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
  from bert import modeling
  from bert import optimization
  from bert import tokenization
  import six
  import tensorflow as tf
  from tensorflow.contrib import cluster_resolver as contrib_cluster_resolver
  from tensorflow.contrib import data as contrib_data
  from tensorflow.contrib import tpu as contrib_tpu
  
  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")
  
  ## Other parameters
  flags.DEFINE_string("exp_name", "default_experiment",
                      "Unique experiment name to prevent file collisions")
  
  flags.DEFINE_string(
      "predict_input_file", None,
      "SQuAD json for predictions. E.g., dev-v1.1.json or test-v1.1.json")
  
  flags.DEFINE_string("predict_output_dir", None,
                      "Output directory for prediction outputs.")
  
  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", 5000,
                       "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.")
  
  
  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,
                 orig_answer_text=None,
                 start_position=None,
                 end_position=None,
                 is_impossible=False):
      self.qas_id = qas_id
      self.question_text = question_text
      self.doc_tokens = doc_tokens
      self.orig_answer_text = orig_answer_text
      self.start_position = start_position
      self.end_position = end_position
      self.is_impossible = is_impossible
  
    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.start_position:
        s += ", start_position: %d" % (self.start_position)
      if self.start_position:
        s += ", end_position: %d" % (self.end_position)
      if self.start_position:
        s += ", is_impossible: %r" % (self.is_impossible)
      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,
                 start_position=None,
                 end_position=None,
                 is_impossible=None):
      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.start_position = start_position
      self.end_position = end_position
      self.is_impossible = is_impossible
  
  
  def read_squad_examples(input_file, is_training):
    """Read a SQuAD json file into a list of SquadExample."""
    with tf.gfile.Open(input_file, "r") as reader:
      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
        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"]
          start_position = None
          end_position = None
          orig_answer_text = None
          is_impossible = False
          if is_training:
  
            if FLAGS.version_2_with_negative:
              is_impossible = qa["is_impossible"]
            if (len(qa["answers"]) != 1) and (not is_impossible):
              raise ValueError(
                  "For training, each question should have exactly 1 answer.")
            if not is_impossible:
              answer = qa["answers"][0]
              orig_answer_text = answer["text"]
              answer_offset = answer["answer_start"]
              answer_length = len(orig_answer_text)
              start_position = char_to_word_offset[answer_offset]
              end_position = char_to_word_offset[answer_offset + answer_length -
                                                 1]
              # Only add answers where the text can be exactly recovered from the
              # document. If this CAN'T happen it's likely due to weird Unicode
              # stuff so we will just skip the example.
              #
              # Note that this means for training mode, every example is NOT
              # guaranteed to be preserved.
              actual_text = " ".join(doc_tokens[start_position:(end_position +
                                                                1)])
              cleaned_answer_text = " ".join(
                  tokenization.whitespace_tokenize(orig_answer_text))
              if actual_text.find(cleaned_answer_text) == -1:
                tf.logging.warning("Could not find answer: '%s' vs. '%s'",
                                   actual_text, cleaned_answer_text)
                continue
            else:
              start_position = -1
              end_position = -1
              orig_answer_text = ""
  
          example = SquadExample(
              qas_id=qas_id,
              question_text=question_text,
              doc_tokens=doc_tokens,
              orig_answer_text=orig_answer_text,
              start_position=start_position,
              end_position=end_position,
              is_impossible=is_impossible)
          examples.append(example)
  
    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)
  
      tok_start_position = None
      tok_end_position = None
      if is_training and example.is_impossible:
        tok_start_position = -1
        tok_end_position = -1
      if is_training and not example.is_impossible:
        tok_start_position = orig_to_tok_index[example.start_position]
        if example.end_position < len(example.doc_tokens) - 1:
          tok_end_position = orig_to_tok_index[example.end_position + 1] - 1
        else:
          tok_end_position = len(all_doc_tokens) - 1
        (tok_start_position, tok_end_position) = _improve_answer_span(
            all_doc_tokens, tok_start_position, tok_end_position, tokenizer,
            example.orig_answer_text)
  
      # 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
  
        start_position = None
        end_position = None
        if is_training and not example.is_impossible:
          # For training, if our document chunk does not contain an annotation
          # we throw it out, since there is nothing to predict.
          doc_start = doc_span.start
          doc_end = doc_span.start + doc_span.length - 1
          out_of_span = False
          if not (tok_start_position >= doc_start and
                  tok_end_position <= doc_end):
            out_of_span = True
          if out_of_span:
            start_position = 0
            end_position = 0
          else:
            doc_offset = len(query_tokens) + 2
            start_position = tok_start_position - doc_start + doc_offset
            end_position = tok_end_position - doc_start + doc_offset
  
        if is_training and example.is_impossible:
          start_position = 0
          end_position = 0
  
        if example_index < 20:
          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 and example.is_impossible:
            tf.logging.info("impossible example")
          if is_training and not example.is_impossible:
            answer_text = " ".join(tokens[start_position:(end_position + 1)])
            tf.logging.info("start_position: %d" % (start_position))
            tf.logging.info("end_position: %d" % (end_position))
            tf.logging.info("answer: %s" %
                            (tokenization.printable_text(answer_text)))
  
        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,
            start_position=start_position,
            end_position=end_position,
            is_impossible=example.is_impossible)
  
        # Run callback
        output_fn(feature)
  
        unique_id += 1
  
  
  def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer,
                           orig_answer_text):
    """Returns tokenized answer spans that better match the annotated answer."""
  
    # The SQuAD annotations are character based. We first project them to
    # whitespace-tokenized words. But then after WordPiece tokenization, we can
    # often find a "better match". For example:
    #
    #   Question: What year was John Smith born?
    #   Context: The leader was John Smith (1895-1943).
    #   Answer: 1895
    #
    # The original whitespace-tokenized answer will be "(1895-1943).". However
    # after tokenization, our tokens will be "( 1895 - 1943 ) .". So we can match
    # the exact answer, 1895.
    #
    # However, this is not always possible. Consider the following:
    #
    #   Question: What country is the top exporter of electornics?
    #   Context: The Japanese electronics industry is the lagest in the world.
    #   Answer: Japan
    #
    # In this case, the annotator chose "Japan" as a character sub-span of
    # the word "Japanese". Since our WordPiece tokenizer does not split
    # "Japanese", we just use "Japanese" as the annotation. This is fairly rare
    # in SQuAD, but does happen.
    tok_answer_text = " ".join(tokenizer.tokenize(orig_answer_text))
  
    for new_start in range(input_start, input_end + 1):
      for new_end in range(input_end, new_start - 1, -1):
        text_span = " ".join(doc_tokens[new_start:(new_end + 1)])
        if text_span == tok_answer_text:
          return (new_start, new_end)
  
    return (input_start, input_end)
  
  
  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]
  
    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())
  
    final_hidden_matrix = tf.reshape(final_hidden,
                                     [batch_size * seq_length, hidden_size])
    logits = tf.matmul(final_hidden_matrix, output_weights, transpose_b=True)
    logits = tf.nn.bias_add(logits, output_bias)
  
    logits = tf.reshape(logits, [batch_size, seq_length, 2])
    logits = tf.transpose(logits, [2, 0, 1])
  
    unstacked_logits = tf.unstack(logits, axis=0)
  
    (start_logits, end_logits) = (unstacked_logits[0], unstacked_logits[1])
  
    return (start_logits, end_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)
  
      (start_logits, end_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)
  
      output_spec = None
      if mode == tf.estimator.ModeKeys.TRAIN:
        seq_length = modeling.get_shape_list(input_ids)[1]
  
        def compute_loss(logits, positions):
          one_hot_positions = tf.one_hot(
              positions, depth=seq_length, 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))
          return loss
  
        start_positions = features["start_positions"]
        end_positions = features["end_positions"]
  
        start_loss = compute_loss(start_logits, start_positions)
        end_loss = compute_loss(end_logits, end_positions)
  
        total_loss = (start_loss + end_loss) / 2.0
  
        train_op = optimization.create_optimizer(total_loss, learning_rate,
                                                 num_train_steps,
                                                 num_warmup_steps, use_tpu)
  
        output_spec = contrib_tpu.TPUEstimatorSpec(
            mode=mode,
            loss=total_loss,
            train_op=train_op,
            scaffold_fn=scaffold_fn)
      elif mode == tf.estimator.ModeKeys.PREDICT:
        predictions = {
            "unique_ids": unique_ids,
            "start_logits": start_logits,
            "end_logits": end_logits,
        }
        output_spec = 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["start_positions"] = tf.FixedLenFeature([], tf.int64)
      name_to_features["end_positions"] = 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(
          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", "start_logits", "end_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", "start_index", "end_index", "start_logit", "end_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
      min_null_feature_index = 0  # the paragraph slice with min mull score
      null_start_logit = 0  # the start logit at the slice with min null score
      null_end_logit = 0  # the end logit at the slice with min null score
      for (feature_index, feature) in enumerate(features):
        result = unique_id_to_result[feature.unique_id]
        start_indexes = _get_best_indexes(result.start_logits, n_best_size)
        end_indexes = _get_best_indexes(result.end_logits, n_best_size)
        # if we could have irrelevant answers, get the min score of irrelevant
        if FLAGS.version_2_with_negative:
          feature_null_score = result.start_logits[0] + result.end_logits[0]
          if feature_null_score < score_null:
            score_null = feature_null_score
            min_null_feature_index = feature_index
            null_start_logit = result.start_logits[0]
            null_end_logit = result.end_logits[0]
        for start_index in start_indexes:
          for end_index in end_indexes:
            # We could hypothetically create invalid predictions, e.g., predict
            # that the start of the span is in the question. We throw out all
            # invalid predictions.
            if start_index >= len(feature.tokens):
              continue
            if end_index >= len(feature.tokens):
              continue
            if start_index not in feature.token_to_orig_map:
              continue
            if end_index not in feature.token_to_orig_map:
              continue
            if not feature.token_is_max_context.get(start_index, False):
              continue
            if end_index < start_index:
              continue
            length = end_index - start_index + 1
            if length > max_answer_length:
              continue
            prelim_predictions.append(
                _PrelimPrediction(
                    feature_index=feature_index,
                    start_index=start_index,
                    end_index=end_index,
                    start_logit=result.start_logits[start_index],
                    end_logit=result.end_logits[end_index]))
  
      if FLAGS.version_2_with_negative:
        prelim_predictions.append(
            _PrelimPrediction(
                feature_index=min_null_feature_index,
                start_index=0,
                end_index=0,
                start_logit=null_start_logit,
                end_logit=null_end_logit))
      prelim_predictions = sorted(
          prelim_predictions,
          key=lambda x: (x.start_logit + x.end_logit),
          reverse=True)
  
      _NbestPrediction = collections.namedtuple(  # pylint: disable=invalid-name
          "NbestPrediction", ["text", "start_logit", "end_logit"])
  
      seen_predictions = {}
      nbest = []
      for pred in prelim_predictions:
        if len(nbest) >= n_best_size:
          break
        feature = features[pred.feature_index]
        if pred.start_index > 0:  # this is a non-null prediction
          tok_tokens = feature.tokens[pred.start_index:(pred.end_index + 1)]
          orig_doc_start = feature.token_to_orig_map[pred.start_index]
          orig_doc_end = feature.token_to_orig_map[pred.end_index]
          orig_tokens = example.doc_tokens[orig_doc_start:(orig_doc_end + 1)]
          tok_text = " ".join(tok_tokens)
  
          # De-tokenize WordPieces that have been split off.
          tok_text = tok_text.replace(" ##", "")
          tok_text = tok_text.replace("##", "")
  
          # Clean whitespace
          tok_text = tok_text.strip()
          tok_text = " ".join(tok_text.split())
          orig_text = " ".join(orig_tokens)
  
          final_text = get_final_text(tok_text, orig_text, do_lower_case)
          if final_text in seen_predictions:
            continue
  
          seen_predictions[final_text] = True
        else:
          final_text = ""
          seen_predictions[final_text] = True
  
        nbest.append(
            _NbestPrediction(
                text=final_text,
                start_logit=pred.start_logit,
                end_logit=pred.end_logit))
  
      # if we didn't inlude the empty option in the n-best, inlcude it
      if FLAGS.version_2_with_negative:
        if "" not in seen_predictions:
          nbest.append(
              _NbestPrediction(
                  text="", start_logit=null_start_logit,
                  end_logit=null_end_logit))
      # In very rare edge cases we could have no valid predictions. So we
      # just create a nonce prediction in this case to avoid failure.
      if not nbest:
        nbest.append(
            _NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0))
  
      assert len(nbest) >= 1
  
      total_scores = []
      best_non_null_entry = None
      for entry in nbest:
        total_scores.append(entry.start_logit + entry.end_logit)
        if not best_non_null_entry:
          if entry.text:
            best_non_null_entry = entry
  
      probs = _compute_softmax(total_scores)
  
      nbest_json = []
      for (i, entry) in enumerate(nbest):
        output = collections.OrderedDict()
        output["text"] = entry.text
        output["probability"] = probs[i]
        output["start_logit"] = entry.start_logit
        output["end_logit"] = entry.end_logit
        nbest_json.append(output)
  
      assert len(nbest_json) >= 1
  
      if not FLAGS.version_2_with_negative:
        all_predictions[example.qas_id] = nbest_json[0]["text"]
      else:
        # predict "" iff the null score - the score of best non-null > threshold
        score_diff = score_null - best_non_null_entry.start_logit - (
            best_non_null_entry.end_logit)
        scores_diff_json[example.qas_id] = score_diff
        if score_diff > FLAGS.null_score_diff_threshold:
          all_predictions[example.qas_id] = ""
        else:
          all_predictions[example.qas_id] = best_non_null_entry.text
  
      all_nbest_json[example.qas_id] = nbest_json
  
    with tf.gfile.GFile(output_prediction_file, "w") as writer:
      writer.write(json.dumps(all_predictions, indent=4) + "\n")
  
    with tf.gfile.GFile(output_nbest_file, "w") as writer:
      writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
  
    if FLAGS.version_2_with_negative:
      with tf.gfile.GFile(output_null_log_odds_file, "w") as writer:
        writer.write(json.dumps(scores_diff_json, 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 _get_best_indexes(logits, n_best_size):
    """Get the n-best logits from a list."""
    index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True)
  
    best_indexes = []
    for i in range(len(index_and_score)):
      if i >= n_best_size:
        break
      best_indexes.append(index_and_score[i][0])
    return best_indexes
  
  
  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._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
  
      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["start_positions"] = create_int_feature([feature.start_position])
        features["end_positions"] = create_int_feature([feature.end_position])
        impossible = 0
        if feature.is_impossible:
          impossible = 1
        features["is_impossible"] = create_int_feature([impossible])
  
      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_input_file:
        raise ValueError(
            "If `do_predict` is True, then specify `predict_input_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))
  
    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(_):
    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 = contrib_cluster_resolver.TPUClusterResolver(
          FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
  
    is_per_host = contrib_tpu.InputPipelineConfig.PER_HOST_V2
    run_config = contrib_tpu.RunConfig(
        cluster=tpu_cluster_resolver,
        master=FLAGS.master,
        model_dir=FLAGS.output_dir,
        save_checkpoints_steps=FLAGS.save_checkpoints_steps,
        tpu_config=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 = read_squad_examples(
          input_file=FLAGS.train_file, is_training=True)
      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)
  
      # Pre-shuffle the input to avoid having to make a very large shuffle
      # buffer in in the `input_fn`.
      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 = 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." + FLAGS.exp_name),
          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))
      tf.logging.info("  Num split examples = %d", train_writer.num_features)
      tf.logging.info("  Batch size = %d", FLAGS.train_batch_size)
      tf.logging.info("  Num steps = %d", num_train_steps)
      del train_examples
  
      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)
  
    if FLAGS.do_predict:
      eval_examples = read_squad_examples(
          input_file=FLAGS.predict_input_file, is_training=False)
  
      eval_writer = FeatureWriter(
          filename=os.path.join(FLAGS.output_dir,
                                "eval.tf_record." + FLAGS.exp_name),
          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) % 1000 == 0:
          tf.logging.info("Processing example: %d" % (len(all_results)))
        unique_id = int(result["unique_ids"])
        start_logits = [float(x) for x in result["start_logits"].flat]
        end_logits = [float(x) for x in result["end_logits"].flat]
        all_results.append(
            RawResult(
                unique_id=unique_id,
                start_logits=start_logits,
                end_logits=end_logits))
  
      if FLAGS.predict_output_dir:
        tf.gfile.MakeDirs(FLAGS.predict_output_dir)
        output_prediction_file = os.path.join(FLAGS.predict_output_dir,
                                              "predictions.json")
        output_nbest_file = os.path.join(FLAGS.predict_output_dir,
                                         "nbest_predictions.json")
        output_null_log_odds_file = os.path.join(FLAGS.predict_output_dir,
                                                 "null_odds.json")
      else:
        output_prediction_file = os.path.join(FLAGS.output_dir,
                                              "predictions.json")
        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()
  