
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__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_=256	)							->						Optional[int]:
			lowerCAmelCase__ : Any   =						min(img.size	)

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

			lowerCAmelCase__ : Dict   =						target_image_size / s
			lowerCAmelCase__ : Union[str, Any]   =						(round(r * img.size[1]	), round(r * img.size[0]	))
			lowerCAmelCase__ : Union[str, Any]   =						TF.resize(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				interpolation=PIL.Image.LANCZOS	)
			lowerCAmelCase__ : int   =						TF.center_crop(SCREAMING_SNAKE_CASE_			,				output_size=2 * [target_image_size]	)
			lowerCAmelCase__ : str   =						torch.unsqueeze(T.ToTensor()(SCREAMING_SNAKE_CASE_	)			,				0	)
			return img
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_	)							->						List[str]:
			lowerCAmelCase__ : Tuple   =						2.0 * x - 1.0
			return x
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_=True			,				SCREAMING_SNAKE_CASE_="RGB"	)							->						List[Any]:
			lowerCAmelCase__ : Dict   =						x.detach().cpu()
			if process:
						lowerCAmelCase__ : int   =						post_process_tensor(SCREAMING_SNAKE_CASE_	)
			lowerCAmelCase__ : List[str]   =						x.numpy()
			if process:
						lowerCAmelCase__ : Optional[int]   =						(255 * x).astype(np.uinta	)
			lowerCAmelCase__ : Optional[Any]   =						Image.fromarray(SCREAMING_SNAKE_CASE_	)
			if not x.mode == mode:
						lowerCAmelCase__ : Union[str, Any]   =						x.convert(SCREAMING_SNAKE_CASE_	)
			return x
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_	)							->						Tuple:
			lowerCAmelCase__ : Any   =						torch.clamp(SCREAMING_SNAKE_CASE_			,				-1.0			,				1.0	)
			lowerCAmelCase__ : str   =						(x + 1.0) / 2.0
			lowerCAmelCase__ : str   =						x.permute(1			,				2			,				0	)
			return x







def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_	)							->						Optional[int]:
			lowerCAmelCase__ : Dict   =						post_process_tensor(x.squeeze()	)
			return x.permute(2			,				0			,				1	).unsqueeze(0	)

import math
from decimal import Decimal, getcontext
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_ = 4_000_000	)							->						int:

			try:
						lowerCAmelCase__ : str   =						int(SCREAMING_SNAKE_CASE_	)
			except (TypeError, ValueError):
						raise TypeError('Parameter n must be int or castable to int.'	)
			if n <= 0:
						raise ValueError('Parameter n must be greater than or equal to one.'	)
			lowerCAmelCase__ : Union[str, Any]   =						100
			lowerCAmelCase__ : List[Any]   =						(Decimal(5	) ** Decimal(0.5	) + 1) / Decimal(2	)

			lowerCAmelCase__ : Tuple   =						(math.floor(math.log(n * (phi + 2)			,				SCREAMING_SNAKE_CASE_	) - 1	) // 3) * 3 + 2
			lowerCAmelCase__ : Optional[int]   =						Decimal(round(phi ** Decimal(index + 1	)	)	) / (phi + 2)
			lowerCAmelCase__ : Tuple   =						num // 2
			return int(SCREAMING_SNAKE_CASE_	)


if __name__ == "__main__":
					print(F"""{solution() = }""")