def inner(type_, options):
if is_dataclass(type_):
if _issubclass_safe(type_, mixin):
options['field_many'] = bool(
_is_supported_generic(field.type)
and _is_collection(field.type))
return fields.Nested(type_.schema(), **options)
else:
warnings.warn(f"Nested dataclass field {field.name} of type "
f"{field.type} detected in "
f"{cls.__name__} that is not an instance of "
f"dataclass_json. Did you mean to recursively "
f"serialize this field? If so, make sure to "
f"augment {type_} with either the "
f"`dataclass_json` decorator or mixin.")
return fields.Field(**options)
origin = getattr(type_, '__origin__', type_)
args = [inner(a, {}) for a in getattr(type_, '__args__', [])]
if origin in TYPES:
return TYPES[origin](*args, **options)
warnings.warn(
f"Unknown type {type_} at {cls.__name__}.{field.name}: {field.type} "
f"It's advised to pass the correct marshmallow type to `mm_field`."
)
return fields.Field(**options)
def _inspect_nested_fields(fields_):
nested_dc_fields_and_is_many = []
for field in fields_:
if _is_supported_generic(field.type):
t_arg = field.type.__args__[0]
if is_dataclass(t_arg):
if _is_collection(field.type):
nested_dc_fields_and_is_many.append((field, t_arg, True))
else:
nested_dc_fields_and_is_many.append((field, t_arg, False))
elif is_dataclass(field.type):
nested_dc_fields_and_is_many.append((field, field.type, False))
return nested_dc_fields_and_is_many
def __coerce_value(cls, typ: type, value):
if isinstance(value, np.generic):
# Handle numpy types
return value.item()
elif hasattr(value, 'tolist'):
# tolist converts scalar or array to native python type if not already native.
return value()
elif typ in (DictBase, Optional[DictBase]) and isinstance(value, Base):
return value.to_dict()
if _is_supported_generic(typ):
return _decode_generic(typ, value, False)
else:
return value
def inner(type_, options):
while True:
if not _is_new_type(type_):
break
type_ = type_.__supertype__
if is_dataclass(type_):
if _issubclass_safe(type_, mixin):
options['field_many'] = bool(
_is_supported_generic(field.type)
and _is_collection(field.type))
return fields.Nested(type_.schema(), **options)
else:
warnings.warn(f"Nested dataclass field {field.name} of type "
f"{field.type} detected in "
f"{cls.__name__} that is not an instance of "
f"dataclass_json. Did you mean to recursively "
f"serialize this field? If so, make sure to "
f"augment {type_} with either the "
f"`dataclass_json` decorator or mixin.")
return fields.Field(**options)
origin = getattr(type_, '__origin__', type_)
args = [
inner(a, {}) for a in getattr(type_, '__args__', [])
if a is not type(None)
]
if _is_optional(type_):
options["allow_none"] = True
if origin in TYPES:
return TYPES[origin](*args, **options)
if _issubclass_safe(origin, Enum):
return EnumField(enum=origin, by_value=True, *args, **options)
if is_union_type(type_):
union_types = [
a for a in getattr(type_, '__args__', [])
if a is not type(None)
]
union_desc = dict(zip(union_types, args))
return _UnionField(union_desc, cls, field, **options)
warnings.warn(
f"Unknown type {type_} at {cls.__name__}.{field.name}: {field.type} "
f"It's advised to pass the correct marshmallow type to `mm_field`."
)
return fields.Field(**options)
def _decode_generic_subsets(type_, value, infer_missing):
if value is None:
res = value
elif _issubclass_safe(type_, Enum):
# Convert to an Enum using the type as a constructor.
# Assumes a direct match is found.
res = type_(value)
# FIXME this is a hack to fix a deeper underlying issue. A refactor is due.
elif _is_collection(type_):
if _is_mapping(type_):
k_type, v_type = getattr(type_, "__args__", (Any, Any))
# a mapping type has `.keys()` and `.values()`
# (see collections.abc)
ks = _decode_dict_keys(k_type, value.keys(), infer_missing)
vs = _decode_items(v_type, value.values(), infer_missing)
xs = zip(ks, vs)
else:
xs = (_decode_dataclass(getSubclass(type_, v), v, infer_missing)
for v in value)
# get the constructor if using corresponding generic type in `typing`
# otherwise fallback on constructing using type_ itself
try:
res = _get_type_cons(type_)(xs)
except (TypeError, AttributeError):
res = type_(xs)
else: # Optional or Union
if not hasattr(type_, "__args__"):
# Any, just accept
res = value
elif _is_optional(type_) and len(type_.__args__) == 2: # Optional
type_arg = type_.__args__[0]
if is_dataclass(type_arg) or is_dataclass(value):
res = _decode_dataclass(type_arg, value, infer_missing)
elif _is_supported_generic(type_arg):
res = _decode_generic(type_arg, value, infer_missing)
else:
res = _support_extended_types(type_arg, value)
else: # Union (already decoded or unsupported 'from_json' used)
res = value
return res
def _decode_dataclass(cls, kvs, infer_missing):
if isinstance(kvs, cls):
return kvs
# >>> OVERRIDE TYPE
if "__cls__" in kvs and kvs["__cls__"] != cls.__module__ + ":" + cls.__name__:
module, name = kvs["__cls__"].split(":")
cls = getattr(importlib.import_module(module), name)
# <<< END
overrides = _user_overrides_or_exts(cls)
kvs = {} if kvs is None and infer_missing else kvs
field_names = [field.name for field in fields(cls)]
decode_names = _decode_letter_case_overrides(field_names, overrides)
kvs = {decode_names.get(k, k): v for k, v in kvs.items()}
missing_fields = {field for field in fields(cls) if field.name not in kvs}
for field in missing_fields:
if field.default is not MISSING:
kvs[field.name] = field.default
elif field.default_factory is not MISSING:
kvs[field.name] = field.default_factory()
elif infer_missing:
kvs[field.name] = None
# Perform undefined parameter action
kvs = _handle_undefined_parameters_safe(cls, kvs, usage="from")
init_kwargs = {}
types = get_type_hints(cls)
for field in fields(cls):
# The field should be skipped from being added
# to init_kwargs as it's not intended as a constructor argument.
if not field.init:
continue
field_value = kvs[field.name]
field_type = types[field.name]
# >>> Support for Generic Types
if isinstance(field_type, TypeVar):
if not hasattr(field_type, "__bound__"):
warnings.warn(f"If using TypeVars, set the bound field for obtaining the default type. ")
else:
field_type = field_type.__bound__
# <<< Support for Generic Types
if field_value is None and not _is_optional(field_type):
warning = f"value of non-optional type {field.name} detected " f"when decoding {cls.__name__}"
if infer_missing:
warnings.warn(
f"Missing {warning} and was defaulted to None by "
f"infer_missing=True. "
f"Set infer_missing=False (the default) to prevent this "
f"behavior.",
RuntimeWarning,
)
else:
warnings.warn(f"`NoneType` object {warning}.", RuntimeWarning)
init_kwargs[field.name] = field_value
continue
while True:
if not _is_new_type(field_type):
break
field_type = field_type.__supertype__
if field.name in overrides and overrides[field.name].decoder is not None:
# FIXME hack
if field_type is type(field_value):
init_kwargs[field.name] = field_value
else:
init_kwargs[field.name] = overrides[field.name].decoder(field_value)
elif is_dataclass(field_type):
# FIXME this is a band-aid to deal with the value already being
# serialized when handling nested marshmallow schema
# proper fix is to investigate the marshmallow schema generation
# code
if is_dataclass(field_value):
value = field_value
else:
value = _decode_dataclass(field_type, field_value, infer_missing)
init_kwargs[field.name] = value
elif _is_supported_generic(field_type) and field_type != str:
init_kwargs[field.name] = _decode_generic(field_type, field_value, infer_missing)
else:
init_kwargs[field.name] = _support_extended_types(field_type, field_value)
return cls(**init_kwargs)
def _decode_dataclass(cls, kvs, infer_missing):
if isinstance(kvs, cls):
return kvs
overrides = _user_overrides_or_exts(cls)
kvs = {} if kvs is None and infer_missing else kvs
field_names = [field.name for field in fields(cls)]
decode_names = _decode_letter_case_overrides(field_names, overrides)
kvs = {decode_names.get(k, k): v for k, v in kvs.items()}
missing_fields = {field for field in fields(cls) if field.name not in kvs}
for field in missing_fields:
if field.default is not MISSING:
kvs[field.name] = field.default
elif field.default_factory is not MISSING:
kvs[field.name] = field.default_factory()
elif infer_missing:
kvs[field.name] = None
# Perform undefined parameter action
kvs = _handle_undefined_parameters_safe(cls, kvs, usage="from")
init_kwargs = {}
types = get_type_hints(cls)
for field in fields(cls):
# The field should be skipped from being added
# to init_kwargs as it's not intended as a constructor argument.
if not field.init:
continue
from typing import GenericMeta
field_value = kvs[field.name]
field_type = types[field.name]
if _is_supported_generic(field_type) and field_type.__args__[0] != str:
type_param = 'type' in [
f.name for f in fields(field_type.__args__[0])
]
elif 'type' in field_names:
type_param = True
else:
type_param = False
if field_value is None and not _is_optional(field_type):
warning = (f"value of non-optional type {field.name} detected "
f"when decoding {cls.__name__}")
if infer_missing:
warnings.warn(
f"Missing {warning} and was defaulted to None by "
f"infer_missing=True. "
f"Set infer_missing=False (the default) to prevent this "
f"behavior.", RuntimeWarning)
else:
pass
init_kwargs[field.name] = field_value
continue
while True:
if not _is_new_type(field_type):
break
field_type = field_type.__supertype__
if (field.name in overrides
and overrides[field.name].decoder is not None):
# FIXME hack
if field_type is type(field_value):
init_kwargs[field.name] = field_value
else:
init_kwargs[field.name] = overrides[field.name].decoder(
field_value)
elif is_dataclass(field_type):
# FIXME this is a band-aid to deal with the value already being
# serialized when handling nested marshmallow schema
# proper fix is to investigate the marshmallow schema generation
# code
if is_dataclass(field_value):
value = field_value
else:
value = _decode_dataclass(field_type, field_value,
infer_missing)
init_kwargs[field.name] = value
elif _is_supported_generic(
field_type) and field_type != str and not type_param:
init_kwargs[field.name] = _decode_generic(field_type, field_value,
infer_missing)
elif _is_supported_generic(
field_type) and field_type.__args__[0] != str and type_param:
init_kwargs[field.name] = _decode_generic_subsets(
field_type, field_value, infer_missing)
else:
init_kwargs[field.name] = _support_extended_types(
field_type, field_value)
return cls(**init_kwargs)
