# coding=utf-8
# Copyright 2022 The TensorFlow datasets Authors, the HuggingFace datalab Authors and the DataLab 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
"""The Stanford Natural Language Inference (SNLI) Corpus."""


import csv
import os

import datalabs
from datalabs import get_task, TaskType

_CITATION = """\
@inproceedings{snli:emnlp2015,
    Author = {Bowman, Samuel R. and Angeli, Gabor and Potts, Christopher, and Manning, Christopher D.},
    Booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
    Publisher = {Association for Computational Linguistics},
    Title = {A large annotated corpus for learning natural language inference},
    Year = {2015}
}
"""

_DESCRIPTION = """\
The SNLI corpus (version 1.0) is a collection of 570k human-written English
sentence pairs manually labeled for balanced classification with the labels
entailment, contradiction, and neutral, supporting the task of natural language
inference (NLI), also known as recognizing textual entailment (RTE).
"""

_DATA_URL = "https://nlp.stanford.edu/projects/snli/snli_1.0.zip"


class Snli(datalabs.GeneratorBasedBuilder):
    """The Stanford Natural Language Inference (SNLI) Corpus."""

    # def __init__(self,*args, **kwargs):
    #     super(Snli, self).__init__(*args, **kwargs)
    #     self.dataset_class = TextMatchingDataset

    def _info(self):
        return datalabs.DatasetInfo(
            description=_DESCRIPTION,
            features=datalabs.Features(
                {
                    "text1": datalabs.Value("string"),
                    "text2": datalabs.Value("string"),
                    "label": datalabs.features.ClassLabel(
                        names=["entailment", "neutral", "contradiction"]
                    ),
                }
            ),
            # No default supervised_keys (as we have to pass both premise
            # and hypothesis as input).
            supervised_keys=None,
            homepage="https://nlp.stanford.edu/projects/snli/",
            citation=_CITATION,
            task_templates=[
                get_task(TaskType.natural_language_inference)(
                    text1_column="text1", text2_column="text2", label_column="label"
                ),
            ],
        )

    def _split_generators(self, dl_manager):
        dl_dir = dl_manager.download_and_extract(_DATA_URL)
        data_dir = os.path.join(dl_dir, "snli_1.0")
        return [
            datalabs.SplitGenerator(
                name=datalabs.Split.TEST,
                gen_kwargs={"filepath": os.path.join(data_dir, "snli_1.0_test.txt")},
            ),
            datalabs.SplitGenerator(
                name=datalabs.Split.VALIDATION,
                gen_kwargs={"filepath": os.path.join(data_dir, "snli_1.0_dev.txt")},
            ),
            datalabs.SplitGenerator(
                name=datalabs.Split.TRAIN,
                gen_kwargs={"filepath": os.path.join(data_dir, "snli_1.0_train.txt")},
            ),
        ]

    def _generate_examples(self, filepath):
        """This function returns the examples in the raw (text) form."""
        with open(filepath, encoding="utf-8") as f:
            reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
            for idx, row in enumerate(reader):
                if row["gold_label"] == "-":
                    continue
                label = row["gold_label"]
                yield idx, {
                    "text1": row["sentence1"],
                    "text2": row["sentence2"],
                    "label": label,
                }
