
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class   __lowercase						(						__snake_case       ):
	_A       =   42
	_A       =   42






	def __init__(self				: Any      ,	snake_case				: UNetaDModel      ,	snake_case				: ScoreSdeVeScheduler    ) ->			Any:
							super().__init__()
							self.register_modules(unet=snake_case      ,	scheduler=snake_case    )






	@torch.no_grad()
	def __call__(self				: List[Any]      ,	snake_case				: int = 1      ,	snake_case				: int = 2000      ,	snake_case				: Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,	snake_case				: Optional[str] = "pil"      ,	snake_case				: bool = True      ,	**snake_case				: List[Any]      ,	) ->			Union[ImagePipelineOutput, Tuple]:
							_lowercase       :	Optional[Any]           =   self.unet.config.sample_size
							_lowercase       :	Union[str, Any]           =   (batch_size, 3, img_size, img_size)

							_lowercase       :	str           =   self.unet

							_lowercase       :	Optional[int]           =   randn_tensor(snake_case      ,	generator=snake_case    ) * self.scheduler.init_noise_sigma
							_lowercase       :	Optional[int]           =   sample.to(self.device    )

							self.scheduler.set_timesteps(snake_case    )
							self.scheduler.set_sigmas(snake_case    )

							for i, t in enumerate(self.progress_bar(self.scheduler.timesteps    )    ):
													_lowercase       :	int           =   self.scheduler.sigmas[i] * torch.ones(shape[0]      ,	device=self.device    )

													# correction step
													for _ in range(self.scheduler.config.correct_steps    ):
																			_lowercase       :	Union[str, Any]           =   self.unet(snake_case      ,	snake_case    ).sample
																			_lowercase       :	List[Any]           =   self.scheduler.step_correct(snake_case      ,	snake_case      ,	generator=snake_case    ).prev_sample

													# prediction step
													_lowercase       :	str           =   model(snake_case      ,	snake_case    ).sample
													_lowercase       :	Any           =   self.scheduler.step_pred(snake_case      ,	snake_case      ,	snake_case      ,	generator=snake_case    )

													_lowercase      ,    _lowercase       :	Dict           =   output.prev_sample, output.prev_sample_mean

							_lowercase       :	List[str]           =   sample_mean.clamp(0      ,	1    )
							_lowercase       :	Union[str, Any]           =   sample.cpu().permute(0      ,	2      ,	3      ,	1    ).numpy()
							if output_type == "pil":
													_lowercase       :	Tuple           =   self.numpy_to_pil(snake_case    )

							if not return_dict:
													return (sample,)

							return ImagePipelineOutput(images=snake_case    )






import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

UpperCamelCase_						:     List[Any]	       =							3



def 		UpperCamelCase     (       _UpperCAmelCase  :   int  )   ->			int:






						'''simple docstring'''

						print("Generating primitive root of p"  )
						while True:
												_lowercase       :	Any           =   random.randrange(3 ,			_UpperCAmelCase  )
												if pow(_UpperCAmelCase ,			2 ,			_UpperCAmelCase  ) == 1:
																		continue
												if pow(_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase  ) == 1:
																		continue
												return g



def 		UpperCamelCase     (       _UpperCAmelCase  :   int  )   ->			tuple[tuple[int, int, int, int], tuple[int, int]]:






						'''simple docstring'''

						print("Generating prime p..."  )
						_lowercase       :	Optional[int]           =   rabin_miller.generate_large_prime(_UpperCAmelCase  )  # select large prime number.
						_lowercase       :	Dict           =   primitive_root(_UpperCAmelCase  )  # one primitive root on modulo p.
						_lowercase       :	Any           =   random.randrange(3 ,			_UpperCAmelCase  )  # private_key -> have to be greater than 2 for safety.
						_lowercase       :	Tuple           =   cryptomath.find_mod_inverse(pow(_UpperCAmelCase ,			_UpperCAmelCase ,			_UpperCAmelCase  ) ,			_UpperCAmelCase  )

						_lowercase       :	List[Any]           =   (key_size, e_a, e_a, p)
						_lowercase       :	Union[str, Any]           =   (key_size, d)

						return public_key, private_key



def 		UpperCamelCase     (       _UpperCAmelCase  :   str ,			_UpperCAmelCase  :   int  )   ->			None:






						'''simple docstring'''

						if os.path.exists(f"""{name}_pubkey.txt"""  ) or os.path.exists(f"""{name}_privkey.txt"""  ):
												print("\nWARNING:"  )
												print(
												    f"""\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n"""
												    "Use a different name or delete these files and re-run this program."  )
												sys.exit()

						_lowercase      ,    _lowercase       :	Optional[Any]           =   generate_key(_UpperCAmelCase  )
						print(f"""\nWriting public key to file {name}_pubkey.txt..."""  )
						with open(f"""{name}_pubkey.txt""" ,			"w"  ) as fo:
												fo.write(f"""{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}"""  )

						print(f"""Writing private key to file {name}_privkey.txt..."""  )
						with open(f"""{name}_privkey.txt""" ,			"w"  ) as fo:
												fo.write(f"""{private_key[0]},{private_key[1]}"""  )



def 		UpperCamelCase     (       )   ->			None:






						'''simple docstring'''

						print("Making key files..."  )
						make_key_files("elgamal" ,			2048  )
						print("Key files generation successful"  )


if __name__ == "__main__":
							main()




