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95 lines
4.1 KiB
Python
95 lines
4.1 KiB
Python
import _random
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import sys
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from _typeshed import SupportsLenAndGetItem
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from collections.abc import Callable, Iterable, MutableSequence, Sequence, Set as AbstractSet
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from fractions import Fraction
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from typing import Any, ClassVar, NoReturn, Tuple, TypeVar
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_T = TypeVar("_T")
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class Random(_random.Random):
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VERSION: ClassVar[int]
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def __init__(self, x: Any = ...) -> None: ...
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def seed(self, a: Any = ..., version: int = ...) -> None: ...
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def getstate(self) -> Tuple[Any, ...]: ...
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def setstate(self, state: Tuple[Any, ...]) -> None: ...
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def getrandbits(self, __k: int) -> int: ...
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def randrange(self, start: int, stop: int | None = ..., step: int = ...) -> int: ...
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def randint(self, a: int, b: int) -> int: ...
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if sys.version_info >= (3, 9):
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def randbytes(self, n: int) -> bytes: ...
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def choice(self, seq: SupportsLenAndGetItem[_T]) -> _T: ...
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def choices(
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self,
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population: SupportsLenAndGetItem[_T],
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weights: Sequence[float | Fraction] | None = ...,
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*,
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cum_weights: Sequence[float | Fraction] | None = ...,
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k: int = ...,
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) -> list[_T]: ...
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def shuffle(self, x: MutableSequence[Any], random: Callable[[], float] | None = ...) -> None: ...
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if sys.version_info >= (3, 9):
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def sample(
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self, population: Sequence[_T] | AbstractSet[_T], k: int, *, counts: Iterable[_T] | None = ...
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) -> list[_T]: ...
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else:
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def sample(self, population: Sequence[_T] | AbstractSet[_T], k: int) -> list[_T]: ...
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def random(self) -> float: ...
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def uniform(self, a: float, b: float) -> float: ...
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def triangular(self, low: float = ..., high: float = ..., mode: float | None = ...) -> float: ...
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def betavariate(self, alpha: float, beta: float) -> float: ...
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def expovariate(self, lambd: float) -> float: ...
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def gammavariate(self, alpha: float, beta: float) -> float: ...
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def gauss(self, mu: float, sigma: float) -> float: ...
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def lognormvariate(self, mu: float, sigma: float) -> float: ...
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def normalvariate(self, mu: float, sigma: float) -> float: ...
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def vonmisesvariate(self, mu: float, kappa: float) -> float: ...
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def paretovariate(self, alpha: float) -> float: ...
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def weibullvariate(self, alpha: float, beta: float) -> float: ...
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# SystemRandom is not implemented for all OS's; good on Windows & Linux
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class SystemRandom(Random):
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def getrandbits(self, k: int) -> int: ... # k can be passed by keyword
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def getstate(self, *args: Any, **kwds: Any) -> NoReturn: ...
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def setstate(self, *args: Any, **kwds: Any) -> NoReturn: ...
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# ----- random function stubs -----
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def seed(a: Any = ..., version: int = ...) -> None: ...
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def getstate() -> object: ...
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def setstate(state: object) -> None: ...
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def getrandbits(__k: int) -> int: ...
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def randrange(start: int, stop: None | int = ..., step: int = ...) -> int: ...
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def randint(a: int, b: int) -> int: ...
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if sys.version_info >= (3, 9):
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def randbytes(n: int) -> bytes: ...
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def choice(seq: SupportsLenAndGetItem[_T]) -> _T: ...
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def choices(
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population: SupportsLenAndGetItem[_T],
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weights: Sequence[float] | None = ...,
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*,
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cum_weights: Sequence[float] | None = ...,
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k: int = ...,
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) -> list[_T]: ...
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def shuffle(x: MutableSequence[Any], random: Callable[[], float] | None = ...) -> None: ...
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if sys.version_info >= (3, 9):
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def sample(population: Sequence[_T] | AbstractSet[_T], k: int, *, counts: Iterable[_T] | None = ...) -> list[_T]: ...
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else:
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def sample(population: Sequence[_T] | AbstractSet[_T], k: int) -> list[_T]: ...
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def random() -> float: ...
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def uniform(a: float, b: float) -> float: ...
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def triangular(low: float = ..., high: float = ..., mode: float | None = ...) -> float: ...
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def betavariate(alpha: float, beta: float) -> float: ...
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def expovariate(lambd: float) -> float: ...
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def gammavariate(alpha: float, beta: float) -> float: ...
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def gauss(mu: float, sigma: float) -> float: ...
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def lognormvariate(mu: float, sigma: float) -> float: ...
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def normalvariate(mu: float, sigma: float) -> float: ...
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def vonmisesvariate(mu: float, kappa: float) -> float: ...
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def paretovariate(alpha: float) -> float: ...
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def weibullvariate(alpha: float, beta: float) -> float: ...
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