#-*-coding:utf-8-*-


"""
Copyright 2018 The Google AI Language Team Authors.
BASED ON Google_BERT.
@Author:zhoukaiyin
Adjust code for chinese ner
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import collections
import os
from bert import modeling
from bert import optimization
from bert import tokenization
import tensorflow as tf
from sklearn.metrics import f1_score, precision_score, recall_score
from tensorflow.python.ops import math_ops
from train import tf_metrics
import pickle

flags = tf.flags

FLAGS = flags.FLAGS

flags.DEFINE_string(
  "data_dir", None,
  "The input datadir.",
)

flags.DEFINE_string(
  "bert_config_file", None,
  "The config json file corresponding to the pre-trained BERT model."
)

flags.DEFINE_string(
  "task_name", "NER", "The name of the task to train."
)
flags.DEFINE_bool("crf", False, "use crf!")
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."
)

flags.DEFINE_integer(
  "max_seq_length", 256,
  "The maximum total input sequence length after WordPiece tokenization."
)

flags.DEFINE_bool(
  "do_train", True,
  "Whether to run training."
)
flags.DEFINE_bool("use_tpu", False, "Whether to use TPU or GPU/CPU.")

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_bool('do_export', False, 'Export model ')

flags.DEFINE_integer("train_batch_size", 16, "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", 4.0, "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_string("vocab_file", None,
                    "The vocabulary file that the BERT model was trained on.")
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, 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.
      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 = text
    self.label = label


class InputFeatures(object):
  """A single set of features of data."""

  def __init__(self, input_ids, input_mask, segment_ids, label_ids, ):
    self.input_ids = input_ids
    self.input_mask = input_mask
    self.segment_ids = segment_ids
    self.label_ids = label_ids
    # self.label_mask = label_mask


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_labels(self):
    """Gets the list of labels for this data set."""
    raise NotImplementedError()

  @classmethod
  def _read_data(cls, input_file):
    """Reads a BIO data."""
    with open(input_file) as f:
      lines = []
      words = []
      labels = []
      for line in f:
        contends = line.strip()
        word = line.strip().split(' ')[0]
        label = line.strip().split(' ')[-1]
        if contends.startswith("-DOCSTART-"):
          words.append('')
          continue
        # if len(contends) == 0 and words[-1] == '。':
        if len(contends) == 0:
          l = ' '.join([label for label in labels if len(label) > 0])
          w = ' '.join([word for word in words if len(word) > 0])
          lines.append([l, w])
          words = []
          labels = []
          continue
        words.append(word)
        labels.append(label)
      return lines


class NerProcessor(DataProcessor):
  def get_train_examples(self, data_dir):
    return self._create_example(
      self._read_data(os.path.join(data_dir, "train.txt")), "train"
    )

  def get_dev_examples(self, data_dir):
    return self._create_example(
      self._read_data(os.path.join(data_dir, "dev.txt")), "dev"
    )

  def get_test_examples(self, data_dir):
    return self._create_example(
      self._read_data(os.path.join(data_dir, "test.txt")), "test")

  def get_labels(self):
    # prevent potential bug for chinese text mixed with english text
    # return ["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "[CLS]","[SEP]"]
    # labels = ["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "X","[CLS]","[SEP]"]
    labels = ['O', "B-COMAPNY", "I-COMAPNY", "B-REAL", "I-REAL", "B-AMOUT", "I-AMOUT", "[CLS]", "[SEP]"]
    return labels

  def _create_example(self, lines, set_type):
    examples = []
    for (i, line) in enumerate(lines):
      guid = "%s-%s" % (set_type, i)
      text = tokenization.convert_to_unicode(line[1])
      label = tokenization.convert_to_unicode(line[0])
      examples.append(InputExample(guid=guid, text=text, label=label))
    return examples


def write_tokens(tokens, mode):
  if mode == "test":
    path = os.path.join(FLAGS.output_dir, "token_" + mode + ".txt")
    wf = open(path, 'a')
    for token in tokens:
      if token != "**NULL**":
        wf.write(token + '\n')
    wf.close()


def convert_single_example(ex_index, example, label_map, max_seq_length, tokenizer, mode):
  textlist = example.text.split(' ')
  labellist = example.label.split(' ')
  tokens = []
  labels = []
  # print(textlist)
  for i, word in enumerate(textlist):
    token = tokenizer.tokenize(word)
    # print(token)
    tokens.extend(token)
    label_1 = labellist[i]
    # print(label_1)
    for m in range(len(token)):
      if m == 0:
        labels.append(label_1)
      else:
        labels.append("X")
    # print(tokens, labels)
  # tokens = tokenizer.tokenize(example.text)
  if len(tokens) >= max_seq_length - 1:
    tokens = tokens[0:(max_seq_length - 2)]
    labels = labels[0:(max_seq_length - 2)]
  ntokens = []
  segment_ids = []
  label_ids = []
  ntokens.append("[CLS]")
  segment_ids.append(0)
  # append("O") or append("[CLS]") not sure!
  label_ids.append(label_map["[CLS]"])
  for i, token in enumerate(tokens):
    ntokens.append(token)
    segment_ids.append(0)
    label_ids.append(label_map[labels[i]])
  ntokens.append("[SEP]")
  segment_ids.append(0)
  # append("O") or append("[SEP]") not sure!
  label_ids.append(label_map["[SEP]"])
  input_ids = tokenizer.convert_tokens_to_ids(ntokens)
  input_mask = [1] * len(input_ids)
  # label_mask = [1] * len(input_ids)
  while len(input_ids) < max_seq_length:
    input_ids.append(0)
    input_mask.append(0)
    segment_ids.append(0)
    # we don't concerned about it!
    label_ids.append(0)
    ntokens.append("**NULL**")
    # label_mask.append(0)
  # print(len(input_ids))
  assert len(input_ids) == max_seq_length
  assert len(input_mask) == max_seq_length
  assert len(segment_ids) == max_seq_length
  assert len(label_ids) == max_seq_length
  # assert len(label_mask) == max_seq_length

  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_ids: %s" % " ".join([str(x) for x in label_ids]))
    # tf.logging.info("label_mask: %s" % " ".join([str(x) for x in label_mask]))

  feature = InputFeatures(
    input_ids=input_ids,
    input_mask=input_mask,
    segment_ids=segment_ids,
    label_ids=label_ids,
    # label_mask = label_mask
  )
  write_tokens(ntokens, mode)
  return feature


def filed_based_convert_examples_to_features(
  examples, label_list, max_seq_length, tokenizer, output_file, mode=None
):
  if os.path.exists(output_file):
    return
  label_map = {}
  for (i, label) in enumerate(label_list, 1):
    label_map[label] = i
  with open('./output/label2id.pkl', 'wb') as w:
    pickle.dump(label_map, w)

  writer = tf.python_io.TFRecordWriter(output_file)
  for (ex_index, example) in enumerate(examples):
    if ex_index % 5000 == 0:
      tf.logging.info("Writing example %d of %d" % (ex_index, len(examples)))
    feature = convert_single_example(ex_index, example, label_map, max_seq_length, tokenizer, mode)

    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_ids)
    # features["label_mask"] = create_int_feature(feature.label_mask)
    tf_example = tf.train.Example(features=tf.train.Features(feature=features))
    writer.write(tf_example.SerializeToString())


def serving_input_receiver_fn():
  input_str = tf.placeholder(tf.string, name='inputs')
  seq_length = 256
  name_to_features = {
    "input_ids": tf.VarLenFeature(tf.int64),
    "input_mask": tf.VarLenFeature(tf.int64),
    "segment_ids": tf.VarLenFeature(tf.int64),
    # "label_ids": tf.FixedLenFeature([seq_length], tf.int64),
  }

  def _decode_record(record, name_to_features):
    example = tf.parse_single_example(record, name_to_features)
    for name in list(example.keys()):
      t = example[name]
      t = tf.sparse_tensor_to_dense(t, default_value=0)
      t = tf.expand_dims(t, axis=0)
      if t.dtype == tf.int64:
        t = tf.to_int32(t)
      example[name] = t
    return example

  features = _decode_record(input_str, name_to_features)
  receiver_tensors = {"inputs": input_str}
  return tf.estimator.export.ServingInputReceiver(features, receiver_tensors)


def file_based_input_fn_builder(input_file, seq_length, is_training, drop_remainder):
  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([seq_length], tf.int64),
    # "label_ids":tf.VarLenFeature(tf.int64),
    # "label_mask": tf.FixedLenFeature([seq_length], tf.int64),
  }

  def _decode_record(record, name_to_features):
    example = tf.parse_single_example(record, name_to_features)
    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):
    batch_size = params["batch_size"]
    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 create_model(bert_config, is_training, input_ids, input_mask,
                 segment_ids, num_labels, use_one_hot_embeddings):
  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
  )

  output_layer = model.get_sequence_output()

  hidden_size = output_layer.shape[-1].value

  output_weight = 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()
  )
  if is_training:
    output_layer = tf.nn.dropout(output_layer, keep_prob=0.9)
  output_layer = tf.reshape(output_layer, [-1, hidden_size])
  logits = tf.matmul(output_layer, output_weight, transpose_b=True)
  logits = tf.nn.bias_add(logits, output_bias)
  print("logits====>", logits)
  return logits
  ##########################################################################


def crf_loss(logits, labels, mask, num_labels, mask2len):
  """
  :param logits:
  :param labels:
  :param mask2len:each sample's length
  :return:
  """
  # TODO
  with tf.variable_scope("crf_loss"):
    trans = tf.get_variable(
      "transition",
      shape=[num_labels, num_labels],
      initializer=tf.contrib.layers.xavier_initializer()
    )

  log_likelihood, transition = tf.contrib.crf.crf_log_likelihood(logits, labels, transition_params=trans,
                                                                 sequence_lengths=mask2len)
  loss = tf.math.reduce_mean(-log_likelihood)
  return loss


def softmax_loss(logits, labels, num_labels, mask):
  print("logits ===>", logits)
  print("labels===>", labels)
  logits = tf.reshape(logits, [-1, num_labels])
  labels = tf.reshape(labels, [-1])
  mask = tf.cast(mask, dtype=tf.float32)
  one_hot_labels = tf.one_hot(labels, depth=num_labels, dtype=tf.float32)
  loss = tf.losses.softmax_cross_entropy(logits=logits, onehot_labels=one_hot_labels)
  loss *= tf.reshape(mask, [-1])
  loss = tf.reduce_sum(loss)
  total_size = tf.reduce_sum(mask)
  total_size += 1e-12  # to avoid division by 0 for all-0 weights
  loss /= total_size
  return loss


def model_fn_builder(bert_config, num_labels, init_checkpoint, learning_rate,
                     num_train_steps, num_warmup_steps, use_tpu,
                     use_one_hot_embeddings):
  def model_fn(features, mode, params):
    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_mask = features["label_mask"]
    is_training = (mode == tf.estimator.ModeKeys.TRAIN)
    if mode != tf.estimator.ModeKeys.PREDICT:
      label_ids = features["label_ids"]

    logits = create_model(
      bert_config, is_training, input_ids, input_mask, segment_ids,
      num_labels, use_one_hot_embeddings)
    if mode != tf.estimator.ModeKeys.PREDICT:
      logits = tf.reshape(logits, [-1, FLAGS.max_seq_length, num_labels])
    else:
      shapes = input_ids.get_shape().as_list()
      print("input shapes:", shapes)
      logits = tf.reshape(logits, [1, -1, num_labels])
    tvars = tf.trainable_variables()
    scaffold_fn = None
    if init_checkpoint:
      (assignment_map, initialized_variable_names) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)
      tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
      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.PREDICT:
      if FLAGS.crf:
        mask2len = tf.reduce_sum(input_mask, axis=1)
        loss = crf_loss(logits, label_ids, input_mask, num_labels, mask2len)
      else:
        loss = softmax_loss(logits, label_ids, num_labels, input_mask)
    if mode == tf.estimator.ModeKeys.TRAIN:
      train_op = optimization.create_optimizer(
        loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu)
      output_spec = tf.contrib.tpu.TPUEstimatorSpec(
        mode=mode,
        loss=loss,
        train_op=train_op,
        scaffold_fn=scaffold_fn)
    elif mode == tf.estimator.ModeKeys.EVAL:
      def metric_fn(label_ids, logits):
        predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)
        precision = tf_metrics.precision(label_ids, predictions, num_labels, [2, 3, 4, 5, 6, 7], average="macro")
        recall = tf_metrics.recall(label_ids, predictions, num_labels, [2, 3, 4, 5, 6, 7], average="macro")
        f = tf_metrics.f1(label_ids, predictions, num_labels, [2, 3, 4, 5, 6, 7], average="macro")
        return {
          "eval_precision": precision,
          "eval_recall": recall,
          "eval_f": f,
          # "eval_loss": loss,
        }

      eval_metrics = (metric_fn, [label_ids, logits])
      # eval_metrics = (metric_fn, [label_ids, logits])
      output_spec = tf.contrib.tpu.TPUEstimatorSpec(
        mode=mode,
        loss=loss,
        eval_metrics=eval_metrics,
        scaffold_fn=scaffold_fn)
    else:
      predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)
      output_spec = tf.contrib.tpu.TPUEstimatorSpec(
        mode=mode, predictions=predictions, scaffold_fn=scaffold_fn
      )
    return output_spec

  return model_fn


def main(_):
  tf.logging.set_verbosity(tf.logging.INFO)
  processors = {
    "ner": NerProcessor
  }
  if not FLAGS.do_train and not FLAGS.do_eval:
    raise ValueError("At least one of `do_train` or `do_eval` 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))

  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
  if not os.path.exists(FLAGS.output_dir):
    os.mkdir(FLAGS.output_dir)
  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) + 1,
    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)

  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")
    filed_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)
    eval_file = os.path.join(FLAGS.output_dir, "eval.tf_record")
    filed_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", len(eval_examples))
    tf.logging.info("  Batch size = %d", FLAGS.eval_batch_size)
    eval_steps = None
    if FLAGS.use_tpu:
      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 open(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:
    token_path = os.path.join(FLAGS.output_dir, "token_test.txt")
    with open('./output/label2id.pkl', 'rb') as rf:
      label2id = pickle.load(rf)
      id2label = {value: key for key, value in label2id.items()}
    if os.path.exists(token_path):
      os.remove(token_path)
    predict_examples = processor.get_test_examples(FLAGS.data_dir)

    predict_file = os.path.join(FLAGS.output_dir, "predict.tf_record")
    filed_based_convert_examples_to_features(predict_examples, label_list,
                                             FLAGS.max_seq_length, tokenizer,
                                             predict_file, mode="test")

    tf.logging.info("***** Running prediction*****")
    tf.logging.info("  Num examples = %d", len(predict_examples))
    tf.logging.info("  Batch size = %d", FLAGS.predict_batch_size)
    if FLAGS.use_tpu:
      # Warning: According to tpu_estimator.py Prediction on TPU is an
      # experimental feature and hence not supported here
      raise ValueError("Prediction in TPU not supported")
    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, "label_test.txt")
    with open(output_predict_file, 'w') as writer:
      for prediction in result:
        output_line = "\n".join(id2label[id] for id in prediction if id != 0) + "\n"
        writer.write(output_line)
  if FLAGS.do_export and FLAGS.do_predict:
    estimator.export_saved_model('./export_models', serving_input_receiver_fn)


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()