Source code for transformers.models.bert_generation.tokenization_bert_generation

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""" Tokenization class for model BertGeneration."""


import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


logger = logging.get_logger(__name__)

VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"}

PRETRAINED_VOCAB_FILES_MAP = {
    "vocab_file": {
        "bert_for_seq_generation": "https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model",
    }
}

PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {"bert_for_seq_generation": 512}


[docs]class BertGenerationTokenizer(PreTrainedTokenizer): """ Construct a BertGeneration tokenizer. Based on `SentencePiece <https://github.com/google/sentencepiece>`__. This tokenizer inherits from :class:`~transformers.PreTrainedTokenizer` which contains most of the main methods. Users should refer to this superclass for more information regarding those methods. Args: vocab_file (:obj:`str`): `SentencePiece <https://github.com/google/sentencepiece>`__ file (generally has a `.spm` extension) that contains the vocabulary necessary to instantiate a tokenizer. eos_token (:obj:`str`, `optional`, defaults to :obj:`"</s>"`): The end of sequence token. bos_token (:obj:`str`, `optional`, defaults to :obj:`"<s>"`): The begin of sequence token. unk_token (:obj:`str`, `optional`, defaults to :obj:`"<unk>"`): The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. pad_token (:obj:`str`, `optional`, defaults to :obj:`"<pad>"`): The token used for padding, for example when batching sequences of different lengths. sp_model_kwargs (:obj:`dict`, `optional`): Will be passed to the ``SentencePieceProcessor.__init__()`` method. The `Python wrapper for SentencePiece <https://github.com/google/sentencepiece/tree/master/python>`__ can be used, among other things, to set: - ``enable_sampling``: Enable subword regularization. - ``nbest_size``: Sampling parameters for unigram. Invalid for BPE-Dropout. - ``nbest_size = {0,1}``: No sampling is performed. - ``nbest_size > 1``: samples from the nbest_size results. - ``nbest_size < 0``: assuming that nbest_size is infinite and samples from the all hypothesis (lattice) using forward-filtering-and-backward-sampling algorithm. - ``alpha``: Smoothing parameter for unigram sampling, and dropout probability of merge operations for BPE-dropout. """ vocab_files_names = VOCAB_FILES_NAMES pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES prefix_tokens: List[int] = [] model_input_names = ["input_ids", "attention_mask"] def __init__( self, vocab_file, bos_token="<s>", eos_token="</s>", unk_token="<unk>", pad_token="<pad>", sep_token="<::::>", sp_model_kwargs: Optional[Dict[str, Any]] = None, **kwargs ) -> None: self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs # Add extra_ids to the special token list super().__init__( bos_token=bos_token, eos_token=eos_token, unk_token=unk_token, pad_token=pad_token, sep_token=sep_token, sp_model_kwargs=self.sp_model_kwargs, **kwargs, ) 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 self.sp_model.get_piece_size() 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 _tokenize(self, text: str) -> List[str]: """Take as input a string and return a list of strings (tokens) for words/sub-words""" return self.sp_model.encode(text, out_type=str) def _convert_token_to_id(self, token): """Converts a token (str) in an id using the vocab.""" 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.""" token = self.sp_model.IdToPiece(index) return token def convert_tokens_to_string(self, tokens): """Converts a sequence of tokens (string) in a single string.""" out_string = self.sp_model.decode_pieces(tokens) return out_string
[docs] def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: if not os.path.isdir(save_directory): logger.error(f"Vocabulary path ({save_directory}) should be a directory") return out_vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file): copyfile(self.vocab_file, out_vocab_file) return (out_vocab_file,)