text: # 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 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()
  