import json
from pathlib import Path

import httpx
import pytest
from django.test import override_settings

from apps.jobs.models import AiAnalysisCache
from apps.jobs.services import ai as ai_service
from apps.jobs.services.ai import analyze_job_text

pytestmark = pytest.mark.django_db


def _evaluation_cases() -> list[dict[str, object]]:
    path = Path(__file__).resolve().parents[2] / "fixtures" / "ai" / "evaluation_set.json"
    return json.loads(path.read_text(encoding="utf-8-sig"))["cases"]


@pytest.mark.parametrize("case", _evaluation_cases(), ids=lambda case: case["id"])
def test_ai_evaluation_cases(monkeypatch, case):
    if case["response_type"] == "disabled":
        with override_settings(OLLAMA_ENABLED=False, OLLAMA_MODEL=""):
            result = analyze_job_text(
                title=case["title"],
                description=case["description"],
                content_hash=case["content_hash"],
            )
        assert result.status == ai_service.AiAnalysisCache.Status.DISABLED
        assert result.error_category == "ollama_disabled"
        assert result.cached is False
        assert result.summary_nl == ""
        return

    if case["response_type"] == "timeout":

        class FakeClient:
            def __init__(self, *args, **kwargs):
                pass

            def __enter__(self):
                return self

            def __exit__(self, exc_type, exc, tb):
                return False

            def post(self, *args, **kwargs):
                raise httpx.TimeoutException("timeout")

        monkeypatch.setattr(httpx, "Client", FakeClient)
    else:

        class FakeClient:
            def __init__(self, *args, **kwargs):
                pass

            def __enter__(self):
                return self

            def __exit__(self, exc_type, exc, tb):
                return False

            def post(self, *args, **kwargs):
                return _fake_response(case["response"])

        monkeypatch.setattr(httpx, "Client", FakeClient)

    with override_settings(
        OLLAMA_ENABLED=True,
        OLLAMA_MODEL="local-model",
        OLLAMA_BASE_URL="http://ollama:11434",
        OLLAMA_TIMEOUT_SECONDS=2,
    ):
        result = analyze_job_text(
            title=case["title"],
            description=case["description"],
            content_hash=case["content_hash"],
        )

    assert result.status == case["expected_status"]
    assert result.error_category == case["expected_error_category"]
    assert result.schema_version == "1.0.0"
    if result.status == ai_service.AiAnalysisCache.Status.OK:
        assert result.summary_nl == case["expected_summary"]
        assert round(result.features.get("support_ratio", 0), 3) == case["expected_support_ratio"]
        assert result.features.get("evidence") == case["expected_evidence"]
    if result.status != ai_service.AiAnalysisCache.Status.OK:
        assert result.features == {}


def _fake_response(response_text: object):
    class FakeResponse:
        def raise_for_status(self):
            return None

        def json(self):
            return {"response": response_text}

    return FakeResponse()


def test_ai_cache_reuses_result(monkeypatch):
    calls = 0

    class FakeClient:
        def __init__(self, *args, **kwargs):
            pass

        def __enter__(self):
            return self

        def __exit__(self, exc_type, exc, tb):
            return False

        def post(self, *args, **kwargs):
            nonlocal calls
            calls += 1
            return _fake_response(
                '{"summary_nl":"Gevalideerde IT-ops advertentie.",'
                '"features":{"support_ratio":0.03,"consultancy_ratio":0.01,'
                '"travel_ratio":0.02,"seniority":"senior","evidence":["IT"]},'
                '"warnings":[]}'
            )

    monkeypatch.setattr(httpx, "Client", FakeClient)

    with override_settings(
        OLLAMA_ENABLED=True,
        OLLAMA_MODEL="local-model",
        OLLAMA_BASE_URL="http://ollama:11434",
    ):
        first = analyze_job_text(
            "Cloud Engineer",
            "Cloud Engineer vacature. IT-ops taken incl. Azure en Linux.",
            content_hash="cache-hit-case",
        )
        second = analyze_job_text(
            "Cloud Engineer",
            "Cloud Engineer vacature. IT-ops taken incl. Azure en Linux.",
            content_hash="cache-hit-case",
        )

    assert first.status == ai_service.AiAnalysisCache.Status.OK
    assert second.status == ai_service.AiAnalysisCache.Status.OK
    assert first.cached is False
    assert second.cached is True
    assert calls == 1
    assert AiAnalysisCache.objects.count() == 1