




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				(					lowercase_   ,       lowercase_=2_56 )    ->		Optional[int]:

    """simple docstring"""


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




def    __SCREAMING_SNAKE_CASE				(					lowercase_ )    ->		Dict:

    """simple docstring"""


    __UpperCamelCase											=  2.0 * x - 1.0
    return x




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_=True   ,       lowercase_="RGB" )    ->		List[Any]:

    """simple docstring"""


    __UpperCamelCase											=  x.detach().cpu()
    if process:
        __UpperCamelCase											=  post_process_tensor(lowercase_ )
    __UpperCamelCase											=  x.numpy()
    if process:
        __UpperCamelCase											=  (2_55 * x).astype(np.uinta )
    __UpperCamelCase											=  Image.fromarray(lowercase_ )
    if not x.mode == mode:
        __UpperCamelCase											=  x.convert(lowercase_ )
    return x




def    __SCREAMING_SNAKE_CASE				(					lowercase_ )    ->		Union[str, Any]:

    """simple docstring"""


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




def    __SCREAMING_SNAKE_CASE				(					lowercase_ )    ->		Tuple:

    """simple docstring"""


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












def    __SCREAMING_SNAKE_CASE				(					lowercase_ )    ->		int:

    """simple docstring"""


    if not isinstance(lowercase_   ,       lowercase_ ):
        raise TypeError('''recursive_lucas_number accepts only integer arguments.''' )
    if n_th_number == 0:
        return 2
    if n_th_number == 1:
        return 1

    return recursive_lucas_number(n_th_number - 1 ) + recursive_lucas_number(
        n_th_number - 2 )




def    __SCREAMING_SNAKE_CASE				(					lowercase_ )    ->		int:

    """simple docstring"""


    if not isinstance(lowercase_   ,       lowercase_ ):
        raise TypeError('''dynamic_lucas_number accepts only integer arguments.''' )
    __UpperCamelCase						, __UpperCamelCase											=  2, 1
    for _ in range(lowercase_ ):
        __UpperCamelCase						, __UpperCamelCase											=  b, a + b
    return a


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

 testmod()
 a_										=       int(input("Enter the number of terms in lucas series:\n").strip())
 print("Using recursive function to calculate lucas series:")
 print(" ".join(str(recursive_lucas_number(i)) for i in range(n)))
 print("\nUsing dynamic function to calculate lucas series:")
 print(" ".join(str(dynamic_lucas_number(i)) for i in range(n)))






