# 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.
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# limitations under the License.
from typing import List, Optional
from .file_utils import add_start_docstrings
from .tokenization_utils import BatchEncoding
from .tokenization_utils_base import PREPARE_SEQ2SEQ_BATCH_DOCSTRING
from .tokenization_xlm_roberta import XLMRobertaTokenizer
from .utils import logging
logger = logging.get_logger(__name__)
_all_mbart_models = ["facebook/mbart-large-en-ro", "facebook/mbart-large-cc25"]
SPM_URL = "https://s3.amazonaws.com/models.huggingface.co/bert/facebook/mbart-large-en-ro/sentence.bpe.model"
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",
]
[docs]class MBartTokenizer(XLMRobertaTokenizer):
"""
This inherits from XLMRobertaTokenizer. ``prepare_seq2seq_batch`` should be used to encode inputs.
Other tokenizer methods like ``encode`` do not work properly.
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')
>>> 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"
>>> batch: dict = tokenizer.prepare_seq2seq_batch(
... example_english_phrase, src_lang="en_XX", tgt_lang="ro_RO", tgt_texts=expected_translation_romanian
... )
"""
vocab_files_names = {"vocab_file": "sentencepiece.bpe.model"}
max_model_input_sizes = {m: 1024 for m in _all_mbart_models}
pretrained_vocab_files_map = {"vocab_file": {m: SPM_URL for m in _all_mbart_models}}
prefix_tokens: List[int] = []
suffix_tokens: List[int] = []
def __init__(self, *args, **kwargs):
super().__init__(*args, **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.cur_lang_code = self.lang_code_to_id["en_XX"]
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.set_src_lang_special_tokens(kwargs.get("src_lang", "en_XX"))
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]:
"""
Retrieves 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`` methods.
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`):
Set to True if 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 formated 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
[docs] @add_start_docstrings(PREPARE_SEQ2SEQ_BATCH_DOCSTRING)
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",
max_length: Optional[int] = None,
max_target_length: Optional[int] = None,
truncation: bool = True,
padding: str = "longest",
return_tensors: str = "pt",
add_prefix_space: bool = False, # ignored
**kwargs,
) -> BatchEncoding:
if max_length is None:
max_length = self.max_len
self.set_src_lang_special_tokens(src_lang)
model_inputs: BatchEncoding = self(
src_texts,
add_special_tokens=True,
return_tensors=return_tensors,
max_length=max_length,
padding=padding,
truncation=truncation,
**kwargs,
)
if tgt_texts is None:
return model_inputs
# Process tgt_texts
if max_target_length is None:
max_target_length = max_length
self.set_tgt_lang_special_tokens(tgt_lang)
labels = self(
tgt_texts,
add_special_tokens=True,
return_tensors=return_tensors,
padding=padding,
max_length=max_target_length,
truncation=True,
**kwargs,
)["input_ids"]
model_inputs["labels"] = labels
self.set_src_lang_special_tokens(src_lang) # sets to src_lang
return model_inputs
def set_src_lang_special_tokens(self, src_lang) -> None:
"""Reset the special tokens to the source lang setting. No prefix and suffix=[eos, cur_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. Prefix [tgt_lang_code], suffix =[eos]."""
self.cur_lang_code = self.lang_code_to_id[lang]
self.prefix_tokens = []
self.suffix_tokens = [self.eos_token_id, self.cur_lang_code]