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import os |
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import re |
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import warnings |
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from shutil import copyfile |
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from typing import Any, Dict, List, Optional, Tuple |
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import sentencepiece as spm |
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from transformers.tokenization_utils import PreTrainedTokenizer |
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from transformers.utils import logging |
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logger = logging.get_logger(__name__) |
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VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"} |
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class OpenBATokenizer(PreTrainedTokenizer): |
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vocab_files_names = VOCAB_FILES_NAMES |
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model_input_names = ["input_ids", "attention_mask"] |
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def __init__( |
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self, |
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vocab_file, |
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eos_token="</s>", |
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unk_token="<unk>", |
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pad_token="<pad>", |
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extra_ids=100, |
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additional_special_tokens=None, |
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sp_model_kwargs: Optional[Dict[str, Any]] = None, |
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**kwargs, |
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) -> None: |
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if extra_ids > 0 and additional_special_tokens is None: |
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additional_special_tokens = [f"<extra_id_{i}>" for i in range(extra_ids)] |
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elif extra_ids > 0 and additional_special_tokens is not None: |
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extra_tokens = len(set(filter(lambda x: bool("extra_id" in str(x)), additional_special_tokens))) |
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if extra_tokens != extra_ids: |
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raise ValueError( |
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f"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are" |
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" provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids" |
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" tokens" |
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) |
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self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs |
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super().__init__( |
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eos_token=eos_token, |
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unk_token=unk_token, |
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pad_token=pad_token, |
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extra_ids=extra_ids, |
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additional_special_tokens=additional_special_tokens, |
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sp_model_kwargs=self.sp_model_kwargs, |
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**kwargs, |
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) |
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self.vocab_file = vocab_file |
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self._extra_ids = extra_ids |
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self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) |
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self.sp_model.Load(vocab_file) |
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@staticmethod |
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def _eventually_correct_t5_max_length(pretrained_model_name_or_path, max_model_length, init_max_model_length): |
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if pretrained_model_name_or_path in OpenBATokenizer.max_model_input_sizes: |
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deprecated_max_model_length = OpenBATokenizer.max_model_input_sizes[pretrained_model_name_or_path] |
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if init_max_model_length is not None and init_max_model_length != max_model_length: |
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return init_max_model_length |
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elif init_max_model_length is None: |
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warnings.warn( |
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"This tokenizer was incorrectly instantiated with a model max length of" |
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f" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this" |
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" behavior is kept to avoid breaking backwards compatibility when padding/encoding with" |
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" `truncation is True`.\n- Be aware that you SHOULD NOT rely on" |
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f" {pretrained_model_name_or_path} automatically truncating your input to" |
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f" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences" |
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f" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with" |
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" `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please" |
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" instantiate this tokenizer with `model_max_length` set to your preferred value.", |
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FutureWarning, |
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) |
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return max_model_length |
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@property |
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def vocab_size(self): |
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return self.sp_model.get_piece_size() + self._extra_ids |
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def get_vocab(self): |
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vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)} |
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vocab.update(self.added_tokens_encoder) |
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return vocab |
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def get_special_tokens_mask( |
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False |
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) -> List[int]: |
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""" |
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Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding |
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special tokens using the tokenizer `prepare_for_model` method. |
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Args: |
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token_ids_0 (`List[int]`): |
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List of IDs. |
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token_ids_1 (`List[int]`, *optional*): |
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Optional second list of IDs for sequence pairs. |
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already_has_special_tokens (`bool`, *optional*, defaults to `False`): |
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Whether or not the token list is already formatted with special tokens for the model. |
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Returns: |
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`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. |
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""" |
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if already_has_special_tokens: |
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return super().get_special_tokens_mask( |
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token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True |
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) |
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if token_ids_1 is None: |
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return ([0] * len(token_ids_0)) + [1] |
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return ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1] |
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def get_sentinel_tokens(self): |
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return list( |
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set(filter(lambda x: bool(re.search(r"<extra_id_\d+>", x)) is not None, self.additional_special_tokens)) |
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) |
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def get_sentinel_token_ids(self): |
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return [self._convert_token_to_id(token) for token in self.get_sentinel_tokens()] |
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def _add_eos_if_not_present(self, token_ids: List[int]) -> List[int]: |
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"""Do not add eos again if user already added it.""" |
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if len(token_ids) > 0 and token_ids[-1] == self.eos_token_id: |
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warnings.warn( |
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f"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated" |
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" eos tokens being added." |
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) |
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return token_ids |
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else: |
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return token_ids + [self.eos_token_id] |
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def create_token_type_ids_from_sequences( |
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None |
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) -> List[int]: |
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""" |
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Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make |
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use of token type ids, therefore a list of zeros is returned. |
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Args: |
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token_ids_0 (`List[int]`): |
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List of IDs. |
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token_ids_1 (`List[int]`, *optional*): |
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Optional second list of IDs for sequence pairs. |
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Returns: |
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`List[int]`: List of zeros. |
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""" |
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eos = [self.eos_token_id] |
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if token_ids_1 is None: |
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return len(token_ids_0 + eos) * [0] |
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return len(token_ids_0 + eos + token_ids_1 + eos) * [0] |
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def build_inputs_with_special_tokens( |
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self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None |
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) -> List[int]: |
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""" |
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Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and |
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adding special tokens. A sequence has the following format: |
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- single sequence: `X </s>` |
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- pair of sequences: `A </s> B </s>` |
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Args: |
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token_ids_0 (`List[int]`): |
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List of IDs to which the special tokens will be added. |
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token_ids_1 (`List[int]`, *optional*): |
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Optional second list of IDs for sequence pairs. |
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Returns: |
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`List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. |
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""" |
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token_ids_0 = self._add_eos_if_not_present(token_ids_0) |
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if token_ids_1 is None: |
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return token_ids_0 |
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else: |
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token_ids_1 = self._add_eos_if_not_present(token_ids_1) |
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return token_ids_0 + token_ids_1 |
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def __getstate__(self): |
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state = self.__dict__.copy() |
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state["sp_model"] = None |
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return state |
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def __setstate__(self, d): |
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self.__dict__ = d |
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if not hasattr(self, "sp_model_kwargs"): |
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self.sp_model_kwargs = {} |
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self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) |
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self.sp_model.Load(self.vocab_file) |
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def _tokenize(self, text: str) -> List[str]: |
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"""Take as input a string and return a list of strings (tokens) for words/sub-words""" |
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return self.sp_model.encode(text, out_type=str) |
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def _convert_token_to_id(self, token): |
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"""Converts a token (str) in an id using the vocab.""" |
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if token.startswith("<extra_id_"): |
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match = re.match(r"<extra_id_(\d+)>", token) |
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num = int(match.group(1)) |
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return self.vocab_size - num - 1 |
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return self.sp_model.piece_to_id(token) |
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def _convert_id_to_token(self, index): |
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"""Converts an index (integer) in a token (str) using the vocab.""" |
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if index < self.sp_model.get_piece_size(): |
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token = self.sp_model.IdToPiece(index) |
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else: |
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token = f"<extra_id_{self.vocab_size - 1 - index}>" |
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return token |
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def convert_tokens_to_string(self, tokens): |
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"""Converts a sequence of tokens (string) in a single string.""" |
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current_sub_tokens = [] |
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out_string = "" |
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prev_is_special = False |
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for token in tokens: |
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if token in self.all_special_tokens: |
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if not prev_is_special: |
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out_string += " " |
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out_string += self.sp_model.decode(current_sub_tokens) + token |
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prev_is_special = True |
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current_sub_tokens = [] |
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else: |
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current_sub_tokens.append(token) |
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prev_is_special = False |
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out_string += self.sp_model.decode(current_sub_tokens) |
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return out_string.strip() |
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def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: |
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if not os.path.isdir(save_directory): |
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logger.error(f"Vocabulary path ({save_directory}) should be a directory") |
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return |
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out_vocab_file = os.path.join( |
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save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] |
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) |
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if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file): |
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copyfile(self.vocab_file, out_vocab_file) |
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elif not os.path.isfile(self.vocab_file): |
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with open(out_vocab_file, "wb") as fi: |
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content_spiece_model = self.sp_model.serialized_model_proto() |
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fi.write(content_spiece_model) |
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return (out_vocab_file,) |
