"""Unit tests for sequence equivalence mapping covering scenarios CAN-001 to CAN-010."""

from src.runtime.candidate.equivalence import (
    compute_sequence_similarity,
    map_candidate_equivalences,
    normalize_text_for_comparison,
)
from src.runtime.candidate.models import CandidateObject


def test_text_normalization():
    text = "  São Paulo   Futebol   Clube\n\t "
    norm = normalize_text_for_comparison(text)
    assert norm == "são paulo futebol clube"


def test_sequence_similarity():
    t1 = "River Plate empató sin goles ante Independiente Santa Fe."
    t2 = "River Plate empató 0-0 con Independiente Santa Fe."
    sim = compute_sequence_similarity(t1, t2)
    assert sim > 0.6


def test_map_candidate_equivalences():
    c1 = CandidateObject(
        id="traf_01",
        type="paragraph",
        text="El partido finalizó 0 a 0 en Bogotá.",
        extractor="trafilatura",
        position=1,
    )
    c2 = CandidateObject(
        id="news_01",
        type="paragraph",
        text="El partido finalizó 0 a 0 en Bogotá.",
        extractor="newspaper4k",
        position=1,
    )

    map_candidate_equivalences([c1], [c2], similarity_threshold=0.9)
    assert "news_01" in c1.equivalent_ids
    assert "traf_01" in c2.equivalent_ids