'''
Paper: Pan, X., Luo, P., Shi, J. and Tang, X., 2018. Two at once: Enhancing learning and generalization capacities via ibn-net.
       In Proceedings of the European Conference on Computer Vision (ECCV) (pp. 464-479).
Reference code: https://github.com/thuml/Transfer-Learning-Library
'''
import torch
import logging
import torch.nn as nn
from tqdm import tqdm
import torch.nn.functional as F

import modules
from train_utils import TrainerBase


class IBN(nn.Module):
    def __init__(self, in_channel):
        super(IBN, self).__init__()
        self.half_channel = int(in_channel/2)
        self.IN = nn.InstanceNorm1d(self.half_channel)
        self.BN = nn.BatchNorm1d(self.half_channel)
    
    def forward(self, x):
        split = torch.split(x, self.half_channel, dim=1)
        out1 = self.IN(split[0].contiguous())
        out2 = self.BN(split[1].contiguous())
        out = torch.cat((out1, out2), dim=1)
        return out


class IBNlayer(nn.Module):
    def __init__(self,
                 in_channel=1, 
                 kernel_size=3, 
                 stride=1, 
                 padding=1,
                 mp_kernel_size=2, 
                 mp_stride=2,
                 num_layer=5):
        super(IBNlayer, self).__init__()
        
        layers = []
        current_in_channels = in_channel
        predefined_output_channels = [4, 16, 32, 64, 128]
        num_predefined_layers = len(predefined_output_channels)

        for i in range(num_layer):
            if i < num_predefined_layers:
                current_out_channels = predefined_output_channels[i]
            else:
                current_out_channels = 128  # Use 128 for any additional layers
            
            layers.append(nn.Conv1d(current_in_channels, current_out_channels, kernel_size=kernel_size,
                                    stride=stride, padding=padding))
            if i <= 1:
                layers.append(IBN(current_out_channels))
            else:
                layers.append(nn.BatchNorm1d(current_out_channels))
            layers.append(nn.ReLU(inplace=True))
            if i < num_layer - 1:  # For all but the last layer
                layers.append(nn.MaxPool1d(kernel_size=mp_kernel_size, stride=mp_stride))
            current_in_channels = current_out_channels

        layers.append(nn.AdaptiveMaxPool1d(1))  # Final adaptive max pool
        layers.append(nn.Flatten())  # Flatten for the output

        self.net = nn.Sequential(*layers)
        self.out_dim = current_out_channels

    def forward(self, input):
        output = self.net(input)
        return output


class Trainer(TrainerBase):
    
    def __init__(self, args):
        super(Trainer, self).__init__(args)
        assert args.backbone == 'CNN', "IBN is only implemented in CNN backbone"
        self.G = modules.MSCNN(in_channel=1, block=IBNlayer)
        self.C = modules.MLP(self.G.out_dim, args.num_classes[0], args.dropout, last=None)
        self.model = nn.Sequential(self.G, self.C).to(self.device)
        self._init_data()

        if args.train_mode == 'single_source':
            self.src = args.source_name[0]
        elif args.train_mode == 'source_combine':
            self.src = 'concat_source'
        elif args.train_mode == 'multi_source':
            raise Exception("This model cannot be trained in multi_source mode.")

        self.num_iter = len(self.dataloaders[self.src])
        self.optimizer = self._get_optimizer(self.model)
        self.lr_scheduler = self._get_lr_scheduler(self.optimizer)
    
    def save_model(self):
        torch.save({
            'model': self.model.state_dict()
            }, self.args.save_path + '.pth')
        logging.info('Model saved to {}'.format(self.args.save_path + '.pth'))
    
    def load_model(self):
        logging.info('Loading model from {}'.format(self.args.load_path))
        ckpt = torch.load(self.args.load_path)
        self.model.load_state_dict(ckpt['model'])
    
    def _set_to_train(self):
        self.model.train()
    
    def _set_to_eval(self):
        self.model.eval()
    
    def _train_one_epoch(self, epoch_acc, epoch_loss):
        for _ in tqdm(range(self.num_iter), ascii=True):
            # obtain data
            source_data, source_labels = self._get_next_batch(self.src)
            
            # forward
            self.optimizer.zero_grad()
            pred = self.model(source_data)

            # compute loss
            loss = F.cross_entropy(pred, source_labels)

            # log information
            epoch_acc['Source Data']  += self._get_accuracy(pred, source_labels)
            epoch_loss['Source Classifier'] += loss

            # backward
            loss.backward()
            self.optimizer.step()
        return epoch_acc, epoch_loss
    
    def _eval(self, data, actual_labels, correct, total):
        pred = self.model(data)
        actual_pred = self._get_actual_label(pred, idx=0)
        output = self._get_accuracy(actual_pred, actual_labels, return_acc=False)
        correct['acc'] += output[0]; total['acc'] += output[1]
        if self.args.da_scenario in ['open-set', 'universal']:
            output = self._get_accuracy(actual_pred, actual_labels, return_acc=False, idx=0, mode='closed-set')
            correct['Closed-set-acc'] += output[0]; total['Closed-set-acc'] += output[1]
        return correct, total