

from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available





@dataclass
class      snake_case_       (		UpperCAmelCase__ ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE					:       List[str]						      =    {
			    'sld_warmup_steps': 15,
			    'sld_guidance_scale': 20,
			    'sld_threshold': 0.0,
			    'sld_momentum_scale': 0.0,
			    'sld_mom_beta': 0.0,
			}
			SCREAMING_SNAKE_CASE					:       Union[str, Any]						      =    {
			    'sld_warmup_steps': 10,
			    'sld_guidance_scale': 1000,
			    'sld_threshold': 0.01,
			    'sld_momentum_scale': 0.3,
			    'sld_mom_beta': 0.4,
			}
			SCREAMING_SNAKE_CASE					:       Union[str, Any]						      =    {
			    'sld_warmup_steps': 7,
			    'sld_guidance_scale': 2000,
			    'sld_threshold': 0.025,
			    'sld_momentum_scale': 0.5,
			    'sld_mom_beta': 0.7,
			}
			SCREAMING_SNAKE_CASE					:       Tuple						      =    {
			    'sld_warmup_steps': 0,
			    'sld_guidance_scale': 5000,
			    'sld_threshold': 1.0,
			    'sld_momentum_scale': 0.5,
			    'sld_mom_beta': 0.7,
			}






@dataclass
class      snake_case_       (		UpperCAmelCase__ ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE					:       Union[List[PIL.Image.Image], np.ndarray]
			SCREAMING_SNAKE_CASE					:       Optional[List[bool]]
			SCREAMING_SNAKE_CASE					:       Optional[Union[List[PIL.Image.Image], np.ndarray]]
			SCREAMING_SNAKE_CASE					:       Optional[str]


if is_transformers_available() and is_torch_available():
						from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
						from .safety_checker import SafeStableDiffusionSafetyChecker


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()