'''simple docstring'''

import os
import sys

from . import rsa_key_generator as rkg

lowercase_  		=			128
lowercase_  		=			256



def 				UpperCamelCase__      (      a__      ,   a__ = DEFAULT_BLOCK_SIZE   ):


						'''simple docstring'''






						_lowerCAmelCase					    =message.encode('ascii'   )

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



def 				UpperCamelCase__      (      a__      ,   a__      ,   a__ = DEFAULT_BLOCK_SIZE   ):


						'''simple docstring'''






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



def 				UpperCamelCase__      (      a__      ,   a__      ,   a__ = DEFAULT_BLOCK_SIZE   ):


						'''simple docstring'''






						_lowerCAmelCase					    =[]
						_lowerCAmelCase    ,					_lowerCAmelCase					    =key

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



def 				UpperCamelCase__      (      a__      ,   a__      ,   a__      ,   a__ = DEFAULT_BLOCK_SIZE      ,   ):


						'''simple docstring'''






						_lowerCAmelCase					    =[]
						_lowerCAmelCase    ,					_lowerCAmelCase					    =key
						for block in encrypted_blocks:
												decrypted_blocks.append(pow(a__      ,   a__      ,   a__   )   )
						return get_text_from_blocks(a__      ,   a__      ,   a__   )



def 				UpperCamelCase__      (      a__   ):


						'''simple docstring'''






						with open(a__   ) as fo:
												_lowerCAmelCase					    =fo.read()
						_lowerCAmelCase    ,					_lowerCAmelCase    ,					_lowerCAmelCase					    =content.split(','   )
						return (int(a__   ), int(a__   ), int(a__   ))



def 				UpperCamelCase__      (      a__      ,   a__      ,   a__      ,   a__ = DEFAULT_BLOCK_SIZE      ,   ):


						'''simple docstring'''






						_lowerCAmelCase    ,					_lowerCAmelCase    ,					_lowerCAmelCase					    =read_key_file(a__   )
						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      ,   a__   )   )

						_lowerCAmelCase					    =[str(a__   ) for i in encrypt_message(a__      ,   (n, e)      ,   a__   )]

						_lowerCAmelCase					    =','.join(a__   )
						_lowerCAmelCase					    =F'''{len(a__   )}_{block_size}_{encrypted_content}'''
						with open(a__      ,   'w'   ) as fo:
												fo.write(a__   )
						return encrypted_content



def 				UpperCamelCase__      (      a__      ,   a__   ):


						'''simple docstring'''






						_lowerCAmelCase    ,					_lowerCAmelCase    ,					_lowerCAmelCase					    =read_key_file(a__   )
						with open(a__   ) as fo:
												_lowerCAmelCase					    =fo.read()
						_lowerCAmelCase    ,					_lowerCAmelCase    ,					_lowerCAmelCase					    =content.split('_'   )
						_lowerCAmelCase					    =int(a__   )
						_lowerCAmelCase					    =int(a__   )

						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      ,   a__   )   )

						_lowerCAmelCase					    =[]
						for block in encrypted_message.split(','   ):
												encrypted_blocks.append(int(a__   )   )

						return decrypt_message(a__      ,   a__      ,   (n, d)      ,   a__   )



def 				UpperCamelCase__      (      ):


						'''simple docstring'''






						_lowerCAmelCase					    ='encrypted_file.txt'
						_lowerCAmelCase					    =input(r'Encrypt\Decrypt [e\d]: '   )

						if response.lower().startswith('e'   ):
												_lowerCAmelCase					    ='encrypt'
						elif response.lower().startswith('d'   ):
												_lowerCAmelCase					    ='decrypt'

						if mode == "encrypt":
												if not os.path.exists('rsa_pubkey.txt'   ):
																		rkg.make_key_files('rsa'      ,   1_0_2_4   )

												_lowerCAmelCase					    =input('\nEnter message: '   )
												_lowerCAmelCase					    ='rsa_pubkey.txt'
												print(F'''Encrypting and writing to {filename}...'''   )
												_lowerCAmelCase					    =encrypt_and_write_to_file(a__      ,   a__      ,   a__   )

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

						elif mode == "decrypt":
												_lowerCAmelCase					    ='rsa_privkey.txt'
												print(F'''Reading from {filename} and decrypting...'''   )
												_lowerCAmelCase					    =read_from_file_and_decrypt(a__      ,   a__   )
												print('writing decryption to rsa_decryption.txt...'   )
												with open('rsa_decryption.txt'      ,   'w'   ) as dec:
																		dec.write(a__   )

												print('\nDecryption:'   )
												print(a__   )


if __name__ == "__main__":
							main()






'''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 	SCREAMING_SNAKE_CASE      (    __lowercase):







		"""simple docstring"""






		def __init__( self		,    __A		,    __A		,    __A		,    __A		,    __A		,    )			->  Any:
								super().__init__()

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






		def 		UpperCamelCase__							( self		,    __A					)			->  List[Any]:
								with torch.no_grad():
														_lowerCAmelCase					    =self.vae.encode(tfms.ToTensor()(__A					).unsqueeze(0					).to(self.device					) * 2 - 1					)
														_lowerCAmelCase					    =0.18_215 * latent.latent_dist.sample()
								return latent






		def 		UpperCamelCase__							( self		,    __A					)			->  Dict:
								_lowerCAmelCase					    =(1 / 0.18_215) * latent
								with torch.no_grad():
														_lowerCAmelCase					    =self.vae.decode(__A					).sample
								_lowerCAmelCase					    =(img / 2 + 0.5).clamp(0		,    1					)
								_lowerCAmelCase					    =img.detach().cpu().permute(0		,    2		,    3		,    1					).numpy()
								_lowerCAmelCase					    =(img * 255).round().astype('uint8'					)
								return Image.fromarray(img[0]					)






		def 		UpperCamelCase__							( self		,    __A					)			->  str:
								_lowerCAmelCase					    =self.tokenizer(
								    __A		,    padding='max_length'		,    max_length=self.tokenizer.model_max_length		,    truncation=__A		,    return_tensors='pt'		,    )

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

								_lowerCAmelCase					    =self.tokenizer(
								    ''		,    padding='max_length'		,    max_length=self.tokenizer.model_max_length		,    truncation=__A		,    return_tensors='pt'		,    )

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

								return torch.cat([uncond_embedding, text_embedding]					)






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

								_lowerCAmelCase					    =self.prep_text(__A					)

								self.scheduler.set_timesteps(__A					)

								_lowerCAmelCase    ,					_lowerCAmelCase					    =img.size
								_lowerCAmelCase					    =self.encode(__A					)

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

								_lowerCAmelCase					    =self.scheduler.add_noise(
								    __A		,    __A		,    timesteps=self.scheduler.timesteps[tmax]		,    )

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

								_lowerCAmelCase					    =self.scheduler.scale_model_input(__A		,    self.scheduler.timesteps[tmax]					)

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

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

								_lowerCAmelCase					    =self.scheduler.step(__A		,    self.scheduler.timesteps[tmax]		,    __A					).prev_sample

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

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

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

																				_lowerCAmelCase					    =self.scheduler.scale_model_input(__A		,    __A					)

																				with torch.no_grad():
																										_lowerCAmelCase					    =self.unet(
																										    __A		,    __A		,    encoder_hidden_states=__A		,    ).sample

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

																				_lowerCAmelCase					    =self.scheduler.step(__A		,    __A		,    __A					).prev_sample

								return self.decode(__A					)





