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





class      UpperCamelCase_			:







						'''simple docstring'''



						def __init__( self       :Optional[Any]		,						lowerCAmelCase__       :Optional[int]		,						lowerCAmelCase__       :int    )     ->Any:
										lowercase						    =			0
										# max greyscale value for #FFFFFF
										lowercase						    =			int(self.get_greyscale(255		,						255		,						255    )    )

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

										lowercase						    =			input_img
										lowercase						    =			threshold
										lowercase    ,  lowercase						    =			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						    =			[
										    [0 for _ in range(self.height + 4    )] for __ in range(self.width + 1    )
										]
										lowercase						    =			np.ones((self.width, self.height, 3)		,						np.uinta    ) * 255





						@classmethod
						def 					SCREAMING_SNAKE_CASE( cls       :Optional[int]		,						lowerCAmelCase__       :int		,						lowerCAmelCase__       :int		,						lowerCAmelCase__       :int    )     ->float:
										return 0.1_14 * blue + 0.5_87 * green + 0.21_26 * red





						def 					SCREAMING_SNAKE_CASE( self       :Union[str, Any]    )     ->None:
										for y in range(self.height    ):
														for x in range(self.width    ):
																		lowercase						    =			int(self.get_greyscale(*self.input_img[y][x]    )    )
																		if self.threshold > greyscale + self.error_table[y][x]:
																						lowercase						    =			(0, 0, 0)
																						lowercase						    =			greyscale + self.error_table[x][y]
																		else:
																						lowercase						    =			(255, 255, 255)
																						lowercase						    =			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
						_snake_case			:       List[Any]      =						[
						    Burkes(imread("image_data/lena.jpg", 1), threshold)
						    for threshold in (1, 1_26, 1_30, 1_40)
						]

						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()


from transformers import PretrainedConfig





class      UpperCamelCase_			(			__a		):







						'''simple docstring'''



						UpperCamelCase			:     List[str]							      =				'''custom'''





						def __init__( self       :Tuple		,						lowerCAmelCase__       :int=1		,						**lowerCAmelCase__       :List[Any]    )     ->int:
										lowercase						    =			attribute
										super().__init__(**lowerCAmelCase__    )





class      UpperCamelCase_			(			__a		):







						'''simple docstring'''



						UpperCamelCase			:     Union[str, Any]							      =				'''custom'''





						def __init__( self       :Union[str, Any]		,						lowerCAmelCase__       :Dict=1		,						**lowerCAmelCase__       :Dict    )     ->List[str]:
										lowercase						    =			attribute
