0084e94f78
## Summary This is a bugfix for subtyping of `type[Any]` / `type[T]` and protocols. ## Test Plan Regression test that will only be really meaningful once https://github.com/astral-sh/ruff/pull/21553 lands.
2.0 KiB
2.0 KiB
numpy
[environment]
python-version = "3.14"
numpy's dtype
numpy functions often accept a dtype parameter. For example, one of np.array's overloads accepts
a dtype parameter of type DTypeLike | None. Here, we build up something that resembles numpy's
internals in order to model the type DTypeLike. Many details have been left out.
mini_numpy.py:
from typing import TypeVar, Generic, Any, Protocol, TypeAlias, runtime_checkable, final
import builtins
_ItemT_co = TypeVar("_ItemT_co", default=Any, covariant=True)
class generic(Generic[_ItemT_co]):
@property
def dtype(self) -> _DTypeT_co:
raise NotImplementedError
_BoolItemT_co = TypeVar("_BoolItemT_co", bound=builtins.bool, default=builtins.bool, covariant=True)
class bool(generic[_BoolItemT_co], Generic[_BoolItemT_co]): ...
@final
class object_(generic): ...
_ScalarT = TypeVar("_ScalarT", bound=generic)
_ScalarT_co = TypeVar("_ScalarT_co", bound=generic, default=Any, covariant=True)
@final
class dtype(Generic[_ScalarT_co]): ...
_DTypeT_co = TypeVar("_DTypeT_co", bound=dtype, default=dtype, covariant=True)
@runtime_checkable
class _SupportsDType(Protocol[_DTypeT_co]):
@property
def dtype(self) -> _DTypeT_co: ...
# TODO: no errors here
# error: [invalid-type-arguments] "Type `typing.TypeVar` is not assignable to upper bound `generic[Any]` of type variable `_ScalarT_co@dtype`"
# error: [invalid-type-arguments] "Type `typing.TypeVar` is not assignable to upper bound `generic[Any]` of type variable `_ScalarT_co@dtype`"
_DTypeLike: TypeAlias = type[_ScalarT] | dtype[_ScalarT] | _SupportsDType[dtype[_ScalarT]]
DTypeLike: TypeAlias = _DTypeLike[Any] | str | None
Now we can make sure that a function which accepts DTypeLike | None works as expected:
import mini_numpy as np
def accepts_dtype(dtype: np.DTypeLike | None) -> None: ...
accepts_dtype(dtype=np.bool)
accepts_dtype(dtype=np.dtype[np.bool])
accepts_dtype(dtype=object)
accepts_dtype(dtype=np.object_)
accepts_dtype(dtype="U")