

from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)



class 						a_			(				lowerCamelCase_	):



      """simple docstring"""







      def __init__( self	:				Union[str, Any]     ,snake_case	:				AutoencoderKL     ,snake_case	:				CLIPTextModel     ,snake_case	:				CLIPTokenizer     ,snake_case	:				UNetaDConditionModel     ,snake_case	:				DDIMScheduler     ,snake_case	:				float = 0.93     ,snake_case	:				bool = True     ,):
           SCREAMING_SNAKE_CASE 		=mixing_coeff
           SCREAMING_SNAKE_CASE 		=leapfrog_steps

           super().__init__()
           self.register_modules(
               vae=snake_case     ,text_encoder=snake_case     ,tokenizer=snake_case     ,unet=snake_case     ,scheduler=snake_case     ,)

           SCREAMING_SNAKE_CASE 		=2 ** (len(self.vae.config.block_out_channels							) - 1)
           SCREAMING_SNAKE_CASE 		=VaeImageProcessor(vae_scale_factor=self.vae_scale_factor							)
      def 			_lowerCAmelCase	( self	:				List[Any]     ,snake_case	:				str     ,snake_case	:				Optional[str] = None     ,snake_case	:				bool = False							):
           SCREAMING_SNAKE_CASE 		=self.tokenizer(
               snake_case     ,padding='max_length'     ,max_length=self.tokenizer.model_max_length     ,truncation=snake_case     ,return_tensors='pt'     ,)

           SCREAMING_SNAKE_CASE 		=self.text_encoder(text_inputs.input_ids.to(self.device							)							).last_hidden_state

           SCREAMING_SNAKE_CASE 		=prompt_embeds.to(dtype=self.text_encoder.dtype     ,device=self.device							)

           if do_classifier_free_guidance:
                SCREAMING_SNAKE_CASE 		='' if negative_prompt is None else negative_prompt

                SCREAMING_SNAKE_CASE 		=self.tokenizer(
                    snake_case     ,padding='max_length'     ,max_length=self.tokenizer.model_max_length     ,truncation=snake_case     ,return_tensors='pt'     ,)

                SCREAMING_SNAKE_CASE 		=self.text_encoder(uncond_input.input_ids.to(self.device							)							).last_hidden_state

                SCREAMING_SNAKE_CASE 		=torch.cat([negative_prompt_embeds, prompt_embeds]							)

           return prompt_embeds
      def 			_lowerCAmelCase	( self	:				Tuple     ,snake_case	:				torch.Tensor     ,snake_case	:				torch.Tensor							):
           SCREAMING_SNAKE_CASE 		=self.mixing_coeff * x + (1 - self.mixing_coeff) * y
           SCREAMING_SNAKE_CASE 		=self.mixing_coeff * y + (1 - self.mixing_coeff) * x

           return [x, y]
      def 			_lowerCAmelCase	( self	:				Tuple     ,snake_case	:				torch.Tensor     ,snake_case	:				torch.Tensor							):
           SCREAMING_SNAKE_CASE 		=(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
           SCREAMING_SNAKE_CASE 		=(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

           return [x, y]
      def 			_lowerCAmelCase	( self	:				Dict     ,snake_case	:				torch.Tensor							):
           # as self.alphas_cumprod is always in cpu
           SCREAMING_SNAKE_CASE 		=int(snake_case							)

           SCREAMING_SNAKE_CASE 		=self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

           return alpha_prod, 1 - alpha_prod
      def 			_lowerCAmelCase	( self	:				Any     ,snake_case	:				torch.Tensor     ,snake_case	:				torch.Tensor     ,snake_case	:				torch.Tensor     ,snake_case	:				torch.Tensor     ,):
           SCREAMING_SNAKE_CASE 		=timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

           SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE 		=self._get_alpha_and_beta(snake_case							)
           SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE 		=self._get_alpha_and_beta(snake_case							)

           SCREAMING_SNAKE_CASE 		=(alpha_prod_t_prev / alpha_prod_t) ** 0.5
           SCREAMING_SNAKE_CASE 		=-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

           SCREAMING_SNAKE_CASE 		=(base - b_t * model_output) / a_t

           return model_input, next_model_input.to(base.dtype							)
      def 			_lowerCAmelCase	( self	:				int     ,snake_case	:				torch.Tensor     ,snake_case	:				torch.Tensor     ,snake_case	:				torch.Tensor     ,snake_case	:				torch.Tensor     ,):
           SCREAMING_SNAKE_CASE 		=timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

           SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE 		=self._get_alpha_and_beta(snake_case							)
           SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE 		=self._get_alpha_and_beta(snake_case							)

           SCREAMING_SNAKE_CASE 		=(alpha_prod_t_prev / alpha_prod_t) ** 0.5
           SCREAMING_SNAKE_CASE 		=-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
           SCREAMING_SNAKE_CASE 		=a_t * base + b_t * model_output

           return model_input, next_model_input.to(base.dtype							)
      @torch.no_grad()
      def 			_lowerCAmelCase	( self	:				int     ,snake_case	:				torch.Tensor							):
           SCREAMING_SNAKE_CASE 		=1 / self.vae.config.scaling_factor * latents
           SCREAMING_SNAKE_CASE 		=self.vae.decode(snake_case							).sample
           SCREAMING_SNAKE_CASE 		=(image / 2 + 0.5).clamp(0     ,1							)
           return image
      @torch.no_grad()
      def 			_lowerCAmelCase	( self	:				Dict     ,snake_case	:				Image.Image     ,snake_case	:				torch.Tensor     ,snake_case	:				torch.Tensor     ,snake_case	:				float     ,snake_case	:				Optional[torch.Generator] = None     ,):
           SCREAMING_SNAKE_CASE 		=guidance_scale > 1.0

           SCREAMING_SNAKE_CASE 		=image.to(device=self.device     ,dtype=text_embeds.dtype							)
           SCREAMING_SNAKE_CASE 		=self.vae.encode(snake_case							).latent_dist.sample(snake_case							)

           SCREAMING_SNAKE_CASE 		=self.vae.config.scaling_factor * latent

           SCREAMING_SNAKE_CASE 		=[latent.clone(), latent.clone()]

           for i, t in tqdm(enumerate(snake_case							)     ,total=len(snake_case							)							):
                SCREAMING_SNAKE_CASE 		=self.noise_mixing_layer(x=coupled_latents[0]     ,y=coupled_latents[1]							)

                # j - model_input index, k - base index
                for j in range(2							):
                     SCREAMING_SNAKE_CASE 		=j ^ 1

                     if self.leapfrog_steps:
                          if i % 2 == 0:
                               SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE 		=j, k

                     SCREAMING_SNAKE_CASE 		=coupled_latents[j]
                     SCREAMING_SNAKE_CASE 		=coupled_latents[k]

                     SCREAMING_SNAKE_CASE 		=torch.cat([model_input] * 2							) if do_classifier_free_guidance else model_input

                     SCREAMING_SNAKE_CASE 		=self.unet(snake_case     ,snake_case     ,encoder_hidden_states=snake_case							).sample

                     if do_classifier_free_guidance:
                          SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE 		=noise_pred.chunk(2							)
                          SCREAMING_SNAKE_CASE 		=noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                     SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE 		=self.noise_step(
                         base=snake_case     ,model_input=snake_case     ,model_output=snake_case     ,timestep=snake_case     ,)

                     SCREAMING_SNAKE_CASE 		=model_input

           return coupled_latents


      @torch.no_grad()
      def __call__( self	:				str     ,snake_case	:				str     ,snake_case	:				str     ,snake_case	:				Image.Image     ,snake_case	:				float = 3.0     ,snake_case	:				int = 50     ,snake_case	:				float = 0.8     ,snake_case	:				Optional[str] = None     ,snake_case	:				Optional[torch.Generator] = None     ,snake_case	:				Optional[str] = "pil"     ,):
           SCREAMING_SNAKE_CASE 		=guidance_scale > 1.0

           SCREAMING_SNAKE_CASE 		=self.image_processor.preprocess(snake_case							)

           SCREAMING_SNAKE_CASE 		=self._encode_prompt(snake_case     ,snake_case     ,snake_case							)
           SCREAMING_SNAKE_CASE 		=self._encode_prompt(snake_case     ,snake_case     ,snake_case							)

           self.scheduler.set_timesteps(snake_case     ,self.device							)

           SCREAMING_SNAKE_CASE 		=num_inference_steps - int(num_inference_steps * strength							)
           SCREAMING_SNAKE_CASE 		=self.scheduler.timesteps[t_limit:]
           SCREAMING_SNAKE_CASE 		=fwd_timesteps.flip(0							)

           SCREAMING_SNAKE_CASE 		=self.prepare_latents(snake_case     ,snake_case     ,snake_case     ,snake_case     ,snake_case							)

           for i, t in tqdm(enumerate(snake_case							)     ,total=len(snake_case							)							):
                # j - model_input index, k - base index
                for k in range(2							):
                     SCREAMING_SNAKE_CASE 		=k ^ 1

                     if self.leapfrog_steps:
                          if i % 2 == 1:
                               SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE 		=j, k

                     SCREAMING_SNAKE_CASE 		=coupled_latents[j]
                     SCREAMING_SNAKE_CASE 		=coupled_latents[k]

                     SCREAMING_SNAKE_CASE 		=torch.cat([model_input] * 2							) if do_classifier_free_guidance else model_input

                     SCREAMING_SNAKE_CASE 		=self.unet(snake_case     ,snake_case     ,encoder_hidden_states=snake_case							).sample

                     if do_classifier_free_guidance:
                          SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE 		=noise_pred.chunk(2							)
                          SCREAMING_SNAKE_CASE 		=noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                     SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE 		=self.denoise_step(
                         base=snake_case     ,model_input=snake_case     ,model_output=snake_case     ,timestep=snake_case     ,)

                     SCREAMING_SNAKE_CASE 		=model_input

                SCREAMING_SNAKE_CASE 		=self.denoise_mixing_layer(x=coupled_latents[0]     ,y=coupled_latents[1]							)

           # either one is fine
           SCREAMING_SNAKE_CASE 		=coupled_latents[0]

           if output_type not in ["latent", "pt", "np", "pil"]:
                SCREAMING_SNAKE_CASE 		=(
                    f'the output_type {output_type} is outdated. Please make sure to set it to one of these instead: '
                    '`pil`, `np`, `pt`, `latent`'
                )
                deprecate('Unsupported output_type'     ,'1.0.0'     ,snake_case     ,standard_warn=snake_case							)
                SCREAMING_SNAKE_CASE 		='np'

           if output_type == "latent":
                SCREAMING_SNAKE_CASE 		=final_latent
           else:
                SCREAMING_SNAKE_CASE 		=self.decode_latents(snake_case							)
                SCREAMING_SNAKE_CASE 		=self.image_processor.postprocess(snake_case     ,output_type=snake_case							)

           return image




def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_     ):





     """simple docstring"""







     if not isinstance(lowerCAmelCase_,       lowerCAmelCase_     ):
          raise TypeError('plaintext must be a string'     )
     if not isinstance(lowerCAmelCase_,       lowerCAmelCase_     ):
          raise TypeError('key must be a string'     )

     if not plaintext:
          raise ValueError('plaintext is empty'     )
     if not key:
          raise ValueError('key is empty'     )

     key += plaintext
     SCREAMING_SNAKE_CASE 		=plaintext.lower()
     SCREAMING_SNAKE_CASE 		=key.lower()
     SCREAMING_SNAKE_CASE 		=0
     SCREAMING_SNAKE_CASE 		=0
     SCREAMING_SNAKE_CASE 		=''
     while plaintext_iterator < len(lowerCAmelCase_     ):
          if (
              ord(plaintext[plaintext_iterator]     ) < 97
              or ord(plaintext[plaintext_iterator]     ) > 122
          ):
               ciphertext += plaintext[plaintext_iterator]
               plaintext_iterator += 1
          elif ord(key[key_iterator]     ) < 97 or ord(key[key_iterator]     ) > 122:
               key_iterator += 1
          else:
               ciphertext += chr(
                   (
                       (ord(plaintext[plaintext_iterator]     ) - 97 + ord(key[key_iterator]     ))
                       - 97
                   )
                   % 26
                   + 97     )
               key_iterator += 1
               plaintext_iterator += 1
     return ciphertext







def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_     ):





     """simple docstring"""







     if not isinstance(lowerCAmelCase_,       lowerCAmelCase_     ):
          raise TypeError('ciphertext must be a string'     )
     if not isinstance(lowerCAmelCase_,       lowerCAmelCase_     ):
          raise TypeError('key must be a string'     )

     if not ciphertext:
          raise ValueError('ciphertext is empty'     )
     if not key:
          raise ValueError('key is empty'     )

     SCREAMING_SNAKE_CASE 		=key.lower()
     SCREAMING_SNAKE_CASE 		=0
     SCREAMING_SNAKE_CASE 		=0
     SCREAMING_SNAKE_CASE 		=''
     while ciphertext_iterator < len(lowerCAmelCase_     ):
          if (
              ord(ciphertext[ciphertext_iterator]     ) < 97
              or ord(ciphertext[ciphertext_iterator]     ) > 122
          ):
               plaintext += ciphertext[ciphertext_iterator]
          else:
               plaintext += chr(
                   (ord(ciphertext[ciphertext_iterator]     ) - ord(key[key_iterator]     )) % 26
                   + 97     )
               key += chr(
                   (ord(ciphertext[ciphertext_iterator]     ) - ord(key[key_iterator]     )) % 26
                   + 97     )
               key_iterator += 1
          ciphertext_iterator += 1
     return plaintext


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 _lowerCamelCase							=int(input("Type 1 to encrypt or 2 to decrypt:"))
 if operation == 1:
  _lowerCamelCase							=input("Typeplaintext to be encrypted:\n")
  _lowerCamelCase							=input("Type the key:\n")
  print(encrypt(plaintext, key))
 elif operation == 2:
  _lowerCamelCase							=input("Type the ciphertext to be decrypted:\n")
  _lowerCamelCase							=input("Type the key:\n")
  print(decrypt(ciphertext, key))
 decrypt("jsqqs avvwo", "coffee")

