"""YOLO 耗材/手部 + configured reference ActionFormer 实时段落检测默认配置。"""

from __future__ import annotations

from pathlib import Path

from app.algorithm_runner.reference_bundle_runtime import default_reference_bundle_dir

_PACKAGE_DIR = Path(__file__).resolve().parent.parent


def default_consumable_classifier_weights_path() -> str:
    return str(_PACKAGE_DIR / "resources" / "consumable_classifier.pt")


def default_consumable_classifier_labels_yaml_path() -> str:
    return str(_PACKAGE_DIR / "resources" / "consumable_classifier_labels.yaml")


def default_or_site_config_sample_path() -> str:
    """站点配置示例：video_rtsp_urls + voice_or_room_bindings。"""
    return str(_PACKAGE_DIR / "resources" / "or_site_config.sample.json")


def default_actionformer_hand_det_weights_path() -> str:
    return str(default_reference_bundle_dir() / "weights" / "hand_detect.pt")


def default_actionformer_goodbad_weights_path() -> str:
    return str(default_reference_bundle_dir() / "weights" / "goodbad_frame.pt")


def default_actionformer_haocai_weights_path() -> str:
    return str(default_reference_bundle_dir() / "weights" / "haocai_classify.pt")


def default_actionformer_ckpt_path() -> str:
    return str(default_reference_bundle_dir() / "weights" / "actionformer_epoch_045.pth.tar")


def default_actionformer_tear_weights_path() -> str:
    return str(default_reference_bundle_dir() / "weights" / "tear_classify.pt")


# --- 耗材分类（YOLO-cls）---
CONSUMABLE_CLASSIFIER_WEIGHTS: str = default_consumable_classifier_weights_path()
CONSUMABLE_CLASSIFIER_IMGSZ: int = 224
CONSUMABLE_CLASSIFIER_DEVICE: str = ""
CONSUMABLE_CLASSIFIER_TOPK: int = 5
CONSUMABLE_MIN_CLS_CONFIDENCE: float = 0.5
CONSUMABLE_CLASSIFIER_LABELS_YAML_PATH: str = default_consumable_classifier_labels_yaml_path()
CONSUMABLE_VISION_WINDOW_SEC: float = 15.0

# --- 手部检测；全空路径则整帧分类 ---
HAND_DETECTION_WEIGHTS: str = ""
HAND_DETECTION_IMGSZ: int = 640
HAND_DETECTION_CONF: float = 0.25
HAND_DETECTION_PAD_RATIO: float = 0.30
HAND_DETECTION_MIN_CROP_PX: int = 64
HAND_DETECTION_DEVICE: str = ""


# --- algorithm_subprocesses/5.15 ActionFormer 实时版（VideoSwin → ActionFormer + 手检/好坏帧/耗材投票）---
# 默认开启并与常见 demo 主摄 id 对齐；若缺少配置引用包 weights 下权重，开录时该路会记录异常。
ACTIONFORMER_ENABLED: bool = True
# 检查期：单路算法固定拉流 or-cam-03（恢复按请求首路时请改回 "" 或按需配置）。
ACTIONFORMER_PRIMARY_CAMERA_ID: str = "or-cam-03"

# RTSP 帧率假设（用于 frame_idx → t_abs 推算与 ActionFormer fps 入参；线上需与摄像机一致）。
ACTIONFORMER_ASSUMED_FPS: float = 25.0

# 与 algorithm_subprocesses/5.15 ``run_haocai_actionformer_consumables_e2e`` 写出的 infer_single.yaml 同语义的 VideoSwin 参数：
# clip_len 32, frame_stride 2, feat_stride_frames 32 → feat_num_frames=64。
VIDEOSWIN_CLIP_LEN: int = 32
VIDEOSWIN_FRAME_STRIDE: int = 2
VIDEOSWIN_FEAT_STRIDE_FRAMES: int = 32
VIDEOSWIN_INPUT_DIM: int = 768
VIDEOSWIN_IMAGE_SIZE: int = 224

# 模型权重（默认读取配置引用包 weights；文件名与 configs/default_config.yaml 对齐）
ACTIONFORMER_CKPT: str = default_actionformer_ckpt_path()
ACTIONFORMER_HAND_DET_WEIGHTS: str = default_actionformer_hand_det_weights_path()
ACTIONFORMER_GOODBAD_WEIGHTS: str = default_actionformer_goodbad_weights_path()
ACTIONFORMER_HAOCAI_WEIGHTS: str = default_actionformer_haocai_weights_path()
ACTIONFORMER_TEAR_WEIGHTS: str = default_actionformer_tear_weights_path()
ACTIONFORMER_NUM_CLASSES: int = 1  # 单类（Action）划段，与 e2e infer_single.yaml 对齐

# 与 algorithm_subprocesses/5.15 e2e 默认对齐的运行时阈值
ACTIONFORMER_DET_CONF: float = 0.5
ACTIONFORMER_PAD_RATIO: float = 0.30
ACTIONFORMER_DET_IMGSZ: int = 640
ACTIONFORMER_GOODBAD_IMGSZ: int = 224
ACTIONFORMER_HAOCAI_IMGSZ: int = 224
ACTIONFORMER_GOOD_TOP1_CONF_THRESHOLD: float = 0.9
ACTIONFORMER_HAOCAI_MIN_CONF: float = 0.8

# ActionFormer 候选段 score 下限（不含等于）；与 e2e ``--af-min-score`` 默认对齐
ACTIONFORMER_AF_MIN_SCORE: float = 0.1

# 候选耗材 → softmax mask（按会话候选耗材索引集做 mask，避免命中候选外类别）
ACTIONFORMER_WHITELIST_SOFTMAX: bool = True

# 周期性 ActionFormer + 稳定段发布
# 周期间隔（墙钟秒）：每过这么久才考虑跑一次 ActionFormer，避免逐帧打 forward
ACTIONFORMER_PERIOD_SEC: float = 10.0
# 至少累积多少特征才有第一次有效推理（32 帧/特征 @ 25 fps ≈ 1.28 秒/特征）
ACTIONFORMER_MIN_FEATURES_FOR_INFER: int = 8
# 段尾时间 + lookahead < now 才视为「冻结」，可以增量落库；越大越接近全量批处理。
ACTIONFORMER_INCREMENTAL_STABLE_FLUSH: bool = True
ACTIONFORMER_FREEZE_LOOKAHEAD_S: float = 12.0
# 新旧配置兼容；未显式使用时以 ACTIONFORMER_FREEZE_LOOKAHEAD_S 为准。
ACTIONFORMER_STABLE_FLUSH_MARGIN_S: float = ACTIONFORMER_FREEZE_LOOKAHEAD_S
# 已发布段去重：新段与已发布段的较短段覆盖率超过该阈值时视为同一段。
ACTIONFORMER_DEDUPE_OVERLAP_RATIO: float = 0.5

# 设备：空字符串走 resolve_inference_device（cuda 优先、mac 上再 mps、否则 CPU）
ACTIONFORMER_VIDEOSWIN_DEVICE: str = ""
ACTIONFORMER_DEVICE: str = ""
ACTIONFORMER_HAND_DET_DEVICE: str = ""
ACTIONFORMER_GOODBAD_DEVICE: str = ""
ACTIONFORMER_HAOCAI_DEVICE: str = ""