
"""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_						)




"""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'))


