


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

import torch_xla.distributed.xla_multiprocessing as xmp






def      _lowercase(				):
							a__     						=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      _lowercase(				):
							a__     						=parse_args()

							# Import training_script as a module.
							a__     						=Path(args.training_script    )
							sys.path.append(str(script_fpath.parent.resolve()    )    )
							a__     						=script_fpath.stem
							a__     						=importlib.import_module(__a    )

							# Patch sys.argv
							a__     						=[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()





import numpy as np
from PIL import Image






def      _lowercase(				__a							:	np.array    ):
							a__						,    a__						,    a__     						=rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
							return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b






def      _lowercase(				__a							:	np.array    ):
							return (gray > 127) & (gray <= 255)






def      _lowercase(				__a							:	np.array						,    __a							:	np.array    ):
							a__     						=np.zeros_like(__a    )
							a__     						=np.zeros(
							    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)    )

							# Copy image to padded image
							a__     						=image

							# Iterate over image & apply kernel
							for x in range(image.shape[1]    ):
														for y in range(image.shape[0]    ):
																					a__     						=(
																					    kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
																					).sum()
																					a__     						=int(summation == 5    )
							return output


# kernel to be applied
_lowerCAmelCase:	Dict      							=					np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
	# read original image
	_lowerCAmelCase:	Optional[int]      							=					np.array(Image.open(R'..\image_data\lena.jpg'))
	# Apply erosion operation to a binary image
	_lowerCAmelCase:	Any      							=					erosion(grayabinary(rgbagray(image)), structuring_element)
	# Save the output image
	_lowerCAmelCase:	Dict      							=					Image.fromarray(output).convert('RGB')
	pil_img.save('result_erosion.png')

