import sys
import types
import unittest
from pathlib import Path

import numpy as np

sys.path.insert(0, str(Path(__file__).resolve().parents[2] / "src"))

omegaconf_stub = types.ModuleType("omegaconf")
omegaconf_stub.OmegaConf = object
omegaconf_stub.DictConfig = object
sys.modules.setdefault("omegaconf", omegaconf_stub)

data_stub = types.ModuleType("data")
octflow_stub = types.ModuleType("data.octflow_dataset")
octflow_stub.OCTFlowDataset = object
sys.modules.setdefault("data", data_stub)
sys.modules.setdefault("data.octflow_dataset", octflow_stub)

from train_sd3_t2i import build_balanced_sampling_weights


class TaxonomySamplingTest(unittest.TestCase):
    def test_temperature_sampling_gives_each_group_equal_total_weight_at_zero_temperature(self):
        labels = np.array(["fundus"] * 8 + ["ubm"] * 2)

        weights, counts, targets = build_balanced_sampling_weights(
            labels,
            temperature=0.0,
            min_count=1,
        )

        self.assertEqual(counts, {"fundus": 8, "ubm": 2})
        self.assertAlmostEqual(weights[:8].sum(), weights[8:].sum(), places=8)
        self.assertEqual(set(targets), {"fundus", "ubm"})

    def test_min_count_excludes_tiny_groups(self):
        labels = np.array(["fundus"] * 8 + ["ir_slo"] * 2)

        weights, counts, targets = build_balanced_sampling_weights(
            labels,
            temperature=0.5,
            min_count=3,
        )

        self.assertEqual(counts, {"fundus": 8, "ir_slo": 2})
        self.assertGreater(weights[:8].sum(), 0.0)
        self.assertEqual(weights[8:].sum(), 0.0)
        self.assertEqual(targets, {"fundus": 1.0})


if __name__ == "__main__":
    unittest.main()