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tokenization_mbart.py
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tokenization_mbart.py
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# 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.
# 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.
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"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"facebook/mbart-large-en-ro": "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model",
"facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model",
}
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"facebook/mbart-large-en-ro": 1024,
"facebook/mbart-large-cc25": 1024,
}
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",
]
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, **kwargs):
super().__init__(*args, tokenizer_file=tokenizer_file, src_lang=src_lang, tgt_lang=tgt_lang, **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())
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:
if token_ids_1 is not None:
raise ValueError(
"You should not supply a second sequence if the provided sequence of "
"ids is already formatted with special tokens for the model."
)
return list(map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0))
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 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 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)
@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]

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