# 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 os
import tempfile
import unittest

import numpy as np
import torch
from parameterized import parameterized

from monai.data.meta_tensor import MetaTensor
from monai.transforms import SaveImage
from monai.utils import optional_import

_, has_itk = optional_import("itk", allow_namespace_pkg=True)
nib, has_nibabel = optional_import("nibabel")

TEST_CASE_1 = [torch.randint(0, 255, (1, 2, 3, 4)), {"filename_or_obj": "testfile0.nii.gz"}, ".nii.gz", False]

TEST_CASE_2 = [torch.randint(0, 255, (1, 2, 3, 4)), None, ".nii.gz", False]

TEST_CASE_3 = [torch.randint(0, 255, (1, 2, 3, 4)), {"filename_or_obj": "testfile0.nrrd"}, ".nrrd", False]

TEST_CASE_4 = [
    torch.randint(0, 255, (3, 2, 4, 5), dtype=torch.uint8),
    {"filename_or_obj": "testfile0.dcm"},
    ".dcm",
    False,
]

TEST_CASE_5 = [torch.randint(0, 255, (3, 2, 4, 5), dtype=torch.uint8), ".dcm", False]


@unittest.skipUnless(has_itk, "itk not installed")
class TestSaveImage(unittest.TestCase):

    @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4])
    def test_saved_content(self, test_data, meta_data, output_ext, resample):
        if meta_data is not None:
            test_data = MetaTensor(test_data, meta=meta_data)

        with tempfile.TemporaryDirectory() as tempdir:
            trans = SaveImage(
                output_dir=tempdir,
                output_ext=output_ext,
                resample=resample,
                separate_folder=False,  # test saving into the same folder
                output_name_formatter=lambda x, xform: dict(subject=x["filename_or_obj"] if x else "0"),
            )
            trans(test_data)

            filepath = "testfile0" if meta_data is not None else "0"
            self.assertTrue(os.path.exists(os.path.join(tempdir, filepath + "_trans" + output_ext)))

    @parameterized.expand([TEST_CASE_5])
    def test_saved_content_with_filename(self, test_data, output_ext, resample):
        with tempfile.TemporaryDirectory() as tempdir:
            trans = SaveImage(
                output_dir=tempdir,
                output_ext=output_ext,
                resample=resample,
                separate_folder=False,  # test saving into the same folder
            )
            filename = str(os.path.join(tempdir, "test"))
            trans(test_data, filename=filename)

            self.assertTrue(os.path.exists(filename + output_ext))


@unittest.skipUnless(has_nibabel, "nibabel not installed")
class TestSaveImageChannelDim(unittest.TestCase):

    @parameterized.expand(
        [
            ("numpy_single", "numpy", 1, 0, True),
            ("numpy_no_metadata", "numpy", 1, 0, False),
            ("tensor", "tensor", 1, 0, True),
            ("metatensor", "metatensor", 1, 0, True),
            ("multiple_channels", "numpy", 2, 0, True),
            ("channel_last", "numpy", 2, -1, True),
        ]
    )
    def test_reuse_after_channel_less(self, _, input_type, channels, channel_dim, with_metadata):
        spatial_shape = (2, 3, 4)
        first = np.arange(24, dtype=np.float32).reshape(spatial_shape)
        second = np.arange(channels * 24, dtype=np.float32).reshape((channels, *spatial_shape)) + 100
        expected = second[0] if channels == 1 else np.moveaxis(second, 0, -1)
        second = np.moveaxis(second, 0, channel_dim)
        metadata = {"spatial_shape": spatial_shape}
        if input_type == "tensor":
            first, second = torch.as_tensor(first), torch.as_tensor(second)
        elif input_type == "metatensor":
            first, second = MetaTensor(first, meta=metadata), MetaTensor(second, meta=metadata)

        with tempfile.TemporaryDirectory() as tempdir:
            saver = SaveImage(writer="NibabelWriter", resample=False, print_log=False)
            saver.set_options(data_kwargs={"channel_dim": channel_dim})
            original_options = saver.data_kwargs.copy()
            saver(first, meta_data=metadata, filename=os.path.join(tempdir, "first"))
            np.testing.assert_array_equal(
                nib.load(os.path.join(tempdir, "first.nii.gz")).get_fdata(), np.arange(24).reshape(spatial_shape)
            )
            saver(second, meta_data=metadata if with_metadata else None, filename=os.path.join(tempdir, "second"))
            np.testing.assert_array_equal(nib.load(os.path.join(tempdir, "second.nii.gz")).get_fdata(), expected)
            self.assertEqual(saver.data_kwargs, original_options)


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