fastapi_13207

{"status": "ok", "stdout": "fastapi/applications.py:791:        separate_input_output_schemas: Annotated[\nfastapi/applications.py:875:        self.separate_input_output_schemas = separate_input_output_schemas\nfastapi/applications.py:1078:                separate_input_output_schemas=self.separate_input_output_schemas,\nfastapi/openapi/utils.py:102:    separate_input_output_schemas: bool = True,\nfastapi/openapi/utils.py:133:                separate_input_output_schemas=separate_input_output_schemas,\nfastapi/openapi/utils.py:175:    separate_input_output_schemas: bool = True,\nfastapi/openapi/utils.py:184:        separate_input_output_schemas=separate_input_output_schemas,\nfastapi/openapi/utils.py:258:    separate_input_output_schemas: bool = True,\nfastapi/openapi/utils.py:288:                separate_input_output_schemas=separate_input_output_schemas,\nfastapi/openapi/utils.py:309:                    separate_input_output_schemas=separate_input_output_schemas,\nfastapi/openapi/utils.py:326:                            separate_input_output_schemas=separate_input_output_schemas,\nfastapi/openapi/utils.py:356:                            separate_input_output_schemas=separate_input_output_schemas,\nfastapi/openapi/utils.py:389:                            separate_input_output_schemas=separate_input_output_schemas,\nfastapi/openapi/utils.py:481:    separate_input_output_schemas: bool = True,\nfastapi/openapi/utils.py:507:        separate_input_output_schemas=separate_input_output_schemas,\nfastapi/openapi/utils.py:516:                separate_input_output_schemas=separate_input_output_schemas,\nfastapi/openapi/utils.py:535:                separate_input_output_schemas=separate_input_output_schemas,\nfastapi/_compat/v1.py:243:    separate_input_output_schemas: bool = True,\nfastapi/_compat/v1.py:259:    separate_input_output_schemas: bool = True,\nfastapi/_compat/may_v1.py:65:        separate_input_output_schemas: bool = True,\n", "stderr": "", "exit_code": 0}

{"status": "error", "error_type": "FileReadError", "error_message": "'>' not supported between instances of 'int' and 'str'"}

{"status": "error", "error_type": "FileReadError", "error_message": "'>' not supported between instances of 'int' and 'str'"}

{"status": "error", "error_type": "FileReadError", "error_message": "'>' not supported between instances of 'int' and 'str'"}

{"status": "ok", "stdout": "__init__.py\nconstants.py\ndocs.py\nmodels.py\nutils.py\n", "stderr": "", "exit_code": 0}

{"status": "ok", "stdout": "import http.client\nimport inspect\nimport warnings\nfrom typing import Any, Dict, List, Optional, Sequence, Set, Tuple, Type, Union, cast\n\nfrom fastapi import routing\nfrom fastapi._compat import (\n    JsonSchemaValue,\n    ModelField,\n    Undefined,\n    get_compat_model_name_map,\n    get_definitions,\n    get_schema_from_model_field,\n    lenient_issubclass,\n)\nfrom fastapi.datastructures import DefaultPlaceholder\nfrom fastapi.dependencies.models import Dependant\nfrom fastapi.dependencies.utils import (\n    _get_flat_fields_from_params,\n    get_flat_dependant,\n    get_flat_params,\n)\nfrom fastapi.encoders import jsonable_encoder\nfrom fastapi.openapi.constants import METHODS_WITH_BODY, REF_PREFIX\nfrom fastapi.openapi.models import OpenAPI\nfrom fastapi.params import Body, ParamTypes\nfrom fastapi.responses import Response\nfrom fastapi.types import ModelNameMap\nfrom fastapi.utils import (\n    deep_dict_update,\n    generate_operation_id_for_path,\n    is_body_allowed_for_status_code,\n)\nfrom pydantic import BaseModel\nfrom starlette.responses import JSONResponse\nfrom starlette.routing import BaseRoute\nfrom typing_extensions import Literal\n\nfrom .._compat import _is_model_field\n\nvalidation_error_definition = {\n    \"title\": \"ValidationError\",\n    \"type\": \"object\",\n    \"properties\": {\n        \"loc\": {\n            \"title\": \"Location\",\n            \"type\": \"array\",\n            \"items\": {\"anyOf\": [{\"type\": \"string\"}, {\"type\": \"integer\"}]},\n        },\n        \"msg\": {\"title\": \"Message\", \"type\": \"string\"},\n        \"type\": {\"title\": \"Error Type\", \"type\": \"string\"},\n    },\n    \"required\": [\"loc\", \"msg\", \"type\"],\n}\n\nvalidation_error_response_definition = {\n    \"title\": \"HTTPValidationError\",\n    \"type\": \"object\",\n    \"properties\": {\n        \"detail\": {\n            \"title\": \"Detail\",\n            \"type\": \"array\",\n            \"

{"status": "ok", "stdout": "102:    separate_input_output_schemas: bool = True,\n133:                separate_input_output_schemas=separate_input_output_schemas,\n175:    separate_input_output_schemas: bool = True,\n184:        separate_input_output_schemas=separate_input_output_schemas,\n258:    separate_input_output_schemas: bool = True,\n288:                separate_input_output_schemas=separate_input_output_schemas,\n309:                    separate_input_output_schemas=separate_input_output_schemas,\n326:                            separate_input_output_schemas=separate_input_output_schemas,\n356:                            separate_input_output_schemas=separate_input_output_schemas,\n389:                            separate_input_output_schemas=separate_input_output_schemas,\n481:    separate_input_output_schemas: bool = True,\n507:        separate_input_output_schemas=separate_input_output_schemas,\n516:                separate_input_output_schemas=separate_input_output_schemas,\n535:                separate_input_output_schemas=separate_input_output_schemas,\n", "stderr": "", "exit_code": 0}

{"status": "ok", "stdout": "        Tuple[ModelField, Literal[\"validation\", \"serialization\"]], JsonSchemaValue\n    ],\n    separate_input_output_schemas: bool = True,\n) -> List[Dict[str, Any]]:\n    parameters = []\n    flat_dependant = get_flat_dependant(dependant, skip_repeats=True)\n    path_params = _get_flat_fields_from_params(flat_dependant.path_params)\n    query_params = _get_flat_fields_from_params(flat_dependant.query_params)\n    header_params = _get_flat_fields_from_params(flat_dependant.header_params)\n    cookie_params = _get_flat_fields_from_params(flat_dependant.cookie_params)\n    parameter_groups = [\n        (ParamTypes.path, path_params),\n        (ParamTypes.query, query_params),\n        (ParamTypes.header, header_params),\n        (ParamTypes.cookie, cookie_params),\n    ]\n    default_convert_underscores = True\n    if len(flat_dependant.header_params) == 1:\n        first_field = flat_dependant.header_params[0]\n        if lenient_issubclass(first_field.type_, BaseModel):\n            default_convert_underscores = getattr(\n                first_field.field_info, \"convert_underscores\", True\n            )\n    for param_type, param_group in parameter_groups:\n        for param in param_group:\n            field_info = param.field_info\n            # field_info = cast(Param, field_info)\n            if not getattr(field_info, \"include_in_schema\", True):\n                continue\n            param_schema = get_schema_from_model_field(\n                field=param,\n                model_name_map=model_name_map,\n                field_mapping=field_mapping,\n                separate_input_output_schemas=separate_input_output_schemas,\n            )\n            name = param.alias\n            convert_underscores = getattr(\n                param.field_info,\n                \"convert_underscores\",\n                default_convert_underscores,\n            )\n            if (\n                param_type == ParamTypes.header\n        

{"status": "ok", "stdout": "    body_field: Optional[ModelField],\n    model_name_map: ModelNameMap,\n    field_mapping: Dict[\n        Tuple[ModelField, Literal[\"validation\", \"serialization\"]], JsonSchemaValue\n    ],\n    separate_input_output_schemas: bool = True,\n) -> Optional[Dict[str, Any]]:\n    if not body_field:\n        return None\n    assert _is_model_field(body_field)\n    body_schema = get_schema_from_model_field(\n        field=body_field,\n        model_name_map=model_name_map,\n        field_mapping=field_mapping,\n        separate_input_output_schemas=separate_input_output_schemas,\n    )\n    field_info = cast(Body, body_field.field_info)\n    request_media_type = field_info.media_type\n    required = body_field.required\n    request_body_oai: Dict[str, Any] = {}\n    if required:\n        request_body_oai[\"required\"] = required\n    request_media_content: Dict[str, Any] = {\"schema\": body_schema}\n    if field_info.openapi_examples:\n        request_media_content[\"examples\"] = jsonable_encoder(\n            field_info.openapi_examples\n        )\n    elif field_info.example != Undefined:\n        request_media_content[\"example\"] = jsonable_encoder(field_info.example)\n    request_body_oai[\"content\"] = {request_media_type: request_media_content}\n    return request_body_oai\n\n\ndef generate_operation_id(\n    *, route: routing.APIRoute, method: str\n) -> str:  # pragma: nocover\n    warnings.warn(\n        \"fastapi.openapi.utils.generate_operation_id() was deprecated, \"\n        \"it is not used internally, and will be removed soon\",\n        DeprecationWarning,\n        stacklevel=2,\n    )\n    if route.operation_id:\n        return route.operation_id\n    path: str = route.path_format\n    return generate_operation_id_for_path(name=route.name, path=path, method=method)\n\n\ndef generate_operation_summary(*, route: routing.APIRoute, method: str) -> str:\n    if route.summary:\n        return route.summary\n", "stderr": "", "exi

{"status": "ok", "stdout": "def get_openapi_path(\n    *,\n    route: routing.APIRoute,\n    operation_ids: Set[str],\n    model_name_map: ModelNameMap,\n    field_mapping: Dict[\n        Tuple[ModelField, Literal[\"validation\", \"serialization\"]], JsonSchemaValue\n    ],\n    separate_input_output_schemas: bool = True,\n) -> Tuple[Dict[str, Any], Dict[str, Any], Dict[str, Any]]:\n    path = {}\n    security_schemes: Dict[str, Any] = {}\n    definitions: Dict[str, Any] = {}\n    assert route.methods is not None, \"Methods must be a list\"\n    if isinstance(route.response_class, DefaultPlaceholder):\n        current_response_class: Type[Response] = route.response_class.value\n    else:\n        current_response_class = route.response_class\n    assert current_response_class, \"A response class is needed to generate OpenAPI\"\n    route_response_media_type: Optional[str] = current_response_class.media_type\n    if route.include_in_schema:\n        for method in route.methods:\n            operation = get_openapi_operation_metadata(\n                route=route, method=method, operation_ids=operation_ids\n            )\n            parameters: List[Dict[str, Any]] = []\n            flat_dependant = get_flat_dependant(route.dependant, skip_repeats=True)\n            security_definitions, operation_security = get_openapi_security_definitions(\n                flat_dependant=flat_dependant\n            )\n            if operation_security:\n                operation.setdefault(\"security\", []).extend(operation_security)\n            if security_definitions:\n                security_schemes.update(security_definitions)\n            operation_parameters = _get_openapi_operation_parameters(\n                dependant=route.dependant,\n                model_name_map=model_name_map,\n                field_mapping=field_mapping,\n                separate_input_output_schemas=separate_input_output_schemas,\n            )\n            parameters.extend(operation_parameters)\

{"status": "ok", "stdout": "    version: str,\n    openapi_version: str = \"3.1.0\",\n    summary: Optional[str] = None,\n    description: Optional[str] = None,\n    routes: Sequence[BaseRoute],\n    webhooks: Optional[Sequence[BaseRoute]] = None,\n    tags: Optional[List[Dict[str, Any]]] = None,\n    servers: Optional[List[Dict[str, Union[str, Any]]]] = None,\n    terms_of_service: Optional[str] = None,\n    contact: Optional[Dict[str, Union[str, Any]]] = None,\n    license_info: Optional[Dict[str, Union[str, Any]]] = None,\n    separate_input_output_schemas: bool = True,\n    external_docs: Optional[Dict[str, Any]] = None,\n) -> Dict[str, Any]:\n    info: Dict[str, Any] = {\"title\": title, \"version\": version}\n    if summary:\n        info[\"summary\"] = summary\n    if description:\n        info[\"description\"] = description\n    if terms_of_service:\n        info[\"termsOfService\"] = terms_of_service\n    if contact:\n        info[\"contact\"] = contact\n    if license_info:\n        info[\"license\"] = license_info\n    output: Dict[str, Any] = {\"openapi\": openapi_version, \"info\": info}\n    if servers:\n        output[\"servers\"] = servers\n    components: Dict[str, Dict[str, Any]] = {}\n    paths: Dict[str, Dict[str, Any]] = {}\n    webhook_paths: Dict[str, Dict[str, Any]] = {}\n    operation_ids: Set[str] = set()\n    all_fields = get_fields_from_routes(list(routes or []) + list(webhooks or []))\n    model_name_map = get_compat_model_name_map(all_fields)\n    field_mapping, definitions = get_definitions(\n        fields=all_fields,\n        model_name_map=model_name_map,\n        separate_input_output_schemas=separate_input_output_schemas,\n    )\n    for route in routes or []:\n        if isinstance(route, routing.APIRoute):\n            result = get_openapi_path(\n                route=route,\n                operation_ids=operation_ids,\n                model_name_map=model_name_map,\n                field_mapping=field_mapping,\n               

{"status": "ok", "stdout": "255:def get_definitions(\n", "stderr": "", "exit_code": 0}

{"status": "ok", "stdout": "def get_definitions(\n    *,\n    fields: List[ModelField],\n    model_name_map: ModelNameMap,\n    separate_input_output_schemas: bool = True,\n) -> Tuple[\n    Dict[Tuple[ModelField, Literal[\"validation\", \"serialization\"]], JsonSchemaValue],\n    Dict[str, Dict[str, Any]],\n]:\n    models = get_flat_models_from_fields(fields, known_models=set())\n    return {}, get_model_definitions(flat_models=models, model_name_map=model_name_map)\n\n\ndef is_scalar_field(field: ModelField) -> bool:\n    return is_pv1_scalar_field(field)\n\n\ndef is_sequence_field(field: ModelField) -> bool:\n    return field.shape in sequence_shapes or shared._annotation_is_sequence(field.type_)\n\n\ndef is_scalar_sequence_field(field: ModelField) -> bool:\n    return is_pv1_scalar_sequence_field(field)\n\n\ndef is_bytes_field(field: ModelField) -> bool:\n    return lenient_issubclass(field.type_, bytes)  # type: ignore[no-any-return]\n\n\ndef is_bytes_sequence_field(field: ModelField) -> bool:\n    return field.shape in sequence_shapes and lenient_issubclass(field.type_, bytes)\n\n\ndef copy_field_info(*, field_info: FieldInfo, annotation: Any) -> FieldInfo:\n    return copy(field_info)\n\n\ndef serialize_sequence_value(*, field: ModelField, value: Any) -> Sequence[Any]:\n    return sequence_shape_to_type[field.shape](value)  # type: ignore[no-any-return]\n\n\ndef get_missing_field_error(loc: Tuple[str, ...]) -> Dict[str, Any]:\n    missing_field_error = ErrorWrapper(MissingError(), loc=loc)\n    new_error = ValidationError([missing_field_error], RequestErrorModel)\n    return new_error.errors()[0]  # type: ignore[return-value]\n\n\ndef create_body_model(\n    *, fields: Sequence[ModelField], model_name: str\n) -> Type[BaseModel]:\n    BodyModel = create_model(model_name)\n    for f in fields:\n        BodyModel.__fields__[f.name] = f  # type: ignore[index]\n    return BodyModel\n\n\ndef get_model_fields(model: Type[BaseModel]) -> List[ModelField]:\n    return l

{"status": "ok", "stdout": "170:def get_model_definitions(\n265:    return {}, get_model_definitions(flat_models=models, model_name_map=model_name_map)\n", "stderr": "", "exit_code": 0}

{"status": "ok", "stdout": "def get_model_definitions(\n    *,\n    flat_models: Set[Union[Type[BaseModel], Type[Enum]]],\n    model_name_map: Dict[Union[Type[BaseModel], Type[Enum]], str],\n) -> Dict[str, Any]:\n    definitions: Dict[str, Dict[str, Any]] = {}\n    for model in flat_models:\n        m_schema, m_definitions, m_nested_models = model_process_schema(\n            model, model_name_map=model_name_map, ref_prefix=REF_PREFIX\n        )\n        definitions.update(m_definitions)\n        model_name = model_name_map[model]\n        definitions[model_name] = m_schema\n    for m_schema in definitions.values():\n        if \"description\" in m_schema:\n            m_schema[\"description\"] = m_schema[\"description\"].split(\"\\f\")[0]\n    return definitions\n\n\ndef is_pv1_scalar_field(field: ModelField) -> bool:\n    from fastapi import params\n\n    field_info = field.field_info\n    if not (\n        field.shape == SHAPE_SINGLETON\n        and not lenient_issubclass(field.type_, BaseModel)\n        and not lenient_issubclass(field.type_, dict)\n        and not shared.field_annotation_is_sequence(field.type_)\n        and not is_dataclass(field.type_)\n        and not isinstance(field_info, params.Body)\n    ):\n        return False\n    if field.sub_fields:\n        if not all(is_pv1_scalar_field(f) for f in field.sub_fields):\n            return False\n    return True\n\n\ndef is_pv1_scalar_sequence_field(field: ModelField) -> bool:\n    if (field.shape in sequence_shapes) and not lenient_issubclass(\n        field.type_, BaseModel\n    ):\n        if field.sub_fields is not None:\n            for sub_field in field.sub_fields:\n                if not is_pv1_scalar_field(sub_field):\n                    return False\n        return True\n    if shared._annotation_is_sequence(field.type_):\n        return True\n    return False\n\n\ndef _model_rebuild(model: Type[BaseModel]) -> None:\n    model.update_forward_refs()\n\n\ndef _model_dump(\n    model: BaseMod

{"status": "ok", "stdout": "", "stderr": "", "exit_code": 0}

{"status": "ok", "stdout": "./tests/test_computed_fields.py:12:    from pydantic import BaseModel, computed_field\n./tests/test_computed_fields.py:18:        @computed_field\n./docs/en/docs/release-notes.md:2807:* \u2728 Enable Pydantic's serialization mode for responses, add support for Pydantic's `computed_field`, better OpenAPI for response models, proper required attributes, better generated clients. PR [#10011](https://github.com/tiangolo/fastapi/pull/10011) by [@tiangolo](https://github.com/tiangolo).\nBinary file ./.git/index matches\n", "stderr": "", "exit_code": 0}

{"status": "ok", "stdout": "import pytest\nfrom fastapi import FastAPI\nfrom fastapi.testclient import TestClient\n\nfrom .utils import needs_pydanticv2\n\n\n@pytest.fixture(name=\"client\")\ndef get_client():\n    app = FastAPI()\n\n    from pydantic import BaseModel, computed_field\n\n    class Rectangle(BaseModel):\n        width: int\n        length: int\n\n        @computed_field\n        @property\n        def area(self) -> int:\n            return self.width * self.length\n\n    @app.get(\"/\")\n    def read_root() -> Rectangle:\n        return Rectangle(width=3, length=4)\n\n    @app.get(\"/responses\", responses={200: {\"model\": Rectangle}})\n    def read_responses() -> Rectangle:\n        return Rectangle(width=3, length=4)\n\n    client = TestClient(app)\n    return client\n\n\n@pytest.mark.parametrize(\"path\", [\"/\", \"/responses\"])\n@needs_pydanticv2\ndef test_get(client: TestClient, path: str):\n    response = client.get(path)\n    assert response.status_code == 200, response.text\n    assert response.json() == {\"width\": 3, \"length\": 4, \"area\": 12}\n\n\n@needs_pydanticv2\ndef test_openapi_schema(client: TestClient):\n    response = client.get(\"/openapi.json\")\n    assert response.status_code == 200, response.text\n    assert response.json() == {\n        \"openapi\": \"3.1.0\",\n        \"info\": {\"title\": \"FastAPI\", \"version\": \"0.1.0\"},\n        \"paths\": {\n            \"/\": {\n                \"get\": {\n                    \"summary\": \"Read Root\",\n                    \"operationId\": \"read_root__get\",\n                    \"responses\": {\n                        \"200\": {\n                            \"description\": \"Successful Response\",\n                            \"content\": {\n                                \"application/json\": {\n                                    \"schema\": {\"$ref\": \"#/components/schemas/Rectangle\"}\n                                }\n                            },\n                 

{"status": "error", "error_type": "FileWriteError", "error_message": "Path traversal detected: '/tmp/test_repro.py' escapes workspace root."}

{"status": "ok", "filepath": "repro.py", "size": 1086}

{"status": "ok", "stdout": "", "stderr": "", "exit_code": 0}

{"status": "error", "error_type": "CommandError", "error_message": "============================= test session starts ==============================\nplatform darwin -- Python 3.13.15, pytest-9.1.1, pluggy-1.6.0\ncodspeed: 5.0.3 (disabled, mode: walltime, callgraph: not supported, timer_resolution: 41.7ns)\nrootdir: /private/workspace\nconfigfile: pytest.ini (WARNING: ignoring pytest config in pyproject.toml!)\nplugins: cov-7.1.0, xdist-3.8.0, timeout-2.4.0, codspeed-5.0.3, sugar-1.1.1, anyio-4.15.1, inline-snapshot-0.35.4\ncollected 1 item\n\nrepro.py F                                                               [100%]\n\n=================================== FAILURES ===================================\n___________ test_computed_fields_separate_input_output_schemas_false ___________\n\n    def test_computed_fields_separate_input_output_schemas_false():\n        app = FastAPI(separate_input_output_schemas=False)\n    \n        class Rectangle(BaseModel):\n            width: int\n            length: int\n    \n            @computed_field\n            @property\n            def area(self) -> int:\n                return self.width * self.length\n    \n        @app.get(\"/\")\n        def read_root() -> Rectangle:\n            return Rectangle(width=3, length=4)\n    \n        client = TestClient(app)\n        response = client.get(\"/openapi.json\")\n        assert response.status_code == 200\n        schema = response.json()\n    \n        # The Rectangle schema should be in components/schemas\n        rectangle_schema = schema[\"components\"][\"schemas\"][\"Rectangle\"]\n    \n        # It should contain 'area' even when separate_input_output_schemas=False\n>       assert \"area\" in rectangle_schema[\"properties\"]\nE       AssertionError: assert 'area' in {'width': {'type': 'integer', 'title': 'Width'}, 'length': {'type': 'integer', 'title': 'Length'}}\n\nrepro.py:31: AssertionError\n=============================== warnings summary ===============================

{"status": "ok", "stdout": "fastapi/_compat/v1.py:170:def get_model_definitions(\nfastapi/_compat/v1.py:265:    return {}, get_model_definitions(flat_models=models, model_name_map=model_name_map)\n", "stderr": "", "exit_code": 0}

{"status": "ok", "stdout": "fastapi/_compat/v1.py:57:        model_process_schema,\nfastapi/_compat/v1.py:104:        model_process_schema,\nfastapi/_compat/v1.py:177:        m_schema, m_definitions, m_nested_models = model_process_schema(\n", "stderr": "", "exit_code": 0}

{"status": "ok", "stdout": "from copy import copy\nfrom dataclasses import dataclass, is_dataclass\nfrom enum import Enum\nfrom typing import (\n    Any,\n    Callable,\n    Dict,\n    List,\n    Sequence,\n    Set,\n    Tuple,\n    Type,\n    Union,\n)\n\nfrom fastapi._compat import shared\nfrom fastapi.openapi.constants import REF_PREFIX as REF_PREFIX\nfrom fastapi.types import ModelNameMap\nfrom pydantic.version import VERSION as PYDANTIC_VERSION\nfrom typing_extensions import Literal\n\nPYDANTIC_VERSION_MINOR_TUPLE = tuple(int(x) for x in PYDANTIC_VERSION.split(\".\")[:2])\nPYDANTIC_V2 = PYDANTIC_VERSION_MINOR_TUPLE[0] == 2\n# Keeping old \"Required\" functionality from Pydantic V1, without\n# shadowing typing.Required.\nRequiredParam: Any = Ellipsis\n\nif not PYDANTIC_V2:\n    from pydantic import BaseConfig as BaseConfig\n    from pydantic import BaseModel as BaseModel\n    from pydantic import ValidationError as ValidationError\n    from pydantic import create_model as create_model\n    from pydantic.class_validators import Validator as Validator\n    from pydantic.color import Color as Color\n    from pydantic.error_wrappers import ErrorWrapper as ErrorWrapper\n    from pydantic.errors import MissingError\n    from pydantic.fields import (  # type: ignore[attr-defined]\n        SHAPE_FROZENSET,\n        SHAPE_LIST,\n        SHAPE_SEQUENCE,\n        SHAPE_SET,\n        SHAPE_SINGLETON,\n        SHAPE_TUPLE,\n        SHAPE_TUPLE_ELLIPSIS,\n    )\n    from pydantic.fields import FieldInfo as FieldInfo\n    from pydantic.fields import ModelField as ModelField  # type: ignore[attr-defined]\n    from pydantic.fields import Undefined as Undefined  # type: ignore[attr-defined]\n    from pydantic.fields import (  # type: ignore[attr-defined]\n        UndefinedType as UndefinedType,\n    )\n    from pydantic.networks import AnyUrl as AnyUrl\n    from pydantic.networks import NameEmail as NameEmail\n    from pydantic.schema import TypeModelSet as TypeModelSet\n    from

{"status": "ok", "stdout": "    )\n    from pydantic.v1.schema import TypeModelSet as TypeModelSet\n    from pydantic.v1.schema import (\n        field_schema,\n        model_process_schema,\n    )\n    from pydantic.v1.schema import (\n        get_annotation_from_field_info as get_annotation_from_field_info,\n    )\n    from pydantic.v1.schema import (\n        get_flat_models_from_field as get_flat_models_from_field,\n    )\n    from pydantic.v1.schema import (\n        get_flat_models_from_fields as get_flat_models_from_fields,\n    )\n    from pydantic.v1.schema import get_model_name_map as get_model_name_map\n    from pydantic.v1.types import (  # type: ignore[assignment]\n        SecretBytes as SecretBytes,\n    )\n    from pydantic.v1.types import (  # type: ignore[assignment]\n        SecretStr as SecretStr,\n    )\n    from pydantic.v1.typing import evaluate_forwardref as evaluate_forwardref\n    from pydantic.v1.utils import lenient_issubclass as lenient_issubclass\n\n\nGetJsonSchemaHandler = Any\nJsonSchemaValue = Dict[str, Any]\nCoreSchema = Any\nUrl = AnyUrl\n\nsequence_shapes = {\n    SHAPE_LIST,\n    SHAPE_SET,\n    SHAPE_FROZENSET,\n    SHAPE_TUPLE,\n    SHAPE_SEQUENCE,\n    SHAPE_TUPLE_ELLIPSIS,\n}\nsequence_shape_to_type = {\n    SHAPE_LIST: list,\n    SHAPE_SET: set,\n    SHAPE_TUPLE: tuple,\n    SHAPE_SEQUENCE: list,\n    SHAPE_TUPLE_ELLIPSIS: list,\n}\n\n\n@dataclass\nclass GenerateJsonSchema:\n    ref_template: str\n", "stderr": "", "exit_code": 0}

{"status": "ok", "stdout": "fastapi/_compat/v1.py:170:def get_model_definitions(\nfastapi/_compat/v1.py:265:    return {}, get_model_definitions(flat_models=models, model_name_map=model_name_map)\n", "stderr": "", "exit_code": 0}

{"status": "ok", "stdout": "def get_model_definitions(\n    *,\n    flat_models: Set[Union[Type[BaseModel], Type[Enum]]],\n    model_name_map: Dict[Union[Type[BaseModel], Type[Enum]], str],\n) -> Dict[str, Any]:\n    definitions: Dict[str, Dict[str, Any]] = {}\n    for model in flat_models:\n        m_schema, m_definitions, m_nested_models = model_process_schema(\n            model, model_name_map=model_name_map, ref_prefix=REF_PREFIX\n        )\n        definitions.update(m_definitions)\n        model_name = model_name_map[model]\n        definitions[model_name] = m_schema\n    for m_schema in definitions.values():\n        if \"description\" in m_schema:\n            m_schema[\"description\"] = m_schema[\"description\"].split(\"\\f\")[0]\n", "stderr": "", "exit_code": 0}

{"status": "ok", "stdout": "fastapi/_compat/v1.py:255:def get_definitions(\n", "stderr": "", "exit_code": 0}

{"status": "ok", "stdout": "def get_definitions(\n    *,\n    fields: List[ModelField],\n    model_name_map: ModelNameMap,\n    separate_input_output_schemas: bool = True,\n) -> Tuple[\n    Dict[Tuple[ModelField, Literal[\"validation\", \"serialization\"]], JsonSchemaValue],\n    Dict[str, Dict[str, Any]],\n]:\n    models = get_flat_models_from_fields(fields, known_models=set())\n    return {}, get_model_definitions(flat_models=models, model_name_map=model_name_map)\n\n\ndef is_scalar_field(field: ModelField) -> bool:\n    return is_pv1_scalar_field(field)\n\n", "stderr": "", "exit_code": 0}

{"status": "ok", "stdout": "fastapi/_compat/v1.py:170:def get_model_definitions(\nfastapi/_compat/v1.py:265:    return {}, get_model_definitions(flat_models=models, model_name_map=model_name_map)\n", "stderr": "", "exit_code": 0}

{"status": "ok", "stdout": "fastapi/_compat/v1.py:57:        model_process_schema,\nfastapi/_compat/v1.py:104:        model_process_schema,\nfastapi/_compat/v1.py:177:        m_schema, m_definitions, m_nested_models = model_process_schema(\n", "stderr": "", "exit_code": 0}

{"status": "ok", "stdout": "fastapi/_compat/v1.py:57:        model_process_schema,\nfastapi/_compat/v1.py:104:        model_process_schema,\nfastapi/_compat/v1.py:177:        m_schema, m_definitions, m_nested_models = model_process_schema(\n", "stderr": "", "exit_code": 0}

{"status": "ok", "stdout": "def get_model_definitions(\n    *,\n    flat_models: Set[Union[Type[BaseModel], Type[Enum]]],\n    model_name_map: Dict[Union[Type[BaseModel], Type[Enum]], str],\n) -> Dict[str, Any]:\n    definitions: Dict[str, Dict[str, Any]] = {}\n    for model in flat_models:\n        m_schema, m_definitions, m_nested_models = model_process_schema(\n            model, model_name_map=model_name_map, ref_prefix=REF_PREFIX\n        )\n        definitions.update(m_definitions)\n        model_name = model_name_map[model]\n        definitions[model_name] = m_schema\n    for m_schema in definitions.values():\n        if \"description\" in m_schema:\n            m_schema[\"description\"] = m_schema[\"description\"].split(\"\\f\")[0]\n", "stderr": "", "exit_code": 0}

{"status": "ok", "stdout": "fastapi/_compat/v1.py:255:def get_definitions(\n", "stderr": "", "exit_code": 0}

{"status": "ok", "stdout": "def get_definitions(\n    *,\n    fields: List[ModelField],\n    model_name_map: ModelNameMap,\n    separate_input_output_schemas: bool = True,\n) -> Tuple[\n    Dict[Tuple[ModelField, Literal[\"validation\", \"serialization\"]], JsonSchemaValue],\n    Dict[str, Dict[str, Any]],\n]:\n    models = get_flat_models_from_fields(fields, known_models=set())\n    return {}, get_model_definitions(flat_models=models, model_name_map=model_name_map)\n\n\ndef is_scalar_field(field: ModelField) -> bool:\n    return is_pv1_scalar_field(field)\n\n", "stderr": "", "exit_code": 0}

{"status": "ok", "stdout": "fastapi/_compat/v1.py:170:def get_model_definitions(\nfastapi/_compat/v1.py:265:    return {}, get_model_definitions(flat_models=models, model_name_map=model_name_map)\n", "stderr": "", "exit_code": 0}

{"status": "ok", "stdout": "def get_model_definitions(\n    *,\n    flat_models: Set[Union[Type[BaseModel], Type[Enum]]],\n    model_name_map: Dict[Union[Type[BaseModel], Type[Enum]], str],\n) -> Dict[str, Any]:\n    definitions: Dict[str, Dict[str, Any]] = {}\n    for model in flat_models:\n        m_schema, m_definitions, m_nested_models = model_process_schema(\n            model, model_name_map=model_name_map, ref_prefix=REF_PREFIX\n        )\n        definitions.update(m_definitions)\n        model_name = model_name_map[model]\n        definitions[model_name] = m_schema\n    for m_schema in definitions.values():\n        if \"description\" in m_schema:\n            m_schema[\"description\"] = m_schema[\"description\"].split(\"\\f\")[0]\n", "stderr": "", "exit_code": 0}

{"status": "ok", "stdout": "fastapi/_compat/v1.py:57:        model_process_schema,\nfastapi/_compat/v1.py:104:        model_process_schema,\nfastapi/_compat/v1.py:177:        m_schema, m_definitions, m_nested_models = model_process_schema(\n", "stderr": "", "exit_code": 0}

{"status": "ok", "stdout": "from copy import copy\nfrom dataclasses import dataclass, is_dataclass\nfrom enum import Enum\nfrom typing import (\n    Any,\n    Callable,\n    Dict,\n    List,\n    Sequence,\n    Set,\n    Tuple,\n    Type,\n    Union,\n)\n\nfrom fastapi._compat import shared\nfrom fastapi.openapi.constants import REF_PREFIX as REF_PREFIX\nfrom fastapi.types import ModelNameMap\nfrom pydantic.version import VERSION as PYDANTIC_VERSION\nfrom typing_extensions import Literal\n\nPYDANTIC_VERSION_MINOR_TUPLE = tuple(int(x) for x in PYDANTIC_VERSION.split(\".\")[:2])\nPYDANTIC_V2 = PYDANTIC_VERSION_MINOR_TUPLE[0] == 2\n# Keeping old \"Required\" functionality from Pydantic V1, without\n# shadowing typing.Required.\nRequiredParam: Any = Ellipsis\n\nif not PYDANTIC_V2:\n    from pydantic import BaseConfig as BaseConfig\n    from pydantic import BaseModel as BaseModel\n    from pydantic import ValidationError as ValidationError\n    from pydantic import create_model as create_model\n    from pydantic.class_validators import Validator as Validator\n    from pydantic.color import Color as Color\n    from pydantic.error_wrappers import ErrorWrapper as ErrorWrapper\n    from pydantic.errors import MissingError\n    from pydantic.fields import (  # type: ignore[attr-defined]\n        SHAPE_FROZENSET,\n        SHAPE_LIST,\n        SHAPE_SEQUENCE,\n        SHAPE_SET,\n        SHAPE_SINGLETON,\n        SHAPE_TUPLE,\n        SHAPE_TUPLE_ELLIPSIS,\n    )\n    from pydantic.fields import FieldInfo as FieldInfo\n    from pydantic.fields import ModelField as ModelField  # type: ignore[attr-defined]\n    from pydantic.fields import Undefined as Undefined  # type: ignore[attr-defined]\n    from pydantic.fields import (  # type: ignore[attr-defined]\n        UndefinedType as UndefinedType,\n    )\n    from pydantic.networks import AnyUrl as AnyUrl\n    from pydantic.networks import NameEmail as NameEmail\n    from pydantic.schema import TypeModelSet as TypeModelSet\n    from

{"status": "ok", "stdout": "fastapi/_compat/v1.py:170:def get_model_definitions(\nfastapi/_compat/v1.py:265:    return {}, get_model_definitions(flat_models=models, model_name_map=model_name_map)\n", "stderr": "", "exit_code": 0}

{"status": "ok", "stdout": "def get_model_definitions(\n    *,\n    flat_models: Set[Union[Type[BaseModel], Type[Enum]]],\n    model_name_map: Dict[Union[Type[BaseModel], Type[Enum]], str],\n) -> Dict[str, Any]:\n    definitions: Dict[str, Dict[str, Any]] = {}\n    for model in flat_models:\n        m_schema, m_definitions, m_nested_models = model_process_schema(\n            model, model_name_map=model_name_map, ref_prefix=REF_PREFIX\n        )\n        definitions.update(m_definitions)\n        model_name = model_name_map[model]\n        definitions[model_name] = m_schema\n    for m_schema in definitions.values():\n        if \"description\" in m_schema:\n            m_schema[\"description\"] = m_schema[\"description\"].split(\"\\f\")[0]\n", "stderr": "", "exit_code": 0}

{"status": "ok", "stdout": "def get_definitions(\n    *,\n    fields: List[ModelField],\n    model_name_map: ModelNameMap,\n    separate_input_output_schemas: bool = True,\n) -> Tuple[\n    Dict[Tuple[ModelField, Literal[\"validation\", \"serialization\"]], JsonSchemaValue],\n    Dict[str, Dict[str, Any]],\n]:\n    models = get_flat_models_from_fields(fields, known_models=set())\n    return {}, get_model_definitions(flat_models=models, model_name_map=model_name_map)\n\n\ndef is_scalar_field(field: ModelField) -> bool:\n    return is_pv1_scalar_field(field)\n\n", "stderr": "", "exit_code": 0}

{"status": "ok", "stdout": ") -> Tuple[\n    Dict[Tuple[ModelField, Literal[\"validation\", \"serialization\"]], JsonSchemaValue],\n    Dict[str, Dict[str, Any]],\n]:\n    models = get_flat_models_from_fields(fields, known_models=set())\n    return {}, get_model_definitions(flat_models=models, model_name_map=model_name_map)\n\n\ndef is_scalar_field(field: ModelField) -> bool:\n    return is_pv1_scalar_field(field)\n\n", "stderr": "", "exit_code": 0}

{"status": "ok", "stdout": "def get_model_definitions(\n    *,\n    flat_models: Set[Union[Type[BaseModel], Type[Enum]]],\n    model_name_map: Dict[Union[Type[BaseModel], Type[Enum]], str],\n) -> Dict[str, Any]:\n    definitions: Dict[str, Dict[str, Any]] = {}\n    for model in flat_models:\n        m_schema, m_definitions, m_nested_models = model_process_schema(\n            model, model_name_map=model_name_map, ref_prefix=REF_PREFIX\n        )\n        definitions.update(m_definitions)\n        model_name = model_name_map[model]\n        definitions[model_name] = m_schema\n    for m_schema in definitions.values():\n        if \"description\" in m_schema:\n            m_schema[\"description\"] = m_schema[\"description\"].split(\"\\f\")[0]\n", "stderr": "", "exit_code": 0}

{"status": "ok", "stdout": "fastapi/_compat/v1.py:170:def get_model_definitions(\nfastapi/_compat/v1.py:265:    return {}, get_model_definitions(flat_models=models, model_name_map=model_name_map)\n", "stderr": "", "exit_code": 0}

{"status": "ok", "stdout": "def get_model_definitions(\n    *,\n    flat_models: Set[Union[Type[BaseModel], Type[Enum]]],\n    model_name_map: Dict[Union[Type[BaseModel], Type[Enum]], str],\n) -> Dict[str, Any]:\n    definitions: Dict[str, Dict[str, Any]] = {}\n    for model in flat_models:\n        m_schema, m_definitions, m_nested_models = model_process_schema(\n            model, model_name_map=model_name_map, ref_prefix=REF_PREFIX\n        )\n        definitions.update(m_definitions)\n        model_name = model_name_map[model]\n        definitions[model_name] = m_schema\n    for m_schema in definitions.values():\n        if \"description\" in m_schema:\n            m_schema[\"description\"] = m_schema[\"description\"].split(\"\\f\")[0]\n", "stderr": "", "exit_code": 0}

{"status": "ok", "stdout": "def get_definitions(\n    *,\n    fields: List[ModelField],\n    model_name_map: ModelNameMap,\n    separate_input_output_schemas: bool = True,\n) -> Tuple[\n    Dict[Tuple[ModelField, Literal[\"validation\", \"serialization\"]], JsonSchemaValue],\n    Dict[str, Dict[str, Any]],\n]:\n    models = get_flat_models_from_fields(fields, known_models=set())\n    return {}, get_model_definitions(flat_models=models, model_name_map=model_name_map)\n\n\ndef is_scalar_field(field: ModelField) -> bool:\n    return is_pv1_scalar_field(field)\n\n", "stderr": "", "exit_code": 0}

{"status": "ok", "stdout": ") -> Tuple[\n    Dict[Tuple[ModelField, Literal[\"validation\", \"serialization\"]], JsonSchemaValue],\n    Dict[str, Dict[str, Any]],\n]:\n    models = get_flat_models_from_fields(fields, known_models=set())\n    return {}, get_model_definitions(flat_models=models, model_name_map=model_name_map)\n\n\ndef is_scalar_field(field: ModelField) -> bool:\n    return is_pv1_scalar_field(field)\n\n", "stderr": "", "exit_code": 0}

{"status": "ok", "stdout": "def get_model_definitions(\n    *,\n    flat_models: Set[Union[Type[BaseModel], Type[Enum]]],\n    model_name_map: Dict[Union[Type[BaseModel], Type[Enum]], str],\n) -> Dict[str, Any]:\n    definitions: Dict[str, Dict[str, Any]] = {}\n    for model in flat_models:\n        m_schema, m_definitions, m_nested_models = model_process_schema(\n            model, model_name_map=model_name_map, ref_prefix=REF_PREFIX\n        )\n        definitions.update(m_definitions)\n        model_name = model_name_map[model]\n        definitions[model_name] = m_schema\n    for m_schema in definitions.values():\n        if \"description\" in m_schema:\n            m_schema[\"description\"] = m_schema[\"description\"].split(\"\\f\")[0]\n", "stderr": "", "exit_code": 0, "budget_warning": "Only 10 tool call(s) remaining (50/60 used). Finalize your edits and call submit_patch soon."}