



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




def       UpperCAmelCase__	(							__snake_case  ,			__snake_case=256							)				->	Dict:
  _A          =      min(img.size							)

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

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




def       UpperCAmelCase__	(							__snake_case							)				->	Any:
  _A          =      2.0 * x - 1.0
  return x




def       UpperCAmelCase__	(							__snake_case  ,			__snake_case=True  ,			__snake_case="RGB"							)				->	int:
  _A          =      x.detach().cpu()
  if process:
    _A          =      post_process_tensor(__snake_case							)
  _A          =      x.numpy()
  if process:
    _A          =      (255 * x).astype(np.uinta							)
  _A          =      Image.fromarray(__snake_case							)
  if not x.mode == mode:
    _A          =      x.convert(__snake_case							)
  return x




def       UpperCAmelCase__	(							__snake_case							)				->	int:
  _A          =      torch.clamp(__snake_case  ,			-1.0  ,			1.0							)
  _A          =      (x + 1.0) / 2.0
  _A          =      x.permute(1  ,			2  ,			0							)
  return x




def       UpperCAmelCase__	(							__snake_case							)				->	int:
  _A          =      post_process_tensor(x.squeeze()							)
  return x.permute(2  ,			0  ,			1							).unsqueeze(0							)




import math
from timeit import timeit




def       UpperCAmelCase__	(							__snake_case							)				->	int:
  _A          =      0
  _A          =      abs(__snake_case							)
  while True:
    _A          =      n // 10
    digits += 1
    if n == 0:
      break
  return digits




def       UpperCAmelCase__	(							__snake_case							)				->	int:
  return 1 if n == 0 else math.floor(math.log(abs(__snake_case							)  ,			10							) + 1							)




def       UpperCAmelCase__	(							__snake_case							)				->	int:
  return len(str(abs(__snake_case							)							)							)




def       UpperCAmelCase__	(							)				->	None:
  from collections.abc import Callable

  def benchmark_a_function(__snake_case  ,			__snake_case							) -> None:
    _A          =      F'''{func.__name__}({value})'''
    _A          =      timeit(F'''__main__.{call}'''  ,			setup='''import __main__'''							)
    print(F'''{call}: {func(__snake_case							)} -- {timing} seconds'''							)

  for value in (262_144, 1_125_899_906_842_624, 1_267_650_600_228_229_401_496_703_205_376):
    for func in (num_digits, num_digits_fast, num_digits_faster):
      benchmark_a_function(__snake_case  ,			__snake_case							)
    print()


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    benchmark()