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#     http://www.apache.org/licenses/LICENSE-2.0
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# Lint as: python3
"""MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics"""


import json
import os

import datasets


logger = datasets.logging.get_logger(__name__)


_CITATION = """\
@inproceedings{Chen2020MOCHAAD,
    author={Anthony Chen and Gabriel Stanovsky and Sameer Singh and Matt Gardner},
    title={MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics},
    booktitle={EMNLP},
    year={2020}
}
"""

_DESCRIPTION = """\
Posing reading comprehension as a generation problem provides a great deal of flexibility, allowing for \
open-ended questions with few restrictions on possible answers. However, progress is impeded by existing \
generation metrics, which rely on token overlap and are agnostic to the nuances of reading comprehension. \
To address this, we introduce a benchmark for training and evaluating generative reading comprehension metrics: \
MOdeling Correctness with Human Annotations. MOCHA contains 40K human judgement scores on model outputs from \
6 diverse question answering datasets and an additional set of minimal pairs for evaluation. Using MOCHA, \
we train an evaluation metric: LERC, a Learned Evaluation metric for Reading Comprehension, to mimic human \
judgement scores.
"""

_HOMEPAGE = "https://allennlp.org/mocha"

_LICENSE = "https://creativecommons.org/licenses/by-sa/4.0/legalcode"

_URL = "https://github.com/anthonywchen/MOCHA/raw/main/data/mocha.tar.gz"

_MINIMAL_PAIRS_SPLIT = "minimal_pairs"

SPLIT_FILENAMES = {
    datasets.Split.TRAIN: "train.json",
    datasets.Split.VALIDATION: "dev.json",
    datasets.Split.TEST: "test_no_labels.json",
    _MINIMAL_PAIRS_SPLIT: "minimal_pairs.json",
}


class Mocha(datasets.GeneratorBasedBuilder):
    """MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics"""

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "constituent_dataset": datasets.Value("string"),
                    "id": datasets.Value("string"),
                    "context": datasets.Value("string"),
                    "question": datasets.Value("string"),
                    "reference": datasets.Value("string"),
                    "candidate": datasets.Value("string"),
                    "score": datasets.Value("float"),
                    "metadata": {
                        "scores": datasets.features.Sequence(datasets.Value("int32")),
                        "source": datasets.Value("string"),
                    },
                    # features for minimal pairs
                    "candidate2": datasets.Value("string"),
                    "score2": datasets.Value("float"),
                }
            ),
            supervised_keys=None,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        dl_path = os.path.join(dl_manager.download_and_extract(_URL), "mocha")

        return [
            datasets.SplitGenerator(
                name=split,
                gen_kwargs={"filepath": os.path.join(dl_path, SPLIT_FILENAMES[split]), "split": split},
            )
            for split in SPLIT_FILENAMES
        ]

    def _generate_examples(self, filepath, split):
        """This function returns the examples in the raw (text) form."""
        logger.info("Generating examples from = %s", filepath)
        with open(filepath, encoding="utf-8") as f:
            mocha = json.load(f)
            for constituent_dataset, samples in mocha.items():
                for id_, sample in samples.items():
                    sample["id"] = id_
                    sample["constituent_dataset"] = constituent_dataset

                    # Add default values
                    if split == _MINIMAL_PAIRS_SPLIT:
                        sample["candidate"] = sample["candidate1"]
                        sample["score"] = sample["score1"]
                        del sample["candidate1"], sample["score1"]
                        sample["metadata"] = {"scores": [], "source": ""}
                    else:
                        if "score" not in sample:
                            sample["score"] = -1.0
                            sample["metadata"]["scores"] = []
                        sample["candidate2"] = ""
                        sample["score2"] = -1.0

                    yield id_, sample