3d7bd70355
- 新增 Alembic 初始迁移、领域明细模型及归档持久化与重试链路\n- 拆分视频会话注册表、分类处理、推理时间窗聚合与流处理\n- 消耗日志：TSV/Markdown 含 top2/top3；item_id 优先产品编码；待确认记「待确认」行，语音确认后落正式行并更新汇总\n- 待确认时内存/DB 明细为占位行，确认后替换；拒绝时移除占位\n- 分类 probs 先 detach/cpu 再转 NumPy，修复 MPS/CUDA 上推理被静默跳过\n- 补充集成测试、归档与设备张量等单测 Made-with: Cursor
212 lines
6.9 KiB
Python
212 lines
6.9 KiB
Python
"""视觉分类结果处理：把 ``PredictionResult`` 转成自动记账 or 待人工确认。
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从 ``CameraSessionManager`` 抽出，保持原先行为：
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- 置信度低于 ``video_voice_confirm_min_confidence`` → 丢弃。
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- 会话状态中候选清单为空 → 丢弃（开录时通常会由空请求解析为全量目录/模型类名）。
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- 置信度 ≥ ``video_auto_confirm_confidence`` 且 Top1 在候选内 → 自动追加 vision 明细，并写消耗 TSV（记具体耗材）。
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- 置信度 ≥ 自动阈值但 Top1 不在候选内 → 视 voice_confirmation_enabled 入 pending。
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- 中等置信度 → 入 pending（若有可展示候选项）。
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需医生确认时：消耗 TSV / 内存明细记「待确认」（不写模型 top1 商品名）；语音确认后再落最终耗材并更新汇总。
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"""
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from __future__ import annotations
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from loguru import logger
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from app.config import Settings
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from app.services.consumable_vision_algorithm import (
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PredictionCandidate,
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PredictionResult,
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)
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from app.services.consumption_tsv_log import (
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append_consumption_pending_window,
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append_consumption_window,
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resolve_consumption_item_id,
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)
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from app.services.video.inference_aggregator import WindowInferenceReady
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from app.services.video.session_registry import (
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SurgerySessionRegistry,
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SurgerySessionState,
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)
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def rank_topk_for_candidates(
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topk: list[PredictionCandidate],
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ordered_candidates: list[str],
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*,
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limit: int = 5,
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) -> list[PredictionCandidate]:
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if not topk:
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return []
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stripped_order = [c.strip() for c in ordered_candidates if c.strip()]
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if not stripped_order:
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return topk[:limit]
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order_index = {name: i for i, name in enumerate(stripped_order)}
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picked = [c for c in topk if c.label.strip() in order_index]
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picked.sort(key=lambda c: order_index[c.label.strip()])
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return picked[:limit]
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class VisionClassificationHandler:
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"""把分类结果转化为 registry 上的状态变更（追加明细 / 入队待确认）。"""
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def __init__(
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self,
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*,
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settings: Settings,
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registry: SurgerySessionRegistry,
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) -> None:
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self._s = settings
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self._registry = registry
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def _append_vision_consumption_window_if_ready(
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self,
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state: SurgerySessionState,
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ready: WindowInferenceReady | None,
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surgery_id: str,
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camera_id: str,
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) -> None:
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if (
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ready is None
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or not surgery_id
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or not camera_id
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or (
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not self._s.consumption_tsv_log_enabled
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and not self._s.consumption_log_markdown_terminal
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)
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):
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return
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append_consumption_window(
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surgery_id=surgery_id,
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name_to_code=state.name_to_code,
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best=ready.best,
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doctor_id=self._s.video_result_doctor_id,
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camera_id=camera_id,
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wall_start_epoch=ready.wall_lo,
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wall_end_epoch=ready.wall_hi,
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running_totals=state.consumption_log_totals,
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)
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async def handle(
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self,
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*,
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state: SurgerySessionState,
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cls_res: PredictionResult,
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ready: WindowInferenceReady | None = None,
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surgery_id: str = "",
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camera_id: str = "",
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) -> None:
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conf = cls_res.confidence
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label = (cls_res.label or "").strip()
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item_id = resolve_consumption_item_id(label, "", state.name_to_code)
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voice_floor = self._s.video_voice_confirm_min_confidence
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if conf < voice_floor:
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return
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cand_order = [c.strip() for c in state.candidate_consumables if c.strip()]
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if not cand_order:
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return
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cand_set = set(cand_order)
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ranked = rank_topk_for_candidates(cls_res.topk, cand_order)
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auto_th = self._s.video_auto_confirm_confidence
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def in_allowed(name: str) -> bool:
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return name in cand_set
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if conf >= auto_th and in_allowed(label):
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self._append_vision_consumption_window_if_ready(
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state, ready, surgery_id, camera_id
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)
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await self._registry.append_confirmed_detail(
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state=state,
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item_id=item_id or "unknown",
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item_name=label or "unknown",
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doctor_id=self._s.video_result_doctor_id,
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source="vision",
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)
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return
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if conf >= auto_th and not in_allowed(label):
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if ranked and self._s.voice_confirmation_enabled:
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await self._enqueue(
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state,
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ranked,
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label,
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conf,
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ready=ready,
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surgery_id=surgery_id,
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camera_id=camera_id,
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)
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return
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if not self._s.voice_confirmation_enabled:
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return
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if ranked:
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await self._enqueue(
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state,
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ranked,
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label,
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conf,
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ready=ready,
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surgery_id=surgery_id,
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camera_id=camera_id,
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)
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elif in_allowed(label):
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await self._enqueue(
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state,
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[PredictionCandidate(label=label, confidence=conf)],
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label,
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conf,
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ready=ready,
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surgery_id=surgery_id,
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camera_id=camera_id,
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)
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async def _enqueue(
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self,
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state: SurgerySessionState,
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ranked: list[PredictionCandidate],
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top_key: str,
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top_confidence: float,
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*,
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ready: WindowInferenceReady | None = None,
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surgery_id: str = "",
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camera_id: str = "",
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) -> None:
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cid = await self._registry.enqueue_pending_confirmation(
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state,
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ranked,
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top_key=top_key,
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top_confidence=top_confidence,
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)
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if cid is None:
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return
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logger.info(
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"Enqueued pending consumable confirmation id={} top_key={}",
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cid,
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top_key,
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)
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if ready is not None and surgery_id and camera_id and (
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self._s.consumption_tsv_log_enabled
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or self._s.consumption_log_markdown_terminal
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):
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append_consumption_pending_window(
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surgery_id=surgery_id,
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confirmation_id=cid,
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model_snap=ready.best,
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doctor_id=self._s.video_result_doctor_id,
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camera_id=camera_id,
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wall_start_epoch=ready.wall_lo,
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wall_end_epoch=ready.wall_hi,
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tsv_enabled=self._s.consumption_tsv_log_enabled,
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markdown_terminal=self._s.consumption_log_markdown_terminal,
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)
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await self._registry.append_pending_consumption_detail(
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state=state,
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confirmation_id=cid,
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doctor_id=self._s.video_result_doctor_id,
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)
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