peacock-data-public-datasets-idc-cronscript
/
venv
/lib
/python3.10
/site-packages
/pydantic
/root_model.py
"""RootModel class and type definitions.""" | |
from __future__ import annotations as _annotations | |
import typing | |
from copy import copy, deepcopy | |
from pydantic_core import PydanticUndefined | |
from . import PydanticUserError | |
from ._internal import _model_construction, _repr | |
from .main import BaseModel, _object_setattr | |
if typing.TYPE_CHECKING: | |
from typing import Any | |
from typing_extensions import Literal, dataclass_transform | |
from .fields import Field as PydanticModelField | |
# dataclass_transform could be applied to RootModel directly, but `ModelMetaclass`'s dataclass_transform | |
# takes priority (at least with pyright). We trick type checkers into thinking we apply dataclass_transform | |
# on a new metaclass. | |
class _RootModelMetaclass(_model_construction.ModelMetaclass): | |
... | |
else: | |
_RootModelMetaclass = _model_construction.ModelMetaclass | |
__all__ = ('RootModel',) | |
Model = typing.TypeVar('Model', bound='BaseModel') | |
RootModelRootType = typing.TypeVar('RootModelRootType') | |
class RootModel(BaseModel, typing.Generic[RootModelRootType], metaclass=_RootModelMetaclass): | |
"""Usage docs: https://docs.pydantic.dev/2.7/concepts/models/#rootmodel-and-custom-root-types | |
A Pydantic `BaseModel` for the root object of the model. | |
Attributes: | |
root: The root object of the model. | |
__pydantic_root_model__: Whether the model is a RootModel. | |
__pydantic_private__: Private fields in the model. | |
__pydantic_extra__: Extra fields in the model. | |
""" | |
__pydantic_root_model__ = True | |
__pydantic_private__ = None | |
__pydantic_extra__ = None | |
root: RootModelRootType | |
def __init_subclass__(cls, **kwargs): | |
extra = cls.model_config.get('extra') | |
if extra is not None: | |
raise PydanticUserError( | |
"`RootModel` does not support setting `model_config['extra']`", code='root-model-extra' | |
) | |
super().__init_subclass__(**kwargs) | |
def __init__(self, /, root: RootModelRootType = PydanticUndefined, **data) -> None: # type: ignore | |
__tracebackhide__ = True | |
if data: | |
if root is not PydanticUndefined: | |
raise ValueError( | |
'"RootModel.__init__" accepts either a single positional argument or arbitrary keyword arguments' | |
) | |
root = data # type: ignore | |
self.__pydantic_validator__.validate_python(root, self_instance=self) | |
__init__.__pydantic_base_init__ = True # pyright: ignore[reportFunctionMemberAccess] | |
def model_construct(cls: type[Model], root: RootModelRootType, _fields_set: set[str] | None = None) -> Model: # type: ignore | |
"""Create a new model using the provided root object and update fields set. | |
Args: | |
root: The root object of the model. | |
_fields_set: The set of fields to be updated. | |
Returns: | |
The new model. | |
Raises: | |
NotImplemented: If the model is not a subclass of `RootModel`. | |
""" | |
return super().model_construct(root=root, _fields_set=_fields_set) | |
def __getstate__(self) -> dict[Any, Any]: | |
return { | |
'__dict__': self.__dict__, | |
'__pydantic_fields_set__': self.__pydantic_fields_set__, | |
} | |
def __setstate__(self, state: dict[Any, Any]) -> None: | |
_object_setattr(self, '__pydantic_fields_set__', state['__pydantic_fields_set__']) | |
_object_setattr(self, '__dict__', state['__dict__']) | |
def __copy__(self: Model) -> Model: | |
"""Returns a shallow copy of the model.""" | |
cls = type(self) | |
m = cls.__new__(cls) | |
_object_setattr(m, '__dict__', copy(self.__dict__)) | |
_object_setattr(m, '__pydantic_fields_set__', copy(self.__pydantic_fields_set__)) | |
return m | |
def __deepcopy__(self: Model, memo: dict[int, Any] | None = None) -> Model: | |
"""Returns a deep copy of the model.""" | |
cls = type(self) | |
m = cls.__new__(cls) | |
_object_setattr(m, '__dict__', deepcopy(self.__dict__, memo=memo)) | |
# This next line doesn't need a deepcopy because __pydantic_fields_set__ is a set[str], | |
# and attempting a deepcopy would be marginally slower. | |
_object_setattr(m, '__pydantic_fields_set__', copy(self.__pydantic_fields_set__)) | |
return m | |
if typing.TYPE_CHECKING: | |
def model_dump( # type: ignore | |
self, | |
*, | |
mode: Literal['json', 'python'] | str = 'python', | |
include: Any = None, | |
exclude: Any = None, | |
context: dict[str, Any] | None = None, | |
by_alias: bool = False, | |
exclude_unset: bool = False, | |
exclude_defaults: bool = False, | |
exclude_none: bool = False, | |
round_trip: bool = False, | |
warnings: bool | Literal['none', 'warn', 'error'] = True, | |
serialize_as_any: bool = False, | |
) -> Any: | |
"""This method is included just to get a more accurate return type for type checkers. | |
It is included in this `if TYPE_CHECKING:` block since no override is actually necessary. | |
See the documentation of `BaseModel.model_dump` for more details about the arguments. | |
Generally, this method will have a return type of `RootModelRootType`, assuming that `RootModelRootType` is | |
not a `BaseModel` subclass. If `RootModelRootType` is a `BaseModel` subclass, then the return | |
type will likely be `dict[str, Any]`, as `model_dump` calls are recursive. The return type could | |
even be something different, in the case of a custom serializer. | |
Thus, `Any` is used here to catch all of these cases. | |
""" | |
... | |
def __eq__(self, other: Any) -> bool: | |
if not isinstance(other, RootModel): | |
return NotImplemented | |
return self.model_fields['root'].annotation == other.model_fields['root'].annotation and super().__eq__(other) | |
def __repr_args__(self) -> _repr.ReprArgs: | |
yield 'root', self.root | |