


import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
			import transformers
def      _a					( SCREAMING_SNAKE_CASE	):


		"""simple docstring"""




		random.seed(SCREAMING_SNAKE_CASE	)
		np.random.seed(SCREAMING_SNAKE_CASE	)
		torch.manual_seed(SCREAMING_SNAKE_CASE	)
		torch.cuda.manual_seed_all(SCREAMING_SNAKE_CASE	)
		# ^^ safe to call this function even if cuda is not available


class 			_a				:

							def __init__(				self:      List[str]       ,		UpperCamelCase_:      Iterable[torch.nn.Parameter]       ,		UpperCamelCase_:      float = 0.9999       ,		UpperCamelCase_:      float = 0.0       ,		UpperCamelCase_:      int = 0       ,		UpperCamelCase_:      bool = False       ,		UpperCamelCase_:      Union[float, int] = 1.0       ,		UpperCamelCase_:      Union[float, int] = 2 / 3       ,		UpperCamelCase_:      Optional[Any] = None       ,		UpperCamelCase_:      Dict[str, Any] = None       ,		**UpperCamelCase_:      List[Any]       ,		)     ->  Tuple:





									"""simple docstring"""







									if isinstance(UpperCamelCase_       ,		torch.nn.Module				):
											lowercase__				= (
											    '''Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. '''
											    '''Please pass the parameters of the module instead.'''
											)
											deprecate(
											    '''passing a `torch.nn.Module` to `ExponentialMovingAverage`'''       ,		'''1.0.0'''       ,		UpperCamelCase_       ,		standard_warn=UpperCamelCase_       ,		)
											lowercase__				= parameters.parameters()

											# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
											lowercase__				= True

									if kwargs.get('''max_value'''       ,		UpperCamelCase_				) is not None:
											lowercase__				= '''The `max_value` argument is deprecated. Please use `decay` instead.'''
											deprecate('''max_value'''       ,		'''1.0.0'''       ,		UpperCamelCase_       ,		standard_warn=UpperCamelCase_				)
											lowercase__				= kwargs['''max_value''']

									if kwargs.get('''min_value'''       ,		UpperCamelCase_				) is not None:
											lowercase__				= '''The `min_value` argument is deprecated. Please use `min_decay` instead.'''
											deprecate('''min_value'''       ,		'''1.0.0'''       ,		UpperCamelCase_       ,		standard_warn=UpperCamelCase_				)
											lowercase__				= kwargs['''min_value''']

									lowercase__				= list(UpperCamelCase_				)
									lowercase__				= [p.clone().detach() for p in parameters]

									if kwargs.get('''device'''       ,		UpperCamelCase_				) is not None:
											lowercase__				= '''The `device` argument is deprecated. Please use `to` instead.'''
											deprecate('''device'''       ,		'''1.0.0'''       ,		UpperCamelCase_       ,		standard_warn=UpperCamelCase_				)
											self.to(device=kwargs['''device''']				)

									lowercase__				= None

									lowercase__				= decay
									lowercase__				= min_decay
									lowercase__				= update_after_step
									lowercase__				= use_ema_warmup
									lowercase__				= inv_gamma
									lowercase__				= power
									lowercase__				= 0
									lowercase__				= None  # set in `step()`

									lowercase__				= model_cls
									lowercase__				= model_config

							@classmethod
							def  lowerCamelCase_					(				cls:      Union[str, Any]       ,		UpperCamelCase_:      List[Any]       ,		UpperCamelCase_:      int				)     ->  "EMAModel":





									"""simple docstring"""







									lowercase__	,						lowercase__				= model_cls.load_config(UpperCamelCase_       ,		return_unused_kwargs=UpperCamelCase_				)
									lowercase__				= model_cls.from_pretrained(UpperCamelCase_				)

									lowercase__				= cls(model.parameters()       ,		model_cls=UpperCamelCase_       ,		model_config=model.config				)

									ema_model.load_state_dict(UpperCamelCase_				)
									return ema_model

							def  lowerCamelCase_					(				self:      Optional[int]       ,		UpperCamelCase_:      Optional[Any]				)     ->  List[str]:





									"""simple docstring"""







									if self.model_cls is None:
											raise ValueError('''`save_pretrained` can only be used if `model_cls` was defined at __init__.'''				)

									if self.model_config is None:
											raise ValueError('''`save_pretrained` can only be used if `model_config` was defined at __init__.'''				)

									lowercase__				= self.model_cls.from_config(self.model_config				)
									lowercase__				= self.state_dict()
									state_dict.pop('''shadow_params'''       ,		UpperCamelCase_				)

									model.register_to_config(**UpperCamelCase_				)
									self.copy_to(model.parameters()				)
									model.save_pretrained(UpperCamelCase_				)

							def  lowerCamelCase_					(				self:      Tuple       ,		UpperCamelCase_:      int				)     ->  float:





									"""simple docstring"""







									lowercase__				= max(0       ,		optimization_step - self.update_after_step - 1				)

									if step <= 0:
											return 0.0

									if self.use_ema_warmup:
											lowercase__				= 1 - (1 + step / self.inv_gamma) ** -self.power
									else:
											lowercase__				= (1 + step) / (10 + step)

									lowercase__				= min(UpperCamelCase_       ,		self.decay				)
									# make sure decay is not smaller than min_decay
									lowercase__				= max(UpperCamelCase_       ,		self.min_decay				)
									return cur_decay_value

							@torch.no_grad()
							def  lowerCamelCase_					(				self:      int       ,		UpperCamelCase_:      Iterable[torch.nn.Parameter]				)     ->  int:





									"""simple docstring"""







									if isinstance(UpperCamelCase_       ,		torch.nn.Module				):
											lowercase__				= (
											    '''Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. '''
											    '''Please pass the parameters of the module instead.'''
											)
											deprecate(
											    '''passing a `torch.nn.Module` to `ExponentialMovingAverage.step`'''       ,		'''1.0.0'''       ,		UpperCamelCase_       ,		standard_warn=UpperCamelCase_       ,		)
											lowercase__				= parameters.parameters()

									lowercase__				= list(UpperCamelCase_				)

									self.optimization_step += 1

									# Compute the decay factor for the exponential moving average.
									lowercase__				= self.get_decay(self.optimization_step				)
									lowercase__				= decay
									lowercase__				= 1 - decay

									lowercase__				= contextlib.nullcontext
									if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
											import deepspeed

									for s_param, param in zip(self.shadow_params       ,		UpperCamelCase_				):
											if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
													lowercase__				= deepspeed.zero.GatheredParameters(UpperCamelCase_       ,		modifier_rank=UpperCamelCase_				)

											with context_manager():
													if param.requires_grad:
															s_param.sub_(one_minus_decay * (s_param - param)				)
													else:
															s_param.copy_(UpperCamelCase_				)

							def  lowerCamelCase_					(				self:      Any       ,		UpperCamelCase_:      Iterable[torch.nn.Parameter]				)     ->  None:





									"""simple docstring"""







									lowercase__				= list(UpperCamelCase_				)
									for s_param, param in zip(self.shadow_params       ,		UpperCamelCase_				):
											param.data.copy_(s_param.to(param.device				).data				)

							def  lowerCamelCase_					(				self:      Union[str, Any]       ,		UpperCamelCase_:      Tuple=None       ,		UpperCamelCase_:      Optional[int]=None				)     ->  None:





									"""simple docstring"""







									lowercase__				= [
									    p.to(device=UpperCamelCase_       ,		dtype=UpperCamelCase_				) if p.is_floating_point() else p.to(device=UpperCamelCase_				)
									    for p in self.shadow_params
									]

							def  lowerCamelCase_					(				self:      Union[str, Any]				)     ->  dict:





									"""simple docstring"""







									return {
									    "decay": self.decay,
									    "min_decay": self.min_decay,
									    "optimization_step": self.optimization_step,
									    "update_after_step": self.update_after_step,
									    "use_ema_warmup": self.use_ema_warmup,
									    "inv_gamma": self.inv_gamma,
									    "power": self.power,
									    "shadow_params": self.shadow_params,
									}

							def  lowerCamelCase_					(				self:      Union[str, Any]       ,		UpperCamelCase_:      Iterable[torch.nn.Parameter]				)     ->  None:





									"""simple docstring"""







									lowercase__				= [param.detach().cpu().clone() for param in parameters]

							def  lowerCamelCase_					(				self:      str       ,		UpperCamelCase_:      Iterable[torch.nn.Parameter]				)     ->  None:





									"""simple docstring"""







									if self.temp_stored_params is None:
											raise RuntimeError('''This ExponentialMovingAverage has no `store()`ed weights ''' '''to `restore()`'''				)
									for c_param, param in zip(self.temp_stored_params       ,		UpperCamelCase_				):
											param.data.copy_(c_param.data				)

									# Better memory-wise.
									lowercase__				= None


							def  lowerCamelCase_					(				self:      Dict       ,		UpperCamelCase_:      dict				)     ->  None:





									"""simple docstring"""







									lowercase__				= copy.deepcopy(UpperCamelCase_				)

									lowercase__				= state_dict.get('''decay'''       ,		self.decay				)
									if self.decay < 0.0 or self.decay > 1.0:
											raise ValueError('''Decay must be between 0 and 1'''				)

									lowercase__				= state_dict.get('''min_decay'''       ,		self.min_decay				)
									if not isinstance(self.min_decay       ,		UpperCamelCase_				):
											raise ValueError('''Invalid min_decay'''				)

									lowercase__				= state_dict.get('''optimization_step'''       ,		self.optimization_step				)
									if not isinstance(self.optimization_step       ,		UpperCamelCase_				):
											raise ValueError('''Invalid optimization_step'''				)

									lowercase__				= state_dict.get('''update_after_step'''       ,		self.update_after_step				)
									if not isinstance(self.update_after_step       ,		UpperCamelCase_				):
											raise ValueError('''Invalid update_after_step'''				)

									lowercase__				= state_dict.get('''use_ema_warmup'''       ,		self.use_ema_warmup				)
									if not isinstance(self.use_ema_warmup       ,		UpperCamelCase_				):
											raise ValueError('''Invalid use_ema_warmup'''				)

									lowercase__				= state_dict.get('''inv_gamma'''       ,		self.inv_gamma				)
									if not isinstance(self.inv_gamma       ,		(float, int)				):
											raise ValueError('''Invalid inv_gamma'''				)

									lowercase__				= state_dict.get('''power'''       ,		self.power				)
									if not isinstance(self.power       ,		(float, int)				):
											raise ValueError('''Invalid power'''				)

									lowercase__				= state_dict.get('''shadow_params'''       ,		UpperCamelCase_				)
									if shadow_params is not None:
											lowercase__				= shadow_params
											if not isinstance(self.shadow_params       ,		UpperCamelCase_				):
													raise ValueError('''shadow_params must be a list'''				)
											if not all(isinstance(UpperCamelCase_       ,		torch.Tensor				) for p in self.shadow_params				):
													raise ValueError('''shadow_params must all be Tensors'''				)










'''simple docstring'''







from binascii import hexlify
from hashlib import shaaaa
from os import urandom

# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526

__lowerCAmelCase									=   {
    # 1536-bit
    5: {
        'prime': int(
            'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'
            + '29024E088A67CC74020BBEA63B139B22514A08798E3404DD'
            + 'EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'
            + 'E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'
            + 'EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'
            + 'C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'
            + '83655D23DCA3AD961C62F356208552BB9ED529077096966D'
            + '670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF',
            base=16,
        ),
        'generator': 2,
    },
    # 2048-bit
    14: {
        'prime': int(
            'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'
            + '29024E088A67CC74020BBEA63B139B22514A08798E3404DD'
            + 'EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'
            + 'E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'
            + 'EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'
            + 'C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'
            + '83655D23DCA3AD961C62F356208552BB9ED529077096966D'
            + '670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'
            + 'E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'
            + 'DE2BCBF6955817183995497CEA956AE515D2261898FA0510'
            + '15728E5A8AACAA68FFFFFFFFFFFFFFFF',
            base=16,
        ),
        'generator': 2,
    },
    # 3072-bit
    15: {
        'prime': int(
            'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'
            + '29024E088A67CC74020BBEA63B139B22514A08798E3404DD'
            + 'EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'
            + 'E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'
            + 'EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'
            + 'C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'
            + '83655D23DCA3AD961C62F356208552BB9ED529077096966D'
            + '670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'
            + 'E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'
            + 'DE2BCBF6955817183995497CEA956AE515D2261898FA0510'
            + '15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64'
            + 'ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7'
            + 'ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B'
            + 'F12FFA06D98A0864D87602733EC86A64521F2B18177B200C'
            + 'BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31'
            + '43DB5BFCE0FD108E4B82D120A93AD2CAFFFFFFFFFFFFFFFF',
            base=16,
        ),
        'generator': 2,
    },
    # 4096-bit
    16: {
        'prime': int(
            'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'
            + '29024E088A67CC74020BBEA63B139B22514A08798E3404DD'
            + 'EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'
            + 'E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'
            + 'EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'
            + 'C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'
            + '83655D23DCA3AD961C62F356208552BB9ED529077096966D'
            + '670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'
            + 'E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'
            + 'DE2BCBF6955817183995497CEA956AE515D2261898FA0510'
            + '15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64'
            + 'ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7'
            + 'ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B'
            + 'F12FFA06D98A0864D87602733EC86A64521F2B18177B200C'
            + 'BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31'
            + '43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7'
            + '88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA'
            + '2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6'
            + '287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED'
            + '1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9'
            + '93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934063199'
            + 'FFFFFFFFFFFFFFFF',
            base=16,
        ),
        'generator': 2,
    },
    # 6144-bit
    17: {
        'prime': int(
            'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E08'
            + '8A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B'
            + '302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9'
            + 'A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE6'
            + '49286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8'
            + 'FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D'
            + '670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C'
            + '180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF695581718'
            + '3995497CEA956AE515D2261898FA051015728E5A8AAAC42DAD33170D'
            + '04507A33A85521ABDF1CBA64ECFB850458DBEF0A8AEA71575D060C7D'
            + 'B3970F85A6E1E4C7ABF5AE8CDB0933D71E8C94E04A25619DCEE3D226'
            + '1AD2EE6BF12FFA06D98A0864D87602733EC86A64521F2B18177B200C'
            + 'BBE117577A615D6C770988C0BAD946E208E24FA074E5AB3143DB5BFC'
            + 'E0FD108E4B82D120A92108011A723C12A787E6D788719A10BDBA5B26'
            + '99C327186AF4E23C1A946834B6150BDA2583E9CA2AD44CE8DBBBC2DB'
            + '04DE8EF92E8EFC141FBECAA6287C59474E6BC05D99B2964FA090C3A2'
            + '233BA186515BE7ED1F612970CEE2D7AFB81BDD762170481CD0069127'
            + 'D5B05AA993B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492'
            + '36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BDF8FF9406'
            + 'AD9E530EE5DB382F413001AEB06A53ED9027D831179727B0865A8918'
            + 'DA3EDBEBCF9B14ED44CE6CBACED4BB1BDB7F1447E6CC254B33205151'
            + '2BD7AF426FB8F401378CD2BF5983CA01C64B92ECF032EA15D1721D03'
            + 'F482D7CE6E74FEF6D55E702F46980C82B5A84031900B1C9E59E7C97F'
            + 'BEC7E8F323A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA'
            + 'CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE32806A1D58B'
            + 'B7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55CDA56C9EC2EF29632'
            + '387FE8D76E3C0468043E8F663F4860EE12BF2D5B0B7474D6E694F91E'
            + '6DCC4024FFFFFFFFFFFFFFFF',
            base=16,
        ),
        'generator': 2,
    },
    # 8192-bit
    18: {
        'prime': int(
            'FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1'
            + '29024E088A67CC74020BBEA63B139B22514A08798E3404DD'
            + 'EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245'
            + 'E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED'
            + 'EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D'
            + 'C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F'
            + '83655D23DCA3AD961C62F356208552BB9ED529077096966D'
            + '670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B'
            + 'E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9'
            + 'DE2BCBF6955817183995497CEA956AE515D2261898FA0510'
            + '15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64'
            + 'ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7'
            + 'ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B'
            + 'F12FFA06D98A0864D87602733EC86A64521F2B18177B200C'
            + 'BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31'
            + '43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7'
            + '88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA'
            + '2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6'
            + '287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED'
            + '1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9'
            + '93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492'
            + '36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BD'
            + 'F8FF9406AD9E530EE5DB382F413001AEB06A53ED9027D831'
            + '179727B0865A8918DA3EDBEBCF9B14ED44CE6CBACED4BB1B'
            + 'DB7F1447E6CC254B332051512BD7AF426FB8F401378CD2BF'
            + '5983CA01C64B92ECF032EA15D1721D03F482D7CE6E74FEF6'
            + 'D55E702F46980C82B5A84031900B1C9E59E7C97FBEC7E8F3'
            + '23A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA'
            + 'CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE328'
            + '06A1D58BB7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55C'
            + 'DA56C9EC2EF29632387FE8D76E3C0468043E8F663F4860EE'
            + '12BF2D5B0B7474D6E694F91E6DBE115974A3926F12FEE5E4'
            + '38777CB6A932DF8CD8BEC4D073B931BA3BC832B68D9DD300'
            + '741FA7BF8AFC47ED2576F6936BA424663AAB639C5AE4F568'
            + '3423B4742BF1C978238F16CBE39D652DE3FDB8BEFC848AD9'
            + '22222E04A4037C0713EB57A81A23F0C73473FC646CEA306B'
            + '4BCBC8862F8385DDFA9D4B7FA2C087E879683303ED5BDD3A'
            + '062B3CF5B3A278A66D2A13F83F44F82DDF310EE074AB6A36'
            + '4597E899A0255DC164F31CC50846851DF9AB48195DED7EA1'
            + 'B1D510BD7EE74D73FAF36BC31ECFA268359046F4EB879F92'
            + '4009438B481C6CD7889A002ED5EE382BC9190DA6FC026E47'
            + '9558E4475677E9AA9E3050E2765694DFC81F56E880B96E71'
            + '60C980DD98EDD3DFFFFFFFFFFFFFFFFF',
            base=16,
        ),
        'generator': 2,
    },
}





class 						SCREAMING_SNAKE_CASE       :






			def __init__(			self   :      Any	,    __SCREAMING_SNAKE_CASE   :      int = 14       )      ->      None:
							if group not in primes:
											raise ValueError('''Unsupported Group'''       )
							a_		:	Union[str, Any]									=   primes[group]['''prime''']
							a_		:	List[str]									=   primes[group]['''generator''']

							a_		:	str									=   int(hexlify(urandom(32       )       )	,    base=16       )






			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      str:
							return hex(self.__private_key       )[2:]






			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]       )      ->      str:
							a_		:	Dict									=   pow(self.generator	,    self.__private_key	,    self.prime       )
							return hex(__SCREAMING_SNAKE_CASE       )[2:]






			def       SCREAMING_SNAKE_CASE   (			self   :      Any	,    __SCREAMING_SNAKE_CASE   :      int       )      ->      bool:
							# check if the other public key is valid based on NIST SP800-56
							return (
							    2 <= key <= self.prime - 2
							    and pow(__SCREAMING_SNAKE_CASE	,    (self.prime - 1) // 2	,    self.prime       ) == 1
							)






			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[Any]	,    __SCREAMING_SNAKE_CASE   :      str       )      ->      str:
							a_		:	str									=   int(__SCREAMING_SNAKE_CASE	,    base=16       )
							if not self.is_valid_public_key(__SCREAMING_SNAKE_CASE       ):
											raise ValueError('''Invalid public key'''       )
							a_		:	Optional[int]									=   pow(__SCREAMING_SNAKE_CASE	,    self.__private_key	,    self.prime       )
							return shaaaa(str(__SCREAMING_SNAKE_CASE       ).encode()       ).hexdigest()






			@staticmethod
			def       SCREAMING_SNAKE_CASE   (			__SCREAMING_SNAKE_CASE   :      int	,    __SCREAMING_SNAKE_CASE   :      int       )      ->      bool:
							# check if the other public key is valid based on NIST SP800-56
							return (
							    2 <= remote_public_key_str <= prime - 2
							    and pow(__SCREAMING_SNAKE_CASE	,    (prime - 1) // 2	,    __SCREAMING_SNAKE_CASE       ) == 1
							)







			@staticmethod
			def       SCREAMING_SNAKE_CASE   (			__SCREAMING_SNAKE_CASE   :      str	,    __SCREAMING_SNAKE_CASE   :      str	,    __SCREAMING_SNAKE_CASE   :      int = 14       )      ->      str:
							a_		:	Tuple									=   int(__SCREAMING_SNAKE_CASE	,    base=16       )
							a_		:	Any									=   int(__SCREAMING_SNAKE_CASE	,    base=16       )
							a_		:	List[str]									=   primes[group]['''prime''']
							if not DiffieHellman.is_valid_public_key_static(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       ):
											raise ValueError('''Invalid public key'''       )
							a_		:	Optional[int]									=   pow(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
							return shaaaa(str(__SCREAMING_SNAKE_CASE       ).encode()       ).hexdigest()


if __name__ == "__main__":
		import doctest

		doctest.testmod()




