"""
    IBN-ResNeXt for ImageNet-1K, implemented in PyTorch.
    Original paper: 'Aggregated Residual Transformations for Deep Neural Networks,' http://arxiv.org/abs/1611.05431.
"""

__all__ = ['IBNResNeXt', 'ibn_resnext50_32x4d', 'ibn_resnext101_32x4d', 'ibn_resnext101_64x4d']

import os
import math
import torch.nn as nn
import torch.nn.init as init
from .common import conv1x1_block, conv3x3_block
from .resnet import ResInitBlock
from .ibnresnet import ibn_conv1x1_block


class IBNResNeXtBottleneck(nn.Module):
    """
    IBN-ResNeXt bottleneck block for residual path in IBN-ResNeXt unit.

    Parameters:
    ----------
    in_channels : int
        Number of input channels.
    out_channels : int
        Number of output channels.
    stride : int or tuple/list of 2 int
        Strides of the convolution.
    cardinality: int
        Number of groups.
    bottleneck_width: int
        Width of bottleneck block.
    conv1_ibn : bool
        Whether to use IBN normalization in the first convolution layer of the block.
    """
    def __init__(self,
                 in_channels,
                 out_channels,
                 stride,
                 cardinality,
                 bottleneck_width,
                 conv1_ibn):
        super(IBNResNeXtBottleneck, self).__init__()
        mid_channels = out_channels // 4
        D = int(math.floor(mid_channels * (bottleneck_width / 64.0)))
        group_width = cardinality * D

        self.conv1 = ibn_conv1x1_block(
            in_channels=in_channels,
            out_channels=group_width,
            use_ibn=conv1_ibn)
        self.conv2 = conv3x3_block(
            in_channels=group_width,
            out_channels=group_width,
            stride=stride,
            groups=cardinality)
        self.conv3 = conv1x1_block(
            in_channels=group_width,
            out_channels=out_channels,
            activation=None)

    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = self.conv3(x)
        return x


class IBNResNeXtUnit(nn.Module):
    """
    IBN-ResNeXt unit.

    Parameters:
    ----------
    in_channels : int
        Number of input channels.
    out_channels : int
        Number of output channels.
    stride : int or tuple/list of 2 int
        Strides of the convolution.
    cardinality: int
        Number of groups.
    bottleneck_width: int
        Width of bottleneck block.
    conv1_ibn : bool
        Whether to use IBN normalization in the first convolution layer of the block.
    """
    def __init__(self,
                 in_channels,
                 out_channels,
                 stride,
                 cardinality,
                 bottleneck_width,
                 conv1_ibn):
        super(IBNResNeXtUnit, self).__init__()
        self.resize_identity = (in_channels != out_channels) or (stride != 1)

        self.body = IBNResNeXtBottleneck(
            in_channels=in_channels,
            out_channels=out_channels,
            stride=stride,
            cardinality=cardinality,
            bottleneck_width=bottleneck_width,
            conv1_ibn=conv1_ibn)
        if self.resize_identity:
            self.identity_conv = conv1x1_block(
                in_channels=in_channels,
                out_channels=out_channels,
                stride=stride,
                activation=None)
        self.activ = nn.ReLU(inplace=True)

    def forward(self, x):
        if self.resize_identity:
            identity = self.identity_conv(x)
        else:
            identity = x
        x = self.body(x)
        x = x + identity
        x = self.activ(x)
        return x


class IBNResNeXt(nn.Module):
    """
    IBN-ResNeXt model from 'Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net,'
    https://arxiv.org/abs/1807.09441.

    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.
    cardinality: int
        Number of groups.
    bottleneck_width: int
        Width of bottleneck block.
    in_channels : int, default 3
        Number of input channels.
    in_size : tuple of two ints, default (224, 224)
        Spatial size of the expected input image.
    num_classes : int, default 1000
        Number of classification classes.
    """
    def __init__(self,
                 channels,
                 init_block_channels,
                 cardinality,
                 bottleneck_width,
                 in_channels=3,
                 in_size=(224, 224),
                 num_classes=1000):
        super(IBNResNeXt, self).__init__()
        self.in_size = in_size
        self.num_classes = num_classes

        self.features = nn.Sequential()
        self.features.add_module("init_block", ResInitBlock(
            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()
            for j, out_channels in enumerate(channels_per_stage):
                stride = 2 if (j == 0) and (i != 0) else 1
                conv1_ibn = (out_channels < 2048)
                stage.add_module("unit{}".format(j + 1), IBNResNeXtUnit(
                    in_channels=in_channels,
                    out_channels=out_channels,
                    stride=stride,
                    cardinality=cardinality,
                    bottleneck_width=bottleneck_width,
                    conv1_ibn=conv1_ibn))
                in_channels = out_channels
            self.features.add_module("stage{}".format(i + 1), stage)
        self.features.add_module("final_pool", nn.AvgPool2d(
            kernel_size=7,
            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_ibnresnext(blocks,
                   cardinality,
                   bottleneck_width,
                   model_name=None,
                   pretrained=False,
                   root=os.path.join("~", ".torch", "models"),
                   **kwargs):
    """
    Create IBN-ResNeXt model with specific parameters.

    Parameters:
    ----------
    blocks : int
        Number of blocks.
    cardinality: int
        Number of groups.
    bottleneck_width: int
        Width of bottleneck block.
    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.
    """

    if blocks == 50:
        layers = [3, 4, 6, 3]
    elif blocks == 101:
        layers = [3, 4, 23, 3]
    else:
        raise ValueError("Unsupported IBN-ResNeXt with number of blocks: {}".format(blocks))

    init_block_channels = 64
    channels_per_layers = [256, 512, 1024, 2048]

    channels = [[ci] * li for (ci, li) in zip(channels_per_layers, layers)]

    net = IBNResNeXt(
        channels=channels,
        init_block_channels=init_block_channels,
        cardinality=cardinality,
        bottleneck_width=bottleneck_width,
        **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 ibn_resnext50_32x4d(**kwargs):
    """
    IBN-ResNeXt-50 (32x4d) model from 'Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net,'
    https://arxiv.org/abs/1807.09441.

    Parameters:
    ----------
    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_ibnresnext(blocks=50, cardinality=32, bottleneck_width=4, model_name="ibn_resnext50_32x4d", **kwargs)


def ibn_resnext101_32x4d(**kwargs):
    """
    IBN-ResNeXt-101 (32x4d) model from 'Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net,'
    https://arxiv.org/abs/1807.09441.

    Parameters:
    ----------
    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_ibnresnext(blocks=101, cardinality=32, bottleneck_width=4, model_name="ibn_resnext101_32x4d", **kwargs)


def ibn_resnext101_64x4d(**kwargs):
    """
    IBN-ResNeXt-101 (64x4d) model from 'Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net,'
    https://arxiv.org/abs/1807.09441.

    Parameters:
    ----------
    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_ibnresnext(blocks=101, cardinality=64, bottleneck_width=4, model_name="ibn_resnext101_64x4d", **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

    pretrained = False

    models = [
        ibn_resnext50_32x4d,
        ibn_resnext101_32x4d,
        ibn_resnext101_64x4d,
    ]

    for model in models:

        net = model(pretrained=pretrained)

        # net.train()
        net.eval()
        weight_count = _calc_width(net)
        print("m={}, {}".format(model.__name__, weight_count))
        assert (model != ibn_resnext50_32x4d or weight_count == 25028904)
        assert (model != ibn_resnext101_32x4d or weight_count == 44177704)
        assert (model != ibn_resnext101_64x4d or weight_count == 83455272)

        x = torch.randn(1, 3, 224, 224)
        y = net(x)
        y.sum().backward()
        assert (tuple(y.size()) == (1, 1000))


if __name__ == "__main__":
    _test()
