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/summarization/run_summarization.py

@@ -25,9 +25,7 @@ logger = logging.getLogger(__name__)
 logging.basicConfig(stream=sys.stdout, level=logging.INFO)
 
 
-Batch = namedtuple(
-    "Batch", ["document_names", "batch_size", "src", "segs", "mask_src", "tgt_str"]
-)
+Batch = namedtuple("Batch", ["document_names", "batch_size", "src", "segs", "mask_src", "tgt_str"])
 
 
 def evaluate(args):
@@ -48,13 +46,14 @@ def evaluate(args):
 
         import rouge
         import nltk
-        nltk.download('punkt')
+
+        nltk.download("punkt")
         rouge_evaluator = rouge.Rouge(
-            metrics=['rouge-n', 'rouge-l'],
+            metrics=["rouge-n", "rouge-l"],
             max_n=2,
             limit_length=True,
             length_limit=args.beam_size,
-            length_limit_type='words',
+            length_limit_type="words",
             apply_avg=True,
             apply_best=False,
             alpha=0.5,  # Default F1_score
@@ -161,15 +160,15 @@ Recall    >> {:.3f}
 F1        >> {:.3f}
 Precision >> {:.3f}
 Recall    >> {:.3f}""".format(
-        scores['rouge-1']['f'],
-        scores['rouge-1']['p'],
-        scores['rouge-1']['r'],
-        scores['rouge-2']['f'],
-        scores['rouge-2']['p'],
-        scores['rouge-2']['r'],
-        scores['rouge-l']['f'],
-        scores['rouge-l']['p'],
-        scores['rouge-l']['r'],
+        scores["rouge-1"]["f"],
+        scores["rouge-1"]["p"],
+        scores["rouge-1"]["r"],
+        scores["rouge-2"]["f"],
+        scores["rouge-2"]["p"],
+        scores["rouge-2"]["r"],
+        scores["rouge-l"]["f"],
+        scores["rouge-l"]["p"],
+        scores["rouge-l"]["r"],
     )
 
 
@@ -187,9 +186,7 @@ def build_data_iterator(args, tokenizer):
     dataset = load_and_cache_examples(args, tokenizer)
     sampler = SequentialSampler(dataset)
     collate_fn = lambda data: collate(data, tokenizer, block_size=512, device=args.device)
-    iterator = DataLoader(
-        dataset, sampler=sampler, batch_size=args.batch_size, collate_fn=collate_fn,
-    )
+    iterator = DataLoader(dataset, sampler=sampler, batch_size=args.batch_size, collate_fn=collate_fn,)
 
     return iterator
 
@@ -210,14 +207,9 @@ def collate(data, tokenizer, block_size, device):
     names = [name for name, _, _ in data]
     summaries = [" ".join(summary_list) for _, _, summary_list in data]
 
-    encoded_text = [
-        encode_for_summarization(story, summary, tokenizer) for _, story, summary in data
-    ]
+    encoded_text = [encode_for_summarization(story, summary, tokenizer) for _, story, summary in data]
     encoded_stories = torch.tensor(
-        [
-            fit_to_block_size(story, block_size, tokenizer.pad_token_id)
-            for story, _ in encoded_text
-        ]
+        [fit_to_block_size(story, block_size, tokenizer.pad_token_id) for story, _ in encoded_text]
     )
     encoder_token_type_ids = compute_token_type_ids(encoded_stories, tokenizer.cls_token_id)
     encoder_mask = build_mask(encoded_stories, tokenizer.pad_token_id)
@@ -272,38 +264,23 @@ def main():
     )
     # EVALUATION options
     parser.add_argument(
-        "--no_cuda",
-        default=False,
-        type=bool,
-        help="Whether to force the execution on CPU.",
+        "--no_cuda", default=False, type=bool, help="Whether to force the execution on CPU.",
     )
     parser.add_argument(
         "--batch_size", default=4, type=int, help="Batch size per GPU/CPU for training.",
     )
     # BEAM SEARCH arguments
     parser.add_argument(
-        "--min_length",
-        default=50,
-        type=int,
-        help="Minimum number of tokens for the summaries.",
+        "--min_length", default=50, type=int, help="Minimum number of tokens for the summaries.",
     )
     parser.add_argument(
-        "--max_length",
-        default=200,
-        type=int,
-        help="Maixmum number of tokens for the summaries.",
+        "--max_length", default=200, type=int, help="Maixmum number of tokens for the summaries.",
     )
     parser.add_argument(
-        "--beam_size",
-        default=5,
-        type=int,
-        help="The number of beams to start with for each example.",
+        "--beam_size", default=5, type=int, help="The number of beams to start with for each example.",
     )
     parser.add_argument(
-        "--alpha",
-        default=0.95,
-        type=float,
-        help="The value of alpha for the length penalty in the beam search.",
+        "--alpha", default=0.95, type=float, help="The value of alpha for the length penalty in the beam search.",
     )
     parser.add_argument(
         "--block_trigram",