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# coding=utf-8
# Copyright 2025-present, the HuggingFace Inc. Team and AIRAS 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.
from typing import List, Optional, Tuple, Union
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers import AutoTokenizer
import json
import regex as re
from pathlib import Path
from typing import Dict, List, Optional, Union
BYTES_TO_UNICODE_REGEX = re.compile(r"'([^']+)':\s*([0-9]+)")
def bytes_to_unicode():
bs = list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1))
cs = bs[:]
n = 0
for b in range(2**8):
if b not in bs:
bs.append(b)
cs.append(2**8 + n)
n += 1
cs = [chr(n) for n in cs]
return dict(zip(bs, cs))
def get_pairs(word):
pairs = set()
prev_char = word[0]
for char in word[1:]:
pairs.add((prev_char, char))
prev_char = char
return pairs
class SapnousTokenizer(PreTrainedTokenizer):
model_input_names = ["input_ids", "attention_mask"]
def __init__(
self,
vocab_file: str,
merges_file: Optional[str] = None,
unk_token: str = "<|endoftext|>",
bos_token: str = "<|startoftext|>",
eos_token: str = "<|endoftext|>",
pad_token: str = "<|pad|>",
vision_start_token: str = "<|vision_start|>",
vision_end_token: str = "<|vision_end|>",
image_token: str = "<|image|>",
video_token: str = "<|video|>",
add_prefix_space: bool = False,
**kwargs
):
super().__init__(
unk_token=unk_token,
bos_token=bos_token,
eos_token=eos_token,
pad_token=pad_token,
**kwargs,
)
self.vocab_file = vocab_file
self.merges_file = merges_file
self.add_prefix_space = add_prefix_space
self.special_tokens = {
"unk_token": unk_token,
"bos_token": bos_token,
"eos_token": eos_token,
"pad_token": pad_token,
"vision_start_token": vision_start_token,
"vision_end_token": vision_end_token,
"image_token": image_token,
"video_token": video_token,
}
with Path(vocab_file).open(encoding="utf-8") as f:
self.encoder = json.load(f)
self.decoder = {v: k for k, v in self.encoder.items()}
if merges_file:
with Path(merges_file).open(encoding="utf-8") as f:
bpe_merges = f.read().strip().split('\n')[1:]
bpe_merges = [tuple(merge.split()) for merge in bpe_merges]
self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
else:
self.bpe_ranks = {}
self.byte_encoder = bytes_to_unicode()
self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\w+| ?\d+| ?[^\s\w\d]+|\s+(?!\S)|\s+""")
def bpe(self, token: str) -> str:
if token in self.special_tokens.values():
return token
word = tuple(token)
pairs = get_pairs(word)
if not pairs:
return token
while True:
bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float('inf')))
if bigram not in self.bpe_ranks:
break
first, second = bigram
new_word = []
i = 0
while i < len(word):
try:
j = word.index(first, i)
new_word.extend(word[i:j])
if word[j + 1] == second:
new_word.append(first + second)
i = j + 2
else:
new_word.append(word[j])
i = j + 1
except ValueError:
new_word.extend(word[i:])
break
word = tuple(new_word)
if len(word) == 1:
break
pairs = get_pairs(word)
return ' '.join(word)
def _tokenize(self, text: str) -> List[str]:
if self.add_prefix_space:
text = ' ' + text
bpe_tokens = []
for token in re.findall(self.pat, text):
token = ''.join(self.byte_encoder[ord(b)] for b in token)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(' '))
return bpe_tokens
def _convert_token_to_id(self, token: str) -> int:
return self.encoder.get(token, self.encoder.get(self.unk_token))
def _convert_id_to_token(self, index: int) -> str:
return self.decoder.get(index, self.unk_token)
def convert_tokens_to_string(self, tokens: List[str]) -> str:
text = ''.join(tokens)
text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors='replace')
return text
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str, str]:
if not filename_prefix:
filename_prefix = ""
vocab_file = Path(save_directory) / f"{filename_prefix}vocab.json"
merge_file = Path(save_directory) / f"{filename_prefix}merges.txt"
with vocab_file.open('w', encoding='utf-8') as f:
json.dump(self.encoder, f, ensure_ascii=False)
if self.merges_file:
with merge_file.open('w', encoding='utf-8') as f:
for merge in self.bpe_ranks:
f.write(f"{merge[0]} {merge[1]}\n")
return str(vocab_file), str(merge_file)
return str(vocab_file)
def prepare_for_vision(self, text: str) -> str:
"""Prepare text for vision tasks by adding special tokens."""
return f"{self.vision_start_token}{text}{self.vision_end_token}"
def prepare_for_image(self, text: str) -> str:
"""Prepare text for image tasks."""
return f"{self.image_token}{text}"
def prepare_for_video(self, text: str) -> str:
"""Prepare text for video tasks."""
return f"{self.video_token}{text}"
@property
def vocab_size(self) -> int:
return len(self.encoder)
def get_vocab(self) -> Dict[str, int]:
return self.encoder.copy()
# Register the tokenizer
AutoTokenizer.register(SapnousTokenizer, "sapnous") |