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.
