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fields.py
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import copy import re from collections import Counter as CollectionCounter, defaultdict, deque from collections.abc import Callable, Hashable as CollectionsHashable, Iterable as CollectionsIterable from typing import ( TYPE_CHECKING, Any, Counter, DefaultDict, Deque, Dict, ForwardRef, FrozenSet, Generator, Iterable, Iterator, List, Mapping, Optional, Pattern, Sequence, Set, Tuple, Type, TypeVar, Union, ) from typing_extensions import Annotated, Final from pydantic.v1 import errors as errors_ from pydantic.v1.class_validators import Validator, make_generic_validator, prep_validators from pydantic.v1.error_wrappers import ErrorWrapper from pydantic.v1.errors import ConfigError, InvalidDiscriminator, MissingDiscriminator, NoneIsNotAllowedError from pydantic.v1.types import Json, JsonWrapper from pydantic.v1.typing import ( NoArgAnyCallable, convert_generics, display_as_type, get_args, get_origin, is_finalvar, is_literal_type, is_new_type, is_none_type, is_typeddict, is_typeddict_special, is_union, new_type_supertype, ) from pydantic.v1.utils import ( PyObjectStr, Representation, ValueItems, get_discriminator_alias_and_values, get_unique_discriminator_alias, lenient_isinstance, lenient_issubclass, sequence_like, smart_deepcopy, ) from pydantic.v1.validators import constant_validator, dict_validator, find_validators, validate_json Required: Any = Ellipsis T = TypeVar('T') class UndefinedType: def __repr__(self) -> str: return 'PydanticUndefined' def __copy__(self: T) -> T: return self def __reduce__(self) -> str: return 'Undefined' def __deepcopy__(self: T, _: Any) -> T: return self Undefined = UndefinedType() if TYPE_CHECKING: from pydantic.v1.class_validators import ValidatorsList from pydantic.v1.config import BaseConfig from pydantic.v1.error_wrappers import ErrorList from pydantic.v1.types import ModelOrDc from pydantic.v1.typing import AbstractSetIntStr, MappingIntStrAny, ReprArgs ValidateReturn = Tuple[Optional[Any], Optional[ErrorList]] LocStr = Union[Tuple[Union[int, str], ...], str] BoolUndefined = Union[bool, UndefinedType] class FieldInfo(Representation): """ Captures extra information about a field. """ __slots__ = ( 'default', 'default_factory', 'alias', 'alias_priority', 'title', 'description', 'exclude', 'include', 'const', 'gt', 'ge', 'lt', 'le', 'multiple_of', 'allow_inf_nan', 'max_digits', 'decimal_places', 'min_items', 'max_items', 'unique_items', 'min_length', 'max_length', 'allow_mutation', 'repr', 'regex', 'discriminator', 'extra', ) # field constraints with the default value, it's also used in update_from_config below __field_constraints__ = { 'min_length': None, 'max_length': None, 'regex': None, 'gt': None, 'lt': None, 'ge': None, 'le': None, 'multiple_of': None, 'allow_inf_nan': None, 'max_digits': None, 'decimal_places': None, 'min_items': None, 'max_items': None, 'unique_items': None, 'allow_mutation': True, } def __init__(self, default: Any = Undefined, **kwargs: Any) -> None: self.default = default self.default_factory = kwargs.pop('default_factory', None) self.alias = kwargs.pop('alias', None) self.alias_priority = kwargs.pop('alias_priority', 2 if self.alias is not None else None) self.title = kwargs.pop('title', None) self.description = kwargs.pop('description', None) self.exclude = kwargs.pop('exclude', None) self.include = kwargs.pop('include', None) self.const = kwargs.pop('const', None) self.gt = kwargs.pop('gt', None) self.ge = kwargs.pop('ge', None) self.lt = kwargs.pop('lt', None) self.le = kwargs.pop('le', None) self.multiple_of = kwargs.pop('multiple_of', None) self.allow_inf_nan = kwargs.pop('allow_inf_nan', None) self.max_digits = kwargs.pop('max_digits', None) self.decimal_places = kwargs.pop('decimal_places', None) self.min_items = kwargs.pop('min_items', None) self.max_items = kwargs.pop('max_items', None) self.unique_items = kwargs.pop('unique_items', None) self.min_length = kwargs.pop('min_length', None) self.max_length = kwargs.pop('max_length', None) self.allow_mutation = kwargs.pop('allow_mutation', True) self.regex = kwargs.pop('regex', None) self.discriminator = kwargs.pop('discriminator', None) self.repr = kwargs.pop('repr', True) self.extra = kwargs def __repr_args__(self) -> 'ReprArgs': field_defaults_to_hide: Dict[str, Any] = { 'repr': True, **self.__field_constraints__, } attrs = ((s, getattr(self, s)) for s in self.__slots__) return [(a, v) for a, v in attrs if v != field_defaults_to_hide.get(a, None)] def get_constraints(self) -> Set[str]: """ Gets the constraints set on the field by comparing the constraint value with its default value :return: the constraints set on field_info """ return {attr for attr, default in self.__field_constraints__.items() if getattr(self, attr) != default} def update_from_config(self, from_config: Dict[str, Any]) -> None: """ Update this FieldInfo based on a dict from get_field_info, only fields which have not been set are dated. """ for attr_name, value in from_config.items(): try: current_value = getattr(self, attr_name) except AttributeError: # attr_name is not an attribute of FieldInfo, it should therefore be added to extra # (except if extra already has this value!) self.extra.setdefault(attr_name, value) else: if current_value is self.__field_constraints__.get(attr_name, None): setattr(self, attr_name, value) elif attr_name == 'exclude': self.exclude = ValueItems.merge(value, current_value) elif attr_name == 'include': self.include = ValueItems.merge(value, current_value, intersect=True) def _validate(self) -> None: if self.default is not Undefined and self.default_factory is not None: raise ValueError('cannot specify both default and default_factory') def Field( default: Any = Undefined, *, default_factory: Optional[NoArgAnyCallable] = None, alias: Optional[str] = None, title: Optional[str] = None, description: Optional[str] = None, exclude: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny', Any]] = None, include: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny', Any]] = None, const: Optional[bool] = None, gt: Optional[float] = None, ge: Optional[float] = None, lt: Optional[float] = None, le: Optional[float] = None, multiple_of: Optional[float] = None, allow_inf_nan: Optional[bool] = None, max_digits: Optional[int] = None, decimal_places: Optional[int] = None, min_items: Optional[int] = None, max_items: Optional[int] = None, unique_items: Optional[bool] = None, min_length: Optional[int] = None, max_length: Optional[int] = None, allow_mutation: bool = True, regex: Optional[str] = None, discriminator: Optional[str] = None, repr: bool = True, **extra: Any, ) -> Any: """ Used to provide extra information about a field, either for the model schema or complex validation. Some arguments apply only to number fields (``int``, ``float``, ``Decimal``) and some apply only to ``str``. :param default: since this is replacing the field’s default, its first argument is used to set the default, use ellipsis (``...``) to indicate the field is required :param default_factory: callable that will be called when a default value is needed for this field If both `default` and `default_factory` are set, an error is raised. :param alias: the public name of the field :param title: can be any string, used in the schema :param description: can be any string, used in the schema :param exclude: exclude this field while dumping. Takes same values as the ``include`` and ``exclude`` arguments on the ``.dict`` method. :param include: include this field while dumping. Takes same values as the ``include`` and ``exclude`` arguments on the ``.dict`` method. :param const: this field is required and *must* take it's default value :param gt: only applies to numbers, requires the field to be "greater than". The schema will have an ``exclusiveMinimum`` validation keyword :param ge: only applies to numbers, requires the field to be "greater than or equal to". The schema will have a ``minimum`` validation keyword :param lt: only applies to numbers, requires the field to be "less than". The schema will have an ``exclusiveMaximum`` validation keyword :param le: only applies to numbers, requires the field to be "less than or equal to". The schema will have a ``maximum`` validation keyword :param multiple_of: only applies to numbers, requires the field to be "a multiple of". The schema will have a ``multipleOf`` validation keyword :param allow_inf_nan: only applies to numbers, allows the field to be NaN or infinity (+inf or -inf), which is a valid Python float. Default True, set to False for compatibility with JSON. :param max_digits: only applies to Decimals, requires the field to have a maximum number of digits within the decimal. It does not include a zero before the decimal point or trailing decimal zeroes. :param decimal_places: only applies to Decimals, requires the field to have at most a number of decimal places allowed. It does not include trailing decimal zeroes. :param min_items: only applies to lists, requires the field to have a minimum number of elements. The schema will have a ``minItems`` validation keyword :param max_items: only applies to lists, requires the field to have a maximum number of elements. The schema will have a ``maxItems`` validation keyword :param unique_items: only applies to lists, requires the field not to have duplicated elements. The schema will have a ``uniqueItems`` validation keyword :param min_length: only applies to strings, requires the field to have a minimum length. The schema will have a ``minLength`` validation keyword :param max_length: only applies to strings, requires the field to have a maximum length. The schema will have a ``maxLength`` validation keyword :param allow_mutation: a boolean which defaults to True. When False, the field raises a TypeError if the field is assigned on an instance. The BaseModel Config must set validate_assignment to True :param regex: only applies to strings, requires the field match against a regular expression pattern string. The schema will have a ``pattern`` validation keyword :param discriminator: only useful with a (discriminated a.k.a. tagged) `Union` of sub models with a common field. The `discriminator` is the name of this common field to shorten validation and improve generated schema :param repr: show this field in the representation :param **extra: any additional keyword arguments will be added as is to the schema """ field_info = FieldInfo( default, default_factory=default_factory, alias=alias, title=title, description=description, exclude=exclude, include=include, const=const, gt=gt, ge=ge, lt=lt, le=le, multiple_of=multiple_of, allow_inf_nan=allow_inf_nan, max_digits=max_digits, decimal_places=decimal_places, min_items=min_items, max_items=max_items, unique_items=unique_items, min_length=min_length, max_length=max_length, allow_mutation=allow_mutation, regex=regex, discriminator=discriminator, repr=repr, **extra, ) field_info._validate() return field_info # used to be an enum but changed to int's for small performance improvement as less access overhead SHAPE_SINGLETON = 1 SHAPE_LIST = 2 SHAPE_SET = 3 SHAPE_MAPPING = 4 SHAPE_TUPLE = 5 SHAPE_TUPLE_ELLIPSIS = 6 SHAPE_SEQUENCE = 7 SHAPE_FROZENSET = 8 SHAPE_ITERABLE = 9 SHAPE_GENERIC = 10 SHAPE_DEQUE = 11 SHAPE_DICT = 12 SHAPE_DEFAULTDICT = 13 SHAPE_COUNTER = 14 SHAPE_NAME_LOOKUP = { SHAPE_LIST: 'List[{}]', SHAPE_SET: 'Set[{}]', SHAPE_TUPLE_ELLIPSIS: 'Tuple[{}, ...]', SHAPE_SEQUENCE: 'Sequence[{}]', SHAPE_FROZENSET: 'FrozenSet[{}]', SHAPE_ITERABLE: 'Iterable[{}]', SHAPE_DEQUE: 'Deque[{}]', SHAPE_DICT: 'Dict[{}]', SHAPE_DEFAULTDICT: 'DefaultDict[{}]', SHAPE_COUNTER: 'Counter[{}]', } MAPPING_LIKE_SHAPES: Set[int] = {SHAPE_DEFAULTDICT, SHAPE_DICT, SHAPE_MAPPING, SHAPE_COUNTER} class ModelField(Representation): __slots__ = ( 'type_', 'outer_type_', 'annotation', 'sub_fields', 'sub_fields_mapping', 'key_field', 'validators', 'pre_validators', 'post_validators', 'default', 'default_factory', 'required', 'final', 'model_config', 'name', 'alias', 'has_alias', 'field_info', 'discriminator_key', 'discriminator_alias', 'validate_always', 'allow_none', 'shape', 'class_validators', 'parse_json', ) def __init__( self, *, name: str, type_: Type[Any], class_validators: Optional[Dict[str, Validator]], model_config: Type['BaseConfig'], default: Any = None, default_factory: Optional[NoArgAnyCallable] = None, required: 'BoolUndefined' = Undefined, final: bool = False, alias: Optional[str] = None, field_info: Optional[FieldInfo] = None, ) -> None: self.name: str = name self.has_alias: bool = alias is not None self.alias: str = alias if alias is not None else name self.annotation = type_ self.type_: Any = convert_generics(type_) self.outer_type_: Any = type_ self.class_validators = class_validators or {} self.default: Any = default self.default_factory: Optional[NoArgAnyCallable] = default_factory self.required: 'BoolUndefined' = required self.final: bool = final self.model_config = model_config self.field_info: FieldInfo = field_info or FieldInfo(default) self.discriminator_key: Optional[str] = self.field_info.discriminator self.discriminator_alias: Optional[str] = self.discriminator_key self.allow_none: bool = False self.validate_always: bool = False self.sub_fields: Optional[List[ModelField]] = None self.sub_fields_mapping: Optional[Dict[str, 'ModelField']] = None # used for discriminated union self.key_field: Optional[ModelField] = None self.validators: 'ValidatorsList' = [] self.pre_validators: Optional['ValidatorsList'] = None self.post_validators: Optional['ValidatorsList'] = None self.parse_json: bool = False self.shape: int = SHAPE_SINGLETON self.model_config.prepare_field(self) self.prepare() def get_default(self) -> Any: return smart_deepcopy(self.default) if self.default_factory is None else self.default_factory() @staticmethod def _get_field_info( field_name: str, annotation: Any, value: Any, config: Type['BaseConfig'] ) -> Tuple[FieldInfo, Any]: """ Get a FieldInfo from a root typing.Annotated annotation, value, or config default. The FieldInfo may be set in typing.Annotated or the value, but not both. If neither contain a FieldInfo, a new one will be created using the config. :param field_name: name of the field for use in error messages :param annotation: a type hint such as `str` or `Annotated[str, Field(..., min_length=5)]` :param value: the field's assigned value :param config: the model's config object :return: the FieldInfo contained in the `annotation`, the value, or a new one from the config. """ field_info_from_config = config.get_field_info(field_name) field_info = None if get_origin(annotation) is Annotated: field_infos = [arg for arg in get_args(annotation)[1:] if isinstance(arg, FieldInfo)] if len(field_infos) > 1: raise ValueError(f'cannot specify multiple `Annotated` `Field`s for {field_name!r}') field_info = next(iter(field_infos), None) if field_info is not None: field_info = copy.copy(field_info) field_info.update_from_config(field_info_from_config) if field_info.default not in (Undefined, Required): raise ValueError(f'`Field` default cannot be set in `Annotated` for {field_name!r}') if value is not Undefined and value is not Required: # check also `Required` because of `validate_arguments` that sets `...` as default value field_info.default = value if isinstance(value, FieldInfo): if field_info is not None: raise ValueError(f'cannot specify `Annotated` and value `Field`s together for {field_name!r}') field_info = value field_info.update_from_config(field_info_from_config) elif field_info is None: field_info = FieldInfo(value, **field_info_from_config) value = None if field_info.default_factory is not None else field_info.default field_info._validate() return field_info, value @classmethod def infer( cls, *, name: str, value: Any, annotation: Any, class_validators: Optional[Dict[str, Validator]], config: Type['BaseConfig'], ) -> 'ModelField': from pydantic.v1.schema import get_annotation_from_field_info field_info, value = cls._get_field_info(name, annotation, value, config) required: 'BoolUndefined' = Undefined if value is Required: required = True value = None elif value is not Undefined: required = False annotation = get_annotation_from_field_info(annotation, field_info, name, config.validate_assignment) return cls( name=name, type_=annotation, alias=field_info.alias, class_validators=class_validators, default=value, default_factory=field_info.default_factory, required=required, model_config=config, field_info=field_info, ) def set_config(self, config: Type['BaseConfig']) -> None: self.model_config = config info_from_config = config.get_field_info(self.name) config.prepare_field(self) new_alias = info_from_config.get('alias') new_alias_priority = info_from_config.get('alias_priority') or 0 if new_alias and new_alias_priority >= (self.field_info.alias_priority or 0): self.field_info.alias = new_alias self.field_info.alias_priority = new_alias_priority self.alias = new_alias new_exclude = info_from_config.get('exclude') if new_exclude is not None: self.field_info.exclude = ValueItems.merge(self.field_info.exclude, new_exclude) new_include = info_from_config.get('include') if new_include is not None: self.field_info.include = ValueItems.merge(self.field_info.include, new_include, intersect=True) @property def alt_alias(self) -> bool: return self.name != self.alias def prepare(self) -> None: """ Prepare the field but inspecting self.default, self.type_ etc. Note: this method is **not** idempotent (because _type_analysis is not idempotent), e.g. calling it it multiple times may modify the field and configure it incorrectly. """ self._set_default_and_type() if self.type_.__class__ is ForwardRef or self.type_.__class__ is DeferredType: # self.type_ is currently a ForwardRef and there's nothing we can do now, # user will need to call model.update_forward_refs() return self._type_analysis() if self.required is Undefined: self.required = True if self.default is Undefined and self.default_factory is None: self.default = None self.populate_validators() def _set_default_and_type(self) -> None: """ Set the default value, infer the type if needed and check if `None` value is valid. """ if self.default_factory is not None: if self.type_ is Undefined: raise errors_.ConfigError( f'you need to set the type of field {self.name!r} when using `default_factory`' ) return default_value = self.get_default() if default_value is not None and self.type_ is Undefined: self.type_ = default_value.__class__ self.outer_type_ = self.type_ self.annotation = self.type_ if self.type_ is Undefined: raise errors_.ConfigError(f'unable to infer type for attribute "{self.name}"') if self.required is False and default_value is None: self.allow_none = True def _type_analysis(self) -> None: # noqa: C901 (ignore complexity) # typing interface is horrible, we have to do some ugly checks if lenient_issubclass(self.type_, JsonWrapper): self.type_ = self.type_.inner_type self.parse_json = True elif lenient_issubclass(self.type_, Json): self.type_ = Any self.parse_json = True elif isinstance(self.type_, TypeVar): if self.type_.__bound__: self.type_ = self.type_.__bound__ elif self.type_.__constraints__: self.type_ = Union[self.type_.__constraints__] else: self.type_ = Any elif is_new_type(self.type_): self.type_ = new_type_supertype(self.type_) if self.type_ is Any or self.type_ is object: if self.required is Undefined: self.required = False self.allow_none = True return elif self.type_ is Pattern or self.type_ is re.Pattern: # python 3.7 only, Pattern is a typing object but without sub fields return elif is_literal_type(self.type_): return elif is_typeddict(self.type_): return if is_finalvar(self.type_): self.final = True if self.type_ is Final: self.type_ = Any else: self.type_ = get_args(self.type_)[0] self._type_analysis() return origin = get_origin(self.type_) if origin is Annotated or is_typeddict_special(origin): self.type_ = get_args(self.type_)[0] self._type_analysis() return if self.discriminator_key is not None and not is_union(origin): raise TypeError('`discriminator` can only be used with `Union` type with more than one variant') # add extra check for `collections.abc.Hashable` for python 3.10+ where origin is not `None` if origin is None or origin is CollectionsHashable: # field is not "typing" object eg. Union, Dict, List etc. # allow None for virtual superclasses of NoneType, e.g. Hashable if isinstance(self.type_, type) and isinstance(None, self.type_): self.allow_none = True return elif origin is Callable: return elif is_union(origin): types_ = [] for type_ in get_args(self.type_): if is_none_type(type_) or type_ is Any or type_ is object: if self.required is Undefined: self.required = False self.allow_none = True if is_none_type(type_): continue types_.append(type_) if len(types_) == 1: # Optional[] self.type_ = types_[0] # this is the one case where the "outer type" isn't just the original type self.outer_type_ = self.type_ # re-run to correctly interpret the new self.type_ self._type_analysis() else: self.sub_fields = [self._create_sub_type(t, f'{self.name}_{display_as_type(t)}') for t in types_] if self.discriminator_key is not None: self.prepare_discriminated_union_sub_fields() return elif issubclass(origin, Tuple): # type: ignore # origin == Tuple without item type args = get_args(self.type_) if not args: # plain tuple self.type_ = Any self.shape = SHAPE_TUPLE_ELLIPSIS elif len(args) == 2 and args[1] is Ellipsis: # e.g. Tuple[int, ...] self.type_ = args[0] self.shape = SHAPE_TUPLE_ELLIPSIS self.sub_fields = [self._create_sub_type(args[0], f'{self.name}_0')] elif args == ((),): # Tuple[()] means empty tuple self.shape = SHAPE_TUPLE self.type_ = Any self.sub_fields = [] else: self.shape = SHAPE_TUPLE self.sub_fields = [self._create_sub_type(t, f'{self.name}_{i}') for i, t in enumerate(args)] return elif issubclass(origin, List): # Create self validators get_validators = getattr(self.type_, '__get_validators__', None) if get_validators: self.class_validators.update( {f'list_{i}': Validator(validator, pre=True) for i, validator in enumerate(get_validators())} ) self.type_ = get_args(self.type_)[0] self.shape = SHAPE_LIST elif issubclass(origin, Set): # Create self validators get_validators = getattr(self.type_, '__get_validators__', None) if get_validators: self.class_validators.update( {f'set_{i}': Validator(validator, pre=True) for i, validator in enumerate(get_validators())} ) self.type_ = get_args(self.type_)[0] self.shape = SHAPE_SET elif issubclass(origin, FrozenSet): # Create self validators get_validators = getattr(self.type_, '__get_validators__', None) if get_validators: self.class_validators.update( {f'frozenset_{i}': Validator(validator, pre=True) for i, validator in enumerate(get_validators())} ) self.type_ = get_args(self.type_)[0] self.shape = SHAPE_FROZENSET elif issubclass(origin, Deque): self.type_ = get_args(self.type_)[0] self.shape = SHAPE_DEQUE elif issubclass(origin, Sequence): self.type_ = get_args(self.type_)[0] self.shape = SHAPE_SEQUENCE # priority to most common mapping: dict elif origin is dict or origin is Dict: self.key_field = self._create_sub_type(get_args(self.type_)[0], 'key_' + self.name, for_keys=True) self.type_ = get_args(self.type_)[1] self.shape = SHAPE_DICT elif issubclass(origin, DefaultDict): self.key_field = self._create_sub_type(get_args(self.type_)[0], 'key_' + self.name, for_keys=True) self.type_ = get_args(self.type_)[1] self.shape = SHAPE_DEFAULTDICT elif issubclass(origin, Counter): self.key_field = self._create_sub_type(get_args(self.type_)[0], 'key_' + self.name, for_keys=True) self.type_ = int self.shape = SHAPE_COUNTER elif issubclass(origin, Mapping): self.key_field = self._create_sub_type(get_args(self.type_)[0], 'key_' + self.name, for_keys=True) self.type_ = get_args(self.type_)[1] self.shape = SHAPE_MAPPING # Equality check as almost everything inherits form Iterable, including str # check for Iterable and CollectionsIterable, as it could receive one even when declared with the other elif origin in {Iterable, CollectionsIterable}: self.type_ = get_args(self.type_)[0] self.shape = SHAPE_ITERABLE self.sub_fields = [self._create_sub_type(self.type_, f'{self.name}_type')] elif issubclass(origin, Type): # type: ignore return elif hasattr(origin, '__get_validators__') or self.model_config.arbitrary_types_allowed: # Is a Pydantic-compatible generic that handles itself # or we have arbitrary_types_allowed = True self.shape = SHAPE_GENERIC self.sub_fields = [self._create_sub_type(t, f'{self.name}_{i}') for i, t in enumerate(get_args(self.type_))] self.type_ = origin return else: raise TypeError(f'Fields of type "{origin}" are not supported.') # type_ has been refined eg. as the type of a List and sub_fields needs to be populated self.sub_fields = [self._create_sub_type(self.type_, '_' + self.name)] def prepare_discriminated_union_sub_fields(self) -> None: """ Prepare the mapping <discriminator key> -> <ModelField> and update `sub_fields` Note that this process can be aborted if a `ForwardRef` is encountered """ assert self.discriminator_key is not None if self.type_.__class__ is DeferredType: return assert self.sub_fields is not None sub_fields_mapping: Dict[str, 'ModelField'] = {} all_aliases: Set[str] = set() for sub_field in self.sub_fields: t = sub_field.type_ if t.__class__ is ForwardRef: # Stopping everything...will need to call `update_forward_refs` return alias, discriminator_values = get_discriminator_alias_and_values(t, self.discriminator_key) all_aliases.add(alias) for discriminator_value in discriminator_values: sub_fields_mapping[discriminator_value] = sub_field self.sub_fields_mapping = sub_fields_mapping self.discriminator_alias = get_unique_discriminator_alias(all_aliases, self.discriminator_key) def _create_sub_type(self, type_: Type[Any], name: str, *, for_keys: bool = False) -> 'ModelField': if for_keys: class_validators = None else: # validators for sub items should not have `each_item` as we want to check only the first sublevel class_validators = { k: Validator( func=v.func, pre=v.pre, each_item=False, always=v.always, check_fields=v.check_fields, skip_on_failure=v.skip_on_failure, ) for k, v in self.class_validators.items() if v.each_item } field_info, _ = self._get_field_info(name, type_, None, self.model_config) return self.__class__( type_=type_, name=name, class_validators=class_validators, model_config=self.model_config, field_info=field_info, ) def populate_validators(self) -> None: """ Prepare self.pre_validators, self.validators, and self.post_validators based on self.type_'s __get_validators__ and class validators. This method should be idempotent, e.g. it should be safe to call multiple times without mis-configuring the field. """ self.validate_always = getattr(self.type_, 'validate_always', False) or any( v.always for v in self.class_validators.values() ) class_validators_ = self.class_validators.values() if not self.sub_fields or self.shape == SHAPE_GENERIC: get_validators = getattr(self.type_, '__get_validators__', None) v_funcs = ( *[v.func for v in class_validators_ if v.each_item and v.pre], *(get_validators() if get_validators else list(find_validators(self.type_, self.model_config))), *[v.func for v in class_validators_ if v.each_item and not v.pre], ) self.validators = prep_validators(v_funcs) self.pre_validators = [] self.post_validators = [] if self.field_info and self.field_info.const: self.post_validators.append(make_generic_validator(constant_validator)) if class_validators_: self.pre_validators += prep_validators(v.func for v in class_validators_ if not v.each_item and v.pre) self.post_validators += prep_validators(v.func for v in class_validators_ if not v.each_item and not v.pre) if self.parse_json: self.pre_validators.append(make_generic_validator(validate_json)) self.pre_validators = self.pre_validators or None self.post_validators = self.post_validators or None def validate( self, v: Any, values: Dict[str, Any], *, loc: 'LocStr', cls: Optional['ModelOrDc'] = None ) -> 'ValidateReturn': assert self.type_.__class__ is not DeferredType if self.type_.__class__ is ForwardRef: assert cls is not None raise ConfigError( f'field "{self.name}" not yet prepared so type is still a ForwardRef, ' f'you might need to call {cls.__name__}.update_forward_refs().' ) errors: Optional['ErrorList'] if self.pre_validators: v, errors = self._apply_validators(v, values, loc, cls, self.pre_validators) if errors: return v, errors if v is None: if is_none_type(self.type_): # keep validating pass elif self.allow_none: if self.post_validators: return self._apply_validators(v, values, loc, cls, self.post_validators) else: return None, None else: return v, ErrorWrapper(NoneIsNotAllowedError(), loc) if self.shape == SHAPE_SINGLETON: v, errors = self._validate_singleton(v, values, loc, cls) elif self.shape in MAPPING_LIKE_SHAPES: v, errors = self._validate_mapping_like(v, values, loc, cls) elif self.shape == SHAPE_TUPLE: v, errors = self._validate_tuple(v, values, loc, cls) elif self.shape == SHAPE_ITERABLE: v, errors = self._validate_iterable(v, values, loc, cls) elif self.shape == SHAPE_GENERIC: v, errors = self._apply_validators(v, values, loc, cls, self.validators) else: # sequence, list, set, generator, tuple with ellipsis, frozen set v, errors = self._validate_sequence_like(v, values, loc, cls) if not errors and self.post_validators: v, errors = self._apply_validators(v, values, loc, cls, self.post_validators) return v, errors def _validate_sequence_like( # noqa: C901 (ignore complexity) self, v: Any, values: Dict[str, Any], loc: 'LocStr', cls: Optional['ModelOrDc'] ) -> 'ValidateReturn': """ Validate sequence-like containers: lists, tuples, sets and generators Note that large if-else blocks are necessary to enable Cython optimization, which is why we disable the complexity check above. """ if not sequence_like(v): e: errors_.PydanticTypeError if self.shape == SHAPE_LIST: e = errors_.ListError() elif self.shape in (SHAPE_TUPLE, SHAPE_TUPLE_ELLIPSIS): e = errors_.TupleError() elif self.shape == SHAPE_SET: e = errors_.SetError() elif self.shape == SHAPE_FROZENSET: e = errors_.FrozenSetError() else: e = errors_.SequenceError() return v, ErrorWrapper(e, loc) loc = loc if isinstance(loc, tuple) else (loc,) result = [] errors: List[ErrorList] = [] for i, v_ in enumerate(v): v_loc = *loc, i r, ee = self._validate_singleton(v_, values, v_loc, cls) if ee: errors.append(ee) else: result.append(r) if errors: return v, errors converted: Union[List[Any], Set[Any], FrozenSet[Any], Tuple[Any, ...], Iterator[Any], Deque[Any]] = result if self.shape == SHAPE_SET: converted = set(result) elif self.shape == SHAPE_FROZENSET: converted = frozenset(result) elif self.shape == SHAPE_TUPLE_ELLIPSIS: converted = tuple(result) elif self.shape == SHAPE_DEQUE: converted = deque(result, maxlen=getattr(v, 'maxlen', None)) elif self.shape == SHAPE_SEQUENCE: if isinstance(v, tuple): converted = tuple(result) elif isinstance(v, set): converted = set(result) elif isinstance(v, Generator): converted = iter(result) elif isinstance(v, deque): converted = deque(result, maxlen=getattr(v, 'maxlen', None)) return converted, None def _validate_iterable( self, v: Any, values: Dict[str, Any], loc: 'LocStr', cls: Optional['ModelOrDc'] ) -> 'ValidateReturn': """ Validate Iterables. This intentionally doesn't validate values to allow infinite generators. """ try: iterable = iter(v) except TypeError: return v, ErrorWrapper(errors_.IterableError(), loc) return iterable, None def _validate_tuple( self, v: Any, values: Dict[str, Any], loc: 'LocStr', cls: Optional['ModelOrDc'] ) -> 'ValidateReturn': e: Optional[Exception] = None if not sequence_like(v): e = errors_.TupleError() else: actual_length, expected_length = len(v), len(self.sub_fields) # type: ignore if actual_length != expected_length: e = errors_.TupleLengthError(actual_length=actual_length, expected_length=expected_length) if e: return v, ErrorWrapper(e, loc) loc = loc if isinstance(loc, tuple) else (loc,) result = [] errors: List[ErrorList] = [] for i, (v_, field) in enumerate(zip(v, self.sub_fields)): # type: ignore v_loc = *loc, i r, ee = field.validate(v_, values, loc=v_loc, cls=cls) if ee: errors.append(ee) else: result.append(r) if errors: return v, errors else: return tuple(result), None def _validate_mapping_like( self, v: Any, values: Dict[str, Any], loc: 'LocStr', cls: Optional['ModelOrDc'] ) -> 'ValidateReturn': try: v_iter = dict_validator(v) except TypeError as exc: return v, ErrorWrapper(exc, loc) loc = loc if isinstance(loc, tuple) else (loc,) result, errors = {}, [] for k, v_ in v_iter.items(): v_loc = *loc, '__key__' key_result, key_errors = self.key_field.validate(k, values, loc=v_loc, cls=cls) # type: ignore if key_errors: errors.append(key_errors) continue v_loc = *loc, k value_result, value_errors = self._validate_singleton(v_, values, v_loc, cls) if value_errors: errors.append(value_errors) continue result[key_result] = value_result if errors: return v, errors elif self.shape == SHAPE_DICT: return result, None elif self.shape == SHAPE_DEFAULTDICT: return defaultdict(self.type_, result), None elif self.shape == SHAPE_COUNTER: return CollectionCounter(result), None else: return self._get_mapping_value(v, result), None def _get_mapping_value(self, original: T, converted: Dict[Any, Any]) -> Union[T, Dict[Any, Any]]: """ When type is `Mapping[KT, KV]` (or another unsupported mapping), we try to avoid coercing to `dict` unwillingly. """ original_cls = original.__class__ if original_cls == dict or original_cls == Dict: return converted elif original_cls in {defaultdict, DefaultDict}: return defaultdict(self.type_, converted) else: try: # Counter, OrderedDict, UserDict, ... return original_cls(converted) # type: ignore except TypeError: raise RuntimeError(f'Could not convert dictionary to {original_cls.__name__!r}') from None def _validate_singleton( self, v: Any, values: Dict[str, Any], loc: 'LocStr', cls: Optional['ModelOrDc'] ) -> 'ValidateReturn': if self.sub_fields: if self.discriminator_key is not None: return self._validate_discriminated_union(v, values, loc, cls) errors = [] if self.model_config.smart_union and is_union(get_origin(self.type_)): # 1st pass: check if the value is an exact instance of one of the Union types # (e.g. to avoid coercing a bool into an int) for field in self.sub_fields: if v.__class__ is field.outer_type_: return v, None # 2nd pass: check if the value is an instance of any subclass of the Union types for field in self.sub_fields: # This whole logic will be improved later on to support more complex `isinstance` checks # It will probably be done once a strict mode is added and be something like: # ``` # value, error = field.validate(v, values, strict=True) # if error is None: # return value, None # ``` try: if isinstance(v, field.outer_type_): return v, None except TypeError: # compound type if lenient_isinstance(v, get_origin(field.outer_type_)): value, error = field.validate(v, values, loc=loc, cls=cls) if not error: return value, None # 1st pass by default or 3rd pass with `smart_union` enabled: # check if the value can be coerced into one of the Union types for field in self.sub_fields: value, error = field.validate(v, values, loc=loc, cls=cls) if error: errors.append(error) else: return value, None return v, errors else: return self._apply_validators(v, values, loc, cls, self.validators) def _validate_discriminated_union( self, v: Any, values: Dict[str, Any], loc: 'LocStr', cls: Optional['ModelOrDc'] ) -> 'ValidateReturn': assert self.discriminator_key is not None assert self.discriminator_alias is not None try: try: discriminator_value = v[self.discriminator_alias] except KeyError: if self.model_config.allow_population_by_field_name: discriminator_value = v[self.discriminator_key] else: raise except KeyError: return v, ErrorWrapper(MissingDiscriminator(discriminator_key=self.discriminator_key), loc) except TypeError: try: # BaseModel or dataclass discriminator_value = getattr(v, self.discriminator_key) except (AttributeError, TypeError): return v, ErrorWrapper(MissingDiscriminator(discriminator_key=self.discriminator_key), loc) if self.sub_fields_mapping is None: assert cls is not None raise ConfigError( f'field "{self.name}" not yet prepared so type is still a ForwardRef, ' f'you might need to call {cls.__name__}.update_forward_refs().' ) try: sub_field = self.sub_fields_mapping[discriminator_value] except (KeyError, TypeError): # KeyError: `discriminator_value` is not in the dictionary. # TypeError: `discriminator_value` is unhashable. assert self.sub_fields_mapping is not None return v, ErrorWrapper( InvalidDiscriminator( discriminator_key=self.discriminator_key, discriminator_value=discriminator_value, allowed_values=list(self.sub_fields_mapping), ), loc, ) else: if not isinstance(loc, tuple): loc = (loc,) return sub_field.validate(v, values, loc=(*loc, display_as_type(sub_field.type_)), cls=cls) def _apply_validators( self, v: Any, values: Dict[str, Any], loc: 'LocStr', cls: Optional['ModelOrDc'], validators: 'ValidatorsList' ) -> 'ValidateReturn': for validator in validators: try: v = validator(cls, v, values, self, self.model_config) except (ValueError, TypeError, AssertionError) as exc: return v, ErrorWrapper(exc, loc) return v, None def is_complex(self) -> bool: """ Whether the field is "complex" eg. env variables should be parsed as JSON. """ from pydantic.v1.main import BaseModel return ( self.shape != SHAPE_SINGLETON or hasattr(self.type_, '__pydantic_model__') or lenient_issubclass(self.type_, (BaseModel, list, set, frozenset, dict)) ) def _type_display(self) -> PyObjectStr: t = display_as_type(self.type_) if self.shape in MAPPING_LIKE_SHAPES: t = f'Mapping[{display_as_type(self.key_field.type_)}, {t}]' # type: ignore elif self.shape == SHAPE_TUPLE: t = 'Tuple[{}]'.format(', '.join(display_as_type(f.type_) for f in self.sub_fields)) # type: ignore elif self.shape == SHAPE_GENERIC: assert self.sub_fields t = '{}[{}]'.format( display_as_type(self.type_), ', '.join(display_as_type(f.type_) for f in self.sub_fields) ) elif self.shape != SHAPE_SINGLETON: t = SHAPE_NAME_LOOKUP[self.shape].format(t) if self.allow_none and (self.shape != SHAPE_SINGLETON or not self.sub_fields): t = f'Optional[{t}]' return PyObjectStr(t) def __repr_args__(self) -> 'ReprArgs': args = [('name', self.name), ('type', self._type_display()), ('required', self.required)] if not self.required: if self.default_factory is not None: args.append(('default_factory', f'<function {self.default_factory.__name__}>')) else: args.append(('default', self.default)) if self.alt_alias: args.append(('alias', self.alias)) return args class ModelPrivateAttr(Representation): __slots__ = ('default', 'default_factory') def __init__(self, default: Any = Undefined, *, default_factory: Optional[NoArgAnyCallable] = None) -> None: self.default = default self.default_factory = default_factory def get_default(self) -> Any: return smart_deepcopy(self.default) if self.default_factory is None else self.default_factory() def __eq__(self, other: Any) -> bool: return isinstance(other, self.__class__) and (self.default, self.default_factory) == ( other.default, other.default_factory, ) def PrivateAttr( default: Any = Undefined, *, default_factory: Optional[NoArgAnyCallable] = None, ) -> Any: """ Indicates that attribute is only used internally and never mixed with regular fields. Types or values of private attrs are not checked by pydantic and it's up to you to keep them relevant. Private attrs are stored in model __slots__. :param default: the attribute’s default value :param default_factory: callable that will be called when a default value is needed for this attribute If both `default` and `default_factory` are set, an error is raised. """ if default is not Undefined and default_factory is not None: raise ValueError('cannot specify both default and default_factory') return ModelPrivateAttr( default, default_factory=default_factory, ) class DeferredType: """ Used to postpone field preparation, while creating recursive generic models. """ def is_finalvar_with_default_val(type_: Type[Any], val: Any) -> bool: return is_finalvar(type_) and val is not Undefined and not isinstance(val, FieldInfo)