def test_config_optional_sections():
config = Config().from_str(nlp_config_string)
config = DEFAULT_CONFIG.merge(config)
assert "pretraining" not in config
filled = registry.fill(config, schema=ConfigSchema, validate=False)
# Make sure that optional "pretraining" block doesn't default to None,
# which would (rightly) cause error because it'd result in a top-level
# key that's not a section (dict). Note that the following roundtrip is
# also how Config.interpolate works under the hood.
new_config = Config().from_str(filled.to_str())
assert new_config["pretraining"] == {}
def __init__(self,
udpipe_model: UDPipeModel,
meta: Optional[Dict] = None,
**kwargs):
"""Initialize the Language class.
The language is called "udpipe_en" instead of "en" in order to
avoid any potential conflicts with spaCy's built-in languages.
Using entry points, this enables serializing and deserializing
the language class and "lang": "udpipe_en" in the meta.json will
automatically instantiate this class if this package is available.
udpipe_model: The loaded UDPipe model.
meta: spaCy model metadata.
kwargs: Optional config parameters.
"""
self.udpipe = udpipe_model
self.Defaults = get_defaults(lang=udpipe_model._lang)
self.lang = f"udpipe_{udpipe_model._lang}"
ignore_tag_map = kwargs.get("ignore_tag_map", False)
if ignore_tag_map:
self.Defaults.tag_map = {} # workaround for ValueError: [E167]
if SPACY_V3:
from spacy.vocab import create_vocab
from spacy.language import DEFAULT_CONFIG
self.vocab = create_vocab(udpipe_model._lang, self.Defaults)
self.batch_size = 1000
self._components = []
self._disabled = set()
self._config = DEFAULT_CONFIG.merge(self.default_config)
else:
self.vocab = self.Defaults.create_vocab()
self.pipeline = []
self.tokenizer = UDPipeTokenizer(model=self.udpipe, vocab=self.vocab)
self.max_length = kwargs.get("max_length", 10**6)
self._meta = self.udpipe._meta if meta is None else dict(meta)
self._path = None
self._optimizer = None