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#
# 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.


import os
from random import shuffle

from .....utils.file_interface import custom_open


def split_dataset(dataset_root, train_rate, val_rate):
    """
    将图像数据集按照比例分成训练集、验证集和测试集，并生成对应的.txt文件。

    Args:
        dataset_root (str): 数据集根目录路径。
        train_rate (int): 训练集占总数据集的比例（%）。
        val_rate (int): 验证集占总数据集的比例（%）。

    Returns:
        str: 数据划分结果信息。
    """
    sum_rate = train_rate + val_rate
    if sum_rate != 100:
        return "训练集、验证集比例之和需要等于100，请修改后重试"
    tags = ["train", "val"]

    valid_path = False
    image_files = []
    for tag in tags:
        split_image_list = os.path.abspath(os.path.join(dataset_root, f"{tag}.txt"))
        rename_image_list = os.path.abspath(
            os.path.join(dataset_root, f"{tag}.txt.bak")
        )
        if os.path.exists(split_image_list):
            with custom_open(split_image_list, "r") as f:
                lines = f.readlines()
            image_files = image_files + lines
            valid_path = True
            if not os.path.exists(rename_image_list):
                os.rename(split_image_list, rename_image_list)

    if not valid_path:
        return f"数据集目录下保存待划分文件{tags[0]}.txt或{tags[1]}.txt不存在，请检查后重试"

    shuffle(image_files)
    start = 0
    image_num = len(image_files)
    rate_list = [train_rate, val_rate]
    for i, tag in enumerate(tags):

        rate = rate_list[i]
        if rate == 0:
            continue
        if rate > 100 or rate < 0:
            return f"{tag} 数据集的比例应该在0~100之间."

        end = start + round(image_num * rate / 100)
        if sum(rate_list[i + 1 :]) == 0:
            end = image_num

        txt_file = os.path.abspath(os.path.join(dataset_root, tag + ".txt"))
        with custom_open(txt_file, "w") as f:
            m = 0
            for id in range(start, end):
                m += 1
                f.write(image_files[id])
        start = end
    return dataset_root