# Copyright (c) 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.

from __future__ import annotations

import unittest

import numpy as np
from parameterized import parameterized

from monai.transforms import LabelToMask
from tests.utils import TEST_NDARRAYS, assert_allclose

TESTS = []
for p in TEST_NDARRAYS:
    TESTS.append(
        [
            {"select_labels": [2, 3], "merge_channels": False},
            p(np.array([[[1, 1, 1], [2, 2, 2], [3, 3, 3], [4, 4, 4], [5, 5, 5], [6, 6, 6]]])),
            np.array([[[0, 0, 0], [1, 1, 1], [1, 1, 1], [0, 0, 0], [0, 0, 0], [0, 0, 0]]]),
        ]
    )
    TESTS.append(
        [
            {"select_labels": 2, "merge_channels": False},
            p(np.array([[[1, 1, 1], [2, 2, 2], [3, 3, 3], [4, 4, 4], [5, 5, 5], [6, 6, 6]]])),
            np.array([[[0, 0, 0], [1, 1, 1], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]]]),
        ]
    )
    TESTS.append(
        [
            {"select_labels": [1, 2], "merge_channels": False},
            p(np.array([[[0, 0, 1], [0, 1, 0]], [[1, 0, 0], [0, 1, 1]], [[1, 0, 1], [1, 1, 0]]])),
            np.array([[[1, 0, 0], [0, 1, 1]], [[1, 0, 1], [1, 1, 0]]]),
        ]
    )
    TESTS.append(
        [
            {"select_labels": 2, "merge_channels": False},
            p(np.array([[[0, 0, 1], [0, 1, 0]], [[1, 0, 0], [0, 1, 1]], [[1, 0, 1], [1, 1, 0]]])),
            np.array([[[1, 0, 1], [1, 1, 0]]]),
        ]
    )
    TESTS.append(
        [
            {"select_labels": [1, 2], "merge_channels": True},
            p(np.array([[[0, 0, 1], [0, 1, 0]], [[1, 0, 0], [0, 1, 1]], [[1, 0, 1], [1, 1, 0]]])),
            np.array([[[1, 0, 1], [1, 1, 1]]]),
        ]
    )


class TestLabelToMask(unittest.TestCase):
    @parameterized.expand(TESTS)
    def test_value(self, arguments, image, expected_data):
        result = LabelToMask(**arguments)(image)
        assert_allclose(result, expected_data, type_test="tensor")


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