Use TypeIs in is_dataclass

[NeilGirdhar]

Fixes #9723

[NeilGirdhar]

@erictraut Is this a problem with Pyright?

[erictraut]

@NeilGirdhar, can you be more specific? I'm not sure what you're asking.

[JelleZijlstra]

I think it's about this CI failure:

/home/runner/work/typeshed/typeshed/stdlib/@tests/test_cases/check_dataclasses.py
  /home/runner/work/typeshed/typeshed/stdlib/@tests/test_cases/check_dataclasses.py:60:21 - error: "assert_type" mismatch: expected "type[DataclassInstance]" but received "<subclass of type and DataclassInstance>" (reportAssertTypeFailure)

I haven't looked at this code in detail to figure out whether pyright is correct here.

[NeilGirdhar]

So, I removed one of the overloads, but from my testing it didn't seem necessary anymore. I'll quickly try replacing it to see if it makes a difference. (Edit: still broken. Removing it since it doesn't belong anymore IMO.)

The failure in PyRIght seems to be because Pyright isn't evaluating the overloads for TypeIs functions? Or else it's picking the wrong one. But even if it's picking the wrong one, why is type & (DataclassInstance | type[DataclassInstance]) equal to type & DataclassInstanc (or Pyright's approximation to the intersection). Seems odd to me.

[erictraut]

@NeilGirdhar, PEP 742 allows some variation between type checkers. What you're seeing here is an example of this. Pyright's evaluation is arguably more accurate (narrower) and more consistent than mypy's, but both results are defensible. I think you'll need to change your test case to accommodate this variation.

PEP 742 says:

As a rule of thumb, a type checker should use the same type narrowing logic – and get results that are consistent with – its handling of isinstance().

If you try this self-contained code sample in both pyright and mypy, you'll see that mypy is inconsistent but pyright is consistent.

from dataclasses import Field
from typing import Any, ClassVar, Protocol, runtime_checkable
from typing_extensions import TypeIs

@runtime_checkable
class DataclassInstance(Protocol):
    __dataclass_fields__: ClassVar[dict[str, Field[Any]]]

def is_dataclass(obj: type) -> TypeIs[type[DataclassInstance]]: ...

def test1(x: type) -> None:
    if isinstance(x, DataclassInstance):
        reveal_type(x) # Both mypy and pyright reveal <subclass of "type" and "DataclassInstance">

def test2(x: type) -> None:
    if is_dataclass(x):
        reveal_type(x) # Mypy reveals "DataclassInstance" but pyright gives results consistent with "isinstance"

[NeilGirdhar]

@erictraut Sorry, but I don't understand how these two functions are supposed to be the same. In the first function, you are checking against DataclassInstance whereas in the second you are checking against type[DataclassInstance].

If you change is_dataclass to return TypeIs[DataclassInstance], then MyPy and PyRight agree with each other and are consistent between the two functions.

Similarly, if you change the isinstance line to issubclass(x, DataclassInstance), then you will see that MyPy is consistent between the two functions whereas Pyright is not.

Therefore, I believe this is a bug in Pyright even if as you say PEP 742 doesn't specify this case.

[erictraut]

Ah yes, you're correct. This is a bug in pyright.

[erictraut]

@NeilGirdhar, this will be fixed in the next release of pyright. I typically publish a new version every Tuesday evening PST. You could either wait until then to land this PR or you could temporarily comment out this test case.

[NeilGirdhar]

@JelleZijlstra There appear to be some errors with updating PyRight. Would you mind updating PyRight when you have a chance?

[srittau]

Dependencies are updated automatically by renovate each UTC night.

[github-actions]

Diff from mypy_primer, showing the effect of this PR on open source code:

pydantic (https://github.com/samuelcolvin/pydantic)
+ pydantic/v1/json.py:80: error: No overload variant of "asdict" matches argument type "type[DataclassInstance]"  [call-overload]
+ pydantic/v1/json.py:80: note: Possible overload variants:
+ pydantic/v1/json.py:80: note:     def asdict(obj: DataclassInstance) -> dict[str, Any]
+ pydantic/v1/json.py:80: note:     def [_T] asdict(obj: DataclassInstance, *, dict_factory: Callable[[list[tuple[str, Any]]], _T]) -> _T
+ pydantic/deprecated/json.py:98: error: No overload variant of "asdict" matches argument type "type[DataclassInstance]"  [call-overload]
+ pydantic/deprecated/json.py:98: note: Possible overload variants:
+ pydantic/deprecated/json.py:98: note:     def asdict(obj: DataclassInstance) -> dict[str, Any]
+ pydantic/deprecated/json.py:98: note:     def [_T] asdict(obj: DataclassInstance, *, dict_factory: Callable[[list[tuple[str, Any]]], _T]) -> _T

hydra-zen (https://github.com/mit-ll-responsible-ai/hydra-zen)
- src/hydra_zen/structured_configs/_implementations.py:1129: error: Argument 2 to "builds" of "BuildsFn" has incompatible type "**dict[str, int | float | Path | DataClass_ | type[DataClass_] | Enum | Any | Sequence[HydraSupportedType] | Mapping[Any, HydraSupportedType] | None]"; expected "Literal[False] | None"  [arg-type]
- src/hydra_zen/structured_configs/_implementations.py:1129: error: Argument 2 to "builds" of "BuildsFn" has incompatible type "**dict[str, int | float | Path | DataClass_ | type[DataClass_] | Enum | Any | Sequence[HydraSupportedType] | Mapping[Any, HydraSupportedType] | None]"; expected "bool"  [arg-type]
- src/hydra_zen/structured_configs/_implementations.py:1129: error: Argument 2 to "builds" of "BuildsFn" has incompatible type "**dict[str, int | float | Path | DataClass_ | type[DataClass_] | Enum | Any | Sequence[HydraSupportedType] | Mapping[Any, HydraSupportedType] | None]"; expected "Builds[Callable[[Callable[..., Any]], Callable[..., Any]]] | ZenPartialBuilds[Callable[[Callable[..., Any]], Callable[..., Any]]] | HydraPartialBuilds[Callable[[Callable[..., Any]], Callable[..., Any]]] | Just[Callable[[Callable[..., Any]], Callable[..., Any]]] | type[Builds[Callable[[Callable[..., Any]], Callable[..., Any]]]] | type[ZenPartialBuilds[Callable[[Callable[..., Any]], Callable[..., Any]]]] | type[HydraPartialBuilds[Callable[[Callable[..., Any]], Callable[..., Any]]]] | type[Just[Callable[[Callable[..., Any]], Callable[..., Any]]]] | Callable[[Callable[..., Any]], Callable[..., Any]] | str | None | Sequence[Builds[Callable[[Callable[..., Any]], Callable[..., Any]]] | ZenPartialBuilds[Callable[[Callable[..., Any]], Callable[..., Any]]] | HydraPartialBuilds[Callable[[Callable[..., Any]], Callable[..., Any]]] | Just[Callable[[Callable[..., Any]], Callable[..., Any]]] | type[Builds[Callable[[Callable[..., Any]], Callable[..., Any]]]] | type[ZenPartialBuilds[Callable[[Callable[..., Any]], Callable[..., Any]]]] | type[HydraPartialBuilds[Callable[[Callable[..., Any]], Callable[..., Any]]]] | type[Just[Callable[[Callable[..., Any]], Callable[..., Any]]]] | Callable[[Callable[..., Any]], Callable[..., Any]] | str | None]"  [arg-type]
- src/hydra_zen/structured_configs/_implementations.py:1129: error: Argument 2 to "builds" of "BuildsFn" has incompatible type "**dict[str, int | float | Path | DataClass_ | type[DataClass_] | Enum | Any | Sequence[HydraSupportedType] | Mapping[Any, HydraSupportedType] | None]"; expected "Mapping[str, SupportedPrimitive] | None"  [arg-type]
- src/hydra_zen/structured_configs/_implementations.py:1129: error: Argument 2 to "builds" of "BuildsFn" has incompatible type "**dict[str, int | float | Path | DataClass_ | type[DataClass_] | Enum | Any | Sequence[HydraSupportedType] | Mapping[Any, HydraSupportedType] | None]"; expected "list[str | DataClass_ | type[DataClass_] | Mapping[str, str | Sequence[str] | None]] | None"  [arg-type]
- src/hydra_zen/structured_configs/_implementations.py:1129: error: Argument 2 to "builds" of "BuildsFn" has incompatible type "**dict[str, int | float | Path | DataClass_ | type[DataClass_] | Enum | Any | Sequence[HydraSupportedType] | Mapping[Any, HydraSupportedType] | None]"; expected "str | None"  [arg-type]
- src/hydra_zen/structured_configs/_implementations.py:1129: error: Argument 2 to "builds" of "BuildsFn" has incompatible type "**dict[str, int | float | Path | DataClass_ | type[DataClass_] | Enum | Any | Sequence[HydraSupportedType] | Mapping[Any, HydraSupportedType] | None]"; expected "tuple[type[DataClass_], ...]"  [arg-type]
- src/hydra_zen/structured_configs/_implementations.py:1129: error: Argument 2 to "builds" of "BuildsFn" has incompatible type "**dict[str, int | float | Path | DataClass_ | type[DataClass_] | Enum | Any | Sequence[HydraSupportedType] | Mapping[Any, HydraSupportedType] | None]"; expected "ZenConvert | None"  [arg-type]
- src/hydra_zen/structured_configs/_implementations.py:1129: error: Argument 2 to "builds" of "BuildsFn" has incompatible type "**dict[str, int | float | Path | DataClass_ | type[DataClass_] | Enum | Any | Sequence[HydraSupportedType] | Mapping[Any, HydraSupportedType] | None]"; expected "T"  [arg-type]
- src/hydra_zen/structured_configs/_implementations.py:1161: error: Incompatible types in assignment (expression has type "Just", variable has type "type[Builds[Any]]")  [assignment]

pytest (https://github.com/pytest-dev/pytest)
+ src/_pytest/_io/pprint.py:117: error: Right operand of "and" is never evaluated  [unreachable]
+ src/_pytest/_io/pprint.py:125: error: Statement is unreachable  [unreachable]

streamlit (https://github.com/streamlit/streamlit)
+ lib/streamlit/runtime/caching/hashing.py: note: In member "_to_bytes" of class "_CacheFuncHasher":
+ lib/streamlit/runtime/caching/hashing.py:409:34: error: No overload variant of "asdict" matches argument type "Type[DataclassInstance]"  [call-overload]
+ lib/streamlit/runtime/caching/hashing.py:409:34: note: Possible overload variants:
+ lib/streamlit/runtime/caching/hashing.py:409:34: note:     def asdict(obj: DataclassInstance) -> Dict[str, Any]
+ lib/streamlit/runtime/caching/hashing.py:409:34: note:     def [_T] asdict(obj: DataclassInstance, *, dict_factory: Callable[[List[Tuple[str, Any]]], _T]) -> _T

[NeilGirdhar]

Updated. Ready for pull.

[srittau]

Looking at the primer output: pydantic and streamlit are true positives. They use is_dataclass() and immediately call asdict() if true. This can lead to errors if is_dataclass() is a dataclass and not a dataclass instance. (Weird design choice to have one function to check for both types and instances ...)

pytest is a false positive:

https://github.com/pytest-dev/pytest/blob/889d9b28d786c75b66f4d1acb80123bdb341639c/src/_pytest/_io/pprint.py#L117C13-L118C45

It's basically complaining about this code (where object is Any):

if is_dataclass(object) and not isinstance(object, type): ...

I'm unsure why that is. Maybe it's assuming that is_dataclass(Any) is always True?

[NeilGirdhar]

(Weird design choice to have one function to check for both types and instances ...)

Definitely a weird choice.

So, can the PR be pulled, or should we wait for the dependent projects to fix bugs?

[srittau]

I'm fine with merging this, despite the false positive, but I'll leave it open for a day or two if someone has an idea how to fix/work around it.

[NeilGirdhar]

Sounds good 😄 !

[NeilGirdhar]

Thanks for the quick merge. If anyone has time, it may be worth mentioning that most of the other uses of TypeGuard should actually be changed to TypeIs.