from pathlib import Path

import pytest

from gpu_rent.llm_runtime import (
    already_have_ollama_tag,
    decide_runtime,
    normalize_runtime,
    parse_ollama_models,
    preferred_ollama_model,
    write_ollama_models_preset,
)


def test_normalize_runtime():
    assert normalize_runtime(None) == "none"
    assert normalize_runtime("OLLAMA") == "ollama"
    with pytest.raises(ValueError):
        normalize_runtime("llamacpp")
    with pytest.raises(ValueError):
        normalize_runtime("foo")


def test_decide_runtime_flags_win():
    assert (
        decide_runtime(flag=None, ollama_flag=True, from_config="none") == "ollama"
    )
    assert (
        decide_runtime(flag="ollama", ollama_flag=False, from_config="none") == "ollama"
    )
    assert decide_runtime(flag=None, ollama_flag=False, from_config="ollama") == "ollama"


def test_parse_ollama_models(tmp_path: Path):
    path = tmp_path / "m.yaml"
    path.write_text(
        "models:\n  - name: huihui_ai/qwen2.5-abliterate:7b\n    default: true\n  - qwen2.5:3b\n",
        encoding="utf-8",
    )
    entries = parse_ollama_models(path)
    assert [e.name for e in entries] == [
        "huihui_ai/qwen2.5-abliterate:7b",
        "qwen2.5:3b",
    ]
    assert entries[0].default is True


def test_parse_empty_manifest(tmp_path: Path):
    path = tmp_path / "empty.yaml"
    path.write_text("models: []\n", encoding="utf-8")
    assert parse_ollama_models(path) == []
    assert parse_ollama_models(tmp_path / "missing.yaml") == []


def test_write_preset(tmp_path: Path):
    path = tmp_path / "out.yaml"
    write_ollama_models_preset(path, "recommended")
    entries = parse_ollama_models(path)
    assert entries[0].name == "huihui_ai/qwen3-vl-abliterated:8b-instruct"
    assert entries[0].default is True
    assert entries[1].name == "huihui_ai/qwen2.5-vl-abliterated:7b"
    write_ollama_models_preset(path, "big")
    assert parse_ollama_models(path)[0].name == "huihui_ai/qwen2.5-vl-abliterated:32b"
    write_ollama_models_preset(path, "text")
    assert parse_ollama_models(path)[0].name == "huihui_ai/qwen2.5-abliterate:7b"


def test_already_have_ollama_tag_exact_only():
    have = {"qwen2.5:7b", "foo:latest"}
    assert already_have_ollama_tag(have, "qwen2.5:7b")
    assert not already_have_ollama_tag(have, "qwen2.5:3b")
    assert already_have_ollama_tag(have, "foo")
    assert already_have_ollama_tag(have, "foo:latest")


def test_preferred_ollama_model(tmp_path: Path):
    path = tmp_path / "m.yaml"
    path.write_text(
        "models:\n  - name: a:3b\n  - name: b:7b\n    default: true\n",
        encoding="utf-8",
    )
    assert preferred_ollama_model(path) == "b:7b"
    path.write_text('models:\n  - "only:7b"\n', encoding="utf-8")
    assert preferred_ollama_model(path) == "only:7b"
    assert preferred_ollama_model(tmp_path / "missing.yaml") is None


def test_warmup_ollama_http_skips_loaded(monkeypatch):
    from gpu_rent.llm_runtime import warmup_ollama_http

    class Resp:
        def raise_for_status(self) -> None:
            return None

        def json(self):
            return {"models": [{"name": "foo:7b"}]}

    class Client:
        def __init__(self, *a, **k):
            pass

        def __enter__(self):
            return self

        def __exit__(self, *a):
            return False

        def get(self, url, timeout=None):
            assert url.endswith("/api/ps")
            return Resp()

        def post(self, *_a, **_k):
            raise AssertionError("must not chat when already loaded")

    monkeypatch.setattr("gpu_rent.llm_runtime.httpx.Client", Client)
    msg = warmup_ollama_http("http://127.0.0.1:17811", "foo:7b")
    assert "skip" in msg


def test_maybe_warmup_skips_when_runtime_none():
    from gpu_rent.llm_runtime import maybe_warmup_ollama_local

    logs: list[str] = []

    class Cfg:
        llm_runtime = "none"
        ollama_models_manifest = Path("missing.yaml")
        ollama_local_port = 17811

    maybe_warmup_ollama_local(Cfg(), logs.append)
    assert logs == []