accesspath3r/cli.py

#!/usr/bin/env python3
"""AccessibilityAmodal one-command mask, 2D completion, and 3D inference.

This is a lightweight login-node launcher.  GPU work is submitted to Slurm via
``slurm/run_accesspath_demo.sbatch``; this process can wait for the
job and report the persistent result directory.

The third-party Accessibility3D model is accessed only through the project adapter
``tools/accessibility_3d_completion.py``.
"""

from __future__ import annotations

import argparse
import json
import os
import re
import shutil
import subprocess
import sys
import time
from datetime import datetime
from pathlib import Path

from accesspath3r.privacy import public_path, sanitize_text


PROJECT_ROOT = Path(__file__).resolve().parents[1]
SBATCH_SCRIPT = PROJECT_ROOT / "slurm" / "run_accesspath_demo.sbatch"
PRIMARY_3D_DIRNAME = "accessibility3d"
DEFAULT_PROMPT_CONFIG = (
    PROJECT_ROOT / "configs" / "accessibility_mask_prompts_e5_v2.json"
)
STRUCTURAL_STAIRS_PROMPT_CONFIG = (
    PROJECT_ROOT / "configs" / "accessibility_stairs_structural_occlusion_prompts.json"
)
OUTDOOR_STAIRS_NO_OCCLUSION_PROMPT_CONFIG = (
    PROJECT_ROOT / "configs" / "accessibility_stairs_outdoor_no_occlusion_prompts.json"
)
CATEGORIES = ("curb_cut", "ramp", "stairs", "tactile_paving", "walkway")
GEOMETRY_ALLOWED_2D_STATUSES = {
    "candidate_selected_for_review",
    "skipped_empty_removal_mask",
}
TERMINAL_STATES = {
    "BOOT_FAIL",
    "CANCELLED",
    "COMPLETED",
    "DEADLINE",
    "FAILED",
    "NODE_FAIL",
    "OUT_OF_MEMORY",
    "PREEMPTED",
    "REVOKED",
    "TIMEOUT",
}


def safe_sample_id(value: str) -> str:
    result = re.sub(r"[^A-Za-z0-9_-]+", "_", value).strip("_")
    return result or "single_image"


def normalized_state(value: str) -> str:
    return value.strip().split()[0].split("+")[0] if value.strip() else ""


def run_command(command: list[str]) -> subprocess.CompletedProcess[str]:
    return subprocess.run(
        command,
        cwd=PROJECT_ROOT,
        check=False,
        capture_output=True,
        text=True,
    )


def query_job_state(job_id: str) -> str | None:
    queued = run_command(["squeue", "-h", "-j", job_id, "-o", "%T"])
    if queued.returncode == 0 and queued.stdout.strip():
        return normalized_state(queued.stdout.splitlines()[0])

    accounting = run_command(
        [
            "sacct",
            "-n",
            "-P",
            "-j",
            job_id,
            "--format=JobIDRaw,State,ExitCode",
        ]
    )
    if accounting.returncode != 0:
        return None
    for line in accounting.stdout.splitlines():
        fields = line.split("|")
        if len(fields) >= 2 and fields[0] == job_id:
            return normalized_state(fields[1])
    return None


def format_clock(seconds: float) -> str:
    seconds = max(0, int(round(seconds)))
    hours, remainder = divmod(seconds, 3600)
    minutes, seconds = divmod(remainder, 60)
    if hours:
        return f"{hours:d}:{minutes:02d}:{seconds:02d}"
    return f"{minutes:02d}:{seconds:02d}"


def parse_timestamp(value: object) -> float | None:
    if not isinstance(value, str):
        return None
    try:
        return datetime.fromisoformat(value).timestamp()
    except ValueError:
        return None


def read_progress(output_dir: Path) -> dict | None:
    try:
        value = json.loads((output_dir / "progress.json").read_text(encoding="utf-8"))
    except (FileNotFoundError, json.JSONDecodeError, OSError):
        return None
    return value if isinstance(value, dict) else None


def infer_progress_from_artifacts(
    output_dir: Path,
    sample_id: str,
    include_generative_3d: bool,
) -> dict | None:
    """Provide useful progress for jobs launched before progress.json existed."""
    presentation_manifest = (
        output_dir
        / "04_3d_completion"
        / "geometry"
        / "presentation_models"
        / "manifest.json"
    )
    legacy_variants_manifest = (
        output_dir
        / "04_3d_completion"
        / "geometry"
        / "3d_variants"
        / "manifest.json"
    )
    if not presentation_manifest.is_file() and legacy_variants_manifest.is_file():
        presentation_manifest = legacy_variants_manifest
    learned_manifest = output_dir / PRIMARY_3D_DIRNAME / "manifest.json"
    current_compatibility_manifest = (
        output_dir / "04_3d_completion" / "visual_candidate" / "manifest.json"
    )
    if not learned_manifest.is_file() and current_compatibility_manifest.is_file():
        learned_manifest = current_compatibility_manifest
    final_marker = learned_manifest if include_generative_3d else presentation_manifest
    if final_marker.is_file():
        return {
            "status": "completed",
            "percent": 100.0,
            "stage_label": "All outputs completed",
        }
    milestones = (
        (
            learned_manifest,
            96.0,
            "Accessibility3D CUDA Gaussian rotation and dense triangle mesh",
        ),
        (
            presentation_manifest,
            74.0,
            "Diagnostic category-constrained geometry",
        ),
        (
            output_dir / "04_3d_completion" / "geometry" / "completed_mesh_turntable_slow.gif",
            44.0,
            "Continuous-surface and solid 3D views",
        ),
        (
            output_dir / "04_3d_completion" / "geometry" / "geometry_manifest.json",
            43.0,
            "Basic 3D turntable preview",
        ),
        (
            output_dir / "03_2d_completion" / "completed_rgb_selected.png",
            34.0,
            "Depth and 3D geometry reconstruction",
        ),
        (
            output_dir / "02_masks" / "metadata.json",
            20.0,
            "GPU generative 2D completion and candidate selection",
        ),
        (
            output_dir / "01_sam3" / "summary.json",
            16.0,
            "Visible, hidden, amodal, and obstacle masks",
        ),
    )
    for marker, percent, label in milestones:
        if marker.is_file():
            return {"status": "running", "percent": percent, "stage_label": label}
    return None


def interpolated_percent(progress: dict, now: float) -> float:
    try:
        percent = float(progress.get("percent", 0.0))
    except (TypeError, ValueError):
        percent = 0.0
    if progress.get("status") != "running":
        return max(0.0, min(100.0, percent))
    try:
        end_percent = float(progress.get("stage_end_percent", percent))
        expected = float(progress.get("stage_expected_seconds", 0.0))
    except (TypeError, ValueError):
        return max(0.0, min(99.0, percent))
    stage_started = parse_timestamp(progress.get("stage_started_at"))
    if stage_started is None or expected <= 0 or end_percent <= percent:
        return max(0.0, min(99.0, percent))
    fraction = max(0.0, min(0.98, (now - stage_started) / expected))
    interpolated = percent + (end_percent - percent) * fraction
    return max(0.0, min(99.0, interpolated))


def estimated_remaining_seconds(
    progress: dict | None,
    now: float,
    elapsed: float,
    base_total: float,
) -> float:
    """Keep ETA conservative when a stage or an earlier stage runs long."""
    adjusted_total = max(1.0, base_total)
    if not progress or progress.get("status") != "running":
        return max(0.0, adjusted_total - elapsed)
    overall_started = parse_timestamp(progress.get("started_at"))
    stage_started = parse_timestamp(progress.get("stage_started_at"))
    try:
        start_percent = float(progress.get("percent", 0.0))
        stage_expected = float(progress.get("stage_expected_seconds", 0.0))
    except (TypeError, ValueError):
        return max(0.0, adjusted_total - elapsed)
    if overall_started is not None and stage_started is not None:
        planned_stage_start = max(0.0, start_percent) / 100.0 * base_total
        actual_stage_start = max(0.0, stage_started - overall_started)
        adjusted_total += max(0.0, actual_stage_start - planned_stage_start)
        adjusted_total += max(0.0, now - stage_started - stage_expected)
    return max(0.0, adjusted_total - elapsed)


class ProgressDisplay:
    def __init__(self, job_id: str) -> None:
        self.job_id = job_id
        self.is_tty = sys.stdout.isatty()
        self.last_key: tuple[str, str] | None = None
        self.last_width = 0

    def update(
        self,
        percent: float,
        label: str,
        elapsed: float,
        eta: float | None,
        state: str,
        final: bool = False,
    ) -> None:
        width = 28
        filled = min(width, max(0, int(round(width * percent / 100.0))))
        bar = "#" * filled + "-" * (width - filled)
        if state == "PENDING":
            timing = f"queue {format_clock(elapsed)} | GPU ETA ~{format_clock(eta or 0)}"
        elif state == "FAILED":
            timing = f"elapsed {format_clock(elapsed)} | failed"
        elif eta is None:
            timing = f"elapsed {format_clock(elapsed)} | ETA calculating"
        elif eta <= 0 and not final:
            timing = f"elapsed {format_clock(elapsed)} | finishing"
        else:
            timing = f"elapsed {format_clock(elapsed)} | ETA ~{format_clock(eta)}"
        line = (
            f"[Slurm {self.job_id}] [{bar}] {percent:5.1f}% | "
            f"{label} | {timing}"
        )
        key = (state, label)
        if self.is_tty:
            padding = " " * max(0, self.last_width - len(line))
            print(f"\r{line}{padding}", end="\n" if final else "", flush=True)
            self.last_width = len(line)
        elif key != self.last_key or final:
            print(line, flush=True)
        self.last_key = key


def wait_for_job(
    job_id: str,
    poll_seconds: float,
    output_dir: Path,
    sample_id: str,
    include_generative_3d: bool,
    estimated_seconds: float,
) -> str:
    missing_polls = 0
    submitted_at = time.monotonic()
    running_at: float | None = None
    display = ProgressDisplay(job_id)
    while True:
        state = query_job_state(job_id)
        now_wall = time.time()
        now_monotonic = time.monotonic()
        progress = read_progress(output_dir) or infer_progress_from_artifacts(
            output_dir,
            sample_id,
            include_generative_3d,
        )

        if state:
            missing_polls = 0
            if state == "RUNNING" and running_at is None:
                running_at = now_monotonic
        else:
            # sacct can lag briefly after a job leaves squeue.
            missing_polls += 1
            if missing_polls >= 12 and not progress:
                raise RuntimeError(
                    "Unable to read the job state from squeue or sacct. "
                    f"Check manually with: squeue -j {job_id}"
                )

        progress_status = progress.get("status") if progress else None
        if progress_status == "completed":
            runtime_started = parse_timestamp(progress.get("started_at"))
            elapsed = now_wall - runtime_started if runtime_started else 0.0
            display.update(100.0, "All outputs completed", elapsed, 0.0, "COMPLETED", final=True)
            return "COMPLETED"
        if progress_status == "failed":
            runtime_started = parse_timestamp(progress.get("started_at"))
            elapsed = now_wall - runtime_started if runtime_started else 0.0
            display.update(
                interpolated_percent(progress, now_wall),
                str(progress.get("message") or progress.get("stage_label") or "GPU stage failed"),
                elapsed,
                None,
                "FAILED",
                final=True,
            )
            return "FAILED"

        if state == "PENDING":
            display.update(
                0.0,
                "Waiting for a GPU",
                now_monotonic - submitted_at,
                estimated_seconds,
                state,
            )
        else:
            if running_at is None:
                running_at = now_monotonic
            runtime_started = parse_timestamp(progress.get("started_at")) if progress else None
            elapsed = (
                max(0.0, now_wall - runtime_started)
                if runtime_started is not None
                else now_monotonic - running_at
            )
            total_estimate = estimated_seconds
            if progress:
                try:
                    total_estimate = float(progress.get("total_estimated_seconds", estimated_seconds))
                except (TypeError, ValueError):
                    pass
            percent = interpolated_percent(progress, now_wall) if progress else 0.0
            label = (
                str(progress.get("stage_label") or "Initializing GPU job")
                if progress
                else "Initializing GPU job"
            )
            display.update(
                percent,
                label,
                elapsed,
                estimated_remaining_seconds(progress, now_wall, elapsed, total_estimate),
                state or "UNKNOWN",
                final=bool(state in TERMINAL_STATES),
            )

        if state in TERMINAL_STATES:
            return state
        time.sleep(poll_seconds)


def estimate_runtime_seconds(args: argparse.Namespace) -> int:
    # Broad SAM3 prompt evaluation and the presentation-model renderer scale
    # with image size.  These values are calibrated from the 1368x1824 9528
    # run; GLB texture baking is accounted for separately because it dominated
    # that job's final stage instead of behaving like the normal mesh export.
    seconds = (5 if args.reviewed_visible_workspace else 150) + 13 + (24 if not args.no_depth else 6) + 3 + 420
    if not args.no_2d:
        seconds += 40
    if args.fast_2d_baseline:
        seconds += 12
    if not args.no_generative_3d:
        seconds += 75
        if args.export_glb:
            seconds += 540
    return seconds


def build_parser() -> argparse.ArgumentParser:
    parser = argparse.ArgumentParser(
        description=(
            "Submit one accessibility image to SAM3 mask inference, amodal/hidden "
            "completion, 2D completion, Depth Anything geometry reconstruction, "
            "and Accessibility3D CUDA Gaussian rendering with a dense triangle mesh."
        )
    )
    parser.add_argument("image_pos", nargs="?", help="Input RGB image (positional form).")
    parser.add_argument("--image", dest="image_opt", help="Input RGB image.")
    parser.add_argument("--category", required=True, choices=CATEGORIES)
    parser.add_argument("--sample-id", default=None)
    parser.add_argument(
        "--output-dir",
        default=None,
        help=(
            "Persistent output directory. "
            "Default: output/inference/<sample>_<time>."
        ),
    )
    parser.add_argument(
        "--prompt-config",
        default=None,
        help=(
            "Optional expert override for the mask prompt JSON. By default, a unified "
            "target/occluder prompt bank is evaluated against the input image, so the "
            "caller does not need to choose a scene-specific JSON."
        ),
    )
    parser.add_argument(
        "--reviewed-visible-workspace",
        default=None,
        help=(
            "A one-image workspace saved by serve_accessibility_visible_mask_review.py. "
            "Requires explicit human approval; replaces the SAM3 visible-target proposal only."
        ),
    )
    parser.add_argument(
        "--structural-occluder",
        action="store_true",
        help=(
            "For stairs occluded by a pillar, newel post, banister, or railing. "
            "Selects the dedicated structural-occlusion prompt config."
        ),
    )
    parser.add_argument(
        "--no-target-occluder",
        action="store_true",
        help=(
            "For a visible outdoor stair flight with no target-surface occluder. "
            "Uses prompts that exclude permanent handrails and side walls from the obstacle mask."
        ),
    )
    parser.add_argument(
        "--sd-2d",
        action="store_true",
        help="Legacy compatibility flag; GPU generative 2D completion is enabled by default.",
    )
    parser.add_argument(
        "--fast-2d-baseline",
        action="store_true",
        help="Also produce the old OpenCV 2D baseline for comparison (not a primary result).",
    )
    parser.add_argument("--no-2d", action="store_true", help="Skip GPU generative 2D completion.")
    parser.add_argument("--no-depth", action="store_true", help="Use debug perspective geometry instead of Depth Anything.")
    parser.add_argument(
        "--no-generative-3d",
        action="store_true",
        help="Skip the primary Accessibility3D CUDA rotation and dense learned mesh.",
    )
    parser.add_argument(
        "--export-glb",
        action="store_true",
        help="Also export the Accessibility3D dense triangle result as mesh.glb.",
    )
    parser.add_argument("--allow-large-2d", action="store_true", help="Allow 2D completion masks larger than the normal safety gate.")
    parser.add_argument("--no-wait", action="store_true", help="Submit and return immediately instead of waiting for results.")
    parser.add_argument("--poll-seconds", type=float, default=5.0, help=argparse.SUPPRESS)
    parser.add_argument("--dry-run", action="store_true", help="Validate and print the planned submission without submitting.")
    return parser


def resolve_args(args: argparse.Namespace, parser: argparse.ArgumentParser) -> dict[str, str]:
    if bool(args.image_pos) == bool(args.image_opt):
        parser.error("provide exactly one image, either positional or with --image")
    image = Path(args.image_opt or args.image_pos).expanduser().resolve()
    if not image.is_file():
        parser.error(
            "input image does not exist: "
            f"{public_path(image, project_root=PROJECT_ROOT)}"
        )
    if args.poll_seconds < 2:
        parser.error("--poll-seconds must be at least 2")
    if args.structural_occluder and args.category != "stairs":
        parser.error("--structural-occluder currently applies only to --category stairs")
    if args.no_target_occluder and args.category != "stairs":
        parser.error("--no-target-occluder currently applies only to --category stairs")
    if args.structural_occluder and args.no_target_occluder:
        parser.error("--structural-occluder and --no-target-occluder are mutually exclusive")
    if (args.structural_occluder or args.no_target_occluder) and args.prompt_config:
        parser.error("choose one of --structural-occluder, --no-target-occluder, or --prompt-config")
    reviewed_visible_workspace = None
    if args.reviewed_visible_workspace:
        reviewed_visible_workspace = Path(args.reviewed_visible_workspace).expanduser().resolve()
        if not reviewed_visible_workspace.is_dir():
            parser.error(
                "reviewed-visible workspace does not exist: "
                f"{public_path(reviewed_visible_workspace, project_root=PROJECT_ROOT)}"
            )

    if args.structural_occluder:
        prompt_config = STRUCTURAL_STAIRS_PROMPT_CONFIG
    elif args.no_target_occluder:
        prompt_config = OUTDOOR_STAIRS_NO_OCCLUSION_PROMPT_CONFIG
    elif args.prompt_config:
        prompt_config = Path(args.prompt_config).expanduser().resolve()
    else:
        prompt_config = DEFAULT_PROMPT_CONFIG
    if not prompt_config.is_file():
        parser.error(
            "prompt config does not exist: "
            f"{public_path(prompt_config, project_root=PROJECT_ROOT)}"
        )
    if not SBATCH_SCRIPT.is_file():
        parser.error(
            "Slurm script does not exist: "
            f"{public_path(SBATCH_SCRIPT, project_root=PROJECT_ROOT)}"
        )
    if shutil.which("sbatch") is None:
        parser.error("sbatch is unavailable; run this command on the configured Slurm server")

    sample_id = safe_sample_id(args.sample_id or image.stem)
    if args.output_dir:
        output_dir = Path(args.output_dir).expanduser().resolve()
    else:
        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
        output_dir = PROJECT_ROOT / "output" / "inference" / f"{sample_id}_{timestamp}"

    return {
        "IMAGE": str(image),
        "CATEGORY": args.category,
        "SAMPLE_ID": sample_id,
        "OUTPUT_DIR": str(output_dir),
        "PROMPT_CONFIG": str(prompt_config),
        "RUN_GPU_2D": "0" if args.no_2d else "1",
        "RUN_FAST_2D": "1" if args.fast_2d_baseline else "0",
        "ALLOW_LARGE_2D": "1" if args.allow_large_2d else "0",
        "RUN_DEPTH": "0" if args.no_depth else "1",
        "RUN_ACCESSIBILITYAMODAL_VISUAL_3D": "0" if args.no_generative_3d else "1",
        "EXPORT_ACCESSIBILITYAMODAL_VISUAL_GLB": "1" if args.export_glb else "0",
        "NO_TARGET_OCCLUDER": "1" if args.no_target_occluder else "0",
        "REVIEWED_VISIBLE_WORKSPACE": str(reviewed_visible_workspace) if reviewed_visible_workspace else "",
    }


def print_result_paths(
    output_dir: Path,
    sample_id: str,
    include_gpu_2d: bool,
    include_generative_3d: bool,
    include_glb: bool,
    reviewed_visible: bool = False,
) -> None:
    def shown(path: Path) -> str:
        return public_path(
            path,
            project_root=PROJECT_ROOT,
            output_root=output_dir,
        )

    print("\nResults:")
    print(f"  output root:       {shown(output_dir)}")
    if include_gpu_2d:
        print(
            f"  quick review:      "
            f"{shown(output_dir / '00_quick_review' / 'overview.jpg')}"
        )
    if reviewed_visible:
        print(f"  reviewed visible:  {shown(output_dir / '01_reviewed_visible' / 'metadata.json')}")
    else:
        print(f"  SAM3 overlay:      {shown(output_dir / '01_sam3' / 'samples' / sample_id / 'overlay.png')}")
    print(f"  mask overlay:      {shown(output_dir / '02_masks' / 'mask_overlay.png')}")
    print(f"  visible mask:      {shown(output_dir / '02_masks' / 'target_visible.png')}")
    print(f"  hidden mask:       {shown(output_dir / '02_masks' / 'hidden.png')}")
    print(f"  amodal mask:       {shown(output_dir / '02_masks' / 'target_amodal.png')}")
    print(f"  obstacle mask:     {shown(output_dir / '02_masks' / 'obstacle.png')}")
    if include_gpu_2d:
        print(f"  GPU 2D selected:   {shown(output_dir / '03_2d_completion' / 'completed_rgb_selected.png')}")
        print(f"  GPU 2D candidates: {shown(output_dir / '03_2d_completion' / 'candidate_comparison.jpg')}")
    print(f"  depth diagnostic:  {shown(output_dir / '04_3d_completion' / 'geometry' / 'completed_depth_vis.png')}")
    print(f"  point-cloud debug: {shown(output_dir / '04_3d_completion' / 'geometry' / 'completed_point_cloud.ply')}")
    print(f"  geometry debug:    {shown(output_dir / '04_3d_completion' / 'geometry' / 'completed_mesh.ply')}")
    print(f"  geometry fallback: {shown(output_dir / '04_3d_completion' / 'turntable' / 'turntable.gif')}")
    print(
        "  presentation 3D:   "
        f"{shown(output_dir / '04_3d_completion' / 'geometry' / 'presentation_models')}"
    )
    if include_generative_3d:
        visual_dir = output_dir / PRIMARY_3D_DIRNAME
        print(f"  quality checks:   {shown(output_dir / '05_quality_checks' / 'verification.json')}")
        print(f"  primary 3D GIF:   {shown(visual_dir / 'sample_gaussian.gif')}")
        print(f"  mesh normal diag: {shown(visual_dir / 'sample_mesh.gif')}")
        print(f"  primary 3D views: {shown(visual_dir / 'multiview_contact_sheet.jpg')}")
        print(f"  dense face mesh:  {shown(visual_dir / 'mesh.ply')}")
        if include_glb:
            print(f"  textured 3D GLB:  {shown(visual_dir / 'mesh.glb')}")


def main(argv: list[str] | None = None) -> int:
    parser = build_parser()
    args = parser.parse_args(argv)
    job_env = resolve_args(args, parser)
    output_dir = Path(job_env["OUTPUT_DIR"])
    sample_id = job_env["SAMPLE_ID"]

    print("AccessibilityAmodal one-command inference")
    print(
        "  image:         "
        f"{public_path(job_env['IMAGE'], project_root=PROJECT_ROOT)}"
    )
    print(f"  category:      {job_env['CATEGORY']}")
    if job_env["REVIEWED_VISIBLE_WORKSPACE"]:
        print("  visible mask:   human-reviewed workspace (SAM3 proposal bypassed)")
        print("  prompt config:  not used after reviewed-mask validation")
    else:
        print(
            "  prompt config: "
            f"{public_path(job_env['PROMPT_CONFIG'], project_root=PROJECT_ROOT)}"
        )
    print(
        "  output:        "
        f"{public_path(output_dir, project_root=PROJECT_ROOT, output_root=output_dir)}"
    )
    print("  GPU execution: Slurm, one GPU (5090 partition)")
    print(
        "  3D outputs:    diagnostic depth/point cloud"
        + (
            " + primary Accessibility3D CUDA Gaussian rotation/dense mesh"
            if not args.no_generative_3d
            else " + category-geometry fallback"
        )
    )
    estimated_seconds = estimate_runtime_seconds(args)
    print(
        f"  estimated time: about {format_clock(estimated_seconds)} after GPU starts "
        "(Slurm queue excluded)"
    )

    command = ["sbatch", "--parsable", "--export=ALL", str(SBATCH_SCRIPT)]
    if args.dry_run:
        print("Dry run; no job submitted.")
        shown_command = [*command[:-1], public_path(command[-1], project_root=PROJECT_ROOT)]
        print("  " + " ".join(shown_command))
        return 0

    environment = os.environ.copy()
    environment.update(job_env)
    submitted = subprocess.run(
        command,
        cwd=PROJECT_ROOT,
        env=environment,
        check=False,
        capture_output=True,
        text=True,
    )
    if submitted.returncode != 0:
        detail = submitted.stderr.strip() or submitted.stdout.strip()
        raise RuntimeError(
            "Slurm submission failed: "
            + sanitize_text(
                detail,
                project_root=PROJECT_ROOT,
                output_root=output_dir,
                sensitive_paths=(job_env["IMAGE"], job_env["PROMPT_CONFIG"]),
            )
        )
    job_id = submitted.stdout.strip().split(";")[0]
    if not job_id.isdigit():
        raise RuntimeError(f"Could not parse Slurm job ID from: {submitted.stdout!r}")

    print(f"Submitted Slurm job {job_id}")
    print(f"  stdout: Logs/slurm-{job_id}.out")
    print(f"  stderr: Logs/slurm-{job_id}.err")
    if args.no_wait:
        print(f"Monitor with: squeue -j {job_id}")
        print_result_paths(
            output_dir,
            sample_id,
            not args.no_2d,
            not args.no_generative_3d,
            args.export_glb,
            bool(job_env["REVIEWED_VISIBLE_WORKSPACE"]),
        )
        return 0

    try:
        state = wait_for_job(
            job_id,
            args.poll_seconds,
            output_dir,
            sample_id,
            not args.no_generative_3d,
            estimated_seconds,
        )
    except KeyboardInterrupt:
        print(
            f"\nStopped waiting; Slurm job {job_id} is still managed separately. "
            f"Cancel it only if needed with: scancel {job_id}",
            file=sys.stderr,
        )
        return 130

    if state != "COMPLETED":
        raise RuntimeError(
            f"Slurm job {job_id} ended with state {state}. "
            f"Inspect Logs/slurm-{job_id}.err"
        )
    expected = output_dir / "02_masks" / "mask_overlay.png"
    if not expected.is_file():
        raise RuntimeError(
            f"Slurm job {job_id} completed but the expected mask output is missing: "
            f"{public_path(expected, project_root=PROJECT_ROOT, output_root=output_dir)}"
        )
    preflight_path = output_dir / "05_quality_checks" / "preflight.json"
    if preflight_path.is_file():
        try:
            preflight = json.loads(preflight_path.read_text(encoding="utf-8"))
        except (json.JSONDecodeError, OSError):
            preflight = {}
        if preflight.get("decision") != "accept":
            print(
                "\n3D generation was intentionally withheld because the mask/structure "
                "preflight requires re-prompting or human review."
            )
            print(
                "  verification: "
                f"{public_path(preflight_path, project_root=PROJECT_ROOT, output_root=output_dir)}"
            )
            print("Automatic masks are review candidates, not ground truth.")
            return 0
    completion_manifest_path = output_dir / "03_2d_completion" / "manifest.json"
    if not args.no_2d:
        if not completion_manifest_path.is_file():
            raise RuntimeError(
                "Slurm job completed but the 2D completion manifest is missing: "
                f"{public_path(completion_manifest_path, project_root=PROJECT_ROOT, output_root=output_dir)}"
            )
        try:
            completion_manifest = json.loads(
                completion_manifest_path.read_text(encoding="utf-8")
            )
        except (json.JSONDecodeError, OSError) as exc:
            raise RuntimeError("The 2D completion manifest is unreadable") from exc
        completion_status = completion_manifest.get("status")
        if completion_status not in GEOMETRY_ALLOWED_2D_STATUSES:
            print(
                "\nThe 2D result needs review and was not used to condition 3D: "
                f"{completion_status!r}. Accessibility3D used the original RGB and strict "
                "three-value mask instead."
            )
            comparison = output_dir / "03_2d_completion" / "candidate_comparison.jpg"
            if comparison.is_file():
                print(
                    "  candidates: "
                    f"{public_path(comparison, project_root=PROJECT_ROOT, output_root=output_dir)}"
                )

    if not args.no_generative_3d:
        verification_path = output_dir / "05_quality_checks" / "verification.json"
        if not verification_path.is_file():
            raise RuntimeError(
                "Slurm job completed but the generated-3D verification report is missing: "
                f"{public_path(verification_path, project_root=PROJECT_ROOT, output_root=output_dir)}"
            )
        try:
            verification = json.loads(verification_path.read_text(encoding="utf-8"))
        except (json.JSONDecodeError, OSError) as exc:
            raise RuntimeError("Generated-3D verification report is unreadable") from exc
        if verification.get("decision") != "accept":
            print(
                "\nThe Accessibility3D result remains available for human review, but its "
                "structured verification did not accept automatic use."
            )
            print(
                "  verification: "
                f"{public_path(verification_path, project_root=PROJECT_ROOT, output_root=output_dir)}"
            )

    print_result_paths(
        output_dir,
        sample_id,
        not args.no_2d,
        not args.no_generative_3d,
        args.export_glb,
        bool(job_env["REVIEWED_VISIBLE_WORKSPACE"]),
    )
    print("\nAutomatic masks are review candidates, not ground truth.")
    return 0


if __name__ == "__main__":
    raise SystemExit(main())