# 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 = "https://arxiv.org/abs/2310.09550"

_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 = ['polysemy_resolution',
    'poetry_sentiment_analysis',
    'named_entity_recognition',
    'basic_ancient_chinese',
    'poetry_context_prediction',
    'sentence_segmentation',
    'couplet_prediction',
    'poetry_appreciate',
    'ancient_chinese_culture',
    'ancient_phonetics',
    'homographic_character_resolution',
    'ancient_literature',
    'ancient_medical',
    'poetry_quality_assessment',
    'reading_comprehension']
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.DEVELOPMENT,
                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