# The implementation is adopted from HRNet, made publicly available under the MIT License License
# at https://github.com/HRNet/HRNet-Semantic-Segmentation
import logging
import os

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F

BatchNorm2d = nn.BatchNorm2d

BN_MOMENTUM = 0.1
logger = logging.getLogger(__name__)


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


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = BatchNorm2d(planes, momentum=BN_MOMENTUM)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = BatchNorm2d(planes, momentum=BN_MOMENTUM)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = 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:
            residual = self.downsample(x)

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

        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = BatchNorm2d(planes, momentum=BN_MOMENTUM)
        self.conv2 = nn.Conv2d(
            planes,
            planes,
            kernel_size=3,
            stride=stride,
            padding=1,
            bias=False)
        self.bn2 = BatchNorm2d(planes, momentum=BN_MOMENTUM)
        self.conv3 = nn.Conv2d(
            planes, planes * self.expansion, kernel_size=1, bias=False)
        self.bn3 = BatchNorm2d(planes * self.expansion, momentum=BN_MOMENTUM)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = 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:
            residual = self.downsample(x)

        out += residual
        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):
        super(HighResolutionModule, self).__init__()
        self._check_branches(num_branches, blocks, num_blocks, num_inchannels,
                             num_channels)

        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),
                BatchNorm2d(
                    num_channels[branch_index] * block.expansion,
                    momentum=BN_MOMENTUM),
            )

        layers = []
        layers.append(
            block(self.num_inchannels[branch_index],
                  num_channels[branch_index], stride, downsample))
        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]))

        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  # tuple
        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),
                            BatchNorm2d(
                                num_inchannels[i], momentum=BN_MOMENTUM)))
                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),
                                    BatchNorm2d(
                                        num_outchannels_conv3x3,
                                        momentum=BN_MOMENTUM)))
                        else:
                            num_outchannels_conv3x3 = num_inchannels[j]
                            conv3x3s.append(
                                nn.Sequential(
                                    nn.Conv2d(
                                        num_inchannels[j],
                                        num_outchannels_conv3x3,
                                        3,
                                        2,
                                        1,
                                        bias=False),
                                    BatchNorm2d(
                                        num_outchannels_conv3x3,
                                        momentum=BN_MOMENTUM),
                                    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


model_w18v1 = {
    'STAGE1': {
        'NUM_MODULES': 1,
        'NUM_BRANCHES': 1,
        'BLOCK': 'BOTTLENECK',
        'NUM_BLOCKS': (1),
        'NUM_CHANNELS': (32),
        'FUSE_METHOD': 'SUM'
    },
    'STAGE2': {
        'NUM_MODULES': 1,
        'NUM_BRANCHES': 2,
        'BLOCK': 'BASIC',
        'NUM_BLOCKS': (2, 2),
        'NUM_CHANNELS': (16, 32),
        'FUSE_METHOD': 'SUM'
    },
    'STAGE3': {
        'NUM_MODULES': 1,
        'NUM_BRANCHES': 3,
        'BLOCK': 'BASIC',
        'NUM_BLOCKS': (2, 2, 2),
        'NUM_CHANNELS': (16, 32, 64),
        'FUSE_METHOD': 'SUM'
    },
    'STAGE4': {
        'NUM_MODULES': 1,
        'NUM_BRANCHES': 4,
        'BLOCK': 'BASIC',
        'NUM_BLOCKS': (2, 2, 2, 2),
        'NUM_CHANNELS': (16, 32, 64, 128),
        'FUSE_METHOD': 'SUM'
    },
    'FINAL_CONV_KERNEL': 1
}

model_w18v2 = {
    'STAGE1': {
        'NUM_MODULES': 1,
        'NUM_BRANCHES': 1,
        'BLOCK': 'BOTTLENECK',
        'NUM_BLOCKS': (2),
        'NUM_CHANNELS': (64),
        'FUSE_METHOD': 'SUM'
    },
    'STAGE2': {
        'NUM_MODULES': 1,
        'NUM_BRANCHES': 2,
        'BLOCK': 'BASIC',
        'NUM_BLOCKS': (2, 2),
        'NUM_CHANNELS': (18, 36),
        'FUSE_METHOD': 'SUM'
    },
    'STAGE3': {
        'NUM_MODULES': 3,
        'NUM_BRANCHES': 3,
        'BLOCK': 'BASIC',
        'NUM_BLOCKS': (2, 2, 2),
        'NUM_CHANNELS': (18, 36, 72),
        'FUSE_METHOD': 'SUM'
    },
    'STAGE4': {
        'NUM_MODULES': 2,
        'NUM_BRANCHES': 4,
        'BLOCK': 'BASIC',
        'NUM_BLOCKS': (2, 2, 2, 2),
        'NUM_CHANNELS': (18, 36, 72, 144),
        'FUSE_METHOD': 'SUM'
    },
    'FINAL_CONV_KERNEL': 1
}

model_w48 = {
    'STAGE1': {
        'NUM_MODULES': 1,
        'NUM_BRANCHES': 1,
        'BLOCK': 'BOTTLENECK',
        'NUM_BLOCKS': (4),
        'NUM_CHANNELS': (64),
        'FUSE_METHOD': 'SUM'
    },
    'STAGE2': {
        'NUM_MODULES': 1,
        'NUM_BRANCHES': 2,
        'BLOCK': 'BASIC',
        'NUM_BLOCKS': (4, 4),
        'NUM_CHANNELS': (48, 96),
        'FUSE_METHOD': 'SUM'
    },
    'STAGE3': {
        'NUM_MODULES': 4,
        'NUM_BRANCHES': 3,
        'BLOCK': 'BASIC',
        'NUM_BLOCKS': (4, 4, 4),
        'NUM_CHANNELS': (48, 96, 192),
        'FUSE_METHOD': 'SUM'
    },
    'STAGE4': {
        'NUM_MODULES': 3,
        'NUM_BRANCHES': 4,
        'BLOCK': 'BASIC',
        'NUM_BLOCKS': (4, 4, 4, 4),
        'NUM_CHANNELS': (48, 96, 192, 384),
        'FUSE_METHOD': 'SUM'
    },
    'FINAL_CONV_KERNEL': 1
}

model_version_dict = {}
model_version_dict['w48'] = model_w48
model_version_dict['w18v1'] = model_w18v1
model_version_dict['w18v2'] = model_w18v2

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


class HrnetBackBone(nn.Module):

    def __init__(self, **kwargs):
        super(HrnetBackBone, self).__init__()

        assert 'version' in kwargs, 'hrnet not exist model version'
        extra = model_version_dict[kwargs['version']]

        # stem net
        self.conv1 = nn.Conv2d(
            3, 64, kernel_size=3, stride=2, padding=1, bias=False)
        self.bn1 = BatchNorm2d(64, momentum=BN_MOMENTUM)
        self.conv2 = nn.Conv2d(
            64, 64, kernel_size=3, stride=2, padding=1, bias=False)
        self.bn2 = BatchNorm2d(64, momentum=BN_MOMENTUM)
        self.relu = nn.ReLU(inplace=True)

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

        self.stage2_cfg = extra['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)

        self.stage3_cfg = extra['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)

        self.stage4_cfg = extra['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)

        self.backbone_last_inp_channels = np.int(np.sum(pre_stage_channels))

    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),
                            BatchNorm2d(
                                num_channels_cur_layer[i],
                                momentum=BN_MOMENTUM), 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),
                            BatchNorm2d(outchannels, momentum=BN_MOMENTUM),
                            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),
                BatchNorm2d(planes * block.expansion, momentum=BN_MOMENTUM),
            )

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

        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))
            num_inchannels = modules[-1].get_num_inchannels()

        return nn.Sequential(*modules), num_inchannels

    def _backbone_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:
                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:
                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=True)
        x2 = F.interpolate(
            x[2], size=(x0_h, x0_w), mode='bilinear', align_corners=True)
        x3 = F.interpolate(
            x[3], size=(x0_h, x0_w), mode='bilinear', align_corners=True)

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

    def init_weights(self, url, cache_file=''):
        pretrained_dict = load_state(url, model_dir=cache_file)
        model_dict = self.state_dict()

        model_len = len(model_dict)
        pretrain_len = len(pretrained_dict)
        common_dict = {}
        valid_layer_num = 0
        for k, v in pretrained_dict.items():
            if k in model_dict:
                common_dict[k] = v
                valid_layer_num += 1

        print('*' * 50)
        print('Model Param Num:{}    Pretrained Param Num:{}   '
              'Commmon Num:{}'.format(model_len, pretrain_len,
                                      valid_layer_num))
        print('-' * 50)
        print('Model Extra Param Names:\n\t{}'.format(
            set(model_dict) - set(pretrained_dict)))
        print('-' * 50)
        print('Pretrained Extra Param Names:\n\t{}'.format(
            set(pretrained_dict) - set(model_dict)))
        print('*' * 50)

        model_dict.update(common_dict)
        self.load_state_dict(model_dict)
