import json
import os
import datasets

_CITATION = """\
@inproceedings{lin-etal-2020-commongen,
    title = "{C}ommon{G}en: A Constrained Text Generation Challenge for Generative Commonsense Reasoning",
    author = "Lin, Bill Yuchen  and
      Zhou, Wangchunshu  and
      Shen, Ming  and
      Zhou, Pei  and
      Bhagavatula, Chandra  and
      Choi, Yejin  and
      Ren, Xiang",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.165",
    pages = "1823--1840",
}
"""

_DESCRIPTION = """\
CommonGen is a constrained text generation task, associated with a benchmark
dataset, to explicitly test machines for the ability of generative commonsense
reasoning. Given a set of common concepts; the task is to generate a coherent
sentence describing an everyday scenario using these concepts.
"""

_URLs = {
    "data": "https://storage.googleapis.com/huggingface-nlp/datasets/common_gen/commongen_data.zip",
    "challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/common_gen.zip",
}


class CommonGen(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.0.0")
    DEFAULT_CONFIG_NAME = "common_gen"

    def _info(self):
        features = datasets.Features(
            {
                "gem_id": datasets.Value("string"),
                "gem_parent_id": datasets.Value("string"),
                "concept_set_id": datasets.Value("int32"),
                "concepts": [datasets.Value("string")],
                "target": datasets.Value("string"),  # single target for train
                "references": [
                    datasets.Value("string")
                ],  # multiple references for validation
            }
        )

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            supervised_keys=datasets.info.SupervisedKeysData(
                input="concepts", output="target"
            ),
            homepage="https://inklab.usc.edu/CommonGen/",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        dl_dir = dl_manager.download_and_extract(_URLs)
        challenge_sets = [
            ("challenge_train_sample", "train_common_gen_RandomSample500.json"),
            (
                "challenge_validation_sample",
                "validation_common_gen_RandomSample500.json",
            ),
            (
                "challenge_test_scramble",
                "test_common_gen_ScrambleInputStructure500.json",
            ),
        ]
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": os.path.join(dl_dir["data"], "commongen.train.jsonl"),
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "filepath": os.path.join(dl_dir["data"], "commongen.dev.jsonl"),
                    "split": "validation",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "filepath": os.path.join(
                        dl_dir["data"], "commongen.test_noref.jsonl"
                    ),
                    "split": "test",
                },
            ),
        ] + [
            datasets.SplitGenerator(
                name=challenge_split,
                gen_kwargs={
                    "filepath": os.path.join(
                        dl_dir["challenge_set"], "common_gen", filename
                    ),
                    "split": challenge_split,
                },
            )
            for challenge_split, filename in challenge_sets
        ]

    def _generate_examples(self, filepath, split, filepaths=None, lang=None):
        """Yields examples."""
        if split.startswith("challenge"):
            exples = json.load(open(filepath, encoding="utf-8"))
            if isinstance(exples, dict):
                assert len(exples) == 1, "multiple entries found"
                exples = list(exples.values())[0]
            for id_, exple in enumerate(exples):
                if len(exple) == 0:
                    continue
                exple["gem_parent_id"] = exple["gem_id"]
                exple["gem_id"] = f"common_gen-{split}-{id_}"
                yield id_, exple
        else:
            with open(filepath, encoding="utf-8") as f:
                id_ = -1
                i = -1
                for row in f:
                    row = row.replace(", }", "}")  # Fix possible JSON format error
                    data = json.loads(row)
                    concepts = [word for word in data["concept_set"].split("#")]
                    if split == "train":
                        i += 1
                        for scene in data["scene"]:
                            id_ += 1
                            yield id_, {
                                "gem_id": f"common_gen-{split}-{id_}",
                                "gem_parent_id": f"common_gen-{split}-{id_}",
                                "concept_set_id": i,
                                "concepts": concepts,
                                "target": scene,
                                "references": [],
                            }
                    else:
                        id_ += 1
                        yield id_, {
                            "gem_id": f"common_gen-{split}-{id_}",
                            "gem_parent_id": f"common_gen-{split}-{id_}",
                            "concept_set_id": id_,
                            "concepts": concepts,
                            "target": "" if split == "test" else data["scene"][0],
                            "references": [] if split == "test" else data["scene"],
                        }