PaddleX/paddlex/inference/models/common/tokenizer/bert_tokenizer.py

# Copyright (c) 2024 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.

import os
import unicodedata

from .tokenizer_utils import (
    PretrainedTokenizer,
    _is_control,
    _is_punctuation,
    _is_symbol,
    _is_whitespace,
    convert_to_unicode,
    whitespace_tokenize,
)

__all__ = [
    "BasicTokenizer",
    "BertTokenizer",
    "WordpieceTokenizer",
]


class BasicTokenizer(object):
    """
    Runs basic tokenization (punctuation splitting, lower casing, etc.).

    Args:
        do_lower_case (bool):
            Whether to lowercase the input when tokenizing.
            Defaults to `True`.
        never_split (Iterable):
            Collection of tokens which will never be split during tokenization. Only has an effect when
            `do_basic_tokenize=True`
        tokenize_chinese_chars (bool):
            Whether to tokenize Chinese characters.
        strip_accents: (bool):
            Whether to strip all accents. If this option is not specified, then it will be determined by the
            value for `lowercase` (as in the original BERT).
    """

    def __init__(
        self,
        do_lower_case=True,
        never_split=None,
        tokenize_chinese_chars=True,
        strip_accents=None,
    ):
        """Constructs a BasicTokenizer."""
        if never_split is None:
            never_split = []
        self.do_lower_case = do_lower_case
        self.never_split = set(never_split)
        self.tokenize_chinese_chars = tokenize_chinese_chars
        self.strip_accents = strip_accents

    def tokenize(self, text, never_split=None):
        """
        Tokenizes a piece of text using basic tokenizer.

        Args:
            text (str): A piece of text.
            never_split (List[str]): List of token not to split.

        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)
        never_split = (
            self.never_split.union(set(never_split))
            if never_split
            else self.never_split
        )
        text = self._clean_text(text)

        if self.tokenize_chinese_chars:
            text = self._tokenize_chinese_chars(text)
        orig_tokens = whitespace_tokenize(text)
        split_tokens = []
        for token in orig_tokens:
            if token not in never_split:
                if self.do_lower_case:
                    token = token.lower()
                    if self.strip_accents is not False:
                        token = self._run_strip_accents(token)
                elif self.strip_accents:
                    token = self._run_strip_accents(token)
            split_tokens.extend(self._run_split_on_punc(token, never_split))

        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, never_split=None):
        """
        Splits punctuation on a piece of text.
        """
        if never_split is not None and text in never_split:
            return [text]
        chars = list(text)
        i = 0
        start_new_word = True
        output = []
        while i < len(chars):
            char = chars[i]
            # punctuation and symbol should be treat as single char.
            if _is_punctuation(char) or _is_symbol(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 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, optional):
            Whether to lowercase the input when tokenizing.
            Defaults to `True`.
        do_basic_tokenize (bool, optional):
            Whether to use a basic tokenizer before a WordPiece tokenizer.
            Defaults to `True`.
        never_split (Iterable, optional):
            Collection of tokens which will never be split during tokenization. Only has an effect when
            `do_basic_tokenize=True`. Defaults to `None`.
        unk_token (str, optional):
            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, optional):
            A special token separating two different sentences in the same input.
            Defaults to "[SEP]".
        pad_token (str, optional):
            A special token used to make arrays of tokens the same size for batching purposes.
            Defaults to "[PAD]".
        cls_token (str, optional):
            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, optional):
            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]".
        tokenize_chinese_chars (bool, optional):
            Whether to tokenize Chinese characters.
            Defaults to `True`.
        strip_accents: (bool, optional):
            Whether to strip all accents. If this option is not specified, then it will be determined by the
            value for `lowercase` (as in the original BERT).
            Defaults to `None`.

    Examples:
        .. code-block::

            from paddlenlp.transformers import BertTokenizer
            tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

            inputs = tokenizer('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://bj.bcebos.com/paddle-hapi/models/bert/bert-base-uncased-vocab.txt",
            "bert-large-uncased": "https://bj.bcebos.com/paddle-hapi/models/bert/bert-large-uncased-vocab.txt",
            "bert-base-cased": "https://bj.bcebos.com/paddle-hapi/models/bert/bert-base-cased-vocab.txt",
            "bert-large-cased": "https://bj.bcebos.com/paddle-hapi/models/bert/bert-large-cased-vocab.txt",
            "bert-base-multilingual-uncased": "https://bj.bcebos.com/paddle-hapi/models/bert/bert-base-multilingual-uncased-vocab.txt",
            "bert-base-multilingual-cased": "https://bj.bcebos.com/paddle-hapi/models/bert/bert-base-multilingual-cased-vocab.txt",
            "bert-base-chinese": "https://bj.bcebos.com/paddle-hapi/models/bert/bert-base-chinese-vocab.txt",
            "bert-wwm-chinese": "http://bj.bcebos.com/paddlenlp/models/transformers/bert/bert-wwm-chinese-vocab.txt",
            "bert-wwm-ext-chinese": "http://bj.bcebos.com/paddlenlp/models/transformers/bert/bert-wwm-ext-chinese-vocab.txt",
            "macbert-large-chinese": "https://bj.bcebos.com/paddle-hapi/models/bert/bert-base-chinese-vocab.txt",
            "macbert-base-chinese": "https://bj.bcebos.com/paddle-hapi/models/bert/bert-base-chinese-vocab.txt",
            "simbert-base-chinese": "https://bj.bcebos.com/paddlenlp/models/transformers/simbert/vocab.txt",
            "uer/chinese-roberta-base": "https://bj.bcebos.com/paddlenlp/models/transformers/uer/chinese_roberta_vocab.txt",
            "uer/chinese-roberta-medium": "https://bj.bcebos.com/paddlenlp/models/transformers/uer/chinese_roberta_vocab.txt",
            "uer/chinese-roberta-6l-768h": "https://bj.bcebos.com/paddlenlp/models/transformers/uer/chinese_roberta_vocab.txt",
            "uer/chinese-roberta-small": "https://bj.bcebos.com/paddlenlp/models/transformers/uer/chinese_roberta_vocab.txt",
            "uer/chinese-roberta-mini": "https://bj.bcebos.com/paddlenlp/models/transformers/uer/chinese_roberta_vocab.txt",
            "uer/chinese-roberta-tiny": "https://bj.bcebos.com/paddlenlp/models/transformers/uer/chinese_roberta_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},
        "uer/chinese-roberta-base": {"do_lower_case": True},
        "uer/chinese-roberta-medium": {"do_lower_case": True},
        "uer/chinese-roberta-6l-768h": {"do_lower_case": True},
        "uer/chinese-roberta-small": {"do_lower_case": True},
        "uer/chinese-roberta-mini": {"do_lower_case": True},
        "uer/chinese-roberta-tiny": {"do_lower_case": True},
    }
    max_model_input_sizes = {
        "bert-base-uncased": 512,
        "bert-large-uncased": 512,
        "bert-base-cased": 512,
        "bert-large-cased": 512,
        "bert-base-multilingual-uncased": 512,
        "bert-base-multilingual-cased": 512,
        "bert-base-chinese": 512,
        "bert-wwm-chinese": 512,
        "bert-wwm-ext-chinese": 512,
        "macbert-large-chinese": 512,
        "macbert-base-chinese": 512,
        "simbert-base-chinese": 512,
        "uer/chinese-roberta-base": 512,
        "uer/chinese-roberta-medium": 512,
        "uer/chinese-roberta-6l-768h": 512,
        "uer/chinese-roberta-small": 512,
        "uer/chinese-roberta-mini": 512,
        "uer/chinese-roberta-tiny": 512,
    }
    padding_side = "right"

    def __init__(
        self,
        vocab_file,
        do_lower_case=True,
        do_basic_tokenize=True,
        never_split=None,
        unk_token="[UNK]",
        sep_token="[SEP]",
        pad_token="[PAD]",
        cls_token="[CLS]",
        mask_token="[MASK]",
        tokenize_chinese_chars=True,
        strip_accents=None,
        **kwargs
    ):

        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.do_lower_case = do_lower_case
        self.vocab = self.load_vocabulary(vocab_file, unk_token=unk_token)
        self.do_basic_tokenize = do_basic_tokenize
        if do_basic_tokenize:
            self.basic_tokenizer = BasicTokenizer(
                do_lower_case=do_lower_case,
                never_split=never_split,
                tokenize_chinese_chars=tokenize_chinese_chars,
                strip_accents=strip_accents,
            )
        self.wordpiece_tokenizer = WordpieceTokenizer(
            vocab=self.vocab, unk_token=unk_token
        )

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

        Returns:
            int: The size of vocabulary.
        """

        return len(self.vocab)

    def get_vocab(self):
        return dict(self.vocab.token_to_idx, **self.added_tokens_encoder)

    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 = []
        if self.do_basic_tokenize:
            for token in self.basic_tokenizer.tokenize(
                text, never_split=self.all_special_tokens
            ):
                # If the token is part of the never_split set
                if token in self.basic_tokenizer.never_split:
                    split_tokens.append(token)
                else:
                    split_tokens += self.wordpiece_tokenizer.tokenize(token)
        else:
            split_tokens = self.wordpiece_tokenizer.tokenize(text)
        return split_tokens

    def convert_tokens_to_string(self, tokens):
        """
        Converts a sequence of tokens (list of string) to a single string. Since
        the usage of WordPiece introducing `##` to concat subwords, also removes
        `##` when converting.

        Args:
            tokens (list): A list of string representing tokens to be converted.

        Returns:
            str: Converted string from 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']
                '''
                strings = tokenizer.convert_tokens_to_string(tokens)
                '''
                he was a puppeteer
                '''
        """

        out_string = " ".join(tokens).replace(" ##", "").strip()
        return out_string

    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 build_offset_mapping_with_special_tokens(
        self, offset_mapping_0, offset_mapping_1=None
    ):
        """
        Build offset map from a pair of offset map by concatenating and adding offsets of special tokens.

        A BERT offset_mapping has the following format:

        - single sequence:      ``(0,0) X (0,0)``
        - pair of sequences:        ``(0,0) A (0,0) B (0,0)``

        Args:
            offset_mapping_ids_0 (List[tuple]):
                List of wordpiece offsets to which the special tokens will be added.
            offset_mapping_ids_1 (List[tuple], optional):
                Optional second list of wordpiece offsets for offset mapping pairs. Defaults to None.

        Returns:
            List[tuple]: A list of wordpiece offsets with the appropriate offsets of special tokens.
        """
        if offset_mapping_1 is None:
            return [(0, 0)] + offset_mapping_0 + [(0, 0)]

        return [(0, 0)] + offset_mapping_0 + [(0, 0)] + offset_mapping_1 + [(0, 0)]

    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 `token_ids_1` is `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], optional):
                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.all_special_ids 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]

    def _convert_id_to_token(self, index):
        """Converts an index (integer) in a token (str) using the vocab."""
        return self.vocab._idx_to_token.get(index, self.unk_token)