"""The rules are well formed and say the same thing everywhere they appear."""
import pytest

from conftest import load

NAMES = sorted(load('rules.json')['rules'])


@pytest.mark.parametrize('name', NAMES)
def test_the_two_dims_differ(name, rules):
    r = rules['rules'][name]
    assert r['pos_dim'] != r['neg_dim'], f'{name} compares a channel with itself'


@pytest.mark.parametrize('name', NAMES)
def test_dims_are_in_range(name, rules):
    r = rules['rules'][name]
    for d in (r['pos_dim'], r['neg_dim']):
        assert 0 <= d < 768


@pytest.mark.parametrize('name', NAMES)
def test_category_ids_are_coco(name, rules):
    assert 1 <= rules['rules'][name]['cat_id'] <= 90


def test_category_ids_are_unique(rules):
    ids = [r['cat_id'] for r in rules['rules'].values()]
    assert len(set(ids)) == len(ids)


@pytest.mark.parametrize('name', NAMES)
def test_eval_carries_the_same_rule(name, rules, evaluation):
    r, e = rules['rules'][name], evaluation['rules'][name]
    assert (e['pos_dim'], e['neg_dim']) == (r['pos_dim'], r['neg_dim'])
    assert e['cat_id'] == r['cat_id']
    assert e['train_balanced_accuracy'] == r['train_balanced_accuracy']


@pytest.mark.parametrize('name', NAMES)
def test_every_rule_beats_chance(name, evaluation):
    """Balanced accuracy on a balanced subsample; 0.5 is chance for any rate."""
    assert evaluation['rules'][name]['balanced_accuracy'] > 0.5


@pytest.mark.parametrize('name', NAMES)
def test_scoring_set_is_balanced_and_large_enough(name, evaluation):
    e = evaluation['rules'][name]
    assert e['n_balanced'] % 2 == 0
    assert e['n_balanced'] >= 40


def test_distribution_matches_the_rules(evaluation):
    accs = sorted(v['balanced_accuracy'] for v in evaluation['rules'].values())
    d = evaluation['distribution']
    assert d['n_categories'] == len(accs)
    assert d['min'] == accs[0] and d['max'] == accs[-1]
    for t, n in d['at_least'].items():
        assert n == sum(a >= float(t) for a in accs)


def test_skipped_categories_are_recorded(rules):
    """Categories dropped for lack of validation images are named, not silent."""
    skipped = rules['provenance']['skipped']
    assert len(rules['rules']) + len(skipped) == 80
    for s in skipped:
        assert s['val_positives'] < rules['min_val_images']