code: """
  This is the Data Loading Pipeline for Sentence Classifier Task from
  https://github.com/google-research/bert/blob/master/run_classifier.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.
  
  import os
  import csv
  import collections
  import sys
  sys.path.append(os.path.dirname(__file__))
  import tokenization
  import tensorflow as tf
  
  class InputExample():
      """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():
      """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()
  
      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 SSTProcessor(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 = []
          if set_type == 'train' or set_type == 'dev':
              for (i, line) in enumerate(lines):
                  if i == 0:
                      continue
                  guid = "%s-%s" % (set_type, i)
                  text_a = tokenization.convert_to_unicode(line[0])
                  # Single sentence classification, text_b doesn't exist
                  text_b = None
                  label = tokenization.convert_to_unicode(line[1])
                  examples.append(InputExample(guid=guid, text_a=text_a,
                                               text_b=text_b, label=label))
          if set_type == 'test':
              for (i, line) in enumerate(lines):
                  if i == 0:
                      continue
                  guid = "%s-%s" % (set_type, i)
                  text_a = tokenization.convert_to_unicode(line[1])
                  # Single sentence classification, text_b doesn't exist
                  text_b = None
                  label = '0' # arbitrary set as 0
                  examples.append(InputExample(guid=guid, text_a=text_a,
                                               text_b=text_b, label=label))
          return examples
  
  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`."""
      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 rule is:
      # (a) For sequence pairs:
      #   tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
      #    segment_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]
      #   sigment_ids: 0 0 0 0 0 0 0
      #
      # Where "segment_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]
  
      # here we disable the verbose printing of the data
      if ex_index < 0:
          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_ids length: %d" % len(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)
      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):
  
          feature = convert_single_example(ex_index, example, label_list,
                                           max_seq_length, tokenizer)
  
          def create_int_feature(values):
              return tf.train.Feature(
                  int64_list=tf.train.Int64List(value=list(values)))
  
          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])
  
          tf_example = tf.train.Example(
              features=tf.train.Features(feature=features))
          writer.write(tf_example.SerializeToString())
  
  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 prepare_TFRecord_data(processor, tokenizer,
                            data_dir, max_seq_length, output_dir):
      """
      Args:
          processor: Data Preprocessor, which must have get_lables,
              get_train/dev/test/examples methods defined.
          tokenizer: The Sentence Tokenizer. Generally should be
              SentencePiece Model.
          data_dir: The input data directory.
          max_seq_length: Max sequence length.
          batch_size: mini-batch size.
          model: `train`, `eval` or `test`.
          output_dir: The directory to save the TFRecord in.
      """
      label_list = processor.get_labels()
  
      train_examples = processor.get_train_examples(data_dir)
      train_file = os.path.join(output_dir, "train.tf_record")
      file_based_convert_examples_to_features(
          train_examples, label_list, max_seq_length,
          tokenizer, train_file)
  
      eval_examples = processor.get_dev_examples(data_dir)
      eval_file = os.path.join(output_dir, "eval.tf_record")
      file_based_convert_examples_to_features(
          eval_examples, label_list,
          max_seq_length, tokenizer, eval_file)
  
      test_examples = processor.get_test_examples(data_dir)
      test_file = os.path.join(output_dir, "predict.tf_record")
      file_based_convert_examples_to_features(
          test_examples, label_list,
          max_seq_length, tokenizer, test_file)
  
apis: tensorflow.train.Features tensorflow.gfile.Open tensorflow.logging.info tensorflow.python_io.TFRecordWriter