[dataclass_transform] detect transform spec changes in incremental mode #14695
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mypy/server/astdiff.py
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| @@ -280,6 +283,7 @@ def snapshot_definition(node: SymbolNode | None, common: SymbolSnapshot) -> Symb | |||
| tuple(snapshot_type(tdef) for tdef in node.defn.type_vars), | |||
| [snapshot_type(base) for base in node.bases], | |||
| [snapshot_type(p) for p in node._promote], | |||
| node.dataclass_transform_spec or find_dataclass_transform_spec(node), | |||
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this may be somewhat expensive; an alternative may be to attach the relevant spec to each class in the hierarchy during semantic analysis rather than only attaching the spec to the transformer class
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Class hierarchies are almost always quite shallow, so this shouldn't be expensive. Similar to above, the snapshots must support equality. Using the serialized form should do the trick.
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mypy/server/astdiff.py
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| @@ -239,6 +241,7 @@ def snapshot_definition(node: SymbolNode | None, common: SymbolSnapshot) -> Symb | |||
| node.is_static, | |||
| signature, | |||
| is_trivial_body, | |||
| dataclass_transform_spec, | |||
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This doesn't support equality and can't be used in snapshots. You can probably use the serialized form instead.
mypy/server/astdiff.py
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| @@ -280,6 +283,7 @@ def snapshot_definition(node: SymbolNode | None, common: SymbolSnapshot) -> Symb | |||
| tuple(snapshot_type(tdef) for tdef in node.defn.type_vars), | |||
| [snapshot_type(base) for base in node.bases], | |||
| [snapshot_type(p) for p in node._promote], | |||
| node.dataclass_transform_spec or find_dataclass_transform_spec(node), | |||
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Class hierarchies are almost always quite shallow, so this shouldn't be expensive. Similar to above, the snapshots must support equality. Using the serialized form should do the trick.
| @dataclass_transform() | ||
| class Dataclass: ... | ||
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| class A(Dataclass): |
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Here the change in MRO will trigger dependencies, so this doesn't perfectly test how applying dataclass transform behaves. You can work around this by having a Dataclass base class in the original version, but omit the dataclass_transform decorator.
wesleywright
changed the title
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According to mypy_primer, this change has no effect on the checked open source code. 🤖🎉 |
… incremental mode (python#14695) Adds support for triggering rechecking of downstream classes when `@dataclass_transform` is added or removed from a function/class, as well as when parameters to `dataclass_transform` are updated. These changes aren't propagated normally since they don't change the type signature of the `dataclass_transform` decorator. Also adds new a new `find-grained-dataclass-transform.test` file to test the new logic. (cherry picked from commit 29bcc7f)
… incremental mode (#14695) (#14768) Adds support for triggering rechecking of downstream classes when `@dataclass_transform` is added or removed from a function/class, as well as when parameters to `dataclass_transform` are updated. These changes aren't propagated normally since they don't change the type signature of the `dataclass_transform` decorator. Also adds new a new `find-grained-dataclass-transform.test` file to test the new logic. (cherry picked from commit 29bcc7f) Co-authored-by: Wesley Collin Wright <[email protected]>
Adds support for triggering rechecking of downstream classes when
@dataclass_transformis added or removed from a function/class, as well as when parameters todataclass_transformare updated. These changes aren't propagated normally since they don't change the type signature of thedataclass_transformdecorator.Also adds new a new
find-grained-dataclass-transform.testfile to test the new logic.