

from __future__ import annotations







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

     """simple docstring"""







     _lowerCAmelCase :       List[str]	   =     {1}
     _lowerCAmelCase :       List[str]	   =     [1]
     for _ in range(2		,				depth + 1					):
          _lowerCAmelCase :       List[str]	   =     [*previous_coefficients, 0]
          _lowerCAmelCase :       Union[str, Any]	   =     [0, *previous_coefficients]
          _lowerCAmelCase :       List[Any]	   =     []
          for x, y in zip(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					):
               coefficients.add(x + y					)
               previous_coefficients.append(x + y					)
     return coefficients







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

     """simple docstring"""







     _lowerCAmelCase :       Tuple	   =     set()
     for number in unique_coefficients:
          _lowerCAmelCase :       Tuple	   =     2
          _lowerCAmelCase :       List[str]	   =     number
          while divisor**2 <= copy_number:
               _lowerCAmelCase :       Any	   =     0
               while copy_number % divisor == 0:
                    copy_number //= divisor
                    multiplicity += 1
               if multiplicity >= 2:
                    non_squarefrees.add(SCREAMING_SNAKE_CASE					)
                    break
               divisor += 1

     return unique_coefficients.difference(SCREAMING_SNAKE_CASE					)







def 						_snake_case		(     SCREAMING_SNAKE_CASE = 51					)							->   int:

     """simple docstring"""







     _lowerCAmelCase :       List[Any]	   =     get_pascal_triangle_unique_coefficients(SCREAMING_SNAKE_CASE					)
     _lowerCAmelCase :       Dict	   =     get_squarefrees(SCREAMING_SNAKE_CASE					)
     return sum(SCREAMING_SNAKE_CASE					)


if __name__ == "__main__":
  print(f'''{solution() = }''')









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					)






