import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.optim import AdamW
import numpy as np
import itertools
from .warplayer import warp
from torch.nn.parallel import DistributedDataParallel as DDP
from .IFNet_HDv3 import *
from .loss import *
import devicetorch
device = devicetorch.get(torch)


class Model:
    def __init__(self, local_rank=-1):
        self.flownet = IFNet()
        self.device()
        self.optimG = AdamW(self.flownet.parameters(), lr=1e-6, weight_decay=1e-4)
        self.epe = EPE()
        # self.vgg = VGGPerceptualLoss().to(device)
        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):
            if rank == -1:
                return {k.replace("module.", ""): v for k, v in param.items() if "module." in k}
            else:
                return param

        if rank <= 0:
            model_path = "{}/flownet.pkl".format(path)
            # Check PyTorch version to safely use weights_only
            from packaging import version
            use_weights_only = version.parse(torch.__version__) >= version.parse("1.13")
            
            load_kwargs = {}
            if not torch.cuda.is_available():
                load_kwargs['map_location'] = "cpu"

            if use_weights_only:
                # For modern PyTorch, be explicit and safe
                load_kwargs['weights_only'] = True
                # print(f"PyTorch >= 1.13 detected. Loading RIFE model with weights_only=True.")
                state_dict = torch.load(model_path, **load_kwargs)
            else:
                # For older PyTorch, load the old way
                print(f"PyTorch < 1.13 detected. Loading RIFE model using legacy method.")
                state_dict = torch.load(model_path, **load_kwargs)
            
            self.flownet.load_state_dict(convert(state_dict))

    def inference(self, img0, img1, scale=1.0):
        imgs = torch.cat((img0, img1), 1)
        scale_list = [4 / scale, 2 / scale, 1 / scale]
        flow, mask, merged = self.flownet(imgs, scale_list)
        return merged[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()
        scale = [4, 2, 1]
        flow, mask, merged = self.flownet(torch.cat((imgs, gt), 1), scale=scale, training=training)
        loss_l1 = (merged[2] - gt).abs().mean()
        loss_smooth = self.sobel(flow[2], flow[2] * 0).mean()
        # loss_vgg = self.vgg(merged[2], gt)
        if training:
            self.optimG.zero_grad()
            loss_G = loss_cons + loss_smooth * 0.1
            loss_G.backward()
            self.optimG.step()
        else:
            flow_teacher = flow[2]
        return merged[2], {
            "mask": mask,
            "flow": flow[2][:, :2],
            "loss_l1": loss_l1,
            "loss_cons": loss_cons,
            "loss_smooth": loss_smooth,
        }