

from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class      snake_case_       (		__UpperCAmelCase ):







			'''simple docstring'''

			def __init__(					self			:    List[Any]							,       _UpperCamelCase			:    Optional[Any]							,       _UpperCamelCase			:    Any							)				->List[str]:
									super().__init__()
									self.register_modules(unet=lowerCamelCase__							,       scheduler=lowerCamelCase__							)







			@torch.no_grad()
			def __call__(					self			:    Optional[int]							,       _UpperCamelCase			:    Optional[Any] = 1							,       _UpperCamelCase			:    Tuple = None							,       _UpperCamelCase			:    Union[str, Any] = 5_0							,       _UpperCamelCase			:    Optional[int] = "pil"							,       _UpperCamelCase			:    List[str] = True							,       **_UpperCamelCase			:    List[Any]							,       )				->Union[ImagePipelineOutput, Tuple]:
									snake_case_       =     torch.randn(
									    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)							,       generator=lowerCamelCase__							,       )
									snake_case_       =     image.to(self.device							)

									# set step values
									self.scheduler.set_timesteps(lowerCamelCase__							)

									for t in self.progress_bar(self.scheduler.timesteps							):
															# 1. predict noise model_output
															snake_case_       =     self.unet(lowerCamelCase__							,       lowerCamelCase__							).sample

															# 2. predict previous mean of image x_t-1 and add variance depending on eta
															# eta corresponds to η in paper and should be between [0, 1]
															# do x_t -> x_t-1
															snake_case_       =     self.scheduler.step(lowerCamelCase__							,       lowerCamelCase__							,       lowerCamelCase__							).prev_sample

									snake_case_       =     (image / 2 + 0.5).clamp(0							,       1							)
									snake_case_       =     image.cpu().permute(0							,       2							,       3							,       1							).numpy()
									if output_type == "pil":
															snake_case_       =     self.numpy_to_pil(lowerCamelCase__							)

									if not return_dict:
															return (image,), "This is a local test"

									return ImagePipelineOutput(images=lowerCamelCase__							), "This is a local test"


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





class      snake_case_       :







			'''simple docstring'''

			def __init__(					self			:    Tuple							,       _UpperCamelCase			:    Optional[Any]							,       _UpperCamelCase			:    int							)				->List[Any]:
									snake_case_       =     0
									# max greyscale value for #FFFFFF
									snake_case_       =     int(self.get_greyscale(2_5_5							,       2_5_5							,       2_5_5							)							)

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

									snake_case_       =     input_img
									snake_case_       =     threshold
									snake_case_,   snake_case_       =     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
									snake_case_       =     [
									    [0 for _ in range(self.height + 4							)] for __ in range(self.width + 1							)
									]
									snake_case_       =     np.ones((self.width, self.height, 3)							,       np.uinta							) * 2_5_5







			@classmethod
			def     snake_case__(					cls			:    Optional[Any]							,       _UpperCamelCase			:    int							,       _UpperCamelCase			:    int							,       _UpperCamelCase			:    int							)				->float:
									return 0.114 * blue + 0.587 * green + 0.2126 * red







			def     snake_case__(					self			:    Any							)				->None:
									for y in range(self.height							):
															for x in range(self.width							):
																					snake_case_       =     int(self.get_greyscale(*self.input_img[y][x]							)							)
																					if self.threshold > greyscale + self.error_table[y][x]:
																											snake_case_       =     (0, 0, 0)
																											snake_case_       =     greyscale + self.error_table[x][y]
																					else:
																											snake_case_       =     (2_5_5, 2_5_5, 2_5_5)
																											snake_case_       =     greyscale + self.error_table[x][y] - 2_5_5
																					self.error_table[y][x + 1] += int(8 / 3_2 * current_error							)
																					self.error_table[y][x + 2] += int(4 / 3_2 * current_error							)
																					self.error_table[y + 1][x] += int(8 / 3_2 * current_error							)
																					self.error_table[y + 1][x + 1] += int(4 / 3_2 * current_error							)
																					self.error_table[y + 1][x + 2] += int(2 / 3_2 * current_error							)
																					self.error_table[y + 1][x - 1] += int(4 / 3_2 * current_error							)
																					self.error_table[y + 1][x - 2] += int(2 / 3_2 * current_error							)


if __name__ == "__main__":
					# create Burke's instances with original images in greyscale
					lowerCAmelCase_	     =     [
					    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()