def convert_examples_to_features(examples, tokenizer, max_seq_length,
                                 doc_stride, max_query_length, is_training):
    """Loads a data file into a list of `InputBatch`s."""

    unique_id = 1000000000

    features = []
    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:
            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:
                # 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
                if (example.start_position < doc_start
                        or example.end_position < doc_start
                        or example.start_position > doc_end
                        or example.end_position > doc_end):
                    continue

                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 example_index < 20:
                logger.info("*** Example ***")
                logger.info("unique_id: %s" % (unique_id))
                logger.info("example_index: %s" % (example_index))
                logger.info("doc_span_index: %s" % (doc_span_index))
                logger.info("tokens: %s" % " ".join(tokens))
                logger.info("token_to_orig_map: %s" % " ".join(
                    ["%d:%d" % (x, y)
                     for (x, y) in token_to_orig_map.items()]))
                logger.info("token_is_max_context: %s" % " ".join([
                    "%d:%s" % (x, y)
                    for (x, y) in token_is_max_context.items()
                ]))
                logger.info("input_ids: %s" %
                            " ".join([str(x) for x in input_ids]))
                logger.info("input_mask: %s" %
                            " ".join([str(x) for x in input_mask]))
                logger.info("segment_ids: %s" %
                            " ".join([str(x) for x in segment_ids]))
                if is_training:
                    answer_text = " ".join(
                        tokens[start_position:(end_position + 1)])
                    logger.info("start_position: %d" % (start_position))
                    logger.info("end_position: %d" % (end_position))
                    logger.info("answer: %s" % (answer_text))

            features.append(
                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))
            unique_id += 1

    return features
Loads a data file into a list of `InputBatch`s.
training/BingBertSquad/nvidia_run_squad_baseline.py