codingutf8 2018 the google ai language team s 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 orglicenseslicense2 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 tokenization classes import os from shutil import copyfile from typing import list optional tuple from tokenizationutilsfast import addedtoken pretrainedtokenizerfast from utils import logging logger logging getloggername vocabfilesnames vocabfile spiece model tokenizerfile tokenizer json pretrainedvocabfilesmap vocabfile tsinghuaaicpmgenerate https huggingface cotsinghuaaicpmgenerateresolvemainspiece model tokenizerfile tsinghuaaicpmgenerate https huggingface cotsinghuaaicpmgenerateresolvemaintokenizer json class cpmtokenizerfastpretrainedtokenizerfast construct a cpm tokenizer based on jiebahttps pypi orgprojectjieba and sentencepiecehttps github comgooglesentencepiece this tokenizer inherits from pretrainedtokenizer which contains most of the main methods users should refer to this superclass for more information regarding those methods args vocabfile str sentencepiecehttps github comgooglesentencepiece file generally has a spm extension that contains the vocabulary necessary to instantiate a tokenizer dolowercase bool optional defaults to true whether to lowercase the input when tokenizing removespace bool optional defaults to true whether to strip the text when tokenizing removing excess spaces before and after the string keepaccents bool optional defaults to false whether to keep accents when tokenizing bostoken str optional defaults to s the beginning of sequence token that was used during pretraining can be used a sequence classifier token tip when building a sequence using special tokens this is not the token that is used for the beginning of sequence the token used is the clstoken tip eostoken str optional defaults to s the end of sequence token tip when building a sequence using special tokens this is not the token that is used for the end of sequence the token used is the septoken tip unktoken str optional defaults to unk the unknown token a token that is not in the vocabulary cannot be converted to an id and is set to be this token instead septoken str optional defaults to sep the separator token which is used when building a sequence from multiple sequences e g two sequences for sequence classification or for a text and a question for question answering it is also used as the last token of a sequence built with special tokens padtoken str optional defaults to pad the token used for padding for example when batching sequences of different lengths clstoken str optional defaults to cls the classifier token which is used when doing sequence classification classification of the whole sequence instead of pertoken classification it is the first token of the sequence when built with special tokens masktoken str optional defaults to mask the token used for masking values this is the token used when training this model with masked language modeling this is the token which the model will try to predict additionalspecialtokens liststr optional defaults to eop eod additional special tokens used by the tokenizer attributes spmodel sentencepieceprocessor the sentencepiece processor that is used for every conversion string tokens and ids mask token behave like a normal word i e include the space before it copied from transformers models xlnet tokenizationxlnetfast xlnettokenizerfast buildinputswithspecialtokens build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens an xlnet sequence has the following format single sequence x sep cls pair of sequences a sep b sep cls args tokenids0 listint list of ids to which the special tokens will be added tokenids1 listint optional optional second list of ids for sequence pairs returns listint list of input ids glossaryinputids with the appropriate special tokens copied from transformers models xlnet tokenizationxlnetfast xlnettokenizerfast createtokentypeidsfromsequences create a mask from the two sequences passed to be used in a sequencepair classification task an xlnet 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 tokenids1 is none this method only returns the first portion of the mask 0s args tokenids0 listint list of ids tokenids1 listint optional optional second list of ids for sequence pairs returns listint list of token type ids glossarytokentypeids according to the given sequences copied from transformers models xlnet tokenizationxlnetfast xlnettokenizerfast savevocabulary coding utf 8 2018 the google ai language team s 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 tokenization classes runs pre tokenization with jieba segmentation tool it is used in cpm models construct a cpm tokenizer based on jieba https pypi org project jieba and sentencepiece https github com google sentencepiece this tokenizer inherits from pretrainedtokenizer which contains most of the main methods users should refer to this superclass for more information regarding those methods args vocab_file str sentencepiece https github com google sentencepiece file generally has a spm extension that contains the vocabulary necessary to instantiate a tokenizer do_lower_case bool optional defaults to true whether to lowercase the input when tokenizing remove_space bool optional defaults to true whether to strip the text when tokenizing removing excess spaces before and after the string keep_accents bool optional defaults to false whether to keep accents when tokenizing bos_token str optional defaults to s the beginning of sequence token that was used during pretraining can be used a sequence classifier token tip when building a sequence using special tokens this is not the token that is used for the beginning of sequence the token used is the cls_token tip eos_token str optional defaults to s the end of sequence token tip when building a sequence using special tokens this is not the token that is used for the end of sequence the token used is the sep_token tip unk_token str optional defaults to unk the unknown token a token that is not in the vocabulary cannot be converted to an id and is set to be this token instead sep_token str optional defaults to sep the separator token which is used when building a sequence from multiple sequences e g two sequences for sequence classification or for a text and a question for question answering it is also used as the last token of a sequence built with special tokens pad_token str optional defaults to pad the token used for padding for example when batching sequences of different lengths cls_token str optional defaults to cls the classifier token which is used when doing sequence classification classification of the whole sequence instead of per token classification it is the first token of the sequence when built with special tokens mask_token str optional defaults to mask the token used for masking values this is the token used when training this model with masked language modeling this is the token which the model will try to predict additional_special_tokens list str optional defaults to eop eod additional special tokens used by the tokenizer attributes sp_model sentencepieceprocessor the sentencepiece processor that is used for every conversion string tokens and ids mask token behave like a normal word i e include the space before it copied from transformers models xlnet tokenization_xlnet_fast xlnettokenizerfast build_inputs_with_special_tokens build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens an xlnet sequence has the following format single sequence x sep cls pair of sequences a sep b sep cls 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 returns list int list of input ids glossary input ids with the appropriate special tokens copied from transformers models xlnet tokenization_xlnet_fast xlnettokenizerfast create_token_type_ids_from_sequences create a mask from the two sequences passed to be used in a sequence pair classification task an xlnet 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 list of ids token_ids_1 list int optional optional second list of ids for sequence pairs returns list int list of token type ids glossary token type ids according to the given sequence s copied from transformers models xlnet tokenization_xlnet_fast xlnettokenizerfast save_vocabulary
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
from ...utils import logging


logger = logging.get_logger(__name__)

VOCAB_FILES_NAMES = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

PRETRAINED_VOCAB_FILES_MAP = {
    "vocab_file": {
        "TsinghuaAI/CPM-Generate": "https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "TsinghuaAI/CPM-Generate": "https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json",
    },
}


class CpmTokenizerFast(PreTrainedTokenizerFast):
    

    def __init__(
        self,
        vocab_file=None,
        tokenizer_file=None,
        do_lower_case=False,
        remove_space=True,
        keep_accents=False,
        bos_token="<s>",
        eos_token="</s>",
        unk_token="<unk>",
        sep_token="<sep>",
        pad_token="<pad>",
        cls_token="<cls>",
        mask_token="<mask>",
        additional_special_tokens=["<eop>", "<eod>"],
        **kwargs,
    ):
        
        
        mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token

        super().__init__(
            vocab_file=vocab_file,
            tokenizer_file=tokenizer_file,
            do_lower_case=do_lower_case,
            remove_space=remove_space,
            keep_accents=keep_accents,
            bos_token=bos_token,
            eos_token=eos_token,
            unk_token=unk_token,
            sep_token=sep_token,
            pad_token=pad_token,
            cls_token=cls_token,
            mask_token=mask_token,
            additional_special_tokens=additional_special_tokens,
            **kwargs,
        )

        self._pad_token_type_id = 3
        self.do_lower_case = do_lower_case
        self.remove_space = remove_space
        self.keep_accents = keep_accents
        self.vocab_file = vocab_file

        try:
            import jieba
        except ModuleNotFoundError as error:
            raise error.__class__(
                "You need to install jieba to use CpmTokenizer or CpmTokenizerFast. "
                "See https://pypi.org/project/jieba/ for installation."
            )
        self.jieba = jieba
        self.translator = str.maketrans(" \n", "\u2582\u2583")

    @property
    def can_save_slow_tokenizer(self) -> bool:
        return os.path.isfile(self.vocab_file) if self.vocab_file else False

    
    def build_inputs_with_special_tokens(
        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
    ) -> List[int]:
        
        sep = [self.sep_token_id]
        cls = [self.cls_token_id]
        if token_ids_1 is None:
            return token_ids_0 + sep + cls
        return token_ids_0 + sep + token_ids_1 + sep + cls

    
    def create_token_type_ids_from_sequences(
        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
    ) -> List[int]:
        
        sep = [self.sep_token_id]
        cls_segment_id = [2]

        if token_ids_1 is None:
            return len(token_ids_0 + sep) * [0] + cls_segment_id
        return len(token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1] + cls_segment_id

    
    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
        if not self.can_save_slow_tokenizer:
            raise ValueError(
                "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                "tokenizer."
            )

        if not os.path.isdir(save_directory):
            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
            return
        out_vocab_file = os.path.join(
            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
        )

        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
            copyfile(self.vocab_file, out_vocab_file)

        return (out_vocab_file,)

    def _batch_encode_plus(self, batch_text_or_text_pairs, *args, **kwargs):
        batch_text_or_text_pairs = [
            " ".join([x.translate(self.translator) for x in self.jieba.cut(text, cut_all=False)])
            for text in batch_text_or_text_pairs
        ]
        return super()._batch_encode_plus(batch_text_or_text_pairs, *args, **kwargs)

    def _decode(self, *args, **kwargs):
        text = super()._decode(*args, **kwargs)
        text = text.replace(" ", "").replace("\u2582", " ").replace("\u2583", "\n")
        return text