
"""simple docstring"""







import numpy as np




def 			A_			(					lowercase						, lowercase      )     ->							Dict:


					"""simple docstring"""







					UpperCAmelCase_						:    Optional[int]    					=  np.array(lowercase      )
					UpperCAmelCase_						:    Optional[Any]    					=  np.array(lowercase      )

					UpperCAmelCase_						:    Dict    					=  abs(predict - actual      )
					UpperCAmelCase_						:    Tuple    					=  difference.mean()

					return score




def 			A_			(					lowercase						, lowercase      )     ->							str:


					"""simple docstring"""







					UpperCAmelCase_						:    Optional[Any]    					=  np.array(lowercase      )
					UpperCAmelCase_						:    List[str]    					=  np.array(lowercase      )

					UpperCAmelCase_						:    str    					=  predict - actual
					UpperCAmelCase_						:    Optional[Any]    					=  np.square(lowercase      )

					UpperCAmelCase_						:    Optional[Any]    					=  square_diff.mean()
					return score




def 			A_			(					lowercase						, lowercase      )     ->							List[Any]:


					"""simple docstring"""







					UpperCAmelCase_						:    Union[str, Any]    					=  np.array(lowercase      )
					UpperCAmelCase_						:    int    					=  np.array(lowercase      )

					UpperCAmelCase_						:    Optional[int]    					=  predict - actual
					UpperCAmelCase_						:    Optional[int]    					=  np.square(lowercase      )
					UpperCAmelCase_						:    str    					=  square_diff.mean()
					UpperCAmelCase_						:    Any    					=  np.sqrt(lowercase      )
					return score




def 			A_			(					lowercase						, lowercase      )     ->							List[Any]:


					"""simple docstring"""







					UpperCAmelCase_						:    Optional[int]    					=  np.array(lowercase      )
					UpperCAmelCase_						:    List[Any]    					=  np.array(lowercase      )

					UpperCAmelCase_						:    Any    					=  np.log(predict + 1      )
					UpperCAmelCase_						:    Tuple    					=  np.log(actual + 1      )

					UpperCAmelCase_						:    int    					=  log_predict - log_actual
					UpperCAmelCase_						:    Optional[Any]    					=  np.square(lowercase      )
					UpperCAmelCase_						:    List[Any]    					=  square_diff.mean()

					UpperCAmelCase_						:    str    					=  np.sqrt(lowercase      )

					return score




def 			A_			(					lowercase						, lowercase      )     ->							List[str]:


					"""simple docstring"""







					UpperCAmelCase_						:    Dict    					=  np.array(lowercase      )
					UpperCAmelCase_						:    Dict    					=  np.array(lowercase      )

					UpperCAmelCase_						:    List[str]    					=  predict - actual
					UpperCAmelCase_						:    Dict    					=  np.sum(lowercase      ) / len(lowercase      )
					UpperCAmelCase_						:    Optional[Any]    					=  np.sum(lowercase      ) / len(lowercase      )
					# print(numerator, denumerator)
					UpperCAmelCase_						:    int    					=  float(lowercase      ) / denumerator * 100

					return score




def 			A_			(					lowercase						, lowercase      )     ->							Tuple:


					"""simple docstring"""







					return np.mean(np.array(lowercase      ) == np.array(lowercase      )      )



"""simple docstring"""







import unittest

from transformers.image_processing_utils import get_size_dict



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




 def   a      ( self       : Union[str, Any] )->					Optional[Any]:



      """simple docstring"""

      UpperCAmelCase_						:    Any    					=  {"""wrong_key""": 2_24}
      with self.assertRaises(a_ ):
           get_size_dict(a_ )

      UpperCAmelCase_						:    Union[str, Any]    					=  {"""height""": 2_24}
      with self.assertRaises(a_ ):
           get_size_dict(a_ )

      UpperCAmelCase_						:    Union[str, Any]    					=  {"""width""": 2_24, """shortest_edge""": 2_24}
      with self.assertRaises(a_ ):
           get_size_dict(a_ )

      # Test a dict with the correct keys is returned as is
      UpperCAmelCase_						:    int    					=  {"""height""": 2_24, """width""": 2_24}
      UpperCAmelCase_						:    Dict    					=  get_size_dict(a_ )
      self.assertEqual(a_     ,   a_ )

      UpperCAmelCase_						:    Optional[int]    					=  {"""shortest_edge""": 2_24}
      UpperCAmelCase_						:    Tuple    					=  get_size_dict(a_ )
      self.assertEqual(a_     ,   {"""shortest_edge""": 2_24} )

      UpperCAmelCase_						:    str    					=  {"""longest_edge""": 2_24, """shortest_edge""": 2_24}
      UpperCAmelCase_						:    Any    					=  get_size_dict(a_ )
      self.assertEqual(a_     ,   {"""longest_edge""": 2_24, """shortest_edge""": 2_24} )

      # Test a single int value which  represents (size, size)
      UpperCAmelCase_						:    Optional[Any]    					=  get_size_dict(2_24 )
      self.assertEqual(a_     ,   {"""height""": 2_24, """width""": 2_24} )

      # Test a single int value which represents the shortest edge
      UpperCAmelCase_						:    List[str]    					=  get_size_dict(2_24     ,   default_to_square=a_ )
      self.assertEqual(a_     ,   {"""shortest_edge""": 2_24} )

      # Test a tuple of ints which represents (height, width)
      UpperCAmelCase_						:    List[Any]    					=  get_size_dict((1_50, 2_00) )
      self.assertEqual(a_     ,   {"""height""": 1_50, """width""": 2_00} )

      # Test a tuple of ints which represents (width, height)
      UpperCAmelCase_						:    List[str]    					=  get_size_dict((1_50, 2_00)     ,   height_width_order=a_ )
      self.assertEqual(a_     ,   {"""height""": 2_00, """width""": 1_50} )

      # Test an int representing the shortest edge and max_size which represents the longest edge
      UpperCAmelCase_						:    List[Any]    					=  get_size_dict(2_24     ,   max_size=2_56     ,   default_to_square=a_ )
      self.assertEqual(a_     ,   {"""shortest_edge""": 2_24, """longest_edge""": 2_56} )

      # Test int with default_to_square=True and max_size fails
      with self.assertRaises(a_ ):
           get_size_dict(2_24     ,   max_size=2_56     ,   default_to_square=a_ )

