'''
Author: Xiang Pan
Date: 2021-09-20 01:38:55
LastEditTime: 2021-09-20 01:45:46
LastEditors: Xiang Pan
Description: 
FilePath: /SNLI_OOD/cached_datasets/snli_break/snli_break.py
xiangpan@nyu.edu
'''
import csv
import os

import datasets

_DESCRIPTION = """\
The SNLI corpus (version 1.0) is a collection of 570k human-written English
sentence pairs manually labeled for balanced classification with the labels
entailment, contradiction, and neutral, supporting the task of natural language
inference (NLI), also known as recognizing textual entailment (RTE).
"""

# _DATA_URL = "https://nlp.stanford.edu/projects/snli/snli_1.0.zip"


class snli_break(datasets.GeneratorBasedBuilder):
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="plain_text",
            version=datasets.Version("1.0.0", ""),
            description="Plain text import of SNLI",
        )
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            # features=datasets.Features(
            #     {
            #         "premise": datasets.Value("string"),
            #         "hypothesis": datasets.Value("string"),
            #         "label": datasets.features.ClassLabel(names=["entailment", "neutral", "contradiction"]),
            #     }
            # ),
            # # No default supervised_keys (as we have to pass both premise
            # # and hypothesis as input).
            # supervised_keys=None,
            # homepage="https://nlp.stanford.edu/projects/snli/",
            # # citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        # dl_dir = dl_manager.download_and_extract(_DATA_URL)
        data_dir = ("./")
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TEST, gen_kwargs={"filepath": os.path.join(data_dir, "dataset.jsonl")}
            ),
            # datasets.SplitGenerator(
            #     name=datasets.Split.VALIDATION, gen_kwargs={"filepath": os.path.join(data_dir, "snli_1.0_dev.txt")}
            # ),
            # datasets.SplitGenerator(
            #     name=datasets.Split.TRAIN, gen_kwargs={"filepath": os.path.join(data_dir, "snli_1.0_train.txt")}
            # ),
        ]

    def _generate_examples(self, filepath):
        """This function returns the examples in the raw (text) form."""
        with open(filepath, encoding="utf-8") as f:
            reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
            for idx, row in enumerate(reader):
                label = -1 if row["gold_label"] == "-" else row["gold_label"]
                yield idx, {
                    "premise": row["sentence1"],
                    "hypothesis": row["sentence2"],
                    "label": label,
                }