#!/usr/bin/env python3

"""
Match local video filenames against canonical WCX movie names.

The script is deliberately conservative and read-only.

Matching sources:

- Current canonical name from movie.name
- Verified aliases from movie_name_alias
- Current duration from movie.duration_seconds
- Historical durations from movie_history.duration_seconds
- Actual file duration read with ffprobe

Important principles:

- Duration never creates a candidate without a name or alias match.
- A close duration may resolve an otherwise ambiguous name match.
- The least-wrong duration does not win when all candidates have poor matches.
- The current canonical movie name is always displayed.
- Files and database contents are never modified.

Examples:

    match_filenames.py /storage/disk1/X \
        --database /path/to/wcx.db

    match_filenames.py /storage/disk1/X \
        --database /path/to/wcx.db \
        --recursive

    match_filenames.py /storage/disk1/X \
        --database /path/to/wcx.db \
        --ending mp4,avi

    match_filenames.py /storage/disk1/X \
        --database /path/to/wcx.db \
        --ending mp4 \
        --ending avi \
        --debug
"""

import argparse
import json
import os
import shutil
import sqlite3
import subprocess
import sys
import unicodedata
from dataclasses import dataclass, replace
from pathlib import Path


VIDEO_EXTENSIONS = {
    ".avi",
    ".m4v",
    ".mkv",
    ".mov",
    ".mp4",
    ".mpeg",
    ".mpg",
    ".ts",
    ".webm",
    ".wmv",
}

MIN_NAME_SCORE = 100
MIN_NAME_MARGIN = 15

# At least this score is considered credible duration support.
MIN_CREDIBLE_DURATION_SCORE = 10

# Difference between duration scores required to resolve several name matches.
MIN_DURATION_SCORE_MARGIN = 15


@dataclass(frozen=True)
class MatchName:
    """
    One searchable name belonging to a movie.

    source is normally:
        current
        manual
        history
        csv
    """

    display_name: str
    normalized_name: str
    normalized_persons: tuple[str, ...]
    source: str


@dataclass(frozen=True)
class DurationVersion:
    duration_seconds: int
    source: str
    archived_at: str | None = None


@dataclass(frozen=True)
class Movie:
    movie_id: str
    name: str
    match_names: tuple[MatchName, ...]
    duration_versions: tuple[DurationVersion, ...]


@dataclass(frozen=True)
class DurationMatch:
    score: int
    classification: str
    matched_duration: int | None
    difference_seconds: int | None
    source: str | None
    archived_at: str | None


@dataclass(frozen=True)
class MatchCandidate:
    movie: Movie
    matched_name: MatchName

    name_score: int
    duration_score: int
    total_score: int

    name_reason: str
    duration_reason: str

    full_name_occurrences: int
    all_persons_matched: bool
    duration_match: DurationMatch


@dataclass(frozen=True)
class MatchResult:
    status: str
    best: MatchCandidate | None
    margin: int
    reason: str
    detected_movies: tuple[str, ...]


def parse_arguments() -> argparse.Namespace:
    parser = argparse.ArgumentParser(
        description=(
            "Match local video filenames against canonical WCX movie names."
        )
    )

    parser.add_argument(
        "directory",
        type=Path,
        help="Directory containing video files.",
    )

    parser.add_argument(
        "--database",
        type=Path,
        required=True,
        help="SQLite database",
    )

    parser.add_argument(
        "--recursive",
        action="store_true",
        help="Search recursively below the input directory.",
    )

    parser.add_argument(
        "--ending",
        action="append",
        help=(
            "Video file endings to include. Examples: "
            "'--ending mp4,avi' or '--ending mp4 --ending avi'. "
            "A leading dot is optional. "
            "Default: all supported video endings."
        ),
    )

    parser.add_argument(
        "--limit",
        type=int,
        help="Analyze at most N files.",
    )

    parser.add_argument(
        "--debug",
        action="store_true",
        help="Show candidates, scores, durations and classification details.",
    )

    parser.add_argument(
        "--detection-report",
        type=Path,
        help=(
            "JSON detection report; only files classified as wcx "
            "will be matched."
        ),
    )

    return parser.parse_args()


def normalize_text(value: str) -> str:
    decomposed = unicodedata.normalize("NFKD", value)

    without_diacritics = "".join(
        character
        for character in decomposed
        if not unicodedata.combining(character)
    )

    return "".join(
        character.casefold()
        for character in without_diacritics
        if character.isalnum()
    )


def split_person_names(value: str) -> tuple[str, ...]:
    persons = []

    for part in value.split("+"):
        normalized = normalize_text(part)

        if normalized:
            persons.append(normalized)

    return tuple(persons)


def normalize_duration(value: object) -> int | None:
    if value is None:
        return None

    try:
        duration = int(value)
    except (TypeError, ValueError):
        return None

    if duration <= 0:
        return None

    return duration


def parse_video_extensions(
    ending_arguments: list[str] | None,
) -> set[str]:
    """
    Parse --ending arguments.

    Supported examples:

        --ending mp4,avi
        --ending .mp4,.avi
        --ending mp4 --ending avi

    Without --ending, all extensions in VIDEO_EXTENSIONS are used.
    """
    if not ending_arguments:
        return set(VIDEO_EXTENSIONS)

    extensions: set[str] = set()

    for argument in ending_arguments:
        for value in argument.split(","):
            extension = value.strip().casefold()

            if not extension:
                continue

            if not extension.startswith("."):
                extension = f".{extension}"

            extensions.add(extension)

    if not extensions:
        raise ValueError(
            "--ending did not contain any valid file endings."
        )

    return extensions


def normalize_absolute_path(path: Path) -> Path:
    return Path(os.path.abspath(path))


def load_detection_report(
    report_file: Path,
) -> dict[Path, str]:
    if not report_file.is_file():
        raise FileNotFoundError(
            f"Detection report does not exist: {report_file}"
        )

    try:
        with report_file.open(encoding="utf-8") as file:
            report = json.load(file)
    except json.JSONDecodeError as error:
        raise ValueError(
            f"Detection report is not valid JSON: {report_file}: {error}"
        ) from error

    if not isinstance(report, dict):
        raise ValueError(
            "Detection report top level must be a JSON object."
        )

    schema_version = report.get("schema_version")
    if type(schema_version) is not int or schema_version != 1:
        raise ValueError(
            "Detection report schema_version must be 1."
        )

    entries = report.get("files")
    if not isinstance(entries, list):
        raise ValueError(
            "Detection report files must be a list."
        )

    allowed_classifications = {
        "wcx",
        "uncertain",
        "not_wcx",
        "error",
    }
    classifications_by_path = {}

    for index, entry in enumerate(entries):
        if not isinstance(entry, dict):
            raise ValueError(
                f"Detection report files[{index}] must be an object."
            )

        if "path" not in entry:
            raise ValueError(
                f"Detection report files[{index}] is missing path."
            )

        path_value = entry["path"]
        if not isinstance(path_value, str):
            raise ValueError(
                f"Detection report files[{index}].path must be a string."
            )

        if "classification" not in entry:
            raise ValueError(
                f"Detection report files[{index}] is missing classification."
            )

        classification = entry["classification"]
        if not isinstance(classification, str):
            raise ValueError(
                f"Detection report files[{index}].classification "
                f"must be a string."
            )

        if classification not in allowed_classifications:
            raise ValueError(
                f"Detection report files[{index}] has unknown "
                f"classification: {classification}"
            )

        normalized_path = normalize_absolute_path(
            Path(path_value)
        )
        if normalized_path in classifications_by_path:
            raise ValueError(
                f"Detection report contains duplicate path: "
                f"{normalized_path}"
            )

        classifications_by_path[normalized_path] = classification

    return classifications_by_path


def select_wcx_files(
    inventoried_files: list[Path],
    report_entries: dict[Path, str],
) -> list[Path]:
    wcx_paths = {
        path
        for path, classification in report_entries.items()
        if classification == "wcx"
    }
    return [
        path
        for path in inventoried_files
        if normalize_absolute_path(path) in wcx_paths
    ]


def load_movies(database_file: Path) -> list[Movie]:
    if not database_file.is_file():
        raise FileNotFoundError(
            f"Database file does not exist: {database_file}"
        )

    database_uri = f"{database_file.resolve().as_uri()}?mode=ro"

    with sqlite3.connect(database_uri, uri=True) as connection:
        connection.row_factory = sqlite3.Row

        movie_rows = connection.execute(
            """
            SELECT
                id,
                name,
                duration_seconds
            FROM movie
            WHERE name IS NOT NULL
              AND TRIM(name) <> ''
            ORDER BY name, id
            """
        ).fetchall()

        alias_rows = connection.execute(
            """
            SELECT
                movie_id,
                alias,
                normalized_alias,
                source
            FROM movie_name_alias
            WHERE alias IS NOT NULL
              AND TRIM(alias) <> ''
            ORDER BY movie_id, alias
            """
        ).fetchall()

        history_rows = connection.execute(
            """
            SELECT
                movie_id,
                duration_seconds,
                archived_at
            FROM movie_history
            WHERE duration_seconds IS NOT NULL
              AND duration_seconds > 0
            ORDER BY movie_id, archived_at
            """
        ).fetchall()

    aliases_by_movie: dict[str, list[MatchName]] = {}

    for row in alias_rows:
        movie_id = str(row["movie_id"])
        alias = str(row["alias"])

        normalized_alias = (
            str(row["normalized_alias"]).strip()
            if row["normalized_alias"]
            else normalize_text(alias)
        )

        if not normalized_alias:
            continue

        aliases_by_movie.setdefault(
            movie_id,
            [],
        ).append(
            MatchName(
                display_name=alias,
                normalized_name=normalized_alias,
                normalized_persons=split_person_names(alias),
                source=str(row["source"] or "alias"),
            )
        )

    historical_durations_by_movie: dict[
        str,
        list[DurationVersion],
    ] = {}

    for row in history_rows:
        duration = normalize_duration(
            row["duration_seconds"]
        )

        if duration is None:
            continue

        movie_id = str(row["movie_id"])

        historical_durations_by_movie.setdefault(
            movie_id,
            [],
        ).append(
            DurationVersion(
                duration_seconds=duration,
                source="historical",
                archived_at=(
                    str(row["archived_at"])
                    if row["archived_at"] is not None
                    else None
                ),
            )
        )

    movies = []

    for row in movie_rows:
        movie_id = str(row["id"])
        current_name = str(row["name"])

        current_match_name = MatchName(
            display_name=current_name,
            normalized_name=normalize_text(current_name),
            normalized_persons=split_person_names(current_name),
            source="current",
        )

        match_names = [current_match_name]

        seen_normalized_names = {
            current_match_name.normalized_name
        }

        for alias in aliases_by_movie.get(movie_id, []):
            if alias.normalized_name in seen_normalized_names:
                continue

            seen_normalized_names.add(
                alias.normalized_name
            )

            match_names.append(alias)

        duration_versions: list[DurationVersion] = []
        seen_durations: set[int] = set()

        current_duration = normalize_duration(
            row["duration_seconds"]
        )

        if current_duration is not None:
            duration_versions.append(
                DurationVersion(
                    duration_seconds=current_duration,
                    source="current",
                )
            )

            seen_durations.add(current_duration)

        for historical in historical_durations_by_movie.get(
            movie_id,
            [],
        ):
            if historical.duration_seconds in seen_durations:
                continue

            seen_durations.add(
                historical.duration_seconds
            )

            duration_versions.append(historical)

        movies.append(
            Movie(
                movie_id=movie_id,
                name=current_name,
                match_names=tuple(match_names),
                duration_versions=tuple(duration_versions),
            )
        )

    return movies


def should_ignore_file(path: Path) -> bool:
    """
    Ignore common operating-system metadata and resource-fork files.

    macOS commonly creates files such as:

        ._Video.mp4
        .DS_Store

    AppleDouble files may have a valid video extension but are not videos.
    """
    name = path.name

    if name.startswith("._"):
        return True

    if name in {
        ".DS_Store",
        "Thumbs.db",
        "desktop.ini",
    }:
        return True

    return False


def find_video_files(
    directory: Path,
    recursive: bool,
    extensions: set[str],
) -> list[Path]:
    if not directory.is_dir():
        raise NotADirectoryError(
            f"Input directory does not exist: {directory}"
        )

    iterator = (
        directory.rglob("*")
        if recursive
        else directory.glob("*")
    )

    files = []

    for path in iterator:
        if not path.is_file():
            continue

        if should_ignore_file(path):
            continue

        if path.suffix.casefold() not in extensions:
            continue

        files.append(path)

    return sorted(
        files,
        key=lambda path: str(path).casefold(),
    )


def read_file_duration(file_path: Path) -> int | None:
    result = subprocess.run(
        [
            "ffprobe",
            "-v",
            "error",
            "-show_entries",
            "format=duration",
            "-of",
            "default=noprint_wrappers=1:nokey=1",
            str(file_path),
        ],
        capture_output=True,
        text=True,
        check=False,
    )

    if result.returncode != 0:
        return None

    output = result.stdout.strip()

    if not output:
        return None

    try:
        duration = round(float(output))
    except ValueError:
        return None

    return duration if duration > 0 else None


def calculate_duration_score(
    difference_seconds: int,
    reference_duration: int,
) -> int:
    relative_difference = (
        difference_seconds / reference_duration
        if reference_duration > 0
        else 1.0
    )

    if difference_seconds <= 15:
        return 50

    if difference_seconds <= 60:
        return 40

    if difference_seconds <= 180:
        return 25

    if relative_difference <= 0.05:
        return 20

    if difference_seconds <= 600:
        return 10

    return 0


def classify_poor_duration(
    file_duration: int,
    reference_duration: int,
) -> str:
    if file_duration < reference_duration:
        return "shorter_than_known_version"

    if file_duration > reference_duration:
        return "longer_than_known_version"

    return "duration_mismatch"


def match_duration(
    file_duration: int | None,
    movie: Movie,
) -> DurationMatch:
    if file_duration is None:
        return DurationMatch(
            score=0,
            classification="file_duration_unknown",
            matched_duration=None,
            difference_seconds=None,
            source=None,
            archived_at=None,
        )

    if not movie.duration_versions:
        return DurationMatch(
            score=0,
            classification="database_duration_unknown",
            matched_duration=None,
            difference_seconds=None,
            source=None,
            archived_at=None,
        )

    scored_versions = []

    for version in movie.duration_versions:
        difference = abs(
            file_duration - version.duration_seconds
        )

        score = calculate_duration_score(
            difference_seconds=difference,
            reference_duration=version.duration_seconds,
        )

        scored_versions.append(
            (
                score,
                -difference,
                version.source == "current",
                version,
                difference,
            )
        )

    (
        score,
        _negative_difference,
        _prefer_current,
        best_version,
        difference,
    ) = max(scored_versions)

    if score > 0:
        classification = (
            "current_version_match"
            if best_version.source == "current"
            else "historical_version_match"
        )
    else:
        classification = classify_poor_duration(
            file_duration=file_duration,
            reference_duration=best_version.duration_seconds,
        )

    return DurationMatch(
        score=score,
        classification=classification,
        matched_duration=best_version.duration_seconds,
        difference_seconds=difference,
        source=best_version.source,
        archived_at=best_version.archived_at,
    )


def score_match_name(
    normalized_filename: str,
    movie: Movie,
    match_name: MatchName,
) -> MatchCandidate | None:
    full_name_occurrences = normalized_filename.count(
        match_name.normalized_name
    )

    matched_persons = [
        person
        for person in match_name.normalized_persons
        if person in normalized_filename
    ]

    all_persons_matched = (
        bool(match_name.normalized_persons)
        and len(matched_persons)
        == len(match_name.normalized_persons)
    )

    if full_name_occurrences == 0 and not all_persons_matched:
        return None

    name_score = 0
    reasons = []

    if full_name_occurrences:
        name_score += 80

        if match_name.source == "current":
            reasons.append("exact current-name match")
        else:
            reasons.append(
                f"exact alias match: {match_name.display_name}"
            )

        if full_name_occurrences > 1:
            repetition_bonus = min(
                15,
                (full_name_occurrences - 1) * 5,
            )

            name_score += repetition_bonus

            reasons.append(
                f"matched name occurs "
                f"{full_name_occurrences} times"
            )

    if all_persons_matched:
        name_score += 20
        reasons.append("all persons matched")

        if len(match_name.normalized_persons) > 1:
            name_score += 10
            reasons.append("multi-person name matched")

    specificity_bonus = min(
        10,
        len(match_name.normalized_name) // 5,
    )

    name_score += specificity_bonus

    reasons.append(
        f"specificity bonus {specificity_bonus}"
    )

    empty_duration_match = DurationMatch(
        score=0,
        classification="not_evaluated",
        matched_duration=None,
        difference_seconds=None,
        source=None,
        archived_at=None,
    )

    return MatchCandidate(
        movie=movie,
        matched_name=match_name,
        name_score=name_score,
        duration_score=0,
        total_score=name_score,
        name_reason=", ".join(reasons),
        duration_reason="duration not evaluated",
        full_name_occurrences=full_name_occurrences,
        all_persons_matched=all_persons_matched,
        duration_match=empty_duration_match,
    )


def best_name_candidate_for_movie(
    normalized_filename: str,
    movie: Movie,
) -> MatchCandidate | None:
    candidates = []

    for match_name in movie.match_names:
        candidate = score_match_name(
            normalized_filename=normalized_filename,
            movie=movie,
            match_name=match_name,
        )

        if candidate is not None:
            candidates.append(candidate)

    if not candidates:
        return None

    return max(
        candidates,
        key=lambda candidate: (
            candidate.name_score,
            candidate.matched_name.source == "current",
            len(candidate.matched_name.normalized_persons),
            len(candidate.matched_name.normalized_name),
        ),
    )


def describe_duration_match(
    duration_match: DurationMatch,
) -> str:
    classification = duration_match.classification

    if classification == "file_duration_unknown":
        return "file duration unavailable"

    if classification == "database_duration_unknown":
        return "database duration unavailable"

    if duration_match.matched_duration is None:
        return classification

    difference = duration_match.difference_seconds or 0

    if classification == "current_version_match":
        return (
            f"current duration match, difference {difference}s"
        )

    if classification == "historical_version_match":
        archived = (
            f", archived {duration_match.archived_at}"
            if duration_match.archived_at
            else ""
        )

        return (
            f"historical duration match, "
            f"difference {difference}s{archived}"
        )

    return (
        f"{classification}, difference {difference}s"
    )


def apply_duration_score(
    candidate: MatchCandidate,
    file_duration: int | None,
) -> MatchCandidate:
    duration_match = match_duration(
        file_duration=file_duration,
        movie=candidate.movie,
    )

    return replace(
        candidate,
        duration_score=duration_match.score,
        total_score=(
            candidate.name_score
            + duration_match.score
        ),
        duration_reason=describe_duration_match(
            duration_match
        ),
        duration_match=duration_match,
    )


def find_candidates(
    filename: str,
    movies: list[Movie],
    file_duration: int | None,
) -> list[MatchCandidate]:
    filename_stem = Path(filename).stem
    normalized_filename = normalize_text(filename_stem)

    candidates = []

    for movie in movies:
        candidate = best_name_candidate_for_movie(
            normalized_filename=normalized_filename,
            movie=movie,
        )

        if candidate is None:
            continue

        candidates.append(
            apply_duration_score(
                candidate=candidate,
                file_duration=file_duration,
            )
        )

    return sorted(
        candidates,
        key=lambda candidate: (
            candidate.total_score,
            candidate.duration_score,
            candidate.name_score,
            len(candidate.matched_name.normalized_persons),
            len(candidate.matched_name.normalized_name),
        ),
        reverse=True,
    )


def get_detected_movies(
    candidates: list[MatchCandidate],
) -> dict[str, str]:
    detected: dict[str, str] = {}

    for candidate in candidates:
        if candidate.full_name_occurrences == 0:
            continue

        detected[candidate.movie.movie_id] = (
            candidate.movie.name
        )

    return detected


def candidate_covers_detected_movies(
    candidate: MatchCandidate,
    candidates: list[MatchCandidate],
    detected_movie_ids: set[str],
) -> bool:
    candidate_persons = set(
        candidate.matched_name.normalized_persons
    )

    if len(candidate_persons) <= 1:
        return False

    detected_names: set[str] = set()

    for other in candidates:
        if other.movie.movie_id not in detected_movie_ids:
            continue

        if other.full_name_occurrences == 0:
            continue

        if len(other.matched_name.normalized_persons) != 1:
            continue

        detected_names.add(
            other.matched_name.normalized_persons[0]
        )

    return (
        bool(detected_names)
        and detected_names.issubset(candidate_persons)
    )


def calculate_total_margin(
    candidates: list[MatchCandidate],
) -> int:
    if not candidates:
        return 0

    if len(candidates) == 1:
        return candidates[0].total_score

    return (
        candidates[0].total_score
        - candidates[1].total_score
    )


def resolve_with_duration(
    candidates: list[MatchCandidate],
) -> MatchCandidate | None:
    """
    Resolve several exact name candidates using duration.

    Exactly one candidate must have credible duration support, or the best
    duration-supported candidate must have a clear duration-score margin.
    """
    credible = [
        candidate
        for candidate in candidates
        if candidate.duration_score
        >= MIN_CREDIBLE_DURATION_SCORE
    ]

    if not credible:
        return None

    credible = sorted(
        credible,
        key=lambda candidate: (
            candidate.duration_score,
            candidate.total_score,
            candidate.name_score,
        ),
        reverse=True,
    )

    if len(credible) == 1:
        return credible[0]

    duration_margin = (
        credible[0].duration_score
        - credible[1].duration_score
    )

    if duration_margin >= MIN_DURATION_SCORE_MARGIN:
        return credible[0]

    return None


def classify_candidates(
    candidates: list[MatchCandidate],
) -> MatchResult:
    if not candidates:
        return MatchResult(
            status="unmatched",
            best=None,
            margin=0,
            reason="no exact database name or alias found",
            detected_movies=(),
        )

    best = candidates[0]
    margin = calculate_total_margin(candidates)

    detected = get_detected_movies(candidates)
    detected_movie_ids = set(detected)

    if len(detected_movie_ids) > 1:
        covering_candidates = [
            candidate
            for candidate in candidates
            if candidate_covers_detected_movies(
                candidate=candidate,
                candidates=candidates,
                detected_movie_ids=detected_movie_ids,
            )
        ]

        if covering_candidates:
            covering_candidates.sort(
                key=lambda candidate: (
                    candidate.total_score,
                    candidate.duration_score,
                    candidate.name_score,
                ),
                reverse=True,
            )

            covering_best = covering_candidates[0]

            other_scores = [
                candidate.total_score
                for candidate in candidates
                if candidate.movie.movie_id
                != covering_best.movie.movie_id
            ]

            covering_margin = (
                covering_best.total_score
                - max(other_scores, default=0)
            )

            if (
                covering_best.name_score >= MIN_NAME_SCORE
                and covering_margin >= MIN_NAME_MARGIN
            ):
                return MatchResult(
                    status="matched",
                    best=covering_best,
                    margin=covering_margin,
                    reason=(
                        "multi-person database entry covers all "
                        "detected movie names"
                    ),
                    detected_movies=tuple(
                        sorted(
                            detected.values(),
                            key=str.casefold,
                        )
                    ),
                )

        duration_winner = resolve_with_duration(
            candidates
        )

        if duration_winner is not None:
            other_scores = [
                candidate.total_score
                for candidate in candidates
                if candidate.movie.movie_id
                != duration_winner.movie.movie_id
            ]

            winner_margin = (
                duration_winner.total_score
                - max(other_scores, default=0)
            )

            return MatchResult(
                status="matched",
                best=duration_winner,
                margin=winner_margin,
                reason=(
                    "multiple names matched, but duration "
                    "clearly supports one candidate"
                ),
                detected_movies=tuple(
                    sorted(
                        detected.values(),
                        key=str.casefold,
                    )
                ),
            )

        return MatchResult(
            status="ambiguous",
            best=best,
            margin=margin,
            reason=(
                "multiple distinct database movies matched; "
                "duration does not clearly resolve them"
            ),
            detected_movies=tuple(
                sorted(
                    detected.values(),
                    key=str.casefold,
                )
            ),
        )

    if (
        best.name_score >= MIN_NAME_SCORE
        and (
            len(candidates) == 1
            or margin >= MIN_NAME_MARGIN
        )
    ):
        return MatchResult(
            status="matched",
            best=best,
            margin=margin,
            reason=(
                "best candidate exceeds name and margin thresholds"
            ),
            detected_movies=tuple(
                sorted(
                    detected.values(),
                    key=str.casefold,
                )
            ),
        )

    return MatchResult(
        status="ambiguous",
        best=best,
        margin=margin,
        reason="score or margin is insufficient",
        detected_movies=tuple(
            sorted(
                detected.values(),
                key=str.casefold,
            )
        ),
    )


def format_duration(seconds: int | None) -> str:
    if seconds is None:
        return "unknown"

    hours, remainder = divmod(seconds, 3600)
    minutes, seconds = divmod(remainder, 60)

    if hours:
        return f"{hours}:{minutes:02d}:{seconds:02d}"

    return f"{minutes}:{seconds:02d}"


def format_match_source(
    candidate: MatchCandidate,
) -> str:
    if candidate.matched_name.source == "current":
        return "current name"

    return (
        f"{candidate.matched_name.source} alias "
        f"{candidate.matched_name.display_name!r}"
    )


def print_result(
    path: Path,
    file_duration: int | None,
    result: MatchResult,
    debug: bool,
    candidates: list[MatchCandidate],
) -> None:
    if result.best is None:
        name = "<no match>"
        score = 0
        version = "-"
    else:
        name = result.best.movie.name
        score = result.best.total_score
        version = result.best.duration_match.classification

    print(
        f"{path.name:<55} "
        f"{name:<35} "
        f"{score:>5} "
        f"{result.status:<10} "
        f"{version}"
    )

    if not debug:
        return

    print(
        f"    file duration: "
        f"{format_duration(file_duration)}"
    )

    if not candidates:
        print("    no candidates")
        print(f"    reason: {result.reason}")
        return

    if result.detected_movies:
        print(
            "    detected movies: "
            + ", ".join(result.detected_movies)
        )

    for position, candidate in enumerate(
        candidates[:5],
        start=1,
    ):
        matched_duration = (
            candidate.duration_match.matched_duration
        )

        print(
            f"    {position}. "
            f"{candidate.movie.name} "
            f"[name={candidate.name_score}, "
            f"duration={candidate.duration_score}, "
            f"total={candidate.total_score}]"
        )

        print(
            f"       via {format_match_source(candidate)}"
        )

        print(
            f"       name: {candidate.name_reason}"
        )

        print(
            f"       duration: "
            f"{candidate.duration_reason}; "
            f"reference="
            f"{format_duration(matched_duration)}"
        )

    print(f"    margin: {result.margin}")
    print(f"    reason: {result.reason}")


def main() -> None:
    args = parse_arguments()

    if args.limit is not None and args.limit <= 0:
        raise ValueError(
            "--limit must be greater than zero."
        )

    if args.detection_report is None:
        if shutil.which("ffprobe") is None:
            raise FileNotFoundError(
                "ffprobe was not found in PATH."
            )

        extensions = parse_video_extensions(
            args.ending
        )

        movies = load_movies(args.database)

        video_files = find_video_files(
            directory=args.directory,
            recursive=args.recursive,
            extensions=extensions,
        )

        if args.limit is not None:
            video_files = video_files[:args.limit]
    else:
        extensions = parse_video_extensions(
            args.ending
        )
        inventoried_files = find_video_files(
            directory=args.directory,
            recursive=args.recursive,
            extensions=extensions,
        )

        if args.limit is not None:
            inventoried_files = inventoried_files[:args.limit]

        report_entries = load_detection_report(
            args.detection_report
        )
        video_files = select_wcx_files(
            inventoried_files,
            report_entries,
        )
        report_wcx_files = sum(
            classification == "wcx"
            for classification in report_entries.values()
        )

        print(
            f"detection_report: "
            f"{normalize_absolute_path(args.detection_report)}"
        )
        print(f"inventoried_files: {len(inventoried_files)}")
        print(f"report_wcx_files: {report_wcx_files}")
        print(f"selected_files: {len(video_files)}")
        print()

        if not video_files:
            print(
                "No inventoried files were classified as wcx; "
                "nothing to match."
            )
            return

        if shutil.which("ffprobe") is None:
            raise FileNotFoundError(
                "ffprobe was not found in PATH."
            )

        movies = load_movies(args.database)

    print(
        f"{'Filename':<55} "
        f"{'Likely database name':<35} "
        f"{'Score':>5} "
        f"{'Status':<10} "
        f"Version"
    )

    print(
        f"{'-' * 55} "
        f"{'-' * 35} "
        f"{'-' * 5} "
        f"{'-' * 10} "
        f"{'-' * 26}"
    )

    matched = 0
    ambiguous = 0
    unmatched = 0
    ffprobe_failures = 0
    historical_matches = 0

    for video_file in video_files:
        file_duration = read_file_duration(
            video_file
        )

        if file_duration is None:
            ffprobe_failures += 1

        candidates = find_candidates(
            filename=video_file.name,
            movies=movies,
            file_duration=file_duration,
        )

        result = classify_candidates(candidates)

        if result.status == "matched":
            matched += 1
        elif result.status == "ambiguous":
            ambiguous += 1
        else:
            unmatched += 1

        if (
            result.best is not None
            and result.best.duration_match.classification
            == "historical_version_match"
        ):
            historical_matches += 1

        print_result(
            path=video_file,
            file_duration=file_duration,
            result=result,
            debug=args.debug,
            candidates=candidates,
        )

    print()
    print("Summary")
    print("-------")
    print(f"Files analyzed:      {len(video_files)}")
    print(f"Matched:             {matched}")
    print(f"Ambiguous:           {ambiguous}")
    print(f"Unmatched:           {unmatched}")
    print(f"Historical versions: {historical_matches}")
    print(f"ffprobe failures:    {ffprobe_failures}")


if __name__ == "__main__":
    try:
        main()
    except (
        FileNotFoundError,
        NotADirectoryError,
        ValueError,
        sqlite3.Error,
    ) as error:
        print(f"Error: {error}", file=sys.stderr)
        sys.exit(1)