def __new__(cls, cls_name, bases, class_dict):
name = naming.camelcase_to_snakecase(cls_name)
class_dict["name"] = name
builder_cls = super(RegisteredDataset, cls).__new__( # pylint: disable=too-many-function-args
cls, cls_name, bases, class_dict)
if py_utils.is_notebook(): # On Colab/Jupyter, we allow overwriting
pass
elif name in _DATASET_REGISTRY:
raise ValueError("Dataset with name %s already registered." % name)
elif name in _IN_DEVELOPMENT_REGISTRY:
raise ValueError(
"Dataset with name %s already registered as in development." % name)
elif name in _ABSTRACT_DATASET_REGISTRY:
raise ValueError(
"Dataset with name %s already registered as abstract." % name)
if _skip_registration:
pass # Skip dataset registration within the contextmanager
elif inspect.isabstract(builder_cls):
_ABSTRACT_DATASET_REGISTRY[name] = builder_cls
elif class_dict.get("IN_DEVELOPMENT"):
_IN_DEVELOPMENT_REGISTRY[name] = builder_cls
else:
_DATASET_REGISTRY[name] = builder_cls
return builder_cls
def __new__(mcs, cls_name, bases, class_dict):
name = naming.camelcase_to_snakecase(cls_name)
class_dict["name"] = name
cls = super(RegisteredDataset, mcs).__new__(mcs, cls_name, bases,
class_dict)
if py_utils.is_notebook(): # On Colab/Jupyter, we allow overwriting
pass
elif name in _DATASET_REGISTRY:
raise ValueError("Dataset with name %s already registered." % name)
elif name in _IN_DEVELOPMENT_REGISTRY:
raise ValueError(
"Dataset with name %s already registered as in development." %
name)
elif name in _ABSTRACT_DATASET_REGISTRY:
raise ValueError(
"Dataset with name %s already registered as abstract." % name)
if inspect.isabstract(cls):
_ABSTRACT_DATASET_REGISTRY[name] = cls
elif class_dict.get("IN_DEVELOPMENT"):
_IN_DEVELOPMENT_REGISTRY[name] = cls
else:
_DATASET_REGISTRY[name] = cls
return cls
def __init_subclass__(cls, skip_registration=False, **kwargs): # pylint: disable=redefined-outer-name
super().__init_subclass__(**kwargs)
# @tag-tfdatasets-datasetbuilder-012
# Set the name if the dataset does not define it.
# Use __dict__ rather than getattr so subclasses are not affected.
if not cls.__dict__.get('name'):
cls.name = naming.camelcase_to_snakecase(cls.__name__)
is_abstract = inspect.isabstract(cls)
# Capture all concrete datasets, including when skip registration is True.
# This ensure that `builder_cls_from_module` can load the datasets
# even when the module has been imported inside a `skip_registration`
# context.
if not is_abstract:
_MODULE_TO_DATASETS[cls.__module__].append(cls)
# Skip dataset registration within contextmanager, or if skip_registration
# is passed as meta argument.
if skip_registration or _skip_registration:
return
# Check for name collisions
if py_utils.is_notebook(): # On Colab/Jupyter, we allow overwriting
pass
elif cls.name in _DATASET_REGISTRY:
raise ValueError(
f'Dataset with name {cls.name} already registered.')
elif cls.name in _IN_DEVELOPMENT_REGISTRY:
raise ValueError(
f'Dataset with name {cls.name} already registered as in development.'
)
elif cls.name in _ABSTRACT_DATASET_REGISTRY:
raise ValueError(
f'Dataset with name {cls.name} already registered as abstract.'
)
# Add the dataset to the registers
if is_abstract:
_ABSTRACT_DATASET_REGISTRY[cls.name] = cls
elif cls.IN_DEVELOPMENT:
_IN_DEVELOPMENT_REGISTRY[cls.name] = cls
else:
_DATASET_REGISTRY[cls.name] = cls
def __new__(cls, cls_name, bases, class_dict):
name = naming.camelcase_to_snakecase(cls_name)
class_dict["name"] = name
builder_cls = super(RegisteredDataset, cls).__new__( # pylint: disable=too-many-function-args,redefined-outer-name
cls, cls_name, bases, class_dict)
if py_utils.is_notebook(): # On Colab/Jupyter, we allow overwriting
pass
elif name in _DATASET_REGISTRY:
raise ValueError("Dataset with name %s already registered." % name)
elif name in _IN_DEVELOPMENT_REGISTRY:
raise ValueError(
"Dataset with name %s already registered as in development." %
name)
elif name in _ABSTRACT_DATASET_REGISTRY:
raise ValueError(
"Dataset with name %s already registered as abstract." % name)
is_abstract = inspect.isabstract(builder_cls)
# Capture all concrete datasets, including when skip registration is True.
# This ensure that `builder_cls_from_module` can load the datasets
# even when the module has been imported inside a `skip_registration`
# context.
if not is_abstract:
_MODULE_TO_DATASETS[builder_cls.__module__].append(builder_cls)
if _skip_registration:
pass # Skip dataset registration within the contextmanager
elif is_abstract:
_ABSTRACT_DATASET_REGISTRY[name] = builder_cls
elif class_dict.get("IN_DEVELOPMENT"):
_IN_DEVELOPMENT_REGISTRY[name] = builder_cls
else:
_DATASET_REGISTRY[name] = builder_cls
return builder_cls
def test_is_notebook(self):
self.assertFalse(py_utils.is_notebook())
