231 lines
9.5 KiB
Python
231 lines
9.5 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""Merge Multilingual-ai (post-edit) into term_*.xlsx + apply term-review.md fixes."""
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import sys, io, os
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sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')
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from openpyxl import load_workbook
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TOOLS = r'C:\TH1\TH1\Tools'
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AI_BACKUP = os.path.join(TOOLS, 'Multilingual-ai.backup-2026-04-28.xlsx')
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TERM_DIR = os.path.join(TOOLS, '术语表')
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# ============================================================
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# STEP 1: Multilingual-ai-review.md fixes (rebuild post-edit)
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# Format: ID -> {EN/JP/KR: new_value}
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# ============================================================
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AI_FIXES = {
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'17831': {'EN': 'Dharma', 'KR': '정법'},
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'17857': {'EN': 'Wago-Araga Bond', 'KR': '화혼과 황혼의 연리'},
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'17858': {'EN': 'Cursed Bunrei'},
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'17863': {'KR': '바람타기'},
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'17865': {'EN': 'Omikuji'},
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'17890': {'EN': '[Ability: Leave it to Lord Mishaguji!]',
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'JP': '[能力:ミシャグチ様に任せて!]',
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'KR': '[능력: 미샤구지님께 맡겨!]'},
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'17896': {'EN': 'Chaotic Omikuji Barrage'},
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'17897': {'EN': '[Ability: Omikuji]'},
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'17908': {'EN': '[Ability: Crimson Leaf Herald]'},
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'17916': {'EN': '[Bishop] Class Hero'},
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'17939': {'KR': '오니족 호걸'},
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'18062': {'EN': 'Gross Body'},
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'18127': {'EN': 'Statue of the Queen', 'JP': '后の彫像'},
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'18130': {'EN': 'Statue of the Bishop'},
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'18133': {'EN': 'Statue of the Rook'},
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'18138': {'EN': 'Statue of the Knight'},
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'18154': {'EN': 'Karana-sharira', 'JP': '原因身', 'KR': '원인신'},
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'18168': {'EN': 'Maha-Vetala', 'JP': 'マハー・ヴェターラ', 'KR': '마하베탈라'},
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'18179': {'EN': 'Awe-Strike'},
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'18213': {'KR': '바루나함'},
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'18217': {'EN': 'Rudra Fire Chariot'},
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'18311': {'EN': '[Bishop] Class', 'KR': '[상] 클래스'},
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'18314': {'EN': '[Rook] Class'},
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'18447': {'KR': '월토끼 환영'},
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'18520': {'EN': 'Scarlet Sleepless Castle'},
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'18523': {'EN': "The Empire's Blood Sovereign", 'KR': '제국의 피의 군왕'},
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'18525': {'EN': 'Perfect & Elegant Maid'},
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'18534': {'EN': '[Ability: Yellow Quake Stomp]', 'KR': '[능력: 황진각]'},
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'18549': {'EN': 'Sea of Trees of Hourai'},
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'18567': {'EN': 'Peerless Elixir of the Realm'},
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'18613': {'KR': '마법석{화}'},
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'18614': {'KR': '마법석{목}'},
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'18615': {'KR': '마법석{금}'},
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'18626': {'JP': '----[バグと最適化]----'},
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'18717': {'EN': 'Statue of the King'},
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'18840': {'EN': 'Statue of the King'},
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'19450': {'KR': '중국어'},
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}
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# ============================================================
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# STEP 2: Build {ZH: (EN, JP, KR)} from AI backup + AI_FIXES
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# ============================================================
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def build_ai_data():
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wb = load_workbook(AI_BACKUP, read_only=True)
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ws = wb.active
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data = {} # ZH -> [EN, JP, KR]
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for row in ws.iter_rows(min_row=2, values_only=True):
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rid = str(row[0] or '').lstrip('')
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zh, en, jp, kr = row[2], row[4], row[5], row[6]
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if not zh:
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continue
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# Apply fixes if any
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f = AI_FIXES.get(rid, {})
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en2 = f.get('EN', en)
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jp2 = f.get('JP', jp)
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kr2 = f.get('KR', kr)
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data[zh] = [en2, jp2, kr2]
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wb.close()
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return data
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# ============================================================
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# STEP 3: term-review.md row-specific fixes by ZH
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# Applied after AI merge (so they override anything from AI merge)
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# ============================================================
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TERM_FIXES = {
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# E1
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'绝不再把你留在阿斯佩恩': {
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'EN': 'Never leave you in Aspern again',
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'JP': '二度とアスペルンに置いていかない',
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'KR': '다시는 널 아스페른에 두고 가지 않을게'},
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# E2 (ZH not changed; per user instruction)
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'御住古战场': {
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'EN': 'Onbashira Ancient Battlefield',
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'KR': '온바시라 고대 전장'},
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# E3 拼音 → 英文棋子
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'英雄神像[后]': {'EN': 'Hero Statue [Queen]', 'JP': '英雄神像[后]', 'KR': '영웅 신상 [후]'},
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'英雄神像[相]': {'EN': 'Hero Statue [Bishop]'},
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'英雄神像[王]': {'EN': 'Hero Statue [King]'},
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'英雄神像[马]': {'EN': 'Hero Statue [Knight]'},
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'英雄神像[车]': {'EN': 'Hero Statue [Rook]'},
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# E4 赤口 → Mishaguji
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'赤口大人的作祟': {'EN': "Lord Mishaguji's Curse",
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'JP': 'ミシャグチ様の祟り',
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'KR': '미샤구지님의 저주'},
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'交给赤口大人吧!': {'EN': 'Leave it to Lord Mishaguji!',
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'JP': 'ミシャグチ様に任せて!',
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'KR': '미샤구지님께 맡겨!'},
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# E5
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'召唤御射宫司大人': {'EN': 'Summon Lord Mishaguji', 'KR': '미샤구지님 소환'},
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# E7 挖掘 verb
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'挖掘': {'JP': '発掘', 'KR': '발굴'},
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# §二 sync (overwrite if AI merge didn't already cover)
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'国士无双之药': {'EN': 'Peerless Elixir of the Realm'},
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'完美女仆': {'EN': 'Perfect & Elegant Maid'},
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'红色不夜城': {'EN': 'Scarlet Sleepless Castle'},
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'红叶传令': {'EN': 'Crimson Leaf Herald'},
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# §三 C1 (建造英雄神像系列)
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'建造英雄神像[马]': {'EN': 'Build Hero Statue [Knight]', 'KR': '영웅 신상 [마] 건설'},
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'建造英雄神像[后]': {'EN': 'Build Hero Statue [Queen]', 'KR': '영웅 신상 [후] 건설'},
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'建造英雄神像[车]': {'EN': 'Build Hero Statue [Rook]', 'KR': '영웅 신상 [차] 건설'},
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'建造英雄神像[相]': {'EN': 'Build Hero Statue [Bishop]', 'JP': '英雄神像[相]', 'KR': '영웅 신상 [상] 건설'},
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'建造英雄神像[王]': {'EN': 'Build Hero Statue [King]', 'KR': '영웅 신상 [왕] 건설'},
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# §C2 红雾 KR unify (주홍 안개)
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'清除红雾': {'KR': '주홍 안개 제거'},
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'回收红雾': {'KR': '주홍 안개 회수'},
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'红雾攻击': {'KR': '주홍 안개 공격'},
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'红雾弥漫': {'KR': '주홍 안개 자욱'},
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'红雾领地': {'KR': '주홍 안개 영지'},
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'红雾防御': {'KR': '주홍 안개 방어'},
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# §C3 风祝 KR (바람 무녀)
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'铁血风祝': {'KR': '철혈의 바람 무녀'},
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'风祝': {'KR': '바람 무녀'},
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# §C4 大团长 → Grand Master
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'大团长的将棋时间': {'EN': "Grand Master's Shogi Time"},
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# §D3 核能 → 核エネルギー / 핵 에너지
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'土豆大王与核能尊神': {'JP': '芋大王と核エネルギーの尊神',
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'KR': '감자 대왕과 핵 에너지 존신'},
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# §D5 punctuation
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'地毯、咖啡与香料': {'JP': '絨毯・コーヒー・香辛料'},
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# §D7 미조우
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'未遇见': {'KR': '만난 적 없음'},
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'未相遇的玩家': {'KR': '만난 적 없는 플레이어'},
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# §D10 竹林狼 KR space
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'召唤竹林狼': {'KR': '대나무 숲 늑대 소환'},
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'竹林狼上校': {'KR': '대나무 숲 늑대 대령'},
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# §D11 简体中文 KR
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'简体中文': {'KR': '간체 중국어'},
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# §D12 血流成河 EN
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'我要看到血流成河!': {'EN': 'I want to see rivers of blood!'},
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'无聊！我要看到血流成河！': {'EN': 'Boring! I want to see rivers of blood!'},
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}
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# ============================================================
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# STEP 4: Merge into a single term file
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# ============================================================
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LANG_IDX = {'EN': 0, 'JP': 1, 'KR': 2}
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def merge_term(term_path, lang):
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"""lang in EN/JP/KR. Loads term_*.xlsx, merges AI data + term fixes, saves."""
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ai_data = build_ai_data() # ZH -> [EN, JP, KR]
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li = LANG_IDX[lang]
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wb = load_workbook(term_path)
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ws = wb.active
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# Build existing ZH -> row index map
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existing = {}
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for r in range(2, ws.max_row + 1):
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zh = ws.cell(row=r, column=1).value
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if zh:
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existing[zh] = r
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# Counters
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overwrote = 0
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appended = 0
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term_fixed = 0
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# Pass 1: Merge AI data
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for zh, vals in ai_data.items():
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new_val = vals[li]
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if new_val is None:
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continue
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if zh in existing:
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row = existing[zh]
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old = ws.cell(row=row, column=2).value
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if old != new_val:
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ws.cell(row=row, column=2).value = new_val
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overwrote += 1
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else:
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new_row = ws.max_row + 1
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ws.cell(row=new_row, column=1).value = zh
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ws.cell(row=new_row, column=2).value = new_val
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existing[zh] = new_row
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appended += 1
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# Pass 2: Apply term-review fixes (override)
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for zh, fix_map in TERM_FIXES.items():
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if lang not in fix_map:
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continue
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new_val = fix_map[lang]
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if zh in existing:
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row = existing[zh]
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old = ws.cell(row=row, column=2).value
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if old != new_val:
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ws.cell(row=row, column=2).value = new_val
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term_fixed += 1
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else:
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print(f' [WARN] term-fix ZH not found in {lang}: {zh!r}')
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wb.save(term_path)
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print(f' {os.path.basename(term_path)} ({lang}): overwrote={overwrote} appended={appended} term_fixed={term_fixed} new_total_rows={ws.max_row}')
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# ============================================================
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# RUN
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# ============================================================
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if __name__ == '__main__':
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print('=== Merging Multilingual-ai post-edit data into term_*.xlsx ===')
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merge_term(os.path.join(TERM_DIR, 'term_en.xlsx'), 'EN')
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merge_term(os.path.join(TERM_DIR, 'term_jp.xlsx'), 'JP')
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merge_term(os.path.join(TERM_DIR, 'term_kr.xlsx'), 'KR')
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print('Done.')
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