


from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin


@flax.struct.dataclass
class        lowerCAmelCase_			:
						# setable values
						lowerCamelCase_    =  None
						lowerCamelCase_    =  None
						lowerCamelCase_    =  None  # sigma(t_i)




						@classmethod
						def 			_snake_case       (						cls    : Any    )   ->str:






									"""simple docstring"""






									return cls()






@dataclass
class        lowerCAmelCase_			(							__a):
						lowerCamelCase_    =  42
						lowerCamelCase_    =  42
						lowerCamelCase_    =  42






class        lowerCAmelCase_			(							__a      ,__a):




						@property
						def 			_snake_case       (						self    : str    )   ->Union[str, Any]:






									"""simple docstring"""






									return True




						@register_to_config
						def __init__(						self    : Optional[int] ,  __A    : int = 0.02 ,  __A    : List[str] = 100 ,  __A    : str = 1.007 ,  __A    : List[Any] = 80 ,  __A    : List[str] = 0.05 ,  __A    : Optional[int] = 50 ,  )   ->Any:






									"""simple docstring"""






									pass




						def 			_snake_case       (						self    : Optional[int]    )   ->Dict:






									"""simple docstring"""






									return KarrasVeSchedulerState.create()




						def 			_snake_case       (						self    : List[Any] ,  __A    : Any ,  __A    : Optional[int] ,  __A    : str = ()    )   ->KarrasVeSchedulerState:






									"""simple docstring"""






									a__			:str    	=     jnp.arange(0 ,  __UpperCamelCase    )[::-1].copy()
									a__			:Optional[int]    	=     [
									    (
									        self.config.sigma_max**2
									        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
									    )
									    for i in timesteps
									]

									return state.replace(
									    num_inference_steps=__UpperCamelCase ,  schedule=jnp.array(__UpperCamelCase ,  dtype=jnp.floataa    ) ,  timesteps=__UpperCamelCase ,  )




						def 			_snake_case       (						self    : Tuple ,  __A    : Dict ,  __A    : Dict ,  __A    : str ,  __A    : Optional[Any] ,  )   ->Tuple[jnp.ndarray, float]:






									"""simple docstring"""






									if self.config.s_min <= sigma <= self.config.s_max:
												a__			:Optional[Any]    	=     min(self.config.s_churn / state.num_inference_steps ,  2**0.5 - 1    )
									else:
												a__			:List[Any]    	=     0

									# sample eps ~ N(0, S_noise^2 * I)
									a__			:Tuple    	=     random.split(__UpperCamelCase ,  num=1    )
									a__			:Optional[int]    	=     self.config.s_noise * random.normal(key=__UpperCamelCase ,  shape=sample.shape    )
									a__			:int    	=     sigma + gamma * sigma
									a__			:int    	=     sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

									return sample_hat, sigma_hat




						def 			_snake_case       (						self    : Dict ,  __A    : List[str] ,  __A    : Any ,  __A    : int ,  __A    : Tuple ,  __A    : Tuple ,  __A    : Any = True ,  )   ->Union[FlaxKarrasVeOutput, Tuple]:






									"""simple docstring"""






									a__			:Optional[int]    	=     sample_hat + sigma_hat * model_output
									a__			:Dict    	=     (sample_hat - pred_original_sample) / sigma_hat
									a__			:List[str]    	=     sample_hat + (sigma_prev - sigma_hat) * derivative

									if not return_dict:
												return (sample_prev, derivative, state)

									return FlaxKarrasVeOutput(prev_sample=__UpperCamelCase ,  derivative=__UpperCamelCase ,  state=__UpperCamelCase    )




						def 			_snake_case       (						self    : str ,  __A    : Optional[Any] ,  __A    : Optional[Any] ,  __A    : Any ,  __A    : List[str] ,  __A    : Any ,  __A    : List[str] ,  __A    : List[str] ,  __A    : Optional[int] = True ,  )   ->Union[FlaxKarrasVeOutput, Tuple]:






									"""simple docstring"""






									a__			:Union[str, Any]    	=     sample_prev + sigma_prev * model_output
									a__			:str    	=     (sample_prev - pred_original_sample) / sigma_prev
									a__			:Any    	=     sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

									if not return_dict:
												return (sample_prev, derivative, state)

									return FlaxKarrasVeOutput(prev_sample=__UpperCamelCase ,  derivative=__UpperCamelCase ,  state=__UpperCamelCase    )






						def 			_snake_case       (						self    : List[str] ,  __A    : Any ,  __A    : Dict ,  __A    : Optional[Any] ,  __A    : str    )   ->Dict:






									"""simple docstring"""






									raise NotImplementedError()



import os
import random
import sys

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






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






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				int			):
						print("""Generating prime p..."""			)
						UpperCamelCase_		:							Tuple  =		rabinMiller.generate_large_prime(_SCREAMING_SNAKE_CASE			)
						print("""Generating prime q..."""			)
						UpperCamelCase_		:							List[Any]  =		rabinMiller.generate_large_prime(_SCREAMING_SNAKE_CASE			)
						UpperCamelCase_		:							List[str]  =		p * q

						print("""Generating e that is relatively prime to (p - 1) * (q - 1)..."""			)
						while True:
												UpperCamelCase_		:							Union[str, Any]  =		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..."""			)
						UpperCamelCase_		:							Tuple  =		cryptoMath.find_mod_inverse(_SCREAMING_SNAKE_CASE		, (p - 1) * (q - 1)			)

						UpperCamelCase_		:							Tuple  =		(n, e)
						UpperCamelCase_		:							int  =		(n, d)
						return (public_key, private_key)






def     lowerCAmelCase_			(   _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()

						UpperCamelCase_,UpperCamelCase_		:							str  =		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()






