import torch
import torch.nn as nn
import numpy as np
from torch.optim import AdamW
import torch.optim as optim
import itertools
from model.warplayer import warp
from torch.nn.parallel import DistributedDataParallel as DDP
from model.IFNet import *
from model.IFNet_m import *
import torch.nn.functional as F
from model.loss import *
from model.laplacian import *
from model.refine import *

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    
class Model:
    def __init__(self, local_rank=-1, arbitrary=False):
        if arbitrary == True:
            self.flownet = IFNet_m()
        else:
            self.flownet = IFNet()
        self.device()
        self.optimG = AdamW(self.flownet.parameters(), lr=1e-6, weight_decay=1e-3) # use large weight decay may avoid NaN loss
        self.epe = EPE()
        self.lap = LapLoss()
        self.sobel = SOBEL()
        if local_rank != -1:
            self.flownet = DDP(self.flownet, device_ids=[local_rank], output_device=local_rank)

    def train(self):
        self.flownet.train()

    def eval(self):
        self.flownet.eval()

    def device(self):
        self.flownet.to(device)

    def load_model(self, path, rank=0):
        def convert(param):
            return {
            k.replace("module.", ""): v
                for k, v in param.items()
                if "module." in k
            }
            
        if rank <= 0:
            self.flownet.load_state_dict(convert(torch.load('{}/flownet.pkl'.format(path))))
        
    def save_model(self, path, rank=0):
        if rank == 0:
            torch.save(self.flownet.state_dict(),'{}/flownet.pkl'.format(path))

    def inference(self, img0, img1, scale=1, scale_list=[4, 2, 1], TTA=False, timestep=0.5):
        for i in range(3):
            scale_list[i] = scale_list[i] * 1.0 / scale
        imgs = torch.cat((img0, img1), 1)
        flow, mask, merged, flow_teacher, merged_teacher, loss_distill = self.flownet(imgs, scale_list, timestep=timestep)
        if TTA == False:
            return merged[2]
        else:
            flow2, mask2, merged2, flow_teacher2, merged_teacher2, loss_distill2 = self.flownet(imgs.flip(2).flip(3), scale_list, timestep=timestep)
            return (merged[2] + merged2[2].flip(2).flip(3)) / 2
    
    def update(self, imgs, gt, learning_rate=0, mul=1, training=True, flow_gt=None):
        for param_group in self.optimG.param_groups:
            param_group['lr'] = learning_rate
        img0 = imgs[:, :3]
        img1 = imgs[:, 3:]
        if training:
            self.train()
        else:
            self.eval()
        flow, mask, merged, flow_teacher, merged_teacher, loss_distill = self.flownet(torch.cat((imgs, gt), 1), scale=[4, 2, 1])
        loss_l1 = (self.lap(merged[2], gt)).mean()
        loss_tea = (self.lap(merged_teacher, gt)).mean()
        if training:
            self.optimG.zero_grad()
            loss_G = loss_l1 + loss_tea + loss_distill * 0.01 # when training RIFEm, the weight of loss_distill should be 0.005 or 0.002
            loss_G.backward()
            self.optimG.step()
        else:
            flow_teacher = flow[2]
        return merged[2], {
            'merged_tea': merged_teacher,
            'mask': mask,
            'mask_tea': mask,
            'flow': flow[2][:, :2],
            'flow_tea': flow_teacher,
            'loss_l1': loss_l1,
            'loss_tea': loss_tea,
            'loss_distill': loss_distill,
            }