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

import datasets
import jsonlines

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

_CITATION = """\
@article{,
  author    = {Audah, Hanif Arkan and Yuliawati, Arlisa and Alfina, Ika},
  title     = {A Comparison Between SymSpell and a Combination of Damerau-Levenshtein Distance With the Trie Data Structure},
  journal   = {2023 10th International Conference on Advanced Informatics: Concept, Theory and Application (ICAICTA)},
  volume    = {},
  year      = {2023},
  url       = {https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10390399&casa_token=HtJUCIGGlWYAAAAA:q8ll1RWmpHtSAq2Qp5uQAE1NJETx7tUYFZIvTO1IWoaYy4eqFETSsm9p6C7tJwLZBGq5y8zc3A&tag=1},
  doi       = {},
  biburl    = {https://github.com/ir-nlp-csui/saltik?tab=readme-ov-file#references},
  bibsource = {https://github.com/ir-nlp-csui/saltik?tab=readme-ov-file#references}
}
"""

_DATASETNAME = "saltik"
_DESCRIPTION = """\
Saltik is a dataset for benchmarking non-word error correction method accuracy in evaluating Indonesian words.
It consists of 58,532 non-word errors generated from 3,000 of the most popular Indonesian words.
"""
_HOMEPAGE = "https://github.com/ir-nlp-csui/saltik"
_LANGUAGES = ["ind"]
_LICENSE = Licenses.AGPL_3_0.value
_LOCAL = False
_URLS = {
    _DATASETNAME: "https://raw.githubusercontent.com/ir-nlp-csui/saltik/main/saltik.json",
}
_SUPPORTED_TASKS = []
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"


class Saltik(datasets.GeneratorBasedBuilder):
    """It consists of 58,532 non-word errors generated from 3,000 of the most popular Indonesian words."""

    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}",
        ),
    ]

    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"

    def _info(self) -> datasets.DatasetInfo:

        if self.config.schema == "source":
            # EX: Arbitrary NER type dataset
            features = datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "word": datasets.Value("string"),
                    "errors": [
                        {
                            "typo": datasets.Value("string"),
                            "error_type": datasets.Value("string"),
                        }
                    ],
                }
            )
        else:
            raise NotImplementedError()

        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]
        file_path = dl_manager.download(urls)
        data = self._read_jsonl(file_path)
        all_words = list(data.keys())
        processed_data = []
        id = 0
        for word in all_words:
            processed_data.append({"id": id, "word": word, "errors": data[word]})
            id += 1
        self._write_jsonl(file_path + ".jsonl", processed_data)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # Whatever you put in gen_kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": file_path + ".jsonl",
                    "split": "train",
                },
            ),
        ]

    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
        """Yields examples as (key, example) tuples."""
        if self.config.schema == "source":
            i = 0
            with jsonlines.open(filepath) as f:
                for each_data in f.iter():
                    ex = {
                        "id": each_data["id"],
                        "word": each_data["word"],
                        "errors": each_data["errors"],
                    }

                    yield i, ex
                    i += 1

    def _read_jsonl(self, filepath: Path):
        with open(filepath) as user_file:
            parsed_json = json.load(user_file)
            return parsed_json

    def _write_jsonl(self, filepath, values):
        with jsonlines.open(filepath, "w") as writer:
            for line in values:
                writer.write(line)