Initial toolkit: scripts, references, skills, and term database
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iacore
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"""Generate bilingual.dj from DOCX for 「生命也可以被设计的」.
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One-pass approach: walk interleaved paragraphs, handle multi-CN sequences.
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"""
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import re, subprocess
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from pathlib import Path
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ROOT = Path(__file__).resolve().parent.parent
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DOCX = ROOT / "translate-files/生命也可以被设计的/中英文定稿-260324-生命也是可以被设计的-妙一宽山静雅初翻 慈鎏妙一审议 宽山定稿.docx"
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OUT_DIR = ROOT / "translate-files/生命也可以被设计的"
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def has_cjk(s):
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return any('\u4e00' <= c <= '\u9fff' for c in s)
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def pandoc(path):
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r = subprocess.run(['pandoc', path, '-f', 'docx', '-t', 'plain', '--wrap=none'],
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capture_output=True, text=True)
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return r.stdout
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def split_toc_line(line):
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s = line.strip()
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s = re.sub(r'\s+\d+\s*$', '', s)
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m = re.match(r'^(.+[\u4e00-\u9fff\u3000-\u303f\uff00-\uffef）\)])\s+([A-Z].+)$', s)
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if m:
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return m.group(1).strip(), m.group(2).strip()
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return None, None
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def extract_toc_entries(text):
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lines = text.split('\n')
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toc_start = None
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toc_end = None
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for i, l in enumerate(lines):
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s = l.strip()
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if s.startswith('一、') and ('EDUCATION' in s or 'NURTURING' in s):
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if toc_start is None:
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toc_start = i
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if toc_start is not None and s and has_cjk(s) and re.search(r'\d+$', s):
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toc_end = i
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elif toc_start is not None and toc_end is not None and s and not re.search(r'\d+$', s) and has_cjk(s):
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break
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cn_entries = []
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en_entries = []
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for i in range(toc_start, toc_end + 1):
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cn, en = split_toc_line(lines[i])
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if cn and en:
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cn_entries.append(cn)
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en_entries.append(en)
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return cn_entries, en_entries
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def extract_body_pairs(text):
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"""One-pass: walk interleaved paras, joining consecutive same-language lines."""
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lines = text.split('\n')
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# Find body start
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body_start = None
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for i, l in enumerate(lines):
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if '现在是一个浮躁的时代' in l:
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body_start = i
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break
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# Extract non-blank paragraphs with language tags
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tagged = []
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for l in lines[body_start:]:
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s = l.strip()
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if s:
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tagged.append(('cn' if has_cjk(s) else 'en', s))
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# Accumulate consecutive same-language paragraphs (page-break splits only, not headings)
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merged = []
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for lang, text in tagged:
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# Heading-like patterns that should not be merged
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prev_is_heading = (merged and merged[-1][0] == lang
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and bool(re.match(r'^[\dIVX]+[\.\s]', merged[-1][1].strip())
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and len(merged[-1][1].strip()) < 60))
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if (merged and merged[-1][0] == lang
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and not prev_is_heading
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and len(merged[-1][1]) > 30
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and not re.search(r'[。！？：）\u201d\u2019\uff0c\uff0e\.!\?]$', merged[-1][1])):
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# Long previous line, doesn't end naturally → page-break split, join
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merged[-1] = (lang, merged[-1][1].rstrip() + text.lstrip())
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else:
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merged.append((lang, text))
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# Build pairs: group consecutive same-language items into blocks, then zip
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blocks = []
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for lang, text in merged:
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if blocks and blocks[-1][0] == lang:
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blocks[-1][1].append(text)
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else:
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blocks.append((lang, [text]))
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pairs = []
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i = 0
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while i < len(blocks):
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if blocks[i][0] == 'cn':
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cn_block = blocks[i][1]
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# Find next EN block
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if i + 1 < len(blocks) and blocks[i+1][0] == 'en':
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en_block = blocks[i+1][1]
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n = min(len(cn_block), len(en_block))
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for j in range(n):
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pairs.append((cn_block[j], en_block[j]))
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if len(cn_block) != len(en_block):
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print(f" WARNING: block mismatch CN={len(cn_block)} EN={len(en_block)} at CN[{j}]: {cn_block[j][:60]}...")
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i += 2
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else:
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print(f" WARNING: CN block without EN block: {cn_block[0][:60]}...")
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i += 1
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else:
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print(f" WARNING: orphan EN block: {blocks[i][1][0][:60]}...")
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i += 1
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return pairs
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SANSKRIT = [
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'bodhisattva', 'bodhicitta', 'samsara', 'Dharma', 'karma',
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'nirvana', 'Sangha', 'sutra', 'Mahayana', 'Sravaka',
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'Vinaya', 'Lamrim', 'Ksitigarbha', 'Samantabhadra',
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'Chan', 'Arhatship', 'Theravada', 'buddha', 'Buddha',
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'buddhas', 'Buddhas', 'Bodhisattva', 'Bodhisattvas',
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]
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def apply_fixes(en_text, italicized):
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en_text = re.sub(r'(\d)\.([A-Z][a-z])', r'\1. \2', en_text)
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en_text = re.sub(r'(said|says),\"', r'\1, "', en_text)
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en_text = re.sub(r'\.([A-Z][a-z])', r'. \1', en_text)
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for term in SANSKRIT:
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if term not in italicized:
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pattern = re.compile(r'\b' + re.escape(term) + r'\b')
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m = pattern.search(en_text)
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if m:
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s, e = m.start(), m.end()
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en_text = en_text[:s] + '*' + en_text[s:e] + '*' + en_text[e:]
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italicized.add(term)
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return en_text
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def generate(toc_cn, toc_en, pairs, out_path):
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italicized = set()
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lines = []
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lines.append('# 生命也是可以被设计的')
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lines.append('# Life Can Also Be Designed')
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lines.append('')
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lines.append('济群法师 2025年冬为母爱书院开示')
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lines.append('A teaching given by the Master Jiqun in the winter of 2025 at Amrita Retreat Center for Motherly Love Academy')
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lines.append('')
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for e in toc_cn:
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lines.append(f'- {e}')
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lines.append('')
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for e in toc_en:
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lines.append(f'- {e}')
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lines.append('')
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for cn, en in pairs:
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en_fixed = apply_fixes(en, italicized)
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lines.append(cn)
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lines.append(en_fixed)
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lines.append('')
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with open(out_path, 'w') as f:
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f.write('\n'.join(lines))
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print(f"Written: {out_path}")
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print(f" TOC entries: {len(toc_cn)}")
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print(f" Body pairs: {len(pairs)}")
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print(f" Sanskrit italicized: {sorted(italicized)}")
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if __name__ == '__main__':
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print("Extracting DOCX...")
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text = pandoc(DOCX)
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print("Extracting TOC...")
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toc_cn, toc_en = extract_toc_entries(text)
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for cn, en in zip(toc_cn, toc_en):
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print(f" {cn} → {en}")
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print("Extracting body...")
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pairs = extract_body_pairs(text)
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print(f" Pairs: {len(pairs)}")
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out = OUT_DIR / "bilingual.dj"
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generate(toc_cn, toc_en, pairs, out)
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