110 lines
3.3 KiB
Python
110 lines
3.3 KiB
Python
|
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: ...
|