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/distillation/utils.py

@@ -23,9 +23,12 @@ import torch
 import numpy as np
 
 import logging
-logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s',
-                    datefmt = '%m/%d/%Y %H:%M:%S',
-                    level = logging.INFO)
+
+logging.basicConfig(
+    format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s",
+    datefmt="%m/%d/%Y %H:%M:%S",
+    level=logging.INFO,
+)
 logger = logging.getLogger(__name__)
 
 
@@ -35,12 +38,12 @@ def git_log(folder_path: str):
     """
     repo = git.Repo(search_parent_directories=True)
     repo_infos = {
-        'repo_id': str(repo),
-        'repo_sha': str(repo.head.object.hexsha),
-        'repo_branch': str(repo.active_branch)
+        "repo_id": str(repo),
+        "repo_sha": str(repo.head.object.hexsha),
+        "repo_branch": str(repo.active_branch),
     }
 
-    with open(os.path.join(folder_path, 'git_log.json'), 'w') as f:
+    with open(os.path.join(folder_path, "git_log.json"), "w") as f:
         json.dump(repo_infos, f, indent=4)
 
 
@@ -57,21 +60,21 @@ def init_gpu_params(params):
 
     assert torch.cuda.is_available()
 
-    logger.info('Initializing GPUs')
+    logger.info("Initializing GPUs")
     if params.n_gpu > 1:
         assert params.local_rank != -1
 
-        params.world_size = int(os.environ['WORLD_SIZE'])
-        params.n_gpu_per_node = int(os.environ['N_GPU_NODE'])
-        params.global_rank = int(os.environ['RANK'])
+        params.world_size = int(os.environ["WORLD_SIZE"])
+        params.n_gpu_per_node = int(os.environ["N_GPU_NODE"])
+        params.global_rank = int(os.environ["RANK"])
 
         # number of nodes / node ID
         params.n_nodes = params.world_size // params.n_gpu_per_node
         params.node_id = params.global_rank // params.n_gpu_per_node
         params.multi_gpu = True
 
-        assert params.n_nodes == int(os.environ['N_NODES'])
-        assert params.node_id == int(os.environ['NODE_RANK'])
+        assert params.n_nodes == int(os.environ["N_NODES"])
+        assert params.node_id == int(os.environ["NODE_RANK"])
 
     # local job (single GPU)
     else:
@@ -114,8 +117,7 @@ def init_gpu_params(params):
     if params.multi_gpu:
         logger.info("Initializing PyTorch distributed")
         torch.distributed.init_process_group(
-            init_method='env://',
-            backend='nccl',
+            init_method="env://", backend="nccl",
         )