# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # Copyright 2018 Google AI, Google Brain and Carnegie Mellon University 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. """Tokenization class for XLNet model.""" import os import unicodedata from shutil import copyfile import sentencepiece as spm from .. import AddedToken, PretrainedTokenizer __all__ = ["XLNetTokenizer"] SENTENCEPIECE_UNDERLINE = "▁" SPIECE_UNDERLINE = SENTENCEPIECE_UNDERLINE # Kept for backward compatibility # Segments (not really needed) SEG_ID_A = 0 SEG_ID_B = 1 SEG_ID_CLS = 2 SEG_ID_SEP = 3 SEG_ID_PAD = 4 class XLNetTokenizer(PretrainedTokenizer): """ Constructs an XLNet tokenizer based on `SentencePiece `__. This tokenizer inherits from :class:`~paddlenlp.transformers.tokenizer_utils.PretrainedTokenizer` which contains most of the main methods. For more information regarding those methods, please refer to this superclass. Args: vocab_file (str): The vocabulary file (ends with '.spm') required to instantiate a `SentencePiece `__ tokenizer. do_lower_case (bool, optional): Whether or not to lowercase the input when tokenizing. Defaults to `False` and **does not** lowercase the input. remove_space (bool, optional): Whether or not to strip the text when tokenizing. Defaults to `True` and removes excess spaces before and after the string. keep_accents (bool, optional): Whether or not to keep accents when tokenizing. Defaults to `False` and **does not** keep accents. bos_token (str, optional): A special token representing the beginning of a sequence that was used during pretraining. Defaults to `""`. eos_token (str, optional): A special token representing the end of a sequence that was used during pretraining. Defaults to `""`. unk_token (str, optional): A special token representing the *unknown (out-of-vocabulary)* token. An unknown token is set to be `unk_token` inorder to be converted to an ID. Defaults to `""`. sep_token (str, optional): A special token separating two different sentences in the same input. Defaults to `""`. pad_token (str, optional): A special token used to make arrays of tokens the same size for batching purposes. Defaults to `""`. cls_token (str, optional): A special token used for sequence classification. It is the last token of the sequence when built with special tokens. Defaults to `""`. mask_token (str, optional): A special token representing a masked token. This is the token used in the masked language modeling task which the model tries to predict the original unmasked ones. Defaults to `""`. additional_special_tokens (List[str], optional): A list of additional special tokens to be used by the tokenizer. Defaults to `["", ""]`. Attributes: sp_model (SentencePieceProcessor): The *SentencePiece* processor that is used for every conversion (string, tokens and IDs). """ resource_files_names = {"vocab_file": "spiece.model"} pretrained_resource_files_map = { "vocab_file": { "xlnet-base-cased": "https://bj.bcebos.com/paddlenlp/models/transformers/xlnet/xlnet-base-cased-spiece.model", "xlnet-large-cased": "https://bj.bcebos.com/paddlenlp/models/transformers/xlnet/xlnet-large-cased-spiece.model", "chinese-xlnet-base": "https://bj.bcebos.com/paddlenlp/models/transformers/xlnet/chinese-xlnet-base-spiece.model", "chinese-xlnet-mid": "https://bj.bcebos.com/paddlenlp/models/transformers/xlnet/chinese-xlnet-mid-spiece.model", "chinese-xlnet-large": "https://bj.bcebos.com/paddlenlp/models/transformers/xlnet/chinese-xlnet-large-spiece.model", } } pretrained_init_configuration = { "xlnet-base-cased": {"do_lower_case": False}, "xlnet-large-cased": {"do_lower_case": False}, "chinese-xlnet-base": {"do_lower_case": False}, "chinese-xlnet-mid": {"do_lower_case": False}, "chinese-xlnet-large": {"do_lower_case": False}, } pretrained_positional_embedding_sizes = { "xlnet-base-cased": None, "xlnet-large-cased": None, "chinese-xlnet-base": None, "chinese-xlnet-mid": None, "chinese-xlnet-large": None, } max_model_input_sizes = pretrained_positional_embedding_sizes padding_side = "left" pad_token_type_id = 3 def __init__( self, vocab_file, do_lower_case=False, remove_space=True, keep_accents=False, bos_token="", eos_token="", unk_token="", sep_token="", pad_token="", cls_token="", mask_token="", additional_special_tokens=["", ""], sp_model_kwargs=None, **kwargs ): # 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(mask_token=mask_token) self._pad_token_type_id = 3 self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs self.do_lower_case = do_lower_case self.remove_space = remove_space self.keep_accents = keep_accents self.vocab_file = vocab_file self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) self.sp_model.Load(vocab_file) @property def vocab_size(self): return len(self.sp_model) def get_vocab(self): vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)} vocab.update(self.added_tokens_encoder) return vocab def __getstate__(self): state = self.__dict__.copy() state["sp_model"] = None return state def __setstate__(self, d): self.__dict__ = d # for backward compatibility if not hasattr(self, "sp_model_kwargs"): self.sp_model_kwargs = {} self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) self.sp_model.Load(self.vocab_file) def preprocess_text(self, inputs): if self.remove_space: outputs = " ".join(inputs.strip().split()) else: outputs = inputs outputs = outputs.replace("``", '"').replace("''", '"') if not self.keep_accents: outputs = unicodedata.normalize("NFKD", outputs) outputs = "".join([c for c in outputs if not unicodedata.combining(c)]) if self.do_lower_case: outputs = outputs.lower() return outputs def _tokenize(self, text): """Tokenize a string.""" text = self.preprocess_text(text) pieces = self.sp_model.encode(text, out_type=str) new_pieces = [] for piece in pieces: if len(piece) > 1 and piece[-1] == str(",") and piece[-2].isdigit(): cur_pieces = self.sp_model.EncodeAsPieces(piece[:-1].replace(SPIECE_UNDERLINE, "")) if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE: if len(cur_pieces[0]) == 1: cur_pieces = cur_pieces[1:] else: cur_pieces[0] = cur_pieces[0][1:] cur_pieces.append(piece[-1]) new_pieces.extend(cur_pieces) else: new_pieces.append(piece) return new_pieces def _convert_token_to_id(self, token): """Converts a token (str) to an id using the vocab.""" return self.sp_model.PieceToId(token) def _convert_id_to_token(self, index): """Converts an index (integer) to a token (str) using the vocab.""" return self.sp_model.IdToPiece(index) def convert_tokens_to_string(self, tokens): # Converts a sequence of tokens (strings for sub-words) in a single string. out_string = "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip() return out_string def num_special_tokens_to_add(self, pair=False): """ Returns the number of added tokens when encoding a sequence with special tokens. Args: pair (bool, optional): Whether the input is a sequence pair or a single sequence. Defaults to `False` and the input is a single sequence. Returns: int: Number of tokens added to sequences. """ token_ids_0 = [] token_ids_1 = [] return len(self.build_inputs_with_special_tokens(token_ids_0, token_ids_1 if pair else None)) def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): """ Builds model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. An XLNet sequence has the following format: - single sequence: ``X `` - pair of sequences: ``A B `` Args: token_ids_0 (List[int]): List of IDs for the first sequence. token_ids_1 (List[int], optional): Optional second list of IDs for the second sequenze. Defaults to `None`. Returns: List[int]: List of input IDs with the appropriate special tokens. """ sep = [self.sep_token_id] cls = [self.cls_token_id] if token_ids_1 is None: return token_ids_0 + sep + cls return token_ids_0 + sep + token_ids_1 + sep + cls def build_offset_mapping_with_special_tokens(self, offset_mapping_0, offset_mapping_1=None): """ Builds offset map from a pair of offset map by concatenating and adding offsets of special tokens. An XLNet offset_mapping has the following format: - single sequence: ``X (0,0) (0,0)`` - pair of sequences: ``A (0,0) B (0,0) (0,0)`` Args: offset_mapping_0 (List[tuple]): List of char offsets to which the special tokens will be added. offset_mapping_1 (List[tuple], optional): Optional second list of char offsets for offset mapping pairs. Defaults to `None`. Returns: List[tuple]: A list of char offsets with the appropriate offsets of special tokens. """ if offset_mapping_1 is None: return offset_mapping_0 + [(0, 0)] + [(0, 0)] return offset_mapping_0 + [(0, 0)] + offset_mapping_1 + [(0, 0)] + [(0, 0)] def get_special_tokens_mask(self, token_ids_0, token_ids_1=None, already_has_special_tokens=False): """ Creates a special tokens mask from the input sequences. This method is called when adding special tokens using the tokenizer `encode` method. 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 `inputs_ids` for the second sequence. Defaults to `None`. already_has_special_tokens (bool, optional): Whether or not the token list already contains special tokens for the model. Defaults to `False`. Returns: List[int]: A list of integers which is either 0 or 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)) if token_ids_1 is not None: return ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1, 1] return ([0] * len(token_ids_0)) + [1, 1] def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None): """ Creates a token_type mask from the input sequences. If `token_ids_1` is not `None`, then a sequence pair token_type mask has the following format: :: 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 2 | first sequence | second sequence | Else if `token_ids_1` is `None`, then a single sequence token_type mask has the following format: :: 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 | first sequence | - 0 stands for the segment id of **first segment tokens**, - 1 stands for the segment id of **second segment tokens**, - 2 stands for the segment id of **cls_token**. 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 `inputs_ids` for the second sequence. Defaults to `None`. Returns: List[int]: List of token type IDs according to the given sequence(s). """ sep = [self.sep_token_id] cls_segment_id = [2] if token_ids_1 is None: return len(token_ids_0 + sep) * [0] + cls_segment_id return len(token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1] + cls_segment_id def save_resources(self, save_directory): for name, file_name in self.resource_files_names.items(): save_path = os.path.join(save_directory, file_name) if os.path.abspath(self.vocab_file) != os.path.abspath(save_path) and os.path.isfile(self.vocab_file): copyfile(self.vocab_file, save_path) elif not os.path.isfile(self.vocab_file): with open(save_path, "wb") as fi: content_spiece_model = self.sp_model.serialized_model_proto() fi.write(content_spiece_model)