# Copyright (c) 2019  PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os

import paddle_hub as hub
from paddle_hub.tools.downloader import default_downloader


class ImageClassificationDataset:
    def __init__(self):
        self.base_path = None
        self.train_list_file = None
        self.test_list_file = None
        self.validate_list_file = None
        self.num_labels = 0

    def _download_dataset(self, dataset_path, url):
        if not os.path.exists(dataset_path):
            result, tips, dataset_path = default_downloader.download_file_and_uncompress(
                url=url,
                save_path=hub.dir.DATA_HOME,
                print_progress=True,
                replace=True)
            if not result:
                print(tips)
                exit()
        return dataset_path

    def _parse_data(self, data_path, shuffle=False):
        def _base_reader():
            data = []
            with open(data_path, "r") as file:
                while True:
                    line = file.readline()
                    if not line:
                        break
                    line = line.strip()
                    items = line.split(" ")
                    image_path = os.path.join(self.base_path, items[0])
                    label = items[1]
                    data.append((image_path, items[1]))

            if shuffle:
                np.random.shuffle(data)

            for item in data:
                yield item

        return _base_reader()

    def train_data(self, shuffle=True):
        train_data_path = os.path.join(self.base_path, self.train_list_file)
        return self._parse_data(train_data_path, shuffle)

    def test_data(self, shuffle=False):
        test_data_path = os.path.join(self.base_path, self.test_list_file)
        return self._parse_data(test_data_path, shuffle)

    def validate_data(self, shuffle=False):
        validate_data_path = os.path.join(self.base_path,
                                          self.validate_list_file)
        return self._parse_data(validate_data_path, shuffle)