# 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 monai.transforms import ShiftIntensityd, StdShiftIntensityd
from monai.utils import dtype_numpy_to_torch
from tests.test_utils import NumpyImageTestCase2D


class TestStdShiftIntensityd(NumpyImageTestCase2D):
    def test_value(self):
        key = "img"
        factor = np.random.rand()
        offset = np.std(self.imt) * factor
        shifter = ShiftIntensityd(keys=[key], offset=offset)
        expected = shifter({key: self.imt})
        std_shifter = StdShiftIntensityd(keys=[key], factor=factor)
        result = std_shifter({key: self.imt})
        np.testing.assert_allclose(result[key], expected[key], rtol=1e-5)

    def test_zerostd(self):
        key = "img"
        image = np.ones([2, 3, 3])
        for nonzero in [True, False]:
            for channel_wise in [True, False]:
                factor = np.random.rand()
                std_shifter = StdShiftIntensityd(keys=[key], factor=factor, nonzero=nonzero, channel_wise=channel_wise)
                result = std_shifter({key: image})
                np.testing.assert_allclose(result[key], image, rtol=1e-5)

    def test_nonzero(self):
        key = "img"
        image = np.asarray([[4.0, 0.0, 2.0], [0, 2, 4]])  # std = 1
        factor = np.random.rand()
        std_shifter = StdShiftIntensityd(keys=[key], factor=factor, nonzero=True)
        result = std_shifter({key: image})
        expected = np.asarray([[4 + factor, 0, 2 + factor], [0, 2 + factor, 4 + factor]])
        np.testing.assert_allclose(result[key], expected, rtol=1e-5)

    def test_channel_wise(self):
        key = "img"
        image = np.stack((np.asarray([1.0, 2.0]), np.asarray([1.0, 1.0])))  # std: 0.5, 0
        factor = np.random.rand()
        std_shifter = StdShiftIntensityd(keys=[key], factor=factor, channel_wise=True)
        result = std_shifter({key: image})
        expected = np.stack((np.asarray([1 + 0.5 * factor, 2 + 0.5 * factor]), np.asarray([1, 1])))
        np.testing.assert_allclose(result[key], expected, rtol=1e-5)

    def test_dtype(self):
        key = "img"
        trans_dtype = np.float32
        for dtype in [int, np.float32, np.float64]:
            image = np.random.rand(2, 2, 2).astype(dtype)
            factor = np.random.rand()
            std_shifter = StdShiftIntensityd(keys=[key], factor=factor, dtype=trans_dtype)
            result = std_shifter({key: image})
            np.testing.assert_equal(result[key].dtype, dtype_numpy_to_torch(trans_dtype))


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