[issue43923] Can't create generic NamedTuple as of py3.9

[Alex Waygood]

Alex Waygood <Alex.Waygood at Gmail.com> added the comment:

Consider the typeshed stub for `concurrent.futures.DoneAndNotDoneFutures`. At runtime this is a `collections.namedtuple`, but in the stub, we need it to be generic to allow precise type inference. But we can't have a generic NamedTuple, so the stub is currently this:

```
class DoneAndNotDoneFutures(Sequence[set[Future[_T]]]):
    @property
    def done(self) -> set[Future[_T]]: ...
    @property
    def not_done(self) -> set[Future[_T]]: ...
    def __new__(_cls, done: set[Future[_T]], not_done: set[Future[_T]]) -> DoneAndNotDoneFutures[_T]: ...
    def __len__(self) -> int: ...
    @overload
    def __getitem__(self, __i: SupportsIndex) -> set[Future[_T]]: ...
    @overload
    def __getitem__(self, __s: slice) -> DoneAndNotDoneFutures[_T]: ...
```

Until two days ago, this stub actually had a bug: `done` and `not_done` were both given as writeable attributes, whereas they are read-only properties at runtime.

With generic NamedTuples, we could write the stub for the class far more simply (and more accurately) like this:

```
class DoneAndNotDoneFutures(NamedTuple, Generic[_T]):
    done: set[Future[_T]]
    not_done: set[Future[_T]]
```

And in code that actually needs to run at runtime, I frequently find it frustrating that I have to use dataclasses instead of NamedTuples if I want a simple class that just happens to be generic. dataclasses are great, but for small, lightweight classes, I prefer to use NamedTuples where possible. I often find that I don't need to use the full range of features dataclasses provide; and NamedTuples are often more performant than dataclasses, especially in cases where there's a lot of tuple unpacking.

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Python tracker <report at bugs.python.org>
<https://bugs.python.org/issue43923>
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