import logging
from collections import OrderedDict

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
from torch.nn.parallel import DataParallel, DistributedDataParallel

import models.lr_scheduler as lr_scheduler
import models.networks as networks
from models.modules.loss import CharbonnierLoss

from .base_model import BaseModel

logger = logging.getLogger("base")


class B_Model(BaseModel):
    def __init__(self, opt):
        super(B_Model, self).__init__(opt)

        if opt["dist"]:
            self.rank = torch.distributed.get_rank()
        else:
            self.rank = -1  # non dist training

        # define network and load pretrained models
        self.netG = networks.define_G(opt).to(self.device)
        if opt["dist"]:
            self.netG = DistributedDataParallel(
                self.netG, device_ids=[torch.cuda.current_device()]
            )
        else:
            self.netG = DataParallel(self.netG)
        # print network
        self.print_network()
        self.load()

        if self.is_train:
            train_opt = opt["train"]
            # self.init_model() # Not use init is OK, since Pytorch has its owen init (by default)
            self.netG.train()

            # loss
            loss_type = train_opt["pixel_criterion"]
            if loss_type == "l1":
                self.cri_pix = nn.L1Loss().to(self.device)
            elif loss_type == "l2":
                self.cri_pix = nn.MSELoss().to(self.device)
            elif loss_type == "cb":
                self.cri_pix = CharbonnierLoss().to(self.device)
            else:
                raise NotImplementedError(
                    "Loss type [{:s}] is not recognized.".format(loss_type)
                )
            self.l_pix_w = train_opt["pixel_weight"]

            # optimizers
            wd_G = train_opt["weight_decay_G"] if train_opt["weight_decay_G"] else 0
            optim_restorer = []
            optim_estimator = []
            for (
                k,
                v,
            ) in self.netG.named_parameters():  # can optimize for a part of the model
                if v.requires_grad:
                    if "Restorer" in k:
                        optim_restorer.append(v)
                    elif "Estimator" in k:
                        optim_estimator.append(v)
                else:
                    if self.rank <= 0:
                        logger.warning("Params [{:s}] will not optimize.".format(k))
            self.optimizer_G = torch.optim.Adam(
                [
                    {"params": optim_restorer, "lr": train_opt["lr_G"]},
                    {"params": optim_estimator, "lr": train_opt["lr_E"]},
                ],
                weight_decay=wd_G,
                betas=(train_opt["beta1"], train_opt["beta2"]),
            )
            # self.optimizer_G = torch.optim.SGD(optim_params, lr=train_opt['lr_G'], momentum=0.9)
            self.optimizers.append(self.optimizer_G)

            # schedulers
            if train_opt["lr_scheme"] == "MultiStepLR":
                for optimizer in self.optimizers:
                    self.schedulers.append(
                        lr_scheduler.MultiStepLR_Restart(
                            optimizer,
                            train_opt["lr_steps"],
                            restarts=train_opt["restarts"],
                            weights=train_opt["restart_weights"],
                            gamma=train_opt["lr_gamma"],
                            clear_state=train_opt["clear_state"],
                        )
                    )
            elif train_opt["lr_scheme"] == "CosineAnnealingLR_Restart":
                for optimizer in self.optimizers:
                    self.schedulers.append(
                        lr_scheduler.CosineAnnealingLR_Restart(
                            optimizer,
                            train_opt["T_period"],
                            eta_min=train_opt["eta_min"],
                            restarts=train_opt["restarts"],
                            weights=train_opt["restart_weights"],
                        )
                    )
            else:
                print("MultiStepLR learning rate scheme is enough.")

            self.log_dict = OrderedDict()

    def init_model(self, scale=0.1):
        # Common practise for initialization.
        for layer in self.netG.modules():
            if isinstance(layer, nn.Conv2d):
                init.kaiming_normal_(layer.weight, a=0, mode="fan_in")
                layer.weight.data *= scale  # for residual block
                if layer.bias is not None:
                    layer.bias.data.zero_()
            elif isinstance(layer, nn.Linear):
                init.kaiming_normal_(layer.weight, a=0, mode="fan_in")
                layer.weight.data *= scale
                if layer.bias is not None:
                    layer.bias.data.zero_()
            elif isinstance(layer, nn.BatchNorm2d):
                init.constant_(layer.weight, 1)
                init.constant_(layer.bias.data, 0.0)

    def feed_data(self, LR_img, GT_img=None, kernel=None, ker_map=None):
        self.var_L = LR_img.to(self.device)
        if not (GT_img is None):
            self.real_H = GT_img.to(self.device)
        if not (ker_map is None):
            self.real_ker_map = ker_map.to(self.device)
        if not (kernel is None):
            self.real_kernel = kernel.to(self.device)

    def optimize_parameters(self, step):
        self.optimizer_G.zero_grad()
        srs, ker_maps, kernels = self.netG(self.var_L)

        self.fake_SR = srs[-1]
        self.fake_ker = kernels[-1]

        total_loss = 0
        for ind in range(len(srs)):

            d_kr = self.cri_pix(
                kernels[ind], self.real_kernel.view(*kernels[ind].shape)
            )
            # d_kr = self.cri_pix(ker_maps[ind], self.real_ker_map)

            d_sr = self.cri_pix(srs[ind], self.real_H)

            self.log_dict["l_pix%d" % ind] = d_sr.item()
            self.log_dict["l_ker%d" % ind] = d_kr.item()

        total_loss += d_sr
        total_loss += d_kr

        total_loss.backward()
        self.optimizer_G.step()

    def test(self):
        self.netG.eval()
        with torch.no_grad():
            srs, kermaps, kernels = self.netG(self.var_L)
            self.fake_SR = srs[-1]
            self.fake_ker = kernels[-1]
        self.netG.train()

    def test_x8(self):
        # from https://github.com/thstkdgus35/EDSR-PyTorch
        self.netG.eval()

        def _transform(v, op):
            # if self.precision != 'single': v = v.float()
            v2np = v.data.cpu().numpy()
            if op == "v":
                tfnp = v2np[:, :, :, ::-1].copy()
            elif op == "h":
                tfnp = v2np[:, :, ::-1, :].copy()
            elif op == "t":
                tfnp = v2np.transpose((0, 1, 3, 2)).copy()

            ret = torch.Tensor(tfnp).to(self.device)
            # if self.precision == 'half': ret = ret.half()

            return ret

        lr_list = [self.var_L]
        for tf in "v", "h", "t":
            lr_list.extend([_transform(t, tf) for t in lr_list])
        with torch.no_grad():
            sr_list = [self.netG(aug)[0][-1] for aug in lr_list]
        for i in range(len(sr_list)):
            if i > 3:
                sr_list[i] = _transform(sr_list[i], "t")
            if i % 4 > 1:
                sr_list[i] = _transform(sr_list[i], "h")
            if (i % 4) % 2 == 1:
                sr_list[i] = _transform(sr_list[i], "v")

        output_cat = torch.cat(sr_list, dim=0)
        self.fake_H = output_cat.mean(dim=0, keepdim=True)
        self.netG.train()

    def get_current_log(self):
        return self.log_dict

    def get_current_visuals(self):
        out_dict = OrderedDict()
        out_dict["LQ"] = self.var_L.detach()[0].float().cpu()
        out_dict["SR"] = self.fake_SR.detach()[0].float().cpu()
        out_dict["GT"] = self.real_H.detach()[0].float().cpu()
        out_dict["ker"] = self.fake_ker.detach()[0].float().cpu()
        out_dict["Batch_SR"] = (
            self.fake_SR.detach().float().cpu()
        )  # Batch SR, for train
        return out_dict

    def print_network(self):
        s, n = self.get_network_description(self.netG)
        if isinstance(self.netG, nn.DataParallel) or isinstance(
            self.netG, DistributedDataParallel
        ):
            net_struc_str = "{} - {}".format(
                self.netG.__class__.__name__, self.netG.module.__class__.__name__
            )
        else:
            net_struc_str = "{}".format(self.netG.__class__.__name__)
        if self.rank <= 0:
            logger.info(
                "Network G structure: {}, with parameters: {:,d}".format(
                    net_struc_str, n
                )
            )
            logger.info(s)

    def load(self):
        load_path_G = self.opt["path"]["pretrain_model_G"]
        if load_path_G is not None:
            logger.info("Loading model for G [{:s}] ...".format(load_path_G))
            self.load_network(load_path_G, self.netG, self.opt["path"]["strict_load"])

    def save(self, iter_label):
        self.save_network(self.netG, "G", iter_label)
