import os
import random

import numpy as np
from paddle.io import Dataset

from .imaug import create_operators, transform


class PGDataSet(Dataset):
    def __init__(self, config, mode, logger, seed=None):
        super(PGDataSet, self).__init__()

        self.logger = logger
        self.seed = seed
        self.mode = mode
        global_config = config["Global"]
        dataset_config = config[mode]["dataset"]
        loader_config = config[mode]["loader"]

        self.delimiter = dataset_config.get("delimiter", "\t")
        label_file_list = dataset_config.pop("label_file_list")
        data_source_num = len(label_file_list)
        ratio_list = dataset_config.get("ratio_list", [1.0])
        if isinstance(ratio_list, (float, int)):
            ratio_list = [float(ratio_list)] * int(data_source_num)
        assert (
            len(ratio_list) == data_source_num
        ), "The length of ratio_list should be the same as the file_list."
        self.data_dir = dataset_config["data_dir"]
        self.do_shuffle = loader_config["shuffle"]

        logger.info("Initialize indexs of datasets:%s" % label_file_list)
        self.data_lines = self.get_image_info_list(label_file_list, ratio_list)
        self.data_idx_order_list = list(range(len(self.data_lines)))
        if mode.lower() == "train":
            self.shuffle_data_random()

        self.ops = create_operators(dataset_config["transforms"], global_config)

        self.need_reset = True in [x < 1 for x in ratio_list]

    def shuffle_data_random(self):
        if self.do_shuffle:
            random.seed(self.seed)
            random.shuffle(self.data_lines)
        return

    def get_image_info_list(self, file_list, ratio_list):
        if isinstance(file_list, str):
            file_list = [file_list]
        data_lines = []
        for idx, file in enumerate(file_list):
            with open(file, "rb") as f:
                lines = f.readlines()
                if self.mode == "train" or ratio_list[idx] < 1.0:
                    random.seed(self.seed)
                    lines = random.sample(lines, round(len(lines) * ratio_list[idx]))
                data_lines.extend(lines)
        return data_lines

    def __getitem__(self, idx):
        file_idx = self.data_idx_order_list[idx]
        data_line = self.data_lines[file_idx]
        img_id = 0
        try:
            data_line = data_line.decode("utf-8")
            substr = data_line.strip("\n").split(self.delimiter)
            file_name = substr[0]
            label = substr[1]
            img_path = os.path.join(self.data_dir, file_name)
            if self.mode.lower() == "eval":
                try:
                    img_id = int(data_line.split(".")[0][7:])
                except:
                    img_id = 0
            data = {"img_path": img_path, "label": label, "img_id": img_id}
            if not os.path.exists(img_path):
                raise Exception("{} does not exist!".format(img_path))
            with open(data["img_path"], "rb") as f:
                img = f.read()
                data["image"] = img
            outs = transform(data, self.ops)
        except Exception as e:
            self.logger.error(
                "When parsing line {}, error happened with msg: {}".format(
                    self.data_idx_order_list[idx], e
                )
            )
            outs = None
        if outs is None:
            return self.__getitem__(np.random.randint(self.__len__()))
        return outs

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