add new features: info about underscores in path, info about lib location, info about context of original file(extension type, psi parents, size of project),
info about already existing imports from the same library(in this file, in other opened files, in other files in the same directory)
GitOrigin-RevId: ca8206d4d7db6bc79e8f1a78502bf33696a653e9
`dataclass_transform` support posed a number of challenges to the current
AST/PSI stubs separation in our architecture. For the standard "dataclasses"
module and the "attrs" package API, we could rely on well-known names
and defaults to recognize and preserve the information about decorator
arguments and field specifier arguments in PSI stubs. With `dataclass_transform`
however, effectively any decorator call or extending any base class can indicate
using a "magical" API that should generate dataclass-like entities.
At the moment of building PSI stubs we can't be sure, because we can't leave
the boundaries of the current file to resolve these names and determine if these
decorators and base classes are decorated themselves with `dataclass_transform`.
To support that, we instead rely on well-known keyword argument names documented
in the spec, e.g. "kw_only" or "frozen". In other words, whenever we encounter
any decorator call or a superclass list with such keyword arguments, we generate
a `PyDataclassStub` stub for the corresponding class.
The same thing is happening with class attribute initializers, i.e. whenever
we see a function call with argument such as "default" or "kw_only" in their
RHS, we generate a `PyDataclassFieldStub` for the corresponding target expression.
Both of these stub interfaces now can contain null values for the corresponding
properties if they were not specified directly in the class definition.
Finally, for the `dataclass_transform` decorator itself, a new custom decorator stub
was introduced -- `PyDataclassTransformDecoratorStub`, it preserves its keyword
arguments, such as "keyword_only_default" or "frozen_default" controlling
the default properties of generated dataclasses.
Later, when we need concluded information about specific dataclass properties,
e.g. in `PyDataclassTypeProvider` to generate a constructor signature, or in
`PyDataclassInspection`, we try to "resolve" this incomplete information from stubs
into finalized `PyDataclassParameters` and `PyDataclassFieldParameters` that
contain non-null versions of the same fields. The main entry points for that
are `resolveDataclassParameters` and `resolveDataclassFieldParameters`.
These methods additionally handle the situations where decorators, superclass
lists and field specifiers lack any keyword arguments, and thus, there were no
automatically created custom stubs for them.
All the existing usages of `PyDataclassStub` and `PyDataclassFieldStub`
were updated to operate on `PyDataclassParameters` and `PyDataclassFieldParameters`
instead.
Counterparts of the tests on various inspection checks for the standard dataclasses
definitions were added for dataclasses created with `dataclass_transform`, even
though the spec is unclear on some aspects the expected type checker semantics, e.g.
if combining "eq=False" and "order=True" or specifying both "default" and
"default_factory" for a field should be reported.
I tried to follow common sense when enabling existing checks for such arbitrary
user-defined dataclass APIs.
GitOrigin-RevId: 4180a1e32b5e4025fc4e3ed49bb8d67af0d60e66
These changes make PyStatementList elements (which are function and class bodies, cycle bodies, if-else branches, etc.) lazy-parseable which means they can now be reparsed without reparsing the whole file if changes are happened inside them accepted as safe
The main reason behind these changes is to improve performance
GitOrigin-RevId: 892acbe0c95fde6aec74b7595b0a58f902c426f5
Otherwise, incomplete stubs break our custom logic for resolving tensorflow
modules re-exported from its _api.v2 subpackage, such as tensorflow.audio,
tensorflow.image, tensorflow.random, etc.
We did the same for Numpy stubs in the past, enabling them back once they
became mature enough.
GitOrigin-RevId: b1e46067406a592761f56b7d296a287e5282b079
Assume that such decorators as well as "well-known" decorators, which we special-case,
don't change signatures of decorated functions and classes.
This change effectively stops the long-standing policy of safe-listing a few recognized
"well-known" decorators and assuming everything else can change a definition in any
way. This approach doesn't apply well to the current state of the Python world, where most
of the common side effects of decorators, such as adding new parameters, can be expressed
in type hints.
In 2021.1 we added PyDecoratedFunctionTypeProvider that was able to infer a return type of
decorator over its body, as for any other function, and then correctly apply this information
to a decorated definition. It led to a number of problems.
First of all, depending on whether TypeEvalContext allowed us to access AST of a decorator's
body, we inferred different signatures for functions decorated with an imported decorator in
inspections and in user-initiated actions, such as Parameter Info.
Secondly, we started inferring useless `(*args, **kwargs)` signatures in case of decorators
defined following the common pattern of returning a wrapper function accepting arbitrary
parameters and itself decorated with @functools.wraps (PY-48338). In some sense, our code
analysis was "too smart" in its type inference in this case.
Lastly, we diluted the return types of functions decorated with unknown decorators, even
fully typed, by uniting these types with Any (so-called "weak" types). This logic
existed before PyDecoratedFunctionTypeProvider, but it became more problematic now
than we were able to propagate this artificial union through generic decorators.
This change in behavior might lead to some false positives for untyped Python code
with non-pure decorators. However, given that other type checkers are also likely to hit these
problems, there is now a stronger incentive to add type hints for such problematic APIs.
In the worst case, we can special-case some heavily requested decorators as we did before.
GitOrigin-RevId: db11fb3573bda5da155cb921a30adc31d5c841e2
Inspection covers such cases:
* Extending typing.Generic in new-style generic classes
* Extending parameterized typing.Protocol in new-style generic classes
* Using generic upper bounds and constraints with type parameters for ParamSpec and TypeVarTuple
* Mixing traditional and new-style type variables
* Using traditional type variables in new-style type aliases
GitOrigin-RevId: 8812959f64d2d87e1b72f713405edb86936220b9
We turned them off as part of PY-48166. Now these stubs are much more complete
and fix a number of problems with the code insight for the library (PY-35164,
PY-37461, PY-50394, PY-59347, PY-60224), most of which are caused by the lack
of information in skeletons automatically generated for its binary modules or
incorrect type information being extracted from docstrings.
I leave the possibility to disable the stubs for the time being and will remove
the registry option once it become clear that they don't cause serious problems.
GitOrigin-RevId: 0df09ddb8ca40f88b908e19c0f49f5b005abaa58
Precise types can be inferred only over AST if it's accessible.
I had to move PyStdlibTypeProvider higher in the provider's hierarchy
so that it could override types coming from Typeshed, otherwise we
infer enum.property type for the "value" attribute.
GitOrigin-RevId: 8727e080cfc06d0edda13eccfd601601dc661da9
* Dedicated inspections for `ClassVar` variables in variable declarations, variable reassignments, function parameters, local and return variables
* Types of `ClassVar` variables now resolves correctly
* Tests for `ClassVar` inspections
GitOrigin-RevId: 0fd0ef0126ba2c2801ef82bcbeca4ea9b0c48c73
By running the completion contributor for non-imported modules after everything
else, including the LegacyCompletionContributor offering names present in
the scope, and filtering out duplicate variants.
GitOrigin-RevId: 98982b2c33d1fc11d70b144de556a6825bd3febc
this makes accessing decorator arguments possible without needing to parse the file again
(cherry picked from commit a9854dbf181f3c7bf488d6ecc18205bb29df2422)
IJ-MR-15304
GitOrigin-RevId: 1031d888a4a08a706c4fa9e2dc48e2f97462c95e
Both are implemented other the type of the corresponding class.
References resolve to any readable attribute of a class, however
some obviously wrong variants such as special "dunder" attributes
and methods are intentionally excluded from completion suggestions.
GitOrigin-RevId: 5edac14f47cba39840b15b0dd7f21e2e46077261
