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[dataclass_transform] include __dataclass_fields__ in transformed types #14752

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Feb 23, 2023
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[dataclass_transform] include __dataclass_fields__ in transformed types #14752

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19 changes: 10 additions & 9 deletions mypy/plugins/dataclasses.py
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Original file line number Diff line number Diff line change
Expand Up @@ -648,17 +648,18 @@ def _is_kw_only_type(self, node: Type | None) -> bool:
return node_type.type.fullname == "dataclasses.KW_ONLY"

def _add_dataclass_fields_magic_attribute(self) -> None:
# Only add if the class is a dataclasses dataclass, and omit it for dataclass_transform
# classes.
# It would be nice if this condition were reified rather than using an `is` check.
# Only add if the class is a dataclasses dataclass, and omit it for dataclass_transform
# classes.
if self._spec is not _TRANSFORM_SPEC_FOR_DATACLASSES:
return

attr_name = "__dataclass_fields__"
any_type = AnyType(TypeOfAny.explicit)
field_type = self._api.named_type_or_none("dataclasses.Field", [any_type]) or any_type
# For `dataclasses`, use the type `dict[str, Field[Any]]` for accuracy. For dataclass
# transforms, it's inaccurate to use `Field` since a given transform may use a completely
# different type (or none); fall back to `Any` there.
#
# In either case, we're aiming to match the Typeshed stub for `is_dataclass`, which expects
# the instance to have a `__dataclass_fields__` attribute of type `dict[str, Field[Any]]`.
if self._spec is _TRANSFORM_SPEC_FOR_DATACLASSES:
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The is check probably doesn't work in incremental mode, when the spec has been deserialized. Another way to do this would be to add an internal boolean flag to the spec that is only true for regular dataclasses.

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@wesleywright wesleywright Feb 22, 2023 •
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I believe it shouldn't hit any serialization issues because _TRANSFORM_SPEC_FOR_DATACLASSES is never actually attached to an AST node; we return it from another function in this plugin here when we fail to find a spec in the AST.

That said, it's definitely opaque and not very elegant; I should probably make a helper function or something similar at least

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Yes, you are right.

field_type = self._api.named_type_or_none("dataclasses.Field", [any_type]) or any_type
else:
field_type = any_type
attr_type = self._api.named_type(
"builtins.dict", [self._api.named_type("builtins.str"), field_type]
)
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3 changes: 1 addition & 2 deletions test-data/unit/check-dataclass-transform.test
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Expand Up @@ -279,8 +279,7 @@ class Bad:
bad1: int = field(alias=some_str()) # E: "alias" argument to dataclass field must be a string literal
bad2: int = field(kw_only=some_bool()) # E: "kw_only" argument must be a boolean literal

# this metadata should only exist for dataclasses.dataclass classes
Foo.__dataclass_fields__ # E: "Type[Foo]" has no attribute "__dataclass_fields__"
reveal_type(Foo.__dataclass_fields__) # N: Revealed type is "builtins.dict[builtins.str, Any]"

[typing fixtures/typing-full.pyi]
[builtins fixtures/dataclasses.pyi]
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