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.
  """Run BERT on SQuAD 1.1 and SQuAD 2.0."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import collections
  import json
  import math
  import os
  import random
  import modeling
  import optimization
  import tokenization
  import six
  import numpy as np
  import tensorflow as tf
  
  flags = tf.flags
  
  FLAGS = flags.FLAGS
  
  ## Required parameters
  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("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("train_file", None,
                      "SQuAD json for training. E.g., train-v1.1.json")
  
  flags.DEFINE_string(
      "predict_file", None,
      "SQuAD json for predictions. E.g., dev-v1.1.json or test-v1.1.json")
  
  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", 384,
      "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_integer(
      "doc_stride", 128,
      "When splitting up a long document into chunks, how much stride to "
      "take between chunks.")
  
  flags.DEFINE_integer(
      "max_query_length", 64,
      "The maximum number of tokens for the question. Questions longer than "
      "this will be truncated to this length.")
  
  flags.DEFINE_bool("do_train", False, "Whether to run training.")
  
  flags.DEFINE_bool("do_predict", False, "Whether to run eval on the dev set.")
  
  flags.DEFINE_integer("train_batch_size", 32, "Total batch size for training.")
  
  flags.DEFINE_integer("predict_batch_size", 8,
                       "Total batch size for predictions.")
  
  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_integer(
      "n_best_size", 20,
      "The total number of n-best predictions to generate in the "
      "nbest_predictions.json output file.")
  
  flags.DEFINE_integer(
      "max_answer_length", 30,
      "The maximum length of an answer that can be generated. This is needed "
      "because the start and end predictions are not conditioned on one another.")
  
  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_bool(
      "verbose_logging", False,
      "If true, all of the warnings related to data processing will be printed. "
      "A number of warnings are expected for a normal SQuAD evaluation.")
  
  flags.DEFINE_bool(
      "version_2_with_negative", False,
      "If true, the SQuAD examples contain some that do not have an answer.")
  
  flags.DEFINE_float(
      "null_score_diff_threshold", 0.0,
      "If null_score - best_non_null is greater than the threshold predict null.")
  
  tf.flags.DEFINE_string("bioasq_version", None, "[Optional] TensorFlow master URL.")
  tf.flags.DEFINE_string("bioasq_batch", None, "[Optional] TensorFlow master URL.")
  tf.flags.DEFINE_string("bioasq_task", None, "[Optional] TensorFlow master URL.")
  tf.flags.DEFINE_string("bioasq_snippet", None, "[Optional] TensorFlow master URL.")
  
  
  class SquadExample(object):
    """A single training/test example for simple sequence classification.
  
       For examples without an answer, the start and end position are -1.
    """
  
    def __init__(self,
                 qas_id,
                 question_text,
                 doc_tokens,
                 answer):
      self.qas_id = qas_id
      self.question_text = question_text
      self.doc_tokens = doc_tokens
      self.answer = answer
  
    def __str__(self):
      return self.__repr__()
  
    def __repr__(self):
      s = ""
      s += "qas_id: %s" % (tokenization.printable_text(self.qas_id))
      s += ", question_text: %s" % (
          tokenization.printable_text(self.question_text))
      s += ", doc_tokens: [%s]" % (" ".join(self.doc_tokens))
      if self.answer:
        s += ", answer: %r" % (self.answer)
      return s
  
  
  class InputFeatures(object):
    """A single set of features of data."""
  
    def __init__(self,
                 unique_id,
                 example_index,
                 doc_span_index,
                 tokens,
                 token_to_orig_map,
                 token_is_max_context,
                 input_ids,
                 input_mask,
                 segment_ids,
                 target):
      self.unique_id = unique_id
      self.example_index = example_index
      self.doc_span_index = doc_span_index
      self.tokens = tokens
      self.token_to_orig_map = token_to_orig_map
      self.token_is_max_context = token_is_max_context
      self.input_ids = input_ids
      self.input_mask = input_mask
      self.segment_ids = segment_ids
      self.target = target
  
  
  def read_squad_examples(input_file, is_training):
    """Read a SQuAD json file into a list of SquadExample."""
    is_bioasq=True # for BioASQ
  
    with tf.gfile.Open(input_file, "r") as reader:
      #if is_bioasq:
        #input_data = [{u'paragraphs':json.load(reader)["questions"], u'title':'bioASQ'}] # to fit the shape of squad code
      #else:
      input_data = json.load(reader)["data"]
  
    def is_whitespace(c):
      if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F:
        return True
      return False
  
    examples = []
    for entry in input_data:
      for paragraph in entry["paragraphs"]:
        paragraph_text = paragraph["context"]
        doc_tokens = []
        char_to_word_offset = []
        prev_is_whitespace = True
        if is_bioasq:
          paragraph_text.replace('/',' ')  # need review
        for c in paragraph_text:
          if is_whitespace(c):
            prev_is_whitespace = True
          else:
            if prev_is_whitespace:
              doc_tokens.append(c)
            else:
              doc_tokens[-1] += c
            prev_is_whitespace = False
          char_to_word_offset.append(len(doc_tokens) - 1)
  
        for qa in paragraph["qas"]:
          qas_id = qa["id"]
          question_text = qa["question"]
          answer = None
          if is_training:
            assert (qa["is_impossible"] == True) != (qa["answers"] == "yes")
            assert qa["answers"] in ["yes", "no"]
            answer = 1 if qa["answers"] == 'yes' else 0
  
          example = SquadExample(
              qas_id=qas_id,
              question_text=question_text,
              doc_tokens=doc_tokens,
              answer=answer
          )
          examples.append(example)
  
    target_cnt = 500
    if is_training:
      pos_cnt = sum([1 for example in examples if example.answer == 1])
      neg_cnt = sum([1 for example in examples if example.answer == 0])
      print()
      print('Imbalance btw {} vs {}'.format(pos_cnt, neg_cnt))
      # rng = random.Random(12345)
      random.shuffle(examples)
  
      new_examples = []
      new_pos_cnt = 0
      new_neg_cnt = 0
      for example in examples:
        if example.answer == 1 and new_pos_cnt >= target_cnt:
          continue
        if example.answer == 0 and new_neg_cnt >= target_cnt:
          continue
        else:
          new_examples.append(example)
          new_pos_cnt += (1 if example.answer == 1 else 0)
          new_neg_cnt += (1 if example.answer == 0 else 0)
  
      pos_cnt = sum([1 for example in new_examples if example.answer == 1])
      neg_cnt = sum([1 for example in new_examples if example.answer == 0])
      random.shuffle(new_examples)
      print('Balanced as {} vs {}'.format(pos_cnt, neg_cnt))
      print('Sample: {}'.format(new_examples[0]))
      return new_examples
    else:
      return examples
  
  
  def convert_examples_to_features(examples, tokenizer, max_seq_length,
                                   doc_stride, max_query_length, is_training,
                                   output_fn):
    """Loads a data file into a list of `InputBatch`s."""
  
    unique_id = 1000000000
  
    for (example_index, example) in enumerate(examples):
      query_tokens = tokenizer.tokenize(example.question_text)
  
      if len(query_tokens) > max_query_length:
        query_tokens = query_tokens[0:max_query_length]
  
      tok_to_orig_index = []
      orig_to_tok_index = []
      all_doc_tokens = []
      for (i, token) in enumerate(example.doc_tokens):
        orig_to_tok_index.append(len(all_doc_tokens))
        sub_tokens = tokenizer.tokenize(token)
        for sub_token in sub_tokens:
          tok_to_orig_index.append(i)
          all_doc_tokens.append(sub_token)
  
      target = example.answer
  
      # The -3 accounts for [CLS], [SEP] and [SEP]
      max_tokens_for_doc = max_seq_length - len(query_tokens) - 3
  
      # We can have documents that are longer than the maximum sequence length.
      # To deal with this we do a sliding window approach, where we take chunks
      # of the up to our max length with a stride of `doc_stride`.
      _DocSpan = collections.namedtuple(  # pylint: disable=invalid-name
          "DocSpan", ["start", "length"])
      doc_spans = []
      start_offset = 0
      while start_offset < len(all_doc_tokens):
        length = len(all_doc_tokens) - start_offset
        if length > max_tokens_for_doc:
          length = max_tokens_for_doc
        doc_spans.append(_DocSpan(start=start_offset, length=length))
        if start_offset + length == len(all_doc_tokens):
          break
        start_offset += min(length, doc_stride)
  
      for (doc_span_index, doc_span) in enumerate(doc_spans):
        tokens = []
        token_to_orig_map = {}
        token_is_max_context = {}
        segment_ids = []
        tokens.append("[CLS]")
        segment_ids.append(0)
        for token in query_tokens:
          tokens.append(token)
          segment_ids.append(0)
        tokens.append("[SEP]")
        segment_ids.append(0)
  
        for i in range(doc_span.length):
          split_token_index = doc_span.start + i
          token_to_orig_map[len(tokens)] = tok_to_orig_index[split_token_index]
  
          is_max_context = _check_is_max_context(doc_spans, doc_span_index,
                                                 split_token_index)
          token_is_max_context[len(tokens)] = is_max_context
          tokens.append(all_doc_tokens[split_token_index])
          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
  
        if example_index < 1:
          tf.logging.info("*** Example ***")
          tf.logging.info("unique_id: %s" % (unique_id))
          # tf.logging.info("example_index: %s" % (example_index))
          # tf.logging.info("doc_span_index: %s" % (doc_span_index))
          tf.logging.info("tokens: %s" % " ".join(
              [tokenization.printable_text(x) for x in tokens]))
          # tf.logging.info("token_to_orig_map: %s" % " ".join(
          #     ["%d:%d" % (x, y) for (x, y) in six.iteritems(token_to_orig_map)]))
          # tf.logging.info("token_is_max_context: %s" % " ".join([
          #     "%d:%s" % (x, y) for (x, y) in six.iteritems(token_is_max_context)
          # ]))
          # 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]))
          if is_training:
            tf.logging.info(
                "target: %d" % (target))
  
        feature = InputFeatures(
            unique_id=unique_id,
            example_index=example_index,
            doc_span_index=doc_span_index,
            tokens=tokens,
            token_to_orig_map=token_to_orig_map,
            token_is_max_context=token_is_max_context,
            input_ids=input_ids,
            input_mask=input_mask,
            segment_ids=segment_ids,
            target=target)
  
        # Run callback
        output_fn(feature)
        unique_id += 1
  
  
  def _check_is_max_context(doc_spans, cur_span_index, position):
    """Check if this is the 'max context' doc span for the token."""
  
    # Because of the sliding window approach taken to scoring documents, a single
    # token can appear in multiple documents. E.g.
    #  Doc: the man went to the store and bought a gallon of milk
    #  Span A: the man went to the
    #  Span B: to the store and bought
    #  Span C: and bought a gallon of
    #  ...
    #
    # Now the word 'bought' will have two scores from spans B and C. We only
    # want to consider the score with "maximum context", which we define as
    # the *minimum* of its left and right context (the *sum* of left and
    # right context will always be the same, of course).
    #
    # In the example the maximum context for 'bought' would be span C since
    # it has 1 left context and 3 right context, while span B has 4 left context
    # and 0 right context.
    best_score = None
    best_span_index = None
    for (span_index, doc_span) in enumerate(doc_spans):
      end = doc_span.start + doc_span.length - 1
      if position < doc_span.start:
        continue
      if position > end:
        continue
      num_left_context = position - doc_span.start
      num_right_context = end - position
      score = min(num_left_context, num_right_context) + 0.01 * doc_span.length
      if best_score is None or score > best_score:
        best_score = score
        best_span_index = span_index
  
    return cur_span_index == best_span_index
  
  
  def create_model(bert_config, is_training, input_ids, input_mask, segment_ids,
                   use_one_hot_embeddings):
    """Creates a classification model."""
    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)
  
    final_hidden = model.get_sequence_output()
  
    final_hidden_shape = modeling.get_shape_list(final_hidden, expected_rank=3)
    batch_size = final_hidden_shape[0]
    seq_length = final_hidden_shape[1]
    hidden_size = final_hidden_shape[2]
  
    # Standard weight (for start/end)
    '''
    output_weights = tf.get_variable(
        "cls/squad/output_weights", [2, hidden_size],
        initializer=tf.truncated_normal_initializer(stddev=0.02))
    output_bias = tf.get_variable(
        "cls/squad/output_bias", [2], initializer=tf.zeros_initializer())
  
    '''
    # Sigmoid weight
    output_weights_sgm = tf.get_variable(
        "cls/squad/output_weights_sgm", [1, hidden_size],
        initializer=tf.truncated_normal_initializer(stddev=0.02))
    output_bias_sgm = tf.get_variable(
        "cls/squad/output_bias_sgm", [1], initializer=tf.zeros_initializer())
  
    # Get first CLS token
    cls_hidden = final_hidden[:,0,:]
    # cls_hidden = tf.layers.dropout(cls_hidden, rate=0.5, training=is_training)
  
    # Standard logit
    '''
    logits = tf.matmul(cls_hidden, output_weights, transpose_b=True)
    logits = tf.nn.bias_add(logits, output_bias)
    '''
  
    # Sigmoid logit
    logits = tf.matmul(cls_hidden, output_weights_sgm, transpose_b=True)
    logits = tf.nn.bias_add(logits, output_bias_sgm)
  
    return logits
  
  
  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))
  
      unique_ids = features["unique_ids"]
      input_ids = features["input_ids"]
      input_mask = features["input_mask"]
      segment_ids = features["segment_ids"]
  
      is_training = (mode == tf.estimator.ModeKeys.TRAIN)
  
      logits = create_model(
          bert_config=bert_config,
          is_training=is_training,
          input_ids=input_ids,
          input_mask=input_mask,
          segment_ids=segment_ids,
          use_one_hot_embeddings=use_one_hot_embeddings)
  
      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)
        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)
        break
  
      output_spec = None
      if mode == tf.estimator.ModeKeys.TRAIN:
        seq_length = modeling.get_shape_list(input_ids)[1]
  
        def compute_loss(logits, targets):
          '''
          one_hot_positions = tf.one_hot(
              targets, depth=2, dtype=tf.float32)
          log_probs = tf.nn.log_softmax(logits, axis=-1)
          loss = -tf.reduce_mean(
              tf.reduce_sum(one_hot_positions * log_probs, axis=-1))
          '''
          logits = tf.squeeze(logits, axis=1)
          loss = tf.nn.sigmoid_cross_entropy_with_logits(
              labels=tf.cast(targets, tf.float32),
              logits=logits
          )
          loss = tf.reduce_mean(loss)
          return loss
  
        targets = features["target"]
        total_loss = compute_loss(logits, targets)
  
        train_op = optimization.create_optimizer(
            total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu)
        logging_hook = tf.train.LoggingTensorHook({"loss" : total_loss}, every_n_iter=50)
  
        output_spec = tf.contrib.tpu.TPUEstimatorSpec(
            mode=mode,
            loss=total_loss,
            train_op=train_op,
            scaffold_fn=scaffold_fn,
            training_hooks=[logging_hook])
        print("new model!!")
      elif mode == tf.estimator.ModeKeys.PREDICT:
        predictions = {
            "unique_ids": unique_ids,
            "logits": logits,
        }
        output_spec = tf.contrib.tpu.TPUEstimatorSpec(
            mode=mode, predictions=predictions, scaffold_fn=scaffold_fn)
      else:
        raise ValueError(
            "Only TRAIN and PREDICT modes are supported: %s" % (mode))
  
      return output_spec
  
    return model_fn
  
  
  def input_fn_builder(input_file, seq_length, is_training, drop_remainder):
    """Creates an `input_fn` closure to be passed to TPUEstimator."""
  
    name_to_features = {
        "unique_ids": tf.FixedLenFeature([], tf.int64),
        "input_ids": tf.FixedLenFeature([seq_length], tf.int64),
        "input_mask": tf.FixedLenFeature([seq_length], tf.int64),
        "segment_ids": tf.FixedLenFeature([seq_length], tf.int64),
    }
  
    if is_training:
      name_to_features["target"] = 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(None)
        # 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
  
  
  RawResult = collections.namedtuple("RawResult",
                                     ["unique_id", "logits"])
  
  
  def write_predictions(all_examples, all_features, all_results, n_best_size,
                        max_answer_length, do_lower_case, output_prediction_file,
                        output_nbest_file, output_null_log_odds_file):
    """Write final predictions to the json file and log-odds of null if needed."""
    tf.logging.info("Writing predictions to: %s" % (output_prediction_file))
    tf.logging.info("Writing nbest to: %s" % (output_nbest_file))
  
    example_index_to_features = collections.defaultdict(list)
    for feature in all_features:
      example_index_to_features[feature.example_index].append(feature)
  
    unique_id_to_result = {}
    for result in all_results:
      unique_id_to_result[result.unique_id] = result
  
    _PrelimPrediction = collections.namedtuple(  # pylint: disable=invalid-name
        "PrelimPrediction",
        ["feature_index", "answer", "logit"])
  
    all_predictions = collections.OrderedDict()
    all_nbest_json = collections.OrderedDict()
    scores_diff_json = collections.OrderedDict()
  
    for (example_index, example) in enumerate(all_examples):
      features = example_index_to_features[example_index]
  
      prelim_predictions = []
      # keep track of the minimum score of null start+end of position 0
      score_null = 1000000  # large and positive
      for (feature_index, feature) in enumerate(features):
        result = unique_id_to_result[feature.unique_id]
        # logits = result.logits[0]
        logits = result.logits
        answer = 'yes' if logits[0] > 0.5 else 'no'
        # answer = 'yes' if np.argmax(logits) == 1 else 'no'
  
        prelim_predictions.append(
            _PrelimPrediction(
                feature_index=feature_index,
                answer=answer,
                # logit=max(logits)))
                logit=logits))
  
        break
  
      assert len(prelim_predictions) == 1
      # Not used..
      # probs = _compute_softmax(logits)
      probs = logits
      for (i, entry) in enumerate(prelim_predictions):
        output = collections.OrderedDict()
        output["text"] = entry.answer
        output["probability"] = probs
        output["logit"] = entry.logit
        output["question_text"] = entry.question_text
  
      all_predictions[example.qas_id] = [prelim_predictions[0].answer, probs]
  
    with tf.gfile.GFile(output_prediction_file, "w") as writer:
      writer.write(json.dumps(all_predictions, indent=4) + "\n")
  
  def get_final_text(pred_text, orig_text, do_lower_case):
    """Project the tokenized prediction back to the original text."""
  
    # When we created the data, we kept track of the alignment between original
    # (whitespace tokenized) tokens and our WordPiece tokenized tokens. So
    # now `orig_text` contains the span of our original text corresponding to the
    # span that we predicted.
    #
    # However, `orig_text` may contain extra characters that we don't want in
    # our prediction.
    #
    # For example, let's say:
    #   pred_text = steve smith
    #   orig_text = Steve Smith's
    #
    # We don't want to return `orig_text` because it contains the extra "'s".
    #
    # We don't want to return `pred_text` because it's already been normalized
    # (the SQuAD eval script also does punctuation stripping/lower casing but
    # our tokenizer does additional normalization like stripping accent
    # characters).
    #
    # What we really want to return is "Steve Smith".
    #
    # Therefore, we have to apply a semi-complicated alignment heruistic between
    # `pred_text` and `orig_text` to get a character-to-charcter alignment. This
    # can fail in certain cases in which case we just return `orig_text`.
  
    def _strip_spaces(text):
      ns_chars = []
      ns_to_s_map = collections.OrderedDict()
      for (i, c) in enumerate(text):
        if c == " ":
          continue
        ns_to_s_map[len(ns_chars)] = i
        ns_chars.append(c)
      ns_text = "".join(ns_chars)
      return (ns_text, ns_to_s_map)
  
    # We first tokenize `orig_text`, strip whitespace from the result
    # and `pred_text`, and check if they are the same length. If they are
    # NOT the same length, the heuristic has failed. If they are the same
    # length, we assume the characters are one-to-one aligned.
    tokenizer = tokenization.BasicTokenizer(do_lower_case=do_lower_case)
  
    tok_text = " ".join(tokenizer.tokenize(orig_text))
  
    start_position = tok_text.find(pred_text)
    if start_position == -1:
      if FLAGS.verbose_logging:
        tf.logging.info(
            "Unable to find text: '%s' in '%s'" % (pred_text, orig_text))
      return orig_text
    end_position = start_position + len(pred_text) - 1
  
    (orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text)
    (tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text)
  
    if len(orig_ns_text) != len(tok_ns_text):
      if FLAGS.verbose_logging:
        tf.logging.info("Length not equal after stripping spaces: '%s' vs '%s'",
                        orig_ns_text, tok_ns_text)
      return orig_text
  
    # We then project the characters in `pred_text` back to `orig_text` using
    # the character-to-character alignment.
    tok_s_to_ns_map = {}
    for (i, tok_index) in six.iteritems(tok_ns_to_s_map):
      tok_s_to_ns_map[tok_index] = i
  
    orig_start_position = None
    if start_position in tok_s_to_ns_map:
      ns_start_position = tok_s_to_ns_map[start_position]
      if ns_start_position in orig_ns_to_s_map:
        orig_start_position = orig_ns_to_s_map[ns_start_position]
  
    if orig_start_position is None:
      if FLAGS.verbose_logging:
        tf.logging.info("Couldn't map start position")
      return orig_text
  
    orig_end_position = None
    if end_position in tok_s_to_ns_map:
      ns_end_position = tok_s_to_ns_map[end_position]
      if ns_end_position in orig_ns_to_s_map:
        orig_end_position = orig_ns_to_s_map[ns_end_position]
  
    if orig_end_position is None:
      if FLAGS.verbose_logging:
        tf.logging.info("Couldn't map end position")
      return orig_text
  
    output_text = orig_text[orig_start_position:(orig_end_position + 1)]
    return output_text
  
  
  def _compute_softmax(scores):
    """Compute softmax probability over raw logits."""
    if not scores:
      return []
  
    max_score = None
    for score in scores:
      if max_score is None or score > max_score:
        max_score = score
  
    exp_scores = []
    total_sum = 0.0
    for score in scores:
      x = math.exp(score - max_score)
      exp_scores.append(x)
      total_sum += x
  
    probs = []
    for score in exp_scores:
      probs.append(score / total_sum)
    return probs
  
  
  class FeatureWriter(object):
    """Writes InputFeature to TF example file."""
  
    def __init__(self, filename, is_training):
      self.filename = filename
      self.is_training = is_training
      self.num_features = 0
      self.num_pos_features = 0
      self._writer = tf.python_io.TFRecordWriter(filename)
  
    def process_feature(self, feature):
      """Write a InputFeature to the TFRecordWriter as a tf.train.Example."""
      self.num_features += 1
      self.num_pos_features += 1 if feature.target == 1 else 0
  
      def create_int_feature(values):
        feature = tf.train.Feature(
            int64_list=tf.train.Int64List(value=list(values)))
        return feature
  
      features = collections.OrderedDict()
      features["unique_ids"] = create_int_feature([feature.unique_id])
      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)
  
      if self.is_training:
        features["target"] = create_int_feature([feature.target])
  
      tf_example = tf.train.Example(features=tf.train.Features(feature=features))
      self._writer.write(tf_example.SerializeToString())
  
    def close(self):
      self._writer.close()
  
  
  def validate_flags_or_throw(bert_config):
    """Validate the input FLAGS or throw an exception."""
    tokenization.validate_case_matches_checkpoint(FLAGS.do_lower_case,
                                                  FLAGS.init_checkpoint)
  
    if not FLAGS.do_train and not FLAGS.do_predict:
      raise ValueError("At least one of `do_train` or `do_predict` must be True.")
  
    if FLAGS.do_train:
      if not FLAGS.train_file:
        raise ValueError(
            "If `do_train` is True, then `train_file` must be specified.")
    if FLAGS.do_predict:
      if not FLAGS.predict_file:
        raise ValueError(
            "If `do_predict` is True, then `predict_file` must be specified.")
  
    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))
  
    if FLAGS.max_seq_length <= FLAGS.max_query_length + 3:
      raise ValueError(
          "The max_seq_length (%d) must be greater than max_query_length "
          "(%d) + 3" % (FLAGS.max_seq_length, FLAGS.max_query_length))
  
  
  def main(_):
    #seed = 1111
    #random.seed(seed)
    #np.random.seed(seed)
    #tf.set_random_seed(seed)
    #tf.logging.set_verbosity(tf.logging.INFO)
  
    bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
  
    validate_flags_or_throw(bert_config)
  
    tf.gfile.MakeDirs(FLAGS.output_dir)
  
    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
    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))
  
    num_train_steps = None
    num_warmup_steps = None
    if FLAGS.do_train:
      train_examples = read_squad_examples(
          input_file=FLAGS.train_file, is_training=True)
      num_train_steps = int(math.ceil(len(train_examples) / FLAGS.train_batch_size) * FLAGS.num_train_epochs)
      # num_train_steps = FLAGS.save_checkpoints_steps
      num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion)
  
      # Re-define run_config for training
      run_config = tf.contrib.tpu.RunConfig(
          cluster=tpu_cluster_resolver,
          master=FLAGS.master,
          model_dir=FLAGS.output_dir,
          save_checkpoints_steps=int(num_train_steps/FLAGS.num_train_epochs),
          tpu_config=tf.contrib.tpu.TPUConfig(
              iterations_per_loop=int(num_train_steps/FLAGS.num_train_epochs),
              num_shards=FLAGS.num_tpu_cores,
              per_host_input_for_training=is_per_host))
  
      rng = random.Random(12345)
      rng.shuffle(train_examples)
  
    model_fn = model_fn_builder(
        bert_config=bert_config,
        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)
  
    # 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,
        train_batch_size=FLAGS.train_batch_size,
        predict_batch_size=FLAGS.predict_batch_size)
  
    if FLAGS.do_train:
      # We write to a temporary file to avoid storing very large constant tensors
      # in memory.
      train_writer = FeatureWriter(
          filename=os.path.join(FLAGS.output_dir, "train.tf_record"),
          is_training=True)
  
      # Bootstrap
      for epoch_idx in range(int(FLAGS.num_train_epochs)):
          train_examples = read_squad_examples(
              input_file=FLAGS.train_file, is_training=True)
  
          convert_examples_to_features(
              examples=train_examples,
              tokenizer=tokenizer,
              max_seq_length=FLAGS.max_seq_length,
              doc_stride=FLAGS.doc_stride,
              max_query_length=FLAGS.max_query_length,
              is_training=True,
              output_fn=train_writer.process_feature)
  
      train_writer.close()
      tf.logging.info("***** Running training *****")
      tf.logging.info("  Num orig examples = %d", len(train_examples) * int(FLAGS.num_train_epochs))
      tf.logging.info("  Num split examples = %d", train_writer.num_features)
      tf.logging.info("  Num pos examples = %d", train_writer.num_pos_features)
      tf.logging.info("  Batch size = %d", FLAGS.train_batch_size)
      tf.logging.info("  Num steps = %d", num_train_steps)
      tf.logging.info("  Num warmup steps = %d", num_warmup_steps)
  
      train_input_fn = input_fn_builder(
          input_file=train_writer.filename,
          seq_length=FLAGS.max_seq_length,
          is_training=True,
          drop_remainder=True)
  
      # estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)
      estimator.train(input_fn=train_input_fn, steps=num_train_steps)
  
    if FLAGS.do_predict:
      eval_examples = read_squad_examples(
          input_file=FLAGS.predict_file, is_training=False)
  
      eval_writer = FeatureWriter(
          filename=os.path.join(FLAGS.output_dir, "eval.tf_record"),
          is_training=False)
      eval_features = []
  
      def append_feature(feature):
        eval_features.append(feature)
        eval_writer.process_feature(feature)
  
      convert_examples_to_features(
          examples=eval_examples,
          tokenizer=tokenizer,
          max_seq_length=FLAGS.max_seq_length,
          doc_stride=FLAGS.doc_stride,
          max_query_length=FLAGS.max_query_length,
          is_training=False,
          output_fn=append_feature)
      eval_writer.close()
  
      tf.logging.info("***** Running predictions *****")
      tf.logging.info("  Num orig examples = %d", len(eval_examples))
      tf.logging.info("  Num split examples = %d", len(eval_features))
      tf.logging.info("  Batch size = %d", FLAGS.predict_batch_size)
  
      all_results = []
  
      predict_input_fn = input_fn_builder(
          input_file=eval_writer.filename,
          seq_length=FLAGS.max_seq_length,
          is_training=False,
          drop_remainder=False)
  
      # If running eval on the TPU, you will need to specify the number of
      # steps.
      all_results = []
      for result in estimator.predict(
          predict_input_fn, yield_single_examples=True):
        if len(all_results) % 100 == 0:
          tf.logging.info("Processing example: %d" % (len(all_results)))
        unique_id = int(result["unique_ids"])
        logits = [float(x) for x in result["logits"].flat]
        all_results.append(
            RawResult(
                unique_id=unique_id,
                logits=logits))
  
      output_prediction_file = os.path.join(FLAGS.output_dir, "predictions.json")
      #output_prediction_file = os.path.join(FLAGS.output_dir, "predictions_{}b{}_{}{}.json".format(
      #    FLAGS.bioasq_version, FLAGS.bioasq_batch, FLAGS.bioasq_task, FLAGS.bioasq_snippet))
      output_nbest_file = os.path.join(FLAGS.output_dir, "nbest_predictions.json")
      output_null_log_odds_file = os.path.join(FLAGS.output_dir, "null_odds.json")
  
      write_predictions(eval_examples, eval_features, all_results,
                        FLAGS.n_best_size, FLAGS.max_answer_length,
                        FLAGS.do_lower_case, output_prediction_file,
                        output_nbest_file, output_null_log_odds_file)
  
  
  if __name__ == "__main__":
    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()
  