import math
import logging

import torch
import torch.nn as nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from torch.nn.modules.utils import _pair

from . import deform_conv_cuda

logger = logging.getLogger('base')


class DeformConvFunction(Function):
    @staticmethod
    def forward(ctx, input, offset, weight, stride=1, padding=0, dilation=1, groups=1,
                deformable_groups=1, im2col_step=64):
        if input is not None and input.dim() != 4:
            raise ValueError("Expected 4D tensor as input, got {}D tensor instead.".format(
                input.dim()))
        ctx.stride = _pair(stride)
        ctx.padding = _pair(padding)
        ctx.dilation = _pair(dilation)
        ctx.groups = groups
        ctx.deformable_groups = deformable_groups
        ctx.im2col_step = im2col_step

        ctx.save_for_backward(input, offset, weight)

        output = input.new_empty(
            DeformConvFunction._output_size(input, weight, ctx.padding, ctx.dilation, ctx.stride))

        ctx.bufs_ = [input.new_empty(0), input.new_empty(0)]  # columns, ones

        if not input.is_cuda:
            raise NotImplementedError
        else:
            cur_im2col_step = min(ctx.im2col_step, input.shape[0])
            assert (input.shape[0] % cur_im2col_step) == 0, 'im2col step must divide batchsize'
            deform_conv_cuda.deform_conv_forward_cuda(input, weight, offset, output,
                                                      ctx.bufs_[0], ctx.bufs_[1], weight.size(3),
                                                      weight.size(2), ctx.stride[1], ctx.stride[0],
                                                      ctx.padding[1], ctx.padding[0],
                                                      ctx.dilation[1], ctx.dilation[0], ctx.groups,
                                                      ctx.deformable_groups, cur_im2col_step)
        return output

    @staticmethod
    @once_differentiable
    def backward(ctx, grad_output):
        input, offset, weight = ctx.saved_tensors

        grad_input = grad_offset = grad_weight = None

        if not grad_output.is_cuda:
            raise NotImplementedError
        else:
            cur_im2col_step = min(ctx.im2col_step, input.shape[0])
            assert (input.shape[0] % cur_im2col_step) == 0, 'im2col step must divide batchsize'

            if ctx.needs_input_grad[0] or ctx.needs_input_grad[1]:
                grad_input = torch.zeros_like(input)
                grad_offset = torch.zeros_like(offset)
                deform_conv_cuda.deform_conv_backward_input_cuda(
                    input, offset, grad_output, grad_input, grad_offset, weight, ctx.bufs_[0],
                    weight.size(3), weight.size(2), ctx.stride[1], ctx.stride[0], ctx.padding[1],
                    ctx.padding[0], ctx.dilation[1], ctx.dilation[0], ctx.groups,
                    ctx.deformable_groups, cur_im2col_step)

            if ctx.needs_input_grad[2]:
                grad_weight = torch.zeros_like(weight)
                deform_conv_cuda.deform_conv_backward_parameters_cuda(
                    input, offset, grad_output, grad_weight, ctx.bufs_[0], ctx.bufs_[1],
                    weight.size(3), weight.size(2), ctx.stride[1], ctx.stride[0], ctx.padding[1],
                    ctx.padding[0], ctx.dilation[1], ctx.dilation[0], ctx.groups,
                    ctx.deformable_groups, 1, cur_im2col_step)

        return (grad_input, grad_offset, grad_weight, None, None, None, None, None)

    @staticmethod
    def _output_size(input, weight, padding, dilation, stride):
        channels = weight.size(0)
        output_size = (input.size(0), channels)
        for d in range(input.dim() - 2):
            in_size = input.size(d + 2)
            pad = padding[d]
            kernel = dilation[d] * (weight.size(d + 2) - 1) + 1
            stride_ = stride[d]
            output_size += ((in_size + (2 * pad) - kernel) // stride_ + 1, )
        if not all(map(lambda s: s > 0, output_size)):
            raise ValueError("convolution input is too small (output would be {})".format('x'.join(
                map(str, output_size))))
        return output_size


class ModulatedDeformConvFunction(Function):
    @staticmethod
    def forward(ctx, input, offset, mask, weight, bias=None, stride=1, padding=0, dilation=1,
                groups=1, deformable_groups=1):
        ctx.stride = stride
        ctx.padding = padding
        ctx.dilation = dilation
        ctx.groups = groups
        ctx.deformable_groups = deformable_groups
        ctx.with_bias = bias is not None
        if not ctx.with_bias:
            bias = input.new_empty(1)  # fake tensor
        if not input.is_cuda:
            raise NotImplementedError
        if weight.requires_grad or mask.requires_grad or offset.requires_grad \
                or input.requires_grad:
            ctx.save_for_backward(input, offset, mask, weight, bias)
        output = input.new_empty(ModulatedDeformConvFunction._infer_shape(ctx, input, weight))
        ctx._bufs = [input.new_empty(0), input.new_empty(0)]
        deform_conv_cuda.modulated_deform_conv_cuda_forward(
            input, weight, bias, ctx._bufs[0], offset, mask, output, ctx._bufs[1], weight.shape[2],
            weight.shape[3], ctx.stride, ctx.stride, ctx.padding, ctx.padding, ctx.dilation,
            ctx.dilation, ctx.groups, ctx.deformable_groups, ctx.with_bias)
        return output

    @staticmethod
    @once_differentiable
    def backward(ctx, grad_output):
        if not grad_output.is_cuda:
            raise NotImplementedError
        input, offset, mask, weight, bias = ctx.saved_tensors
        grad_input = torch.zeros_like(input)
        grad_offset = torch.zeros_like(offset)
        grad_mask = torch.zeros_like(mask)
        grad_weight = torch.zeros_like(weight)
        grad_bias = torch.zeros_like(bias)
        deform_conv_cuda.modulated_deform_conv_cuda_backward(
            input, weight, bias, ctx._bufs[0], offset, mask, ctx._bufs[1], grad_input, grad_weight,
            grad_bias, grad_offset, grad_mask, grad_output, weight.shape[2], weight.shape[3],
            ctx.stride, ctx.stride, ctx.padding, ctx.padding, ctx.dilation, ctx.dilation,
            ctx.groups, ctx.deformable_groups, ctx.with_bias)
        if not ctx.with_bias:
            grad_bias = None

        return (grad_input, grad_offset, grad_mask, grad_weight, grad_bias, None, None, None, None,
                None)

    @staticmethod
    def _infer_shape(ctx, input, weight):
        n = input.size(0)
        channels_out = weight.size(0)
        height, width = input.shape[2:4]
        kernel_h, kernel_w = weight.shape[2:4]
        height_out = (height + 2 * ctx.padding - (ctx.dilation *
                                                  (kernel_h - 1) + 1)) // ctx.stride + 1
        width_out = (width + 2 * ctx.padding - (ctx.dilation *
                                                (kernel_w - 1) + 1)) // ctx.stride + 1
        return n, channels_out, height_out, width_out


deform_conv = DeformConvFunction.apply
modulated_deform_conv = ModulatedDeformConvFunction.apply


class DeformConv(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1,
                 groups=1, deformable_groups=1, bias=False):
        super(DeformConv, self).__init__()

        assert not bias
        assert in_channels % groups == 0, \
            'in_channels {} cannot be divisible by groups {}'.format(
                in_channels, groups)
        assert out_channels % groups == 0, \
            'out_channels {} cannot be divisible by groups {}'.format(
                out_channels, groups)

        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = _pair(kernel_size)
        self.stride = _pair(stride)
        self.padding = _pair(padding)
        self.dilation = _pair(dilation)
        self.groups = groups
        self.deformable_groups = deformable_groups

        self.weight = nn.Parameter(
            torch.Tensor(out_channels, in_channels // self.groups, *self.kernel_size))

        self.reset_parameters()

    def reset_parameters(self):
        n = self.in_channels
        for k in self.kernel_size:
            n *= k
        stdv = 1. / math.sqrt(n)
        self.weight.data.uniform_(-stdv, stdv)

    def forward(self, x, offset):
        return deform_conv(x, offset, self.weight, self.stride, self.padding, self.dilation,
                           self.groups, self.deformable_groups)


class DeformConvPack(DeformConv):
    def __init__(self, *args, **kwargs):
        super(DeformConvPack, self).__init__(*args, **kwargs)

        self.conv_offset = nn.Conv2d(
            self.in_channels,
            self.deformable_groups * 2 * self.kernel_size[0] * self.kernel_size[1],
            kernel_size=self.kernel_size, stride=_pair(self.stride), padding=_pair(self.padding),
            bias=True)
        self.init_offset()

    def init_offset(self):
        self.conv_offset.weight.data.zero_()
        self.conv_offset.bias.data.zero_()

    def forward(self, x):
        offset = self.conv_offset(x)
        return deform_conv(x, offset, self.weight, self.stride, self.padding, self.dilation,
                           self.groups, self.deformable_groups)


class ModulatedDeformConv(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1,
                 groups=1, deformable_groups=1, bias=True):
        super(ModulatedDeformConv, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = _pair(kernel_size)
        self.stride = stride
        self.padding = padding
        self.dilation = dilation
        self.groups = groups
        self.deformable_groups = deformable_groups
        self.with_bias = bias

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

    def reset_parameters(self):
        n = self.in_channels
        for k in self.kernel_size:
            n *= k
        stdv = 1. / math.sqrt(n)
        self.weight.data.uniform_(-stdv, stdv)
        if self.bias is not None:
            self.bias.data.zero_()

    def forward(self, x, offset, mask):
        return modulated_deform_conv(x, offset, mask, self.weight, self.bias, self.stride,
                                     self.padding, self.dilation, self.groups,
                                     self.deformable_groups)


class ModulatedDeformConvPack(ModulatedDeformConv):
    def __init__(self, *args, extra_offset_mask=False, **kwargs):
        super(ModulatedDeformConvPack, self).__init__(*args, **kwargs)

        self.extra_offset_mask = extra_offset_mask
        self.conv_offset_mask = nn.Conv2d(
            self.in_channels,
            self.deformable_groups * 3 * self.kernel_size[0] * self.kernel_size[1],
            kernel_size=self.kernel_size, stride=_pair(self.stride), padding=_pair(self.padding),
            bias=True)
        self.init_offset()

    def init_offset(self):
        self.conv_offset_mask.weight.data.zero_()
        self.conv_offset_mask.bias.data.zero_()

    def forward(self, x):
        if self.extra_offset_mask:
            # x = [input, features]
            out = self.conv_offset_mask(x[1])
            x = x[0]
        else:
            out = self.conv_offset_mask(x)
        o1, o2, mask = torch.chunk(out, 3, dim=1)
        offset = torch.cat((o1, o2), dim=1)
        mask = torch.sigmoid(mask)

        offset_mean = torch.mean(torch.abs(offset))
        if offset_mean > 100:
            logger.warning('Offset mean is {}, larger than 100.'.format(offset_mean))

        return modulated_deform_conv(x, offset, mask, self.weight, self.bias, self.stride,
                                     self.padding, self.dilation, self.groups,
                                     self.deformable_groups)
#==============================================================================#
#==============================================================================#
class ModulatedDeformConvPack2(ModulatedDeformConv):
    def __init__(self, *args, extra_offset_mask=False, offset_in_channel=32, **kwargs):
        super(ModulatedDeformConvPack2, self).__init__(*args, **kwargs)

        self.extra_offset_mask = extra_offset_mask
        self.conv_offset_mask = nn.Conv2d(
            offset_in_channel,
            self.deformable_groups * 3 * self.kernel_size[0] * self.kernel_size[1],
            kernel_size=self.kernel_size, stride=_pair(self.stride), padding=_pair(self.padding),
            bias=True)
        self.init_offset()

    def init_offset(self):
        self.conv_offset_mask.weight.data.zero_()
        self.conv_offset_mask.bias.data.zero_()

    def forward(self, x):
        if self.extra_offset_mask:
            # x = [input, features]
            out = self.conv_offset_mask(x[1])
            x = x[0]
        else:
            out = self.conv_offset_mask(x)
        o1, o2, mask = torch.chunk(out, 3, dim=1)
        #print(o1[0,:,o1.size()[2]//2,o1.size()[3]//2])
        #print(o2[0,:,o1.size()[2]//2,o1.size()[3]//2])
        offset = torch.cat((o1, o2), dim=1)
        mask = torch.sigmoid(mask)

        offset_mean = torch.mean(torch.abs(offset))
        if offset_mean > 100:
            logger.warning('Offset mean is {}, larger than 100.'.format(offset_mean))

        return modulated_deform_conv(x, offset, mask, self.weight, self.bias, self.stride,
                                     self.padding, self.dilation, self.groups,
                                     self.deformable_groups)
