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==========================================
WWHO Encoder
==========================================
"""
from __future__ import annotations
import argparse
import json
from typing import Optional
from linguis_trie import LinguisTrie
def _is_boundary_token(token: str, segmenter) -> bool:
for ch in token:
if segmenter:
lang = segmenter._get_char_language(ch)
if lang is not None and lang != "latin":
return False
return True
def segment_into_words(syllables: list[str], segmenter) -> list[list[str]]:
words: list[list[str]] = []
current: list[str] = []
for tok in syllables:
if _is_boundary_token(tok, segmenter):
if current:
words.append(current)
current = []
words.append([tok])
else:
if tok[0] in (' ', '\t', '\n', '\r') and current:
words.append(current)
current = []
current.append(tok)
if current:
words.append(current)
return words
class SGPEEncoder:
def __init__(self, vocab_path: str):
with open(vocab_path, "r", encoding="utf-8") as f:
data = json.load(f)
self.vocab: dict[str, int] = data["vocab"]
self.merges: list[tuple[str, str]] = [tuple(m) for m in data["merges"]]
self.special_tokens: list[str] = data["special_tokens"]
self.leading_space: bool = data.get("leading_space", False)
script_mode = data.get("script_mode", "mixed")
from linguis_trie import load_dfa_map
from router import CodeSwitchSegmenter
self._dfa_map = load_dfa_map(script_mode)
language_blocks = {lang: dfa.unicode_blocks for lang, dfa in self._dfa_map.items()}
self._segmenter = CodeSwitchSegmenter(language_blocks)
self._merge_priority: dict[tuple[str, str], int] = {
(a, b): rank for rank, (a, b) in enumerate(self.merges)
}
def encode(self, text: str) -> list[int]:
tokens = self.tokenize(text)
return [self.vocab.get(t, self.unk_id) for t in tokens]
def _apply_merges_to_word(self, tokens: list[str]) -> list[str]:
if len(tokens) <= 1:
return tokens
while True:
best_rank = len(self.merges)
best_idx = -1
for i in range(len(tokens) - 1):
pair = (tokens[i], tokens[i + 1])
rank = self._merge_priority.get(pair)
if rank is not None and rank < best_rank:
best_rank = rank
best_idx = i
if best_idx == -1:
break
merged = tokens[best_idx] + tokens[best_idx + 1]
tokens = tokens[:best_idx] + [merged] + tokens[best_idx + 2:]
return tokens
def tokenize(self, text: str) -> list[str]:
tokens: list[str] = []
for seg in self._segmenter.segment(text):
if seg.language == "latin":
tokens.append(seg.text)
else:
dfa = self._dfa_map.get(seg.language)
if not dfa:
tokens.append(seg.text)
continue
syllables = dfa.tokenize(seg.text, leading_space=seg.has_leading_space)
words = segment_into_words(syllables, self._segmenter)
for word_toks in words:
if len(word_toks) == 1 and _is_boundary_token(word_toks[0], self._segmenter):
tokens.append(word_toks[0])
continue
cleaned = [t if t in self.vocab else "[UNK]" for t in word_toks]
tokens.extend(self._apply_merges_to_word(cleaned))
return tokens
def decode(self, ids: list[int]) -> str:
id_to_token = {v: k for k, v in self.vocab.items()}
return "".join(id_to_token.get(i, "") for i in ids)
# ============================================================================
# MetaVocab — unified ID space
# ============================================================================
class MetaVocab:
def __init__(self, sgpe_vocab: dict[str, int], tiktoken_size: int):
self.tiktoken_size: int = tiktoken_size
self._sgpe_raw: dict[str, int] = sgpe_vocab
self._sgpe_offset: dict[str, int] = {
tok: idx + tiktoken_size for tok, idx in sgpe_vocab.items()
}
self._sgpe_reverse: dict[int, str] = {
v: k for k, v in self._sgpe_offset.items()
}
@property
def total_size(self) -> int:
return self.tiktoken_size + len(self._sgpe_raw)
def encode_sgpe_token(self, token: str, unk_id_raw: int) -> int:
return self._sgpe_offset.get(token, unk_id_raw + self.tiktoken_size)
def decode_id(self, uid: int) -> Optional[str]:
if uid < self.tiktoken_size:
return None
return self._sgpe_reverse.get(uid)
def is_tiktoken_id(self, uid: int) -> bool:
return uid < self.tiktoken_size
def sgpe_unk_id(self, raw_unk: int) -> int:
return raw_unk + self.tiktoken_size
# ============================================================================
# WWHOMetaEncoder
# ============================================================================
class WWHOMetaEncoder:
def __init__(self, vocab_path: str, tiktoken_model: str = "o200k_base"):
# Load SGPE vocab
with open(vocab_path, "r", encoding="utf-8") as f:
data = json.load(f)
sgpe_vocab: dict[str, int] = data["vocab"]
self._merges: list[tuple[str, str]] = [tuple(m) for m in data["merges"]]
self._special_tokens: list[str] = data["special_tokens"]
self._leading_space: bool = data.get("leading_space", False)
self._raw_unk_id: int = sgpe_vocab.get("[UNK]", 1)
if " " not in sgpe_vocab:
next_id = max(sgpe_vocab.values()) + 1
sgpe_vocab[" "] = next_id
try:
from router import _INDIC_PUNCT_CHARS
for ch in _INDIC_PUNCT_CHARS:
if ch not in sgpe_vocab:
next_id = max(sgpe_vocab.values()) + 1
sgpe_vocab[ch] = next_id
except ImportError:
pass
self._merge_priority: dict[tuple[str, str], int] = {
(a, b): rank for rank, (a, b) in enumerate(self._merges)
}
# tiktoken
try:
import tiktoken as _tiktoken
self._tik = _tiktoken.get_encoding(tiktoken_model)
except Exception as e:
raise RuntimeError(
f"tiktoken ({tiktoken_model!r}) unavailable: {e}. "
)
# Unified vocab
self._meta = MetaVocab(sgpe_vocab, self._tik.n_vocab)
self._space_id: int = self._meta._sgpe_offset[" "]
# Indic LinguisTries
from linguis_trie import load_dfa_map, LinguisTrie
self._dfa_map: dict[str, LinguisTrie] = load_dfa_map("mixed")
# Router Segmenter
from router import CodeSwitchSegmenter
language_blocks = {lang: dfa.unicode_blocks for lang, dfa in self._dfa_map.items()}
self._segmenter = CodeSwitchSegmenter(language_blocks)
# ------------------------------------------------------------------
# Public API
# ------------------------------------------------------------------
@property
def vocab_size(self) -> int:
return self._meta.total_size
@property
def tiktoken_size(self) -> int:
return self._meta.tiktoken_size
@property
def vocab(self) -> dict[str, int]:
return self._meta._sgpe_raw
def encode(self, text: str) -> list[int]:
ids: list[int] = []
for seg in self._segmenter.segment(text):
if seg.language == "latin":
ids.extend(self._tik.encode(seg.text))
else:
dfa = self._dfa_map.get(seg.language)
if not dfa:
ids.extend(self._tik.encode(seg.text))
continue
syllables = dfa.tokenize(seg.text, leading_space=seg.has_leading_space)
words = segment_into_words(syllables, self._segmenter)
for word_toks in words:
if len(word_toks) == 1 and _is_boundary_token(word_toks[0], self._segmenter):
ids.extend(self._tik.encode(word_toks[0]))
continue
merged = self._apply_merges(word_toks)
for tok in merged:
ids.append(self._meta.encode_sgpe_token(tok, self._raw_unk_id))
return ids
def decode(self, ids: list[int]) -> str:
tik_buf: list[int] = []
result_parts: list[str] = []
def _flush_tik():
if tik_buf:
result_parts.append(self._tik.decode(tik_buf))
tik_buf.clear()
for uid in ids:
if self._meta.is_tiktoken_id(uid):
tik_buf.append(uid)
else:
_flush_tik()
tok = self._meta.decode_id(uid)
result_parts.append(tok if tok is not None else "")
_flush_tik()
return "".join(result_parts)
def tokenize(self, text: str) -> list[str]:
tokens: list[str] = []
for seg in self._segmenter.segment(text):
if seg.language == "latin":
ids = self._tik.encode(seg.text)
tokens.extend(self._tik.decode([i]) for i in ids)
else:
dfa = self._dfa_map.get(seg.language)
if not dfa:
ids = self._tik.encode(seg.text)
tokens.extend(self._tik.decode([i]) for i in ids)
continue
syllables = dfa.tokenize(seg.text, leading_space=seg.has_leading_space)
words = segment_into_words(syllables, self._segmenter)
for word_toks in words:
if len(word_toks) == 1 and _is_boundary_token(word_toks[0], self._segmenter):
ids = self._tik.encode(word_toks[0])
tokens.extend(self._tik.decode([i]) for i in ids)
continue
tokens.extend(self._apply_merges(word_toks))
return tokens
def _apply_merges(self, tokens: list[str]) -> list[str]:
if len(tokens) <= 1:
return tokens
sgpe = self._meta._sgpe_raw
cleaned = [t if t in sgpe else "[UNK]" for t in tokens]
while True:
best_rank = len(self._merges)
best_idx = -1
for i in range(len(cleaned) - 1):
pair = (cleaned[i], cleaned[i + 1])
rank = self._merge_priority.get(pair)
if rank is not None and rank < best_rank:
best_rank = rank
best_idx = i
if best_idx == -1:
break
merged = cleaned[best_idx] + cleaned[best_idx + 1]
cleaned = cleaned[:best_idx] + [merged] + cleaned[best_idx + 2:]
return cleaned
# ============================================================================
# CLI
# ============================================================================
def main():
parser = argparse.ArgumentParser(description="WWHO Encoder (Unified Meta-Vocabulary)")
parser.add_argument("--vocab", type=str, default="output/vocab.json",
help="Path to WWHO vocab.json")
parser.add_argument("--text", type=str, required=True,
help="Text to encode (supports mixed Latin + Indic)")
parser.add_argument("--mode", type=str, default="meta",
choices=["sgpe", "meta"],
help="'sgpe' = pure SGPE encoder; 'meta' = unified meta-encoder")
parser.add_argument("--tiktoken_model", type=str, default="o200k_base")
args = parser.parse_args()
if args.mode == "sgpe":
enc = SGPEEncoder(args.vocab)
tokens = enc.tokenize(args.text)
ids = enc.encode(args.text)
print(f"[SGPEEncoder]")
print(f" tokens : {tokens}")
print(f" ids : {ids}")
print(f" count : {len(tokens)}")
else:
enc = WWHOMetaEncoder(args.vocab, tiktoken_model=args.tiktoken_model)
tokens = enc.tokenize(args.text)
ids = enc.encode(args.text)
decoded = enc.decode(ids)
print(f"[WWHOMetaEncoder]")
print(f" vocab_size : {enc.vocab_size:,} "
f"(tiktoken={enc.tiktoken_size:,} + SGPE={enc.vocab_size - enc.tiktoken_size:,})")
print(f" tokens : {tokens}")
print(f" ids : {ids}")
print(f" count : {len(tokens)}")
print(f" decoded: {decoded!r}")
print(f" lossless: {decoded == args.text}")
if __name__ == "__main__":
main()
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