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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: ...
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