Black 20 release

examples/multiple-choice/utils_multiple_choice.py

@@ -100,7 +100,12 @@ if is_torch_available():
 
             cached_features_file = os.path.join(
                 data_dir,
-                "cached_{}_{}_{}_{}".format(mode.value, tokenizer.__class__.__name__, str(max_seq_length), task,),
+                "cached_{}_{}_{}_{}".format(
+                    mode.value,
+                    tokenizer.__class__.__name__,
+                    str(max_seq_length),
+                    task,
+                ),
             )
 
             # Make sure only the first process in distributed training processes the dataset,
@@ -121,7 +126,12 @@ if is_torch_available():
                     else:
                         examples = processor.get_train_examples(data_dir)
                     logger.info("Training examples: %s", len(examples))
-                    self.features = convert_examples_to_features(examples, label_list, max_seq_length, tokenizer,)
+                    self.features = convert_examples_to_features(
+                        examples,
+                        label_list,
+                        max_seq_length,
+                        tokenizer,
+                    )
                     logger.info("Saving features into cached file %s", cached_features_file)
                     torch.save(self.features, cached_features_file)
 
@@ -164,7 +174,12 @@ if is_tf_available():
                 examples = processor.get_train_examples(data_dir)
             logger.info("Training examples: %s", len(examples))
 
-            self.features = convert_examples_to_features(examples, label_list, max_seq_length, tokenizer,)
+            self.features = convert_examples_to_features(
+                examples,
+                label_list,
+                max_seq_length,
+                tokenizer,
+            )
 
             def gen():
                 for (ex_index, ex) in tqdm.tqdm(enumerate(self.features), desc="convert examples to features"):
@@ -491,7 +506,10 @@ class ArcProcessor(DataProcessor):
 
 
 def convert_examples_to_features(
-    examples: List[InputExample], label_list: List[str], max_length: int, tokenizer: PreTrainedTokenizer,
+    examples: List[InputExample],
+    label_list: List[str],
+    max_length: int,
+    tokenizer: PreTrainedTokenizer,
 ) -> List[InputFeatures]:
     """
     Loads a data file into a list of `InputFeatures`