# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors.
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# Licensed under the Apache License, Version 2.0 (the "License");
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Lint as: python3
"""Opinosis Opinion Dataset."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os

import tensorflow.compat.v2 as tf
import tensorflow_datasets.public_api as tfds

_CITATION = """
@inproceedings{ganesan2010opinosis,
  title={Opinosis: a graph-based approach to abstractive summarization of highly redundant opinions},
  author={Ganesan, Kavita and Zhai, ChengXiang and Han, Jiawei},
  booktitle={Proceedings of the 23rd International Conference on Computational Linguistics},
  pages={340--348},
  year={2010},
  organization={Association for Computational Linguistics}
}
"""

_DESCRIPTION = """
The Opinosis Opinion Dataset consists of sentences extracted from reviews for 51 topics.
Topics and opinions are obtained from Tripadvisor, Edmunds.com and Amazon.com.
"""

_URL = "https://github.com/kavgan/opinosis-summarization/raw/master/OpinosisDataset1.0_0.zip"

_REVIEW_SENTS = "review_sents"
_SUMMARIES = "summaries"


class Opinosis(tfds.core.GeneratorBasedBuilder):
  """Opinosis Opinion Dataset."""

  VERSION = tfds.core.Version("1.0.0")

  def _info(self):
    return tfds.core.DatasetInfo(
        builder=self,
        description=_DESCRIPTION,
        features=tfds.features.FeaturesDict({
            _REVIEW_SENTS: tfds.features.Text(),
            _SUMMARIES: tfds.features.Sequence(tfds.features.Text())
        }),
        supervised_keys=(_REVIEW_SENTS, _SUMMARIES),
        homepage="http://kavita-ganesan.com/opinosis/",
        citation=_CITATION,
    )

  def _split_generators(self, dl_manager):
    """Returns SplitGenerators."""
    extract_path = dl_manager.download_and_extract(_URL)
    return [
        tfds.core.SplitGenerator(
            name=tfds.Split.TRAIN,
            gen_kwargs={"path": extract_path},
        ),
    ]

  def _generate_examples(self, path=None):
    """Yields examples."""
    topics_path = os.path.join(path, "topics")
    filenames = tf.io.gfile.listdir(topics_path)
    for filename in filenames:
      file_path = os.path.join(topics_path, filename)
      topic_name = filename.split(".txt")[0]
      with tf.io.gfile.GFile(file_path, "rb") as src_f:
        input_data = src_f.read()
      summaries_path = os.path.join(path, "summaries-gold", topic_name)
      summary_lst = []
      for summ_filename in sorted(tf.io.gfile.listdir(summaries_path)):
        file_path = os.path.join(summaries_path, summ_filename)
        with tf.io.gfile.GFile(file_path, "rb") as tgt_f:
          data = tgt_f.read().strip()
          summary_lst.append(data)
      summary_data = summary_lst
      yield filename, {_REVIEW_SENTS: input_data, _SUMMARIES: summary_data}