




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      )











from __future__ import annotations




def 					SCREAMING_SNAKE_CASE	(       __UpperCamelCase			:						list[int | float]      )  ->							int | float:
    if len(__UpperCamelCase      ) == 0:
        raise ValueError('''find_max() arg is an empty sequence'''      )
    UpperCAmelCase_							 =     nums[0]
    for x in nums:
        if x > max_num:
            UpperCAmelCase_							 =     x
    return max_num


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)





