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.
  """BERT finetuning runner."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import collections
  import csv
  import os
  import modeling
  import optimization
  import tokenization
  import tensorflow as tf
  import json
  import numpy as np
  import sys
  
  flags = tf.flags
  
  FLAGS = flags.FLAGS
  
  ## Required parameters
  flags.DEFINE_string(
      "data_dir", None,
      "The input data dir. Should contain the .tsv files (or other data files) "
      "for the task.")
  
  flags.DEFINE_string(
      "bert_config_file", None,
      "The config json file corresponding to the pre-trained BERT model. "
      "This specifies the model architecture.")
  
  flags.DEFINE_string("task_name", None, "The name of the task to train.")
  
  flags.DEFINE_string("vocab_file", None,
                      "The vocabulary file that the BERT model was trained on.")
  
  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. Should be True for uncased "
      "models and False for cased models.")
  
  flags.DEFINE_integer(
      "max_seq_length", 128,
      "The maximum total input sequence length after WordPiece tokenization. "
      "Sequences longer than this will be truncated, and sequences shorter "
      "than this will be padded.")
  
  flags.DEFINE_bool("do_train", True, "Whether to run training.")
  
  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_integer("train_batch_size", 32, "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", 3.0,
                     "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_bool("use_tpu", False, "Whether to use TPU or GPU/CPU.")
  
  tf.flags.DEFINE_string(
      "tpu_name", None,
      "The Cloud TPU to use for training. This should be either the name "
      "used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 "
      "url.")
  
  tf.flags.DEFINE_string(
      "tpu_zone", None,
      "[Optional] GCE zone where the Cloud TPU is located in. If not "
      "specified, we will attempt to automatically detect the GCE project from "
      "metadata.")
  
  tf.flags.DEFINE_string(
      "gcp_project", None,
      "[Optional] Project name for the Cloud TPU-enabled project. If not "
      "specified, we will attempt to automatically detect the GCE project from "
      "metadata.")
  
  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.")
  
  flags.DEFINE_string(
      "mode", None,
      "mode")
  
  class InputExample(object):
      """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 PaddingInputExample(object):
      """Fake example so the num input examples is a multiple of the batch size.
  
      When running eval/predict on the TPU, we need to pad the number of examples
      to be a multiple of the batch size, because the TPU requires a fixed batch
      size. The alternative is to drop the last batch, which is bad because it means
      the entire output data won't be generated.
  
      We use this class instead of `None` because treating `None` as padding
      battches could cause silent errors.
      """
  
  
  class InputFeatures(object):
      """A single set of features of data."""
  
      def __init__(self,
                   input_ids,
                   input_mask,
                   segment_ids,
                   label_ids,
                   is_real_example=True):
          self.input_ids = input_ids
          self.input_mask = input_mask
          self.segment_ids = segment_ids
          self.label_ids = label_ids
          self.is_real_example = is_real_example
  
  
  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 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
  
  
  class sortProcessor(DataProcessor):
      """Processor for the MRPC data set (GLUE version)."""
  
      def _read_txt(self, path):
          with open(path, 'r', encoding='utf-8') as f:
              lines = f.read().strip().split('\n')
              lines = [t.split('\t') for t in lines]
          return lines
  
      def get_train_examples(self, data_dir):
          """See base class."""
          return self._create_examples(
              self._read_txt(data_dir), "train")
  
      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[0])
              text_b = tokenization.convert_to_unicode(line[1])
              if set_type == "test":
                  labels = "0"
              else:
                  labels = tokenization.convert_to_unicode(line[-1])
              examples.append(
                  InputExample(guid=guid, text_a=text_a, text_b=text_b, label=labels))
              if i%1000==0:
                  print('dataprocess line {} from {}'.format(i,len(lines)))
          return examples
  
  
  def convert_single_example(ex_index, example, label_list, max_seq_length,
                             tokenizer):
      """Converts a single `InputExample` into a single `InputFeatures`."""
  
      if isinstance(example, PaddingInputExample):
          return InputFeatures(
              input_ids=[0] * max_seq_length,
              input_mask=[0] * max_seq_length,
              segment_ids=[0] * max_seq_length,
              label_id=0,
              is_real_example=False)
  
      label_map = {}
      for (i, label) in enumerate(label_list):
          label_map[label] = i
  
      # tokens_a = tokenizer.tokenize(example.text_a)
      tokens_a = tokenizer.tokenize(example.text_a)
      tokens_b = None
      if example.text_b:
          # tokens_b = tokenizer.tokenize(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 in BERT is:
      # (a) For sequence pairs:
      #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
      #  type_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]
      #  type_ids: 0     0   0   0  0     0 0
      #
      # Where "type_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 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]
      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: %s (id = %d)" % (example.label, label_id))
  
      feature = InputFeatures(
          input_ids=input_ids,
          input_mask=input_mask,
          segment_ids=segment_ids,
          label_ids=label_id,
          is_real_example=True)
      return feature
  
  
  def file_based_convert_examples_to_features(
          examples, label_lists, 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):
          if ex_index % 10000 == 0:
              tf.logging.info("Writing example %d of %d" % (ex_index, len(examples)))
          feature = convert_single_example(ex_index, example, label_lists,
                                           max_seq_length, tokenizer)
          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["is_real_example"] = create_int_feature(
              [int(feature.is_real_example)])
          tf_example = tf.train.Example(features=tf.train.Features(feature=features))
          writer.write(tf_example.SerializeToString())
      writer.close()
  
  
  def file_based_input_fn_builder(input_file, seq_length, is_training,
                                  drop_remainder):
      """Creates an `input_fn` closure to be passed to TPUEstimator."""
  
      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),
          "is_real_example": tf.FixedLenFeature([], tf.int64),
      }
      name_to_features["label_ids"] = tf.FixedLenFeature([], tf.int64)
  
      def _decode_record(record, name_to_features):
          """Decodes a record to a TensorFlow example."""
          example = tf.parse_single_example(record, name_to_features)
  
          # tf.Example only supports tf.int64, but the TPU only supports tf.int32.
          # So cast all int64 to int32.
          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):
          """The actual input function."""
          batch_size = params["batch_size"]
  
          # For training, we want a lot of parallel reading and shuffling.
          # For eval, we want no shuffling and parallel reading doesn't matter.
          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 _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 cosine(q,a):
      pooled_len_1 = tf.sqrt(tf.reduce_sum(q * q, 1))
      pooled_len_2 = tf.sqrt(tf.reduce_sum(a * a, 1))
      pooled_mul_12 = tf.reduce_sum(q * a, 1)
      score = tf.div(pooled_mul_12, pooled_len_1 * pooled_len_2 +1e-8, name="scores")
      return score
  
  def create_model(bert_config, is_training, input_ids, input_mask, segment_ids,
                   labels, use_one_hot_embeddings):
      """Creates a classification model."""
      output_layer = []
      for k in range(len(input_ids)):
          with tf.variable_scope('lm', reuse=k > 0):
              model = modeling.BertModel(
                  config=bert_config,
                  is_training=is_training,
                  input_ids=input_ids[k],
                  input_mask=input_mask[k],
                  token_type_ids=segment_ids,
                  use_one_hot_embeddings=use_one_hot_embeddings)
              output_layer.append(model.get_pooled_output())
      feature0 = output_layer[0]
      feature1 = output_layer[1]
      feature_qr = tf.layers.dense(inputs=output_layer[0], units = 256, activation = tf.nn.relu)
      feature_dc = tf.layers.dense(inputs=output_layer[1], units = 256, activation = tf.nn.relu)
      score = cosine(feature_qr,feature_dc)
      c = tf.square(score - tf.cast(labels, dtype=tf.float32))
      per_example_loss = tf.reduce_mean(c,axis=-1)
      loss = tf.reduce_mean(per_example_loss)
      return (loss,score,per_example_loss,feature_qr,feature_dc,feature0,feature1)
  
  def get_assignment_map_from_checkpoint(tvars, init_checkpoint):
    import re,collections
    """Compute the union of the current variables and checkpoint variables."""
    initialized_variable_names = {}
    name_to_variable = collections.OrderedDict()
    for var in tvars:
      name = var.name
      m = re.match("^(.*):\\d+$", name)
      if m is not None:
        name = m.group(1)
      name_to_variable[name] = var
    init_vars = tf.train.list_variables(init_checkpoint)
    print('init_variable:',init_vars)
    print('train_variable:',tvars)
    print('name_to_variable',name_to_variable)
    assignment_map = collections.OrderedDict()
    vars_others = []
    for x in init_vars:
      (name, var) = (x[0], x[1])
      if 'lm/'+name in name_to_variable:
        assignment_map[name] = 'lm/'+name
      elif name in name_to_variable:
        assignment_map[name] = name
      else:
        vars_others.append(name)
        continue
      initialized_variable_names[name] = 1
      initialized_variable_names[name + ":0"] = 1
    return (assignment_map, initialized_variable_names,vars_others)
  
  def model_fn_builder(bert_config, init_checkpoint, learning_rate,
                       num_train_steps, num_warmup_steps, use_tpu,
                       use_one_hot_embeddings):
      """Returns `model_fn` closure for TPUEstimator."""
      def model_fn(features, labels, mode, params):  # pylint: disable=unused-argument
          """The `model_fn` for TPUEstimator."""
          tf.logging.info("*** Features ***")
          for name in sorted(features.keys()):
              tf.logging.info("  name = %s, shape = %s" % (name, features[name].shape))
          print(features)
          input_ids = features["input_ids"]
          input_mask = features["input_mask"]
          segment_ids = features["segment_ids"]
          label_ids = features["label_ids"]
          is_real_example = None
          if "is_real_example" in features:
              is_real_example = tf.cast(features["is_real_example"], dtype=tf.float32)
          else:
              is_real_example = tf.ones(tf.shape(label_ids), dtype=tf.float32)
          is_training = (mode == tf.estimator.ModeKeys.TRAIN)
          (loss,score,per_example_loss,feature_qr,feature_dc,feature0,feature1) = create_model(bert_config, is_training, input_ids, input_mask, segment_ids,label_ids, use_one_hot_embeddings)
          total_loss = loss
          tvars = tf.trainable_variables()
          initialized_variable_names = {}
          scaffold_fn = None
          if init_checkpoint:
              (assignment_map, initialized_variable_names
               ) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)
              print('TEST:assignment_map0',assignment_map)
              if len(assignment_map)==0:
                  (assignment_map, initialized_variable_names, vars_others
                   ) = get_assignment_map_from_checkpoint(tvars, init_checkpoint)
                  print("assignment_map", 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.TRAIN:
              train_op = optimization.create_optimizer(
                  total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu)
              output_spec = tf.contrib.tpu.TPUEstimatorSpec(
                  mode=mode,
                  loss=total_loss,
                  train_op=train_op,
                  scaffold_fn=scaffold_fn)
          return output_spec
      return model_fn
  
  
  # This function is not used by this file but is still used by the Colab and
  # people who depend on it.
  def input_fn_builder(features, seq_length, is_training, drop_remainder,Num_labels):
      """Creates an `input_fn` closure to be passed to TPUEstimator."""
  
      all_input_ids = []
      all_input_mask = []
      all_segment_ids = []
      all_label_ids = []
  
      for feature in features:
          all_input_ids.append(feature.input_ids)
          all_input_mask.append(feature.input_mask)
          all_segment_ids.append(feature.segment_ids)
          all_label_ids.append(feature.label_id)
  
      def input_fn(params):
          """The actual input function."""
          batch_size = params["batch_size"]
  
          num_examples = len(features)
  
          # This is for demo purposes and does NOT scale to large data sets. We do
          # not use Dataset.from_generator() because that uses tf.py_func which is
          # not TPU compatible. The right way to load data is with TFRecordReader.
          d0 = {
              "input_ids":
                  tf.constant(
                      all_input_ids, shape=[num_examples, seq_length],
                      dtype=tf.int32),
              "input_mask":
                  tf.constant(
                      all_input_mask,
                      shape=[num_examples, seq_length],
                      dtype=tf.int32),
              "segment_ids":
                  tf.constant(
                      all_segment_ids,
                      shape=[num_examples, seq_length],
                      dtype=tf.int32),
          }
          d0["label_ids"] = tf.constant(all_label_ids, shape=[num_examples], dtype=tf.float32)
          d = tf.data.Dataset.from_tensor_slices(d0)
  
          if is_training:
              d = d.repeat()
              d = d.shuffle(buffer_size=100)
  
          d = d.batch(batch_size=batch_size, drop_remainder=drop_remainder)
          return d
  
      return input_fn
  
  
  # This function is not used by this file but is still used by the Colab and
  # people who depend on it.
  def convert_examples_to_features(examples, label_list, max_seq_length,
                                   tokenizer):
      """Convert a set of `InputExample`s to a list of `InputFeatures`."""
  
      features = []
      for (ex_index, example) in enumerate(examples):
          if ex_index % 10000 == 0:
              tf.logging.info("Writing example %d of %d" % (ex_index, len(examples)))
  
          feature = convert_single_example(ex_index, example, label_list,
                                           max_seq_length, tokenizer)
  
          features.append(feature)
      return features
  
  def netConfig():
      import numpy as np
      print('参数量：%d'%(np.sum([np.prod(v.get_shape().as_list()) for v in tf.trainable_variables()])))
      tvars = [v for v in tf.trainable_variables()]
      for t in tvars:
          print(t)
  def main(_):
      # FLAGS.data_dir = "/search/odin/guobk/vpa/vpa-studio-research/sort/data"
      # FLAGS.task_name = "sort"
      # FLAGS.bert_config_file = "/search/odin/guobk/vpa/roberta_zh/model/roberta_zh_l12/bert_config.json"
      # FLAGS.vocab_file = "/search/odin/guobk/vpa/roberta_zh/model/roberta_zh_l12/vocab.txt"
      # FLAGS.init_checkpoint = "/search/odin/guobk/vpa/roberta_zh/model/roberta_zh_l12/bert_model.ckpt"
      label_lists = ['0','1']
      tokenization.validate_case_matches_checkpoint(FLAGS.do_lower_case,
                                                    FLAGS.init_checkpoint)
  
      if not FLAGS.do_train and not FLAGS.do_eval and not FLAGS.do_predict:
          raise ValueError(
              "At least one of `do_train`, `do_eval` or `do_predict' 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))
  
      #processor = processors[task_name]()
      processor = sortProcessor()
      tokenizer = tokenization.FullTokenizer(
          vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case)
      train_examples = None
      num_train_steps = None
      num_warmup_steps = None
      print('Data processing')
      tf.logging.info("***** Data processing *****")
      train_examples = processor.get_train_examples(FLAGS.data_dir)
      num_train_steps = int(
          len(train_examples) / FLAGS.train_batch_size * FLAGS.num_train_epochs)
      tf.logging.info("num_train_samples is {} and num_train_steps is {}".format(len(train_examples),num_train_steps))
      tf.logging.info("***** Data processing over *****")
      file_based_convert_examples_to_features(
          train_examples, label_lists,FLAGS.max_seq_length, tokenizer, FLAGS.output_dir)
  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()
      main(0)
  