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459 lines
16 KiB
459 lines
16 KiB
"""
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The main purpose is to enhance stdlib dataclasses by adding validation
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A pydantic dataclass can be generated from scratch or from a stdlib one.
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Behind the scene, a pydantic dataclass is just like a regular one on which we attach
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a `BaseModel` and magic methods to trigger the validation of the data.
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`__init__` and `__post_init__` are hence overridden and have extra logic to be
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able to validate input data.
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When a pydantic dataclass is generated from scratch, it's just a plain dataclass
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with validation triggered at initialization
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The tricky part if for stdlib dataclasses that are converted after into pydantic ones e.g.
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```py
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@dataclasses.dataclass
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class M:
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x: int
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ValidatedM = pydantic.dataclasses.dataclass(M)
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```
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We indeed still want to support equality, hashing, repr, ... as if it was the stdlib one!
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```py
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assert isinstance(ValidatedM(x=1), M)
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assert ValidatedM(x=1) == M(x=1)
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```
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This means we **don't want to create a new dataclass that inherits from it**
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The trick is to create a wrapper around `M` that will act as a proxy to trigger
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validation without altering default `M` behaviour.
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"""
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import sys
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from contextlib import contextmanager
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from functools import wraps
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from typing import TYPE_CHECKING, Any, Callable, ClassVar, Dict, Generator, Optional, Type, TypeVar, Union, overload
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from typing_extensions import dataclass_transform
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from .class_validators import gather_all_validators
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from .config import BaseConfig, ConfigDict, Extra, get_config
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from .error_wrappers import ValidationError
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from .errors import DataclassTypeError
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from .fields import Field, FieldInfo, Required, Undefined
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from .main import create_model, validate_model
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from .utils import ClassAttribute
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if TYPE_CHECKING:
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from .main import BaseModel
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from .typing import CallableGenerator, NoArgAnyCallable
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DataclassT = TypeVar('DataclassT', bound='Dataclass')
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DataclassClassOrWrapper = Union[Type['Dataclass'], 'DataclassProxy']
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class Dataclass:
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# stdlib attributes
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__dataclass_fields__: ClassVar[Dict[str, Any]]
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__dataclass_params__: ClassVar[Any] # in reality `dataclasses._DataclassParams`
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__post_init__: ClassVar[Callable[..., None]]
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# Added by pydantic
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__pydantic_run_validation__: ClassVar[bool]
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__post_init_post_parse__: ClassVar[Callable[..., None]]
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__pydantic_initialised__: ClassVar[bool]
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__pydantic_model__: ClassVar[Type[BaseModel]]
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__pydantic_validate_values__: ClassVar[Callable[['Dataclass'], None]]
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__pydantic_has_field_info_default__: ClassVar[bool] # whether a `pydantic.Field` is used as default value
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def __init__(self, *args: object, **kwargs: object) -> None:
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pass
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@classmethod
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def __get_validators__(cls: Type['Dataclass']) -> 'CallableGenerator':
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pass
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@classmethod
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def __validate__(cls: Type['DataclassT'], v: Any) -> 'DataclassT':
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pass
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__all__ = [
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'dataclass',
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'set_validation',
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'create_pydantic_model_from_dataclass',
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'is_builtin_dataclass',
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'make_dataclass_validator',
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]
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_T = TypeVar('_T')
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if sys.version_info >= (3, 10):
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@dataclass_transform(kw_only_default=True, field_descriptors=(Field, FieldInfo))
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@overload
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def dataclass(
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*,
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init: bool = True,
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repr: bool = True,
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eq: bool = True,
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order: bool = False,
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unsafe_hash: bool = False,
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frozen: bool = False,
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config: Union[ConfigDict, Type[object], None] = None,
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validate_on_init: Optional[bool] = None,
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kw_only: bool = ...,
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) -> Callable[[Type[_T]], 'DataclassClassOrWrapper']:
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...
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@dataclass_transform(kw_only_default=True, field_descriptors=(Field, FieldInfo))
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@overload
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def dataclass(
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_cls: Type[_T],
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*,
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init: bool = True,
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repr: bool = True,
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eq: bool = True,
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order: bool = False,
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unsafe_hash: bool = False,
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frozen: bool = False,
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config: Union[ConfigDict, Type[object], None] = None,
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validate_on_init: Optional[bool] = None,
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kw_only: bool = ...,
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) -> 'DataclassClassOrWrapper':
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...
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else:
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@dataclass_transform(kw_only_default=True, field_descriptors=(Field, FieldInfo))
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@overload
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def dataclass(
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*,
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init: bool = True,
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repr: bool = True,
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eq: bool = True,
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order: bool = False,
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unsafe_hash: bool = False,
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frozen: bool = False,
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config: Union[ConfigDict, Type[object], None] = None,
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validate_on_init: Optional[bool] = None,
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) -> Callable[[Type[_T]], 'DataclassClassOrWrapper']:
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...
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@dataclass_transform(kw_only_default=True, field_descriptors=(Field, FieldInfo))
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@overload
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def dataclass(
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_cls: Type[_T],
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*,
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init: bool = True,
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repr: bool = True,
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eq: bool = True,
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order: bool = False,
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unsafe_hash: bool = False,
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frozen: bool = False,
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config: Union[ConfigDict, Type[object], None] = None,
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validate_on_init: Optional[bool] = None,
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) -> 'DataclassClassOrWrapper':
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...
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@dataclass_transform(kw_only_default=True, field_descriptors=(Field, FieldInfo))
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def dataclass(
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_cls: Optional[Type[_T]] = None,
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*,
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init: bool = True,
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repr: bool = True,
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eq: bool = True,
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order: bool = False,
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unsafe_hash: bool = False,
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frozen: bool = False,
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config: Union[ConfigDict, Type[object], None] = None,
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validate_on_init: Optional[bool] = None,
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kw_only: bool = False,
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) -> Union[Callable[[Type[_T]], 'DataclassClassOrWrapper'], 'DataclassClassOrWrapper']:
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"""
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Like the python standard lib dataclasses but with type validation.
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The result is either a pydantic dataclass that will validate input data
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or a wrapper that will trigger validation around a stdlib dataclass
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to avoid modifying it directly
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"""
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the_config = get_config(config)
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def wrap(cls: Type[Any]) -> 'DataclassClassOrWrapper':
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import dataclasses
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if is_builtin_dataclass(cls):
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dc_cls_doc = ''
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dc_cls = DataclassProxy(cls)
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default_validate_on_init = False
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else:
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dc_cls_doc = cls.__doc__ or '' # needs to be done before generating dataclass
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if sys.version_info >= (3, 10):
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dc_cls = dataclasses.dataclass(
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cls,
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init=init,
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repr=repr,
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eq=eq,
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order=order,
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unsafe_hash=unsafe_hash,
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frozen=frozen,
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kw_only=kw_only,
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)
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else:
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dc_cls = dataclasses.dataclass( # type: ignore
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cls, init=init, repr=repr, eq=eq, order=order, unsafe_hash=unsafe_hash, frozen=frozen
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)
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default_validate_on_init = True
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should_validate_on_init = default_validate_on_init if validate_on_init is None else validate_on_init
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_add_pydantic_validation_attributes(cls, the_config, should_validate_on_init, dc_cls_doc)
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dc_cls.__pydantic_model__.__try_update_forward_refs__(**{cls.__name__: cls})
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return dc_cls
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if _cls is None:
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return wrap
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return wrap(_cls)
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@contextmanager
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def set_validation(cls: Type['DataclassT'], value: bool) -> Generator[Type['DataclassT'], None, None]:
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original_run_validation = cls.__pydantic_run_validation__
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try:
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cls.__pydantic_run_validation__ = value
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yield cls
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finally:
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cls.__pydantic_run_validation__ = original_run_validation
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class DataclassProxy:
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__slots__ = '__dataclass__'
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def __init__(self, dc_cls: Type['Dataclass']) -> None:
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object.__setattr__(self, '__dataclass__', dc_cls)
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def __call__(self, *args: Any, **kwargs: Any) -> Any:
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with set_validation(self.__dataclass__, True):
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return self.__dataclass__(*args, **kwargs)
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def __getattr__(self, name: str) -> Any:
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return getattr(self.__dataclass__, name)
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def __instancecheck__(self, instance: Any) -> bool:
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return isinstance(instance, self.__dataclass__)
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def _add_pydantic_validation_attributes( # noqa: C901 (ignore complexity)
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dc_cls: Type['Dataclass'],
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config: Type[BaseConfig],
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validate_on_init: bool,
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dc_cls_doc: str,
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) -> None:
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"""
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We need to replace the right method. If no `__post_init__` has been set in the stdlib dataclass
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it won't even exist (code is generated on the fly by `dataclasses`)
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By default, we run validation after `__init__` or `__post_init__` if defined
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"""
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init = dc_cls.__init__
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@wraps(init)
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def handle_extra_init(self: 'Dataclass', *args: Any, **kwargs: Any) -> None:
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if config.extra == Extra.ignore:
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init(self, *args, **{k: v for k, v in kwargs.items() if k in self.__dataclass_fields__})
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elif config.extra == Extra.allow:
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for k, v in kwargs.items():
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self.__dict__.setdefault(k, v)
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init(self, *args, **{k: v for k, v in kwargs.items() if k in self.__dataclass_fields__})
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else:
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init(self, *args, **kwargs)
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if hasattr(dc_cls, '__post_init__'):
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post_init = dc_cls.__post_init__
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@wraps(post_init)
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def new_post_init(self: 'Dataclass', *args: Any, **kwargs: Any) -> None:
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if config.post_init_call == 'before_validation':
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post_init(self, *args, **kwargs)
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if self.__class__.__pydantic_run_validation__:
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self.__pydantic_validate_values__()
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if hasattr(self, '__post_init_post_parse__'):
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self.__post_init_post_parse__(*args, **kwargs)
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if config.post_init_call == 'after_validation':
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post_init(self, *args, **kwargs)
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setattr(dc_cls, '__init__', handle_extra_init)
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setattr(dc_cls, '__post_init__', new_post_init)
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else:
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@wraps(init)
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def new_init(self: 'Dataclass', *args: Any, **kwargs: Any) -> None:
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handle_extra_init(self, *args, **kwargs)
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if self.__class__.__pydantic_run_validation__:
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self.__pydantic_validate_values__()
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if hasattr(self, '__post_init_post_parse__'):
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# We need to find again the initvars. To do that we use `__dataclass_fields__` instead of
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# public method `dataclasses.fields`
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import dataclasses
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# get all initvars and their default values
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initvars_and_values: Dict[str, Any] = {}
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for i, f in enumerate(self.__class__.__dataclass_fields__.values()):
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if f._field_type is dataclasses._FIELD_INITVAR: # type: ignore[attr-defined]
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try:
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# set arg value by default
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initvars_and_values[f.name] = args[i]
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except IndexError:
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initvars_and_values[f.name] = kwargs.get(f.name, f.default)
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self.__post_init_post_parse__(**initvars_and_values)
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setattr(dc_cls, '__init__', new_init)
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setattr(dc_cls, '__pydantic_run_validation__', ClassAttribute('__pydantic_run_validation__', validate_on_init))
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setattr(dc_cls, '__pydantic_initialised__', False)
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setattr(dc_cls, '__pydantic_model__', create_pydantic_model_from_dataclass(dc_cls, config, dc_cls_doc))
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setattr(dc_cls, '__pydantic_validate_values__', _dataclass_validate_values)
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setattr(dc_cls, '__validate__', classmethod(_validate_dataclass))
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setattr(dc_cls, '__get_validators__', classmethod(_get_validators))
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if dc_cls.__pydantic_model__.__config__.validate_assignment and not dc_cls.__dataclass_params__.frozen:
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setattr(dc_cls, '__setattr__', _dataclass_validate_assignment_setattr)
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def _get_validators(cls: 'DataclassClassOrWrapper') -> 'CallableGenerator':
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yield cls.__validate__
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def _validate_dataclass(cls: Type['DataclassT'], v: Any) -> 'DataclassT':
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with set_validation(cls, True):
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if isinstance(v, cls):
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v.__pydantic_validate_values__()
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return v
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elif isinstance(v, (list, tuple)):
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return cls(*v)
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elif isinstance(v, dict):
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return cls(**v)
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else:
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raise DataclassTypeError(class_name=cls.__name__)
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def create_pydantic_model_from_dataclass(
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dc_cls: Type['Dataclass'],
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config: Type[Any] = BaseConfig,
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dc_cls_doc: Optional[str] = None,
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) -> Type['BaseModel']:
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import dataclasses
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field_definitions: Dict[str, Any] = {}
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for field in dataclasses.fields(dc_cls):
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default: Any = Undefined
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default_factory: Optional['NoArgAnyCallable'] = None
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field_info: FieldInfo
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if field.default is not dataclasses.MISSING:
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default = field.default
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elif field.default_factory is not dataclasses.MISSING:
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default_factory = field.default_factory
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else:
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default = Required
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if isinstance(default, FieldInfo):
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field_info = default
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dc_cls.__pydantic_has_field_info_default__ = True
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else:
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field_info = Field(default=default, default_factory=default_factory, **field.metadata)
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field_definitions[field.name] = (field.type, field_info)
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validators = gather_all_validators(dc_cls)
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model: Type['BaseModel'] = create_model(
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dc_cls.__name__,
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__config__=config,
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__module__=dc_cls.__module__,
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__validators__=validators,
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__cls_kwargs__={'__resolve_forward_refs__': False},
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**field_definitions,
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)
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model.__doc__ = dc_cls_doc if dc_cls_doc is not None else dc_cls.__doc__ or ''
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return model
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def _dataclass_validate_values(self: 'Dataclass') -> None:
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# validation errors can occur if this function is called twice on an already initialised dataclass.
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# for example if Extra.forbid is enabled, it would consider __pydantic_initialised__ an invalid extra property
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if getattr(self, '__pydantic_initialised__'):
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return
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if getattr(self, '__pydantic_has_field_info_default__', False):
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# We need to remove `FieldInfo` values since they are not valid as input
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# It's ok to do that because they are obviously the default values!
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input_data = {k: v for k, v in self.__dict__.items() if not isinstance(v, FieldInfo)}
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else:
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input_data = self.__dict__
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d, _, validation_error = validate_model(self.__pydantic_model__, input_data, cls=self.__class__)
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if validation_error:
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raise validation_error
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self.__dict__.update(d)
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object.__setattr__(self, '__pydantic_initialised__', True)
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def _dataclass_validate_assignment_setattr(self: 'Dataclass', name: str, value: Any) -> None:
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if self.__pydantic_initialised__:
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d = dict(self.__dict__)
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d.pop(name, None)
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known_field = self.__pydantic_model__.__fields__.get(name, None)
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if known_field:
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value, error_ = known_field.validate(value, d, loc=name, cls=self.__class__)
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if error_:
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raise ValidationError([error_], self.__class__)
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object.__setattr__(self, name, value)
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def is_builtin_dataclass(_cls: Type[Any]) -> bool:
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"""
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Whether a class is a stdlib dataclass
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|
(useful to discriminated a pydantic dataclass that is actually a wrapper around a stdlib dataclass)
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we check that
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- `_cls` is a dataclass
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- `_cls` is not a processed pydantic dataclass (with a basemodel attached)
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- `_cls` is not a pydantic dataclass inheriting directly from a stdlib dataclass
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e.g.
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```
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@dataclasses.dataclass
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|
class A:
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x: int
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@pydantic.dataclasses.dataclass
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|
class B(A):
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y: int
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|
```
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|
In this case, when we first check `B`, we make an extra check and look at the annotations ('y'),
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|
which won't be a superset of all the dataclass fields (only the stdlib fields i.e. 'x')
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"""
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|
import dataclasses
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|
return (
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dataclasses.is_dataclass(_cls)
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|
and not hasattr(_cls, '__pydantic_model__')
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and set(_cls.__dataclass_fields__).issuperset(set(getattr(_cls, '__annotations__', {})))
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)
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|
|
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|
def make_dataclass_validator(dc_cls: Type['Dataclass'], config: Type[BaseConfig]) -> 'CallableGenerator':
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|
"""
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|
Create a pydantic.dataclass from a builtin dataclass to add type validation
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|
and yield the validators
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|
It retrieves the parameters of the dataclass and forwards them to the newly created dataclass
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|
"""
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|
yield from _get_validators(dataclass(dc_cls, config=config, validate_on_init=False))
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