import random

import lmdb
import numpy as np
import torch
import torch.utils.data as data

import data.util as util


class SRkerDataset(data.Dataset):
    """Read LR images to Predictor."""

    def __init__(self, opt, ker_map_list, SR_img_list):
        super(SRKerDataset, self).__init__()
        self.opt = opt
        self.opt_C = opt

        self.LR_paths = None
        self.LR_sizes = None  # environment for lmdb
        self.LR_env = None
        self.LR_size = opt["LR_size"]

        self.SR_env = None
        self.SR_img_list = SR_img_list
        self.SR_size = opt["GT_size"]

        self.ker_map_list = ker_map_list
        self.real_ker_path = "/mnt/yjchai/SR_data/Flickr2K/kermap.pt"
        self.real_ker_map_list = util.load_ker_map_list(self.real_ker_path)

        # read image list from lmdb or image files
        # if opt['data_type'] == 'lmdb':
        #    self.LR_paths, self.LR_sizes = util.get_image_paths(opt['data_type'], opt['dataroot_LQ'])
        # elif opt['data_type'] == 'img':
        #    self.LR_paths = util.get_image_paths(opt['data_type'], opt['dataroot_LQ']) #LR_list
        # else:
        #    print('Error: data_type is not matched in Dataset')
        # assert self.LR_paths, 'Error: LR paths are empty.'

    def _init_lmdb(self):
        # https://github.com/chainer/chainermn/issues/129
        self.LR_env = lmdb.open(
            self.opt["dataroot_LQ"],
            readonly=True,
            lock=False,
            readahead=False,
            meminit=False,
        )

    def __getitem__(self, index):
        if self.opt["data_type"] == "lmdb":
            if self.LR_env is None:
                self._init_lmdb()

        LR_size = self.LR_size
        SR_size = self.SR_size
        scale = self.opt["scale"]

        # get real kernel map
        real_ker_map = self.real_ker_map_list[index]
        # get each kernel map
        ker_map = self.ker_map_list[index]

        # get LR image
        LR_path = self.LR_paths[index]
        if self.opt["data_type"] == "lmdb":
            resolution = [int(s) for s in self.LR_sizes[index].split("_")]
        else:
            resolution = None
        img_LR = util.read_img(self.LR_env, LR_path, resolution)
        H, W, C = img_LR.shape

        # get SR image
        img_SR = self.SR_img_list[index]

        if self.opt["phase"] == "train":
            # randomly crop
            rnd_h = random.randint(0, max(0, H - LR_size))
            rnd_w = random.randint(0, max(0, W - LR_size))
            rnd_h_SR, rnd_w_SR = int(rnd_h * scale), int(rnd_w * scale)
            img_SR = img_SR[
                rnd_h_SR : rnd_h_SR + SR_size, rnd_w_SR : rnd_w_SR + SR_size, :
            ]

            # augmentation - flip, rotate
            img_SR = util.augment(
                img_SR, self.opt["use_flip"], self.opt["use_rot"], self.opt["mode"]
            )

        # change color space if necessary
        if self.opt["color"]:
            img_SR = util.channel_convert(C, self.opt["color"], [img_SR])[0]

        # BGR to RGB, HWC to CHW, numpy to tensor
        if img_SR.shape[2] == 3:
            img_SR = img_SR[:, :, [2, 1, 0]]
        img_SR = torch.from_numpy(
            np.ascontiguousarray(np.transpose(img_SR, (2, 0, 1)))
        ).float()

        return {"SR": img_SR, "real_ker": real_ker_map, "ker": ker_map}

    def __len__(self):
        return len(self.SR_img_list)
