from __future__ import annotations import os from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple import sentencepiece as spm import transformers from transformers.tokenization_utils import PreTrainedTokenizer from transformers.utils import logging VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"} logger = logging.get_logger(__name__) class PlamoTokenizer(PreTrainedTokenizer): # type: ignore vocab_files_names = VOCAB_FILES_NAMES model_input_names = ["input_ids", "attention_mask"] def __init__( self, vocab_file: str, unk_token: str = "", bos_token: str = "", eos_token: str = "", pad_token: str = "", cls_token: str = "", sep_token: str = "", mask_token: str = "", sp_model_kwargs: Optional[Dict[str, Any]] = None, clean_up_tokenization_spaces: bool = False, **kwargs: Any, ) -> None: if "add_bos_token" not in kwargs: kwargs["add_bos_token"] = False if "add_eos_token" not in kwargs: kwargs["add_eos_token"] = False super().__init__( vocab_file=vocab_file, unk_token=unk_token, bos_token=bos_token, eos_token=eos_token, pad_token=pad_token, cls_token=cls_token, sep_token=sep_token, mask_token=mask_token, sp_model_kwargs=sp_model_kwargs, clean_up_tokenization_spaces=clean_up_tokenization_spaces, **kwargs, ) self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs self.vocab_file = vocab_file self.add_bos_token = kwargs["add_bos_token"] self.add_eos_token = kwargs["add_eos_token"] self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) self.sp_model.Load(vocab_file) self.add_tokens(self.all_special_tokens_extended, special_tokens=True) # the functions below are copied from hf transformers LlamaTokenizer's implementation to fix the behaviour of the tokenizer # https://github.com/huggingface/transformers/blob/v4.30.2/src/transformers/models/llama/tokenization_llama.py def __getstate__(self) -> dict[str, Any]: state = self.__dict__.copy() state["sp_model"] = None state["sp_model_proto"] = self.sp_model.serialized_model_proto() return state def __setstate__(self, d: dict[str, Any]) -> None: self.__dict__ = d self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs) self.sp_model.LoadFromSerializedProto(self.sp_model_proto) @property def vocab_size(self) -> Any: """Returns vocab size""" return self.sp_model.get_piece_size() def get_vocab(self) -> dict[str, int]: """Returns vocab as a dict""" vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)} vocab.update(self.added_tokens_encoder) return vocab def convert_tokens_to_string(self, tokens: List[int]) -> str: """Converts a sequence of tokens (string) in a single string.""" current_sub_tokens: List[int] = [] out_string = "" prev_is_special = False for i, token in enumerate(tokens): # make sure that special tokens are not decoded using sentencepiece model if token in self.all_special_tokens: if not prev_is_special and i != 0: out_string += " " out_string += self.sp_model.decode(current_sub_tokens) + token prev_is_special = True current_sub_tokens = [] else: current_sub_tokens.append(token) prev_is_special = False out_string += self.sp_model.decode(current_sub_tokens) return out_string def _tokenize(self, text: str) -> Any: """Returns a tokenized string.""" return self.sp_model.encode(text, out_type=str) def _convert_token_to_id(self, token: str) -> Any: """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: int) -> Any: """Converts an index (integer) in a token (str) using the vocab.""" token = self.sp_model.IdToPiece(index) return token def build_inputs_with_special_tokens( self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None ) -> List[int]: bos_token_id = [self.bos_token_id] if self.add_bos_token else [] eos_token_id = [self.eos_token_id] if self.add_eos_token else [] output = bos_token_id + token_ids_0 + eos_token_id if token_ids_1 is not None: output = output + bos_token_id + token_ids_1 + eos_token_id return output def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: """ Save the vocabulary and special tokens file to a directory. Args: save_directory (`str`): The directory in which to save the vocabulary. Returns: `Tuple(str)`: Paths to the files saved. """ 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) and os.path.isfile(self.vocab_file): copyfile(self.vocab_file, out_vocab_file) elif not os.path.isfile(self.vocab_file): with open(out_vocab_file, "wb") as fi: content_spiece_model = self.sp_model.serialized_model_proto() fi.write(content_spiece_model) return (out_vocab_file,) class PlamoConfig(transformers.LlamaConfig): # type: ignore model_type = "plamo"