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 a fast mbart tokenizer backed by huggingface s tokenizers library based on bpehttps huggingface codocstokenizerspythonlatestcomponents html highlightbpemodels this tokenizer inherits from pretrainedtokenizerfast which contains most of the main methods users should refer to this superclass for more information regarding those methods 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 mbarttokenizerfast tokenizer mbarttokenizerfast frompretrained facebookmbartlargeenro 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 slowtokenizerclass mbarttokenizer prefixtokens listint suffixtokens listint def init self vocabfilenone tokenizerfilenone bostokens eostokens septokens clstokens unktokenunk padtokenpad masktokenmask srclangnone tgtlangnone additionalspecialtokensnone kwargs mask token behave like a normal word i e include the space before it masktoken addedtokenmasktoken lstriptrue rstripfalse if isinstancemasktoken str else masktoken additionalspecialtokens fairseqlanguagecodes copy 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 vocabfilevocabfile tokenizerfiletokenizerfile bostokenbostoken eostokeneostoken septokenseptoken clstokenclstoken unktokenunktoken padtokenpadtoken masktokenmasktoken srclangsrclang tgtlangtgtlang additionalspecialtokensadditionalspecialtokens kwargs self vocabfile vocabfile self langcodetoid langcode self converttokenstoidslangcode for langcode in fairseqlanguagecodes self srclang srclang if srclang is not none else enxx self curlangcode self converttokenstoidsself srclang self tgtlang tgtlang self setsrclangspecialtokensself srclang property def cansaveslowtokenizerself bool return os path isfileself vocabfile if self vocabfile else false property def srclangself str return self srclang srclang setter def srclangself newsrclang str none self srclang newsrclang self setsrclangspecialtokensself srclang 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 reset the special tokens to the source lang setting no prefix and suffixeos srclangcode self curlangcode self converttokenstoidssrclang self prefixtokens self suffixtokens self eostokenid self curlangcode prefixtokensstr self convertidstotokensself prefixtokens suffixtokensstr self convertidstotokensself suffixtokens self tokenizer postprocessor processors templateprocessing singleprefixtokensstr a suffixtokensstr pairprefixtokensstr a b suffixtokensstr specialtokenslistzipprefixtokensstr suffixtokensstr self prefixtokens self suffixtokens def settgtlangspecialtokensself lang str none 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 a fast mbart tokenizer backed by huggingface s tokenizers library based on bpe https huggingface co docs tokenizers python latest components html highlight bpe models this tokenizer inherits from pretrainedtokenizerfast which contains most of the main methods users should refer to this superclass for more information regarding those methods 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 mbarttokenizerfast tokenizer mbarttokenizerfast 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 only add those special tokens if they are not already there build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens the special tokens depend on calling set_lang 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 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 List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
    from .tokenization_mbart import MBartTokenizer
else:
    MBartTokenizer = None


logger = logging.get_logger(__name__)


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

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"
        ),
    },
    "tokenizer_file": {
        "facebook/mbart-large-en-ro": "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json",
        "facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json",
    },
}

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 MBartTokenizerFast(PreTrainedTokenizerFast):
    

    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"]
    slow_tokenizer_class = MBartTokenizer

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

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

        _additional_special_tokens = FAIRSEQ_LANGUAGE_CODES.copy()

        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__(
            vocab_file=vocab_file,
            tokenizer_file=tokenizer_file,
            bos_token=bos_token,
            eos_token=eos_token,
            sep_token=sep_token,
            cls_token=cls_token,
            unk_token=unk_token,
            pad_token=pad_token,
            mask_token=mask_token,
            src_lang=src_lang,
            tgt_lang=tgt_lang,
            additional_special_tokens=_additional_special_tokens,
            **kwargs,
        )

        self.vocab_file = vocab_file
        self.lang_code_to_id = {
            lang_code: self.convert_tokens_to_ids(lang_code) for lang_code in FAIRSEQ_LANGUAGE_CODES
        }

        self._src_lang = src_lang if src_lang is not None else "en_XX"
        self.cur_lang_code = self.convert_tokens_to_ids(self._src_lang)
        self.tgt_lang = tgt_lang
        self.set_src_lang_special_tokens(self._src_lang)

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

    @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 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 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.convert_tokens_to_ids(src_lang)
        self.prefix_tokens = []
        self.suffix_tokens = [self.eos_token_id, self.cur_lang_code]

        prefix_tokens_str = self.convert_ids_to_tokens(self.prefix_tokens)
        suffix_tokens_str = self.convert_ids_to_tokens(self.suffix_tokens)

        self._tokenizer.post_processor = processors.TemplateProcessing(
            single=prefix_tokens_str + ["$A"] + suffix_tokens_str,
            pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str,
            special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str, self.prefix_tokens + self.suffix_tokens)),
        )

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

        prefix_tokens_str = self.convert_ids_to_tokens(self.prefix_tokens)
        suffix_tokens_str = self.convert_ids_to_tokens(self.suffix_tokens)

        self._tokenizer.post_processor = processors.TemplateProcessing(
            single=prefix_tokens_str + ["$A"] + suffix_tokens_str,
            pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str,
            special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str, self.prefix_tokens + self.suffix_tokens)),
        )

    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,)