# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # Copyright 2018 The HuggingFace Inc. team, Microsoft Corporation. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # 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 ..bert.tokenizer import BertTokenizer from .. import AddedToken __all__ = ["MPNetTokenizer"] PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"mpnet-base": 514} class MPNetTokenizer(BertTokenizer): """ Construct a MPNet tokenizer which is almost identical to `BertTokenizer`. For more information regarding those methods, please refer to this superclass. """ resource_files_names = {"vocab_file": "vocab.txt"} # for save_pretrained pretrained_resource_files_map = { "vocab_file": { "mpnet-base": "https://bj.bcebos.com/paddlenlp/models/transformers/mpnet/mpnet-base/vocab.txt", } } pretrained_init_configuration = {"mpnet-base": {"do_lower_case": True}} max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES def __init__( self, vocab_file, do_lower_case=True, bos_token="", eos_token="", unk_token="[UNK]", sep_token="", pad_token="", cls_token="", mask_token="", **kwargs ): super().__init__( vocab_file=vocab_file, do_lower_case=do_lower_case, unk_token=unk_token, sep_token=sep_token, pad_token=pad_token, cls_token=cls_token, mask_token=mask_token, ) bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token # 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._build_special_tokens_map_extended( bos_token=bos_token, eos_token=eos_token, sep_token=sep_token, cls_token=cls_token, unk_token=unk_token, pad_token=pad_token, mask_token=mask_token, ) def __call__( self, text, text_pair=None, max_seq_len=None, stride=0, is_split_into_words=False, pad_to_max_seq_len=False, truncation_strategy="longest_first", return_position_ids=False, return_token_type_ids=False, return_attention_mask=False, return_length=False, return_overflowing_tokens=False, return_special_tokens_mask=False, ): return super().__call__( text, text_pair=text_pair, max_seq_len=max_seq_len, stride=stride, is_split_into_words=is_split_into_words, pad_to_max_seq_len=pad_to_max_seq_len, truncation_strategy=truncation_strategy, return_position_ids=return_position_ids, return_token_type_ids=return_token_type_ids, return_attention_mask=return_attention_mask, return_length=return_length, return_overflowing_tokens=return_overflowing_tokens, return_special_tokens_mask=return_special_tokens_mask, ) def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): """ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A MPNet sequence has the following format: - single sequence: `` X `` - pair of sequences: `` A B `` Args: token_ids_0 (List[int]): List of IDs to which the special tokens will be added. token_ids_1 (List[int], optional): Optional second list of IDs for sequence pairs. Defaults to None. Returns: List[int]: List of input_id with the appropriate special tokens. """ if token_ids_1 is None: return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] cls = [self.cls_token_id] sep = [self.sep_token_id] return cls + token_ids_0 + sep + sep + token_ids_1 + sep def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False): """ Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer ``encode`` methods. Args: token_ids_0 (List[int]): A list of `inputs_ids` for the first sequence. token_ids_1 (List[int], optional): Optional second list of IDs for sequence pairs. Defaults to None. already_has_special_tokens (bool, optional): Whether or not the token list is already formatted with special tokens for the model. Defaults to None. Returns: List[int]: The list of integers either be 0 or 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 ) if token_ids_1 is None: return [1] + ([0] * len(token_ids_0)) + [1] return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1] def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None): """ Creates a mask from the two sequences passed to be used in a sequence-pair classification task. MPNet does not make use of token type ids, therefore a list of zeros is returned. Args: token_ids_0 (List[int]): A list of `inputs_ids` for the first sequence. token_ids_1 (List[int], optional): Optional second list of IDs for sequence pairs. Defaults to None. Returns: List[int]: List of token_type_id according to the given sequence(s). """ sep = [self.sep_token_id] cls = [self.cls_token_id] if token_ids_1 is None: return len(cls + token_ids_0 + sep) * [0] return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]