o ɝif3@sUdZddlmZddlZddlZddlZddlZddlm Z ddl m Z ddl m Z ddlmZmZmZmZmZddlmZmZmZmZdd lmZmZd d lmZd d lmZd d l m!Z!m"Z"d dlm#Z#ddl$m%Z%m&Z&ddl'm(Z(ddl)m*Z*m+Z+ddl,m-Z-ddl.m/Z/ddl0m1Z1ddl2m3Z3ddl4m5Z5erddl6m7Z8d dl9m:Z:Gddde8eZ; dd-d.Z=d?d0d1Z>d@d5d6Z?e@eAejBefZCd7eDd8<e dAd:d;ZEdS)Bz0Private logic for creating pydantic dataclasses.) annotationsN) Generator)contextmanager)partial) TYPE_CHECKINGAnyClassVarProtocolcast) ArgsKwargsSchemaSerializerSchemaValidator core_schema) TypeAliasTypeIs)PydanticUndefinedAnnotation) FieldInfo)PluggableSchemaValidatorcreate_schema_validator)PydanticDeprecatedSince20)_config _decorators)collect_dataclass_fields)GenerateSchemaInvalidSchemaError)get_standard_typevars_map)set_dataclass_mocks) NsResolver)generate_pydantic_signature)LazyClassAttribute)DataclassInstance) ConfigDictc@sXeZdZUdZded<ded<ded<ded <d ed <d ed <ded<edddZdS)PydanticDataclassaiA protocol containing attributes only available once a class has been decorated as a Pydantic dataclass. Attributes: __pydantic_config__: Pydantic-specific configuration settings for the dataclass. __pydantic_complete__: Whether dataclass building is completed, or if there are still undefined fields. __pydantic_core_schema__: The pydantic-core schema used to build the SchemaValidator and SchemaSerializer. __pydantic_decorators__: Metadata containing the decorators defined on the dataclass. __pydantic_fields__: Metadata about the fields defined on the dataclass. __pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the dataclass. __pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the dataclass. zClassVar[ConfigDict]__pydantic_config__zClassVar[bool]__pydantic_complete__z ClassVar[core_schema.CoreSchema]__pydantic_core_schema__z$ClassVar[_decorators.DecoratorInfos]__pydantic_decorators__zClassVar[dict[str, FieldInfo]]__pydantic_fields__zClassVar[SchemaSerializer]__pydantic_serializer__z4ClassVar[SchemaValidator | PluggableSchemaValidator]__pydantic_validator__returnboolcCsdSNclsr/r/[/var/www/html/ocr-read/venv/lib/python3.10/site-packages/pydantic/_internal/_dataclasses.py__pydantic_fields_complete__=sz.PydanticDataclass.__pydantic_fields_complete__N)r,r-)__name__ __module__ __qualname____doc____annotations__ classmethodr3r/r/r/r2r$(s  r$r1type[StandardDataclass]config_wrapper_config.ConfigWrapper ns_resolverNsResolver | Noner,NonecCs"t|}t||||d}||_dS)zCollect and set `cls.__pydantic_fields__`. Args: cls: The class. config_wrapper: The config wrapper instance. ns_resolver: Namespace resolver to use when getting dataclass annotations. )r= typevars_mapr;N)rrr))r1r;r=r@fieldsr/r/r2set_dataclass_fieldsAs   rBTF) raise_errorsr= _force_build type[Any]rCr-rDc Csj|j}ddd }|jd |_||_|j|_t|||d |s(|jr(t|d St|d r3t dt t |}t |||d}t dtt||j|j|jdd|_z||} Wn tyv} z|rat|d| jdWYd} ~ d Sd} ~ ww|j|jd} z|| } Wntyt|Yd Swtd|}| |_t| ||j|jd| |j|_ t!| | |_"d|_#dS)aFinish building a pydantic dataclass. This logic is called on a class which has already been wrapped in `dataclasses.dataclass()`. This is somewhat analogous to `pydantic._internal._model_construction.complete_model_class`. Args: cls: The class. config_wrapper: The config wrapper instance. raise_errors: Whether to raise errors, defaults to `True`. ns_resolver: The namespace resolver instance to use when collecting dataclass fields and during schema building. _force_build: Whether to force building the dataclass, no matter if [`defer_build`][pydantic.config.ConfigDict.defer_build] is set. 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. __dataclass_self__r$argsrkwargsr,r?c_s"d}|}|jjt|||ddS)NT) self_instance)r+validate_pythonr )rFrGrH__tracebackhide__sr/r/r2__init__vsz$complete_dataclass..__init__z .__init__)r;r=F__post_init_post_parse__zVSupport for `__post_init_post_parse__` has been dropped, the method will not be called)r=r@ __signature__T)initrAvalidate_by_nameextra is_dataclass`N)titleztype[PydanticDataclass] dataclass)rFr$rGrrHrr,r?)$rMr6 config_dictr%rB defer_buildrhasattrwarningswarnrrrr!rr r)rQrRrOgenerate_schemarname core_configr4 clean_schemarr r'rr5plugin_settingsr+r r*r&) r1r;rCr=rD original_initrMr@ gen_schemaschemaer^r/r/r2complete_dataclassUsn       reTypeIs[type[StandardDataclass]]cCsd|jvo t|d S)afReturns `True` if the class is a stdlib dataclass and *not* a Pydantic dataclass. Unlike the stdlib `dataclasses.is_dataclass()` function, this does *not* include subclasses of a dataclass that are themselves not dataclasses. Args: cls: The class. Returns: `True` if the class is a stdlib dataclass, `False` otherwise. __dataclass_fields__r+)__dict__rYr0r/r/r2is_stdlib_dataclasss ripydantic_fieldrdataclasses.Field[Any]cCsbd|i}tjdkr|jdur|j|d<tjdkr|jrd|d<|jdur)|j|d<tjdi|S) Ndefault)docrm Tkw_onlyreprr/)sys version_info descriptionrrrs dataclassesfield)rj field_argsr/r/r2as_dataclass_fields   rzrDcFieldsGenerator[None]c csg}|jddD]G}|jdi}dd|D}|rQ|||f|D]'\}}tt|j}t|}t j dkrC|j rCd|_ |j durL|j |_ |||<q)q zdVW|D]\} } | D]\}} | | |<qaqYdS|D]\} } | D]\}} | | |<qwqow)aTemporarily patch the stdlib dataclasses bases of `cls` if the Pydantic `Field()` function is used. When creating a Pydantic dataclass, it is possible to inherit from stdlib dataclasses, where the Pydantic `Field()` function is used. To create this Pydantic dataclass, we first apply the stdlib `@dataclass` decorator on it. During the construction of the stdlib dataclass, the `kw_only` and `repr` field arguments need to be understood by the stdlib *during* the dataclass construction. To do so, we temporarily patch the fields dictionary of the affected bases. For instance, with the following example: ```python {test="skip" lint="skip"} import dataclasses as stdlib_dc import pydantic import pydantic.dataclasses as pydantic_dc @stdlib_dc.dataclass class A: a: int = pydantic.Field(repr=False) # Notice that the `repr` attribute of the dataclass field is `True`: A.__dataclass_fields__['a'] #> dataclass.Field(default=FieldInfo(repr=False), repr=True, ...) @pydantic_dc.dataclass class B(A): b: int = pydantic.Field(repr=False) ``` When passing `B` to the stdlib `@dataclass` decorator, it will look for fields in the parent classes and reuse them directly. When this context manager is active, `A` will be temporarily patched to be equivalent to: ```python {test="skip" lint="skip"} @stdlib_dc.dataclass class A: a: int = stdlib_dc.field(default=Field(repr=False), repr=False) ``` !!! note This is only applied to the bases of `cls`, and not `cls` itself. The reason is that the Pydantic dataclass decorator "owns" `cls` (in the previous example, `B`). As such, we instead modify the fields directly (in the previous example, we simply do `setattr(B, 'b', as_dataclass_field(pydantic_field))`). !!! note This approach is far from ideal, and can probably be the source of unwanted side effects/race conditions. The previous implemented approach was mutating the `__annotations__` dict of `cls`, which is no longer a safe operation in Python 3.14+, and resulted in unexpected behavior with field ordering anyway. rNrgcSsBi|]\}}t|jtr|jjdus|jjs|jjdur||qS)NT) isinstancerlrrvrrrs).0 field_namerxr/r/r2 s  z%patch_base_fields..rpT) __mro__rhgetitemsappendr rrlcopyrtrurrrs) r1original_fields_listbase dc_fields&dc_fields_with_pydantic_field_defaultsrrxrl new_dc_fieldrAoriginal_fieldsoriginal_fieldr/r/r2patch_base_fieldss:7       rr.)r1r:r;r<r=r>r,r?) r1rEr;r<rCr-r=r>rDr-r,r-)r1rEr,rf)rjrr,rk)r1rEr,r|)Fr7 __future__r _annotationsrrwrtrZcollections.abcr contextlibr functoolsrtypingrrrr r pydantic_corer r r rtyping_extensionsrrerrorsrrArplugin._schema_validatorrrrrr_fieldsr_generate_schemarr _genericsr _mock_val_serr_namespace_utilsr _signaturer _utilsr! _typeshedr"StandardDataclassconfigr#r$rBrerirzdictstrFieldr{r8rr/r/r/r2sN                 l