Tidy up and auto-format

spacy/lang/tr/lex_attrs.py

@@ -62,6 +62,7 @@ _ordinal_words = [
 
 _ordinal_endings = ("inci", "ıncı", "nci", "ncı", "uncu", "üncü")
 
+
 def like_num(text):
     if text.startswith(("+", "-", "±", "~")):
         text = text[1:]
@@ -75,11 +76,11 @@ def like_num(text):
 
     text_lower = text.lower()
 
-    #Check cardinal number
+    # Check cardinal number
     if text_lower in _num_words:
         return True
 
-    #Check ordinal number
+    # Check ordinal number
     if text_lower in _ordinal_words:
         return True
     if text_lower.endswith(_ordinal_endings):

spacy/lang/tr/syntax_iterators.py

@@ -51,9 +51,8 @@ def noun_chunks(doclike):
         elif word.dep == conj:
             cc_token = word.left_edge
             prev_end = cc_token.i
-            yield cc_token.right_edge.i + 1, extend_right(word), np_label  # Shave off cc tokens from the NP
+            # Shave off cc tokens from the NP
-
+            yield cc_token.right_edge.i + 1, extend_right(word), np_label
-
 
 
 SYNTAX_ITERATORS = {"noun_chunks": noun_chunks}

spacy/language.py

@@ -1,5 +1,5 @@
 from typing import Optional, Any, Dict, Callable, Iterable, Union, List, Pattern
-from typing import Tuple, Iterator
+from typing import Tuple
 from dataclasses import dataclass
 import random
 import itertools
@@ -1197,7 +1197,9 @@ class Language:
             doc = Doc(self.vocab, words=["x", "y", "z"])
             get_examples = lambda: [Example.from_dict(doc, {})]
         if not hasattr(get_examples, "__call__"):
-            err = Errors.E930.format(method="Language.initialize", obj=type(get_examples))
+            err = Errors.E930.format(
+                method="Language.initialize", obj=type(get_examples)
+            )
             raise TypeError(err)
         # Make sure the config is interpolated so we can resolve subsections
         config = self.config.interpolate()

spacy/tests/conftest.py

@@ -239,10 +239,12 @@ def th_tokenizer():
 def tr_tokenizer():
     return get_lang_class("tr")().tokenizer
 
+
 @pytest.fixture(scope="session")
 def tr_vocab():
     return get_lang_class("tr").Defaults.create_vocab()
 
+
 @pytest.fixture(scope="session")
 def tt_tokenizer():
     return get_lang_class("tt")().tokenizer

spacy/tests/lang/tr/test_parser.py

@@ -225,7 +225,7 @@ def test_tr_noun_chunks_acl_nmod(tr_tokenizer):
     assert chunks[0].text_with_ws == "en sevdiğim ses sanatçısı "
 
 
-def test_tr_noun_chunks_acl_nmod(tr_tokenizer):
+def test_tr_noun_chunks_acl_nmod2(tr_tokenizer):
     text = "bildiğim bir turizm şirketi"
     heads = [3, 3, 3, 3]
     deps = ["acl", "det", "nmod", "ROOT"]
@@ -326,7 +326,7 @@ def test_tr_noun_chunks_np_recursive_no_nmod(tr_tokenizer):
 def test_tr_noun_chunks_np_recursive_long_two_acls(tr_tokenizer):
     text = "içine Simge'nin bahçesinden toplanmış birkaç çiçeğin konmuş olduğu bir vazo"
     heads = [6, 2, 3, 5, 5, 6, 9, 6, 9, 9]
-    deps = ["obl", "nmod" , "obl", "acl", "det", "nsubj", "acl", "aux", "det", "ROOT"]
+    deps = ["obl", "nmod", "obl", "acl", "det", "nsubj", "acl", "aux", "det", "ROOT"]
     pos = ["ADP", "PROPN", "NOUN", "VERB", "DET", "NOUN", "VERB", "AUX", "DET", "NOUN"]
     tokens = tr_tokenizer(text)
     doc = Doc(
@@ -334,7 +334,10 @@ def test_tr_noun_chunks_np_recursive_long_two_acls(tr_tokenizer):
     )
     chunks = list(doc.noun_chunks)
     assert len(chunks) == 1
-    assert chunks[0].text_with_ws == "içine Simge'nin bahçesinden toplanmış birkaç çiçeğin konmuş olduğu bir vazo "
+    assert (
+        chunks[0].text_with_ws
+        == "içine Simge'nin bahçesinden toplanmış birkaç çiçeğin konmuş olduğu bir vazo "
+    )
 
 
 def test_tr_noun_chunks_two_nouns_in_nmod(tr_tokenizer):
@@ -350,7 +353,8 @@ def test_tr_noun_chunks_two_nouns_in_nmod(tr_tokenizer):
     assert len(chunks) == 1
     assert chunks[0].text_with_ws == "kız ve erkek çocuklar "
 
-def test_tr_noun_chunks_two_nouns_in_nmod(tr_tokenizer):
+
+def test_tr_noun_chunks_two_nouns_in_nmod2(tr_tokenizer):
     text = "tatlı ve gürbüz çocuklar"
     heads = [3, 2, 0, 3]
     deps = ["amod", "cc", "conj", "ROOT"]
@@ -378,6 +382,7 @@ def test_tr_noun_chunks_conj_simple(tr_tokenizer):
     assert chunks[0].text_with_ws == "ben "
     assert chunks[1].text_with_ws == "Sen "
 
+
 def test_tr_noun_chunks_conj_three(tr_tokenizer):
     text = "sen, ben ve ondan"
     heads = [0, 2, 0, 4, 0]
@@ -394,7 +399,7 @@ def test_tr_noun_chunks_conj_three(tr_tokenizer):
     assert chunks[2].text_with_ws == "sen "
 
 
-def test_tr_noun_chunks_conj_three(tr_tokenizer):
+def test_tr_noun_chunks_conj_three2(tr_tokenizer):
     text = "ben ya da sen ya da onlar"
     heads = [0, 3, 1, 0, 6, 4, 3]
     deps = ["ROOT", "cc", "fixed", "conj", "cc", "fixed", "conj"]
@@ -499,7 +504,7 @@ def test_tr_noun_chunks_flat_names_and_title(tr_tokenizer):
     assert chunks[0].text_with_ws == "Gazi Mustafa Kemal "
 
 
-def test_tr_noun_chunks_flat_names_and_title(tr_tokenizer):
+def test_tr_noun_chunks_flat_names_and_title2(tr_tokenizer):
     text = "Ahmet Vefik Paşa"
     heads = [2, 0, 2]
     deps = ["nmod", "flat", "ROOT"]

spacy/tests/lang/tr/test_text.py

@@ -15,8 +15,8 @@ from spacy.lang.tr.lex_attrs import like_num
         "üçüncü",
         "beşinci",
         "100üncü",
-        "8inci"
+        "8inci",
-    ]
+    ],
 )
 def test_tr_lex_attrs_like_number_cardinal_ordinal(word):
     assert like_num(word)
@@ -26,4 +26,3 @@ def test_tr_lex_attrs_like_number_cardinal_ordinal(word):
 def test_tr_lex_attrs_capitals(word):
     assert like_num(word)
     assert like_num(word.upper())
-

spacy/tests/pipeline/test_entity_linker.py

@@ -446,7 +446,7 @@ def test_overfitting_IO():
         return mykb
 
     # Create the Entity Linker component and add it to the pipeline
-    entity_linker = nlp.add_pipe("entity_linker", last=True,)
+    entity_linker = nlp.add_pipe("entity_linker", last=True)
     entity_linker.set_kb(create_kb)
     assert "Q2146908" in entity_linker.vocab.strings
     assert "Q2146908" in entity_linker.kb.vocab.strings

spacy/tests/regression/test_issue6207.py

@@ -6,8 +6,8 @@ def test_issue6207(en_tokenizer):
 
     # Make spans
     s1 = doc[:4]
-    s2 = doc[3:6]   # overlaps with s1
+    s2 = doc[3:6]  # overlaps with s1
-    s3 = doc[5:7]   # overlaps with s2, not s1
+    s3 = doc[5:7]  # overlaps with s2, not s1
 
     result = filter_spans((s1, s2, s3))
     assert s1 in result

spacy/tests/test_models.py

@@ -1,10 +1,8 @@
 from typing import List
-
 import pytest
 from thinc.api import fix_random_seed, Adam, set_dropout_rate
 from numpy.testing import assert_array_equal
 import numpy
-
 from spacy.ml.models import build_Tok2Vec_model, MultiHashEmbed, MaxoutWindowEncoder
 from spacy.ml.models import build_text_classifier, build_simple_cnn_text_classifier
 from spacy.ml.staticvectors import StaticVectors
@@ -188,12 +186,7 @@ def test_models_update_consistently(seed, dropout, model_func, kwargs, get_X):
     assert_array_equal(get_all_params(model1), get_all_params(model2))
 
 
-@pytest.mark.parametrize(
+@pytest.mark.parametrize("model_func,kwargs", [(StaticVectors, {"nO": 128, "nM": 300})])
-    "model_func,kwargs",
-    [
-        (StaticVectors, {"nO": 128, "nM": 300}),
-    ]
-)
 def test_empty_docs(model_func, kwargs):
     nlp = English()
     model = model_func(**kwargs).initialize()
@@ -201,7 +194,7 @@ def test_empty_docs(model_func, kwargs):
     for n_docs in range(3):
         docs = [nlp("") for _ in range(n_docs)]
         # Test predict
-        _ = model.predict(docs)
+        model.predict(docs)
         # Test backprop
         output, backprop = model.begin_update(docs)
-        _ = backprop(output)
+        backprop(output)