This primarily comes up with annotated `self` parameters in
constructors:
```py
class C[T]:
def __init__(self: C[int]): ...
```
Here, we want infer a specialization of `{T = int}` for a call that hits
this overload.
Normally when inferring a specialization of a function call, typevars
appear in the parameter annotations, and not in the argument types. In
this case, this is reversed: we need to verify that the `self` argument
(`C[T]`, as we have not yet completed specialization inference) is
assignable to the parameter type `C[int]`.
To do this, we simply look for a typevar/type in both directions when
performing inference, and apply the inferred specialization to argument
types as well as parameter types before verifying assignability.
As a wrinkle, this exposed that we were not checking
subtyping/assignability for function literals correctly. Our function
literal representation includes an optional specialization that should
be applied to the signature. Before, function literals were considered
subtypes of (assignable to) each other only if they were identical Salsa
objects. Two function literals with different specializations should
still be considered subtypes of (assignable to) each other if those
specializations result in the same function signature (typically because
the function doesn't use the typevars in the specialization).
Closes https://github.com/astral-sh/ty/issues/370
Closes https://github.com/astral-sh/ty/issues/100
Closes https://github.com/astral-sh/ty/issues/258
---------
Co-authored-by: Carl Meyer <carl@astral.sh>
Follows on from (and depends on)
https://github.com/astral-sh/ruff/pull/18021.
This updates our function specialization inference to infer type
mappings from parameters that are generic protocols.
For now, this only works when the argument _explicitly_ implements the
protocol by listing it as a base class. (We end up using exactly the
same logic as for generic classes in #18021.) For this to work with
classes that _implicitly_ implement the protocol, we will have to check
the types of the protocol members (which we are not currently doing), so
that we can infer the specialization of the protocol that the class
implements.
---------
Co-authored-by: Alex Waygood <Alex.Waygood@Gmail.com>
This updates our function specialization inference to infer type
mappings from parameters that are generic aliases, e.g.:
```py
def f[T](x: list[T]) -> T: ...
reveal_type(f(["a", "b"])) # revealed: str
```
Though note that we're still inferring the type of list literals as
`list[Unknown]`, so for now we actually need something like the
following in our tests:
```py
def _(x: list[str]):
reveal_type(f(x)) # revealed: str
```
We were not inducting into instance types and subclass-of types when
looking for legacy typevars, nor when apply specializations.
This addresses
https://github.com/astral-sh/ruff/pull/17832#discussion_r2081502056
```py
from __future__ import annotations
from typing import TypeVar, Any, reveal_type
S = TypeVar("S")
class Foo[T]:
def method(self, other: Foo[S]) -> Foo[T | S]: ... # type: ignore[invalid-return-type]
def f(x: Foo[Any], y: Foo[Any]):
reveal_type(x.method(y)) # revealed: `Foo[Any | S]`, but should be `Foo[Any]`
```
We were not detecting that `S` made `method` generic, since we were not
finding it when searching the function signature for legacy typevars.
Function literals have an optional specialization, which is applied to
the parameter/return type annotations lazily when the function's
signature is requested. We were previously only applying this
specialization to the final overload of an overloaded function.
This manifested most visibly for `list.__add__`, which has an overloaded
definition in the typeshed:
b398b83631/crates/ty_vendored/vendor/typeshed/stdlib/builtins.pyi (L1069-L1072)
Closes https://github.com/astral-sh/ty/issues/314
It's possible for a typevar to list another typevar as its default
value:
```py
class C[T, U = T]: ...
```
When specializing this class, if a type isn't provided for `U`, we would
previously use the default as-is, leaving an unspecialized `T` typevar
in the specialization. Instead, we want to use what `T` is mapped to as
the type of `U`.
```py
reveal_type(C()) # revealed: C[Unknown, Unknown]
reveal_type(C[int]()) # revealed: C[int, int]
reveal_type(C[int, str]()) # revealed: C[int, str]
```
This is especially important for the `slice` built-in type.
#17897 added variance handling for legacy typevars — but they were only
being considered when checking generic aliases of the same class:
```py
class A: ...
class B(A): ...
class C[T]: ...
static_assert(is_subtype_of(C[B], C[A]))
```
and not for generic subclasses:
```py
class D[U](C[U]): ...
static_assert(is_subtype_of(D[B], C[A]))
```
Now we check those too!
Closes https://github.com/astral-sh/ty/issues/101
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>
@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.
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>
