# 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.
# TODO: Address all TODOs and remove all explanatory comments
"""DUVEL : the Detection of Unlimited Variant Ensemble in Literature"""


import csv
import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""


_DESCRIPTION = """\
This dataset was created to identity oligogenic variant combinations, i.e. relation between several genes and their mutations, \
causing genetic diseases in scientific articles written in english. At the moment, it contains only digenic variant combinations, \
i.e. relations between two genes and at least two variants. The dataset is intended for binary relation extraction where the \
entities are masked within the text.
"""

_HOMEPAGE = "https://github.com/cnachteg/DUVEL"

_LICENSE = "cc-by-nc-sa-4.0"

# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URL = "https://raw.githubusercontent.com/cnachteg/DUVEL/main/"
_URLS = {
    "train": _URL + "data/train.csv",
    "dev": _URL + "data/validation.csv",
    "test": _URL + "data/test.csv"
}



class DUVEL(datasets.GeneratorBasedBuilder):
    """DUVEL : the Detection of Unlimited Variant Ensemble in Literature - Version 1.1."""

    VERSION = datasets.Version("1.1.0")
    
    def _info(self):
        features = datasets.Features(
                {
                    'sentence': datasets.Value('string'),
                    'pmcid': datasets.Value('int32'), 
                    'gene1': datasets.Value('string'),
                    'gene2': datasets.Value('string'), 
                    'variant1': datasets.Value('string'), 
                    'variant2': datasets.Value('string'),
                    'label': datasets.ClassLabel(names=[0,1])
                }
            )
        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=features,  # Here we define them above because they are different between the two configurations
            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
            # supervised_keys=("sentence", "label"),
            # Homepage of the dataset for documentation
            homepage=_HOMEPAGE,
            # License for the dataset if available
            license=_LICENSE,
            # Citation for the dataset
            citation=_CITATION,
            task_templates=[
                datasets.tasks.TextClassification(
                    text_column='sentence', label_column='label'
                )
            ],
        )

    def _split_generators(self, dl_manager):
        downloaded_files = dl_manager.download_and_extract(_URLS)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": downloaded_files['train']
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "filepath": downloaded_files['dev'],
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "filepath": downloaded_files['test'],
                },
            ),
        ]

    def _generate_examples(self, filepath):
        with open(filepath, encoding="utf-8") as f:
            reader = csv.DictReader(f)
            for key, row in enumerate(reader):
                yield key, row