

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__							(  lowerCAmelCase_,       lowerCAmelCase_=256     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=min(img.size     )

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

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







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=2.0 * x - 1.0
     return x







def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_=True,       lowerCAmelCase_="RGB"     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=x.detach().cpu()
     if process:
          SCREAMING_SNAKE_CASE 		=post_process_tensor(lowerCAmelCase_     )
     SCREAMING_SNAKE_CASE 		=x.numpy()
     if process:
          SCREAMING_SNAKE_CASE 		=(255 * x).astype(np.uinta     )
     SCREAMING_SNAKE_CASE 		=Image.fromarray(lowerCAmelCase_     )
     if not x.mode == mode:
          SCREAMING_SNAKE_CASE 		=x.convert(lowerCAmelCase_     )
     return x







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=torch.clamp(lowerCAmelCase_,       -1.0,       1.0     )
     SCREAMING_SNAKE_CASE 		=(x + 1.0) / 2.0
     SCREAMING_SNAKE_CASE 		=x.permute(1,       2,       0     )
     return x







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=post_process_tensor(x.squeeze()     )
     return x.permute(2,       0,       1     ).unsqueeze(0     )




import math



class 						a_			:



      """simple docstring"""







      def __init__( self	:				str     ,snake_case	:				List[Any]     ,snake_case	:				Union[str, Any]     ,snake_case	:				Optional[int]							):
           SCREAMING_SNAKE_CASE 		=x
           SCREAMING_SNAKE_CASE 		=y
           SCREAMING_SNAKE_CASE 		=z


      def __repr__( self	:				Any							):
           return f'Point({self.x}, {self.y}, {self.z})'







def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_     ):





     """simple docstring"""







     return math.sqrt(abs((b.x - a.x) ** 2 + (b.y - a.y) ** 2 + (b.z - a.z) ** 2     )     )


if __name__ == "__main__":
 import doctest

 doctest.testmod()

