# Copyright 2020 The HuggingFace Datasets Authors and Cory Paik
#
# 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.
# ==============================================================================
"""TODO: Add a description here."""

import json
import datasets

# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
"""

# TODO: Add description of the dataset here
_DESCRIPTION = """\
*Physical Reasoning about Objects Through Space and Time* (PROST) is a probing dataset to evaluate the ability of pretrained LMs to understand and reason about the physical world. PROST consists of 18,736 cloze-style multiple choice questions from 14 manually curated templates, covering 10 physical reasoning concepts:  direction, mass, height, circumference, stackable, rollable, graspable, breakable, slideable, and bounceable.
"""

_HOMEPAGE = 'https://github.com/nala-cub/prost'
_LICENSE = 'Apache 2.0'

_URL = 'https://huggingface.co/datasets/corypaik/prost/resolve/main/data'

_URLs = {'default': f'{_URL}/default.jsonl'}

MC_LABELS = list('ABCD')


class Prost(datasets.GeneratorBasedBuilder):
  """TODO: Short description of my dataset."""

  VERSION = datasets.Version('1.0.0')

  def _info(self):
    features = datasets.Features({
      'A': datasets.Value('string'),
      'B': datasets.Value('string'),
      'C': datasets.Value('string'),
      'D': datasets.Value('string'),
      'context': datasets.Value('string'),
      'question': datasets.Value('string'),
      'ex_question': datasets.Value('string'),
      'group': datasets.Value('string'),
      'label': datasets.ClassLabel(names=MC_LABELS),
      'name': datasets.Value('string'),})
    return datasets.DatasetInfo(description=_DESCRIPTION, features=features,
                                supervised_keys=None, homepage=_HOMEPAGE,
                                license=_LICENSE, citation=_CITATION)

  def _split_generators(self, dl_manager):
    """ Returns SplitGenerators."""
    path = dl_manager.download_and_extract(_URLs[self.config.name])
    kwargs = {'path': path}
    return [datasets.SplitGenerator(datasets.Split.TEST, gen_kwargs=kwargs)]

  def _generate_examples(self, path):
    with open(path, 'r') as f:
      for _id, line in enumerate(f.readlines()):
        yield _id, json.loads(line)