



'''simple docstring'''
import json
import logging
import os
import subprocess
from argparse import ArgumentParser


_UpperCamelCase											=			logging.getLogger(__name__)


def   lowercase_	(   ):

			"""simple docstring"""





			__UpperCAmelCase   :      str										=     ArgumentParser()
			__UpperCAmelCase				,  __UpperCAmelCase   :      str										=     parser.parse_known_args()
			for arg in unknown:
						if arg.startswith(("""-""", """--""") ):
									parser.add_argument(arg.split("""=""" )[0] )

			return parser.parse_args()


def   lowercase_	(   ):

			"""simple docstring"""





			__UpperCAmelCase   :      str										=     parse_args()
			__UpperCAmelCase   :      Optional[Any]										=     8888
			__UpperCAmelCase   :      str										=     int(os.environ["""SM_NUM_GPUS"""] )
			__UpperCAmelCase   :      Any										=     json.loads(os.environ["""SM_HOSTS"""] )
			__UpperCAmelCase   :      str										=     len(lowerCAmelCase__ )
			__UpperCAmelCase   :      Optional[Any]										=     os.environ["""SM_CURRENT_HOST"""]
			__UpperCAmelCase   :      Optional[int]										=     hosts.index(lowerCAmelCase__ )
			__UpperCAmelCase   :      List[Any]										=     """INFO"""

			if num_nodes > 1:
						__UpperCAmelCase   :      List[str]										=     f'python -m torch.distributed.launch \\n                --nnodes={num_nodes}  \\n                --node_rank={rank}  \\n                --nproc_per_node={num_gpus}  \\n                --master_addr={hosts[0]}  \\n                --master_port={port} \\n                ./run_glue.py \\n                {"".join(["nested fstring" for parameter,value in args.__dict__.items()] )}'
			else:
						__UpperCAmelCase   :      Union[str, Any]										=     f'python -m torch.distributed.launch \\n            --nproc_per_node={num_gpus}  \\n            ./run_glue.py \\n            {"".join(["nested fstring" for parameter,value in args.__dict__.items()] )}'
			try:
						subprocess.run(lowerCAmelCase__			,							shell=lowerCAmelCase__ )
			except Exception as e:
						logger.info(lowerCAmelCase__ )


if __name__ == "__main__":
					main()









'''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey



class _A  :





		def __init__(     self ,       __UpperCAmelCase ,       __UpperCAmelCase				)       ->     Union[str, Any]:

					'''simple docstring'''
					__UpperCAmelCase   :      Optional[int]										=     0
					# max greyscale value for #FFFFFF
					__UpperCAmelCase   :      List[Any]										=     int(self.get_greyscale(255 ,       255 ,       255				)				)

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

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





		@classmethod
		def 							__A       (     cls ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase				)       ->     float:

					'''simple docstring'''
					return 0.114 * blue + 0.587 * green + 0.2126 * red





		def 							__A       (     self				)       ->     None:

					'''simple docstring'''
					for y in range(self.height				):
								for x in range(self.width				):
											__UpperCAmelCase   :      Optional[Any]										=     int(self.get_greyscale(*self.input_img[y][x]				)				)
											if self.threshold > greyscale + self.error_table[y][x]:
														__UpperCAmelCase   :      List[Any]										=     (0, 0, 0)
														__UpperCAmelCase   :      Union[str, Any]										=     greyscale + self.error_table[x][y]
											else:
														__UpperCAmelCase   :      Optional[Any]										=     (255, 255, 255)
														__UpperCAmelCase   :      Dict										=     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
					_UpperCamelCase											=			[
					    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()




