# 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 csv
import json
import os
import datasets


_CITATION = """\
@InProceedings{huggingface:dataset,
title = {Ko-LIMA: Korean LIMA Dataset},
author={Hahn, Taeseung},
year={2023}
}
"""
_DESCRIPTION = """\
A high-quality korean dataset for efficient instruction tuning.
"""
_HOMEPAGE = ""
_LICENSE = ""
_URLS = {
    "plain": "koLIMA-plain.zip", # 
    "vicuna": "koLIMA-vicuna.zip", # 
}


class KoLima(datasets.GeneratorBasedBuilder):
    """A high-quality korean dataset for efficient instruction tuning."""

    VERSION = datasets.Version("1.0.0")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="plain", version=VERSION, description="Korean LIMA dataset in a plain format"),
        datasets.BuilderConfig(name="vicuna", version=VERSION, description="Korean LIMA dataset in Vicuna format"),
    ]

    DEFAULT_CONFIG_NAME = "plain"

    def _info(self):
        if self.config.name == "vicuna":
            features = datasets.Features(
                {
                    'id': datasets.Value(dtype='string', id=None),
                    'conversations': [
                        {
                            'from': datasets.Value(dtype='string', id=None),
                            'value': datasets.Value(dtype='string', id=None)
                        }
                    ]
                }
            )
        else:
            features = datasets.Features(
                {
                    'conversations': datasets.Sequence(feature=datasets.Value(dtype='string', id=None), length=-1, id=None),
                    'source': datasets.Value(dtype='string', id=None)
                }
            )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        urls = _URLS[self.config.name]
        data_dir = dl_manager.download_and_extract(urls)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": os.path.join(data_dir, "train.jsonl"),
                    "split": "train",
                },
            ),
            # datasets.SplitGenerator(
            #     name=datasets.Split.VALIDATION,
            #     gen_kwargs={
            #         "filepath": os.path.join(data_dir, "dev.jsonl"),
            #         "split": "dev",
            #     },
            # ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": os.path.join(data_dir, "test.jsonl"),
                    "split": "test"
                },
            ),
        ]

    def _generate_examples(self, filepath, split):
        with open(filepath, encoding="utf-8") as f:
            for key, row in enumerate(f):
                instance = json.loads(row)
                if self.config.name == "vicuna":
                    yield key, instance
                else:
                    yield key, instance