# Part of the implementation is borrowed and modified from ControlNet,
# publicly available at https://github.com/lllyasviel/ControlNet
import os
import sys

import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
from torch.nn import functional as F


class BlockTypeA(nn.Module):

    def __init__(self, in_c1, in_c2, out_c1, out_c2, upscale=True):
        super(BlockTypeA, self).__init__()
        self.conv1 = nn.Sequential(
            nn.Conv2d(in_c2, out_c2, kernel_size=1), nn.BatchNorm2d(out_c2),
            nn.ReLU(inplace=True))
        self.conv2 = nn.Sequential(
            nn.Conv2d(in_c1, out_c1, kernel_size=1), nn.BatchNorm2d(out_c1),
            nn.ReLU(inplace=True))
        self.upscale = upscale

    def forward(self, a, b):
        b = self.conv1(b)
        a = self.conv2(a)
        if self.upscale:
            b = F.interpolate(
                b, scale_factor=2.0, mode='bilinear', align_corners=True)
        return torch.cat((a, b), dim=1)


class BlockTypeB(nn.Module):

    def __init__(self, in_c, out_c):
        super(BlockTypeB, self).__init__()
        self.conv1 = nn.Sequential(
            nn.Conv2d(in_c, in_c, kernel_size=3, padding=1),
            nn.BatchNorm2d(in_c), nn.ReLU())
        self.conv2 = nn.Sequential(
            nn.Conv2d(in_c, out_c, kernel_size=3, padding=1),
            nn.BatchNorm2d(out_c), nn.ReLU())

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


class BlockTypeC(nn.Module):

    def __init__(self, in_c, out_c):
        super(BlockTypeC, self).__init__()
        self.conv1 = nn.Sequential(
            nn.Conv2d(in_c, in_c, kernel_size=3, padding=5, dilation=5),
            nn.BatchNorm2d(in_c), nn.ReLU())
        self.conv2 = nn.Sequential(
            nn.Conv2d(in_c, in_c, kernel_size=3, padding=1),
            nn.BatchNorm2d(in_c), nn.ReLU())
        self.conv3 = nn.Conv2d(in_c, out_c, kernel_size=1)

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


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 ConvBNReLU(nn.Sequential):

    def __init__(self,
                 in_planes,
                 out_planes,
                 kernel_size=3,
                 stride=1,
                 groups=1):
        self.channel_pad = out_planes - in_planes
        self.stride = stride
        # padding = (kernel_size - 1) // 2

        # TFLite uses slightly different padding than PyTorch
        if stride == 2:
            padding = 0
        else:
            padding = (kernel_size - 1) // 2

        super(ConvBNReLU, self).__init__(
            nn.Conv2d(
                in_planes,
                out_planes,
                kernel_size,
                stride,
                padding,
                groups=groups,
                bias=False), nn.BatchNorm2d(out_planes),
            nn.ReLU6(inplace=True))
        self.max_pool = nn.MaxPool2d(kernel_size=stride, stride=stride)

    def forward(self, x):
        # TFLite uses  different padding
        if self.stride == 2:
            x = F.pad(x, (0, 1, 0, 1), 'constant', 0)
            # print(x.shape)

        for module in self:
            if not isinstance(module, nn.MaxPool2d):
                x = module(x)
        return x


class InvertedResidual(nn.Module):

    def __init__(self, inp, oup, stride, expand_ratio):
        super(InvertedResidual, self).__init__()
        self.stride = stride
        assert stride in [1, 2]

        hidden_dim = int(round(inp * expand_ratio))
        self.use_res_connect = self.stride == 1 and inp == oup

        layers = []
        if expand_ratio != 1:
            # pw
            layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1))
        layers.extend([
            # dw
            ConvBNReLU(
                hidden_dim, hidden_dim, stride=stride, groups=hidden_dim),
            # pw-linear
            nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
            nn.BatchNorm2d(oup),
        ])
        self.conv = nn.Sequential(*layers)

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


class MobileNetV2(nn.Module):

    def __init__(self, pretrained=True):
        """
        MobileNet V2 main class
        Args:
            num_classes (int): Number of classes
            width_mult (float): Width multiplier - adjusts number of channels in each layer by this amount
            inverted_residual_setting: Network structure
            round_nearest (int): Round the number of channels in each layer to be a multiple of this number
            Set to 1 to turn off rounding
            block: Module specifying inverted residual building block for mobilenet
        """
        super(MobileNetV2, self).__init__()

        block = InvertedResidual
        input_channel = 32
        last_channel = 1280
        width_mult = 1.0
        round_nearest = 8

        inverted_residual_setting = [
            # t, c, n, s
            [1, 16, 1, 1],
            [6, 24, 2, 2],
            [6, 32, 3, 2],
            [6, 64, 4, 2],
            [6, 96, 3, 1],
            # [6, 160, 3, 2],
            # [6, 320, 1, 1],
        ]

        # only check the first element, assuming user knows t,c,n,s are required
        if len(inverted_residual_setting) == 0 or len(
                inverted_residual_setting[0]) != 4:
            raise ValueError('inverted_residual_setting should be non-empty '
                             'or a 4-element list, got {}'.format(
                                 inverted_residual_setting))

        # building first layer
        input_channel = _make_divisible(input_channel * width_mult,
                                        round_nearest)
        self.last_channel = _make_divisible(
            last_channel * max(1.0, width_mult), round_nearest)
        features = [ConvBNReLU(4, input_channel, stride=2)]
        # building inverted residual blocks
        for t, c, n, s in inverted_residual_setting:
            output_channel = _make_divisible(c * width_mult, round_nearest)
            for i in range(n):
                stride = s if i == 0 else 1
                features.append(
                    block(
                        input_channel, output_channel, stride, expand_ratio=t))
                input_channel = output_channel

        self.features = nn.Sequential(*features)
        self.fpn_selected = [1, 3, 6, 10, 13]
        # weight initialization
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out')
                if m.bias is not None:
                    nn.init.zeros_(m.bias)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.ones_(m.weight)
                nn.init.zeros_(m.bias)
            elif isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, 0, 0.01)
                nn.init.zeros_(m.bias)
        if pretrained:
            self._load_pretrained_model()

    def _forward_impl(self, x):
        # This exists since TorchScript doesn't support inheritance, so the superclass method
        # (this one) needs to have a name other than `forward` that can be accessed in a subclass
        fpn_features = []
        for i, f in enumerate(self.features):
            if i > self.fpn_selected[-1]:
                break
            x = f(x)
            if i in self.fpn_selected:
                fpn_features.append(x)

        c1, c2, c3, c4, c5 = fpn_features
        return c1, c2, c3, c4, c5

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

    def _load_pretrained_model(self):
        pretrain_dict = model_zoo.load_url(
            'https://download.pytorch.org/models/mobilenet_v2-b0353104.pth')
        model_dict = {}
        state_dict = self.state_dict()
        for k, v in pretrain_dict.items():
            if k in state_dict:
                model_dict[k] = v
        state_dict.update(model_dict)
        self.load_state_dict(state_dict)


class MobileV2_MLSD_Large(nn.Module):

    def __init__(self):
        super(MobileV2_MLSD_Large, self).__init__()

        self.backbone = MobileNetV2(pretrained=False)
        # A, B
        self.block15 = BlockTypeA(
            in_c1=64, in_c2=96, out_c1=64, out_c2=64, upscale=False)
        self.block16 = BlockTypeB(128, 64)

        # A, B
        self.block17 = BlockTypeA(in_c1=32, in_c2=64, out_c1=64, out_c2=64)
        self.block18 = BlockTypeB(128, 64)

        # A, B
        self.block19 = BlockTypeA(in_c1=24, in_c2=64, out_c1=64, out_c2=64)
        self.block20 = BlockTypeB(128, 64)

        # A, B, C
        self.block21 = BlockTypeA(in_c1=16, in_c2=64, out_c1=64, out_c2=64)
        self.block22 = BlockTypeB(128, 64)

        self.block23 = BlockTypeC(64, 16)

    def forward(self, x):
        c1, c2, c3, c4, c5 = self.backbone(x)

        x = self.block15(c4, c5)
        x = self.block16(x)

        x = self.block17(c3, x)
        x = self.block18(x)

        x = self.block19(c2, x)
        x = self.block20(x)

        x = self.block21(c1, x)
        x = self.block22(x)
        x = self.block23(x)
        x = x[:, 7:, :, :]

        return x
