


'''simple docstring'''


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







def       snake_case_       (  SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__=256					):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE			:	Any							=				min(img.size					)

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

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







def       snake_case_       (  SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				2.0 * x - 1.0
				return x







def       snake_case_       (  SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__=True							,				SCREAMING_SNAKE_CASE__="RGB"					):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE			:	Tuple							=				x.detach().cpu()
				if process:
								_SCREAMING_SNAKE_CASE			:	Dict							=				post_process_tensor(SCREAMING_SNAKE_CASE__					)
				_SCREAMING_SNAKE_CASE			:	Any							=				x.numpy()
				if process:
								_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				(255 * x).astype(np.uinta					)
				_SCREAMING_SNAKE_CASE			:	Optional[int]							=				Image.fromarray(SCREAMING_SNAKE_CASE__					)
				if not x.mode == mode:
								_SCREAMING_SNAKE_CASE			:	List[Any]							=				x.convert(SCREAMING_SNAKE_CASE__					)
				return x







def       snake_case_       (  SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				torch.clamp(SCREAMING_SNAKE_CASE__							,				-1.0							,				1.0					)
				_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				(x + 1.0) / 2.0
				_SCREAMING_SNAKE_CASE			:	List[Any]							=				x.permute(1							,				2							,				0					)
				return x







def       snake_case_       (  SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





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








'''simple docstring'''


import sys







def       snake_case_       (  SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





				if not numbers:
								return 0
				if target == 0 and target in numbers:
								return 0
				if not isinstance(SCREAMING_SNAKE_CASE__							,				(list, tuple)					) or not all(
				    isinstance(SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					) for number in numbers					):
								raise ValueError("""numbers must be an iterable of integers"""					)

				_SCREAMING_SNAKE_CASE			:	str							=				0
				_SCREAMING_SNAKE_CASE			:	int							=				sys.maxsize

				while right < len(SCREAMING_SNAKE_CASE__					):
								curr_sum += numbers[right]
								while curr_sum >= target and left <= right:
												_SCREAMING_SNAKE_CASE			:	Any							=				min(SCREAMING_SNAKE_CASE__							,				right - left + 1					)
												curr_sum -= numbers[left]
												left += 1
								right += 1

				return 0 if min_len == sys.maxsize else min_len




