import datasets
import json
import os
from datasets import Value, Sequence

_CITATION = """\
@inproceedings{chalkidis-etal-2019-neural,
    title = "Neural Legal Judgment Prediction in {E}nglish",
    author = "Chalkidis, Ilias  and
      Androutsopoulos, Ion  and
      Aletras, Nikolaos",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P19-1424",
    doi = "10.18653/v1/P19-1424",
    pages = "4317--4323",
}
"""

_HOMEPAGE = "https://archive.org/details/ECHR-ACL2019"
_DESCRIPTION = """\
The ECHR Cases dataset is designed for experimentation of neural judgment prediction, as in the original 2019 ACL paper "Neural Legal Judgment Prediction in English".
"""


ARTICLES = {
    "2": "Right to life",
    "3": "Prohibition of torture",
    "4": "Prohibition of slavery and forced labour",
    "5": "Right to liberty and security",
    "6": "Right to a fair trial",
    "7": "No punishment without law",
    "8": "Right to respect for private and family life",
    "9": "Freedom of thought, conscience and religion",
    "10": "Freedom of expression",
    "11": "Freedom of assembly and association",
    "12": "Right to marry",
    "13": "Right to an effective remedy",
    "14": "Prohibition of discrimination",
    "15": "Derogation in time of emergency",
    "16": "Restrictions on political activity of aliens",
    "17": "Prohibition of abuse of rights",
    "18": "Limitation on use of restrictions on rights",
    "34": "Individual applications",
    "38": "Examination of the case",
    "39": "Friendly settlements",
    "46": "Binding force and execution of judgments",
    "P1-1": "Protection of property",
    "P1-2": "Right to education",
    "P1-3": "Right to free elections",
    "P3-1": "Right to free elections",
    "P4-1": "Prohibition of imprisonment for debt",
    "P4-2": "Freedom of movement",
    "P4-3": "Prohibition of expulsion of nationals",
    "P4-4": "Prohibition of collective expulsion of aliens",
    "P6-1": "Abolition of the death penalty",
    "P6-2": "Death penalty in time of war",
    "P6-3": "Prohibition of derogations",
    "P7-1": "Procedural safeguards relating to expulsion of aliens",
    "P7-2": "Right of appeal in criminal matters",
    "P7-3": "Compensation for wrongful conviction",
    "P7-4": "Right not to be tried or punished twice",
    "P7-5": "Equality between spouses",
    "P12-1": "General prohibition of discrimination",
    "P13-1": "Abolition of the death penalty",
    "P13-2": "Prohibition of derogations",
    "P13-3": "Prohibition of reservations",
}


class Echr(datasets.GeneratorBasedBuilder):
    """ECHR dataset."""

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="non-anon", data_dir="data"),
        datasets.BuilderConfig(name="anon", data_dir="data_anon"),
    ]

    def _info(self):
        features = datasets.Features(
            {
                "itemid": Value(dtype="string"),
                "languageisocode": Value(dtype="string"),
                "respondent": Value(dtype="string"),
                "branch": Value(dtype="string"),
                "date": Value(dtype="int64"),
                "docname": Value(dtype="string"),
                "importance": Value(dtype="int64"),
                "conclusion": Value(dtype="string"),
                "judges": Value(dtype="string"),
                "text": Sequence(feature=Value(dtype="string")),
                "violated_articles": Sequence(feature=Value(dtype="string")),
                "violated_paragraphs": Sequence(feature=Value(dtype="string")),
                "violated_bulletpoints": Sequence(feature=Value(dtype="string")),
                "non_violated_articles": Sequence(feature=Value(dtype="string")),
                "non_violated_paragraphs": Sequence(feature=Value(dtype="string")),
                "non_violated_bulletpoints": Sequence(feature=Value(dtype="string")),
                "violated": Value(dtype="bool"),
            }
        )

        return datasets.DatasetInfo(
            features=features,
            homepage=_HOMEPAGE,
            description=_DESCRIPTION,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        path_prefix = self.config.data_dir
        data_dir = dl_manager.download([os.path.join(path_prefix, f"{f}.jsonl") for f in ["train", "test", "dev"]])
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": data_dir[0],
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": data_dir[1],
                    "split": "test",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": data_dir[2],
                    "split": "dev",
                },
            ),
        ]

    def _generate_examples(self, filepath, split):
        with open(filepath, encoding="utf-8") as f:
            for id_, row in enumerate(f):
                data = json.loads(row)
                yield id_, data