This PR adds a specialization inference special case that lets us handle
the following examples better:
```py
def f[T](t: T | None) -> T: ...
def g[T](t: T | int | None) -> T | int: ...
def _(x: str | None):
reveal_type(f(x)) # revealed: str (previously str | None)
def _(y: str | int | None):
reveal_type(g(x)) # revealed: str | int (previously str | int | None)
```
We already have a special case for when the formal is a union where one
element is a typevar, but it maps the entire actual type to the typevar
(as you can see in the "previously" results above).
The new special case kicks in when the actual is also a union. Now, we
filter out any actual union elements that are already subtypes of the
formal, and only bind whatever types remain to the typevar. (The `|
None` pattern appears quite often in the ecosystem results, but it's
more general and works with any number of non-typevar union elements.)
The new constraint solver should handle this case as well, but it's
worth adding this heuristic now with the old solver because it
eliminates some false positives from the ecosystem report, and makes the
ecosystem report less noisy on the other constraint solver PRs.
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.
## 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
The union `T | U` can be validly simplified to `U` iff:
1. `T` is a subtype of `U` OR
2. `T` is equivalent to `U` OR
3. `U` is a union and contains a type that is equivalent to `T` OR
4. `T` is an intersection and contains a type that is equivalent to `U`
(In practice, the only situation in which 2, 3 or 4 would be true when
(1) was not true would be if `T` or `U` is a dynamic type.)
Currently we achieve these simplifications in the union builder by doing
something along the lines of `t.is_subtype_of(db, u) ||
t.is_equivalent_to_(db, u) ||
t.into_intersection().is_some_and(|intersection|
intersection.positive(db).contains(&u)) ||
u.into_union().is_some_and(|union| union.elements(db).contains(&t))`.
But this is both slow and misses some cases (it doesn't simplify the
union `Any | (Unknown & ~None)` to `Any`, for example). We can improve
the consistency and performance of our union simplifications by adding a
third type relation that sits in between `TypeRelation::Subtyping` and
`TypeRelation::Assignability`: `TypeRelation::UnionSimplification`.
This change leads to simpler, more user-friendly types due to the more
consistent simplification. It also lead to a pretty huge performance
improvement!
## Test Plan
Existing tests, plus some new ones.
## Summary
closes: https://github.com/astral-sh/ty/issues/247
This PR adds support for variadic arguments to overload call evaluation.
This basically boils down to making sure that the overloads are not
filtered out incorrectly during the step 5 in the overload call
evaluation algorithm. For context, the step 5 tries to filter out the
remaining overloads after finding an overload where the materialization
of argument types are assignable to the parameter types.
The issue with the previous implementation was that it wouldn't unpack
the variadic argument and wouldn't consider the many-to-one (multiple
arguments mapping to a single variadic parameter) correctly. This PR
fixes that.
## Test Plan
Update existing test cases and resolve the TODOs.
## Summary
Currently we do not emit an error on this code:
```py
from ty_extensions import Not
def f[T](x: T, y: Not[T]) -> T:
x = y
return x
```
But we should do! `~T` should never be assignable to `T`.
This fixes a small regression introduced in
14fe1228e7 (diff-8049ab5af787dba29daa389bbe2b691560c15461ef536f122b1beab112a4b48aR1443-R1446),
where a branch that previously returned `false` was replaced with a
branch that returns `C::always_satisfiable` -- the opposite of what it
used to be! The regression occurred because we didn't have any tests for
this -- so I added some tests in this PR that fail on `main`. I only
spotted the problem because I was going through the code of
`has_relation_to_impl` with a fine toothcomb for
https://github.com/astral-sh/ruff/pull/20602😄
## Summary
Quoting from the newly added comment:
Module-level globals can be mutated externally. A `MY_CONSTANT = 1`
global might be changed to `"some string"` from code outside of the
module that we're looking at, and so from a gradual-guarantee
perspective, it makes sense to infer a type of `Literal[1] | Unknown`
for global symbols. This allows the code that does the mutation to type
check correctly, and for code that uses the global, it accurately
reflects the lack of knowledge about the type.
External modifications (or modifications through `global` statements)
that would require a wider type are relatively rare. From a practical
perspective, we can therefore achieve a better user experience by
trusting the inferred type. Users who need the external mutation to work
can always annotate the global with the wider type. And everyone else
benefits from more precise type inference.
I initially implemented this by applying literal promotion to the type
of the unannotated module globals (as suggested in
https://github.com/astral-sh/ty/issues/1069), but the ecosystem impact
showed a lot of problems (https://github.com/astral-sh/ruff/pull/20643).
I fixed/patched some of these problems, but this PR seems like a good
first step, and it seems sensible to apply the literal promotion change
in a second step that can be evaluated separately.
closes https://github.com/astral-sh/ty/issues/1069
## Ecosystem impact
This seems like an (unexpectedly large) net positive with 650 fewer
diagnostics overall.. even though this change will certainly catch more
true positives.
* There are 666 removed `type-assertion-failure` diagnostics, where we
were previously used the correct type already, but removing the
`Unknown` now leads to an "exact" match.
* 1464 of the 1805 total new diagnostics are `unresolved-attribute`
errors, most (1365) of which were previously
`possibly-missing-attribute` errors. So they could also be counted as
"changed" diagnostics.
* For code that uses constants like
```py
IS_PYTHON_AT_LEAST_3_10 = sys.version_info >= (3, 10)
```
where we would have previously inferred a type of `Literal[True/False] |
Unknown`, removing the `Unknown` now allows us to do reachability
analysis on branches that use these constants, and so we get a lot of
favorable ecosystem changes because of that.
* There is code like the following, where we previously emitted
`conflicting-argument-forms` diagnostics on calls to the aliased
`assert_type`, because its type was `Unknown | def …` (and the call to
`Unknown` "used" the type form argument in a non type-form way):
```py
if sys.version_info >= (3, 11):
import typing
assert_type = typing.assert_type
else:
import typing_extensions
assert_type = typing_extensions.assert_type
```
* ~100 new `invalid-argument-type` false positives, due to missing
`**kwargs` support (https://github.com/astral-sh/ty/issues/247)
## Typing conformance
```diff
+protocols_modules.py:25:1: error[invalid-assignment] Object of type `<module '_protocols_modules1'>` is not assignable to `Options1`
```
This diagnostic should apparently not be there, but it looks like we
also fail other tests in that file, so it seems to be a limitation that
was previously hidden by `Unknown` somehow.
## Test Plan
Updated tests and relatively thorough ecosystem analysis.
## Summary
Reformulation of the public symbol type inference test suite to use
class scopes instead of module scopes. This is in preparation for an
upcoming change to module-global scopes (#20664).
## Test Plan
Updated tests
This doesn't seem to be flaky in the sense of tests failing
non-deterministically, but they are flaky in the sense of unrelated
changes causing testing failures from the clauses of a constraint set
being rendered in different orders. This flakiness is because we're
using Salsa IDs to determine the order in which typevars appear in a
constraint set BDD, and those IDs are assigned non-deterministically.
The fix is ham-fisted but effective: sort the constraints in each
clause, and the clauses in each set, as part of the rendering process.
Constraint sets are only rendered in our test cases, so we don't need to
over-optimize this.
## Summary
Not sure if this was the original intention, but it looks to me like the
previous `Type::literal_promotion_type` was more of an implementation
detail for the actual operation of promoting all literals in a
possibly-nested position of a type.
This is not a pure refactor, as I'm technically changing the behavior
for that protocols diagnostic message suggestion.
## Test Plan
New Markdown test
## Summary
Add two simple tests that we recently discussed with @dcreager. They
demonstrate that the `TypeMapping::MarkTypeVarsInferable` operation
really does need to keep track of the binding context.
## Test Plan
Made sure that those tests fail if we create
`TypeMapping::MarkTypeVarsInferable(None)`s everywhere.
## Summary
Modify the (external) signature of instance methods such that the first
parameter uses `Self` unless it is explicitly annotated. This allows us
to correctly type-check more code, and allows us to infer correct return
types for many functions that return `Self`. For example:
```py
from pathlib import Path
from datetime import datetime, timedelta
reveal_type(Path(".config") / ".ty") # now Path, previously Unknown
def _(dt: datetime, delta: timedelta):
reveal_type(dt - delta) # now datetime, previously Unknown
```
part of https://github.com/astral-sh/ty/issues/159
## Performance
I ran benchmarks locally on `attrs`, `freqtrade` and `colour`, the
projects with the largest regressions on CodSpeed. I see much smaller
effects locally, but can definitely reproduce the regression on `attrs`.
From looking at the profiling results (on Codspeed), it seems that we
simply do more type inference work, which seems plausible, given that we
now understand much more return types (of many stdlib functions). In
particular, whenever a function uses an implicit `self` and returns
`Self` (without mentioning `Self` anywhere else in its signature), we
will now infer the correct type, whereas we would previously return
`Unknown`. This also means that we need to invoke the generics solver in
more cases. Comparing half a million lines of log output on attrs, I can
see that we do 5% more "work" (number of lines in the log), and have a
lot more `apply_specialization` events (7108 vs 4304). On freqtrade, I
see similar numbers for `apply_specialization` (11360 vs 5138 calls).
Given these results, I'm not sure if it's generally worth doing more
performance work, especially since none of the code modifications
themselves seem to be likely candidates for regressions.
| Command | Mean [ms] | Min [ms] | Max [ms] | Relative |
|:---|---:|---:|---:|---:|
| `./ty_main check /home/shark/ecosystem/attrs` | 92.6 ± 3.6 | 85.9 |
102.6 | 1.00 |
| `./ty_self check /home/shark/ecosystem/attrs` | 101.7 ± 3.5 | 96.9 |
113.8 | 1.10 ± 0.06 |
| Command | Mean [ms] | Min [ms] | Max [ms] | Relative |
|:---|---:|---:|---:|---:|
| `./ty_main check /home/shark/ecosystem/freqtrade` | 599.0 ± 20.2 |
568.2 | 627.5 | 1.00 |
| `./ty_self check /home/shark/ecosystem/freqtrade` | 607.9 ± 11.5 |
594.9 | 626.4 | 1.01 ± 0.04 |
| Command | Mean [ms] | Min [ms] | Max [ms] | Relative |
|:---|---:|---:|---:|---:|
| `./ty_main check /home/shark/ecosystem/colour` | 423.9 ± 17.9 | 394.6
| 447.4 | 1.00 |
| `./ty_self check /home/shark/ecosystem/colour` | 426.9 ± 24.9 | 373.8
| 456.6 | 1.01 ± 0.07 |
## Test Plan
New Markdown tests
## Ecosystem report
* apprise: ~300 new diagnostics related to problematic stubs in apprise
😩
* attrs: a new true positive, since [this
function](4e2c89c823/tests/test_make.py (L2135))
is missing a `@staticmethod`?
* Some legitimate true positives
* sympy: lots of new `invalid-operator` false positives in [matrix
multiplication](cf9f4b6805/sympy/matrices/matrixbase.py (L3267-L3269))
due to our limited understanding of [generic `Callable[[Callable[[T1,
T2], T3]], Callable[[T1, T2], T3]]` "identity"
types](cf9f4b6805/sympy/core/decorators.py (L83-L84))
of decorators. This is not related to type-of-self.
## Typing conformance results
The changes are all correct, except for
```diff
+generics_self_usage.py:50:5: error[invalid-assignment] Object of type `def foo(self) -> int` is not assignable to `(typing.Self, /) -> int`
```
which is related to an assignability problem involving type variables on
both sides:
```py
class CallableAttribute:
def foo(self) -> int:
return 0
bar: Callable[[Self], int] = foo # <- we currently error on this assignment
```
---------
Co-authored-by: Shaygan Hooshyari <sh.hooshyari@gmail.com>
## Summary
Fixes a bug observed by @AlexWaygood where `C[Any] <: C[object]` should
hold for a class that is covariant in its type parameter (and similar
subtyping relations involving dynamic types for other variance
configurations).
## Test Plan
New and updated Markdown tests
While working on #20093, I kept running into test failures due to
constraint sets not simplifying as much as they could, and therefore not
being easily testable against "always true" and "always false".
This PR updates our constraint set representation to use BDDs. Because
BDDs are reduced and ordered, they are canonical — equivalent boolean
formulas are represented by the same interned BDD node.
That said, there is a wrinkle, in that the "variables" that we use in
these BDDs — the individual constraints like `Lower ≤ T ≤ Upper` are not
always independent of each other.
As an example, given types `A ≤ B ≤ C ≤ D` and a typevar `T`, the
constraints `A ≤ T ≤ C` and `B ≤ T ≤ D` "overlap" — their intersection
is non-empty. So we should be able to simplify
```
(A ≤ T ≤ C) ∧ (B ≤ T ≤ D) == (B ≤ T ≤ C)
```
That's not a simplification that the BDD structure can perform itself,
since those three constraints are modeled as separate BDD variables, and
are therefore "opaque" to the BDD algorithms.
That means we need to perform this kind of simplification ourselves. We
look at pairs of constraints that appear in a BDD and see if they can be
simplified relative to each other, and if so, replace the pair with the
simplification. A large part of the toil of getting this PR to work was
identifying all of those patterns and getting that substitution logic
correct.
With this new representation, all existing tests pass, as well as some
new ones that represent test failures that were occuring on #20093.
---------
Co-authored-by: Carl Meyer <carl@astral.sh>
## Summary
Closes: https://github.com/astral-sh/ty/issues/551
This PR adds support for step 4 of the overload call evaluation
algorithm which states that:
> If the argument list is compatible with two or more overloads,
determine whether one or more of the overloads has a variadic parameter
(either `*args` or `**kwargs`) that maps to a corresponding argument
that supplies an indeterminate number of positional or keyword
arguments. If so, eliminate overloads that do not have a variadic
parameter.
And, with that, the overload call evaluation algorithm has been
implemented completely end to end as stated in the typing spec.
## Test Plan
Expand the overload call test suite.
## Summary
This removes a hack in the protocol satisfiability check that was
previously needed to work around missing assignability-modeling of
inferable type variables. Assignability of type variables is not
implemented fully, but some recent changes allow us to remove that hack
with limited impact on the ecosystem (and the test suite). The change in
the typing conformance test is favorable.
## Test Plan
* Adapted Markdown tests
* Made sure that this change works in combination with
https://github.com/astral-sh/ruff/pull/20517
## Summary
Closes: https://github.com/astral-sh/ty/issues/1236
This PR fixes a bug where the variadic argument wouldn't match against
the variadic parameter in certain scenarios.
This was happening because I didn't realize that the `all_elements`
iterator wouldn't keep on returning the variable element (which is
correct, I just didn't realize it back then).
I don't think we can use the `resize` method here because we don't know
how many parameters this variadic argument is matching against as this
is where the actual parameter matching occurs.
## Test Plan
Expand test cases to consider a few more combinations of arguments and
parameters which are variadic.
## Summary
This applies the trick that we use for `builtins.open` to similar
functions that have the same problem. The reason is that the problem
would otherwise become even more pronounced once we add understanding of
the implicit type of `self` parameters, because then something like
`(base_path / "test.bin").open("rb")` also leads to a wrong return type
and can result in false positives.
## Test Plan
New Markdown tests
## Summary
I found this bug while working on #20528.
The minimum reproducible code is:
```python
from __future__ import annotations
from typing import NamedTuple
from ty_extensions import is_disjoint_from, static_assert
class Path(NamedTuple):
prev: Path | None
key: str
static_assert(not is_disjoint_from(Path, Path))
```
A stack overflow occurs when a nominal instance type inherits from
`NamedTuple` and is defined recursively.
This PR fixes this bug.
## Test Plan
mdtest updated
### Summary
This PR includes two changes, both of which are necessary to resolve
https://github.com/astral-sh/ty/issues/1196:
* For a generic class `C[T]`, we previously used `C[Unknown]` as the
upper bound of the `Self` type variable. There were two problems with
this. For one, when `Self` appeared in contravariant position, we would
materialize its upper bound to `Bottom[C[Unknown]]` (which might
simplify to `C[Never]` if `C` is covariant in `T`) when accessing
methods on `Top[C[Unknown]]`. This would result in `invalid-argument`
errors on the `self` parameter. Also, using an upper bound of
`C[Unknown]` would mean that inside methods, references to `T` would be
treated as `Unknown`. This could lead to false negatives. To fix this,
we now use `C[T]` (with a "nested" typevar) as the upper bound for
`Self` on `C[T]`.
* In order to make this work, we needed to allow assignability/subtyping
of inferable typevars to other types, since we now check assignability
of e.g. `C[int]` to `C[T]` (when checking assignability to the upper
bound of `Self`) when calling an instance-method on `C[int]` whose
`self` parameter is annotated as `self: Self` (or implicitly `Self`,
following https://github.com/astral-sh/ruff/pull/18007).
closes https://github.com/astral-sh/ty/issues/1196
closes https://github.com/astral-sh/ty/issues/1208
### Test Plan
Regression tests for both issues.
## Summary
@ibraheemdev notes this example failed
```py
from typing import Callable
class X:
...
def f(callable: Callable[[], X]) -> X:
return callable()
x = f(X)
```
Resolves https://github.com/astral-sh/ty/issues/1210
The issue was that we set the `Self` to the class type instead of the
instance type of the class.
## Test Plan
Fix tests in `is_subtype_of.md`
## Summary
Fixes https://github.com/astral-sh/ty/issues/1218.
This bug doesn't currently cause us any real-world issues, because we
don't yet understand the signatures typeshed gives us for `isinstance()`
and `issubclass()` (typeshed's annotations there use PEP-613 type
aliases). #20107 demonstrates that this will start causing us issues as
soon as we add support for PEP-613 aliases, however, so it makes sense
to fix it now.
## Test Plan
Added mdtests
## 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.
## Summary
Part of https://github.com/astral-sh/ty/issues/168. Infer more precise types for collection literals (currently, only `list` and `set`). For example,
```py
x = [1, 2, 3] # revealed: list[Unknown | int]
y: list[int] = [1, 2, 3] # revealed: list[int]
```
This could easily be extended to `dict` literals, but I am intentionally limiting scope for now.
Fixes: https://github.com/astral-sh/ty/issues/1173
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## Summary
This PR will change the logic of binding Self type variables to bind
self to the immediate function that it's used on.
Since we are binding `self` to methods and not the class itself we need
to ensure that we bind self consistently.
The fix is to traverse scopes containing the self and find the first
function inside a class and use that function to bind the typevar for
self.
If no such scope is found we fallback to the normal behavior. Using Self
outside of a class scope is not legal anyway.
## Test Plan
Added a new mdtest.
Checked the diagnostics that are not emitted anymore in [primer
results](https://github.com/astral-sh/ruff/pull/20366#issuecomment-3289411424).
It looks good altough I don't completely understand what was wrong
before.
---------
Co-authored-by: Douglas Creager <dcreager@dcreager.net>
## Summary
Catch infinite recursion in binary-compare inference.
Fixes the stack overflow in `graphql-core` in mypy-primer.
## Test Plan
Added two tests that stack-overflowed before this PR.
## Summary
Use `Type::Divergent` to short-circuit diverging types in type
expressions. This avoids panicking in a wide variety of cases of
recursive type expressions.
Avoids many panics (but not yet all -- I'll be tracking down the rest)
from https://github.com/astral-sh/ty/issues/256 by falling back to
Divergent. For many of these recursive type aliases, we'd like to
support them properly (i.e. really understand the recursive nature of
the type, not just fall back to Divergent) but that will be future work.
This switches `Type::has_divergent_type` from using `any_over_type` to a
custom set of visit methods, because `any_over_type` visits more than we
need to visit, and exercises some lazy attributes of type, causing
significantly more work. This change means this diff doesn't regress
perf; it even reclaims some of the perf regression from
https://github.com/astral-sh/ruff/pull/20333.
## Test Plan
Added mdtest for recursive type alias that panics on main.
Verified that we can now type-check `packaging` (and projects depending
on it) without panic; this will allow moving a number of mypy-primer
projects from `bad.txt` to `good.txt` in a subsequent PR.
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## Summary
This PR implements F406
https://docs.astral.sh/ruff/rules/undefined-local-with-nested-import-star-usage/
as a semantic syntax error
## Test Plan
I have written inline tests as directed in #17412
---------
Signed-off-by: 11happy <soni5happy@gmail.com>
Previously, we used a very fine-grained representation for individual
constraints: each constraint was _either_ a range constraint, a
not-equivalent constraint, or an incomparable constraint. These three
pieces are enough to represent all of the "real" constraints we need to
create — range constraints and their negation.
However, it meant that we weren't picking up as many chances to simplify
constraint sets as we could. Our simplification logic depends on being
able to look at _pairs_ of constraints or clauses to see if they
simplify relative to each other. With our fine-grained representation,
we could easily encounter situations that we should have been able to
simplify, but that would require looking at three or more individual
constraints.
For instance, negating a range constraint would produce:
```
¬(Base ≤ T ≤ Super) = ((T ≤ Base) ∧ (T ≠ Base)) ∨ (T ≁ Base) ∨
((Super ≤ T) ∧ (T ≠ Super)) ∨ (T ≁ Super)
```
That is, `T` must be (strictly) less than `Base`, (strictly) greater
than `Super`, or incomparable to either.
If we tried to union those back together, we should get `always`, since
`x ∨ ¬x` should always be true, no matter what `x` is. But instead we
would get:
```
(Base ≤ T ≤ Super) ∨ ((T ≤ Base) ∧ (T ≠ Base)) ∨ (T ≁ Base) ∨ ((Super ≤ T) ∧ (T ≠
Super)) ∨ (T ≁ Super)
```
Nothing would simplify relative to each other, because we'd have to look
at all five union elements to see that together they do in fact combine
to `always`.
The fine-grained representation was nice, because it made it easier to
[work out the math](https://dcreager.net/theory/constraints/) for
intersections and unions of each kind of constraint. But being able to
simplify is more important, since the example above comes up immediately
in #20093 when trying to handle constrained typevars.
The fix in this PR is to go back to a more coarse-grained
representation, where each individual constraint consists of a positive
range (which might be `always` / `Never ≤ T ≤ object`), and zero or more
negative ranges. The intuition is to think of a constraint as a region
of the type space (representable as a range) with zero or more "holes"
removed from it.
With this representation, negating a range constraint produces:
```
¬(Base ≤ T ≤ Super) = (always ∧ ¬(Base ≤ T ≤ Super))
```
(That looks trivial, because it is! We just move the positive range to
the negative side.)
The math is not that much harder than before, because there are only
three combinations to consider (each for intersection and union) —
though the fact that there can be multiple holes in a constraint does
require some nested loops. But the mdtest suite gives me confidence that
this is not introducing any new issues, and it definitely removes a
troublesome TODO.
(As an aside, this change also means that we are back to having each
clause contain no more than one individual constraint for any typevar.
This turned out to be important, because part of our simplification
logic was also depending on that!)
---------
Co-authored-by: Carl Meyer <carl@astral.sh>
## Summary
Fixes https://github.com/astral-sh/ty/issues/1161
Include `NamedTupleFallback` members in `NamedTuple` instance
completions.
- Augment instance attribute completions when completing on NamedTuple
instances by merging members from
`_typeshed._type_checker_internals.NamedTupleFallback`
## Test Plan
Adds a minimal completion test `namedtuple_fallback_instance_methods`
---------
Co-authored-by: David Peter <mail@david-peter.de>
## Summary
This project was [recently removed from
mypy_primer](https://github.com/astral-sh/ruff/pull/20378), so we need
to remove it from `good.txt` in order for ecosystem-analyzer to work
correctly.
## Test Plan
Run mypy_primer and ecosystem-analyzer on this branch.
## Summary
https://github.com/astral-sh/ruff/pull/20165 added a lot of false
positives around calls to `builtins.open()`, because our missing support
for PEP-613 type aliases means that we don't understand typeshed's
overloads for `builtins.open()` at all yet, and therefore always select
the first overload. This didn't use to matter very much, but now that we
have a much stricter implementation of protocol assignability/subtyping
it matters a lot, because most of the stdlib functions dealing with I/O
(`pickle`, `marshal`, `io`, `json`, etc.) are annotated in typeshed as
taking in protocols of some kind.
In lieu of full PEP-613 support, which is blocked on various things and
might not land in time for our next alpha release, this PR adds some
temporary special-casing for `builtins.open()` to avoid the false
positives. We just infer `Todo` for anything that isn't meant to match
typeshed's first `open()` overload. This should be easy to rip out again
once we have proper support for PEP-613 type aliases, which hopefully
should be pretty soon!
## Test Plan
Added an mdtest
## Summary
Fixes https://github.com/astral-sh/ty/issues/377.
We were treating any function as being assignable to any callback
protocol, because we were trying to figure out a type's `Callable`
supertype by looking up the `__call__` attribute on the type's
meta-type. But a function-literal's meta-type is `types.FunctionType`,
and `types.FunctionType.__call__` is `(...) -> Any`, which is not very
helpful!
While working on this PR, I also realised that assignability between
class-literals and callback protocols was somewhat broken too, so I
fixed that at the same time.
## Test Plan
Added mdtests
## Summary
This PR addresses an issue for a variadic argument when involved in
argument type expansion of overload call evaluation.
The issue is that the expansion of the variadic argument could result in
argument list of different arity. For example, in `*args: tuple[int] |
tuple[int, str]`, the expansion would lead to the variadic argument
being unpacked into 1 and 2 element respectively. This means that the
parameter matching that was performed initially isn't sufficient and
each expanded argument list would need to redo the parameter matching
again.
This is currently done by redoing the parameter matching directly,
maintaining the state of argument forms (and the conflicting forms), and
updating the `Bindings` values if it changes.
Closes: astral-sh/ty#735
## Test Plan
Update existing mdtest.
## Summary
Use `Type::Divergent` to avoid "too many iterations" panic on an
infinitely-nested tuple in an implicit instance attribute.
The regression here is from checking all tuple elements to see if they
contain a Divergent type. It's 5% on one project, 1% on another, and
zero on the rest. I spent some time looking into eliminating this
regression by tracking a flag on inference results to note if they could
possibly contain any Divergent type, but this doesn't really work --
there are too many different ways a type containing a Divergent type
could enter an inference result. Still thinking about whether there are
other ways to reduce this. One option is if we see certain kinds of
non-atomic types that are commonly expensive to check for Divergent, we
could make `has_divergent_type` a Salsa query on those types.
## Test Plan
Added mdtest.
Co-authored-by: Alex Waygood <Alex.Waygood@Gmail.com>
## Summary
`CallableTypeOf[bound_method]` would previously bind `self` to the
bound method type itself, instead of binding it to the instance type
stored inside the bound method type.
## Test Plan
Added regression test
This PR adds a new `ty_extensions.ConstraintSet` class, which is used to
expose constraint sets to our mdtest framework. This lets us write a
large collection of unit tests that exercise the invariants and rewrite
rules of our constraint set implementation.
As part of this, `is_assignable_to` and friends are updated to return a
`ConstraintSet` instead of a `bool`, and we implement
`ConstraintSet.__bool__` to return when a constraint set is always
satisfied. That lets us still use
`static_assert(is_assignable_to(...))`, since the assertion will coerce
the constraint set to a bool, and also lets us
`reveal_type(is_assignable_to(...))` to see more detail about
whether/when the two types are assignable. That lets us get rid of
`reveal_when_assignable_to` and friends, since they are now redundant
with the expanded capabilities of `is_assignable_to`.
## Summary
When adding an enum literal `E = Literal[Color.RED]` to a union which
already contained a subtype of that enum literal(!), we were previously
not simplifying the union correctly. My assumption is that our property
tests didn't catch that earlier, because the only possible non-trivial
subytpe of an enum literal that I can think of is `Any & E`. And in
order for that to be detected by the property tests, it would have to
randomly generate `Any & E | E` and then also compare that with `E` on
the other side (in an equivalence test, or the subtyping-antisymmetry
test).
closes https://github.com/astral-sh/ty/issues/1155
## Test Plan
* Added a regression test.
* I also ran the property tests for a while, but probably not for two
months worth of daily CI runs.
## Summary
This is a follow-up to https://github.com/astral-sh/ruff/pull/19321.
Now lazy snapshots are updated to take into account new bindings on
every symbol reassignment.
```python
def outer(x: A | None):
if x is None:
x = A()
reveal_type(x) # revealed: A
def inner() -> None:
# lazy snapshot: {x: A}
reveal_type(x) # revealed: A
inner()
def outer() -> None:
x = None
x = 1
def inner() -> None:
# lazy snapshot: {x: Literal[1]} -> {x: Literal[1, 2]}
reveal_type(x) # revealed: Literal[1, 2]
inner()
x = 2
```
Closesastral-sh/ty#559.
## Test Plan
Some TODOs in `public_types.md` now work properly.
---------
Co-authored-by: Carl Meyer <carl@astral.sh>
## Summary
Adds support for generic PEP695 type aliases, e.g.,
```python
type A[T] = T
reveal_type(A[int]) # A[int]
```
Resolves https://github.com/astral-sh/ty/issues/677.
## Summary
Support cases like the following, where we need the generic context to
include both `Self` and `T` (not just `T`):
```py
from typing import Self
class C:
def method[T](self: Self, arg: T): ...
C().method(1)
```
closes https://github.com/astral-sh/ty/issues/1131
## Test Plan
Added regression test
## Summary
The sub-checks for assignability and subtyping of materializations
performed in `has_relation_in_invariant_position` and
`is_subtype_in_invariant_position` need to propagate the
`HasRelationToVisitor`, or we can stack overflow.
A side effect of this change is that we also propagate the
`ConstraintSet` through, rather than using `C::from_bool`, which I think
may also become important for correctness in cases involving type
variables (though it isn't testable yet, since we aren't yet actually
creating constraints other than always-true and always-false.)
## Test Plan
Added mdtest (derived from code found in pydantic) which
stack-overflowed before this PR.
With this change incorporated, pydantic now checks successfully on my
draft PR for PEP 613 TypeAlias support.
Now that https://github.com/astral-sh/ruff/pull/20263 is merged, we can
update mypy_primer and add the new `egglog-python` project to
`good.txt`. The ecosystem-analyzer run shows that we now add 1,356
diagnostics (where we had over 5,000 previously, due to the unsupported
project layout).
## Summary
Add backreferences to the original item declaration in TypedDict
diagnostics.
Thanks to @AlexWaygood for the suggestion.
## Test Plan
Updated snapshots
## Summary
An annotated assignment `name: annotation` without a right-hand side was
previously not covered by the range returned from
`DefinitionKind::full_range`, because we did expand the range to include
the right-hand side (if there was one), but failed to include the
annotation.
## Test Plan
Updated snapshot tests
## Summary
Add support for `typing.ReadOnly` as a type qualifier to mark
`TypedDict` fields as being read-only. If you try to mutate them, you
get a new diagnostic:
<img width="787" height="234" alt="image"
src="https://github.com/user-attachments/assets/f62fddf9-4961-4bcd-ad1c-747043ebe5ff"
/>
## Test Plan
* New Markdown tests
* The typing conformance changes are all correct. There are some false
negatives, but those are related to the missing support for the
functional form of `TypedDict`, or to overriding of fields via
inheritance. Both of these topics are tracked in
https://github.com/astral-sh/ty/issues/154
Closesastral-sh/ty#456. Part of astral-sh/ty#994.
After all the foundational work, this is only a small change, but let's
see if it exposes any unresolved issues.
## Summary
Part of astral-sh/ty#994. The goal of this PR was to add correct
behavior for attribute access on the top and bottom materializations.
This is necessary for the end goal of using the top materialization for
narrowing generics (`isinstance(x, list)`): we want methods like
`x.append` to work correctly in that case.
It turned out to be convenient to represent materialization as a
TypeMapping, so it can be stashed in the `type_mappings` list of a
function object. This also allowed me to remove most concrete
`materialize` methods, since they usually just delegate to the subparts
of the type, the same as other type mappings. That is why the net effect
of this PR is to remove a few hundred lines.
## Test Plan
I added a few more tests. Much of this PR is refactoring and covered by
existing tests.
## Followups
Assigning to attributes of top materializations is not yet covered. This
seems less important so I'd like to defer it.
I noticed that the `materialize` implementation of `Parameters` was
wrong; it did the same for the top and bottom materializations. This PR
makes the bottom materialization slightly more reasonable, but
implementing this correctly will require extending the struct.
## Summary
A small set of additional tests for `TypedDict` that I wrote while going
through the spec. Note that this certainly doesn't make the test suite
exhaustive (see remaining open points in the updated list here:
https://github.com/astral-sh/ty/issues/154).
This PR adds two new `ty_extensions` functions,
`reveal_when_assignable_to` and `reveal_when_subtype_of`. These are
closely related to the existing `is_assignable_to` and `is_subtype_of`,
but instead of returning when the property (always) holds, it produces a
diagnostic that describes _when_ the property holds. (This will let us
construct mdtests that print out constraints that are not always true or
always false — though we don't currently have any instances of those.)
I did not replace _every_ occurrence of the `is_property` variants in
the mdtest suite, instead focusing on the generics-related tests where
it will be important to see the full detail of the constraint sets.
As part of this, I also updated the mdtest harness to accept the shorter
`# revealed:` assertion format for more than just `reveal_type`, and
updated the existing uses of `reveal_protocol_interface` to take
advantage of this.
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## Summary
This PR implements
https://docs.astral.sh/ruff/rules/yield-from-in-async-function/ as a
syntax semantic error
## Test Plan
<!-- How was it tested? -->
I have written a simple inline test as directed in
[https://github.com/astral-sh/ruff/issues/17412](https://github.com/astral-sh/ruff/issues/17412)
---------
Signed-off-by: 11happy <soni5happy@gmail.com>
Co-authored-by: Alex Waygood <alex.waygood@gmail.com>
Co-authored-by: Brent Westbrook <36778786+ntBre@users.noreply.github.com>
## Summary
Thread visitors through the rest of `apply_type_mapping`: callable and
protocol types.
## Test Plan
Added mdtest that previously stack overflowed.
## Summary
We have the ability to defer type inference of some parts of
definitions, so as to allow us to create a type that may need to be
recursively referenced in those other parts of the definition.
We also have the ability to do type inference in a context where all
name resolution should be deferred (that is, names should be looked up
from all-reachable-definitions rather than from the location of use.)
This is used for all annotations in stubs, or if `from __future__ import
annotations` is active.
Previous to this PR, these two concepts were linked: deferred-inference
always implied deferred-name-resolution, though we also supported
deferred-name-resolution without deferred-inference, via
`DeferredExpressionState`.
For the upcoming `typing.TypeAlias` support, I will defer inference of
the entire RHS of the alias (so as to support cycles), but that doesn't
imply deferred name resolution; at runtime, the RHS of a name annotated
as `typing.TypeAlias` is executed eagerly.
So this PR fully de-couples the two concepts, instead explicitly setting
the `DeferredExpressionState` in those cases where we should defer name
resolution.
It also fixes a long-standing related bug, where we were deferring name
resolution of all names in class bases, if any of the class bases
contained a stringified annotation.
## Test Plan
Added test that failed before this PR.
Reverts astral-sh/ruff#20156. As @sharkdp noted in his post-merge
review, there were several issues with that PR that I didn't spot before
merging — but I'm out for four days now, and would rather not leave
things in an inconsistent state for that long. I'll revisit this on
Wednesday.
## Summary
These projects all check successfully now.
(Pandas still takes 9s, as the comment in `bad.txt` said, but I don't
think this is slow enough to exclude it; mypy-primer overall still runs
in 4 minutes, faster than e.g. the test suite on Windows.)
## Test Plan
mypy-primer CI.
## Summary
This error is about assigning to attributes rather than reading
attributes, so I think `invalid-assignment` makes more sense than
`invalid-attribute-access`
## Test Plan
existing mdtests updated
## Summary
Now that we have `Type::TypeAlias`, which can wrap a union, and the
possibility of unions including non-unpacked type aliases (which is
necessary to support recursive type aliases), we can no longer assume in
`UnionType::normalized_impl` that normalizing each element of an
existing union will result in a set of elements that we can order and
then place raw into `UnionType` to create a normalized union. It's now
possible for those elements to themselves include union types (unpacked
from an alias). So instead, we need to feed those elements into the full
`UnionBuilder` (with alias-unpacking turned on) to flatten/normalize
them, and then order them.
## Test Plan
Added mdtest.
---------
Co-authored-by: Alex Waygood <Alex.Waygood@Gmail.com>
## Summary
This PR fixes various TODOs around overload call when a variadic
argument is used.
The reason this bug existed is because the specialization wouldn't
account for unpacking the type of the variadic argument.
This is fixed by expanding `MatchedArgument` to contain the type of that
argument _only_ when it is a variadic argument. The reason is that
there's a split for when the argument type is inferred -- the
non-variadic arguments are inferred using `infer_argument_types` _after_
parameter matching while the variadic argument type is inferred _during_
the parameter matching. And, the `MatchedArgument` is populated _during_
parameter matching which means the unpacking would need to happen during
parameter matching.
This split seems a bit inconsistent but I don't want to spend a lot of
time on trying to merge them such that all argument type inference
happens in a single place. I might look into it while adding support for
`**kwargs`.
## Test Plan
Update existing tests by resolving the todos.
The ecosystem changes looks correct to me except for the `slice` call
but it seems that it's unrelated to this PR as we infer `slice[Any, Any,
Any]` for a `slice(1, 2, 3)` call on `main` as well
([playground](https://play.ty.dev/9eacce00-c7d5-4dd5-a932-4265cb2bb4f6)).
## Summary
Decrease the maximum number of literals in a union before we collapse to
the supertype. The better fix for this will be
https://github.com/astral-sh/ty/issues/957, but it is very tempting to
solve this for now by simply decreasing the limit by one, to get below
the salsa limit of 200.
closes https://github.com/astral-sh/ty/issues/660
## Test Plan
Added a regression test that would previously lead to a "too many cycle
iterations" panic.
## Summary
With this PR, we stop performing boundness analysis for implicit
instance attributes:
```py
class C:
def __init__(self):
if False:
self.x = 1
C().x # would previously show an error, with this PR we pretend the attribute exists
```
This PR is potentially just a temporary measure until we find a better
fix. But I have already invested a lot of time trying to find the root
cause of https://github.com/astral-sh/ty/issues/758 (and [this
example](https://github.com/astral-sh/ty/issues/758#issuecomment-3206108262),
which I'm not entirely sure is related) and I still don't understand
what is going on. This PR fixes the performance problems in both of
these problems (in a rather crude way).
The impact of the proposed change on the ecosystem is small, and the
three new diagnostics are arguably true positives (previously hidden
because we considered the code unreachable, based on e.g. `assert`ions
that depended on implicit instance attributes). So this seems like a
reasonable fix for now.
Note that we still support cases like these:
```py
class D:
if False: # or any other expression that statically evaluates to `False`
x: int = 1
D().x # still an error
class E:
if False: # or any other expression that statically evaluates to `False`
def f(self):
self.x = 1
E().x # still an error
```
closes https://github.com/astral-sh/ty/issues/758
## Test Plan
Updated tests, benchmark results
## Summary
closes https://github.com/astral-sh/ty/issues/692
If the expression (or any child expressions) is not definitely bound the
reachability constraint evaluation is determined as ambiguous.
This fixes the infinite cycles panic in the following code:
```py
from typing import Literal
class Toggle:
def __init__(self: "Toggle"):
if not self.x:
self.x: Literal[True] = True
```
Credit of this solution is for David.
## Test Plan
- Added a test case with too many cycle iterations panic.
- Previous tests.
---------
Co-authored-by: David Peter <mail@david-peter.de>
Part of #994. This adds a new field to the Specialization struct to
record when we're dealing with the top or bottom materialization of an
invariant generic. It also implements subtyping and assignability for
these objects.
Next planned steps after this is done are to implement other operations
on top/bottom materializations; probably attribute access is an
important one.
---------
Co-authored-by: Carl Meyer <carl@astral.sh>
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
Properly preserve type qualifiers when accessing attributes on unions
and intersections. This is a prerequisite for
https://github.com/astral-sh/ruff/pull/19579.
Also fix a completely wrong implementation of
`map_with_boundness_and_qualifiers`. It now closely follows
`map_with_boundness` (just above).
## Test Plan
I thought about it, but didn't find any easy way to test this. This only
affected `Type::member`. Things like validation of attribute writes
(where type qualifiers like `ClassVar` and `Final` are important) were
already handling things correctly.
## Summary
Add a subtly different test case for recursive PEP 695 type aliases,
which does require that we relax our union simplification, so we don't
eagerly unpack aliases from user-provided union annotations.
## Test Plan
Added mdtest.
## Summary
This has been here for awhile (since our initial PEP 695 type alias
support) but isn't really correct. The right-hand-side of a PEP 695 type
alias is a distinct scope, and we don't mark it as an "eager" nested
scope, so it automatically gets "deferred" resolution of names from
outer scopes (just like a nested function). Thus it's
redundant/unnecessary for us to use `DeferredExpressionState::Deferred`
for resolving that RHS expression -- that's for deferring resolution of
individual names within a scope. Using it here causes us to wrongly
ignore applicable outer-scope narrowing.
## Test Plan
Added mdtest that failed before this PR (the second snippet -- the first
snippet always passed.)
## 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 limits the argument type expansion size for an overload call
evaluation to 512.
The limit chosen is arbitrary but I've taken the 256 limit from Pyright
into account and bumped it x2 to start with.
Initially, I actually started out by trying to refactor the entire
argument type expansion to be lazy. Currently, expanding a single
argument at any position eagerly creates the combination (argument
lists) and returns that (`Vec<CallArguments>`) but I thought we could
make it lazier by converting the return type of `expand` from
`Iterator<Item = Vec<CallArguments>>` to `Iterator<Item = Iterator<Item
= CallArguments>>` but that's proving to be difficult to implement
mainly because we **need** to maintain the previous expansion to
generate the next expansion which is the main reason to use
`std::iter::successors` in the first place.
Another approach would be to eagerly expand all the argument types and
then use the `combinations` from `itertools` to generate the
combinations but we would need to find the "boundary" between arguments
lists produced from expanding argument at position 1 and position 2
because that's important for the algorithm.
Closes: https://github.com/astral-sh/ty/issues/868
## Test Plan
Add test case to demonstrate the limit along with the diagnostic
snapshot stating that the limit has been reached.
Part of astral-sh/ty#994
## Summary
Add new special forms to `ty_extensions`, `Top[T]` and `Bottom[T]`.
Remove `ty_extensions.top_materialization` and
`ty_extensions.bottom_materialization`.
## Test Plan
Converted the existing `materialization.md` mdtest to the new syntax.
Added some tests for invalid use of the new special form.
## Summary
Previously we held off from doing this because we weren't sure that it
was worth the added complexity cost. But our code has changed in the
months since we made that initial decision, and I think the structure of
the code is such that it no longer really leads to much added complexity
to add precise inference when unpacking a string literal or a bytes
literal.
The improved inference we gain from this has real benefits to users (see
the mypy_primer report), and this PR doesn't appear to have a
performance impact.
## Test plan
mdtests
"Why would you do this? This looks like you just replaced `bool` with an
overly complex trait"
Yes that's correct!
This should be a no-op refactoring. It replaces all of the logic in our
assignability, subtyping, equivalence, and disjointness methods to work
over an arbitrary `Constraints` trait instead of only working on `bool`.
The methods that `Constraints` provides looks very much like what we get
from `bool`. But soon we will add a new impl of this trait, and some new
methods, that let us express "fuzzy" constraints that aren't always true
or false. (In particular, a constraint will express the upper and lower
bounds of the allowed specializations of a typevar.)
Even once we have that, most of the operations that we perform on
constraint sets will be the usual boolean operations, just on sets.
(`false` becomes empty/never; `true` becomes universe/always; `or`
becomes union; `and` becomes intersection; `not` becomes negation.) So
it's helpful to have this separate PR to refactor how we invoke those
operations without introducing the new functionality yet.
Note that we also have translations of `Option::is_some_and` and
`is_none_or`, and of `Iterator::any` and `all`, and that the `and`,
`or`, `when_any`, and `when_all` methods are meant to short-circuit,
just like the corresponding boolean operations. For constraint sets,
that depends on being able to implement the `is_always` and `is_never`
trait methods.
---------
Co-authored-by: Carl Meyer <carl@astral.sh>
Co-authored-by: Alex Waygood <Alex.Waygood@Gmail.com>
## Summary
Part of: https://github.com/astral-sh/ty/issues/868
This PR adds a heuristic to avoid argument type expansion if it's going
to eventually lead to no matching overload.
This is done by checking whether the non-expandable argument types are
assignable to the corresponding annotated parameter type. If one of them
is not assignable to all of the remaining overloads, then argument type
expansion isn't going to help.
## Test Plan
Add mdtest that would otherwise take a long time because of the number
of arguments that it would need to expand (30).
This commit corrects the type checker's behavior when handling
`dataclass_transform` decorators that don't explicitly specify
`field_specifiers`. According to [PEP 681 (Data Class
Transforms)](https://peps.python.org/pep-0681/#dataclass-transform-parameters),
when `field_specifiers` is not provided, it defaults to an empty tuple,
meaning no field specifiers are supported and
`dataclasses.field`/`dataclasses.Field` calls should be ignored.
Fixes https://github.com/astral-sh/ty/issues/980
## Summary
Closes: https://github.com/astral-sh/ty/issues/669
(This turned out to be simpler that I thought :))
## Test Plan
Update existing test cases.
### Ecosystem report
Most of them are basically because ty has now started inferring more
precise types for the return type to an overloaded call and a lot of the
types are defined using type aliases, here's some examples:
<details><summary>Details</summary>
<p>
> attrs (https://github.com/python-attrs/attrs)
> + tests/test_make.py:146:14: error[unresolved-attribute] Type
`Literal[42]` has no attribute `default`
> - Found 555 diagnostics
> + Found 556 diagnostics
This is accurate now that we infer the type as `Literal[42]` instead of
`Unknown` (Pyright infers it as `int`)
> optuna (https://github.com/optuna/optuna)
> + optuna/_gp/search_space.py:181:53: error[invalid-argument-type]
Argument to function `_round_one_normalized_param` is incorrect:
Expected `tuple[int | float, int | float]`, found `tuple[Unknown |
ndarray[Unknown, <class 'float'>], Unknown | ndarray[Unknown, <class
'float'>]]`
> + optuna/_gp/search_space.py:181:83: error[invalid-argument-type]
Argument to function `_round_one_normalized_param` is incorrect:
Expected `int | float`, found `Unknown | ndarray[Unknown, <class
'float'>]`
> + tests/gp_tests/test_search_space.py:109:13:
error[invalid-argument-type] Argument to function
`_unnormalize_one_param` is incorrect: Expected `tuple[int | float, int
| float]`, found `Unknown | ndarray[Unknown, <class 'float'>]`
> + tests/gp_tests/test_search_space.py:110:13:
error[invalid-argument-type] Argument to function
`_unnormalize_one_param` is incorrect: Expected `int | float`, found
`Unknown | ndarray[Unknown, <class 'float'>]`
> - Found 559 diagnostics
> + Found 563 diagnostics
Same as above where ty is now inferring a more precise type like
`Unknown | ndarray[tuple[int, int], <class 'float'>]` instead of just
`Unknown` as before
> jinja (https://github.com/pallets/jinja)
> + src/jinja2/bccache.py:298:39: error[invalid-argument-type] Argument
to bound method `write_bytecode` is incorrect: Expected `IO[bytes]`,
found `_TemporaryFileWrapper[str]`
> - Found 186 diagnostics
> + Found 187 diagnostics
This requires support for type aliases to match the correct overload.
> hydra-zen (https://github.com/mit-ll-responsible-ai/hydra-zen)
> + src/hydra_zen/wrapper/_implementations.py:945:16:
error[invalid-return-type] Return type does not match returned value:
expected `DataClass_ | type[@Todo(type[T] for protocols)] | ListConfig |
DictConfig`, found `@Todo(unsupported type[X] special form) | (((...) ->
Any) & dict[Unknown, Unknown]) | (DataClass_ & dict[Unknown, Unknown]) |
dict[Any, Any] | (ListConfig & dict[Unknown, Unknown]) | (DictConfig &
dict[Unknown, Unknown]) | (((...) -> Any) & list[Unknown]) | (DataClass_
& list[Unknown]) | list[Any] | (ListConfig & list[Unknown]) |
(DictConfig & list[Unknown])`
> + tests/annotations/behaviors.py:60:28: error[call-non-callable]
Object of type `Path` is not callable
> + tests/annotations/behaviors.py:64:21: error[call-non-callable]
Object of type `Path` is not callable
> + tests/annotations/declarations.py:167:17: error[call-non-callable]
Object of type `Path` is not callable
> + tests/annotations/declarations.py:524:17:
error[unresolved-attribute] Type `<class 'int'>` has no attribute
`_target_`
> - Found 561 diagnostics
> + Found 566 diagnostics
Same as above, this requires support for type aliases to match the
correct overload.
> paasta (https://github.com/yelp/paasta)
> + paasta_tools/utils.py:4188:19: warning[redundant-cast] Value is
already of type `list[str]`
> - Found 888 diagnostics
> + Found 889 diagnostics
This is correct.
> colour (https://github.com/colour-science/colour)
> + colour/plotting/diagrams.py:448:13: error[invalid-argument-type]
Argument to bound method `__init__` is incorrect: Expected
`Sequence[@Todo(Support for `typing.TypeAlias`)]`, found
`ndarray[tuple[int, int, int], dtype[Unknown]]`
> + colour/plotting/diagrams.py:462:13: error[invalid-argument-type]
Argument to bound method `__init__` is incorrect: Expected
`Sequence[@Todo(Support for `typing.TypeAlias`)]`, found
`ndarray[tuple[int, int, int], dtype[Unknown]]`
> + colour/plotting/models.py:419:13: error[invalid-argument-type]
Argument to bound method `__init__` is incorrect: Expected
`Sequence[@Todo(Support for `typing.TypeAlias`)]`, found
`ndarray[tuple[int, int, int], dtype[Unknown]]`
> + colour/plotting/temperature.py:230:9: error[invalid-argument-type]
Argument to bound method `__init__` is incorrect: Expected
`Sequence[@Todo(Support for `typing.TypeAlias`)]`, found
`ndarray[tuple[int, int, int], dtype[Unknown]]`
> + colour/plotting/temperature.py:474:13: error[invalid-argument-type]
Argument to bound method `__init__` is incorrect: Expected
`Sequence[@Todo(Support for `typing.TypeAlias`)]`, found
`ndarray[tuple[int, int, int], dtype[Unknown]]`
> + colour/plotting/temperature.py:495:17: error[invalid-argument-type]
Argument to bound method `__init__` is incorrect: Expected
`Sequence[@Todo(Support for `typing.TypeAlias`)]`, found
`ndarray[tuple[int, int, int], dtype[Unknown]]`
> + colour/plotting/temperature.py:513:13: error[invalid-argument-type]
Argument to bound method `text` is incorrect: Expected `int | float`,
found `ndarray[@Todo(Support for `typing.TypeAlias`), dtype[Unknown]]`
> + colour/plotting/temperature.py:514:13: error[invalid-argument-type]
Argument to bound method `text` is incorrect: Expected `int | float`,
found `ndarray[@Todo(Support for `typing.TypeAlias`), dtype[Unknown]]`
> - Found 480 diagnostics
> + Found 488 diagnostics
Most of them are correct except for the last two diagnostics which I'm
not sure
what's happening, it's trying to index into an `np.ndarray` type (which
is
inferred correctly) but I think it might be picking up an incorrect
overload
for the `__getitem__` method.
Scipy's diagnostics also requires support for type alises to pick the
correct overload.
</p>
</details>
In implementing partial stubs I had observed that this continue in the
namespace package code seemed erroneous since the same continue for
partial stubs didn't work. Unfortunately I wasn't confident enough to
push on that hunch. Fortunately I remembered that hunch to make this an
easy fix.
The issue with the continue is that it bails out of the current
search-path without testing any .py files. This breaks when for example
`google` and `google-stubs`/`types-google` are both in the same
site-packages dir -- failing to find a module in `types-google` has us
completely skip over `google`!
Fixes https://github.com/astral-sh/ty/issues/520
fix https://github.com/astral-sh/ty/issues/1047
## Summary
This PR fixes how `KW_ONLY` is applied in dataclasses. Previously, the
sentinel leaked into subclasses and incorrectly marked their fields as
keyword-only; now it only affects fields declared in the same class.
```py
from dataclasses import dataclass, KW_ONLY
@dataclass
class D:
x: int
_: KW_ONLY
y: str
@dataclass
class E(D):
z: bytes
# This should work: x=1 (positional), z=b"foo" (positional), y="foo" (keyword-only)
E(1, b"foo", y="foo")
reveal_type(E.__init__) # revealed: (self: E, x: int, z: bytes, *, y: str) -> None
```
<!-- What's the purpose of the change? What does it do, and why? -->
## Test Plan
<!-- How was it tested? -->
mdtests
## Summary
Fixes https://github.com/astral-sh/ty/issues/1046
We special-case iteration of certain types because they may have a more
detailed tuple-spec. Now that type aliases are a distinct type variant,
we need to handle them as well.
I don't love that `Type::TypeAlias` means we have to remember to add a
case for it basically anywhere we are special-casing a certain kind of
type, but at the moment I don't have a better plan. It's another
argument for avoiding fallback cases in `Type` matches, which we usually
prefer; I've updated this match statement to be comprehensive.
## Test Plan
Added mdtest.
`Type::TypeVar` now distinguishes whether the typevar in question is
inferable or not.
A typevar is _not inferable_ inside the body of the generic class or
function that binds it:
```py
def f[T](t: T) -> T:
return t
```
The infered type of `t` in the function body is `TypeVar(T,
NotInferable)`. This represents how e.g. assignability checks need to be
valid for all possible specializations of the typevar. Most of the
existing assignability/etc logic only applies to non-inferable typevars.
Outside of the function body, the typevar is _inferable_:
```py
f(4)
```
Here, the parameter type of `f` is `TypeVar(T, Inferable)`. This
represents how e.g. assignability doesn't need to hold for _all_
specializations; instead, we need to find the constraints under which
this specific assignability check holds.
This is in support of starting to perform specialization inference _as
part of_ performing the assignability check at the call site.
In the [[POPL2015][]] paper, this concept is called _monomorphic_ /
_polymorphic_, but I thought _non-inferable_ / _inferable_ would be
clearer for us.
Depends on #19784
[POPL2015]: https://doi.org/10.1145/2676726.2676991
---------
Co-authored-by: Carl Meyer <carl@astral.sh>
## 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
For PEP 695 generic functions and classes, there is an extra "type
params scope" (a child of the outer scope, and wrapping the body scope)
in which the type parameters are defined; class bases and function
parameter/return annotations are resolved in that type-params scope.
This PR fixes some longstanding bugs in how we resolve name loads from
inside these PEP 695 type parameter scopes, and also defers type
inference of PEP 695 typevar bounds/constraints/default, so we can
handle cycles without panicking.
We were previously treating these type-param scopes as lazy nested
scopes, which is wrong. In fact they are eager nested scopes; the class
`C` here inherits `int`, not `str`, and previously we got that wrong:
```py
Base = int
class C[T](Base): ...
Base = str
```
But certain syntactic positions within type param scopes (typevar
bounds/constraints/defaults) are lazy at runtime, and we should use
deferred name resolution for them. This also means they can have cycles;
in order to handle that without panicking in type inference, we need to
actually defer their type inference until after we have constructed the
`TypeVarInstance`.
PEP 695 does specify that typevar bounds and constraints cannot be
generic, and that typevar defaults can only reference prior typevars,
not later ones. This reduces the scope of (valid from the type-system
perspective) cycles somewhat, although cycles are still possible (e.g.
`class C[T: list[C]]`). And this is a type-system-only restriction; from
the runtime perspective an "invalid" case like `class C[T: T]` actually
works fine.
I debated whether to implement the PEP 695 restrictions as a way to
avoid some cycles up-front, but I ended up deciding against that; I'd
rather model the runtime name-resolution semantics accurately, and
implement the PEP 695 restrictions as a separate diagnostic on top.
(This PR doesn't yet implement those diagnostics, thus some `# TODO:
error` in the added tests.)
Introducing the possibility of cyclic typevars made typevar display
potentially stack overflow. For now I've handled this by simply removing
typevar details (bounds/constraints/default) from typevar display. This
impacts display of two kinds of types. If you `reveal_type(T)` on an
unbound `T` you now get just `typing.TypeVar` instead of
`typing.TypeVar("T", ...)` where `...` is the bound/constraints/default.
This matches pyright and mypy; pyrefly uses `type[TypeVar[T]]` which
seems a bit confusing, but does include the name. (We could easily
include the name without cycle issues, if there's a syntax we like for
that.)
It also means that displaying a generic function type like `def f[T:
int](x: T) -> T: ...` now displays as `f[T](x: T) -> T` instead of `f[T:
int](x: T) -> T`. This matches pyright and pyrefly; mypy does include
bound/constraints/defaults of typevars in function/callable type
display. If we wanted to add this, we would either need to thread a
visitor through all the type display code, or add a `decycle` type
transformation that replaced recursive reoccurrence of a type with a
marker.
## Test Plan
Added mdtests and modified existing tests to improve their correctness.
After this PR, there's only a single remaining py-fuzzer seed in the
0-500 range that panics! (Before this PR, there were 10; the fuzzer
likes to generate cyclic PEP 695 syntax.)
## Ecosystem report
It's all just the changes to `TypeVar` display.
## Summary
A [passing
comment](https://github.com/astral-sh/ruff/pull/19711#issuecomment-3169312014)
led me to explore why we didn't report a class attribute as possibly
unbound if it was a method and defined in two different conditional
branches.
I found that the reason was because of our handling of "conflicting
declarations" in `place_from_declarations`. It returned a `Result` which
would be `Err` in case of conflicting declarations.
But we only actually care about conflicting declarations when we are
actually doing type inference on that scope and might emit a diagnostic
about it. And in all cases (including that one), we want to otherwise
proceed with the union of the declared types, as if there was no
conflict.
In several cases we were failing to handle the union of declared types
in the same way as a normal declared type if there was a declared-types
conflict. The `Result` return type made this mistake really easy to
make, as we'd match on e.g. `Ok(Place::Type(...))` and do one thing,
then match on `Err(...)` and do another, even though really both of
those cases should be handled the same.
This PR refactors `place_from_declarations` to instead return a struct
which always represents the declared type we should use in the same way,
as well as carrying the conflicting declared types, if any. This struct
has a method to allow us to explicitly ignore the declared-types
conflict (which is what we want in most cases), as well as a method to
get the declared type and the conflict information, in the case where we
want to emit a diagnostic on the conflict.
## Test Plan
Existing CI; added a test showing that we now understand a
multiply-conditionally-defined method as possibly-unbound.
This does trigger issues on a couple new fuzzer seeds, but the issues
are just new instances of an already-known (and rarely occurring)
problem which I already plan to address in a future PR, so I think it's
OK to land as-is.
I happened to build this initially on top of
https://github.com/astral-sh/ruff/pull/19711, which adds invalid-await
diagnostics, so I also updated some invalid-syntax tests to not await on
an invalid type, since the purpose of those tests is to check the
syntactic location of the `await`, not the validity of the awaited type.
## Summary
Support recursive type aliases by adding a `Type::TypeAlias` type
variant, which allows referring to a type alias directly as a type
without eagerly unpacking it to its value.
We still unpack type aliases when they are added to intersections and
unions, so that we can simplify the intersection/union appropriately
based on the unpacked value of the type alias.
This introduces new possible recursive types, and so also requires
expanding our usage of recursion-detecting visitors in Type methods. The
use of these visitors is still not fully comprehensive in this PR, and
will require further expansion to support recursion in more kinds of
types (I already have further work on this locally), but I think it may
be better to do this incrementally in multiple PRs.
## Test Plan
Added some recursive type-alias tests and made them pass.
fix https://github.com/astral-sh/ty/issues/943
## Summary
Add module-level `__getattr__` support for ty's type checker, fixing
issue https://github.com/astral-sh/ty/issues/943.
Module-level `__getattr__` functions ([PEP
562](https://peps.python.org/pep-0562/)) are now respected when
resolving dynamic attributes, matching the behavior of mypy and pyright.
## Implementation
Thanks @sharkdp for the guidance in
https://github.com/astral-sh/ty/issues/943#issuecomment-3157566579
- Adds module-specific `__getattr__` resolution in
`ModuleLiteral.static_member()`
- Maintains proper attribute precedence: explicit attributes >
submodules > `__getattr__`
## Test Plan
- New mdtest covering basic functionality, type annotations, attribute
precedence, and edge cases
(run ```cargo nextest run -p ty_python_semantic
mdtest__import_module_getattr```)
- All new tests pass, verifying `__getattr__` is called correctly and
returns proper types
- Existing test suite passes, ensuring no regressions introduced
## Summary
Reported in:
https://github.com/astral-sh/ruff/pull/19795#issuecomment-3161981945
If a root expression is reassigned, narrowing on the member should be
invalidated, but there was an oversight in the current implementation.
This PR fixes that, and also removes some unnecessary handling.
## Test Plan
New tests cases in `narrow/conditionals/nested.md`.
## 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
This fixes our logic for binding a legacy typevar with its binding
context. (To recap, a legacy typevar starts out "unbound" when it is
first created, and each time it's used in a generic class or function,
we "bind" it with the corresponding `Definition`.)
We treat `typing.Self` the same as a legacy typevar, and so we apply
this binding logic to it too. Before, we were using the enclosing class
as its binding context. But that's not correct — it's the method where
`typing.Self` is used that binds the typevar. (Each invocation of the
method will find a new specialization of `Self` based on the specific
instance type containing the invoked method.)
This required plumbing through some additional state to the
`in_type_expression` method.
This also revealed that we weren't handling `Self`-typed instance
attributes correctly (but were coincidentally not getting the expected
false positive diagnostics).
## Summary
Disallow `typing.TypedDict` in type expressions.
Related reference: https://github.com/python/mypy/issues/11030
## Test Plan
New Markdown tests, checked ecosystem and conformance test impact.
## Summary
This PR improves the `is_safe_mutable_class` function in `infer.rs` in
several ways:
- It uses `KnownClass::to_instance()` for all "safe mutable classes".
Previously, we were using `SpecialFormType::instance_fallback()` for
some variants -- I'm not totally sure why. Switching to
`KnownClass::to_instance()` for all "safe mutable classes" fixes a
number of TODOs in the `assignment.md` mdtest suite
- Rather than eagerly calling `.to_instance(db)` on all "safe mutable
classes" every time `is_safe_mutable_class` is called, we now only call
it lazily on each element, allowing us to short-circuit more
effectively.
- I removed the entry entirely for `TypedDict` from the list of "safe
mutable classes", as it's not correct.
`SpecialFormType::TypedDict.instance_fallback(db)` just returns an
instance type representing "any instance of `typing._SpecialForm`",
which I don't think was the intent of this code. No tests fail as a
result of removing this entry, as we already check separately whether an
object is an inhabitant of a `TypedDict` type (and consider that object
safe-mutable if so!).
## Test Plan
mdtests updated
## 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 fixes a few inaccuracies in attribute access on `TypedDict`s. It
also changes the return type of `type(person)` to `type[dict[str,
object]]` if `person: Person` is an inhabitant of a `TypedDict`
`Person`. We still use `type[Person]` as the *meta type* of Person,
however (see reasoning
[here](https://github.com/astral-sh/ruff/pull/19733#discussion_r2253297926)).
## Test Plan
Updated Markdown tests.
## Summary
This PR adds a new `Type::TypedDict` variant. Before this PR, we treated
`TypedDict`-based types as dynamic Todo-types, and I originally planned
to make this change a no-op. And we do in fact still treat that new
variant similar to a dynamic type when it comes to type properties such
as assignability and subtyping. But then I somehow tricked myself into
implementing some of the things correctly, so here we are. The two main
behavioral changes are: (1) we now also detect generic `TypedDict`s,
which removes a few false positives in the ecosystem, and (2) we now
support *attribute* access (not key-based indexing!) on these types,
i.e. we infer proper types for something like
`MyTypedDict.__required_keys__`. Nothing exciting yet, but gets the
infrastructure into place.
Note that with this PR, the type of (the type) `MyTypedDict` itself is
still represented as a `Type::ClassLiteral` or `Type::GenericAlias` (in
case `MyTypedDict` is generic). Only inhabitants of `MyTypedDict`
(instances of `dict` at runtime) are represented by `Type::TypedDict`.
We may want to revisit this decision in the future, if this turns out to
be too error-prone. Right now, we need to use `.is_typed_dict(db)` in
all the right places to distinguish between actual (generic) classes and
`TypedDict`s. But so far, it seemed unnecessary to add additional `Type`
variants for these as well.
part of https://github.com/astral-sh/ty/issues/154
## Ecosystem impact
The new diagnostics on `cloud-init` look like true positives to me.
## Test Plan
Updated and new Markdown tests
## Summary
This is a follow-up to #19321.
Narrowing constraints introduced in a class scope were not applied even
when they can be applied in lazy nested scopes. This PR fixes so that
they are now applied.
Conversely, there were cases where narrowing constraints were being
applied in places where they should not, so it is also fixed.
## Test Plan
Some TODOs in `narrow/conditionals/nested.md` are now work correctly.
## Summary
This is a follow-up to #19321.
If we try to access a class variable before it is defined, the variable
is looked up in the global scope, rather than in any enclosing scopes.
Closes https://github.com/astral-sh/ty/issues/875.
## Test Plan
New tests in `narrow/conditionals/nested.md`.
## Summary
Support `as` patterns in reachability analysis:
```py
from typing import assert_never
def f(subject: str | int):
match subject:
case int() as x:
pass
case str():
pass
case _:
assert_never(subject) # would previously emit an error
```
Note that we still don't support inferring correct types for the bound
name (`x`).
Closes https://github.com/astral-sh/ty/issues/928
## Test Plan
New 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.
This is subtle, and the root cause became more apparent with #19604,
since we now have many more cases of superclasses and subclasses using
different typevars. The issue is easiest to see in the following:
```py
class C[T]:
def __init__(self, t: T) -> None: ...
class D[U](C[T]):
pass
reveal_type(C(1)) # revealed: C[int]
reveal_type(D(1)) # should be: D[int]
```
When instantiating a generic class, the `__init__` method inherits the
generic context of that class. This lets our call binding machinery
infer a specialization for that context.
Prior to this PR, the instantiation of `C` worked just fine. Its
`__init__` method would inherit the `[T]` generic context, and we would
infer `{T = int}` as the specialization based on the argument
parameters.
It didn't work for `D`. The issue is that the `__init__` method was
inheriting the generic context of the class where `__init__` was defined
(here, `C` and `[T]`). At the call site, we would then infer `{T = int}`
as the specialization — but that wouldn't help us specialize `D[U]`,
since `D` does not have `T` in its generic context!
Instead, the `__init__` method should inherit the generic context of the
class that we are performing the lookup on (here, `D` and `[U]`). That
lets us correctly infer `{U = int}` as the specialization, which we can
successfully apply to `D[U]`.
(Note that `__init__` refers to `C`'s typevars in its signature, but
that's okay; our member lookup logic already applies the `T = U`
specialization when returning a member of `C` while performing a lookup
on `D`, transforming its signature from `(Self, T) -> None` to `(Self,
U) -> None`.)
Closes https://github.com/astral-sh/ty/issues/588
This PR introduces a few related changes:
- We now keep track of each time a legacy typevar is bound in a
different generic context (e.g. class, function), and internally create
a new `TypeVarInstance` for each usage. This means the rest of the code
can now assume that salsa-equivalent `TypeVarInstance`s refer to the
same typevar, even taking into account that legacy typevars can be used
more than once.
- We also go ahead and track the binding context of PEP 695 typevars.
That's _much_ easier to track since we have the binding context right
there during type inference.
- With that in place, we can now include the name of the binding context
when rendering typevars (e.g. `T@f` instead of `T`)
## Summary
Adds an initial set of tests based on the highest-priority items in
https://github.com/astral-sh/ty/issues/154. This is certainly not yet
exhaustive (required/non-required, `total`, and other things are
missing), but will be useful to measure progress on this feature.
## Test Plan
Checked intended behavior against runtime and other type checkers.
## Summary
Adds validation to subscript assignment expressions.
```py
class Foo: ...
class Bar:
__setattr__ = None
class Baz:
def __setitem__(self, index: str, value: int) -> None:
pass
# We now emit a diagnostic on these statements
Foo()[1] = 2
Bar()[1] = 2
Baz()[1] = 2
```
Also improves error messages on invalid `__getitem__` expressions
## Test Plan
Update mdtests and add more to `subscript/instance.md`
---------
Co-authored-by: David Peter <sharkdp@users.noreply.github.com>
Co-authored-by: David Peter <mail@david-peter.de>
Summary
--
Fixes#19640. I'm not sure these are the exact fixes we really want, but
I
reproduced the issue in a 32-bit Docker container and tracked down the
causes,
so I figured I'd open a PR.
As I commented on the issue, the `goto_references` test depends on the
iteration
order of the files in an `FxHashSet` in `Indexed`. In this case, we can
just
sort the output in test code.
Similarly, the tuple case depended on the order of overloads inserted in
an
`FxHashMap`. `FxIndexMap` seemed like a convenient drop-in replacement,
but I
don't know if that will have other detrimental effects. I did have to
change the
assertion for the tuple test, but I think it should now be stable across
architectures.
Test Plan
--
Running the tests in the aforementioned Docker container
## Summary
This PR improves our generics solver such that we are able to solve the
`TypeVar` in this snippet to `int | str` (the union of the elements in
the heterogeneous tuple) by upcasting the heterogeneous tuple to its
pure-homogeneous-tuple supertype:
```py
def f[T](x: tuple[T, ...]) -> T:
return x[0]
def g(x: tuple[int, str]):
reveal_type(f(x))
```
## Test Plan
Mdtests. Some TODOs remain in the mdtest regarding solving `TypeVar`s
for mixed tuples, but I think this PR on its own is a significant step
forward for our generics solver when it comes to tuple types.
---------
Co-authored-by: Douglas Creager <dcreager@dcreager.net>
## Summary
Add support for `async for` loops and async iterables.
part of https://github.com/astral-sh/ty/issues/151
## Ecosystem impact
```diff
- boostedblob/listing.py:445:54: warning[unused-ignore-comment] Unused blanket `type: ignore` directive
```
This is correct. We now find a true positive in the `# type: ignore`'d
code.
All of the other ecosystem hits are of the type
```diff
trio (https://github.com/python-trio/trio)
+ src/trio/_core/_tests/test_guest_mode.py:532:24: error[not-iterable] Object of type `MemorySendChannel[int] | MemoryReceiveChannel[int]` may not be iterable
```
The message is correct, because only `MemoryReceiveChannel` has an
`__aiter__` method, but `MemorySendChannel` does not. What's not correct
is our inferred type here. It should be `MemoryReceiveChannel[int]`, not
the union of the two. This is due to missing unpacking support for tuple
subclasses, which @AlexWaygood is working on. I don't think this should
block merging this PR, because those wrong types are already there,
without this PR.
## Test Plan
New Markdown tests and snapshot tests for diagnostics.
## Summary
- Add support for the return types of `async` functions
- Add type inference for `await` expressions
- Add support for `async with` / async context managers
- Add support for `yield from` expressions
This PR is generally lacking proper error handling in some cases (e.g.
illegal `__await__` attributes). I'm planning to work on this in a
follow-up.
part of https://github.com/astral-sh/ty/issues/151
closes https://github.com/astral-sh/ty/issues/736
## Ecosystem
There are a lot of true positives on `prefect` which look similar to:
```diff
prefect (https://github.com/PrefectHQ/prefect)
+ src/integrations/prefect-aws/tests/workers/test_ecs_worker.py:406:12: error[unresolved-attribute] Type `str` has no attribute `status_code`
```
This is due to a wrong return type annotation
[here](e926b8c4c1/src/integrations/prefect-aws/tests/workers/test_ecs_worker.py (L355-L391)).
```diff
mitmproxy (https://github.com/mitmproxy/mitmproxy)
+ test/mitmproxy/addons/test_clientplayback.py:18:1: error[invalid-argument-type] Argument to function `asynccontextmanager` is incorrect: Expected `(...) -> AsyncIterator[Unknown]`, found `def tcp_server(handle_conn, **server_args) -> Unknown | tuple[str, int]`
```
[This](a4d794c59a/test/mitmproxy/addons/test_clientplayback.py (L18-L19))
is a true positive. That function should return
`AsyncIterator[Address]`, not `Address`.
I looked through almost all of the other new diagnostics and they all
look like known problems or true positives.
## Typing conformance
The typing conformance diff looks good.
## Test Plan
New Markdown tests
## Summary
Split the "Generator functions" tests into two parts. The first part
(synchronous) refers to a function called `i` from a function `i2`. But
`i` is later redeclared in the asynchronous part, which was probably not
intended.
We now correctly exclude legacy typevars from enclosing scopes when
constructing the generic context for a generic function.
more detail:
A function is generic if it refers to legacy typevars in its signature:
```py
from typing import TypeVar
T = TypeVar("T")
def f(t: T) -> T:
return t
```
Generic functions are allowed to appear inside of other generic
contexts. When they do, they can refer to the typevars of those
enclosing generic contexts, and that should not rebind the typevar:
```py
from typing import TypeVar, Generic
T = TypeVar("T")
U = TypeVar("U")
class C(Generic[T]):
@staticmethod
def method(t: T, u: U) -> None: ...
# revealed: def method(t: int, u: U) -> None
reveal_type(C[int].method)
```
This substitution was already being performed correctly, but we were
also still including the enclosing legacy typevars in the method's own
generic context, which can be seen via `ty_extensions.generic_context`
(which has been updated to work on generic functions and methods):
```py
from ty_extensions import generic_context
# before: tuple[T, U]
# after: tuple[U]
reveal_type(generic_context(C[int].method))
```
---------
Co-authored-by: Carl Meyer <carl@astral.sh>
Co-authored-by: Alex Waygood <Alex.Waygood@Gmail.com>
## Summary
We currently infer a `@Todo` type whenever we access an attribute on an
intersection type with negative components. This can happen very
naturally. Consequently, this `@Todo` type is rather pervasive and hides
a lot of true positives that ty could otherwise detect:
```py
class Foo:
attr: int = 1
def _(f: Foo | None):
if f:
reveal_type(f) # Foo & ~AlwaysFalsy
reveal_type(f.attr) # now: int, previously: @Todo
```
The changeset here proposes to handle member access on these
intersection types by simply ignoring all negative contributions. This
is not always ideal: a negative contribution like `~<Protocol with
members 'attr'>` could be a hint that `.attr` should not be accessible
on the full intersection type. The behavior can certainly be improved in
the future, but this seems like a reasonable initial step to get rid of
this unnecessary `@Todo` type.
## Ecosystem analysis
There are quite a few changes here. I spot-checked them and found one
bug where attribute access on pure negation types (`~P == object & ~P`)
would not allow attributes on `object` to be accessed. After that was
fixed, I only see true positives and known problems. The fact that a lot
of `unused-ignore-comment` diagnostics go away are also evidence for the
fact that this touches a sensitive area, where static analysis clashes
with dynamically adding attributes to objects:
```py
… # type: ignore # Runtime attribute access
```
## Test Plan
Updated tests.
## Summary
Add basic support for `dataclasses.field`:
* remove fields with `init=False` from the signature of the synthesized
`__init__` method
* infer correct default value types from `default` or `default_factory`
arguments
```py
from dataclasses import dataclass, field
def default_roles() -> list[str]:
return ["user"]
@dataclass
class Member:
name: str
roles: list[str] = field(default_factory=default_roles)
tag: str | None = field(default=None, init=False)
# revealed: (self: Member, name: str, roles: list[str] = list[str]) -> None
reveal_type(Member.__init__)
```
Support for `kw_only` has **not** been added.
part of https://github.com/astral-sh/ty/issues/111
## Test Plan
New Markdown tests
Co-authored-by: David Peter <sharkdp@users.noreply.github.com>
Co-authored-by: Carl Meyer <carl@oddbird.net>
Co-authored-by: Micha Reiser <micha@reiser.io>
## Summary
I saw that this creates a lot of false positives in the ecosystem, and
it seemed to be relatively easy to add basic support for this.
Some preliminary work on this was done by @InSyncWithFoo — thank you.
part of https://github.com/astral-sh/ty/issues/111
## Ecosystem analysis
The results look good.
## Test Plan
New Markdown tests
---------
Co-authored-by: InSync <insyncwithfoo@gmail.com>
Co-authored-by: Alex Waygood <Alex.Waygood@Gmail.com>
This PR updates our iterator protocol machinery to return a tuple spec
describing the elements that are returned, instead of a type. That
allows us to track heterogeneous iterators more precisely, and
consolidates the logic in unpacking and splatting, which are the two
places where we can take advantage of that more precise information.
(Other iterator consumers, like `for` loops, have to collapse the
iterated elements down to a single type regardless, and we provide a new
helper method on `TupleSpec` to perform that summarization.)
## Summary
Implements proper reachability analysis and — in effect — exhaustiveness
checking for `match` statements. This allows us to check the following
code without any errors (leads to *"can implicitly return `None`"* on
`main`):
```py
from enum import Enum, auto
class Color(Enum):
RED = auto()
GREEN = auto()
BLUE = auto()
def hex(color: Color) -> str:
match color:
case Color.RED:
return "#ff0000"
case Color.GREEN:
return "#00ff00"
case Color.BLUE:
return "#0000ff"
```
Note that code like this already worked fine if there was a
`assert_never(color)` statement in a catch-all case, because we would
then consider that `assert_never` call terminal. But now this also works
without the wildcard case. Adding a member to the enum would still lead
to an error here, if that case would not be handled in `hex`.
What needed to happen to support this is a new way of evaluating match
pattern constraints. Previously, we would simply compare the type of the
subject expression against the patterns. For the last case here, the
subject type would still be `Color` and the value type would be
`Literal[Color.BLUE]`, so we would infer an ambiguous truthiness.
Now, before we compare the subject type against the pattern, we first
generate a union type that corresponds to the set of all values that
would have *definitely been matched* by previous patterns. Then, we
build a "narrowed" subject type by computing `subject_type &
~already_matched_type`, and compare *that* against the pattern type. For
the example here, `already_matched_type = Literal[Color.RED] |
Literal[Color.GREEN]`, and so we have a narrowed subject type of `Color
& ~(Literal[Color.RED] | Literal[Color.GREEN]) = Literal[Color.BLUE]`,
which allows us to infer a reachability of `AlwaysTrue`.
<details>
<summary>A note on negated reachability constraints</summary>
It might seem that we now perform duplicate work, because we also record
*negated* reachability constraints. But that is still important for
cases like the following (and possibly also for more realistic
scenarios):
```py
from typing import Literal
def _(x: int | str):
match x:
case None:
pass # never reachable
case _:
y = 1
y
```
</details>
closes https://github.com/astral-sh/ty/issues/99
## Test Plan
* I verified that this solves all examples from the linked ticket (the
first example needs a PEP 695 type alias, because we don't support
legacy type aliases yet)
* Verified that the ecosystem changes are all because of removed false
positives
* Updated tests
## Summary
I noticed that our type narrowing and reachability analysis was
incorrect for class patterns that are not irrefutable. The test cases
below compare the old and the new behavior:
```py
from dataclasses import dataclass
@dataclass
class Point:
x: int
y: int
class Other: ...
def _(target: Point):
y = 1
match target:
case Point(0, 0):
y = 2
case Point(x=0, y=1):
y = 3
case Point(x=1, y=0):
y = 4
reveal_type(y) # revealed: Literal[1, 2, 3, 4] (previously: Literal[2])
def _(target: Point | Other):
match target:
case Point(0, 0):
reveal_type(target) # revealed: Point
case Point(x=0, y=1):
reveal_type(target) # revealed: Point (previously: Never)
case Point(x=1, y=0):
reveal_type(target) # revealed: Point (previously: Never)
case Other():
reveal_type(target) # revealed: Other (previously: Other & ~Point)
```
## Test Plan
New Markdown test
## Summary
We previously didn't recognize `Literal[Color.RED]` as single-valued, if
the enum also derived from `str` or `int`:
```py
from enum import Enum
class Color(str, Enum):
RED = "red"
GREEN = "green"
BLUE = "blue"
def _(color: Color):
if color == Color.RED:
reveal_type(color) # previously: Color, now: Literal[Color.RED]
```
The reason for that was that `int` and `str` have "custom" `__eq__` and
`__ne__` implementations that return `bool`. We do not treat enum
literals from classes with custom `__eq__` and `__ne__` implementations
as single-valued, but of course we know that `int.__eq__` and
`str.__eq__` are well-behaved.
## Test Plan
New Markdown tests.
## Summary
Add more precise type inference for a limited set of `isinstance(…)`
calls, i.e. return `Literal[True]` if we can be sure that this is the
correct result. This improves exhaustiveness checking / reachability
analysis for if-elif-else chains with `isinstance` checks. For example:
```py
def is_number(x: int | str) -> bool: # no "can implicitly return `None` error here anymore
if isinstance(x, int):
return True
elif isinstance(x, str):
return False
# code here is now detected as being unreachable
```
This PR also adds a new test suite for exhaustiveness checking.
## Test Plan
New Markdown tests
### Ecosystem analysis
The removed diagnostics look good. There's [one
case](f52c4f1afd/torchvision/io/video_reader.py (L125-L143))
where a "true positive" is removed in unreachable code. `src` is
annotated as being of type `str`, but there is an `elif isinstance(src,
bytes)` branch, which we now detect as unreachable. And so the
diagnostic inside that branch is silenced. I don't think this is a
problem, especially once we have a "graying out" feature, or a lint that
warns about unreachable code.
## Summary
Fixes https://github.com/astral-sh/ty/issues/874
Labeling this as `internal`, since we haven't released the
enum-expansion feature.
## Test Plan
New Markdown tests
## Summary
This PR implements the following section from the [typing spec on
enums](https://typing.python.org/en/latest/spec/enums.html#enum-definition):
> Enum classes can also be defined using a subclass of `enum.Enum` **or
any class that uses `enum.EnumType` (or a subclass thereof) as a
metaclass**. Note that `enum.EnumType` was named `enum.EnumMeta` prior
to Python 3.11.
part of https://github.com/astral-sh/ty/issues/183
## Test Plan
New Markdown tests
This PR updates our call binding logic to handle splatted arguments.
Complicating matters is that we have separated call bind analysis into
two phases: parameter matching and type checking. Parameter matching
looks at the arity of the function signature and call site, and assigns
arguments to parameters. Importantly, we don't yet know the type of each
argument! This is needed so that we can decide whether to infer the type
of each argument as a type form or value form, depending on the
requirements of the parameter that the argument was matched to.
This is an issue when splatting an argument, since we need to know how
many elements the splatted argument contains to know how many positional
parameters to match it against. And to know how many elements the
splatted argument has, we need to know its type.
To get around this, we now make the assumption that splatted arguments
can only be used with value-form parameters. (If you end up splatting an
argument into a type-form parameter, we will silently pass in its
value-form type instead.) That allows us to preemptively infer the
(value-form) type of any splatted argument, so that we have its arity
available during parameter matching. We defer inference of non-splatted
arguments until after parameter matching has finished, as before.
We reuse a lot of the new tuple machinery to make this happen — in
particular resizing the tuple spec representing the number of arguments
passed in with the tuple length representing the number of parameters
the splat was matched with.
This work also shows that we might need to change how we are performing
argument expansion during overload resolution. At the moment, when we
expand parameters, we assume that each argument will still be matched to
the same parameters as before, and only retry the type-checking phase.
With splatted arguments, this is no longer the case, since the inferred
arity of each union element might be different than the arity of the
union as a whole, which can affect how many parameters the splatted
argument is matched to. See the regression test case in
`mdtest/call/function.md` for more details.
## Summary
Infer the correct type in a scenario like this:
```py
class Color(Enum):
RED = 1
GREEN = 2
BLUE = 3
for color in Color:
reveal_type(color) # revealed: Color
```
We should eventually support this out-of-the-box when
https://github.com/astral-sh/ty/issues/501 is implemented. For this
reason, @AlexWaygood would prefer to keep things as they are (we
currently infer `Unknown`, so false positives seem unlikely). But it
seemed relatively easy to support, so I'm opening this for discussion.
part of https://github.com/astral-sh/ty/issues/183
## Test Plan
Adapted existing test.
## Ecosystem analysis
```diff
- warning[unused-ignore-comment] rotkehlchen/chain/aggregator.py:591:82: Unused blanket `type: ignore` directive
```
This `unused-ignore-comment` goes away due to a new true positive.
## Summary
Fixes pull-types panics for illegal annotations like
`Literal[object[index]]`.
Originally reported by @AlexWaygood
## Test Plan
* Verified that this caused panics in the playground, when typing (and
potentially hovering over) `x: Literal[obj[0]]`.
* Added a regression test
## Summary
It was faster to implement this then to write the ticket: Disallow
`ClassVar` annotations almost everywhere outside of class body scopes.
## Test Plan
New Markdown tests
## Summary
Disallow `Final` in function parameter- and return-type annotations.
[Typing
spec](https://typing.python.org/en/latest/spec/qualifiers.html#uppercase-final):
> `Final` may only be used in assignments or variable annotations. Using
it in any other position is an error. In particular, `Final` can’t be
used in annotations for function arguments
## Test Plan
Updated MD test
## Summary
Adds proper type inference for implicit instance attributes that are
declared with a "bare" `Final` and adds `invalid-assignment` diagnostics
for all implicit instance attributes that are declared `Final` or
`Final[…]`.
## Test Plan
New and updated MD tests.
## Ecosystem analysis
```diff
pytest (https://github.com/pytest-dev/pytest)
+ error[invalid-return-type] src/_pytest/fixtures.py:1662:24: Return type does not match returned value: expected `Scope`, found `Scope | (Unknown & ~None & ~((...) -> object) & ~str) | (((str, Config, /) -> Unknown) & ~((...) -> object) & ~str) | (Unknown & ~str)
```
The definition of the `scope` attribute is [here](
5f99385635/src/_pytest/fixtures.py (L1020-L1028)).
Looks like this is a new false positive due to missing `TypeAlias`
support that is surfaced here because we now infer a more precise type
for `FixtureDef._scope`.
## Summary
Implement expansion of enums into unions of enum literals (and the
reverse operation). For the enum below, this allows us to understand
that `Color = Literal[Color.RED, Color.GREEN, Color.BLUE]`, or that
`Color & ~Literal[Color.RED] = Literal[Color.GREEN, Color.BLUE]`. This
helps in exhaustiveness checking, which is why we see some removed
`assert_never` false positives. And since exhaustiveness checking also
helps with understanding terminal control flow, we also see a few
removed `invalid-return-type` and `possibly-unresolved-reference` false
positives. This PR also adds expansion of enums in overload resolution
and type narrowing constructs.
```py
from enum import Enum
from typing_extensions import Literal, assert_never
from ty_extensions import Intersection, Not, static_assert, is_equivalent_to
class Color(Enum):
RED = 1
GREEN = 2
BLUE = 3
type Red = Literal[Color.RED]
type Green = Literal[Color.GREEN]
type Blue = Literal[Color.BLUE]
static_assert(is_equivalent_to(Red | Green | Blue, Color))
static_assert(is_equivalent_to(Intersection[Color, Not[Red]], Green | Blue))
def color_name(color: Color) -> str: # no error here (we detect that this can not implicitly return None)
if color is Color.RED:
return "Red"
elif color is Color.GREEN:
return "Green"
elif color is Color.BLUE:
return "Blue"
else:
assert_never(color) # no error here
```
## Performance
I avoided an initial regression here for large enums, but the
`UnionBuilder` and `IntersectionBuilder` parts can certainly still be
optimized. We might want to use the same technique that we also use for
unions of other literals. I didn't see any problems in our benchmarks so
far, so this is not included yet.
## Test Plan
Many new Markdown tests
## Summary
Emit errors for the following assignments:
```py
class C:
CLASS_LEVEL_CONSTANT: Final[int] = 1
C.CLASS_LEVEL_CONSTANT = 2
C().CLASS_LEVEL_CONSTANT = 2
```
## Test Plan
Updated and new MD tests
* [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
Douglas Creager
Brent Westbrook
David Peter
Ibraheem Ahmed
Alex Waygood
Dhruv Manilawala
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