def _convert_from_pydantic_to_strawberry_type(
type_: Union[StrawberryType, type], data_from_model=None, extra=None
):
data = data_from_model if data_from_model is not None else extra
if isinstance(type_, StrawberryOptional):
if data is None:
return data
return _convert_from_pydantic_to_strawberry_type(
type_.of_type, data_from_model=data, extra=extra
)
if isinstance(type_, StrawberryUnion):
for option_type in type_.types:
if hasattr(option_type, "_pydantic_type"):
source_type = option_type._pydantic_type # type: ignore
else:
source_type = cast(type, option_type)
if isinstance(data, source_type):
return _convert_from_pydantic_to_strawberry_type(
option_type, data_from_model=data, extra=extra
)
if isinstance(type_, EnumDefinition):
return data
if isinstance(type_, StrawberryList):
items = []
for index, item in enumerate(data):
items.append(
_convert_from_pydantic_to_strawberry_type(
type_.of_type,
data_from_model=item,
extra=extra[index] if extra else None,
)
)
return items
elif is_scalar(type_):
return data
else:
# in the case of an interface, the concrete type may be more specific
# than the type in the field definition
if hasattr(type(data), "_strawberry_type"):
type_ = type(data)._strawberry_type
return convert_pydantic_model_to_strawberry_class(
type_, model_instance=data_from_model, extra=extra
)
def _convert_from_pydantic_to_strawberry_field(field: FieldDefinition,
data_from_model=None,
extra=None):
data = data_from_model or extra
if field.is_list:
child = field.child
items = [None for _ in data]
for index, item in enumerate(data):
items[index] = _convert_from_pydantic_to_strawberry_field(
cast(FieldDefinition, child),
data_from_model=item,
extra=extra[index] if extra else None,
)
return items
elif is_scalar(field.type): # type: ignore
return data
else:
return convert_pydantic_model_to_strawberry_class(
field.type, model_instance=data_from_model, extra=extra)
def _convert_from_pydantic_to_strawberry_field(field: StrawberryField,
data_from_model=None,
extra=None):
data = data_from_model if data_from_model is not None else extra
if field.is_list:
assert field.child is not None
items = [None for _ in data]
for index, item in enumerate(data):
items[index] = _convert_from_pydantic_to_strawberry_field(
field.child,
data_from_model=item,
extra=extra[index] if extra else None,
)
return items
elif is_scalar(field.type): # type: ignore
return data
else:
return convert_pydantic_model_to_strawberry_class(
field.type, model_instance=data_from_model, extra=extra)
def get_type_for_annotation(annotation: Type,
type_map: TypeMap) -> GraphQLType:
graphql_type: Optional[GraphQLType] = None
# this adds support for type annotations that use
# strings, without having to use get_type_hints
if type(annotation) == str and annotation in type_map:
return type_map[annotation].implementation # type: ignore
if is_scalar(annotation):
graphql_type = get_scalar_type(annotation, type_map)
elif hasattr(annotation, "_enum_definition"):
graphql_type = get_enum_type(annotation._enum_definition, type_map)
elif hasattr(annotation, "_type_definition"):
from .object_type import get_object_type
graphql_type = get_object_type(annotation, type_map)
if not graphql_type:
raise ValueError(f"Unable to get GraphQL type for {annotation}")
return graphql_type
def _is_scalar(type_: Union[StrawberryType, type]) -> TypeGuard[type]:
# isinstance(type_, StrawberryScalar) # noqa: E800
return is_scalar(type_)
def _is_scalar(type_: Type) -> bool:
# isinstance(type_, StrawberryScalar) # noqa: E800
return is_scalar(type_)
def _is_scalar(
type_: Union[StrawberryType, type],
scalar_registry: Dict[object, Union[ScalarWrapper, ScalarDefinition]],
) -> TypeGuard[type]:
# isinstance(type_, StrawberryScalar) # noqa: E800
return is_scalar(type_, scalar_registry)
