I gave up trying to do this one lint at a time and just (mostly)
mechanically translated this entire file in one go.
Generally the messages stay the same (with most moving from an
annotation message to the diagnostic's main message). I added a couple
of `info` sub-diagnostics where it seemed to be the obvious intent.
This finishes the migration for the `INVALID_ASSIGNMENT` lint.
Notice how I'm steadily losing steam in terms of actually improving the
diagnostics. This change is more mechanical, because taking the time to
revamp every diagnostic is a ton of effort. Probably future migrations
will be similar unless there are easy pickings.
We mostly keep things the same here, but the message has been moved from
the annotation to the diagnostic's top-line message. I think this is
perhaps a little worse, but some bigger improvements could be made here.
Indeed, we could perhaps even add a "fix" here.
This moves all INVALID_ASSIGNMENT lints related to unpacking over to the new
diagnostic model.
While we're here, we improve the diagnostic a bit by adding a secondary
annotation covering where the value is. We also split apart the original
singular message into one message for the diagnostic and the "expected
versus got" into annotation messages.
This tests the diagnostic rendering of a case that wasn't previously
covered by snapshots: when unpacking fails because there are too few
values, but where the left hand side can tolerate "N or more." In the
code, this is a distinct diagnostic, so we capture it here.
(Sorry about the diff here, but it made sense to rename the other
sections and that changes the name of the snapshot file.)
## Summary
I ran red-knot on every project in mypy-primer. I moved every project
where red-knot ran to completion (fast enough, and mypy-primer could
handle its output) into `good.txt`, so it will run in our CI.
The remaining projects I left listed in `bad.txt`, with a comment
summarizing the failure mode (a few don't fail, they are just slow -- on
a debug build, at least -- or output too many diagnostics for
mypy-primer to handle.)
We will now run CI on 109 projects; 34 are left in `bad.txt`.
## Test Plan
CI on this PR!
---------
Co-authored-by: David Peter <mail@david-peter.de>
## Summary
Takes the `good.txt` changes from #17474, and removes the following
projects:
- arrow (not part of mypy_primer upstream)
- freqtrade, hydpy, ibis, pandera, xarray (saw panics locally, all
related to try_metaclass cycles)
Increases the mypy_primer CI run time to ~4 min.
## Test Plan
Three successful CI runs.
## Summary
* Add initial support for `typing.dataclass_transform`
* Support decorating a function decorator with `@dataclass_transform(…)`
(used by `attrs`, `strawberry`)
* Support decorating a metaclass with `@dataclass_transform(…)` (used by
`pydantic`, but doesn't work yet, because we don't seem to model
`__new__` calls correctly?)
* *No* support yet for decorating base classes with
`@dataclass_transform(…)`. I haven't figured out how this even supposed
to work. And haven't seen it being used.
* Add `strawberry` as an ecosystem project, as it makes heavy use of
`@dataclass_transform`
## Test Plan
New Markdown tests
## Summary
This PR is a follow-up to #16852.
Instance variables bound in comprehensions are recorded, allowing type
inference to work correctly.
This required adding support for unpacking in comprehension which
resolves https://github.com/astral-sh/ruff/issues/15369.
## Test Plan
One TODO in `mdtest/attributes.md` is now resolved, and some new test
cases are added.
---------
Co-authored-by: Dhruv Manilawala <dhruvmanila@gmail.com>
## Summary
If two types are gradually-equivalent, that means they share the same
set of possible materializations. There's no need to keep two such types
in the same union or intersection; we should simplify them.
Fixes https://github.com/astral-sh/ruff/issues/17465
The one downside here is that now we will simplify e.g. `Unknown |
Todo(...)` to just `Unknown`, if `Unknown` was added to the union first.
This is correct from a type perspective (they are equivalent types), but
it can mean we lose visibility into part of the cause for the type
inferring as unknown. I think this is OK, but if we think it's important
to avoid this, I can add a special case to try to preserve `Todo` over
`Unknown`, if we see them both in the same union or intersection.
## Test Plan
Added and updated mdtests.
## Summary
The long line of projects in `mypy_primer.yaml` is hard to work with
when adding projects or checking whether they are currently run. Use a
one-per-line text file instead.
## Test Plan
Ecosystem check on this PR.
## Summary
Member lookup can be cyclic, with type inference of implicit members. A
sample case is shown in the added mdtest.
There's no clear way to handle such cases other than to fixpoint-iterate
the cycle.
Fixes#17457.
## Test Plan
Added test.
You can now use subscript expressions in a type expression to explicitly
specialize generic classes, just like you could already do in value
expressions.
This still does not implement bidirectional checking, so a type
annotation on an assignment does not influence how we infer a
specialization for a (not explicitly specialized) constructor call. You
might get an `invalid-assignment` error if (a) we cannot infer a class
specialization from the constructor call (in which case you end up e.g.
trying to assign `C[Unknown]` to `C[int]`) or if (b) we can infer a
specialization, but it doesn't match the annotation.
Closes https://github.com/astral-sh/ruff/issues/17432
## Summary
There was some narrowing constraints not covered from the previous PR
```py
def _(x: object):
if (type(y := x)) is bool:
reveal_type(y) # revealed: bool
```
Also, refactored a bit
## Test Plan
Update type_api.md
In #17403 I added a comment asserting that all same-kind literal types
share all the same super-types. This is true, with two notable
exceptions: the types `AlwaysTruthy` and `AlwaysFalsy`. These two types
are super-types of some literal types within a given kind and not
others: `Literal[0]`, `Literal[""]`, and `Literal[b""]` inhabit
`AlwaysFalsy`, while other literals inhabit `AlwaysTruthy`.
This PR updates the literal-unions optimization to handle these types
correctly.
Fixes https://github.com/astral-sh/ruff/issues/17447
Verified locally that `QUICKCHECK_TESTS=100000 cargo test -p
red_knot_python_semantic -- --ignored types::property_tests::stable` now
passes again.
## Summary
Fixes#17147.
This was landed in #17149 and then reverted in #17335 because it caused
cycle panics in checking pybind11. #17456 fixed the cause of that panic.
## Test Plan
Add new narrow/assert.md test file
Co-authored-by: Matthew Mckee <matthewmckee04@yahoo.co.uk>
## Summary
We were over-conflating the conditions for deferred name resolution.
`from __future__ import annotations` defers annotations, but not class
bases. In stub files, class bases are also deferred. Modeling this
correctly also reduces likelihood of cycles in Python files using `from
__future__ import annotations` (since deferred resolution is inherently
cycle-prone). The same cycles are still possible in `.pyi` files, but
much less likely, since typically there isn't anything in a `pyi` file
that would cause an early return from a scope, or otherwise cause
visibility constraints to persist to end of scope. Usually there is only
code at module global scope and class scope, which can't have `return`
statements, and `raise` or `assert` statements in a stub file would be
very strange. (Technically according to the spec we'd be within our
rights to just forbid a whole bunch of syntax outright in a stub file,
but I kinda like minimizing unnecessary differences between the handling
of Python files and stub files.)
## Test Plan
Added mdtests.
## Summary
Part of #15383, this PR adds support for overloaded callables.
Typing spec: https://typing.python.org/en/latest/spec/overload.html
Specifically, it does the following:
1. Update the `FunctionType::signature` method to return signatures from
a possibly overloaded callable using a new `FunctionSignature` enum
2. Update `CallableType` to accommodate overloaded callable by updating
the inner type to `Box<[Signature]>`
3. Update the relation methods on `CallableType` with logic specific to
overloads
4. Update the display of callable type to display a list of signatures
enclosed by parenthesis
5. Update `CallableTypeOf` special form to recognize overloaded callable
6. Update subtyping, assignability and fully static check to account for
callables (equivalence is planned to be done as a follow-up)
For (2), it is required to be done in this PR because otherwise I'd need
to add some workaround for `into_callable_type` and I though it would be
best to include it in here.
For (2), another possible design would be convert `CallableType` in an
enum with two variants `CallableType::Single` and
`CallableType::Overload` but I decided to go with `Box<[Signature]>` for
now to (a) mirror it to be equivalent to `overload` field on
`CallableSignature` and (b) to avoid any refactor in this PR. This could
be done in a follow-up to better split the two kind of callables.
### Design
There were two main candidates on how to represent the overloaded
definition:
1. To include it in the existing infrastructure which is what this PR is
doing by recognizing all the signatures within the
`FunctionType::signature` method
2. To create a new `Overload` type variant
<details><summary>For context, this is what I had in mind with the new
type variant:</summary>
<p>
```rs
pub enum Type {
FunctionLiteral(FunctionType),
Overload(OverloadType),
BoundMethod(BoundMethodType),
...
}
pub struct OverloadType {
// FunctionLiteral or BoundMethod
overloads: Box<[Type]>,
// FunctionLiteral or BoundMethod
implementation: Option<Type>
}
pub struct BoundMethodType {
kind: BoundMethodKind,
self_instance: Type,
}
pub enum BoundMethodKind {
Function(FunctionType),
Overload(OverloadType),
}
```
</p>
</details>
The main reasons to choose (1) are the simplicity in the implementation,
reusing the existing infrastructure, avoiding any complications that the
new type variant has specifically around the different variants between
function and methods which would require the overload type to use `Type`
instead.
### Implementation
The core logic is how to collect all the overloaded functions. The way
this is done in this PR is by recording a **use** on the `Identifier`
node that represents the function name in the use-def map. This is then
used to fetch the previous symbol using the same name. This way the
signatures are going to be propagated from top to bottom (from first
overload to the final overload or the implementation) with each function
/ method. For example:
```py
from typing import overload
@overload
def foo(x: int) -> int: ...
@overload
def foo(x: str) -> str: ...
def foo(x: int | str) -> int | str:
return x
```
Here, each definition of `foo` knows about all the signatures that comes
before itself. So, the first overload would only see itself, the second
would see the first and itself and so on until the implementation or the
final overload.
This approach required some updates specifically recognizing
`Identifier` node to record the function use because it doesn't use
`ExprName`.
## Test Plan
Update existing test cases which were limited by the overload support
and add test cases for the following cases:
* Valid overloads as functions, methods, generics, version specific
* Invalid overloads as stated in
https://typing.python.org/en/latest/spec/overload.html#invalid-overload-definitions
(implementation will be done in a follow-up)
* Various relation: fully static, subtyping, and assignability (others
in a follow-up)
## Ecosystem changes
_WIP_
After going through the ecosystem changes (there are a lot!), here's
what I've found:
We need assignability check between a callable type and a class literal
because a lot of builtins are defined as classes in typeshed whose
constructor method is overloaded e.g., `map`, `sorted`, `list.sort`,
`max`, `min` with the `key` parameter, `collections.abc.defaultdict`,
etc. (https://github.com/astral-sh/ruff/issues/17343). This makes up
most of the ecosystem diff **roughly 70 diagnostics**. For example:
```py
from collections import defaultdict
# red-knot: No overload of bound method `__init__` matches arguments [lint:no-matching-overload]
defaultdict(int)
# red-knot: No overload of bound method `__init__` matches arguments [lint:no-matching-overload]
defaultdict(list)
class Foo:
def __init__(self, x: int):
self.x = x
# red-knot: No overload of function `__new__` matches arguments [lint:no-matching-overload]
map(Foo, ["a", "b", "c"])
```
Duplicate diagnostics in unpacking
(https://github.com/astral-sh/ruff/issues/16514) has **~16
diagnostics**.
Support for the `callable` builtin which requires `TypeIs` support. This
is **5 diagnostics**. For example:
```py
from typing import Any
def _(x: Any | None) -> None:
if callable(x):
# red-knot: `Any | None`
# Pyright: `(...) -> object`
# mypy: `Any`
# pyrefly: `(...) -> object`
reveal_type(x)
```
Narrowing on `assert` which has **11 diagnostics**. This is being worked
on in https://github.com/astral-sh/ruff/pull/17345. For example:
```py
import re
match = re.search("", "")
assert match
match.group() # error: [possibly-unbound-attribute]
```
Others:
* `Self`: 2
* Type aliases: 6
* Generics: 3
* Protocols: 13
* Unpacking in comprehension: 1
(https://github.com/astral-sh/ruff/pull/17396)
## Performance
Refer to
https://github.com/astral-sh/ruff/pull/17366#issuecomment-2814053046.
## Summary
Add more narrowing analysis for match statements:
* add narrowing constraints from guard expressions
* add negated constraints from previous predicates and guards to
subsequent cases
This PR doesn't address that guards can mutate your subject, and so
theoretically invalidate some of these narrowing constraints that you've
previously accumulated. Some prior art on this issue [here][mutable
guards].
[mutable guards]:
https://www.irif.fr/~scherer/research/mutable-patterns/mutable-patterns-mlworkshop2024-abstract.pdf
## Test Plan
Add some new tests, and update some existing ones
---------
Co-authored-by: Carl Meyer <carl@astral.sh>
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## Summary
Fixes#14866Fixes#17437
## Test Plan
Update mdtests in `narrow/`
## Summary
This PR extends version-related syntax error detection to red-knot. The
main changes here are:
1. Passing `ParseOptions` specifying a `PythonVersion` to parser calls
2. Adding a `python_version` method to the `Db` trait to make this
possible
3. Converting `UnsupportedSyntaxError`s to `Diagnostic`s
4. Updating existing mdtests to avoid unrelated syntax errors
My initial draft of (1) and (2) in #16090 instead tried passing a
`PythonVersion` down to every parser call, but @MichaReiser suggested
the `Db` approach instead
[here](https://github.com/astral-sh/ruff/pull/16090#discussion_r1969198407),
and I think it turned out much nicer.
All of the new `python_version` methods look like this:
```rust
fn python_version(&self) -> ruff_python_ast::PythonVersion {
Program::get(self).python_version(self)
}
```
with the exception of the `TestDb` in `ruff_db`, which hard-codes
`PythonVersion::latest()`.
## Test Plan
Existing mdtests, plus a new mdtest to see at least one of the new
diagnostics.
## Summary
This changeset allows us to generate the signature of synthesized
`__init__` functions in dataclasses by analyzing the fields on the class
(and its superclasses). There are certain things that I have not yet
attempted to model in this PR, like `kw_only`,
[`dataclasses.KW_ONLY`](https://docs.python.org/3/library/dataclasses.html#dataclasses.KW_ONLY)
or functionality around
[`dataclasses.field`](https://docs.python.org/3/library/dataclasses.html#dataclasses.field).
ticket: https://github.com/astral-sh/ruff/issues/16651
## Ecosystem analysis
These two seem to depend on missing features in generics (see [relevant
code
here](9898ccbb78/tests/core/test_generics.py (L54))):
> ```diff
> + error[lint:unknown-argument]
/tmp/mypy_primer/projects/dacite/tests/core/test_generics.py:54:24:
Argument `x` does not match any known parameter
> + error[lint:unknown-argument]
/tmp/mypy_primer/projects/dacite/tests/core/test_generics.py:54:38:
Argument `y` does not match any known parameter
> ```
These two are true positives. See [relevant code
here](9898ccbb78/tests/core/test_config.py (L154-L161)).
> ```diff
> + error[lint:invalid-argument-type]
/tmp/mypy_primer/projects/dacite/tests/core/test_config.py:161:24:
Argument to this function is incorrect: Expected `int`, found
`Literal["test"]`
> + error[lint:invalid-argument-type]
/tmp/mypy_primer/projects/dacite/tests/core/test_config.py:172:24:
Argument to this function is incorrect: Expected `int | float`, found
`Literal["test"]`
> ```
This one depends on `**` unpacking of dictionaries, which we don't
support yet:
> ```diff
> + error[lint:missing-argument]
/tmp/mypy_primer/projects/mypy_primer/mypy_primer/globals.py:218:11: No
arguments provided for required parameters `new`, `old`, `repo`,
`type_checker`, `mypyc_compile_level`, `custom_typeshed_repo`,
`new_typeshed`, `old_typeshed`, `new_prepend_path`, `old_prepend_path`,
`additional_flags`, `project_selector`, `known_dependency_selector`,
`local_project`, `expected_success`, `project_date`, `shard_index`,
`num_shards`, `output`, `old_success`, `coverage`, `bisect`,
`bisect_output`, `validate_expected_success`,
`measure_project_runtimes`, `concurrency`, `base_dir`, `debug`, `clear`
> ```
## Test Plan
New Markdown tests.
## Summary
Support dataclasses with `order=True`:
```py
@dataclass(order=True)
class WithOrder:
x: int
WithOrder(1) < WithOrder(2) # no error
```
Also adds some additional tests to `dataclasses.md`.
ticket: #16651
## Test Plan
New Markdown tests
This PR adds **_very_** basic inference of generic typevars at call
sites. It does not bring in a full unification algorithm, and there are
a few TODOs in the test suite that are not discharged by this. But it
handles a good number of useful cases! And the PR does not add anything
that would go away with a more sophisticated constraint solver.
In short, we just look for typevars in the formal parameters, and assume
that the inferred type of the corresponding argument is what that
typevar should map to. If a typevar appears more than once, we union
together the corresponding argument types.
Cases we are not yet handling:
- We are not widening literals.
- We are not recursing into parameters that are themselves generic
aliases.
- We are not being very clever with parameters that are union types.
---------
Co-authored-by: Alex Waygood <Alex.Waygood@Gmail.com>
Co-authored-by: Carl Meyer <carl@astral.sh>
## Summary
This is similar to https://github.com/astral-sh/ruff/pull/17095, it adds
assignability check for bound methods to callables.
## Test Plan
Add test cases to for assignability; specifically it uses gradual types
because otherwise it would just delegate to `is_subtype_of`.
## Summary
closes#16615
This PR includes:
- Introduces a new type: `Type::BoundSuper`
- Implements member lookup for `Type::BoundSuper`, resolving attributes
by traversing the MRO starting from the specified class
- Adds support for inferring appropriate arguments (`pivot_class` and
`owner`) for `super()` when it is used without arguments
When `super(..)` appears in code, it can be inferred into one of the
following:
- `Type::Unknown`: when a runtime error would occur (e.g. calling
`super()` out of method scope, or when parameter validation inside
`super` fails)
- `KnownClass::Super::to_instance()`: when the result is an *unbound
super object* or when a dynamic type is used as parameters (MRO
traversing is meaningless)
- `Type::BoundSuper`: the common case, representing a properly
constructed `super` instance that is ready for MRO traversal and
attribute resolution
### Terminology
Python defines the terms *bound super object* and *unbound super
object*.
An **unbound super object** is created when `super` is called with only
one argument (e.g.
`super(A)`). This object may later be bound via the `super.__get__`
method. However, this form is rarely used in practice.
A **bound super object** is created either by calling
`super(pivot_class, owner)` or by using the implicit form `super()`,
where both arguments are inferred from the context. This is the most
common usage.
### Follow-ups
- Add diagnostics for `super()` calls that would result in runtime
errors (marked as TODO)
- Add property tests for `Type::BoundSuper`
## Test Plan
- Added `mdtest/class/super.md`
---------
Co-authored-by: Carl Meyer <carl@astral.sh>
## Summary
Until we optimize our full union/intersection representation to
efficiently handle large numbers of same-kind literal types "as a
block", set a fairly low limit on the size of unions of literals.
We will want to increase this limit once we've made the broader
efficiency improvement (tracked in
https://github.com/astral-sh/ruff/issues/17420).
## Test Plan
`cargo bench --bench red_knot`
## Summary
Special-case literal types in `UnionBuilder` to speed up building large
unions of literals.
This optimization is extremely effective at speeding up building even a
very large union (it improves the large-unions benchmark by 41x!). The
problem we can run into is that it is easy to then run into another
operation on the very large union (for instance, narrowing may add it to
an intersection, which then distributes it over the intersection) which
is still slow.
I think it is possible to avoid this by extending this optimized
"grouped" representation throughout not just `UnionBuilder`, but all of
our union and intersection representations. I have some work in this
direction, but rather than spending more time on it right now, I'd
rather just land this much, along with a limit on the size of these
unions (to avoid building really big unions quickly and then hitting
issues where they are used.)
## Test Plan
Existing tests and benchmarks.
---------
Co-authored-by: Alex Waygood <Alex.Waygood@Gmail.com>
## Summary
Fixes incorrect negated type eq and ne assertions in
infer_binary_intersection_type_comparison
fixes#17360
## Test Plan
Remove and update some now incorrect tests
This reworks the assignability/subtyping relations a bit to handle
typevars better:
1. For the most part, types are not assignable to typevars, since
there's no guarantee what type the typevar will be specialized to.
2. An intersection is an exception, if it contains the typevar itself as
one of the positive elements. This should fall out from the other
clauses automatically, since a typevar is assignable to itself, and an
intersection is assignable to something if any positive element is
assignable to that something.
3. Constrained typevars are an exception, since they must be specialized
to _exactly_ one of the constraints, not to a _subtype_ of a constraint.
If a type is assignable to every constraint, then the type is also
assignable to the constrained typevar.
We already had a special case for (3), but the ordering of it relative
to the intersection clauses meant we weren't catching (2) correctly. To
fix this, we keep the special case for (3), but fall through to the
other match arms for non-constrained typevars and if the special case
isn't true for a constrained typevar.
Closes https://github.com/astral-sh/ruff/issues/17364
## Summary
Add very early support for dataclasses. This is mostly to make sure that
we do not emit false positives on dataclass construction, but it also
lies some foundations for future extensions.
This seems like a good initial step to merge to me, as it basically
removes all false positives on dataclass constructor calls. This allows
us to use the ecosystem checks for making sure we don't introduce new
false positives as we continue to work on dataclasses.
## Ecosystem analysis
I re-ran the mypy_primer evaluation of [the `__init__`
PR](https://github.com/astral-sh/ruff/pull/16512) locally with our
current mypy_primer version and project selection. It introduced 1597
new diagnostics. Filtering those by searching for `__init__` and
rejecting those that contain `invalid-argument-type` (those could not
possibly be solved by this PR) leaves 1281 diagnostics. The current
version of this PR removes 1171 diagnostics, which leaves 110
unaccounted for. I extracted the lint + file path for all of these
diagnostics and generated a diff (of diffs), to see which
`__init__`-diagnostics remain. I looked at a subset of these: There are
a lot of `SomeClass(*args)` calls where we don't understand the
unpacking yet (this is not even related to `__init__`). Some others are
related to `NamedTuple`, which we also don't support yet. And then there
are some errors related to `@attrs.define`-decorated classes, which
would probably require support for `dataclass_transform`, which I made
no attempt to include in this PR.
## Test Plan
New Markdown tests.
## Summary
* Partial #17238
* Flyby from discord discussion - `todo_type!` now statically checks for
no parens in the message to avoid issues between debug & release build
tests
## Test Plan
many mdtests are changing
## Summary
Infer precise Boolean literal types for `str.startswith` calls where the
instance and the prefix are both string literals. This allows us to
understand `sys.platform.startswith(…)` branches.
## Test Plan
New Markdown tests
## Summary
For silencing `invalid-type-form` diagnostics in unreachable code, we
use the same approach that we use before and check the reachability that
we already record.
For silencing `invalid-bases`, we simply check if the type of the base
is `Never`. If so, we silence the diagnostic with the argument that the
class construction would never happen.
## Test Plan
Updated Markdown tests.
## Summary
Similar to what we did for `unresolved-reference` and
`unresolved-attribute`, we now also silence `unresolved-import`
diagnostics if the corresponding `import` statement is unreachable.
This addresses the (already closed) issue #17049.
## Test Plan
Adapted Markdown tests.
This finally completes the deletion of all old diagnostic types.
We do this by migrating the second (and last) use of secondary
diagnostic messages: to highlight the return type of a function
definition when its return value is inconsistent with the type.
Like the last diagnostic, we do actually change the message here a bit.
We don't need a sub-diagnostic here, and we can instead just add a
secondary annotation to highlight the return type.
This is the first use of the new `lint()` reporter.
I somewhat skipped a step here and also modified the actual diagnostic
message itself. The snapshots should tell the story.
We couldn't do this before because we had no way of differentiating
between "message for the diagnostic as a whole" and "message for a
specific code annotation." Now we can, so we can write more precise
messages based on the assumption that users are also seeing the code
snippet.
The downside here is that the actual message text can become quite vague
in the absence of the code snippet. This occurs, for example, with
concise diagnostic formatting. It's unclear if we should do anything
about it. I don't really see a way to make it better that doesn't
involve creating diagnostics with messages for each mode, which I think
would be a major PITA.
The upside is that this code gets a bit simpler, and we very
specifically avoid doing extra work if this specific lint is disabled.
This required a bit of surgery in the diagnostic matching and more
faffing about using a "concise" message from a diagnostic instead of
only printing the "primary" message.
