nlp_reader.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

import collections
import numpy as np
import six
from collections import namedtuple

import paddle.fluid as fluid

from paddlehub.reader import tokenization
from paddlehub.common.logger import logger
from paddlehub.common.utils import sys_stdout_encoding
from paddlehub.dataset.dataset import InputExample
from .batching import pad_batch_data
import paddlehub as hub
from .base_reader import BaseReader


class BaseNLPReader(BaseReader):
    def __init__(self,
                 vocab_path,
                 dataset=None,
                 label_map_config=None,
                 max_seq_len=512,
                 do_lower_case=True,
                 random_seed=None,
                 use_task_id=False,
                 sp_model_path=None,
                 word_dict_path=None,
                 in_tokens=False):
        super(BaseNLPReader, self).__init__(dataset, random_seed)
        self.max_seq_len = max_seq_len
        if sp_model_path and word_dict_path:
            self.tokenizer = tokenization.WSSPTokenizer(
                vocab_path, sp_model_path, word_dict_path, ws=True, lower=True)
        else:
            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.use_task_id = use_task_id

        if self.use_task_id:
            logger.warning(
                "use_task_id has been de discarded since PaddleHub v1.4.0, it's no necessary to feed task_ids now."
            )
            self.task_id = 0

        self.Record_With_Label_Id = namedtuple(
            'Record',
            ['token_ids', 'text_type_ids', 'position_ids', 'label_id'])
        self.Record_Wo_Label_Id = namedtuple(
            'Record', ['token_ids', 'text_type_ids', 'position_ids'])

    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,
                                   phase=None):
        """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
        if example.text_b is not None:
            #if "text_b" in example._fields:
            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)))

        if self.label_map:
            if example.label not in self.label_map:
                raise KeyError(
                    "example.label = {%s} not in label" % example.label)
            label_id = self.label_map[example.label]
        else:
            label_id = example.label

        if phase != "predict":
            record = self.Record_With_Label_Id(
                token_ids=token_ids,
                text_type_ids=text_type_ids,
                position_ids=position_ids,
                label_id=label_id)
        else:
            record = self.Record_Wo_Label_Id(
                token_ids=token_ids,
                text_type_ids=text_type_ids,
                position_ids=position_ids)

        return record

    def _pad_batch_records(self, batch_records, phase):
        raise NotImplementedError

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

        if batch_records:
            yield self._pad_batch_records(batch_records, phase)

    def data_generator(self,
                       batch_size=1,
                       phase='train',
                       shuffle=True,
                       data=None,
                       return_list=True):
        if phase != 'predict' and not self.dataset:
            raise ValueError("The dataset is None ! It isn't allowed.")
        if phase == 'train':
            shuffle = True
            examples = self.get_train_examples()
            self.num_examples['train'] = len(examples)
        elif phase == 'val' or phase == 'dev':
            shuffle = False
            examples = self.get_dev_examples()
            self.num_examples['dev'] = len(examples)
        elif phase == 'test':
            shuffle = False
            examples = self.get_test_examples()
            self.num_examples['test'] = len(examples)
        elif phase == 'predict':
            shuffle = False
            examples = []
            seq_id = 0

            for item in data:
                # set label in order to run the program
                if self.dataset:
                    label = list(self.label_map.keys())[0]
                else:
                    label = 0
                if len(item) == 1:
                    item_i = InputExample(
                        guid=seq_id, text_a=item[0], label=label)
                elif len(item) == 2:
                    item_i = InputExample(
                        guid=seq_id,
                        text_a=item[0],
                        text_b=item[1],
                        label=label)
                else:
                    raise ValueError(
                        "The length of input_text is out of handling, which must be 1 or 2!"
                    )
                examples.append(item_i)
                seq_id += 1
        else:
            raise ValueError(
                "Unknown phase, which should be in ['train', 'dev', 'test', 'predict']."
            )

        def wrapper():
            if shuffle:
                np.random.shuffle(examples)

            for batch_data in self._prepare_batch_data(
                    examples, batch_size, phase=phase):
                if return_list:
                    # for DataFeeder
                    yield [batch_data]
                else:
                    # for DataLoader
                    yield batch_data

        return wrapper


class ClassifyReader(BaseNLPReader):
    def _pad_batch_records(self, batch_records, phase=None):
        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]

        padded_token_ids, input_mask = pad_batch_data(
            batch_token_ids,
            max_seq_len=self.max_seq_len,
            pad_idx=self.pad_id,
            return_input_mask=True)
        padded_text_type_ids = pad_batch_data(
            batch_text_type_ids,
            max_seq_len=self.max_seq_len,
            pad_idx=self.pad_id)
        padded_position_ids = pad_batch_data(
            batch_position_ids,
            max_seq_len=self.max_seq_len,
            pad_idx=self.pad_id)

        if phase != "predict":
            batch_labels = [record.label_id for record in batch_records]
            batch_labels = np.array(batch_labels).astype("int64").reshape(
                [-1, 1])

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

            if self.use_task_id:
                padded_task_ids = np.ones_like(
                    padded_token_ids, dtype="int64") * self.task_id
                return_list = [
                    padded_token_ids, padded_position_ids, padded_text_type_ids,
                    input_mask, padded_task_ids, batch_labels
                ]
        else:
            return_list = [
                padded_token_ids, padded_position_ids, padded_text_type_ids,
                input_mask
            ]

            if self.use_task_id:
                padded_task_ids = np.ones_like(
                    padded_token_ids, dtype="int64") * self.task_id
                return_list = [
                    padded_token_ids, padded_position_ids, padded_text_type_ids,
                    input_mask, padded_task_ids
                ]
        return return_list


class SequenceLabelReader(BaseNLPReader):
    def __init__(self,
                 vocab_path,
                 dataset=None,
                 label_map_config=None,
                 max_seq_len=512,
                 do_lower_case=True,
                 random_seed=None,
                 use_task_id=False,
                 sp_model_path=None,
                 word_dict_path=None,
                 in_tokens=False):
        super(SequenceLabelReader, self).__init__(
            vocab_path=vocab_path,
            dataset=dataset,
            label_map_config=label_map_config,
            max_seq_len=max_seq_len,
            do_lower_case=do_lower_case,
            random_seed=random_seed,
            use_task_id=use_task_id,
            sp_model_path=sp_model_path,
            word_dict_path=word_dict_path,
            in_tokens=in_tokens)
        if sp_model_path and word_dict_path:
            self.tokenizer = tokenization.FullTokenizer(
                vocab_file=vocab_path,
                do_lower_case=do_lower_case,
                use_sentence_piece_vocab=True)

    def _pad_batch_records(self, batch_records, phase=None):
        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, batch_seq_lens = pad_batch_data(
            batch_token_ids,
            pad_idx=self.pad_id,
            max_seq_len=self.max_seq_len,
            return_input_mask=True,
            return_seq_lens=True)
        padded_text_type_ids = pad_batch_data(
            batch_text_type_ids,
            max_seq_len=self.max_seq_len,
            pad_idx=self.pad_id)
        padded_position_ids = pad_batch_data(
            batch_position_ids,
            max_seq_len=self.max_seq_len,
            pad_idx=self.pad_id)

        if phase != "predict":
            batch_label_ids = [record.label_id for record in batch_records]
            padded_label_ids = pad_batch_data(
                batch_label_ids,
                max_seq_len=self.max_seq_len,
                pad_idx=len(self.label_map) - 1)

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

            if self.use_task_id:
                padded_task_ids = np.ones_like(
                    padded_token_ids, dtype="int64") * self.task_id
                return_list = [
                    padded_token_ids, padded_position_ids, padded_text_type_ids,
                    input_mask, padded_task_ids, padded_label_ids,
                    batch_seq_lens
                ]

        else:
            return_list = [
                padded_token_ids, padded_position_ids, padded_text_type_ids,
                input_mask, batch_seq_lens
            ]

            if self.use_task_id:
                padded_task_ids = np.ones_like(
                    padded_token_ids, dtype="int64") * self.task_id
                return_list = [
                    padded_token_ids, padded_position_ids, padded_text_type_ids,
                    input_mask, padded_task_ids, batch_seq_lens
                ]

        return return_list

    def _reseg_token_label(self, tokens, tokenizer, phase, labels=None):
        if phase != "predict":
            if len(tokens) != len(labels):
                raise ValueError(
                    "The length of tokens must be same with 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)
                ret_labels.append(label)
                if len(sub_token) < 2:
                    continue
                sub_label = label
                if label.startswith("B-"):
                    sub_label = "I-" + label[2:]
                ret_labels.extend([sub_label] * (len(sub_token) - 1))

            if len(ret_tokens) != len(ret_labels):
                raise ValueError(
                    "The length of ret_tokens can't match with labels")
            return ret_tokens, ret_labels
        else:
            ret_tokens = []
            for token in tokens:
                sub_token = tokenizer.tokenize(token)
                if len(sub_token) == 0:
                    continue
                ret_tokens.extend(sub_token)
                if len(sub_token) < 2:
                    continue

            return ret_tokens

    def _convert_example_to_record(self,
                                   example,
                                   max_seq_length,
                                   tokenizer,
                                   phase=None):

        tokens = tokenization.convert_to_unicode(example.text_a).split(u"")

        if phase != "predict":
            labels = tokenization.convert_to_unicode(example.label).split(u"")
            tokens, labels = self._reseg_token_label(
                tokens=tokens, labels=labels, tokenizer=tokenizer, phase=phase)

            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
            label_ids = [no_entity_id
                         ] + [self.label_map[label]
                              for label in labels] + [no_entity_id]
            record = self.Record_With_Label_Id(
                token_ids=token_ids,
                text_type_ids=text_type_ids,
                position_ids=position_ids,
                label_id=label_ids)
        else:
            tokens = self._reseg_token_label(
                tokens=tokens, tokenizer=tokenizer, phase=phase)

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

            record = self.Record_Wo_Label_Id(
                token_ids=token_ids,
                text_type_ids=text_type_ids,
                position_ids=position_ids,
            )

        return record


class MultiLabelClassifyReader(BaseNLPReader):
    def _pad_batch_records(self, batch_records, phase=None):
        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 = pad_batch_data(
            batch_token_ids,
            pad_idx=self.pad_id,
            max_seq_len=self.max_seq_len,
            return_input_mask=True)
        padded_text_type_ids = pad_batch_data(
            batch_text_type_ids,
            max_seq_len=self.max_seq_len,
            pad_idx=self.pad_id)
        padded_position_ids = pad_batch_data(
            batch_position_ids,
            max_seq_len=self.max_seq_len,
            pad_idx=self.pad_id)

        if phase != "predict":
            batch_labels_ids = [record.label_id for record in batch_records]
            num_label = len(self.dataset.get_labels())
            batch_labels = np.array(batch_labels_ids).astype("int64").reshape(
                [-1, num_label])

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

            if self.use_task_id:
                padded_task_ids = np.ones_like(
                    padded_token_ids, dtype="int64") * self.task_id
                return_list = [
                    padded_token_ids, padded_position_ids, padded_text_type_ids,
                    input_mask, padded_task_ids, batch_labels
                ]
        else:
            return_list = [
                padded_token_ids, padded_position_ids, padded_text_type_ids,
                input_mask
            ]

            if self.use_task_id:
                padded_task_ids = np.ones_like(
                    padded_token_ids, dtype="int64") * self.task_id
                return_list = [
                    padded_token_ids, padded_position_ids, padded_text_type_ids,
                    input_mask, padded_task_ids
                ]
        return return_list

    def _convert_example_to_record(self,
                                   example,
                                   max_seq_length,
                                   tokenizer,
                                   phase=None):
        """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
        if example.text_b is not None:
            #if "text_b" in example._fields:
            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)]

        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)))

        label_ids = []
        if phase == "predict":
            label_ids = [0, 0, 0, 0, 0, 0]
        else:
            for label in example.label:
                label_ids.append(int(label))

        if phase != "predict":
            record = self.Record_With_Label_Id(
                token_ids=token_ids,
                text_type_ids=text_type_ids,
                position_ids=position_ids,
                label_id=label_ids)
        else:
            record = self.Record_Wo_Label_Id(
                token_ids=token_ids,
                text_type_ids=text_type_ids,
                position_ids=position_ids)

        return record


class RegressionReader(BaseNLPReader):
    def _pad_batch_records(self, batch_records, phase=None):
        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]

        padded_token_ids, input_mask = pad_batch_data(
            batch_token_ids,
            max_seq_len=self.max_seq_len,
            pad_idx=self.pad_id,
            return_input_mask=True)
        padded_text_type_ids = pad_batch_data(
            batch_text_type_ids,
            max_seq_len=self.max_seq_len,
            pad_idx=self.pad_id)
        padded_position_ids = pad_batch_data(
            batch_position_ids,
            max_seq_len=self.max_seq_len,
            pad_idx=self.pad_id)

        if phase != "predict":
            batch_labels = [record.label_id for record in batch_records]
            # the only diff with ClassifyReader: astype("float32")
            batch_labels = np.array(batch_labels).astype("float32").reshape(
                [-1, 1])

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

            if self.use_task_id:
                padded_task_ids = np.ones_like(
                    padded_token_ids, dtype="int64") * self.task_id
                return_list = [
                    padded_token_ids, padded_position_ids, padded_text_type_ids,
                    input_mask, padded_task_ids, batch_labels
                ]
        else:
            return_list = [
                padded_token_ids, padded_position_ids, padded_text_type_ids,
                input_mask
            ]

            if self.use_task_id:
                padded_task_ids = np.ones_like(
                    padded_token_ids, dtype="int64") * self.task_id
                return_list = [
                    padded_token_ids, padded_position_ids, padded_text_type_ids,
                    input_mask, padded_task_ids
                ]

        return return_list

    def data_generator(self,
                       batch_size=1,
                       phase='train',
                       shuffle=True,
                       data=None,
                       return_list=True):
        if phase != 'predict' and not self.dataset:
            raise ValueError("The dataset is none and it's not allowed.")
        if phase == 'train':
            shuffle = True
            examples = self.get_train_examples()
            self.num_examples['train'] = len(examples)
        elif phase == 'val' or phase == 'dev':
            shuffle = False
            examples = self.get_dev_examples()
            self.num_examples['dev'] = len(examples)
        elif phase == 'test':
            shuffle = False
            examples = self.get_test_examples()
            self.num_examples['test'] = len(examples)
        elif phase == 'predict':
            shuffle = False
            examples = []
            seq_id = 0

            for item in data:
                # set label in order to run the program
                label = -1  # different from BaseNLPReader
                if len(item) == 1:
                    item_i = InputExample(
                        guid=seq_id, text_a=item[0], label=label)
                elif len(item) == 2:
                    item_i = InputExample(
                        guid=seq_id,
                        text_a=item[0],
                        text_b=item[1],
                        label=label)
                else:
                    raise ValueError(
                        "The length of input_text is out of handling, which must be 1 or 2!"
                    )
                examples.append(item_i)
                seq_id += 1
        else:
            raise ValueError(
                "Unknown phase, which should be in ['train', 'dev', 'test', 'predict']."
            )

        def wrapper():
            if shuffle:
                np.random.shuffle(examples)

            for batch_data in self._prepare_batch_data(
                    examples, batch_size, phase=phase):
                if return_list:
                    # for DataFeeder
                    yield [batch_data]
                else:
                    # for DataLoader
                    yield batch_data

        return wrapper


class Features(object):
    """A single set of features of squad_data."""

    def __init__(
            self,
            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=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.token_ids = token_ids
        self.position_ids = position_ids
        self.text_type_ids = text_type_ids
        self.start_position = start_position
        self.end_position = end_position
        self.is_impossible = is_impossible

    def __repr__(self):
        s = ""
        s += "unique_id: %s " % self.unique_id
        s += "example_index: %s " % self.example_index
        s += "start_position: %s " % self.start_position
        s += "end_position: %s " % self.end_position
        s += "is_impossible: %s " % self.is_impossible
        # s += "tokens: %s" % self.tokens
        # s += "token_to_orig_map %s" % self.token_to_orig_map
        return s


class ReadingComprehensionReader(BaseNLPReader):
    def __init__(self,
                 dataset,
                 vocab_path,
                 do_lower_case=True,
                 max_seq_len=512,
                 doc_stride=128,
                 max_query_length=64,
                 random_seed=None,
                 use_task_id=False,
                 sp_model_path=None,
                 word_dict_path=None,
                 in_tokens=False):
        super(ReadingComprehensionReader, self).__init__(
            vocab_path=vocab_path,
            dataset=dataset,
            label_map_config=None,
            max_seq_len=max_seq_len,
            do_lower_case=do_lower_case,
            random_seed=random_seed,
            use_task_id=use_task_id,
            sp_model_path=sp_model_path,
            word_dict_path=word_dict_path,
            in_tokens=in_tokens)

        self.doc_stride = doc_stride
        self.max_query_length = max_query_length
        self._DocSpan = collections.namedtuple("DocSpan", ["start", "length"])
        # self.all_examples[phase] and self.all_features[phase] will be used
        # in write_prediction in reading_comprehension_task
        self.all_features = {"train": [], "dev": [], "test": [], "predict": []}
        self.all_examples = {"train": [], "dev": [], "test": [], "predict": []}

    def _pad_batch_records(self, batch_records, phase):
        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_unique_ids = [record.unique_id for record in batch_records]
        batch_unique_ids = np.array(batch_unique_ids).astype("int64").reshape(
            [-1, 1])

        # padding
        padded_token_ids, input_mask = pad_batch_data(
            batch_token_ids,
            pad_idx=self.pad_id,
            return_input_mask=True,
            max_seq_len=self.max_seq_len)
        padded_text_type_ids = pad_batch_data(
            batch_text_type_ids,
            pad_idx=self.pad_id,
            max_seq_len=self.max_seq_len)
        padded_position_ids = pad_batch_data(
            batch_position_ids,
            pad_idx=self.pad_id,
            max_seq_len=self.max_seq_len)

        if phase != "predict":
            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, 1])
            batch_end_position = np.array(batch_end_position).astype(
                "int64").reshape([-1, 1])

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

            if self.use_task_id:
                padded_task_ids = np.ones_like(
                    padded_token_ids, dtype="int64") * self.task_id
                return_list = [
                    padded_token_ids, padded_position_ids, padded_text_type_ids,
                    input_mask, padded_task_ids, batch_unique_ids,
                    batch_start_position, batch_end_position
                ]

        else:
            return_list = [
                padded_token_ids, padded_position_ids, padded_text_type_ids,
                input_mask, batch_unique_ids
            ]
            if self.use_task_id:
                padded_task_ids = np.ones_like(
                    padded_token_ids, dtype="int64") * self.task_id
                return_list = [
                    padded_token_ids, padded_position_ids, padded_text_type_ids,
                    input_mask, padded_task_ids, batch_unique_ids
                ]
        return return_list

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

        if batch_records:
            yield self._pad_batch_records(batch_records, phase)

    def data_generator(self,
                       batch_size=1,
                       phase='train',
                       shuffle=False,
                       data=None,
                       return_list=True):
        # we need all_examples and  all_features in write_prediction in reading_comprehension_task
        # we can also use all_examples and all_features to avoid duplicate long-time preprocessing
        examples = None
        if self.all_examples[phase]:
            examples = self.all_examples[phase]
        else:
            if phase == 'train':
                examples = self.get_train_examples()
            elif phase == 'dev':
                examples = self.get_dev_examples()
            elif phase == 'test':
                examples = self.get_test_examples()
            elif phase == 'predict':
                examples = data
            else:
                raise ValueError(
                    "Unknown phase, which should be in ['train', 'dev', 'test', 'predict']."
                )
            self.all_examples[phase] = examples
        shuffle = True if phase == 'train' else False

        # As reading comprehension task will divide a long context into several doc_spans and then get multiple features
        # To get the real total steps, we need to know the features' length
        # So we use _convert_examples_to_records rather than _convert_example_to_record in this task
        if self.all_features[phase]:
            features = self.all_features[phase]
        else:
            features = self._convert_examples_to_records(
                examples, self.max_seq_len, self.tokenizer, phase)
            self.all_features[phase] = features

        # self.num_examples["train"] use in strategy.py to show the total steps,
        # we need to cover it with correct len(features)
        self.num_examples[phase] = len(features)

        def wrapper():
            if shuffle:
                np.random.shuffle(features)

            for batch_data in self._prepare_batch_data(
                    features, batch_size, phase=phase):
                if return_list:
                    # for DataFeeder
                    yield [batch_data]
                else:
                    # for DataLoader
                    yield batch_data

        return wrapper

    def _convert_examples_to_records(self,
                                     examples,
                                     max_seq_length,
                                     tokenizer,
                                     phase=None):
        """Loads a data file into a list of `InputBatch`s."""
        features = []
        unique_id = 1000000000

        for (example_index, example) in enumerate(examples):
            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
            is_impossible = example.is_impossible if hasattr(
                example, "is_impossible") else False

            if phase != "predict" and is_impossible:
                tok_start_position = -1
                tok_end_position = -1
            if phase != "predict" and not 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) = self.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`.
            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 phase != "predict" and not 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 phase != "predict" and is_impossible:
                    start_position = 0
                    end_position = 0

                feature = Features(
                    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,
                    is_impossible=is_impossible)
                features.append(feature)

                unique_id += 1

        return features

    def improve_answer_span(self, 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(self, doc_spans, cur_span_index, position):
        """Check if this is the 'max context' doc span for the token."""

        # Because of the sliding window approach taken to scoring documents, a single
        # token can appear in multiple documents. E.g.
        #  Doc: the man went to the store and bought a gallon of milk
        #  Span A: the man went to the
        #  Span B: to the store and bought
        #  Span C: and bought a gallon of
        #  ...
        #
        # Now the word 'bought' will have two scores from spans B and C. We only
        # want to consider the score with "maximum context", which we define as
        # the *minimum* of its left and right context (the *sum* of left and
        # right context will always be the same, of course).
        #
        # In the example the maximum context for 'bought' would be span C since
        # it has 1 left context and 3 right context, while span B has 4 left context
        # and 0 right context.
        best_score = None
        best_span_index = None
        for (span_index, doc_span) in enumerate(doc_spans):
            end = doc_span.start + doc_span.length - 1
            if position < doc_span.start:
                continue
            if position > end:
                continue
            num_left_context = position - doc_span.start
            num_right_context = end - position
            score = min(num_left_context,
                        num_right_context) + 0.01 * doc_span.length
            if best_score is None or score > best_score:
                best_score = score
                best_span_index = span_index

        return cur_span_index == best_span_index


class LACClassifyReader(BaseReader):
    def __init__(self, vocab_path, dataset=None, in_tokens=False):
        super(LACClassifyReader, self).__init__(dataset)
        self.in_tokens = in_tokens

        self.lac = hub.Module(name="lac")
        self.tokenizer = tokenization.FullTokenizer(
            vocab_file=vocab_path, do_lower_case=False)
        self.vocab = self.tokenizer.vocab
        self.has_processed = {
            "train": False,
            "dev": False,
            "val": False,
            "test": False,
            "predict": False
        }

    def data_generator(self,
                       batch_size=1,
                       phase="train",
                       shuffle=False,
                       data=None,
                       return_list=True):
        if phase != "predict" and not self.dataset:
            raise ValueError("The dataset is None and it isn't allowed.")
        if phase == "train":
            shuffle = True
            data = self.dataset.get_train_examples()
            self.num_examples['train'] = len(data)
        elif phase == "test":
            shuffle = False
            data = self.dataset.get_test_examples()
            self.num_examples['test'] = len(data)
        elif phase == "val" or phase == "dev":
            shuffle = False
            data = self.dataset.get_dev_examples()
            self.num_examples['dev'] = len(data)
        elif phase == "predict":
            data = data
        else:
            raise ValueError(
                "Unknown phase, which should be in ['train', 'dev', 'test'].")

        def preprocess(text):
            data_dict = {'text': [text]}
            processed = self.lac.lexical_analysis(data=data_dict)
            processed = [
                self.vocab[word] for word in processed[0]['word']
                if word in self.vocab
            ]

            if len(processed) == 0:
                if six.PY2:
                    text = text.encode(sys_stdout_encoding())
                logger.warning(
                    "The words in text %s can't be found in the vocabulary." %
                    (text))

            return processed

        if not self.has_processed[phase]:
            logger.info(
                "processing %s data now... this may take a few minutes" % phase)
            for i in range(len(data)):
                if phase == "predict":
                    data[i] = preprocess(data[i])
                else:
                    data[i].text_a = preprocess(data[i].text_a)
                    if self.label_map:
                        if data[i].label not in self.label_map:
                            raise KeyError("example.label = {%s} not in label" %
                                           data[i].label)
                        label_id = self.label_map[data[i].label]
                    else:
                        label_id = data[i].label
                    data[i].label = label_id
            self.has_processed[phase] = True

        def _data_reader():
            if shuffle:
                np.random.shuffle(data)
            texts = []
            labels = []
            if phase == "predict":
                for text in data:
                    if not text:
                        continue
                    texts.append(text)
                    if len(texts) == batch_size:
                        if return_list:
                            # for DataFeeder
                            # if you want to use high-performance predictor, yield [[[t] for t in texts]]
                            yield [[t] for t in texts]
                        else:
                            # for DataLoader
                            # cannot use in high-performance predictor, as PaddleTensor rejects lod_tensor
                            texts = fluid.create_lod_tensor(
                                texts, [[len(seq) for seq in texts]],
                                fluid.CPUPlace())
                            yield [texts]
                        texts = []
                if texts:
                    if return_list:
                        yield [[t] for t in texts]
                    else:
                        texts = fluid.create_lod_tensor(
                            texts, [[len(seq) for seq in texts]],
                            fluid.CPUPlace())

                        yield [texts]
                    texts = []
            else:
                for item in data:
                    text = item.text_a
                    if not text:
                        continue
                    texts.append(text)
                    labels.append([item.label])
                    if len(texts) == batch_size:
                        if return_list:
                            yield list(zip(texts, labels))
                        else:
                            texts = fluid.create_lod_tensor(
                                texts, [[len(seq) for seq in texts]],
                                fluid.CPUPlace())
                            yield [texts, labels]
                        texts = []
                        labels = []
                if texts:
                    if return_list:
                        yield list(zip(texts, labels))
                    else:
                        texts = fluid.create_lod_tensor(
                            texts, [[len(seq) for seq in texts]],
                            fluid.CPUPlace())
                        yield [texts, labels]
                    texts = []
                    labels = []

        return _data_reader


if __name__ == '__main__':
    pass