codingutf8 2021 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 a mbart50 tokenizer based on 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 path to the vocabulary file srclang str optional a string representing the source language tgtlang str optional a string representing the target language eostoken str optional defaults to s the end of sequence token septoken str optional defaults to s 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 clstoken str optional defaults to s 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 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 padtoken str optional defaults to pad the token used for padding for example when batching sequences of different lengths 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 spmodelkwargs dict optional will be passed to the sentencepieceprocessor init method the python wrapper for sentencepiecehttps github comgooglesentencepiecetreemasterpython can be used among other things to set enablesampling enable subword regularization nbestsize sampling parameters for unigram invalid for bpedropout nbestsize 0 1 no sampling is performed nbestsize 1 samples from the nbestsize results nbestsize 0 assuming that nbestsize is infinite and samples from the all hypothesis lattice using forwardfilteringandbackwardsampling algorithm alpha smoothing parameter for unigram sampling and dropout probability of merge operations for bpedropout examples python from transformers import mbart50tokenizer tokenizer mbart50tokenizer frompretrainedfacebookmbartlarge50 srclangenxx tgtlangroro srctext un chief says there is no military solution in syria tgttext eful onu declar c nu exist o soluie militar n siria modelinputs tokenizersrctext texttargettgttext returntensorspt modelmodelinputs should work vocabfilesnames vocabfilesnames maxmodelinputsizes pretrainedpositionalembeddingssizes pretrainedvocabfilesmap pretrainedvocabfilesmap modelinputnames inputids attentionmask prefixtokens listint suffixtokens listint def init self vocabfile srclangnone tgtlangnone eostokens septokens clstokens unktokenunk padtokenpad masktokenmask spmodelkwargs optionaldictstr any none kwargs none mask token behave like a normal word i e include the space before it masktoken addedtokenmasktoken lstriptrue rstripfalse if isinstancemasktoken str else masktoken self spmodelkwargs if spmodelkwargs is none else spmodelkwargs kwargsadditionalspecialtokens kwargs getadditionalspecialtokens or kwargsadditionalspecialtokens code for code in fairseqlanguagecodes if code not in kwargsadditionalspecialtokens 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 super init srclangsrclang tgtlangtgtlang eostokeneostoken unktokenunktoken septokenseptoken clstokenclstoken padtokenpadtoken masktokenmasktoken spmodelkwargsself spmodelkwargs kwargs self srclang srclang if srclang is not none else enxx self curlangcodeid self langcodetoidself srclang self tgtlang tgtlang self setsrclangspecialtokensself srclang property def vocabsizeself int 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 getstateself dict state self dict copy statespmodel none return state def setstateself d dict none self dict d for backward compatibility if not hasattrself spmodelkwargs self spmodelkwargs self spmodel spm sentencepieceprocessorself spmodelkwargs self spmodel loadself vocabfile def getvocabself dict vocab self convertidstotokensi i for i in rangeself vocabsize vocab updateself addedtokensencoder return vocab def tokenizeself text str liststr return self spmodel encodetext outtypestr def converttokentoidself token str int 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 make sure that special tokens are not decoded using sentencepiece model retrieve sequence ids from a token list that has no special tokens added this method is called when adding special tokens using the tokenizer prepareformodel method args tokenids0 listint list of ids tokenids1 listint optional optional second list of ids for sequence pairs alreadyhasspecialtokens bool optional defaults to false whether or not the token list is already formatted with special tokens for the model returns listint 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 mbart50 sequence has the following format where x represents the sequence inputids for encoder srclangcode x eos labels for decoder tgtlangcode x eos bos is never used pairs of sequences are not the expected use case but they will be handled without a separator 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 we don t expect to process pairs but leave the pair logic for api consistency used by translation pipeline to prepare inputs for the generate function if srclang is none or tgtlang is none raise valueerrortranslation requires a srclang and a tgtlang for this model self srclang srclang inputs selfrawinputs addspecialtokenstrue returntensorsreturntensors extrakwargs tgtlangid self converttokenstoidstgtlang inputsforcedbostokenid tgtlangid return inputs 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 str none reset the special tokens to the target language setting prefixtgtlangcode and suffixeos self curlangcodeid self langcodetoidtgtlang self prefixtokens self curlangcodeid self suffixtokens self eostokenid coding utf 8 2021 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 a mbart50 tokenizer 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 src_lang str optional a string representing the source language tgt_lang str optional a string representing the target language eos_token str optional defaults to s the end of sequence token sep_token str optional defaults to s 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 cls_token str optional defaults to s 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 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 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 examples python from transformers import mbart50tokenizer tokenizer mbart50tokenizer from_pretrained facebook mbart large 50 src_lang en_xx tgt_lang ro_ro src_text un chief says there is no military solution in syria tgt_text şeful onu declară că nu există o soluţie militară în siria model_inputs tokenizer src_text text_target tgt_text return_tensors pt model model_inputs should work 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 plus 1 for the mask token for backward compatibility 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 string in a single string make sure that special tokens are not decoded using sentencepiece model 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 50 sequence has the following format where x represents the sequence input_ids for encoder src_lang_code x eos labels for decoder tgt_lang_code x eos 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 used by translation pipeline to prepare inputs for the generate function reset the special tokens to the source lang setting prefix src_lang_code and suffix eos reset the special tokens to the target language setting prefix tgt_lang_code and suffix eos
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-50-one-to-many-mmt": (
            "https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model"
        ),
    }
}

PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
    "facebook/mbart-large-50-one-to-many-mmt": 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", "af_ZA", "az_AZ", "bn_IN", "fa_IR", "he_IL", "hr_HR", "id_ID", "ka_GE", "km_KH", "mk_MK", "ml_IN", "mn_MN", "mr_IN", "pl_PL", "ps_AF", "pt_XX", "sv_SE", "sw_KE", "ta_IN", "te_IN", "th_TH", "tl_XX", "uk_UA", "ur_PK", "xh_ZA", "gl_ES", "sl_SI"]  


class MBart50Tokenizer(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,
        src_lang=None,
        tgt_lang=None,
        eos_token="</s>",
        sep_token="</s>",
        cls_token="<s>",
        unk_token="<unk>",
        pad_token="<pad>",
        mask_token="<mask>",
        sp_model_kwargs: Optional[Dict[str, Any]] = None,
        **kwargs,
    ) -> None:
        
        mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token

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

        kwargs["additional_special_tokens"] = kwargs.get("additional_special_tokens", []) or []
        kwargs["additional_special_tokens"] += [
            code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
        ]

        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()}

        super().__init__(
            src_lang=src_lang,
            tgt_lang=tgt_lang,
            eos_token=eos_token,
            unk_token=unk_token,
            sep_token=sep_token,
            cls_token=cls_token,
            pad_token=pad_token,
            mask_token=mask_token,
            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)

    @property
    def vocab_size(self) -> int:
        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 __getstate__(self) -> Dict:
        state = self.__dict__.copy()
        state["sp_model"] = None
        return state

    def __setstate__(self, d: Dict) -> None:
        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.Load(self.vocab_file)

    def get_vocab(self) -> Dict:
        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: str) -> int:
        
        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: int) -> str:
        
        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):
        
        current_sub_tokens = []
        out_string = ""
        prev_is_special = False
        for token in tokens:
            
            if token in self.all_special_tokens:
                if not prev_is_special:
                    out_string += " "
                out_string += self.sp_model.decode(current_sub_tokens) + token
                prev_is_special = True
                current_sub_tokens = []
            else:
                current_sub_tokens.append(token)
                prev_is_special = False
        out_string += self.sp_model.decode(current_sub_tokens)
        return out_string.strip()

    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 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 _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 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: str) -> None:
        
        self.cur_lang_code_id = self.lang_code_to_id[src_lang]
        self.prefix_tokens = [self.cur_lang_code_id]
        self.suffix_tokens = [self.eos_token_id]

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