# coding=utf-8
# Copyright 2020 HuggingFace Datasets Authors.
# Modified by Vésteinn Snæbjarnarson 2021
#
# 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






LABELS = [
]



import datasets


logger = datasets.logging.get_logger(__name__)


_CITATION = """\
@misc{sosialurin-ner,
 title = {},
 author = {},
 url = {},
 year = {2022} }
"""

_DESCRIPTION = """\
The corpus that has been created consists of ca. 100.000 words of text from the [Faroese] newspaper Sosialurin. Each word is tagged with named entity information
"""

_URL = "https://huggingface.co/datasets/vesteinn/sosialurin-faroese-ner/raw/main/"
_TRAINING_FILE = "sosialurin.faroese.ner.train.txt"


class SosialurinNERConfig(datasets.BuilderConfig):
    """BuilderConfig for sosialurin-faroese-ner"""

    def __init__(self, **kwargs):
        """BuilderConfig for sosialurin-faroese-ner.
        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(SosialurinNERConfig, self).__init__(**kwargs)


class SosialurinNER(datasets.GeneratorBasedBuilder):
    """sosialurin-faroese-ner dataset."""

    BUILDER_CONFIGS = [
        SosialurinNERConfig(name="sosialurin-faroese-ner", version=datasets.Version("0.1.0"), description="sosialurin-faroese-ner dataset"),
    ]

    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=LABELS
                        )
                    ),
                }
            ),
            supervised_keys=None,
            homepage="",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        urls_to_download = {
            "train": f"{_URL}{_TRAINING_FILE}",
        }
        downloaded_files = dl_manager.download_and_extract(urls_to_download)

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
        ]

    def _generate_examples(self, filepath):
        logger.info("⏳ Generating examples from = %s", filepath)
        with open(filepath, encoding="utf-8") as f:
            guid = 0
            tokens = []
            ner_tags = []
            for line in f:
                if line.startswith("-DOCSTART-") or line == "" or line == "\n":
                    if tokens:
                        yield guid, {
                            "id": str(guid),
                            "tokens": tokens,
                            "ner_tags": ner_tags,
                        }
                        guid += 1
                        tokens = []
                        ner_tags = []
                else:
                    # tokens are tab separated
                    splits = line.split("\t")
                    tokens.append(splits[0])
                    try:
                       ner_tags.append(splits[1].rstrip())
                    except:
                        print(splits)
                        raise
            # last example
            yield guid, {
                "id": str(guid),
                "tokens": tokens,
                "ner_tags": ner_tags,
            }