# LICENSE HEADER MANAGED BY add-license-header
#
# Copyright 2018 Kornia Team
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#

import pytest
import torch

import kornia

from testing.base import BaseTester


class TestHistMatch(BaseTester):
    def test_interp(self, device, dtype):
        xp = torch.tensor([1, 2, 3], device=device, dtype=dtype)
        fp = torch.tensor([4, 2, 0], device=device, dtype=dtype)
        x = torch.tensor([4, 5, 6], device=device, dtype=dtype)
        x_hat_expected = torch.tensor([-2.0, -4.0, -6.0], device=device, dtype=dtype)
        x_hat = kornia.contrib.interp(x, xp, fp)
        self.assert_close(x_hat_expected, x_hat)

    def test_histmatch(self, device, dtype):
        torch.manual_seed(44)
        # generate random value by CPU.
        src = torch.randn(1, 4, 4).to(device=device, dtype=dtype)
        dst = torch.randn(1, 16, 16).to(device=device, dtype=dtype)
        out = kornia.contrib.histogram_matching(src, dst)
        exp = torch.tensor(
            [
                [
                    [1.5902, 0.9295, 2.9409, 0.1211],
                    [0.2472, 1.2137, -0.1098, -0.4272],
                    [-0.2644, -1.1983, -0.6065, -0.8091],
                    [-1.4999, 0.6370, -0.9800, 0.4474],
                ]
            ],
            device=device,
            dtype=dtype,
        )
        assert exp.shape == out.shape
        self.assert_close(out, exp, rtol=1e-4, atol=1e-4)

    @pytest.mark.skip(reason="not differentiable now.")
    def test_grad(self, device):
        B, C, H, W = 1, 3, 32, 32
        src = torch.rand(B, C, H, W, device=device, dtype=torch.float64, requires_grad=True)
        dst = torch.rand(B, C, H, W, device=device, dtype=torch.float64, requires_grad=True)
        self.gradcheck(kornia.contrib.histogram_matching, (src, dst))