Delete create_pretraining_data.py

create_pretraining_data.py

@@ -1,718 +0,0 @@
-# Copyright 2024 The TensorFlow Authors. All Rights Reserved.
-#
-# 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."""
-
-import collections
-import itertools
-import random
-
-# Import libraries
-
-from absl import app
-from absl import flags
-from absl import logging
-import tensorflow as tf, tf_keras
-
-from official.nlp.tools import tokenization
-
-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_enum(
-    "tokenization",
-    "WordPiece",
-    ["WordPiece", "SentencePiece"],
-    "Specifies the tokenizer implementation, i.e., whether to use WordPiece "
-    "or SentencePiece tokenizer. Canonical BERT uses WordPiece tokenizer, "
-    "while ALBERT uses SentencePiece tokenizer.",
-)
-
-flags.DEFINE_string(
-    "vocab_file",
-    None,
-    "For WordPiece tokenization, the vocabulary file of the tokenizer.",
-)
-
-flags.DEFINE_string(
-    "sp_model_file",
-    "",
-    "For SentencePiece tokenization, the path to the model of the tokenizer.",
-)
-
-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_bool(
-    "do_whole_word_mask",
-    False,
-    "Whether to use whole word masking rather than per-token masking.",
-)
-
-flags.DEFINE_integer(
-    "max_ngram_size", None,
-    "Mask contiguous whole words (n-grams) of up to `max_ngram_size` using a "
-    "weighting scheme to favor shorter n-grams. "
-    "Note: `--do_whole_word_mask=True` must also be set when n-gram masking.")
-
-flags.DEFINE_bool(
-    "gzip_compress", False,
-    "Whether to use `GZIP` compress option to get compressed TFRecord files.")
-
-flags.DEFINE_bool(
-    "use_v2_feature_names", False,
-    "Whether to use the feature names consistent with the 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,
-                                    gzip_compress, use_v2_feature_names):
-  """Creates TF example files from `TrainingInstance`s."""
-  writers = []
-  for output_file in output_files:
-    writers.append(
-        tf.io.TFRecordWriter(
-            output_file, options="GZIP" if gzip_compress else ""))
-
-  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()
-    if use_v2_feature_names:
-      features["input_word_ids"] = create_int_feature(input_ids)
-      features["input_type_ids"] = create_int_feature(segment_ids)
-    else:
-      features["input_ids"] = create_int_feature(input_ids)
-      features["segment_ids"] = create_int_feature(segment_ids)
-
-    features["input_mask"] = create_int_feature(input_mask)
-    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:
-      logging.info("*** Example ***")
-      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
-        logging.info("%s: %s", feature_name, " ".join([str(x) for x in values]))
-
-  for writer in writers:
-    writer.close()
-
-  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,
-    processor_text_fn,
-    max_seq_length,
-    dupe_factor,
-    short_seq_prob,
-    masked_lm_prob,
-    max_predictions_per_seq,
-    rng,
-    do_whole_word_mask=False,
-    max_ngram_size=None,
-):
-  """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, "rb") as reader:
-      for line in reader:
-        line = processor_text_fn(line)
-
-        # 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,
-              do_whole_word_mask, max_ngram_size))
-
-  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,
-    do_whole_word_mask=False,
-    max_ngram_size=None):
-  """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,
-             do_whole_word_mask, max_ngram_size)
-        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
-
-
-MaskedLmInstance = collections.namedtuple("MaskedLmInstance",
-                                          ["index", "label"])
-
-# A _Gram is a [half-open) interval of token indices which form a word.
-# E.g.,
-#   words:  ["The", "doghouse"]
-#   tokens: ["The", "dog", "##house"]
-#   grams:  [(0,1), (1,3)]
-_Gram = collections.namedtuple("_Gram", ["begin", "end"])
-
-
-def _window(iterable, size):
-  """Helper to create a sliding window iterator with a given size.
-
-  E.g.,
-    input = [1, 2, 3, 4]
-    _window(input, 1) => [1], [2], [3], [4]
-    _window(input, 2) => [1, 2], [2, 3], [3, 4]
-    _window(input, 3) => [1, 2, 3], [2, 3, 4]
-    _window(input, 4) => [1, 2, 3, 4]
-    _window(input, 5) => None
-
-  Args:
-    iterable: elements to iterate over.
-    size: size of the window.
-
-  Yields:
-    Elements of `iterable` batched into a sliding window of length `size`.
-  """
-  i = iter(iterable)
-  window = []
-  try:
-    for e in range(0, size):
-      window.append(next(i))
-    yield window
-  except StopIteration:
-    # handle the case where iterable's length is less than the window size.
-    return
-  for e in i:
-    window = window[1:] + [e]
-    yield window
-
-
-def _contiguous(sorted_grams):
-  """Test whether a sequence of grams is contiguous.
-
-  Args:
-    sorted_grams: _Grams which are sorted in increasing order.
-  Returns:
-    True if `sorted_grams` are touching each other.
-
-  E.g.,
-    _contiguous([(1, 4), (4, 5), (5, 10)]) == True
-    _contiguous([(1, 2), (4, 5)]) == False
-  """
-  for a, b in _window(sorted_grams, 2):
-    if a.end != b.begin:
-      return False
-  return True
-
-
-def _masking_ngrams(grams, max_ngram_size, max_masked_tokens, rng):
-  """Create a list of masking {1, ..., n}-grams from a list of one-grams.
-
-  This is an extension of 'whole word masking' to mask multiple, contiguous
-  words such as (e.g., "the red boat").
-
-  Each input gram represents the token indices of a single word,
-     words:  ["the", "red", "boat"]
-     tokens: ["the", "red", "boa", "##t"]
-     grams:  [(0,1), (1,2), (2,4)]
-
-  For a `max_ngram_size` of three, possible outputs masks include:
-    1-grams: (0,1), (1,2), (2,4)
-    2-grams: (0,2), (1,4)
-    3-grams; (0,4)
-
-  Output masks will not overlap and contain less than `max_masked_tokens` total
-  tokens.  E.g., for the example above with `max_masked_tokens` as three,
-  valid outputs are,
-       [(0,1), (1,2)]  # "the", "red" covering two tokens
-       [(1,2), (2,4)]  # "red", "boa", "##t" covering three tokens
-
-  The length of the selected n-gram follows a zipf weighting to
-  favor shorter n-gram sizes (weight(1)=1, weight(2)=1/2, weight(3)=1/3, ...).
-
-  Args:
-    grams: List of one-grams.
-    max_ngram_size: Maximum number of contiguous one-grams combined to create
-      an n-gram.
-    max_masked_tokens: Maximum total number of tokens to be masked.
-    rng: `random.Random` generator.
-
-  Returns:
-    A list of n-grams to be used as masks.
-  """
-  if not grams:
-    return None
-
-  grams = sorted(grams)
-  num_tokens = grams[-1].end
-
-  # Ensure our grams are valid (i.e., they don't overlap).
-  for a, b in _window(grams, 2):
-    if a.end > b.begin:
-      raise ValueError("overlapping grams: {}".format(grams))
-
-  # Build map from n-gram length to list of n-grams.
-  ngrams = {i: [] for i in range(1, max_ngram_size+1)}
-  for gram_size in range(1, max_ngram_size+1):
-    for g in _window(grams, gram_size):
-      if _contiguous(g):
-        # Add an n-gram which spans these one-grams.
-        ngrams[gram_size].append(_Gram(g[0].begin, g[-1].end))
-
-  # Shuffle each list of n-grams.
-  for v in ngrams.values():
-    rng.shuffle(v)
-
-  # Create the weighting for n-gram length selection.
-  # Stored cumulatively for `random.choices` below.
-  cummulative_weights = list(
-      itertools.accumulate([1./n for n in range(1, max_ngram_size+1)]))
-
-  output_ngrams = []
-  # Keep a bitmask of which tokens have been masked.
-  masked_tokens = [False] * num_tokens
-  # Loop until we have enough masked tokens or there are no more candidate
-  # n-grams of any length.
-  # Each code path should ensure one or more elements from `ngrams` are removed
-  # to guarantee this loop terminates.
-  while (sum(masked_tokens) < max_masked_tokens and
-         sum(len(s) for s in ngrams.values())):
-    # Pick an n-gram size based on our weights.
-    sz = random.choices(range(1, max_ngram_size+1),
-                        cum_weights=cummulative_weights)[0]
-
-    # Ensure this size doesn't result in too many masked tokens.
-    # E.g., a two-gram contains _at least_ two tokens.
-    if sum(masked_tokens) + sz > max_masked_tokens:
-      # All n-grams of this length are too long and can be removed from
-      # consideration.
-      ngrams[sz].clear()
-      continue
-
-    # All of the n-grams of this size have been used.
-    if not ngrams[sz]:
-      continue
-
-    # Choose a random n-gram of the given size.
-    gram = ngrams[sz].pop()
-    num_gram_tokens = gram.end-gram.begin
-
-    # Check if this would add too many tokens.
-    if num_gram_tokens + sum(masked_tokens) > max_masked_tokens:
-      continue
-
-    # Check if any of the tokens in this gram have already been masked.
-    if sum(masked_tokens[gram.begin:gram.end]):
-      continue
-
-    # Found a usable n-gram!  Mark its tokens as masked and add it to return.
-    masked_tokens[gram.begin:gram.end] = [True] * (gram.end-gram.begin)
-    output_ngrams.append(gram)
-  return output_ngrams
-
-
-def _tokens_to_grams(tokens):
-  """Reconstitue grams (words) from `tokens`.
-
-  E.g.,
-     tokens: ['[CLS]', 'That', 'lit', '##tle', 'blue', 'tru', '##ck', '[SEP]']
-      grams: [          [1,2), [2,         4),  [4,5) , [5,       6)]
-
-  Args:
-    tokens: list of tokens (word pieces or sentence pieces).
-
-  Returns:
-    List of _Grams representing spans of whole words
-    (without "[CLS]" and "[SEP]").
-  """
-  grams = []
-  gram_start_pos = None
-  for i, token in enumerate(tokens):
-    if gram_start_pos is not None and token.startswith("##"):
-      continue
-    if gram_start_pos is not None:
-      grams.append(_Gram(gram_start_pos, i))
-    if token not in ["[CLS]", "[SEP]"]:
-      gram_start_pos = i
-    else:
-      gram_start_pos = None
-  if gram_start_pos is not None:
-    grams.append(_Gram(gram_start_pos, len(tokens)))
-  return grams
-
-
-def create_masked_lm_predictions(tokens, masked_lm_prob,
-                                 max_predictions_per_seq, vocab_words, rng,
-                                 do_whole_word_mask,
-                                 max_ngram_size=None):
-  """Creates the predictions for the masked LM objective."""
-  if do_whole_word_mask:
-    grams = _tokens_to_grams(tokens)
-  else:
-    # Here we consider each token to be a word to allow for sub-word masking.
-    if max_ngram_size:
-      raise ValueError("cannot use ngram masking without whole word masking")
-    grams = [_Gram(i, i+1) for i in range(0, len(tokens))
-             if tokens[i] not in ["[CLS]", "[SEP]"]]
-
-  num_to_predict = min(max_predictions_per_seq,
-                       max(1, int(round(len(tokens) * masked_lm_prob))))
-  # Generate masks.  If `max_ngram_size` in [0, None] it means we're doing
-  # whole word masking or token level masking.  Both of these can be treated
-  # as the `max_ngram_size=1` case.
-  masked_grams = _masking_ngrams(grams, max_ngram_size or 1,
-                                 num_to_predict, rng)
-  masked_lms = []
-  output_tokens = list(tokens)
-  for gram in masked_grams:
-    # 80% of the time, replace all n-gram tokens with [MASK]
-    if rng.random() < 0.8:
-      replacement_action = lambda idx: "[MASK]"
-    else:
-      # 10% of the time, keep all the original n-gram tokens.
-      if rng.random() < 0.5:
-        replacement_action = lambda idx: tokens[idx]
-      # 10% of the time, replace each n-gram token with a random word.
-      else:
-        replacement_action = lambda idx: rng.choice(vocab_words)
-
-    for idx in range(gram.begin, gram.end):
-      output_tokens[idx] = replacement_action(idx)
-      masked_lms.append(MaskedLmInstance(index=idx, label=tokens[idx]))
-
-  assert len(masked_lms) <= num_to_predict
-  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 get_processor_text_fn(is_sentence_piece, do_lower_case):
-  def processor_text_fn(text):
-    text = tokenization.convert_to_unicode(text)
-    if is_sentence_piece:
-      # Additional preprocessing specific to the SentencePiece tokenizer.
-      text = tokenization.preprocess_text(text, lower=do_lower_case)
-
-    return text.strip()
-
-  return processor_text_fn
-
-
-def main(_):
-  if FLAGS.tokenization == "WordPiece":
-    tokenizer = tokenization.FullTokenizer(
-        vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case
-    )
-    processor_text_fn = get_processor_text_fn(False, FLAGS.do_lower_case)
-  else:
-    assert FLAGS.tokenization == "SentencePiece"
-    tokenizer = tokenization.FullSentencePieceTokenizer(FLAGS.sp_model_file)
-    processor_text_fn = get_processor_text_fn(True, FLAGS.do_lower_case)
-
-  input_files = []
-  for input_pattern in FLAGS.input_file.split(","):
-    input_files.extend(tf.io.gfile.glob(input_pattern))
-
-  logging.info("*** Reading from input files ***")
-  for input_file in input_files:
-    logging.info("  %s", input_file)
-
-  rng = random.Random(FLAGS.random_seed)
-  instances = create_training_instances(
-      input_files,
-      tokenizer,
-      processor_text_fn,
-      FLAGS.max_seq_length,
-      FLAGS.dupe_factor,
-      FLAGS.short_seq_prob,
-      FLAGS.masked_lm_prob,
-      FLAGS.max_predictions_per_seq,
-      rng,
-      FLAGS.do_whole_word_mask,
-      FLAGS.max_ngram_size,
-  )
-
-  output_files = FLAGS.output_file.split(",")
-  logging.info("*** Writing to output files ***")
-  for output_file in output_files:
-    logging.info("  %s", output_file)
-
-  write_instance_to_example_files(instances, tokenizer, FLAGS.max_seq_length,
-                                  FLAGS.max_predictions_per_seq, output_files,
-                                  FLAGS.gzip_compress,
-                                  FLAGS.use_v2_feature_names)
-
-
-if __name__ == "__main__":
-  flags.mark_flag_as_required("input_file")
-  flags.mark_flag_as_required("output_file")
-  app.run(main)