"""simple docstring"""







from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Tuple  ,       _SCREAMING_SNAKE_CASE				:				Dict=3						)    ->     Optional[int]:







     '''simple docstring'''


     __A					:					List[Any]								=		int(mask / 2						)
     # copy image size
     __A					:					Optional[Any]								=		zeros_like(_SCREAMING_SNAKE_CASE						)
     for i in range(_SCREAMING_SNAKE_CASE  ,       gray_img.shape[0] - bd						):
          for j in range(_SCREAMING_SNAKE_CASE  ,       gray_img.shape[1] - bd						):
               # get mask according with mask
               __A					:					Tuple								=		ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]						)
               # calculate mask median
               __A					:					List[str]								=		sort(_SCREAMING_SNAKE_CASE						)[inta(divide((multiply(_SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE						))  ,       2						) + 1						)]
               __A					:					Optional[Any]								=		median
     return median_img


if __name__ == "__main__":
       # read original image
       lowerCamelCase      : Optional[Any]       =imread('''../image_data/lena.jpg''')
       # turn image in gray scale value
       lowerCamelCase      : str       =cvtColor(img, COLOR_BGR2GRAY)

       # get values with two different mask size
       lowerCamelCase      : str       =median_filter(gray, 3)
       lowerCamelCase      : str       =median_filter(gray, 5)

       # show result images
       imshow('''median filter with 3x3 mask''', medianaxa)
       imshow('''median filter with 5x5 mask''', medianaxa)
       waitKey(0)




"""simple docstring"""







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

import torch_xla.distributed.xla_multiprocessing as xmp




def 						_lowercase				(  )    ->     List[str]:







     '''simple docstring'''


     __A					:					int								=		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=_SCREAMING_SNAKE_CASE  ,       default=1  ,       help='Number of TPU cores to use (1 or 8).'						)

     # positional
     parser.add_argument(
         'training_script'  ,       type=_SCREAMING_SNAKE_CASE  ,       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=_SCREAMING_SNAKE_CASE						)

     return parser.parse_args()




def 						_lowercase				(  )    ->     List[str]:







     '''simple docstring'''


     __A					:					List[str]								=		parse_args()

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

     # Patch sys.argv
     __A					:					Union[str, Any]								=		[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()



