def convert_examples_to_features(examples, tokenizer, max_seq_length,
                                 doc_stride, max_query_length, is_training,
                                 cls_token_at_end=False,
                                 cls_token='[CLS]', sep_token='[SEP]', pad_token=0,
                                 sequence_a_segment_id=0, sequence_b_segment_id=1,
                                 cls_token_segment_id=0, pad_token_segment_id=0,
                                 mask_padding_with_zero=True):
    """Loads a data file into a list of `InputBatch`s."""

    unique_id = 1000000000
    # cnt_pos, cnt_neg = 0, 0
    # max_N, max_M = 1024, 1024
    # f = np.zeros((max_N, max_M), dtype=np.float32)

    features = []
    for (example_index, example) in enumerate(examples):

        # if example_index % 100 == 0:
        #     logger.info('Converting %s/%s pos %s neg %s', example_index, len(examples), cnt_pos, cnt_neg)

        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 = []

            # p_mask: mask with 1 for token than cannot be in the answer (0 for token which can be in an answer)
            # Original TF implem also keep the classification token (set to 0) (not sure why...)
            p_mask = []

            # CLS token at the beginning
            if not cls_token_at_end:
                tokens.append(cls_token)
                segment_ids.append(cls_token_segment_id)
                p_mask.append(0)
                cls_index = 0

            # Query
            for token in query_tokens:
                tokens.append(token)
                segment_ids.append(sequence_a_segment_id)
                p_mask.append(1)

            # SEP token
            tokens.append(sep_token)
            segment_ids.append(sequence_a_segment_id)
            p_mask.append(1)

            # Paragraph
            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(sequence_b_segment_id)
                p_mask.append(0)
            paragraph_len = doc_span.length

            # SEP token
            tokens.append(sep_token)
            segment_ids.append(sequence_b_segment_id)
            p_mask.append(1)

            # CLS token at the end
            if cls_token_at_end:
                tokens.append(cls_token)
                segment_ids.append(cls_token_segment_id)
                p_mask.append(0)
                cls_index = len(tokens) - 1  # Index of classification token

            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 if mask_padding_with_zero else 0] * len(input_ids)

            # Zero-pad up to the sequence length.
            while len(input_ids) < max_seq_length:
                input_ids.append(pad_token)
                input_mask.append(0 if mask_padding_with_zero else 1)
                segment_ids.append(pad_token_segment_id)
                p_mask.append(1)

            assert len(input_ids) == max_seq_length
            assert len(input_mask) == max_seq_length
            assert len(segment_ids) == max_seq_length

            span_is_impossible = example.is_impossible
            start_position = None
            end_position = None
            if is_training and not span_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
                    span_is_impossible = True
                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 span_is_impossible:
                start_position = cls_index
                end_position = cls_index

            if example_index < 2:
                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 and span_is_impossible:
                    logger.info("impossible example")
                if is_training and not span_is_impossible:
                    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,
                    cls_index=cls_index,
                    p_mask=p_mask,
                    paragraph_len=paragraph_len,
                    start_position=start_position,
                    end_position=end_position,
                    is_impossible=span_is_impossible))
            unique_id += 1

    return features
Loads a data file into a list of `InputBatch`s.
xtune/src/pequod/data/utils_squad.py