get_greyscale(255 ,       255 ,       255				)				)

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

					__UpperCAmelCase   :      Tuple										=     input_img
					__UpperCAmelCase   :      Optional[Any]										=     threshold
					__UpperCAmelCase				,  __UpperCAmelCase   :      List[Any]										=     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
					__UpperCAmelCase   :      Optional[Any]										=     [
					    [0 for _ in range(self.height + 4				)] for __ in range(self.width + 1				)
					]
					__UpperCAmelCase   :      Any										=     np.ones((self.width, self.height, 3) ,       np.uinta				) * 255





		@classmethod
		def 							__A       (     cls ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase				)       ->     float:

					'''simple docstring'''
					return 0.114 * blue + 0.587 * green + 0.2126 * red





		def 							__A       (     self				)       ->     None:

					'''simple docstring'''
					for y in range(self.height				):
								for x in range(self.width				):
											__UpperCAmelCase   :      Optional[Any]										=     int(self.get_greyscale(*self.input_img[y][x]				)				)
											if self.threshold > greyscale + self.error_table[y][x]:
														__UpperCAmelCase   :      List[Any]										=     (0, 0, 0)
														__UpperCAmelCase   :      Union[str, Any]										=     greyscale + self.error_table[x][y]
											else:
														__UpperCAmelCase   :      Optional[Any]										=     (255, 255, 255)
														__UpperCAmelCase   :      Dict										=     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
					_UpperCamelCase											=			[
					    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()




