


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 							A					( _SCREAMING_SNAKE_CASE							):
					'''simple docstring'''






					def __init__(self					:		List[str]   ,      _UpperCAmelCase					:		Optional[Any]   ,      _UpperCAmelCase					:		List[Any]   ,      _UpperCAmelCase					:		Optional[Any]   ,      _UpperCAmelCase					:		Union[str, Any]   ,      _UpperCAmelCase					:		Optional[Any]   ,      )		->     List[str]:




							"""simple docstring"""


							super().__init__()

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

					def 						lowerCamelCase__  (self					:		Optional[int]   ,      _UpperCAmelCase					:		int	)		->     str:




							"""simple docstring"""


							with torch.no_grad():
									lowercase__      =							self.vae.encode(tfms.ToTensor()(__A	).unsqueeze(0	).to(self.device	) * 2 - 1	)
									lowercase__      =							0.18_215 * latent.latent_dist.sample()
							return latent

					def 						lowerCamelCase__  (self					:		int   ,      _UpperCAmelCase					:		Tuple	)		->     Union[str, Any]:




							"""simple docstring"""


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

					def 						lowerCamelCase__  (self					:		str   ,      _UpperCAmelCase					:		Dict	)		->     Dict:




							"""simple docstring"""


							lowercase__      =							self.tokenizer(
							    __A   ,      padding="""max_length"""   ,      max_length=self.tokenizer.model_max_length   ,      truncation=__A   ,      return_tensors="""pt"""   ,      )

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

							lowercase__      =							self.tokenizer(
							    """"""   ,      padding="""max_length"""   ,      max_length=self.tokenizer.model_max_length   ,      truncation=__A   ,      return_tensors="""pt"""   ,      )

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

							return torch.cat([uncond_embedding, text_embedding]	)


					def __call__(self					:		Any   ,      _UpperCAmelCase					:		Dict   ,      _UpperCAmelCase					:		Tuple   ,      _UpperCAmelCase					:		Dict = 0.3   ,      _UpperCAmelCase					:		List[Any] = 0.6   ,      _UpperCAmelCase					:		List[str] = 0.5   ,      _UpperCAmelCase					:		Dict = 42   ,      _UpperCAmelCase					:		Any = 50   ,      _UpperCAmelCase					:		Any = 7.5   ,      )		->     Image.Image:




							"""simple docstring"""


							lowercase__      =							steps - int(kmin * steps	)
							lowercase__      =							steps - int(kmax * steps	)

							lowercase__      =							self.prep_text(__A	)

							self.scheduler.set_timesteps(__A	)

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

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

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

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

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

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

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

							lowercase__      =							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
													lowercase__      =							self.scheduler.add_noise(
													    __A   ,      __A   ,      timesteps=__A   ,      )

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

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

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

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

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

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

							return self.decode(__A	)





import os
import sys

from . import rsa_key_generator as rkg

A :	List[str]												=     1_2_8
A :	Optional[Any]												=     2_5_6






def 				UpperCamelCase      (			__magic_name__  :							str					,					__magic_name__  :							int = DEFAULT_BLOCK_SIZE  )     ->						list[int]:
		"""simple docstring"""




		lowercase__      =	message.encode("""ascii"""  )

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






def 				UpperCamelCase      (			__magic_name__  :							list[int]					,					__magic_name__  :							int					,					__magic_name__  :							int = DEFAULT_BLOCK_SIZE  )     ->						str:
		"""simple docstring"""




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






def 				UpperCamelCase      (			__magic_name__  :							str					,					__magic_name__  :							tuple[int, int]					,					__magic_name__  :							int = DEFAULT_BLOCK_SIZE  )     ->						list[int]:
		"""simple docstring"""




		lowercase__      =	[]
		lowercase__    ,			lowercase__      =	key

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






def 				UpperCamelCase      (			__magic_name__  :							list[int]					,					__magic_name__  :							int					,					__magic_name__  :							tuple[int, int]					,					__magic_name__  :							int = DEFAULT_BLOCK_SIZE					,					)     ->						str:
		"""simple docstring"""




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






def 				UpperCamelCase      (			__magic_name__  :							str  )     ->						tuple[int, int, int]:
		"""simple docstring"""




		with open(__magic_name__  ) as fo:
				lowercase__      =	fo.read()
		lowercase__    ,			lowercase__    ,			lowercase__      =	content.split(""","""  )
		return (int(__magic_name__  ), int(__magic_name__  ), int(__magic_name__  ))






def 				UpperCamelCase      (			__magic_name__  :							str					,					__magic_name__  :							str					,					__magic_name__  :							str					,					__magic_name__  :							int = DEFAULT_BLOCK_SIZE					,					)     ->						str:
		"""simple docstring"""




		lowercase__    ,			lowercase__    ,			lowercase__      =	read_key_file(__magic_name__  )
		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					,					__magic_name__  )  )

		lowercase__      =	[str(__magic_name__  ) for i in encrypt_message(__magic_name__					,					(n, e)					,					__magic_name__  )]

		lowercase__      =	""",""".join(__magic_name__  )
		lowercase__      =	f'''{len(__magic_name__  )}_{block_size}_{encrypted_content}'''
		with open(__magic_name__					,					"""w"""  ) as fo:
				fo.write(__magic_name__  )
		return encrypted_content






def 				UpperCamelCase      (			__magic_name__  :							str					,					__magic_name__  :							str  )     ->						str:
		"""simple docstring"""




		lowercase__    ,			lowercase__    ,			lowercase__      =	read_key_file(__magic_name__  )
		with open(__magic_name__  ) as fo:
				lowercase__      =	fo.read()
		lowercase__    ,			lowercase__    ,			lowercase__      =	content.split("""_"""  )
		lowercase__      =	int(__magic_name__  )
		lowercase__      =	int(__magic_name__  )

		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					,					__magic_name__  )  )

		lowercase__      =	[]
		for block in encrypted_message.split(""","""  ):
				encrypted_blocks.append(int(__magic_name__  )  )

		return decrypt_message(__magic_name__					,					__magic_name__					,					(n, d)					,					__magic_name__  )






def 				UpperCamelCase      (			)     ->						None:
		"""simple docstring"""




		lowercase__      =	"""encrypted_file.txt"""
		lowercase__      =	input(R"""Encrypt\Decrypt [e\d]: """  )

		if response.lower().startswith("""e"""  ):
				lowercase__      =	"""encrypt"""
		elif response.lower().startswith("""d"""  ):
				lowercase__      =	"""decrypt"""

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

				lowercase__      =	input("""\nEnter message: """  )
				lowercase__      =	"""rsa_pubkey.txt"""
				print(f'''Encrypting and writing to {filename}...'''  )
				lowercase__      =	encrypt_and_write_to_file(__magic_name__					,					__magic_name__					,					__magic_name__  )

				print("""\nEncrypted text:"""  )
				print(__magic_name__  )

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

				print("""\nDecryption:"""  )
				print(__magic_name__  )


if __name__ == "__main__":
	main()

