Source code for transformers.models.mbart.tokenization_mbart

# coding=utf-8
# Copyright 2020 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.
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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from contextlib import contextmanager
from typing import List, Optional

from ...tokenization_utils import BatchEncoding
from ...utils import logging
from ..xlm_roberta.tokenization_xlm_roberta import XLMRobertaTokenizer

logger = logging.get_logger(__name__)

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

    "vocab_file": {
        "facebook/mbart-large-en-ro": "",
        "facebook/mbart-large-cc25": "",

    "facebook/mbart-large-en-ro": 1024,
    "facebook/mbart-large-cc25": 1024,


[docs]class MBartTokenizer(XLMRobertaTokenizer): """ Construct an MBART tokenizer. :class:`~transformers.MBartTokenizer` is a subclass of :class:`~transformers.XLMRobertaTokenizer`. Refer to superclass :class:`~transformers.XLMRobertaTokenizer` for usage examples and documentation concerning the initialization parameters and other methods. The tokenization method is ``<tokens> <eos> <language code>`` for source language documents, and ``<language code> <tokens> <eos>``` for target language documents. Examples:: >>> from transformers import MBartTokenizer >>> tokenizer = MBartTokenizer.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, return_tensors="pt) >>> with tokenizer.as_target_tokenizer(): ... labels = tokenizer(expected_translation_romanian, return_tensors="pt") >>> inputs["labels"] = labels["input_ids"] """ vocab_files_names = VOCAB_FILES_NAMES max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP prefix_tokens: List[int] = [] suffix_tokens: List[int] = [] def __init__( self, *args, tokenizer_file=None, src_lang=None, tgt_lang=None, additional_special_tokens=None, **kwargs ): super().__init__( *args, tokenizer_file=tokenizer_file, src_lang=src_lang, tgt_lang=tgt_lang, additional_special_tokens=additional_special_tokens, **kwargs, ) 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._additional_special_tokens = list(self.lang_code_to_id.keys()) if additional_special_tokens is not None: # Only add those special tokens if they are not already there. self._additional_special_tokens.extend( [t for t in additional_special_tokens if t not in self._additional_special_tokens] ) 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): 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 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 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 (: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 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)
def set_src_lang_special_tokens(self, src_lang) -> None: """Reset the special tokens to the source lang setting. No prefix and suffix=[eos, src_lang_code].""" self.cur_lang_code = self.lang_code_to_id[src_lang] self.prefix_tokens = [] self.suffix_tokens = [self.eos_token_id, self.cur_lang_code] def set_tgt_lang_special_tokens(self, lang: str) -> None: """Reset the special tokens to the target language setting. No prefix and suffix=[eos, tgt_lang_code].""" self.cur_lang_code = self.lang_code_to_id[lang] self.prefix_tokens = [] self.suffix_tokens = [self.eos_token_id, self.cur_lang_code]