






"""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        A	(							__snake_case:   Tuple   ,    __snake_case:   Tuple=2_5_6     )						->		Any:

							"""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(__snake_case   ,    __snake_case   ,    interpolation=PIL.Image.LANCZOS     )
							__magic_name__										=						TF.center_crop(__snake_case   ,    output_size=2 * [target_image_size]     )
							__magic_name__										=						torch.unsqueeze(T.ToTensor()(__snake_case     )   ,    0     )
							return img


def        A	(							__snake_case:   int     )						->		Any:

							"""simple docstring"""





							__magic_name__										=						2.0 * x - 1.0
							return x


def        A	(							__snake_case:   Any   ,    __snake_case:   List[str]=True   ,    __snake_case:   Union[str, Any]="RGB"     )						->		int:

							"""simple docstring"""





							__magic_name__										=						x.detach().cpu()
							if process:
														__magic_name__										=						post_process_tensor(__snake_case     )
							__magic_name__										=						x.numpy()
							if process:
														__magic_name__										=						(2_5_5 * x).astype(np.uinta     )
							__magic_name__										=						Image.fromarray(__snake_case     )
							if not x.mode == mode:
														__magic_name__										=						x.convert(__snake_case     )
							return x


def        A	(							__snake_case:   Optional[Any]     )						->		Optional[int]:

							"""simple docstring"""





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






def        A	(							__snake_case:   Union[str, Any]     )						->		Union[str, Any]:

							"""simple docstring"""





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







"""simple docstring"""
def        A	(							__snake_case:   Dict   ,    __snake_case:   Tuple     )						->		int:

       """simple docstring"""





       sequence.append(__snake_case     )

       __magic_name__										=						0
       while sequence[index] != target:
              index += 1

       sequence.pop()

       if index == len(__snake_case     ):
              return None

       return index


if __name__ == "__main__":
 snake_case   :   Dict    		=		input("""Enter numbers separated by comma:\n""").strip()
 snake_case   :   List[str]    		=		[int(item) for item in user_input.split(""",""")]

 snake_case   :   Union[str, Any]    		=		input("""Enter a single number to be found in the list:\n""")
 snake_case   :   str    		=		int(target_input)
 snake_case   :   Any    		=		sentinel_linear_search(sequence, target)
 if result is not None:
  print(f"""{target} found at positions: {result}""")
 else:
  print("""Not found""")