"""Integration tests for the /estimate and /health endpoints."""

from __future__ import annotations

from dataclasses import dataclass
from unittest.mock import patch

import pytest
from fastapi.testclient import TestClient


# -- helpers to build fake ecologits responses (same as unit tests) --

@dataclass
class _RangeValue:
    min: float
    max: float


@dataclass
class _Metric:
    value: _RangeValue


@dataclass
class _FakeImpactsOutput:
    gwp: _Metric | None = None
    energy: _Metric | None = None
    warnings: list | None = None
    errors: list | None = None


def _make_impact(gwp_min: float = 0.001, gwp_max: float = 0.003):
    return _FakeImpactsOutput(
        gwp=_Metric(value=_RangeValue(min=gwp_min, max=gwp_max)),
        energy=_Metric(value=_RangeValue(min=0.01, max=0.02)),
        warnings=[],
        errors=[],
    )


# -- tests --

def test_health(client: TestClient):
    resp = client.get("/health")
    assert resp.status_code == 200
    assert resp.json() == {"status": "ok"}


@patch("app.services.impact.llm_impacts")
def test_estimate_success(mock_llm_impacts, client: TestClient):
    mock_llm_impacts.return_value = _make_impact()

    resp = client.post("/estimate", json={
        "provider": "openai",
        "model": "gpt-4o",
        "output_tokens": 500,
        "request_latency": 1.2,
    })

    assert resp.status_code == 200
    body = resp.json()
    assert body["status"] == "ok"
    assert body["data"] is not None
    assert body["data"]["gwp_avg_kgco2eq"] == pytest.approx(0.002)
    assert body["data"]["gwp_gco2eq"] == pytest.approx(2.0)
    assert body["data"]["equivalences"]["car_km"] > 0
    assert body["data"]["equivalences"]["tgv_km"] > 0


@patch("app.services.impact.llm_impacts")
def test_estimate_unknown_model(mock_llm_impacts, client: TestClient):
    """Unknown provider/model returns an error status, not HTTP 500."""
    mock_llm_impacts.return_value = _FakeImpactsOutput(
        gwp=None, energy=None, warnings=[], errors=["Model not found"],
    )

    resp = client.post("/estimate", json={
        "provider": "unknown",
        "model": "no-model",
        "output_tokens": 100,
        "request_latency": 0.5,
    })

    assert resp.status_code == 200
    body = resp.json()
    assert body["status"] == "error"
    assert body["data"] is None
    assert len(body["errors"]) > 0


def test_estimate_missing_fields(client: TestClient):
    """Missing required fields should return 422."""
    resp = client.post("/estimate", json={"provider": "openai"})
    assert resp.status_code == 422


def test_estimate_invalid_token_count(client: TestClient):
    """output_tokens must be > 0."""
    resp = client.post("/estimate", json={
        "provider": "openai",
        "model": "gpt-4o",
        "output_tokens": 0,
        "request_latency": 1.0,
    })
    assert resp.status_code == 422


@patch("app.services.impact.llm_impacts")
def test_estimate_ecologits_crash(mock_llm_impacts, client: TestClient):
    """If ecologits raises, the API should return HTTP 500."""
    mock_llm_impacts.side_effect = RuntimeError("unexpected")

    resp = client.post("/estimate", json={
        "provider": "openai",
        "model": "gpt-4o",
        "output_tokens": 500,
        "request_latency": 1.0,
    })

    assert resp.status_code == 500