示例#1
0
    def add_fields(cls, **field_definitions: Tuple[str, Any]) -> None:
        """Add fields to the model.

        Adapted from here :
        https://github.com/samuelcolvin/pydantic/issues/1937#issuecomment-695313040
        """
        new_fields: Dict[str, ModelField] = {}
        new_annotations: Dict[str, Optional[type]] = {}
        validators = None

        for f_name, f_def in field_definitions.items():
            f_annotation, f_value = f_def

            if cls.__vg__:
                validators = cls.__vg__.get_validators(f_name)

            new_fields[f_name] = ModelField.infer(
                name=f_name,
                value=f_value,
                annotation=f_annotation,
                class_validators=validators,
                config=cls.__config__,
            )

        cls.__fields__.update(new_fields)
        cls.__annotations__.update(new_annotations)
示例#2
0
    def add_fields(cls, **field_definitions: Any):
        """
        This class lets a caller define new custom fields in the container model by
        dynamically inserting fields inplace, so that when the service parses an incoming container
        that container can be parsed with the new fields in mind, applying validation, etc.
        """
        new_fields: Dict[str, ModelField] = {}
        new_annotations: Dict[str, Optional[type]] = {}

        for f_name, f_annotation in field_definitions.items():
            new_annotations[f_name] = f_annotation
            new_fields[f_name] = ModelField.infer(name=f_name,
                                                  value=None,
                                                  annotation=f_annotation,
                                                  class_validators=None,
                                                  config=cls.__config__)

        cls.__fields__.update(new_fields)
        cls.__annotations__.update(new_annotations)
示例#3
0
        def __new__(mcs, name, bases, namespace, **kwargs):
            from pydantic.fields import Undefined
            from pydantic.class_validators import extract_validators, inherit_validators
            from pydantic.types import PyObject
            from pydantic.typing import is_classvar, resolve_annotations
            from pydantic.utils import lenient_issubclass, validate_field_name
            from pydantic.main import inherit_config, prepare_config, UNTOUCHED_TYPES

            fields: Dict[str, ModelField] = {}
            config = BaseConfig
            validators: Dict[str, List[Validator]] = {}

            for base in reversed(bases):
                if issubclass(base, AbstractCheckedSession) and base != AbstractCheckedSession:
                    config = inherit_config(base.__config__, config)
                    fields.update(deepcopy(base.__fields__))
                    validators = inherit_validators(base.__validators__, validators)

            config = inherit_config(namespace.get('Config'), config)
            validators = inherit_validators(extract_validators(namespace), validators)

            # update fields inherited from base classes
            for field in fields.values():
                field.set_config(config)
                extra_validators = validators.get(field.name, [])
                if extra_validators:
                    field.class_validators.update(extra_validators)
                    # re-run prepare to add extra validators
                    field.populate_validators()

            prepare_config(config, name)

            # extract and build fields
            class_vars = set()
            if (namespace.get('__module__'), namespace.get('__qualname__')) != \
                    ('larray.core.checked', 'CheckedSession'):
                untouched_types = UNTOUCHED_TYPES + config.keep_untouched

                # annotation only fields need to come first in fields
                annotations = resolve_annotations(namespace.get('__annotations__', {}),
                                                  namespace.get('__module__', None))
                for ann_name, ann_type in annotations.items():
                    if is_classvar(ann_type):
                        class_vars.add(ann_name)
                    elif not ann_name.startswith('_'):
                        validate_field_name(bases, ann_name)
                        value = namespace.get(ann_name, Undefined)
                        if (isinstance(value, untouched_types) and ann_type != PyObject
                                and not lenient_issubclass(getattr(ann_type, '__origin__', None), Type)):
                            continue
                        fields[ann_name] = ModelField.infer(name=ann_name, value=value, annotation=ann_type,
                                                            class_validators=validators.get(ann_name, []),
                                                            config=config)

                for var_name, value in namespace.items():
                    # 'var_name not in annotations' because namespace.items() contains annotated fields
                    # with default values
                    # 'var_name not in class_vars' to avoid to update a field if it was redeclared (by mistake)
                    if (var_name not in annotations and not var_name.startswith('_')
                            and not isinstance(value, untouched_types) and var_name not in class_vars):
                        validate_field_name(bases, var_name)
                        # the method ModelField.infer() fails to infer the type of Group objects
                        # (which are interpreted as ndarray objects)
                        annotation = type(value) if isinstance(value, Group) else annotations.get(var_name)
                        inferred = ModelField.infer(name=var_name, value=value, annotation=annotation,
                                                    class_validators=validators.get(var_name, []), config=config)
                        if var_name in fields and inferred.type_ != fields[var_name].type_:
                            raise TypeError(f'The type of {name}.{var_name} differs from the new default value; '
                                            f'if you wish to change the type of this field, please use a type '
                                            f'annotation')
                        fields[var_name] = inferred

            new_namespace = {
                '__config__': config,
                '__fields__': fields,
                '__field_defaults__': {n: f.default for n, f in fields.items() if not f.required},
                '__validators__': validators,
                **{n: v for n, v in namespace.items() if n not in fields},
            }
            return super().__new__(mcs, name, bases, new_namespace, **kwargs)