Tidy up and auto-format

spacy/cli/convert.py

@@ -9,7 +9,8 @@ import sys
 from ._util import app, Arg, Opt
 from ..training import docs_to_json
 from ..tokens import DocBin
-from ..training.converters import iob_to_docs, conll_ner_to_docs, json_to_docs, conllu_to_docs
+from ..training.converters import iob_to_docs, conll_ner_to_docs, json_to_docs
+from ..training.converters import conllu_to_docs
 
 
 # Converters are matched by file extension except for ner/iob, which are

spacy/cli/debug_data.py

@@ -27,7 +27,7 @@ BLANK_MODEL_THRESHOLD = 2000
 
 
 @debug_cli.command(
-    "data", context_settings={"allow_extra_args": True, "ignore_unknown_options": True},
+    "data", context_settings={"allow_extra_args": True, "ignore_unknown_options": True}
 )
 @app.command(
     "debug-data",

spacy/cli/project/dvc.py

@@ -134,7 +134,7 @@ def update_dvc_config(
 
 
 def run_dvc_commands(
-    commands: Iterable[str] = SimpleFrozenList(), flags: Dict[str, bool] = {},
+    commands: Iterable[str] = SimpleFrozenList(), flags: Dict[str, bool] = {}
 ) -> None:
     """Run a sequence of DVC commands in a subprocess, in order.
 

spacy/lang/en/lemmatizer.py

@@ -3,8 +3,7 @@ from ...tokens import Token
 
 
 class EnglishLemmatizer(Lemmatizer):
-    """English lemmatizer. Only overrides is_base_form.
-    """
+    """English lemmatizer. Only overrides is_base_form."""
 
     def is_base_form(self, token: Token) -> bool:
         """

spacy/lang/es/syntax_iterators.py

@@ -58,7 +58,7 @@ def noun_bounds(
                 doc, token, np_left_deps, np_right_deps, stop_deps
             )
             filter_func = lambda t: is_verb_token(t) or t.dep in stop_deps
-            if list(filter(filter_func, doc[left_bound.i : right.i],)):
+            if list(filter(filter_func, doc[left_bound.i : right.i])):
                 break
             else:
                 right_bound = right

spacy/lang/sa/lex_attrs.py

@@ -108,8 +108,8 @@ _num_words = [
 
 def like_num(text):
     """
-   Check if text resembles a number
-   """
+    Check if text resembles a number
+    """
     if text.startswith(("+", "-", "±", "~")):
         text = text[1:]
     text = text.replace(",", "").replace(".", "")

spacy/lang/vi/__init__.py

@@ -17,7 +17,7 @@ use_pyvi = true
 
 
 @registry.tokenizers("spacy.vi.VietnameseTokenizer")
-def create_vietnamese_tokenizer(use_pyvi: bool = True,):
+def create_vietnamese_tokenizer(use_pyvi: bool = True):
     def vietnamese_tokenizer_factory(nlp):
         return VietnameseTokenizer(nlp, use_pyvi=use_pyvi)
 

spacy/language.py

@@ -1189,7 +1189,7 @@ class Language:
         # These are the settings provided in the [initialize] block in the config
         I = registry.resolve(config["initialize"], schema=ConfigSchemaInit)
         init_vocab(
-            self, data=I["vocab_data"], lookups=I["lookups"], vectors=I["vectors"],
+            self, data=I["vocab_data"], lookups=I["lookups"], vectors=I["vectors"]
         )
         pretrain_cfg = config.get("pretraining")
         if pretrain_cfg:

spacy/pipe_analysis.py

@@ -78,7 +78,7 @@ def get_attr_info(nlp: "Language", attr: str) -> Dict[str, List[str]]:
 
 
 def analyze_pipes(
-    nlp: "Language", *, keys: List[str] = DEFAULT_KEYS,
+    nlp: "Language", *, keys: List[str] = DEFAULT_KEYS
 ) -> Dict[str, Union[List[str], Dict[str, List[str]]]]:
     """Print a formatted summary for the current nlp object's pipeline. Shows
     a table with the pipeline components and why they assign and require, as

spacy/pipeline/attributeruler.py

@@ -82,8 +82,7 @@ class AttributeRuler(Pipe):
         matches = self.matcher(doc, allow_missing=True)
         # Sort by the attribute ID, so that later rules have precendence
         matches = [
-            (int(self.vocab.strings[m_id]), m_id, s, e)
-            for m_id, s, e in matches
+            (int(self.vocab.strings[m_id]), m_id, s, e) for m_id, s, e in matches
         ]
         matches.sort()
         for attr_id, match_id, start, end in matches:
@@ -93,7 +92,7 @@ class AttributeRuler(Pipe):
             try:
                 # The index can be negative, which makes it annoying to do
                 # the boundscheck. Let Span do it instead.
-                token = span[index]
+                token = span[index]  # noqa: F841
             except IndexError:
                 # The original exception is just our conditional logic, so we
                 # raise from.
@@ -103,7 +102,7 @@ class AttributeRuler(Pipe):
                         span=[t.text for t in span],
                         index=index,
                     )
-                ) from None 
+                ) from None
             set_token_attrs(span[index], attrs)
         return doc
 

spacy/pipeline/lemmatizer.py

@@ -67,7 +67,7 @@ class Lemmatizer(Pipe):
         return {}
 
     @classmethod
-    def load_lookups(cls, lang: str, mode: str, lookups: Optional[Lookups],) -> Lookups:
+    def load_lookups(cls, lang: str, mode: str, lookups: Optional[Lookups]) -> Lookups:
         """Load and validate lookups tables. If the provided lookups is None,
         load the default lookups tables according to the language and mode
         settings. Confirm that all required tables for the language and mode

spacy/pipeline/textcat.py

@@ -347,7 +347,7 @@ class TextCategorizer(Pipe):
         get_examples: Callable[[], Iterable[Example]],
         *,
         nlp: Optional[Language] = None,
-        labels: Optional[Dict] = None
+        labels: Optional[Dict] = None,
     ):
         """Initialize the pipe for training, using a representative set
         of data examples.

spacy/schemas.py

@@ -132,7 +132,7 @@ def validate_init_settings(
         block = "initialize" if not section else f"initialize.{section}"
         title = f"Error validating initialization settings in [{block}]"
         raise ConfigValidationError(
-            title=title, errors=e.errors(), config=settings, parent=name,
+            title=title, errors=e.errors(), config=settings, parent=name
         ) from None
 
 

spacy/scorer.py

@@ -32,9 +32,7 @@ class PRFScore:
 
     def __add__(self, other):
         return PRFScore(
-            tp=self.tp+other.tp,
-            fp=self.fp+other.fp,
-            fn=self.fn+other.fn
+            tp=self.tp + other.tp, fp=self.fp + other.fp, fn=self.fn + other.fn
         )
 
     def score_set(self, cand: set, gold: set) -> None:
@@ -485,7 +483,7 @@ class Scorer:
                     (pred_ent.start_char, pred_ent.end_char), None
                 )
                 label = gold_span.label_
-                if not label in f_per_type:
+                if label not in f_per_type:
                     f_per_type[label] = PRFScore()
                 gold = gold_span.kb_id_
                 # only evaluating entities that overlap between gold and pred,
@@ -632,7 +630,6 @@ def get_ner_prf(examples: Iterable[Example]) -> Dict[str, PRFScore]:
             continue
         golds = {(e.label_, e.start, e.end) for e in eg.y.ents}
         align_x2y = eg.alignment.x2y
-        preds = set()
         for pred_ent in eg.x.ents:
             if pred_ent.label_ not in scores:
                 scores[pred_ent.label_] = PRFScore()

spacy/tests/doc/test_doc_api.py

@@ -19,7 +19,7 @@ def test_doc_api_init(en_vocab):
     assert [t.is_sent_start for t in doc] == [True, False, True, False]
     # heads override sent_starts
     doc = Doc(
-        en_vocab, words=words, sent_starts=[True] * 4, heads=heads, deps=["dep"] * 4,
+        en_vocab, words=words, sent_starts=[True] * 4, heads=heads, deps=["dep"] * 4
     )
     assert [t.is_sent_start for t in doc] == [True, False, True, False]
 

spacy/tests/lang/de/test_noun_chunks.py

@@ -2,8 +2,7 @@ import pytest
 
 
 def test_noun_chunks_is_parsed_de(de_tokenizer):
-    """Test that noun_chunks raises Value Error for 'de' language if Doc is not parsed.
-    """
+    """Test that noun_chunks raises Value Error for 'de' language if Doc is not parsed."""
     doc = de_tokenizer("Er lag auf seinem")
     with pytest.raises(ValueError):
         list(doc.noun_chunks)

spacy/tests/lang/el/test_noun_chunks.py

@@ -2,8 +2,7 @@ import pytest
 
 
 def test_noun_chunks_is_parsed_el(el_tokenizer):
-    """Test that noun_chunks raises Value Error for 'el' language if Doc is not parsed.
-    """
+    """Test that noun_chunks raises Value Error for 'el' language if Doc is not parsed."""
     doc = el_tokenizer("είναι χώρα της νοτιοανατολικής")
     with pytest.raises(ValueError):
         list(doc.noun_chunks)

spacy/tests/lang/en/test_noun_chunks.py

@@ -7,8 +7,7 @@ import pytest
 
 
 def test_noun_chunks_is_parsed(en_tokenizer):
-    """Test that noun_chunks raises Value Error for 'en' language if Doc is not parsed.
-    """
+    """Test that noun_chunks raises Value Error for 'en' language if Doc is not parsed."""
     doc = en_tokenizer("This is a sentence")
     with pytest.raises(ValueError):
         list(doc.noun_chunks)

spacy/tests/lang/es/test_noun_chunks.py

@@ -2,8 +2,7 @@ import pytest
 
 
 def test_noun_chunks_is_parsed_es(es_tokenizer):
-    """Test that noun_chunks raises Value Error for 'es' language if Doc is not parsed.
-    """
+    """Test that noun_chunks raises Value Error for 'es' language if Doc is not parsed."""
     doc = es_tokenizer("en Oxford este verano")
     with pytest.raises(ValueError):
         list(doc.noun_chunks)

spacy/tests/lang/fa/test_noun_chunks.py

@@ -2,8 +2,7 @@ import pytest
 
 
 def test_noun_chunks_is_parsed_fa(fa_tokenizer):
-    """Test that noun_chunks raises Value Error for 'fa' language if Doc is not parsed.
-    """
+    """Test that noun_chunks raises Value Error for 'fa' language if Doc is not parsed."""
 
     doc = fa_tokenizer("این یک جمله نمونه می باشد.")
     with pytest.raises(ValueError):

spacy/tests/lang/fr/test_exceptions.py

@@ -36,9 +36,7 @@ def test_fr_tokenizer_infix_exceptions(fr_tokenizer, text):
     assert len(tokens) == 1
 
 
-@pytest.mark.parametrize(
-    "text", ["janv.", "juill.", "Dr.", "av.", "sept."],
-)
+@pytest.mark.parametrize("text", ["janv.", "juill.", "Dr.", "av.", "sept."])
 def test_fr_tokenizer_handles_abbr(fr_tokenizer, text):
     tokens = fr_tokenizer(text)
     assert len(tokens) == 1

spacy/tests/lang/fr/test_noun_chunks.py

@@ -2,8 +2,7 @@ import pytest
 
 
 def test_noun_chunks_is_parsed_fr(fr_tokenizer):
-    """Test that noun_chunks raises Value Error for 'fr' language if Doc is not parsed.
-    """
+    """Test that noun_chunks raises Value Error for 'fr' language if Doc is not parsed."""
     doc = fr_tokenizer("trouver des travaux antérieurs")
     with pytest.raises(ValueError):
         list(doc.noun_chunks)

spacy/tests/lang/id/test_noun_chunks.py

@@ -2,8 +2,7 @@ import pytest
 
 
 def test_noun_chunks_is_parsed_id(id_tokenizer):
-    """Test that noun_chunks raises Value Error for 'id' language if Doc is not parsed.
-    """
+    """Test that noun_chunks raises Value Error for 'id' language if Doc is not parsed."""
     doc = id_tokenizer("sebelas")
     with pytest.raises(ValueError):
         list(doc.noun_chunks)

spacy/tests/lang/ja/test_tokenizer.py

@@ -112,7 +112,7 @@ def test_ja_tokenizer_split_modes(ja_tokenizer, text, len_a, len_b, len_c):
 
 
 @pytest.mark.parametrize(
-    "text,sub_tokens_list_a,sub_tokens_list_b,sub_tokens_list_c", SUB_TOKEN_TESTS,
+    "text,sub_tokens_list_a,sub_tokens_list_b,sub_tokens_list_c", SUB_TOKEN_TESTS
 )
 def test_ja_tokenizer_sub_tokens(
     ja_tokenizer, text, sub_tokens_list_a, sub_tokens_list_b, sub_tokens_list_c

spacy/tests/lang/nb/test_noun_chunks.py

@@ -2,8 +2,7 @@ import pytest
 
 
 def test_noun_chunks_is_parsed_nb(nb_tokenizer):
-    """Test that noun_chunks raises Value Error for 'nb' language if Doc is not parsed.
-    """
+    """Test that noun_chunks raises Value Error for 'nb' language if Doc is not parsed."""
     doc = nb_tokenizer("Smørsausen brukes bl.a. til")
     with pytest.raises(ValueError):
         list(doc.noun_chunks)

spacy/tests/lang/ne/test_text.py

@@ -8,7 +8,7 @@ def test_ne_tokenizer_handlers_long_text(ne_tokenizer):
 
 
 @pytest.mark.parametrize(
-    "text,length", [("समय जान कति पनि बेर लाग्दैन ।", 7), ("म ठूलो हुँदै थिएँ ।", 5)],
+    "text,length", [("समय जान कति पनि बेर लाग्दैन ।", 7), ("म ठूलो हुँदै थिएँ ।", 5)]
 )
 def test_ne_tokenizer_handles_cnts(ne_tokenizer, text, length):
     tokens = ne_tokenizer(text)

spacy/tests/lang/sa/test_text.py

@@ -10,7 +10,7 @@ def test_sa_tokenizer_handles_long_text(sa_tokenizer):
 @pytest.mark.parametrize(
     "text,length",
     [
-        ("श्री भगवानुवाच पश्य मे पार्थ रूपाणि शतशोऽथ सहस्रशः।", 9,),
+        ("श्री भगवानुवाच पश्य मे पार्थ रूपाणि शतशोऽथ सहस्रशः।", 9),
         ("गुणान् सर्वान् स्वभावो मूर्ध्नि वर्तते ।", 6),
     ],
 )

spacy/tests/lang/sv/test_noun_chunks.py

@@ -3,8 +3,7 @@ from spacy.tokens import Doc
 
 
 def test_noun_chunks_is_parsed_sv(sv_tokenizer):
-    """Test that noun_chunks raises Value Error for 'sv' language if Doc is not parsed.
-    """
+    """Test that noun_chunks raises Value Error for 'sv' language if Doc is not parsed."""
     doc = sv_tokenizer("Studenten läste den bästa boken")
     with pytest.raises(ValueError):
         list(doc.noun_chunks)

spacy/tests/pipeline/test_entity_linker.py

@@ -254,14 +254,12 @@ def test_vocab_serialization(nlp):
     mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
 
     # adding entities
-    q1_hash = mykb.add_entity(entity="Q1", freq=27, entity_vector=[1])
+    mykb.add_entity(entity="Q1", freq=27, entity_vector=[1])
     q2_hash = mykb.add_entity(entity="Q2", freq=12, entity_vector=[2])
-    q3_hash = mykb.add_entity(entity="Q3", freq=5, entity_vector=[3])
+    mykb.add_entity(entity="Q3", freq=5, entity_vector=[3])
 
     # adding aliases
-    douglas_hash = mykb.add_alias(
-        alias="douglas", entities=["Q2", "Q3"], probabilities=[0.4, 0.1]
-    )
+    mykb.add_alias(alias="douglas", entities=["Q2", "Q3"], probabilities=[0.4, 0.1])
     adam_hash = mykb.add_alias(alias="adam", entities=["Q2"], probabilities=[0.9])
 
     candidates = mykb.get_alias_candidates("adam")

spacy/tests/pipeline/test_textcat.py

@@ -226,6 +226,7 @@ def test_positive_class_not_binary():
     with pytest.raises(ValueError):
         verify_textcat_config(nlp, pipe_config)
 
+
 def test_textcat_evaluation():
     train_examples = []
     nlp = English()
@@ -241,15 +242,17 @@ def test_textcat_evaluation():
     pred2.cats = {"winter": 1.0, "summer": 0.0, "spring": 0.0, "autumn": 1.0}
     train_examples.append(Example(pred2, ref2))
 
-    scores = Scorer().score_cats(train_examples, "cats", labels=["winter", "summer", "spring", "autumn"])
-    assert scores["cats_f_per_type"]["winter"]["p"] == 1/2
-    assert scores["cats_f_per_type"]["winter"]["r"] == 1/1
+    scores = Scorer().score_cats(
+        train_examples, "cats", labels=["winter", "summer", "spring", "autumn"]
+    )
+    assert scores["cats_f_per_type"]["winter"]["p"] == 1 / 2
+    assert scores["cats_f_per_type"]["winter"]["r"] == 1 / 1
     assert scores["cats_f_per_type"]["summer"]["p"] == 0
-    assert scores["cats_f_per_type"]["summer"]["r"] == 0/1
-    assert scores["cats_f_per_type"]["spring"]["p"] == 1/1
-    assert scores["cats_f_per_type"]["spring"]["r"] == 1/2
-    assert scores["cats_f_per_type"]["autumn"]["p"] == 2/2
-    assert scores["cats_f_per_type"]["autumn"]["r"] == 2/2
+    assert scores["cats_f_per_type"]["summer"]["r"] == 0 / 1
+    assert scores["cats_f_per_type"]["spring"]["p"] == 1 / 1
+    assert scores["cats_f_per_type"]["spring"]["r"] == 1 / 2
+    assert scores["cats_f_per_type"]["autumn"]["p"] == 2 / 2
+    assert scores["cats_f_per_type"]["autumn"]["r"] == 2 / 2
 
-    assert scores["cats_micro_p"] == 4/5
-    assert scores["cats_micro_r"] == 4/6
+    assert scores["cats_micro_p"] == 4 / 5
+    assert scores["cats_micro_r"] == 4 / 6

spacy/tests/pipeline/test_tok2vec.py

@@ -73,8 +73,7 @@ def test_tok2vec_configs(width, embed_arch, embed_config, encode_arch, encode_co
     encode_config["width"] = width
     docs = get_batch(3)
     tok2vec = build_Tok2Vec_model(
-        embed_arch(**embed_config),
-        encode_arch(**encode_config)
+        embed_arch(**embed_config), encode_arch(**encode_config)
     )
     tok2vec.initialize(docs)
     vectors, backprop = tok2vec.begin_update(docs)

spacy/tests/regression/test_issue3501-4000.py

@@ -229,9 +229,7 @@ def test_issue3611():
             batches = minibatch(train_data, size=compounding(4.0, 32.0, 1.001))
 
             for batch in batches:
-                nlp.update(
-                    examples=batch, sgd=optimizer, drop=0.1, losses=losses,
-                )
+                nlp.update(examples=batch, sgd=optimizer, drop=0.1, losses=losses)
 
 
 def test_issue3625():
@@ -390,7 +388,7 @@ def test_issue3959():
 
 
 def test_issue3962(en_vocab):
-    """ Ensure that as_doc does not result in out-of-bound access of tokens.
+    """Ensure that as_doc does not result in out-of-bound access of tokens.
     This is achieved by setting the head to itself if it would lie out of the span otherwise."""
     # fmt: off
     words = ["He", "jests", "at", "scars", ",", "that", "never", "felt", "a", "wound", "."]
@@ -428,7 +426,7 @@ def test_issue3962(en_vocab):
 
 
 def test_issue3962_long(en_vocab):
-    """ Ensure that as_doc does not result in out-of-bound access of tokens.
+    """Ensure that as_doc does not result in out-of-bound access of tokens.
     This is achieved by setting the head to itself if it would lie out of the span otherwise."""
     # fmt: off
     words = ["He", "jests", "at", "scars", ".", "They", "never", "felt", "a", "wound", "."]
@@ -463,8 +461,7 @@ def test_issue3962_long(en_vocab):
 
 
 def test_issue3972(en_vocab):
-    """Test that the PhraseMatcher returns duplicates for duplicate match IDs.
-    """
+    """Test that the PhraseMatcher returns duplicates for duplicate match IDs."""
     matcher = PhraseMatcher(en_vocab)
     matcher.add("A", [Doc(en_vocab, words=["New", "York"])])
     matcher.add("B", [Doc(en_vocab, words=["New", "York"])])

spacy/tests/regression/test_issue4001-4500.py

@@ -19,8 +19,7 @@ from ..util import make_tempdir
 
 
 def test_issue4002(en_vocab):
-    """Test that the PhraseMatcher can match on overwritten NORM attributes.
-    """
+    """Test that the PhraseMatcher can match on overwritten NORM attributes."""
     matcher = PhraseMatcher(en_vocab, attr="NORM")
     pattern1 = Doc(en_vocab, words=["c", "d"])
     assert [t.norm_ for t in pattern1] == ["c", "d"]
@@ -72,9 +71,7 @@ def test_issue4030():
             batches = minibatch(train_data, size=compounding(4.0, 32.0, 1.001))
 
             for batch in batches:
-                nlp.update(
-                    examples=batch, sgd=optimizer, drop=0.1, losses=losses,
-                )
+                nlp.update(examples=batch, sgd=optimizer, drop=0.1, losses=losses)
     # processing of an empty doc should result in 0.0 for all categories
     doc = nlp("")
     assert doc.cats["offensive"] == 0.0

spacy/tests/test_cli.py

@@ -7,7 +7,7 @@ from spacy.cli.init_config import init_config, RECOMMENDATIONS
 from spacy.cli._util import validate_project_commands, parse_config_overrides
 from spacy.cli._util import load_project_config, substitute_project_variables
 from spacy.cli._util import string_to_list, OVERRIDES_ENV_VAR
-from thinc.api import ConfigValidationError, Config
+from thinc.api import ConfigValidationError
 import srsly
 import os
 

spacy/tests/test_language.py

@@ -290,9 +290,7 @@ def test_spacy_blank():
     assert nlp.meta["name"] == "my_custom_model"
 
 
-@pytest.mark.parametrize(
-    "value", [False, None, ["x", "y"], Language, Vocab],
-)
+@pytest.mark.parametrize("value", [False, None, ["x", "y"], Language, Vocab])
 def test_language_init_invalid_vocab(value):
     err_fragment = "invalid value"
     with pytest.raises(ValueError) as e:

spacy/tests/test_models.py

@@ -64,7 +64,7 @@ def get_tok2vec_kwargs():
             width=32, rows=500, also_embed_subwords=True, also_use_static_vectors=False
         ),
         "encode": MaxoutWindowEncoder(
-            width=32, depth=2, maxout_pieces=2, window_size=1,
+            width=32, depth=2, maxout_pieces=2, window_size=1
         ),
     }
 

spacy/tests/test_scorer.py

@@ -137,7 +137,7 @@ 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"]}
         example = Example.from_dict(doc, gold)

spacy/tests/training/test_training.py

@@ -496,8 +496,10 @@ def test_make_orth_variants(doc):
         output_file = tmpdir / "roundtrip.spacy"
         DocBin(docs=[doc]).to_disk(output_file)
         # due to randomness, test only that this runs with no errors for now
-        reader = Corpus(output_file, augmenter=create_orth_variants_augmenter(level=0.2, lower=0.5))
-        train_examples = list(reader(nlp))
+        reader = Corpus(
+            output_file, augmenter=create_orth_variants_augmenter(level=0.2, lower=0.5)
+        )
+        list(reader(nlp))
 
 
 @pytest.mark.skip("Outdated")

spacy/training/augment.py

@@ -23,7 +23,7 @@ def dont_augment(nlp, example):
     yield example
 
 
-def orth_variants_augmenter(nlp, example, *, level: float = 0.0, lower: float=0.0):
+def orth_variants_augmenter(nlp, example, *, level: float = 0.0, lower: float = 0.0):
     if random.random() >= level:
         yield example
     else:
@@ -36,14 +36,14 @@ def orth_variants_augmenter(nlp, example, *, level: float = 0.0, lower: float=0.
                 nlp,
                 raw_text,
                 orig_dict["token_annotation"],
-                lower=raw_text is not None and random.random() < lower
+                lower=raw_text is not None and random.random() < lower,
             )
             doc = nlp.make_doc(variant_text)
             orig_dict["token_annotation"] = variant_token_annot
             yield example.from_dict(doc, orig_dict)
 
 
-def make_orth_variants(nlp, raw, token_dict, *, lower: bool=False):
+def make_orth_variants(nlp, raw, token_dict, *, lower: bool = False):
     orig_token_dict = copy.deepcopy(token_dict)
     orth_variants = nlp.vocab.lookups.get_table("orth_variants", {})
     ndsv = orth_variants.get("single", [])

spacy/training/initialize.py

@@ -188,8 +188,8 @@ def verify_textcat_config(nlp: "Language", pipe_config: Dict[str, Any]) -> None:
 
 def get_sourced_components(config: Union[Dict[str, Any], Config]) -> List[str]:
     """RETURNS (List[str]): All sourced components in the original config,
-        e.g. {"source": "en_core_web_sm"}. If the config contains a key
-        "factory", we assume it refers to a component factory.
+    e.g. {"source": "en_core_web_sm"}. If the config contains a key
+    "factory", we assume it refers to a component factory.
     """
     return [
         name

spacy/training/pretrain.py

@@ -94,7 +94,7 @@ def ensure_docs(examples_or_docs: Iterable[Union[Doc, Example]]) -> List[Doc]:
 
 
 def _resume_model(
-    model: Model, resume_path: Path, epoch_resume: int, silent: bool = True,
+    model: Model, resume_path: Path, epoch_resume: int, silent: bool = True
 ) -> None:
     msg = Printer(no_print=silent)
     msg.info(f"Resume training tok2vec from: {resume_path}")

spacy/util.py

@@ -488,7 +488,7 @@ def load_config_from_str(
     RETURNS (Config): The loaded config.
     """
     return Config(section_order=CONFIG_SECTION_ORDER).from_str(
-        text, overrides=overrides, interpolate=interpolate,
+        text, overrides=overrides, interpolate=interpolate
     )