import pytest
import torch
from torch.autograd import gradcheck

from kornia.geometry.calibration.distort import distort_points
from kornia.testing import assert_close


class TestDistortPoints:
    def test_smoke(self, device, dtype):
        points = torch.rand(1, 2, device=device, dtype=dtype)
        K = torch.rand(3, 3, device=device, dtype=dtype)
        distCoeff = torch.rand(4, device=device, dtype=dtype)
        pointsu = distort_points(points, K, distCoeff)
        assert points.shape == pointsu.shape

        new_K = torch.rand(3, 3, device=device, dtype=dtype)
        pointsu = distort_points(points, K, distCoeff, new_K)
        assert points.shape == pointsu.shape

    def test_smoke_batch(self, device, dtype):
        points = torch.rand(1, 1, 2, device=device, dtype=dtype)
        K = torch.rand(1, 3, 3, device=device, dtype=dtype)
        distCoeff = torch.rand(1, 4, device=device, dtype=dtype)
        pointsu = distort_points(points, K, distCoeff)
        assert points.shape == pointsu.shape

        new_K = torch.rand(1, 3, 3, device=device, dtype=dtype)
        pointsu = distort_points(points, K, distCoeff, new_K)
        assert points.shape == pointsu.shape

    @pytest.mark.parametrize(
        "batch_size, num_points, num_distcoeff", [(1, 3, 4), (2, 4, 5), (3, 5, 8), (4, 6, 12), (5, 7, 14)]
    )
    def test_shape(self, batch_size, num_points, num_distcoeff, device, dtype):
        B, N, Ndist = batch_size, num_points, num_distcoeff

        points = torch.rand(B, N, 2, device=device, dtype=dtype)
        K = torch.rand(B, 3, 3, device=device, dtype=dtype)
        new_K = torch.rand(B, 3, 3, device=device, dtype=dtype)
        distCoeff = torch.rand(B, Ndist, device=device, dtype=dtype)

        pointsu = distort_points(points, K, distCoeff, new_K)
        assert pointsu.shape == (B, N, 2)

    def test_gradcheck(self, device):
        points = torch.rand(1, 8, 2, device=device, dtype=torch.float64, requires_grad=True)
        K = torch.rand(1, 3, 3, device=device, dtype=torch.float64)
        new_K = torch.rand(1, 3, 3, device=device, dtype=torch.float64)
        distCoeff = torch.rand(1, 4, device=device, dtype=torch.float64)

        assert gradcheck(distort_points, (points, K, distCoeff, new_K), raise_exception=True, fast_mode=True)

    def test_jit(self, device, dtype):
        points = torch.rand(1, 1, 2, device=device, dtype=dtype)
        K = torch.rand(1, 3, 3, device=device, dtype=dtype)
        new_K = torch.rand(1, 3, 3, device=device, dtype=dtype)
        distCoeff = torch.rand(1, 4, device=device, dtype=dtype)
        inputs = (points, K, distCoeff, new_K)

        op = distort_points
        op_jit = torch.jit.script(op)
        assert_close(op(*inputs), op_jit(*inputs))
