

from __future__ import annotations







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				)							->   list[float | int]:

     """simple docstring"""







     if not all((nth_term, start_term_a, common_ratio_r)					):
          return []
     _lowerCAmelCase :       list[float | int]	   =     []
     _lowerCAmelCase :       List[str]	   =     1
     _lowerCAmelCase :       Optional[int]	   =     common_ratio_r
     for _ in range(int(SCREAMING_SNAKE_CASE					)					):
          if not series:
               series.append(SCREAMING_SNAKE_CASE					)
          else:
               power += 1
               series.append(float(start_term_a * multiple					)					)
               _lowerCAmelCase :       Optional[Any]	   =     pow(float(SCREAMING_SNAKE_CASE					)		,				SCREAMING_SNAKE_CASE					)
     return series


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  __UpperCAmelCase     					=  float(input('Enter the last number (n term) of the Geometric Series'))
  __UpperCAmelCase     					=  float(input('Enter the starting term (a) of the Geometric Series'))
  __UpperCAmelCase     					=  float(
      input('Enter the common ratio between two terms (r) of the Geometric Series')
  )
  print('Formula of Geometric Series => a + ar + ar^2 ... +ar^n')
  print(geometric_series(nth_term, start_term_a, common_ratio_r))









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					)							->   Optional[int]:

     """simple docstring"""







     _lowerCAmelCase :       Any	   =     min(img.size					)

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

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







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   List[str]:

     """simple docstring"""







     _lowerCAmelCase :       List[Any]	   =     2.0 * x - 1.0
     return x







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=True		,				SCREAMING_SNAKE_CASE="RGB"					)							->   int:

     """simple docstring"""







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







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   Union[str, Any]:

     """simple docstring"""







     _lowerCAmelCase :       Any	   =     torch.clamp(SCREAMING_SNAKE_CASE		,				-1.0		,				1.0					)
     _lowerCAmelCase :       Any	   =     (x + 1.0) / 2.0
     _lowerCAmelCase :       List[Any]	   =     x.permute(1		,				2		,				0					)
     return x







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   Tuple:

     """simple docstring"""







     _lowerCAmelCase :       str	   =     post_process_tensor(x.squeeze()					)
     return x.permute(2		,				0		,				1					).unsqueeze(0					)






