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: ... @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: ...