# 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 typing import List, Optional
from transformers import add_start_docstrings
from ...tokenization_utils_base import PREPARE_SEQ2SEQ_BATCH_DOCSTRING, BatchEncoding
from ...utils import logging
from ..roberta.tokenization_roberta import RobertaTokenizer
logger = logging.get_logger(__name__)
# vocab and merges same as roberta
vocab_url = "https://huggingface.co/roberta-large/resolve/main/vocab.json"
merges_url = "https://huggingface.co/roberta-large/resolve/main/merges.txt"
_all_bart_models = [
"facebook/bart-base",
"facebook/bart-large",
"facebook/bart-large-mnli",
"facebook/bart-large-cnn",
"facebook/bart-large-xsum",
"yjernite/bart_eli5",
# This is not exhaustive: see https://huggingface.co/models?filter=bart
]
[docs]class BartTokenizer(RobertaTokenizer):
r"""
Construct a BART tokenizer.
:class:`~transformers.BartTokenizer` is identical to :class:`~transformers.RobertaTokenizer` and adds a new
:meth:`~transformers.BartTokenizer.prepare_seq2seq_batch`
Refer to superclass :class:`~transformers.RobertaTokenizer` for usage examples and documentation concerning the
initialization parameters and other methods.
"""
# merges and vocab same as Roberta
max_model_input_sizes = {m: 1024 for m in _all_bart_models}
pretrained_vocab_files_map = {
"vocab_file": {m: vocab_url for m in _all_bart_models},
"merges_file": {m: merges_url for m in _all_bart_models},
}
[docs] @add_start_docstrings(PREPARE_SEQ2SEQ_BATCH_DOCSTRING)
def prepare_seq2seq_batch(
self,
src_texts: List[str],
tgt_texts: Optional[List[str]] = None,
max_length: Optional[int] = None,
max_target_length: Optional[int] = None,
padding: str = "longest",
return_tensors: str = None,
truncation=True,
**kwargs,
) -> BatchEncoding:
kwargs.pop("src_lang", None)
kwargs.pop("tgt_lang", None)
if max_length is None:
max_length = self.model_max_length
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
labels = self(
tgt_texts,
add_special_tokens=True,
return_tensors=return_tensors,
padding=padding,
max_length=max_target_length,
truncation=truncation,
**kwargs,
)["input_ids"]
model_inputs["labels"] = labels
return model_inputs