Auto-format [ci skip]

spacy/lang/hi/lex_attrs.py

@@ -74,7 +74,7 @@ _eleven_to_beyond = [
     "बावन",
     "तिरपन", "तिरेपन",
     "चौवन", "चउवन",
-    "पचपन", 
+    "पचपन",
     "छप्पन",
     "सतावन", "सत्तावन",
     "अठावन",
@@ -91,7 +91,7 @@ _eleven_to_beyond = [
     "उनहत्तर",
     "सत्तर",
     "इकहत्तर"
-    "बहत्तर", 
+    "बहत्तर",
     "तिहत्तर",
     "चौहत्तर",
     "पचहत्तर",
@@ -144,6 +144,7 @@ _ordinal_words_one_to_ten = [
 _ordinal_suffix = "वाँ"
 # fmt: on
 
+
 def norm(string):
     # normalise base exceptions,  e.g. punctuation or currency symbols
     if string in BASE_NORMS:
@@ -180,7 +181,7 @@ def like_num(text):
     if text in _ordinal_words_one_to_ten:
         return True
     if text.endswith(_ordinal_suffix):
-        if text[:-len(_ordinal_suffix)] in _eleven_to_beyond:
+        if text[: -len(_ordinal_suffix)] in _eleven_to_beyond:
             return True
     return False
 

spacy/tests/conftest.py

@@ -297,7 +297,7 @@ def zh_tokenizer_pkuseg():
                 "segmenter": "pkuseg",
             }
         },
-        "initialize": {"tokenizer": {"pkuseg_model": "web",}},
+        "initialize": {"tokenizer": {"pkuseg_model": "web"}},
     }
     nlp = get_lang_class("zh").from_config(config)
     nlp.initialize()

spacy/tests/lang/hi/test_lex_attrs.py

@@ -28,14 +28,16 @@ def test_hi_norm(word, word_norm):
 
 
 @pytest.mark.parametrize(
-    "word", ["१९८७", "1987", "१२,२६७", "उन्नीस", "पाँच", "नवासी", "५/१०"],
+    "word",
+    ["१९८७", "1987", "१२,२६७", "उन्नीस", "पाँच", "नवासी", "५/१०"],
 )
 def test_hi_like_num(word):
     assert like_num(word)
 
 
 @pytest.mark.parametrize(
-    "word", ["पहला", "तृतीय", "निन्यानवेवाँ", "उन्नीस", "तिहत्तरवाँ", "छत्तीसवाँ",],
+    "word",
+    ["पहला", "तृतीय", "निन्यानवेवाँ", "उन्नीस", "तिहत्तरवाँ", "छत्तीसवाँ"],
 )
 def test_hi_like_num_ordinal_words(word):
     assert like_num(word)