# 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.

import os
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 = False
_LOCAL = True
_CITATION = """\
@misc{ask9medicaldata,
  author    = {Khan, Arbaaz},
  title     = {Sentiment Analysis for Medical Drugs},
  year      = {2019},
  url       = {https://www.kaggle.com/datasets/arbazkhan971/analyticvidhyadatasetsentiment},
}
"""

_DATASETNAME = "medical_data"
_DISPLAYNAME = "Medical Data"

_DESCRIPTION = """\
    This dataset is designed to do multiclass classification on medical drugs
"""

_HOMEPAGE = ""

_LICENSE = Licenses.UNKNOWN

_URLS = {}

_SUPPORTED_TASKS = [Tasks.TEXTUAL_ENTAILMENT]


_SOURCE_VERSION = "1.0.0"

_BIGBIO_VERSION = "1.0.0"


class MedicaldataDatatset(datasets.GeneratorBasedBuilder):
    """This dataset contains comments about patients and the sentiment in those comments about a specific drug that's mentioned.
    1 - Negative sentiment
    2 - Positive sentiment
    0 - Neutral"""

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

    BUILDER_CONFIGS = [
        BigBioConfig(
            name=f"{_DATASETNAME}_source",
            version=SOURCE_VERSION,
            description=f"{_DATASETNAME} source schema",
            schema="source",
            subset_id=f"{_DATASETNAME}",
        ),
        BigBioConfig(
            name=f"{_DATASETNAME}_bigbio_te",
            version=BIGBIO_VERSION,
            description=f"{_DATASETNAME} BigBio schema",
            schema="bigbio_te",
            subset_id=f"{_DATASETNAME}",
        ),
    ]

    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"

    def _info(self) -> datasets.DatasetInfo:

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

            features = datasets.Features(
                {
                    "hash": datasets.Value("string"),
                    "text": datasets.Value("string"),
                    "drug_name": datasets.Value("string"),
                    "sentiment": datasets.Value("string"),
                }
            )

        elif self.config.schema == "bigbio_te":
            features = schemas.entailment_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."""

        if self.config.data_dir is None:
            raise ValueError(
                "This is a local dataset. Please pass the data_dir kwarg to load_dataset."
            )
        else:
            data_dir = self.config.data_dir

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": os.path.join(data_dir, "train_F3WbcTw.csv"),
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "filepath": os.path.join(data_dir, "test_tOlRoBf.csv"),
                    "split": "test",
                },
            ),
        ]

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

        if self.config.schema == "source":
            csv_reader = pd.read_csv(filepath, dtype="object")
            # Train and test splits handled differently as test split is missing values
            if split == "train":
                for _cols, line in csv_reader.iterrows():
                    document = {}
                    document["hash"] = line["unique_hash"]
                    document["text"] = line["text"]
                    document["drug_name"] = line["drug"]
                    document["sentiment"] = line["sentiment"]
                    yield document["hash"], document
            else:
                for _cols, line in csv_reader.iterrows():
                    document = {}
                    document["hash"] = line["unique_hash"]
                    document["text"] = line["text"]
                    document["drug_name"] = line["drug"]
                    document["sentiment"] = None
                    yield document["hash"], document

        elif self.config.schema == "bigbio_te":
            csv_reader = pd.read_csv(filepath, dtype="object")
            # Train and test splits handled differently as test split is missing values
            if split == "train":
                for _cols, line in csv_reader.iterrows():
                    document = {}
                    document["id"] = line["unique_hash"]
                    document["premise"] = line["text"]
                    document["hypothesis"] = line["drug"]
                    document["label"] = line["sentiment"]
                    yield document["id"], document
            else:
                for _cols, line in csv_reader.iterrows():
                    document = {}
                    document["id"] = line["unique_hash"]
                    document["premise"] = line["text"]
                    document["hypothesis"] = line["drug"]
                    document["label"] = None
                    yield document["id"], document
