DM-40366: Use default pydantic model for testing

python/lsst/daf/butler/formatters/yaml.py

@@ -152,15 +152,26 @@
         This will fail for data structures that have complex python classes
         without a registered YAML representer.
         """
+        converted = False
         if hasattr(inMemoryDataset, "model_dump") and hasattr(inMemoryDataset, "model_dump_json"):
-            # Pydantic-like model if both dump() and json() exist.
+            # Pydantic v2-like model if both model_dump() and model_json()
+            # exist.
             with contextlib.suppress(Exception):
                 inMemoryDataset = inMemoryDataset.model_dump()
+                converted = True
+
+        if not converted and hasattr(inMemoryDataset, "dict") and hasattr(inMemoryDataset, "json"):
+            # Pydantic v1-like model if both dict() and json() exist.
+            with contextlib.suppress(Exception):
+                inMemoryDataset = inMemoryDataset.dict()
+                converted = True
+
+        if not converted:
+            if dataclasses.is_dataclass(inMemoryDataset):
+                inMemoryDataset = dataclasses.asdict(inMemoryDataset)
+            elif hasattr(inMemoryDataset, "_asdict"):
+                inMemoryDataset = inMemoryDataset._asdict()
 
-        if dataclasses.is_dataclass(inMemoryDataset):
-            inMemoryDataset = dataclasses.asdict(inMemoryDataset)
-        elif hasattr(inMemoryDataset, "_asdict"):
-            inMemoryDataset = inMemoryDataset._asdict()
         unsafe_dump = self.writeParameters.get("unsafe_dump", False)
         if unsafe_dump:
             serialized = yaml.dump(inMemoryDataset)

python/lsst/daf/butler/tests/_examplePythonTypes.py

@@ -44,7 +44,7 @@
 from typing import TYPE_CHECKING, Any
 
 from lsst.daf.butler import StorageClass, StorageClassDelegate
-from lsst.daf.butler._compat import _BaseModelCompat
+from pydantic import BaseModel
 
 if TYPE_CHECKING:
     from lsst.daf.butler import Butler, Datastore, FormatterFactory
@@ -264,7 +264,7 @@
         return cls(exportDict["summary"], exportDict["output"], data)
 
 
-class MetricsExampleModel(_BaseModelCompat):
+class MetricsExampleModel(BaseModel):
     """A variant of `MetricsExample` based on model."""
 
     summary: dict[str, Any] | None = None
@@ -274,7 +274,12 @@
     @classmethod
     def from_metrics(cls, metrics: MetricsExample) -> MetricsExampleModel:
         """Create a model based on an example."""
-        return cls.model_validate(metrics.exportAsDict())
+        d = metrics.exportAsDict()
+        # Assume pydantic v2 but fallback to v1
+        try:
+            return cls.model_validate(d)
+        except AttributeError:
+            return cls.parse_obj(d)
 
 
 @dataclasses.dataclass