## Summary
This PR allows our generics solver to find a solution for `T` in cases
like the following:
```py
def extract_t[T](x: P[T] | Q[T]) -> T:
raise NotImplementedError
reveal_type(extract_t(P[int]())) # revealed: int
reveal_type(extract_t(Q[str]())) # revealed: str
```
closes https://github.com/astral-sh/ty/issues/1772
closes https://github.com/astral-sh/ty/issues/1314
## Ecosystem
The impact here looks very good!
It took me a long time to figure this out, but the new diagnostics on
bokeh are actually true positives. I should have tested with another
type-checker immediately, I guess. All other type checkers also emit
errors on these `__init__` calls. MRE
[here](https://play.ty.dev/5c19d260-65e2-4f70-a75e-1a25780843a2) (no
error on main, diagnostic on this branch)
A lot of false positives on home-assistant go away for calls to
functions like
[`async_listen`](180053fe98/homeassistant/core.py (L1581-L1587))
which take a `event_type: EventType[_DataT] | str` parameter. We can now
solve for `_DataT` here, which was previously falling back to its
default value, and then caused problems because it was used as an
argument to an invariant generic class.
## Test Plan
New Markdown tests
## Summary
fixes: https://github.com/astral-sh/ty/issues/1809
I took this chance to add some debug level tracing logs for overload
call evaluation similar to Doug's implementation in `constraints.rs`.
## Test Plan
- Add new mdtests
- Tested it against `sqlalchemy.select` in pyx which results in the
correct overload being matched
While still under development, it's far enough along now that we think
it's worth enabling it by default. This should also help give us
feedback for how it behaves.
This PR adds a "completion settings" grouping similar to inlay hints. We
only have an auto-import setting there now, but I expect we'll add more
options to configure completion behavior in the future.
Closesastral-sh/ty#1765
This adds autocomplete suggestions for function arguments. For example,
`okay` in:
```python
def foo(okay=None):
foo(o<CURSOR>
```
This also ensures that we don't suggest a keyword argument if it has
already been used.
Closesastral-sh/issues#1550
Closes issue #21565
## Summary
As pointed out in the issue, slices are currently flagged by B008 but
this behavior is incorrect because slices are immutable.
## Test Plan
Added a test case in the "B006_B008.py" fixture. Sorry for the diff in
the snapshots, the only thing that changes in those flies is the line
numbers, though.
You can also test this manually with this file:
```py
# test_slice.py
def c(d=slice(0, 3)): ...
```
```sh
> target/debug/ruff check tmp/test_slice.py --no-cache --select B008
All checks passed!
```
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## Summary
Fixes https://github.com/astral-sh/ruff/issues/8774
This PR fixes `pydocstyle` incorrectly flagging missing argument for
arguments with `Unpack` type annotation by extracting the `kwarg` `D417`
suppression logic into a helper function for future rules as needed.
## Problem Statement
The below example was incorrectly triggering `D417` error for missing
`**kwargs` doc.
```python
class User(TypedDict):
id: int
name: str
def do_something(some_arg: str, **kwargs: Unpack[User]):
"""Some doc
Args:
some_arg: Some argument
"""
```
<img width="1135" height="276" alt="image"
src="https://github.com/user-attachments/assets/42fa4bb9-61a5-4a70-a79c-0c8922a3ee66"
/>
`**kwargs: Unpack[User]` indicates the function expects keyword
arguments that will be unpacked. Ideally, the individual fields of the
User `TypedDict` should be documented, not in the `**kwargs` itself. The
`**kwargs` parameter acts as a semantic grouping rather than a parameter
requiring documentation.
## Solution
As discussed in the linked issue, it makes sense to suppress the `D417`
for parameters with `Unpack` annotation. I extract a helper function to
solely check `D417` should be suppressed with `**kwarg: Unpack[T]`
parameter, this function can also be unit tested independently and
reduce complexity of current `missing_args` check function. This also
makes it easier to add additional rules in the future.
_✏️ Note:_ This is my first PR in this repo, as I've learned a ton from
it, please call out anything that could be improved. Thanks for making
this excellent tool 👏
## Test Plan
Add 2 test cases in `D417.py` and update snapshots.
---------
Co-authored-by: Brent Westbrook <36778786+ntBre@users.noreply.github.com>
## Summary
By taking a purely syntactic approach to the problem of trivial
initializer calls we can supress `x: T = T()`, `x: T = x.y.T()` and `x:
MyNewType = MyNewType(0)` but still display `x: T[U] = T()`.
The place where we drop a ball is this does not compose with our
analysis for supressing `x = (0, "hello")` as `x = (0, T())` and `x =
(T(), T())` will still get inlay hints (I don't think this is a huge
deal).
* fixes https://github.com/astral-sh/ty/issues/1516
## Test Plan
Existing snapshots cover this well.
## Summary
If you pass a non-tuple to `Annotated`, we end up running inference on
it twice. I _think_ the only case here is `Annotated[]`, where we insert
a (fake) empty `Name` node in the slice.
Closes https://github.com/astral-sh/ty/issues/1801.
## Summary
Increase our SQLAlchemy test coverage to make sure we understand
`Session.scalar`, `Session.scalars`, `Session.execute` (and their async
equivalents), as well as `Result.tuples`, `Result.one_or_none`,
`Row._tuple`.
## Summary
This PR adds the possibility to write mdtests that specify external
dependencies in a `project` section of TOML blocks. For example, here is
a test that makes sure that we understand Pydantic's dataclass-transform
setup:
````markdown
```toml
[environment]
python-version = "3.12"
python-platform = "linux"
[project]
dependencies = ["pydantic==2.12.2"]
```
```py
from pydantic import BaseModel
class User(BaseModel):
id: int
name: str
user = User(id=1, name="Alice")
reveal_type(user.id) # revealed: int
reveal_type(user.name) # revealed: str
# error: [missing-argument] "No argument provided for required parameter
`name`"
invalid_user = User(id=2)
```
````
## How?
Using the `python-version` and the `dependencies` fields from the
Markdown section, we generate a `pyproject.toml` file, write it to a
temporary directory, and use `uv sync` to install the dependencies into
a virtual environment. We then copy the Python source files from that
venv's `site-packages` folder to a corresponding directory structure in
the in-memory filesystem. Finally, we configure the search paths
accordingly, and run the mdtest as usual.
I fully understand that there are valid concerns here:
* Doesn't this require network access? (yes, it does)
* Is this fast enough? (`uv` caching makes this almost unnoticeable,
actually)
* Is this deterministic? ~~(probably not, package resolution can depend
on the platform you're on)~~ (yes, hopefully)
For this reason, this first version is opt-in, locally. ~~We don't even
run these tests in CI (even though they worked fine in a previous
iteration of this PR).~~ You need to set `MDTEST_EXTERNAL=1`, or use the
new `-e/--enable-external` command line option of the `mdtest.py`
runner. For example:
```bash
# Skip mdtests with external dependencies (default):
uv run crates/ty_python_semantic/mdtest.py
# Run all mdtests, including those with external dependencies:
uv run crates/ty_python_semantic/mdtest.py -e
# Only run the `pydantic` tests. Use `-e` to make sure it is not skipped:
uv run crates/ty_python_semantic/mdtest.py -e pydantic
```
## Why?
I believe that this can be a useful addition to our testing strategy,
which lies somewhere between ecosystem tests and normal mdtests.
Ecosystem tests cover much more code, but they have the disadvantage
that we only see second- or third-order effects via diagnostic diffs. If
we unexpectedly gain or lose type coverage somewhere, we might not even
notice (assuming the gradual guarantee holds, and ecosystem code is
mostly correct). Another disadvantage of ecosystem checks is that they
only test checked-in code that is usually correct. However, we also want
to test what happens on wrong code, like the code that is momentarily
written in an editor, before fixing it. On the other end of the spectrum
we have normal mdtests, which have the disadvantage that they do not
reflect the reality of complex real-world code. We experience this
whenever we're surprised by an ecosystem report on a PR.
That said, these tests should not be seen as a replacement for either of
these things. For example, we should still strive to write detailed
self-contained mdtests for user-reported issues. But we might use this
new layer for regression tests, or simply as a debugging tool. It can
also serve as a tool to document our support for popular third-party
libraries.
## Test Plan
* I've been locally using this for a couple of weeks now.
* `uv run crates/ty_python_semantic/mdtest.py -e`
## Summary
As-is, a single-element tuple gets destructured via:
```rust
let arguments = if let ast::Expr::Tuple(tuple) = slice {
&*tuple.elts
} else {
std::slice::from_ref(slice)
};
```
But then, because it's a single element, we call
`infer_annotation_expression_impl`, passing in the tuple, rather than
the first element.
Closes https://github.com/astral-sh/ty/issues/1793.
Closes https://github.com/astral-sh/ty/issues/1768.
---------
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
This PR adds the same `minimal-size` profile as `uv` repo workspace has
```toml
# Profile to build a minimally sized binary for uv-build
[profile.minimal-size]
inherits = "release"
opt-level = "z"
# This will still show a panic message, we only skip the unwind
panic = "abort"
codegen-units = 1
```
but removes its `panic = "abort"` setting
- As discussed in #21825
Compared to the ones pre-built via `uv tool install`, this builds 35%
smaller ruff and 24% smaller ty binaries
(as measured
[here](https://github.com/lmmx/just-pre-commit/blob/master/refresh_binaries.sh))
* origin/main:
[ty] Allow `tuple[Any, ...]` to assign to `tuple[int, *tuple[int, ...]]` (#21803)
[ty] Support renaming import aliases (#21792)
[ty] Add redeclaration LSP tests (#21812)
[ty] more detailed description of "Size limit on unions of literals" in mdtest (#21804)
[ty] Complete support for `ParamSpec` (#21445)
[ty] Update benchmark dependencies (#21815)
Andrew Gallant
David Peter
Dhruv Manilawala
Douglas Creager
Micha Reiser
Amethyst Reese
Aria Desires
Rasmus Nygren
Loïc Riegel
Phong Do
Alex Waygood
Charlie Marsh
Prakhar Pratyush
Louis Maddox
Shunsuke Shibayama