






import unittest

from transformers.image_processing_utils import get_size_dict



class 							_A (    unittest.TestCase							):
    """simple docstring"""

    def _a       (							self			:    Optional[Any]			)			->       str:
           # Test a dict with the wrong keys raises an error
           __UpperCAmelCase    							={"""wrong_key""": 224}
           with self.assertRaises(__SCREAMING_SNAKE_CASE			):
                  get_size_dict(__SCREAMING_SNAKE_CASE			)

           __UpperCAmelCase    							={"""height""": 224}
           with self.assertRaises(__SCREAMING_SNAKE_CASE			):
                  get_size_dict(__SCREAMING_SNAKE_CASE			)

           __UpperCAmelCase    							={"""width""": 224, """shortest_edge""": 224}
           with self.assertRaises(__SCREAMING_SNAKE_CASE			):
                  get_size_dict(__SCREAMING_SNAKE_CASE			)

           # Test a dict with the correct keys is returned as is
           __UpperCAmelCase    							={"""height""": 224, """width""": 224}
           __UpperCAmelCase    							=get_size_dict(__SCREAMING_SNAKE_CASE			)
           self.assertEqual(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			)

           __UpperCAmelCase    							={"""shortest_edge""": 224}
           __UpperCAmelCase    							=get_size_dict(__SCREAMING_SNAKE_CASE			)
           self.assertEqual(__SCREAMING_SNAKE_CASE   ,    {"""shortest_edge""": 224}			)

           __UpperCAmelCase    							={"""longest_edge""": 224, """shortest_edge""": 224}
           __UpperCAmelCase    							=get_size_dict(__SCREAMING_SNAKE_CASE			)
           self.assertEqual(__SCREAMING_SNAKE_CASE   ,    {"""longest_edge""": 224, """shortest_edge""": 224}			)

           # Test a single int value which  represents (size, size)
           __UpperCAmelCase    							=get_size_dict(224			)
           self.assertEqual(__SCREAMING_SNAKE_CASE   ,    {"""height""": 224, """width""": 224}			)

           # Test a single int value which represents the shortest edge
           __UpperCAmelCase    							=get_size_dict(224   ,    default_to_square=__SCREAMING_SNAKE_CASE			)
           self.assertEqual(__SCREAMING_SNAKE_CASE   ,    {"""shortest_edge""": 224}			)

           # Test a tuple of ints which represents (height, width)
           __UpperCAmelCase    							=get_size_dict((150, 200)			)
           self.assertEqual(__SCREAMING_SNAKE_CASE   ,    {"""height""": 150, """width""": 200}			)

           # Test a tuple of ints which represents (width, height)
           __UpperCAmelCase    							=get_size_dict((150, 200)   ,    height_width_order=__SCREAMING_SNAKE_CASE			)
           self.assertEqual(__SCREAMING_SNAKE_CASE   ,    {"""height""": 200, """width""": 150}			)

           # Test an int representing the shortest edge and max_size which represents the longest edge
           __UpperCAmelCase    							=get_size_dict(224   ,    max_size=256   ,    default_to_square=__SCREAMING_SNAKE_CASE			)
           self.assertEqual(__SCREAMING_SNAKE_CASE   ,    {"""shortest_edge""": 224, """longest_edge""": 256}			)

           # Test int with default_to_square=True and max_size fails
           with self.assertRaises(__SCREAMING_SNAKE_CASE			):
                  get_size_dict(224   ,    max_size=256   ,    default_to_square=__SCREAMING_SNAKE_CASE			)












from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like







def 			lowercase__  (				A_:	int     ,			A_:	Any=3		)  ->							Dict:







       """simple docstring"""






       __UpperCAmelCase    							=int(mask / 2		)
       # copy image size
       __UpperCAmelCase    							=zeros_like(A_		)
       for i in range(A_     ,			gray_img.shape[0] - bd		):
              for j in range(A_     ,			gray_img.shape[1] - bd		):
                     # get mask according with mask
                     __UpperCAmelCase    							=ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]		)
                     # calculate mask median
                     __UpperCAmelCase    							=sort(A_		)[inta(divide((multiply(A_     ,			A_		))     ,			2		) + 1		)]
                     __UpperCAmelCase    							=median
       return median_img


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

   # get values with two different mask size
   __A  							=  median_filter(gray, 3)
   __A  							=  median_filter(gray, 5)

   # show result images
   imshow("median filter with 3x3 mask", medianaxa)
   imshow("median filter with 5x5 mask", medianaxa)
   waitKey(0)




