Tidy up and auto-format [ci skip]

spacy/cli/debug_model.py

@@ -5,7 +5,8 @@ from thinc.api import require_gpu, fix_random_seed, set_dropout_rate, Adam
 from thinc.api import Model, data_validation
 import typer
 
-from ._util import Arg, Opt, debug_cli, show_validation_error, parse_config_overrides, string_to_list
+from ._util import Arg, Opt, debug_cli, show_validation_error
+from ._util import parse_config_overrides, string_to_list
 from .. import util
 
 

spacy/cli/init_model.py

@@ -277,7 +277,7 @@ def read_vectors(msg: Printer, vectors_loc: Path, truncate_vectors: int):
 
 def ensure_shape(lines):
     """Ensure that the first line of the data is the vectors shape.
-    
+
     If it's not, we read in the data and output the shape as the first result,
     so that the reader doesn't have to deal with the problem.
     """

spacy/language.py

@@ -244,7 +244,8 @@ class Language:
         self._config["nlp"]["disabled"] = list(self.disabled)
         self._config["components"] = pipeline
         if not self._config["training"].get("score_weights"):
-            self._config["training"]["score_weights"] = combine_score_weights(score_weights)
+            combined_score_weights = combine_score_weights(score_weights)
+            self._config["training"]["score_weights"] = combined_score_weights
         if not srsly.is_json_serializable(self._config):
             raise ValueError(Errors.E961.format(config=self._config))
         return self._config

spacy/tests/doc/test_add_entities.py

@@ -9,7 +9,10 @@ from spacy.pipeline.ner import DEFAULT_NER_MODEL
 
 
 def _ner_example(ner):
-    doc = Doc(ner.vocab, words=["Joe", "loves", "visiting", "London", "during", "the", "weekend"])
+    doc = Doc(
+        ner.vocab,
+        words=["Joe", "loves", "visiting", "London", "during", "the", "weekend"],
+    )
     gold = {"entities": [(0, 3, "PERSON"), (19, 25, "LOC")]}
     return Example.from_dict(doc, gold)
 

spacy/tests/matcher/test_dependency_matcher.py

@@ -78,7 +78,7 @@ def patterns(en_vocab):
             "REL_OP": ">",
             "RIGHT_ID": "fox",
             "RIGHT_ATTRS": {"ORTH": "fox"},
-        }
+        },
     ]
 
     pattern5 = [
@@ -233,9 +233,7 @@ def test_dependency_matcher_callback(en_vocab, doc):
     assert matches == matches2
 
 
-@pytest.mark.parametrize(
-    "op,num_matches", [(".", 8), (".*", 20), (";", 8), (";*", 20),]
-)
+@pytest.mark.parametrize("op,num_matches", [(".", 8), (".*", 20), (";", 8), (";*", 20)])
 def test_dependency_matcher_precedence_ops(en_vocab, op, num_matches):
     # two sentences to test that all matches are within the same sentence
     doc = get_doc(
@@ -248,7 +246,7 @@ def test_dependency_matcher_precedence_ops(en_vocab, op, num_matches):
     for text in ["a", "b", "c", "d", "e"]:
         pattern = [
             {"RIGHT_ID": "1", "RIGHT_ATTRS": {"ORTH": text}},
-            {"LEFT_ID": "1", "REL_OP": op, "RIGHT_ID": "2", "RIGHT_ATTRS": {},},
+            {"LEFT_ID": "1", "REL_OP": op, "RIGHT_ID": "2", "RIGHT_ATTRS": {}},
         ]
         matcher = DependencyMatcher(en_vocab)
         matcher.add("A", [pattern])

spacy/tests/parser/test_add_label.py

@@ -54,7 +54,10 @@ def _parser_example(parser):
 
 
 def _ner_example(ner):
-    doc = Doc(ner.vocab, words=["Joe", "loves", "visiting", "London", "during", "the", "weekend"])
+    doc = Doc(
+        ner.vocab,
+        words=["Joe", "loves", "visiting", "London", "during", "the", "weekend"],
+    )
     gold = {"entities": [(0, 3, "PERSON"), (19, 25, "LOC")]}
     return Example.from_dict(doc, gold)
 

spacy/tests/pipeline/test_senter.py

@@ -30,9 +30,10 @@ TRAIN_DATA = [
     ),
 ]
 
+
 def test_begin_training_examples():
     nlp = Language()
-    senter = nlp.add_pipe("senter")
+    nlp.add_pipe("senter")
     train_examples = []
     for t in TRAIN_DATA:
         train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))

spacy/tests/pipeline/test_textcat.py

@@ -89,7 +89,7 @@ def test_no_label():
 
 def test_implicit_label():
     nlp = Language()
-    textcat = nlp.add_pipe("textcat")
+    nlp.add_pipe("textcat")
     train_examples = []
     for t in TRAIN_DATA:
         train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))