"""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      UpperCamelCase__(      __lowerCAmelCase			):





						def __init__(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,)       ->					Tuple:
													super().__init__()

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





						def 					snake_case__						(							self		,__UpperCAmelCase  )       ->					int:





													with torch.no_grad():
																				A__						  =				self.vae.encode(tfms.ToTensor()(__UpperCAmelCase  ).unsqueeze(0  ).to(self.device  ) * 2 - 1  )
																				A__						  =				0.1_8_2_1_5 * latent.latent_dist.sample()
													return latent





						def 					snake_case__						(							self		,__UpperCAmelCase  )       ->					List[str]:
													A__						  =				(1 / 0.1_8_2_1_5) * latent
													with torch.no_grad():
																				A__						  =				self.vae.decode(__UpperCAmelCase  ).sample
													A__						  =				(img / 2 + 0.5).clamp(0		,1  )
													A__						  =				img.detach().cpu().permute(0		,2		,3		,1  ).numpy()
													A__						  =				(img * 2_55).round().astype('uint8'  )
													return Image.fromarray(img[0]  )





						def 					snake_case__						(							self		,__UpperCAmelCase  )       ->					Tuple:
													A__						  =				self.tokenizer(
													    __UpperCAmelCase		,padding='max_length'		,max_length=self.tokenizer.model_max_length		,truncation=__UpperCAmelCase		,return_tensors='pt'		,)

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

													A__						  =				self.tokenizer(
													    ''		,padding='max_length'		,max_length=self.tokenizer.model_max_length		,truncation=__UpperCAmelCase		,return_tensors='pt'		,)

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

													return torch.cat([uncond_embedding, text_embedding]  )





						def __call__(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase = 0.3		,__UpperCAmelCase = 0.6		,__UpperCAmelCase = 0.5		,__UpperCAmelCase = 42		,__UpperCAmelCase = 50		,__UpperCAmelCase = 7.5		,)       ->					Image.Image:
													A__						  =				steps - int(kmin * steps  )
													A__						  =				steps - int(kmax * steps  )

													A__						  =				self.prep_text(__UpperCAmelCase  )

													self.scheduler.set_timesteps(__UpperCAmelCase  )

													A__						  =				img.size
													A__						  =				self.encode(__UpperCAmelCase  )

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

													A__						  =				self.scheduler.add_noise(
													    __UpperCAmelCase		,__UpperCAmelCase		,timesteps=self.scheduler.timesteps[tmax]		,)

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

													A__						  =				self.scheduler.scale_model_input(__UpperCAmelCase		,self.scheduler.timesteps[tmax]  )

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

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

													A__						  =				self.scheduler.step(__UpperCAmelCase		,self.scheduler.timesteps[tmax]		,__UpperCAmelCase  ).prev_sample

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

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

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

																											A__						  =				self.scheduler.scale_model_input(__UpperCAmelCase		,__UpperCAmelCase  )

																											with torch.no_grad():
																																		A__						  =				self.unet(
																																		    __UpperCAmelCase		,__UpperCAmelCase		,encoder_hidden_states=__UpperCAmelCase		,).sample

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

																											A__						  =				self.scheduler.step(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  ).prev_sample

													return self.decode(__UpperCAmelCase  )






"""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()


