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from typing import Callable, Collection, Optional
from .class_helper import (get_meta, CLASS_TO_LOAD_FUNC,
CLASS_TO_LOADER, CLASS_TO_V1_LOADER,
set_class_loader, create_new_class, CLASS_TO_DUMP_FUNC, CLASS_TO_V1_DUMPER, set_class_dumper,
CLASS_TO_DUMPER)
from .constants import _LOAD_HOOKS, _DUMP_HOOKS
from .type_def import T, JSONObject
def asdict(o: T,
*, cls=None,
dict_factory=dict,
exclude: 'Collection[str] | None' = None,
**kwargs) -> JSONObject:
# noinspection PyUnresolvedReferences
"""Return the fields of a dataclass instance as a new dictionary mapping
field names to field values.
Example usage:
@dataclass
class C:
x: int
y: int
c = C(1, 2)
assert asdict(c) == {'x': 1, 'y': 2}
When directly invoking this function, an optional Meta configuration for
the dataclass can be specified via ``DumpMeta``; by default, this will
apply recursively to any nested dataclasses. Here's a sample usage of this
below::
>>> DumpMeta(key_transform='CAMEL').bind_to(MyClass)
>>> asdict(MyClass(my_str="value"))
If given, 'dict_factory' will be used instead of built-in dict.
The function applies recursively to field values that are
dataclass instances. This will also look into built-in containers:
tuples, lists, and dicts.
"""
# This likely won't be needed, as ``dataclasses.fields`` already has this
# check.
# if not _is_dataclass_instance(obj):
# raise TypeError("asdict() should be called on dataclass instances")
cls = cls or type(o)
try:
dump = CLASS_TO_DUMP_FUNC[cls]
except KeyError:
dump = _get_dump_fn_for_dataclass(cls)
return dump(o, dict_factory, exclude, **kwargs)
def fromdict(cls: type[T], d: JSONObject) -> T:
"""
Converts a Python dictionary object to a dataclass instance.
Iterates over each dataclass field recursively; lists, dicts, and nested
dataclasses will likewise be initialized as expected.
When directly invoking this function, an optional Meta configuration for
the dataclass can be specified via ``LoadMeta``; by default, this will
apply recursively to any nested dataclasses. Here's a sample usage of this
below::
>>> LoadMeta(key_transform='CAMEL').bind_to(MyClass)
>>> fromdict(MyClass, {"myStr": "value"})
"""
try:
load = CLASS_TO_LOAD_FUNC[cls]
except KeyError:
load = _get_load_fn_for_dataclass(cls)
return load(d)
def fromlist(cls: type[T], list_of_dict: list[JSONObject]) -> list[T]:
"""
Converts a Python list object to a list of dataclass instances.
Iterates over each dataclass field recursively; lists, dicts, and nested
dataclasses will likewise be initialized as expected.
"""
try:
load = CLASS_TO_LOAD_FUNC[cls]
except KeyError:
load = _get_load_fn_for_dataclass(cls)
return [load(d) for d in list_of_dict]
def _get_load_fn_for_dataclass(cls: type[T], v1=None) -> Callable[[JSONObject], T]:
meta = get_meta(cls)
if v1 is None:
v1 = getattr(meta, 'v1', False)
if v1:
from .v1.loaders import load_func_for_dataclass as V1_load_func_for_dataclass
# noinspection PyTypeChecker
load = V1_load_func_for_dataclass(cls)
else:
from .loaders import load_func_for_dataclass
load = load_func_for_dataclass(cls)
# noinspection PyTypeChecker
return load
def _get_dump_fn_for_dataclass(cls: type[T], v1=None) -> Callable[[JSONObject], T]:
if v1 is None:
v1 = getattr(get_meta(cls), 'v1', False)
if v1:
from .v1.dumpers import dump_func_for_dataclass as V1_dump_func_for_dataclass
# noinspection PyTypeChecker
dump = V1_dump_func_for_dataclass(cls)
else:
from .dumpers import dump_func_for_dataclass
dump = dump_func_for_dataclass(cls)
# noinspection PyTypeChecker
return dump
def get_dumper(class_or_instance=None, create=True,
base_cls: T = None,
v1: Optional[bool] = None) -> type[T]:
"""
Get the dumper for the class, using the following logic:
* Return the class if it's already a sub-class of :class:`DumpMixin`
* If `create` is enabled (which is the default), a new sub-class of
:class:`DumpMixin` for the class will be generated and cached on the
initial run.
* Otherwise, we will return the base loader, :class:`DumpMixin`, which
can potentially be shared by more than one dataclass.
"""
if v1 is None:
v1 = getattr(get_meta(class_or_instance), 'v1', False)
if v1:
cls_to_dumper = CLASS_TO_V1_DUMPER
if base_cls is None:
from .v1.dumpers import DumpMixin as V1_DumpMixin
base_cls = V1_DumpMixin
else:
cls_to_dumper = CLASS_TO_DUMPER
if base_cls is None:
from .dumpers import DumpMixin
base_cls = DumpMixin
try:
return cls_to_dumper[class_or_instance]
except KeyError:
# TODO figure out type errors
if hasattr(class_or_instance, _DUMP_HOOKS):
return set_class_dumper(
cls_to_dumper, class_or_instance, class_or_instance)
elif create:
cls_loader = create_new_class(class_or_instance, (base_cls, ))
return set_class_dumper(
cls_to_dumper, class_or_instance, cls_loader)
return set_class_dumper(
cls_to_dumper, class_or_instance, base_cls)
def get_loader(class_or_instance=None, create=True,
base_cls: T = None,
v1: Optional[bool] = None) -> type[T]:
"""
Get the loader for the class, using the following logic:
* Return the class if it's already a sub-class of :class:`LoadMixin`
* If `create` is enabled (which is the default), a new sub-class of
:class:`LoadMixin` for the class will be generated and cached on the
initial run.
* Otherwise, we will return the base loader, :class:`LoadMixin`, which
can potentially be shared by more than one dataclass.
"""
if v1 is None:
v1 = getattr(get_meta(class_or_instance), 'v1', False)
if v1:
cls_to_loader = CLASS_TO_V1_LOADER
if base_cls is None:
from .v1.loaders import LoadMixin as V1_LoadMixin
base_cls = V1_LoadMixin
else:
cls_to_loader = CLASS_TO_LOADER
if base_cls is None:
from .loaders import LoadMixin
base_cls = LoadMixin
try:
return cls_to_loader[class_or_instance]
except KeyError:
if hasattr(class_or_instance, _LOAD_HOOKS):
return set_class_loader(
cls_to_loader, class_or_instance, class_or_instance)
elif create:
cls_loader = create_new_class(class_or_instance, (base_cls, ))
return set_class_loader(
cls_to_loader, class_or_instance, cls_loader)
return set_class_loader(
cls_to_loader, class_or_instance, base_cls)
|
