#!/usr/bin/env python3

"""
The script used to load the dataset from the original source.
"""


import json
import datasets
import glob
import os

_CITATION = """\
@inproceedings{suadaa-etal-2021-towards,
    title = "Towards Table-to-Text Generation with Numerical Reasoning",
    author = "Suadaa, Lya Hulliyyatus  and
      Kamigaito, Hidetaka  and
      Funakoshi, Kotaro  and
      Okumura, Manabu  and
      Takamura, Hiroya",
    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.acl-long.115",
    doi = "10.18653/v1/2021.acl-long.115",
    pages = "1451--1465"
}
"""
_DESCRIPTION = """\
NumericNLG is a dataset for table-totext generation focusing on numerical reasoning. 
The dataset consists of textual descriptions of numerical tables from scientific papers.
"""

_URL = "https://github.com/titech-nlp/numeric-nlg"
_LICENSE = "CC BY-SA 4.0"

class NumericNLG(datasets.GeneratorBasedBuilder):
    VERSION = "1.0.0"
    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features({
                "table_id_paper": datasets.Value(dtype='string'),
                "caption": datasets.Value(dtype='string'),
                "row_header_level" : datasets.Value(dtype='int32'),
                "row_headers" : datasets.Value(dtype='large_string'),
                "column_header_level": datasets.Value(dtype='int32'),
                "column_headers" : datasets.Value(dtype='large_string'),
                "contents"  : datasets.Value(dtype='large_string'),
                "metrics_loc" : datasets.Value(dtype='string'),
                "metrics_type" :  datasets.Value(dtype='large_string'),
                "target_entity":  datasets.Value(dtype='large_string'),
                "table_html_clean": datasets.Value(dtype='large_string'),
                "table_name": datasets.Value(dtype='string'),
                "table_id": datasets.Value(dtype='string'),
                "paper_id": datasets.Value(dtype='string'),
                "page_no": datasets.Value(dtype='int32'),
                "dir": datasets.Value(dtype='string'),
                "valid": datasets.Value(dtype='int32'),
            }),
            supervised_keys=None,
            homepage="https://github.com/titech-nlp/numeric-nlg",
            citation=_CITATION,
            license=_LICENSE,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": "data", "split" : "train"}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": "data", "split" : "dev"}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": "data", "split" : "test"}),
        ]

    def _generate_examples(self, filepath, split):
        filename = split if split != "dev" else "val"

        with open(os.path.join(filepath, f"table_{filename}.json")) as f:
            j = json.load(f)

        for example_idx, entry in enumerate(j):
            yield example_idx, {key: str(value) for key, value in entry.items()}

if __name__ == '__main__':
    dataset = datasets.load_dataset(__file__)
    dataset.push_to_hub("kasnerz/numericnlg")