from __future__ import annotations

from reranker.backends import get_rerank_backend
from reranker.backends.dashscope_rerank import DashScopeRerankBackend


def test_dashscope_backend_factory_loads():
    backend = get_rerank_backend(
        "dashscope_rerank",
        {
            "model_name": "qwen3-rerank",
            "endpoint": "https://dashscope.aliyuncs.com/compatible-api/v1/reranks",
            "api_key": "test-key",
        },
    )
    assert isinstance(backend, DashScopeRerankBackend)


def test_dashscope_backend_score_with_meta_dedup_and_restore(monkeypatch):
    backend = DashScopeRerankBackend(
        {
            "model_name": "qwen3-rerank",
            "endpoint": "https://dashscope.aliyuncs.com/compatible-api/v1/reranks",
            "api_key": "test-key",
            "top_n_cap": 0,
        }
    )

    def _fake_post(query: str, docs: list[str], top_n: int):
        assert query == "wireless mouse"
        # deduplicated docs
        assert docs == ["doc-a", "doc-b"]
        assert top_n == 2
        return {
            "results": [
                {"index": 1, "relevance_score": 0.9},
                {"index": 0, "relevance_score": 0.2},
            ]
        }

    monkeypatch.setattr(backend, "_post_rerank", _fake_post)
    scores, meta = backend.score_with_meta(
        query="wireless mouse",
        docs=["doc-a", "doc-b", "doc-a", "", "   ", None],
        normalize=True,
    )

    assert scores == [0.2, 0.9, 0.2, 0.0, 0.0, 0.0]
    assert meta["input_docs"] == 6
    assert meta["usable_docs"] == 3
    assert meta["unique_docs"] == 2
    assert meta["top_n"] == 2
    assert meta["response_results"] == 2
    assert meta["backend"] == "dashscope_rerank"


def test_dashscope_backend_top_n_cap_and_normalize_fallback(monkeypatch):
    backend = DashScopeRerankBackend(
        {
            "model_name": "qwen3-rerank",
            "endpoint": "https://dashscope.aliyuncs.com/compatible-api/v1/reranks",
            "api_key": "test-key",
            "top_n_cap": 1,
        }
    )

    def _fake_post(query: str, docs: list[str], top_n: int):
        assert query == "q"
        assert len(docs) == 2
        assert top_n == 1
        # Only top-1 returned, score outside [0,1] to trigger sigmoid fallback
        return {"results": [{"index": 1, "score": 3.0}]}

    monkeypatch.setattr(backend, "_post_rerank", _fake_post)
    scores_norm, _ = backend.score_with_meta(query="q", docs=["a", "b"], normalize=True)
    scores_raw, _ = backend.score_with_meta(query="q", docs=["a", "b"], normalize=False)

    assert scores_norm[0] == 0.0
    assert 0.95 < scores_norm[1] < 0.96
    assert scores_raw == [0.0, 3.0]


def test_dashscope_backend_score_with_meta_topn_request(monkeypatch):
    backend = DashScopeRerankBackend(
        {
            "model_name": "qwen3-rerank",
            "endpoint": "https://dashscope.aliyuncs.com/compatible-api/v1/reranks",
            "api_key": "test-key",
            "top_n_cap": 0,
        }
    )

    def _fake_post(query: str, docs: list[str], top_n: int):
        assert query == "q"
        assert docs == ["d1", "d2", "d3"]
        assert top_n == 2
        return {"results": [{"index": 2, "relevance_score": 0.8}, {"index": 0, "relevance_score": 0.3}]}

    monkeypatch.setattr(backend, "_post_rerank", _fake_post)
    scores, meta = backend.score_with_meta_topn(query="q", docs=["d1", "d2", "d3"], top_n=2)
    assert scores == [0.3, 0.0, 0.8]
    assert meta["top_n"] == 2
    assert meta["requested_top_n"] == 2