"""Shared internal helpers used by tool handlers across categories.

Extracted from server.py — validation, formatting, and small parsing utilities
that more than one server_tools/*.py module needs.
"""

from __future__ import annotations

import json
import os
import re
from pathlib import Path
from typing import Any, Sequence

from mcp.types import TextContent

from fcpxml.media_intel import media_src_to_path
from fcpxml.models import (
    DuplicateGroup,
    FlashFrame,
    FlashFrameSeverity,
    GapInfo,
    Timecode,
    TimeValue,
)
from fcpxml.parser import FCPXMLParser
from fcpxml.rough_cut import RoughCutGenerator
from fcpxml.transcribe import invert_ranges, merge_ranges, transcribe
from fcpxml.writer import FCPXMLModifier

PROJECTS_DIR = os.environ.get("FCP_PROJECTS_DIR", os.path.expanduser("~/Movies"))

_SANDBOX_ENABLED = "FCP_PROJECTS_DIR" in os.environ

MAX_FILE_SIZE = 100 * 1024 * 1024

MAX_MEDIA_FILE_SIZE = 32 * 1024 * 1024 * 1024

_MAX_JSON_DEPTH = 50

def _check_json_depth(obj: object, _depth: int = 0) -> None:
    """Reject JSON structures nested beyond _MAX_JSON_DEPTH.

    Prevents denial-of-service via deeply nested objects that exhaust the
    call stack or memory during downstream processing.  Called after
    json.load() since Python's json module has no built-in depth limit.
    """
    if _depth > _MAX_JSON_DEPTH:
        raise ValueError(
            f"JSON nesting depth exceeds {_MAX_JSON_DEPTH} — "
            "file may be malformed or adversarial"
        )
    if isinstance(obj, dict):
        for v in obj.values():
            _check_json_depth(v, _depth + 1)
    elif isinstance(obj, list):
        for item in obj:
            _check_json_depth(item, _depth + 1)

def _validate_filepath(
    filepath: str,
    allowed_extensions: tuple[str, ...] | None = None,
    max_size: int = MAX_FILE_SIZE,
) -> str:
    """Validate a user-provided file path against traversal and size attacks.

    Resolves symlinks, blocks null bytes, enforces extension whitelist, and
    checks file size before any parsing takes place.

    ``max_size`` defaults to the document limit; callers handling source
    media pass ``MAX_MEDIA_FILE_SIZE``, since media is streamed rather than
    parsed into memory (see the constant for why).

    Raises:
        ValueError: For invalid paths (null bytes, bad extensions, oversized).
        FileNotFoundError: When the resolved path does not exist.
    """
    if '\x00' in filepath:
        raise ValueError("Invalid file path: null byte detected")

    resolved = Path(filepath).resolve()

    if not resolved.exists():
        raise FileNotFoundError(f"File not found: {filepath}")

    # .fcpxmld bundles are directories (a package wrapping Info.fcpxml plus
    # sidecar data files for object tracking / Cinematic mode).  The size
    # check applies to the inner Info.fcpxml, which is what gets parsed.
    if resolved.is_dir():
        if resolved.suffix.lower() != '.fcpxmld':
            raise ValueError(f"Not a regular file: {filepath}")
        inner = resolved / 'Info.fcpxml'
        if not inner.is_file():
            raise ValueError(f"Invalid bundle (no Info.fcpxml): {filepath}")
        size_target = inner
    elif not resolved.is_file():
        raise ValueError(f"Not a regular file: {filepath}")
    else:
        size_target = resolved

    if allowed_extensions and resolved.suffix.lower() not in allowed_extensions:
        raise ValueError(
            f"Invalid file type '{resolved.suffix}'. "
            f"Allowed: {', '.join(allowed_extensions)}"
        )

    if size_target.stat().st_size > max_size:
        size_mb = size_target.stat().st_size / (1024 * 1024)
        raise ValueError(f"File too large ({size_mb:.1f} MB). Maximum: {max_size // (1024 * 1024)} MB")

    return str(resolved)

def _validate_output_path(output_path: str, *, anchor_dir: str | None = None) -> str:
    """Validate an output path with optional sandbox enforcement.

    Resolves traversal, blocks null bytes, ensures parent exists, and — when
    *anchor_dir* is provided — verifies the resolved output lives under that
    directory.  This prevents LLM-generated tool calls from writing to
    arbitrary filesystem locations (e.g. ``/etc/cron.d/backdoor``).

    Args:
        output_path: The raw output path to validate.
        anchor_dir: If set, the resolved output must be a child of this
            directory.  Typically the parent directory of the input file so
            outputs stay co-located with their sources.

    Raises:
        ValueError: For null bytes, missing parent, or sandbox escape.
    """
    if '\x00' in output_path:
        raise ValueError("Invalid output path: null byte detected")

    resolved = Path(output_path).resolve()

    if not resolved.parent.exists():
        raise ValueError(f"Output directory does not exist: {resolved.parent}")

    if anchor_dir is not None:
        anchor = Path(anchor_dir).resolve()
        try:
            resolved.relative_to(anchor)
        except ValueError:
            raise ValueError(
                f"Output path escapes allowed directory: "
                f"{resolved} is not under {anchor}"
            )

    return str(resolved)

def _validate_directory(directory: str, *, allowed_root: str | None = None) -> str:
    """Validate a user-provided directory path against traversal and injection.

    Resolves symlinks, blocks null bytes, and verifies the path is a real
    directory. When *allowed_root* is given, the resolved path must be a
    descendant of (or equal to) that root — preventing filesystem enumeration
    beyond the project workspace.

    Raises:
        ValueError: For invalid paths (null bytes, not a directory, sandbox escape).
    """
    if '\x00' in directory:
        raise ValueError("Invalid directory path: null byte detected")

    resolved = Path(directory).resolve()

    if not resolved.is_dir():
        raise ValueError(f"Not a valid directory: {directory}")

    if allowed_root is not None:
        root = Path(allowed_root).resolve()
        try:
            resolved.relative_to(root)
        except ValueError:
            raise ValueError(
                f"Directory escapes allowed root: "
                f"{resolved} is not under {root}"
            )

    return str(resolved)

def find_fcpxml_files(directory: str) -> list[str]:
    """Find all FCPXML files in a directory."""
    path = Path(directory)
    files = list(str(f) for f in path.rglob("*.fcpxml"))
    files.extend(str(f) for f in path.rglob("*.fcpxmld"))
    return sorted(files)

def format_timecode(tc) -> str:
    """Format a Timecode object to SMPTE string."""
    return tc.to_smpte() if tc else "00:00:00:00"

def format_duration(seconds: float) -> str:
    """Format seconds into human-readable duration."""
    if seconds < 1:
        return f"{seconds*1000:.0f}ms"
    elif seconds < 60:
        return f"{seconds:.2f}s"
    return f"{int(seconds // 60)}m {seconds % 60:.1f}s"

def _format_clip_table(clips: list, header: str) -> str:
    """Render a list of clips as a markdown table with timecodes and durations.

    Shared by handlers that filter clips by duration threshold
    (find_short_cuts, find_long_clips).
    """
    result = f"{header}\n\n| Name | TC | Duration |\n|------|----|---------|\n"
    result += "\n".join(
        f"| {c.name} | {format_timecode(c.start)} | {format_duration(c.duration_seconds)} |"
        for c in clips
    )
    return result

def _markdown_table(headers: list[str], rows: list[list[str]]) -> str:
    """Build a markdown table from headers and rows.

    Returns header row, separator row, and data rows as a single string.
    Callers avoid repeating the ``| H1 | H2 |\\n|---|---|`` boilerplate
    that appears in 15+ handlers.
    """
    header_line = "| " + " | ".join(headers) + " |"
    sep_line = "|" + "|".join("------" for _ in headers) + "|"
    data_lines = "\n".join(
        "| " + " | ".join(str(c) for c in row) + " |" for row in rows
    )
    return f"{header_line}\n{sep_line}\n{data_lines}"

def _format_batch_result(
    title: str,
    summary: dict[str, str],
    headers: list[str],
    rows: list[list[str]],
    output_path: str,
) -> str:
    """Build a standard batch-operation result with summary, table, and save footer.

    Used by batch fix handlers (flash frames, rapid trim, fill gaps) that all
    share the same markdown structure: ``# Title → ## Summary → ## Details table
    → Saved to`` footer.
    """
    summary_lines = "\n".join(f"- **{k}**: {v}" for k, v in summary.items())
    table = _markdown_table(headers, rows)
    return (
        f"# {title}\n\n"
        f"## Summary\n{summary_lines}\n\n"
        f"## Details\n{table}\n\n"
        f"Saved to: `{output_path}`"
    )

def _fmt_suggestions(suggestions: list[str]) -> str:
    """Format pacing suggestions as markdown list (Python 3.10 compatible)."""
    if not suggestions:
        return "- Pacing looks good!"
    nl = "\n"
    return nl.join(f"- {s}" for s in suggestions)

def generate_output_path(input_path: str, suffix: str = "_modified") -> str:
    """Generate output path from input path.

    The suffix is sanitized to prevent path-component injection — only
    alphanumeric, hyphen, underscore, and dot characters survive.
    """
    # Strip anything that could inject path separators or traversal sequences
    clean_suffix = re.sub(r'[^a-zA-Z0-9._-]', '', suffix)
    if not clean_suffix:
        clean_suffix = "_modified"
    p = Path(input_path)
    return str(p.parent / f"{p.stem}{clean_suffix}{p.suffix}")

def _parse_project(filepath: str):
    """Parse an FCPXML file and return the project with its primary timeline."""
    filepath = _validate_filepath(filepath, ('.fcpxml', '.fcpxmld'))
    project = FCPXMLParser().parse_file(filepath)
    if not project.timelines:
        return None, None
    return project, project.primary_timeline

def _text_result(text: str) -> list[TextContent]:
    """Wrap a string in the MCP TextContent list that every tool handler returns."""
    return [TextContent(type="text", text=text)]

def _no_timeline():
    """Standard response when no timelines are found."""
    return _text_result("No timelines found")

def _require_timeline(filepath: str):
    """Parse FCPXML and return (project, timeline), raising if no timeline exists.

    Centralises the repeated _parse_project + _no_timeline guard that
    appears in every read-only timeline handler.  Returns a tuple so
    callers can destructure directly::

        project, tl = _require_timeline(arguments["filepath"])
    """
    project, tl = _parse_project(filepath)
    if not tl:
        raise _NoTimelineError()
    return project, tl

class _NoTimelineError(Exception):
    """Sentinel raised by _require_timeline when no timelines exist."""

def _resolve_io_paths(
    arguments: dict,
    suffix: str = "_modified",
) -> tuple[str, str]:
    """Validate input filepath and resolve the output path.

    Shared foundation for every handler that reads an FCPXML and writes
    a derived file.  Validates the input, falls back to a suffixed
    output name when ``output_path`` is not supplied, and sandbox-checks
    the result.

    Args:
        arguments: Tool arguments dict (must contain ``filepath``; may
            contain ``output_path``).
        suffix: Default output filename suffix when ``output_path`` is
            not provided (e.g. ``"_modified"``, ``"_beats"``).

    Returns:
        ``(filepath, output_path)`` tuple with both paths validated.
    """
    filepath = _validate_filepath(arguments["filepath"], ('.fcpxml', '.fcpxmld'))
    # Anchor write operations to the input file's directory so LLM-generated
    # tool calls cannot write to arbitrary filesystem locations (e.g.
    # /etc/cron.d/backdoor).  When the explicit sandbox is off, the anchor
    # still prevents writes outside the source directory tree.
    # `output_dir` is where the caller wants the file written, not merely a
    # sandbox boundary: the app's "Pasta do projeto" promises that everything
    # generated lands there. Deriving the name from the input but keeping the
    # input's directory made every cross-directory call fail its own anchor
    # check ("output path escapes allowed directory"), so the setting silently
    # only worked when it pointed at the directory the file was already going
    # to. An explicit `output_path` still wins, and still has to sit inside
    # the anchor.
    output_dir = arguments.get("output_dir")
    if output_dir:
        anchor = _validate_directory(str(output_dir))
        default_output = str(Path(anchor) / Path(generate_output_path(filepath, suffix)).name)
    else:
        anchor = str(Path(filepath).resolve().parent)
        default_output = generate_output_path(filepath, suffix)
    output_path = _validate_output_path(
        arguments.get("output_path") or default_output,
        anchor_dir=anchor,
    )
    return filepath, output_path

def _setup_modifier(
    arguments: dict,
    suffix: str = "_modified",
) -> tuple[str, str, "FCPXMLModifier"]:
    """Common setup for write handlers: validate paths and create modifier.

    Consolidates the repeated validate-filepath → resolve-output-path →
    create-modifier boilerplate shared by 18+ write handlers.

    Args:
        arguments: Tool arguments dict (must contain ``filepath``; may
            contain ``output_path``).
        suffix: Default output filename suffix when ``output_path`` is
            not provided (e.g. ``"_modified"``, ``"_flash_fixed"``).

    Returns:
        ``(filepath, output_path, modifier)`` tuple ready for the
        handler's domain-specific operation.
    """
    filepath, output_path = _resolve_io_paths(arguments, suffix)
    modifier = FCPXMLModifier(filepath)
    return filepath, output_path, modifier

def _setup_generator(
    arguments: dict,
    suffix: str = "_roughcut",
) -> tuple[str, str, "RoughCutGenerator"]:
    """Common setup for generation handlers: validate paths and create generator.

    Args:
        arguments: Tool arguments dict (must contain ``filepath`` and
            ``output_path``).
        suffix: Default output filename suffix.

    Returns:
        ``(filepath, output_path, generator)`` tuple.
    """
    filepath, output_path = _resolve_io_paths(arguments, suffix)
    generator = RoughCutGenerator(filepath)
    return filepath, output_path, generator

def _parse_timestamp_parts(
    parts: list[str], *, frame_rate: float = 24.0
) -> float | None:
    """Convert colon-separated timestamp parts to total seconds.

    Handles 2-part (M:SS), 3-part (H:MM:SS / HH:MM:SS.ms), and
    4-part (HH:MM:SS:FF SMPTE) formats.  Returns ``None`` when the
    part count is unrecognised so callers can skip.

    Args:
        parts: Colon-split timestamp components.
        frame_rate: FPS used to convert the frame component of SMPTE
            timecodes into fractional seconds (default 24.0).
    """
    if len(parts) == 2:
        return int(parts[0]) * 60 + float(parts[1])
    elif len(parts) == 3:
        return int(parts[0]) * 3600 + int(parts[1]) * 60 + float(parts[2])
    elif len(parts) == 4:
        # SMPTE: HH:MM:SS:FF — convert frames to fractional seconds
        base = int(parts[0]) * 3600 + int(parts[1]) * 60 + float(parts[2])
        frames = int(parts[3])
        return base + (frames / frame_rate) if frame_rate > 0 else base
    return None

def _raw_markers_to_batch(
    raw_markers: list[dict],
    marker_type: str = "chapter",
    max_label: int | None = None,
) -> list[dict]:
    """Convert raw {seconds, text} marker dicts to batch_add_markers format.

    Shared by import_srt_markers and import_transcript_markers.
    """
    batch = []
    for m in raw_markers:
        label = m["text"]
        if max_label and len(label) > max_label:
            label = label[:max_label]
        batch.append({
            "timecode": f"{m['seconds']}s",
            "name": label,
            "marker_type": marker_type.upper(),
        })
    return batch

def _extract_subtitle_blocks(text: str, *, strip_vtt_tags: bool = False) -> list[dict]:
    """Extract timestamp/text pairs from subtitle cue blocks (SRT or VTT).

    Both SRT and VTT use the same ``start --> end`` cue syntax with
    text lines underneath; only header stripping and tag cleaning differ.
    """
    markers = []
    blocks = re.split(r'\n\s*\n', text.strip())
    for block in blocks:
        lines = block.strip().split('\n')
        if len(lines) < 2:
            continue
        ts_line = None
        text_lines = []
        for line in lines:
            if '-->' in line:
                ts_line = line
            elif ts_line is not None:
                if strip_vtt_tags:
                    line = re.sub(r'<[^>]+>', '', line)
                cleaned = line.strip()
                if cleaned:
                    text_lines.append(cleaned)
        if not ts_line or not text_lines:
            continue
        start_str = ts_line.split('-->')[0].strip().replace(',', '.')
        seconds = _parse_timestamp_parts(start_str.split(':'))
        if seconds is not None:
            markers.append({'seconds': seconds, 'text': ' '.join(text_lines)})
    return markers

def parse_srt(text: str) -> list[dict]:
    """Parse SRT subtitle format into timestamp/text pairs."""
    return _extract_subtitle_blocks(text)

def parse_vtt(text: str) -> list[dict]:
    """Parse WebVTT subtitle format into timestamp/text pairs."""
    text = re.sub(r'^WEBVTT.*?\n', '', text, flags=re.MULTILINE)
    text = re.sub(r'NOTE\n.*?\n\n', '', text, flags=re.DOTALL)
    return _extract_subtitle_blocks(text, strip_vtt_tags=True)

def parse_transcript_timestamps(text: str) -> list[dict]:
    """Parse timestamped text (YouTube description format) into markers.

    Supports formats like:
      0:00 Introduction
      00:01:30 Main Topic
      1:05:30 Conclusion
      00:00:00:00 SMPTE timecode
    """
    markers = []
    for line in text.strip().split('\n'):
        line = line.strip()
        if not line:
            continue
        match = re.match(r'^(\d{1,2}:\d{2}(?::\d{2}){0,2})\s+(.+)$', line)
        if match:
            seconds = _parse_timestamp_parts(match.group(1).split(':'))
            if seconds is not None:
                markers.append({'seconds': seconds, 'text': match.group(2).strip()})
    return markers

def _detect_flash_frames(
    tl: Any, *, critical_threshold: int = 2, warning_threshold: int = 6,
) -> list:
    """Find clips shorter than *warning_threshold* frames.

    Returns a list of ``FlashFrame`` objects sorted by severity.  Shared by
    ``handle_detect_flash_frames`` and ``handle_validate_timeline`` so the
    detection logic lives in exactly one place.
    """
    fps = tl.frame_rate
    flash_frames: list[FlashFrame] = []
    for clip in tl.clips:
        duration_frames = int(clip.duration_seconds * fps)
        if duration_frames < warning_threshold:
            severity = (
                FlashFrameSeverity.CRITICAL
                if duration_frames < critical_threshold
                else FlashFrameSeverity.WARNING
            )
            flash_frames.append(FlashFrame(
                clip_name=clip.name, clip_id=clip.name,
                start=clip.start, duration_frames=duration_frames,
                duration_seconds=clip.duration_seconds, severity=severity,
            ))
    return flash_frames

def _detect_gaps(tl: Any, *, min_gap_frames: int = 1) -> list:
    """Find inter-clip gaps of at least *min_gap_frames* length.

    Returns a list of ``GapInfo`` objects.  Shared by ``handle_detect_gaps``
    and ``handle_validate_timeline``.
    """
    fps = tl.frame_rate
    min_gap_seconds = min_gap_frames / fps
    gaps: list[GapInfo] = []
    sorted_clips = sorted(tl.clips, key=lambda c: c.start.seconds)
    for i in range(len(sorted_clips) - 1):
        current_end = sorted_clips[i].end.seconds
        next_start = sorted_clips[i + 1].start.seconds
        gap_duration = next_start - current_end
        if gap_duration >= min_gap_seconds:
            gaps.append(GapInfo(
                start=Timecode(frames=int(current_end * fps), frame_rate=fps),
                duration_frames=int(gap_duration * fps),
                duration_seconds=gap_duration,
                previous_clip=sorted_clips[i].name,
                next_clip=sorted_clips[i + 1].name,
            ))
    return gaps

def _detect_duplicate_groups(tl: Any, *, mode: str = "same_source") -> list:
    """Group clips that share a source media reference.

    Returns a list of ``DuplicateGroup`` objects.  Shared by
    ``handle_detect_duplicates`` and ``handle_validate_timeline``.
    """
    source_groups: dict[str, list[dict]] = {}
    for clip in tl.clips:
        source_key = clip.media_path or clip.name
        if source_key not in source_groups:
            source_groups[source_key] = []
        source_groups[source_key].append({
            'name': clip.name,
            'start': clip.start.seconds,
            'duration': clip.duration_seconds,
            'source_start': clip.source_start.seconds if clip.source_start else 0,
            'source_duration': clip.duration_seconds,
            'timecode': format_timecode(clip.start),
        })

    duplicates: list[DuplicateGroup] = []
    for source_key, clips in source_groups.items():
        if len(clips) <= 1:
            continue
        group = DuplicateGroup(
            source_ref=source_key,
            source_name=source_key.split('/')[-1] if '/' in source_key else source_key,
            clips=clips,
        )
        if mode == "same_source":
            duplicates.append(group)
        elif mode == "overlapping_ranges" and group.has_overlapping_ranges:
            duplicates.append(group)
        elif mode == "identical":
            seen_ranges: set[tuple] = set()
            identical_clips = []
            for c in clips:
                range_key = (c['source_start'], c['source_duration'])
                if range_key in seen_ranges:
                    identical_clips.append(c)
                seen_ranges.add(range_key)
            if identical_clips:
                group.clips = identical_clips
                duplicates.append(group)
    return duplicates

AUDIO_MEDIA_EXTENSIONS = (
    '.wav', '.aif', '.aiff', '.mp3', '.m4a', '.aac', '.flac', '.mov', '.mp4',
)

_DIARIZATION_INSTALL_HINT = (
    "\n\nInstall the optional diarization extra:\n\n"
    "    pip install 'fcp-mcp-server[diarization]'\n\n"
    "and set a HuggingFace token with access to "
    "pyannote/speaker-diarization-3.1 (pass hf_token= or persist one via "
    "save_hf_token)."
)

_FEATURES_INSTALL_HINT = (
    "\n\nInstall the optional media-intelligence extra:\n\n"
    "    pip install 'fcp-mcp-server[intelligence]'"
)

def _voice_analysis_config_text(config: dict) -> str:
    w = config["emphasis_weights"]
    text = "# Voice Analysis Settings\n\n"
    text += _markdown_table(
        ["Setting", "Value"],
        [
            ["Energy threshold", f"{config['energy_threshold']:.2f}"],
            ["Peak selection", f"top {config['peak_percentile']:.1%} of words"],
            ["Emphasis floor", f"{config['emphasis_floor']:.2f}"],
            ["Emotion detection", "on" if config["emotion_enabled"] else "off"],
            ["Emotion sensitivity", f"{config['emotion_sensitivity']:.2f}"],
        ],
    ) + "\n\n## Emphasis Weights\n"
    text += _markdown_table(
        ["Factor", "Weight"],
        [[k.replace("_", " ").title(), f"{v:.2f}"] for k, v in w.items()],
    )
    return text

def _apply_placed_action(modifier, clip_el, action, clip_start: float) -> str:
    """Apply one non-cut action to the clip that hosts it.

    ``clip_start`` is where that clip begins on the timeline; the writer
    wants times relative to the clip's own head, so the rebase happens here
    — the single place that knows about the conversion. The clip *element*
    is passed through rather than its name: after a cut the pieces share a
    name, and a name lookup would land every edit on the first piece.
    """
    rel_start = action.start - clip_start
    rel_end = action.end - clip_start

    if action.kind == "zoom":
        # Only forward an explicit ease — otherwise add_zoom's own default
        # (a fast ramp in, instant snap back out) is what should apply.
        zoom_args = {}
        if action.params.get("ease") is not None:
            zoom_args["ease"] = float(action.params["ease"])
        if action.params.get("ease_out") is not None:
            zoom_args["ease_out"] = float(action.params["ease_out"])
        modifier.add_zoom(
            clip_id=clip_el,
            start=rel_start,
            end=rel_end,
            scale=float(action.params.get("scale", 1.3)),
            **zoom_args,
        )
        return f"zoom {action.params.get('scale', 1.3):.2f}x"

    if action.kind == "text":
        modifier.add_text_title(
            clip_el,
            action.params["content"],
            offset=modifier.snap_seconds_to_frame(rel_start).to_fcpxml(),
            duration=modifier.snap_seconds_to_frame(action.duration).to_fcpxml(),
        )
        return f"text \"{action.params['content'][:24]}\""

    # marker
    modifier.add_marker(
        clip_id=clip_el,
        timecode=modifier.snap_seconds_to_frame(rel_start).to_fcpxml(),
        name=action.params.get("content") or action.reason or "Voice action",
        note=action.reason or None,
    )
    return "marker"

def _speaker_table(profiles: Sequence[dict]) -> str:
    """Who was detected, ordered by how much of the runtime each holds."""
    return _markdown_table(
        ["ID", "Name", "Share", "Speaking", "Lines", "Avg line"],
        [
            [
                p["id"],
                p.get("name", ""),
                f"{p['share']:.0%}",
                format_duration(p["speaking_seconds"]),
                str(p["segment_count"]),
                f"{p['avg_segment']:.1f}s",
            ]
            for p in profiles
        ],
    )

TRANSCRIBE_MAX_MEDIA = 10

_TRANSCRIBE_INSTALL_HINT = (
    "\n\nInstall the optional transcription extra:\n\n"
    "    pip install 'fcp-mcp-server[transcribe]'\n\n"
    "or run via uvx:\n\n"
    "    uvx --from \"fcp-mcp-server[transcribe]\" fcp-mcp-server"
)

def _transcript_json_path(media_path: str, output_dir: str | None = None) -> Path:
    """Where the ``_transcript.json`` for ``media_path`` lives.

    When ``output_dir`` (the user-selected project folder) is set, the
    transcript is saved/read there instead of next to the source media.
    """
    p = Path(media_path)
    if output_dir:
        directory = Path(output_dir).expanduser()
        directory.mkdir(parents=True, exist_ok=True)
        return directory / f"{p.stem}_transcript.json"
    return p.with_name(p.stem + "_transcript.json")

def _load_or_transcribe(
    media_path: str, model: str, language: str | None, output_dir: str | None = None
) -> tuple[dict | None, str]:
    """Load a cached ``_transcript.json`` for a media file, else transcribe and cache it.

    Returns ``(transcript, "")`` or ``(None, reason)``. The cache makes
    transcription a one-time cost per media file across all transcript tools.
    """
    json_path = _transcript_json_path(media_path, output_dir)
    if json_path.is_file():
        try:
            with open(json_path) as f:
                data = json.load(f)
            if isinstance(data, dict) and isinstance(data.get("words"), list):
                return data, ""
        except (OSError, json.JSONDecodeError, UnicodeDecodeError):
            pass  # unreadable cache falls through to re-transcribe
    result = transcribe(media_path, model_size=model, language=language)
    if result is None:
        return None, "untranscribable (faster-whisper not installed or media unreadable)"
    anchor = str(Path(output_dir).expanduser()) if output_dir else str(Path(media_path).parent)
    out_path = _validate_output_path(str(json_path), anchor_dir=anchor)
    with open(out_path, "w") as f:
        json.dump({"source": Path(media_path).name, **result}, f, indent=2)
    return result, ""

def _cut_transcript_spans(modifier, clip_filter, model, language, padding, spans_fn, keep_only=False, output_dir=None):
    """Shared cut engine for transcript-driven editing.

    ``spans_fn(words) -> [(start, end), ...]`` in source seconds. Spans are
    padded, clamped to each clip's used source window, optionally inverted
    (keep_only), snapped to the frame grid, and cut with ripple.
    """
    to_frame = modifier.snap_seconds_to_frame

    cache: dict[str, tuple] = {}
    cuts_made: list[tuple[str, int, float]] = []
    skipped: list[tuple[str, str]] = []
    spine_clips = [el for _, el in modifier._iter_spine_clips()]
    for el in spine_clips:
        name = el.get("name", "")
        if clip_filter and name != clip_filter:
            continue
        src = modifier.resources.get(el.get("ref", ""), {}).get("src", "")
        media_path = media_src_to_path(src)
        if not media_path or not Path(media_path).is_file():
            skipped.append((name, "media file missing"))
            continue
        if media_path not in cache:
            if len(cache) >= TRANSCRIBE_MAX_MEDIA:
                skipped.append((name, f"transcription cap reached ({TRANSCRIBE_MAX_MEDIA} media files)"))
                continue
            cache[media_path] = _load_or_transcribe(media_path, model, language, output_dir)
        data, reason = cache[media_path]
        if data is None:
            skipped.append((name, reason))
            continue

        clip_source_start = modifier.source_file_start(el).to_seconds()
        clip_duration = modifier._parse_time(el.get("duration", "0s")).to_seconds()
        window_start = clip_source_start
        window_end = clip_source_start + clip_duration

        spans = spans_fn(data.get("words", []))
        padded = merge_ranges([(s - padding, e + padding) for s, e in spans])
        clamped = [
            (max(s, window_start), min(e, window_end))
            for s, e in padded
            if min(e, window_end) > max(s, window_start)
        ]
        if keep_only:
            if not clamped:
                # Never delete a whole clip just because nothing matched in it.
                skipped.append((name, "no phrase matches — left untouched (keep_only)"))
                continue
            cut_source = invert_ranges(clamped, window_start, window_end)
        else:
            cut_source = clamped
        cut_ranges = [
            (to_frame(s - clip_source_start), to_frame(e - clip_source_start))
            for s, e in cut_source
        ]
        cut_ranges = [(a, b) for a, b in cut_ranges if b > a]
        if not cut_ranges:
            continue
        removed = modifier.cut_clip_ranges(el, cut_ranges)
        if removed > TimeValue.zero():
            cuts_made.append((name, len(cut_ranges), removed.to_seconds()))
    return cuts_made, skipped

def _transcript_cut_report(title, summary_lines, cuts_made, skipped, output_path, footer):
    if not cuts_made:
        text = f"# {title}\n\nNo cuts to make — file unchanged (nothing saved)."
        if skipped:
            text += "\n\n## Skipped Clips\n" + _markdown_table(
                ["Clip", "Reason"], [[name, reason] for name, reason in skipped]
            )
        if any("faster-whisper" in reason for _, reason in skipped):
            text += _TRANSCRIBE_INSTALL_HINT
        return _text_result(text)
    total_removed = sum(seconds for _, _, seconds in cuts_made)
    result = f"# {title}\n\n## Summary\n"
    result += "\n".join(summary_lines) + "\n"
    result += f"- **Clips Cut**: {len(cuts_made)}\n- **Total Removed**: {format_duration(total_removed)}\n"
    result += "\n## Cuts\n"
    result += _markdown_table(
        ["Clip", "Ranges Cut", "Removed"],
        [[name, str(count), f"{seconds:.2f}s"] for name, count, seconds in cuts_made],
    ) + "\n"
    if skipped:
        result += "\n## Skipped Clips\n" + _markdown_table(
            ["Clip", "Reason"], [[name, reason] for name, reason in skipped]
        ) + "\n"
    result += f"\nSaved to: {output_path}\n\n{footer}"
    return _text_result(result)