# Copyright 2021 Yan Yan
#
# 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 math
import time
from typing import List, Optional, Tuple, Union

import numpy as np
import torch
from torch import nn
from torch.nn import init
from torch.nn.parameter import Parameter

from spconv import pytorch as spconv
from spconv import SPCONV_VERSION_NUMBERS
from spconv.core import ConvAlgo
from spconv.pytorch import functional as Fsp
from spconv.pytorch import ops
from spconv.cppconstants import CPU_ONLY_BUILD
from spconv.pytorch.core import IndiceData, SparseConvTensor, ImplicitGemmIndiceData
from spconv.pytorch.modules import SparseModule
from spconv.constants import SAVED_WEIGHT_LAYOUT, ALL_WEIGHT_IS_KRSC
from spconv.utils import nullcontext

FILTER_HWIO = False


def expand_nd(val: Union[int, List[int], Tuple[int, ...]], ndim: int) -> List[int]:
    if isinstance(val, int):
        val = [val] * ndim
    elif isinstance(val, list):
        assert len(val) == ndim
    elif isinstance(val, tuple):
        assert len(val) == ndim
        return [*val]
    else:
        raise NotImplementedError
    return val


def _calculate_fan_in_and_fan_out_hwio(tensor, algo: ConvAlgo):
    dimensions = tensor.ndimension()
    if dimensions < 2:
        raise ValueError(
            "Fan in and fan out can not be computed for tensor with fewer than 2 dimensions"
        )

    if dimensions == 2:  # Linear
        fan_in = tensor.size(-2)
        fan_out = tensor.size(-1)
    else:
        if algo == ConvAlgo.Native:
            if FILTER_HWIO:
                num_input_fmaps = tensor.size(-2)
                num_output_fmaps = tensor.size(-1)
            else:
                num_input_fmaps = tensor.size(-1)
                num_output_fmaps = tensor.size(-2)

            receptive_field_size = 1
            if tensor.dim() > 2:
                receptive_field_size = tensor[..., 0, 0].numel()
        else:
            num_input_fmaps = tensor.size(-1)
            num_output_fmaps = tensor.size(0)
            receptive_field_size = 1
            if tensor.dim() > 2:
                receptive_field_size = int(np.prod(tensor.shape[1:-1]))

        fan_in = num_input_fmaps * receptive_field_size
        fan_out = num_output_fmaps * receptive_field_size

    return fan_in, fan_out


class SparseConvolution(SparseModule):
    __constants__ = [
        'stride', 'padding', 'dilation', 'groups', 'bias', 'subm', 'inverse',
        'transposed', 'output_padding'
    ]

    def __init__(self,
                 ndim: int,
                 in_channels: int,
                 out_channels: int,
                 kernel_size: Union[int, List[int], Tuple[int, ...]] = 3,
                 stride: Union[int, List[int], Tuple[int, ...]] = 1,
                 padding: Union[int, List[int], Tuple[int, ...]] = 0,
                 dilation: Union[int, List[int], Tuple[int, ...]] = 1,
                 groups: Union[int, List[int], Tuple[int, ...]] = 1,
                 bias: bool = True,
                 subm: bool = False,
                 output_padding: Union[int, List[int], Tuple[int, ...]] = 0,
                 transposed: bool = False,
                 inverse: bool = False,
                 indice_key: Optional[str] = None,
                 algo: Optional[ConvAlgo] = None,
                 name=None):
        super(SparseConvolution, self).__init__(name=name)
        assert groups == 1, "don't support groups for now"
        if not isinstance(kernel_size, (list, tuple)):
            kernel_size = [kernel_size] * ndim
        if not isinstance(stride, (list, tuple)):
            stride = [stride] * ndim
        if not isinstance(padding, (list, tuple)):
            padding = [padding] * ndim
        if not isinstance(dilation, (list, tuple)):
            dilation = [dilation] * ndim
        if not isinstance(output_padding, (list, tuple)):
            output_padding = [output_padding] * ndim
        self.ndim = ndim
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = kernel_size
        kv = int(np.prod(kernel_size))
        kv_stride = int(np.prod(kernel_size))

        self.conv1x1 = kv == 1 and kv_stride == 1
        self.stride = stride
        self.padding = padding
        self.dilation = dilation
        self.transposed = transposed
        self.inverse = inverse
        self.output_padding = output_padding
        self.groups = groups
        self.subm = subm
        self.indice_key = indice_key
        if algo is None:
            if kv <= 32 and not CPU_ONLY_BUILD:
                if kv < 8:
                    algo = ConvAlgo.MaskImplicitGemm
                else:
                    algo = ConvAlgo.MaskImplicitGemm
            else:
                algo = ConvAlgo.Native
        if kv > 32:
            assert algo == ConvAlgo.Native, "implicit gemm don't support kv >= 32 for now"
        if CPU_ONLY_BUILD:
            assert algo == ConvAlgo.Native, "cpu only build only support native algorithm"
        self.algo = algo
        # self.algo = ConvAlgo.Native
        if self.algo == ConvAlgo.Native and not ALL_WEIGHT_IS_KRSC:
            if FILTER_HWIO:
                # RSCK
                self.weight = Parameter(
                    torch.Tensor(*kernel_size, in_channels, out_channels))
            else:
                # RSKC
                self.weight = Parameter(
                    torch.Tensor(*kernel_size, out_channels, in_channels))
        else:
            # KRSC
            self.weight = Parameter(
                torch.Tensor(out_channels, *kernel_size, in_channels))

        if bias:
            self.bias = Parameter(torch.Tensor(out_channels))
        else:
            self.register_parameter('bias', None)
        self.reset_parameters()

        self._register_load_state_dict_pre_hook(self._load_weight_different_layout)

    def _load_weight_different_layout(
            self, state_dict, prefix, local_metadata, strict,
            missing_keys, unexpected_keys, error_msgs):
        if not SAVED_WEIGHT_LAYOUT:
            return
        key = prefix + "weight"
        assert key in state_dict
        ndim = self.ndim
        if SAVED_WEIGHT_LAYOUT == "RSKC":
            state_dict[key] = state_dict[key].permute(ndim, *range(ndim), ndim + 1).contiguous()
        elif SAVED_WEIGHT_LAYOUT == "RSCK":
            state_dict[key] = state_dict[key].permute(ndim + 1, *range(ndim), ndim).contiguous()

        if ALL_WEIGHT_IS_KRSC or self.algo != ConvAlgo.Native:
            # in spconv 2.2, we only support KRSC layout.
            if SAVED_WEIGHT_LAYOUT == "RSKC":
                state_dict[key] = state_dict[key].permute(ndim, *range(ndim), ndim + 1).contiguous()
            elif SAVED_WEIGHT_LAYOUT == "RSCK":
                state_dict[key] = state_dict[key].permute(ndim + 1, *range(ndim), ndim).contiguous()

        else:
            if self.algo == ConvAlgo.Native:
                # to RSCK
                if SAVED_WEIGHT_LAYOUT == "RSKC":
                    state_dict[key] = state_dict[key].permute(*range(ndim), ndim + 1, ndim).contiguous()
                elif SAVED_WEIGHT_LAYOUT == "KRSC":
                    state_dict[key] = state_dict[key].permute(*range(1, ndim + 1), 0, ndim + 1).contiguous()


    def extra_repr(self):
        s = ('{in_channels}, {out_channels}, kernel_size={kernel_size}'
             ', stride={stride}')
        if self.padding != (0, ) * len(self.padding):
            s += ', padding={padding}'
        if self.dilation != (1, ) * len(self.dilation):
            s += ', dilation={dilation}'
        if self.output_padding != (0, ) * len(self.output_padding):
            s += ', output_padding={output_padding}'
        if self.groups != 1:
            s += ', groups={groups}'
        if self.bias is None:
            s += ', bias=False'
        if self.algo is not None:
            s += f', algo={self.algo}'
        return s.format(**self.__dict__)

    def reset_parameters(self):
        n = self.in_channels
        # following commented code is used to make weight different layout have same value
        # if self.algo != ConvAlgo.Native:
        #     weight2 = self.weight.data.permute(1, 2, 3, 0,
        #                                        4).contiguous().clone()
        #     init.uniform_(weight2, 0, 0.001)
        #     self.weight.data[:] = weight2.permute(3, 0, 1, 2, 4)
        # else:
        #     init.uniform_(self.weight, 0, 0.001)
        init.kaiming_uniform_(self.weight, a=math.sqrt(0.005))
        if self.bias is not None:
            fan_in, _ = _calculate_fan_in_and_fan_out_hwio(
                self.weight, self.algo)
            bound = 1 / math.sqrt(fan_in)
            init.uniform_(self.bias, -bound, bound)

    def forward(self, input: SparseConvTensor):
        assert isinstance(input, SparseConvTensor)
        assert input.features.shape[
            1] == self.in_channels, "channel size mismatch"
        features = input.features
        device = features.device
        indices = input.indices
        spatial_shape = input.spatial_shape
        batch_size = input.batch_size
        if not self.subm:
            if self.transposed:
                out_spatial_shape = ops.get_deconv_output_size(
                    spatial_shape, self.kernel_size, self.stride, self.padding,
                    self.dilation, self.output_padding)
            else:
                out_spatial_shape = ops.get_conv_output_size(
                    spatial_shape, self.kernel_size, self.stride, self.padding,
                    self.dilation)
        else:
            out_spatial_shape = spatial_shape
        # print(self._sparse_unique_name, spatial_shape, out_spatial_shape)
        # input.update_grid(out_spatial_shape)
        # t = time.time()
        out_tensor = input.shadow_copy()
        if input.benchmark:
            if self.name is None:
                raise ValueError(
                    "you need to assign name to spmodules before benchmark (spconv.utils.bench.assign_name_to_spmod)"
                )
            if self.name not in input.benchmark_record:
                input.benchmark_record[self.name] = {
                    "type": "SparseConvolution",
                    "indice_gen_time": [],
                    "time": [],
                    "num_points": [],
                    "num_out_points": [],
                    "params": {
                        "kernel_size": self.kernel_size,
                        "stride": self.stride,
                        "padding": self.padding,
                        "dilation": self.dilation,
                        "output_padding": self.output_padding,
                        "subm": self.subm,
                        "transposed": self.transposed,
                        "input_channels": self.in_channels,
                        "out_channels": self.out_channels,
                    }
                }
        if self.conv1x1:
            if FILTER_HWIO:
                features = torch.mm(
                    input.features,
                    self.weight.view(self.out_channels, self.in_channels).T)
            else:
                features = torch.mm(
                    input.features,
                    self.weight.view(self.in_channels, self.out_channels))

            if self.bias is not None:
                features += self.bias
            out_tensor = out_tensor.replace_feature(features)
            return out_tensor
        indice_dict = input.indice_dict.copy()

        algo = self.algo
        if self.indice_key is not None:
            datas = input.find_indice_pair(self.indice_key)
            if datas is not None:
                msg = "due to limitation of pytorch, you must provide same algo to layers share same indice key."
                assert algo == datas.algo, msg
                # algo = datas.algo
        profile_ctx = nullcontext()
        if input._timer is not None and self._sparse_unique_name:
            profile_ctx = input._timer.namespace(self._sparse_unique_name)
        with profile_ctx:
            if algo == ConvAlgo.Native:
                datas = input.find_indice_pair(self.indice_key)
                if datas is not None:
                    assert isinstance(datas, IndiceData)
                if self.inverse:
                    assert datas is not None and self.indice_key is not None
                    assert datas.is_subm is False, "inverse conv can only be used with standard conv and pool ops."

                    outids = datas.indices
                    indice_pairs = datas.indice_pairs
                    indice_pair_num = datas.indice_pair_num
                    out_spatial_shape = datas.spatial_shape
                    assert indice_pair_num.shape[0] == np.prod(
                        self.kernel_size
                    ), "inverse conv must have same kernel size as its couple conv"
                else:
                    if self.indice_key is not None and datas is not None:
                        outids = datas.out_indices
                        indice_pairs = datas.indice_pairs
                        indice_pair_num = datas.indice_pair_num
                    else:
                        if input.benchmark:
                            torch.cuda.synchronize()
                            t = time.time()
                        outids, indice_pairs, indice_pair_num = ops.get_indice_pairs(
                            indices, batch_size, spatial_shape, algo,
                            self.kernel_size, self.stride, self.padding,
                            self.dilation, self.output_padding, self.subm,
                            self.transposed)
                        if input.benchmark:
                            torch.cuda.synchronize()
                            interval = time.time() - t
                            out_tensor.benchmark_record[
                                self.name]["indice_gen_time"].append(interval)

                        indice_data = IndiceData(outids,
                                                 indices,
                                                 indice_pairs,
                                                 indice_pair_num,
                                                 spatial_shape,
                                                 out_spatial_shape,
                                                 is_subm=self.subm,
                                                 algo=algo)
                        if self.indice_key is not None:
                            msg = f"your indice key {self.indice_key} already exists in this sparse tensor."
                            assert self.indice_key not in indice_dict, msg
                            indice_dict[self.indice_key] = indice_data
                if input.benchmark:
                    torch.cuda.synchronize()
                    t = time.time()
                indice_pairs_calc = indice_pairs
                if indice_pairs.device != features.device:
                    indice_pairs_calc = indice_pairs.to(features.device)
                if self.subm:
                    out_features = Fsp.indice_subm_conv(
                        features, self.weight, indice_pairs_calc,
                        indice_pair_num, outids.shape[0], algo, input._timer)
                else:
                    if self.inverse:
                        out_features = Fsp.indice_inverse_conv(
                            features, self.weight, indice_pairs_calc,
                            indice_pair_num, outids.shape[0], algo)
                    else:
                        out_features = Fsp.indice_conv(features, self.weight,
                                                       indice_pairs_calc,
                                                       indice_pair_num,
                                                       outids.shape[0], algo,
                                                       input._timer)

            else:
                datas = input.find_indice_pair(self.indice_key)
                if datas is not None:
                    assert isinstance(datas, ImplicitGemmIndiceData)
                if self.inverse:
                    assert datas is not None and self.indice_key is not None
                    assert datas.is_subm is False, "inverse conv can only be used with standard conv and pool ops."
                    outids = datas.indices
                    pair_fwd = datas.pair_bwd
                    pair_bwd = datas.pair_fwd
                    pair_mask_fwd_splits = datas.pair_mask_bwd_splits
                    pair_mask_bwd_splits = datas.pair_mask_fwd_splits
                    mask_argsort_fwd_splits = datas.mask_argsort_bwd_splits
                    mask_argsort_bwd_splits = datas.mask_argsort_fwd_splits
                    masks = datas.masks
                    out_spatial_shape = datas.spatial_shape
                    assert datas.pair_fwd.shape[0] == np.prod(
                        self.kernel_size
                    ), "inverse conv must have same kernel size as its couple conv"

                else:
                    if self.indice_key is not None and datas is not None:
                        outids = datas.out_indices
                        pair_fwd = datas.pair_fwd
                        pair_bwd = datas.pair_bwd
                        pair_mask_fwd_splits = datas.pair_mask_fwd_splits
                        pair_mask_bwd_splits = datas.pair_mask_bwd_splits
                        mask_argsort_fwd_splits = datas.mask_argsort_fwd_splits
                        mask_argsort_bwd_splits = datas.mask_argsort_bwd_splits
                        masks = datas.masks
                    else:
                        with input._timer.namespace("gen_pairs"):
                            # we need to gen bwd indices for regular conv
                            # because it may be inversed.
                            res = ops.get_indice_pairs_implicit_gemm(
                                indices,
                                batch_size,
                                spatial_shape,
                                algo,
                                ksize=self.kernel_size,
                                stride=self.stride,
                                padding=self.padding,
                                dilation=self.dilation,
                                out_padding=self.output_padding,
                                subm=self.subm,
                                transpose=self.transposed,
                                is_train=(not self.subm) or self.training,
                                alloc=input.thrust_allocator,
                                timer=input._timer)
                        outids = res[0]
                        num_inds_per_loc = res[1]
                        pair_fwd = res[2]
                        pair_bwd = res[3]
                        pair_mask_fwd_splits = res[4]
                        pair_mask_bwd_splits = res[5]
                        mask_argsort_fwd_splits = res[6]
                        mask_argsort_bwd_splits = res[7]
                        masks = res[8]
                        if self.indice_key is not None:
                            indice_data = ImplicitGemmIndiceData(
                                outids,
                                indices,
                                pair_fwd,
                                pair_bwd,
                                pair_mask_fwd_splits=pair_mask_fwd_splits,
                                pair_mask_bwd_splits=pair_mask_bwd_splits,
                                mask_argsort_fwd_splits=mask_argsort_fwd_splits,
                                mask_argsort_bwd_splits=mask_argsort_bwd_splits,
                                masks=masks,
                                is_subm=self.subm,
                                spatial_shape=spatial_shape,
                                out_spatial_shape=out_spatial_shape,
                                algo=algo)
                            msg = f"your indice key {self.indice_key} already exists in this sparse tensor."
                            assert self.indice_key not in indice_dict, msg
                            indice_dict[self.indice_key] = indice_data
                if input.benchmark:
                    torch.cuda.synchronize()
                    t = time.time()
                num_activate_out = outids.shape[0]
                out_features = Fsp.implicit_gemm(
                    features, self.weight, pair_fwd, pair_bwd,
                    pair_mask_fwd_splits, pair_mask_bwd_splits,
                    mask_argsort_fwd_splits, mask_argsort_bwd_splits,
                    num_activate_out, masks, self.training, self.subm,
                    input._timer)
        if self.bias is not None:
            out_features += self.bias
        if input.benchmark:
            torch.cuda.synchronize()
            interval = time.time() - t
            out_tensor.benchmark_record[self.name]["time"].append(interval)
            out_tensor.benchmark_record[self.name]["num_points"].append(
                features.shape[0])
            out_tensor.benchmark_record[self.name]["num_out_points"].append(
                out_features.shape[0])
        out_tensor = out_tensor.replace_feature(out_features)
        out_tensor.indices = outids
        out_tensor.indice_dict = indice_dict
        out_tensor.spatial_shape = out_spatial_shape
        return out_tensor


class SparseConv1d(SparseConvolution):
    def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size,
                 stride=1,
                 padding=0,
                 dilation=1,
                 groups=1,
                 bias=True,
                 indice_key=None,
                 algo: Optional[ConvAlgo] = None,
                 name=None):
        super(SparseConv1d, self).__init__(1,
                                           in_channels,
                                           out_channels,
                                           kernel_size,
                                           stride,
                                           padding,
                                           dilation,
                                           groups,
                                           bias,
                                           indice_key=indice_key,
                                           algo=algo,
                                           name=name)


class SparseConv2d(SparseConvolution):
    def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size,
                 stride=1,
                 padding=0,
                 dilation=1,
                 groups=1,
                 bias=True,
                 indice_key=None,
                 algo: Optional[ConvAlgo] = None,
                 name=None):
        super(SparseConv2d, self).__init__(2,
                                           in_channels,
                                           out_channels,
                                           kernel_size,
                                           stride,
                                           padding,
                                           dilation,
                                           groups,
                                           bias,
                                           indice_key=indice_key,
                                           algo=algo,
                                           name=name)


class SparseConv3d(SparseConvolution):
    def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size,
                 stride=1,
                 padding=0,
                 dilation=1,
                 groups=1,
                 bias=True,
                 indice_key=None,
                 algo: Optional[ConvAlgo] = None,
                 name=None):
        super(SparseConv3d, self).__init__(3,
                                           in_channels,
                                           out_channels,
                                           kernel_size,
                                           stride,
                                           padding,
                                           dilation,
                                           groups,
                                           bias,
                                           indice_key=indice_key,
                                           algo=algo,
                                           name=name)


class SparseConv4d(SparseConvolution):
    def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size,
                 stride=1,
                 padding=0,
                 dilation=1,
                 groups=1,
                 bias=True,
                 indice_key=None,
                 algo: Optional[ConvAlgo] = None,
                 name=None):
        super(SparseConv4d, self).__init__(4,
                                           in_channels,
                                           out_channels,
                                           kernel_size,
                                           stride,
                                           padding,
                                           dilation,
                                           groups,
                                           bias,
                                           indice_key=indice_key,
                                           algo=algo,
                                           name=name)


class SparseConvTranspose1d(SparseConvolution):
    def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size,
                 stride=1,
                 padding=0,
                 dilation=1,
                 groups=1,
                 bias=True,
                 indice_key=None,
                 algo: Optional[ConvAlgo] = None,
                 name=None):
        super(SparseConvTranspose1d, self).__init__(1,
                                                    in_channels,
                                                    out_channels,
                                                    kernel_size,
                                                    stride,
                                                    padding,
                                                    dilation,
                                                    groups,
                                                    bias,
                                                    transposed=True,
                                                    indice_key=indice_key,
                                                    algo=algo,
                                                    name=name)


class SparseConvTranspose2d(SparseConvolution):
    def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size,
                 stride=1,
                 padding=0,
                 dilation=1,
                 groups=1,
                 bias=True,
                 indice_key=None,
                 algo: Optional[ConvAlgo] = None,
                 name=None):
        super(SparseConvTranspose2d, self).__init__(2,
                                                    in_channels,
                                                    out_channels,
                                                    kernel_size,
                                                    stride,
                                                    padding,
                                                    dilation,
                                                    groups,
                                                    bias,
                                                    transposed=True,
                                                    indice_key=indice_key,
                                                    algo=algo,
                                                    name=name)


class SparseConvTranspose3d(SparseConvolution):
    def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size,
                 stride=1,
                 padding=0,
                 dilation=1,
                 groups=1,
                 bias=True,
                 indice_key=None,
                 algo: Optional[ConvAlgo] = None,
                 name=None):
        super(SparseConvTranspose3d, self).__init__(3,
                                                    in_channels,
                                                    out_channels,
                                                    kernel_size,
                                                    stride,
                                                    padding,
                                                    dilation,
                                                    groups,
                                                    bias,
                                                    transposed=True,
                                                    indice_key=indice_key,
                                                    algo=algo,
                                                    name=name)


class SparseConvTranspose4d(SparseConvolution):
    def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size,
                 stride=1,
                 padding=0,
                 dilation=1,
                 groups=1,
                 bias=True,
                 indice_key=None,
                 algo: Optional[ConvAlgo] = None,
                 name=None):
        super(SparseConvTranspose4d, self).__init__(4,
                                                    in_channels,
                                                    out_channels,
                                                    kernel_size,
                                                    stride,
                                                    padding,
                                                    dilation,
                                                    groups,
                                                    bias,
                                                    transposed=True,
                                                    indice_key=indice_key,
                                                    algo=algo,
                                                    name=name)


class SparseInverseConv1d(SparseConvolution):
    def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size,
                 indice_key,
                 bias=True,
                 algo: Optional[ConvAlgo] = None,
                 name=None):
        super(SparseInverseConv1d, self).__init__(1,
                                                  in_channels,
                                                  out_channels,
                                                  kernel_size,
                                                  bias=bias,
                                                  inverse=True,
                                                  indice_key=indice_key,
                                                  algo=algo,
                                                  name=name)


class SparseInverseConv2d(SparseConvolution):
    def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size,
                 indice_key,
                 bias=True,
                 algo: Optional[ConvAlgo] = None,
                 name=None):
        super(SparseInverseConv2d, self).__init__(2,
                                                  in_channels,
                                                  out_channels,
                                                  kernel_size,
                                                  bias=bias,
                                                  inverse=True,
                                                  indice_key=indice_key,
                                                  algo=algo,
                                                  name=name)


class SparseInverseConv3d(SparseConvolution):
    def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size,
                 indice_key,
                 bias=True,
                 algo: Optional[ConvAlgo] = None,
                 name=None):
        super(SparseInverseConv3d, self).__init__(3,
                                                  in_channels,
                                                  out_channels,
                                                  kernel_size,
                                                  bias=bias,
                                                  inverse=True,
                                                  indice_key=indice_key,
                                                  algo=algo,
                                                  name=name)


class SparseInverseConv4d(SparseConvolution):
    def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size,
                 indice_key,
                 bias=True,
                 algo: Optional[ConvAlgo] = None,
                 name=None):
        super(SparseInverseConv4d, self).__init__(4,
                                                  in_channels,
                                                  out_channels,
                                                  kernel_size,
                                                  bias=bias,
                                                  inverse=True,
                                                  indice_key=indice_key,
                                                  algo=algo,
                                                  name=name)


class SubMConv1d(SparseConvolution):
    def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size,
                 stride=1,
                 padding=0,
                 dilation=1,
                 groups=1,
                 bias=True,
                 indice_key=None,
                 algo: Optional[ConvAlgo] = None,
                 name=None):
        super(SubMConv1d, self).__init__(1,
                                         in_channels,
                                         out_channels,
                                         kernel_size,
                                         stride,
                                         padding,
                                         dilation,
                                         groups,
                                         bias,
                                         True,
                                         indice_key=indice_key,
                                         algo=algo,
                                         name=name)


class SubMConv2d(SparseConvolution):
    def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size,
                 stride=1,
                 padding=0,
                 dilation=1,
                 groups=1,
                 bias=True,
                 indice_key=None,
                 algo: Optional[ConvAlgo] = None,
                 name=None):
        super(SubMConv2d, self).__init__(2,
                                         in_channels,
                                         out_channels,
                                         kernel_size,
                                         stride,
                                         padding,
                                         dilation,
                                         groups,
                                         bias,
                                         True,
                                         indice_key=indice_key,
                                         algo=algo,
                                         name=name)


class SubMConv3d(SparseConvolution):
    def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size,
                 stride=1,
                 padding=0,
                 dilation=1,
                 groups=1,
                 bias=True,
                 indice_key=None,
                 algo: Optional[ConvAlgo] = None,
                 name=None):
        super(SubMConv3d, self).__init__(3,
                                         in_channels,
                                         out_channels,
                                         kernel_size,
                                         stride,
                                         padding,
                                         dilation,
                                         groups,
                                         bias,
                                         True,
                                         indice_key=indice_key,
                                         algo=algo,
                                         name=name)


class SubMConv4d(SparseConvolution):
    def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size,
                 stride=1,
                 padding=0,
                 dilation=1,
                 groups=1,
                 bias=True,
                 indice_key=None,
                 algo: Optional[ConvAlgo] = None,
                 name=None):
        super(SubMConv4d, self).__init__(4,
                                         in_channels,
                                         out_channels,
                                         kernel_size,
                                         stride,
                                         padding,
                                         dilation,
                                         groups,
                                         bias,
                                         True,
                                         indice_key=indice_key,
                                         algo=algo,
                                         name=name)
