Reformat source code with black.

This is the result of:

    $ black --line-length 119 examples templates transformers utils hubconf.py setup.py

There's a lot of fairly long lines in the project. As a consequence, I'm
picking the longest widely accepted line length, 119 characters.

This is also Thomas' preference, because it allows for explicit variable
names, to make the code easier to understand.

examples/contrib/run_transfo_xl.py

@@ -30,44 +30,36 @@ import torch
 
 from transformers import TransfoXLLMHeadModel, TransfoXLCorpus, TransfoXLTokenizer
 
-logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
-                    datefmt = '%m/%d/%Y %H:%M:%S',
-                    level = logging.INFO)
+logging.basicConfig(
+    format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
+)
 logger = logging.getLogger(__name__)
 
+
 def main():
-    parser = argparse.ArgumentParser(description='PyTorch Transformer Language Model')
-    parser.add_argument('--model_name', type=str, default='transfo-xl-wt103',
-                        help='pretrained model name')
-    parser.add_argument('--split', type=str, default='test',
-                        choices=['all', 'valid', 'test'],
-                        help='which split to evaluate')
-    parser.add_argument('--batch_size', type=int, default=10,
-                        help='batch size')
-    parser.add_argument('--tgt_len', type=int, default=128,
-                        help='number of tokens to predict')
-    parser.add_argument('--ext_len', type=int, default=0,
-                        help='length of the extended context')
-    parser.add_argument('--mem_len', type=int, default=1600,
-                        help='length of the retained previous heads')
-    parser.add_argument('--clamp_len', type=int, default=1000,
-                        help='max positional embedding index')
-    parser.add_argument('--no_cuda', action='store_true',
-                        help='Do not use CUDA even though CUA is available')
-    parser.add_argument('--work_dir', type=str, required=True,
-                        help='path to the work_dir')
-    parser.add_argument('--no_log', action='store_true',
-                        help='do not log the eval result')
-    parser.add_argument('--same_length', action='store_true',
-                        help='set same length attention with masking')
-    parser.add_argument('--server_ip', type=str, default='', help="Can be used for distant debugging.")
-    parser.add_argument('--server_port', type=str, default='', help="Can be used for distant debugging.")
+    parser = argparse.ArgumentParser(description="PyTorch Transformer Language Model")
+    parser.add_argument("--model_name", type=str, default="transfo-xl-wt103", help="pretrained model name")
+    parser.add_argument(
+        "--split", type=str, default="test", choices=["all", "valid", "test"], help="which split to evaluate"
+    )
+    parser.add_argument("--batch_size", type=int, default=10, help="batch size")
+    parser.add_argument("--tgt_len", type=int, default=128, help="number of tokens to predict")
+    parser.add_argument("--ext_len", type=int, default=0, help="length of the extended context")
+    parser.add_argument("--mem_len", type=int, default=1600, help="length of the retained previous heads")
+    parser.add_argument("--clamp_len", type=int, default=1000, help="max positional embedding index")
+    parser.add_argument("--no_cuda", action="store_true", help="Do not use CUDA even though CUA is available")
+    parser.add_argument("--work_dir", type=str, required=True, help="path to the work_dir")
+    parser.add_argument("--no_log", action="store_true", help="do not log the eval result")
+    parser.add_argument("--same_length", action="store_true", help="set same length attention with masking")
+    parser.add_argument("--server_ip", type=str, default="", help="Can be used for distant debugging.")
+    parser.add_argument("--server_port", type=str, default="", help="Can be used for distant debugging.")
     args = parser.parse_args()
-    assert args.ext_len >= 0, 'extended context length must be non-negative'
+    assert args.ext_len >= 0, "extended context length must be non-negative"
 
     if args.server_ip and args.server_port:
         # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
         import ptvsd
+
         print("Waiting for debugger attach")
         ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
         ptvsd.wait_for_attach()
@@ -84,17 +76,18 @@ def main():
     corpus = TransfoXLCorpus.from_pretrained(args.model_name)
     ntokens = len(corpus.vocab)
 
-    va_iter = corpus.get_iterator('valid', args.batch_size, args.tgt_len,
-        device=device, ext_len=args.ext_len)
-    te_iter = corpus.get_iterator('test', args.batch_size, args.tgt_len,
-        device=device, ext_len=args.ext_len)
+    va_iter = corpus.get_iterator("valid", args.batch_size, args.tgt_len, device=device, ext_len=args.ext_len)
+    te_iter = corpus.get_iterator("test", args.batch_size, args.tgt_len, device=device, ext_len=args.ext_len)
 
     # Load a pre-trained model
     model = TransfoXLLMHeadModel.from_pretrained(args.model_name)
     model = model.to(device)
 
-    logger.info('Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}'.format(
-        args.batch_size, args.tgt_len, args.ext_len, args.mem_len, args.clamp_len))
+    logger.info(
+        "Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}".format(
+            args.batch_size, args.tgt_len, args.ext_len, args.mem_len, args.clamp_len
+        )
+    )
 
     model.reset_length(args.tgt_len, args.ext_len, args.mem_len)
     if args.clamp_len > 0:
@@ -108,7 +101,7 @@ def main():
     def evaluate(eval_iter):
         # Turn on evaluation mode which disables dropout.
         model.eval()
-        total_len, total_loss = 0, 0.
+        total_len, total_loss = 0, 0.0
         start_time = time.time()
         with torch.no_grad():
             mems = None
@@ -119,35 +112,34 @@ def main():
                 total_loss += seq_len * loss.item()
                 total_len += seq_len
             total_time = time.time() - start_time
-        logger.info('Time : {:.2f}s, {:.2f}ms/segment'.format(
-                total_time, 1000 * total_time / (idx+1)))
+        logger.info("Time : {:.2f}s, {:.2f}ms/segment".format(total_time, 1000 * total_time / (idx + 1)))
         return total_loss / total_len
 
     # Run on test data.
-    if args.split == 'all':
+    if args.split == "all":
         test_loss = evaluate(te_iter)
         valid_loss = evaluate(va_iter)
-    elif args.split == 'valid':
+    elif args.split == "valid":
         valid_loss = evaluate(va_iter)
         test_loss = None
-    elif args.split == 'test':
+    elif args.split == "test":
         test_loss = evaluate(te_iter)
         valid_loss = None
 
     def format_log(loss, split):
-        log_str = '| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '.format(
-            split, loss, math.exp(loss))
+        log_str = "| {0} loss {1:5.2f} | {0} ppl {2:9.3f} ".format(split, loss, math.exp(loss))
         return log_str
 
-    log_str = ''
+    log_str = ""
     if valid_loss is not None:
-        log_str += format_log(valid_loss, 'valid')
+        log_str += format_log(valid_loss, "valid")
     if test_loss is not None:
-        log_str += format_log(test_loss, 'test')
+        log_str += format_log(test_loss, "test")
 
-    logger.info('=' * 100)
+    logger.info("=" * 100)
     logger.info(log_str)
-    logger.info('=' * 100)
+    logger.info("=" * 100)
 
-if __name__ == '__main__':
+
+if __name__ == "__main__":
     main()