"""Source: https://github.com/HRNet/HRNet-Semantic-Segmentation"""
# ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Written by RainbowSecret (yhyuan@pku.edu.cn)
# ------------------------------------------------------------------------------

import torch
import numpy as np
import logging
from typing import Dict
import torch.nn as nn
import torch.nn.functional as F
from torch.hub import load_state_dict_from_url

from utils.dropout import add_dropout
from utils.self_adapt_norm import replace_batchnorm

logger = logging.getLogger('hrnet_backbone')
ALIGN_CORNERS = None

__all__ = ['hrnet18', 'hrnet32', 'hrnet48']

model_urls = {
    'hrnet18': 'https://opr0mq.dm.files.1drv.com/y4mIoWpP2n-LUohHHANpC0jrOixm1FZgO2OsUtP2DwIozH5RsoYVyv_De5wDgR6XuQmirMV3C0AljLeB-zQXevfLlnQpcNeJlT9Q8LwNYDwh3TsECkMTWXCUn3vDGJWpCxQcQWKONr5VQWO1hLEKPeJbbSZ6tgbWwJHgHF7592HY7ilmGe39o5BhHz7P9QqMYLBts6V7QGoaKrr0PL3wvvR4w',
    'hrnet32': 'https://opr74a.dm.files.1drv.com/y4mKOuRSNGQQlp6wm_a9bF-UEQwp6a10xFCLhm4bqjDu6aSNW9yhDRM7qyx0vK0WTh42gEaniUVm3h7pg0H-W0yJff5qQtoAX7Zze4vOsqjoIthp-FW3nlfMD0-gcJi8IiVrMWqVOw2N3MbCud6uQQrTaEAvAdNjtjMpym1JghN-F060rSQKmgtq5R-wJe185IyW4-_c5_ItbhYpCyLxdqdEQ',
    'hrnet48': 'https://optgaw.dm.files.1drv.com/y4mWNpya38VArcDInoPaL7GfPMgcop92G6YRkabO1QTSWkCbo7djk8BFZ6LK_KHHIYE8wqeSAChU58NVFOZEvqFaoz392OgcyBrq_f8XGkusQep_oQsuQ7DPQCUrdLwyze_NlsyDGWot0L9agkQ-M_SfNr10ETlCF5R7BdKDZdupmcMXZc-IE3Ysw1bVHdOH4l-XEbEKFAi6ivPUbeqlYkRMQ'
}

# model_urls = {
#     'resnet18_ibn_a': 'https://github.com/XingangPan/IBN-Net/releases/download/v1.0/resnet18_ibn_a-2f571257.pth',
#     'resnet34_ibn_a': 'https://github.com/XingangPan/IBN-Net/releases/download/v1.0/resnet34_ibn_a-94bc1577.pth',
#     'resnet50_ibn_a': 'https://github.com/XingangPan/IBN-Net/releases/download/v1.0/resnet50_ibn_a-d9d0bb7b.pth',
#     'resnet101_ibn_a': 'https://github.com/XingangPan/IBN-Net/releases/download/v1.0/resnet101_ibn_a-59ea0ac6.pth',
#     'resnet18_ibn_b': 'https://github.com/XingangPan/IBN-Net/releases/download/v1.0/resnet18_ibn_b-bc2f3c11.pth',
#     'resnet34_ibn_b': 'https://github.com/XingangPan/IBN-Net/releases/download/v1.0/resnet34_ibn_b-04134c37.pth',
#     'resnet50_ibn_b': 'https://github.com/XingangPan/IBN-Net/releases/download/v1.0/resnet50_ibn_b-9ca61e85.pth',
#     'resnet101_ibn_b': 'https://github.com/XingangPan/IBN-Net/releases/download/v1.0/resnet101_ibn_b-c55f6dba.pth',
# }


def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=dilation, groups=groups, bias=False, dilation=dilation)


def conv1x1(in_planes, out_planes, stride=1):
    """1x1 convolution"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
                 base_width=64, dilation=1, norm_layer=None):
        super(BasicBlock, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        if groups != 1 or base_width != 64:
            raise ValueError('BasicBlock only supports groups=1 and base_width=64')
        if dilation > 1:
            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = norm_layer(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = norm_layer(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
                 base_width=64, dilation=1, norm_layer=None):
        super(Bottleneck, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        width = int(planes * (base_width / 64.)) * groups
        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv1x1(inplanes, width)
        self.bn1 = norm_layer(width)
        self.conv2 = conv3x3(width, width, stride, groups, dilation)
        self.bn2 = norm_layer(width)
        self.conv3 = conv1x1(width, planes * self.expansion)
        self.bn3 = norm_layer(planes * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out


class HighResolutionModule(nn.Module):
    def __init__(self, num_branches, blocks, num_blocks, num_inchannels,
                 num_channels, fuse_method, multi_scale_output=True, norm_layer=None):
        super(HighResolutionModule, self).__init__()
        self._check_branches(
            num_branches, blocks, num_blocks, num_inchannels, num_channels)

        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self.norm_layer = norm_layer

        self.num_inchannels = num_inchannels
        self.fuse_method = fuse_method
        self.num_branches = num_branches

        self.multi_scale_output = multi_scale_output

        self.branches = self._make_branches(
            num_branches, blocks, num_blocks, num_channels)
        self.fuse_layers = self._make_fuse_layers()
        self.relu = nn.ReLU(inplace=True)

    def _check_branches(self, num_branches, blocks, num_blocks,
                        num_inchannels, num_channels):
        if num_branches != len(num_blocks):
            error_msg = 'NUM_BRANCHES({}) <> NUM_BLOCKS({})'.format(
                num_branches, len(num_blocks))
            logger.error(error_msg)
            raise ValueError(error_msg)

        if num_branches != len(num_channels):
            error_msg = 'NUM_BRANCHES({}) <> NUM_CHANNELS({})'.format(
                num_branches, len(num_channels))
            logger.error(error_msg)
            raise ValueError(error_msg)

        if num_branches != len(num_inchannels):
            error_msg = 'NUM_BRANCHES({}) <> NUM_INCHANNELS({})'.format(
                num_branches, len(num_inchannels))
            logger.error(error_msg)
            raise ValueError(error_msg)

    def _make_one_branch(self, branch_index, block, num_blocks, num_channels,
                         stride=1):
        downsample = None
        if stride != 1 or \
                self.num_inchannels[branch_index] != num_channels[branch_index] * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.num_inchannels[branch_index],
                          num_channels[branch_index] * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                self.norm_layer(num_channels[branch_index] * block.expansion),
            )

        layers = []
        layers.append(block(self.num_inchannels[branch_index],
                            num_channels[branch_index], stride, downsample, norm_layer=self.norm_layer))
        self.num_inchannels[branch_index] = \
            num_channels[branch_index] * block.expansion
        for i in range(1, num_blocks[branch_index]):
            layers.append(block(self.num_inchannels[branch_index],
                                num_channels[branch_index], norm_layer=self.norm_layer))

        return nn.Sequential(*layers)

    def _make_branches(self, num_branches, block, num_blocks, num_channels):
        branches = []

        for i in range(num_branches):
            branches.append(
                self._make_one_branch(i, block, num_blocks, num_channels))

        return nn.ModuleList(branches)

    def _make_fuse_layers(self):
        if self.num_branches == 1:
            return None

        num_branches = self.num_branches
        num_inchannels = self.num_inchannels
        fuse_layers = []
        for i in range(num_branches if self.multi_scale_output else 1):
            fuse_layer = []
            for j in range(num_branches):
                if j > i:
                    fuse_layer.append(nn.Sequential(
                        nn.Conv2d(num_inchannels[j],
                                  num_inchannels[i],
                                  1,
                                  1,
                                  0,
                                  bias=False),
                        self.norm_layer(num_inchannels[i])))
                elif j == i:
                    fuse_layer.append(None)
                else:
                    conv3x3s = []
                    for k in range(i - j):
                        if k == i - j - 1:
                            num_outchannels_conv3x3 = num_inchannels[i]
                            conv3x3s.append(nn.Sequential(
                                nn.Conv2d(num_inchannels[j],
                                          num_outchannels_conv3x3,
                                          3, 2, 1, bias=False),
                                self.norm_layer(num_outchannels_conv3x3)))
                        else:
                            num_outchannels_conv3x3 = num_inchannels[j]
                            conv3x3s.append(nn.Sequential(
                                nn.Conv2d(num_inchannels[j],
                                          num_outchannels_conv3x3,
                                          3, 2, 1, bias=False),
                                self.norm_layer(num_outchannels_conv3x3),
                                nn.ReLU(inplace=True)))
                    fuse_layer.append(nn.Sequential(*conv3x3s))
            fuse_layers.append(nn.ModuleList(fuse_layer))

        return nn.ModuleList(fuse_layers)

    def get_num_inchannels(self):
        return self.num_inchannels

    def forward(self, x):
        if self.num_branches == 1:
            return [self.branches[0](x[0])]

        for i in range(self.num_branches):
            x[i] = self.branches[i](x[i])

        x_fuse = []
        for i in range(len(self.fuse_layers)):
            y = x[0] if i == 0 else self.fuse_layers[i][0](x[0])
            for j in range(1, self.num_branches):
                if i == j:
                    y = y + x[j]
                elif j > i:
                    width_output = x[i].shape[-1]
                    height_output = x[i].shape[-2]
                    y = y + F.interpolate(
                        self.fuse_layers[i][j](x[j]),
                        size=[height_output, width_output],
                        mode='bilinear',
                        align_corners=True
                    )
                else:
                    y = y + self.fuse_layers[i][j](x[j])
            x_fuse.append(self.relu(y))

        return x_fuse


blocks_dict = {
    'BASIC': BasicBlock,
    'BOTTLENECK': Bottleneck
}


class HighResolutionNet(nn.Module):

    def __init__(self,
                 cfg,
                 norm_layer=None,
                 num_classes: int = 19):
        super(HighResolutionNet, self).__init__()

        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self.norm_layer = norm_layer
        # stem network
        # stem net
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1,
                               bias=False)
        self.bn1 = self.norm_layer(64)
        self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1,
                               bias=False)
        self.bn2 = self.norm_layer(64)
        self.relu = nn.ReLU(inplace=True)

        # stage 1
        self.stage1_cfg = cfg['STAGE1']
        num_channels = self.stage1_cfg['NUM_CHANNELS'][0]
        block = blocks_dict[self.stage1_cfg['BLOCK']]
        num_blocks = self.stage1_cfg['NUM_BLOCKS'][0]
        self.layer1 = self._make_layer(block, 64, num_channels, num_blocks)
        stage1_out_channel = block.expansion * num_channels

        # stage 2
        self.stage2_cfg = cfg['STAGE2']
        num_channels = self.stage2_cfg['NUM_CHANNELS']
        block = blocks_dict[self.stage2_cfg['BLOCK']]
        num_channels = [
            num_channels[i] * block.expansion for i in range(len(num_channels))]
        self.transition1 = self._make_transition_layer(
            [stage1_out_channel], num_channels)
        self.stage2, pre_stage_channels = self._make_stage(
            self.stage2_cfg, num_channels)

        # stage 3
        self.stage3_cfg = cfg['STAGE3']
        num_channels = self.stage3_cfg['NUM_CHANNELS']
        block = blocks_dict[self.stage3_cfg['BLOCK']]
        num_channels = [
            num_channels[i] * block.expansion for i in range(len(num_channels))]
        self.transition2 = self._make_transition_layer(
            pre_stage_channels, num_channels)
        self.stage3, pre_stage_channels = self._make_stage(
            self.stage3_cfg, num_channels)

        # stage 4
        self.stage4_cfg = cfg['STAGE4']
        num_channels = self.stage4_cfg['NUM_CHANNELS']
        block = blocks_dict[self.stage4_cfg['BLOCK']]
        num_channels = [
            num_channels[i] * block.expansion for i in range(len(num_channels))]
        self.transition3 = self._make_transition_layer(
            pre_stage_channels, num_channels)
        self.stage4, pre_stage_channels = self._make_stage(
            self.stage4_cfg, num_channels, multi_scale_output=True)

        last_inp_channels = np.int(np.sum(pre_stage_channels))

        self.last_layer = nn.Sequential(
            nn.Conv2d(
                in_channels=last_inp_channels,
                out_channels=last_inp_channels,
                kernel_size=1,
                stride=1,
                padding=0),
            self.norm_layer(last_inp_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(
                in_channels=last_inp_channels,
                out_channels=num_classes,
                kernel_size=1,
                stride=1,
                padding=0)
        )

    def _make_transition_layer(
            self, num_channels_pre_layer, num_channels_cur_layer):
        num_branches_cur = len(num_channels_cur_layer)
        num_branches_pre = len(num_channels_pre_layer)

        transition_layers = []
        for i in range(num_branches_cur):
            if i < num_branches_pre:
                if num_channels_cur_layer[i] != num_channels_pre_layer[i]:
                    transition_layers.append(nn.Sequential(
                        nn.Conv2d(num_channels_pre_layer[i],
                                  num_channels_cur_layer[i],
                                  3,
                                  1,
                                  1,
                                  bias=False),
                        self.norm_layer(num_channels_cur_layer[i]),
                        nn.ReLU(inplace=True)))
                else:
                    transition_layers.append(None)
            else:
                conv3x3s = []
                for j in range(i + 1 - num_branches_pre):
                    inchannels = num_channels_pre_layer[-1]
                    outchannels = num_channels_cur_layer[i] \
                        if j == i - num_branches_pre else inchannels
                    conv3x3s.append(nn.Sequential(
                        nn.Conv2d(
                            inchannels, outchannels, 3, 2, 1, bias=False),
                        self.norm_layer(outchannels),
                        nn.ReLU(inplace=True)))
                transition_layers.append(nn.Sequential(*conv3x3s))

        return nn.ModuleList(transition_layers)

    def _make_layer(self, block, inplanes, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                self.norm_layer(planes * block.expansion),
            )

        layers = []
        layers.append(block(inplanes, planes, stride, downsample, norm_layer=self.norm_layer))
        inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(inplanes, planes, norm_layer=self.norm_layer))

        return nn.Sequential(*layers)

    def _make_stage(self, layer_config, num_inchannels,
                    multi_scale_output=True):
        num_modules = layer_config['NUM_MODULES']
        num_branches = layer_config['NUM_BRANCHES']
        num_blocks = layer_config['NUM_BLOCKS']
        num_channels = layer_config['NUM_CHANNELS']
        block = blocks_dict[layer_config['BLOCK']]
        fuse_method = layer_config['FUSE_METHOD']

        modules = []
        for i in range(num_modules):
            # multi_scale_output is only used last module
            if not multi_scale_output and i == num_modules - 1:
                reset_multi_scale_output = False
            else:
                reset_multi_scale_output = True

            modules.append(
                HighResolutionModule(num_branches,
                                     block,
                                     num_blocks,
                                     num_inchannels,
                                     num_channels,
                                     fuse_method,
                                     reset_multi_scale_output,
                                     norm_layer=self.norm_layer)
            )
            num_inchannels = modules[-1].get_num_inchannels()

        return nn.Sequential(*modules), num_inchannels

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu(x)
        x = self.layer1(x)

        x_list = []
        for i in range(self.stage2_cfg['NUM_BRANCHES']):
            if self.transition1[i] is not None:
                x_list.append(self.transition1[i](x))
            else:
                x_list.append(x)
        y_list = self.stage2(x_list)

        x_list = []
        for i in range(self.stage3_cfg['NUM_BRANCHES']):
            if self.transition2[i] is not None:
                if i < self.stage2_cfg['NUM_BRANCHES']:
                    x_list.append(self.transition2[i](y_list[i]))
                else:
                    x_list.append(self.transition2[i](y_list[-1]))
            else:
                x_list.append(y_list[i])
        y_list = self.stage3(x_list)

        x_list = []
        for i in range(self.stage4_cfg['NUM_BRANCHES']):
            if self.transition3[i] is not None:
                if i < self.stage3_cfg['NUM_BRANCHES']:
                    x_list.append(self.transition3[i](y_list[i]))
                else:
                    x_list.append(self.transition3[i](y_list[-1]))
            else:
                x_list.append(y_list[i])
        x = self.stage4(x_list)

        # Upsampling
        x0_h, x0_w = x[0].size(2), x[0].size(3)
        x1 = F.interpolate(x[1], size=(x0_h, x0_w), mode='bilinear', align_corners=ALIGN_CORNERS)
        x2 = F.interpolate(x[2], size=(x0_h, x0_w), mode='bilinear', align_corners=ALIGN_CORNERS)
        x3 = F.interpolate(x[3], size=(x0_h, x0_w), mode='bilinear', align_corners=ALIGN_CORNERS)

        x = torch.cat([x[0], x1, x2, x3], 1)

        x = self.last_layer(x)

        return x


def _hrnet(arch, pretrained, progress, num_classes: int = 19):
    from models.hrnet_config import MODEL_CONFIGS
    model = HighResolutionNet(MODEL_CONFIGS[arch], num_classes=num_classes)
    if pretrained:
        model_url = model_urls[arch]
        state_dict = load_state_dict_from_url(model_url,
                                              progress=progress)
        model.load_state_dict(state_dict, strict=False)
    return model

class HRNet(torch.nn.Module):

    def __init__(self,
                 hrnet_name: str,
                 num_classes: int = 19,
                 dropout: bool = False,
                 alpha: float = 0.0,
                 update_source_bn: bool = True):
        super(HRNet, self).__init__()
        self.model = _hrnet(hrnet_name, pretrained=True, progress=True, num_classes=num_classes)

        # Add dropout layers after relu
        if dropout:
            add_dropout(model=self)

        # Replace BN layers with SaN layers
        replace_batchnorm(self, alpha=alpha, update_source_bn=update_source_bn)

    def forward(self,
                img: torch.Tensor) -> Dict[str, torch.Tensor]:
        """
        Args:
            img: Batch of input images
        Returns:
            output:
                'pred': Segmentation output of images
        """
        # Create output dict of forward pass
        output_dict = {}

        # Compute probabilities for semantic classes
        x = self.model(img)

        # Upsample to full resolution
        x = torch.nn.functional.interpolate(x, size=img.shape[2:], mode='bilinear', align_corners=True)
        output_dict['pred'] = x

        return output_dict


def hrnet18(backbone_name: str = None,
                  num_classes: int = 19,
                  alpha: float = 0.5,
                  update_source_bn: bool = True,
                  dropout: bool = False):
    return HRNet("hrnet18",
                         num_classes,
                         dropout,
                         alpha=alpha,
                         update_source_bn=update_source_bn)

def hrnet32(backbone_name: str = None,
                  num_classes: int = 19,
                  alpha: float = 0.5,
                  update_source_bn: bool = True,
                  dropout: bool = False):
    return HRNet("hrnet32",
                         num_classes,
                         dropout,
                         alpha=alpha,
                         update_source_bn=update_source_bn)

def hrnet48(backbone_name: str = None,
                  num_classes: int = 19,
                  alpha: float = 0.5,
                  update_source_bn: bool = True,
                  dropout: bool = False):
    return HRNet("hrnet48",
                         num_classes,
                         dropout,
                         alpha=alpha,
                         update_source_bn=update_source_bn)