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