_compat.py•6.56 kB
from __future__ import annotations
from typing import TYPE_CHECKING, Any, Union, Generic, TypeVar, Callable, cast, overload
from datetime import date, datetime
from typing_extensions import Self, Literal
import pydantic
from pydantic.fields import FieldInfo
from ._types import IncEx, StrBytesIntFloat
_T = TypeVar("_T")
_ModelT = TypeVar("_ModelT", bound=pydantic.BaseModel)
# --------------- Pydantic v2, v3 compatibility ---------------
# Pyright incorrectly reports some of our functions as overriding a method when they don't
# pyright: reportIncompatibleMethodOverride=false
PYDANTIC_V1 = pydantic.VERSION.startswith("1.")
if TYPE_CHECKING:
def parse_date(value: date | StrBytesIntFloat) -> date: # noqa: ARG001
...
def parse_datetime(value: Union[datetime, StrBytesIntFloat]) -> datetime: # noqa: ARG001
...
def get_args(t: type[Any]) -> tuple[Any, ...]: # noqa: ARG001
...
def is_union(tp: type[Any] | None) -> bool: # noqa: ARG001
...
def get_origin(t: type[Any]) -> type[Any] | None: # noqa: ARG001
...
def is_literal_type(type_: type[Any]) -> bool: # noqa: ARG001
...
def is_typeddict(type_: type[Any]) -> bool: # noqa: ARG001
...
else:
# v1 re-exports
if PYDANTIC_V1:
from pydantic.typing import (
get_args as get_args,
is_union as is_union,
get_origin as get_origin,
is_typeddict as is_typeddict,
is_literal_type as is_literal_type,
)
from pydantic.datetime_parse import parse_date as parse_date, parse_datetime as parse_datetime
else:
from ._utils import (
get_args as get_args,
is_union as is_union,
get_origin as get_origin,
parse_date as parse_date,
is_typeddict as is_typeddict,
parse_datetime as parse_datetime,
is_literal_type as is_literal_type,
)
# refactored config
if TYPE_CHECKING:
from pydantic import ConfigDict as ConfigDict
else:
if PYDANTIC_V1:
# TODO: provide an error message here?
ConfigDict = None
else:
from pydantic import ConfigDict as ConfigDict
# renamed methods / properties
def parse_obj(model: type[_ModelT], value: object) -> _ModelT:
if PYDANTIC_V1:
return cast(_ModelT, model.parse_obj(value)) # pyright: ignore[reportDeprecated, reportUnnecessaryCast]
else:
return model.model_validate(value)
def field_is_required(field: FieldInfo) -> bool:
if PYDANTIC_V1:
return field.required # type: ignore
return field.is_required()
def field_get_default(field: FieldInfo) -> Any:
value = field.get_default()
if PYDANTIC_V1:
return value
from pydantic_core import PydanticUndefined
if value == PydanticUndefined:
return None
return value
def field_outer_type(field: FieldInfo) -> Any:
if PYDANTIC_V1:
return field.outer_type_ # type: ignore
return field.annotation
def get_model_config(model: type[pydantic.BaseModel]) -> Any:
if PYDANTIC_V1:
return model.__config__ # type: ignore
return model.model_config
def get_model_fields(model: type[pydantic.BaseModel]) -> dict[str, FieldInfo]:
if PYDANTIC_V1:
return model.__fields__ # type: ignore
return model.model_fields
def model_copy(model: _ModelT, *, deep: bool = False) -> _ModelT:
if PYDANTIC_V1:
return model.copy(deep=deep) # type: ignore
return model.model_copy(deep=deep)
def model_json(model: pydantic.BaseModel, *, indent: int | None = None) -> str:
if PYDANTIC_V1:
return model.json(indent=indent) # type: ignore
return model.model_dump_json(indent=indent)
def model_dump(
model: pydantic.BaseModel,
*,
exclude: IncEx | None = None,
exclude_unset: bool = False,
exclude_defaults: bool = False,
warnings: bool = True,
mode: Literal["json", "python"] = "python",
) -> dict[str, Any]:
if (not PYDANTIC_V1) or hasattr(model, "model_dump"):
return model.model_dump(
mode=mode,
exclude=exclude,
exclude_unset=exclude_unset,
exclude_defaults=exclude_defaults,
# warnings are not supported in Pydantic v1
warnings=True if PYDANTIC_V1 else warnings,
)
return cast(
"dict[str, Any]",
model.dict( # pyright: ignore[reportDeprecated, reportUnnecessaryCast]
exclude=exclude,
exclude_unset=exclude_unset,
exclude_defaults=exclude_defaults,
),
)
def model_parse(model: type[_ModelT], data: Any) -> _ModelT:
if PYDANTIC_V1:
return model.parse_obj(data) # pyright: ignore[reportDeprecated]
return model.model_validate(data)
# generic models
if TYPE_CHECKING:
class GenericModel(pydantic.BaseModel): ...
else:
if PYDANTIC_V1:
import pydantic.generics
class GenericModel(pydantic.generics.GenericModel, pydantic.BaseModel): ...
else:
# there no longer needs to be a distinction in v2 but
# we still have to create our own subclass to avoid
# inconsistent MRO ordering errors
class GenericModel(pydantic.BaseModel): ...
# cached properties
if TYPE_CHECKING:
cached_property = property
# we define a separate type (copied from typeshed)
# that represents that `cached_property` is `set`able
# at runtime, which differs from `@property`.
#
# this is a separate type as editors likely special case
# `@property` and we don't want to cause issues just to have
# more helpful internal types.
class typed_cached_property(Generic[_T]):
func: Callable[[Any], _T]
attrname: str | None
def __init__(self, func: Callable[[Any], _T]) -> None: ...
@overload
def __get__(self, instance: None, owner: type[Any] | None = None) -> Self: ...
@overload
def __get__(self, instance: object, owner: type[Any] | None = None) -> _T: ...
def __get__(self, instance: object, owner: type[Any] | None = None) -> _T | Self:
raise NotImplementedError()
def __set_name__(self, owner: type[Any], name: str) -> None: ...
# __set__ is not defined at runtime, but @cached_property is designed to be settable
def __set__(self, instance: object, value: _T) -> None: ...
else:
from functools import cached_property as cached_property
typed_cached_property = cached_property