from __future__ import annotations

import json
import subprocess
import sys
from pathlib import Path

import pytest

from sim.granovetter import cascade, prediction_hit, run_experiments


@pytest.mark.parametrize(("thresholds", "final"), [
    ([0, 0, 5], 2),
    ([1, 1], 0),
    ([0.5], 0),
    ([0, 0.5], 2),
    ([0, 1, 2], 3),
    ([0, 2, 2], 1),
])
def test_small_examples(thresholds, final):
    assert cascade(thresholds)["final"] == final


def test_round_outputs_are_simultaneous_and_one_based():
    assert cascade([0, 0.5, 3]) == {
        "final": 2, "rounds": 2, "series": [1, 2], "stand_round": [1, 2, 0]
    }


@pytest.fixture(scope="module")
def summary():
    return run_experiments()


def test_e1_e2_e3(summary):
    assert summary["ladder"]["final"] == 100
    assert summary["swap"]["final"] == 1
    assert summary["swap_k"]["all_equal_k"] is True
    assert summary["swap_k"]["final"] == list(range(100))


def test_e4_paper_check(summary):
    assert 12.1 <= summary["expected"]["sigma_c"] <= 12.4
    assert 4 <= summary["expected"]["re_below"] <= 8


def stable_json(path: Path):
    value = json.loads(path.read_text(encoding="utf-8"))
    value.pop("generated_at", None)
    value.pop("runtime_s", None)
    return json.dumps(value, ensure_ascii=False, sort_keys=True, indent=1) + "\n"


def test_cli_is_deterministic_except_declared_fields(tmp_path):
    script = Path(__file__).parents[1] / "sim" / "granovetter.py"
    first, second = tmp_path / "first", tmp_path / "second"
    for out in (first, second):
        subprocess.run([sys.executable, str(script), "--out", str(out)], check=True)
    assert stable_json(first / "summary.json") == stable_json(second / "summary.json")
    assert (first / "predictions_check.json").read_bytes() == (second / "predictions_check.json").read_bytes()


@pytest.mark.parametrize(("prediction", "yes", "no"), [
    ({"kind": "eq", "value": 3}, 3, 2),
    ({"kind": "bool", "value": True}, True, False),
    ({"kind": "range", "lo": 2, "hi": 4}, 4, 5),
    ({"kind": "max", "hi": 4}, 4, 5),
    ({"kind": "min", "lo": 2}, 2, 1),
    ({"kind": "in", "values": [2, 4]}, 4, 3),
])
def test_prediction_kinds_positive_and_negative(prediction, yes, no):
    assert prediction_hit(prediction, yes) is True
    assert prediction_hit(prediction, no) is False
    assert prediction_hit(prediction, None) is False