# coding=utf-8
# Copyright 2020 HuggingFace Datasets Authors.
#
# 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.

# Lint as: python3
import datasets


_DESCRIPTION = """\
A dataset of Legal Documents from German federal court decisions for Named Entity Recognition. The dataset is human-annotated with 19 fine-grained entity classes. The dataset consists of approx. 67,000 sentences and contains 54,000 annotated entities.
"""

_HOMEPAGE_URL = "https://github.com/elenanereiss/Legal-Entity-Recognition"
_CITATION = """\
@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}
}
"""
_URL = {
    "train": "https://raw.githubusercontent.com/elenanereiss/Legal-Entity-Recognition/master/data/ler_train.conll",
    "dev": "https://raw.githubusercontent.com/elenanereiss/Legal-Entity-Recognition/master/data/ler_dev.conll",  
    "test": "https://raw.githubusercontent.com/elenanereiss/Legal-Entity-Recognition/master/data/ler_test.conll",
}
_VERSION = "1.0.0"


class German_LER(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version(_VERSION)

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "tokens": datasets.Sequence(datasets.Value("string")),
                    "ner_tags": datasets.Sequence(
                        datasets.features.ClassLabel(
                            names=[
                                "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",
                            ]
                        )
                    ),
                },
            ),
            supervised_keys=None,
            homepage=_HOMEPAGE_URL,
            citation=_CITATION,
        )


    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""

        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
        data_dir = dl_manager.download_and_extract(_URL)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={"datapath": data_dir["train"], "split": "train"},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={"datapath": data_dir["test"], "split": "test"},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={"datapath": data_dir["dev"], "split": "dev"},
            ),
        ]
    
                            
    def _generate_examples(self, datapath):
        sentence_counter = 0
        for filepath in self.config.filepaths:
            filepath = os.path.join(datapath, filepath)
            with open(filepath, encoding="utf-8") as f:
                current_words = []
                current_labels = []
                for row in f:
                    row = row.rstrip()
                    row_split = row.split()
                    if len(row_split) == 2:
                        token, label = row_split
                        current_words.append(token)
                        current_labels.append(label)
                    else:
                        if not current_words:
                            continue
                        assert len(current_words) == len(current_labels), "word len doesnt match label length"
                        sentence = (
                            sentence_counter,
                            {
                                "id": str(sentence_counter),
                                "tokens": current_words,
                                "ner_tags": current_labels,
                            },
                        )
                        sentence_counter += 1
                        current_words = []
                        current_labels = []
                        yield sentence

                # if something remains:
                if current_words:
                    sentence = (
                        sentence_counter,
                        {
                            "id": str(sentence_counter),
                            "tokens": current_words,
                            "ner_tags": current_labels,
                        },
                    )
                    yield sentence