File size: 14,259 Bytes
1d0ef1d
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
"""
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('</w>', ' ')
        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