""" Final Tokenizer wrapper - encode/decode with BPE merges, handles special tokens, sections. Implements token sections for prompt formatting. """ import json import re from typing import List, Dict import os class AresTokenizer: def __init__(self, vocab_file: str = None, vocab_size: int = 128256): self.vocab_file = vocab_file self.vocab = {} self.inverse_vocab = {} self.merges = {} # pair -> rank (lower = earlier) self.merge_ranks = {} self.special_tokens = ["<|pad|>", "<|bos|>", "<|eos|>", "<|unk|>", "<|im_start|>", "<|im_end|>"] self.special_ids = {} # byte encoder mapping from trainer self.byte_encoder = {} self.byte_decoder = {} # GPT-2 style byte mapping fallback if not self.byte_encoder: 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(256): if b not in bs: bs.append(b) cs.append(256+n) n+=1 self.byte_encoder = dict(zip(bs, [chr(c) for c in cs])) 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| ?[A-Za-z]+| ?[0-9]+| ?[^\sA-Za-z0-9]+|\s+(?!\S)|\s+""") if vocab_file and os.path.exists(vocab_file): self.load(vocab_file) else: # Dummy small vocab for tiny config test self._build_dummy_vocab(vocab_size) # Special token ids for tok in self.special_tokens: if tok in self.vocab: self.special_ids[tok] = self.vocab[tok] # Defaults self.pad_token_id = self.special_ids.get("<|pad|>", 0) self.bos_token_id = self.special_ids.get("<|bos|>", 1) self.eos_token_id = self.special_ids.get("<|eos|>", 2) self.unk_token_id = self.special_ids.get("<|unk|>", 3) def _build_dummy_vocab(self, size): # Build minimal byte-level vocab for tests without trained file, but pad to requested size base = {tok:i for i,tok in enumerate(self.special_tokens)} for b in range(256): ch = self.byte_encoder[b] if ch not in base: base[ch] = len(base) # Pad remaining with tok_# idx = len(base) while len(base) < size: tok = f"tok_{idx}" if tok not in base: base[tok] = len(base) idx+=1 if idx>size+10000: break self.vocab = base self.inverse_vocab = {v:k for k,v in base.items()} # No merges self.merges = {} self.merge_ranks = {} def load(self, path): with open(path, 'r', encoding='utf-8') as f: data = json.load(f) self.vocab = data["vocab"] # ensure int ids # merges stored as "a b": rank raw_merges = data.get("merges", {}) self.merges = {} for k,v in raw_merges.items(): parts = k.split(' ') if len(parts)==2: self.merges[(parts[0], parts[1])] = v # sort by rank for BPE self.merge_ranks = {pair: rank for pair, rank in sorted(self.merges.items(), key=lambda x: x[1])} self.special_tokens = data.get("special_tokens", self.special_tokens) self.byte_encoder = data.get("byte_encoder", self.byte_encoder) self.byte_decoder = {v:k for k,v in self.byte_encoder.items()} self.inverse_vocab = {v:k for k,v in self.vocab.items()} def get_pairs(self, word): pairs = set() prev = word[0] for ch in word[1:]: pairs.add((prev, ch)) prev = ch return pairs def bpe(self, token): # token is string of byte-encoded chars if token in self.vocab: return [token] word = list(token) pairs = self.get_pairs(word) if not pairs: return [token] while True: # Find best pair by lowest rank bigram = min(pairs, key=lambda pair: self.merge_ranks.get(pair, float('inf'))) if bigram not in self.merge_ranks: break first, second = bigram new_word = [] i=0 while i < len(word): try: j = word.index(first, i) except: new_word.extend(word[i:]) break new_word.extend(word[i:j]) i=j if i < len(word)-1 and word[i]==first and word[i+1]==second: new_word.append(first+second) i+=2 else: new_word.append(word[i]) i+=1 word = new_word if len(word)==1: break else: pairs = self.get_pairs(word) return word def encode(self, text: str, add_bos=False, add_eos=False) -> List[int]: tokens = [] if add_bos: tokens.append(self.bos_token_id) # handle special tokens as separate sections # Split by special tokens # Simple: iterate over pat for tok in re.findall(self.pat, text): # byte encode try: encoded = ''.join(self.byte_encoder[b] for b in tok.encode('utf-8')) except: encoded = tok bpe_tokens = self.bpe(encoded) for bt in bpe_tokens: tokens.append(self.vocab.get(bt, self.unk_token_id)) if add_eos: tokens.append(self.eos_token_id) return tokens def decode(self, ids: List[int], skip_special=True) -> str: text = '' for i in ids: token = self.inverse_vocab.get(i, "<|unk|>") if skip_special and token in self.special_tokens: continue text += token # byte decode # Convert unicode chars back to bytes try: # text is sequence of byte-encoded unicode chars byte_vals = [] for c in text: if c in self.byte_decoder: byte_vals.append(self.byte_decoder[c]) else: # might be merged token containing multiple bytes encoded chars # Decode each char inside token for ch in c: if ch in self.byte_decoder: byte_vals.append(self.byte_decoder[ch]) decoded = bytes(byte_vals).decode('utf-8', errors='replace') return decoded except Exception: return text def encode_with_sections(self, messages: List[Dict[str,str]]) -> List[int]: """ Token sections for chat: [{"role":"user","content":"..."}] Format: <|im_start|>role\ncontent<|im_end|> """ ids = [] for m in messages: role = m.get("role","user") content = m.get("content","") start = f"<|im_start|>{role}\n" end = "<|im_end|>\n" # Encode sections for part in [start, content, end]: if part in self.special_tokens or part.startswith("<|im_"): # handle special split # start contains special token substring if "<|im_start|>" in part: ids.append(self.vocab.get("<|im_start|>", self.special_ids.get("<|im_start|>", 4))) # remainder after remainder = part.replace("<|im_start|>","") if remainder: ids.extend(self.encode(remainder)) elif "<|im_end|>" in part: # check if content includes im_end if part.strip()=="<|im_end|>": ids.append(self.vocab.get("<|im_end|>", self.special_ids.get("<|im_end|>",5))) else: # split ids.extend(self.encode(part.replace("<|im_end|>",""))) ids.append(self.vocab.get("<|im_end|>",5)) else: ids.append(self.vocab.get(part, self.unk_token_id)) else: ids.extend(self.encode(part)) return ids def save(self, path): data = { "vocab": self.vocab, "merges": {f"{k[0]} {k[1]}": v for k,v in self.merges.items()}, "special_tokens": self.special_tokens, "byte_encoder": self.byte_encoder } with open(path,'w',encoding='utf-8') as f: json.dump(data,f,ensure_ascii=False)