


'''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       (       lowerCamelCase_ :				int	,  lowerCamelCase_ :				int=256						):
				'''simple docstring'''




				__magic_name__	  = min(img.size						)

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

				__magic_name__	  = target_image_size / s
				__magic_name__	  = (round(r * img.size[1]						), round(r * img.size[0]						))
				__magic_name__	  = TF.resize(_UpperCAmelCase	,  _UpperCAmelCase	,  interpolation=PIL.Image.LANCZOS						)
				__magic_name__	  = TF.center_crop(_UpperCAmelCase	,  output_size=2 * [target_image_size]						)
				__magic_name__	  = torch.unsqueeze(T.ToTensor()(_UpperCAmelCase						)	,  0						)
				return img




def      __snake_case       (       lowerCamelCase_ :				Optional[int]						):
				'''simple docstring'''




				__magic_name__	  = 2.0 * x - 1.0
				return x




def      __snake_case       (       lowerCamelCase_ :				Union[str, Any]	,  lowerCamelCase_ :				Dict=True	,  lowerCamelCase_ :				Dict="RGB"						):
				'''simple docstring'''




				__magic_name__	  = x.detach().cpu()
				if process:
								__magic_name__	  = post_process_tensor(_UpperCAmelCase						)
				__magic_name__	  = x.numpy()
				if process:
								__magic_name__	  = (255 * x).astype(np.uinta						)
				__magic_name__	  = Image.fromarray(_UpperCAmelCase						)
				if not x.mode == mode:
								__magic_name__	  = x.convert(_UpperCAmelCase						)
				return x




def      __snake_case       (       lowerCamelCase_ :				str						):
				'''simple docstring'''




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




def      __snake_case       (       lowerCamelCase_ :				Tuple						):
				'''simple docstring'''




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







'''simple docstring'''
from __future__ import annotations




def      __snake_case       (       lowerCamelCase_ :				list[list[int]]	,  lowerCamelCase_ :				list[list[int]]						):
				'''simple docstring'''




				assert len(lowerCamelCase_						) == len(lowerCamelCase_						)

				__magic_name__	  = len(lowerCamelCase_						)
				__magic_name__	  = list(range(lowerCamelCase_						)						)
				__magic_name__	  = [-1] * n  # who the donor has donated to
				__magic_name__	  = [-1] * n  # who the recipient has received from
				__magic_name__	  = [0] * n

				while unmatched_donors:
								__magic_name__	  = unmatched_donors[0]
								__magic_name__	  = donor_pref[donor]
								__magic_name__	  = donor_preference[num_donations[donor]]
								num_donations[donor] += 1
								__magic_name__	  = recipient_pref[recipient]
								__magic_name__	  = rec_record[recipient]

								if prev_donor != -1:
												if rec_preference.index(lowerCamelCase_						) > rec_preference.index(lowerCamelCase_						):
																__magic_name__	  = donor
																__magic_name__	  = recipient
																unmatched_donors.append(lowerCamelCase_						)
																unmatched_donors.remove(lowerCamelCase_						)
								else:
												__magic_name__	  = donor
												__magic_name__	  = recipient
												unmatched_donors.remove(lowerCamelCase_						)
				return donor_record



