

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	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	List[str]									= k_size // 2
	UpperCAmelCase__							,						UpperCAmelCase__:	Tuple									= mgrid[0 - center : k_size - center, 0 - center : k_size - center]
	UpperCAmelCase__:	int									= 1 / (2 * pi * sigma) * exp(-(square(SCREAMING_SNAKE_CASE      ) + square(SCREAMING_SNAKE_CASE      )) / (2 * square(SCREAMING_SNAKE_CASE      ))      )
	return g



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__							,						UpperCAmelCase__:	str									= image.shape[0], image.shape[1]
	# dst image height and width
	UpperCAmelCase__:	int									= height - k_size + 1
	UpperCAmelCase__:	Optional[int]									= width - k_size + 1

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

	#  turn the kernel into shape(k*k, 1)
	UpperCAmelCase__:	Any									= gen_gaussian_kernel(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	Any									= ravel(SCREAMING_SNAKE_CASE      )

	# reshape and get the dst image
	UpperCAmelCase__:	Union[str, Any]									= dot(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ).reshape(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ).astype(SCREAMING_SNAKE_CASE      )

	return dst


if __name__ == "__main__":
					# read original image
					_lowerCAmelCase  :      Optional[Any]			 =imread(r"""../image_data/lena.jpg""")
					# turn image in gray scale value
					_lowerCAmelCase  :      int			 =cvtColor(img, COLOR_BGR2GRAY)

					# get values with two different mask size
					_lowerCAmelCase  :      int			 =gaussian_filter(gray, 3, sigma=1)
					_lowerCAmelCase  :      List[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()


import unittest

from transformers import DonutProcessor


_lowerCAmelCase  :      str			 ="""naver-clova-ix/donut-base"""

class __UpperCamelCase     (				unittest.TestCase		):






					'''simple docstring'''



					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Any									= DonutProcessor.from_pretrained(lowerCamelCase__						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Any									= {
						    "name": "John Doe",
						    "age": "99",
						    "city": "Atlanta",
						    "state": "GA",
						    "zip": "30301",
						    "phone": "123-4567",
						    "nicknames": [{"nickname": "Johnny"}, {"nickname": "JD"}],
						}

						UpperCAmelCase__:	Optional[Any]									= (
						    "<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>"
						    "<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>"
						    "<s_nicknames><s_nickname>Johnny</s_nickname>"
						    "<sep/><s_nickname>JD</s_nickname></s_nicknames>"
						)
						UpperCAmelCase__:	str									= self.processor.tokenajson(lowerCamelCase__						)

						self.assertDictEqual(lowerCamelCase__				,      lowerCamelCase__						)