codingutf8 2020 the facebook ai research 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 construct an mbart tokenizer adapted from robertatokenizer and xlnettokenizer based on sentencepiecehttps github comgooglesentencepiece the tokenization method is tokens eos language code for source language documents and language code tokens eos for target language documents examples python from transformers import mbarttokenizer tokenizer mbarttokenizer frompretrainedfacebookmbartlargeenro srclangenxx tgtlangroro exampleenglishphrase un chief says there is no military solution in syria expectedtranslationromanian eful onu declar c nu exist o soluie militar n siria inputs tokenizerexampleenglishphrase texttargetexpectedtranslationromanian returntensorspt vocabfilesnames vocabfilesnames maxmodelinputsizes pretrainedpositionalembeddingssizes pretrainedvocabfilesmap pretrainedvocabfilesmap modelinputnames inputids attentionmask prefixtokens listint suffixtokens listint def init self vocabfile bostokens eostokens septokens clstokens unktokenunk padtokenpad masktokenmask tokenizerfilenone srclangnone tgtlangnone spmodelkwargs optionaldictstr any none additionalspecialtokensnone kwargs mask token behave like a normal word i e include the space before it masktoken addedtokenmasktoken lstriptrue normalizedfalse if isinstancemasktoken str else masktoken self spmodelkwargs if spmodelkwargs is none else spmodelkwargs 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 mimic fairseq tokentoid alignment for the first 4 token self fairseqtokenstoids s 0 pad 1 s 2 unk 3 the first real token has position 4 in the original fairseq vocab and position 3 in the spm vocab self fairseqoffset 1 self spmodelsize lenself spmodel self langcodetoid code self spmodelsize i self fairseqoffset for i code in enumeratefairseqlanguagecodes self idtolangcode v k for k v in self langcodetoid items self fairseqtokenstoidsmask lenself spmodel lenself langcodetoid self fairseqoffset self fairseqtokenstoids updateself langcodetoid self fairseqidstotokens v k for k v in self fairseqtokenstoids items additionalspecialtokens listself langcodetoid keys if additionalspecialtokens is not none only add those special tokens if they are not already there additionalspecialtokens extend t for t in additionalspecialtokens if t not in additionalspecialtokens super init bostokenbostoken eostokeneostoken unktokenunktoken septokenseptoken clstokenclstoken padtokenpadtoken masktokenmasktoken tokenizerfilenone srclangsrclang tgtlangtgtlang additionalspecialtokensadditionalspecialtokens spmodelkwargsself spmodelkwargs kwargs self srclang srclang if srclang is not none else enxx self curlangcodeid self langcodetoidself srclang self tgtlang tgtlang self setsrclangspecialtokensself srclang def getstateself state self dict copy statespmodel none statespmodelproto self spmodel serializedmodelproto return state def setstateself d self dict d for backward compatibility if not hasattrself spmodelkwargs self spmodelkwargs self spmodel spm sentencepieceprocessorself spmodelkwargs self spmodel loadfromserializedprotoself spmodelproto property def vocabsizeself return lenself spmodel lenself langcodetoid self fairseqoffset 1 plus 1 for the mask token property def srclangself str return self srclang srclang setter def srclangself newsrclang str none self srclang newsrclang self setsrclangspecialtokensself srclang def getspecialtokensmask self tokenids0 listint tokenids1 optionallistint none alreadyhasspecialtokens bool false listint if alreadyhasspecialtokens return super getspecialtokensmask tokenids0tokenids0 tokenids1tokenids1 alreadyhasspecialtokenstrue prefixones 1 lenself prefixtokens suffixones 1 lenself suffixtokens if tokenids1 is none return prefixones 0 lentokenids0 suffixones return prefixones 0 lentokenids0 0 lentokenids1 suffixones def buildinputswithspecialtokens self tokenids0 listint tokenids1 optionallistint none listint if tokenids1 is none return self prefixtokens tokenids0 self suffixtokens we don t expect to process pairs but leave the pair logic for api consistency return self prefixtokens tokenids0 tokenids1 self suffixtokens def createtokentypeidsfromsequences self tokenids0 listint tokenids1 optionallistint none listint sep self septokenid cls self clstokenid if tokenids1 is none return lencls tokenids0 sep 0 return lencls tokenids0 sep sep tokenids1 sep 0 def buildtranslationinputs self rawinputs returntensors str srclang optionalstr tgtlang optionalstr extrakwargs converts a token str in an id using the vocab if token in self fairseqtokenstoids return self fairseqtokenstoidstoken spmid self spmodel piecetoidtoken need to return unknown token if the sp model returned 0 return spmid self fairseqoffset if spmid else self unktokenid def convertidtotokenself index converts a sequence of tokens strings for subwords in a single string outstring jointokens replacespieceunderline strip return outstring def savevocabularyself savedirectory str filenameprefix optionalstr none tuplestr if not os path isdirsavedirectory logger errorfvocabulary path savedirectory should be a directory return outvocabfile os path join savedirectory filenameprefix if filenameprefix else vocabfilesnamesvocabfile if os path abspathself vocabfile os path abspathoutvocabfile and os path isfileself vocabfile copyfileself vocabfile outvocabfile elif not os path isfileself vocabfile with openoutvocabfile wb as fi contentspiecemodel self spmodel serializedmodelproto fi writecontentspiecemodel return outvocabfile def prepareseq2seqbatch self srctexts liststr srclang str enxx tgttexts optionalliststr none tgtlang str roro kwargs batchencoding self srclang srclang self tgtlang tgtlang return super prepareseq2seqbatchsrctexts tgttexts kwargs def switchtoinputmodeself return self setsrclangspecialtokensself srclang def switchtotargetmodeself return self settgtlangspecialtokensself tgtlang def setsrclangspecialtokensself srclang none reset the special tokens to the target language setting no prefix and suffixeos tgtlangcode self curlangcode self langcodetoidlang self prefixtokens self suffixtokens self eostokenid self curlangcode coding utf 8 2020 the facebook ai research 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 fmt skip construct an mbart tokenizer adapted from robertatokenizer and xlnettokenizer based on sentencepiece https github com google sentencepiece the tokenization method is tokens eos language code for source language documents and language code tokens eos for target language documents examples python from transformers import mbarttokenizer tokenizer mbarttokenizer from_pretrained facebook mbart large en ro src_lang en_xx tgt_lang ro_ro example_english_phrase un chief says there is no military solution in syria expected_translation_romanian şeful onu declară că nu există o soluţie militară în siria inputs tokenizer example_english_phrase text_target expected_translation_romanian return_tensors pt mask token behave like a normal word i e include the space before it 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 mimic fairseq token to id alignment for the first 4 token the first real token has position 4 in the original fairseq vocab and position 3 in the spm vocab only add those special tokens if they are not already there for backward compatibility plus 1 for the mask token 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 build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens an mbart sequence has the following format where x represents the sequence input_ids for encoder x eos src_lang_code decoder_input_ids for decoder x eos tgt_lang_code bos is never used pairs of sequences are not the expected use case but they will be handled without a separator 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 we don t expect to process pairs but leave the pair logic for api consistency create a mask from the two sequences passed to be used in a sequence pair classification task mbart 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 used by translation pipeline to prepare inputs for the generate function 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 reset the special tokens to the source lang setting no prefix and suffix eos src_lang_code reset the special tokens to the target language setting no prefix and suffix eos tgt_lang_code
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


logger = logging.get_logger(__name__)

SPIECE_UNDERLINE = "▁"

VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model"}

PRETRAINED_VOCAB_FILES_MAP = {
    "vocab_file": {
        "facebook/mbart-large-en-ro": (
            "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"
        ),
        "facebook/mbart-large-cc25": (
            "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"
        ),
    }
}

PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
    "facebook/mbart-large-en-ro": 1024,
    "facebook/mbart-large-cc25": 1024,
}

FAIRSEQ_LANGUAGE_CODES = ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"]  


class MBartTokenizer(PreTrainedTokenizer):
    

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

    prefix_tokens: List[int] = []
    suffix_tokens: List[int] = []

    def __init__(
        self,
        vocab_file,
        bos_token="<s>",
        eos_token="</s>",
        sep_token="</s>",
        cls_token="<s>",
        unk_token="<unk>",
        pad_token="<pad>",
        mask_token="<mask>",
        tokenizer_file=None,
        src_lang=None,
        tgt_lang=None,
        sp_model_kwargs: Optional[Dict[str, Any]] = None,
        additional_special_tokens=None,
        **kwargs,
    ):
        
        mask_token = (
            AddedToken(mask_token, lstrip=True, normalized=False) if isinstance(mask_token, str) else mask_token
        )

        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs

        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 = {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

        
        self.fairseq_offset = 1

        self.sp_model_size = len(self.sp_model)
        self.lang_code_to_id = {
            code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(FAIRSEQ_LANGUAGE_CODES)
        }
        self.id_to_lang_code = {v: k for k, v in self.lang_code_to_id.items()}
        self.fairseq_tokens_to_ids["<mask>"] = len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset

        self.fairseq_tokens_to_ids.update(self.lang_code_to_id)
        self.fairseq_ids_to_tokens = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
        _additional_special_tokens = list(self.lang_code_to_id.keys())

        if additional_special_tokens is not None:
            
            _additional_special_tokens.extend(
                [t for t in additional_special_tokens if t not in _additional_special_tokens]
            )

        super().__init__(
            bos_token=bos_token,
            eos_token=eos_token,
            unk_token=unk_token,
            sep_token=sep_token,
            cls_token=cls_token,
            pad_token=pad_token,
            mask_token=mask_token,
            tokenizer_file=None,
            src_lang=src_lang,
            tgt_lang=tgt_lang,
            additional_special_tokens=_additional_special_tokens,
            sp_model_kwargs=self.sp_model_kwargs,
            **kwargs,
        )

        self._src_lang = src_lang if src_lang is not None else "en_XX"
        self.cur_lang_code_id = self.lang_code_to_id[self._src_lang]
        self.tgt_lang = tgt_lang
        self.set_src_lang_special_tokens(self._src_lang)

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

    def __setstate__(self, d):
        self.__dict__ = d

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

        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
        self.sp_model.LoadFromSerializedProto(self.sp_model_proto)

    @property
    def vocab_size(self):
        return len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset + 1  

    @property
    def src_lang(self) -> str:
        return self._src_lang

    @src_lang.setter
    def src_lang(self, new_src_lang: str) -> None:
        self._src_lang = new_src_lang
        self.set_src_lang_special_tokens(self._src_lang)

    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
            )

        prefix_ones = [1] * len(self.prefix_tokens)
        suffix_ones = [1] * len(self.suffix_tokens)
        if token_ids_1 is None:
            return prefix_ones + ([0] * len(token_ids_0)) + suffix_ones
        return prefix_ones + ([0] * len(token_ids_0)) + ([0] * len(token_ids_1)) + suffix_ones

    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 self.prefix_tokens + token_ids_0 + self.suffix_tokens
        
        return self.prefix_tokens + token_ids_0 + token_ids_1 + self.suffix_tokens

    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 = [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 + sep + token_ids_1 + sep) * [0]

    def _build_translation_inputs(
        self, raw_inputs, return_tensors: str, src_lang: Optional[str], tgt_lang: Optional[str], **extra_kwargs
    ):
        
        if src_lang is None or tgt_lang is None:
            raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model")
        self.src_lang = src_lang
        inputs = self(raw_inputs, add_special_tokens=True, return_tensors=return_tensors, **extra_kwargs)
        tgt_lang_id = self.convert_tokens_to_ids(tgt_lang)
        inputs["forced_bos_token_id"] = tgt_lang_id
        return inputs

    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) -> List[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 prepare_seq2seq_batch(
        self,
        src_texts: List[str],
        src_lang: str = "en_XX",
        tgt_texts: Optional[List[str]] = None,
        tgt_lang: str = "ro_RO",
        **kwargs,
    ) -> BatchEncoding:
        self.src_lang = src_lang
        self.tgt_lang = tgt_lang
        return super().prepare_seq2seq_batch(src_texts, tgt_texts, **kwargs)

    def _switch_to_input_mode(self):
        return self.set_src_lang_special_tokens(self.src_lang)

    def _switch_to_target_mode(self):
        return self.set_tgt_lang_special_tokens(self.tgt_lang)

    def set_src_lang_special_tokens(self, src_lang) -> None:
        
        self.cur_lang_code = self.lang_code_to_id[src_lang]
        self.prefix_tokens = []
        self.suffix_tokens = [self.eos_token_id, self.cur_lang_code]

    def set_tgt_lang_special_tokens(self, lang: str) -> None:
        
        self.cur_lang_code = self.lang_code_to_id[lang]
        self.prefix_tokens = []
        self.suffix_tokens = [self.eos_token_id, self.cur_lang_code]