# 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.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
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# distributed under the License is distributed on an "AS IS" BASIS,
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# Lint as: python3
"""Movie reviews with human annotated rationales."""

from __future__ import absolute_import, division, print_function

import json
import os

import datasets


_CITATION = """
@unpublished{eraser2019,
    title = {ERASER: A Benchmark to Evaluate Rationalized NLP Models},
    author = {Jay DeYoung and Sarthak Jain and Nazneen Fatema Rajani and Eric Lehman and Caiming Xiong and Richard Socher and Byron C. Wallace}
}
@InProceedings{zaidan-eisner-piatko-2008:nips,
  author    =  {Omar F. Zaidan  and  Jason Eisner  and  Christine Piatko},
  title     =  {Machine Learning with Annotator Rationales to Reduce Annotation Cost},
  booktitle =  {Proceedings of the NIPS*2008 Workshop on Cost Sensitive Learning},
  month     =  {December},
  year      =  {2008}
}
"""

_DESCRIPTION = """
The movie rationale dataset contains human annotated rationales for movie
reviews.
"""

_DOWNLOAD_URL = "http://www.eraserbenchmark.com/zipped/movies.tar.gz"


class MovieRationales(datasets.GeneratorBasedBuilder):
    """Movie reviews with human annotated rationales."""

    VERSION = datasets.Version("0.1.0")

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "review": datasets.Value("string"),
                    "label": datasets.features.ClassLabel(names=["NEG", "POS"]),
                    "evidences": datasets.features.Sequence(datasets.Value("string")),
                }
            ),
            supervised_keys=None,
            homepage="http://www.cs.jhu.edu/~ozaidan/rationales/",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        dl_dir = dl_manager.download_and_extract(_DOWNLOAD_URL)
        data_dir = os.path.join(dl_dir, "movies")

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"data_dir": data_dir, "filepath": os.path.join(data_dir, "train.jsonl")},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={"data_dir": data_dir, "filepath": os.path.join(data_dir, "val.jsonl")},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={"data_dir": data_dir, "filepath": os.path.join(data_dir, "test.jsonl")},
            ),
        ]

    def _generate_examples(self, data_dir, filepath):
        """Yields examples."""
        reviews_dir = os.path.join(data_dir, "docs")

        with open(filepath, encoding="utf-8") as f:
            for line in f:
                row = json.loads(line)
                doc_id = row["annotation_id"]
                review_file = os.path.join(reviews_dir, doc_id)
                with open(review_file, encoding="utf-8") as f1:
                    review_text = f1.read()

                evidences = []
                for evidence in row["evidences"]:
                    for e in evidence:
                        evidences.append(e["text"])

                yield doc_id, {
                    "review": review_text,
                    "label": row["classification"],
                    "evidences": evidences,
                }