# Copyright (c) 2022 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.

import os

from paddlenlp.datasets import load_dataset


def load_local_dataset(data_path, splits, label_list):
    """
    Load dataset for multi-label classification from files, where
    there is one example per line. Text and label are separated
    by '\t', and multiple labels are delimited by ','.

    Args:
        data_path (str):
            Path to the dataset directory, including label.txt, train.txt,
            dev.txt (and data.txt).
        splits (list):
            Which file(s) to load, such as ['train', 'dev', 'test'].
        label_list (dict):
            The dictionary that maps labels to indeces.
    """

    def _reader(data_file, label_list):
        with open(data_file, "r", encoding="utf-8") as fp:
            for idx, line in enumerate(fp):
                data = line.strip().split("\t")
                if len(data) == 1:
                    yield {"text_a": data[0]}
                else:
                    text, label = data
                    label = label.strip().split(",")
                    label = [float(1) if x in label else float(0) for x in label_list]
                    yield {"text_a": text, "labels": label}

    split_map = {"train": "train.txt", "dev": "dev.txt", "test": "test.txt"}
    datasets = []
    for split in splits:
        data_file = os.path.join(data_path, split_map[split])
        datasets.append(load_dataset(_reader, data_file=data_file, label_list=label_list, lazy=False))
    return datasets