nonebot2/nonebot/compat.py

"""本模块为 Pydantic 版本兼容层模块

为兼容 Pydantic V1 与 V2 版本，定义了一系列兼容函数与类供使用。

FrontMatter:
    sidebar_position: 16
    description: nonebot.compat 模块
"""

from dataclasses import dataclass, is_dataclass
from typing_extensions import Self, Annotated, get_args, get_origin, is_typeddict
from typing import (
    TYPE_CHECKING,
    Any,
    Set,
    Dict,
    List,
    Type,
    TypeVar,
    Callable,
    Optional,
    Protocol,
    Generator,
)

from pydantic import VERSION, BaseModel

from nonebot.typing import origin_is_annotated

T = TypeVar("T")

PYDANTIC_V2 = int(VERSION.split(".", 1)[0]) == 2

if TYPE_CHECKING:

    class _CustomValidationClass(Protocol):
        @classmethod
        def __get_validators__(cls) -> Generator[Callable[..., Any], None, None]: ...

    CVC = TypeVar("CVC", bound=_CustomValidationClass)


__all__ = (
    "Required",
    "PydanticUndefined",
    "PydanticUndefinedType",
    "ConfigDict",
    "DEFAULT_CONFIG",
    "FieldInfo",
    "ModelField",
    "extract_field_info",
    "model_field_validate",
    "model_fields",
    "model_config",
    "model_dump",
    "type_validate_python",
    "custom_validation",
)

__autodoc__ = {
    "PydanticUndefined": "Pydantic Undefined object",
    "PydanticUndefinedType": "Pydantic Undefined type",
}


if PYDANTIC_V2:  # pragma: pydantic-v2
    from pydantic_core import CoreSchema, core_schema
    from pydantic._internal._repr import display_as_type
    from pydantic import TypeAdapter, GetCoreSchemaHandler
    from pydantic.fields import FieldInfo as BaseFieldInfo

    Required = Ellipsis
    """Alias of Ellipsis for compatibility with pydantic v1"""

    # Export undefined type
    from pydantic_core import PydanticUndefined as PydanticUndefined
    from pydantic_core import PydanticUndefinedType as PydanticUndefinedType

    # isort: split

    # Export model config dict
    from pydantic import ConfigDict as ConfigDict

    DEFAULT_CONFIG = ConfigDict(extra="allow", arbitrary_types_allowed=True)
    """Default config for validations"""

    class FieldInfo(BaseFieldInfo):
        """FieldInfo class with extra property for compatibility with pydantic v1"""

        # make default can be positional argument
        def __init__(self, default: Any = PydanticUndefined, **kwargs: Any) -> None:
            super().__init__(default=default, **kwargs)

        @property
        def extra(self) -> Dict[str, Any]:
            """Extra data that is not part of the standard pydantic fields.

            For compatibility with pydantic v1.
            """
            # extract extra data from attributes set except used slots
            # we need to call super in advance due to
            # comprehension not inlined in cpython < 3.12
            # https://peps.python.org/pep-0709/
            slots = super().__slots__
            return {k: v for k, v in self._attributes_set.items() if k not in slots}

    @dataclass
    class ModelField:
        """ModelField class for compatibility with pydantic v1"""

        name: str
        """The name of the field."""
        annotation: Any
        """The annotation of the field."""
        field_info: FieldInfo
        """The FieldInfo of the field."""

        @classmethod
        def _construct(cls, name: str, annotation: Any, field_info: FieldInfo) -> Self:
            return cls(name, annotation, field_info)

        @classmethod
        def construct(
            cls, name: str, annotation: Any, field_info: Optional[FieldInfo] = None
        ) -> Self:
            """Construct a ModelField from given infos."""
            return cls._construct(name, annotation, field_info or FieldInfo())

        def _annotation_has_config(self) -> bool:
            """Check if the annotation has config.

            TypeAdapter raise error when annotation has config
            and given config is not None.
            """
            type_is_annotated = origin_is_annotated(get_origin(self.annotation))
            inner_type = (
                get_args(self.annotation)[0] if type_is_annotated else self.annotation
            )
            try:
                return (
                    issubclass(inner_type, BaseModel)
                    or is_dataclass(inner_type)
                    or is_typeddict(inner_type)
                )
            except TypeError:
                return False

        def get_default(self) -> Any:
            """Get the default value of the field."""
            return self.field_info.get_default(call_default_factory=True)

        def _type_display(self):
            """Get the display of the type of the field."""
            return display_as_type(self.annotation)

        def __hash__(self) -> int:
            # Each ModelField is unique for our purposes,
            # to allow store them in a set.
            return id(self)

    def extract_field_info(field_info: BaseFieldInfo) -> Dict[str, Any]:
        """Get FieldInfo init kwargs from a FieldInfo instance."""

        kwargs = field_info._attributes_set.copy()
        kwargs["annotation"] = field_info.rebuild_annotation()
        return kwargs

    def model_field_validate(
        model_field: ModelField, value: Any, config: Optional[ConfigDict] = None
    ) -> Any:
        """Validate the value pass to the field."""
        type: Any = Annotated[model_field.annotation, model_field.field_info]
        return TypeAdapter(
            type, config=None if model_field._annotation_has_config() else config
        ).validate_python(value)

    def model_fields(model: Type[BaseModel]) -> List[ModelField]:
        """Get field list of a model."""

        return [
            ModelField._construct(
                name=name,
                annotation=field_info.rebuild_annotation(),
                field_info=FieldInfo(**extract_field_info(field_info)),
            )
            for name, field_info in model.model_fields.items()
        ]

    def model_config(model: Type[BaseModel]) -> Any:
        """Get config of a model."""
        return model.model_config

    def model_dump(
        model: BaseModel,
        include: Optional[Set[str]] = None,
        exclude: Optional[Set[str]] = None,
    ) -> Dict[str, Any]:
        return model.model_dump(include=include, exclude=exclude)

    def type_validate_python(type_: Type[T], data: Any) -> T:
        """Validate data with given type."""
        return TypeAdapter(type_).validate_python(data)

    def __get_pydantic_core_schema__(
        cls: Type["_CustomValidationClass"],
        source_type: Any,
        handler: GetCoreSchemaHandler,
    ) -> CoreSchema:
        validators = list(cls.__get_validators__())
        if len(validators) == 1:
            return core_schema.no_info_plain_validator_function(validators[0])
        return core_schema.chain_schema(
            [core_schema.no_info_plain_validator_function(func) for func in validators]
        )

    def custom_validation(class_: Type["CVC"]) -> Type["CVC"]:
        """Use pydantic v1 like validator generator in pydantic v2"""

        setattr(
            class_,
            "__get_pydantic_core_schema__",
            classmethod(__get_pydantic_core_schema__),
        )
        return class_

else:  # pragma: pydantic-v1
    from pydantic import Extra
    from pydantic import parse_obj_as
    from pydantic import BaseConfig as PydanticConfig
    from pydantic.fields import FieldInfo as BaseFieldInfo
    from pydantic.fields import ModelField as BaseModelField
    from pydantic.schema import get_annotation_from_field_info

    # isort: split

    from pydantic.fields import Required as Required

    # isort: split

    from pydantic.fields import Undefined as PydanticUndefined
    from pydantic.fields import UndefinedType as PydanticUndefinedType

    class ConfigDict(PydanticConfig):
        """Config class that allow get value with default value."""

        @classmethod
        def get(cls, field: str, default: Any = None) -> Any:
            """Get a config value."""
            return getattr(cls, field, default)

    class DEFAULT_CONFIG(ConfigDict):
        extra = Extra.allow
        arbitrary_types_allowed = True

    class FieldInfo(BaseFieldInfo):
        def __init__(self, default: Any = PydanticUndefined, **kwargs: Any):
            # preprocess default value to make it compatible with pydantic v2
            # when default is Required, set it to PydanticUndefined
            if default is Required:
                default = PydanticUndefined
            super().__init__(default, **kwargs)

    class ModelField(BaseModelField):
        @classmethod
        def _construct(cls, name: str, annotation: Any, field_info: FieldInfo) -> Self:
            return cls(
                name=name,
                type_=annotation,
                class_validators=None,
                model_config=DEFAULT_CONFIG,
                default=field_info.default,
                default_factory=field_info.default_factory,
                required=(
                    field_info.default is PydanticUndefined
                    and field_info.default_factory is None
                ),
                field_info=field_info,
            )

        @classmethod
        def construct(
            cls, name: str, annotation: Any, field_info: Optional[FieldInfo] = None
        ) -> Self:
            """Construct a ModelField from given infos.

            Field annotation is preprocessed with field_info.
            """
            if field_info is not None:
                annotation = get_annotation_from_field_info(
                    annotation, field_info, name
                )
            return cls._construct(name, annotation, field_info or FieldInfo())

    def extract_field_info(field_info: BaseFieldInfo) -> Dict[str, Any]:
        """Get FieldInfo init kwargs from a FieldInfo instance."""

        kwargs = {
            s: getattr(field_info, s) for s in field_info.__slots__ if s != "extra"
        }
        kwargs.update(field_info.extra)
        return kwargs

    def model_field_validate(
        model_field: ModelField, value: Any, config: Optional[Type[ConfigDict]] = None
    ) -> Any:
        """Validate the value pass to the field.

        Set config before validate to ensure validate correctly.
        """

        if model_field.model_config is not config:
            model_field.set_config(config or ConfigDict)

        v, errs_ = model_field.validate(value, {}, loc=())
        if errs_:
            raise ValueError(value, model_field)
        return v

    def model_fields(model: Type[BaseModel]) -> List[ModelField]:
        """Get field list of a model."""

        # construct the model field without preprocess to avoid error
        return [
            ModelField._construct(
                name=model_field.name,
                annotation=model_field.annotation,
                field_info=FieldInfo(
                    **extract_field_info(model_field.field_info),
                ),
            )
            for model_field in model.__fields__.values()
        ]

    def model_config(model: Type[BaseModel]) -> Any:
        """Get config of a model."""
        return model.__config__

    def model_dump(
        model: BaseModel,
        include: Optional[Set[str]] = None,
        exclude: Optional[Set[str]] = None,
    ) -> Dict[str, Any]:
        return model.dict(include=include, exclude=exclude)

    def type_validate_python(type_: Type[T], data: Any) -> T:
        """Validate data with given type."""
        return parse_obj_as(type_, data)

    def custom_validation(class_: Type["CVC"]) -> Type["CVC"]:
        """Do nothing in pydantic v1"""
        return class_