code: 
  
  
  
  
  
  
  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			)
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  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)
  
  
  
  
  