


"""simple docstring"""






from string import ascii_uppercase







def     lowerCamelCase_(						_lowerCamelCase       ,						_lowerCamelCase       ,						_lowerCamelCase = False       ,						_lowerCamelCase = ascii_uppercase    )  ->			str:
   '''simple docstring'''





   _lowerCamelCase					:							List[Any]				=			keyword.upper()
   _lowerCamelCase					:							str				=			plaintext.upper()
   _lowerCamelCase					:							List[Any]				=			set(lowerCAmelCase_    )

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

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

   # create a modified alphabet by splitting the shifted alphabet into rows
   _lowerCamelCase					:							List[str]				=			[
       shifted_alphabet[k : k + num_unique_chars_in_keyword]
       for k in range(0       ,						26       ,						lowerCAmelCase_    )
   ]

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

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

   if verbose:
      print(lowerCAmelCase_    )
   # 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'''))





# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class  __lowerCamelCase       (    UpperCamelCase__    ):




    """simple docstring"""

    def __init__(       self     :     Union[str, Any]   ,	SCREAMING_SNAKE_CASE__     :     Union[str, Any]   ,	SCREAMING_SNAKE_CASE__     :     Any   )      ->       Union[str, Any]:
         super().__init__()
         self.register_modules(unet=SCREAMING_SNAKE_CASE__   ,	scheduler=SCREAMING_SNAKE_CASE__   )


    @torch.no_grad()
    def __call__(       self     :     Dict   ,	SCREAMING_SNAKE_CASE__     :     int = 1   ,	SCREAMING_SNAKE_CASE__     :     Optional[torch.Generator] = None   ,	SCREAMING_SNAKE_CASE__     :     int = 50   ,	SCREAMING_SNAKE_CASE__     :     Optional[str] = "pil"   ,	SCREAMING_SNAKE_CASE__     :     bool = True   ,	**SCREAMING_SNAKE_CASE__     :     Union[str, Any]   ,	)      ->       Union[ImagePipelineOutput, Tuple]:
         lowerCAmelCase__		= torch.randn(
             (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)   ,	generator=SCREAMING_SNAKE_CASE__   ,	)
         lowerCAmelCase__		= image.to(self.device   )

         # set step values
         self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )

         for t in self.progress_bar(self.scheduler.timesteps   ):
              # 1. predict noise model_output
              lowerCAmelCase__		= self.unet(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   ).sample

              # 2. predict previous mean of image x_t-1 and add variance depending on eta
              # eta corresponds to η in paper and should be between [0, 1]
              # do x_t -> x_t-1
              lowerCAmelCase__		= self.scheduler.step(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   ).prev_sample

         lowerCAmelCase__		= (image / 2 + 0.5).clamp(0   ,	1   )
         lowerCAmelCase__		= image.cpu().permute(0   ,	2   ,	3   ,	1   ).numpy()
         if output_type == "pil":
              lowerCAmelCase__		= self.numpy_to_pil(SCREAMING_SNAKE_CASE__   )

         if not return_dict:
              return (image,), "This is a local test"

         return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__   ), "This is a local test"
