"""Tests for the section cache.

Key acceptance criterion: calling generation twice with the same payload
should NOT trigger a second LLM call (verified via mock call-count assertion).
"""

from pathlib import Path

import pytest

from app.cache.section_cache import clear_all, compute_cache_key, get, invalidate, set


@pytest.fixture(autouse=True)
def isolated_cache(tmp_path: Path, monkeypatch: pytest.MonkeyPatch) -> None:
    """Redirect cache to a per-test temporary directory."""
    monkeypatch.setattr("app.cache.section_cache.settings.cache_dir", tmp_path)
    monkeypatch.setattr("app.config.settings.cache_dir", tmp_path)


# ── compute_cache_key ─────────────────────────────────────────────────────────

def test_cache_key_deterministic() -> None:
    """Same inputs should always produce the same key."""
    k1 = compute_cache_key("E4", ["bullet one", "bullet two"], "tenant_a")
    k2 = compute_cache_key("E4", ["bullet one", "bullet two"], "tenant_a")
    assert k1 == k2


def test_cache_key_differs_for_different_inputs() -> None:
    """Different bullets should produce different cache keys."""
    k1 = compute_cache_key("E4", ["bullet one"], "tenant_a")
    k2 = compute_cache_key("E4", ["bullet two"], "tenant_a")
    assert k1 != k2


def test_cache_key_differs_for_different_tenants() -> None:
    """Same template+bullets but different tenant must produce different keys."""
    k1 = compute_cache_key("E4", ["bullet one"], "tenant_a")
    k2 = compute_cache_key("E4", ["bullet one"], "tenant_b")
    assert k1 != k2


def test_cache_key_is_64_hex_chars() -> None:
    """SHA-256 cache key should be 64 hexadecimal characters."""
    key = compute_cache_key("E4", ["fact"], "tenant_a")
    assert len(key) == 64
    assert all(c in "0123456789abcdef" for c in key)


def test_cache_key_differs_for_different_ai_levels() -> None:
    """Different ai_level values must produce different cache keys.

    This prevents a RAG-only result (level 1) being served to a full-AI
    request (level 5) just because the bullets are identical.
    """
    k1 = compute_cache_key("E4", ["fact one"], "tenant_a", ai_level=1)
    k3 = compute_cache_key("E4", ["fact one"], "tenant_a", ai_level=3)
    k5 = compute_cache_key("E4", ["fact one"], "tenant_a", ai_level=5)
    assert k1 != k3
    assert k3 != k5
    assert k1 != k5


def test_cache_key_differs_for_different_ai_percent() -> None:
    """Different ai_percent values must produce different cache keys."""
    k0 = compute_cache_key("E4", ["fact one"], "tenant_a", ai_percent=0)
    k50 = compute_cache_key("E4", ["fact one"], "tenant_a", ai_percent=50)
    k100 = compute_cache_key("E4", ["fact one"], "tenant_a", ai_percent=100)
    assert k0 != k50
    assert k50 != k100
    assert k0 != k100


def test_cache_key_differs_for_different_interference_level() -> None:
    """Same ai_percent but different interference level must not share a cache entry."""
    k_medium = compute_cache_key("E4", ["fact"], "tenant_a", ai_percent=52, interference_level="medium")
    k_max = compute_cache_key("E4", ["fact"], "tenant_a", ai_percent=52, interference_level="maximum")
    k_none = compute_cache_key("E4", ["fact"], "tenant_a", ai_percent=52, interference_level=None)
    assert k_medium != k_max
    assert k_medium != k_none
    assert k_max != k_none


def test_cache_key_default_ai_level_equals_3() -> None:
    """Default ai_level of 3 must match an explicit ai_level=3 call."""
    k_default = compute_cache_key("E4", ["fact"], "tenant_a")
    k_explicit = compute_cache_key("E4", ["fact"], "tenant_a", ai_level=3)
    assert k_default == k_explicit


# ── get / set / invalidate ────────────────────────────────────────────────────

def test_get_returns_none_on_miss() -> None:
    """get should return None for a key that has not been set."""
    assert get("nonexistent-key-abc") is None


def test_set_and_get_roundtrip() -> None:
    """set then get should return the same payload."""
    key = compute_cache_key("E4", ["test bullet"], "tenant_a")
    payload = {"text": "FIXTURE output", "confidence": 0.9, "provenance": []}
    set(key, payload)
    result = get(key)
    assert result is not None
    assert result["text"] == "FIXTURE output"
    assert result["confidence"] == 0.9


def test_invalidate_removes_entry() -> None:
    """invalidate should delete the cache entry so get returns None."""
    key = compute_cache_key("E4", ["bullet"], "tenant_a")
    set(key, {"text": "x", "confidence": 0.0, "provenance": []})
    assert get(key) is not None
    invalidate(key)
    assert get(key) is None


def test_invalidate_nonexistent_returns_false() -> None:
    """invalidate on a missing key should return False."""
    assert invalidate("totally-missing-key") is False


def test_clear_all_removes_entries(tmp_path: Path, monkeypatch: pytest.MonkeyPatch) -> None:
    """clear_all should remove all .json files in the cache directory."""
    monkeypatch.setattr("app.cache.section_cache.settings.cache_dir", tmp_path)
    key1 = compute_cache_key("E2", ["b1"], "tenant_a")
    key2 = compute_cache_key("E4", ["b2"], "tenant_a")
    set(key1, {"text": "a", "confidence": 0.5, "provenance": []})
    set(key2, {"text": "b", "confidence": 0.5, "provenance": []})
    count = clear_all()
    assert count == 2
    assert get(key1) is None
    assert get(key2) is None


# ── Cache prevents duplicate LLM calls (acceptance test) ─────────────────────

def test_cache_hit_prevents_llm_call(
    monkeypatch: pytest.MonkeyPatch,
    tmp_path: Path,
) -> None:
    """Generating the same section twice must only call the LLM once.

    This is the key acceptance criterion for the caching layer.
    """
    from app.generator.adapter import MockLLMAdapter

    call_count = {"n": 0}

    class CountingAdapter(MockLLMAdapter):
        def generate_section(self, skeleton: str, bullets: list[str], snippets: list[str], **kwargs) -> str:
            call_count["n"] += 1
            return "FIXTURE generated text."

    adapter = CountingAdapter()

    bullets = ["fact one", "fact two"]
    template_id = "E4"
    tenant_id = "tenant_a"
    key = compute_cache_key(template_id, bullets, tenant_id)

    def _generate(sk: str, bl: list[str], sn: list[str]) -> str:
        cached = get(key)
        if cached:
            return cached["text"]
        result = adapter.generate_section(sk, bl, sn)
        set(key, {"text": result, "confidence": 0.8, "provenance": []})
        return result

    # First call — LLM should be invoked
    out1 = _generate("skeleton", bullets, [])
    # Second call — should use cache
    out2 = _generate("skeleton", bullets, [])

    assert out1 == out2
    assert call_count["n"] == 1, (
        f"LLM was called {call_count['n']} times; expected exactly 1"
    )