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.
  """Extract pre-computed feature vectors from BERT."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import codecs
  import collections
  import json
  import re
  
  import modeling
  import tokenization
  import tensorflow as tf
  
  flags = tf.flags
  
  FLAGS = flags.FLAGS
  
  flags.DEFINE_string("input_file", "D:\python project me\data\my_bert_English/train.txt", "")
  
  flags.DEFINE_string("output_file", "D:\python project me\data\my_bert_English/feature_mymodel", "")
  
  flags.DEFINE_string("layers", "-1,-2,-3,-4", "")
  
  flags.DEFINE_string(
      "bert_config_file", r"D:\python project me/bert1\uncased/bert_config.json",
      "The config json file corresponding to the pre-trained BERT model. "
      "This specifies the model architecture.")
  
  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_string(
      "init_checkpoint", r"D:\python project me\data\my_bert_English\output/model.ckpt-0",
      "Initial checkpoint (usually from a pre-trained BERT model).")
  
  flags.DEFINE_string("vocab_file", "D:\python project me\data\my_bert_English/vocab2.txt",
                      "The vocabulary file that the BERT model was trained on.")
  
  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("batch_size", 32, "Batch size for predictions.")
  
  flags.DEFINE_bool("use_tpu", False, "Whether to use TPU or GPU/CPU.")
  
  flags.DEFINE_string("master", None,
                      "If using a TPU, the address of the master.")
  
  flags.DEFINE_integer(
      "num_tpu_cores", 8,
      "Only used if `use_tpu` is True. Total number of TPU cores to use.")
  
  flags.DEFINE_bool(
      "use_one_hot_embeddings", False,
      "If True, tf.one_hot will be used for embedding lookups, otherwise "
      "tf.nn.embedding_lookup will be used. On TPUs, this should be True "
      "since it is much faster.")
  
  
  class InputExample(object):
  
    def __init__(self, unique_id, text_a, text_b):
      self.unique_id = unique_id
      self.text_a = text_a
      self.text_b = text_b
  
  
  class InputFeatures(object):
    """A single set of features of data."""
  
    def __init__(self, unique_id, tokens, input_ids, input_mask, input_type_ids):
      self.unique_id = unique_id
      self.tokens = tokens
      self.input_ids = input_ids
      self.input_mask = input_mask
      self.input_type_ids = input_type_ids
  
  
  def input_fn_builder(features, seq_length):
    """Creates an `input_fn` closure to be passed to TPUEstimator."""
  
    all_unique_ids = []
    all_input_ids = []
    all_input_mask = []
    all_input_type_ids = []
  
    for feature in features:
      all_unique_ids.append(feature.unique_id)
      all_input_ids.append(feature.input_ids)
      all_input_mask.append(feature.input_mask)
      all_input_type_ids.append(feature.input_type_ids)
  
    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.
      d = tf.data.Dataset.from_tensor_slices({
          "unique_ids":
              tf.constant(all_unique_ids, shape=[num_examples], dtype=tf.int32),
          "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),
          "input_type_ids":
              tf.constant(
                  all_input_type_ids,
                  shape=[num_examples, seq_length],
                  dtype=tf.int32),
      })
  
      d = d.batch(batch_size=batch_size, drop_remainder=False)
      return d
  
    return input_fn
  
  
  def model_fn_builder(bert_config, init_checkpoint, layer_indexes, 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."""
  
      unique_ids = features["unique_ids"]
      input_ids = features["input_ids"]
      input_mask = features["input_mask"]
      input_type_ids = features["input_type_ids"]
  
      model = modeling.BertModel(
          config=bert_config,
          is_training=False,
          input_ids=input_ids,
          input_mask=input_mask,
          token_type_ids=input_type_ids,
          use_one_hot_embeddings=use_one_hot_embeddings)
  
      if mode != tf.estimator.ModeKeys.PREDICT:
        raise ValueError("Only PREDICT modes are supported: %s" % (mode))
  
      tvars = tf.trainable_variables()
      scaffold_fn = None
      (assignment_map,
       initialized_variable_names) = modeling.get_assignment_map_from_checkpoint(
           tvars, init_checkpoint)
      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)
  
      all_layers = model.get_all_encoder_layers()
  
      predictions = {
          "unique_id": unique_ids,
      }
  
      for (i, layer_index) in enumerate(layer_indexes):
        predictions["layer_output_%d" % i] = all_layers[layer_index]
  
      output_spec = tf.contrib.tpu.TPUEstimatorSpec(
          mode=mode, predictions=predictions, scaffold_fn=scaffold_fn)
      return output_spec
  
    return model_fn
  
  
  def convert_examples_to_features(examples, seq_length, tokenizer):
    """Loads a data file into a list of `InputBatch`s."""
  
    features = []
    for (ex_index, example) in enumerate(examples):
      tokens_a = tokenizer.tokenize(example.text_a)
  
      tokens_b = None
      if 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, seq_length - 3)
      else:
        # Account for [CLS] and [SEP] with "- 2"
        if len(tokens_a) > seq_length - 2:
          tokens_a = tokens_a[0:(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 as the "sentence vector". Note that this only makes sense because
      # the entire model is fine-tuned.
      tokens = []
      input_type_ids = []
      tokens.append("[CLS]")
      input_type_ids.append(0)
      for token in tokens_a:
        tokens.append(token)
        input_type_ids.append(0)
      tokens.append("[SEP]")
      input_type_ids.append(0)
  
      if tokens_b:
        for token in tokens_b:
          tokens.append(token)
          input_type_ids.append(1)
        tokens.append("[SEP]")
        input_type_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) < seq_length:
        input_ids.append(0)
        input_mask.append(0)
        input_type_ids.append(0)
  
      assert len(input_ids) == seq_length
      assert len(input_mask) == seq_length
      assert len(input_type_ids) == seq_length
  
      if ex_index < 5:
        tf.logging.info("*** Example ***")
        tf.logging.info("unique_id: %s" % (example.unique_id))
        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(
            "input_type_ids: %s" % " ".join([str(x) for x in input_type_ids]))
  
      features.append(
          InputFeatures(
              unique_id=example.unique_id,
              tokens=tokens,
              input_ids=input_ids,
              input_mask=input_mask,
              input_type_ids=input_type_ids))
    return features
  
  
  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 read_examples(input_file):
    """Read a list of `InputExample`s from an input file."""
    examples = []
    unique_id = 0
    with tf.gfile.GFile(input_file, "r") as reader:
      while True:
        line = tokenization.convert_to_unicode(reader.readline())
        if not line:
          break
        line = line.strip()
        text_a = None
        text_b = None
        m = re.match(r"^(.*) \|\|\| (.*)$", line)
        if m is None:
          text_a = line
        else:
          text_a = m.group(1)
          text_b = m.group(2)
        examples.append(
            InputExample(unique_id=unique_id, text_a=text_a, text_b=text_b))
        unique_id += 1
    return examples
  
  
  def main(_):
    tf.logging.set_verbosity(tf.logging.INFO)
  
    layer_indexes = [int(x) for x in FLAGS.layers.split(",")]
  
    bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
  
    tokenizer = tokenization.FullTokenizer(
        vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case)
  
    is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2
    run_config = tf.contrib.tpu.RunConfig(
        master=FLAGS.master,
        tpu_config=tf.contrib.tpu.TPUConfig(
            num_shards=FLAGS.num_tpu_cores,
            per_host_input_for_training=is_per_host))
  
    examples = read_examples(FLAGS.input_file)
  
    features = convert_examples_to_features(
        examples=examples, seq_length=FLAGS.max_seq_length, tokenizer=tokenizer)
  
    unique_id_to_feature = {}
    for feature in features:
      unique_id_to_feature[feature.unique_id] = feature
  
    model_fn = model_fn_builder(
        bert_config=bert_config,
        init_checkpoint=FLAGS.init_checkpoint,
        layer_indexes=layer_indexes,
        use_tpu=FLAGS.use_tpu,
        use_one_hot_embeddings=FLAGS.use_one_hot_embeddings)
  
    # If TPU is not available, this will fall back to normal Estimator on CPU
    # or GPU.
    estimator = tf.contrib.tpu.TPUEstimator(
        use_tpu=FLAGS.use_tpu,
        model_fn=model_fn,
        config=run_config,
        predict_batch_size=FLAGS.batch_size)
  
    input_fn = input_fn_builder(
        features=features, seq_length=FLAGS.max_seq_length)
  
    with codecs.getwriter("utf-8")(tf.gfile.Open(FLAGS.output_file,
                                                 "w")) as writer:
      for result in estimator.predict(input_fn, yield_single_examples=True):
        unique_id = int(result["unique_id"])
        feature = unique_id_to_feature[unique_id]
        output_json = collections.OrderedDict()
        output_json["linex_index"] = unique_id
        all_features = []
        for (i, token) in enumerate(feature.tokens):
          all_layers = []
          for (j, layer_index) in enumerate(layer_indexes):
            layer_output = result["layer_output_%d" % j]
            layers = collections.OrderedDict()
            layers["index"] = layer_index
            layers["values"] = [
                round(float(x), 6) for x in layer_output[i:(i + 1)].flat
            ]
            all_layers.append(layers)
          features = collections.OrderedDict()
          features["token"] = token
          features["layers"] = all_layers
          all_features.append(features)
        output_json["features"] = all_features
        writer.write(json.dumps(output_json) + "\n")
  
  
  if __name__ == "__main__":
    flags.mark_flag_as_required("input_file")
    flags.mark_flag_as_required("vocab_file")
    flags.mark_flag_as_required("bert_config_file")
    flags.mark_flag_as_required("init_checkpoint")
    flags.mark_flag_as_required("output_file")
    tf.app.run()
  