Auto-format

spacy/lang/char_classes.py

@@ -206,7 +206,15 @@ _upper = LATIN_UPPER + _russian_upper + _tatar_upper + _greek_upper + _ukrainian
 _lower = LATIN_LOWER + _russian_lower + _tatar_lower + _greek_lower + _ukrainian_lower
 
 _uncased = (
-    _bengali + _hebrew + _persian + _sinhala + _hindi + _kannada + _tamil + _telugu + _hangul
+    _bengali
+    + _hebrew
+    + _persian
+    + _sinhala
+    + _hindi
+    + _kannada
+    + _tamil
+    + _telugu
+    + _hangul
 )
 
 ALPHA = group_chars(LATIN + _russian + _tatar + _greek + _ukrainian + _uncased)

spacy/lang/ko/lex_attrs.py

@@ -45,7 +45,7 @@ _num_words = [
     "천만",
     "일억",
     "십억",
-  "백억"
+    "백억",
 ]
 
 

spacy/lang/lb/tokenizer_exceptions.py

@@ -6,9 +6,7 @@ from ...symbols import ORTH, LEMMA, NORM
 # TODO
 # treat other apostrophes within words as part of the word: [op d'mannst], [fir d'éischt] (= exceptions)
 
-_exc = {
+_exc = {}
-    
-}
 
 # translate / delete what is not necessary
 for exc_data in [

spacy/lang/zh/__init__.py

@@ -14,6 +14,7 @@ from .tag_map import TAG_MAP
 def try_jieba_import(use_jieba):
     try:
         import jieba
+
         return jieba
     except ImportError:
         if use_jieba:
@@ -34,7 +35,9 @@ class ChineseTokenizer(DummyTokenizer):
     def __call__(self, text):
         # use jieba
         if self.use_jieba:
-            jieba_words = list([x for x in self.jieba_seg.cut(text, cut_all=False) if x])
+            jieba_words = list(
+                [x for x in self.jieba_seg.cut(text, cut_all=False) if x]
+            )
             words = [jieba_words[0]]
             spaces = [False]
             for i in range(1, len(jieba_words)):

spacy/scorer.py

@@ -271,7 +271,9 @@ class Scorer(object):
                         self.labelled_per_dep[token.dep_.lower()] = PRFScore()
                     if token.dep_.lower() not in cand_deps_per_dep:
                         cand_deps_per_dep[token.dep_.lower()] = set()
-                    cand_deps_per_dep[token.dep_.lower()].add((gold_i, gold_head, token.dep_.lower()))
+                    cand_deps_per_dep[token.dep_.lower()].add(
+                        (gold_i, gold_head, token.dep_.lower())
+                    )
         if "-" not in [token[-1] for token in gold.orig_annot]:
             # Find all NER labels in gold and doc
             ent_labels = set([x[0] for x in gold_ents] + [k.label_ for k in doc.ents])
@@ -304,7 +306,9 @@ class Scorer(object):
         self.tags.score_set(cand_tags, gold_tags)
         self.labelled.score_set(cand_deps, gold_deps)
         for dep in self.labelled_per_dep:
-            self.labelled_per_dep[dep].score_set(cand_deps_per_dep.get(dep, set()), gold_deps_per_dep.get(dep, set()))
+            self.labelled_per_dep[dep].score_set(
+                cand_deps_per_dep.get(dep, set()), gold_deps_per_dep.get(dep, set())
+            )
         self.unlabelled.score_set(
             set(item[:2] for item in cand_deps), set(item[:2] for item in gold_deps)
         )

spacy/tests/regression/test_issue4590.py

@@ -11,8 +11,14 @@ def test_issue4590(en_vocab):
     """Test that matches param in on_match method are the same as matches run with no on_match method"""
     pattern = [
         {"SPEC": {"NODE_NAME": "jumped"}, "PATTERN": {"ORTH": "jumped"}},
-        {"SPEC": {"NODE_NAME": "fox", "NBOR_RELOP": ">", "NBOR_NAME": "jumped"}, "PATTERN": {"ORTH": "fox"}},
+        {
-        {"SPEC": {"NODE_NAME": "quick", "NBOR_RELOP": ".", "NBOR_NAME": "jumped"}, "PATTERN": {"ORTH": "fox"}},
+            "SPEC": {"NODE_NAME": "fox", "NBOR_RELOP": ">", "NBOR_NAME": "jumped"},
+            "PATTERN": {"ORTH": "fox"},
+        },
+        {
+            "SPEC": {"NODE_NAME": "quick", "NBOR_RELOP": ".", "NBOR_NAME": "jumped"},
+            "PATTERN": {"ORTH": "fox"},
+        },
     ]
 
     on_match = Mock()
@@ -31,4 +37,3 @@ def test_issue4590(en_vocab):
     on_match_args = on_match.call_args
 
     assert on_match_args[0][3] == matches
-

spacy/tests/test_scorer.py

@@ -12,8 +12,22 @@ from .util import get_doc
 test_las_apple = [
     [
         "Apple is looking at buying U.K. startup for $ 1 billion",
-        {"heads": [2, 2, 2, 2, 3, 6, 4, 4, 10, 10, 7],
+        {
-         "deps": ['nsubj', 'aux', 'ROOT', 'prep', 'pcomp', 'compound', 'dobj', 'prep', 'quantmod', 'compound', 'pobj']},
+            "heads": [2, 2, 2, 2, 3, 6, 4, 4, 10, 10, 7],
+            "deps": [
+                "nsubj",
+                "aux",
+                "ROOT",
+                "prep",
+                "pcomp",
+                "compound",
+                "dobj",
+                "prep",
+                "quantmod",
+                "compound",
+                "pobj",
+            ],
+        },
     ]
 ]
 
@@ -59,7 +73,7 @@ def test_las_per_type(en_vocab):
             en_vocab,
             words=input_.split(" "),
             heads=([h - i for i, h in enumerate(annot["heads"])]),
-            deps=annot["deps"]
+            deps=annot["deps"],
         )
         gold = GoldParse(doc, heads=annot["heads"], deps=annot["deps"])
         doc[0].dep_ = "compound"