# encoding: utf-8
# based on:
# https://github.com/zhanghang1989/ResNeSt/blob/master/resnest/torch/resnest.py
"""ResNeSt models"""

import logging
import math

import torch
from torch import nn

from fastreid.layers import (
    IBN,
    Non_local,
    SplAtConv2d,
    get_norm,
)

from fastreid.utils.checkpoint import get_unexpected_parameters_message, get_missing_parameters_message

from .build import BACKBONE_REGISTRY

_url_format = 'https://hangzh.s3.amazonaws.com/encoding/models/{}-{}.pth'

_model_sha256 = {name: checksum for checksum, name in [
    ('528c19ca', 'resnest50'),
    ('22405ba7', 'resnest101'),
    ('75117900', 'resnest200'),
    ('0cc87c48', 'resnest269'),
]}


def short_hash(name):
    if name not in _model_sha256:
        raise ValueError('Pretrained model for {name} is not available.'.format(name=name))
    return _model_sha256[name][:8]


model_urls = {name: _url_format.format(name, short_hash(name)) for
              name in _model_sha256.keys()
              }


class Bottleneck(nn.Module):
    """ResNet Bottleneck
    """
    # pylint: disable=unused-argument
    expansion = 4

    def __init__(self, inplanes, planes, bn_norm, num_splits, with_ibn=False, stride=1, downsample=None,
                 radix=1, cardinality=1, bottleneck_width=64,
                 avd=False, avd_first=False, dilation=1, is_first=False,
                 rectified_conv=False, rectify_avg=False,
                 dropblock_prob=0.0, last_gamma=False):
        super(Bottleneck, self).__init__()
        group_width = int(planes * (bottleneck_width / 64.)) * cardinality
        self.conv1 = nn.Conv2d(inplanes, group_width, kernel_size=1, bias=False)
        if with_ibn:
            self.bn1 = IBN(group_width, bn_norm, num_splits)
        else:
            self.bn1 = get_norm(bn_norm, group_width, num_splits)
        self.dropblock_prob = dropblock_prob
        self.radix = radix
        self.avd = avd and (stride > 1 or is_first)
        self.avd_first = avd_first

        if self.avd:
            self.avd_layer = nn.AvgPool2d(3, stride, padding=1)
            stride = 1

        if radix > 1:
            self.conv2 = SplAtConv2d(
                group_width, group_width, kernel_size=3,
                stride=stride, padding=dilation,
                dilation=dilation, groups=cardinality, bias=False,
                radix=radix, rectify=rectified_conv,
                rectify_avg=rectify_avg,
                norm_layer=bn_norm, num_splits=num_splits,
                dropblock_prob=dropblock_prob)
        elif rectified_conv:
            from rfconv import RFConv2d
            self.conv2 = RFConv2d(
                group_width, group_width, kernel_size=3, stride=stride,
                padding=dilation, dilation=dilation,
                groups=cardinality, bias=False,
                average_mode=rectify_avg)
            self.bn2 = get_norm(bn_norm, group_width, num_splits)
        else:
            self.conv2 = nn.Conv2d(
                group_width, group_width, kernel_size=3, stride=stride,
                padding=dilation, dilation=dilation,
                groups=cardinality, bias=False)
            self.bn2 = get_norm(bn_norm, group_width, num_splits)

        self.conv3 = nn.Conv2d(
            group_width, planes * 4, kernel_size=1, bias=False)
        self.bn3 = get_norm(bn_norm, planes * 4, num_splits)

        if last_gamma:
            from torch.nn.init import zeros_
            zeros_(self.bn3.weight)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.dilation = dilation
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        if self.dropblock_prob > 0.0:
            out = self.dropblock1(out)
        out = self.relu(out)

        if self.avd and self.avd_first:
            out = self.avd_layer(out)

        out = self.conv2(out)
        if self.radix == 1:
            out = self.bn2(out)
            if self.dropblock_prob > 0.0:
                out = self.dropblock2(out)
            out = self.relu(out)

        if self.avd and not self.avd_first:
            out = self.avd_layer(out)

        out = self.conv3(out)
        out = self.bn3(out)
        if self.dropblock_prob > 0.0:
            out = self.dropblock3(out)

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

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

        return out


class ResNest(nn.Module):
    """ResNet Variants ResNest
    Parameters
    ----------
    block : Block
        Class for the residual block. Options are BasicBlockV1, BottleneckV1.
    layers : list of int
        Numbers of layers in each block
    classes : int, default 1000
        Number of classification classes.
    dilated : bool, default False
        Applying dilation strategy to pretrained ResNet yielding a stride-8 model,
        typically used in Semantic Segmentation.
    norm_layer : object
        Normalization layer used in backbone network (default: :class:`mxnet.gluon.nn.BatchNorm`;
        for Synchronized Cross-GPU BachNormalization).
    Reference:
        - He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
        - Yu, Fisher, and Vladlen Koltun. "Multi-scale context aggregation by dilated convolutions."
    """

    # pylint: disable=unused-variable
    def __init__(self, last_stride, bn_norm, num_splits, with_ibn, with_nl, block, layers, non_layers, radix=1, groups=1,
                 bottleneck_width=64,
                 dilated=False, dilation=1,
                 deep_stem=False, stem_width=64, avg_down=False,
                 rectified_conv=False, rectify_avg=False,
                 avd=False, avd_first=False,
                 final_drop=0.0, dropblock_prob=0,
                 last_gamma=False):
        self.cardinality = groups
        self.bottleneck_width = bottleneck_width
        # ResNet-D params
        self.inplanes = stem_width * 2 if deep_stem else 64
        self.avg_down = avg_down
        self.last_gamma = last_gamma
        # ResNeSt params
        self.radix = radix
        self.avd = avd
        self.avd_first = avd_first

        super().__init__()
        self.rectified_conv = rectified_conv
        self.rectify_avg = rectify_avg
        if rectified_conv:
            from rfconv import RFConv2d
            conv_layer = RFConv2d
        else:
            conv_layer = nn.Conv2d
        conv_kwargs = {'average_mode': rectify_avg} if rectified_conv else {}
        if deep_stem:
            self.conv1 = nn.Sequential(
                conv_layer(3, stem_width, kernel_size=3, stride=2, padding=1, bias=False, **conv_kwargs),
                get_norm(bn_norm, stem_width, num_splits),
                nn.ReLU(inplace=True),
                conv_layer(stem_width, stem_width, kernel_size=3, stride=1, padding=1, bias=False, **conv_kwargs),
                get_norm(bn_norm, stem_width, num_splits),
                nn.ReLU(inplace=True),
                conv_layer(stem_width, stem_width * 2, kernel_size=3, stride=1, padding=1, bias=False, **conv_kwargs),
            )
        else:
            self.conv1 = conv_layer(3, 64, kernel_size=7, stride=2, padding=3,
                                    bias=False, **conv_kwargs)
        self.bn1 = get_norm(bn_norm, self.inplanes, num_splits)
        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], 1, bn_norm, num_splits, with_ibn=with_ibn, is_first=False)
        self.layer2 = self._make_layer(block, 128, layers[1], 2, bn_norm, num_splits, with_ibn=with_ibn)
        if dilated or dilation == 4:
            self.layer3 = self._make_layer(block, 256, layers[2], 1, bn_norm, num_splits, with_ibn=with_ibn,
                                           dilation=2, dropblock_prob=dropblock_prob)
            self.layer4 = self._make_layer(block, 512, layers[3], 1, bn_norm, num_splits, with_ibn=with_ibn,
                                           dilation=4, dropblock_prob=dropblock_prob)
        elif dilation == 2:
            self.layer3 = self._make_layer(block, 256, layers[2], 2, bn_norm, num_splits, with_ibn=with_ibn,
                                           dilation=1, dropblock_prob=dropblock_prob)
            self.layer4 = self._make_layer(block, 512, layers[3], 1, bn_norm, num_splits, with_ibn=with_ibn,
                                           dilation=2, dropblock_prob=dropblock_prob)
        else:
            self.layer3 = self._make_layer(block, 256, layers[2], 2, bn_norm, num_splits, with_ibn=with_ibn,
                                           dropblock_prob=dropblock_prob)
            self.layer4 = self._make_layer(block, 512, layers[3], last_stride, bn_norm, num_splits, with_ibn=with_ibn,
                                           dropblock_prob=dropblock_prob)

        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_()

        if with_nl:
            self._build_nonlocal(layers, non_layers, bn_norm, num_splits)
        else:
            self.NL_1_idx = self.NL_2_idx = self.NL_3_idx = self.NL_4_idx = []

    def _make_layer(self, block, planes, blocks, stride=1, bn_norm="BN", num_splits=1, with_ibn=False,
                    dilation=1, dropblock_prob=0.0, is_first=True):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            down_layers = []
            if self.avg_down:
                if dilation == 1:
                    down_layers.append(nn.AvgPool2d(kernel_size=stride, stride=stride,
                                                    ceil_mode=True, count_include_pad=False))
                else:
                    down_layers.append(nn.AvgPool2d(kernel_size=1, stride=1,
                                                    ceil_mode=True, count_include_pad=False))
                down_layers.append(nn.Conv2d(self.inplanes, planes * block.expansion,
                                             kernel_size=1, stride=1, bias=False))
            else:
                down_layers.append(nn.Conv2d(self.inplanes, planes * block.expansion,
                                             kernel_size=1, stride=stride, bias=False))
            down_layers.append(get_norm(bn_norm, planes * block.expansion, num_splits))
            downsample = nn.Sequential(*down_layers)

        layers = []
        if planes == 512:
            with_ibn = False
        if dilation == 1 or dilation == 2:
            layers.append(block(self.inplanes, planes, bn_norm, num_splits, with_ibn, stride, downsample=downsample,
                                radix=self.radix, cardinality=self.cardinality,
                                bottleneck_width=self.bottleneck_width,
                                avd=self.avd, avd_first=self.avd_first,
                                dilation=1, is_first=is_first, rectified_conv=self.rectified_conv,
                                rectify_avg=self.rectify_avg,
                                dropblock_prob=dropblock_prob,
                                last_gamma=self.last_gamma))
        elif dilation == 4:
            layers.append(block(self.inplanes, planes, bn_norm, num_splits, with_ibn, stride, downsample=downsample,
                                radix=self.radix, cardinality=self.cardinality,
                                bottleneck_width=self.bottleneck_width,
                                avd=self.avd, avd_first=self.avd_first,
                                dilation=2, is_first=is_first, rectified_conv=self.rectified_conv,
                                rectify_avg=self.rectify_avg,
                                dropblock_prob=dropblock_prob,
                                last_gamma=self.last_gamma))
        else:
            raise RuntimeError("=> unknown dilation size: {}".format(dilation))

        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes, bn_norm, num_splits, with_ibn,
                                radix=self.radix, cardinality=self.cardinality,
                                bottleneck_width=self.bottleneck_width,
                                avd=self.avd, avd_first=self.avd_first,
                                dilation=dilation, rectified_conv=self.rectified_conv,
                                rectify_avg=self.rectify_avg,
                                dropblock_prob=dropblock_prob,
                                last_gamma=self.last_gamma))

        return nn.Sequential(*layers)

    def _build_nonlocal(self, layers, non_layers, bn_norm, num_splits):
        self.NL_1 = nn.ModuleList(
            [Non_local(256, bn_norm, num_splits) for _ 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, bn_norm, num_splits) for _ 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, bn_norm, num_splits) for _ 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, bn_norm, num_splits) for _ in range(non_layers[3])])
        self.NL_4_idx = sorted([layers[3] - (i + 1) for i in range(non_layers[3])])

    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


@BACKBONE_REGISTRY.register()
def build_resnest_backbone(cfg):
    """
    Create a ResNest instance from config.
    Returns:
        ResNet: a :class:`ResNet` instance.
    """

    # fmt: off
    pretrain = cfg.MODEL.BACKBONE.PRETRAIN
    last_stride = cfg.MODEL.BACKBONE.LAST_STRIDE
    bn_norm = cfg.MODEL.BACKBONE.NORM
    num_splits = cfg.MODEL.BACKBONE.NORM_SPLIT
    with_ibn = cfg.MODEL.BACKBONE.WITH_IBN
    with_se = cfg.MODEL.BACKBONE.WITH_SE
    with_nl = cfg.MODEL.BACKBONE.WITH_NL
    depth = cfg.MODEL.BACKBONE.DEPTH

    num_blocks_per_stage = {50: [3, 4, 6, 3], 101: [3, 4, 23, 3], 200: [3, 24, 36, 3], 269: [3, 30, 48, 8]}[depth]
    nl_layers_per_stage = {50: [0, 2, 3, 0], 101: [0, 2, 3, 0]}[depth]
    stem_width = {50: 32, 101: 64, 200: 64, 269: 64}[depth]
    model = ResNest(last_stride, bn_norm, num_splits, with_ibn, with_nl, Bottleneck, num_blocks_per_stage,
                    nl_layers_per_stage, radix=2, groups=1, bottleneck_width=64,
                    deep_stem=True, stem_width=stem_width, avg_down=True,
                    avd=True, avd_first=False)
    if pretrain:
        # if not with_ibn:
        # original resnet
        state_dict = torch.hub.load_state_dict_from_url(
            model_urls['resnest' + str(depth)], progress=True, check_hash=True)
        # else:
        #     raise KeyError('Not implementation ibn in resnest')
        # # 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 new_k in model.state_dict() and (model.state_dict()[new_k].shape == state_dict[k].shape):
        #         new_state_dict[new_k] = state_dict[k]
        # state_dict = new_state_dict
        incompatible = model.load_state_dict(state_dict, strict=False)
        logger = logging.getLogger(__name__)
        if incompatible.missing_keys:
            logger.info(
                get_missing_parameters_message(incompatible.missing_keys)
            )
        if incompatible.unexpected_keys:
            logger.info(
                get_unexpected_parameters_message(incompatible.unexpected_keys)
            )
    return model