import os

import numpy as np


def _collect_vnormal_sequence(sensor, frames: int) -> np.ndarray:
    seq = []
    for _ in range(frames):
        img = sensor.get_img()
        assert img is not None
        sensor.compute_deformation(check_motion=False)
        (vnormal,) = sensor.read_info(sensor.info.VNORMAL)
        seq.append(vnormal.copy())
    return np.asarray(seq)


def test_available_backends_smoke(sensor_factory, available_backends):
    for backend in available_backends:
        sensor = sensor_factory(backend=backend)
        try:
            img = sensor.get_img()
            assert img is not None
            sensor.compute_deformation(check_motion=False)
            flow, vnormal, vshear = sensor.read_info(
                sensor.info.VRAW,
                sensor.info.VNORMAL,
                sensor.info.VSHEAR,
            )
            assert flow.shape == vnormal.shape == vshear.shape
            assert flow.shape[-1] == 2
            assert np.isfinite(flow).all()
            assert np.isfinite(vnormal).all()
            assert np.isfinite(vshear).all()
        finally:
            sensor.disconnect()


def test_backend_parity_against_cpu(sensor_factory, available_backends):
    if len(available_backends) <= 1:
        return

    compare_frames = int(os.getenv("ORISYS_PARITY_FRAMES", "30"))
    warmup = int(os.getenv("ORISYS_PARITY_WARMUP", "5"))
    tol = float(os.getenv("ORISYS_PARITY_REL_TOL", "2.0"))

    cpu_sensor = sensor_factory(backend="cpu")
    try:
        cpu_seq = _collect_vnormal_sequence(cpu_sensor, compare_frames)
    finally:
        cpu_sensor.disconnect()

    cpu_ref = cpu_seq[warmup:].mean(axis=0)
    cpu_scale = float(np.mean(np.abs(cpu_ref))) + 1e-6

    for backend in available_backends:
        if backend == "cpu":
            continue
        sensor = sensor_factory(backend=backend)
        try:
            seq = _collect_vnormal_sequence(sensor, compare_frames)
        finally:
            sensor.disconnect()

        cand_ref = seq[warmup:].mean(axis=0)
        rel = float(np.mean(np.abs(cand_ref - cpu_ref)) / cpu_scale)
        assert rel < tol, f"{backend} parity drift too high: relative_error={rel:.4f}, tol={tol}"