




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





def 							lowerCamelCase    (							UpperCAmelCase_	:      Dict   ,       UpperCAmelCase_	:      Tuple=256      )->			List[str]:

       """simple docstring"""




       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(UpperCAmelCase_   ,       UpperCAmelCase_   ,       interpolation=PIL.Image.LANCZOS      )
       a			   =TF.center_crop(UpperCAmelCase_   ,       output_size=2 * [target_image_size]      )
       a			   =torch.unsqueeze(T.ToTensor()(UpperCAmelCase_      )   ,       0      )
       return img





def 							lowerCamelCase    (							UpperCAmelCase_	:      Optional[Any]      )->			List[Any]:

       """simple docstring"""




       a			   =2.0 * x - 1.0
       return x





def 							lowerCamelCase    (							UpperCAmelCase_	:      int   ,       UpperCAmelCase_	:      List[Any]=True   ,       UpperCAmelCase_	:      Optional[int]="RGB"      )->			Optional[Any]:

       """simple docstring"""




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





def 							lowerCamelCase    (							UpperCAmelCase_	:      int      )->			str:

       """simple docstring"""




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






def 							lowerCamelCase    (							UpperCAmelCase_	:      Optional[int]      )->			Tuple:

       """simple docstring"""




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






import math
from timeit import timeit





def 							lowerCamelCase    (							UpperCAmelCase_	:      int      )->			int:

       """simple docstring"""




       a			   =0
       a			   =abs(UpperCAmelCase_      )
       while True:
              a			   =n // 10
              digits += 1
              if n == 0:
                     break
       return digits





def 							lowerCamelCase    (							UpperCAmelCase_	:      int      )->			int:

       """simple docstring"""




       return 1 if n == 0 else math.floor(math.log(abs(UpperCAmelCase_      )   ,       10      ) + 1      )





def 							lowerCamelCase    (							UpperCAmelCase_	:      int      )->			int:

       """simple docstring"""




       return len(str(abs(UpperCAmelCase_      )      )      )





def 							lowerCamelCase    (							)->			None:

       """simple docstring"""




       from collections.abc import Callable

       def benchmark_a_function(UpperCAmelCase_	:      Callable   ,       UpperCAmelCase_	:      int      ) -> None:
              a			   =F'''{func.__name__}({value})'''
              a			   =timeit(F'''__main__.{call}'''   ,       setup="""import __main__"""      )
              print(F'''{call}: {func(UpperCAmelCase_      )} -- {timing} seconds'''      )

       for value in (26_2144, 1125_8999_0684_2624, 126_7650_6002_2822_9401_4967_0320_5376):
              for func in (num_digits, num_digits_fast, num_digits_faster):
                     benchmark_a_function(UpperCAmelCase_   ,       UpperCAmelCase_      )
              print()


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    benchmark()
