Format (#9630)

spacy/lang/ja/__init__.py

@@ -203,7 +203,11 @@ class Japanese(Language):
         "extend": True,
         "scorer": {"@scorers": "spacy.morphologizer_scorer.v1"},
     },
-    default_score_weights={"pos_acc": 0.5, "morph_micro_f": 0.5, "morph_per_feat": None},
+    default_score_weights={
+        "pos_acc": 0.5,
+        "morph_micro_f": 0.5,
+        "morph_per_feat": None,
+    },
 )
 def make_morphologizer(
     nlp: Language,

spacy/lang/ru/lemmatizer.py

@@ -33,7 +33,9 @@ class RussianLemmatizer(Lemmatizer):
                 ) from None
             if getattr(self, "_morph", None) is None:
                 self._morph = MorphAnalyzer()
-        super().__init__(vocab, model, name, mode=mode, overwrite=overwrite, scorer=scorer)
+        super().__init__(
+            vocab, model, name, mode=mode, overwrite=overwrite, scorer=scorer
+        )
 
     def pymorphy2_lemmatize(self, token: Token) -> List[str]:
         string = token.text

spacy/lang/ti/lex_attrs.py

@@ -27,7 +27,7 @@ _num_words = [
     "ትሪልዮን",
     "ኳድሪልዮን",
     "ጋዚልዮን",
-    "ባዚልዮን"
+    "ባዚልዮን",
 ]
 
 # Tigrinya ordinals above 10 are the same as _num_words but start with "መበል "
@@ -41,7 +41,7 @@ _ordinal_words = [
     "ሻውዓይ",
     "ሻምናይ",
     "ታሽዓይ",
-    "ዓስራይ"
+    "ዓስራይ",
 ]
 
 

spacy/lang/uk/lemmatizer.py

@@ -29,4 +29,6 @@ class UkrainianLemmatizer(RussianLemmatizer):
                 ) from None
             if getattr(self, "_morph", None) is None:
                 self._morph = MorphAnalyzer(lang="uk")
-        super().__init__(vocab, model, name, mode=mode, overwrite=overwrite, scorer=scorer)
+        super().__init__(
+            vocab, model, name, mode=mode, overwrite=overwrite, scorer=scorer
+        )

spacy/lang/vi/examples.py

@@ -1,4 +1,3 @@
-
 """
 Example sentences to test spaCy and its language models.
 >>> from spacy.lang.vi.examples import sentences

spacy/scorer.py

@@ -303,7 +303,9 @@ class Scorer:
                                     pred_per_feat[field] = set()
                                 pred_per_feat[field].add((gold_i, feat))
             for field in per_feat:
-                micro_score.score_set(pred_per_feat.get(field, set()), gold_per_feat.get(field, set()))
+                micro_score.score_set(
+                    pred_per_feat.get(field, set()), gold_per_feat.get(field, set())
+                )
                 per_feat[field].score_set(
                     pred_per_feat.get(field, set()), gold_per_feat.get(field, set())
                 )

spacy/tests/lang/ja/test_tokenizer.py

@@ -133,11 +133,7 @@ def test_ja_tokenizer_sub_tokens(
             (["五段-ラ行;連用形-促音便"], [], ["下一段-カ行;連用形-一般"], ["助動詞-タ;終止形-一般"]),
             (["トッ"], ["テ"], ["ツケ"], ["タ"]),
         ),
-        (
-            "2=3",
-            ([], [], []),
-            (["ニ"], ["_"], ["サン"])
-        ),
+        ("2=3", ([], [], []), (["ニ"], ["_"], ["サン"])),
     ],
 )
 def test_ja_tokenizer_inflections_reading_forms(

spacy/tests/tokenizer/test_tokenizer.py

@@ -216,8 +216,8 @@ def test_tokenizer_flush_specials(en_vocab):
 
 def test_tokenizer_prefix_suffix_overlap_lookbehind(en_vocab):
     # the prefix and suffix matches overlap in the suffix lookbehind
-    prefixes = ['a(?=.)']
-    suffixes = [r'(?<=\w)\.', r'(?<=a)\d+\.']
+    prefixes = ["a(?=.)"]
+    suffixes = [r"(?<=\w)\.", r"(?<=a)\d+\."]
     prefix_re = compile_prefix_regex(prefixes)
     suffix_re = compile_suffix_regex(suffixes)
     tokenizer = Tokenizer(

spacy/tests/training/test_training.py

@@ -524,6 +524,7 @@ def test_roundtrip_docs_to_docbin(doc):
     assert cats["TRAVEL"] == reloaded_example.reference.cats["TRAVEL"]
     assert cats["BAKING"] == reloaded_example.reference.cats["BAKING"]
 
+
 def test_docbin_user_data_serialized(doc):
     doc.user_data["check"] = True
     nlp = English()
@@ -536,6 +537,7 @@ def test_docbin_user_data_serialized(doc):
 
     assert reloaded_doc.user_data["check"] == True
 
+
 def test_docbin_user_data_not_serialized(doc):
     # this isn't serializable, but that shouldn't cause an error
     doc.user_data["check"] = set()
@@ -549,6 +551,7 @@ def test_docbin_user_data_not_serialized(doc):
 
     assert "check" not in reloaded_doc.user_data
 
+
 @pytest.mark.parametrize(
     "tokens_a,tokens_b,expected",
     [

spacy/training/pretrain.py

@@ -50,7 +50,9 @@ def pretrain(
     # TODO: move this to logger function?
     tracker = ProgressTracker(frequency=10000)
     if P["n_save_epoch"]:
-        msg.divider(f"Pre-training tok2vec layer - starting at epoch {epoch_resume} - saving every {P['n_save_epoch']} epoch")
+        msg.divider(
+            f"Pre-training tok2vec layer - starting at epoch {epoch_resume} - saving every {P['n_save_epoch']} epoch"
+        )
     else:
         msg.divider(f"Pre-training tok2vec layer - starting at epoch {epoch_resume}")
     row_settings = {"widths": (3, 10, 10, 6, 4), "aligns": ("r", "r", "r", "r", "r")}

spacy/util.py

@@ -288,16 +288,17 @@ def find_matching_language(lang: str) -> Optional[str]:
     None
     """
     import spacy.lang  # noqa: F401
-    if lang == 'xx':
-        return 'xx'
+
+    if lang == "xx":
+        return "xx"
 
     # Find out which language modules we have
     possible_languages = []
     for modinfo in pkgutil.iter_modules(spacy.lang.__path__):  # type: ignore
         code = modinfo.name
-        if code == 'xx':
+        if code == "xx":
             # Temporarily make 'xx' into a valid language code
-            possible_languages.append('mul')
+            possible_languages.append("mul")
         elif langcodes.tag_is_valid(code):
             possible_languages.append(code)
 
@@ -306,12 +307,10 @@ def find_matching_language(lang: str) -> Optional[str]:
     # more possibilities, like variants of Chinese like 'wuu', but text that
     # is labeled that way is probably trying to be distinct from 'zh' and
     # shouldn't automatically match.
-    match = langcodes.closest_supported_match(
-        lang, possible_languages, max_distance=9
-    )
-    if match == 'mul':
+    match = langcodes.closest_supported_match(lang, possible_languages, max_distance=9)
+    if match == "mul":
         # Convert 'mul' back to spaCy's 'xx'
-        return 'xx'
+        return "xx"
     else:
         return match