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"""Frozen, local-only IMDb directory expansion.

The three task titles remain defined in :mod:`backend.db` so tasks 901–905
retain byte-for-byte identity.  This module supplies the remainder of the
fixed benchmark directory: 27 more movies, four series, two episodes and
nineteen additional people (Danny Elfman remains the original twentieth
person).  All images resolve to the already-frozen local asset closure.
"""

from __future__ import annotations

import json
from pathlib import Path
from typing import Any


# id, title, year, type, certificate, runtime, aggregate, exact seed votes,
# metascore, popularity, plot, director, stars, genres, parent, season, episode
CATALOG_ROWS = (
    ("tt0111161", "The Shawshank Redemption", "1994", "movie", "R", "2h 22m", 9.3, 3_100_000, "82", "47", "A banker sentenced to life in Shawshank forms an enduring friendship and finds a path to hope.", "Frank Darabont", "Tim Robbins, Morgan Freeman", ["Drama"], None, None, None),
    ("tt0068646", "The Godfather", "1972", "movie", "R", "2h 55m", 9.2, 2_200_000, "100", "58", "The aging patriarch of an organized crime dynasty transfers control to his reluctant son.", "Francis Ford Coppola", "Marlon Brando, Al Pacino", ["Crime", "Drama"], None, None, None),
    ("tt0137523", "Fight Club", "1999", "movie", "R", "2h 19m", 8.8, 2_500_000, "67", "76", "An insomniac office worker and a soap maker form an underground fight club.", "David Fincher", "Brad Pitt, Edward Norton", ["Drama"], None, None, None),
    ("tt0109830", "Forrest Gump", "1994", "movie", "PG-13", "2h 22m", 8.8, 2_400_000, "82", "64", "The life of a kindhearted Alabama man intersects with decades of American history.", "Robert Zemeckis", "Tom Hanks, Robin Wright", ["Drama", "Romance"], None, None, None),
    ("tt1375666", "Inception", "2010", "movie", "PG-13", "2h 28m", 8.8, 2_800_000, "74", "19", "A thief who steals corporate secrets through shared dreams receives an inverse assignment.", "Christopher Nolan", "Leonardo DiCaprio, Joseph Gordon-Levitt", ["Action", "Sci-Fi", "Thriller"], None, None, None),
    ("tt0133093", "The Matrix", "1999", "movie", "R", "2h 16m", 8.7, 2_200_000, "73", "70", "A hacker discovers that the world he knows is a simulated reality.", "Lana Wachowski, Lilly Wachowski", "Keanu Reeves, Laurence Fishburne", ["Action", "Sci-Fi"], None, None, None),
    ("tt0099685", "Goodfellas", "1990", "movie", "R", "2h 25m", 8.7, 1_300_000, "92", "133", "A young man grows up in the mob and works to advance through its ranks.", "Martin Scorsese", "Robert De Niro, Ray Liotta", ["Biography", "Crime", "Drama"], None, None, None),
    ("tt0114369", "Se7en", "1995", "movie", "R", "2h 7m", 8.6, 1_900_000, "65", "35", "Two detectives hunt a serial killer who stages crimes around the seven deadly sins.", "David Fincher", "Morgan Freeman, Brad Pitt", ["Crime", "Drama", "Mystery"], None, None, None),
    ("tt0102926", "The Silence of the Lambs", "1991", "movie", "R", "1h 58m", 8.6, 1_600_000, "86", "122", "A trainee seeks the insight of an imprisoned killer to catch another murderer.", "Jonathan Demme", "Jodie Foster, Anthony Hopkins", ["Crime", "Drama", "Thriller"], None, None, None),
    ("tt0120815", "Saving Private Ryan", "1998", "movie", "R", "2h 49m", 8.6, 1_600_000, "91", "83", "A wartime squad searches behind enemy lines for a paratrooper whose brothers have died.", "Steven Spielberg", "Tom Hanks, Matt Damon", ["Drama", "War"], None, None, None),
    ("tt0245429", "Spirited Away", "2001", "movie", "PG", "2h 4m", 8.6, 900_000, "96", "161", "A girl enters a world ruled by spirits and must save her parents.", "Hayao Miyazaki", "Daveigh Chase, Suzanne Pleshette", ["Animation", "Adventure", "Family"], None, None, None),
    ("tt0120689", "The Green Mile", "1999", "movie", "R", "3h 9m", 8.6, 1_500_000, "61", "116", "A death-row guard encounters a prisoner with an extraordinary gift.", "Frank Darabont", "Tom Hanks, Michael Clarke Duncan", ["Crime", "Drama", "Fantasy"], None, None, None),
    ("tt6751668", "Parasite", "2019", "movie", "R", "2h 12m", 8.5, 1_100_000, "97", "102", "A struggling family gradually enters the household of a wealthy family.", "Bong Joon Ho", "Song Kang-ho, Lee Sun-kyun", ["Drama", "Thriller"], None, None, None),
    ("tt0172495", "Gladiator", "2000", "movie", "R", "2h 35m", 8.5, 1_800_000, "67", "52", "A betrayed Roman general returns as a gladiator to seek justice.", "Ridley Scott", "Russell Crowe, Joaquin Phoenix", ["Action", "Adventure", "Drama"], None, None, None),
    ("tt0482571", "The Prestige", "2006", "movie", "PG-13", "2h 10m", 8.5, 1_500_000, "66", "91", "Two rival magicians pursue the ultimate illusion at devastating cost.", "Christopher Nolan", "Christian Bale, Hugh Jackman", ["Drama", "Mystery", "Sci-Fi"], None, None, None),
    ("tt2582802", "Whiplash", "2014", "movie", "R", "1h 46m", 8.5, 1_100_000, "89", "88", "An ambitious drummer is pushed to his limits by a demanding instructor.", "Damien Chazelle", "Miles Teller, J.K. Simmons", ["Drama", "Music"], None, None, None),
    ("tt0407887", "The Departed", "2006", "movie", "R", "2h 31m", 8.5, 1_500_000, "85", "97", "An undercover officer and a police informant try to identify one another.", "Martin Scorsese", "Leonardo DiCaprio, Matt Damon", ["Crime", "Drama", "Thriller"], None, None, None),
    ("tt0110357", "The Lion King", "1994", "movie", "G", "1h 28m", 8.5, 1_200_000, "88", "148", "A young lion prince flees his kingdom before learning to accept his role.", "Roger Allers, Rob Minkoff", "Matthew Broderick, Jeremy Irons", ["Animation", "Adventure", "Drama"], None, None, None),
    ("tt1853728", "Django Unchained", "2012", "movie", "R", "2h 45m", 8.5, 1_800_000, "81", "104", "A freed man and a bounty hunter journey to rescue his wife.", "Quentin Tarantino", "Jamie Foxx, Christoph Waltz", ["Drama", "Western"], None, None, None),
    ("tt0114814", "The Usual Suspects", "1995", "movie", "R", "1h 46m", 8.5, 1_200_000, "77", "152", "A survivor recounts the events that brought five criminals together.", "Bryan Singer", "Kevin Spacey, Gabriel Byrne", ["Crime", "Drama", "Mystery"], None, None, None),
    ("tt0253474", "The Pianist", "2002", "movie", "R", "2h 30m", 8.5, 1_000_000, "85", "172", "A Polish musician struggles to survive the destruction of Warsaw.", "Roman Polanski", "Adrien Brody, Thomas Kretschmann", ["Biography", "Drama", "Music"], None, None, None),
    ("tt0088763", "Back to the Future", "1985", "movie", "PG", "1h 56m", 8.5, 1_400_000, "87", "99", "A teenager is sent thirty years into the past in a time-traveling car.", "Robert Zemeckis", "Michael J. Fox, Christopher Lloyd", ["Adventure", "Comedy", "Sci-Fi"], None, None, None),
    ("tt0103064", "Terminator 2: Judgment Day", "1991", "movie", "R", "2h 17m", 8.6, 1_200_000, "75", "118", "A reprogrammed cyborg protects a child from a more advanced machine.", "James Cameron", "Arnold Schwarzenegger, Linda Hamilton", ["Action", "Sci-Fi"], None, None, None),
    ("tt0078748", "Alien", "1979", "movie", "R", "1h 57m", 8.5, 1_000_000, "89", "140", "A space crew encounters a deadly life-form aboard its ship.", "Ridley Scott", "Sigourney Weaver, Tom Skerritt", ["Horror", "Sci-Fi"], None, None, None),
    ("tt15398776", "Oppenheimer", "2023", "movie", "R", "3h", 8.3, 900_000, "90", "26", "The physicist who led the Manhattan Project confronts its consequences.", "Christopher Nolan", "Cillian Murphy, Florence Pugh", ["Biography", "Drama", "History"], None, None, None),
    ("tt15239678", "Dune: Part Two", "2024", "movie", "PG-13", "2h 46m", 8.5, 700_000, "79", "23", "Paul Atreides unites with the Fremen while seeking justice for his family.", "Denis Villeneuve", "Timothée Chalamet, Zendaya", ["Action", "Adventure", "Drama"], None, None, None),
    ("tt6710474", "Everything Everywhere All at Once", "2022", "movie", "R", "2h 19m", 7.8, 600_000, "81", "205", "A laundromat owner is swept into a multiverse-spanning struggle.", "Daniel Kwan, Daniel Scheinert", "Michelle Yeoh, Stephanie Hsu", ["Action", "Adventure", "Comedy"], None, None, None),
    ("tt0903747", "Breaking Bad", "2008–2013", "series", "TV-MA", "45m", 9.5, 2_400_000, "87", "8", "A chemistry teacher enters the drug trade after a terminal diagnosis.", "Vince Gilligan", "Bryan Cranston, Aaron Paul", ["Crime", "Drama", "Thriller"], None, None, None),
    ("tt7366338", "Chernobyl", "2019", "series", "TV-MA", "1h", 9.3, 950_000, "82", "29", "Responders confront the 1986 nuclear disaster and its aftermath.", "Craig Mazin", "Jessie Buckley, Jared Harris", ["Drama", "History", "Thriller"], None, None, None),
    ("tt0944947", "Game of Thrones", "2011–2019", "series", "TV-MA", "57m", 9.2, 2_400_000, "86", "12", "Noble families struggle for control of the Iron Throne.", "David Benioff, D.B. Weiss", "Emilia Clarke, Peter Dinklage", ["Action", "Adventure", "Drama"], None, None, None),
    ("tt4574334", "Stranger Things", "2016–2025", "series", "TV-14", "51m", 8.6, 1_500_000, "74", "5", "Friends uncover supernatural forces and secret experiments in their town.", "The Duffer Brothers", "Millie Bobby Brown, Finn Wolfhard", ["Drama", "Fantasy", "Horror"], None, None, None),
    ("tt2301451", "Ozymandias", "2013", "episode", "TV-14", "48m", 10.0, 260_000, "100", "41", "Walt faces the catastrophic consequences of the conflict in the desert.", "Rian Johnson", "Bryan Cranston, Aaron Paul", ["Crime", "Drama", "Thriller"], "tt0903747", 5, 14),
    ("tt4283094", "The Winds of Winter", "2016", "episode", "TV-MA", "1h 8m", 9.9, 190_000, "98", "63", "The great houses confront the aftermath of Cersei's trial.", "Miguel Sapochnik", "Emilia Clarke, Lena Headey", ["Action", "Adventure", "Drama"], "tt0944947", 6, 10),
)


# id, name, professions, known for, search aliases
CATALOG_PEOPLE = (
    ("nm0634240", "Christopher Nolan", "Writer, Producer, Director", "Oppenheimer (2023)", ["inception", "interstellar", "dark knight"]),
    ("nm0000288", "Christian Bale", "Actor, Producer", "The Dark Knight (2008)", ["dark knight", "prestige"]),
    ("nm0005132", "Heath Ledger", "Actor, Director", "The Dark Knight (2008)", ["joker", "dark knight"]),
    ("nm0000190", "Matthew McConaughey", "Actor, Producer", "Interstellar (2014)", ["interstellar"]),
    ("nm0004266", "Anne Hathaway", "Actress, Producer", "Les Misérables (2012)", ["interstellar"]),
    ("nm0000354", "Matt Damon", "Actor, Producer, Writer", "Good Will Hunting (1997)", ["saving private ryan", "departed", "matt damon"]),
    ("nm0000233", "Quentin Tarantino", "Writer, Producer, Director", "Pulp Fiction (1994)", ["pulp fiction", "django"]),
    ("nm0000237", "John Travolta", "Actor, Producer", "Pulp Fiction (1994)", ["pulp fiction"]),
    ("nm0000235", "Uma Thurman", "Actress, Producer", "Kill Bill: Vol. 1 (2003)", ["pulp fiction"]),
    ("nm0000168", "Samuel L. Jackson", "Actor, Producer", "Pulp Fiction (1994)", ["pulp fiction"]),
    ("nm0000151", "Morgan Freeman", "Actor, Producer", "The Shawshank Redemption (1994)", ["shawshank", "se7en"]),
    ("nm0000209", "Tim Robbins", "Actor, Director", "The Shawshank Redemption (1994)", ["shawshank"]),
    ("nm0000199", "Al Pacino", "Actor, Director", "The Godfather (1972)", ["godfather"]),
    ("nm0186505", "Bryan Cranston", "Actor, Producer", "Breaking Bad (2008–2013)", ["breaking bad", "ozymandias"]),
    ("nm3592338", "Emilia Clarke", "Actress, Producer", "Game of Thrones (2011–2019)", ["game of thrones", "winds of winter"]),
    ("nm0614165", "Cillian Murphy", "Actor, Producer", "Oppenheimer (2023)", ["oppenheimer"]),
    ("nm0898288", "Denis Villeneuve", "Director, Producer, Writer", "Dune: Part Two (2024)", ["dune"]),
    ("nm3918035", "Zendaya", "Actress, Producer", "Dune: Part Two (2024)", ["dune"]),
    ("nm6073955", "Florence Pugh", "Actress, Producer", "Oppenheimer (2023)", ["oppenheimer", "dune"]),
)


PERSON_CREDITS = (
    ("tt0468569", "nm0634240", "Director", "Christopher Nolan"),
    ("tt0468569", "nm0000288", "Actor", "Bruce Wayne"),
    ("tt0468569", "nm0005132", "Actor", "Joker"),
    ("tt0816692", "nm0634240", "Director", "Christopher Nolan"),
    ("tt0816692", "nm0000190", "Actor", "Cooper"),
    ("tt0816692", "nm0004266", "Actor", "Brand"),
    ("tt0120815", "nm0000354", "Actor", "Private Ryan"),
    ("tt0407887", "nm0000354", "Actor", "Colin Sullivan"),
    ("tt0110912", "nm0000233", "Director", "Quentin Tarantino"),
    ("tt0110912", "nm0000237", "Actor", "Vincent Vega"),
    ("tt0110912", "nm0000235", "Actor", "Mia Wallace"),
    ("tt0110912", "nm0000168", "Actor", "Jules Winnfield"),
    ("tt0111161", "nm0000209", "Actor", "Andy Dufresne"),
    ("tt0111161", "nm0000151", "Actor", "Ellis Boyd Redding"),
    ("tt0068646", "nm0000199", "Actor", "Michael Corleone"),
    ("tt0114369", "nm0000151", "Actor", "Somerset"),
    ("tt0482571", "nm0634240", "Director", "Christopher Nolan"),
    ("tt0482571", "nm0000288", "Actor", "Alfred Borden"),
    ("tt15398776", "nm0634240", "Director", "Christopher Nolan"),
    ("tt15398776", "nm0614165", "Actor", "J. Robert Oppenheimer"),
    ("tt15398776", "nm6073955", "Actor", "Jean Tatlock"),
    ("tt15239678", "nm0898288", "Director", "Denis Villeneuve"),
    ("tt15239678", "nm3918035", "Actor", "Chani"),
    ("tt15239678", "nm6073955", "Actor", "Princess Irulan"),
    ("tt0903747", "nm0186505", "Actor", "Walter White"),
    ("tt2301451", "nm0186505", "Actor", "Walter White"),
    ("tt0944947", "nm3592338", "Actor", "Daenerys Targaryen"),
    ("tt4283094", "nm3592338", "Actor", "Daenerys Targaryen"),
)


CATALOG_PATH = Path(__file__).resolve().parents[1] / "catalog.json"


def complete_catalog_titles(
    existing_titles: tuple[dict[str, Any], ...],
) -> tuple[dict[str, Any], ...]:
    """Merge the immutable chart contracts into complete local title rows.

    The richer 36-title directory wins on overlap.  Catalog-only rows retain
    exactly the title/year/rank/rating/vote/credit/poster facts present in
    ``catalog.json`` and use explicit unavailable copy for fields that file
    does not claim.
    """

    payload = json.loads(CATALOG_PATH.read_text(encoding="utf-8"))
    top_by_id = {item["id"]: item for item in payload["top"]}
    popular_by_id = {item["id"]: item for item in payload["popular"]}
    merged: dict[str, dict[str, Any]] = {
        item["id"]: dict(item) for item in existing_titles
    }
    for row in [*payload["top"], *payload["popular"]]:
        title_id = row["id"]
        if title_id in merged:
            continue
        top = top_by_id.get(title_id)
        popular = popular_by_id.get(title_id)
        rating = row.get("rating")
        votes = row.get("votes")
        credit = str(row.get("credit") or "Credits unavailable")
        rank_copy = (
            f"Top rated movie #{top['rank']}"
            if top is not None
            else f"Popular movie #{popular['rank']}"
        )
        merged[title_id] = {
            "id": title_id,
            "title": row["title"],
            "year": str(row.get("year") or "Year unavailable"),
            "cert": "Certificate unavailable",
            "runtime": "Runtime unavailable",
            "imdb_rating": str(rating) if rating is not None else "Not rated",
            "votes": str(votes) if votes is not None else "No ratings",
            "metascore": "Unavailable",
            "popularity": str(popular["rank"] if popular is not None else top["rank"]),
            "watchlist_count": "Unavailable",
            "top_rank": rank_copy,
            "plot": (
                f"The local catalog records {row['title']} as a {row.get('year') or 'year-unavailable'} "
                "movie. A synopsis was not included in the preserved catalog."
            ),
            "director": "Director unavailable",
            "writers": "Writers unavailable",
            "stars": "Unavailable",
            "catalog_credit": credit,
            "genres": ["Catalog entry"],
            "poster": row["poster"],
            "card": row["poster"],
            "title_type": "movie",
            "series_id": None,
            "season_number": None,
            "episode_number": None,
            "asset_group": "catalog",
            "seed_vote_count": int(votes) if votes is not None else None,
            "seed_rating_avg": float(rating) if rating is not None else None,
        }
    return tuple(merged.values())


def extra_titles(assets_base: str) -> tuple[dict[str, Any], ...]:
    catalog_base = f"{assets_base.rsplit('/', 1)[0]}/2026-08-10"
    rows: list[dict[str, Any]] = []
    for index, row in enumerate(CATALOG_ROWS, start=1):
        (
            title_id, title, year, title_type, cert, runtime, rating,
            votes, metascore, popularity, plot, director, stars, genres,
            parent, season, episode,
        ) = row
        poster = f"{catalog_base}/catalog-posters/{title_id}.svg"
        rows.append(
            {
                "id": title_id,
                "title": title,
                "year": year,
                "cert": cert,
                "runtime": runtime,
                "imdb_rating": f"{rating:.1f}",
                "votes": str(votes),
                "metascore": metascore,
                "popularity": popularity,
                "watchlist_count": f"{max(votes // 2, 50_000):,}",
                "top_rank": (
                    f"Top rated {title_type} #{index + 3}"
                    if rating >= 8.5 else "Popular title"
                ),
                "plot": plot,
                "director": director,
                "writers": director,
                "stars": stars,
                "genres": genres,
                "poster": poster,
                "card": poster,
                "title_type": title_type,
                "series_id": parent,
                "season_number": season,
                "episode_number": episode,
                "seed_vote_count": votes,
                "seed_rating_avg": rating,
                "asset_group": (
                    "title-dark-knight" if index % 3 == 1
                    else "title-interstellar" if index % 3 == 2
                    else "title-pulp-fiction"
                ),
            }
        )
    return tuple(rows)


def extra_people(assets_base: str) -> tuple[tuple[Any, ...], ...]:
    catalog_base = f"{assets_base.rsplit('/', 1)[0]}/2026-08-10"
    return tuple(
        (*row[:4], f"{catalog_base}/catalog-people/{row[0]}.svg", row[4])
        for row in CATALOG_PEOPLE
    )