## Summary
Fixes#17541
Before this change, in the case of overloaded functions,
`@dataclass_transform` was detected only when applied to the
implementation, not the overloads.
However, the spec also allows this decorator to be applied to any of the
overloads as well.
With this PR, we start handling `@dataclass_transform`s applied to
overloads.
## Test Plan
Fixed existing TODOs in the test suite.
## Summary
This is sort of an anticlimactic resolution to #17863, but now that we
understand what the root cause for the stack overflows was, I think it's
fine to enable running on this project. See the linked ticket for the
full analysis.
closes#17863
## Test Plan
Ran lots of times locally and never observed a crash at worker thread
stack sizes > 8 MiB.
We now track the variance of each typevar, and obey the `covariant` and
`contravariant` parameters to the legacy `TypeVar` constructor. We still
don't yet infer variance for PEP-695 typevars or for the
`infer_variance` legacy constructor parameter.
---------
Co-authored-by: Alex Waygood <Alex.Waygood@Gmail.com>
Co-authored-by: Carl Meyer <carl@astral.sh>
## Summary
A recursive protocol like the following would previously lead to stack
overflows when attempting to create the union type for the `P | None`
member, because `UnionBuilder` checks if element types are fully static,
and the fully-static check on `P` would in turn list all members and
check whether all of them were fully static, leading to a cycle.
```py
from __future__ import annotations
from typing import Protocol
class P(Protocol):
parent: P | None
```
Here, we make the fully-static check on protocols a salsa query and add
fixpoint iteration, starting with `true` as the initial value (assume
that the recursive protocol is fully-static). If the recursive protocol
has any non-fully-static members, we still return `false` when
re-executing the query (see newly added tests).
closes#17861
## Test Plan
Added regression test
@AlexWaygood discovered that even though we've been propagating
specializations to _parent_ base classes correctly, we haven't been
passing them on to _grandparent_ base classes:
https://github.com/astral-sh/ruff/pull/17832#issuecomment-2854360969
```py
class Bar[T]:
x: T
class Baz[T](Bar[T]): ...
class Spam[T](Baz[T]): ...
reveal_type(Spam[int]().x) # revealed: `T`, but should be `int`
```
This PR updates the MRO machinery to apply the current specialization
when starting to iterate the MRO of each base class.
## Summary
This fixes some false positives that showed up in the primer diff for
https://github.com/astral-sh/ruff/pull/17832
## Test Plan
new mdtests added that fail with false-positive diagnostics on `main`
## Summary
This PR fixes#17595.
## Test Plan
New test cases are added to `mdtest/narrow/conditionals/nested.md`.
---------
Co-authored-by: Carl Meyer <carl@astral.sh>
If a typevar is declared as having a default, we shouldn't require a
type to be specified for that typevar when explicitly specializing a
generic class:
```py
class WithDefault[T, U = int]: ...
reveal_type(WithDefault[str]()) # revealed: WithDefault[str, int]
```
---------
Co-authored-by: Alex Waygood <Alex.Waygood@Gmail.com>
Fixes
https://github.com/astral-sh/ruff/pull/17832#issuecomment-2851224968. We
had a comment that we did not need to apply specializations to generic
aliases, or to the bound `self` of a bound method, because they were
already specialized. But they might be specialized with a type variable,
which _does_ need to be specialized, in the case of a "multi-step"
specialization, such as:
```py
class LinkedList[T]: ...
class C[U]:
def method(self) -> LinkedList[U]:
return LinkedList[U]()
```
---------
Co-authored-by: Alex Waygood <Alex.Waygood@Gmail.com>
This fixes cycle panics in several ecosystem projects (moved to
`good.txt` in a following PR
https://github.com/astral-sh/ruff/pull/17834 because our mypy-primer job
doesn't handle it well if we move projects to `good.txt` in the same PR
that fixes `ty` to handle them), as well as in the minimal case in the
added mdtest. It also fixes a number of panicking fuzzer seeds. It
doesn't appear to cause any regression in any ecosystem project or any
fuzzer seed.
Re: #17526
## Summary
Add integration tests for Python Semantic Syntax for
`InvalidStarExpression`, `DuplicateMatchKey`, and
`DuplicateMatchClassAttribute`.
## Note
- Red knot integration tests for `DuplicateMatchKey` exist already in
line 89-101.
<!-- What's the purpose of the change? What does it do, and why? -->
## Test Plan
This is a test.
<!-- How was it tested? -->
## Summary
When entering an `infer_expression_types` cycle from
`TypeInferenceBuilder::infer_standalone_expression`, we might get back a
`TypeInference::cycle_fallback(…)` that doesn't actually contain any new
types, but instead it contains a `cycle_fallback_type` which is set to
`Some(Type::Never)`. When calling `self.extend(…)`, we therefore don't
really pull in a type for the expression we're interested in. This
caused us to panic if we tried to call `self.expression_type(…)` after
`self.extend(…)`.
The proposed fix here is to retrieve that type from the nested
`TypeInferenceBuilder` directly, which will correctly fall back to
`cycle_fallback_type`.
## Details
I minimized the second example from #17792 a bit further and used this
example for debugging:
```py
from __future__ import annotations
class C: ...
def f(arg: C):
pass
x, _ = f(1)
assert x
```
This is self-referential because when we check the assignment statement
`x, _ = f(1)`, we need to look up the signature of `f`. Since evaluation
of annotations is deferred, we look up the public type of `C` for the
`arg` parameter. The public use of `C` is visibility-constraint by "`x`"
via the `assert` statement. While evaluating this constraint, we need to
look up the type of `x`, which in turn leads us back to the `x, _ =
f(1)` definition.
The reason why this only showed up in the relatively peculiar case with
unpack assignments is the code here:
78b4c3ccf1/crates/ty_python_semantic/src/types/infer.rs (L2709-L2718)
For a non-unpack assignment like `x = f(1)`, we would not try to infer
the right-hand side eagerly. Instead, we would enter a
`infer_definition_types` cycle that handles the situation correctly. For
unpack assignments, however, we try to infer the type of `value`
(`f(1)`) and therefore enter the cycle via `standalone_expression_type
=> infer_expression_type`.
closes#17792
## Test Plan
* New regression test
* Made sure that we can now run successfully on scipy => see #17850
