"""providers.py 注册表测试(M1 设计 §7;register_provider 为纯函数,无可变全局)。"""

import pytest
from loguru import logger

from polygateway.providers import (
    DEFAULT_CAPABILITIES,
    DEFAULT_PROFILES,
    ProviderProfile,
    ThinkingCapability,
    get_capability,
    get_provider,
    register_capability,
    register_provider,
    resolve_thinking,
)


class TestDefaultProfiles:
    def test_qwen_profile(self):
        p = get_provider("qwen")
        assert p.thinking_on == {"enable_thinking": True}
        assert p.thinking_off == {"enable_thinking": False}
        assert p.strip_think_tags is True
        assert p.supports_native_schema is False

    def test_deepseek_profile(self):
        p = get_provider("deepseek")
        assert p.thinking_on == {"thinking": {"type": "enabled"}}
        assert p.thinking_off == {"thinking": {"type": "disabled"}}
        assert p.strip_think_tags is False

    def test_openai_slots_are_unknown_not_empty(self):
        """issue #5: 该段名实践中被复用为任意兼容厂商的兜底(下游把 kimi 挂在此),

        故不能下发任何厂商方言参数。None = 形态未知 → 配了 enable_thinking 即报错,
        而不是空字典那种"注入了个寂寞"的静默失效。
        """
        p = get_provider("openai")
        assert p.thinking_on is None and p.thinking_off is None
        assert p.strip_think_tags is False

    def test_minimax_profile_uses_reasoning_effort(self):
        """2026-08-02 实测: reasoning_effort 才是 MiniMax 认的开关。"""
        p = get_provider("minimax")
        assert p.thinking_off == {"reasoning_effort": "none"}
        assert p.thinking_on == {"reasoning_effort": "medium"}
        assert p.strip_think_tags is False

    def test_unknown_provider_fails_loudly(self):
        """消灭子串猜测: 未注册 provider 装配期即报错,不做模糊匹配。"""
        with pytest.raises(ValueError, match="glm"):
            get_provider("glm")
        with pytest.raises(ValueError):
            get_provider("qwen2")  # 子串相似也不放行


class TestPureFunctionRegistration:
    def test_register_returns_new_mapping(self):
        glm = ProviderProfile(name="glm", thinking_on={}, thinking_off={}, strip_think_tags=False)
        table = register_provider(glm)
        assert get_provider("glm", registry=table) is glm
        # 默认表未被污染(无可变全局状态铁律)
        with pytest.raises(ValueError):
            get_provider("glm")

    def test_register_on_custom_base_and_override(self):
        custom_qwen = ProviderProfile(
            name="qwen", thinking_on={"x": 1}, thinking_off={}, strip_think_tags=False
        )
        table = register_provider(custom_qwen, base=DEFAULT_PROFILES)
        assert get_provider("qwen", registry=table).thinking_on == {"x": 1}
        assert get_provider("qwen").thinking_on == {"enable_thinking": True}

    def test_default_profiles_mapping_is_read_only(self):
        with pytest.raises(TypeError):
            DEFAULT_PROFILES["hack"] = None  # type: ignore[index]


def _warnings():
    """捕获库发出的 WARNING;loguru 不经标准 logging,pytest 的 caplog 抓不到。"""
    messages: list[str] = []
    sink_id = logger.add(messages.append, level="WARNING")
    return messages, sink_id


class TestThinkingCapability:
    """issue #5: 能力按 model 登记——同一 provider 内部代际差异是决定性的。"""

    def test_registered_models_carry_evidence(self):
        """登记必须附实测证据: 表会过期,没有出处就无从判断该不该信。"""
        for model in ("MiniMax-M3", "MiniMax-M2.7", "MiniMax-M2.5"):
            cap = get_capability(model)
            assert cap is not None and cap.evidence.strip()

    def test_m3_can_disable_but_m2x_cannot(self):
        assert get_capability("MiniMax-M3").can_disable is True
        assert get_capability("MiniMax-M2.7").can_disable is False
        assert get_capability("MiniMax-M2.5").can_disable is False

    def test_unregistered_model_is_unknown(self):
        assert get_capability("some-brand-new-model") is None

    def test_register_capability_is_pure(self):
        table = register_capability("x-1", ThinkingCapability(True, "实测"))
        assert get_capability("x-1", table=table) is not None
        assert get_capability("x-1") is None  # 默认表未被污染

    def test_default_capabilities_mapping_is_read_only(self):
        with pytest.raises(TypeError):
            DEFAULT_CAPABILITIES["hack"] = None  # type: ignore[index]


class TestResolveThinking:
    """五条判定规则(顺序即语义);设计 §5 真值表。"""

    def test_rule1_none_injects_nothing(self):
        got = resolve_thinking(get_provider("minimax"), None, None, model="MiniMax-M3")
        assert got == {}

    @pytest.mark.parametrize("enable", [True, False])
    def test_rule2_unknown_shape_raises_and_points_the_way(self, enable):
        with pytest.raises(ValueError, match="register_provider") as exc:
            resolve_thinking(get_provider("openai"), None, enable, model="kimi-k3")
        assert "extra_body" in str(exc.value)

    def test_rule3_unregistered_model_warns_but_passes(self):
        messages, sink_id = _warnings()
        try:
            got = resolve_thinking(get_provider("minimax"), None, False, model="MiniMax-M9")
        finally:
            logger.remove(sink_id)
        assert got == {"reasoning_effort": "none"}
        assert any("MiniMax-M9" in m for m in messages)

    def test_rule4_cannot_disable_raises_with_the_model_name(self):
        cap = get_capability("MiniMax-M2.7")
        with pytest.raises(ValueError, match="MiniMax-M2.7"):
            resolve_thinking(get_provider("minimax"), cap, False, model="MiniMax-M2.7")

    def test_rule4_only_blocks_the_off_direction(self):
        """关不掉 ≠ 开不了: M2.x 默认就在推理,开的方向不该被拦。"""
        cap = get_capability("MiniMax-M2.7")
        got = resolve_thinking(get_provider("minimax"), cap, True, model="MiniMax-M2.7")
        assert got == {"reasoning_effort": "medium"}

    def test_rule5_normal_path(self):
        cap = get_capability("MiniMax-M3")
        assert resolve_thinking(get_provider("minimax"), cap, False, model="MiniMax-M3") == {
            "reasoning_effort": "none"
        }

    def test_unknown_shape_beats_capability_check(self):
        """第 2 步先于第 4 步: 形态未知时无从注入,能力如何无关紧要。"""
        cap = ThinkingCapability(can_disable=False, evidence="构造")
        with pytest.raises(ValueError, match="register_provider"):
            resolve_thinking(get_provider("openai"), cap, False, model="whatever")