# 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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"""Dataset loading script for loading the Large-Scale-QASRL (FitzGeralds et. al., ACL 2018) training set, along with the QASRL-GS evaluation dataset (Roit et. al., ACL 2020)."""


import datasets
from pathlib import Path
import gzip
import json


_CITATION = """\
@inproceedings{fitzgerald2018large,
  title={Large-Scale QA-SRL Parsing},
  author={FitzGerald, Nicholas and Michael, Julian and He, Luheng and Zettlemoyer, Luke},
  booktitle={Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  pages={2051--2060},
  year={2018}
}
@inproceedings{roit2020controlled,
  title={Controlled Crowdsourcing for High-Quality QA-SRL Annotation},
  author={Roit, Paul and Klein, Ayal and Stepanov, Daniela and Mamou, Jonathan and Michael, Julian and Stanovsky, Gabriel and Zettlemoyer, Luke and Dagan, Ido},
  booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
  pages={7008--7013},
  year={2020}
}
"""


_DESCRIPTION = """\
The dataset contains question-answer pairs to model verbal predicate-argument structure. 
The questions start with wh-words (Who, What, Where, What, etc.) and contain a verb predicate in the sentence; the answers are phrases in the sentence.
This dataset loads the train split from "QASRL Bank", a.k.a "QASRL-v2" or "QASRL-LS" (Large Scale), 
which was constructed via crowdsourcing and presented at (FitzGeralds et. al., ACL 2018), 
and the dev and test splits from QASRL-GS (Gold Standard), introduced in (Roit et. al., ACL 2020).
"""

_HOMEPAGE = "https://qasrl.org"

# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""

SpanFeatureType = datasets.Sequence(datasets.Value("int32"), length=2)

# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
class QaSrl(datasets.GeneratorBasedBuilder):
    """QA-SRL: Question-Answer Driven Semantic Role Labeling corpus"""

    VERSION = datasets.Version("1.0.0")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="plain_text", version=VERSION, description=""
        ),
    ]

    DEFAULT_CONFIG_NAME = (
        "plain_text"  # It's not mandatory to have a default configuration. Just use one if it make sense.
    )

    def _info(self):
        features = datasets.Features(
            {
                "sentence": datasets.Value("string"),
                "sent_id": datasets.Value("string"),
                "predicate_idx": datasets.Value("int32"),
                "predicate": datasets.Value("string"),
                "is_verbal": datasets.Value("bool"),
                "verb_form": datasets.Value("string"),  
                "question": datasets.Sequence(datasets.Value("string")),
                "answers": datasets.Sequence(datasets.Value("string")),
                "answer_ranges": datasets.Sequence(SpanFeatureType)
            }
        )
        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
            # If there's a common (input, target) tuple from the features,
            # specify them here. They'll be used if as_supervised=True in
            # builder.as_dataset.
            supervised_keys=None,
            # Homepage of the dataset for documentation
            homepage=_HOMEPAGE,
            # License for the dataset if available
            license=_LICENSE,
            # Citation for the dataset
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager: datasets.utils.download_manager.DownloadManager):
        """Returns SplitGenerators."""

        # iterate the tar file of the corpus
        
        # Older version of the corpus (has some format errors):
        # corpus_base_path = Path(dl_manager.download_and_extract(_URLs["qasrl_v2.0"]))
        # corpus_orig = corpus_base_path / "qasrl-v2" / "orig"        
        
        self.qasrl2018 = datasets.load_dataset("biu-nlp/qa_srl2018")
        self.qasrl2020 = datasets.load_dataset("biu-nlp/qa_srl2020")
        
        
        # TODO add optional kwarg for genre (wikinews) 
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "dataset": self.qasrl2018["train"],
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "dataset": self.qasrl2020["validation"],
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "dataset": self.qasrl2020["test"],
                },
            ),
        ]

    def _generate_examples(self, dataset):

        """ Yields examples from a '.jsonl.gz' file ."""
        for idx, instance in enumerate(dataset):
            yield idx, instance