code: 
  """simple docstring"""
  
  
  
  
  
  from typing import Union
  
  import torch
  from PIL import Image
  from torchvision import transforms as tfms
  from tqdm.auto import tqdm
  from transformers import CLIPTextModel, CLIPTokenizer
  
  from diffusers import (
      AutoencoderKL,
      DDIMScheduler,
      DiffusionPipeline,
      LMSDiscreteScheduler,
      PNDMScheduler,
      UNetaDConditionModel,
  )
  
  
  class A_			(lowercase__   ):
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(	self       ,     lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_       ,     ):
  
       """simple docstring"""
       super().__init__()
  
       self.register_modules(vae=lowercase_       ,     text_encoder=lowercase_       ,     tokenizer=lowercase_       ,     unet=lowercase_       ,     scheduler=lowercase_						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_						):
  
       """simple docstring"""
       with torch.no_grad():
          UpperCAmelCase_      :      str      =					self.vae.encode(tfms.ToTensor()(lowercase_						).unsqueeze(0						).to(self.device						) * 2 - 1						)
          UpperCAmelCase_      :      Tuple      =					0.1_82_15 * latent.latent_dist.sample()
       return latent
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Tuple      =					(1 / 0.1_82_15) * latent
       with torch.no_grad():
          UpperCAmelCase_      :      Dict      =					self.vae.decode(lowercase_						).sample
       UpperCAmelCase_      :      int      =					(img / 2 + 0.5).clamp(0       ,     1						)
       UpperCAmelCase_      :      List[str]      =					img.detach().cpu().permute(0       ,     2       ,     3       ,     1						).numpy()
       UpperCAmelCase_      :      int      =					(img * 255).round().astype("uint8"						)
       return Image.fromarray(img[0]						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Union[str, Any]      =					self.tokenizer(
           lowercase_       ,     padding="max_length"       ,     max_length=self.tokenizer.model_max_length       ,     truncation=lowercase_       ,     return_tensors="pt"       ,     )
  
       UpperCAmelCase_      :      Optional[Any]      =					self.text_encoder(text_input.input_ids.to(self.device						)						)[0]
  
       UpperCAmelCase_      :      int      =					self.tokenizer(
           ""       ,     padding="max_length"       ,     max_length=self.tokenizer.model_max_length       ,     truncation=lowercase_       ,     return_tensors="pt"       ,     )
  
       UpperCAmelCase_      :      Tuple      =					self.text_encoder(uncond_input.input_ids.to(self.device						)						)[0]
  
       return torch.cat([uncond_embedding, text_embedding]						)
  
  
  
  
  
  
  
    def __call__(	self       ,     lowercase_       ,     lowercase_       ,     lowercase_ = 0.3       ,     lowercase_ = 0.6       ,     lowercase_ = 0.5       ,     lowercase_ = 42       ,     lowercase_ = 50       ,     lowercase_ = 7.5       ,     ):
  
       """simple docstring"""
       UpperCAmelCase_      :      List[str]      =					steps - int(kmin * steps						)
       UpperCAmelCase_      :      int      =					steps - int(kmax * steps						)
  
       UpperCAmelCase_      :      List[str]      =					self.prep_text(lowercase_						)
  
       self.scheduler.set_timesteps(lowercase_						)
  
       UpperCAmelCase_  ,     UpperCAmelCase_      :      Optional[int]      =					img.size
       UpperCAmelCase_      :      str      =					self.encode(lowercase_						)
  
       torch.manual_seed(lowercase_						)
       UpperCAmelCase_      :      Optional[Any]      =					torch.randn(
           (1, self.unet.config.in_channels, height // 8, width // 8)       ,     ).to(self.device						)
  
       UpperCAmelCase_      :      Any      =					self.scheduler.add_noise(
           lowercase_       ,     lowercase_       ,     timesteps=self.scheduler.timesteps[tmax]       ,     )
  
       UpperCAmelCase_      :      Union[str, Any]      =					torch.cat([latents] * 2						)
  
       UpperCAmelCase_      :      Optional[int]      =					self.scheduler.scale_model_input(lowercase_       ,     self.scheduler.timesteps[tmax]						)
  
       with torch.no_grad():
          UpperCAmelCase_      :      Union[str, Any]      =					self.unet(
              lowercase_       ,     self.scheduler.timesteps[tmax]       ,     encoder_hidden_states=lowercase_       ,     ).sample
  
       UpperCAmelCase_  ,     UpperCAmelCase_      :      Optional[int]      =					pred.chunk(2						)
       UpperCAmelCase_      :      int      =					pred_uncond + guidance_scale * (pred_text - pred_uncond)
  
       UpperCAmelCase_      :      int      =					self.scheduler.step(lowercase_       ,     self.scheduler.timesteps[tmax]       ,     lowercase_						).prev_sample
  
       for i, t in enumerate(tqdm(self.scheduler.timesteps						)						):
          if i > tmax:
             if i < tmin:  # layout generation phase
                UpperCAmelCase_      :      Any      =					self.scheduler.add_noise(
                    lowercase_       ,     lowercase_       ,     timesteps=lowercase_       ,     )
  
                UpperCAmelCase_      :      Union[str, Any]      =					(mix_factor * latents) + (
                    1 - mix_factor
                ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
                UpperCAmelCase_      :      Optional[Any]      =					torch.cat([input] * 2						)
  
             else:  # content generation phase
                UpperCAmelCase_      :      Any      =					torch.cat([latents] * 2						)
  
             UpperCAmelCase_      :      List[Any]      =					self.scheduler.scale_model_input(lowercase_       ,     lowercase_						)
  
             with torch.no_grad():
                UpperCAmelCase_      :      Tuple      =					self.unet(
                    lowercase_       ,     lowercase_       ,     encoder_hidden_states=lowercase_       ,     ).sample
  
             UpperCAmelCase_  ,     UpperCAmelCase_      :      Any      =					pred.chunk(2						)
             UpperCAmelCase_      :      Tuple      =					pred_uncond + guidance_scale * (pred_text - pred_uncond)
  
             UpperCAmelCase_      :      Tuple      =					self.scheduler.step(lowercase_       ,     lowercase_       ,     lowercase_						).prev_sample
  
       return self.decode(lowercase_						)
  
  
  
style_context: 
  """simple docstring"""
  
  
  
  
  
  from string import ascii_uppercase
  
  
  
  
  
  
  
  def 			__a  (	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase = False,	__lowerCamelCase = ascii_uppercase						):
     UpperCAmelCase_      :      List[str]      =					keyword.upper()
     UpperCAmelCase_      :      str      =					plaintext.upper()
     UpperCAmelCase_      :      Tuple      =					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
     UpperCAmelCase_      :      Tuple      =					[]
     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
     UpperCAmelCase_      :      Union[str, Any]      =					len(__lowerCamelCase						)
  
     # create a shifted version of the alphabet
     UpperCAmelCase_      :      Tuple      =					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_      :      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
     UpperCAmelCase_      :      Tuple      =					{}
     UpperCAmelCase_      :      Dict      =					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
           UpperCAmelCase_      :      Tuple      =					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'))
  
  
  