We already had support for homogeneous tuples (`tuple[int, ...]`). This
PR extends this to also support mixed tuples (`tuple[str, str,
*tuple[int, ...], str str]`).
A mixed tuple consists of a fixed-length (possibly empty) prefix and
suffix, and a variable-length portion in the middle. Every element of
the variable-length portion must be of the same type. A homogeneous
tuple is then just a mixed tuple with an empty prefix and suffix.
The new data representation uses different Rust types for a fixed-length
(aka heterogeneous) tuple. Another option would have been to use the
`VariableLengthTuple` representation for all tuples, and to wrap the
"variable + suffix" portion in an `Option`. I don't think that would
simplify the method implementations much, though, since we would still
have a 2×2 case analysis for most of them.
One wrinkle is that the definition of the `tuple` class in the typeshed
has a single typevar, and canonically represents a homogeneous tuple.
When getting the class of a tuple instance, that means that we have to
summarize our detailed mixed tuple type information into its
"homogeneous supertype". (We were already doing this for heterogeneous
types.)
A similar thing happens when concatenating two mixed tuples: the
variable-length portion and suffix of the LHS, and the prefix and
variable-length portion of the RHS, all get unioned into the
variable-length portion of the result. The LHS prefix and RHS suffix
carry through unchanged.
---------
Co-authored-by: Alex Waygood <Alex.Waygood@Gmail.com>
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.
