






"""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_						,       snake_case_=256		):
       """simple docstring"""





       lowercase__:    List[str]        =      min(img.size		)

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

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






def     A(  snake_case_		):
       """simple docstring"""





       lowercase__:    List[str]        =      2.0 * x - 1.0
       return x






def     A(  snake_case_						,       snake_case_=True						,       snake_case_="RGB"		):
       """simple docstring"""





       lowercase__:    Optional[int]        =      x.detach().cpu()
       if process:
              lowercase__:    Optional[Any]        =      post_process_tensor(snake_case_		)
       lowercase__:    Union[str, Any]        =      x.numpy()
       if process:
              lowercase__:    int        =      (255 * x).astype(np.uinta		)
       lowercase__:    Optional[int]        =      Image.fromarray(snake_case_		)
       if not x.mode == mode:
              lowercase__:    Optional[int]        =      x.convert(snake_case_		)
       return x






def     A(  snake_case_		):
       """simple docstring"""





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







def     A(  snake_case_		):
       """simple docstring"""





       lowercase__:    Union[str, Any]        =      post_process_tensor(x.squeeze()		)
       return x.permute(2						,       0						,       1		).unsqueeze(0		)














"""simple docstring"""





from __future__ import annotations






def     A(  snake_case_						,       snake_case_		):
       """simple docstring"""





       lowercase__  ,     lowercase__:    Optional[int]        =      0, len(snake_case_		) - 1
       while start_ind <= end_ind:
              if array[start_ind] == search_item:
                     return start_ind
              elif array[end_ind] == search_item:
                     return end_ind
              else:
                     start_ind += 1
                     end_ind -= 1
    # returns -1 if search_item is not found in array
       return -1


if __name__ == "__main__":
  print(double_linear_search(list(range(1_00)), 40))






