import json

from tft.model.learn import learn


def board_row(placement, unit="TFT17_A", item="TFT_Item_X", trait_tier=1):
    units = [{"character_id": unit, "tier": 2, "itemNames": [item]}]
    traits = [{"name": "TFT17_T", "num_units": 2, "tier_current": trait_tier}]
    return (placement, json.dumps(units), json.dumps(traits), json.dumps([]))


def test_strong_item_gets_multiplier_above_1():
    rows = [board_row(1, item="TFT_Item_Good") for _ in range(200)]
    rows += [board_row(8, item="TFT_Item_Bad") for _ in range(200)]
    learned = learn(rows)
    assert learned["items"]["TFT_Item_Good"] > 1.05
    assert learned["items"]["TFT_Item_Bad"] < 0.95


def test_shrinkage_with_thin_data():
    rows = [board_row(1, item="TFT_Item_Rare") for _ in range(3)]
    learned = learn(rows)
    assert abs(learned["items"]["TFT_Item_Rare"] - 1.0) < 0.06


def test_trait_keys_match_score_format():
    rows = [board_row(2, trait_tier=3) for _ in range(50)]
    learned = learn(rows)
    assert "TFT17_T@3" in learned["traits"]


def test_pair_lift_for_cooccurring_units():
    def pair_row(placement):
        units = [
            {"character_id": "TFT17_A", "tier": 2, "itemNames": []},
            {"character_id": "TFT17_B", "tier": 2, "itemNames": []},
        ]
        return (placement, json.dumps(units), json.dumps([]), json.dumps([]))

    # A und B stehen immer zusammen in Top-4-Boards; C ist überall.
    rows = [pair_row(1) for _ in range(100)]
    solo = [{"character_id": "TFT17_C", "tier": 1, "itemNames": []}]
    rows += [(1, json.dumps(solo), json.dumps([]), json.dumps([])) for _ in range(100)]
    learned = learn(rows)
    assert learned["pairs"]["TFT17_A|TFT17_B"] > 0.5


def test_empty_rows():
    learned = learn([])
    assert learned["traits"] == {}
    assert learned["pairs"] == {}
    assert learned["n_boards"] == 0