from models.micro_operations import *
from misc.utils import drop_path


DEFAULT_PADDINGS = {
    'none': 1,
    'skip_connect': 1,
    'avg_pool_3x3': 1,
    'max_pool_3x3': 1,
    'sep_conv_3x3': 1,
    'sep_conv_5x5': 2,
    'sep_conv_7x7': 3,
    'dil_conv_3x3': 2,
    'dil_conv_5x5': 4,
    'conv_7x1_1x7': 3,
}


class Cell(nn.Module):

    def __init__(self, genotype, C_prev_prev, C_prev, C, reduction, reduction_prev, SE=False):
        super(Cell, self).__init__()
        print(C_prev_prev, C_prev, C)

        self.se_layer = None

        if reduction_prev:
            self.preprocess0 = FactorizedReduce(C_prev_prev, C)
        else:
            self.preprocess0 = ReLUConvBN(C_prev_prev, C, 1, 1, 0)
        self.preprocess1 = ReLUConvBN(C_prev, C, 1, 1, 0)

        if reduction:
            op_names, indices = zip(*genotype.reduce)
            concat = genotype.reduce_concat
        else:
            op_names, indices = zip(*genotype.normal)
            concat = genotype.normal_concat
        self._compile(C, op_names, indices, concat, reduction)

        if SE:
            self.se_layer = SELayer(channel=self.multiplier * C)

    def _compile(self, C, op_names, indices, concat, reduction):
        assert len(op_names) == len(indices)
        self._steps = len(op_names) // 2
        self._concat = concat
        self.multiplier = len(concat)

        self._ops = nn.ModuleList()
        for name, index in zip(op_names, indices):
            stride = 2 if reduction and index < 2 else 1
            op = OPS[name](C, stride, True)
            self._ops += [op]
        self._indices = indices

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

        states = [s0, s1]
        for i in range(self._steps):
            h1 = states[self._indices[2 * i]]
            h2 = states[self._indices[2 * i + 1]]
            op1 = self._ops[2 * i]
            op2 = self._ops[2 * i + 1]
            h1 = op1(h1)
            h2 = op2(h2)
            if self.training and drop_prob > 0.:
                if not isinstance(op1, Identity):
                    h1 = drop_path(h1, drop_prob)
                if not isinstance(op2, Identity):
                    h2 = drop_path(h2, drop_prob)
            s = h1 + h2
            states += [s]

        if self.se_layer is None:
            return torch.cat([states[i] for i in self._concat], dim=1)
        else:
            return self.se_layer(torch.cat([states[i] for i in self._concat], dim=1))


class AuxiliaryHeadCIFAR(nn.Module):

    def __init__(self, C, num_classes):
        """assuming input size 8x8"""
        super(AuxiliaryHeadCIFAR, self).__init__()
        self.features = nn.Sequential(
            nn.ReLU(inplace=True),
            nn.AvgPool2d(5, stride=3, padding=0, count_include_pad=False),  # image size = 2 x 2
            nn.Conv2d(C, 128, 1, bias=False),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.Conv2d(128, 768, 2, bias=False),
            nn.BatchNorm2d(768),
            nn.ReLU(inplace=True)
        )
        self.classifier = nn.Linear(768, num_classes)

    def forward(self, x):
        x = self.features(x)
        x = self.classifier(x.view(x.size(0), -1))
        return x


class AuxiliaryHeadImageNet(nn.Module):

    def __init__(self, C, num_classes):
        """assuming input size 14x14"""
        super(AuxiliaryHeadImageNet, self).__init__()
        self.features = nn.Sequential(
            nn.ReLU(inplace=True),
            nn.AvgPool2d(5, stride=2, padding=0, count_include_pad=False),
            nn.Conv2d(C, 128, 1, bias=False),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.Conv2d(128, 768, 2, bias=False),
            # NOTE: This batchnorm was omitted in my earlier implementation due to a typo.
            # Commenting it out for consistency with the experiments in the paper.
            # nn.BatchNorm2d(768),
            nn.ReLU(inplace=True)
        )
        self.classifier = nn.Linear(768, num_classes)

    def forward(self, x):
        x = self.features(x)
        x = self.classifier(x.view(x.size(0), -1))
        return x


class NetworkCIFAR(nn.Module):

    def __init__(self, C, num_classes, layers, auxiliary, genotype, SE=False):
        super(NetworkCIFAR, self).__init__()
        self._layers = layers
        self._auxiliary = auxiliary

        stem_multiplier = 3
        C_curr = stem_multiplier * C
        self.stem = nn.Sequential(
            nn.Conv2d(3, C_curr, 3, padding=1, bias=False),
            nn.BatchNorm2d(C_curr)
        )

        C_prev_prev, C_prev, C_curr = C_curr, C_curr, C
        self.cells = nn.ModuleList()
        reduction_prev = False
        for i in range(layers):
            if i in [layers // 3, 2 * layers // 3]:
                C_curr *= 2
                reduction = True
            else:
                reduction = False

            cell = Cell(genotype, C_prev_prev, C_prev, C_curr, reduction, reduction_prev, SE=SE)

            reduction_prev = reduction
            self.cells += [cell]
            C_prev_prev, C_prev = C_prev, cell.multiplier * C_curr
            if i == 2 * layers // 3:
                C_to_auxiliary = C_prev

        if auxiliary:
            self.auxiliary_head = AuxiliaryHeadCIFAR(C_to_auxiliary, num_classes)
        self.global_pooling = nn.AdaptiveAvgPool2d(1)
        self.classifier = nn.Linear(C_prev, num_classes)

    def forward(self, input):
        logits_aux = None
        s0 = s1 = self.stem(input)
        for i, cell in enumerate(self.cells):
            s0, s1 = s1, cell(s0, s1, self.droprate)
            if i == 2 * self._layers // 3:
                if self._auxiliary and self.training:
                    logits_aux = self.auxiliary_head(s1)
        out = self.global_pooling(s1)
        logits = self.classifier(out.view(out.size(0), -1))
        return logits, logits_aux


class PyramidNetworkCIFAR(nn.Module):
    def __init__(self, C, num_classes, layers, auxiliary, genotype, increment=4, SE=False):
        super(PyramidNetworkCIFAR, self).__init__()
        self._layers = layers
        self._auxiliary = auxiliary

        stem_multiplier = 3
        C_curr = stem_multiplier * C
        self.stem = nn.Sequential(
            nn.Conv2d(3, C_curr, 3, padding=1, bias=False),
            nn.BatchNorm2d(C_curr)
        )
        C_prev_prev, C_prev, C_curr = C_curr, C_curr, C
        self.cells = nn.ModuleList()
        reduction_prev = False
        for i in range(layers):
            if i in [layers // 3, 2 * layers // 3]:
                # C_curr *= 2
                reduction = True
            else:
                reduction = False

            cell = Cell(genotype, C_prev_prev, C_prev, C_curr, reduction, reduction_prev, SE=SE)

            reduction_prev = reduction
            self.cells += [cell]
            C_prev_prev, C_prev = C_prev, cell.multiplier * C_curr
            if i == 2 * layers // 3:
                C_to_auxiliary = C_prev

            C_curr += increment

        if auxiliary:
            self.auxiliary_head = AuxiliaryHeadCIFAR(C_to_auxiliary, num_classes)
        self.global_pooling = nn.AdaptiveAvgPool2d(1)
        self.classifier = nn.Linear(C_prev, num_classes)

    def forward(self, input):
        logits_aux = None
        s0 = s1 = self.stem(input)
        for i, cell in enumerate(self.cells):
            s0, s1 = s1, cell(s0, s1, self.droprate)
            if i == 2 * self._layers // 3:
                if self._auxiliary and self.training:
                    logits_aux = self.auxiliary_head(s1)
        out = self.global_pooling(s1)
        logits = self.classifier(out.view(out.size(0), -1))
        return logits, logits_aux


class NetworkImageNet(nn.Module):

    def __init__(self, C, num_classes, layers, auxiliary, genotype):
        super(NetworkImageNet, self).__init__()
        self._layers = layers
        self._auxiliary = auxiliary

        self.stem0 = nn.Sequential(
            nn.Conv2d(3, C // 2, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(C // 2),
            nn.ReLU(inplace=True),
            nn.Conv2d(C // 2, C, 3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(C),
        )

        self.stem1 = nn.Sequential(
            nn.ReLU(inplace=True),
            nn.Conv2d(C, C, 3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(C),
        )

        C_prev_prev, C_prev, C_curr = C, C, C

        self.cells = nn.ModuleList()
        reduction_prev = True
        for i in range(layers):
            if i in [layers // 3, 2 * layers // 3]:
                C_curr *= 2
                reduction = True
            else:
                reduction = False
            cell = Cell(genotype, C_prev_prev, C_prev, C_curr, reduction, reduction_prev)
            reduction_prev = reduction
            self.cells += [cell]
            C_prev_prev, C_prev = C_prev, cell.multiplier * C_curr
            if i == 2 * layers // 3:
                C_to_auxiliary = C_prev

        if auxiliary:
            self.auxiliary_head = AuxiliaryHeadImageNet(C_to_auxiliary, num_classes)
        self.global_pooling = nn.AvgPool2d(7)
        self.classifier = nn.Linear(C_prev, num_classes)

    def forward(self, input):
        logits_aux = None
        s0 = self.stem0(input)
        s1 = self.stem1(s0)
        for i, cell in enumerate(self.cells):
            s0, s1 = s1, cell(s0, s1, self.droprate)
            if i == 2 * self._layers // 3:
                if self._auxiliary and self.training:
                    logits_aux = self.auxiliary_head(s1)
        out = self.global_pooling(s1)
        logits = self.classifier(out.view(out.size(0), -1))
        return logits, logits_aux


if __name__ == '__main__':
    import validation.utils as utils
    import models.micro_genotypes as genotypes

    genome = genotypes.NSGANet
    # model = AlterPyramidNetworkCIFAR(30, 10, 20, True, genome, 6, SE=False)
    model = PyramidNetworkCIFAR(48, 10, 20, True, genome, 22, SE=True)
    # model = NetworkCIFAR(34, 10, 20, True, genome, SE=True)
    # model = GradPyramidNetworkCIFAR(34, 10, 20, True, genome, 4)
    model.droprate = 0.0

    # calculate number of trainable parameters
    print("param size = {}MB".format(utils.count_parameters_in_MB(model)))
