# 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 AsDiscreted

TEST_CASE_1 = [
    {
        "keys": ["pred", "label"],
        "argmax": [True, False],
        "to_onehot": True,
        "n_classes": 2,
        "threshold_values": False,
        "logit_thresh": 0.5,
    },
    {"pred": torch.tensor([[[[0.0, 1.0]], [[2.0, 3.0]]]]), "label": torch.tensor([[[[0, 1]]]])},
    {"pred": torch.tensor([[[[0.0, 0.0]], [[1.0, 1.0]]]]), "label": torch.tensor([[[[1.0, 0.0]], [[0.0, 1.0]]]])},
    (1, 2, 1, 2),
]

TEST_CASE_2 = [
    {
        "keys": ["pred", "label"],
        "argmax": False,
        "to_onehot": False,
        "n_classes": None,
        "threshold_values": [True, False],
        "logit_thresh": 0.6,
    },
    {"pred": torch.tensor([[[[0.0, 1.0], [2.0, 3.0]]]]), "label": torch.tensor([[[[0, 1], [1, 1]]]])},
    {"pred": torch.tensor([[[[0.0, 1.0], [1.0, 1.0]]]]), "label": torch.tensor([[[[0.0, 1.0], [1.0, 1.0]]]])},
    (1, 1, 2, 2),
]

TEST_CASE_3 = [
    {
        "keys": ["pred"],
        "argmax": True,
        "to_onehot": True,
        "n_classes": 2,
        "threshold_values": False,
        "logit_thresh": 0.5,
    },
    {"pred": torch.tensor([[[[0.0, 1.0]], [[2.0, 3.0]]]])},
    {"pred": torch.tensor([[[[0.0, 0.0]], [[1.0, 1.0]]]])},
    (1, 2, 1, 2),
]


class TestAsDiscreted(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 = AsDiscreted(**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()