import torch
import torchtestcase

from nde.transforms import splines

class RationalQuadraticSplineTest(torchtestcase.TorchTestCase):
    def test_forward_inverse_are_consistent(self):
            num_bins = 10
            shape = [2,3,4]

            unnormalized_widths = torch.randn(*shape, num_bins)
            unnormalized_heights = torch.randn(*shape, num_bins)
            unnormalized_derivatives = torch.randn(*shape, num_bins + 1)

            def call_spline_fn(inputs, inverse=False):
                return splines.rational_quadratic_spline(
                    inputs=inputs,
                    unnormalized_widths=unnormalized_widths,
                    unnormalized_heights=unnormalized_heights,
                    unnormalized_derivatives=unnormalized_derivatives,
                    inverse=inverse
                )

            inputs = torch.rand(*shape)
            outputs, logabsdet = call_spline_fn(inputs, inverse=False)
            inputs_inv, logabsdet_inv = call_spline_fn(outputs, inverse=True)

            self.eps = 1e-4
            self.assertEqual(inputs, inputs_inv)
            self.assertEqual(logabsdet + logabsdet_inv, torch.zeros_like(logabsdet))

class UnconstrainedRationalQuadraticSplineTest(torchtestcase.TorchTestCase):
    def test_forward_inverse_are_consistent(self):
        num_bins = 10
        shape = [2,3,4]

        unnormalized_widths = torch.randn(*shape, num_bins)
        unnormalized_heights = torch.randn(*shape, num_bins)
        unnormalized_derivatives = torch.randn(*shape, num_bins + 1)

        def call_spline_fn(inputs, inverse=False):
            return splines.unconstrained_rational_quadratic_spline(
                inputs=inputs,
                unnormalized_widths=unnormalized_widths,
                unnormalized_heights=unnormalized_heights,
                unnormalized_derivatives=unnormalized_derivatives,
                inverse=inverse
            )

        inputs = 3 * torch.randn(*shape) # Note inputs are outside [0,1].
        outputs, logabsdet = call_spline_fn(inputs, inverse=False)
        inputs_inv, logabsdet_inv = call_spline_fn(outputs, inverse=True)

        self.eps = 1e-4
        self.assertEqual(inputs, inputs_inv)
        self.assertEqual(logabsdet + logabsdet_inv, torch.zeros_like(logabsdet))
