"""CogText Dataset"""

import datasets
import pandas as pd


_CITATION = """\
@misc{cogtext2022,
author = {Morteza Ansarinia and
Paul Schrater and
Pedro Cardoso-Leite},
title = {Linking Theories and Methods in Cognitive Sciences via Joint Embedding of the Scientific Literature: The Example of Cognitive Control},
year = {2022},
url = {https://arxiv.org/abs/2203.11016}
}
"""

_DESCRIPTION = """\
CogText dataset contains a collection of PubMed abstracts, along with their GPT-3 embeddings and topic embeddings.
"""
_HOMEPAGE = "https://github.com/morteza/cogtext"
_LICENSE = "CC-BY-4.0"

_URLS = [
    "pubmed/abstracts.csv.gz"
]


class CogText(datasets.GeneratorBasedBuilder):
    """S collection of PubMed abstracts, along with their GPT-3 embeddings and topic embeddings."""

    VERSION = datasets.Version("1.0.0")

    # This is an example of a dataset with multiple configurations.
    # If you don't want/need to define several sub-sets in your dataset,
    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

    # If you need to make complex sub-parts in the datasets with configurable options
    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
    # BUILDER_CONFIG_CLASS = MyBuilderConfig

    # You will be able to load one or the other configurations in the following list with
    # data = datasets.load_dataset('my_dataset', 'first_domain')
    # data = datasets.load_dataset('my_dataset', 'second_domain')
    # BUILDER_CONFIGS = [
    #     datasets.BuilderConfig(name="first_domain", version=VERSION, description="This part of my dataset"),
    #     datasets.BuilderConfig(name="second_domain", version=VERSION, description="This part of my dataset"),
    # ]

    DEFAULT_CONFIG_NAME = "abstracts"  # It's not mandatory to have a default configuration.

    def _info(self):
        # This method specifies the datasets.DatasetInfo object which contains information and typings for the dataset

        features = datasets.Features(
            {
                "pmid": datasets.Value("int32"),
                "doi": datasets.Value("string"),
                "year": datasets.Value("int32"),
                "journal_title": datasets.Value("string"),
                "journal_iso_abbreviation": datasets.Value("string"),
                "title": datasets.Value("string"),
                "abstract": datasets.Value("string"),
                "category": datasets.Value("string"),
                "subcategory": datasets.Value("string"),
            }
        )

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            supervised_keys=("abstract", "subcategory"),
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):

        [abstracts_path] = dl_manager.download(_URLS)

        return [
            datasets.SplitGenerator(
                name=datasets.Split("abstracts"),
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": abstracts_path,
                    "split": "abstracts",
                },
            )
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filepath, split):
        # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.

        example_df = pd.read_csv(filepath, compression="gzip")

        for key, row in example_df.iterrows():
            yield key, {
                "pmid": row['pmid'],
                "year": row['year'],
                "journal_title": row['journal_title'],
                "journal_iso_abbreviation": row['journal_iso_abbreviation'],
                'category': row['category'],
                'subcategory': row['subcategory'],
                'doi': row['doi'],
                'title': row['title'],
                'abstract': row['abstract'],
            }