
from __future__ import annotations

import math
import random




def     a						(							snake_case__: int						,       snake_case__: int						,       snake_case__: int ):


				'''simple docstring'''
				lowercase_					  = d * e - 1
				lowercase_					  = 0
				lowercase_					  = 0
				while p == 0:
								lowercase_					  = random.randint(2						,       n - 1 )
								lowercase_					  = k
								while True:
												if t % 2 == 0:
																lowercase_					  = t // 2
																lowercase_					  = (g**t) % n
																lowercase_					  = math.gcd(x - 1						,       snake_case__ )
																if x > 1 and y > 1:
																				lowercase_					  = y
																				lowercase_					  = 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()






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__a        = {
    'configuration_nystromformer': ['NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NystromformerConfig'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a        = [
						    'NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'NystromformerForMaskedLM',
						    'NystromformerForMultipleChoice',
						    'NystromformerForQuestionAnswering',
						    'NystromformerForSequenceClassification',
						    'NystromformerForTokenClassification',
						    'NystromformerLayer',
						    'NystromformerModel',
						    'NystromformerPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_nystromformer import (
												    NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    NystromformerForMaskedLM,
												    NystromformerForMultipleChoice,
												    NystromformerForQuestionAnswering,
												    NystromformerForSequenceClassification,
												    NystromformerForTokenClassification,
												    NystromformerLayer,
												    NystromformerModel,
												    NystromformerPreTrainedModel,
												)


else:
						import sys

						__a        = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




