# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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
import datasets
import pandas as pd

_CITATION = """\
"""

_DESCRIPTION = """\
The Ancient Chinese Language Understanding Evaluation (ACLUE) is an evaluation benchmark focused on ancient Chinese language comprehension. It aims to assess the performance of large-scale language models on understanding ancient Chinese.
"""
_HOMEPAGE = "https://github.com/isen-zhang/ACLUE"

_URL = r"https://huggingface.co/datasets/tyouisen/aclue/resolve/main/aclue_v1_0_0.zip"

task_list = ['古文单字多义',
    '诗词情感分类',
    '古汉语命名体识别',
    '古汉语知识',
    '古诗词上下句预测',
    '古文断句',
    '对联',
    '古诗词曲鉴赏',
    '国学常识',
    '古音学',
    '通假字',
    '古代文学知识',
    'ancient_medical',
    '古诗词质量评估',
    '古文阅读理解']

class ACLUEConfig(datasets.BuilderConfig):
    def __init__(self, **kwargs):
        super().__init__(version=datasets.Version("1.0.0"), **kwargs)
        # V1.0.0 Init version


        
class ACLUE(datasets.GeneratorBasedBuilder):
    BUILDER_CONFIGS = [
        ACLUEConfig(name=task_name) for task_name in task_list
    ]

    def _info(self):
        features = datasets.Features(
            {
                "Question": datasets.Value("string"),
                "A": datasets.Value("string"),
                "B": datasets.Value("string"),
                "C": datasets.Value("string"),
                "D": datasets.Value("string"),
                "Answer": datasets.Value("string"),
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        data_dir = dl_manager.download_and_extract(_URL)
        task_name = self.config.name
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "filepath": os.path.join(data_dir, f"test/{task_name}.csv"),
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split("dev"),
                gen_kwargs={
                    "filepath": os.path.join(data_dir, f"dev/{task_name}.csv"),
                },
            ),
        ]

    def _generate_examples(self, filepath):
        df = pd.read_csv(filepath, header=0, encoding="utf-8")
        for i, instance in enumerate(df.to_dict(orient="records")):
            yield i, instance