# coding=utf-8
# Copyright 2022 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
#
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# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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import json
from pathlib import Path
from typing import Dict, List, Tuple

import datasets

from seacrowd.utils import schemas
from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import Licenses, Tasks

_CITATION = """\
@misc{IndoQA,
  author = {{Jakarta Artificial Intelligence Research}}
  title = {IndoQA: Building Indonesian QA dataset},
  year = {2023}
  url = {https://huggingface.co/datasets/jakartaresearch/indoqa}
}
"""

_DATASETNAME = "indoqa"

_DESCRIPTION = """\
IndoQA is a monolingual question-answering dataset of Indonesian language (ind).
It comprises 4,413 examples with 3:1 split of training and validation sets.
The datasets consists of a context paragraph along with an associated question-answer pair.
"""

_HOMEPAGE = "https://jakartaresearch.com/"
_LICENSE = Licenses.CC_BY_ND_4_0.value

_LANGUAGES = ["ind"]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
_LOCAL = False
_URLS = {
    _DATASETNAME: {
        "train": "https://drive.google.com/uc?id=1ND893H5x2gaPRRMJVajQ4hgqpopHoD0u",
        "validation": "https://drive.google.com/uc?id=1mq_foV72riXb1KVBirJzTFZEe7oa8f4f",
    },
}

_SUPPORTED_TASKS = [Tasks.QUESTION_ANSWERING]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"


class IndoQADataset(datasets.GeneratorBasedBuilder):
    """IndoQA: A monolingual Indonesian question-answering dataset comprises 4,413 instances of QA-pair with context."""

    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)

    BUILDER_CONFIGS = [
        SEACrowdConfig(
            name=f"{_DATASETNAME}_source",
            version=SOURCE_VERSION,
            description=f"{_DATASETNAME} source schema",
            schema="source",
            subset_id=f"{_DATASETNAME}",
        ),
        SEACrowdConfig(
            name=f"{_DATASETNAME}_seacrowd_qa",
            version=SEACROWD_VERSION,
            description=f"{_DATASETNAME} SEACrowd schema",
            schema="seacrowd_qa",
            subset_id=f"{_DATASETNAME}",
        ),
    ]

    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"

    def _info(self) -> datasets.DatasetInfo:

        if self.config.schema == "source":
            features = datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "question": datasets.Value("string"),
                    "answer": datasets.Value("string"),
                    "context": datasets.Value("string"),
                    "category": datasets.Value("string"),
                    "span_start": datasets.Value("int32"),
                    "span_end": datasets.Value("int32"),
                }
            )

        elif self.config.schema == "seacrowd_qa":
            features = schemas.qa_features
            features["meta"]["span_start"] = datasets.Value("int32")
            features["meta"]["span_end"] = datasets.Value("int32")

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
        """Returns SplitGenerators."""
        urls = _URLS[_DATASETNAME]
        data_paths = dl_manager.download_and_extract(urls)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"filepath": data_paths["train"]},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={"filepath": data_paths["validation"]},
            ),
        ]

    def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]:
        """Yields examples as (key, example) tuples."""
        with open(filepath, "r", encoding="utf-8") as file:
            datas = json.load(file)

        if self.config.schema == "source":
            for key, data in enumerate(datas):
                yield key, data

        elif self.config.schema == "seacrowd_qa":
            for key, data in enumerate(datas):
                yield key, {
                    "id": f'{data["id"]}',
                    "question_id": data["id"],
                    "document_id": "",
                    "question": data["question"],
                    "type": data["category"],
                    "choices": [],
                    "context": data["context"],
                    "answer": [data["answer"]],
                    "meta": {
                        "span_start": data["span_start"],
                        "span_end": data["span_end"],
                    },
                }