


'''simple docstring'''

import math
import sys

import cva
import numpy as np





def lowerCamelCase      (      lowerCAmelCase    :     np.ndarray		,		lowerCAmelCase    :     float		):





 """simple docstring"""







 __magic_name__	:   List[str]					     =					math.sqrt(lowerCAmelCase		)
 __magic_name__	:   Optional[Any]					     =					1 / (sigma * math.sqrt(2 * math.pi		))
 return cons * np.exp(-((img / sigma) ** 2) * 0.5		)





def lowerCamelCase      (      lowerCAmelCase    :     np.ndarray		,		lowerCAmelCase    :     int		,		lowerCAmelCase    :     int		,		lowerCAmelCase    :     int		):





 """simple docstring"""







 __magic_name__	:   List[Any]					     =					kernel_size // 2
 return img[x - half : x + half + 1, y - half : y + half + 1]





def lowerCamelCase      (      lowerCAmelCase    :     int		,		lowerCAmelCase    :     float		):





 """simple docstring"""







 __magic_name__	:   Tuple					     =					np.zeros((kernel_size, kernel_size)		)
 for i in range(0		,		lowerCAmelCase		):
  for j in range(0		,		lowerCAmelCase		):
   __magic_name__	:   Optional[int]					     =					math.sqrt(
       abs(i - kernel_size // 2		) ** 2 + abs(j - kernel_size // 2		) ** 2		)
 return vec_gaussian(lowerCAmelCase		,		lowerCAmelCase		)





def lowerCamelCase      (      lowerCAmelCase    :     np.ndarray		,		lowerCAmelCase    :     float		,		lowerCAmelCase    :     float		,		lowerCAmelCase    :     int		,		):





 """simple docstring"""







 __magic_name__	:   Any					     =					np.zeros(img.shape		)
 __magic_name__	:   Dict					     =					get_gauss_kernel(lowerCAmelCase		,		lowerCAmelCase		)
 __magic_name__	:   Tuple					     =					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		):
   __magic_name__	:   List[Any]					     =					get_slice(lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		)
   __magic_name__	:   int					     =					img_s - img_s[kernel_size // 2, kernel_size // 2]
   __magic_name__	:   int					     =					vec_gaussian(lowerCAmelCase		,		lowerCAmelCase		)
   __magic_name__	:   Union[str, Any]					     =					np.multiply(lowerCAmelCase		,		lowerCAmelCase		)
   __magic_name__	:   int					     =					np.multiply(lowerCAmelCase		,		lowerCAmelCase		)
   __magic_name__	:   Optional[int]					     =					np.sum(lowerCAmelCase		) / np.sum(lowerCAmelCase		)
   __magic_name__	:   List[str]					     =					val
 return imga





def lowerCamelCase      (      lowerCAmelCase    :     list		):





 """simple docstring"""







 __magic_name__	:   List[str]					     =					args[1] if args[1:] else '../image_data/lena.jpg'
 __magic_name__	:   Tuple					     =					float(args[2]		) if args[2:] else 1.0
 __magic_name__	:   str					     =					float(args[3]		) if args[3:] else 1.0
 if args[4:]:
  __magic_name__	:   List[str]					     =					int(args[4]		)
  __magic_name__	:   Optional[int]					     =					kernel_size + abs(kernel_size % 2 - 1		)
 else:
  __magic_name__	:   str					     =					5
 return filename, spatial_variance, intensity_variance, kernel_size


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

       lowerCAmelCase				:Tuple        =      img / 2_5_5
       lowerCAmelCase				:Optional[int]        =      out.astype('''float32''')
       lowerCAmelCase				:List[Any]        =      bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
       lowerCAmelCase				:Tuple        =      out * 2_5_5
       lowerCAmelCase				:Union[str, Any]        =      np.uinta(out)
       cva.imshow('''output image''', out)
       cva.waitKey(0)
       cva.destroyAllWindows()



'''simple docstring'''

lowerCAmelCase				:Union[str, Any]        =      '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

lowerCAmelCase				:Union[str, Any]        =      [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
lowerCAmelCase				:Tuple        =      {
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}