import sys
import tempfile
import unittest
from pathlib import Path

import numpy as np


MODULE_DIR = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(MODULE_DIR))

import audit_data
from purpose_data import SourceRecord


def record(index, purpose, slice_name="core", language="en"):
    return SourceRecord(
        value={
            "prompt": f"prompt {index}",
            "purpose": purpose,
            "secondary": None,
            "mixed": False,
            "difficulty": 0.5,
            "slice": slice_name,
            "lang": language,
        },
        source=Path("source.jsonl"),
        line=index + 1,
    )


class ReviewSampleTests(unittest.TestCase):
    def test_sample_is_exact_and_deterministic(self):
        records = [
            record(index, audit_data.LABELS[index % len(audit_data.LABELS)])
            for index in range(101)
        ]
        first = audit_data.stratified_review_sample(records, fraction=0.1, seed=42)
        second = audit_data.stratified_review_sample(records, fraction=0.1, seed=42)
        self.assertEqual(10, len(first))
        self.assertEqual(
            [item.value["prompt"] for item in first],
            [item.value["prompt"] for item in second],
        )

    def test_review_csv_has_blank_reviewer_fields(self):
        records = [record(0, "planning")]
        with tempfile.TemporaryDirectory() as temporary:
            path = Path(temporary) / "review.csv"
            audit_data.write_review_csv(path, records)
            text = path.read_text(encoding="utf-8")
        self.assertIn("reviewedPurpose", text)
        self.assertIn("prompt 0", text)


class SemanticCandidateTests(unittest.TestCase):
    def test_threshold_depends_on_label_agreement(self):
        embeddings = np.asarray(
            [
                [1.0, 0.0],
                [0.98, 0.2],
                [0.98, -0.2],
            ],
            dtype=np.float32,
        )
        candidates = audit_data.semantic_candidates(
            embeddings,
            ["planning", "planning", "writing"],
            same_label_threshold=0.97,
            cross_label_threshold=0.99,
            neighbors=2,
            block_size=2,
        )
        pairs = {(item.left, item.right) for item in candidates}
        self.assertIn((0, 1), pairs)
        self.assertNotIn((0, 2), pairs)

    def test_candidate_pairs_are_deduplicated(self):
        embeddings = np.asarray([[1.0, 0.0], [1.0, 0.0]], dtype=np.float32)
        candidates = audit_data.semantic_candidates(
            embeddings,
            ["review", "review"],
            same_label_threshold=0.9,
            cross_label_threshold=0.9,
            neighbors=1,
            block_size=1,
        )
        self.assertEqual(1, len(candidates))


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