code: #! usr/bin/env python3
  # -*- coding:utf-8 -*-
  """
  Copyright 2018 The Google AI Language Team Authors.
  BASED ON Google_BERT.
  @Author:zhoukaiyin
  """
  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
  import tf_metrics
  
  flags = tf.flags
  
  FLAGS = flags.FLAGS
  
  flags.DEFINE_string(
      "data_dir", './drive/My Drive/ai/NERdata',
      "The input datadir.",
  )
  
  flags.DEFINE_string(
      "bert_config_file", './drive/My Drive/ai/checkpoint/bert_config.json',
      "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_string(
      "output_dir", './drive/My Drive/ai/output/result_dir/',
      "The output directory where the model checkpoints will be written."
  )
  
  flags.DEFINE_string(
      "tpu_name", 'gcp_tpu',
      "Use Google Cloud Colaborator TPU to train"
  )
  
  ## Other parameters
  flags.DEFINE_string(
      "init_checkpoint", './drive/My Drive/ai/checkpoint/bert_model.ckpt',
      "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", 128,
      "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_integer("train_batch_size", 32, "Total batch size for training.")
  
  flags.DEFINE_integer("eval_batch_size", 8, "Total batch size for eval.")
  
  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_string("vocab_file", './drive/My Drive/ai/checkpoint/vocab.txt',
                      "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
  
  
  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] == '.':
                      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_labels(self):
          return ["B-MISC", "I-MISC", "O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "X"]
  
      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 convert_single_example(ex_index, example, label_list, max_seq_length, tokenizer):
      label_map = {}
      for (i, label) in enumerate(label_list, 1):
          label_map[label] = i
      textlist = example.text.split(' ')
      labellist = example.label.split(' ')
      tokens = []
      labels = []
      for i, word in enumerate(textlist):
          token = tokenizer.tokenize(word)
          tokens.extend(token)
          label_1 = labellist[i]
          for m in range(len(token)):
              if m == 0:
                  labels.append(label_1)
              else:
                  labels.append("X")
      # 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)
      label_ids.append(0)
      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)
      label_ids.append(0)
      input_ids = tokenizer.convert_tokens_to_ids(ntokens)
      input_mask = [1] * len(input_ids)
      while len(input_ids) < max_seq_length:
          input_ids.append(0)
          input_mask.append(0)
          segment_ids.append(0)
          label_ids.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
  
      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]))
      feature = InputFeatures(
          input_ids=input_ids,
          input_mask=input_mask,
          segment_ids=segment_ids,
          label_ids=label_ids
      )
      return feature
  
  
  def filed_based_convert_examples_to_features(
          examples, label_list, max_seq_length, tokenizer, output_file
  ):
      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_list, 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)
          tf_example = tf.train.Example(features=tf.train.Features(feature=features))
          writer.write(tf_example.SerializeToString())
  
  
  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),
      }
  
      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, labels, 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()
      )
      with tf.variable_scope("loss"):
          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)
          logits = tf.reshape(logits, [-1, FLAGS.max_seq_length, 11])
          log_probs = tf.nn.log_softmax(logits, axis=-1)
  
          # labels = tf.cast(labels,dtype=tf.float32)
          one_hot_labels = tf.one_hot(labels, depth=num_labels, dtype=tf.float32)
          per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1)
          loss = tf.reduce_sum(per_example_loss)
          return (loss, per_example_loss, logits)
  
  
  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, labels, 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_ids = features["label_ids"]
  
          is_training = (mode == tf.estimator.ModeKeys.TRAIN)
  
          (total_loss, per_example_loss, logits) = create_model(
              bert_config, is_training, input_ids, input_mask, segment_ids, label_ids,
              num_labels, use_one_hot_embeddings)
          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.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)
          elif mode == tf.estimator.ModeKeys.EVAL:
  
              def metric_fn(per_example_loss, label_ids, logits):
                  predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)
                  precision = tf_metrics.precision(label_ids,predictions,11,[1,2,4,5,6,7,8,9],average="macro")
                  recall = tf_metrics.recall(label_ids,predictions,11,[1,2,4,5,6,7,8,9],average="macro")
                  f = tf_metrics.f1(label_ids,predictions,11,[1,2,4,5,6,7,8,9],average="macro")
                  loss = tf.metrics.mean(per_example_loss)
                  return {
                      "eval_precision":precision,
                      "eval_recall":recall,
                      "eval_f": f,
                      "eval_loss": loss,
                  }
  
              eval_metrics = (metric_fn, [per_example_loss, label_ids, logits])
              output_spec = tf.contrib.tpu.TPUEstimatorSpec(
                  mode=mode,
                  loss=total_loss,
                  eval_metrics=eval_metrics,
                  scaffold_fn=scaffold_fn)
          else:
              raise ValueError("Only TRAIN and EVAL modes are supported: %s" % (mode))
  
          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('grpc://' + os.environ['COLAB_TPU_ADDR'])
      is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2
      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)
  
      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 __name__ == "__main__":
  
      tf.app.run()
  
  
  
apis: tensorflow.data.TFRecordDataset tensorflow.contrib.tpu.TPUEstimator tensorflow.reshape tensorflow.logging.set_verbosity tensorflow.variable_scope tensorflow.matmul tensorflow.contrib.tpu.TPUEstimatorSpec tensorflow.contrib.cluster_resolver.TPUClusterResolver tensorflow.one_hot tensorflow.reduce_sum tensorflow.nn.dropout tensorflow.metrics.mean tensorflow.contrib.tpu.TPUConfig tensorflow.truncated_normal_initializer tensorflow.nn.log_softmax tensorflow.train.Features tensorflow.FixedLenFeature tensorflow.train.init_from_checkpoint tensorflow.python_io.TFRecordWriter tensorflow.train.Scaffold tensorflow.app.run tensorflow.parse_single_example tensorflow.zeros_initializer tensorflow.flags.DEFINE_string tensorflow.logging.info tensorflow.nn.bias_add tensorflow.trainable_variables tensorflow.to_int32 tensorflow.argmax