chore: import upstream snapshot with attribution

python/ray/_common/pydantic_compat.py

@@ -0,0 +1,113 @@
+# ruff: noqa
+import packaging.version
+
+# Pydantic is a dependency of `ray["default"]` but not the minimal installation,
+# so handle the case where it isn't installed.
+try:
+    import pydantic
+
+    PYDANTIC_INSTALLED = True
+except ImportError:
+    pydantic = None
+    PYDANTIC_INSTALLED = False
+
+
+if not PYDANTIC_INSTALLED:
+    IS_PYDANTIC_2 = False
+    BaseModel = None
+    Extra = None
+    Field = None
+    NonNegativeFloat = None
+    NonNegativeInt = None
+    PositiveFloat = None
+    PositiveInt = None
+    PrivateAttr = None
+    StrictInt = None
+    ValidationError = None
+    root_validator = None
+    validator = None
+
+    def is_subclass_of_base_model(obj):
+        return False
+
+elif not hasattr(pydantic, "__version__") or packaging.version.parse(
+    pydantic.__version__
+) < packaging.version.parse("2.0"):
+    raise ImportError(
+        "Pydantic v1 is no longer supported in Ray. " "Please upgrade to `pydantic>=2`."
+    )
+else:
+    IS_PYDANTIC_2 = True
+    from pydantic import (
+        BaseModel,
+        Extra,
+        Field,
+        NonNegativeFloat,
+        NonNegativeInt,
+        PositiveFloat,
+        PositiveInt,
+        PrivateAttr,
+        StrictInt,
+        ValidationError,
+        root_validator,
+        validator,
+    )
+
+    def is_subclass_of_base_model(obj):
+        return issubclass(obj, BaseModel)
+
+
+def _iter_model_field_types():
+    model_field_types = []
+
+    try:
+        from pydantic.fields import ModelField as model_field_type
+    except ImportError:
+        pass
+    else:
+        model_field_types.append(model_field_type)
+
+    try:
+        from pydantic.v1.fields import ModelField as compat_model_field_type
+    except ImportError:
+        pass
+    else:
+        if compat_model_field_type not in model_field_types:
+            model_field_types.append(compat_model_field_type)
+
+    return model_field_types
+
+
+def register_pydantic_serializers(serialization_context):
+    if not PYDANTIC_INSTALLED:
+        return
+
+    # Pydantic's Cython validators are not serializable.
+    # https://github.com/cloudpipe/cloudpickle/issues/408
+    #
+    # FastAPI can still surface Pydantic's v1 compatibility ModelField under
+    # Pydantic v2, so we need to register serializers for both types until that
+    # compatibility path is no longer used upstream.
+    for model_field_type in _iter_model_field_types():
+        serialization_context._register_cloudpickle_serializer(
+            model_field_type,
+            custom_serializer=lambda o: {
+                "name": o.name,
+                # outer_type_ is the original type for ModelFields,
+                # while type_ can be updated later with the nested type
+                # like int for List[int].
+                "type_": o.outer_type_,
+                "class_validators": o.class_validators,
+                "model_config": o.model_config,
+                "default": o.default,
+                "default_factory": o.default_factory,
+                "required": o.required,
+                "alias": o.alias,
+                "field_info": o.field_info,
+            },
+            custom_deserializer=(
+                lambda kwargs, model_field_type=model_field_type: model_field_type(
+                    **kwargs
+                )
+            ),
+        )