Auto-format code with black

spacy/lang/ru/lemmatizer.py

@@ -12,7 +12,6 @@ PUNCT_RULES = {"«": '"', "»": '"'}
 
 
 class RussianLemmatizer(Lemmatizer):
-
     def __init__(
         self,
         vocab: Vocab,

spacy/pipeline/spancat.py

@@ -79,7 +79,9 @@ def build_ngram_suggester(sizes: List[int]) -> Callable[[List[Doc]], Ragged]:
 
 
 @registry.misc("spacy.ngram_range_suggester.v1")
-def build_ngram_range_suggester(min_size: int, max_size: int) -> Callable[[List[Doc]], Ragged]:
+def build_ngram_range_suggester(
+    min_size: int, max_size: int
+) -> Callable[[List[Doc]], Ragged]:
     """Suggest all spans of the given lengths between a given min and max value - both inclusive.
     Spans are returned as a ragged array of integers. The array has two columns,
     indicating the start and end position."""

spacy/tests/pipeline/test_spancat.py

@@ -188,7 +188,9 @@ def test_ngram_suggester(en_tokenizer):
 def test_ngram_sizes(en_tokenizer):
     # test that the range suggester works well
     size_suggester = registry.misc.get("spacy.ngram_suggester.v1")(sizes=[1, 2, 3])
-    range_suggester = registry.misc.get("spacy.ngram_range_suggester.v1")(min_size=1, max_size=3)
+    range_suggester = registry.misc.get("spacy.ngram_range_suggester.v1")(
+        min_size=1, max_size=3
+    )
     docs = [
         en_tokenizer(text) for text in ["a", "a b", "a b c", "a b c d", "a b c d e"]
     ]
@@ -199,6 +201,8 @@ def test_ngram_sizes(en_tokenizer):
     assert_equal(ngrams_1.data, ngrams_2.data)
 
     # one more variation
-    range_suggester = registry.misc.get("spacy.ngram_range_suggester.v1")(min_size=2, max_size=4)
+    range_suggester = registry.misc.get("spacy.ngram_range_suggester.v1")(
+        min_size=2, max_size=4
+    )
     ngrams_3 = range_suggester(docs)
     assert_equal(ngrams_3.lengths, [0, 1, 3, 6, 9])