
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			:    List[str]				,    __UpperCamelCase			:    int=2_56							)			->							Dict:

				"""simple docstring"""







				A__	:						Optional[Any]								=				min(img.size							)

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

				A__	:						Union[str, Any]								=				target_image_size / s
				A__	:						int								=				(round(r * img.size[1]							), round(r * img.size[0]							))
				A__	:						List[Any]								=				TF.resize(__UpperCamelCase				,    __UpperCamelCase				,    interpolation=PIL.Image.LANCZOS							)
				A__	:						Optional[int]								=				TF.center_crop(__UpperCamelCase				,    output_size=2 * [target_image_size]							)
				A__	:						Tuple								=				torch.unsqueeze(T.ToTensor()(__UpperCamelCase							)				,    0							)
				return img

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    Any							)			->							Any:

				"""simple docstring"""







				A__	:						List[str]								=				2.0 * x - 1.0
				return x

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    List[str]				,    __UpperCamelCase			:    str=True				,    __UpperCamelCase			:    str="RGB"							)			->							Tuple:

				"""simple docstring"""







				A__	:						Union[str, Any]								=				x.detach().cpu()
				if process:
								A__	:						Optional[int]								=				post_process_tensor(__UpperCamelCase							)
				A__	:						Union[str, Any]								=				x.numpy()
				if process:
								A__	:						int								=				(2_55 * x).astype(np.uinta							)
				A__	:						str								=				Image.fromarray(__UpperCamelCase							)
				if not x.mode == mode:
								A__	:						List[str]								=				x.convert(__UpperCamelCase							)
				return x

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    int							)			->							Optional[int]:

				"""simple docstring"""







				A__	:						Optional[int]								=				torch.clamp(__UpperCamelCase				,    -1.0				,    1.0							)
				A__	:						Optional[int]								=				(x + 1.0) / 2.0
				A__	:						Tuple								=				x.permute(1				,    2				,    0							)
				return x



def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    Union[str, Any]							)			->							Union[str, Any]:

				"""simple docstring"""







				A__	:						int								=				post_process_tensor(x.squeeze()							)
				return x.permute(2				,    0				,    1							).unsqueeze(0							)

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    Dict				,    __UpperCamelCase			:    Dict							)			->							List[Any]:

				"""simple docstring"""







				if len(str(__UpperCamelCase							)							) == 1 or len(str(__UpperCamelCase							)							) == 1:
								return a * b

				A__	:						List[str]								=				max(len(str(__UpperCamelCase							)							)				,    len(str(__UpperCamelCase							)							)							)
				A__	:						Any								=				ma // 2

				A__							,				A__	:						Union[str, Any]								=				divmod(__UpperCamelCase				,    10**ma							)
				A__							,				A__	:						Tuple								=				divmod(__UpperCamelCase				,    10**ma							)

				A__	:						Optional[int]								=				karatsuba(__UpperCamelCase				,    __UpperCamelCase							)
				A__	:						Tuple								=				karatsuba((aa + aa)				,    (ba + ba)							)
				A__	:						Optional[int]								=				karatsuba(__UpperCamelCase				,    __UpperCamelCase							)

				return (z * 10 ** (2 * ma)) + ((y - z - x) * 10 ** (ma)) + (x)

def 					SCREAMING_SNAKE_CASE  (   )			->							Dict:

				"""simple docstring"""







				print(karatsuba(1_54_63				,    2_34_89							)							)


if __name__ == "__main__":
					main()