"""
Unit tests for ReportAgent's similarity-highlight span builder
(plan.md §8.4, Phase 5). The builder turns a SimilarityReport dict into
mapper input spans so the single combined PdfHighlightMapper call can render
both AI-detection and similarity overlays from one invocation.
"""
from agents.report_agent import similarity_highlight_spans


def _report(status="complete", spans_per_source=1, source_count=2):
    return {
        "status": status,
        "sources": [
            {
                "source_index": i + 1,
                "percent": 10 * (i + 1),
                "spans": [
                    {
                        "excerpt": f"shared passage number {j} of source {i + 1}",
                        "doc_char_start": j,
                        "doc_char_end": j + 10,
                    }
                    for j in range(spans_per_source)
                ],
            }
            for i in range(source_count)
        ],
    }


def test_builds_spans_with_kind_and_source_index():
    spans = similarity_highlight_spans(_report(spans_per_source=2, source_count=2))
    assert len(spans) == 4
    assert all(s["kind"] == "similarity" for s in spans)
    assert spans[0]["meta"] == {"source_index": 1}
    assert spans[2]["meta"] == {"source_index": 2}
    assert "shared passage" in spans[0]["text"]


def test_unavailable_report_contributes_nothing():
    assert similarity_highlight_spans(_report(status="unavailable")) == []
    assert similarity_highlight_spans({"status": "partial", "sources": []}) == []


def test_empty_excerpts_are_dropped():
    report = _report(spans_per_source=2, source_count=2)
    report["sources"][0]["spans"] = [
        {"excerpt": "   ", "doc_char_start": 0, "doc_char_end": 1}
    ]
    spans = similarity_highlight_spans(report)
    assert all(s["text"] for s in spans)
    assert len(spans) == 2  # empty excerpt of source 1 dropped, source 2 kept


def test_none_report_returns_empty():
    assert similarity_highlight_spans(None) == []