Merge pull request #17309 from BvB93/numeric-move

MAINT: Move the `fromnumeric` annotations to their own stub file

2 files changed, 576 insertions, 476 deletions

diff --git a/numpy/__init__.pyi b/numpy/__init__.pyi
index bf54207a4..d4eda6b31 100644
--- a/numpy/__init__.pyi
+++ b/numpy/__init__.pyi
@@ -66,10 +66,93 @@ from numpy.core.function_base import (
     geomspace,
 )
 
+from numpy.core.fromnumeric import (
+    take,
+    reshape,
+    choose,
+    repeat,
+    put,
+    swapaxes,
+    transpose,
+    partition,
+    argpartition,
+    sort,
+    argsort,
+    argmax,
+    argmin,
+    searchsorted,
+    resize,
+    squeeze,
+    diagonal,
+    trace,
+    ravel,
+    nonzero,
+    shape,
+    compress,
+    clip,
+    sum,
+    all,
+    any,
+    cumsum,
+    ptp,
+    amax,
+    amin,
+    prod,
+    cumprod,
+    ndim,
+    size,
+    around,
+    mean,
+    std,
+    var,
+)
+
 # Add an object to `__all__` if their stubs are defined in an external file;
 # their stubs will not be recognized otherwise.
 # NOTE: This is redundant for objects defined within this file.
-__all__ = ["linspace", "logspace", "geomspace"]
+__all__ = [
+    "linspace",
+    "logspace",
+    "geomspace",
+    "take",
+    "reshape",
+    "choose",
+    "repeat",
+    "put",
+    "swapaxes",
+    "transpose",
+    "partition",
+    "argpartition",
+    "sort",
+    "argsort",
+    "argmax",
+    "argmin",
+    "searchsorted",
+    "resize",
+    "squeeze",
+    "diagonal",
+    "trace",
+    "ravel",
+    "nonzero",
+    "shape",
+    "compress",
+    "clip",
+    "sum",
+    "all",
+    "any",
+    "cumsum",
+    "ptp",
+    "amax",
+    "amin",
+    "prod",
+    "cumprod",
+    "ndim",
+    "size",
+    "around",
+    "mean",
+    "std",
+    "var",
+]
 
 # TODO: remove when the full numpy namespace is defined
 def __getattr__(name: str) -> Any: ...
@@ -998,481 +1081,6 @@ def find_common_type(
     array_types: Sequence[DtypeLike], scalar_types: Sequence[DtypeLike]
 ) -> dtype: ...
 
-# Functions from np.core.fromnumeric
-_Mode = Literal["raise", "wrap", "clip"]
-_PartitionKind = Literal["introselect"]
-_SortKind = Literal["quicksort", "mergesort", "heapsort", "stable"]
-_Side = Literal["left", "right"]
-
-# Various annotations for scalars
-
-# While dt.datetime and dt.timedelta are not technically part of NumPy,
-# they are one of the rare few builtin scalars which serve as valid return types.
-# See https://github.com/numpy/numpy-stubs/pull/67#discussion_r412604113.
-_ScalarNumpy = Union[generic, dt.datetime, dt.timedelta]
-_ScalarBuiltin = Union[str, bytes, dt.date, dt.timedelta, bool, int, float, complex]
-_Scalar = Union[_ScalarBuiltin, _ScalarNumpy]
-
-# Integers and booleans can generally be used interchangeably
-_ScalarIntOrBool = TypeVar("_ScalarIntOrBool", bound=Union[integer, bool_])
-_ScalarGeneric = TypeVar("_ScalarGeneric", bound=generic)
-_ScalarGenericDT = TypeVar(
-    "_ScalarGenericDT", bound=Union[dt.datetime, dt.timedelta, generic]
-)
-
-_Number = TypeVar('_Number', bound=number)
 _NumberLike = Union[int, float, complex, number, bool_]
-
-# An array-like object consisting of integers
 _IntLike = Union[int, integer]
 _BoolLike = Union[bool, bool_]
-_IntOrBool = Union[_IntLike, _BoolLike]
-_ArrayLikeIntNested = ArrayLike  # TODO: wait for support for recursive types
-_ArrayLikeBoolNested = ArrayLike  # TODO: wait for support for recursive types
-
-# Integers and booleans can generally be used interchangeably
-_ArrayLikeIntOrBool = Union[
-    _IntOrBool,
-    ndarray,
-    Sequence[_IntOrBool],
-    Sequence[_ArrayLikeIntNested],
-    Sequence[_ArrayLikeBoolNested],
-]
-_ArrayLikeBool = Union[
-    _BoolLike,
-    Sequence[_BoolLike],
-    ndarray
-]
-
-# The signature of take() follows a common theme with its overloads:
-# 1. A generic comes in; the same generic comes out
-# 2. A scalar comes in; a generic comes out
-# 3. An array-like object comes in; some keyword ensures that a generic comes out
-# 4. An array-like object comes in; an ndarray or generic comes out
-@overload
-def take(
-    a: _ScalarGenericDT,
-    indices: int,
-    axis: Optional[int] = ...,
-    out: Optional[ndarray] = ...,
-    mode: _Mode = ...,
-) -> _ScalarGenericDT: ...
-@overload
-def take(
-    a: _Scalar,
-    indices: int,
-    axis: Optional[int] = ...,
-    out: Optional[ndarray] = ...,
-    mode: _Mode = ...,
-) -> _ScalarNumpy: ...
-@overload
-def take(
-    a: ArrayLike,
-    indices: int,
-    axis: Optional[int] = ...,
-    out: Optional[ndarray] = ...,
-    mode: _Mode = ...,
-) -> _ScalarNumpy: ...
-@overload
-def take(
-    a: ArrayLike,
-    indices: _ArrayLikeIntOrBool,
-    axis: Optional[int] = ...,
-    out: Optional[ndarray] = ...,
-    mode: _Mode = ...,
-) -> Union[_ScalarNumpy, ndarray]: ...
-def reshape(a: ArrayLike, newshape: _ShapeLike, order: _OrderACF = ...) -> ndarray: ...
-@overload
-def choose(
-    a: _ScalarIntOrBool,
-    choices: ArrayLike,
-    out: Optional[ndarray] = ...,
-    mode: _Mode = ...,
-) -> _ScalarIntOrBool: ...
-@overload
-def choose(
-    a: _IntOrBool, choices: ArrayLike, out: Optional[ndarray] = ..., mode: _Mode = ...
-) -> Union[integer, bool_]: ...
-@overload
-def choose(
-    a: _ArrayLikeIntOrBool,
-    choices: ArrayLike,
-    out: Optional[ndarray] = ...,
-    mode: _Mode = ...,
-) -> ndarray: ...
-def repeat(
-    a: ArrayLike, repeats: _ArrayLikeIntOrBool, axis: Optional[int] = ...
-) -> ndarray: ...
-def put(
-    a: ndarray, ind: _ArrayLikeIntOrBool, v: ArrayLike, mode: _Mode = ...
-) -> None: ...
-def swapaxes(a: ArrayLike, axis1: int, axis2: int) -> ndarray: ...
-def transpose(
-    a: ArrayLike, axes: Union[None, Sequence[int], ndarray] = ...
-) -> ndarray: ...
-def partition(
-    a: ArrayLike,
-    kth: _ArrayLikeIntOrBool,
-    axis: Optional[int] = ...,
-    kind: _PartitionKind = ...,
-    order: Union[None, str, Sequence[str]] = ...,
-) -> ndarray: ...
-@overload
-def argpartition(
-    a: generic,
-    kth: _ArrayLikeIntOrBool,
-    axis: Optional[int] = ...,
-    kind: _PartitionKind = ...,
-    order: Union[None, str, Sequence[str]] = ...,
-) -> integer: ...
-@overload
-def argpartition(
-    a: _ScalarBuiltin,
-    kth: _ArrayLikeIntOrBool,
-    axis: Optional[int] = ...,
-    kind: _PartitionKind = ...,
-    order: Union[None, str, Sequence[str]] = ...,
-) -> ndarray: ...
-@overload
-def argpartition(
-    a: ArrayLike,
-    kth: _ArrayLikeIntOrBool,
-    axis: Optional[int] = ...,
-    kind: _PartitionKind = ...,
-    order: Union[None, str, Sequence[str]] = ...,
-) -> ndarray: ...
-def sort(
-    a: ArrayLike,
-    axis: Optional[int] = ...,
-    kind: Optional[_SortKind] = ...,
-    order: Union[None, str, Sequence[str]] = ...,
-) -> ndarray: ...
-def argsort(
-    a: ArrayLike,
-    axis: Optional[int] = ...,
-    kind: Optional[_SortKind] = ...,
-    order: Union[None, str, Sequence[str]] = ...,
-) -> ndarray: ...
-@overload
-def argmax(a: ArrayLike, axis: None = ..., out: Optional[ndarray] = ...) -> integer: ...
-@overload
-def argmax(
-    a: ArrayLike, axis: int = ..., out: Optional[ndarray] = ...
-) -> Union[integer, ndarray]: ...
-@overload
-def argmin(a: ArrayLike, axis: None = ..., out: Optional[ndarray] = ...) -> integer: ...
-@overload
-def argmin(
-    a: ArrayLike, axis: int = ..., out: Optional[ndarray] = ...
-) -> Union[integer, ndarray]: ...
-@overload
-def searchsorted(
-    a: ArrayLike,
-    v: _Scalar,
-    side: _Side = ...,
-    sorter: Optional[_ArrayLikeIntOrBool] = ...,  # 1D int array
-) -> integer: ...
-@overload
-def searchsorted(
-    a: ArrayLike,
-    v: ArrayLike,
-    side: _Side = ...,
-    sorter: Optional[_ArrayLikeIntOrBool] = ...,  # 1D int array
-) -> ndarray: ...
-def resize(a: ArrayLike, new_shape: _ShapeLike) -> ndarray: ...
-@overload
-def squeeze(a: _ScalarGeneric, axis: Optional[_ShapeLike] = ...) -> _ScalarGeneric: ...
-@overload
-def squeeze(a: ArrayLike, axis: Optional[_ShapeLike] = ...) -> ndarray: ...
-def diagonal(
-    a: ArrayLike, offset: int = ..., axis1: int = ..., axis2: int = ...  # >= 2D array
-) -> ndarray: ...
-def trace(
-    a: ArrayLike,  # >= 2D array
-    offset: int = ...,
-    axis1: int = ...,
-    axis2: int = ...,
-    dtype: DtypeLike = ...,
-    out: Optional[ndarray] = ...,
-) -> Union[number, ndarray]: ...
-def ravel(a: ArrayLike, order: _OrderKACF = ...) -> ndarray: ...
-def nonzero(a: ArrayLike) -> Tuple[ndarray, ...]: ...
-def shape(a: ArrayLike) -> _Shape: ...
-def compress(
-    condition: ArrayLike,  # 1D bool array
-    a: ArrayLike,
-    axis: Optional[int] = ...,
-    out: Optional[ndarray] = ...,
-) -> ndarray: ...
-@overload
-def clip(
-    a: _Number,
-    a_min: ArrayLike,
-    a_max: Optional[ArrayLike],
-    out: Optional[ndarray] = ...,
-    **kwargs: Any,
-) -> _Number: ...
-@overload
-def clip(
-    a: _Number,
-    a_min: None,
-    a_max: ArrayLike,
-    out: Optional[ndarray] = ...,
-    **kwargs: Any,
-) -> _Number: ...
-@overload
-def clip(
-    a: ArrayLike,
-    a_min: ArrayLike,
-    a_max: Optional[ArrayLike],
-    out: Optional[ndarray] = ...,
-    **kwargs: Any,
-) -> Union[number, ndarray]: ...
-@overload
-def clip(
-    a: ArrayLike,
-    a_min: None,
-    a_max: ArrayLike,
-    out: Optional[ndarray] = ...,
-    **kwargs: Any,
-) -> Union[number, ndarray]: ...
-@overload
-def sum(
-    a: _Number,
-    axis: Optional[_ShapeLike] = ...,
-    dtype: DtypeLike = ...,
-    out: Optional[ndarray] = ...,
-    keepdims: bool = ...,
-    initial: _NumberLike = ...,
-    where: _ArrayLikeBool = ...,
-) -> _Number: ...
-@overload
-def sum(
-    a: ArrayLike,
-    axis: _ShapeLike = ...,
-    dtype: DtypeLike = ...,
-    out: Optional[ndarray] = ...,
-    keepdims: bool = ...,
-    initial: _NumberLike = ...,
-    where: _ArrayLikeBool = ...,
-) -> Union[number, ndarray]: ...
-@overload
-def all(
-    a: ArrayLike,
-    axis: None = ...,
-    out: Optional[ndarray] = ...,
-    keepdims: Literal[False] = ...,
-) -> bool_: ...
-@overload
-def all(
-    a: ArrayLike,
-    axis: Optional[_ShapeLike] = ...,
-    out: Optional[ndarray] = ...,
-    keepdims: bool = ...,
-) -> Union[bool_, ndarray]: ...
-@overload
-def any(
-    a: ArrayLike,
-    axis: None = ...,
-    out: Optional[ndarray] = ...,
-    keepdims: Literal[False] = ...,
-) -> bool_: ...
-@overload
-def any(
-    a: ArrayLike,
-    axis: Optional[_ShapeLike] = ...,
-    out: Optional[ndarray] = ...,
-    keepdims: bool = ...,
-) -> Union[bool_, ndarray]: ...
-def cumsum(
-    a: ArrayLike,
-    axis: Optional[int] = ...,
-    dtype: DtypeLike = ...,
-    out: Optional[ndarray] = ...,
-) -> ndarray: ...
-@overload
-def ptp(
-    a: _Number,
-    axis: Optional[_ShapeLike] = ...,
-    out: Optional[ndarray] = ...,
-    keepdims: bool = ...,
-) -> _Number: ...
-@overload
-def ptp(
-    a: ArrayLike,
-    axis: None = ...,
-    out: Optional[ndarray] = ...,
-    keepdims: Literal[False] = ...,
-) -> number: ...
-@overload
-def ptp(
-    a: ArrayLike,
-    axis: Optional[_ShapeLike] = ...,
-    out: Optional[ndarray] = ...,
-    keepdims: bool = ...,
-) -> Union[number, ndarray]: ...
-@overload
-def amax(
-    a: _Number,
-    axis: Optional[_ShapeLike] = ...,
-    out: Optional[ndarray] = ...,
-    keepdims: bool = ...,
-    initial: _NumberLike = ...,
-    where: _ArrayLikeBool = ...,
-) -> _Number: ...
-@overload
-def amax(
-    a: ArrayLike,
-    axis: None = ...,
-    out: Optional[ndarray] = ...,
-    keepdims: Literal[False] = ...,
-    initial: _NumberLike = ...,
-    where: _ArrayLikeBool = ...,
-) -> number: ...
-@overload
-def amax(
-    a: ArrayLike,
-    axis: Optional[_ShapeLike] = ...,
-    out: Optional[ndarray] = ...,
-    keepdims: bool = ...,
-    initial: _NumberLike = ...,
-    where: _ArrayLikeBool = ...,
-) -> Union[number, ndarray]: ...
-@overload
-def amin(
-    a: _Number,
-    axis: Optional[_ShapeLike] = ...,
-    out: Optional[ndarray] = ...,
-    keepdims: bool = ...,
-    initial: _NumberLike = ...,
-    where: _ArrayLikeBool = ...,
-) -> _Number: ...
-@overload
-def amin(
-    a: ArrayLike,
-    axis: None = ...,
-    out: Optional[ndarray] = ...,
-    keepdims: Literal[False] = ...,
-    initial: _NumberLike = ...,
-    where: _ArrayLikeBool = ...,
-) -> number: ...
-@overload
-def amin(
-    a: ArrayLike,
-    axis: Optional[_ShapeLike] = ...,
-    out: Optional[ndarray] = ...,
-    keepdims: bool = ...,
-    initial: _NumberLike = ...,
-    where: _ArrayLikeBool = ...,
-) -> Union[number, ndarray]: ...
-
-# TODO: `np.prod()``: For object arrays `initial` does not necessarily
-# have to be a numerical scalar.
-# The only requirement is that it is compatible
-# with the `.__mul__()` method(s) of the passed array's elements.
-
-# Note that the same situation holds for all wrappers around
-# `np.ufunc.reduce`, e.g. `np.sum()` (`.__add__()`).
-
-@overload
-def prod(
-    a: _Number,
-    axis: Optional[_ShapeLike] = ...,
-    dtype: DtypeLike = ...,
-    out: None = ...,
-    keepdims: bool = ...,
-    initial: _NumberLike = ...,
-    where: _ArrayLikeBool = ...,
-) -> _Number: ...
-@overload
-def prod(
-    a: ArrayLike,
-    axis: None = ...,
-    dtype: DtypeLike = ...,
-    out: None = ...,
-    keepdims: Literal[False] = ...,
-    initial: _NumberLike = ...,
-    where: _ArrayLikeBool = ...,
-) -> number: ...
-@overload
-def prod(
-    a: ArrayLike,
-    axis: Optional[_ShapeLike] = ...,
-    dtype: DtypeLike = ...,
-    out: Optional[ndarray] = ...,
-    keepdims: bool = ...,
-    initial: _NumberLike = ...,
-    where: _ArrayLikeBool = ...,
-) -> Union[number, ndarray]: ...
-def cumprod(
-    a: ArrayLike,
-    axis: Optional[int] = ...,
-    dtype: DtypeLike = ...,
-    out: Optional[ndarray] = ...,
-) -> ndarray: ...
-def ndim(a: ArrayLike) -> int: ...
-def size(a: ArrayLike, axis: Optional[int] = ...) -> int: ...
-@overload
-def around(
-    a: _Number, decimals: int = ..., out: Optional[ndarray] = ...
-) -> _Number: ...
-@overload
-def around(
-    a: _NumberLike, decimals: int = ..., out: Optional[ndarray] = ...
-) -> number: ...
-@overload
-def around(
-    a: ArrayLike, decimals: int = ..., out: Optional[ndarray] = ...
-) -> ndarray: ...
-@overload
-def mean(
-    a: ArrayLike,
-    axis: None = ...,
-    dtype: DtypeLike = ...,
-    out: None = ...,
-    keepdims: Literal[False] = ...,
-) -> number: ...
-@overload
-def mean(
-    a: ArrayLike,
-    axis: Optional[_ShapeLike] = ...,
-    dtype: DtypeLike = ...,
-    out: Optional[ndarray] = ...,
-    keepdims: bool = ...,
-) -> Union[number, ndarray]: ...
-@overload
-def std(
-    a: ArrayLike,
-    axis: None = ...,
-    dtype: DtypeLike = ...,
-    out: None = ...,
-    ddof: int = ...,
-    keepdims: Literal[False] = ...,
-) -> number: ...
-@overload
-def std(
-    a: ArrayLike,
-    axis: Optional[_ShapeLike] = ...,
-    dtype: DtypeLike = ...,
-    out: Optional[ndarray] = ...,
-    ddof: int = ...,
-    keepdims: bool = ...,
-) -> Union[number, ndarray]: ...
-@overload
-def var(
-    a: ArrayLike,
-    axis: None = ...,
-    dtype: DtypeLike = ...,
-    out: None = ...,
-    ddof: int = ...,
-    keepdims: Literal[False] = ...,
-) -> number: ...
-@overload
-def var(
-    a: ArrayLike,
-    axis: Optional[_ShapeLike] = ...,
-    dtype: DtypeLike = ...,
-    out: Optional[ndarray] = ...,
-    ddof: int = ...,
-    keepdims: bool = ...,
-) -> Union[number, ndarray]: ...
diff --git a/numpy/core/fromnumeric.pyi b/numpy/core/fromnumeric.pyi
new file mode 100644
index 000000000..7ad772b07
--- /dev/null
+++ b/numpy/core/fromnumeric.pyi
@@ -0,0 +1,492 @@
+import sys
+import datetime as dt
+from typing import Optional, Union, Sequence, Tuple, Any, overload, TypeVar
+
+from numpy import (
+    ndarray,
+    number,
+    integer,
+    bool_,
+    generic,
+    _OrderKACF,
+    _OrderACF,
+    _IntLike,
+    _BoolLike,
+    _NumberLike,
+)
+from numpy.typing import DtypeLike, ArrayLike, _ShapeLike, _Shape
+
+if sys.version_info >= (3, 8):
+    from typing import Literal
+else:
+    from typing_extensions import Literal
+
+_Mode = Literal["raise", "wrap", "clip"]
+_PartitionKind = Literal["introselect"]
+_SortKind = Literal["quicksort", "mergesort", "heapsort", "stable"]
+_Side = Literal["left", "right"]
+
+# Various annotations for scalars
+
+# While dt.datetime and dt.timedelta are not technically part of NumPy,
+# they are one of the rare few builtin scalars which serve as valid return types.
+# See https://github.com/numpy/numpy-stubs/pull/67#discussion_r412604113.
+_ScalarNumpy = Union[generic, dt.datetime, dt.timedelta]
+_ScalarBuiltin = Union[str, bytes, dt.date, dt.timedelta, bool, int, float, complex]
+_Scalar = Union[_ScalarBuiltin, _ScalarNumpy]
+
+# Integers and booleans can generally be used interchangeably
+_ScalarIntOrBool = TypeVar("_ScalarIntOrBool", bound=Union[integer, bool_])
+_ScalarGeneric = TypeVar("_ScalarGeneric", bound=generic)
+_ScalarGenericDT = TypeVar(
+    "_ScalarGenericDT", bound=Union[dt.datetime, dt.timedelta, generic]
+)
+
+_Number = TypeVar("_Number", bound=number)
+
+# An array-like object consisting of integers
+_IntOrBool = Union[_IntLike, _BoolLike]
+_ArrayLikeIntNested = ArrayLike  # TODO: wait for support for recursive types
+_ArrayLikeBoolNested = ArrayLike  # TODO: wait for support for recursive types
+
+# Integers and booleans can generally be used interchangeably
+_ArrayLikeIntOrBool = Union[
+    _IntOrBool,
+    ndarray,
+    Sequence[_IntOrBool],
+    Sequence[_ArrayLikeIntNested],
+    Sequence[_ArrayLikeBoolNested],
+]
+_ArrayLikeBool = Union[_BoolLike, Sequence[_BoolLike], ndarray]
+
+# The signature of take() follows a common theme with its overloads:
+# 1. A generic comes in; the same generic comes out
+# 2. A scalar comes in; a generic comes out
+# 3. An array-like object comes in; some keyword ensures that a generic comes out
+# 4. An array-like object comes in; an ndarray or generic comes out
+@overload
+def take(
+    a: _ScalarGenericDT,
+    indices: int,
+    axis: Optional[int] = ...,
+    out: Optional[ndarray] = ...,
+    mode: _Mode = ...,
+) -> _ScalarGenericDT: ...
+@overload
+def take(
+    a: _Scalar,
+    indices: int,
+    axis: Optional[int] = ...,
+    out: Optional[ndarray] = ...,
+    mode: _Mode = ...,
+) -> _ScalarNumpy: ...
+@overload
+def take(
+    a: ArrayLike,
+    indices: int,
+    axis: Optional[int] = ...,
+    out: Optional[ndarray] = ...,
+    mode: _Mode = ...,
+) -> _ScalarNumpy: ...
+@overload
+def take(
+    a: ArrayLike,
+    indices: _ArrayLikeIntOrBool,
+    axis: Optional[int] = ...,
+    out: Optional[ndarray] = ...,
+    mode: _Mode = ...,
+) -> Union[_ScalarNumpy, ndarray]: ...
+def reshape(a: ArrayLike, newshape: _ShapeLike, order: _OrderACF = ...) -> ndarray: ...
+@overload
+def choose(
+    a: _ScalarIntOrBool,
+    choices: ArrayLike,
+    out: Optional[ndarray] = ...,
+    mode: _Mode = ...,
+) -> _ScalarIntOrBool: ...
+@overload
+def choose(
+    a: _IntOrBool, choices: ArrayLike, out: Optional[ndarray] = ..., mode: _Mode = ...
+) -> Union[integer, bool_]: ...
+@overload
+def choose(
+    a: _ArrayLikeIntOrBool,
+    choices: ArrayLike,
+    out: Optional[ndarray] = ...,
+    mode: _Mode = ...,
+) -> ndarray: ...
+def repeat(
+    a: ArrayLike, repeats: _ArrayLikeIntOrBool, axis: Optional[int] = ...
+) -> ndarray: ...
+def put(
+    a: ndarray, ind: _ArrayLikeIntOrBool, v: ArrayLike, mode: _Mode = ...
+) -> None: ...
+def swapaxes(a: ArrayLike, axis1: int, axis2: int) -> ndarray: ...
+def transpose(
+    a: ArrayLike, axes: Union[None, Sequence[int], ndarray] = ...
+) -> ndarray: ...
+def partition(
+    a: ArrayLike,
+    kth: _ArrayLikeIntOrBool,
+    axis: Optional[int] = ...,
+    kind: _PartitionKind = ...,
+    order: Union[None, str, Sequence[str]] = ...,
+) -> ndarray: ...
+@overload
+def argpartition(
+    a: generic,
+    kth: _ArrayLikeIntOrBool,
+    axis: Optional[int] = ...,
+    kind: _PartitionKind = ...,
+    order: Union[None, str, Sequence[str]] = ...,
+) -> integer: ...
+@overload
+def argpartition(
+    a: _ScalarBuiltin,
+    kth: _ArrayLikeIntOrBool,
+    axis: Optional[int] = ...,
+    kind: _PartitionKind = ...,
+    order: Union[None, str, Sequence[str]] = ...,
+) -> ndarray: ...
+@overload
+def argpartition(
+    a: ArrayLike,
+    kth: _ArrayLikeIntOrBool,
+    axis: Optional[int] = ...,
+    kind: _PartitionKind = ...,
+    order: Union[None, str, Sequence[str]] = ...,
+) -> ndarray: ...
+def sort(
+    a: ArrayLike,
+    axis: Optional[int] = ...,
+    kind: Optional[_SortKind] = ...,
+    order: Union[None, str, Sequence[str]] = ...,
+) -> ndarray: ...
+def argsort(
+    a: ArrayLike,
+    axis: Optional[int] = ...,
+    kind: Optional[_SortKind] = ...,
+    order: Union[None, str, Sequence[str]] = ...,
+) -> ndarray: ...
+@overload
+def argmax(a: ArrayLike, axis: None = ..., out: Optional[ndarray] = ...) -> integer: ...
+@overload
+def argmax(
+    a: ArrayLike, axis: int = ..., out: Optional[ndarray] = ...
+) -> Union[integer, ndarray]: ...
+@overload
+def argmin(a: ArrayLike, axis: None = ..., out: Optional[ndarray] = ...) -> integer: ...
+@overload
+def argmin(
+    a: ArrayLike, axis: int = ..., out: Optional[ndarray] = ...
+) -> Union[integer, ndarray]: ...
+@overload
+def searchsorted(
+    a: ArrayLike,
+    v: _Scalar,
+    side: _Side = ...,
+    sorter: Optional[_ArrayLikeIntOrBool] = ...,  # 1D int array
+) -> integer: ...
+@overload
+def searchsorted(
+    a: ArrayLike,
+    v: ArrayLike,
+    side: _Side = ...,
+    sorter: Optional[_ArrayLikeIntOrBool] = ...,  # 1D int array
+) -> ndarray: ...
+def resize(a: ArrayLike, new_shape: _ShapeLike) -> ndarray: ...
+@overload
+def squeeze(a: _ScalarGeneric, axis: Optional[_ShapeLike] = ...) -> _ScalarGeneric: ...
+@overload
+def squeeze(a: ArrayLike, axis: Optional[_ShapeLike] = ...) -> ndarray: ...
+def diagonal(
+    a: ArrayLike, offset: int = ..., axis1: int = ..., axis2: int = ...  # >= 2D array
+) -> ndarray: ...
+def trace(
+    a: ArrayLike,  # >= 2D array
+    offset: int = ...,
+    axis1: int = ...,
+    axis2: int = ...,
+    dtype: DtypeLike = ...,
+    out: Optional[ndarray] = ...,
+) -> Union[number, ndarray]: ...
+def ravel(a: ArrayLike, order: _OrderKACF = ...) -> ndarray: ...
+def nonzero(a: ArrayLike) -> Tuple[ndarray, ...]: ...
+def shape(a: ArrayLike) -> _Shape: ...
+def compress(
+    condition: ArrayLike,  # 1D bool array
+    a: ArrayLike,
+    axis: Optional[int] = ...,
+    out: Optional[ndarray] = ...,
+) -> ndarray: ...
+@overload
+def clip(
+    a: _Number,
+    a_min: ArrayLike,
+    a_max: Optional[ArrayLike],
+    out: Optional[ndarray] = ...,
+    **kwargs: Any,
+) -> _Number: ...
+@overload
+def clip(
+    a: _Number,
+    a_min: None,
+    a_max: ArrayLike,
+    out: Optional[ndarray] = ...,
+    **kwargs: Any,
+) -> _Number: ...
+@overload
+def clip(
+    a: ArrayLike,
+    a_min: ArrayLike,
+    a_max: Optional[ArrayLike],
+    out: Optional[ndarray] = ...,
+    **kwargs: Any,
+) -> Union[number, ndarray]: ...
+@overload
+def clip(
+    a: ArrayLike,
+    a_min: None,
+    a_max: ArrayLike,
+    out: Optional[ndarray] = ...,
+    **kwargs: Any,
+) -> Union[number, ndarray]: ...
+@overload
+def sum(
+    a: _Number,
+    axis: Optional[_ShapeLike] = ...,
+    dtype: DtypeLike = ...,
+    out: Optional[ndarray] = ...,
+    keepdims: bool = ...,
+    initial: _NumberLike = ...,
+    where: _ArrayLikeBool = ...,
+) -> _Number: ...
+@overload
+def sum(
+    a: ArrayLike,
+    axis: _ShapeLike = ...,
+    dtype: DtypeLike = ...,
+    out: Optional[ndarray] = ...,
+    keepdims: bool = ...,
+    initial: _NumberLike = ...,
+    where: _ArrayLikeBool = ...,
+) -> Union[number, ndarray]: ...
+@overload
+def all(
+    a: ArrayLike,
+    axis: None = ...,
+    out: Optional[ndarray] = ...,
+    keepdims: Literal[False] = ...,
+) -> bool_: ...
+@overload
+def all(
+    a: ArrayLike,
+    axis: Optional[_ShapeLike] = ...,
+    out: Optional[ndarray] = ...,
+    keepdims: bool = ...,
+) -> Union[bool_, ndarray]: ...
+@overload
+def any(
+    a: ArrayLike,
+    axis: None = ...,
+    out: Optional[ndarray] = ...,
+    keepdims: Literal[False] = ...,
+) -> bool_: ...
+@overload
+def any(
+    a: ArrayLike,
+    axis: Optional[_ShapeLike] = ...,
+    out: Optional[ndarray] = ...,
+    keepdims: bool = ...,
+) -> Union[bool_, ndarray]: ...
+def cumsum(
+    a: ArrayLike,
+    axis: Optional[int] = ...,
+    dtype: DtypeLike = ...,
+    out: Optional[ndarray] = ...,
+) -> ndarray: ...
+@overload
+def ptp(
+    a: _Number,
+    axis: Optional[_ShapeLike] = ...,
+    out: Optional[ndarray] = ...,
+    keepdims: bool = ...,
+) -> _Number: ...
+@overload
+def ptp(
+    a: ArrayLike,
+    axis: None = ...,
+    out: Optional[ndarray] = ...,
+    keepdims: Literal[False] = ...,
+) -> number: ...
+@overload
+def ptp(
+    a: ArrayLike,
+    axis: Optional[_ShapeLike] = ...,
+    out: Optional[ndarray] = ...,
+    keepdims: bool = ...,
+) -> Union[number, ndarray]: ...
+@overload
+def amax(
+    a: _Number,
+    axis: Optional[_ShapeLike] = ...,
+    out: Optional[ndarray] = ...,
+    keepdims: bool = ...,
+    initial: _NumberLike = ...,
+    where: _ArrayLikeBool = ...,
+) -> _Number: ...
+@overload
+def amax(
+    a: ArrayLike,
+    axis: None = ...,
+    out: Optional[ndarray] = ...,
+    keepdims: Literal[False] = ...,
+    initial: _NumberLike = ...,
+    where: _ArrayLikeBool = ...,
+) -> number: ...
+@overload
+def amax(
+    a: ArrayLike,
+    axis: Optional[_ShapeLike] = ...,
+    out: Optional[ndarray] = ...,
+    keepdims: bool = ...,
+    initial: _NumberLike = ...,
+    where: _ArrayLikeBool = ...,
+) -> Union[number, ndarray]: ...
+@overload
+def amin(
+    a: _Number,
+    axis: Optional[_ShapeLike] = ...,
+    out: Optional[ndarray] = ...,
+    keepdims: bool = ...,
+    initial: _NumberLike = ...,
+    where: _ArrayLikeBool = ...,
+) -> _Number: ...
+@overload
+def amin(
+    a: ArrayLike,
+    axis: None = ...,
+    out: Optional[ndarray] = ...,
+    keepdims: Literal[False] = ...,
+    initial: _NumberLike = ...,
+    where: _ArrayLikeBool = ...,
+) -> number: ...
+@overload
+def amin(
+    a: ArrayLike,
+    axis: Optional[_ShapeLike] = ...,
+    out: Optional[ndarray] = ...,
+    keepdims: bool = ...,
+    initial: _NumberLike = ...,
+    where: _ArrayLikeBool = ...,
+) -> Union[number, ndarray]: ...
+
+# TODO: `np.prod()``: For object arrays `initial` does not necessarily
+# have to be a numerical scalar.
+# The only requirement is that it is compatible
+# with the `.__mul__()` method(s) of the passed array's elements.
+
+# Note that the same situation holds for all wrappers around
+# `np.ufunc.reduce`, e.g. `np.sum()` (`.__add__()`).
+@overload
+def prod(
+    a: _Number,
+    axis: Optional[_ShapeLike] = ...,
+    dtype: DtypeLike = ...,
+    out: None = ...,
+    keepdims: bool = ...,
+    initial: _NumberLike = ...,
+    where: _ArrayLikeBool = ...,
+) -> _Number: ...
+@overload
+def prod(
+    a: ArrayLike,
+    axis: None = ...,
+    dtype: DtypeLike = ...,
+    out: None = ...,
+    keepdims: Literal[False] = ...,
+    initial: _NumberLike = ...,
+    where: _ArrayLikeBool = ...,
+) -> number: ...
+@overload
+def prod(
+    a: ArrayLike,
+    axis: Optional[_ShapeLike] = ...,
+    dtype: DtypeLike = ...,
+    out: Optional[ndarray] = ...,
+    keepdims: bool = ...,
+    initial: _NumberLike = ...,
+    where: _ArrayLikeBool = ...,
+) -> Union[number, ndarray]: ...
+def cumprod(
+    a: ArrayLike,
+    axis: Optional[int] = ...,
+    dtype: DtypeLike = ...,
+    out: Optional[ndarray] = ...,
+) -> ndarray: ...
+def ndim(a: ArrayLike) -> int: ...
+def size(a: ArrayLike, axis: Optional[int] = ...) -> int: ...
+@overload
+def around(
+    a: _Number, decimals: int = ..., out: Optional[ndarray] = ...
+) -> _Number: ...
+@overload
+def around(
+    a: _NumberLike, decimals: int = ..., out: Optional[ndarray] = ...
+) -> number: ...
+@overload
+def around(
+    a: ArrayLike, decimals: int = ..., out: Optional[ndarray] = ...
+) -> ndarray: ...
+@overload
+def mean(
+    a: ArrayLike,
+    axis: None = ...,
+    dtype: DtypeLike = ...,
+    out: None = ...,
+    keepdims: Literal[False] = ...,
+) -> number: ...
+@overload
+def mean(
+    a: ArrayLike,
+    axis: Optional[_ShapeLike] = ...,
+    dtype: DtypeLike = ...,
+    out: Optional[ndarray] = ...,
+    keepdims: bool = ...,
+) -> Union[number, ndarray]: ...
+@overload
+def std(
+    a: ArrayLike,
+    axis: None = ...,
+    dtype: DtypeLike = ...,
+    out: None = ...,
+    ddof: int = ...,
+    keepdims: Literal[False] = ...,
+) -> number: ...
+@overload
+def std(
+    a: ArrayLike,
+    axis: Optional[_ShapeLike] = ...,
+    dtype: DtypeLike = ...,
+    out: Optional[ndarray] = ...,
+    ddof: int = ...,
+    keepdims: bool = ...,
+) -> Union[number, ndarray]: ...
+@overload
+def var(
+    a: ArrayLike,
+    axis: None = ...,
+    dtype: DtypeLike = ...,
+    out: None = ...,
+    ddof: int = ...,
+    keepdims: Literal[False] = ...,
+) -> number: ...
+@overload
+def var(
+    a: ArrayLike,
+    axis: Optional[_ShapeLike] = ...,
+    dtype: DtypeLike = ...,
+    out: Optional[ndarray] = ...,
+    ddof: int = ...,
+    keepdims: bool = ...,
+) -> Union[number, ndarray]: ...