feat:Adjust the directory and increase the image production process

Issue-ID: USECASEUI-525
Change-Id: I7bcbf0b48778fd59946483b253f32dda217913c0
Signed-off-by: zhaoyehua <zhaoyh6@asiainfo.com>

1 files changed, 0 insertions, 1028 deletions

diff --git a/scripts/api_squad.py b/scripts/api_squad.py
deleted file mode 100644
index f29a74b..0000000
--- a/scripts/api_squad.py
+++ /dev/null
@@ -1,1028 +0,0 @@
-# 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 modeling
-import optimization
-import tokenization
-import six
-import tensorflow as tf
-import pandas as pd
-from global_setting import 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))
-
-        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, FLAGS_version_2_with_negative):
-    """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()
-
-    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
-        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
-            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))