"""数据集标签目录布局解析。

README 中的 keys 是相对每个 modality 的 ``.tar`` 根目录的扁平路径；
实际解压后常多一层子目录或 clip stem 前缀。解析失败时在 ``FileNotFoundError``
里附带目录列表，便于与 Hugging Face 数据集页面中的说明对照。
"""

from __future__ import annotations

from pathlib import Path


def _norm_name(s: str) -> str:
    return "".join(c for c in s.lower() if c.isalnum())


def _diagnose_labels(labels_dir: Path, folder: str, max_list: int = 50) -> str:
    """列出 ``labels/<clip>/<folder>/`` 下文件采样 + clip 根下一级子目录。"""
    lines: list[str] = []
    sub = labels_dir / folder
    if sub.is_dir():
        files = sorted(p for p in sub.rglob("*") if p.is_file())
        lines.append(f"[{folder}/] 下共 {len(files)} 个文件（最多列出 {max_list} 条相对路径）：")
        for p in files[:max_list]:
            try:
                rel = p.relative_to(labels_dir).as_posix()
            except ValueError:
                rel = str(p)
            lines.append(f"  {rel}")
        if len(files) > max_list:
            lines.append(f"  ... 另有 {len(files) - max_list} 个文件未列出")
    else:
        lines.append(f"[{folder}/] 不存在：{sub}")
    try:
        top = sorted(d.name for d in labels_dir.iterdir() if d.is_dir())
        lines.append(f"[labels/<clip>/] 一级子目录：{top}")
    except OSError as e:
        lines.append(f"[labels/<clip>/] 无法列举：{e}")
    return "\n".join(lines)


def _scan_npy_json_npz(
    labels_dir: Path,
    folder: str,
    fname: str,
    *,
    exts: tuple[str, ...] = (".npy",),
    tokens_norm: list[str],
    name_must_contain: str | None = None,
) -> list[Path]:
    """在整棵 labels/<clip>/ 下找候选文件：扩展名 + 归一化名须含各 token。"""
    root_hint = labels_dir / folder
    search_roots = [root_hint] if root_hint.is_dir() else []
    if not search_roots:
        search_roots = [labels_dir]
    hits: list[Path] = []
    for root in search_roots:
        for p in root.rglob("*"):
            if not p.is_file():
                continue
            if not p.suffix.lower() in [e.lower() for e in exts]:
                continue
            if name_must_contain and name_must_contain.lower() not in p.name.lower():
                continue
            pn = _norm_name(p.name)
            if all(tok in pn for tok in tokens_norm if tok):
                hits.append(p)
    if not hits and root_hint.is_dir():
        for p in labels_dir.rglob("*"):
            if not p.is_file() or p.suffix.lower() not in [e.lower() for e in exts]:
                continue
            if name_must_contain and name_must_contain.lower() not in p.name.lower():
                continue
            pn = _norm_name(p.name)
            if all(tok in pn for tok in tokens_norm if tok):
                hits.append(p)
    return hits


def resolve_clip_file(labels_dir: Path, *parts: str) -> Path:
    """在 ``labels/<clip_id>/`` 下解析 ``parts`` 组成的相对路径（首个元素为一级子文件夹）。"""
    if not parts:
        raise ValueError("parts 不能为空")
    if not labels_dir.is_dir():
        raise FileNotFoundError(f"clip 标签根目录不存在: {labels_dir}")

    direct = labels_dir.joinpath(*parts)
    if direct.is_file():
        return direct
    # NVIDIA 磁盘命名：``{clip_stem}.{README_key}``，clip_stem = ``labels/<clip>/``
    # 解析后的目录名（含 ``uuid_t0_t1``）；README 里的 key 本身不含此前缀。
    clip_stem = labels_dir.resolve().name
    if len(parts) >= 2:
        folder, fname = parts[0], parts[-1]
        if not fname.startswith(f"{clip_stem}."):
            stemmed = labels_dir / folder / f"{clip_stem}.{fname}"
            if stemmed.is_file():
                return stemmed
    if len(parts) >= 2:
        folder = parts[0]
        rest = parts[1:]
        doubled = (labels_dir / folder / folder).joinpath(*rest)
        if doubled.is_file():
            return doubled
    fname = parts[-1]
    folder = parts[0]
    sub = labels_dir / folder
    if sub.is_dir():
        for p in sub.rglob(fname):
            if p.is_file():
                return p

    for p in labels_dir.rglob(fname):
        if p.is_file():
            return p
    fl = fname.lower()
    for p in labels_dir.rglob("*"):
        if p.is_file() and p.name.lower() == fl:
            return p

    # ftheta / pinhole
    if folder in ("ftheta_intrinsic", "pinhole_intrinsic") and fname.endswith(".npy"):
        prefix = folder + "."
        if fname.lower().startswith(prefix.lower()):
            cam = fname[len(prefix) : -len(".npy")]
            cam_n = _norm_name(cam)
            hits = []
            for p in labels_dir.rglob("*.npy"):
                if not p.is_file():
                    continue
                pn = _norm_name(p.name)
                if folder == "ftheta_intrinsic":
                    if "ftheta" not in pn:
                        continue
                else:
                    if "pinhole" not in pn:
                        continue
                if cam_n and cam_n in pn:
                    hits.append(p)
            if len(hits) == 1:
                return hits[0]
            if len(hits) > 1:
                hits.sort(key=lambda x: (len(x.parts), str(x)))
                return hits[0]

    # pose: ``{idx:06d}.pose.{camera}.npy``
    if folder == "pose" and fname.endswith(".npy"):
        base = fname[: -len(".npy")]
        if ".pose." in base:
            idx_part, _, cam_part = base.partition(".pose.")
            hits = _scan_npy_json_npz(
                labels_dir,
                folder,
                fname,
                exts=(".npy",),
                tokens_norm=[_norm_name(idx_part), _norm_name(cam_part)],
                name_must_contain="pose",
            )
            if len(hits) == 1:
                return hits[0]
            if len(hits) > 1:
                hits.sort(key=lambda x: (len(x.parts), -len(x.name), str(x)))
                return hits[0]

    # vehicle_pose: ``{idx:06d}.vehicle_pose.npy``
    if folder == "vehicle_pose" and fname.endswith(".npy"):
        idx_part = fname.split(".")[0]
        hits = _scan_npy_json_npz(
            labels_dir,
            folder,
            fname,
            exts=(".npy",),
            tokens_norm=[_norm_name(idx_part), "vehiclepose"],
            name_must_contain="vehicle",
        )
        if len(hits) == 1:
            return hits[0]
        if len(hits) > 1:
            hits.sort(key=lambda x: (len(x.parts), str(x)))
            return hits[0]

    # all_object_info
    if folder == "all_object_info" and fname.endswith(".json"):
        idx_part = fname.split(".")[0]
        hits = _scan_npy_json_npz(
            labels_dir,
            folder,
            fname,
            exts=(".json",),
            tokens_norm=[_norm_name(idx_part), "allobjectinfo"],
        )
        if len(hits) == 1:
            return hits[0]
        if len(hits) > 1:
            hits.sort(key=lambda x: (len(x.parts), str(x)))
            return hits[0]

    # lidar_raw
    if folder == "lidar_raw" and fname.endswith(".npz"):
        stem = fname[: -len(".npz")]
        hits = _scan_npy_json_npz(
            labels_dir,
            folder,
            fname,
            exts=(".npz",),
            tokens_norm=[_norm_name(stem), "lidar", "raw"],
        )
        if len(hits) == 1:
            return hits[0]
        if len(hits) > 1:
            hits.sort(key=lambda x: (len(x.parts), str(x)))
            return hits[0]

    detail = _diagnose_labels(labels_dir, folder)
    raise FileNotFoundError(
        f"在 {labels_dir} 下未找到 {'/'.join(parts)}（已尝试 README 扁平路径、双嵌套、"
        f"rglob、按帧索引+相机的扫描匹配）。\n{detail}"
    )