Tidy up and auto-format

spacy/cli/debug_data.py

@@ -171,7 +171,7 @@ def debug_data(
         n_missing_vectors = sum(gold_train_data["words_missing_vectors"].values())
         msg.warn(
             "{} words in training data without vectors ({:0.2f}%)".format(
-                n_missing_vectors, n_missing_vectors / gold_train_data["n_words"],
+                n_missing_vectors, n_missing_vectors / gold_train_data["n_words"]
             ),
         )
         msg.text(

spacy/lang/pl/__init__.py

@@ -8,7 +8,6 @@ from .stop_words import STOP_WORDS
 from .lex_attrs import LEX_ATTRS
 from .lemmatizer import PolishLemmatizer
 from ..tokenizer_exceptions import BASE_EXCEPTIONS
-from ...lookups import Lookups
 from ...language import Language
 
 

spacy/lang/zh/__init__.py

@@ -47,7 +47,7 @@ class Segmenter(str, Enum):
 
 
 @registry.tokenizers("spacy.zh.ChineseTokenizer")
-def create_chinese_tokenizer(segmenter: Segmenter = Segmenter.char,):
+def create_chinese_tokenizer(segmenter: Segmenter = Segmenter.char):
     def chinese_tokenizer_factory(nlp):
         return ChineseTokenizer(nlp, segmenter=segmenter)
 

spacy/ml/models/tok2vec.py

@@ -165,8 +165,12 @@ def MultiHashEmbed(
 
 @registry.architectures.register("spacy.CharacterEmbed.v1")
 def CharacterEmbed(
-    width: int, rows: int, nM: int, nC: int, also_use_static_vectors: bool,
-    feature: Union[int, str]="LOWER"
+    width: int,
+    rows: int,
+    nM: int,
+    nC: int,
+    also_use_static_vectors: bool,
+    feature: Union[int, str] = "LOWER",
 ) -> Model[List[Doc], List[Floats2d]]:
     """Construct an embedded representation based on character embeddings, using
     a feed-forward network. A fixed number of UTF-8 byte characters are used for

spacy/pipeline/textcat.py

@@ -70,7 +70,7 @@ subword_features = true
     },
 )
 def make_textcat(
-    nlp: Language, name: str, model: Model[List[Doc], List[Floats2d]], threshold: float,
+    nlp: Language, name: str, model: Model[List[Doc], List[Floats2d]], threshold: float
 ) -> "TextCategorizer":
     """Create a TextCategorizer compoment. The text categorizer predicts categories
     over a whole document. It can learn one or more labels, and the labels can

spacy/tests/conftest.py

@@ -294,7 +294,8 @@ def zh_tokenizer_pkuseg():
                 "segmenter": "pkuseg",
             }
         },
-        "initialize": {"tokenizer": {
+        "initialize": {
+            "tokenizer": {
                 "pkuseg_model": "default",
             }
         },

spacy/tests/doc/test_morphanalysis.py

@@ -5,12 +5,14 @@ import pytest
 def i_has(en_tokenizer):
     doc = en_tokenizer("I has")
     doc[0].set_morph({"PronType": "prs"})
-    doc[1].set_morph({
+    doc[1].set_morph(
+        {
             "VerbForm": "fin",
             "Tense": "pres",
             "Number": "sing",
             "Person": "three",
-    })
+        }
+    )
 
     return doc
 

spacy/tests/lang/zh/test_serialize.py

@@ -34,7 +34,8 @@ def test_zh_tokenizer_serialize_pkuseg_with_processors(zh_tokenizer_pkuseg):
                 "segmenter": "pkuseg",
             }
         },
-        "initialize": {"tokenizer": {
+        "initialize": {
+            "tokenizer": {
                 "pkuseg_model": "medicine",
             }
         },

spacy/tests/pipeline/test_textcat.py

@@ -139,7 +139,8 @@ def test_overfitting_IO():
     nlp = English()
     nlp.config["initialize"]["components"]["textcat"] = {"positive_label": "POSITIVE"}
     # Set exclusive labels
-    textcat = nlp.add_pipe("textcat", config={"model": {"exclusive_classes": True}},)
+    config = {"model": {"exclusive_classes": True}}
+    textcat = nlp.add_pipe("textcat", config=config)
     train_examples = []
     for text, annotations in TRAIN_DATA:
         train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
@@ -226,7 +227,9 @@ def test_positive_class_not_binary():
     textcat = nlp.add_pipe("textcat")
     get_examples = make_get_examples(nlp)
     with pytest.raises(ValueError):
-        textcat.initialize(get_examples, labels=["SOME", "THING", "POS"], positive_label="POS")
+        textcat.initialize(
+            get_examples, labels=["SOME", "THING", "POS"], positive_label="POS"
+        )
 
 
 def test_textcat_evaluation():

spacy/tests/serialize/test_serialize_doc.py

@@ -92,7 +92,13 @@ def test_serialize_doc_bin_unknown_spaces(en_vocab):
 
 
 @pytest.mark.parametrize(
-    "writer_flag,reader_flag,reader_value", [(True, True, "bar"), (True, False, "bar"), (False, True, "nothing"), (False, False, "nothing")]
+    "writer_flag,reader_flag,reader_value",
+    [
+        (True, True, "bar"),
+        (True, False, "bar"),
+        (False, True, "nothing"),
+        (False, False, "nothing"),
+    ],
 )
 def test_serialize_custom_extension(en_vocab, writer_flag, reader_flag, reader_value):
     """Test that custom extensions are correctly serialized in DocBin."""

spacy/tests/test_scorer.py

@@ -158,7 +158,7 @@ def test_las_per_type(en_vocab):
     examples = []
     for input_, annot in test_las_apple:
         doc = Doc(
-            en_vocab, words=input_.split(" "), heads=annot["heads"], deps=annot["deps"],
+            en_vocab, words=input_.split(" "), heads=annot["heads"], deps=annot["deps"]
         )
         gold = {"heads": annot["heads"], "deps": annot["deps"]}
         doc[0].dep_ = "compound"
@@ -182,9 +182,7 @@ def test_ner_per_type(en_vocab):
     examples = []
     for input_, annot in test_ner_cardinal:
         doc = Doc(
-            en_vocab,
-            words=input_.split(" "),
-            ents=["B-CARDINAL", "O", "B-CARDINAL"],
+            en_vocab, words=input_.split(" "), ents=["B-CARDINAL", "O", "B-CARDINAL"]
         )
         entities = offsets_to_biluo_tags(doc, annot["entities"])
         example = Example.from_dict(doc, {"entities": entities})

spacy/training/augment.py

@@ -30,7 +30,7 @@ class OrthVariants(BaseModel):
 
 @registry.augmenters("spacy.orth_variants.v1")
 def create_orth_variants_augmenter(
-    level: float, lower: float, orth_variants: OrthVariants,
+    level: float, lower: float, orth_variants: OrthVariants
 ) -> Callable[["Language", Example], Iterator[Example]]:
     """Create a data augmentation callback that uses orth-variant replacement.
     The callback can be added to a corpus or other data iterator during training.

spacy/training/loop.py

@@ -21,8 +21,8 @@ def train(
     output_path: Optional[Path] = None,
     *,
     use_gpu: int = -1,
-    stdout: IO=sys.stdout,
-    stderr: IO=sys.stderr
+    stdout: IO = sys.stdout,
+    stderr: IO = sys.stderr,
 ) -> None:
     """Train a pipeline.
 

spacy/vocab.pyx

@@ -16,7 +16,7 @@ from .errors import Errors
 from .attrs import intify_attrs, NORM, IS_STOP
 from .vectors import Vectors
 from .util import registry
-from .lookups import Lookups, load_lookups
+from .lookups import Lookups
 from . import util
 from .lang.norm_exceptions import BASE_NORMS
 from .lang.lex_attrs import LEX_ATTRS, is_stop, get_lang