2021 the fairseq 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 for m2m100 import json import os from pathlib import path from shutil import copyfile from typing import any dict list optional tuple union import sentencepiece from tokenizationutils import batchencoding pretrainedtokenizer from utils import logging logger logging getloggername spieceunderline vocabfilesnames vocabfile vocab json spmfile sentencepiece bpe model tokenizerconfigfile tokenizerconfig json pretrainedvocabfilesmap vocabfile facebookm2m100418m https huggingface cofacebookm2m100418mresolvemainvocab json facebookm2m1001 2b https huggingface cofacebookm2m1001 2bresolvemainvocab json spmfile facebookm2m100418m https huggingface cofacebookm2m100418mresolvemainsentencepiece bpe model facebookm2m1001 2b https huggingface cofacebookm2m1001 2bresolvemainsentencepiece bpe model tokenizerconfigfile facebookm2m100418m https huggingface cofacebookm2m100418mresolvemaintokenizerconfig json facebookm2m1001 2b https huggingface cofacebookm2m1001 2bresolvemaintokenizerconfig json pretrainedpositionalembeddingssizes facebookm2m100418m 1024 fmt off fairseqlanguagecodes m2m100 af am ar ast az ba be bg bn br bs ca ceb cs cy da de el en es et fa ff fi fr fy ga gd gl gu ha he hi hr ht hu hy id ig ilo is it ja jv ka kk km kn ko lb lg ln lo lt lv mg mk ml mn mr ms my ne nl no ns oc or pa pl ps pt ro ru sd si sk sl so sq sr ss su sv sw ta th tl tn tr uk ur uz vi wo xh yi yo zh zu wmt21 en ha is ja cs ru zh de fmt on class m2m100tokenizerpretrainedtokenizer converts an index integer in a token str using the decoder if index in self idtolangtoken return self idtolangtokenindex return self decoder getindex self unktoken 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 mbart sequence has the following format where x represents the sequence inputids for encoder x eos srclangcode decoderinputids for decoder x eos tgtlangcode 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 for backward compatibility 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 extrakwargs tgtlangid self getlangidtgtlang inputsforcedbostokenid tgtlangid return inputs def switchtoinputmodeself self setsrclangspecialtokensself srclang def switchtotargetmodeself self settgtlangspecialtokensself tgtlang def setsrclangspecialtokensself srclang str none reset the special tokens to the target language setting no prefix and suffixeos tgtlangcode langtoken self getlangtokentgtlang self curlangid self langtokentoidlangtoken self prefixtokens self curlangid self suffixtokens self eostokenid def getlangtokenself lang str str return self langcodetotokenlang def getlangidself lang str int langtoken self getlangtokenlang return self langtokentoidlangtoken def loadspmpath str spmodelkwargs dictstr any sentencepiece sentencepieceprocessor spm sentencepiece sentencepieceprocessorspmodelkwargs spm loadstrpath return spm def loadjsonpath str uniondict list with openpath r as f return json loadf def savejsondata path str none with openpath w as f json dumpdata f indent2 2021 the fairseq 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 for m2m100 fmt off fmt on construct an m2m100 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 spm_file str path to sentencepiece https github com google sentencepiece file generally has a spm extension that contains the vocabulary 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 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 language_codes str optional defaults to m2m100 what language codes to use should be one of m2m100 or wmt21 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 m2m100forconditionalgeneration m2m100tokenizer model m2m100forconditionalgeneration from_pretrained facebook m2m100_418m tokenizer m2m100tokenizer from_pretrained facebook m2m100_418m src_lang en tgt_lang 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 outputs model model_inputs should work converts an index integer in a token str using the decoder 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 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 for backward compatibility 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 json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


logger = logging.get_logger(__name__)

SPIECE_UNDERLINE = "▁"

VOCAB_FILES_NAMES = {
    "vocab_file": "vocab.json",
    "spm_file": "sentencepiece.bpe.model",
    "tokenizer_config_file": "tokenizer_config.json",
}

PRETRAINED_VOCAB_FILES_MAP = {
    "vocab_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json",
    },
    "spm_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_config_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json",
    },
}

PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
    "facebook/m2m100_418M": 1024,
}


FAIRSEQ_LANGUAGE_CODES = {
    "m2m100": ["af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "lb", "lg", "ln", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "ns", "oc", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "so", "sq", "sr", "ss", "su", "sv", "sw", "ta", "th", "tl", "tn", "tr", "uk", "ur", "uz", "vi", "wo", "xh", "yi", "yo", "zh", "zu"],
    "wmt21": ['en', 'ha', 'is', 'ja', 'cs', 'ru', 'zh', 'de']
}



class M2M100Tokenizer(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,
        spm_file,
        src_lang=None,
        tgt_lang=None,
        bos_token="<s>",
        eos_token="</s>",
        sep_token="</s>",
        pad_token="<pad>",
        unk_token="<unk>",
        language_codes="m2m100",
        sp_model_kwargs: Optional[Dict[str, Any]] = None,
        num_madeup_words=8,
        **kwargs,
    ) -> None:
        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs

        self.language_codes = language_codes
        fairseq_language_code = FAIRSEQ_LANGUAGE_CODES[language_codes]
        self.lang_code_to_token = {lang_code: f"__{lang_code}__" for lang_code in fairseq_language_code}

        additional_special_tokens = kwargs.pop("additional_special_tokens", [])
        for lang_code in fairseq_language_code:
            token = self.get_lang_token(lang_code)
            if token not in additional_special_tokens and lang_code not in str(token) not in self.added_tokens_encoder:
                additional_special_tokens.append(token)

        self.vocab_file = vocab_file
        self.encoder = load_json(vocab_file)
        self.decoder = {v: k for k, v in self.encoder.items()}
        self.spm_file = spm_file
        self.sp_model = load_spm(spm_file, self.sp_model_kwargs)

        self.encoder_size = len(self.encoder)

        self.lang_token_to_id = {
            self.get_lang_token(lang_code): self.encoder_size + i for i, lang_code in enumerate(fairseq_language_code)
        }
        self.lang_code_to_id = {lang_code: self.encoder_size + i for i, lang_code in enumerate(fairseq_language_code)}
        self.id_to_lang_token = {v: k for k, v in self.lang_token_to_id.items()}

        self._src_lang = src_lang if src_lang is not None else "en"
        self.tgt_lang = tgt_lang
        self.cur_lang_id = self.get_lang_id(self._src_lang)

        self.num_madeup_words = num_madeup_words

        super().__init__(
            src_lang=src_lang,
            tgt_lang=tgt_lang,
            bos_token=bos_token,
            eos_token=eos_token,
            sep_token=sep_token,
            unk_token=unk_token,
            pad_token=pad_token,
            language_codes=language_codes,
            sp_model_kwargs=self.sp_model_kwargs,
            additional_special_tokens=additional_special_tokens,
            num_madeup_words=num_madeup_words,
            **kwargs,
        )
        self.set_src_lang_special_tokens(self._src_lang)

    @property
    def vocab_size(self) -> int:
        return len(self.encoder)

    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

    @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 _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.lang_token_to_id:
            return self.lang_token_to_id[token]
        return self.encoder.get(token, self.encoder[self.unk_token])

    def _convert_id_to_token(self, index: int) -> str:
        
        if index in self.id_to_lang_token:
            return self.id_to_lang_token[index]
        return self.decoder.get(index, self.unk_token)

    def convert_tokens_to_string(self, tokens):
        
        current_sub_tokens = []
        out_string = ""
        for token in tokens:
            
            if token in self.all_special_tokens:
                out_string += self.sp_model.decode(current_sub_tokens) + token
                current_sub_tokens = []
            else:
                current_sub_tokens.append(token)
        out_string += self.sp_model.decode(current_sub_tokens)
        return out_string.strip()

    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 __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 = load_spm(self.spm_file, self.sp_model_kwargs)

    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
        save_dir = Path(save_directory)
        if not save_dir.is_dir():
            raise OSError(f"{save_directory} should be a directory")
        vocab_save_path = save_dir / (
            (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
        )
        spm_save_path = save_dir / (
            (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"]
        )

        save_json(self.encoder, vocab_save_path)

        if os.path.abspath(self.spm_file) != os.path.abspath(spm_save_path) and os.path.isfile(self.spm_file):
            copyfile(self.spm_file, spm_save_path)
        elif not os.path.isfile(self.spm_file):
            with open(spm_save_path, "wb") as fi:
                content_spiece_model = self.sp_model.serialized_model_proto()
                fi.write(content_spiece_model)

        return (str(vocab_save_path), str(spm_save_path))

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

    def _build_translation_inputs(self, raw_inputs, 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, **extra_kwargs)
        tgt_lang_id = self.get_lang_id(tgt_lang)
        inputs["forced_bos_token_id"] = tgt_lang_id
        return inputs

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

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

    def set_src_lang_special_tokens(self, src_lang: str) -> None:
        
        lang_token = self.get_lang_token(src_lang)
        self.cur_lang_id = self.lang_token_to_id[lang_token]
        self.prefix_tokens = [self.cur_lang_id]
        self.suffix_tokens = [self.eos_token_id]

    def set_tgt_lang_special_tokens(self, tgt_lang: str) -> None:
        
        lang_token = self.get_lang_token(tgt_lang)
        self.cur_lang_id = self.lang_token_to_id[lang_token]
        self.prefix_tokens = [self.cur_lang_id]
        self.suffix_tokens = [self.eos_token_id]

    def get_lang_token(self, lang: str) -> str:
        return self.lang_code_to_token[lang]

    def get_lang_id(self, lang: str) -> int:
        lang_token = self.get_lang_token(lang)
        return self.lang_token_to_id[lang_token]


def load_spm(path: str, sp_model_kwargs: Dict[str, Any]) -> sentencepiece.SentencePieceProcessor:
    spm = sentencepiece.SentencePieceProcessor(**sp_model_kwargs)
    spm.Load(str(path))
    return spm


def load_json(path: str) -> Union[Dict, List]:
    with open(path, "r") as f:
        return json.load(f)


def save_json(data, path: str) -> None:
    with open(path, "w") as f:
        json.dump(data, f, indent=2)