Move function is_pydantic_dataclass from pydantic._internal._dataclasses to pydantic.dataclasses

docs/usage/dataclasses.md

@@ -356,6 +356,34 @@ print(repr(m))
 #> Model(dc=DC(a=ArbitraryType(value=3), b='qwe'), other='other')
 ```
 
+### Checking if a dataclass is a pydantic dataclass
+
+Pydantic dataclasses are still considered dataclasses, so using `dataclasses.is_dataclass` will return `True`. To check if a type is specifically a pydantic dataclass you can use `pydantic.dataclasses.is_pydantic_dataclass`.
+
+```py
+import dataclasses
+
+import pydantic
+
+
+@dataclasses.dataclass
+class StdLibDataclass:
+    id: int
+
+
+PydanticDataclass = pydantic.dataclasses.dataclass(StdLibDataclass)
+
+print(dataclasses.is_dataclass(StdLibDataclass))
+#> True
+print(pydantic.dataclasses.is_pydantic_dataclass(StdLibDataclass))
+#> False
+
+print(dataclasses.is_dataclass(PydanticDataclass))
+#> True
+print(pydantic.dataclasses.is_pydantic_dataclass(PydanticDataclass))
+#> True
+```
+
 ## Initialization hooks
 
 When you initialize a dataclass, it is possible to execute code *before* or *after* validation

pydantic/_internal/_dataclasses.py

@@ -259,15 +259,3 @@ class B(A):
         and not hasattr(_cls, '__pydantic_validator__')
         and set(_cls.__dataclass_fields__).issuperset(set(getattr(_cls, '__annotations__', {})))
     )
-
-
-def is_pydantic_dataclass(_cls: type[Any]) -> TypeGuard[type[PydanticDataclass]]:
-    """Whether a class is a pydantic dataclass.
-
-    Args:
-        cls: The class.
-
-    Returns:
-        `True` if the class is a pydantic dataclass, `False` otherwise.
-    """
-    return dataclasses.is_dataclass(_cls) and '__pydantic_validator__' in _cls.__dict__

pydantic/_internal/_generate_schema.py

@@ -1308,7 +1308,7 @@ def _dataclass_schema(
 
                 self = self._current_generate_schema
 
-                from ._dataclasses import is_pydantic_dataclass
+                from ..dataclasses import is_pydantic_dataclass
 
                 if is_pydantic_dataclass(dataclass):
                     fields = dataclass.__pydantic_fields__

pydantic/dataclasses.py

@@ -6,7 +6,7 @@
 import types
 from typing import TYPE_CHECKING, Any, Callable, Generic, NoReturn, TypeVar, overload
 
-from typing_extensions import Literal, dataclass_transform
+from typing_extensions import Literal, TypeGuard, dataclass_transform
 
 from ._internal import _config, _decorators, _typing_extra
 from ._internal import _dataclasses as _pydantic_dataclasses
@@ -276,3 +276,15 @@ def rebuild_dataclass(
             raise_errors=raise_errors,
             types_namespace=types_namespace,
         )
+
+
+def is_pydantic_dataclass(_cls: type[Any]) -> TypeGuard[type[PydanticDataclass]]:
+    """Whether a class is a pydantic dataclass.
+
+    Args:
+        _cls: The class.
+
+    Returns:
+        `True` if the class is a pydantic dataclass, `False` otherwise.
+    """
+    return dataclasses.is_dataclass(_cls) and '__pydantic_validator__' in _cls.__dict__

tests/test_dataclasses.py

@@ -33,7 +33,7 @@
     model_validator,
 )
 from pydantic._internal._mock_validator import MockValidator
-from pydantic.dataclasses import rebuild_dataclass
+from pydantic.dataclasses import is_pydantic_dataclass, rebuild_dataclass
 from pydantic.fields import Field, FieldInfo
 from pydantic.json_schema import model_json_schema
 
@@ -2487,3 +2487,16 @@ class A:
     assert count == 0
     TypeAdapter(A).validate_python({'a': 123})
     assert count == 1
+
+
+def test_is_pydantic_dataclass():
+    @pydantic.dataclasses.dataclass
+    class PydanticDataclass:
+        a: int
+
+    @dataclasses.dataclass
+    class StdLibDataclass:
+        b: int
+
+    assert is_pydantic_dataclass(PydanticDataclass) is True
+    assert is_pydantic_dataclass(StdLibDataclass) is False