aitrailer/cli.py

"""
cli.py — AI Trailer Generator v2 — Command-Line Interface

Usage:
    python cli.py analyze   [--config CONFIG] [--no-audio] [--no-llm]
    python cli.py match     [--config CONFIG] [--force-reindex]
    python cli.py rematch   --beat N [--threshold F] [--refine]
    python cli.py report    [--config CONFIG]
    python cli.py run       [--config CONFIG] [--force-reindex] [--no-audio] [--no-llm]
    python cli.py export    [--config CONFIG] [--format fcpxml|edl|both]

On --no-audio / --no-llm:
    These flags do NOT affect matching quality.
    Whisper and the LLM only assign narrative labels (HOOK/SETUP/CLIMAX)
    to beats in the export metadata. The CV pipeline is identical either way.
    Use them for fast iterations: they skip large model downloads.

All heavy imports are deferred so --help is instant.
"""

from __future__ import annotations

import argparse
import json
import logging
import sys
from pathlib import Path


# ---------------------------------------------------------------------------
# Logging setup
# ---------------------------------------------------------------------------

def _setup_logging(level: str = "INFO") -> None:
    # Force UTF-8 for Windows console emoji printing
    if sys.stdout.encoding != 'utf-8':
        sys.stdout.reconfigure(encoding='utf-8')
    logging.basicConfig(
        format="%(asctime)s  %(levelname)-8s  %(name)s — %(message)s",
        datefmt="%H:%M:%S",
        level=getattr(logging, level.upper(), logging.INFO),
        stream=sys.stdout,
    )
    logging.getLogger("PIL").setLevel(logging.WARNING)


def _ensure_utf8_console() -> None:
    """Make argparse help safe on Windows before logging is configured."""
    if sys.stdout.encoding != "utf-8":
        sys.stdout.reconfigure(encoding="utf-8")


# ---------------------------------------------------------------------------
# Cache helpers (match results ↔ JSON)
# ---------------------------------------------------------------------------

def _results_cache_path(cfg: "AppConfig") -> Path:  # type: ignore[name-defined]
    return cfg.paths.cache_dir / "match_results.json"


def _save_results(results: list, cfg: "AppConfig") -> None:  # type: ignore[name-defined]
    from src.core.models import MatchResult
    data = [
        {
            "beat_id":        r.beat_id,
            "scene_id":       r.scene_id,
            "source_path":    str(r.source_path),
            "in_point_s":     r.in_point_s,
            "out_point_s":    r.out_point_s,
            "in_point_frame": r.in_point_frame,
            "match_score":    r.match_score,
            "match_location": list(r.match_location),
            "is_confirmed":   r.is_confirmed,
            "segments": [
                {
                    "trailer_offset_s": s.trailer_offset_s,
                    "duration_s": s.duration_s,
                    "scene_id": s.scene_id,
                    "in_point_s": s.in_point_s,
                    "out_point_s": s.out_point_s,
                    "match_score": s.match_score,
                    "is_confirmed": s.is_confirmed,
                }
                for s in getattr(r, "segments", ())
            ],
        }
        for r in results
    ]
    p = _results_cache_path(cfg)
    p.parent.mkdir(parents=True, exist_ok=True)
    p.write_text(json.dumps(data, indent=2), encoding="utf-8")
    logging.getLogger(__name__).info("Match results cached → %s", p)


def _regenerate_cutter_report(cfg: "AppConfig") -> None:  # type: ignore[name-defined]
    """Re-render CUTTER_REPORT.md after each cache write so it stays in sync."""
    try:
        from scripts.generate_cutter_report import render_report
    except Exception as exc:
        logging.getLogger(__name__).warning("Cutter report regen skipped: %s", exc)
        return
    try:
        project_root = cfg.paths.cache_dir.parent
        out = project_root / "CUTTER_REPORT.md"
        out.write_text(render_report(project_root), encoding="utf-8")
        logging.getLogger(__name__).info("Cutter report regenerated → %s", out)
    except Exception as exc:
        logging.getLogger(__name__).warning("Cutter report regen failed: %s", exc)


def _load_results(cfg: "AppConfig") -> list:  # type: ignore[name-defined]
    from src.core.models import MatchResult, MatchSegment
    p = _results_cache_path(cfg)
    if not p.exists():
        raise FileNotFoundError(f"No cached results at {p}. Run 'match' first.")
    raw = json.loads(p.read_text(encoding="utf-8"))
    return [
        MatchResult(
            beat_id=d["beat_id"],
            scene_id=d["scene_id"],
            source_path=Path(d["source_path"]),
            in_point_s=d["in_point_s"],
            out_point_s=d["out_point_s"],
            in_point_frame=d["in_point_frame"],
            match_score=d["match_score"],
            match_location=tuple(d["match_location"]),
            is_confirmed=d.get("is_confirmed", True),
            segments=tuple(
                MatchSegment(
                    trailer_offset_s=float(s["trailer_offset_s"]),
                    duration_s=float(s["duration_s"]),
                    scene_id=int(s["scene_id"]),
                    in_point_s=float(s["in_point_s"]),
                    out_point_s=float(s["out_point_s"]),
                    match_score=float(s["match_score"]),
                    is_confirmed=bool(s.get("is_confirmed", True)),
                )
                for s in d.get("segments", ())
            ),
        )
        for d in raw
    ]


def _load_scene_cache_light(cfg) -> list[dict]:
    p = cfg.paths.cache_dir / "scene_index.json"
    if not p.exists():
        return []
    return json.loads(p.read_text(encoding="utf-8"))


def _scene_fps_light(scene: dict, cfg) -> float:
    duration_s = max(0.0, float(scene["end_s"]) - float(scene["start_s"]))
    frame_count = max(0, int(scene["end_frame"]) - int(scene["start_frame"]))
    return frame_count / duration_s if duration_s > 0 and frame_count > 0 else cfg.export.edl_frame_rate


def _scene_for_time_light(scenes: list[dict], t_sec: float, cfg) -> dict | None:
    for idx, scene in enumerate(scenes):
        if float(scene["start_s"]) <= t_sec < float(scene["end_s"]):
            if (
                float(scene["end_s"]) - t_sec <= cfg.cv.deep_scan.scene_boundary_epsilon_s
                and idx + 1 < len(scenes)
            ):
                return scenes[idx + 1]
            return scene
    return None


def _scene_by_id_light(scenes: list[dict], scene_id: int) -> dict | None:
    return next((s for s in scenes if int(s["scene_id"]) == scene_id), None)


def _contiguous_duration_light(beat, in_point_s: float, scenes: list[dict], cfg, matchable_duration_s: float) -> float:
    if matchable_duration_s <= 0:
        return 0.0
    try:
        from src.cv.global_scan import _reference_internal_cut_offsets
        cut_offsets = _reference_internal_cut_offsets(beat, cfg)
    except Exception:
        cut_offsets = []

    start_idx = None
    for idx, scene in enumerate(scenes):
        if float(scene["start_s"]) <= in_point_s < float(scene["end_s"]):
            start_idx = idx
            break
    if start_idx is None:
        return 0.0

    target_end = in_point_s + matchable_duration_s
    current_end = in_point_s
    for scene in scenes[start_idx:]:
        scene_end = float(scene["end_s"])
        if target_end <= scene_end:
            return matchable_duration_s

        boundary_offset = scene_end - in_point_s
        if not any(
            abs(boundary_offset - cut_offset) <= cfg.vision.multi_shot_boundary_tolerance_s
            for cut_offset in cut_offsets
        ):
            tail_s = max(0.0, cfg.cv.deep_scan.trim_tail_frames / _scene_fps_light(scene, cfg))
            return max(0.0, scene_end - in_point_s - tail_s)
        current_end = scene_end

    return max(0.0, current_end - in_point_s)


def _normalize_cached_results(beats: list, results: list, cfg) -> list:
    """
    Re-apply current generic timing rules to cached results.

    This keeps old automatic cache entries from preserving obsolete scene-boundary
    or tail-trim behavior without introducing manual per-beat truth.
    """
    from dataclasses import replace

    scenes = _load_scene_cache_light(cfg)
    if not scenes:
        return results

    beats_by_id = {b.beat_id: b for b in beats}
    normalized = []
    for result in results:
        beat = beats_by_id.get(result.beat_id)
        if getattr(result, "segments", ()):
            segment_duration = sum(max(0.0, float(s.duration_s)) for s in result.segments)
            weighted_score = (
                sum(max(0.0, float(s.duration_s)) * float(s.match_score) for s in result.segments)
                / segment_duration
                if segment_duration > 0 else result.match_score
            )
            if weighted_score < cfg.cv.deep_scan.provisional_match_threshold:
                continue
            if beat is not None and beat.duration_s > 0:
                visible_duration = sum(
                    max(0.0, end_s - start_s)
                    for start_s, end_s in _reference_scoreable_segments(beat, cfg)
                )
                coverage_target = visible_duration if visible_duration > 0 else beat.duration_s
                coverage = segment_duration / coverage_target
                if coverage < cfg.cv.deep_scan.min_duration_coverage:
                    continue
            normalized.append(replace(result, match_score=weighted_score))
            continue

        if result.match_score < cfg.cv.deep_scan.provisional_match_threshold:
            continue

        scene = _scene_for_time_light(scenes, result.in_point_s, cfg)
        declared_scene = _scene_by_id_light(scenes, result.scene_id)

        # If the automatic matcher selected a scene but its in-point sits just
        # before that scene's detected start, treat this as scene-boundary drift
        # and clamp to the declared scene. This is generic: no beat IDs, no
        # manual timestamps, just consistent scene/time reconciliation.
        if declared_scene is not None:
            declared_start = float(declared_scene["start_s"])
            declared_end = float(declared_scene["end_s"])
            declared_fps = _scene_fps_light(declared_scene, cfg)
            boundary_tolerance_s = (
                cfg.cv.deep_scan.scene_boundary_epsilon_s
                + cfg.cv.deep_scan.start_preroll_frames / declared_fps
            )
            if declared_start - boundary_tolerance_s <= result.in_point_s < declared_end:
                scene = declared_scene

        if beat is None or scene is None:
            normalized.append(result)
            continue

        fps = _scene_fps_light(scene, cfg)
        adjusted_in_s = result.in_point_s
        scene_changed = int(scene["scene_id"]) != result.scene_id
        starts_before_scene = result.in_point_s < float(scene["start_s"])
        if scene_changed or starts_before_scene or result.duration_s <= 0.12:
            adjusted_in_s = max(0.0, result.in_point_s - (cfg.cv.deep_scan.start_preroll_frames / fps))
            adjusted_in_s = max(float(scene["start_s"]), adjusted_in_s)
            scene = _scene_for_time_light(scenes, adjusted_in_s, cfg) or scene
            fps = _scene_fps_light(scene, cfg)

        matchable_duration_s = beat.duration_s
        try:
            from src.cv.global_scan import estimate_matchable_reference_duration
            matchable_duration_s = estimate_matchable_reference_duration(beat, cfg)
        except Exception:
            pass

        tail_s = max(0.0, cfg.cv.deep_scan.trim_tail_frames / fps)
        single_scene_duration_s = max(0.0, min(beat.duration_s, float(scene["end_s"]) - adjusted_in_s) - tail_s)
        contiguous_duration_s = _contiguous_duration_light(
            beat,
            adjusted_in_s,
            scenes,
            cfg,
            matchable_duration_s,
        )
        max_duration_s = max(single_scene_duration_s, min(beat.duration_s, contiguous_duration_s))

        normalized_result = result
        if (
            scene_changed
            or starts_before_scene
            or result.duration_s <= 0.12
            or result.out_point_s > adjusted_in_s + max_duration_s + (1.0 / fps)
        ):
            normalized_result = replace(
                result,
                scene_id=int(scene["scene_id"]),
                in_point_s=adjusted_in_s,
                out_point_s=adjusted_in_s + max_duration_s,
                in_point_frame=int(adjusted_in_s * fps),
            )

        coverage = (
            max(0.0, normalized_result.duration_s) / matchable_duration_s
            if matchable_duration_s > 0 else 0.0
        )
        if coverage < cfg.cv.deep_scan.min_duration_coverage:
            continue

        try:
            from src.cv.content_align import align_cached_match_by_content
            _, content_score = align_cached_match_by_content(
                beat,
                normalized_result.in_point_s,
                cfg,
                search_window_s=min(0.8, cfg.cv.deep_scan.content_align_window_seconds),
                fps=12.5,
            )
            content_gate = (
                cfg.cv.deep_scan.provisional_content_threshold
                if normalized_result.is_confirmed
                else min(cfg.cv.deep_scan.provisional_content_threshold, cfg.vision.content_threshold)
            )
            if content_score < content_gate:
                continue
            if content_score < cfg.cv.deep_scan.match_threshold and normalized_result.is_confirmed:
                normalized_result = replace(
                    normalized_result,
                    match_score=min(normalized_result.match_score, content_score),
                    is_confirmed=False,
                )
        except Exception:
            pass

        normalized.append(normalized_result)

    return normalized


# ---------------------------------------------------------------------------
# Command handlers
# ---------------------------------------------------------------------------

def _build_transcribe_callback(cfg):
    """Return a transcribe_callback closure, or None if audio is disabled."""
    from src.audio.transcriber import transcribe_video

    def _cb(path, start_s, end_s, offset_s):
        return transcribe_video(path, cfg, start_s=start_s, end_s=end_s, time_offset_s=offset_s)

    return _cb


def _build_classify_callback(cfg):
    """Return a classify_callback closure."""
    from src.llm.dramaturg import classify_beats

    def _cb(beats):
        return classify_beats(beats, cfg)

    return _cb


def cmd_analyze(args: argparse.Namespace, cfg) -> list:
    from src.pipeline.trailer_analyzer import analyze_reference_trailer

    transcribe_cb = _build_transcribe_callback(cfg) if not args.no_audio else None
    classify_cb   = _build_classify_callback(cfg)   if not args.no_llm   else None

    beats = analyze_reference_trailer(
        cfg,
        transcribe_callback=transcribe_cb,
        classify_callback=classify_cb,
    )

    # Persist beats for downstream commands (including histogram bytes as hex)
    beats_cache = cfg.paths.cache_dir / "trailer_beats.json"
    beats_cache.parent.mkdir(parents=True, exist_ok=True)
    beats_data = [
        {
            "beat_id":     b.beat_id,
            "start_s":     b.start_s,
            "end_s":       b.end_s,
            "start_frame": b.start_frame,
            "end_frame":   b.end_frame,
            "beat_type":   b.beat_type.name,
            "dialogue":    [{"start_s": d.start_s, "end_s": d.end_s, "text": d.text} for d in b.dialogue],
            "phash":       b.phash,
            "luma_hist":   b.luma_hist.hex() if b.luma_hist else None,
            "sat_hist":    b.sat_hist.hex()  if b.sat_hist  else None,
        }
        for b in beats
    ]
    beats_cache.write_text(json.dumps(beats_data, indent=2, ensure_ascii=False), encoding="utf-8")
    print(f"\n\u2705  {len(beats)} beats analyzed \u2192 {beats_cache}")
    return beats


def _load_beats(cfg) -> list:
    from src.core.models import BeatType, DialogueLine, TrailerBeat

    p = cfg.paths.cache_dir / "trailer_beats.json"
    if not p.exists():
        raise FileNotFoundError(f"No cached beats at {p}. Run 'analyze' first.")

    raw = json.loads(p.read_text(encoding="utf-8"))
    beats = []
    for d in raw:
        dialogue = tuple(
            DialogueLine(start_s=x["start_s"], end_s=x["end_s"], text=x["text"])
            for x in d.get("dialogue", [])
        )
        beats.append(TrailerBeat(
            beat_id=d["beat_id"],
            trailer_path=cfg.paths.reference_trailer,
            start_s=d["start_s"],
            end_s=d["end_s"],
            start_frame=d["start_frame"],
            end_frame=d["end_frame"],
            beat_type=BeatType[d.get("beat_type", "UNKNOWN")],
            dialogue=dialogue,
            phash=d.get("phash"),
            luma_hist=bytes.fromhex(d["luma_hist"]) if d.get("luma_hist") else None,
            sat_hist= bytes.fromhex(d["sat_hist"])  if d.get("sat_hist")  else None,
        ))
    return beats


def _select_beats(beats: list, beat_id: int | None) -> list:
    """Return all beats or exactly one requested beat."""
    if beat_id is None:
        return beats
    selected = [b for b in beats if b.beat_id == beat_id]
    if not selected:
        raise ValueError(f"Beat {beat_id} not found. Run 'analyze' first.")
    return selected


def _select_results(results: list, beat_ids: set[int] | None) -> list:
    """Return all results or only results for the requested beats."""
    if beat_ids is None:
        return results
    return [r for r in results if r.beat_id in beat_ids]


def _find_scene_for_in_point(cfg, in_point_s: float):
    from src.cv.scene_indexer import build_scene_index

    scenes = build_scene_index(cfg)
    for idx, scene in enumerate(scenes):
        if scene.start_s <= in_point_s < scene.end_s:
            if (
                scene.end_s - in_point_s <= cfg.cv.deep_scan.scene_boundary_epsilon_s
                and idx + 1 < len(scenes)
            ):
                return scenes[idx + 1]
            return scene
    return None


def _reference_scoreable_segments(beat, cfg) -> list[tuple[float, float]]:
    """Find visible source-matchable islands inside a trailer beat."""
    from src.cv.frame_extractor import grab_frame_at_path
    from src.cv.global_scan import (
        _corr_same_size,
        _is_scoreable_reference_frame,
        _prepare_haystack,
        _reference_visibility_stats,
    )

    def is_visible(frame) -> bool:
        if frame is None:
            return False
        mean_luma, p90_luma, contrast = _reference_visibility_stats(frame, cfg)
        visible_luma = (
            mean_luma >= cfg.cv.deep_scan.scoreable_luma_mean_min * 0.45
            or p90_luma >= cfg.cv.deep_scan.scoreable_luma_p90_min * 0.50
        )
        visible_contrast = contrast >= max(8.0, cfg.cv.deep_scan.scoreable_contrast_min * 0.30)
        return visible_luma and visible_contrast

    step_s = max(0.08, cfg.cv.deep_scan.span_sample_step_s)
    min_segment_s = max(0.32, step_s * 3.0)
    bridge_gap_s = max(0.18, step_s * 2.0)
    raw: list[tuple[float, float]] = []
    start: float | None = None
    last_seen: float | None = None
    t = 0.0
    while t <= beat.duration_s:
        frame = grab_frame_at_path(beat.trailer_path, beat.start_s + t)
        scoreable = frame is not None and _is_scoreable_reference_frame(frame, cfg)
        if scoreable:
            if start is None:
                start = t
            last_seen = t
        elif start is not None and last_seen is not None and t - last_seen > bridge_gap_s:
            end = min(beat.duration_s, last_seen + step_s)
            if end - start >= min_segment_s:
                raw.append((start, end))
            start = None
            last_seen = None
        t = round(t + step_s, 6)

    if start is not None and last_seen is not None:
        end = min(beat.duration_s, last_seen + step_s)
        if end - start >= min_segment_s:
            raw.append((start, end))

    expanded: list[tuple[float, float]] = []
    same_shot_corr_min = 0.72
    for start_s, end_s in raw:
        start_anchor = grab_frame_at_path(beat.trailer_path, beat.start_s + start_s)
        end_anchor = grab_frame_at_path(beat.trailer_path, beat.start_s + max(start_s, end_s - step_s))
        start_feature = _prepare_haystack(start_anchor, cfg) if start_anchor is not None else None
        end_feature = _prepare_haystack(end_anchor, cfg) if end_anchor is not None else None

        soft_start = start_s
        t = round(start_s - step_s, 6)
        while t >= 0.0:
            frame = grab_frame_at_path(beat.trailer_path, beat.start_s + t)
            if not is_visible(frame):
                break
            if start_feature is not None and _corr_same_size(_prepare_haystack(frame, cfg), start_feature) < same_shot_corr_min:
                break
            soft_start = max(0.0, t)
            t = round(t - step_s, 6)

        soft_end = end_s
        t = round(end_s, 6)
        while t <= beat.duration_s + 1e-6:
            frame = grab_frame_at_path(beat.trailer_path, beat.start_s + t)
            if not is_visible(frame):
                break
            if end_feature is not None and _corr_same_size(_prepare_haystack(frame, cfg), end_feature) < same_shot_corr_min:
                break
            soft_end = min(beat.duration_s, t + step_s)
            t = round(t + step_s, 6)

        if soft_end - soft_start >= min_segment_s:
            expanded.append((soft_start, soft_end))

    merged: list[tuple[float, float]] = []
    for start_s, end_s in expanded:
        if merged and start_s - merged[-1][1] <= bridge_gap_s:
            merged[-1] = (merged[-1][0], max(merged[-1][1], end_s))
        else:
            merged.append((start_s, end_s))
    return merged


def _trim_beats_to_single_visual_island(beats: list, cfg) -> tuple[list, dict[int, tuple[float, float]]]:
    """Use a single visible island as the primary match target for faded beats."""
    from dataclasses import replace

    trimmed = []
    trims: dict[int, tuple[float, float]] = {}
    frame_s = 1.0 / max(1.0, float(cfg.export.edl_frame_rate))
    for beat in beats:
        islands = _reference_scoreable_segments(beat, cfg)
        if len(islands) == 1:
            start_s, end_s = islands[0]
            island_duration_s = max(0.0, end_s - start_s)
            has_real_trim = (
                start_s > frame_s * 1.5
                or beat.duration_s - end_s > frame_s * 1.5
            )
            if island_duration_s > 0.0 and has_real_trim:
                trimmed.append(
                    replace(
                        beat,
                        start_s=beat.start_s + start_s,
                        end_s=beat.start_s + end_s,
                    )
                )
                trims[beat.beat_id] = (start_s, island_duration_s)
                continue
        trimmed.append(beat)
    return trimmed, trims


def _apply_single_island_segments(results: list, trims: dict[int, tuple[float, float]]) -> list:
    """Restore beat-relative segment metadata after matching a trimmed island."""
    if not trims:
        return results

    from dataclasses import replace
    from src.core.models import MatchSegment

    expanded = []
    for result in results:
        trim = trims.get(result.beat_id)
        if trim is None or getattr(result, "segments", ()):
            expanded.append(result)
            continue
        trailer_offset_s, island_duration_s = trim
        duration_s = min(max(0.0, island_duration_s), max(0.0, result.duration_s))
        segment = MatchSegment(
            trailer_offset_s=trailer_offset_s,
            duration_s=duration_s,
            scene_id=result.scene_id,
            in_point_s=result.in_point_s,
            out_point_s=result.in_point_s + duration_s,
            match_score=result.match_score,
            is_confirmed=result.is_confirmed,
        )
        expanded.append(
            replace(
                result,
                out_point_s=result.in_point_s + duration_s,
                segments=(segment,),
            )
        )
    return expanded


def _merge_best_results(existing: list, candidates: list, cfg) -> list:
    """Merge matches by beat, preferring confirmed or higher-scoring results."""
    by_id = {r.beat_id: r for r in existing}
    for candidate in candidates:
        old = by_id.get(candidate.beat_id)
        if old is None:
            by_id[candidate.beat_id] = candidate
            continue
        candidate_confirmed = candidate.match_score >= cfg.cv.deep_scan.match_threshold or candidate.is_confirmed
        old_confirmed = old.match_score >= cfg.cv.deep_scan.match_threshold or old.is_confirmed
        if (
            candidate_confirmed and not old_confirmed
            or candidate.match_score > old.match_score + cfg.cv.deep_scan.duration_tie_break_score_delta
            or (
                candidate.match_score >= old.match_score - cfg.cv.deep_scan.duration_tie_break_score_delta
                and candidate.duration_s > old.duration_s
            )
        ):
            by_id[candidate.beat_id] = candidate
    return sorted(by_id.values(), key=lambda r: r.beat_id)


def _recover_unmatched_beats_via_vision(results: list, beats: list, cfg) -> list:
    """Try a vision-led search for beats that ended up without a match.

    For each unmatched beat that has scoreable visual content (i.e. not pure
    fade/title-card material), this pass:
      1. Asks the vibe-check (CV histogram + pHash) for the top-K candidate
         scenes.
      2. For each candidate, runs the semantic action-window search with the
         beat's own description, prefering windows whose phase matches the
         visible part of the beat.
      3. Refines the in-point with the regular CV content/motion aligner.
      4. Validates the resulting window with the vision phase check, exactly
         like the main filter.
      5. Adds the best validated candidate as a provisional MatchResult.

    Confirmed and provisional matches both stay subject to the same thresholds
    used elsewhere; this only adds matches that pass the same quality gates.
    """
    if not cfg.vision.enabled or not beats:
        return results

    from dataclasses import replace
    from src.cv.global_scan import align_in_point_by_content_and_motion, estimate_usable_source_duration
    from src.cv.scene_indexer import build_scene_index
    from src.cv.vibe_check import run_vibe_check
    from src.core.models import MatchResult
    from src.llm.vision_cache import find_action_window_in_scene, validate_match_window_with_vision

    logger = logging.getLogger(__name__)
    matched_ids = {r.beat_id for r in results}
    unmatched = [b for b in beats if b.beat_id not in matched_ids]
    if not unmatched:
        return results

    scenes = build_scene_index(cfg)
    if not scenes:
        return results

    new_results = list(results)
    for beat in unmatched:
        try:
            islands = _reference_scoreable_segments(beat, cfg)
        except Exception:
            islands = []

        # Anchor selection: prefer the longest visible island; if none exists,
        # fall back to the full beat. The latter handles dark / low-contrast
        # close-ups that drop below the scoreable luma/contrast thresholds but
        # are still semantically describable. The strict vision phase
        # validation later in this pass keeps us from accepting pure title-card
        # or logo material.
        from dataclasses import replace as _replace
        if islands:
            anchor_start_s, anchor_end_s = max(islands, key=lambda iv: iv[1] - iv[0])
            anchor_beat = _replace(
                beat,
                start_s=beat.start_s + anchor_start_s,
                end_s=beat.start_s + anchor_end_s,
            )
        else:
            anchor_beat = beat

        try:
            hits = run_vibe_check(
                beat,
                scenes,
                top_k=max(cfg.cv.deep_scan.scene_seed_top_k, cfg.cv.vibe_check.top_k_candidates),
                hist_method=cfg.cv.vibe_check.hist_compare_method,
                phash_max_distance=64,
            )
        except Exception as exc:
            logger.warning("Beat %d: recovery vibe-check failed (%s)", beat.beat_id, exc)
            continue

        scenes_by_id = {s.scene_id: s for s in scenes}
        best = None  # (score, scene, in_s, dur_s, reason)
        seen = set()
        for hit in hits[: cfg.cv.deep_scan.scene_seed_top_k]:
            scene = scenes_by_id.get(hit.scene_id)
            if scene is None or scene.scene_id in seen:
                continue
            seen.add(scene.scene_id)

            try:
                found = find_action_window_in_scene(anchor_beat, scene, cfg)
            except Exception as exc:
                logger.debug("Beat %d: action window failed for scene %d (%s)", beat.beat_id, scene.scene_id, exc)
                continue
            if found is None:
                continue
            start_s, end_s, semantic_score, reason = found

            window_s = max(3.0, min(8.0, (end_s - start_s) * 4.0))
            try:
                aligned_in_s, combined_score, content_score, motion_score = align_in_point_by_content_and_motion(
                    anchor_beat,
                    start_s,
                    cfg,
                    search_window_s=window_s,
                )
            except Exception as exc:
                logger.debug("Beat %d: align failed for scene %d (%s)", beat.beat_id, scene.scene_id, exc)
                continue
            aligned_in_s = max(scene.start_s, min(aligned_in_s, max(scene.start_s, scene.end_s - anchor_beat.duration_s)))

            try:
                usable_duration_s, usable_score = estimate_usable_source_duration(anchor_beat, aligned_in_s, cfg)
            except Exception:
                usable_duration_s, usable_score = anchor_beat.duration_s, 0.0
            usable_duration_s = max(0.0, min(anchor_beat.duration_s, usable_duration_s))
            if usable_duration_s < max(0.32, anchor_beat.duration_s * 0.45):
                usable_duration_s = anchor_beat.duration_s

            try:
                ok, verify_reason = validate_match_window_with_vision(
                    anchor_beat,
                    source_path=scene.source_path,
                    scene_id=scene.scene_id,
                    in_point_s=aligned_in_s,
                    out_point_s=aligned_in_s + usable_duration_s,
                    cfg=cfg,
                )
            except Exception as exc:
                logger.debug("Beat %d: validate failed scene=%d (%s)", beat.beat_id, scene.scene_id, exc)
                continue
            if not ok:
                continue

            final_score = max(
                combined_score,
                min(0.99, semantic_score * 0.65 + motion_score * 0.18 + content_score * 0.09 + usable_score * 0.08),
            )
            if final_score < cfg.cv.deep_scan.provisional_match_threshold:
                continue
            candidate = (final_score, scene, aligned_in_s, usable_duration_s, f"recovery; {reason}; {verify_reason}")
            if best is None or candidate[0] > best[0]:
                best = candidate

        if best is None:
            continue
        score, scene, aligned_in_s, usable_duration_s, repair_reason = best
        logger.info(
            "Beat %d: recovered via vision action search scene=%d in=%.3fs score=%.3f (%s)",
            beat.beat_id,
            scene.scene_id,
            aligned_in_s,
            score,
            repair_reason,
        )
        new_results.append(MatchResult(
            beat_id=beat.beat_id,
            scene_id=scene.scene_id,
            source_path=scene.source_path,
            in_point_s=aligned_in_s,
            out_point_s=aligned_in_s + usable_duration_s,
            in_point_frame=int(aligned_in_s * cfg.export.edl_frame_rate),
            match_score=score,
            match_location=(0, 0),
            is_confirmed=score >= cfg.cv.deep_scan.match_threshold,
            segments=tuple(),
        ))

    return sorted(new_results, key=lambda r: r.beat_id)


def _filter_semantically_invalid_vision_matches(results: list, beats: list, cfg) -> list:
    """Drop vision-enabled matches whose final action phase contradicts the beat."""
    if not cfg.vision.enabled or not results:
        return results

    from dataclasses import replace
    from src.llm.vision_cache import find_action_window_in_scene, validate_match_window_with_vision
    from src.cv.scene_indexer import build_scene_index
    from src.cv.global_scan import align_in_point_by_content_and_motion, estimate_usable_source_duration

    logger = logging.getLogger(__name__)
    beats_by_id = {beat.beat_id: beat for beat in beats}
    scenes_by_id = {scene.scene_id: scene for scene in build_scene_index(cfg)}

    def visible_content_offset(action_beat, segment_start_offset_s: float) -> float:
        content_offset_s = 0.0
        for start_s, end_s in _reference_scoreable_segments(action_beat, cfg):
            if end_s <= segment_start_offset_s:
                content_offset_s += max(0.0, end_s - start_s)
            elif start_s < segment_start_offset_s:
                content_offset_s += max(0.0, segment_start_offset_s - start_s)
                break
            else:
                break
        return content_offset_s

    def realign_window(check_beat, scene_id: int, action_beat=None):
        scene = scenes_by_id.get(scene_id)
        if scene is None:
            return None
        segment_window = find_action_window_in_scene(check_beat, scene, cfg)
        if action_beat is not None and action_beat is not check_beat:
            beat_window = find_action_window_in_scene(action_beat, scene, cfg)
        else:
            beat_window = None
        use_beat_context = False
        if segment_window is None:
            found = beat_window
            use_beat_context = beat_window is not None
        elif beat_window is None:
            found = segment_window
        elif beat_window[2] > segment_window[2] + 0.06:
            found = beat_window
            use_beat_context = True
        else:
            found = segment_window
        if found is None:
            return None
        start_s, end_s, semantic_score, reason = found
        if use_beat_context:
            segment_start_offset_s = max(0.0, check_beat.start_s - action_beat.start_s)
            content_offset_s = visible_content_offset(action_beat, segment_start_offset_s)
            start_s += content_offset_s
            end_s += content_offset_s
        window_s = max(3.0, min(8.0, (end_s - start_s) * 4.0))
        aligned_in_s, combined_score, content_score, motion_score = align_in_point_by_content_and_motion(
            check_beat,
            start_s,
            cfg,
            search_window_s=window_s,
        )
        aligned_in_s = max(scene.start_s, min(aligned_in_s, max(scene.start_s, scene.end_s - check_beat.duration_s)))
        usable_duration_s, usable_score = estimate_usable_source_duration(check_beat, aligned_in_s, cfg)
        usable_duration_s = max(0.0, min(check_beat.duration_s, usable_duration_s))
        if usable_duration_s < max(0.32, check_beat.duration_s * 0.45):
            usable_duration_s = check_beat.duration_s
        ok, verify_reason = validate_match_window_with_vision(
            check_beat,
            source_path=scene.source_path,
            scene_id=scene.scene_id,
            in_point_s=aligned_in_s,
            out_point_s=aligned_in_s + usable_duration_s,
            cfg=cfg,
        )
        if not ok:
            logger.info(
                "Beat %d: action-window realign rejected scene=%d in=%.3fs (%s)",
                check_beat.beat_id,
                scene.scene_id,
                aligned_in_s,
                verify_reason,
            )
            return None
        score = max(
            combined_score,
            min(0.99, semantic_score * 0.65 + motion_score * 0.18 + content_score * 0.09 + usable_score * 0.08),
        )
        return scene, aligned_in_s, usable_duration_s, score, f"{reason}; {verify_reason}"

    kept = []
    for result in results:
        beat = beats_by_id.get(result.beat_id)
        if beat is None:
            kept.append(result)
            continue

        kept_before = len(kept)
        try:
            _filter_repair_one(result, beat, beats_by_id, scenes_by_id, kept, cfg, realign_window, validate_match_window_with_vision, logger)
        except Exception as exc:
            logger.warning(
                "Beat %d: vision filter/repair failed (%s); keeping previous cached match.",
                result.beat_id,
                exc,
            )
            del kept[kept_before:]
            kept.append(result)
    return kept


def _filter_repair_one(result, beat, beats_by_id, scenes_by_id, kept, cfg, realign_window, validate_match_window_with_vision, logger):
    from dataclasses import replace
    if True:
        windows = []
        if getattr(result, "segments", ()):
            for segment in result.segments:
                segment_beat = replace(
                    beat,
                    start_s=beat.start_s + segment.trailer_offset_s,
                    end_s=beat.start_s + segment.trailer_offset_s + segment.duration_s,
                )
                windows.append((
                    segment_beat,
                    segment.scene_id,
                    segment.in_point_s,
                    segment.out_point_s,
                ))
        else:
            windows.append((beat, result.scene_id, result.in_point_s, result.out_point_s))

        valid = True
        reasons: list[str] = []
        for check_beat, scene_id, in_point_s, out_point_s in windows:
            ok, reason = validate_match_window_with_vision(
                check_beat,
                source_path=result.source_path,
                scene_id=scene_id,
                in_point_s=in_point_s,
                out_point_s=out_point_s,
                cfg=cfg,
            )
            reasons.append(reason)
            if not ok:
                valid = False
                break
        if valid:
            repaired = False
            if getattr(result, "segments", ()):
                new_segments = []
                repair_reasons = []
                changed = False
                for segment in result.segments:
                    scene = scenes_by_id.get(segment.scene_id)
                    # Allow phase-realign whenever the scene has any meaningful
                    # slack beyond the segment, not only for "long" scenes.
                    # Short scenes don't need realigning because the segment
                    # essentially is the scene.
                    if scene is None or scene.duration_s <= segment.duration_s + 0.5:
                        new_segments.append(segment)
                        continue
                    # For already-confirmed segments, skip the realign to avoid
                    # destabilizing a strong original match.
                    if segment.is_confirmed and scene.duration_s <= max(segment.duration_s * 1.6, 6.0):
                        new_segments.append(segment)
                        continue
                    segment_beat = replace(
                        beat,
                        start_s=beat.start_s + segment.trailer_offset_s,
                        end_s=beat.start_s + segment.trailer_offset_s + segment.duration_s,
                    )
                    repair = realign_window(segment_beat, segment.scene_id, action_beat=beat)
                    if repair is None:
                        new_segments.append(segment)
                        continue
                    repair_scene, aligned_in_s, usable_duration_s, score, repair_reason = repair
                    if abs(aligned_in_s - segment.in_point_s) <= 1.0 / cfg.export.edl_frame_rate:
                        new_segments.append(segment)
                        continue
                    # Don't commit a repair that scores meaningfully worse than
                    # the original; phase realign should improve, not regress.
                    if score < segment.match_score - 0.02:
                        new_segments.append(segment)
                        continue
                    changed = True
                    repair_reasons.append(repair_reason)
                    new_segments.append(replace(
                        segment,
                        scene_id=repair_scene.scene_id,
                        in_point_s=aligned_in_s,
                        out_point_s=aligned_in_s + usable_duration_s,
                        duration_s=usable_duration_s,
                        match_score=score,
                        is_confirmed=score >= cfg.cv.deep_scan.match_threshold,
                    ))
                if changed and new_segments:
                    first = new_segments[0]
                    repaired_score = min(seg.match_score for seg in new_segments)
                    logger.info(
                        "Beat %d: realigned semantically valid long scene by motion/action windows (%s)",
                        result.beat_id,
                        "; ".join(repair_reasons),
                    )
                    kept.append(replace(
                        result,
                        scene_id=first.scene_id,
                        in_point_s=first.in_point_s,
                        out_point_s=first.out_point_s,
                        in_point_frame=int(first.in_point_s * cfg.export.edl_frame_rate),
                        match_score=repaired_score,
                        is_confirmed=repaired_score >= cfg.cv.deep_scan.match_threshold,
                        segments=tuple(new_segments),
                    ))
                    repaired = True
            else:
                scene = scenes_by_id.get(result.scene_id)
                wide_scene = (
                    scene is not None
                    and scene.duration_s > result.duration_s + 0.5
                )
                already_confirmed_in_tight_scene = (
                    result.is_confirmed
                    and scene is not None
                    and scene.duration_s <= max(result.duration_s * 1.6, 6.0)
                )
                if wide_scene and not already_confirmed_in_tight_scene:
                    repair = realign_window(beat, result.scene_id)
                    if repair is not None:
                        repair_scene, aligned_in_s, usable_duration_s, score, repair_reason = repair
                        moved = abs(aligned_in_s - result.in_point_s) > 1.0 / cfg.export.edl_frame_rate
                        improved = score >= result.match_score - 0.02
                        if moved and improved:
                            logger.info(
                                "Beat %d: realigned semantically valid long scene by motion/action window (%s)",
                                result.beat_id,
                                repair_reason,
                            )
                            kept.append(replace(
                                result,
                                scene_id=repair_scene.scene_id,
                                in_point_s=aligned_in_s,
                                out_point_s=aligned_in_s + usable_duration_s,
                                in_point_frame=int(aligned_in_s * cfg.export.edl_frame_rate),
                                match_score=score,
                                is_confirmed=score >= cfg.cv.deep_scan.match_threshold,
                            ))
                            repaired = True
            if not repaired:
                kept.append(result)
        else:
            if getattr(result, "segments", ()):
                new_segments = []
                all_repaired = True
                repair_reasons = []
                for segment in result.segments:
                    segment_beat = replace(
                        beat,
                        start_s=beat.start_s + segment.trailer_offset_s,
                        end_s=beat.start_s + segment.trailer_offset_s + segment.duration_s,
                    )
                    repair = realign_window(segment_beat, segment.scene_id, action_beat=beat)
                    if repair is None:
                        all_repaired = False
                        break
                    scene, aligned_in_s, usable_duration_s, score, repair_reason = repair
                    repair_reasons.append(repair_reason)
                    new_segments.append(replace(
                        segment,
                        scene_id=scene.scene_id,
                        in_point_s=aligned_in_s,
                        out_point_s=aligned_in_s + usable_duration_s,
                        duration_s=usable_duration_s,
                        match_score=score,
                        is_confirmed=score >= cfg.cv.deep_scan.match_threshold,
                    ))
                if all_repaired and new_segments:
                    first = new_segments[0]
                    repaired_score = min(seg.match_score for seg in new_segments)
                    logger.info(
                        "Beat %d: realigned inside matched scene by vision action windows (%s)",
                        result.beat_id,
                        "; ".join(repair_reasons),
                    )
                    kept.append(replace(
                        result,
                        scene_id=first.scene_id,
                        in_point_s=first.in_point_s,
                        out_point_s=first.out_point_s,
                        in_point_frame=int(first.in_point_s * cfg.export.edl_frame_rate),
                        match_score=repaired_score,
                        is_confirmed=repaired_score >= cfg.cv.deep_scan.match_threshold,
                        segments=tuple(new_segments),
                    ))
                    return
            else:
                repair = realign_window(beat, result.scene_id)
                if repair is not None:
                    scene, aligned_in_s, usable_duration_s, score, repair_reason = repair
                    logger.info(
                        "Beat %d: realigned inside matched scene by vision action window (%s)",
                        result.beat_id,
                        repair_reason,
                    )
                    kept.append(replace(
                        result,
                        scene_id=scene.scene_id,
                        in_point_s=aligned_in_s,
                        out_point_s=aligned_in_s + usable_duration_s,
                        in_point_frame=int(aligned_in_s * cfg.export.edl_frame_rate),
                        match_score=score,
                        is_confirmed=score >= cfg.cv.deep_scan.match_threshold,
                    ))
                    return
            logger.warning(
                "Beat %d: rejected by vision action-phase verification (%s)",
                result.beat_id,
                "; ".join(reasons),
            )


def _attach_visual_segments(results: list, beats: list, cfg) -> list:
    """Attach automatic sub-shot matches for multi-island trailer beats."""
    from dataclasses import replace
    from src.core.models import MatchResult, MatchSegment
    from src.cv.global_scan import run_global_scan

    by_id = {b.beat_id: b for b in beats}
    expanded: list[MatchResult] = []
    for result in results:
        beat = by_id.get(result.beat_id)
        if beat is None:
            expanded.append(result)
            continue
        if getattr(result, "segments", ()):
            expanded.append(result)
            continue

        islands = _reference_scoreable_segments(beat, cfg)
        if len(islands) <= 1:
            primary = MatchSegment(
                trailer_offset_s=0.0,
                duration_s=max(0.0, result.duration_s),
                scene_id=result.scene_id,
                in_point_s=result.in_point_s,
                out_point_s=result.out_point_s,
                match_score=result.match_score,
                is_confirmed=result.is_confirmed,
            )
            expanded.append(replace(result, segments=(primary,)))
            continue

        segments: list[MatchSegment] = []
        first_start, first_end = islands[0]
        first_duration = min(max(0.0, result.duration_s), max(0.0, first_end - first_start))
        segments.append(
            MatchSegment(
                trailer_offset_s=first_start,
                duration_s=first_duration,
                scene_id=result.scene_id,
                in_point_s=result.in_point_s,
                out_point_s=result.in_point_s + first_duration,
                match_score=result.match_score,
                is_confirmed=result.is_confirmed,
            )
        )

        for start_s, end_s in islands[1:]:
            segment_beat = replace(
                beat,
                start_s=beat.start_s + start_s,
                end_s=beat.start_s + end_s,
            )
            segment_matches = run_global_scan([segment_beat], cfg, seed_in_points=None)
            if not segment_matches:
                continue
            seg = segment_matches[0]
            seg_dur = min(max(0.0, end_s - start_s), max(0.0, seg.duration_s))
            segments.append(
                MatchSegment(
                    trailer_offset_s=start_s,
                    duration_s=seg_dur,
                    scene_id=seg.scene_id,
                    in_point_s=seg.in_point_s,
                    out_point_s=seg.in_point_s + seg_dur,
                    match_score=seg.match_score,
                    is_confirmed=seg.is_confirmed,
                )
            )

        expanded.append(replace(result, segments=tuple(segments)))
    return expanded


def _fast_vision_match_cfg(cfg):
    """Return a vision-seed prepass config that still keeps quality settings."""
    from dataclasses import replace

    return replace(
        cfg,
        cv=replace(
            cfg.cv,
            deep_scan=replace(cfg.cv.deep_scan, skip_coarse_scan_with_weighted_seeds=True),
        ),
        vision=replace(
            cfg.vision,
            fullscan_fallback=False,
        ),
    )


def _run_segment_match(segment_beat, continuity, cfg, allow_fullscan: bool = True):
    """Match one visual island with the same generic staged strategy as a beat."""
    from src.pipeline.matcher import run_matching

    if cfg.vision.enabled:
        fast_cfg = _fast_vision_match_cfg(cfg)
        fast_matches = run_matching(
            fast_cfg,
            [segment_beat],
            seed_in_points=continuity,
        )
        if fast_matches:
            if not allow_fullscan or all(
                m.is_confirmed or m.match_score >= cfg.cv.deep_scan.match_threshold
                for m in fast_matches
            ):
                return fast_matches

    if not allow_fullscan:
        return fast_matches if cfg.vision.enabled else []

    full_matches = run_matching(
        cfg,
        [segment_beat],
        seed_in_points=continuity,
    )
    return _merge_best_results(fast_matches if cfg.vision.enabled else [], full_matches, cfg)


def _match_unmatched_visual_segments(
    results: list,
    beats: list,
    cached: list,
    cfg,
    skip_global_segment_scan_for: set[int] | None = None,
) -> list:
    """Create segmented provisional matches when a whole beat has no single match."""
    from dataclasses import replace
    from src.core.models import MatchResult, MatchSegment
    from src.cv.frame_extractor import get_video_info

    matched_ids = {r.beat_id for r in results}
    expanded = list(results)
    skip_global_segment_scan_for = skip_global_segment_scan_for or set()
    try:
        fps = float(get_video_info(cfg.paths.source_movie)["fps"]) or cfg.export.edl_frame_rate
    except Exception:
        fps = cfg.export.edl_frame_rate

    for beat in beats:
        if beat.beat_id in matched_ids:
            continue

        islands = _reference_scoreable_segments(beat, cfg)
        if not islands:
            continue

        segments: list[MatchSegment] = []
        for start_s, end_s in islands:
            segment_beat = replace(
                beat,
                start_s=beat.start_s + start_s,
                end_s=beat.start_s + end_s,
            )
            continuity = _continuity_seed_in_points(
                beat.beat_id,
                [b if b.beat_id != beat.beat_id else segment_beat for b in beats],
                cached + expanded,
                cfg,
            )
            segment_matches = []
            if beat.beat_id not in skip_global_segment_scan_for:
                segment_matches = _run_segment_match(segment_beat, continuity, cfg, allow_fullscan=True)
            if not segment_matches:
                local_segment = _local_same_scene_segment_match(
                    segment_beat,
                    beat,
                    start_s,
                    cached + expanded,
                    cfg,
                )
                if local_segment is not None:
                    segments.append(local_segment)
                continue
            seg = segment_matches[0]
            seg_dur = min(max(0.0, end_s - start_s), max(0.0, seg.duration_s))
            segments.append(
                MatchSegment(
                    trailer_offset_s=start_s,
                    duration_s=seg_dur,
                    scene_id=seg.scene_id,
                    in_point_s=seg.in_point_s,
                    out_point_s=seg.in_point_s + seg_dur,
                    match_score=seg.match_score,
                    is_confirmed=seg.is_confirmed,
                )
            )

        if not segments:
            continue

        first = segments[0]
        total_segment_duration = sum(max(0.0, s.duration_s) for s in segments)
        score = (
            sum(max(0.0, s.duration_s) * s.match_score for s in segments) / total_segment_duration
            if total_segment_duration > 0 else min(s.match_score for s in segments)
        )
        expanded.append(
            MatchResult(
                beat_id=beat.beat_id,
                scene_id=first.scene_id,
                source_path=cfg.paths.source_movie,
                in_point_s=first.in_point_s,
                out_point_s=first.out_point_s,
                in_point_frame=int(max(0.0, first.in_point_s) * fps),
                match_score=score,
                is_confirmed=all(s.is_confirmed for s in segments),
                segments=tuple(segments),
            )
        )

    return expanded


def _local_same_scene_segment_match(segment_beat, beat, segment_offset_s: float, cached: list, cfg):
    """Find a short trailer island inside scenes adjacent to neighbouring beat matches."""
    from src.core.models import MatchSegment
    from src.cv.frame_extractor import open_video
    from src.cv.global_scan import _content_alignment_score, _content_alignment_templates

    scenes = _load_scene_cache_light(cfg)
    if not scenes:
        return None

    by_id = {r.beat_id: r for r in cached}
    scene_ids: list[int] = []
    for neighbour_id in (beat.beat_id - 1, beat.beat_id + 1):
        result = by_id.get(neighbour_id)
        if result is None:
            continue
        ids = [getattr(s, "scene_id", result.scene_id) for s in getattr(result, "segments", ())] or [result.scene_id]
        for scene_id in ids:
            if scene_id not in scene_ids:
                scene_ids.append(scene_id)

    if not scene_ids:
        return None

    templates = _content_alignment_templates(segment_beat, cfg)
    if not templates:
        return None

    min_score = min(
        cfg.cv.deep_scan.provisional_content_threshold * 0.70,
        cfg.cv.deep_scan.provisional_match_threshold,
    )
    step_s = max(1.0 / cfg.export.edl_frame_rate, 0.04)
    best: tuple[float, float, int] | None = None
    with open_video(cfg.paths.source_movie) as cap:
        for scene_id in scene_ids:
            scene = next((s for s in scenes if int(s["scene_id"]) == int(scene_id)), None)
            if scene is None:
                continue
            start_s = max(0.0, float(scene["start_s"]) - 0.25)
            end_s = max(start_s, float(scene["end_s"]) - max(0.04, segment_beat.duration_s) + 0.25)
            t = start_s
            while t <= end_s:
                score = _content_alignment_score(cap, t, templates, cfg)
                if best is None or score > best[0]:
                    best = (score, t, int(scene_id))
                t = round(t + step_s, 6)

    if best is None or best[0] < min_score:
        return None

    score, in_point_s, scene_id = best
    duration_s = max(0.0, min(segment_beat.duration_s, segment_beat.end_s - segment_beat.start_s))
    return MatchSegment(
        trailer_offset_s=segment_offset_s,
        duration_s=duration_s,
        scene_id=scene_id,
        in_point_s=in_point_s,
        out_point_s=in_point_s + duration_s,
        match_score=score,
        is_confirmed=score >= cfg.cv.deep_scan.match_threshold,
    )


def cmd_match(args: argparse.Namespace, cfg) -> list:
    from src.pipeline.matcher import run_matching
    from dataclasses import replace

    if getattr(args, "vision", False):
        cfg = replace(cfg, vision=replace(cfg.vision, enabled=True))
    if getattr(args, "no_vision", False):
        cfg = replace(cfg, vision=replace(cfg.vision, enabled=False))

    all_beats = _load_beats(cfg)
    beats = _select_beats(all_beats, getattr(args, "beat", None))
    cached = _normalize_cached_results(all_beats, _load_results(cfg), cfg) if _results_cache_path(cfg).exists() else []
    multi_island_beat_ids = {
        beat.beat_id
        for beat in beats
        if len(_reference_scoreable_segments(beat, cfg)) > 1
    }
    scan_beats, single_island_trims = _trim_beats_to_single_visual_island(beats, cfg)
    scan_beats = [b for b in scan_beats if b.beat_id not in multi_island_beat_ids]
    seed_in_points = (
        _continuity_seed_in_points(args.beat, all_beats, cached, cfg)
        if getattr(args, "beat", None) is not None
        else None
    )
    results = []
    if cfg.vision.enabled:
        fast_cfg = _fast_vision_match_cfg(cfg)
        results = run_matching(
            fast_cfg,
            scan_beats,
            force_reindex=args.force_reindex,
            seed_in_points=seed_in_points,
        )

    if len(results) < len(scan_beats) or any(
        not r.is_confirmed and r.match_score < cfg.cv.deep_scan.match_threshold
        for r in results
    ):
        results_by_id = {r.beat_id: r for r in results}
        remaining_beats = [
            b for b in scan_beats
            if (
                b.beat_id not in results_by_id
                or (
                    not results_by_id[b.beat_id].is_confirmed
                    and results_by_id[b.beat_id].match_score < cfg.cv.deep_scan.match_threshold
                )
            )
        ]
        if remaining_beats:
            full_results = run_matching(
                cfg,
                remaining_beats,
                force_reindex=args.force_reindex,
                seed_in_points=seed_in_points,
            )
            results = _merge_best_results(results, full_results, cfg)
    results = _apply_single_island_segments(results, single_island_trims)
    results = _match_unmatched_visual_segments(
        results,
        beats,
        cached,
        cfg,
        skip_global_segment_scan_for=set(single_island_trims),
    )
    results = _attach_visual_segments(results, beats, cfg)
    results = _filter_semantically_invalid_vision_matches(results, beats, cfg)
    results = _recover_unmatched_beats_via_vision(results, beats, cfg)

    # A targeted one-beat match should improve the cache without deleting
    # automatic matches for other beats.
    if getattr(args, "beat", None) is not None and _results_cache_path(cfg).exists():
        cached = [r for r in cached if r.beat_id != args.beat]
        for result in results:
            cached = _update_result(result, cached)
        results_to_save = cached
    else:
        results_to_save = results

    _save_results(results_to_save, cfg)
    _regenerate_cutter_report(cfg)

    print(f"\n✅  {len(results)} / {len(beats)} beats matched.")
    for r in results:
        print(f"   Beat {r.beat_id:03d} → scene {r.scene_id:04d}  "
              f"in={r.in_point_s:>8.3f}s  score={r.match_score:.3f}")
    return results


def _update_result(new_result, results: list) -> list:
    """Replace or insert a MatchResult in the list (by beat_id)."""
    updated = [r for r in results if r.beat_id != new_result.beat_id]
    updated.append(new_result)
    return sorted(updated, key=lambda r: r.beat_id)


def _continuity_seed_in_points(beat_id: int, beats: list, results: list, cfg) -> dict[int, list[float | tuple[float, float]]]:
    beats_by_id = {b.beat_id: b for b in beats}
    results_by_id = {r.beat_id: r for r in results}
    target = beats_by_id.get(beat_id)
    if target is None:
        return {}

    seeds: list[tuple[float, float]] = []
    base_score = max(cfg.cv.deep_scan.coarse_candidate_threshold + 0.08, 0.92)
    prev_matches = [
        (b, results_by_id[b.beat_id])
        for b in beats
        if b.beat_id < beat_id and b.beat_id in results_by_id
    ]
    if prev_matches:
        prev_beat, prev_result = max(prev_matches, key=lambda item: item[0].beat_id)
        trailer_gap_s = max(0.0, target.start_s - prev_beat.end_s)
        expected = prev_result.out_point_s + trailer_gap_s
        for offset in cfg.cv.deep_scan.continuity_seed_offsets_s:
            offset_score = max(
                cfg.cv.deep_scan.coarse_candidate_threshold,
                base_score - abs(offset) * 0.06,
            )
            seeds.append((expected + offset, offset_score))

    next_matches = [
        (b, results_by_id[b.beat_id])
        for b in beats
        if b.beat_id > beat_id and b.beat_id in results_by_id
    ]
    if next_matches:
        next_beat, next_result = min(next_matches, key=lambda item: item[0].beat_id)
        trailer_gap_s = max(0.0, next_beat.start_s - target.end_s)
        expected = next_result.in_point_s - trailer_gap_s - target.duration_s
        for offset in cfg.cv.deep_scan.continuity_seed_offsets_s:
            offset_score = max(
                cfg.cv.deep_scan.coarse_candidate_threshold,
                base_score - abs(offset) * 0.06,
            )
            seeds.append((expected - offset, offset_score))

    unique: dict[float, float] = {}
    for seed_t, seed_score in seeds:
        rounded = round(max(0.0, seed_t), 3)
        unique[rounded] = max(unique.get(rounded, 0.0), seed_score)
    points = [(seed_t, score) for seed_t, score in sorted(unique.items())]
    return {beat_id: points} if points else {}


def cmd_rematch(args: argparse.Namespace, cfg) -> None:
    """
    Re-run automatic matching for ONE beat.

    python cli.py rematch --beat 5               # re-scan CV for beat 5
    python cli.py rematch --beat 5 --threshold 0.40  # relax threshold
    """

    beat_id = args.beat
    beats   = _load_beats(cfg)
    results = _load_results(cfg) if _results_cache_path(cfg).exists() else []

    beat = next((b for b in beats if b.beat_id == beat_id), None)
    if beat is None:
        print(f"\u274c  Beat {beat_id} not found. Run 'analyze' first.")
        return

    # ---- Refine an already acceptable cached match -------------------------
    if args.refine:
        current = next((r for r in results if r.beat_id == beat_id), None)
        if current is None:
            print(f"❌  Beat {beat_id} has no cached match to refine. Run 'match --beat {beat_id}' first.")
            return

        from src.cv.content_align import align_cached_match_by_content
        refined_in_s, sequence_score = align_cached_match_by_content(
            beat,
            current.in_point_s,
            cfg,
            search_window_s=args.refine_window,
        )
        usable_duration_s = max(0.0, current.out_point_s - current.in_point_s)
        span_score = sequence_score
        scene_data = _scene_for_time_light(_load_scene_cache_light(cfg), refined_in_s, cfg)
        out_point_s = refined_in_s + usable_duration_s
        if scene_data is not None:
            out_point_s = min(out_point_s, float(scene_data["end_s"]))
        matchable_duration_s = beat.duration_s
        duration_coverage = (
            max(0.0, out_point_s - refined_in_s) / matchable_duration_s
            if matchable_duration_s > 0 else 0.0
        )
        if duration_coverage < cfg.cv.deep_scan.min_duration_coverage:
            print(
                f"❌  Beat {beat_id} refined candidate rejected: "
                f"duration coverage {duration_coverage:.0%} < "
                f"{cfg.cv.deep_scan.min_duration_coverage:.0%}"
            )
            return

        try:
            from src.cv.frame_extractor import get_video_info
            fps = float(get_video_info(cfg.paths.source_movie)["fps"]) or cfg.export.edl_frame_rate
        except Exception:
            fps = cfg.export.edl_frame_rate

        from src.core.models import MatchResult
        refined = MatchResult(
            beat_id=beat_id,
            scene_id=int(scene_data["scene_id"]) if scene_data is not None else current.scene_id,
            source_path=current.source_path,
            in_point_s=max(0.0, refined_in_s),
            out_point_s=out_point_s,
            in_point_frame=int(max(0.0, refined_in_s) * fps),
            match_score=max(sequence_score, span_score),
            match_location=current.match_location,
            is_confirmed=max(sequence_score, span_score) >= cfg.cv.deep_scan.match_threshold,
        )
        results = _update_result(refined, results)
        _save_results(results, cfg)
        print(
            f"✅  Beat {beat_id} refined → "
            f"in={refined.in_point_s:.3f}s, out={refined.out_point_s:.3f}s, "
            f"sequence_score={refined.match_score:.3f}"
        )
        return

    # ---- Re-run CV with optional threshold override ------------------------
    from dataclasses import replace as dc_replace
    run_cfg = cfg
    if args.threshold is not None:
        run_cfg = dc_replace(
            cfg,
            cv=dc_replace(
                cfg.cv,
                deep_scan=dc_replace(cfg.cv.deep_scan, match_threshold=args.threshold),
            ),
        )
        print(f"ℹ️   threshold overridden to {args.threshold} for beat {beat_id}")

    from src.cv.global_scan import run_global_scan
    seed_in_points = _continuity_seed_in_points(beat_id, beats, results, run_cfg)
    matches = run_global_scan([beat], run_cfg, seed_in_points=seed_in_points)
    
    if not matches:
        print(f"❌  Beat {beat_id}: no match. Try --threshold 0.40.")
        return
        
    match = matches[0]
    results = _update_result(match, results)
    _save_results(results, cfg)
    print(f"✅  Beat {beat_id} rematched → (in={match.in_point_s:.3f}s, score={match.match_score:.3f})")


def cmd_report(args: argparse.Namespace, cfg) -> None:
    from src.pipeline.reporter import generate_report
    beats = _select_beats(_load_beats(cfg), getattr(args, "beat", None))
    beat_ids = {b.beat_id for b in beats} if getattr(args, "beat", None) is not None else None
    results = _select_results(_normalize_cached_results(_load_beats(cfg), _load_results(cfg), cfg), beat_ids)
    out     = generate_report(beats, results, cfg)
    if getattr(args, "beat", None) is not None and not results:
        print(
            f"\n⚠️   Beat {args.beat} has no cached match yet. "
            f"Run: python cli.py match --beat {args.beat}"
        )
    print(f"\n\u2705  Report \u2192 {out}")


def cmd_export(args: argparse.Namespace, cfg) -> None:
    from src.export.edl_writer   import write_edl
    from src.export.fcpxml_writer import write_fcpxml
    from src.pipeline.matcher    import build_timeline

    beats = _select_beats(_load_beats(cfg), getattr(args, "beat", None))
    beat_ids = {b.beat_id for b in beats} if getattr(args, "beat", None) is not None else None
    results = _select_results(_normalize_cached_results(_load_beats(cfg), _load_results(cfg), cfg), beat_ids)
    if getattr(args, "beat", None) is not None and not results:
        print(f"❌  Beat {args.beat} has no cached match. Run 'match --beat {args.beat}' first.")
        return
    timeline = build_timeline(beats, results, cfg)

    fmt = args.format or cfg.export.output_format
    beat_id = getattr(args, "beat", None)
    out_stem = (
        f"{cfg.paths.reference_trailer.stem}_beat_{beat_id:03d}"
        if beat_id is not None
        else timeline.title
    )

    if fmt in ("fcpxml", "both"):
        out = write_fcpxml(timeline, cfg, output_path=cfg.paths.output_dir / f"{out_stem}.fcpxml")
        print(f"✅  FCPXML → {out}")

    if fmt in ("edl", "both"):
        out = write_edl(timeline, cfg, output_path=cfg.paths.output_dir / f"{out_stem}.edl")
        print(f"✅  EDL    → {out}")


def cmd_run(args: argparse.Namespace, cfg) -> None:
    """Full pipeline: analyze → match → report → export."""
    cmd_analyze(args, cfg)
    cmd_match(args, cfg)
    cmd_report(args, cfg)
    cmd_export(args, cfg)


# ---------------------------------------------------------------------------
# Argument parser
# ---------------------------------------------------------------------------

def _build_parser() -> argparse.ArgumentParser:
    parser = argparse.ArgumentParser(
        prog="ai-trailer",
        description="AI Trailer Generator v2 — Pure CV scene matching",
        formatter_class=argparse.RawDescriptionHelpFormatter,
    )
    parser.add_argument(
        "--config", type=Path, default=Path("config.toml"),
        metavar="CONFIG", help="Path to config.toml (default: ./config.toml)",
    )
    parser.add_argument(
        "--log-level", default="INFO",
        choices=["DEBUG", "INFO", "WARNING", "ERROR"],
        help="Logging verbosity (default: INFO)",
    )

    sub = parser.add_subparsers(dest="command", required=True)

    # analyze
    p_analyze = sub.add_parser("analyze", help="Detect trailer beats + fingerprint")
    p_analyze.add_argument("--no-audio", action="store_true",
                           help="Skip Whisper (only affects beat labels, not matching)")
    p_analyze.add_argument("--no-llm", action="store_true",
                           help="Skip LLM classification (only affects beat labels)")

    # match
    p_match = sub.add_parser("match", help="Run 2-phase CV matching")
    p_match.add_argument("--force-reindex", action="store_true",
                         help="Ignore scene cache and re-run PySceneDetect")
    p_match.add_argument("--beat", type=int,
                         help="Match only one beat and merge it into the cached results")
    p_match.add_argument("--vision", action="store_true",
                         help="Enable cached vision descriptions for extra automatic search seeds")
    p_match.add_argument("--no-vision", action="store_true",
                         help="Disable vision seeding even if [vision].enabled is true")

    # rematch
    p_rematch = sub.add_parser("rematch", help="Re-run or override matching for one beat")
    p_rematch.add_argument("--beat",      type=int,   required=True,  help="Beat ID to rematch")
    p_rematch.add_argument("--threshold", type=float, default=None,   help="Override match_threshold")
    p_rematch.add_argument("--refine",    action="store_true",
            help="Refine the cached match by measuring a local image-content offset")
    p_rematch.add_argument("--refine-window", type=float, default=None,
                           help="Seconds to search around the cached in-point when using --refine")

    # report
    p_report = sub.add_parser("report", help="Generate HTML visual comparison report")
    p_report.add_argument("--beat", type=int, help="Report only one beat")

    # export
    p_export = sub.add_parser("export", help="Export timeline from cached results")
    p_export.add_argument("--format", choices=["fcpxml", "edl", "both"],
                          help="Override [export] output_format from config")
    p_export.add_argument("--beat", type=int, help="Export only one beat")

    # run
    p_run = sub.add_parser("run", help="Full pipeline: analyze → match → export")
    p_run.add_argument("--no-audio",      action="store_true")
    p_run.add_argument("--no-llm",        action="store_true")
    p_run.add_argument("--force-reindex", action="store_true")
    p_run.add_argument("--vision",        action="store_true")
    p_run.add_argument("--no-vision",     action="store_true")
    p_run.add_argument("--format", choices=["fcpxml", "edl", "both"])
    p_run.add_argument("--beat",          type=int,
                       help="Run match/report/export for only one cached beat")

    return parser


# ---------------------------------------------------------------------------
# Entry point
# ---------------------------------------------------------------------------

def main() -> None:
    _ensure_utf8_console()
    parser = _build_parser()
    args   = parser.parse_args()

    _setup_logging(args.log_level)

    from src.core.config import load_config
    cfg = load_config(args.config)

    dispatch = {
        "analyze": cmd_analyze,
        "match":   cmd_match,
        "rematch": cmd_rematch,
        "report":  cmd_report,
        "export":  cmd_export,
        "run":     cmd_run,
    }

    handler = dispatch[args.command]
    handler(args, cfg)


if __name__ == "__main__":
    main()