






"""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__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE=2_5_6			)    ->List[Any]:
 UpperCAmelCase__ 						=				min(img.size			)

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

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




def       snake_case__		(   _SCREAMING_SNAKE_CASE			)    ->List[Any]:
 UpperCAmelCase__ 						=				2.0 * x - 1.0
 return x




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE=True       ,	_SCREAMING_SNAKE_CASE="RGB"			)    ->List[Any]:
 UpperCAmelCase__ 						=				x.detach().cpu()
 if process:
  UpperCAmelCase__ 						=				post_process_tensor(_SCREAMING_SNAKE_CASE			)
 UpperCAmelCase__ 						=				x.numpy()
 if process:
  UpperCAmelCase__ 						=				(2_5_5 * x).astype(np.uinta			)
 UpperCAmelCase__ 						=				Image.fromarray(_SCREAMING_SNAKE_CASE			)
 if not x.mode == mode:
  UpperCAmelCase__ 						=				x.convert(_SCREAMING_SNAKE_CASE			)
 return x




def       snake_case__		(   _SCREAMING_SNAKE_CASE			)    ->Optional[Any]:
 UpperCAmelCase__ 						=				torch.clamp(_SCREAMING_SNAKE_CASE       ,	-1.0       ,	1.0			)
 UpperCAmelCase__ 						=				(x + 1.0) / 2.0
 UpperCAmelCase__ 						=				x.permute(1       ,	2       ,	0			)
 return x




def       snake_case__		(   _SCREAMING_SNAKE_CASE			)    ->List[str]:
 UpperCAmelCase__ 						=				post_process_tensor(x.squeeze()			)
 return x.permute(2       ,	0       ,	1			).unsqueeze(0			)











"""simple docstring"""



from string import ascii_uppercase




def       snake_case__		(   _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE = False       ,	_SCREAMING_SNAKE_CASE = ascii_uppercase			)    ->str:
 UpperCAmelCase__ 						=				keyword.upper()
 UpperCAmelCase__ 						=				plaintext.upper()
 UpperCAmelCase__ 						=				set(_SCREAMING_SNAKE_CASE			)

 # create a list of unique characters in the keyword - their order matters
 # it determines how we will map plaintext characters to the ciphertext
 UpperCAmelCase__ 						=				[]
 for char in keyword:
  if char in alphabet_set and char not in unique_chars:
   unique_chars.append(_SCREAMING_SNAKE_CASE			)
    # the number of those unique characters will determine the number of rows
 UpperCAmelCase__ 						=				len(_SCREAMING_SNAKE_CASE			)

 # create a shifted version of the alphabet
 UpperCAmelCase__ 						=				unique_chars + [
     char for char in alphabet if char not in unique_chars
 ]

 # create a modified alphabet by splitting the shifted alphabet into rows
 UpperCAmelCase__ 						=				[
     shifted_alphabet[k : k + num_unique_chars_in_keyword]
     for k in range(0       ,	2_6       ,	_SCREAMING_SNAKE_CASE			)
 ]

 # map the alphabet characters to the modified alphabet characters
 # going 'vertically' through the modified alphabet - consider columns first
 UpperCAmelCase__ 						=				{}
 UpperCAmelCase__ 						=				0
 for column in range(_SCREAMING_SNAKE_CASE			):
  for row in modified_alphabet:
   # if current row (the last one) is too short, break out of loop
   if len(_SCREAMING_SNAKE_CASE			) <= column:
    break

   # map current letter to letter in modified alphabet
   UpperCAmelCase__ 						=				row[column]
   letter_index += 1

 if verbose:
  print(_SCREAMING_SNAKE_CASE			)
 # create the encrypted text by mapping the plaintext to the modified alphabet
 return "".join(mapping[char] if char in mapping else char for char in plaintext			)


if __name__ == "__main__":
 # example use
 print(mixed_keyword('''college''', '''UNIVERSITY'''))



