
'''simple docstring'''







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 			lowerCAmelCase (				__UpperCAmelCase			):
		a							:					UNetaDModel
		a							:					ScoreSdeVeScheduler




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase      ):
								super().__init__()
								self.register_modules(unet=UpperCamelCase					,						scheduler=UpperCamelCase      )




		@torch.no_grad()
		def __call__(				self					,						UpperCamelCase = 1					,						UpperCamelCase = 2_000					,						UpperCamelCase = None					,						UpperCamelCase = "pil"					,						UpperCamelCase = True					,						**UpperCamelCase					,						):
								_SCREAMING_SNAKE_CASE				= self.unet.config.sample_size
								_SCREAMING_SNAKE_CASE				= (batch_size, 3, img_size, img_size)

								_SCREAMING_SNAKE_CASE				= self.unet

								_SCREAMING_SNAKE_CASE				= randn_tensor(UpperCamelCase					,						generator=UpperCamelCase      ) * self.scheduler.init_noise_sigma
								_SCREAMING_SNAKE_CASE				= sample.to(self.device      )

								self.scheduler.set_timesteps(UpperCamelCase      )
								self.scheduler.set_sigmas(UpperCamelCase      )

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

														# correction step
														for _ in range(self.scheduler.config.correct_steps      ):
																				_SCREAMING_SNAKE_CASE				= self.unet(UpperCamelCase					,						UpperCamelCase      ).sample
																				_SCREAMING_SNAKE_CASE				= self.scheduler.step_correct(UpperCamelCase					,						UpperCamelCase					,						generator=UpperCamelCase      ).prev_sample

														# prediction step
														_SCREAMING_SNAKE_CASE				= model(UpperCamelCase					,						UpperCamelCase      ).sample
														_SCREAMING_SNAKE_CASE				= self.scheduler.step_pred(UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						generator=UpperCamelCase      )

														_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= output.prev_sample, output.prev_sample_mean

								_SCREAMING_SNAKE_CASE				= sample_mean.clamp(0					,						1      )
								_SCREAMING_SNAKE_CASE				= sample.cpu().permute(0					,						2					,						3					,						1      ).numpy()
								if output_type == "pil":
														_SCREAMING_SNAKE_CASE				= self.numpy_to_pil(UpperCamelCase      )

								if not return_dict:
														return (sample,)

								return ImagePipelineOutput(images=UpperCamelCase      )

'''simple docstring'''







import os
import random
import sys

from . import cryptomath_module as cryptoMath  # noqa: N812
from . import rabin_miller as rabinMiller  # noqa: N812



def 			_a		(	):
						print("Making key files..."  )
						make_key_files("rsa"	,			1024  )
						print("Key files generation successful."  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   int  ):
						print("Generating prime p..."  )
						_SCREAMING_SNAKE_CASE				= rabinMiller.generate_large_prime(_SCREAMING_SNAKE_CASE  )
						print("Generating prime q..."  )
						_SCREAMING_SNAKE_CASE				= rabinMiller.generate_large_prime(_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= p * q

						print("Generating e that is relatively prime to (p - 1) * (q - 1)..."  )
						while True:
												_SCREAMING_SNAKE_CASE				= random.randrange(2 ** (key_size - 1)	,			2 ** (key_size)  )
												if cryptoMath.gcd(_SCREAMING_SNAKE_CASE	,			(p - 1) * (q - 1)  ) == 1:
																		break

						print("Calculating d that is mod inverse of e..."  )
						_SCREAMING_SNAKE_CASE				= cryptoMath.find_mod_inverse(_SCREAMING_SNAKE_CASE	,			(p - 1) * (q - 1)  )

						_SCREAMING_SNAKE_CASE				= (n, e)
						_SCREAMING_SNAKE_CASE				= (n, d)
						return (public_key, private_key)



def 			_a		(	_SCREAMING_SNAKE_CASE		:   str	,			_SCREAMING_SNAKE_CASE		:   int  ):
						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()

						_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= generate_key(_SCREAMING_SNAKE_CASE  )
						print(F'\nWriting public key to file {name}_pubkey.txt...'  )
						with open(F'{name}_pubkey.txt'	,			"w"  ) as out_file:
												out_file.write(F'{key_size},{public_key[0]},{public_key[1]}'  )

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


if __name__ == "__main__":
						main()