# 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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from pathlib import Path
from typing import Dict, List, Tuple

import datasets
import pandas as pd

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

_CITATION = """\
@article{Tuhenay2021,
  title = {Perbandingan Klasifikasi Bahasa Menggunakan Metode Naïve Bayes Classifier (NBC) Dan Support Vector Machine (SVM)},
  volume = {4},
  ISSN = {2656-1948},
  url = {http://dx.doi.org/10.33387/jiko.v4i2.2958},
  DOI = {10.33387/jiko.v4i2.2958},
  number = {2},
  journal = {JIKO (Jurnal Informatika dan Komputer)},
  publisher = {LPPM Universitas Khairun},
  author = {Tuhenay,  Deglorians},
  year = {2021},
  month = aug,
  pages = {105-111}
}
"""

_DATASETNAME = "identifikasi_bahasa"

_DESCRIPTION = """\
The identifikasi-bahasa dataset includes text samples in Indonesian, Ambonese, and Javanese. \
Each entry is comprised of cleantext, representing the sentence content, and a label identifying the language. \
The manual input process involved grouping the data by language categories, \
with labels for language identification and cleantext representing sentence content. The dataset, excluding punctuation and numbers, \
consists of a minimum of 3,000 Ambonese, 10,000 Javanese, \
and 3,500 Indonesian language entries, meeting the research's minimum standard for effective language identification.
"""

_HOMEPAGE = "https://github.com/joanitolopo/identifikasi-bahasa"
_LANGUAGES = ["ind", "jav", "abs"]

_LICENSE = Licenses.APACHE_2_0.value
_LOCAL = False

_URLS = {
    _DATASETNAME: "https://github.com/joanitolopo/identifikasi-bahasa/raw/main/DataKlasifikasi.xlsx",
}

_SUPPORTED_TASKS = [Tasks.LANGUAGE_IDENTIFICATION]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
_TAGS = ["Ambon", "Indo", "Jawa"]


class IdentifikasiBahasaDataset(datasets.GeneratorBasedBuilder):
    """The "identifikasi-bahasa" dataset, manually grouped by language, \
    contains labeled Indonesian, Ambonese, and Javanese text entries, excluding \
    punctuation and numbers, with a minimum of 3,000 Ambonese, 10,000 Javanese, \
    and 3,500 Indonesian entries for effective language identification."""

    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
    SEACROWD_SCHEMA_NAME = "text"

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

    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"

    def _info(self) -> datasets.DatasetInfo:
        if self.config.schema == "source":
            features = datasets.Features({"cleanText": datasets.Value("string"), "label": datasets.Value("string")})
        elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
            features = schemas.text_features(_TAGS)

        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_dir = dl_manager.download(urls)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": data_dir,
                    "split": "train",
                },
            )
        ]

    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
        """Yields examples as (key, example) tuples."""
        dataset = pd.read_excel(filepath)

        if self.config.schema == "source":
            for i, row in dataset.iterrows():
                yield i, {"cleanText": row["cleanText"], "label": row["label"]}

        elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
            for i, row in dataset.iterrows():
                yield i, {"id": i, "text": row["cleanText"], "label": row["label"]}