ROLLOUT 3 · caikit/caikit
caikit__caikit-233
Not solved
477 stotal time
10tool calls
10model calls
6,416tokens generated
77,335prompt tokens read
Empty agent patch
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System instructions 4,822 characters
You are a principal Python software engineer resolving a repository issue in `/workspace`. Your sole objective is to produce a minimal, root-cause source patch that passes every hidden fail-to-pass and pass-to-pass regression test on the first grading run. ## Critical Environment & Grader Rules 1. **Air-Gapped Sandbox & Available CLI Tools**: No network access. Never run `pip`, `uv`, `conda`, or `git fetch`. Note: `rg` (`ripgrep`) is NOT installed in the container — always use `grep -rn --include="*.py"` for fast exact searches so you never waste a tool call on `command not found`. 2. **Protected Grading Paths**: Before running official verification, the grading harness **strips and reverts all changes** to `tests/`, `test/`, `testing/`, `test_*.py`, `*_test.py`, `conftest.py`, `pyproject.toml`, `setup.cfg`, `pytest.ini`, and `tox.ini`. Every part of your fix MUST reside in non-test production source files. 3. **Untracked File Trap (`git add -N .`)**: `submit_patch` stages ALL untracked files inside `/workspace`! Never leave reproduction or scratch files inside `/workspace` — run reproductions inline via `python3 - <<'PY'` or inside `/tmp/repro.py` in a single `run_command`. 4. **Stdout Truncation Rule**: `run_command` truncates stdout after the first 5,000 characters from the TOP. Always run pytest quietly with short tracebacks: `pytest path/to/test_file.py -k pattern -q --tb=short --no-header` If a test file is large, redirect output to `/tmp/pytest.log` and inspect `tail -n 80 /tmp/pytest.log` so failure tracebacks at the bottom are never cut off. 5. **Strict Pacing Discipline**: `get_status` and `submit_patch` are free and do NOT count toward `max_tool_calls`. Target **16-22 counted tool calls total**, and **begin your first `edit_file` change no later than tool call 15**. Reserve at least 4 calls for verification, `git status --short && git diff`, and submission. ## Execution Protocol ### Phase 1 — Pinpoint Code & Tests Immediately (1-2 tool calls) - The user message already contains `Workspace Layout` — do NOT waste a turn running `ls` on top-level directories. - Run a single compound `run_command` with `grep -rn --include="*.py"` to locate: (a) the exact symbols, error strings, or functions from the problem description, (b) **all parallel or sibling implementations** (for example sync + async variants, `Client` + `AsyncClient`, serializer + deserializer, multiple backends/dialects, base + subclass methods, `__init__.py` / `__all__` exports), and (c) the closest existing unit test files under `tests/`. ### Phase 2 — Deep Read & Spec Anchor (2-5 tool calls) - Use `read_file` with generous `start_line` and `end_line` windows (180-300 lines per call so you read whole modules in fewer calls) on the target function/class, callers, type annotations, and neighboring tests. - Before editing, verify every explicit and implicit requirement from the issue: exact parameter names, defaults, boundary/domain edge cases, exception types/messages, `None` vs empty collection vs falsy (`0`, `False`, `""`) semantics, mutability/copying, and return types. ### Phase 3 — Reproduce Bug in One Call (1 tool call) - Run ONE concise command (`python3 - <<'PY' ... PY` or targeted `pytest ... -q --tb=short --no-header`) via `run_command` to observe the exact pre-fix failure. Do NOT spend multiple tool calls writing separate reproduction scripts. ### Phase 4 — Surgical Implementation Across All Parallel Paths (1-4 tool calls) - Begin editing by tool call 15! Use `edit_file` with 4-6 lines of exact surrounding context copied from `read_file`. - Update ALL parallel codepaths (sync/async, multiple classes/dialects, type hints) consistently so partial fixes do not fail sibling tests. - **2-Strike Edit Rule**: If `edit_file` fails twice on the same block due to whitespace or escaping, do not retry `edit_file` a third time — apply an atomic Python transformation via `run_command` and immediately verify `python3 -m py_compile path/to/file.py`. ### Phase 5 — Edge-Case Verification, Diff Hygiene & Clean Exit (2-4 tool calls) - Run a single compound `run_command` testing both your reproduction + boundary edge cases (`None`, empty input, leading/trailing delimiters, subdomain/prefix boundaries, subclass/async parity) AND the relevant existing `pytest` module(s) with `-q --tb=short --no-header`. - Run `git status --short && git diff` via `run_command`. Confirm: - ZERO untracked (`??`) files in `/workspace` (`rm -f` any untracked workspace files immediately), - ZERO modifications to `tests/` or protected config files (`git checkout -- tests/` if touched), - Minimal, clean diff with valid imports and indentation. - Call `submit_patch` once verification passes, then respond with a brief 2-sentence plain-text summary and make NO further tool calls.
Task prompt 4,175 characters
You are evaluating a software engineering task for repository caikit/caikit.
Problem Statement:
Support Functional Inheritance
## Describe the bug
When creating a `class` that inherits from a `DataObjectBase` and overrides parent functions without adding or changing any of the schema, an exception is raised:
```
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
File "/Users/ghart/Projects/github/caikit/caikit2/caikit/core/data_model/dataobject.py", line 88, in __new__
raise TypeError(
TypeError: All DataObjectBase classes must follow dataclass syntax
```
## Sample Code
```py
from caikit.core import DataObjectBase, dataobject
@dataobject
class Base(DataObjectBase):
foo: int
def doit(self):
return self.foo * 2
@dataobject
class Derived(Base):
def doit(self):
return self.foo * 3
```
## Expected behavior
```py
b_inst = Base(1)
assert b_inst.doit() == 2
d_inst = Derived(1)
assert d_inst.doit() == 3
```
## Observed behavior
```
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
File "/Users/ghart/Projects/github/caikit/caikit2/caikit/core/data_model/dataobject.py", line 88, in __new__
raise TypeError(
TypeError: All DataObjectBase classes must follow dataclass syntax
```
## Additional context
This will be very important for allowing content libraries to share common data structures with custom backend, construction, and functional interfaces
## Task Budget (Session terminates when any budget is exhausted)
- Time allowance: 7.5 minutes
- Tool calls allowance: 30 calls
- Max loop iterations: 40 turns
## Execution Environment Rules
- Single command timeout: 90 seconds (commands exceeding this fail without ending the session)
- Command output limit: 5000 characters
- File view limit: 150 lines per read_file call
- File character limit: 10000 characters per read_file call
- Environment is offline (no network/PyPI access). All repository and test dependencies are ALREADY pre-installed. Do NOT attempt to run pip install or download packages.
## Instructions:
0. All source code is under `/workspace`. Do NOT search outside `/workspace` (e.g. `/usr/`, `/wheels/`, system site-packages). If imports fail, the issue is in the source code under `/workspace`, not in missing system packages.
1. Analyze the problem statement and any provided hints carefully to identify all requested script paths, CLI subcommands, or Python modules.
2. Inspect existing codebase conventions and test files before making edits.
3. Verify your implementation using targeted tests or inline assertions before submitting.
4. Call submit_patch only after your implementation is complete and verified.
5. As your final action, you must return a text-only response reporting your completion to terminate the session.
## Workspace Layout
The repository is located at `/workspace`. Here is the directory tree (up to 3 levels):
```
./CODEOWNERS
./CONTRIBUTING.md
./LICENSE
./README.md
./SECURITY.md
./caikit
./caikit-overview.png
./caikit/__init__.py
./caikit/config
./caikit/config/__init__.py
./caikit/config/config.py
./caikit/config/config.yml
./caikit/core
./caikit/core/__init__.py
./caikit/core/augmentors
./caikit/core/data_model
./caikit/core/model_manager.py
./caikit/core/module_backends
./caikit/core/modules
./caikit/core/registries.py
./caikit/core/signature_parsing
./caikit/core/task.py
./caikit/core/toolkit
./caikit/interfaces
./caikit/interfaces/__init__.py
./caikit/interfaces/common
./caikit/interfaces/runtime
./caikit/runtime
./caikit/runtime/__init__.py
./caikit/runtime/dump_services.py
./caikit/runtime/grpc_server.py
./caikit/runtime/interceptors
./caikit/runtime/metrics
./caikit/runtime/model_management
./caikit/runtime/protobufs
./caikit/runtime/service_factory.py
./caikit/runtime/service_generation
./caikit/runtime/servicers
./caikit/runtime/types
./caikit/runtime/utils
./caikit/runtime/work_management
./code-of-conduct.md
./docs
./docs-requirements.txt
./examples
./pyproject.toml
./releases.md
./scripts
./scripts/check_deps.sh
./scripts/fmt.sh
./setup_requirements.txt
./tests
./tox.ini
```
Turn 1at 46 s261 tokens out3,251 prompt tokens
The user wants to support functional inheritance in `DataObjectBase` classes.
When a class inherits from a `DataObjectBase` and overrides functions without changing the schema (i.e., no new dataclass fields), a `TypeError` is raised: "All DataObjectBase classes must follow dataclass syntax".
Looking at the sample code:
```python
from caikit.core import DataObjectBase, dataobject
@dataobject
class Base(DataObjectBase):
foo: int
def doit(self):
return self.foo * 2
@dataobject
class Derived(Base):
def doit(self):
return self.foo * 3
```
The `Derived` class overrides `doit` but doesn't add any new fields. It seems the `@dataobject` decorator or `DataObjectBase.__new__` checks for dataclass-like fields and fails if none are found.
Let's locate the offending code in `/workspace/caikit/core/data_model/dataobject.py`.
TOOL CALL · run_command
{
"command": "grep -rn --include=\"*.py\" \"All DataObjectBase classes must follow dataclass syntax\" ."
}Result of run_command 183 characters
{
"status": "ok",
"stdout": "./caikit/core/data_model/dataobject.py:89: \"All DataObjectBase classes must follow dataclass syntax\"\n",
"stderr": "",
"exit_code": 0
}Generated tokens 261 tokens · stopped: STOP
<|channel>thought
The user wants to support functional inheritance in `DataObjectBase` classes.
When a class inherits from a `DataObjectBase` and overrides functions without changing the schema (i.e., no new dataclass fields), a `TypeError` is raised: "All DataObjectBase classes must follow dataclass syntax".
Looking at the sample code:
```python
from caikit.core import DataObjectBase, dataobject
@dataobject
class Base(DataObjectBase):
foo: int
def doit(self):
return self.foo * 2
@dataobject
class Derived(Base):
def doit(self):
return self.foo * 3
```
The `Derived` class overrides `doit` but doesn't add any new fields. It seems the `@dataobject` decorator or `DataObjectBase.__new__` checks for dataclass-like fields and fails if none are found.
Let's locate the offending code in `/workspace/caikit/core/data_model/dataobject.py`.<channel|><|tool_call>call:run_command{command:<|"|>grep -rn --include="*.py" "All DataObjectBase classes must follow dataclass syntax" .<|"|>}<tool_call|><|tool_response>
Turn 2at 52 s79 tokens out3,582 prompt tokens
The error is in `caikit/core/data_model/dataobject.py` at line 89. Let's examine the code around that area.
TOOL CALL · read_file
{
"end_line": 110,
"filepath": "caikit/core/data_model/dataobject.py",
"start_line": 60
}Result of read_file 2,196 characters
{
"status": "ok",
"filepath": "caikit/core/data_model/dataobject.py",
"content": "# Common package prefix\nCAIKIT_DATA_MODEL = \"caikit_data_model\"\n\n# Registry of auto-generated protos so that they can be rendered to .proto\n_AUTO_GEN_PROTO_CLASSES = []\n\n# Special attribute used to indicate which defaults are user provided\n_USER_DEFINED_DEFAULTS = \"__user_defined_defaults__\"\n\n## Public ######################################################################\n\n\nclass _DataObjectBaseMetaClass(_DataBaseMetaClass):\n \"\"\"This metaclass is used for the DataObject base class so that all data\n objects can delay the creation of their proto class until after the\n metaclass has been instantiated.\n \"\"\"\n\n def __new__(mcs, name, bases, attrs):\n \"\"\"When instantiating a new DataObject class, the proto class will not\n yet have been generated, but the set of fields will be known since the\n class will be the raw input representation of a @dataclass\n \"\"\"\n\n # Get the annotations that will go into the dataclass\n if name != \"DataObjectBase\":\n field_names = attrs.get(\"__annotations__\")\n if field_names is None:\n raise TypeError(\n \"All DataObjectBase classes must follow dataclass syntax\"\n )\n attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names\n\n # Delegate to the base metaclass\n return super().__new__(mcs, name, bases, attrs)\n\n\nclass DataObjectBase(DataBase, metaclass=_DataObjectBaseMetaClass):\n \"\"\"A DataObject is a data model class that is backed by a @dataclass.\n\n Data model classes that use the @dataobject decorator must derive from this\n base class.\n \"\"\"\n\n\ndef dataobject(*args, **kwargs) -> Callable[[Type], Type[DataBase]]:\n \"\"\"The @dataobject decorator can be used to define a Data Model object's\n schema inline with the definition of the python class rather than needing to\n bind to a pre-compiled protobufs class. For example:\n\n @dataobject(\"foo.bar\")",
"start_line": 60,
"end_line": 110,
"total_lines": 386,
"is_truncated": false
}Generated tokens 79 tokens · stopped: STOP
The error is in `caikit/core/data_model/dataobject.py` at line 89.
Let's examine the code around that area.<channel|><|tool_call>call:read_file{filepath:<|"|>caikit/core/data_model/dataobject.py<|"|>,start_line:60,end_line:110}<tool_call|><|tool_response>
Turn 3at 91 s570 tokens out4,263 prompt tokens
The `_DataObjectBaseMetaClass.__new__` method is where the `TypeError` is raised.
```python
def __new__(mcs, name, bases, attrs):
"""When instantiating a new DataObject class, the proto class will not
yet have been generated, but the set of fields will be known since the
class will be the raw input representation of a @dataclass
"""
# Get the annotations that will go into the dataclass
if name != "DataObjectBase":
field_names = attrs.get("__annotations__")
if field_names is None:
raise TypeError(
"All DataObjectBase classes must follow dataclass syntax"
)
attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names
# Delegate to the base metaclass
return super().__new__(mcs, name, bases, attrs)
```
It checks if `__annotations__` exists in `attrs`. If it doesn't, it raises `TypeError`.
When inheriting from `Base` (which has annotations) and creating `Derived` (which doesn't add any new annotations), `attrs` for `Derived` will not contain `__annotations__` because annotations are not inherited in `attrs` of the child class unless the child also defines some.
Wait, in Python, `__annotations__` is NOT inherited. If `Derived` doesn't define any annotated fields, `Derived.__dict__` (which is what `attrs` is during `__new__`) will not have `__annotations__`.
To support functional inheritance, it should check if any of the base classes have annotations if the current class doesn't. Or more accurately, it should collect annotations from the bases.
Actually, the purpose here is to store `_FWD_DECL_FIELDS` in `attrs`.
If `Derived` doesn't have its own annotations, it should probably inherit the annotations from its parent `Base`.
Let's see how `_FWD_DECL_FIELDS` is used.
I'll read more of `caikit/core/data_model/dataobject.py` and also `caikit/core/data_model/base.py` if it exists (since `_DataObjectBaseMetaClass` inherits from `_DataBaseMetaClass`).
Wait, `_DataObjectBaseMetaClass` inherits from `_DataBaseMetaClass`. Let's find where `_DataBaseMetaClass` is defined.
TOOL CALL · run_command
{
"command": "grep -rn \"class _DataBaseMetaClass\" caikit/core/data_model/"
}Result of run_command 127 characters
{
"status": "ok",
"stdout": "caikit/core/data_model/base.py:46:class _DataBaseMetaClass(type):\n",
"stderr": "",
"exit_code": 0
}Generated tokens 570 tokens · stopped: STOP
The `_DataObjectBaseMetaClass.__new__` method is where the `TypeError` is raised.
```python
def __new__(mcs, name, bases, attrs):
"""When instantiating a new DataObject class, the proto class will not
yet have been generated, but the set of fields will be known since the
class will be the raw input representation of a @dataclass
"""
# Get the annotations that will go into the dataclass
if name != "DataObjectBase":
field_names = attrs.get("__annotations__")
if field_names is None:
raise TypeError(
"All DataObjectBase classes must follow dataclass syntax"
)
attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names
# Delegate to the base metaclass
return super().__new__(mcs, name, bases, attrs)
```
It checks if `__annotations__` exists in `attrs`. If it doesn't, it raises `TypeError`.
When inheriting from `Base` (which has annotations) and creating `Derived` (which doesn't add any new annotations), `attrs` for `Derived` will not contain `__annotations__` because annotations are not inherited in `attrs` of the child class unless the child also defines some.
Wait, in Python, `__annotations__` is NOT inherited. If `Derived` doesn't define any annotated fields, `Derived.__dict__` (which is what `attrs` is during `__new__`) will not have `__annotations__`.
To support functional inheritance, it should check if any of the base classes have annotations if the current class doesn't. Or more accurately, it should collect annotations from the bases.
Actually, the purpose here is to store `_FWD_DECL_FIELDS` in `attrs`.
If `Derived` doesn't have its own annotations, it should probably inherit the annotations from its parent `Base`.
Let's see how `_FWD_DECL_FIELDS` is used.
I'll read more of `caikit/core/data_model/dataobject.py` and also `caikit/core/data_model/base.py` if it exists (since `_DataObjectBaseMetaClass` inherits from `_DataBaseMetaClass`).
Wait, `_DataObjectBaseMetaClass` inherits from `_DataBaseMetaClass`. Let's find where `_DataBaseMetaClass` is defined.
<channel|><|tool_call>call:run_command{command:<|"|>grep -rn "class _DataBaseMetaClass" caikit/core/data_model/<|"|>}<tool_call|><|tool_response>
Turn 4at 94 s46 tokens out4,896 prompt tokens
Let's read `caikit/core/data_model/base.py`.
TOOL CALL · read_file
{
"filepath": "caikit/core/data_model/base.py"
}Result of read_file 6,238 characters
{
"status": "ok",
"filepath": "caikit/core/data_model/base.py",
"content": "# Copyright The Caikit Authors\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\n\"\"\"Base classes and functionality for all data structures.\n\"\"\"\n\n# metaclass-generated field members cannot be detected by pylint\n# pylint: disable=no-member\n\n# Standard\nfrom dataclasses import dataclass\nfrom enum import Enum\nfrom typing import Any, Dict, Optional, Type, Union\nimport base64\nimport json\n\n# Third Party\nfrom google.protobuf import json_format\nfrom google.protobuf.descriptor import Descriptor\nfrom google.protobuf.internal import type_checkers as proto_type_checkers\nfrom google.protobuf.message import Message as ProtoMessageType\n\n# First Party\nimport alog\n\n# Local\nfrom ..toolkit.errors import error_handler\nfrom . import enums, json_dict\n\nlog = alog.use_channel(\"DATAM\")\nerror = error_handler.get(log)\n\n\nclass _DataBaseMetaClass(type):\n \"\"\"Meta class for all structures in the data model.\"\"\"\n\n # store a registry of all classes that use this metaclass, i.e.,\n # all classes that extend DataBase. This is used for constructing new\n # instances by name without having to introspect all modules in data_model.\n class_registry = {}\n\n # This sentinel value is used to determine whether a given attribute is\n # present on a class without doing `getattr` twice in the case where the\n # attribute does exist.\n _MISSING_ATTRIBUTE = \"missing attribute\"\n\n # Special attribute used to communicate that the proto fields are forward\n # declared and will be populated after the metaclass has completed\n # construction.\n _FWD_DECL_FIELDS = \"__fwd_decl_fields__\"\n\n # Special instance attributes that an instance of a class derived from\n # DataBase may have. These are added to __slots__.\n _BACKEND_ATTR = \"_backend\"\n _WHICH_ONEOF_ATTR = \"_which_oneof\"\n\n def __new__(mcs, name, bases, attrs):\n \"\"\"When constructing a new data model class, we set the 'fields' class variable from the\n protobufs descriptor and then set the '__slots__' magic class attribute to fields. This\n provides two benefits: (a) performance is improved since the classes only need to know\n about these attributes (b) it helps to enforce that all member variables in these classes\n are described in the protobufs.\n\n Note: If you want to add a variable for internal use that is not described in the\n protobufs, it can be named in the tuple class variable _private_slots and will\n automatically be added to __slots__.\n \"\"\"\n\n # Protobufs fields can be divided into these categories, which are used\n # to automatically determine appropriate behavior in a number of methods\n attrs[\"full_name\"] = name\n attrs[\"fields_enum_map\"] = {}\n attrs[\"fields_enum_rev\"] = {}\n attrs[\"_fields_oneofs_map\"] = {}\n attrs[\"_fields_to_oneof\"] = {}\n attrs[\"_fields_map\"] = ()\n attrs[\"_fields_message\"] = ()\n attrs[\"_fields_message_repeated\"] = ()\n attrs[\"_fields_enum\"] = ()\n attrs[\"_fields_enum_repeated\"] = ()\n attrs[\"_fields_primitive\"] = ()\n attrs[\"_fields_primitive_repeated\"] = ()\n\n # Look for the set of fields either from a predefined protobuf class or\n # from a forward declaration from @dataobject\n fields = ()\n proto_class = None\n if name not in [\"DataBase\", \"DataObjectBase\"]:\n # Look for a precompiled proto class and if found, parse its\n # descriptor\n proto_class = attrs.get(\"_proto_class\")\n if proto_class is not None:\n all_oneof_fields = [\n field.name\n for oneof in proto_class.DESCRIPTOR.oneofs\n for field in oneof.fields\n ]\n fields = tuple(\n (\n field\n for field in proto_class.DESCRIPTOR.fields_by_name\n if field not in all_oneof_fields\n )\n ) + tuple(proto_class.DESCRIPTOR.oneofs_by_name)\n\n # Otherwise, we need to get the fields from a \"special\" attribute\n else:\n fields = attrs.pop(mcs._FWD_DECL_FIELDS, None)\n log.debug4(\n \"Using dataclass forward declaration fields %s for %s\", fields, name\n )\n error.value_check(\n \"<COR49310991E>\",\n fields is not None,\n \"No proto class found for {}\",\n name,\n )\n attrs[\"fields\"] = fields\n attrs[\"_proto_class\"] = proto_class\n\n # Look if any private slots are declared as class variables\n private_slots = attrs.setdefault(\"_private_slots\", ())\n\n # Class slots are fields + private slots, this prevents other\n # member attributes from being set and also improves performance\n attrs[\"__slots__\"] = tuple(\n [f\"_{field}\" for field in fields]\n + list(private_slots)\n + [mcs._BACKEND_ATTR, mcs._WHICH_ONEOF_ATTR]\n )\n\n # Create the instance of the type\n instance = super().__new__(mcs, name, bases, attrs)\n\n # If there's a valid proto class, perform proto descriptor parsing\n if proto_class is not None:\n mcs.parse_proto_descriptor(instance)\n",
"start_line": 1,
"end_line": 150,
"total_lines": 990,
"is_truncated": true
}Generated tokens 46 tokens · stopped: STOP
Let's read `caikit/core/data_model/base.py`.
<channel|><|tool_call>call:read_file{filepath:<|"|>caikit/core/data_model/base.py<|"|>}<tool_call|><|tool_response>
Turn 5at 169 s1,232 tokens out6,574 prompt tokens
In `_DataBaseMetaClass.__new__`:
```python
# Otherwise, we need to get the fields from a "special" attribute
else:
fields = attrs.pop(mcs._FWD_DECL_FIELDS, None)
log.debug4(
"Using dataclass forward declaration fields %s for %s", fields, name
)
error.value_check(
"<COR49310991E>",
fields is not None,
"No proto class found for {}",
name,
)
```
It expects `_FWD_DECL_FIELDS` to be present in `attrs`.
Now back to `_DataObjectBaseMetaClass.__new__`:
```python
def __new__(mcs, name, bases, attrs):
# ...
# Get the annotations that will go into the dataclass
if name != "DataObjectBase":
field_names = attrs.get("__annotations__")
if field_names is None:
raise TypeError(
"All DataObjectBase classes must follow dataclass syntax"
)
attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names
# ...
```
If `Derived` inherits from `Base`, `Base` has `__annotations__` and will have `_FWD_DECL_FIELDS` set on the resulting class. But when `Derived` is being created, its `attrs` won't have `__annotations__`.
If we want to allow `Derived` to inherit fields from `Base`, we should:
1. Check if `attrs` has `__annotations__`.
2. If it doesn't, check if any of the `bases` have `_FWD_DECL_FIELDS` (which was set by the metaclass of the base).
3. If it still doesn't have any fields, and it's not `DataObjectBase`, then maybe it should raise the error. But wait, if it's a purely functional override, it should just keep the fields of the base.
Wait, if `Derived(Base)` is created, and `Base` was a `DataObjectBase`, then `Base` will have `_FWD_DECL_FIELDS` in its `__dict__` (or it will be a class attribute).
Let's see if we can retrieve `_FWD_DECL_FIELDS` from `bases`.
Actually, if `Derived` doesn't have any annotations, it just inherits everything from `Base`. If `Base` is a `DataObjectBase`, it already has the fields defined.
However, `_DataBaseMetaClass.__new__` calls `attrs.pop(mcs._FWD_DECL_FIELDS, None)`. This removes it from the `attrs` of the class being created. But it doesn't remove it from the base class.
Wait, `_DataBaseMetaClass.__new__` does:
```python
# Look for the set of fields either from a predefined protobuf class or
# from a forward declaration from @dataobject
fields = ()
proto_class = None
if name not in ["DataBase", "DataObjectBase"]:
# ...
# Otherwise, we need to get the fields from a "special" attribute
else:
fields = attrs.pop(mcs._FWD_DECL_FIELDS, None)
# ...
error.value_check(
"<COR49310991E>",
fields is not None,
"No proto class found for {}",
name,
)
```
If `Derived` is being created, `attrs` does not contain `_FWD_DECL_FIELDS` because it was not put there by `_DataObjectBaseMetaClass.__new__` (because `__annotations__` was missing). Then `fields` becomes `None` (via `pop`), and `error.value_check` fails.
So we need to make sure `_FWD_DECL_FIELDS` is present in `attrs` even if `__annotations__` is missing, as long as one of the bases has it.
Wait, `_FWD_DECL_FIELDS` is used to tell `_DataBaseMetaClass` what the fields are.
If `Derived` inherits from `Base`, it should have the same fields as `Base` (unless it adds more).
If `Derived` has its own `__annotations__`, we should probably merge them with `Base`'s annotations? The issue says "without adding or changing any of the schema", so maybe for now we only need to handle the case where it doesn't add any. But usually, dataclass inheritance merges fields.
Let's check the sample code again.
```python
@dataobject
class Base(DataObjectBase):
foo: int
def doit(self):
return self.foo * 2
@dataobject
class Derived(Base):
def doit(self):
return self.foo * 3
```
`Base` is a `DataObjectBase`. `Derived` is also decorated with `@dataobject`.
When `@dataobject` is used, it probably calls `type(...)` or uses the metaclass.
Wait, `dataobject` is a decorator.
```python
def dataobject(*args, **kwargs) -> Callable[[Type], Type[DataBase]]:
# ...
```
Let's see the implementation of `dataobject` decorator.
TOOL CALL · read_file
{
"filepath": "caikit/core/data_model/dataobject.py",
"start_line": 112
}Result of read_file 6,434 characters
{
"status": "ok",
"filepath": "caikit/core/data_model/dataobject.py",
"content": " class MyDataObject(DataObjectBase):\n '''My Custom Data Object'''\n foo: str\n bar: int\n\n NOTE: The wrapped class must NOT inherit directly from DataBase. That\n inheritance will be added by this decorator, but if it is written\n directly, the metaclass that links protobufs to the class will be called\n before this decorator can auto-gen the protobufs class.\n\n Args:\n package: str\n The package name to use for the generated protobufs class\n\n Returns:\n decorator: Callable[[Type], Type[DataBase]]\n The decorator function that will wrap the given class\n \"\"\"\n\n def decorator(cls: Type) -> Type[DataBase]:\n # Make sure that the wrapped class does NOT inherit from DataBase\n error.value_check(\n \"<COR95184230E>\",\n issubclass(cls, (DataObjectBase, Enum)),\n \"{} must inherit from DataObjectBase/Enum when using @dataobject\",\n cls.__name__,\n )\n\n # Add the package to the kwargs\n kwargs.setdefault(\"package\", package)\n\n # If it's not an enum, fill in any missing field defaults as None\n # and make sure it's a dataclass\n if not issubclass(cls, Enum):\n log.debug2(\"Wrapping data class %s\", cls)\n user_defined_defaults = {}\n for annotation in getattr(cls, \"__annotations__\", {}):\n user_defined_default = getattr(cls, annotation, dataclasses.MISSING)\n if user_defined_default == dataclasses.MISSING:\n log.debug3(\"Filling in None default for %s.%s\", cls, annotation)\n setattr(cls, annotation, None)\n else:\n user_defined_defaults[annotation] = user_defined_default\n # If the current __init__ is auto-generated by dataclass, remove\n # it so that a new one is created with the new defaults\n if _has_dataclass_init(cls):\n log.debug3(\"Resetting default dataclass init\")\n delattr(cls, \"__init__\")\n cls = dataclasses.dataclass(cls)\n setattr(cls, _USER_DEFINED_DEFAULTS, user_defined_defaults)\n\n descriptor = _dataobject_to_proto(dataclass_=cls, **kwargs)\n\n # Create the message class from the dataclass\n proto_class = py_to_proto.descriptor_to_message_class(descriptor)\n _AUTO_GEN_PROTO_CLASSES.append(proto_class)\n\n # Add enums to the global enums module\n for enum_class in _get_all_enums(proto_class):\n log.debug2(\"Importing enum [%s]\", enum_class.DESCRIPTOR.name)\n enums.import_enum(enum_class)\n\n # Declare the merged class that binds DataBase to the wrapped class with\n # this generated proto class\n if isinstance(proto_class, type):\n setattr(cls, \"_proto_class\", proto_class)\n cls = _make_data_model_class(proto_class, cls)\n\n # If this was a default-generated dataclass __init__ and there are\n # any oneofs, we need to augment the __init__ to support kwargs for\n # the individual fields\n if _has_dataclass_init(cls) and cls._fields_oneofs_map:\n setattr(cls, \"__init__\", _make_oneof_init(cls))\n\n else:\n enums.import_enum(proto_class, cls)\n setattr(cls, \"_proto_enum\", proto_class)\n\n # Return the decorated class\n return cls\n\n # If called without the function invocation, fill in the default argument\n if args and callable(args[0]):\n assert not kwargs, \"This shouldn't happen!\"\n package = CAIKIT_DATA_MODEL\n return decorator(args[0])\n\n # Pull the package as an arg or a keyword arg\n if args:\n package = args[0]\n if \"package\" in kwargs:\n raise TypeError(\"Got multiple values for argument 'package'\")\n else:\n package = kwargs.get(\"package\", CAIKIT_DATA_MODEL)\n return decorator\n\n\ndef render_dataobject_protos(interfaces_dir: str):\n \"\"\"Write out protobufs files for all proto classes generated from dataobjects\n to the target interfaces directory\n\n Args:\n interfaces_dir: str\n The target directory (must already exist)\n \"\"\"\n for proto_class in _AUTO_GEN_PROTO_CLASSES:\n proto_class.write_proto_file(interfaces_dir)\n\n\n## Implementation Details ######################################################\n\n\ndef _dataobject_to_proto(*args, **kwargs):\n kwargs.setdefault(\"type_mapping\", DATAOBJECT_PY_TO_PROTO_TYPES)\n return _DataobjectConverter(*args, **kwargs).descriptor\n\n\nclass _DataobjectConverter(DataclassConverter):\n \"\"\"Augment the dataclass converter to be able to pull descriptors from\n existing data objects\n \"\"\"\n\n def get_concrete_type(self, entry: Any) -> Any:\n \"\"\"Also include data model classes and enums as concrete types\"\"\"\n unwrapped = self._resolve_wrapped_type(entry)\n if (\n isinstance(unwrapped, type)\n and issubclass(unwrapped, DataBase)\n and unwrapped._proto_class is not None\n ) or hasattr(unwrapped, \"_proto_enum\"):\n return entry\n return super().get_concrete_type(entry)\n\n def get_descriptor(self, entry: Any) -> Any:\n \"\"\"Unpack data model classes and enums to their descriptors\"\"\"\n entry = self._resolve_wrapped_type(entry)\n if isinstance(entry, type) and issubclass(entry, DataBase):\n return entry._proto_class.DESCRIPTOR\n proto_enum = getattr(entry, \"_proto_enum\", None)\n if proto_enum is not None:\n return proto_enum.DESCRIPTOR\n return super().get_descriptor(entry)\n\n def get_optional_field_names(self, entry: Any) -> List[str]:\n \"\"\"Get the names of any fields which are optional. This will be any\n field that has a user-defined default or is marked as Optional[]\n \"\"\"\n optional_fields = list(getattr(entry, _USER_DEFINED_DEFAULTS, {}))\n for field_name, field in entry.__dataclass_fields__.items():\n if (",
"start_line": 112,
"end_line": 261,
"total_lines": 386,
"is_truncated": true
}Generated tokens 1,232 tokens · stopped: STOP
In `_DataBaseMetaClass.__new__`:
```python
# Otherwise, we need to get the fields from a "special" attribute
else:
fields = attrs.pop(mcs._FWD_DECL_FIELDS, None)
log.debug4(
"Using dataclass forward declaration fields %s for %s", fields, name
)
error.value_check(
"<COR49310991E>",
fields is not None,
"No proto class found for {}",
name,
)
```
It expects `_FWD_DECL_FIELDS` to be present in `attrs`.
Now back to `_DataObjectBaseMetaClass.__new__`:
```python
def __new__(mcs, name, bases, attrs):
# ...
# Get the annotations that will go into the dataclass
if name != "DataObjectBase":
field_names = attrs.get("__annotations__")
if field_names is None:
raise TypeError(
"All DataObjectBase classes must follow dataclass syntax"
)
attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names
# ...
```
If `Derived` inherits from `Base`, `Base` has `__annotations__` and will have `_FWD_DECL_FIELDS` set on the resulting class. But when `Derived` is being created, its `attrs` won't have `__annotations__`.
If we want to allow `Derived` to inherit fields from `Base`, we should:
1. Check if `attrs` has `__annotations__`.
2. If it doesn't, check if any of the `bases` have `_FWD_DECL_FIELDS` (which was set by the metaclass of the base).
3. If it still doesn't have any fields, and it's not `DataObjectBase`, then maybe it should raise the error. But wait, if it's a purely functional override, it should just keep the fields of the base.
Wait, if `Derived(Base)` is created, and `Base` was a `DataObjectBase`, then `Base` will have `_FWD_DECL_FIELDS` in its `__dict__` (or it will be a class attribute).
Let's see if we can retrieve `_FWD_DECL_FIELDS` from `bases`.
Actually, if `Derived` doesn't have any annotations, it just inherits everything from `Base`. If `Base` is a `DataObjectBase`, it already has the fields defined.
However, `_DataBaseMetaClass.__new__` calls `attrs.pop(mcs._FWD_DECL_FIELDS, None)`. This removes it from the `attrs` of the class being created. But it doesn't remove it from the base class.
Wait, `_DataBaseMetaClass.__new__` does:
```python
# Look for the set of fields either from a predefined protobuf class or
# from a forward declaration from @dataobject
fields = ()
proto_class = None
if name not in ["DataBase", "DataObjectBase"]:
# ...
# Otherwise, we need to get the fields from a "special" attribute
else:
fields = attrs.pop(mcs._FWD_DECL_FIELDS, None)
# ...
error.value_check(
"<COR49310991E>",
fields is not None,
"No proto class found for {}",
name,
)
```
If `Derived` is being created, `attrs` does not contain `_FWD_DECL_FIELDS` because it was not put there by `_DataObjectBaseMetaClass.__new__` (because `__annotations__` was missing). Then `fields` becomes `None` (via `pop`), and `error.value_check` fails.
So we need to make sure `_FWD_DECL_FIELDS` is present in `attrs` even if `__annotations__` is missing, as long as one of the bases has it.
Wait, `_FWD_DECL_FIELDS` is used to tell `_DataBaseMetaClass` what the fields are.
If `Derived` inherits from `Base`, it should have the same fields as `Base` (unless it adds more).
If `Derived` has its own `__annotations__`, we should probably merge them with `Base`'s annotations? The issue says "without adding or changing any of the schema", so maybe for now we only need to handle the case where it doesn't add any. But usually, dataclass inheritance merges fields.
Let's check the sample code again.
```python
@dataobject
class Base(DataObjectBase):
foo: int
def doit(self):
return self.foo * 2
@dataobject
class Derived(Base):
def doit(self):
return self.foo * 3
```
`Base` is a `DataObjectBase`. `Derived` is also decorated with `@dataobject`.
When `@dataobject` is used, it probably calls `type(...)` or uses the metaclass.
Wait, `dataobject` is a decorator.
```python
def dataobject(*args, **kwargs) -> Callable[[Type], Type[DataBase]]:
# ...
```
Let's see the implementation of `dataobject` decorator.
<channel|><|tool_call>call:read_file{filepath:<|"|>caikit/core/data_model/dataobject.py<|"|>,start_line:112}<tool_call|><|tool_response>
Turn 6at 171 s54 tokens out9,564 prompt tokens
And `_make_data_model_class`:
TOOL CALL · run_command
{
"command": "grep -n \"def _make_data_model_class\" caikit/core/data_model/dataobject.py"
}Result of run_command 111 characters
{
"status": "ok",
"stdout": "294:def _make_data_model_class(proto_class, cls):\n",
"stderr": "",
"exit_code": 0
}Generated tokens 54 tokens · stopped: STOP
And `_make_data_model_class`:
<channel|><|tool_call>call:run_command{command:<|"|>grep -n "def _make_data_model_class" caikit/core/data_model/dataobject.py<|"|>}<tool_call|><|tool_response>
Turn 7at 177 s44 tokens out9,677 prompt tokens
TOOL CALL · read_file
{
"end_line": 320,
"filepath": "caikit/core/data_model/dataobject.py",
"start_line": 294
}Result of read_file 1,238 characters
{
"status": "ok",
"filepath": "caikit/core/data_model/dataobject.py",
"content": "def _make_data_model_class(proto_class, cls):\n if issubclass(cls, DataObjectBase):\n _DataBaseMetaClass.parse_proto_descriptor(cls)\n\n # Recursively make all nested message wrappers\n for nested_message_descriptor in proto_class.DESCRIPTOR.nested_types:\n nested_message_name = nested_message_descriptor.name\n nested_proto_class = getattr(proto_class, nested_message_name)\n setattr(\n cls,\n nested_message_name,\n _make_data_model_class(\n nested_proto_class,\n _DataBaseMetaClass.__new__(\n _DataBaseMetaClass,\n name=nested_message_name,\n bases=(DataBase,),\n attrs={\"_proto_class\": getattr(proto_class, nested_message_name)},\n ),\n ),\n )\n for nested_enum_descriptor in proto_class.DESCRIPTOR.enum_types:\n setattr(\n cls,\n nested_enum_descriptor.name,\n getattr(enums, nested_enum_descriptor.name),\n )",
"start_line": 294,
"end_line": 320,
"total_lines": 386,
"is_truncated": false
}Generated tokens 44 tokens · stopped: STOP
<channel|><|tool_call>call:read_file{filepath:<|"|>caikit/core/data_model/dataobject.py<|"|>,start_line:294,end_line:320}<tool_call|><|tool_response>
Turn 8at 236 s1,003 tokens out10,065 prompt tokens
Wait, let's look at the `decorator` function again:
```python
def decorator(cls: Type) -> Type[DataBase]:
# Make sure that the wrapped class does NOT inherit from DataBase
error.value_check(
"<COR95184230E>",
issubclass(cls, (DataObjectBase, Enum)),
"{} must inherit from DataObjectBase/Enum when using @dataobject",
cls.__name__,
)
```
Wait, the sample code says:
```python
@dataobject
class Base(DataObjectBase):
foo: int
```
Wait, `Base` inherits from `DataObjectBase`.
So `issubclass(Base, (DataObjectBase, Enum))` is `True`.
This matches `<COR95184230E>`.
Wait, the `decorator` code I read:
```python
error.value_check(
"<COR95184230E>",
issubclass(cls, (DataObjectBase, Enum)),
"{} must inherit from DataObjectBase/Enum when using @dataobject",
cls.__name__,
)
```
Wait, if `issubclass(cls, (DataObjectBase, Enum))` is `True`, then the check passes.
But `Base` is passed to `decorator`. `Base` was defined as `class Base(DataObjectBase)`.
So when `Base` is created, `_DataObjectBaseMetaClass.__new__` is called.
```python
class _DataObjectBaseMetaClass(_DataBaseMetaClass):
def __new__(mcs, name, bases, attrs):
# ...
# Get the annotations that will go into the dataclass
if name != "DataObjectBase":
field_names = attrs.get("__annotations__")
if field_names is None:
raise TypeError(
"All DataObjectBase classes must follow dataclass syntax"
)
attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names
```
For `Base`, `attrs` contains `foo: int`, so `__annotations__` is `{'foo': <class 'int'>}`. It works.
For `Derived(Base)`, `Base` is in `bases`. `Derived` doesn't have `__annotations__` in its `attrs`. So it raises `TypeError`.
To fix this, `_DataObjectBaseMetaClass.__new__` should check if any of the base classes have `_FWD_DECL_FIELDS` if `__annotations__` is missing.
Wait, if `Derived` inherits from `Base`, and `Base` is a `DataObjectBase`, then `Base` already has its fields defined.
If we want `Derived` to also be a `DataObjectBase` (which it is, by inheritance), it should also have these fields.
If we look at `_DataBaseMetaClass.__new__`, it looks for `_FWD_DECL_FIELDS` in `attrs`.
If we can't find `__annotations__` in `attrs` for `Derived`, we should check if any of the base classes have `_FWD_DECL_FIELDS`. Actually, they would have it as a class attribute if it was passed to `super().__new__` in `_DataObjectBaseMetaClass.__new__`.
Wait, `attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names`. This adds it to the class being created.
For `Derived`, if we don't find `__annotations__`, we should see if we can find `_FWD_DECL_FIELDS` in any of the `bases`.
But wait, if we just do that, then `_DataBaseMetaClass.__new__` will see it in `attrs` (since we put it there) and `fields` will be set.
Let's try to reproduce this first to be sure.
TOOL CALL · run_command
{
"command": "python3 - <<'PY'\nfrom caikit.core import DataObjectBase, dataobject\n\ntry:\n @dataobject\n class Base(DataObjectBase):\n foo: int\n def doit(self):\n return self.foo * 2\n\n print(\"Base created successfully\")\n\n @dataobject\n class Derived(Base):\n def doit(self):\n return self.foo * 3\n \n print(\"Derived created successfully\")\nexcept Exception as e:\n import traceback\n traceback.print_exc()\nPY\n"
}Result of run_command 438 characters
{
"status": "ok",
"stdout": "Base created successfully\n",
"stderr": "<function register_backend_type at 0x750f0b99e170> is still in the BETA phase and subject to change!\nTraceback (most recent call last):\n File \"<stdin>\", line 13, in <module>\n File \"/workspace/caikit/core/data_model/dataobject.py\", line 88, in __new__\n raise TypeError(\nTypeError: All DataObjectBase classes must follow dataclass syntax\n",
"exit_code": 0
}Generated tokens 1,003 tokens · stopped: STOP
Wait, let's look at the `decorator` function again:
```python
def decorator(cls: Type) -> Type[DataBase]:
# Make sure that the wrapped class does NOT inherit from DataBase
error.value_check(
"<COR95184230E>",
issubclass(cls, (DataObjectBase, Enum)),
"{} must inherit from DataObjectBase/Enum when using @dataobject",
cls.__name__,
)
```
Wait, the sample code says:
```python
@dataobject
class Base(DataObjectBase):
foo: int
```
Wait, `Base` inherits from `DataObjectBase`.
So `issubclass(Base, (DataObjectBase, Enum))` is `True`.
This matches `<COR95184230E>`.
Wait, the `decorator` code I read:
```python
error.value_check(
"<COR95184230E>",
issubclass(cls, (DataObjectBase, Enum)),
"{} must inherit from DataObjectBase/Enum when using @dataobject",
cls.__name__,
)
```
Wait, if `issubclass(cls, (DataObjectBase, Enum))` is `True`, then the check passes.
But `Base` is passed to `decorator`. `Base` was defined as `class Base(DataObjectBase)`.
So when `Base` is created, `_DataObjectBaseMetaClass.__new__` is called.
```python
class _DataObjectBaseMetaClass(_DataBaseMetaClass):
def __new__(mcs, name, bases, attrs):
# ...
# Get the annotations that will go into the dataclass
if name != "DataObjectBase":
field_names = attrs.get("__annotations__")
if field_names is None:
raise TypeError(
"All DataObjectBase classes must follow dataclass syntax"
)
attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names
```
For `Base`, `attrs` contains `foo: int`, so `__annotations__` is `{'foo': <class 'int'>}`. It works.
For `Derived(Base)`, `Base` is in `bases`. `Derived` doesn't have `__annotations__` in its `attrs`. So it raises `TypeError`.
To fix this, `_DataObjectBaseMetaClass.__new__` should check if any of the base classes have `_FWD_DECL_FIELDS` if `__annotations__` is missing.
Wait, if `Derived` inherits from `Base`, and `Base` is a `DataObjectBase`, then `Base` already has its fields defined.
If we want `Derived` to also be a `DataObjectBase` (which it is, by inheritance), it should also have these fields.
If we look at `_DataBaseMetaClass.__new__`, it looks for `_FWD_DECL_FIELDS` in `attrs`.
If we can't find `__annotations__` in `attrs` for `Derived`, we should check if any of the base classes have `_FWD_DECL_FIELDS`. Actually, they would have it as a class attribute if it was passed to `super().__new__` in `_DataObjectBaseMetaClass.__new__`.
Wait, `attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names`. This adds it to the class being created.
For `Derived`, if we don't find `__annotations__`, we should see if we can find `_FWD_DECL_FIELDS` in any of the `bases`.
But wait, if we just do that, then `_DataBaseMetaClass.__new__` will see it in `attrs` (since we put it there) and `fields` will be set.
Let's try to reproduce this first to be sure.
<channel|><|tool_call>call:run_command{command:<|"|>python3 - <<'PY'
from caikit.core import DataObjectBase, dataobject
try:
@dataobject
class Base(DataObjectBase):
foo: int
def doit(self):
return self.foo * 2
print("Base created successfully")
@dataobject
class Derived(Base):
def doit(self):
return self.foo * 3
print("Derived created successfully")
except Exception as e:
import traceback
traceback.print_exc()
PY
<|"|>}<tool_call|><|tool_response>
Turn 9at 321 s1,405 tokens out11,223 prompt tokens
The reproduction confirms the bug.
Now, how to fix it. We should allow `Derived` to not have `__annotations__` if it inherits from a class that does.
In `_DataObjectBaseMetaClass.__new__`:
```python
def __new__(mcs, name, bases, attrs):
# ...
# Get the annotations that will go into the dataclass
if name != "DataObjectBase":
field_names = attrs.get("__annotations__")
if field_names is None:
# Check if any base has _FWD_DECL_FIELDS
for base in bases:
if hasattr(base, _DataBaseMetaClass._FWD_DECL_FIELDS):
field_names = getattr(base, _DataBaseMetaClass._FWD_DECL_FIELDS)
break
if field_names is None:
raise TypeError(
"All DataObjectBase classes must follow dataclass syntax"
)
attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names
```
Wait, if we do this, `field_names` will be the same as `Base`'s.
Then `attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names` will set it for `Derived`.
Then `_DataBaseMetaClass.__new__` will pop it and use it to set `fields`.
Is this correct for dataclass inheritance?
If `Derived` adds fields, it will have its own `__annotations__`.
If it adds fields, we should probably combine them with `Base`'s fields.
The issue says: "When creating a class that inherits from a DataObjectBase and overrides parent functions without adding or changing any of the schema, an exception is raised".
It doesn't explicitly ask for field additions in derived classes, but that's normally how inheritance works.
However, let's see if `dataclasses.dataclass` handles this.
The `dataobject` decorator calls `dataclasses.dataclass(cls)`.
```python
cls = dataclasses.dataclass(cls)
```
If `Derived` inherits from `Base` (which is already a dataclass), then `dataclasses.dataclass(Derived)` will include fields from `Base`.
But the `_DataObjectBaseMetaClass.__new__` is called *before* the `dataobject` decorator is fully applied?
No, let's see:
1. `class Base(DataObjectBase)`: `_DataObjectBaseMetaClass.__new__` is called.
2. `@dataobject` is applied to `Base`.
- `decorator(Base)` is called.
- `dataclasses.dataclass(Base)` is called.
- `_make_data_model_class` is called.
For `Derived`:
1. `class Derived(Base)`: `_DataObjectBaseMetaClass.__new__` is called. (This is where it crashes).
So it crashes before `@dataobject` decorator for `Derived` is even executed.
If `Derived` doesn't have `__annotations__`, we should probably look for `__annotations__` in the base classes, or just look for `_FWD_DECL_FIELDS`.
Actually, if `Derived` does have `__annotations__`, the current code takes them and puts them in `_FWD_DECL_FIELDS`. But it doesn't include fields from `Base`.
Wait, does `_DataBaseMetaClass` handle field inheritance?
```python
# Look for the set of fields either from a predefined protobuf class or
# from a forward declaration from @dataobject
fields = ()
proto_class = None
if name not in ["DataBase", "DataObjectBase"]:
# ...
# Otherwise, we need to get the fields from a "special" attribute
else:
fields = attrs.pop(mcs._FWD_DECL_FIELDS, None)
```
It just pops `_FWD_DECL_FIELDS` from `attrs`. It doesn't look at base classes.
So if `Derived` has `__annotations__`, `_DataObjectBaseMetaClass` will put those in `_FWD_DECL_FIELDS`, and `_DataBaseMetaClass` will use those as the fields. The fields from `Base` will be lost!
This seems like another bug, or at least a limitation. But the current task is specifically about "Functional Inheritance" (overriding functions without changing schema).
If we just want to fix the requested bug, we should allow `field_names` to be inherited.
Let's refine the fix:
In `_DataObjectBaseMetaClass.__new__`:
1. Try to get `__annotations__` from `attrs`.
2. If not found, try to find any base that has `_FWD_DECL_FIELDS` (meaning it's a `DataObjectBase`).
3. If still not found, raise `TypeError`.
Wait, if `Derived` has `__annotations__`, and `Base` also has them, should we merge them?
Standard `dataclass` behavior is to merge them.
If we want our `fields` to match what `dataclass` will produce, we should merge them.
But the `_FWD_DECL_FIELDS` is used to create the protobufs. And the `dataobject` decorator uses `_dataobject_to_proto(dataclass_=cls)`.
Let's see `_dataobject_to_proto`.
```python
def _dataobject_to_proto(*args, **kwargs):
kwargs.setdefault("type_mapping", DATAOBJECT_PY_TO_PROTO_TYPES)
return _DataobjectConverter(*args, **kwargs).descriptor
```
If `cls` is a dataclass, `DataclassConverter` (which `_DataobjectConverter` inherits from) should be handling the field extraction.
Wait, I don't see `DataclassConverter` definition here. It must be imported.
Actually, I don't see the import for `DataclassConverter` in `caikit/core/data_model/dataobject.py`.
Let's check the imports in `caikit/core/data_model/dataobject.py`.
TOOL CALL · read_file
{
"filepath": "caikit/core/data_model/dataobject.py",
"start_line": 1
}Result of read_file 5,896 characters
{
"status": "ok",
"filepath": "caikit/core/data_model/dataobject.py",
"content": "# Copyright The Caikit Authors\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"This module defines the @schema decorator which can be used to declare data\nmodel objects inline without manually defining the protobufs representation\n\"\"\"\n\n\n# Standard\nfrom enum import Enum\nfrom typing import Any, Callable, List, Type, Union, get_args, get_origin\nimport dataclasses\n\n# Third Party\nfrom google.protobuf import descriptor as _descriptor\nfrom google.protobuf import message as _message\nfrom google.protobuf import struct_pb2\nfrom google.protobuf.internal.enum_type_wrapper import EnumTypeWrapper\nimport numpy as np\n\n# First Party\nfrom py_to_proto.dataclass_to_proto import PY_TO_PROTO_TYPES, DataclassConverter\nimport alog\nimport py_to_proto\n\n# Local\nfrom ..toolkit.errors import error_handler\nfrom . import enums\nfrom .base import DataBase, _DataBaseMetaClass\nfrom .json_dict import JsonDict\n\n## Globals #####################################################################\n\nlog = alog.use_channel(\"SCHEMA\")\nerror = error_handler.get(log)\n\n# Type mapping for type hints in @dataobject classes\nDATAOBJECT_PY_TO_PROTO_TYPES = {\n JsonDict: struct_pb2.Struct,\n np.int32: _descriptor.FieldDescriptor.TYPE_INT32,\n np.int64: _descriptor.FieldDescriptor.TYPE_INT64,\n np.uint32: _descriptor.FieldDescriptor.TYPE_UINT32,\n np.uint64: _descriptor.FieldDescriptor.TYPE_UINT64,\n np.float32: _descriptor.FieldDescriptor.TYPE_FLOAT,\n np.float64: _descriptor.FieldDescriptor.TYPE_DOUBLE,\n **PY_TO_PROTO_TYPES,\n}\n\n# Common package prefix\nCAIKIT_DATA_MODEL = \"caikit_data_model\"\n\n# Registry of auto-generated protos so that they can be rendered to .proto\n_AUTO_GEN_PROTO_CLASSES = []\n\n# Special attribute used to indicate which defaults are user provided\n_USER_DEFINED_DEFAULTS = \"__user_defined_defaults__\"\n\n## Public ######################################################################\n\n\nclass _DataObjectBaseMetaClass(_DataBaseMetaClass):\n \"\"\"This metaclass is used for the DataObject base class so that all data\n objects can delay the creation of their proto class until after the\n metaclass has been instantiated.\n \"\"\"\n\n def __new__(mcs, name, bases, attrs):\n \"\"\"When instantiating a new DataObject class, the proto class will not\n yet have been generated, but the set of fields will be known since the\n class will be the raw input representation of a @dataclass\n \"\"\"\n\n # Get the annotations that will go into the dataclass\n if name != \"DataObjectBase\":\n field_names = attrs.get(\"__annotations__\")\n if field_names is None:\n raise TypeError(\n \"All DataObjectBase classes must follow dataclass syntax\"\n )\n attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names\n\n # Delegate to the base metaclass\n return super().__new__(mcs, name, bases, attrs)\n\n\nclass DataObjectBase(DataBase, metaclass=_DataObjectBaseMetaClass):\n \"\"\"A DataObject is a data model class that is backed by a @dataclass.\n\n Data model classes that use the @dataobject decorator must derive from this\n base class.\n \"\"\"\n\n\ndef dataobject(*args, **kwargs) -> Callable[[Type], Type[DataBase]]:\n \"\"\"The @dataobject decorator can be used to define a Data Model object's\n schema inline with the definition of the python class rather than needing to\n bind to a pre-compiled protobufs class. For example:\n\n @dataobject(\"foo.bar\")\n @dataclass\n class MyDataObject(DataObjectBase):\n '''My Custom Data Object'''\n foo: str\n bar: int\n\n NOTE: The wrapped class must NOT inherit directly from DataBase. That\n inheritance will be added by this decorator, but if it is written\n directly, the metaclass that links protobufs to the class will be called\n before this decorator can auto-gen the protobufs class.\n\n Args:\n package: str\n The package name to use for the generated protobufs class\n\n Returns:\n decorator: Callable[[Type], Type[DataBase]]\n The decorator function that will wrap the given class\n \"\"\"\n\n def decorator(cls: Type) -> Type[DataBase]:\n # Make sure that the wrapped class does NOT inherit from DataBase\n error.value_check(\n \"<COR95184230E>\",\n issubclass(cls, (DataObjectBase, Enum)),\n \"{} must inherit from DataObjectBase/Enum when using @dataobject\",\n cls.__name__,\n )\n\n # Add the package to the kwargs\n kwargs.setdefault(\"package\", package)\n\n # If it's not an enum, fill in any missing field defaults as None\n # and make sure it's a dataclass\n if not issubclass(cls, Enum):\n log.debug2(\"Wrapping data class %s\", cls)\n user_defined_defaults = {}\n for annotation in getattr(cls, \"__annotations__\", {}):\n user_defined_default = getattr(cls, annotation, dataclasses.MISSING)\n if user_defined_default == dataclasses.MISSING:",
"start_line": 1,
"end_line": 150,
"total_lines": 386,
"is_truncated": true
}Generated tokens 1,405 tokens · stopped: STOP
The reproduction confirms the bug.
Now, how to fix it. We should allow `Derived` to not have `__annotations__` if it inherits from a class that does.
In `_DataObjectBaseMetaClass.__new__`:
```python
def __new__(mcs, name, bases, attrs):
# ...
# Get the annotations that will go into the dataclass
if name != "DataObjectBase":
field_names = attrs.get("__annotations__")
if field_names is None:
# Check if any base has _FWD_DECL_FIELDS
for base in bases:
if hasattr(base, _DataBaseMetaClass._FWD_DECL_FIELDS):
field_names = getattr(base, _DataBaseMetaClass._FWD_DECL_FIELDS)
break
if field_names is None:
raise TypeError(
"All DataObjectBase classes must follow dataclass syntax"
)
attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names
```
Wait, if we do this, `field_names` will be the same as `Base`'s.
Then `attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names` will set it for `Derived`.
Then `_DataBaseMetaClass.__new__` will pop it and use it to set `fields`.
Is this correct for dataclass inheritance?
If `Derived` adds fields, it will have its own `__annotations__`.
If it adds fields, we should probably combine them with `Base`'s fields.
The issue says: "When creating a class that inherits from a DataObjectBase and overrides parent functions without adding or changing any of the schema, an exception is raised".
It doesn't explicitly ask for field additions in derived classes, but that's normally how inheritance works.
However, let's see if `dataclasses.dataclass` handles this.
The `dataobject` decorator calls `dataclasses.dataclass(cls)`.
```python
cls = dataclasses.dataclass(cls)
```
If `Derived` inherits from `Base` (which is already a dataclass), then `dataclasses.dataclass(Derived)` will include fields from `Base`.
But the `_DataObjectBaseMetaClass.__new__` is called *before* the `dataobject` decorator is fully applied?
No, let's see:
1. `class Base(DataObjectBase)`: `_DataObjectBaseMetaClass.__new__` is called.
2. `@dataobject` is applied to `Base`.
- `decorator(Base)` is called.
- `dataclasses.dataclass(Base)` is called.
- `_make_data_model_class` is called.
For `Derived`:
1. `class Derived(Base)`: `_DataObjectBaseMetaClass.__new__` is called. (This is where it crashes).
So it crashes before `@dataobject` decorator for `Derived` is even executed.
If `Derived` doesn't have `__annotations__`, we should probably look for `__annotations__` in the base classes, or just look for `_FWD_DECL_FIELDS`.
Actually, if `Derived` does have `__annotations__`, the current code takes them and puts them in `_FWD_DECL_FIELDS`. But it doesn't include fields from `Base`.
Wait, does `_DataBaseMetaClass` handle field inheritance?
```python
# Look for the set of fields either from a predefined protobuf class or
# from a forward declaration from @dataobject
fields = ()
proto_class = None
if name not in ["DataBase", "DataObjectBase"]:
# ...
# Otherwise, we need to get the fields from a "special" attribute
else:
fields = attrs.pop(mcs._FWD_DECL_FIELDS, None)
```
It just pops `_FWD_DECL_FIELDS` from `attrs`. It doesn't look at base classes.
So if `Derived` has `__annotations__`, `_DataObjectBaseMetaClass` will put those in `_FWD_DECL_FIELDS`, and `_DataBaseMetaClass` will use those as the fields. The fields from `Base` will be lost!
This seems like another bug, or at least a limitation. But the current task is specifically about "Functional Inheritance" (overriding functions without changing schema).
If we just want to fix the requested bug, we should allow `field_names` to be inherited.
Let's refine the fix:
In `_DataObjectBaseMetaClass.__new__`:
1. Try to get `__annotations__` from `attrs`.
2. If not found, try to find any base that has `_FWD_DECL_FIELDS` (meaning it's a `DataObjectBase`).
3. If still not found, raise `TypeError`.
Wait, if `Derived` has `__annotations__`, and `Base` also has them, should we merge them?
Standard `dataclass` behavior is to merge them.
If we want our `fields` to match what `dataclass` will produce, we should merge them.
But the `_FWD_DECL_FIELDS` is used to create the protobufs. And the `dataobject` decorator uses `_dataobject_to_proto(dataclass_=cls)`.
Let's see `_dataobject_to_proto`.
```python
def _dataobject_to_proto(*args, **kwargs):
kwargs.setdefault("type_mapping", DATAOBJECT_PY_TO_PROTO_TYPES)
return _DataobjectConverter(*args, **kwargs).descriptor
```
If `cls` is a dataclass, `DataclassConverter` (which `_DataobjectConverter` inherits from) should be handling the field extraction.
Wait, I don't see `DataclassConverter` definition here. It must be imported.
Actually, I don't see the import for `DataclassConverter` in `caikit/core/data_model/dataobject.py`.
Let's check the imports in `caikit/core/data_model/dataobject.py`.
<channel|><|tool_call>call:read_file{filepath:<|"|>caikit/core/data_model/dataobject.py<|"|>,start_line:1}<tool_call|><|tool_response>
Turn 10at 409 s1,722 tokens out14,240 prompt tokens
Wait, `_DataObjectBaseMetaClass` is used for `DataObjectBase` and its subclasses.
When `class Derived(Base)` is executed, `_DataObjectBaseMetaClass.__new__` is called.
If we allow it to not have `__annotations__` if a base class has `_FWD_DECL_FIELDS`, we solve the reported bug.
What if `Derived` *does* have `__annotations__`?
Example:
```python
@dataobject
class Base(DataObjectBase):
foo: int
@dataobject
class Derived(Base):
bar: int
```
1. `Base` is created. `_DataObjectBaseMetaClass.__new__` sets `_FWD_DECL_FIELDS = {'foo': int}`.
2. `Base` is decorated.
3. `Derived` is created. `_DataObjectBaseMetaClass.__new__` finds `__annotations__ = {'bar': int}`. It sets `_FWD_DECL_FIELDS = {'bar': int}`.
4. `_DataBaseMetaClass.__new__` pops `_FWD_DECL_FIELDS` from `attrs`. So `Derived.fields` will only contain `bar`.
5. `Derived` is decorated. `dataclasses.dataclass(Derived)` will include both `foo` and `bar`.
6. `_dataobject_to_proto(dataclass_=Derived)` is called. It uses `DataclassConverter`.
If `DataclassConverter` uses `dataclasses.fields(cls)`, then it will find both `foo` and `bar` regardless of what `_FWD_DECL_FIELDS` was.
Wait, let's check if `DataclassConverter` uses `dataclasses.fields`.
I don't have the source for `py_to_proto`. But it's likely.
The purpose of `_FWD_DECL_FIELDS` seems to be for `_DataBaseMetaClass` to set `__slots__` and avoid errors before the decorator is applied.
In `_DataBaseMetaClass.__new__`:
```python
# Look for the set of fields either from a predefined protobuf class or
# from a forward declaration from @dataobject
fields = ()
proto_class = None
if name not in ["DataBase", "DataObjectBase"]:
# ...
# Otherwise, we need to get the fields from a "special" attribute
else:
fields = attrs.pop(mcs._FWD_DECL_FIELDS, None)
# ...
error.value_check(
"<COR49310991E>",
fields is not None,
"No proto class found for {}",
name,
)
attrs["fields"] = fields
```
If `fields` is `None`, it raise an error. This is exactly what happens for `Derived` if we don't provide `_FWD_DECL_FIELDS`.
So we must provide `_FWD_DECL_FIELDS` in `_DataObjectBaseMetaClass.__new__`.
If we want to support adding fields, we should probably merge annotations.
But for the "Functional Inheritance" bug, we just need to ensure it doesn't crash when there are no annotations.
Let's see if we can just inherit `_FWD_DECL_FIELDS` from bases if it's missing in `attrs`.
```python
# Get the annotations that will go into the dataclass
if name != "DataObjectBase":
field_names = attrs.get("__annotations__")
if field_names is None:
# Look for _FWD_DECL_FIELDS in any base class
for base in bases:
if hasattr(base, _DataBaseMetaClass._FWD_DECL_FIELDS):
field_names = getattr(base, _DataBaseMetaClass._FWD_DECL_FIELDS)
break
if field_names is None:
raise TypeError(
"All DataObjectBase classes must follow dataclass syntax"
)
attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names
```
Wait, if I do this, then `_DataBaseMetaClass.__new__` will pop it from `attrs`.
Let's test this fix with the reproduction script and also a case where fields are added.
Wait, if I merge annotations, it's better.
But `attrs` in `__new__` only has the annotations of the class being created. To merge, I'd need to look at bases.
Actually, if I just look for `_FWD_DECL_FIELDS` in bases when `__annotations__` is missing, it solves the bug.
If `__annotations__` is present, we use it. If it's not, we try to inherit.
What if `Derived` has `__annotations__` and we only use those?
Then `_DataBaseMetaClass` will set `fields` to only the new fields.
Then `__slots__` will only have new fields.
But `Derived` inherits from `Base`, so it should have all fields in `__slots__`?
Actually, `__slots__` are additive in Python. If `Base` has `__slots__`, and `Derived` has `__slots__`, then `Derived` has both.
So if `Derived` only adds `bar`, and `Base` had `foo`, `Derived`'s `__slots__` will have `_bar` and `Base`'s will have `_foo`. This is correct.
So the only problem is when `__annotations__` is missing, `_DataBaseMetaClass` fails because `_FWD_DECL_FIELDS` is missing.
Let's try this fix in a repro script first.
TOOL CALL · run_command
{
"command": "python3 - <<'PY'\nimport dataclasses\nfrom typing import Any, Type\n\nclass _DataBaseMetaClass(type):\n _FWD_DECL_FIELDS = \"__fwd_decl_fields__\"\n def __new__(mcs, name, bases, attrs):\n fields = ()\n if name not in [\"DataBase\", \"DataObjectBase\"]:\n fields = attrs.pop(mcs._FWD_DECL_FIELDS, None)\n if fields is None:\n print(f\"Error: No proto class fields found for {name}\")\n # return super().__new__(mcs, name, bases, attrs) # to avoid crashing the script\n attrs[\"fields\"] = fields\n return super().__new__(mcs, name, bases, attrs)\n\nclass _DataObjectBaseMetaClass(_DataBaseMetaClass):\n def __new__(mcs, name, bases, attrs):\n if name != \"DataObjectBase\":\n field_names = attrs.get(\"__annotations__\")\n if field_names is None:\n # FIX: Try to inherit from base\n for base in bases:\n if hasattr(base, _DataBaseMetaClass._FWD_DECL_FIELDS):\n field_names = getattr(base, _DataBaseMetaClass._FWD_DECL_FIELDS)\n break\n if field_names is None:\n print(f\"TypeError: All DataObjectBase classes must follow dataclass syntax for {name}\")\n return super().__new__(mcs, name, bases, attrs)\n attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names\n return super().__new__(mcs, name, bases, attrs)\n\nclass DataObjectBase(metaclass=_DataObjectBaseMetaClass):\n pass\n\nclass Base(DataObjectBase):\n foo: int\n\nprint(\"Base created\")\nclass Derived(Base):\n def doit(self):\n return self.foo * 3\n\nprint(\"Derived created\")\nPY\n"
}Result of run_command 219 characters
{
"status": "ok",
"stdout": "Base created\nTypeError: All DataObjectBase classes must follow dataclass syntax for Derived\nError: No proto class fields found for Derived\nDerived created\n",
"stderr": "",
"exit_code": 0
}Generated tokens 1,722 tokens · stopped: STOP
Wait, `_DataObjectBaseMetaClass` is used for `DataObjectBase` and its subclasses.
When `class Derived(Base)` is executed, `_DataObjectBaseMetaClass.__new__` is called.
If we allow it to not have `__annotations__` if a base class has `_FWD_DECL_FIELDS`, we solve the reported bug.
What if `Derived` *does* have `__annotations__`?
Example:
```python
@dataobject
class Base(DataObjectBase):
foo: int
@dataobject
class Derived(Base):
bar: int
```
1. `Base` is created. `_DataObjectBaseMetaClass.__new__` sets `_FWD_DECL_FIELDS = {'foo': int}`.
2. `Base` is decorated.
3. `Derived` is created. `_DataObjectBaseMetaClass.__new__` finds `__annotations__ = {'bar': int}`. It sets `_FWD_DECL_FIELDS = {'bar': int}`.
4. `_DataBaseMetaClass.__new__` pops `_FWD_DECL_FIELDS` from `attrs`. So `Derived.fields` will only contain `bar`.
5. `Derived` is decorated. `dataclasses.dataclass(Derived)` will include both `foo` and `bar`.
6. `_dataobject_to_proto(dataclass_=Derived)` is called. It uses `DataclassConverter`.
If `DataclassConverter` uses `dataclasses.fields(cls)`, then it will find both `foo` and `bar` regardless of what `_FWD_DECL_FIELDS` was.
Wait, let's check if `DataclassConverter` uses `dataclasses.fields`.
I don't have the source for `py_to_proto`. But it's likely.
The purpose of `_FWD_DECL_FIELDS` seems to be for `_DataBaseMetaClass` to set `__slots__` and avoid errors before the decorator is applied.
In `_DataBaseMetaClass.__new__`:
```python
# Look for the set of fields either from a predefined protobuf class or
# from a forward declaration from @dataobject
fields = ()
proto_class = None
if name not in ["DataBase", "DataObjectBase"]:
# ...
# Otherwise, we need to get the fields from a "special" attribute
else:
fields = attrs.pop(mcs._FWD_DECL_FIELDS, None)
# ...
error.value_check(
"<COR49310991E>",
fields is not None,
"No proto class found for {}",
name,
)
attrs["fields"] = fields
```
If `fields` is `None`, it raise an error. This is exactly what happens for `Derived` if we don't provide `_FWD_DECL_FIELDS`.
So we must provide `_FWD_DECL_FIELDS` in `_DataObjectBaseMetaClass.__new__`.
If we want to support adding fields, we should probably merge annotations.
But for the "Functional Inheritance" bug, we just need to ensure it doesn't crash when there are no annotations.
Let's see if we can just inherit `_FWD_DECL_FIELDS` from bases if it's missing in `attrs`.
```python
# Get the annotations that will go into the dataclass
if name != "DataObjectBase":
field_names = attrs.get("__annotations__")
if field_names is None:
# Look for _FWD_DECL_FIELDS in any base class
for base in bases:
if hasattr(base, _DataBaseMetaClass._FWD_DECL_FIELDS):
field_names = getattr(base, _DataBaseMetaClass._FWD_DECL_FIELDS)
break
if field_names is None:
raise TypeError(
"All DataObjectBase classes must follow dataclass syntax"
)
attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names
```
Wait, if I do this, then `_DataBaseMetaClass.__new__` will pop it from `attrs`.
Let's test this fix with the reproduction script and also a case where fields are added.
Wait, if I merge annotations, it's better.
But `attrs` in `__new__` only has the annotations of the class being created. To merge, I'd need to look at bases.
Actually, if I just look for `_FWD_DECL_FIELDS` in bases when `__annotations__` is missing, it solves the bug.
If `__annotations__` is present, we use it. If it's not, we try to inherit.
What if `Derived` has `__annotations__` and we only use those?
Then `_DataBaseMetaClass` will set `fields` to only the new fields.
Then `__slots__` will only have new fields.
But `Derived` inherits from `Base`, so it should have all fields in `__slots__`?
Actually, `__slots__` are additive in Python. If `Base` has `__slots__`, and `Derived` has `__slots__`, then `Derived` has both.
So if `Derived` only adds `bar`, and `Base` had `foo`, `Derived`'s `__slots__` will have `_bar` and `Base`'s will have `_foo`. This is correct.
So the only problem is when `__annotations__` is missing, `_DataBaseMetaClass` fails because `_FWD_DECL_FIELDS` is missing.
Let's try this fix in a repro script first.
<channel|><|tool_call>call:run_command{command:<|"|>python3 - <<'PY'
import dataclasses
from typing import Any, Type
class _DataBaseMetaClass(type):
_FWD_DECL_FIELDS = "__fwd_decl_fields__"
def __new__(mcs, name, bases, attrs):
fields = ()
if name not in ["DataBase", "DataObjectBase"]:
fields = attrs.pop(mcs._FWD_DECL_FIELDS, None)
if fields is None:
print(f"Error: No proto class fields found for {name}")
# return super().__new__(mcs, name, bases, attrs) # to avoid crashing the script
attrs["fields"] = fields
return super().__new__(mcs, name, bases, attrs)
class _DataObjectBaseMetaClass(_DataBaseMetaClass):
def __new__(mcs, name, bases, attrs):
if name != "DataObjectBase":
field_names = attrs.get("__annotations__")
if field_names is None:
# FIX: Try to inherit from base
for base in bases:
if hasattr(base, _DataBaseMetaClass._FWD_DECL_FIELDS):
field_names = getattr(base, _DataBaseMetaClass._FWD_DECL_FIELDS)
break
if field_names is None:
print(f"TypeError: All DataObjectBase classes must follow dataclass syntax for {name}")
return super().__new__(mcs, name, bases, attrs)
attrs[_DataBaseMetaClass._FWD_DECL_FIELDS] = field_names
return super().__new__(mcs, name, bases, attrs)
class DataObjectBase(metaclass=_DataObjectBaseMetaClass):
pass
class Base(DataObjectBase):
foo: int
print("Base created")
class Derived(Base):
def doit(self):
return self.foo * 3
print("Derived created")
PY
<|"|>}<tool_call|><|tool_response>
Patch, tests and logs
Final code change Included
Test output Included
============================= test session starts ==============================
collected 126 items
tests/core/data_model/test_dataobject.py ............................... [ 24%]
........................................................................ [ 81%]
....................... [100%]
=============================== warnings summary ===============================
tests/core/data_model/test_dataobject.py::test_render_dataobject_protos_valid_dir
tests/core/data_model/test_dataobject.py::test_render_dataobject_protos_no_dir
/usr/local/lib/python3.10/site-packages/py_to_proto/descriptor_to_file.py:86: RuntimeWarning: descriptor.syntax is deprecated. It will be removed soon. Most usages are checking field descriptors. Consider to use has_presence, is_packed on field descriptors.
proto_file_lines.append(f'syntax = "{descriptor.syntax}";')
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
==================================== PASSES ====================================
_________________________ test_dataobject_native_types _________________________
---------------------------- Captured stderr setup -----------------------------
{"channel": "WIPDC", "exception": null, "level": "warning", "message": "<function register_backend_type at 0x77a04bd7e950> is still in the BETA phase and subject to change!", "num_indent": 0, "thread_id": 131530502495104, "timestamp": "2026-11-14T02:49:52.812535"}
{"channel": "WIPDC", "exception": null, "level": "warning", "message": "<function register_backend_type at 0x77a04bd7e950> is still in the BETA phase and subject to change!", "num_indent": 0, "thread_id": 131530502495104, "timestamp": "2026-11-14T02:49:52.812678"}
------------------------------ Captured log setup ------------------------------
WARNING WIPDC:wip_decorator.py:213 <function register_backend_type at 0x77a04bd7e950> is still in the BETA phase and subject to change!
WARNING WIPDC:wip_decorator.py:213 <function register_backend_type at 0x77a04bd7e950> is still in the BETA phase and subject to change!
__________________________ test_dataobject_with_oneof __________________________
----------------------------- Captured stderr call -----------------------------
{"channel": "SCHEMA", "exception": null, "level": "error", "log_code": "<COR09282193E>", "message": "exception raised: TypeError('Received conflicting oneof args/kwargs for data_stream/foo')", "num_indent": 0, "thread_id": 131530502495104, "timestamp": "2026-11-14T02:49:52.830757"}
{"channel": "SCHEMA", "exception": null, "level": "error", "log_code": "<COR59933157E>", "message": "exception raised: TypeError('Received multiple keyword arguments for oneof data_stream')", "num_indent": 0, "thread_id": 131530502495104, "timestamp": "2026-11-14T02:49:52.830859"}
{"channel": "SCHEMA", "exception": null, "level": "error", "log_code": "<COR59933157E>", "message": "exception raised: TypeError('Received multiple keyword arguments for oneof data_stream')", "num_indent": 0, "thread_id": 131530502495104, "timestamp": "2026-11-14T02:49:52.830914"}
------------------------------ Captured log call -------------------------------
ERROR SCHEMA:error_handler.py:80 {'log_code': '<COR09282193E>', 'message': "exception raised: TypeError('Received conflicting oneof args/kwargs for data_stream/foo')", 'args': None}
ERROR SCHEMA:error_handler.py:80 {'log_code': '<COR59933157E>', 'message': "exception raised: TypeError('Received multiple keyword arguments for oneof data_stream')", 'args': None}
ERROR SCHEMA:error_handler.py:80 {'log_code': '<COR59933157E>', 'message': "exception raised: TypeError('Received multiple keyword arguments for oneof data_stream')", 'args': None}
______________________ test_dataobject_invocation_flavors ______________________
----------------------------- Captured stderr call -----------------------------
{"channel": "SCHEMA", "exception": null, "level": "error", "log_code": "<COR95184230E>", "message": "exception raised: ValueError('value check failed: Foo4 must inherit from DataObjectBase/Enum when using @dataobject')", "num_indent": 0, "thread_id": 131530502495104, "timestamp": "2026-11-14T02:49:52.849468"}
------------------------------ Captured log call -------------------------------
ERROR SCHEMA:error_handler.py:80 {'log_code': '<COR95184230E>', 'message': "exception raised: ValueError('value check failed: Foo4 must inherit from DataObjectBase/Enum when using @dataobject')", 'args': None}
- generated xml file: /tmp/rebench-grade-0e29661f92cd4c0489aef2394c61e8fb.xml --
=========================== short test summary info ============================
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_native_types
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_nested_objects
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_nested_enums
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_top_level_enums
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_arrays
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_obj_refs_no_opt_types
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_obj_refs_with_optional_types
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_additional_methods
PASSED tests/core/data_model/test_dataobject.py::test_render_dataobject_protos_valid_dir
PASSED tests/core/data_model/test_dataobject.py::test_render_dataobject_protos_no_dir
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_with_oneof
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_with_same_type_of_oneof
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_primitive_oneof_round_trips
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_oneof_from_backend
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_round_trip_json
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_round_trip_proto
PASSED tests/core/data_model/test_dataobject.py::test_dir_on_instance
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_invocation_flavors
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_pre_existing_dataclass
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_dataclass_non_default_init
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_dataclass_default_factory
PASSED tests/core/data_model/test_dataobject.py::test_enum_value_dereference
PASSED tests/core/data_model/test_dataobject.py::test_np_dtypes
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[0]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[1]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[2]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[3]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[4]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[5]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[6]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[7]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[8]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[9]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[10]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[11]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[12]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[13]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[14]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[15]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[16]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[17]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[18]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[19]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[20]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[21]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[22]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[23]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[24]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[25]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[26]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[27]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[28]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[29]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[30]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[31]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[32]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[33]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[34]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[35]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[36]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[37]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[38]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[39]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[40]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[41]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[42]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[43]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[44]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[45]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[46]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[47]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[48]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[49]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[50]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[51]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[52]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[53]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[54]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[55]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[56]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[57]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[58]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[59]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[60]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[61]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[62]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[63]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[64]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[65]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[66]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[67]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[68]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[69]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[70]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[71]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[72]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[73]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[74]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[75]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[76]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[77]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[78]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[79]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[80]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[81]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[82]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[83]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[84]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[85]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[86]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[87]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[88]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[89]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[90]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[91]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[92]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[93]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[94]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[95]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[96]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[97]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[98]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_jsondict[99]
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_to_kwargs
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_inheritance
PASSED tests/core/data_model/test_dataobject.py::test_dataobject_function_inheritance
======================= 126 passed, 2 warnings in 0.20s ========================
Test errors Included
Test report (JUnit) Included
<?xml version="1.0" encoding="utf-8"?><testsuites name="pytest tests"><testsuite name="pytest" errors="0" failures="0" skipped="0" tests="126" time="0.203" timestamp="2026-11-14T02:49:52.758753+00:00" hostname="6c9f90e1c359"><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_native_types" time="0.005" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_nested_objects" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_nested_enums" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_top_level_enums" time="0.000" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_arrays" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_obj_refs_no_opt_types" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_obj_refs_with_optional_types" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_additional_methods" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_render_dataobject_protos_valid_dir" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_render_dataobject_protos_no_dir" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_with_oneof" time="0.002" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_with_same_type_of_oneof" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_primitive_oneof_round_trips" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_oneof_from_backend" time="0.011" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_round_trip_json" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_round_trip_proto" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dir_on_instance" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_invocation_flavors" time="0.002" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_pre_existing_dataclass" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_dataclass_non_default_init" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_dataclass_default_factory" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_enum_value_dereference" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_np_dtypes" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[0]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[1]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[2]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[3]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[4]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[5]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[6]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[7]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[8]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[9]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[10]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[11]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[12]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[13]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[14]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[15]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[16]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[17]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[18]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[19]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[20]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[21]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[22]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[23]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[24]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[25]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[26]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[27]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[28]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[29]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[30]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[31]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[32]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[33]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[34]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[35]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[36]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[37]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[38]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[39]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[40]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[41]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[42]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[43]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[44]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[45]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[46]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[47]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[48]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[49]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[50]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[51]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[52]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[53]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[54]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[55]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[56]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[57]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[58]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[59]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[60]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[61]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_jsondict[62]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" 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name="test_dataobject_jsondict[99]" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_to_kwargs" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_inheritance" time="0.001" /><testcase classname="tests.core.data_model.test_dataobject" name="test_dataobject_function_inheritance" time="0.001" /></testsuite></testsuites>
Agent log Included
[ 0.0s] 📋 System Prompt: You are a principal Python software engineer resolving a repository issue in `/workspace`.
[ 0.0s] 🎯 Task Prompt: You are evaluating a software engineering task for repository caikit/caikit.
[ 20.7s] 💭 (853 chars) "The user wants to support functional inheritance in `DataObjectBase` classes.
When a class inherits from a `DataObjectBa..."
[ 20.7s] 🔧 run_command grep -rn --include="*.py" "All DataObjectBase classes must follow dataclass synt
[ 20.8s] → ./caikit/core/data_model/dataobject.py:89: "All DataObjectBase classes must follow
dataclass syntax"
[ 26.6s] 💭 (107 chars) "The error is in `caikit/core/data_model/dataobject.py` at line 89.
Let's examine the code around that area."
[ 26.6s] 📖 read_file (caikit/core/data_model/dataobject.py)
[ 26.7s] ✓ {'status': 'ok', 'filepath': 'caikit/core/data_model/dataobject.py', 'content': '# Common package
prefix\nCAIKIT_DATA_MO…
[ 66.5s] 💭 (2151 chars) "The `_DataObjectBaseMetaClass.__new__` method is where the `TypeError` is raised.
```python
def __new__(mcs, name, ..."
[ 66.5s] 🔧 run_command grep -rn "class _DataBaseMetaClass" caikit/core/data_model/
[ 66.6s] → caikit/core/data_model/base.py:46:class _DataBaseMetaClass(type):
[ 68.9s] 💭 (44 chars) "Let's read `caikit/core/data_model/base.py`."
[ 68.9s] 📖 read_file (caikit/core/data_model/base.py)
[ 69.0s] ✓ {'status': 'ok', 'filepath': 'caikit/core/data_model/base.py', 'content': '# Copyright The Caikit
Authors\n#\n# Licensed…
[ 144.2s] 💭 (4448 chars) "In `_DataBaseMetaClass.__new__`:
```python
# Otherwise, we need to get the fields from a "special" attribute
..."
[ 144.2s] 📖 read_file (caikit/core/data_model/dataobject.py)
[ 144.2s] ✓ {'status': 'ok', 'filepath': 'caikit/core/data_model/dataobject.py', 'content': ' class
MyDataObject(DataObjectBase):…
[ 146.5s] 💭 (29 chars) "And `_make_data_model_class`:"
[ 146.5s] 🔧 run_command grep -n "def _make_data_model_class" caikit/core/data_model/dataobject.py
[ 146.6s] → 294:def _make_data_model_class(proto_class, cls):
[ 151.7s] 📖 read_file (caikit/core/data_model/dataobject.py)
[ 151.7s] ✓ {'status': 'ok', 'filepath': 'caikit/core/data_model/dataobject.py', 'content': 'def
_make_data_model_class(proto_class,…
[ 211.4s] 💭 (3050 chars) "Wait, let's look at the `decorator` function again:
```python
def decorator(cls: Type) -> Type[DataBase]:
#..."
[ 211.4s] 🔧 run_command (22 lines) python3 - <<'PY'
[ 212.1s] → Base created successfully
[ 296.4s] 💭 (5134 chars) "The reproduction confirms the bug.
Now, how to fix it. We should allow `Derived` to not have `__annotations__` if it in..."
[ 296.4s] 📖 read_file (caikit/core/data_model/dataobject.py)
[ 296.5s] ✓ {'status': 'ok', 'filepath': 'caikit/core/data_model/dataobject.py', 'content': '# Copyright The Caikit
Authors\n#\n# Li…
[ 384.2s] 💭 (4616 chars) "Wait, `_DataObjectBaseMetaClass` is used for `DataObjectBase` and its subclasses.
When `class Derived(Base)` is executed..."
[ 384.2s] 🔧 run_command (45 lines) python3 - <<'PY'
[ 384.3s] → Base created
Native result row
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"duration_seconds": 477.22,
"error": "Empty agent patch",
"grade": {
"error": "Empty agent patch",
"resolved": false,
"strict_clean_patch": false,
"test_exit_code": -1
},
"instance_id": "caikit__caikit-233",
"pass_idx": 0,
"patch_chars": 0,
"phase1_seconds": 477.22,
"repo": "caikit/caikit",
"request_log_path": "/kaggle-root/working/r31/rollouts_X/requests/caikit__caikit-233__p0__r3.jsonl",
"resolved": false,
"rollout_idx": 3,
"run_key": "caikit__caikit-233__p0__r3",
"strict_clean_patch": false,
"tool_calls": 10,
"total_llm_calls": 10,
"trace_path": "/kaggle-root/working/r31/rollouts_X/traces/trace_caikit__caikit-233__p0__r3.json"
}