from pathlib import Path
import shutil

helper = '''    def _wait_for_database_voice(self, fp, *, visible_voice=False):
        """Yield this task while local ASR works; unrelated conversations continue."""
        from voice_messages import voice_batch_status
        state = self._pending_reply_state(fp)
        context = getattr(self, "_last_database_read", None)
        bound = isinstance(context, dict) and context.get("fp_hex") == bytes(fp or b"").hex()
        voice = voice_batch_status(context) if bound else {"status": "none"}
        if visible_voice and voice["status"] == "none":
            voice = {"status": "pending", "key": "visible-voice:" + bytes(fp or b"").hex(),
                     "reason": "等待语音对应的本机消息库和音频文件"}
        if voice["status"] in {"none", "ready"}:
            if state:
                state.pop("voice_wait_key", None)
                state.pop("voice_wait_started_at", None)
            return False
        if state is None:
            return True  # No durable task identity: never guess or answer an unheard voice.
        now = time.time()
        if state.get("voice_wait_key") != voice.get("key"):
            state.update(voice_wait_key=voice.get("key"), voice_wait_started_at=now)
        waiting = voice["status"] == "pending" and now - float(state.get("voice_wait_started_at") or now) < 180
        if waiting:
            state["ready_at"] = now + 2
            self._set_task_stage(fp, "voice_transcribing", detail=voice.get("reason") or "语音正在本机转文字")
        else:
            reason = voice.get("reason") if voice["status"] == "error" else "语音转文字等待超时，尚未取得可核验文字，请人工处理"
            state.update(manual_required=True, manual_reason=reason, awaiting_review=False,
                         reply_text="", staged_reply_text="", chat_text=str((context or {}).get("text") or ""))
            self._set_task_stage(fp, "error", detail="语音需要人工核对", error=reason)
            self._register_assistant_review(fp)
        self._persist_pending_replies()
        return True

'''

for label, project in [('source', Path('C:/kefu/wechat_rpa')), ('production', Path('C:/wechat_rpa'))]:
    path = project / 'wechat_bot.py'
    backup = Path(__file__).parent / 'before-voice-integration' / label
    backup.mkdir(parents=True, exist_ok=True)
    shutil.copy2(path, backup / path.name)
    text = path.read_text(encoding='utf-8')
    needle = '    def _database_message_direction(self, fp: bytes, text: str):\n'
    assert text.count(needle) == 1
    text = text.replace(needle, helper + needle)
    needle = '''            database_text = self._database_chat_text(fp)
            if database_text:
                chat_text = database_text
'''
    assert text.count(needle) == 1
    text = text.replace(needle, needle + '''            if self._wait_for_database_voice(fp):
                return None
            from voice_messages import voice_batch_status
            database_voice_ready = bool(database_text and voice_batch_status(getattr(self, "_last_database_read", None))["status"] == "ready")
''')
    needle = '''            first_untrusted_batch = bool(
                first_record_batch and not first_explicit_customer_text
            )
'''
    assert text.count(needle) == 1
    text = text.replace(needle, '''            first_untrusted_batch = bool(
                first_record_batch and not first_explicit_customer_text and not database_voice_ready
            )
            if database_voice_ready:
                # Exact database identity and successful ASR make voice text available to normal model/risk processing.
                reliable_text_pending = True
                force_media_check = False
''')
    needle = '''                self.report_operation("AI 准备", 2, 3, "语音没有转写，生成安全追问", fp)
                print("    [AI] 检测到未转写语音，使用不猜测内容的安全回复")
                ai_reply = safe_media_reply(media_types)
'''
    assert text.count(needle) == 1
    text = text.replace(needle, '''                self._wait_for_database_voice(fp, visible_voice=True)
                return None
''')
    path.write_text(text, encoding='utf-8')
print('Visual voice waiting/model flow integrated with targeted edits in both variants.')