'''simple docstring'''




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





class        __magic_name__		:







			def __init__(      self							:    str							,     lowercase_							:    Union[str, Any]							,     lowercase_							:    int       ):
										lowercase_   :     Dict									=			0
										# max greyscale value for #FFFFFF
										lowercase_   :     int									=			int(self.get_greyscale(255							,     255							,     255       )       )

										if not self.min_threshold < threshold < self.max_threshold:
																	lowercase_   :     Optional[Any]									=			f'''Factor value should be from 0 to {self.max_threshold}'''
																	raise ValueError(lowercase_       )

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







			@classmethod
			def       SCREAMING_SNAKE_CASE_	(      cls							:    Dict							,     lowercase_							:    int							,     lowercase_							:    int							,     lowercase_							:    int       ):
										return 0.1_14 * blue + 0.5_87 * green + 0.21_26 * red







			def       SCREAMING_SNAKE_CASE_	(      self							:    Tuple       ):
										for y in range(self.height       ):
																	for x in range(self.width       ):
																								lowercase_   :     Tuple									=			int(self.get_greyscale(*self.input_img[y][x]       )       )
																								if self.threshold > greyscale + self.error_table[y][x]:
																															lowercase_   :     Optional[int]									=			(0, 0, 0)
																															lowercase_   :     Optional[Any]									=			greyscale + self.error_table[x][y]
																								else:
																															lowercase_   :     str									=			(255, 255, 255)
																															lowercase_   :     List[Any]									=			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
						_lowercase		:					List[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()






'''simple docstring'''




_lowercase		:					List[str]													=		"\n# Diffusers installation\n! pip install diffusers transformers datasets accelerate\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/diffusers.git\n"

_lowercase		:					Union[str, Any]													=		[{"type": "code", "content": INSTALL_CONTENT}]





