import json
import os

import datasets

_CITATION = """\
@article{scialom2020mlsum,
  title={MLSUM: The Multilingual Summarization Corpus},
  author={Scialom, Thomas and Dray, Paul-Alexis and Lamprier, Sylvain and Piwowarski, Benjamin and Staiano, Jacopo},
  journal={arXiv preprint arXiv:2004.14900},
  year={2020}
}
"""

_DESCRIPTION = """\
This is the MLSUM subset of the GEM benchmark. MLSUM is the first large-scale MultiLingual SUMmarization dataset.
Obtained from online newspapers, it contains 1.5M+ article/summary pairs in five different languages -- namely, French, German, Spanish, Russian, Turkish.
Together with English newspapers from the popular CNN/Daily mail dataset, the collected data form a large scale multilingual dataset which can enable new research directions for the text summarization community.
We report cross-lingual comparative analyses based on state-of-the-art systems.
These highlight existing biases which motivate the use of a multi-lingual dataset.
"""
_URL = "https://gitlab.lip6.fr/scialom/mlsum_data/-/raw/master/MLSUM/"
_LANG = ["de", "es"]
_URLs = {
    "de": {
        "train": "https://gitlab.lip6.fr/scialom/mlsum_data/-/raw/master/MLSUM/de_train.zip",
        "validation": "https://gitlab.lip6.fr/scialom/mlsum_data/-/raw/master/MLSUM/de_val.zip",
        "test": "https://gitlab.lip6.fr/scialom/mlsum_data/-/raw/master/MLSUM/de_test.zip",
        "bad_ids": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_mlsum_bad_ids_fixed.json",
        "challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/mlsum_de.zip",
    },
    "es": {
        "train": "https://gitlab.lip6.fr/scialom/mlsum_data/-/raw/master/MLSUM/es_train.zip",
        "validation": "https://gitlab.lip6.fr/scialom/mlsum_data/-/raw/master/MLSUM/es_val.zip",
        "test": "https://gitlab.lip6.fr/scialom/mlsum_data/-/raw/master/MLSUM/es_test.zip",
        "bad_ids": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_mlsum_bad_ids_fixed.json",
        "challenge_set": "https://storage.googleapis.com/huggingface-nlp/datasets/gem/gem_challenge_sets/mlsum_es.zip",
    },
}


class Mlsum(datasets.GeneratorBasedBuilder):

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name=lang,
            version=datasets.Version("1.0.0"),
            description="",
        )
        for lang in _LANG
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "gem_id": datasets.Value("string"),
                    "gem_parent_id": datasets.Value("string"),
                    "text": datasets.Value("string"),
                    "topic": datasets.Value("string"),
                    "url": datasets.Value("string"),
                    "title": datasets.Value("string"),
                    "date": datasets.Value("string"),
                    "target": datasets.Value("string"),
                    "references": [datasets.Value("string")],
                }
            ),
            supervised_keys=None,
            homepage="",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        dl_dir = dl_manager.download_and_extract(_URLs[self.config.name])
        lang = str(self.config.name)
        challenge_sets = [
            ("challenge_train_sample", f"train_mlsum_{lang}_RandomSample500.json"),
            ("challenge_validation_sample", f"validation_mlsum_{lang}_RandomSample500.json"),
            ("challenge_test_covid", f"{lang}_test_covid19_cleaned.jsonl"),
        ]
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": os.path.join(dl_dir["train"], lang + "_train.jsonl"),
                    "split": "train",
                    "lang": lang,
                    "filepaths": dl_dir["bad_ids"],
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "filepath": os.path.join(dl_dir["validation"], lang + "_val.jsonl"),
                    "split": "validation",
                    "lang": lang,
                    "filepaths": dl_dir["bad_ids"],
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "filepath": os.path.join(dl_dir["test"], lang + "_test.jsonl"),
                    "split": "test",
                    "lang": lang,
                    "filepaths": dl_dir["bad_ids"],
                },
            ),
        ] + [
            datasets.SplitGenerator(
                name=challenge_split,
                gen_kwargs={
                    "filepath": os.path.join(dl_dir["challenge_set"], f"mlsum_{self.config.name}", 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 in ["train", "validation", "test", "challenge_test_covid"]:
            if split == "challenge_test_covid":
                bad_ids = {}
            else:
                bad_ids_dct = json.load(open(filepaths, encoding="utf-8"))
                bad_ids = dict((bad_url, True) for _, bad_url in bad_ids_dct[f"{lang}-{split}"])
            with open(filepath, encoding="utf-8") as f:
                id_ = -1
                for line in f:
                    data = json.loads(line)
                    if data["url"] in bad_ids:
                        continue
                    else:
                        id_ += 1
                        yield id_, {
                            "gem_id": f"mlsum_{self.config.name}-{split}-{id_}",
                            "gem_parent_id": f"mlsum_{self.config.name}-{split}-{id_}",
                            "text": data["text"],
                            "target": data["summary"],
                            "references": [] if split == "train" else [data["summary"]],
                            "topic": data["topic"],
                            "url": data["url"],
                            "title": data["title"],
                            "date": data["date"],
                        }
        else:
            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"mlsum_{self.config.name}-{split}-{id_}"
                yield id_, exple