



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 						UpperCamelCase      (  snake_case__			:       Optional[int]			,	snake_case__			:       Optional[Any]      )      ->	Dict:
   UpperCamelCase       :    Optional[Any]		      =      k_size // 2
   UpperCamelCase      ,	UpperCamelCase       :    List[str]		      =      mgrid[0 - center : k_size - center, 0 - center : k_size - center]
   UpperCamelCase       :    str		      =      1 / (2 * pi * sigma) * exp(-(square(snake_case__      ) + square(snake_case__      )) / (2 * square(snake_case__      ))      )
   return g
def 						UpperCamelCase      (  snake_case__			:       str			,	snake_case__			:       Optional[Any]			,	snake_case__			:       Optional[int]      )      ->	Optional[Any]:
   UpperCamelCase      ,	UpperCamelCase       :    List[str]		      =      image.shape[0], image.shape[1]
   # dst image height and width
   UpperCamelCase       :    Optional[Any]		      =      height - k_size + 1
   UpperCamelCase       :    Dict		      =      width - k_size + 1

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

   #  turn the kernel into shape(k*k, 1)
   UpperCamelCase       :    Optional[int]		      =      gen_gaussian_kernel(snake_case__			,	snake_case__      )
   UpperCamelCase       :    Optional[int]		      =      ravel(snake_case__      )

   # reshape and get the dst image
   UpperCamelCase       :    Dict		      =      dot(snake_case__			,	snake_case__      ).reshape(snake_case__			,	snake_case__      ).astype(snake_case__      )

   return dst


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

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





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

import torch_xla.distributed.xla_multiprocessing as xmp

def  __snake_case							(	):







				'''simple docstring'''


				lowercase         =      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=__magic_name__		,    default=1		,    help="Number of TPU cores to use (1 or 8)."						)

				# positional
				parser.add_argument(
				    "training_script"		,    type=__magic_name__		,    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=__magic_name__						)

				return parser.parse_args()

def  __snake_case							(	):







				'''simple docstring'''


				lowercase         =      parse_args()

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

				# Patch sys.argv
				lowercase         =      [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()
