# coding=utf-8
# Copyright 2022 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.

from typing import Dict, List, Tuple

import datasets
import pandas as pd

from bigbio.utils import schemas
from bigbio.utils.configs import BigBioConfig
from bigbio.utils.constants import Lang, Tasks
from bigbio.utils.license import Licenses

_LANGUAGES = [Lang.EN]
_PUBMED = True
_LOCAL = False
_CITATION = """\
@inproceedings{chen2021overview,
  title        = {
    Overview of the BioCreative VII LitCovid Track: multi-label topic
    classification for COVID-19 literature annotation
  },
  author       = {
    Chen, Qingyu and Allot, Alexis and Leaman, Robert and Do{\\u{g}}an, Rezarta
    Islamaj and Lu, Zhiyong
  },
  year         = 2021,
  booktitle    = {Proceedings of the seventh BioCreative challenge evaluation workshop}
}

"""

_DATASETNAME = "bc7_litcovid"
_DISPLAYNAME = "BC7-LitCovid"

_DESCRIPTION = """\
The training and development datasets contain the publicly-available \
text of over 30 thousand COVID-19-related articles and their metadata \
(e.g., title, abstract, journal). Articles in both datasets have been \
manually reviewed and articles annotated by in-house models.
"""

_HOMEPAGE = "https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-5/"

_LICENSE = Licenses.UNKNOWN

_BASE = "https://ftp.ncbi.nlm.nih.gov/pub/lu/LitCovid/biocreative/BC7-LitCovid-"

_URLS = {
    _DATASETNAME: {
        "train": _BASE + "Train.csv",
        "validation": _BASE + "Dev.csv",
        "test": _BASE + "Test-GS.csv",
    },
}

_SUPPORTED_TASKS = [Tasks.TEXT_CLASSIFICATION]

_SOURCE_VERSION = "1.0.0"
_BIGBIO_VERSION = "1.0.0"

_CLASS_NAMES = [
    "Epidemic Forecasting",
    "Treatment",
    "Prevention",
    "Mechanism",
    "Case Report",
    "Transmission",
    "Diagnosis",
]


class BC7LitCovidDataset(datasets.GeneratorBasedBuilder):
    """
    Track 5 - LitCovid track Multi-label topic classification for
    COVID-19 literature annotation
    """

    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)

    BUILDER_CONFIGS = [
        BigBioConfig(
            name="bc7_litcovid_source",
            version=SOURCE_VERSION,
            description="bc7_litcovid source schema",
            schema="source",
            subset_id="bc7_litcovid",
        ),
        BigBioConfig(
            name="bc7_litcovid_bigbio_text",
            version=BIGBIO_VERSION,
            description="bc7_litcovid BigBio schema",
            schema="bigbio_text",
            subset_id="bc7_litcovid",
        ),
    ]

    DEFAULT_CONFIG_NAME = "bc7_litcovid_source"

    def _info(self) -> datasets.DatasetInfo:

        if self.config.schema == "source":

            features = datasets.Features(
                {
                    "pmid": datasets.Value("string"),
                    "journal": datasets.Value("string"),
                    "title": datasets.Value("string"),
                    "abstract": datasets.Value("string"),
                    "keywords": datasets.Sequence(datasets.Value("string")),
                    "pub_type": datasets.Sequence(datasets.Value("string")),
                    "authors": datasets.Sequence(datasets.Value("string")),
                    "doi": datasets.Value("string"),
                    "labels": datasets.Sequence(
                        datasets.ClassLabel(names=_CLASS_NAMES)
                    ),
                }
            )

        elif self.config.schema == "bigbio_text":
            features = schemas.text_features

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

    def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:
        """Returns SplitGenerators."""

        # Download all the CSV
        urls = _URLS[_DATASETNAME]
        path_train = dl_manager.download(urls["train"])
        path_validation = dl_manager.download(urls["validation"])
        path_test = dl_manager.download(urls["test"])

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": path_train,
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "filepath": path_validation,
                    "split": "test",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "filepath": path_test,
                    "split": "dev",
                },
            ),
        ]

    def _generate_examples(self, filepath, split: str) -> Tuple[int, Dict]:
        """Yields examples as (key, example) tuples."""

        idx = 0

        # Load the CSV and convert it to the string format
        df = pd.read_csv(filepath, sep=",").astype(str).replace({"nan": None})

        for index, e in df.iterrows():

            if self.config.schema == "source":

                yield idx, {
                    "pmid": e["pmid"],
                    "journal": e["journal"],
                    "title": e["title"],
                    "abstract": e["abstract"],
                    "keywords": e["keywords"].split(";")
                    if e["keywords"] is not None
                    else [],
                    "pub_type": e["pub_type"].split(";")
                    if e["pub_type"] is not None
                    else [],
                    "authors": e["authors"].split(";")
                    if e["authors"] is not None
                    else [],
                    "doi": e["doi"],
                    "labels": e["label"].split(";"),
                }

            elif self.config.schema == "bigbio_text":

                yield idx, {
                    "id": idx,
                    "document_id": e["pmid"],
                    "text": e["abstract"],
                    "labels": e["label"].split(";"),
                }

            idx += 1
