fastapi_13207

{"status": "ok", "stdout": "./tests/test_openapi_separate_input_output_schemas.py:27:def get_app_client(separate_input_output_schemas: bool = True) -> TestClient:\n./tests/test_openapi_separate_input_output_schemas.py:28:    app = FastAPI(separate_input_output_schemas=separate_input_output_schemas)\n./tests/test_openapi_separate_input_output_schemas.py:55:    client_no = get_app_client(separate_input_output_schemas=False)\n./tests/test_openapi_separate_input_output_schemas.py:68:    client_no = get_app_client(separate_input_output_schemas=False)\n./tests/test_openapi_separate_input_output_schemas.py:89:    client_no = get_app_client(separate_input_output_schemas=False)\n./tests/test_openapi_separate_input_output_schemas.py:116:    client_no = get_app_client(separate_input_output_schemas=False)\n./tests/test_openapi_separate_input_output_schemas.py:360:    client = get_app_client(separate_input_output_schemas=False)\n./docs/ru/docs/how-to/separate-openapi-schemas.md:86:\u0412 \u0442\u0430\u043a\u043e\u043c \u0441\u043b\u0443\u0447\u0430\u0435 \u0432\u044b \u043c\u043e\u0436\u0435\u0442\u0435 \u043e\u0442\u043a\u043b\u044e\u0447\u0438\u0442\u044c \u044d\u0442\u0443 \u0444\u0443\u043d\u043a\u0446\u0438\u043e\u043d\u0430\u043b\u044c\u043d\u043e\u0441\u0442\u044c \u0432 FastAPI \u0441 \u043f\u043e\u043c\u043e\u0449\u044c\u044e \u043f\u0430\u0440\u0430\u043c\u0435\u0442\u0440\u0430 `separate_input_output_schemas=False`.\n./docs/ru/docs/how-to/separate-openapi-schemas.md:90:\u041f\u043e\u0434\u0434\u0435\u0440\u0436\u043a\u0430 `separate_input_output_schemas` \u043f\u043e\u044f\u0432\u0438\u043b\u0430\u0441\u044c \u0432 FastAPI `0.102.0`. \ud83e\udd13\n./docs/pt/docs/how-to/separate-openapi-schemas.md:86:Nesse caso, voc\u00ea pode desativar esse recurso no **FastAPI**, com o par\u00e2metro `separate_input_output_schemas=False`.\n./docs/pt/docs/how-to/separate-openapi-schemas.md:90:O suporte para `separate_input_output_schemas` foi adicionado no FastAPI `0.102.0`. \ud83e\ud

{"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": "ok", "stdout": "from typing import List, Optional\n\nfrom fastapi import FastAPI\nfrom fastapi.testclient import TestClient\nfrom inline_snapshot import snapshot\nfrom pydantic import BaseModel\n\nfrom .utils import PYDANTIC_V2, needs_pydanticv2\n\n\nclass SubItem(BaseModel):\n    subname: str\n    sub_description: Optional[str] = None\n    tags: List[str] = []\n    if PYDANTIC_V2:\n        model_config = {\"json_schema_serialization_defaults_required\": True}\n\n\nclass Item(BaseModel):\n    name: str\n    description: Optional[str] = None\n    sub: Optional[SubItem] = None\n    if PYDANTIC_V2:\n        model_config = {\"json_schema_serialization_defaults_required\": True}\n\n\ndef get_app_client(separate_input_output_schemas: bool = True) -> TestClient:\n    app = FastAPI(separate_input_output_schemas=separate_input_output_schemas)\n\n    @app.post(\"/items/\", responses={402: {\"model\": Item}})\n    def create_item(item: Item) -> Item:\n        return item\n\n    @app.post(\"/items-list/\")\n    def create_item_list(item: List[Item]):\n        return item\n\n    @app.get(\"/items/\")\n    def read_items() -> List[Item]:\n        return [\n            Item(\n                name=\"Portal Gun\",\n                description=\"Device to travel through the multi-rick-verse\",\n                sub=SubItem(subname=\"subname\"),\n            ),\n            Item(name=\"Plumbus\"),\n        ]\n\n    client = TestClient(app)\n    return client\n\n\ndef test_create_item():\n    client = get_app_client()\n    client_no = get_app_client(separate_input_output_schemas=False)\n    response = client.post(\"/items/\", json={\"name\": \"Plumbus\"})\n    response2 = client_no.post(\"/items/\", json={\"name\": \"Plumbus\"})\n    assert response.status_code == response2.status_code == 200, response.text\n    assert (\n        response.json()\n        == response2.json()\n        == {\"name\": \"Plumbus\", \"description\": None, \"sub\": None}\n    )\n\n\ndef test_create_

{"status": "ok", "stdout": "Properties: ['name', 'description']\nFAILURE: name_upper is missing from the schema\n", "stderr": "", "exit_code": 0}

{"status": "ok", "stdout": "('title', 'version', 'openapi_version', 'summary', 'description', 'routes', 'webhooks', 'tags', 'servers', 'terms_of_service', 'contact', 'license_info', 'separate_input_output_schemas', 'external_docs', 'info', 'output', 'components', 'paths', 'webhook_paths', 'operation_ids', 'all_fields', 'model_name_map', 'field_mapping', 'definitions', 'route', 'result', 'path', 'security_schemes', 'path_definitions', 'webhook', 'k')\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": "13:    get_schema_from_model_field,\n129:            param_schema = get_schema_from_model_field(\n180:    body_schema = get_schema_from_model_field(\n352:                        response_schema = get_schema_from_model_field(\n385:                        additional_field_schema = get_schema_from_model_field(\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": "fastapi/_compat/v1.py:236:def get_schema_from_model_field(\n", "stderr": "", "exit_code": 0}

{"status": "ok", "stdout": "def get_schema_from_model_field(\n    *,\n    field: 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) -> Dict[str, Any]:\n    return field_schema(  # type: ignore[no-any-return]\n        field, model_name_map=model_name_map, ref_prefix=REF_PREFIX\n    )[0]\n\n\n# def get_compat_model_name_map(fields: List[ModelField]) -> ModelNameMap:\n#     models = get_flat_models_from_fields(fields, known_models=set())\n#     return get_model_name_map(models)  # type: ignore[no-any-return]\n\n\ndef 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

{"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": "57:        model_process_schema,\n104:        model_process_schema,\n177:        m_schema, m_definitions, m_nested_models = model_process_schema(\n", "stderr": "", "exit_code": 0}

{"status": "ok", "stdout": "        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 pydantic.schema import (\n        field_schema,\n        model_process_schema,\n    )\n    from pydantic.schema import (\n        get_annotation_from_field_info as get_annotation_from_field_info,\n    )\n    from pydantic.schema import get_flat_models_from_field as get_flat_models_from_field\n    from pydantic.schema import (\n        get_flat_models_from_fields as get_flat_models_from_fields,\n    )\n    from pydantic.schema import get_model_name_map as get_model_name_map\n    from pydantic.types import SecretBytes as SecretBytes\n    from pydantic.types import SecretStr as SecretStr\n    from pydantic.typing import evaluate_forwardref as evaluate_forwardref\n    from pydantic.utils import lenient_issubclass as lenient_issubclass\n\n\nelse:\n    from pydantic.v1 import BaseConfig as BaseConfig  # type: ignore[assignment]\n    from pydantic.v1 import BaseModel as BaseModel  # type: ignore[assignment]\n    from pydantic.v1 import (  # type: ignore[assignment]\n        ValidationError as ValidationError,\n    )\n    from pydantic.v1 import create_model as create_model  # type: ignore[no-redef]\n    from pydantic.v1.class_validators import Validator as Validator\n    from pydantic.v1.color import Color as Color  # type: ignore[assignment]\n    from pydantic.v1.error_wrappers import ErrorWrapper as ErrorWrapper\n    from pydantic.v1.errors import MissingError\n    from pydantic.v1.fields import (\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.v1.fields import FieldInfo as FieldInfo  # type: ignore[assignment]\n    from pydantic.v1.fields import ModelField as ModelField\n

{"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": "error", "error_type": "CommandError", "error_message": "", "details": {"stdout": "", "stderr": "", "exit_code": 1}}

{"status": "ok", "stdout": "fastapi/openapi/utils.py:12:    get_definitions,\nfastapi/openapi/utils.py:504:    field_mapping, definitions = get_definitions(\n", "stderr": "", "exit_code": 0}

{"status": "ok", "stdout": "    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                separate_input_output_schemas=separate_input_output_schemas,\n            )\n            if result:\n                path, security_schemes, path_definitions = result\n                if path:\n", "stderr": "", "exit_code": 0}

{"status": "error", "error_type": "CommandError", "error_message": "", "details": {"stdout": "", "stderr": "", "exit_code": 1}}

{"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", "stderr": "", "exit_code": 0}

{"status": "ok", "stdout": "61:    def get_definitions(\n", "stderr": "", "exit_code": 0}

{"status": "ok", "stdout": "\n    def get_definitions(\n        *,\n        fields: List[ModelField],\n        model_name_map: ModelNameMap,\n        separate_input_output_schemas: bool = True,\n    ) -> Tuple[\n        Dict[\n            Tuple[ModelField, Literal[\"validation\", \"serialization\"]], JsonSchemaValue\n        ],\n        Dict[str, Dict[str, Any]],\n    ]:\n        return {}, {}  # pragma: no cover\n\n\nelse:\n    from .v1 import AnyUrl as AnyUrl\n    from .v1 import BaseConfig as BaseConfig\n    from .v1 import BaseModel as BaseModel\n    from .v1 import Color as Color\n    from .v1 import CoreSchema as CoreSchema\n", "stderr": "", "exit_code": 0}

{"status": "error", "error_type": "CommandError", "error_message": "", "details": {"stdout": "", "stderr": "", "exit_code": 1}}

{"status": "ok", "stdout": "from .main import BaseConfig as BaseConfig\nfrom .main import PydanticSchemaGenerationError as PydanticSchemaGenerationError\nfrom .main import RequiredParam as RequiredParam\nfrom .main import Undefined as Undefined\nfrom .main import UndefinedType as UndefinedType\nfrom .main import Url as Url\nfrom .main import Validator as Validator\nfrom .main import _get_model_config as _get_model_config\nfrom .main import _is_error_wrapper as _is_error_wrapper\nfrom .main import _is_model_class as _is_model_class\nfrom .main import _is_model_field as _is_model_field\nfrom .main import _is_undefined as _is_undefined\nfrom .main import _model_dump as _model_dump\nfrom .main import _model_rebuild as _model_rebuild\nfrom .main import copy_field_info as copy_field_info\nfrom .main import create_body_model as create_body_model\nfrom .main import evaluate_forwardref as evaluate_forwardref\nfrom .main import get_annotation_from_field_info as get_annotation_from_field_info\nfrom .main import get_cached_model_fields as get_cached_model_fields\nfrom .main import get_compat_model_name_map as get_compat_model_name_map\nfrom .main import get_definitions as get_definitions\nfrom .main import get_missing_field_error as get_missing_field_error\nfrom .main import get_schema_from_model_field as get_schema_from_model_field\nfrom .main import is_bytes_field as is_bytes_field\nfrom .main import is_bytes_sequence_field as is_bytes_sequence_field\nfrom .main import is_scalar_field as is_scalar_field\nfrom .main import is_scalar_sequence_field as is_scalar_sequence_field\nfrom .main import is_sequence_field as is_sequence_field\nfrom .main import serialize_sequence_value as serialize_sequence_value\nfrom .main import (\n    with_info_plain_validator_function as with_info_plain_validator_function,\n)\nfrom .may_v1 import CoreSchema as CoreSchema\nfrom .may_v1 import GetJsonSchemaHandler as GetJsonSchemaHandler\nfrom .may_v1 import JsonSchemaValue as JsonSchemaValue\nfrom .m

{"status": "ok", "stdout": "import sys\nfrom functools import lru_cache\nfrom typing import (\n    Any,\n    Dict,\n    List,\n    Sequence,\n    Tuple,\n    Type,\n)\n\nfrom fastapi._compat import may_v1\nfrom fastapi._compat.shared import PYDANTIC_V2, lenient_issubclass\nfrom fastapi.types import ModelNameMap\nfrom pydantic import BaseModel\nfrom typing_extensions import Literal\n\nfrom .model_field import ModelField\n\nif PYDANTIC_V2:\n    from .v2 import BaseConfig as BaseConfig\n    from .v2 import FieldInfo as FieldInfo\n    from .v2 import PydanticSchemaGenerationError as PydanticSchemaGenerationError\n    from .v2 import RequiredParam as RequiredParam\n    from .v2 import Undefined as Undefined\n    from .v2 import UndefinedType as UndefinedType\n    from .v2 import Url as Url\n    from .v2 import Validator as Validator\n    from .v2 import evaluate_forwardref as evaluate_forwardref\n    from .v2 import get_missing_field_error as get_missing_field_error\n    from .v2 import (\n        with_info_plain_validator_function as with_info_plain_validator_function,\n    )\nelse:\n    from .v1 import BaseConfig as BaseConfig  # type: ignore[assignment]\n    from .v1 import FieldInfo as FieldInfo\n    from .v1 import (  # type: ignore[assignment]\n        PydanticSchemaGenerationError as PydanticSchemaGenerationError,\n    )\n    from .v1 import RequiredParam as RequiredParam\n    from .v1 import Undefined as Undefined\n    from .v1 import UndefinedType as UndefinedType\n    from .v1 import Url as Url  # type: ignore[assignment]\n    from .v1 import Validator as Validator\n    from .v1 import evaluate_forwardref as evaluate_forwardref\n    from .v1 import get_missing_field_error as get_missing_field_error\n    from .v1 import (  # type: ignore[assignment]\n        with_info_plain_validator_function as with_info_plain_validator_function,\n    )\n\n\n@lru_cache\ndef get_cached_model_fields(model: Type[BaseModel]) -> List[ModelField]:\n    if lenient_issubclass(model, ma

{"status": "ok", "stdout": "\n\ndef is_bytes_field(field: ModelField) -> bool:\n    if isinstance(field, may_v1.ModelField):\n        from fastapi._compat import v1\n\n        return v1.is_bytes_field(field)\n    else:\n        assert PYDANTIC_V2\n        from . import v2\n\n        return v2.is_bytes_field(field)  # type: ignore[arg-type]\n\n\ndef is_bytes_sequence_field(field: ModelField) -> bool:\n    if isinstance(field, may_v1.ModelField):\n        from fastapi._compat import v1\n\n        return v1.is_bytes_sequence_field(field)\n    else:\n        assert PYDANTIC_V2\n        from . import v2\n\n        return v2.is_bytes_sequence_field(field)  # type: ignore[arg-type]\n\n\ndef is_scalar_field(field: ModelField) -> bool:\n    if isinstance(field, may_v1.ModelField):\n        from fastapi._compat import v1\n\n        return v1.is_scalar_field(field)\n    else:\n        assert PYDANTIC_V2\n        from . import v2\n\n        return v2.is_scalar_field(field)  # type: ignore[arg-type]\n\n\ndef is_scalar_sequence_field(field: ModelField) -> bool:\n    if isinstance(field, may_v1.ModelField):\n        from fastapi._compat import v1\n\n        return v1.is_scalar_sequence_field(field)\n    else:\n        assert PYDANTIC_V2\n        from . import v2\n\n        return v2.is_scalar_sequence_field(field)  # type: ignore[arg-type]\n\n\ndef is_sequence_field(field: ModelField) -> bool:\n", "stderr": "", "exit_code": 0}

{"status": "ok", "stdout": "def is_sequence_field(field: ModelField) -> bool:\n    if isinstance(field, may_v1.ModelField):\n        from fastapi._compat import v1\n\n        return v1.is_sequence_field(field)\n    else:\n        assert PYDANTIC_V2\n        from . import v2\n\n        return v2.is_sequence_field(field)  # type: ignore[arg-type]\n\n\ndef serialize_sequence_value(*, field: ModelField, value: Any) -> Sequence[Any]:\n    if isinstance(field, may_v1.ModelField):\n        from fastapi._compat import v1\n\n        return v1.serialize_sequence_value(field=field, value=value)\n    else:\n        assert PYDANTIC_V2\n        from . import v2\n\n        return v2.serialize_sequence_value(field=field, value=value)  # type: ignore[arg-type]\n\n\ndef _model_rebuild(model: Type[BaseModel]) -> None:\n    if lenient_issubclass(model, may_v1.BaseModel):\n        from fastapi._compat import v1\n\n        v1._model_rebuild(model)\n    elif PYDANTIC_V2:\n        from . import v2\n\n        v2._model_rebuild(model)\n\n\ndef get_compat_model_name_map(fields: List[ModelField]) -> ModelNameMap:\n    v1_model_fields = [\n        field for field in fields if isinstance(field, may_v1.ModelField)\n    ]\n    if v1_model_fields:\n        from fastapi._compat import v1\n\n        v1_flat_models = v1.get_flat_models_from_fields(\n            v1_model_fields, known_models=set()\n        )\n        all_flat_models = v1_flat_models\n    else:\n        all_flat_models = set()\n    if PYDANTIC_V2:\n        from . import v2\n\n", "stderr": "", "exit_code": 0}

{"status": "ok", "stdout": "\n        v2_model_fields = [\n            field for field in fields if isinstance(field, v2.ModelField)\n        ]\n        v2_flat_models = v2.get_flat_models_from_fields(\n            v2_model_fields, known_models=set()\n        )\n        all_flat_models = all_flat_models.union(v2_flat_models)\n\n        model_name_map = v2.get_model_name_map(all_flat_models)\n        return model_name_map\n    from fastapi._compat import v1\n\n    model_name_map = v1.get_model_name_map(all_flat_models)\n    return model_name_map\n\n\ndef get_definitions(\n    *,\n    fields: List[ModelField],\n    model_name_map: ModelNameMap,\n    separate_input_output_schemas: bool = True,\n) -> Tuple[\n    Dict[\n        Tuple[ModelField, Literal[\"validation\", \"serialization\"]],\n        may_v1.JsonSchemaValue,\n    ],\n    Dict[str, Dict[str, Any]],\n]:\n    if sys.version_info < (3, 14):\n        v1_fields = [field for field in fields if isinstance(field, may_v1.ModelField)]\n        v1_field_maps, v1_definitions = may_v1.get_definitions(\n            fields=v1_fields,\n            model_name_map=model_name_map,\n            separate_input_output_schemas=separate_input_output_schemas,\n        )\n        if not PYDANTIC_V2:\n            return v1_field_maps, v1_definitions\n        else:\n            from . import v2\n\n            v2_fields = [field for field in fields if isinstance(field, v2.ModelField)]\n            v2_field_maps, v2_definitions = v2.get_definitions(\n                fields=v2_fields,\n                model_name_map=model_name_map,\n                separate_input_output_schemas=separate_input_output_schemas,\n            )\n            all_definitions = {**v1_definitions, **v2_definitions}\n            all_field_maps = {**v1_field_maps, **v2_field_maps}\n            return all_field_maps, all_definitions\n\n", "stderr": "", "exit_code": 0}

{"status": "ok", "stdout": "import re\nimport warnings\nfrom copy import copy, deepcopy\nfrom dataclasses import dataclass\nfrom enum import Enum\nfrom typing import (\n    Any,\n    Dict,\n    List,\n    Sequence,\n    Set,\n    Tuple,\n    Type,\n    Union,\n    cast,\n)\n\nfrom fastapi._compat import may_v1, shared\nfrom fastapi.openapi.constants import REF_TEMPLATE\nfrom fastapi.types import IncEx, ModelNameMap\nfrom pydantic import BaseModel, TypeAdapter, create_model\nfrom pydantic import PydanticSchemaGenerationError as PydanticSchemaGenerationError\nfrom pydantic import PydanticUndefinedAnnotation as PydanticUndefinedAnnotation\nfrom pydantic import ValidationError as ValidationError\nfrom pydantic._internal._schema_generation_shared import (  # type: ignore[attr-defined]\n    GetJsonSchemaHandler as GetJsonSchemaHandler,\n)\nfrom pydantic._internal._typing_extra import eval_type_lenient\nfrom pydantic._internal._utils import lenient_issubclass as lenient_issubclass\nfrom pydantic.fields import FieldInfo as FieldInfo\nfrom pydantic.json_schema import GenerateJsonSchema as GenerateJsonSchema\nfrom pydantic.json_schema import JsonSchemaValue as JsonSchemaValue\nfrom pydantic_core import CoreSchema as CoreSchema\nfrom pydantic_core import PydanticUndefined, PydanticUndefinedType\nfrom pydantic_core import Url as Url\nfrom typing_extensions import Annotated, Literal, get_args, get_origin\n\ntry:\n    from pydantic_core.core_schema import (\n        with_info_plain_validator_function as with_info_plain_validator_function,\n    )\nexcept ImportError:  # pragma: no cover\n    from pydantic_core.core_schema import (\n        general_plain_validator_function as with_info_plain_validator_function,  # noqa: F401\n    )\n\nRequiredParam = PydanticUndefined\nUndefined = PydanticUndefined\nUndefinedType = PydanticUndefinedType\nevaluate_forwardref = eval_type_lenient\nValidator = Any\n\n\nclass BaseConfig:\n    pass\n\n\nclass ErrorWrapper(Exception):\n    pass\n\n\n@data

{"status": "ok", "stdout": ") -> Tuple[\n    Dict[Tuple[ModelField, Literal[\"validation\", \"serialization\"]], JsonSchemaValue],\n    Dict[str, Any],\n]:\n    old_name_to_new_name_map = {}\n    for field_key, schema in field_mapping.items():\n        model = field_key[0].type_\n        if model not in model_name_map or \"$ref\" not in schema:\n            continue\n        new_name = model_name_map[model]\n        old_name = schema[\"$ref\"].split(\"/\")[-1]\n        if old_name in {f\"{new_name}-Input\", f\"{new_name}-Output\"}:\n            continue\n        old_name_to_new_name_map[old_name] = new_name\n\n    new_field_mapping: Dict[\n        Tuple[ModelField, Literal[\"validation\", \"serialization\"]], JsonSchemaValue\n    ] = {}\n    for field_key, schema in field_mapping.items():\n        new_schema = _replace_refs(\n            schema=schema,\n            old_name_to_new_name_map=old_name_to_new_name_map,\n        )\n        new_field_mapping[field_key] = new_schema\n\n    new_definitions = {}\n    for key, value in definitions.items():\n        if key in old_name_to_new_name_map:\n            new_key = old_name_to_new_name_map[key]\n        else:\n            new_key = key\n        new_value = _replace_refs(\n            schema=value,\n            old_name_to_new_name_map=old_name_to_new_name_map,\n        )\n        new_definitions[new_key] = new_value\n    return new_field_mapping, new_definitions\n\n\ndef is_scalar_field(field: ModelField) -> bool:\n    from fastapi import params\n\n    return shared.field_annotation_is_scalar(\n        field.field_info.annotation\n    ) and not isinstance(field.field_info, params.Body)\n\n\ndef is_sequence_field(field: ModelField) -> bool:\n    return shared.field_annotation_is_sequence(field.field_info.annotation)\n\n\ndef is_scalar_sequence_field(field: ModelField) -> bool:\n    return shared.field_annotation_is_scalar_sequence(field.field_info.annotation)\n\n\ndef is_bytes_field(field: ModelField) -> bool:\n    ret

{"status": "ok", "stdout": "197:def get_definitions(\n", "stderr": "", "exit_code": 0}

{"status": "ok", "stdout": "def get_definitions(\n    *,\n    fields: Sequence[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    schema_generator = GenerateJsonSchema(ref_template=REF_TEMPLATE)\n    override_mode: Union[Literal[\"validation\"], None] = (\n        None if separate_input_output_schemas else \"validation\"\n    )\n    validation_fields = [field for field in fields if field.mode == \"validation\"]\n    serialization_fields = [field for field in fields if field.mode == \"serialization\"]\n    flat_validation_models = get_flat_models_from_fields(\n        validation_fields, known_models=set()\n    )\n    flat_serialization_models = get_flat_models_from_fields(\n        serialization_fields, known_models=set()\n    )\n    flat_validation_model_fields = [\n        ModelField(\n            field_info=FieldInfo(annotation=model),\n            name=model.__name__,\n            mode=\"validation\",\n        )\n        for model in flat_validation_models\n    ]\n    flat_serialization_model_fields = [\n        ModelField(\n            field_info=FieldInfo(annotation=model),\n            name=model.__name__,\n            mode=\"serialization\",\n        )\n        for model in flat_serialization_models\n    ]\n    flat_model_fields = flat_validation_model_fields + flat_serialization_model_fields\n    input_types = {f.type_ for f in fields}\n    unique_flat_model_fields = {\n        f for f in flat_model_fields if f.type_ not in input_types\n    }\n\n    inputs = [\n        (field, override_mode or field.mode, field._type_adapter.core_schema)\n        for field in list(fields) + list(unique_flat_model_fields)\n    ]\n    field_mapping, definitions = schema_generator.generate_definitions(inputs=inputs)\n    for item_def in cast(Dict[str, Dict[str, Any]], definitions).values():

{"status": "ok", "stdout": "    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    schema_generator = GenerateJsonSchema(ref_template=REF_TEMPLATE)\n    override_mode: Union[Literal[\"validation\"], None] = (\n        None if separate_input_output_schemas else \"validation\"\n    )\n    validation_fields = [field for field in fields if field.mode == \"validation\"]\n    serialization_fields = [field for field in fields if field.mode == \"serialization\"]\n    flat_validation_models = get_flat_models_from_fields(\n        validation_fields, known_models=set()\n    )\n    flat_serialization_models = get_flat_models_from_fields(\n        serialization_fields, known_models=set()\n    )\n    flat_validation_model_fields = [\n        ModelField(\n            field_info=FieldInfo(annotation=model),\n", "stderr": "", "exit_code": 0}

{"status": "error", "error_type": "CommandError", "error_message": "Traceback (most recent call last):\n  File \"<string>\", line 1, in <module>\n    from pydantic import BaseModel, computed_field; import pydantic_core; print(pydantic_core.core_schema.generate_schema_from_mode(computed_field(lambda x: x), 'validation'))\n                                                                                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/Users/jp/repos/kaggle-gemini-coding-agent-post-training/.envs/fastapi/lib/python3.13/site-packages/pydantic_core/core_schema.py\", line 4455, in __getattr__\n    raise AttributeError(f\"module 'pydantic_core' has no attribute '{attr_name}'\")\nAttributeError: module 'pydantic_core' has no attribute 'generate_schema_from_mode'\n", "details": {"stdout": "", "stderr": "Traceback (most recent call last):\n  File \"<string>\", line 1, in <module>\n    from pydantic import BaseModel, computed_field; import pydantic_core; print(pydantic_core.core_schema.generate_schema_from_mode(computed_field(lambda x: x), 'validation'))\n                                                                                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/Users/jp/repos/kaggle-gemini-coding-agent-post-training/.envs/fastapi/lib/python3.13/site-packages/pydantic_core/core_schema.py\", line 4455, in __getattr__\n    raise AttributeError(f\"module 'pydantic_core' has no attribute '{attr_name}'\")\nAttributeError: module 'pydantic_core' has no attribute 'generate_schema_from_mode'\n", "exit_code": 1}}