from __future__ import annotations

import json
from pathlib import Path
from typing import Generator

import datasets

from .base import BaseDatasetProcessor
from .chabsa import ChabsaDatasetProcessor
from .coreference import WikipediaCoreferenceDatasetProcessor
from .dependency import WikipediaDependencyDatasetProcessor
from .jamp import JAMPDatasetProcessor
from .janli import JaNLIDatasetProcessor
from .jcommonsenseqa import JCommonsenseQADatasetProcessor
from .jemhopqa import JEMHopQADatasetProcessor
from .jnli import JNLIDatasetProcessor
from .jsem import JSeMDatasetProcessor
from .jsick import JSICKDatasetProcessor
from .jsquad import JSQuADDatasetProcessor
from .jsts import JSTSDatasetProcessor
from .mawps import MawpsDatasetProcessor
from .ner import WikipediaNERDatasetProcessor
from .niilc import NIILCDatasetProcessor
from .pas import WikipediaPASDatasetProcessor
from .reading import WikipediaReadingDatasetProcessor
from .wiki_base import WikipediaBaseDatasetProcessor

_CITATION = ""
_DESCRIPTION = "These are datasets including the benchmark 'llm-jp-eval.'"
_HOMEPAGE = "https://github.com/llm-jp/llm-jp-eval/tree/v1.3.0"
_LICENSE = "This work is license under Apache-2.0 license"
_VERSION = datasets.Version("1.3.0")


class LlmJpEvalConfig(datasets.BuilderConfig):
    def __init__(
        self,
        name: str = "default",
        version: datasets.Version | str | None = _VERSION,
        data_dir: str | None = None,
        data_files: datasets.data_files.DataFilesDict | None = None,
        description: str | None = _DESCRIPTION,
        output_dir: str = "./data_llm-jp-eval",
    ) -> None:
        super().__init__(
            name=name,
            version=version,
            data_dir=data_dir,
            data_files=data_files,
            description=description,
        )
        self.output_dir = output_dir


class LlmJpEval(datasets.GeneratorBasedBuilder):

    BUILDER_CONFIGS = [
        LlmJpEvalConfig(name="jamp"),
        LlmJpEvalConfig(name="janli"),
        LlmJpEvalConfig(name="jnli"),
        LlmJpEvalConfig(name="jsem"),
        LlmJpEvalConfig(name="jsick"),
        LlmJpEvalConfig(name="jemhopqa"),
        LlmJpEvalConfig(name="niilc"),
        LlmJpEvalConfig(name="jsquad"),
        LlmJpEvalConfig(name="jcommonsenseqa"),
        LlmJpEvalConfig(name="chabsa"),
        LlmJpEvalConfig(name="wiki_reading"),
        LlmJpEvalConfig(name="wiki_ner"),
        LlmJpEvalConfig(name="wiki_dependency"),
        LlmJpEvalConfig(name="wiki_pas"),
        LlmJpEvalConfig(name="wiki_coreference"),
        LlmJpEvalConfig(name="mawps"),
        LlmJpEvalConfig(name="jsts"),
    ]

    def _info(self) -> datasets.DatasetInfo:
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            citation=_CITATION,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            features=datasets.Features(
                {
                    "input": datasets.Value("string"),
                    "output": datasets.Value("string"),
                }
            ),
        )

    def _split_generators(
        self, dl_manager: datasets.DownloadManager
    ) -> list[datasets.SplitGenerator]:
        output_dir = Path(self.config.output_dir)
        version_name = self.config.version.version_str
        dataset_name = self.config.name

        if dataset_name == "jamp":
            processor = JAMPDatasetProcessor
        elif dataset_name == "janli":
            processor = JaNLIDatasetProcessor
        elif dataset_name == "jnli":
            processor = JNLIDatasetProcessor
        elif dataset_name == "jsem":
            processor = JSeMDatasetProcessor
        elif dataset_name == "jsick":
            processor = JSICKDatasetProcessor
        elif dataset_name == "jemhopqa":
            processor = JEMHopQADatasetProcessor
        elif dataset_name == "niilc":
            processor = NIILCDatasetProcessor
        elif dataset_name == "jsquad":
            processor = JSQuADDatasetProcessor
        elif dataset_name == "jcommonsenseqa":
            processor = JCommonsenseQADatasetProcessor
        elif dataset_name == "chabsa":
            processor = ChabsaDatasetProcessor
        elif dataset_name == "wiki_reading":
            processor = WikipediaReadingDatasetProcessor
        elif dataset_name == "wiki_ner":
            processor = WikipediaNERDatasetProcessor
        elif dataset_name == "wiki_dependency":
            processor = WikipediaDependencyDatasetProcessor
        elif dataset_name == "wiki_pas":
            processor = WikipediaPASDatasetProcessor
        elif dataset_name == "wiki_coreference":
            processor = WikipediaCoreferenceDatasetProcessor
        elif dataset_name == "mawps":
            processor = MawpsDatasetProcessor
        elif dataset_name == "jsts":
            processor = JSTSDatasetProcessor

        processor(output_dir, version_name).pipeline()

        eval_dir = output_dir / version_name / "evaluation"
        with (eval_dir / "train" / f"{dataset_name}.json").open(encoding="utf-8") as f:
            train_data = json.load(f)
        with (eval_dir / "dev" / f"{dataset_name}.json").open(encoding="utf-8") as f:
            validation_data = json.load(f)
        with (eval_dir / "test" / f"{dataset_name}.json").open(encoding="utf-8") as f:
            test_data = json.load(f)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN, gen_kwargs={"data": train_data["samples"]}
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={"data": validation_data["samples"]},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST, gen_kwargs={"data": test_data["samples"]}
            ),
        ]

    def _generate_examples(self, data: list[dict[str, str]]) -> Generator:
        for i, d in enumerate(data):
            yield i, d