






"""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 							snake_case (  ):
			print("Making key files..."		)
			make_key_files("rsa"      ,10_24		)
			print("Key files generation successful."		)

def 							snake_case (  A__		):
			print("Generating prime p..."		)
			UpperCAmelCase_	:					int     	=   rabinMiller.generate_large_prime(A__		)
			print("Generating prime q..."		)
			UpperCAmelCase_	:					Dict     	=   rabinMiller.generate_large_prime(A__		)
			UpperCAmelCase_	:					Union[str, Any]     	=   p * q

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

			print("Calculating d that is mod inverse of e..."		)
			UpperCAmelCase_	:					Union[str, Any]     	=   cryptoMath.find_mod_inverse(A__      ,(p - 1) * (q - 1)		)

			UpperCAmelCase_	:					List[str]     	=   (n, e)
			UpperCAmelCase_	:					List[str]     	=   (n, d)
			return (public_key, private_key)

def 							snake_case (  A__      ,A__		):
			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()

			UpperCAmelCase_	,      UpperCAmelCase_	:					Any     	=   generate_key(A__		)
			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()





from typing import List, Optional, Tuple, Union

import torch

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


class 			lowercase_		(      A       ):
       __lowerCamelCase				       =						42
       __lowerCamelCase				       =						42


       def __init__(    self						,       __A						,       __A				)						->    List[str]:
              super().__init__()
              self.register_modules(unet=__A						,       scheduler=__A				)





       @torch.no_grad()
       def __call__(    self						,       __A = 1						,       __A = 50						,       __A = None						,       __A = "pil"						,       __A = True						,       **__A						,       )						->    Union[Tuple, ImagePipelineOutput]:
              SCREAMING_SNAKE_CASE_							:				Dict		=self.unet.config.sample_size
              SCREAMING_SNAKE_CASE_							:				Tuple		=(batch_size, 3, img_size, img_size)

              SCREAMING_SNAKE_CASE_							:				int		=self.unet

              # sample x_0 ~ N(0, sigma_0^2 * I)
              SCREAMING_SNAKE_CASE_							:				Tuple		=randn_tensor(__A						,       generator=__A						,       device=self.device				) * self.scheduler.init_noise_sigma

              self.scheduler.set_timesteps(__A				)

              for t in self.progress_bar(self.scheduler.timesteps				):
                     # here sigma_t == t_i from the paper
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.scheduler.schedule[t]
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=self.scheduler.schedule[t - 1] if t > 0 else 0

                     # 1. Select temporarily increased noise level sigma_hat
                     # 2. Add new noise to move from sample_i to sample_hat
                     SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Dict		=self.scheduler.add_noise_to_input(__A						,       __A						,       generator=__A				)

                     # 3. Predict the noise residual given the noise magnitude `sigma_hat`
                     # The model inputs and output are adjusted by following eq. (213) in [1].
                     SCREAMING_SNAKE_CASE_							:				Tuple		=(sigma_hat / 2) * model((sample_hat + 1) / 2						,       sigma_hat / 2				).sample

                     # 4. Evaluate dx/dt at sigma_hat
                     # 5. Take Euler step from sigma to sigma_prev
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=self.scheduler.step(__A						,       __A						,       __A						,       __A				)

                     if sigma_prev != 0:
                            # 6. Apply 2nd order correction
                            # The model inputs and output are adjusted by following eq. (213) in [1].
                            SCREAMING_SNAKE_CASE_							:				int		=(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2						,       sigma_prev / 2				).sample
                            SCREAMING_SNAKE_CASE_							:				Dict		=self.scheduler.step_correct(
                                __A						,       __A						,       __A						,       __A						,       step_output.prev_sample						,       step_output['''derivative''']						,       )
                     SCREAMING_SNAKE_CASE_							:				Any		=step_output.prev_sample

              SCREAMING_SNAKE_CASE_							:				List[Any]		=(sample / 2 + 0.5).clamp(0						,       1				)
              SCREAMING_SNAKE_CASE_							:				Any		=sample.cpu().permute(0						,       2						,       3						,       1				).numpy()
              if output_type == "pil":
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.numpy_to_pil(__A				)

              if not return_dict:
                     return (image,)

              return ImagePipelineOutput(images=__A				)
