9tj3UdZddlmZddlZddlZddlZddlZddlm Z ddl m Z ddl m Z ddlmZmZmZmZmZddlmZmZmZmZdd lmZmZd d lmZd d lmZd d l m!Z!m"Z"d dlm#Z#ddl$m%Z%m&Z&ddl'm(Z(ddl)m*Z*m+Z+ddl,m-Z-ddl.m/Z/ddl0m1Z1ddl2m3Z3ddl4m5Z5erddl6m7Z8d dl9m:Z:Gdde8eZ; d7d8d$Zd;d2Z?e@eAejBefZCd3eDd4<e d.__init__vs:    00D&1I1IYZ0[[[[[r7z .__init__)r?rAF__post_init_post_parse__zVSupport for `__post_init_post_parse__` has been dropped, the method will not be called)rArE __signature__T)initrFvalidate_by_nameextra is_dataclass`N)titleztype[PydanticDataclass] dataclass)rLr&rMrrNrr.rC)$rTr: config_dictr'rG defer_buildrhasattrwarningswarnrrrr"rr!r+rXrYrVgenerate_schemarname core_configr8 clean_schemarr r)rr9plugin_settingsr-r r,r() r4r?rHrArI original_initrTrE gen_schemaschemaeres r5complete_dataclassrlUsX:LM\\\\ #/:::HCL,8C^UUUU N6C   us.//  d %   -S11L!J+ '*+< &   C++C00 &   CQV///uuuuu  !,,3<,@@K((00 C   uu (# . .C#)C !8S^S%5{KQ_Qo""C#36;"G"GC $C 4s*"C88 D)D$$D)EE;:E;TypeIs[type[StandardDataclass]]c6d|jvot|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__r`r3s r5is_stdlib_dataclassrqs$ "S\ 1 `'#G_:`:`6``r7pydantic_fieldrdataclasses.Field[Any]cd|i}tjdkr|j |j|d<tjdkr|j |j|d<|jdur |j|d<t jdi|S) Ndefault)docrv kw_onlyTreprr2)sys version_info descriptionr{r| dataclassesfield)rr field_argss r5as_dataclass_fieldrs"+^! 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. rNroci|]H\}}t|jtr)|jj|jjs|jjduE||IS)NT) isinstancerurrr{r|).0 field_namers r5 z%patch_base_fields.. si2 2 2 ! E%-332 *6%-:O6SXS`SemqSqSq TrSqSqr7ryT) __mro__rpgetitemsappendr rrucopyr}r~r{r|) r4original_fields_listbase dc_fields&dc_fields_with_pydantic_field_defaultsrrru new_dc_fieldrForiginal_fieldsoriginal_fields r5patch_base_fieldsrsn=? ABB557;}7H7HI_ac7d7d 2 2 %.__%6%62 2 2 . 2 5 ' '4Z([ \ \ \%K%Q%Q%S%S 5 5! Ey%-88 $y// #w..7?.+/L(r?r@rArBr.rC) r4rJr?r@rHr/rArBrIr/r.r/)r4rJr.rm)rrrr.rs)r4rJr.r)Fr; __future__r _annotationsrrr}racollections.abcr contextlibr functoolsrtypingrrr r r pydantic_corer r rrtyping_extensionsrrerrorsrrFrplugin._schema_validatorrrrrr_fieldsr_generate_schemarr _genericsr _mock_val_serr_namespace_utilsr _signaturer!_utilsr" _typeshedr#StandardDataclassconfigr$r&rGrlrqrdictstrFieldrr<rr2r7r5rs666222222  %%%%%%%%%%%%?????????????? 0///////000000XXXXXXXX000000""""""""------@@@@@@@@000000......((((((333333&&&&&&;@@@@@@######;;;;;-x;;;8&*%%%%%0%) iiiiiiX a a a a++++$3 1# 6677777U4U4U4U4U4U4r7