## Summary
Add support for generic PEP 613 type aliases and generic implicit type
aliases:
```py
from typing import TypeVar
T = TypeVar("T")
ListOrSet = list[T] | set[T]
def _(xs: ListOrSet[int]):
reveal_type(xs) # list[int] | set[int]
```
closes https://github.com/astral-sh/ty/issues/1643
closes https://github.com/astral-sh/ty/issues/1629
closes https://github.com/astral-sh/ty/issues/1596
closes https://github.com/astral-sh/ty/issues/573
closes https://github.com/astral-sh/ty/issues/221
## Typing conformance
```diff
-aliases_explicit.py:52:5: error[type-assertion-failure] Type `list[int]` does not match asserted type `@Todo(specialized generic alias in type expression)`
-aliases_explicit.py:53:5: error[type-assertion-failure] Type `tuple[str, ...] | list[str]` does not match asserted type `@Todo(Generic specialization of types.UnionType)`
-aliases_explicit.py:54:5: error[type-assertion-failure] Type `tuple[int, int, int, str]` does not match asserted type `@Todo(specialized generic alias in type expression)`
-aliases_explicit.py:56:5: error[type-assertion-failure] Type `(int, str, /) -> str` does not match asserted type `@Todo(Generic specialization of typing.Callable)`
-aliases_explicit.py:59:5: error[type-assertion-failure] Type `int | str | None | list[list[int]]` does not match asserted type `int | str | None | list[@Todo(specialized generic alias in type expression)]`
```
New true negatives ✔️
```diff
+aliases_explicit.py:41:36: error[invalid-type-arguments] Too many type arguments: expected 1, got 2
-aliases_explicit.py:57:5: error[type-assertion-failure] Type `(int, str, str, /) -> None` does not match asserted type `@Todo(Generic specialization of typing.Callable)`
+aliases_explicit.py:57:5: error[type-assertion-failure] Type `(int, str, str, /) -> None` does not match asserted type `(...) -> Unknown`
```
These require `ParamSpec`
```diff
+aliases_explicit.py:67:24: error[invalid-type-arguments] Too many type arguments: expected 0, got 1
+aliases_explicit.py:68:24: error[invalid-type-arguments] Too many type arguments: expected 0, got 1
+aliases_explicit.py:69:29: error[invalid-type-arguments] Too many type arguments: expected 1, got 2
+aliases_explicit.py:70:29: error[invalid-type-arguments] Too many type arguments: expected 1, got 2
+aliases_explicit.py:71:29: error[invalid-type-arguments] Too many type arguments: expected 1, got 2
+aliases_explicit.py:102:20: error[invalid-type-arguments] Too many type arguments: expected 0, got 1
```
New true positives ✔️
```diff
-aliases_implicit.py:63:5: error[type-assertion-failure] Type `list[int]` does not match asserted type `@Todo(specialized generic alias in type expression)`
-aliases_implicit.py:64:5: error[type-assertion-failure] Type `tuple[str, ...] | list[str]` does not match asserted type `@Todo(Generic specialization of types.UnionType)`
-aliases_implicit.py:65:5: error[type-assertion-failure] Type `tuple[int, int, int, str]` does not match asserted type `@Todo(specialized generic alias in type expression)`
-aliases_implicit.py:67:5: error[type-assertion-failure] Type `(int, str, /) -> str` does not match asserted type `@Todo(Generic specialization of typing.Callable)`
-aliases_implicit.py:70:5: error[type-assertion-failure] Type `int | str | None | list[list[int]]` does not match asserted type `int | str | None | list[@Todo(specialized generic alias in type expression)]`
-aliases_implicit.py:71:5: error[type-assertion-failure] Type `list[bool]` does not match asserted type `@Todo(specialized generic alias in type expression)`
```
New true negatives ✔️
```diff
+aliases_implicit.py:54:36: error[invalid-type-arguments] Too many type arguments: expected 1, got 2
-aliases_implicit.py:68:5: error[type-assertion-failure] Type `(int, str, str, /) -> None` does not match asserted type `@Todo(Generic specialization of typing.Callable)`
+aliases_implicit.py:68:5: error[type-assertion-failure] Type `(int, str, str, /) -> None` does not match asserted type `(...) -> Unknown`
```
These require `ParamSpec`
```diff
+aliases_implicit.py:76:24: error[invalid-type-arguments] Too many type arguments: expected 0, got 1
+aliases_implicit.py:77:24: error[invalid-type-arguments] Too many type arguments: expected 0, got 1
+aliases_implicit.py:78:29: error[invalid-type-arguments] Too many type arguments: expected 1, got 2
+aliases_implicit.py:79:29: error[invalid-type-arguments] Too many type arguments: expected 1, got 2
+aliases_implicit.py:80:29: error[invalid-type-arguments] Too many type arguments: expected 1, got 2
+aliases_implicit.py:81:25: error[invalid-type-arguments] Type `str` is not assignable to upper bound `int | float` of type variable `TFloat@GoodTypeAlias12`
+aliases_implicit.py:135:20: error[invalid-type-arguments] Too many type arguments: expected 0, got 1
```
New true positives ✔️
```diff
+callables_annotation.py:172:19: error[invalid-type-arguments] Too many type arguments: expected 0, got 1
+callables_annotation.py:175:19: error[invalid-type-arguments] Too many type arguments: expected 0, got 1
+callables_annotation.py:188:25: error[invalid-type-arguments] Too many type arguments: expected 0, got 1
+callables_annotation.py:189:25: error[invalid-type-arguments] Too many type arguments: expected 0, got 1
```
These require `ParamSpec` and `Concatenate`.
```diff
-generics_defaults_specialization.py:26:5: error[type-assertion-failure] Type `SomethingWithNoDefaults[int, str]` does not match asserted type `SomethingWithNoDefaults[int, typing.TypeVar]`
+generics_defaults_specialization.py:26:5: error[type-assertion-failure] Type `SomethingWithNoDefaults[int, str]` does not match asserted type `SomethingWithNoDefaults[int, DefaultStrT]`
```
Favorable diagnostic change ✔️
```diff
-generics_defaults_specialization.py:27:5: error[type-assertion-failure] Type `SomethingWithNoDefaults[int, bool]` does not match asserted type `@Todo(specialized generic alias in type expression)`
```
New true negative ✔️
```diff
-generics_defaults_specialization.py:30:1: error[non-subscriptable] Cannot subscript object of type `<class 'SomethingWithNoDefaults[int, typing.TypeVar]'>` with no `__class_getitem__` method
+generics_defaults_specialization.py:30:15: error[invalid-type-arguments] Too many type arguments: expected between 0 and 1, got 2
```
Correct new diagnostic ✔️
```diff
-generics_variance.py:175:25: error[non-subscriptable] Cannot subscript object of type `<class 'Contra[typing.TypeVar]'>` with no `__class_getitem__` method
-generics_variance.py:175:35: error[non-subscriptable] Cannot subscript object of type `<class 'Co[typing.TypeVar]'>` with no `__class_getitem__` method
-generics_variance.py:179:29: error[non-subscriptable] Cannot subscript object of type `<class 'Contra[typing.TypeVar]'>` with no `__class_getitem__` method
-generics_variance.py:179:39: error[non-subscriptable] Cannot subscript object of type `<class 'Contra[typing.TypeVar]'>` with no `__class_getitem__` method
-generics_variance.py:183:21: error[non-subscriptable] Cannot subscript object of type `<class 'Co[typing.TypeVar]'>` with no `__class_getitem__` method
-generics_variance.py:183:27: error[non-subscriptable] Cannot subscript object of type `<class 'Co[typing.TypeVar]'>` with no `__class_getitem__` method
-generics_variance.py:187:25: error[non-subscriptable] Cannot subscript object of type `<class 'Co[typing.TypeVar]'>` with no `__class_getitem__` method
-generics_variance.py:187:31: error[non-subscriptable] Cannot subscript object of type `<class 'Contra[typing.TypeVar]'>` with no `__class_getitem__` method
-generics_variance.py:191:33: error[non-subscriptable] Cannot subscript object of type `<class 'Contra[typing.TypeVar]'>` with no `__class_getitem__` method
-generics_variance.py:191:43: error[non-subscriptable] Cannot subscript object of type `<class 'Co[typing.TypeVar]'>` with no `__class_getitem__` method
-generics_variance.py:191:49: error[non-subscriptable] Cannot subscript object of type `<class 'Contra[typing.TypeVar]'>` with no `__class_getitem__` method
-generics_variance.py:196:5: error[non-subscriptable] Cannot subscript object of type `<class 'Contra[typing.TypeVar]'>` with no `__class_getitem__` method
-generics_variance.py:196:15: error[non-subscriptable] Cannot subscript object of type `<class 'Contra[typing.TypeVar]'>` with no `__class_getitem__` method
-generics_variance.py:196:25: error[non-subscriptable] Cannot subscript object of type `<class 'Contra[typing.TypeVar]'>` with no `__class_getitem__` method
```
One of these should apparently be an error, but not of this kind, so
this is good ✔️
```diff
-specialtypes_type.py:152:16: error[invalid-type-form] `typing.TypeVar` is not a generic class
-specialtypes_type.py:156:16: error[invalid-type-form] `typing.TypeVar` is not a generic class
```
Good, those were false positives. ✔️
I skipped the analysis for everything involving `TypeVarTuple`.
## Ecosystem impact
**[Full report with detailed
diff](https://david-generic-implicit-alias.ecosystem-663.pages.dev/diff)**
Previous iterations of this PR showed all kinds of problems. In it's
current state, I do not see any large systematic problems, but it is
hard to tell with 5k diagnostic changes.
## Performance
* There is a huge 4x regression in `colour-science/colour`, related to
[this large
file](https://github.com/colour-science/colour/blob/develop/colour/io/luts/tests/test_lut.py)
with [many assignments of hard-coded arrays (lists of lists) to
`np.NDArray`
types](83e754c8b6/colour/io/luts/tests/test_lut.py (L701-L781))
that we now understand. We now take ~2 seconds to check this file, so
definitely not great, but maybe acceptable for now.
## Test Plan
Updated and new Markdown tests
## Summary
Previously if an explicit specialization failed (e.g. wrong number of
type arguments or violates an upper bound) we just inferred `Unknown`
for the entire type. This actually caused us to panic on an a case of a
recursive upper bound with invalid specialization; the upper bound would
oscillate indefinitely in fixpoint iteration between `Unknown` and the
given specialization. This could be fixed with a cycle recovery
function, but in this case there's a simpler fix: if we infer
`C[Unknown]` instead of `Unknown` for an invalid attempt to specialize
`C`, that allows fixpoint iteration to quickly converge, as well as
giving a more precise type inference.
Other type checkers actually just go with the attempted specialization
even if it's invalid. So if `C` has a type parameter with upper bound
`int`, and you say `C[str]`, they'll emit a diagnostic but just go with
`C[str]`. Even weirder, if `C` has a single type parameter and you say
`C[str, bytes]`, they'll just go with `C[str]` as the type. I'm not
convinced by this approach; it seems odd to have specializations
floating around that explicitly violate the declared upper bound, or in
the latter case aren't even the specialization the annotation requested.
I prefer `C[Unknown]` for this case.
Fixing this revealed an issue with `collections.namedtuple`, which
returns `type[tuple[Any, ...]]`. Due to
https://github.com/astral-sh/ty/issues/1649 we consider that to be an
invalid specialization. So previously we returned `Unknown`; after this
PR it would be `type[tuple[Unknown]]`, leading to more false positives
from our lack of functional namedtuple support. To avoid that I added an
explicit Todo type for functional namedtuples for now.
## Test Plan
Added and updated mdtests.
The conformance suite changes have to do with `ParamSpec`, so no
meaningful signal there.
The ecosystem changes appear to be the expected effects of having more
precise type information (including occurrences of known issues such as
https://github.com/astral-sh/ty/issues/1495 ). Most effects are just
changes to types in diagnostics.
Refs https://github.com/astral-sh/ty/issues/544
## Summary
Takes a more incremental approach to PEP 613 type alias support (vs
https://github.com/astral-sh/ruff/pull/20107). Instead of eagerly
inferring the RHS of a PEP 613 type alias as a type expression, infer it
as a value expression, just like we do for implicit type aliases, taking
advantage of the same support for e.g. unions and other type special
forms.
The main reason I'm following this path instead of the one in
https://github.com/astral-sh/ruff/pull/20107 is that we've realized that
people do sometimes use PEP 613 type aliases as values, not just as
types (because they are just a normal runtime assignment, unlike PEP 695
type aliases which create an opaque `TypeAliasType`).
This PR doesn't yet provide full support for recursive type aliases
(they don't panic, but they just fall back to `Unknown` at the recursion
point). This is future work.
## Test Plan
Added mdtests.
Many new ecosystem diagnostics, mostly because we
understand new types in lots of places.
Conformance suite changes are correct.
Performance regression is due to understanding lots of new
types; nothing we do in this PR is inherently expensive.
## Summary
Get rid of the catch-all todo type from subscripting a base type we
haven't implemented handling for yet in a type expression, and turn it
into a diagnostic instead.
Handle a few more cases explicitly, to avoid false positives from the
above change:
1. Subscripting any dynamic type (not just a todo type) in a type
expression should just result in that same dynamic type. This is
important for gradual guarantee, and matches other type checkers.
2. Subscripting a generic alias may be an error or not, depending
whether the specialization itself contains typevars. Don't try to handle
this yet (it should be handled in a later PR for specializing generic
non-PEP695 type aliases), just use a dedicated todo type for it.
3. Add a temporary todo branch to avoid false positives from string PEP
613 type aliases. This can be removed in the next PR, with PEP 613 type
alias support.
## Test Plan
Adjusted mdtests, ecosystem.
All new diagnostics in conformance suite are supposed to be diagnostics,
so this PR is a strict improvement there.
New diagnostics in the ecosystem are surfacing cases where we already
don't understand an annotation, but now we emit a diagnostic about it.
They are mostly intentional choices. Analysis of particular cases:
* `attrs`, `bokeh`, `django-stubs`, `dulwich`, `ibis`, `kornia`,
`mitmproxy`, `mongo-python-driver`, `mypy`, `pandas`, `poetry`,
`prefect`, `pydantic`, `pytest`, `scrapy`, `trio`, `werkzeug`, and
`xarray` are all cases where under `from __future__ import annotations`
or Python 3.14 deferred-annotations semantics, we follow normal
name-scoping rules, whereas some other type checkers prefer global names
over local names. This means we don't like it if e.g. you have a class
with a method or attribute named `type` or `tuple`, and you also try to
use `type` or `tuple` in method/attribute annotations of that class.
This PR isn't changing those semantics, just revealing them in more
cases where previously we just silently fell back to `Unknown`. I think
failing with a diagnostic (so authors can alias names as needed to avoid
relying on scoping rules that differ between type checkers) is better
than failing silently here.
* `beartype` assumes we support `TypeForm` (because it only supports
mypy and pyright, it uses `if MYPY:` to hide the `TypeForm` from mypy,
and pyright supports `TypeForm`), and we don't yet.
* `graphql-core` likes to use a `try: ... except ImportError: ...`
pattern for importing special forms from `typing` with fallback to
`typing_extensions`, instead of using `sys.version_info` checks. We
don't handle this well when type checking under an older Python version
(where the import from `typing` is not found); we see the imported name
as of type e.g. `Unknown | SpecialFormType(...)`, and because of the
union with `Unknown` we fail to handle it as the special form type. Mypy
and pyright also don't seem to support this pattern. They don't complain
about subscripting such special forms, but they do silently fail to
treat them as the desired special form. Again here, if we are going to
fail I'd rather fail with a diagnostic rather than silently.
* `ibis` is [trying to
use](https://github.com/ibis-project/ibis/blob/main/ibis/common/collections.py#L372)
`frozendict: type[FrozenDict]` as a way to create a "type alias" to
`FrozenDict`, but this is wrong: that means `frozendict:
type[FrozenDict[Any, Any]]`.
* `mypy` has some errors due to the fact that type-checking `typing.pyi`
itself (without knowing that it's the real `typing.pyi`) doesn't work
very well.
* `mypy-protobuf` imports some types from the protobufs library that end
up unioned with `Unknown` for some reason, and so we don't allow
explicit-specialization of them. Depending on the reason they end up
unioned with `Unknown`, we might want to better support this? But it's
orthogonal to this PR -- we aren't failing any worse here, just alerting
the author that we didn't understand their annotation.
* `pwndbg` has unresolved references due to star-importing from a
dependency that isn't installed, and uses un-imported names like `Dict`
in annotation expressions. Some of the unresolved references were hidden
by
https://github.com/astral-sh/ruff/blob/main/crates/ty_python_semantic/src/types/infer/builder.rs#L7223-L7228
when some annotations previously resolved to a Todo type that no longer
do.
## Summary
We synthesize a (potentially large) set of `__setitem__` overloads for
every item in a `TypedDict`. Previously, validation of subscript
assignments on `TypedDict`s relied on actually calling `__setitem__`
with the provided key and value types, which implied that we needed to
do the full overload call evaluation for this large set of overloads.
This PR improves the performance of subscript assignment checks on
`TypedDict`s by validating the assignment directly instead of calling
`__setitem__`.
This PR also adds better handling for assignments to subscripts on union
and intersection types (but does not attempt to make it perfect). It
achieves this by distributing the check over unions and intersections,
instead of calling `__setitem__` on the union/intersection directly. We
already do something similar when validating *attribute* assignments.
## Ecosystem impact
* A lot of diagnostics change their rule type, and/or split into
multiple diagnostics. The new version is more verbose, but easier to
understand, in my opinion
* Almost all of the invalid-key diagnostics come from pydantic, and they
should all go away (including many more) when we implement
https://github.com/astral-sh/ty/issues/1479
* Everything else looks correct to me. There may be some new diagnostics
due to the fact that we now check intersections.
## Test Plan
New Markdown tests.
## Summary
Add support for `typing.Union` in implicit type aliases / in value
position.
## Typing conformance tests
Two new tests are passing
## Ecosystem impact
* The 2k new `invalid-key` diagnostics on pydantic are caused by
https://github.com/astral-sh/ty/issues/1479#issuecomment-3513854645.
* Everything else I've checked is either a known limitation (often
related to type narrowing, because union types are often narrowed down
to a subset of options), or a true positive.
## Test Plan
New Markdown tests
I don't know why, but it always takes me an eternity to find the failing
project name a few lines below in the output. So I'm suggesting we just
add the project name to the assertion message.
## Summary
Add support for implicit type aliases that use PEP 604 unions:
```py
IntOrStr = int | str
reveal_type(IntOrStr) # UnionType
def _(int_or_str: IntOrStr):
reveal_type(int_or_str) # int | str
```
## Typing conformance
The changes are either removed false positives, or new diagnostics due
to known limitations unrelated to this PR.
## Ecosystem impact
Spot checked, a mix of true positives and known limitations.
## Test Plan
New Markdown tests.
## Summary
Infer a type of unannotated `self` parameters in decorated methods /
properties.
closes https://github.com/astral-sh/ty/issues/1448
## Test Plan
Existing tests, some new tests.
## Summary
Infer a type of `Self` for unannotated `self` parameters in methods of
classes.
part of https://github.com/astral-sh/ty/issues/159
closes https://github.com/astral-sh/ty/issues/1081
## Conformance tests changes
```diff
+enums_member_values.py:85:9: error[invalid-assignment] Object of type `int` is not assignable to attribute `_value_` of type `str`
```
A true positive ✔️
```diff
-generics_self_advanced.py:35:9: error[type-assertion-failure] Argument does not have asserted type `Self@method2`
-generics_self_basic.py:14:9: error[type-assertion-failure] Argument does not have asserted type `Self@set_scale
```
Two false positives going away ✔️
```diff
+generics_syntax_infer_variance.py:82:9: error[invalid-assignment] Cannot assign to final attribute `x` on type `Self@__init__`
```
This looks like a true positive to me, even if it's not marked with `#
E` ✔️
```diff
+protocols_explicit.py:56:9: error[invalid-assignment] Object of type `tuple[int, int, str]` is not assignable to attribute `rgb` of type `tuple[int, int, int]`
```
True positive ✔️
```
+protocols_explicit.py:85:9: error[invalid-attribute-access] Cannot assign to ClassVar `cm1` from an instance of type `Self@__init__`
```
This looks like a true positive to me, even if it's not marked with `#
E`. But this is consistent with our understanding of `ClassVar`, I
think. ✔️
```py
+qualifiers_final_annotation.py:52:9: error[invalid-assignment] Cannot assign to final attribute `ID4` on type `Self@__init__`
+qualifiers_final_annotation.py:65:9: error[invalid-assignment] Cannot assign to final attribute `ID7` on type `Self@method1`
```
New true positives ✔️
```py
+qualifiers_final_annotation.py:52:9: error[invalid-assignment] Cannot assign to final attribute `ID4` on type `Self@__init__`
+qualifiers_final_annotation.py:57:13: error[invalid-assignment] Cannot assign to final attribute `ID6` on type `Self@__init__`
+qualifiers_final_annotation.py:59:13: error[invalid-assignment] Cannot assign to final attribute `ID6` on type `Self@__init__`
```
This is a new false positive, but that's a pre-existing issue on main
(if you annotate with `Self`):
https://play.ty.dev/3ee1c56d-7e13-43bb-811a-7a81e236e6ab❌ => reported
as https://github.com/astral-sh/ty/issues/1409
## Ecosystem
* There are 5931 new `unresolved-attribute` and 3292 new
`possibly-missing-attribute` attribute errors, way too many to look at
all of them. I randomly sampled 15 of these errors and found:
* 13 instances where there was simply no such attribute that we could
plausibly see. Sometimes [I didn't find it
anywhere](8644d886c6/openlibrary/plugins/openlibrary/tests/test_listapi.py (L33)).
Sometimes it was set externally on the object. Sometimes there was some
[`setattr` dynamicness going
on](a49f6b927d/setuptools/wheel.py (L88-L94)).
I would consider all of them to be true positives.
* 1 instance where [attribute was set on `obj` in
`__new__`](9e87b44fd4/sympy/tensor/array/array_comprehension.py (L45C1-L45C36)),
which we don't support yet
* 1 instance [where the attribute was defined via `__slots__`
](e250ec0fc8/lib/spack/spack/vendor/pyrsistent/_pdeque.py (L48C5-L48C14))
* I see 44 instances [of the false positive
above](https://github.com/astral-sh/ty/issues/1409) with `Final`
instance attributes being set in `__init__`. I don't think this should
block this PR.
## Test Plan
New Markdown tests.
---------
Co-authored-by: Shaygan Hooshyari <sh.hooshyari@gmail.com>
## Summary
Use the type annotation of function parameters as bidirectional type
context when inferring the argument expression. For example, the
following example now type-checks:
```py
class TD(TypedDict):
x: int
def f(_: TD): ...
f({ "x": 1 })
```
Part of https://github.com/astral-sh/ty/issues/168.
## 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 adds support for unpacking `**kwargs` argument.
This can be matched against any standard (positional or keyword),
keyword-only, or keyword variadic parameter that haven't been matched
yet.
This PR also takes care of special casing `TypedDict` because the key
names and the corresponding value type is known, so we can be more
precise in our matching and type checking step. In the future, this
special casing would be extended to include `ParamSpec` as well.
Part of astral-sh/ty#247
## Test Plan
Add test cases for various scenarios.
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## Summary
<!-- What's the purpose of the change? What does it do, and why? -->
This PR adds support for building loongarch64 binaries in CI. As such
support has been merged in uv (astral-sh/uv#15387) it's time to consider
adding it to ruff.
Please note that as Ubuntu is not yet available for loongarch64, I have
elected to use a Debian Trixie container maintained by community
members. In addition, as Debian's pip does not allow installing modules
system-wide, I have modified the workflow to install additional modules
in a virtual environment.
Since the workflow is shared between all targets, the only way to handle
this difference (between Debian and Ubuntu) is just to install pip in a
venv for all targets. If there is a better (and less intrusive) way to
work around this, please let me know.
## Test Plan
Tests are included in CI and the loongarch64 artifacts built in [this
workflow](https://github.com/SkyBird233/ruff/actions/runs/17640270032/job/50125471548)
has been smoke tested.
Summary
--
To take advantage of the new diagnostics, we need to update our caching
model to include all of the information supported by `ruff_db`'s
diagnostic type. Instead of trying to serialize all of this information,
Micha suggested simply not caching files with diagnostics, like we
already do for files with syntax errors. This PR is an attempt at that
approach.
This has the added benefit of trimming down our `Rule` derives since
this was the last place the `FromStr`/`strum_macros::EnumString`
implementation was used, as well as the (de)serialization macros and
`CacheKey`.
Test Plan
--
Existing tests, with their input updated not to include a diagnostic,
plus a new test showing that files with lint diagnostics are not cached.
Benchmarks
--
In addition to tests, we wanted to check that this doesn't degrade
performance too much. I posted part of this new analysis in
https://github.com/astral-sh/ruff/issues/18198#issuecomment-3175048672,
but I'll duplicate it here. In short, there's not much difference
between `main` and this branch for projects with few diagnostics
(`home-assistant`, `airflow`), as expected. The difference for projects
with many diagnostics (`cpython`) is quite a bit bigger (~300 ms vs ~220
ms), but most projects that run ruff regularly are likely to have very
few diagnostics, so this may not be a problem practically.
I guess GitHub isn't really rendering this as I intended, but the extra
separator line is meant to separate the benchmarks on `main` (above the
line) from this branch (below the line).
| Command | Mean [ms] | Min [ms] | Max [ms] |
|:--------------------------------------------------------------|----------:|---------:|---------:|
| `ruff check cpython --no-cache --isolated --exit-zero` | 322.0 | 317.5
| 326.2 |
| `ruff check cpython --isolated --exit-zero` | 217.3 | 209.8 | 237.9 |
| `ruff check home-assistant --no-cache --isolated --exit-zero` | 279.5
| 277.0 | 283.6 |
| `ruff check home-assistant --isolated --exit-zero` | 37.2 | 35.7 |
40.6 |
| `ruff check airflow --no-cache --isolated --exit-zero` | 133.1 | 130.4
| 146.4 |
| `ruff check airflow --isolated --exit-zero` | 34.7 | 32.9 | 41.6 |
|:--------------------------------------------------------------|----------:|---------:|---------:|
| `ruff check cpython --no-cache --isolated --exit-zero` | 330.1 | 324.5
| 333.6 |
| `ruff check cpython --isolated --exit-zero` | 309.2 | 306.1 | 314.7 |
| `ruff check home-assistant --no-cache --isolated --exit-zero` | 288.6
| 279.4 | 302.3 |
| `ruff check home-assistant --isolated --exit-zero` | 39.8 | 36.9 |
42.4 |
| `ruff check airflow --no-cache --isolated --exit-zero` | 134.5 | 131.3
| 140.6 |
| `ruff check airflow --isolated --exit-zero` | 39.1 | 37.2 | 44.3 |
I had Claude adapt one of the
[scripts](https://github.com/sharkdp/hyperfine/blob/master/scripts/plot_whisker.py)
from the hyperfine repo to make this plot, so it's not quite perfect,
but maybe it's still useful. The table is probably more reliable for
close comparisons. I'll put more details about the benchmarks below for
the sake of future reproducibility.
<img width="4472" height="2368" alt="image"
src="https://github.com/user-attachments/assets/1c42d13e-818a-44e7-b34c-247340a936d7"
/>
<details><summary>Benchmark details</summary>
<p>
The versions of each project:
- CPython: 6322edd260e8cad4b09636e05ddfb794a96a0451, the 3.10 branch
from the contributing docs
- `home-assistant`: 5585376b406f099fb29a970b160877b57e5efcb0
- `airflow`: 29a1cb0cfde9d99b1774571688ed86cb60123896
The last two are just the main branches at the time I cloned the repos.
I don't think our Ruff config should be applied since I used
`--isolated`, but these are cloned into my copy of Ruff at
`crates/ruff_linter/resources/test`, and I trimmed the
`./target/release/` prefix from each of the commands, but these are
builds of Ruff in release mode.
And here's the script with the `hyperfine` invocation:
```shell
#!/bin/bash
cargo build --release --bin ruff
# git clone --depth 1 https://github.com/home-assistant/core crates/ruff_linter/resources/test/home-assistant
# git clone --depth 1 https://github.com/apache/airflow crates/ruff_linter/resources/test/airflow
bin=./target/release/ruff
resources=./crates/ruff_linter/resources/test
cpython=$resources/cpython
home_assistant=$resources/home-assistant
airflow=$resources/airflow
base=${1:-bench}
hyperfine --warmup 10 --export-json $base.json --export-markdown $base.md \
"$bin check $cpython --no-cache --isolated --exit-zero" \
"$bin check $cpython --isolated --exit-zero" \
"$bin check $home_assistant --no-cache --isolated --exit-zero" \
"$bin check $home_assistant --isolated --exit-zero" \
"$bin check $airflow --no-cache --isolated --exit-zero" \
"$bin check $airflow --isolated --exit-zero"
```
I ran this once on `main` (`baseline` in the graph, top half of the
table) and once on this branch (`nocache` and bottom of the table).
</p>
</details>
* [x] basic handling
* [x] parse and discover `@warnings.deprecated` attributes
* [x] associate them with function definitions
* [x] associate them with class definitions
* [x] add a new "deprecated" diagnostic
* [x] ensure diagnostic is styled appropriately for LSPs
(DiagnosticTag::Deprecated)
* [x] functions
* [x] fire on calls
* [x] fire on arbitrary references
* [x] classes
* [x] fire on initializers
* [x] fire on arbitrary references
* [x] methods
* [x] fire on calls
* [x] fire on arbitrary references
* [ ] overloads
* [ ] fire on calls
* [ ] fire on arbitrary references(??? maybe not ???)
* [ ] only fire if the actual selected overload is deprecated
* [ ] dunder desugarring (warn on deprecated `__add__` if `+` is
invoked)
* [ ] alias supression? (don't warn on uses of variables that deprecated
items were assigned to)
* [ ] import logic
* [x] fire on imports of deprecated items
* [ ] suppress subsequent diagnostics if the import diagnostic fired (is
this handled by alias supression?)
* [x] fire on all qualified references (`module.mydeprecated`)
* [x] fire on all references that depend on a `*` import
Fixes https://github.com/astral-sh/ty/issues/153
## Summary
This PR updates the server to keep track of open files both system and
virtual files.
This is done by updating the project by adding the file in the open file
set in `didOpen` notification and removing it in `didClose`
notification.
This does mean that for workspace diagnostics, ty will only check open
files because the behavior of different diagnostic builder is to first
check `is_file_open` and only add diagnostics for open files. So, this
required updating the `is_file_open` model to be `should_check_file`
model which validates whether the file needs to be checked based on the
`CheckMode`. If the check mode is open files only then it will check
whether the file is open. If it's all files then it'll return `true` by
default.
Closes: astral-sh/ty#619
## Test Plan
### Before
There are two files in the project: `__init__.py` and `diagnostics.py`.
In the video, I'm demonstrating the old behavior where making changes to
the (open) `diagnostics.py` file results in re-parsing the file:
https://github.com/user-attachments/assets/c2ac0ecd-9c77-42af-a924-c3744b146045
### After
Same setup as above.
In the video, I'm demonstrating the new behavior where making changes to
the (open) `diagnostics.py` file doesn't result in re-parting the file:
https://github.com/user-attachments/assets/7b82fe92-f330-44c7-b527-c841c4545f8f
## Summary
Add a new `Type::EnumLiteral(…)` variant and infer this type for member
accesses on enums.
**Example**: No more `@Todo` types here:
```py
from enum import Enum
class Answer(Enum):
YES = 1
NO = 2
def is_yes(self) -> bool:
return self == Answer.YES
reveal_type(Answer.YES) # revealed: Literal[Answer.YES]
reveal_type(Answer.YES == Answer.NO) # revealed: Literal[False]
reveal_type(Answer.YES.is_yes()) # revealed: bool
```
## Test Plan
* Many new Markdown tests for the new type variant
* Added enum literal types to property tests, ran property tests
## Ecosystem analysis
Summary:
Lots of false positives removed. All of the new diagnostics are
either new true positives (the majority) or known problems. Click for
detailed analysis</summary>
Details:
```diff
AutoSplit (https://github.com/Toufool/AutoSplit)
+ error[call-non-callable] src/capture_method/__init__.py:137:9: Method `__getitem__` of type `bound method CaptureMethodDict.__getitem__(key: Never, /) -> type[CaptureMethodBase]` is not callable on object of type `CaptureMethodDict`
+ error[call-non-callable] src/capture_method/__init__.py:147:9: Method `__getitem__` of type `bound method CaptureMethodDict.__getitem__(key: Never, /) -> type[CaptureMethodBase]` is not callable on object of type `CaptureMethodDict`
+ error[call-non-callable] src/capture_method/__init__.py:148:1: Method `__getitem__` of type `bound method CaptureMethodDict.__getitem__(key: Never, /) -> type[CaptureMethodBase]` is not callable on object of type `CaptureMethodDict`
```
New true positives. That `__getitem__` method is apparently annotated
with `Never` to prevent developers from using it.
```diff
dd-trace-py (https://github.com/DataDog/dd-trace-py)
+ error[invalid-assignment] ddtrace/vendor/psutil/_common.py:29:5: Object of type `None` is not assignable to `Literal[AddressFamily.AF_INET6]`
+ error[invalid-assignment] ddtrace/vendor/psutil/_common.py:33:5: Object of type `None` is not assignable to `Literal[AddressFamily.AF_UNIX]`
```
Arguably true positives:
e0a772c28b/ddtrace/vendor/psutil/_common.py (L29)
```diff
ignite (https://github.com/pytorch/ignite)
+ error[invalid-argument-type] tests/ignite/engine/test_custom_events.py:190:34: Argument to bound method `__call__` is incorrect: Expected `((...) -> Unknown) | None`, found `Literal["123"]`
+ error[invalid-argument-type] tests/ignite/engine/test_custom_events.py:220:37: Argument to function `default_event_filter` is incorrect: Expected `Engine`, found `None`
+ error[invalid-argument-type] tests/ignite/engine/test_custom_events.py:220:43: Argument to function `default_event_filter` is incorrect: Expected `int`, found `None`
+ error[call-non-callable] tests/ignite/engine/test_custom_events.py:561:9: Object of type `CustomEvents` is not callable
+ error[invalid-argument-type] tests/ignite/metrics/test_frequency.py:50:38: Argument to bound method `attach` is incorrect: Expected `Events`, found `CallableEventWithFilter`
```
All true positives. Some of them are inside `pytest.raises(TypeError,
…)` blocks 🙃
```diff
meson (https://github.com/mesonbuild/meson)
+ error[invalid-argument-type] unittests/internaltests.py:243:51: Argument to bound method `__init__` is incorrect: Expected `bool`, found `Literal[MachineChoice.HOST]`
+ error[invalid-argument-type] unittests/internaltests.py:271:51: Argument to bound method `__init__` is incorrect: Expected `bool`, found `Literal[MachineChoice.HOST]`
```
New true positives. Enum literals can not be assigned to `bool`, even if
their value types are `0` and `1`.
```diff
poetry (https://github.com/python-poetry/poetry)
+ error[invalid-assignment] src/poetry/console/exceptions.py:101:5: Object of type `Literal[""]` is not assignable to `InitVar[str]`
```
New false positive, missing support for `InitVar`.
```diff
prefect (https://github.com/PrefectHQ/prefect)
+ error[invalid-argument-type] src/integrations/prefect-dask/tests/test_task_runners.py:193:17: Argument is incorrect: Expected `StateType`, found `Literal[StateType.COMPLETED]`
```
This is confusing. There are two definitions
([one](74d8cd93ee/src/prefect/client/schemas/objects.py (L89-L100)),
[two](https://github.com/PrefectHQ/prefect/blob/main/src/prefect/server/schemas/states.py#L40))
of the `StateType` enum. Here, we're trying to assign one to the other.
I don't think that should be allowed, so this is a true positive (?).
```diff
python-htmlgen (https://github.com/srittau/python-htmlgen)
+ error[invalid-assignment] test_htmlgen/form.py:51:9: Object of type `str` is not assignable to attribute `autocomplete` of type `Autocomplete | None`
+ error[invalid-assignment] test_htmlgen/video.py:38:9: Object of type `str` is not assignable to attribute `preload` of type `Preload | None`
```
True positives. [The stubs are
wrong](01e3b911ac/htmlgen/form.pyi (L8-L10)).
These should not contain type annotations, but rather just `OFF = ...`.
```diff
rotki (https://github.com/rotki/rotki)
+ error[invalid-argument-type] rotkehlchen/tests/unit/test_serialization.py:62:30: Argument to bound method `deserialize` is incorrect: Expected `str`, found `Literal[15]`
```
New true positive.
```diff
vision (https://github.com/pytorch/vision)
+ error[unresolved-attribute] test/test_extended_models.py:302:17: Type `type[WeightsEnum]` has no attribute `DEFAULT`
+ error[unresolved-attribute] test/test_extended_models.py:302:58: Type `type[WeightsEnum]` has no attribute `DEFAULT`
```
Also new true positives. No `DEFAULT` member exists on `WeightsEnum`.
## Summary
I played with those numbers a bit locally and `sample_size=3,
sample_count=8` seemed like a rather stable setup. This means a single
sample consistents of 3 iterations of checking pydantic multithreaded.
And this is repeated 8 times for statistics. A single check took ~300 ms
previously on the runners, so this should only take 7 s.
## Summary
The [`DateType`](https://github.com/glyph/DateType) library has some
very large protocols in it. Currently we type-check it quite quickly,
but the current version of https://github.com/astral-sh/ruff/pull/18659
makes our execution time on this library pathologically slow. That PR
doesn't seem to have a big impact on any of our current benchmarks,
however, so it seems we have some missing coverage in this area; I
therefore propose that we add `DateType` as a benchmark.
Currently the benchmark runs pretty quickly (about half the runtime of
attrs, which is our fastest real-world benchmark currently), and the
library has 0 third-party dependencies, so the benchmark is quick to
setup.
## Test Plan
`cargo bench -p ruff_benchmark --bench=ty`
The benchmark is currently very noisy (± 10%). This leads to codspeed
reports on PRs, because we often exceed the trigger threshold. This is
confusing to ty contributors who are not aware about the flakiness.
Let's disable it for now.
## Summary
Adds a new micro-benchmark as a regression test for
https://github.com/astral-sh/ty/issues/627.
## Test Plan
Ran the benchmark on the parent commit of
89d915a1e3,
and verified that it took > 1s, while it takes ~10 ms after the fix.
## Summary
Add a micro-benchmark for the code pattern observed in
https://github.com/astral-sh/ty/issues/362.
This currently takes around 1 second on my machine.
## Test Plan
```bash
cargo bench -p ruff_benchmark -- 'ty_micro\[many_tuple' --sample-size 10
```
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.
## Summary
Adds a simple progress bar for the `ty check` CLI command. The style is
taken from uv, and like uv the bar is always shown - for smaller
projects it is fast enough that it isn't noticeable. We could
alternatively hide it completely based on some heuristic for the number
of files, or only show it after some amount of time.
I also disabled it when `--watch` is passed, cancelling inflight checks
was leading to zombie progress bars. I think we can fix this by using
[`MultiProgress`](https://docs.rs/indicatif/latest/indicatif/struct.MultiProgress.html)
and managing all the bars globally, but I left that out for now.
Resolves https://github.com/astral-sh/ty/issues/98.
## Summary
This PR is a first step toward integration of the new `Diagnostic` type
into ruff. There are two main changes:
- A new `UnifiedFile` enum wrapping `File` for red-knot and a
`SourceFile` for ruff
- ruff's `Message::SyntaxError` variant is now a `Diagnostic` instead of
a `SyntaxErrorMessage`
The second of these changes was mostly just a proof of concept for the
first, and it went pretty smoothly. Converting `DiagnosticMessage`s will
be most of the work in replacing `Message` entirely.
## Test Plan
Existing tests, which show no changes.
---------
Co-authored-by: Carl Meyer <carl@astral.sh>
Co-authored-by: Micha Reiser <micha@reiser.io>
David Peter
Carl Meyer
Dhruv Manilawala
Micha Reiser
Alex Waygood
Ibraheem Ahmed
Takayuki Maeda
William Woodruff
SkyBird
ffgan
Brent Westbrook
Aria Desires
justin
Douglas Creager
Andrew Gallant