## Summary
The exact behavior around what's allowed vs. disallowed was partly
detected through trial and error in the runtime.
I was a little confused by [this
comment](https://github.com/python/cpython/pull/129352) that says
"`NamedTuple` subclasses cannot be inherited from" because in practice
that doesn't appear to error at runtime.
Closes [#1683](https://github.com/astral-sh/ty/issues/1683).
## Summary
Lots of Ruff rules encourage you to make changes that might then cause
ty to start complaining about Liskov violations. Most of these Ruff
rules already refrain from complaining about a method if they see that
the method is decorated with `@override`, but this usually isn't
documented. This PR updates the docs of many Ruff rules to note that
they refrain from complaining about `@override`-decorated methods, and
also adds a similar note to the ty `invalid-method-override`
documentation.
Helps with
https://github.com/astral-sh/ty/issues/1644#issuecomment-3581663859
## Test Plan
- `uvx prek run -a` locally
- CI on this PR
## Summary
This PR updates the explicit specialization logic to avoid using the
call machinery.
Previously, the logic would use the call machinery by converting the
list of type variables into a `Binding` with a single `Signature` where
all the type variables are positional-only parameters with bounds and
constraints as the annotated type and the default type as the default
parameter value. This has the advantage that it doesn't need to
implement any specific logic but the disadvantages are subpar diagnostic
messages as it would use the ones specific to a function call. But, an
important disadvantage is that the kind of type variable is lost in this
translation which becomes important in #21445 where a `ParamSpec` can
specialize into a list of types which is provided using list literal.
For example,
```py
class Foo[T, **P]: ...
Foo[int, [int, str]]
```
This PR converts the logic to use a simple loop using `zip_longest` as
all type variables and their corresponding type argument maps on a 1-1
basis. They cannot be specified using keyword argument either e.g.,
`dict[_VT=str, _KT=int]` is invalid.
This PR also makes an initial attempt to improve the diagnostic message
to specifically target the specialization part by using words like "type
argument" instead of just "argument" and including information like the
type variable, bounds, and constraints. Further improvements can be made
by highlighting the type variable definition or the bounds / constraints
as a sub-diagnostic but I'm going to leave that as a follow-up.
## Test Plan
Update messages in existing test cases.
## Summary
Derived from #17371Fixesastral-sh/ty#256
Fixes https://github.com/astral-sh/ty/issues/1415
Fixes https://github.com/astral-sh/ty/issues/1433
Fixes https://github.com/astral-sh/ty/issues/1524
Properly handles any kind of recursive inference and prevents panics.
---
Let me explain techniques for converging fixed-point iterations during
recursive type inference.
There are two types of type inference that naively don't converge
(causing salsa to panic): divergent type inference and oscillating type
inference.
### Divergent type inference
Divergent type inference occurs when eagerly expanding a recursive type.
A typical example is this:
```python
class C:
def f(self, other: "C"):
self.x = (other.x, 1)
reveal_type(C().x) # revealed: Unknown | tuple[Unknown | tuple[Unknown | tuple[..., Literal[1]], Literal[1]], Literal[1]]
```
To solve this problem, we have already introduced `Divergent` types
(https://github.com/astral-sh/ruff/pull/20312). `Divergent` types are
treated as a kind of dynamic type [^1].
```python
Unknown | tuple[Unknown | tuple[Unknown | tuple[..., Literal[1]], Literal[1]], Literal[1]]
=> Unknown | tuple[Divergent, Literal[1]]
```
When a query function that returns a type enters a cycle, it sets
`Divergent` as the cycle initial value (instead of `Never`). Then, in
the cycle recovery function, it reduces the nesting of types containing
`Divergent` to converge.
```python
0th: Divergent
1st: Unknown | tuple[Divergent, Literal[1]]
2nd: Unknown | tuple[Unknown | tuple[Divergent, Literal[1]], Literal[1]]
=> Unknown | tuple[Divergent, Literal[1]]
```
Each cycle recovery function for each query should operate only on the
`Divergent` type originating from that query.
For this reason, while `Divergent` appears the same as `Any` to the
user, it internally carries some information: the location where the
cycle occurred. Previously, we roughly identified this by having the
scope where the cycle occurred, but with the update to salsa, functions
that create cycle initial values can now receive a `salsa::Id`
(https://github.com/salsa-rs/salsa/pull/1012). This is an opaque ID that
uniquely identifies the cycle head (the query that is the starting point
for the fixed-point iteration). `Divergent` now has this `salsa::Id`.
### Oscillating type inference
Now, another thing to consider is oscillating type inference.
Oscillating type inference arises from the fact that monotonicity is
broken. Monotonicity here means that for a query function, if it enters
a cycle, the calculation must start from a "bottom value" and progress
towards the final result with each cycle. Monotonicity breaks down in
type systems that have features like overloading and overriding.
```python
class Base:
def flip(self) -> "Sub":
return Sub()
class Sub(Base):
def flip(self) -> "Base":
return Base()
class C:
def __init__(self, x: Sub):
self.x = x
def replace_with(self, other: "C"):
self.x = other.x.flip()
reveal_type(C(Sub()).x)
```
Naive fixed-point iteration results in `Divergent -> Sub -> Base -> Sub
-> ...`, which oscillates forever without diverging or converging. To
address this, the salsa API has been modified so that the cycle recovery
function receives the value of the previous cycle
(https://github.com/salsa-rs/salsa/pull/1012).
The cycle recovery function returns the union type of the current cycle
and the previous cycle. In the above example, the result type for each
cycle is `Divergent -> Sub -> Base (= Sub | Base) -> Base`, which
converges.
The final result of oscillating type inference does not contain
`Divergent` because `Divergent` that appears in a union type can be
removed, as is clear from the expansion. This simplification is
performed at the same time as nesting reduction.
```
T | Divergent = T | (T | (T | ...)) = T
```
[^1]: In theory, it may be possible to strictly treat types containing
`Divergent` types as recursive types, but we probably shouldn't go that
deep yet. (AFAIK, there are no PEPs that specify how to handle
implicitly recursive types that aren't named by type aliases)
## Performance analysis
A happy side effect of this PR is that we've observed widespread
performance improvements!
This is likely due to the removal of the `ITERATIONS_BEFORE_FALLBACK`
and max-specialization depth trick
(https://github.com/astral-sh/ty/issues/1433,
https://github.com/astral-sh/ty/issues/1415), which means we reach a
fixed point much sooner.
## Ecosystem analysis
The changes look good overall.
You may notice changes in the converged values for recursive types,
this is because the way recursive types are normalized has been changed.
Previously, types containing `Divergent` types were normalized by
replacing them with the `Divergent` type itself, but in this PR, types
with a nesting level of 2 or more that contain `Divergent` types are
normalized by replacing them with a type with a nesting level of 1. This
means that information about the non-divergent parts of recursive types
is no longer lost.
```python
# previous
tuple[tuple[Divergent, int], int] => Divergent
# now
tuple[tuple[Divergent, int], int] => tuple[Divergent, int]
```
The false positive error introduced in this PR occurs in class
definitions with self-referential base classes, such as the one below.
```python
from typing_extensions import Generic, TypeVar
T = TypeVar("T")
U = TypeVar("U")
class Base2(Generic[T, U]): ...
# TODO: no error
# error: [unsupported-base] "Unsupported class base with type `<class 'Base2[Sub2, U@Sub2]'> | <class 'Base2[Sub2[Unknown], U@Sub2]'>`"
class Sub2(Base2["Sub2", U]): ...
```
This is due to the lack of support for unions of MROs, or because cyclic
legacy generic types are not inferred as generic types early in the
query cycle.
## Test Plan
All samples listed in astral-sh/ty#256 are tested and passed without any
panic!
## Acknowledgments
Thanks to @MichaReiser for working on bug fixes and improvements to
salsa for this PR. @carljm also contributed early on to the discussion
of the query convergence mechanism proposed in this PR.
---------
Co-authored-by: Carl Meyer <carl@astral.sh>
## Summary
This PR adds a code action to remove unused ignore comments.
This PR also includes some infrastructure boilerplate to set up code
actions in the editor:
* Extend `snapshot-diagnostics` to render fixes
* Render fixes when using `--output-format=full`
* Hook up edits and the code action request in the LSP
* Add the `Unnecessary` tag to `unused-ignore-comment` diagnostics
* Group multiple unused codes into a single diagnostic
The same fix can be used on the CLI once we add `ty fix`
Note: `unused-ignore-comment` is currently disabled by default.
https://github.com/user-attachments/assets/f9e21087-3513-4156-85d7-a90b1a7a3489
## Summary
Building on https://github.com/astral-sh/ruff/pull/21436.
There's nothing conceptually more complicated about this, it just
requires its own set of tests and its own subdiagnostic hint.
I also uncovered another inconsistency between mypy/pyright/pyrefly,
which is fun. In this case, I suggest we go with pyright's behaviour.
## Test Plan
mdtests/snapshots
## Summary
This PR adds support for understanding the legacy definition and PEP 695
definition for `ParamSpec`.
This is still very initial and doesn't really implement any of the
semantics.
Part of https://github.com/astral-sh/ty/issues/157
## Test Plan
Add mdtest cases.
## Ecosystem analysis
Most of the diagnostics in `starlette` are due to the fact that ty now
understands `ParamSpec` is not a `Todo` type, so the assignability check
fails. The code looks something like:
```py
class _MiddlewareFactory(Protocol[P]):
def __call__(self, app: ASGIApp, /, *args: P.args, **kwargs: P.kwargs) -> ASGIApp: ... # pragma: no cover
class Middleware:
def __init__(
self,
cls: _MiddlewareFactory[P],
*args: P.args,
**kwargs: P.kwargs,
) -> None:
self.cls = cls
self.args = args
self.kwargs = kwargs
# ty complains that `ServerErrorMiddleware` is not assignable to `_MiddlewareFactory[P]`
Middleware(ServerErrorMiddleware, handler=error_handler, debug=debug)
```
There are multiple diagnostics where there's an attribute access on the
`Wrapped` object of `functools` which Pyright also raises:
```py
from functools import wraps
def my_decorator(f):
@wraps(f)
def wrapper(*args, **kwds):
return f(*args, **kwds)
# Pyright: Cannot access attribute "__signature__" for class "_Wrapped[..., Unknown, ..., Unknown]"
Attribute "__signature__" is unknown [reportAttributeAccessIssue]
# ty: Object of type `_Wrapped[Unknown, Unknown, Unknown, Unknown]` has no attribute `__signature__` [unresolved-attribute]
wrapper.__signature__
return wrapper
```
There are additional diagnostics that is due to the assignability checks
failing because ty now infers the `ParamSpec` instead of using the
`Todo` type which would always succeed. This results in a few
`no-matching-overload` diagnostics because the assignability checks
fail.
There are a few diagnostics related to
https://github.com/astral-sh/ty/issues/491 where there's a variable
which is either a bound method or a variable that's annotated with
`Callable` that doesn't contain the instance as the first parameter.
Another set of (valid) diagnostics are where the code hasn't provided
all the type variables. ty is now raising diagnostics for these because
we include `ParamSpec` type variable in the signature. For example,
`staticmethod[Any]` which contains two type variables.
## Summary
Before this PR, we would emit diagnostics like "Invalid key access" for
a TypedDict literal with invalid key, which doesn't make sense since
there's no "access" in that case. This PR just adjusts the wording to be
more general, and adjusts the documentation of the lint rule too.
I noticed this in the playground and thought it would be a quick fix. As
usual, it turned out to be a bit more subtle than I expected, but for
now I chose to punt on the complexity. We may ultimately want to have
different rules for invalid subscript vs invalid TypedDict literal,
because an invalid key in a TypedDict literal is low severity: it's a
typo detector, but not actually a type error. But then there's another
wrinkle there: if the TypedDict is `closed=True`, then it _is_ a type
error. So would we want to separate the open and closed cases into
separate rules, too? I decided to leave this as a question for future.
If we wanted to use separate rules, or use specific wording for each
case instead of the generalized wording I chose here, that would also
involve a bit of extra work to distinguish the cases, since we use a
generic set of functions for reporting these errors.
## Test Plan
Added and updated mdtests.
This is the ultra-minimal implementation of
* https://github.com/astral-sh/ty/issues/296
that was previously discussed as a good starting point. In particular we
don't actually bother trying to figure out the exact python versions,
but we still mention "hey btw for No Reason At All... you're on python
3.10" when you try to access something that has a definition rooted in
the stdlib that we believe exists sometimes.
## Summary
Bump the latest supported Python version of ty to 3.14 and updates some
references from 3.13 to 3.14.
This also fixes a bug with `dataclasses.field` on 3.14 (which adds a new
keyword-only parameter to that function, breaking our previously naive
matching on the parameter structure of that function).
## Test Plan
A `ty check` on a file with template strings (without any further
configuration) doesn't raise errors anymore.
Summary
--
Closes#19467 and also removes the warning about using Python 3.14
without
preview enabled.
I also bumped `PythonVersion::default` to 3.9 because it reaches EOL
this month,
but we could also defer that for now if we wanted.
The first three commits are related to the `latest` bump to 3.14; the
fourth commit
bumps the default to 3.10.
Note that this PR also bumps the default Python version for ty to 3.10
because
there was a test asserting that it stays in sync with
`ast::PythonVersion`.
Test Plan
--
Existing tests
I spot-checked the ecosystem report, and I believe these are all
expected. Inbits doesn't specify a target Python version, so I guess
we're applying the default. UP007, UP035, and UP045 all use the new
default value to emit new diagnostics.
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## Summary
#19990 didn't completely fix the base vs. child conda environment
distinction, since it detected slightly different behavior than what I
usually see in conda. E.g., I see something like the following:
```
(didn't yet activate conda, but base is active)
➜ printenv | grep CONDA
CONDA_PYTHON_EXE=/opt/anaconda3/bin/python
CONDA_PREFIX=/opt/anaconda3
CONDA_DEFAULT_ENV=base
CONDA_EXE=/opt/anaconda3/bin/conda
CONDA_SHLVL=1
CONDA_PROMPT_MODIFIER=(base)
(activating conda)
➜ conda activate test
(test is an active conda environment)
❯ printenv | grep CONDA
CONDA_PREFIX=/opt/anaconda3/envs/test
CONDA_PYTHON_EXE=/opt/anaconda3/bin/python
CONDA_SHLVL=2
CONDA_PREFIX_1=/opt/anaconda3
CONDA_DEFAULT_ENV=test
CONDA_PROMPT_MODIFIER=(test)
CONDA_EXE=/opt/anaconda3/bin/conda
```
But the current behavior looks for `CONDA_DEFAULT_ENV =
basename(CONDA_PREFIX)` for the base environment instead of the child
environment, where we actually see this equality.
This pull request fixes that and updates the tests correspondingly.
## Test Plan
I updated the existing tests with the new behavior. Let me know if you
want more tests. Note: It shouldn't be necessary to test for the case
where we have `conda/envs/base`, since one should not be able to create
such an environment (one with the name of `CONDA_DEFAULT_ENV`).
---------
Co-authored-by: Aria Desires <aria.desires@gmail.com>
## Summary
This PR wires up the GitHub output format moved to `ruff_db` in #20320
to the ty CLI.
It's a bit smaller than the GitLab version (#20155) because some of the
helpers were already in place, but I did factor out a few
`DisplayDiagnosticConfig` constructor calls in Ruff. I also exposed the
`GithubRenderer` and a wrapper `DisplayGithubDiagnostics` type because
we needed a way to configure the program name displayed in the GitHub
diagnostics. This was previously hard-coded to `Ruff`:
<img width="675" height="247" alt="image"
src="https://github.com/user-attachments/assets/592da860-d2f5-4abd-bc5a-66071d742509"
/>
Another option would be to drop the program name in the output format,
but I think it can be helpful in workflows with multiple programs
emitting annotations (such as Ruff and ty!)
## Test Plan
New CLI test, and a manual test with `--config 'terminal.output-format =
"github"'`
## Summary
I felt it was safer to add the `python` folder *in addition* to a
possibly-existing `src` folder, even though the `src` folder only
contains Rust code for `maturin`-based projects. There might be
non-maturin projects where a `python` folder exists for other reasons,
next to a normal `src` layout.
closes https://github.com/astral-sh/ty/issues/1120
## Test Plan
Tested locally on the egglog-python project.
## Summary
Add backreferences to the original item declaration in TypedDict
diagnostics.
Thanks to @AlexWaygood for the suggestion.
## Test Plan
Updated snapshots
## Summary
Two minor cleanups:
- Return `Option<ClassType>` rather than `Option<ClassLiteral>` from
`TypeInferenceBuilder::class_context_of_current_method`. Now that
`ClassType::is_protocol` exists as a method as well as
`ClassLiteral::is_protocol`, this simplifies most of the call-sites of
the `class_context_of_current_method()` method.
- Make more use of the `MethodDecorator::try_from_fn_type` method in
`class.rs`. Under the hood, this method uses the new methods
`FunctionType::is_classmethod()` and `FunctionType::is_staticmethod()`
that @sharkdp recently added, so it gets the semantics more precisely
correct than the code it's replacing in `infer.rs` (by accounting for
implicit staticmethods/classmethods as well as explicit ones). By using
these methods we can delete some code elsewhere (the
`FunctionDecorators::from_decorator_types()` constructor)
## Test Plan
Existing tests
## Summary
This wires up the GitLab output format moved into `ruff_db` in
https://github.com/astral-sh/ruff/pull/20117 to the ty CLI.
While I was here, I made one unrelated change to the CLI docs. Clap was
rendering the escapes around the `\[default\]` brackets for the `full`
output, so I just switched those to parentheses:
```
--output-format <OUTPUT_FORMAT>
The format to use for printing diagnostic messages
Possible values:
- full: Print diagnostics verbosely, with context and helpful hints \[default\]
- concise: Print diagnostics concisely, one per line
- gitlab: Print diagnostics in the JSON format expected by GitLab Code Quality reports
```
## Test Plan
New CLI test, and a manual test with `--config 'terminal.output-format =
"gitlab"'` to make sure this works as a configuration option too. I also
tried piping the output through jq to make sure it's at least valid JSON
There are some situations that we have a confusing diagnostics due to
identical class names.
## Class with same name from different modules
```python
import pandas
import polars
df: pandas.DataFrame = polars.DataFrame()
```
This yields the following error:
**Actual:**
error: [invalid-assignment] "Object of type `DataFrame` is not
assignable to `DataFrame`"
**Expected**:
error: [invalid-assignment] "Object of type `polars.DataFrame` is not
assignable to `pandas.DataFrame`"
## Nested classes
```python
from enum import Enum
class A:
class B(Enum):
ACTIVE = "active"
INACTIVE = "inactive"
class C:
class B(Enum):
ACTIVE = "active"
INACTIVE = "inactive"
```
**Actual**:
error: [invalid-assignment] "Object of type `Literal[B.ACTIVE]` is not
assignable to `B`"
**Expected**:
error: [invalid-assignment] "Object of type
`Literal[my_module.C.B.ACTIVE]` is not assignable to `my_module.A.B`"
## Solution
In this MR we added an heuristics to detect when to use a fully
qualified name:
- There is an invalid assignment and;
- They are two different classes and;
- They have the same name
The fully qualified name always includes:
- module name
- nested classes name
- actual class name
There was no `QualifiedDisplay` so I had to implement it from scratch.
I'm very new to the codebase, so I might have done things inefficiently,
so I appreciate feedback.
Should we pre-compute the fully qualified name or do it on demand?
## Not implemented
### Function-local classes
Should we approach this in a different PR?
**Example**:
```python
# t.py
from __future__ import annotations
def function() -> A:
class A:
pass
return A()
class A:
pass
a: A = function()
```
#### mypy
```console
t.py:8: error: Incompatible return value type (got "t.A@5", expected "t.A") [return-value]
```
From my testing the 5 in `A@5` comes from the like number.
#### ty
```console
error[invalid-return-type]: Return type does not match returned value
--> t.py:4:19
|
4 | def function() -> A:
| - Expected `A` because of return type
5 | class A:
6 | pass
7 |
8 | return A()
| ^^^ expected `A`, found `A`
|
info: rule `invalid-return-type` is enabled by default
```
Fixes https://github.com/astral-sh/ty/issues/848
---------
Co-authored-by: Alex Waygood <Alex.Waygood@Gmail.com>
Co-authored-by: Carl Meyer <carl@astral.sh>
## Summary
Implement validation for `TypedDict` constructor calls and dictionary
literal assignments, including support for `total=False` and proper
field management.
Also add support for `Required` and `NotRequired` type qualifiers in
`TypedDict` classes, along with proper inheritance behavior and the
`total=` parameter.
Support both constructor calls and dict literal syntax
part of https://github.com/astral-sh/ty/issues/154
### Basic Required Field Validation
```py
class Person(TypedDict):
name: str
age: int | None
# Error: Missing required field 'name' in TypedDict `Person` constructor
incomplete = Person(age=25)
# Error: Invalid argument to key "name" with declared type `str` on TypedDict `Person`
wrong_type = Person(name=123, age=25)
# Error: Invalid key access on TypedDict `Person`: Unknown key "extra"
extra_field = Person(name="Bob", age=25, extra=True)
```
<img width="773" height="191" alt="Screenshot 2025-08-07 at 17 59 22"
src="https://github.com/user-attachments/assets/79076d98-e85f-4495-93d6-a731aa72a5c9"
/>
### Support for `total=False`
```py
class OptionalPerson(TypedDict, total=False):
name: str
age: int | None
# All valid - all fields are optional with total=False
charlie = OptionalPerson()
david = OptionalPerson(name="David")
emily = OptionalPerson(age=30)
frank = OptionalPerson(name="Frank", age=25)
# But type validation and extra fields still apply
invalid_type = OptionalPerson(name=123) # Error: Invalid argument type
invalid_extra = OptionalPerson(extra=True) # Error: Invalid key access
```
### Dictionary Literal Validation
```py
# Type checking works for both constructors and dict literals
person: Person = {"name": "Alice", "age": 30}
reveal_type(person["name"]) # revealed: str
reveal_type(person["age"]) # revealed: int | None
# Error: Invalid key access on TypedDict `Person`: Unknown key "non_existing"
reveal_type(person["non_existing"]) # revealed: Unknown
```
### `Required`, `NotRequired`, `total`
```python
from typing import TypedDict
from typing_extensions import Required, NotRequired
class PartialUser(TypedDict, total=False):
name: Required[str] # Required despite total=False
age: int # Optional due to total=False
email: NotRequired[str] # Explicitly optional (redundant)
class User(TypedDict):
name: Required[str] # Explicitly required (redundant)
age: int # Required due to total=True
bio: NotRequired[str] # Optional despite total=True
# Valid constructions
partial = PartialUser(name="Alice") # name required, age optional
full = User(name="Bob", age=25) # name and age required, bio optional
# Inheritance maintains original field requirements
class Employee(PartialUser):
department: str # Required (new field)
# name: still Required (inherited)
# age: still optional (inherited)
emp = Employee(name="Charlie", department="Engineering") # ✅
Employee(department="Engineering") # ❌
e: Employee = {"age": 1} # ❌
```
<img width="898" height="683" alt="Screenshot 2025-08-11 at 22 02 57"
src="https://github.com/user-attachments/assets/4c1b18cd-cb2e-493a-a948-51589d121738"
/>
## Implementation
The implementation reuses existing validation logic done in
https://github.com/astral-sh/ruff/pull/19782
### ℹ️ Why I did NOT synthesize an `__init__` for `TypedDict`:
`TypedDict` inherits `dict.__init__(self, *args, **kwargs)` that accepts
all arguments.
The type resolution system finds this inherited signature **before**
looking for synthesized members.
So `own_synthesized_member()` is never called because a signature
already exists.
To force synthesis, you'd have to override Python’s inheritance
mechanism, which would break compatibility with the existing ecosystem.
This is why I went with ad-hoc validation. IMO it's the only viable
approach that respects Python’s
inheritance semantics while providing the required validation.
### Refacto of `Field`
**Before:**
```rust
struct Field<'db> {
declared_ty: Type<'db>,
default_ty: Option<Type<'db>>, // NamedTuple and dataclass only
init_only: bool, // dataclass only
init: bool, // dataclass only
is_required: Option<bool>, // TypedDict only
}
```
**After:**
```rust
struct Field<'db> {
declared_ty: Type<'db>,
kind: FieldKind<'db>,
}
enum FieldKind<'db> {
NamedTuple { default_ty: Option<Type<'db>> },
Dataclass { default_ty: Option<Type<'db>>, init_only: bool, init: bool },
TypedDict { is_required: bool },
}
```
## Test Plan
Updated Markdown tests
---------
Co-authored-by: David Peter <mail@david-peter.de>
## Summary
This PR adds a new lint, `invalid-await`, for all sorts of reasons why
an object may not be `await`able, as discussed in astral-sh/ty#919.
Precisely, `__await__` is guarded against being missing, possibly
unbound, or improperly defined (expects additional arguments or doesn't
return an iterator).
Of course, diagnostics need to be fine-tuned. If `__await__` cannot be
called with no extra arguments, it indicates an error (or a quirk?) in
the method signature, not at the call site. Without any doubt, such an
object is not `Awaitable`, but I feel like talking about arguments for
an *implicit* call is a bit leaky.
I didn't reference any actual diagnostic messages in the lint
definition, because I want to hear feedback first.
Also, there's no mention of the actual required method signature for
`__await__` anywhere in the docs. The only reference I had is the
`typing` stub. I basically ended up linking `[Awaitable]` to ["must
implement
`__await__`"](https://docs.python.org/3/library/collections.abc.html#collections.abc.Awaitable),
which is insufficient on its own.
## Test Plan
The following code was tested:
```python
import asyncio
import typing
class Awaitable:
def __await__(self) -> typing.Generator[typing.Any, None, int]:
yield None
return 5
class NoDunderMethod:
pass
class InvalidAwaitArgs:
def __await__(self, value: int) -> int:
return value
class InvalidAwaitReturn:
def __await__(self) -> int:
return 5
class InvalidAwaitReturnImplicit:
def __await__(self):
pass
async def main() -> None:
result = await Awaitable() # valid
result = await NoDunderMethod() # `__await__` is missing
result = await InvalidAwaitReturn() # `__await__` returns `int`, which is not a valid iterator
result = await InvalidAwaitArgs() # `__await__` expects additional arguments and cannot be called implicitly
result = await InvalidAwaitReturnImplicit() # `__await__` returns `Unknown`, which is not a valid iterator
asyncio.run(main())
```
---------
Co-authored-by: Carl Meyer <carl@astral.sh>
## Summary
Validates writes to `TypedDict` keys, for example:
```py
class Person(TypedDict):
name: str
age: int | None
def f(person: Person):
person["naem"] = "Alice" # error: [invalid-key]
person["age"] = "42" # error: [invalid-assignment]
```
The new specialized `invalid-assignment` diagnostic looks like this:
<img width="1160" height="279" alt="image"
src="https://github.com/user-attachments/assets/51259455-3501-4829-a84e-df26ff90bd89"
/>
## Ecosystem analysis
As far as I can tell, all true positives!
There are some extremely long diagnostic messages. We should truncate
our display of overload sets somehow.
## Test Plan
New Markdown tests
## Summary
This PR adds type inference for key-based access on `TypedDict`s and a
new diagnostic for invalid subscript accesses:
```py
class Person(TypedDict):
name: str
age: int | None
alice = Person(name="Alice", age=25)
reveal_type(alice["name"]) # revealed: str
reveal_type(alice["age"]) # revealed: int | None
alice["naem"] # Unknown key "naem" - did you mean "name"?
```
## Test Plan
Updated Markdown tests
## Summary
This PR reduces the virality of some of the `Todo` types in
`infer_tuple_type_expression`. Rather than inferring `Todo`, we instead
infer `tuple[Todo, ...]`. This reflects the fact that whatever the
contents of the slice in a `tuple[]` type expression, we would always
infer some kind of tuple type as the result of the type expression. Any
tuple type should be assignable to `tuple[Todo, ...]`, so this shouldn't
introduce any new false positives; this can be seen in the ecosystem
report.
As a result of the change, we are now able to enforce in the signature
of `Type::infer_tuple_type_expression` that it returns an
`Option<TupleType<'db>>`, which is more strongly typed and expresses
clearly the invariant that a tuple type expression should always be
inferred as a `tuple` type. To enable this, it was necessary to refactor
several `TupleType` constructors in `tuple.rs` so that they return
`Option<TupleType>` rather than `Type`; this means that callers of these
constructor functions are now free to either propagate the
`Option<TupleType<'db>>` or convert it to a `Type<'db>`.
## Test Plan
Mdtests updated.
Denys Zhak
Alex Waygood
Charlie Marsh
Dhruv Manilawala
Shunsuke Shibayama
Micha Reiser
David Peter
Jack O'Connor
Carl Meyer
Aria Desires
Brent Westbrook
Daniel Kongsgaard
Renkai Ge
fgiacome
Leandro Braga
Eric Jolibois
Andrii Turov