Tidy up and auto-format [ci skip]

spacy/cli/convert.py

@@ -88,12 +88,21 @@ def convert(
             msg.info("Auto-detected sentence-per-line NER format")
             converter = converter_autodetect
         else:
-            msg.warn("Can't automatically detect NER format. Conversion may not succeed. See https://spacy.io/api/cli#convert")
+            msg.warn(
+                "Can't automatically detect NER format. Conversion may not succeed. See https://spacy.io/api/cli#convert"
+            )
     if converter not in CONVERTERS:
         msg.fail("Can't find converter for {}".format(converter), exits=1)
     # Use converter function to convert data
     func = CONVERTERS[converter]
-    data = func(input_data, n_sents=n_sents, seg_sents=seg_sents, use_morphology=morphology, lang=lang, model=model)
+    data = func(
+        input_data,
+        n_sents=n_sents,
+        seg_sents=seg_sents,
+        use_morphology=morphology,
+        lang=lang,
+        model=model,
+    )
     if output_dir != "-":
         # Export data to a file
         suffix = ".{}".format(file_type)
@@ -104,7 +113,9 @@ def convert(
             srsly.write_jsonl(output_file, data)
         elif file_type == "msg":
             srsly.write_msgpack(output_file, data)
-        msg.good("Generated output file ({} documents): {}".format(len(data), output_file))
+        msg.good(
+            "Generated output file ({} documents): {}".format(len(data), output_file)
+        )
     else:
         # Print to stdout
         if file_type == "json":

spacy/cli/converters/conll_ner2json.py

@@ -38,32 +38,50 @@ def conll_ner2json(input_data, n_sents=10, seg_sents=False, model=None, **kwargs
     doc_delimiter = "-DOCSTART- -X- O O"
     # check for existing delimiters, which should be preserved
     if "\n\n" in input_data and seg_sents:
-        msg.warn("Sentence boundaries found, automatic sentence segmentation with `-s` disabled.")
+        msg.warn(
+            "Sentence boundaries found, automatic sentence segmentation with "
+            "`-s` disabled."
+        )
         seg_sents = False
     if doc_delimiter in input_data and n_sents:
-        msg.warn("Document delimiters found, automatic document segmentation with `-n` disabled.")
+        msg.warn(
+            "Document delimiters found, automatic document segmentation with "
+            "`-n` disabled."
+        )
         n_sents = 0
     # do document segmentation with existing sentences
-    if "\n\n" in input_data and not doc_delimiter in input_data and n_sents:
+    if "\n\n" in input_data and doc_delimiter not in input_data and n_sents:
         n_sents_info(msg, n_sents)
         input_data = segment_docs(input_data, n_sents, doc_delimiter)
     # do sentence segmentation with existing documents
-    if not "\n\n" in input_data and doc_delimiter in input_data and seg_sents:
+    if "\n\n" not in input_data and doc_delimiter in input_data and seg_sents:
         input_data = segment_sents_and_docs(input_data, 0, "", model=model, msg=msg)
     # do both sentence segmentation and document segmentation according
     # to options
-    if not "\n\n" in input_data and not doc_delimiter in input_data:
+    if "\n\n" not in input_data and doc_delimiter not in input_data:
         # sentence segmentation required for document segmentation
         if n_sents > 0 and not seg_sents:
-            msg.warn("No sentence boundaries found to use with option `-n {}`. Use `-s` to automatically segment sentences or `-n 0` to disable.".format(n_sents))
+            msg.warn(
+                "No sentence boundaries found to use with option `-n {}`. "
+                "Use `-s` to automatically segment sentences or `-n 0` "
+                "to disable.".format(n_sents)
+            )
         else:
             n_sents_info(msg, n_sents)
-            input_data = segment_sents_and_docs(input_data, n_sents, doc_delimiter, model=model, msg=msg)
+            input_data = segment_sents_and_docs(
+                input_data, n_sents, doc_delimiter, model=model, msg=msg
+            )
     # provide warnings for problematic data
-    if not "\n\n" in input_data:
-        msg.warn("No sentence boundaries found. Use `-s` to automatically segment sentences.")
-    if not doc_delimiter in input_data:
-        msg.warn("No document delimiters found. Use `-n` to automatically group sentences into documents.")
+    if "\n\n" not in input_data:
+        msg.warn(
+            "No sentence boundaries found. Use `-s` to automatically segment "
+            "sentences."
+        )
+    if doc_delimiter not in input_data:
+        msg.warn(
+            "No document delimiters found. Use `-n` to automatically group "
+            "sentences into documents."
+        )
     output_docs = []
     for doc in input_data.strip().split(doc_delimiter):
         doc = doc.strip()
@@ -78,7 +96,9 @@ def conll_ner2json(input_data, n_sents=10, seg_sents=False, model=None, **kwargs
             cols = list(zip(*[line.split() for line in lines]))
             if len(cols) < 2:
                 raise ValueError(
-                    "The token-per-line NER file is not formatted correctly. Try checking whitespace and delimiters. See https://spacy.io/api/cli#convert"
+                    "The token-per-line NER file is not formatted correctly. "
+                    "Try checking whitespace and delimiters. See "
+                    "https://spacy.io/api/cli#convert"
                 )
             words = cols[0]
             iob_ents = cols[-1]
@@ -110,7 +130,10 @@ def segment_sents_and_docs(doc, n_sents, doc_delimiter, model=None, msg=None):
             msg.info("Segmenting sentences with parser from model '{}'.".format(model))
             sentencizer = nlp.get_pipe("parser")
     if not sentencizer:
-        msg.info("Segmenting sentences with sentencizer. (Use `-b model` for improved parser-based sentence segmentation.)")
+        msg.info(
+            "Segmenting sentences with sentencizer. (Use `-b model` for "
+            "improved parser-based sentence segmentation.)"
+        )
         nlp = MultiLanguage()
         sentencizer = nlp.create_pipe("sentencizer")
     lines = doc.strip().split("\n")
@@ -143,4 +166,7 @@ def segment_docs(input_data, n_sents, doc_delimiter):
 def n_sents_info(msg, n_sents):
     msg.info("Grouping every {} sentences into a document.".format(n_sents))
     if n_sents == 1:
-        msg.warn("To generate better training data, you may want to group sentences into documents with `-n 10`.")
+        msg.warn(
+            "To generate better training data, you may want to group "
+            "sentences into documents with `-n 10`."
+        )

spacy/cli/converters/iob2json.py

@@ -34,7 +34,7 @@ def read_iob(raw_sents):
     for line in raw_sents:
         if not line.strip():
             continue
-        tokens = [t.split('|') for t in line.split()]
+        tokens = [t.split("|") for t in line.split()]
         if len(tokens[0]) == 3:
             words, pos, iob = zip(*tokens)
         elif len(tokens[0]) == 2:

spacy/language.py

@@ -38,8 +38,8 @@ from . import about
 class BaseDefaults(object):
     @classmethod
     def create_lemmatizer(cls, nlp=None, lookups=None):
-        lemma_rules, lemma_index, lemma_exc, lemma_lookup = util.get_lemma_tables(lookups)
-        return Lemmatizer(lemma_index, lemma_exc, lemma_rules, lemma_lookup)
+        rules, index, exc, lookup = util.get_lemma_tables(lookups)
+        return Lemmatizer(index, exc, rules, lookup)
 
     @classmethod
     def create_lookups(cls, nlp=None):

spacy/matcher/_schemas.py

@@ -89,10 +89,7 @@ TOKEN_PATTERN_SCHEMA = {
                 "title": "Fine-grained part-of-speech tag",
                 "$ref": "#/definitions/string_value",
             },
-            "DEP": {
-                "title": "Dependency label",
-                "$ref": "#/definitions/string_value"
-            },
+            "DEP": {"title": "Dependency label", "$ref": "#/definitions/string_value"},
             "LEMMA": {
                 "title": "Lemma (base form)",
                 "$ref": "#/definitions/string_value",

spacy/tests/lang/sr/test_еxceptions.py

@@ -6,8 +6,13 @@ import pytest
 
 @pytest.mark.parametrize(
     "text,norms,lemmas",
-    [("о.г.", ["ове године"], ["ова година"]), ("чет.", ["четвртак"], ["четвртак"]),
-     ("гђа", ["госпођа"], ["госпођа"]), ("ил'", ["или"], ["или"])])
+    [
+        ("о.г.", ["ове године"], ["ова година"]),
+        ("чет.", ["четвртак"], ["четвртак"]),
+        ("гђа", ["госпођа"], ["госпођа"]),
+        ("ил'", ["или"], ["или"]),
+    ],
+)
 def test_sr_tokenizer_abbrev_exceptions(sr_tokenizer, text, norms, lemmas):
     tokens = sr_tokenizer(text)
     assert len(tokens) == 1

spacy/tests/matcher/test_matcher_api.py

@@ -394,7 +394,7 @@ def test_attr_pipeline_checks(en_vocab):
         ([{"IS_PUNCT": True}], "."),
         ([{"IS_SPACE": True}], "\n"),
         ([{"IS_BRACKET": True}], "["),
-        ([{"IS_QUOTE": True}], "\""),
+        ([{"IS_QUOTE": True}], '"'),
         ([{"IS_LEFT_PUNCT": True}], "``"),
         ([{"IS_RIGHT_PUNCT": True}], "''"),
         ([{"IS_STOP": True}], "the"),
@@ -405,7 +405,7 @@ def test_attr_pipeline_checks(en_vocab):
 )
 def test_matcher_schema_token_attributes(en_vocab, pattern, text):
     matcher = Matcher(en_vocab)
-    doc = Doc(en_vocab, words=text.split(' '))
+    doc = Doc(en_vocab, words=text.split(" "))
     matcher.add("Rule", None, pattern)
     assert len(matcher) == 1
     matches = matcher(doc)

spacy/tests/test_cli.py

@@ -49,8 +49,10 @@ def test_cli_converters_iob2json():
         sent = converted[0]["paragraphs"][0]["sentences"][i]
         assert len(sent["tokens"]) == 8
         tokens = sent["tokens"]
+        # fmt: off
         assert [t["orth"] for t in tokens] == ["I", "like", "London", "and", "New", "York", "City", "."]
         assert [t["ner"] for t in tokens] == ["O", "O", "U-GPE", "O", "B-GPE", "I-GPE", "L-GPE", "O"]
+        # fmt: on
 
 
 def test_cli_converters_conll_ner2json():
@@ -113,8 +115,10 @@ def test_cli_converters_conll_ner2json():
         sent = converted[0]["paragraphs"][0]["sentences"][i]
         assert len(sent["tokens"]) == 8
         tokens = sent["tokens"]
+        # fmt: off
         assert [t["orth"] for t in tokens] == ["I", "like", "London", "and", "New", "York", "City", "."]
         assert [t["ner"] for t in tokens] == ["O", "O", "U-GPE", "O", "B-GPE", "I-GPE", "L-GPE", "O"]
+        # fmt: on
 
 
 def test_pretrain_make_docs():