일단 커밋. 오랫동안 커밋을 안해서 꼬였다.

리팩토리 중.

.venv/lib/python3.12/site-packages/numpy/typing/__init__.py

@@ -0,0 +1,201 @@
+"""
+============================
+Typing (:mod:`numpy.typing`)
+============================
+
+.. versionadded:: 1.20
+
+Large parts of the NumPy API have :pep:`484`-style type annotations. In
+addition a number of type aliases are available to users, most prominently
+the two below:
+
+- `ArrayLike`: objects that can be converted to arrays
+- `DTypeLike`: objects that can be converted to dtypes
+
+.. _typing-extensions: https://pypi.org/project/typing-extensions/
+
+Mypy plugin
+-----------
+
+.. versionadded:: 1.21
+
+.. automodule:: numpy.typing.mypy_plugin
+
+.. currentmodule:: numpy.typing
+
+Differences from the runtime NumPy API
+--------------------------------------
+
+NumPy is very flexible. Trying to describe the full range of
+possibilities statically would result in types that are not very
+helpful. For that reason, the typed NumPy API is often stricter than
+the runtime NumPy API. This section describes some notable
+differences.
+
+ArrayLike
+~~~~~~~~~
+
+The `ArrayLike` type tries to avoid creating object arrays. For
+example,
+
+.. code-block:: python
+
+    >>> np.array(x**2 for x in range(10))
+    array(<generator object <genexpr> at ...>, dtype=object)
+
+is valid NumPy code which will create a 0-dimensional object
+array. Type checkers will complain about the above example when using
+the NumPy types however. If you really intended to do the above, then
+you can either use a ``# type: ignore`` comment:
+
+.. code-block:: python
+
+    >>> np.array(x**2 for x in range(10))  # type: ignore
+
+or explicitly type the array like object as `~typing.Any`:
+
+.. code-block:: python
+
+    >>> from typing import Any
+    >>> array_like: Any = (x**2 for x in range(10))
+    >>> np.array(array_like)
+    array(<generator object <genexpr> at ...>, dtype=object)
+
+ndarray
+~~~~~~~
+
+It's possible to mutate the dtype of an array at runtime. For example,
+the following code is valid:
+
+.. code-block:: python
+
+    >>> x = np.array([1, 2])
+    >>> x.dtype = np.bool
+
+This sort of mutation is not allowed by the types. Users who want to
+write statically typed code should instead use the `numpy.ndarray.view`
+method to create a view of the array with a different dtype.
+
+DTypeLike
+~~~~~~~~~
+
+The `DTypeLike` type tries to avoid creation of dtype objects using
+dictionary of fields like below:
+
+.. code-block:: python
+
+    >>> x = np.dtype({"field1": (float, 1), "field2": (int, 3)})
+
+Although this is valid NumPy code, the type checker will complain about it,
+since its usage is discouraged.
+Please see : :ref:`Data type objects <arrays.dtypes>`
+
+Number precision
+~~~~~~~~~~~~~~~~
+
+The precision of `numpy.number` subclasses is treated as a invariant generic
+parameter (see :class:`~NBitBase`), simplifying the annotating of processes
+involving precision-based casting.
+
+.. code-block:: python
+
+    >>> from typing import TypeVar
+    >>> import numpy as np
+    >>> import numpy.typing as npt
+
+    >>> T = TypeVar("T", bound=npt.NBitBase)
+    >>> def func(a: "np.floating[T]", b: "np.floating[T]") -> "np.floating[T]":
+    ...     ...
+
+Consequently, the likes of `~numpy.float16`, `~numpy.float32` and
+`~numpy.float64` are still sub-types of `~numpy.floating`, but, contrary to
+runtime, they're not necessarily considered as sub-classes.
+
+Timedelta64
+~~~~~~~~~~~
+
+The `~numpy.timedelta64` class is not considered a subclass of
+`~numpy.signedinteger`, the former only inheriting from `~numpy.generic`
+while static type checking.
+
+0D arrays
+~~~~~~~~~
+
+During runtime numpy aggressively casts any passed 0D arrays into their
+corresponding `~numpy.generic` instance. Until the introduction of shape
+typing (see :pep:`646`) it is unfortunately not possible to make the
+necessary distinction between 0D and >0D arrays. While thus not strictly
+correct, all operations that can potentially perform a 0D-array -> scalar
+cast are currently annotated as exclusively returning an `~numpy.ndarray`.
+
+If it is known in advance that an operation *will* perform a
+0D-array -> scalar cast, then one can consider manually remedying the
+situation with either `typing.cast` or a ``# type: ignore`` comment.
+
+Record array dtypes
+~~~~~~~~~~~~~~~~~~~
+
+The dtype of `numpy.recarray`, and the :ref:`routines.array-creation.rec`
+functions in general, can be specified in one of two ways:
+
+* Directly via the ``dtype`` argument.
+* With up to five helper arguments that operate via `numpy.rec.format_parser`:
+  ``formats``, ``names``, ``titles``, ``aligned`` and ``byteorder``.
+
+These two approaches are currently typed as being mutually exclusive,
+*i.e.* if ``dtype`` is specified than one may not specify ``formats``.
+While this mutual exclusivity is not (strictly) enforced during runtime,
+combining both dtype specifiers can lead to unexpected or even downright
+buggy behavior.
+
+API
+---
+
+"""
+# NOTE: The API section will be appended with additional entries
+# further down in this file
+
+# pyright: reportDeprecated=false
+
+from numpy._typing import ArrayLike, DTypeLike, NBitBase, NDArray
+
+__all__ = ["ArrayLike", "DTypeLike", "NBitBase", "NDArray"]
+
+
+__DIR = __all__ + [k for k in globals() if k.startswith("__") and k.endswith("__")]
+__DIR_SET = frozenset(__DIR)
+
+
+def __dir__() -> list[str]:
+    return __DIR
+
+def __getattr__(name: str):
+    if name == "NBitBase":
+        import warnings
+
+        # Deprecated in NumPy 2.3, 2025-05-01
+        warnings.warn(
+            "`NBitBase` is deprecated and will be removed from numpy.typing in the "
+            "future. Use `@typing.overload` or a `TypeVar` with a scalar-type as upper "
+            "bound, instead. (deprecated in NumPy 2.3)",
+            DeprecationWarning,
+            stacklevel=2,
+        )
+        return NBitBase
+
+    if name in __DIR_SET:
+        return globals()[name]
+
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
+
+
+if __doc__ is not None:
+    from numpy._typing._add_docstring import _docstrings
+    __doc__ += _docstrings
+    __doc__ += '\n.. autoclass:: numpy.typing.NBitBase\n'
+    del _docstrings
+
+from numpy._pytesttester import PytestTester
+
+test = PytestTester(__name__)
+del PytestTester

.venv/lib/python3.12/site-packages/numpy/typing/mypy_plugin.py

@@ -0,0 +1,195 @@
+"""A mypy_ plugin for managing a number of platform-specific annotations.
+Its functionality can be split into three distinct parts:
+
+* Assigning the (platform-dependent) precisions of certain `~numpy.number`
+  subclasses, including the likes of `~numpy.int_`, `~numpy.intp` and
+  `~numpy.longlong`. See the documentation on
+  :ref:`scalar types <arrays.scalars.built-in>` for a comprehensive overview
+  of the affected classes. Without the plugin the precision of all relevant
+  classes will be inferred as `~typing.Any`.
+* Removing all extended-precision `~numpy.number` subclasses that are
+  unavailable for the platform in question. Most notably this includes the
+  likes of `~numpy.float128` and `~numpy.complex256`. Without the plugin *all*
+  extended-precision types will, as far as mypy is concerned, be available
+  to all platforms.
+* Assigning the (platform-dependent) precision of `~numpy.ctypeslib.c_intp`.
+  Without the plugin the type will default to `ctypes.c_int64`.
+
+  .. versionadded:: 1.22
+
+.. deprecated:: 2.3
+
+Examples
+--------
+To enable the plugin, one must add it to their mypy `configuration file`_:
+
+.. code-block:: ini
+
+    [mypy]
+    plugins = numpy.typing.mypy_plugin
+
+.. _mypy: https://mypy-lang.org/
+.. _configuration file: https://mypy.readthedocs.io/en/stable/config_file.html
+
+"""
+
+from collections.abc import Callable, Iterable
+from typing import TYPE_CHECKING, Final, TypeAlias, cast
+
+import numpy as np
+
+__all__: list[str] = []
+
+
+def _get_precision_dict() -> dict[str, str]:
+    names = [
+        ("_NBitByte", np.byte),
+        ("_NBitShort", np.short),
+        ("_NBitIntC", np.intc),
+        ("_NBitIntP", np.intp),
+        ("_NBitInt", np.int_),
+        ("_NBitLong", np.long),
+        ("_NBitLongLong", np.longlong),
+
+        ("_NBitHalf", np.half),
+        ("_NBitSingle", np.single),
+        ("_NBitDouble", np.double),
+        ("_NBitLongDouble", np.longdouble),
+    ]
+    ret: dict[str, str] = {}
+    for name, typ in names:
+        n = 8 * np.dtype(typ).itemsize
+        ret[f"{_MODULE}._nbit.{name}"] = f"{_MODULE}._nbit_base._{n}Bit"
+    return ret
+
+
+def _get_extended_precision_list() -> list[str]:
+    extended_names = [
+        "float96",
+        "float128",
+        "complex192",
+        "complex256",
+    ]
+    return [i for i in extended_names if hasattr(np, i)]
+
+def _get_c_intp_name() -> str:
+    # Adapted from `np.core._internal._getintp_ctype`
+    return {
+        "i": "c_int",
+        "l": "c_long",
+        "q": "c_longlong",
+    }.get(np.dtype("n").char, "c_long")
+
+
+_MODULE: Final = "numpy._typing"
+
+#: A dictionary mapping type-aliases in `numpy._typing._nbit` to
+#: concrete `numpy.typing.NBitBase` subclasses.
+_PRECISION_DICT: Final = _get_precision_dict()
+
+#: A list with the names of all extended precision `np.number` subclasses.
+_EXTENDED_PRECISION_LIST: Final = _get_extended_precision_list()
+
+#: The name of the ctypes equivalent of `np.intp`
+_C_INTP: Final = _get_c_intp_name()
+
+
+try:
+    if TYPE_CHECKING:
+        from mypy.typeanal import TypeAnalyser
+
+    import mypy.types
+    from mypy.build import PRI_MED
+    from mypy.nodes import ImportFrom, MypyFile, Statement
+    from mypy.plugin import AnalyzeTypeContext, Plugin
+
+except ModuleNotFoundError as e:
+
+    def plugin(version: str) -> type:
+        raise e
+
+else:
+
+    _HookFunc: TypeAlias = Callable[[AnalyzeTypeContext], mypy.types.Type]
+
+    def _hook(ctx: AnalyzeTypeContext) -> mypy.types.Type:
+        """Replace a type-alias with a concrete ``NBitBase`` subclass."""
+        typ, _, api = ctx
+        name = typ.name.split(".")[-1]
+        name_new = _PRECISION_DICT[f"{_MODULE}._nbit.{name}"]
+        return cast("TypeAnalyser", api).named_type(name_new)
+
+    def _index(iterable: Iterable[Statement], id: str) -> int:
+        """Identify the first ``ImportFrom`` instance the specified `id`."""
+        for i, value in enumerate(iterable):
+            if getattr(value, "id", None) == id:
+                return i
+        raise ValueError("Failed to identify a `ImportFrom` instance "
+                         f"with the following id: {id!r}")
+
+    def _override_imports(
+        file: MypyFile,
+        module: str,
+        imports: list[tuple[str, str | None]],
+    ) -> None:
+        """Override the first `module`-based import with new `imports`."""
+        # Construct a new `from module import y` statement
+        import_obj = ImportFrom(module, 0, names=imports)
+        import_obj.is_top_level = True
+
+        # Replace the first `module`-based import statement with `import_obj`
+        for lst in [file.defs, cast("list[Statement]", file.imports)]:
+            i = _index(lst, module)
+            lst[i] = import_obj
+
+    class _NumpyPlugin(Plugin):
+        """A mypy plugin for handling versus numpy-specific typing tasks."""
+
+        def get_type_analyze_hook(self, fullname: str) -> _HookFunc | None:
+            """Set the precision of platform-specific `numpy.number`
+            subclasses.
+
+            For example: `numpy.int_`, `numpy.longlong` and `numpy.longdouble`.
+            """
+            if fullname in _PRECISION_DICT:
+                return _hook
+            return None
+
+        def get_additional_deps(
+            self, file: MypyFile
+        ) -> list[tuple[int, str, int]]:
+            """Handle all import-based overrides.
+
+            * Import platform-specific extended-precision `numpy.number`
+              subclasses (*e.g.* `numpy.float96` and `numpy.float128`).
+            * Import the appropriate `ctypes` equivalent to `numpy.intp`.
+
+            """
+            fullname = file.fullname
+            if fullname == "numpy":
+                _override_imports(
+                    file,
+                    f"{_MODULE}._extended_precision",
+                    imports=[(v, v) for v in _EXTENDED_PRECISION_LIST],
+                )
+            elif fullname == "numpy.ctypeslib":
+                _override_imports(
+                    file,
+                    "ctypes",
+                    imports=[(_C_INTP, "_c_intp")],
+                )
+            return [(PRI_MED, fullname, -1)]
+
+    def plugin(version: str) -> type:
+        import warnings
+
+        plugin = "numpy.typing.mypy_plugin"
+        # Deprecated 2025-01-10, NumPy 2.3
+        warn_msg = (
+            f"`{plugin}` is deprecated, and will be removed in a future "
+            f"release. Please remove `plugins = {plugin}` in your mypy config."
+            f"(deprecated in NumPy 2.3)"
+        )
+        warnings.warn(warn_msg, DeprecationWarning, stacklevel=3)
+
+        return _NumpyPlugin

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/fail/arithmetic.pyi

@@ -0,0 +1,126 @@
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+
+b_ = np.bool()
+dt = np.datetime64(0, "D")
+td = np.timedelta64(0, "D")
+
+AR_b: npt.NDArray[np.bool]
+AR_u: npt.NDArray[np.uint32]
+AR_i: npt.NDArray[np.int64]
+AR_f: npt.NDArray[np.longdouble]
+AR_c: npt.NDArray[np.complex128]
+AR_m: npt.NDArray[np.timedelta64]
+AR_M: npt.NDArray[np.datetime64]
+
+ANY: Any
+
+AR_LIKE_b: list[bool]
+AR_LIKE_u: list[np.uint32]
+AR_LIKE_i: list[int]
+AR_LIKE_f: list[float]
+AR_LIKE_c: list[complex]
+AR_LIKE_m: list[np.timedelta64]
+AR_LIKE_M: list[np.datetime64]
+
+# Array subtraction
+
+# NOTE: mypys `NoReturn` errors are, unfortunately, not that great
+_1 = AR_b - AR_LIKE_b  # type: ignore[var-annotated]
+_2 = AR_LIKE_b - AR_b  # type: ignore[var-annotated]
+AR_i - bytes()  # type: ignore[operator]
+
+AR_f - AR_LIKE_m  # type: ignore[operator]
+AR_f - AR_LIKE_M  # type: ignore[operator]
+AR_c - AR_LIKE_m  # type: ignore[operator]
+AR_c - AR_LIKE_M  # type: ignore[operator]
+
+AR_m - AR_LIKE_f  # type: ignore[operator]
+AR_M - AR_LIKE_f  # type: ignore[operator]
+AR_m - AR_LIKE_c  # type: ignore[operator]
+AR_M - AR_LIKE_c  # type: ignore[operator]
+
+AR_m - AR_LIKE_M  # type: ignore[operator]
+AR_LIKE_m - AR_M  # type: ignore[operator]
+
+# array floor division
+
+AR_M // AR_LIKE_b  # type: ignore[operator]
+AR_M // AR_LIKE_u  # type: ignore[operator]
+AR_M // AR_LIKE_i  # type: ignore[operator]
+AR_M // AR_LIKE_f  # type: ignore[operator]
+AR_M // AR_LIKE_c  # type: ignore[operator]
+AR_M // AR_LIKE_m  # type: ignore[operator]
+AR_M // AR_LIKE_M  # type: ignore[operator]
+
+AR_b // AR_LIKE_M  # type: ignore[operator]
+AR_u // AR_LIKE_M  # type: ignore[operator]
+AR_i // AR_LIKE_M  # type: ignore[operator]
+AR_f // AR_LIKE_M  # type: ignore[operator]
+AR_c // AR_LIKE_M  # type: ignore[operator]
+AR_m // AR_LIKE_M  # type: ignore[operator]
+AR_M // AR_LIKE_M  # type: ignore[operator]
+
+_3 = AR_m // AR_LIKE_b  # type: ignore[var-annotated]
+AR_m // AR_LIKE_c  # type: ignore[operator]
+
+AR_b // AR_LIKE_m  # type: ignore[operator]
+AR_u // AR_LIKE_m  # type: ignore[operator]
+AR_i // AR_LIKE_m  # type: ignore[operator]
+AR_f // AR_LIKE_m  # type: ignore[operator]
+AR_c // AR_LIKE_m  # type: ignore[operator]
+
+# regression tests for https://github.com/numpy/numpy/issues/28957
+AR_c // 2  # type: ignore[operator]
+AR_c // AR_i  # type: ignore[operator]
+AR_c // AR_c  # type: ignore[operator]
+
+# Array multiplication
+
+AR_b *= AR_LIKE_u  # type: ignore[arg-type]
+AR_b *= AR_LIKE_i  # type: ignore[arg-type]
+AR_b *= AR_LIKE_f  # type: ignore[arg-type]
+AR_b *= AR_LIKE_c  # type: ignore[arg-type]
+AR_b *= AR_LIKE_m  # type: ignore[arg-type]
+
+AR_u *= AR_LIKE_f  # type: ignore[arg-type]
+AR_u *= AR_LIKE_c  # type: ignore[arg-type]
+AR_u *= AR_LIKE_m  # type: ignore[arg-type]
+
+AR_i *= AR_LIKE_f  # type: ignore[arg-type]
+AR_i *= AR_LIKE_c  # type: ignore[arg-type]
+AR_i *= AR_LIKE_m  # type: ignore[arg-type]
+
+AR_f *= AR_LIKE_c  # type: ignore[arg-type]
+AR_f *= AR_LIKE_m  # type: ignore[arg-type]
+
+# Array power
+
+AR_b **= AR_LIKE_b  # type: ignore[misc]
+AR_b **= AR_LIKE_u  # type: ignore[misc]
+AR_b **= AR_LIKE_i  # type: ignore[misc]
+AR_b **= AR_LIKE_f  # type: ignore[misc]
+AR_b **= AR_LIKE_c  # type: ignore[misc]
+
+AR_u **= AR_LIKE_f  # type: ignore[arg-type]
+AR_u **= AR_LIKE_c  # type: ignore[arg-type]
+
+AR_i **= AR_LIKE_f  # type: ignore[arg-type]
+AR_i **= AR_LIKE_c  # type: ignore[arg-type]
+
+AR_f **= AR_LIKE_c  # type: ignore[arg-type]
+
+# Scalars
+
+b_ - b_  # type: ignore[operator]
+
+dt + dt  # type: ignore[operator]
+td - dt  # type: ignore[operator]
+td % 1  # type: ignore[operator]
+td / dt  # type: ignore[operator]
+td % dt  # type: ignore[operator]
+
+-b_  # type: ignore[operator]
++b_  # type: ignore[operator]

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/fail/array_constructors.pyi

@@ -0,0 +1,34 @@
+import numpy as np
+import numpy.typing as npt
+
+a: npt.NDArray[np.float64]
+generator = (i for i in range(10))
+
+np.require(a, requirements=1)  # type: ignore[call-overload]
+np.require(a, requirements="TEST")  # type: ignore[arg-type]
+
+np.zeros("test")  # type: ignore[arg-type]
+np.zeros()  # type: ignore[call-overload]
+
+np.ones("test")  # type: ignore[arg-type]
+np.ones()  # type: ignore[call-overload]
+
+np.array(0, float, True)  # type: ignore[call-overload]
+
+np.linspace(None, 'bob')  # type: ignore[call-overload]
+np.linspace(0, 2, num=10.0)  # type: ignore[call-overload]
+np.linspace(0, 2, endpoint='True')  # type: ignore[call-overload]
+np.linspace(0, 2, retstep=b'False')  # type: ignore[call-overload]
+np.linspace(0, 2, dtype=0)  # type: ignore[call-overload]
+np.linspace(0, 2, axis=None)  # type: ignore[call-overload]
+
+np.logspace(None, 'bob')  # type: ignore[call-overload]
+np.logspace(0, 2, base=None)  # type: ignore[call-overload]
+
+np.geomspace(None, 'bob')  # type: ignore[call-overload]
+
+np.stack(generator)  # type: ignore[call-overload]
+np.hstack({1, 2})  # type: ignore[call-overload]
+np.vstack(1)  # type: ignore[call-overload]
+
+np.array([1], like=1)  # type: ignore[call-overload]

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/fail/array_like.pyi

@@ -0,0 +1,15 @@
+import numpy as np
+from numpy._typing import ArrayLike
+
+class A: ...
+
+x1: ArrayLike = (i for i in range(10))  # type: ignore[assignment]
+x2: ArrayLike = A()  # type: ignore[assignment]
+x3: ArrayLike = {1: "foo", 2: "bar"}  # type: ignore[assignment]
+
+scalar = np.int64(1)
+scalar.__array__(dtype=np.float64)  # type: ignore[call-overload]
+array = np.array([1])
+array.__array__(dtype=np.float64)  # type: ignore[call-overload]
+
+array.setfield(np.eye(1), np.int32, (0, 1))  # type: ignore[arg-type]

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/fail/array_pad.pyi

@@ -0,0 +1,6 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_i8: npt.NDArray[np.int64]
+
+np.pad(AR_i8, 2, mode="bob")  # type: ignore[call-overload]

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/fail/arrayprint.pyi

@@ -0,0 +1,16 @@
+from collections.abc import Callable
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+
+AR: npt.NDArray[np.float64]
+func1: Callable[[Any], str]
+func2: Callable[[np.integer], str]
+
+np.array2string(AR, style=None)  # type: ignore[call-overload]
+np.array2string(AR, legacy="1.14")  # type: ignore[call-overload]
+np.array2string(AR, sign="*")  # type: ignore[call-overload]
+np.array2string(AR, floatmode="default")  # type: ignore[call-overload]
+np.array2string(AR, formatter={"A": func1})  # type: ignore[call-overload]
+np.array2string(AR, formatter={"float": func2})  # type: ignore[call-overload]

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/fail/arrayterator.pyi

@@ -0,0 +1,14 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_i8: npt.NDArray[np.int64]
+ar_iter = np.lib.Arrayterator(AR_i8)
+
+np.lib.Arrayterator(np.int64())  # type: ignore[arg-type]
+ar_iter.shape = (10, 5)  # type: ignore[misc]
+ar_iter[None]  # type: ignore[index]
+ar_iter[None, 1]  # type: ignore[index]
+ar_iter[np.intp()]  # type: ignore[index]
+ar_iter[np.intp(), ...]  # type: ignore[index]
+ar_iter[AR_i8]  # type: ignore[index]
+ar_iter[AR_i8, :]  # type: ignore[index]

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/fail/bitwise_ops.pyi

@@ -0,0 +1,17 @@
+import numpy as np
+
+i8 = np.int64()
+i4 = np.int32()
+u8 = np.uint64()
+b_ = np.bool()
+i = int()
+
+f8 = np.float64()
+
+b_ >> f8  # type: ignore[operator]
+i8 << f8  # type: ignore[operator]
+i | f8  # type: ignore[operator]
+i8 ^ f8  # type: ignore[operator]
+u8 & f8  # type: ignore[operator]
+~f8  # type: ignore[operator]
+# TODO: Certain mixes like i4 << u8 go to float and thus should fail

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/fail/char.pyi

@@ -0,0 +1,65 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_U: npt.NDArray[np.str_]
+AR_S: npt.NDArray[np.bytes_]
+
+np.char.equal(AR_U, AR_S)  # type: ignore[arg-type]
+np.char.not_equal(AR_U, AR_S)  # type: ignore[arg-type]
+
+np.char.greater_equal(AR_U, AR_S)  # type: ignore[arg-type]
+np.char.less_equal(AR_U, AR_S)  # type: ignore[arg-type]
+np.char.greater(AR_U, AR_S)  # type: ignore[arg-type]
+np.char.less(AR_U, AR_S)  # type: ignore[arg-type]
+
+np.char.encode(AR_S)  # type: ignore[arg-type]
+np.char.decode(AR_U)  # type: ignore[arg-type]
+
+np.char.join(AR_U, b"_")  # type: ignore[arg-type]
+np.char.join(AR_S, "_")  # type: ignore[arg-type]
+
+np.char.ljust(AR_U, 5, fillchar=b"a")  # type: ignore[arg-type]
+np.char.ljust(AR_S, 5, fillchar="a")  # type: ignore[arg-type]
+np.char.rjust(AR_U, 5, fillchar=b"a")  # type: ignore[arg-type]
+np.char.rjust(AR_S, 5, fillchar="a")  # type: ignore[arg-type]
+
+np.char.lstrip(AR_U, chars=b"a")  # type: ignore[arg-type]
+np.char.lstrip(AR_S, chars="a")  # type: ignore[arg-type]
+np.char.strip(AR_U, chars=b"a")  # type: ignore[arg-type]
+np.char.strip(AR_S, chars="a")  # type: ignore[arg-type]
+np.char.rstrip(AR_U, chars=b"a")  # type: ignore[arg-type]
+np.char.rstrip(AR_S, chars="a")  # type: ignore[arg-type]
+
+np.char.partition(AR_U, b"a")  # type: ignore[arg-type]
+np.char.partition(AR_S, "a")  # type: ignore[arg-type]
+np.char.rpartition(AR_U, b"a")  # type: ignore[arg-type]
+np.char.rpartition(AR_S, "a")  # type: ignore[arg-type]
+
+np.char.replace(AR_U, b"_", b"-")  # type: ignore[arg-type]
+np.char.replace(AR_S, "_", "-")  # type: ignore[arg-type]
+
+np.char.split(AR_U, b"_")  # type: ignore[arg-type]
+np.char.split(AR_S, "_")  # type: ignore[arg-type]
+np.char.rsplit(AR_U, b"_")  # type: ignore[arg-type]
+np.char.rsplit(AR_S, "_")  # type: ignore[arg-type]
+
+np.char.count(AR_U, b"a", start=[1, 2, 3])  # type: ignore[arg-type]
+np.char.count(AR_S, "a", end=9)  # type: ignore[arg-type]
+
+np.char.endswith(AR_U, b"a", start=[1, 2, 3])  # type: ignore[arg-type]
+np.char.endswith(AR_S, "a", end=9)  # type: ignore[arg-type]
+np.char.startswith(AR_U, b"a", start=[1, 2, 3])  # type: ignore[arg-type]
+np.char.startswith(AR_S, "a", end=9)  # type: ignore[arg-type]
+
+np.char.find(AR_U, b"a", start=[1, 2, 3])  # type: ignore[arg-type]
+np.char.find(AR_S, "a", end=9)  # type: ignore[arg-type]
+np.char.rfind(AR_U, b"a", start=[1, 2, 3])  # type: ignore[arg-type]
+np.char.rfind(AR_S, "a", end=9)  # type: ignore[arg-type]
+
+np.char.index(AR_U, b"a", start=[1, 2, 3])  # type: ignore[arg-type]
+np.char.index(AR_S, "a", end=9)  # type: ignore[arg-type]
+np.char.rindex(AR_U, b"a", start=[1, 2, 3])  # type: ignore[arg-type]
+np.char.rindex(AR_S, "a", end=9)  # type: ignore[arg-type]
+
+np.char.isdecimal(AR_S)  # type: ignore[arg-type]
+np.char.isnumeric(AR_S)  # type: ignore[arg-type]

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/fail/chararray.pyi

@@ -0,0 +1,62 @@
+from typing import Any
+import numpy as np
+
+AR_U: np.char.chararray[tuple[Any, ...], np.dtype[np.str_]]
+AR_S: np.char.chararray[tuple[Any, ...], np.dtype[np.bytes_]]
+
+AR_S.encode()  # type: ignore[misc]
+AR_U.decode()  # type: ignore[misc]
+
+AR_U.join(b"_")  # type: ignore[arg-type]
+AR_S.join("_")  # type: ignore[arg-type]
+
+AR_U.ljust(5, fillchar=b"a")  # type: ignore[arg-type]
+AR_S.ljust(5, fillchar="a")  # type: ignore[arg-type]
+AR_U.rjust(5, fillchar=b"a")  # type: ignore[arg-type]
+AR_S.rjust(5, fillchar="a")  # type: ignore[arg-type]
+
+AR_U.lstrip(chars=b"a")  # type: ignore[arg-type]
+AR_S.lstrip(chars="a")  # type: ignore[arg-type]
+AR_U.strip(chars=b"a")  # type: ignore[arg-type]
+AR_S.strip(chars="a")  # type: ignore[arg-type]
+AR_U.rstrip(chars=b"a")  # type: ignore[arg-type]
+AR_S.rstrip(chars="a")  # type: ignore[arg-type]
+
+AR_U.partition(b"a")  # type: ignore[arg-type]
+AR_S.partition("a")  # type: ignore[arg-type]
+AR_U.rpartition(b"a")  # type: ignore[arg-type]
+AR_S.rpartition("a")  # type: ignore[arg-type]
+
+AR_U.replace(b"_", b"-")  # type: ignore[arg-type]
+AR_S.replace("_", "-")  # type: ignore[arg-type]
+
+AR_U.split(b"_")  # type: ignore[arg-type]
+AR_S.split("_")  # type: ignore[arg-type]
+AR_S.split(1)  # type: ignore[arg-type]
+AR_U.rsplit(b"_")  # type: ignore[arg-type]
+AR_S.rsplit("_")  # type: ignore[arg-type]
+
+AR_U.count(b"a", start=[1, 2, 3])  # type: ignore[arg-type]
+AR_S.count("a", end=9)  # type: ignore[arg-type]
+
+AR_U.endswith(b"a", start=[1, 2, 3])  # type: ignore[arg-type]
+AR_S.endswith("a", end=9)  # type: ignore[arg-type]
+AR_U.startswith(b"a", start=[1, 2, 3])  # type: ignore[arg-type]
+AR_S.startswith("a", end=9)  # type: ignore[arg-type]
+
+AR_U.find(b"a", start=[1, 2, 3])  # type: ignore[arg-type]
+AR_S.find("a", end=9)  # type: ignore[arg-type]
+AR_U.rfind(b"a", start=[1, 2, 3])  # type: ignore[arg-type]
+AR_S.rfind("a", end=9)  # type: ignore[arg-type]
+
+AR_U.index(b"a", start=[1, 2, 3])  # type: ignore[arg-type]
+AR_S.index("a", end=9)  # type: ignore[arg-type]
+AR_U.rindex(b"a", start=[1, 2, 3])  # type: ignore[arg-type]
+AR_S.rindex("a", end=9)  # type: ignore[arg-type]
+
+AR_U == AR_S  # type: ignore[operator]
+AR_U != AR_S  # type: ignore[operator]
+AR_U >= AR_S  # type: ignore[operator]
+AR_U <= AR_S  # type: ignore[operator]
+AR_U > AR_S  # type: ignore[operator]
+AR_U < AR_S  # type: ignore[operator]

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/fail/comparisons.pyi

@@ -0,0 +1,27 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_i: npt.NDArray[np.int64]
+AR_f: npt.NDArray[np.float64]
+AR_c: npt.NDArray[np.complex128]
+AR_m: npt.NDArray[np.timedelta64]
+AR_M: npt.NDArray[np.datetime64]
+
+AR_f > AR_m  # type: ignore[operator]
+AR_c > AR_m  # type: ignore[operator]
+
+AR_m > AR_f  # type: ignore[operator]
+AR_m > AR_c  # type: ignore[operator]
+
+AR_i > AR_M  # type: ignore[operator]
+AR_f > AR_M  # type: ignore[operator]
+AR_m > AR_M  # type: ignore[operator]
+
+AR_M > AR_i  # type: ignore[operator]
+AR_M > AR_f  # type: ignore[operator]
+AR_M > AR_m  # type: ignore[operator]
+
+AR_i > str()  # type: ignore[operator]
+AR_i > bytes()  # type: ignore[operator]
+str() > AR_M  # type: ignore[operator]
+bytes() > AR_M  # type: ignore[operator]

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/fail/constants.pyi

@@ -0,0 +1,3 @@
+import numpy as np
+
+np.little_endian = np.little_endian  # type: ignore[misc]

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/fail/datasource.pyi

@@ -0,0 +1,15 @@
+from pathlib import Path
+import numpy as np
+
+path: Path
+d1: np.lib.npyio.DataSource
+
+d1.abspath(path)  # type: ignore[arg-type]
+d1.abspath(b"...")  # type: ignore[arg-type]
+
+d1.exists(path)  # type: ignore[arg-type]
+d1.exists(b"...")  # type: ignore[arg-type]
+
+d1.open(path, "r")  # type: ignore[arg-type]
+d1.open(b"...", encoding="utf8")  # type: ignore[arg-type]
+d1.open(None, newline="/n")  # type: ignore[arg-type]

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/fail/dtype.pyi

@@ -0,0 +1,17 @@
+import numpy as np
+
+class Test1:
+    not_dtype = np.dtype(float)
+
+class Test2:
+    dtype = float
+
+np.dtype(Test1())  # type: ignore[call-overload]
+np.dtype(Test2())  # type: ignore[arg-type]
+
+np.dtype(  # type: ignore[call-overload]
+    {
+        "field1": (float, 1),
+        "field2": (int, 3),
+    }
+)

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/fail/einsumfunc.pyi

@@ -0,0 +1,12 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_i: npt.NDArray[np.int64]
+AR_f: npt.NDArray[np.float64]
+AR_m: npt.NDArray[np.timedelta64]
+AR_U: npt.NDArray[np.str_]
+
+np.einsum("i,i->i", AR_i, AR_m)  # type: ignore[arg-type]
+np.einsum("i,i->i", AR_f, AR_f, dtype=np.int32)  # type: ignore[arg-type]
+np.einsum("i,i->i", AR_i, AR_i, out=AR_U)  # type: ignore[type-var]
+np.einsum("i,i->i", AR_i, AR_i, out=AR_U, casting="unsafe")  # type: ignore[call-overload]

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/fail/flatiter.pyi

@@ -0,0 +1,20 @@
+import numpy as np
+import numpy._typing as npt
+
+class Index:
+    def __index__(self) -> int: ...
+
+a: np.flatiter[npt.NDArray[np.float64]]
+supports_array: npt._SupportsArray[np.dtype[np.float64]]
+
+a.base = object()  # type: ignore[assignment, misc]
+a.coords = object()  # type: ignore[assignment, misc]
+a.index = object()  # type: ignore[assignment, misc]
+a.copy(order='C')  # type: ignore[call-arg]
+
+# NOTE: Contrary to `ndarray.__getitem__` its counterpart in `flatiter`
+# does not accept objects with the `__array__` or `__index__` protocols;
+# boolean indexing is just plain broken (gh-17175)
+a[np.bool()]  # type: ignore[index]
+a[Index()]  # type: ignore[call-overload]
+a[supports_array]  # type: ignore[index]

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/fail/fromnumeric.pyi

@@ -0,0 +1,148 @@
+"""Tests for :mod:`numpy._core.fromnumeric`."""
+
+import numpy as np
+import numpy.typing as npt
+
+A = np.array(True, ndmin=2, dtype=bool)
+A.setflags(write=False)
+AR_U: npt.NDArray[np.str_]
+AR_M: npt.NDArray[np.datetime64]
+AR_f4: npt.NDArray[np.float32]
+
+a = np.bool(True)
+
+np.take(a, None)  # type: ignore[call-overload]
+np.take(a, axis=1.0)  # type: ignore[call-overload]
+np.take(A, out=1)  # type: ignore[call-overload]
+np.take(A, mode="bob")  # type: ignore[call-overload]
+
+np.reshape(a, None)  # type: ignore[call-overload]
+np.reshape(A, 1, order="bob")  # type: ignore[call-overload]
+
+np.choose(a, None)  # type: ignore[call-overload]
+np.choose(a, out=1.0)  # type: ignore[call-overload]
+np.choose(A, mode="bob")  # type: ignore[call-overload]
+
+np.repeat(a, None)  # type: ignore[call-overload]
+np.repeat(A, 1, axis=1.0)  # type: ignore[call-overload]
+
+np.swapaxes(A, None, 1)  # type: ignore[call-overload]
+np.swapaxes(A, 1, [0])  # type: ignore[call-overload]
+
+np.transpose(A, axes=1.0)  # type: ignore[call-overload]
+
+np.partition(a, None)  # type: ignore[call-overload]
+np.partition(a, 0, axis="bob") # type: ignore[call-overload]
+np.partition(A, 0, kind="bob")  # type: ignore[call-overload]
+np.partition(A, 0, order=range(5))  # type: ignore[arg-type]
+
+np.argpartition(a, None)  # type: ignore[arg-type]
+np.argpartition(a, 0, axis="bob")  # type: ignore[arg-type]
+np.argpartition(A, 0, kind="bob") # type: ignore[arg-type]
+np.argpartition(A, 0, order=range(5))  # type: ignore[arg-type]
+
+np.sort(A, axis="bob")  # type: ignore[call-overload]
+np.sort(A, kind="bob")  # type: ignore[call-overload]
+np.sort(A, order=range(5)) # type: ignore[arg-type]
+
+np.argsort(A, axis="bob")  # type: ignore[arg-type]
+np.argsort(A, kind="bob")  # type: ignore[arg-type]
+np.argsort(A, order=range(5))  # type: ignore[arg-type]
+
+np.argmax(A, axis="bob")  # type: ignore[call-overload]
+np.argmax(A, kind="bob")  # type: ignore[call-overload]
+np.argmax(A, out=AR_f4)  # type: ignore[type-var]
+
+np.argmin(A, axis="bob")  # type: ignore[call-overload]
+np.argmin(A, kind="bob")  # type: ignore[call-overload]
+np.argmin(A, out=AR_f4)  # type: ignore[type-var]
+
+np.searchsorted(A[0], 0, side="bob")  # type: ignore[call-overload]
+np.searchsorted(A[0], 0, sorter=1.0)  # type: ignore[call-overload]
+
+np.resize(A, 1.0)  # type: ignore[call-overload]
+
+np.squeeze(A, 1.0)  # type: ignore[call-overload]
+
+np.diagonal(A, offset=None)  # type: ignore[call-overload]
+np.diagonal(A, axis1="bob")  # type: ignore[call-overload]
+np.diagonal(A, axis2=[])  # type: ignore[call-overload]
+
+np.trace(A, offset=None)  # type: ignore[call-overload]
+np.trace(A, axis1="bob")  # type: ignore[call-overload]
+np.trace(A, axis2=[])  # type: ignore[call-overload]
+
+np.ravel(a, order="bob")  # type: ignore[call-overload]
+
+np.nonzero(0)  # type: ignore[arg-type]
+
+np.compress([True], A, axis=1.0)  # type: ignore[call-overload]
+
+np.clip(a, 1, 2, out=1)  # type: ignore[call-overload]
+
+np.sum(a, axis=1.0)  # type: ignore[call-overload]
+np.sum(a, keepdims=1.0)  # type: ignore[call-overload]
+np.sum(a, initial=[1])  # type: ignore[call-overload]
+
+np.all(a, axis=1.0)  # type: ignore[call-overload]
+np.all(a, keepdims=1.0)  # type: ignore[call-overload]
+np.all(a, out=1.0)  # type: ignore[call-overload]
+
+np.any(a, axis=1.0)  # type: ignore[call-overload]
+np.any(a, keepdims=1.0)  # type: ignore[call-overload]
+np.any(a, out=1.0)  # type: ignore[call-overload]
+
+np.cumsum(a, axis=1.0)  # type: ignore[call-overload]
+np.cumsum(a, dtype=1.0)  # type: ignore[call-overload]
+np.cumsum(a, out=1.0)  # type: ignore[call-overload]
+
+np.ptp(a, axis=1.0)  # type: ignore[call-overload]
+np.ptp(a, keepdims=1.0)  # type: ignore[call-overload]
+np.ptp(a, out=1.0)  # type: ignore[call-overload]
+
+np.amax(a, axis=1.0)  # type: ignore[call-overload]
+np.amax(a, keepdims=1.0)  # type: ignore[call-overload]
+np.amax(a, out=1.0)  # type: ignore[call-overload]
+np.amax(a, initial=[1.0])  # type: ignore[call-overload]
+np.amax(a, where=[1.0])  # type: ignore[arg-type]
+
+np.amin(a, axis=1.0)  # type: ignore[call-overload]
+np.amin(a, keepdims=1.0)  # type: ignore[call-overload]
+np.amin(a, out=1.0)  # type: ignore[call-overload]
+np.amin(a, initial=[1.0])  # type: ignore[call-overload]
+np.amin(a, where=[1.0])  # type: ignore[arg-type]
+
+np.prod(a, axis=1.0)  # type: ignore[call-overload]
+np.prod(a, out=False)  # type: ignore[call-overload]
+np.prod(a, keepdims=1.0)  # type: ignore[call-overload]
+np.prod(a, initial=int)  # type: ignore[call-overload]
+np.prod(a, where=1.0)  # type: ignore[call-overload]
+np.prod(AR_U)  # type: ignore[arg-type]
+
+np.cumprod(a, axis=1.0)  # type: ignore[call-overload]
+np.cumprod(a, out=False)  # type: ignore[call-overload]
+np.cumprod(AR_U)  # type: ignore[arg-type]
+
+np.size(a, axis=1.0)  # type: ignore[arg-type]
+
+np.around(a, decimals=1.0)  # type: ignore[call-overload]
+np.around(a, out=type)  # type: ignore[call-overload]
+np.around(AR_U)  # type: ignore[arg-type]
+
+np.mean(a, axis=1.0)  # type: ignore[call-overload]
+np.mean(a, out=False)  # type: ignore[call-overload]
+np.mean(a, keepdims=1.0)  # type: ignore[call-overload]
+np.mean(AR_U)  # type: ignore[arg-type]
+np.mean(AR_M)  # type: ignore[arg-type]
+
+np.std(a, axis=1.0)  # type: ignore[call-overload]
+np.std(a, out=False)  # type: ignore[call-overload]
+np.std(a, ddof='test')  # type: ignore[call-overload]
+np.std(a, keepdims=1.0)  # type: ignore[call-overload]
+np.std(AR_U)  # type: ignore[arg-type]
+
+np.var(a, axis=1.0)  # type: ignore[call-overload]
+np.var(a, out=False)  # type: ignore[call-overload]
+np.var(a, ddof='test')  # type: ignore[call-overload]
+np.var(a, keepdims=1.0)  # type: ignore[call-overload]
+np.var(AR_U)  # type: ignore[arg-type]

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/fail/histograms.pyi

@@ -0,0 +1,12 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_i8: npt.NDArray[np.int64]
+AR_f8: npt.NDArray[np.float64]
+
+np.histogram_bin_edges(AR_i8, range=(0, 1, 2))  # type: ignore[arg-type]
+
+np.histogram(AR_i8, range=(0, 1, 2))  # type: ignore[arg-type]
+
+np.histogramdd(AR_i8, range=(0, 1))  # type: ignore[arg-type]
+np.histogramdd(AR_i8, range=[(0, 1, 2)])  # type: ignore[list-item]

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/fail/index_tricks.pyi

@@ -0,0 +1,14 @@
+import numpy as np
+
+AR_LIKE_i: list[int]
+AR_LIKE_f: list[float]
+
+np.ndindex([1, 2, 3])  # type: ignore[call-overload]
+np.unravel_index(AR_LIKE_f, (1, 2, 3))  # type: ignore[arg-type]
+np.ravel_multi_index(AR_LIKE_i, (1, 2, 3), mode="bob")  # type: ignore[call-overload]
+np.mgrid[1]  # type: ignore[index]
+np.mgrid[...]  # type: ignore[index]
+np.ogrid[1]  # type: ignore[index]
+np.ogrid[...]  # type: ignore[index]
+np.fill_diagonal(AR_LIKE_f, 2)  # type: ignore[arg-type]
+np.diag_indices(1.0)  # type: ignore[arg-type]

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/fail/lib_function_base.pyi

@@ -0,0 +1,62 @@
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+
+AR_f8: npt.NDArray[np.float64]
+AR_c16: npt.NDArray[np.complex128]
+AR_m: npt.NDArray[np.timedelta64]
+AR_M: npt.NDArray[np.datetime64]
+AR_O: npt.NDArray[np.object_]
+AR_b_list: list[npt.NDArray[np.bool]]
+
+def fn_none_i(a: None, /) -> npt.NDArray[Any]: ...
+def fn_ar_i(a: npt.NDArray[np.float64], posarg: int, /) -> npt.NDArray[Any]: ...
+
+np.average(AR_m)  # type: ignore[arg-type]
+np.select(1, [AR_f8])  # type: ignore[arg-type]
+np.angle(AR_m)  # type: ignore[arg-type]
+np.unwrap(AR_m)  # type: ignore[arg-type]
+np.unwrap(AR_c16)  # type: ignore[arg-type]
+np.trim_zeros(1)  # type: ignore[arg-type]
+np.place(1, [True], 1.5)  # type: ignore[arg-type]
+np.vectorize(1)  # type: ignore[arg-type]
+np.place(AR_f8, slice(None), 5)  # type: ignore[arg-type]
+
+np.piecewise(AR_f8, True, [fn_ar_i], 42)  # type: ignore[call-overload]
+# TODO: enable these once mypy actually supports ParamSpec (released in 2021)
+# NOTE: pyright correctly reports errors for these (`reportCallIssue`)
+# np.piecewise(AR_f8, AR_b_list, [fn_none_i])  # type: ignore[call-overload]s
+# np.piecewise(AR_f8, AR_b_list, [fn_ar_i])  # type: ignore[call-overload]
+# np.piecewise(AR_f8, AR_b_list, [fn_ar_i], 3.14)  # type: ignore[call-overload]
+# np.piecewise(AR_f8, AR_b_list, [fn_ar_i], 42, None)  # type: ignore[call-overload]
+# np.piecewise(AR_f8, AR_b_list, [fn_ar_i], 42, _=None)  # type: ignore[call-overload]
+
+np.interp(AR_f8, AR_c16, AR_f8)  # type: ignore[arg-type]
+np.interp(AR_c16, AR_f8, AR_f8)  # type: ignore[arg-type]
+np.interp(AR_f8, AR_f8, AR_f8, period=AR_c16)  # type: ignore[call-overload]
+np.interp(AR_f8, AR_f8, AR_O)  # type: ignore[arg-type]
+
+np.cov(AR_m)  # type: ignore[arg-type]
+np.cov(AR_O)  # type: ignore[arg-type]
+np.corrcoef(AR_m)  # type: ignore[arg-type]
+np.corrcoef(AR_O)  # type: ignore[arg-type]
+np.corrcoef(AR_f8, bias=True)  # type: ignore[call-overload]
+np.corrcoef(AR_f8, ddof=2)  # type: ignore[call-overload]
+np.blackman(1j)  # type: ignore[arg-type]
+np.bartlett(1j)  # type: ignore[arg-type]
+np.hanning(1j)  # type: ignore[arg-type]
+np.hamming(1j)  # type: ignore[arg-type]
+np.hamming(AR_c16)  # type: ignore[arg-type]
+np.kaiser(1j, 1)  # type: ignore[arg-type]
+np.sinc(AR_O)  # type: ignore[arg-type]
+np.median(AR_M)  # type: ignore[arg-type]
+
+np.percentile(AR_f8, 50j)  # type: ignore[call-overload]
+np.percentile(AR_f8, 50, interpolation="bob")  # type: ignore[call-overload]
+np.quantile(AR_f8, 0.5j)  # type: ignore[call-overload]
+np.quantile(AR_f8, 0.5, interpolation="bob")  # type: ignore[call-overload]
+np.meshgrid(AR_f8, AR_f8, indexing="bob")  # type: ignore[call-overload]
+np.delete(AR_f8, AR_f8)  # type: ignore[arg-type]
+np.insert(AR_f8, AR_f8, 1.5)  # type: ignore[arg-type]
+np.digitize(AR_f8, 1j)  # type: ignore[call-overload]

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/fail/lib_polynomial.pyi

@@ -0,0 +1,29 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_f8: npt.NDArray[np.float64]
+AR_c16: npt.NDArray[np.complex128]
+AR_O: npt.NDArray[np.object_]
+AR_U: npt.NDArray[np.str_]
+
+poly_obj: np.poly1d
+
+np.polymul(AR_f8, AR_U)  # type: ignore[arg-type]
+np.polydiv(AR_f8, AR_U)  # type: ignore[arg-type]
+
+5**poly_obj  # type: ignore[operator]
+
+np.polyint(AR_U)  # type: ignore[arg-type]
+np.polyint(AR_f8, m=1j)  # type: ignore[call-overload]
+
+np.polyder(AR_U)  # type: ignore[arg-type]
+np.polyder(AR_f8, m=1j)  # type: ignore[call-overload]
+
+np.polyfit(AR_O, AR_f8, 1)  # type: ignore[arg-type]
+np.polyfit(AR_f8, AR_f8, 1, rcond=1j)  # type: ignore[call-overload]
+np.polyfit(AR_f8, AR_f8, 1, w=AR_c16)  # type: ignore[arg-type]
+np.polyfit(AR_f8, AR_f8, 1, cov="bob")  # type: ignore[call-overload]
+
+np.polyval(AR_f8, AR_U)  # type: ignore[arg-type]
+np.polyadd(AR_f8, AR_U)  # type: ignore[arg-type]
+np.polysub(AR_f8, AR_U)  # type: ignore[arg-type]

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/fail/lib_utils.pyi

@@ -0,0 +1,3 @@
+import numpy.lib.array_utils as array_utils
+
+array_utils.byte_bounds(1)  # type: ignore[arg-type]

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/fail/lib_version.pyi

@@ -0,0 +1,6 @@
+from numpy.lib import NumpyVersion
+
+version: NumpyVersion
+
+NumpyVersion(b"1.8.0")  # type: ignore[arg-type]
+version >= b"1.8.0"  # type: ignore[operator]

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/fail/linalg.pyi

@@ -0,0 +1,48 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_f8: npt.NDArray[np.float64]
+AR_O: npt.NDArray[np.object_]
+AR_M: npt.NDArray[np.datetime64]
+
+np.linalg.tensorsolve(AR_O, AR_O)  # type: ignore[arg-type]
+
+np.linalg.solve(AR_O, AR_O)  # type: ignore[arg-type]
+
+np.linalg.tensorinv(AR_O)  # type: ignore[arg-type]
+
+np.linalg.inv(AR_O)  # type: ignore[arg-type]
+
+np.linalg.matrix_power(AR_M, 5)  # type: ignore[arg-type]
+
+np.linalg.cholesky(AR_O)  # type: ignore[arg-type]
+
+np.linalg.qr(AR_O)  # type: ignore[arg-type]
+np.linalg.qr(AR_f8, mode="bob")  # type: ignore[call-overload]
+
+np.linalg.eigvals(AR_O)  # type: ignore[arg-type]
+
+np.linalg.eigvalsh(AR_O)  # type: ignore[arg-type]
+np.linalg.eigvalsh(AR_O, UPLO="bob")  # type: ignore[call-overload]
+
+np.linalg.eig(AR_O)  # type: ignore[arg-type]
+
+np.linalg.eigh(AR_O)  # type: ignore[arg-type]
+np.linalg.eigh(AR_O, UPLO="bob")  # type: ignore[call-overload]
+
+np.linalg.svd(AR_O)  # type: ignore[arg-type]
+
+np.linalg.cond(AR_O)  # type: ignore[arg-type]
+np.linalg.cond(AR_f8, p="bob")  # type: ignore[arg-type]
+
+np.linalg.matrix_rank(AR_O)  # type: ignore[arg-type]
+
+np.linalg.pinv(AR_O)  # type: ignore[arg-type]
+
+np.linalg.slogdet(AR_O)  # type: ignore[arg-type]
+
+np.linalg.det(AR_O)  # type: ignore[arg-type]
+
+np.linalg.norm(AR_f8, ord="bob")  # type: ignore[call-overload]
+
+np.linalg.multi_dot([AR_M])  # type: ignore[list-item]

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/fail/ma.pyi

@@ -0,0 +1,143 @@
+from typing import TypeAlias, TypeVar
+
+import numpy as np
+import numpy.typing as npt
+from numpy._typing import _Shape
+
+_ScalarT = TypeVar("_ScalarT", bound=np.generic)
+MaskedArray: TypeAlias = np.ma.MaskedArray[_Shape, np.dtype[_ScalarT]]
+
+MAR_1d_f8: np.ma.MaskedArray[tuple[int], np.dtype[np.float64]]
+MAR_b: MaskedArray[np.bool]
+MAR_c: MaskedArray[np.complex128]
+MAR_td64: MaskedArray[np.timedelta64]
+
+AR_b: npt.NDArray[np.bool]
+
+MAR_1d_f8.shape = (3, 1)  # type: ignore[assignment]
+MAR_1d_f8.dtype = np.bool  # type: ignore[assignment]
+
+np.ma.min(MAR_1d_f8, axis=1.0)  # type: ignore[call-overload]
+np.ma.min(MAR_1d_f8, keepdims=1.0)  # type: ignore[call-overload]
+np.ma.min(MAR_1d_f8, out=1.0)  # type: ignore[call-overload]
+np.ma.min(MAR_1d_f8, fill_value=lambda x: 27)  # type: ignore[call-overload]
+
+MAR_1d_f8.min(axis=1.0)  # type: ignore[call-overload]
+MAR_1d_f8.min(keepdims=1.0)  # type: ignore[call-overload]
+MAR_1d_f8.min(out=1.0)  # type: ignore[call-overload]
+MAR_1d_f8.min(fill_value=lambda x: 27)  # type: ignore[call-overload]
+
+np.ma.max(MAR_1d_f8, axis=1.0)  # type: ignore[call-overload]
+np.ma.max(MAR_1d_f8, keepdims=1.0)  # type: ignore[call-overload]
+np.ma.max(MAR_1d_f8, out=1.0)  # type: ignore[call-overload]
+np.ma.max(MAR_1d_f8, fill_value=lambda x: 27)  # type: ignore[call-overload]
+
+MAR_1d_f8.max(axis=1.0)  # type: ignore[call-overload]
+MAR_1d_f8.max(keepdims=1.0)  # type: ignore[call-overload]
+MAR_1d_f8.max(out=1.0)  # type: ignore[call-overload]
+MAR_1d_f8.max(fill_value=lambda x: 27)  # type: ignore[call-overload]
+
+np.ma.ptp(MAR_1d_f8, axis=1.0)  # type: ignore[call-overload]
+np.ma.ptp(MAR_1d_f8, keepdims=1.0)  # type: ignore[call-overload]
+np.ma.ptp(MAR_1d_f8, out=1.0)  # type: ignore[call-overload]
+np.ma.ptp(MAR_1d_f8, fill_value=lambda x: 27)  # type: ignore[call-overload]
+
+MAR_1d_f8.ptp(axis=1.0)  # type: ignore[call-overload]
+MAR_1d_f8.ptp(keepdims=1.0)  # type: ignore[call-overload]
+MAR_1d_f8.ptp(out=1.0)  # type: ignore[call-overload]
+MAR_1d_f8.ptp(fill_value=lambda x: 27)  # type: ignore[call-overload]
+
+MAR_1d_f8.argmin(axis=1.0)  # type: ignore[call-overload]
+MAR_1d_f8.argmin(keepdims=1.0)  # type: ignore[call-overload]
+MAR_1d_f8.argmin(out=1.0)  # type: ignore[call-overload]
+MAR_1d_f8.argmin(fill_value=lambda x: 27)  # type: ignore[call-overload]
+
+np.ma.argmin(MAR_1d_f8, axis=1.0)  # type: ignore[call-overload]
+np.ma.argmin(MAR_1d_f8, axis=(1,))  # type: ignore[call-overload]
+np.ma.argmin(MAR_1d_f8, keepdims=1.0)  # type: ignore[call-overload]
+np.ma.argmin(MAR_1d_f8, out=1.0)  # type: ignore[call-overload]
+np.ma.argmin(MAR_1d_f8, fill_value=lambda x: 27)  # type: ignore[call-overload]
+
+MAR_1d_f8.argmax(axis=1.0)  # type: ignore[call-overload]
+MAR_1d_f8.argmax(keepdims=1.0)  # type: ignore[call-overload]
+MAR_1d_f8.argmax(out=1.0)  # type: ignore[call-overload]
+MAR_1d_f8.argmax(fill_value=lambda x: 27)  # type: ignore[call-overload]
+
+np.ma.argmax(MAR_1d_f8, axis=1.0)  # type: ignore[call-overload]
+np.ma.argmax(MAR_1d_f8, axis=(0,))  # type: ignore[call-overload]
+np.ma.argmax(MAR_1d_f8, keepdims=1.0)  # type: ignore[call-overload]
+np.ma.argmax(MAR_1d_f8, out=1.0)  # type: ignore[call-overload]
+np.ma.argmax(MAR_1d_f8, fill_value=lambda x: 27)  # type: ignore[call-overload]
+
+MAR_1d_f8.all(axis=1.0)  # type: ignore[call-overload]
+MAR_1d_f8.all(keepdims=1.0)  # type: ignore[call-overload]
+MAR_1d_f8.all(out=1.0)  # type: ignore[call-overload]
+
+MAR_1d_f8.any(axis=1.0)  # type: ignore[call-overload]
+MAR_1d_f8.any(keepdims=1.0)  # type: ignore[call-overload]
+MAR_1d_f8.any(out=1.0)  # type: ignore[call-overload]
+
+MAR_1d_f8.sort(axis=(0,1))  # type: ignore[arg-type]
+MAR_1d_f8.sort(axis=None)  # type: ignore[arg-type]
+MAR_1d_f8.sort(kind='cabbage')  # type: ignore[arg-type]
+MAR_1d_f8.sort(order=lambda: 'cabbage')  # type: ignore[arg-type]
+MAR_1d_f8.sort(endwith='cabbage')  # type: ignore[arg-type]
+MAR_1d_f8.sort(fill_value=lambda: 'cabbage')  # type: ignore[arg-type]
+MAR_1d_f8.sort(stable='cabbage')  # type: ignore[arg-type]
+MAR_1d_f8.sort(stable=True)  # type: ignore[arg-type]
+
+MAR_1d_f8.take(axis=1.0)  # type: ignore[call-overload]
+MAR_1d_f8.take(out=1)  # type: ignore[call-overload]
+MAR_1d_f8.take(mode="bob")  # type: ignore[call-overload]
+
+np.ma.take(None)  # type: ignore[call-overload]
+np.ma.take(axis=1.0)  # type: ignore[call-overload]
+np.ma.take(out=1)  # type: ignore[call-overload]
+np.ma.take(mode="bob")  # type: ignore[call-overload]
+
+MAR_1d_f8.partition(['cabbage'])  # type: ignore[arg-type]
+MAR_1d_f8.partition(axis=(0,1))  # type: ignore[arg-type, call-arg]
+MAR_1d_f8.partition(kind='cabbage')  # type: ignore[arg-type, call-arg]
+MAR_1d_f8.partition(order=lambda: 'cabbage')  # type: ignore[arg-type, call-arg]
+MAR_1d_f8.partition(AR_b)  # type: ignore[arg-type]
+
+MAR_1d_f8.argpartition(['cabbage'])  # type: ignore[arg-type]
+MAR_1d_f8.argpartition(axis=(0,1))  # type: ignore[arg-type, call-arg]
+MAR_1d_f8.argpartition(kind='cabbage')  # type: ignore[arg-type, call-arg]
+MAR_1d_f8.argpartition(order=lambda: 'cabbage')  # type: ignore[arg-type, call-arg]
+MAR_1d_f8.argpartition(AR_b)  # type: ignore[arg-type]
+
+np.ma.ndim(lambda: 'lambda')  # type: ignore[arg-type]
+
+np.ma.size(AR_b, axis='0')  # type: ignore[arg-type]
+
+MAR_1d_f8 >= (lambda x: 'mango') # type: ignore[operator]
+MAR_1d_f8 > (lambda x: 'mango') # type: ignore[operator]
+MAR_1d_f8 <= (lambda x: 'mango') # type: ignore[operator]
+MAR_1d_f8 < (lambda x: 'mango') # type: ignore[operator]
+
+MAR_1d_f8.count(axis=0.)  # type: ignore[call-overload]
+
+np.ma.count(MAR_1d_f8, axis=0.)  # type: ignore[call-overload]
+
+MAR_1d_f8.put(4, 999, mode='flip')  # type: ignore[arg-type]
+
+np.ma.put(MAR_1d_f8, 4, 999, mode='flip')  # type: ignore[arg-type]
+
+np.ma.put([1,1,3], 0, 999)  # type: ignore[arg-type]
+
+np.ma.compressed(lambda: 'compress me')  # type: ignore[call-overload]
+
+np.ma.allequal(MAR_1d_f8, [1,2,3], fill_value=1.5)  # type: ignore[arg-type]
+
+np.ma.allclose(MAR_1d_f8, [1,2,3], masked_equal=4.5)  # type: ignore[arg-type]
+np.ma.allclose(MAR_1d_f8, [1,2,3], rtol='.4')  # type: ignore[arg-type]
+np.ma.allclose(MAR_1d_f8, [1,2,3], atol='.5')  # type: ignore[arg-type]
+
+MAR_1d_f8.__setmask__('mask')  # type: ignore[arg-type]
+
+MAR_b *= 2  # type: ignore[arg-type]
+MAR_c //= 2  # type: ignore[misc]
+MAR_td64 **= 2  # type: ignore[misc]
+
+MAR_1d_f8.swapaxes(axis1=1, axis2=0)  # type: ignore[call-arg]

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/fail/memmap.pyi

@@ -0,0 +1,5 @@
+import numpy as np
+
+with open("file.txt", "r") as f:
+    np.memmap(f)  # type: ignore[call-overload]
+np.memmap("test.txt", shape=[10, 5])  # type: ignore[call-overload]

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/fail/modules.pyi

@@ -0,0 +1,17 @@
+import numpy as np
+
+np.testing.bob  # type: ignore[attr-defined]
+np.bob  # type: ignore[attr-defined]
+
+# Stdlib modules in the namespace by accident
+np.warnings  # type: ignore[attr-defined]
+np.sys  # type: ignore[attr-defined]
+np.os  # type: ignore[attr-defined]
+np.math  # type: ignore[attr-defined]
+
+# Public sub-modules that are not imported to their parent module by default;
+# e.g. one must first execute `import numpy.lib.recfunctions`
+np.lib.recfunctions  # type: ignore[attr-defined]
+
+np.__deprecated_attrs__  # type: ignore[attr-defined]
+np.__expired_functions__  # type: ignore[attr-defined]

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/fail/multiarray.pyi

@@ -0,0 +1,52 @@
+import numpy as np
+import numpy.typing as npt
+
+i8: np.int64
+
+AR_b: npt.NDArray[np.bool]
+AR_u1: npt.NDArray[np.uint8]
+AR_i8: npt.NDArray[np.int64]
+AR_f8: npt.NDArray[np.float64]
+AR_M: npt.NDArray[np.datetime64]
+
+M: np.datetime64
+
+AR_LIKE_f: list[float]
+
+def func(a: int) -> None: ...
+
+np.where(AR_b, 1)  # type: ignore[call-overload]
+
+np.can_cast(AR_f8, 1)  # type: ignore[arg-type]
+
+np.vdot(AR_M, AR_M)  # type: ignore[arg-type]
+
+np.copyto(AR_LIKE_f, AR_f8)  # type: ignore[arg-type]
+
+np.putmask(AR_LIKE_f, [True, True, False], 1.5)  # type: ignore[arg-type]
+
+np.packbits(AR_f8)  # type: ignore[arg-type]
+np.packbits(AR_u1, bitorder=">")  # type: ignore[arg-type]
+
+np.unpackbits(AR_i8)  # type: ignore[arg-type]
+np.unpackbits(AR_u1, bitorder=">")  # type: ignore[arg-type]
+
+np.shares_memory(1, 1, max_work=i8)  # type: ignore[arg-type]
+np.may_share_memory(1, 1, max_work=i8)  # type: ignore[arg-type]
+
+np.arange(stop=10)  # type: ignore[call-overload]
+
+np.datetime_data(int)  # type: ignore[arg-type]
+
+np.busday_offset("2012", 10)  # type: ignore[call-overload]
+
+np.datetime_as_string("2012")  # type: ignore[call-overload]
+
+np.char.compare_chararrays("a", b"a", "==", False)  # type: ignore[call-overload]
+
+np.nested_iters([AR_i8, AR_i8])  # type: ignore[call-arg]
+np.nested_iters([AR_i8, AR_i8], 0)  # type: ignore[arg-type]
+np.nested_iters([AR_i8, AR_i8], [0])  # type: ignore[list-item]
+np.nested_iters([AR_i8, AR_i8], [[0], [1]], flags=["test"])  # type: ignore[list-item]
+np.nested_iters([AR_i8, AR_i8], [[0], [1]], op_flags=[["test"]])  # type: ignore[list-item]
+np.nested_iters([AR_i8, AR_i8], [[0], [1]], buffersize=1.0)  # type: ignore[arg-type]

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/fail/ndarray.pyi

@@ -0,0 +1,11 @@
+import numpy as np
+
+# Ban setting dtype since mutating the type of the array in place
+# makes having ndarray be generic over dtype impossible. Generally
+# users should use `ndarray.view` in this situation anyway. See
+#
+# https://github.com/numpy/numpy-stubs/issues/7
+#
+# for more context.
+float_array = np.array([1.0])
+float_array.dtype = np.bool  # type: ignore[assignment, misc]

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/fail/ndarray_misc.pyi

@@ -0,0 +1,36 @@
+"""
+Tests for miscellaneous (non-magic) ``np.ndarray``/``np.generic`` methods.
+
+More extensive tests are performed for the methods'
+function-based counterpart in `../from_numeric.py`.
+
+"""
+
+import numpy as np
+import numpy.typing as npt
+
+f8: np.float64
+AR_f8: npt.NDArray[np.float64]
+AR_M: npt.NDArray[np.datetime64]
+AR_b: npt.NDArray[np.bool]
+
+ctypes_obj = AR_f8.ctypes
+
+f8.argpartition(0)  # type: ignore[attr-defined]
+f8.diagonal()  # type: ignore[attr-defined]
+f8.dot(1)  # type: ignore[attr-defined]
+f8.nonzero()  # type: ignore[attr-defined]
+f8.partition(0)  # type: ignore[attr-defined]
+f8.put(0, 2)  # type: ignore[attr-defined]
+f8.setfield(2, np.float64)  # type: ignore[attr-defined]
+f8.sort()  # type: ignore[attr-defined]
+f8.trace()  # type: ignore[attr-defined]
+
+AR_M.__complex__()  # type: ignore[misc]
+AR_b.__index__()  # type: ignore[misc]
+
+AR_f8[1.5]  # type: ignore[call-overload]
+AR_f8["field_a"]  # type: ignore[call-overload]
+AR_f8[["field_a", "field_b"]]  # type: ignore[index]
+
+AR_f8.__array_finalize__(object())  # type: ignore[arg-type]

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/fail/nditer.pyi

@@ -0,0 +1,8 @@
+import numpy as np
+
+class Test(np.nditer): ...  # type: ignore[misc]
+
+np.nditer([0, 1], flags=["test"])  # type: ignore[list-item]
+np.nditer([0, 1], op_flags=[["test"]])  # type: ignore[list-item]
+np.nditer([0, 1], itershape=(1.0,))  # type: ignore[arg-type]
+np.nditer([0, 1], buffersize=1.0)  # type: ignore[arg-type]

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/fail/nested_sequence.pyi

@@ -0,0 +1,16 @@
+from collections.abc import Sequence
+from numpy._typing import _NestedSequence
+
+a: Sequence[float]
+b: list[complex]
+c: tuple[str, ...]
+d: int
+e: str
+
+def func(a: _NestedSequence[int]) -> None: ...
+
+reveal_type(func(a))  # type: ignore[arg-type, misc]
+reveal_type(func(b))  # type: ignore[arg-type, misc]
+reveal_type(func(c))  # type: ignore[arg-type, misc]
+reveal_type(func(d))  # type: ignore[arg-type, misc]
+reveal_type(func(e))  # type: ignore[arg-type, misc]

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/fail/npyio.pyi

@@ -0,0 +1,24 @@
+import pathlib
+from typing import IO
+
+import numpy.typing as npt
+import numpy as np
+
+str_path: str
+bytes_path: bytes
+pathlib_path: pathlib.Path
+str_file: IO[str]
+AR_i8: npt.NDArray[np.int64]
+
+np.load(str_file)  # type: ignore[arg-type]
+
+np.save(bytes_path, AR_i8)  # type: ignore[call-overload]
+np.save(str_path, AR_i8, fix_imports=True)  # type: ignore[deprecated]  # pyright: ignore[reportDeprecated]
+
+np.savez(bytes_path, AR_i8)  # type: ignore[arg-type]
+
+np.savez_compressed(bytes_path, AR_i8)  # type: ignore[arg-type]
+
+np.loadtxt(bytes_path)  # type: ignore[arg-type]
+
+np.fromregex(bytes_path, ".", np.int64)  # type: ignore[call-overload]

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/fail/numerictypes.pyi

@@ -0,0 +1,5 @@
+import numpy as np
+
+np.isdtype(1, np.int64)  # type: ignore[arg-type]
+
+np.issubdtype(1, np.int64)  # type: ignore[arg-type]

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/fail/random.pyi

@@ -0,0 +1,62 @@
+import numpy as np
+import numpy.typing as npt
+
+SEED_FLOAT: float = 457.3
+SEED_ARR_FLOAT: npt.NDArray[np.float64] = np.array([1.0, 2, 3, 4])
+SEED_ARRLIKE_FLOAT: list[float] = [1.0, 2.0, 3.0, 4.0]
+SEED_SEED_SEQ: np.random.SeedSequence = np.random.SeedSequence(0)
+SEED_STR: str = "String seeding not allowed"
+
+# default rng
+np.random.default_rng(SEED_FLOAT)  # type: ignore[arg-type]
+np.random.default_rng(SEED_ARR_FLOAT)  # type: ignore[arg-type]
+np.random.default_rng(SEED_ARRLIKE_FLOAT)  # type: ignore[arg-type]
+np.random.default_rng(SEED_STR)  # type: ignore[arg-type]
+
+# Seed Sequence
+np.random.SeedSequence(SEED_FLOAT)  # type: ignore[arg-type]
+np.random.SeedSequence(SEED_ARR_FLOAT)  # type: ignore[arg-type]
+np.random.SeedSequence(SEED_ARRLIKE_FLOAT)  # type: ignore[arg-type]
+np.random.SeedSequence(SEED_SEED_SEQ)  # type: ignore[arg-type]
+np.random.SeedSequence(SEED_STR)  # type: ignore[arg-type]
+
+seed_seq: np.random.bit_generator.SeedSequence = np.random.SeedSequence()
+seed_seq.spawn(11.5)  # type: ignore[arg-type]
+seed_seq.generate_state(3.14)  # type: ignore[arg-type]
+seed_seq.generate_state(3, np.uint8)  # type: ignore[arg-type]
+seed_seq.generate_state(3, "uint8")  # type: ignore[arg-type]
+seed_seq.generate_state(3, "u1")  # type: ignore[arg-type]
+seed_seq.generate_state(3, np.uint16)  # type: ignore[arg-type]
+seed_seq.generate_state(3, "uint16")  # type: ignore[arg-type]
+seed_seq.generate_state(3, "u2")  # type: ignore[arg-type]
+seed_seq.generate_state(3, np.int32)  # type: ignore[arg-type]
+seed_seq.generate_state(3, "int32")  # type: ignore[arg-type]
+seed_seq.generate_state(3, "i4")  # type: ignore[arg-type]
+
+# Bit Generators
+np.random.MT19937(SEED_FLOAT)  # type: ignore[arg-type]
+np.random.MT19937(SEED_ARR_FLOAT)  # type: ignore[arg-type]
+np.random.MT19937(SEED_ARRLIKE_FLOAT)  # type: ignore[arg-type]
+np.random.MT19937(SEED_STR)  # type: ignore[arg-type]
+
+np.random.PCG64(SEED_FLOAT)  # type: ignore[arg-type]
+np.random.PCG64(SEED_ARR_FLOAT)  # type: ignore[arg-type]
+np.random.PCG64(SEED_ARRLIKE_FLOAT)  # type: ignore[arg-type]
+np.random.PCG64(SEED_STR)  # type: ignore[arg-type]
+
+np.random.Philox(SEED_FLOAT)  # type: ignore[arg-type]
+np.random.Philox(SEED_ARR_FLOAT)  # type: ignore[arg-type]
+np.random.Philox(SEED_ARRLIKE_FLOAT)  # type: ignore[arg-type]
+np.random.Philox(SEED_STR)  # type: ignore[arg-type]
+
+np.random.SFC64(SEED_FLOAT)  # type: ignore[arg-type]
+np.random.SFC64(SEED_ARR_FLOAT)  # type: ignore[arg-type]
+np.random.SFC64(SEED_ARRLIKE_FLOAT)  # type: ignore[arg-type]
+np.random.SFC64(SEED_STR)  # type: ignore[arg-type]
+
+# Generator
+np.random.Generator(None)  # type: ignore[arg-type]
+np.random.Generator(12333283902830213)  # type: ignore[arg-type]
+np.random.Generator("OxFEEDF00D")  # type: ignore[arg-type]
+np.random.Generator([123, 234])  # type: ignore[arg-type]
+np.random.Generator(np.array([123, 234], dtype="u4"))  # type: ignore[arg-type]

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/fail/rec.pyi

@@ -0,0 +1,17 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_i8: npt.NDArray[np.int64]
+
+np.rec.fromarrays(1)  # type: ignore[call-overload]
+np.rec.fromarrays([1, 2, 3], dtype=[("f8", "f8")], formats=["f8", "f8"])  # type: ignore[call-overload]
+
+np.rec.fromrecords(AR_i8)  # type: ignore[arg-type]
+np.rec.fromrecords([(1.5,)], dtype=[("f8", "f8")], formats=["f8", "f8"])  # type: ignore[call-overload]
+
+np.rec.fromstring("string", dtype=[("f8", "f8")])  # type: ignore[call-overload]
+np.rec.fromstring(b"bytes")  # type: ignore[call-overload]
+np.rec.fromstring(b"(1.5,)", dtype=[("f8", "f8")], formats=["f8", "f8"])  # type: ignore[call-overload]
+
+with open("test", "r") as f:
+    np.rec.fromfile(f, dtype=[("f8", "f8")])  # type: ignore[call-overload]

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/fail/scalars.pyi

@@ -0,0 +1,87 @@
+import sys
+import numpy as np
+
+f2: np.float16
+f8: np.float64
+c8: np.complex64
+
+# Construction
+
+np.float32(3j)  # type: ignore[arg-type]
+
+# Technically the following examples are valid NumPy code. But they
+# are not considered a best practice, and people who wish to use the
+# stubs should instead do
+#
+# np.array([1.0, 0.0, 0.0], dtype=np.float32)
+# np.array([], dtype=np.complex64)
+#
+# See e.g. the discussion on the mailing list
+#
+# https://mail.python.org/pipermail/numpy-discussion/2020-April/080566.html
+#
+# and the issue
+#
+# https://github.com/numpy/numpy-stubs/issues/41
+#
+# for more context.
+np.float32([1.0, 0.0, 0.0])  # type: ignore[arg-type]
+np.complex64([])  # type: ignore[call-overload]
+
+# TODO: protocols (can't check for non-existent protocols w/ __getattr__)
+
+np.datetime64(0)  # type: ignore[call-overload]
+
+class A:
+    def __float__(self) -> float: ...
+
+np.int8(A())  # type: ignore[arg-type]
+np.int16(A())  # type: ignore[arg-type]
+np.int32(A())  # type: ignore[arg-type]
+np.int64(A())  # type: ignore[arg-type]
+np.uint8(A())  # type: ignore[arg-type]
+np.uint16(A())  # type: ignore[arg-type]
+np.uint32(A())  # type: ignore[arg-type]
+np.uint64(A())  # type: ignore[arg-type]
+
+np.void("test")  # type: ignore[call-overload]
+np.void("test", dtype=None)  # type: ignore[call-overload]
+
+np.generic(1)  # type: ignore[abstract, call-arg]
+np.number(1)  # type: ignore[abstract, call-arg]
+np.integer(1)  # type: ignore[abstract, call-arg]
+np.inexact(1)  # type: ignore[abstract, call-arg]
+np.character("test")  # type: ignore[abstract, call-arg]
+np.flexible(b"test")  # type: ignore[abstract, call-arg]
+
+np.float64(value=0.0)  # type: ignore[call-arg]
+np.int64(value=0)  # type: ignore[call-arg]
+np.uint64(value=0)  # type: ignore[call-arg]
+np.complex128(value=0.0j)  # type: ignore[call-overload]
+np.str_(value='bob')  # type: ignore[call-overload]
+np.bytes_(value=b'test')  # type: ignore[call-overload]
+np.void(value=b'test')  # type: ignore[call-overload]
+np.bool(value=True)  # type: ignore[call-overload]
+np.datetime64(value="2019")  # type: ignore[call-overload]
+np.timedelta64(value=0)  # type: ignore[call-overload]
+
+np.bytes_(b"hello", encoding='utf-8')  # type: ignore[call-overload]
+np.str_("hello", encoding='utf-8')  # type: ignore[call-overload]
+
+f8.item(1)  # type: ignore[call-overload]
+f8.item((0, 1))  # type: ignore[arg-type]
+f8.squeeze(axis=1)  # type: ignore[arg-type]
+f8.squeeze(axis=(0, 1))  # type: ignore[arg-type]
+f8.transpose(1)  # type: ignore[arg-type]
+
+def func(a: np.float32) -> None: ...
+
+func(f2)  # type: ignore[arg-type]
+func(f8)  # type: ignore[arg-type]
+
+c8.__getnewargs__()  # type: ignore[attr-defined]
+f2.__getnewargs__()  # type: ignore[attr-defined]
+f2.hex()  # type: ignore[attr-defined]
+np.float16.fromhex("0x0.0p+0")  # type: ignore[attr-defined]
+f2.__trunc__()  # type: ignore[attr-defined]
+f2.__getformat__("float")  # type: ignore[attr-defined]

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/fail/shape.pyi

@@ -0,0 +1,6 @@
+from typing import Any
+import numpy as np
+
+# test bounds of _ShapeT_co
+
+np.ndarray[tuple[str, str], Any]  # type: ignore[type-var]

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/fail/shape_base.pyi

@@ -0,0 +1,8 @@
+import numpy as np
+
+class DTypeLike:
+    dtype: np.dtype[np.int_]
+
+dtype_like: DTypeLike
+
+np.expand_dims(dtype_like, (5, 10))  # type: ignore[call-overload]

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/fail/stride_tricks.pyi

@@ -0,0 +1,9 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_f8: npt.NDArray[np.float64]
+
+np.lib.stride_tricks.as_strided(AR_f8, shape=8)  # type: ignore[call-overload]
+np.lib.stride_tricks.as_strided(AR_f8, strides=8)  # type: ignore[call-overload]
+
+np.lib.stride_tricks.sliding_window_view(AR_f8, axis=(1,))  # type: ignore[call-overload]

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/fail/strings.pyi

@@ -0,0 +1,52 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_U: npt.NDArray[np.str_]
+AR_S: npt.NDArray[np.bytes_]
+
+np.strings.equal(AR_U, AR_S)  # type: ignore[arg-type]
+np.strings.not_equal(AR_U, AR_S)  # type: ignore[arg-type]
+
+np.strings.greater_equal(AR_U, AR_S)  # type: ignore[arg-type]
+np.strings.less_equal(AR_U, AR_S)  # type: ignore[arg-type]
+np.strings.greater(AR_U, AR_S)  # type: ignore[arg-type]
+np.strings.less(AR_U, AR_S)  # type: ignore[arg-type]
+
+np.strings.encode(AR_S)  # type: ignore[arg-type]
+np.strings.decode(AR_U)  # type: ignore[arg-type]
+
+np.strings.lstrip(AR_U, b"a")  # type: ignore[arg-type]
+np.strings.lstrip(AR_S, "a")  # type: ignore[arg-type]
+np.strings.strip(AR_U, b"a")  # type: ignore[arg-type]
+np.strings.strip(AR_S, "a")  # type: ignore[arg-type]
+np.strings.rstrip(AR_U, b"a")  # type: ignore[arg-type]
+np.strings.rstrip(AR_S, "a")  # type: ignore[arg-type]
+
+np.strings.partition(AR_U, b"a")  # type: ignore[arg-type]
+np.strings.partition(AR_S, "a")  # type: ignore[arg-type]
+np.strings.rpartition(AR_U, b"a")  # type: ignore[arg-type]
+np.strings.rpartition(AR_S, "a")  # type: ignore[arg-type]
+
+np.strings.count(AR_U, b"a", [1, 2, 3], [1, 2, 3])  # type: ignore[arg-type]
+np.strings.count(AR_S, "a", 0, 9)  # type: ignore[arg-type]
+
+np.strings.endswith(AR_U, b"a", [1, 2, 3], [1, 2, 3])  # type: ignore[arg-type]
+np.strings.endswith(AR_S, "a", 0, 9)  # type: ignore[arg-type]
+np.strings.startswith(AR_U, b"a", [1, 2, 3], [1, 2, 3])  # type: ignore[arg-type]
+np.strings.startswith(AR_S, "a", 0, 9)  # type: ignore[arg-type]
+
+np.strings.find(AR_U, b"a", [1, 2, 3], [1, 2, 3])  # type: ignore[arg-type]
+np.strings.find(AR_S, "a", 0, 9)  # type: ignore[arg-type]
+np.strings.rfind(AR_U, b"a", [1, 2, 3], [1, 2, 3])  # type: ignore[arg-type]
+np.strings.rfind(AR_S, "a", 0, 9)  # type: ignore[arg-type]
+
+np.strings.index(AR_U, b"a", start=[1, 2, 3])  # type: ignore[arg-type]
+np.strings.index(AR_S, "a", end=9)  # type: ignore[arg-type]
+np.strings.rindex(AR_U, b"a", start=[1, 2, 3])  # type: ignore[arg-type]
+np.strings.rindex(AR_S, "a", end=9)  # type: ignore[arg-type]
+
+np.strings.isdecimal(AR_S)  # type: ignore[arg-type]
+np.strings.isnumeric(AR_S)  # type: ignore[arg-type]
+
+np.strings.replace(AR_U, b"_", b"-", 10)  # type: ignore[arg-type]
+np.strings.replace(AR_S, "_", "-", 1)  # type: ignore[arg-type]

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/fail/testing.pyi

@@ -0,0 +1,28 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_U: npt.NDArray[np.str_]
+
+def func(x: object) -> bool: ...
+
+np.testing.assert_(True, msg=1)  # type: ignore[arg-type]
+np.testing.build_err_msg(1, "test")  # type: ignore[arg-type]
+np.testing.assert_almost_equal(AR_U, AR_U)  # type: ignore[arg-type]
+np.testing.assert_approx_equal([1, 2, 3], [1, 2, 3])  # type: ignore[arg-type]
+np.testing.assert_array_almost_equal(AR_U, AR_U)  # type: ignore[arg-type]
+np.testing.assert_array_less(AR_U, AR_U)  # type: ignore[arg-type]
+np.testing.assert_string_equal(b"a", b"a")  # type: ignore[arg-type]
+
+np.testing.assert_raises(expected_exception=TypeError, callable=func)  # type: ignore[call-overload]
+np.testing.assert_raises_regex(expected_exception=TypeError, expected_regex="T", callable=func)  # type: ignore[call-overload]
+
+np.testing.assert_allclose(AR_U, AR_U)  # type: ignore[arg-type]
+np.testing.assert_array_almost_equal_nulp(AR_U, AR_U)  # type: ignore[arg-type]
+np.testing.assert_array_max_ulp(AR_U, AR_U)  # type: ignore[arg-type]
+
+np.testing.assert_warns(RuntimeWarning, func)  # type: ignore[call-overload]
+np.testing.assert_no_warnings(func=func)  # type: ignore[call-overload]
+np.testing.assert_no_warnings(func)  # type: ignore[call-overload]
+np.testing.assert_no_warnings(func, y=None)  # type: ignore[call-overload]
+
+np.testing.assert_no_gc_cycles(func=func)  # type: ignore[call-overload]

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/fail/twodim_base.pyi

@@ -0,0 +1,32 @@
+from typing import Any, TypeVar
+
+import numpy as np
+import numpy.typing as npt
+
+def func1(ar: npt.NDArray[Any], a: int) -> npt.NDArray[np.str_]: ...
+
+def func2(ar: npt.NDArray[Any], a: float) -> float: ...
+
+AR_b: npt.NDArray[np.bool]
+AR_m: npt.NDArray[np.timedelta64]
+
+AR_LIKE_b: list[bool]
+
+np.eye(10, M=20.0)  # type: ignore[call-overload]
+np.eye(10, k=2.5, dtype=int)  # type: ignore[call-overload]
+
+np.diag(AR_b, k=0.5)  # type: ignore[call-overload]
+np.diagflat(AR_b, k=0.5)  # type: ignore[call-overload]
+
+np.tri(10, M=20.0)  # type: ignore[call-overload]
+np.tri(10, k=2.5, dtype=int)  # type: ignore[call-overload]
+
+np.tril(AR_b, k=0.5)  # type: ignore[call-overload]
+np.triu(AR_b, k=0.5)  # type: ignore[call-overload]
+
+np.vander(AR_m)  # type: ignore[arg-type]
+
+np.histogram2d(AR_m)  # type: ignore[call-overload]
+
+np.mask_indices(10, func1)  # type: ignore[arg-type]
+np.mask_indices(10, func2, 10.5)  # type: ignore[arg-type]

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/fail/type_check.pyi

@@ -0,0 +1,13 @@
+import numpy as np
+import numpy.typing as npt
+
+DTYPE_i8: np.dtype[np.int64]
+
+np.mintypecode(DTYPE_i8)  # type: ignore[arg-type]
+np.iscomplexobj(DTYPE_i8)  # type: ignore[arg-type]
+np.isrealobj(DTYPE_i8)  # type: ignore[arg-type]
+
+np.typename(DTYPE_i8)  # type: ignore[call-overload]
+np.typename("invalid")  # type: ignore[call-overload]
+
+np.common_type(np.timedelta64())  # type: ignore[arg-type]

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/fail/ufunc_config.pyi

@@ -0,0 +1,21 @@
+"""Typing tests for `numpy._core._ufunc_config`."""
+
+import numpy as np
+
+def func1(a: str, b: int, c: float) -> None: ...
+def func2(a: str, *, b: int) -> None: ...
+
+class Write1:
+    def write1(self, a: str) -> None: ...
+
+class Write2:
+    def write(self, a: str, b: str) -> None: ...
+
+class Write3:
+    def write(self, *, a: str) -> None: ...
+
+np.seterrcall(func1)  # type: ignore[arg-type]
+np.seterrcall(func2)  # type: ignore[arg-type]
+np.seterrcall(Write1())  # type: ignore[arg-type]
+np.seterrcall(Write2())  # type: ignore[arg-type]
+np.seterrcall(Write3())  # type: ignore[arg-type]

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/fail/ufunclike.pyi

@@ -0,0 +1,21 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_c: npt.NDArray[np.complex128]
+AR_m: npt.NDArray[np.timedelta64]
+AR_M: npt.NDArray[np.datetime64]
+AR_O: npt.NDArray[np.object_]
+
+np.fix(AR_c)  # type: ignore[arg-type]
+np.fix(AR_m)  # type: ignore[arg-type]
+np.fix(AR_M)  # type: ignore[arg-type]
+
+np.isposinf(AR_c)  # type: ignore[arg-type]
+np.isposinf(AR_m)  # type: ignore[arg-type]
+np.isposinf(AR_M)  # type: ignore[arg-type]
+np.isposinf(AR_O)  # type: ignore[arg-type]
+
+np.isneginf(AR_c)  # type: ignore[arg-type]
+np.isneginf(AR_m)  # type: ignore[arg-type]
+np.isneginf(AR_M)  # type: ignore[arg-type]
+np.isneginf(AR_O)  # type: ignore[arg-type]

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/fail/ufuncs.pyi

@@ -0,0 +1,17 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_f8: npt.NDArray[np.float64]
+
+np.sin.nin + "foo"  # type: ignore[operator]
+np.sin(1, foo="bar")  # type: ignore[call-overload]
+
+np.abs(None)  # type: ignore[call-overload]
+
+np.add(1, 1, 1)  # type: ignore[call-overload]
+np.add(1, 1, axis=0)  # type: ignore[call-overload]
+
+np.matmul(AR_f8, AR_f8, where=True)  # type: ignore[call-overload]
+
+np.frexp(AR_f8, out=None)  # type: ignore[call-overload]
+np.frexp(AR_f8, out=AR_f8)  # type: ignore[call-overload]

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/fail/warnings_and_errors.pyi

@@ -0,0 +1,5 @@
+import numpy.exceptions as ex
+
+ex.AxisError(1.0)  # type: ignore[call-overload]
+ex.AxisError(1, ndim=2.0)  # type: ignore[call-overload]
+ex.AxisError(2, msg_prefix=404)  # type: ignore[call-overload]

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/misc/extended_precision.pyi

@@ -0,0 +1,9 @@
+import numpy as np
+from numpy._typing import _96Bit, _128Bit
+
+from typing import assert_type
+
+assert_type(np.float96(), np.floating[_96Bit])
+assert_type(np.float128(), np.floating[_128Bit])
+assert_type(np.complex192(), np.complexfloating[_96Bit, _96Bit])
+assert_type(np.complex256(), np.complexfloating[_128Bit, _128Bit])

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/mypy.ini

@@ -0,0 +1,9 @@
+[mypy]
+enable_error_code = deprecated, ignore-without-code, truthy-bool
+strict_bytes = True
+warn_unused_ignores = True
+implicit_reexport = False
+disallow_any_unimported = True
+disallow_any_generics = True
+show_absolute_path = True
+pretty = True

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/pass/arithmetic.py

@@ -0,0 +1,612 @@
+from __future__ import annotations
+
+from typing import Any, cast
+import numpy as np
+import numpy.typing as npt
+import pytest
+
+c16 = np.complex128(1)
+f8 = np.float64(1)
+i8 = np.int64(1)
+u8 = np.uint64(1)
+
+c8 = np.complex64(1)
+f4 = np.float32(1)
+i4 = np.int32(1)
+u4 = np.uint32(1)
+
+dt = np.datetime64(1, "D")
+td = np.timedelta64(1, "D")
+
+b_ = np.bool(1)
+
+b = bool(1)
+c = complex(1)
+f = float(1)
+i = int(1)
+
+
+class Object:
+    def __array__(self, dtype: np.typing.DTypeLike = None,
+                  copy: bool | None = None) -> np.ndarray[Any, np.dtype[np.object_]]:
+        ret = np.empty((), dtype=object)
+        ret[()] = self
+        return ret
+
+    def __sub__(self, value: Any) -> Object:
+        return self
+
+    def __rsub__(self, value: Any) -> Object:
+        return self
+
+    def __floordiv__(self, value: Any) -> Object:
+        return self
+
+    def __rfloordiv__(self, value: Any) -> Object:
+        return self
+
+    def __mul__(self, value: Any) -> Object:
+        return self
+
+    def __rmul__(self, value: Any) -> Object:
+        return self
+
+    def __pow__(self, value: Any) -> Object:
+        return self
+
+    def __rpow__(self, value: Any) -> Object:
+        return self
+
+
+AR_b: npt.NDArray[np.bool] = np.array([True])
+AR_u: npt.NDArray[np.uint32] = np.array([1], dtype=np.uint32)
+AR_i: npt.NDArray[np.int64] = np.array([1])
+AR_integer: npt.NDArray[np.integer] = cast(npt.NDArray[np.integer], AR_i)
+AR_f: npt.NDArray[np.float64] = np.array([1.0])
+AR_c: npt.NDArray[np.complex128] = np.array([1j])
+AR_m: npt.NDArray[np.timedelta64] = np.array([np.timedelta64(1, "D")])
+AR_M: npt.NDArray[np.datetime64] = np.array([np.datetime64(1, "D")])
+AR_O: npt.NDArray[np.object_] = np.array([Object()])
+
+AR_LIKE_b = [True]
+AR_LIKE_u = [np.uint32(1)]
+AR_LIKE_i = [1]
+AR_LIKE_f = [1.0]
+AR_LIKE_c = [1j]
+AR_LIKE_m = [np.timedelta64(1, "D")]
+AR_LIKE_M = [np.datetime64(1, "D")]
+AR_LIKE_O = [Object()]
+
+# Array subtractions
+
+AR_b - AR_LIKE_u
+AR_b - AR_LIKE_i
+AR_b - AR_LIKE_f
+AR_b - AR_LIKE_c
+AR_b - AR_LIKE_m
+AR_b - AR_LIKE_O
+
+AR_LIKE_u - AR_b
+AR_LIKE_i - AR_b
+AR_LIKE_f - AR_b
+AR_LIKE_c - AR_b
+AR_LIKE_m - AR_b
+AR_LIKE_M - AR_b
+AR_LIKE_O - AR_b
+
+AR_u - AR_LIKE_b
+AR_u - AR_LIKE_u
+AR_u - AR_LIKE_i
+AR_u - AR_LIKE_f
+AR_u - AR_LIKE_c
+AR_u - AR_LIKE_m
+AR_u - AR_LIKE_O
+
+AR_LIKE_b - AR_u
+AR_LIKE_u - AR_u
+AR_LIKE_i - AR_u
+AR_LIKE_f - AR_u
+AR_LIKE_c - AR_u
+AR_LIKE_m - AR_u
+AR_LIKE_M - AR_u
+AR_LIKE_O - AR_u
+
+AR_i - AR_LIKE_b
+AR_i - AR_LIKE_u
+AR_i - AR_LIKE_i
+AR_i - AR_LIKE_f
+AR_i - AR_LIKE_c
+AR_i - AR_LIKE_m
+AR_i - AR_LIKE_O
+
+AR_LIKE_b - AR_i
+AR_LIKE_u - AR_i
+AR_LIKE_i - AR_i
+AR_LIKE_f - AR_i
+AR_LIKE_c - AR_i
+AR_LIKE_m - AR_i
+AR_LIKE_M - AR_i
+AR_LIKE_O - AR_i
+
+AR_f - AR_LIKE_b
+AR_f - AR_LIKE_u
+AR_f - AR_LIKE_i
+AR_f - AR_LIKE_f
+AR_f - AR_LIKE_c
+AR_f - AR_LIKE_O
+
+AR_LIKE_b - AR_f
+AR_LIKE_u - AR_f
+AR_LIKE_i - AR_f
+AR_LIKE_f - AR_f
+AR_LIKE_c - AR_f
+AR_LIKE_O - AR_f
+
+AR_c - AR_LIKE_b
+AR_c - AR_LIKE_u
+AR_c - AR_LIKE_i
+AR_c - AR_LIKE_f
+AR_c - AR_LIKE_c
+AR_c - AR_LIKE_O
+
+AR_LIKE_b - AR_c
+AR_LIKE_u - AR_c
+AR_LIKE_i - AR_c
+AR_LIKE_f - AR_c
+AR_LIKE_c - AR_c
+AR_LIKE_O - AR_c
+
+AR_m - AR_LIKE_b
+AR_m - AR_LIKE_u
+AR_m - AR_LIKE_i
+AR_m - AR_LIKE_m
+
+AR_LIKE_b - AR_m
+AR_LIKE_u - AR_m
+AR_LIKE_i - AR_m
+AR_LIKE_m - AR_m
+AR_LIKE_M - AR_m
+
+AR_M - AR_LIKE_b
+AR_M - AR_LIKE_u
+AR_M - AR_LIKE_i
+AR_M - AR_LIKE_m
+AR_M - AR_LIKE_M
+
+AR_LIKE_M - AR_M
+
+AR_O - AR_LIKE_b
+AR_O - AR_LIKE_u
+AR_O - AR_LIKE_i
+AR_O - AR_LIKE_f
+AR_O - AR_LIKE_c
+AR_O - AR_LIKE_O
+
+AR_LIKE_b - AR_O
+AR_LIKE_u - AR_O
+AR_LIKE_i - AR_O
+AR_LIKE_f - AR_O
+AR_LIKE_c - AR_O
+AR_LIKE_O - AR_O
+
+AR_u += AR_b
+AR_u += AR_u
+AR_u += 1  # Allowed during runtime as long as the object is 0D and >=0
+
+# Array floor division
+
+AR_b // AR_LIKE_b
+AR_b // AR_LIKE_u
+AR_b // AR_LIKE_i
+AR_b // AR_LIKE_f
+AR_b // AR_LIKE_O
+
+AR_LIKE_b // AR_b
+AR_LIKE_u // AR_b
+AR_LIKE_i // AR_b
+AR_LIKE_f // AR_b
+AR_LIKE_O // AR_b
+
+AR_u // AR_LIKE_b
+AR_u // AR_LIKE_u
+AR_u // AR_LIKE_i
+AR_u // AR_LIKE_f
+AR_u // AR_LIKE_O
+
+AR_LIKE_b // AR_u
+AR_LIKE_u // AR_u
+AR_LIKE_i // AR_u
+AR_LIKE_f // AR_u
+AR_LIKE_m // AR_u
+AR_LIKE_O // AR_u
+
+AR_i // AR_LIKE_b
+AR_i // AR_LIKE_u
+AR_i // AR_LIKE_i
+AR_i // AR_LIKE_f
+AR_i // AR_LIKE_O
+
+AR_LIKE_b // AR_i
+AR_LIKE_u // AR_i
+AR_LIKE_i // AR_i
+AR_LIKE_f // AR_i
+AR_LIKE_m // AR_i
+AR_LIKE_O // AR_i
+
+AR_f // AR_LIKE_b
+AR_f // AR_LIKE_u
+AR_f // AR_LIKE_i
+AR_f // AR_LIKE_f
+AR_f // AR_LIKE_O
+
+AR_LIKE_b // AR_f
+AR_LIKE_u // AR_f
+AR_LIKE_i // AR_f
+AR_LIKE_f // AR_f
+AR_LIKE_m // AR_f
+AR_LIKE_O // AR_f
+
+AR_m // AR_LIKE_u
+AR_m // AR_LIKE_i
+AR_m // AR_LIKE_f
+AR_m // AR_LIKE_m
+
+AR_LIKE_m // AR_m
+
+AR_m /= f
+AR_m //= f
+AR_m /= AR_f
+AR_m /= AR_LIKE_f
+AR_m //= AR_f
+AR_m //= AR_LIKE_f
+
+AR_O // AR_LIKE_b
+AR_O // AR_LIKE_u
+AR_O // AR_LIKE_i
+AR_O // AR_LIKE_f
+AR_O // AR_LIKE_O
+
+AR_LIKE_b // AR_O
+AR_LIKE_u // AR_O
+AR_LIKE_i // AR_O
+AR_LIKE_f // AR_O
+AR_LIKE_O // AR_O
+
+# Inplace multiplication
+
+AR_b *= AR_LIKE_b
+
+AR_u *= AR_LIKE_b
+AR_u *= AR_LIKE_u
+
+AR_i *= AR_LIKE_b
+AR_i *= AR_LIKE_u
+AR_i *= AR_LIKE_i
+
+AR_integer *= AR_LIKE_b
+AR_integer *= AR_LIKE_u
+AR_integer *= AR_LIKE_i
+
+AR_f *= AR_LIKE_b
+AR_f *= AR_LIKE_u
+AR_f *= AR_LIKE_i
+AR_f *= AR_LIKE_f
+
+AR_c *= AR_LIKE_b
+AR_c *= AR_LIKE_u
+AR_c *= AR_LIKE_i
+AR_c *= AR_LIKE_f
+AR_c *= AR_LIKE_c
+
+AR_m *= AR_LIKE_b
+AR_m *= AR_LIKE_u
+AR_m *= AR_LIKE_i
+AR_m *= AR_LIKE_f
+
+AR_O *= AR_LIKE_b
+AR_O *= AR_LIKE_u
+AR_O *= AR_LIKE_i
+AR_O *= AR_LIKE_f
+AR_O *= AR_LIKE_c
+AR_O *= AR_LIKE_O
+
+# Inplace power
+
+AR_u **= AR_LIKE_b
+AR_u **= AR_LIKE_u
+
+AR_i **= AR_LIKE_b
+AR_i **= AR_LIKE_u
+AR_i **= AR_LIKE_i
+
+AR_integer **= AR_LIKE_b
+AR_integer **= AR_LIKE_u
+AR_integer **= AR_LIKE_i
+
+AR_f **= AR_LIKE_b
+AR_f **= AR_LIKE_u
+AR_f **= AR_LIKE_i
+AR_f **= AR_LIKE_f
+
+AR_c **= AR_LIKE_b
+AR_c **= AR_LIKE_u
+AR_c **= AR_LIKE_i
+AR_c **= AR_LIKE_f
+AR_c **= AR_LIKE_c
+
+AR_O **= AR_LIKE_b
+AR_O **= AR_LIKE_u
+AR_O **= AR_LIKE_i
+AR_O **= AR_LIKE_f
+AR_O **= AR_LIKE_c
+AR_O **= AR_LIKE_O
+
+# unary ops
+
+-c16
+-c8
+-f8
+-f4
+-i8
+-i4
+with pytest.warns(RuntimeWarning):
+    -u8
+    -u4
+-td
+-AR_f
+
++c16
++c8
++f8
++f4
++i8
++i4
++u8
++u4
++td
++AR_f
+
+abs(c16)
+abs(c8)
+abs(f8)
+abs(f4)
+abs(i8)
+abs(i4)
+abs(u8)
+abs(u4)
+abs(td)
+abs(b_)
+abs(AR_f)
+
+# Time structures
+
+dt + td
+dt + i
+dt + i4
+dt + i8
+dt - dt
+dt - i
+dt - i4
+dt - i8
+
+td + td
+td + i
+td + i4
+td + i8
+td - td
+td - i
+td - i4
+td - i8
+td / f
+td / f4
+td / f8
+td / td
+td // td
+td % td
+
+
+# boolean
+
+b_ / b
+b_ / b_
+b_ / i
+b_ / i8
+b_ / i4
+b_ / u8
+b_ / u4
+b_ / f
+b_ / f8
+b_ / f4
+b_ / c
+b_ / c16
+b_ / c8
+
+b / b_
+b_ / b_
+i / b_
+i8 / b_
+i4 / b_
+u8 / b_
+u4 / b_
+f / b_
+f8 / b_
+f4 / b_
+c / b_
+c16 / b_
+c8 / b_
+
+# Complex
+
+c16 + c16
+c16 + f8
+c16 + i8
+c16 + c8
+c16 + f4
+c16 + i4
+c16 + b_
+c16 + b
+c16 + c
+c16 + f
+c16 + i
+c16 + AR_f
+
+c16 + c16
+f8 + c16
+i8 + c16
+c8 + c16
+f4 + c16
+i4 + c16
+b_ + c16
+b + c16
+c + c16
+f + c16
+i + c16
+AR_f + c16
+
+c8 + c16
+c8 + f8
+c8 + i8
+c8 + c8
+c8 + f4
+c8 + i4
+c8 + b_
+c8 + b
+c8 + c
+c8 + f
+c8 + i
+c8 + AR_f
+
+c16 + c8
+f8 + c8
+i8 + c8
+c8 + c8
+f4 + c8
+i4 + c8
+b_ + c8
+b + c8
+c + c8
+f + c8
+i + c8
+AR_f + c8
+
+# Float
+
+f8 + f8
+f8 + i8
+f8 + f4
+f8 + i4
+f8 + b_
+f8 + b
+f8 + c
+f8 + f
+f8 + i
+f8 + AR_f
+
+f8 + f8
+i8 + f8
+f4 + f8
+i4 + f8
+b_ + f8
+b + f8
+c + f8
+f + f8
+i + f8
+AR_f + f8
+
+f4 + f8
+f4 + i8
+f4 + f4
+f4 + i4
+f4 + b_
+f4 + b
+f4 + c
+f4 + f
+f4 + i
+f4 + AR_f
+
+f8 + f4
+i8 + f4
+f4 + f4
+i4 + f4
+b_ + f4
+b + f4
+c + f4
+f + f4
+i + f4
+AR_f + f4
+
+# Int
+
+i8 + i8
+i8 + u8
+i8 + i4
+i8 + u4
+i8 + b_
+i8 + b
+i8 + c
+i8 + f
+i8 + i
+i8 + AR_f
+
+u8 + u8
+u8 + i4
+u8 + u4
+u8 + b_
+u8 + b
+u8 + c
+u8 + f
+u8 + i
+u8 + AR_f
+
+i8 + i8
+u8 + i8
+i4 + i8
+u4 + i8
+b_ + i8
+b + i8
+c + i8
+f + i8
+i + i8
+AR_f + i8
+
+u8 + u8
+i4 + u8
+u4 + u8
+b_ + u8
+b + u8
+c + u8
+f + u8
+i + u8
+AR_f + u8
+
+i4 + i8
+i4 + i4
+i4 + i
+i4 + b_
+i4 + b
+i4 + AR_f
+
+u4 + i8
+u4 + i4
+u4 + u8
+u4 + u4
+u4 + i
+u4 + b_
+u4 + b
+u4 + AR_f
+
+i8 + i4
+i4 + i4
+i + i4
+b_ + i4
+b + i4
+AR_f + i4
+
+i8 + u4
+i4 + u4
+u8 + u4
+u4 + u4
+b_ + u4
+b + u4
+i + u4
+AR_f + u4

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/pass/array_constructors.py

@@ -0,0 +1,137 @@
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+
+class Index:
+    def __index__(self) -> int:
+        return 0
+
+
+class SubClass(npt.NDArray[np.float64]):
+    pass
+
+
+def func(i: int, j: int, **kwargs: Any) -> SubClass:
+    return B
+
+
+i8 = np.int64(1)
+
+A = np.array([1])
+B = A.view(SubClass).copy()
+B_stack = np.array([[1], [1]]).view(SubClass)
+C = [1]
+
+np.ndarray(Index())
+np.ndarray([Index()])
+
+np.array(1, dtype=float)
+np.array(1, copy=None)
+np.array(1, order='F')
+np.array(1, order=None)
+np.array(1, subok=True)
+np.array(1, ndmin=3)
+np.array(1, str, copy=True, order='C', subok=False, ndmin=2)
+
+np.asarray(A)
+np.asarray(B)
+np.asarray(C)
+
+np.asanyarray(A)
+np.asanyarray(B)
+np.asanyarray(B, dtype=int)
+np.asanyarray(C)
+
+np.ascontiguousarray(A)
+np.ascontiguousarray(B)
+np.ascontiguousarray(C)
+
+np.asfortranarray(A)
+np.asfortranarray(B)
+np.asfortranarray(C)
+
+np.require(A)
+np.require(B)
+np.require(B, dtype=int)
+np.require(B, requirements=None)
+np.require(B, requirements="E")
+np.require(B, requirements=["ENSUREARRAY"])
+np.require(B, requirements={"F", "E"})
+np.require(B, requirements=["C", "OWNDATA"])
+np.require(B, requirements="W")
+np.require(B, requirements="A")
+np.require(C)
+
+np.linspace(0, 2)
+np.linspace(0.5, [0, 1, 2])
+np.linspace([0, 1, 2], 3)
+np.linspace(0j, 2)
+np.linspace(0, 2, num=10)
+np.linspace(0, 2, endpoint=True)
+np.linspace(0, 2, retstep=True)
+np.linspace(0j, 2j, retstep=True)
+np.linspace(0, 2, dtype=bool)
+np.linspace([0, 1], [2, 3], axis=Index())
+
+np.logspace(0, 2, base=2)
+np.logspace(0, 2, base=2)
+np.logspace(0, 2, base=[1j, 2j], num=2)
+
+np.geomspace(1, 2)
+
+np.zeros_like(A)
+np.zeros_like(C)
+np.zeros_like(B)
+np.zeros_like(B, dtype=np.int64)
+
+np.ones_like(A)
+np.ones_like(C)
+np.ones_like(B)
+np.ones_like(B, dtype=np.int64)
+
+np.empty_like(A)
+np.empty_like(C)
+np.empty_like(B)
+np.empty_like(B, dtype=np.int64)
+
+np.full_like(A, i8)
+np.full_like(C, i8)
+np.full_like(B, i8)
+np.full_like(B, i8, dtype=np.int64)
+
+np.ones(1)
+np.ones([1, 1, 1])
+
+np.full(1, i8)
+np.full([1, 1, 1], i8)
+
+np.indices([1, 2, 3])
+np.indices([1, 2, 3], sparse=True)
+
+np.fromfunction(func, (3, 5))
+
+np.identity(10)
+
+np.atleast_1d(C)
+np.atleast_1d(A)
+np.atleast_1d(C, C)
+np.atleast_1d(C, A)
+np.atleast_1d(A, A)
+
+np.atleast_2d(C)
+
+np.atleast_3d(C)
+
+np.vstack([C, C])
+np.vstack([C, A])
+np.vstack([A, A])
+
+np.hstack([C, C])
+
+np.stack([C, C])
+np.stack([C, C], axis=0)
+np.stack([C, C], out=B_stack)
+
+np.block([[C, C], [C, C]])
+np.block(A)

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/pass/array_like.py

@@ -0,0 +1,43 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+if TYPE_CHECKING:
+    from numpy._typing import NDArray, ArrayLike, _SupportsArray
+
+x1: ArrayLike = True
+x2: ArrayLike = 5
+x3: ArrayLike = 1.0
+x4: ArrayLike = 1 + 1j
+x5: ArrayLike = np.int8(1)
+x6: ArrayLike = np.float64(1)
+x7: ArrayLike = np.complex128(1)
+x8: ArrayLike = np.array([1, 2, 3])
+x9: ArrayLike = [1, 2, 3]
+x10: ArrayLike = (1, 2, 3)
+x11: ArrayLike = "foo"
+x12: ArrayLike = memoryview(b'foo')
+
+
+class A:
+    def __array__(self, dtype: np.dtype | None = None) -> NDArray[np.float64]:
+        return np.array([1.0, 2.0, 3.0])
+
+
+x13: ArrayLike = A()
+
+scalar: _SupportsArray[np.dtype[np.int64]] = np.int64(1)
+scalar.__array__()
+array: _SupportsArray[np.dtype[np.int_]] = np.array(1)
+array.__array__()
+
+a: _SupportsArray[np.dtype[np.float64]] = A()
+a.__array__()
+a.__array__()
+
+# Escape hatch for when you mean to make something like an object
+# array.
+object_array_scalar: object = (i for i in range(10))
+np.array(object_array_scalar)

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/pass/arrayprint.py

@@ -0,0 +1,37 @@
+import numpy as np
+
+AR = np.arange(10)
+AR.setflags(write=False)
+
+with np.printoptions():
+    np.set_printoptions(
+        precision=1,
+        threshold=2,
+        edgeitems=3,
+        linewidth=4,
+        suppress=False,
+        nanstr="Bob",
+        infstr="Bill",
+        formatter={},
+        sign="+",
+        floatmode="unique",
+    )
+    np.get_printoptions()
+    str(AR)
+
+    np.array2string(
+        AR,
+        max_line_width=5,
+        precision=2,
+        suppress_small=True,
+        separator=";",
+        prefix="test",
+        threshold=5,
+        floatmode="fixed",
+        suffix="?",
+        legacy="1.13",
+    )
+    np.format_float_scientific(1, precision=5)
+    np.format_float_positional(1, trim="k")
+    np.array_repr(AR)
+    np.array_str(AR)

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/pass/arrayterator.py

@@ -0,0 +1,27 @@
+
+from __future__ import annotations
+
+from typing import Any
+import numpy as np
+
+AR_i8: np.ndarray[Any, np.dtype[np.int_]] = np.arange(10)
+ar_iter = np.lib.Arrayterator(AR_i8)
+
+ar_iter.var
+ar_iter.buf_size
+ar_iter.start
+ar_iter.stop
+ar_iter.step
+ar_iter.shape
+ar_iter.flat
+
+ar_iter.__array__()
+
+for i in ar_iter:
+    pass
+
+ar_iter[0]
+ar_iter[...]
+ar_iter[:]
+ar_iter[0, 0, 0]
+ar_iter[..., 0, :]

.venv/lib/python3.12/site-packages/numpy/typing/tests/data/pass/bitwise_ops.py

@@ -0,0 +1,131 @@
+import numpy as np
+
+i8 = np.int64(1)
+u8 = np.uint64(1)
+
+i4 = np.int32(1)
+u4 = np.uint32(1)
+
+b_ = np.bool(1)
+
+b = bool(1)
+i = int(1)
+
+AR = np.array([0, 1, 2], dtype=np.int32)
+AR.setflags(write=False)
+
+
+i8 << i8
+i8 >> i8
+i8 | i8
+i8 ^ i8
+i8 & i8
+
+i << AR
+i >> AR
+i | AR
+i ^ AR
+i & AR
+
+i8 << AR
+i8 >> AR
+i8 | AR
+i8 ^ AR
+i8 & AR
+
+i4 << i4
+i4 >> i4
+i4 | i4
+i4 ^ i4
+i4 & i4
+
+i8 << i4
+i8 >> i4
+i8 | i4
+i8 ^ i4
+i8 & i4
+
+i8 << i
+i8 >> i
+i8 | i
+i8 ^ i
+i8 & i
+
+i8 << b_
+i8 >> b_
+i8 | b_
+i8 ^ b_
+i8 & b_
+
+i8 << b
+i8 >> b
+i8 | b
+i8 ^ b
+i8 & b
+
+u8 << u8
+u8 >> u8
+u8 | u8
+u8 ^ u8
+u8 & u8
+
+u4 << u4
+u4 >> u4
+u4 | u4
+u4 ^ u4
+u4 & u4
+
+u4 << i4
+u4 >> i4
+u4 | i4
+u4 ^ i4
+u4 & i4
+
+u4 << i
+u4 >> i
+u4 | i
+u4 ^ i
+u4 & i
+
+u8 << b_
+u8 >> b_
+u8 | b_
+u8 ^ b_
+u8 & b_
+
+u8 << b
+u8 >> b
+u8 | b
+u8 ^ b
+u8 & b
+
+b_ << b_
+b_ >> b_
+b_ | b_
+b_ ^ b_
+b_ & b_
+
+b_ << AR
+b_ >> AR
+b_ | AR
+b_ ^ AR
+b_ & AR
+
+b_ << b
+b_ >> b
+b_ | b
+b_ ^ b
+b_ & b
+
+b_ << i
+b_ >> i
+b_ | i
+b_ ^ i
+b_ & i
+
+~i8
+~i4
+~u8
+~u4
+~b_
+~AR