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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))
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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))
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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,
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)
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self.vocab_file = vocab_file
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self.merges_file = merges_file
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self.add_prefix_space = add_prefix_space
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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+""")
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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')))
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if bigram not in self.bpe_ranks:
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break
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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
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word = tuple(new_word)
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if len(word) == 1:
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break
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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
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def _convert_token_to_id(self, token: str) -> int:
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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:
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text = ''.join(tokens)
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text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors='replace')
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return text
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def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str, str]:
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if not filename_prefix:
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filename_prefix = ""
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vocab_file = Path(save_directory) / f"{filename_prefix}vocab.json"
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merge_file = Path(save_directory) / f"{filename_prefix}merges.txt"
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with vocab_file.open('w', encoding='utf-8') as f:
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json.dump(self.encoder, f, ensure_ascii=False)
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if self.merges_file:
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with merge_file.open('w', encoding='utf-8') as f:
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for merge in self.bpe_ranks:
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f.write(f"{merge[0]} {merge[1]}\n")
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return str(vocab_file), str(merge_file)
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return str(vocab_file)
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def prepare_for_vision(self, text: str) -> str:
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"""Prepare text for vision tasks by adding special tokens."""
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return f"{self.vision_start_token}{text}{self.vision_end_token}"
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def prepare_for_image(self, text: str) -> str:
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"""Prepare text for image tasks."""
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return f"{self.image_token}{text}"
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def prepare_for_video(self, text: str) -> str:
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"""Prepare text for video tasks."""
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return f"{self.video_token}{text}"
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@property
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def vocab_size(self) -> int:
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return len(self.encoder)
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def get_vocab(self) -> Dict[str, int]:
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return self.encoder.copy()
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AutoTokenizer.register(SapnousTokenizer, "sapnous") |
