code: # coding=utf-8
  """BERT finetuning runner."""
  
  # @time    : 2019/5/17 19:01
  # @author  : alchemistlee
  # @fileName: multi_lable_classifier_v1.py
  # @abstract:
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import collections
  import csv
  import os
  import modeling
  import optimization
  import tokenization
  import tensorflow as tf
  
  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_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.")
  
  flags.DEFINE_integer("eval_batch_size", 8, "Total batch size for eval.")
  
  flags.DEFINE_integer("predict_batch_size", 8, "Total batch size for predict.")
  
  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 PaddingInputExample(object):
    """Fake example so the num input examples is a multiple of the batch size.
  
    When running eval/predict on the TPU, we need to pad the number of examples
    to be a multiple of the batch size, because the TPU requires a fixed batch
    size. The alternative is to drop the last batch, which is bad because it means
    the entire output data won't be generated.
  
    We use this class instead of `None` because treating `None` as padding
    battches could cause silent errors.
    """
  
  
  class InputFeatures(object):
    """A single set of features of data."""
  
    def __init__(self,
                 input_ids,
                 input_mask,
                 segment_ids,
                 label_id,
                 is_real_example=True):
      self.input_ids = input_ids
      self.input_mask = input_mask
      self.segment_ids = segment_ids
      self.label_id = label_id
      self.is_real_example = is_real_example
  
  
  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()
  
    def get_test_examples(self, data_dir):
      """Gets a collection of `InputExample`s for prediction."""
      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
  
  
  class XnliProcessor(DataProcessor):
    """Processor for the XNLI data set."""
  
    def __init__(self):
      self.language = "zh"
  
    def get_train_examples(self, data_dir):
      """See base class."""
      lines = self._read_tsv(
          os.path.join(data_dir, "multinli",
                       "multinli.train.%s.tsv" % self.language))
      examples = []
      for (i, line) in enumerate(lines):
        if i == 0:
          continue
        guid = "train-%d" % (i)
        text_a = tokenization.convert_to_unicode(line[0])
        text_b = tokenization.convert_to_unicode(line[1])
        label = tokenization.convert_to_unicode(line[2])
        if label == tokenization.convert_to_unicode("contradictory"):
          label = tokenization.convert_to_unicode("contradiction")
        examples.append(
            InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
      return examples
  
    def get_dev_examples(self, data_dir):
      """See base class."""
      lines = self._read_tsv(os.path.join(data_dir, "xnli.dev.tsv"))
      examples = []
      for (i, line) in enumerate(lines):
        if i == 0:
          continue
        guid = "dev-%d" % (i)
        language = tokenization.convert_to_unicode(line[0])
        if language != tokenization.convert_to_unicode(self.language):
          continue
        text_a = tokenization.convert_to_unicode(line[6])
        text_b = tokenization.convert_to_unicode(line[7])
        label = tokenization.convert_to_unicode(line[1])
        examples.append(
            InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
      return examples
  
    def get_labels(self):
      """See base class."""
      return ["contradiction", "entailment", "neutral"]
  
  
  class MnliProcessor(DataProcessor):
    """Processor for the MultiNLI data set (GLUE 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_matched.tsv")),
          "dev_matched")
  
    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")
  
    def get_labels(self):
      """See base class."""
      return ["contradiction", "entailment", "neutral"]
  
    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[8])
        text_b = tokenization.convert_to_unicode(line[9])
        if set_type == "test":
          label = "contradiction"
        else:
          label = tokenization.convert_to_unicode(line[-1])
        examples.append(
            InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
      return examples
  
  
  class MrpcProcessor(DataProcessor):
    """Processor for the MRPC data set (GLUE 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")
  
    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 get_labels(self):
      """See base class."""
      return ["0", "1"]
  
    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, i)
        text_a = tokenization.convert_to_unicode(line[3])
        text_b = tokenization.convert_to_unicode(line[4])
        if set_type == "test":
          label = "0"
        else:
          label = tokenization.convert_to_unicode(line[0])
        examples.append(
            InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
      return examples
  
  
  class ColaProcessor(DataProcessor):
    """Processor for the CoLA data set (GLUE 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")
  
    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 get_labels(self):
      """See base class."""
      return ["0", "1"]
  
    def _create_examples(self, lines, set_type):
      """Creates examples for the training and dev sets."""
      examples = []
      for (i, line) in enumerate(lines):
        # Only the test set has a header
        if set_type == "test" and i == 0:
          continue
        guid = "%s-%s" % (set_type, i)
        if set_type == "test":
          text_a = tokenization.convert_to_unicode(line[1])
          label = "0"
        else:
          text_a = tokenization.convert_to_unicode(line[3])
          label = tokenization.convert_to_unicode(line[1])
        examples.append(
            InputExample(guid=guid, text_a=text_a, text_b=None, label=label))
      return examples
  
  
  def convert_single_example(ex_index, example, label_list, max_seq_length,
                             tokenizer):
    """Converts a single `InputExample` into a single `InputFeatures`."""
  
    if isinstance(example, PaddingInputExample):
      return InputFeatures(
          input_ids=[0] * max_seq_length,
          input_mask=[0] * max_seq_length,
          segment_ids=[0] * max_seq_length,
          label_id=0,
          is_real_example=False)
  
    label_map = {}
    for (i, label) in enumerate(label_list):
      label_map[label] = i
  
    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 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 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: %s (id = %d)" % (example.label, label_id))
  
    feature = InputFeatures(
        input_ids=input_ids,
        input_mask=input_mask,
        segment_ids=segment_ids,
        label_id=label_id,
        is_real_example=True)
    return feature
  
  
  def file_based_convert_examples_to_features(
      examples, label_list, max_seq_length, tokenizer, output_file):
    """Convert a set of `InputExample`s to a TFRecord file."""
  
    writer = tf.python_io.TFRecordWriter(output_file)
  
    for (ex_index, example) in enumerate(examples):
      if ex_index % 10000 == 0:
        tf.logging.info("Writing example %d of %d" % (ex_index, len(examples)))
  
      feature = convert_single_example(ex_index, example, label_list,
                                       max_seq_length, tokenizer)
  
      def create_int_feature(values):
        f = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
        return f
  
      features = collections.OrderedDict()
      features["input_ids"] = create_int_feature(feature.input_ids)
      features["input_mask"] = create_int_feature(feature.input_mask)
      features["segment_ids"] = create_int_feature(feature.segment_ids)
      features["label_ids"] = create_int_feature([feature.label_id])
      features["is_real_example"] = create_int_feature(
          [int(feature.is_real_example)])
  
      tf_example = tf.train.Example(features=tf.train.Features(feature=features))
      writer.write(tf_example.SerializeToString())
    writer.close()
  
  
  def file_based_input_fn_builder(input_file, seq_length, is_training,
                                  drop_remainder):
    """Creates an `input_fn` closure to be passed to TPUEstimator."""
  
    name_to_features = {
        "input_ids": tf.FixedLenFeature([seq_length], tf.int64),
        "input_mask": tf.FixedLenFeature([seq_length], tf.int64),
        "segment_ids": tf.FixedLenFeature([seq_length], tf.int64),
        "label_ids": tf.FixedLenFeature([], tf.int64),
        "is_real_example": tf.FixedLenFeature([], tf.int64),
    }
  
    def _decode_record(record, name_to_features):
      """Decodes a record to a TensorFlow example."""
      example = tf.parse_single_example(record, name_to_features)
  
      # tf.Example only supports tf.int64, but the TPU only supports tf.int32.
      # So cast all int64 to int32.
      for name in list(example.keys()):
        t = example[name]
        if t.dtype == tf.int64:
          t = tf.to_int32(t)
        example[name] = t
  
      return example
  
    def input_fn(params):
      """The actual input function."""
      batch_size = params["batch_size"]
  
      # For training, we want a lot of parallel reading and shuffling.
      # For eval, we want no shuffling and parallel reading doesn't matter.
      d = tf.data.TFRecordDataset(input_file)
      if is_training:
        d = d.repeat()
        d = d.shuffle(buffer_size=100)
  
      d = d.apply(
          tf.contrib.data.map_and_batch(
              lambda record: _decode_record(record, name_to_features),
              batch_size=batch_size,
              drop_remainder=drop_remainder))
  
      return d
  
    return input_fn
  
  
  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, num_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", [num_labels, hidden_size],
        initializer=tf.truncated_normal_initializer(stddev=0.02))
  
    output_bias = tf.get_variable(
        "output_bias", [num_labels], 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)
      probabilities = tf.nn.softmax(logits, axis=-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.reduce_sum(one_hot_labels * log_probs, axis=-1)
      loss = tf.reduce_mean(per_example_loss)
  
      return (loss, per_example_loss, logits, probabilities)
  
  
  def model_fn_builder(bert_config, num_labels, 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_real_example = None
      if "is_real_example" in features:
        is_real_example = tf.cast(features["is_real_example"], dtype=tf.float32)
      else:
        is_real_example = tf.ones(tf.shape(label_ids), dtype=tf.float32)
  
      is_training = (mode == tf.estimator.ModeKeys.TRAIN)
  
      (total_loss, per_example_loss, logits, probabilities) = create_model(
          bert_config, is_training, input_ids, input_mask, segment_ids, label_ids,
          num_labels, use_one_hot_embeddings)
  
      tvars = tf.trainable_variables()
      initialized_variable_names = {}
      scaffold_fn = None
      if init_checkpoint:
        (assignment_map, initialized_variable_names
        ) = modeling.get_assignment_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, is_real_example):
          predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)
          accuracy = tf.metrics.accuracy(
              labels=label_ids, predictions=predictions, weights=is_real_example)
          loss = tf.metrics.mean(values=per_example_loss, weights=is_real_example)
          return {
              "eval_accuracy": accuracy,
              "eval_loss": loss,
          }
  
        eval_metrics = (metric_fn,
                        [per_example_loss, label_ids, logits, is_real_example])
        output_spec = tf.contrib.tpu.TPUEstimatorSpec(
            mode=mode,
            loss=total_loss,
            eval_metrics=eval_metrics,
            scaffold_fn=scaffold_fn)
      else:
        output_spec = tf.contrib.tpu.TPUEstimatorSpec(
            mode=mode,
            predictions={"probabilities": probabilities},
            scaffold_fn=scaffold_fn)
      return output_spec
  
    return model_fn
  
  
  # This function is not used by this file but is still used by the Colab and
  # people who depend on it.
  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], dtype=tf.int32),
      })
  
      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
  
  
  # This function is not used by this file but is still used by the Colab and
  # people who depend on it.
  def convert_examples_to_features(examples, label_list, max_seq_length,
                                   tokenizer):
    """Convert a set of `InputExample`s to a list of `InputFeatures`."""
  
    features = []
    for (ex_index, example) in enumerate(examples):
      if ex_index % 10000 == 0:
        tf.logging.info("Writing example %d of %d" % (ex_index, len(examples)))
  
      feature = convert_single_example(ex_index, example, label_list,
                                       max_seq_length, tokenizer)
  
      features.append(feature)
    return features
  
  
  def main(_):
    tf.logging.set_verbosity(tf.logging.INFO)
  
    processors = {
        "cola": ColaProcessor,
        "mnli": MnliProcessor,
        "mrpc": MrpcProcessor,
        "xnli": XnliProcessor,
    }
  
    tokenization.validate_case_matches_checkpoint(FLAGS.do_lower_case,
                                                  FLAGS.init_checkpoint)
  
    if not FLAGS.do_train and not FLAGS.do_eval and not FLAGS.do_predict:
      raise ValueError(
          "At least one of `do_train`, `do_eval` or `do_predict' 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()
  
    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,
        num_labels=len(label_list),
        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,
        predict_batch_size=FLAGS.predict_batch_size)
  
    if FLAGS.do_train:
      train_file = os.path.join(FLAGS.output_dir, "train.tf_record")
      file_based_convert_examples_to_features(
          train_examples, label_list, FLAGS.max_seq_length, tokenizer, train_file)
      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 = file_based_input_fn_builder(
          input_file=train_file,
          seq_length=FLAGS.max_seq_length,
          is_training=True,
          drop_remainder=True)
      estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)
  
    if FLAGS.do_eval:
      eval_examples = processor.get_dev_examples(FLAGS.data_dir)
      num_actual_eval_examples = len(eval_examples)
      if FLAGS.use_tpu:
        # TPU requires a fixed batch size for all batches, therefore the number
        # of examples must be a multiple of the batch size, or else examples
        # will get dropped. So we pad with fake examples which are ignored
        # later on. These do NOT count towards the metric (all tf.metrics
        # support a per-instance weight, and these get a weight of 0.0).
        while len(eval_examples) % FLAGS.eval_batch_size != 0:
          eval_examples.append(PaddingInputExample())
  
      eval_file = os.path.join(FLAGS.output_dir, "eval.tf_record")
      file_based_convert_examples_to_features(
          eval_examples, label_list, FLAGS.max_seq_length, tokenizer, eval_file)
  
      tf.logging.info("***** Running evaluation *****")
      tf.logging.info("  Num examples = %d (%d actual, %d padding)",
                      len(eval_examples), num_actual_eval_examples,
                      len(eval_examples) - num_actual_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:
        assert len(eval_examples) % FLAGS.eval_batch_size == 0
        eval_steps = int(len(eval_examples) // FLAGS.eval_batch_size)
  
      eval_drop_remainder = True if FLAGS.use_tpu else False
      eval_input_fn = file_based_input_fn_builder(
          input_file=eval_file,
          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_predict:
      predict_examples = processor.get_test_examples(FLAGS.data_dir)
      num_actual_predict_examples = len(predict_examples)
      if FLAGS.use_tpu:
        # TPU requires a fixed batch size for all batches, therefore the number
        # of examples must be a multiple of the batch size, or else examples
        # will get dropped. So we pad with fake examples which are ignored
        # later on.
        while len(predict_examples) % FLAGS.predict_batch_size != 0:
          predict_examples.append(PaddingInputExample())
  
      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)
  
      tf.logging.info("***** Running prediction*****")
      tf.logging.info("  Num examples = %d (%d actual, %d padding)",
                      len(predict_examples), num_actual_predict_examples,
                      len(predict_examples) - num_actual_predict_examples)
      tf.logging.info("  Batch size = %d", FLAGS.predict_batch_size)
  
      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)
  
      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:
        num_written_lines = 0
        tf.logging.info("***** Predict results *****")
        for (i, prediction) in enumerate(result):
          probabilities = prediction["probabilities"]
          if i >= num_actual_predict_examples:
            break
          output_line = "\t".join(
              str(class_probability)
              for class_probability in probabilities) + "\n"
          writer.write(output_line)
          num_written_lines += 1
      assert num_written_lines == num_actual_predict_examples
  
  
  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()
  
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