"""
Creates a MobileNetV3 Model as defined in:
Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, Quoc V. Le, Hartwig Adam. (2019).
Searching for MobileNetV3
arXiv preprint arXiv:1905.02244.
"""

import torch
import torch.nn as nn
import numpy as np
import math


def _make_divisible(v, divisor, min_value=None):
    """
    This function is taken from the original tf repo.
    It ensures that all layers have a channel number that is divisible by 8
    It can be seen here:
    https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
    :param v:
    :param divisor:
    :param min_value:
    :return:
    """
    if min_value is None:
        min_value = divisor
    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
    # Make sure that round down does not go down by more than 10%.
    if new_v < 0.9 * v:
        new_v += divisor
    return new_v


class h_sigmoid(nn.Module):
    def __init__(self, inplace=True):
        super(h_sigmoid, self).__init__()
        self.relu = nn.ReLU6(inplace=inplace)

    def forward(self, x):
        return self.relu(x + 3) / 6


class h_swish(nn.Module):
    def __init__(self, inplace=True):
        super(h_swish, self).__init__()
        self.sigmoid = h_sigmoid(inplace=inplace)

    def forward(self, x):
        return x * self.sigmoid(x)


class SELayer(nn.Module):
    def __init__(self, channel, reduction=4):
        super(SELayer, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.fc = nn.Sequential(
            nn.Linear(channel, _make_divisible(channel // reduction, 8)),
            nn.ReLU(inplace=True),
            nn.Linear(_make_divisible(channel // reduction, 8), channel),
            h_sigmoid()
        )

    def forward(self, x):
        b, c, _, _ = x.size()
        y = self.avg_pool(x).view(b, c)
        y = self.fc(y).view(b, c, 1, 1)
        return x * y


def conv_3x3_bn(inp, oup, stride):
    return nn.Sequential(
        nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
        nn.BatchNorm2d(oup),
        h_swish()
    )


def conv_1x1_bn(inp, oup):
    return nn.Sequential(
        nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
        nn.BatchNorm2d(oup),
        h_swish()
    )


class InvertedResidual(nn.Module):
    def __init__(self, inp, hidden_dim, oup, kernel_size, stride, use_se, use_hs):
        super(InvertedResidual, self).__init__()
        assert stride in [1, 2]

        self.identity = stride == 1 and inp == oup

        if inp == hidden_dim:
            self.conv = nn.Sequential(
                # dw
                nn.Conv2d(hidden_dim, hidden_dim, kernel_size, stride, (kernel_size - 1) // 2, groups=hidden_dim,
                          bias=False),
                nn.BatchNorm2d(hidden_dim),
                h_swish() if use_hs else nn.ReLU(inplace=True),
                # Squeeze-and-Excite
                SELayer(hidden_dim) if use_se else nn.Identity(),
                # pw-linear
                nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
                nn.BatchNorm2d(oup),
            )
        else:
            self.conv = nn.Sequential(
                # pw
                nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False),
                nn.BatchNorm2d(hidden_dim),
                h_swish() if use_hs else nn.ReLU(inplace=True),
                # dw
                nn.Conv2d(hidden_dim, hidden_dim, kernel_size, stride, (kernel_size - 1) // 2, groups=hidden_dim,
                          bias=False),
                nn.BatchNorm2d(hidden_dim),
                # Squeeze-and-Excite
                SELayer(hidden_dim) if use_se else nn.Identity(),
                h_swish() if use_hs else nn.ReLU(inplace=True),
                # pw-linear
                nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
                nn.BatchNorm2d(oup),
            )

    def forward(self, x):
        if self.identity:
            return x + self.conv(x)
        else:
            return self.conv(x)


class MobileNetV3(nn.Module):
    def __init__(self, width_mult=1.):
        super(MobileNetV3, self).__init__()
        # setting of inverted residual blocks
        cfgs = [
        # k, t, c, SE, HS, s
        [3, 1, 16, 0, 0, 1],
        [3, 4, 24, 0, 0, 2], # feature 2
        [3, 3, 24, 0, 0, 1],
        [5, 3, 40, 1, 0, 2], # feature 3
        [5, 3, 40, 1, 0, 1],
        [5, 3, 40, 1, 0, 1],
        [3, 6, 80, 0, 1, 2], # feature 4
        [3, 2.5, 80, 0, 1, 1],
        [3, 2.3, 80, 0, 1, 1],
        [3, 2.3, 80, 0, 1, 1],
        [3, 6, 112, 1, 1, 1],
        [3, 6, 112, 1, 1, 1],
        [5, 6, 160, 1, 1, 2],
        [5, 6, 160, 1, 1, 1],
        [5, 6, 160, 1, 1, 1] # feature 5
        ]

        # building first layer
        input_channel = _make_divisible(16 * width_mult, 8)
        layers = [conv_3x3_bn(3, input_channel, 2)]
        # building inverted residual blocks
        block = InvertedResidual
        for k, t, c, use_se, use_hs, s in cfgs:
            output_channel = _make_divisible(c * width_mult, 8)
            exp_size = _make_divisible(input_channel * t, 8)
            layers.append(block(input_channel, exp_size, output_channel, k, s, use_se, use_hs))
            input_channel = output_channel
        self.features = nn.Sequential(*layers)

class MobileEncoder(nn.Module):
    def __init__(self, pretrained):
        super(MobileEncoder, self).__init__()

        self.num_ch_enc = np.array([16, 24, 40, 80, 160])
        self.encoder = MobileNetV3()
        if pretrained:
            state_dict = torch.load("pretrain_model/mobilenetV3/mobilenetv3-large-1cd25616.pth")
            filter_dict_enc = {k: v for k, v in state_dict.items() if k in self.encoder.state_dict()}
            self.encoder.load_state_dict(filter_dict_enc)

    def forward(self, input_image):
        return_features = []
        x = (input_image - 0.45) / 0.225
        return_features.append(self.encoder.features[0](x))
        return_features.append(self.encoder.features[1:3](return_features[-1]))
        return_features.append(self.encoder.features[3:5](return_features[-1]))
        return_features.append(self.encoder.features[5:8](return_features[-1]))
        return_features.append(self.encoder.features[8:](return_features[-1]))
        return return_features