



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

def 	a						(				_UpperCAmelCase							: Any			,       _UpperCAmelCase							: Tuple=2_56						):







		'''simple docstring'''





		__UpperCAmelCase   :					Union[str, Any]													=     min(img.size						)

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

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

def 	a						(				_UpperCAmelCase							: List[Any]						):







		'''simple docstring'''





		__UpperCAmelCase   :					str													=     2.0 * x - 1.0
		return x

def 	a						(				_UpperCAmelCase							: List[Any]			,       _UpperCAmelCase							: Any=True			,       _UpperCAmelCase							: int="RGB"						):







		'''simple docstring'''





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

def 	a						(				_UpperCAmelCase							: Tuple						):







		'''simple docstring'''





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

def 	a						(				_UpperCAmelCase							: Any						):







		'''simple docstring'''





		__UpperCAmelCase   :					List[str]													=     post_process_tensor(x.squeeze()						)
		return x.permute(2			,       0			,       1						).unsqueeze(0						)








def 	a						(				_UpperCAmelCase							: str						):







		'''simple docstring'''





		__UpperCAmelCase   :					Optional[Any]													=     53_81
		for x in s:
				__UpperCAmelCase   :					str													=     ((hash_value << 5) + hash_value) + ord(_UpperCAmelCase						)
		return hash_value & 0xFFFF_FFFF





