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


def SCREAMING_SNAKE_CASE					(  lowercase_:						Optional[int]		,    lowercase_:						Any=256					):
       _UpperCamelCase  :							List[str]	    =	min(img.size					)

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

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


def SCREAMING_SNAKE_CASE					(  lowercase_:						List[str]					):
       _UpperCamelCase  :							Tuple	    =	2.0 * x - 1.0
       return x


def SCREAMING_SNAKE_CASE					(  lowercase_:						Optional[int]		,    lowercase_:						Union[str, Any]=True		,    lowercase_:						Dict="RGB"					):
       _UpperCamelCase  :							List[str]	    =	x.detach().cpu()
       if process:
              _UpperCamelCase  :							Optional[int]	    =	post_process_tensor(lowercase__					)
       _UpperCamelCase  :							List[str]	    =	x.numpy()
       if process:
              _UpperCamelCase  :							Optional[Any]	    =	(255 * x).astype(np.uinta					)
       _UpperCamelCase  :							Dict	    =	Image.fromarray(lowercase__					)
       if not x.mode == mode:
              _UpperCamelCase  :							Dict	    =	x.convert(lowercase__					)
       return x


def SCREAMING_SNAKE_CASE					(  lowercase_:						Optional[int]					):
       _UpperCamelCase  :							List[Any]	    =	torch.clamp(lowercase__		,    -1.0		,    1.0					)
       _UpperCamelCase  :							Dict	    =	(x + 1.0) / 2.0
       _UpperCamelCase  :							List[str]	    =	x.permute(1		,    2		,    0					)
       return x





def SCREAMING_SNAKE_CASE					(  lowercase_:						Dict					):
       _UpperCamelCase  :							List[str]	    =	post_process_tensor(x.squeeze()					)
       return x.permute(2		,    0		,    1					).unsqueeze(0					)











'''simple docstring'''





def  lowerCamelCase_  (     lowercase__):
 return int(lowercase__) if x - int(lowercase__) <= 0 else int(lowercase__) + 1


if __name__ == "__main__":
 import doctest

 doctest.testmod()

