## Summary
Add two simple tests that we recently discussed with @dcreager. They
demonstrate that the `TypeMapping::MarkTypeVarsInferable` operation
really does need to keep track of the binding context.
## Test Plan
Made sure that those tests fail if we create
`TypeMapping::MarkTypeVarsInferable(None)`s everywhere.
## Summary
Modify the (external) signature of instance methods such that the first
parameter uses `Self` unless it is explicitly annotated. This allows us
to correctly type-check more code, and allows us to infer correct return
types for many functions that return `Self`. For example:
```py
from pathlib import Path
from datetime import datetime, timedelta
reveal_type(Path(".config") / ".ty") # now Path, previously Unknown
def _(dt: datetime, delta: timedelta):
reveal_type(dt - delta) # now datetime, previously Unknown
```
part of https://github.com/astral-sh/ty/issues/159
## Performance
I ran benchmarks locally on `attrs`, `freqtrade` and `colour`, the
projects with the largest regressions on CodSpeed. I see much smaller
effects locally, but can definitely reproduce the regression on `attrs`.
From looking at the profiling results (on Codspeed), it seems that we
simply do more type inference work, which seems plausible, given that we
now understand much more return types (of many stdlib functions). In
particular, whenever a function uses an implicit `self` and returns
`Self` (without mentioning `Self` anywhere else in its signature), we
will now infer the correct type, whereas we would previously return
`Unknown`. This also means that we need to invoke the generics solver in
more cases. Comparing half a million lines of log output on attrs, I can
see that we do 5% more "work" (number of lines in the log), and have a
lot more `apply_specialization` events (7108 vs 4304). On freqtrade, I
see similar numbers for `apply_specialization` (11360 vs 5138 calls).
Given these results, I'm not sure if it's generally worth doing more
performance work, especially since none of the code modifications
themselves seem to be likely candidates for regressions.
| Command | Mean [ms] | Min [ms] | Max [ms] | Relative |
|:---|---:|---:|---:|---:|
| `./ty_main check /home/shark/ecosystem/attrs` | 92.6 ± 3.6 | 85.9 |
102.6 | 1.00 |
| `./ty_self check /home/shark/ecosystem/attrs` | 101.7 ± 3.5 | 96.9 |
113.8 | 1.10 ± 0.06 |
| Command | Mean [ms] | Min [ms] | Max [ms] | Relative |
|:---|---:|---:|---:|---:|
| `./ty_main check /home/shark/ecosystem/freqtrade` | 599.0 ± 20.2 |
568.2 | 627.5 | 1.00 |
| `./ty_self check /home/shark/ecosystem/freqtrade` | 607.9 ± 11.5 |
594.9 | 626.4 | 1.01 ± 0.04 |
| Command | Mean [ms] | Min [ms] | Max [ms] | Relative |
|:---|---:|---:|---:|---:|
| `./ty_main check /home/shark/ecosystem/colour` | 423.9 ± 17.9 | 394.6
| 447.4 | 1.00 |
| `./ty_self check /home/shark/ecosystem/colour` | 426.9 ± 24.9 | 373.8
| 456.6 | 1.01 ± 0.07 |
## Test Plan
New Markdown tests
## Ecosystem report
* apprise: ~300 new diagnostics related to problematic stubs in apprise
😩
* attrs: a new true positive, since [this
function](4e2c89c823/tests/test_make.py (L2135))
is missing a `@staticmethod`?
* Some legitimate true positives
* sympy: lots of new `invalid-operator` false positives in [matrix
multiplication](cf9f4b6805/sympy/matrices/matrixbase.py (L3267-L3269))
due to our limited understanding of [generic `Callable[[Callable[[T1,
T2], T3]], Callable[[T1, T2], T3]]` "identity"
types](cf9f4b6805/sympy/core/decorators.py (L83-L84))
of decorators. This is not related to type-of-self.
## Typing conformance results
The changes are all correct, except for
```diff
+generics_self_usage.py:50:5: error[invalid-assignment] Object of type `def foo(self) -> int` is not assignable to `(typing.Self, /) -> int`
```
which is related to an assignability problem involving type variables on
both sides:
```py
class CallableAttribute:
def foo(self) -> int:
return 0
bar: Callable[[Self], int] = foo # <- we currently error on this assignment
```
---------
Co-authored-by: Shaygan Hooshyari <sh.hooshyari@gmail.com>
### Summary
This PR includes two changes, both of which are necessary to resolve
https://github.com/astral-sh/ty/issues/1196:
* For a generic class `C[T]`, we previously used `C[Unknown]` as the
upper bound of the `Self` type variable. There were two problems with
this. For one, when `Self` appeared in contravariant position, we would
materialize its upper bound to `Bottom[C[Unknown]]` (which might
simplify to `C[Never]` if `C` is covariant in `T`) when accessing
methods on `Top[C[Unknown]]`. This would result in `invalid-argument`
errors on the `self` parameter. Also, using an upper bound of
`C[Unknown]` would mean that inside methods, references to `T` would be
treated as `Unknown`. This could lead to false negatives. To fix this,
we now use `C[T]` (with a "nested" typevar) as the upper bound for
`Self` on `C[T]`.
* In order to make this work, we needed to allow assignability/subtyping
of inferable typevars to other types, since we now check assignability
of e.g. `C[int]` to `C[T]` (when checking assignability to the upper
bound of `Self`) when calling an instance-method on `C[int]` whose
`self` parameter is annotated as `self: Self` (or implicitly `Self`,
following https://github.com/astral-sh/ruff/pull/18007).
closes https://github.com/astral-sh/ty/issues/1196
closes https://github.com/astral-sh/ty/issues/1208
### Test Plan
Regression tests for both issues.
## Summary
Support cases like the following, where we need the generic context to
include both `Self` and `T` (not just `T`):
```py
from typing import Self
class C:
def method[T](self: Self, arg: T): ...
C().method(1)
```
closes https://github.com/astral-sh/ty/issues/1131
## Test Plan
Added regression test
This fixes our logic for binding a legacy typevar with its binding
context. (To recap, a legacy typevar starts out "unbound" when it is
first created, and each time it's used in a generic class or function,
we "bind" it with the corresponding `Definition`.)
We treat `typing.Self` the same as a legacy typevar, and so we apply
this binding logic to it too. Before, we were using the enclosing class
as its binding context. But that's not correct — it's the method where
`typing.Self` is used that binds the typevar. (Each invocation of the
method will find a new specialization of `Self` based on the specific
instance type containing the invoked method.)
This required plumbing through some additional state to the
`in_type_expression` method.
This also revealed that we weren't handling `Self`-typed instance
attributes correctly (but were coincidentally not getting the expected
false positive diagnostics).
This PR introduces a few related changes:
- We now keep track of each time a legacy typevar is bound in a
different generic context (e.g. class, function), and internally create
a new `TypeVarInstance` for each usage. This means the rest of the code
can now assume that salsa-equivalent `TypeVarInstance`s refer to the
same typevar, even taking into account that legacy typevars can be used
more than once.
- We also go ahead and track the binding context of PEP 695 typevars.
That's _much_ easier to track since we have the binding context right
there during type inference.
- With that in place, we can now include the name of the binding context
when rendering typevars (e.g. `T@f` instead of `T`)
## Summary
This PR improves our generics solver such that we are able to solve the
`TypeVar` in this snippet to `int | str` (the union of the elements in
the heterogeneous tuple) by upcasting the heterogeneous tuple to its
pure-homogeneous-tuple supertype:
```py
def f[T](x: tuple[T, ...]) -> T:
return x[0]
def g(x: tuple[int, str]):
reveal_type(f(x))
```
## Test Plan
Mdtests. Some TODOs remain in the mdtest regarding solving `TypeVar`s
for mixed tuples, but I think this PR on its own is a significant step
forward for our generics solver when it comes to tuple types.
---------
Co-authored-by: Douglas Creager <dcreager@dcreager.net>
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.
