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e8aab00 | 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 | import json
from collections import Counter
from collections import defaultdict
from typing import Dict
from typing import List
from typing import Tuple
class Tokenizer:
def __init__(self, vocab_size: int = 1000):
self.special_tokens = ['<PAD>', '<UNK>', '<SOS>', '<EOS>']
self.char2idx: Dict[str, int] = {}
self.idx2char: Dict[int, str] = {}
self.vocab_size: int = 0
self.target_vocab_size: int = vocab_size
self.bpe_ranks: Dict[Tuple[str, str], int] = {}
for idx, token in enumerate(self.special_tokens):
self.char2idx[token] = idx
self.idx2char[idx] = token
self.vocab_size = len(self.special_tokens)
def _get_stats(self, words: Dict[Tuple[str, ...], int]) -> Counter:
pairs = Counter()
for word, freq in words.items():
for i in range(len(word) - 1):
pairs[(word[i], word[i + 1])] += freq
return pairs
def _merge_vocab(
self, pair: Tuple[str, str], words: Dict[Tuple[str, ...], int]
) -> Dict[Tuple[str, ...], int]:
new_words = {}
replacement = "".join(pair)
for word in words:
new_word = []
i = 0
while i < len(word):
if (
i < len(word) - 1
and word[i] == pair[0]
and word[i + 1] == pair[1]
):
new_word.append(replacement)
i += 2
else:
new_word.append(word[i])
i += 1
new_words[tuple(new_word)] = words[word]
return new_words
def build_vocab(self, texts: List[str]) -> None:
print(f"Building BPE vocabulary from {len(texts)} texts...")
vocab = set()
for text in texts:
vocab.update(text)
for char in sorted(vocab):
if char not in self.char2idx:
self.char2idx[char] = self.vocab_size
self.idx2char[self.vocab_size] = char
self.vocab_size += 1
print(
f"Initial character vocabulary: "
f"{self.vocab_size - len(self.special_tokens)} characters"
)
words = defaultdict(int)
for text in texts:
word = tuple(text)
words[word] += 1
num_merges = self.target_vocab_size - self.vocab_size
print(f"Learning {num_merges} BPE merges...")
for i in range(num_merges):
pairs = self._get_stats(words)
if not pairs:
break
best_pair = max(pairs, key=pairs.get)
words = self._merge_vocab(best_pair, words)
new_token = ''.join(best_pair)
if new_token not in self.char2idx:
self.char2idx[new_token] = self.vocab_size
self.idx2char[self.vocab_size] = new_token
self.vocab_size += 1
self.bpe_ranks[best_pair] = i
if (i + 1) % 100 == 0:
print(
f" Learned {i + 1} merges, "
f"vocab size: {self.vocab_size}"
)
print(f"BPE Vocabulary built! Total tokens: {self.vocab_size}")
print(f" - Special tokens: {len(self.special_tokens)}")
print(f" - Base characters: {len(vocab)}")
print(f" - BPE subwords: {len(self.bpe_ranks)}")
print(f" - Sample subwords: {list(self.bpe_ranks.keys())[:5]}")
def _tokenize(self, text: str) -> List[str]:
if not text:
return []
word = tuple(text)
while len(word) > 1:
pairs = [(word[i], word[i + 1]) for i in range(len(word) - 1)]
valid_pairs = [p for p in pairs if p in self.bpe_ranks]
if not valid_pairs:
break
bigram = min(valid_pairs, key=lambda p: self.bpe_ranks[p])
new_word = []
i = 0
while i < len(word):
if (
i < len(word) - 1
and word[i] == bigram[0]
and word[i + 1] == bigram[1]
):
new_word.append("".join(bigram))
i += 2
else:
new_word.append(word[i])
i += 1
word = tuple(new_word)
return list(word)
def add_token(self, token: str) -> None:
if token not in self.char2idx:
idx = self.vocab_size
self.char2idx[token] = idx
self.idx2char[idx] = token
self.vocab_size += 1
def encode(
self, text: str, max_length: int, add_special_tokens: bool = True
) -> List[int]:
tokens = self._tokenize(text)
indices = []
if add_special_tokens:
indices.append(self.char2idx['<SOS>'])
for token in tokens[:max_length - (2 if add_special_tokens else 0)]:
indices.append(self.char2idx.get(token, self.char2idx['<UNK>']))
if add_special_tokens:
indices.append(self.char2idx['<EOS>'])
while len(indices) < max_length:
indices.append(self.char2idx['<PAD>'])
return indices
def decode(self, indices: List[int]) -> str:
chars = []
for idx in indices:
token = self.idx2char.get(idx, '<UNK>')
if token == '<EOS>':
break
if token not in ['<PAD>', '<SOS>', '<UNK>']:
chars.append(token)
return ''.join(chars)
def save(self, filepath: str) -> None:
state = {
"char2idx": self.char2idx,
"special_tokens": self.special_tokens,
"vocab_size": self.vocab_size,
"target_vocab_size": self.target_vocab_size,
"bpe_ranks": {
f"{k[0]}_{k[1]}": v for k, v in self.bpe_ranks.items()
},
}
with open(filepath, "w") as f:
json.dump(state, f, indent=2)
print(f"BPE Tokenizer saved to {filepath}")
def load(self, filepath: str) -> "Tokenizer":
with open(filepath, "r") as f:
state = json.load(f)
self.char2idx = state["char2idx"]
self.special_tokens = state["special_tokens"]
self.vocab_size = state["vocab_size"]
self.target_vocab_size = state.get("target_vocab_size", 1000)
self.idx2char = {v: k for k, v in self.char2idx.items()}
if "bpe_ranks" in state:
self.bpe_ranks = {}
for key, value in state["bpe_ranks"].items():
parts = key.split("_", 1)
if len(parts) == 2:
self.bpe_ranks[(parts[0], parts[1])] = value
print(f"BPE Tokenizer loaded from {filepath}")
print(f" - Vocab size: {self.vocab_size}")
print(f" - BPE merges: {len(self.bpe_ranks)}")
return self |