run_scorer.py

# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""BERT finetuning runner."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import csv
import os
import modeling
import optimization
import tokenization
import tensorflow as tf
import scipy

flags = tf.flags

FLAGS = flags.FLAGS

## Required parameters
flags.DEFINE_string(
    "data_dir", None,
    "The input data dir. Should contain the .tsv files (or other data files) "
    "for the task.")

flags.DEFINE_string(
    "bert_config_file", None,
    "The config json file corresponding to the pre-trained BERT model. "
    "This specifies the model architecture.")

flags.DEFINE_string("task_name", None, "The name of the task to train.")

flags.DEFINE_string("vocab_file", None,
                    "The vocabulary file that the BERT model was trained on.")

flags.DEFINE_string(
    "output_dir", None,
    "The output directory where the model checkpoints will be written.")

## Other parameters

flags.DEFINE_string(
    "init_checkpoint", None,
    "Initial checkpoint (usually from a pre-trained BERT model).")

flags.DEFINE_bool(
    "do_lower_case", True,
    "Whether to lower case the input text. Should be True for uncased "
    "models and False for cased models.")

flags.DEFINE_integer(
    "max_seq_length", 128,
    "The maximum total input sequence length after WordPiece tokenization. "
    "Sequences longer than this will be truncated, and sequences shorter "
    "than this will be padded.")

flags.DEFINE_bool("do_train", False, "Whether to run training.")

flags.DEFINE_bool("do_eval", False, "Whether to run eval on the dev set.")

flags.DEFINE_bool("do_test", False, "Whether to run test on the test set.")    # ADDED

flags.DEFINE_bool("benchmark", False, "Whether to run a quick benchmark on prediction or not.")    # ADDED

flags.DEFINE_integer("train_batch_size", 32, "Total batch size for training.")

flags.DEFINE_integer("eval_batch_size", 8, "Total batch size for eval.")

flags.DEFINE_integer("test_batch_size", 8, "Total batch size for eval.")

flags.DEFINE_float("learning_rate", 5e-5, "The initial learning rate for Adam.")

flags.DEFINE_float("num_train_epochs", 3.0,
                   "Total number of training epochs to perform.")

flags.DEFINE_float(
    "warmup_proportion", 0.1,
    "Proportion of training to perform linear learning rate warmup for. "
    "E.g., 0.1 = 10% of training.")

flags.DEFINE_integer("save_checkpoints_steps", 1000,
                     "How often to save the model checkpoint.")

flags.DEFINE_integer("iterations_per_loop", 1000,
                     "How many steps to make in each estimator call.")

flags.DEFINE_bool("use_tpu", False, "Whether to use TPU or GPU/CPU.")

tf.flags.DEFINE_string(
    "tpu_name", None,
    "The Cloud TPU to use for training. This should be either the name "
    "used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 "
    "url.")

tf.flags.DEFINE_string(
    "tpu_zone", None,
    "[Optional] GCE zone where the Cloud TPU is located in. If not "
    "specified, we will attempt to automatically detect the GCE project from "
    "metadata.")

tf.flags.DEFINE_string(
    "gcp_project", None,
    "[Optional] Project name for the Cloud TPU-enabled project. If not "
    "specified, we will attempt to automatically detect the GCE project from "
    "metadata.")

tf.flags.DEFINE_string("master", None, "[Optional] TensorFlow master URL.")

flags.DEFINE_integer(
    "num_tpu_cores", 8,
    "Only used if `use_tpu` is True. Total number of TPU cores to use.")


class InputExample(object):
  """A single training/test example for simple sequence classification."""

  def __init__(self, guid, text_a, text_b=None, label=None):
    """Constructs a InputExample.

    Args:
      guid: Unique id for the example.
      text_a: string. The untokenized text of the first sequence. For single
        sequence tasks, only this sequence must be specified.
      text_b: (Optional) string. The untokenized text of the second sequence.
        Only must be specified for sequence pair tasks.
      label: (Optional) string. The label of the example. This should be
        specified for train and dev examples, but not for test examples.
    """
    self.guid = guid
    self.text_a = text_a
    self.text_b = text_b
    self.label = label


class InputFeatures(object):
  """A single set of features of data."""

  def __init__(self, input_ids, input_mask, segment_ids, label_id):
    self.input_ids = input_ids
    self.input_mask = input_mask
    self.segment_ids = segment_ids
    self.label_id = label_id


class DataProcessor(object):
  """Base class for data converters for sequence classification data sets."""

  def get_train_examples(self, data_dir):
    """Gets a collection of `InputExample`s for the train set."""
    raise NotImplementedError()

  def get_dev_examples(self, data_dir):
    """Gets a collection of `InputExample`s for the dev set."""
    raise NotImplementedError()
  
  # ADDED
  def get_test_examples(self, data_dir):
    """Gets a collection of `InputExample`s for the dev set."""
    raise NotImplementedError()

  def get_labels(self):
    """Gets the list of labels for this data set."""
    raise NotImplementedError()

  @classmethod
  def _read_tsv(cls, input_file, quotechar=None):
    """Reads a tab separated value file."""
    with tf.gfile.Open(input_file, "r") as f:
      reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
      lines = []
      for line in reader:
        lines.append(line)
      return lines

### NOT FROM THE OFFICIAL REPO
class SickProcessor(DataProcessor):
  """Processor for the SICK data set (normalized version)."""

  def get_train_examples(self, data_dir):
    """See base class."""
    return self._create_examples(
        self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")

  def get_dev_examples(self, data_dir):
    """See base class."""
    return self._create_examples(
        self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")

  # ADDED
  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")

  def _create_examples(self, lines, set_type):
    """Creates examples for the training and dev sets."""
    examples = []
    for (i, line) in enumerate(lines):
        if i == 0:
            continue
        guid = "%s-%s" % (set_type, tokenization.convert_to_unicode(line[0]))
        text_a = tokenization.convert_to_unicode(line[1])
        text_b = tokenization.convert_to_unicode(line[2])
        label = float(line[4])
        examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
    return examples

class StsProcessor(DataProcessor):
  """Processor for the STS-B data set."""

  def get_train_examples(self, data_dir):
    """See base class."""
    return self._create_examples(
        self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")

  def get_dev_examples(self, data_dir):
    """See base class."""
    return self._create_examples(
        self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")

  # ADDED
  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")

  def _create_examples(self, lines, set_type):
    """Creates examples for the training and dev sets."""
    examples = []
    for (i, line) in enumerate(lines):
        if i == 0:
            continue
        guid = "%s-%s" % (set_type, tokenization.convert_to_unicode(line[0]))
        text_a = tokenization.convert_to_unicode(line[-3])
        text_b = tokenization.convert_to_unicode(line[-2])
        label = float(line[-1])
        examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
    return examples
###

def convert_examples_to_features(examples, label_list, max_seq_length,
                                 tokenizer, shut_up=False):
  """Loads a data file into a list of `InputBatch`s."""

#   label_map = {}
#   for (i, label) in enumerate(label_list):
#     label_map[label] = i

  features = []
  for (ex_index, example) in enumerate(examples):
    tokens_a = tokenizer.tokenize(example.text_a)

    tokens_b = None
    if example.text_b:
      tokens_b = tokenizer.tokenize(example.text_b)

    if tokens_b:
      # Modifies `tokens_a` and `tokens_b` in place so that the total
      # length is less than the specified length.
      # Account for [CLS], [SEP], [SEP] with "- 3"
      _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
    else:
      # Account for [CLS] and [SEP] with "- 2"
      if len(tokens_a) > max_seq_length - 2:
        tokens_a = tokens_a[0:(max_seq_length - 2)]

    # The convention in BERT is:
    # (a) For sequence pairs:
    #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
    #  type_ids: 0   0  0    0    0     0       0 0    1  1  1  1   1 1
    # (b) For single sequences:
    #  tokens:   [CLS] the dog is hairy . [SEP]
    #  type_ids: 0   0   0   0  0     0 0
    #
    # Where "type_ids" are used to indicate whether this is the first
    # sequence or the second sequence. The embedding vectors for `type=0` and
    # `type=1` were learned during pre-training and are added to the wordpiece
    # embedding vector (and position vector). This is not *strictly* necessary
    # since the [SEP] token unambiguously separates the sequences, but it makes
    # it easier for the model to learn the concept of sequences.
    #
    # For classification tasks, the first vector (corresponding to [CLS]) is
    # used as as the "sentence vector". Note that this only makes sense because
    # the entire model is fine-tuned.
    tokens = []
    segment_ids = []
    tokens.append("[CLS]")
    segment_ids.append(0)
    for token in tokens_a:
      tokens.append(token)
      segment_ids.append(0)
    tokens.append("[SEP]")
    segment_ids.append(0)

    if tokens_b:
      for token in tokens_b:
        tokens.append(token)
        segment_ids.append(1)
      tokens.append("[SEP]")
      segment_ids.append(1)

    input_ids = tokenizer.convert_tokens_to_ids(tokens)

    # The mask has 1 for real tokens and 0 for padding tokens. Only real
    # tokens are attended to.
    input_mask = [1] * len(input_ids)

    # Zero-pad up to the sequence length.
    while len(input_ids) < max_seq_length:
      input_ids.append(0)
      input_mask.append(0)
      segment_ids.append(0)

    assert len(input_ids) == max_seq_length
    assert len(input_mask) == max_seq_length
    assert len(segment_ids) == max_seq_length

    # label_id = label_map[example.label]
    if not shut_up:
        label_id = example.label
        if ex_index < 5:
          tf.logging.info("*** Example ***")
          tf.logging.info("guid: %s" % (example.guid))
          tf.logging.info("tokens: %s" % " ".join(
              [tokenization.printable_text(x) for x in tokens]))
          tf.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
          tf.logging.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
          tf.logging.info(
              "segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
          tf.logging.info("label: {} (id = {})".format(example.label, label_id))

    features.append(
        InputFeatures(
            input_ids=input_ids,
            input_mask=input_mask,
            segment_ids=segment_ids,
            label_id=label_id))
  return features


def _truncate_seq_pair(tokens_a, tokens_b, max_length):
  """Truncates a sequence pair in place to the maximum length."""

  # This is a simple heuristic which will always truncate the longer sequence
  # one token at a time. This makes more sense than truncating an equal percent
  # of tokens from each, since if one sequence is very short then each token
  # that's truncated likely contains more information than a longer sequence.
  while True:
    total_length = len(tokens_a) + len(tokens_b)
    if total_length <= max_length:
      break
    if len(tokens_a) > len(tokens_b):
      tokens_a.pop()
    else:
      tokens_b.pop()


def create_model(bert_config, is_training, input_ids, input_mask, segment_ids,
                 labels, use_one_hot_embeddings):
  """Creates a classification model."""
  model = modeling.BertModel(
      config=bert_config,
      is_training=is_training,
      input_ids=input_ids,
      input_mask=input_mask,
      token_type_ids=segment_ids,
      use_one_hot_embeddings=use_one_hot_embeddings)

  # In the demo, we are doing a simple classification task on the entire
  # segment.
  #
  # If you want to use the token-level output, use model.get_sequence_output()
  # instead.
  output_layer = model.get_pooled_output()

  hidden_size = output_layer.shape[-1].value

  output_weights = tf.get_variable(
      "output_weights", [1, hidden_size],
      initializer=tf.truncated_normal_initializer(stddev=0.02))

  output_bias = tf.get_variable(
      "output_bias", [1], initializer=tf.zeros_initializer())

  with tf.variable_scope("loss"):
    if is_training:
      # I.e., 0.1 dropout
      output_layer = tf.nn.dropout(output_layer, keep_prob=0.9)

    logits = tf.matmul(output_layer, output_weights, transpose_b=True)
    logits = tf.nn.bias_add(logits, output_bias)
    logits = tf.squeeze(logits, [-1])
    # log_probs = tf.nn.log_softmax(logits, axis=-1)
    
    # one_hot_labels = tf.one_hot(labels, depth=num_labels, dtype=tf.float32)
    per_example_loss = tf.square(logits - labels)
    
    # per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1)
    loss = tf.reduce_mean(per_example_loss)

    return (loss, per_example_loss, logits)


def model_fn_builder(bert_config, init_checkpoint, learning_rate,
                     num_train_steps, num_warmup_steps, use_tpu,
                     use_one_hot_embeddings):
  """Returns `model_fn` closure for TPUEstimator."""

  def model_fn(features, labels, mode, params):  # pylint: disable=unused-argument
    """The `model_fn` for TPUEstimator."""

    tf.logging.info("*** Features ***")
    for name in sorted(features.keys()):
      tf.logging.info("  name = %s, shape = %s" % (name, features[name].shape))

    input_ids = features["input_ids"]
    input_mask = features["input_mask"]
    segment_ids = features["segment_ids"]
    label_ids = features["label_ids"]

    is_training = (mode == tf.estimator.ModeKeys.TRAIN)

    (total_loss, per_example_loss, logits) = create_model(
        bert_config, is_training, input_ids, input_mask, segment_ids, label_ids,
        use_one_hot_embeddings)

    tvars = tf.trainable_variables()

    scaffold_fn = None
    if init_checkpoint:
      (assignment_map,
       initialized_variable_names) = modeling.get_assigment_map_from_checkpoint(
           tvars, init_checkpoint)
      if use_tpu:

        def tpu_scaffold():
          tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
          return tf.train.Scaffold()

        scaffold_fn = tpu_scaffold
      else:
        tf.train.init_from_checkpoint(init_checkpoint, assignment_map)

    tf.logging.info("**** Trainable Variables ****")
    for var in tvars:
      init_string = ""
      if var.name in initialized_variable_names:
        init_string = ", *INIT_FROM_CKPT*"
      tf.logging.info("  name = %s, shape = %s%s", var.name, var.shape,
                      init_string)

    output_spec = None
    if mode == tf.estimator.ModeKeys.TRAIN:

      train_op = optimization.create_optimizer(
          total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu)

      output_spec = tf.contrib.tpu.TPUEstimatorSpec(
          mode=mode,
          loss=total_loss,
          train_op=train_op,
          scaffold_fn=scaffold_fn)
    elif mode == tf.estimator.ModeKeys.EVAL:

#       def metric_fn(per_example_loss, label_ids, logits):
#         predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)
#         accuracy = tf.metrics.accuracy(label_ids, predictions)
#         loss = tf.metrics.mean(per_example_loss)
#         return {
#             "eval_accuracy": accuracy,
#             "eval_loss": loss,
#         }
      def metric_fn(per_example_loss, label_ids, logits):       
        # Display labels and predictions
        concat1 = tf.contrib.metrics.streaming_concat(logits)
        concat2 = tf.contrib.metrics.streaming_concat(label_ids)
        
        # Compute Pearson correlation
        pearson = tf.contrib.metrics.streaming_pearson_correlation(logits, label_ids)
        
        # Compute MSE
        # mse = tf.metrics.mean(per_example_loss)    
        mse = tf.metrics.mean_squared_error(label_ids, logits)
        
        # Compute Spearman correlation
        size = tf.size(logits)
        indice_of_ranks_pred = tf.nn.top_k(logits, k=size)[1]
        indice_of_ranks_label = tf.nn.top_k(label_ids, k=size)[1]
        rank_pred = tf.nn.top_k(-indice_of_ranks_pred, k=size)[1]
        rank_label = tf.nn.top_k(-indice_of_ranks_label, k=size)[1]
        rank_pred = tf.to_float(rank_pred)
        rank_label = tf.to_float(rank_label)
        spearman = tf.contrib.metrics.streaming_pearson_correlation(rank_pred, rank_label)
        
        return {'pred': concat1, 'label_ids': concat2, 'pearson': pearson, 'spearman': spearman, 'MSE': mse}

      eval_metrics = (metric_fn, [per_example_loss, label_ids, logits])
      output_spec = tf.contrib.tpu.TPUEstimatorSpec(
          mode=mode,
          loss=total_loss,
          eval_metrics=eval_metrics,
          scaffold_fn=scaffold_fn)
    else:
      raise ValueError("Only TRAIN and EVAL modes are supported: %s" % (mode))

    return output_spec

  return model_fn


def input_fn_builder(features, seq_length, is_training, drop_remainder):
  """Creates an `input_fn` closure to be passed to TPUEstimator."""

  all_input_ids = []
  all_input_mask = []
  all_segment_ids = []
  all_label_ids = []

  for feature in features:
    all_input_ids.append(feature.input_ids)
    all_input_mask.append(feature.input_mask)
    all_segment_ids.append(feature.segment_ids)
    all_label_ids.append(feature.label_id)

  def input_fn(params):
    """The actual input function."""
    batch_size = params["batch_size"]

    num_examples = len(features)

    # This is for demo purposes and does NOT scale to large data sets. We do
    # not use Dataset.from_generator() because that uses tf.py_func which is
    # not TPU compatible. The right way to load data is with TFRecordReader.
    d = tf.data.Dataset.from_tensor_slices({
        "input_ids":
            tf.constant(
                all_input_ids, shape=[num_examples, seq_length],
                dtype=tf.int32),
        "input_mask":
            tf.constant(
                all_input_mask,
                shape=[num_examples, seq_length],
                dtype=tf.int32),
        "segment_ids":
            tf.constant(
                all_segment_ids,
                shape=[num_examples, seq_length],
                dtype=tf.int32),
        "label_ids":
            # tf.constant(all_label_ids, shape=[num_examples, 0], dtype=tf.float32),
            tf.constant(all_label_ids, shape=[num_examples], dtype=tf.float32),
    })

    if is_training:
      d = d.repeat()
      d = d.shuffle(buffer_size=100)

    d = d.batch(batch_size=batch_size, drop_remainder=drop_remainder)
    return d

  return input_fn


def main(_):
  tf.logging.set_verbosity(tf.logging.INFO)

  processors = {
      "sick": SickProcessor,
      "sts": StsProcessor
  }

  if not FLAGS.do_train and not FLAGS.do_eval:
    raise ValueError("At least one of `do_train` or `do_eval` must be True.")

  bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)

  if FLAGS.max_seq_length > bert_config.max_position_embeddings:
    raise ValueError(
        "Cannot use sequence length %d because the BERT model "
        "was only trained up to sequence length %d" %
        (FLAGS.max_seq_length, bert_config.max_position_embeddings))

  tf.gfile.MakeDirs(FLAGS.output_dir)

  task_name = FLAGS.task_name.lower()

  if task_name not in processors:
    raise ValueError("Task not found: %s" % (task_name))

  processor = processors[task_name]()

  # label_list = processor.get_labels()
  label_list = None

  tokenizer = tokenization.FullTokenizer(
      vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case)

  tpu_cluster_resolver = None
  if FLAGS.use_tpu and FLAGS.tpu_name:
    tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(
        FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)

  is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2
  run_config = tf.contrib.tpu.RunConfig(
      cluster=tpu_cluster_resolver,
      master=FLAGS.master,
      model_dir=FLAGS.output_dir,
      save_checkpoints_steps=FLAGS.save_checkpoints_steps,
      tpu_config=tf.contrib.tpu.TPUConfig(
          iterations_per_loop=FLAGS.iterations_per_loop,
          num_shards=FLAGS.num_tpu_cores,
          per_host_input_for_training=is_per_host))

  train_examples = None
  num_train_steps = None
  num_warmup_steps = None
  if FLAGS.do_train:
    train_examples = processor.get_train_examples(FLAGS.data_dir)
    num_train_steps = int(
        len(train_examples) / FLAGS.train_batch_size * FLAGS.num_train_epochs)
    num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion)

  model_fn = model_fn_builder(
      bert_config=bert_config,
      init_checkpoint=FLAGS.init_checkpoint,
      learning_rate=FLAGS.learning_rate,
      num_train_steps=num_train_steps,
      num_warmup_steps=num_warmup_steps,
      use_tpu=FLAGS.use_tpu,
      use_one_hot_embeddings=FLAGS.use_tpu)

  # If TPU is not available, this will fall back to normal Estimator on CPU
  # or GPU.
  estimator = tf.contrib.tpu.TPUEstimator(
      use_tpu=FLAGS.use_tpu,
      model_fn=model_fn,
      config=run_config,
      train_batch_size=FLAGS.train_batch_size,
      eval_batch_size=FLAGS.eval_batch_size)

  if FLAGS.do_train:
    import time
    train_t0 = time.time()
    train_features = convert_examples_to_features(
        train_examples, label_list, FLAGS.max_seq_length, tokenizer)
    tf.logging.info("***** Running training *****")
    tf.logging.info("  Num examples = %d", len(train_examples))
    tf.logging.info("  Batch size = %d", FLAGS.train_batch_size)
    tf.logging.info("  Num steps = %d", num_train_steps)
    train_input_fn = input_fn_builder(
        features=train_features,
        seq_length=FLAGS.max_seq_length,
        is_training=True,
        drop_remainder=True)
    estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)
    train_t1 = time.time()
    

  if FLAGS.do_eval:
    eval_examples = processor.get_dev_examples(FLAGS.data_dir)
    eval_features = convert_examples_to_features(
        eval_examples, label_list, FLAGS.max_seq_length, tokenizer)

    tf.logging.info("***** Running evaluation *****")
    tf.logging.info("  Num examples = %d", len(eval_examples))
    tf.logging.info("  Batch size = %d", FLAGS.eval_batch_size)

    # This tells the estimator to run through the entire set.
    eval_steps = None
    # However, if running eval on the TPU, you will need to specify the
    # number of steps.
    if FLAGS.use_tpu:
      # Eval will be slightly WRONG on the TPU because it will truncate
      # the last batch.
      eval_steps = int(len(eval_examples) / FLAGS.eval_batch_size)

    eval_drop_remainder = True if FLAGS.use_tpu else False
    eval_input_fn = input_fn_builder(
        features=eval_features,
        seq_length=FLAGS.max_seq_length,
        is_training=False,
        drop_remainder=eval_drop_remainder)

    result = estimator.evaluate(input_fn=eval_input_fn, steps=eval_steps)

    output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt")
    with tf.gfile.GFile(output_eval_file, "w") as writer:
      tf.logging.info("***** Eval results *****")
      for key in sorted(result.keys()):
        tf.logging.info("  %s = %s", key, str(result[key]))
        writer.write("%s = %s\n" % (key, str(result[key])))
        
  if FLAGS.do_test:
    test_examples = processor.get_test_examples(FLAGS.data_dir)
    test_features = convert_examples_to_features(
        test_examples, label_list, FLAGS.max_seq_length, tokenizer)

    tf.logging.info("***** Running test *****")
    tf.logging.info("  Num examples = %d", len(test_examples))
    tf.logging.info("  Batch size = %d", FLAGS.test_batch_size)

    # This tells the estimator to run through the entire set.
    test_steps = None
    # However, if running eval on the TPU, you will need to specify the
    # number of steps.
    if FLAGS.use_tpu:
      # Eval will be slightly WRONG on the TPU because it will truncate
      # the last batch.
      test_steps = int(len(test_examples) / FLAGS.test_batch_size)

    test_drop_remainder = True if FLAGS.use_tpu else False
    test_input_fn = input_fn_builder(
        features=test_features,
        seq_length=FLAGS.max_seq_length,
        is_training=False,
        drop_remainder=test_drop_remainder)

    result = estimator.evaluate(input_fn=test_input_fn, steps=test_steps)

    output_test_file = os.path.join(FLAGS.output_dir, "test_results.txt")
    with tf.gfile.GFile(output_test_file, "w") as writer:
      tf.logging.info("***** Test results *****")
      for key in sorted(result.keys()):
        tf.logging.info("  %s = %s", key, str(result[key]))
        writer.write("%s = %s\n" % (key, str(result[key])))
        
  # Benchmark how long it takes for prediction
  if FLAGS.benchmark:
      import time
      t0 = time.time()
      for i in range(100):
          predict_example = [InputExample(guid=2, text_a='He is a smart and experienced person', text_b='He\'s a truly wise man')]
          predict_features = convert_examples_to_features(predict_example, label_list, FLAGS.max_seq_length, tokenizer, shut_up=True)
          input_fn = input_fn_builder(features=predict_features, seq_length=FLAGS.max_seq_length, is_training=False, drop_remainder=False)
          result = estimator.predict(input_fn=input_fn)
      t1 = time.time()
      print("Time necessary for 1 prediction : {}".format((t1 - t0) / 100))
      print("Time necessary for training : {}".format(train_t1 - train_t0))

if __name__ == "__main__":
  flags.mark_flag_as_required("data_dir")
  flags.mark_flag_as_required("task_name")
  flags.mark_flag_as_required("vocab_file")
  flags.mark_flag_as_required("bert_config_file")
  flags.mark_flag_as_required("output_dir")
  tf.app.run()