# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Create masked LM/next sentence masked_lm examples for BERT."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import sys
import os
import ipdb

sys.path.append(os.getcwd())
import collections
import random
import argparse
import h5py
import numpy as np
from tqdm import tqdm

from transformers import BertTokenizer


class TrainingInstance(object):
    """A single training instance (sentence pair)."""
    
    def __init__(self, tokens, segment_ids, masked_lm_positions, masked_lm_labels,
                 is_random_next):
        self.tokens = tokens
        self.segment_ids = segment_ids
        self.is_random_next = is_random_next
        self.masked_lm_positions = masked_lm_positions
        self.masked_lm_labels = masked_lm_labels

MaskedLmInstance = collections.namedtuple("MaskedLmInstance", ["index", "label"])


class CreateBertPretrainingData(object):
    def __init__(self, args):
        self.args = args
        self._bert_tokenizer = BertTokenizer.from_pretrained(args.bert_pretrained)
        if args.dataset in ['ubuntu']:
            self._bert_tokenizer.add_tokens(['__number__', '__url__', '__path__'])
    
    def create_training_instances(self, input_file, max_seq_length,
                                  dupe_factor, short_seq_prob, masked_lm_prob,
                                  max_predictions_per_seq, rng, special_tok=None):
        """Create `TrainingInstance`s from raw text."""
        all_documents = [[]]

        # Input file format:
        # (1) One sentence per line. These should ideally be actual sentences, not
        # entire paragraphs or arbitrary spans of text. (Because we use the
        # sentence boundaries for the "next sentence prediction" task).
        # (2) Blank lines between documents. Document boundaries are needed so
        # that the "next sentence prediction" task doesn't span between documents.
        document_cnt = 0
        with open(input_file, "r", encoding="utf=8") as fr_handle:
            for line in tqdm(fr_handle):
                line = line.strip()
                
                # Empty lines are used as document delimiters
                if len(line) == 0:
                    all_documents.append([])
                    document_cnt += 1
                    if document_cnt % 50000 == 0:
                        print(f"{document_cnt} samples have been tokenized!")

                tokens = self._bert_tokenizer.tokenize(line)
                if tokens:
                    all_documents[-1].append(tokens)
        # Remove empty documents
        all_documents = [x for x in all_documents if x]
        rng.shuffle(all_documents)

        vocab_words = list(self._bert_tokenizer.vocab.keys())

        self.feature_keys = ["input_ids", "attention_mask", "token_type_ids", "masked_lm_positions", "masked_lm_ids", "next_sentence_labels"]

        print("Total number of documents : %d" % len(all_documents))
        hf = h5py.File(self.args.output_file, "w")
        for d in range(dupe_factor):
            rng.shuffle(all_documents)
            
            self.all_doc_feat_dict = dict()
            for feat_key in self.feature_keys:
                self.all_doc_feat_dict[feat_key] = []

            for document_index in tqdm(range(len(all_documents))):
                instances = self.create_instances_from_document(
                    all_documents, 
                    document_index, 
                    max_seq_length, 
                    short_seq_prob,
                    masked_lm_prob, 
                    max_predictions_per_seq, 
                    vocab_words, 
                    rng
                )
                self.instance_to_example_feature(instances, self.args.max_seq_length, self.args.max_predictions_per_seq)
            self.build_h5_data(hf, d_idx=d),
            print("Current Dupe Factor : %d" % (d + 1))
        print("Pretraining Data Creation Completes!")
        hf.close()

    def build_h5_data(self, hf, d_idx=0):
        """
          features["input_ids"] = torch.tensor(input_ids).long()
          features["attention_mask"] = torch.tensor(input_mask).long()
          features["token_type_ids"] = torch.tensor(segment_ids).long()
          features["masked_lm_positions"] = torch.tensor(masked_lm_positions).long()
          features["masked_lm_ids"] = torch.tensor(masked_lm_ids).long() # masked_lm_ids
          features["next_sentence_labels"] = torch.tensor([next_sentence_label]).long()
        """
    
        h5_key_dict = {}
        print("Number of documents features", len(self.all_doc_feat_dict["next_sentence_labels"]))
        if d_idx == 0:
            for feat_key in self.feature_keys:
                key_size = [len(self.all_doc_feat_dict["next_sentence_labels"])] + [len(self.all_doc_feat_dict[feat_key][0])]
                h5_key_dict[feat_key] = hf.create_dataset(
                    feat_key, tuple(key_size), dtype='i8', chunks=True,
                    maxshape=(None, tuple(key_size)[1]),
                    data=np.array(self.all_doc_feat_dict[feat_key])
                )
        else:
            for feat_key in self.feature_keys:
                hf[feat_key].resize((hf[feat_key].shape[0] + len(self.all_doc_feat_dict["next_sentence_labels"])), axis=0)
                hf[feat_key][-len(self.all_doc_feat_dict["next_sentence_labels"]):] = np.array(self.all_doc_feat_dict[feat_key])

    def create_instances_from_document(self, all_documents, document_index, max_seq_length, short_seq_prob, masked_lm_prob, max_predictions_per_seq, vocab_words, rng):
        """Creates `TrainingInstance`s for a single document."""
        document = all_documents[document_index]
        
        # Account for [CLS], [SEP], [SEP]
        max_num_tokens = max_seq_length - 3

        # We *usually* want to fill up the entire sequence since we are padding
        # to `max_seq_length` anyways, so short sequences are generally wasted
        # computation. However, we *sometimes*
        # (i.e., short_seq_prob == 0.1 == 10% of the time) want to use shorter
        # sequences to minimize the mismatch between pre-training and fine-tuning.
        # The `target_seq_length` is just a rough target however, whereas
        # `max_seq_length` is a hard limit.
        target_seq_length = max_num_tokens
        if rng.random() < short_seq_prob:
            target_seq_length = rng.randint(2, max_num_tokens)

        # We DON'T just concatenate all of the tokens from a document into a long
        # sequence and choose an arbitrary split point because this would make the
        # next sentence prediction task too easy. Instead, we split the input into
        # segments "A" and "B" based on the actual "sentences" provided by the user
        # input.
        instances = []
        current_chunk = []
        current_length = 0
        i = 0

        while i < len(document):
            segment = document[i]
            current_chunk.append(segment)
            current_length += len(segment)
            if i == len(document) - 1 or current_length >= target_seq_length:
                if current_chunk:
                    # `a_end` is how many segments from `current_chunk` go into the `A`
                    # (first) sentence.
                    a_end = 1
                    if len(current_chunk) >= 2:
                        a_end = rng.randint(1, len(current_chunk) - 1)

                    tokens_a = []
                    for j in range(a_end):
                        tokens_a.extend(current_chunk[j])

                    tokens_b = []
                    # Random next
                    is_random_next = False
                    if len(current_chunk) == 1 or rng.random() < 0.5:
                        is_random_next = True
                        target_b_length = target_seq_length - len(tokens_a)

                        # This should rarely go for more than one iteration for large
                        # corpora. However, just to be careful, we try to make sure that
                        # the random document is not the same as the document
                        # we're processing.
                        for _ in range(10):
                            random_document_index = rng.randint(0, len(all_documents) - 1)
                            if random_document_index != document_index:
                                break

                        random_document = all_documents[random_document_index]
                        random_start = rng.randint(0, len(random_document) - 1)
                        for j in range(random_start, len(random_document)):
                            tokens_b.extend(random_document[j])
                            if len(tokens_b) >= target_b_length:
                                break
                        # We didn't actually use these segments so we "put them back" so
                        # they don't go to waste.
                        num_unused_segments = len(current_chunk) - a_end
                        i -= num_unused_segments
                    # Actual next
                    else:
                        is_random_next = False
                        for j in range(a_end, len(current_chunk)):
                            tokens_b.extend(current_chunk[j])
                    self.truncate_seq_pair(tokens_a, tokens_b, max_num_tokens, rng)

                    assert len(tokens_a) >= 1
                    assert len(tokens_b) >= 1

                    tokens = []
                    segment_ids = []
                    tokens.append("[CLS]")
                    segment_ids.append(0)
                    for token in tokens_a:
                        tokens.append(token)
                        segment_ids.append(0)

                    tokens.append("[SEP]")
                    segment_ids.append(0)

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

                    (tokens, masked_lm_positions, masked_lm_labels) = self.create_masked_lm_predictions(
            tokens, masked_lm_prob, max_predictions_per_seq, vocab_words, rng)
                    instance = TrainingInstance(
                        tokens=tokens,
                        segment_ids=segment_ids,
                        is_random_next=is_random_next,
                        masked_lm_positions=masked_lm_positions,
                        masked_lm_labels=masked_lm_labels)
                    instances.append(instance)

                current_chunk = []
                current_length = 0
            i += 1

        return instances

    def create_masked_lm_predictions(self, tokens, masked_lm_prob, max_predictions_per_seq, vocab_words, rng):
        """Creates the predictions for the masked LM objective."""

        cand_indexes = []
        for (i, token) in enumerate(tokens):
            if token == "[CLS]" or token == "[SEP]":
                continue
            # Whole Word Masking means that if we mask all of the wordpiecesdk
            # corresponding to an original word. When a word has been split into
            # WordPieces, the first token does not have any marker and any subsequence
            # tokens are prefixed with ##. So whenever we see the ## token, we
            # append it to the previous set of word indexes.
            #
            # Note that Whole Word Masking does *not* change the training code
            # at all -- we still predict each WordPiece independently, softmaxed
            # over the entire vocabulary.
            if (self.args.do_whole_word_mask and len(cand_indexes) >= 1 and token.startswith("##")):
                cand_indexes[-1].append(i)
            else:
                cand_indexes.append([i])

        rng.shuffle(cand_indexes)

        output_tokens = list(tokens)

        num_to_predict = min(max_predictions_per_seq, max(1, int(round(len(tokens) * masked_lm_prob))))

        masked_lms = []
        covered_indexes = set()
        for index_set in cand_indexes:
            if len(masked_lms) >= num_to_predict:
                break
            # If adding a whole-word mask would exceed the maximum number of
            # predictions, then just skip this candidate.
            if len(masked_lms) + len(index_set) > num_to_predict:
                continue
            is_any_index_covered = False
            for index in index_set:
                if index in covered_indexes:
                    is_any_index_covered = True
                    break
            if is_any_index_covered:
                continue
            for index in index_set:
                covered_indexes.add(index)

                masked_token = None
                # 80% of the time, replace with [MASK]
                if rng.random() < 0.8:
                    masked_token = "[MASK]"
                else:
                    # 10% of the time, keep original
                    if rng.random() < 0.5:
                        masked_token = tokens[index]
                    # 10% of the time, replace with random word
                    else:
                        masked_token = vocab_words[rng.randint(0, len(vocab_words) - 1)]

                output_tokens[index] = masked_token

                masked_lms.append(MaskedLmInstance(index=index, label=tokens[index]))

        assert len(masked_lms) <= num_to_predict
        masked_lms = sorted(masked_lms, key=lambda x: x.index)
        
        masked_lm_positions = []
        masked_lm_labels = []
        for p in masked_lms:
            masked_lm_positions.append(p.index)
            masked_lm_labels.append(p.label)

        return (output_tokens, masked_lm_positions, masked_lm_labels)

    def truncate_seq_pair(self, tokens_a, tokens_b, max_num_tokens, rng):
        """Truncates a pair of sequences to a maximum sequence length."""
        while True:
            total_length = len(tokens_a) + len(tokens_b)
            if total_length <= max_num_tokens:
                break

            trunc_tokens = tokens_a if len(tokens_a) > len(tokens_b) else tokens_b
            assert len(trunc_tokens) >= 1

            # We want to sometimes truncate from the front and sometimes from the
            # back to add more randomness and avoid biases.
            if rng.random() < 0.5:
                del trunc_tokens[0]
            else:
                trunc_tokens.pop()

    def instance_to_example_feature(self, instances, max_seq_length, max_predictions_per_seq):
        for instance in instances:
            input_ids = self._bert_tokenizer.convert_tokens_to_ids(instance.tokens)
            input_mask = [1] * len(input_ids)
            segment_ids = list(instance.segment_ids)

            assert len(input_ids) <= max_seq_length

            while len(input_ids) < max_seq_length:
                input_ids.append(0)
                input_mask.append(0)
                segment_ids.append(0)

        assert len(input_ids) == max_seq_length
        assert len(input_mask) == max_seq_length
        assert len(segment_ids) == max_seq_length

        masked_lm_positions = list(instance.masked_lm_positions)
        masked_lm_ids = self._bert_tokenizer.convert_tokens_to_ids(instance.masked_lm_labels)
        masked_lm_weights = [1.0] * len(masked_lm_ids)

        while len(masked_lm_positions) < max_predictions_per_seq:
            masked_lm_positions.append(0)
            masked_lm_ids.append(0)
            masked_lm_weights.append(0.0)

        next_sentence_label = 1 if instance.is_random_next else 0

        self.all_doc_feat_dict["input_ids"].append(input_ids)
        self.all_doc_feat_dict["attention_mask"].append(input_mask)
        self.all_doc_feat_dict["token_type_ids"].append(segment_ids)
        self.all_doc_feat_dict["masked_lm_positions"].append(masked_lm_positions)
        self.all_doc_feat_dict["masked_lm_ids"].append(masked_lm_ids)
        self.all_doc_feat_dict["next_sentence_labels"].append([next_sentence_label])


if __name__ == "__main__":
    arg_parser = argparse.ArgumentParser(description="Bert / Create Pretraining Data")
    arg_parser.add_argument("--input_file", dest="input_file", type=str, required=True,
                            help="Input raw text file (or comma-separated list of files). "
                               "e.g., ./data/ubuntu_corpus_v1/ubuntu_post_training.txt")
    arg_parser.add_argument("--output_file", dest="output_file", type=str, required=True,
                          help="Output example pkl. e.g., ./data/ubuntu_corpus_v1/ubuntu_post_training.hdf5")
    arg_parser.add_argument("--dataset", dest="dataset", type=str, required=True,
                          help="ecommerce; douban; ubuntu")
    arg_parser.add_argument("--bert_pretrained", dest="bert_pretrained", type=str, required=True,
                          help="bert-base-wwm-chinese")
    arg_parser.add_argument("--do_lower_case", dest="do_lower_case", type=bool, default=True,
                          help="Whether to lower case the input text. Should be True for uncased.")
    arg_parser.add_argument("--do_whole_word_mask", dest="do_whole_word_mask", type=bool, default=True,
                          help="Whether to use whole word masking rather than per-WordPiece masking.")
    arg_parser.add_argument("--max_seq_length", dest="max_seq_length", type=int, default=512,
                          help="Maximum sequence length.")
    arg_parser.add_argument("--max_predictions_per_seq", dest="max_predictions_per_seq", type=int, default=70,
                          help="Maximum number of masked LM predictions per sequence.")
    arg_parser.add_argument("--random_seed", dest="random_seed", type=int, default=12345,
                          help="Random seed for data generation.")
    arg_parser.add_argument("--dupe_factor", dest="dupe_factor", type=int, default=10,
                          help="Number of times to duplicate the input data (with different masks).")
    arg_parser.add_argument("--masked_lm_prob", dest="masked_lm_prob", type=float, default=0.15,
                          help="Masked LM probability.")
    arg_parser.add_argument("--short_seq_prob", dest="short_seq_prob", type=float, default=0.1,
                          help="Probability of creating sequences which are shorter than the maximum length.")
    arg_parser.add_argument("--special_tok", dest="special_tok", type=str, default="[EOT]",
                          help="Special Token.")
    args = arg_parser.parse_args()

    create_data = CreateBertPretrainingData(args)

    rng = random.Random(args.random_seed)
    create_data.create_training_instances(
        args.input_file, args.max_seq_length, args.dupe_factor,
        args.short_seq_prob, args.masked_lm_prob, 
        args.max_predictions_per_seq, rng, args.special_tok
    )

# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Create masked LM/next sentence masked_lm examples for BERT."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import sys
import os
import ipdb

sys.path.append(os.getcwd())
import collections
import random
import argparse
import h5py
import numpy as np
from tqdm import tqdm

from transformers import BertTokenizer


class TrainingInstance(object):
    """A single training instance (sentence pair)."""
    
    def __init__(self, tokens, segment_ids, masked_lm_positions, masked_lm_labels,
                 is_random_next):
        self.tokens = tokens
        self.segment_ids = segment_ids
        self.is_random_next = is_random_next
        self.masked_lm_positions = masked_lm_positions
        self.masked_lm_labels = masked_lm_labels

MaskedLmInstance = collections.namedtuple("MaskedLmInstance", ["index", "label"])


class CreateBertPretrainingData(object):
    def __init__(self, args):
        self.args = args
        self._bert_tokenizer = BertTokenizer.from_pretrained(args.bert_pretrained)
        if args.dataset in ['ubuntu']:
            self._bert_tokenizer.add_tokens(['__number__', '__url__', '__path__'])
    
    def create_training_instances(self, input_file, max_seq_length,
                                  dupe_factor, short_seq_prob, masked_lm_prob,
                                  max_predictions_per_seq, rng, special_tok=None):
        """Create `TrainingInstance`s from raw text."""
        all_documents = [[]]

        # Input file format:
        # (1) One sentence per line. These should ideally be actual sentences, not
        # entire paragraphs or arbitrary spans of text. (Because we use the
        # sentence boundaries for the "next sentence prediction" task).
        # (2) Blank lines between documents. Document boundaries are needed so
        # that the "next sentence prediction" task doesn't span between documents.
        document_cnt = 0
        with open(input_file, "r", encoding="utf=8") as fr_handle:
            for line in tqdm(fr_handle):
                line = line.strip()
                
                # Empty lines are used as document delimiters
                if len(line) == 0:
                    all_documents.append([])
                    document_cnt += 1
                    if document_cnt % 50000 == 0:
                        print(f"{document_cnt} samples have been tokenized!")

                tokens = self._bert_tokenizer.tokenize(line)
                if tokens:
                    all_documents[-1].append(tokens)
        # Remove empty documents
        all_documents = [x for x in all_documents if x]
        rng.shuffle(all_documents)

        vocab_words = list(self._bert_tokenizer.vocab.keys())

        self.feature_keys = ["input_ids", "attention_mask", "token_type_ids", "masked_lm_positions", "masked_lm_ids", "next_sentence_labels"]

        print("Total number of documents : %d" % len(all_documents))
        hf = h5py.File(self.args.output_file, "w")
        for d in range(dupe_factor):
            rng.shuffle(all_documents)
            
            self.all_doc_feat_dict = dict()
            for feat_key in self.feature_keys:
                self.all_doc_feat_dict[feat_key] = []

            for document_index in tqdm(range(len(all_documents))):
                instances = self.create_instances_from_document(
                    all_documents, 
                    document_index, 
                    max_seq_length, 
                    short_seq_prob,
                    masked_lm_prob, 
                    max_predictions_per_seq, 
                    vocab_words, 
                    rng
                )
                self.instance_to_example_feature(instances, self.args.max_seq_length, self.args.max_predictions_per_seq)
            self.build_h5_data(hf, d_idx=d),
            print("Current Dupe Factor : %d" % (d + 1))
        print("Pretraining Data Creation Completes!")
        hf.close()

    def build_h5_data(self, hf, d_idx=0):
        """
          features["input_ids"] = torch.tensor(input_ids).long()
          features["attention_mask"] = torch.tensor(input_mask).long()
          features["token_type_ids"] = torch.tensor(segment_ids).long()
          features["masked_lm_positions"] = torch.tensor(masked_lm_positions).long()
          features["masked_lm_ids"] = torch.tensor(masked_lm_ids).long() # masked_lm_ids
          features["next_sentence_labels"] = torch.tensor([next_sentence_label]).long()
        """
    
        h5_key_dict = {}
        print("Number of documents features", len(self.all_doc_feat_dict["next_sentence_labels"]))
        if d_idx == 0:
            for feat_key in self.feature_keys:
                key_size = [len(self.all_doc_feat_dict["next_sentence_labels"])] + [len(self.all_doc_feat_dict[feat_key][0])]
                h5_key_dict[feat_key] = hf.create_dataset(
                    feat_key, tuple(key_size), dtype='i8', chunks=True,
                    maxshape=(None, tuple(key_size)[1]),
                    data=np.array(self.all_doc_feat_dict[feat_key])
                )
        else:
            for feat_key in self.feature_keys:
                hf[feat_key].resize((hf[feat_key].shape[0] + len(self.all_doc_feat_dict["next_sentence_labels"])), axis=0)
                hf[feat_key][-len(self.all_doc_feat_dict["next_sentence_labels"]):] = np.array(self.all_doc_feat_dict[feat_key])

    def create_instances_from_document(self, all_documents, document_index, max_seq_length, short_seq_prob, masked_lm_prob, max_predictions_per_seq, vocab_words, rng):
        """Creates `TrainingInstance`s for a single document."""
        document = all_documents[document_index]
        
        # Account for [CLS], [SEP], [SEP]
        max_num_tokens = max_seq_length - 3

        # We *usually* want to fill up the entire sequence since we are padding
        # to `max_seq_length` anyways, so short sequences are generally wasted
        # computation. However, we *sometimes*
        # (i.e., short_seq_prob == 0.1 == 10% of the time) want to use shorter
        # sequences to minimize the mismatch between pre-training and fine-tuning.
        # The `target_seq_length` is just a rough target however, whereas
        # `max_seq_length` is a hard limit.
        target_seq_length = max_num_tokens
        if rng.random() < short_seq_prob:
            target_seq_length = rng.randint(2, max_num_tokens)

        # We DON'T just concatenate all of the tokens from a document into a long
        # sequence and choose an arbitrary split point because this would make the
        # next sentence prediction task too easy. Instead, we split the input into
        # segments "A" and "B" based on the actual "sentences" provided by the user
        # input.
        instances = []
        current_chunk = []
        current_length = 0
        i = 0

        while i < len(document):
            segment = document[i]
            current_chunk.append(segment)
            current_length += len(segment)
            if i == len(document) - 1 or current_length >= target_seq_length:
                if current_chunk:
                    # `a_end` is how many segments from `current_chunk` go into the `A`
                    # (first) sentence.
                    a_end = 1
                    if len(current_chunk) >= 2:
                        a_end = rng.randint(1, len(current_chunk) - 1)

                    tokens_a = []
                    for j in range(a_end):
                        tokens_a.extend(current_chunk[j])

                    tokens_b = []
                    # Random next
                    is_random_next = False
                    if len(current_chunk) == 1 or rng.random() < 0.5:
                        is_random_next = True
                        target_b_length = target_seq_length - len(tokens_a)

                        # This should rarely go for more than one iteration for large
                        # corpora. However, just to be careful, we try to make sure that
                        # the random document is not the same as the document
                        # we're processing.
                        for _ in range(10):
                            random_document_index = rng.randint(0, len(all_documents) - 1)
                            if random_document_index != document_index:
                                break

                        random_document = all_documents[random_document_index]
                        random_start = rng.randint(0, len(random_document) - 1)
                        for j in range(random_start, len(random_document)):
                            tokens_b.extend(random_document[j])
                            if len(tokens_b) >= target_b_length:
                                break
                        # We didn't actually use these segments so we "put them back" so
                        # they don't go to waste.
                        num_unused_segments = len(current_chunk) - a_end
                        i -= num_unused_segments
                    # Actual next
                    else:
                        is_random_next = False
                        for j in range(a_end, len(current_chunk)):
                            tokens_b.extend(current_chunk[j])
                    self.truncate_seq_pair(tokens_a, tokens_b, max_num_tokens, rng)

                    assert len(tokens_a) >= 1
                    assert len(tokens_b) >= 1

                    tokens = []
                    segment_ids = []
                    tokens.append("[CLS]")
                    segment_ids.append(0)
                    for token in tokens_a:
                        tokens.append(token)
                        segment_ids.append(0)

                    tokens.append("[SEP]")
                    segment_ids.append(0)

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

                    (tokens, masked_lm_positions, masked_lm_labels) = self.create_masked_lm_predictions(
            tokens, masked_lm_prob, max_predictions_per_seq, vocab_words, rng)
                    instance = TrainingInstance(
                        tokens=tokens,
                        segment_ids=segment_ids,
                        is_random_next=is_random_next,
                        masked_lm_positions=masked_lm_positions,
                        masked_lm_labels=masked_lm_labels)
                    instances.append(instance)

                current_chunk = []
                current_length = 0
            i += 1

        return instances

    def create_masked_lm_predictions(self, tokens, masked_lm_prob, max_predictions_per_seq, vocab_words, rng):
        """Creates the predictions for the masked LM objective."""

        cand_indexes = []
        for (i, token) in enumerate(tokens):
            if token == "[CLS]" or token == "[SEP]":
                continue
            # Whole Word Masking means that if we mask all of the wordpiecesdk
            # corresponding to an original word. When a word has been split into
            # WordPieces, the first token does not have any marker and any subsequence
            # tokens are prefixed with ##. So whenever we see the ## token, we
            # append it to the previous set of word indexes.
            #
            # Note that Whole Word Masking does *not* change the training code
            # at all -- we still predict each WordPiece independently, softmaxed
            # over the entire vocabulary.
            if (self.args.do_whole_word_mask and len(cand_indexes) >= 1 and token.startswith("##")):
                cand_indexes[-1].append(i)
            else:
                cand_indexes.append([i])

        rng.shuffle(cand_indexes)

        output_tokens = list(tokens)

        num_to_predict = min(max_predictions_per_seq, max(1, int(round(len(tokens) * masked_lm_prob))))

        masked_lms = []
        covered_indexes = set()
        for index_set in cand_indexes:
            if len(masked_lms) >= num_to_predict:
                break
            # If adding a whole-word mask would exceed the maximum number of
            # predictions, then just skip this candidate.
            if len(masked_lms) + len(index_set) > num_to_predict:
                continue
            is_any_index_covered = False
            for index in index_set:
                if index in covered_indexes:
                    is_any_index_covered = True
                    break
            if is_any_index_covered:
                continue
            for index in index_set:
                covered_indexes.add(index)

                masked_token = None
                # 80% of the time, replace with [MASK]
                if rng.random() < 0.8:
                    masked_token = "[MASK]"
                else:
                    # 10% of the time, keep original
                    if rng.random() < 0.5:
                        masked_token = tokens[index]
                    # 10% of the time, replace with random word
                    else:
                        masked_token = vocab_words[rng.randint(0, len(vocab_words) - 1)]

                output_tokens[index] = masked_token

                masked_lms.append(MaskedLmInstance(index=index, label=tokens[index]))

        assert len(masked_lms) <= num_to_predict
        masked_lms = sorted(masked_lms, key=lambda x: x.index)
        
        masked_lm_positions = []
        masked_lm_labels = []
        for p in masked_lms:
            masked_lm_positions.append(p.index)
            masked_lm_labels.append(p.label)

        return (output_tokens, masked_lm_positions, masked_lm_labels)

    def truncate_seq_pair(self, tokens_a, tokens_b, max_num_tokens, rng):
        """Truncates a pair of sequences to a maximum sequence length."""
        while True:
            total_length = len(tokens_a) + len(tokens_b)
            if total_length <= max_num_tokens:
                break

            trunc_tokens = tokens_a if len(tokens_a) > len(tokens_b) else tokens_b
            assert len(trunc_tokens) >= 1

            # We want to sometimes truncate from the front and sometimes from the
            # back to add more randomness and avoid biases.
            if rng.random() < 0.5:
                del trunc_tokens[0]
            else:
                trunc_tokens.pop()

    def instance_to_example_feature(self, instances, max_seq_length, max_predictions_per_seq):
        for instance in instances:
            input_ids = self._bert_tokenizer.convert_tokens_to_ids(instance.tokens)
            input_mask = [1] * len(input_ids)
            segment_ids = list(instance.segment_ids)

            assert len(input_ids) <= max_seq_length

            while len(input_ids) < max_seq_length:
                input_ids.append(0)
                input_mask.append(0)
                segment_ids.append(0)

        assert len(input_ids) == max_seq_length
        assert len(input_mask) == max_seq_length
        assert len(segment_ids) == max_seq_length

        masked_lm_positions = list(instance.masked_lm_positions)
        masked_lm_ids = self._bert_tokenizer.convert_tokens_to_ids(instance.masked_lm_labels)
        masked_lm_weights = [1.0] * len(masked_lm_ids)

        while len(masked_lm_positions) < max_predictions_per_seq:
            masked_lm_positions.append(0)
            masked_lm_ids.append(0)
            masked_lm_weights.append(0.0)

        next_sentence_label = 1 if instance.is_random_next else 0

        self.all_doc_feat_dict["input_ids"].append(input_ids)
        self.all_doc_feat_dict["attention_mask"].append(input_mask)
        self.all_doc_feat_dict["token_type_ids"].append(segment_ids)
        self.all_doc_feat_dict["masked_lm_positions"].append(masked_lm_positions)
        self.all_doc_feat_dict["masked_lm_ids"].append(masked_lm_ids)
        self.all_doc_feat_dict["next_sentence_labels"].append([next_sentence_label])


if __name__ == "__main__":
    arg_parser = argparse.ArgumentParser(description="Bert / Create Pretraining Data")
    arg_parser.add_argument("--input_file", dest="input_file", type=str, required=True,
                            help="Input raw text file (or comma-separated list of files). "
                               "e.g., ./data/ubuntu_corpus_v1/ubuntu_post_training.txt")
    arg_parser.add_argument("--output_file", dest="output_file", type=str, required=True,
                          help="Output example pkl. e.g., ./data/ubuntu_corpus_v1/ubuntu_post_training.hdf5")
    arg_parser.add_argument("--dataset", dest="dataset", type=str, required=True,
                          help="ecommerce; douban; ubuntu")
    arg_parser.add_argument("--bert_pretrained", dest="bert_pretrained", type=str, required=True,
                          help="bert-base-wwm-chinese")
    arg_parser.add_argument("--do_lower_case", dest="do_lower_case", type=bool, default=True,
                          help="Whether to lower case the input text. Should be True for uncased.")
    arg_parser.add_argument("--do_whole_word_mask", dest="do_whole_word_mask", type=bool, default=True,
                          help="Whether to use whole word masking rather than per-WordPiece masking.")
    arg_parser.add_argument("--max_seq_length", dest="max_seq_length", type=int, default=512,
                          help="Maximum sequence length.")
    arg_parser.add_argument("--max_predictions_per_seq", dest="max_predictions_per_seq", type=int, default=70,
                          help="Maximum number of masked LM predictions per sequence.")
    arg_parser.add_argument("--random_seed", dest="random_seed", type=int, default=12345,
                          help="Random seed for data generation.")
    arg_parser.add_argument("--dupe_factor", dest="dupe_factor", type=int, default=10,
                          help="Number of times to duplicate the input data (with different masks).")
    arg_parser.add_argument("--masked_lm_prob", dest="masked_lm_prob", type=float, default=0.15,
                          help="Masked LM probability.")
    arg_parser.add_argument("--short_seq_prob", dest="short_seq_prob", type=float, default=0.1,
                          help="Probability of creating sequences which are shorter than the maximum length.")
    arg_parser.add_argument("--special_tok", dest="special_tok", type=str, default="[EOT]",
                          help="Special Token.")
    args = arg_parser.parse_args()

    create_data = CreateBertPretrainingData(args)

    rng = random.Random(args.random_seed)
    create_data.create_training_instances(
        args.input_file, args.max_seq_length, args.dupe_factor,
        args.short_seq_prob, args.masked_lm_prob, 
        args.max_predictions_per_seq, rng, args.special_tok
    )

# coding=utf-8 # Copyright 2018 The Google AI Language Team Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # #     http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. Create masked LM/next sentence masked_lm examples for BERT. A single training instance (sentence pair). Create `TrainingInstance`s from raw text. # Input file format: # (1) One sentence per line. These should ideally be actual sentences, not # entire paragraphs or arbitrary spans of text. (Because we use the # sentence boundaries for the "next sentence prediction" task). # (2) Blank lines between documents. Document boundaries are needed so # that the "next sentence prediction" task doesn't span between documents. # Empty lines are used as document delimiters # Remove empty documents features["input_ids"] = torch.tensor(input_ids).long()           features["attention_mask"] = torch.tensor(input_mask).long()           features["token_type_ids"] = torch.tensor(segment_ids).long()           features["masked_lm_positions"] = torch.tensor(masked_lm_positions).long()           features["masked_lm_ids"] = torch.tensor(masked_lm_ids).long() # masked_lm_ids           features["next_sentence_labels"] = torch.tensor([next_sentence_label]).long() Creates `TrainingInstance`s for a single document. # Account for [CLS], [SEP], [SEP] # We *usually* want to fill up the entire sequence since we are padding # to `max_seq_length` anyways, so short sequences are generally wasted # computation. However, we *sometimes* # (i.e., short_seq_prob == 0.1 == 10% of the time) want to use shorter # sequences to minimize the mismatch between pre-training and fine-tuning. # The `target_seq_length` is just a rough target however, whereas # `max_seq_length` is a hard limit. # We DON'T just concatenate all of the tokens from a document into a long # sequence and choose an arbitrary split point because this would make the # next sentence prediction task too easy. Instead, we split the input into # segments "A" and "B" based on the actual "sentences" provided by the user # input. # `a_end` is how many segments from `current_chunk` go into the `A` # (first) sentence. # Random next # This should rarely go for more than one iteration for large # corpora. However, just to be careful, we try to make sure that # the random document is not the same as the document # we're processing. # We didn't actually use these segments so we "put them back" so # they don't go to waste. # Actual next Creates the predictions for the masked LM objective. # Whole Word Masking means that if we mask all of the wordpiecesdk # corresponding to an original word. When a word has been split into # WordPieces, the first token does not have any marker and any subsequence # tokens are prefixed with ##. So whenever we see the ## token, we # append it to the previous set of word indexes. # # Note that Whole Word Masking does *not* change the training code # at all -- we still predict each WordPiece independently, softmaxed # over the entire vocabulary. #")): # If adding a whole-word mask would exceed the maximum number of # predictions, then just skip this candidate. # 80% of the time, replace with [MASK] # 10% of the time, keep original # 10% of the time, replace with random word Truncates a pair of sequences to a maximum sequence length. # We want to sometimes truncate from the front and sometimes from the # back to add more randomness and avoid biases.