# 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.
# See the License for the specific language governing permissions and
# limitations under the License.

# Lint as: python3

import json

import datasets

_CITATION = '''
@inproceedings{Ammanabrolu2020AAAI, 
title={Story Realization: Expanding Plot Events into Sentences}, 
author={Prithviraj Ammanabrolu and Ethan Tien and Wesley Cheung and Zhaochen Luo and William Ma and Lara J. Martin and Mark O. Riedl}, 
journal={Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)}, 
year={2020}, 
volume={34},
number={05},
url={https://ojs.aaai.org//index.php/AAAI/article/view/6232}
}
'''

_DESCRIPTION = 'Loading script for the science fiction TV show plot dataset.'

URL = 'https://huggingface.co/datasets/lara-martin/Scifi_TV_Shows/blob/main/'
_URLS = {
'test':URL+'Test-Train-Val/all-sci-fi-data-test.txt',
'train':URL+'Test-Train-Val/all-sci-fi-data-train.txt',
'val':URL+'Test-Train-Val/all-sci-fi-data-val.txt',
'all':URL+'all-sci-fi-data.txt',
}

_INPUT_OUTPUT = ["all-sci-fi-data-test_input.txt", "all-sci-fi-data-test_output.txt", "all-sci-fi-data-train_input.txt", "all-sci-fi-data-train_output.txt", "all-sci-fi-data-val_input.txt", "all-sci-fi-data-val_output.txt"]


class Scifi_TV_Shows(datasets.GeneratorBasedBuilder):
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            version=datasets.Version('1.1.0'),
            name="Scifi_TV_Shows", 
            description=f'Science fiction TV show plot summaries.',
        )
    ]

    def _info(self):
        features = datasets.Features({
            'story_num': datasets.Value('int16'),
            'event': datasets.Sequence(datasets.Value('string')), 
            'gen_event': datasets.Sequence(datasets.Value('string')),
            'sent': datasets.Value('string'),
            'gen_sent': datasets.Value('string'),            
            'entities': datasets.Value('string'),
        })

        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=features,  # Here we define them above because they are different between the two configurations
            supervised_keys=None,
            # Homepage of the dataset for documentation
            homepage='https://github.com/rajammanabrolu/StoryRealization',
            # License for the dataset if available
            license='The Creative Commons Attribution 4.0 International License. https://creativecommons.org/licenses/by/4.0/',
            # Citation for the dataset
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        downloaded_files = dl_manager.download(_URLS)
        return[
                datasets.SplitGenerator(
                    name=datasets.Split.TRAIN,
                    gen_kwargs={
                        'filepath': downloaded_files['train'],
                        "split": "train",
                    },
                ),
                datasets.SplitGenerator(
                    name=datasets.Split.TEST,
                    gen_kwargs={
                        'filepath': downloaded_files['test'],
                        "split": "test",
                    },
                ),
                datasets.SplitGenerator(
                    name=datasets.Split.VALIDATION,
                    gen_kwargs={
                        'filepath': downloaded_files['val'],
                        "split": "val",
                    },
                ),
                datasets.SplitGenerator(
                    name="all",
                    gen_kwargs={
                        'filepath': downloaded_files['all'],
                        "split": "all",
                    },
                ),     
         ]                                                                             

    def _generate_examples(self, filepath):
        story_count = 0
        with open(filepath, encoding="utf-8") as f:
            story = []                 
            for id_, line in enumerate(f.readlines()):
                line = line.strip()
                if "%%%%%%" in line:
                    for l in story:
                        event, gen_event, sent, gen_sent = l.split("|||")
                        line = line.replace("%%%%%%%%%%%%%%%%%", "")
                        entities = line.replace("defaultdict(<type 'list'>, ", "")[:-1]
                        yield id_, {
                        'story_num': story_count,
                        'event': eval(event),
                        'gen_event': eval(gen_event),
                        'sent': sent,
                        'gen_sent': gen_sent,                       
                        'entities': entities,
                        }
                    story = []
                    story_count+=1
                elif "<EOS>" in line:
                    continue
                else:
                    story.append(line)