Running through pyformat to meet Google code standards

extract_features.py

@@ -170,8 +170,9 @@ def model_fn(features, labels, mode, params):  # pylint: disable=unused-argument
 
     tvars = tf.trainable_variables()
     scaffold_fn = None
-    (assignment_map, initialized_variable_names
-    ) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)
+    (assignment_map,
+     initialized_variable_names) = modeling.get_assignment_map_from_checkpoint(
+         tvars, init_checkpoint)
     if use_tpu:
 
       def tpu_scaffold():

run_classifier.py

@@ -71,7 +71,9 @@
 
 flags.DEFINE_bool("do_eval", False, "Whether to run eval on the dev set.")
 
-flags.DEFINE_bool("do_predict", False, "Whether to run the model in inference mode on the test set.")
+flags.DEFINE_bool(
+    "do_predict", False,
+    "Whether to run the model in inference mode on the test set.")
 
 flags.DEFINE_integer("train_batch_size", 32, "Total batch size for training.")
 
@@ -248,8 +250,7 @@ def get_dev_examples(self, data_dir):
   def get_test_examples(self, data_dir):
     """See base class."""
     return self._create_examples(
-      self._read_tsv(os.path.join(data_dir, "test_matched.tsv")),
-      "test")
+        self._read_tsv(os.path.join(data_dir, "test_matched.tsv")), "test")
 
   def get_labels(self):
     """See base class."""
@@ -289,7 +290,7 @@ def get_dev_examples(self, data_dir):
   def get_test_examples(self, data_dir):
     """See base class."""
     return self._create_examples(
-      self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
+        self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
 
   def get_labels(self):
     """See base class."""
@@ -329,7 +330,7 @@ def get_dev_examples(self, data_dir):
   def get_test_examples(self, data_dir):
     """See base class."""
     return self._create_examples(
-      self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
+        self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
 
   def get_labels(self):
     """See base class."""
@@ -659,9 +660,7 @@ def metric_fn(per_example_loss, label_ids, logits):
           scaffold_fn=scaffold_fn)
     else:
       output_spec = tf.contrib.tpu.TPUEstimatorSpec(
-          mode=mode,
-          predictions=probabilities,
-          scaffold_fn=scaffold_fn)
+          mode=mode, predictions=probabilities, scaffold_fn=scaffold_fn)
     return output_spec
 
   return model_fn
@@ -874,7 +873,8 @@ def main(_):
     predict_examples = processor.get_test_examples(FLAGS.data_dir)
     predict_file = os.path.join(FLAGS.output_dir, "predict.tf_record")
     file_based_convert_examples_to_features(predict_examples, label_list,
-                                 FLAGS.max_seq_length, tokenizer, predict_file)
+                                            FLAGS.max_seq_length, tokenizer,
+                                            predict_file)
 
     tf.logging.info("***** Running prediction*****")
     tf.logging.info("  Num examples = %d", len(predict_examples))
@@ -887,20 +887,22 @@ def main(_):
 
     predict_drop_remainder = True if FLAGS.use_tpu else False
     predict_input_fn = file_based_input_fn_builder(
-      input_file=predict_file,
-      seq_length=FLAGS.max_seq_length,
-      is_training=False,
-      drop_remainder=predict_drop_remainder)
+        input_file=predict_file,
+        seq_length=FLAGS.max_seq_length,
+        is_training=False,
+        drop_remainder=predict_drop_remainder)
 
     result = estimator.predict(input_fn=predict_input_fn)
 
     output_predict_file = os.path.join(FLAGS.output_dir, "test_results.tsv")
     with tf.gfile.GFile(output_predict_file, "w") as writer:
       tf.logging.info("***** Predict results *****")
       for prediction in result:
-        output_line = "\t".join(str(class_probability) for class_probability in prediction) + "\n"
+        output_line = "\t".join(
+            str(class_probability) for class_probability in prediction) + "\n"
         writer.write(output_line)
 
+
 if __name__ == "__main__":
   flags.mark_flag_as_required("data_dir")
   flags.mark_flag_as_required("task_name")

tokenization.py

@@ -86,7 +86,7 @@ def convert_by_vocab(vocab, items):
   """Converts a sequence of [tokens|ids] using the vocab."""
   output = []
   for item in items:
-      output.append(vocab[item])
+    output.append(vocab[item])
   return output