import os
import torch
import torch.nn as nn


# a complex model consisted of several nets, and each net will be explicitly defined in other py class files
class BaseModel(nn.Module):
    def __init__(self):
        super(BaseModel, self).__init__()

    def init(self, opt):
        self.opt = opt
        self.gpu_ids = opt.gpu_ids
        self.save_dir = opt.model_folder
        self.device = torch.device('cuda:{}'.format(self.gpu_ids[0])) if self.gpu_ids else torch.device('cpu')
        self.model_names = []

    def setInput(self, img, mask):
        self.input = img
        self.mask = mask

    def forward(self):
        pass

    def optimize_parameters(self):
        pass

    def get_current_visuals(self):
        pass

    def get_current_losses(self):
        pass

    def update_learning_rate(self):
        pass

    def test(self):
        with torch.no_grad():
            self.forward()

    # save models to the disk
    def save_networks(self, which_epoch):
        for name in self.model_names:
            if isinstance(name, str):
                save_filename = '%s_net_%s.pth' % (which_epoch, name)
                save_path = os.path.join(self.save_dir, save_filename)
                net = getattr(self, 'net' + name)

                if len(self.gpu_ids) > 0 and torch.cuda.is_available():
                    torch.save(net.state_dict(), save_path)
                    # net.cuda(self.gpu_ids[0])
                else:
                    torch.save(net.state_dict(), save_path)

    def __patch_instance_norm_state_dict(self, state_dict, module, keys, i=0):
        key = keys[i]
        if i + 1 == len(keys):  # at the end, pointing to a parameter/buffer
            if module.__class__.__name__.startswith('InstanceNorm') and \
                    (key == 'running_mean' or key == 'running_var'):
                if getattr(module, key) is None:
                    state_dict.pop('.'.join(keys))
        else:
            self.__patch_instance_norm_state_dict(state_dict, getattr(module, key), keys, i + 1)

    # load models from the disk
    def load_networks(self, load_path):
        for name in self.model_names:
            if isinstance(name, str):
                net = getattr(self, 'net' + name)
                if isinstance(net, torch.nn.DataParallel):
                    net = net.module
                print('loading the model from %s' % load_path)
                # if you are using PyTorch newer than 0.4 (e.g., built from
                # GitHub source), you can remove str() on self.device
                state_dict = torch.load(load_path)
                # patch InstanceNorm checkpoints prior to 0.4
                for key in list(state_dict.keys()):  # need to copy keys here because we mutate in loop
                    self.__patch_instance_norm_state_dict(state_dict, net, key.split('.'))
                net.load_state_dict(state_dict)

    # print network information
    def print_networks(self, verbose=True):
        print('---------- Networks initialized -------------')
        for name in self.model_names:
            if isinstance(name, str):
                net = getattr(self, 'net' + name)
                num_params = 0
                for param in net.parameters():
                    num_params += param.numel()
                if verbose:
                    print(net)
                print('[Network %s] Total number of parameters : %.3f M' % (name, num_params / 1e6))
        print('-----------------------------------------------')

    # set requies_grad=Fasle to avoid computation
    def set_requires_grad(self, nets, requires_grad=False):
        if not isinstance(nets, list):
            nets = [nets]
        for net in nets:
            if net is not None:
                for param in net.parameters():
                    param.requires_grad = requires_grad