+DCf+9UdZddlmZddlZddlmZddlmZmZm Z m Z m Z m Z ddl mZmZddl mZddlmZmZmZd d lmZd d lmZmZd d lmZej8d)iej:d diGddZej8d)iej:d diGddZerQe e eeefe!eefeefZ"de#d<e ejHe"fZ%e ejLe"fZ'e de%Z(e de'Z)e dddd d*dZ*e dddd d+dZ*e dddd d,dZ*deddd d-d Z*e d!e defZ+e d.d"Z,e dddd# d/d$Z, d0dded# d1d%Z,e d&Z-eree-dfZ.yej8d)iej:Gd'd(Z.y)2zEThis module contains related classes and functions for serialization.) annotationsN) partialmethod) TYPE_CHECKINGAnyCallableTypeVarUnionoverload)PydanticUndefined core_schema)r ) AnnotatedLiteral TypeAlias)PydanticUndefinedAnnotation) _decorators_internal_dataclass)GetCoreSchemaHandlerfrozenTc@eZdZUdZded<eZded<dZded<d d Zy ) PlainSerializeraCPlain serializers use a function to modify the output of serialization. This is particularly helpful when you want to customize the serialization for annotated types. Consider an input of `list`, which will be serialized into a space-delimited string. ```python from typing import List from typing_extensions import Annotated from pydantic import BaseModel, PlainSerializer CustomStr = Annotated[ List, PlainSerializer(lambda x: ' '.join(x), return_type=str) ] class StudentModel(BaseModel): courses: CustomStr student = StudentModel(courses=['Math', 'Chemistry', 'English']) print(student.model_dump()) #> {'courses': 'Math Chemistry English'} ``` Attributes: func: The serializer function. return_type: The return type for the function. If omitted it will be inferred from the type annotation. when_used: Determines when this serializer should be used. Accepts a string with values `'always'`, `'unless-none'`, `'json'`, and `'json-unless-none'`. Defaults to 'always'. zcore_schema.SerializerFunctionfuncr return_typealways> 4++W-I-I-KK !,/@ @gF]F]^iFj "-"R"RYY ==diiQ'nn #    H-==a@a G H9B.. C7C  CNr-rr.rreturnzcore_schema.CoreSchema __name__ __module__ __qualname____doc____annotations__r rrr2r1rrs)> )((K(NVIKVr=rc@eZdZUdZded<eZded<dZded<d d Zy ) WrapSerializera Wrap serializers receive the raw inputs along with a handler function that applies the standard serialization logic, and can modify the resulting value before returning it as the final output of serialization. For example, here's a scenario in which a wrap serializer transforms timezones to UTC **and** utilizes the existing `datetime` serialization logic. ```python from datetime import datetime, timezone from typing import Any, Dict from typing_extensions import Annotated from pydantic import BaseModel, WrapSerializer class EventDatetime(BaseModel): start: datetime end: datetime def convert_to_utc(value: Any, handler, info) -> Dict[str, datetime]: # Note that `helper` can actually help serialize the `value` for further custom serialization in case it's a subclass. partial_result = handler(value, info) if info.mode == 'json': return { k: datetime.fromisoformat(v).astimezone(timezone.utc) for k, v in partial_result.items() } return {k: v.astimezone(timezone.utc) for k, v in partial_result.items()} UTCEventDatetime = Annotated[EventDatetime, WrapSerializer(convert_to_utc)] class EventModel(BaseModel): event_datetime: UTCEventDatetime dt = EventDatetime( start='2024-01-01T07:00:00-08:00', end='2024-01-03T20:00:00+06:00' ) event = EventModel(event_datetime=dt) print(event.model_dump()) ''' { 'event_datetime': { 'start': datetime.datetime( 2024, 1, 1, 15, 0, tzinfo=datetime.timezone.utc ), 'end': datetime.datetime( 2024, 1, 3, 14, 0, tzinfo=datetime.timezone.utc ), } } ''' print(event.model_dump_json()) ''' {"event_datetime":{"start":"2024-01-01T15:00:00Z","end":"2024-01-03T14:00:00Z"}} ''' ``` Attributes: func: The serializer function to be wrapped. return_type: The return type for the function. If omitted it will be inferred from the type annotation. when_used: Determines when this serializer should be used. Accepts a string with values `'always'`, `'unless-none'`, `'json'`, and `'json-unless-none'`. Defaults to 'always'. z"core_schema.WrapSerializerFunctionrrrrrrc||} tj|j|j|j }|turdn|j|}tj|jtj|jd||j|d<|S#t $r}t j||d}~wwxYw)zThis method is used to get the Pydantic core schema of the class. Args: source_type: Source type. handler: Core schema handler. Returns: The generated core schema of the class. Nwraprr#)rr$rrr%r&rr'r r(r #wrap_serializer_function_ser_schemar*rr+s r1r2z+WrapSerializer.__get_pydantic_core_schema__s% H%>> 4++W-I-I-KK !,/@ @gF]F]^iFj "-"Q"QYY ==diiP'nn #    H-==a@a G Hr3Nr4r6r<r=r1r?r?Qs*=~ -,(K(NVIKVr=r?r_PartialClsOrStaticMethod_PlainSerializeMethodType)bound_WrapSerializeMethodType.)rr check_fieldscyNr<)fieldrrrGfieldss r1field_serializerrLsr=cyrIr<rJmoderrrGrKs r1rLrLr=cyrIr<rNs r1rLrLrPr=rr)rOrrrGc, dfd }|S)aDecorator that enables custom field serialization. In the below example, a field of type `set` is used to mitigate duplication. A `field_serializer` is used to serialize the data as a sorted list. ```python from typing import Set from pydantic import BaseModel, field_serializer class StudentModel(BaseModel): name: str = 'Jane' courses: Set[str] @field_serializer('courses', when_used='json') def serialize_courses_in_order(courses: Set[str]): return sorted(courses) student = StudentModel(courses={'Math', 'Chemistry', 'English'}) print(student.model_dump_json()) #> {"name":"Jane","courses":["Chemistry","English","Math"]} ``` See [Custom serializers](../concepts/serialization.md#custom-serializers) for more information. Four signatures are supported: - `(self, value: Any, info: FieldSerializationInfo)` - `(self, value: Any, nxt: SerializerFunctionWrapHandler, info: FieldSerializationInfo)` - `(value: Any, info: SerializationInfo)` - `(value: Any, nxt: SerializerFunctionWrapHandler, info: SerializationInfo)` Args: fields: Which field(s) the method should be called on. mode: The serialization mode. - `plain` means the function will be called instead of the default serialization logic, - `wrap` means the function will be called with an argument to optionally call the default serialization logic. return_type: Optional return type for the function, if omitted it will be inferred from the type annotation. when_used: Determines the serializer will be used for serialization. check_fields: Whether to check that the fields actually exist on the model. Returns: The decorator function. cdtj}tj||S)N)rKrOrrrG)rFieldSerializerDecoratorInfoPydanticDescriptorProxy)fdec_inforGrKrOrrs r1deczfield_serializer..decs9;;#%  221h??r=)rVzHCallable[..., Any] | staticmethod[Any, Any] | classmethod[Any, Any, Any]r5(_decorators.PydanticDescriptorProxy[Any]r<)rOrrrGrKrXs````` r1rLrLs)j @ S @ 1 @ @ Jr=FuncTypecyrIr<)__fs r1model_serializerr]&sr=rOrrcyrIr<r^s r1r]r]+sr=c0dfd }||S||S)aDecorator that enables custom model serialization. This is useful when a model need to be serialized in a customized manner, allowing for flexibility beyond just specific fields. An example would be to serialize temperature to the same temperature scale, such as degrees Celsius. ```python from typing import Literal from pydantic import BaseModel, model_serializer class TemperatureModel(BaseModel): unit: Literal['C', 'F'] value: int @model_serializer() def serialize_model(self): if self.unit == 'F': return {'unit': 'C', 'value': int((self.value - 32) / 1.8)} return {'unit': self.unit, 'value': self.value} temperature = TemperatureModel(unit='F', value=212) print(temperature.model_dump()) #> {'unit': 'C', 'value': 100} ``` See [Custom serializers](../concepts/serialization.md#custom-serializers) for more information. Args: f: The function to be decorated. mode: The serialization mode. - `'plain'` means the function will be called instead of the default serialization logic - `'wrap'` means the function will be called with an argument to optionally call the default serialization logic. when_used: Determines when this serializer should be used. return_type: The return type for the function. If omitted it will be inferred from the type annotation. Returns: The decorator function. c`tj}tj||S)N)rOrr)rModelSerializerDecoratorInforU)rVrWrOrrs r1rXzmodel_serializer..decgs,;;S^jst221h??r=)rVzCallable[..., Any]r5rYr<)rVrOrrrXs ``` r1r]r]5sd@ y 1v r=AnyTypec@eZdZddZ ddZej Zy)SerializeAsAnyc(t|tfSrI)r re)clsitems r1__class_getitem__z SerializeAsAny.__class_getitem__sT>#334 4r=c||}|}|ddk(r|j}|d}|ddk(rtjdtj|d<|S)Ntype definitionsr/c||SrIr<)xhs r1z=SerializeAsAny.__get_pydantic_core_schema__..s QqTr=)r/r#)copyr rB any_schema)r,r-r.r/schema_to_updates r1r2z+SerializeAsAny.__get_pydantic_core_schema__sw[)F% "6*m;#3#8#8#: #3H#= #6*m;1<0_0_!+*@*@*B1 _ -Mr=N)rhrr5rr4)r7r8r9rir2object__hash__r<r=r1rere}s/ 5 " -A  # ??r=rer<) rJstrrKrvrrrrrG bool | Noner5@Callable[[_PlainSerializeMethodType], _PlainSerializeMethodType])rJrvrKrvrOzLiteral['plain']rrrrrGrwr5rx)rJrvrKrvrOzLiteral['wrap']rrrrrGrwr5z>Callable[[_WrapSerializeMethodType], _WrapSerializeMethodType]) rKrvrOLiteral['plain', 'wrap']rrrrrGrwr5Callable[[Any], Any])r\rZr5rZ)rOryrrrrr5zCallable[[FuncType], FuncType]rI) rVzCallable[..., Any] | NonerOryrrrrr5rz)/r: __future__r dataclasses functoolsrtypingrrrrr r pydantic_corer r _core_schematyping_extensionsr rrr _internalrrannotated_handlersr dataclass slots_truerr? classmethod staticmethodrCr;SerializerFunction_PlainSerializationFunctionWrapSerializerFunction_WrapSerializationFunctionrDrFrLrZr]rcrer<r=r1rsK"#II85;;)74E,77EE<<F<~E,77EE\\F\~+0S#s]1K\Z]_bZbMcersvew1w+xyx"' (G(GIb(b"c!&|'J'JLe'e!f '(CKf g&'AIcd NQ #    L   F  NQ #         L  F    NQ #         L  D   &-(NV $ A A "AAL A  A  AH :Xc3h%7 8   &)NV  "L  $  $(9&-NV( 9 9 # 9 L 9  99x ) w|,N[<0;;<##=#r=