

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_(_UpperCamelCase				, _UpperCamelCase=256  )							->			Union[str, Any]:



  """simple docstring"""






  _snake_case		=							min(img.size  )

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

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



def    snake_case_(_UpperCamelCase  )							->			str:



  """simple docstring"""






  _snake_case		=							2.0 * x - 1.0
  return x



def    snake_case_(_UpperCamelCase				, _UpperCamelCase=True				, _UpperCamelCase="RGB"  )							->			Dict:



  """simple docstring"""






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



def    snake_case_(_UpperCamelCase  )							->			Any:



  """simple docstring"""






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



def    snake_case_(_UpperCamelCase  )							->			List[Any]:



  """simple docstring"""






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




from __future__ import annotations



def    snake_case_(_UpperCamelCase  )							->			int | float:



  """simple docstring"""






  if len(_UpperCamelCase  ) == 0:
    raise ValueError('''find_max() arg is an empty sequence'''  )
  _snake_case		=							nums[0]
  for x in nums:
    if x > max_num:
      _snake_case		=							x
  return max_num


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)

