Python - Black auto formatting

bindings/python/examples/train_bert_wordpiece.py

@@ -4,21 +4,24 @@ import glob
 from tokenizers import BertWordPieceTokenizer
 
 parser = argparse.ArgumentParser()
-parser.add_argument("--files",
-                    default=None,
-                    metavar="path",
-                    type=str,
-                    required=True,
-                    help="The files to use as training; accept '**/*.txt' type of patterns \
-                          if enclosed in quotes")
-parser.add_argument("--out",
-                    default="./",
-                    type=str,
-                    help="Path to the output directory, where the files will be saved")
-parser.add_argument("--name",
-                    default="bert-wordpiece",
-                    type=str,
-                    help="The name of the output vocab files")
+parser.add_argument(
+    "--files",
+    default=None,
+    metavar="path",
+    type=str,
+    required=True,
+    help="The files to use as training; accept '**/*.txt' type of patterns \
+                          if enclosed in quotes",
+)
+parser.add_argument(
+    "--out",
+    default="./",
+    type=str,
+    help="Path to the output directory, where the files will be saved",
+)
+parser.add_argument(
+    "--name", default="bert-wordpiece", type=str, help="The name of the output vocab files"
+)
 args = parser.parse_args()
 
 files = glob.glob(args.files)
@@ -29,11 +32,7 @@ if not files:
 
 # Initialize an empty tokenizer
 tokenizer = BertWordPieceTokenizer(
-    clean_text=True,
-    handle_chinese_chars=True,
-    strip_accents=True,
-    lowercase=True,
-
+    clean_text=True, handle_chinese_chars=True, strip_accents=True, lowercase=True,
 )
 
 # And then train
@@ -44,7 +43,7 @@ trainer = tokenizer.train(
     show_progress=True,
     special_tokens=["[PAD]", "[UNK]", "[CLS]", "[SEP]", "[MASK]"],
     limit_alphabet=1000,
-    wordpieces_prefix="##"
+    wordpieces_prefix="##",
 )
 
 # Save the files