




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	(       __UpperCamelCase			:						Optional[Any]       ,     __UpperCamelCase			:						Dict=256      )  ->							Union[str, Any]:
    UpperCAmelCase_							 =     min(img.size      )

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

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




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						Optional[int]      )  ->							str:
    UpperCAmelCase_							 =     2.0 * x - 1.0
    return x




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




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						Union[str, Any]      )  ->							List[Any]:
    UpperCAmelCase_							 =     torch.clamp(__UpperCamelCase       ,     -1.0       ,     1.0      )
    UpperCAmelCase_							 =     (x + 1.0) / 2.0
    UpperCAmelCase_							 =     x.permute(1       ,     2       ,     0      )
    return x




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						List[Any]      )  ->							Union[str, Any]:
    UpperCAmelCase_							 =     post_process_tensor(x.squeeze()      )
    return x.permute(2       ,     0       ,     1      ).unsqueeze(0      )












"""simple docstring"""


def SCREAMING_SNAKE_CASE_   (       snake_case   :					str		)->		list:
       _lowerCamelCase    	=					[]
       _lowerCamelCase    	=					1

       for i in range(len(snake_case		)		):
              if i + 1 < len(snake_case		) and text[i] == text[i + 1]:
                     count += 1
              else:
                     encoded.append((text[i], count)		)
                     _lowerCamelCase    	=					1

       return encoded





def SCREAMING_SNAKE_CASE_   (       snake_case   :					list		)->		str:
       return "".join(char * length for char, length in encoded		)


if __name__ == "__main__":
      from doctest import testmod

      testmod(name="""run_length_encode""", verbose=True)
      testmod(name="""run_length_decode""", verbose=True)






