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import ctypes as ct import re from collections.abc import Callable, Iterable from typing import Any, Final, Generic, Self, overload from typing_extensions import TypeVar import numpy as np import numpy.typing as npt from numpy.ctypeslib import c_intp _CastT = TypeVar("_CastT", bound=ct._CanCastTo) _T_co = TypeVar("_T_co", covariant=True) _CT = TypeVar("_CT", bound=ct._CData) _PT_co = TypeVar("_PT_co", bound=int | None, default=None, covariant=True) ### IS_PYPY: Final[bool] = ... format_re: Final[re.Pattern[str]] = ... sep_re: Final[re.Pattern[str]] = ... space_re: Final[re.Pattern[str]] = ... ### # TODO: Let the likes of `shape_as` and `strides_as` return `None` # for 0D arrays once we've got shape-support class _ctypes(Generic[_PT_co]): @overload def __init__(self: _ctypes[None], /, array: npt.NDArray[Any], ptr: None = None) -> None: ... @overload def __init__(self, /, array: npt.NDArray[Any], ptr: _PT_co) -> None: ... # @property def data(self) -> _PT_co: ... @property def shape(self) -> ct.Array[c_intp]: ... @property def strides(self) -> ct.Array[c_intp]: ... @property def _as_parameter_(self) -> ct.c_void_p: ... # def data_as(self, /, obj: type[_CastT]) -> _CastT: ... def shape_as(self, /, obj: type[_CT]) -> ct.Array[_CT]: ... def strides_as(self, /, obj: type[_CT]) -> ct.Array[_CT]: ... class dummy_ctype(Generic[_T_co]): _cls: type[_T_co] def __init__(self, /, cls: type[_T_co]) -> None: ... def __eq__(self, other: Self, /) -> bool: ... # type: ignore[override] # pyright: ignore[reportIncompatibleMethodOverride] def __ne__(self, other: Self, /) -> bool: ... # type: ignore[override] # pyright: ignore[reportIncompatibleMethodOverride] def __mul__(self, other: object, /) -> Self: ... def __call__(self, /, *other: object) -> _T_co: ... def array_ufunc_errmsg_formatter(dummy: object, ufunc: np.ufunc, method: str, *inputs: object, **kwargs: object) -> str: ... def array_function_errmsg_formatter(public_api: Callable[..., object], types: Iterable[str]) -> str: ... def npy_ctypes_check(cls: type) -> bool: ...