reader4ernie.py

# -*- coding: UTF-8 -*-
#   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 absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from __future__ import absolute_import

import sys
import os
import json
import random
import logging
import numpy as np
import six
from io import open
from collections import namedtuple

from . import gpu_dev_count
import paddlepalm as palm
import paddlepalm.tokenizer.ernie_tokenizer as tokenization
from paddlepalm.reader.utils.batching4ernie import pad_batch_data
from paddlepalm.reader.utils.mlm_batching import prepare_batch_data



log = logging.getLogger(__name__)

if six.PY3:
    import io
    sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')
    sys.stderr = io.TextIOWrapper(sys.stderr.buffer, encoding='utf-8')


def csv_reader(fd, delimiter='\t'):
    def gen():
        for i in fd:
            yield i.rstrip('\n').split(delimiter)
    return gen()


class BaseReader(object):
    def __init__(self,
                 vocab_path,
                 label_map_config=None,
                 max_seq_len=512,
                 do_lower_case=True,
                 in_tokens=False,
                 is_inference=False,
                 learning_strategy='pointwise',
                 random_seed=None,
                 tokenizer="FullTokenizer",
                 phase='train',
                 is_classify=True,
                 is_regression=False,
                 for_cn=True,
                 task_id=0):
        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.pad_id = self.vocab["[PAD]"]
        self.cls_id = self.vocab["[CLS]"]
        self.sep_id = self.vocab["[SEP]"]
        self.mask_id = self.vocab["[MASK]"]
        self.in_tokens = in_tokens
        self.phase = phase
        self.is_inference = is_inference
        self.learning_strategy = learning_strategy
        self.for_cn = for_cn
        self.task_id = task_id

        np.random.seed(random_seed)

        self.is_classify = is_classify
        self.is_regression = is_regression
        self.current_example = 0
        self.current_epoch = 0
        self.num_examples = 0

        self.examples = {}

        if label_map_config:
            with open(label_map_config, encoding='utf8') as f: 
                self.label_map = json.load(f)
        else:
            self.label_map = None

    def get_train_progress(self):
        """Gets progress for training phase."""
        return self.current_example, self.current_epoch

    def _read_tsv(self, input_file, quotechar=None):
        """Reads a tab separated value file."""
        with open(input_file, 'r', encoding='utf8') as f:
            reader = csv_reader(f)
            headers = next(reader)
            Example = namedtuple('Example', headers)

            examples = []
            for line in reader:
                example = Example(*line)
                examples.append(example)
            return examples

    def _truncate_seq_pair(self, 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()
    

    def _convert_example_to_record(self, example, max_seq_length, tokenizer):
        """Converts a single `Example` into a single `Record`."""

        text_a = tokenization.convert_to_unicode(example.text_a)
        tokens_a = tokenizer.tokenize(text_a)
        tokens_b = None
        has_text_b = False
        has_text_b_neg = False
        if isinstance(example, dict):
            has_text_b = "text_b" in example.keys()
            has_text_b_neg = "text_b_neg" in example.keys()
        else:
            has_text_b = "text_b" in example._fields
            has_text_b_neg = "text_b_neg" in example._fields

        if has_text_b:
            text_b = tokenization.convert_to_unicode(example.text_b)
            tokens_b = tokenizer.tokenize(text_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"
            self._truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
           
            if has_text_b_neg and self.phase == 'train':
                tokens_a_neg = tokenizer.tokenize(text_a)
                text_b_neg = tokenization.convert_to_unicode(example.text_b_neg)
                tokens_b_neg = tokenizer.tokenize(text_b_neg)
                self._truncate_seq_pair(tokens_a_neg, tokens_b_neg, 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/ERNIE 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 = []
        text_type_ids = []
        tokens.append("[CLS]")
        
        text_type_ids.append(0)
        for token in tokens_a:
            tokens.append(token)
            text_type_ids.append(0)
        tokens.append("[SEP]")
        text_type_ids.append(0)

        if tokens_b:
            for token in tokens_b:
                tokens.append(token)
                text_type_ids.append(1)
            tokens.append("[SEP]")
            text_type_ids.append(1)

        token_ids = tokenizer.convert_tokens_to_ids(tokens)
        position_ids = list(range(len(token_ids)))


        if has_text_b_neg and self.phase == 'train':
            tokens_neg = []
            text_type_ids_neg = []
            tokens_neg.append("[CLS]")
            text_type_ids_neg.append(0)
            for token in tokens_a_neg:
                tokens_neg.append(token)
                text_type_ids_neg.append(0)
            tokens_neg.append("[SEP]")
            text_type_ids_neg.append(0)

            if tokens_b_neg:
                for token in tokens_b_neg:
                    tokens_neg.append(token)
                    text_type_ids_neg.append(1)
                tokens_neg.append("[SEP]")
                text_type_ids_neg.append(1)

            token_ids_neg = tokenizer.convert_tokens_to_ids(tokens_neg)
            position_ids_neg = list(range(len(token_ids_neg)))


        if self.is_inference:
            Record = namedtuple('Record',
                                ['token_ids', 'text_type_ids', 'position_ids'])
            record = Record(
                token_ids=token_ids,
                text_type_ids=text_type_ids,
                position_ids=position_ids)
        else:
            qid = None
            if "qid" in example._fields:
                qid = example.qid
            if self.learning_strategy == 'pairwise' and self.phase == 'train':
                Record = namedtuple('Record',
                                    ['token_ids', 'text_type_ids', 'position_ids', 'token_ids_neg', 'text_type_ids_neg', 'position_ids_neg', 'qid'])
                
                record = Record(
                    token_ids=token_ids,
                    text_type_ids=text_type_ids,
                    position_ids=position_ids,
                    token_ids_neg=token_ids_neg,
                    text_type_ids_neg=text_type_ids_neg,
                    position_ids_neg=position_ids_neg,
                    qid=qid)
 
            else:
                if self.label_map:
                    label_id = self.label_map[example.label]
                else:
                    label_id = example.label

                Record = namedtuple('Record', [
                    'token_ids', 'text_type_ids', 'position_ids', 'label_id', 'qid'
                ])

                record = Record(
                    token_ids=token_ids,
                    text_type_ids=text_type_ids,
                    position_ids=position_ids,
                    label_id=label_id,
                    qid=qid)
        return record

    def _prepare_batch_data(self, examples, batch_size, phase=None):
        """generate batch records"""
        batch_records, max_len = [], 0
        if len(examples) < batch_size:
            raise Exception('CLS dataset contains too few samples. Expect more than '+str(batch_size))
        for index, example in enumerate(examples):
            if phase == "train":
                self.current_example = index
            record = self._convert_example_to_record(example, self.max_seq_len,
                                                     self.tokenizer)                                       
            max_len = max(max_len, len(record.token_ids))
            if self.in_tokens:
                to_append = (len(batch_records) + 1) * max_len <= batch_size
            else:
                to_append = len(batch_records) < batch_size
            if to_append:
                batch_records.append(record)
            else:
                yield self._pad_batch_records(batch_records)
                batch_records, max_len = [record], len(record.token_ids)

        if phase == 'pred' and batch_records:
            yield self._pad_batch_records(batch_records)

    def get_num_examples(self, input_file=None, phase=None):
        if self.examples is not None:
            if phase is None:
                phase = 'all'
            return len(self.examples[phase])
        else:
            assert input_file is not None, "Argument input_file should be given or the data_generator should be created when this func is called."
            examples = self._read_tsv(input_file)
            return len(examples)

    def data_generator(self,
                       input_file,
                       batch_size,
                       epoch,
                       dev_count=1,
                       shuffle=True,
                       phase=None):
        examples = self._read_tsv(input_file)
        if phase is None:
            phase = 'all'
        self.examples[phase] = examples

        def wrapper():
            all_dev_batches = []
            if epoch is None:
                num_epochs = 99999999
            else:
                num_epochs = epoch
            for epoch_index in range(num_epochs):
                if phase == "train":
                    self.current_example = 0
                    self.current_epoch = epoch_index
                if shuffle:
                    np.random.shuffle(examples)

                for batch_data in self._prepare_batch_data(
                        examples, batch_size, phase=phase):
                    if len(all_dev_batches) < dev_count:
                        all_dev_batches.append(batch_data)
                    if len(all_dev_batches) == dev_count:
                        for batch in all_dev_batches:
                            yield batch
                        
                        all_dev_batches = []
        def f():
            for i in wrapper():
                yield i

        # def f():
        #     try:
        #         for i in wrapper():
        #             yield i
        #     except Exception as e:
        #         import traceback
        #         traceback.print_exc()

        return f


class MaskLMReader(BaseReader):

    def _convert_example_to_record(self, example, max_seq_length, tokenizer):
        """Converts a single `Example` into a single `Record`."""

        text_a = tokenization.convert_to_unicode(example.text_a)
        tokens_a = tokenizer.tokenize(text_a)
        tokens_b = None 

        has_text_b = False
        if isinstance(example, dict):
            has_text_b = "text_b" in example.keys()
        else:
            has_text_b = "text_b" in example._fields

        if has_text_b:
            text_b = tokenization.convert_to_unicode(example.text_b)
            tokens_b = tokenizer.tokenize(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"
            self._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/ERNIE 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 = []
        text_type_ids = []
        tokens.append("[CLS]")
        text_type_ids.append(0)
        for token in tokens_a:
            tokens.append(token)
            text_type_ids.append(0)
        tokens.append("[SEP]")
        text_type_ids.append(0)

        if tokens_b:
            for token in tokens_b:
                tokens.append(token)
                text_type_ids.append(1)
            tokens.append("[SEP]")
            text_type_ids.append(1)

        token_ids = tokenizer.convert_tokens_to_ids(tokens)
        position_ids = list(range(len(token_ids)))

        return [token_ids, text_type_ids, position_ids]

    def batch_reader(self, examples, batch_size, in_tokens, phase):
        batch = []
        total_token_num = 0
        if len(examples) < batch_size:
            raise Exception('MaskLM dataset contains too few samples. Expect more than '+str(batch_size))
        for e in examples:
            parsed_line = self._convert_example_to_record(e, self.max_seq_len, self.tokenizer)
            to_append = len(batch) < batch_size
            if to_append:
                batch.append(parsed_line)
                total_token_num += len(parsed_line[0])
            else:
                yield batch, total_token_num
                batch = [parsed_line]
                total_token_num = len(parsed_line[0])

        if len(batch) > 0 and phase == 'pred':
            yield batch, total_token_num

    def data_generator(self,
                       input_file,
                       batch_size,
                       epoch,
                       dev_count=1,
                       shuffle=True,
                       phase=None):
        examples = self._read_tsv(input_file)
        if phase is None:
            phase = 'all'
        self.examples[phase] = examples

        def wrapper():
            all_dev_batches = []
            if epoch is None:
                num_epochs = 99999999
            else:
                num_epochs = epoch
            for epoch_index in range(num_epochs):
                if phase == "train":
                    self.current_example = 0
                    self.current_epoch = epoch_index
                if shuffle:
                    np.random.shuffle(examples)

                all_dev_batches = []
                for batch_data, num_tokens in self.batch_reader(examples, 
                                                    batch_size, self.in_tokens, phase=phase):
                    batch_data = prepare_batch_data(
                        batch_data,
                        num_tokens,
                        voc_size=len(self.vocab),
                        pad_id=self.pad_id,
                        cls_id=self.cls_id,
                        sep_id=self.sep_id,
                        mask_id=self.mask_id,
                        # max_len=self.max_seq_len, # 注意，如果padding到最大长度，会导致mask_pos与实际位置不对应。因为mask pos是基于batch内最大长度来计算的。
                        return_input_mask=True,
                        return_max_len=False,
                        return_num_token=False,
                        dev_count=gpu_dev_count)

                    # yield batch
                    for piece in palm.distribute.yield_pieces(batch_data, ['s', 's', 's', 's', 's', 'u', 'u'], batch_size):
                        yield piece

        return wrapper


class ClassifyReader(BaseReader):
    def _read_tsv(self, input_file, quotechar=None):
        """Reads a tab separated value file."""
        with open(input_file, 'r', encoding='utf8') as f:
            reader = csv_reader(f)
            headers = next(reader)
            text_indices = [
                index for index, h in enumerate(headers) if h != "label"
            ]
            Example = namedtuple('Example', headers)
            examples = []
            for line in reader:
                for index, text in enumerate(line):
                    if index in text_indices:
                        if self.for_cn:
                            line[index] = text.replace(' ', '')
                        else:
                            line[index] = text
                example = Example(*line)
                examples.append(example)
            return examples

    def _pad_batch_records(self, batch_records):
        batch_token_ids = [record.token_ids for record in batch_records]
        batch_text_type_ids = [record.text_type_ids for record in batch_records]
        batch_position_ids = [record.position_ids for record in batch_records]
        if self.phase=='train' and self.learning_strategy == 'pairwise':
            batch_token_ids_neg = [record.token_ids_neg for record in batch_records]
            batch_text_type_ids_neg = [record.text_type_ids_neg for record in batch_records]
            batch_position_ids_neg = [record.position_ids_neg for record in batch_records]

        if not self.is_inference:
            if not self.learning_strategy == 'pairwise':
                batch_labels = [record.label_id for record in batch_records]
                if self.is_classify:
                    batch_labels = np.array(batch_labels).astype("int64").reshape(
                        [-1])
                elif self.is_regression:
                    batch_labels = np.array(batch_labels).astype("float32").reshape(
                        [-1])

            if batch_records[0].qid:
                batch_qids = [record.qid for record in batch_records]
                batch_qids = np.array(batch_qids).astype("int64").reshape(
                    [-1])
            else:
                batch_qids = np.array([]).astype("int64").reshape([-1])

        # padding
        padded_token_ids, input_mask = pad_batch_data(
            batch_token_ids, pad_idx=self.pad_id, return_input_mask=True)
        padded_text_type_ids = pad_batch_data(
            batch_text_type_ids, pad_idx=self.pad_id)
        padded_position_ids = pad_batch_data(
            batch_position_ids, pad_idx=self.pad_id)
        padded_task_ids = np.ones_like(
            padded_token_ids, dtype="int64") * self.task_id

        return_list = [
            padded_token_ids, padded_text_type_ids, padded_position_ids,
            padded_task_ids, input_mask
        ]

        if self.phase=='train':
            if self.learning_strategy == 'pairwise':
                padded_token_ids_neg, input_mask_neg = pad_batch_data(
                    batch_token_ids_neg, pad_idx=self.pad_id, return_input_mask=True)
                padded_text_type_ids_neg = pad_batch_data(
                    batch_text_type_ids_neg, pad_idx=self.pad_id)
                padded_position_ids_neg = pad_batch_data(
                    batch_position_ids_neg, pad_idx=self.pad_id)
                padded_task_ids_neg = np.ones_like(
                    padded_token_ids_neg, dtype="int64") * self.task_id

                return_list += [padded_token_ids_neg, padded_text_type_ids_neg, \
                                padded_position_ids_neg, padded_task_ids_neg, input_mask_neg]

            elif self.learning_strategy == 'pointwise':
                return_list += [batch_labels]

        return return_list


class SequenceLabelReader(BaseReader):
    def _pad_batch_records(self, batch_records):
        batch_token_ids = [record.token_ids for record in batch_records]
        batch_text_type_ids = [record.text_type_ids for record in batch_records]
        batch_position_ids = [record.position_ids for record in batch_records]
        batch_label_ids = [record.label_ids for record in batch_records]

        # padding
        padded_token_ids, input_mask, batch_seq_lens = pad_batch_data(
            batch_token_ids,
            pad_idx=self.pad_id,
            return_input_mask=True,
            return_seq_lens=True)
        padded_text_type_ids = pad_batch_data(
            batch_text_type_ids, pad_idx=self.pad_id)
        padded_position_ids = pad_batch_data(
            batch_position_ids, pad_idx=self.pad_id)
        padded_label_ids = pad_batch_data(
            batch_label_ids, pad_idx=len(self.label_map) - 1)
        padded_task_ids = np.ones_like(
            padded_token_ids, dtype="int64") * self.task_id

        return_list = [
            padded_token_ids, padded_text_type_ids, padded_position_ids,
            padded_task_ids, input_mask, padded_label_ids, batch_seq_lens
        ]
        return return_list

    def _reseg_token_label(self, tokens, labels, tokenizer):
        assert len(tokens) == len(labels)
        ret_tokens = []
        ret_labels = []
        for token, label in zip(tokens, labels):
            sub_token = tokenizer.tokenize(token)
            if len(sub_token) == 0:
                continue
            ret_tokens.extend(sub_token)
            if len(sub_token) == 1:
                ret_labels.append(label)
                continue

            ret_labels.extend([label] * len(sub_token))

        assert len(ret_tokens) == len(ret_labels)
        return ret_tokens, ret_labels

    def _convert_example_to_record(self, example, max_seq_length, tokenizer):
        tokens = tokenization.convert_to_unicode(example.text_a).split(u"")
        labels = tokenization.convert_to_unicode(example.label).split(u"")
        tokens, labels = self._reseg_token_label(tokens, labels, tokenizer)

        if len(tokens) > max_seq_length - 2:
            tokens = tokens[0:(max_seq_length - 2)]
            labels = labels[0:(max_seq_length - 2)]

        tokens = ["[CLS]"] + tokens + ["[SEP]"]
        token_ids = tokenizer.convert_tokens_to_ids(tokens)
        position_ids = list(range(len(token_ids)))
        text_type_ids = [0] * len(token_ids)
        no_entity_id = len(self.label_map) - 1
        labels = [
            label if label in self.label_map else u"O" for label in labels
        ]
        label_ids = [no_entity_id] + [
            self.label_map[label] for label in labels
        ] + [no_entity_id]

        Record = namedtuple(
            'Record',
            ['token_ids', 'text_type_ids', 'position_ids', 'label_ids'])
        record = Record(
            token_ids=token_ids,
            text_type_ids=text_type_ids,
            position_ids=position_ids,
            label_ids=label_ids)
        return record


class ExtractEmbeddingReader(BaseReader):
    def _pad_batch_records(self, batch_records):
        batch_token_ids = [record.token_ids for record in batch_records]
        batch_text_type_ids = [record.text_type_ids for record in batch_records]
        batch_position_ids = [record.position_ids for record in batch_records]

        # padding
        padded_token_ids, input_mask, seq_lens = pad_batch_data(
            batch_token_ids,
            pad_idx=self.pad_id,
            return_input_mask=True,
            return_seq_lens=True)
        padded_text_type_ids = pad_batch_data(
            batch_text_type_ids, pad_idx=self.pad_id)
        padded_position_ids = pad_batch_data(
            batch_position_ids, pad_idx=self.pad_id)
        padded_task_ids = np.ones_like(
            padded_token_ids, dtype="int64") * self.task_id

        return_list = [
            padded_token_ids, padded_text_type_ids, padded_position_ids,
            padded_task_ids, input_mask, seq_lens
        ]

        return return_list


class MRCReader(BaseReader):
    def __init__(self,
                 vocab_path,
                 label_map_config=None,
                 max_seq_len=512,
                 do_lower_case=True,
                 in_tokens=False,
                 random_seed=None,
                 tokenizer="FullTokenizer",
                 is_classify=True,
                 is_regression=False,
                 for_cn=True,
                 task_id=0,
                 doc_stride=128,
                 max_query_length=64,
                 remove_noanswer=True):
        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.pad_id = self.vocab["[PAD]"]
        self.cls_id = self.vocab["[CLS]"]
        self.sep_id = self.vocab["[SEP]"]
        self.in_tokens = in_tokens
        self.for_cn = for_cn
        self.task_id = task_id
        self.doc_stride = doc_stride
        self.max_query_length = max_query_length
        self.examples = {}
        self.features = {}
        self.remove_noanswer = remove_noanswer

        if random_seed is not None:
            np.random.seed(random_seed)

        self.current_example = 0
        self.current_epoch = 0
        self.num_examples = 0

        self.Example = namedtuple('Example',
                ['qas_id', 'question_text', 'doc_tokens', 'orig_answer_text',
                'start_position', 'end_position'])
        self.Feature = namedtuple("Feature", ["unique_id", "example_index", "doc_span_index",
                "tokens", "token_to_orig_map", "token_is_max_context",
                "token_ids", "position_ids", "text_type_ids",
                "start_position", "end_position"])
        self.DocSpan = namedtuple("DocSpan", ["start", "length"])

    def _read_json(self, input_file, is_training):
        examples = []
        with open(input_file, "r", encoding='utf8') as f:
            input_data = json.load(f)["data"]
            for entry in input_data:
                for paragraph in entry["paragraphs"]:
                    paragraph_text = paragraph["context"]
                    for qa in paragraph["qas"]:
                        qas_id = qa["id"]
                        question_text = qa["question"]
                        start_pos = None
                        end_pos = None
                        orig_answer_text = None

                        if is_training:
                            if len(qa["answers"]) != 1:
                                raise ValueError(
                                    "For training, each question should have exactly 1 answer."
                                )

                            answer = qa["answers"][0]
                            orig_answer_text = answer["text"]
                            answer_offset = answer["answer_start"]
                            answer_length = len(orig_answer_text)
                            doc_tokens = [
                                paragraph_text[:answer_offset],
                                paragraph_text[answer_offset:answer_offset +
                                               answer_length],
                                paragraph_text[answer_offset + answer_length:]
                            ]

                            start_pos = 1
                            end_pos = 1

                            actual_text = " ".join(doc_tokens[start_pos:(end_pos
                                                                         + 1)])
                            if actual_text.find(orig_answer_text) == -1:
                                log.info("Could not find answer: '%s' vs. '%s'",
                                      actual_text, orig_answer_text)
                                continue
                        else:
                            doc_tokens = tokenization.tokenize_chinese_chars(
                                paragraph_text)

                        example = self.Example(
                            qas_id=qas_id,
                            question_text=question_text,
                            doc_tokens=doc_tokens,
                            orig_answer_text=orig_answer_text,
                            start_position=start_pos,
                            end_position=end_pos)
                        examples.append(example)

        return examples

    def _improve_answer_span(self, doc_tokens, input_start, input_end,
                             tokenizer, orig_answer_text):
        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(self, doc_spans, cur_span_index, position):
        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 _convert_example_to_feature(self, examples, max_seq_length, tokenizer,
                                    is_training, remove_noanswer=True):
        features = []
        unique_id = 1000000000

        print('converting examples to features...')
        for (example_index, example) in enumerate(examples):
            if example_index % 1000 == 0:
                print('processing {}th example...'.format(example_index))
            query_tokens = tokenizer.tokenize(example.question_text)
            if len(query_tokens) > self.max_query_length:
                query_tokens = query_tokens[0:self.max_query_length]
            tok_to_orig_index = []
            orig_to_tok_index = []
            all_doc_tokens = []
            for (i, token) in enumerate(example.doc_tokens):
                orig_to_tok_index.append(len(all_doc_tokens))
                sub_tokens = tokenizer.tokenize(token)
                for sub_token in sub_tokens:
                    tok_to_orig_index.append(i)
                    all_doc_tokens.append(sub_token)

            tok_start_position = None
            tok_end_position = None
            if is_training:
                tok_start_position = orig_to_tok_index[example.start_position]
                if example.end_position < len(example.doc_tokens) - 1:
                    tok_end_position = orig_to_tok_index[example.end_position +
                                                         1] - 1
                else:
                    tok_end_position = len(all_doc_tokens) - 1
                (tok_start_position,
                 tok_end_position) = self._improve_answer_span(
                     all_doc_tokens, tok_start_position, tok_end_position,
                     tokenizer, example.orig_answer_text)

            max_tokens_for_doc = max_seq_length - len(query_tokens) - 3
            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(self.DocSpan(start=start_offset, length=length))
                if start_offset + length == len(all_doc_tokens):
                    break
                start_offset += min(length, self.doc_stride)

            for (doc_span_index, doc_span) in enumerate(doc_spans):
                tokens = []
                token_to_orig_map = {}
                token_is_max_context = {}
                text_type_ids = []
                tokens.append("[CLS]")
                text_type_ids.append(0)
                for token in query_tokens:
                    tokens.append(token)
                    text_type_ids.append(0)
                tokens.append("[SEP]")
                text_type_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 = self._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])
                    text_type_ids.append(1)
                tokens.append("[SEP]")
                text_type_ids.append(1)

                token_ids = tokenizer.convert_tokens_to_ids(tokens)
                position_ids = list(range(len(token_ids)))
                start_position = None
                end_position = None
                if is_training:
                    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
                        if remove_noanswer:
                            continue
                    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

                feature = self.Feature(
                    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,
                    token_ids=token_ids,
                    position_ids=position_ids,
                    text_type_ids=text_type_ids,
                    start_position=start_position,
                    end_position=end_position)
                features.append(feature)

                unique_id += 1

        return features

    def _prepare_batch_data(self, records, batch_size, phase=None):
        """generate batch records"""
        batch_records, max_len = [], 0

        if len(records) < batch_size:
            raise Exception('mrc dataset contains too few samples. Expect more than '+str(batch_size))

        for index, record in enumerate(records):
            if phase == "train":
                self.current_example = index
            max_len = max(max_len, len(record.token_ids))
            if self.in_tokens:
                to_append = (len(batch_records) + 1) * max_len <= batch_size
            else:
                to_append = len(batch_records) < batch_size
            if to_append:
                batch_records.append(record)
            else:
                # yield self._pad_batch_records(batch_records, phase == "train")
                ds = ['s'] * 8
                for piece in palm.distribute.yield_pieces(\
                        self._pad_batch_records(batch_records, phase == 'train'),
                        ds, batch_size):
                    yield piece
                batch_records, max_len = [record], len(record.token_ids)

        if phase == 'pred' and batch_records:
            for piece in palm.distribute.yield_pieces(\
                        self._pad_batch_records(batch_records, phase == 'train'),
                        ds, batch_size):
                yield piece


    def _pad_batch_records(self, batch_records, is_training):
        batch_token_ids = [record.token_ids for record in batch_records]
        batch_text_type_ids = [record.text_type_ids for record in batch_records]
        batch_position_ids = [record.position_ids for record in batch_records]
        if is_training:
            batch_start_position = [
                record.start_position for record in batch_records
            ]
            batch_end_position = [
                record.end_position for record in batch_records
            ]
            batch_start_position = np.array(batch_start_position).astype(
                "int64").reshape([-1])
            batch_end_position = np.array(batch_end_position).astype(
                "int64").reshape([-1])

        else:
            batch_size = len(batch_token_ids)
            batch_start_position = np.zeros(
                shape=[batch_size], dtype="int64")
            batch_end_position = np.zeros(shape=[batch_size], dtype="int64")

        batch_unique_ids = [record.unique_id for record in batch_records]
        batch_unique_ids = np.array(batch_unique_ids).astype("int64").reshape(
            [-1])

        # padding
        padded_token_ids, input_mask = pad_batch_data(
            batch_token_ids, pad_idx=self.pad_id, return_input_mask=True)
        padded_text_type_ids = pad_batch_data(
            batch_text_type_ids, pad_idx=self.pad_id)
        padded_position_ids = pad_batch_data(
            batch_position_ids, pad_idx=self.pad_id)
        padded_task_ids = np.ones_like(
            padded_token_ids, dtype="int64") * self.task_id

        return_list = [
            padded_token_ids, padded_text_type_ids, padded_position_ids,
            padded_task_ids, input_mask, batch_start_position,
            batch_end_position, batch_unique_ids
        ]

        return return_list

    def get_num_examples(self, phase):
        return len(self.features[phase])

    def get_features(self, phase):
        return self.features[phase]

    def get_examples(self, phase):
        return self.examples[phase]

    def data_generator(self,
                       input_file,
                       batch_size,
                       epoch,
                       dev_count=1,
                       shuffle=True,
                       phase=None):

        examples = self.examples.get(phase, None)
        features = self.features.get(phase, None)
        if not examples:
            examples = self._read_json(input_file, phase == "train")
            features = self._convert_example_to_feature(
                examples, self.max_seq_len, self.tokenizer, phase == "train", remove_noanswer=self.remove_noanswer)
            self.examples[phase] = examples
            self.features[phase] = features

        def wrapper():
            all_dev_batches = []
            if epoch is None:
                num_epochs = 99999999
            else:
                num_epochs = epoch
            for epoch_index in range(num_epochs):
                if phase == "train":
                    self.current_example = 0
                    self.current_epoch = epoch_index
                if phase == "train" and shuffle:
                    np.random.shuffle(features)

                for batch_data in self._prepare_batch_data(
                        features, batch_size, phase=phase):

                    yield batch_data

        return wrapper


if __name__ == '__main__':
    pass