# 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

from parameterized import parameterized

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

TESTS_CASES = []
for p in TEST_NDARRAYS:
    TESTS_CASES.append(
        [
            # test Argmax data
            {"keys": "label", "num_classes": 3, "image_threshold": 0.0},
            {"label": p([[[0, 1, 2], [2, 0, 1], [1, 2, 0]]])},
            [p([0, 4, 8]), p([1, 5, 6]), p([2, 3, 7])],
        ]
    )

    TESTS_CASES.append(
        [
            {"keys": "label", "image_key": "image", "num_classes": 3, "image_threshold": 60},
            {
                "label": p([[[0, 1, 2], [2, 0, 1], [1, 2, 0]]]),
                "image": p([[[132, 1434, 51], [61, 0, 133], [523, 44, 232]]]),
            },
            [p([0, 8]), p([1, 5, 6]), p([3])],
        ]
    )

    TESTS_CASES.append(
        [
            # test One-Hot data
            {"keys": "label", "image_threshold": 0.0},
            {
                "label": p(
                    [
                        [[1, 0, 0], [0, 1, 0], [0, 0, 1]],
                        [[0, 1, 0], [0, 0, 1], [1, 0, 0]],
                        [[0, 0, 1], [1, 0, 0], [0, 1, 0]],
                    ]
                )
            },
            [p([0, 4, 8]), p([1, 5, 6]), p([2, 3, 7])],
        ]
    )

    TESTS_CASES.append(
        [
            {
                "keys": "label",
                "image_key": "image",
                "num_classes": None,
                "image_threshold": 60,
                "max_samples_per_class": 2,
            },
            {
                "label": p(
                    [
                        [[1, 0, 0], [0, 1, 0], [0, 0, 1]],
                        [[0, 1, 0], [0, 0, 1], [1, 0, 0]],
                        [[0, 0, 1], [1, 0, 0], [0, 1, 0]],
                    ]
                ),
                "image": p([[[132, 1434, 51], [61, 0, 133], [523, 44, 232]]]),
            },
            [p([0, 8]), p([1, 6]), p([3])],
        ]
    )

    TESTS_CASES.append(
        [
            # test output_shape
            {
                "keys": "label",
                "indices_postfix": "cls",
                "num_classes": 3,
                "image_threshold": 0.0,
                "output_shape": [3, 3],
            },
            {"label": p([[[0, 1, 2], [2, 0, 1], [1, 2, 0]]])},
            [p([[0, 0], [1, 1], [2, 2]]), p([[0, 1], [1, 2], [2, 0]]), p([[0, 2], [1, 0], [2, 1]])],
        ]
    )


class TestClassesToIndicesd(unittest.TestCase):
    @parameterized.expand(TESTS_CASES)
    def test_value(self, input_args, input_data, expected_indices):
        result = ClassesToIndicesd(**input_args)(input_data)
        key_postfix = input_args.get("indices_postfix")
        key_postfix = "_cls_indices" if key_postfix is None else key_postfix
        for i, e in zip(result["label" + key_postfix], expected_indices):
            assert_allclose(i, e)


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