PEP8 and formatting cleanups

examples/lm_finetuning/finetune_on_pregenerated.py

@@ -9,7 +9,7 @@ from collections import namedtuple
 
 from torch.utils.data import DataLoader, Dataset, RandomSampler
 from torch.utils.data.distributed import DistributedSampler
-from tqdm import tqdm, trange
+from tqdm import tqdm
 
 from pytorch_pretrained_bert.modeling import BertForPreTraining
 from pytorch_pretrained_bert.tokenization import BertTokenizer
@@ -149,7 +149,8 @@ def main():
                         help="random seed for initialization")
     args = parser.parse_args()
 
-    assert args.pregenerated_data.is_dir(), "--pregenerated_data should point to the folder of files made by pregenerate_training_data.py!"
+    assert args.pregenerated_data.is_dir(), \
+        "--pregenerated_data should point to the folder of files made by pregenerate_training_data.py!"
 
     samples_per_epoch = []
     for i in range(args.epochs):
@@ -237,7 +238,8 @@ def main():
             from apex.optimizers import FP16_Optimizer
             from apex.optimizers import FusedAdam
         except ImportError:
-            raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.")
+            raise ImportError(
+                "Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.")
 
         optimizer = FusedAdam(optimizer_grouped_parameters,
                               lr=args.learning_rate,
@@ -293,7 +295,8 @@ def main():
                     if args.fp16:
                         # modify learning rate with special warm up BERT uses
                         # if args.fp16 is False, BertAdam is used that handles this automatically
-                        lr_this_step = args.learning_rate * warmup_linear(global_step/num_train_optimization_steps, args.warmup_proportion)
+                        lr_this_step = args.learning_rate * warmup_linear(global_step/num_train_optimization_steps,
+                                                                          args.warmup_proportion)
                         for param_group in optimizer.param_groups:
                             param_group['lr'] = lr_this_step
                     optimizer.step()

examples/lm_finetuning/pregenerate_training_data.py

@@ -269,6 +269,5 @@ def main():
             metrics_file.write(json.dumps(metrics))
 
 
-
 if __name__ == '__main__':
     main()