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Move imports

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This commit is contained in:
Matthew Honnibal 2025-05-20 18:34:01 +02:00
parent fd91e8a0e4
commit 216ef7ee7a
1 changed files with 36 additions and 36 deletions
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72
spacy/registrations.py
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@ -25,6 +25,40 @@ from .pipeline.dep_parser import DependencyParser
from .pipeline.tagger import Tagger
from .pipeline.multitask import MultitaskObjective
from .pipeline.senter import SentenceRecognizer
from .language import Language
from .pipeline.sentencizer import Sentencizer
# Import factory default configurations
from .pipeline.entity_linker import DEFAULT_NEL_MODEL
from .pipeline.entityruler import DEFAULT_ENT_ID_SEP
from .pipeline.tok2vec import DEFAULT_TOK2VEC_MODEL
from .pipeline.senter import DEFAULT_SENTER_MODEL
from .pipeline.morphologizer import DEFAULT_MORPH_MODEL
from .pipeline.spancat import (
DEFAULT_SPANCAT_MODEL,
DEFAULT_SPANCAT_SINGLELABEL_MODEL,
DEFAULT_SPANS_KEY,
)
from .pipeline.span_ruler import DEFAULT_SPANS_KEY as SPAN_RULER_DEFAULT_SPANS_KEY
from .pipeline.edit_tree_lemmatizer import DEFAULT_EDIT_TREE_LEMMATIZER_MODEL
from .pipeline.textcat_multilabel import DEFAULT_MULTI_TEXTCAT_MODEL
from .pipeline.span_finder import DEFAULT_SPAN_FINDER_MODEL
from .pipeline.ner import DEFAULT_NER_MODEL
from .pipeline.dep_parser import DEFAULT_PARSER_MODEL
from .pipeline.tagger import DEFAULT_TAGGER_MODEL
from .pipeline.multitask import DEFAULT_MT_MODEL
from .pipeline.textcat import DEFAULT_SINGLE_TEXTCAT_MODEL
from .pipeline.entity_linker import EntityLinker, EntityLinker_v1
from .pipeline.attributeruler import AttributeRuler
from .pipeline.spancat import SpanCategorizer
from .pipeline.lemmatizer import Lemmatizer
from .pipeline.functions import TokenSplitter
from .pipeline.functions import DocCleaner
from .pipeline.span_ruler import SpanRuler, prioritize_new_ents_filter, prioritize_existing_ents_filter
from .pipeline.span_ruler import SpanRuler
from .pipeline.edit_tree_lemmatizer import EditTreeLemmatizer
from .pipeline.morphologizer import Morphologizer
# Global flag to track if registry has been populated
REGISTRY_POPULATED = False
@ -135,30 +169,6 @@ def register_factories() -> None:
if FACTORIES_REGISTERED:
return
from .language import Language
from .pipeline.sentencizer import Sentencizer
# Import factory default configurations
from .pipeline.entity_linker import DEFAULT_NEL_MODEL
from .pipeline.entityruler import DEFAULT_ENT_ID_SEP
from .pipeline.tok2vec import DEFAULT_TOK2VEC_MODEL
from .pipeline.senter import DEFAULT_SENTER_MODEL
from .pipeline.morphologizer import DEFAULT_MORPH_MODEL
from .pipeline.spancat import (
DEFAULT_SPANCAT_MODEL,
DEFAULT_SPANCAT_SINGLELABEL_MODEL,
DEFAULT_SPANS_KEY,
)
from .pipeline.span_ruler import DEFAULT_SPANS_KEY as SPAN_RULER_DEFAULT_SPANS_KEY
from .pipeline.edit_tree_lemmatizer import DEFAULT_EDIT_TREE_LEMMATIZER_MODEL
from .pipeline.textcat_multilabel import DEFAULT_MULTI_TEXTCAT_MODEL
from .pipeline.span_finder import DEFAULT_SPAN_FINDER_MODEL
from .pipeline.ner import DEFAULT_NER_MODEL
from .pipeline.dep_parser import DEFAULT_PARSER_MODEL
from .pipeline.tagger import DEFAULT_TAGGER_MODEL
from .pipeline.multitask import DEFAULT_MT_MODEL
from .pipeline.textcat import DEFAULT_SINGLE_TEXTCAT_MODEL
# We can't have function implementations for these factories in Cython, because
# we need to build a Pydantic model for them dynamically, reading their argument
# structure from the signature. In Cython 3, this doesn't work because the
@ -178,7 +188,6 @@ def register_factories() -> None:
def make_attribute_ruler(
nlp: Language, name: str, validate: bool, scorer: Optional[Callable]
):
from .pipeline.attributeruler import AttributeRuler
return AttributeRuler(nlp.vocab, name, validate=validate, scorer=scorer)
def make_entity_linker(
@ -202,7 +211,6 @@ def register_factories() -> None:
candidates_batch_size: int,
threshold: Optional[float] = None,
):
from .pipeline.entity_linker import EntityLinker, EntityLinker_v1
if not model.attrs.get("include_span_maker", False):
# The only difference in arguments here is that use_gold_ents and threshold aren't available.
@ -246,7 +254,6 @@ def register_factories() -> None:
overwrite: bool,
scorer: Optional[Callable],
):
from .pipeline.lemmatizer import Lemmatizer
return Lemmatizer(
nlp.vocab, model, name, mode=mode, overwrite=overwrite, scorer=scorer
)
@ -263,11 +270,9 @@ def register_factories() -> None:
def make_token_splitter(
nlp: Language, name: str, *, min_length: int = 0, split_length: int = 0
):
from .pipeline.functions import TokenSplitter
return TokenSplitter(min_length=min_length, split_length=split_length)
def make_doc_cleaner(nlp: Language, name: str, *, attrs: Dict[str, Any], silent: bool):
from .pipeline.functions import DocCleaner
return DocCleaner(attrs, silent=silent)
def make_tok2vec(nlp: Language, name: str, model: Model) -> Tok2Vec:
@ -306,8 +311,7 @@ def register_factories() -> None:
negative_weight: float,
allow_overlap: bool,
scorer: Optional[Callable],
) -> "SpanCategorizer":
from .pipeline.spancat import SpanCategorizer
) -> SpanCategorizer:
return SpanCategorizer(
nlp.vocab,
model=model,
@ -332,8 +336,7 @@ def register_factories() -> None:
scorer: Optional[Callable],
ent_id_sep: str,
):
from .pipeline.span_ruler import SpanRuler, prioritize_new_ents_filter, prioritize_existing_ents_filter
if overwrite_ents:
if overwrite_ents:
ents_filter = prioritize_new_ents_filter
else:
ents_filter = prioritize_existing_ents_filter
@ -385,7 +388,6 @@ def register_factories() -> None:
overwrite: bool,
scorer: Optional[Callable],
):
from .pipeline.span_ruler import SpanRuler
return SpanRuler(
nlp,
name,
@ -410,7 +412,6 @@ def register_factories() -> None:
top_k: int,
scorer: Optional[Callable],
):
from .pipeline.edit_tree_lemmatizer import EditTreeLemmatizer
return EditTreeLemmatizer(
nlp.vocab,
model,
@ -583,7 +584,6 @@ def register_factories() -> None:
label_smoothing: float,
scorer: Optional[Callable],
):
from .pipeline.morphologizer import Morphologizer
return Morphologizer(
nlp.vocab, model, name,
overwrite=overwrite,