"""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   __snake_case			(	__lowerCAmelCase							):



      def __init__(		self       ,     lowercase       ,     lowercase       ,     lowercase       ,     lowercase       ,     lowercase       ,     )      ->     Tuple:





        '''simple docstring'''





        super().__init__()

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



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     int:





        '''simple docstring'''





        with torch.no_grad():
          a__:	List[str]     				=      self.vae.encode(tfms.ToTensor()(lowercase).unsqueeze(0).to(self.device) * 2 - 1)
          a__:	Optional[Any]     				=      0.18215 * latent.latent_dist.sample()
        return latent



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     List[str]:





        '''simple docstring'''





        a__:	Optional[Any]     				=      (1 / 0.18215) * latent
        with torch.no_grad():
          a__:	Optional[int]     				=      self.vae.decode(lowercase).sample
        a__:	Dict     				=      (img / 2 + 0.5).clamp(0       ,     1)
        a__:	List[Any]     				=      img.detach().cpu().permute(0       ,     2       ,     3       ,     1).numpy()
        a__:	Tuple     				=      (img * 2_55).round().astype('uint8')
        return Image.fromarray(img[0])



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     Tuple:





        '''simple docstring'''





        a__:	Any     				=      self.tokenizer(
            lowercase       ,     padding='max_length'       ,     max_length=self.tokenizer.model_max_length       ,     truncation=lowercase       ,     return_tensors='pt'       ,     )

        a__:	Optional[Any]     				=      self.text_encoder(text_input.input_ids.to(self.device))[0]

        a__:	str     				=      self.tokenizer(
            ''       ,     padding='max_length'       ,     max_length=self.tokenizer.model_max_length       ,     truncation=lowercase       ,     return_tensors='pt'       ,     )

        a__:	Optional[int]     				=      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       ,     )      ->     Image.Image:





        '''simple docstring'''





        a__:	Optional[Any]     				=      steps - int(kmin * steps)
        a__:	Optional[int]     				=      steps - int(kmax * steps)

        a__:	Optional[int]     				=      self.prep_text(lowercase)

        self.scheduler.set_timesteps(lowercase)

        a__				,       a__:	Union[str, Any]     				=      img.size
        a__:	str     				=      self.encode(lowercase)

        torch.manual_seed(lowercase)
        a__:	Optional[int]     				=      torch.randn(
            (1, self.unet.config.in_channels, height // 8, width // 8)       ,     ).to(self.device)

        a__:	Tuple     				=      self.scheduler.add_noise(
            lowercase       ,     lowercase       ,     timesteps=self.scheduler.timesteps[tmax]       ,     )

        a__:	List[Any]     				=      torch.cat([latents] * 2)

        a__:	List[str]     				=      self.scheduler.scale_model_input(lowercase       ,     self.scheduler.timesteps[tmax])

        with torch.no_grad():
          a__:	Optional[Any]     				=      self.unet(
              lowercase       ,     self.scheduler.timesteps[tmax]       ,     encoder_hidden_states=lowercase       ,     ).sample

        a__				,       a__:	List[str]     				=      pred.chunk(2)
        a__:	Union[str, Any]     				=      pred_uncond + guidance_scale * (pred_text - pred_uncond)

        a__:	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
              a__:	List[Any]     				=      self.scheduler.add_noise(
                  lowercase       ,     lowercase       ,     timesteps=lowercase       ,     )

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

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

            a__:	Union[str, Any]     				=      self.scheduler.scale_model_input(lowercase       ,     lowercase)

            with torch.no_grad():
              a__:	Union[str, Any]     				=      self.unet(
                  lowercase       ,     lowercase       ,     encoder_hidden_states=lowercase       ,     ).sample

            a__				,       a__:	Union[str, Any]     				=      pred.chunk(2)
            a__:	Optional[Any]     				=      pred_uncond + guidance_scale * (pred_text - pred_uncond)

            a__:	List[str]     				=      self.scheduler.step(lowercase       ,     lowercase       ,     lowercase).prev_sample

        return self.decode(lowercase)



"""simple docstring"""
import random
import sys

from . import cryptomath_module as cryptomath

lowercase__   			=      (
    r' !"#$%&\'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`'
    r'abcdefghijklmnopqrstuvwxyz{|}~'
)







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->None:
  if mode == "encrypt":
    if key_a == 1:
      sys.exit(
          'The affine cipher becomes weak when key '
          'A is set to 1. Choose different key'  )
    if key_b == 0:
      sys.exit(
          'The affine cipher becomes weak when key '
          'B is set to 0. Choose different key'  )
  if key_a < 0 or key_b < 0 or key_b > len(_SCREAMING_SNAKE_CASE  ) - 1:
    sys.exit(
        'Key A must be greater than 0 and key B must '
        F'be between 0 and {len(_SCREAMING_SNAKE_CASE  ) - 1}.'  )
  if cryptomath.gcd(_SCREAMING_SNAKE_CASE						,	len(_SCREAMING_SNAKE_CASE  )  ) != 1:
    sys.exit(
        F'Key A {key_a} and the symbol set size {len(_SCREAMING_SNAKE_CASE  )} '
        'are not relatively prime. Choose a different key.'  )







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->str:
  a__				,       a__:	Optional[Any]     				=      divmod(_SCREAMING_SNAKE_CASE						,	len(_SCREAMING_SNAKE_CASE  )  )
  check_keys(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	'encrypt'  )
  a__:	Dict     				=      ''
  for symbol in message:
    if symbol in SYMBOLS:
      a__:	int     				=      SYMBOLS.find(_SCREAMING_SNAKE_CASE  )
      cipher_text += SYMBOLS[(sym_index * key_a + key_b) % len(_SCREAMING_SNAKE_CASE  )]
    else:
      cipher_text += symbol
  return cipher_text







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->str:
  a__				,       a__:	List[str]     				=      divmod(_SCREAMING_SNAKE_CASE						,	len(_SCREAMING_SNAKE_CASE  )  )
  check_keys(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	'decrypt'  )
  a__:	List[Any]     				=      ''
  a__:	str     				=      cryptomath.find_mod_inverse(_SCREAMING_SNAKE_CASE						,	len(_SCREAMING_SNAKE_CASE  )  )
  for symbol in message:
    if symbol in SYMBOLS:
      a__:	List[str]     				=      SYMBOLS.find(_SCREAMING_SNAKE_CASE  )
      plain_text += SYMBOLS[
          (sym_index - key_b) * mod_inverse_of_key_a % len(_SCREAMING_SNAKE_CASE  )
      ]
    else:
      plain_text += symbol
  return plain_text







def 		__a  (      )      ->int:
  while True:
    a__:	int     				=      random.randint(2						,	len(_SCREAMING_SNAKE_CASE  )  )
    a__:	List[Any]     				=      random.randint(2						,	len(_SCREAMING_SNAKE_CASE  )  )
    if cryptomath.gcd(_SCREAMING_SNAKE_CASE						,	len(_SCREAMING_SNAKE_CASE  )  ) == 1 and key_b % len(_SCREAMING_SNAKE_CASE  ) != 0:
      return key_b * len(_SCREAMING_SNAKE_CASE  ) + key_b







def 		__a  (      )      ->None:
  a__:	Union[str, Any]     				=      input('Enter message: '  ).strip()
  a__:	List[Any]     				=      int(input('Enter key [2000 - 9000]: '  ).strip()  )
  a__:	List[Any]     				=      input('Encrypt/Decrypt [E/D]: '  ).strip().lower()

  if mode.startswith('e'  ):
    a__:	int     				=      'encrypt'
    a__:	List[str]     				=      encrypt_message(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )
  elif mode.startswith('d'  ):
    a__:	Dict     				=      'decrypt'
    a__:	Any     				=      decrypt_message(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )
  print(F'\n{mode.title()}ed text: \n{translated}'  )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 # main()


