import unittest

import torch
from numpy.testing import assert_equal, assert_almost_equal

if torch.cuda.is_available():
    from .compute_spline_basis import compute_spline_basis
    from .compute_spline_basis import get_basis_kernel


class SplineQuadraticGPUTest(unittest.TestCase):
    @unittest.skipIf(not torch.cuda.is_available(), 'no GPU')
    def test_open_spline(self):
        input = torch.cuda.FloatTensor([0, 0.05, 0.25, 0.5, 0.75, 0.95, 1])
        kernel_size = torch.cuda.LongTensor([6])
        is_open_spline = torch.cuda.LongTensor([1])
        k_max = 3
        K = 6
        dim = 1
        basis_kernel = get_basis_kernel(k_max, K, dim, 2)

        a1, i1 = compute_spline_basis(input, kernel_size, is_open_spline, 6,
                                      basis_kernel)

        a2 = [[0.5, 0.5, 0], [0.32, 0.66, 0.02], [0.5, 0.5, 0], [0.5, 0.5, 0],
              [0.5, 0.5, 0], [0.02, 0.66, 0.32], [0.5, 0.5, 0]]
        i2 = [[0, 1, 2], [0, 1, 2], [1, 2, 3], [2, 3, 4], [3, 4, 5], [3, 4, 5],
              [4, 5, 0]]

        assert_almost_equal(a1.cpu().numpy(), a2, 2)
        assert_equal(i1.cpu().numpy(), i2)

    @unittest.skipIf(not torch.cuda.is_available(), 'no GPU')
    def test_closed_spline(self):
        input = torch.cuda.FloatTensor([0, 0.05, 0.25, 0.5, 0.75, 0.95, 1])
        kernel_size = torch.cuda.LongTensor([4])
        is_open_spline = torch.cuda.LongTensor([0])
        k_max = 3
        K = 4
        dim = 1
        basis_kernel = get_basis_kernel(k_max, K, dim, 2)
        a1, i1 = compute_spline_basis(input, kernel_size, is_open_spline, 4,
                                      basis_kernel)

        a2 = [[0.5, 0.5, 0], [0.32, 0.66, 0.02], [0.5, 0.5, 0], [0.5, 0.5, 0],
              [0.5, 0.5, 0], [0.02, 0.66, 0.32], [0.5, 0.5, 0]]
        i2 = [[0, 1, 2], [0, 1, 2], [1, 2, 3], [2, 3, 0], [3, 0, 1], [3, 0, 1],
              [0, 1, 2]]

        assert_almost_equal(a1.cpu().numpy(), a2, 2)
        assert_equal(i1.cpu().numpy(), i2)