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models

Definition of Model, it's parents NewBaseModel and mixins used by models. Also defines a Metaclass that handles all constructions and relations registration, ass well as vast number of helper functions for pydantic, sqlalchemy and relations.

ExcludableItems

Keeps a dictionary of Excludables by alias + model_name keys to allow quick lookup by nested models without need to travers deeply nested dictionaries and passing include/exclude around

Source code in ormar\models\excludable.py 
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class ExcludableItems:
    """
    Keeps a dictionary of Excludables by alias + model_name keys
    to allow quick lookup by nested models without need to travers
    deeply nested dictionaries and passing include/exclude around
    """

    def __init__(self) -> None:
        self.items: Dict[str, Excludable] = dict()

    @classmethod
    def from_excludable(cls, other: "ExcludableItems") -> "ExcludableItems":
        """
        Copy passed ExcludableItems to avoid inplace modifications.

        :param other: other excludable items to be copied
        :type other: ormar.models.excludable.ExcludableItems
        :return: copy of other
        :rtype: ormar.models.excludable.ExcludableItems
        """
        new_excludable = cls()
        for key, value in other.items.items():
            new_excludable.items[key] = value.get_copy()
        return new_excludable

    def include_entry_count(self) -> int:
        """
        Returns count of include items inside
        """
        count = 0
        for key in self.items.keys():
            count += len(self.items[key].include)
        return count

    def get(self, model_cls: Type["Model"], alias: str = "") -> Excludable:
        """
        Return Excludable for given model and alias.

        :param model_cls: target model to check
        :type model_cls: ormar.models.metaclass.ModelMetaclass
        :param alias: table alias from relation manager
        :type alias: str
        :return: Excludable for given model and alias
        :rtype: ormar.models.excludable.Excludable
        """
        key = f"{alias + '_' if alias else ''}{model_cls.get_name(lower=True)}"
        excludable = self.items.get(key)
        if not excludable:
            excludable = Excludable()
            self.items[key] = excludable
        return excludable

    def build(
        self,
        items: Union[List[str], str, Tuple[str], Set[str], Dict],
        model_cls: Type["Model"],
        is_exclude: bool = False,
    ) -> None:
        """
        Receives the one of the types of items and parses them as to achieve
        a end situation with one excludable per alias/model in relation.

        Each excludable has two sets of values - one to include, one to exclude.

        :param items: values to be included or excluded
        :type items: Union[List[str], str, Tuple[str], Set[str], Dict]
        :param model_cls: source model from which relations are constructed
        :type model_cls: ormar.models.metaclass.ModelMetaclass
        :param is_exclude: flag if items should be included or excluded
        :type is_exclude: bool
        """
        if isinstance(items, str):
            items = {items}

        if isinstance(items, Dict):
            self._traverse_dict(
                values=items,
                source_model=model_cls,
                model_cls=model_cls,
                is_exclude=is_exclude,
            )

        else:
            items = set(items)
            nested_items = set(x for x in items if "__" in x)
            items.difference_update(nested_items)
            self._set_excludes(
                items=items,
                model_name=model_cls.get_name(lower=True),
                is_exclude=is_exclude,
            )
            if nested_items:
                self._traverse_list(
                    values=nested_items, model_cls=model_cls, is_exclude=is_exclude
                )

    def _set_excludes(
        self, items: Set, model_name: str, is_exclude: bool, alias: str = ""
    ) -> None:
        """
        Sets set of values to be included or excluded for given key and model.

        :param items: items to include/exclude
        :type items: set
        :param model_name: name of model to construct key
        :type model_name: str
        :param is_exclude: flag if values should be included or excluded
        :type is_exclude: bool
        :param alias:
        :type alias: str
        """
        key = f"{alias + '_' if alias else ''}{model_name}"
        excludable = self.items.get(key)
        if not excludable:
            excludable = Excludable()
        excludable.set_values(value=items, is_exclude=is_exclude)
        self.items[key] = excludable

    def _traverse_dict(  # noqa: CFQ002
        self,
        values: Dict,
        source_model: Type["Model"],
        model_cls: Type["Model"],
        is_exclude: bool,
        related_items: List = None,
        alias: str = "",
    ) -> None:
        """
        Goes through dict of nested values and construct/update Excludables.

        :param values: items to include/exclude
        :type values: Dict
        :param source_model: source model from which relations are constructed
        :type source_model: ormar.models.metaclass.ModelMetaclass
        :param model_cls: model from which current relation is constructed
        :type model_cls: ormar.models.metaclass.ModelMetaclass
        :param is_exclude: flag if values should be included or excluded
        :type is_exclude: bool
        :param related_items: list of names of related fields chain
        :type related_items: List
        :param alias: alias of relation
        :type alias: str
        """
        self_fields = set()
        related_items = related_items[:] if related_items else []
        for key, value in values.items():
            if value is ...:
                self_fields.add(key)
            elif isinstance(value, set):
                (
                    table_prefix,
                    target_model,
                    _,
                    _,
                ) = get_relationship_alias_model_and_str(
                    source_model=source_model, related_parts=related_items + [key]
                )
                self._set_excludes(
                    items=value,
                    model_name=target_model.get_name(),
                    is_exclude=is_exclude,
                    alias=table_prefix,
                )
            else:
                # dict
                related_items.append(key)
                (
                    table_prefix,
                    target_model,
                    _,
                    _,
                ) = get_relationship_alias_model_and_str(
                    source_model=source_model, related_parts=related_items
                )
                self._traverse_dict(
                    values=value,
                    source_model=source_model,
                    model_cls=target_model,
                    is_exclude=is_exclude,
                    related_items=related_items,
                    alias=table_prefix,
                )
        if self_fields:
            self._set_excludes(
                items=self_fields,
                model_name=model_cls.get_name(),
                is_exclude=is_exclude,
                alias=alias,
            )

    def _traverse_list(
        self, values: Set[str], model_cls: Type["Model"], is_exclude: bool
    ) -> None:
        """
        Goes through list of values and construct/update Excludables.

        :param values: items to include/exclude
        :type values: set
        :param model_cls: model from which current relation is constructed
        :type model_cls: ormar.models.metaclass.ModelMetaclass
        :param is_exclude: flag if values should be included or excluded
        :type is_exclude: bool
        """
        # here we have only nested related keys
        for key in values:
            key_split = key.split("__")
            related_items, field_name = key_split[:-1], key_split[-1]
            (table_prefix, target_model, _, _) = get_relationship_alias_model_and_str(
                source_model=model_cls, related_parts=related_items
            )
            self._set_excludes(
                items={field_name},
                model_name=target_model.get_name(),
                is_exclude=is_exclude,
                alias=table_prefix,
            )

build(items, model_cls, is_exclude=False)

Receives the one of the types of items and parses them as to achieve a end situation with one excludable per alias/model in relation.

Each excludable has two sets of values - one to include, one to exclude.

Parameters:

Name Type Description Default
items Union[List[str], str, Tuple[str], Set[str], Dict]

values to be included or excluded

 required
model_cls Type[Model]

source model from which relations are constructed

 required
is_exclude bool

flag if items should be included or excluded

 False
Source code in ormar\models\excludable.py 
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def build(
    self,
    items: Union[List[str], str, Tuple[str], Set[str], Dict],
    model_cls: Type["Model"],
    is_exclude: bool = False,
) -> None:
    """
    Receives the one of the types of items and parses them as to achieve
    a end situation with one excludable per alias/model in relation.

    Each excludable has two sets of values - one to include, one to exclude.

    :param items: values to be included or excluded
    :type items: Union[List[str], str, Tuple[str], Set[str], Dict]
    :param model_cls: source model from which relations are constructed
    :type model_cls: ormar.models.metaclass.ModelMetaclass
    :param is_exclude: flag if items should be included or excluded
    :type is_exclude: bool
    """
    if isinstance(items, str):
        items = {items}

    if isinstance(items, Dict):
        self._traverse_dict(
            values=items,
            source_model=model_cls,
            model_cls=model_cls,
            is_exclude=is_exclude,
        )

    else:
        items = set(items)
        nested_items = set(x for x in items if "__" in x)
        items.difference_update(nested_items)
        self._set_excludes(
            items=items,
            model_name=model_cls.get_name(lower=True),
            is_exclude=is_exclude,
        )
        if nested_items:
            self._traverse_list(
                values=nested_items, model_cls=model_cls, is_exclude=is_exclude
            )

from_excludable(other) classmethod

Copy passed ExcludableItems to avoid inplace modifications.

Parameters:

Name Type Description Default
other ExcludableItems

other excludable items to be copied

 required

Returns:

Type Description
ormar.models.excludable.ExcludableItems

copy of other
Source code in ormar\models\excludable.py 
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@classmethod
def from_excludable(cls, other: "ExcludableItems") -> "ExcludableItems":
    """
    Copy passed ExcludableItems to avoid inplace modifications.

    :param other: other excludable items to be copied
    :type other: ormar.models.excludable.ExcludableItems
    :return: copy of other
    :rtype: ormar.models.excludable.ExcludableItems
    """
    new_excludable = cls()
    for key, value in other.items.items():
        new_excludable.items[key] = value.get_copy()
    return new_excludable

get(model_cls, alias='')

Return Excludable for given model and alias.

Parameters:

Name Type Description Default
model_cls Type[Model]

target model to check

 required
alias str

table alias from relation manager

 ''

Returns:

Type Description
ormar.models.excludable.Excludable

Excludable for given model and alias
Source code in ormar\models\excludable.py 
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def get(self, model_cls: Type["Model"], alias: str = "") -> Excludable:
    """
    Return Excludable for given model and alias.

    :param model_cls: target model to check
    :type model_cls: ormar.models.metaclass.ModelMetaclass
    :param alias: table alias from relation manager
    :type alias: str
    :return: Excludable for given model and alias
    :rtype: ormar.models.excludable.Excludable
    """
    key = f"{alias + '_' if alias else ''}{model_cls.get_name(lower=True)}"
    excludable = self.items.get(key)
    if not excludable:
        excludable = Excludable()
        self.items[key] = excludable
    return excludable

include_entry_count()

Returns count of include items inside

Source code in ormar\models\excludable.py 
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def include_entry_count(self) -> int:
    """
    Returns count of include items inside
    """
    count = 0
    for key in self.items.keys():
        count += len(self.items[key].include)
    return count

Model

Bases: ModelRow

Source code in ormar\models\model.py 
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class Model(ModelRow):
    __abstract__ = False
    if TYPE_CHECKING:  # pragma nocover
        Meta: ModelMeta

    def __repr__(self) -> str:  # pragma nocover
        _repr = {
            k: getattr(self, k)
            for k, v in self.Meta.model_fields.items()
            if not v.skip_field
        }
        return f"{self.__class__.__name__}({str(_repr)})"

    async def upsert(self: T, **kwargs: Any) -> T:
        """
        Performs either a save or an update depending on the presence of the pk.
        If the pk field is filled it's an update, otherwise the save is performed.
        For save kwargs are ignored, used only in update if provided.

        :param kwargs: list of fields to update
        :type kwargs: Any
        :return: saved Model
        :rtype: Model
        """

        force_save = kwargs.pop("__force_save__", False)
        if force_save:
            expr = self.Meta.table.select().where(self.pk_column == self.pk)
            row = await self.Meta.database.fetch_one(expr)
            if not row:
                return await self.save()
            return await self.update(**kwargs)

        if not self.pk:
            return await self.save()
        return await self.update(**kwargs)

    async def save(self: T) -> T:
        """
        Performs a save of given Model instance.
        If primary key is already saved, db backend will throw integrity error.

        Related models are saved by pk number, reverse relation and many to many fields
        are not saved - use corresponding relations methods.

        If there are fields with server_default set and those fields
        are not already filled save will trigger also a second query
        to refreshed the fields populated server side.

        Does not recognize if model was previously saved.
        If you want to perform update or insert depending on the pk
        fields presence use upsert.

        Sends pre_save and post_save signals.

        Sets model save status to True.

        :return: saved Model
        :rtype: Model
        """
        await self.signals.pre_save.send(sender=self.__class__, instance=self)
        self_fields = self._extract_model_db_fields()

        if not self.pk and self.Meta.model_fields[self.Meta.pkname].autoincrement:
            self_fields.pop(self.Meta.pkname, None)
        self_fields = self.populate_default_values(self_fields)
        self.update_from_dict(
            {
                k: v
                for k, v in self_fields.items()
                if k not in self.extract_related_names()
            }
        )

        self_fields = self.translate_columns_to_aliases(self_fields)
        expr = self.Meta.table.insert()
        expr = expr.values(**self_fields)

        pk = await self.Meta.database.execute(expr)
        if pk and isinstance(pk, self.pk_type()):
            setattr(self, self.Meta.pkname, pk)

        self.set_save_status(True)
        # refresh server side defaults
        if any(
            field.server_default is not None
            for name, field in self.Meta.model_fields.items()
            if name not in self_fields
        ):
            await self.load()

        await self.signals.post_save.send(sender=self.__class__, instance=self)
        return self

    async def save_related(  # noqa: CCR001, CFQ002
        self,
        follow: bool = False,
        save_all: bool = False,
        relation_map: Dict = None,
        exclude: Union[Set, Dict] = None,
        update_count: int = 0,
        previous_model: "Model" = None,
        relation_field: Optional["ForeignKeyField"] = None,
    ) -> int:
        """
        Triggers a upsert method on all related models
        if the instances are not already saved.
        By default saves only the directly related ones.

        If follow=True is set it saves also related models of related models.

        To not get stuck in an infinite loop as related models also keep a relation
        to parent model visited models set is kept.

        That way already visited models that are nested are saved, but the save do not
        follow them inside. So Model A -> Model B -> Model A -> Model C will save second
        Model A but will never follow into Model C.
        Nested relations of those kind need to be persisted manually.

        :param relation_field: field with relation leading to this model
        :type relation_field: Optional[ForeignKeyField]
        :param previous_model: previous model from which method came
        :type previous_model: Model
        :param exclude: items to exclude during saving of relations
        :type exclude: Union[Set, Dict]
        :param relation_map: map of relations to follow
        :type relation_map: Dict
        :param save_all: flag if all models should be saved or only not saved ones
        :type save_all: bool
        :param follow: flag to trigger deep save -
        by default only directly related models are saved
        with follow=True also related models of related models are saved
        :type follow: bool
        :param update_count: internal parameter for recursive calls -
        number of updated instances
        :type update_count: int
        :return: number of updated/saved models
        :rtype: int
        """
        relation_map = (
            relation_map
            if relation_map is not None
            else translate_list_to_dict(self._iterate_related_models())
        )
        if exclude and isinstance(exclude, Set):
            exclude = translate_list_to_dict(exclude)
        relation_map = subtract_dict(relation_map, exclude or {})

        if relation_map:
            fields_to_visit = {
                field
                for field in self.extract_related_fields()
                if field.name in relation_map
            }
            pre_save = {
                field
                for field in fields_to_visit
                if not field.virtual and not field.is_multi
            }

            update_count = await self._update_relation_list(
                fields_list=pre_save,
                follow=follow,
                save_all=save_all,
                relation_map=relation_map,
                update_count=update_count,
            )

            update_count = await self._upsert_model(
                instance=self,
                save_all=save_all,
                previous_model=previous_model,
                relation_field=relation_field,
                update_count=update_count,
            )

            post_save = fields_to_visit - pre_save

            update_count = await self._update_relation_list(
                fields_list=post_save,
                follow=follow,
                save_all=save_all,
                relation_map=relation_map,
                update_count=update_count,
            )

        else:
            update_count = await self._upsert_model(
                instance=self,
                save_all=save_all,
                previous_model=previous_model,
                relation_field=relation_field,
                update_count=update_count,
            )

        return update_count

    async def update(self: T, _columns: List[str] = None, **kwargs: Any) -> T:
        """
        Performs update of Model instance in the database.
        Fields can be updated before or you can pass them as kwargs.

        Sends pre_update and post_update signals.

        Sets model save status to True.

        :param _columns: list of columns to update, if None all are updated
        :type _columns: List
        :raises ModelPersistenceError: If the pk column is not set

        :param kwargs: list of fields to update as field=value pairs
        :type kwargs: Any
        :return: updated Model
        :rtype: Model
        """
        if kwargs:
            self.update_from_dict(kwargs)

        if not self.pk:
            raise ModelPersistenceError(
                "You cannot update not saved model! Use save or upsert method."
            )

        await self.signals.pre_update.send(
            sender=self.__class__, instance=self, passed_args=kwargs
        )
        self_fields = self._extract_model_db_fields()
        self_fields.pop(self.get_column_name_from_alias(self.Meta.pkname))
        if _columns:
            self_fields = {k: v for k, v in self_fields.items() if k in _columns}
        self_fields = self.translate_columns_to_aliases(self_fields)
        expr = self.Meta.table.update().values(**self_fields)
        expr = expr.where(self.pk_column == getattr(self, self.Meta.pkname))

        await self.Meta.database.execute(expr)
        self.set_save_status(True)
        await self.signals.post_update.send(sender=self.__class__, instance=self)
        return self

    async def delete(self) -> int:
        """
        Removes the Model instance from the database.

        Sends pre_delete and post_delete signals.

        Sets model save status to False.

        Note it does not delete the Model itself (python object).
        So you can delete and later save (since pk is deleted no conflict will arise)
        or update and the Model will be saved in database again.

        :return: number of deleted rows (for some backends)
        :rtype: int
        """
        await self.signals.pre_delete.send(sender=self.__class__, instance=self)
        expr = self.Meta.table.delete()
        expr = expr.where(self.pk_column == (getattr(self, self.Meta.pkname)))
        result = await self.Meta.database.execute(expr)
        self.set_save_status(False)
        await self.signals.post_delete.send(sender=self.__class__, instance=self)
        return result

    async def load(self: T) -> T:
        """
        Allow to refresh existing Models fields from database.
        Be careful as the related models can be overwritten by pk_only models in load.
        Does NOT refresh the related models fields if they were loaded before.

        :raises NoMatch: If given pk is not found in database.

        :return: reloaded Model
        :rtype: Model
        """
        expr = self.Meta.table.select().where(self.pk_column == self.pk)
        row = await self.Meta.database.fetch_one(expr)
        if not row:  # pragma nocover
            raise NoMatch("Instance was deleted from database and cannot be refreshed")
        kwargs = dict(row)
        kwargs = self.translate_aliases_to_columns(kwargs)
        self.update_from_dict(kwargs)
        self.set_save_status(True)
        return self

    async def load_all(
        self: T,
        follow: bool = False,
        exclude: Union[List, str, Set, Dict] = None,
        order_by: Union[List, str] = None,
    ) -> T:
        """
        Allow to refresh existing Models fields from database.
        Performs refresh of the related models fields.

        By default, loads only self and the directly related ones.

        If follow=True is set it loads also related models of related models.

        To not get stuck in an infinite loop as related models also keep a relation
        to parent model visited models set is kept.

        That way already visited models that are nested are loaded, but the load do not
        follow them inside. So Model A -> Model B -> Model C -> Model A -> Model X
        will load second Model A but will never follow into Model X.
        Nested relations of those kind need to be loaded manually.

        :param order_by: columns by which models should be sorted
        :type order_by: Union[List, str]
        :raises NoMatch: If given pk is not found in database.

        :param exclude: related models to exclude
        :type exclude: Union[List, str, Set, Dict]
        :param follow: flag to trigger deep save -
        by default only directly related models are saved
        with follow=True also related models of related models are saved
        :type follow: bool
        :return: reloaded Model
        :rtype: Model
        """
        relations = list(self.extract_related_names())
        if follow:
            relations = self._iterate_related_models()
        queryset = self.__class__.objects
        if exclude:
            queryset = queryset.exclude_fields(exclude)
        if order_by:
            queryset = queryset.order_by(order_by)
        instance = await queryset.select_related(relations).get(pk=self.pk)
        self._orm.clear()
        self.update_from_dict(instance.dict())
        return self

delete() async

Removes the Model instance from the database.

Sends pre_delete and post_delete signals.

Sets model save status to False.

Note it does not delete the Model itself (python object). So you can delete and later save (since pk is deleted no conflict will arise) or update and the Model will be saved in database again.

Returns:

Type Description
int

number of deleted rows (for some backends)
Source code in ormar\models\model.py 
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async def delete(self) -> int:
    """
    Removes the Model instance from the database.

    Sends pre_delete and post_delete signals.

    Sets model save status to False.

    Note it does not delete the Model itself (python object).
    So you can delete and later save (since pk is deleted no conflict will arise)
    or update and the Model will be saved in database again.

    :return: number of deleted rows (for some backends)
    :rtype: int
    """
    await self.signals.pre_delete.send(sender=self.__class__, instance=self)
    expr = self.Meta.table.delete()
    expr = expr.where(self.pk_column == (getattr(self, self.Meta.pkname)))
    result = await self.Meta.database.execute(expr)
    self.set_save_status(False)
    await self.signals.post_delete.send(sender=self.__class__, instance=self)
    return result

load() async

Allow to refresh existing Models fields from database. Be careful as the related models can be overwritten by pk_only models in load. Does NOT refresh the related models fields if they were loaded before.

Returns:

Type Description
Model

reloaded Model

Raises:

Type Description
NoMatch

If given pk is not found in database.
Source code in ormar\models\model.py 
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async def load(self: T) -> T:
    """
    Allow to refresh existing Models fields from database.
    Be careful as the related models can be overwritten by pk_only models in load.
    Does NOT refresh the related models fields if they were loaded before.

    :raises NoMatch: If given pk is not found in database.

    :return: reloaded Model
    :rtype: Model
    """
    expr = self.Meta.table.select().where(self.pk_column == self.pk)
    row = await self.Meta.database.fetch_one(expr)
    if not row:  # pragma nocover
        raise NoMatch("Instance was deleted from database and cannot be refreshed")
    kwargs = dict(row)
    kwargs = self.translate_aliases_to_columns(kwargs)
    self.update_from_dict(kwargs)
    self.set_save_status(True)
    return self

load_all(follow=False, exclude=None, order_by=None) async

Allow to refresh existing Models fields from database. Performs refresh of the related models fields.

By default, loads only self and the directly related ones.

If follow=True is set it loads also related models of related models.

To not get stuck in an infinite loop as related models also keep a relation to parent model visited models set is kept.

That way already visited models that are nested are loaded, but the load do not follow them inside. So Model A -> Model B -> Model C -> Model A -> Model X will load second Model A but will never follow into Model X. Nested relations of those kind need to be loaded manually.

Parameters:

Name Type Description Default
order_by Union[List, str]

columns by which models should be sorted

 None
exclude Union[List, str, Set, Dict]

related models to exclude

 None
follow bool

flag to trigger deep save - by default only directly related models are saved with follow=True also related models of related models are saved

 False

Returns:

Type Description
Model

reloaded Model

Raises:

Type Description
NoMatch

If given pk is not found in database.
Source code in ormar\models\model.py 
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async def load_all(
    self: T,
    follow: bool = False,
    exclude: Union[List, str, Set, Dict] = None,
    order_by: Union[List, str] = None,
) -> T:
    """
    Allow to refresh existing Models fields from database.
    Performs refresh of the related models fields.

    By default, loads only self and the directly related ones.

    If follow=True is set it loads also related models of related models.

    To not get stuck in an infinite loop as related models also keep a relation
    to parent model visited models set is kept.

    That way already visited models that are nested are loaded, but the load do not
    follow them inside. So Model A -> Model B -> Model C -> Model A -> Model X
    will load second Model A but will never follow into Model X.
    Nested relations of those kind need to be loaded manually.

    :param order_by: columns by which models should be sorted
    :type order_by: Union[List, str]
    :raises NoMatch: If given pk is not found in database.

    :param exclude: related models to exclude
    :type exclude: Union[List, str, Set, Dict]
    :param follow: flag to trigger deep save -
    by default only directly related models are saved
    with follow=True also related models of related models are saved
    :type follow: bool
    :return: reloaded Model
    :rtype: Model
    """
    relations = list(self.extract_related_names())
    if follow:
        relations = self._iterate_related_models()
    queryset = self.__class__.objects
    if exclude:
        queryset = queryset.exclude_fields(exclude)
    if order_by:
        queryset = queryset.order_by(order_by)
    instance = await queryset.select_related(relations).get(pk=self.pk)
    self._orm.clear()
    self.update_from_dict(instance.dict())
    return self

save() async

Performs a save of given Model instance. If primary key is already saved, db backend will throw integrity error.

Related models are saved by pk number, reverse relation and many to many fields are not saved - use corresponding relations methods.

If there are fields with server_default set and those fields are not already filled save will trigger also a second query to refreshed the fields populated server side.

Does not recognize if model was previously saved. If you want to perform update or insert depending on the pk fields presence use upsert.

Sends pre_save and post_save signals.

Sets model save status to True.

Returns:

Type Description
Model

saved Model
Source code in ormar\models\model.py 
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async def save(self: T) -> T:
    """
    Performs a save of given Model instance.
    If primary key is already saved, db backend will throw integrity error.

    Related models are saved by pk number, reverse relation and many to many fields
    are not saved - use corresponding relations methods.

    If there are fields with server_default set and those fields
    are not already filled save will trigger also a second query
    to refreshed the fields populated server side.

    Does not recognize if model was previously saved.
    If you want to perform update or insert depending on the pk
    fields presence use upsert.

    Sends pre_save and post_save signals.

    Sets model save status to True.

    :return: saved Model
    :rtype: Model
    """
    await self.signals.pre_save.send(sender=self.__class__, instance=self)
    self_fields = self._extract_model_db_fields()

    if not self.pk and self.Meta.model_fields[self.Meta.pkname].autoincrement:
        self_fields.pop(self.Meta.pkname, None)
    self_fields = self.populate_default_values(self_fields)
    self.update_from_dict(
        {
            k: v
            for k, v in self_fields.items()
            if k not in self.extract_related_names()
        }
    )

    self_fields = self.translate_columns_to_aliases(self_fields)
    expr = self.Meta.table.insert()
    expr = expr.values(**self_fields)

    pk = await self.Meta.database.execute(expr)
    if pk and isinstance(pk, self.pk_type()):
        setattr(self, self.Meta.pkname, pk)

    self.set_save_status(True)
    # refresh server side defaults
    if any(
        field.server_default is not None
        for name, field in self.Meta.model_fields.items()
        if name not in self_fields
    ):
        await self.load()

    await self.signals.post_save.send(sender=self.__class__, instance=self)
    return self

Triggers a upsert method on all related models if the instances are not already saved. By default saves only the directly related ones.

If follow=True is set it saves also related models of related models.

To not get stuck in an infinite loop as related models also keep a relation to parent model visited models set is kept.

That way already visited models that are nested are saved, but the save do not follow them inside. So Model A -> Model B -> Model A -> Model C will save second Model A but will never follow into Model C. Nested relations of those kind need to be persisted manually.

Parameters:

Name Type Description Default
relation_field Optional[ForeignKeyField]

field with relation leading to this model

 None
previous_model Model

previous model from which method came

 None
exclude Union[Set, Dict]

items to exclude during saving of relations

 None
relation_map Dict

map of relations to follow

 None
save_all bool

flag if all models should be saved or only not saved ones

 False
follow bool

flag to trigger deep save - by default only directly related models are saved with follow=True also related models of related models are saved

 False
update_count int

internal parameter for recursive calls - number of updated instances

 0

Returns:

Type Description
int

number of updated/saved models
Source code in ormar\models\model.py 
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async def save_related(  # noqa: CCR001, CFQ002
    self,
    follow: bool = False,
    save_all: bool = False,
    relation_map: Dict = None,
    exclude: Union[Set, Dict] = None,
    update_count: int = 0,
    previous_model: "Model" = None,
    relation_field: Optional["ForeignKeyField"] = None,
) -> int:
    """
    Triggers a upsert method on all related models
    if the instances are not already saved.
    By default saves only the directly related ones.

    If follow=True is set it saves also related models of related models.

    To not get stuck in an infinite loop as related models also keep a relation
    to parent model visited models set is kept.

    That way already visited models that are nested are saved, but the save do not
    follow them inside. So Model A -> Model B -> Model A -> Model C will save second
    Model A but will never follow into Model C.
    Nested relations of those kind need to be persisted manually.

    :param relation_field: field with relation leading to this model
    :type relation_field: Optional[ForeignKeyField]
    :param previous_model: previous model from which method came
    :type previous_model: Model
    :param exclude: items to exclude during saving of relations
    :type exclude: Union[Set, Dict]
    :param relation_map: map of relations to follow
    :type relation_map: Dict
    :param save_all: flag if all models should be saved or only not saved ones
    :type save_all: bool
    :param follow: flag to trigger deep save -
    by default only directly related models are saved
    with follow=True also related models of related models are saved
    :type follow: bool
    :param update_count: internal parameter for recursive calls -
    number of updated instances
    :type update_count: int
    :return: number of updated/saved models
    :rtype: int
    """
    relation_map = (
        relation_map
        if relation_map is not None
        else translate_list_to_dict(self._iterate_related_models())
    )
    if exclude and isinstance(exclude, Set):
        exclude = translate_list_to_dict(exclude)
    relation_map = subtract_dict(relation_map, exclude or {})

    if relation_map:
        fields_to_visit = {
            field
            for field in self.extract_related_fields()
            if field.name in relation_map
        }
        pre_save = {
            field
            for field in fields_to_visit
            if not field.virtual and not field.is_multi
        }

        update_count = await self._update_relation_list(
            fields_list=pre_save,
            follow=follow,
            save_all=save_all,
            relation_map=relation_map,
            update_count=update_count,
        )

        update_count = await self._upsert_model(
            instance=self,
            save_all=save_all,
            previous_model=previous_model,
            relation_field=relation_field,
            update_count=update_count,
        )

        post_save = fields_to_visit - pre_save

        update_count = await self._update_relation_list(
            fields_list=post_save,
            follow=follow,
            save_all=save_all,
            relation_map=relation_map,
            update_count=update_count,
        )

    else:
        update_count = await self._upsert_model(
            instance=self,
            save_all=save_all,
            previous_model=previous_model,
            relation_field=relation_field,
            update_count=update_count,
        )

    return update_count

update(_columns=None, **kwargs) async

Performs update of Model instance in the database. Fields can be updated before or you can pass them as kwargs.

Sends pre_update and post_update signals.

Sets model save status to True.

Parameters:

Name Type Description Default
_columns List[str]

list of columns to update, if None all are updated

 None
kwargs Any

list of fields to update as field=value pairs

 {}

Returns:

Type Description
Model

updated Model

Raises:

Type Description
ModelPersistenceError

If the pk column is not set
Source code in ormar\models\model.py 
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async def update(self: T, _columns: List[str] = None, **kwargs: Any) -> T:
    """
    Performs update of Model instance in the database.
    Fields can be updated before or you can pass them as kwargs.

    Sends pre_update and post_update signals.

    Sets model save status to True.

    :param _columns: list of columns to update, if None all are updated
    :type _columns: List
    :raises ModelPersistenceError: If the pk column is not set

    :param kwargs: list of fields to update as field=value pairs
    :type kwargs: Any
    :return: updated Model
    :rtype: Model
    """
    if kwargs:
        self.update_from_dict(kwargs)

    if not self.pk:
        raise ModelPersistenceError(
            "You cannot update not saved model! Use save or upsert method."
        )

    await self.signals.pre_update.send(
        sender=self.__class__, instance=self, passed_args=kwargs
    )
    self_fields = self._extract_model_db_fields()
    self_fields.pop(self.get_column_name_from_alias(self.Meta.pkname))
    if _columns:
        self_fields = {k: v for k, v in self_fields.items() if k in _columns}
    self_fields = self.translate_columns_to_aliases(self_fields)
    expr = self.Meta.table.update().values(**self_fields)
    expr = expr.where(self.pk_column == getattr(self, self.Meta.pkname))

    await self.Meta.database.execute(expr)
    self.set_save_status(True)
    await self.signals.post_update.send(sender=self.__class__, instance=self)
    return self

upsert(**kwargs) async

Performs either a save or an update depending on the presence of the pk. If the pk field is filled it's an update, otherwise the save is performed. For save kwargs are ignored, used only in update if provided.

Parameters:

Name Type Description Default
kwargs Any

list of fields to update

 {}

Returns:

Type Description
Model

saved Model
Source code in ormar\models\model.py 
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async def upsert(self: T, **kwargs: Any) -> T:
    """
    Performs either a save or an update depending on the presence of the pk.
    If the pk field is filled it's an update, otherwise the save is performed.
    For save kwargs are ignored, used only in update if provided.

    :param kwargs: list of fields to update
    :type kwargs: Any
    :return: saved Model
    :rtype: Model
    """

    force_save = kwargs.pop("__force_save__", False)
    if force_save:
        expr = self.Meta.table.select().where(self.pk_column == self.pk)
        row = await self.Meta.database.fetch_one(expr)
        if not row:
            return await self.save()
        return await self.update(**kwargs)

    if not self.pk:
        return await self.save()
    return await self.update(**kwargs)

ModelRow

Bases: NewBaseModel

Source code in ormar\models\model_row.py 
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class ModelRow(NewBaseModel):
    @classmethod
    def from_row(  # noqa: CFQ002
        cls,
        row: ResultProxy,
        source_model: Type["Model"],
        select_related: List = None,
        related_models: Any = None,
        related_field: "ForeignKeyField" = None,
        excludable: ExcludableItems = None,
        current_relation_str: str = "",
        proxy_source_model: Optional[Type["Model"]] = None,
        used_prefixes: List[str] = None,
    ) -> Optional["Model"]:
        """
        Model method to convert raw sql row from database into ormar.Model instance.
        Traverses nested models if they were specified in select_related for query.

        Called recurrently and returns model instance if it's present in the row.
        Note that it's processing one row at a time, so if there are duplicates of
        parent row that needs to be joined/combined
        (like parent row in sql join with 2+ child rows)
        instances populated in this method are later combined in the QuerySet.
        Other method working directly on raw database results is in prefetch_query,
        where rows are populated in a different way as they do not have
        nested models in result.

        :param used_prefixes: list of already extracted prefixes
        :type used_prefixes: List[str]
        :param proxy_source_model: source model from which querysetproxy is constructed
        :type proxy_source_model: Optional[Type["ModelRow"]]
        :param excludable: structure of fields to include and exclude
        :type excludable: ExcludableItems
        :param current_relation_str: name of the relation field
        :type current_relation_str: str
        :param source_model: model on which relation was defined
        :type source_model: Type[Model]
        :param row: raw result row from the database
        :type row: ResultProxy
        :param select_related: list of names of related models fetched from database
        :type select_related: List
        :param related_models: list or dict of related models
        :type related_models: Union[List, Dict]
        :param related_field: field with relation declaration
        :type related_field: ForeignKeyField
        :return: returns model if model is populated from database
        :rtype: Optional[Model]
        """
        item: Dict[str, Any] = {}
        select_related = select_related or []
        related_models = related_models or []
        table_prefix = ""
        used_prefixes = used_prefixes if used_prefixes is not None else []
        excludable = excludable or ExcludableItems()

        if select_related:
            related_models = group_related_list(select_related)

        if related_field:
            table_prefix = cls._process_table_prefix(
                source_model=source_model,
                current_relation_str=current_relation_str,
                related_field=related_field,
                used_prefixes=used_prefixes,
            )

        item = cls._populate_nested_models_from_row(
            item=item,
            row=row,
            related_models=related_models,
            excludable=excludable,
            current_relation_str=current_relation_str,
            source_model=source_model,  # type: ignore
            proxy_source_model=proxy_source_model,  # type: ignore
            table_prefix=table_prefix,
            used_prefixes=used_prefixes,
        )
        item = cls.extract_prefixed_table_columns(
            item=item, row=row, table_prefix=table_prefix, excludable=excludable
        )

        instance: Optional["Model"] = None
        if item.get(cls.Meta.pkname, None) is not None:
            item["__excluded__"] = cls.get_names_to_exclude(
                excludable=excludable, alias=table_prefix
            )
            instance = cast("Model", cls(**item))
            instance.set_save_status(True)
        return instance

    @classmethod
    def _process_table_prefix(
        cls,
        source_model: Type["Model"],
        current_relation_str: str,
        related_field: "ForeignKeyField",
        used_prefixes: List[str],
    ) -> str:
        """

        :param source_model: model on which relation was defined
        :type source_model: Type[Model]
        :param current_relation_str: current relation string
        :type current_relation_str: str
        :param related_field: field with relation declaration
        :type related_field: "ForeignKeyField"
        :param used_prefixes: list of already extracted prefixes
        :type used_prefixes: List[str]
        :return: table_prefix to use
        :rtype: str
        """
        if related_field.is_multi:
            previous_model = related_field.through
        else:
            previous_model = related_field.owner
        table_prefix = cls.Meta.alias_manager.resolve_relation_alias(
            from_model=previous_model, relation_name=related_field.name
        )
        if not table_prefix or table_prefix in used_prefixes:
            manager = cls.Meta.alias_manager
            table_prefix = manager.resolve_relation_alias_after_complex(
                source_model=source_model,
                relation_str=current_relation_str,
                relation_field=related_field,
            )
        used_prefixes.append(table_prefix)
        return table_prefix

    @classmethod
    def _populate_nested_models_from_row(  # noqa: CFQ002
        cls,
        item: dict,
        row: ResultProxy,
        source_model: Type["Model"],
        related_models: Any,
        excludable: ExcludableItems,
        table_prefix: str,
        used_prefixes: List[str],
        current_relation_str: str = None,
        proxy_source_model: Type["Model"] = None,
    ) -> dict:
        """
        Traverses structure of related models and populates the nested models
        from the database row.
        Related models can be a list if only directly related models are to be
        populated, converted to dict if related models also have their own related
        models to be populated.

        Recurrently calls from_row method on nested instances and create nested
        instances. In the end those instances are added to the final model dictionary.

        :param proxy_source_model: source model from which querysetproxy is constructed
        :type proxy_source_model: Optional[Type["ModelRow"]]
        :param excludable: structure of fields to include and exclude
        :type excludable: ExcludableItems
        :param source_model: source model from which relation started
        :type source_model: Type[Model]
        :param current_relation_str: joined related parts into one string
        :type current_relation_str: str
        :param item: dictionary of already populated nested models, otherwise empty dict
        :type item: Dict
        :param row: raw result row from the database
        :type row: ResultProxy
        :param related_models: list or dict of related models
        :type related_models: Union[Dict, List]
        :return: dictionary with keys corresponding to model fields names
        and values are database values
        :rtype: Dict
        """

        for related in related_models:
            field = cls.Meta.model_fields[related]
            field = cast("ForeignKeyField", field)
            model_cls = field.to
            model_excludable = excludable.get(
                model_cls=cast(Type["Model"], cls), alias=table_prefix
            )
            if model_excludable.is_excluded(related):
                continue

            relation_str, remainder = cls._process_remainder_and_relation_string(
                related_models=related_models,
                current_relation_str=current_relation_str,
                related=related,
            )
            child = model_cls.from_row(
                row,
                related_models=remainder,
                related_field=field,
                excludable=excludable,
                current_relation_str=relation_str,
                source_model=source_model,
                proxy_source_model=proxy_source_model,
                used_prefixes=used_prefixes,
            )
            item[model_cls.get_column_name_from_alias(related)] = child
            if (
                field.is_multi
                and child
                and not model_excludable.is_excluded(field.through.get_name())
            ):
                cls._populate_through_instance(
                    row=row,
                    item=item,
                    related=related,
                    excludable=excludable,
                    child=child,
                    proxy_source_model=proxy_source_model,
                )

        return item

    @staticmethod
    def _process_remainder_and_relation_string(
        related_models: Union[Dict, List],
        current_relation_str: Optional[str],
        related: str,
    ) -> Tuple[str, Optional[Union[Dict, List]]]:
        """
        Process remainder models and relation string

        :param related_models: list or dict of related models
        :type related_models: Union[Dict, List]
        :param current_relation_str: current relation string
        :type current_relation_str: Optional[str]
        :param related: name of the relation
        :type related: str
        """
        relation_str = (
            "__".join([current_relation_str, related])
            if current_relation_str
            else related
        )

        remainder = None
        if isinstance(related_models, dict) and related_models[related]:
            remainder = related_models[related]
        return relation_str, remainder

    @classmethod
    def _populate_through_instance(  # noqa: CFQ002
        cls,
        row: ResultProxy,
        item: Dict,
        related: str,
        excludable: ExcludableItems,
        child: "Model",
        proxy_source_model: Optional[Type["Model"]],
    ) -> None:
        """
        Populates the through model on reverse side of current query.
        Normally it's child class, unless the query is from queryset.

        :param row: row from db result
        :type row: ResultProxy
        :param item: parent item dict
        :type item: Dict
        :param related: current relation name
        :type related: str
        :param excludable: structure of fields to include and exclude
        :type excludable: ExcludableItems
        :param child: child item of parent
        :type child: "Model"
        :param proxy_source_model: source model from which querysetproxy is constructed
        :type proxy_source_model: Type["Model"]
        """
        through_name = cls.Meta.model_fields[related].through.get_name()
        through_child = cls._create_through_instance(
            row=row, related=related, through_name=through_name, excludable=excludable
        )

        if child.__class__ != proxy_source_model:
            setattr(child, through_name, through_child)
        else:
            item[through_name] = through_child
        child.set_save_status(True)

    @classmethod
    def _create_through_instance(
        cls,
        row: ResultProxy,
        through_name: str,
        related: str,
        excludable: ExcludableItems,
    ) -> "ModelRow":
        """
        Initialize the through model from db row.
        Excluded all relation fields and other exclude/include set in excludable.

        :param row: loaded row from database
        :type row: sqlalchemy.engine.ResultProxy
        :param through_name: name of the through field
        :type through_name: str
        :param related: name of the relation
        :type related: str
        :param excludable: structure of fields to include and exclude
        :type excludable: ExcludableItems
        :return: initialized through model without relation
        :rtype: "ModelRow"
        """
        model_cls = cls.Meta.model_fields[through_name].to
        table_prefix = cls.Meta.alias_manager.resolve_relation_alias(
            from_model=cls, relation_name=related
        )
        # remove relations on through field
        model_excludable = excludable.get(model_cls=model_cls, alias=table_prefix)
        model_excludable.set_values(
            value=model_cls.extract_related_names(), is_exclude=True
        )
        child_dict = model_cls.extract_prefixed_table_columns(
            item={}, row=row, excludable=excludable, table_prefix=table_prefix
        )
        child_dict["__excluded__"] = model_cls.get_names_to_exclude(
            excludable=excludable, alias=table_prefix
        )
        child = model_cls(**child_dict)  # type: ignore
        return child

    @classmethod
    def extract_prefixed_table_columns(
        cls,
        item: dict,
        row: ResultProxy,
        table_prefix: str,
        excludable: ExcludableItems,
    ) -> Dict:
        """
        Extracts own fields from raw sql result, using a given prefix.
        Prefix changes depending on the table's position in a join.

        If the table is a main table, there is no prefix.
        All joined tables have prefixes to allow duplicate column names,
        as well as duplicated joins to the same table from multiple different tables.

        Extracted fields populates the related dict later used to construct a Model.

        Used in Model.from_row and PrefetchQuery._populate_rows methods.

        :param excludable: structure of fields to include and exclude
        :type excludable: ExcludableItems
        :param item: dictionary of already populated nested models, otherwise empty dict
        :type item: Dict
        :param row: raw result row from the database
        :type row: sqlalchemy.engine.result.ResultProxy
        :param table_prefix: prefix of the table from AliasManager
        each pair of tables have own prefix (two of them depending on direction) -
        used in joins to allow multiple joins to the same table.
        :type table_prefix: str
        :return: dictionary with keys corresponding to model fields names
        and values are database values
        :rtype: Dict
        """
        selected_columns = cls.own_table_columns(
            model=cls, excludable=excludable, alias=table_prefix, use_alias=False
        )

        column_prefix = table_prefix + "_" if table_prefix else ""
        for column in cls.Meta.table.columns:
            alias = cls.get_column_name_from_alias(column.name)
            if alias not in item and alias in selected_columns:
                prefixed_name = f"{column_prefix}{column.name}"
                item[alias] = row[prefixed_name]

        return item

extract_prefixed_table_columns(item, row, table_prefix, excludable) classmethod

Extracts own fields from raw sql result, using a given prefix. Prefix changes depending on the table's position in a join.

If the table is a main table, there is no prefix. All joined tables have prefixes to allow duplicate column names, as well as duplicated joins to the same table from multiple different tables.

Extracted fields populates the related dict later used to construct a Model.

Used in Model.from_row and PrefetchQuery._populate_rows methods.

Parameters:

Name Type Description Default
excludable ExcludableItems

structure of fields to include and exclude

 required
item dict

dictionary of already populated nested models, otherwise empty dict

 required
row ResultProxy

raw result row from the database

 required
table_prefix str

prefix of the table from AliasManager each pair of tables have own prefix (two of them depending on direction) - used in joins to allow multiple joins to the same table.

 required

Returns:

Type Description
Dict

dictionary with keys corresponding to model fields names and values are database values
Source code in ormar\models\model_row.py 
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@classmethod
def extract_prefixed_table_columns(
    cls,
    item: dict,
    row: ResultProxy,
    table_prefix: str,
    excludable: ExcludableItems,
) -> Dict:
    """
    Extracts own fields from raw sql result, using a given prefix.
    Prefix changes depending on the table's position in a join.

    If the table is a main table, there is no prefix.
    All joined tables have prefixes to allow duplicate column names,
    as well as duplicated joins to the same table from multiple different tables.

    Extracted fields populates the related dict later used to construct a Model.

    Used in Model.from_row and PrefetchQuery._populate_rows methods.

    :param excludable: structure of fields to include and exclude
    :type excludable: ExcludableItems
    :param item: dictionary of already populated nested models, otherwise empty dict
    :type item: Dict
    :param row: raw result row from the database
    :type row: sqlalchemy.engine.result.ResultProxy
    :param table_prefix: prefix of the table from AliasManager
    each pair of tables have own prefix (two of them depending on direction) -
    used in joins to allow multiple joins to the same table.
    :type table_prefix: str
    :return: dictionary with keys corresponding to model fields names
    and values are database values
    :rtype: Dict
    """
    selected_columns = cls.own_table_columns(
        model=cls, excludable=excludable, alias=table_prefix, use_alias=False
    )

    column_prefix = table_prefix + "_" if table_prefix else ""
    for column in cls.Meta.table.columns:
        alias = cls.get_column_name_from_alias(column.name)
        if alias not in item and alias in selected_columns:
            prefixed_name = f"{column_prefix}{column.name}"
            item[alias] = row[prefixed_name]

    return item

from_row(row, source_model, select_related=None, related_models=None, related_field=None, excludable=None, current_relation_str='', proxy_source_model=None, used_prefixes=None) classmethod

Model method to convert raw sql row from database into ormar.Model instance. Traverses nested models if they were specified in select_related for query.

Called recurrently and returns model instance if it's present in the row. Note that it's processing one row at a time, so if there are duplicates of parent row that needs to be joined/combined (like parent row in sql join with 2+ child rows) instances populated in this method are later combined in the QuerySet. Other method working directly on raw database results is in prefetch_query, where rows are populated in a different way as they do not have nested models in result.

Parameters:

Name Type Description Default
used_prefixes List[str]

list of already extracted prefixes

 None
proxy_source_model Optional[Type[Model]]

source model from which querysetproxy is constructed

 None
excludable ExcludableItems

structure of fields to include and exclude

 None
current_relation_str str

name of the relation field

 ''
source_model Type[Model]

model on which relation was defined

 required
row ResultProxy

raw result row from the database

 required
select_related List

list of names of related models fetched from database

 None
related_models Any

list or dict of related models

 None
related_field ForeignKeyField

field with relation declaration

 None

Returns:

Type Description
Optional[Model]

returns model if model is populated from database
Source code in ormar\models\model_row.py 
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@classmethod
def from_row(  # noqa: CFQ002
    cls,
    row: ResultProxy,
    source_model: Type["Model"],
    select_related: List = None,
    related_models: Any = None,
    related_field: "ForeignKeyField" = None,
    excludable: ExcludableItems = None,
    current_relation_str: str = "",
    proxy_source_model: Optional[Type["Model"]] = None,
    used_prefixes: List[str] = None,
) -> Optional["Model"]:
    """
    Model method to convert raw sql row from database into ormar.Model instance.
    Traverses nested models if they were specified in select_related for query.

    Called recurrently and returns model instance if it's present in the row.
    Note that it's processing one row at a time, so if there are duplicates of
    parent row that needs to be joined/combined
    (like parent row in sql join with 2+ child rows)
    instances populated in this method are later combined in the QuerySet.
    Other method working directly on raw database results is in prefetch_query,
    where rows are populated in a different way as they do not have
    nested models in result.

    :param used_prefixes: list of already extracted prefixes
    :type used_prefixes: List[str]
    :param proxy_source_model: source model from which querysetproxy is constructed
    :type proxy_source_model: Optional[Type["ModelRow"]]
    :param excludable: structure of fields to include and exclude
    :type excludable: ExcludableItems
    :param current_relation_str: name of the relation field
    :type current_relation_str: str
    :param source_model: model on which relation was defined
    :type source_model: Type[Model]
    :param row: raw result row from the database
    :type row: ResultProxy
    :param select_related: list of names of related models fetched from database
    :type select_related: List
    :param related_models: list or dict of related models
    :type related_models: Union[List, Dict]
    :param related_field: field with relation declaration
    :type related_field: ForeignKeyField
    :return: returns model if model is populated from database
    :rtype: Optional[Model]
    """
    item: Dict[str, Any] = {}
    select_related = select_related or []
    related_models = related_models or []
    table_prefix = ""
    used_prefixes = used_prefixes if used_prefixes is not None else []
    excludable = excludable or ExcludableItems()

    if select_related:
        related_models = group_related_list(select_related)

    if related_field:
        table_prefix = cls._process_table_prefix(
            source_model=source_model,
            current_relation_str=current_relation_str,
            related_field=related_field,
            used_prefixes=used_prefixes,
        )

    item = cls._populate_nested_models_from_row(
        item=item,
        row=row,
        related_models=related_models,
        excludable=excludable,
        current_relation_str=current_relation_str,
        source_model=source_model,  # type: ignore
        proxy_source_model=proxy_source_model,  # type: ignore
        table_prefix=table_prefix,
        used_prefixes=used_prefixes,
    )
    item = cls.extract_prefixed_table_columns(
        item=item, row=row, table_prefix=table_prefix, excludable=excludable
    )

    instance: Optional["Model"] = None
    if item.get(cls.Meta.pkname, None) is not None:
        item["__excluded__"] = cls.get_names_to_exclude(
            excludable=excludable, alias=table_prefix
        )
        instance = cast("Model", cls(**item))
        instance.set_save_status(True)
    return instance

NewBaseModel

Bases: pydantic.BaseModel, ModelTableProxy

Main base class of ormar Model. Inherits from pydantic BaseModel and has all mixins combined in ModelTableProxy. Constructed with ModelMetaclass which in turn also inherits pydantic metaclass.

Abstracts away all internals and helper functions, so final Model class has only the logic concerned with database connection and data persistence.

Source code in ormar\models\newbasemodel.py 
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class NewBaseModel(pydantic.BaseModel, ModelTableProxy, metaclass=ModelMetaclass):
    """
    Main base class of ormar Model.
    Inherits from pydantic BaseModel and has all mixins combined in ModelTableProxy.
    Constructed with ModelMetaclass which in turn also inherits pydantic metaclass.

    Abstracts away all internals and helper functions, so final Model class has only
    the logic concerned with database connection and data persistence.
    """

    __slots__ = (
        "_orm_id",
        "_orm_saved",
        "_orm",
        "_pk_column",
        "__pk_only__",
        "__cached_hash__",
    )

    if TYPE_CHECKING:  # pragma no cover
        pk: Any
        __model_fields__: Dict[str, BaseField]
        __table__: sqlalchemy.Table
        __pydantic_model__: Type[BaseModel]
        __pkname__: str
        __tablename__: str
        __metadata__: sqlalchemy.MetaData
        __database__: databases.Database
        __relation_map__: Optional[List[str]]
        __cached_hash__: Optional[int]
        _orm_relationship_manager: AliasManager
        _orm: RelationsManager
        _orm_id: int
        _orm_saved: bool
        _related_names: Optional[Set]
        _through_names: Optional[Set]
        _related_names_hash: str
        _choices_fields: Set
        _pydantic_fields: Set
        _quick_access_fields: Set
        _json_fields: Set
        _bytes_fields: Set
        Meta: ModelMeta

    # noinspection PyMissingConstructor
    def __init__(self, *args: Any, **kwargs: Any) -> None:  # type: ignore
        """
        Initializer that creates a new ormar Model that is also pydantic Model at the
        same time.

        Passed keyword arguments can be only field names and their corresponding values
        as those will be passed to pydantic validation that will complain if extra
        params are passed.

        If relations are defined each relation is expanded and children models are also
        initialized and validated. Relation from both sides is registered so you can
        access related models from both sides.

        Json fields are automatically loaded/dumped if needed.

        Models marked as abstract=True in internal Meta class cannot be initialized.

        Accepts also special __pk_only__ flag that indicates that Model is constructed
        only with primary key value (so no other fields, it's a child model on other
        Model), that causes skipping the validation, that's the only case when the
        validation can be skipped.

        Accepts also special __excluded__ parameter that contains a set of fields that
        should be explicitly set to None, as otherwise pydantic will try to populate
        them with their default values if default is set.

        :raises ModelError: if abstract model is initialized, model has ForwardRefs
         that has not been updated or unknown field is passed
        :param args: ignored args
        :type args: Any
        :param kwargs: keyword arguments - all fields values and some special params
        :type kwargs: Any
        """
        self._verify_model_can_be_initialized()
        self._initialize_internal_attributes()

        pk_only = kwargs.pop("__pk_only__", False)
        object.__setattr__(self, "__pk_only__", pk_only)

        new_kwargs, through_tmp_dict = self._process_kwargs(kwargs)

        if not pk_only:
            values, fields_set, validation_error = pydantic.validate_model(
                self, new_kwargs  # type: ignore
            )
            if validation_error:
                raise validation_error
        else:
            fields_set = {self.Meta.pkname}
            values = new_kwargs

        object.__setattr__(self, "__dict__", values)
        object.__setattr__(self, "__fields_set__", fields_set)

        # add back through fields
        new_kwargs.update(through_tmp_dict)
        model_fields = object.__getattribute__(self, "Meta").model_fields
        # register the columns models after initialization
        for related in self.extract_related_names().union(self.extract_through_names()):
            model_fields[related].expand_relationship(
                new_kwargs.get(related), self, to_register=True
            )

        if hasattr(self, "_init_private_attributes"):
            # introduced in pydantic 1.7
            self._init_private_attributes()

    def __setattr__(self, name: str, value: Any) -> None:  # noqa CCR001
        """
        Overwrites setattr in pydantic parent as otherwise descriptors are not called.

        :param name: name of the attribute to set
        :type name: str
        :param value: value of the attribute to set
        :type value: Any
        :return: None
        :rtype: None
        """
        prev_hash = hash(self)

        if hasattr(self, name):
            object.__setattr__(self, name, value)
        else:
            # let pydantic handle errors for unknown fields
            super().__setattr__(name, value)

        # In this case, the hash could have changed, so update it
        if name == self.Meta.pkname or self.pk is None:
            object.__setattr__(self, "__cached_hash__", None)
            new_hash = hash(self)

            if prev_hash != new_hash:
                self._update_relation_cache(prev_hash, new_hash)

    def __getattr__(self, item: str) -> Any:
        """
        Used only to silence mypy errors for Through models and reverse relations.
        Not used in real life as in practice calls are intercepted
        by RelationDescriptors

        :param item: name of attribute
        :type item: str
        :return: Any
        :rtype: Any
        """
        return super().__getattribute__(item)

    def __getstate__(self) -> Dict[Any, Any]:
        state = super().__getstate__()
        self_dict = self.dict()
        state["__dict__"].update(**self_dict)
        return state

    def __setstate__(self, state: Dict[Any, Any]) -> None:
        relations = {
            k: v
            for k, v in state["__dict__"].items()
            if k in self.extract_related_names()
        }
        basic_state = {
            k: v
            for k, v in state["__dict__"].items()
            if k not in self.extract_related_names()
        }
        state["__dict__"] = basic_state
        super().__setstate__(state)
        self._initialize_internal_attributes()
        for name, value in relations.items():
            setattr(self, name, value)

    def _update_relation_cache(self, prev_hash: int, new_hash: int) -> None:
        """
        Update all relation proxy caches with different hash if we have changed

        :param prev_hash: The previous hash to update
        :type prev_hash: int
        :param new_hash: The hash to update to
        :type new_hash: int
        """

        def _update_cache(relations: List[Relation], recurse: bool = True) -> None:
            for relation in relations:
                relation_proxy = relation.get()

                if hasattr(relation_proxy, "update_cache"):
                    relation_proxy.update_cache(prev_hash, new_hash)  # type: ignore
                elif recurse and hasattr(relation_proxy, "_orm"):
                    _update_cache(
                        relation_proxy._orm._relations.values(),  # type: ignore
                        recurse=False,
                    )

        _update_cache(list(self._orm._relations.values()))

    def _internal_set(self, name: str, value: Any) -> None:
        """
        Delegates call to pydantic.

        :param name: name of param
        :type name: str
        :param value: value to set
        :type value: Any
        """
        super().__setattr__(name, value)

    def _verify_model_can_be_initialized(self) -> None:
        """
        Raises exception if model is abstract or has ForwardRefs in relation fields.

        :return: None
        :rtype: None
        """
        if self.Meta.abstract:
            raise ModelError(f"You cannot initialize abstract model {self.get_name()}")
        if self.Meta.requires_ref_update:
            raise ModelError(
                f"Model {self.get_name()} has not updated "
                f"ForwardRefs. \nBefore using the model you "
                f"need to call update_forward_refs()."
            )

    def _process_kwargs(self, kwargs: Dict) -> Tuple[Dict, Dict]:  # noqa: CCR001
        """
        Initializes nested models.

        Removes property_fields

        Checks if field is in the model fields or pydatnic fields.

        Nullifies fields that should be excluded.

        Extracts through models from kwargs into temporary dict.

        :param kwargs: passed to init keyword arguments
        :type kwargs: Dict
        :return: modified kwargs
        :rtype: Tuple[Dict, Dict]
        """
        property_fields = self.Meta.property_fields
        model_fields = self.Meta.model_fields
        pydantic_fields = set(self.__fields__.keys())

        # remove property fields
        for prop_filed in property_fields:
            kwargs.pop(prop_filed, None)

        excluded: Set[str] = kwargs.pop("__excluded__", set())
        if "pk" in kwargs:
            kwargs[self.Meta.pkname] = kwargs.pop("pk")

        # extract through fields
        through_tmp_dict = dict()
        for field_name in self.extract_through_names():
            through_tmp_dict[field_name] = kwargs.pop(field_name, None)

        kwargs = self._remove_extra_parameters_if_they_should_be_ignored(
            kwargs=kwargs, model_fields=model_fields, pydantic_fields=pydantic_fields
        )
        try:
            new_kwargs: Dict[str, Any] = {
                k: self._convert_to_bytes(
                    k,
                    self._convert_json(
                        k,
                        model_fields[k].expand_relationship(v, self, to_register=False)
                        if k in model_fields
                        else (v if k in pydantic_fields else model_fields[k]),
                    ),
                )
                for k, v in kwargs.items()
            }
        except KeyError as e:
            raise ModelError(
                f"Unknown field '{e.args[0]}' for model {self.get_name(lower=False)}"
            )

        # explicitly set None to excluded fields
        # as pydantic populates them with default if set
        for field_to_nullify in excluded:
            new_kwargs[field_to_nullify] = None

        return new_kwargs, through_tmp_dict

    def _remove_extra_parameters_if_they_should_be_ignored(
        self, kwargs: Dict, model_fields: Dict, pydantic_fields: Set
    ) -> Dict:
        """
        Removes the extra fields from kwargs if they should be ignored.

        :param kwargs: passed arguments
        :type kwargs: Dict
        :param model_fields: dictionary of model fields
        :type model_fields: Dict
        :param pydantic_fields: set of pydantic fields names
        :type pydantic_fields: Set
        :return: dict without extra fields
        :rtype: Dict
        """
        if self.Meta.extra == Extra.ignore:
            kwargs = {
                k: v
                for k, v in kwargs.items()
                if k in model_fields or k in pydantic_fields
            }
        return kwargs

    def _initialize_internal_attributes(self) -> None:
        """
        Initializes internal attributes during __init__()
        :rtype: None
        """
        # object.__setattr__(self, "_orm_id", uuid.uuid4().hex)
        object.__setattr__(self, "_orm_saved", False)
        object.__setattr__(self, "_pk_column", None)
        object.__setattr__(
            self,
            "_orm",
            RelationsManager(
                related_fields=self.extract_related_fields(), owner=cast("Model", self)
            ),
        )

    def __eq__(self, other: object) -> bool:
        """
        Compares other model to this model. when == is called.
        :param other: other model to compare
        :type other: object
        :return: result of comparison
        :rtype: bool
        """
        if isinstance(other, NewBaseModel):
            return self.__same__(other)
        return super().__eq__(other)  # pragma no cover

    def __hash__(self) -> int:
        if getattr(self, "__cached_hash__", None) is not None:
            return self.__cached_hash__ or 0

        if self.pk is not None:
            ret = hash(str(self.pk) + self.__class__.__name__)
        else:
            vals = {
                k: v
                for k, v in self.__dict__.items()
                if k not in self.extract_related_names()
            }
            ret = hash(str(vals) + self.__class__.__name__)

        object.__setattr__(self, "__cached_hash__", ret)
        return ret

    def __same__(self, other: "NewBaseModel") -> bool:
        """
        Used by __eq__, compares other model to this model.
        Compares:
        * _orm_ids,
        * primary key values if it's set
        * dictionary of own fields (excluding relations)
        :param other: model to compare to
        :type other: NewBaseModel
        :return: result of comparison
        :rtype: bool
        """
        if (self.pk is None and other.pk is not None) or (
            self.pk is not None and other.pk is None
        ):
            return False
        else:
            return hash(self) == other.__hash__()

    def _copy_and_set_values(
        self: "NewBaseModel", values: "DictStrAny", fields_set: "SetStr", *, deep: bool
    ) -> "NewBaseModel":
        """
        Overwrite related models values with dict representation to avoid infinite
        recursion through related fields.
        """
        self_dict = values
        self_dict.update(self.dict(exclude_list=True))
        return cast(
            "NewBaseModel",
            super()._copy_and_set_values(
                values=self_dict, fields_set=fields_set, deep=deep
            ),
        )

    @classmethod
    def get_name(cls, lower: bool = True) -> str:
        """
        Returns name of the Model class, by default lowercase.

        :param lower: flag if name should be set to lowercase
        :type lower: bool
        :return: name of the model
        :rtype: str
        """
        name = cls.__name__
        if lower:
            name = name.lower()
        return name

    @property
    def pk_column(self) -> sqlalchemy.Column:
        """
        Retrieves primary key sqlalchemy column from models Meta.table.
        Each model has to have primary key.
        Only one primary key column is allowed.

        :return: primary key sqlalchemy column
        :rtype: sqlalchemy.Column
        """
        if object.__getattribute__(self, "_pk_column") is not None:
            return object.__getattribute__(self, "_pk_column")
        pk_columns = self.Meta.table.primary_key.columns.values()
        pk_col = pk_columns[0]
        object.__setattr__(self, "_pk_column", pk_col)
        return pk_col

    @property
    def saved(self) -> bool:
        """Saved status of the model. Changed by setattr and loading from db"""
        return self._orm_saved

    @property
    def signals(self) -> "SignalEmitter":
        """Exposes signals from model Meta"""
        return self.Meta.signals

    @classmethod
    def pk_type(cls) -> Any:
        """Shortcut to models primary key field type"""
        return cls.Meta.model_fields[cls.Meta.pkname].__type__

    @classmethod
    def db_backend_name(cls) -> str:
        """Shortcut to database dialect,
        cause some dialect require different treatment"""
        return cls.Meta.database._backend._dialect.name

    def remove(self, parent: "Model", name: str) -> None:
        """Removes child from relation with given name in RelationshipManager"""
        self._orm.remove_parent(self, parent, name)

    def set_save_status(self, status: bool) -> None:
        """Sets value of the save status"""
        object.__setattr__(self, "_orm_saved", status)

    @classmethod
    def get_properties(
        cls, include: Union[Set, Dict, None], exclude: Union[Set, Dict, None]
    ) -> Set[str]:
        """
        Returns a set of names of functions/fields decorated with
        @property_field decorator.

        They are added to dictionary when called directly and therefore also are
        present in fastapi responses.

        :param include: fields to include
        :type include: Union[Set, Dict, None]
        :param exclude: fields to exclude
        :type exclude: Union[Set, Dict, None]
        :return: set of property fields names
        :rtype: Set[str]
        """

        props = cls.Meta.property_fields
        if include:
            props = {prop for prop in props if prop in include}
        if exclude:
            props = {prop for prop in props if prop not in exclude}
        return props

    @classmethod
    def update_forward_refs(cls, **localns: Any) -> None:
        """
        Processes fields that are ForwardRef and need to be evaluated into actual
        models.

        Expands relationships, register relation in alias manager and substitutes
        sqlalchemy columns with new ones with proper column type (null before).

        Populates Meta table of the Model which is left empty before.

        Sets self_reference flag on models that links to themselves.

        Calls the pydantic method to evaluate pydantic fields.

        :param localns: local namespace
        :type localns: Any
        :return: None
        :rtype: None
        """
        globalns = sys.modules[cls.__module__].__dict__.copy()
        globalns.setdefault(cls.__name__, cls)
        fields_to_check = cls.Meta.model_fields.copy()
        for field in fields_to_check.values():
            if field.has_unresolved_forward_refs():
                field = cast(ForeignKeyField, field)
                field.evaluate_forward_ref(globalns=globalns, localns=localns)
                field.set_self_reference_flag()
                if field.is_multi and not field.through:
                    field = cast(ormar.ManyToManyField, field)
                    field.create_default_through_model()
                expand_reverse_relationship(model_field=field)
                register_relation_in_alias_manager(field=field)
                update_column_definition(model=cls, field=field)
        populate_meta_sqlalchemy_table_if_required(meta=cls.Meta)
        super().update_forward_refs(**localns)
        cls.Meta.requires_ref_update = False

    @staticmethod
    def _get_not_excluded_fields(
        fields: Union[List, Set], include: Optional[Dict], exclude: Optional[Dict]
    ) -> List:
        """
        Returns related field names applying on them include and exclude set.

        :param include: fields to include
        :type include: Union[Set, Dict, None]
        :param exclude: fields to exclude
        :type exclude: Union[Set, Dict, None]
        :return:
        :rtype: List of fields with relations that is not excluded
        """
        fields = [*fields] if not isinstance(fields, list) else fields
        if include:
            fields = [field for field in fields if field in include]
        if exclude:
            fields = [
                field
                for field in fields
                if field not in exclude
                or (
                    exclude.get(field) is not Ellipsis
                    and exclude.get(field) != {"__all__"}
                )
            ]
        return fields

    @staticmethod
    def _extract_nested_models_from_list(
        relation_map: Dict,
        models: MutableSequence,
        include: Union[Set, Dict, None],
        exclude: Union[Set, Dict, None],
        exclude_primary_keys: bool,
        exclude_through_models: bool,
    ) -> List:
        """
        Converts list of models into list of dictionaries.

        :param models: List of models
        :type models: List
        :param include: fields to include
        :type include: Union[Set, Dict, None]
        :param exclude: fields to exclude
        :type exclude: Union[Set, Dict, None]
        :return: list of models converted to dictionaries
        :rtype: List[Dict]
        """
        result = []
        for model in models:
            try:
                result.append(
                    model.dict(
                        relation_map=relation_map,
                        include=include,
                        exclude=exclude,
                        exclude_primary_keys=exclude_primary_keys,
                        exclude_through_models=exclude_through_models,
                    )
                )
            except ReferenceError:  # pragma no cover
                continue
        return result

    @classmethod
    def _skip_ellipsis(
        cls, items: Union[Set, Dict, None], key: str, default_return: Any = None
    ) -> Union[Set, Dict, None]:
        """
        Helper to traverse the include/exclude dictionaries.
        In dict() Ellipsis should be skipped as it indicates all fields required
        and not the actual set/dict with fields names.

        :param items: current include/exclude value
        :type items: Union[Set, Dict, None]
        :param key: key for nested relations to check
        :type key: str
        :return: nested value of the items
        :rtype: Union[Set, Dict, None]
        """
        result = cls.get_child(items, key)
        return result if result is not Ellipsis else default_return

    @staticmethod
    def _convert_all(items: Union[Set, Dict, None]) -> Union[Set, Dict, None]:
        """
        Helper to convert __all__ pydantic special index to ormar which does not
        support index based exclusions.

        :param items: current include/exclude value
        :type items: Union[Set, Dict, None]
        """
        if isinstance(items, dict) and "__all__" in items:
            return items.get("__all__")
        return items

    def _extract_nested_models(  # noqa: CCR001, CFQ002
        self,
        relation_map: Dict,
        dict_instance: Dict,
        include: Optional[Dict],
        exclude: Optional[Dict],
        exclude_primary_keys: bool,
        exclude_through_models: bool,
        exclude_list: bool,
    ) -> Dict:
        """
        Traverse nested models and converts them into dictionaries.
        Calls itself recursively if needed.

        :param nested: flag if current instance is nested
        :type nested: bool
        :param dict_instance: current instance dict
        :type dict_instance: Dict
        :param include: fields to include
        :type include: Optional[Dict]
        :param exclude: fields to exclude
        :type exclude: Optional[Dict]
        :param exclude: whether to exclude lists
        :type exclude: bool
        :return: current model dict with child models converted to dictionaries
        :rtype: Dict
        """
        fields = self._get_not_excluded_fields(
            fields=self.extract_related_names(), include=include, exclude=exclude
        )

        for field in fields:
            if not relation_map or field not in relation_map:
                continue
            try:
                nested_model = getattr(self, field)
                if isinstance(nested_model, MutableSequence):
                    if exclude_list:
                        continue

                    dict_instance[field] = self._extract_nested_models_from_list(
                        relation_map=self._skip_ellipsis(  # type: ignore
                            relation_map, field, default_return=dict()
                        ),
                        models=nested_model,
                        include=self._convert_all(self._skip_ellipsis(include, field)),
                        exclude=self._convert_all(self._skip_ellipsis(exclude, field)),
                        exclude_primary_keys=exclude_primary_keys,
                        exclude_through_models=exclude_through_models,
                    )
                elif nested_model is not None:

                    dict_instance[field] = nested_model.dict(
                        relation_map=self._skip_ellipsis(
                            relation_map, field, default_return=dict()
                        ),
                        include=self._convert_all(self._skip_ellipsis(include, field)),
                        exclude=self._convert_all(self._skip_ellipsis(exclude, field)),
                        exclude_primary_keys=exclude_primary_keys,
                        exclude_through_models=exclude_through_models,
                    )
                else:
                    dict_instance[field] = None
            except ReferenceError:
                dict_instance[field] = None
        return dict_instance

    def dict(  # type: ignore # noqa A003
        self,
        *,
        include: Union[Set, Dict] = None,
        exclude: Union[Set, Dict] = None,
        by_alias: bool = False,
        skip_defaults: bool = None,
        exclude_unset: bool = False,
        exclude_defaults: bool = False,
        exclude_none: bool = False,
        exclude_primary_keys: bool = False,
        exclude_through_models: bool = False,
        exclude_list: bool = False,
        relation_map: Dict = None,
    ) -> "DictStrAny":  # noqa: A003'
        """

        Generate a dictionary representation of the model,
        optionally specifying which fields to include or exclude.

        Nested models are also parsed to dictionaries.

        Additionally fields decorated with @property_field are also added.

        :param exclude_through_models: flag to exclude through models from dict
        :type exclude_through_models: bool
        :param exclude_primary_keys: flag to exclude primary keys from dict
        :type exclude_primary_keys: bool
        :param include: fields to include
        :type include: Union[Set, Dict, None]
        :param exclude: fields to exclude
        :type exclude: Union[Set, Dict, None]
        :param by_alias: flag to get values by alias - passed to pydantic
        :type by_alias: bool
        :param skip_defaults: flag to not set values - passed to pydantic
        :type skip_defaults: bool
        :param exclude_unset: flag to exclude not set values - passed to pydantic
        :type exclude_unset: bool
        :param exclude_defaults: flag to exclude default values - passed to pydantic
        :type exclude_defaults: bool
        :param exclude_none: flag to exclude None values - passed to pydantic
        :type exclude_none: bool
        :param exclude_list: flag to exclude lists of nested values models from dict
        :type exclude_list: bool
        :param relation_map: map of the relations to follow to avoid circural deps
        :type relation_map: Dict
        :return:
        :rtype:
        """
        pydantic_exclude = self._update_excluded_with_related(exclude)
        pydantic_exclude = self._update_excluded_with_pks_and_through(
            exclude=pydantic_exclude,
            exclude_primary_keys=exclude_primary_keys,
            exclude_through_models=exclude_through_models,
        )
        dict_instance = super().dict(
            include=include,
            exclude=pydantic_exclude,
            by_alias=by_alias,
            skip_defaults=skip_defaults,
            exclude_unset=exclude_unset,
            exclude_defaults=exclude_defaults,
            exclude_none=exclude_none,
        )

        dict_instance = {
            k: self._convert_bytes_to_str(column_name=k, value=v)
            for k, v in dict_instance.items()
        }

        if include and isinstance(include, Set):
            include = translate_list_to_dict(include)
        if exclude and isinstance(exclude, Set):
            exclude = translate_list_to_dict(exclude)

        relation_map = (
            relation_map
            if relation_map is not None
            else translate_list_to_dict(self._iterate_related_models())
        )
        pk_only = getattr(self, "__pk_only__", False)
        if relation_map and not pk_only:
            dict_instance = self._extract_nested_models(
                relation_map=relation_map,
                dict_instance=dict_instance,
                include=include,  # type: ignore
                exclude=exclude,  # type: ignore
                exclude_primary_keys=exclude_primary_keys,
                exclude_through_models=exclude_through_models,
                exclude_list=exclude_list,
            )

        # include model properties as fields in dict
        if object.__getattribute__(self, "Meta").property_fields:
            props = self.get_properties(include=include, exclude=exclude)
            if props:
                dict_instance.update({prop: getattr(self, prop) for prop in props})

        return dict_instance

    def json(  # type: ignore # noqa A003
        self,
        *,
        include: Union[Set, Dict] = None,
        exclude: Union[Set, Dict] = None,
        by_alias: bool = False,
        skip_defaults: bool = None,
        exclude_unset: bool = False,
        exclude_defaults: bool = False,
        exclude_none: bool = False,
        encoder: Optional[Callable[[Any], Any]] = None,
        exclude_primary_keys: bool = False,
        exclude_through_models: bool = False,
        **dumps_kwargs: Any,
    ) -> str:
        """
        Generate a JSON representation of the model, `include` and `exclude`
        arguments as per `dict()`.

        `encoder` is an optional function to supply as `default` to json.dumps(),
        other arguments as per `json.dumps()`.
        """
        if skip_defaults is not None:  # pragma: no cover
            warnings.warn(
                f'{self.__class__.__name__}.json(): "skip_defaults" is deprecated '
                f'and replaced by "exclude_unset"',
                DeprecationWarning,
            )
            exclude_unset = skip_defaults
        encoder = cast(Callable[[Any], Any], encoder or self.__json_encoder__)
        data = self.dict(
            include=include,
            exclude=exclude,
            by_alias=by_alias,
            exclude_unset=exclude_unset,
            exclude_defaults=exclude_defaults,
            exclude_none=exclude_none,
            exclude_primary_keys=exclude_primary_keys,
            exclude_through_models=exclude_through_models,
        )
        if self.__custom_root_type__:  # pragma: no cover
            data = data["__root__"]
        return self.__config__.json_dumps(data, default=encoder, **dumps_kwargs)

    @classmethod
    def construct(
        cls: Type["T"], _fields_set: Optional["SetStr"] = None, **values: Any
    ) -> "T":
        own_values = {
            k: v for k, v in values.items() if k not in cls.extract_related_names()
        }
        model = cls.__new__(cls)
        fields_values: Dict[str, Any] = {}
        for name, field in cls.__fields__.items():
            if name in own_values:
                fields_values[name] = own_values[name]
            elif not field.required:
                fields_values[name] = field.get_default()
        fields_values.update(own_values)
        object.__setattr__(model, "__dict__", fields_values)
        model._initialize_internal_attributes()
        cls._construct_relations(model=model, values=values)
        if _fields_set is None:
            _fields_set = set(values.keys())
        object.__setattr__(model, "__fields_set__", _fields_set)
        return model

    @classmethod
    def _construct_relations(cls: Type["T"], model: "T", values: Dict) -> None:
        present_relations = [
            relation for relation in cls.extract_related_names() if relation in values
        ]
        for relation in present_relations:
            value_to_set = values[relation]
            if not isinstance(value_to_set, list):
                value_to_set = [value_to_set]
            relation_field = cls.Meta.model_fields[relation]
            relation_value = [
                relation_field.expand_relationship(x, model, to_register=False)
                for x in value_to_set
                if x is not None
            ]

            for child in relation_value:
                model._orm.add(
                    parent=cast("Model", child),
                    child=cast("Model", model),
                    field=cast("ForeignKeyField", relation_field),
                )

    def update_from_dict(self, value_dict: Dict) -> "NewBaseModel":
        """
        Updates self with values of fields passed in the dictionary.

        :param value_dict: dictionary of fields names and values
        :type value_dict: Dict
        :return: self
        :rtype: NewBaseModel
        """
        for key, value in value_dict.items():
            setattr(self, key, value)
        return self

    def _convert_to_bytes(self, column_name: str, value: Any) -> Union[str, Dict]:
        """
        Converts value to bytes from string

        :param column_name: name of the field
        :type column_name: str
        :param value: value fo the field
        :type value: Any
        :return: converted value if needed, else original value
        :rtype: Any
        """
        if column_name not in self._bytes_fields:
            return value
        field = self.Meta.model_fields[column_name]
        if not isinstance(value, bytes) and value is not None:
            if field.represent_as_base64_str:
                value = base64.b64decode(value)
            else:
                value = value.encode("utf-8")
        return value

    def _convert_bytes_to_str(self, column_name: str, value: Any) -> Union[str, Dict]:
        """
        Converts value to str from bytes for represent_as_base64_str columns.

        :param column_name: name of the field
        :type column_name: str
        :param value: value fo the field
        :type value: Any
        :return: converted value if needed, else original value
        :rtype: Any
        """
        if column_name not in self._bytes_fields:
            return value
        field = self.Meta.model_fields[column_name]
        if (
            value is not None
            and not isinstance(value, str)
            and field.represent_as_base64_str
        ):
            return base64.b64encode(value).decode()
        return value

    def _convert_json(self, column_name: str, value: Any) -> Union[str, Dict, None]:
        """
        Converts value to/from json if needed (for Json columns).

        :param column_name: name of the field
        :type column_name: str
        :param value: value fo the field
        :type value: Any
        :return: converted value if needed, else original value
        :rtype: Any
        """
        if column_name not in self._json_fields:
            return value
        return encode_json(value)

    def _extract_own_model_fields(self) -> Dict:
        """
        Returns a dictionary with field names and values for fields that are not
        relations fields (ForeignKey, ManyToMany etc.)

        :return: dictionary of fields names and values.
        :rtype: Dict
        """
        related_names = self.extract_related_names()
        self_fields = {k: v for k, v in self.__dict__.items() if k not in related_names}
        return self_fields

    def _extract_model_db_fields(self) -> Dict:
        """
        Returns a dictionary with field names and values for fields that are stored in
        current model's table.

        That includes own non-relational fields ang foreign key fields.

        :return: dictionary of fields names and values.
        :rtype: Dict
        """
        # TODO: Cache this dictionary?
        self_fields = self._extract_own_model_fields()
        self_fields = {
            k: v
            for k, v in self_fields.items()
            if self.get_column_alias(k) in self.Meta.table.columns
        }
        for field in self._extract_db_related_names():
            relation_field = self.Meta.model_fields[field]
            target_pk_name = relation_field.to.Meta.pkname
            target_field = getattr(self, field)
            self_fields[field] = getattr(target_field, target_pk_name, None)
            if not relation_field.nullable and not self_fields[field]:
                raise ModelPersistenceError(
                    f"You cannot save {relation_field.to.get_name()} "
                    f"model without pk set!"
                )
        return self_fields

    def get_relation_model_id(self, target_field: "BaseField") -> Optional[int]:
        """
        Returns an id of the relation side model to use in prefetch query.

        :param target_field: field with relation definition
        :type target_field: "BaseField"
        :return: value of pk if set
        :rtype: Optional[int]
        """
        if target_field.virtual or target_field.is_multi:
            return self.pk
        related_name = target_field.name
        related_model = getattr(self, related_name)
        return None if not related_model else related_model.pk

pk_column: sqlalchemy.Column property

Retrieves primary key sqlalchemy column from models Meta.table. Each model has to have primary key. Only one primary key column is allowed.

Returns:

Type Description
sqlalchemy.Column

primary key sqlalchemy column

saved: bool property

Saved status of the model. Changed by setattr and loading from db

signals: SignalEmitter property

Exposes signals from model Meta

__eq__(other)

Compares other model to this model. when == is called.

Parameters:

Name Type Description Default
other object

other model to compare

 required

Returns:

Type Description
bool

result of comparison
Source code in ormar\models\newbasemodel.py 
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def __eq__(self, other: object) -> bool:
    """
    Compares other model to this model. when == is called.
    :param other: other model to compare
    :type other: object
    :return: result of comparison
    :rtype: bool
    """
    if isinstance(other, NewBaseModel):
        return self.__same__(other)
    return super().__eq__(other)  # pragma no cover

__getattr__(item)

Used only to silence mypy errors for Through models and reverse relations. Not used in real life as in practice calls are intercepted by RelationDescriptors

Parameters:

Name Type Description Default
item str

name of attribute

 required

Returns:

Type Description
Any

Any
Source code in ormar\models\newbasemodel.py 
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def __getattr__(self, item: str) -> Any:
    """
    Used only to silence mypy errors for Through models and reverse relations.
    Not used in real life as in practice calls are intercepted
    by RelationDescriptors

    :param item: name of attribute
    :type item: str
    :return: Any
    :rtype: Any
    """
    return super().__getattribute__(item)

__init__(*args, **kwargs)

Initializer that creates a new ormar Model that is also pydantic Model at the same time.

Passed keyword arguments can be only field names and their corresponding values as those will be passed to pydantic validation that will complain if extra params are passed.

If relations are defined each relation is expanded and children models are also initialized and validated. Relation from both sides is registered so you can access related models from both sides.

Json fields are automatically loaded/dumped if needed.

Models marked as abstract=True in internal Meta class cannot be initialized.

Accepts also special pk_only flag that indicates that Model is constructed only with primary key value (so no other fields, it's a child model on other Model), that causes skipping the validation, that's the only case when the validation can be skipped.

Accepts also special excluded parameter that contains a set of fields that should be explicitly set to None, as otherwise pydantic will try to populate them with their default values if default is set.

Parameters:

Name Type Description Default
args Any

ignored args

 ()
kwargs Any

keyword arguments - all fields values and some special params

 {}

Raises:

Type Description
ModelError

if abstract model is initialized, model has ForwardRefs that has not been updated or unknown field is passed
Source code in ormar\models\newbasemodel.py 
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def __init__(self, *args: Any, **kwargs: Any) -> None:  # type: ignore
    """
    Initializer that creates a new ormar Model that is also pydantic Model at the
    same time.

    Passed keyword arguments can be only field names and their corresponding values
    as those will be passed to pydantic validation that will complain if extra
    params are passed.

    If relations are defined each relation is expanded and children models are also
    initialized and validated. Relation from both sides is registered so you can
    access related models from both sides.

    Json fields are automatically loaded/dumped if needed.

    Models marked as abstract=True in internal Meta class cannot be initialized.

    Accepts also special __pk_only__ flag that indicates that Model is constructed
    only with primary key value (so no other fields, it's a child model on other
    Model), that causes skipping the validation, that's the only case when the
    validation can be skipped.

    Accepts also special __excluded__ parameter that contains a set of fields that
    should be explicitly set to None, as otherwise pydantic will try to populate
    them with their default values if default is set.

    :raises ModelError: if abstract model is initialized, model has ForwardRefs
     that has not been updated or unknown field is passed
    :param args: ignored args
    :type args: Any
    :param kwargs: keyword arguments - all fields values and some special params
    :type kwargs: Any
    """
    self._verify_model_can_be_initialized()
    self._initialize_internal_attributes()

    pk_only = kwargs.pop("__pk_only__", False)
    object.__setattr__(self, "__pk_only__", pk_only)

    new_kwargs, through_tmp_dict = self._process_kwargs(kwargs)

    if not pk_only:
        values, fields_set, validation_error = pydantic.validate_model(
            self, new_kwargs  # type: ignore
        )
        if validation_error:
            raise validation_error
    else:
        fields_set = {self.Meta.pkname}
        values = new_kwargs

    object.__setattr__(self, "__dict__", values)
    object.__setattr__(self, "__fields_set__", fields_set)

    # add back through fields
    new_kwargs.update(through_tmp_dict)
    model_fields = object.__getattribute__(self, "Meta").model_fields
    # register the columns models after initialization
    for related in self.extract_related_names().union(self.extract_through_names()):
        model_fields[related].expand_relationship(
            new_kwargs.get(related), self, to_register=True
        )

    if hasattr(self, "_init_private_attributes"):
        # introduced in pydantic 1.7
        self._init_private_attributes()

__same__(other)

Used by eq, compares other model to this model. Compares: * _orm_ids, * primary key values if it's set * dictionary of own fields (excluding relations)

Parameters:

Name Type Description Default
other NewBaseModel

model to compare to

 required

Returns:

Type Description
bool

result of comparison
Source code in ormar\models\newbasemodel.py 
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def __same__(self, other: "NewBaseModel") -> bool:
    """
    Used by __eq__, compares other model to this model.
    Compares:
    * _orm_ids,
    * primary key values if it's set
    * dictionary of own fields (excluding relations)
    :param other: model to compare to
    :type other: NewBaseModel
    :return: result of comparison
    :rtype: bool
    """
    if (self.pk is None and other.pk is not None) or (
        self.pk is not None and other.pk is None
    ):
        return False
    else:
        return hash(self) == other.__hash__()

__setattr__(name, value)

Overwrites setattr in pydantic parent as otherwise descriptors are not called.

Parameters:

Name Type Description Default
name str

name of the attribute to set

 required
value Any

value of the attribute to set

 required

Returns:

Type Description
None

None
Source code in ormar\models\newbasemodel.py 
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def __setattr__(self, name: str, value: Any) -> None:  # noqa CCR001
    """
    Overwrites setattr in pydantic parent as otherwise descriptors are not called.

    :param name: name of the attribute to set
    :type name: str
    :param value: value of the attribute to set
    :type value: Any
    :return: None
    :rtype: None
    """
    prev_hash = hash(self)

    if hasattr(self, name):
        object.__setattr__(self, name, value)
    else:
        # let pydantic handle errors for unknown fields
        super().__setattr__(name, value)

    # In this case, the hash could have changed, so update it
    if name == self.Meta.pkname or self.pk is None:
        object.__setattr__(self, "__cached_hash__", None)
        new_hash = hash(self)

        if prev_hash != new_hash:
            self._update_relation_cache(prev_hash, new_hash)

db_backend_name() classmethod

Shortcut to database dialect, cause some dialect require different treatment

Source code in ormar\models\newbasemodel.py 
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@classmethod
def db_backend_name(cls) -> str:
    """Shortcut to database dialect,
    cause some dialect require different treatment"""
    return cls.Meta.database._backend._dialect.name

dict(*, include=None, exclude=None, by_alias=False, skip_defaults=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, exclude_primary_keys=False, exclude_through_models=False, exclude_list=False, relation_map=None)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Nested models are also parsed to dictionaries.

Additionally fields decorated with @property_field are also added.

Parameters:

Name Type Description Default
exclude_through_models bool

flag to exclude through models from dict

 False
exclude_primary_keys bool

flag to exclude primary keys from dict

 False
include Union[Set, Dict]

fields to include

 None
exclude Union[Set, Dict]

fields to exclude

 None
by_alias bool

flag to get values by alias - passed to pydantic

 False
skip_defaults bool

flag to not set values - passed to pydantic

 None
exclude_unset bool

flag to exclude not set values - passed to pydantic

 False
exclude_defaults bool

flag to exclude default values - passed to pydantic

 False
exclude_none bool

flag to exclude None values - passed to pydantic

 False
exclude_list bool

flag to exclude lists of nested values models from dict

 False
relation_map Dict

map of the relations to follow to avoid circural deps

 None

Returns:

Type Description
Source code in ormar\models\newbasemodel.py 
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def dict(  # type: ignore # noqa A003
    self,
    *,
    include: Union[Set, Dict] = None,
    exclude: Union[Set, Dict] = None,
    by_alias: bool = False,
    skip_defaults: bool = None,
    exclude_unset: bool = False,
    exclude_defaults: bool = False,
    exclude_none: bool = False,
    exclude_primary_keys: bool = False,
    exclude_through_models: bool = False,
    exclude_list: bool = False,
    relation_map: Dict = None,
) -> "DictStrAny":  # noqa: A003'
    """

    Generate a dictionary representation of the model,
    optionally specifying which fields to include or exclude.

    Nested models are also parsed to dictionaries.

    Additionally fields decorated with @property_field are also added.

    :param exclude_through_models: flag to exclude through models from dict
    :type exclude_through_models: bool
    :param exclude_primary_keys: flag to exclude primary keys from dict
    :type exclude_primary_keys: bool
    :param include: fields to include
    :type include: Union[Set, Dict, None]
    :param exclude: fields to exclude
    :type exclude: Union[Set, Dict, None]
    :param by_alias: flag to get values by alias - passed to pydantic
    :type by_alias: bool
    :param skip_defaults: flag to not set values - passed to pydantic
    :type skip_defaults: bool
    :param exclude_unset: flag to exclude not set values - passed to pydantic
    :type exclude_unset: bool
    :param exclude_defaults: flag to exclude default values - passed to pydantic
    :type exclude_defaults: bool
    :param exclude_none: flag to exclude None values - passed to pydantic
    :type exclude_none: bool
    :param exclude_list: flag to exclude lists of nested values models from dict
    :type exclude_list: bool
    :param relation_map: map of the relations to follow to avoid circural deps
    :type relation_map: Dict
    :return:
    :rtype:
    """
    pydantic_exclude = self._update_excluded_with_related(exclude)
    pydantic_exclude = self._update_excluded_with_pks_and_through(
        exclude=pydantic_exclude,
        exclude_primary_keys=exclude_primary_keys,
        exclude_through_models=exclude_through_models,
    )
    dict_instance = super().dict(
        include=include,
        exclude=pydantic_exclude,
        by_alias=by_alias,
        skip_defaults=skip_defaults,
        exclude_unset=exclude_unset,
        exclude_defaults=exclude_defaults,
        exclude_none=exclude_none,
    )

    dict_instance = {
        k: self._convert_bytes_to_str(column_name=k, value=v)
        for k, v in dict_instance.items()
    }

    if include and isinstance(include, Set):
        include = translate_list_to_dict(include)
    if exclude and isinstance(exclude, Set):
        exclude = translate_list_to_dict(exclude)

    relation_map = (
        relation_map
        if relation_map is not None
        else translate_list_to_dict(self._iterate_related_models())
    )
    pk_only = getattr(self, "__pk_only__", False)
    if relation_map and not pk_only:
        dict_instance = self._extract_nested_models(
            relation_map=relation_map,
            dict_instance=dict_instance,
            include=include,  # type: ignore
            exclude=exclude,  # type: ignore
            exclude_primary_keys=exclude_primary_keys,
            exclude_through_models=exclude_through_models,
            exclude_list=exclude_list,
        )

    # include model properties as fields in dict
    if object.__getattribute__(self, "Meta").property_fields:
        props = self.get_properties(include=include, exclude=exclude)
        if props:
            dict_instance.update({prop: getattr(self, prop) for prop in props})

    return dict_instance

get_name(lower=True) classmethod

Returns name of the Model class, by default lowercase.

Parameters:

Name Type Description Default
lower bool

flag if name should be set to lowercase

 True

Returns:

Type Description
str

name of the model
Source code in ormar\models\newbasemodel.py 
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@classmethod
def get_name(cls, lower: bool = True) -> str:
    """
    Returns name of the Model class, by default lowercase.

    :param lower: flag if name should be set to lowercase
    :type lower: bool
    :return: name of the model
    :rtype: str
    """
    name = cls.__name__
    if lower:
        name = name.lower()
    return name

get_properties(include, exclude) classmethod

Returns a set of names of functions/fields decorated with @property_field decorator.

They are added to dictionary when called directly and therefore also are present in fastapi responses.

Parameters:

Name Type Description Default
include Union[Set, Dict, None]

fields to include

 required
exclude Union[Set, Dict, None]

fields to exclude

 required

Returns:

Type Description
Set[str]

set of property fields names
Source code in ormar\models\newbasemodel.py 
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@classmethod
def get_properties(
    cls, include: Union[Set, Dict, None], exclude: Union[Set, Dict, None]
) -> Set[str]:
    """
    Returns a set of names of functions/fields decorated with
    @property_field decorator.

    They are added to dictionary when called directly and therefore also are
    present in fastapi responses.

    :param include: fields to include
    :type include: Union[Set, Dict, None]
    :param exclude: fields to exclude
    :type exclude: Union[Set, Dict, None]
    :return: set of property fields names
    :rtype: Set[str]
    """

    props = cls.Meta.property_fields
    if include:
        props = {prop for prop in props if prop in include}
    if exclude:
        props = {prop for prop in props if prop not in exclude}
    return props

get_relation_model_id(target_field)

Returns an id of the relation side model to use in prefetch query.

Parameters:

Name Type Description Default
target_field BaseField

field with relation definition

 required

Returns:

Type Description
Optional[int]

value of pk if set
Source code in ormar\models\newbasemodel.py 
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def get_relation_model_id(self, target_field: "BaseField") -> Optional[int]:
    """
    Returns an id of the relation side model to use in prefetch query.

    :param target_field: field with relation definition
    :type target_field: "BaseField"
    :return: value of pk if set
    :rtype: Optional[int]
    """
    if target_field.virtual or target_field.is_multi:
        return self.pk
    related_name = target_field.name
    related_model = getattr(self, related_name)
    return None if not related_model else related_model.pk

json(*, include=None, exclude=None, by_alias=False, skip_defaults=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=None, exclude_primary_keys=False, exclude_through_models=False, **dumps_kwargs)

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

Source code in ormar\models\newbasemodel.py 
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def json(  # type: ignore # noqa A003
    self,
    *,
    include: Union[Set, Dict] = None,
    exclude: Union[Set, Dict] = None,
    by_alias: bool = False,
    skip_defaults: bool = None,
    exclude_unset: bool = False,
    exclude_defaults: bool = False,
    exclude_none: bool = False,
    encoder: Optional[Callable[[Any], Any]] = None,
    exclude_primary_keys: bool = False,
    exclude_through_models: bool = False,
    **dumps_kwargs: Any,
) -> str:
    """
    Generate a JSON representation of the model, `include` and `exclude`
    arguments as per `dict()`.

    `encoder` is an optional function to supply as `default` to json.dumps(),
    other arguments as per `json.dumps()`.
    """
    if skip_defaults is not None:  # pragma: no cover
        warnings.warn(
            f'{self.__class__.__name__}.json(): "skip_defaults" is deprecated '
            f'and replaced by "exclude_unset"',
            DeprecationWarning,
        )
        exclude_unset = skip_defaults
    encoder = cast(Callable[[Any], Any], encoder or self.__json_encoder__)
    data = self.dict(
        include=include,
        exclude=exclude,
        by_alias=by_alias,
        exclude_unset=exclude_unset,
        exclude_defaults=exclude_defaults,
        exclude_none=exclude_none,
        exclude_primary_keys=exclude_primary_keys,
        exclude_through_models=exclude_through_models,
    )
    if self.__custom_root_type__:  # pragma: no cover
        data = data["__root__"]
    return self.__config__.json_dumps(data, default=encoder, **dumps_kwargs)

pk_type() classmethod

Shortcut to models primary key field type

Source code in ormar\models\newbasemodel.py 
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@classmethod
def pk_type(cls) -> Any:
    """Shortcut to models primary key field type"""
    return cls.Meta.model_fields[cls.Meta.pkname].__type__

remove(parent, name)

Removes child from relation with given name in RelationshipManager

Source code in ormar\models\newbasemodel.py 
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def remove(self, parent: "Model", name: str) -> None:
    """Removes child from relation with given name in RelationshipManager"""
    self._orm.remove_parent(self, parent, name)

set_save_status(status)

Sets value of the save status

Source code in ormar\models\newbasemodel.py 
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def set_save_status(self, status: bool) -> None:
    """Sets value of the save status"""
    object.__setattr__(self, "_orm_saved", status)

update_forward_refs(**localns) classmethod

Processes fields that are ForwardRef and need to be evaluated into actual models.

Expands relationships, register relation in alias manager and substitutes sqlalchemy columns with new ones with proper column type (null before).

Populates Meta table of the Model which is left empty before.

Sets self_reference flag on models that links to themselves.

Calls the pydantic method to evaluate pydantic fields.

Parameters:

Name Type Description Default
localns Any

local namespace

 {}

Returns:

Type Description
None

None
Source code in ormar\models\newbasemodel.py 
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@classmethod
def update_forward_refs(cls, **localns: Any) -> None:
    """
    Processes fields that are ForwardRef and need to be evaluated into actual
    models.

    Expands relationships, register relation in alias manager and substitutes
    sqlalchemy columns with new ones with proper column type (null before).

    Populates Meta table of the Model which is left empty before.

    Sets self_reference flag on models that links to themselves.

    Calls the pydantic method to evaluate pydantic fields.

    :param localns: local namespace
    :type localns: Any
    :return: None
    :rtype: None
    """
    globalns = sys.modules[cls.__module__].__dict__.copy()
    globalns.setdefault(cls.__name__, cls)
    fields_to_check = cls.Meta.model_fields.copy()
    for field in fields_to_check.values():
        if field.has_unresolved_forward_refs():
            field = cast(ForeignKeyField, field)
            field.evaluate_forward_ref(globalns=globalns, localns=localns)
            field.set_self_reference_flag()
            if field.is_multi and not field.through:
                field = cast(ormar.ManyToManyField, field)
                field.create_default_through_model()
            expand_reverse_relationship(model_field=field)
            register_relation_in_alias_manager(field=field)
            update_column_definition(model=cls, field=field)
    populate_meta_sqlalchemy_table_if_required(meta=cls.Meta)
    super().update_forward_refs(**localns)
    cls.Meta.requires_ref_update = False

update_from_dict(value_dict)

Updates self with values of fields passed in the dictionary.

Parameters:

Name Type Description Default
value_dict Dict

dictionary of fields names and values

 required

Returns:

Type Description
NewBaseModel

self
Source code in ormar\models\newbasemodel.py 
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def update_from_dict(self, value_dict: Dict) -> "NewBaseModel":
    """
    Updates self with values of fields passed in the dictionary.

    :param value_dict: dictionary of fields names and values
    :type value_dict: Dict
    :return: self
    :rtype: NewBaseModel
    """
    for key, value in value_dict.items():
        setattr(self, key, value)
    return self