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"""
## adapt to transformer tokenizer
https://github.com/huggingface/transformers/blob/v4.40.1/src/transformers/tokenization_utils.py#L379
## usage
- grok
## 风险评估
- 可能会干扰 sentencepiece.SentencePieceProcessor的正常使用，比如 .vocab_size 原来是个方法，patch后是个property
## TODO
不用patch，改用wrapper。常见的 tokenizer通常是封装的 sentencepiece，
"""
import sentencepiece
@property
def vocab_size(self):
"""Returns vocab size"""
return self.get_piece_size()
def get_vocab(self):
"""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 _tokenize(self, text):
"""Returns a tokenized string."""
return self.encode(text, out_type=str)
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
return self.piece_to_id(token)
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
token = self.IdToPiece(index)
return token
def convert_ids_to_tokens(self, ids, skip_special_tokens=False):
""" copy from transformers.PreTrainedTokenizer
Converts a single index or a sequence of indices in a token or a sequence of tokens, using the vocabulary and
added tokens.
Args:
ids (`int` or `List[int]`):
The token id (or token ids) to convert to tokens.
skip_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not to remove special tokens in the decoding.
Returns:
`str` or `List[str]`: The decoded token(s).
"""
self._added_tokens_decoder = {} # add by xs
if isinstance(ids, int):
if ids in self._added_tokens_decoder:
return self._added_tokens_decoder[ids].content
else:
return self._convert_id_to_token(ids)
tokens = []
for index in ids:
index = int(index)
if skip_special_tokens and index in self.all_special_ids:
continue
if index in self._added_tokens_decoder:
tokens.append(self._added_tokens_decoder[index].content)
else:
tokens.append(self._convert_id_to_token(index))
return tokens
def encode(self, *args, **kwargs):
"""
add_special_token 是为了兼容 hf_tokenizer
"""
kwargs.pop("add_special_tokens", None)
kwargs.pop("allowed_special", None)
return self.Encode(*args, **kwargs)
def decode(self, *args, **kwargs):
kwargs.pop("skip_special_tokens", None)
return self.Decode(*args, **kwargs)
sentencepiece.SentencePieceProcessor.vocab_size = vocab_size #
sentencepiece.SentencePieceProcessor.get_vocab = get_vocab
sentencepiece.SentencePieceProcessor._convert_id_to_token = _convert_id_to_token
sentencepiece.SentencePieceProcessor.convert_ids_to_tokens = convert_ids_to_tokens
# sentencepiece.SentencePieceProcessor.tokenize = _tokenize
sentencepiece.SentencePieceProcessor.encode = encode
sentencepiece.SentencePieceProcessor.decode = decode
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