Reformat with black 25

spacy/cli/_util.py

@@ -225,13 +225,11 @@ def get_git_version(
 
 
 @overload
-def string_to_list(value: str, intify: Literal[False] = ...) -> List[str]:
-    ...
+def string_to_list(value: str, intify: Literal[False] = ...) -> List[str]: ...
 
 
 @overload
-def string_to_list(value: str, intify: Literal[True]) -> List[int]:
-    ...
+def string_to_list(value: str, intify: Literal[True]) -> List[int]: ...
 
 
 def string_to_list(value: str, intify: bool = False) -> Union[List[str], List[int]]:

spacy/cli/debug_data.py

@@ -968,16 +968,14 @@ def _compile_gold(
 
 
 @overload
-def _format_labels(labels: Iterable[str], counts: Literal[False] = False) -> str:
-    ...
+def _format_labels(labels: Iterable[str], counts: Literal[False] = False) -> str: ...
 
 
 @overload
 def _format_labels(
     labels: Iterable[Tuple[str, int]],
     counts: Literal[True],
-) -> str:
-    ...
+) -> str: ...
 
 
 def _format_labels(

spacy/cli/find_threshold.py

@@ -157,9 +157,11 @@ def find_threshold(
                 exits=1,
             )
         return {
-            keys[0]: filter_config(config[keys[0]], keys[1:], full_key)
-            if len(keys) > 1
-            else config[keys[0]]
+            keys[0]: (
+                filter_config(config[keys[0]], keys[1:], full_key)
+                if len(keys) > 1
+                else config[keys[0]]
+            )
         }
 
     # Evaluate with varying threshold values.
@@ -216,12 +218,14 @@ def find_threshold(
     if len(set(scores.values())) == 1:
         wasabi.msg.warn(
             title="All scores are identical. Verify that all settings are correct.",
-            text=""
-            if (
-                not isinstance(pipe, MultiLabel_TextCategorizer)
-                or scores_key in ("cats_macro_f", "cats_micro_f")
-            )
-            else "Use `cats_macro_f` or `cats_micro_f` when optimizing the threshold for `textcat_multilabel`.",
+            text=(
+                ""
+                if (
+                    not isinstance(pipe, MultiLabel_TextCategorizer)
+                    or scores_key in ("cats_macro_f", "cats_micro_f")
+                )
+                else "Use `cats_macro_f` or `cats_micro_f` when optimizing the threshold for `textcat_multilabel`."
+            ),
         )
 
     else:

spacy/cli/init_config.py

@@ -195,9 +195,11 @@ def init_config(
         "Pipeline": ", ".join(pipeline),
         "Optimize for": optimize,
         "Hardware": variables["hardware"].upper(),
-        "Transformer": template_vars.transformer.get("name")  # type: ignore[attr-defined]
-        if template_vars.use_transformer  # type: ignore[attr-defined]
-        else None,
+        "Transformer": (
+            template_vars.transformer.get("name")  # type: ignore[attr-defined]
+            if template_vars.use_transformer  # type: ignore[attr-defined]
+            else None
+        ),
     }
     msg.info("Generated config template specific for your use case")
     for label, value in use_case.items():

spacy/compat.py

@@ -1,4 +1,5 @@
 """Helpers for Python and platform compatibility."""
+
 import sys
 
 from thinc.util import copy_array

spacy/displacy/__init__.py

@@ -4,6 +4,7 @@ spaCy's built in visualization suite for dependencies and named entities.
 DOCS: https://spacy.io/api/top-level#displacy
 USAGE: https://spacy.io/usage/visualizers
 """
+
 import warnings
 from typing import Any, Callable, Dict, Iterable, Optional, Union
 

spacy/lang/am/examples.py

@@ -5,7 +5,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "አፕል የዩኬን ጅምር ድርጅት በ 1 ቢሊዮን ዶላር ለመግዛት አስቧል።",
     "የራስ ገዝ መኪኖች የኢንሹራንስ ኃላፊነትን ወደ አምራቾች ያዛውራሉ",

spacy/lang/az/examples.py

@@ -4,7 +4,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "Bu bir cümlədir.",
     "Necəsən?",

spacy/lang/bg/stop_words.py

@@ -3,6 +3,7 @@ References:
     https://github.com/Alir3z4/stop-words - Original list, serves as a base.
     https://postvai.com/books/stop-dumi.pdf - Additions to the original list in order to improve it.
 """
+
 STOP_WORDS = set(
     """
 а автентичен аз ако ала

spacy/lang/bn/examples.py

@@ -5,5 +5,4 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = ["তুই খুব ভালো", "আজ আমরা ডাক্তার দেখতে যাবো", "আমি জানি না "]

spacy/lang/bo/examples.py

@@ -5,7 +5,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "དོན་དུ་རྒྱ་མཚོ་བླ་མ་ཞེས་བྱ་ཞིང༌།",
     "ཏཱ་ལའི་ཞེས་པ་ནི་སོག་སྐད་ཡིན་པ་དེ་བོད་སྐད་དུ་རྒྱ་མཚོའི་དོན་དུ་འཇུག",

spacy/lang/ca/examples.py

@@ -5,7 +5,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "Apple està buscant comprar una startup del Regne Unit per mil milions de dòlars",
     "Els cotxes autònoms deleguen la responsabilitat de l'assegurança als seus fabricants",

spacy/lang/char_classes.py

@@ -277,10 +277,10 @@ _currency = (
 # These expressions contain various unicode variations, including characters
 # used in Chinese (see #1333, #1340, #1351) – unless there are cross-language
 # conflicts, spaCy's base tokenizer should handle all of those by default
-_punct = (
-    r"… …… , : ; \! \? ¿ ؟ ¡ \( \) \[ \] \{ \} < > _ # \* & 。 ？ ！ ， 、 ； ： ～ · । ، ۔ ؛ ٪"
+_punct = r"… …… , : ; \! \? ¿ ؟ ¡ \( \) \[ \] \{ \} < > _ # \* & 。 ？ ！ ， 、 ； ： ～ · । ، ۔ ؛ ٪"
+_quotes = (
+    r'\' " ” “ ` ‘ ´ ’ ‚ , „ » « 「 」 『 』 （ ） 〔 〕 【 】 《 》 〈 〉 〈 〉  ⟦ ⟧'
 )
-_quotes = r'\' " ” “ ` ‘ ´ ’ ‚ , „ » « 「 」 『 』 （ ） 〔 〕 【 】 《 》 〈 〉 〈 〉  ⟦ ⟧'
 _hyphens = "- – — -- --- —— ~"
 
 # Various symbols like dingbats, but also emoji

spacy/lang/cs/examples.py

@@ -4,7 +4,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "Máma mele maso.",
     "Příliš žluťoučký kůň úpěl ďábelské ódy.",

spacy/lang/da/tokenizer_exceptions.py

@@ -2,6 +2,7 @@
 Tokenizer Exceptions.
 Source: https://forkortelse.dk/ and various others.
 """
+
 from ...symbols import NORM, ORTH
 from ...util import update_exc
 from ..tokenizer_exceptions import BASE_EXCEPTIONS

spacy/lang/de/examples.py

@@ -5,7 +5,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "Die ganze Stadt ist ein Startup: Shenzhen ist das Silicon Valley für Hardware-Firmen",
     "Wie deutsche Startups die Technologie vorantreiben wollen: Künstliche Intelligenz",

spacy/lang/dsb/examples.py

@@ -5,7 +5,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "Z tym stwori so wuměnjenje a zakład za dalše wobdźěłanje přez analyzu tekstoweje struktury a semantisku anotaciju a z tym tež za tu předstajenu digitalnu online-wersiju.",
     "Mi so tu jara derje spodoba.",

spacy/lang/en/examples.py

@@ -5,7 +5,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "Apple is looking at buying U.K. startup for $1 billion",
     "Autonomous cars shift insurance liability toward manufacturers",

spacy/lang/es/examples.py

@@ -5,7 +5,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "Apple está buscando comprar una startup del Reino Unido por mil millones de dólares.",
     "Los coches autónomos delegan la responsabilidad del seguro en sus fabricantes.",

spacy/lang/fa/examples.py

@@ -5,7 +5,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "این یک جمله نمونه می باشد.",
     "قرار ما، امروز ساعت ۲:۳۰ بعدازظهر هست!",

spacy/lang/fi/tokenizer_exceptions.py

@@ -100,9 +100,9 @@ conj_contraction_negations = [
     ("eivat", "eivät"),
     ("eivät", "eivät"),
 ]
-for (base_lower, base_norm) in conj_contraction_bases:
+for base_lower, base_norm in conj_contraction_bases:
     for base in [base_lower, base_lower.title()]:
-        for (suffix, suffix_norm) in conj_contraction_negations:
+        for suffix, suffix_norm in conj_contraction_negations:
             _exc[base + suffix] = [
                 {ORTH: base, NORM: base_norm},
                 {ORTH: suffix, NORM: suffix_norm},

spacy/lang/fr/examples.py

@@ -5,7 +5,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "Apple cherche à acheter une start-up anglaise pour 1 milliard de dollars",
     "Les voitures autonomes déplacent la responsabilité de l'assurance vers les constructeurs",

spacy/lang/grc/examples.py

@@ -5,7 +5,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "ἐρᾷ μὲν ἁγνὸς οὐρανὸς τρῶσαι χθόνα, ἔρως δὲ γαῖαν λαμβάνει γάμου τυχεῖν·",
     "εὐδαίμων Χαρίτων καὶ Μελάνιππος ἔφυ, θείας ἁγητῆρες ἐφαμερίοις φιλότατος.",

spacy/lang/gu/examples.py

@@ -5,7 +5,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "લોકશાહી એ સરકારનું એક એવું તંત્ર છે જ્યાં નાગરિકો મત દ્વારા સત્તાનો ઉપયોગ કરે છે.",
     "તે ગુજરાત રાજ્યના ધરમપુર શહેરમાં આવેલું હતું",

spacy/lang/he/examples.py

@@ -5,7 +5,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "סין מקימה קרן של 440 מיליון דולר להשקעה בהייטק בישראל",
     'רה"מ הודיע כי יחרים טקס בחסותו',

spacy/lang/hi/examples.py

@@ -5,7 +5,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "एप्पल 1 अरब डॉलर के लिए यू.के. स्टार्टअप खरीदने पर विचार कर रहा है।",
     "स्वायत्त कारें निर्माताओं की ओर बीमा दायित्व रखतीं हैं।",

spacy/lang/hsb/examples.py

@@ -5,7 +5,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "To běšo wjelgin raźone a jo se wót luźi derje pśiwzeło. Tak som dožywiła wjelgin",
     "Jogo pśewóźowarce stej groniłej, až how w serbskich stronach njama Santa Claus nic pytaś.",

spacy/lang/ht/__init__.py

@@ -22,10 +22,12 @@ class HaitianCreoleDefaults(BaseDefaults):
     stop_words = STOP_WORDS
     tag_map = TAG_MAP
 
+
 class HaitianCreole(Language):
     lang = "ht"
     Defaults = HaitianCreoleDefaults
 
+
 @HaitianCreole.factory(
     "lemmatizer",
     assigns=["token.lemma"],
@@ -49,4 +51,5 @@ def make_lemmatizer(
         nlp.vocab, model, name, mode=mode, overwrite=overwrite, scorer=scorer
     )
 
+
 __all__ = ["HaitianCreole"]

spacy/lang/ht/examples.py

@@ -5,7 +5,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "Apple ap panse achte yon demaraj nan Wayòm Ini pou $1 milya dola",
     "Machin otonòm fè responsablite asirans lan ale sou men fabrikan yo",

spacy/lang/ht/lex_attrs.py

@@ -49,6 +49,7 @@ NORM_MAP = {
     "P": "Pa",
 }
 
+
 def like_num(text):
     text = text.strip().lower()
     if text.startswith(("+", "-", "±", "~")):
@@ -69,9 +70,11 @@ def like_num(text):
         return True
     return False
 
+
 def norm_custom(text):
     return NORM_MAP.get(text, text.lower())
 
+
 LEX_ATTRS = {
     LIKE_NUM: like_num,
     NORM: norm_custom,

spacy/lang/ht/punctuation.py

@@ -16,28 +16,43 @@ ELISION = "'’".replace(" ", "")
 _prefixes_elision = "m n l y t k w"
 _prefixes_elision += " " + _prefixes_elision.upper()
 
-TOKENIZER_PREFIXES = LIST_PUNCT + LIST_QUOTES + [
-    r"(?:({pe})[{el}])(?=[{a}])".format(
-        a=ALPHA, el=ELISION, pe=merge_chars(_prefixes_elision)
-    )
-]
+TOKENIZER_PREFIXES = (
+    LIST_PUNCT
+    + LIST_QUOTES
+    + [
+        r"(?:({pe})[{el}])(?=[{a}])".format(
+            a=ALPHA, el=ELISION, pe=merge_chars(_prefixes_elision)
+        )
+    ]
+)
 
-TOKENIZER_SUFFIXES = LIST_PUNCT + LIST_QUOTES + LIST_ELLIPSES + [
-    r"(?<=[0-9])%",  # numbers like 10%
-    r"(?<=[0-9])(?:{h})".format(h=HYPHENS),  # hyphens after numbers
-    r"(?<=[{a}])['’]".format(a=ALPHA),  # apostrophes after letters
-    r"(?<=[{a}])['’][mwlnytk](?=\s|$)".format(a=ALPHA),  # contractions
-    r"(?<=[{a}0-9])\)",  # right parenthesis after letter/number
-    r"(?<=[{a}])\.(?=\s|$)".format(a=ALPHA),  # period after letter if space or end of string
-    r"(?<=\))[\.\?!]",  # punctuation immediately after right parenthesis
-]
+TOKENIZER_SUFFIXES = (
+    LIST_PUNCT
+    + LIST_QUOTES
+    + LIST_ELLIPSES
+    + [
+        r"(?<=[0-9])%",  # numbers like 10%
+        r"(?<=[0-9])(?:{h})".format(h=HYPHENS),  # hyphens after numbers
+        r"(?<=[{a}])['’]".format(a=ALPHA),  # apostrophes after letters
+        r"(?<=[{a}])['’][mwlnytk](?=\s|$)".format(a=ALPHA),  # contractions
+        r"(?<=[{a}0-9])\)",  # right parenthesis after letter/number
+        r"(?<=[{a}])\.(?=\s|$)".format(
+            a=ALPHA
+        ),  # period after letter if space or end of string
+        r"(?<=\))[\.\?!]",  # punctuation immediately after right parenthesis
+    ]
+)
 
-TOKENIZER_INFIXES = LIST_ELLIPSES + LIST_ICONS + [
-    r"(?<=[0-9])[+\-\*^](?=[0-9-])",
-    r"(?<=[{al}{q}])\.(?=[{au}{q}])".format(
-        al=ALPHA_LOWER, au=ALPHA_UPPER, q=CONCAT_QUOTES
-    ),
-    r"(?<=[{a}]),(?=[{a}])".format(a=ALPHA),
-    r"(?<=[{a}0-9])(?:{h})(?=[{a}])".format(a=ALPHA, h=HYPHENS),
-    r"(?<=[{a}][{el}])(?=[{a}])".format(a=ALPHA, el=ELISION),
-]
+TOKENIZER_INFIXES = (
+    LIST_ELLIPSES
+    + LIST_ICONS
+    + [
+        r"(?<=[0-9])[+\-\*^](?=[0-9-])",
+        r"(?<=[{al}{q}])\.(?=[{au}{q}])".format(
+            al=ALPHA_LOWER, au=ALPHA_UPPER, q=CONCAT_QUOTES
+        ),
+        r"(?<=[{a}]),(?=[{a}])".format(a=ALPHA),
+        r"(?<=[{a}0-9])(?:{h})(?=[{a}])".format(a=ALPHA, h=HYPHENS),
+        r"(?<=[{a}][{el}])(?=[{a}])".format(a=ALPHA, el=ELISION),
+    ]
+)

spacy/lang/ht/stop_words.py

@@ -39,8 +39,7 @@ sa san si swa si
 
 men mèsi oswa osinon
 
-"""
-.split()
+""".split()
 )
 
 # Add common contractions, with and without apostrophe variants

spacy/lang/ht/tag_map.py

@@ -1,4 +1,22 @@
-from spacy.symbols import NOUN, VERB, AUX, ADJ, ADV, PRON, DET, ADP, SCONJ, CCONJ, PART, INTJ, NUM, PROPN, PUNCT, SYM, X
+from spacy.symbols import (
+    NOUN,
+    VERB,
+    AUX,
+    ADJ,
+    ADV,
+    PRON,
+    DET,
+    ADP,
+    SCONJ,
+    CCONJ,
+    PART,
+    INTJ,
+    NUM,
+    PROPN,
+    PUNCT,
+    SYM,
+    X,
+)
 
 TAG_MAP = {
     "NOUN": {"pos": NOUN},

spacy/lang/ht/tokenizer_exceptions.py

@@ -1,5 +1,6 @@
 from spacy.symbols import ORTH, NORM
 
+
 def make_variants(base, first_norm, second_orth, second_norm):
     return {
         base: [
@@ -7,14 +8,16 @@ def make_variants(base, first_norm, second_orth, second_norm):
             {ORTH: second_orth, NORM: second_norm},
         ],
         base.capitalize(): [
-            {ORTH: base.split("'")[0].capitalize() + "'", NORM: first_norm.capitalize()},
+            {
+                ORTH: base.split("'")[0].capitalize() + "'",
+                NORM: first_norm.capitalize(),
+            },
             {ORTH: second_orth, NORM: second_norm},
-        ]
+        ],
     }
 
-TOKENIZER_EXCEPTIONS = {
-    "Dr.": [{ORTH: "Dr."}]
-}
+
+TOKENIZER_EXCEPTIONS = {"Dr.": [{ORTH: "Dr."}]}
 
 # Apostrophe forms
 TOKENIZER_EXCEPTIONS.update(make_variants("m'ap", "mwen", "ap", "ap"))
@@ -29,93 +32,95 @@ TOKENIZER_EXCEPTIONS.update(make_variants("p'ap", "pa", "ap", "ap"))
 TOKENIZER_EXCEPTIONS.update(make_variants("t'ap", "te", "ap", "ap"))
 
 # Non-apostrophe contractions (with capitalized variants)
-TOKENIZER_EXCEPTIONS.update({
-    "map": [
-        {ORTH: "m", NORM: "mwen"},
-        {ORTH: "ap", NORM: "ap"},
-    ],
-    "Map": [
-        {ORTH: "M", NORM: "Mwen"},
-        {ORTH: "ap", NORM: "ap"},
-    ],
-    "lem": [
-        {ORTH: "le", NORM: "le"},
-        {ORTH: "m", NORM: "mwen"},
-    ],
-    "Lem": [
-        {ORTH: "Le", NORM: "Le"},
-        {ORTH: "m", NORM: "mwen"},
-    ],
-    "lew": [
-        {ORTH: "le", NORM: "le"},
-        {ORTH: "w", NORM: "ou"},
-    ],
-    "Lew": [
-        {ORTH: "Le", NORM: "Le"},
-        {ORTH: "w", NORM: "ou"},
-    ],
-    "nap": [
-        {ORTH: "n", NORM: "nou"},
-        {ORTH: "ap", NORM: "ap"},
-    ],
-    "Nap": [
-        {ORTH: "N", NORM: "Nou"},
-        {ORTH: "ap", NORM: "ap"},
-    ],
-    "lap": [
-        {ORTH: "l", NORM: "li"},
-        {ORTH: "ap", NORM: "ap"},
-    ],
-    "Lap": [
-        {ORTH: "L", NORM: "Li"},
-        {ORTH: "ap", NORM: "ap"},
-    ],
-    "yap": [
-        {ORTH: "y", NORM: "yo"},
-        {ORTH: "ap", NORM: "ap"},
-    ],
-    "Yap": [
-        {ORTH: "Y", NORM: "Yo"},
-        {ORTH: "ap", NORM: "ap"},
-    ],
-    "mte": [
-        {ORTH: "m", NORM: "mwen"},
-        {ORTH: "te", NORM: "te"},
-    ],
-    "Mte": [
-        {ORTH: "M", NORM: "Mwen"},
-        {ORTH: "te", NORM: "te"},
-    ],
-    "mpral": [
-        {ORTH: "m", NORM: "mwen"},
-        {ORTH: "pral", NORM: "pral"},
-    ],
-    "Mpral": [
-        {ORTH: "M", NORM: "Mwen"},
-        {ORTH: "pral", NORM: "pral"},
-    ],
-    "wap": [
-        {ORTH: "w", NORM: "ou"},
-        {ORTH: "ap", NORM: "ap"},
-    ],
-    "Wap": [
-        {ORTH: "W", NORM: "Ou"},
-        {ORTH: "ap", NORM: "ap"},
-    ],
-    "kap": [
-        {ORTH: "k", NORM: "ki"},
-        {ORTH: "ap", NORM: "ap"},
-    ],
-    "Kap": [
-        {ORTH: "K", NORM: "Ki"},
-        {ORTH: "ap", NORM: "ap"},
-    ],
-    "tap": [
-        {ORTH: "t", NORM: "te"},
-        {ORTH: "ap", NORM: "ap"},
-    ],
-    "Tap": [
-        {ORTH: "T", NORM: "Te"},
-        {ORTH: "ap", NORM: "ap"},
-    ],
-})
+TOKENIZER_EXCEPTIONS.update(
+    {
+        "map": [
+            {ORTH: "m", NORM: "mwen"},
+            {ORTH: "ap", NORM: "ap"},
+        ],
+        "Map": [
+            {ORTH: "M", NORM: "Mwen"},
+            {ORTH: "ap", NORM: "ap"},
+        ],
+        "lem": [
+            {ORTH: "le", NORM: "le"},
+            {ORTH: "m", NORM: "mwen"},
+        ],
+        "Lem": [
+            {ORTH: "Le", NORM: "Le"},
+            {ORTH: "m", NORM: "mwen"},
+        ],
+        "lew": [
+            {ORTH: "le", NORM: "le"},
+            {ORTH: "w", NORM: "ou"},
+        ],
+        "Lew": [
+            {ORTH: "Le", NORM: "Le"},
+            {ORTH: "w", NORM: "ou"},
+        ],
+        "nap": [
+            {ORTH: "n", NORM: "nou"},
+            {ORTH: "ap", NORM: "ap"},
+        ],
+        "Nap": [
+            {ORTH: "N", NORM: "Nou"},
+            {ORTH: "ap", NORM: "ap"},
+        ],
+        "lap": [
+            {ORTH: "l", NORM: "li"},
+            {ORTH: "ap", NORM: "ap"},
+        ],
+        "Lap": [
+            {ORTH: "L", NORM: "Li"},
+            {ORTH: "ap", NORM: "ap"},
+        ],
+        "yap": [
+            {ORTH: "y", NORM: "yo"},
+            {ORTH: "ap", NORM: "ap"},
+        ],
+        "Yap": [
+            {ORTH: "Y", NORM: "Yo"},
+            {ORTH: "ap", NORM: "ap"},
+        ],
+        "mte": [
+            {ORTH: "m", NORM: "mwen"},
+            {ORTH: "te", NORM: "te"},
+        ],
+        "Mte": [
+            {ORTH: "M", NORM: "Mwen"},
+            {ORTH: "te", NORM: "te"},
+        ],
+        "mpral": [
+            {ORTH: "m", NORM: "mwen"},
+            {ORTH: "pral", NORM: "pral"},
+        ],
+        "Mpral": [
+            {ORTH: "M", NORM: "Mwen"},
+            {ORTH: "pral", NORM: "pral"},
+        ],
+        "wap": [
+            {ORTH: "w", NORM: "ou"},
+            {ORTH: "ap", NORM: "ap"},
+        ],
+        "Wap": [
+            {ORTH: "W", NORM: "Ou"},
+            {ORTH: "ap", NORM: "ap"},
+        ],
+        "kap": [
+            {ORTH: "k", NORM: "ki"},
+            {ORTH: "ap", NORM: "ap"},
+        ],
+        "Kap": [
+            {ORTH: "K", NORM: "Ki"},
+            {ORTH: "ap", NORM: "ap"},
+        ],
+        "tap": [
+            {ORTH: "t", NORM: "te"},
+            {ORTH: "ap", NORM: "ap"},
+        ],
+        "Tap": [
+            {ORTH: "T", NORM: "Te"},
+            {ORTH: "ap", NORM: "ap"},
+        ],
+    }
+)

spacy/lang/hu/examples.py

@@ -5,7 +5,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "Az Apple egy brit startup vásárlását tervezi 1 milliárd dollár értékben.",
     "San Francisco vezetése mérlegeli a járdát használó szállító robotok betiltását.",

spacy/lang/hu/punctuation.py

@@ -11,7 +11,7 @@ from ..char_classes import (
 )
 
 # removing ° from the special icons to keep e.g. 99° as one token
-_concat_icons = CONCAT_ICONS.replace("\u00B0", "")
+_concat_icons = CONCAT_ICONS.replace("\u00b0", "")
 
 _currency = r"\$¢£€¥฿"
 _quotes = CONCAT_QUOTES.replace("'", "")

spacy/lang/hy/examples.py

@@ -4,7 +4,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "Լոնդոնը Միացյալ Թագավորության մեծ քաղաք է։",
     "Ո՞վ է Ֆրանսիայի նախագահը։",

spacy/lang/id/examples.py

@@ -5,7 +5,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "Indonesia merupakan negara kepulauan yang kaya akan budaya.",
     "Berapa banyak warga yang dibutuhkan saat kerja bakti?",

spacy/lang/it/examples.py

@@ -5,7 +5,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "Apple vuole comprare una startup del Regno Unito per un miliardo di dollari",
     "Le automobili a guida autonoma spostano la responsabilità assicurativa verso i produttori",

spacy/lang/ja/__init__.py

@@ -102,9 +102,9 @@ class JapaneseTokenizer(DummyTokenizer):
                 token.dictionary_form(),  # lemma
                 token.normalized_form(),
                 token.reading_form(),
-                sub_tokens_list[idx]
-                if sub_tokens_list
-                else None,  # user_data['sub_tokens']
+                (
+                    sub_tokens_list[idx] if sub_tokens_list else None
+                ),  # user_data['sub_tokens']
             )
             for idx, token in enumerate(sudachipy_tokens)
             if len(token.surface()) > 0

spacy/lang/ja/examples.py

@@ -5,7 +5,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "アップルがイギリスの新興企業を１０億ドルで購入を検討",
     "自動運転車の損害賠償責任、自動車メーカーに一定の負担を求める",

spacy/lang/ja/tag_map.py

@@ -25,7 +25,9 @@ TAG_MAP = {
     # Universal Dependencies Mapping: (Some of the entries in this mapping are updated to v2.6 in the list below)
     # http://universaldependencies.org/ja/overview/morphology.html
     # http://universaldependencies.org/ja/pos/all.html
-    "記号-一般": {POS: NOUN},  # this includes characters used to represent sounds like ドレミ
+    "記号-一般": {
+        POS: NOUN
+    },  # this includes characters used to represent sounds like ドレミ
     "記号-文字": {
         POS: NOUN
     },  # this is for Greek and Latin characters having some meanings, or used as symbols, as in math
@@ -72,7 +74,9 @@ TAG_MAP = {
     "名詞-固有名詞-地名-国": {POS: PROPN},  # country name
     "名詞-助動詞語幹": {POS: AUX},
     "名詞-数詞": {POS: NUM},  # includes Chinese numerals
-    "名詞-普通名詞-サ変可能": {POS: NOUN},  # XXX: sometimes VERB in UDv2; suru-verb noun
+    "名詞-普通名詞-サ変可能": {
+        POS: NOUN
+    },  # XXX: sometimes VERB in UDv2; suru-verb noun
     "名詞-普通名詞-サ変形状詞可能": {POS: NOUN},
     "名詞-普通名詞-一般": {POS: NOUN},
     "名詞-普通名詞-形状詞可能": {POS: NOUN},  # XXX: sometimes ADJ in UDv2

spacy/lang/kn/examples.py

@@ -5,7 +5,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "ಆಪಲ್ ಒಂದು ಯು.ಕೆ. ಸ್ಟಾರ್ಟ್ಅಪ್ ಅನ್ನು ೧ ಶತಕೋಟಿ ಡಾಲರ್ಗಳಿಗೆ ಖರೀದಿಸಲು ನೋಡುತ್ತಿದೆ.",
     "ಸ್ವಾಯತ್ತ ಕಾರುಗಳು ವಿಮಾ ಹೊಣೆಗಾರಿಕೆಯನ್ನು ತಯಾರಕರ ಕಡೆಗೆ ಬದಲಾಯಿಸುತ್ತವೆ.",

spacy/lang/lij/examples.py

@@ -5,7 +5,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "Sciusciâ e sciorbî no se peu.",
     "Graçie di çetroin, che me son arrivæ.",

spacy/lang/lt/examples.py

@@ -5,7 +5,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "Jaunikis pirmąją vestuvinę naktį iškeitė į areštinės gultą",
     "Bepiločiai automobiliai išnaikins vairavimo mokyklas, autoservisus ir eismo nelaimes",

spacy/lang/ml/examples.py

@@ -5,7 +5,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "അനാവശ്യമായി കണ്ണിലും മൂക്കിലും വായിലും സ്പർശിക്കാതിരിക്കുക",
     "പൊതുരംഗത്ത് മലയാള ഭാഷയുടെ സമഗ്രപുരോഗതി ലക്ഷ്യമാക്കി പ്രവർത്തിക്കുന്ന സംഘടനയായ മലയാളഐക്യവേദിയുടെ വിദ്യാർത്ഥിക്കൂട്ടായ്മയാണ് വിദ്യാർത്ഥി മലയാളവേദി",

spacy/lang/ms/examples.py

@@ -5,7 +5,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "Malaysia ialah sebuah negara yang terletak di Asia Tenggara.",
     "Berapa banyak pelajar yang akan menghadiri majlis perpisahan sekolah?",

spacy/lang/nb/examples.py

@@ -5,7 +5,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "Apple vurderer å kjøpe britisk oppstartfirma for en milliard dollar.",
     "Selvkjørende biler flytter forsikringsansvaret over på produsentene.",

spacy/lang/ne/examples.py

@@ -5,7 +5,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "एप्पलले अमेरिकी स्टार्टअप १ अर्ब डलरमा किन्ने सोच्दै छ",
     "स्वायत्त कारहरूले बीमा दायित्व निर्माताहरु तिर बदल्छन्",

spacy/lang/nl/examples.py

@@ -5,7 +5,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "Apple overweegt om voor 1 miljard een U.K. startup te kopen",
     "Autonome auto's verschuiven de verzekeringverantwoordelijkheid naar producenten",

spacy/lang/nn/examples.py

@@ -5,7 +5,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 # sentences taken from Omsetjingsminne frå Nynorsk pressekontor 2022 (https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-80/)
 sentences = [
     "Konseptet går ut på at alle tre omgangar tel, alle hopparar må stille i kvalifiseringa og poengsummen skal telje.",

spacy/lang/pl/examples.py

@@ -5,7 +5,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "Poczuł przyjemną woń mocnej kawy.",
     "Istnieje wiele dróg oddziaływania substancji psychoaktywnej na układ nerwowy.",

spacy/lang/pt/examples.py

@@ -5,7 +5,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "Apple está querendo comprar uma startup do Reino Unido por 100 milhões de dólares",
     "Carros autônomos empurram a responsabilidade do seguro para os fabricantes."

spacy/lang/ro/examples.py

@@ -7,7 +7,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "Apple plănuiește să cumpere o companie britanică pentru un miliard de dolari",
     "Municipalitatea din San Francisco ia în calcul interzicerea roboților curieri pe trotuar",

spacy/lang/ru/examples.py

@@ -5,7 +5,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     # Translations from English:
     "Apple рассматривает возможность покупки стартапа из Соединённого Королевства за $1 млрд",

spacy/lang/sa/examples.py

@@ -5,7 +5,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "अभ्यावहति कल्याणं विविधं वाक् सुभाषिता ।",
     "मनसि व्याकुले चक्षुः पश्यन्नपि न पश्यति ।",

spacy/lang/si/examples.py

@@ -5,7 +5,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "මෙය වාක්‍යයකි.",
     "ඔබ කවුද?",

spacy/lang/sk/examples.py

@@ -5,7 +5,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "Ardevop, s.r.o. je malá startup firma na území SR.",
     "Samojazdiace autá presúvajú poistnú zodpovednosť na výrobcov automobilov.",

spacy/lang/sl/examples.py

@@ -5,7 +5,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "Apple načrtuje nakup britanskega startupa za 1 bilijon dolarjev",
     "France Prešeren je umrl 8. februarja 1849 v Kranju",

spacy/lang/sq/examples.py

@@ -5,7 +5,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "Apple po shqyrton blerjen e nje shoqërie të U.K. për 1 miliard dollarë",
     "Makinat autonome ndryshojnë përgjegjësinë e sigurimit ndaj prodhuesve",

spacy/lang/sr/examples.py

@@ -5,7 +5,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     # Translations from English
     "Apple планира куповину америчког стартапа за $1 милијарду.",

spacy/lang/sv/examples.py

@@ -5,7 +5,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "Apple överväger att köpa brittisk startup för 1 miljard dollar.",
     "Självkörande bilar förskjuter försäkringsansvar mot tillverkare.",

spacy/lang/ta/examples.py

@@ -5,7 +5,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "கிறிஸ்துமஸ் மற்றும் இனிய புத்தாண்டு வாழ்த்துக்கள்",
     "எனக்கு என் குழந்தைப் பருவம் நினைவிருக்கிறது",

spacy/lang/te/examples.py

@@ -7,7 +7,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "ఆపిల్ 1 బిలియన్ డాలర్స్ కి యూ.కె. స్టార్ట్అప్ ని కొనాలని అనుకుంటుంది.",
     "ఆటోనోమోస్ కార్లు భీమా బాధ్యతను తయారీదారులపైకి మళ్లిస్తాయి.",

spacy/lang/ti/examples.py

@@ -5,7 +5,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "አፕል ብዩኬ ትርከብ ንግድ ብ1 ቢሊዮን ዶላር ንምግዛዕ ሐሲባ።",
     "ፈላማይ ክታበት ኮቪድ 19 ተጀሚሩ፤ሓዱሽ ተስፋ ሂቡ ኣሎ",

spacy/lang/tn/examples.py

@@ -4,7 +4,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "Apple e nyaka go reka JSE ka tlhwatlhwa ta R1 billion",
     "Johannesburg ke toropo e kgolo mo Afrika Borwa.",

spacy/lang/tr/examples.py

@@ -4,7 +4,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "Neredesin?",
     "Neredesiniz?",

spacy/lang/uk/examples.py

@@ -5,7 +5,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "Ніч на середу буде морозною.",
     "Чим кращі книги ти читав, тим гірше спиш.",  # Serhiy Zhadan

spacy/lang/ur/examples.py

@@ -5,7 +5,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "اردو ہے جس کا نام ہم جانتے ہیں داغ",
     "سارے جہاں میں دھوم ہماری زباں کی ہے",

spacy/lang/vi/examples.py

@@ -4,7 +4,6 @@ Example sentences to test spaCy and its language models.
 >>> docs = nlp.pipe(sentences)
 """
 
-
 sentences = [
     "Đây là đâu, tôi là ai?",
     "Căn phòng có nhiều cửa sổ nên nó khá sáng",

spacy/language.py

@@ -1519,8 +1519,7 @@ class Language:
         disable: Iterable[str] = ...,
         component_cfg: Optional[Dict[str, Dict[str, Any]]] = ...,
         n_process: int = ...,
-    ) -> Iterator[Doc]:
-        ...
+    ) -> Iterator[Doc]: ...
 
     @overload
     def pipe(  # noqa: F811
@@ -1532,8 +1531,7 @@ class Language:
         disable: Iterable[str] = ...,
         component_cfg: Optional[Dict[str, Dict[str, Any]]] = ...,
         n_process: int = ...,
-    ) -> Iterator[Tuple[Doc, _AnyContext]]:
-        ...
+    ) -> Iterator[Tuple[Doc, _AnyContext]]: ...
 
     def pipe(  # noqa: F811
         self,
@@ -1641,7 +1639,7 @@ class Language:
         batch_size: int,
     ) -> Iterator[Doc]:
         def prepare_input(
-            texts: Iterable[Union[str, Doc]]
+            texts: Iterable[Union[str, Doc]],
         ) -> Iterable[Tuple[Union[str, bytes], _AnyContext]]:
             # Serialize Doc inputs to bytes to avoid incurring pickling
             # overhead when they are passed to child processes. Also yield
@@ -1943,9 +1941,9 @@ class Language:
                         )
                     if "_sourced_vectors_hashes" not in nlp.meta:
                         nlp.meta["_sourced_vectors_hashes"] = {}
-                    nlp.meta["_sourced_vectors_hashes"][
-                        pipe_name
-                    ] = source_nlp_vectors_hashes[model]
+                    nlp.meta["_sourced_vectors_hashes"][pipe_name] = (
+                        source_nlp_vectors_hashes[model]
+                    )
                     # Delete from cache if listeners were replaced
                     if listeners_replaced:
                         del source_nlps[model]

spacy/matcher/dependencymatcher.pyi

@@ -51,9 +51,7 @@ class DependencyMatcher:
         ] = ...
     ) -> None: ...
     def has_key(self, key: Union[str, int]) -> bool: ...
-    def get(
-        self, key: Union[str, int], default: Optional[Any] = ...
-    ) -> Tuple[
+    def get(self, key: Union[str, int], default: Optional[Any] = ...) -> Tuple[
         Optional[
             Callable[[DependencyMatcher, Doc, int, List[Tuple[int, List[int]]]], Any]
         ],

spacy/ml/featureextractor.py

@@ -7,7 +7,7 @@ from ..tokens import Doc
 
 
 def FeatureExtractor(
-    columns: Union[List[str], List[int], List[Union[int, str]]]
+    columns: Union[List[str], List[int], List[Union[int, str]]],
 ) -> Model[List[Doc], List[Ints2d]]:
     return Model("extract_features", forward, attrs={"columns": columns})
 

spacy/ml/models/entity_linker.py

@@ -122,7 +122,7 @@ def create_candidates() -> Callable[[KnowledgeBase, Span], Iterable[Candidate]]:
     return get_candidates
 
 
-def create_candidates_batch() -> Callable[
-    [KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]
-]:
+def create_candidates_batch() -> (
+    Callable[[KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]]
+):
     return get_candidates_batch

spacy/pipeline/edit_tree_lemmatizer.py

@@ -93,7 +93,7 @@ class EditTreeLemmatizer(TrainablePipe):
         truths = []
         for eg in examples:
             eg_truths = []
-            for (predicted, gold_lemma) in zip(
+            for predicted, gold_lemma in zip(
                 eg.predicted, eg.get_aligned("LEMMA", as_string=True)
             ):
                 if gold_lemma is None or gold_lemma == "":

spacy/pipeline/spancat.py

@@ -80,8 +80,7 @@ DEFAULT_SPANCAT_SINGLELABEL_MODEL = Config().from_str(
 
 @runtime_checkable
 class Suggester(Protocol):
-    def __call__(self, docs: Iterable[Doc], *, ops: Optional[Ops] = None) -> Ragged:
-        ...
+    def __call__(self, docs: Iterable[Doc], *, ops: Optional[Ops] = None) -> Ragged: ...
 
 
 def ngram_suggester(

spacy/registrations.py

@@ -6,6 +6,7 @@ remain in their original locations, but decoration is moved here.
 
 Component definitions and registrations are in spacy/pipeline/factories.py
 """
+
 # Global flag to track if registry has been populated
 REGISTRY_POPULATED = False
 

spacy/tests/doc/test_doc_api.py

@@ -141,7 +141,8 @@ def test_issue3869(sentence):
 @pytest.mark.issue(3962)
 def test_issue3962(en_vocab):
     """Ensure that as_doc does not result in out-of-bound access of tokens.
-    This is achieved by setting the head to itself if it would lie out of the span otherwise."""
+    This is achieved by setting the head to itself if it would lie out of the span otherwise.
+    """
     # fmt: off
     words = ["He", "jests", "at", "scars", ",", "that", "never", "felt", "a", "wound", "."]
     heads = [1, 7, 1, 2, 7, 7, 7, 7, 9, 7, 7]
@@ -180,7 +181,8 @@ def test_issue3962(en_vocab):
 @pytest.mark.issue(3962)
 def test_issue3962_long(en_vocab):
     """Ensure that as_doc does not result in out-of-bound access of tokens.
-    This is achieved by setting the head to itself if it would lie out of the span otherwise."""
+    This is achieved by setting the head to itself if it would lie out of the span otherwise.
+    """
     # fmt: off
     words = ["He", "jests", "at", "scars", ".", "They", "never", "felt", "a", "wound", "."]
     heads = [1, 1, 1, 2, 1, 7, 7, 7, 9, 7, 7]

spacy/tests/lang/ht/test_exceptions.py

@@ -29,4 +29,16 @@ def test_ht_tokenizer_handles_basic_abbreviation(ht_tokenizer, text):
 def test_ht_tokenizer_full_sentence(ht_tokenizer):
     text = "Si'm ka vini, m'ap pale ak li."
     tokens = [t.text for t in ht_tokenizer(text)]
-    assert tokens == ["Si", "'m", "ka", "vini", ",", "m'", "ap", "pale", "ak", "li", "."]
+    assert tokens == [
+        "Si",
+        "'m",
+        "ka",
+        "vini",
+        ",",
+        "m'",
+        "ap",
+        "pale",
+        "ak",
+        "li",
+        ".",
+    ]

spacy/tests/lang/ht/test_prefix_suffix_infix.py

@@ -37,7 +37,9 @@ def test_ht_tokenizer_splits_uneven_wrap(ht_tokenizer, text):
     assert len(tokens) == 5
 
 
-@pytest.mark.parametrize("text,length", [("Ozetazini.", 2), ("Frans.", 2), ("(Ozetazini.", 3)])
+@pytest.mark.parametrize(
+    "text,length", [("Ozetazini.", 2), ("Frans.", 2), ("(Ozetazini.", 3)]
+)
 def test_ht_tokenizer_splits_prefix_interact(ht_tokenizer, text, length):
     tokens = ht_tokenizer(text)
     assert len(tokens) == length

spacy/tests/lang/ht/test_text.py

@@ -16,7 +16,6 @@ Nan Washington, Depatman Deta Etazini pibliye yon deklarasyon ki eksprime "regre
     assert len(tokens) == 84
 
 
-
 @pytest.mark.parametrize(
     "text,length",
     [
@@ -66,14 +65,14 @@ def test_ht_lex_attrs_capitals(word):
 
 
 @pytest.mark.parametrize(
-    "word, expected", [
+    "word, expected",
+    [
         ("'m", "mwen"),
         ("'n", "nou"),
         ("'l", "li"),
         ("'y", "yo"),
         ("'w", "ou"),
-    ]
+    ],
 )
 def test_ht_lex_attrs_norm_custom(word, expected):
     assert norm_custom(word) == expected
-

spacy/tests/lang/hu/test_tokenizer.py

@@ -304,9 +304,11 @@ TESTS.extend([x for i, x in enumerate(EXTRA_TESTS) if i % 10 == 0])
 SLOW_TESTS = [x for i, x in enumerate(EXTRA_TESTS) if i % 10 != 0]
 TESTS.extend(
     [
-        pytest.param(x[0], x[1], marks=pytest.mark.slow())
-        if not isinstance(x[0], tuple)
-        else x
+        (
+            pytest.param(x[0], x[1], marks=pytest.mark.slow())
+            if not isinstance(x[0], tuple)
+            else x
+        )
         for x in SLOW_TESTS
     ]
 )

spacy/tests/matcher/test_matcher_logic.py

@@ -544,7 +544,7 @@ def test_greedy_matching_longest(doc, text, pattern, longest):
     matcher = Matcher(doc.vocab)
     matcher.add("RULE", [pattern], greedy="LONGEST")
     matches = matcher(doc)
-    for (key, s, e) in matches:
+    for key, s, e in matches:
         assert doc[s:e].text == longest
 
 

spacy/tests/pipeline/test_entity_linker.py

@@ -496,15 +496,15 @@ def test_el_pipe_configuration(nlp):
         return [get_lowercased_candidates(kb, span) for span in spans]
 
     @registry.misc("spacy.LowercaseCandidateGenerator.v1")
-    def create_candidates() -> Callable[
-        [InMemoryLookupKB, "Span"], Iterable[Candidate]
-    ]:
+    def create_candidates() -> (
+        Callable[[InMemoryLookupKB, "Span"], Iterable[Candidate]]
+    ):
         return get_lowercased_candidates
 
     @registry.misc("spacy.LowercaseCandidateBatchGenerator.v1")
-    def create_candidates_batch() -> Callable[
-        [InMemoryLookupKB, Iterable["Span"]], Iterable[Iterable[Candidate]]
-    ]:
+    def create_candidates_batch() -> (
+        Callable[[InMemoryLookupKB, Iterable["Span"]], Iterable[Iterable[Candidate]]]
+    ):
         return get_lowercased_candidates_batch
 
     # replace the pipe with a new one with with a different candidate generator

spacy/tests/pipeline/test_pipe_factories.py

@@ -279,20 +279,17 @@ def test_pipe_factories_wrong_formats():
     with pytest.raises(ValueError):
         # Decorator is not called
         @Language.component
-        def component(foo: int, bar: str):
-            ...
+        def component(foo: int, bar: str): ...
 
     with pytest.raises(ValueError):
         # Decorator is not called
         @Language.factory
-        def factory1(foo: int, bar: str):
-            ...
+        def factory1(foo: int, bar: str): ...
 
     with pytest.raises(ValueError):
         # Factory function is missing "nlp" and "name" arguments
         @Language.factory("test_pipe_factories_missing_args")
-        def factory2(foo: int, bar: str):
-            ...
+        def factory2(foo: int, bar: str): ...
 
 
 def test_pipe_factory_meta_config_cleanup():
@@ -329,8 +326,7 @@ def test_pipe_factories_empty_dict_default():
     name = "test_pipe_factories_empty_dict_default"
 
     @Language.factory(name, default_config={"foo": {}})
-    def factory(nlp: Language, name: str, foo: dict):
-        ...
+    def factory(nlp: Language, name: str, foo: dict): ...
 
     nlp = Language()
     nlp.create_pipe(name)
@@ -549,11 +545,9 @@ def test_pipe_factories_from_source_config():
 
 
 class PipeFactoriesIdempotent:
-    def __init__(self, nlp, name):
-        ...
+    def __init__(self, nlp, name): ...
 
-    def __call__(self, doc):
-        ...
+    def __call__(self, doc): ...
 
 
 @pytest.mark.parametrize(

spacy/tests/pipeline/test_textcat.py

@@ -874,7 +874,8 @@ def test_textcat_eval_missing(multi_label: bool, spring_p: float):
 def test_textcat_loss(multi_label: bool, expected_loss: float):
     """
     multi-label: the missing 'spring' in gold_doc_2 doesn't incur an increase in loss
-    exclusive labels: the missing 'spring' in gold_doc_2 is interpreted as 0.0 and adds to the loss"""
+    exclusive labels: the missing 'spring' in gold_doc_2 is interpreted as 0.0 and adds to the loss
+    """
     train_examples = []
     nlp = English()
 

spacy/tests/test_cli.py

@@ -890,7 +890,7 @@ def test_cli_find_threshold(capsys):
         return docs
 
     def init_nlp(
-        components: Tuple[Tuple[str, Dict[str, Any]], ...] = ()
+        components: Tuple[Tuple[str, Dict[str, Any]], ...] = (),
     ) -> Tuple[Language, List[Example]]:
         new_nlp = English()
         new_nlp.add_pipe(  # type: ignore

spacy/tokens/doc.pyi

@@ -57,9 +57,7 @@ class Doc:
         force: bool = ...,
     ) -> None: ...
     @classmethod
-    def get_extension(
-        cls, name: str
-    ) -> Tuple[
+    def get_extension(cls, name: str) -> Tuple[
         Optional[Any],
         Optional[DocMethod],
         Optional[Callable[[Doc], Any]],
@@ -68,9 +66,7 @@ class Doc:
     @classmethod
     def has_extension(cls, name: str) -> bool: ...
     @classmethod
-    def remove_extension(
-        cls, name: str
-    ) -> Tuple[
+    def remove_extension(cls, name: str) -> Tuple[
         Optional[Any],
         Optional[DocMethod],
         Optional[Callable[[Doc], Any]],

spacy/tokens/span.pyi

@@ -23,9 +23,7 @@ class Span:
         force: bool = ...,
     ) -> None: ...
     @classmethod
-    def get_extension(
-        cls, name: str
-    ) -> Tuple[
+    def get_extension(cls, name: str) -> Tuple[
         Optional[Any],
         Optional[SpanMethod],
         Optional[Callable[[Span], Any]],
@@ -34,9 +32,7 @@ class Span:
     @classmethod
     def has_extension(cls, name: str) -> bool: ...
     @classmethod
-    def remove_extension(
-        cls, name: str
-    ) -> Tuple[
+    def remove_extension(cls, name: str) -> Tuple[
         Optional[Any],
         Optional[SpanMethod],
         Optional[Callable[[Span], Any]],

spacy/tokens/token.pyi

@@ -27,9 +27,7 @@ class Token:
         force: bool = ...,
     ) -> None: ...
     @classmethod
-    def get_extension(
-        cls, name: str
-    ) -> Tuple[
+    def get_extension(cls, name: str) -> Tuple[
         Optional[Any],
         Optional[TokenMethod],
         Optional[Callable[[Token], Any]],
@@ -38,9 +36,7 @@ class Token:
     @classmethod
     def has_extension(cls, name: str) -> bool: ...
     @classmethod
-    def remove_extension(
-        cls, name: str
-    ) -> Tuple[
+    def remove_extension(cls, name: str) -> Tuple[
         Optional[Any],
         Optional[TokenMethod],
         Optional[Callable[[Token], Any]],

spacy/training/loop.py

@@ -354,7 +354,7 @@ def update_meta(
 
 
 def create_before_to_disk_callback(
-    callback: Optional[Callable[["Language"], "Language"]]
+    callback: Optional[Callable[["Language"], "Language"]],
 ) -> Callable[["Language"], "Language"]:
     from ..language import Language  # noqa: F811
 

spacy/ty.py

@@ -30,11 +30,9 @@ class TrainableComponent(Protocol):
         drop: float = 0.0,
         sgd: Optional[Optimizer] = None,
         losses: Optional[Dict[str, float]] = None
-    ) -> Dict[str, float]:
-        ...
+    ) -> Dict[str, float]: ...
 
-    def finish_update(self, sgd: Optimizer) -> None:
-        ...
+    def finish_update(self, sgd: Optimizer) -> None: ...
 
 
 @runtime_checkable
@@ -44,8 +42,7 @@ class InitializableComponent(Protocol):
         get_examples: Callable[[], Iterable["Example"]],
         nlp: "Language",
         **kwargs: Any
-    ):
-        ...
+    ): ...
 
 
 @runtime_checkable
@@ -55,11 +52,8 @@ class ListenedToComponent(Protocol):
     listener_map: Dict[str, Sequence[Model]]
     listening_components: List[str]
 
-    def add_listener(self, listener: Model, component_name: str) -> None:
-        ...
+    def add_listener(self, listener: Model, component_name: str) -> None: ...
 
-    def remove_listener(self, listener: Model, component_name: str) -> bool:
-        ...
+    def remove_listener(self, listener: Model, component_name: str) -> bool: ...
 
-    def find_listeners(self, component) -> None:
-        ...
+    def find_listeners(self, component) -> None: ...

spacy/util.py

@@ -657,7 +657,7 @@ def load_model_from_config(
 
 
 def get_sourced_components(
-    config: Union[Dict[str, Any], Config]
+    config: Union[Dict[str, Any], Config],
 ) -> Dict[str, Dict[str, Any]]:
     """RETURNS (List[str]): All sourced components in the original config,
     e.g. {"source": "en_core_web_sm"}. If the config contains a key