first commit

.venv/Lib/site-packages/numpy/typing/__init__.py

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+"""
+============================
+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 covariant 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 are that can potentially perform a 0D-array -> scalar
+cast are currently annotated as exclusively returning an `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 `numpy.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.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
+
+from __future__ import annotations
+
+from numpy import ufunc
+from typing import TYPE_CHECKING, final
+
+if not TYPE_CHECKING:
+    __all__ = ["ArrayLike", "DTypeLike", "NBitBase", "NDArray"]
+else:
+    # Ensure that all objects within this module are accessible while
+    # static type checking. This includes private ones, as we need them
+    # for internal use.
+    #
+    # Declare to mypy that `__all__` is a list of strings without assigning
+    # an explicit value
+    __all__: list[str]
+    __path__: list[str]
+
+
+@final  # Disallow the creation of arbitrary `NBitBase` subclasses
+class NBitBase:
+    """
+    A type representing `numpy.number` precision during static type checking.
+
+    Used exclusively for the purpose static type checking, `NBitBase`
+    represents the base of a hierarchical set of subclasses.
+    Each subsequent subclass is herein used for representing a lower level
+    of precision, *e.g.* ``64Bit > 32Bit > 16Bit``.
+
+    .. versionadded:: 1.20
+
+    Examples
+    --------
+    Below is a typical usage example: `NBitBase` is herein used for annotating
+    a function that takes a float and integer of arbitrary precision
+    as arguments and returns a new float of whichever precision is largest
+    (*e.g.* ``np.float16 + np.int64 -> np.float64``).
+
+    .. code-block:: python
+
+        >>> from __future__ import annotations
+        >>> from typing import TypeVar, TYPE_CHECKING
+        >>> import numpy as np
+        >>> import numpy.typing as npt
+
+        >>> T1 = TypeVar("T1", bound=npt.NBitBase)
+        >>> T2 = TypeVar("T2", bound=npt.NBitBase)
+
+        >>> def add(a: np.floating[T1], b: np.integer[T2]) -> np.floating[T1 | T2]:
+        ...     return a + b
+
+        >>> a = np.float16()
+        >>> b = np.int64()
+        >>> out = add(a, b)
+
+        >>> if TYPE_CHECKING:
+        ...     reveal_locals()
+        ...     # note: Revealed local types are:
+        ...     # note:     a: numpy.floating[numpy.typing._16Bit*]
+        ...     # note:     b: numpy.signedinteger[numpy.typing._64Bit*]
+        ...     # note:     out: numpy.floating[numpy.typing._64Bit*]
+
+    """
+
+    def __init_subclass__(cls) -> None:
+        allowed_names = {
+            "NBitBase", "_256Bit", "_128Bit", "_96Bit", "_80Bit",
+            "_64Bit", "_32Bit", "_16Bit", "_8Bit",
+        }
+        if cls.__name__ not in allowed_names:
+            raise TypeError('cannot inherit from final class "NBitBase"')
+        super().__init_subclass__()
+
+
+# Silence errors about subclassing a `@final`-decorated class
+class _256Bit(NBitBase):  # type: ignore[misc]
+    pass
+
+class _128Bit(_256Bit):  # type: ignore[misc]
+    pass
+
+class _96Bit(_128Bit):  # type: ignore[misc]
+    pass
+
+class _80Bit(_96Bit):  # type: ignore[misc]
+    pass
+
+class _64Bit(_80Bit):  # type: ignore[misc]
+    pass
+
+class _32Bit(_64Bit):  # type: ignore[misc]
+    pass
+
+class _16Bit(_32Bit):  # type: ignore[misc]
+    pass
+
+class _8Bit(_16Bit):  # type: ignore[misc]
+    pass
+
+
+from ._nested_sequence import (
+    _NestedSequence as _NestedSequence,
+)
+from ._nbit import (
+    _NBitByte as _NBitByte,
+    _NBitShort as _NBitShort,
+    _NBitIntC as _NBitIntC,
+    _NBitIntP as _NBitIntP,
+    _NBitInt as _NBitInt,
+    _NBitLongLong as _NBitLongLong,
+    _NBitHalf as _NBitHalf,
+    _NBitSingle as _NBitSingle,
+    _NBitDouble as _NBitDouble,
+    _NBitLongDouble as _NBitLongDouble,
+)
+from ._char_codes import (
+    _BoolCodes as _BoolCodes,
+    _UInt8Codes as _UInt8Codes,
+    _UInt16Codes as _UInt16Codes,
+    _UInt32Codes as _UInt32Codes,
+    _UInt64Codes as _UInt64Codes,
+    _Int8Codes as _Int8Codes,
+    _Int16Codes as _Int16Codes,
+    _Int32Codes as _Int32Codes,
+    _Int64Codes as _Int64Codes,
+    _Float16Codes as _Float16Codes,
+    _Float32Codes as _Float32Codes,
+    _Float64Codes as _Float64Codes,
+    _Complex64Codes as _Complex64Codes,
+    _Complex128Codes as _Complex128Codes,
+    _ByteCodes as _ByteCodes,
+    _ShortCodes as _ShortCodes,
+    _IntCCodes as _IntCCodes,
+    _IntPCodes as _IntPCodes,
+    _IntCodes as _IntCodes,
+    _LongLongCodes as _LongLongCodes,
+    _UByteCodes as _UByteCodes,
+    _UShortCodes as _UShortCodes,
+    _UIntCCodes as _UIntCCodes,
+    _UIntPCodes as _UIntPCodes,
+    _UIntCodes as _UIntCodes,
+    _ULongLongCodes as _ULongLongCodes,
+    _HalfCodes as _HalfCodes,
+    _SingleCodes as _SingleCodes,
+    _DoubleCodes as _DoubleCodes,
+    _LongDoubleCodes as _LongDoubleCodes,
+    _CSingleCodes as _CSingleCodes,
+    _CDoubleCodes as _CDoubleCodes,
+    _CLongDoubleCodes as _CLongDoubleCodes,
+    _DT64Codes as _DT64Codes,
+    _TD64Codes as _TD64Codes,
+    _StrCodes as _StrCodes,
+    _BytesCodes as _BytesCodes,
+    _VoidCodes as _VoidCodes,
+    _ObjectCodes as _ObjectCodes,
+)
+from ._scalars import (
+    _CharLike_co as _CharLike_co,
+    _BoolLike_co as _BoolLike_co,
+    _UIntLike_co as _UIntLike_co,
+    _IntLike_co as _IntLike_co,
+    _FloatLike_co as _FloatLike_co,
+    _ComplexLike_co as _ComplexLike_co,
+    _TD64Like_co as _TD64Like_co,
+    _NumberLike_co as _NumberLike_co,
+    _ScalarLike_co as _ScalarLike_co,
+    _VoidLike_co as _VoidLike_co,
+)
+from ._shape import (
+    _Shape as _Shape,
+    _ShapeLike as _ShapeLike,
+)
+from ._dtype_like import (
+    DTypeLike as DTypeLike,
+    _SupportsDType as _SupportsDType,
+    _VoidDTypeLike as _VoidDTypeLike,
+    _DTypeLikeBool as _DTypeLikeBool,
+    _DTypeLikeUInt as _DTypeLikeUInt,
+    _DTypeLikeInt as _DTypeLikeInt,
+    _DTypeLikeFloat as _DTypeLikeFloat,
+    _DTypeLikeComplex as _DTypeLikeComplex,
+    _DTypeLikeTD64 as _DTypeLikeTD64,
+    _DTypeLikeDT64 as _DTypeLikeDT64,
+    _DTypeLikeObject as _DTypeLikeObject,
+    _DTypeLikeVoid as _DTypeLikeVoid,
+    _DTypeLikeStr as _DTypeLikeStr,
+    _DTypeLikeBytes as _DTypeLikeBytes,
+    _DTypeLikeComplex_co as _DTypeLikeComplex_co,
+)
+from ._array_like import (
+    ArrayLike as ArrayLike,
+    _ArrayLike as _ArrayLike,
+    _FiniteNestedSequence as _FiniteNestedSequence,
+    _SupportsArray as _SupportsArray,
+    _ArrayLikeInt as _ArrayLikeInt,
+    _ArrayLikeBool_co as _ArrayLikeBool_co,
+    _ArrayLikeUInt_co as _ArrayLikeUInt_co,
+    _ArrayLikeInt_co as _ArrayLikeInt_co,
+    _ArrayLikeFloat_co as _ArrayLikeFloat_co,
+    _ArrayLikeComplex_co as _ArrayLikeComplex_co,
+    _ArrayLikeNumber_co as _ArrayLikeNumber_co,
+    _ArrayLikeTD64_co as _ArrayLikeTD64_co,
+    _ArrayLikeDT64_co as _ArrayLikeDT64_co,
+    _ArrayLikeObject_co as _ArrayLikeObject_co,
+    _ArrayLikeVoid_co as _ArrayLikeVoid_co,
+    _ArrayLikeStr_co as _ArrayLikeStr_co,
+    _ArrayLikeBytes_co as _ArrayLikeBytes_co,
+)
+from ._generic_alias import (
+    NDArray as NDArray,
+    _DType as _DType,
+    _GenericAlias as _GenericAlias,
+)
+
+if TYPE_CHECKING:
+    from ._ufunc import (
+        _UFunc_Nin1_Nout1 as _UFunc_Nin1_Nout1,
+        _UFunc_Nin2_Nout1 as _UFunc_Nin2_Nout1,
+        _UFunc_Nin1_Nout2 as _UFunc_Nin1_Nout2,
+        _UFunc_Nin2_Nout2 as _UFunc_Nin2_Nout2,
+        _GUFunc_Nin2_Nout1 as _GUFunc_Nin2_Nout1,
+    )
+else:
+    # Declare the (type-check-only) ufunc subclasses as ufunc aliases during
+    # runtime; this helps autocompletion tools such as Jedi (numpy/numpy#19834)
+    _UFunc_Nin1_Nout1 = ufunc
+    _UFunc_Nin2_Nout1 = ufunc
+    _UFunc_Nin1_Nout2 = ufunc
+    _UFunc_Nin2_Nout2 = ufunc
+    _GUFunc_Nin2_Nout1 = ufunc
+
+# Clean up the namespace
+del TYPE_CHECKING, final, ufunc
+
+if __doc__ is not None:
+    from ._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/site-packages/numpy/typing/_add_docstring.py

@@ -0,0 +1,152 @@
+"""A module for creating docstrings for sphinx ``data`` domains."""
+
+import re
+import textwrap
+
+from ._generic_alias import NDArray
+
+_docstrings_list = []
+
+
+def add_newdoc(name: str, value: str, doc: str) -> None:
+    """Append ``_docstrings_list`` with a docstring for `name`.
+
+    Parameters
+    ----------
+    name : str
+        The name of the object.
+    value : str
+        A string-representation of the object.
+    doc : str
+        The docstring of the object.
+
+    """
+    _docstrings_list.append((name, value, doc))
+
+
+def _parse_docstrings() -> str:
+    """Convert all docstrings in ``_docstrings_list`` into a single
+    sphinx-legible text block.
+
+    """
+    type_list_ret = []
+    for name, value, doc in _docstrings_list:
+        s = textwrap.dedent(doc).replace("\n", "\n    ")
+
+        # Replace sections by rubrics
+        lines = s.split("\n")
+        new_lines = []
+        indent = ""
+        for line in lines:
+            m = re.match(r'^(\s+)[-=]+\s*$', line)
+            if m and new_lines:
+                prev = textwrap.dedent(new_lines.pop())
+                if prev == "Examples":
+                    indent = ""
+                    new_lines.append(f'{m.group(1)}.. rubric:: {prev}')
+                else:
+                    indent = 4 * " "
+                    new_lines.append(f'{m.group(1)}.. admonition:: {prev}')
+                new_lines.append("")
+            else:
+                new_lines.append(f"{indent}{line}")
+
+        s = "\n".join(new_lines)
+        s_block = f""".. data:: {name}\n    :value: {value}\n    {s}"""
+        type_list_ret.append(s_block)
+    return "\n".join(type_list_ret)
+
+
+add_newdoc('ArrayLike', 'typing.Union[...]',
+    """
+    A `~typing.Union` representing objects that can be coerced
+    into an `~numpy.ndarray`.
+
+    Among others this includes the likes of:
+
+    * Scalars.
+    * (Nested) sequences.
+    * Objects implementing the `~class.__array__` protocol.
+
+    .. versionadded:: 1.20
+
+    See Also
+    --------
+    :term:`array_like`:
+        Any scalar or sequence that can be interpreted as an ndarray.
+
+    Examples
+    --------
+    .. code-block:: python
+
+        >>> import numpy as np
+        >>> import numpy.typing as npt
+
+        >>> def as_array(a: npt.ArrayLike) -> np.ndarray:
+        ...     return np.array(a)
+
+    """)
+
+add_newdoc('DTypeLike', 'typing.Union[...]',
+    """
+    A `~typing.Union` representing objects that can be coerced
+    into a `~numpy.dtype`.
+
+    Among others this includes the likes of:
+
+    * :class:`type` objects.
+    * Character codes or the names of :class:`type` objects.
+    * Objects with the ``.dtype`` attribute.
+
+    .. versionadded:: 1.20
+
+    See Also
+    --------
+    :ref:`Specifying and constructing data types <arrays.dtypes.constructing>`
+        A comprehensive overview of all objects that can be coerced
+        into data types.
+
+    Examples
+    --------
+    .. code-block:: python
+
+        >>> import numpy as np
+        >>> import numpy.typing as npt
+
+        >>> def as_dtype(d: npt.DTypeLike) -> np.dtype:
+        ...     return np.dtype(d)
+
+    """)
+
+add_newdoc('NDArray', repr(NDArray),
+    """
+    A :term:`generic <generic type>` version of
+    `np.ndarray[Any, np.dtype[+ScalarType]] <numpy.ndarray>`.
+
+    Can be used during runtime for typing arrays with a given dtype
+    and unspecified shape.
+
+    .. versionadded:: 1.21
+
+    Examples
+    --------
+    .. code-block:: python
+
+        >>> import numpy as np
+        >>> import numpy.typing as npt
+
+        >>> print(npt.NDArray)
+        numpy.ndarray[typing.Any, numpy.dtype[+ScalarType]]
+
+        >>> print(npt.NDArray[np.float64])
+        numpy.ndarray[typing.Any, numpy.dtype[numpy.float64]]
+
+        >>> NDArrayInt = npt.NDArray[np.int_]
+        >>> a: NDArrayInt = np.arange(10)
+
+        >>> def func(a: npt.ArrayLike) -> npt.NDArray[Any]:
+        ...     return np.array(a)
+
+    """)
+
+_docstrings = _parse_docstrings()

.venv/Lib/site-packages/numpy/typing/_array_like.py

@@ -0,0 +1,123 @@
+from __future__ import annotations
+
+from typing import Any, Sequence, Protocol, Union, TypeVar
+from numpy import (
+    ndarray,
+    dtype,
+    generic,
+    bool_,
+    unsignedinteger,
+    integer,
+    floating,
+    complexfloating,
+    number,
+    timedelta64,
+    datetime64,
+    object_,
+    void,
+    str_,
+    bytes_,
+)
+from ._nested_sequence import _NestedSequence
+
+_T = TypeVar("_T")
+_ScalarType = TypeVar("_ScalarType", bound=generic)
+_DType = TypeVar("_DType", bound="dtype[Any]")
+_DType_co = TypeVar("_DType_co", covariant=True, bound="dtype[Any]")
+
+# The `_SupportsArray` protocol only cares about the default dtype
+# (i.e. `dtype=None` or no `dtype` parameter at all) of the to-be returned
+# array.
+# Concrete implementations of the protocol are responsible for adding
+# any and all remaining overloads
+class _SupportsArray(Protocol[_DType_co]):
+    def __array__(self) -> ndarray[Any, _DType_co]: ...
+
+
+# TODO: Wait until mypy supports recursive objects in combination with typevars
+_FiniteNestedSequence = Union[
+    _T,
+    Sequence[_T],
+    Sequence[Sequence[_T]],
+    Sequence[Sequence[Sequence[_T]]],
+    Sequence[Sequence[Sequence[Sequence[_T]]]],
+]
+
+# A union representing array-like objects; consists of two typevars:
+# One representing types that can be parametrized w.r.t. `np.dtype`
+# and another one for the rest
+_ArrayLike = Union[
+    _SupportsArray[_DType],
+    _NestedSequence[_SupportsArray[_DType]],
+    _T,
+    _NestedSequence[_T],
+]
+
+# TODO: support buffer protocols once
+#
+# https://bugs.python.org/issue27501
+#
+# is resolved. See also the mypy issue:
+#
+# https://github.com/python/typing/issues/593
+ArrayLike = _ArrayLike[
+    dtype,
+    Union[bool, int, float, complex, str, bytes],
+]
+
+# `ArrayLike<X>_co`: array-like objects that can be coerced into `X`
+# given the casting rules `same_kind`
+_ArrayLikeBool_co = _ArrayLike[
+    "dtype[bool_]",
+    bool,
+]
+_ArrayLikeUInt_co = _ArrayLike[
+    "dtype[Union[bool_, unsignedinteger[Any]]]",
+    bool,
+]
+_ArrayLikeInt_co = _ArrayLike[
+    "dtype[Union[bool_, integer[Any]]]",
+    Union[bool, int],
+]
+_ArrayLikeFloat_co = _ArrayLike[
+    "dtype[Union[bool_, integer[Any], floating[Any]]]",
+    Union[bool, int, float],
+]
+_ArrayLikeComplex_co = _ArrayLike[
+    "dtype[Union[bool_, integer[Any], floating[Any], complexfloating[Any, Any]]]",
+    Union[bool, int, float, complex],
+]
+_ArrayLikeNumber_co = _ArrayLike[
+    "dtype[Union[bool_, number[Any]]]",
+    Union[bool, int, float, complex],
+]
+_ArrayLikeTD64_co = _ArrayLike[
+    "dtype[Union[bool_, integer[Any], timedelta64]]",
+    Union[bool, int],
+]
+_ArrayLikeDT64_co = Union[
+    _SupportsArray["dtype[datetime64]"],
+    _NestedSequence[_SupportsArray["dtype[datetime64]"]],
+]
+_ArrayLikeObject_co = Union[
+    _SupportsArray["dtype[object_]"],
+    _NestedSequence[_SupportsArray["dtype[object_]"]],
+]
+
+_ArrayLikeVoid_co = Union[
+    _SupportsArray["dtype[void]"],
+    _NestedSequence[_SupportsArray["dtype[void]"]],
+]
+_ArrayLikeStr_co = _ArrayLike[
+    "dtype[str_]",
+    str,
+]
+_ArrayLikeBytes_co = _ArrayLike[
+    "dtype[bytes_]",
+    bytes,
+]
+
+_ArrayLikeInt = _ArrayLike[
+    "dtype[integer[Any]]",
+    int,
+]

.venv/Lib/site-packages/numpy/typing/_callable.pyi

@@ -0,0 +1,327 @@
+"""
+A module with various ``typing.Protocol`` subclasses that implement
+the ``__call__`` magic method.
+
+See the `Mypy documentation`_ on protocols for more details.
+
+.. _`Mypy documentation`: https://mypy.readthedocs.io/en/stable/protocols.html#callback-protocols
+
+"""
+
+from __future__ import annotations
+
+from typing import (
+    Union,
+    TypeVar,
+    overload,
+    Any,
+    Tuple,
+    NoReturn,
+    Protocol,
+)
+
+from numpy import (
+    ndarray,
+    dtype,
+    generic,
+    bool_,
+    timedelta64,
+    number,
+    integer,
+    unsignedinteger,
+    signedinteger,
+    int8,
+    int_,
+    floating,
+    float64,
+    complexfloating,
+    complex128,
+)
+from ._nbit import _NBitInt, _NBitDouble
+from ._scalars import (
+    _BoolLike_co,
+    _IntLike_co,
+    _FloatLike_co,
+    _NumberLike_co,
+)
+from . import NBitBase
+from ._generic_alias import NDArray
+
+_T1 = TypeVar("_T1")
+_T2 = TypeVar("_T2")
+_T1_contra = TypeVar("_T1_contra", contravariant=True)
+_T2_contra = TypeVar("_T2_contra", contravariant=True)
+_2Tuple = Tuple[_T1, _T1]
+
+_NBit1 = TypeVar("_NBit1", bound=NBitBase)
+_NBit2 = TypeVar("_NBit2", bound=NBitBase)
+
+_IntType = TypeVar("_IntType", bound=integer)
+_FloatType = TypeVar("_FloatType", bound=floating)
+_NumberType = TypeVar("_NumberType", bound=number)
+_NumberType_co = TypeVar("_NumberType_co", covariant=True, bound=number)
+_GenericType_co = TypeVar("_GenericType_co", covariant=True, bound=generic)
+
+class _BoolOp(Protocol[_GenericType_co]):
+    @overload
+    def __call__(self, other: _BoolLike_co, /) -> _GenericType_co: ...
+    @overload  # platform dependent
+    def __call__(self, other: int, /) -> int_: ...
+    @overload
+    def __call__(self, other: float, /) -> float64: ...
+    @overload
+    def __call__(self, other: complex, /) -> complex128: ...
+    @overload
+    def __call__(self, other: _NumberType, /) -> _NumberType: ...
+
+class _BoolBitOp(Protocol[_GenericType_co]):
+    @overload
+    def __call__(self, other: _BoolLike_co, /) -> _GenericType_co: ...
+    @overload  # platform dependent
+    def __call__(self, other: int, /) -> int_: ...
+    @overload
+    def __call__(self, other: _IntType, /) -> _IntType: ...
+
+class _BoolSub(Protocol):
+    # Note that `other: bool_` is absent here
+    @overload
+    def __call__(self, other: bool, /) -> NoReturn: ...
+    @overload  # platform dependent
+    def __call__(self, other: int, /) -> int_: ...
+    @overload
+    def __call__(self, other: float, /) -> float64: ...
+    @overload
+    def __call__(self, other: complex, /) -> complex128: ...
+    @overload
+    def __call__(self, other: _NumberType, /) -> _NumberType: ...
+
+class _BoolTrueDiv(Protocol):
+    @overload
+    def __call__(self, other: float | _IntLike_co, /) -> float64: ...
+    @overload
+    def __call__(self, other: complex, /) -> complex128: ...
+    @overload
+    def __call__(self, other: _NumberType, /) -> _NumberType: ...
+
+class _BoolMod(Protocol):
+    @overload
+    def __call__(self, other: _BoolLike_co, /) -> int8: ...
+    @overload  # platform dependent
+    def __call__(self, other: int, /) -> int_: ...
+    @overload
+    def __call__(self, other: float, /) -> float64: ...
+    @overload
+    def __call__(self, other: _IntType, /) -> _IntType: ...
+    @overload
+    def __call__(self, other: _FloatType, /) -> _FloatType: ...
+
+class _BoolDivMod(Protocol):
+    @overload
+    def __call__(self, other: _BoolLike_co, /) -> _2Tuple[int8]: ...
+    @overload  # platform dependent
+    def __call__(self, other: int, /) -> _2Tuple[int_]: ...
+    @overload
+    def __call__(self, other: float, /) -> _2Tuple[floating[_NBit1 | _NBitDouble]]: ...
+    @overload
+    def __call__(self, other: _IntType, /) -> _2Tuple[_IntType]: ...
+    @overload
+    def __call__(self, other: _FloatType, /) -> _2Tuple[_FloatType]: ...
+
+class _TD64Div(Protocol[_NumberType_co]):
+    @overload
+    def __call__(self, other: timedelta64, /) -> _NumberType_co: ...
+    @overload
+    def __call__(self, other: _BoolLike_co, /) -> NoReturn: ...
+    @overload
+    def __call__(self, other: _FloatLike_co, /) -> timedelta64: ...
+
+class _IntTrueDiv(Protocol[_NBit1]):
+    @overload
+    def __call__(self, other: bool, /) -> floating[_NBit1]: ...
+    @overload
+    def __call__(self, other: int, /) -> floating[_NBit1 | _NBitInt]: ...
+    @overload
+    def __call__(self, other: float, /) -> floating[_NBit1 | _NBitDouble]: ...
+    @overload
+    def __call__(
+        self, other: complex, /,
+    ) -> complexfloating[_NBit1 | _NBitDouble, _NBit1 | _NBitDouble]: ...
+    @overload
+    def __call__(self, other: integer[_NBit2], /) -> floating[_NBit1 | _NBit2]: ...
+
+class _UnsignedIntOp(Protocol[_NBit1]):
+    # NOTE: `uint64 + signedinteger -> float64`
+    @overload
+    def __call__(self, other: bool, /) -> unsignedinteger[_NBit1]: ...
+    @overload
+    def __call__(
+        self, other: int | signedinteger[Any], /
+    ) -> Any: ...
+    @overload
+    def __call__(self, other: float, /) -> floating[_NBit1 | _NBitDouble]: ...
+    @overload
+    def __call__(
+        self, other: complex, /,
+    ) -> complexfloating[_NBit1 | _NBitDouble, _NBit1 | _NBitDouble]: ...
+    @overload
+    def __call__(
+        self, other: unsignedinteger[_NBit2], /
+    ) -> unsignedinteger[_NBit1 | _NBit2]: ...
+
+class _UnsignedIntBitOp(Protocol[_NBit1]):
+    @overload
+    def __call__(self, other: bool, /) -> unsignedinteger[_NBit1]: ...
+    @overload
+    def __call__(self, other: int, /) -> signedinteger[Any]: ...
+    @overload
+    def __call__(self, other: signedinteger[Any], /) -> signedinteger[Any]: ...
+    @overload
+    def __call__(
+        self, other: unsignedinteger[_NBit2], /
+    ) -> unsignedinteger[_NBit1 | _NBit2]: ...
+
+class _UnsignedIntMod(Protocol[_NBit1]):
+    @overload
+    def __call__(self, other: bool, /) -> unsignedinteger[_NBit1]: ...
+    @overload
+    def __call__(
+        self, other: int | signedinteger[Any], /
+    ) -> Any: ...
+    @overload
+    def __call__(self, other: float, /) -> floating[_NBit1 | _NBitDouble]: ...
+    @overload
+    def __call__(
+        self, other: unsignedinteger[_NBit2], /
+    ) -> unsignedinteger[_NBit1 | _NBit2]: ...
+
+class _UnsignedIntDivMod(Protocol[_NBit1]):
+    @overload
+    def __call__(self, other: bool, /) -> _2Tuple[signedinteger[_NBit1]]: ...
+    @overload
+    def __call__(
+        self, other: int | signedinteger[Any], /
+    ) -> _2Tuple[Any]: ...
+    @overload
+    def __call__(self, other: float, /) -> _2Tuple[floating[_NBit1 | _NBitDouble]]: ...
+    @overload
+    def __call__(
+        self, other: unsignedinteger[_NBit2], /
+    ) -> _2Tuple[unsignedinteger[_NBit1 | _NBit2]]: ...
+
+class _SignedIntOp(Protocol[_NBit1]):
+    @overload
+    def __call__(self, other: bool, /) -> signedinteger[_NBit1]: ...
+    @overload
+    def __call__(self, other: int, /) -> signedinteger[_NBit1 | _NBitInt]: ...
+    @overload
+    def __call__(self, other: float, /) -> floating[_NBit1 | _NBitDouble]: ...
+    @overload
+    def __call__(
+        self, other: complex, /,
+    ) -> complexfloating[_NBit1 | _NBitDouble, _NBit1 | _NBitDouble]: ...
+    @overload
+    def __call__(
+        self, other: signedinteger[_NBit2], /,
+    ) -> signedinteger[_NBit1 | _NBit2]: ...
+
+class _SignedIntBitOp(Protocol[_NBit1]):
+    @overload
+    def __call__(self, other: bool, /) -> signedinteger[_NBit1]: ...
+    @overload
+    def __call__(self, other: int, /) -> signedinteger[_NBit1 | _NBitInt]: ...
+    @overload
+    def __call__(
+        self, other: signedinteger[_NBit2], /,
+    ) -> signedinteger[_NBit1 | _NBit2]: ...
+
+class _SignedIntMod(Protocol[_NBit1]):
+    @overload
+    def __call__(self, other: bool, /) -> signedinteger[_NBit1]: ...
+    @overload
+    def __call__(self, other: int, /) -> signedinteger[_NBit1 | _NBitInt]: ...
+    @overload
+    def __call__(self, other: float, /) -> floating[_NBit1 | _NBitDouble]: ...
+    @overload
+    def __call__(
+        self, other: signedinteger[_NBit2], /,
+    ) -> signedinteger[_NBit1 | _NBit2]: ...
+
+class _SignedIntDivMod(Protocol[_NBit1]):
+    @overload
+    def __call__(self, other: bool, /) -> _2Tuple[signedinteger[_NBit1]]: ...
+    @overload
+    def __call__(self, other: int, /) -> _2Tuple[signedinteger[_NBit1 | _NBitInt]]: ...
+    @overload
+    def __call__(self, other: float, /) -> _2Tuple[floating[_NBit1 | _NBitDouble]]: ...
+    @overload
+    def __call__(
+        self, other: signedinteger[_NBit2], /,
+    ) -> _2Tuple[signedinteger[_NBit1 | _NBit2]]: ...
+
+class _FloatOp(Protocol[_NBit1]):
+    @overload
+    def __call__(self, other: bool, /) -> floating[_NBit1]: ...
+    @overload
+    def __call__(self, other: int, /) -> floating[_NBit1 | _NBitInt]: ...
+    @overload
+    def __call__(self, other: float, /) -> floating[_NBit1 | _NBitDouble]: ...
+    @overload
+    def __call__(
+        self, other: complex, /,
+    ) -> complexfloating[_NBit1 | _NBitDouble, _NBit1 | _NBitDouble]: ...
+    @overload
+    def __call__(
+        self, other: integer[_NBit2] | floating[_NBit2], /
+    ) -> floating[_NBit1 | _NBit2]: ...
+
+class _FloatMod(Protocol[_NBit1]):
+    @overload
+    def __call__(self, other: bool, /) -> floating[_NBit1]: ...
+    @overload
+    def __call__(self, other: int, /) -> floating[_NBit1 | _NBitInt]: ...
+    @overload
+    def __call__(self, other: float, /) -> floating[_NBit1 | _NBitDouble]: ...
+    @overload
+    def __call__(
+        self, other: integer[_NBit2] | floating[_NBit2], /
+    ) -> floating[_NBit1 | _NBit2]: ...
+
+class _FloatDivMod(Protocol[_NBit1]):
+    @overload
+    def __call__(self, other: bool, /) -> _2Tuple[floating[_NBit1]]: ...
+    @overload
+    def __call__(self, other: int, /) -> _2Tuple[floating[_NBit1 | _NBitInt]]: ...
+    @overload
+    def __call__(self, other: float, /) -> _2Tuple[floating[_NBit1 | _NBitDouble]]: ...
+    @overload
+    def __call__(
+        self, other: integer[_NBit2] | floating[_NBit2], /
+    ) -> _2Tuple[floating[_NBit1 | _NBit2]]: ...
+
+class _ComplexOp(Protocol[_NBit1]):
+    @overload
+    def __call__(self, other: bool, /) -> complexfloating[_NBit1, _NBit1]: ...
+    @overload
+    def __call__(self, other: int, /) -> complexfloating[_NBit1 | _NBitInt, _NBit1 | _NBitInt]: ...
+    @overload
+    def __call__(
+        self, other: complex, /,
+    ) -> complexfloating[_NBit1 | _NBitDouble, _NBit1 | _NBitDouble]: ...
+    @overload
+    def __call__(
+        self,
+        other: Union[
+            integer[_NBit2],
+            floating[_NBit2],
+            complexfloating[_NBit2, _NBit2],
+        ], /,
+    ) -> complexfloating[_NBit1 | _NBit2, _NBit1 | _NBit2]: ...
+
+class _NumberOp(Protocol):
+    def __call__(self, other: _NumberLike_co, /) -> Any: ...
+
+class _ComparisonOp(Protocol[_T1_contra, _T2_contra]):
+    @overload
+    def __call__(self, other: _T1_contra, /) -> bool_: ...
+    @overload
+    def __call__(self, other: _T2_contra, /) -> NDArray[bool_]: ...

.venv/Lib/site-packages/numpy/typing/_char_codes.py

@@ -0,0 +1,111 @@
+from typing import Literal
+
+_BoolCodes = Literal["?", "=?", "<?", ">?", "bool", "bool_", "bool8"]
+
+_UInt8Codes = Literal["uint8", "u1", "=u1", "<u1", ">u1"]
+_UInt16Codes = Literal["uint16", "u2", "=u2", "<u2", ">u2"]
+_UInt32Codes = Literal["uint32", "u4", "=u4", "<u4", ">u4"]
+_UInt64Codes = Literal["uint64", "u8", "=u8", "<u8", ">u8"]
+
+_Int8Codes = Literal["int8", "i1", "=i1", "<i1", ">i1"]
+_Int16Codes = Literal["int16", "i2", "=i2", "<i2", ">i2"]
+_Int32Codes = Literal["int32", "i4", "=i4", "<i4", ">i4"]
+_Int64Codes = Literal["int64", "i8", "=i8", "<i8", ">i8"]
+
+_Float16Codes = Literal["float16", "f2", "=f2", "<f2", ">f2"]
+_Float32Codes = Literal["float32", "f4", "=f4", "<f4", ">f4"]
+_Float64Codes = Literal["float64", "f8", "=f8", "<f8", ">f8"]
+
+_Complex64Codes = Literal["complex64", "c8", "=c8", "<c8", ">c8"]
+_Complex128Codes = Literal["complex128", "c16", "=c16", "<c16", ">c16"]
+
+_ByteCodes = Literal["byte", "b", "=b", "<b", ">b"]
+_ShortCodes = Literal["short", "h", "=h", "<h", ">h"]
+_IntCCodes = Literal["intc", "i", "=i", "<i", ">i"]
+_IntPCodes = Literal["intp", "int0", "p", "=p", "<p", ">p"]
+_IntCodes = Literal["long", "int", "int_", "l", "=l", "<l", ">l"]
+_LongLongCodes = Literal["longlong", "q", "=q", "<q", ">q"]
+
+_UByteCodes = Literal["ubyte", "B", "=B", "<B", ">B"]
+_UShortCodes = Literal["ushort", "H", "=H", "<H", ">H"]
+_UIntCCodes = Literal["uintc", "I", "=I", "<I", ">I"]
+_UIntPCodes = Literal["uintp", "uint0", "P", "=P", "<P", ">P"]
+_UIntCodes = Literal["uint", "L", "=L", "<L", ">L"]
+_ULongLongCodes = Literal["ulonglong", "Q", "=Q", "<Q", ">Q"]
+
+_HalfCodes = Literal["half", "e", "=e", "<e", ">e"]
+_SingleCodes = Literal["single", "f", "=f", "<f", ">f"]
+_DoubleCodes = Literal["double", "float", "float_", "d", "=d", "<d", ">d"]
+_LongDoubleCodes = Literal["longdouble", "longfloat", "g", "=g", "<g", ">g"]
+
+_CSingleCodes = Literal["csingle", "singlecomplex", "F", "=F", "<F", ">F"]
+_CDoubleCodes = Literal["cdouble", "complex", "complex_", "cfloat", "D", "=D", "<D", ">D"]
+_CLongDoubleCodes = Literal["clongdouble", "clongfloat", "longcomplex", "G", "=G", "<G", ">G"]
+
+_StrCodes = Literal["str", "str_", "str0", "unicode", "unicode_", "U", "=U", "<U", ">U"]
+_BytesCodes = Literal["bytes", "bytes_", "bytes0", "S", "=S", "<S", ">S"]
+_VoidCodes = Literal["void", "void0", "V", "=V", "<V", ">V"]
+_ObjectCodes = Literal["object", "object_", "O", "=O", "<O", ">O"]
+
+_DT64Codes = Literal[
+    "datetime64", "=datetime64", "<datetime64", ">datetime64",
+    "datetime64[Y]", "=datetime64[Y]", "<datetime64[Y]", ">datetime64[Y]",
+    "datetime64[M]", "=datetime64[M]", "<datetime64[M]", ">datetime64[M]",
+    "datetime64[W]", "=datetime64[W]", "<datetime64[W]", ">datetime64[W]",
+    "datetime64[D]", "=datetime64[D]", "<datetime64[D]", ">datetime64[D]",
+    "datetime64[h]", "=datetime64[h]", "<datetime64[h]", ">datetime64[h]",
+    "datetime64[m]", "=datetime64[m]", "<datetime64[m]", ">datetime64[m]",
+    "datetime64[s]", "=datetime64[s]", "<datetime64[s]", ">datetime64[s]",
+    "datetime64[ms]", "=datetime64[ms]", "<datetime64[ms]", ">datetime64[ms]",
+    "datetime64[us]", "=datetime64[us]", "<datetime64[us]", ">datetime64[us]",
+    "datetime64[ns]", "=datetime64[ns]", "<datetime64[ns]", ">datetime64[ns]",
+    "datetime64[ps]", "=datetime64[ps]", "<datetime64[ps]", ">datetime64[ps]",
+    "datetime64[fs]", "=datetime64[fs]", "<datetime64[fs]", ">datetime64[fs]",
+    "datetime64[as]", "=datetime64[as]", "<datetime64[as]", ">datetime64[as]",
+    "M", "=M", "<M", ">M",
+    "M8", "=M8", "<M8", ">M8",
+    "M8[Y]", "=M8[Y]", "<M8[Y]", ">M8[Y]",
+    "M8[M]", "=M8[M]", "<M8[M]", ">M8[M]",
+    "M8[W]", "=M8[W]", "<M8[W]", ">M8[W]",
+    "M8[D]", "=M8[D]", "<M8[D]", ">M8[D]",
+    "M8[h]", "=M8[h]", "<M8[h]", ">M8[h]",
+    "M8[m]", "=M8[m]", "<M8[m]", ">M8[m]",
+    "M8[s]", "=M8[s]", "<M8[s]", ">M8[s]",
+    "M8[ms]", "=M8[ms]", "<M8[ms]", ">M8[ms]",
+    "M8[us]", "=M8[us]", "<M8[us]", ">M8[us]",
+    "M8[ns]", "=M8[ns]", "<M8[ns]", ">M8[ns]",
+    "M8[ps]", "=M8[ps]", "<M8[ps]", ">M8[ps]",
+    "M8[fs]", "=M8[fs]", "<M8[fs]", ">M8[fs]",
+    "M8[as]", "=M8[as]", "<M8[as]", ">M8[as]",
+]
+_TD64Codes = Literal[
+    "timedelta64", "=timedelta64", "<timedelta64", ">timedelta64",
+    "timedelta64[Y]", "=timedelta64[Y]", "<timedelta64[Y]", ">timedelta64[Y]",
+    "timedelta64[M]", "=timedelta64[M]", "<timedelta64[M]", ">timedelta64[M]",
+    "timedelta64[W]", "=timedelta64[W]", "<timedelta64[W]", ">timedelta64[W]",
+    "timedelta64[D]", "=timedelta64[D]", "<timedelta64[D]", ">timedelta64[D]",
+    "timedelta64[h]", "=timedelta64[h]", "<timedelta64[h]", ">timedelta64[h]",
+    "timedelta64[m]", "=timedelta64[m]", "<timedelta64[m]", ">timedelta64[m]",
+    "timedelta64[s]", "=timedelta64[s]", "<timedelta64[s]", ">timedelta64[s]",
+    "timedelta64[ms]", "=timedelta64[ms]", "<timedelta64[ms]", ">timedelta64[ms]",
+    "timedelta64[us]", "=timedelta64[us]", "<timedelta64[us]", ">timedelta64[us]",
+    "timedelta64[ns]", "=timedelta64[ns]", "<timedelta64[ns]", ">timedelta64[ns]",
+    "timedelta64[ps]", "=timedelta64[ps]", "<timedelta64[ps]", ">timedelta64[ps]",
+    "timedelta64[fs]", "=timedelta64[fs]", "<timedelta64[fs]", ">timedelta64[fs]",
+    "timedelta64[as]", "=timedelta64[as]", "<timedelta64[as]", ">timedelta64[as]",
+    "m", "=m", "<m", ">m",
+    "m8", "=m8", "<m8", ">m8",
+    "m8[Y]", "=m8[Y]", "<m8[Y]", ">m8[Y]",
+    "m8[M]", "=m8[M]", "<m8[M]", ">m8[M]",
+    "m8[W]", "=m8[W]", "<m8[W]", ">m8[W]",
+    "m8[D]", "=m8[D]", "<m8[D]", ">m8[D]",
+    "m8[h]", "=m8[h]", "<m8[h]", ">m8[h]",
+    "m8[m]", "=m8[m]", "<m8[m]", ">m8[m]",
+    "m8[s]", "=m8[s]", "<m8[s]", ">m8[s]",
+    "m8[ms]", "=m8[ms]", "<m8[ms]", ">m8[ms]",
+    "m8[us]", "=m8[us]", "<m8[us]", ">m8[us]",
+    "m8[ns]", "=m8[ns]", "<m8[ns]", ">m8[ns]",
+    "m8[ps]", "=m8[ps]", "<m8[ps]", ">m8[ps]",
+    "m8[fs]", "=m8[fs]", "<m8[fs]", ">m8[fs]",
+    "m8[as]", "=m8[as]", "<m8[as]", ">m8[as]",
+]

.venv/Lib/site-packages/numpy/typing/_dtype_like.py

@@ -0,0 +1,236 @@
+from typing import (
+    Any,
+    List,
+    Sequence,
+    Tuple,
+    Union,
+    Type,
+    TypeVar,
+    Protocol,
+    TypedDict,
+)
+
+import numpy as np
+
+from ._shape import _ShapeLike
+from ._generic_alias import _DType as DType
+
+from ._char_codes import (
+    _BoolCodes,
+    _UInt8Codes,
+    _UInt16Codes,
+    _UInt32Codes,
+    _UInt64Codes,
+    _Int8Codes,
+    _Int16Codes,
+    _Int32Codes,
+    _Int64Codes,
+    _Float16Codes,
+    _Float32Codes,
+    _Float64Codes,
+    _Complex64Codes,
+    _Complex128Codes,
+    _ByteCodes,
+    _ShortCodes,
+    _IntCCodes,
+    _IntPCodes,
+    _IntCodes,
+    _LongLongCodes,
+    _UByteCodes,
+    _UShortCodes,
+    _UIntCCodes,
+    _UIntPCodes,
+    _UIntCodes,
+    _ULongLongCodes,
+    _HalfCodes,
+    _SingleCodes,
+    _DoubleCodes,
+    _LongDoubleCodes,
+    _CSingleCodes,
+    _CDoubleCodes,
+    _CLongDoubleCodes,
+    _DT64Codes,
+    _TD64Codes,
+    _StrCodes,
+    _BytesCodes,
+    _VoidCodes,
+    _ObjectCodes,
+)
+
+_DTypeLikeNested = Any  # TODO: wait for support for recursive types
+_DType_co = TypeVar("_DType_co", covariant=True, bound=DType[Any])
+
+# Mandatory keys
+class _DTypeDictBase(TypedDict):
+    names: Sequence[str]
+    formats: Sequence[_DTypeLikeNested]
+
+
+# Mandatory + optional keys
+class _DTypeDict(_DTypeDictBase, total=False):
+    # Only `str` elements are usable as indexing aliases,
+    # but `titles` can in principle accept any object
+    offsets: Sequence[int]
+    titles: Sequence[Any]
+    itemsize: int
+    aligned: bool
+
+
+# A protocol for anything with the dtype attribute
+class _SupportsDType(Protocol[_DType_co]):
+    @property
+    def dtype(self) -> _DType_co: ...
+
+
+# Would create a dtype[np.void]
+_VoidDTypeLike = Union[
+    # (flexible_dtype, itemsize)
+    Tuple[_DTypeLikeNested, int],
+    # (fixed_dtype, shape)
+    Tuple[_DTypeLikeNested, _ShapeLike],
+    # [(field_name, field_dtype, field_shape), ...]
+    #
+    # The type here is quite broad because NumPy accepts quite a wide
+    # range of inputs inside the list; see the tests for some
+    # examples.
+    List[Any],
+    # {'names': ..., 'formats': ..., 'offsets': ..., 'titles': ...,
+    #  'itemsize': ...}
+    _DTypeDict,
+    # (base_dtype, new_dtype)
+    Tuple[_DTypeLikeNested, _DTypeLikeNested],
+]
+
+# Anything that can be coerced into numpy.dtype.
+# Reference: https://docs.scipy.org/doc/numpy/reference/arrays.dtypes.html
+DTypeLike = Union[
+    DType[Any],
+    # default data type (float64)
+    None,
+    # array-scalar types and generic types
+    Type[Any],  # NOTE: We're stuck with `Type[Any]` due to object dtypes
+    # anything with a dtype attribute
+    _SupportsDType[DType[Any]],
+    # character codes, type strings or comma-separated fields, e.g., 'float64'
+    str,
+    _VoidDTypeLike,
+]
+
+# NOTE: while it is possible to provide the dtype as a dict of
+# dtype-like objects (e.g. `{'field1': ..., 'field2': ..., ...}`),
+# this syntax is officially discourged and
+# therefore not included in the Union defining `DTypeLike`.
+#
+# See https://github.com/numpy/numpy/issues/16891 for more details.
+
+# Aliases for commonly used dtype-like objects.
+# Note that the precision of `np.number` subclasses is ignored herein.
+_DTypeLikeBool = Union[
+    Type[bool],
+    Type[np.bool_],
+    DType[np.bool_],
+    _SupportsDType[DType[np.bool_]],
+    _BoolCodes,
+]
+_DTypeLikeUInt = Union[
+    Type[np.unsignedinteger],
+    DType[np.unsignedinteger],
+    _SupportsDType[DType[np.unsignedinteger]],
+    _UInt8Codes,
+    _UInt16Codes,
+    _UInt32Codes,
+    _UInt64Codes,
+    _UByteCodes,
+    _UShortCodes,
+    _UIntCCodes,
+    _UIntPCodes,
+    _UIntCodes,
+    _ULongLongCodes,
+]
+_DTypeLikeInt = Union[
+    Type[int],
+    Type[np.signedinteger],
+    DType[np.signedinteger],
+    _SupportsDType[DType[np.signedinteger]],
+    _Int8Codes,
+    _Int16Codes,
+    _Int32Codes,
+    _Int64Codes,
+    _ByteCodes,
+    _ShortCodes,
+    _IntCCodes,
+    _IntPCodes,
+    _IntCodes,
+    _LongLongCodes,
+]
+_DTypeLikeFloat = Union[
+    Type[float],
+    Type[np.floating],
+    DType[np.floating],
+    _SupportsDType[DType[np.floating]],
+    _Float16Codes,
+    _Float32Codes,
+    _Float64Codes,
+    _HalfCodes,
+    _SingleCodes,
+    _DoubleCodes,
+    _LongDoubleCodes,
+]
+_DTypeLikeComplex = Union[
+    Type[complex],
+    Type[np.complexfloating],
+    DType[np.complexfloating],
+    _SupportsDType[DType[np.complexfloating]],
+    _Complex64Codes,
+    _Complex128Codes,
+    _CSingleCodes,
+    _CDoubleCodes,
+    _CLongDoubleCodes,
+]
+_DTypeLikeDT64 = Union[
+    Type[np.timedelta64],
+    DType[np.timedelta64],
+    _SupportsDType[DType[np.timedelta64]],
+    _TD64Codes,
+]
+_DTypeLikeTD64 = Union[
+    Type[np.datetime64],
+    DType[np.datetime64],
+    _SupportsDType[DType[np.datetime64]],
+    _DT64Codes,
+]
+_DTypeLikeStr = Union[
+    Type[str],
+    Type[np.str_],
+    DType[np.str_],
+    _SupportsDType[DType[np.str_]],
+    _StrCodes,
+]
+_DTypeLikeBytes = Union[
+    Type[bytes],
+    Type[np.bytes_],
+    DType[np.bytes_],
+    _SupportsDType[DType[np.bytes_]],
+    _BytesCodes,
+]
+_DTypeLikeVoid = Union[
+    Type[np.void],
+    DType[np.void],
+    _SupportsDType[DType[np.void]],
+    _VoidCodes,
+    _VoidDTypeLike,
+]
+_DTypeLikeObject = Union[
+    type,
+    DType[np.object_],
+    _SupportsDType[DType[np.object_]],
+    _ObjectCodes,
+]
+
+_DTypeLikeComplex_co = Union[
+    _DTypeLikeBool,
+    _DTypeLikeUInt,
+    _DTypeLikeInt,
+    _DTypeLikeFloat,
+    _DTypeLikeComplex,
+]

.venv/Lib/site-packages/numpy/typing/_extended_precision.py

@@ -0,0 +1,43 @@
+"""A module with platform-specific extended precision
+`numpy.number` subclasses.
+
+The subclasses are defined here (instead of ``__init__.pyi``) such
+that they can be imported conditionally via the numpy's mypy plugin.
+"""
+
+from typing import TYPE_CHECKING
+
+import numpy as np
+from . import (
+    _80Bit,
+    _96Bit,
+    _128Bit,
+    _256Bit,
+)
+
+if TYPE_CHECKING:
+    uint128 = np.unsignedinteger[_128Bit]
+    uint256 = np.unsignedinteger[_256Bit]
+    int128 = np.signedinteger[_128Bit]
+    int256 = np.signedinteger[_256Bit]
+    float80 = np.floating[_80Bit]
+    float96 = np.floating[_96Bit]
+    float128 = np.floating[_128Bit]
+    float256 = np.floating[_256Bit]
+    complex160 = np.complexfloating[_80Bit, _80Bit]
+    complex192 = np.complexfloating[_96Bit, _96Bit]
+    complex256 = np.complexfloating[_128Bit, _128Bit]
+    complex512 = np.complexfloating[_256Bit, _256Bit]
+else:
+    uint128 = Any
+    uint256 = Any
+    int128 = Any
+    int256 = Any
+    float80 = Any
+    float96 = Any
+    float128 = Any
+    float256 = Any
+    complex160 = Any
+    complex192 = Any
+    complex256 = Any
+    complex512 = Any

.venv/Lib/site-packages/numpy/typing/_generic_alias.py

@@ -0,0 +1,215 @@
+from __future__ import annotations
+
+import sys
+import types
+from typing import (
+    Any,
+    ClassVar,
+    FrozenSet,
+    Generator,
+    Iterable,
+    Iterator,
+    List,
+    NoReturn,
+    Tuple,
+    Type,
+    TypeVar,
+    TYPE_CHECKING,
+)
+
+import numpy as np
+
+__all__ = ["_GenericAlias", "NDArray"]
+
+_T = TypeVar("_T", bound="_GenericAlias")
+
+
+def _to_str(obj: object) -> str:
+    """Helper function for `_GenericAlias.__repr__`."""
+    if obj is Ellipsis:
+        return '...'
+    elif isinstance(obj, type) and not isinstance(obj, _GENERIC_ALIAS_TYPE):
+        if obj.__module__ == 'builtins':
+            return obj.__qualname__
+        else:
+            return f'{obj.__module__}.{obj.__qualname__}'
+    else:
+        return repr(obj)
+
+
+def _parse_parameters(args: Iterable[Any]) -> Generator[TypeVar, None, None]:
+    """Search for all typevars and typevar-containing objects in `args`.
+
+    Helper function for `_GenericAlias.__init__`.
+
+    """
+    for i in args:
+        if hasattr(i, "__parameters__"):
+            yield from i.__parameters__
+        elif isinstance(i, TypeVar):
+            yield i
+
+
+def _reconstruct_alias(alias: _T, parameters: Iterator[TypeVar]) -> _T:
+    """Recursively replace all typevars with those from `parameters`.
+
+    Helper function for `_GenericAlias.__getitem__`.
+
+    """
+    args = []
+    for i in alias.__args__:
+        if isinstance(i, TypeVar):
+            value: Any = next(parameters)
+        elif isinstance(i, _GenericAlias):
+            value = _reconstruct_alias(i, parameters)
+        elif hasattr(i, "__parameters__"):
+            prm_tup = tuple(next(parameters) for _ in i.__parameters__)
+            value = i[prm_tup]
+        else:
+            value = i
+        args.append(value)
+
+    cls = type(alias)
+    return cls(alias.__origin__, tuple(args))
+
+
+class _GenericAlias:
+    """A python-based backport of the `types.GenericAlias` class.
+
+    E.g. for ``t = list[int]``, ``t.__origin__`` is ``list`` and
+    ``t.__args__`` is ``(int,)``.
+
+    See Also
+    --------
+    :pep:`585`
+        The PEP responsible for introducing `types.GenericAlias`.
+
+    """
+
+    __slots__ = ("__weakref__", "_origin", "_args", "_parameters", "_hash")
+
+    @property
+    def __origin__(self) -> type:
+        return super().__getattribute__("_origin")
+
+    @property
+    def __args__(self) -> Tuple[object, ...]:
+        return super().__getattribute__("_args")
+
+    @property
+    def __parameters__(self) -> Tuple[TypeVar, ...]:
+        """Type variables in the ``GenericAlias``."""
+        return super().__getattribute__("_parameters")
+
+    def __init__(
+        self,
+        origin: type,
+        args: object | Tuple[object, ...],
+    ) -> None:
+        self._origin = origin
+        self._args = args if isinstance(args, tuple) else (args,)
+        self._parameters = tuple(_parse_parameters(self.__args__))
+
+    @property
+    def __call__(self) -> type:
+        return self.__origin__
+
+    def __reduce__(self: _T) -> Tuple[
+        Type[_T],
+        Tuple[type, Tuple[object, ...]],
+    ]:
+        cls = type(self)
+        return cls, (self.__origin__, self.__args__)
+
+    def __mro_entries__(self, bases: Iterable[object]) -> Tuple[type]:
+        return (self.__origin__,)
+
+    def __dir__(self) -> List[str]:
+        """Implement ``dir(self)``."""
+        cls = type(self)
+        dir_origin = set(dir(self.__origin__))
+        return sorted(cls._ATTR_EXCEPTIONS | dir_origin)
+
+    def __hash__(self) -> int:
+        """Return ``hash(self)``."""
+        # Attempt to use the cached hash
+        try:
+            return super().__getattribute__("_hash")
+        except AttributeError:
+            self._hash: int = hash(self.__origin__) ^ hash(self.__args__)
+            return super().__getattribute__("_hash")
+
+    def __instancecheck__(self, obj: object) -> NoReturn:
+        """Check if an `obj` is an instance."""
+        raise TypeError("isinstance() argument 2 cannot be a "
+                        "parameterized generic")
+
+    def __subclasscheck__(self, cls: type) -> NoReturn:
+        """Check if a `cls` is a subclass."""
+        raise TypeError("issubclass() argument 2 cannot be a "
+                        "parameterized generic")
+
+    def __repr__(self) -> str:
+        """Return ``repr(self)``."""
+        args = ", ".join(_to_str(i) for i in self.__args__)
+        origin = _to_str(self.__origin__)
+        return f"{origin}[{args}]"
+
+    def __getitem__(self: _T, key: object | Tuple[object, ...]) -> _T:
+        """Return ``self[key]``."""
+        key_tup = key if isinstance(key, tuple) else (key,)
+
+        if len(self.__parameters__) == 0:
+            raise TypeError(f"There are no type variables left in {self}")
+        elif len(key_tup) > len(self.__parameters__):
+            raise TypeError(f"Too many arguments for {self}")
+        elif len(key_tup) < len(self.__parameters__):
+            raise TypeError(f"Too few arguments for {self}")
+
+        key_iter = iter(key_tup)
+        return _reconstruct_alias(self, key_iter)
+
+    def __eq__(self, value: object) -> bool:
+        """Return ``self == value``."""
+        if not isinstance(value, _GENERIC_ALIAS_TYPE):
+            return NotImplemented
+        return (
+            self.__origin__ == value.__origin__ and
+            self.__args__ == value.__args__
+        )
+
+    _ATTR_EXCEPTIONS: ClassVar[FrozenSet[str]] = frozenset({
+        "__origin__",
+        "__args__",
+        "__parameters__",
+        "__mro_entries__",
+        "__reduce__",
+        "__reduce_ex__",
+        "__copy__",
+        "__deepcopy__",
+    })
+
+    def __getattribute__(self, name: str) -> Any:
+        """Return ``getattr(self, name)``."""
+        # Pull the attribute from `__origin__` unless its
+        # name is in `_ATTR_EXCEPTIONS`
+        cls = type(self)
+        if name in cls._ATTR_EXCEPTIONS:
+            return super().__getattribute__(name)
+        return getattr(self.__origin__, name)
+
+
+# See `_GenericAlias.__eq__`
+if sys.version_info >= (3, 9):
+    _GENERIC_ALIAS_TYPE = (_GenericAlias, types.GenericAlias)
+else:
+    _GENERIC_ALIAS_TYPE = (_GenericAlias,)
+
+ScalarType = TypeVar("ScalarType", bound=np.generic, covariant=True)
+
+if TYPE_CHECKING or sys.version_info >= (3, 9):
+    _DType = np.dtype[ScalarType]
+    NDArray = np.ndarray[Any, np.dtype[ScalarType]]
+else:
+    _DType = _GenericAlias(np.dtype, (ScalarType,))
+    NDArray = _GenericAlias(np.ndarray, (Any, _DType))

.venv/Lib/site-packages/numpy/typing/_nbit.py

@@ -0,0 +1,16 @@
+"""A module with the precisions of platform-specific `~numpy.number`s."""
+
+from typing import Any
+
+# To-be replaced with a `npt.NBitBase` subclass by numpy's mypy plugin
+_NBitByte = Any
+_NBitShort = Any
+_NBitIntC = Any
+_NBitIntP = Any
+_NBitInt = Any
+_NBitLongLong = Any
+
+_NBitHalf = Any
+_NBitSingle = Any
+_NBitDouble = Any
+_NBitLongDouble = Any

.venv/Lib/site-packages/numpy/typing/_nested_sequence.py

@@ -0,0 +1,90 @@
+"""A module containing the `_NestedSequence` protocol."""
+
+from __future__ import annotations
+
+from typing import (
+    Any,
+    Iterator,
+    overload,
+    TypeVar,
+    Protocol,
+)
+
+__all__ = ["_NestedSequence"]
+
+_T_co = TypeVar("_T_co", covariant=True)
+
+
+class _NestedSequence(Protocol[_T_co]):
+    """A protocol for representing nested sequences.
+
+    Warning
+    -------
+    `_NestedSequence` currently does not work in combination with typevars,
+    *e.g.* ``def func(a: _NestedSequnce[T]) -> T: ...``.
+
+    See Also
+    --------
+    `collections.abc.Sequence`
+        ABCs for read-only and mutable :term:`sequences`.
+
+    Examples
+    --------
+    .. code-block:: python
+
+        >>> from __future__ import annotations
+
+        >>> from typing import TYPE_CHECKING
+        >>> import numpy as np
+        >>> from numpy.typing import _NestedSequnce
+
+        >>> def get_dtype(seq: _NestedSequnce[float]) -> np.dtype[np.float64]:
+        ...     return np.asarray(seq).dtype
+
+        >>> a = get_dtype([1.0])
+        >>> b = get_dtype([[1.0]])
+        >>> c = get_dtype([[[1.0]]])
+        >>> d = get_dtype([[[[1.0]]]])
+
+        >>> if TYPE_CHECKING:
+        ...     reveal_locals()
+        ...     # note: Revealed local types are:
+        ...     # note:     a: numpy.dtype[numpy.floating[numpy.typing._64Bit]]
+        ...     # note:     b: numpy.dtype[numpy.floating[numpy.typing._64Bit]]
+        ...     # note:     c: numpy.dtype[numpy.floating[numpy.typing._64Bit]]
+        ...     # note:     d: numpy.dtype[numpy.floating[numpy.typing._64Bit]]
+
+    """
+
+    def __len__(self, /) -> int:
+        """Implement ``len(self)``."""
+        raise NotImplementedError
+
+    @overload
+    def __getitem__(self, index: int, /) -> _T_co | _NestedSequence[_T_co]: ...
+    @overload
+    def __getitem__(self, index: slice, /) -> _NestedSequence[_T_co]: ...
+
+    def __getitem__(self, index, /):
+        """Implement ``self[x]``."""
+        raise NotImplementedError
+
+    def __contains__(self, x: object, /) -> bool:
+        """Implement ``x in self``."""
+        raise NotImplementedError
+
+    def __iter__(self, /) -> Iterator[_T_co | _NestedSequence[_T_co]]:
+        """Implement ``iter(self)``."""
+        raise NotImplementedError
+
+    def __reversed__(self, /) -> Iterator[_T_co | _NestedSequence[_T_co]]:
+        """Implement ``reversed(self)``."""
+        raise NotImplementedError
+
+    def count(self, value: Any, /) -> int:
+        """Return the number of occurrences of `value`."""
+        raise NotImplementedError
+
+    def index(self, value: Any, /) -> int:
+        """Return the first index of `value`."""
+        raise NotImplementedError

.venv/Lib/site-packages/numpy/typing/_scalars.py

@@ -0,0 +1,30 @@
+from typing import Union, Tuple, Any
+
+import numpy as np
+
+# NOTE: `_StrLike_co` and `_BytesLike_co` are pointless, as `np.str_` and
+# `np.bytes_` are already subclasses of their builtin counterpart
+
+_CharLike_co = Union[str, bytes]
+
+# The 6 `<X>Like_co` type-aliases below represent all scalars that can be
+# coerced into `<X>` (with the casting rule `same_kind`)
+_BoolLike_co = Union[bool, np.bool_]
+_UIntLike_co = Union[_BoolLike_co, np.unsignedinteger]
+_IntLike_co = Union[_BoolLike_co, int, np.integer]
+_FloatLike_co = Union[_IntLike_co, float, np.floating]
+_ComplexLike_co = Union[_FloatLike_co, complex, np.complexfloating]
+_TD64Like_co = Union[_IntLike_co, np.timedelta64]
+
+_NumberLike_co = Union[int, float, complex, np.number, np.bool_]
+_ScalarLike_co = Union[
+    int,
+    float,
+    complex,
+    str,
+    bytes,
+    np.generic,
+]
+
+# `_VoidLike_co` is technically not a scalar, but it's close enough
+_VoidLike_co = Union[Tuple[Any, ...], np.void]

.venv/Lib/site-packages/numpy/typing/_shape.py

@@ -0,0 +1,6 @@
+from typing import Sequence, Tuple, Union, SupportsIndex
+
+_Shape = Tuple[int, ...]
+
+# Anything that can be coerced to a shape tuple
+_ShapeLike = Union[SupportsIndex, Sequence[SupportsIndex]]

.venv/Lib/site-packages/numpy/typing/_ufunc.pyi

@@ -0,0 +1,405 @@
+"""A module with private type-check-only `numpy.ufunc` subclasses.
+
+The signatures of the ufuncs are too varied to reasonably type
+with a single class. So instead, `ufunc` has been expanded into
+four private subclasses, one for each combination of
+`~ufunc.nin` and `~ufunc.nout`.
+
+"""
+
+from typing import (
+    Any,
+    Generic,
+    List,
+    overload,
+    Tuple,
+    TypeVar,
+    Literal,
+    SupportsIndex,
+)
+
+from numpy import ufunc, _CastingKind, _OrderKACF
+from numpy.typing import NDArray
+
+from ._shape import _ShapeLike
+from ._scalars import _ScalarLike_co
+from ._array_like import ArrayLike, _ArrayLikeBool_co, _ArrayLikeInt_co
+from ._dtype_like import DTypeLike
+
+_T = TypeVar("_T")
+_2Tuple = Tuple[_T, _T]
+_3Tuple = Tuple[_T, _T, _T]
+_4Tuple = Tuple[_T, _T, _T, _T]
+
+_NTypes = TypeVar("_NTypes", bound=int)
+_IDType = TypeVar("_IDType", bound=Any)
+_NameType = TypeVar("_NameType", bound=str)
+
+# NOTE: In reality `extobj` should be a length of list 3 containing an
+# int, an int, and a callable, but there's no way to properly express
+# non-homogenous lists.
+# Use `Any` over `Union` to avoid issues related to lists invariance.
+
+# NOTE: `reduce`, `accumulate`, `reduceat` and `outer` raise a ValueError for
+# ufuncs that don't accept two input arguments and return one output argument.
+# In such cases the respective methods are simply typed as `None`.
+
+# NOTE: Similarly, `at` won't be defined for ufuncs that return
+# multiple outputs; in such cases `at` is typed as `None`
+
+# NOTE: If 2 output types are returned then `out` must be a
+# 2-tuple of arrays. Otherwise `None` or a plain array are also acceptable
+
+class _UFunc_Nin1_Nout1(ufunc, Generic[_NameType, _NTypes, _IDType]):
+    @property
+    def __name__(self) -> _NameType: ...
+    @property
+    def ntypes(self) -> _NTypes: ...
+    @property
+    def identity(self) -> _IDType: ...
+    @property
+    def nin(self) -> Literal[1]: ...
+    @property
+    def nout(self) -> Literal[1]: ...
+    @property
+    def nargs(self) -> Literal[2]: ...
+    @property
+    def signature(self) -> None: ...
+    @property
+    def reduce(self) -> None: ...
+    @property
+    def accumulate(self) -> None: ...
+    @property
+    def reduceat(self) -> None: ...
+    @property
+    def outer(self) -> None: ...
+
+    @overload
+    def __call__(
+        self,
+        __x1: _ScalarLike_co,
+        out: None = ...,
+        *,
+        where: None | _ArrayLikeBool_co = ...,
+        casting: _CastingKind = ...,
+        order: _OrderKACF = ...,
+        dtype: DTypeLike = ...,
+        subok: bool = ...,
+        signature: str | _2Tuple[None | str] = ...,
+        extobj: List[Any] = ...,
+    ) -> Any: ...
+    @overload
+    def __call__(
+        self,
+        __x1: ArrayLike,
+        out: None | NDArray[Any] | Tuple[NDArray[Any]] = ...,
+        *,
+        where: None | _ArrayLikeBool_co = ...,
+        casting: _CastingKind = ...,
+        order: _OrderKACF = ...,
+        dtype: DTypeLike = ...,
+        subok: bool = ...,
+        signature: str | _2Tuple[None | str] = ...,
+        extobj: List[Any] = ...,
+    ) -> NDArray[Any]: ...
+
+    def at(
+        self,
+        a: NDArray[Any],
+        indices: _ArrayLikeInt_co,
+        /,
+    ) -> None: ...
+
+class _UFunc_Nin2_Nout1(ufunc, Generic[_NameType, _NTypes, _IDType]):
+    @property
+    def __name__(self) -> _NameType: ...
+    @property
+    def ntypes(self) -> _NTypes: ...
+    @property
+    def identity(self) -> _IDType: ...
+    @property
+    def nin(self) -> Literal[2]: ...
+    @property
+    def nout(self) -> Literal[1]: ...
+    @property
+    def nargs(self) -> Literal[3]: ...
+    @property
+    def signature(self) -> None: ...
+
+    @overload
+    def __call__(
+        self,
+        __x1: _ScalarLike_co,
+        __x2: _ScalarLike_co,
+        out: None = ...,
+        *,
+        where: None | _ArrayLikeBool_co = ...,
+        casting: _CastingKind = ...,
+        order: _OrderKACF = ...,
+        dtype: DTypeLike = ...,
+        subok: bool = ...,
+        signature: str | _3Tuple[None | str] = ...,
+        extobj: List[Any] = ...,
+    ) -> Any: ...
+    @overload
+    def __call__(
+        self,
+        __x1: ArrayLike,
+        __x2: ArrayLike,
+        out: None | NDArray[Any] | Tuple[NDArray[Any]] = ...,
+        *,
+        where: None | _ArrayLikeBool_co = ...,
+        casting: _CastingKind = ...,
+        order: _OrderKACF = ...,
+        dtype: DTypeLike = ...,
+        subok: bool = ...,
+        signature: str | _3Tuple[None | str] = ...,
+        extobj: List[Any] = ...,
+    ) -> NDArray[Any]: ...
+
+    def at(
+        self,
+        a: NDArray[Any],
+        indices: _ArrayLikeInt_co,
+        b: ArrayLike,
+        /,
+    ) -> None: ...
+
+    def reduce(
+        self,
+        array: ArrayLike,
+        axis: None | _ShapeLike = ...,
+        dtype: DTypeLike = ...,
+        out: None | NDArray[Any] = ...,
+        keepdims: bool = ...,
+        initial: Any = ...,
+        where: _ArrayLikeBool_co = ...,
+    ) -> Any: ...
+
+    def accumulate(
+        self,
+        array: ArrayLike,
+        axis: SupportsIndex = ...,
+        dtype: DTypeLike = ...,
+        out: None | NDArray[Any] = ...,
+    ) -> NDArray[Any]: ...
+
+    def reduceat(
+        self,
+        array: ArrayLike,
+        indices: _ArrayLikeInt_co,
+        axis: SupportsIndex = ...,
+        dtype: DTypeLike = ...,
+        out: None | NDArray[Any] = ...,
+    ) -> NDArray[Any]: ...
+
+    # Expand `**kwargs` into explicit keyword-only arguments
+    @overload
+    def outer(
+        self,
+        A: _ScalarLike_co,
+        B: _ScalarLike_co,
+        /, *,
+        out: None = ...,
+        where: None | _ArrayLikeBool_co = ...,
+        casting: _CastingKind = ...,
+        order: _OrderKACF = ...,
+        dtype: DTypeLike = ...,
+        subok: bool = ...,
+        signature: str | _3Tuple[None | str] = ...,
+        extobj: List[Any] = ...,
+    ) -> Any: ...
+    @overload
+    def outer(  # type: ignore[misc]
+        self,
+        A: ArrayLike,
+        B: ArrayLike,
+        /, *,
+        out: None | NDArray[Any] | Tuple[NDArray[Any]] = ...,
+        where: None | _ArrayLikeBool_co = ...,
+        casting: _CastingKind = ...,
+        order: _OrderKACF = ...,
+        dtype: DTypeLike = ...,
+        subok: bool = ...,
+        signature: str | _3Tuple[None | str] = ...,
+        extobj: List[Any] = ...,
+    ) -> NDArray[Any]: ...
+
+class _UFunc_Nin1_Nout2(ufunc, Generic[_NameType, _NTypes, _IDType]):
+    @property
+    def __name__(self) -> _NameType: ...
+    @property
+    def ntypes(self) -> _NTypes: ...
+    @property
+    def identity(self) -> _IDType: ...
+    @property
+    def nin(self) -> Literal[1]: ...
+    @property
+    def nout(self) -> Literal[2]: ...
+    @property
+    def nargs(self) -> Literal[3]: ...
+    @property
+    def signature(self) -> None: ...
+    @property
+    def at(self) -> None: ...
+    @property
+    def reduce(self) -> None: ...
+    @property
+    def accumulate(self) -> None: ...
+    @property
+    def reduceat(self) -> None: ...
+    @property
+    def outer(self) -> None: ...
+
+    @overload
+    def __call__(
+        self,
+        __x1: _ScalarLike_co,
+        __out1: None = ...,
+        __out2: None = ...,
+        *,
+        where: None | _ArrayLikeBool_co = ...,
+        casting: _CastingKind = ...,
+        order: _OrderKACF = ...,
+        dtype: DTypeLike = ...,
+        subok: bool = ...,
+        signature: str | _3Tuple[None | str] = ...,
+        extobj: List[Any] = ...,
+    ) -> _2Tuple[Any]: ...
+    @overload
+    def __call__(
+        self,
+        __x1: ArrayLike,
+        __out1: None | NDArray[Any] = ...,
+        __out2: None | NDArray[Any] = ...,
+        *,
+        out: _2Tuple[NDArray[Any]] = ...,
+        where: None | _ArrayLikeBool_co = ...,
+        casting: _CastingKind = ...,
+        order: _OrderKACF = ...,
+        dtype: DTypeLike = ...,
+        subok: bool = ...,
+        signature: str | _3Tuple[None | str] = ...,
+        extobj: List[Any] = ...,
+    ) -> _2Tuple[NDArray[Any]]: ...
+
+class _UFunc_Nin2_Nout2(ufunc, Generic[_NameType, _NTypes, _IDType]):
+    @property
+    def __name__(self) -> _NameType: ...
+    @property
+    def ntypes(self) -> _NTypes: ...
+    @property
+    def identity(self) -> _IDType: ...
+    @property
+    def nin(self) -> Literal[2]: ...
+    @property
+    def nout(self) -> Literal[2]: ...
+    @property
+    def nargs(self) -> Literal[4]: ...
+    @property
+    def signature(self) -> None: ...
+    @property
+    def at(self) -> None: ...
+    @property
+    def reduce(self) -> None: ...
+    @property
+    def accumulate(self) -> None: ...
+    @property
+    def reduceat(self) -> None: ...
+    @property
+    def outer(self) -> None: ...
+
+    @overload
+    def __call__(
+        self,
+        __x1: _ScalarLike_co,
+        __x2: _ScalarLike_co,
+        __out1: None = ...,
+        __out2: None = ...,
+        *,
+        where: None | _ArrayLikeBool_co = ...,
+        casting: _CastingKind = ...,
+        order: _OrderKACF = ...,
+        dtype: DTypeLike = ...,
+        subok: bool = ...,
+        signature: str | _4Tuple[None | str] = ...,
+        extobj: List[Any] = ...,
+    ) -> _2Tuple[Any]: ...
+    @overload
+    def __call__(
+        self,
+        __x1: ArrayLike,
+        __x2: ArrayLike,
+        __out1: None | NDArray[Any] = ...,
+        __out2: None | NDArray[Any] = ...,
+        *,
+        out: _2Tuple[NDArray[Any]] = ...,
+        where: None | _ArrayLikeBool_co = ...,
+        casting: _CastingKind = ...,
+        order: _OrderKACF = ...,
+        dtype: DTypeLike = ...,
+        subok: bool = ...,
+        signature: str | _4Tuple[None | str] = ...,
+        extobj: List[Any] = ...,
+    ) -> _2Tuple[NDArray[Any]]: ...
+
+class _GUFunc_Nin2_Nout1(ufunc, Generic[_NameType, _NTypes, _IDType]):
+    @property
+    def __name__(self) -> _NameType: ...
+    @property
+    def ntypes(self) -> _NTypes: ...
+    @property
+    def identity(self) -> _IDType: ...
+    @property
+    def nin(self) -> Literal[2]: ...
+    @property
+    def nout(self) -> Literal[1]: ...
+    @property
+    def nargs(self) -> Literal[3]: ...
+
+    # NOTE: In practice the only gufunc in the main name is `matmul`,
+    # so we can use its signature here
+    @property
+    def signature(self) -> Literal["(n?,k),(k,m?)->(n?,m?)"]: ...
+    @property
+    def reduce(self) -> None: ...
+    @property
+    def accumulate(self) -> None: ...
+    @property
+    def reduceat(self) -> None: ...
+    @property
+    def outer(self) -> None: ...
+    @property
+    def at(self) -> None: ...
+
+    # Scalar for 1D array-likes; ndarray otherwise
+    @overload
+    def __call__(
+        self,
+        __x1: ArrayLike,
+        __x2: ArrayLike,
+        out: None = ...,
+        *,
+        casting: _CastingKind = ...,
+        order: _OrderKACF = ...,
+        dtype: DTypeLike = ...,
+        subok: bool = ...,
+        signature: str | _3Tuple[None | str] = ...,
+        extobj: List[Any] = ...,
+        axes: List[_2Tuple[SupportsIndex]] = ...,
+    ) -> Any: ...
+    @overload
+    def __call__(
+        self,
+        __x1: ArrayLike,
+        __x2: ArrayLike,
+        out: NDArray[Any] | Tuple[NDArray[Any]],
+        *,
+        casting: _CastingKind = ...,
+        order: _OrderKACF = ...,
+        dtype: DTypeLike = ...,
+        subok: bool = ...,
+        signature: str | _3Tuple[None | str] = ...,
+        extobj: List[Any] = ...,
+        axes: List[_2Tuple[SupportsIndex]] = ...,
+    ) -> NDArray[Any]: ...

.venv/Lib/site-packages/numpy/typing/mypy_plugin.py

@@ -0,0 +1,197 @@
+"""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
+
+Examples
+--------
+To enable the plugin, one must add it to their mypy `configuration file`_:
+
+.. code-block:: ini
+
+    [mypy]
+    plugins = numpy.typing.mypy_plugin
+
+.. _mypy: http://mypy-lang.org/
+.. _configuration file: https://mypy.readthedocs.io/en/stable/config_file.html
+
+"""
+
+from __future__ import annotations
+
+from collections.abc import Iterable
+from typing import Final, TYPE_CHECKING, Callable
+
+import numpy as np
+
+try:
+    import mypy.types
+    from mypy.types import Type
+    from mypy.plugin import Plugin, AnalyzeTypeContext
+    from mypy.nodes import MypyFile, ImportFrom, Statement
+    from mypy.build import PRI_MED
+
+    _HookFunc = Callable[[AnalyzeTypeContext], Type]
+    MYPY_EX: None | ModuleNotFoundError = None
+except ModuleNotFoundError as ex:
+    MYPY_EX = ex
+
+__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_),
+        ("_NBitLongLong", np.longlong),
+
+        ("_NBitHalf", np.half),
+        ("_NBitSingle", np.single),
+        ("_NBitDouble", np.double),
+        ("_NBitLongDouble", np.longdouble),
+    ]
+    ret = {}
+    for name, typ in names:
+        n: int = 8 * typ().dtype.itemsize
+        ret[f'numpy.typing._nbit.{name}'] = f"numpy._{n}Bit"
+    return ret
+
+
+def _get_extended_precision_list() -> list[str]:
+    extended_types = [np.ulonglong, np.longlong, np.longdouble, np.clongdouble]
+    extended_names = {
+        "uint128",
+        "uint256",
+        "int128",
+        "int256",
+        "float80",
+        "float96",
+        "float128",
+        "float256",
+        "complex160",
+        "complex192",
+        "complex256",
+        "complex512",
+    }
+    return [i.__name__ for i in extended_types if i.__name__ in extended_names]
+
+
+def _get_c_intp_name() -> str:
+    # Adapted from `np.core._internal._getintp_ctype`
+    char = np.dtype('p').char
+    if char == 'i':
+        return "c_int"
+    elif char == 'l':
+        return "c_long"
+    elif char == 'q':
+        return "c_longlong"
+    else:
+        return "c_long"
+
+
+#: 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 quivalent of `np.intp`
+_C_INTP: Final = _get_c_intp_name()
+
+
+def _hook(ctx: AnalyzeTypeContext) -> Type:
+    """Replace a type-alias with a concrete ``NBitBase`` subclass."""
+    typ, _, api = ctx
+    name = typ.name.split(".")[-1]
+    name_new = _PRECISION_DICT[f"numpy.typing._nbit.{name}"]
+    return api.named_type(name_new)
+
+
+if TYPE_CHECKING or MYPY_EX is None:
+    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, None | str]],
+    ) -> 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, file.imports]:  # type: list[Statement]
+            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) -> None | _HookFunc:
+            """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`, `numpy.float128` and
+              `numpy.complex256`).
+            * Import the appropriate `ctypes` equivalent to `numpy.intp`.
+
+            """
+            ret = [(PRI_MED, file.fullname, -1)]
+
+            if file.fullname == "numpy":
+                _override_imports(
+                    file, "numpy.typing._extended_precision",
+                    imports=[(v, v) for v in _EXTENDED_PRECISION_LIST],
+                )
+            elif file.fullname == "numpy.ctypeslib":
+                _override_imports(
+                    file, "ctypes",
+                    imports=[(_C_INTP, "_c_intp")],
+                )
+            return ret
+
+    def plugin(version: str) -> type[_NumpyPlugin]:
+        """An entry-point for mypy."""
+        return _NumpyPlugin
+
+else:
+    def plugin(version: str) -> type[_NumpyPlugin]:
+        """An entry-point for mypy."""
+        raise MYPY_EX

.venv/Lib/site-packages/numpy/typing/setup.py

@@ -0,0 +1,12 @@
+def configuration(parent_package='', top_path=None):
+    from numpy.distutils.misc_util import Configuration
+    config = Configuration('typing', parent_package, top_path)
+    config.add_subpackage('tests')
+    config.add_data_dir('tests/data')
+    config.add_data_files('*.pyi')
+    return config
+
+
+if __name__ == '__main__':
+    from numpy.distutils.core import setup
+    setup(configuration=configuration)

.venv/Lib/site-packages/numpy/typing/tests/data/fail/arithmetic.pyi

@@ -0,0 +1,121 @@
+from typing import List, Any
+import numpy as np
+
+b_ = np.bool_()
+dt = np.datetime64(0, "D")
+td = np.timedelta64(0, "D")
+
+AR_b: np.ndarray[Any, np.dtype[np.bool_]]
+AR_u: np.ndarray[Any, np.dtype[np.uint32]]
+AR_i: np.ndarray[Any, np.dtype[np.int64]]
+AR_f: np.ndarray[Any, np.dtype[np.float64]]
+AR_c: np.ndarray[Any, np.dtype[np.complex128]]
+AR_m: np.ndarray[Any, np.dtype[np.timedelta64]]
+AR_M: np.ndarray[Any, np.dtype[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  # E: Need type annotation
+_2 = AR_LIKE_b - AR_b  # E: Need type annotation
+AR_i - bytes()  # E: No overload variant
+
+AR_f - AR_LIKE_m  # E: Unsupported operand types
+AR_f - AR_LIKE_M  # E: Unsupported operand types
+AR_c - AR_LIKE_m  # E: Unsupported operand types
+AR_c - AR_LIKE_M  # E: Unsupported operand types
+
+AR_m - AR_LIKE_f  # E: Unsupported operand types
+AR_M - AR_LIKE_f  # E: Unsupported operand types
+AR_m - AR_LIKE_c  # E: Unsupported operand types
+AR_M - AR_LIKE_c  # E: Unsupported operand types
+
+AR_m - AR_LIKE_M  # E: Unsupported operand types
+AR_LIKE_m - AR_M  # E: Unsupported operand types
+
+# array floor division
+
+AR_M // AR_LIKE_b  # E: Unsupported operand types
+AR_M // AR_LIKE_u  # E: Unsupported operand types
+AR_M // AR_LIKE_i  # E: Unsupported operand types
+AR_M // AR_LIKE_f  # E: Unsupported operand types
+AR_M // AR_LIKE_c  # E: Unsupported operand types
+AR_M // AR_LIKE_m  # E: Unsupported operand types
+AR_M // AR_LIKE_M  # E: Unsupported operand types
+
+AR_b // AR_LIKE_M  # E: Unsupported operand types
+AR_u // AR_LIKE_M  # E: Unsupported operand types
+AR_i // AR_LIKE_M  # E: Unsupported operand types
+AR_f // AR_LIKE_M  # E: Unsupported operand types
+AR_c // AR_LIKE_M  # E: Unsupported operand types
+AR_m // AR_LIKE_M  # E: Unsupported operand types
+AR_M // AR_LIKE_M  # E: Unsupported operand types
+
+_3 = AR_m // AR_LIKE_b  # E: Need type annotation
+AR_m // AR_LIKE_c  # E: Unsupported operand types
+
+AR_b // AR_LIKE_m  # E: Unsupported operand types
+AR_u // AR_LIKE_m  # E: Unsupported operand types
+AR_i // AR_LIKE_m  # E: Unsupported operand types
+AR_f // AR_LIKE_m  # E: Unsupported operand types
+AR_c // AR_LIKE_m  # E: Unsupported operand types
+
+# Array multiplication
+
+AR_b *= AR_LIKE_u  # E: incompatible type
+AR_b *= AR_LIKE_i  # E: incompatible type
+AR_b *= AR_LIKE_f  # E: incompatible type
+AR_b *= AR_LIKE_c  # E: incompatible type
+AR_b *= AR_LIKE_m  # E: incompatible type
+
+AR_u *= AR_LIKE_i  # E: incompatible type
+AR_u *= AR_LIKE_f  # E: incompatible type
+AR_u *= AR_LIKE_c  # E: incompatible type
+AR_u *= AR_LIKE_m  # E: incompatible type
+
+AR_i *= AR_LIKE_f  # E: incompatible type
+AR_i *= AR_LIKE_c  # E: incompatible type
+AR_i *= AR_LIKE_m  # E: incompatible type
+
+AR_f *= AR_LIKE_c  # E: incompatible type
+AR_f *= AR_LIKE_m  # E: incompatible type
+
+# Array power
+
+AR_b **= AR_LIKE_b  # E: Invalid self argument
+AR_b **= AR_LIKE_u  # E: Invalid self argument
+AR_b **= AR_LIKE_i  # E: Invalid self argument
+AR_b **= AR_LIKE_f  # E: Invalid self argument
+AR_b **= AR_LIKE_c  # E: Invalid self argument
+
+AR_u **= AR_LIKE_i  # E: incompatible type
+AR_u **= AR_LIKE_f  # E: incompatible type
+AR_u **= AR_LIKE_c  # E: incompatible type
+
+AR_i **= AR_LIKE_f  # E: incompatible type
+AR_i **= AR_LIKE_c  # E: incompatible type
+
+AR_f **= AR_LIKE_c  # E: incompatible type
+
+# Scalars
+
+b_ - b_  # E: No overload variant
+
+dt + dt  # E: Unsupported operand types
+td - dt  # E: Unsupported operand types
+td % 1  # E: Unsupported operand types
+td / dt  # E: No overload
+td % dt  # E: Unsupported operand types
+
+-b_  # E: Unsupported operand type
++b_  # E: Unsupported operand type

.venv/Lib/site-packages/numpy/typing/tests/data/fail/array_constructors.pyi

@@ -0,0 +1,31 @@
+import numpy as np
+
+a: np.ndarray
+generator = (i for i in range(10))
+
+np.require(a, requirements=1)  # E: No overload variant
+np.require(a, requirements="TEST")  # E: incompatible type
+
+np.zeros("test")  # E: incompatible type
+np.zeros()  # E: require at least one argument
+
+np.ones("test")  # E: incompatible type
+np.ones()  # E: require at least one argument
+
+np.array(0, float, True)  # E: No overload variant
+
+np.linspace(None, 'bob')  # E: No overload variant
+np.linspace(0, 2, num=10.0)  # E: No overload variant
+np.linspace(0, 2, endpoint='True')  # E: No overload variant
+np.linspace(0, 2, retstep=b'False')  # E: No overload variant
+np.linspace(0, 2, dtype=0)  # E: No overload variant
+np.linspace(0, 2, axis=None)  # E: No overload variant
+
+np.logspace(None, 'bob')  # E: Argument 1
+np.logspace(0, 2, base=None)  # E: Argument "base"
+
+np.geomspace(None, 'bob')  # E: Argument 1
+
+np.stack(generator)  # E: No overload variant
+np.hstack({1, 2})  # E: No overload variant
+np.vstack(1)  # E: No overload variant

.venv/Lib/site-packages/numpy/typing/tests/data/fail/array_like.pyi

@@ -0,0 +1,16 @@
+import numpy as np
+from numpy.typing import ArrayLike
+
+
+class A:
+    pass
+
+
+x1: ArrayLike = (i for i in range(10))  # E: Incompatible types in assignment
+x2: ArrayLike = A()  # E: Incompatible types in assignment
+x3: ArrayLike = {1: "foo", 2: "bar"}  # E: Incompatible types in assignment
+
+scalar = np.int64(1)
+scalar.__array__(dtype=np.float64)  # E: No overload variant
+array = np.array([1])
+array.__array__(dtype=np.float64)  # E: No overload variant

.venv/Lib/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")  # E: No overload variant

.venv/Lib/site-packages/numpy/typing/tests/data/fail/arrayprint.pyi

@@ -0,0 +1,13 @@
+from typing import Callable, Any
+import numpy as np
+
+AR: np.ndarray
+func1: Callable[[Any], str]
+func2: Callable[[np.integer[Any]], str]
+
+np.array2string(AR, style=None)  # E: Unexpected keyword argument
+np.array2string(AR, legacy="1.14")  # E: incompatible type
+np.array2string(AR, sign="*")  # E: incompatible type
+np.array2string(AR, floatmode="default")  # E: incompatible type
+np.array2string(AR, formatter={"A": func1})  # E: incompatible type
+np.array2string(AR, formatter={"float": func2})  # E: Incompatible types

.venv/Lib/site-packages/numpy/typing/tests/data/fail/arrayterator.pyi

@@ -0,0 +1,14 @@
+from typing import Any
+import numpy as np
+
+AR_i8: np.ndarray[Any, np.dtype[np.int64]]
+ar_iter = np.lib.Arrayterator(AR_i8)
+
+np.lib.Arrayterator(np.int64())  # E: incompatible type
+ar_iter.shape = (10, 5)  # E: is read-only
+ar_iter[None]  # E: Invalid index type
+ar_iter[None, 1]  # E: Invalid index type
+ar_iter[np.intp()]  # E: Invalid index type
+ar_iter[np.intp(), ...]  # E: Invalid index type
+ar_iter[AR_i8]  # E: Invalid index type
+ar_iter[AR_i8, :]  # E: Invalid index type

.venv/Lib/site-packages/numpy/typing/tests/data/fail/bitwise_ops.pyi

@@ -0,0 +1,20 @@
+import numpy as np
+
+i8 = np.int64()
+i4 = np.int32()
+u8 = np.uint64()
+b_ = np.bool_()
+i = int()
+
+f8 = np.float64()
+
+b_ >> f8  # E: No overload variant
+i8 << f8  # E: No overload variant
+i | f8  # E: Unsupported operand types
+i8 ^ f8  # E: No overload variant
+u8 & f8  # E: No overload variant
+~f8  # E: Unsupported operand type
+
+# mypys' error message for `NoReturn` is unfortunately pretty bad
+# TODO: Re-enable this once we add support for numerical precision for `number`s
+# a = u8 | 0  # E: Need type annotation

.venv/Lib/site-packages/numpy/typing/tests/data/fail/char.pyi

@@ -0,0 +1,66 @@
+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)  # E: incompatible type
+
+np.char.not_equal(AR_U, AR_S)  # E: incompatible type
+
+np.char.greater_equal(AR_U, AR_S)  # E: incompatible type
+
+np.char.less_equal(AR_U, AR_S)  # E: incompatible type
+
+np.char.greater(AR_U, AR_S)  # E: incompatible type
+
+np.char.less(AR_U, AR_S)  # E: incompatible type
+
+np.char.encode(AR_S)  # E: incompatible type
+np.char.decode(AR_U)  # E: incompatible type
+
+np.char.join(AR_U, b"_")  # E: incompatible type
+np.char.join(AR_S, "_")  # E: incompatible type
+
+np.char.ljust(AR_U, 5, fillchar=b"a")  # E: incompatible type
+np.char.ljust(AR_S, 5, fillchar="a")  # E: incompatible type
+np.char.rjust(AR_U, 5, fillchar=b"a")  # E: incompatible type
+np.char.rjust(AR_S, 5, fillchar="a")  # E: incompatible type
+
+np.char.lstrip(AR_U, chars=b"a")  # E: incompatible type
+np.char.lstrip(AR_S, chars="a")  # E: incompatible type
+np.char.strip(AR_U, chars=b"a")  # E: incompatible type
+np.char.strip(AR_S, chars="a")  # E: incompatible type
+np.char.rstrip(AR_U, chars=b"a")  # E: incompatible type
+np.char.rstrip(AR_S, chars="a")  # E: incompatible type
+
+np.char.partition(AR_U, b"a")  # E: incompatible type
+np.char.partition(AR_S, "a")  # E: incompatible type
+np.char.rpartition(AR_U, b"a")  # E: incompatible type
+np.char.rpartition(AR_S, "a")  # E: incompatible type
+
+np.char.replace(AR_U, b"_", b"-")  # E: incompatible type
+np.char.replace(AR_S, "_", "-")  # E: incompatible type
+
+np.char.split(AR_U, b"_")  # E: incompatible type
+np.char.split(AR_S, "_")  # E: incompatible type
+np.char.rsplit(AR_U, b"_")  # E: incompatible type
+np.char.rsplit(AR_S, "_")  # E: incompatible type
+
+np.char.count(AR_U, b"a", start=[1, 2, 3])  # E: incompatible type
+np.char.count(AR_S, "a", end=9)  # E: incompatible type
+
+np.char.endswith(AR_U, b"a", start=[1, 2, 3])  # E: incompatible type
+np.char.endswith(AR_S, "a", end=9)  # E: incompatible type
+np.char.startswith(AR_U, b"a", start=[1, 2, 3])  # E: incompatible type
+np.char.startswith(AR_S, "a", end=9)  # E: incompatible type
+
+np.char.find(AR_U, b"a", start=[1, 2, 3])  # E: incompatible type
+np.char.find(AR_S, "a", end=9)  # E: incompatible type
+np.char.rfind(AR_U, b"a", start=[1, 2, 3])  # E: incompatible type
+np.char.rfind(AR_S, "a", end=9)  # E: incompatible type
+
+np.char.index(AR_U, b"a", start=[1, 2, 3])  # E: incompatible type
+np.char.index(AR_S, "a", end=9)  # E: incompatible type
+np.char.rindex(AR_U, b"a", start=[1, 2, 3])  # E: incompatible type
+np.char.rindex(AR_S, "a", end=9)  # E: incompatible type

.venv/Lib/site-packages/numpy/typing/tests/data/fail/chararray.pyi

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

.venv/Lib/site-packages/numpy/typing/tests/data/fail/comparisons.pyi

@@ -0,0 +1,27 @@
+from typing import Any
+import numpy as np
+
+AR_i: np.ndarray[Any, np.dtype[np.int64]]
+AR_f: np.ndarray[Any, np.dtype[np.float64]]
+AR_c: np.ndarray[Any, np.dtype[np.complex128]]
+AR_m: np.ndarray[Any, np.dtype[np.timedelta64]]
+AR_M: np.ndarray[Any, np.dtype[np.datetime64]]
+
+AR_f > AR_m  # E: Unsupported operand types
+AR_c > AR_m  # E: Unsupported operand types
+
+AR_m > AR_f  # E: Unsupported operand types
+AR_m > AR_c  # E: Unsupported operand types
+
+AR_i > AR_M  # E: Unsupported operand types
+AR_f > AR_M  # E: Unsupported operand types
+AR_m > AR_M  # E: Unsupported operand types
+
+AR_M > AR_i  # E: Unsupported operand types
+AR_M > AR_f  # E: Unsupported operand types
+AR_M > AR_m  # E: Unsupported operand types
+
+AR_i > str()  # E: No overload variant
+AR_i > bytes()  # E: No overload variant
+str() > AR_M  # E: Unsupported operand types
+bytes() > AR_M  # E: Unsupported operand types

.venv/Lib/site-packages/numpy/typing/tests/data/fail/constants.pyi

@@ -0,0 +1,7 @@
+import numpy as np
+
+np.Inf = np.Inf  # E: Cannot assign to final
+np.ALLOW_THREADS = np.ALLOW_THREADS  # E: Cannot assign to final
+np.little_endian = np.little_endian  # E: Cannot assign to final
+np.UFUNC_PYVALS_NAME = "bob"  # E: Incompatible types
+np.CLIP = 2  # E: Incompatible types

.venv/Lib/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.DataSource
+
+d1.abspath(path)  # E: incompatible type
+d1.abspath(b"...")  # E: incompatible type
+
+d1.exists(path)  # E: incompatible type
+d1.exists(b"...")  # E: incompatible type
+
+d1.open(path, "r")  # E: incompatible type
+d1.open(b"...", encoding="utf8")  # E: incompatible type
+d1.open(None, newline="/n")  # E: incompatible type

.venv/Lib/site-packages/numpy/typing/tests/data/fail/dtype.pyi

@@ -0,0 +1,20 @@
+import numpy as np
+
+
+class Test1:
+    not_dtype = np.dtype(float)
+
+
+class Test2:
+    dtype = float
+
+
+np.dtype(Test1())  # E: No overload variant of "dtype" matches
+np.dtype(Test2())  # E: incompatible type
+
+np.dtype(  # E: No overload variant of "dtype" matches
+    {
+        "field1": (float, 1),
+        "field2": (int, 3),
+    }
+)

.venv/Lib/site-packages/numpy/typing/tests/data/fail/einsumfunc.pyi

@@ -0,0 +1,15 @@
+from typing import List, Any
+import numpy as np
+
+AR_i: np.ndarray[Any, np.dtype[np.int64]]
+AR_f: np.ndarray[Any, np.dtype[np.float64]]
+AR_m: np.ndarray[Any, np.dtype[np.timedelta64]]
+AR_O: np.ndarray[Any, np.dtype[np.object_]]
+AR_U: np.ndarray[Any, np.dtype[np.str_]]
+
+np.einsum("i,i->i", AR_i, AR_m)  # E: incompatible type
+np.einsum("i,i->i", AR_O, AR_O)  # E: incompatible type
+np.einsum("i,i->i", AR_f, AR_f, dtype=np.int32)  # E: incompatible type
+np.einsum("i,i->i", AR_i, AR_i, dtype=np.timedelta64, casting="unsafe")  # E: No overload variant
+np.einsum("i,i->i", AR_i, AR_i, out=AR_U)  # E: Value of type variable "_ArrayType" of "einsum" cannot be
+np.einsum("i,i->i", AR_i, AR_i, out=AR_U, casting="unsafe")  # E: No overload variant

.venv/Lib/site-packages/numpy/typing/tests/data/fail/false_positives.pyi

@@ -0,0 +1,11 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_f8: npt.NDArray[np.float64]
+
+# NOTE: Mypy bug presumably due to the special-casing of heterogeneous tuples;
+# xref numpy/numpy#20901
+#
+# The expected output should be no different than, e.g., when using a
+# list instead of a tuple
+np.concatenate(([1], AR_f8))  # E: Argument 1 to "concatenate" has incompatible type

.venv/Lib/site-packages/numpy/typing/tests/data/fail/flatiter.pyi

@@ -0,0 +1,25 @@
+from typing import Any
+
+import numpy as np
+from numpy.typing import _SupportsArray
+
+
+class Index:
+    def __index__(self) -> int:
+        ...
+
+
+a: "np.flatiter[np.ndarray]"
+supports_array: _SupportsArray
+
+a.base = Any  # E: Property "base" defined in "flatiter" is read-only
+a.coords = Any  # E: Property "coords" defined in "flatiter" is read-only
+a.index = Any  # E: Property "index" defined in "flatiter" is read-only
+a.copy(order='C')  # E: Unexpected keyword argument
+
+# 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_()]  # E: No overload variant of "__getitem__"
+a[Index()]  # E: No overload variant of "__getitem__"
+a[supports_array]  # E: No overload variant of "__getitem__"

.venv/Lib/site-packages/numpy/typing/tests/data/fail/fromnumeric.pyi

@@ -0,0 +1,154 @@
+"""Tests for :mod:`numpy.core.fromnumeric`."""
+
+import numpy as np
+
+A = np.array(True, ndmin=2, dtype=bool)
+A.setflags(write=False)
+
+a = np.bool_(True)
+
+np.take(a, None)  # E: incompatible type
+np.take(a, axis=1.0)  # E: incompatible type
+np.take(A, out=1)  # E: incompatible type
+np.take(A, mode="bob")  # E: incompatible type
+
+np.reshape(a, None)  # E: Argument 2 to "reshape" has incompatible type
+np.reshape(A, 1, order="bob")  # E: Argument "order" to "reshape" has incompatible type
+
+np.choose(a, None)  # E: incompatible type
+np.choose(a, out=1.0)  # E: incompatible type
+np.choose(A, mode="bob")  # E: incompatible type
+
+np.repeat(a, None)  # E: Argument 2 to "repeat" has incompatible type
+np.repeat(A, 1, axis=1.0)  # E: Argument "axis" to "repeat" has incompatible type
+
+np.swapaxes(A, None, 1)  # E: Argument 2 to "swapaxes" has incompatible type
+np.swapaxes(A, 1, [0])  # E: Argument 3 to "swapaxes" has incompatible type
+
+np.transpose(A, axes=1.0)  # E: Argument "axes" to "transpose" has incompatible type
+
+np.partition(a, None)  # E: Argument 2 to "partition" has incompatible type
+np.partition(
+    a, 0, axis="bob"  # E: Argument "axis" to "partition" has incompatible type
+)
+np.partition(
+    A, 0, kind="bob"  # E: Argument "kind" to "partition" has incompatible type
+)
+np.partition(
+    A, 0, order=range(5)  # E: Argument "order" to "partition" has incompatible type
+)
+
+np.argpartition(
+    a, None  # E: incompatible type
+)
+np.argpartition(
+    a, 0, axis="bob"  # E: incompatible type
+)
+np.argpartition(
+    A, 0, kind="bob"  # E: incompatible type
+)
+np.argpartition(
+    A, 0, order=range(5)  # E: Argument "order" to "argpartition" has incompatible type
+)
+
+np.sort(A, axis="bob")  # E: Argument "axis" to "sort" has incompatible type
+np.sort(A, kind="bob")  # E: Argument "kind" to "sort" has incompatible type
+np.sort(A, order=range(5))  # E: Argument "order" to "sort" has incompatible type
+
+np.argsort(A, axis="bob")  # E: Argument "axis" to "argsort" has incompatible type
+np.argsort(A, kind="bob")  # E: Argument "kind" to "argsort" has incompatible type
+np.argsort(A, order=range(5))  # E: Argument "order" to "argsort" has incompatible type
+
+np.argmax(A, axis="bob")  # E: No overload variant of "argmax" matches argument type
+np.argmax(A, kind="bob")  # E: No overload variant of "argmax" matches argument type
+
+np.argmin(A, axis="bob")  # E: No overload variant of "argmin" matches argument type
+np.argmin(A, kind="bob")  # E: No overload variant of "argmin" matches argument type
+
+np.searchsorted(  # E: No overload variant of "searchsorted" matches argument type
+    A[0], 0, side="bob"
+)
+np.searchsorted(  # E: No overload variant of "searchsorted" matches argument type
+    A[0], 0, sorter=1.0
+)
+
+np.resize(A, 1.0)  # E: Argument 2 to "resize" has incompatible type
+
+np.squeeze(A, 1.0)  # E: No overload variant of "squeeze" matches argument type
+
+np.diagonal(A, offset=None)  # E: Argument "offset" to "diagonal" has incompatible type
+np.diagonal(A, axis1="bob")  # E: Argument "axis1" to "diagonal" has incompatible type
+np.diagonal(A, axis2=[])  # E: Argument "axis2" to "diagonal" has incompatible type
+
+np.trace(A, offset=None)  # E: Argument "offset" to "trace" has incompatible type
+np.trace(A, axis1="bob")  # E: Argument "axis1" to "trace" has incompatible type
+np.trace(A, axis2=[])  # E: Argument "axis2" to "trace" has incompatible type
+
+np.ravel(a, order="bob")  # E: Argument "order" to "ravel" has incompatible type
+
+np.compress(
+    [True], A, axis=1.0  # E: Argument "axis" to "compress" has incompatible type
+)
+
+np.clip(a, 1, 2, out=1)  # E: No overload variant of "clip" matches argument type
+np.clip(1, None, None)  # E: No overload variant of "clip" matches argument type
+
+np.sum(a, axis=1.0)  # E: incompatible type
+np.sum(a, keepdims=1.0)  # E: incompatible type
+np.sum(a, initial=[1])  # E: incompatible type
+
+np.all(a, axis=1.0)  # E: No overload variant
+np.all(a, keepdims=1.0)  # E: No overload variant
+np.all(a, out=1.0)  # E: No overload variant
+
+np.any(a, axis=1.0)  # E: No overload variant
+np.any(a, keepdims=1.0)  # E: No overload variant
+np.any(a, out=1.0)  # E: No overload variant
+
+np.cumsum(a, axis=1.0)  # E: incompatible type
+np.cumsum(a, dtype=1.0)  # E: incompatible type
+np.cumsum(a, out=1.0)  # E: incompatible type
+
+np.ptp(a, axis=1.0)  # E: incompatible type
+np.ptp(a, keepdims=1.0)  # E: incompatible type
+np.ptp(a, out=1.0)  # E: incompatible type
+
+np.amax(a, axis=1.0)  # E: incompatible type
+np.amax(a, keepdims=1.0)  # E: incompatible type
+np.amax(a, out=1.0)  # E: incompatible type
+np.amax(a, initial=[1.0])  # E: incompatible type
+np.amax(a, where=[1.0])  # E: incompatible type
+
+np.amin(a, axis=1.0)  # E: incompatible type
+np.amin(a, keepdims=1.0)  # E: incompatible type
+np.amin(a, out=1.0)  # E: incompatible type
+np.amin(a, initial=[1.0])  # E: incompatible type
+np.amin(a, where=[1.0])  # E: incompatible type
+
+np.prod(a, axis=1.0)  # E: incompatible type
+np.prod(a, out=False)  # E: incompatible type
+np.prod(a, keepdims=1.0)  # E: incompatible type
+np.prod(a, initial=int)  # E: incompatible type
+np.prod(a, where=1.0)  # E: incompatible type
+
+np.cumprod(a, axis=1.0)  # E: Argument "axis" to "cumprod" has incompatible type
+np.cumprod(a, out=False)  # E: Argument "out" to "cumprod" has incompatible type
+
+np.size(a, axis=1.0)  # E: Argument "axis" to "size" has incompatible type
+
+np.around(a, decimals=1.0)  # E: incompatible type
+np.around(a, out=type)  # E: incompatible type
+
+np.mean(a, axis=1.0)  # E: incompatible type
+np.mean(a, out=False)  # E: incompatible type
+np.mean(a, keepdims=1.0)  # E: incompatible type
+
+np.std(a, axis=1.0)  # E: incompatible type
+np.std(a, out=False)  # E: incompatible type
+np.std(a, ddof='test')  # E: incompatible type
+np.std(a, keepdims=1.0)  # E: incompatible type
+
+np.var(a, axis=1.0)  # E: incompatible type
+np.var(a, out=False)  # E: incompatible type
+np.var(a, ddof='test')  # E: incompatible type
+np.var(a, keepdims=1.0)  # E: incompatible type

.venv/Lib/site-packages/numpy/typing/tests/data/fail/histograms.pyi

@@ -0,0 +1,13 @@
+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))  # E: incompatible type
+
+np.histogram(AR_i8, range=(0, 1, 2))  # E: incompatible type
+np.histogram(AR_i8, normed=True)  # E: incompatible type
+
+np.histogramdd(AR_i8, range=(0, 1))  # E: incompatible type
+np.histogramdd(AR_i8, range=[(0, 1, 2)])  # E: incompatible type

.venv/Lib/site-packages/numpy/typing/tests/data/fail/index_tricks.pyi

@@ -0,0 +1,15 @@
+from typing import List
+import numpy as np
+
+AR_LIKE_i: List[int]
+AR_LIKE_f: List[float]
+
+np.ndindex([1, 2, 3])  # E: No overload variant
+np.unravel_index(AR_LIKE_f, (1, 2, 3))  # E: incompatible type
+np.ravel_multi_index(AR_LIKE_i, (1, 2, 3), mode="bob")  # E: No overload variant
+np.mgrid[1]  # E: Invalid index type
+np.mgrid[...]  # E: Invalid index type
+np.ogrid[1]  # E: Invalid index type
+np.ogrid[...]  # E: Invalid index type
+np.fill_diagonal(AR_LIKE_f, 2)  # E: incompatible type
+np.diag_indices(1.0)  # E: incompatible type

.venv/Lib/site-packages/numpy/typing/tests/data/fail/lib_function_base.pyi

@@ -0,0 +1,53 @@
+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_]
+
+def func(a: int) -> None: ...
+
+np.average(AR_m)  # E: incompatible type
+np.select(1, [AR_f8])  # E: incompatible type
+np.angle(AR_m)  # E: incompatible type
+np.unwrap(AR_m)  # E: incompatible type
+np.unwrap(AR_c16)  # E: incompatible type
+np.trim_zeros(1)  # E: incompatible type
+np.place(1, [True], 1.5)  # E: incompatible type
+np.vectorize(1)  # E: incompatible type
+np.add_newdoc("__main__", 1.5, "docstring")  # E: incompatible type
+np.place(AR_f8, slice(None), 5)  # E: incompatible type
+
+np.interp(AR_f8, AR_c16, AR_f8)  # E: incompatible type
+np.interp(AR_c16, AR_f8, AR_f8)  # E: incompatible type
+np.interp(AR_f8, AR_f8, AR_f8, period=AR_c16)  # E: No overload variant
+np.interp(AR_f8, AR_f8, AR_O)  # E: incompatible type
+
+np.cov(AR_m)  # E: incompatible type
+np.cov(AR_O)  # E: incompatible type
+np.corrcoef(AR_m)  # E: incompatible type
+np.corrcoef(AR_O)  # E: incompatible type
+np.corrcoef(AR_f8, bias=True)  # E: No overload variant
+np.corrcoef(AR_f8, ddof=2)  # E: No overload variant
+np.blackman(1j)  # E: incompatible type
+np.bartlett(1j)  # E: incompatible type
+np.hanning(1j)  # E: incompatible type
+np.hamming(1j)  # E: incompatible type
+np.hamming(AR_c16)  # E: incompatible type
+np.kaiser(1j, 1)  # E: incompatible type
+np.sinc(AR_O)  # E: incompatible type
+np.median(AR_M)  # E: incompatible type
+
+np.add_newdoc_ufunc(func, "docstring")  # E: incompatible type
+np.percentile(AR_f8, 50j)  # E: No overload variant
+np.percentile(AR_f8, 50, interpolation="bob")  # E: No overload variant
+np.quantile(AR_f8, 0.5j)  # E: No overload variant
+np.quantile(AR_f8, 0.5, interpolation="bob")  # E: No overload variant
+np.meshgrid(AR_f8, AR_f8, indexing="bob")  # E: incompatible type
+np.delete(AR_f8, AR_f8)  # E: incompatible type
+np.insert(AR_f8, AR_f8, 1.5)  # E: incompatible type
+np.digitize(AR_f8, 1j)  # E: No overload variant

.venv/Lib/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.polyint(AR_U)  # E: incompatible type
+np.polyint(AR_f8, m=1j)  # E: No overload variant
+
+np.polyder(AR_U)  # E: incompatible type
+np.polyder(AR_f8, m=1j)  # E: No overload variant
+
+np.polyfit(AR_O, AR_f8, 1)  # E: incompatible type
+np.polyfit(AR_f8, AR_f8, 1, rcond=1j)  # E: No overload variant
+np.polyfit(AR_f8, AR_f8, 1, w=AR_c16)  # E: incompatible type
+np.polyfit(AR_f8, AR_f8, 1, cov="bob")  # E: No overload variant
+
+np.polyval(AR_f8, AR_U)  # E: incompatible type
+np.polyadd(AR_f8, AR_U)  # E: incompatible type
+np.polysub(AR_f8, AR_U)  # E: incompatible type
+np.polymul(AR_f8, AR_U)  # E: incompatible type
+np.polydiv(AR_f8, AR_U)  # E: incompatible type
+
+5**poly_obj  # E: No overload variant
+hash(poly_obj)

.venv/Lib/site-packages/numpy/typing/tests/data/fail/lib_utils.pyi

@@ -0,0 +1,13 @@
+import numpy as np
+
+np.deprecate(1)  # E: No overload variant
+
+np.deprecate_with_doc(1)  # E: incompatible type
+
+np.byte_bounds(1)  # E: incompatible type
+
+np.who(1)  # E: incompatible type
+
+np.lookfor(None)  # E: incompatible type
+
+np.safe_eval(None)  # E: incompatible type

.venv/Lib/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")  # E: incompatible type
+version >= b"1.8.0"  # E: Unsupported operand types

.venv/Lib/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)  # E: incompatible type
+
+np.linalg.solve(AR_O, AR_O)  # E: incompatible type
+
+np.linalg.tensorinv(AR_O)  # E: incompatible type
+
+np.linalg.inv(AR_O)  # E: incompatible type
+
+np.linalg.matrix_power(AR_M, 5)  # E: incompatible type
+
+np.linalg.cholesky(AR_O)  # E: incompatible type
+
+np.linalg.qr(AR_O)  # E: incompatible type
+np.linalg.qr(AR_f8, mode="bob")  # E: No overload variant
+
+np.linalg.eigvals(AR_O)  # E: incompatible type
+
+np.linalg.eigvalsh(AR_O)  # E: incompatible type
+np.linalg.eigvalsh(AR_O, UPLO="bob")  # E: No overload variant
+
+np.linalg.eig(AR_O)  # E: incompatible type
+
+np.linalg.eigh(AR_O)  # E: incompatible type
+np.linalg.eigh(AR_O, UPLO="bob")  # E: No overload variant
+
+np.linalg.svd(AR_O)  # E: incompatible type
+
+np.linalg.cond(AR_O)  # E: incompatible type
+np.linalg.cond(AR_f8, p="bob")  # E: incompatible type
+
+np.linalg.matrix_rank(AR_O)  # E: incompatible type
+
+np.linalg.pinv(AR_O)  # E: incompatible type
+
+np.linalg.slogdet(AR_O)  # E: incompatible type
+
+np.linalg.det(AR_O)  # E: incompatible type
+
+np.linalg.norm(AR_f8, ord="bob")  # E: No overload variant
+
+np.linalg.multi_dot([AR_M])  # E: incompatible type

.venv/Lib/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)  # E: No overload variant
+np.memmap("test.txt", shape=[10, 5])  # E: No overload variant

.venv/Lib/site-packages/numpy/typing/tests/data/fail/modules.pyi

@@ -0,0 +1,18 @@
+import numpy as np
+
+np.testing.bob  # E: Module has no attribute
+np.bob  # E: Module has no attribute
+
+# Stdlib modules in the namespace by accident
+np.warnings  # E: Module has no attribute
+np.sys  # E: Module has no attribute
+np.os  # E: Module has no attribute
+np.math  # E: Module has no attribute
+
+# 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  # E: Module has no attribute
+
+np.__NUMPY_SETUP__  # E: Module has no attribute
+np.__deprecated_attrs__  # E: Module has no attribute
+np.__expired_functions__  # E: Module has no attribute

.venv/Lib/site-packages/numpy/typing/tests/data/fail/multiarray.pyi

@@ -0,0 +1,56 @@
+from typing import List
+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)  # E: No overload variant
+
+np.can_cast(AR_f8, 1)  # E: incompatible type
+
+np.vdot(AR_M, AR_M)  # E: incompatible type
+
+np.copyto(AR_LIKE_f, AR_f8)  # E: incompatible type
+
+np.putmask(AR_LIKE_f, [True, True, False], 1.5)  # E: incompatible type
+
+np.packbits(AR_f8)  # E: incompatible type
+np.packbits(AR_u1, bitorder=">")  # E: incompatible type
+
+np.unpackbits(AR_i8)  # E: incompatible type
+np.unpackbits(AR_u1, bitorder=">")  # E: incompatible type
+
+np.shares_memory(1, 1, max_work=i8)  # E: incompatible type
+np.may_share_memory(1, 1, max_work=i8)  # E: incompatible type
+
+np.arange(M)  # E: No overload variant
+np.arange(stop=10)  # E: No overload variant
+
+np.datetime_data(int)  # E: incompatible type
+
+np.busday_offset("2012", 10)  # E: incompatible type
+
+np.datetime_as_string("2012")  # E: No overload variant
+
+np.compare_chararrays("a", b"a", "==", False)  # E: No overload variant
+
+np.add_docstring(func, None)  # E: incompatible type
+
+np.nested_iters([AR_i8, AR_i8])  # E: Missing positional argument
+np.nested_iters([AR_i8, AR_i8], 0)  # E: incompatible type
+np.nested_iters([AR_i8, AR_i8], [0])  # E: incompatible type
+np.nested_iters([AR_i8, AR_i8], [[0], [1]], flags=["test"])  # E: incompatible type
+np.nested_iters([AR_i8, AR_i8], [[0], [1]], op_flags=[["test"]])  # E: incompatible type
+np.nested_iters([AR_i8, AR_i8], [[0], [1]], buffersize=1.0)  # E: incompatible type

.venv/Lib/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_  # E: Property "dtype" defined in "ndarray" is read-only

.venv/Lib/site-packages/numpy/typing/tests/data/fail/ndarray_misc.pyi

@@ -0,0 +1,41 @@
+"""
+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`.
+
+"""
+
+from typing import Any
+import numpy as np
+
+f8: np.float64
+AR_f8: np.ndarray[Any, np.dtype[np.float64]]
+AR_M: np.ndarray[Any, np.dtype[np.datetime64]]
+AR_b: np.ndarray[Any, np.dtype[np.bool_]]
+
+ctypes_obj = AR_f8.ctypes
+
+reveal_type(ctypes_obj.get_data())  # E: has no attribute
+reveal_type(ctypes_obj.get_shape())  # E: has no attribute
+reveal_type(ctypes_obj.get_strides())  # E: has no attribute
+reveal_type(ctypes_obj.get_as_parameter())  # E: has no attribute
+
+f8.argpartition(0)  # E: has no attribute
+f8.diagonal()  # E: has no attribute
+f8.dot(1)  # E: has no attribute
+f8.nonzero()  # E: has no attribute
+f8.partition(0)  # E: has no attribute
+f8.put(0, 2)  # E: has no attribute
+f8.setfield(2, np.float64)  # E: has no attribute
+f8.sort()  # E: has no attribute
+f8.trace()  # E: has no attribute
+
+AR_M.__int__()  # E: Invalid self argument
+AR_M.__float__()  # E: Invalid self argument
+AR_M.__complex__()  # E: Invalid self argument
+AR_b.__index__()  # E: Invalid self argument
+
+AR_f8[1.5]  # E: No overload variant
+AR_f8["field_a"]  # E: No overload variant
+AR_f8[["field_a", "field_b"]]  # E: Invalid index type

.venv/Lib/site-packages/numpy/typing/tests/data/fail/nditer.pyi

@@ -0,0 +1,8 @@
+import numpy as np
+
+class Test(np.nditer): ...  # E: Cannot inherit from final class
+
+np.nditer([0, 1], flags=["test"])  # E: incompatible type
+np.nditer([0, 1], op_flags=[["test"]])  # E: incompatible type
+np.nditer([0, 1], itershape=(1.0,))  # E: incompatible type
+np.nditer([0, 1], buffersize=1.0)  # E: incompatible type

.venv/Lib/site-packages/numpy/typing/tests/data/fail/nested_sequence.pyi

@@ -0,0 +1,17 @@
+from typing import Sequence, Tuple, List
+import numpy.typing as npt
+
+a: Sequence[float]
+b: List[complex]
+c: Tuple[str, ...]
+d: int
+e: str
+
+def func(a: npt._NestedSequence[int]) -> None:
+    ...
+
+reveal_type(func(a))  # E: incompatible type
+reveal_type(func(b))  # E: incompatible type
+reveal_type(func(c))  # E: incompatible type
+reveal_type(func(d))  # E: incompatible type
+reveal_type(func(e))  # E: incompatible type

.venv/Lib/site-packages/numpy/typing/tests/data/fail/npyio.pyi

@@ -0,0 +1,30 @@
+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)  # E: incompatible type
+
+np.save(bytes_path, AR_i8)  # E: incompatible type
+np.save(str_file, AR_i8)  # E: incompatible type
+
+np.savez(bytes_path, AR_i8)  # E: incompatible type
+np.savez(str_file, AR_i8)  # E: incompatible type
+
+np.savez_compressed(bytes_path, AR_i8)  # E: incompatible type
+np.savez_compressed(str_file, AR_i8)  # E: incompatible type
+
+np.loadtxt(bytes_path)  # E: incompatible type
+
+np.fromregex(bytes_path, ".", np.int64)  # E: No overload variant
+
+np.recfromtxt(bytes_path)  # E: incompatible type
+
+np.recfromcsv(bytes_path)  # E: incompatible type

.venv/Lib/site-packages/numpy/typing/tests/data/fail/numerictypes.pyi

@@ -0,0 +1,13 @@
+import numpy as np
+
+# Technically this works, but probably shouldn't. See
+#
+# https://github.com/numpy/numpy/issues/16366
+#
+np.maximum_sctype(1)  # E: No overload variant
+
+np.issubsctype(1, np.int64)  # E: incompatible type
+
+np.issubdtype(1, np.int64)  # E: incompatible type
+
+np.find_common_type(np.int64, np.int64)  # E: incompatible type

.venv/Lib/site-packages/numpy/typing/tests/data/fail/random.pyi

@@ -0,0 +1,61 @@
+import numpy as np
+from typing import Any, List
+
+SEED_FLOAT: float = 457.3
+SEED_ARR_FLOAT: np.ndarray[Any, np.dtype[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)  # E: incompatible type
+np.random.default_rng(SEED_ARR_FLOAT)  # E: incompatible type
+np.random.default_rng(SEED_ARRLIKE_FLOAT)  # E: incompatible type
+np.random.default_rng(SEED_STR)  # E: incompatible type
+
+# Seed Sequence
+np.random.SeedSequence(SEED_FLOAT)  # E: incompatible type
+np.random.SeedSequence(SEED_ARR_FLOAT)  # E: incompatible type
+np.random.SeedSequence(SEED_ARRLIKE_FLOAT)  # E: incompatible type
+np.random.SeedSequence(SEED_SEED_SEQ)  # E: incompatible type
+np.random.SeedSequence(SEED_STR)  # E: incompatible type
+
+seed_seq: np.random.bit_generator.SeedSequence = np.random.SeedSequence()
+seed_seq.spawn(11.5)  # E: incompatible type
+seed_seq.generate_state(3.14)  # E: incompatible type
+seed_seq.generate_state(3, np.uint8)  # E: incompatible type
+seed_seq.generate_state(3, "uint8")  # E: incompatible type
+seed_seq.generate_state(3, "u1")  # E: incompatible type
+seed_seq.generate_state(3, np.uint16)  # E: incompatible type
+seed_seq.generate_state(3, "uint16")  # E: incompatible type
+seed_seq.generate_state(3, "u2")  # E: incompatible type
+seed_seq.generate_state(3, np.int32)  # E: incompatible type
+seed_seq.generate_state(3, "int32")  # E: incompatible type
+seed_seq.generate_state(3, "i4")  # E: incompatible type
+
+# Bit Generators
+np.random.MT19937(SEED_FLOAT)  # E: incompatible type
+np.random.MT19937(SEED_ARR_FLOAT)  # E: incompatible type
+np.random.MT19937(SEED_ARRLIKE_FLOAT)  # E: incompatible type
+np.random.MT19937(SEED_STR)  # E: incompatible type
+
+np.random.PCG64(SEED_FLOAT)  # E: incompatible type
+np.random.PCG64(SEED_ARR_FLOAT)  # E: incompatible type
+np.random.PCG64(SEED_ARRLIKE_FLOAT)  # E: incompatible type
+np.random.PCG64(SEED_STR)  # E: incompatible type
+
+np.random.Philox(SEED_FLOAT)  # E: incompatible type
+np.random.Philox(SEED_ARR_FLOAT)  # E: incompatible type
+np.random.Philox(SEED_ARRLIKE_FLOAT)  # E: incompatible type
+np.random.Philox(SEED_STR)  # E: incompatible type
+
+np.random.SFC64(SEED_FLOAT)  # E: incompatible type
+np.random.SFC64(SEED_ARR_FLOAT)  # E: incompatible type
+np.random.SFC64(SEED_ARRLIKE_FLOAT)  # E: incompatible type
+np.random.SFC64(SEED_STR)  # E: incompatible type
+
+# Generator
+np.random.Generator(None)  # E: incompatible type
+np.random.Generator(12333283902830213)  # E: incompatible type
+np.random.Generator("OxFEEDF00D")  # E: incompatible type
+np.random.Generator([123, 234])  # E: incompatible type
+np.random.Generator(np.array([123, 234], dtype="u4"))  # E: incompatible type

.venv/Lib/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)  # E: No overload variant
+np.rec.fromarrays([1, 2, 3], dtype=[("f8", "f8")], formats=["f8", "f8"])  # E: No overload variant
+
+np.rec.fromrecords(AR_i8)  # E: incompatible type
+np.rec.fromrecords([(1.5,)], dtype=[("f8", "f8")], formats=["f8", "f8"])  # E: No overload variant
+
+np.rec.fromstring("string", dtype=[("f8", "f8")])  # E: No overload variant
+np.rec.fromstring(b"bytes")  # E: No overload variant
+np.rec.fromstring(b"(1.5,)", dtype=[("f8", "f8")], formats=["f8", "f8"])  # E: No overload variant
+
+with open("test", "r") as f:
+    np.rec.fromfile(f, dtype=[("f8", "f8")])  # E: No overload variant

.venv/Lib/site-packages/numpy/typing/tests/data/fail/scalars.pyi

@@ -0,0 +1,93 @@
+import sys
+import numpy as np
+
+f2: np.float16
+f8: np.float64
+c8: np.complex64
+
+# Construction
+
+np.float32(3j)  # E: incompatible 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])  # E: incompatible type
+np.complex64([])  # E: incompatible type
+
+np.complex64(1, 2)  # E: Too many arguments
+# TODO: protocols (can't check for non-existent protocols w/ __getattr__)
+
+np.datetime64(0)  # E: No overload variant
+
+class A:
+    def __float__(self):
+        return 1.0
+
+
+np.int8(A())  # E: incompatible type
+np.int16(A())  # E: incompatible type
+np.int32(A())  # E: incompatible type
+np.int64(A())  # E: incompatible type
+np.uint8(A())  # E: incompatible type
+np.uint16(A())  # E: incompatible type
+np.uint32(A())  # E: incompatible type
+np.uint64(A())  # E: incompatible type
+
+np.void("test")  # E: incompatible type
+
+np.generic(1)  # E: Cannot instantiate abstract class
+np.number(1)  # E: Cannot instantiate abstract class
+np.integer(1)  # E: Cannot instantiate abstract class
+np.inexact(1)  # E: Cannot instantiate abstract class
+np.character("test")  # E: Cannot instantiate abstract class
+np.flexible(b"test")  # E: Cannot instantiate abstract class
+
+np.float64(value=0.0)  # E: Unexpected keyword argument
+np.int64(value=0)  # E: Unexpected keyword argument
+np.uint64(value=0)  # E: Unexpected keyword argument
+np.complex128(value=0.0j)  # E: Unexpected keyword argument
+np.str_(value='bob')  # E: No overload variant
+np.bytes_(value=b'test')  # E: No overload variant
+np.void(value=b'test')  # E: Unexpected keyword argument
+np.bool_(value=True)  # E: Unexpected keyword argument
+np.datetime64(value="2019")  # E: No overload variant
+np.timedelta64(value=0)  # E: Unexpected keyword argument
+
+np.bytes_(b"hello", encoding='utf-8')  # E: No overload variant
+np.str_("hello", encoding='utf-8')  # E: No overload variant
+
+complex(np.bytes_("1"))  # E: No overload variant
+
+f8.item(1)  # E: incompatible type
+f8.item((0, 1))  # E: incompatible type
+f8.squeeze(axis=1)  # E: incompatible type
+f8.squeeze(axis=(0, 1))  # E: incompatible type
+f8.transpose(1)  # E: incompatible type
+
+def func(a: np.float32) -> None: ...
+
+func(f2)  # E: incompatible type
+func(f8)  # E: incompatible type
+
+round(c8)  # E: No overload variant
+
+c8.__getnewargs__()  # E: Invalid self argument
+f2.__getnewargs__()  # E: Invalid self argument
+f2.hex()  # E: Invalid self argument
+np.float16.fromhex("0x0.0p+0")  # E: Invalid self argument
+f2.__trunc__()  # E: Invalid self argument
+f2.__getformat__("float")  # E: Invalid self argument

.venv/Lib/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))  # E: No overload variant

.venv/Lib/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)  # E: No overload variant
+np.lib.stride_tricks.as_strided(AR_f8, strides=8)  # E: No overload variant
+
+np.lib.stride_tricks.sliding_window_view(AR_f8, axis=(1,))  # E: No overload variant

.venv/Lib/site-packages/numpy/typing/tests/data/fail/testing.pyi

@@ -0,0 +1,26 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_U: npt.NDArray[np.str_]
+
+def func() -> bool: ...
+
+np.testing.assert_(True, msg=1)  # E: incompatible type
+np.testing.build_err_msg(1, "test")  # E: incompatible type
+np.testing.assert_almost_equal(AR_U, AR_U)  # E: incompatible type
+np.testing.assert_approx_equal([1, 2, 3], [1, 2, 3])  # E: incompatible type
+np.testing.assert_array_almost_equal(AR_U, AR_U)  # E: incompatible type
+np.testing.assert_array_less(AR_U, AR_U)  # E: incompatible type
+np.testing.assert_string_equal(b"a", b"a")  # E: incompatible type
+
+np.testing.assert_raises(expected_exception=TypeError, callable=func)  # E: No overload variant
+np.testing.assert_raises_regex(expected_exception=TypeError, expected_regex="T", callable=func)  # E: No overload variant
+
+np.testing.assert_allclose(AR_U, AR_U)  # E: incompatible type
+np.testing.assert_array_almost_equal_nulp(AR_U, AR_U)  # E: incompatible type
+np.testing.assert_array_max_ulp(AR_U, AR_U)  # E: incompatible type
+
+np.testing.assert_warns(warning_class=RuntimeWarning, func=func)  # E: No overload variant
+np.testing.assert_no_warnings(func=func)  # E: No overload variant
+
+np.testing.assert_no_gc_cycles(func=func)  # E: No overload variant

.venv/Lib/site-packages/numpy/typing/tests/data/fail/twodim_base.pyi

@@ -0,0 +1,37 @@
+from typing import Any, List, TypeVar
+
+import numpy as np
+import numpy.typing as npt
+
+
+def func1(ar: npt.NDArray[Any], a: int) -> npt.NDArray[np.str_]:
+    pass
+
+
+def func2(ar: npt.NDArray[Any], a: float) -> float:
+    pass
+
+
+AR_b: npt.NDArray[np.bool_]
+AR_m: npt.NDArray[np.timedelta64]
+
+AR_LIKE_b: List[bool]
+
+np.eye(10, M=20.0)  # E: No overload variant
+np.eye(10, k=2.5, dtype=int)  # E: No overload variant
+
+np.diag(AR_b, k=0.5)  # E: No overload variant
+np.diagflat(AR_b, k=0.5)  # E: No overload variant
+
+np.tri(10, M=20.0)  # E: No overload variant
+np.tri(10, k=2.5, dtype=int)  # E: No overload variant
+
+np.tril(AR_b, k=0.5)  # E: No overload variant
+np.triu(AR_b, k=0.5)  # E: No overload variant
+
+np.vander(AR_m)  # E: incompatible type
+
+np.histogram2d(AR_m)  # E: No overload variant
+
+np.mask_indices(10, func1)  # E: incompatible type
+np.mask_indices(10, func2, 10.5)  # E: incompatible type

.venv/Lib/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)  # E: incompatible type
+np.iscomplexobj(DTYPE_i8)  # E: incompatible type
+np.isrealobj(DTYPE_i8)  # E: incompatible type
+
+np.typename(DTYPE_i8)  # E: No overload variant
+np.typename("invalid")  # E: No overload variant
+
+np.common_type(np.timedelta64())  # E: incompatible type

.venv/Lib/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)  # E: Argument 1 to "seterrcall" has incompatible type
+np.seterrcall(func2)  # E: Argument 1 to "seterrcall" has incompatible type
+np.seterrcall(Write1())  # E: Argument 1 to "seterrcall" has incompatible type
+np.seterrcall(Write2())  # E: Argument 1 to "seterrcall" has incompatible type
+np.seterrcall(Write3())  # E: Argument 1 to "seterrcall" has incompatible type

.venv/Lib/site-packages/numpy/typing/tests/data/fail/ufunclike.pyi

@@ -0,0 +1,21 @@
+from typing import List, Any
+import numpy as np
+
+AR_c: np.ndarray[Any, np.dtype[np.complex128]]
+AR_m: np.ndarray[Any, np.dtype[np.timedelta64]]
+AR_M: np.ndarray[Any, np.dtype[np.datetime64]]
+AR_O: np.ndarray[Any, np.dtype[np.object_]]
+
+np.fix(AR_c)  # E: incompatible type
+np.fix(AR_m)  # E: incompatible type
+np.fix(AR_M)  # E: incompatible type
+
+np.isposinf(AR_c)  # E: incompatible type
+np.isposinf(AR_m)  # E: incompatible type
+np.isposinf(AR_M)  # E: incompatible type
+np.isposinf(AR_O)  # E: incompatible type
+
+np.isneginf(AR_c)  # E: incompatible type
+np.isneginf(AR_m)  # E: incompatible type
+np.isneginf(AR_M)  # E: incompatible type
+np.isneginf(AR_O)  # E: incompatible type

.venv/Lib/site-packages/numpy/typing/tests/data/fail/ufuncs.pyi

@@ -0,0 +1,41 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_f8: npt.NDArray[np.float64]
+
+np.sin.nin + "foo"  # E: Unsupported operand types
+np.sin(1, foo="bar")  # E: No overload variant
+
+np.abs(None)  # E: No overload variant
+
+np.add(1, 1, 1)  # E: No overload variant
+np.add(1, 1, axis=0)  # E: No overload variant
+
+np.matmul(AR_f8, AR_f8, where=True)  # E: No overload variant
+
+np.frexp(AR_f8, out=None)  # E: No overload variant
+np.frexp(AR_f8, out=AR_f8)  # E: No overload variant
+
+np.absolute.outer()  # E: "None" not callable
+np.frexp.outer()  # E: "None" not callable
+np.divmod.outer()  # E: "None" not callable
+np.matmul.outer()  # E: "None" not callable
+
+np.absolute.reduceat()  # E: "None" not callable
+np.frexp.reduceat()  # E: "None" not callable
+np.divmod.reduceat()  # E: "None" not callable
+np.matmul.reduceat()  # E: "None" not callable
+
+np.absolute.reduce()  # E: "None" not callable
+np.frexp.reduce()  # E: "None" not callable
+np.divmod.reduce()  # E: "None" not callable
+np.matmul.reduce()  # E: "None" not callable
+
+np.absolute.accumulate()  # E: "None" not callable
+np.frexp.accumulate()  # E: "None" not callable
+np.divmod.accumulate()  # E: "None" not callable
+np.matmul.accumulate()  # E: "None" not callable
+
+np.frexp.at()  # E: "None" not callable
+np.divmod.at()  # E: "None" not callable
+np.matmul.at()  # E: "None" not callable

.venv/Lib/site-packages/numpy/typing/tests/data/fail/warnings_and_errors.pyi

@@ -0,0 +1,5 @@
+import numpy as np
+
+np.AxisError(1.0)  # E: No overload variant
+np.AxisError(1, ndim=2.0)  # E: No overload variant
+np.AxisError(2, msg_prefix=404)  # E: No overload variant

.venv/Lib/site-packages/numpy/typing/tests/data/misc/extended_precision.pyi

@@ -0,0 +1,17 @@
+import numpy as np
+
+reveal_type(np.uint128())
+reveal_type(np.uint256())
+
+reveal_type(np.int128())
+reveal_type(np.int256())
+
+reveal_type(np.float80())
+reveal_type(np.float96())
+reveal_type(np.float128())
+reveal_type(np.float256())
+
+reveal_type(np.complex160())
+reveal_type(np.complex192())
+reveal_type(np.complex256())
+reveal_type(np.complex512())

.venv/Lib/site-packages/numpy/typing/tests/data/mypy.ini

@@ -0,0 +1,10 @@
+[mypy]
+plugins = numpy.typing.mypy_plugin
+show_absolute_path = True
+implicit_reexport = False
+
+[mypy-numpy]
+ignore_errors = True
+
+[mypy-numpy.*]
+ignore_errors = True

.venv/Lib/site-packages/numpy/typing/tests/data/pass/arithmetic.py

@@ -0,0 +1,588 @@
+from __future__ import annotations
+
+from typing import Any
+import numpy as np
+
+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) -> 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: np.ndarray[Any, np.dtype[np.bool_]] = np.array([True])
+AR_u: np.ndarray[Any, np.dtype[np.uint32]] = np.array([1], dtype=np.uint32)
+AR_i: np.ndarray[Any, np.dtype[np.int64]] = np.array([1])
+AR_f: np.ndarray[Any, np.dtype[np.float64]] = np.array([1.0])
+AR_c: np.ndarray[Any, np.dtype[np.complex128]] = np.array([1j])
+AR_m: np.ndarray[Any, np.dtype[np.timedelta64]] = np.array([np.timedelta64(1, "D")])
+AR_M: np.ndarray[Any, np.dtype[np.datetime64]] = np.array([np.datetime64(1, "D")])
+AR_O: np.ndarray[Any, np.dtype[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
+
+# 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_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_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_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
+-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/site-packages/numpy/typing/tests/data/pass/array_constructors.py

@@ -0,0 +1,138 @@
+import sys
+from typing import Any
+import numpy as np
+
+
+class Index:
+    def __index__(self) -> int:
+        return 0
+
+
+class SubClass(np.ndarray):
+    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]
+
+if sys.version_info >= (3, 8):
+    np.ndarray(Index())
+    np.ndarray([Index()])
+
+np.array(1, dtype=float)
+np.array(1, copy=False)
+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/site-packages/numpy/typing/tests/data/pass/array_like.py

@@ -0,0 +1,39 @@
+from typing import Any, Optional
+
+import numpy as np
+from numpy.typing import 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: Optional[np.dtype] = None) -> np.ndarray:
+        return np.array([1, 2, 3])
+
+
+x13: ArrayLike = A()
+
+scalar: _SupportsArray = np.int64(1)
+scalar.__array__()
+array: _SupportsArray = np.array(1)
+array.__array__()
+
+a: _SupportsArray = A()
+a.__array__()
+a.__array__()
+
+# Escape hatch for when you mean to make something like an object
+# array.
+object_array_scalar: Any = (i for i in range(10))
+np.array(object_array_scalar)

.venv/Lib/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/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/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
+
+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
+
+u8 << AR
+u8 >> AR
+u8 | AR
+u8 ^ AR
+u8 & AR
+
+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

.venv/Lib/site-packages/numpy/typing/tests/data/pass/comparisons.py

@@ -0,0 +1,301 @@
+from __future__ import annotations
+
+from typing import Any
+import numpy as np
+
+c16 = np.complex128()
+f8 = np.float64()
+i8 = np.int64()
+u8 = np.uint64()
+
+c8 = np.complex64()
+f4 = np.float32()
+i4 = np.int32()
+u4 = np.uint32()
+
+dt = np.datetime64(0, "D")
+td = np.timedelta64(0, "D")
+
+b_ = np.bool_()
+
+b = bool()
+c = complex()
+f = float()
+i = int()
+
+SEQ = (0, 1, 2, 3, 4)
+
+AR_b: np.ndarray[Any, np.dtype[np.bool_]] = np.array([True])
+AR_u: np.ndarray[Any, np.dtype[np.uint32]] = np.array([1], dtype=np.uint32)
+AR_i: np.ndarray[Any, np.dtype[np.int_]] = np.array([1])
+AR_f: np.ndarray[Any, np.dtype[np.float_]] = np.array([1.0])
+AR_c: np.ndarray[Any, np.dtype[np.complex_]] = np.array([1.0j])
+AR_m: np.ndarray[Any, np.dtype[np.timedelta64]] = np.array([np.timedelta64("1")])
+AR_M: np.ndarray[Any, np.dtype[np.datetime64]] = np.array([np.datetime64("1")])
+AR_O: np.ndarray[Any, np.dtype[np.object_]] = np.array([1], dtype=object)
+
+# Arrays
+
+AR_b > AR_b
+AR_b > AR_u
+AR_b > AR_i
+AR_b > AR_f
+AR_b > AR_c
+
+AR_u > AR_b
+AR_u > AR_u
+AR_u > AR_i
+AR_u > AR_f
+AR_u > AR_c
+
+AR_i > AR_b
+AR_i > AR_u
+AR_i > AR_i
+AR_i > AR_f
+AR_i > AR_c
+
+AR_f > AR_b
+AR_f > AR_u
+AR_f > AR_i
+AR_f > AR_f
+AR_f > AR_c
+
+AR_c > AR_b
+AR_c > AR_u
+AR_c > AR_i
+AR_c > AR_f
+AR_c > AR_c
+
+AR_m > AR_b
+AR_m > AR_u
+AR_m > AR_i
+AR_b > AR_m
+AR_u > AR_m
+AR_i > AR_m
+
+AR_M > AR_M
+
+AR_O > AR_O
+1 > AR_O
+AR_O > 1
+
+# Time structures
+
+dt > dt
+
+td > td
+td > i
+td > i4
+td > i8
+td > AR_i
+td > SEQ
+
+# 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_ > AR_i
+b_ > SEQ
+
+# 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_i
+c16 > SEQ
+
+c16 > c16
+f8 > c16
+i8 > c16
+c8 > c16
+f4 > c16
+i4 > c16
+b_ > c16
+b > c16
+c > c16
+f > c16
+i > c16
+AR_i > c16
+SEQ > 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_i
+c8 > SEQ
+
+c16 > c8
+f8 > c8
+i8 > c8
+c8 > c8
+f4 > c8
+i4 > c8
+b_ > c8
+b > c8
+c > c8
+f > c8
+i > c8
+AR_i > c8
+SEQ > c8
+
+# Float
+
+f8 > f8
+f8 > i8
+f8 > f4
+f8 > i4
+f8 > b_
+f8 > b
+f8 > c
+f8 > f
+f8 > i
+f8 > AR_i
+f8 > SEQ
+
+f8 > f8
+i8 > f8
+f4 > f8
+i4 > f8
+b_ > f8
+b > f8
+c > f8
+f > f8
+i > f8
+AR_i > f8
+SEQ > f8
+
+f4 > f8
+f4 > i8
+f4 > f4
+f4 > i4
+f4 > b_
+f4 > b
+f4 > c
+f4 > f
+f4 > i
+f4 > AR_i
+f4 > SEQ
+
+f8 > f4
+i8 > f4
+f4 > f4
+i4 > f4
+b_ > f4
+b > f4
+c > f4
+f > f4
+i > f4
+AR_i > f4
+SEQ > f4
+
+# Int
+
+i8 > i8
+i8 > u8
+i8 > i4
+i8 > u4
+i8 > b_
+i8 > b
+i8 > c
+i8 > f
+i8 > i
+i8 > AR_i
+i8 > SEQ
+
+u8 > u8
+u8 > i4
+u8 > u4
+u8 > b_
+u8 > b
+u8 > c
+u8 > f
+u8 > i
+u8 > AR_i
+u8 > SEQ
+
+i8 > i8
+u8 > i8
+i4 > i8
+u4 > i8
+b_ > i8
+b > i8
+c > i8
+f > i8
+i > i8
+AR_i > i8
+SEQ > i8
+
+u8 > u8
+i4 > u8
+u4 > u8
+b_ > u8
+b > u8
+c > u8
+f > u8
+i > u8
+AR_i > u8
+SEQ > u8
+
+i4 > i8
+i4 > i4
+i4 > i
+i4 > b_
+i4 > b
+i4 > AR_i
+i4 > SEQ
+
+u4 > i8
+u4 > i4
+u4 > u8
+u4 > u4
+u4 > i
+u4 > b_
+u4 > b
+u4 > AR_i
+u4 > SEQ
+
+i8 > i4
+i4 > i4
+i > i4
+b_ > i4
+b > i4
+AR_i > i4
+SEQ > i4
+
+i8 > u4
+i4 > u4
+u8 > u4
+u4 > u4
+b_ > u4
+b > u4
+i > u4
+AR_i > u4
+SEQ > u4

.venv/Lib/site-packages/numpy/typing/tests/data/pass/dtype.py

@@ -0,0 +1,57 @@
+import numpy as np
+
+dtype_obj = np.dtype(np.str_)
+void_dtype_obj = np.dtype([("f0", np.float64), ("f1", np.float32)])
+
+np.dtype(dtype=np.int64)
+np.dtype(int)
+np.dtype("int")
+np.dtype(None)
+
+np.dtype((int, 2))
+np.dtype((int, (1,)))
+
+np.dtype({"names": ["a", "b"], "formats": [int, float]})
+np.dtype({"names": ["a"], "formats": [int], "titles": [object]})
+np.dtype({"names": ["a"], "formats": [int], "titles": [object()]})
+
+np.dtype([("name", np.unicode_, 16), ("grades", np.float64, (2,)), ("age", "int32")])
+
+np.dtype(
+    {
+        "names": ["a", "b"],
+        "formats": [int, float],
+        "itemsize": 9,
+        "aligned": False,
+        "titles": ["x", "y"],
+        "offsets": [0, 1],
+    }
+)
+
+np.dtype((np.float_, float))
+
+
+class Test:
+    dtype = np.dtype(float)
+
+
+np.dtype(Test())
+
+# Methods and attributes
+dtype_obj.base
+dtype_obj.subdtype
+dtype_obj.newbyteorder()
+dtype_obj.type
+dtype_obj.name
+dtype_obj.names
+
+dtype_obj * 0
+dtype_obj * 2
+
+0 * dtype_obj
+2 * dtype_obj
+
+void_dtype_obj["f0"]
+void_dtype_obj[0]
+void_dtype_obj[["f0", "f1"]]
+void_dtype_obj[["f0"]]

.venv/Lib/site-packages/numpy/typing/tests/data/pass/einsumfunc.py

@@ -0,0 +1,36 @@
+from __future__ import annotations
+
+from typing import Any
+
+import numpy as np
+
+AR_LIKE_b = [True, True, True]
+AR_LIKE_u = [np.uint32(1), np.uint32(2), np.uint32(3)]
+AR_LIKE_i = [1, 2, 3]
+AR_LIKE_f = [1.0, 2.0, 3.0]
+AR_LIKE_c = [1j, 2j, 3j]
+AR_LIKE_U = ["1", "2", "3"]
+
+OUT_f: np.ndarray[Any, np.dtype[np.float64]] = np.empty(3, dtype=np.float64)
+OUT_c: np.ndarray[Any, np.dtype[np.complex128]] = np.empty(3, dtype=np.complex128)
+
+np.einsum("i,i->i", AR_LIKE_b, AR_LIKE_b)
+np.einsum("i,i->i", AR_LIKE_u, AR_LIKE_u)
+np.einsum("i,i->i", AR_LIKE_i, AR_LIKE_i)
+np.einsum("i,i->i", AR_LIKE_f, AR_LIKE_f)
+np.einsum("i,i->i", AR_LIKE_c, AR_LIKE_c)
+np.einsum("i,i->i", AR_LIKE_b, AR_LIKE_i)
+np.einsum("i,i,i,i->i", AR_LIKE_b, AR_LIKE_u, AR_LIKE_i, AR_LIKE_c)
+
+np.einsum("i,i->i", AR_LIKE_f, AR_LIKE_f, dtype="c16")
+np.einsum("i,i->i", AR_LIKE_U, AR_LIKE_U, dtype=bool, casting="unsafe")
+np.einsum("i,i->i", AR_LIKE_f, AR_LIKE_f, out=OUT_c)
+np.einsum("i,i->i", AR_LIKE_U, AR_LIKE_U, dtype=int, casting="unsafe", out=OUT_f)
+
+np.einsum_path("i,i->i", AR_LIKE_b, AR_LIKE_b)
+np.einsum_path("i,i->i", AR_LIKE_u, AR_LIKE_u)
+np.einsum_path("i,i->i", AR_LIKE_i, AR_LIKE_i)
+np.einsum_path("i,i->i", AR_LIKE_f, AR_LIKE_f)
+np.einsum_path("i,i->i", AR_LIKE_c, AR_LIKE_c)
+np.einsum_path("i,i->i", AR_LIKE_b, AR_LIKE_i)
+np.einsum_path("i,i,i,i->i", AR_LIKE_b, AR_LIKE_u, AR_LIKE_i, AR_LIKE_c)

.venv/Lib/site-packages/numpy/typing/tests/data/pass/flatiter.py

@@ -0,0 +1,16 @@
+import numpy as np
+
+a = np.empty((2, 2)).flat
+
+a.base
+a.copy()
+a.coords
+a.index
+iter(a)
+next(a)
+a[0]
+a[[0, 1, 2]]
+a[...]
+a[:]
+a.__array__()
+a.__array__(np.dtype(np.float64))

.venv/Lib/site-packages/numpy/typing/tests/data/pass/fromnumeric.py

@@ -0,0 +1,260 @@
+"""Tests for :mod:`numpy.core.fromnumeric`."""
+
+import numpy as np
+
+A = np.array(True, ndmin=2, dtype=bool)
+B = np.array(1.0, ndmin=2, dtype=np.float32)
+A.setflags(write=False)
+B.setflags(write=False)
+
+a = np.bool_(True)
+b = np.float32(1.0)
+c = 1.0
+d = np.array(1.0, dtype=np.float32)  # writeable
+
+np.take(a, 0)
+np.take(b, 0)
+np.take(c, 0)
+np.take(A, 0)
+np.take(B, 0)
+np.take(A, [0])
+np.take(B, [0])
+
+np.reshape(a, 1)
+np.reshape(b, 1)
+np.reshape(c, 1)
+np.reshape(A, 1)
+np.reshape(B, 1)
+
+np.choose(a, [True, True])
+np.choose(A, [1.0, 1.0])
+
+np.repeat(a, 1)
+np.repeat(b, 1)
+np.repeat(c, 1)
+np.repeat(A, 1)
+np.repeat(B, 1)
+
+np.swapaxes(A, 0, 0)
+np.swapaxes(B, 0, 0)
+
+np.transpose(a)
+np.transpose(b)
+np.transpose(c)
+np.transpose(A)
+np.transpose(B)
+
+np.partition(a, 0, axis=None)
+np.partition(b, 0, axis=None)
+np.partition(c, 0, axis=None)
+np.partition(A, 0)
+np.partition(B, 0)
+
+np.argpartition(a, 0)
+np.argpartition(b, 0)
+np.argpartition(c, 0)
+np.argpartition(A, 0)
+np.argpartition(B, 0)
+
+np.sort(A, 0)
+np.sort(B, 0)
+
+np.argsort(A, 0)
+np.argsort(B, 0)
+
+np.argmax(A)
+np.argmax(B)
+np.argmax(A, axis=0)
+np.argmax(B, axis=0)
+
+np.argmin(A)
+np.argmin(B)
+np.argmin(A, axis=0)
+np.argmin(B, axis=0)
+
+np.searchsorted(A[0], 0)
+np.searchsorted(B[0], 0)
+np.searchsorted(A[0], [0])
+np.searchsorted(B[0], [0])
+
+np.resize(a, (5, 5))
+np.resize(b, (5, 5))
+np.resize(c, (5, 5))
+np.resize(A, (5, 5))
+np.resize(B, (5, 5))
+
+np.squeeze(a)
+np.squeeze(b)
+np.squeeze(c)
+np.squeeze(A)
+np.squeeze(B)
+
+np.diagonal(A)
+np.diagonal(B)
+
+np.trace(A)
+np.trace(B)
+
+np.ravel(a)
+np.ravel(b)
+np.ravel(c)
+np.ravel(A)
+np.ravel(B)
+
+np.nonzero(A)
+np.nonzero(B)
+
+np.shape(a)
+np.shape(b)
+np.shape(c)
+np.shape(A)
+np.shape(B)
+
+np.compress([True], a)
+np.compress([True], b)
+np.compress([True], c)
+np.compress([True], A)
+np.compress([True], B)
+
+np.clip(a, 0, 1.0)
+np.clip(b, -1, 1)
+np.clip(a, 0, None)
+np.clip(b, None, 1)
+np.clip(c, 0, 1)
+np.clip(A, 0, 1)
+np.clip(B, 0, 1)
+np.clip(B, [0, 1], [1, 2])
+
+np.sum(a)
+np.sum(b)
+np.sum(c)
+np.sum(A)
+np.sum(B)
+np.sum(A, axis=0)
+np.sum(B, axis=0)
+
+np.all(a)
+np.all(b)
+np.all(c)
+np.all(A)
+np.all(B)
+np.all(A, axis=0)
+np.all(B, axis=0)
+np.all(A, keepdims=True)
+np.all(B, keepdims=True)
+
+np.any(a)
+np.any(b)
+np.any(c)
+np.any(A)
+np.any(B)
+np.any(A, axis=0)
+np.any(B, axis=0)
+np.any(A, keepdims=True)
+np.any(B, keepdims=True)
+
+np.cumsum(a)
+np.cumsum(b)
+np.cumsum(c)
+np.cumsum(A)
+np.cumsum(B)
+
+np.ptp(b)
+np.ptp(c)
+np.ptp(B)
+np.ptp(B, axis=0)
+np.ptp(B, keepdims=True)
+
+np.amax(a)
+np.amax(b)
+np.amax(c)
+np.amax(A)
+np.amax(B)
+np.amax(A, axis=0)
+np.amax(B, axis=0)
+np.amax(A, keepdims=True)
+np.amax(B, keepdims=True)
+
+np.amin(a)
+np.amin(b)
+np.amin(c)
+np.amin(A)
+np.amin(B)
+np.amin(A, axis=0)
+np.amin(B, axis=0)
+np.amin(A, keepdims=True)
+np.amin(B, keepdims=True)
+
+np.prod(a)
+np.prod(b)
+np.prod(c)
+np.prod(A)
+np.prod(B)
+np.prod(a, dtype=None)
+np.prod(A, dtype=None)
+np.prod(A, axis=0)
+np.prod(B, axis=0)
+np.prod(A, keepdims=True)
+np.prod(B, keepdims=True)
+np.prod(b, out=d)
+np.prod(B, out=d)
+
+np.cumprod(a)
+np.cumprod(b)
+np.cumprod(c)
+np.cumprod(A)
+np.cumprod(B)
+
+np.ndim(a)
+np.ndim(b)
+np.ndim(c)
+np.ndim(A)
+np.ndim(B)
+
+np.size(a)
+np.size(b)
+np.size(c)
+np.size(A)
+np.size(B)
+
+np.around(a)
+np.around(b)
+np.around(c)
+np.around(A)
+np.around(B)
+
+np.mean(a)
+np.mean(b)
+np.mean(c)
+np.mean(A)
+np.mean(B)
+np.mean(A, axis=0)
+np.mean(B, axis=0)
+np.mean(A, keepdims=True)
+np.mean(B, keepdims=True)
+np.mean(b, out=d)
+np.mean(B, out=d)
+
+np.std(a)
+np.std(b)
+np.std(c)
+np.std(A)
+np.std(B)
+np.std(A, axis=0)
+np.std(B, axis=0)
+np.std(A, keepdims=True)
+np.std(B, keepdims=True)
+np.std(b, out=d)
+np.std(B, out=d)
+
+np.var(a)
+np.var(b)
+np.var(c)
+np.var(A)
+np.var(B)
+np.var(A, axis=0)
+np.var(B, axis=0)
+np.var(A, keepdims=True)
+np.var(B, keepdims=True)
+np.var(b, out=d)
+np.var(B, out=d)

.venv/Lib/site-packages/numpy/typing/tests/data/pass/index_tricks.py

@@ -0,0 +1,64 @@
+from __future__ import annotations
+from typing import Any
+import numpy as np
+
+AR_LIKE_b = [[True, True], [True, True]]
+AR_LIKE_i = [[1, 2], [3, 4]]
+AR_LIKE_f = [[1.0, 2.0], [3.0, 4.0]]
+AR_LIKE_U = [["1", "2"], ["3", "4"]]
+
+AR_i8: np.ndarray[Any, np.dtype[np.int64]] = np.array(AR_LIKE_i, dtype=np.int64)
+
+np.ndenumerate(AR_i8)
+np.ndenumerate(AR_LIKE_f)
+np.ndenumerate(AR_LIKE_U)
+
+np.ndenumerate(AR_i8).iter
+np.ndenumerate(AR_LIKE_f).iter
+np.ndenumerate(AR_LIKE_U).iter
+
+next(np.ndenumerate(AR_i8))
+next(np.ndenumerate(AR_LIKE_f))
+next(np.ndenumerate(AR_LIKE_U))
+
+iter(np.ndenumerate(AR_i8))
+iter(np.ndenumerate(AR_LIKE_f))
+iter(np.ndenumerate(AR_LIKE_U))
+
+iter(np.ndindex(1, 2, 3))
+next(np.ndindex(1, 2, 3))
+
+np.unravel_index([22, 41, 37], (7, 6))
+np.unravel_index([31, 41, 13], (7, 6), order='F')
+np.unravel_index(1621, (6, 7, 8, 9))
+
+np.ravel_multi_index(AR_LIKE_i, (7, 6))
+np.ravel_multi_index(AR_LIKE_i, (7, 6), order='F')
+np.ravel_multi_index(AR_LIKE_i, (4, 6), mode='clip')
+np.ravel_multi_index(AR_LIKE_i, (4, 4), mode=('clip', 'wrap'))
+np.ravel_multi_index((3, 1, 4, 1), (6, 7, 8, 9))
+
+np.mgrid[1:1:2]
+np.mgrid[1:1:2, None:10]
+
+np.ogrid[1:1:2]
+np.ogrid[1:1:2, None:10]
+
+np.index_exp[0:1]
+np.index_exp[0:1, None:3]
+np.index_exp[0, 0:1, ..., [0, 1, 3]]
+
+np.s_[0:1]
+np.s_[0:1, None:3]
+np.s_[0, 0:1, ..., [0, 1, 3]]
+
+np.ix_(AR_LIKE_b[0])
+np.ix_(AR_LIKE_i[0], AR_LIKE_f[0])
+np.ix_(AR_i8[0])
+
+np.fill_diagonal(AR_i8, 5)
+
+np.diag_indices(4)
+np.diag_indices(2, 3)
+
+np.diag_indices_from(AR_i8)

.venv/Lib/site-packages/numpy/typing/tests/data/pass/lib_utils.py

@@ -0,0 +1,25 @@
+from __future__ import annotations
+
+from io import StringIO
+
+import numpy as np
+
+FILE = StringIO()
+AR = np.arange(10, dtype=np.float64)
+
+def func(a: int) -> bool: ...
+
+np.deprecate(func)
+np.deprecate()
+
+np.deprecate_with_doc("test")
+np.deprecate_with_doc(None)
+
+np.byte_bounds(AR)
+np.byte_bounds(np.float64())
+
+np.info(1, output=FILE)
+
+np.source(np.interp, output=FILE)
+
+np.lookfor("binary representation", output=FILE)

.venv/Lib/site-packages/numpy/typing/tests/data/pass/lib_version.py

@@ -0,0 +1,18 @@
+from numpy.lib import NumpyVersion
+
+version = NumpyVersion("1.8.0")
+
+version.vstring
+version.version
+version.major
+version.minor
+version.bugfix
+version.pre_release
+version.is_devversion
+
+version == version
+version != version
+version < "1.8.0"
+version <= version
+version > version
+version >= "1.8.0"

.venv/Lib/site-packages/numpy/typing/tests/data/pass/literal.py

@@ -0,0 +1,45 @@
+from functools import partial
+from typing import Callable, List, Tuple
+
+import pytest  # type: ignore
+import numpy as np
+
+AR = np.array(0)
+AR.setflags(write=False)
+
+KACF = frozenset({None, "K", "A", "C", "F"})
+ACF = frozenset({None, "A", "C", "F"})
+CF = frozenset({None, "C", "F"})
+
+order_list: List[Tuple[frozenset, Callable]] = [
+    (KACF, partial(np.ndarray, 1)),
+    (KACF, AR.tobytes),
+    (KACF, partial(AR.astype, int)),
+    (KACF, AR.copy),
+    (ACF, partial(AR.reshape, 1)),
+    (KACF, AR.flatten),
+    (KACF, AR.ravel),
+    (KACF, partial(np.array, 1)),
+    (CF, partial(np.zeros, 1)),
+    (CF, partial(np.ones, 1)),
+    (CF, partial(np.empty, 1)),
+    (CF, partial(np.full, 1, 1)),
+    (KACF, partial(np.zeros_like, AR)),
+    (KACF, partial(np.ones_like, AR)),
+    (KACF, partial(np.empty_like, AR)),
+    (KACF, partial(np.full_like, AR, 1)),
+    (KACF, partial(np.add, 1, 1)),  # i.e. np.ufunc.__call__
+    (ACF, partial(np.reshape, AR, 1)),
+    (KACF, partial(np.ravel, AR)),
+    (KACF, partial(np.asarray, 1)),
+    (KACF, partial(np.asanyarray, 1)),
+]
+
+for order_set, func in order_list:
+    for order in order_set:
+        func(order=order)
+
+    invalid_orders = KACF - order_set
+    for order in invalid_orders:
+        with pytest.raises(ValueError):
+            func(order=order)

.venv/Lib/site-packages/numpy/typing/tests/data/pass/mod.py

@@ -0,0 +1,149 @@
+import numpy as np
+
+f8 = np.float64(1)
+i8 = np.int64(1)
+u8 = np.uint64(1)
+
+f4 = np.float32(1)
+i4 = np.int32(1)
+u4 = np.uint32(1)
+
+td = np.timedelta64(1, "D")
+b_ = np.bool_(1)
+
+b = bool(1)
+f = float(1)
+i = int(1)
+
+AR = np.array([1], dtype=np.bool_)
+AR.setflags(write=False)
+
+AR2 = np.array([1], dtype=np.timedelta64)
+AR2.setflags(write=False)
+
+# Time structures
+
+td % td
+td % AR2
+AR2 % td
+
+divmod(td, td)
+divmod(td, AR2)
+divmod(AR2, td)
+
+# Bool
+
+b_ % b
+b_ % i
+b_ % f
+b_ % b_
+b_ % i8
+b_ % u8
+b_ % f8
+b_ % AR
+
+divmod(b_, b)
+divmod(b_, i)
+divmod(b_, f)
+divmod(b_, b_)
+divmod(b_, i8)
+divmod(b_, u8)
+divmod(b_, f8)
+divmod(b_, AR)
+
+b % b_
+i % b_
+f % b_
+b_ % b_
+i8 % b_
+u8 % b_
+f8 % b_
+AR % b_
+
+divmod(b, b_)
+divmod(i, b_)
+divmod(f, b_)
+divmod(b_, b_)
+divmod(i8, b_)
+divmod(u8, b_)
+divmod(f8, b_)
+divmod(AR, b_)
+
+# int
+
+i8 % b
+i8 % i
+i8 % f
+i8 % i8
+i8 % f8
+i4 % i8
+i4 % f8
+i4 % i4
+i4 % f4
+i8 % AR
+
+divmod(i8, b)
+divmod(i8, i)
+divmod(i8, f)
+divmod(i8, i8)
+divmod(i8, f8)
+divmod(i8, i4)
+divmod(i8, f4)
+divmod(i4, i4)
+divmod(i4, f4)
+divmod(i8, AR)
+
+b % i8
+i % i8
+f % i8
+i8 % i8
+f8 % i8
+i8 % i4
+f8 % i4
+i4 % i4
+f4 % i4
+AR % i8
+
+divmod(b, i8)
+divmod(i, i8)
+divmod(f, i8)
+divmod(i8, i8)
+divmod(f8, i8)
+divmod(i4, i8)
+divmod(f4, i8)
+divmod(i4, i4)
+divmod(f4, i4)
+divmod(AR, i8)
+
+# float
+
+f8 % b
+f8 % i
+f8 % f
+i8 % f4
+f4 % f4
+f8 % AR
+
+divmod(f8, b)
+divmod(f8, i)
+divmod(f8, f)
+divmod(f8, f8)
+divmod(f8, f4)
+divmod(f4, f4)
+divmod(f8, AR)
+
+b % f8
+i % f8
+f % f8
+f8 % f8
+f8 % f8
+f4 % f4
+AR % f8
+
+divmod(b, f8)
+divmod(i, f8)
+divmod(f, f8)
+divmod(f8, f8)
+divmod(f4, f8)
+divmod(f4, f4)
+divmod(AR, f8)

.venv/Lib/site-packages/numpy/typing/tests/data/pass/modules.py

@@ -0,0 +1,43 @@
+import numpy as np
+from numpy import f2py
+
+np.char
+np.ctypeslib
+np.emath
+np.fft
+np.lib
+np.linalg
+np.ma
+np.matrixlib
+np.polynomial
+np.random
+np.rec
+np.testing
+np.version
+
+np.lib.format
+np.lib.mixins
+np.lib.scimath
+np.lib.stride_tricks
+np.ma.extras
+np.polynomial.chebyshev
+np.polynomial.hermite
+np.polynomial.hermite_e
+np.polynomial.laguerre
+np.polynomial.legendre
+np.polynomial.polynomial
+
+np.__path__
+np.__version__
+np.__git_version__
+
+np.__all__
+np.char.__all__
+np.ctypeslib.__all__
+np.emath.__all__
+np.lib.__all__
+np.ma.__all__
+np.random.__all__
+np.rec.__all__
+np.testing.__all__
+f2py.__all__

.venv/Lib/site-packages/numpy/typing/tests/data/pass/multiarray.py

@@ -0,0 +1,76 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_f8: npt.NDArray[np.float64] = np.array([1.0])
+AR_i4 = np.array([1], dtype=np.int32)
+AR_u1 = np.array([1], dtype=np.uint8)
+
+AR_LIKE_f = [1.5]
+AR_LIKE_i = [1]
+
+b_f8 = np.broadcast(AR_f8)
+b_i4_f8_f8 = np.broadcast(AR_i4, AR_f8, AR_f8)
+
+next(b_f8)
+b_f8.reset()
+b_f8.index
+b_f8.iters
+b_f8.nd
+b_f8.ndim
+b_f8.numiter
+b_f8.shape
+b_f8.size
+
+next(b_i4_f8_f8)
+b_i4_f8_f8.reset()
+b_i4_f8_f8.ndim
+b_i4_f8_f8.index
+b_i4_f8_f8.iters
+b_i4_f8_f8.nd
+b_i4_f8_f8.numiter
+b_i4_f8_f8.shape
+b_i4_f8_f8.size
+
+np.inner(AR_f8, AR_i4)
+
+np.where([True, True, False])
+np.where([True, True, False], 1, 0)
+
+np.lexsort([0, 1, 2])
+
+np.can_cast(np.dtype("i8"), int)
+np.can_cast(AR_f8, "f8")
+np.can_cast(AR_f8, np.complex128, casting="unsafe")
+
+np.min_scalar_type([1])
+np.min_scalar_type(AR_f8)
+
+np.result_type(int, AR_i4)
+np.result_type(AR_f8, AR_u1)
+np.result_type(AR_f8, np.complex128)
+
+np.dot(AR_LIKE_f, AR_i4)
+np.dot(AR_u1, 1)
+np.dot(1.5j, 1)
+np.dot(AR_u1, 1, out=AR_f8)
+
+np.vdot(AR_LIKE_f, AR_i4)
+np.vdot(AR_u1, 1)
+np.vdot(1.5j, 1)
+
+np.bincount(AR_i4)
+
+np.copyto(AR_f8, [1.6])
+
+np.putmask(AR_f8, [True], 1.5)
+
+np.packbits(AR_i4)
+np.packbits(AR_u1)
+
+np.unpackbits(AR_u1)
+
+np.shares_memory(1, 2)
+np.shares_memory(AR_f8, AR_f8, max_work=1)
+
+np.may_share_memory(1, 2)
+np.may_share_memory(AR_f8, AR_f8, max_work=1)

.venv/Lib/site-packages/numpy/typing/tests/data/pass/ndarray_conversion.py

@@ -0,0 +1,94 @@
+import os
+import tempfile
+
+import numpy as np
+
+nd = np.array([[1, 2], [3, 4]])
+scalar_array = np.array(1)
+
+# item
+scalar_array.item()
+nd.item(1)
+nd.item(0, 1)
+nd.item((0, 1))
+
+# tolist is pretty simple
+
+# itemset
+scalar_array.itemset(3)
+nd.itemset(3, 0)
+nd.itemset((0, 0), 3)
+
+# tobytes
+nd.tobytes()
+nd.tobytes("C")
+nd.tobytes(None)
+
+# tofile
+if os.name != "nt":
+    with tempfile.NamedTemporaryFile(suffix=".txt") as tmp:
+        nd.tofile(tmp.name)
+        nd.tofile(tmp.name, "")
+        nd.tofile(tmp.name, sep="")
+
+        nd.tofile(tmp.name, "", "%s")
+        nd.tofile(tmp.name, format="%s")
+
+        nd.tofile(tmp)
+
+# dump is pretty simple
+# dumps is pretty simple
+
+# astype
+nd.astype("float")
+nd.astype(float)
+
+nd.astype(float, "K")
+nd.astype(float, order="K")
+
+nd.astype(float, "K", "unsafe")
+nd.astype(float, casting="unsafe")
+
+nd.astype(float, "K", "unsafe", True)
+nd.astype(float, subok=True)
+
+nd.astype(float, "K", "unsafe", True, True)
+nd.astype(float, copy=True)
+
+# byteswap
+nd.byteswap()
+nd.byteswap(True)
+
+# copy
+nd.copy()
+nd.copy("C")
+
+# view
+nd.view()
+nd.view(np.int64)
+nd.view(dtype=np.int64)
+nd.view(np.int64, np.matrix)
+nd.view(type=np.matrix)
+
+# getfield
+complex_array = np.array([[1 + 1j, 0], [0, 1 - 1j]], dtype=np.complex128)
+
+complex_array.getfield("float")
+complex_array.getfield(float)
+
+complex_array.getfield("float", 8)
+complex_array.getfield(float, offset=8)
+
+# setflags
+nd.setflags()
+
+nd.setflags(True)
+nd.setflags(write=True)
+
+nd.setflags(True, True)
+nd.setflags(write=True, align=True)
+
+nd.setflags(True, True, False)
+nd.setflags(write=True, align=True, uic=False)
+
+# fill is pretty simple

.venv/Lib/site-packages/numpy/typing/tests/data/pass/ndarray_misc.py

@@ -0,0 +1,185 @@
+"""
+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`.
+
+"""
+
+from __future__ import annotations
+
+import operator
+from typing import cast, Any
+
+import numpy as np
+
+class SubClass(np.ndarray): ...
+
+i4 = np.int32(1)
+A: np.ndarray[Any, np.dtype[np.int32]] = np.array([[1]], dtype=np.int32)
+B0 = np.empty((), dtype=np.int32).view(SubClass)
+B1 = np.empty((1,), dtype=np.int32).view(SubClass)
+B2 = np.empty((1, 1), dtype=np.int32).view(SubClass)
+C: np.ndarray[Any, np.dtype[np.int32]] = np.array([0, 1, 2], dtype=np.int32)
+D = np.empty(3).view(SubClass)
+
+i4.all()
+A.all()
+A.all(axis=0)
+A.all(keepdims=True)
+A.all(out=B0)
+
+i4.any()
+A.any()
+A.any(axis=0)
+A.any(keepdims=True)
+A.any(out=B0)
+
+i4.argmax()
+A.argmax()
+A.argmax(axis=0)
+A.argmax(out=B0)
+
+i4.argmin()
+A.argmin()
+A.argmin(axis=0)
+A.argmin(out=B0)
+
+i4.argsort()
+A.argsort()
+
+i4.choose([()])
+_choices = np.array([[0, 1, 2], [3, 4, 5], [6, 7, 8]], dtype=np.int32)
+C.choose(_choices)
+C.choose(_choices, out=D)
+
+i4.clip(1)
+A.clip(1)
+A.clip(None, 1)
+A.clip(1, out=B2)
+A.clip(None, 1, out=B2)
+
+i4.compress([1])
+A.compress([1])
+A.compress([1], out=B1)
+
+i4.conj()
+A.conj()
+B0.conj()
+
+i4.conjugate()
+A.conjugate()
+B0.conjugate()
+
+i4.cumprod()
+A.cumprod()
+A.cumprod(out=B1)
+
+i4.cumsum()
+A.cumsum()
+A.cumsum(out=B1)
+
+i4.max()
+A.max()
+A.max(axis=0)
+A.max(keepdims=True)
+A.max(out=B0)
+
+i4.mean()
+A.mean()
+A.mean(axis=0)
+A.mean(keepdims=True)
+A.mean(out=B0)
+
+i4.min()
+A.min()
+A.min(axis=0)
+A.min(keepdims=True)
+A.min(out=B0)
+
+i4.newbyteorder()
+A.newbyteorder()
+B0.newbyteorder('|')
+
+i4.prod()
+A.prod()
+A.prod(axis=0)
+A.prod(keepdims=True)
+A.prod(out=B0)
+
+i4.ptp()
+A.ptp()
+A.ptp(axis=0)
+A.ptp(keepdims=True)
+A.astype(int).ptp(out=B0)
+
+i4.round()
+A.round()
+A.round(out=B2)
+
+i4.repeat(1)
+A.repeat(1)
+B0.repeat(1)
+
+i4.std()
+A.std()
+A.std(axis=0)
+A.std(keepdims=True)
+A.std(out=B0.astype(np.float64))
+
+i4.sum()
+A.sum()
+A.sum(axis=0)
+A.sum(keepdims=True)
+A.sum(out=B0)
+
+i4.take(0)
+A.take(0)
+A.take([0])
+A.take(0, out=B0)
+A.take([0], out=B1)
+
+i4.var()
+A.var()
+A.var(axis=0)
+A.var(keepdims=True)
+A.var(out=B0)
+
+A.argpartition([0])
+
+A.diagonal()
+
+A.dot(1)
+A.dot(1, out=B0)
+
+A.nonzero()
+
+C.searchsorted(1)
+
+A.trace()
+A.trace(out=B0)
+
+void = cast(np.void, np.array(1, dtype=[("f", np.float64)]).take(0))
+void.setfield(10, np.float64)
+
+A.item(0)
+C.item(0)
+
+A.ravel()
+C.ravel()
+
+A.flatten()
+C.flatten()
+
+A.reshape(1)
+C.reshape(3)
+
+int(np.array(1.0, dtype=np.float64))
+int(np.array("1", dtype=np.str_))
+
+float(np.array(1.0, dtype=np.float64))
+float(np.array("1", dtype=np.str_))
+
+complex(np.array(1.0, dtype=np.float64))
+
+operator.index(np.array(1, dtype=np.int64))

.venv/Lib/site-packages/numpy/typing/tests/data/pass/ndarray_shape_manipulation.py

@@ -0,0 +1,47 @@
+import numpy as np
+
+nd1 = np.array([[1, 2], [3, 4]])
+
+# reshape
+nd1.reshape(4)
+nd1.reshape(2, 2)
+nd1.reshape((2, 2))
+
+nd1.reshape((2, 2), order="C")
+nd1.reshape(4, order="C")
+
+# resize
+nd1.resize()
+nd1.resize(4)
+nd1.resize(2, 2)
+nd1.resize((2, 2))
+
+nd1.resize((2, 2), refcheck=True)
+nd1.resize(4, refcheck=True)
+
+nd2 = np.array([[1, 2], [3, 4]])
+
+# transpose
+nd2.transpose()
+nd2.transpose(1, 0)
+nd2.transpose((1, 0))
+
+# swapaxes
+nd2.swapaxes(0, 1)
+
+# flatten
+nd2.flatten()
+nd2.flatten("C")
+
+# ravel
+nd2.ravel()
+nd2.ravel("C")
+
+# squeeze
+nd2.squeeze()
+
+nd3 = np.array([[1, 2]])
+nd3.squeeze(0)
+
+nd4 = np.array([[[1, 2]]])
+nd4.squeeze((0, 1))

.venv/Lib/site-packages/numpy/typing/tests/data/pass/numeric.py

@@ -0,0 +1,89 @@
+"""
+Tests for :mod:`numpy.core.numeric`.
+
+Does not include tests which fall under ``array_constructors``.
+
+"""
+
+from typing import List
+import numpy as np
+
+class SubClass(np.ndarray):
+    ...
+
+i8 = np.int64(1)
+
+A = np.arange(27).reshape(3, 3, 3)
+B: List[List[List[int]]] = A.tolist()
+C = np.empty((27, 27)).view(SubClass)
+
+np.count_nonzero(i8)
+np.count_nonzero(A)
+np.count_nonzero(B)
+np.count_nonzero(A, keepdims=True)
+np.count_nonzero(A, axis=0)
+
+np.isfortran(i8)
+np.isfortran(A)
+
+np.argwhere(i8)
+np.argwhere(A)
+
+np.flatnonzero(i8)
+np.flatnonzero(A)
+
+np.correlate(B[0][0], A.ravel(), mode="valid")
+np.correlate(A.ravel(), A.ravel(), mode="same")
+
+np.convolve(B[0][0], A.ravel(), mode="valid")
+np.convolve(A.ravel(), A.ravel(), mode="same")
+
+np.outer(i8, A)
+np.outer(B, A)
+np.outer(A, A)
+np.outer(A, A, out=C)
+
+np.tensordot(B, A)
+np.tensordot(A, A)
+np.tensordot(A, A, axes=0)
+np.tensordot(A, A, axes=(0, 1))
+
+np.isscalar(i8)
+np.isscalar(A)
+np.isscalar(B)
+
+np.roll(A, 1)
+np.roll(A, (1, 2))
+np.roll(B, 1)
+
+np.rollaxis(A, 0, 1)
+
+np.moveaxis(A, 0, 1)
+np.moveaxis(A, (0, 1), (1, 2))
+
+np.cross(B, A)
+np.cross(A, A)
+
+np.indices([0, 1, 2])
+np.indices([0, 1, 2], sparse=False)
+np.indices([0, 1, 2], sparse=True)
+
+np.binary_repr(1)
+
+np.base_repr(1)
+
+np.allclose(i8, A)
+np.allclose(B, A)
+np.allclose(A, A)
+
+np.isclose(i8, A)
+np.isclose(B, A)
+np.isclose(A, A)
+
+np.array_equal(i8, A)
+np.array_equal(B, A)
+np.array_equal(A, A)
+
+np.array_equiv(i8, A)
+np.array_equiv(B, A)
+np.array_equiv(A, A)

.venv/Lib/site-packages/numpy/typing/tests/data/pass/numerictypes.py

@@ -0,0 +1,47 @@
+import numpy as np
+
+np.maximum_sctype("S8")
+np.maximum_sctype(object)
+
+np.issctype(object)
+np.issctype("S8")
+
+np.obj2sctype(list)
+np.obj2sctype(list, default=None)
+np.obj2sctype(list, default=np.string_)
+
+np.issubclass_(np.int32, int)
+np.issubclass_(np.float64, float)
+np.issubclass_(np.float64, (int, float))
+
+np.issubsctype("int64", int)
+np.issubsctype(np.array([1]), np.array([1]))
+
+np.issubdtype("S1", np.string_)
+np.issubdtype(np.float64, np.float32)
+
+np.sctype2char("S1")
+np.sctype2char(list)
+
+np.find_common_type([], [np.int64, np.float32, complex])
+np.find_common_type((), (np.int64, np.float32, complex))
+np.find_common_type([np.int64, np.float32], [])
+np.find_common_type([np.float32], [np.int64, np.float64])
+
+np.cast[int]
+np.cast["i8"]
+np.cast[np.int64]
+
+np.nbytes[int]
+np.nbytes["i8"]
+np.nbytes[np.int64]
+
+np.ScalarType
+np.ScalarType[0]
+np.ScalarType[4]
+np.ScalarType[9]
+np.ScalarType[11]
+
+np.typecodes["Character"]
+np.typecodes["Complex"]
+np.typecodes["All"]

.venv/Lib/site-packages/numpy/typing/tests/data/pass/scalars.py

@@ -0,0 +1,254 @@
+import sys
+import datetime as dt
+
+import pytest
+import numpy as np
+
+b =  np.bool_()
+u8 = np.uint64()
+i8 = np.int64()
+f8 = np.float64()
+c16 = np.complex128()
+U = np.str_()
+S = np.bytes_()
+
+
+# Construction
+class D:
+    def __index__(self) -> int:
+        return 0
+
+
+class C:
+    def __complex__(self) -> complex:
+        return 3j
+
+
+class B:
+    def __int__(self) -> int:
+        return 4
+
+
+class A:
+    def __float__(self) -> float:
+        return 4.0
+
+
+np.complex64(3j)
+np.complex64(A())
+np.complex64(C())
+np.complex128(3j)
+np.complex128(C())
+np.complex128(None)
+np.complex64("1.2")
+np.complex128(b"2j")
+
+np.int8(4)
+np.int16(3.4)
+np.int32(4)
+np.int64(-1)
+np.uint8(B())
+np.uint32()
+np.int32("1")
+np.int64(b"2")
+
+np.float16(A())
+np.float32(16)
+np.float64(3.0)
+np.float64(None)
+np.float32("1")
+np.float16(b"2.5")
+
+if sys.version_info >= (3, 8):
+    np.uint64(D())
+    np.float32(D())
+    np.complex64(D())
+
+np.bytes_(b"hello")
+np.bytes_("hello", 'utf-8')
+np.bytes_("hello", encoding='utf-8')
+np.str_("hello")
+np.str_(b"hello", 'utf-8')
+np.str_(b"hello", encoding='utf-8')
+
+# Array-ish semantics
+np.int8().real
+np.int16().imag
+np.int32().data
+np.int64().flags
+
+np.uint8().itemsize * 2
+np.uint16().ndim + 1
+np.uint32().strides
+np.uint64().shape
+
+# Time structures
+np.datetime64()
+np.datetime64(0, "D")
+np.datetime64(0, b"D")
+np.datetime64(0, ('ms', 3))
+np.datetime64("2019")
+np.datetime64(b"2019")
+np.datetime64("2019", "D")
+np.datetime64(np.datetime64())
+np.datetime64(dt.datetime(2000, 5, 3))
+np.datetime64(dt.date(2000, 5, 3))
+np.datetime64(None)
+np.datetime64(None, "D")
+
+np.timedelta64()
+np.timedelta64(0)
+np.timedelta64(0, "D")
+np.timedelta64(0, ('ms', 3))
+np.timedelta64(0, b"D")
+np.timedelta64("3")
+np.timedelta64(b"5")
+np.timedelta64(np.timedelta64(2))
+np.timedelta64(dt.timedelta(2))
+np.timedelta64(None)
+np.timedelta64(None, "D")
+
+np.void(1)
+np.void(np.int64(1))
+np.void(True)
+np.void(np.bool_(True))
+np.void(b"test")
+np.void(np.bytes_("test"))
+
+# Protocols
+i8 = np.int64()
+u8 = np.uint64()
+f8 = np.float64()
+c16 = np.complex128()
+b_ = np.bool_()
+td = np.timedelta64()
+U = np.str_("1")
+S = np.bytes_("1")
+AR = np.array(1, dtype=np.float64)
+
+int(i8)
+int(u8)
+int(f8)
+int(b_)
+int(td)
+int(U)
+int(S)
+int(AR)
+with pytest.warns(np.ComplexWarning):
+    int(c16)
+
+float(i8)
+float(u8)
+float(f8)
+float(b_)
+float(td)
+float(U)
+float(S)
+float(AR)
+with pytest.warns(np.ComplexWarning):
+    float(c16)
+
+complex(i8)
+complex(u8)
+complex(f8)
+complex(c16)
+complex(b_)
+complex(td)
+complex(U)
+complex(AR)
+
+
+# Misc
+c16.dtype
+c16.real
+c16.imag
+c16.real.real
+c16.real.imag
+c16.ndim
+c16.size
+c16.itemsize
+c16.shape
+c16.strides
+c16.squeeze()
+c16.byteswap()
+c16.transpose()
+
+# Aliases
+np.str0()
+np.bool8()
+np.bytes0()
+np.string_()
+np.object0()
+np.void0(0)
+
+np.byte()
+np.short()
+np.intc()
+np.intp()
+np.int0()
+np.int_()
+np.longlong()
+
+np.ubyte()
+np.ushort()
+np.uintc()
+np.uintp()
+np.uint0()
+np.uint()
+np.ulonglong()
+
+np.half()
+np.single()
+np.double()
+np.float_()
+np.longdouble()
+np.longfloat()
+
+np.csingle()
+np.singlecomplex()
+np.cdouble()
+np.complex_()
+np.cfloat()
+np.clongdouble()
+np.clongfloat()
+np.longcomplex()
+
+b.item()
+i8.item()
+u8.item()
+f8.item()
+c16.item()
+U.item()
+S.item()
+
+b.tolist()
+i8.tolist()
+u8.tolist()
+f8.tolist()
+c16.tolist()
+U.tolist()
+S.tolist()
+
+b.ravel()
+i8.ravel()
+u8.ravel()
+f8.ravel()
+c16.ravel()
+U.ravel()
+S.ravel()
+
+b.flatten()
+i8.flatten()
+u8.flatten()
+f8.flatten()
+c16.flatten()
+U.flatten()
+S.flatten()
+
+b.reshape(1)
+i8.reshape(1)
+u8.reshape(1)
+f8.reshape(1)
+c16.reshape(1)
+U.reshape(1)
+S.reshape(1)

.venv/Lib/site-packages/numpy/typing/tests/data/pass/simple.py

@@ -0,0 +1,165 @@
+"""Simple expression that should pass with mypy."""
+import operator
+
+import numpy as np
+from typing import Iterable  # noqa: F401
+
+# Basic checks
+array = np.array([1, 2])
+
+
+def ndarray_func(x):
+    # type: (np.ndarray) -> np.ndarray
+    return x
+
+
+ndarray_func(np.array([1, 2]))
+array == 1
+array.dtype == float
+
+# Dtype construction
+np.dtype(float)
+np.dtype(np.float64)
+np.dtype(None)
+np.dtype("float64")
+np.dtype(np.dtype(float))
+np.dtype(("U", 10))
+np.dtype((np.int32, (2, 2)))
+# Define the arguments on the previous line to prevent bidirectional
+# type inference in mypy from broadening the types.
+two_tuples_dtype = [("R", "u1"), ("G", "u1"), ("B", "u1")]
+np.dtype(two_tuples_dtype)
+
+three_tuples_dtype = [("R", "u1", 2)]
+np.dtype(three_tuples_dtype)
+
+mixed_tuples_dtype = [("R", "u1"), ("G", np.unicode_, 1)]
+np.dtype(mixed_tuples_dtype)
+
+shape_tuple_dtype = [("R", "u1", (2, 2))]
+np.dtype(shape_tuple_dtype)
+
+shape_like_dtype = [("R", "u1", (2, 2)), ("G", np.unicode_, 1)]
+np.dtype(shape_like_dtype)
+
+object_dtype = [("field1", object)]
+np.dtype(object_dtype)
+
+np.dtype((np.int32, (np.int8, 4)))
+
+# Dtype comparison
+np.dtype(float) == float
+np.dtype(float) != np.float64
+np.dtype(float) < None
+np.dtype(float) <= "float64"
+np.dtype(float) > np.dtype(float)
+np.dtype(float) >= np.dtype(("U", 10))
+
+# Iteration and indexing
+def iterable_func(x):
+    # type: (Iterable) -> Iterable
+    return x
+
+
+iterable_func(array)
+[element for element in array]
+iter(array)
+zip(array, array)
+array[1]
+array[:]
+array[...]
+array[:] = 0
+
+array_2d = np.ones((3, 3))
+array_2d[:2, :2]
+array_2d[..., 0]
+array_2d[:2, :2] = 0
+
+# Other special methods
+len(array)
+str(array)
+array_scalar = np.array(1)
+int(array_scalar)
+float(array_scalar)
+# currently does not work due to https://github.com/python/typeshed/issues/1904
+# complex(array_scalar)
+bytes(array_scalar)
+operator.index(array_scalar)
+bool(array_scalar)
+
+# comparisons
+array < 1
+array <= 1
+array == 1
+array != 1
+array > 1
+array >= 1
+1 < array
+1 <= array
+1 == array
+1 != array
+1 > array
+1 >= array
+
+# binary arithmetic
+array + 1
+1 + array
+array += 1
+
+array - 1
+1 - array
+array -= 1
+
+array * 1
+1 * array
+array *= 1
+
+nonzero_array = np.array([1, 2])
+array / 1
+1 / nonzero_array
+float_array = np.array([1.0, 2.0])
+float_array /= 1
+
+array // 1
+1 // nonzero_array
+array //= 1
+
+array % 1
+1 % nonzero_array
+array %= 1
+
+divmod(array, 1)
+divmod(1, nonzero_array)
+
+array ** 1
+1 ** array
+array **= 1
+
+array << 1
+1 << array
+array <<= 1
+
+array >> 1
+1 >> array
+array >>= 1
+
+array & 1
+1 & array
+array &= 1
+
+array ^ 1
+1 ^ array
+array ^= 1
+
+array | 1
+1 | array
+array |= 1
+
+# unary arithmetic
+-array
++array
+abs(array)
+~array
+
+# Other methods
+np.array([1, 2]).transpose()

.venv/Lib/site-packages/numpy/typing/tests/data/pass/simple_py3.py

@@ -0,0 +1,6 @@
+import numpy as np
+
+array = np.array([1, 2])
+
+# The @ operator is not in python 2
+array @ array

.venv/Lib/site-packages/numpy/typing/tests/data/pass/ufunc_config.py

@@ -0,0 +1,50 @@
+"""Typing tests for `numpy.core._ufunc_config`."""
+
+import numpy as np
+
+def func1(a: str, b: int) -> None: ...
+def func2(a: str, b: int, c: float = ...) -> None: ...
+def func3(a: str, b: int) -> int: ...
+
+class Write1:
+    def write(self, a: str) -> None: ...
+
+class Write2:
+    def write(self, a: str, b: int = ...) -> None: ...
+
+class Write3:
+    def write(self, a: str) -> int: ...
+
+
+_err_default = np.geterr()
+_bufsize_default = np.getbufsize()
+_errcall_default = np.geterrcall()
+
+try:
+    np.seterr(all=None)
+    np.seterr(divide="ignore")
+    np.seterr(over="warn")
+    np.seterr(under="call")
+    np.seterr(invalid="raise")
+    np.geterr()
+
+    np.setbufsize(4096)
+    np.getbufsize()
+
+    np.seterrcall(func1)
+    np.seterrcall(func2)
+    np.seterrcall(func3)
+    np.seterrcall(Write1())
+    np.seterrcall(Write2())
+    np.seterrcall(Write3())
+    np.geterrcall()
+
+    with np.errstate(call=func1, all="call"):
+        pass
+    with np.errstate(call=Write1(), divide="log", over="log"):
+        pass
+
+finally:
+    np.seterr(**_err_default)
+    np.setbufsize(_bufsize_default)
+    np.seterrcall(_errcall_default)

.venv/Lib/site-packages/numpy/typing/tests/data/pass/ufunclike.py

@@ -0,0 +1,46 @@
+from __future__ import annotations
+from typing import Any
+import numpy as np
+
+
+class Object:
+    def __ceil__(self) -> Object:
+        return self
+
+    def __floor__(self) -> Object:
+        return self
+
+    def __ge__(self, value: object) -> bool:
+        return True
+
+    def __array__(self) -> np.ndarray[Any, np.dtype[np.object_]]:
+        ret = np.empty((), dtype=object)
+        ret[()] = self
+        return ret
+
+
+AR_LIKE_b = [True, True, False]
+AR_LIKE_u = [np.uint32(1), np.uint32(2), np.uint32(3)]
+AR_LIKE_i = [1, 2, 3]
+AR_LIKE_f = [1.0, 2.0, 3.0]
+AR_LIKE_O = [Object(), Object(), Object()]
+AR_U: np.ndarray[Any, np.dtype[np.str_]] = np.zeros(3, dtype="U5")
+
+np.fix(AR_LIKE_b)
+np.fix(AR_LIKE_u)
+np.fix(AR_LIKE_i)
+np.fix(AR_LIKE_f)
+np.fix(AR_LIKE_O)
+np.fix(AR_LIKE_f, out=AR_U)
+
+np.isposinf(AR_LIKE_b)
+np.isposinf(AR_LIKE_u)
+np.isposinf(AR_LIKE_i)
+np.isposinf(AR_LIKE_f)
+np.isposinf(AR_LIKE_f, out=AR_U)
+
+np.isneginf(AR_LIKE_b)
+np.isneginf(AR_LIKE_u)
+np.isneginf(AR_LIKE_i)
+np.isneginf(AR_LIKE_f)
+np.isneginf(AR_LIKE_f, out=AR_U)

.venv/Lib/site-packages/numpy/typing/tests/data/pass/ufuncs.py

@@ -0,0 +1,17 @@
+import numpy as np
+
+np.sin(1)
+np.sin([1, 2, 3])
+np.sin(1, out=np.empty(1))
+np.matmul(np.ones((2, 2, 2)), np.ones((2, 2, 2)), axes=[(0, 1), (0, 1), (0, 1)])
+np.sin(1, signature="D->D")
+np.sin(1, extobj=[16, 1, lambda: None])
+# NOTE: `np.generic` subclasses are not guaranteed to support addition;
+# re-enable this we can infer the exact return type of `np.sin(...)`.
+#
+# np.sin(1) + np.sin(1)
+np.sin.types[0]
+np.sin.__name__
+np.sin.__doc__
+
+np.abs(np.array([1]))

.venv/Lib/site-packages/numpy/typing/tests/data/pass/warnings_and_errors.py

@@ -0,0 +1,6 @@
+import numpy as np
+
+np.AxisError("test")
+np.AxisError(1, ndim=2)
+np.AxisError(1, ndim=2, msg_prefix="error")
+np.AxisError(1, ndim=2, msg_prefix=None)

.venv/Lib/site-packages/numpy/typing/tests/data/reveal/arithmetic.pyi

@@ -0,0 +1,522 @@
+from typing import Any, List
+import numpy as np
+import numpy.typing as npt
+
+# Can't directly import `np.float128` as it is not available on all platforms
+f16: np.floating[npt._128Bit]
+
+c16 = np.complex128()
+f8 = np.float64()
+i8 = np.int64()
+u8 = np.uint64()
+
+c8 = np.complex64()
+f4 = np.float32()
+i4 = np.int32()
+u4 = np.uint32()
+
+dt = np.datetime64(0, "D")
+td = np.timedelta64(0, "D")
+
+b_ = np.bool_()
+
+b = bool()
+c = complex()
+f = float()
+i = int()
+
+AR_b: np.ndarray[Any, np.dtype[np.bool_]]
+AR_u: np.ndarray[Any, np.dtype[np.uint32]]
+AR_i: np.ndarray[Any, np.dtype[np.int64]]
+AR_f: np.ndarray[Any, np.dtype[np.float64]]
+AR_c: np.ndarray[Any, np.dtype[np.complex128]]
+AR_m: np.ndarray[Any, np.dtype[np.timedelta64]]
+AR_M: np.ndarray[Any, np.dtype[np.datetime64]]
+AR_O: np.ndarray[Any, np.dtype[np.object_]]
+
+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]
+AR_LIKE_O: List[np.object_]
+
+# Array subtraction
+
+reveal_type(AR_b - AR_LIKE_u)  # E: ndarray[Any, dtype[unsignedinteger[Any]]]
+reveal_type(AR_b - AR_LIKE_i)  # E: ndarray[Any, dtype[signedinteger[Any]]]
+reveal_type(AR_b - AR_LIKE_f)  # E: ndarray[Any, dtype[floating[Any]]]
+reveal_type(AR_b - AR_LIKE_c)  # E: ndarray[Any, dtype[complexfloating[Any, Any]]]
+reveal_type(AR_b - AR_LIKE_m)  # E: ndarray[Any, dtype[timedelta64]]
+reveal_type(AR_b - AR_LIKE_O)  # E: Any
+
+reveal_type(AR_LIKE_u - AR_b)  # E: ndarray[Any, dtype[unsignedinteger[Any]]]
+reveal_type(AR_LIKE_i - AR_b)  # E: ndarray[Any, dtype[signedinteger[Any]]]
+reveal_type(AR_LIKE_f - AR_b)  # E: ndarray[Any, dtype[floating[Any]]]
+reveal_type(AR_LIKE_c - AR_b)  # E: ndarray[Any, dtype[complexfloating[Any, Any]]]
+reveal_type(AR_LIKE_m - AR_b)  # E: ndarray[Any, dtype[timedelta64]]
+reveal_type(AR_LIKE_M - AR_b)  # E: ndarray[Any, dtype[datetime64]]
+reveal_type(AR_LIKE_O - AR_b)  # E: Any
+
+reveal_type(AR_u - AR_LIKE_b)  # E: ndarray[Any, dtype[unsignedinteger[Any]]]
+reveal_type(AR_u - AR_LIKE_u)  # E: ndarray[Any, dtype[unsignedinteger[Any]]]
+reveal_type(AR_u - AR_LIKE_i)  # E: ndarray[Any, dtype[signedinteger[Any]]]
+reveal_type(AR_u - AR_LIKE_f)  # E: ndarray[Any, dtype[floating[Any]]]
+reveal_type(AR_u - AR_LIKE_c)  # E: ndarray[Any, dtype[complexfloating[Any, Any]]]
+reveal_type(AR_u - AR_LIKE_m)  # E: ndarray[Any, dtype[timedelta64]]
+reveal_type(AR_u - AR_LIKE_O)  # E: Any
+
+reveal_type(AR_LIKE_b - AR_u)  # E: ndarray[Any, dtype[unsignedinteger[Any]]]
+reveal_type(AR_LIKE_u - AR_u)  # E: ndarray[Any, dtype[unsignedinteger[Any]]]
+reveal_type(AR_LIKE_i - AR_u)  # E: ndarray[Any, dtype[signedinteger[Any]]]
+reveal_type(AR_LIKE_f - AR_u)  # E: ndarray[Any, dtype[floating[Any]]]
+reveal_type(AR_LIKE_c - AR_u)  # E: ndarray[Any, dtype[complexfloating[Any, Any]]]
+reveal_type(AR_LIKE_m - AR_u)  # E: ndarray[Any, dtype[timedelta64]]
+reveal_type(AR_LIKE_M - AR_u)  # E: ndarray[Any, dtype[datetime64]]
+reveal_type(AR_LIKE_O - AR_u)  # E: Any
+
+reveal_type(AR_i - AR_LIKE_b)  # E: ndarray[Any, dtype[signedinteger[Any]]]
+reveal_type(AR_i - AR_LIKE_u)  # E: ndarray[Any, dtype[signedinteger[Any]]]
+reveal_type(AR_i - AR_LIKE_i)  # E: ndarray[Any, dtype[signedinteger[Any]]]
+reveal_type(AR_i - AR_LIKE_f)  # E: ndarray[Any, dtype[floating[Any]]]
+reveal_type(AR_i - AR_LIKE_c)  # E: ndarray[Any, dtype[complexfloating[Any, Any]]]
+reveal_type(AR_i - AR_LIKE_m)  # E: ndarray[Any, dtype[timedelta64]]
+reveal_type(AR_i - AR_LIKE_O)  # E: Any
+
+reveal_type(AR_LIKE_b - AR_i)  # E: ndarray[Any, dtype[signedinteger[Any]]]
+reveal_type(AR_LIKE_u - AR_i)  # E: ndarray[Any, dtype[signedinteger[Any]]]
+reveal_type(AR_LIKE_i - AR_i)  # E: ndarray[Any, dtype[signedinteger[Any]]]
+reveal_type(AR_LIKE_f - AR_i)  # E: ndarray[Any, dtype[floating[Any]]]
+reveal_type(AR_LIKE_c - AR_i)  # E: ndarray[Any, dtype[complexfloating[Any, Any]]]
+reveal_type(AR_LIKE_m - AR_i)  # E: ndarray[Any, dtype[timedelta64]]
+reveal_type(AR_LIKE_M - AR_i)  # E: ndarray[Any, dtype[datetime64]]
+reveal_type(AR_LIKE_O - AR_i)  # E: Any
+
+reveal_type(AR_f - AR_LIKE_b)  # E: ndarray[Any, dtype[floating[Any]]]
+reveal_type(AR_f - AR_LIKE_u)  # E: ndarray[Any, dtype[floating[Any]]]
+reveal_type(AR_f - AR_LIKE_i)  # E: ndarray[Any, dtype[floating[Any]]]
+reveal_type(AR_f - AR_LIKE_f)  # E: ndarray[Any, dtype[floating[Any]]]
+reveal_type(AR_f - AR_LIKE_c)  # E: ndarray[Any, dtype[complexfloating[Any, Any]]]
+reveal_type(AR_f - AR_LIKE_O)  # E: Any
+
+reveal_type(AR_LIKE_b - AR_f)  # E: ndarray[Any, dtype[floating[Any]]]
+reveal_type(AR_LIKE_u - AR_f)  # E: ndarray[Any, dtype[floating[Any]]]
+reveal_type(AR_LIKE_i - AR_f)  # E: ndarray[Any, dtype[floating[Any]]]
+reveal_type(AR_LIKE_f - AR_f)  # E: ndarray[Any, dtype[floating[Any]]]
+reveal_type(AR_LIKE_c - AR_f)  # E: ndarray[Any, dtype[complexfloating[Any, Any]]]
+reveal_type(AR_LIKE_O - AR_f)  # E: Any
+
+reveal_type(AR_c - AR_LIKE_b)  # E: ndarray[Any, dtype[complexfloating[Any, Any]]]
+reveal_type(AR_c - AR_LIKE_u)  # E: ndarray[Any, dtype[complexfloating[Any, Any]]]
+reveal_type(AR_c - AR_LIKE_i)  # E: ndarray[Any, dtype[complexfloating[Any, Any]]]
+reveal_type(AR_c - AR_LIKE_f)  # E: ndarray[Any, dtype[complexfloating[Any, Any]]]
+reveal_type(AR_c - AR_LIKE_c)  # E: ndarray[Any, dtype[complexfloating[Any, Any]]]
+reveal_type(AR_c - AR_LIKE_O)  # E: Any
+
+reveal_type(AR_LIKE_b - AR_c)  # E: ndarray[Any, dtype[complexfloating[Any, Any]]]
+reveal_type(AR_LIKE_u - AR_c)  # E: ndarray[Any, dtype[complexfloating[Any, Any]]]
+reveal_type(AR_LIKE_i - AR_c)  # E: ndarray[Any, dtype[complexfloating[Any, Any]]]
+reveal_type(AR_LIKE_f - AR_c)  # E: ndarray[Any, dtype[complexfloating[Any, Any]]]
+reveal_type(AR_LIKE_c - AR_c)  # E: ndarray[Any, dtype[complexfloating[Any, Any]]]
+reveal_type(AR_LIKE_O - AR_c)  # E: Any
+
+reveal_type(AR_m - AR_LIKE_b)  # E: ndarray[Any, dtype[timedelta64]]
+reveal_type(AR_m - AR_LIKE_u)  # E: ndarray[Any, dtype[timedelta64]]
+reveal_type(AR_m - AR_LIKE_i)  # E: ndarray[Any, dtype[timedelta64]]
+reveal_type(AR_m - AR_LIKE_m)  # E: ndarray[Any, dtype[timedelta64]]
+reveal_type(AR_m - AR_LIKE_O)  # E: Any
+
+reveal_type(AR_LIKE_b - AR_m)  # E: ndarray[Any, dtype[timedelta64]]
+reveal_type(AR_LIKE_u - AR_m)  # E: ndarray[Any, dtype[timedelta64]]
+reveal_type(AR_LIKE_i - AR_m)  # E: ndarray[Any, dtype[timedelta64]]
+reveal_type(AR_LIKE_m - AR_m)  # E: ndarray[Any, dtype[timedelta64]]
+reveal_type(AR_LIKE_M - AR_m)  # E: ndarray[Any, dtype[datetime64]]
+reveal_type(AR_LIKE_O - AR_m)  # E: Any
+
+reveal_type(AR_M - AR_LIKE_b)  # E: ndarray[Any, dtype[datetime64]]
+reveal_type(AR_M - AR_LIKE_u)  # E: ndarray[Any, dtype[datetime64]]
+reveal_type(AR_M - AR_LIKE_i)  # E: ndarray[Any, dtype[datetime64]]
+reveal_type(AR_M - AR_LIKE_m)  # E: ndarray[Any, dtype[datetime64]]
+reveal_type(AR_M - AR_LIKE_M)  # E: ndarray[Any, dtype[timedelta64]]
+reveal_type(AR_M - AR_LIKE_O)  # E: Any
+
+reveal_type(AR_LIKE_M - AR_M)  # E: ndarray[Any, dtype[timedelta64]]
+reveal_type(AR_LIKE_O - AR_M)  # E: Any
+
+reveal_type(AR_O - AR_LIKE_b)  # E: Any
+reveal_type(AR_O - AR_LIKE_u)  # E: Any
+reveal_type(AR_O - AR_LIKE_i)  # E: Any
+reveal_type(AR_O - AR_LIKE_f)  # E: Any
+reveal_type(AR_O - AR_LIKE_c)  # E: Any
+reveal_type(AR_O - AR_LIKE_m)  # E: Any
+reveal_type(AR_O - AR_LIKE_M)  # E: Any
+reveal_type(AR_O - AR_LIKE_O)  # E: Any
+
+reveal_type(AR_LIKE_b - AR_O)  # E: Any
+reveal_type(AR_LIKE_u - AR_O)  # E: Any
+reveal_type(AR_LIKE_i - AR_O)  # E: Any
+reveal_type(AR_LIKE_f - AR_O)  # E: Any
+reveal_type(AR_LIKE_c - AR_O)  # E: Any
+reveal_type(AR_LIKE_m - AR_O)  # E: Any
+reveal_type(AR_LIKE_M - AR_O)  # E: Any
+reveal_type(AR_LIKE_O - AR_O)  # E: Any
+
+# Array floor division
+
+reveal_type(AR_b // AR_LIKE_b)  # E: ndarray[Any, dtype[{int8}]]
+reveal_type(AR_b // AR_LIKE_u)  # E: ndarray[Any, dtype[unsignedinteger[Any]]]
+reveal_type(AR_b // AR_LIKE_i)  # E: ndarray[Any, dtype[signedinteger[Any]]]
+reveal_type(AR_b // AR_LIKE_f)  # E: ndarray[Any, dtype[floating[Any]]]
+reveal_type(AR_b // AR_LIKE_O)  # E: Any
+
+reveal_type(AR_LIKE_b // AR_b)  # E: ndarray[Any, dtype[{int8}]]
+reveal_type(AR_LIKE_u // AR_b)  # E: ndarray[Any, dtype[unsignedinteger[Any]]]
+reveal_type(AR_LIKE_i // AR_b)  # E: ndarray[Any, dtype[signedinteger[Any]]]
+reveal_type(AR_LIKE_f // AR_b)  # E: ndarray[Any, dtype[floating[Any]]]
+reveal_type(AR_LIKE_O // AR_b)  # E: Any
+
+reveal_type(AR_u // AR_LIKE_b)  # E: ndarray[Any, dtype[unsignedinteger[Any]]]
+reveal_type(AR_u // AR_LIKE_u)  # E: ndarray[Any, dtype[unsignedinteger[Any]]]
+reveal_type(AR_u // AR_LIKE_i)  # E: ndarray[Any, dtype[signedinteger[Any]]]
+reveal_type(AR_u // AR_LIKE_f)  # E: ndarray[Any, dtype[floating[Any]]]
+reveal_type(AR_u // AR_LIKE_O)  # E: Any
+
+reveal_type(AR_LIKE_b // AR_u)  # E: ndarray[Any, dtype[unsignedinteger[Any]]]
+reveal_type(AR_LIKE_u // AR_u)  # E: ndarray[Any, dtype[unsignedinteger[Any]]]
+reveal_type(AR_LIKE_i // AR_u)  # E: ndarray[Any, dtype[signedinteger[Any]]]
+reveal_type(AR_LIKE_f // AR_u)  # E: ndarray[Any, dtype[floating[Any]]]
+reveal_type(AR_LIKE_m // AR_u)  # E: ndarray[Any, dtype[timedelta64]]
+reveal_type(AR_LIKE_O // AR_u)  # E: Any
+
+reveal_type(AR_i // AR_LIKE_b)  # E: ndarray[Any, dtype[signedinteger[Any]]]
+reveal_type(AR_i // AR_LIKE_u)  # E: ndarray[Any, dtype[signedinteger[Any]]]
+reveal_type(AR_i // AR_LIKE_i)  # E: ndarray[Any, dtype[signedinteger[Any]]]
+reveal_type(AR_i // AR_LIKE_f)  # E: ndarray[Any, dtype[floating[Any]]]
+reveal_type(AR_i // AR_LIKE_O)  # E: Any
+
+reveal_type(AR_LIKE_b // AR_i)  # E: ndarray[Any, dtype[signedinteger[Any]]]
+reveal_type(AR_LIKE_u // AR_i)  # E: ndarray[Any, dtype[signedinteger[Any]]]
+reveal_type(AR_LIKE_i // AR_i)  # E: ndarray[Any, dtype[signedinteger[Any]]]
+reveal_type(AR_LIKE_f // AR_i)  # E: ndarray[Any, dtype[floating[Any]]]
+reveal_type(AR_LIKE_m // AR_i)  # E: ndarray[Any, dtype[timedelta64]]
+reveal_type(AR_LIKE_O // AR_i)  # E: Any
+
+reveal_type(AR_f // AR_LIKE_b)  # E: ndarray[Any, dtype[floating[Any]]]
+reveal_type(AR_f // AR_LIKE_u)  # E: ndarray[Any, dtype[floating[Any]]]
+reveal_type(AR_f // AR_LIKE_i)  # E: ndarray[Any, dtype[floating[Any]]]
+reveal_type(AR_f // AR_LIKE_f)  # E: ndarray[Any, dtype[floating[Any]]]
+reveal_type(AR_f // AR_LIKE_O)  # E: Any
+
+reveal_type(AR_LIKE_b // AR_f)  # E: ndarray[Any, dtype[floating[Any]]]
+reveal_type(AR_LIKE_u // AR_f)  # E: ndarray[Any, dtype[floating[Any]]]
+reveal_type(AR_LIKE_i // AR_f)  # E: ndarray[Any, dtype[floating[Any]]]
+reveal_type(AR_LIKE_f // AR_f)  # E: ndarray[Any, dtype[floating[Any]]]
+reveal_type(AR_LIKE_m // AR_f)  # E: ndarray[Any, dtype[timedelta64]]
+reveal_type(AR_LIKE_O // AR_f)  # E: Any
+
+reveal_type(AR_m // AR_LIKE_u)  # E: ndarray[Any, dtype[timedelta64]]
+reveal_type(AR_m // AR_LIKE_i)  # E: ndarray[Any, dtype[timedelta64]]
+reveal_type(AR_m // AR_LIKE_f)  # E: ndarray[Any, dtype[timedelta64]]
+reveal_type(AR_m // AR_LIKE_m)  # E: ndarray[Any, dtype[{int64}]]
+reveal_type(AR_m // AR_LIKE_O)  # E: Any
+
+reveal_type(AR_LIKE_m // AR_m)  # E: ndarray[Any, dtype[{int64}]]
+reveal_type(AR_LIKE_O // AR_m)  # E: Any
+
+reveal_type(AR_O // AR_LIKE_b)  # E: Any
+reveal_type(AR_O // AR_LIKE_u)  # E: Any
+reveal_type(AR_O // AR_LIKE_i)  # E: Any
+reveal_type(AR_O // AR_LIKE_f)  # E: Any
+reveal_type(AR_O // AR_LIKE_m)  # E: Any
+reveal_type(AR_O // AR_LIKE_M)  # E: Any
+reveal_type(AR_O // AR_LIKE_O)  # E: Any
+
+reveal_type(AR_LIKE_b // AR_O)  # E: Any
+reveal_type(AR_LIKE_u // AR_O)  # E: Any
+reveal_type(AR_LIKE_i // AR_O)  # E: Any
+reveal_type(AR_LIKE_f // AR_O)  # E: Any
+reveal_type(AR_LIKE_m // AR_O)  # E: Any
+reveal_type(AR_LIKE_M // AR_O)  # E: Any
+reveal_type(AR_LIKE_O // AR_O)  # E: Any
+
+# unary ops
+
+reveal_type(-f16)  # E: {float128}
+reveal_type(-c16)  # E: {complex128}
+reveal_type(-c8)  # E: {complex64}
+reveal_type(-f8)  # E: {float64}
+reveal_type(-f4)  # E: {float32}
+reveal_type(-i8)  # E: {int64}
+reveal_type(-i4)  # E: {int32}
+reveal_type(-u8)  # E: {uint64}
+reveal_type(-u4)  # E: {uint32}
+reveal_type(-td)  # E: timedelta64
+reveal_type(-AR_f)  # E: Any
+
+reveal_type(+f16)  # E: {float128}
+reveal_type(+c16)  # E: {complex128}
+reveal_type(+c8)  # E: {complex64}
+reveal_type(+f8)  # E: {float64}
+reveal_type(+f4)  # E: {float32}
+reveal_type(+i8)  # E: {int64}
+reveal_type(+i4)  # E: {int32}
+reveal_type(+u8)  # E: {uint64}
+reveal_type(+u4)  # E: {uint32}
+reveal_type(+td)  # E: timedelta64
+reveal_type(+AR_f)  # E: Any
+
+reveal_type(abs(f16))  # E: {float128}
+reveal_type(abs(c16))  # E: {float64}
+reveal_type(abs(c8))  # E: {float32}
+reveal_type(abs(f8))  # E: {float64}
+reveal_type(abs(f4))  # E: {float32}
+reveal_type(abs(i8))  # E: {int64}
+reveal_type(abs(i4))  # E: {int32}
+reveal_type(abs(u8))  # E: {uint64}
+reveal_type(abs(u4))  # E: {uint32}
+reveal_type(abs(td))  # E: timedelta64
+reveal_type(abs(b_))  # E: bool_
+reveal_type(abs(AR_f))  # E: Any
+
+# Time structures
+
+reveal_type(dt + td)  # E: datetime64
+reveal_type(dt + i)  # E: datetime64
+reveal_type(dt + i4)  # E: datetime64
+reveal_type(dt + i8)  # E: datetime64
+reveal_type(dt - dt)  # E: timedelta64
+reveal_type(dt - i)  # E: datetime64
+reveal_type(dt - i4)  # E: datetime64
+reveal_type(dt - i8)  # E: datetime64
+
+reveal_type(td + td)  # E: timedelta64
+reveal_type(td + i)  # E: timedelta64
+reveal_type(td + i4)  # E: timedelta64
+reveal_type(td + i8)  # E: timedelta64
+reveal_type(td - td)  # E: timedelta64
+reveal_type(td - i)  # E: timedelta64
+reveal_type(td - i4)  # E: timedelta64
+reveal_type(td - i8)  # E: timedelta64
+reveal_type(td / f)  # E: timedelta64
+reveal_type(td / f4)  # E: timedelta64
+reveal_type(td / f8)  # E: timedelta64
+reveal_type(td / td)  # E: {float64}
+reveal_type(td // td)  # E: {int64}
+
+# boolean
+
+reveal_type(b_ / b)  # E: {float64}
+reveal_type(b_ / b_)  # E: {float64}
+reveal_type(b_ / i)  # E: {float64}
+reveal_type(b_ / i8)  # E: {float64}
+reveal_type(b_ / i4)  # E: {float64}
+reveal_type(b_ / u8)  # E: {float64}
+reveal_type(b_ / u4)  # E: {float64}
+reveal_type(b_ / f)  # E: {float64}
+reveal_type(b_ / f16)  # E: {float128}
+reveal_type(b_ / f8)  # E: {float64}
+reveal_type(b_ / f4)  # E: {float32}
+reveal_type(b_ / c)  # E: {complex128}
+reveal_type(b_ / c16)  # E: {complex128}
+reveal_type(b_ / c8)  # E: {complex64}
+
+reveal_type(b / b_)  # E: {float64}
+reveal_type(b_ / b_)  # E: {float64}
+reveal_type(i / b_)  # E: {float64}
+reveal_type(i8 / b_)  # E: {float64}
+reveal_type(i4 / b_)  # E: {float64}
+reveal_type(u8 / b_)  # E: {float64}
+reveal_type(u4 / b_)  # E: {float64}
+reveal_type(f / b_)  # E: {float64}
+reveal_type(f16 / b_)  # E: {float128}
+reveal_type(f8 / b_)  # E: {float64}
+reveal_type(f4 / b_)  # E: {float32}
+reveal_type(c / b_)  # E: {complex128}
+reveal_type(c16 / b_)  # E: {complex128}
+reveal_type(c8 / b_)  # E: {complex64}
+
+# Complex
+
+reveal_type(c16 + f16)  # E: {complex256}
+reveal_type(c16 + c16)  # E: {complex128}
+reveal_type(c16 + f8)  # E: {complex128}
+reveal_type(c16 + i8)  # E: {complex128}
+reveal_type(c16 + c8)  # E: {complex128}
+reveal_type(c16 + f4)  # E: {complex128}
+reveal_type(c16 + i4)  # E: {complex128}
+reveal_type(c16 + b_)  # E: {complex128}
+reveal_type(c16 + b)  # E: {complex128}
+reveal_type(c16 + c)  # E: {complex128}
+reveal_type(c16 + f)  # E: {complex128}
+reveal_type(c16 + i)  # E: {complex128}
+reveal_type(c16 + AR_f)  # E: Any
+
+reveal_type(f16 + c16)  # E: {complex256}
+reveal_type(c16 + c16)  # E: {complex128}
+reveal_type(f8 + c16)  # E: {complex128}
+reveal_type(i8 + c16)  # E: {complex128}
+reveal_type(c8 + c16)  # E: {complex128}
+reveal_type(f4 + c16)  # E: {complex128}
+reveal_type(i4 + c16)  # E: {complex128}
+reveal_type(b_ + c16)  # E: {complex128}
+reveal_type(b + c16)  # E: {complex128}
+reveal_type(c + c16)  # E: {complex128}
+reveal_type(f + c16)  # E: {complex128}
+reveal_type(i + c16)  # E: {complex128}
+reveal_type(AR_f + c16)  # E: Any
+
+reveal_type(c8 + f16)  # E: {complex256}
+reveal_type(c8 + c16)  # E: {complex128}
+reveal_type(c8 + f8)  # E: {complex128}
+reveal_type(c8 + i8)  # E: {complex128}
+reveal_type(c8 + c8)  # E: {complex64}
+reveal_type(c8 + f4)  # E: {complex64}
+reveal_type(c8 + i4)  # E: {complex64}
+reveal_type(c8 + b_)  # E: {complex64}
+reveal_type(c8 + b)  # E: {complex64}
+reveal_type(c8 + c)  # E: {complex128}
+reveal_type(c8 + f)  # E: {complex128}
+reveal_type(c8 + i)  # E: complexfloating[{_NBitInt}, {_NBitInt}]
+reveal_type(c8 + AR_f)  # E: Any
+
+reveal_type(f16 + c8)  # E: {complex256}
+reveal_type(c16 + c8)  # E: {complex128}
+reveal_type(f8 + c8)  # E: {complex128}
+reveal_type(i8 + c8)  # E: {complex128}
+reveal_type(c8 + c8)  # E: {complex64}
+reveal_type(f4 + c8)  # E: {complex64}
+reveal_type(i4 + c8)  # E: {complex64}
+reveal_type(b_ + c8)  # E: {complex64}
+reveal_type(b + c8)  # E: {complex64}
+reveal_type(c + c8)  # E: {complex128}
+reveal_type(f + c8)  # E: {complex128}
+reveal_type(i + c8)  # E: complexfloating[{_NBitInt}, {_NBitInt}]
+reveal_type(AR_f + c8)  # E: Any
+
+# Float
+
+reveal_type(f8 + f16)  # E: {float128}
+reveal_type(f8 + f8)  # E: {float64}
+reveal_type(f8 + i8)  # E: {float64}
+reveal_type(f8 + f4)  # E: {float64}
+reveal_type(f8 + i4)  # E: {float64}
+reveal_type(f8 + b_)  # E: {float64}
+reveal_type(f8 + b)  # E: {float64}
+reveal_type(f8 + c)  # E: {complex128}
+reveal_type(f8 + f)  # E: {float64}
+reveal_type(f8 + i)  # E: {float64}
+reveal_type(f8 + AR_f)  # E: Any
+
+reveal_type(f16 + f8)  # E: {float128}
+reveal_type(f8 + f8)  # E: {float64}
+reveal_type(i8 + f8)  # E: {float64}
+reveal_type(f4 + f8)  # E: {float64}
+reveal_type(i4 + f8)  # E: {float64}
+reveal_type(b_ + f8)  # E: {float64}
+reveal_type(b + f8)  # E: {float64}
+reveal_type(c + f8)  # E: {complex128}
+reveal_type(f + f8)  # E: {float64}
+reveal_type(i + f8)  # E: {float64}
+reveal_type(AR_f + f8)  # E: Any
+
+reveal_type(f4 + f16)  # E: {float128}
+reveal_type(f4 + f8)  # E: {float64}
+reveal_type(f4 + i8)  # E: {float64}
+reveal_type(f4 + f4)  # E: {float32}
+reveal_type(f4 + i4)  # E: {float32}
+reveal_type(f4 + b_)  # E: {float32}
+reveal_type(f4 + b)  # E: {float32}
+reveal_type(f4 + c)  # E: {complex128}
+reveal_type(f4 + f)  # E: {float64}
+reveal_type(f4 + i)  # E: floating[{_NBitInt}]
+reveal_type(f4 + AR_f)  # E: Any
+
+reveal_type(f16 + f4)  # E: {float128}
+reveal_type(f8 + f4)  # E: {float64}
+reveal_type(i8 + f4)  # E: {float64}
+reveal_type(f4 + f4)  # E: {float32}
+reveal_type(i4 + f4)  # E: {float32}
+reveal_type(b_ + f4)  # E: {float32}
+reveal_type(b + f4)  # E: {float32}
+reveal_type(c + f4)  # E: {complex128}
+reveal_type(f + f4)  # E: {float64}
+reveal_type(i + f4)  # E: floating[{_NBitInt}]
+reveal_type(AR_f + f4)  # E: Any
+
+# Int
+
+reveal_type(i8 + i8)  # E: {int64}
+reveal_type(i8 + u8)  # E: Any
+reveal_type(i8 + i4)  # E: {int64}
+reveal_type(i8 + u4)  # E: Any
+reveal_type(i8 + b_)  # E: {int64}
+reveal_type(i8 + b)  # E: {int64}
+reveal_type(i8 + c)  # E: {complex128}
+reveal_type(i8 + f)  # E: {float64}
+reveal_type(i8 + i)  # E: {int64}
+reveal_type(i8 + AR_f)  # E: Any
+
+reveal_type(u8 + u8)  # E: {uint64}
+reveal_type(u8 + i4)  # E: Any
+reveal_type(u8 + u4)  # E: {uint64}
+reveal_type(u8 + b_)  # E: {uint64}
+reveal_type(u8 + b)  # E: {uint64}
+reveal_type(u8 + c)  # E: {complex128}
+reveal_type(u8 + f)  # E: {float64}
+reveal_type(u8 + i)  # E: Any
+reveal_type(u8 + AR_f)  # E: Any
+
+reveal_type(i8 + i8)  # E: {int64}
+reveal_type(u8 + i8)  # E: Any
+reveal_type(i4 + i8)  # E: {int64}
+reveal_type(u4 + i8)  # E: Any
+reveal_type(b_ + i8)  # E: {int64}
+reveal_type(b + i8)  # E: {int64}
+reveal_type(c + i8)  # E: {complex128}
+reveal_type(f + i8)  # E: {float64}
+reveal_type(i + i8)  # E: {int64}
+reveal_type(AR_f + i8)  # E: Any
+
+reveal_type(u8 + u8)  # E: {uint64}
+reveal_type(i4 + u8)  # E: Any
+reveal_type(u4 + u8)  # E: {uint64}
+reveal_type(b_ + u8)  # E: {uint64}
+reveal_type(b + u8)  # E: {uint64}
+reveal_type(c + u8)  # E: {complex128}
+reveal_type(f + u8)  # E: {float64}
+reveal_type(i + u8)  # E: Any
+reveal_type(AR_f + u8)  # E: Any
+
+reveal_type(i4 + i8)  # E: {int64}
+reveal_type(i4 + i4)  # E: {int32}
+reveal_type(i4 + i)  # E: {int_}
+reveal_type(i4 + b_)  # E: {int32}
+reveal_type(i4 + b)  # E: {int32}
+reveal_type(i4 + AR_f)  # E: Any
+
+reveal_type(u4 + i8)  # E: Any
+reveal_type(u4 + i4)  # E: Any
+reveal_type(u4 + u8)  # E: {uint64}
+reveal_type(u4 + u4)  # E: {uint32}
+reveal_type(u4 + i)  # E: Any
+reveal_type(u4 + b_)  # E: {uint32}
+reveal_type(u4 + b)  # E: {uint32}
+reveal_type(u4 + AR_f)  # E: Any
+
+reveal_type(i8 + i4)  # E: {int64}
+reveal_type(i4 + i4)  # E: {int32}
+reveal_type(i + i4)  # E: {int_}
+reveal_type(b_ + i4)  # E: {int32}
+reveal_type(b + i4)  # E: {int32}
+reveal_type(AR_f + i4)  # E: Any
+
+reveal_type(i8 + u4)  # E: Any
+reveal_type(i4 + u4)  # E: Any
+reveal_type(u8 + u4)  # E: {uint64}
+reveal_type(u4 + u4)  # E: {uint32}
+reveal_type(b_ + u4)  # E: {uint32}
+reveal_type(b + u4)  # E: {uint32}
+reveal_type(i + u4)  # E: Any
+reveal_type(AR_f + u4)  # E: Any

.venv/Lib/site-packages/numpy/typing/tests/data/reveal/array_constructors.pyi

@@ -0,0 +1,187 @@
+from typing import List, Any, TypeVar
+from pathlib import Path
+
+import numpy as np
+import numpy.typing as npt
+
+_SCT = TypeVar("_SCT", bound=np.generic, covariant=True)
+
+class SubClass(np.ndarray[Any, np.dtype[_SCT]]): ...
+
+i8: np.int64
+
+A: npt.NDArray[np.float64]
+B: SubClass[np.float64]
+C: List[int]
+
+def func(i: int, j: int, **kwargs: Any) -> SubClass[np.float64]: ...
+
+reveal_type(np.empty_like(A))  # E: ndarray[Any, dtype[{float64}]]
+reveal_type(np.empty_like(B))  # E: SubClass[{float64}]
+reveal_type(np.empty_like([1, 1.0]))  # E: ndarray[Any, dtype[Any]]
+reveal_type(np.empty_like(A, dtype=np.int64))  # E: ndarray[Any, dtype[{int64}]]
+reveal_type(np.empty_like(A, dtype='c16'))  # E: ndarray[Any, dtype[Any]]
+
+reveal_type(np.array(A))  # E: ndarray[Any, dtype[{float64}]]
+reveal_type(np.array(B))  # E: ndarray[Any, dtype[{float64}]]
+reveal_type(np.array(B, subok=True))  # E: SubClass[{float64}]
+reveal_type(np.array([1, 1.0]))  # E: ndarray[Any, dtype[Any]]
+reveal_type(np.array(A, dtype=np.int64))  # E: ndarray[Any, dtype[{int64}]]
+reveal_type(np.array(A, dtype='c16'))  # E: ndarray[Any, dtype[Any]]
+
+reveal_type(np.zeros([1, 5, 6]))  # E: ndarray[Any, dtype[{float64}]]
+reveal_type(np.zeros([1, 5, 6], dtype=np.int64))  # E: ndarray[Any, dtype[{int64}]]
+reveal_type(np.zeros([1, 5, 6], dtype='c16'))  # E: ndarray[Any, dtype[Any]]
+
+reveal_type(np.empty([1, 5, 6]))  # E: ndarray[Any, dtype[{float64}]]
+reveal_type(np.empty([1, 5, 6], dtype=np.int64))  # E: ndarray[Any, dtype[{int64}]]
+reveal_type(np.empty([1, 5, 6], dtype='c16'))  # E: ndarray[Any, dtype[Any]]
+
+reveal_type(np.concatenate(A))  # E: ndarray[Any, dtype[{float64}]]
+reveal_type(np.concatenate([A, A]))  # E: Any
+reveal_type(np.concatenate([[1], A]))  # E: ndarray[Any, dtype[Any]]
+reveal_type(np.concatenate([[1], [1]]))  # E: ndarray[Any, dtype[Any]]
+reveal_type(np.concatenate((A, A)))  # E: ndarray[Any, dtype[{float64}]]
+reveal_type(np.concatenate(([1], [1])))  # E: ndarray[Any, dtype[Any]]
+reveal_type(np.concatenate([1, 1.0]))  # E: ndarray[Any, dtype[Any]]
+reveal_type(np.concatenate(A, dtype=np.int64))  # E: ndarray[Any, dtype[{int64}]]
+reveal_type(np.concatenate(A, dtype='c16'))  # E: ndarray[Any, dtype[Any]]
+reveal_type(np.concatenate([1, 1.0], out=A))  # E: ndarray[Any, dtype[{float64}]]
+
+reveal_type(np.asarray(A))  # E: ndarray[Any, dtype[{float64}]]
+reveal_type(np.asarray(B))  # E: ndarray[Any, dtype[{float64}]]
+reveal_type(np.asarray([1, 1.0]))  # E: ndarray[Any, dtype[Any]]
+reveal_type(np.asarray(A, dtype=np.int64))  # E: ndarray[Any, dtype[{int64}]]
+reveal_type(np.asarray(A, dtype='c16'))  # E: ndarray[Any, dtype[Any]]
+
+reveal_type(np.asanyarray(A))  # E: ndarray[Any, dtype[{float64}]]
+reveal_type(np.asanyarray(B))  # E: SubClass[{float64}]
+reveal_type(np.asanyarray([1, 1.0]))  # E: ndarray[Any, dtype[Any]]
+reveal_type(np.asanyarray(A, dtype=np.int64))  # E: ndarray[Any, dtype[{int64}]]
+reveal_type(np.asanyarray(A, dtype='c16'))  # E: ndarray[Any, dtype[Any]]
+
+reveal_type(np.ascontiguousarray(A))  # E: ndarray[Any, dtype[{float64}]]
+reveal_type(np.ascontiguousarray(B))  # E: ndarray[Any, dtype[{float64}]]
+reveal_type(np.ascontiguousarray([1, 1.0]))  # E: ndarray[Any, dtype[Any]]
+reveal_type(np.ascontiguousarray(A, dtype=np.int64))  # E: ndarray[Any, dtype[{int64}]]
+reveal_type(np.ascontiguousarray(A, dtype='c16'))  # E: ndarray[Any, dtype[Any]]
+
+reveal_type(np.asfortranarray(A))  # E: ndarray[Any, dtype[{float64}]]
+reveal_type(np.asfortranarray(B))  # E: ndarray[Any, dtype[{float64}]]
+reveal_type(np.asfortranarray([1, 1.0]))  # E: ndarray[Any, dtype[Any]]
+reveal_type(np.asfortranarray(A, dtype=np.int64))  # E: ndarray[Any, dtype[{int64}]]
+reveal_type(np.asfortranarray(A, dtype='c16'))  # E: ndarray[Any, dtype[Any]]
+
+reveal_type(np.fromstring("1 1 1", sep=" "))  # E: ndarray[Any, dtype[{float64}]]
+reveal_type(np.fromstring(b"1 1 1", sep=" "))  # E: ndarray[Any, dtype[{float64}]]
+reveal_type(np.fromstring("1 1 1", dtype=np.int64, sep=" "))  # E: ndarray[Any, dtype[{int64}]]
+reveal_type(np.fromstring(b"1 1 1", dtype=np.int64, sep=" "))  # E: ndarray[Any, dtype[{int64}]]
+reveal_type(np.fromstring("1 1 1", dtype="c16", sep=" "))  # E: ndarray[Any, dtype[Any]]
+reveal_type(np.fromstring(b"1 1 1", dtype="c16", sep=" "))  # E: ndarray[Any, dtype[Any]]
+
+reveal_type(np.fromfile("test.txt", sep=" "))  # E: ndarray[Any, dtype[{float64}]]
+reveal_type(np.fromfile("test.txt", dtype=np.int64, sep=" "))  # E: ndarray[Any, dtype[{int64}]]
+reveal_type(np.fromfile("test.txt", dtype="c16", sep=" "))  # E: ndarray[Any, dtype[Any]]
+with open("test.txt") as f:
+    reveal_type(np.fromfile(f, sep=" "))  # E: ndarray[Any, dtype[{float64}]]
+    reveal_type(np.fromfile(b"test.txt", sep=" "))  # E: ndarray[Any, dtype[{float64}]]
+    reveal_type(np.fromfile(Path("test.txt"), sep=" "))  # E: ndarray[Any, dtype[{float64}]]
+
+reveal_type(np.fromiter("12345", np.float64))  # E: ndarray[Any, dtype[{float64}]]
+reveal_type(np.fromiter("12345", float))  # E: ndarray[Any, dtype[Any]]
+
+reveal_type(np.frombuffer(A))  # E: ndarray[Any, dtype[{float64}]]
+reveal_type(np.frombuffer(A, dtype=np.int64))  # E: ndarray[Any, dtype[{int64}]]
+reveal_type(np.frombuffer(A, dtype="c16"))  # E: ndarray[Any, dtype[Any]]
+
+reveal_type(np.arange(False, True))  # E: ndarray[Any, dtype[signedinteger[Any]]]
+reveal_type(np.arange(10))  # E: ndarray[Any, dtype[signedinteger[Any]]]
+reveal_type(np.arange(0, 10, step=2))  # E: ndarray[Any, dtype[signedinteger[Any]]]
+reveal_type(np.arange(10.0))  # E: ndarray[Any, dtype[floating[Any]]]
+reveal_type(np.arange(start=0, stop=10.0))  # E: ndarray[Any, dtype[floating[Any]]]
+reveal_type(np.arange(np.timedelta64(0)))  # E: ndarray[Any, dtype[timedelta64]]
+reveal_type(np.arange(0, np.timedelta64(10)))  # E: ndarray[Any, dtype[timedelta64]]
+reveal_type(np.arange(np.datetime64("0"), np.datetime64("10")))  # E: ndarray[Any, dtype[datetime64]]
+reveal_type(np.arange(10, dtype=np.float64))  # E: ndarray[Any, dtype[{float64}]]
+reveal_type(np.arange(0, 10, step=2, dtype=np.int16))  # E: ndarray[Any, dtype[{int16}]]
+reveal_type(np.arange(10, dtype=int))  # E: ndarray[Any, dtype[Any]]
+reveal_type(np.arange(0, 10, dtype="f8"))  # E: ndarray[Any, dtype[Any]]
+
+reveal_type(np.require(A))  # E: ndarray[Any, dtype[{float64}]]
+reveal_type(np.require(B))  # E: SubClass[{float64}]
+reveal_type(np.require(B, requirements=None))  # E: SubClass[{float64}]
+reveal_type(np.require(B, dtype=int))  # E: ndarray[Any, Any]
+reveal_type(np.require(B, requirements="E"))  # E: ndarray[Any, Any]
+reveal_type(np.require(B, requirements=["ENSUREARRAY"]))  # E: ndarray[Any, Any]
+reveal_type(np.require(B, requirements={"F", "E"}))  # E: ndarray[Any, Any]
+reveal_type(np.require(B, requirements=["C", "OWNDATA"]))  # E: SubClass[{float64}]
+reveal_type(np.require(B, requirements="W"))  # E: SubClass[{float64}]
+reveal_type(np.require(B, requirements="A"))  # E: SubClass[{float64}]
+reveal_type(np.require(C))  # E: ndarray[Any, Any]
+
+reveal_type(np.linspace(0, 10))  # E: ndarray[Any, Any]
+reveal_type(np.linspace(0, 10, retstep=True))  # E: Tuple[ndarray[Any, Any], Any]
+reveal_type(np.logspace(0, 10))  # E: ndarray[Any, Any]
+reveal_type(np.geomspace(1, 10))  # E: ndarray[Any, Any]
+
+reveal_type(np.zeros_like(A))  # E: ndarray[Any, dtype[{float64}]]
+reveal_type(np.zeros_like(C))  # E: ndarray[Any, dtype[Any]]
+reveal_type(np.zeros_like(A, dtype=float))  # E: ndarray[Any, dtype[Any]]
+reveal_type(np.zeros_like(B))  # E: SubClass[{float64}]
+reveal_type(np.zeros_like(B, dtype=np.int64))  # E: ndarray[Any, dtype[{int64}]]
+
+reveal_type(np.ones_like(A))  # E: ndarray[Any, dtype[{float64}]]
+reveal_type(np.ones_like(C))  # E: ndarray[Any, dtype[Any]]
+reveal_type(np.ones_like(A, dtype=float))  # E: ndarray[Any, dtype[Any]]
+reveal_type(np.ones_like(B))  # E: SubClass[{float64}]
+reveal_type(np.ones_like(B, dtype=np.int64))  # E: ndarray[Any, dtype[{int64}]]
+
+reveal_type(np.full_like(A, i8))  # E: ndarray[Any, dtype[{float64}]]
+reveal_type(np.full_like(C, i8))  # E: ndarray[Any, dtype[Any]]
+reveal_type(np.full_like(A, i8, dtype=int))  # E: ndarray[Any, dtype[Any]]
+reveal_type(np.full_like(B, i8))  # E: SubClass[{float64}]
+reveal_type(np.full_like(B, i8, dtype=np.int64))  # E: ndarray[Any, dtype[{int64}]]
+
+reveal_type(np.ones(1))  # E: ndarray[Any, dtype[{float64}]]
+reveal_type(np.ones([1, 1, 1]))  # E: ndarray[Any, dtype[{float64}]]
+reveal_type(np.ones(5, dtype=np.int64))  # E: ndarray[Any, dtype[{int64}]]
+reveal_type(np.ones(5, dtype=int))  # E: ndarray[Any, dtype[Any]]
+
+reveal_type(np.full(1, i8))  # E: ndarray[Any, dtype[Any]]
+reveal_type(np.full([1, 1, 1], i8))  # E: ndarray[Any, dtype[Any]]
+reveal_type(np.full(1, i8, dtype=np.float64))  # E: ndarray[Any, dtype[{float64}]]
+reveal_type(np.full(1, i8, dtype=float))  # E: ndarray[Any, dtype[Any]]
+
+reveal_type(np.indices([1, 2, 3]))  # E: ndarray[Any, dtype[{int_}]]
+reveal_type(np.indices([1, 2, 3], sparse=True))  # E: tuple[ndarray[Any, dtype[{int_}]], ...]
+
+reveal_type(np.fromfunction(func, (3, 5)))  # E: SubClass[{float64}]
+
+reveal_type(np.identity(10))  # E: ndarray[Any, dtype[{float64}]]
+reveal_type(np.identity(10, dtype=np.int64))  # E: ndarray[Any, dtype[{int64}]]
+reveal_type(np.identity(10, dtype=int))  # E: ndarray[Any, dtype[Any]]
+
+reveal_type(np.atleast_1d(A))  # E: ndarray[Any, dtype[{float64}]]
+reveal_type(np.atleast_1d(C))  # E: ndarray[Any, dtype[Any]]
+reveal_type(np.atleast_1d(A, A))  # E: list[ndarray[Any, dtype[Any]]]
+reveal_type(np.atleast_1d(A, C))  # E: list[ndarray[Any, dtype[Any]]]
+reveal_type(np.atleast_1d(C, C))  # E: list[ndarray[Any, dtype[Any]]]
+
+reveal_type(np.atleast_2d(A))  # E: ndarray[Any, dtype[{float64}]]
+
+reveal_type(np.atleast_3d(A))  # E: ndarray[Any, dtype[{float64}]]
+
+reveal_type(np.vstack([A, A]))  # E: ndarray[Any, Any]
+reveal_type(np.vstack([A, C]))  # E: ndarray[Any, dtype[Any]]
+reveal_type(np.vstack([C, C]))  # E: ndarray[Any, dtype[Any]]
+
+reveal_type(np.hstack([A, A]))  # E: ndarray[Any, Any]
+
+reveal_type(np.stack([A, A]))  # E: Any
+reveal_type(np.stack([A, C]))  # E: ndarray[Any, dtype[Any]]
+reveal_type(np.stack([C, C]))  # E: ndarray[Any, dtype[Any]]
+reveal_type(np.stack([A, A], axis=0))  # E: Any
+reveal_type(np.stack([A, A], out=B))  # E: SubClass[{float64}]
+
+reveal_type(np.block([[A, A], [A, A]]))  # E: ndarray[Any, Any]
+reveal_type(np.block(C))  # E: ndarray[Any, dtype[Any]]

.venv/Lib/site-packages/numpy/typing/tests/data/reveal/arraypad.pyi

@@ -0,0 +1,21 @@
+from typing import List, Any, Mapping, Tuple, SupportsIndex
+
+import numpy as np
+import numpy.typing as npt
+
+def mode_func(
+    ar: npt.NDArray[np.number[Any]],
+    width: Tuple[int, int],
+    iaxis: SupportsIndex,
+    kwargs: Mapping[str, Any],
+) -> None: ...
+
+AR_i8: npt.NDArray[np.int64]
+AR_f8: npt.NDArray[np.float64]
+AR_LIKE: List[int]
+
+reveal_type(np.pad(AR_i8, (2, 3), "constant"))  # E: ndarray[Any, dtype[{int64}]]
+reveal_type(np.pad(AR_LIKE, (2, 3), "constant"))  # E: ndarray[Any, dtype[Any]]
+
+reveal_type(np.pad(AR_f8, (2, 3), mode_func))  # E: ndarray[Any, dtype[{float64}]]
+reveal_type(np.pad(AR_f8, (2, 3), mode_func, a=1, b=2))  # E: ndarray[Any, dtype[{float64}]]

.venv/Lib/site-packages/numpy/typing/tests/data/reveal/arrayprint.pyi

@@ -0,0 +1,19 @@
+from typing import Any, Callable
+import numpy as np
+
+AR: np.ndarray[Any, Any]
+func_float: Callable[[np.floating[Any]], str]
+func_int: Callable[[np.integer[Any]], str]
+
+reveal_type(np.get_printoptions())  # E: TypedDict
+reveal_type(np.array2string(  # E: str
+    AR, formatter={'float_kind': func_float, 'int_kind': func_int}
+))
+reveal_type(np.format_float_scientific(1.0))  # E: str
+reveal_type(np.format_float_positional(1))  # E: str
+reveal_type(np.array_repr(AR))  # E: str
+reveal_type(np.array_str(AR))  # E: str
+
+reveal_type(np.printoptions())  # E: contextlib._GeneratorContextManager
+with np.printoptions() as dct:
+    reveal_type(dct)  # E: TypedDict

.venv/Lib/site-packages/numpy/typing/tests/data/reveal/arraysetops.pyi

@@ -0,0 +1,60 @@
+import numpy as np
+import numpy.typing as npt
+
+AR_b: npt.NDArray[np.bool_]
+AR_i8: npt.NDArray[np.int64]
+AR_f8: npt.NDArray[np.float64]
+AR_M: npt.NDArray[np.datetime64]
+AR_O: npt.NDArray[np.object_]
+
+AR_LIKE_f8: list[float]
+
+reveal_type(np.ediff1d(AR_b))  # E: ndarray[Any, dtype[{int8}]]
+reveal_type(np.ediff1d(AR_i8, to_end=[1, 2, 3]))  # E: ndarray[Any, dtype[{int64}]]
+reveal_type(np.ediff1d(AR_M))  # E: ndarray[Any, dtype[timedelta64]]
+reveal_type(np.ediff1d(AR_O))  # E: ndarray[Any, dtype[object_]]
+reveal_type(np.ediff1d(AR_LIKE_f8, to_begin=[1, 1.5]))  # E: ndarray[Any, dtype[Any]]
+
+reveal_type(np.intersect1d(AR_i8, AR_i8))  # E: ndarray[Any, dtype[{int64}]]
+reveal_type(np.intersect1d(AR_M, AR_M, assume_unique=True))  # E: ndarray[Any, dtype[datetime64]]
+reveal_type(np.intersect1d(AR_f8, AR_i8))  # E: ndarray[Any, dtype[Any]]
+reveal_type(np.intersect1d(AR_f8, AR_f8, return_indices=True))  # E: Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{intp}]], ndarray[Any, dtype[{intp}]]]
+
+reveal_type(np.setxor1d(AR_i8, AR_i8))  # E: ndarray[Any, dtype[{int64}]]
+reveal_type(np.setxor1d(AR_M, AR_M, assume_unique=True))  # E: ndarray[Any, dtype[datetime64]]
+reveal_type(np.setxor1d(AR_f8, AR_i8))  # E: ndarray[Any, dtype[Any]]
+
+reveal_type(np.in1d(AR_i8, AR_i8))  # E: ndarray[Any, dtype[bool_]]
+reveal_type(np.in1d(AR_M, AR_M, assume_unique=True))  # E: ndarray[Any, dtype[bool_]]
+reveal_type(np.in1d(AR_f8, AR_i8))  # E: ndarray[Any, dtype[bool_]]
+reveal_type(np.in1d(AR_f8, AR_LIKE_f8, invert=True))  # E: ndarray[Any, dtype[bool_]]
+
+reveal_type(np.isin(AR_i8, AR_i8))  # E: ndarray[Any, dtype[bool_]]
+reveal_type(np.isin(AR_M, AR_M, assume_unique=True))  # E: ndarray[Any, dtype[bool_]]
+reveal_type(np.isin(AR_f8, AR_i8))  # E: ndarray[Any, dtype[bool_]]
+reveal_type(np.isin(AR_f8, AR_LIKE_f8, invert=True))  # E: ndarray[Any, dtype[bool_]]
+
+reveal_type(np.union1d(AR_i8, AR_i8))  # E: ndarray[Any, dtype[{int64}]]
+reveal_type(np.union1d(AR_M, AR_M))  # E: ndarray[Any, dtype[datetime64]]
+reveal_type(np.union1d(AR_f8, AR_i8))  # E: ndarray[Any, dtype[Any]]
+
+reveal_type(np.setdiff1d(AR_i8, AR_i8))  # E: ndarray[Any, dtype[{int64}]]
+reveal_type(np.setdiff1d(AR_M, AR_M, assume_unique=True))  # E: ndarray[Any, dtype[datetime64]]
+reveal_type(np.setdiff1d(AR_f8, AR_i8))  # E: ndarray[Any, dtype[Any]]
+
+reveal_type(np.unique(AR_f8))  # E: ndarray[Any, dtype[{float64}]]
+reveal_type(np.unique(AR_LIKE_f8, axis=0))  # E: ndarray[Any, dtype[Any]]
+reveal_type(np.unique(AR_f8, return_index=True))  # E: Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{intp}]]]
+reveal_type(np.unique(AR_LIKE_f8, return_index=True))  # E: Tuple[ndarray[Any, dtype[Any]], ndarray[Any, dtype[{intp}]]]
+reveal_type(np.unique(AR_f8, return_inverse=True))  # E: Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{intp}]]]
+reveal_type(np.unique(AR_LIKE_f8, return_inverse=True))  # E: Tuple[ndarray[Any, dtype[Any]], ndarray[Any, dtype[{intp}]]]
+reveal_type(np.unique(AR_f8, return_counts=True))  # E: Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{intp}]]]
+reveal_type(np.unique(AR_LIKE_f8, return_counts=True))  # E: Tuple[ndarray[Any, dtype[Any]], ndarray[Any, dtype[{intp}]]]
+reveal_type(np.unique(AR_f8, return_index=True, return_inverse=True))  # E: Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{intp}]], ndarray[Any, dtype[{intp}]]]
+reveal_type(np.unique(AR_LIKE_f8, return_index=True, return_inverse=True))  # E: Tuple[ndarray[Any, dtype[Any]], ndarray[Any, dtype[{intp}]], ndarray[Any, dtype[{intp}]]]
+reveal_type(np.unique(AR_f8, return_index=True, return_counts=True))  # E: Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{intp}]], ndarray[Any, dtype[{intp}]]]
+reveal_type(np.unique(AR_LIKE_f8, return_index=True, return_counts=True))  # E: Tuple[ndarray[Any, dtype[Any]], ndarray[Any, dtype[{intp}]], ndarray[Any, dtype[{intp}]]]
+reveal_type(np.unique(AR_f8, return_inverse=True, return_counts=True))  # E: Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{intp}]], ndarray[Any, dtype[{intp}]]]
+reveal_type(np.unique(AR_LIKE_f8, return_inverse=True, return_counts=True))  # E: Tuple[ndarray[Any, dtype[Any]], ndarray[Any, dtype[{intp}]], ndarray[Any, dtype[{intp}]]]
+reveal_type(np.unique(AR_f8, return_index=True, return_inverse=True, return_counts=True))  # E: Tuple[ndarray[Any, dtype[{float64}]], ndarray[Any, dtype[{intp}]], ndarray[Any, dtype[{intp}]], ndarray[Any, dtype[{intp}]]]
+reveal_type(np.unique(AR_LIKE_f8, return_index=True, return_inverse=True, return_counts=True))  # E: Tuple[ndarray[Any, dtype[Any]], ndarray[Any, dtype[{intp}]], ndarray[Any, dtype[{intp}]], ndarray[Any, dtype[{intp}]]]

.venv/Lib/site-packages/numpy/typing/tests/data/reveal/arrayterator.pyi

@@ -0,0 +1,24 @@
+from typing import Any
+import numpy as np
+
+AR_i8: np.ndarray[Any, np.dtype[np.int64]]
+ar_iter = np.lib.Arrayterator(AR_i8)
+
+reveal_type(ar_iter.var)  # E: ndarray[Any, dtype[{int64}]]
+reveal_type(ar_iter.buf_size)  # E: Union[None, builtins.int]
+reveal_type(ar_iter.start)  # E: builtins.list[builtins.int]
+reveal_type(ar_iter.stop)  # E: builtins.list[builtins.int]
+reveal_type(ar_iter.step)  # E: builtins.list[builtins.int]
+reveal_type(ar_iter.shape)  # E: builtins.tuple[builtins.int, ...]
+reveal_type(ar_iter.flat)  # E: typing.Generator[{int64}, None, None]
+
+reveal_type(ar_iter.__array__())  # E: ndarray[Any, dtype[{int64}]]
+
+for i in ar_iter:
+    reveal_type(i)  # E: ndarray[Any, dtype[{int64}]]
+
+reveal_type(ar_iter[0])  # E: lib.arrayterator.Arrayterator[Any, dtype[{int64}]]
+reveal_type(ar_iter[...])  # E: lib.arrayterator.Arrayterator[Any, dtype[{int64}]]
+reveal_type(ar_iter[:])  # E: lib.arrayterator.Arrayterator[Any, dtype[{int64}]]
+reveal_type(ar_iter[0, 0, 0])  # E: lib.arrayterator.Arrayterator[Any, dtype[{int64}]]
+reveal_type(ar_iter[..., 0, :])  # E: lib.arrayterator.Arrayterator[Any, dtype[{int64}]]