"""
    X-DenseNet for CIFAR, implemented in PyTorch.
    Original paper: 'Deep Expander Networks: Efficient Deep Networks from Graph Theory,'
    https://arxiv.org/abs/1711.08757.
"""

__all__ = ['CIFARXDenseNet', 'xdensenet40_2_k24_bc_cifar10', 'xdensenet40_2_k24_bc_cifar100',
           'xdensenet40_2_k36_bc_cifar10', 'xdensenet40_2_k36_bc_cifar100']

import os
import torch
import torch.nn as nn
import torch.nn.init as init
from .common import conv3x3
from .preresnet import PreResActivation
from .densenet import TransitionBlock
from .xdensenet import pre_xconv3x3_block, XDenseUnit


class XDenseSimpleUnit(nn.Module):
    """
    X-DenseNet simple unit for CIFAR.

    Parameters:
    ----------
    in_channels : int
        Number of input channels.
    out_channels : int
        Number of output channels.
    dropout_rate : bool
        Parameter of Dropout layer. Faction of the input units to drop.
    expand_ratio : int
        Ratio of expansion.
    """

    def __init__(self,
                 in_channels,
                 out_channels,
                 dropout_rate,
                 expand_ratio):
        super(XDenseSimpleUnit, self).__init__()
        self.use_dropout = (dropout_rate != 0.0)
        inc_channels = out_channels - in_channels

        self.conv = pre_xconv3x3_block(
            in_channels=in_channels,
            out_channels=inc_channels,
            expand_ratio=expand_ratio)
        if self.use_dropout:
            self.dropout = nn.Dropout(p=dropout_rate)

    def forward(self, x):
        identity = x
        x = self.conv(x)
        if self.use_dropout:
            x = self.dropout(x)
        x = torch.cat((identity, x), dim=1)
        return x


class CIFARXDenseNet(nn.Module):
    """
    X-DenseNet model for CIFAR from 'Deep Expander Networks: Efficient Deep Networks from Graph Theory,'
    https://arxiv.org/abs/1711.08757.

    Parameters:
    ----------
    channels : list of list of int
        Number of output channels for each unit.
    init_block_channels : int
        Number of output channels for the initial unit.
    bottleneck : bool
        Whether to use a bottleneck or simple block in units.
    dropout_rate : float, default 0.0
        Parameter of Dropout layer. Faction of the input units to drop.
    expand_ratio : int, default 2
        Ratio of expansion.
    in_channels : int, default 3
        Number of input channels.
    in_size : tuple of two ints, default (32, 32)
        Spatial size of the expected input image.
    num_classes : int, default 10
        Number of classification classes.
    """
    def __init__(self,
                 channels,
                 init_block_channels,
                 bottleneck,
                 dropout_rate=0.0,
                 expand_ratio=2,
                 in_channels=3,
                 in_size=(32, 32),
                 num_classes=10):
        super(CIFARXDenseNet, self).__init__()
        self.in_size = in_size
        self.num_classes = num_classes
        unit_class = XDenseUnit if bottleneck else XDenseSimpleUnit

        self.features = nn.Sequential()
        self.features.add_module("init_block", conv3x3(
            in_channels=in_channels,
            out_channels=init_block_channels))
        in_channels = init_block_channels
        for i, channels_per_stage in enumerate(channels):
            stage = nn.Sequential()
            if i != 0:
                stage.add_module("trans{}".format(i + 1), TransitionBlock(
                    in_channels=in_channels,
                    out_channels=(in_channels // 2)))
                in_channels = in_channels // 2
            for j, out_channels in enumerate(channels_per_stage):
                stage.add_module("unit{}".format(j + 1), unit_class(
                    in_channels=in_channels,
                    out_channels=out_channels,
                    dropout_rate=dropout_rate,
                    expand_ratio=expand_ratio))
                in_channels = out_channels
            self.features.add_module("stage{}".format(i + 1), stage)
        self.features.add_module("post_activ", PreResActivation(in_channels=in_channels))
        self.features.add_module("final_pool", nn.AvgPool2d(
            kernel_size=8,
            stride=1))

        self.output = nn.Linear(
            in_features=in_channels,
            out_features=num_classes)

        self._init_params()

    def _init_params(self):
        for name, module in self.named_modules():
            if isinstance(module, nn.Conv2d):
                init.kaiming_uniform_(module.weight)
                if module.bias is not None:
                    init.constant_(module.bias, 0)

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


def get_xdensenet_cifar(num_classes,
                        blocks,
                        growth_rate,
                        bottleneck,
                        expand_ratio=2,
                        model_name=None,
                        pretrained=False,
                        root=os.path.join('~', '.torch', 'models'),
                        **kwargs):
    """
    Create X-DenseNet model for CIFAR with specific parameters.

    Parameters:
    ----------
    num_classes : int
        Number of classification classes.
    blocks : int
        Number of blocks.
    growth_rate : int
        Growth rate.
    bottleneck : bool
        Whether to use a bottleneck or simple block in units.
    expand_ratio : int, default 2
        Ratio of expansion.
    model_name : str or None, default None
        Model name for loading pretrained model.
    pretrained : bool, default False
        Whether to load the pretrained weights for model.
    root : str, default '~/.torch/models'
        Location for keeping the model parameters.
    """
    assert (num_classes in [10, 100])

    if bottleneck:
        assert ((blocks - 4) % 6 == 0)
        layers = [(blocks - 4) // 6] * 3
    else:
        assert ((blocks - 4) % 3 == 0)
        layers = [(blocks - 4) // 3] * 3
    init_block_channels = 2 * growth_rate

    from functools import reduce
    channels = reduce(
        lambda xi, yi: xi + [reduce(
            lambda xj, yj: xj + [xj[-1] + yj],
            [growth_rate] * yi,
            [xi[-1][-1] // 2])[1:]],
        layers,
        [[init_block_channels * 2]])[1:]

    net = CIFARXDenseNet(
        channels=channels,
        init_block_channels=init_block_channels,
        num_classes=num_classes,
        bottleneck=bottleneck,
        expand_ratio=expand_ratio,
        **kwargs)

    if pretrained:
        if (model_name is None) or (not model_name):
            raise ValueError("Parameter `model_name` should be properly initialized for loading pretrained model.")
        from .model_store import download_model
        download_model(
            net=net,
            model_name=model_name,
            local_model_store_dir_path=root)

    return net


def xdensenet40_2_k24_bc_cifar10(num_classes=10, **kwargs):
    """
    X-DenseNet-BC-40-2 (k=24) model for CIFAR-10 from 'Deep Expander Networks: Efficient Deep Networks from Graph
    Theory,' https://arxiv.org/abs/1711.08757.

    Parameters:
    ----------
    num_classes : int, default 10
        Number of classification classes.
    pretrained : bool, default False
        Whether to load the pretrained weights for model.
    root : str, default '~/.torch/models'
        Location for keeping the model parameters.
    """
    return get_xdensenet_cifar(num_classes=num_classes, blocks=40, growth_rate=24, bottleneck=True,
                               model_name="xdensenet40_2_k24_bc_cifar10", **kwargs)


def xdensenet40_2_k24_bc_cifar100(num_classes=100, **kwargs):
    """
    X-DenseNet-BC-40-2 (k=24) model for CIFAR-100 from 'Deep Expander Networks: Efficient Deep Networks from Graph
    Theory,' https://arxiv.org/abs/1711.08757.

    Parameters:
    ----------
    num_classes : int, default 100
        Number of classification classes.
    pretrained : bool, default False
        Whether to load the pretrained weights for model.
    root : str, default '~/.torch/models'
        Location for keeping the model parameters.
    """
    return get_xdensenet_cifar(num_classes=num_classes, blocks=40, growth_rate=24, bottleneck=True,
                               model_name="xdensenet40_2_k24_bc_cifar100", **kwargs)


def xdensenet40_2_k36_bc_cifar10(num_classes=10, **kwargs):
    """
    X-DenseNet-BC-40-2 (k=36) model for CIFAR-10 from 'Deep Expander Networks: Efficient Deep Networks from Graph
    Theory,' https://arxiv.org/abs/1711.08757.

    Parameters:
    ----------
    num_classes : int, default 10
        Number of classification classes.
    pretrained : bool, default False
        Whether to load the pretrained weights for model.
    root : str, default '~/.torch/models'
        Location for keeping the model parameters.
    """
    return get_xdensenet_cifar(num_classes=num_classes, blocks=40, growth_rate=36, bottleneck=True,
                               model_name="xdensenet40_2_k36_bc_cifar10", **kwargs)


def xdensenet40_2_k36_bc_cifar100(num_classes=100, **kwargs):
    """
    X-DenseNet-BC-40-2 (k=36) model for CIFAR-100 from 'Deep Expander Networks: Efficient Deep Networks from Graph
    Theory,' https://arxiv.org/abs/1711.08757.

    Parameters:
    ----------
    num_classes : int, default 100
        Number of classification classes.
    pretrained : bool, default False
        Whether to load the pretrained weights for model.
    root : str, default '~/.torch/models'
        Location for keeping the model parameters.
    """
    return get_xdensenet_cifar(num_classes=num_classes, blocks=40, growth_rate=36, bottleneck=True,
                               model_name="xdensenet40_2_k36_bc_cifar100", **kwargs)


def _calc_width(net):
    import numpy as np
    net_params = filter(lambda p: p.requires_grad, net.parameters())
    weight_count = 0
    for param in net_params:
        weight_count += np.prod(param.size())
    return weight_count


def _test():
    import torch
    from torch.autograd import Variable

    pretrained = False

    models = [
        (xdensenet40_2_k24_bc_cifar10, 10),
        (xdensenet40_2_k24_bc_cifar100, 100),
        (xdensenet40_2_k36_bc_cifar10, 10),
        (xdensenet40_2_k36_bc_cifar100, 100),
    ]

    for model, num_classes in models:

        net = model(pretrained=pretrained)

        # net.train()
        net.eval()
        weight_count = _calc_width(net)
        print("m={}, {}".format(model.__name__, weight_count))
        assert (model != xdensenet40_2_k24_bc_cifar10 or weight_count == 690346)
        assert (model != xdensenet40_2_k24_bc_cifar100 or weight_count == 714196)
        assert (model != xdensenet40_2_k36_bc_cifar10 or weight_count == 1542682)
        assert (model != xdensenet40_2_k36_bc_cifar100 or weight_count == 1578412)

        x = Variable(torch.randn(1, 3, 32, 32))
        y = net(x)
        assert (tuple(y.size()) == (1, num_classes))


if __name__ == "__main__":
    _test()
