import random
import uuid
from datetime import UTC, datetime, timedelta

from app.clips_feed import (
    Affinity,
    Candidate,
    apply_event,
    cap_weights,
    compose_page,
    decay_weights,
    decode_cursor,
    encode_cursor,
    event_kind_for_weights,
    pick_affinity,
    pick_explore,
    score_candidate,
    split_counts,
)


def test_cursor_roundtrip_and_garbage():
    pk = uuid.uuid4()
    c = encode_cursor("abc123", pk, 7)
    assert decode_cursor(c) == ("abc123", pk, 7)
    assert decode_cursor("not-a-cursor") is None
    assert decode_cursor("") is None


def test_event_kind_mapping():
    assert event_kind_for_weights("impression", 12000) == "dwell"
    assert event_kind_for_weights("impression", 3000) is None
    assert event_kind_for_weights("skip", 500) == "skip"
    assert event_kind_for_weights("skip", 5000) is None
    assert event_kind_for_weights("like", None) == "like"
    assert event_kind_for_weights("autoswipe", None) is None


def test_apply_event_like_and_not_interested_reason():
    aff = Affinity()
    changed = apply_event(aff, kind="like", dwell_ms=None, reason=None, tags=["big-tits", "amateur"], niches=["just-boobs"], creator="kitsune")
    assert changed
    assert aff.tags["big-tits"] == 5.0 and aff.niches["just-boobs"] == 5.0 and aff.creators["kitsune"] == 4.0
    apply_event(aff, kind="not_interested", dwell_ms=None, reason="tag:amateur", tags=["amateur"], niches=[], creator=None)
    # -4 za zdarzenie i -8 za wskazany tag
    assert aff.tags["amateur"] == 5.0 - 4.0 - 8.0
    assert not aff.is_empty()
    assert aff.top("tags", 1) == ["big-tits"]


def test_cap_and_decay():
    d = {f"t{i}": float(i) for i in range(100)}
    capped = cap_weights(d, 10)
    assert len(capped) == 10 and "t99" in capped and "t0" not in capped
    decayed = decay_weights({"a": 1.0, "b": 0.2}, factor=0.5, floor=0.2)
    assert decayed == {"a": 0.5}  # b spada pod prog i znika


def _cand(creator, likes=0, tags=(), niches=(), days_old=30):
    return Candidate(
        id=uuid.uuid4(), creator=creator, likes=likes,
        published_at=datetime.now(UTC) - timedelta(days=days_old), tags=list(tags), niches=list(niches),
    )


def test_pick_affinity_respects_creator_cap_and_order():
    aff = Affinity(tags={"x": 10.0})
    cands = [_cand("a", tags=["x"]) for _ in range(5)] + [_cand("b", tags=["x"], likes=1)]
    picked = pick_affinity(cands, aff, 4, max_per_creator=2)
    creators = [c.creator for c in picked]
    assert creators.count("a") == 2 and "b" in creators and len(picked) == 3


def test_score_prefers_fresh_and_liked():
    aff = Affinity()
    old = _cand("a", likes=0, days_old=100)
    fresh = _cand("a", likes=0, days_old=1)
    liked = _cand("a", likes=1000, days_old=100)
    assert score_candidate(fresh, aff) > score_candidate(old, aff)
    assert score_candidate(liked, aff) > score_candidate(old, aff)


def test_split_and_compose_deterministic():
    assert split_counts(10, False) == (0, 10)
    assert split_counts(10, True) == (8, 2)
    a = [_cand("a") for _ in range(8)]
    e = [_cand("e") for _ in range(2)]
    p1 = compose_page(list(a), list(e), random.Random("s:1"))
    p2 = compose_page(list(a), list(e), random.Random("s:1"))
    assert [c.id for c, _ in p1] == [c.id for c, _ in p2]
    assert sum(1 for _, slot in p1 if slot == "explore") == 2


def test_pick_explore_weighted_but_complete():
    cands = [_cand("a", likes=0), _cand("b", likes=10**6)]
    out = pick_explore(cands, 2, random.Random(1))
    assert {c.creator for c in out} == {"a", "b"}