





'''simple docstring'''

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  a__    (    lowercase :					List[str], lowercase :					Any				)		->		List[str]:





     """simple docstring"""

     _UpperCamelCase       =	k_size // 2
     _UpperCamelCase   ,    _UpperCamelCase       =	mgrid[0 - center : k_size - center, 0 - center : k_size - center]
     _UpperCamelCase       =	1 / (2 * pi * sigma) * exp(-(square(lowercase				) + square(lowercase				)) / (2 * square(lowercase				))				)
     return g







def  a__    (    lowercase :					str, lowercase :					Union[str, Any], lowercase :					List[str]				)		->		int:





     """simple docstring"""

     _UpperCamelCase   ,    _UpperCamelCase       =	image.shape[0], image.shape[1]
     # dst image height and width
     _UpperCamelCase       =	height - k_size + 1
     _UpperCamelCase       =	width - k_size + 1

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

     #  turn the kernel into shape(k*k, 1)
     _UpperCamelCase       =	gen_gaussian_kernel(lowercase, lowercase				)
     _UpperCamelCase       =	ravel(lowercase				)

     # reshape and get the dst image
     _UpperCamelCase       =	dot(lowercase, lowercase				).reshape(lowercase, lowercase				).astype(lowercase				)

     return dst


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

   # get values with two different mask size
   lowercase__						:      int	=    gaussian_filter(gray, 3, sigma=1)
   lowercase__						:      Union[str, Any]	=    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()










'''simple docstring'''

from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase__						:      Optional[Any]	=    {
    'configuration_autoformer': [
        'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'AutoformerConfig',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__						:      List[Any]	=    [
       'AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'AutoformerForPrediction',
       'AutoformerModel',
       'AutoformerPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_autoformer import (
       AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       AutoformerConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_autoformer import (
          AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          AutoformerForPrediction,
          AutoformerModel,
          AutoformerPreTrainedModel,
      )

else:
   import sys

   lowercase__						:      Tuple	=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



