Source code for transformers.models.mbart50.tokenization_mbart50

# coding=utf-8
# Copyright 2021 The Facebook AI Research Team Authors and The HuggingFace Inc. team.
# Licensed under the Apache License, Version 2.0 (the "License");
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import os
from contextlib import contextmanager
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__)


VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model"}

    "vocab_file": {
        "facebook/mbart-large-50-one-to-many-mmt": "",

    "facebook/mbart-large-50-one-to-many-mmt": 1024,

# fmt: off
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"]
# fmt: on

[docs]class MBart50Tokenizer(PreTrainedTokenizer): """ Construct a MBart50 tokenizer. Based on `SentencePiece <>`__. This tokenizer inherits from :class:`~transformers.PreTrainedTokenizer` which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: vocab_file (:obj:`str`): Path to the vocabulary file. src_lang (:obj:`str`, `optional`): A string representing the source language. tgt_lang (:obj:`str`, `optional`): A string representing the target language. eos_token (:obj:`str`, `optional`, defaults to :obj:`"</s>"`): The end of sequence token. sep_token (:obj:`str`, `optional`, defaults to :obj:`"</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 (:obj:`str`, `optional`, defaults to :obj:`"<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 (:obj:`str`, `optional`, defaults to :obj:`"<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 (:obj:`str`, `optional`, defaults to :obj:`"<pad>"`): The token used for padding, for example when batching sequences of different lengths. mask_token (:obj:`str`, `optional`, defaults to :obj:`"<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 (:obj:`dict`, `optional`): Will be passed to the ``SentencePieceProcessor.__init__()`` method. The `Python wrapper for SentencePiece <>`__ 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:: >>> 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, return_tensors="pt") >>> with tokenizer.as_target_tokenizer(): ... labels = tokenizer(tgt_text, return_tensors="pt").input_ids >>> # model(**model_inputs, labels=labels) should work """ 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 behave like a normal word, i.e. include the space before it 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", []) kwargs["additional_special_tokens"] += [ code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"] ] 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.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 | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-' # spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a' # Mimic fairseq token-to-id alignment for the first 4 token self.fairseq_tokens_to_ids = {"<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.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()} 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 # Plus 1 for the mask token @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 # 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.Load(self.vocab_file)
[docs] 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: """Converts a token (str) in an id using the vocab.""" if token in self.fairseq_tokens_to_ids: return self.fairseq_tokens_to_ids[token] 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: int) -> str: """Converts an index (integer) in a token (str) using the vocab.""" if index in self.fairseq_ids_to_tokens: return self.fairseq_ids_to_tokens[index] return self.sp_model.IdToPiece(index - self.fairseq_offset)
[docs] def convert_tokens_to_string(self, tokens: List[str]) -> str: """Converts a sequence of tokens (strings for sub-words) in a single string.""" return self.sp_model.decode(tokens)
[docs] 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): copyfile(self.vocab_file, out_vocab_file) return (out_vocab_file,)
[docs] 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 (:obj:`List[int]`): List of IDs. token_ids_1 (:obj:`List[int]`, `optional`): Optional second list of IDs for sequence pairs. already_has_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not the token list is already formatted with special tokens for the model. Returns: :obj:`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
[docs] 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 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 (:obj:`List[int]`): List of IDs to which the special tokens will be added. token_ids_1 (:obj:`List[int]`, `optional`): Optional second list of IDs for sequence pairs. Returns: :obj:`List[int]`: List of `input IDs <../glossary.html#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 _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
[docs] 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)
[docs] @contextmanager def as_target_tokenizer(self): """ Temporarily sets the tokenizer for encoding the targets. Useful for tokenizer associated to sequence-to-sequence models that need a slightly different processing for the labels. """ self.set_tgt_lang_special_tokens(self.tgt_lang) yield self.set_src_lang_special_tokens(self.src_lang)
[docs] def set_src_lang_special_tokens(self, src_lang: str) -> None: """Reset the special tokens to the source lang setting. prefix=[src_lang_code] and suffix=[eos].""" 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]
[docs] def set_tgt_lang_special_tokens(self, tgt_lang: str) -> None: """Reset the special tokens to the target language setting. prefix=[tgt_lang_code] and suffix=[eos].""" 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]