"""simple docstring"""





import os
import sys

from . import rsa_key_generator as rkg

_UpperCamelCase       :						Dict     =   1_28
_UpperCamelCase       :						List[Any]     =   2_56





def     a_					(				_lowerCAmelCase  :			str       ,			_lowerCAmelCase  :			int = DEFAULT_BLOCK_SIZE					):





       '''simple docstring'''

       lowercase__      :		Tuple          =     message.encode('ascii'					)

       lowercase__      :		Optional[Any]          =     []
       for block_start in range(0       ,			len(_lowerCAmelCase					)       ,			_lowerCAmelCase					):
              lowercase__      :		Dict          =     0
              for i in range(_lowerCAmelCase       ,			min(block_start + block_size       ,			len(_lowerCAmelCase					)					)					):
                     block_int += message_bytes[i] * (BYTE_SIZE ** (i % block_size))
              block_ints.append(_lowerCAmelCase					)
       return block_ints





def     a_					(				_lowerCAmelCase  :			list[int]       ,			_lowerCAmelCase  :			int       ,			_lowerCAmelCase  :			int = DEFAULT_BLOCK_SIZE					):





       '''simple docstring'''

       lowercase__      :		list[str]          =     []
       for block_int in block_ints:
              lowercase__      :		list[str]          =     []
              for i in range(block_size - 1       ,			-1       ,			-1					):
                     if len(_lowerCAmelCase					) + i < message_length:
                            lowercase__      :		Any          =     block_int // (BYTE_SIZE**i)
                            lowercase__      :		List[str]          =     block_int % (BYTE_SIZE**i)
                            block_message.insert(0       ,			chr(_lowerCAmelCase					)					)
              message.extend(_lowerCAmelCase					)
       return "".join(_lowerCAmelCase					)





def     a_					(				_lowerCAmelCase  :			str       ,			_lowerCAmelCase  :			tuple[int, int]       ,			_lowerCAmelCase  :			int = DEFAULT_BLOCK_SIZE					):





       '''simple docstring'''

       lowercase__      :		Optional[Any]          =     []
       lowercase__   ,				lowercase__      :		List[Any]          =     key

       for block in get_blocks_from_text(_lowerCAmelCase       ,			_lowerCAmelCase					):
              encrypted_blocks.append(pow(_lowerCAmelCase       ,			_lowerCAmelCase       ,			_lowerCAmelCase					)					)
       return encrypted_blocks





def     a_					(				_lowerCAmelCase  :			list[int]       ,			_lowerCAmelCase  :			int       ,			_lowerCAmelCase  :			tuple[int, int]       ,			_lowerCAmelCase  :			int = DEFAULT_BLOCK_SIZE       ,			):





       '''simple docstring'''

       lowercase__      :		Optional[int]          =     []
       lowercase__   ,				lowercase__      :		str          =     key
       for block in encrypted_blocks:
              decrypted_blocks.append(pow(_lowerCAmelCase       ,			_lowerCAmelCase       ,			_lowerCAmelCase					)					)
       return get_text_from_blocks(_lowerCAmelCase       ,			_lowerCAmelCase       ,			_lowerCAmelCase					)





def     a_					(				_lowerCAmelCase  :			str					):





       '''simple docstring'''

       with open(_lowerCAmelCase					) as fo:
              lowercase__      :		List[Any]          =     fo.read()
       lowercase__   ,				lowercase__   ,				lowercase__      :		str          =     content.split(','					)
       return (int(_lowerCAmelCase					), int(_lowerCAmelCase					), int(_lowerCAmelCase					))





def     a_					(				_lowerCAmelCase  :			str       ,			_lowerCAmelCase  :			str       ,			_lowerCAmelCase  :			str       ,			_lowerCAmelCase  :			int = DEFAULT_BLOCK_SIZE       ,			):





       '''simple docstring'''

       lowercase__   ,				lowercase__   ,				lowercase__      :		List[str]          =     read_key_file(_lowerCAmelCase					)
       if key_size < block_size * 8:
              sys.exit(
                  'ERROR: Block size is {} bits and key size is {} bits. The RSA cipher '
                  'requires the block size to be equal to or greater than the key size. '
                  'Either decrease the block size or use different keys.'.format(
                      block_size * 8       ,			_lowerCAmelCase					)					)

       lowercase__      :		Optional[int]          =     [str(_lowerCAmelCase					) for i in encrypt_message(_lowerCAmelCase       ,			(n, e)       ,			_lowerCAmelCase					)]

       lowercase__      :		Any          =     ','.join(_lowerCAmelCase					)
       lowercase__      :		List[str]          =     f"""{len(_lowerCAmelCase					)}_{block_size}_{encrypted_content}"""
       with open(_lowerCAmelCase       ,			'w'					) as fo:
              fo.write(_lowerCAmelCase					)
       return encrypted_content





def     a_					(				_lowerCAmelCase  :			str       ,			_lowerCAmelCase  :			str					):





       '''simple docstring'''

       lowercase__   ,				lowercase__   ,				lowercase__      :		int          =     read_key_file(_lowerCAmelCase					)
       with open(_lowerCAmelCase					) as fo:
              lowercase__      :		Dict          =     fo.read()
       lowercase__   ,				lowercase__   ,				lowercase__      :		int          =     content.split('_'					)
       lowercase__      :		List[str]          =     int(_lowerCAmelCase					)
       lowercase__      :		Union[str, Any]          =     int(_lowerCAmelCase					)

       if key_size < block_size * 8:
              sys.exit(
                  'ERROR: Block size is {} bits and key size is {} bits. The RSA cipher '
                  'requires the block size to be equal to or greater than the key size. '
                  'Did you specify the correct key file and encrypted file?'.format(
                      block_size * 8       ,			_lowerCAmelCase					)					)

       lowercase__      :		Union[str, Any]          =     []
       for block in encrypted_message.split(','					):
              encrypted_blocks.append(int(_lowerCAmelCase					)					)

       return decrypt_message(_lowerCAmelCase       ,			_lowerCAmelCase       ,			(n, d)       ,			_lowerCAmelCase					)





def     a_					(				):





       '''simple docstring'''

       lowercase__      :		Optional[int]          =     'encrypted_file.txt'
       lowercase__      :		Dict          =     input(R'Encrypt\Decrypt [e\d]: '					)

       if response.lower().startswith('e'					):
              lowercase__      :		List[str]          =     'encrypt'
       elif response.lower().startswith('d'					):
              lowercase__      :		Any          =     'decrypt'

       if mode == "encrypt":
              if not os.path.exists('rsa_pubkey.txt'					):
                     rkg.make_key_files('rsa'       ,			1024					)

              lowercase__      :		Dict          =     input('\nEnter message: '					)
              lowercase__      :		Optional[int]          =     'rsa_pubkey.txt'
              print(f"""Encrypting and writing to {filename}..."""					)
              lowercase__      :		Union[str, Any]          =     encrypt_and_write_to_file(_lowerCAmelCase       ,			_lowerCAmelCase       ,			_lowerCAmelCase					)

              print('\nEncrypted text:'					)
              print(_lowerCAmelCase					)

       elif mode == "decrypt":
              lowercase__      :		Tuple          =     'rsa_privkey.txt'
              print(f"""Reading from {filename} and decrypting..."""					)
              lowercase__      :		Union[str, Any]          =     read_from_file_and_decrypt(_lowerCAmelCase       ,			_lowerCAmelCase					)
              print('writing decryption to rsa_decryption.txt...'					)
              with open('rsa_decryption.txt'       ,			'w'					) as dec:
                     dec.write(_lowerCAmelCase					)

              print('\nDecryption:'					)
              print(_lowerCAmelCase					)


if __name__ == "__main__":
       main()


"""simple docstring"""





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 					UpperCAmelCase_     (   _a):


      def __init__(       self		,      a		,      a		,      a		,      a		,      a		,      a = 0.93		,      a = True		,      )	-> Any:
             lowercase__      :		Union[str, Any]          =     mixing_coeff
             lowercase__      :		Any          =     leapfrog_steps

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

             lowercase__      :		List[str]          =     2 ** (len(self.vae.config.block_out_channels							) - 1)
             lowercase__      :		Union[str, Any]          =     VaeImageProcessor(vae_scale_factor=self.vae_scale_factor							)


      def 		_UpperCAmelCase	(       self		,      a		,      a = None		,      a = False							)	-> Optional[int]:
             lowercase__      :		str          =     self.tokenizer(
                 a		,      padding='max_length'		,      max_length=self.tokenizer.model_max_length		,      truncation=a		,      return_tensors='pt'		,      )

             lowercase__      :		str          =     self.text_encoder(text_inputs.input_ids.to(self.device							)							).last_hidden_state

             lowercase__      :		Any          =     prompt_embeds.to(dtype=self.text_encoder.dtype		,      device=self.device							)

             if do_classifier_free_guidance:
                    lowercase__      :		Tuple          =     '' if negative_prompt is None else negative_prompt

                    lowercase__      :		int          =     self.tokenizer(
                        a		,      padding='max_length'		,      max_length=self.tokenizer.model_max_length		,      truncation=a		,      return_tensors='pt'		,      )

                    lowercase__      :		str          =     self.text_encoder(uncond_input.input_ids.to(self.device							)							).last_hidden_state

                    lowercase__      :		str          =     torch.cat([negative_prompt_embeds, prompt_embeds]							)

             return prompt_embeds


      def 		_UpperCAmelCase	(       self		,      a		,      a							)	-> Optional[Any]:
             lowercase__      :		int          =     self.mixing_coeff * x + (1 - self.mixing_coeff) * y
             lowercase__      :		Tuple          =     self.mixing_coeff * y + (1 - self.mixing_coeff) * x

             return [x, y]


      def 		_UpperCAmelCase	(       self		,      a		,      a							)	-> List[str]:
             lowercase__      :		str          =     (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
             lowercase__      :		Union[str, Any]          =     (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

             return [x, y]


      def 		_UpperCAmelCase	(       self		,      a							)	-> List[str]:
             # as self.alphas_cumprod is always in cpu
             lowercase__      :		Any          =     int(a							)

             lowercase__      :		Any          =     self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

             return alpha_prod, 1 - alpha_prod


      def 		_UpperCAmelCase	(       self		,      a		,      a		,      a		,      a		,      )	-> int:
             lowercase__      :		List[Any]          =     timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

             lowercase__   ,				lowercase__      :		Optional[Any]          =     self._get_alpha_and_beta(a							)
             lowercase__   ,				lowercase__      :		str          =     self._get_alpha_and_beta(a							)

             lowercase__      :		Tuple          =     (alpha_prod_t_prev / alpha_prod_t) ** 0.5
             lowercase__      :		Optional[int]          =     -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

             lowercase__      :		int          =     (base - b_t * model_output) / a_t

             return model_input, next_model_input.to(base.dtype							)


      def 		_UpperCAmelCase	(       self		,      a		,      a		,      a		,      a		,      )	-> Tuple:
             lowercase__      :		Optional[int]          =     timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

             lowercase__   ,				lowercase__      :		Optional[int]          =     self._get_alpha_and_beta(a							)
             lowercase__   ,				lowercase__      :		List[Any]          =     self._get_alpha_and_beta(a							)

             lowercase__      :		Union[str, Any]          =     (alpha_prod_t_prev / alpha_prod_t) ** 0.5
             lowercase__      :		Optional[Any]          =     -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
             lowercase__      :		List[str]          =     a_t * base + b_t * model_output

             return model_input, next_model_input.to(base.dtype							)


      @torch.no_grad()
      def 		_UpperCAmelCase	(       self		,      a							)	-> Optional[Any]:
             lowercase__      :		Union[str, Any]          =     1 / self.vae.config.scaling_factor * latents
             lowercase__      :		Union[str, Any]          =     self.vae.decode(a							).sample
             lowercase__      :		int          =     (image / 2 + 0.5).clamp(0		,      1							)
             return image


      @torch.no_grad()
      def 		_UpperCAmelCase	(       self		,      a		,      a		,      a		,      a		,      a = None		,      )	-> Dict:
             lowercase__      :		List[str]          =     guidance_scale > 1.0

             lowercase__      :		Union[str, Any]          =     image.to(device=self.device		,      dtype=text_embeds.dtype							)
             lowercase__      :		Tuple          =     self.vae.encode(a							).latent_dist.sample(a							)

             lowercase__      :		Optional[Any]          =     self.vae.config.scaling_factor * latent

             lowercase__      :		Tuple          =     [latent.clone(), latent.clone()]

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

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

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

                           lowercase__      :		Union[str, Any]          =     coupled_latents[j]
                           lowercase__      :		Tuple          =     coupled_latents[k]

                           lowercase__      :		Dict          =     torch.cat([model_input] * 2							) if do_classifier_free_guidance else model_input

                           lowercase__      :		Union[str, Any]          =     self.unet(a		,      a		,      encoder_hidden_states=a							).sample

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

                           lowercase__   ,				lowercase__      :		Optional[Any]          =     self.noise_step(
                               base=a		,      model_input=a		,      model_output=a		,      timestep=a		,      )

                           lowercase__      :		str          =     model_input

             return coupled_latents


      @torch.no_grad()
      def __call__(       self		,      a		,      a		,      a		,      a = 3.0		,      a = 5_0		,      a = 0.8		,      a = None		,      a = None		,      a = "pil"		,      )	-> Dict:
             lowercase__      :		List[Any]          =     guidance_scale > 1.0

             lowercase__      :		List[str]          =     self.image_processor.preprocess(a							)

             lowercase__      :		Union[str, Any]          =     self._encode_prompt(a		,      a		,      a							)
             lowercase__      :		int          =     self._encode_prompt(a		,      a		,      a							)

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

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

             lowercase__      :		int          =     self.prepare_latents(a		,      a		,      a		,      a		,      a							)

             for i, t in tqdm(enumerate(a							)		,      total=len(a							)							):
                    # j - model_input index, k - base index
                    for k in range(2							):
                           lowercase__      :		Optional[Any]          =     k ^ 1

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

                           lowercase__      :		Optional[int]          =     coupled_latents[j]
                           lowercase__      :		int          =     coupled_latents[k]

                           lowercase__      :		List[Any]          =     torch.cat([model_input] * 2							) if do_classifier_free_guidance else model_input

                           lowercase__      :		List[str]          =     self.unet(a		,      a		,      encoder_hidden_states=a							).sample

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

                           lowercase__   ,				lowercase__      :		List[str]          =     self.denoise_step(
                               base=a		,      model_input=a		,      model_output=a		,      timestep=a		,      )

                           lowercase__      :		List[str]          =     model_input

                    lowercase__      :		Union[str, Any]          =     self.denoise_mixing_layer(x=coupled_latents[0]		,      y=coupled_latents[1]							)

             # either one is fine
             lowercase__      :		Optional[Any]          =     coupled_latents[0]

             if output_type not in ["latent", "pt", "np", "pil"]:
                    lowercase__      :		Any          =     (
                        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'		,      a		,      standard_warn=a							)
                    lowercase__      :		str          =     'np'

             if output_type == "latent":
                    lowercase__      :		List[str]          =     final_latent
             else:
                    lowercase__      :		Optional[int]          =     self.decode_latents(a							)
                    lowercase__      :		Optional[int]          =     self.image_processor.postprocess(a		,      output_type=a							)

             return image

