






'''simple docstring'''




import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


lowerCAmelCase__          =						logging.get_logger(__name__)







class       __lowercase					(__lowerCamelCase    ):
	_lowerCamelCase              =  '''linear'''
	_lowerCamelCase              =  '''cosine'''
	_lowerCamelCase              =  '''cosine_with_restarts'''
	_lowerCamelCase              =  '''polynomial'''
	_lowerCamelCase              =  '''constant'''
	_lowerCamelCase              =  '''constant_with_warmup'''
	_lowerCamelCase              =  '''piecewise_constant'''







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_ = -1):
		return LambdaLR(lowerCamelCase_     ,      lambda lowerCamelCase_: 1     ,      last_epoch=lowerCamelCase_)







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_ = -1):

		def lr_lambda(lowerCamelCase_):
				if current_step < num_warmup_steps:
						return float(lowerCamelCase_) / float(max(1.0     ,      lowerCamelCase_))
				return 1.0

		return LambdaLR(lowerCamelCase_     ,      lowerCamelCase_     ,      last_epoch=lowerCamelCase_)







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_ = -1):
		UpperCamelCase__				:					Union[str, Any]					=					{}
		UpperCamelCase__				:					List[str]					=					step_rules.split(',')
		for rule_str in rule_list[:-1]:
				UpperCamelCase__				:					str					=					rule_str.split(':')
				UpperCamelCase__				:					Optional[int]					=					int(lowerCamelCase_)
				UpperCamelCase__				:					Optional[int]					=					float(lowerCamelCase_)
				UpperCamelCase__				:					Optional[int]					=					value
		UpperCamelCase__				:					str					=					float(rule_list[-1])

		def create_rules_function(lowerCamelCase_     ,      lowerCamelCase_):
				def rule_func(lowerCamelCase_) -> float:
						UpperCamelCase__				:					Dict					=					sorted(rules_dict.keys())
						for i, sorted_step in enumerate(lowerCamelCase_):
								if steps < sorted_step:
										return rules_dict[sorted_steps[i]]
						return last_lr_multiple

				return rule_func

		UpperCamelCase__				:					str					=					create_rules_function(lowerCamelCase_     ,      lowerCamelCase_)

		return LambdaLR(lowerCamelCase_     ,      lowerCamelCase_     ,      last_epoch=lowerCamelCase_)







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_=-1):

		def lr_lambda(lowerCamelCase_):
				if current_step < num_warmup_steps:
						return float(lowerCamelCase_) / float(max(1     ,      lowerCamelCase_))
				return max(
				    0.0     ,      float(num_training_steps - current_step) / float(max(1     ,      num_training_steps - num_warmup_steps)))

		return LambdaLR(lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_ = 0.5     ,      lowerCamelCase_ = -1):

		def lr_lambda(lowerCamelCase_):
				if current_step < num_warmup_steps:
						return float(lowerCamelCase_) / float(max(1     ,      lowerCamelCase_))
				UpperCamelCase__				:					List[Any]					=					float(current_step - num_warmup_steps) / float(max(1     ,      num_training_steps - num_warmup_steps))
				return max(0.0     ,      0.5 * (1.0 + math.cos(math.pi * float(lowerCamelCase_) * 2.0 * progress)))

		return LambdaLR(lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_ = 1     ,      lowerCamelCase_ = -1):

		def lr_lambda(lowerCamelCase_):
				if current_step < num_warmup_steps:
						return float(lowerCamelCase_) / float(max(1     ,      lowerCamelCase_))
				UpperCamelCase__				:					str					=					float(current_step - num_warmup_steps) / float(max(1     ,      num_training_steps - num_warmup_steps))
				if progress >= 1.0:
						return 0.0
				return max(0.0     ,      0.5 * (1.0 + math.cos(math.pi * ((float(lowerCamelCase_) * progress) % 1.0))))

		return LambdaLR(lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_=1e-7     ,      lowerCamelCase_=1.0     ,      lowerCamelCase_=-1):
		UpperCamelCase__				:					Dict					=					optimizer.defaults['lr']
		if not (lr_init > lr_end):
				raise ValueError(f'lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})')

		def lr_lambda(lowerCamelCase_):
				if current_step < num_warmup_steps:
						return float(lowerCamelCase_) / float(max(1     ,      lowerCamelCase_))
				elif current_step > num_training_steps:
						return lr_end / lr_init  # as LambdaLR multiplies by lr_init
				else:
						UpperCamelCase__				:					Union[str, Any]					=					lr_init - lr_end
						UpperCamelCase__				:					int					=					num_training_steps - num_warmup_steps
						UpperCamelCase__				:					str					=					1 - (current_step - num_warmup_steps) / decay_steps
						UpperCamelCase__				:					Dict					=					lr_range * pct_remaining**power + lr_end
						return decay / lr_init  # as LambdaLR multiplies by lr_init

		return LambdaLR(lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)


lowerCAmelCase__          =						{
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_ = None     ,      lowerCamelCase_ = None     ,      lowerCamelCase_ = None     ,      lowerCamelCase_ = 1     ,      lowerCamelCase_ = 1.0     ,      lowerCamelCase_ = -1     ,      ):
		UpperCamelCase__				:					Union[str, Any]					=					SchedulerType(lowerCamelCase_)
		UpperCamelCase__				:					Tuple					=					TYPE_TO_SCHEDULER_FUNCTION[name]
		if name == SchedulerType.CONSTANT:
				return schedule_func(lowerCamelCase_     ,      last_epoch=lowerCamelCase_)

		if name == SchedulerType.PIECEWISE_CONSTANT:
				return schedule_func(lowerCamelCase_     ,      step_rules=lowerCamelCase_     ,      last_epoch=lowerCamelCase_)

		# All other schedulers require `num_warmup_steps`
		if num_warmup_steps is None:
				raise ValueError(f'{name} requires `num_warmup_steps`, please provide that argument.')

		if name == SchedulerType.CONSTANT_WITH_WARMUP:
				return schedule_func(lowerCamelCase_     ,      num_warmup_steps=lowerCamelCase_     ,      last_epoch=lowerCamelCase_)

		# All other schedulers require `num_training_steps`
		if num_training_steps is None:
				raise ValueError(f'{name} requires `num_training_steps`, please provide that argument.')

		if name == SchedulerType.COSINE_WITH_RESTARTS:
				return schedule_func(
				    lowerCamelCase_     ,      num_warmup_steps=lowerCamelCase_     ,      num_training_steps=lowerCamelCase_     ,      num_cycles=lowerCamelCase_     ,      last_epoch=lowerCamelCase_     ,      )

		if name == SchedulerType.POLYNOMIAL:
				return schedule_func(
				    lowerCamelCase_     ,      num_warmup_steps=lowerCamelCase_     ,      num_training_steps=lowerCamelCase_     ,      power=lowerCamelCase_     ,      last_epoch=lowerCamelCase_     ,      )

		return schedule_func(
		    lowerCamelCase_     ,      num_warmup_steps=lowerCamelCase_     ,      num_training_steps=lowerCamelCase_     ,      last_epoch=lowerCamelCase_)












'''simple docstring'''




import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

lowerCAmelCase__          =						3







def       __UpperCAmelCase						(  lowerCamelCase_)							->     int:
		print('Generating primitive root of p')
		while True:
				UpperCamelCase__				:					Any					=					random.randrange(3     ,      lowerCamelCase_)
				if pow(lowerCamelCase_     ,      2     ,      lowerCamelCase_) == 1:
						continue
				if pow(lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_) == 1:
						continue
				return g







def       __UpperCAmelCase						(  lowerCamelCase_)							->     tuple[tuple[int, int, int, int], tuple[int, int]]:
		print('Generating prime p...')
		UpperCamelCase__				:					List[str]					=					rabin_miller.generate_large_prime(lowerCamelCase_)  # select large prime number.
		UpperCamelCase__				:					Any					=					primitive_root(lowerCamelCase_)  # one primitive root on modulo p.
		UpperCamelCase__				:					Union[str, Any]					=					random.randrange(3     ,      lowerCamelCase_)  # private_key -> have to be greater than 2 for safety.
		UpperCamelCase__				:					Dict					=					cryptomath.find_mod_inverse(pow(lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)     ,      lowerCamelCase_)

		UpperCamelCase__				:					List[Any]					=					(key_size, e_a, e_a, p)
		UpperCamelCase__				:					Optional[Any]					=					(key_size, d)

		return public_key, private_key







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_)							->     None:
		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__				:					Union[str, Any]					=					generate_key(lowerCamelCase_)
		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:
		print('Making key files...')
		make_key_files('elgamal'     ,      2_048)
		print('Key files generation successful')


if __name__ == "__main__":
		main()




