






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        snake_case_				(							lowercase_   ):







   """simple docstring"""



   def __init__(     self:						List[str] ,							snake_case__:						AutoencoderKL ,							snake_case__:						CLIPTextModel ,							snake_case__:						CLIPTokenizer ,							snake_case__:						UNetaDConditionModel ,							snake_case__:						Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler] ,							)	->		Tuple:


       """simple docstring"""



       super().__init__()

       self.register_modules(vae=snake_case__ ,							text_encoder=snake_case__ ,							tokenizer=snake_case__ ,							unet=snake_case__ ,							scheduler=snake_case__						)

   def       SCREAMING_SNAKE_CASE__					(     self:						Any ,							snake_case__:						Union[str, Any]						)	->		Optional[int]:


       """simple docstring"""



       with torch.no_grad():
           _lowercase     			=						self.vae.encode(tfms.ToTensor()(snake_case__						).unsqueeze(0						).to(self.device						) * 2 - 1						)
           _lowercase     			=						0.1_82_15 * latent.latent_dist.sample()
       return latent

   def       SCREAMING_SNAKE_CASE__					(     self:						Union[str, Any] ,							snake_case__:						Optional[int]						)	->		Dict:


       """simple docstring"""



       _lowercase     			=						(1 / 0.1_82_15) * latent
       with torch.no_grad():
           _lowercase     			=						self.vae.decode(snake_case__						).sample
       _lowercase     			=						(img / 2 + 0.5).clamp(0 ,							1						)
       _lowercase     			=						img.detach().cpu().permute(0 ,							2 ,							3 ,							1						).numpy()
       _lowercase     			=						(img * 255).round().astype('uint8'						)
       return Image.fromarray(img[0]						)

   def       SCREAMING_SNAKE_CASE__					(     self:						Tuple ,							snake_case__:						Tuple						)	->		Any:


       """simple docstring"""



       _lowercase     			=						self.tokenizer(
           snake_case__ ,							padding='max_length' ,							max_length=self.tokenizer.model_max_length ,							truncation=snake_case__ ,							return_tensors='pt' ,							)

       _lowercase     			=						self.text_encoder(text_input.input_ids.to(self.device						)						)[0]

       _lowercase     			=						self.tokenizer(
           '' ,							padding='max_length' ,							max_length=self.tokenizer.model_max_length ,							truncation=snake_case__ ,							return_tensors='pt' ,							)

       _lowercase     			=						self.text_encoder(uncond_input.input_ids.to(self.device						)						)[0]

       return torch.cat([uncond_embedding, text_embedding]						)

   def __call__(     self:						Dict ,							snake_case__:						Image.Image ,							snake_case__:						str ,							snake_case__:						float = 0.3 ,							snake_case__:						float = 0.6 ,							snake_case__:						float = 0.5 ,							snake_case__:						int = 42 ,							snake_case__:						int = 50 ,							snake_case__:						float = 7.5 ,							)	->		Image.Image:


       """simple docstring"""



       _lowercase     			=						steps - int(kmin * steps						)
       _lowercase     			=						steps - int(kmax * steps						)

       _lowercase     			=						self.prep_text(snake_case__						)

       self.scheduler.set_timesteps(snake_case__						)

       _lowercase			,     _lowercase     			=						img.size
       _lowercase     			=						self.encode(snake_case__						)

       torch.manual_seed(snake_case__						)
       _lowercase     			=						torch.randn(
           (1, self.unet.config.in_channels, height // 8, width // 8) ,							).to(self.device						)

       _lowercase     			=						self.scheduler.add_noise(
           snake_case__ ,							snake_case__ ,							timesteps=self.scheduler.timesteps[tmax] ,							)

       _lowercase     			=						torch.cat([latents] * 2						)

       _lowercase     			=						self.scheduler.scale_model_input(snake_case__ ,							self.scheduler.timesteps[tmax]						)

       with torch.no_grad():
           _lowercase     			=						self.unet(
               snake_case__ ,							self.scheduler.timesteps[tmax] ,							encoder_hidden_states=snake_case__ ,							).sample

       _lowercase			,     _lowercase     			=						pred.chunk(2						)
       _lowercase     			=						pred_uncond + guidance_scale * (pred_text - pred_uncond)

       _lowercase     			=						self.scheduler.step(snake_case__ ,							self.scheduler.timesteps[tmax] ,							snake_case__						).prev_sample

       for i, t in enumerate(tqdm(self.scheduler.timesteps						)						):
           if i > tmax:
               if i < tmin:  # layout generation phase
                   _lowercase     			=						self.scheduler.add_noise(
                       snake_case__ ,							snake_case__ ,							timesteps=snake_case__ ,							)

                   _lowercase     			=						(mix_factor * latents) + (
                       1 - mix_factor
                   ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
                   _lowercase     			=						torch.cat([input] * 2						)

               else:  # content generation phase
                   _lowercase     			=						torch.cat([latents] * 2						)

               _lowercase     			=						self.scheduler.scale_model_input(snake_case__ ,							snake_case__						)

               with torch.no_grad():
                   _lowercase     			=						self.unet(
                       snake_case__ ,							snake_case__ ,							encoder_hidden_states=snake_case__ ,							).sample

               _lowercase			,     _lowercase     			=						pred.chunk(2						)
               _lowercase     			=						pred_uncond + guidance_scale * (pred_text - pred_uncond)

               _lowercase     			=						self.scheduler.step(snake_case__ ,							snake_case__ ,							snake_case__						).prev_sample

       return self.decode(snake_case__						)








from typing import Literal

lowerCamelCase					    =	'''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''
def 							UpperCAmelCase   ( _lowercase,   _lowercase,   _lowercase   ):


    '''simple docstring'''



    _lowercase     			=						LETTERS if mode == 'decrypt' else key
    _lowercase     			=						key if mode == 'decrypt' else LETTERS
    _lowercase     			=						''
    # loop through each symbol in the message
    for symbol in message:
        if symbol.upper() in chars_a:
            # encrypt/decrypt the symbol
            _lowercase     			=						chars_a.find(symbol.upper()   )
            if symbol.isupper():
                translated += chars_b[sym_index].upper()
            else:
                translated += chars_b[sym_index].lower()
        else:
            # symbol is not in LETTERS, just add it
            translated += symbol
    return translated
def 							UpperCAmelCase   ( _lowercase,   _lowercase   ):


    '''simple docstring'''



    return translate_message(_lowercase,   _lowercase,   'encrypt'   )
def 							UpperCAmelCase   ( _lowercase,   _lowercase   ):


    '''simple docstring'''



    return translate_message(_lowercase,   _lowercase,   'decrypt'   )
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    _lowercase     			=						'Hello World'
    _lowercase     			=						'QWERTYUIOPASDFGHJKLZXCVBNM'
    _lowercase     			=						'decrypt'  # set to 'encrypt' or 'decrypt'

    if mode == "encrypt":
        _lowercase     			=						encrypt_message(_lowercase,   _lowercase   )
    elif mode == "decrypt":
        _lowercase     			=						decrypt_message(_lowercase,   _lowercase   )
    print(f'''Using the key {key}, the {mode}ed message is: {translated}'''   )


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()
