##################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
##################################################
import torch
import torch.nn as nn

__all__ = ['OPS', 'RAW_OP_CLASSES', 'ResNetBasicblock', 'SearchSpaceNames']

OPS = {
    'none': lambda C_in, C_out, stride, affine, track_running_stats: Zero(C_in, C_out, stride),
    'avg_pool_3x3': lambda C_in, C_out, stride, affine, track_running_stats: POOLING(C_in, C_out, stride, 'avg', affine,
                                                                                     track_running_stats),
    'max_pool_3x3': lambda C_in, C_out, stride, affine, track_running_stats: POOLING(C_in, C_out, stride, 'max', affine,
                                                                                     track_running_stats),
    'nor_conv_7x7': lambda C_in, C_out, stride, affine, track_running_stats: ReLUConvBN(C_in, C_out, (7, 7),
                                                                                        (stride, stride), (3, 3),
                                                                                        (1, 1), affine,
                                                                                        track_running_stats),
    'nor_conv_3x3': lambda C_in, C_out, stride, affine, track_running_stats: ReLUConvBN(C_in, C_out, (3, 3),
                                                                                        (stride, stride), (1, 1),
                                                                                        (1, 1), affine,
                                                                                        track_running_stats),
    'nor_conv_1x1': lambda C_in, C_out, stride, affine, track_running_stats: ReLUConvBN(C_in, C_out, (1, 1),
                                                                                        (stride, stride), (0, 0),
                                                                                        (1, 1), affine,
                                                                                        track_running_stats),
    'dua_sepc_3x3': lambda C_in, C_out, stride, affine, track_running_stats: DualSepConv(C_in, C_out, (3, 3),
                                                                                         (stride, stride), (1, 1),
                                                                                         (1, 1), affine,
                                                                                         track_running_stats),
    'dua_sepc_5x5': lambda C_in, C_out, stride, affine, track_running_stats: DualSepConv(C_in, C_out, (5, 5),
                                                                                         (stride, stride), (2, 2),
                                                                                         (1, 1), affine,
                                                                                         track_running_stats),
    'dil_sepc_3x3': lambda C_in, C_out, stride, affine, track_running_stats: SepConv(C_in, C_out, (3, 3),
                                                                                     (stride, stride), (2, 2), (2, 2),
                                                                                     affine, track_running_stats),
    'dil_sepc_5x5': lambda C_in, C_out, stride, affine, track_running_stats: SepConv(C_in, C_out, (5, 5),
                                                                                     (stride, stride), (4, 4), (2, 2),
                                                                                     affine, track_running_stats),
    'skip_connect': lambda C_in, C_out, stride, affine,
                           track_running_stats: Identity() if stride == 1 and C_in == C_out else FactorizedReduce(C_in,
                                                                                                                  C_out,
                                                                                                                  stride,
                                                                                                                  affine,
                                                                                                                  track_running_stats),
}

CONNECT_NAS_BENCHMARK = ['none', 'skip_connect', 'nor_conv_3x3']
NAS_BENCH_201 = ['none', 'skip_connect', 'nor_conv_1x1', 'nor_conv_3x3', 'avg_pool_3x3']
DARTS_SPACE = ['none', 'skip_connect', 'dua_sepc_3x3', 'dua_sepc_5x5', 'dil_sepc_3x3', 'dil_sepc_5x5', 'avg_pool_3x3',
               'max_pool_3x3']

SearchSpaceNames = {'connect-nas': CONNECT_NAS_BENCHMARK,
                    'nats-bench': NAS_BENCH_201,
                    'nas-bench-201': NAS_BENCH_201,
                    'darts': DARTS_SPACE}


class ReLUConvBN(nn.Module):

    def __init__(self, C_in, C_out, kernel_size, stride, padding, dilation, affine, track_running_stats=True):
        super(ReLUConvBN, self).__init__()
        self.op = nn.Sequential(
            nn.ReLU(inplace=False),
            nn.Conv2d(C_in, C_out, kernel_size, stride=stride, padding=padding, dilation=dilation, bias=not affine),
            nn.BatchNorm2d(C_out, affine=affine, track_running_stats=track_running_stats)
        )

    def forward(self, x):
        return self.op(x)


class SepConv(nn.Module):

    def __init__(self, C_in, C_out, kernel_size, stride, padding, dilation, affine, track_running_stats=True):
        super(SepConv, self).__init__()
        self.op = nn.Sequential(
            nn.ReLU(inplace=False),
            nn.Conv2d(C_in, C_in, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation,
                      groups=C_in, bias=False),
            nn.Conv2d(C_in, C_out, kernel_size=1, padding=0, bias=not affine),
            nn.BatchNorm2d(C_out, affine=affine, track_running_stats=track_running_stats),
        )

    def forward(self, x):
        return self.op(x)


class DualSepConv(nn.Module):

    def __init__(self, C_in, C_out, kernel_size, stride, padding, dilation, affine, track_running_stats=True):
        super(DualSepConv, self).__init__()
        self.op_a = SepConv(C_in, C_in, kernel_size, stride, padding, dilation, affine, track_running_stats)
        self.op_b = SepConv(C_in, C_out, kernel_size, 1, padding, dilation, affine, track_running_stats)

    def forward(self, x):
        x = self.op_a(x)
        x = self.op_b(x)
        return x


class ResNetBasicblock(nn.Module):

    def __init__(self, inplanes, planes, stride, affine=True, track_running_stats=True):
        super(ResNetBasicblock, self).__init__()
        assert stride == 1 or stride == 2, 'invalid stride {:}'.format(stride)
        self.conv_a = ReLUConvBN(inplanes, planes, 3, stride, 1, 1, affine, track_running_stats)
        self.conv_b = ReLUConvBN(planes, planes, 3, 1, 1, 1, affine, track_running_stats)
        if stride == 2:
            self.downsample = nn.Sequential(
                nn.AvgPool2d(kernel_size=2, stride=2, padding=0),
                nn.Conv2d(inplanes, planes, kernel_size=1, stride=1, padding=0, bias=False))
        elif inplanes != planes:
            self.downsample = ReLUConvBN(inplanes, planes, 1, 1, 0, 1, affine, track_running_stats)
        else:
            self.downsample = None
        self.in_dim = inplanes
        self.out_dim = planes
        self.stride = stride
        self.num_conv = 2

    def extra_repr(self):
        string = '{name}(inC={in_dim}, outC={out_dim}, stride={stride})'.format(name=self.__class__.__name__,
                                                                                **self.__dict__)
        return string

    def forward(self, inputs):

        basicblock = self.conv_a(inputs)
        basicblock = self.conv_b(basicblock)

        if self.downsample is not None:
            residual = self.downsample(inputs)
        else:
            residual = inputs
        return residual + basicblock


class POOLING(nn.Module):

    def __init__(self, C_in, C_out, stride, mode, affine=True, track_running_stats=True):
        super(POOLING, self).__init__()
        if C_in == C_out:
            self.preprocess = None
        else:
            self.preprocess = ReLUConvBN(C_in, C_out, 1, 1, 0, 1, affine, track_running_stats)
        if mode == 'avg':
            self.op = nn.AvgPool2d(3, stride=stride, padding=1, count_include_pad=False)
        elif mode == 'max':
            self.op = nn.MaxPool2d(3, stride=stride, padding=1)
        else:
            raise ValueError('Invalid mode={:} in POOLING'.format(mode))

    def forward(self, inputs):
        if self.preprocess:
            x = self.preprocess(inputs)
        else:
            x = inputs
        return self.op(x)


class Identity(nn.Module):

    def __init__(self):
        super(Identity, self).__init__()

    def forward(self, x):
        return x


class Zero(nn.Module):

    def __init__(self, C_in, C_out, stride):
        super(Zero, self).__init__()
        self.C_in = C_in
        self.C_out = C_out
        self.stride = stride
        self.is_zero = True

    def forward(self, x):
        if self.C_in == self.C_out:
            if self.stride == 1:
                return x.mul(0.)
            else:
                return x[:, :, ::self.stride, ::self.stride].mul(0.)
        else:
            shape = list(x.shape)
            shape[1] = self.C_out
            zeros = x.new_zeros(shape, dtype=x.dtype, device=x.device)
            return zeros

    def extra_repr(self):
        return 'C_in={C_in}, C_out={C_out}, stride={stride}'.format(**self.__dict__)


class FactorizedReduce(nn.Module):

    def __init__(self, C_in, C_out, stride, affine, track_running_stats):
        super(FactorizedReduce, self).__init__()
        self.stride = stride
        self.C_in = C_in
        self.C_out = C_out
        self.relu = nn.ReLU(inplace=False)
        if stride == 2:
            # assert C_out % 2 == 0, 'C_out : {:}'.format(C_out)
            C_outs = [C_out // 2, C_out - C_out // 2]
            self.convs = nn.ModuleList()
            for i in range(2):
                self.convs.append(nn.Conv2d(C_in, C_outs[i], 1, stride=stride, padding=0, bias=not affine))
            self.pad = nn.ConstantPad2d((0, 1, 0, 1), 0)
        elif stride == 1:
            self.conv = nn.Conv2d(C_in, C_out, 1, stride=stride, padding=0, bias=not affine)
        else:
            raise ValueError('Invalid stride : {:}'.format(stride))
        self.bn = nn.BatchNorm2d(C_out, affine=affine, track_running_stats=track_running_stats)

    def forward(self, x):
        if self.stride == 2:
            x = self.relu(x)
            y = self.pad(x)
            out = torch.cat([self.convs[0](x), self.convs[1](y[:, :, 1:, 1:])], dim=1)
        else:
            out = self.conv(x)
        out = self.bn(out)
        return out

    def extra_repr(self):
        return 'C_in={C_in}, C_out={C_out}, stride={stride}'.format(**self.__dict__)


# Auto-ReID: Searching for a Part-Aware ConvNet for Person Re-Identification, ICCV 2019
class PartAwareOp(nn.Module):

    def __init__(self, C_in, C_out, stride, part=4):
        super().__init__()
        self.part = 4
        self.hidden = C_in // 3
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.local_conv_list = nn.ModuleList()
        for i in range(self.part):
            self.local_conv_list.append(
                nn.Sequential(nn.ReLU(), nn.Conv2d(C_in, self.hidden, 1), nn.BatchNorm2d(self.hidden, affine=True))
            )
        self.W_K = nn.Linear(self.hidden, self.hidden)
        self.W_Q = nn.Linear(self.hidden, self.hidden)

        if stride == 2:
            self.last = FactorizedReduce(C_in + self.hidden, C_out, 2)
        elif stride == 1:
            self.last = FactorizedReduce(C_in + self.hidden, C_out, 1)
        else:
            raise ValueError('Invalid Stride : {:}'.format(stride))

    def forward(self, x):
        batch, C, H, W = x.size()
        assert H >= self.part, 'input size too small : {:} vs {:}'.format(x.shape, self.part)
        IHs = [0]
        for i in range(self.part): IHs.append(min(H, int((i + 1) * (float(H) / self.part))))
        local_feat_list = []
        for i in range(self.part):
            feature = x[:, :, IHs[i]:IHs[i + 1], :]
            xfeax = self.avg_pool(feature)
            xfea = self.local_conv_list[i](xfeax)
            local_feat_list.append(xfea)
        part_feature = torch.cat(local_feat_list, dim=2).view(batch, -1, self.part)
        part_feature = part_feature.transpose(1, 2).contiguous()
        part_K = self.W_K(part_feature)
        part_Q = self.W_Q(part_feature).transpose(1, 2).contiguous()
        weight_att = torch.bmm(part_K, part_Q)
        attention = torch.softmax(weight_att, dim=2)
        aggreateF = torch.bmm(attention, part_feature).transpose(1, 2).contiguous()
        features = []
        for i in range(self.part):
            feature = aggreateF[:, :, i:i + 1].expand(batch, self.hidden, IHs[i + 1] - IHs[i])
            feature = feature.view(batch, self.hidden, IHs[i + 1] - IHs[i], 1)
            features.append(feature)
        features = torch.cat(features, dim=2).expand(batch, self.hidden, H, W)
        final_fea = torch.cat((x, features), dim=1)
        outputs = self.last(final_fea)
        return outputs


def drop_path(x, drop_prob):
    if drop_prob > 0.:
        keep_prob = 1. - drop_prob
        mask = x.new_zeros(x.size(0), 1, 1, 1)
        mask = mask.bernoulli_(keep_prob)
        x = torch.div(x, keep_prob)
        x.mul_(mask)
    return x


# Searching for A Robust Neural Architecture in Four GPU Hours
class GDAS_Reduction_Cell(nn.Module):

    def __init__(self, C_prev_prev, C_prev, C, reduction_prev, affine, track_running_stats):
        super(GDAS_Reduction_Cell, self).__init__()
        if reduction_prev:
            self.preprocess0 = FactorizedReduce(C_prev_prev, C, 2, affine, track_running_stats)
        else:
            self.preprocess0 = ReLUConvBN(C_prev_prev, C, 1, 1, 0, 1, affine, track_running_stats)
        self.preprocess1 = ReLUConvBN(C_prev, C, 1, 1, 0, 1, affine, track_running_stats)

        self.reduction = True
        self.ops1 = nn.ModuleList(
            [nn.Sequential(
                nn.ReLU(inplace=False),
                nn.Conv2d(C, C, (1, 3), stride=(1, 2), padding=(0, 1), groups=8, bias=not affine),
                nn.Conv2d(C, C, (3, 1), stride=(2, 1), padding=(1, 0), groups=8, bias=not affine),
                nn.BatchNorm2d(C, affine=affine, track_running_stats=track_running_stats),
                nn.ReLU(inplace=False),
                nn.Conv2d(C, C, 1, stride=1, padding=0, bias=not affine),
                nn.BatchNorm2d(C, affine=affine, track_running_stats=track_running_stats)),
                nn.Sequential(
                    nn.ReLU(inplace=False),
                    nn.Conv2d(C, C, (1, 3), stride=(1, 2), padding=(0, 1), groups=8, bias=not affine),
                    nn.Conv2d(C, C, (3, 1), stride=(2, 1), padding=(1, 0), groups=8, bias=not affine),
                    nn.BatchNorm2d(C, affine=affine, track_running_stats=track_running_stats),
                    nn.ReLU(inplace=False),
                    nn.Conv2d(C, C, 1, stride=1, padding=0, bias=not affine),
                    nn.BatchNorm2d(C, affine=affine, track_running_stats=track_running_stats))])

        self.ops2 = nn.ModuleList(
            [nn.Sequential(
                nn.MaxPool2d(3, stride=2, padding=1),
                nn.BatchNorm2d(C, affine=affine, track_running_stats=track_running_stats)),
                nn.Sequential(
                    nn.MaxPool2d(3, stride=2, padding=1),
                    nn.BatchNorm2d(C, affine=affine, track_running_stats=track_running_stats))])

    @property
    def multiplier(self):
        return 4

    def forward(self, s0, s1, drop_prob=-1):
        s0 = self.preprocess0(s0)
        s1 = self.preprocess1(s1)

        X0 = self.ops1[0](s0)
        X1 = self.ops1[1](s1)
        if self.training and drop_prob > 0.:
            X0, X1 = drop_path(X0, drop_prob), drop_path(X1, drop_prob)

        # X2 = self.ops2[0] (X0+X1)
        X2 = self.ops2[0](s0)
        X3 = self.ops2[1](s1)
        if self.training and drop_prob > 0.:
            X2, X3 = drop_path(X2, drop_prob), drop_path(X3, drop_prob)
        return torch.cat([X0, X1, X2, X3], dim=1)


# To manage the useful classes in this file.
RAW_OP_CLASSES = {
    'gdas_reduction': GDAS_Reduction_Cell
}
