AmpelBaseModel: add switch for serializer errors

ampel/base/AmpelBaseModel.py

@@ -90,12 +90,14 @@ def dict(
 		exclude_unset: bool = False,
 		exclude_defaults: bool = False,
 		exclude_none: bool = False,
+		warnings: bool = True,
 	) -> dict[str, Any]:
 		return self.model_dump(
 			include=include,
 			exclude=exclude,
 			by_alias=by_alias,
 			exclude_unset=exclude_unset,
 			exclude_defaults=exclude_defaults,
-			exclude_none=exclude_none
+			exclude_none=exclude_none,
+			warnings=warnings,
 		)

pyproject.toml

@@ -45,8 +45,6 @@ profile = "black"
 minversion = "6.0"
 filterwarnings = [
     "error",
-    # Expected `list[definition-ref]` but got `tuple` - serialized value may not be as expected
-    "ignore:Pydantic serializer warnings:UserWarning",
 ]
 
 [tool.mypy]

tests/test_AmpelBaseModel.py

@@ -1,3 +1,5 @@
+import re
+from collections.abc import Sequence
 from types import UnionType
 from typing import Generic, TypeVar, Union, get_origin
 
@@ -122,3 +124,31 @@ class Base(AmpelUnit):
         class Derived(Base, AmpelBaseModel): ...
 
     assert Base().dict() == {"base": 1}
+
+
+def test_serialize_sequence_field_with_tuple():
+    class M(AmpelBaseModel):
+        a: Sequence[int]
+
+    model = M(a=(1,))
+    assert model.dict(warnings=False) == {"a": (1,)}
+
+    # We sometimes use `Sequence[T]` as shorthand for `list[T] | tuple[T]`, but
+    # pydantic uses a list serializer for all Sequence types and complains when
+    # it sees a tuple. We suppress these warnings by setting warnings=False by
+    # default, and rely on validators to ensure that our model fields have the
+    # correct types. See https://github.com/pydantic/pydantic-core/issues/133
+    # for a discussion of why pydantic doesn't directly support Sequence.
+    with pytest.warns(
+        UserWarning,
+        match=re.escape(
+            "Pydantic serializer warnings:\n  Expected `list[int]` but got `tuple` - serialized value may not be as expected"
+        ),
+    ):
+        assert model.dict(warnings=True) == {"a": (1,)}
+
+    class M(AmpelBaseModel):
+        a: list[int] | tuple[int, ...]
+
+    model = M(a=(1,))
+    assert model.dict(warnings=True) == {"a": (1,)}