# Copyright 2019 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

import numpy as np
import spconv
import spconv.functional as Fsp
import torch
from spconv import ops
from spconv.modules import SparseModule
from torch import nn
from torch.nn import init
from torch.nn.parameter import Parameter


def _calculate_fan_in_and_fan_out_hwio(tensor):
    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:
        num_input_fmaps = tensor.size(-2)
        num_output_fmaps = tensor.size(-1)
        receptive_field_size = 1
        if tensor.dim() > 2:
            receptive_field_size = tensor[..., 0, 0].numel()
        fan_in = num_input_fmaps * receptive_field_size
        fan_out = num_output_fmaps * receptive_field_size

    return fan_in, fan_out


class SparseConvolution(SparseModule):
    def __init__(self,
                 ndim,
                 in_channels,
                 out_channels,
                 kernel_size=3,
                 stride=1,
                 padding=0,
                 dilation=1,
                 groups=1,
                 bias=True,
                 subm=False,
                 output_padding=0,
                 transposed=False,
                 inverse=False,
                 indice_key=None):
        super(SparseConvolution, self).__init__()
        assert groups == 1
        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

        for d, s in zip(dilation, stride):
            assert any([s == 1, d == 1]), "don't support this."

        self.ndim = ndim
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = kernel_size
        self.conv1x1 = np.prod(kernel_size) == 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

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

    def reset_parameters(self):
        n = self.in_channels
        init.kaiming_uniform_(self.weight, a=math.sqrt(5))
        if self.bias is not None:
            fan_in, _ = _calculate_fan_in_and_fan_out_hwio(self.weight)
            bound = 1 / math.sqrt(fan_in)
            init.uniform_(self.bias, -bound, bound)

    def forward(self, input):
        assert isinstance(input, spconv.SparseConvTensor)
        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
        # input.update_grid(out_spatial_shape)
        # t = time.time()
        if self.conv1x1:
            input.features = torch.mm(
                input.features,
                self.weight.view(self.in_channels, self.out_channels))
            if self.bias is not None:
                input.features += self.bias
            return input
        datas = input.find_indice_pair(self.indice_key)
        if self.inverse:
            assert datas is not None and self.indice_key is not None
            _, outids, indice_pairs, indice_pair_num, out_spatial_shape = datas
            assert indice_pairs.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, _, indice_pairs, indice_pair_num, _ = datas
            else:
                outids, indice_pairs, indice_pair_num = ops.get_indice_pairs(
                    indices, batch_size, spatial_shape, self.kernel_size,
                    self.stride, self.padding, self.dilation, self.output_padding, self.subm, self.transposed, grid=input.grid)
                input.indice_dict[self.indice_key] = (outids, indices, indice_pairs, indice_pair_num, spatial_shape)
        if self.subm:
            out_features = Fsp.indice_subm_conv(features, self.weight,
                                              indice_pairs.to(device),
                                              indice_pair_num,
                                              outids.shape[0])
        else:
            if self.inverse:
                out_features = Fsp.indice_inverse_conv(features,
                                            self.weight, indice_pairs.to(device),
                                            indice_pair_num, outids.shape[0])
            else:
                out_features = Fsp.indice_conv(features,
                                            self.weight, indice_pairs.to(device),
                                            indice_pair_num, outids.shape[0])

        if self.bias is not None:
            out_features += self.bias
        out_tensor = spconv.SparseConvTensor(out_features, outids,
                                             out_spatial_shape, batch_size)
        out_tensor.indice_dict = input.indice_dict
        out_tensor.grid = input.grid
        return out_tensor


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):
        super(SparseConv2d, self).__init__(
            2,
            in_channels,
            out_channels,
            kernel_size,
            stride,
            padding,
            dilation,
            groups,
            bias,
            indice_key=indice_key)


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):
        super(SparseConv3d, self).__init__(
            3,
            in_channels,
            out_channels,
            kernel_size,
            stride,
            padding,
            dilation,
            groups,
            bias,
            indice_key=indice_key)

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):
        super(SparseConvTranspose2d, self).__init__(
            2,
            in_channels,
            out_channels,
            kernel_size,
            stride,
            padding,
            dilation,
            groups,
            bias,
            transposed=True,
            indice_key=indice_key)


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):
        super(SparseConvTranspose3d, self).__init__(
            3,
            in_channels,
            out_channels,
            kernel_size,
            stride,
            padding,
            dilation,
            groups,
            bias,
            transposed=True,
            indice_key=indice_key)

class SparseInverseConv2d(SparseConvolution):
    def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size,
                 indice_key,
                 bias=True):
        super(SparseInverseConv2d, self).__init__(
            2,
            in_channels,
            out_channels,
            kernel_size,
            bias=bias,
            inverse=True,
            indice_key=indice_key)


class SparseInverseConv3d(SparseConvolution):
    def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size,
                 indice_key,
                 bias=True):
        super(SparseInverseConv3d, self).__init__(
            3,
            in_channels,
            out_channels,
            kernel_size,
            bias=bias,
            inverse=True,
            indice_key=indice_key)


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):
        super(SubMConv2d, self).__init__(
            2,
            in_channels,
            out_channels,
            kernel_size,
            stride,
            padding,
            dilation,
            groups,
            bias,
            True,
            indice_key=indice_key)


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):
        super(SubMConv3d, self).__init__(
            3,
            in_channels,
            out_channels,
            kernel_size,
            stride,
            padding,
            dilation,
            groups,
            bias,
            True,
            indice_key=indice_key)
