"""Medieval Latin."""

from typing import List
from functools import partial

import datasets

import pandas


VERSION = datasets.Version("1.0.0")
_ORIGINAL_FEATURE_NAMES = [
    "epistola",
    "author",
    "content"
]
_BASE_FEATURE_NAMES = [
    "epistola",
    "author",
    "content"
]

DESCRIPTION = "MedievalLatin dataset from the Gungor thesis.\"."
_HOMEPAGE = "https://openportal.isti.cnr.it/doc?id=people______::37b90c87470ef85c78e72b8a3c753293"
_URLS = ("https://openportal.isti.cnr.it/doc?id=people______::37b90c87470ef85c78e72b8a3c753293")
_CITATION = """
@techreport{oai:it.cnr:prodotti:438795,
	title = {MedLatin1 and MedLatin2: Two Datasets for the Computational Authorship Analysis of Medieval Latin Texts},
	author = {Corbara S. and Moreo A. and Sebastiani F. and Tavoni M.},
	institution = {Research report, 2020},
	year = {2020}
}"""

# Dataset info
urls_per_split = {
    "train": "https://huggingface.co/datasets/mstz/medieval_latin/raw/main/epistolas.json",
}
features_types_per_config = {
    "authorship": {
        "epistola": datasets.Value("string"),
        "author": datasets.Value("string"),
        "content": datasets.Value("string")
    }
}
features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}


class MedievalLatinConfig(datasets.BuilderConfig):
    def __init__(self, **kwargs):
        super(MedievalLatinConfig, self).__init__(version=VERSION, **kwargs)
        self.features = features_per_config[kwargs["name"]]


class MedievalLatin(datasets.GeneratorBasedBuilder):
    # dataset versions
    DEFAULT_CONFIG = "authorship"
    BUILDER_CONFIGS = [
        MedievalLatinConfig(name="authorship",
                        description="authorship"),
    ]


    def _info(self):
        info = datasets.DatasetInfo(description=DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE,
                                    features=features_per_config[self.config.name])

        return info
    
    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
        downloads = dl_manager.download_and_extract(urls_per_split)

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloads["train"]})
        ]
    
    def _generate_examples(self, filepath: str):       
        data = pandas.read_json(filepath)

        for row_id, row in data.iterrows():
            data_row = dict(row)

            yield row_id, data_row