# Lint as: python3
"""REBEL"""

from __future__ import absolute_import, division, print_function

import datasets
import os
import re 
import json
import logging

_DESCRIPTION = """\
REBEL is a silver dataset created for the paper REBEL: Relation Extraction By End-to-end Language generation
"""

_URL = "https://huggingface.co/datasets/Babelscape/rebel-dataset/resolve/main/rebel_dataset.zip"
_URLS = {
    "train": _URL + "en_train.jsonl",
    "dev": _URL + "en_val.jsonl",
    "test": _URL + "en_test.jsonl",
}
_LICENSE = "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)"
_CITATION = """\
    @inproceedings{huguet-cabot-navigli-2021-rebel,
    title = "REBEL: Relation Extraction By End-to-end Language generation",
    author = "Huguet Cabot, Pere-Llu{\'\i}s  and
      Navigli, Roberto",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
    month = nov,
    year = "2021",
    address = "Online and in the Barceló Bávaro Convention Centre, Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://github.com/Babelscape/rebel/blob/main/docs/EMNLP_2021_REBEL__Camera_Ready_.pdf",
    }
"""
_HOMEPAGE = "https://github.com/Babelscape/rebel"


class RebelConfig(datasets.BuilderConfig):
    """BuilderConfig for REBEL."""

    def __init__(self, **kwargs):
        """BuilderConfig for REBEL.
        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(RebelConfig, self).__init__(**kwargs)


class Rebel(datasets.GeneratorBasedBuilder):
    """Rebel 1.0"""

    BUILDER_CONFIGS = [
        RebelConfig(
            name="REBEL",
            version=datasets.Version("1.0.0"),
            description=_DESCRIPTION,
        ),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "title": datasets.Value("string"),
                    "context": datasets.Value("string"),
                    "triplets": datasets.Value("string"),
                }
            ),
            supervised_keys=None,
            homepage=_HOMEPAGE,
            citation=_CITATION,
            license=_LICENSE,
        )

    def _split_generators(self, dl_manager):
        if self.config.data_dir:
            data_dir = self.config.data_dir
        else:
            data_dir = dl_manager.download_and_extract(_URL)

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

    def _generate_examples(self, filepath):
        """This function returns the examples in the raw (text) form."""
        logging.info("generating examples from = %s", filepath)

        with open(filepath, encoding="utf-8") as f:
            for id_, row in enumerate(f):
                article = json.loads(row)
                prev_len = 0
                if len(article['triples']) == 0:
                    continue
                count = 0
                for text_paragraph in article['text'].split('\n'):
                    if len(text_paragraph) == 0:
                        continue
                    sentences = re.split(r'(?<=[.])\s', text_paragraph)
                    text = ''
                    for sentence in sentences:
                        text += sentence + ' '
                        if any([entity['boundaries'][0] < len(text) + prev_len < entity['boundaries'][1] for entity in article['entities']]):
                            continue
                        entities = sorted([entity for entity in article['entities'] if prev_len < entity['boundaries'][1] <= len(text)+prev_len], key=lambda tup: tup['boundaries'][0])
                        decoder_output = '<triplet> '
                        for int_ent, entity in enumerate(entities):
                            triplets = sorted([triplet for triplet in article['triples'] if triplet['subject'] == entity and prev_len< triplet['subject']['boundaries'][1]<=len(text) + prev_len and prev_len< triplet['object']['boundaries'][1]<=len(text)+ prev_len], key=lambda tup: tup['object']['boundaries'][0])
                            if len(triplets) == 0:
                                continue
                            decoder_output += entity['surfaceform'] + ' <subj> '
                            for triplet in triplets:
                                decoder_output += triplet['object']['surfaceform'] + ' <obj> '  + triplet['predicate']['surfaceform'] + ' <subj> '
                            decoder_output = decoder_output[:-len(' <subj> ')]
                            decoder_output += ' <triplet> '
                        decoder_output = decoder_output[:-len(' <triplet> ')]
                        count += 1
                        prev_len += len(text)

                        if len(decoder_output) == 0:
                            text = ''
                            continue

                        text = re.sub('([\[\].,!?()])', r' \1 ', text.replace('()', ''))
                        text = re.sub('\s{2,}', ' ', text)

                        yield article['docid'] + '-' + str(count), {
                            "title": article['title'],
                            "context": text,
                            "id": article['uri'] + '-' + str(count),
                            "triplets": decoder_output,
                        }
                        text = ''