# Copyright 2020 MONAI Consortium
# 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 unittest
import time
import sys
import torch
import numpy as np
from parameterized import parameterized
from monai.transforms import CopyItemsd
from monai.utils import ensure_tuple

TEST_CASE_1 = ["img", 1, "img_1"]

TEST_CASE_2 = [["img", "seg"], 1, ["img_1", "seg_1"]]

TEST_CASE_3 = ["img", 2, ["img_1", "img_2"]]

TEST_CASE_4 = [["img", "seg"], 2, ["img_1", "seg_1", "img_2", "seg_2"]]


class TestCopyItemsd(unittest.TestCase):
    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4])
    def test_numpy_values(self, keys, times, names):
        input_data = {"img": np.array([[0, 1], [1, 2]]), "seg": np.array([[0, 1], [1, 2]])}
        result = CopyItemsd(keys=keys, times=times, names=names)(input_data)
        for name in ensure_tuple(names):
            self.assertTrue(name in result)
            result[name] += 1
            np.testing.assert_allclose(result[name], np.array([[1, 2], [2, 3]]))
        np.testing.assert_allclose(result["img"], np.array([[0, 1], [1, 2]]))

    def test_tensor_values(self):
        device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu:0")
        input_data = {
            "img": torch.tensor([[0, 1], [1, 2]], device=device),
            "seg": torch.tensor([[0, 1], [1, 2]], device=device),
        }
        result = CopyItemsd(keys="img", times=1, names="img_1")(input_data)
        self.assertTrue("img_1" in result)
        result["img_1"] += 1
        torch.testing.assert_allclose(result["img"], torch.tensor([[0, 1], [1, 2]], device=device))
        torch.testing.assert_allclose(result["img_1"], torch.tensor([[1, 2], [2, 3]], device=device))

    def test_array_values(self):
        input_data = {"img": [[0, 1], [1, 2]], "seg": [[0, 1], [1, 2]]}
        result = CopyItemsd(keys="img", times=1, names="img_1")(input_data)
        self.assertTrue("img_1" in result)
        result["img_1"][0][0] += 1
        np.testing.assert_allclose(result["img"], [[0, 1], [1, 2]])
        np.testing.assert_allclose(result["img_1"], [[1, 1], [1, 2]])


if __name__ == "__main__":
    unittest.main()

torch.device torch.cuda.is_available torch.tensor