
"""simple docstring"""


from __future__ import annotations

import math
import random






def        lowerCAmelCase_       (		snake_case_		:      str							,   snake_case_		:      int							,   snake_case_		:      str      )       ->Union[str, Any]:
			lowerCamelCase__     :					Dict		=d * e - 1
			lowerCamelCase__     :					Optional[int]		=0
			lowerCamelCase__     :					Union[str, Any]		=0
			while p == 0:
						lowerCamelCase__     :					List[Any]		=random.randint(2							,   n - 1      )
						lowerCamelCase__     :					str		=k
						while True:
									if t % 2 == 0:
												lowerCamelCase__     :					int		=t // 2
												lowerCamelCase__     :					Optional[Any]		=(g**t) % n
												lowerCamelCase__     :					Tuple		=math.gcd(x - 1							,   _UpperCamelCase      )
												if x > 1 and y > 1:
															lowerCamelCase__     :					str		=y
															lowerCamelCase__     :					int		=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_torch_available


lowerCAmelCase								=						{
    """configuration_decision_transformer""": [
        """DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """DecisionTransformerConfig""",
    ],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase								=						[
							    """DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """DecisionTransformerGPT2Model""",
							    """DecisionTransformerGPT2PreTrainedModel""",
							    """DecisionTransformerModel""",
							    """DecisionTransformerPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_decision_transformer import (
							    DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    DecisionTransformerConfig,
							)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_decision_transformer import (
														    DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DecisionTransformerGPTaModel,
														    DecisionTransformerGPTaPreTrainedModel,
														    DecisionTransformerModel,
														    DecisionTransformerPreTrainedModel,
														)


else:
							import sys

							lowerCAmelCase								=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)