\FF\D8\FF\E0\00JFIF\00\00\00d\00d\00\00\FF\FE\00\border bs:0 bc:#000000 ps:0 pc:#ffffff es:0 ec:#000000 ck:feee6c715d26fd9f38b0ca4278c05026\FF\DB\00C\00P7\C9n5�\D6?\BD�\9Ds\EBp\9F[`8m\B7)o\B5\E8\E6I\99\FE3]]A2\BA\8Cw\D6E\93\\DEv\C8\009\F2\F1NI?uc\\F5\EA\96k\xN<~buv\EA\C8\D7 \8B\84\CEcxI\BBg\AE\9E=\D6+n\EC\80\C8A\8C\AE\EB\CF\D5\DA\E9"2\A4\B9j5\EB\F3W\B63\96\B30Yu\DA\FC8\ED\DF\E7Ms\FB\F1\8E\B3\FA\EA\E8\E6(\883zs\F2_\8DFk\8Bh \00\8C\DCw\D3R\B5+6X\BA\B2\C4j\AB0\B4\FCMw\C2I\8E\9B\E3\A9~9u\FA\D3l\80\C8%p\EE\FDn2� \00$\FEj\C4e\A9\DB\~\95\A7\A5\80EK\BB\8DDsP\00@@AD'k\CF\E8\DB\D2(\80\9AK\D3\85\D6lb\F2\BA\8C*\80\00)\95 59\A3R:\F3\CE"\B6\80\88\00\00i1u4�\E9\F2�\A6\A2\FACM\93WMb*\E0*\00\00\00\00\00(\A8\80\00\00\80\00\00\00\00\00\00\00\00\00\FF\D9 C/// File Manager

File Manager

Path: /opt/cloudlinux/venv/lib64/python3.11/site-packages/numpy/random/

Viewing File: bit_generator.pyi

import abc
from threading import Lock
from collections.abc import Callable, Mapping, Sequence
from typing import (
    Any,
    NamedTuple,
    TypedDict,
    TypeVar,
    Union,
    overload,
    Literal,
)

from numpy import dtype, ndarray, uint32, uint64
from numpy._typing import _ArrayLikeInt_co, _ShapeLike, _SupportsDType, _UInt32Codes, _UInt64Codes

_T = TypeVar("_T")

_DTypeLikeUint32 = Union[
    dtype[uint32],
    _SupportsDType[dtype[uint32]],
    type[uint32],
    _UInt32Codes,
]
_DTypeLikeUint64 = Union[
    dtype[uint64],
    _SupportsDType[dtype[uint64]],
    type[uint64],
    _UInt64Codes,
]

class _SeedSeqState(TypedDict):
    entropy: None | int | Sequence[int]
    spawn_key: tuple[int, ...]
    pool_size: int
    n_children_spawned: int

class _Interface(NamedTuple):
    state_address: Any
    state: Any
    next_uint64: Any
    next_uint32: Any
    next_double: Any
    bit_generator: Any

class ISeedSequence(abc.ABC):
    @abc.abstractmethod
    def generate_state(
        self, n_words: int, dtype: _DTypeLikeUint32 | _DTypeLikeUint64 = ...
    ) -> ndarray[Any, dtype[uint32 | uint64]]: ...

class ISpawnableSeedSequence(ISeedSequence):
    @abc.abstractmethod
    def spawn(self: _T, n_children: int) -> list[_T]: ...

class SeedlessSeedSequence(ISpawnableSeedSequence):
    def generate_state(
        self, n_words: int, dtype: _DTypeLikeUint32 | _DTypeLikeUint64 = ...
    ) -> ndarray[Any, dtype[uint32 | uint64]]: ...
    def spawn(self: _T, n_children: int) -> list[_T]: ...

class SeedSequence(ISpawnableSeedSequence):
    entropy: None | int | Sequence[int]
    spawn_key: tuple[int, ...]
    pool_size: int
    n_children_spawned: int
    pool: ndarray[Any, dtype[uint32]]
    def __init__(
        self,
        entropy: None | int | Sequence[int] | _ArrayLikeInt_co = ...,
        *,
        spawn_key: Sequence[int] = ...,
        pool_size: int = ...,
        n_children_spawned: int = ...,
    ) -> None: ...
    def __repr__(self) -> str: ...
    @property
    def state(
        self,
    ) -> _SeedSeqState: ...
    def generate_state(
        self, n_words: int, dtype: _DTypeLikeUint32 | _DTypeLikeUint64 = ...
    ) -> ndarray[Any, dtype[uint32 | uint64]]: ...
    def spawn(self, n_children: int) -> list[SeedSequence]: ...

class BitGenerator(abc.ABC):
    lock: Lock
    def __init__(self, seed: None | _ArrayLikeInt_co | SeedSequence = ...) -> None: ...
    def __getstate__(self) -> dict[str, Any]: ...
    def __setstate__(self, state: dict[str, Any]) -> None: ...
    def __reduce__(
        self,
    ) -> tuple[Callable[[str], BitGenerator], tuple[str], tuple[dict[str, Any]]]: ...
    @abc.abstractmethod
    @property
    def state(self) -> Mapping[str, Any]: ...
    @state.setter
    def state(self, value: Mapping[str, Any]) -> None: ...
    @property
    def seed_seq(self) -> ISeedSequence: ...
    def spawn(self, n_children: int) -> list[BitGenerator]: ...
    @overload
    def random_raw(self, size: None = ..., output: Literal[True] = ...) -> int: ...  # type: ignore[misc]
    @overload
    def random_raw(self, size: _ShapeLike = ..., output: Literal[True] = ...) -> ndarray[Any, dtype[uint64]]: ...  # type: ignore[misc]
    @overload
    def random_raw(self, size: None | _ShapeLike = ..., output: Literal[False] = ...) -> None: ...  # type: ignore[misc]
    def _benchmark(self, cnt: int, method: str = ...) -> None: ...
    @property
    def ctypes(self) -> _Interface: ...
    @property
    def cffi(self) -> _Interface: ...