Source code for paddlenlp.transformers.reformer.tokenizer

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
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import re
import warnings

import sentencepiece as spm

from ..albert.tokenizer import AlbertEnglishTokenizer

__all__ = ["ReformerTokenizer"]

PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"reformer-enwik8": 65536, "reformer-crime-and-punishment": 524288}

[docs] class ReformerTokenizer(AlbertEnglishTokenizer): """ Constructs a Reformer 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: sentencepiece_model_file (str): The vocabulary file (ends with '.spm') required to instantiate a `SentencePiece <>`__ tokenizer. do_lower_case (bool): Whether or not to lowercase the input when tokenizing. Defaults to `False`. remove_space (bool): Whether or note to remove space when tokenizing. Defaults to `True`. keep_accents (bool): Whether or note to keep accents when tokenizing. Defaults to `False`. eos_token (str): A special token representing the *eos (end-of-sentence)* token. Defaults to "</s>". unk_token (str): 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 "<unk>". pad_token (str): A special token used to make arrays of tokens the same size for batching purposes. Defaults to "<unk>". """ resource_files_names = { "sentencepiece_model_file": "spiece.model", } pretrained_resource_files_map = { "sentencepiece_model_file": { "reformer-crime-and-punishment": "", }, } pretrained_init_configuration = { "reformer-crime-and-punishment": {"do_lower_case": False}, } max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES def __init__( self, sentencepiece_model_file, do_lower_case=False, remove_space=True, keep_accents=True, eos_token="</s>", unk_token="<unk>", pad_token="<pad>", extra_ids=100, additional_special_tokens=[], sp_model_kwargs=None, **kwargs ): # Add extra_ids to the special token list if extra_ids > 0 and len(additional_special_tokens) == 0: self._additional_special_tokens = [f"<extra_id_{i}>" for i in range(extra_ids)] elif extra_ids > 0 and len(additional_special_tokens) != 0: # Check that we have the right number of extra_id special tokens extra_tokens = len(set(filter(lambda x: bool("extra_id" in str(x)), additional_special_tokens))) if extra_tokens != extra_ids: raise ValueError( f"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are provided to ReformerTokenizer. " "In this case the additional_special_tokens must include the extra_ids tokens" ) self.do_lower_case = do_lower_case self.remove_space = remove_space self.keep_accents = keep_accents self.extra_ids = extra_ids self.sentencepiece_model_file = sentencepiece_model_file self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) self.sp_model.Load(sentencepiece_model_file) def __call__( self, text, text_pair=None, max_length=None, stride=0, is_split_into_words=False, padding=None, truncation="longest_first", return_position_ids=False, return_token_type_ids=False, return_attention_mask=True, return_length=False, return_overflowing_tokens=False, return_special_tokens_mask=False, **kwargs ): if "pad_to_max_seq_len" in kwargs and padding is None: pad_to_max_seq_len = kwargs.pop("pad_to_max_seq_len") padding = "max_length" if pad_to_max_seq_len else False elif padding is None: padding = False if "max_seq_len" in kwargs and max_length is None: max_length = kwargs["max_seq_len"] if "truncation_strategy" in kwargs and kwargs["truncation_strategy"] != "longest_first": truncation = kwargs["truncation_strategy"] return super(ReformerTokenizer, self).__call__( text=text, text_pair=text_pair, max_length=max_length, stride=stride, is_split_into_words=is_split_into_words, padding=padding, truncation=truncation, 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, **kwargs, ) @property def vocab_size(self): return len(self.sp_model) + self.extra_ids def _add_eos_if_not_present(self, token_ids): """Do not add eos again if user already added it.""" if len(token_ids) > 0 and token_ids[-1] == self.eos_token_id: warnings.warn( f"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated eos tokens being added." ) return token_ids else: return token_ids + [self.eos_token_id]
[docs] def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1): """ Build model inputs from a sequence or a pair of sequence. An Reformer sequence has the following format: - single sequence: ``X </s>`` - pair of sequences: ``A </s> B </s>`` 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. """ token_ids_0 = self._add_eos_if_not_present(token_ids_0) if token_ids_1 is None: return token_ids_0 else: token_ids_1 = self._add_eos_if_not_present(token_ids_1) return token_ids_0 + token_ids_1
[docs] def build_offset_mapping_with_special_tokens(self, offset_mapping_0, offset_mapping_1=None): """ Build offset map from a pair of offset map by concatenating and adding offsets of special tokens. Should be overridden in a subclass if the model has a special way of building those. 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. Returns: List[tuple]: List of char offsets with the appropriate offsets of special tokens. """ if offset_mapping_1 is None: return offset_mapping_0 + [(0, 0)] return offset_mapping_0 + [(0, 0)] + offset_mapping_1 + [(0, 0)]
[docs] def create_token_type_ids_from_sequences(self, token_ids_0, token_ids_1=None): """ Create a mask from the two sequences. If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s). Args: token_ids_0 (List[int]): List of IDs. token_ids_1 (List[int], optional): Optional second list of IDs for sequence pairs. Returns: List[int]: List of token_type_id according to the given sequence(s). """ eos = [self.eos_token_id] if token_ids_1 is None: return len(token_ids_0 + eos) * [0] return len(token_ids_0 + eos + token_ids_1 + eos) * [0]
[docs] 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]): List of ids of the first sequence. token_ids_1 (List[int], optional): List of ids of the second sequence. 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 in the range [0, 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, ) # normal case: some special tokens if token_ids_1 is None: return ([0] * len(token_ids_0)) + [1] return ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
[docs] def convert_tokens_to_string(self, tokens): """Converts a sequence of tokens (string) in a single string.""" current_sub_tokens = [] out_string = "" for token in tokens: # make sure that special tokens are not decoded using sentencepiece model if token in self.all_special_tokens: out_string += self.sp_model.decode_pieces(current_sub_tokens) + token + " " current_sub_tokens = [] else: current_sub_tokens.append(token) out_string += self.sp_model.decode_pieces(current_sub_tokens) return out_string.strip()
def _convert_token_to_id(self, token): """Converts a token (str) in an id using the vocab.""" if token.startswith("<extra_id_"): match = re.match(r"<extra_id_(\d+)>", token) num = int( return self.vocab_size - num - 1 return self.sp_model.piece_to_id(token) def _convert_id_to_token(self, index): """Converts an index (integer) in a token (str) using the vocab.""" if index < self.sp_model.get_piece_size(): token = self.sp_model.IdToPiece(index) else: token = f"<extra_id_{self.vocab_size - 1 - index}>" return token