bert_tokenizer.py

# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
#
# 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.

import copy
import io
import json
import os
import six
import unicodedata

from tokenizer_utils import PretrainedTokenizer
from tokenizer_utils import convert_to_unicode, whitespace_tokenize, _is_whitespace, _is_control, _is_punctuation


class BasicTokenizer(object):
    """
    Runs basic tokenization (punctuation splitting, lower casing, etc.).
    Args:
        do_lower_case (bool):
            Whether or not to lowercase the input when tokenizing.
            Defaults to `True`.
    """

    def __init__(self, do_lower_case=True):
        """Constructs a BasicTokenizer."""

        self.do_lower_case = do_lower_case

    def tokenize(self, text):
        """
        Tokenizes a piece of text using basic tokenizer.
        Args:
            text (str): A piece of text.
        Returns:
            list(str): A list of tokens.
        Examples:
            .. code-block::
                from paddlenlp.transformers import BasicTokenizer
                basictokenizer = BasicTokenizer()
                tokens = basictokenizer.tokenize('He was a puppeteer')
                '''
                ['he', 'was', 'a', 'puppeteer']
                '''
        """

        text = convert_to_unicode(text)
        text = self._clean_text(text)
        text = self._tokenize_chinese_chars(text)

        orig_tokens = whitespace_tokenize(text)
        split_tokens = []
        for token in orig_tokens:
            if self.do_lower_case:
                token = token.lower()
                token = self._run_strip_accents(token)
            split_tokens.extend(self._run_split_on_punc(token))

        output_tokens = whitespace_tokenize(" ".join(split_tokens))
        return output_tokens

    def _run_strip_accents(self, text):
        """
        Strips accents from a piece of text.
        """
        text = unicodedata.normalize("NFD", text)
        output = []
        for char in text:
            cat = unicodedata.category(char)
            if cat == "Mn":
                continue
            output.append(char)
        return "".join(output)

    def _run_split_on_punc(self, text):
        """
        Splits punctuation on a piece of text.
        """
        chars = list(text)
        i = 0
        start_new_word = True
        output = []
        while i < len(chars):
            char = chars[i]
            if _is_punctuation(char):
                output.append([char])
                start_new_word = True
            else:
                if start_new_word:
                    output.append([])
                start_new_word = False
                output[-1].append(char)
            i += 1

        return ["".join(x) for x in output]

    def _tokenize_chinese_chars(self, text):
        """
        Adds whitespace around any CJK character.
        """
        output = []
        for char in text:
            cp = ord(char)
            if self._is_chinese_char(cp):
                output.append(" ")
                output.append(char)
                output.append(" ")
            else:
                output.append(char)
        return "".join(output)

    def _is_chinese_char(self, cp):
        """
        Checks whether CP is the codepoint of a CJK character.
        """

        # This defines a "chinese character" as anything in the CJK Unicode block:
        #     https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
        #
        # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
        # despite its name. The modern Korean Hangul alphabet is a different block,
        # as is Japanese Hiragana and Katakana. Those alphabets are used to write
        # space-separated words, so they are not treated specially and handled
        # like the all of the other languages.
        if ((cp >= 0x4E00 and cp <= 0x9FFF) or  #
            (cp >= 0x3400 and cp <= 0x4DBF) or  #
            (cp >= 0x20000 and cp <= 0x2A6DF) or  #
            (cp >= 0x2A700 and cp <= 0x2B73F) or  #
            (cp >= 0x2B740 and cp <= 0x2B81F) or  #
            (cp >= 0x2B820 and cp <= 0x2CEAF) or
            (cp >= 0xF900 and cp <= 0xFAFF) or  #
            (cp >= 0x2F800 and cp <= 0x2FA1F)):  #
            return True

        return False

    def _clean_text(self, text):
        """
        Performs invalid character removal and whitespace cleanup on text.
        """
        output = []
        for char in text:
            cp = ord(char)
            if cp == 0 or cp == 0xfffd or _is_control(char):
                continue
            if _is_whitespace(char):
                output.append(" ")
            else:
                output.append(char)
        return "".join(output)


class WordpieceTokenizer(object):
    """
    Runs WordPiece tokenization.
    Args:
        vocab (Vocab|dict):
            Vocab of the word piece tokenizer.
        unk_token (str):
            A specific token to replace all unknown tokens.
        max_input_chars_per_word (int):
            If a word's length is more than
            max_input_chars_per_word, it will be dealt as unknown word.
            Defaults to 100.
    """

    def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
        self.vocab = vocab
        self.unk_token = unk_token
        self.max_input_chars_per_word = max_input_chars_per_word

    def tokenize(self, text):
        """
        Tokenizes a piece of text into its word pieces.
        This uses a greedy longest-match-first algorithm to perform tokenization
        using the given vocabulary.
        Args:
            text: A single token or whitespace separated tokens. This should have
                already been passed through `BasicTokenizer`.
        Returns:
            list (str): A list of wordpiece tokens.
        Examples:
            .. code-block::
                from paddlenlp.transformers import BertTokenizer, WordpieceTokenizer
                berttokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
                vocab  = berttokenizer.vocab
                unk_token = berttokenizer.unk_token
                wordpiecetokenizer = WordpieceTokenizer(vocab,unk_token)
                inputs = wordpiecetokenizer.tokenize("unaffable")
                print(inputs)
                '''
                ["un", "##aff", "##able"]
                '''
        """

        output_tokens = []
        for token in whitespace_tokenize(text):
            chars = list(token)
            if len(chars) > self.max_input_chars_per_word:
                output_tokens.append(self.unk_token)
                continue

            is_bad = False
            start = 0
            sub_tokens = []
            while start < len(chars):
                end = len(chars)
                cur_substr = None
                while start < end:
                    substr = "".join(chars[start:end])
                    if start > 0:
                        substr = "##" + substr
                    if substr in self.vocab:
                        cur_substr = substr
                        break
                    end -= 1
                if cur_substr is None:
                    is_bad = True
                    break
                sub_tokens.append(cur_substr)
                start = end

            if is_bad:
                output_tokens.append(self.unk_token)
            else:
                output_tokens.extend(sub_tokens)
        return output_tokens


class BertTokenizer(PretrainedTokenizer):
    """
    Constructs a BERT tokenizer. It uses a basic tokenizer to do punctuation
    splitting, lower casing and so on, and follows a WordPiece tokenizer to
    tokenize as subwords.
    Args:
        vocab_file (str):
            The vocabulary file path (ends with '.txt') required to instantiate
            a `WordpieceTokenizer`.
        do_lower_case (bool):
            Whether or not to lowercase the input when tokenizing.
            Defaults to`True`.
        unk_token (str):
            A special token representing the *unknown (out-of-vocabulary)* token.
            An unknown token is set to be `unk_token` inorder to be converted to an ID.
            Defaults to "[UNK]".
        sep_token (str):
            A special token separating two different sentences in the same input.
            Defaults to "[SEP]".
        pad_token (str):
            A special token used to make arrays of tokens the same size for batching purposes.
            Defaults to "[PAD]".
        cls_token (str):
            A special token used for sequence classification. It is the last token
            of the sequence when built with special tokens. Defaults to "[CLS]".
        mask_token (str):
            A special token representing a masked token. This is the token used
            in the masked language modeling task which the model tries to predict the original unmasked ones.
            Defaults to "[MASK]".
    Examples:
        .. code-block::
            from paddlenlp.transformers import BertTokenizer
            berttokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
            inputs = berttokenizer.tokenize('He was a puppeteer')
            print(inputs)
            '''
            {'input_ids': [101, 2002, 2001, 1037, 13997, 11510, 102], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0]}
            '''
    """
    resource_files_names = {"vocab_file": "vocab.txt"}  # for save_pretrained
    pretrained_resource_files_map = {
        "vocab_file": {
            "bert-base-uncased":
            "https://paddle-hapi.bj.bcebos.com/models/bert/bert-base-uncased-vocab.txt",
            "bert-large-uncased":
            "https://paddle-hapi.bj.bcebos.com/models/bert/bert-large-uncased-vocab.txt",
            "bert-base-cased":
            "https://paddle-hapi.bj.bcebos.com/models/bert/bert-base-cased-vocab.txt",
            "bert-large-cased":
            "https://paddle-hapi.bj.bcebos.com/models/bert/bert-large-cased-vocab.txt",
            "bert-base-multilingual-uncased":
            "https://paddle-hapi.bj.bcebos.com/models/bert/bert-base-multilingual-uncased-vocab.txt",
            "bert-base-multilingual-cased":
            "https://paddle-hapi.bj.bcebos.com/models/bert/bert-base-multilingual-cased-vocab.txt",
            "bert-base-chinese":
            "https://paddle-hapi.bj.bcebos.com/models/bert/bert-base-chinese-vocab.txt",
            "bert-wwm-chinese":
            "http://paddlenlp.bj.bcebos.com/models/transformers/bert/bert-wwm-chinese-vocab.txt",
            "bert-wwm-ext-chinese":
            "http://paddlenlp.bj.bcebos.com/models/transformers/bert/bert-wwm-ext-chinese-vocab.txt",
            "macbert-large-chinese":
            "https://paddle-hapi.bj.bcebos.com/models/bert/bert-base-chinese-vocab.txt",
            "macbert-base-chinese":
            "https://paddle-hapi.bj.bcebos.com/models/bert/bert-base-chinese-vocab.txt",
            "simbert-base-chinese":
            "https://paddlenlp.bj.bcebos.com/models/transformers/simbert/vocab.txt",
        }
    }
    pretrained_init_configuration = {
        "bert-base-uncased": {
            "do_lower_case": True
        },
        "bert-large-uncased": {
            "do_lower_case": True
        },
        "bert-base-cased": {
            "do_lower_case": False
        },
        "bert-large-cased": {
            "do_lower_case": False
        },
        "bert-base-multilingual-uncased": {
            "do_lower_case": True
        },
        "bert-base-multilingual-cased": {
            "do_lower_case": False
        },
        "bert-base-chinese": {
            "do_lower_case": False
        },
        "bert-wwm-chinese": {
            "do_lower_case": False
        },
        "bert-wwm-ext-chinese": {
            "do_lower_case": False
        },
        "macbert-large-chinese": {
            "do_lower_case": False
        },
        "macbert-base-chinese": {
            "do_lower_case": False
        },
        "simbert-base-chinese": {
            "do_lower_case": True
        },
    }
    padding_side = 'right'

    def __init__(self,
                 vocab_file,
                 do_lower_case=True,
                 unk_token="[UNK]",
                 sep_token="[SEP]",
                 pad_token="[PAD]",
                 cls_token="[CLS]",
                 mask_token="[MASK]"):

        if not os.path.isfile(vocab_file):
            raise ValueError(
                "Can't find a vocabulary file at path '{}'. To load the "
                "vocabulary from a pretrained model please use "
                "`tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
                .format(vocab_file))
        self.vocab = self.load_vocabulary(vocab_file, unk_token=unk_token)
        self.do_lower_case = do_lower_case
        self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case)
        self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab,
                                                      unk_token=unk_token)
        self.special_tokens_map = {
            'unk_token': unk_token,
            'sep_token': sep_token,
            'pad_token': pad_token,
            'cls_token': cls_token,
            'mask_token': mask_token
        }

    @property
    def vocab_size(self):
        """
        Return the size of vocabulary.
        Returns:
            int: The size of vocabulary.
        """

        return len(self.vocab)

    def _tokenize(self, text):
        """
        End-to-end tokenization for BERT models.
        Args:
            text (str): The text to be tokenized.

        Returns:
            list: A list of string representing converted tokens.
        """
        split_tokens = []
        for token in self.basic_tokenizer.tokenize(text):
            for sub_token in self.wordpiece_tokenizer.tokenize(token):
                split_tokens.append(sub_token)
        return split_tokens

    def tokenize(self, text):
        """
        Converts a string to a list of tokens.
        Args:
            text (str): The text to be tokenized.

        Returns:
            List(str): A list of string representing converted tokens.
        Examples:
            .. code-block::
                from paddlenlp.transformers import BertTokenizer
                berttokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
                tokens = berttokenizer.tokenize('He was a puppeteer')

                '''
                ['he', 'was', 'a', 'puppet', '##eer']
                '''
        """

        return self._tokenize(text)

    def num_special_tokens_to_add(self, pair=False):
        """
        Returns the number of added tokens when encoding a sequence with special tokens.
        Args:
            pair(bool):
                Whether the input is a sequence pair or a single sequence.
                Defaults to `False` and the input is a single sequence.
        Returns:
            int: Number of tokens added to sequences.
        """
        token_ids_0 = []
        token_ids_1 = []
        return len(
            self.build_inputs_with_special_tokens(
                token_ids_0, token_ids_1 if pair else None))

    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
        """
        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
        adding special tokens.

        A BERT sequence has the following format:
        - single sequence:      ``[CLS] X [SEP]``
        - pair of sequences:        ``[CLS] A [SEP] B [SEP]``
        Args:
            token_ids_0 (List[int]):
                List of IDs to which the special tokens will be added.
            token_ids_1 (List[int], optional):
                Optional second list of IDs for sequence pairs. Defaults to None.
        Returns:
            List[int]: List of input_id with the appropriate special tokens.
        """
        if token_ids_1 is None:
            return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
        _cls = [self.cls_token_id]
        _sep = [self.sep_token_id]
        return _cls + token_ids_0 + _sep + token_ids_1 + _sep

    def create_token_type_ids_from_sequences(self,
                                             token_ids_0,
                                             token_ids_1=None):
        """
        Create a mask from the two sequences passed to be used in a sequence-pair classification task.
        A BERT sequence pair mask has the following format:
        ::
            0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
            | first sequence    | second sequence |
        If :obj:`token_ids_1` is :obj:`None`, this method only returns the first portion of the mask (0s).
        Args:
            token_ids_0 (List[int]):
                A list of `inputs_ids` for the first sequence.
            token_ids_1 (List[int], optional):
                Optional second list of IDs for sequence pairs. Defaults to None.
        Returns:
            List[int]: List of token_type_id according to the given sequence(s).
        """
        _sep = [self.sep_token_id]
        _cls = [self.cls_token_id]
        if token_ids_1 is None:
            return len(_cls + token_ids_0 + _sep) * [0]
        return len(_cls + token_ids_0 + _sep) * [0] + len(token_ids_1 +
                                                          _sep) * [1]

    def get_special_tokens_mask(self,
                                token_ids_0,
                                token_ids_1=None,
                                already_has_special_tokens=False):
        """
        Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
        special tokens using the tokenizer ``encode`` methods.
        Args:
            token_ids_0 (List[int]):
                A list of `inputs_ids` for the first sequence.
            token_ids_1 (List[int], optinal):
                Optional second list of IDs for sequence pairs. Defaults to None.
            already_has_special_tokens (bool, optional): Whether or not the token list is already
                formatted with special tokens for the model. Defaults to None.
        Returns:
            List[int]: The list of integers either be 0 or 1: 1 for a special token, 0 for a sequence token.
        """

        if already_has_special_tokens:
            if token_ids_1 is not None:
                raise ValueError(
                    "You should not supply a second sequence if the provided sequence of "
                    "ids is already formatted with special tokens for the model."
                )
            return list(
                map(
                    lambda x: 1
                    if x in [self.sep_token_id, self.cls_token_id] else 0,
                    token_ids_0))

        if token_ids_1 is not None:
            return [1] + ([0] * len(token_ids_0)) + [1] + (
                [0] * len(token_ids_1)) + [1]
        return [1] + ([0] * len(token_ids_0)) + [1]