

"""simple docstring"""






from __future__ import annotations

import math
import random
def  UpperCamelCase   (     _lowerCAmelCase       :  int,					_lowerCAmelCase       :  int,					_lowerCAmelCase       :  int	)			->     list[int]:
	_UpperCAmelCase      :							Dict       =   d * e - 1
	_UpperCAmelCase      :							Optional[int]       =   0
	_UpperCAmelCase      :							Dict       =   0
	while p == 0:
		_UpperCAmelCase      :							Optional[Any]       =   random.randint(2,					n - 1	)
		_UpperCAmelCase      :							Any       =   k
		while True:
			if t % 2 == 0:
				_UpperCAmelCase      :							List[Any]       =   t // 2
				_UpperCAmelCase      :							Dict       =   (g**t) % n
				_UpperCAmelCase      :							List[str]       =   math.gcd(x - 1,					_lowerCAmelCase	)
				if x > 1 and y > 1:
					_UpperCAmelCase      :							List[str]       =   y
					_UpperCAmelCase      :							Tuple       =   n // y
					break  # find the correct factors
			else:
				break  # t is not divisible by 2, break and choose another g
	return sorted([p, q]	)


if __name__ == "__main__":
						import doctest

						doctest.testmod()








"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCamelCase__			:					Union[str, Any]							  =					{
    '''configuration_unispeech_sat''': ['''UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechSatConfig'''],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__			:					List[str]							  =					[
						    '''UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''UniSpeechSatForAudioFrameClassification''',
						    '''UniSpeechSatForCTC''',
						    '''UniSpeechSatForPreTraining''',
						    '''UniSpeechSatForSequenceClassification''',
						    '''UniSpeechSatForXVector''',
						    '''UniSpeechSatModel''',
						    '''UniSpeechSatPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_unispeech_sat import (
												    UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    UniSpeechSatForAudioFrameClassification,
												    UniSpeechSatForCTC,
												    UniSpeechSatForPreTraining,
												    UniSpeechSatForSequenceClassification,
												    UniSpeechSatForXVector,
												    UniSpeechSatModel,
												    UniSpeechSatPreTrainedModel,
												)

else:
						import sys

						lowerCamelCase__			:					Any							  =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





