# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.
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"""Story Cloze datasets."""


import csv
import os

import datasets


_DESCRIPTION = """
Story Cloze Test' is a commonsense reasoning framework for evaluating story understanding,
story generation, and script learning.This test requires a system to choose the correct ending
to a four-sentence story.
"""

_CITATION = """\
@article{DBLP:journals/corr/abs-2112-10668,
  author    = {Xi Victoria Lin and
               Todor Mihaylov and
               Mikel Artetxe and
               Tianlu Wang and
               Shuohui Chen and
               Daniel Simig and
               Myle Ott and
               Naman Goyal and
               Shruti Bhosale and
               Jingfei Du and
               Ramakanth Pasunuru and
               Sam Shleifer and
               Punit Singh Koura and
               Vishrav Chaudhary and
               Brian O'Horo and
               Jeff Wang and
               Luke Zettlemoyer and
               Zornitsa Kozareva and
               Mona T. Diab and
               Veselin Stoyanov and
               Xian Li},
  title     = {Few-shot Learning with Multilingual Language Models},
  journal   = {CoRR},
  volume    = {abs/2112.10668},
  year      = {2021},
  url       = {https://arxiv.org/abs/2112.10668},
  eprinttype = {arXiv},
  eprint    = {2112.10668},
  timestamp = {Tue, 04 Jan 2022 15:59:27 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2112-10668.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
"""


_LANG = ["ru", "zh", "es", "ar", "hi", "id", "te", "sw", "eu", "my"]
_VERSION = "1.0.0"


class XStoryCloze(datasets.GeneratorBasedBuilder):
    """Story Cloze."""

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name=lang,
            description=f"XStory Cloze {lang}",
            version=_VERSION,
        )
        for lang in _LANG
    ]

    @property
    def manual_download_instructions(self):
        return (
            "To use Story Cloze you have to download it manually. Please fill this "
            "google form (http://goo.gl/forms/aQz39sdDrO). Complete the form. "
            "Then you will receive a download link for the dataset. Load it using: "
            "`datasets.load_dataset('story_cloze', data_dir='path/to/folder/folder_name')`"
        )

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "story_id": datasets.Value("string"),
                    "input_sentence_1": datasets.Value("string"),
                    "input_sentence_2": datasets.Value("string"),
                    "input_sentence_3": datasets.Value("string"),
                    "input_sentence_4": datasets.Value("string"),
                    "sentence_quiz1": datasets.Value("string"),
                    "sentence_quiz2": datasets.Value("string"),
                    "answer_right_ending": datasets.Value("int32"),
                }
            ),
            homepage="https://cs.rochester.edu/nlp/rocstories/",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        path_to_manual_folder = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))

        train_file = os.path.join(path_to_manual_folder, f"spring2016.val.{self.config.name}.tsv.split_20_80_train.tsv")
        val_file = os.path.join(path_to_manual_folder, f"spring2016.val.{self.config.name}.tsv.split_20_80_eval.tsv")

        return [
                datasets.SplitGenerator(
                    name=datasets.Split.TRAIN,
                    gen_kwargs={
                        "filepath": train_file,
                    },
                ),
                datasets.SplitGenerator(
                    name=datasets.Split.VALIDATION,
                    gen_kwargs={
                        "filepath": val_file,
                    },
                ),
            ]

    def _generate_examples(self, filepath):
        """Generate Story Cloze examples."""
        with open(filepath, encoding="utf-8") as tsv_file:
            csv_reader = csv.reader(
                tsv_file, quotechar='"', delimiter="\t", quoting=csv.QUOTE_ALL, skipinitialspace=True
            )
            _ = next(csv_reader)
            for id_, row in enumerate(csv_reader):
                # The Spanish XStory Cloze has 10 columns with the last two being not needed
                if row and len(row) >= 8:
                    yield id_, {
                        "story_id": row[0],
                        "input_sentence_1": row[1],
                        "input_sentence_2": row[2],
                        "input_sentence_3": row[3],
                        "input_sentence_4": row[4],
                        "sentence_quiz1": row[5],
                        "sentence_quiz2": row[6],
                        "answer_right_ending": int(row[7]),
                    }
                else:
                    raise ValueError(f"Unexpected row: {row}")