434 lines
21 KiB
Python
434 lines
21 KiB
Python
# coding=utf-8
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# Copyright 2022 The Facebook AI Research Team Authors and The HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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from shutil import copyfile
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from typing import Any, Dict, List, Optional, Tuple
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import sentencepiece as spm
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from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
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from ...utils import logging
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logger = logging.get_logger(__name__)
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SPIECE_UNDERLINE = "▁"
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VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model"}
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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
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class NllbTokenizer(PreTrainedTokenizer):
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"""
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Construct an NLLB tokenizer.
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Adapted from [`RobertaTokenizer`] and [`XLNetTokenizer`]. Based on
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[SentencePiece](https://github.com/google/sentencepiece).
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The tokenization method is `<tokens> <eos> <language code>` for source language documents, and `<language code>
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<tokens> <eos>` for target language documents.
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Examples:
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```python
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>>> from transformers import NllbTokenizer
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>>> tokenizer = NllbTokenizer.from_pretrained(
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... "facebook/nllb-200-distilled-600M", src_lang="eng_Latn", tgt_lang="fra_Latn"
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... )
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>>> example_english_phrase = " UN Chief Says There Is No Military Solution in Syria"
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>>> expected_translation_french = "Le chef de l'ONU affirme qu'il n'y a pas de solution militaire en Syrie."
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>>> inputs = tokenizer(example_english_phrase, text_target=expected_translation_french, return_tensors="pt")
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```
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Args:
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vocab_file (`str`):
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Path to the vocabulary file.
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bos_token (`str`, *optional*, defaults to `"<s>"`):
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The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
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<Tip>
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When building a sequence using special tokens, this is not the token that is used for the beginning of
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sequence. The token used is the `cls_token`.
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</Tip>
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eos_token (`str`, *optional*, defaults to `"</s>"`):
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The end of sequence token.
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<Tip>
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When building a sequence using special tokens, this is not the token that is used for the end of sequence.
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The token used is the `sep_token`.
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</Tip>
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sep_token (`str`, *optional*, defaults to `"</s>"`):
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The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
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sequence classification or for a text and a question for question answering. It is also used as the last
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token of a sequence built with special tokens.
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cls_token (`str`, *optional*, defaults to `"<s>"`):
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The classifier token which is used when doing sequence classification (classification of the whole sequence
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instead of per-token classification). It is the first token of the sequence when built with special tokens.
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unk_token (`str`, *optional*, defaults to `"<unk>"`):
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The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
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token instead.
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pad_token (`str`, *optional*, defaults to `"<pad>"`):
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The token used for padding, for example when batching sequences of different lengths.
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mask_token (`str`, *optional*, defaults to `"<mask>"`):
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The token used for masking values. This is the token used when training this model with masked language
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modeling. This is the token which the model will try to predict.
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tokenizer_file (`str`, *optional*):
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The path to a tokenizer file to use instead of the vocab file.
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src_lang (`str`, *optional*):
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The language to use as source language for translation.
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tgt_lang (`str`, *optional*):
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The language to use as target language for translation.
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sp_model_kwargs (`Dict[str, str]`):
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Additional keyword arguments to pass to the model initialization.
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"""
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vocab_files_names = VOCAB_FILES_NAMES
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model_input_names = ["input_ids", "attention_mask"]
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prefix_tokens: List[int] = []
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suffix_tokens: List[int] = []
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def __init__(
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self,
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vocab_file,
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bos_token="<s>",
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eos_token="</s>",
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sep_token="</s>",
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cls_token="<s>",
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unk_token="<unk>",
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pad_token="<pad>",
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mask_token="<mask>",
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tokenizer_file=None,
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src_lang=None,
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tgt_lang=None,
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sp_model_kwargs: Optional[Dict[str, Any]] = None,
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additional_special_tokens=None,
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legacy_behaviour=False,
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**kwargs,
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):
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if additional_special_tokens is None:
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additional_special_tokens = FAIRSEQ_LANGUAGE_CODES
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bos_token = AddedToken(bos_token, normalized=False, special=True) if isinstance(bos_token, str) else bos_token
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pad_token = AddedToken(pad_token, normalized=False, special=True) if isinstance(pad_token, str) else pad_token
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eos_token = AddedToken(eos_token, normalized=False, special=True) if isinstance(eos_token, str) else eos_token
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unk_token = AddedToken(unk_token, normalized=False, special=True) if isinstance(unk_token, str) else unk_token
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# Mask token behave like a normal word, i.e. include the space before it
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mask_token = (
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AddedToken(mask_token, normalized=True, lstrip=True, special=True)
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if isinstance(mask_token, str)
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else mask_token
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)
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self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
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self.legacy_behaviour = legacy_behaviour
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self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
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self.sp_model.Load(str(vocab_file))
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self.vocab_file = vocab_file
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# Original fairseq vocab and spm vocab must be "aligned":
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# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
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# -------- | ------- | ------- | ------ | ------- | ---- | ---- | ---- | ---- | ---- | ----
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# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
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# spm | '<unk>' | '<s>' | '</s>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'
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# unk token needs to be in the vocab with correct index
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self._added_tokens_decoder = {0: bos_token, 1: pad_token, 2: eos_token, 3: unk_token}
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# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
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self.fairseq_offset = 1
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self.sp_model_size = len(self.sp_model)
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# Everything that follows is kept for BC and will be removed in v4.38
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self._fairseq_tokens_to_ids = {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}
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language_codes = FAIRSEQ_LANGUAGE_CODES if additional_special_tokens is None else additional_special_tokens
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self._lang_code_to_id = {
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code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(language_codes)
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}
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self._id_to_lang_code = {v: k for k, v in self._lang_code_to_id.items()}
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self._fairseq_tokens_to_ids["<mask>"] = len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset
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self._fairseq_tokens_to_ids.update(self.lang_code_to_id)
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self._fairseq_ids_to_tokens = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
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super().__init__(
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bos_token=bos_token,
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eos_token=eos_token,
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unk_token=unk_token,
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sep_token=sep_token,
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cls_token=cls_token,
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pad_token=pad_token,
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mask_token=mask_token,
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tokenizer_file=tokenizer_file,
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src_lang=src_lang,
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tgt_lang=tgt_lang,
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additional_special_tokens=additional_special_tokens,
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sp_model_kwargs=self.sp_model_kwargs,
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legacy_behaviour=legacy_behaviour,
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**kwargs,
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)
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self._src_lang = src_lang if src_lang is not None else "eng_Latn"
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self.cur_lang_code_id = self.convert_tokens_to_ids(self._src_lang)
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self.tgt_lang = tgt_lang
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self.set_src_lang_special_tokens(self._src_lang)
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def __getstate__(self):
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state = self.__dict__.copy()
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state["sp_model"] = None
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state["sp_model_proto"] = self.sp_model.serialized_model_proto()
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return state
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def __setstate__(self, d):
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self.__dict__ = d
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# for backward compatibility
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if not hasattr(self, "sp_model_kwargs"):
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self.sp_model_kwargs = {}
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self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
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self.sp_model.LoadFromSerializedProto(self.sp_model_proto)
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@property
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def vocab_size(self):
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return len(self.sp_model) + self.fairseq_offset
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@property
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def src_lang(self) -> str:
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return self._src_lang
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@property
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def lang_code_to_id(self):
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logger.warning_once(
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"the `lang_code_to_id` attribute is deprecated. The logic is natively handled in the `tokenizer.adder_tokens_decoder`"
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" this attribute will be removed in `transformers` v4.38"
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)
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return self._lang_code_to_id
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@property
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def fairseq_tokens_to_ids(self):
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logger.warning_once(
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"the `fairseq_tokens_to_ids` attribute is deprecated. The logic is natively handled in the `tokenizer.adder_tokens_decoder`"
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" this attribute will be removed in `transformers` v4.38"
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)
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return self._fairseq_tokens_to_ids
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@property
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def id_to_lang_code(self):
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logger.warning_once(
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"the `id_to_lang_code` attribute is deprecated. The logic is natively handled in the `tokenizer.adder_tokens_decoder`"
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" this attribute will be removed in `transformers` v4.38"
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)
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return self._id_to_lang_code
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@property
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def fairseq_ids_to_tokens(self):
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logger.warning_once(
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"the `_fairseq_ids_to_tokens` attribute is deprecated. The logic is natively handled in the `tokenizer.adder_tokens_decoder`"
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" this attribute will be removed in `transformers` v4.38"
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)
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return self._fairseq_ids_to_tokens
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@src_lang.setter
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def src_lang(self, new_src_lang: str) -> None:
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self._src_lang = new_src_lang
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self.set_src_lang_special_tokens(self._src_lang)
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def get_special_tokens_mask(
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
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) -> List[int]:
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"""
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Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
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special tokens using the tokenizer `prepare_for_model` method.
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Args:
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token_ids_0 (`List[int]`):
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List of IDs.
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token_ids_1 (`List[int]`, *optional*):
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Optional second list of IDs for sequence pairs.
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already_has_special_tokens (`bool`, *optional*, defaults to `False`):
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Whether or not the token list is already formatted with special tokens for the model.
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Returns:
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`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
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"""
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if already_has_special_tokens:
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return super().get_special_tokens_mask(
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token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
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)
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prefix_ones = [1] * len(self.prefix_tokens)
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suffix_ones = [1] * len(self.suffix_tokens)
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if token_ids_1 is None:
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return prefix_ones + ([0] * len(token_ids_0)) + suffix_ones
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return prefix_ones + ([0] * len(token_ids_0)) + ([0] * len(token_ids_1)) + suffix_ones
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def build_inputs_with_special_tokens(
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
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) -> List[int]:
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"""
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Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
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adding special tokens. An NLLB sequence has the following format, where `X` represents the sequence:
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- `input_ids` (for encoder) `X [eos, src_lang_code]`
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- `decoder_input_ids`: (for decoder) `X [eos, tgt_lang_code]`
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BOS is never used. Pairs of sequences are not the expected use case, but they will be handled without a
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separator.
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Args:
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token_ids_0 (`List[int]`):
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List of IDs to which the special tokens will be added.
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token_ids_1 (`List[int]`, *optional*):
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Optional second list of IDs for sequence pairs.
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Returns:
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`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
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"""
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if token_ids_1 is None:
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return self.prefix_tokens + token_ids_0 + self.suffix_tokens
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# We don't expect to process pairs, but leave the pair logic for API consistency
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return self.prefix_tokens + token_ids_0 + token_ids_1 + self.suffix_tokens
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def create_token_type_ids_from_sequences(
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
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) -> List[int]:
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"""
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Create a mask from the two sequences passed to be used in a sequence-pair classification task. nllb does not
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make use of token type ids, therefore a list of zeros is returned.
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Args:
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token_ids_0 (`List[int]`):
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List of IDs.
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token_ids_1 (`List[int]`, *optional*):
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Optional second list of IDs for sequence pairs.
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Returns:
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`List[int]`: List of zeros.
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"""
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sep = [self.sep_token_id]
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cls = [self.cls_token_id]
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if token_ids_1 is None:
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return len(cls + token_ids_0 + sep) * [0]
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return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
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def _build_translation_inputs(
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self, raw_inputs, return_tensors: str, src_lang: Optional[str], tgt_lang: Optional[str], **extra_kwargs
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):
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"""Used by translation pipeline, to prepare inputs for the generate function"""
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if src_lang is None or tgt_lang is None:
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raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model")
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self.src_lang = src_lang
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inputs = self(raw_inputs, add_special_tokens=True, return_tensors=return_tensors, **extra_kwargs)
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tgt_lang_id = self.convert_tokens_to_ids(tgt_lang)
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inputs["forced_bos_token_id"] = tgt_lang_id
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return inputs
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def get_vocab(self):
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vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
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vocab.update(self.added_tokens_encoder)
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return vocab
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def _tokenize(self, text: str) -> List[str]:
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return self.sp_model.encode(text, out_type=str)
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def _convert_token_to_id(self, token):
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"""Converts a token (str) in an id using the vocab."""
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spm_id = self.sp_model.PieceToId(token)
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# Need to return unknown token if the SP model returned 0
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return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
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def _convert_id_to_token(self, index):
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"""Converts an index (integer) in a token (str) using the vocab."""
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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]
|