223 lines
6.2 KiB
Python
223 lines
6.2 KiB
Python
from __future__ import annotations
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from typing import TYPE_CHECKING, Any, Union, Generic, TypeVar, Callable, cast, overload
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from datetime import date, datetime
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from typing_extensions import Self
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import pydantic
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from pydantic.fields import FieldInfo
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from ._types import StrBytesIntFloat
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_T = TypeVar("_T")
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_ModelT = TypeVar("_ModelT", bound=pydantic.BaseModel)
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# --------------- Pydantic v2 compatibility ---------------
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# Pyright incorrectly reports some of our functions as overriding a method when they don't
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# pyright: reportIncompatibleMethodOverride=false
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PYDANTIC_V2 = pydantic.VERSION.startswith("2.")
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# v1 re-exports
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if TYPE_CHECKING:
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def parse_date(value: date | StrBytesIntFloat) -> date: # noqa: ARG001
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...
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def parse_datetime(value: Union[datetime, StrBytesIntFloat]) -> datetime: # noqa: ARG001
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...
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def get_args(t: type[Any]) -> tuple[Any, ...]: # noqa: ARG001
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...
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def is_union(tp: type[Any] | None) -> bool: # noqa: ARG001
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...
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def get_origin(t: type[Any]) -> type[Any] | None: # noqa: ARG001
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...
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def is_literal_type(type_: type[Any]) -> bool: # noqa: ARG001
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...
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def is_typeddict(type_: type[Any]) -> bool: # noqa: ARG001
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...
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else:
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if PYDANTIC_V2:
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from pydantic.v1.typing import (
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get_args as get_args,
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is_union as is_union,
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get_origin as get_origin,
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is_typeddict as is_typeddict,
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is_literal_type as is_literal_type,
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)
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from pydantic.v1.datetime_parse import parse_date as parse_date, parse_datetime as parse_datetime
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else:
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from pydantic.typing import (
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get_args as get_args,
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is_union as is_union,
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get_origin as get_origin,
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is_typeddict as is_typeddict,
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is_literal_type as is_literal_type,
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)
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from pydantic.datetime_parse import parse_date as parse_date, parse_datetime as parse_datetime
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# refactored config
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if TYPE_CHECKING:
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from pydantic import ConfigDict as ConfigDict
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else:
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if PYDANTIC_V2:
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from pydantic import ConfigDict
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else:
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# TODO: provide an error message here?
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ConfigDict = None
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# renamed methods / properties
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def parse_obj(model: type[_ModelT], value: object) -> _ModelT:
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if PYDANTIC_V2:
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return model.model_validate(value)
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else:
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return cast(_ModelT, model.parse_obj(value)) # pyright: ignore[reportDeprecated, reportUnnecessaryCast]
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def field_is_required(field: FieldInfo) -> bool:
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if PYDANTIC_V2:
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return field.is_required()
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return field.required # type: ignore
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def field_get_default(field: FieldInfo) -> Any:
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value = field.get_default()
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if PYDANTIC_V2:
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from pydantic_core import PydanticUndefined
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if value == PydanticUndefined:
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return None
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return value
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return value
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def field_outer_type(field: FieldInfo) -> Any:
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if PYDANTIC_V2:
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return field.annotation
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return field.outer_type_ # type: ignore
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def get_model_config(model: type[pydantic.BaseModel]) -> Any:
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if PYDANTIC_V2:
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return model.model_config
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return model.__config__ # type: ignore
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def get_model_fields(model: type[pydantic.BaseModel]) -> dict[str, FieldInfo]:
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if PYDANTIC_V2:
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return model.model_fields
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return model.__fields__ # type: ignore
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def model_copy(model: _ModelT) -> _ModelT:
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if PYDANTIC_V2:
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return model.model_copy()
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return model.copy() # type: ignore
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def model_json(model: pydantic.BaseModel, *, indent: int | None = None) -> str:
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if PYDANTIC_V2:
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return model.model_dump_json(indent=indent)
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return model.json(indent=indent) # type: ignore
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def model_dump(
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model: pydantic.BaseModel,
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*,
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exclude_unset: bool = False,
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exclude_defaults: bool = False,
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) -> dict[str, Any]:
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if PYDANTIC_V2:
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return model.model_dump(
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exclude_unset=exclude_unset,
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exclude_defaults=exclude_defaults,
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)
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return cast(
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"dict[str, Any]",
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model.dict( # pyright: ignore[reportDeprecated, reportUnnecessaryCast]
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exclude_unset=exclude_unset,
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exclude_defaults=exclude_defaults,
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),
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)
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def model_parse(model: type[_ModelT], data: Any) -> _ModelT:
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if PYDANTIC_V2:
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return model.model_validate(data)
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return model.parse_obj(data) # pyright: ignore[reportDeprecated]
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# generic models
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if TYPE_CHECKING:
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class GenericModel(pydantic.BaseModel):
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...
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else:
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if PYDANTIC_V2:
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# there no longer needs to be a distinction in v2 but
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# we still have to create our own subclass to avoid
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# inconsistent MRO ordering errors
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class GenericModel(pydantic.BaseModel):
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...
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else:
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import pydantic.generics
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class GenericModel(pydantic.generics.GenericModel, pydantic.BaseModel):
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...
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# cached properties
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if TYPE_CHECKING:
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cached_property = property
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# we define a separate type (copied from typeshed)
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# that represents that `cached_property` is `set`able
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# at runtime, which differs from `@property`.
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#
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# this is a separate type as editors likely special case
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# `@property` and we don't want to cause issues just to have
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# more helpful internal types.
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class typed_cached_property(Generic[_T]):
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func: Callable[[Any], _T]
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attrname: str | None
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def __init__(self, func: Callable[[Any], _T]) -> None:
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...
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@overload
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def __get__(self, instance: None, owner: type[Any] | None = None) -> Self:
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...
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@overload
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def __get__(self, instance: object, owner: type[Any] | None = None) -> _T:
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...
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def __get__(self, instance: object, owner: type[Any] | None = None) -> _T | Self:
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raise NotImplementedError()
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def __set_name__(self, owner: type[Any], name: str) -> None:
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...
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# __set__ is not defined at runtime, but @cached_property is designed to be settable
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def __set__(self, instance: object, value: _T) -> None:
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...
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else:
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try:
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from functools import cached_property as cached_property
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except ImportError:
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from cached_property import cached_property as cached_property
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typed_cached_property = cached_property
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