# 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.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
#
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# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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"""
MayoSRS consists of 101 clinical term pairs whose relatedness was determined by
nine medical coders and three physicians from the Mayo Clinic.
"""

from typing import Dict, List, Tuple

import datasets
import pandas as pd

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

_LANGUAGES = ['English']
_PUBMED = False
_LOCAL = False
_CITATION = """\
@article{pedersen2007measures,
  title={Measures of semantic similarity and relatedness in the biomedical domain},
  author={Pedersen, Ted and Pakhomov, Serguei VS and Patwardhan, Siddharth and Chute, Christopher G},
  journal={Journal of biomedical informatics},
  volume={40},
  number={3},
  pages={288--299},
  year={2007},
  publisher={Elsevier}
}
"""

_DATASETNAME = "minimayosrs"
_DISPLAYNAME = "MiniMayoSRS"

_DESCRIPTION = """\
MiniMayoSRS is a subset of the MayoSRS and consists of 30 term pairs on which a higher inter-annotator agreement was
achieved. The average correlation between physicians is 0.68. The average correlation between medical coders is 0.78.
"""

_HOMEPAGE = "https://conservancy.umn.edu/handle/11299/196265"

_LICENSE = 'Creative Commons Zero v1.0 Universal'

_URLS = {
    _DATASETNAME: "https://conservancy.umn.edu/bitstream/handle/11299/196265/MiniMayoSRS.csv?sequence=2&isAllowed=y"
}

_SUPPORTED_TASKS = [Tasks.SEMANTIC_SIMILARITY]

_SOURCE_VERSION = "1.0.0"

_BIGBIO_VERSION = "1.0.0"


class MinimayosrsDataset(datasets.GeneratorBasedBuilder):
    """MiniMayoSRS is a subset of the MayoSRS and consists of 30 term pairs on which a higher inter-annotator agreement
    was achieved. The average correlation between physicians is 0.68. The average correlation between medical coders
    is 0.78.
    """

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

    BUILDER_CONFIGS = [
        BigBioConfig(
            name="minimayosrs_source",
            version=SOURCE_VERSION,
            description="MiniMayoSRS source schema",
            schema="source",
            subset_id="minimayosrs",
        ),
        BigBioConfig(
            name="minimayosrs_bigbio_pairs",
            version=BIGBIO_VERSION,
            description="MiniMayoSRS BigBio schema",
            schema="bigbio_pairs",
            subset_id="minimayosrs",
        ),
    ]

    DEFAULT_CONFIG_NAME = "minimayosrs_source"

    def _info(self) -> datasets.DatasetInfo:

        if self.config.schema == "source":
            features = datasets.Features(
                {
                    "text_1": datasets.Value("string"),
                    "text_2": datasets.Value("string"),
                    "code_1": datasets.Value("string"),
                    "code_2": datasets.Value("string"),
                    "label_physicians": datasets.Value("float32"),
                    "label_coders": 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."""

        urls = _URLS[_DATASETNAME]
        filepath = dl_manager.download_and_extract(urls)

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

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

        data = pd.read_csv(
            filepath,
            sep=",",
            header=0,
            names=[
                "label_physicians",
                "label_coders",
                "code_1",
                "code_2",
                "text_1",
                "text_2",
            ],
        )

        if self.config.schema == "source":
            for id_, row in data.iterrows():
                yield id_, row.to_dict()

        elif self.config.schema == "bigbio_pairs":
            for id_, row in data.iterrows():
                yield id_, {
                    "id": id_,
                    "document_id": id_,
                    "text_1": row["text_1"],
                    "text_2": row["text_2"],
                    "label": str((row["label_physicians"] + row["label_coders"]) / 2),
                }
