# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""LegalGLUE: A Benchmark Dataset for Legal NLP models."""

import csv
import json
import textwrap
import os

import datasets


_DESCRIPTION = """\
Legal General Language Understanding Evaluation (LegalGLUE) benchmark is
a collection of datasets for evaluating model performance across a diverse set of legal NLP tasks
"""

GERMAN_LER = [
    "B-AN",
    "B-EUN",
    "B-GRT",
    "B-GS",
    "B-INN",
    "B-LD",
    "B-LDS",
    "B-LIT",
    "B-MRK",
    "B-ORG",
    "B-PER",
    "B-RR",
    "B-RS",
    "B-ST",
    "B-STR",
    "B-UN",
    "B-VO",
    "B-VS",
    "B-VT",
    "I-AN",
    "I-EUN",
    "I-GRT",
    "I-GS",
    "I-INN",
    "I-LD",
    "I-LDS",
    "I-LIT",
    "I-MRK",
    "I-ORG",
    "I-PER",
    "I-RR",
    "I-RS",
    "I-ST",
    "I-STR",
    "I-UN",
    "I-VO",
    "I-VS",
    "I-VT",
    "O"]


class LegalGlueConfig(datasets.BuilderConfig):
    """BuilderConfig for LegalGLUE."""

    def __init__(
        self,
        label_classes,  #the list of classes of the labels
        multi_label,    #boolean, if the task is multi-label
        homepage,       #homepage of the original dataset
        citation,       #citation for the dataset
        data_url,
        data_files,
        **kwargs,
    ):
        super(LegalGlueConfig, self).__init__(version=datasets.Version("1.1.0", ""), **kwargs)
        self.label_classes = label_classes
        self.multi_label = multi_label
        self.homepage = homepage
        self.citation = citation
        self.data_url = data_url
        self.data_files = data_files



class LegalGLUE(datasets.GeneratorBasedBuilder):
    """LegalGLUE: A Benchmark Dataset for Legal Language Understanding"""

    BUILDER_CONFIGS = [
        LegalGlueConfig(
            name="german_ler",
            description=textwrap.dedent(
                """\
            description"""
            ),
            label_classes=GERMAN_LER,
            multi_label=False,
            data_url="https://raw.githubusercontent.com/elenanereiss/Legal-Entity-Recognition/master/data/dataset_courts.zip",
            data_files=["bag.conll", "bfh.conll", "bgh.conll", "bpatg.conll", "bsg.conll", "bverfg.conll", "bverwg.conll"],
            homepage="https://github.com/elenanereiss/Legal-Entity-Recognition",
            citation=textwrap.dedent("""\
                @inproceedings{leitner2019fine,
                author = {Elena Leitner and Georg Rehm and Julian Moreno-Schneider},
                title = {{Fine-grained Named Entity Recognition in Legal Documents}},
                booktitle = {Semantic Systems. The Power of AI and Knowledge
                  Graphs. Proceedings of the 15th International Conference
                  (SEMANTiCS 2019)},
                year = 2019,
                editor = {Maribel Acosta and Philippe Cudré-Mauroux and Maria
                  Maleshkova and Tassilo Pellegrini and Harald Sack and York
                  Sure-Vetter},
                keywords = {aip},
                publisher = {Springer},
                series = {Lecture Notes in Computer Science},
                number = {11702},
                address = {Karlsruhe, Germany},
                month = 9,
                note = {10/11 September 2019},
                pages = {272--287},
                pdf = {https://link.springer.com/content/pdf/10.1007%2F978-3-030-33220-4_20.pdf}}
            """)
        )
    ]

    def _info(self):
        if self.config.name == "german_ler":
            features = {
                "id": datasets.Value("string"),
                "tokens": datasets.Sequence(datasets.Value("string")),
                "ner_tags": datasets.Sequence(
                    datasets.features.ClassLabel(
                        names=self.config.label_classes
                    )
                )
            }

        return datasets.DatasetInfo(
            description=self.config.description,
            features=datasets.Features(features),
            homepage=self.config.homepage,
            citation=self.config.citation,
        )

    def _split_generators(self, dl_manager):
        #archive = dl_manager.download(self.config.data_url)
        if self.config_name == "german_ler":
            archive = dl_manager.download_and_extract(self.config.data_url)
            return datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": [os.path.join(archive, file) for file in self.config.data_files],
                    "split": "train",
                    #"files": dl_manager.iter_archive(archive),
                },
            )
        # else:
        #     return [
        #         datasets.SplitGenerator(
        #             name=datasets.Split.TRAIN,
        #             # These kwargs will be passed to _generate_examples
        #             gen_kwargs={
        #                 "filepath": self.config.data_files,
        #                 "split": "train",
        #                 "files": dl_manager.iter_archive(archive),
        #                 },
        #             ),
        #         datasets.SplitGenerator(
        #             name=datasets.Split.TEST,
        #             # These kwargs will be passed to _generate_examples
        #             gen_kwargs={
        #                 "filepath": self.config.data_files,
        #                 "split": "test",
        #                 "files": dl_manager.iter_archive(archive),
        #             },
        #         ),
        #         datasets.SplitGenerator(
        #             name=datasets.Split.VALIDATION,
        #             # These kwargs will be passed to _generate_examples
        #             gen_kwargs={
        #                 "filepath": self.config.data_files,
        #                 "split": "validation",
        #                 "files": dl_manager.iter_archive(archive),
        #             },
        #         ),
        #     ]

    def _generate_examples(self, filepath, split):
        if self.config_name == "german_ler":
            texts, labels = [], []
            for path in filepath:
                with open(path, encoding="utf-8") as f:
                    tokens = []
                    tags = []
                    for line in f:
                        if line == "" or line == "\n":
                            if tokens:
                                texts.append(tokens)
                                labels.append(tags)
                                tokens = []
                                tags = []
                            else:
                                token, tag = line.split()
                                tokens.append(token)
                                tags.append(tag.rstrip())
                    texts.append(tokens)
                    labels.append(tags)

            for i in enumerate(texts):
                tokens = text[i]
                ner_tags = labels[i]
                yield i, {
                        "id": str(i),
                        "tokens": tokens,
                        "ner_tags": ner_tags,
                        }