# Copyright (c) Chris Choy (chrischoy@ai.stanford.edu).
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
# of the Software, and to permit persons to whom the Software is furnished to do
# so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
#
# Please cite "4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural
# Networks", CVPR'19 (https://arxiv.org/abs/1904.08755) if you use any part
# of the code.
import torch
import unittest

from MinkowskiEngine import SparseTensor, MinkowskiConvolution, \
    MinkowskiConvolutionTranspose
import MinkowskiEngine as ME


from tests.common import data_loader


def get_random_coords(dimension=2, tensor_stride=2):
    torch.manual_seed(0)
    # Create random coordinates with tensor stride == 2
    coords = torch.rand(10, dimension + 1)
    coords[:, :dimension] *= 5  # random coords
    coords[:, -1] *= 2  # random batch index
    coords = coords.floor().int()
    coords = ME.utils.sparse_quantize(coords)
    coords[:, :dimension] *= tensor_stride  # make the tensor stride 2
    return coords, tensor_stride


class TestConvolution(unittest.TestCase):

    def test(self):
        print(f"{self.__class__.__name__}: test")
        in_channels, out_channels, D = 2, 3, 2
        coords, feats, labels = data_loader(in_channels, batch_size=2)

        # Create random coordinates with tensor stride == 2
        out_coords, tensor_stride = get_random_coords()

        feats = feats.double()
        feats.requires_grad_()
        input = SparseTensor(feats, coords=coords)
        cm = input.coords_man
        print(cm._get_coords_key(1))

        conv = MinkowskiConvolution(
            in_channels,
            out_channels,
            kernel_size=3,
            stride=1,
            bias=False,
            dimension=D).double()

        print('Initial input: ', input)
        print('Specified output coords: ', out_coords)
        output = conv(input, out_coords)

        # To specify the tensor stride
        out_coords_key = cm.create_coords_key(out_coords, tensor_stride=2)
        output = conv(input, out_coords_key)
        print('Conv output: ', output)

        output.F.sum().backward()
        print(input.F.grad)

    def test_tr(self):
        print(f"{self.__class__.__name__}: test_tr")
        in_channels, out_channels, D = 2, 3, 2
        coords, feats, labels = data_loader(in_channels, batch_size=2)
        # tensor stride must be at least 2 for convolution transpose with stride 2
        coords[:, :2] *= 2
        out_coords = torch.rand(10, 3)
        out_coords[:, :2] *= 10  # random coords
        out_coords[:, 2] *= 2  # random batch index
        out_coords = out_coords.floor().int()

        feats = feats.double()
        feats.requires_grad_()

        input = SparseTensor(feats, coords=coords, tensor_stride=2)
        cm = input.coords_man
        print(cm._get_coords_key(2))

        conv_tr = MinkowskiConvolutionTranspose(
            in_channels,
            out_channels,
            kernel_size=3,
            stride=2,
            bias=False,
            dimension=D).double()
        print('Initial input: ', input)
        print('Specified output coords: ', out_coords)
        output = conv_tr(input, out_coords)
        print('Conv output: ', output)

        output.F.sum().backward()
        print(input.F.grad)


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