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| from typing import List, Optional, Tuple, Union
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| from transformers.tokenization_utils import PreTrainedTokenizer
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| from transformers import AutoTokenizer
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| import json
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| import regex as re
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| from pathlib import Path
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| from typing import Dict, List, Optional, Union
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|
|
| BYTES_TO_UNICODE_REGEX = re.compile(r"'([^']+)':\s*([0-9]+)")
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|
|
| def bytes_to_unicode():
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| bs = list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1))
|
| cs = bs[:]
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| n = 0
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| for b in range(2**8):
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| if b not in bs:
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| bs.append(b)
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| cs.append(2**8 + n)
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| n += 1
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| cs = [chr(n) for n in cs]
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| return dict(zip(bs, cs))
|
|
|
| def get_pairs(word):
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| pairs = set()
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| prev_char = word[0]
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| for char in word[1:]:
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| pairs.add((prev_char, char))
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| prev_char = char
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| return pairs
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|
|
| class SapnousTokenizer(PreTrainedTokenizer):
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| model_input_names = ["input_ids", "attention_mask"]
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|
|
| def __init__(
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| self,
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| vocab_file: str,
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| merges_file: Optional[str] = None,
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| unk_token: str = "<|endoftext|>",
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| bos_token: str = "<|startoftext|>",
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| eos_token: str = "<|endoftext|>",
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| pad_token: str = "<|pad|>",
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| vision_start_token: str = "<|vision_start|>",
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| vision_end_token: str = "<|vision_end|>",
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| image_token: str = "<|image|>",
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| video_token: str = "<|video|>",
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| add_prefix_space: bool = False,
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| **kwargs
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| ):
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| super().__init__(
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| unk_token=unk_token,
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| bos_token=bos_token,
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| eos_token=eos_token,
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| pad_token=pad_token,
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| **kwargs,
|
| )
|
|
|
| self.vocab_file = vocab_file
|
| self.merges_file = merges_file
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| self.add_prefix_space = add_prefix_space
|
|
|
| self.special_tokens = {
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| "unk_token": unk_token,
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| "bos_token": bos_token,
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| "eos_token": eos_token,
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| "pad_token": pad_token,
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| "vision_start_token": vision_start_token,
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| "vision_end_token": vision_end_token,
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| "image_token": image_token,
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| "video_token": video_token,
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| }
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|
|
| with Path(vocab_file).open(encoding="utf-8") as f:
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| self.encoder = json.load(f)
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| self.decoder = {v: k for k, v in self.encoder.items()}
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|
|
| if merges_file:
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| with Path(merges_file).open(encoding="utf-8") as f:
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| bpe_merges = f.read().strip().split('\n')[1:]
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| bpe_merges = [tuple(merge.split()) for merge in bpe_merges]
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| self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
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| else:
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| self.bpe_ranks = {}
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|
|
| self.byte_encoder = bytes_to_unicode()
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| self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
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| 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:
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| if token in self.special_tokens.values():
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| return token
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|
|
| word = tuple(token)
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| pairs = get_pairs(word)
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|
|
| if not pairs:
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| return token
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|
|
| while True:
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| bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float('inf')))
|
| if bigram not in self.bpe_ranks:
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| break
|
| first, second = bigram
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| new_word = []
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| i = 0
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| while i < len(word):
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| try:
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| j = word.index(first, i)
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| new_word.extend(word[i:j])
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| if word[j + 1] == second:
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| new_word.append(first + second)
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| i = j + 2
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| else:
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| new_word.append(word[j])
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| i = j + 1
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| except ValueError:
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| new_word.extend(word[i:])
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| break
|
| word = tuple(new_word)
|
| if len(word) == 1:
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| break
|
| pairs = get_pairs(word)
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| return ' '.join(word)
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|
|
| def _tokenize(self, text: str) -> List[str]:
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| if self.add_prefix_space:
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| text = ' ' + text
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|
|
| bpe_tokens = []
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| for token in re.findall(self.pat, text):
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| token = ''.join(self.byte_encoder[ord(b)] for b in token)
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| bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(' '))
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| return bpe_tokens
|
|
|
| def _convert_token_to_id(self, token: str) -> int:
|
| return self.encoder.get(token, self.encoder.get(self.unk_token))
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|
|
| def _convert_id_to_token(self, index: int) -> str:
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| return self.decoder.get(index, self.unk_token)
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|
|
| def convert_tokens_to_string(self, tokens: List[str]) -> str:
|
| text = ''.join(tokens)
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| 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()
|
|
|
|
|
| AutoTokenizer.register(SapnousTokenizer, "sapnous") |
