from lyra_worker.adapters.fakes import FakeMbBrowser, FakeSimilaritySource
from lyra_worker.discovery import DiscoveryConfig, run_discovery
from lyra_worker.similarity.base import SimilarArtist
from tests.conftest import insert_discovery_suggestion, insert_watched_artist


def _artist_rows(conn):
    with conn.cursor() as cur:
        cur.execute('SELECT "artistMbid", "artistName", score, "seedCount", sources, status::text '
                    'FROM "DiscoverySuggestion" WHERE kind = \'artist\' ORDER BY score DESC')
        return cur.fetchall()


def _insert_library_item(conn, artist, artist_mbid, album="Owned Album"):
    with conn.cursor() as cur:
        cur.execute(
            'INSERT INTO "LibraryItem" (id, artist, album, path, source, format, "qualityClass", '
            '"artistMbid", "importedAt") '
            "VALUES (gen_random_uuid()::text, %s, %s, %s, 'scan', 'FLAC', 2, %s, now())",
            (artist, album, f"/music/{artist}/{album}", artist_mbid),
        )
    conn.commit()


def test_seeds_discovery_from_owned_album_artists(conn):
    # An artist you own an album by but don't follow becomes a discovery seed.
    _insert_library_item(conn, "Owned Artist", "lib-seed")
    src = FakeSimilaritySource(similar={"lib-seed": [SimilarArtist("c1", "New Find", 0.9)]})
    result = run_discovery(conn, [src], FakeMbBrowser(), DiscoveryConfig())
    assert result.seeds == 1
    assert [r[0] for r in _artist_rows(conn)] == ["c1"]  # surfaced from the library seed
    with conn.cursor() as cur:  # library seed throttled via DiscoverySeed, not WatchedArtist
        cur.execute('SELECT "lastDiscoveredAt" IS NOT NULL FROM "DiscoverySeed" WHERE mbid = %s', ("lib-seed",))
        assert cur.fetchone()[0] is True


def test_library_seeding_can_be_disabled(conn):
    _insert_library_item(conn, "Owned Artist", "lib-seed")
    src = FakeSimilaritySource(similar={"lib-seed": [SimilarArtist("c1", "New Find", 0.9)]})
    result = run_discovery(conn, [src], FakeMbBrowser(), DiscoveryConfig(seed_from_library=False))
    assert result.seeds == 0 and _artist_rows(conn) == []  # library not seeded


def test_owned_artist_is_not_suggested_as_a_candidate(conn):
    # You own an album by "c1"; a followed seed finds c1 similar. c1 must not be suggested back.
    insert_watched_artist(conn, mbid="s1", name="Seed")
    _insert_library_item(conn, "Owned", "c1")
    src = FakeSimilaritySource(similar={
        "s1": [SimilarArtist("c1", "Owned", 0.9), SimilarArtist("c2", "New", 0.8)],
        "c1": [],  # the library seed itself finds nothing new here
    })
    run_discovery(conn, [src], FakeMbBrowser(), DiscoveryConfig())
    assert [r[0] for r in _artist_rows(conn)] == ["c2"]  # c1 excluded (already owned)


def test_aggregates_scores_across_seeds(conn):
    insert_watched_artist(conn, mbid="s1", name="Seed One")
    insert_watched_artist(conn, mbid="s2", name="Seed Two")
    src = FakeSimilaritySource(similar={
        "s1": [SimilarArtist("c1", "Shared", 0.6), SimilarArtist("c2", "Only1", 0.4)],
        "s2": [SimilarArtist("c1", "Shared", 0.5)],
    })
    result = run_discovery(conn, [src], FakeMbBrowser(), DiscoveryConfig())
    assert result.artists == 2
    rows = _artist_rows(conn)
    # c1 recommended by both seeds; each seed max-normalizes its top score to 1.0, so
    # c1 = 1.0 + 1.0 = 2.0, seedCount 2; c2 (0.4/0.6 within s1) by one seed
    assert rows[0][0] == "c1" and abs(rows[0][2] - 2.0) < 1e-6 and rows[0][3] == 2
    assert rows[0][4] == ["fake"] and rows[0][5] == "pending"
    assert rows[1][0] == "c2" and rows[1][3] == 1


def test_normalizes_across_sources_of_different_scales(conn):
    # ListenBrainz emits co-occurrence counts in the thousands; Last.fm emits 0–1 match
    # scores. Max-normalizing each source before summing keeps the small-scale source's
    # candidates from being drowned.
    insert_watched_artist(conn, mbid="s1", name="Seed")
    lb = FakeSimilaritySource(similar={"s1": [
        SimilarArtist("c_lb", "LB Only", 5000.0), SimilarArtist("c_shared", "Shared", 4000.0)]})
    lb.name = "listenbrainz"
    lfm = FakeSimilaritySource(similar={"s1": [
        SimilarArtist("c_lfm", "LFM Only", 0.9), SimilarArtist("c_shared", "Shared", 0.5)]})
    lfm.name = "lastfm"
    run_discovery(conn, [lb, lfm], FakeMbBrowser(), DiscoveryConfig())

    lb_only = _artist_score(conn, "c_lb")[0]    # 5000/5000 = 1.0
    lfm_only = _artist_score(conn, "c_lfm")[0]  # 0.9/0.9  = 1.0
    shared = _artist_score(conn, "c_shared")[0]  # 4000/5000 + 0.5/0.9 = 0.8 + 0.556
    assert abs(lb_only - lfm_only) < 1e-6         # small-scale source not drowned — ranks equal
    assert shared > lb_only                        # co-sourced candidate outranks a single-source one


def test_skips_already_followed_artists(conn):
    insert_watched_artist(conn, mbid="s1", name="Seed")
    insert_watched_artist(conn, mbid="c1", name="Already Followed")
    src = FakeSimilaritySource(similar={"s1": [SimilarArtist("c1", "X", 0.9),
                                                SimilarArtist("c2", "New", 0.8)]})
    run_discovery(conn, [src], FakeMbBrowser(), DiscoveryConfig())
    assert [r[0] for r in _artist_rows(conn)] == ["c2"]  # c1 filtered (followed)


def test_min_score_filters_weak_candidates(conn):
    insert_watched_artist(conn, mbid="s1", name="Seed")
    src = FakeSimilaritySource(similar={"s1": [SimilarArtist("c1", "Strong", 0.9),
                                                SimilarArtist("c2", "Weak", 0.1)]})
    run_discovery(conn, [src], FakeMbBrowser(), DiscoveryConfig(min_score=0.5))
    assert [r[0] for r in _artist_rows(conn)] == ["c1"]


def test_dismissed_suggestion_is_never_revived(conn):
    insert_watched_artist(conn, mbid="s1", name="Seed")
    insert_discovery_suggestion(conn, kind="artist", artist_mbid="c1", status="dismissed", score=0.0)
    src = FakeSimilaritySource(similar={"s1": [SimilarArtist("c1", "X", 0.9)]})
    run_discovery(conn, [src], FakeMbBrowser(), DiscoveryConfig())
    with conn.cursor() as cur:
        cur.execute('SELECT status::text, score FROM "DiscoverySuggestion" WHERE "artistMbid" = %s', ("c1",))
        assert cur.fetchone() == ("dismissed", 0.0)  # untouched — WHERE status='pending' blocked the update


def test_stamps_last_discovered_and_throttles(conn):
    insert_watched_artist(conn, mbid="s1", name="Seed")
    src = FakeSimilaritySource(similar={"s1": [SimilarArtist("c1", "X", 0.9)]})
    assert run_discovery(conn, [src], FakeMbBrowser(), DiscoveryConfig()).artists == 1
    with conn.cursor() as cur:
        cur.execute('SELECT "lastDiscoveredAt" IS NOT NULL FROM "WatchedArtist" WHERE mbid = %s', ("s1",))
        assert cur.fetchone()[0] is True
    # second run: seed not due (lastDiscoveredAt recent) -> no seeds -> nothing new
    assert run_discovery(conn, [src], FakeMbBrowser(), DiscoveryConfig()).artists == 0


def test_unhealthy_source_contributes_nothing(conn):
    insert_watched_artist(conn, mbid="s1", name="Seed")
    src = FakeSimilaritySource(similar={"s1": [SimilarArtist("c1", "X", 0.9)]}, healthy=False)
    assert run_discovery(conn, [src], FakeMbBrowser(), DiscoveryConfig()).artists == 0
    assert _artist_rows(conn) == []


def test_source_with_raising_health_is_skipped_not_fatal(conn):
    insert_watched_artist(conn, mbid="s1", name="Seed")

    class BoomSource:
        name = "boom"
        def health(self):
            raise RuntimeError("boom")
        def similar_artists(self, mbid):
            return []

    good = FakeSimilaritySource(similar={"s1": [SimilarArtist("c1", "X", 0.9)]})
    result = run_discovery(conn, [BoomSource(), good], FakeMbBrowser(), DiscoveryConfig())
    assert result.artists == 1  # good source still processed; boom skipped, no crash


def test_config_from_config_parses_and_defaults():
    cfg = DiscoveryConfig.from_config({"discover.enabled": "true", "discover.chunkSize": "10",
                                       "discover.minScore": "0.3"})
    assert cfg.enabled is True and cfg.chunk_size == 10 and cfg.min_score == 0.3
    d = DiscoveryConfig.from_config({})
    assert (d.enabled, d.interval_hours, d.chunk_size, d.similar_per_seed,
            d.albums_per_artist, d.min_score) == (False, 168, 5, 20, 1, 0.0)


def _artist_score(conn, mbid):
    with conn.cursor() as cur:
        cur.execute('SELECT score, "seedCount" FROM "DiscoverySuggestion" WHERE "artistMbid" = %s', (mbid,))
        return cur.fetchone()


def test_score_sums_across_separate_chunks(conn):
    # Two seeds, both eligible, forced into separate one-seed chunks by chunk_size=1.
    insert_watched_artist(conn, mbid="s1", name="Seed One",
                          last_discovered_sql="now() - interval '200 hours'")
    insert_watched_artist(conn, mbid="s2", name="Seed Two",
                          last_discovered_sql="now() - interval '190 hours'")
    src = FakeSimilaritySource(similar={
        "s1": [SimilarArtist("c1", "Shared", 0.6)],
        "s2": [SimilarArtist("c1", "Shared", 0.5)],
    })
    cfg = DiscoveryConfig(chunk_size=1)
    run_discovery(conn, [src], FakeMbBrowser(), cfg)   # processes s1 (older)
    run_discovery(conn, [src], FakeMbBrowser(), cfg)   # processes s2
    # replace-not-accumulate would leave 0.5/seedCount1; contributions give the sum.
    assert _artist_score(conn, "c1") == (2.0, 2)  # each seed max-normalizes to 1.0


def test_partial_sweep_preserves_other_seeds_contribution(conn):
    # Full sweep: c1 gets s1(0.6)+s2(0.5)=1.1. Then only s1 is due again with a new
    # score; s2's contribution must survive untouched.
    insert_watched_artist(conn, mbid="s1", name="Seed One",
                          last_discovered_sql="now() - interval '200 hours'")
    insert_watched_artist(conn, mbid="s2", name="Seed Two",
                          last_discovered_sql="now() - interval '200 hours'")
    src = FakeSimilaritySource(similar={
        "s1": [SimilarArtist("c1", "Shared", 0.6)],
        "s2": [SimilarArtist("c1", "Shared", 0.5)],
    })
    run_discovery(conn, [src], FakeMbBrowser(), DiscoveryConfig())  # both -> 1.1/2
    assert _artist_score(conn, "c1") == (2.0, 2)  # each seed max-normalizes to 1.0

    # s1 due again (older than interval), s2 fresh; s1 now scores c1 at 0.7.
    with conn.cursor() as cur:
        cur.execute('UPDATE "WatchedArtist" SET "lastDiscoveredAt" = now() - interval \'200 hours\' WHERE mbid = %s', ("s1",))
        cur.execute('UPDATE "WatchedArtist" SET "lastDiscoveredAt" = now() WHERE mbid = %s', ("s2",))
    conn.commit()
    src2 = FakeSimilaritySource(similar={"s1": [SimilarArtist("c1", "Shared", 0.7)]})
    run_discovery(conn, [src2], FakeMbBrowser(), DiscoveryConfig())
    assert _artist_score(conn, "c1") == (2.0, 2)  # 1.0 (new s1) + 1.0 (kept s2), max-normalized


def test_seed_dropoff_lowers_seedcount(conn):
    insert_watched_artist(conn, mbid="s1", name="Seed One",
                          last_discovered_sql="now() - interval '200 hours'")
    insert_watched_artist(conn, mbid="s2", name="Seed Two",
                          last_discovered_sql="now() - interval '200 hours'")
    src = FakeSimilaritySource(similar={
        "s1": [SimilarArtist("c1", "Shared", 0.6)],
        "s2": [SimilarArtist("c1", "Shared", 0.5)],
    })
    run_discovery(conn, [src], FakeMbBrowser(), DiscoveryConfig())
    assert _artist_score(conn, "c1") == (2.0, 2)  # each seed max-normalizes to 1.0

    # s1 re-swept but no longer finds c1 -> its contribution is removed, c1 recomputed.
    with conn.cursor() as cur:
        cur.execute('UPDATE "WatchedArtist" SET "lastDiscoveredAt" = now() - interval \'200 hours\' WHERE mbid = %s', ("s1",))
        cur.execute('UPDATE "WatchedArtist" SET "lastDiscoveredAt" = now() WHERE mbid = %s', ("s2",))
    conn.commit()
    src2 = FakeSimilaritySource(similar={"s1": [SimilarArtist("c9", "Other", 0.9)]})
    run_discovery(conn, [src2], FakeMbBrowser(), DiscoveryConfig())
    assert _artist_score(conn, "c1") == (1.0, 1)  # only s2 remains (max-normalized to 1.0)


def test_evicts_contributions_from_removed_seed(conn):
    # Two live seeds contribute to c1 -> 1.1/2. Then s1 is unfollowed (deleted from
    # WatchedArtist, simulating DELETE /api/artists/[id]) without any FK cascade on
    # DiscoverySeedContribution. The next sweep must evict s1's orphaned contribution
    # rather than leaving it to inflate c1's score forever.
    insert_watched_artist(conn, mbid="s1", name="Seed One",
                          last_discovered_sql="now() - interval '200 hours'")
    insert_watched_artist(conn, mbid="s2", name="Seed Two",
                          last_discovered_sql="now() - interval '200 hours'")
    src = FakeSimilaritySource(similar={
        "s1": [SimilarArtist("c1", "Shared", 0.6)],
        "s2": [SimilarArtist("c1", "Shared", 0.5)],
    })
    run_discovery(conn, [src], FakeMbBrowser(), DiscoveryConfig())
    assert _artist_score(conn, "c1") == (2.0, 2)  # each seed max-normalizes to 1.0

    # Unfollow s1 (no FK cascade on DiscoverySeedContribution).
    with conn.cursor() as cur:
        cur.execute('DELETE FROM "WatchedArtist" WHERE mbid = %s', ("s1",))
    conn.commit()

    # Make s2 due again and sweep; s1 can no longer be a seed since it's gone.
    with conn.cursor() as cur:
        cur.execute('UPDATE "WatchedArtist" SET "lastDiscoveredAt" = now() - interval \'200 hours\' WHERE mbid = %s', ("s2",))
    conn.commit()
    src2 = FakeSimilaritySource(similar={"s2": [SimilarArtist("c1", "Shared", 0.5)]})
    run_discovery(conn, [src2], FakeMbBrowser(), DiscoveryConfig())

    assert _artist_score(conn, "c1") == (1.0, 1)  # s1's stale contribution evicted; s2 alone -> 1.0
    with conn.cursor() as cur:
        cur.execute('SELECT count(*) FROM "DiscoverySeedContribution" WHERE "seedMbid" = %s', ("s1",))
        assert cur.fetchone()[0] == 0


def test_run_discovery_processes_at_most_chunk_size_seeds(conn):
    from lyra_worker.discovery import DiscoveryConfig, run_discovery
    from lyra_worker.adapters.fakes import FakeMbBrowser, FakeSimilaritySource
    from lyra_worker.similarity.base import SimilarArtist
    from tests.conftest import insert_watched_artist
    for i in range(4):
        insert_watched_artist(conn, mbid=f"s{i}", name=f"Seed{i}")
    src = FakeSimilaritySource(similar={f"s{i}": [SimilarArtist(f"c{i}", f"Cand{i}", 0.9)] for i in range(4)})
    cfg = DiscoveryConfig.from_config({"discover.chunkSize": "2"})
    result = run_discovery(conn, [src], FakeMbBrowser(), cfg)
    assert result.seeds == 2          # only chunk_size seeds this call
    # exactly 2 seeds now have lastDiscoveredAt set
    with conn.cursor() as cur:
        cur.execute('SELECT count(*) FROM "WatchedArtist" WHERE "lastDiscoveredAt" IS NOT NULL')
        assert cur.fetchone()[0] == 2


def test_reset_pending_clears_state_and_marks_seeds_due(conn):
    from lyra_worker.discovery import reset_pending
    from tests.conftest import insert_discovery_suggestion
    insert_watched_artist(conn, mbid="s1", name="Seed", last_discovered_sql="now()")
    insert_discovery_suggestion(conn, kind="artist", artist_mbid="c1", status="pending")
    insert_discovery_suggestion(conn, kind="artist", artist_mbid="c2", status="dismissed")
    insert_discovery_suggestion(conn, kind="artist", artist_mbid="c3", status="wanted")
    with conn.cursor() as cur:
        cur.execute('INSERT INTO "DiscoverySeedContribution" (id, "candidateMbid", "candidateName", '
                    '"seedMbid", score, sources, "updatedAt") VALUES '
                    "(gen_random_uuid()::text, 'c1', 'C1', 's1', 1.0, '{}', now())")
        cur.execute('INSERT INTO "DiscoverySeed" (mbid, "lastDiscoveredAt") VALUES (%s, now())', ("libseed",))
    conn.commit()

    reset_pending(conn)

    with conn.cursor() as cur:
        cur.execute("SELECT count(*) FROM \"DiscoverySuggestion\" WHERE status='pending'")
        assert cur.fetchone()[0] == 0                       # pending cleared
        cur.execute("SELECT count(*) FROM \"DiscoverySuggestion\" WHERE status IN ('dismissed','wanted')")
        assert cur.fetchone()[0] == 2                       # user decisions kept
        cur.execute('SELECT count(*) FROM "DiscoverySeedContribution"')
        assert cur.fetchone()[0] == 0
        cur.execute('SELECT count(*) FROM "DiscoverySeed"')
        assert cur.fetchone()[0] == 0
        cur.execute('SELECT count(*) FROM "WatchedArtist" WHERE "lastDiscoveredAt" IS NULL')
        assert cur.fetchone()[0] == 1                       # seed due again