feat:add dockerfile

Issue-ID: USECASEUI-525
Signed-off-by: wangy122 <wangy122@chinatelecom.cn>
Change-Id: Ifca8abdfff479216bb0ea6b84d2c61fb640039f5

1 files changed, 1045 insertions, 0 deletions

diff --git a/scripts/api_squad.py b/scripts/api_squad.py
new file mode 100644
index 0000000..239bbd6
--- /dev/null
+++ b/scripts/api_squad.py
@@ -0,0 +1,1045 @@
+# coding=utf-8
+# squad interface
+# Required parameters： 
+#          FLAGS_output_dir ：the output path of the model training during training process, the output of the trained model, etc.; the output path of the model prediction during predicting process
+#          FLAGS_init_checkpoint_squad ： model initialization path,  use bert pre-trained model for training; use the output path during training for prediction
+#          FLAGS_predict_file ： the file to be predicted, csv file
+#          FLAGS_train_file : file to be trained, csv file
+#          FLAGS_do_predict : whether to predict or not
+#          FLAGS_do_train ： whether to train or not
+#          FLAGS_train_batch_size ： the batch_size for training, default : 16
+#          FLAGS_predict_batch_size : the batch_size when predicting, default: 8
+#          FLAGS_learning_rate ： the learning_rate at training time, default: 5e-5
+#          FLAGS_num_train_epochs ： epochs at training time, default: 3
+#          FLAGS_max_answer_length ： the maximum length of the answer, default: 100 characters
+#          FLAGS_max_query_length ： the maximum length of the question, default: 64
+#          FLAGS_version_2_with_negative ： whether there is no answer to the question, default false, must be set to False when reasoning
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import collections
+import json
+import math
+import os
+import random
+import modeling
+import optimization
+import tokenization
+import six
+import tensorflow as tf
+import pandas as pd
+from global_setting import FLAGS_bert_config_file, FLAGS_vocab_file, FLAGS_init_checkpoint_squad
+
+
+
+
+FLAGS_max_seq_length = 512
+FLAGS_do_lower_case = True
+FLAGS_doc_stride = 128
+
+
+FLAGS_save_checkpoints_steps = 1000
+FLAGS_iterations_per_loop = 1000
+FLAGS_n_best_size = 20
+FLAGS_tpu_zone = None
+FLAGS_tpu_name = None
+FLAGS_num_tpu_cores = 8
+FLAGS_verbose_logging = False
+FLAGS_master = None
+FLAGS_use_tpu = False
+FLAGS_warmup_proportion = 0.1
+FLAGS_gcp_project = None
+FLAGS_null_score_diff_threshold = 0.0
+
+def make_json(input_file,questions):
+    print(input_file)
+    data_train = pd.read_excel(input_file)
+    print(444)
+    data_train.fillna(0,inplace=True)
+    data_train.index = [i for i in range(len(data_train))]
+    question = questions
+    res = {}
+    res['data'] = []
+    data_inside = {}
+    for i in data_train.index:
+        data_inside['title'] = 'Not available'
+        data_inside['paragraphs'] = []
+        paragraphs_inside = {}
+        paragraphs_inside['context'] = data_train.loc[i,'text']
+        paragraphs_inside['qas'] = []
+        for ques in question:       
+            qas_inside = {}
+            qas_inside['answers'] = []
+            if data_train.loc[i,ques]:
+                answer_inside = {}
+                answer_inside['text'] = str(data_train.loc[i,ques])
+                answer_inside['answer_start'] = paragraphs_inside['context'].find(answer_inside['text'])
+                qas_inside['is_impossible'] = 0
+                
+            else:
+                qas_inside['is_impossible'] = 1
+                answer_inside = {}
+            qas_inside['id'] = str(i) + ques
+            qas_inside['question'] = ques
+            qas_inside['answers'].append(answer_inside.copy())
+            paragraphs_inside['qas'].append(qas_inside.copy())
+        data_inside['paragraphs'].append(paragraphs_inside.copy())
+
+        res['data'].append(data_inside.copy())
+    print('make json done')
+    return json.dumps(res)
+
+
+
+
+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,questions,FLAGS_version_2_with_negative):
+    """Read a SQuAD json file into a list of SquadExample."""
+    data = make_json(input_file,questions)
+    input_data = json.loads(data)["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 = tf.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 = tf.contrib.tpu.TPUEstimatorSpec(
+                mode=mode, predictions=predictions, scaffold_fn=scaffold_fn)
+        else:
+            raise ValueError(
+                "Only TRAIN and PREDICT modes are supported: %s" % (mode))
+
+        return output_spec
+
+    return model_fn
+
+
+def input_fn_builder(input_file, seq_length, is_training, drop_remainder):
+    """Creates an `input_fn` closure to be passed to TPUEstimator."""
+
+    name_to_features = {
+        "unique_ids": tf.FixedLenFeature([], tf.int64),
+        "input_ids": tf.FixedLenFeature([seq_length], tf.int64),
+        "input_mask": tf.FixedLenFeature([seq_length], tf.int64),
+        "segment_ids": tf.FixedLenFeature([seq_length], tf.int64),
+    }
+
+    if is_training:
+        name_to_features["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(
+            tf.contrib.data.map_and_batch(
+                lambda record: _decode_record(record, name_to_features),
+                batch_size=batch_size,
+                drop_remainder=drop_remainder))
+
+        return d
+
+    return input_fn
+
+
+RawResult = collections.namedtuple("RawResult",
+                                   ["unique_id", "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]))
+
+        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))
+
+        # 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
+
+        all_predictions[example.qas_id] = nbest_json[0]["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")
+
+
+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_squad)
+
+    # if not FLAGS_do_train and not FLAGS_do_predict:
+    #     raise ValueError(
+    #         "At least one of `do_train` or `do_predict` must be True.")
+
+    # if FLAGS_do_train:
+    #     if not FLAGS_train_file:
+    #         raise ValueError(
+    #             "If `do_train` is True, then `train_file` must be specified.")
+    # if FLAGS_do_predict:
+    #     if not FLAGS_predict_file:
+    #         raise ValueError(
+    #             "If `do_predict` is True, then `predict_file` must be specified.")
+
+    # if FLAGS_max_seq_length > bert_config.max_position_embeddings:
+    #     raise ValueError(
+    #         "Cannot use sequence length %d because the BERT model "
+    #         "was only trained up to sequence length %d" %
+    #         (FLAGS_max_seq_length, bert_config.max_position_embeddings))
+
+    # if FLAGS_max_seq_length <= FLAGS_max_query_length + 3:
+    #     raise ValueError(
+    #         "The max_seq_length (%d) must be greater than max_query_length "
+    #         "(%d) + 3" % (FLAGS_max_seq_length, FLAGS_max_query_length))