import torch
import torch.nn as nn
from collections import OrderedDict
from torchvision.models import vgg16, vgg16_bn, VGG16_Weights, VGG16_BN_Weights, resnet50, ResNet50_Weights
from BiRefNet_legacy.backbones.pvt_v2 import pvt_v2_b2, pvt_v2_b5
from BiRefNet_legacy.backbones.swin_v1 import swin_v1_t, swin_v1_s, swin_v1_b, swin_v1_l
from ..config import Config


config = Config()

def build_backbone(bb_name, pretrained=True, params_settings=''):
    if bb_name == 'vgg16':
        bb_net = list(vgg16(pretrained=VGG16_Weights.DEFAULT if pretrained else None).children())[0]
        bb = nn.Sequential(OrderedDict({'conv1': bb_net[:4], 'conv2': bb_net[4:9], 'conv3': bb_net[9:16], 'conv4': bb_net[16:23]}))
    elif bb_name == 'vgg16bn':
        bb_net = list(vgg16_bn(pretrained=VGG16_BN_Weights.DEFAULT if pretrained else None).children())[0]
        bb = nn.Sequential(OrderedDict({'conv1': bb_net[:6], 'conv2': bb_net[6:13], 'conv3': bb_net[13:23], 'conv4': bb_net[23:33]}))
    elif bb_name == 'resnet50':
        bb_net = list(resnet50(pretrained=ResNet50_Weights.DEFAULT if pretrained else None).children())
        bb = nn.Sequential(OrderedDict({'conv1': nn.Sequential(*bb_net[0:3]), 'conv2': bb_net[4], 'conv3': bb_net[5], 'conv4': bb_net[6]}))
    else:
        bb = eval('{}({})'.format(bb_name, params_settings))
        if pretrained:
            bb = load_weights(bb, bb_name)
    return bb

def load_weights(model, model_name):
    # save_model = torch.load(config.weights[model_name])
    save_model = torch.load(config.weights[model_name], map_location=torch.device('cpu'))
    model_dict = model.state_dict()
    state_dict = {k: v if v.size() == model_dict[k].size() else model_dict[k] for k, v in save_model.items() if k in model_dict.keys()}
    # to ignore the weights with mismatched size when I modify the backbone itself.
    if not state_dict:
        save_model_keys = list(save_model.keys())
        sub_item = save_model_keys[0] if len(save_model_keys) == 1 else None
        state_dict = {k: v if v.size() == model_dict[k].size() else model_dict[k] for k, v in save_model[sub_item].items() if k in model_dict.keys()}
        if not state_dict or not sub_item:
            print('Weights are not successully loaded. Check the state dict of weights file.')
            return None
        else:
            print('Found correct weights in the "{}" item of loaded state_dict.'.format(sub_item))
    model_dict.update(state_dict)
    model.load_state_dict(model_dict)
    return model
