# 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.

"""
Bio-SimLex enables intrinsic evaluation of word representations. This evaluation
can serve as a predictor of performance on various downstream tasks in the
biomedical domain. The results on Bio-SimLex using standard word representation
models highlight the importance of developing dedicated evaluation resources for
NLP in biomedicine for particular word classes (e.g. verbs).
"""

from typing import Dict, List, Tuple

import datasets

from .bigbiohub import pairs_features
from .bigbiohub import BigBioConfig
from .bigbiohub import Tasks

_LANGUAGES = ['English']
_PUBMED = True
_LOCAL = False
_CITATION = """\
@article{article,
  title        = {
    Bio-SimVerb and Bio-SimLex: Wide-coverage evaluation sets of word
    similarity in biomedicine
  },
  author       = {Chiu, Billy and Pyysalo, Sampo and Vulić, Ivan and Korhonen, Anna},
  year         = 2018,
  month        = {02},
  journal      = {BMC Bioinformatics},
  volume       = 19,
  pages        = {},
  doi          = {10.1186/s12859-018-2039-z}
}
"""

_DATASETNAME = "bio_simlex"
_DISPLAYNAME = "Bio-SimLex"

_DESCRIPTION = """\
Bio-SimLex enables intrinsic evaluation of word representations. This evaluation \
can serve as a predictor of performance on various downstream tasks in the \
biomedical domain. The results on Bio-SimLex using standard word representation \
models highlight the importance of developing dedicated evaluation resources for \
NLP in biomedicine for particular word classes (e.g. verbs).
"""

_HOMEPAGE = "https://github.com/cambridgeltl/bio-simverb"

_LICENSE = 'License information unavailable'

_URLS = {
    _DATASETNAME: "https://github.com/cambridgeltl/bio-simverb/blob/master/wvlib/word-similarities/\
bio-simlex/Bio-SimLex.txt?raw=true"
}

_SUPPORTED_TASKS = [Tasks.SEMANTIC_SIMILARITY]

_SOURCE_VERSION = "1.0.0"
_BIGBIO_VERSION = "1.0.0"


class BioSimlexDataset(datasets.GeneratorBasedBuilder):
    """
    Bio-SimLex enables intrinsic evaluation of word representations. Config schema
    as source gives score between 0-10 for pairs of words. The source schema casts
    labels as `float`, but the bigbio schema casts them as `str`.
    """

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

    BUILDER_CONFIGS = [
        BigBioConfig(
            name="bio_simlex_source",
            version=SOURCE_VERSION,
            description="bio_simlex source schema",
            schema="source",
            subset_id="bio_simlex",
        ),
        BigBioConfig(
            name="bio_simlex_bigbio_pairs",
            version=BIGBIO_VERSION,
            description="bio_simlex BigBio schema",
            schema="bigbio_pairs",
            subset_id="bio_simlex",
        ),
    ]

    DEFAULT_CONFIG_NAME = "bio_simlex_source"

    def _info(self) -> datasets.DatasetInfo:

        if self.config.schema == "source":
            features = datasets.Features(
                {
                    "text_1": datasets.Value("string"),
                    "text_2": datasets.Value("string"),
                    "score": datasets.Value("float32"),
                }
            )

        elif self.config.schema == "bigbio_pairs":
            features = pairs_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."""

        url = _URLS[_DATASETNAME]
        data_dir = dl_manager.download_and_extract(url)

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

    def _generate_examples(self, filepath, split: str) -> Tuple[int, Dict]:
        """Yields examples as (key, example) tuples."""
        with open(filepath, "r", encoding="utf-8") as f:
            for id_, line in enumerate(f):
                word1, word2, score = line.split("\t")
                if self.config.schema == "source":
                    yield id_, {
                        "text_1": word1,
                        "text_2": word2,
                        "score": float(score),
                    }

                elif self.config.schema == "bigbio_pairs":
                    yield id_, {
                        "id": str(id_),
                        "document_id": str(id_),
                        "text_1": word1,
                        "text_2": word2,
                        "label": str(score.strip()),
                    }