
'''simple docstring'''







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






class 					lowerCAmelCase__  :






					def __init__(  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											lowercase_      :	Dict						=			0
											# max greyscale value for #FFFFFF
											lowercase_      :	List[str]						=			int(self.get_greyscale(2_55							,				2_55							,				2_55						)						)

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

											lowercase_      :	Any						=			input_img
											lowercase_      :	Any						=			threshold
											lowercase_      :	Optional[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_      :	str						=			[
											    [0 for _ in range(self.height + 4						)] for __ in range(self.width + 1						)
											]
											lowercase_      :	Dict						=			np.ones((self.width, self.height, 3)							,				np.uinta						) * 2_55






					@classmethod
					def       _snake_case       (  cls							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											return 0.114 * blue + 0.587 * green + 0.2_126 * red






					def       _snake_case       (  self						):


											"""simple docstring"""





											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_      :	Tuple						=			(0, 0, 0)
																													lowercase_      :	Optional[Any]						=			greyscale + self.error_table[x][y]
																							else:
																													lowercase_      :	List[Any]						=			(2_55, 2_55, 2_55)
																													lowercase_      :	Optional[int]						=			greyscale + self.error_table[x][y] - 2_55
																							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					:    Union[str, Any]       				=			[
							    Burkes(imread("image_data/lena.jpg", 1), threshold)
							    for threshold in (1, 1_2_6, 1_3_0, 1_4_0)
							]

							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'''

import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp






def 						_UpperCAmelCase     (	)      ->      Optional[Any]:





						"""simple docstring"""
						lowercase_   : List[str]   	=      ArgumentParser(
						    description=(
						        'PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes'
						    )				)

						# Optional arguments for the launch helper
						parser.add_argument('--num_cores'    ,      type=a    ,      default=1    ,      help='Number of TPU cores to use (1 or 8).'				)

						# positional
						parser.add_argument(
						    'training_script'    ,      type=a    ,      help=(
						        'The full path to the single TPU training '
						        'program/script to be launched in parallel, '
						        'followed by all the arguments for the '
						        'training script'
						    )    ,      )

						# rest from the training program
						parser.add_argument('training_script_args'    ,      nargs=a				)

						return parser.parse_args()






def 						_UpperCAmelCase     (	)      ->      str:





						"""simple docstring"""
						lowercase_   : Optional[int]   	=      parse_args()

						# Import training_script as a module.
						lowercase_   : str   	=      Path(args.training_script				)
						sys.path.append(str(script_fpath.parent.resolve()				)				)
						lowercase_   : int   	=      script_fpath.stem
						lowercase_   : List[Any]   	=      importlib.import_module(a				)

						# Patch sys.argv
						lowercase_   : List[str]   	=      [args.training_script] + args.training_script_args + ['--tpu_num_cores', str(args.num_cores				)]

						xmp.spawn(mod._mp_fn    ,      args=()    ,      nprocs=args.num_cores				)


if __name__ == "__main__":
						main()
