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 poros.bert_model import tokenization
  import tensorflow as tf
  
  
  FLAGS = collections.namedtuple("FLAGS", ["input_file", "output_file", "vocab_file", "do_lower_case", "max_seq_length",
                                           "max_predictions_per_seq", "random_seed", "dupe_factor", "masked_lm_prob",
                                           "short_seq_prob"])
  
  """
  flags.DEFINE_string("input_file", None,
                      "Input raw text file (or comma-separated list of files).")
  """
  FLAGS.input_file = None
  
  """
  flags.DEFINE_string(
      "output_file", None,
      "Output TF example file (or comma-separated list of files).")
  """
  FLAGS.output_file = None
  
  """
  flags.DEFINE_string("vocab_file", None,
                      "The vocabulary file that the BERT model was trained on.")
  """
  FLAGS.vocab_file = None
  """
  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.do_lower_case = True
  
  #flags.DEFINE_integer("max_seq_length", 128, "Maximum sequence length.")
  FLAGS.max_seq_length = 128
  
  """
  flags.DEFINE_integer("max_predictions_per_seq", 20,
                       "Maximum number of masked LM predictions per sequence.")
  """
  FLAGS.max_predictions_per_seq = 20
  """
  flags.DEFINE_integer("random_seed", 12345, "Random seed for data generation.")
  """
  FLAGS.random_seed = 12345
  
  """
  flags.DEFINE_integer(
      "dupe_factor", 10,
      "Number of times to duplicate the input data (with different masks).")
  """
  FLAGS.dupe_factor = 10
  
  """
  flags.DEFINE_float("masked_lm_prob", 0.15, "Masked LM probability.")
  """
  FLAGS.masked_lm_prob = 0.15
  
  """
  flags.DEFINE_float(
      "short_seq_prob", 0.1,
      "Probability of creating sequences which are shorter than the "
      "maximum length.")
  """
  FLAGS.short_seq_prob = 0.1
  
  
  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.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.compat.v1.logging.info("*** Example ***")
              tf.compat.v1.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.compat.v1.logging.info(
                      "%s: %s" % (feature_name, " ".join([str(x) for x in values])))
  
      for writer in writers:
          writer.close()
  
      tf.compat.v1.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, constraint_mode=False):
      """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.io.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, constraint_mode))
  
      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, constraint_mode=False):
      """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(20):
                          random_document_index = rng.randint(0, len(all_documents) - 1)
                          if not constraint_mode:
                              if random_document_index != document_index:
                                 break
                          else:
                              if all_documents[random_document_index] != all_documents[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 create_data(input_file, output_file, vocab_file, do_lower_case=True, random_seed=12345, max_seq_length=128,
                  max_predictions_per_seq=20, dupe_factor=10, masked_lm_prob=0.15, short_seq_prob=0.1, constraint_mode=False):
      """
      :param input_file: Input raw text file (or comma-separated list of files).
      :param output_file: all data which has tfrecord format will be written into this file
      :param vocab_file:
      :param do_lower_case:
      :param random_seed
      :param max_seq_length
      :param max_predictions_per_seq
      :param dupe_factor
      :param masked_lm_prob
      :param short_seq_prob
      :param constraint_mode discriminant if two sentences is the same by compare the text(if true) or the text_id(if false)
      :return:
      """
      tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO)
      FLAGS.max_seq_length = max_seq_length
      FLAGS.random_seed = random_seed
      FLAGS.max_predictions_per_seq = max_predictions_per_seq
      FLAGS.dupe_factor = dupe_factor
      FLAGS.masked_lm_prob = masked_lm_prob
      FLAGS.short_seq_prob = short_seq_prob
  
      tokenizer = tokenization.FullTokenizer(vocab_file=vocab_file, do_lower_case=do_lower_case)
  
      input_files = []
      for input_pattern in input_file.split(","):
          input_files.extend(tf.io.gfile.glob(input_pattern))
  
      tf.compat.v1.logging.info("*** Reading from input files ***")
      for input_file in input_files:
          tf.compat.v1.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, constraint_mode)
  
      output_files = output_file.split(",")
      tf.compat.v1.logging.info("*** Writing to output files ***")
      for output_file in output_files:
          tf.compat.v1.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__":
      tf.io.gfile.makedirs("./data")
      create_data(input_file="./test_data/sample_text.txt", output_file="./data/output", vocab_file="./test_data/vocab.txt", constraint_mode=True)
  