




import json
import logging
import os
import subprocess
from argparse import ArgumentParser


snake_case__ :						Any     =			logging.getLogger(__name__)


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

  return parser.parse_args()


def 						lowercase		(     ):
  UpperCAmelCase__    						=   parse_args()
  UpperCAmelCase__    						=   8888
  UpperCAmelCase__    						=   int(os.environ["""SM_NUM_GPUS"""]      )
  UpperCAmelCase__    						=   json.loads(os.environ["""SM_HOSTS"""]      )
  UpperCAmelCase__    						=   len(lowercase_      )
  UpperCAmelCase__    						=   os.environ['''SM_CURRENT_HOST''']
  UpperCAmelCase__    						=   hosts.index(lowercase_      )
  UpperCAmelCase__    						=   '''INFO'''

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


if __name__ == "__main__":
   main()










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


def 						lowercase		(     _lowerCAmelCase   ,     _lowerCAmelCase=3      ):
  UpperCAmelCase__    						=   int(mask / 2      )
  # copy image size
  UpperCAmelCase__    						=   zeros_like(_lowerCAmelCase      )
  for i in range(_lowerCAmelCase   ,     gray_img.shape[0] - bd      ):
    for j in range(_lowerCAmelCase   ,     gray_img.shape[1] - bd      ):
      # get mask according with mask
      UpperCAmelCase__    						=   ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]      )
      # calculate mask median
      UpperCAmelCase__    						=   sort(_lowerCAmelCase      )[inta(divide((multiply(_lowerCAmelCase   ,     _lowerCAmelCase      ))   ,     2      ) + 1      )]
      UpperCAmelCase__    						=   median
  return median_img


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

   # get values with two different mask size
   snake_case__ :						str     =			median_filter(gray, 3)
   snake_case__ :						List[str]     =			median_filter(gray, 5)

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




