## Summary
Bump the latest supported Python version of ty to 3.14 and updates some
references from 3.13 to 3.14.
This also fixes a bug with `dataclasses.field` on 3.14 (which adds a new
keyword-only parameter to that function, breaking our previously naive
matching on the parameter structure of that function).
## Test Plan
A `ty check` on a file with template strings (without any further
configuration) doesn't raise errors anymore.
Summary
--
Closes#19467 and also removes the warning about using Python 3.14
without
preview enabled.
I also bumped `PythonVersion::default` to 3.9 because it reaches EOL
this month,
but we could also defer that for now if we wanted.
The first three commits are related to the `latest` bump to 3.14; the
fourth commit
bumps the default to 3.10.
Note that this PR also bumps the default Python version for ty to 3.10
because
there was a test asserting that it stays in sync with
`ast::PythonVersion`.
Test Plan
--
Existing tests
I spot-checked the ecosystem report, and I believe these are all
expected. Inbits doesn't specify a target Python version, so I guess
we're applying the default. UP007, UP035, and UP045 all use the new
default value to emit new diagnostics.
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## Summary
#19990 didn't completely fix the base vs. child conda environment
distinction, since it detected slightly different behavior than what I
usually see in conda. E.g., I see something like the following:
```
(didn't yet activate conda, but base is active)
➜ printenv | grep CONDA
CONDA_PYTHON_EXE=/opt/anaconda3/bin/python
CONDA_PREFIX=/opt/anaconda3
CONDA_DEFAULT_ENV=base
CONDA_EXE=/opt/anaconda3/bin/conda
CONDA_SHLVL=1
CONDA_PROMPT_MODIFIER=(base)
(activating conda)
➜ conda activate test
(test is an active conda environment)
❯ printenv | grep CONDA
CONDA_PREFIX=/opt/anaconda3/envs/test
CONDA_PYTHON_EXE=/opt/anaconda3/bin/python
CONDA_SHLVL=2
CONDA_PREFIX_1=/opt/anaconda3
CONDA_DEFAULT_ENV=test
CONDA_PROMPT_MODIFIER=(test)
CONDA_EXE=/opt/anaconda3/bin/conda
```
But the current behavior looks for `CONDA_DEFAULT_ENV =
basename(CONDA_PREFIX)` for the base environment instead of the child
environment, where we actually see this equality.
This pull request fixes that and updates the tests correspondingly.
## Test Plan
I updated the existing tests with the new behavior. Let me know if you
want more tests. Note: It shouldn't be necessary to test for the case
where we have `conda/envs/base`, since one should not be able to create
such an environment (one with the name of `CONDA_DEFAULT_ENV`).
---------
Co-authored-by: Aria Desires <aria.desires@gmail.com>
This PR ensures that we always put `./src` before `.` in our list of
first-party search paths. This better emulates the fact that at runtime,
the module name of a file `src/foo.py` would almost certainly be `foo`
rather than `src.foo`.
I wondered if fixing this might fix
https://github.com/astral-sh/ruff/pull/20603#issuecomment-3345317444. It
seems like that's not the case, but it also seems like it leads to
better diagnostics because we report much more intuitive module names to
the user in our error messages -- so, it's probably a good change
anyway.
## Summary
Fixes https://github.com/astral-sh/ty/issues/1242
From finding references with the LSP, `FileResolver::path` is only
called once, in `UnifiedFile::path`, so I went through those references,
and it looked safe to make this change in every case. Most of the
references are in the various output formats, where we inherited the
absolute vs relative path decision from Ruff. Two other uses are as
fallbacks if converting a relativized path to a string fails. Finally,
we use the path for sorting and in `UnifiedFile::relative_path`.
## Test Plan
Existing tests, with snapshots updated to show absolute paths (in the
`TestDb` this just added a `/` in front of the file names). I also
updated the GitLab CLI test to set the `CI_PROJECT_DIR` environment
variable and ran a test in GitLab CI:
<img width="613" height="114" alt="image"
src="https://github.com/user-attachments/assets/8ab81dba-54fd-4a24-9110-77ef89293cff"
/>
The names of the submodules returned should be *complete*. This
is the contract of `Module::name`. However, we were previously
only returning the basename of the submodule.
## Summary
This PR wires up the GitHub output format moved to `ruff_db` in #20320
to the ty CLI.
It's a bit smaller than the GitLab version (#20155) because some of the
helpers were already in place, but I did factor out a few
`DisplayDiagnosticConfig` constructor calls in Ruff. I also exposed the
`GithubRenderer` and a wrapper `DisplayGithubDiagnostics` type because
we needed a way to configure the program name displayed in the GitHub
diagnostics. This was previously hard-coded to `Ruff`:
<img width="675" height="247" alt="image"
src="https://github.com/user-attachments/assets/592da860-d2f5-4abd-bc5a-66071d742509"
/>
Another option would be to drop the program name in the output format,
but I think it can be helpful in workflows with multiple programs
emitting annotations (such as Ruff and ty!)
## Test Plan
New CLI test, and a manual test with `--config 'terminal.output-format =
"github"'`
## Summary
I felt it was safer to add the `python` folder *in addition* to a
possibly-existing `src` folder, even though the `src` folder only
contains Rust code for `maturin`-based projects. There might be
non-maturin projects where a `python` folder exists for other reasons,
next to a normal `src` layout.
closes https://github.com/astral-sh/ty/issues/1120
## Test Plan
Tested locally on the egglog-python project.
## Summary
Add backreferences to the original item declaration in TypedDict
diagnostics.
Thanks to @AlexWaygood for the suggestion.
## Test Plan
Updated snapshots
## Summary
Two minor cleanups:
- Return `Option<ClassType>` rather than `Option<ClassLiteral>` from
`TypeInferenceBuilder::class_context_of_current_method`. Now that
`ClassType::is_protocol` exists as a method as well as
`ClassLiteral::is_protocol`, this simplifies most of the call-sites of
the `class_context_of_current_method()` method.
- Make more use of the `MethodDecorator::try_from_fn_type` method in
`class.rs`. Under the hood, this method uses the new methods
`FunctionType::is_classmethod()` and `FunctionType::is_staticmethod()`
that @sharkdp recently added, so it gets the semantics more precisely
correct than the code it's replacing in `infer.rs` (by accounting for
implicit staticmethods/classmethods as well as explicit ones). By using
these methods we can delete some code elsewhere (the
`FunctionDecorators::from_decorator_types()` constructor)
## Test Plan
Existing tests
This is to facilitate recursive traversal of all modules in an
environment. This way, we can keep asking for submodules.
This also simplifies how this is used in completions, and probably makes
it faster. Namely, since we return the `Module` itself, callers don't
need to invoke the full module resolver just to get the module type.
Note that this doesn't include namespace packages. (Which were
previously not supported in `Module::all_submodules`.) Given how they
can be spread out across multiple search paths, they will likely require
special consideration here.
## Summary
This wires up the GitLab output format moved into `ruff_db` in
https://github.com/astral-sh/ruff/pull/20117 to the ty CLI.
While I was here, I made one unrelated change to the CLI docs. Clap was
rendering the escapes around the `\[default\]` brackets for the `full`
output, so I just switched those to parentheses:
```
--output-format <OUTPUT_FORMAT>
The format to use for printing diagnostic messages
Possible values:
- full: Print diagnostics verbosely, with context and helpful hints \[default\]
- concise: Print diagnostics concisely, one per line
- gitlab: Print diagnostics in the JSON format expected by GitLab Code Quality reports
```
## Test Plan
New CLI test, and a manual test with `--config 'terminal.output-format =
"gitlab"'` to make sure this works as a configuration option too. I also
tried piping the output through jq to make sure it's at least valid JSON
There are some situations that we have a confusing diagnostics due to
identical class names.
## Class with same name from different modules
```python
import pandas
import polars
df: pandas.DataFrame = polars.DataFrame()
```
This yields the following error:
**Actual:**
error: [invalid-assignment] "Object of type `DataFrame` is not
assignable to `DataFrame`"
**Expected**:
error: [invalid-assignment] "Object of type `polars.DataFrame` is not
assignable to `pandas.DataFrame`"
## Nested classes
```python
from enum import Enum
class A:
class B(Enum):
ACTIVE = "active"
INACTIVE = "inactive"
class C:
class B(Enum):
ACTIVE = "active"
INACTIVE = "inactive"
```
**Actual**:
error: [invalid-assignment] "Object of type `Literal[B.ACTIVE]` is not
assignable to `B`"
**Expected**:
error: [invalid-assignment] "Object of type
`Literal[my_module.C.B.ACTIVE]` is not assignable to `my_module.A.B`"
## Solution
In this MR we added an heuristics to detect when to use a fully
qualified name:
- There is an invalid assignment and;
- They are two different classes and;
- They have the same name
The fully qualified name always includes:
- module name
- nested classes name
- actual class name
There was no `QualifiedDisplay` so I had to implement it from scratch.
I'm very new to the codebase, so I might have done things inefficiently,
so I appreciate feedback.
Should we pre-compute the fully qualified name or do it on demand?
## Not implemented
### Function-local classes
Should we approach this in a different PR?
**Example**:
```python
# t.py
from __future__ import annotations
def function() -> A:
class A:
pass
return A()
class A:
pass
a: A = function()
```
#### mypy
```console
t.py:8: error: Incompatible return value type (got "t.A@5", expected "t.A") [return-value]
```
From my testing the 5 in `A@5` comes from the like number.
#### ty
```console
error[invalid-return-type]: Return type does not match returned value
--> t.py:4:19
|
4 | def function() -> A:
| - Expected `A` because of return type
5 | class A:
6 | pass
7 |
8 | return A()
| ^^^ expected `A`, found `A`
|
info: rule `invalid-return-type` is enabled by default
```
Fixes https://github.com/astral-sh/ty/issues/848
---------
Co-authored-by: Alex Waygood <Alex.Waygood@Gmail.com>
Co-authored-by: Carl Meyer <carl@astral.sh>
## Summary
Implement validation for `TypedDict` constructor calls and dictionary
literal assignments, including support for `total=False` and proper
field management.
Also add support for `Required` and `NotRequired` type qualifiers in
`TypedDict` classes, along with proper inheritance behavior and the
`total=` parameter.
Support both constructor calls and dict literal syntax
part of https://github.com/astral-sh/ty/issues/154
### Basic Required Field Validation
```py
class Person(TypedDict):
name: str
age: int | None
# Error: Missing required field 'name' in TypedDict `Person` constructor
incomplete = Person(age=25)
# Error: Invalid argument to key "name" with declared type `str` on TypedDict `Person`
wrong_type = Person(name=123, age=25)
# Error: Invalid key access on TypedDict `Person`: Unknown key "extra"
extra_field = Person(name="Bob", age=25, extra=True)
```
<img width="773" height="191" alt="Screenshot 2025-08-07 at 17 59 22"
src="https://github.com/user-attachments/assets/79076d98-e85f-4495-93d6-a731aa72a5c9"
/>
### Support for `total=False`
```py
class OptionalPerson(TypedDict, total=False):
name: str
age: int | None
# All valid - all fields are optional with total=False
charlie = OptionalPerson()
david = OptionalPerson(name="David")
emily = OptionalPerson(age=30)
frank = OptionalPerson(name="Frank", age=25)
# But type validation and extra fields still apply
invalid_type = OptionalPerson(name=123) # Error: Invalid argument type
invalid_extra = OptionalPerson(extra=True) # Error: Invalid key access
```
### Dictionary Literal Validation
```py
# Type checking works for both constructors and dict literals
person: Person = {"name": "Alice", "age": 30}
reveal_type(person["name"]) # revealed: str
reveal_type(person["age"]) # revealed: int | None
# Error: Invalid key access on TypedDict `Person`: Unknown key "non_existing"
reveal_type(person["non_existing"]) # revealed: Unknown
```
### `Required`, `NotRequired`, `total`
```python
from typing import TypedDict
from typing_extensions import Required, NotRequired
class PartialUser(TypedDict, total=False):
name: Required[str] # Required despite total=False
age: int # Optional due to total=False
email: NotRequired[str] # Explicitly optional (redundant)
class User(TypedDict):
name: Required[str] # Explicitly required (redundant)
age: int # Required due to total=True
bio: NotRequired[str] # Optional despite total=True
# Valid constructions
partial = PartialUser(name="Alice") # name required, age optional
full = User(name="Bob", age=25) # name and age required, bio optional
# Inheritance maintains original field requirements
class Employee(PartialUser):
department: str # Required (new field)
# name: still Required (inherited)
# age: still optional (inherited)
emp = Employee(name="Charlie", department="Engineering") # ✅
Employee(department="Engineering") # ❌
e: Employee = {"age": 1} # ❌
```
<img width="898" height="683" alt="Screenshot 2025-08-11 at 22 02 57"
src="https://github.com/user-attachments/assets/4c1b18cd-cb2e-493a-a948-51589d121738"
/>
## Implementation
The implementation reuses existing validation logic done in
https://github.com/astral-sh/ruff/pull/19782
### ℹ️ Why I did NOT synthesize an `__init__` for `TypedDict`:
`TypedDict` inherits `dict.__init__(self, *args, **kwargs)` that accepts
all arguments.
The type resolution system finds this inherited signature **before**
looking for synthesized members.
So `own_synthesized_member()` is never called because a signature
already exists.
To force synthesis, you'd have to override Python’s inheritance
mechanism, which would break compatibility with the existing ecosystem.
This is why I went with ad-hoc validation. IMO it's the only viable
approach that respects Python’s
inheritance semantics while providing the required validation.
### Refacto of `Field`
**Before:**
```rust
struct Field<'db> {
declared_ty: Type<'db>,
default_ty: Option<Type<'db>>, // NamedTuple and dataclass only
init_only: bool, // dataclass only
init: bool, // dataclass only
is_required: Option<bool>, // TypedDict only
}
```
**After:**
```rust
struct Field<'db> {
declared_ty: Type<'db>,
kind: FieldKind<'db>,
}
enum FieldKind<'db> {
NamedTuple { default_ty: Option<Type<'db>> },
Dataclass { default_ty: Option<Type<'db>>, init_only: bool, init: bool },
TypedDict { is_required: bool },
}
```
## Test Plan
Updated Markdown tests
---------
Co-authored-by: David Peter <mail@david-peter.de>
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
This PR adds a new lint, `invalid-await`, for all sorts of reasons why
an object may not be `await`able, as discussed in astral-sh/ty#919.
Precisely, `__await__` is guarded against being missing, possibly
unbound, or improperly defined (expects additional arguments or doesn't
return an iterator).
Of course, diagnostics need to be fine-tuned. If `__await__` cannot be
called with no extra arguments, it indicates an error (or a quirk?) in
the method signature, not at the call site. Without any doubt, such an
object is not `Awaitable`, but I feel like talking about arguments for
an *implicit* call is a bit leaky.
I didn't reference any actual diagnostic messages in the lint
definition, because I want to hear feedback first.
Also, there's no mention of the actual required method signature for
`__await__` anywhere in the docs. The only reference I had is the
`typing` stub. I basically ended up linking `[Awaitable]` to ["must
implement
`__await__`"](https://docs.python.org/3/library/collections.abc.html#collections.abc.Awaitable),
which is insufficient on its own.
## Test Plan
The following code was tested:
```python
import asyncio
import typing
class Awaitable:
def __await__(self) -> typing.Generator[typing.Any, None, int]:
yield None
return 5
class NoDunderMethod:
pass
class InvalidAwaitArgs:
def __await__(self, value: int) -> int:
return value
class InvalidAwaitReturn:
def __await__(self) -> int:
return 5
class InvalidAwaitReturnImplicit:
def __await__(self):
pass
async def main() -> None:
result = await Awaitable() # valid
result = await NoDunderMethod() # `__await__` is missing
result = await InvalidAwaitReturn() # `__await__` returns `int`, which is not a valid iterator
result = await InvalidAwaitArgs() # `__await__` expects additional arguments and cannot be called implicitly
result = await InvalidAwaitReturnImplicit() # `__await__` returns `Unknown`, which is not a valid iterator
asyncio.run(main())
```
---------
Co-authored-by: Carl Meyer <carl@astral.sh>
## Summary
Validates writes to `TypedDict` keys, for example:
```py
class Person(TypedDict):
name: str
age: int | None
def f(person: Person):
person["naem"] = "Alice" # error: [invalid-key]
person["age"] = "42" # error: [invalid-assignment]
```
The new specialized `invalid-assignment` diagnostic looks like this:
<img width="1160" height="279" alt="image"
src="https://github.com/user-attachments/assets/51259455-3501-4829-a84e-df26ff90bd89"
/>
## Ecosystem analysis
As far as I can tell, all true positives!
There are some extremely long diagnostic messages. We should truncate
our display of overload sets somehow.
## Test Plan
New Markdown tests
## Summary
This PR implements support for providing LSP client settings.
The complementary PR in the ty VS Code extension:
astral-sh/ty-vscode#106.
Notes for the previous iteration of this PR is in
https://github.com/astral-sh/ruff/pull/19614#issuecomment-3136477864
(click on "Details").
Specifically, this PR splits the client settings into 3 distinct groups.
Keep in mind that these groups are not visible to the user, they're
merely an implementation detail. The groups are:
1. `GlobalOptions` - these are the options that are global to the
language server and will be the same for all the workspaces that are
handled by the server
2. `WorkspaceOptions` - these are the options that are specific to a
workspace and will be applied only when running any logic for that
workspace
3. `InitializationOptions` - these are the options that can be specified
during initialization
The initialization options are a superset that contains both the global
and workspace options flattened into a 1-dimensional structure. This
means that the user can specify any and all fields present in
`GlobalOptions` and `WorkspaceOptions` in the initialization options in
addition to the fields that are _specific_ to initialization options.
From the current set of available settings, following are only available
during initialization because they are required at that time, are static
during the runtime of the server and changing their values require a
restart to take effect:
- `logLevel`
- `logFile`
And, following are available under `GlobalOptions`:
- `diagnosticMode`
And, following under `WorkspaceOptions`:
- `disableLanguageServices`
- `pythonExtension` (Python environment information that is populated by
the ty VS Code extension)
### `workspace/configuration`
This request allows server to ask the client for configuration to a
specific workspace. But, this is only supported by the client that has
the `workspace.configuration` client capability set to `true`. What to
do for clients that don't support pulling configurations?
In that case, the settings needs to be provided in the initialization
options and updating the values of those settings can only be done by
restarting the server. With the way this is implemented, this means that
if the client does not support pulling workspace configuration then
there's no way to specify settings specific to a workspace. Earlier,
this would've been possible by providing an array of client options with
an additional field which specifies which workspace the options belong
to but that adds complexity and clients that actually do not support
`workspace/configuration` would usually not support multiple workspaces
either.
Now, for the clients that do support this, the server will initiate the
request to get the configuration for all the workspaces at the start of
the server. Once the server receives these options, it will resolve them
for each workspace as follows:
1. Combine the client options sent during initialization with the
options specific to the workspace creating the final client options
that's specific to this workspace
2. Create a global options by combining the global options from (1) for
all workspaces which in turn will also combine the global options sent
during initialization
The global options are resolved into the global settings and are
available on the `Session` which is initialized with the default global
settings. The workspace options are resolved into the workspace settings
and are available on the respective `Workspace`.
The `SessionSnapshot` contains the global settings while the document
snapshot contains the workspace settings. We could add the global
settings to the document snapshot but that's currently not needed.
### Document diagnostic dynamic registration
Currently, the document diagnostic server capability is created based on
the `diagnosticMode` sent during initialization. But, that wouldn't
provide us with the complete picture. This means the server needs to
defer registering the document diagnostic capability at a later point
once the settings have been resolved.
This is done using dynamic registration for clients that support it. For
clients that do not support dynamic registration for document diagnostic
capability, the server advertises itself as always supporting workspace
diagnostics and work done progress token.
This dynamic registration now allows us to change the server capability
for workspace diagnostics based on the resolved `diagnosticMode` value.
In the future, once `workspace/didChangeConfiguration` is supported, we
can avoid the server restart when users have changed any client
settings.
## Test Plan
Add integration tests and recorded videos on the user experience in
various editors:
### VS Code
For VS Code users, the settings experience is unchanged because the
extension defines it's own interface on how the user can specify the
server setting. This means everything is under the `ty.*` namespace as
usual.
https://github.com/user-attachments/assets/c2e5ba5c-7617-406e-a09d-e397ce9c3b93
### Zed
For Zed, the settings experience has changed. Users can specify settings
during initialization:
```json
{
"lsp": {
"ty": {
"initialization_options": {
"logLevel": "debug",
"logFile": "~/.cache/ty.log",
"diagnosticMode": "workspace",
"disableLanguageServices": true
}
},
}
}
```
Or, can specify the options under the `settings` key:
```json
{
"lsp": {
"ty": {
"settings": {
"ty": {
"diagnosticMode": "openFilesOnly",
"disableLanguageServices": true
}
},
"initialization_options": {
"logLevel": "debug",
"logFile": "~/.cache/ty.log"
}
},
}
}
```
The `logLevel` and `logFile` setting still needs to go under the
initialization options because they're required by the server during
initialization.
We can remove the nesting of the settings under the "ty" namespace by
updating the return type of
db9ea0cdfd/src/tychecker.rs (L45-L49)
to be wrapped inside `ty` directly so that users can avoid doing the
double nesting.
There's one issue here which is that if the `diagnosticMode` is
specified in both the initialization option and settings key, then the
resolution is a bit different - if either of them is set to be
`workspace`, then it wins which means that in the following
configuration, the diagnostic mode is `workspace`:
```json
{
"lsp": {
"ty": {
"settings": {
"ty": {
"diagnosticMode": "openFilesOnly"
}
},
"initialization_options": {
"diagnosticMode": "workspace"
}
},
}
}
```
This behavior is mainly a result of combining global options from
various workspace configuration results. Users should not be able to
provide global options in multiple workspaces but that restriction
cannot be done on the server side. The ty VS Code extension restricts
these global settings to only be set in the user settings and not in
workspace settings but we do not control extensions in other editors.
https://github.com/user-attachments/assets/8e2d6c09-18e6-49e5-ab78-6cf942fe1255
### Neovim
Same as in Zed.
### Other
Other editors that do not support `workspace/configuration`, the users
would need to provide the server settings during initialization.
## Summary
This PR adds type inference for key-based access on `TypedDict`s and a
new diagnostic for invalid subscript accesses:
```py
class Person(TypedDict):
name: str
age: int | None
alice = Person(name="Alice", age=25)
reveal_type(alice["name"]) # revealed: str
reveal_type(alice["age"]) # revealed: int | None
alice["naem"] # Unknown key "naem" - did you mean "name"?
```
## Test Plan
Updated Markdown tests
## Summary
This PR reduces the virality of some of the `Todo` types in
`infer_tuple_type_expression`. Rather than inferring `Todo`, we instead
infer `tuple[Todo, ...]`. This reflects the fact that whatever the
contents of the slice in a `tuple[]` type expression, we would always
infer some kind of tuple type as the result of the type expression. Any
tuple type should be assignable to `tuple[Todo, ...]`, so this shouldn't
introduce any new false positives; this can be seen in the ecosystem
report.
As a result of the change, we are now able to enforce in the signature
of `Type::infer_tuple_type_expression` that it returns an
`Option<TupleType<'db>>`, which is more strongly typed and expresses
clearly the invariant that a tuple type expression should always be
inferred as a `tuple` type. To enable this, it was necessary to refactor
several `TupleType` constructors in `tuple.rs` so that they return
`Option<TupleType>` rather than `Type`; this means that callers of these
constructor functions are now free to either propagate the
`Option<TupleType<'db>>` or convert it to a `Type<'db>`.
## Test Plan
Mdtests updated.
## Summary
This PR moves most of the work of rendering concise diagnostics in Ruff
into `ruff_db`, where the code is shared with ty. To accomplish this
without breaking backwards compatibility in Ruff, there are two main
changes on the `ruff_db`/ty side:
- Added the logic from Ruff for remapping notebook line numbers to cells
- Reordered the fields in the diagnostic to match Ruff and rustc
```text
# old
error[invalid-assignment] try.py:3:1: Object of type `Literal[1]` is not
assignable to `str`
# new
try.py:3:1: error[invalid-assignment]: Object of type `Literal[1]` is
not assignable to `str`
```
I don't think the notebook change failed any tests on its own, and only
a handful of snaphots changed in ty after reordering the fields, but
this will obviously affect any other uses of the concise format, outside
of tests, too.
The other big change should only affect Ruff:
- Added three new `DisplayDiagnosticConfig` options
Micha and I hoped that we could get by with one option
(`hide_severity`), but Ruff also toggles `show_fix_status` itself,
independently (there are cases where we want neither severity nor the
fix status), and during the implementation I realized we also needed
access to an `Applicability`. The main goal here is to suppress the
severity (`error` above) because ruff only uses the `error` severity and
to use the secondary/noqa code instead of the line name
(`invalid-assignment` above).
```text
# ty - same as "new" above
try.py:3:1: error[invalid-assignment]: Object of type `Literal[1]` is
not assignable to `str`
# ruff
try.py:3:1: RUF123 [*] Object of type `Literal[1]` is not assignable to
`str`
```
This part of the concise diagnostic is actually shared with the `full`
output format in Ruff, but with the settings above, there are no
snapshot changes to either format.
## Test Plan
Existing tests with the handful of updates mentioned above, as well as
some new tests in the `concise` module.
Also this PR. Swapping the fields might have broken mypy_primer, unless
it occasionally times out on its own.
I also ran this script in the root of my Ruff checkout, which also has
CPython in it:
```shell
flags=(--isolated --no-cache --no-respect-gitignore --output-format concise .)
diff <(target/release/ruff check ${flags[@]} 2> /dev/null) \
<(ruff check ${flags[@]} 2> /dev/null)
```
This yielded an expected diff due to some t-string error changes on main
since 0.12.4:
```diff
33622c33622
< crates/ruff_python_parser/resources/inline/err/f_string_lambda_without_parentheses.py:1:15: SyntaxError: Expected an element of or the end of the f-string
---
> crates/ruff_python_parser/resources/inline/err/f_string_lambda_without_parentheses.py:1:15: SyntaxError: Expected an f-string or t-string element or the end of the f-string or t-string
33742c33742
< crates/ruff_python_parser/resources/inline/err/implicitly_concatenated_unterminated_string_multiline.py:4:1: SyntaxError: Expected an element of or the end of the f-string
---
> crates/ruff_python_parser/resources/inline/err/implicitly_concatenated_unterminated_string_multiline.py:4:1: SyntaxError: Expected an f-string or t-string element or the end of the f-string or t-string
34131c34131
< crates/ruff_python_parser/resources/inline/err/t_string_lambda_without_parentheses.py:2:15: SyntaxError: Expected an element of or the end of the t-string
---
> crates/ruff_python_parser/resources/inline/err/t_string_lambda_without_parentheses.py:2:15: SyntaxError: Expected an f-string or t-string element or the end of the f-string or t-string
```
So modulo color, the results are identical on 38,186 errors in our test
suite and CPython 3.10.
---------
Co-authored-by: David Peter <mail@david-peter.de>
* [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
This change makes it so we aren't doing a directory traversal every time
we ask for completions from a module. Specifically, submodules that
aren't attributes of their parent module can only be discovered by
looking at the directory tree. But we want to avoid doing a directory
scan unless we think there are changes.
To make this work, this change does a little bit of surgery to
`FileRoot`. Previously, a `FileRoot` was only used for library search
paths. Its revision was bumped whenever a file in that tree was added,
deleted or even modified (to support the discovery of `pth` files and
changes to its contents). This generally seems fine since these are
presumably dependency paths that shouldn't change frequently.
In this change, we add a `FileRoot` for the project. But having the
`FileRoot`'s revision bumped for every change in the project makes
caching based on that `FileRoot` rather ineffective. That is, cache
invalidation will occur too aggressively. To the point that there is
little point in adding caching in the first place. To mitigate this, a
`FileRoot`'s revision is only bumped on a change to a child file's
contents when the `FileRoot` is a `LibrarySearchPath`. Otherwise, we
only bump the revision when a file is created or added.
The effect is that, at least in VS Code, when a new module is added or
removed, this change is picked up and the cache is properly invalidated.
Other LSP clients with worse support for file watching (which seems to
be the case for the CoC vim plugin that I use) don't work as well. Here,
the cache is less likely to be invalidated which might cause completions
to have stale results. Unless there's an obvious way to fix or improve
this, I propose punting on improvements here for now.
## Summary
This was originally stacked on #19129, but some of the changes I made
for JSON also impacted the Azure format, so I went ahead and combined
them. The main changes here are:
- Implementing `FileResolver` for Ruff's `EmitterContext`
- Adding `FileResolver::notebook_index` and `FileResolver::is_notebook`
methods
- Adding a `DisplayDiagnostics` (with an "s") type for rendering a group
of diagnostics at once
- Adding `Azure`, `Json`, and `JsonLines` as new `DiagnosticFormat`s
I tried a couple of alternatives to the `FileResolver::notebook` methods
like passing down the `NotebookIndex` separately and trying to reparse a
`Notebook` from Ruff's `SourceFile`. The latter seemed promising, but
the `SourceFile` only stores the concatenated plain text of the
notebook, not the re-parsable JSON. I guess the current version is just
a variation on passing the `NotebookIndex`, but at least we can reuse
the existing `resolver` argument. I think a lot of this can be cleaned
up once Ruff has its own actual file resolver.
As suggested, I also tried deleting the corresponding `Emitter` files in
`ruff_linter`, but it doesn't look like git was able to follow this as a
rename. It did, however, track that the tests were moved, so the
snapshots should be easy to review.
## Test Plan
Existing Ruff tests ported to tests in `ruff_db`. I think some other
existing ruff tests also cover parts of this refactor.
---------
Co-authored-by: Micha Reiser <micha@reiser.io>
## Summary
Print the [new salsa memory usage
dumps](https://github.com/astral-sh/ruff/pull/18928) in mypy primer CI
runs to help us catch memory regressions. The numbers are rounded to the
nearest power of 1.1 (about a 5% threshold between buckets) to avoid overly sensitive diffs.
## Summary
Setting `TY_MEMORY_REPORT=full` will generate and print a memory usage
report to the CLI after a `ty check` run:
```
=======SALSA STRUCTS=======
`Definition` metadata=7.24MB fields=17.38MB count=181062
`Expression` metadata=4.45MB fields=5.94MB count=92804
`member_lookup_with_policy_::interned_arguments` metadata=1.97MB fields=2.25MB count=35176
...
=======SALSA QUERIES=======
`File -> ty_python_semantic::semantic_index::SemanticIndex`
metadata=11.46MB fields=88.86MB count=1638
`Definition -> ty_python_semantic::types::infer::TypeInference`
metadata=24.52MB fields=86.68MB count=146018
`File -> ruff_db::parsed::ParsedModule`
metadata=0.12MB fields=69.06MB count=1642
...
=======SALSA SUMMARY=======
TOTAL MEMORY USAGE: 577.61MB
struct metadata = 29.00MB
struct fields = 35.68MB
memo metadata = 103.87MB
memo fields = 409.06MB
```
Eventually, we should integrate these numbers into CI in some form. The
one limitation currently is that heap allocations in salsa structs (e.g.
interned values) are not tracked, but memoized values should have full
coverage. We may also want a peak memory usage counter (that accounts
for non-salsa memory), but that is relatively simple to profile manually
(e.g. `time -v ty check`) and would require a compile-time option to
avoid runtime overhead.
## Summary
Format conflicting declared types as
```
`str`, `int` and `bytes`
```
Thanks to @AlexWaygood for the initial draft.
@dcreager, looking forward to your one-character follow-up PR.
## Summary
Having a recursive type method to check whether a type is fully static
is inefficient, unnecessary, and makes us overly strict about subtyping
relations.
It's inefficient because we end up re-walking the same types many times
to check for fully-static-ness.
It's unnecessary because we can check relations involving the dynamic
type appropriately, depending whether the relation is subtyping or
assignability.
We use the subtyping relation to simplify unions and intersections. We
can usefully consider that `S <: T` for gradual types also, as long as
it remains true that `S | T` is equivalent to `T` and `S & T` is
equivalent to `S`.
One conservative definition (implemented here) that satisfies this
requirement is that we consider `S <: T` if, for every possible pair of
materializations `S'` and `T'`, `S' <: T'`. Or put differently the top
materialization of `S` (`S+` -- the union of all possible
materializations of `S`) is a subtype of the bottom materialization of
`T` (`T-` -- the intersection of all possible materializations of `T`).
In the most basic cases we can usefully say that `Any <: object` and
that `Never <: Any`, and we can handle more complex cases inductively
from there.
This definition of subtyping for gradual subtypes is not reflexive
(`Any` is not a subtype of `Any`).
As a corollary, we also remove `is_gradual_equivalent_to` --
`is_equivalent_to` now has the meaning that `is_gradual_equivalent_to`
used to have. If necessary, we could restore an
`is_fully_static_equivalent_to` or similar (which would not do an
`is_fully_static` pre-check of the types, but would instead pass a
relation-kind enum down through a recursive equivalence check, similar
to `has_relation_to`), but so far this doesn't appear to be necessary.
Credit to @JelleZijlstra for the observation that `is_fully_static` is
unnecessary and overly restrictive on subtyping.
There is another possible definition of gradual subtyping: instead of
requiring that `S+ <: T-`, we could instead require that `S+ <: T+` and
`S- <: T-`. In other words, instead of requiring all materializations of
`S` to be a subtype of every materialization of `T`, we just require
that every materialization of `S` be a subtype of _some_ materialization
of `T`, and that every materialization of `T` be a supertype of some
materialization of `S`. This definition also preserves the core
invariant that `S <: T` implies that `S | T = T` and `S & T = S`, and it
restores reflexivity: under this definition, `Any` is a subtype of
`Any`, and for any equivalent types `S` and `T`, `S <: T` and `T <: S`.
But unfortunately, this definition breaks transitivity of subtyping,
because nominal subclasses in Python use assignability ("consistent
subtyping") to define acceptable overrides. This means that we may have
a class `A` with `def method(self) -> Any` and a subtype `B(A)` with
`def method(self) -> int`, since `int` is assignable to `Any`. This
means that if we have a protocol `P` with `def method(self) -> Any`, we
would have `B <: A` (from nominal subtyping) and `A <: P` (`Any` is a
subtype of `Any`), but not `B <: P` (`int` is not a subtype of `Any`).
Breaking transitivity of subtyping is not tenable, so we don't use this
definition of subtyping.
## Test Plan
Existing tests (modified in some cases to account for updated
semantics.)
Stable property tests pass at a million iterations:
`QUICKCHECK_TESTS=1000000 cargo test -p ty_python_semantic -- --ignored
types::property_tests::stable`
### Changes to property test type generation
Since we no longer have a method of categorizing built types as
fully-static or not-fully-static, I had to add a previously-discussed
feature to the property tests so that some tests can build types that
are known by construction to be fully static, because there are still
properties that only apply to fully-static types (for example,
reflexiveness of subtyping.)
## Changes to handling of `*args, **kwargs` signatures
This PR "discovered" that, once we allow non-fully-static types to
participate in subtyping under the above definitions, `(*args: Any,
**kwargs: Any) -> Any` is now a subtype of `() -> object`. This is true,
if we take a literal interpretation of the former signature: all
materializations of the parameters `*args: Any, **kwargs: Any` can
accept zero arguments, making the former signature a subtype of the
latter. But the spec actually says that `*args: Any, **kwargs: Any`
should be interpreted as equivalent to `...`, and that makes a
difference here: `(...) -> Any` is not a subtype of `() -> object`,
because (unlike a literal reading of `(*args: Any, **kwargs: Any)`),
`...` can materialize to _any_ signature, including a signature with
required positional arguments.
This matters for this PR because it makes the "any two types are both
assignable to their union" property test fail if we don't implement the
equivalence to `...`. Because `FunctionType.__call__` has the signature
`(*args: Any, **kwargs: Any) -> Any`, and if we take that at face value
it's a subtype of `() -> object`, making `FunctionType` a subtype of `()
-> object)` -- but then a function with a required argument is also a
subtype of `FunctionType`, but not a subtype of `() -> object`. So I
went ahead and implemented the equivalence to `...` in this PR.
## Ecosystem analysis
* Most of the ecosystem report are cases of improved union/intersection
simplification. For example, we can now simplify a union like `bool |
(bool & Unknown) | Unknown` to simply `bool | Unknown`, because we can
now observe that every possible materialization of `bool & Unknown` is
still a subtype of `bool` (whereas before we would set aside `bool &
Unknown` as a not-fully-static type.) This is clearly an improvement.
* The `possibly-unresolved-reference` errors in sockeye, pymongo,
ignite, scrapy and others are true positives for conditional imports
that were formerly silenced by bogus conflicting-declarations (which we
currently don't issue a diagnostic for), because we considered two
different declarations of `Unknown` to be conflicting (we used
`is_equivalent_to` not `is_gradual_equivalent_to`). In this PR that
distinction disappears and all equivalence is gradual, so a declaration
of `Unknown` no longer conflicts with a declaration of `Unknown`, which
then results in us surfacing the possibly-unbound error.
* We will now issue "redundant cast" for casting from a typevar with a
gradual bound to the same typevar (the hydra-zen diagnostic). This seems
like an improvement.
* The new diagnostics in bandersnatch are interesting. For some reason
primer in CI seems to be checking bandersnatch on Python 3.10 (not yet
sure why; this doesn't happen when I run it locally). But bandersnatch
uses `enum.StrEnum`, which doesn't exist on 3.10. That makes the `class
SimpleDigest(StrEnum)` a class that inherits from `Unknown` (and
bypasses our current TODO handling for accessing attributes on enum
classes, since we don't recognize it as an enum class at all). This PR
improves our understanding of assignability to classes that inherit from
`Any` / `Unknown`, and we now recognize that a string literal is not
assignable to a class inheriting `Any` or `Unknown`.
## Summary
Fixes https://github.com/astral-sh/ty/issues/640. If a user passes
`--python=<some-virtual-environment>/bin/python`, we must avoid
canonicalizing the path until we've traversed upwards to find the
`sys.prefix` directory (`<some-virtual-environment>`). On Unix systems,
`<sys.prefix>/bin/python` is often a symlink to a system interpreter; if
we resolve the symlink too easily then we'll add the system
interpreter's `site-packages` directory as a search path rather than the
virtual environment's directory.
## Test Plan
I added an integration test to
`crates/ty/tests/cli/python_environment.rs` which fails on `main`. I
also manually tested locally that running `cargo run -p ty check foo.py
--python=.venv/bin/python -vv` now prints this log to the terminal
```
2025-06-20 18:35:24.57702 DEBUG Resolved site-packages directories for this virtual environment are: SitePackagesPaths({"/Users/alexw/dev/ruff/.venv/lib/python3.13/site-packages"})
```
Whereas it previously resolved `site-packages` to my system
intallation's `site-packages` directory
We already had support for homogeneous tuples (`tuple[int, ...]`). This
PR extends this to also support mixed tuples (`tuple[str, str,
*tuple[int, ...], str str]`).
A mixed tuple consists of a fixed-length (possibly empty) prefix and
suffix, and a variable-length portion in the middle. Every element of
the variable-length portion must be of the same type. A homogeneous
tuple is then just a mixed tuple with an empty prefix and suffix.
The new data representation uses different Rust types for a fixed-length
(aka heterogeneous) tuple. Another option would have been to use the
`VariableLengthTuple` representation for all tuples, and to wrap the
"variable + suffix" portion in an `Option`. I don't think that would
simplify the method implementations much, though, since we would still
have a 2×2 case analysis for most of them.
One wrinkle is that the definition of the `tuple` class in the typeshed
has a single typevar, and canonically represents a homogeneous tuple.
When getting the class of a tuple instance, that means that we have to
summarize our detailed mixed tuple type information into its
"homogeneous supertype". (We were already doing this for heterogeneous
types.)
A similar thing happens when concatenating two mixed tuples: the
variable-length portion and suffix of the LHS, and the prefix and
variable-length portion of the RHS, all get unioned into the
variable-length portion of the result. The LHS prefix and RHS suffix
carry through unchanged.
---------
Co-authored-by: Alex Waygood <Alex.Waygood@Gmail.com>
## Summary
Part of [#111](https://github.com/astral-sh/ty/issues/111).
After this change, dataclasses with two or more `KW_ONLY` field will be
reported as invalid. The duplicate fields will simply be ignored when
computing `__init__`'s signature.
## Test Plan
Markdown tests.
## Summary
Part of [#117](https://github.com/astral-sh/ty/issues/117).
`TypeIs[]` is a special form that allows users to define their own
narrowing functions. Despite the syntax, `TypeIs` is not a generic and,
on its own, it is meaningless as a type.
[Officially](https://typing.python.org/en/latest/spec/narrowing.html#typeis),
a function annotated as returning a `TypeIs[T]` is a <i>type narrowing
function</i>, where `T` is called the <i>`TypeIs` return type</i>.
A `TypeIs[T]` may or may not be bound to a symbol. Only bound types have
narrowing effect:
```python
def f(v: object = object()) -> TypeIs[int]: ...
a: str = returns_str()
if reveal_type(f()): # Unbound: TypeIs[int]
reveal_type(a) # str
if reveal_type(f(a)): # Bound: TypeIs[a, int]
reveal_type(a) # str & int
```
Delayed usages of a bound type has no effect, however:
```python
b = f(a)
if b:
reveal_type(a) # str
```
A `TypeIs[T]` type:
* Is fully static when `T` is fully static.
* Is a singleton/single-valued when it is bound.
* Has exactly two runtime inhabitants when it is unbound: `True` and
`False`.
In other words, an unbound type have ambiguous truthiness.
It is possible to infer more precise truthiness for bound types;
however, that is not part of this change.
`TypeIs[T]` is a subtype of or otherwise assignable to `bool`. `TypeIs`
is invariant with respect to the `TypeIs` return type: `TypeIs[int]` is
neither a subtype nor a supertype of `TypeIs[bool]`. When ty sees a
function marked as returning `TypeIs[T]`, its `return`s will be checked
against `bool` instead. ty will also report such functions if they don't
accept a positional argument. Addtionally, a type narrowing function
call with no positional arguments (e.g., `f()` in the example above)
will be considered invalid.
## Test Plan
Markdown tests.
---------
Co-authored-by: Carl Meyer <carl@astral.sh>
## Summary
Minor documentation update to make `mypy_primer` instructions a bit more
verbose/helpful for running against a local branch
## Test Plan
N/A
## Summary
https://github.com/astral-sh/ty/issues/214 will require a couple
invasive changes that I would like to get merged even before garbage
collection is fully implemented (to avoid rebasing):
- `ParsedModule` can no longer be dereferenced directly. Instead you
need to load a `ParsedModuleRef` to access the AST, which requires a
reference to the salsa database (as it may require re-parsing the AST if
it was collected).
- `AstNodeRef` can only be dereferenced with the `node` method, which
takes a reference to the `ParsedModuleRef`. This allows us to encode the
fact that ASTs do not live as long as the database and may be collected
as soon a given instance of a `ParsedModuleRef` is dropped. There are a
number of places where we currently merge the `'db` and `'ast`
lifetimes, so this requires giving some types/functions two separate
lifetime parameters.
## Summary
Fixes https://github.com/astral-sh/ty/issues/556.
On Windows, system installations have different layouts to virtual
environments. In Windows virtual environments, the Python executable is
found at `<sys.prefix>/Scripts/python.exe`. But in Windows system
installations, the Python executable is found at
`<sys.prefix>/python.exe`. That means that Windows users were able to
point to Python executables inside virtual environments with the
`--python` flag, but they weren't able to point to Python executables
inside system installations.
This PR fixes that issue. It also makes a couple of other changes:
- Nearly all `sys.prefix` resolution is moved inside `site_packages.rs`.
That was the original design of the `site-packages` resolution logic,
but features implemented since the initial implementation have added
some resolution and validation to `resolver.rs` inside the module
resolver. That means that we've ended up with a somewhat confusing code
structure and a situation where several checks are unnecessarily
duplicated between the two modules.
- I noticed that we had quite bad error messages if you e.g. pointed to
a path that didn't exist on disk with `--python` (we just gave a
somewhat impenetrable message saying that we "failed to canonicalize"
the path). I improved the error messages here and added CLI tests for
`--python` and the `environment.python` configuration setting.
## Test Plan
- Existing tests pass
- Added new CLI tests
- I manually checked that virtual-environment discovery still works if
no configuration is given
- Micha did some manual testing to check that pointing `--python` to a
system-installation executable now works on Windows
## Summary
This optimizes some of the logic added in
https://github.com/astral-sh/ruff/pull/18444. In general, we only
calculate information for subdiagnostics if we know we'll actually emit
the diagnostic. The check to see whether we'll emit the diagnostic is
work we'll definitely have to do whereas the the work to gather
information for a subdiagnostic isn't work we necessarily have to do if
the diagnostic isn't going to be emitted at all.
This PR makes us lazier about gathering the information we need for the
subdiagnostic, and moves all the subdiagnostic logic into one function
rather than having some `unresolved-reference` subdiagnostic logic in
`infer.rs` and some in `diagnostic.rs`.
## Test Plan
`cargo test -p ty_python_semantic`
This updates our representation of functions to more closely match our
representation of classes.
The new `OverloadLiteral` and `FunctionLiteral` classes represent a
function definition in the AST. If a function is generic, this is
unspecialized. `FunctionType` has been updated to represent a function
type, which is specialized if the function is generic. (These names are
chosen to match `ClassLiteral` and `ClassType` on the class side.)
This PR does not add a separate `Type` variant for `FunctionLiteral`.
Maybe we should? Possibly as a follow-on PR?
Part of https://github.com/astral-sh/ty/issues/462
---------
Co-authored-by: Micha Reiser <micha@reiser.io>
David Peter
Alex Waygood
Brent Westbrook
Daniel Kongsgaard
Micha Reiser
Renkai Ge
fgiacome
Manuel Mendez
Andrew Gallant
Leandro Braga
Renkai Ge
Eric Jolibois
Aria Desires
Andrii Turov
Dhruv Manilawala
Nathaniel Roman
Jack O'Connor
Zanie Blue
renovate[bot]
Charlie Marsh
Ibraheem Ahmed
Carl Meyer
Josiah Kane
Douglas Creager
InSync
justin
DetachHead