# 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 torch
from parameterized import parameterized

from monai.transforms import Activationsd

TEST_CASE_1 = [
    {"keys": ["pred", "label"], "sigmoid": False, "softmax": [True, False], "other": None},
    {"pred": torch.tensor([[[[0.0, 1.0]], [[2.0, 3.0]]]]), "label": torch.tensor([[[[0.0, 1.0]], [[2.0, 3.0]]]])},
    {
        "pred": torch.tensor([[[[0.1192, 0.1192]], [[0.8808, 0.8808]]]]),
        "label": torch.tensor([[[[0.0, 1.0]], [[2.0, 3.0]]]]),
    },
    (1, 2, 1, 2),
]

TEST_CASE_2 = [
    {"keys": ["pred", "label"], "sigmoid": False, "softmax": False, "other": [lambda x: torch.tanh(x), None]},
    {"pred": torch.tensor([[[[0.0, 1.0], [2.0, 3.0]]]]), "label": torch.tensor([[[[0.0, 1.0], [2.0, 3.0]]]])},
    {
        "pred": torch.tensor([[[[0.0000, 0.7616], [0.9640, 0.9951]]]]),
        "label": torch.tensor([[[[0.0, 1.0], [2.0, 3.0]]]]),
    },
    (1, 1, 2, 2),
]

TEST_CASE_3 = [
    {"keys": "pred", "sigmoid": False, "softmax": False, "other": lambda x: torch.tanh(x)},
    {"pred": torch.tensor([[[[0.0, 1.0], [2.0, 3.0]]]])},
    {"pred": torch.tensor([[[[0.0000, 0.7616], [0.9640, 0.9951]]]])},
    (1, 1, 2, 2),
]


class TestActivationsd(unittest.TestCase):
    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3])
    def test_value_shape(self, input_param, test_input, output, expected_shape):
        result = Activationsd(**input_param)(test_input)
        torch.testing.assert_allclose(result["pred"], output["pred"])
        self.assertTupleEqual(result["pred"].shape, expected_shape)
        if "label" in result:
            torch.testing.assert_allclose(result["label"], output["label"])
            self.assertTupleEqual(result["label"].shape, expected_shape)


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