def test_instantiate(self):
bad = PreTokenizer.custom(TestCustomPreTokenizer.BadCustomPretok())
good = PreTokenizer.custom(TestCustomPreTokenizer.GoodCustomPretok())
assert isinstance(bad, PreTokenizer)
assert isinstance(good, PreTokenizer)
with pytest.raises(Exception, match="TypeError: pre_tokenize()"):
bad.pre_tokenize_str("Hey there!")
assert good.pre_tokenize_str("Hey there!") == [
("Hey there!", (0, 10)),
("Hey there!", (0, 10)),
]
def load_custom_tokenizer(tokenizer_file: str) -> Tokenizer:
""" Tokenizerのロード処理：tokenizer.json からTokenizerをロードし、custome PreTokenizerをセットする。
"""
tokenizer = Tokenizer.from_file(tokenizer_file)
# ダミー注入したRustベースのPreTokenizerを、custom PreTokenizerで上書き。
tokenizer.pre_tokenizer = PreTokenizer.custom(MecabPreTokenizer())
return tokenizer
def train_custom_tokenizer(files: List[str], tokenizer_file: str,
**kwargs) -> BertWordPieceTokenizer:
""" Tokenizerの学習・保存処理：custom PreTokenizer付きのTokenizerを学習・保存する。
"""
tokenizer = BertWordPieceTokenizer(
handle_chinese_chars=False, # for japanese
strip_accents=False, # for japanese
)
tokenizer._tokenizer.pre_tokenizer = PreTokenizer.custom(
MecabPreTokenizer())
# 与えられたコーパスファイル集合からサブワード分割を学習
tokenizer.train(files, **kwargs)
# vocab情報に加えて、前処理等パラメータ情報を含んだトークナイザ設定のJSONを保存
# NOTE: Pythonで書かれたcustom PreTokenizerはシリアライズできないので、RustベースのPreTokenizerをダミー注入してシリアライズ
# JSONにはダミーのPreTokenizerが記録されるので、ロード時にcustom PreTokenizerを再設定する必要がある。
tokenizer._tokenizer.pre_tokenizer = BertPreTokenizer()
tokenizer.save(tokenizer_file)
# (Optional) .txt形式のvocabファイルは f"vocab-{filename}.txt" で保存される（外部の処理で欲しい場合）
filename = "wordpiece"
model_files = tokenizer._tokenizer.model.save(
str(pathlib.Path(tokenizer_file).parent), filename)
return tokenizer
def custom_tokenizer_from_pretrained(
tokenizer_file_or_name: str,
cache_dir: Optional[str] = None) -> PreTrainedTokenizerFast:
"""Load BertWordPieceTokenizer from tokenizer.json.
This is necessary due to the following reasons:
- BertWordPieceTokenizer cannot load from tokenizer.json via .from_file() method
- Tokenizer.from_file(tokenizer_file) cannot be used because MecabPretokenizer is not a valid native PreTokenizer.
"""
if os.path.exists(tokenizer_file_or_name):
if not os.path.isdir(tokenizer_file_or_name):
tokenizer_dir = os.path.dirname(tokenizer_file_or_name)
pt_tokenizer = AutoTokenizer.from_pretrained(
tokenizer_dir,
cache_dir=cache_dir,
)
# This is necessary for pt_tokenizer.save_pretrained(save_path)
_tokenizer = Tokenizer.from_file(tokenizer_file_or_name)
_tokenizer.pre_tokenizer = PreTokenizer.custom(MecabPreTokenizer())
pt_tokenizer._tokenizer = _tokenizer
else:
pt_tokenizer = AutoTokenizer.from_pretrained(
tokenizer_file_or_name,
cache_dir=cache_dir,
)
else:
# trf>=4.0.0: PreTrainedTokenizerFast by default
# NOTE: AutoTokenizer doesn't load PreTrainedTokenizerFast...
pt_tokenizer = BertTokenizerFast.from_pretrained(
tokenizer_file_or_name,
cache_dir=cache_dir,
)
return pt_tokenizer
def load_janome_tokenizer(tokenizer_path) -> Tokenizer:
tokenizer = Tokenizer.from_file(str(tokenizer_path))
tokenizer.pre_tokenizer = Sequence([
Whitespace(),
PreTokenizer.custom(JanomePreTokenizer()),
])
tokenizer.decoder = Decoder.custom(JanomeDecoder())
return tokenizer
def test_camel_case(self):
class CamelCasePretok:
def get_state(self, c):
if c.islower():
return "lower"
elif c.isupper():
return "upper"
elif c.isdigit():
return "digit"
else:
return "rest"
def split(self, n, normalized):
i = 0
# states = {"any", "lower", "upper", "digit", "rest"}
state = "any"
pieces = []
for j, c in enumerate(normalized.normalized):
c_state = self.get_state(c)
if state == "any":
state = c_state
if state != "rest" and state == c_state:
pass
elif state == "upper" and c_state == "lower":
pass
else:
pieces.append(normalized[i:j])
i = j
state = c_state
pieces.append(normalized[i:])
return pieces
def pre_tokenize(self, pretok):
pretok.split(self.split)
camel = PreTokenizer.custom(CamelCasePretok())
assert camel.pre_tokenize_str("HeyThere!?-ThisIsLife") == [
("Hey", (0, 3)),
("There", (3, 8)),
("!", (8, 9)),
("?", (9, 10)),
("-", (10, 11)),
("This", (11, 15)),
("Is", (15, 17)),
("Life", (17, 21)),
]
def build_hf_tokenizer(kmer_length: int,
kmer_stride: int,
alphabet: str,
unk_token: str = "?") -> Tokenizer:
"""Build a full huggingface tokenizer from the inputs.
Note:
Same arguments taken as KmerPreTokenizer.
"""
kmer_pre = KmerPreTokenizer(kmer_length=kmer_length,
kmer_stride=kmer_stride,
alphabet=alphabet,
unk_token=unk_token)
tokenizer = Tokenizer(
models.WordLevel(vocab=kmer_pre.kmer_tokenzier.vocab.stoi,
unk_token=unk_token))
tokenizer.pre_tokenizer = PreTokenizer.custom(kmer_pre)
tokenizer.decoder = ByteLevel()
return tokenizer
def load_pretrained_tokenizer(
tokenizer_file: str,
cache_dir: Optional[str] = None) -> PreTrainedTokenizerFast:
"""Load BertWordPieceTokenizer from tokenizer.json.
This is necessary due to the following reasons:
- BertWordPieceTokenizer cannot load from tokenizer.json via .from_file() method
- Tokenizer.from_file(tokenizer_file) cannot be used because MecabPretokenizer is not a valid native PreTokenizer.
"""
tokenizer = Tokenizer.from_file(tokenizer_file)
tokenizer.pre_tokenizer = PreTokenizer.custom(MecabPreTokenizer())
tokenizer_dir = os.path.dirname(tokenizer_file)
pt_tokenizer: PreTrainedTokenizerFast = AutoTokenizer.from_pretrained(
tokenizer_dir,
cache_dir=cache_dir,
)
# This is necessary for pt_tokenizer.save_pretrained(save_path)
pt_tokenizer._tokenizer = tokenizer # ._tokenizer
return pt_tokenizer
class CustomNormalizer:
def normalize(self, normalized: NormalizedString):
# Most of these can be replaced by a `Sequence` combining some provided Normalizer,
# (ie Sequence([ NFKC(), Replace(Regex("\s+"), " "), Lowercase() ])
# and it should be the prefered way. That being said, here is an example of the kind
# of things that can be done here:
normalized.nfkc()
normalized.filter(lambda char: not char.isnumeric())
normalized.replace(Regex("\s+"), " ")
normalized.lowercase()
# This section shows how to attach these custom components to the Tokenizer
tok = Tokenizer(BPE())
tok.normalizer = Normalizer.custom(CustomNormalizer())
tok.pre_tokenizer = PreTokenizer.custom(JiebaPreTokenizer())
tok.decoder = Decoder.custom(CustomDecoder())
input = "永和服装饰品有限公司"
print("PreTokenize:", input)
print(tok.pre_tokenizer.pre_tokenize_str(input))
# [('永和', (0, 2)), ('服装', (2, 4)), ('饰品', (4, 6)), ('有限公司', (6, 10))]
input = "112233"
print("PreTokenize:", input)
print(tok.pre_tokenizer.pre_tokenize_str(input))
# [('1', (0, 1)), ('122', (1, 4)), ('3', (4, 5)), ('3', (5, 6))]
input = "1234 ℌ𝔢𝔩𝔩𝔬 𝔱𝔥𝔢𝔯𝔢 𝓂𝓎 𝒹ℯ𝒶𝓇 𝕕𝕖𝕒𝕣 𝕗𝕣𝕚𝕖𝕟𝕕!"
print("Normalize:", input)
print(tok.normalizer.normalize_str(input))
def load_jieba_tokenizer(tokenizer_path) -> Tokenizer:
tokenizer = Tokenizer.from_file(str(tokenizer_path))
tokenizer.pre_tokenizer = PreTokenizer.custom(JiebaPreTokenizer())
tokenizer.decoder = Decoder.custom(JiebaDecoder())
return tokenizer
def __setstate__(self, d):
self.__dict__ = d
vocab = self.__dict__["_tokenizer"].get_vocab()
self.__dict__["_tokenizer"].pre_tokenizer = PreTokenizer.custom(
JiebaPreTokenizer(vocab))
