tokenizer.py

# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2018 The Google AI Language 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.

import os

from paddlenlp.transformers import BasicTokenizer, PretrainedTokenizer, WordpieceTokenizer

__all__ = ['SqueezeBertTokenizer', ]


class SqueezeBertTokenizer(PretrainedTokenizer):
    """
    Constructs a SqueezeBert tokenizer. It uses a basic tokenizer to do punctuation
    splitting, lower casing and so on, and follows a WordPiece tokenizer to
    tokenize as subwords.
    Args:
        vocab_file (str): file path of the vocabulary
        do_lower_case (bool): Whether the text strips accents and convert to
            lower case. Default: `True`.
            Default: True.
        unk_token (str): The special token for unkown words. Default: "[UNK]".
        sep_token (str): The special token for separator token . Default: "[SEP]".
        pad_token (str): The special token for padding. Default: "[PAD]".
        cls_token (str): The special token for cls. Default: "[CLS]".
        mask_token (str): The special token for mask. Default: "[MASK]".
    Examples:
        .. code-block:: python
            from paddlenlp.transformers import SqueezeBertTokenizer
            tokenizer = SqueezeBertTokenizer.from_pretrained('SqueezeBert-small-discriminator')
            # the following line get: ['he', 'was', 'a', 'puppet', '##eer']
            tokens = tokenizer('He was a puppeteer')
            # the following line get: 'he was a puppeteer'
            tokenizer.convert_tokens_to_string(tokens)
    """
    resource_files_names = {"vocab_file": "vocab.txt"}  # for save_pretrained
    pretrained_resource_files_map = {
        "vocab_file": {
            "squeezebert-uncased":
                "squeezebert-uncased-vocab.txt",
            "squeezebert-mnli":
                "squeezebert-mnli-vocab.txt",
            "queezebert-mnli-headless":
                "queezebert-mnli-headless-vocab.txt",
        }
    }
    pretrained_init_configuration = {
        "squeezebert-uncased": {
            "do_lower_case": True
        },
        "squeezebert-mnli": {
            "do_lower_case": True
        },
        "queezebert-mnli-headless": {
            "do_lower_case": True
        }
    }

    def __init__(self,
                 vocab_file,
                 do_lower_case=True,
                 unk_token="[UNK]",
                 sep_token="[SEP]",
                 pad_token="[PAD]",
                 cls_token="[CLS]",
                 mask_token="[MASK]"):

        if not os.path.isfile(vocab_file):
            raise ValueError(
                "Can't find a vocabulary file at path '{}'. To load the "
                "vocabulary from a pretrained model please use "
                "`tokenizer = SqueezeBertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
                    .format(vocab_file))
        self.vocab = self.load_vocabulary(vocab_file, unk_token=unk_token)
        self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case)
        self.wordpiece_tokenizer = WordpieceTokenizer(
            vocab=self.vocab, unk_token=unk_token)

    @property
    def vocab_size(self):
        """
        return the size of vocabulary.
        Returns:
            int: the size of vocabulary.
        """
        return len(self.vocab)

    def _tokenize(self, text):
        """
        End-to-end tokenization for SqueezeBert models.
        Args:
            text (str): The text to be tokenized.
        Returns:
            list: A list of string representing converted tokens.
        """
        split_tokens = []
        for token in self.basic_tokenizer.tokenize(text):
            for sub_token in self.wordpiece_tokenizer.tokenize(token):
                split_tokens.append(sub_token)
        return split_tokens

    def tokenize(self, text):
        """
        End-to-end tokenization for SqueezeBert models.
        Args:
            text (str): The text to be tokenized.
        Returns:
            list: A list of string representing converted tokens.
        """
        return self._tokenize(text)

    def convert_tokens_to_string(self, tokens):
        """
        Converts a sequence of tokens (list of string) in a single string. Since
        the usage of WordPiece introducing `##` to concat subwords, also remove
        `##` when converting.
        Args:
            tokens (list): A list of string representing tokens to be converted.
        Returns:
            str: Converted string from tokens.
        """
        out_string = " ".join(tokens).replace(" ##", "").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.
        Note:
            This encodes inputs and checks the number of added tokens, and is therefore not efficient. Do not put this
            inside your training loop.
        Args:
            pair: Returns the number of added tokens in the case of a sequence pair if set to True, returns the
                number of added tokens in the case of a single sequence if set to False.
        Returns:
            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):
        """
        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
        adding special tokens.
        A SqueezeBert sequence has the following format:
        ::
            - single sequence: ``[CLS] X [SEP]``
            - pair of sequences: ``[CLS] A [SEP] B [SEP]``
        Args:
            token_ids_0 (:obj:`List[int]`):
                List of IDs to which the special tokens will be added.
            token_ids_1 (:obj:`List[int]`, `optional`):
                Optional second list of IDs for sequence pairs.
        Returns:
            :obj:`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 + token_ids_1 + _sep

    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.
        A SqueezeBert offset_mapping has the following format:
        ::
            - single sequence: ``(0,0) X (0,0)``
            - pair of sequences: `(0,0) A (0,0) B (0,0)``
        Args:
            offset_mapping_ids_0 (:obj:`List[tuple]`):
                List of char offsets to which the special tokens will be added.
            offset_mapping_ids_1 (:obj:`List[tuple]`, `optional`):
                Optional second list of char offsets for offset mapping pairs.
        Returns:
            :obj:`List[tuple]`: List of char offsets with the appropriate offsets of special tokens.
        """
        if offset_mapping_1 is None:
            return [(0, 0)] + offset_mapping_0 + [(0, 0)]

        return [(0, 0)] + offset_mapping_0 + [(0, 0)
                                              ] + offset_mapping_1 + [(0, 0)]

    def create_token_type_ids_from_sequences(self,
                                             token_ids_0,
                                             token_ids_1=None):
        """
        Create a mask from the two sequences passed to be used in a sequence-pair classification task.
        A SqueezeBert sequence pair 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
            | first sequence    | second sequence |
        If :obj:`token_ids_1` is :obj:`None`, this method only returns the first portion of the mask (0s).
        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.
        Returns:
            :obj:`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) * [0] + len(token_ids_1 +
                                                          _sep) * [1]

    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], optinal): 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:
            results (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:
            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 [1] + ([0] * len(token_ids_0)) + [1] + (
                    [0] * len(token_ids_1)) + [1]
        return [1] + ([0] * len(token_ids_0)) + [1]