codingutf8 2020 the microsoft 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 loads a vocabulary file into a dictionary vocab collections ordereddict with openvocabfile r encodingutf8 as reader tokens reader readlines for index token in enumeratetokens token token rstripn vocabtoken index return vocab class xlmprophetnettokenizerpretrainedtokenizer vocabfilesnames vocabfilesnames pretrainedvocabfilesmap pretrainedvocabfilesmap maxmodelinputsizes pretrainedpositionalembeddingssizes modelinputnames inputids attentionmask def init self vocabfile bostokensep eostokensep septokensep unktokenunk padtokenpad clstokencls masktokenmask spmodelkwargs optionaldictstr any none kwargs none self spmodelkwargs if spmodelkwargs is none else spmodelkwargs try import sentencepiece as spm except importerror logger warning you need to install sentencepiece to use xlmrobertatokenizer https github comgooglesentencepiece pip install sentencepiece raise self spmodel spm sentencepieceprocessorself spmodelkwargs self spmodel loadstrvocabfile self vocabfile vocabfile original fairseq vocab and spm vocab must be aligned vocab 0 1 2 3 4 5 6 7 8 9 fairseq s pad s unk s de spm unk s s s de a put special tokens and unused tokens into the vocab self fairseqtokenstoids pad 0 cls 1 sep 2 unk 3 mask 4 for i in range10 tok funusedi self fairseqtokenstoidstok 5 i the first real token has position 15 in the embedding vocab and position 3 in the spm vocab self fairseqoffset 12 self fairseqidstotokens v k for k v in self fairseqtokenstoids items todo arthurz fairseqidstotokens should be removed super init bostokenbostoken eostokeneostoken septokenseptoken unktokenunktoken padtokenpadtoken clstokenclstoken masktokenmasktoken spmodelkwargsself spmodelkwargs kwargs property def cansaveslowtokenizerself bool return os path isfileself vocabfile if self vocabfile else false def getstateself state self dict copy statespmodel none return state def setstateself d self dict d try import sentencepiece as spm except importerror logger warning you need to install sentencepiece to use xlmrobertatokenizer https github comgooglesentencepiece pip install sentencepiece raise for backward compatibility if not hasattrself spmodelkwargs self spmodelkwargs self spmodel spm sentencepieceprocessorself spmodelkwargs self spmodel loadself vocabfile def getspecialtokensmask self tokenids0 listint tokenids1 optionallistint none alreadyhasspecialtokens bool false listint if alreadyhasspecialtokens return super getspecialtokensmask tokenids0tokenids0 tokenids1tokenids1 alreadyhasspecialtokenstrue if tokenids1 is none return 0 lentokenids0 1 return 0 lentokenids0 1 0 lentokenids1 1 def createtokentypeidsfromsequences self tokenids0 listint tokenids1 optionallistint none listint sep self septokenid if tokenids1 is none return lentokenids0 sep 0 return lentokenids0 sep sep tokenids1 sep 0 property def vocabsizeself return lenself spmodel self fairseqoffset def getvocabself vocab self convertidstotokensi i for i in rangeself vocabsize vocab updateself addedtokensencoder return vocab def tokenizeself text str str return self spmodel encodetext outtypestr def converttokentoidself token need to return unknown token if the sp model returned 0 converts an index integer in a token str using the vocab if index in self fairseqidstotokens return self fairseqidstotokensindex return self spmodel idtopieceindex self fairseqoffset def converttokenstostringself tokens build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens a xlmprophetnet sequence has the following format single sequence x sep pair of sequences a sep b sep 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 coding utf 8 2020 the microsoft 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 loads a vocabulary file into a dictionary adapted from robertatokenizer and xlnettokenizer based on 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 path to the vocabulary file bos_token str optional defaults to sep 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 sep 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 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 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 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 sp_model_kwargs dict optional will be passed to the sentencepieceprocessor __init__ method the python wrapper for sentencepiece https github com google sentencepiece tree master python can be used among other things to set enable_sampling enable subword regularization nbest_size sampling parameters for unigram invalid for bpe dropout nbest_size 0 1 no sampling is performed nbest_size 1 samples from the nbest_size results nbest_size 0 assuming that nbest_size is infinite and samples from the all hypothesis lattice using forward filtering and backward sampling algorithm alpha smoothing parameter for unigram sampling and dropout probability of merge operations for bpe dropout attributes sp_model sentencepieceprocessor the sentencepiece processor that is used for every conversion string tokens and ids original fairseq vocab and spm vocab must be aligned vocab 0 1 2 3 4 5 6 7 8 9 fairseq s pad s unk s de spm unk s s s de a put special tokens and unused tokens into the vocab the first real token has position 15 in the embedding vocab and position 3 in the spm vocab todo arthurz fairseq_ids_to_tokens should be removed for backward compatibility retrieve sequence ids from a token list that has no special tokens added this method is called when adding special tokens using the tokenizer prepare_for_model method args token_ids_0 list int list of ids token_ids_1 list int optional optional second list of ids for sequence pairs already_has_special_tokens bool optional defaults to false whether or not the token list is already formatted with special tokens for the model returns list int a list of integers in the range 0 1 1 for a special token 0 for a sequence token create a mask from the two sequences passed to be used in a sequence pair classification task xlmprophetnet does not make use of token type ids therefore a list of zeros is returned 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 zeros converts a token str in an id using the vocab need to return unknown token if the sp model returned 0 converts an index integer in a token str using the vocab converts a sequence of tokens strings for sub words in a single string build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens a xlmprophetnet sequence has the following format single sequence x sep pair of sequences 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 returns list int list of input ids glossary input ids with the appropriate special tokens
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


logger = logging.get_logger(__name__)

SPIECE_UNDERLINE = "▁"

VOCAB_FILES_NAMES = {"vocab_file": "prophetnet.tokenizer"}

PRETRAINED_VOCAB_FILES_MAP = {
    "vocab_file": {
        "microsoft/xprophetnet-large-wiki100-cased": (
            "https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"
        ),
    }
}

PRETRAINED_INIT_CONFIGURATION = {
    "microsoft/xprophetnet-large-wiki100-cased": {"do_lower_case": False},
}

PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
    "microsoft/xprophetnet-large-wiki100-cased": 512,
}


def load_vocab(vocab_file):
    
    vocab = collections.OrderedDict()
    with open(vocab_file, "r", encoding="utf-8") as reader:
        tokens = reader.readlines()
    for index, token in enumerate(tokens):
        token = token.rstrip("\n")
        vocab[token] = index
    return vocab


class XLMProphetNetTokenizer(PreTrainedTokenizer):
    

    vocab_files_names = VOCAB_FILES_NAMES
    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    model_input_names = ["input_ids", "attention_mask"]

    def __init__(
        self,
        vocab_file,
        bos_token="[SEP]",
        eos_token="[SEP]",
        sep_token="[SEP]",
        unk_token="[UNK]",
        pad_token="[PAD]",
        cls_token="[CLS]",
        mask_token="[MASK]",
        sp_model_kwargs: Optional[Dict[str, Any]] = None,
        **kwargs,
    ) -> None:
        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs

        try:
            import sentencepiece as spm
        except ImportError:
            logger.warning(
                "You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
                " pip install sentencepiece"
            )
            raise

        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
        self.sp_model.Load(str(vocab_file))
        self.vocab_file = vocab_file

        
        
        
        
        

        
        self.fairseq_tokens_to_ids = {"[PAD]": 0, "[CLS]": 1, "[SEP]": 2, "[UNK]": 3, "[MASK]": 4}

        for i in range(10):
            tok = f"[unused{i}]"
            self.fairseq_tokens_to_ids[tok] = 5 + i

        
        self.fairseq_offset = 12
        self.fairseq_ids_to_tokens = {v: k for k, v in self.fairseq_tokens_to_ids.items()}

        

        super().__init__(
            bos_token=bos_token,
            eos_token=eos_token,
            sep_token=sep_token,
            unk_token=unk_token,
            pad_token=pad_token,
            cls_token=cls_token,
            mask_token=mask_token,
            sp_model_kwargs=self.sp_model_kwargs,
            **kwargs,
        )

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

    def __getstate__(self):
        state = self.__dict__.copy()
        state["sp_model"] = None
        return state

    def __setstate__(self, d):
        self.__dict__ = d
        try:
            import sentencepiece as spm
        except ImportError:
            logger.warning(
                "You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
                " pip install sentencepiece"
            )
            raise

        
        if not hasattr(self, "sp_model_kwargs"):
            self.sp_model_kwargs = {}

        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
        self.sp_model.Load(self.vocab_file)

    def get_special_tokens_mask(
        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
    ) -> List[int]:
        

        if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
            )

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

    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]

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

    @property
    def vocab_size(self):
        return len(self.sp_model) + self.fairseq_offset

    def get_vocab(self):
        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
        vocab.update(self.added_tokens_encoder)
        return vocab

    def _tokenize(self, text: str) -> str:
        return self.sp_model.encode(text, out_type=str)

    def _convert_token_to_id(self, token):
        
        if token in self.fairseq_tokens_to_ids:
            return self.fairseq_tokens_to_ids[token]
        spm_id = self.sp_model.PieceToId(token)

        
        return spm_id + self.fairseq_offset if spm_id else self.unk_token_id

    def _convert_id_to_token(self, index):
        
        if index in self.fairseq_ids_to_tokens:
            return self.fairseq_ids_to_tokens[index]
        return self.sp_model.IdToPiece(index - self.fairseq_offset)

    def convert_tokens_to_string(self, tokens):
        
        out_string = "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip()
        return out_string

    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
        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) and os.path.isfile(self.vocab_file):
            copyfile(self.vocab_file, out_vocab_file)
        elif not os.path.isfile(self.vocab_file):
            with open(out_vocab_file, "wb") as fi:
                content_spiece_model = self.sp_model.serialized_model_proto()
                fi.write(content_spiece_model)

        return (out_vocab_file,)

    def build_inputs_with_special_tokens(
        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
    ) -> List[int]:
        

        if token_ids_1 is None:
            return token_ids_0 + [self.sep_token_id]
        sep = [self.sep_token_id]
        return token_ids_0 + sep + token_ids_1 + sep