from _typeshed import ConvertibleToInt, Incomplete
from collections.abc import Callable, Iterable, Sequence
from typing import (
    Any,
    Concatenate,
    Literal as L,
    Never,
    Protocol,
    SupportsIndex,
    SupportsInt,
    overload,
    type_check_only,
)
from typing_extensions import TypeIs

import numpy as np
from numpy import _OrderKACF
from numpy._core.multiarray import bincount
from numpy._globals import _NoValueType
from numpy._typing import (
    ArrayLike,
    DTypeLike,
    NDArray,
    _ArrayLike,
    _ArrayLikeBool_co,
    _ArrayLikeComplex_co,
    _ArrayLikeFloat_co,
    _ArrayLikeInt_co,
    _ArrayLikeNumber_co,
    _ArrayLikeObject_co,
    _ComplexLike_co,
    _DTypeLike,
    _FloatLike_co,
    _NestedSequence as _SeqND,
    _NumberLike_co,
    _ScalarLike_co,
    _Shape,
    _ShapeLike,
    _SupportsArray,
)

__all__ = [
    "select",
    "piecewise",
    "trim_zeros",
    "copy",
    "iterable",
    "percentile",
    "diff",
    "gradient",
    "angle",
    "unwrap",
    "sort_complex",
    "flip",
    "rot90",
    "extract",
    "place",
    "vectorize",
    "asarray_chkfinite",
    "average",
    "bincount",
    "digitize",
    "cov",
    "corrcoef",
    "median",
    "sinc",
    "hamming",
    "hanning",
    "bartlett",
    "blackman",
    "kaiser",
    "trapezoid",
    "i0",
    "meshgrid",
    "delete",
    "insert",
    "append",
    "interp",
    "quantile",
]

type _ArrayLike1D[ScalarT: np.generic] = _SupportsArray[np.dtype[ScalarT]] | Sequence[ScalarT]

type _integer_co = np.integer | np.bool
type _float64_co = np.float64 | _integer_co
type _floating_co = np.floating | _integer_co

# non-trivial scalar-types that will become `complex128` in `sort_complex()`,
# i.e. all numeric scalar types except for `[u]int{8,16} | longdouble`
type _SortsToComplex128 = (
    np.bool
    | np.int32
    | np.uint32
    | np.int64
    | np.uint64
    | np.float16
    | np.float32
    | np.float64
    | np.timedelta64
    | np.object_
)
type _ScalarNumeric = np.inexact | np.timedelta64 | np.object_
type _InexactDouble = np.float64 | np.longdouble | np.complex128 | np.clongdouble

type _Array[ShapeT: _Shape, ScalarT: np.generic] = np.ndarray[ShapeT, np.dtype[ScalarT]]
type _Array0D[ScalarT: np.generic] = np.ndarray[tuple[()], np.dtype[ScalarT]]
type _Array1D[ScalarT: np.generic] = np.ndarray[tuple[int], np.dtype[ScalarT]]
type _Array2D[ScalarT: np.generic] = np.ndarray[tuple[int, int], np.dtype[ScalarT]]
type _Array3D[ScalarT: np.generic] = np.ndarray[tuple[int, int, int], np.dtype[ScalarT]]
type _ArrayMax2D[ScalarT: np.generic] = np.ndarray[tuple[int] | tuple[int, int], np.dtype[ScalarT]]
# workaround for mypy and pyright not following the typing spec for overloads
type _ArrayNoD[ScalarT: np.generic] = np.ndarray[tuple[Never, Never, Never, Never], np.dtype[ScalarT]]

type _Seq1D[T] = Sequence[T]
type _Seq2D[T] = Sequence[Sequence[T]]
type _Seq3D[T] = Sequence[Sequence[Sequence[T]]]
type _ListSeqND[T] = list[T] | _SeqND[list[T]]

type _Tuple2[T] = tuple[T, T]
type _Tuple3[T] = tuple[T, T, T]
type _Tuple4[T] = tuple[T, T, T, T]

type _Mesh1[ScalarT: np.generic] = tuple[_Array1D[ScalarT]]
type _Mesh2[ScalarT: np.generic, ScalarT1: np.generic] = tuple[_Array2D[ScalarT], _Array2D[ScalarT1]]
type _Mesh3[ScalarT: np.generic, ScalarT1: np.generic, ScalarT2: np.generic] = tuple[
    _Array3D[ScalarT], _Array3D[ScalarT1], _Array3D[ScalarT2]
]

type _IndexLike = slice | _ArrayLikeInt_co

type _Indexing = L["ij", "xy"]
type _InterpolationMethod = L[
    "inverted_cdf",
    "averaged_inverted_cdf",
    "closest_observation",
    "interpolated_inverted_cdf",
    "hazen",
    "weibull",
    "linear",
    "median_unbiased",
    "normal_unbiased",
    "lower",
    "higher",
    "midpoint",
    "nearest",
]

# The resulting value will be used as `y[cond] = func(vals, *args, **kw)`, so in can
# return any (usually 1d) array-like or scalar-like compatible with the input.
type _PiecewiseFunction[ScalarT: np.generic, **Tss] = Callable[Concatenate[NDArray[ScalarT], Tss], ArrayLike]
type _PiecewiseFunctions[ScalarT: np.generic, **Tss] = _SizedIterable[_PiecewiseFunction[ScalarT, Tss] | _ScalarLike_co]

@type_check_only
class _TrimZerosSequence[T](Protocol):
    def __len__(self, /) -> int: ...
    @overload
    def __getitem__(self, key: int, /) -> object: ...
    @overload
    def __getitem__(self, key: slice, /) -> T: ...

@type_check_only
class _SupportsRMulFloat[T](Protocol):
    def __rmul__(self, other: float, /) -> T: ...

@type_check_only
class _SizedIterable[T](Protocol):
    def __iter__(self) -> Iterable[T]: ...
    def __len__(self) -> int: ...

###

class vectorize:
    __doc__: str | None
    __module__: L["numpy"] = "numpy"  # pyrefly: ignore[bad-override]
    pyfunc: Callable[..., Incomplete]
    cache: bool
    signature: str | None
    otypes: str | None
    excluded: set[int | str]

    def __init__(
        self,
        /,
        pyfunc: Callable[..., Incomplete] | _NoValueType = ...,  # = _NoValue
        otypes: str | Iterable[DTypeLike] | None = None,
        doc: str | None = None,
        excluded: Iterable[int | str] | None = None,
        cache: bool = False,
        signature: str | None = None,
    ) -> None: ...
    def __call__(self, /, *args: Incomplete, **kwargs: Incomplete) -> Incomplete: ...

@overload
def rot90[ArrayT: np.ndarray](m: ArrayT, k: int = 1, axes: tuple[int, int] = (0, 1)) -> ArrayT: ...
@overload
def rot90[ScalarT: np.generic](m: _ArrayLike[ScalarT], k: int = 1, axes: tuple[int, int] = (0, 1)) -> NDArray[ScalarT]: ...
@overload
def rot90(m: ArrayLike, k: int = 1, axes: tuple[int, int] = (0, 1)) -> NDArray[Incomplete]: ...

# NOTE: Technically `flip` also accept scalars, but that has no effect and complicates
# the overloads significantly, so we ignore that case here.
@overload
def flip[ArrayT: np.ndarray](m: ArrayT, axis: int | tuple[int, ...] | None = None) -> ArrayT: ...
@overload
def flip[ScalarT: np.generic](m: _ArrayLike[ScalarT], axis: int | tuple[int, ...] | None = None) -> NDArray[ScalarT]: ...
@overload
def flip(m: ArrayLike, axis: int | tuple[int, ...] | None = None) -> NDArray[Incomplete]: ...

#
def iterable(y: object) -> TypeIs[Iterable[Any]]: ...

# NOTE: This assumes that if `axis` is given the input is at least 2d, and will
# therefore always return an array.
# NOTE: This assumes that if `keepdims=True` the input is at least 1d, and will
# therefore always return an array.
@overload  # inexact array, keepdims=True
def average[ArrayT: NDArray[np.inexact]](
    a: ArrayT,
    axis: int | tuple[int, ...] | None = None,
    weights: _ArrayLikeNumber_co | None = None,
    returned: L[False] = False,
    *,
    keepdims: L[True],
) -> ArrayT: ...
@overload  # inexact array, returned=True keepdims=True
def average[ArrayT: NDArray[np.inexact]](
    a: ArrayT,
    axis: int | tuple[int, ...] | None = None,
    weights: _ArrayLikeNumber_co | None = None,
    *,
    returned: L[True],
    keepdims: L[True],
) -> _Tuple2[ArrayT]: ...
@overload  # inexact array-like, axis=None
def average[ScalarT: np.inexact](
    a: _ArrayLike[ScalarT],
    axis: None = None,
    weights: _ArrayLikeNumber_co | None = None,
    returned: L[False] = False,
    *,
    keepdims: L[False] | _NoValueType = ...,
) -> ScalarT: ...
@overload  # inexact array-like, axis=<given>
def average[ScalarT: np.inexact](
    a: _ArrayLike[ScalarT],
    axis: int | tuple[int, ...],
    weights: _ArrayLikeNumber_co | None = None,
    returned: L[False] = False,
    *,
    keepdims: L[False] | _NoValueType = ...,
) -> NDArray[ScalarT]: ...
@overload  # inexact array-like, keepdims=True
def average[ScalarT: np.inexact](
    a: _ArrayLike[ScalarT],
    axis: int | tuple[int, ...] | None = None,
    weights: _ArrayLikeNumber_co | None = None,
    returned: L[False] = False,
    *,
    keepdims: L[True],
) -> NDArray[ScalarT]: ...
@overload  # inexact array-like, axis=None, returned=True
def average[ScalarT: np.inexact](
    a: _ArrayLike[ScalarT],
    axis: None = None,
    weights: _ArrayLikeNumber_co | None = None,
    *,
    returned: L[True],
    keepdims: L[False] | _NoValueType = ...,
) -> _Tuple2[ScalarT]: ...
@overload  # inexact array-like, axis=<given>, returned=True
def average[ScalarT: np.inexact](
    a: _ArrayLike[ScalarT],
    axis: int | tuple[int, ...],
    weights: _ArrayLikeNumber_co | None = None,
    *,
    returned: L[True],
    keepdims: L[False] | _NoValueType = ...,
) -> _Tuple2[NDArray[ScalarT]]: ...
@overload  # inexact array-like, returned=True, keepdims=True
def average[ScalarT: np.inexact](
    a: _ArrayLike[ScalarT],
    axis: int | tuple[int, ...] | None = None,
    weights: _ArrayLikeNumber_co | None = None,
    *,
    returned: L[True],
    keepdims: L[True],
) -> _Tuple2[NDArray[ScalarT]]: ...
@overload  # bool or integer array-like, axis=None
def average(
    a: _SeqND[float] | _ArrayLikeInt_co,
    axis: None = None,
    weights: _ArrayLikeFloat_co | None = None,
    returned: L[False] = False,
    *,
    keepdims: L[False] | _NoValueType = ...,
) -> np.float64: ...
@overload  # bool or integer array-like, axis=<given>
def average(
    a: _SeqND[float] | _ArrayLikeInt_co,
    axis: int | tuple[int, ...],
    weights: _ArrayLikeFloat_co | None = None,
    returned: L[False] = False,
    *,
    keepdims: L[False] | _NoValueType = ...,
) -> NDArray[np.float64]: ...
@overload  # bool or integer array-like, keepdims=True
def average(
    a: _SeqND[float] | _ArrayLikeInt_co,
    axis: int | tuple[int, ...] | None = None,
    weights: _ArrayLikeFloat_co | None = None,
    returned: L[False] = False,
    *,
    keepdims: L[True],
) -> NDArray[np.float64]: ...
@overload  # bool or integer array-like, axis=None, returned=True
def average(
    a: _SeqND[float] | _ArrayLikeInt_co,
    axis: None = None,
    weights: _ArrayLikeFloat_co | None = None,
    *,
    returned: L[True],
    keepdims: L[False] | _NoValueType = ...,
) -> _Tuple2[np.float64]: ...
@overload  # bool or integer array-like, axis=<given>, returned=True
def average(
    a: _SeqND[float] | _ArrayLikeInt_co,
    axis: int | tuple[int, ...],
    weights: _ArrayLikeFloat_co | None = None,
    *,
    returned: L[True],
    keepdims: L[False] | _NoValueType = ...,
) -> _Tuple2[NDArray[np.float64]]: ...
@overload  # bool or integer array-like, returned=True, keepdims=True
def average(
    a: _SeqND[float] | _ArrayLikeInt_co,
    axis: int | tuple[int, ...] | None = None,
    weights: _ArrayLikeFloat_co | None = None,
    *,
    returned: L[True],
    keepdims: L[True],
) -> _Tuple2[NDArray[np.float64]]: ...
@overload  # complex array-like, axis=None
def average(
    a: _ListSeqND[complex],
    axis: None = None,
    weights: _ArrayLikeComplex_co | None = None,
    returned: L[False] = False,
    *,
    keepdims: L[False] | _NoValueType = ...,
) -> np.complex128: ...
@overload  # complex array-like, axis=<given>
def average(
    a: _ListSeqND[complex],
    axis: int | tuple[int, ...],
    weights: _ArrayLikeComplex_co | None = None,
    returned: L[False] = False,
    *,
    keepdims: L[False] | _NoValueType = ...,
) -> NDArray[np.complex128]: ...
@overload  # complex array-like, keepdims=True
def average(
    a: _ListSeqND[complex],
    axis: int | tuple[int, ...] | None = None,
    weights: _ArrayLikeComplex_co | None = None,
    returned: L[False] = False,
    *,
    keepdims: L[True],
) -> NDArray[np.complex128]: ...
@overload  # complex array-like, axis=None, returned=True
def average(
    a: _ListSeqND[complex],
    axis: None = None,
    weights: _ArrayLikeComplex_co | None = None,
    *,
    returned: L[True],
    keepdims: L[False] | _NoValueType = ...,
) -> _Tuple2[np.complex128]: ...
@overload  # complex array-like, axis=<given>, returned=True
def average(
    a: _ListSeqND[complex],
    axis: int | tuple[int, ...],
    weights: _ArrayLikeComplex_co | None = None,
    *,
    returned: L[True],
    keepdims: L[False] | _NoValueType = ...,
) -> _Tuple2[NDArray[np.complex128]]: ...
@overload  # complex array-like, keepdims=True, returned=True
def average(
    a: _ListSeqND[complex],
    axis: int | tuple[int, ...] | None = None,
    weights: _ArrayLikeComplex_co | None = None,
    *,
    returned: L[True],
    keepdims: L[True],
) -> _Tuple2[NDArray[np.complex128]]: ...
@overload  # unknown, axis=None
def average(
    a: _ArrayLikeNumber_co | _ArrayLikeObject_co,
    axis: None = None,
    weights: _ArrayLikeNumber_co | None = None,
    returned: L[False] = False,
    *,
    keepdims: L[False] | _NoValueType = ...,
) -> Any: ...
@overload  # unknown, axis=<given>
def average(
    a: _ArrayLikeNumber_co | _ArrayLikeObject_co,
    axis: int | tuple[int, ...],
    weights: _ArrayLikeNumber_co | None = None,
    returned: L[False] = False,
    *,
    keepdims: L[False] | _NoValueType = ...,
) -> np.ndarray: ...
@overload  # unknown, keepdims=True
def average(
    a: _ArrayLikeNumber_co | _ArrayLikeObject_co,
    axis: int | tuple[int, ...] | None = None,
    weights: _ArrayLikeNumber_co | None = None,
    returned: L[False] = False,
    *,
    keepdims: L[True],
) -> np.ndarray: ...
@overload  # unknown, axis=None, returned=True
def average(
    a: _ArrayLikeNumber_co | _ArrayLikeObject_co,
    axis: None = None,
    weights: _ArrayLikeNumber_co | None = None,
    *,
    returned: L[True],
    keepdims: L[False] | _NoValueType = ...,
) -> _Tuple2[Any]: ...
@overload  # unknown, axis=<given>, returned=True
def average(
    a: _ArrayLikeNumber_co | _ArrayLikeObject_co,
    axis: int | tuple[int, ...],
    weights: _ArrayLikeNumber_co | None = None,
    *,
    returned: L[True],
    keepdims: L[False] | _NoValueType = ...,
) -> _Tuple2[np.ndarray]: ...
@overload  # unknown, returned=True, keepdims=True
def average(
    a: _ArrayLikeNumber_co | _ArrayLikeObject_co,
    axis: int | tuple[int, ...] | None = None,
    weights: _ArrayLikeNumber_co | None = None,
    *,
    returned: L[True],
    keepdims: L[True],
) -> _Tuple2[np.ndarray]: ...

#
@overload
def asarray_chkfinite[ArrayT: np.ndarray](a: ArrayT, dtype: None = None, order: _OrderKACF = None) -> ArrayT: ...
@overload
def asarray_chkfinite[ShapeT: _Shape, ScalarT: np.generic](
    a: np.ndarray[ShapeT], dtype: _DTypeLike[ScalarT], order: _OrderKACF = None
) -> _Array[ShapeT, ScalarT]: ...
@overload
def asarray_chkfinite[ScalarT: np.generic](
    a: _ArrayLike[ScalarT], dtype: None = None, order: _OrderKACF = None
) -> NDArray[ScalarT]: ...
@overload
def asarray_chkfinite[ScalarT: np.generic](
    a: object, dtype: _DTypeLike[ScalarT], order: _OrderKACF = None
) -> NDArray[ScalarT]: ...
@overload
def asarray_chkfinite(a: object, dtype: DTypeLike | None = None, order: _OrderKACF = None) -> NDArray[Incomplete]: ...

# NOTE: Contrary to the documentation, scalars are also accepted and treated as
# `[condlist]`. And even though the documentation says these should be boolean, in
# practice anything that `np.array(condlist, dtype=bool)` accepts will work, i.e. any
# array-like.
@overload  # this overload is required to avoid pyright rejecting all-scalar `funclist`
def piecewise[ShapeT: _Shape, ScalarT: np.generic](
    x: _Array[ShapeT, ScalarT],
    condlist: ArrayLike,
    funclist: _SizedIterable[_ScalarLike_co],
) -> _Array[ShapeT, ScalarT]: ...
@overload
def piecewise[ShapeT: _Shape, ScalarT: np.generic, **Tss](
    x: _Array[ShapeT, ScalarT],
    condlist: ArrayLike,
    funclist: _PiecewiseFunctions[Any, Tss],
    *args: Tss.args,
    **kw: Tss.kwargs,
) -> _Array[ShapeT, ScalarT]: ...
@overload
def piecewise[ScalarT: np.generic, **Tss](
    x: _ArrayLike[ScalarT],
    condlist: ArrayLike,
    funclist: _PiecewiseFunctions[Any, Tss],
    *args: Tss.args,
    **kw: Tss.kwargs,
) -> NDArray[ScalarT]: ...
@overload
def piecewise[ScalarT: np.generic, **Tss](
    x: ArrayLike,
    condlist: ArrayLike,
    funclist: _PiecewiseFunctions[ScalarT, Tss],
    *args: Tss.args,
    **kw: Tss.kwargs,
) -> NDArray[ScalarT]: ...

# NOTE: condition is usually boolean, but anything with zero/non-zero semantics works
@overload
def extract[ScalarT: np.generic](condition: ArrayLike, arr: _ArrayLike[ScalarT]) -> _Array1D[ScalarT]: ...
@overload
def extract(condition: ArrayLike, arr: _SeqND[bool]) -> _Array1D[np.bool]: ...
@overload
def extract(condition: ArrayLike, arr: _ListSeqND[int]) -> _Array1D[np.int_]: ...
@overload
def extract(condition: ArrayLike, arr: _ListSeqND[float]) -> _Array1D[np.float64]: ...
@overload
def extract(condition: ArrayLike, arr: _ListSeqND[complex]) -> _Array1D[np.complex128]: ...
@overload
def extract(condition: ArrayLike, arr: _SeqND[bytes]) -> _Array1D[np.bytes_]: ...
@overload
def extract(condition: ArrayLike, arr: _SeqND[str]) -> _Array1D[np.str_]: ...
@overload
def extract(condition: ArrayLike, arr: ArrayLike) -> _Array1D[Incomplete]: ...

# NOTE: unlike `extract`, passing non-boolean conditions for `condlist` will raise an
# error at runtime
@overload
def select[ArrayT: np.ndarray](
    condlist: _SizedIterable[_ArrayLikeBool_co],
    choicelist: Sequence[ArrayT],
    default: ArrayLike = 0,
) -> ArrayT: ...
@overload
def select[ScalarT: np.generic](
    condlist: _SizedIterable[_ArrayLikeBool_co],
    choicelist: Sequence[_ArrayLike[ScalarT]] | NDArray[ScalarT],
    default: ArrayLike = 0,
) -> NDArray[ScalarT]: ...
@overload
def select(
    condlist: _SizedIterable[_ArrayLikeBool_co],
    choicelist: Sequence[ArrayLike],
    default: ArrayLike = 0,
) -> np.ndarray: ...

# keep roughly in sync with `ma.core.copy`
@overload
def copy[ArrayT: np.ndarray](a: ArrayT, order: _OrderKACF, subok: L[True]) -> ArrayT: ...
@overload
def copy[ArrayT: np.ndarray](a: ArrayT, order: _OrderKACF = "K", *, subok: L[True]) -> ArrayT: ...
@overload
def copy[ScalarT: np.generic](a: _ArrayLike[ScalarT], order: _OrderKACF = "K", subok: L[False] = False) -> NDArray[ScalarT]: ...
@overload
def copy(a: ArrayLike, order: _OrderKACF = "K", subok: L[False] = False) -> NDArray[Incomplete]: ...

#
@overload  # ?d, known inexact scalar-type
def gradient[ScalarT: np.inexact | np.timedelta64](
    f: _ArrayNoD[ScalarT],
    *varargs: _ArrayLikeNumber_co,
    axis: _ShapeLike | None = None,
    edge_order: L[1, 2] = 1,
    # `| Any` instead of ` | tuple` is returned to avoid several mypy_primer errors
) -> _Array1D[ScalarT] | Any: ...
@overload  # 1d, known inexact scalar-type
def gradient[ScalarT: np.inexact | np.timedelta64](
    f: _Array1D[ScalarT],
    *varargs: _ArrayLikeNumber_co,
    axis: _ShapeLike | None = None,
    edge_order: L[1, 2] = 1,
) -> _Array1D[ScalarT]: ...
@overload  # 2d, known inexact scalar-type
def gradient[ScalarT: np.inexact | np.timedelta64](
    f: _Array2D[ScalarT],
    *varargs: _ArrayLikeNumber_co,
    axis: _ShapeLike | None = None,
    edge_order: L[1, 2] = 1,
) -> _Mesh2[ScalarT, ScalarT]: ...
@overload  # 3d, known inexact scalar-type
def gradient[ScalarT: np.inexact | np.timedelta64](
    f: _Array3D[ScalarT],
    *varargs: _ArrayLikeNumber_co,
    axis: _ShapeLike | None = None,
    edge_order: L[1, 2] = 1,
) -> _Mesh3[ScalarT, ScalarT, ScalarT]: ...
@overload  # ?d, datetime64 scalar-type
def gradient(
    f: _ArrayNoD[np.datetime64],
    *varargs: _ArrayLikeNumber_co,
    axis: _ShapeLike | None = None,
    edge_order: L[1, 2] = 1,
) -> _Array1D[np.timedelta64] | tuple[NDArray[np.timedelta64], ...]: ...
@overload  # 1d, datetime64 scalar-type
def gradient(
    f: _Array1D[np.datetime64],
    *varargs: _ArrayLikeNumber_co,
    axis: _ShapeLike | None = None,
    edge_order: L[1, 2] = 1,
) -> _Array1D[np.timedelta64]: ...
@overload  # 2d, datetime64 scalar-type
def gradient(
    f: _Array2D[np.datetime64],
    *varargs: _ArrayLikeNumber_co,
    axis: _ShapeLike | None = None,
    edge_order: L[1, 2] = 1,
) -> _Mesh2[np.timedelta64, np.timedelta64]: ...
@overload  # 3d, datetime64 scalar-type
def gradient(
    f: _Array3D[np.datetime64],
    *varargs: _ArrayLikeNumber_co,
    axis: _ShapeLike | None = None,
    edge_order: L[1, 2] = 1,
) -> _Mesh3[np.timedelta64, np.timedelta64, np.timedelta64]: ...
@overload  # 1d float-like
def gradient(
    f: _Seq1D[float],
    *varargs: _ArrayLikeNumber_co,
    axis: _ShapeLike | None = None,
    edge_order: L[1, 2] = 1,
) -> _Array1D[np.float64]: ...
@overload  # 2d float-like
def gradient(
    f: _Seq2D[float],
    *varargs: _ArrayLikeNumber_co,
    axis: _ShapeLike | None = None,
    edge_order: L[1, 2] = 1,
) -> _Mesh2[np.float64, np.float64]: ...
@overload  # 3d float-like
def gradient(
    f: _Seq3D[float],
    *varargs: _ArrayLikeNumber_co,
    axis: _ShapeLike | None = None,
    edge_order: L[1, 2] = 1,
) -> _Mesh3[np.float64, np.float64, np.float64]: ...
@overload  # 1d complex-like  (the `list` avoids overlap with the float-like overload)
def gradient(
    f: list[complex],
    *varargs: _ArrayLikeNumber_co,
    axis: _ShapeLike | None = None,
    edge_order: L[1, 2] = 1,
) -> _Array1D[np.complex128]: ...
@overload  # 2d float-like
def gradient(
    f: _Seq1D[list[complex]],
    *varargs: _ArrayLikeNumber_co,
    axis: _ShapeLike | None = None,
    edge_order: L[1, 2] = 1,
) -> _Mesh2[np.complex128, np.complex128]: ...
@overload  # 3d float-like
def gradient(
    f: _Seq2D[list[complex]],
    *varargs: _ArrayLikeNumber_co,
    axis: _ShapeLike | None = None,
    edge_order: L[1, 2] = 1,
) -> _Mesh3[np.complex128, np.complex128, np.complex128]: ...
@overload  # fallback
def gradient(
    f: ArrayLike,
    *varargs: _ArrayLikeNumber_co,
    axis: _ShapeLike | None = None,
    edge_order: L[1, 2] = 1,
) -> Incomplete: ...

# keep in sync with `ma.core.diff`
@overload  # n == 0; return input unchanged
def diff[T](
    a: T,
    n: L[0],
    axis: SupportsIndex = -1,
    prepend: ArrayLike | _NoValueType = ...,  # = _NoValue
    append: ArrayLike | _NoValueType = ...,  # = _NoValue
) -> T: ...
@overload  # known array-type
def diff[ArrayT: NDArray[_ScalarNumeric]](
    a: ArrayT,
    n: int = 1,
    axis: SupportsIndex = -1,
    prepend: ArrayLike | _NoValueType = ...,
    append: ArrayLike | _NoValueType = ...,
) -> ArrayT: ...
@overload  # known shape, datetime64
def diff[ShapeT: _Shape](
    a: _Array[ShapeT, np.datetime64],
    n: int = 1,
    axis: SupportsIndex = -1,
    prepend: ArrayLike | _NoValueType = ...,
    append: ArrayLike | _NoValueType = ...,
) -> _Array[ShapeT, np.timedelta64]: ...
@overload  # unknown shape, known scalar-type
def diff[ScalarT: _ScalarNumeric](
    a: _ArrayLike[ScalarT],
    n: int = 1,
    axis: SupportsIndex = -1,
    prepend: ArrayLike | _NoValueType = ...,
    append: ArrayLike | _NoValueType = ...,
) -> NDArray[ScalarT]: ...
@overload  # unknown shape, datetime64
def diff(
    a: _ArrayLike[np.datetime64],
    n: int = 1,
    axis: SupportsIndex = -1,
    prepend: ArrayLike | _NoValueType = ...,
    append: ArrayLike | _NoValueType = ...,
) -> NDArray[np.timedelta64]: ...
@overload  # 1d int
def diff(
    a: _Seq1D[int],
    n: int = 1,
    axis: SupportsIndex = -1,
    prepend: ArrayLike | _NoValueType = ...,
    append: ArrayLike | _NoValueType = ...,
) -> _Array1D[np.int_]: ...
@overload  # 2d int
def diff(
    a: _Seq2D[int],
    n: int = 1,
    axis: SupportsIndex = -1,
    prepend: ArrayLike | _NoValueType = ...,
    append: ArrayLike | _NoValueType = ...,
) -> _Array2D[np.int_]: ...
@overload  # 1d float  (the `list` avoids overlap with the `int` overloads)
def diff(
    a: list[float],
    n: int = 1,
    axis: SupportsIndex = -1,
    prepend: ArrayLike | _NoValueType = ...,
    append: ArrayLike | _NoValueType = ...,
) -> _Array1D[np.float64]: ...
@overload  # 2d float
def diff(
    a: _Seq1D[list[float]],
    n: int = 1,
    axis: SupportsIndex = -1,
    prepend: ArrayLike | _NoValueType = ...,
    append: ArrayLike | _NoValueType = ...,
) -> _Array2D[np.float64]: ...
@overload  # 1d complex  (the `list` avoids overlap with the `int` overloads)
def diff(
    a: list[complex],
    n: int = 1,
    axis: SupportsIndex = -1,
    prepend: ArrayLike | _NoValueType = ...,
    append: ArrayLike | _NoValueType = ...,
) -> _Array1D[np.complex128]: ...
@overload  # 2d complex
def diff(
    a: _Seq1D[list[complex]],
    n: int = 1,
    axis: SupportsIndex = -1,
    prepend: ArrayLike | _NoValueType = ...,
    append: ArrayLike | _NoValueType = ...,
) -> _Array2D[np.complex128]: ...
@overload  # unknown shape, unknown scalar-type
def diff(
    a: ArrayLike,
    n: int = 1,
    axis: SupportsIndex = -1,
    prepend: ArrayLike | _NoValueType = ...,
    append: ArrayLike | _NoValueType = ...,
) -> NDArray[Incomplete]: ...

#
@overload  # float scalar
def interp(
    x: _FloatLike_co,
    xp: _ArrayLikeFloat_co,
    fp: _ArrayLikeFloat_co,
    left: _FloatLike_co | None = None,
    right: _FloatLike_co | None = None,
    period: _FloatLike_co | None = None,
) -> np.float64: ...
@overload  # complex scalar
def interp(
    x: _FloatLike_co,
    xp: _ArrayLikeFloat_co,
    fp: _ArrayLike1D[np.complexfloating] | list[complex],
    left: _NumberLike_co | None = None,
    right: _NumberLike_co | None = None,
    period: _FloatLike_co | None = None,
) -> np.complex128: ...
@overload  # float array
def interp[ShapeT: _Shape](
    x: _Array[ShapeT, _floating_co],
    xp: _ArrayLikeFloat_co,
    fp: _ArrayLikeFloat_co,
    left: _FloatLike_co | None = None,
    right: _FloatLike_co | None = None,
    period: _FloatLike_co | None = None,
) -> _Array[ShapeT, np.float64]: ...
@overload  # complex array
def interp[ShapeT: _Shape](
    x: _Array[ShapeT, _floating_co],
    xp: _ArrayLikeFloat_co,
    fp: _ArrayLike1D[np.complexfloating] | list[complex],
    left: _NumberLike_co | None = None,
    right: _NumberLike_co | None = None,
    period: _FloatLike_co | None = None,
) -> _Array[ShapeT, np.complex128]: ...
@overload  # float sequence
def interp(
    x: _Seq1D[_FloatLike_co],
    xp: _ArrayLikeFloat_co,
    fp: _ArrayLikeFloat_co,
    left: _FloatLike_co | None = None,
    right: _FloatLike_co | None = None,
    period: _FloatLike_co | None = None,
) -> _Array1D[np.float64]: ...
@overload  # complex sequence
def interp(
    x: _Seq1D[_FloatLike_co],
    xp: _ArrayLikeFloat_co,
    fp: _ArrayLike1D[np.complexfloating] | list[complex],
    left: _NumberLike_co | None = None,
    right: _NumberLike_co | None = None,
    period: _FloatLike_co | None = None,
) -> _Array1D[np.complex128]: ...
@overload  # float array-like
def interp(
    x: _SeqND[_FloatLike_co],
    xp: _ArrayLikeFloat_co,
    fp: _ArrayLikeFloat_co,
    left: _FloatLike_co | None = None,
    right: _FloatLike_co | None = None,
    period: _FloatLike_co | None = None,
) -> NDArray[np.float64]: ...
@overload  # complex array-like
def interp(
    x: _SeqND[_FloatLike_co],
    xp: _ArrayLikeFloat_co,
    fp: _ArrayLike1D[np.complexfloating] | list[complex],
    left: _NumberLike_co | None = None,
    right: _NumberLike_co | None = None,
    period: _FloatLike_co | None = None,
) -> NDArray[np.complex128]: ...
@overload  # float scalar/array-like
def interp(
    x: _ArrayLikeFloat_co,
    xp: _ArrayLikeFloat_co,
    fp: _ArrayLikeFloat_co,
    left: _FloatLike_co | None = None,
    right: _FloatLike_co | None = None,
    period: _FloatLike_co | None = None,
) -> NDArray[np.float64] | np.float64: ...
@overload  # complex scalar/array-like
def interp(
    x: _ArrayLikeFloat_co,
    xp: _ArrayLikeFloat_co,
    fp: _ArrayLike1D[np.complexfloating],
    left: _NumberLike_co | None = None,
    right: _NumberLike_co | None = None,
    period: _FloatLike_co | None = None,
) -> NDArray[np.complex128] | np.complex128: ...
@overload  # float/complex scalar/array-like
def interp(
    x: _ArrayLikeFloat_co,
    xp: _ArrayLikeFloat_co,
    fp: _ArrayLikeNumber_co,
    left: _NumberLike_co | None = None,
    right: _NumberLike_co | None = None,
    period: _FloatLike_co | None = None,
) -> NDArray[np.complex128 | np.float64] | np.complex128 | np.float64: ...

#
@overload  # 0d T: floating -> 0d T
def angle[FloatingT: np.floating](z: FloatingT, deg: bool = False) -> FloatingT: ...
@overload  # 0d complex | float | ~integer -> 0d float64
def angle(z: complex | _integer_co, deg: bool = False) -> np.float64: ...
@overload  # 0d complex64 -> 0d float32
def angle(z: np.complex64, deg: bool = False) -> np.float32: ...
@overload  # 0d clongdouble -> 0d longdouble
def angle(z: np.clongdouble, deg: bool = False) -> np.longdouble: ...
@overload  # T: nd floating -> T
def angle[ArrayFloatingT: NDArray[np.floating]](z: ArrayFloatingT, deg: bool = False) -> ArrayFloatingT: ...
@overload  # nd T: complex128 | ~integer -> nd float64
def angle[ShapeT: _Shape](z: _Array[ShapeT, np.complex128 | _integer_co], deg: bool = False) -> _Array[ShapeT, np.float64]: ...
@overload  # nd T: complex64 -> nd float32
def angle[ShapeT: _Shape](z: _Array[ShapeT, np.complex64], deg: bool = False) -> _Array[ShapeT, np.float32]: ...
@overload  # nd T: clongdouble -> nd longdouble
def angle[ShapeT: _Shape](z: _Array[ShapeT, np.clongdouble], deg: bool = False) -> _Array[ShapeT, np.longdouble]: ...
@overload  # 1d complex -> 1d float64
def angle(z: _Seq1D[complex], deg: bool = False) -> _Array1D[np.float64]: ...
@overload  # 2d complex -> 2d float64
def angle(z: _Seq2D[complex], deg: bool = False) -> _Array2D[np.float64]: ...
@overload  # 3d complex -> 3d float64
def angle(z: _Seq3D[complex], deg: bool = False) -> _Array3D[np.float64]: ...
@overload  # fallback
def angle(z: _ArrayLikeComplex_co, deg: bool = False) -> NDArray[np.floating] | Any: ...

#
@overload  # known array-type
def unwrap[ArrayT: NDArray[np.floating | np.object_]](
    p: ArrayT,
    discont: float | None = None,
    axis: int = -1,
    *,
    period: float = ...,  # = τ
) -> ArrayT: ...
@overload  # known shape, float64
def unwrap[ShapeT: _Shape](
    p: _Array[ShapeT, _float64_co],
    discont: float | None = None,
    axis: int = -1,
    *,
    period: float = ...,  # = τ
) -> _Array[ShapeT, np.float64]: ...
@overload  # 1d float64-like
def unwrap(
    p: _Seq1D[float | _float64_co],
    discont: float | None = None,
    axis: int = -1,
    *,
    period: float = ...,  # = τ
) -> _Array1D[np.float64]: ...
@overload  # 2d float64-like
def unwrap(
    p: _Seq2D[float | _float64_co],
    discont: float | None = None,
    axis: int = -1,
    *,
    period: float = ...,  # = τ
) -> _Array2D[np.float64]: ...
@overload  # 3d float64-like
def unwrap(
    p: _Seq3D[float | _float64_co],
    discont: float | None = None,
    axis: int = -1,
    *,
    period: float = ...,  # = τ
) -> _Array3D[np.float64]: ...
@overload  # ?d, float64
def unwrap(
    p: _SeqND[float] | _ArrayLike[_float64_co],
    discont: float | None = None,
    axis: int = -1,
    *,
    period: float = ...,  # = τ
) -> NDArray[np.float64]: ...
@overload  # fallback
def unwrap(
    p: _ArrayLikeFloat_co | _ArrayLikeObject_co,
    discont: float | None = None,
    axis: int = -1,
    *,
    period: float = ...,  # = τ
) -> np.ndarray: ...

#
@overload
def sort_complex[ArrayT: NDArray[np.complexfloating]](a: ArrayT) -> ArrayT: ...
@overload  # complex64, shape known
def sort_complex[ShapeT: _Shape](a: _Array[ShapeT, np.int8 | np.uint8 | np.int16 | np.uint16]) -> _Array[ShapeT, np.complex64]: ...
@overload  # complex64, shape unknown
def sort_complex(a: _ArrayLike[np.int8 | np.uint8 | np.int16 | np.uint16]) -> NDArray[np.complex64]: ...
@overload  # complex128, shape known
def sort_complex[ShapeT: _Shape](a: _Array[ShapeT, _SortsToComplex128]) -> _Array[ShapeT, np.complex128]: ...
@overload  # complex128, shape unknown
def sort_complex(a: _ArrayLike[_SortsToComplex128]) -> NDArray[np.complex128]: ...
@overload  # clongdouble, shape known
def sort_complex[ShapeT: _Shape](a: _Array[ShapeT, np.longdouble]) -> _Array[ShapeT, np.clongdouble]: ...
@overload  # clongdouble, shape unknown
def sort_complex(a: _ArrayLike[np.longdouble]) -> NDArray[np.clongdouble]: ...
@overload  # builtin +complex, shape unknown
def sort_complex(a: _SeqND[complex]) -> NDArray[np.complex128]: ...

#
def trim_zeros[T](filt: _TrimZerosSequence[T], trim: L["f", "b", "fb", "bf"] = "fb", axis: _ShapeLike | None = None) -> T: ...

# NOTE: keep in sync with `corrcoef`
@overload  # ?d, known inexact scalar-type >=64 precision, y=<given>.
def cov[ScalarT: _InexactDouble](
    m: _ArrayLike[ScalarT],
    y: _ArrayLike[ScalarT],
    rowvar: bool = True,
    bias: bool = False,
    ddof: SupportsIndex | SupportsInt | None = None,
    fweights: _ArrayLikeInt_co | None = None,
    aweights: _ArrayLikeFloat_co | None = None,
    *,
    dtype: None = None,
) -> _Array2D[ScalarT]: ...
@overload  # ?d, known inexact scalar-type >=64 precision, y=None -> 0d or 2d
def cov[ScalarT: _InexactDouble](
    m: _ArrayNoD[ScalarT],
    y: None = None,
    rowvar: bool = True,
    bias: bool = False,
    ddof: SupportsIndex | SupportsInt | None = None,
    fweights: _ArrayLikeInt_co | None = None,
    aweights: _ArrayLikeFloat_co | None = None,
    *,
    dtype: _DTypeLike[ScalarT] | None = None,
) -> NDArray[ScalarT]: ...
@overload  # 1d, known inexact scalar-type >=64 precision, y=None
def cov[ScalarT: _InexactDouble](
    m: _Array1D[ScalarT],
    y: None = None,
    rowvar: bool = True,
    bias: bool = False,
    ddof: SupportsIndex | SupportsInt | None = None,
    fweights: _ArrayLikeInt_co | None = None,
    aweights: _ArrayLikeFloat_co | None = None,
    *,
    dtype: _DTypeLike[ScalarT] | None = None,
) -> _Array0D[ScalarT]: ...
@overload  # nd, known inexact scalar-type >=64 precision, y=None -> 0d or 2d
def cov[ScalarT: _InexactDouble](
    m: _ArrayLike[ScalarT],
    y: None = None,
    rowvar: bool = True,
    bias: bool = False,
    ddof: SupportsIndex | SupportsInt | None = None,
    fweights: _ArrayLikeInt_co | None = None,
    aweights: _ArrayLikeFloat_co | None = None,
    *,
    dtype: _DTypeLike[ScalarT] | None = None,
) -> NDArray[ScalarT]: ...
@overload  # nd, casts to float64, y=<given>
def cov(
    m: NDArray[np.float32 | np.float16 | _integer_co] | _Seq1D[float] | _Seq2D[float],
    y: NDArray[np.float32 | np.float16 | _integer_co] | _Seq1D[float] | _Seq2D[float],
    rowvar: bool = True,
    bias: bool = False,
    ddof: SupportsIndex | SupportsInt | None = None,
    fweights: _ArrayLikeInt_co | None = None,
    aweights: _ArrayLikeFloat_co | None = None,
    *,
    dtype: _DTypeLike[np.float64] | None = None,
) -> _Array2D[np.float64]: ...
@overload  # ?d or 2d, casts to float64, y=None -> 0d or 2d
def cov(
    m: _ArrayNoD[np.float32 | np.float16 | _integer_co] | _Seq2D[float],
    y: None = None,
    rowvar: bool = True,
    bias: bool = False,
    ddof: SupportsIndex | SupportsInt | None = None,
    fweights: _ArrayLikeInt_co | None = None,
    aweights: _ArrayLikeFloat_co | None = None,
    *,
    dtype: _DTypeLike[np.float64] | None = None,
) -> NDArray[np.float64]: ...
@overload  # 1d, casts to float64, y=None
def cov(
    m:  _Array1D[np.float32 | np.float16 | _integer_co] | _Seq1D[float],
    y: None = None,
    rowvar: bool = True,
    bias: bool = False,
    ddof: SupportsIndex | SupportsInt | None = None,
    fweights: _ArrayLikeInt_co | None = None,
    aweights: _ArrayLikeFloat_co | None = None,
    *,
    dtype: _DTypeLike[np.float64] | None = None,
) -> _Array0D[np.float64]: ...
@overload  # nd, casts to float64, y=None -> 0d or 2d
def cov(
    m:  _ArrayLike[np.float32 | np.float16 | _integer_co],
    y: None = None,
    rowvar: bool = True,
    bias: bool = False,
    ddof: SupportsIndex | SupportsInt | None = None,
    fweights: _ArrayLikeInt_co | None = None,
    aweights: _ArrayLikeFloat_co | None = None,
    *,
    dtype: _DTypeLike[np.float64] | None = None,
) -> NDArray[np.float64]: ...
@overload  # 1d complex, y=<given>  (`list` avoids overlap with float overloads)
def cov(
    m: list[complex] | _Seq1D[list[complex]],
    y: list[complex] | _Seq1D[list[complex]],
    rowvar: bool = True,
    bias: bool = False,
    ddof: SupportsIndex | SupportsInt | None = None,
    fweights: _ArrayLikeInt_co | None = None,
    aweights: _ArrayLikeFloat_co | None = None,
    *,
    dtype: _DTypeLike[np.complex128] | None = None,
) -> _Array2D[np.complex128]: ...
@overload  # 1d complex, y=None
def cov(
    m: list[complex],
    y: None = None,
    rowvar: bool = True,
    bias: bool = False,
    ddof: SupportsIndex | SupportsInt | None = None,
    fweights: _ArrayLikeInt_co | None = None,
    aweights: _ArrayLikeFloat_co | None = None,
    *,
    dtype: _DTypeLike[np.complex128] | None = None,
) -> _Array0D[np.complex128]: ...
@overload  # 2d complex, y=None -> 0d or 2d
def cov(
    m: _Seq1D[list[complex]],
    y: None = None,
    rowvar: bool = True,
    bias: bool = False,
    ddof: SupportsIndex | SupportsInt | None = None,
    fweights: _ArrayLikeInt_co | None = None,
    aweights: _ArrayLikeFloat_co | None = None,
    *,
    dtype: _DTypeLike[np.complex128] | None = None,
) -> NDArray[np.complex128]: ...
@overload  # 1d complex-like, y=None, dtype=<known>
def cov[ScalarT: np.generic](
    m: _Seq1D[_ComplexLike_co],
    y: None = None,
    rowvar: bool = True,
    bias: bool = False,
    ddof: SupportsIndex | SupportsInt | None = None,
    fweights: _ArrayLikeInt_co | None = None,
    aweights: _ArrayLikeFloat_co | None = None,
    *,
    dtype: _DTypeLike[ScalarT],
) -> _Array0D[ScalarT]: ...
@overload  # nd complex-like, y=<given>, dtype=<known>
def cov[ScalarT: np.generic](
    m: _ArrayLikeComplex_co,
    y: _ArrayLikeComplex_co,
    rowvar: bool = True,
    bias: bool = False,
    ddof: SupportsIndex | SupportsInt | None = None,
    fweights: _ArrayLikeInt_co | None = None,
    aweights: _ArrayLikeFloat_co | None = None,
    *,
    dtype: _DTypeLike[ScalarT],
) -> _Array2D[ScalarT]: ...
@overload  # nd complex-like, y=None, dtype=<known> -> 0d or 2d
def cov[ScalarT: np.generic](
    m: _ArrayLikeComplex_co,
    y: None = None,
    rowvar: bool = True,
    bias: bool = False,
    ddof: SupportsIndex | SupportsInt | None = None,
    fweights: _ArrayLikeInt_co | None = None,
    aweights: _ArrayLikeFloat_co | None = None,
    *,
    dtype: _DTypeLike[ScalarT],
) -> NDArray[ScalarT]: ...
@overload  # nd complex-like, y=<given>, dtype=?
def cov(
    m: _ArrayLikeComplex_co,
    y: _ArrayLikeComplex_co,
    rowvar: bool = True,
    bias: bool = False,
    ddof: SupportsIndex | SupportsInt | None = None,
    fweights: _ArrayLikeInt_co | None = None,
    aweights: _ArrayLikeFloat_co | None = None,
    *,
    dtype: DTypeLike | None = None,
) -> _Array2D[Incomplete]: ...
@overload  # 1d complex-like, y=None, dtype=?
def cov(
    m: _Seq1D[_ComplexLike_co],
    y: None = None,
    rowvar: bool = True,
    bias: bool = False,
    ddof: SupportsIndex | SupportsInt | None = None,
    fweights: _ArrayLikeInt_co | None = None,
    aweights: _ArrayLikeFloat_co | None = None,
    *,
    dtype: DTypeLike | None = None,
) -> _Array0D[Incomplete]: ...
@overload  # nd complex-like, dtype=?
def cov(
    m: _ArrayLikeComplex_co,
    y: _ArrayLikeComplex_co | None = None,
    rowvar: bool = True,
    bias: bool = False,
    ddof: SupportsIndex | SupportsInt | None = None,
    fweights: _ArrayLikeInt_co | None = None,
    aweights: _ArrayLikeFloat_co | None = None,
    *,
    dtype: DTypeLike | None = None,
) -> NDArray[Incomplete]: ...

# NOTE: If only `x` is given and the resulting array has shape (1,1), a bare scalar
# is returned instead of a 2D array. When y is given, a 2D array is always returned.
# This differs from `cov`, which returns 0-D arrays instead of scalars in such cases.
# NOTE: keep in sync with `cov`
@overload  # ?d, known inexact scalar-type >=64 precision, y=<given>.
def corrcoef[ScalarT: _InexactDouble](
    x: _ArrayLike[ScalarT],
    y: _ArrayLike[ScalarT],
    rowvar: bool = True,
    *,
    dtype: _DTypeLike[ScalarT] | None = None,
) -> _Array2D[ScalarT]: ...
@overload  # ?d, known inexact scalar-type >=64 precision, y=None
def corrcoef[ScalarT: _InexactDouble](
    x: _ArrayNoD[ScalarT],
    y: None = None,
    rowvar: bool = True,
    *,
    dtype: _DTypeLike[ScalarT] | None = None,
) -> _Array2D[ScalarT] | ScalarT: ...
@overload  # 1d, known inexact scalar-type >=64 precision, y=None
def corrcoef[ScalarT: _InexactDouble](
    x: _Array1D[ScalarT],
    y: None = None,
    rowvar: bool = True,
    *,
    dtype: _DTypeLike[ScalarT] | None = None,
) -> ScalarT: ...
@overload  # nd, known inexact scalar-type >=64 precision, y=None
def corrcoef[ScalarT: _InexactDouble](
    x: _ArrayLike[ScalarT],
    y: None = None,
    rowvar: bool = True,
    *,
    dtype: _DTypeLike[ScalarT] | None = None,
) -> _Array2D[ScalarT] | ScalarT: ...
@overload  # nd, casts to float64, y=<given>
def corrcoef(
    x: NDArray[np.float32 | np.float16 | _integer_co] | _Seq1D[float] | _Seq2D[float],
    y: NDArray[np.float32 | np.float16 | _integer_co] | _Seq1D[float] | _Seq2D[float],
    rowvar: bool = True,
    *,
    dtype: _DTypeLike[np.float64] | None = None,
) -> _Array2D[np.float64]: ...
@overload  # ?d or 2d, casts to float64, y=None
def corrcoef(
    x: _ArrayNoD[np.float32 | np.float16 | _integer_co] | _Seq2D[float],
    y: None = None,
    rowvar: bool = True,
    *,
    dtype: _DTypeLike[np.float64] | None = None,
) -> _Array2D[np.float64] | np.float64: ...
@overload  # 1d, casts to float64, y=None
def corrcoef(
    x: _Array1D[np.float32 | np.float16 | _integer_co] | _Seq1D[float],
    y: None = None,
    rowvar: bool = True,
    *,
    dtype: _DTypeLike[np.float64] | None = None,
) -> np.float64: ...
@overload  # nd, casts to float64, y=None
def corrcoef(
    x: _ArrayLike[np.float32 | np.float16 | _integer_co],
    y: None = None,
    rowvar: bool = True,
    *,
    dtype: _DTypeLike[np.float64] | None = None,
) -> _Array2D[np.float64] | np.float64: ...
@overload  # 1d complex, y=<given>  (`list` avoids overlap with float overloads)
def corrcoef(
    x: list[complex] | _Seq1D[list[complex]],
    y: list[complex] | _Seq1D[list[complex]],
    rowvar: bool = True,
    *,
    dtype: _DTypeLike[np.complex128] | None = None,
) -> _Array2D[np.complex128]: ...
@overload  # 1d complex, y=None
def corrcoef(
    x: list[complex],
    y: None = None,
    rowvar: bool = True,
    *,
    dtype: _DTypeLike[np.complex128] | None = None,
) -> np.complex128: ...
@overload  # 2d complex, y=None
def corrcoef(
    x: _Seq1D[list[complex]],
    y: None = None,
    rowvar: bool = True,
    *,
    dtype: _DTypeLike[np.complex128] | None = None,
) -> _Array2D[np.complex128] | np.complex128: ...
@overload  # 1d complex-like, y=None, dtype=<known>
def corrcoef[ScalarT: np.generic](
    x: _Seq1D[_ComplexLike_co],
    y: None = None,
    rowvar: bool = True,
    *,
    dtype: _DTypeLike[ScalarT],
) -> ScalarT: ...
@overload  # nd complex-like, y=<given>, dtype=<known>
def corrcoef[ScalarT: np.generic](
    x: _ArrayLikeComplex_co,
    y: _ArrayLikeComplex_co,
    rowvar: bool = True,
    *,
    dtype: _DTypeLike[ScalarT],
) -> _Array2D[ScalarT]: ...
@overload  # nd complex-like, y=None, dtype=<known>
def corrcoef[ScalarT: np.generic](
    x: _ArrayLikeComplex_co,
    y: None = None,
    rowvar: bool = True,
    *,
    dtype: _DTypeLike[ScalarT],
) -> _Array2D[ScalarT] | ScalarT: ...
@overload  # nd complex-like, y=<given>, dtype=?
def corrcoef(
    x: _ArrayLikeComplex_co,
    y: _ArrayLikeComplex_co,
    rowvar: bool = True,
    *,
    dtype: DTypeLike | None = None,
) -> _Array2D[Incomplete]: ...
@overload  # 1d complex-like, y=None, dtype=?
def corrcoef(
    x: _Seq1D[_ComplexLike_co],
    y: None = None,
    rowvar: bool = True,
    *,
    dtype: DTypeLike | None = None,
) -> Incomplete: ...
@overload  # nd complex-like, dtype=?
def corrcoef(
    x: _ArrayLikeComplex_co,
    y: _ArrayLikeComplex_co | None = None,
    rowvar: bool = True,
    *,
    dtype: DTypeLike | None = None,
) -> _Array2D[Incomplete] | Incomplete: ...

# note that floating `M` are accepted, but their fractional part is ignored
def blackman(M: _FloatLike_co) -> _Array1D[np.float64]: ...
def bartlett(M: _FloatLike_co) -> _Array1D[np.float64]: ...
def hanning(M: _FloatLike_co) -> _Array1D[np.float64]: ...
def hamming(M: _FloatLike_co) -> _Array1D[np.float64]: ...
def kaiser(M: _FloatLike_co, beta: _FloatLike_co) -> _Array1D[np.float64]: ...

#
@overload
def i0[ShapeT: _Shape](x: _Array[ShapeT, np.floating | np.integer]) -> _Array[ShapeT, np.float64]: ...
@overload
def i0(x: _FloatLike_co) -> _Array0D[np.float64]: ...
@overload
def i0(x: _Seq1D[_FloatLike_co]) -> _Array1D[np.float64]: ...
@overload
def i0(x: _Seq2D[_FloatLike_co]) -> _Array2D[np.float64]: ...
@overload
def i0(x: _Seq3D[_FloatLike_co]) -> _Array3D[np.float64]: ...
@overload
def i0(x: _ArrayLikeFloat_co) -> NDArray[np.float64]: ...

#
@overload
def sinc[ScalarT: np.inexact](x: ScalarT) -> ScalarT: ...
@overload
def sinc(x: float | _float64_co) -> np.float64: ...
@overload
def sinc(x: complex) -> np.complex128 | Any: ...
@overload
def sinc[ArrayT: NDArray[np.inexact]](x: ArrayT) -> ArrayT: ...
@overload
def sinc[ShapeT: _Shape](x: _Array[ShapeT, _integer_co]) -> _Array[ShapeT, np.float64]: ...
@overload
def sinc(x: _Seq1D[float]) -> _Array1D[np.float64]: ...
@overload
def sinc(x: _Seq2D[float]) -> _Array2D[np.float64]: ...
@overload
def sinc(x: _Seq3D[float]) -> _Array3D[np.float64]: ...
@overload
def sinc(x: _SeqND[float]) -> NDArray[np.float64]: ...
@overload
def sinc(x: list[complex]) -> _Array1D[np.complex128]: ...
@overload
def sinc(x: _Seq1D[list[complex]]) -> _Array2D[np.complex128]: ...
@overload
def sinc(x: _Seq2D[list[complex]]) -> _Array3D[np.complex128]: ...
@overload
def sinc(x: _ArrayLikeComplex_co) -> np.ndarray | Any: ...

# NOTE: We assume that `axis` is only provided for >=1-D arrays because for <1-D arrays
# it has no effect, and would complicate the overloads significantly.
@overload  # known scalar-type, keepdims=False (default)
def median[ScalarT: np.inexact | np.timedelta64](
    a: _ArrayLike[ScalarT],
    axis: None = None,
    out: None = None,
    overwrite_input: bool = False,
    keepdims: L[False] = False,
) -> ScalarT: ...
@overload  # float array-like, keepdims=False (default)
def median(
    a: _ArrayLikeInt_co | _SeqND[float] | float,
    axis: None = None,
    out: None = None,
    overwrite_input: bool = False,
    keepdims: L[False] = False,
) -> np.float64: ...
@overload  # complex array-like, keepdims=False (default)
def median(
    a: _ListSeqND[complex],
    axis: None = None,
    out: None = None,
    overwrite_input: bool = False,
    keepdims: L[False] = False,
) -> np.complex128: ...
@overload  # complex scalar, keepdims=False (default)
def median(
    a: complex,
    axis: None = None,
    out: None = None,
    overwrite_input: bool = False,
    keepdims: L[False] = False,
) -> np.complex128 | Any: ...
@overload  # known array-type, keepdims=True
def median[ArrayT: NDArray[_ScalarNumeric]](
    a: ArrayT,
    axis: _ShapeLike | None = None,
    out: None = None,
    overwrite_input: bool = False,
    *,
    keepdims: L[True],
) -> ArrayT: ...
@overload  # known scalar-type, keepdims=True
def median[ScalarT: _ScalarNumeric](
    a: _ArrayLike[ScalarT],
    axis: _ShapeLike | None = None,
    out: None = None,
    overwrite_input: bool = False,
    *,
    keepdims: L[True],
) -> NDArray[ScalarT]: ...
@overload  # known scalar-type, axis=<given>
def median[ScalarT: _ScalarNumeric](
    a: _ArrayLike[ScalarT],
    axis: _ShapeLike,
    out: None = None,
    overwrite_input: bool = False,
    keepdims: bool = False,
) -> NDArray[ScalarT]: ...
@overload  # float array-like, keepdims=True
def median(
    a: _SeqND[float],
    axis: _ShapeLike | None = None,
    out: None = None,
    overwrite_input: bool = False,
    *,
    keepdims: L[True],
) -> NDArray[np.float64]: ...
@overload  # float array-like, axis=<given>
def median(
    a: _SeqND[float],
    axis: _ShapeLike,
    out: None = None,
    overwrite_input: bool = False,
    keepdims: bool = False,
) -> NDArray[np.float64]: ...
@overload  # complex array-like, keepdims=True
def median(
    a: _ListSeqND[complex],
    axis: _ShapeLike | None = None,
    out: None = None,
    overwrite_input: bool = False,
    *,
    keepdims: L[True],
) -> NDArray[np.complex128]: ...
@overload  # complex array-like, axis=<given>
def median(
    a: _ListSeqND[complex],
    axis: _ShapeLike,
    out: None = None,
    overwrite_input: bool = False,
    keepdims: bool = False,
) -> NDArray[np.complex128]: ...
@overload  # out=<given> (keyword)
def median[ArrayT: np.ndarray](
    a: _ArrayLikeComplex_co | _ArrayLike[np.timedelta64 | np.object_],
    axis: _ShapeLike | None = None,
    *,
    out: ArrayT,
    overwrite_input: bool = False,
    keepdims: bool = False,
) -> ArrayT: ...
@overload  # out=<given> (positional)
def median[ArrayT: np.ndarray](
    a: _ArrayLikeComplex_co | _ArrayLike[np.timedelta64 | np.object_],
    axis: _ShapeLike | None,
    out: ArrayT,
    overwrite_input: bool = False,
    keepdims: bool = False,
) -> ArrayT: ...
@overload  # fallback
def median(
    a: _ArrayLikeComplex_co | _ArrayLike[np.timedelta64 | np.object_],
    axis: _ShapeLike | None = None,
    out: None = None,
    overwrite_input: bool = False,
    keepdims: bool = False,
) -> Incomplete: ...

# NOTE: keep in sync with `quantile`
@overload  # inexact, scalar, axis=None
def percentile[ScalarT: np.inexact | np.timedelta64 | np.datetime64](
    a: _ArrayLike[ScalarT],
    q: _FloatLike_co,
    axis: None = None,
    out: None = None,
    overwrite_input: bool = False,
    method: _InterpolationMethod = "linear",
    keepdims: L[False] = False,
    *,
    weights: _ArrayLikeFloat_co | None = None,
) -> ScalarT: ...
@overload  # inexact, scalar, axis=<given>
def percentile[ScalarT: np.inexact | np.timedelta64 | np.datetime64](
    a: _ArrayLike[ScalarT],
    q: _FloatLike_co,
    axis: _ShapeLike,
    out: None = None,
    overwrite_input: bool = False,
    method: _InterpolationMethod = "linear",
    keepdims: L[False] = False,
    *,
    weights: _ArrayLikeFloat_co | None = None,
) -> NDArray[ScalarT]: ...
@overload  # inexact, scalar, keepdims=True
def percentile[ScalarT: np.inexact | np.timedelta64 | np.datetime64](
    a: _ArrayLike[ScalarT],
    q: _FloatLike_co,
    axis: _ShapeLike | None = None,
    out: None = None,
    overwrite_input: bool = False,
    method: _InterpolationMethod = "linear",
    *,
    keepdims: L[True],
    weights: _ArrayLikeFloat_co | None = None,
) -> NDArray[ScalarT]: ...
@overload  # inexact, array, axis=None
def percentile[ScalarT: np.inexact | np.timedelta64 | np.datetime64, ShapeT: _Shape](
    a: _ArrayLike[ScalarT],
    q: _Array[ShapeT, _floating_co],
    axis: None = None,
    out: None = None,
    overwrite_input: bool = False,
    method: _InterpolationMethod = "linear",
    keepdims: L[False] = False,
    *,
    weights: _ArrayLikeFloat_co | None = None,
) -> _Array[ShapeT, ScalarT]: ...
@overload  # inexact, array-like
def percentile[ScalarT: np.inexact | np.timedelta64 | np.datetime64](
    a: _ArrayLike[ScalarT],
    q: NDArray[_floating_co] | _SeqND[_FloatLike_co],
    axis: _ShapeLike | None = None,
    out: None = None,
    overwrite_input: bool = False,
    method: _InterpolationMethod = "linear",
    keepdims: bool = False,
    *,
    weights: _ArrayLikeFloat_co | None = None,
) -> NDArray[ScalarT]: ...
@overload  # float, scalar, axis=None
def percentile(
    a: _SeqND[float] | _ArrayLikeInt_co,
    q: _FloatLike_co,
    axis: None = None,
    out: None = None,
    overwrite_input: bool = False,
    method: _InterpolationMethod = "linear",
    keepdims: L[False] = False,
    *,
    weights: _ArrayLikeFloat_co | None = None,
) -> np.float64: ...
@overload  # float, scalar, axis=<given>
def percentile(
    a: _SeqND[float] | _ArrayLikeInt_co,
    q: _FloatLike_co,
    axis: _ShapeLike,
    out: None = None,
    overwrite_input: bool = False,
    method: _InterpolationMethod = "linear",
    keepdims: L[False] = False,
    *,
    weights: _ArrayLikeFloat_co | None = None,
) -> NDArray[np.float64]: ...
@overload  # float, scalar, keepdims=True
def percentile(
    a: _SeqND[float] | _ArrayLikeInt_co,
    q: _FloatLike_co,
    axis: _ShapeLike | None = None,
    out: None = None,
    overwrite_input: bool = False,
    method: _InterpolationMethod = "linear",
    *,
    keepdims: L[True],
    weights: _ArrayLikeFloat_co | None = None,
) -> NDArray[np.float64]: ...
@overload  # float, array, axis=None
def percentile[ShapeT: _Shape](
    a: _SeqND[float] | _ArrayLikeInt_co,
    q: _Array[ShapeT, _floating_co],
    axis: None = None,
    out: None = None,
    overwrite_input: bool = False,
    method: _InterpolationMethod = "linear",
    keepdims: L[False] = False,
    *,
    weights: _ArrayLikeFloat_co | None = None,
) -> _Array[ShapeT, np.float64]: ...
@overload  # float, array-like
def percentile(
    a: _SeqND[float] | _ArrayLikeInt_co,
    q: NDArray[_floating_co] | _SeqND[_FloatLike_co],
    axis: _ShapeLike | None = None,
    out: None = None,
    overwrite_input: bool = False,
    method: _InterpolationMethod = "linear",
    keepdims: bool = False,
    *,
    weights: _ArrayLikeFloat_co | None = None,
) -> NDArray[np.float64]: ...
@overload  # complex, scalar, axis=None
def percentile(
    a: _ListSeqND[complex],
    q: _FloatLike_co,
    axis: None = None,
    out: None = None,
    overwrite_input: bool = False,
    method: _InterpolationMethod = "linear",
    keepdims: L[False] = False,
    *,
    weights: _ArrayLikeFloat_co | None = None,
) -> np.complex128: ...
@overload  # complex, scalar, axis=<given>
def percentile(
    a: _ListSeqND[complex],
    q: _FloatLike_co,
    axis: _ShapeLike,
    out: None = None,
    overwrite_input: bool = False,
    method: _InterpolationMethod = "linear",
    keepdims: L[False] = False,
    *,
    weights: _ArrayLikeFloat_co | None = None,
) -> NDArray[np.complex128]: ...
@overload  # complex, scalar, keepdims=True
def percentile(
    a: _ListSeqND[complex],
    q: _FloatLike_co,
    axis: _ShapeLike | None = None,
    out: None = None,
    overwrite_input: bool = False,
    method: _InterpolationMethod = "linear",
    *,
    keepdims: L[True],
    weights: _ArrayLikeFloat_co | None = None,
) -> NDArray[np.complex128]: ...
@overload  # complex, array, axis=None
def percentile[ShapeT: _Shape](
    a: _ListSeqND[complex],
    q: _Array[ShapeT, _floating_co],
    axis: None = None,
    out: None = None,
    overwrite_input: bool = False,
    method: _InterpolationMethod = "linear",
    keepdims: L[False] = False,
    *,
    weights: _ArrayLikeFloat_co | None = None,
) -> _Array[ShapeT, np.complex128]: ...
@overload  # complex, array-like
def percentile(
    a: _ListSeqND[complex],
    q: NDArray[_floating_co] | _SeqND[_FloatLike_co],
    axis: _ShapeLike | None = None,
    out: None = None,
    overwrite_input: bool = False,
    method: _InterpolationMethod = "linear",
    keepdims: bool = False,
    *,
    weights: _ArrayLikeFloat_co | None = None,
) -> NDArray[np.complex128]: ...
@overload  # object_, scalar, axis=None
def percentile(
    a: _ArrayLikeObject_co,
    q: _FloatLike_co,
    axis: None = None,
    out: None = None,
    overwrite_input: bool = False,
    method: _InterpolationMethod = "linear",
    keepdims: L[False] = False,
    *,
    weights: _ArrayLikeFloat_co | None = None,
) -> Any: ...
@overload  # object_, scalar, axis=<given>
def percentile(
    a: _ArrayLikeObject_co,
    q: _FloatLike_co,
    axis: _ShapeLike,
    out: None = None,
    overwrite_input: bool = False,
    method: _InterpolationMethod = "linear",
    keepdims: L[False] = False,
    *,
    weights: _ArrayLikeFloat_co | None = None,
) -> NDArray[np.object_]: ...
@overload  # object_, scalar, keepdims=True
def percentile(
    a: _ArrayLikeObject_co,
    q: _FloatLike_co,
    axis: _ShapeLike | None = None,
    out: None = None,
    overwrite_input: bool = False,
    method: _InterpolationMethod = "linear",
    *,
    keepdims: L[True],
    weights: _ArrayLikeFloat_co | None = None,
) -> NDArray[np.object_]: ...
@overload  # object_, array, axis=None
def percentile[ShapeT: _Shape](
    a: _ArrayLikeObject_co,
    q: _Array[ShapeT, _floating_co],
    axis: None = None,
    out: None = None,
    overwrite_input: bool = False,
    method: _InterpolationMethod = "linear",
    keepdims: L[False] = False,
    *,
    weights: _ArrayLikeFloat_co | None = None,
) -> _Array[ShapeT, np.object_]: ...
@overload  # object_, array-like
def percentile(
    a: _ArrayLikeObject_co,
    q: NDArray[_floating_co] | _SeqND[_FloatLike_co],
    axis: _ShapeLike | None = None,
    out: None = None,
    overwrite_input: bool = False,
    method: _InterpolationMethod = "linear",
    keepdims: bool = False,
    *,
    weights: _ArrayLikeFloat_co | None = None,
) -> NDArray[np.object_]: ...
@overload  # out=<given> (keyword)
def percentile[ArrayT: np.ndarray](
    a: ArrayLike,
    q: _ArrayLikeFloat_co,
    axis: _ShapeLike | None,
    out: ArrayT,
    overwrite_input: bool = False,
    method: _InterpolationMethod = "linear",
    keepdims: bool = False,
    *,
    weights: _ArrayLikeFloat_co | None = None,
) -> ArrayT: ...
@overload  # out=<given> (positional)
def percentile[ArrayT: np.ndarray](
    a: ArrayLike,
    q: _ArrayLikeFloat_co,
    axis: _ShapeLike | None = None,
    *,
    out: ArrayT,
    overwrite_input: bool = False,
    method: _InterpolationMethod = "linear",
    keepdims: bool = False,
    weights: _ArrayLikeFloat_co | None = None,
) -> ArrayT: ...
@overload  # fallback
def percentile(
    a: _ArrayLikeNumber_co | _ArrayLikeObject_co,
    q: _ArrayLikeFloat_co,
    axis: _ShapeLike | None = None,
    out: None = None,
    overwrite_input: bool = False,
    method: _InterpolationMethod = "linear",
    keepdims: bool = False,
    *,
    weights: _ArrayLikeFloat_co | None = None,
) -> Incomplete: ...

# NOTE: keep in sync with `percentile`
@overload  # inexact, scalar, axis=None
def quantile[ScalarT: np.inexact | np.timedelta64 | np.datetime64](
    a: _ArrayLike[ScalarT],
    q: _FloatLike_co,
    axis: None = None,
    out: None = None,
    overwrite_input: bool = False,
    method: _InterpolationMethod = "linear",
    keepdims: L[False] = False,
    *,
    weights: _ArrayLikeFloat_co | None = None,
) -> ScalarT: ...
@overload  # inexact, scalar, axis=<given>
def quantile[ScalarT: np.inexact | np.timedelta64 | np.datetime64](
    a: _ArrayLike[ScalarT],
    q: _FloatLike_co,
    axis: _ShapeLike,
    out: None = None,
    overwrite_input: bool = False,
    method: _InterpolationMethod = "linear",
    keepdims: L[False] = False,
    *,
    weights: _ArrayLikeFloat_co | None = None,
) -> NDArray[ScalarT]: ...
@overload  # inexact, scalar, keepdims=True
def quantile[ScalarT: np.inexact | np.timedelta64 | np.datetime64](
    a: _ArrayLike[ScalarT],
    q: _FloatLike_co,
    axis: _ShapeLike | None = None,
    out: None = None,
    overwrite_input: bool = False,
    method: _InterpolationMethod = "linear",
    *,
    keepdims: L[True],
    weights: _ArrayLikeFloat_co | None = None,
) -> NDArray[ScalarT]: ...
@overload  # inexact, array, axis=None
def quantile[ScalarT: np.inexact | np.timedelta64 | np.datetime64, ShapeT: _Shape](
    a: _ArrayLike[ScalarT],
    q: _Array[ShapeT, _floating_co],
    axis: None = None,
    out: None = None,
    overwrite_input: bool = False,
    method: _InterpolationMethod = "linear",
    keepdims: L[False] = False,
    *,
    weights: _ArrayLikeFloat_co | None = None,
) -> _Array[ShapeT, ScalarT]: ...
@overload  # inexact, array-like
def quantile[ScalarT: np.inexact | np.timedelta64 | np.datetime64](
    a: _ArrayLike[ScalarT],
    q: NDArray[_floating_co] | _SeqND[_FloatLike_co],
    axis: _ShapeLike | None = None,
    out: None = None,
    overwrite_input: bool = False,
    method: _InterpolationMethod = "linear",
    keepdims: bool = False,
    *,
    weights: _ArrayLikeFloat_co | None = None,
) -> NDArray[ScalarT]: ...
@overload  # float, scalar, axis=None
def quantile(
    a: _SeqND[float] | _ArrayLikeInt_co,
    q: _FloatLike_co,
    axis: None = None,
    out: None = None,
    overwrite_input: bool = False,
    method: _InterpolationMethod = "linear",
    keepdims: L[False] = False,
    *,
    weights: _ArrayLikeFloat_co | None = None,
) -> np.float64: ...
@overload  # float, scalar, axis=<given>
def quantile(
    a: _SeqND[float] | _ArrayLikeInt_co,
    q: _FloatLike_co,
    axis: _ShapeLike,
    out: None = None,
    overwrite_input: bool = False,
    method: _InterpolationMethod = "linear",
    keepdims: L[False] = False,
    *,
    weights: _ArrayLikeFloat_co | None = None,
) -> NDArray[np.float64]: ...
@overload  # float, scalar, keepdims=True
def quantile(
    a: _SeqND[float] | _ArrayLikeInt_co,
    q: _FloatLike_co,
    axis: _ShapeLike | None = None,
    out: None = None,
    overwrite_input: bool = False,
    method: _InterpolationMethod = "linear",
    *,
    keepdims: L[True],
    weights: _ArrayLikeFloat_co | None = None,
) -> NDArray[np.float64]: ...
@overload  # float, array, axis=None
def quantile[ShapeT: _Shape](
    a: _SeqND[float] | _ArrayLikeInt_co,
    q: _Array[ShapeT, _floating_co],
    axis: None = None,
    out: None = None,
    overwrite_input: bool = False,
    method: _InterpolationMethod = "linear",
    keepdims: L[False] = False,
    *,
    weights: _ArrayLikeFloat_co | None = None,
) -> _Array[ShapeT, np.float64]: ...
@overload  # float, array-like
def quantile(
    a: _SeqND[float] | _ArrayLikeInt_co,
    q: NDArray[_floating_co] | _SeqND[_FloatLike_co],
    axis: _ShapeLike | None = None,
    out: None = None,
    overwrite_input: bool = False,
    method: _InterpolationMethod = "linear",
    keepdims: bool = False,
    *,
    weights: _ArrayLikeFloat_co | None = None,
) -> NDArray[np.float64]: ...
@overload  # complex, scalar, axis=None
def quantile(
    a: _ListSeqND[complex],
    q: _FloatLike_co,
    axis: None = None,
    out: None = None,
    overwrite_input: bool = False,
    method: _InterpolationMethod = "linear",
    keepdims: L[False] = False,
    *,
    weights: _ArrayLikeFloat_co | None = None,
) -> np.complex128: ...
@overload  # complex, scalar, axis=<given>
def quantile(
    a: _ListSeqND[complex],
    q: _FloatLike_co,
    axis: _ShapeLike,
    out: None = None,
    overwrite_input: bool = False,
    method: _InterpolationMethod = "linear",
    keepdims: L[False] = False,
    *,
    weights: _ArrayLikeFloat_co | None = None,
) -> NDArray[np.complex128]: ...
@overload  # complex, scalar, keepdims=True
def quantile(
    a: _ListSeqND[complex],
    q: _FloatLike_co,
    axis: _ShapeLike | None = None,
    out: None = None,
    overwrite_input: bool = False,
    method: _InterpolationMethod = "linear",
    *,
    keepdims: L[True],
    weights: _ArrayLikeFloat_co | None = None,
) -> NDArray[np.complex128]: ...
@overload  # complex, array, axis=None
def quantile[ShapeT: _Shape](
    a: _ListSeqND[complex],
    q: _Array[ShapeT, _floating_co],
    axis: None = None,
    out: None = None,
    overwrite_input: bool = False,
    method: _InterpolationMethod = "linear",
    keepdims: L[False] = False,
    *,
    weights: _ArrayLikeFloat_co | None = None,
) -> _Array[ShapeT, np.complex128]: ...
@overload  # complex, array-like
def quantile(
    a: _ListSeqND[complex],
    q: NDArray[_floating_co] | _SeqND[_FloatLike_co],
    axis: _ShapeLike | None = None,
    out: None = None,
    overwrite_input: bool = False,
    method: _InterpolationMethod = "linear",
    keepdims: bool = False,
    *,
    weights: _ArrayLikeFloat_co | None = None,
) -> NDArray[np.complex128]: ...
@overload  # object_, scalar, axis=None
def quantile(
    a: _ArrayLikeObject_co,
    q: _FloatLike_co,
    axis: None = None,
    out: None = None,
    overwrite_input: bool = False,
    method: _InterpolationMethod = "linear",
    keepdims: L[False] = False,
    *,
    weights: _ArrayLikeFloat_co | None = None,
) -> Any: ...
@overload  # object_, scalar, axis=<given>
def quantile(
    a: _ArrayLikeObject_co,
    q: _FloatLike_co,
    axis: _ShapeLike,
    out: None = None,
    overwrite_input: bool = False,
    method: _InterpolationMethod = "linear",
    keepdims: L[False] = False,
    *,
    weights: _ArrayLikeFloat_co | None = None,
) -> NDArray[np.object_]: ...
@overload  # object_, scalar, keepdims=True
def quantile(
    a: _ArrayLikeObject_co,
    q: _FloatLike_co,
    axis: _ShapeLike | None = None,
    out: None = None,
    overwrite_input: bool = False,
    method: _InterpolationMethod = "linear",
    *,
    keepdims: L[True],
    weights: _ArrayLikeFloat_co | None = None,
) -> NDArray[np.object_]: ...
@overload  # object_, array, axis=None
def quantile[ShapeT: _Shape](
    a: _ArrayLikeObject_co,
    q: _Array[ShapeT, _floating_co],
    axis: None = None,
    out: None = None,
    overwrite_input: bool = False,
    method: _InterpolationMethod = "linear",
    keepdims: L[False] = False,
    *,
    weights: _ArrayLikeFloat_co | None = None,
) -> _Array[ShapeT, np.object_]: ...
@overload  # object_, array-like
def quantile(
    a: _ArrayLikeObject_co,
    q: NDArray[_floating_co] | _SeqND[_FloatLike_co],
    axis: _ShapeLike | None = None,
    out: None = None,
    overwrite_input: bool = False,
    method: _InterpolationMethod = "linear",
    keepdims: bool = False,
    *,
    weights: _ArrayLikeFloat_co | None = None,
) -> NDArray[np.object_]: ...
@overload  # out=<given> (keyword)
def quantile[ArrayT: np.ndarray](
    a: ArrayLike,
    q: _ArrayLikeFloat_co,
    axis: _ShapeLike | None,
    out: ArrayT,
    overwrite_input: bool = False,
    method: _InterpolationMethod = "linear",
    keepdims: bool = False,
    *,
    weights: _ArrayLikeFloat_co | None = None,
) -> ArrayT: ...
@overload  # out=<given> (positional)
def quantile[ArrayT: np.ndarray](
    a: ArrayLike,
    q: _ArrayLikeFloat_co,
    axis: _ShapeLike | None = None,
    *,
    out: ArrayT,
    overwrite_input: bool = False,
    method: _InterpolationMethod = "linear",
    keepdims: bool = False,
    weights: _ArrayLikeFloat_co | None = None,
) -> ArrayT: ...
@overload  # fallback
def quantile(
    a: _ArrayLikeNumber_co | _ArrayLikeObject_co,
    q: _ArrayLikeFloat_co,
    axis: _ShapeLike | None = None,
    out: None = None,
    overwrite_input: bool = False,
    method: _InterpolationMethod = "linear",
    keepdims: bool = False,
    *,
    weights: _ArrayLikeFloat_co | None = None,
) -> Incomplete: ...

#
@overload  # ?d, known inexact/timedelta64 scalar-type
def trapezoid[ScalarT: np.inexact | np.timedelta64](
    y: _ArrayNoD[ScalarT],
    x: _ArrayLike[ScalarT] | _ArrayLikeFloat_co | None = None,
    dx: float = 1.0,
    axis: SupportsIndex = -1,
) -> NDArray[ScalarT] | ScalarT: ...
@overload  # ?d, casts to float64
def trapezoid(
    y: _ArrayNoD[_integer_co],
    x: _ArrayLikeFloat_co | None = None,
    dx: float = 1.0,
    axis: SupportsIndex = -1,
) -> NDArray[np.float64] | np.float64: ...
@overload  # strict 1d, known inexact/timedelta64 scalar-type
def trapezoid[ScalarT: np.inexact | np.timedelta64](
    y: _Array1D[ScalarT],
    x: _Array1D[ScalarT] | _Seq1D[float] | None = None,
    dx: float = 1.0,
    axis: SupportsIndex = -1,
) -> ScalarT: ...
@overload  # strict 1d, casts to float64
def trapezoid(
    y: _Array1D[_float64_co] | _Seq1D[float],
    x: _Array1D[_float64_co] | _Seq1D[float] | None = None,
    dx: float = 1.0,
    axis: SupportsIndex = -1,
) -> np.float64: ...
@overload  # strict 1d, casts to complex128 (`list` prevents overlapping overloads)
def trapezoid(
    y: list[complex],
    x: _Seq1D[complex] | None = None,
    dx: complex = 1.0,
    axis: SupportsIndex = -1,
) -> np.complex128: ...
@overload  # strict 1d, casts to complex128
def trapezoid(
    y: _Seq1D[complex],
    x: list[complex],
    dx: complex = 1.0,
    axis: SupportsIndex = -1,
) -> np.complex128: ...
@overload  # strict 2d, known inexact/timedelta64 scalar-type
def trapezoid[ScalarT: np.inexact | np.timedelta64](
    y: _Array2D[ScalarT],
    x: _ArrayMax2D[ScalarT] | _Seq2D[float] | _Seq1D[float] | None = None,
    dx: float = 1.0,
    axis: SupportsIndex = -1,
) -> ScalarT: ...
@overload  # strict 2d, casts to float64
def trapezoid(
    y: _Array2D[_float64_co] | _Seq2D[float],
    x: _ArrayMax2D[_float64_co] | _Seq2D[float] | _Seq1D[float] | None = None,
    dx: float = 1.0,
    axis: SupportsIndex = -1,
) -> np.float64: ...
@overload  # strict 2d, casts to complex128 (`list` prevents overlapping overloads)
def trapezoid(
    y: _Seq1D[list[complex]],
    x: _Seq2D[complex] | _Seq1D[complex] | None = None,
    dx: complex = 1.0,
    axis: SupportsIndex = -1,
) -> np.complex128: ...
@overload  # strict 2d, casts to complex128
def trapezoid(
    y: _Seq2D[complex] | _Seq1D[complex],
    x: _Seq1D[list[complex]],
    dx: complex = 1.0,
    axis: SupportsIndex = -1,
) -> np.complex128: ...
@overload
def trapezoid[ScalarT: np.inexact | np.timedelta64](
    y: _ArrayLike[ScalarT],
    x: _ArrayLike[ScalarT] | _ArrayLikeInt_co | None = None,
    dx: complex = 1.0,
    axis: SupportsIndex = -1,
) -> NDArray[ScalarT] | ScalarT: ...
@overload
def trapezoid(
    y: _ArrayLike[_float64_co],
    x: _ArrayLikeFloat_co | None = None,
    dx: float = 1.0,
    axis: SupportsIndex = -1,
) -> NDArray[np.float64] | np.float64: ...
@overload
def trapezoid(
    y: _ArrayLike[np.complex128],
    x: _ArrayLikeComplex_co | None = None,
    dx: float = 1.0,
    axis: SupportsIndex = -1,
) -> NDArray[np.complex128] | np.complex128: ...
@overload
def trapezoid(
    y: _ArrayLikeComplex_co,
    x: _ArrayLike[np.complex128],
    dx: float = 1.0,
    axis: SupportsIndex = -1,
) -> NDArray[np.complex128] | np.complex128: ...
@overload
def trapezoid(
    y: _ArrayLikeObject_co,
    x: _ArrayLikeObject_co | _ArrayLikeFloat_co | None = None,
    dx: float = 1.0,
    axis: SupportsIndex = -1,
) -> NDArray[np.object_] | Any: ...
@overload
def trapezoid[T](
    y: _Seq1D[_SupportsRMulFloat[T]],
    x: _Seq1D[_SupportsRMulFloat[T] | T] | None = None,
    dx: complex = 1.0,
    axis: SupportsIndex = -1,
) -> T: ...
@overload
def trapezoid(
    y: _ArrayLikeComplex_co | _ArrayLike[np.timedelta64 | np.object_],
    x: _ArrayLikeComplex_co | _ArrayLike[np.timedelta64 | np.object_] | None = None,
    dx: complex = 1.0,
    axis: SupportsIndex = -1,
) -> Incomplete: ...

#
@overload  # 0d
def meshgrid(*, copy: bool = True, sparse: bool = False, indexing: _Indexing = "xy") -> tuple[()]: ...
@overload  # 1d, known scalar-type
def meshgrid[ScalarT: np.generic](
    x1: _ArrayLike[ScalarT],
    /,
    *,
    copy: bool = True,
    sparse: bool = False,
    indexing: _Indexing = "xy",
) -> _Mesh1[ScalarT]: ...
@overload  # 1d, unknown scalar-type
def meshgrid(
    x1: ArrayLike,
    /,
    *,
    copy: bool = True,
    sparse: bool = False,
    indexing: _Indexing = "xy",
) -> _Mesh1[Any]: ...
@overload  # 2d, known scalar-types
def meshgrid[ScalarT1: np.generic, ScalarT2: np.generic](
    x1: _ArrayLike[ScalarT1],
    x2: _ArrayLike[ScalarT2],
    /,
    *,
    copy: bool = True,
    sparse: bool = False,
    indexing: _Indexing = "xy",
) -> _Mesh2[ScalarT1, ScalarT2]: ...
@overload  # 2d, known/unknown scalar-types
def meshgrid[ScalarT: np.generic](
    x1: _ArrayLike[ScalarT],
    x2: ArrayLike,
    /,
    *,
    copy: bool = True,
    sparse: bool = False,
    indexing: _Indexing = "xy",
) -> _Mesh2[ScalarT, Any]: ...
@overload  # 2d, unknown/known scalar-types
def meshgrid[ScalarT: np.generic](
    x1: ArrayLike,
    x2: _ArrayLike[ScalarT],
    /,
    *,
    copy: bool = True,
    sparse: bool = False,
    indexing: _Indexing = "xy",
) -> _Mesh2[Any, ScalarT]: ...
@overload  # 2d, unknown scalar-types
def meshgrid(
    x1: ArrayLike,
    x2: ArrayLike,
    /,
    *,
    copy: bool = True,
    sparse: bool = False,
    indexing: _Indexing = "xy",
) -> _Mesh2[Any, Any]: ...
@overload  # 3d, known scalar-types
def meshgrid[ScalarT1: np.generic, ScalarT2: np.generic, ScalarT3: np.generic](
    x1: _ArrayLike[ScalarT1],
    x2: _ArrayLike[ScalarT2],
    x3: _ArrayLike[ScalarT3],
    /,
    *,
    copy: bool = True,
    sparse: bool = False,
    indexing: _Indexing = "xy",
) -> _Mesh3[ScalarT1, ScalarT2, ScalarT3]: ...
@overload  # 3d, unknown scalar-types
def meshgrid(
    x1: ArrayLike,
    x2: ArrayLike,
    x3: ArrayLike,
    /,
    *,
    copy: bool = True,
    sparse: bool = False,
    indexing: _Indexing = "xy",
) -> _Mesh3[Any, Any, Any]: ...
@overload  # ?d, known scalar-types
def meshgrid[ScalarT: np.generic](
    *xi: _ArrayLike[ScalarT],
    copy: bool = True,
    sparse: bool = False,
    indexing: _Indexing = "xy",
) -> tuple[NDArray[ScalarT], ...]: ...
@overload  # ?d, unknown scalar-types
def meshgrid(
    *xi: ArrayLike,
    copy: bool = True,
    sparse: bool = False,
    indexing: _Indexing = "xy",
) -> tuple[NDArray[Any], ...]: ...

#
def place(arr: np.ndarray, mask: ConvertibleToInt | Sequence[ConvertibleToInt], vals: ArrayLike) -> None: ...

# keep in sync with `insert`
@overload  # known scalar-type, axis=None (default)
def delete[ScalarT: np.generic](arr: _ArrayLike[ScalarT], obj: _IndexLike, axis: None = None) -> _Array1D[ScalarT]: ...
@overload  # known array-type, axis specified
def delete[ArrayT: np.ndarray](arr: ArrayT, obj: _IndexLike, axis: SupportsIndex) -> ArrayT: ...
@overload  # known scalar-type, axis specified
def delete[ScalarT: np.generic](arr: _ArrayLike[ScalarT], obj: _IndexLike, axis: SupportsIndex) -> NDArray[ScalarT]: ...
@overload  # known scalar-type, axis=None (default)
def delete(arr: ArrayLike, obj: _IndexLike, axis: None = None) -> _Array1D[Any]: ...
@overload  # unknown scalar-type, axis specified
def delete(arr: ArrayLike, obj: _IndexLike, axis: SupportsIndex) -> NDArray[Any]: ...

# keep in sync with `delete`
@overload  # known scalar-type, axis=None (default)
def insert[ScalarT: np.generic](arr: _ArrayLike[ScalarT], obj: _IndexLike, values: ArrayLike, axis: None = None) -> _Array1D[ScalarT]: ...
@overload  # known array-type, axis specified
def insert[ArrayT: np.ndarray](arr: ArrayT, obj: _IndexLike, values: ArrayLike, axis: SupportsIndex) -> ArrayT: ...
@overload  # known scalar-type, axis specified
def insert[ScalarT: np.generic](arr: _ArrayLike[ScalarT], obj: _IndexLike, values: ArrayLike, axis: SupportsIndex) -> NDArray[ScalarT]: ...
@overload  # known scalar-type, axis=None (default)
def insert(arr: ArrayLike, obj: _IndexLike, values: ArrayLike, axis: None = None) -> _Array1D[Any]: ...
@overload  # unknown scalar-type, axis specified
def insert(arr: ArrayLike, obj: _IndexLike, values: ArrayLike, axis: SupportsIndex) -> NDArray[Any]: ...

# keep in sync with `ma.core.append`
@overload  # known array type, axis specified
def append[ArrayT: np.ndarray](arr: ArrayT, values: ArrayT, axis: SupportsIndex) -> ArrayT: ...
@overload  # 1d, known scalar type, axis specified
def append[ScalarT: np.generic](arr: _Seq1D[ScalarT], values: _Seq1D[ScalarT], axis: SupportsIndex) -> _Array1D[ScalarT]: ...
@overload  # 2d, known scalar type, axis specified
def append[ScalarT: np.generic](arr: _Seq2D[ScalarT], values: _Seq2D[ScalarT], axis: SupportsIndex) -> _Array2D[ScalarT]: ...
@overload  # 3d, known scalar type, axis specified
def append[ScalarT: np.generic](arr: _Seq3D[ScalarT], values: _Seq3D[ScalarT], axis: SupportsIndex) -> _Array3D[ScalarT]: ...
@overload  # ?d, known scalar type, axis specified
def append[ScalarT: np.generic](arr: _SeqND[ScalarT], values: _SeqND[ScalarT], axis: SupportsIndex) -> NDArray[ScalarT]: ...
@overload  # ?d, unknown scalar type, axis specified
def append(arr: np.ndarray | _SeqND[_ScalarLike_co], values: _SeqND[_ScalarLike_co], axis: SupportsIndex) -> np.ndarray: ...
@overload  # known scalar type, axis=None
def append[ScalarT: np.generic](arr: _ArrayLike[ScalarT], values: _ArrayLike[ScalarT], axis: None = None) -> _Array1D[ScalarT]: ...
@overload  # unknown scalar type, axis=None
def append(arr: ArrayLike, values: ArrayLike, axis: None = None) -> _Array1D[Any]: ...

#
@overload
def digitize[ShapeT: _Shape](
    x: _Array[ShapeT, np.floating | np.integer], bins: _ArrayLikeFloat_co, right: bool = False
) -> _Array[ShapeT, np.int_]: ...
@overload
def digitize(x: _FloatLike_co, bins: _ArrayLikeFloat_co, right: bool = False) -> np.int_: ...
@overload
def digitize(x: _Seq1D[_FloatLike_co], bins: _ArrayLikeFloat_co, right: bool = False) -> _Array1D[np.int_]: ...
@overload
def digitize(x: _Seq2D[_FloatLike_co], bins: _ArrayLikeFloat_co, right: bool = False) -> _Array2D[np.int_]: ...
@overload
def digitize(x: _Seq3D[_FloatLike_co], bins: _ArrayLikeFloat_co, right: bool = False) -> _Array3D[np.int_]: ...
@overload
def digitize(x: _ArrayLikeFloat_co, bins: _ArrayLikeFloat_co, right: bool = False) -> NDArray[np.int_] | Any: ...
