




'''simple docstring'''
import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image





def  lowerCamelCase(       SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_=256    )		->		Dict:
   A_       		=   min(img.size    )

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

   A_       		=   target_image_size / s
   A_       		=   (round(r * img.size[1]    ), round(r * img.size[0]    ))
   A_       		=   TF.resize(SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_					,interpolation=PIL.Image.LANCZOS    )
   A_       		=   TF.center_crop(SCREAMING_SNAKE_CASE_					,output_size=2 * [target_image_size]    )
   A_       		=   torch.unsqueeze(T.ToTensor()(SCREAMING_SNAKE_CASE_    )					,0    )
   return img





def  lowerCamelCase(       SCREAMING_SNAKE_CASE_    )		->		List[str]:
   A_       		=   2.0 * x - 1.0
   return x





def  lowerCamelCase(       SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_=True					,SCREAMING_SNAKE_CASE_="RGB"    )		->		Any:
   A_       		=   x.detach().cpu()
   if process:
      A_       		=   post_process_tensor(SCREAMING_SNAKE_CASE_    )
   A_       		=   x.numpy()
   if process:
      A_       		=   (255 * x).astype(np.uinta    )
   A_       		=   Image.fromarray(SCREAMING_SNAKE_CASE_    )
   if not x.mode == mode:
      A_       		=   x.convert(SCREAMING_SNAKE_CASE_    )
   return x





def  lowerCamelCase(       SCREAMING_SNAKE_CASE_    )		->		Optional[int]:
   A_       		=   torch.clamp(SCREAMING_SNAKE_CASE_					,-1.0					,1.0    )
   A_       		=   (x + 1.0) / 2.0
   A_       		=   x.permute(1					,2					,0    )
   return x






def  lowerCamelCase(       SCREAMING_SNAKE_CASE_    )		->		Optional[Any]:
   A_       		=   post_process_tensor(x.squeeze()    )
   return x.permute(2					,0					,1    ).unsqueeze(0    )










'''simple docstring'''
def  lowerCamelCase(       SCREAMING_SNAKE_CASE_    )		->		list:
			A_       		=   [True for i in range(n + 1    )]
			A_       		=   [i - 1 for i in range(n + 1    )]
			A_       		=   []
			for i in range(2					,n + 1    ):
						if is_prime[i]:
									primes.append(SCREAMING_SNAKE_CASE_    )
						for j in range(0					,len(SCREAMING_SNAKE_CASE_    )    ):
									if i * primes[j] >= n:
												break
									A_       		=   False

									if i % primes[j] == 0:
												A_       		=   totients[i] * primes[j]
												break

									A_       		=   totients[i] * (primes[j] - 1)

			return totients


if __name__ == "__main__":
							import doctest

							doctest.testmod()




