

from math import sqrt







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   list[int]:

     """simple docstring"""







     _lowerCAmelCase :       Tuple	   =     [1]
     for i in range(2		,				int(sqrt(SCREAMING_SNAKE_CASE					)					) + 1		,				1					):
          if number % i == 0:
               values.append(SCREAMING_SNAKE_CASE					)
               if int(number // i					) != i:
                    values.append(int(number // i					)					)
     return sorted(SCREAMING_SNAKE_CASE					)







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   bool:

     """simple docstring"""







     return sum(factors(SCREAMING_SNAKE_CASE					)					) > n







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   bool:

     """simple docstring"""







     _lowerCAmelCase :       int	   =     factors(SCREAMING_SNAKE_CASE					)
     _lowerCAmelCase :       Any	   =     len(SCREAMING_SNAKE_CASE					)
     _lowerCAmelCase :       Optional[Any]	   =     [[0 for i in range(number + 1					)] for j in range(r + 1					)]
     for i in range(r + 1					):
          _lowerCAmelCase :       List[str]	   =     True

     for i in range(1		,				number + 1					):
          _lowerCAmelCase :       List[Any]	   =     False

     for i in range(1		,				r + 1					):
          for j in range(1		,				number + 1					):
               if j < values[i - 1]:
                    _lowerCAmelCase :       Optional[int]	   =     subset[i - 1][j]
               else:
                    _lowerCAmelCase :       Dict	   =     subset[i - 1][j] or subset[i - 1][j - values[i - 1]]

     return subset[r][number] != 0







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   bool:

     """simple docstring"""







     return abundant(SCREAMING_SNAKE_CASE					) and not semi_perfect(SCREAMING_SNAKE_CASE					)


if __name__ == "__main__":
  import doctest

  doctest.testmod(verbose=True)
  for number in (6_9, 7_0, 7_1):
    print(f'''{number} is {'' if weird(number) else 'not '}weird.''')









import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=256					)							->   Optional[int]:

     """simple docstring"""







     _lowerCAmelCase :       Any	   =     min(img.size					)

     if s < target_image_size:
          raise ValueError(f'''min dim for image {s} < {target_image_size}'''					)

     _lowerCAmelCase :       Dict	   =     target_image_size / s
     _lowerCAmelCase :       Dict	   =     (round(r * img.size[1]					), round(r * img.size[0]					))
     _lowerCAmelCase :       Any	   =     TF.resize(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				interpolation=PIL.Image.LANCZOS					)
     _lowerCAmelCase :       str	   =     TF.center_crop(SCREAMING_SNAKE_CASE		,				output_size=2 * [target_image_size]					)
     _lowerCAmelCase :       int	   =     torch.unsqueeze(T.ToTensor()(SCREAMING_SNAKE_CASE					)		,				0					)
     return img







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   List[str]:

     """simple docstring"""







     _lowerCAmelCase :       List[Any]	   =     2.0 * x - 1.0
     return x







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=True		,				SCREAMING_SNAKE_CASE="RGB"					)							->   int:

     """simple docstring"""







     _lowerCAmelCase :       Optional[int]	   =     x.detach().cpu()
     if process:
          _lowerCAmelCase :       List[Any]	   =     post_process_tensor(SCREAMING_SNAKE_CASE					)
     _lowerCAmelCase :       Optional[int]	   =     x.numpy()
     if process:
          _lowerCAmelCase :       List[Any]	   =     (255 * x).astype(np.uinta					)
     _lowerCAmelCase :       Optional[int]	   =     Image.fromarray(SCREAMING_SNAKE_CASE					)
     if not x.mode == mode:
          _lowerCAmelCase :       str	   =     x.convert(SCREAMING_SNAKE_CASE					)
     return x







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   Union[str, Any]:

     """simple docstring"""







     _lowerCAmelCase :       Any	   =     torch.clamp(SCREAMING_SNAKE_CASE		,				-1.0		,				1.0					)
     _lowerCAmelCase :       Any	   =     (x + 1.0) / 2.0
     _lowerCAmelCase :       List[Any]	   =     x.permute(1		,				2		,				0					)
     return x







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   Tuple:

     """simple docstring"""







     _lowerCAmelCase :       str	   =     post_process_tensor(x.squeeze()					)
     return x.permute(2		,				0		,				1					).unsqueeze(0					)






