python_env/lib/site-packages/pydantic/functional_serializers.py

"""This module contains related classes and functions for serialization."""

from __future__ import annotations

import dataclasses
from functools import partial, partialmethod
from typing import TYPE_CHECKING, Annotated, Any, Callable, Literal, TypeVar, overload

from pydantic_core import PydanticUndefined, core_schema
from pydantic_core.core_schema import SerializationInfo, SerializerFunctionWrapHandler, WhenUsed
from typing_extensions import TypeAlias

from . import PydanticUndefinedAnnotation
from ._internal import _decorators, _internal_dataclass
from .annotated_handlers import GetCoreSchemaHandler


@dataclasses.dataclass(**_internal_dataclass.slots_true, frozen=True)
class PlainSerializer:
    """Plain 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'.
    """

    func: core_schema.SerializerFunction
    return_type: Any = PydanticUndefined
    when_used: WhenUsed = 'always'

    def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
        """Gets the Pydantic core schema.

        Args:
            source_type: The source type.
            handler: The `GetCoreSchemaHandler` instance.

        Returns:
            The Pydantic core schema.
        """
        schema = handler(source_type)
        if self.return_type is not PydanticUndefined:
            return_type = self.return_type
        else:
            try:
                # Do not pass in globals as the function could be defined in a different module.
                # Instead, let `get_callable_return_type` infer the globals to use, but still pass
                # in locals that may contain a parent/rebuild namespace:
                return_type = _decorators.get_callable_return_type(
                    self.func,
                    localns=handler._get_types_namespace().locals,
                )
            except NameError as e:
                raise PydanticUndefinedAnnotation.from_name_error(e) from e

        return_schema = None if return_type is PydanticUndefined else handler.generate_schema(return_type)
        schema['serialization'] = core_schema.plain_serializer_function_ser_schema(
            function=self.func,
            info_arg=_decorators.inspect_annotated_serializer(self.func, 'plain'),
            return_schema=return_schema,
            when_used=self.when_used,
        )
        return schema


@dataclasses.dataclass(**_internal_dataclass.slots_true, frozen=True)
class WrapSerializer:
    """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 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'.
    """

    func: core_schema.WrapSerializerFunction
    return_type: Any = PydanticUndefined
    when_used: WhenUsed = 'always'

    def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
        """This 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.
        """
        schema = handler(source_type)
        if self.return_type is not PydanticUndefined:
            return_type = self.return_type
        else:
            try:
                # Do not pass in globals as the function could be defined in a different module.
                # Instead, let `get_callable_return_type` infer the globals to use, but still pass
                # in locals that may contain a parent/rebuild namespace:
                return_type = _decorators.get_callable_return_type(
                    self.func,
                    localns=handler._get_types_namespace().locals,
                )
            except NameError as e:
                raise PydanticUndefinedAnnotation.from_name_error(e) from e

        return_schema = None if return_type is PydanticUndefined else handler.generate_schema(return_type)
        schema['serialization'] = core_schema.wrap_serializer_function_ser_schema(
            function=self.func,
            info_arg=_decorators.inspect_annotated_serializer(self.func, 'wrap'),
            return_schema=return_schema,
            when_used=self.when_used,
        )
        return schema


if TYPE_CHECKING:
    _Partial: TypeAlias = 'partial[Any] | partialmethod[Any]'

    FieldPlainSerializer: TypeAlias = 'core_schema.SerializerFunction | _Partial'
    """A field serializer method or function in `plain` mode."""

    FieldWrapSerializer: TypeAlias = 'core_schema.WrapSerializerFunction | _Partial'
    """A field serializer method or function in `wrap` mode."""

    FieldSerializer: TypeAlias = 'FieldPlainSerializer | FieldWrapSerializer'
    """A field serializer method or function."""

    _FieldPlainSerializerT = TypeVar('_FieldPlainSerializerT', bound=FieldPlainSerializer)
    _FieldWrapSerializerT = TypeVar('_FieldWrapSerializerT', bound=FieldWrapSerializer)


@overload
def field_serializer(
    field: str,
    /,
    *fields: str,
    mode: Literal['wrap'],
    return_type: Any = ...,
    when_used: WhenUsed = ...,
    check_fields: bool | None = ...,
) -> Callable[[_FieldWrapSerializerT], _FieldWrapSerializerT]: ...


@overload
def field_serializer(
    field: str,
    /,
    *fields: str,
    mode: Literal['plain'] = ...,
    return_type: Any = ...,
    when_used: WhenUsed = ...,
    check_fields: bool | None = ...,
) -> Callable[[_FieldPlainSerializerT], _FieldPlainSerializerT]: ...


def field_serializer(
    *fields: str,
    mode: Literal['plain', 'wrap'] = 'plain',
    # TODO PEP 747 (grep for 'return_type' on the whole code base):
    return_type: Any = PydanticUndefined,
    when_used: WhenUsed = 'always',
    check_fields: bool | None = None,
) -> (
    Callable[[_FieldWrapSerializerT], _FieldWrapSerializerT]
    | Callable[[_FieldPlainSerializerT], _FieldPlainSerializerT]
):
    """Decorator 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 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 [the usage documentation](../concepts/serialization.md#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.
    """

    def dec(f: FieldSerializer) -> _decorators.PydanticDescriptorProxy[Any]:
        dec_info = _decorators.FieldSerializerDecoratorInfo(
            fields=fields,
            mode=mode,
            return_type=return_type,
            when_used=when_used,
            check_fields=check_fields,
        )
        return _decorators.PydanticDescriptorProxy(f, dec_info)  # pyright: ignore[reportArgumentType]

    return dec  # pyright: ignore[reportReturnType]


if TYPE_CHECKING:
    # The first argument in the following callables represent the `self` type:

    ModelPlainSerializerWithInfo: TypeAlias = Callable[[Any, SerializationInfo[Any]], Any]
    """A model serializer method with the `info` argument, in `plain` mode."""

    ModelPlainSerializerWithoutInfo: TypeAlias = Callable[[Any], Any]
    """A model serializer method without the `info` argument, in `plain` mode."""

    ModelPlainSerializer: TypeAlias = 'ModelPlainSerializerWithInfo | ModelPlainSerializerWithoutInfo'
    """A model serializer method in `plain` mode."""

    ModelWrapSerializerWithInfo: TypeAlias = Callable[[Any, SerializerFunctionWrapHandler, SerializationInfo[Any]], Any]
    """A model serializer method with the `info` argument, in `wrap` mode."""

    ModelWrapSerializerWithoutInfo: TypeAlias = Callable[[Any, SerializerFunctionWrapHandler], Any]
    """A model serializer method without the `info` argument, in `wrap` mode."""

    ModelWrapSerializer: TypeAlias = 'ModelWrapSerializerWithInfo | ModelWrapSerializerWithoutInfo'
    """A model serializer method in `wrap` mode."""

    ModelSerializer: TypeAlias = 'ModelPlainSerializer | ModelWrapSerializer'

    _ModelPlainSerializerT = TypeVar('_ModelPlainSerializerT', bound=ModelPlainSerializer)
    _ModelWrapSerializerT = TypeVar('_ModelWrapSerializerT', bound=ModelWrapSerializer)


@overload
def model_serializer(f: _ModelPlainSerializerT, /) -> _ModelPlainSerializerT: ...


@overload
def model_serializer(
    *, mode: Literal['wrap'], when_used: WhenUsed = 'always', return_type: Any = ...
) -> Callable[[_ModelWrapSerializerT], _ModelWrapSerializerT]: ...


@overload
def model_serializer(
    *,
    mode: Literal['plain'] = ...,
    when_used: WhenUsed = 'always',
    return_type: Any = ...,
) -> Callable[[_ModelPlainSerializerT], _ModelPlainSerializerT]: ...


def model_serializer(
    f: _ModelPlainSerializerT | _ModelWrapSerializerT | None = None,
    /,
    *,
    mode: Literal['plain', 'wrap'] = 'plain',
    when_used: WhenUsed = 'always',
    return_type: Any = PydanticUndefined,
) -> (
    _ModelPlainSerializerT
    | Callable[[_ModelWrapSerializerT], _ModelWrapSerializerT]
    | Callable[[_ModelPlainSerializerT], _ModelPlainSerializerT]
):
    """Decorator 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}
    ```

    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 [the usage documentation](../concepts/serialization.md#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.
    """

    def dec(f: ModelSerializer) -> _decorators.PydanticDescriptorProxy[Any]:
        dec_info = _decorators.ModelSerializerDecoratorInfo(mode=mode, return_type=return_type, when_used=when_used)
        return _decorators.PydanticDescriptorProxy(f, dec_info)

    if f is None:
        return dec  # pyright: ignore[reportReturnType]
    else:
        return dec(f)  # pyright: ignore[reportReturnType]


AnyType = TypeVar('AnyType')


if TYPE_CHECKING:
    SerializeAsAny = Annotated[AnyType, ...]  # SerializeAsAny[list[str]] will be treated by type checkers as list[str]
    """Annotation used to mark a type as having duck-typing serialization behavior.

    See [usage documentation](../concepts/serialization.md#serializing-with-duck-typing) for more details.
    """
else:

    @dataclasses.dataclass(**_internal_dataclass.slots_true)
    class SerializeAsAny:
        """Annotation used to mark a type as having duck-typing serialization behavior.

        See [usage documentation](../concepts/serialization.md#serializing-with-duck-typing) for more details.
        """

        def __class_getitem__(cls, item: Any) -> Any:
            return Annotated[item, SerializeAsAny()]

        def __get_pydantic_core_schema__(
            self, source_type: Any, handler: GetCoreSchemaHandler
        ) -> core_schema.CoreSchema:
            schema = handler(source_type)
            schema_to_update = schema
            while schema_to_update['type'] == 'definitions':
                schema_to_update = schema_to_update.copy()
                schema_to_update = schema_to_update['schema']
            schema_to_update['serialization'] = core_schema.simple_ser_schema('any')
            return schema

        __hash__ = object.__hash__