add bert-reader

demo/bert-cls/reader/cls.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+
+import os
+import types
+import csv
+import numpy as np
+import tokenization
+from batching import prepare_batch_data
+
+
+class DataProcessor(object):
+    """Base class for data converters for sequence classification data sets."""
+
+    def __init__(self,
+                 data_dir,
+                 vocab_path,
+                 max_seq_len,
+                 do_lower_case,
+                 in_tokens,
+                 random_seed=None):
+        self.data_dir = data_dir
+        self.max_seq_len = max_seq_len
+        self.tokenizer = tokenization.FullTokenizer(
+            vocab_file=vocab_path, do_lower_case=do_lower_case)
+        self.vocab = self.tokenizer.vocab
+        self.in_tokens = in_tokens
+
+        np.random.seed(random_seed)
+
+        self.current_train_example = -1
+        self.num_examples = {'train': -1, 'dev': -1, 'test': -1}
+        self.current_train_epoch = -1
+
+    def get_train_examples(self, data_dir):
+        """Gets a collection of `InputExample`s for the train set."""
+        raise NotImplementedError()
+
+    def get_dev_examples(self, data_dir):
+        """Gets a collection of `InputExample`s for the dev set."""
+        raise NotImplementedError()
+
+    def get_test_examples(self, data_dir):
+        """Gets a collection of `InputExample`s for prediction."""
+        raise NotImplementedError()
+
+    def get_labels(self):
+        """Gets the list of labels for this data set."""
+        raise NotImplementedError()
+
+    def convert_example(self, index, example, labels, max_seq_len, tokenizer):
+        """Converts a single `InputExample` into a single `InputFeatures`."""
+        feature = convert_single_example(index, example, labels, max_seq_len,
+                                         tokenizer)
+        return feature
+
+    def generate_instance(self, feature):
+        """
+        generate instance with given feature
+
+        Args:
+            feature: InputFeatures(object). A single set of features of data.
+        """
+        input_pos = list(range(len(feature.input_ids)))
+        return [
+            feature.input_ids, feature.segment_ids, input_pos, feature.label_id
+        ]
+
+    def generate_batch_data(self,
+                            batch_data,
+                            total_token_num,
+                            voc_size=-1,
+                            mask_id=-1,
+                            return_input_mask=True,
+                            return_max_len=False,
+                            return_num_token=False):
+        return prepare_batch_data(
+            batch_data,
+            total_token_num,
+            voc_size=-1,
+            pad_id=self.vocab["[PAD]"],
+            cls_id=self.vocab["[CLS]"],
+            sep_id=self.vocab["[SEP]"],
+            mask_id=-1,
+            return_input_mask=True,
+            return_max_len=False,
+            return_num_token=False)
+
+    @classmethod
+    def _read_tsv(cls, input_file, quotechar=None):
+        """Reads a tab separated value file."""
+        with open(input_file, "r") as f:
+            reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
+            lines = []
+            for line in reader:
+                lines.append(line)
+            return lines
+
+    def get_num_examples(self, phase):
+        """Get number of examples for train, dev or test."""
+        if phase not in ['train', 'dev', 'test']:
+            raise ValueError(
+                "Unknown phase, which should be in ['train', 'dev', 'test'].")
+        return self.num_examples[phase]
+
+    def get_train_progress(self):
+        """Gets progress for training phase."""
+        return self.current_train_example, self.current_train_epoch
+
+    def data_generator(self, batch_size, phase='train', epoch=1, shuffle=True):
+        """
+        Generate data for train, dev or test.
+
+        Args:
+          batch_size: int. The batch size of generated data.
+          phase: string. The phase for which to generate data.
+          epoch: int. Total epoches to generate data.
+          shuffle: bool. Whether to shuffle examples.
+        """
+        if phase == 'train':
+            examples = self.get_train_examples(self.data_dir)
+            self.num_examples['train'] = len(examples)
+        elif phase == 'dev':
+            examples = self.get_dev_examples(self.data_dir)
+            self.num_examples['dev'] = len(examples)
+        elif phase == 'test':
+            examples = self.get_test_examples(self.data_dir)
+            self.num_examples['test'] = len(examples)
+        else:
+            raise ValueError(
+                "Unknown phase, which should be in ['train', 'dev', 'test'].")
+
+        def instance_reader():
+            for epoch_index in range(epoch):
+                if shuffle:
+                    np.random.shuffle(examples)
+                if phase == 'train':
+                    self.current_train_epoch = epoch_index
+                for (index, example) in enumerate(examples):
+                    if phase == 'train':
+                        self.current_train_example = index + 1
+                    feature = self.convert_example(index, example,
+                                                   self.get_labels(),
+                                                   self.max_seq_len,
+                                                   self.tokenizer)
+
+                    instance = self.generate_instance(feature)
+                    yield instance
+
+        def batch_reader(reader, batch_size, in_tokens):
+            batch, total_token_num, max_len = [], 0, 0
+            for instance in reader():
+                token_ids, sent_ids, pos_ids, label = instance[:4]
+                max_len = max(max_len, len(token_ids))
+                if in_tokens:
+                    to_append = (len(batch) + 1) * max_len <= batch_size
+                else:
+                    to_append = len(batch) < batch_size
+                if to_append:
+                    batch.append(instance)
+                    total_token_num += len(token_ids)
+                else:
+                    yield batch, total_token_num
+                    batch, total_token_num, max_len = [
+                        instance
+                    ], len(token_ids), len(token_ids)
+
+            if len(batch) > 0:
+                yield batch, total_token_num
+
+        def wrapper():
+            for batch_data, total_token_num in batch_reader(
+                    instance_reader, batch_size, self.in_tokens):
+                batch_data = self.generate_batch_data(
+                    batch_data,
+                    total_token_num,
+                    voc_size=-1,
+                    mask_id=-1,
+                    return_input_mask=True,
+                    return_max_len=False,
+                    return_num_token=False)
+                yield batch_data
+
+        return wrapper
+
+
+class InputExample(object):
+    """A single training/test example for simple sequence classification."""
+
+    def __init__(self, guid, text_a, text_b=None, label=None):
+        """Constructs a InputExample.
+
+    Args:
+      guid: Unique id for the example.
+      text_a: string. The untokenized text of the first sequence. For single
+        sequence tasks, only this sequence must be specified.
+      text_b: (Optional) string. The untokenized text of the second sequence.
+        Only must be specified for sequence pair tasks.
+      label: (Optional) string. The label of the example. This should be
+        specified for train and dev examples, but not for test examples.
+    """
+        self.guid = guid
+        self.text_a = text_a
+        self.text_b = text_b
+        self.label = label
+
+
+def _truncate_seq_pair(tokens_a, tokens_b, max_length):
+    """Truncates a sequence pair in place to the maximum length."""
+
+    # This is a simple heuristic which will always truncate the longer sequence
+    # one token at a time. This makes more sense than truncating an equal percent
+    # of tokens from each, since if one sequence is very short then each token
+    # that's truncated likely contains more information than a longer sequence.
+    while True:
+        total_length = len(tokens_a) + len(tokens_b)
+        if total_length <= max_length:
+            break
+        if len(tokens_a) > len(tokens_b):
+            tokens_a.pop()
+        else:
+            tokens_b.pop()
+
+
+class InputFeatures(object):
+    """A single set of features of data."""
+
+    def __init__(self, input_ids, input_mask, segment_ids, label_id):
+        self.input_ids = input_ids
+        self.input_mask = input_mask
+        self.segment_ids = segment_ids
+        self.label_id = label_id
+
+
+class XnliProcessor(DataProcessor):
+    """Processor for the XNLI data set."""
+
+    def get_train_examples(self, data_dir):
+        """See base class."""
+        self.language = "zh"
+        lines = self._read_tsv(
+            os.path.join(data_dir, "multinli",
+                         "multinli.train.%s.tsv" % self.language))
+        examples = []
+        for (i, line) in enumerate(lines):
+            if i == 0:
+                continue
+            guid = "train-%d" % (i)
+            text_a = tokenization.convert_to_unicode(line[0])
+            text_b = tokenization.convert_to_unicode(line[1])
+            label = tokenization.convert_to_unicode(line[2])
+            if label == tokenization.convert_to_unicode("contradictory"):
+                label = tokenization.convert_to_unicode("contradiction")
+            examples.append(
+                InputExample(
+                    guid=guid, text_a=text_a, text_b=text_b, label=label))
+        return examples
+
+    def get_dev_examples(self, data_dir):
+        """See base class."""
+        self.language = "zh"
+        lines = self._read_tsv(os.path.join(data_dir, "xnli.dev.tsv"))
+        examples = []
+        for (i, line) in enumerate(lines):
+            if i == 0:
+                continue
+            guid = "dev-%d" % (i)
+            language = tokenization.convert_to_unicode(line[0])
+            if language != tokenization.convert_to_unicode(self.language):
+                continue
+            text_a = tokenization.convert_to_unicode(line[6])
+            text_b = tokenization.convert_to_unicode(line[7])
+            label = tokenization.convert_to_unicode(line[1])
+            examples.append(
+                InputExample(
+                    guid=guid, text_a=text_a, text_b=text_b, label=label))
+        return examples
+
+    def get_test_examples(self, data_dir):
+        """See base class."""
+        self.language = "zh"
+        lines = self._read_tsv(os.path.join(data_dir, "xnli.test.tsv"))
+        examples = []
+        for (i, line) in enumerate(lines):
+            if i == 0:
+                continue
+            guid = "test-%d" % (i)
+            language = tokenization.convert_to_unicode(line[0])
+            if language != tokenization.convert_to_unicode(self.language):
+                continue
+            text_a = tokenization.convert_to_unicode(line[6])
+            text_b = tokenization.convert_to_unicode(line[7])
+            label = tokenization.convert_to_unicode(line[1])
+            examples.append(
+                InputExample(
+                    guid=guid, text_a=text_a, text_b=text_b, label=label))
+        return examples
+
+    def get_labels(self):
+        """See base class."""
+        return ["contradiction", "entailment", "neutral"]
+
+
+class MnliProcessor(DataProcessor):
+    """Processor for the MultiNLI data set (GLUE version)."""
+
+    def get_train_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
+
+    def get_dev_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "dev_matched.tsv")),
+            "dev_matched")
+
+    def get_test_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "test_matched.tsv")), "test")
+
+    def get_labels(self):
+        """See base class."""
+        return ["contradiction", "entailment", "neutral"]
+
+    def _create_examples(self, lines, set_type):
+        """Creates examples for the training and dev sets."""
+        examples = []
+        for (i, line) in enumerate(lines):
+            if i == 0:
+                continue
+            guid = "%s-%s" % (set_type, tokenization.convert_to_unicode(
+                line[0]))
+            text_a = tokenization.convert_to_unicode(line[8])
+            text_b = tokenization.convert_to_unicode(line[9])
+            if set_type == "test":
+                label = "contradiction"
+            else:
+                label = tokenization.convert_to_unicode(line[-1])
+            examples.append(
+                InputExample(
+                    guid=guid, text_a=text_a, text_b=text_b, label=label))
+        return examples
+
+
+class MrpcProcessor(DataProcessor):
+    """Processor for the MRPC data set (GLUE version)."""
+
+    def get_train_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
+
+    def get_dev_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
+
+    def get_test_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
+
+    def get_labels(self):
+        """See base class."""
+        return ["0", "1"]
+
+    def _create_examples(self, lines, set_type):
+        """Creates examples for the training and dev sets."""
+        examples = []
+        for (i, line) in enumerate(lines):
+            if i == 0:
+                continue
+            guid = "%s-%s" % (set_type, i)
+            text_a = tokenization.convert_to_unicode(line[3])
+            text_b = tokenization.convert_to_unicode(line[4])
+            if set_type == "test":
+                label = "0"
+            else:
+                label = tokenization.convert_to_unicode(line[0])
+            examples.append(
+                InputExample(
+                    guid=guid, text_a=text_a, text_b=text_b, label=label))
+        return examples
+
+
+class ColaProcessor(DataProcessor):
+    """Processor for the CoLA data set (GLUE version)."""
+
+    def get_train_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
+
+    def get_dev_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
+
+    def get_test_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
+
+    def get_labels(self):
+        """See base class."""
+        return ["0", "1"]
+
+    def _create_examples(self, lines, set_type):
+        """Creates examples for the training and dev sets."""
+        examples = []
+        for (i, line) in enumerate(lines):
+            # Only the test set has a header
+            if set_type == "test" and i == 0:
+                continue
+            guid = "%s-%s" % (set_type, i)
+            if set_type == "test":
+                text_a = tokenization.convert_to_unicode(line[1])
+                label = "0"
+            else:
+                text_a = tokenization.convert_to_unicode(line[3])
+                label = tokenization.convert_to_unicode(line[1])
+            examples.append(
+                InputExample(
+                    guid=guid, text_a=text_a, text_b=None, label=label))
+        return examples
+
+
+class ChnsenticorpProcessor(DataProcessor):
+    """Processor for the Chnsenticorp data set."""
+
+    def get_train_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
+
+    def get_dev_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
+
+    def get_test_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
+
+    def get_labels(self):
+        """See base class."""
+        return ["0", "1"]
+
+    def _create_examples(self, lines, set_type):
+        """Creates examples for the training and dev sets."""
+        examples = []
+        for (i, line) in enumerate(lines):
+            guid = "%s-%s" % (set_type, i)
+            text_a = tokenization.convert_to_unicode(line[1])
+            label = tokenization.convert_to_unicode(line[0])
+            examples.append(
+                InputExample(
+                    guid=guid, text_a=text_a, text_b=None, label=label))
+        return examples
+
+
+def convert_single_example_to_unicode(guid, single_example):
+    text_a = tokenization.convert_to_unicode(single_example[0])
+    text_b = tokenization.convert_to_unicode(single_example[1])
+    label = tokenization.convert_to_unicode(single_example[2])
+    return InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)
+
+
+def convert_single_example(ex_index, example, label_list, max_seq_length,
+                           tokenizer):
+    """Converts a single `InputExample` into a single `InputFeatures`."""
+    label_map = {}
+    for (i, label) in enumerate(label_list):
+        label_map[label] = i
+
+    tokens_a = tokenizer.tokenize(example.text_a)
+    tokens_b = None
+    if example.text_b:
+        tokens_b = tokenizer.tokenize(example.text_b)
+
+    if tokens_b:
+        # Modifies `tokens_a` and `tokens_b` in place so that the total
+        # length is less than the specified length.
+        # Account for [CLS], [SEP], [SEP] with "- 3"
+        _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
+    else:
+        # Account for [CLS] and [SEP] with "- 2"
+        if len(tokens_a) > max_seq_length - 2:
+            tokens_a = tokens_a[0:(max_seq_length - 2)]
+
+    # The convention in BERT is:
+    # (a) For sequence pairs:
+    #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
+    #  type_ids: 0     0  0    0    0     0       0 0     1  1  1  1   1 1
+    # (b) For single sequences:
+    #  tokens:   [CLS] the dog is hairy . [SEP]
+    #  type_ids: 0     0   0   0  0     0 0
+    #
+    # Where "type_ids" are used to indicate whether this is the first
+    # sequence or the second sequence. The embedding vectors for `type=0` and
+    # `type=1` were learned during pre-training and are added to the wordpiece
+    # embedding vector (and position vector). This is not *strictly* necessary
+    # since the [SEP] token unambiguously separates the sequences, but it makes
+    # it easier for the model to learn the concept of sequences.
+    #
+    # For classification tasks, the first vector (corresponding to [CLS]) is
+    # used as as the "sentence vector". Note that this only makes sense because
+    # the entire model is fine-tuned.
+    tokens = []
+    segment_ids = []
+    tokens.append("[CLS]")
+    segment_ids.append(0)
+    for token in tokens_a:
+        tokens.append(token)
+        segment_ids.append(0)
+    tokens.append("[SEP]")
+    segment_ids.append(0)
+
+    if tokens_b:
+        for token in tokens_b:
+            tokens.append(token)
+            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)
+
+    label_id = label_map[example.label]
+
+    feature = InputFeatures(
+        input_ids=input_ids,
+        input_mask=input_mask,
+        segment_ids=segment_ids,
+        label_id=label_id)
+    return feature
+
+
+def convert_examples_to_features(examples, label_list, max_seq_length,
+                                 tokenizer):
+    """Convert a set of `InputExample`s to a list of `InputFeatures`."""
+
+    features = []
+    for (ex_index, example) in enumerate(examples):
+        if ex_index % 10000 == 0:
+            print("Writing example %d of %d" % (ex_index, len(examples)))
+
+        feature = convert_single_example(ex_index, example, label_list,
+                                         max_seq_length, tokenizer)
+
+        features.append(feature)
+    return features
+
+
+if __name__ == '__main__':
+    pass

demo/bert-cls/reader/pretraining.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+
+from __future__ import print_function
+from __future__ import division
+
+import os
+import numpy as np
+import types
+import gzip
+import logging
+import re
+import six
+import collections
+import tokenization
+
+import paddle
+import paddle.fluid as fluid
+
+from batching import prepare_batch_data
+
+
+class DataReader(object):
+    def __init__(self,
+                 data_dir,
+                 vocab_path,
+                 batch_size=4096,
+                 in_tokens=True,
+                 max_seq_len=512,
+                 shuffle_files=True,
+                 epoch=100,
+                 voc_size=0,
+                 is_test=False,
+                 generate_neg_sample=False):
+
+        self.vocab = self.load_vocab(vocab_path)
+        self.data_dir = data_dir
+        self.batch_size = batch_size
+        self.in_tokens = in_tokens
+        self.shuffle_files = shuffle_files
+        self.epoch = epoch
+        self.current_epoch = 0
+        self.current_file_index = 0
+        self.total_file = 0
+        self.current_file = None
+        self.voc_size = voc_size
+        self.max_seq_len = max_seq_len
+        self.pad_id = self.vocab["[PAD]"]
+        self.cls_id = self.vocab["[CLS]"]
+        self.sep_id = self.vocab["[SEP]"]
+        self.mask_id = self.vocab["[MASK]"]
+        self.is_test = is_test
+        self.generate_neg_sample = generate_neg_sample
+        if self.in_tokens:
+            assert self.batch_size >= self.max_seq_len, "The number of " \
+                   "tokens in batch should not be smaller than max seq length."
+
+        if self.is_test:
+            self.epoch = 1
+            self.shuffle_files = False
+
+    def get_progress(self):
+        """return current progress of traning data
+        """
+        return self.current_epoch, self.current_file_index, self.total_file, self.current_file
+
+    def parse_line(self, line, max_seq_len=512):
+        """ parse one line to token_ids, sentence_ids, pos_ids, label
+        """
+        line = line.strip().split(";")
+        assert len(line) == 4, "One sample must have 4 fields!"
+        (token_ids, sent_ids, pos_ids, label) = line
+        token_ids = [int(token) for token in token_ids.split(" ")]
+        sent_ids = [int(token) for token in sent_ids.split(" ")]
+        pos_ids = [int(token) for token in pos_ids.split(" ")]
+        assert len(token_ids) == len(sent_ids) == len(
+            pos_ids
+        ), "[Must be true]len(token_ids) == len(sent_ids) == len(pos_ids)"
+        label = int(label)
+        if len(token_ids) > max_seq_len:
+            return None
+        return [token_ids, sent_ids, pos_ids, label]
+
+    def read_file(self, file):
+        assert file.endswith('.gz'), "[ERROR] %s is not a gzip file" % file
+        file_path = self.data_dir + "/" + file
+        with gzip.open(file_path, "rb") as f:
+            for line in f:
+                parsed_line = self.parse_line(
+                    line, max_seq_len=self.max_seq_len)
+                if parsed_line is None:
+                    continue
+                yield parsed_line
+
+    def convert_to_unicode(self, text):
+        """Converts `text` to Unicode (if it's not already), assuming utf-8 input."""
+        if six.PY3:
+            if isinstance(text, str):
+                return text
+            elif isinstance(text, bytes):
+                return text.decode("utf-8", "ignore")
+            else:
+                raise ValueError("Unsupported string type: %s" % (type(text)))
+        elif six.PY2:
+            if isinstance(text, str):
+                return text.decode("utf-8", "ignore")
+            elif isinstance(text, unicode):
+                return text
+            else:
+                raise ValueError("Unsupported string type: %s" % (type(text)))
+        else:
+            raise ValueError("Not running on Python2 or Python 3?")
+
+    def load_vocab(self, vocab_file):
+        """Loads a vocabulary file into a dictionary."""
+        vocab = collections.OrderedDict()
+        fin = open(vocab_file)
+        for num, line in enumerate(fin):
+            items = self.convert_to_unicode(line.strip()).split("\t")
+            if len(items) > 2:
+                break
+            token = items[0]
+            index = items[1] if len(items) == 2 else num
+            token = token.strip()
+            vocab[token] = int(index)
+        return vocab
+
+    def random_pair_neg_samples(self, pos_samples):
+        """ randomly generate negtive samples using pos_samples
+
+            Args:
+                pos_samples: list of positive samples
+
+            Returns:
+                neg_samples: list of negtive samples
+        """
+        np.random.shuffle(pos_samples)
+        num_sample = len(pos_samples)
+        neg_samples = []
+        miss_num = 0
+
+        for i in range(num_sample):
+            pair_index = (i + 1) % num_sample
+            origin_src_ids = pos_samples[i][0]
+            origin_sep_index = origin_src_ids.index(2)
+            pair_src_ids = pos_samples[pair_index][0]
+            pair_sep_index = pair_src_ids.index(2)
+
+            src_ids = origin_src_ids[:origin_sep_index +
+                                     1] + pair_src_ids[pair_sep_index + 1:]
+            if len(src_ids) >= self.max_seq_len:
+                miss_num += 1
+                continue
+            sent_ids = [0] * len(origin_src_ids[:origin_sep_index + 1]) + [
+                1
+            ] * len(pair_src_ids[pair_sep_index + 1:])
+            pos_ids = list(range(len(src_ids)))
+            neg_sample = [src_ids, sent_ids, pos_ids, 0]
+            assert len(src_ids) == len(sent_ids) == len(
+                pos_ids
+            ), "[ERROR]len(src_id) == lne(sent_id) == len(pos_id) must be True"
+            neg_samples.append(neg_sample)
+        return neg_samples, miss_num
+
+    def mixin_negtive_samples(self, pos_sample_generator, buffer=1000):
+        """ 1. generate negtive samples by randomly group sentence_1 and sentence_2 of positive samples
+            2. combine negtive samples and positive samples
+
+            Args:
+                pos_sample_generator: a generator producing a parsed positive sample, which is a list: [token_ids, sent_ids, pos_ids, 1]
+
+            Returns:
+                sample: one sample from shuffled positive samples and negtive samples
+        """
+        pos_samples = []
+        num_total_miss = 0
+        pos_sample_num = 0
+        try:
+            while True:
+                while len(pos_samples) < buffer:
+                    pos_sample = next(pos_sample_generator)
+                    label = pos_sample[3]
+                    assert label == 1, "positive sample's label must be 1"
+                    pos_samples.append(pos_sample)
+                    pos_sample_num += 1
+
+                neg_samples, miss_num = self.random_pair_neg_samples(
+                    pos_samples)
+                num_total_miss += miss_num
+                samples = pos_samples + neg_samples
+                pos_samples = []
+                np.random.shuffle(samples)
+                for sample in samples:
+                    yield sample
+        except StopIteration:
+            print("stopiteration: reach end of file")
+            if len(pos_samples) == 1:
+                yield pos_samples[0]
+            elif len(pos_samples) == 0:
+                yield None
+            else:
+                neg_samples, miss_num = self.random_pair_neg_samples(
+                    pos_samples)
+                num_total_miss += miss_num
+                samples = pos_samples + neg_samples
+                pos_samples = []
+                np.random.shuffle(samples)
+                for sample in samples:
+                    yield sample
+            print("miss_num:%d\tideal_total_sample_num:%d\tmiss_rate:%f" %
+                  (num_total_miss, pos_sample_num * 2, num_total_miss /
+                   (pos_sample_num * 2)))
+
+    def data_generator(self):
+        """
+        data_generator
+        """
+        files = os.listdir(self.data_dir)
+        self.total_file = len(files)
+        assert self.total_file > 0, "[Error] data_dir is empty"
+
+        def wrapper():
+            def reader():
+                for epoch in range(self.epoch):
+                    self.current_epoch = epoch + 1
+                    if self.shuffle_files:
+                        np.random.shuffle(files)
+                    for index, file in enumerate(files):
+                        self.current_file_index = index + 1
+                        self.current_file = file
+                        sample_generator = self.read_file(file)
+                        if not self.is_test and self.generate_neg_sample:
+                            sample_generator = self.mixin_negtive_samples(
+                                sample_generator)
+                        for sample in sample_generator:
+                            if sample is None:
+                                continue
+                            yield sample
+
+            def batch_reader(reader, batch_size, in_tokens):
+                batch, total_token_num, max_len = [], 0, 0
+                for parsed_line in reader():
+                    token_ids, sent_ids, pos_ids, label = parsed_line
+                    max_len = max(max_len, len(token_ids))
+                    if in_tokens:
+                        to_append = (len(batch) + 1) * max_len <= batch_size
+                    else:
+                        to_append = len(batch) < batch_size
+                    if to_append:
+                        batch.append(parsed_line)
+                        total_token_num += len(token_ids)
+                    else:
+                        yield batch, total_token_num
+                        batch, total_token_num, max_len = [
+                            parsed_line
+                        ], len(token_ids), len(token_ids)
+
+                if len(batch) > 0:
+                    yield batch, total_token_num
+
+            for batch_data, total_token_num in batch_reader(
+                    reader, self.batch_size, self.in_tokens):
+                yield prepare_batch_data(
+                    batch_data,
+                    total_token_num,
+                    voc_size=self.voc_size,
+                    pad_id=self.pad_id,
+                    cls_id=self.cls_id,
+                    sep_id=self.sep_id,
+                    mask_id=self.mask_id,
+                    return_input_mask=True,
+                    return_max_len=False,
+                    return_num_token=False)
+
+        return wrapper
+
+
+if __name__ == "__main__":
+    pass

demo/bert-cls/reader/squad.py

+# 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."""
+
+import six
+import math
+import json
+import random
+import collections
+import tokenization
+from batching import prepare_batch_data
+
+
+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, version_2_with_negative=False):
+    """Read a SQuAD json file into a list of SquadExample."""
+    with 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 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:
+                            print("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 < 3:
+                print("*** Example ***")
+                print("unique_id: %s" % (unique_id))
+                print("example_index: %s" % (example_index))
+                print("doc_span_index: %s" % (doc_span_index))
+                print("tokens: %s" % " ".join(
+                    [tokenization.printable_text(x) for x in tokens]))
+                print("token_to_orig_map: %s" % " ".join([
+                    "%d:%d" % (x, y)
+                    for (x, y) in six.iteritems(token_to_orig_map)
+                ]))
+                print("token_is_max_context: %s" % " ".join([
+                    "%d:%s" % (x, y)
+                    for (x, y) in six.iteritems(token_is_max_context)
+                ]))
+                print("input_ids: %s" % " ".join([str(x) for x in input_ids]))
+                print("input_mask: %s" % " ".join([str(x) for x in input_mask]))
+                print(
+                    "segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
+                if is_training and example.is_impossible:
+                    print("impossible example")
+                if is_training and not example.is_impossible:
+                    answer_text = " ".join(
+                        tokens[start_position:(end_position + 1)])
+                    print("start_position: %d" % (start_position))
+                    print("end_position: %d" % (end_position))
+                    print("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)
+
+            unique_id += 1
+
+            yield feature
+
+
+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
+
+
+class DataProcessor(object):
+    def __init__(self, vocab_path, do_lower_case, max_seq_length, in_tokens,
+                 doc_stride, max_query_length):
+        self._tokenizer = tokenization.FullTokenizer(
+            vocab_file=vocab_path, do_lower_case=do_lower_case)
+        self._max_seq_length = max_seq_length
+        self._doc_stride = doc_stride
+        self._max_query_length = max_query_length
+        self._in_tokens = in_tokens
+
+        self.vocab = self._tokenizer.vocab
+        self.vocab_size = len(self.vocab)
+        self.pad_id = self.vocab["[PAD]"]
+        self.cls_id = self.vocab["[CLS]"]
+        self.sep_id = self.vocab["[SEP]"]
+        self.mask_id = self.vocab["[MASK]"]
+
+        self.current_train_example = -1
+        self.num_train_examples = -1
+        self.current_train_epoch = -1
+
+        self.train_examples = None
+        self.predict_examples = None
+        self.num_examples = {'train': -1, 'predict': -1}
+
+    def get_train_progress(self):
+        """Gets progress for training phase."""
+        return self.current_train_example, self.current_train_epoch
+
+    def get_examples(self,
+                     data_path,
+                     is_training,
+                     version_2_with_negative=False):
+        examples = read_squad_examples(
+            input_file=data_path,
+            is_training=is_training,
+            version_2_with_negative=version_2_with_negative)
+        return examples
+
+    def get_num_examples(self, phase):
+        if phase not in ['train', 'predict']:
+            raise ValueError(
+                "Unknown phase, which should be in ['train', 'predict'].")
+        return self.num_examples[phase]
+
+    def get_features(self, examples, is_training):
+        features = convert_examples_to_features(
+            examples=examples,
+            tokenizer=self._tokenizer,
+            max_seq_length=self._max_seq_length,
+            doc_stride=self._doc_stride,
+            max_query_length=self._max_query_length,
+            is_training=is_training)
+        return features
+
+    def data_generator(self,
+                       data_path,
+                       batch_size,
+                       phase='train',
+                       shuffle=False,
+                       version_2_with_negative=False,
+                       epoch=1):
+        if phase == 'train':
+            self.train_examples = self.get_examples(
+                data_path,
+                is_training=True,
+                version_2_with_negative=version_2_with_negative)
+            examples = self.train_examples
+            self.num_examples['train'] = len(self.train_examples)
+        elif phase == 'predict':
+            self.predict_examples = self.get_examples(
+                data_path,
+                is_training=False,
+                version_2_with_negative=version_2_with_negative)
+            examples = self.predict_examples
+            self.num_examples['predict'] = len(self.predict_examples)
+        else:
+            raise ValueError(
+                "Unknown phase, which should be in ['train', 'predict'].")
+
+        def batch_reader(features, batch_size, in_tokens):
+            batch, total_token_num, max_len = [], 0, 0
+            for (index, feature) in enumerate(features):
+                if phase == 'train':
+                    self.current_train_example = index + 1
+                seq_len = len(feature.input_ids)
+                labels = [feature.unique_id
+                          ] if feature.start_position is None else [
+                              feature.start_position, feature.end_position
+                          ]
+                example = [
+                    feature.input_ids, feature.segment_ids,
+                    range(seq_len)
+                ] + labels
+                max_len = max(max_len, seq_len)
+
+                #max_len = max(max_len, len(token_ids))
+                if in_tokens:
+                    to_append = (len(batch) + 1) * max_len <= batch_size
+                else:
+                    to_append = len(batch) < batch_size
+
+                if to_append:
+                    batch.append(example)
+                    total_token_num += seq_len
+                else:
+                    yield batch, total_token_num
+                    batch, total_token_num, max_len = [example
+                                                       ], seq_len, seq_len
+            if len(batch) > 0:
+                yield batch, total_token_num
+
+        def wrapper():
+            for epoch_index in range(epoch):
+                if shuffle:
+                    random.shuffle(examples)
+                if phase == 'train':
+                    self.current_train_epoch = epoch_index
+                    features = self.get_features(examples, is_training=True)
+                else:
+                    features = self.get_features(examples, is_training=False)
+
+                for batch_data, total_token_num in batch_reader(
+                        features, batch_size, self._in_tokens):
+                    yield prepare_batch_data(
+                        batch_data,
+                        total_token_num,
+                        voc_size=-1,
+                        pad_id=self.pad_id,
+                        cls_id=self.cls_id,
+                        sep_id=self.sep_id,
+                        mask_id=-1,
+                        return_input_mask=True,
+                        return_max_len=False,
+                        return_num_token=False)
+
+        return wrapper
+
+
+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,
+                      version_2_with_negative, null_score_diff_threshold,
+                      verbose):
+    """Write final predictions to the json file and log-odds of null if needed."""
+    print("Writing predictions to: %s" % (output_prediction_file))
+    print("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 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 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,
+                                            verbose)
+                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 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
+        # debug
+        if best_non_null_entry is None:
+            print("Emmm..., sth wrong")
+
+        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 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 > 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 open(output_prediction_file, "w") as writer:
+        writer.write(json.dumps(all_predictions, indent=4) + "\n")
+
+    with open(output_nbest_file, "w") as writer:
+        writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
+
+    if version_2_with_negative:
+        with open(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, verbose):
+    """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 verbose:
+            print("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 verbose:
+            print("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 verbose:
+            print("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 verbose:
+            print("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
+
+
+if __name__ == '__main__':
+    train_file = 'squad/train-v1.1.json'
+    vocab_file = 'uncased_L-12_H-768_A-12/vocab.txt'
+    do_lower_case = True
+    tokenizer = tokenization.FullTokenizer(
+        vocab_file=vocab_file, do_lower_case=do_lower_case)
+    train_examples = read_squad_examples(
+        input_file=train_file, is_training=True)
+    print("begin converting")
+    for (index, feature) in enumerate(
+            convert_examples_to_features(
+                examples=train_examples,
+                tokenizer=tokenizer,
+                max_seq_length=384,
+                doc_stride=128,
+                max_query_length=64,
+                is_training=True,
+                #output_fn=train_writer.process_feature
+            )):
+        if index < 10:
+            print(index, feature.input_ids, feature.input_mask,
+                  feature.segment_ids)
+    #for (index, example) in enumerate(train_examples):
+    #    if index < 5:
+    #        print(example)