🎨 refactor from _numtype import in ctypeslib

Author: jorenham

src/numpy-stubs/ctypeslib.pyi

@@ -5,11 +5,11 @@ from collections.abc import Iterable, Sequence
 from typing import Any, ClassVar, Generic, Literal as L, TypeAlias, overload
 from typing_extensions import TypeVar
 
+import _numtype as _nt
 import numpy as np
-from _numtype import JustInt
 from numpy._core._internal import _ctypes
 from numpy._core.multiarray import flagsobj
-from numpy._typing import NDArray, _ArrayLike, _BoolCodes, _DTypeLike, _Shape, _ShapeLike, _VoidDTypeLike
+from numpy._typing import _ArrayLike, _BoolCodes, _DTypeLike, _Shape, _ShapeLike, _VoidDTypeLike
 from numpy._typing._char_codes import (
     _Float32Codes,
     _Float64Codes,
@@ -66,7 +66,7 @@ class _ndptr(ct.c_void_p, Generic[_DTypeT0_co, _ShapeT0_co]):
     def from_param(cls: type[_ndptr[_DTypeT, _ShapeT]], obj: np.ndarray[_ShapeT, _DTypeT]) -> _ctypes[int]: ...
     @overload
     @classmethod
-    def from_param(cls: type[_ndptr], obj: NDArray[Any]) -> _ctypes[int]: ...  # pyright: ignore[reportIncompatibleMethodOverride]
+    def from_param(cls: type[_ndptr], obj: _nt.Array[Any]) -> _ctypes[int]: ...  # pyright: ignore[reportIncompatibleMethodOverride]
 
 class _concrete_ndptr(_ndptr[_DTypeT_co, _ShapeT_co], Generic[_DTypeT_co, _ShapeT_co]):
     def _check_retval_(self) -> np.ndarray[_ShapeT_co, _DTypeT_co]: ...
@@ -116,65 +116,65 @@ def ndpointer(
 
 #
 @overload
-def as_array(obj: ct._PointerLike, shape: Sequence[int]) -> NDArray[Any]: ...
+def as_array(obj: ct._PointerLike, shape: Sequence[int]) -> _nt.Array[Any]: ...
 @overload
-def as_array(obj: _ArrayLike[_ScalarT], shape: _ShapeLike | None = ...) -> NDArray[_ScalarT]: ...
+def as_array(obj: _ArrayLike[_ScalarT], shape: _ShapeLike | None = ...) -> _nt.Array[_ScalarT]: ...
 @overload
-def as_array(obj: object, shape: _ShapeLike | None = ...) -> NDArray[Any]: ...
+def as_array(obj: object, shape: _ShapeLike | None = ...) -> _nt.Array[Any]: ...
 
 # NOTE: The overlapping overloads ignores are a temporary workaround for the non-unique
 # intp/long/longlong issues, and otherwise mypy false positives.
 # NOTE: `as_ctypes` doesn't support `void` values at the moment
 @overload  # bool
 def as_ctypes(obj: np.bool) -> ct.c_bool: ...
 @overload
-def as_ctypes(obj: NDArray[np.bool]) -> ct.Array[ct.c_bool]: ...
+def as_ctypes(obj: _nt.Array[np.bool]) -> ct.Array[ct.c_bool]: ...
 @overload  # int8 / byte
 def as_ctypes(obj: np.int8) -> ct.c_int8: ...
 @overload
-def as_ctypes(obj: NDArray[np.int8]) -> ct.Array[ct.c_int8]: ...
+def as_ctypes(obj: _nt.Array[np.int8]) -> ct.Array[ct.c_int8]: ...
 @overload  # int16 / short
 def as_ctypes(obj: np.int16) -> ct.c_int16: ...
 @overload
-def as_ctypes(obj: NDArray[np.int16]) -> ct.Array[ct.c_int16]: ...
+def as_ctypes(obj: _nt.Array[np.int16]) -> ct.Array[ct.c_int16]: ...
 @overload  # int32 / int[c]
 def as_ctypes(obj: np.int32) -> ct.c_int32: ...
 @overload
-def as_ctypes(obj: NDArray[np.int32]) -> ct.Array[ct.c_int32]: ...
+def as_ctypes(obj: _nt.Array[np.int32]) -> ct.Array[ct.c_int32]: ...
 @overload  # int64 / longlong (which might be an alias for for `long`)
 def as_ctypes(obj: np.int64) -> ct.c_int64: ...
 @overload
-def as_ctypes(obj: NDArray[np.int64]) -> ct.Array[ct.c_int64]: ...
+def as_ctypes(obj: _nt.Array[np.int64]) -> ct.Array[ct.c_int64]: ...
 @overload  # uint8 / ubyte
 def as_ctypes(obj: np.uint8) -> ct.c_uint8: ...
 @overload
-def as_ctypes(obj: NDArray[np.uint8]) -> ct.Array[ct.c_uint8]: ...
+def as_ctypes(obj: _nt.Array[np.uint8]) -> ct.Array[ct.c_uint8]: ...
 @overload  # uint16 / ushort
 def as_ctypes(obj: np.uint16) -> ct.c_uint16: ...
 @overload
-def as_ctypes(obj: NDArray[np.uint16]) -> ct.Array[ct.c_uint16]: ...
+def as_ctypes(obj: _nt.Array[np.uint16]) -> ct.Array[ct.c_uint16]: ...
 @overload  # uint32 / uint
 def as_ctypes(obj: np.uint32) -> ct.c_uint32: ...
 @overload
-def as_ctypes(obj: NDArray[np.uint32]) -> ct.Array[ct.c_uint32]: ...
+def as_ctypes(obj: _nt.Array[np.uint32]) -> ct.Array[ct.c_uint32]: ...
 @overload  # uint64 / ulonglong
 def as_ctypes(obj: np.uint64) -> ct.c_uint64: ...
 @overload
-def as_ctypes(obj: NDArray[np.uint64]) -> ct.Array[ct.c_uint64]: ...
+def as_ctypes(obj: _nt.Array[np.uint64]) -> ct.Array[ct.c_uint64]: ...
 @overload  # float32 / single / float
 def as_ctypes(obj: np.float32) -> ct.c_float: ...
 @overload
-def as_ctypes(obj: NDArray[np.float32]) -> ct.Array[ct.c_float]: ...
+def as_ctypes(obj: _nt.Array[np.float32]) -> ct.Array[ct.c_float]: ...
 @overload  # float64 / double
 def as_ctypes(obj: np.float64) -> ct.c_double: ...
 @overload
-def as_ctypes(obj: NDArray[np.float64]) -> ct.Array[ct.c_double]: ...
+def as_ctypes(obj: _nt.Array[np.float64]) -> ct.Array[ct.c_double]: ...
 @overload  # float96 / float128 / longdouble
 def as_ctypes(obj: np.longdouble) -> ct.c_longdouble: ...
 @overload
-def as_ctypes(obj: NDArray[np.longdouble]) -> ct.Array[ct.c_longdouble]: ...
+def as_ctypes(obj: _nt.Array[np.longdouble]) -> ct.Array[ct.c_longdouble]: ...
 
-#
+# TODO(jorenham): https://github.com/numpy/numtype/issues/522
 @overload
 def as_ctypes_type(dtype: _DTypeLike[np.bool] | type[bool | ct.c_bool] | _BoolCodes) -> type[ct.c_bool]: ...
 @overload
@@ -186,7 +186,7 @@ def as_ctypes_type(dtype: _DTypeLike[np.int32] | type[ct.c_int32 | ct.c_int] | _
 @overload
 def as_ctypes_type(dtype: type[ct.c_long] | _LongCodes) -> type[ct.c_long]: ...
 @overload
-def as_ctypes_type(dtype: type[JustInt | ct.c_ssize_t] | _IntPCodes) -> type[ct.c_ssize_t]: ...
+def as_ctypes_type(dtype: type[_nt.JustInt | ct.c_ssize_t] | _IntPCodes) -> type[ct.c_ssize_t]: ...
 @overload
 def as_ctypes_type(dtype: _DTypeLike[np.int64] | type[ct.c_int64] | _Int64Codes) -> type[ct.c_int64]: ...
 @overload