import pytest


def test_chunk_text_single_short_text():
    from axon.services.reranker import chunk_text
    chunks = chunk_text("hello world", max_tokens=512)
    assert chunks == ["hello world"]


def test_chunk_text_splits_long_text():
    from axon.services.reranker import chunk_text
    # 384 words per chunk at max_tokens=512 (512 * 0.75)
    text = " ".join([f"word{i}" for i in range(500)])
    chunks = chunk_text(text, max_tokens=512)
    assert len(chunks) == 2
    assert chunks[0].startswith("word0")


def test_chunk_text_empty_string():
    from axon.services.reranker import chunk_text
    chunks = chunk_text("", max_tokens=512)
    assert chunks == [""]


def test_chunk_text_exact_boundary():
    from axon.services.reranker import chunk_text
    # 384 words should be exactly 1 chunk
    text = " ".join(["x"] * 384)
    chunks = chunk_text(text, max_tokens=512)
    assert len(chunks) == 1


@pytest.mark.asyncio
async def test_rerank_results_sorts_by_score(monkeypatch):
    import numpy as np

    call_count = [0]

    class FakeModel:
        def predict(self, pairs):
            # First pair gets low score, second gets high score
            return np.array([0.1, 0.9])

    monkeypatch.setattr("axon.services.reranker._model", FakeModel())

    from axon.services.reranker import rerank_results
    contents = [("https://low.com", "low relevance text"), ("https://high.com", "high relevance text")]
    ranked = await rerank_results("query", contents)
    assert ranked[0][0] == "https://high.com"
    assert ranked[1][0] == "https://low.com"
    assert ranked[0][2] > ranked[1][2]


@pytest.mark.asyncio
async def test_rerank_results_empty_input():
    from axon.services.reranker import rerank_results
    result = await rerank_results("query", [])
    assert result == []


@pytest.mark.asyncio
async def test_rerank_results_handles_empty_content(monkeypatch):
    import numpy as np

    class FakeModel:
        def predict(self, pairs):
            return np.array([0.5] * len(pairs))

    monkeypatch.setattr("axon.services.reranker._model", FakeModel())

    from axon.services.reranker import rerank_results
    contents = [("https://a.com", ""), ("https://b.com", "some text")]
    ranked = await rerank_results("query", contents)
    assert len(ranked) == 2