
'''simple docstring'''


import math
import sys

import cva
import numpy as np



def  __snake_case     (    _UpperCAmelCase					:       np.ndarray,      _UpperCAmelCase					:       float):
   # For applying gaussian function for each element in matrix.
   UpperCamelCase       =  math.sqrt(_UpperCAmelCase)
   UpperCamelCase       =  1 / (sigma * math.sqrt(2 * math.pi))
   return cons * np.exp(-((img / sigma) ** 2) * 0.5)



def  __snake_case     (    _UpperCAmelCase					:       np.ndarray,      _UpperCAmelCase					:       int,      _UpperCAmelCase					:       int,      _UpperCAmelCase					:       int):
   UpperCamelCase       =  kernel_size // 2
   return img[x - half : x + half + 1, y - half : y + half + 1]



def  __snake_case     (    _UpperCAmelCase					:       int,      _UpperCAmelCase					:       float):
   # Creates a gaussian kernel of given dimension.
   UpperCamelCase       =  np.zeros((kernel_size, kernel_size))
   for i in range(0,      _UpperCAmelCase):
      for j in range(0,      _UpperCAmelCase):
         UpperCamelCase       =  math.sqrt(
             abs(i - kernel_size // 2) ** 2 + abs(j - kernel_size // 2) ** 2)
   return vec_gaussian(_UpperCAmelCase,      _UpperCAmelCase)



def  __snake_case     (    _UpperCAmelCase					:       np.ndarray,      _UpperCAmelCase					:       float,      _UpperCAmelCase					:       float,      _UpperCAmelCase					:       int,      ):
   UpperCamelCase       =  np.zeros(img.shape)
   UpperCamelCase       =  get_gauss_kernel(_UpperCAmelCase,      _UpperCAmelCase)
   UpperCamelCase     , UpperCamelCase       =  img.shape
   for i in range(kernel_size // 2,      size_x - kernel_size // 2):
      for j in range(kernel_size // 2,      size_y - kernel_size // 2):
         UpperCamelCase       =  get_slice(_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase)
         UpperCamelCase       =  img_s - img_s[kernel_size // 2, kernel_size // 2]
         UpperCamelCase       =  vec_gaussian(_UpperCAmelCase,      _UpperCAmelCase)
         UpperCamelCase       =  np.multiply(_UpperCAmelCase,      _UpperCAmelCase)
         UpperCamelCase       =  np.multiply(_UpperCAmelCase,      _UpperCAmelCase)
         UpperCamelCase       =  np.sum(_UpperCAmelCase) / np.sum(_UpperCAmelCase)
         UpperCamelCase       =  val
   return imga



def  __snake_case     (    _UpperCAmelCase					:       list):
   UpperCamelCase       =  args[1] if args[1:] else '''../image_data/lena.jpg'''
   UpperCamelCase       =  float(args[2]) if args[2:] else 1.0
   UpperCamelCase       =  float(args[3]) if args[3:] else 1.0
   if args[4:]:
      UpperCamelCase       =  int(args[4])
      UpperCamelCase       =  kernel_size + abs(kernel_size % 2 - 1)
   else:
      UpperCamelCase       =  5
   return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
   snake_case_  , snake_case_  , snake_case_  , snake_case_ :  List[str]     =					parse_args(sys.argv)
   snake_case_ :  str     =					cva.imread(filename, 0)
   cva.imshow('input image', img)

   snake_case_ :  Any     =					img / 255
   snake_case_ :  Dict     =					out.astype('float32')
   snake_case_ :  List[str]     =					bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
   snake_case_ :  Optional[int]     =					out * 255
   snake_case_ :  Optional[Any]     =					np.uinta(out)
   cva.imshow('output image', out)
   cva.waitKey(0)
   cva.destroyAllWindows()







'''simple docstring'''


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

import torch_xla.distributed.xla_multiprocessing as xmp



def  __snake_case     (    ):
   UpperCamelCase       =  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=_UpperCAmelCase,      default=1,      help='''Number of TPU cores to use (1 or 8).''')

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

   return parser.parse_args()



def  __snake_case     (    ):
   UpperCamelCase       =  parse_args()

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

   # Patch sys.argv
   UpperCamelCase       =  [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()





