125 lines
3.5 KiB
Python
125 lines
3.5 KiB
Python
|
import abc
|
||
|
|
from collections.abc import Callable, Mapping, Sequence
|
||
|
|
from threading import Lock
|
||
|
|
from typing import (
|
||
|
|
Any,
|
||
|
|
ClassVar,
|
||
|
|
Literal,
|
||
|
|
NamedTuple,
|
||
|
|
Self,
|
||
|
|
TypeAlias,
|
||
|
|
TypedDict,
|
||
|
|
overload,
|
||
|
|
type_check_only,
|
||
|
|
)
|
||
|
|
|
||
|
|
from _typeshed import Incomplete
|
||
|
|
from typing_extensions import CapsuleType
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
from numpy._typing import (
|
||
|
|
NDArray,
|
||
|
|
_ArrayLikeInt_co,
|
||
|
|
_DTypeLike,
|
||
|
|
_ShapeLike,
|
||
|
|
_UInt32Codes,
|
||
|
|
_UInt64Codes,
|
||
|
|
)
|
||
|
|
|
||
|
|
__all__ = ["BitGenerator", "SeedSequence"]
|
||
|
|
|
||
|
|
###
|
||
|
|
|
||
|
|
_DTypeLikeUint_: TypeAlias = _DTypeLike[np.uint32 | np.uint64] | _UInt32Codes | _UInt64Codes
|
||
|
|
|
||
|
|
@type_check_only
|
||
|
|
class _SeedSeqState(TypedDict):
|
||
|
|
entropy: int | Sequence[int] | None
|
||
|
|
spawn_key: tuple[int, ...]
|
||
|
|
pool_size: int
|
||
|
|
n_children_spawned: int
|
||
|
|
|
||
|
|
@type_check_only
|
||
|
|
class _Interface(NamedTuple):
|
||
|
|
state_address: Incomplete
|
||
|
|
state: Incomplete
|
||
|
|
next_uint64: Incomplete
|
||
|
|
next_uint32: Incomplete
|
||
|
|
next_double: Incomplete
|
||
|
|
bit_generator: Incomplete
|
||
|
|
|
||
|
|
@type_check_only
|
||
|
|
class _CythonMixin:
|
||
|
|
def __setstate_cython__(self, pyx_state: object, /) -> None: ...
|
||
|
|
def __reduce_cython__(self) -> Any: ... # noqa: ANN401
|
||
|
|
|
||
|
|
@type_check_only
|
||
|
|
class _GenerateStateMixin(_CythonMixin):
|
||
|
|
def generate_state(self, /, n_words: int, dtype: _DTypeLikeUint_ = ...) -> NDArray[np.uint32 | np.uint64]: ...
|
||
|
|
|
||
|
|
###
|
||
|
|
|
||
|
|
class ISeedSequence(abc.ABC):
|
||
|
|
@abc.abstractmethod
|
||
|
|
def generate_state(self, /, n_words: int, dtype: _DTypeLikeUint_ = ...) -> NDArray[np.uint32 | np.uint64]: ...
|
||
|
|
|
||
|
|
class ISpawnableSeedSequence(ISeedSequence, abc.ABC):
|
||
|
|
@abc.abstractmethod
|
||
|
|
def spawn(self, /, n_children: int) -> list[Self]: ...
|
||
|
|
|
||
|
|
class SeedlessSeedSequence(_GenerateStateMixin, ISpawnableSeedSequence):
|
||
|
|
def spawn(self, /, n_children: int) -> list[Self]: ...
|
||
|
|
|
||
|
|
class SeedSequence(_GenerateStateMixin, ISpawnableSeedSequence):
|
||
|
|
__pyx_vtable__: ClassVar[CapsuleType] = ...
|
||
|
|
|
||
|
|
entropy: int | Sequence[int] | None
|
||
|
|
spawn_key: tuple[int, ...]
|
||
|
|
pool_size: int
|
||
|
|
n_children_spawned: int
|
||
|
|
pool: NDArray[np.uint32]
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
/,
|
||
|
|
entropy: _ArrayLikeInt_co | None = None,
|
||
|
|
*,
|
||
|
|
spawn_key: Sequence[int] = (),
|
||
|
|
pool_size: int = 4,
|
||
|
|
n_children_spawned: int = ...,
|
||
|
|
) -> None: ...
|
||
|
|
def spawn(self, /, n_children: int) -> list[Self]: ...
|
||
|
|
@property
|
||
|
|
def state(self) -> _SeedSeqState: ...
|
||
|
|
|
||
|
|
class BitGenerator(_CythonMixin, abc.ABC):
|
||
|
|
lock: Lock
|
||
|
|
@property
|
||
|
|
def state(self) -> Mapping[str, Any]: ...
|
||
|
|
@state.setter
|
||
|
|
def state(self, value: Mapping[str, Any], /) -> None: ...
|
||
|
|
@property
|
||
|
|
def seed_seq(self) -> ISeedSequence: ...
|
||
|
|
@property
|
||
|
|
def ctypes(self) -> _Interface: ...
|
||
|
|
@property
|
||
|
|
def cffi(self) -> _Interface: ...
|
||
|
|
@property
|
||
|
|
def capsule(self) -> CapsuleType: ...
|
||
|
|
|
||
|
|
#
|
||
|
|
def __init__(self, /, seed: _ArrayLikeInt_co | SeedSequence | None = None) -> None: ...
|
||
|
|
def __reduce__(self) -> tuple[Callable[[str], Self], tuple[str], tuple[Mapping[str, Any], ISeedSequence]]: ...
|
||
|
|
def spawn(self, /, n_children: int) -> list[Self]: ...
|
||
|
|
def _benchmark(self, /, cnt: int, method: str = "uint64") -> None: ...
|
||
|
|
|
||
|
|
#
|
||
|
|
@overload
|
||
|
|
def random_raw(self, /, size: None = None, output: Literal[True] = True) -> int: ...
|
||
|
|
@overload
|
||
|
|
def random_raw(self, /, size: _ShapeLike, output: Literal[True] = True) -> NDArray[np.uint64]: ...
|
||
|
|
@overload
|
||
|
|
def random_raw(self, /, size: _ShapeLike | None, output: Literal[False]) -> None: ...
|
||
|
|
@overload
|
||
|
|
def random_raw(self, /, size: _ShapeLike | None = None, *, output: Literal[False]) -> None: ...
|