diff options
Diffstat (limited to '.venv/lib/python3.12/site-packages/pydantic/v1/dataclasses.py')
| -rw-r--r-- | .venv/lib/python3.12/site-packages/pydantic/v1/dataclasses.py | 500 |
1 files changed, 500 insertions, 0 deletions
diff --git a/.venv/lib/python3.12/site-packages/pydantic/v1/dataclasses.py b/.venv/lib/python3.12/site-packages/pydantic/v1/dataclasses.py new file mode 100644 index 00000000..bd167029 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/pydantic/v1/dataclasses.py @@ -0,0 +1,500 @@ +""" +The main purpose is to enhance stdlib dataclasses by adding validation +A pydantic dataclass can be generated from scratch or from a stdlib one. + +Behind the scene, a pydantic dataclass is just like a regular one on which we attach +a `BaseModel` and magic methods to trigger the validation of the data. +`__init__` and `__post_init__` are hence overridden and have extra logic to be +able to validate input data. + +When a pydantic dataclass is generated from scratch, it's just a plain dataclass +with validation triggered at initialization + +The tricky part if for stdlib dataclasses that are converted after into pydantic ones e.g. + +```py +@dataclasses.dataclass +class M: + x: int + +ValidatedM = pydantic.dataclasses.dataclass(M) +``` + +We indeed still want to support equality, hashing, repr, ... as if it was the stdlib one! + +```py +assert isinstance(ValidatedM(x=1), M) +assert ValidatedM(x=1) == M(x=1) +``` + +This means we **don't want to create a new dataclass that inherits from it** +The trick is to create a wrapper around `M` that will act as a proxy to trigger +validation without altering default `M` behaviour. +""" +import copy +import dataclasses +import sys +from contextlib import contextmanager +from functools import wraps + +try: + from functools import cached_property +except ImportError: + # cached_property available only for python3.8+ + pass + +from typing import TYPE_CHECKING, Any, Callable, ClassVar, Dict, Generator, Optional, Type, TypeVar, Union, overload + +from typing_extensions import dataclass_transform + +from pydantic.v1.class_validators import gather_all_validators +from pydantic.v1.config import BaseConfig, ConfigDict, Extra, get_config +from pydantic.v1.error_wrappers import ValidationError +from pydantic.v1.errors import DataclassTypeError +from pydantic.v1.fields import Field, FieldInfo, Required, Undefined +from pydantic.v1.main import create_model, validate_model +from pydantic.v1.utils import ClassAttribute + +if TYPE_CHECKING: + from pydantic.v1.main import BaseModel + from pydantic.v1.typing import CallableGenerator, NoArgAnyCallable + + DataclassT = TypeVar('DataclassT', bound='Dataclass') + + DataclassClassOrWrapper = Union[Type['Dataclass'], 'DataclassProxy'] + + class Dataclass: + # stdlib attributes + __dataclass_fields__: ClassVar[Dict[str, Any]] + __dataclass_params__: ClassVar[Any] # in reality `dataclasses._DataclassParams` + __post_init__: ClassVar[Callable[..., None]] + + # Added by pydantic + __pydantic_run_validation__: ClassVar[bool] + __post_init_post_parse__: ClassVar[Callable[..., None]] + __pydantic_initialised__: ClassVar[bool] + __pydantic_model__: ClassVar[Type[BaseModel]] + __pydantic_validate_values__: ClassVar[Callable[['Dataclass'], None]] + __pydantic_has_field_info_default__: ClassVar[bool] # whether a `pydantic.Field` is used as default value + + def __init__(self, *args: object, **kwargs: object) -> None: + pass + + @classmethod + def __get_validators__(cls: Type['Dataclass']) -> 'CallableGenerator': + pass + + @classmethod + def __validate__(cls: Type['DataclassT'], v: Any) -> 'DataclassT': + pass + + +__all__ = [ + 'dataclass', + 'set_validation', + 'create_pydantic_model_from_dataclass', + 'is_builtin_dataclass', + 'make_dataclass_validator', +] + +_T = TypeVar('_T') + +if sys.version_info >= (3, 10): + + @dataclass_transform(field_specifiers=(dataclasses.field, Field)) + @overload + def dataclass( + *, + init: bool = True, + repr: bool = True, + eq: bool = True, + order: bool = False, + unsafe_hash: bool = False, + frozen: bool = False, + config: Union[ConfigDict, Type[object], None] = None, + validate_on_init: Optional[bool] = None, + use_proxy: Optional[bool] = None, + kw_only: bool = ..., + ) -> Callable[[Type[_T]], 'DataclassClassOrWrapper']: + ... + + @dataclass_transform(field_specifiers=(dataclasses.field, Field)) + @overload + def dataclass( + _cls: Type[_T], + *, + init: bool = True, + repr: bool = True, + eq: bool = True, + order: bool = False, + unsafe_hash: bool = False, + frozen: bool = False, + config: Union[ConfigDict, Type[object], None] = None, + validate_on_init: Optional[bool] = None, + use_proxy: Optional[bool] = None, + kw_only: bool = ..., + ) -> 'DataclassClassOrWrapper': + ... + +else: + + @dataclass_transform(field_specifiers=(dataclasses.field, Field)) + @overload + def dataclass( + *, + init: bool = True, + repr: bool = True, + eq: bool = True, + order: bool = False, + unsafe_hash: bool = False, + frozen: bool = False, + config: Union[ConfigDict, Type[object], None] = None, + validate_on_init: Optional[bool] = None, + use_proxy: Optional[bool] = None, + ) -> Callable[[Type[_T]], 'DataclassClassOrWrapper']: + ... + + @dataclass_transform(field_specifiers=(dataclasses.field, Field)) + @overload + def dataclass( + _cls: Type[_T], + *, + init: bool = True, + repr: bool = True, + eq: bool = True, + order: bool = False, + unsafe_hash: bool = False, + frozen: bool = False, + config: Union[ConfigDict, Type[object], None] = None, + validate_on_init: Optional[bool] = None, + use_proxy: Optional[bool] = None, + ) -> 'DataclassClassOrWrapper': + ... + + +@dataclass_transform(field_specifiers=(dataclasses.field, Field)) +def dataclass( + _cls: Optional[Type[_T]] = None, + *, + init: bool = True, + repr: bool = True, + eq: bool = True, + order: bool = False, + unsafe_hash: bool = False, + frozen: bool = False, + config: Union[ConfigDict, Type[object], None] = None, + validate_on_init: Optional[bool] = None, + use_proxy: Optional[bool] = None, + kw_only: bool = False, +) -> Union[Callable[[Type[_T]], 'DataclassClassOrWrapper'], 'DataclassClassOrWrapper']: + """ + Like the python standard lib dataclasses but with type validation. + The result is either a pydantic dataclass that will validate input data + or a wrapper that will trigger validation around a stdlib dataclass + to avoid modifying it directly + """ + the_config = get_config(config) + + def wrap(cls: Type[Any]) -> 'DataclassClassOrWrapper': + should_use_proxy = ( + use_proxy + if use_proxy is not None + else ( + is_builtin_dataclass(cls) + and (cls.__bases__[0] is object or set(dir(cls)) == set(dir(cls.__bases__[0]))) + ) + ) + if should_use_proxy: + dc_cls_doc = '' + dc_cls = DataclassProxy(cls) + default_validate_on_init = False + else: + dc_cls_doc = cls.__doc__ or '' # needs to be done before generating dataclass + if sys.version_info >= (3, 10): + dc_cls = dataclasses.dataclass( + cls, + init=init, + repr=repr, + eq=eq, + order=order, + unsafe_hash=unsafe_hash, + frozen=frozen, + kw_only=kw_only, + ) + else: + dc_cls = dataclasses.dataclass( # type: ignore + cls, init=init, repr=repr, eq=eq, order=order, unsafe_hash=unsafe_hash, frozen=frozen + ) + default_validate_on_init = True + + should_validate_on_init = default_validate_on_init if validate_on_init is None else validate_on_init + _add_pydantic_validation_attributes(cls, the_config, should_validate_on_init, dc_cls_doc) + dc_cls.__pydantic_model__.__try_update_forward_refs__(**{cls.__name__: cls}) + return dc_cls + + if _cls is None: + return wrap + + return wrap(_cls) + + +@contextmanager +def set_validation(cls: Type['DataclassT'], value: bool) -> Generator[Type['DataclassT'], None, None]: + original_run_validation = cls.__pydantic_run_validation__ + try: + cls.__pydantic_run_validation__ = value + yield cls + finally: + cls.__pydantic_run_validation__ = original_run_validation + + +class DataclassProxy: + __slots__ = '__dataclass__' + + def __init__(self, dc_cls: Type['Dataclass']) -> None: + object.__setattr__(self, '__dataclass__', dc_cls) + + def __call__(self, *args: Any, **kwargs: Any) -> Any: + with set_validation(self.__dataclass__, True): + return self.__dataclass__(*args, **kwargs) + + def __getattr__(self, name: str) -> Any: + return getattr(self.__dataclass__, name) + + def __setattr__(self, __name: str, __value: Any) -> None: + return setattr(self.__dataclass__, __name, __value) + + def __instancecheck__(self, instance: Any) -> bool: + return isinstance(instance, self.__dataclass__) + + def __copy__(self) -> 'DataclassProxy': + return DataclassProxy(copy.copy(self.__dataclass__)) + + def __deepcopy__(self, memo: Any) -> 'DataclassProxy': + return DataclassProxy(copy.deepcopy(self.__dataclass__, memo)) + + +def _add_pydantic_validation_attributes( # noqa: C901 (ignore complexity) + dc_cls: Type['Dataclass'], + config: Type[BaseConfig], + validate_on_init: bool, + dc_cls_doc: str, +) -> None: + """ + We need to replace the right method. If no `__post_init__` has been set in the stdlib dataclass + it won't even exist (code is generated on the fly by `dataclasses`) + By default, we run validation after `__init__` or `__post_init__` if defined + """ + init = dc_cls.__init__ + + @wraps(init) + def handle_extra_init(self: 'Dataclass', *args: Any, **kwargs: Any) -> None: + if config.extra == Extra.ignore: + init(self, *args, **{k: v for k, v in kwargs.items() if k in self.__dataclass_fields__}) + + elif config.extra == Extra.allow: + for k, v in kwargs.items(): + self.__dict__.setdefault(k, v) + init(self, *args, **{k: v for k, v in kwargs.items() if k in self.__dataclass_fields__}) + + else: + init(self, *args, **kwargs) + + if hasattr(dc_cls, '__post_init__'): + try: + post_init = dc_cls.__post_init__.__wrapped__ # type: ignore[attr-defined] + except AttributeError: + post_init = dc_cls.__post_init__ + + @wraps(post_init) + def new_post_init(self: 'Dataclass', *args: Any, **kwargs: Any) -> None: + if config.post_init_call == 'before_validation': + post_init(self, *args, **kwargs) + + if self.__class__.__pydantic_run_validation__: + self.__pydantic_validate_values__() + if hasattr(self, '__post_init_post_parse__'): + self.__post_init_post_parse__(*args, **kwargs) + + if config.post_init_call == 'after_validation': + post_init(self, *args, **kwargs) + + setattr(dc_cls, '__init__', handle_extra_init) + setattr(dc_cls, '__post_init__', new_post_init) + + else: + + @wraps(init) + def new_init(self: 'Dataclass', *args: Any, **kwargs: Any) -> None: + handle_extra_init(self, *args, **kwargs) + + if self.__class__.__pydantic_run_validation__: + self.__pydantic_validate_values__() + + if hasattr(self, '__post_init_post_parse__'): + # We need to find again the initvars. To do that we use `__dataclass_fields__` instead of + # public method `dataclasses.fields` + + # get all initvars and their default values + initvars_and_values: Dict[str, Any] = {} + for i, f in enumerate(self.__class__.__dataclass_fields__.values()): + if f._field_type is dataclasses._FIELD_INITVAR: # type: ignore[attr-defined] + try: + # set arg value by default + initvars_and_values[f.name] = args[i] + except IndexError: + initvars_and_values[f.name] = kwargs.get(f.name, f.default) + + self.__post_init_post_parse__(**initvars_and_values) + + setattr(dc_cls, '__init__', new_init) + + setattr(dc_cls, '__pydantic_run_validation__', ClassAttribute('__pydantic_run_validation__', validate_on_init)) + setattr(dc_cls, '__pydantic_initialised__', False) + setattr(dc_cls, '__pydantic_model__', create_pydantic_model_from_dataclass(dc_cls, config, dc_cls_doc)) + setattr(dc_cls, '__pydantic_validate_values__', _dataclass_validate_values) + setattr(dc_cls, '__validate__', classmethod(_validate_dataclass)) + setattr(dc_cls, '__get_validators__', classmethod(_get_validators)) + + if dc_cls.__pydantic_model__.__config__.validate_assignment and not dc_cls.__dataclass_params__.frozen: + setattr(dc_cls, '__setattr__', _dataclass_validate_assignment_setattr) + + +def _get_validators(cls: 'DataclassClassOrWrapper') -> 'CallableGenerator': + yield cls.__validate__ + + +def _validate_dataclass(cls: Type['DataclassT'], v: Any) -> 'DataclassT': + with set_validation(cls, True): + if isinstance(v, cls): + v.__pydantic_validate_values__() + return v + elif isinstance(v, (list, tuple)): + return cls(*v) + elif isinstance(v, dict): + return cls(**v) + else: + raise DataclassTypeError(class_name=cls.__name__) + + +def create_pydantic_model_from_dataclass( + dc_cls: Type['Dataclass'], + config: Type[Any] = BaseConfig, + dc_cls_doc: Optional[str] = None, +) -> Type['BaseModel']: + field_definitions: Dict[str, Any] = {} + for field in dataclasses.fields(dc_cls): + default: Any = Undefined + default_factory: Optional['NoArgAnyCallable'] = None + field_info: FieldInfo + + if field.default is not dataclasses.MISSING: + default = field.default + elif field.default_factory is not dataclasses.MISSING: + default_factory = field.default_factory + else: + default = Required + + if isinstance(default, FieldInfo): + field_info = default + dc_cls.__pydantic_has_field_info_default__ = True + else: + field_info = Field(default=default, default_factory=default_factory, **field.metadata) + + field_definitions[field.name] = (field.type, field_info) + + validators = gather_all_validators(dc_cls) + model: Type['BaseModel'] = create_model( + dc_cls.__name__, + __config__=config, + __module__=dc_cls.__module__, + __validators__=validators, + __cls_kwargs__={'__resolve_forward_refs__': False}, + **field_definitions, + ) + model.__doc__ = dc_cls_doc if dc_cls_doc is not None else dc_cls.__doc__ or '' + return model + + +if sys.version_info >= (3, 8): + + def _is_field_cached_property(obj: 'Dataclass', k: str) -> bool: + return isinstance(getattr(type(obj), k, None), cached_property) + +else: + + def _is_field_cached_property(obj: 'Dataclass', k: str) -> bool: + return False + + +def _dataclass_validate_values(self: 'Dataclass') -> None: + # validation errors can occur if this function is called twice on an already initialised dataclass. + # for example if Extra.forbid is enabled, it would consider __pydantic_initialised__ an invalid extra property + if getattr(self, '__pydantic_initialised__'): + return + if getattr(self, '__pydantic_has_field_info_default__', False): + # We need to remove `FieldInfo` values since they are not valid as input + # It's ok to do that because they are obviously the default values! + input_data = { + k: v + for k, v in self.__dict__.items() + if not (isinstance(v, FieldInfo) or _is_field_cached_property(self, k)) + } + else: + input_data = {k: v for k, v in self.__dict__.items() if not _is_field_cached_property(self, k)} + d, _, validation_error = validate_model(self.__pydantic_model__, input_data, cls=self.__class__) + if validation_error: + raise validation_error + self.__dict__.update(d) + object.__setattr__(self, '__pydantic_initialised__', True) + + +def _dataclass_validate_assignment_setattr(self: 'Dataclass', name: str, value: Any) -> None: + if self.__pydantic_initialised__: + d = dict(self.__dict__) + d.pop(name, None) + known_field = self.__pydantic_model__.__fields__.get(name, None) + if known_field: + value, error_ = known_field.validate(value, d, loc=name, cls=self.__class__) + if error_: + raise ValidationError([error_], self.__class__) + + object.__setattr__(self, name, value) + + +def is_builtin_dataclass(_cls: Type[Any]) -> bool: + """ + Whether a class is a stdlib dataclass + (useful to discriminated a pydantic dataclass that is actually a wrapper around a stdlib dataclass) + + we check that + - `_cls` is a dataclass + - `_cls` is not a processed pydantic dataclass (with a basemodel attached) + - `_cls` is not a pydantic dataclass inheriting directly from a stdlib dataclass + e.g. + ``` + @dataclasses.dataclass + class A: + x: int + + @pydantic.dataclasses.dataclass + class B(A): + y: int + ``` + In this case, when we first check `B`, we make an extra check and look at the annotations ('y'), + which won't be a superset of all the dataclass fields (only the stdlib fields i.e. 'x') + """ + return ( + dataclasses.is_dataclass(_cls) + and not hasattr(_cls, '__pydantic_model__') + and set(_cls.__dataclass_fields__).issuperset(set(getattr(_cls, '__annotations__', {}))) + ) + + +def make_dataclass_validator(dc_cls: Type['Dataclass'], config: Type[BaseConfig]) -> 'CallableGenerator': + """ + Create a pydantic.dataclass from a builtin dataclass to add type validation + and yield the validators + It retrieves the parameters of the dataclass and forwards them to the newly created dataclass + """ + yield from _get_validators(dataclass(dc_cls, config=config, use_proxy=True)) |