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/run_tf_glue.py

@@ -1,7 +1,14 @@
 import os
 import tensorflow as tf
 import tensorflow_datasets
-from transformers import BertTokenizer, TFBertForSequenceClassification, BertConfig, glue_convert_examples_to_features, BertForSequenceClassification, glue_processors
+from transformers import (
+    BertTokenizer,
+    TFBertForSequenceClassification,
+    BertConfig,
+    glue_convert_examples_to_features,
+    BertForSequenceClassification,
+    glue_processors,
+)
 
 # script parameters
 BATCH_SIZE = 32
@@ -16,7 +23,7 @@ if TASK == "sst-2":
     TFDS_TASK = "sst2"
 elif TASK == "sts-b":
     TFDS_TASK = "stsb"
-else: 
+else:
     TFDS_TASK = TASK
 
 num_labels = len(glue_processors[TASK]().get_labels())
@@ -27,29 +34,29 @@ tf.config.optimizer.set_experimental_options({"auto_mixed_precision": USE_AMP})
 
 # Load tokenizer and model from pretrained model/vocabulary. Specify the number of labels to classify (2+: classification, 1: regression)
 config = BertConfig.from_pretrained("bert-base-cased", num_labels=num_labels)
-tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
-model = TFBertForSequenceClassification.from_pretrained('bert-base-cased', config=config)
+tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
+model = TFBertForSequenceClassification.from_pretrained("bert-base-cased", config=config)
 
 # Load dataset via TensorFlow Datasets
-data, info = tensorflow_datasets.load(f'glue/{TFDS_TASK}', with_info=True)
-train_examples = info.splits['train'].num_examples
+data, info = tensorflow_datasets.load(f"glue/{TFDS_TASK}", with_info=True)
+train_examples = info.splits["train"].num_examples
 
 # MNLI expects either validation_matched or validation_mismatched
-valid_examples = info.splits['validation'].num_examples
+valid_examples = info.splits["validation"].num_examples
 
 # Prepare dataset for GLUE as a tf.data.Dataset instance
-train_dataset = glue_convert_examples_to_features(data['train'], tokenizer, 128, TASK)
+train_dataset = glue_convert_examples_to_features(data["train"], tokenizer, 128, TASK)
 
 # MNLI expects either validation_matched or validation_mismatched
-valid_dataset = glue_convert_examples_to_features(data['validation'], tokenizer, 128, TASK)
+valid_dataset = glue_convert_examples_to_features(data["validation"], tokenizer, 128, TASK)
 train_dataset = train_dataset.shuffle(128).batch(BATCH_SIZE).repeat(-1)
 valid_dataset = valid_dataset.batch(EVAL_BATCH_SIZE)
 
-# Prepare training: Compile tf.keras model with optimizer, loss and learning rate schedule 
+# Prepare training: Compile tf.keras model with optimizer, loss and learning rate schedule
 opt = tf.keras.optimizers.Adam(learning_rate=3e-5, epsilon=1e-08)
 if USE_AMP:
     # loss scaling is currently required when using mixed precision
-    opt = tf.keras.mixed_precision.experimental.LossScaleOptimizer(opt, 'dynamic')
+    opt = tf.keras.mixed_precision.experimental.LossScaleOptimizer(opt, "dynamic")
 
 
 if num_labels == 1:
@@ -57,37 +64,42 @@ if num_labels == 1:
 else:
     loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
 
-metric = tf.keras.metrics.SparseCategoricalAccuracy('accuracy')
+metric = tf.keras.metrics.SparseCategoricalAccuracy("accuracy")
 model.compile(optimizer=opt, loss=loss, metrics=[metric])
 
 # Train and evaluate using tf.keras.Model.fit()
-train_steps = train_examples//BATCH_SIZE
-valid_steps = valid_examples//EVAL_BATCH_SIZE
+train_steps = train_examples // BATCH_SIZE
+valid_steps = valid_examples // EVAL_BATCH_SIZE
 
-history = model.fit(train_dataset, epochs=EPOCHS, steps_per_epoch=train_steps,
-                    validation_data=valid_dataset, validation_steps=valid_steps)
+history = model.fit(
+    train_dataset,
+    epochs=EPOCHS,
+    steps_per_epoch=train_steps,
+    validation_data=valid_dataset,
+    validation_steps=valid_steps,
+)
 
 # Save TF2 model
-os.makedirs('./save/', exist_ok=True)
-model.save_pretrained('./save/')
+os.makedirs("./save/", exist_ok=True)
+model.save_pretrained("./save/")
 
 if TASK == "mrpc":
     # Load the TensorFlow model in PyTorch for inspection
-    # This is to demo the interoperability between the two frameworks, you don't have to 
+    # This is to demo the interoperability between the two frameworks, you don't have to
     # do this in real life (you can run the inference on the TF model).
-    pytorch_model = BertForSequenceClassification.from_pretrained('./save/', from_tf=True)
+    pytorch_model = BertForSequenceClassification.from_pretrained("./save/", from_tf=True)
 
     # Quickly test a few predictions - MRPC is a paraphrasing task, let's see if our model learned the task
-    sentence_0 = 'This research was consistent with his findings.'
-    sentence_1 = 'His findings were compatible with this research.'
-    sentence_2 = 'His findings were not compatible with this research.'
-    inputs_1 = tokenizer.encode_plus(sentence_0, sentence_1, add_special_tokens=True, return_tensors='pt')
-    inputs_2 = tokenizer.encode_plus(sentence_0, sentence_2, add_special_tokens=True, return_tensors='pt')
+    sentence_0 = "This research was consistent with his findings."
+    sentence_1 = "His findings were compatible with this research."
+    sentence_2 = "His findings were not compatible with this research."
+    inputs_1 = tokenizer.encode_plus(sentence_0, sentence_1, add_special_tokens=True, return_tensors="pt")
+    inputs_2 = tokenizer.encode_plus(sentence_0, sentence_2, add_special_tokens=True, return_tensors="pt")
 
     del inputs_1["special_tokens_mask"]
     del inputs_2["special_tokens_mask"]
 
     pred_1 = pytorch_model(**inputs_1)[0].argmax().item()
     pred_2 = pytorch_model(**inputs_2)[0].argmax().item()
-    print('sentence_1 is', 'a paraphrase' if pred_1 else 'not a paraphrase', 'of sentence_0')
-    print('sentence_2 is', 'a paraphrase' if pred_2 else 'not a paraphrase', 'of sentence_0')
+    print("sentence_1 is", "a paraphrase" if pred_1 else "not a paraphrase", "of sentence_0")
+    print("sentence_2 is", "a paraphrase" if pred_2 else "not a paraphrase", "of sentence_0")