관리-도구
편집 파일: _dataclasses.cpython-38.pyc
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G dd� dej/�Z0G dd� de0ej/�Z1neZ2d-ddd d d �d d �Z3d!d"�d#d$d%dd%d&�d'd(�Z4d#d)d*�d+d,�Z5dS ).z0Private logic for creating pydantic dataclasses.� )� annotationsN)�partial�wraps)�Any�Callable�ClassVar)� ArgsKwargs�SchemaSerializer�SchemaValidator� core_schema)� TypeGuard� )�PydanticUndefinedAnnotation)� FieldInfo)�PluggableSchemaValidator�create_schema_validator)�PydanticDeprecatedSince20� )�_config� _decorators� _typing_extra)�collect_dataclass_fields)�GenerateSchema)�get_standard_typevars_map)�set_dataclass_mocks)� CallbackGetCoreSchemaHandler)�generate_pydantic_signature)� ConfigDictc @ s8 e Zd ZU ded< ded< ded< dddd �d d �Zd S ) �StandardDataclasszClassVar[dict[str, Any]]�__dataclass_fields__z ClassVar[Any]�__dataclass_params__z$ClassVar[Callable[(Ellipsis, None)]]� __post_init__�object�None)�args�kwargs�returnc O s d S �N� )�selfr$ r% r( r( �M/opt/hc_python/lib/python3.8/site-packages/pydantic/_internal/_dataclasses.py�__init__' s zStandardDataclass.__init__N)�__name__� __module__� __qualname__�__annotations__r+ r( r( r( r* r " s r c @ sJ e Zd ZU dZded< ded< ded< ded < d ed < d ed < ded< dS )�PydanticDataclassai A protocol containing attributes only available once a class has been decorated as a Pydantic dataclass. Attributes: __pydantic_config__: Pydantic-specific configuration settings for the dataclass. __pydantic_complete__: Whether dataclass building is completed, or if there are still undefined fields. __pydantic_core_schema__: The pydantic-core schema used to build the SchemaValidator and SchemaSerializer. __pydantic_decorators__: Metadata containing the decorators defined on the dataclass. __pydantic_fields__: Metadata about the fields defined on the dataclass. __pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the dataclass. __pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the dataclass. zClassVar[ConfigDict]�__pydantic_config__zClassVar[bool]Z__pydantic_complete__z ClassVar[core_schema.CoreSchema]�__pydantic_core_schema__z$ClassVar[_decorators.DecoratorInfos]Z__pydantic_decorators__z ClassVar[dict[str, FieldInfo]]�__pydantic_fields__zClassVar[SchemaSerializer]�__pydantic_serializer__z4ClassVar[SchemaValidator | PluggableSchemaValidator]�__pydantic_validator__N)r, r- r. �__doc__r/ r( r( r( r* r0 * s r0 ztype[StandardDataclass]zdict[str, Any] | Nonez _config.ConfigWrapper | Noner# )�cls�types_namespace�config_wrapperr& c C s" t | �}t| |||d�}|| _dS )z�Collect and set `cls.__pydantic_fields__`. 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Args: cls: The class. config_wrapper: The config wrapper instance. raise_errors: Whether to raise errors, defaults to `True`. types_namespace: The types namespace. Returns: `True` if building a pydantic dataclass is successfully completed, `False` otherwise. Raises: PydanticUndefinedAnnotation: If `raise_error` is `True` and there is an undefined annotations. Z__post_init_post_parse__zVSupport for `__post_init_post_parse__` has been dropped, the method will not be calledN)r9 T)�initr; r9 � is_dataclassr0 r r# )�__dataclass_self__r$ r% r& c _ s" d}| }|j jt||�|d� d S )NT)Z self_instance)r5 Zvalidate_pythonr )rA r$ r% Z__tracebackhide__�sr( r( r* r+ � s z$complete_dataclass.<locals>.__init__z .__init__Z __get_pydantic_core_schema__F)Zfrom_dunder_get_core_schema�unpack)Zref_mode�`zall referenced typesztype[PydanticDataclass]� dataclass�str)�instance�field�valuer& c s � � | ||� d S r' )�validate_assignment)rG rH rI �� validatorr( r* �validated_setattr� s z-complete_dataclass.<locals>.validated_setattr)(�hasattr�warnings�warn�DeprecationWarningr Zget_cls_types_namespacer< r r r r+ r3 r. Z config_dictr1 � __signature__�getattrr r Zgenerate_schemar r r, �name� core_configZ clean_schemaZCollectedInvalid�typing�castr2 r r- Zplugin_settingsr5 r r4 rJ r � __setattr__�__get__) r7 r9 r= r8 r: Z gen_schema�sigr+ Zget_core_schemaZschema�erU rM r( rK r* �complete_dataclassW s� � �� �� � r\ z"TypeGuard[type[StandardDataclass]])�_clsr& c C s2 t �| �o0t| d� o0t| j��tt| di ���S )a> Returns True if a class is a stdlib dataclass and *not* a pydantic dataclass. We check that - `_cls` is a dataclass - `_cls` does not inherit from a processed pydantic dataclass (and thus have a `__pydantic_validator__`) - `_cls` does not have any annotations that are not dataclass fields e.g. ```py import dataclasses import pydantic.dataclasses @dataclasses.dataclass class A: x: int @pydantic.dataclasses.dataclass class B(A): y: int ``` In this case, when we first check `B`, we make an extra check and look at the annotations ('y'), which won't be a superset of all the dataclass fields (only the stdlib fields i.e. 'x') Args: cls: The class. Returns: `True` if the class is a stdlib dataclass, `False` otherwise. r5 r/ )� dataclassesr@ rN �setr � issupersetrS )r] r( r( r* �is_builtin_dataclass� s ��ra )NN)6r6 � __future__r Z _annotationsr^ rV rO � functoolsr r r r r Z pydantic_corer r r r �typing_extensionsr �errorsr r; r Zplugin._schema_validatorr r r � r r r �_fieldsr Z_generate_schemar Z _genericsr Z _mock_val_serr Z_schema_generation_sharedr � _signaturer � TYPE_CHECKING�configr �Protocolr r0 rQ r< r\ ra r( r( r( r* �<module> s< ��n