# 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 torch
from parameterized import parameterized

from monai.networks import eval_mode
from monai.networks.blocks import ChannelSELayer, ResidualSELayer
from tests.utils import test_script_save

TEST_CASES = [  # single channel 3D, batch 16
    [{"spatial_dims": 2, "in_channels": 4, "r": 3}, (7, 4, 64, 48), (7, 4, 64, 48)],  # 4-channel 2D, batch 7
    [  # 4-channel 1D, batch 16
        {"spatial_dims": 1, "in_channels": 4, "r": 3, "acti_type_1": "relu"},
        (16, 4, 63),
        (16, 4, 63),
    ],
]

TEST_CASES_3D = []
for type_1 in {"relu", "relu6", "leakyrelu"}:
    for type_2 in {"prelu", "sigmoid", "relu"}:
        test_case = [
            {"spatial_dims": 3, "in_channels": 10, "r": 3, "acti_type_1": type_1, "acti_type_2": type_2},
            (16, 10, 32, 24, 48),
            (16, 10, 32, 24, 48),
        ]
        TEST_CASES_3D.append(test_case)


class TestChannelSELayer(unittest.TestCase):
    @parameterized.expand(TEST_CASES + TEST_CASES_3D)
    def test_shape(self, input_param, input_shape, expected_shape):
        net = ChannelSELayer(**input_param)
        with eval_mode(net):
            result = net(torch.randn(input_shape))
            self.assertEqual(result.shape, expected_shape)

    def test_script(self):
        input_param, input_shape, _ = TEST_CASES[0]
        net = ChannelSELayer(**input_param)
        test_data = torch.randn(input_shape)
        test_script_save(net, test_data)

    def test_ill_arg(self):
        with self.assertRaises(ValueError):
            ChannelSELayer(spatial_dims=1, in_channels=4, r=100)


class TestResidualSELayer(unittest.TestCase):
    @parameterized.expand(TEST_CASES[:1])
    def test_shape(self, input_param, input_shape, expected_shape):
        net = ResidualSELayer(**input_param)
        with eval_mode(net):
            result = net(torch.randn(input_shape))
            self.assertEqual(result.shape, expected_shape)

    def test_script(self):
        input_param, input_shape, _ = TEST_CASES[0]
        net = ResidualSELayer(**input_param)
        test_data = torch.randn(input_shape)
        test_script_save(net, test_data)


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