# encoding: utf-8

import logging
import math

import torch
from torch import nn

from fastreid.modeling.backbones import BACKBONE_REGISTRY
from fastreid.modeling.backbones.resnet import Bottleneck, model_zoo, model_urls
from .non_local_layer import Non_local


class ResNetNL(nn.Module):
    def __init__(self, last_stride, with_ibn, block=Bottleneck, layers=[3, 4, 6, 3], non_layers=[0, 2, 3, 0]):
        self.inplanes = 64
        super().__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0], with_ibn=with_ibn)
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2, with_ibn=with_ibn)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2, with_ibn=with_ibn)
        self.layer4 = self._make_layer(
            block, 512, layers[3], stride=last_stride)

        self.NL_1 = nn.ModuleList(
            [Non_local(256) for i in range(non_layers[0])])
        self.NL_1_idx = sorted([layers[0] - (i + 1) for i in range(non_layers[0])])
        self.NL_2 = nn.ModuleList(
            [Non_local(512) for i in range(non_layers[1])])
        self.NL_2_idx = sorted([layers[1] - (i + 1) for i in range(non_layers[1])])
        self.NL_3 = nn.ModuleList(
            [Non_local(1024) for i in range(non_layers[2])])
        self.NL_3_idx = sorted([layers[2] - (i + 1) for i in range(non_layers[2])])
        self.NL_4 = nn.ModuleList(
            [Non_local(2048) for i in range(non_layers[3])])
        self.NL_4_idx = sorted([layers[3] - (i + 1) for i in range(non_layers[3])])

    def _make_layer(self, block, planes, blocks, stride=1, with_ibn=False):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        if planes == 512:
            with_ibn = False
        layers.append(block(self.inplanes, planes, with_ibn, stride=stride, downsample=downsample))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes, with_ibn))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        NL1_counter = 0
        if len(self.NL_1_idx) == 0: self.NL_1_idx = [-1]
        for i in range(len(self.layer1)):
            x = self.layer1[i](x)
            if i == self.NL_1_idx[NL1_counter]:
                _, C, H, W = x.shape
                x = self.NL_1[NL1_counter](x)
                NL1_counter += 1
        # Layer 2
        NL2_counter = 0
        if len(self.NL_2_idx) == 0: self.NL_2_idx = [-1]
        for i in range(len(self.layer2)):
            x = self.layer2[i](x)
            if i == self.NL_2_idx[NL2_counter]:
                _, C, H, W = x.shape
                x = self.NL_2[NL2_counter](x)
                NL2_counter += 1
        # Layer 3
        NL3_counter = 0
        if len(self.NL_3_idx) == 0: self.NL_3_idx = [-1]
        for i in range(len(self.layer3)):
            x = self.layer3[i](x)
            if i == self.NL_3_idx[NL3_counter]:
                _, C, H, W = x.shape
                x = self.NL_3[NL3_counter](x)
                NL3_counter += 1
        # Layer 4
        NL4_counter = 0
        if len(self.NL_4_idx) == 0: self.NL_4_idx = [-1]
        for i in range(len(self.layer4)):
            x = self.layer4[i](x)
            if i == self.NL_4_idx[NL4_counter]:
                _, C, H, W = x.shape
                x = self.NL_4[NL4_counter](x)
                NL4_counter += 1

        return x

    def load_param(self, model_path):
        param_dict = torch.load(model_path)
        for i in param_dict:
            if 'fc' in i:
                continue
            self.state_dict()[i].copy_(param_dict[i])

    def random_init(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()


@BACKBONE_REGISTRY.register()
def build_resnetNL_backbone(cfg):
    """
    Create a ResNet Non-local instance from config.
    Returns:
        ResNet: a :class:`ResNet` instance.
    """

    # fmt: off
    pretrain = cfg.MODEL.BACKBONE.PRETRAIN
    pretrain_path = cfg.MODEL.BACKBONE.PRETRAIN_PATH
    last_stride = cfg.MODEL.BACKBONE.LAST_STRIDE
    with_ibn = cfg.MODEL.BACKBONE.WITH_IBN
    with_se = cfg.MODEL.BACKBONE.WITH_SE
    depth = cfg.MODEL.BACKBONE.DEPTH

    num_blocks_per_stage = {50: [3, 4, 6, 3], 101: [3, 4, 23, 3], 152: [3, 8, 36, 3]}[depth]
    nl_layers_per_stage = [0, 2, 3, 0]
    model = ResNetNL(last_stride, with_ibn, Bottleneck, num_blocks_per_stage, nl_layers_per_stage)
    if pretrain:
        if not with_ibn:
            # original resnet
            state_dict = model_zoo.load_url(model_urls[depth])
        else:
            # ibn resnet
            state_dict = torch.load(pretrain_path)['state_dict']
            # remove module in name
            new_state_dict = {}
            for k in state_dict:
                new_k = '.'.join(k.split('.')[1:])
                if model.state_dict()[new_k].shape == state_dict[k].shape:
                    new_state_dict[new_k] = state_dict[k]
            state_dict = new_state_dict
        res = model.load_state_dict(state_dict, strict=False)
        logger = logging.getLogger('fastreid.'+__name__)
        logger.info('missing keys is {}'.format(res.missing_keys))
        logger.info('unexpected keys is {}'.format(res.unexpected_keys))
    return model