# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# 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.

# Lint as: python3
"""OSGD-CD: The OSDG Community Dataset."""


import csv
import json

import datasets
from datasets.tasks import TextClassification


logger = datasets.logging.get_logger(__name__)


_CITATION = """\
@dataset{osdg_2023_8397907,
  author       = {OSDG and
                  UNDP IICPSD SDG AI Lab and
                  PPMI},
  title        = {OSDG Community Dataset (OSDG-CD)},
  month        = oct,
  year         = 2023,
  note         = {{This CSV file uses UTF-8 character encoding. For
                   easy access on MS Excel, open the file using Data
                   → From Text/CSV.  Please split CSV data into
                   different columns by using a TAB delimiter.}},
  publisher    = {Zenodo},
  version      = {2023.10},
  doi          = {10.5281/zenodo.8397907},
  url          = {https://doi.org/10.5281/zenodo.8397907}
}
"""

_HOMEPAGE = "https://zenodo.org/record/8397907"

_LICENSE = "https://creativecommons.org/licenses/by/4.0/"

_DESCRIPTION = """\
The OSDG Community Dataset (OSDG-CD) is a public dataset of thousands of text excerpts, \
which were validated by approximately 1,000 OSDG Community Platform (OSDG-CP) \
citizen scientists from over 110 countries, with respect to the Sustainable Development Goals (SDGs).
"""

_VERSIONS = {
    "2021.09": "1.0.0",
    "2022.01": "1.0.1",
    "2022.04": "1.0.2",
    "2022.07": "1.0.3",
    "2022.10": "1.0.4",
    "2023.01": "1.0.5",
    "2023.04": "1.0.6",
    "2023.07": "1.0.7",
    "2023.10": "1.0.8",
}

_VERSION = _VERSIONS["2023.10"]

_URLS = {
    #"train": "https://zenodo.org/record/8107038/files/osdg-community-data-v2023-07-01.csv",
    "train": "https://zenodo.org/record/8397907/files/osdg-community-data-v2023-10-01.csv",
}


class OSDGCDConfig(datasets.BuilderConfig):
    """BuilderConfig for OSDG-CD."""

    def __init__(self, **kwargs):
        """BuilderConfig for OSDG-CD.

        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(OSDGCDConfig, self).__init__(**kwargs)


class OSDGCD(datasets.GeneratorBasedBuilder):
    """OSDG-CD: The OSDG Community Dataset (OSDG-CD)"""

    # 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 = [
        OSDGCDConfig(
            name="main_config",
            version=datasets.Version(_VERSION, ""),
            description="Main configuration",
        ),
    ]

    DEFAULT_CONFIG_NAME = "main_config"

    def _info(self) -> datasets.DatasetInfo:
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            license=_LICENSE,
            features=datasets.Features(
                {
                    "doi": datasets.Value("string"),
                    "text_id": datasets.Value("string"),
                    "text": datasets.Value("string"),
                    "sdg": datasets.Value("uint16"),
                    "label": datasets.ClassLabel(num_classes=16, names=[f"SDG {sdg}" for sdg in range(1, 17)]),
                    "labels_negative": datasets.Value("uint16"),
                    "labels_positive": datasets.Value("uint16"),
                    "agreement": datasets.Value("float"),
                }
            ),
            homepage=_HOMEPAGE,
            citation=_CITATION,
            task_templates=[
                TextClassification(
                    text_column="text", label_column="label",
                )
            ],
        )

    def _split_generators(self, dl_manager):
        downloaded_files = dl_manager.download_and_extract(_URLS)

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

    def _generate_examples(self, filepath):
        """This function returns the examples in the raw (text) form."""
        logger.info("generating examples from = %s", filepath)
        with open(filepath, encoding="utf-8") as f:
            osdg = csv.DictReader(f, delimiter="\t")
            for row in osdg:
                id_ = row["text_id"]
                sdg = int(row["sdg"])
                row["label"] = f"SDG {sdg}"
                yield id_, row