structure saas with tools

.venv/lib/python3.10/site-packages/numpy/random/bit_generator.pyi

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+import abc
+from collections.abc import Callable, Mapping, Sequence
+from threading import Lock
+from typing import Any, ClassVar, Literal, NamedTuple, TypeAlias, TypedDict, overload, type_check_only
+
+from _typeshed import Incomplete
+from typing_extensions import CapsuleType, Self
+
+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: ...