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.
  """Create masked LM/next sentence masked_lm TF examples for BERT."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import collections
  import random
  
  from bert_attn_viz import tokenization
  import tensorflow as tf
  
  flags = tf.flags
  
  FLAGS = flags.FLAGS
  
  flags.DEFINE_string("input_file", None,
                      "Input raw text file (or comma-separated list of files).")
  
  flags.DEFINE_string(
      "output_file", None,
      "Output TF example file (or comma-separated list of files).")
  
  flags.DEFINE_string("vocab_file", None,
                      "The vocabulary file that the BERT model was trained on.")
  
  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, "Maximum sequence length.")
  
  flags.DEFINE_integer("max_predictions_per_seq", 20,
                       "Maximum number of masked LM predictions per sequence.")
  
  flags.DEFINE_integer("random_seed", 12345, "Random seed for data generation.")
  
  flags.DEFINE_integer(
      "dupe_factor", 10,
      "Number of times to duplicate the input data (with different masks).")
  
  flags.DEFINE_float("masked_lm_prob", 0.15, "Masked LM probability.")
  
  flags.DEFINE_float(
      "short_seq_prob", 0.1,
      "Probability of creating sequences which are shorter than the "
      "maximum length.")
  
  
  class TrainingInstance(object):
    """A single training instance (sentence pair)."""
  
    def __init__(self, tokens, segment_ids, masked_lm_positions, masked_lm_labels,
                 is_random_next):
      self.tokens = tokens
      self.segment_ids = segment_ids
      self.is_random_next = is_random_next
      self.masked_lm_positions = masked_lm_positions
      self.masked_lm_labels = masked_lm_labels
  
    def __str__(self):
      s = ""
      s += "tokens: %s\n" % (" ".join(
          [tokenization.printable_text(x) for x in self.tokens]))
      s += "segment_ids: %s\n" % (" ".join([str(x) for x in self.segment_ids]))
      s += "is_random_next: %s\n" % self.is_random_next
      s += "masked_lm_positions: %s\n" % (" ".join(
          [str(x) for x in self.masked_lm_positions]))
      s += "masked_lm_labels: %s\n" % (" ".join(
          [tokenization.printable_text(x) for x in self.masked_lm_labels]))
      s += "\n"
      return s
  
    def __repr__(self):
      return self.__str__()
  
  
  def write_instance_to_example_files(instances, tokenizer, max_seq_length,
                                      max_predictions_per_seq, output_files):
    """Create TF example files from `TrainingInstance`s."""
    writers = []
    for output_file in output_files:
      writers.append(tf.python_io.TFRecordWriter(output_file))
  
    writer_index = 0
  
    total_written = 0
    for (inst_index, instance) in enumerate(instances):
      input_ids = tokenizer.convert_tokens_to_ids(instance.tokens)
      input_mask = [1] * len(input_ids)
      segment_ids = list(instance.segment_ids)
      assert len(input_ids) <= max_seq_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
  
      masked_lm_positions = list(instance.masked_lm_positions)
      masked_lm_ids = tokenizer.convert_tokens_to_ids(instance.masked_lm_labels)
      masked_lm_weights = [1.0] * len(masked_lm_ids)
  
      while len(masked_lm_positions) < max_predictions_per_seq:
        masked_lm_positions.append(0)
        masked_lm_ids.append(0)
        masked_lm_weights.append(0.0)
  
      next_sentence_label = 1 if instance.is_random_next else 0
  
      features = collections.OrderedDict()
      features["input_ids"] = create_int_feature(input_ids)
      features["input_mask"] = create_int_feature(input_mask)
      features["segment_ids"] = create_int_feature(segment_ids)
      features["masked_lm_positions"] = create_int_feature(masked_lm_positions)
      features["masked_lm_ids"] = create_int_feature(masked_lm_ids)
      features["masked_lm_weights"] = create_float_feature(masked_lm_weights)
      features["next_sentence_labels"] = create_int_feature([next_sentence_label])
  
      tf_example = tf.train.Example(features=tf.train.Features(feature=features))
  
      writers[writer_index].write(tf_example.SerializeToString())
      writer_index = (writer_index + 1) % len(writers)
  
      total_written += 1
  
      if inst_index < 20:
        tf.logging.info("*** Example ***")
        tf.logging.info("tokens: %s" % " ".join(
            [tokenization.printable_text(x) for x in instance.tokens]))
  
        for feature_name in features.keys():
          feature = features[feature_name]
          values = []
          if feature.int64_list.value:
            values = feature.int64_list.value
          elif feature.float_list.value:
            values = feature.float_list.value
          tf.logging.info(
              "%s: %s" % (feature_name, " ".join([str(x) for x in values])))
  
    for writer in writers:
      writer.close()
  
    tf.logging.info("Wrote %d total instances", total_written)
  
  
  def create_int_feature(values):
    feature = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
    return feature
  
  
  def create_float_feature(values):
    feature = tf.train.Feature(float_list=tf.train.FloatList(value=list(values)))
    return feature
  
  
  def create_training_instances(input_files, tokenizer, max_seq_length,
                                dupe_factor, short_seq_prob, masked_lm_prob,
                                max_predictions_per_seq, rng):
    """Create `TrainingInstance`s from raw text."""
    all_documents = [[]]
  
    # Input file format:
    # (1) One sentence per line. These should ideally be actual sentences, not
    # entire paragraphs or arbitrary spans of text. (Because we use the
    # sentence boundaries for the "next sentence prediction" task).
    # (2) Blank lines between documents. Document boundaries are needed so
    # that the "next sentence prediction" task doesn't span between documents.
    for input_file in input_files:
      with tf.gfile.GFile(input_file, "r") as reader:
        while True:
          line = tokenization.convert_to_unicode(reader.readline())
          if not line:
            break
          line = line.strip()
  
          # Empty lines are used as document delimiters
          if not line:
            all_documents.append([])
          tokens = tokenizer.tokenize(line)
          if tokens:
            all_documents[-1].append(tokens)
  
    # Remove empty documents
    all_documents = [x for x in all_documents if x]
    rng.shuffle(all_documents)
  
    vocab_words = list(tokenizer.vocab.keys())
    instances = []
    for _ in range(dupe_factor):
      for document_index in range(len(all_documents)):
        instances.extend(
            create_instances_from_document(
                all_documents, document_index, max_seq_length, short_seq_prob,
                masked_lm_prob, max_predictions_per_seq, vocab_words, rng))
  
    rng.shuffle(instances)
    return instances
  
  
  def create_instances_from_document(
      all_documents, document_index, max_seq_length, short_seq_prob,
      masked_lm_prob, max_predictions_per_seq, vocab_words, rng):
    """Creates `TrainingInstance`s for a single document."""
    document = all_documents[document_index]
  
    # Account for [CLS], [SEP], [SEP]
    max_num_tokens = max_seq_length - 3
  
    # We *usually* want to fill up the entire sequence since we are padding
    # to `max_seq_length` anyways, so short sequences are generally wasted
    # computation. However, we *sometimes*
    # (i.e., short_seq_prob == 0.1 == 10% of the time) want to use shorter
    # sequences to minimize the mismatch between pre-training and fine-tuning.
    # The `target_seq_length` is just a rough target however, whereas
    # `max_seq_length` is a hard limit.
    target_seq_length = max_num_tokens
    if rng.random() < short_seq_prob:
      target_seq_length = rng.randint(2, max_num_tokens)
  
    # We DON'T just concatenate all of the tokens from a document into a long
    # sequence and choose an arbitrary split point because this would make the
    # next sentence prediction task too easy. Instead, we split the input into
    # segments "A" and "B" based on the actual "sentences" provided by the user
    # input.
    instances = []
    current_chunk = []
    current_length = 0
    i = 0
    while i < len(document):
      segment = document[i]
      current_chunk.append(segment)
      current_length += len(segment)
      if i == len(document) - 1 or current_length >= target_seq_length:
        if current_chunk:
          # `a_end` is how many segments from `current_chunk` go into the `A`
          # (first) sentence.
          a_end = 1
          if len(current_chunk) >= 2:
            a_end = rng.randint(1, len(current_chunk) - 1)
  
          tokens_a = []
          for j in range(a_end):
            tokens_a.extend(current_chunk[j])
  
          tokens_b = []
          # Random next
          is_random_next = False
          if len(current_chunk) == 1 or rng.random() < 0.5:
            is_random_next = True
            target_b_length = target_seq_length - len(tokens_a)
  
            # This should rarely go for more than one iteration for large
            # corpora. However, just to be careful, we try to make sure that
            # the random document is not the same as the document
            # we're processing.
            for _ in range(10):
              random_document_index = rng.randint(0, len(all_documents) - 1)
              if random_document_index != document_index:
                break
  
            random_document = all_documents[random_document_index]
            random_start = rng.randint(0, len(random_document) - 1)
            for j in range(random_start, len(random_document)):
              tokens_b.extend(random_document[j])
              if len(tokens_b) >= target_b_length:
                break
            # We didn't actually use these segments so we "put them back" so
            # they don't go to waste.
            num_unused_segments = len(current_chunk) - a_end
            i -= num_unused_segments
          # Actual next
          else:
            is_random_next = False
            for j in range(a_end, len(current_chunk)):
              tokens_b.extend(current_chunk[j])
          truncate_seq_pair(tokens_a, tokens_b, max_num_tokens, rng)
  
          assert len(tokens_a) >= 1
          assert len(tokens_b) >= 1
  
          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)
  
          for token in tokens_b:
            tokens.append(token)
            segment_ids.append(1)
          tokens.append("[SEP]")
          segment_ids.append(1)
  
          (tokens, masked_lm_positions,
           masked_lm_labels) = create_masked_lm_predictions(
               tokens, masked_lm_prob, max_predictions_per_seq, vocab_words, rng)
          instance = TrainingInstance(
              tokens=tokens,
              segment_ids=segment_ids,
              is_random_next=is_random_next,
              masked_lm_positions=masked_lm_positions,
              masked_lm_labels=masked_lm_labels)
          instances.append(instance)
        current_chunk = []
        current_length = 0
      i += 1
  
    return instances
  
  
  def create_masked_lm_predictions(tokens, masked_lm_prob,
                                   max_predictions_per_seq, vocab_words, rng):
    """Creates the predictions for the masked LM objective."""
  
    cand_indexes = []
    for (i, token) in enumerate(tokens):
      if token == "[CLS]" or token == "[SEP]":
        continue
      cand_indexes.append(i)
  
    rng.shuffle(cand_indexes)
  
    output_tokens = list(tokens)
  
    masked_lm = collections.namedtuple("masked_lm", ["index", "label"])  # pylint: disable=invalid-name
  
    num_to_predict = min(max_predictions_per_seq,
                         max(1, int(round(len(tokens) * masked_lm_prob))))
  
    masked_lms = []
    covered_indexes = set()
    for index in cand_indexes:
      if len(masked_lms) >= num_to_predict:
        break
      if index in covered_indexes:
        continue
      covered_indexes.add(index)
  
      masked_token = None
      # 80% of the time, replace with [MASK]
      if rng.random() < 0.8:
        masked_token = "[MASK]"
      else:
        # 10% of the time, keep original
        if rng.random() < 0.5:
          masked_token = tokens[index]
        # 10% of the time, replace with random word
        else:
          masked_token = vocab_words[rng.randint(0, len(vocab_words) - 1)]
  
      output_tokens[index] = masked_token
  
      masked_lms.append(masked_lm(index=index, label=tokens[index]))
  
    masked_lms = sorted(masked_lms, key=lambda x: x.index)
  
    masked_lm_positions = []
    masked_lm_labels = []
    for p in masked_lms:
      masked_lm_positions.append(p.index)
      masked_lm_labels.append(p.label)
  
    return (output_tokens, masked_lm_positions, masked_lm_labels)
  
  
  def truncate_seq_pair(tokens_a, tokens_b, max_num_tokens, rng):
    """Truncates a pair of sequences to a maximum sequence length."""
    while True:
      total_length = len(tokens_a) + len(tokens_b)
      if total_length <= max_num_tokens:
        break
  
      trunc_tokens = tokens_a if len(tokens_a) > len(tokens_b) else tokens_b
      assert len(trunc_tokens) >= 1
  
      # We want to sometimes truncate from the front and sometimes from the
      # back to add more randomness and avoid biases.
      if rng.random() < 0.5:
        del trunc_tokens[0]
      else:
        trunc_tokens.pop()
  
  
  def main(_):
    tf.logging.set_verbosity(tf.logging.INFO)
  
    tokenizer = tokenization.FullTokenizer(
        vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case)
  
    input_files = []
    for input_pattern in FLAGS.input_file.split(","):
      input_files.extend(tf.gfile.Glob(input_pattern))
  
    tf.logging.info("*** Reading from input files ***")
    for input_file in input_files:
      tf.logging.info("  %s", input_file)
  
    rng = random.Random(FLAGS.random_seed)
    instances = create_training_instances(
        input_files, tokenizer, FLAGS.max_seq_length, FLAGS.dupe_factor,
        FLAGS.short_seq_prob, FLAGS.masked_lm_prob, FLAGS.max_predictions_per_seq,
        rng)
  
    output_files = FLAGS.output_file.split(",")
    tf.logging.info("*** Writing to output files ***")
    for output_file in output_files:
      tf.logging.info("  %s", output_file)
  
    write_instance_to_example_files(instances, tokenizer, FLAGS.max_seq_length,
                                    FLAGS.max_predictions_per_seq, output_files)
  
  
  if __name__ == "__main__":
    flags.mark_flag_as_required("input_file")
    flags.mark_flag_as_required("output_file")
    flags.mark_flag_as_required("vocab_file")
    tf.app.run()
  