Building blocks for precise & flexible type hints

Optype is available as optype on PyPI:

pip install optype

For optional NumPy support, ensure that you use the optype[numpy] extra. This ensures that the installed numpy and the required numpy-typing-compat versions are compatible with each other.

pip install "optype[numpy]"

See the optype.numpy docs for more info.

Optype can also be installed with conda from the conda-forge channel:

conda install conda-forge::optype

If you want to use optype.numpy, you should instead install optype-numpy:

conda install conda-forge::optype-numpy

Let's say you're writing a twice(x) function, that evaluates 2 * x. Implementing it is trivial, but what about the type annotations?

Because twice(2) == 4, twice(3.14) == 6.28 and twice('I') = 'II', it might seem like a good idea to type it as twice[T](x: T) -> T: .... However, that wouldn't include cases such as twice(True) == 2 or twice((42, True)) == (42, True, 42, True), where the input- and output types differ. Moreover, twice should accept any type with a custom __rmul__ method that accepts 2 as argument.

This is where optype comes in handy, which has single-method protocols for all the builtin special methods. For twice, we can use optype.CanRMul[T, R], which, as the name suggests, is a protocol with (only) the def __rmul__(self, lhs: T) -> R: ... method. With this, the twice function can written as:

from typing import Literal
from typing import TypeAlias, TypeVar
from optype import CanRMul

R = TypeVar("R")
Two: TypeAlias = Literal[2]
RMul2: TypeAlias = CanRMul[Two, R]


def twice(x: RMul2[R]) -> R:
    return 2 * x

from typing import Literal
from optype import CanRMul

type Two = Literal[2]
type RMul2[R] = CanRMul[Two, R]


def twice[R](x: RMul2[R]) -> R:
    return 2 * x

But what about types that implement __add__ but not __radd__? In this case, we could return x * 2 as fallback (assuming commutativity). Because the optype.Can* protocols are runtime-checkable, the revised twice2 function can be compactly written as:

from optype import CanMul

Mul2: TypeAlias = CanMul[Two, R]
CMul2: TypeAlias = Mul2[R] | RMul2[R]


def twice2(x: CMul2[R]) -> R:
    if isinstance(x, CanRMul):
        return 2 * x
    else:
        return x * 2

from optype import CanMul

type Mul2[R] = CanMul[Two, R]
type CMul2[R] = Mul2[R] | RMul2[R]


def twice2[R](x: CMul2[R]) -> R:
    if isinstance(x, CanRMul):
        return 2 * x
    else:
        return x * 2

See examples/twice.py for the full example.

The API of optype is flat; a single import optype as opt is all you need (except for optype.numpy).

• optype
  • Just
  • Builtin type conversion
  • Rich relations
  • Binary operations
  • Reflected operations
  • Inplace operations
  • Unary operations
  • Rounding
  • Callables
  • Iteration
  • Awaitables
  • Async Iteration
  • Containers
  • Attributes
  • Context managers
  • Descriptors
  • Buffer types
• optype.copy
• optype.dataclasses
• optype.inspect
• optype.io
• optype.json
• optype.pickle
• optype.string
• optype.typing
  • Any* type aliases
  • Empty* type aliases
  • Literal types
• optype.dlpack
• optype.numpy
  • Shape-typing
  • Array-likes
  • Literals
  • compat submodule
  • random submodule
  • Any*Array and Any*DType
  • Low-level interfaces

There are five flavors of things that live within optype,

• The optype.Just[T] and its optype.Just{Int,Float,Complex} subtypes only accept instances of the type itself, while rejecting instances of strict subtypes. This can be used to e.g. work around the float and complex type promotions, annotating object() sentinels with Just[object], rejecting bool in functions that accept int, etc.
• optype.Can{} types describe what can be done with it. For instance, any CanAbs[T] type can be used as argument to the abs() builtin function with return type T. Most Can{} implement a single special method, whose name directly matches that of the type. CanAbs implements __abs__, CanAdd implements __add__, etc.
• optype.Has{} is the analogue of Can{}, but for special attributes. HasName has a __name__ attribute, HasDict has a __dict__, etc.
• optype.Does{} describe the type of operators. So DoesAbs is the type of the abs({}) builtin function, and DoesPos the type of the +{} prefix operator.
• optype.do_{} are the correctly-typed implementations of Does{}. For each do_{} there is a Does{}, and vice-versa. So do_abs: DoesAbs is the typed alias of abs({}), and do_pos: DoesPos is a typed version of operator.pos. The optype.do_ operators are more complete than operators, have runtime-accessible type annotations, and have names you don't need to know by heart.

The reference docs are structured as follows:

All typing protocols here live in the root optype namespace. They are runtime-checkable so that you can do e.g. isinstance('snail', optype.CanAdd), in case you want to check whether snail implements __add__.

Unlikecollections.abc, optype's protocols aren't abstract base classes, i.e. they don't extend abc.ABC, only typing.Protocol. This allows the optype protocols to be used as building blocks for .pyi type stubs.

Just is an invariant type "wrapper", where Just[T] only accepts instances of T, and rejects instances of any strict subtypes of T.

Note that e.g. Literal[""] and LiteralString are not a strict str subtypes, and are therefore assignable to Just[str], but instances of class S(str): ... are not assignable to Just[str].

Disallow passing bool as int:

import optype as op


def assert_int(x: op.Just[int]) -> int:
    assert type(x) is int
    return x


assert_int(42)  # ok
assert_int(False)  # rejected

Annotating a sentinel:

import optype as op

_DEFAULT = object()


def intmap(
    value: int,
    # same as `dict[int, int] | op.Just[object]`
    mapping: dict[int, int] | op.JustObject = _DEFAULT,
    /,
) -> int:
    # same as `type(mapping) is object`
    if isinstance(mapping, op.JustObject):
        return value

    return mapping[value]


intmap(1)  # ok
intmap(1, {1: 42})  # ok
intmap(1, "some object")  # rejected

The return type of these special methods is invariant. Python will raise an error if some other (sub)type is returned. This is why these optype interfaces don't accept generic type arguments.

These formatting methods are allowed to return instances that are a subtype of the str builtin. The same holds for the __format__ argument. So if you're a 10x developer that wants to hack Python's f-strings, but only if your type hints are spot-on; optype is you friend.

Additionally, optype provides protocols for types with (custom) hash or index methods:

The "rich" comparison special methods often return a bool. However, instances of any type can be returned (e.g. a numpy array). This is why the corresponding optype.Can* interfaces accept a second type argument for the return type, that defaults to bool when omitted. The first type parameter matches the passed method argument, i.e. the right-hand side operand, denoted here as x.

In the Python docs, these are referred to as "arithmetic operations". But the operands aren't limited to numeric types, and because the operations aren't required to be commutative, might be non-deterministic, and could have side-effects. Classifying them "arithmetic" is, at the very least, a bit of a stretch.

For the binary infix operators above, optype additionally provides interfaces with reflected (swapped) operands, e.g. __radd__ is a reflected __add__. They are named like the original, but prefixed with CanR prefix, i.e. __name__.replace('Can', 'CanR').

Similar to the reflected ops, the inplace/augmented ops are prefixed with CanI, namely:

These inplace operators usually return themselves (after some in-place mutation). But unfortunately, it currently isn't possible to use Self for this (i.e. something like type MyAlias[T] = optype.CanIAdd[T, Self] isn't allowed). So to help ease this unbearable pain, optype comes equipped with ready-made aliases for you to use. They bear the same name, with an additional *Self suffix, e.g. optype.CanIAddSelf[T].

The Can*Self variants return -> Self instead of R. Since optype 0.12.1 these also accept an optional R type parameter (with a default of Never), which, when provided, will result in a return type of -> Self | R.

The round() built-in function takes an optional second argument. From a typing perspective, round() has two overloads, one with 1 parameter, and one with two. For both overloads, optype provides separate operand interfaces: CanRound1[R] and CanRound2[T, RT]. Additionally, optype also provides their (overloaded) intersection type: CanRound[-T, +R1, +R2] = CanRound1[R1] & CanRound2[T, R2].

For example, type-checkers will mark the following code as valid (tested with pyright in strict mode):

x: float = 3.14
x1: CanRound1[int] = x
x2: CanRound2[int, float] = x
x3: CanRound[int, int, float] = x

Furthermore, there are the alternative rounding functions from the math standard library:

Almost all implementations use int for R. In fact, if no type for R is specified, it will default in int. But technically speaking, these methods can be made to return anything.

Unlike operator, optype provides an operator for callable objects: optype.do_call(f, *args. **kwargs).

CanCall is similar to collections.abc.Callable, but is runtime-checkable, and doesn't use esoteric hacks.

The operand x of iter(_) is within Python known as an iterable, which is what collections.abc.Iterable[V] is often used for (e.g. as base class, or for instance checking).

The optype analogue is CanIter[R], which as the name suggests, also implements __iter__. But unlike Iterable[V], its type parameter R binds to the return type of iter(_) -> R. This makes it possible to annotate the specific type of the iterable that iter(_) returns. Iterable[V] is only able to annotate the type of the iterated value. To see why that isn't possible, see python/typing#548.

The collections.abc.Iterator[V] is even more awkward; it is a subtype of Iterable[V]. For those familiar with collections.abc this might come as a surprise, but an iterator only needs to implement __next__, __iter__ isn't needed. This means that the Iterator[V] is unnecessarily restrictive. Apart from that being theoretically "ugly", it has significant performance implications, because the time-complexity of isinstance on a typing.Protocol is $O(n)$, with the $n$ referring to the amount of members. So even if the overhead of the inheritance and the abc.ABC usage is ignored, collections.abc.Iterator is twice as slow as it needs to be.

That's one of the (many) reasons that optype.CanNext[V] and optype.CanIter[R] are the better alternatives to Iterable and Iterator from the abracadabra collections. This is how they are defined:

For the sake of compatibility with collections.abc, there is optype.CanIterSelf[V], which is a protocol whose __iter__ returns typing.Self, as well as a __next__ method that returns T. I.e. it is equivalent to collections.abc.Iterator[V], but without the abc nonsense.

The optype.CanAwait[R] is almost the same as collections.abc.Awaitable[R], except that optype.CanAwait[R] is a pure interface, whereas Awaitable is also an abstract base class (making it absolutely useless when writing stubs).

Yes, you guessed it right; the abracadabra collections made the exact same mistakes for the async iterablors (or was it "iteramblers"...?).

But fret not; the optype alternatives are right here:

But wait, shouldn't V be a CanAwait? Well, only if you don't want to get fired... Technically speaking, __anext__ can return any type, and anext will pass it along without nagging. For details, see the discussion at python/typeshed#7491. Just because something is legal, doesn't mean it's a good idea (don't eat the yellow snow).

Additionally, there is optype.CanAIterSelf[R], with both the __aiter__() -> Self and the __anext__() -> V methods.

Because CanMissing[K, D] generally doesn't show itself without CanGetitem[K, V] there to hold its hand, optype conveniently stitched them together as optype.CanGetMissing[K, V, D=V].

Similarly, there is optype.CanSequence[K: CanIndex | slice, V], which is the combination of both CanLen and CanItem[I, V], and serves as a more specific and flexible collections.abc.Sequence[V].

Support for the with statement.

CanEnterSelf and CanWithSelf are (runtime-checkable) aliases for CanEnter[Self] and CanWith[Self, R], respectively.

For the async with statement the interfaces look very similar:

Interfaces for descriptors.

Interfaces for emulating buffer types using the buffer protocol.

For the copy standard library, optype.copy provides the following runtime-checkable interfaces:

^[1] copy.replace requires python>=3.13 (but optype.copy.CanReplace doesn't)

In practice, it makes sense that a copy of an instance is the same type as the original. But because typing.Self cannot be used as a type argument, this difficult to properly type. Instead, you can use the optype.copy.Can{}Self types, which are the runtime-checkable equivalents of the following (non-expressible) aliases:

type CanCopySelf = CanCopy[Self]
type CanDeepcopySelf = CanDeepcopy[Self]
type CanReplaceSelf = CanReplace[Self]

For the dataclasses standard library, optype.dataclasses provides the HasDataclassFields[V: Mapping[str, Field]] interface. It can conveniently be used to check whether a type or instance is a dataclass, i.e. isinstance(obj, HasDataclassFields).

A collection of functions for runtime inspection of types, modules, and other objects.

>>> from typing import Literal, TypeAlias, get_args
>>> Falsy: TypeAlias = Literal[None] | Literal[False, 0] | Literal["", b""]
>>> get_args(Falsy)
(typing.Literal[None], typing.Literal[False, 0], typing.Literal['', b''])

>>> import optype as opt
>>> opt.inspect.get_args(Falsy)
(None, False, 0, '', b'')

>>> import typing
>>> import optype as opt
>>> type StringLike = str | bytes
>>> typing.get_args(StringLike)
()
>>> opt.inspect.get_args(StringLike)
(<class 'str'>, <class 'bytes'>)

A collection of protocols and type-aliases that, unlike their analogues in _typeshed, are accessible at runtime, and use a consistent naming scheme.

Type aliases for the json standard library:

The (Any)Value can be any json input, i.e. Value | Array | Object is equivalent to Value. It's also worth noting that Value is a subtype of AnyValue, which means that AnyValue | Value is equivalent to AnyValue.

For the pickle standard library, optype.pickle provides the following interfaces:

The string standard library contains practical constants, but it has two issues:

• The constants contain a collection of characters, but are represented as a single string. This makes it practically impossible to type-hint the individual characters, so typeshed currently types these constants as a LiteralString.
• The names of the constants are inconsistent, and doesn't follow PEP 8.

So instead, optype.string provides an alternative interface, that is compatible with string, but with slight differences:

• For each constant, there is a corresponding Literal type alias for the individual characters. Its name matches the name of the constant, but is singular instead of plural.
• Instead of a single string, optype.string uses a tuple of characters, so that each character has its own typing.Literal annotation. Note that this is only tested with (based)pyright / pylance, so it might not work with mypy (it has more bugs than it has lines of codes).
• The names of the constant are consistent with PEP 8, and use a postfix notation for variants, e.g. DIGITS_HEX instead of hexdigits.
• Unlike string, optype.string has a constant (and type alias) for binary digits '0' and '1'; DIGITS_BIN (and DigitBin). Because besides oct and hex functions in builtins, there's also the builtins.bin function.

Each of the optype.string constants is exactly the same as the corresponding string constant (after concatenation / splitting), e.g.

>>> import string
>>> import optype as opt
>>> "".join(opt.string.PRINTABLE) == string.printable
True
>>> tuple(string.printable) == opt.string.PRINTABLE
True

Similarly, the values within a constant's Literal type exactly match the values of its constant:

>>> import optype as opt
>>> from optype.inspect import get_args
>>> get_args(opt.string.Printable) == opt.string.PRINTABLE
True

The optype.inspect.get_args is a non-broken variant of typing.get_args that correctly flattens nested literals, type-unions, and PEP 695 type aliases, so that it matches the official typing specs. In other words; typing.get_args is yet another fundamentally broken python-typing feature that's useless in the situations where you need it most.

Type aliases for anything that can always be passed to int, float, complex, iter, or typing.Literal

These are builtin types or collections that are empty, i.e. have length 0 or yield no elements.

A collection of low-level types for working DLPack.

CanDLPack[
    +T = int,
    +D: int = int,
]

def __dlpack__(
    *,
    stream: int | None = ...,
    max_version: tuple[int, int] | None = ...,
    dl_device: tuple[T, D] | None = ...,
    copy: bool | None = ...,
) -> types.CapsuleType: ...

CanDLPackDevice[
    +T = int,
    +D: int = int,
]

def __dlpack_device__() -> tuple[T, D]: ...

The + prefix indicates that the type parameter is covariant.

There are also two convenient IntEnums in optype.dlpack: DLDeviceType for the device types, and DLDataTypeCode for the internal type-codes of the DLPack data types.

Optype supports both NumPy 1 and 2. The current minimum supported version is 1.25, following NEP 29 and SPEC 0.

optype.numpy uses numpy-typing-compat package to ensure compatibility for older versions of NumPy. To ensure that the correct versions of numpy and numpy-typing-compat are installed, you should install optype with the numpy extra:

pip install "optype[numpy]"

If you're using conda, the optype-numpy package can be used, which will also install the required numpy and numpy-typing-compat versions:

conda install conda-forge::optype-numpy

from typing import Any, Literal
import numpy as np
import numpy.typing as npt
import optype.numpy as onp

Optype provides the generic onp.Array type alias for np.ndarray. It is similar to npt.NDArray, but includes two (optional) type parameters: one that matches the shape type (ND: tuple[int, ...]), and one that matches the scalar type (ST: np.generic).

When we put the definitions of npt.NDArray and onp.Array side-by-side, their differences become clear:

type NDArray[
    # no shape type
    SCT: generic,  # no default
] = ndarray[Any, dtype[SCT]]

type Array[
    NDT: (int, ...) = (int, ...),
    SCT: generic = generic,
] = ndarray[NDT, dtype[SCT]]

type ArrayND[
    SCT: generic = generic,
    NDT: (int, ...) = (int, ...),
] = ndarray[NDT, dtype[SCT]]

Additionally, there are the four Array{0,1,2,3}D aliases, which are equivalent to Array with tuple[()], tuple[int], tuple[int, int] and tuple[int, int, int] as shape-type, respectively.

In the same way as ArrayND for ndarray (shown for reference), its subtypes np.ma.MaskedArray and np.matrix are also aliased:

type ArrayND[
    SCT: generic = generic,
    NDT: (int, ...) = (int, ...),
] = ndarray[NDT, dtype[SCT]]

type MArray[
    SCT: generic = generic,
    NDT: (int, ...) = (int, ...),
] = ma.MaskedArray[NDT, dtype[SCT]]

type Matrix[
    SCT: generic = generic,
    M: int = int,
    N: int = M,
] = matrix[(M, N), dtype[SCT]]

For masked arrays with specific ndim, you could also use one of the four MArray{0,1,2,3}D aliases.

To check whether a given object is an instance of Array{0,1,2,3,N}D, in a way that static type-checkers also understand it, the following PEP 742 typeguards can be used:

These functions additionally accept an optional dtype argument, that can either be a np.dtype[ST] instance, a type[ST], or something that has a dtype: np.dtype[ST] attribute. The signatures are almost identical to each other, and in the 0d case it roughly looks like this:

_T = TypeVar("_T", bound=np.generic, default=Any)
_ToDType: TypeAlias = type[_T] | np.dtype[_T] | HasDType[np.dtype[_T]]


def is_array_0d(a, /, dtype: _ToDType[_T] | None = None) -> TypeIs[Array0D[_T]]: ...

A shape is nothing more than a tuple of (non-negative) integers, i.e. an instance of tuple[int, ...] such as (42,), (480, 720, 3) or (). The length of a shape is often referred to as the number of dimensions or the dimensionality of the array or scalar. For arrays this is accessible through the np.ndarray.ndim, which is an alias for len(np.ndarray.shape).

To make typing the shape of an array easier, optype provides two families of shape type aliases: AtLeast{N}D and AtMost{N}D. The {N} should be replaced by the number of dimensions, which currently is limited to 0, 1, 2, and 3.

Both of these families are generic, and their (optional) type parameters must be either int (default), or a literal (non-negative) integer, i.e. like typing.Literal[N: int].

The names AtLeast{N}D and AtMost{N}D are pretty much as self-explanatory:

• AtLeast{N}D is a tuple[int, ...] with ndim >= N
• AtMost{N}D is a tuple[int, ...] with ndim <= N

The shape aliases are roughly defined as:

type AtLeast0D = (int, ...)

type AtMost0D = ()

type AtLeast1D = (int, *AtLeast0D)

type AtMost1D = AtMost0D | (int,)

type AtLeast2D = (
    tuple[int, int]
    | AtLeast3D[int]
)

type AtMost2D = AtMost1D | (int, int)

type AtLeast3D = (
    tuple[int, int, int]
    | tuple[int, int, int, int]
    | tuple[int, int, int, int, int]
    # etc...
)

type AtMost3D = AtMost2D | (int, int, int)

The AtLeast{}D optionally accepts a type argument that can either be int (default), or Any. Passing Any turns it from a gradual tuple type, so that they can also be assigned to compatible bounded shape-types. So AtLeast1D[Any] is assignable to tuple[int], whereas AtLeast1D (equiv. AtLeast1D[int]) is not.

However, mypy currently has a bug, causing it to falsely reject such gradual shape-type assignment for N=1 or up.

Similar to the numpy._typing._ArrayLike{}_co coercible array-like types, optype.numpy provides the optype.numpy.To{}ND. Unlike the ones in numpy, these don't accept "bare" scalar types (the __len__ method is required). Additionally, there are the To{}1D, To{}2D, and To{}3D for vector-likes, matrix-likes, and cuboid-likes, and the To{} aliases for "bare" scalar types.

Source code: optype/numpy/_to.py

Compatibility module for supporting a wide range of numpy versions (currently >=1.25). It contains the abstract numeric scalar types, with numpy>=2.2 type-parameter defaults, which I explained in the release notes.

SPEC 7 -compatible type aliases. The optype.numpy.random module provides three type aliases: RNG, ToRNG, and ToSeed.

In general, the most useful one is ToRNG, which describes what can be passed to numpy.random.default_rng. It is defined as the union of RNG, ToSeed, and numpy.random.BitGenerator.

The RNG is the union type of numpy.random.Generator and its legacy dual type, numpy.random.RandomState.

ToSeed accepts integer-like scalars, sequences, and arrays, as well as instances of numpy.random.SeedSequence.

In NumPy, a dtype (data type) object, is an instance of the numpy.dtype[ST: np.generic] type. It's commonly used to convey metadata of a scalar type, e.g. within arrays.

Because the type parameter of np.dtype isn't optional, it could be more convenient to use the alias optype.numpy.DType, which is defined as:

type DType[ST: np.generic = np.generic] = np.dtype[ST]

Apart from the "CamelCase" name, the only difference with np.dtype is that the type parameter can be omitted, in which case it's equivalent to np.dtype[np.generic], but shorter.

The optype.numpy.Scalar interface is a generic runtime-checkable protocol, that can be seen as a "more specific" np.generic, both in name, and from a typing perspective.

Its type signature looks roughly like this:

type Scalar[
    # The "Python type", so that `Scalar.item() -> PT`.
    PT: object,
    # The "N-bits" type (without having to deal with `npt.NBitBase`).
    # It matches the `itemsize: NB` property.
    NB: int = int,
] = ...

It can be used as e.g.

are_birds_real: Scalar[bool, Literal[1]] = np.bool_(True)
the_answer: Scalar[int, Literal[2]] = np.uint16(42)
alpha: Scalar[float, Literal[8]] = np.float64(1 / 137)

A large portion of numpy's public API consists of universal functions, often denoted as ufuncs, which are (callable) instances of np.ufunc.

But np.ufunc has a big issue; it accepts no type parameters. This makes it very difficult to properly annotate its callable signature and its literal attributes (e.g. .nin and .identity).

This is where optype.numpy.UFunc comes into play: It's a runtime-checkable generic typing protocol, that has been thoroughly type- and unit-tested to ensure compatibility with all of numpy's ufunc definitions. Its generic type signature looks roughly like:

type UFunc[
    # The type of the (bound) `__call__` method.
    Fn: CanCall = CanCall,
    # The types of the `nin` and `nout` (readonly) attributes.
    # Within numpy these match either `Literal[1]` or `Literal[2]`.
    Nin: int = int,
    Nout: int = int,
    # The type of the `signature` (readonly) attribute;
    # Must be `None` unless this is a generalized ufunc (gufunc), e.g.
    # `np.matmul`.
    Sig: str | None = str | None,
    # The type of the `identity` (readonly) attribute (used in `.reduce`).
    # Unless `Nin: Literal[2]`, `Nout: Literal[1]`, and `Sig: None`,
    # this should always be `None`.
    # Note that `complex` also includes `bool | int | float`.
    Id: complex | bytes | str | None = float | None,
] = ...

The Any{Scalar}Array type aliases describe array-likes that are coercible to an numpy.ndarray with specific dtype.

Unlike numpy.typing.ArrayLike, these optype.numpy aliases don't accept "bare" scalar types such as float and np.float64. However, arrays of "zero dimensions" like onp.Array[tuple[()], np.float64] will be accepted. This is in line with the behavior of numpy.isscalar on numpy >= 2.

import numpy.typing as npt
import optype.numpy as onp

v_np: npt.ArrayLike = 3.14  # accepted
v_op: onp.AnyArray = 3.14  # rejected

sigma1_np: npt.ArrayLike = [[0, 1], [1, 0]]  # accepted
sigma1_op: onp.AnyArray = [[0, 1], [1, 0]]  # accepted

See the docs for more info on the NumPy scalar type hierarchy.

Scalar types with "flexible" length, whose values have a (constant) length that depends on the specific np.dtype instantiation.

Within optype.numpy there are several Can* (single-method) and Has* (single-attribute) protocols, related to the __array_*__ dunders of the NumPy Python API. These typing protocols are, just like the optype.Can* and optype.Has* ones, runtime-checkable and extensible (i.e. not @final).

class CanArray[
    ND: tuple[int, ...] = ...,
    ST: np.generic = ...,
]: ...

def __array__[RT = ST](
    _,
    dtype: DType[RT] | None = ...,
) -> Array[ND, RT]

class CanArrayUFunc[
    U: UFunc = ...,
    R: object = ...,
]: ...

def __array_ufunc__(
    _,
    ufunc: U,
    method: LiteralString,
    *args: object,
    **kwargs: object,
) -> R: ...

class CanArrayFunction[
    F: CanCall[..., object] = ...,
    R = object,
]: ...

def __array_function__(
    _,
    func: F,
    types: CanIterSelf[type[CanArrayFunction]],
    args: tuple[object, ...],
    kwargs: Mapping[str, object],
) -> R: ...

class CanArrayFinalize[
    T: object = ...,
]: ...

def __array_finalize__(_, obj: T): ...

class CanArrayWrap: ...

def __array_wrap__[ND, ST](
    _,
    array: Array[ND, ST],
    context: (...) | None = ...,
    return_scalar: bool = ...,
) -> Self | Array[ND, ST]

class HasArrayInterface[
    V: Mapping[str, object] = ...,
]: ...

__array_interface__: V

class HasArrayPriority: ...

__array_priority__: float

class HasDType[
    DT: DType = ...,
]: ...

dtype: DT