zhenxun_bot/zhenxun/utils/pydantic_compat.py

96 lines
2.6 KiB
Python
Raw Normal View History

"""
Pydantic V1 & V2 兼容层模块
Pydantic V1 V2 版本提供统一的便捷函数与类
包括 model_dump, model_copy, model_json_schema, parse_as
"""
from typing import Any, TypeVar, get_args, get_origin
from nonebot.compat import PYDANTIC_V2, model_dump
from pydantic import VERSION, BaseModel
T = TypeVar("T", bound=BaseModel)
V = TypeVar("V")
__all__ = [
"PYDANTIC_V2",
"_dump_pydantic_obj",
"_is_pydantic_type",
"compat_computed_field",
"model_copy",
"model_dump",
"model_json_schema",
"parse_as",
]
def model_copy(
model: T, *, update: dict[str, Any] | None = None, deep: bool = False
) -> T:
"""
Pydantic `model.copy()` (v1) `model.model_copy()` (v2) 的兼容函数
"""
if PYDANTIC_V2:
return model.model_copy(update=update, deep=deep)
else:
update_dict = update or {}
return model.copy(update=update_dict, deep=deep)
if PYDANTIC_V2:
from pydantic import computed_field as compat_computed_field
else:
compat_computed_field = property
def model_json_schema(model_class: type[BaseModel], **kwargs: Any) -> dict[str, Any]:
"""
Pydantic `Model.schema()` (v1) `Model.model_json_schema()` (v2) 的兼容函数
"""
if PYDANTIC_V2:
return model_class.model_json_schema(**kwargs)
else:
return model_class.schema(by_alias=kwargs.get("by_alias", True))
def _is_pydantic_type(t: Any) -> bool:
"""
递归检查一个类型注解是否与 Pydantic BaseModel 相关
"""
if t is None:
return False
origin = get_origin(t)
if origin:
return any(_is_pydantic_type(arg) for arg in get_args(t))
return isinstance(t, type) and issubclass(t, BaseModel)
def _dump_pydantic_obj(obj: Any) -> Any:
"""
递归地将一个对象内部的 Pydantic BaseModel 实例转换为字典
支持单个实例实例列表实例字典等情况
"""
if isinstance(obj, BaseModel):
return model_dump(obj)
if isinstance(obj, list):
return [_dump_pydantic_obj(item) for item in obj]
if isinstance(obj, dict):
return {key: _dump_pydantic_obj(value) for key, value in obj.items()}
return obj
def parse_as(type_: type[V], obj: Any) -> V:
"""
一个兼容 Pydantic V1 parse_obj_as 和V2的TypeAdapter.validate_python 的辅助函数
"""
if VERSION.startswith("1"):
from pydantic import parse_obj_as
return parse_obj_as(type_, obj)
else:
from pydantic import TypeAdapter # type: ignore
return TypeAdapter(type_).validate_python(obj)