import pytest
import torch
from torch.autograd import gradcheck

import kornia
from kornia.testing import BaseTester, assert_close
from kornia.utils._compat import torch_version


class TestGrayscaleToRgb(BaseTester):
    def test_smoke(self, device, dtype):
        C, H, W = 1, 4, 5
        img = torch.rand(C, H, W, device=device, dtype=dtype)
        assert isinstance(kornia.color.grayscale_to_rgb(img), torch.Tensor)

    @pytest.mark.parametrize("batch_size, height, width", [(1, 3, 4), (2, 2, 4), (3, 4, 1)])
    def test_cardinality(self, device, dtype, batch_size, height, width):
        img = torch.ones(batch_size, 1, height, width, device=device, dtype=dtype)
        assert kornia.color.grayscale_to_rgb(img).shape == (batch_size, 3, height, width)

    def test_exception(self, device, dtype):
        with pytest.raises(TypeError):
            assert kornia.color.grayscale_to_rgb([0.0])

        with pytest.raises(ValueError):
            img = torch.ones(1, 1, device=device, dtype=dtype)
            assert kornia.color.grayscale_to_rgb(img)

        with pytest.raises(ValueError):
            img = torch.ones(2, 1, 1, device=device, dtype=dtype)
            assert kornia.color.grayscale_to_rgb(img)

        with pytest.raises(ValueError):
            img = torch.ones(1, 3, 1, 1, device=device, dtype=dtype)
            assert kornia.color.grayscale_to_rgb(img)

    def test_opencv(self, device, dtype):
        data = torch.tensor(
            [
                [
                    [0.4684734, 0.8954562, 0.6064363, 0.5236061, 0.6106016],
                    [0.1709944, 0.5133104, 0.7915002, 0.5745703, 0.1680204],
                    [0.5279005, 0.6092287, 0.3034387, 0.5333768, 0.6064113],
                    [0.3503858, 0.5720159, 0.7052018, 0.4558409, 0.3261529],
                    [0.6988886, 0.5897652, 0.6532392, 0.7234108, 0.7218805],
                ]
            ],
            device=device,
            dtype=dtype,
        )

        # Output data generated with OpenCV 4.5.2: cv2.cvtColor(img_np, cv2.COLOR_GRAY2RGB)
        expected = torch.tensor(
            [
                [
                    [0.4684734, 0.8954562, 0.6064363, 0.5236061, 0.6106016],
                    [0.1709944, 0.5133104, 0.7915002, 0.5745703, 0.1680204],
                    [0.5279005, 0.6092287, 0.3034387, 0.5333768, 0.6064113],
                    [0.3503858, 0.5720159, 0.7052018, 0.4558409, 0.3261529],
                    [0.6988886, 0.5897652, 0.6532392, 0.7234108, 0.7218805],
                ],
                [
                    [0.4684734, 0.8954562, 0.6064363, 0.5236061, 0.6106016],
                    [0.1709944, 0.5133104, 0.7915002, 0.5745703, 0.1680204],
                    [0.5279005, 0.6092287, 0.3034387, 0.5333768, 0.6064113],
                    [0.3503858, 0.5720159, 0.7052018, 0.4558409, 0.3261529],
                    [0.6988886, 0.5897652, 0.6532392, 0.7234108, 0.7218805],
                ],
                [
                    [0.4684734, 0.8954562, 0.6064363, 0.5236061, 0.6106016],
                    [0.1709944, 0.5133104, 0.7915002, 0.5745703, 0.1680204],
                    [0.5279005, 0.6092287, 0.3034387, 0.5333768, 0.6064113],
                    [0.3503858, 0.5720159, 0.7052018, 0.4558409, 0.3261529],
                    [0.6988886, 0.5897652, 0.6532392, 0.7234108, 0.7218805],
                ],
            ],
            device=device,
            dtype=dtype,
        )

        img_rgb = kornia.color.grayscale_to_rgb(data)
        assert_close(img_rgb, expected)

    @pytest.mark.grad()
    def test_gradcheck(self, device, dtype):
        B, C, H, W = 2, 1, 4, 4
        img = torch.ones(B, C, H, W, device=device, dtype=torch.float64, requires_grad=True)
        assert gradcheck(kornia.color.grayscale_to_rgb, (img,), raise_exception=True, fast_mode=True)

    def test_dynamo(self, device, dtype, torch_optimizer):
        B, C, H, W = 2, 1, 4, 4
        img = torch.ones(B, C, H, W, device=device, dtype=dtype)
        op = kornia.color.grayscale_to_rgb
        op_optimized = torch_optimizer(op)
        assert_close(op(img), op_optimized(img))

    def test_module(self, device, dtype):
        B, C, H, W = 2, 1, 4, 4
        img = torch.ones(B, C, H, W, device=device, dtype=dtype)
        gray_ops = kornia.color.GrayscaleToRgb().to(device, dtype)
        gray_fcn = kornia.color.grayscale_to_rgb
        assert_close(gray_ops(img), gray_fcn(img))


class TestRgbToGrayscale(BaseTester):
    def test_smoke(self, device, dtype):
        C, H, W = 3, 4, 5
        img = torch.rand(C, H, W, device=device, dtype=dtype)
        out = kornia.color.rgb_to_grayscale(img)
        assert out.device == img.device
        assert out.dtype == img.dtype

    def test_smoke_byte(self, device):
        C, H, W = 3, 4, 5
        img = torch.randint(0, 255, (C, H, W), device=device, dtype=torch.uint8)
        out = kornia.color.rgb_to_grayscale(img)
        assert out.device == img.device
        assert out.dtype == img.dtype

    @pytest.mark.parametrize("batch_size, height, width", [(1, 3, 4), (2, 2, 4), (3, 4, 1)])
    def test_cardinality(self, device, dtype, batch_size, height, width):
        img = torch.ones(batch_size, 3, height, width, device=device, dtype=dtype)
        assert kornia.color.rgb_to_grayscale(img).shape == (batch_size, 1, height, width)

    def test_exception(self, device, dtype):
        with pytest.raises(TypeError):
            assert kornia.color.rgb_to_grayscale([0.0])

        with pytest.raises(ValueError):
            img = torch.ones(1, 1, device=device, dtype=dtype)
            assert kornia.color.rgb_to_grayscale(img)

        with pytest.raises(ValueError):
            img = torch.ones(2, 1, 1, device=device, dtype=dtype)
            assert kornia.color.rgb_to_grayscale(img)

        with pytest.raises(ValueError):
            img = torch.ones(3, 1, 1, device=device, dtype=dtype)
            rgb_weights = torch.tensor([0.2, 0.8])
            assert kornia.color.rgb_to_grayscale(img, rgb_weights=rgb_weights)

    def test_opencv(self, device, dtype):
        data = torch.tensor(
            [
                [
                    [0.3944633, 0.8597369, 0.1670904, 0.2825457, 0.0953912],
                    [0.1251704, 0.8020709, 0.8933256, 0.9170977, 0.1497008],
                    [0.2711633, 0.1111478, 0.0783281, 0.2771807, 0.5487481],
                    [0.0086008, 0.8288748, 0.9647092, 0.8922020, 0.7614344],
                    [0.2898048, 0.1282895, 0.7621747, 0.5657831, 0.9918593],
                ],
                [
                    [0.5414237, 0.9962701, 0.8947155, 0.5900949, 0.9483274],
                    [0.0468036, 0.3933847, 0.8046577, 0.3640994, 0.0632100],
                    [0.6171775, 0.8624780, 0.4126036, 0.7600935, 0.7279997],
                    [0.4237089, 0.5365476, 0.5591233, 0.1523191, 0.1382165],
                    [0.8932794, 0.8517839, 0.7152701, 0.8983801, 0.5905426],
                ],
                [
                    [0.2869580, 0.4700376, 0.2743714, 0.8135023, 0.2229074],
                    [0.9306560, 0.3734594, 0.4566821, 0.7599275, 0.7557513],
                    [0.7415742, 0.6115875, 0.3317572, 0.0379378, 0.1315770],
                    [0.8692724, 0.0809556, 0.7767404, 0.8742208, 0.1522012],
                    [0.7708948, 0.4509611, 0.0481175, 0.2358997, 0.6900532],
                ],
            ],
            device=device,
            dtype=dtype,
        )

        # Output data generated with OpenCV 4.1.1: cv2.cvtColor(img_np, cv2.COLOR_RGB2GRAY)
        expected = torch.tensor(
            [
                [
                    [0.4684734, 0.8954562, 0.6064363, 0.5236061, 0.6106016],
                    [0.1709944, 0.5133104, 0.7915002, 0.5745703, 0.1680204],
                    [0.5279005, 0.6092287, 0.3034387, 0.5333768, 0.6064113],
                    [0.3503858, 0.5720159, 0.7052018, 0.4558409, 0.3261529],
                    [0.6988886, 0.5897652, 0.6532392, 0.7234108, 0.7218805],
                ]
            ],
            device=device,
            dtype=dtype,
        )

        img_gray = kornia.color.rgb_to_grayscale(data)
        assert_close(img_gray, expected)

    def test_custom_rgb_weights(self, device, dtype):
        B, C, H, W = 2, 3, 4, 4
        img = torch.ones(B, C, H, W, device=device, dtype=dtype)

        rgb_weights = torch.tensor([0.5, 0.25, 0.25])
        img_gray = kornia.color.rgb_to_grayscale(img, rgb_weights=rgb_weights)
        assert img_gray.device == device
        assert img_gray.dtype == dtype

    @pytest.mark.grad()
    def test_gradcheck(self, device, dtype):
        B, C, H, W = 2, 3, 4, 4
        img = torch.ones(B, C, H, W, device=device, dtype=torch.float64, requires_grad=True)
        assert gradcheck(kornia.color.rgb_to_grayscale, (img,), raise_exception=True, fast_mode=True)

    @pytest.mark.skipif(torch_version() in {'2.0.0', '2.0.1'}, reason='Not working on 2.0')
    def test_dynamo(self, device, dtype, torch_optimizer):
        # TODO: investigate the problem with dynamo
        B, C, H, W = 2, 3, 4, 4
        img = torch.ones(B, C, H, W, device=device, dtype=dtype)
        op = kornia.color.rgb_to_grayscale
        op_optimized = torch_optimizer(op)
        assert_close(op(img), op_optimized(img))

    def test_module(self, device, dtype):
        B, C, H, W = 2, 3, 4, 4
        img = torch.ones(B, C, H, W, device=device, dtype=dtype)
        gray_ops = kornia.color.RgbToGrayscale().to(device, dtype)
        gray_fcn = kornia.color.rgb_to_grayscale
        assert_close(gray_ops(img), gray_fcn(img))


class TestBgrToGrayscale(BaseTester):
    def test_smoke(self, device, dtype):
        C, H, W = 3, 4, 5
        img = torch.rand(C, H, W, device=device, dtype=dtype)
        assert kornia.color.bgr_to_grayscale(img) is not None

    @pytest.mark.parametrize("batch_size, height, width", [(1, 3, 4), (2, 2, 4), (3, 4, 1)])
    def test_cardinality(self, device, dtype, batch_size, height, width):
        img = torch.ones(batch_size, 3, height, width, device=device, dtype=dtype)
        assert kornia.color.bgr_to_grayscale(img).shape == (batch_size, 1, height, width)

    def test_exception(self, device, dtype):
        with pytest.raises(TypeError):
            assert kornia.color.bgr_to_grayscale([0.0])

        with pytest.raises(ValueError):
            img = torch.ones(1, 1, device=device, dtype=dtype)
            assert kornia.color.bgr_to_grayscale(img)

        with pytest.raises(ValueError):
            img = torch.ones(2, 1, 1, device=device, dtype=dtype)
            assert kornia.color.bgr_to_grayscale(img)

    def test_opencv(self, device, dtype):
        data = torch.tensor(
            [
                [
                    [0.3944633, 0.8597369, 0.1670904, 0.2825457, 0.0953912],
                    [0.1251704, 0.8020709, 0.8933256, 0.9170977, 0.1497008],
                    [0.2711633, 0.1111478, 0.0783281, 0.2771807, 0.5487481],
                    [0.0086008, 0.8288748, 0.9647092, 0.8922020, 0.7614344],
                    [0.2898048, 0.1282895, 0.7621747, 0.5657831, 0.9918593],
                ],
                [
                    [0.5414237, 0.9962701, 0.8947155, 0.5900949, 0.9483274],
                    [0.0468036, 0.3933847, 0.8046577, 0.3640994, 0.0632100],
                    [0.6171775, 0.8624780, 0.4126036, 0.7600935, 0.7279997],
                    [0.4237089, 0.5365476, 0.5591233, 0.1523191, 0.1382165],
                    [0.8932794, 0.8517839, 0.7152701, 0.8983801, 0.5905426],
                ],
                [
                    [0.2869580, 0.4700376, 0.2743714, 0.8135023, 0.2229074],
                    [0.9306560, 0.3734594, 0.4566821, 0.7599275, 0.7557513],
                    [0.7415742, 0.6115875, 0.3317572, 0.0379378, 0.1315770],
                    [0.8692724, 0.0809556, 0.7767404, 0.8742208, 0.1522012],
                    [0.7708948, 0.4509611, 0.0481175, 0.2358997, 0.6900532],
                ],
            ],
            device=device,
            dtype=dtype,
        )

        # Output data generated with OpenCV 4.1.1: cv2.cvtColor(img_np, cv2.COLOR_BGR2GRAY)
        expected = torch.tensor(
            [
                [
                    [0.4485849, 0.8233618, 0.6262833, 0.6218331, 0.6341921],
                    [0.3200093, 0.4340172, 0.7107211, 0.5454938, 0.2801398],
                    [0.6149265, 0.7018101, 0.3503231, 0.4891168, 0.5292346],
                    [0.5096100, 0.4336508, 0.6704276, 0.4525143, 0.2134447],
                    [0.7878902, 0.6494595, 0.5211386, 0.6623823, 0.6660464],
                ]
            ],
            device=device,
            dtype=dtype,
        )

        img_gray = kornia.color.bgr_to_grayscale(data)
        assert_close(img_gray, expected)

    @pytest.mark.grad()
    def test_gradcheck(self, device, dtype):
        B, C, H, W = 2, 3, 4, 4
        img = torch.ones(B, C, H, W, device=device, dtype=torch.float64, requires_grad=True)
        assert gradcheck(kornia.color.bgr_to_grayscale, (img,), raise_exception=True, fast_mode=True)

    @pytest.mark.skipif(torch_version() in {'2.0.0', '2.0.1'}, reason='Not working on 2.0')
    def test_dynamo(self, device, dtype, torch_optimizer):
        # TODO: investigate the problem with dynamo
        B, C, H, W = 2, 3, 4, 4
        img = torch.ones(B, C, H, W, device=device, dtype=dtype)
        op = kornia.color.rgb_to_grayscale
        op_optimized = torch_optimizer(op)
        assert_close(op(img), op_optimized(img))

    def test_module(self, device, dtype):
        B, C, H, W = 2, 3, 4, 4
        img = torch.ones(B, C, H, W, device=device, dtype=dtype)
        gray_ops = kornia.color.BgrToGrayscale().to(device, dtype)
        gray_fcn = kornia.color.bgr_to_grayscale
        assert_close(gray_ops(img), gray_fcn(img))
