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/mm-imdb/utils_mmimdb.py

@@ -25,17 +25,7 @@ import torchvision
 import torchvision.transforms as transforms
 from torch.utils.data import Dataset
 
-POOLING_BREAKDOWN = {
-    1: (1, 1),
-    2: (2, 1),
-    3: (3, 1),
-    4: (2, 2),
-    5: (5, 1),
-    6: (3, 2),
-    7: (7, 1),
-    8: (4, 2),
-    9: (3, 3)
-}
+POOLING_BREAKDOWN = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}
 
 
 class ImageEncoder(nn.Module):
@@ -54,7 +44,6 @@ class ImageEncoder(nn.Module):
         return out  # BxNx2048
 
 
-
 class JsonlDataset(Dataset):
     def __init__(self, data_path, tokenizer, transforms, labels, max_seq_length):
         self.data = [json.loads(l) for l in open(data_path)]
@@ -72,7 +61,7 @@ class JsonlDataset(Dataset):
     def __getitem__(self, index):
         sentence = torch.LongTensor(self.tokenizer.encode(self.data[index]["text"], add_special_tokens=True))
         start_token, sentence, end_token = sentence[0], sentence[1:-1], sentence[-1]
-        sentence = sentence[:self.max_seq_length]
+        sentence = sentence[: self.max_seq_length]
 
         label = torch.zeros(self.n_classes)
         label[[self.labels.index(tgt) for tgt in self.data[index]["label"]]] = 1
@@ -80,8 +69,13 @@ class JsonlDataset(Dataset):
         image = Image.open(os.path.join(self.data_dir, self.data[index]["img"])).convert("RGB")
         image = self.transforms(image)
 
-        return {"image_start_token": start_token, "image_end_token": end_token,
-                "sentence": sentence, "image": image, "label": label}
+        return {
+            "image_start_token": start_token,
+            "image_end_token": end_token,
+            "sentence": sentence,
+            "image": image,
+            "label": label,
+        }
 
     def get_label_frequencies(self):
         label_freqs = Counter()
@@ -110,10 +104,31 @@ def collate_fn(batch):
 
 
 def get_mmimdb_labels():
-    return ['Crime', 'Drama', 'Thriller', 'Action', 'Comedy', 'Romance',
-            'Documentary', 'Short', 'Mystery', 'History', 'Family', 'Adventure',
-            'Fantasy', 'Sci-Fi', 'Western', 'Horror', 'Sport', 'War', 'Music',
-            'Musical', 'Animation', 'Biography', 'Film-Noir']
+    return [
+        "Crime",
+        "Drama",
+        "Thriller",
+        "Action",
+        "Comedy",
+        "Romance",
+        "Documentary",
+        "Short",
+        "Mystery",
+        "History",
+        "Family",
+        "Adventure",
+        "Fantasy",
+        "Sci-Fi",
+        "Western",
+        "Horror",
+        "Sport",
+        "War",
+        "Music",
+        "Musical",
+        "Animation",
+        "Biography",
+        "Film-Noir",
+    ]
 
 
 def get_image_transforms():
@@ -122,9 +137,6 @@ def get_image_transforms():
             transforms.Resize(256),
             transforms.CenterCrop(224),
             transforms.ToTensor(),
-            transforms.Normalize(
-                mean=[0.46777044, 0.44531429, 0.40661017],
-                std=[0.12221994, 0.12145835, 0.14380469],
-            ),
+            transforms.Normalize(mean=[0.46777044, 0.44531429, 0.40661017], std=[0.12221994, 0.12145835, 0.14380469],),
         ]
     )