""" Enhanced Tokenizer for Hugging Face Integration Improved accuracy with comprehensive vocabulary and encoding """ import json import re from typing import List, Dict, Optional, Union from transformers import PreTrainedTokenizer import os class IlluminatorTokenizer(PreTrainedTokenizer): """ Enhanced tokenizer for the Illuminator model with improved accuracy Compatible with Hugging Face transformers """ vocab_files_names = {"vocab_file": "vocab.json", "merges_file": "merges.txt"} def __init__( self, vocab_file=None, merges_file=None, errors="replace", unk_token="<|unk|>", bos_token="<|bos|>", eos_token="<|eos|>", pad_token="<|pad|>", add_prefix_space=False, **kwargs ): super().__init__( errors=errors, unk_token=unk_token, bos_token=bos_token, eos_token=eos_token, pad_token=pad_token, add_prefix_space=add_prefix_space, **kwargs ) self.add_prefix_space = add_prefix_space # Initialize enhanced vocabulary if vocab_file and os.path.isfile(vocab_file): with open(vocab_file, 'r', encoding='utf-8') as f: self.encoder = json.load(f) else: self.encoder = self._build_enhanced_vocabulary() self.decoder = {v: k for k, v in self.encoder.items()} # Enhanced BPE merges for better subword handling self.bpe_merges = [] if merges_file and os.path.isfile(merges_file): with open(merges_file, 'r', encoding='utf-8') as f: self.bpe_merges = [tuple(line.strip().split()) for line in f.readlines()[1:]] else: self.bpe_merges = self._build_enhanced_bpe_merges() self.bpe_merges_dict = dict(self.bpe_merges) self.cache = {} def _build_enhanced_vocabulary(self) -> Dict[str, int]: """Build comprehensive vocabulary for maximum accuracy""" vocab = {} idx = 0 # Special tokens first special_tokens = [ "<|pad|>", "<|unk|>", "<|bos|>", "<|eos|>", "<|mask|>", "<|sep|>", "<|cls|>", "<|endoftext|>" ] for token in special_tokens: vocab[token] = idx idx += 1 # Bytes for all possible byte values (0-255) for i in range(256): vocab[chr(i)] = idx idx += 1 # Enhanced vocabulary for better accuracy enhanced_words = self._get_enhanced_vocabulary_words() for word in enhanced_words: if word not in vocab: vocab[word] = idx idx += 1 # Common subwords and morphemes subwords = self._get_subword_vocabulary() for subword in subwords: if subword not in vocab: vocab[subword] = idx idx += 1 # Technical terms for better domain coverage technical_terms = self._get_technical_vocabulary() for term in technical_terms: if term not in vocab: vocab[term] = idx idx += 1 return vocab def _get_enhanced_vocabulary_words(self) -> List[str]: """Get enhanced vocabulary for better accuracy""" return [ # High-frequency words "the", "be", "to", "of", "and", "a", "in", "that", "have", "i", "it", "for", "not", "on", "with", "he", "as", "you", "do", "at", "this", "but", "his", "by", "from", "they", "we", "say", "her", "she", "or", "an", "will", "my", "one", "all", "would", "there", "their", # AI/ML terms for domain accuracy "artificial", "intelligence", "machine", "learning", "deep", "neural", "network", "algorithm", "model", "training", "data", "dataset", "feature", "prediction", "classification", "regression", "supervised", "unsupervised", "reinforcement", "attention", "transformer", "embedding", "gradient", "optimization", "backpropagation", "epoch", "batch", "loss", "accuracy", "validation", "testing", # Programming terms "python", "javascript", "java", "cpp", "function", "method", "class", "object", "variable", "parameter", "return", "loop", "condition", "array", "list", "dictionary", "string", "integer", "boolean", "algorithm", "structure", "framework", "library", # Science terms "physics", "chemistry", "biology", "mathematics", "quantum", "relativity", "evolution", "genetics", "climate", "environment", "energy", "force", "matter", "atom", "molecule", "cell", "organism", "ecosystem", "theory", "experiment", "research", # Technology terms "computer", "software", "hardware", "internet", "network", "database", "security", "encryption", "server", "client", "protocol", "application", "system", "platform", "technology", "digital", "electronic", "innovation", "development", # Common prefixes and suffixes "un", "re", "in", "dis", "en", "non", "over", "mis", "sub", "pre", "inter", "fore", "de", "trans", "super", "semi", "anti", "ing", "ed", "er", "est", "ly", "tion", "sion", "ness", "ment", "ful", "less", "able", "ible", "ous", "ious", "ive", ] def _get_subword_vocabulary(self) -> List[str]: """Get subword vocabulary for better tokenization""" return [ # Common letter combinations "th", "he", "in", "er", "an", "re", "ed", "nd", "on", "en", "at", "ou", "it", "is", "or", "ti", "as", "te", "et", "ng", "of", "al", "de", "se", "le", "sa", "si", "ar", "ve", "ra", "ld", "ur", "ly", "ta", "ri", "ne", "me", "nt", "ty", "ic", # Programming patterns "def", "class", "import", "from", "return", "if", "else", "elif", "for", "while", "try", "except", "with", "lambda", "self", "init", "len", "str", "int", "float", "bool", "list", "dict", "set", "tuple", "range", "print", "input", # Technical patterns "http", "https", "www", "com", "org", "net", "api", "json", "xml", "html", "css", "sql", "url", "uri", "uuid", "config", "setup", "install", "version", "update", "upgrade", "debug", "error", "warning", "info", "log", ] def _get_technical_vocabulary(self) -> List[str]: """Get technical vocabulary for domain expertise""" return [ # AI/ML frameworks and tools "pytorch", "tensorflow", "keras", "scikit", "pandas", "numpy", "matplotlib", "jupyter", "colab", "huggingface", "openai", "anthropic", "deepmind", "nvidia", "cuda", "gpu", "cpu", "ram", "memory", "storage", # Cloud and infrastructure "aws", "azure", "gcp", "docker", "kubernetes", "linux", "ubuntu", "centos", "debian", "windows", "server", "cluster", "container", "virtual", "machine", "instance", "deployment", "scaling", # Programming languages and frameworks "react", "angular", "vue", "nodejs", "express", "django", "flask", "fastapi", "spring", "laravel", "mongodb", "postgresql", "mysql", "redis", "elasticsearch", "kafka", "rabbitmq", "nginx", # Version control and development "git", "github", "gitlab", "bitbucket", "branch", "commit", "merge", "pull", "push", "clone", "repository", "fork", "issue", "release", "tag", "workflow", "pipeline", "cicd", "devops", ] def _build_enhanced_bpe_merges(self) -> List[tuple]: """Build enhanced BPE merges for better subword tokenization""" return [ # Common English patterns ("t", "h"), ("h", "e"), ("i", "n"), ("e", "r"), ("a", "n"), ("r", "e"), ("e", "d"), ("n", "d"), ("o", "n"), ("e", "n"), ("a", "t"), ("o", "u"), ("i", "t"), ("i", "s"), ("o", "r"), ("t", "i"), ("a", "s"), ("t", "e"), ("e", "t"), ("n", "g"), ("o", "f"), ("a", "l"), ("d", "e"), ("s", "e"), # Programming patterns ("d", "ef"), ("cl", "ass"), ("im", "port"), ("fr", "om"), ("ret", "urn"), ("sel", "f"), ("in", "it"), ("le", "n"), ("st", "r"), ("in", "t"), ("pr", "int"), ("ran", "ge"), # Technical patterns ("ht", "tp"), ("ww", "w"), ("co", "m"), ("or", "g"), ("ne", "t"), ("ap", "i"), ("js", "on"), ("ht", "ml"), ("cs", "s"), ("sq", "l"), ("ur", "l"), ("uu", "id"), # AI/ML patterns ("ne", "ural"), ("net", "work"), ("mod", "el"), ("tra", "in"), ("dat", "a"), ("acc", "uracy"), ("los", "s"), ("gra", "dient"), ("opt", "im"), ("bat", "ch"), ("epo", "ch"), ("val", "id"), ] def get_vocab(self) -> Dict[str, int]: """Return the vocabulary dictionary""" return self.encoder.copy() @property def vocab_size(self) -> int: """Return the size of vocabulary""" return len(self.encoder) def _tokenize(self, text: str) -> List[str]: """Tokenize text using enhanced BPE""" if not text: return [] # Normalize text text = self._normalize_text(text) # Split into words words = re.findall(r'\S+|\s+', text) tokens = [] for word in words: if word.isspace(): continue # Apply BPE to each word word_tokens = self._bpe_encode(word) tokens.extend(word_tokens) return tokens def _normalize_text(self, text: str) -> str: """Normalize text for better tokenization""" # Handle Unicode normalization import unicodedata text = unicodedata.normalize('NFKD', text) # Handle common programming patterns text = re.sub(r'([a-z])([A-Z])', r'\1 \2', text) # camelCase -> camel Case text = re.sub(r'([a-zA-Z])(\d)', r'\1 \2', text) # word123 -> word 123 text = re.sub(r'(\d)([a-zA-Z])', r'\1 \2', text) # 123word -> 123 word return text def _bpe_encode(self, word: str) -> List[str]: """Apply BPE encoding to a word""" if word in self.cache: return self.cache[word] # Convert to list of characters word_chars = list(word) if len(word_chars) == 1: return word_chars # Apply BPE merges while len(word_chars) > 1: pairs = self._get_pairs(word_chars) if not pairs: break # Find the best pair to merge best_pair = min(pairs, key=lambda x: self.bpe_merges_dict.get(x, float('inf'))) if best_pair not in self.bpe_merges_dict: break # Merge the best pair new_word_chars = [] i = 0 while i < len(word_chars): if (i < len(word_chars) - 1 and word_chars[i] == best_pair[0] and word_chars[i + 1] == best_pair[1]): new_word_chars.append(best_pair[0] + best_pair[1]) i += 2 else: new_word_chars.append(word_chars[i]) i += 1 word_chars = new_word_chars self.cache[word] = word_chars return word_chars def _get_pairs(self, word_chars: List[str]) -> set: """Get all adjacent pairs in the word""" pairs = set() for i in range(len(word_chars) - 1): pairs.add((word_chars[i], word_chars[i + 1])) return pairs def _convert_token_to_id(self, token: str) -> int: """Convert token to ID""" return self.encoder.get(token, self.encoder.get(self.unk_token)) def _convert_id_to_token(self, index: int) -> str: """Convert ID to token""" return self.decoder.get(index, self.unk_token) def convert_tokens_to_string(self, tokens: List[str]) -> str: """Convert tokens back to string""" text = ''.join(tokens) # Clean up the text text = text.replace('', ' ') text = re.sub(r' +', ' ', text) return text.strip() def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple: """Save vocabulary files""" if not os.path.isdir(save_directory): os.makedirs(save_directory) vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + "vocab.json" ) merges_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + "merges.txt" ) # Save vocabulary with open(vocab_file, 'w', encoding='utf-8') as f: json.dump(self.encoder, f, indent=2, sort_keys=True, ensure_ascii=False) # Save merges with open(merges_file, 'w', encoding='utf-8') as f: f.write('#version: 0.2\n') for merge in self.bpe_merges: f.write(f'{merge[0]} {merge[1]}\n') return vocab_file, merges_file def build_inputs_with_special_tokens(self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None) -> List[int]: """Build model inputs by adding special tokens""" bos = [self.bos_token_id] if self.bos_token_id is not None else [] eos = [self.eos_token_id] if self.eos_token_id is not None else [] if token_ids_1 is None: return bos + token_ids_0 + eos sep = [self.sep_token_id] if hasattr(self, 'sep_token_id') and self.sep_token_id is not None else [] return bos + token_ids_0 + sep + token_ids_1 + eos