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
While looking at some logging output that I added to
`ReachabilityConstraintBuilder::add_and_constraint` in order to debug
https://github.com/astral-sh/ty/issues/1091, I noticed that it seemed to
suggest that the TDD was built in an imbalanced way for code like the
following, where we have a sequence of non-nested `if` conditions:
```py
def f(t1, t2, t3, t4, …):
x = 0
if t1:
x = 1
if t2:
x = 2
if t3:
x = 3
if t4:
x = 4
…
```
To understand this a bit better, I added some code to the
`ReachabilityConstraintBuilder` to render the resulting TDD. On `main`,
we get a tree that looks like the following, where you can see a pattern
of N sub-trees that grow linearly with N (number of `if` statements).
This results in an overall tree structure that has N² nodes (see graph
below):
<img alt="normal order"
src="https://github.com/user-attachments/assets/aab40ce9-e82a-4fcd-823a-811f05f15f66"
/>
If we zoom in to one of these subgraphs, we can see what the problem is.
When we add new constraints that represent combinations like `t1 AND ~t2
AND ~t3 AND t4 AND …`, they start with the evaluation of "early"
conditions (`t1`, `t2`, …). This means that we have to create new
subgraphs for each new `if` condition because there is little sharing
with the previous structure. We evaluate the Boolean condition in a
right-associative way: `t1 AND (~t2 AND (~t3 AND t4)))`:
<img width="500" align="center"
src="https://github.com/user-attachments/assets/31ea7182-9e00-4975-83df-d980464f545d"
/>
If we change the ordering of TDD atoms, we can change that to a
left-associative evaluation: `(((t1 AND ~t2) AND ~t3) AND t4) …`. This
means that we can re-use previous subgraphs `(t1 AND ~t2)`, which
results in a much more compact graph structure overall (note how "late"
conditions are now at the top, and "early" conditions are further down
in the graph):
<img alt="reverse order"
src="https://github.com/user-attachments/assets/96a6b7c1-3d35-4192-a917-0b2d24c6b144"
/>
If we count the number of TDD nodes for a growing number if `if`
statements, we can see that this change results in a slower growth. It's
worth noting that the growth is still superlinear, though:
<img width="800" height="600" alt="plot"
src="https://github.com/user-attachments/assets/22e8394f-e74e-4a9e-9687-0d41f94f2303"
/>
On the actual code from the referenced ticket (the `t_main.py` file
reduced to its main function, with the main function limited to 2000
lines instead of 11000 to allow the version on `main` to run to
completion), the effect is much more dramatic. Instead of 26 million TDD
nodes (`main`), we now only create 250 thousand (this branch), which is
slightly less than 1%.
The change in this PR allows us to build the semantic index and
type-check the problematic `t_main.py` file in
https://github.com/astral-sh/ty/issues/1091 in 9 seconds. This is still
not great, but an obvious improvement compared to running out of memory
after *minutes* of execution.
An open question remains whether this change is beneficial for all kinds
of code patterns, or just this linear sequence of `if` statements. It
does not seem unreasonable to think that referring to "earlier"
conditions is generally a good idea, but I learned from Doug that it's
generally not possible to find a TDD-construction heuristic that is
non-pathological for all kinds of inputs. Fortunately, it seems like
this change here results in performance improvements across *all of our
benchmarks*, which should increase the confidence in this change:
| Benchmark | Improvement |
|---------------------|-------------------------|
| hydra-zen | +13% |
| DateType | +5% |
| sympy (walltime) | +4% |
| attrs | +4% |
| pydantic (walltime) | +2% |
| pandas (walltime) | +2% |
| altair (walltime) | +2% |
| static-frame | +2% |
| anyio | +1% |
| freqtrade | +1% |
| colour-science | +1% |
| tanjun | +1% |
closes https://github.com/astral-sh/ty/issues/1091
---------
Co-authored-by: Douglas Creager <dcreager@dcreager.net>
## 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
Removes the `module_ptr` field from `AstNodeRef` in release mode, and
change `NodeIndex` to a `NonZeroU32` to reduce the size of
`Option<AstNodeRef<_>>` fields.
I believe CI runs in debug mode, so this won't show up in the memory
report, but this reduces memory by ~2% in release mode.
## 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
## Summary
We use the `System` abstraction in ty to abstract away the host/system
on which ty runs.
This has a few benefits:
* Tests can run in full isolation using a memory system (that uses an
in-memory file system)
* The LSP has a custom implementation where `read_to_string` returns the
content as seen by the editor (e.g. unsaved changes) instead of always
returning the content as it is stored on disk
* We don't require any file system polyfills for wasm in the browser
However, it does require extra care that we don't accidentally use
`std::fs` or `std::env` (etc.) methods in ty's code base (which is very
easy).
This PR sets up Clippy and disallows the most common methods, instead
pointing users towards the corresponding `System` methods.
The setup is a bit awkward because clippy doesn't support inheriting
configurations. That means, a crate can only override the entire
workspace configuration or not at all.
The approach taken in this PR is:
* Configure the disallowed methods at the workspace level
* Allow `disallowed_methods` at the workspace level
* Enable the lint at the crate level using the warn attribute (in code)
The obvious downside is that it won't work if we ever want to disallow
other methods, but we can figure that out once we reach that point.
What about false positives: Just add an `allow` and move on with your
life :) This isn't something that we have to enforce strictly; the goal
is to catch accidental misuse.
## Test Plan
Clippy found a place where we incorrectly used `std::fs::read_to_string`
## Summary
Rename `TypeAliasType::Bare` to `TypeAliasType::ManualPEP695`, and
`BareTypeAliasType` to `ManualPEP695TypeAliasType`.
Why?
Both existing variants of `TypeAliasType` are specific to features added
in PEP 695 (which introduced both the `type` statement and
`types.TypeAliasType`), so it doesn't make sense to name one with the
name `PEP695` and not the other.
A "bare" type alias, in my mind, is a legacy type alias like `IntOrStr =
int | str`, which is "bare" in that there is nothing at all
distinguishing it as a type alias. I will want to use the "bare" name
for this variant, in a future PR.
The renamed variant here describes a type alias created with `IntOrStr =
types.TypeAliasType("IntOrStr", int | str)`, which is not "bare", it's
just "manually" instantiated instead of using the `type` statement
syntax sugar. (This is useful when using the `typing_extensions`
backport of `TypeAliasType` on older Python versions.)
## Test Plan
Pure rename, existing tests pass.
## Summary
This PR fixes https://github.com/astral-sh/ty/issues/1071
The core issue is that `CallableType` is a salsa interned but
`Signature` (which `CallableType` stores) ignores the `Definition` in
its `Eq` and `Hash` implementation.
This PR tries to simplest fix by removing the custom `Eq` and `Hash`
implementation. The main downside of this fix is that it can increase
memory usage because `CallableType`s that are equal except for their
`Definition` are now interned separately.
The alternative is to remove `Definition` from `CallableType` and
instead, call `bindings` directly on the callee (call_expression.func).
However, this would require
addressing the TODO
here
39ee71c2a5/crates/ty_python_semantic/src/types.rs (L4582-L4586)
This might probably be worth addressing anyway, but is the more involved
fix. That's why I opted for removing the custom `Eq` implementation.
We already "ignore" the definition during normalization, thank's to
Alex's work in https://github.com/astral-sh/ruff/pull/19615
## Test Plan
https://github.com/user-attachments/assets/248d1cb1-12fd-4441-adab-b7e0866d23eb
"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
This basically splits `list_modules` into a higher level "aggregation"
routine and a lower level "get modules for one search path" routine.
This permits Salsa to cache the lower level components, e.g., many
search paths refer to directories that rarely change. This saves us
interaction with the system.
This did require a fair bit of surgery in terms of being careful about
adding file roots. Namely, now that we rely even more on file roots
existing for correct handling of cache invalidation, there were several
spots in our code that needed to be updated to add roots (that we
weren't previously doing). This feels Not Great, and it would be better
if we had some kind of abstraction that handled this for us. But it
isn't clear to me at this time what that looks like.
This ensures there is some level of consistency between the APIs.
This did require exposing a couple more things on `Module` for good
error messages. This also motivated a switch to an interned struct
instead of a tracked struct. This ensures that `list_modules` and
`resolve_modules` reuse the same `Module` values when the inputs are the
same.
Ref https://github.com/astral-sh/ruff/pull/19883#discussion_r2272520194
This makes `import <CURSOR>` and `from <CURSOR>` completions work.
This also makes `import os.<CURSOR>` and `from os.<CURSOR>`
completions work. In this case, we are careful to only offer
submodule completions.
The actual implementation wasn't too bad. It's not long
but pretty fiddly. I copied over the tests from the existing
module resolver and adapted them to work with this API. Then
I added a number of my own tests as well.
Previously, if the module was just `foo-stubs`, we'd skip over
stripping the `-stubs` suffix which would lead to us returning
`None`.
This function is now a little convoluted and could be simpler
if we did an intermediate allocation. But I kept the iterative
approach and added a special case to handle `foo-stubs`.
These tests capture existing behavior.
I added these when I stumbled upon what I thought was an
oddity: we prioritize `foo.pyi` over `foo.py`, but
prioritize `foo/__init__.py` over `foo.pyi`.
(I plan to investigate this more closely in follow-up
work. Particularly, to look at other type checkers. It
seems like we may want to change this to always prioritize
stubs.)
This is a port of the logic in https://github.com/astral-sh/uv/pull/7691
The basic idea is we use CONDA_DEFAULT_ENV as a signal for whether
CONDA_PREFIX is just the ambient system conda install, or the user has
explicitly activated a custom one. If the former, then the conda is
treated like a system install (having lowest priority). If the latter,
the conda is treated like an activated venv (having priority over
everything but an Actual activated venv).
Fixes https://github.com/astral-sh/ty/issues/611
## 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
Requires some iteration, but this includes the most tedious part --
threading a new concept of DisplaySettings through every type display
impl. Currently it only holds a boolean for multiline, but in the future
it could also take other things like "render to markdown" or "here's
your base indent if you make a newline".
For types which have exposed display functions I've left the old
signature as a compatibility polyfill to avoid having to audit
everywhere that prints types right off the bat (notably I originally
tried doing multiline functions unconditionally and a ton of things
churned that clearly weren't ready for multi-line (diagnostics).
The only real use of this API in this PR is to multiline render function
types in hovers, which is the highest impact (see snapshot changes).
Fixes https://github.com/astral-sh/ty/issues/1000
