Tidy up and auto-format

spacy/language.py

@@ -1376,8 +1376,6 @@ class Language:
             docs = (self.make_doc(text) for text in texts)
             for pipe in pipes:
                 docs = pipe(docs)
-
-        nr_seen = 0
         for doc in docs:
             yield doc
 

spacy/tests/lang/en/test_text.py

@@ -56,15 +56,7 @@ def test_lex_attrs_like_number(en_tokenizer, text, match):
     assert tokens[0].like_num == match
 
 
-@pytest.mark.parametrize(
-    "word",
-    [
-        "third",
-        "Millionth",
-        "100th",
-        "Hundredth",
-    ]
-)
+@pytest.mark.parametrize("word", ["third", "Millionth", "100th", "Hundredth"])
 def test_en_lex_attrs_like_number_for_ordinal(word):
     assert like_num(word)
 

spacy/tests/pipeline/test_tagger.py

@@ -71,6 +71,6 @@ def test_overfitting_IO():
 
 def test_tagger_requires_labels():
     nlp = English()
-    tagger = nlp.add_pipe("tagger")
+    nlp.add_pipe("tagger")
     with pytest.raises(ValueError):
-        optimizer = nlp.begin_training()
+        nlp.begin_training()

spacy/tests/test_tok2vec.py

@@ -135,6 +135,7 @@ TRAIN_DATA = [
     ("Eat blue ham", {"tags": ["V", "J", "N"]}),
 ]
 
+
 def test_tok2vec_listener():
     orig_config = Config().from_str(cfg_string)
     nlp, config = util.load_model_from_config(orig_config, auto_fill=True, validate=True)