# coding=utf-8
# --- dataset class --
import csv
import datasets
from datasets import load_dataset
from datasets.tasks import TextClassification

_TRAIN_DOWNLOAD_URL = 'https://raw.githubusercontent.com/ZhangLe59151/goginBE/main/wolf_t.csv' #'./corpus.json'
_TEST_DOWNLOAD_URL = 'https://raw.githubusercontent.com/ZhangLe59151/goginBE/main/wolf_v.csv'

_DESCRIPTION = 'My Third Dataset'
_HOMEPAGE = ''
_LICENSE = ''
_CITATION = '''\\n@inproceedings{Casanueva2020,
    author      = Mulin,
    title       = Second Dataset,
    year        = {2021},
    month       = {Sep},
    note        = {},
    url         = {},
    booktitle   = {}
}'''
class MulinThird(datasets.GeneratorBasedBuilder):
  VERSION = datasets.Version('1.1.0')
  def _info(self):
    features = datasets.Features(
      {'text': datasets.Value('string'),
      'label': datasets.features.ClassLabel(
        names=[
          'Ask for ideas',
          'Explain ideas',
          'Ask action',
          'Reject answer',
          'Ask to do',
          'Explain actions',
          'Polite',
          'Reject',
          'Encourage',
          'Check emotion',
          'Explain ideas,Encourage',
          'Explain ideas,Polite',
          'Reject answer,Reject',
          'Ask for ideas,Ask to do',
          'Explain actions,Polite',
          'Explain ideas,Explain actions',
          'Polite,Encourage',
          'Polite,Explain actions',
          'Polite,Ask to do',
          'Polite,Encourage,Explain ideas,Explain actions',
          'Explain ideas,Polite,Explain actions,Encourage'])})
    return datasets.DatasetInfo(
        description=_DESCRIPTION,
        features=features,
        supervised_keys=None,
        homepage=_HOMEPAGE,
        license=_LICENSE,
        citation=_CITATION,
        task_templates=[TextClassification(text_column='text', label_column='label')]
    )
  def _split_generators(self, dl_manager):
    train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)
    test_path = dl_manager.download_and_extract(_TEST_DOWNLOAD_URL)
    print(train_path)
    return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path})
            ]
  
  def _generate_examples(self, filepath):
    with open(filepath, encoding='utf-8') as f:
      csv_reader = csv.reader(f, quotechar='"', delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True)
      next(csv_reader)
      for id_, row in enumerate(csv_reader):
        id, label, text = row
        yield id_, {"text": text, "label": label}