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	# -- coding: utf-8 --
	"""CLUTRR_Dataset Loading Script.ipynb
	Automatically generated by Colaboratory.
	Original file is located at
	https://colab.research.google.com/drive/1q9DdeHA5JbgTHkH6kfZe_KWHQOwHZA97
	"""
	# coding=utf-8
	# Copyright 2019 The CLUTRR Datasets Authors and the HuggingFace Datasets Authors.
	#
	# CLUTRR is CC-BY-NC 4.0 (Attr Non-Commercial Inter.) licensed, as found in the LICENSE file.
	#
	# 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
	"""The CLUTRR (Compositional Language Understanding and Text-based Relational Reasoning) benchmark."""


	import csv
	import os
	import textwrap

	import numpy as np

	import datasets
	import json

	_CLUTRR_CITATION = """\
	@article{sinha2019clutrr,
	Author = {Koustuv Sinha and Shagun Sodhani and Jin Dong and Joelle Pineau and William L. Hamilton},
	Title = {CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text},
	Year = {2019},
	journal = {Empirical Methods of Natural Language Processing (EMNLP)},
	arxiv = {1908.06177}
	}
	"""

	_CLUTRR_DESCRIPTION = """\
	CLUTRR (Compositional Language Understanding and Text-based Relational Reasoning),
	a diagnostic benchmark suite, is first introduced in (https://arxiv.org/abs/1908.06177)
	to test the systematic generalization and inductive reasoning capabilities of NLU systems.
	"""
	_URL = "https://raw.githubusercontent.com/kliang5/CLUTRR_huggingface_dataset/main/"
	_TASK = ["gen_train23_test2to10", "gen_train234_test2to10", "rob_train_clean_23_test_all_23", "rob_train_disc_23_test_all_23", "rob_train_irr_23_test_all_23","rob_train_sup_23_test_all_23"]

	class v1(datasets.GeneratorBasedBuilder):
	"""BuilderConfig for CLUTRR."""

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name=task,
	version=datasets.Version("1.0.0"),
	description="",
	)
	for task in _TASK
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_CLUTRR_DESCRIPTION,
	features=datasets.Features(
	{
	"id": datasets.Value("string"),
	"story": datasets.Value("string"),
	"query": datasets.Value("string"),
	"target": datasets.Value("int32"),
	"target_text": datasets.Value("string"),
	"clean_story": datasets.Value("string"),
	"proof_state": datasets.Value("string"),
	"f_comb": datasets.Value("string"),
	"task_name": datasets.Value("string"),
	"story_edges": datasets.Value("string"),
	"edge_types": datasets.Value("string"),
	"query_edge": datasets.Value("string"),
	"genders": datasets.Value("string"),
	"task_split": datasets.Value("string"),
	}
	),
	# No default supervised_keys (as we have to pass both premise
	# and hypothesis as input).
	supervised_keys=None,
	homepage="https://www.cs.mcgill.ca/~ksinha4/clutrr/",
	citation=_CLUTRR_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	# dl_manager is a datasets.download.DownloadManager that can be used to
	# download and extract URLs

	task = str(self.config.name)
	urls_to_download = {
	"test": _URL + task + "/test.csv",
	"train": _URL + task + "/train.csv",
	"validation": _URL + task + "/validation.csv",
	}
	downloaded_files = dl_manager.download_and_extract(urls_to_download)


	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": downloaded_files["train"],
	"task": task,
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": downloaded_files["validation"],
	"task": task,
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": downloaded_files["test"],
	"task": task,
	},
	),
	]

	def _generate_examples(self, filepath, task):
	"""Yields examples."""
	with open(filepath, encoding="utf-8") as f:
	reader = csv.reader(f)
	for id_, data in enumerate(reader):
	if id_ == 0:
	continue
	# yield id_, data
	# id_ += 1
	yield id_, {
	"id": data[1],
	"story": data[2],
	"query": data[3],
	"target": data[4],
	"target_text": data[5],
	"clean_story": data[6],
	"proof_state": data[7],
	"f_comb": data[8],
	"task_name": data[9],
	"story_edges": data[10],
	"edge_types": data[11],
	"query_edge": data[12],
	"genders": data[13],
	"task_split": data[14],
	}