o ZhB@sUdZddlmZddlZddlmZmZddlmZm Z m Z m Z m Z m Z mZddlmZmZddlmZmZmZddlmZd d lmZd d lmZmZd d lmZejdTiej d diGdddZ!ejdTiej d diGdddZ"erdZ#de$d<dZ%de$d< dZ&de$d< dZ'de$d< e de%dZ(e de&dZ)edddd dUd.d/Z*eddddd0dVd3d/Z*d4ed5dd0dWd8d/Z*ere e ege fZ+de$d9< e e ge fZ,de$d:< d;Z-de$d<< e e eege fZ.de$d=< e e ege fZ/de$d>< d?Z0de$d@< dAZ1de$dB<e dCe-dZ2e dDe0dZ3edXdFdGZ4ed5ddHdYdJdGZ4edd5ddKdZdMdGZ4 d[d4d5edKd\dPdGZ4e dQZ5erXe e5dfZ6dSejdTiej GdRdSdSZ6dS)]zEThis module contains related classes and functions for serialization.) annotationsN)partial partialmethod) TYPE_CHECKING AnnotatedAnyCallableLiteralTypeVaroverload)PydanticUndefined core_schema)SerializationInfoSerializerFunctionWrapHandlerWhenUsed) TypeAlias)PydanticUndefinedAnnotation) _decorators_internal_dataclass)GetCoreSchemaHandlerfrozenTc@<eZdZUdZded<eZded<dZded<dddZdS)PlainSerializeraPlain serializers use a function to modify the output of serialization. This is particularly helpful when you want to customize the serialization for annotated types. Consider an input of `list`, which will be serialized into a space-delimited string. ```python from typing import Annotated from pydantic import BaseModel, PlainSerializer CustomStr = Annotated[ list, PlainSerializer(lambda x: ' '.join(x), return_type=str) ] class StudentModel(BaseModel): courses: CustomStr student = StudentModel(courses=['Math', 'Chemistry', 'English']) print(student.model_dump()) #> {'courses': 'Math Chemistry English'} ``` Attributes: func: The serializer function. return_type: The return type for the function. If omitted it will be inferred from the type annotation. when_used: Determines when this serializer should be used. Accepts a string with values `'always'`, `'unless-none'`, `'json'`, and `'json-unless-none'`. Defaults to 'always'. zcore_schema.SerializerFunctionfuncr return_typealwaysr when_used source_typehandlerrreturncore_schema.CoreSchemac C||}|jtur |j}n z tj|j|jd}Wnty,}zt ||d}~ww|tur3dn| |}t j |jt |jd||jd|d<|S)zGets the Pydantic core schema. Args: source_type: The source type. handler: The `GetCoreSchemaHandler` instance. Returns: The Pydantic core schema. localnsNplainfunctionZinfo_arg return_schemar serialization)rr rget_callable_return_typer_get_types_namespacelocals NameErrorrfrom_name_errorgenerate_schemar Z$plain_serializer_function_ser_schemainspect_annotated_serializerrselfrrschemarer(r5V/var/www/html/lang_env/lib/python3.10/site-packages/pydantic/functional_serializers.py__get_pydantic_core_schema__5(     z,PlainSerializer.__get_pydantic_core_schema__Nrrrrr r! __name__ __module__ __qualname____doc____annotations__r rrr7r5r5r5r6rs   rc@r)WrapSerializeraWrap 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 Annotated, Any 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 `handler` 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.WrapSerializerFunctionrrrrrrrrrr r!c Cr")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. r#Nwrapr&r))rr rr*rr+r,r-rr.r/r #wrap_serializer_function_ser_schemar0rr1r5r5r6r7r8z+WrapSerializer.__get_pydantic_core_schema__Nr9r:r5r5r5r6r@Xs >  r@z!partial[Any] | partialmethod[Any]r_Partialz)core_schema.SerializerFunction | _PartialFieldPlainSerializerz-core_schema.WrapSerializerFunction | _PartialFieldWrapSerializerz*FieldPlainSerializer | FieldWrapSerializerFieldSerializer_FieldPlainSerializerT)bound_FieldWrapSerializerT.)rr check_fieldsfieldstrfieldsmodeLiteral['wrap']rrrrrJ bool | Noner 8Callable[[_FieldWrapSerializerT], _FieldWrapSerializerT]cGdSNr5rKrNrrrJrMr5r5r6field_serializer rU)rNrrrJLiteral['plain']:Callable[[_FieldPlainSerializerT], _FieldPlainSerializerT]cGrRrSr5rTr5r5r6rUrVr%rLiteral['plain', 'wrap']uCallable[[_FieldWrapSerializerT], _FieldWrapSerializerT] | Callable[[_FieldPlainSerializerT], _FieldPlainSerializerT]csdfdd }|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(self, 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. frFr (_decorators.PydanticDescriptorProxy[Any]cs tjd}t||S)N)rMrNrrrJ)rZFieldSerializerDecoratorInfoPydanticDescriptorProxyr[Zdec_inforJrMrNrrr5r6decs zfield_serializer..decN)r[rFr r\r5)rNrrrJrMr`r5r_r6rUs8 ModelPlainSerializerWithInfoModelPlainSerializerWithoutInfoz>ModelPlainSerializerWithInfo | ModelPlainSerializerWithoutInfoModelPlainSerializerModelWrapSerializerWithInfoModelWrapSerializerWithoutInfoz {'unit': 'C', 'value': 100} ``` Two signatures are supported for `mode='plain'`, which is the default: - `(self)` - `(self, info: SerializationInfo)` And two other signatures for `mode='wrap'`: - `(self, nxt: SerializerFunctionWrapHandler)` - `(self, nxt: SerializerFunctionWrapHandler, info: SerializationInfo)` 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. 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