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# 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
"""e-SNLI: Natural Language Inference with Natural Language Explanations."""


import csv

import datasets


_CITATION = """
@incollection{NIPS2018_8163,
title = {e-SNLI: Natural Language Inference with Natural Language Explanations},
author = {Camburu, Oana-Maria and Rockt\"{a}schel, Tim and Lukasiewicz, Thomas and Blunsom, Phil},
booktitle = {Advances in Neural Information Processing Systems 31},
editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
pages = {9539--9549},
year = {2018},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/8163-e-snli-natural-language-inference-with-natural-language-explanations.pdf}
}
"""

_DESCRIPTION = """
The e-SNLI dataset extends the Stanford Natural Language Inference Dataset to
include human-annotated natural language explanations of the entailment
relations.
"""
_URL = "https://raw.githubusercontent.com/OanaMariaCamburu/e-SNLI/master/dataset/"


class Esnli(datasets.GeneratorBasedBuilder):
    """e-SNLI: Natural Language Inference with Natural Language Explanations corpus."""

    # Version History
    # 0.0.2 Added explanation_2, explanation_3 fields which exist in the dev/test
    # splits only.
    # 0.0.1 Initial version
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="plain_text",
            version=datasets.Version("0.0.2"),
            description="Plain text import of e-SNLI",
        )
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "premise": datasets.Value("string"),
                    "hypothesis": datasets.Value("string"),
                    "label": datasets.features.ClassLabel(names=["entailment", "neutral", "contradiction"]),
                    "explanation_1": datasets.Value("string"),
                    "explanation_2": datasets.Value("string"),
                    "explanation_3": datasets.Value("string"),
                }
            ),
            supervised_keys=None,
            homepage="https://github.com/OanaMariaCamburu/e-SNLI",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""

        files = dl_manager.download_and_extract(
            {
                "train": [_URL + "esnli_train_1.csv", _URL + "esnli_train_2.csv"],
                "validation": [_URL + "esnli_dev.csv"],
                "test": [_URL + "esnli_test.csv"],
            }
        )

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"files": files["train"]},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={"files": files["validation"]},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={"files": files["test"]},
            ),
        ]

    def _generate_examples(self, files):
        """Yields examples."""
        for filepath in files:
            with open(filepath, encoding="utf-8") as f:
                reader = csv.DictReader(f)
                for _, row in enumerate(reader):
                    yield row["pairID"], {
                        "premise": row["Sentence1"],
                        "hypothesis": row["Sentence2"],
                        "label": row["gold_label"],
                        "explanation_1": row["Explanation_1"],
                        "explanation_2": row.get("Explanation_2", ""),
                        "explanation_3": row.get("Explanation_3", ""),
                    }

	# 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
	"""e-SNLI: Natural Language Inference with Natural Language Explanations."""


	import csv

	import datasets


	_CITATION = """
	@incollection{NIPS2018_8163,
	title = {e-SNLI: Natural Language Inference with Natural Language Explanations},
	author = {Camburu, Oana-Maria and Rockt\"{a}schel, Tim and Lukasiewicz, Thomas and Blunsom, Phil},
	booktitle = {Advances in Neural Information Processing Systems 31},
	editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
	pages = {9539--9549},
	year = {2018},
	publisher = {Curran Associates, Inc.},
	url = {http://papers.nips.cc/paper/8163-e-snli-natural-language-inference-with-natural-language-explanations.pdf}
	}
	"""

	_DESCRIPTION = """
	The e-SNLI dataset extends the Stanford Natural Language Inference Dataset to
	include human-annotated natural language explanations of the entailment
	relations.
	"""
	_URL = "https://raw.githubusercontent.com/OanaMariaCamburu/e-SNLI/master/dataset/"


	class Esnli(datasets.GeneratorBasedBuilder):
	"""e-SNLI: Natural Language Inference with Natural Language Explanations corpus."""

	# Version History
	# 0.0.2 Added explanation_2, explanation_3 fields which exist in the dev/test
	# splits only.
	# 0.0.1 Initial version
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="plain_text",
	version=datasets.Version("0.0.2"),
	description="Plain text import of e-SNLI",
	)
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"premise": datasets.Value("string"),
	"hypothesis": datasets.Value("string"),
	"label": datasets.features.ClassLabel(names=["entailment", "neutral", "contradiction"]),
	"explanation_1": datasets.Value("string"),
	"explanation_2": datasets.Value("string"),
	"explanation_3": datasets.Value("string"),
	}
	),
	supervised_keys=None,
	homepage="https://github.com/OanaMariaCamburu/e-SNLI",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""

	files = dl_manager.download_and_extract(
	{
	"train": [_URL + "esnli_train_1.csv", _URL + "esnli_train_2.csv"],
	"validation": [_URL + "esnli_dev.csv"],
	"test": [_URL + "esnli_test.csv"],
	}
	)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"files": files["train"]},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={"files": files["validation"]},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={"files": files["test"]},
	),
	]

	def _generate_examples(self, files):
	"""Yields examples."""
	for filepath in files:
	with open(filepath, encoding="utf-8") as f:
	reader = csv.DictReader(f)
	for _, row in enumerate(reader):
	yield row["pairID"], {
	"premise": row["Sentence1"],
	"hypothesis": row["Sentence2"],
	"label": row["gold_label"],
	"explanation_1": row["Explanation_1"],
	"explanation_2": row.get("Explanation_2", ""),
	"explanation_3": row.get("Explanation_3", ""),
	}