





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

import torch_xla.distributed.xla_multiprocessing as xmp


def 							SCREAMING_SNAKE_CASE__  (      ):
	snake_case_						:     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 							SCREAMING_SNAKE_CASE__  (      ):
	snake_case_						:     str     =		parse_args()

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

	# Patch sys.argv
	snake_case_						:     int     =		[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()








from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				):
	snake_case_						:     List[Any]     =		k_size // 2
	snake_case_  ,snake_case_						:     Any     =		mgrid[0 - center : k_size - center, 0 - center : k_size - center]
	snake_case_						:     Optional[Any]     =		1 / (2 * pi * sigma) * exp(-(square(__a				) + square(__a				)) / (2 * square(__a				))				)
	return g


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				):
	snake_case_  ,snake_case_						:     Union[str, Any]     =		image.shape[0], image.shape[1]
	# dst image height and width
	snake_case_						:     int     =		height - k_size + 1
	snake_case_						:     Optional[int]     =		width - k_size + 1

	# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
	snake_case_						:     Optional[Any]     =		zeros((dst_height * dst_width, k_size * k_size)				)
	snake_case_						:     Tuple     =		0
	for i, j in product(range(__a				)				,	range(__a				)				):
		snake_case_						:     Optional[int]     =		ravel(image[i : i + k_size, j : j + k_size]				)
		snake_case_						:     str     =		window
		row += 1

	#  turn the kernel into shape(k*k, 1)
	snake_case_						:     List[Any]     =		gen_gaussian_kernel(__a				,	__a				)
	snake_case_						:     str     =		ravel(__a				)

	# reshape and get the dst image
	snake_case_						:     Optional[int]     =		dot(__a				,	__a				).reshape(__a				,	__a				).astype(__a				)

	return dst


if __name__ == "__main__":
						# read original image
						_SCREAMING_SNAKE_CASE 		=						imread(R"""../image_data/lena.jpg""")
						# turn image in gray scale value
						_SCREAMING_SNAKE_CASE 		=						cvtColor(img, COLOR_BGR2GRAY)

						# get values with two different mask size
						_SCREAMING_SNAKE_CASE 		=						gaussian_filter(gray, 3, sigma=1)
						_SCREAMING_SNAKE_CASE 		=						gaussian_filter(gray, 5, sigma=0.8)

						# show result images
						imshow("""gaussian filter with 3x3 mask""", gaussianaxa)
						imshow("""gaussian filter with 5x5 mask""", gaussianaxa)
						waitKey()

