|
from __future__ import annotations |
|
|
|
import os |
|
from shutil import copyfile |
|
from typing import Any, Dict, List, Optional, Tuple |
|
|
|
import sentencepiece as spm |
|
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): |
|
vocab_files_names = VOCAB_FILES_NAMES |
|
model_input_names = ["input_ids", "attention_mask"] |
|
|
|
def __init__( |
|
self, |
|
vocab_file: str, |
|
unk_token: str = "<unk>", |
|
bos_token: str = "<s>", |
|
eos_token: str = "</s>", |
|
pad_token: str = "<pad>", |
|
cls_token: str = "<cls>", |
|
sep_token: str = "<sep>", |
|
mask_token: str = "<mask>", |
|
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 |
|
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(vocab_file) |
|
self.vocab_file = vocab_file |
|
self.add_bos_token = kwargs["add_bos_token"] |
|
self.add_eos_token = kwargs["add_eos_token"] |
|
|
|
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, |
|
) |
|
|
|
|
|
|
|
|
|
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): |
|
|
|
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,) |
|
|
