# coding=utf-8 # Copyright 2022 The Facebook AI Research Team Authors 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. 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"} FAIRSEQ_LANGUAGE_CODES = ['ace_Arab', 'ace_Latn', 'acm_Arab', 'acq_Arab', 'aeb_Arab', 'afr_Latn', 'ajp_Arab', 'aka_Latn', 'amh_Ethi', 'apc_Arab', 'arb_Arab', 'ars_Arab', 'ary_Arab', 'arz_Arab', 'asm_Beng', 'ast_Latn', 'awa_Deva', 'ayr_Latn', 'azb_Arab', 'azj_Latn', 'bak_Cyrl', 'bam_Latn', 'ban_Latn', 'bel_Cyrl', 'bem_Latn', 'ben_Beng', 'bho_Deva', 'bjn_Arab', 'bjn_Latn', 'bod_Tibt', 'bos_Latn', 'bug_Latn', 'bul_Cyrl', 'cat_Latn', 'ceb_Latn', 'ces_Latn', 'cjk_Latn', 'ckb_Arab', 'crh_Latn', 'cym_Latn', 'dan_Latn', 'deu_Latn', 'dik_Latn', 'dyu_Latn', 'dzo_Tibt', 'ell_Grek', 'eng_Latn', 'epo_Latn', 'est_Latn', 'eus_Latn', 'ewe_Latn', 'fao_Latn', 'pes_Arab', 'fij_Latn', 'fin_Latn', 'fon_Latn', 'fra_Latn', 'fur_Latn', 'fuv_Latn', 'gla_Latn', 'gle_Latn', 'glg_Latn', 'grn_Latn', 'guj_Gujr', 'hat_Latn', 'hau_Latn', 'heb_Hebr', 'hin_Deva', 'hne_Deva', 'hrv_Latn', 'hun_Latn', 'hye_Armn', 'ibo_Latn', 'ilo_Latn', 'ind_Latn', 'isl_Latn', 'ita_Latn', 'jav_Latn', 'jpn_Jpan', 'kab_Latn', 'kac_Latn', 'kam_Latn', 'kan_Knda', 'kas_Arab', 'kas_Deva', 'kat_Geor', 'knc_Arab', 'knc_Latn', 'kaz_Cyrl', 'kbp_Latn', 'kea_Latn', 'khm_Khmr', 'kik_Latn', 'kin_Latn', 'kir_Cyrl', 'kmb_Latn', 'kon_Latn', 'kor_Hang', 'kmr_Latn', 'lao_Laoo', 'lvs_Latn', 'lij_Latn', 'lim_Latn', 'lin_Latn', 'lit_Latn', 'lmo_Latn', 'ltg_Latn', 'ltz_Latn', 'lua_Latn', 'lug_Latn', 'luo_Latn', 'lus_Latn', 'mag_Deva', 'mai_Deva', 'mal_Mlym', 'mar_Deva', 'min_Latn', 'mkd_Cyrl', 'plt_Latn', 'mlt_Latn', 'mni_Beng', 'khk_Cyrl', 'mos_Latn', 'mri_Latn', 'zsm_Latn', 'mya_Mymr', 'nld_Latn', 'nno_Latn', 'nob_Latn', 'npi_Deva', 'nso_Latn', 'nus_Latn', 'nya_Latn', 'oci_Latn', 'gaz_Latn', 'ory_Orya', 'pag_Latn', 'pan_Guru', 'pap_Latn', 'pol_Latn', 'por_Latn', 'prs_Arab', 'pbt_Arab', 'quy_Latn', 'ron_Latn', 'run_Latn', 'rus_Cyrl', 'sag_Latn', 'san_Deva', 'sat_Beng', 'scn_Latn', 'shn_Mymr', 'sin_Sinh', 'slk_Latn', 'slv_Latn', 'smo_Latn', 'sna_Latn', 'snd_Arab', 'som_Latn', 'sot_Latn', 'spa_Latn', 'als_Latn', 'srd_Latn', 'srp_Cyrl', 'ssw_Latn', 'sun_Latn', 'swe_Latn', 'swh_Latn', 'szl_Latn', 'tam_Taml', 'tat_Cyrl', 'tel_Telu', 'tgk_Cyrl', 'tgl_Latn', 'tha_Thai', 'tir_Ethi', 'taq_Latn', 'taq_Tfng', 'tpi_Latn', 'tsn_Latn', 'tso_Latn', 'tuk_Latn', 'tum_Latn', 'tur_Latn', 'twi_Latn', 'tzm_Tfng', 'uig_Arab', 'ukr_Cyrl', 'umb_Latn', 'urd_Arab', 'uzn_Latn', 'vec_Latn', 'vie_Latn', 'war_Latn', 'wol_Latn', 'xho_Latn', 'ydd_Hebr', 'yor_Latn', 'yue_Hant', 'zho_Hans', 'zho_Hant', 'zul_Latn'] # fmt: skip class NllbTokenizer(PreTrainedTokenizer): """ Construct an NLLB tokenizer. Adapted from [`RobertaTokenizer`] and [`XLNetTokenizer`]. Based on [SentencePiece](https://github.com/google/sentencepiece). The tokenization method is ` ` for source language documents, and ` ` for target language documents. Examples: ```python >>> from transformers import NllbTokenizer >>> tokenizer = NllbTokenizer.from_pretrained( ... "facebook/nllb-200-distilled-600M", src_lang="eng_Latn", tgt_lang="fra_Latn" ... ) >>> example_english_phrase = " UN Chief Says There Is No Military Solution in Syria" >>> expected_translation_french = "Le chef de l'ONU affirme qu'il n'y a pas de solution militaire en Syrie." >>> inputs = tokenizer(example_english_phrase, text_target=expected_translation_french, return_tensors="pt") ``` Args: vocab_file (`str`): Path to the vocabulary file. bos_token (`str`, *optional*, defaults to `""`): The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. 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`. eos_token (`str`, *optional*, defaults to `""`): The end of sequence token. 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`. sep_token (`str`, *optional*, defaults to `""`): 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 `""`): 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 `""`): 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 `""`): The token used for padding, for example when batching sequences of different lengths. mask_token (`str`, *optional*, defaults to `""`): 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. tokenizer_file (`str`, *optional*): The path to a tokenizer file to use instead of the vocab file. src_lang (`str`, *optional*): The language to use as source language for translation. tgt_lang (`str`, *optional*): The language to use as target language for translation. sp_model_kwargs (`Dict[str, str]`): Additional keyword arguments to pass to the model initialization. """ vocab_files_names = VOCAB_FILES_NAMES model_input_names = ["input_ids", "attention_mask"] prefix_tokens: List[int] = [] suffix_tokens: List[int] = [] def __init__( self, vocab_file, bos_token="", eos_token="", sep_token="", cls_token="", unk_token="", pad_token="", mask_token="", tokenizer_file=None, src_lang=None, tgt_lang=None, sp_model_kwargs: Optional[Dict[str, Any]] = None, additional_special_tokens=None, legacy_behaviour=False, **kwargs, ): if additional_special_tokens is None: additional_special_tokens = FAIRSEQ_LANGUAGE_CODES bos_token = AddedToken(bos_token, normalized=False, special=True) if isinstance(bos_token, str) else bos_token pad_token = AddedToken(pad_token, normalized=False, special=True) if isinstance(pad_token, str) else pad_token eos_token = AddedToken(eos_token, normalized=False, special=True) if isinstance(eos_token, str) else eos_token unk_token = AddedToken(unk_token, normalized=False, special=True) if isinstance(unk_token, str) else unk_token # Mask token behave like a normal word, i.e. include the space before it mask_token = ( AddedToken(mask_token, normalized=True, lstrip=True, special=True) if isinstance(mask_token, str) else mask_token ) self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs self.legacy_behaviour = legacy_behaviour self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) self.sp_model.Load(str(vocab_file)) self.vocab_file = vocab_file # Original fairseq vocab and spm vocab must be "aligned": # Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 # -------- | ------- | ------- | ------ | ------- | ---- | ---- | ---- | ---- | ---- | ---- # fairseq | '' | '' | '' | '' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' # spm | '' | '' | '' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s' # unk token needs to be in the vocab with correct index self._added_tokens_decoder = {0: bos_token, 1: pad_token, 2: eos_token, 3: unk_token} # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab self.fairseq_offset = 1 self.sp_model_size = len(self.sp_model) # Everything that follows is kept for BC and will be removed in v4.38 self._fairseq_tokens_to_ids = {"": 0, "": 1, "": 2, "": 3} language_codes = FAIRSEQ_LANGUAGE_CODES if additional_special_tokens is None else additional_special_tokens self._lang_code_to_id = { code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(language_codes) } self._id_to_lang_code = {v: k for k, v in self._lang_code_to_id.items()} self._fairseq_tokens_to_ids[""] = 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__( 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=tokenizer_file, src_lang=src_lang, tgt_lang=tgt_lang, additional_special_tokens=additional_special_tokens, sp_model_kwargs=self.sp_model_kwargs, legacy_behaviour=legacy_behaviour, **kwargs, ) self._src_lang = src_lang if src_lang is not None else "eng_Latn" self.cur_lang_code_id = self.convert_tokens_to_ids(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 # for backward compatibility 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) + self.fairseq_offset @property def src_lang(self) -> str: return self._src_lang @property def lang_code_to_id(self): logger.warning_once( "the `lang_code_to_id` attribute is deprecated. The logic is natively handled in the `tokenizer.adder_tokens_decoder`" " this attribute will be removed in `transformers` v4.38" ) return self._lang_code_to_id @property def fairseq_tokens_to_ids(self): logger.warning_once( "the `fairseq_tokens_to_ids` attribute is deprecated. The logic is natively handled in the `tokenizer.adder_tokens_decoder`" " this attribute will be removed in `transformers` v4.38" ) return self._fairseq_tokens_to_ids @property def id_to_lang_code(self): logger.warning_once( "the `id_to_lang_code` attribute is deprecated. The logic is natively handled in the `tokenizer.adder_tokens_decoder`" " this attribute will be removed in `transformers` v4.38" ) return self._id_to_lang_code @property def fairseq_ids_to_tokens(self): logger.warning_once( "the `_fairseq_ids_to_tokens` attribute is deprecated. The logic is natively handled in the `tokenizer.adder_tokens_decoder`" " this attribute will be removed in `transformers` v4.38" ) return self._fairseq_ids_to_tokens @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]: """ 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. """ 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]: """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. An NLLB 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. """ if token_ids_1 is None: return self.prefix_tokens + token_ids_0 + self.suffix_tokens # We don't expect to process pairs, but leave the pair logic for API consistency 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]: """ Create a mask from the two sequences passed to be used in a sequence-pair classification task. nllb 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. """ 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 ): """Used by translation pipeline, to prepare inputs for the generate function""" 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): """Converts a token (str) in an id using the vocab.""" spm_id = self.sp_model.PieceToId(token) # Need to return unknown token if the SP model returned 0 return spm_id + self.fairseq_offset if spm_id else self.unk_token_id def _convert_id_to_token(self, index): """Converts an index (integer) in a token (str) using the vocab.""" return self.sp_model.IdToPiece(index - self.fairseq_offset) def convert_tokens_to_string(self, tokens): """Converts a sequence of tokens (strings for sub-words) in a single string.""" 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 = "eng_Latn", tgt_texts: Optional[List[str]] = None, tgt_lang: str = "fra_Latn", **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: """Reset the special tokens to the source lang setting. - In legacy mode: No prefix and suffix=[eos, src_lang_code]. - In default mode: Prefix=[src_lang_code], suffix = [eos] """ self.cur_lang_code = self.convert_tokens_to_ids(src_lang) if self.legacy_behaviour: self.prefix_tokens = [] self.suffix_tokens = [self.eos_token_id, self.cur_lang_code] else: self.prefix_tokens = [self.cur_lang_code] self.suffix_tokens = [self.eos_token_id] def set_tgt_lang_special_tokens(self, lang: str) -> None: """Reset the special tokens to the target lang setting. - In legacy mode: No prefix and suffix=[eos, tgt_lang_code]. - In default mode: Prefix=[tgt_lang_code], suffix = [eos] """ self.cur_lang_code = self.convert_tokens_to_ids(lang) if self.legacy_behaviour: self.prefix_tokens = [] self.suffix_tokens = [self.eos_token_id, self.cur_lang_code] else: self.prefix_tokens = [self.cur_lang_code] self.suffix_tokens = [self.eos_token_id]