chore: import upstream snapshot with attribution

python/ray/llm/_internal/common/base_pydantic.py

@@ -0,0 +1,31 @@
+from typing import Type, TypeVar
+
+import yaml
+from pydantic import BaseModel, ConfigDict
+
+ModelT = TypeVar("ModelT", bound=BaseModel)
+
+
+class BaseModelExtended(BaseModel):
+    # NOTE(edoakes): Pydantic protects the namespace `model_` by default and prints
+    # warnings if you define fields with that prefix. However, we added such fields
+    # before this behavior existed. To avoid spamming user-facing logs, we mark the
+    # namespace as not protected. This means we need to be careful about overriding
+    # internal attributes starting with `model_`.
+    # See: https://github.com/anyscale/ray-llm/issues/1425
+    model_config = ConfigDict(
+        protected_namespaces=tuple(),
+        extra="forbid",
+    )
+
+    @classmethod
+    def parse_yaml(cls: Type[ModelT], file, **kwargs) -> ModelT:
+        kwargs.setdefault("Loader", yaml.SafeLoader)
+        dict_args = yaml.load(file, **kwargs)
+        return cls.model_validate(dict_args)
+
+    @classmethod
+    def from_file(cls: Type[ModelT], path: str, **kwargs) -> ModelT:
+        """Load a model from a YAML file path."""
+        with open(path, "r") as f:
+            return cls.parse_yaml(f, **kwargs)