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tokenization_gptsan_japanese.py
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tokenization_gptsan_japanese.py
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# coding=utf-8
# Copyright 2023 HuggingFace Inc. team. All rights reserved.
#
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# 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.
"""Tokenization classes for GPTSANJapanese."""
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import (
BatchEncoding,
PreTokenizedInput,
PreTokenizedInputPair,
TextInput,
TextInputPair,
TruncationStrategy,
)
from ...utils import PaddingStrategy, logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt", "emoji_file": "emoji.json"}
PRETRAINED_VOCAB_FILES_MAP = {
"vocab_file": {
"Tanrei/GPTSAN-japanese": "https://huggingface.co/Tanrei/GPTSAN-japanese/blob/main/vocab.txt",
},
"emoji_file": {
"Tanrei/GPTSAN-japanese": "https://huggingface.co/Tanrei/GPTSAN-japanese/blob/main/emoji.json",
},
}
PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
"Tanrei/GPTSAN-japanese": 1280,
}
def load_vocab_and_emoji(vocab_file, emoji_file):
"""Loads a vocabulary file and emoji file into a dictionary."""
with open(emoji_file, "r", encoding="utf-8") as f:
emoji = json.loads(f.read())
vocab = collections.OrderedDict()
raw_vocab = collections.OrderedDict()
ids_to_tokens = collections.OrderedDict()
with open(vocab_file, "r", encoding="utf-8") as f:
token = f.readlines()
token = [[t.rstrip("\n")] if (t == ",\n" or "," not in t) else t.rstrip("\n").split(",") for t in token]
for idx, b in enumerate(token):
ids_to_tokens[idx] = b
raw_vocab[",".join(b)] = idx
for wd in b:
vocab[wd] = idx
return vocab, raw_vocab, ids_to_tokens, emoji
class GPTSanJapaneseTokenizer(PreTrainedTokenizer):
"""
This tokenizer is based on GPTNeoXJapaneseTokenizer and has the following modifications
- Decoding byte0~byte255 tokens correctly
- Added bagofword token handling
- Return token_type_ids for Prefix-LM model
The bagofword token represents a repetition of the previous token and is converted to 3 consecutive tokens when
decoding In addition, the original Japanese special Sub-Word-Encoding has been released in this repository
(https://github.com/tanreinama/Japanese-BPEEncoder_V2). The token_type_ids is a mask indicating the prefix input
position of the Prefix-LM model. To specify a prefix position, specify a prefix input for prefix_text, or specify a
sentence of the prefix part and the part after it as a text pair of batch input.
Example:
```python
>>> from transformers import GPTSanJapaneseTokenizer
>>> tokenizer = GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese")
>>> # You can confirm both 慶応 and 慶應 are encoded to 17750
>>> tokenizer("吾輩は猫である🐯。実は慶応(慶應)大学出身")["input_ids"]
[35993, 35998, 34347, 31459, 30647, 31448, 25, 30659, 35729, 35676, 32417, 30647, 17750, 35589, 17750, 35590, 321, 1281]
>>> # Both 慶応 and 慶應 are decoded to 慶応
>>> tokenizer.decode(tokenizer("吾輩は猫である🐯。実は慶応(慶應)大学出身")["input_ids"])
'吾輩は猫である🐯。実は慶応(慶応)大学出身'
```
Example for Prefix-LM:
```python
>>> from transformers import GPTSanJapaneseTokenizer
>>> tokenizer = GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese")
>>> tokenizer("実は慶応(慶應)大学出身", prefix_text="吾輩は猫である🐯。")["input_ids"]
[35993, 34347, 31459, 30647, 31448, 25, 30659, 35729, 35676, 35998, 32417, 30647, 17750, 35589, 17750, 35590, 321, 1281]
>>> # Mask for Prefix-LM inputs
>>> tokenizer("実は慶応(慶應)大学出身", prefix_text="吾輩は猫である🐯。")["token_type_ids"]
[1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]
```
Example for batch encode:
```python
>>> from transformers import GPTSanJapaneseTokenizer
>>> tokenizer = GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese")
>>> tokenizer([["武田信玄", "は、"], ["織田信長", "の配下の、"]], padding=True)["input_ids"]
[[35993, 8640, 25948, 35998, 30647, 35675, 35999, 35999], [35993, 10382, 9868, 35998, 30646, 9459, 30646, 35675]]
>>> # Mask for Prefix-LM inputs
>>> tokenizer([["武田信玄", "は、"], ["織田信長", "の配下の、"]], padding=True)["token_type_ids"]
[[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
>>> # Mask for padding
>>> tokenizer([["武田信玄", "は、"], ["織田信長", "の配下の、"]], padding=True)["attention_mask"]
[[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
```
Args:
vocab_file (`str`):
File containing the vocabulary.
emoji_file (`str`):
File containing the emoji.
unk_token (`str`, *optional*, defaults to `"<|nottoken|>"`):
The token used for unknown charactor
pad_token (`str`, *optional*, defaults to `"<|separator|>"`):
The token used for padding
bos_token (`str`, *optional*, defaults to `"<|startoftext|>""`):
The beginning of sequence token.
eos_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
The end of sequence token.
sep_token (`str`, *optional*, defaults to `"<|segmenter|>"`):
A special token to separate token to prefix part and general input part.
do_clean_text (`bool`, *optional*, defaults to `False`):
Whether or not to clean text for URL, EMAIL, TEL, Japanese DATE and Japanese PRICE.
"""
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
model_input_names = ["input_ids", "attention_mask", "token_type_ids"]
def __init__(
self,
vocab_file,
emoji_file,
unk_token="<|nottoken|>",
pad_token="<|separator|>",
bos_token="<|startoftext|>",
eos_token="<|endoftext|>",
sep_token="<|segmenter|>",
do_clean_text=False,
**kwargs,
):
super().__init__(
unk_token=unk_token,
pad_token=pad_token,
bos_token=bos_token,
eos_token=eos_token,
sep_token=sep_token,
do_clean_text=do_clean_text,
**kwargs,
)
if not os.path.isfile(vocab_file):
raise ValueError(
f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"
" model use `tokenizer = GPTSanJapaneseTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
)
if not os.path.isfile(emoji_file):
raise ValueError(
f"Can't find a emoji file at path '{emoji_file}'. To load the emoji information from a Google"
" pretrained model use `tokenizer = GPTSanJapaneseTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
)
self.do_clean_text = do_clean_text
self.vocab, self.raw_vocab, self.ids_to_tokens, self.emoji = load_vocab_and_emoji(vocab_file, emoji_file)
self.subword_tokenizer = SubWordJapaneseTokenizer(
vocab=self.vocab, ids_to_tokens=self.ids_to_tokens, emoji=self.emoji
)
@property
# Copied from tokenization_gpt_neox_japanese.GPTNeoXJapaneseTokenizer.vocab_size
def vocab_size(self):
# self.vocab contains support for character fluctuation unique to Japanese, and has a large number of vocab
return len(self.raw_vocab)
# Copied from tokenization_gpt_neox_japanese.GPTNeoXJapaneseTokenizer.get_vocab
def get_vocab(self):
return dict(self.raw_vocab, **self.added_tokens_encoder)
# Copied from tokenization_gpt_neox_japanese.GPTNeoXJapaneseTokenizer._tokenize
def _tokenize(self, text):
return self.subword_tokenizer.tokenize(text, clean=self.do_clean_text)
# Copied from tokenization_gpt_neox_japanese.GPTNeoXJapaneseTokenizer._convert_token_to_id
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
return self.vocab.get(token, self.vocab.get(self.unk_token))
# Copied from tokenization_gpt_neox_japanese.GPTNeoXJapaneseTokenizer._convert_id_to_token
def _convert_id_to_token(self, index):
"""Converts an index (integer) in a token (str) using the vocab."""
return self.subword_tokenizer.convert_id_to_token(index)
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (string) in a single string."""
words = []
byte_tokens = []
for word in tokens:
if word[:6] == "<|byte" and word[-2:] == "|>":
byte_tokens.append(int(word[6:-2]))
else:
if len(byte_tokens) > 0:
words.append(bytearray(byte_tokens).decode("utf-8", errors="replace"))
byte_tokens = []
if word[:7] == "<|emoji" and word[-2:] == "|>":
words.append(self.emoji["emoji_inv"][word])
elif word == "<SP>":
words.append(" ")
elif word == "<BR>":
words.append("\n")
elif word == "<TAB>":
words.append("\t")
elif word == "<BLOCK>":
words.append("▀")
elif word == "<KIGOU>":
words.append("ǀ")
elif word == "<U2000U2BFF>":
words.append("‖")
elif word == "<|bagoftoken|>":
if len(words) > 0:
words.append(words[-1])
words.append(words[-1])
words.append(words[-1])
elif word.startswith("<|") and word.endswith("|>"):
words.append("")
else:
words.append(word)
if len(byte_tokens) > 0:
words.append(bytearray(byte_tokens).decode("utf-8", errors="replace"))
text = "".join(words)
return text
# Copied from tokenization_gpt_neox_japanese.GPTNeoXJapaneseTokenizer._build_conversation_input_ids
def _build_conversation_input_ids(self, conversation: "Conversation") -> List[int]:
"""This corresponds to DialoGPT variants of models."""
input_ids = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(text, add_special_tokens=False) + [self.eos_token_id])
if len(input_ids) > self.model_max_length:
input_ids = input_ids[-self.model_max_length :]
return input_ids
# Copied from tokenization_gpt_neox_japanese.GPTNeoXJapaneseTokenizer.save_vocabulary
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
index = 0
if os.path.isdir(save_directory):
vocab_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
)
emoji_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["emoji_file"]
)
else:
vocab_file = (
(filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["vocab_file"]
)
emoji_file = (
(filename_prefix + "-" if filename_prefix else "") + save_directory + VOCAB_FILES_NAMES["emoji_file"]
)
with open(vocab_file, "w", encoding="utf-8") as writer:
for token_index, token in self.ids_to_tokens.items():
if index != token_index:
logger.warning(
f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
" Please check that the vocabulary is not corrupted!"
)
index = token_index
writer.write(",".join(token) + "\n")
index += 1
with open(emoji_file, "w", encoding="utf-8") as writer:
json.dump(self.emoji, writer)
return vocab_file, emoji_file
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
# docstyle-ignore
"""
The tokenizer returns token_type_ids as separators between the Prefix part and the rest.
token_type_ids is 1 for the Prefix part and 0 for the rest of the token.
Example:
```python
>>> from transformers import GPTSanJapaneseTokenizer
>>> tokenizer = GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese")
>>> x_token = tokenizer("アイウエ")
>>> # input_ids: | SOT | SEG | ア | イ | ウ | エ |
>>> # token_type_ids: | 1 | 0 | 0 | 0 | 0 | 0 |
>>> x_token = tokenizer("", prefix_text="アイウエ")
>>> # input_ids: | SOT | ア | イ | ウ | エ | SEG |
>>> # token_type_ids: | 1 | 1 | 1 | 1 | 1 | 0 |
>>> x_token = tokenizer("ウエ", prefix_text="アイ")
>>> # input_ids: | SOT | ア | イ | SEG | ウ | エ |
>>> # token_type_ids: | 1 | 1 | 1 | 0 | 0 | 0 |
```"""
prefix_len = 0
if self.sep_token in self.vocab:
segid = self.vocab[self.sep_token]
if segid in token_ids_0:
prefix_len = token_ids_0.index(segid)
if token_ids_1 is None:
total_len = len(token_ids_0)
else:
total_len = len(token_ids_0 + token_ids_1)
return prefix_len * [1] + (total_len - prefix_len) * [0]
def prepare_for_tokenization(self, text, prefix_text=None, add_sep_token=None, **kwargs):
# GPTSAN inserts extra SEP tokens in Prefix-LM in addition to SOT for text generation.
# SOT at the beginning of the text, and SEP at the separator between the Prefix part and the rest.
if add_sep_token is None:
add_sep_token = self.sep_token not in text # If insert un-prefix position explicitly
prepared = self.bos_token if self.bos_token in self.vocab else ""
prepared += prefix_text if prefix_text is not None else ""
if add_sep_token:
prepared += self.sep_token if self.sep_token in self.vocab else ""
prepared += text
return (prepared, kwargs)
def _batch_encode_plus(
self,
batch_text_or_text_pairs: Union[
List[TextInput], List[TextInputPair], List[PreTokenizedInput], List[PreTokenizedInputPair]
],
add_special_tokens: bool = True,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
max_length: Optional[int] = None,
stride: int = 0,
is_split_into_words: bool = False,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[str] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
) -> BatchEncoding:
# This tokenizer converts input text pairs into Prefix input and subsequent input
if type(batch_text_or_text_pairs[0]) is tuple or type(batch_text_or_text_pairs[0]) is list:
# As a single text with an explicit un-prefix position
batch_prefix_texts = []
for pref, txt in batch_text_or_text_pairs:
batch_prefix_texts.append(pref + self.sep_token + txt)
batch_text_or_text_pairs = batch_prefix_texts
return super()._batch_encode_plus(
batch_text_or_text_pairs,
add_special_tokens,
padding_strategy,
truncation_strategy,
max_length,
stride,
is_split_into_words,
pad_to_multiple_of,
return_tensors,
return_token_type_ids,
return_attention_mask,
return_overflowing_tokens,
return_special_tokens_mask,
return_offsets_mapping,
return_length,
verbose,
)
class SubWordJapaneseTokenizer(object):
"""
This tokenizer is based on GPTNeoXJapaneseTokenizer and has the following modifications
- Decoding byte0~byte255 tokens correctly
- Added bagofword token handling
https://github.com/tanreinama/Japanese-BPEEncoder_V2 This tokenizer class is under MIT Lisence according to the
original repository.
MIT License
Copyright (c) 2020 tanreinama
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to
permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of
the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO
THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""
# Copied from tokenization_gpt_neox_japanese.SubWordJapaneseTokenizer.__init__
def __init__(self, vocab, ids_to_tokens, emoji):
self.vocab = vocab # same as swe
self.ids_to_tokens = ids_to_tokens # same as bpe
self.emoji = emoji
self.maxlen = np.max([len(w) for w in self.vocab.keys()])
self.content_repatter1 = re.compile(r"(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)")
self.content_repatter2 = re.compile(r"[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*")
self.content_repatter3 = re.compile(r"[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}")
self.content_repatter4 = re.compile(
r"([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*"
)
self.content_repatter5 = re.compile(
r"(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*"
)
self.content_repatter6 = re.compile(
r"((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*"
)
keisen = "─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿"
blocks = "▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟"
self.content_trans1 = str.maketrans({k: "<BLOCK>" for k in keisen + blocks})
# Copied from tokenization_gpt_neox_japanese.SubWordJapaneseTokenizer.__len__
def __len__(self):
return len(self.ids_to_tokens)
# Copied from tokenization_gpt_neox_japanese.SubWordJapaneseTokenizer.clean_text
def clean_text(self, content):
content = self.content_repatter1.sub("<URL>", content)
content = self.content_repatter2.sub("<EMAIL>", content)
content = self.content_repatter3.sub("<TEL>", content)
content = self.content_repatter4.sub("<DATE>", content)
content = self.content_repatter5.sub("<DATE>", content)
content = self.content_repatter6.sub("<PRICE>", content)
content = content.translate(self.content_trans1)
while "<BLOCK><BLOCK>" in content:
content = content.replace("<BLOCK><BLOCK>", "<BLOCK>")
return content
# Copied from tokenization_gpt_neox_japanese.SubWordJapaneseTokenizer.tokenize
def tokenize(self, text, clean=False):
text = text.replace(" ", "<SP>")
text = text.replace(" ", "<SP>")
text = text.replace("\r\n", "<BR>")
text = text.replace("\n", "<BR>")
text = text.replace("\r", "<BR>")
text = text.replace("\t", "<TAB>")
text = text.replace("—", "ー")
text = text.replace("−", "ー")
for k, v in self.emoji["emoji"].items():
if k in text:
text = text.replace(k, v)
if clean:
text = self.clean_text(text)
def check_simbol(x):
e = x.encode()
if len(x) == 1 and len(e) == 2:
c = (int(e[0]) << 8) + int(e[1])
if (
(c >= 0xC2A1 and c <= 0xC2BF)
or (c >= 0xC780 and c <= 0xC783)
or (c >= 0xCAB9 and c <= 0xCBBF)
or (c >= 0xCC80 and c <= 0xCDA2)
):
return True
return False
def checku2e(x):
e = x.encode()
if len(x) == 1 and len(e) == 3:
c = (int(e[0]) << 16) + (int(e[1]) << 8) + int(e[2])
if c >= 0xE28080 and c <= 0xE2B07F:
return True
return False
pos = 0
result = []
while pos < len(text):
end = min(len(text), pos + self.maxlen + 1) if text[pos] == "<" else pos + 3
candidates = [] # (token_id, token, pos)
for e in range(end, pos, -1):
wd = text[pos:e]
if wd in self.vocab:
if wd[0] == "<" and len(wd) > 2:
candidates = [(self.vocab[wd], wd, e)]
break
else:
candidates.append((self.vocab[wd], wd, e))
if len(candidates) > 0:
# the smallest token_id is adopted
_, wd, e = sorted(candidates, key=lambda x: x[0])[0]
result.append(wd)
pos = e
else:
end = pos + 1
wd = text[pos:end]
if check_simbol(wd):
result.append("<KIGOU>")
elif checku2e(wd):
result.append("<U2000U2BFF>")
else:
for i in wd.encode("utf-8"):
result.append("<|byte%d|>" % i)
pos = end
return result
def convert_id_to_token(self, index):
return self.ids_to_tokens[index][0]