# coding=utf-8
#
# Everdoubling LLC.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# The following code is modified from HuggingFace's ByT5 Tokenizer: transformers/models/byt5/tokenization_byt5.py
#
""" Tokenization class for model ByT5."""
import warnings
from typing import Dict, List, Optional, Tuple, Union
from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
from transformers.models.byt5.tokenization_byt5 import ByT5Tokenizer
class ByT5KoreanTokenizer(PreTrainedTokenizer):
"""
Construct a ByT5Korean tokenizer.
On top of ByT5's simple raw bytes utf-8 encoding, ByT5Korean adds extra tokens for Korean jamo.
This tokenizer inherits from :class:`~transformers.PreTrainedTokenizer` which contains most of the main methods.
Users should refer to this superclass for more information regarding those methods.
Args:
eos_token (:obj:`str`, `optional`, defaults to :obj:`"</s>"`):
The end of sequence token.
.. note::
When building a sequence using special tokens, this is not the token that is used for the end of
sequence. The token used is the :obj:`sep_token`.
unk_token (:obj:`str`, `optional`, defaults to :obj:`"<unk>"`):
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.
pad_token (:obj:`str`, `optional`, defaults to :obj:`"<pad>"`):
The token used for padding, for example when batching sequences of different lengths.
extra_ids (:obj:`int`, `optional`, defaults to 100):
Add a number of extra ids added to the end of the vocabulary for use as sentinels. These tokens are
accessible as "<extra_id_{%d}>" where "{%d}" is a number between 0 and extra_ids-1. Extra tokens are
indexed from the end of the vocabulary up to beginning ("<extra_id_0>" is the last token in the vocabulary
like in ByT5 preprocessing see `here
<https://github.com/google-research/text-to-text-transfer-transformer/blob/9fd7b14a769417be33bc6c850f9598764913c833/t5/data/preprocessors.py#L2117>`__).
additional_special_tokens (:obj:`List[str]`, `optional`):
Additional special tokens used by the tokenizer.
"""
model_input_names = ["input_ids", "attention_mask"]
def __init__(
self,
eos_token="</s>",
unk_token="<unk>",
pad_token="<pad>",
extra_ids=57,
additional_special_tokens=None,
**kwargs
) -> None:
# Add extra_ids to the special token list
if extra_ids > 0 and additional_special_tokens is None:
additional_special_tokens = [f"<extra_id_{i}>" for i in range(extra_ids)]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra_id special tokens
extra_tokens = len(set(filter(lambda x: bool("extra_id" in str(x)), additional_special_tokens)))
if extra_tokens != extra_ids:
raise ValueError(
f"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are provided to ByT5Tokenizer. "
"In this case the additional_special_tokens must include the extra_ids tokens"
)
pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
super().__init__(
eos_token=eos_token,
unk_token=unk_token,
pad_token=pad_token,
extra_ids=extra_ids,
additional_special_tokens=additional_special_tokens,
**kwargs,
)
self._extra_ids = extra_ids
# Add the special tokens (including extra_ids)
for token in self.all_special_tokens:
self.tokens_trie.add(token)
self._utf_vocab_size = 2 ** 8 # utf is 8 bits
self._utf_vocab_size += 19 + 21 + 28 # korean jamo
# define special tokens dict
self.special_tokens_encoder: Dict[int, str] = {
self.pad_token: 0,
self.eos_token: 1,
self.unk_token: 2,
}
self._num_special_tokens = len(self.special_tokens_encoder)
n = len(additional_special_tokens)
for i, token in enumerate(additional_special_tokens):
self.special_tokens_encoder[token] = self.vocab_size + i - n
self.special_tokens_decoder: Dict[str, int] = {v: k for k, v in self.special_tokens_encoder.items()}
@property
def vocab_size(self):
return self._utf_vocab_size + self._num_special_tokens + self._extra_ids
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer ``prepare_for_model`` method.
Args:
token_ids_0 (:obj:`List[int]`):
List of IDs.
token_ids_1 (:obj:`List[int]`, `optional`):
Optional second list of IDs for sequence pairs.
already_has_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not the token list is already formatted with special tokens for the model.
Returns:
:obj:`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
)
# normal case: some special tokens
if token_ids_1 is None:
return ([0] * len(token_ids_0)) + [1]
return ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
def _add_eos_if_not_present(self, token_ids: List[int]) -> List[int]:
"""Do not add eos again if user already added it."""
if len(token_ids) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn(
f"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated eos tokens being added."
)
return token_ids
else:
return token_ids + [self.eos_token_id]
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. ByT5 does not
make use of token type ids, therefore a list of zeros is returned.
Args:
token_ids_0 (:obj:`List[int]`):
List of IDs.
token_ids_1 (:obj:`List[int]`, `optional`):
Optional second list of IDs for sequence pairs.
Returns:
:obj:`List[int]`: List of zeros.
"""
eos = [self.eos_token_id]
if token_ids_1 is None:
return len(token_ids_0 + eos) * [0]
return len(token_ids_0 + eos + token_ids_1 + eos) * [0]
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A sequence has the following format:
- single sequence: ``X </s>``
- pair of sequences: ``A </s> B </s>``
Args:
token_ids_0 (:obj:`List[int]`):
List of IDs to which the special tokens will be added.
token_ids_1 (:obj:`List[int]`, `optional`):
Optional second list of IDs for sequence pairs.
Returns:
:obj:`List[int]`: List of `input IDs <../glossary.html#input-ids>`__ with the appropriate special tokens.
"""
token_ids_0 = self._add_eos_if_not_present(token_ids_0)
if token_ids_1 is None:
return token_ids_0
else:
token_ids_1 = self._add_eos_if_not_present(token_ids_1)
return token_ids_0 + token_ids_1
def _convert_char_to_tokens_Korean(self, c):
o = ord(c)
if 44032 <= o and o <= 55203: # 44032: 가, 55203: 힣
o -= 44032
return [chr(256 + (o // 588)), chr(256 + 19 + ((o % 588) // 28)), chr(256 + 19 + 21 + (o % 28))]
return [chr(i) for i in c.encode("utf-8")]
def _tokenize(self, text: str) -> List[str]:
"""Take as input a string and return a list of strings (tokens) for words/sub-words"""
if text in self.all_special_tokens:
return [text]
# return [self.special_tokens_encoder[text]]
# tokens = [chr(i) for i in text.encode("utf-8")]
# return tokens
return sum([self._convert_char_to_tokens_Korean(c) for c in text], [])
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
if token in self.special_tokens_encoder:
token_id = self.special_tokens_encoder[token]
elif token in self.added_tokens_encoder:
token_id = self.added_tokens_encoder[token]
# else:
# token_id = token + self._num_special_tokens
elif len(token) != 1:
token_id = self.unk_token_id
else:
token_id = ord(token) + self._num_special_tokens
return token_id
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
if index in self.special_tokens_decoder:
token = self.special_tokens_decoder[index]
else:
token = chr(index - self._num_special_tokens)
return token
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (string) in a single string."""
bstring = b""
ids = [ord(t[0]) for t in tokens]
for i in range(len(ids)-2):
if 256 <= ids[i] and ids[i] < 256+19 and 256+19 <= ids[i+1] and ids[i+1] < 256+19+21 and 256+19+21 <= ids[i+2] and ids[i+2] < 256+19+21+28:
tokens[i] = chr(44032 + (ids[i]-256)*21*28 + (ids[i+1]-256-19)*28 + (ids[i+2]-256-19-21))
tokens[i+1] = None
tokens[i+2] = None
for token in tokens:
if token == None:
continue
if token in self.special_tokens_decoder:
tok_string = self.special_tokens_decoder[token].encode("utf-8")
elif token in self.added_tokens_decoder:
tok_string = self.special_tokens_decoder[token].encode("utf-8")
elif token in self.special_tokens_encoder:
tok_string = token.encode("utf-8")
elif token in self.added_tokens_encoder:
tok_string = token.encode("utf-8")
else:
if type(token) == str and ord(token) >= 256:
tok_string = token.encode("utf-8")
else:
tok_string = bytes([ord(token) if type(token) == str else min(255, token)])
bstring += tok_string
string = bstring.decode("utf-8", errors="ignore")
return string
# ByT5KoreanTokenizer has no vocab file
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
return ()
if __name__ == "__main__":
tokenizer = ByT5KoreanTokenizer()
text = "This is a test <extra_id_0> of the 가나힣 안녕하세요 <extra_id_1>."
tokenized_text = tokenizer.tokenize(text)
print(tokenized_text)
print(tokenizer(text))
print(tokenizer.convert_tokens_to_ids(tokenized_text))
print(tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids(tokenized_text)))
print(tokenizer.convert_tokens_to_string(tokenized_text))