# coding=utf-8
# Copyright 2022 Leon Derczynski, 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.
# 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
"""Danish Stance Dataset DAST"""

from collections import defaultdict
import glob
import json
import os
import sys

import datasets

logger = datasets.logging.get_logger(__name__)

_CITATION = """\
@inproceedings{lillie-etal-2019-joint,
    title = "Joint Rumour Stance and Veracity Prediction",
    author = "Lillie, Anders Edelbo  and
      Middelboe, Emil Refsgaard  and
      Derczynski, Leon",
    booktitle = "Proceedings of the 22nd Nordic Conference on Computational Linguistics",
    month = sep # "{--}" # oct,
    year = "2019",
    address = "Turku, Finland",
    publisher = {Link{\"o}ping University Electronic Press},
    url = "https://aclanthology.org/W19-6122",
    pages = "208--221",
}
"""

_DESCRIPTION = """\
This dataset presents a series of stories on Reddit and the conversation around
them, annotated for stance. Stories are also annotated for veracity.

For more details see https://aclanthology.org/W19-6122/
"""

_URL = "dast.jsonl"


class DastConfig(datasets.BuilderConfig):
    """BuilderConfig for IPM NEL"""

    def __init__(self, **kwargs):
        """BuilderConfig for IPM NEL.

        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(DastConfig, self).__init__(**kwargs)



class Dast(datasets.GeneratorBasedBuilder):


    """Dast dataset."""

    BUILDER_CONFIGS = [
        DastConfig(name="dkstance", version=datasets.Version("1.0.0"), description="Danish Stance"),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "native_id": datasets.Value("string"),
                    "text": datasets.Value("string"),
                    "parent_id": datasets.Value("string"),
                    "parent_text": datasets.Value("string"),
                    "parent_stance": datasets.features.ClassLabel(
                        names=[
                            "Supporting",
                            "Denying",
                            "Querying",
                            "Commenting",
                        ]
                    ),
                    "source_id": datasets.Value("string"),
                    "source_text": datasets.Value("string"),
                    "source_stance": datasets.features.ClassLabel(
                        names=[
                            "Supporting",
                            "Denying",
                            "Querying",
                            "Commenting",
                        ]
                    ),

                }
            ),
            supervised_keys=None,
            homepage="https://aclanthology.org/W19-6122/",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        downloaded_file = dl_manager.download_and_extract(_URL)
        print(downloaded_file)
        data_files = {
            "dast": downloaded_file,
        }

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_files['dast'], "split":"train"}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_files['dast'], "split":"validation"}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_files['dast'], "split":"test"}),
        ]


    def unpack(self, entry, parent_id = None, source_id = None):

        if isinstance(entry, dict):
            e = entry['comment']
            original_id = e['comment_id']
            text = e['text']
            parent_id = e['parent_id']
            parent_stance = e['SDQC_Parent']
            source_id = e['submission_id']
            source_stance = e['SDQC_Submission']

            self.texts[original_id] = text

            instance = {
                "id":self.guid,
                "native_id":original_id, 
                "text":text, 
                "parent_id":parent_id, 
                "parent_text":self.texts[parent_id], 
                "parent_stance":parent_stance, 
                "source_id":source_id, 
                "source_text":self.texts[source_id], 
                "source_stance":source_stance,
            }
            
            self.id_mapper[e['comment_id']] = self.guid
            self.guid += 1
            yield instance

        elif isinstance(entry, list):
            for sub_entry in entry:
                yield from self.unpack(sub_entry, parent_id=parent_id, source_id=source_id)


    def process_block(self, block):

        j = json.loads(block)
        s = j['redditSubmission']
        descr = s['RumourDescription']
        source_id = s['submission_id']
        #print(i, '', descr, '', '', s['title'], s['SourceSDQC'])
        self.id_mapper[source_id] = self.guid
        self.guid += 1
        self.texts[source_id] = s['title']

        yield from self.unpack(j['branches'], source_id = 0, parent_id = 0)


    def _generate_examples(self, filepath, split):
        logger.info("⏳ Generating %s examples from = %s", (split, filepath))

        def _deleted():
            return "[deleted]"

        self.guid = 0
        self.id_mapper = {}
        self.texts = defaultdict(_deleted)

        partition_sources = ()
        if split == 'train':
            partition_sources = ('8sjevz', 'a0954m', 'a1gsmt', 'a2fpjr', 'a6o3us', 'ax70y5', 'axnshu', 'b23eat', 'b2xrgd', 'b72gok', 'b7aybw', 'b7ohqt', 'bb9iqt')
        elif split == 'validation':
            partition_sources = ('6v1ivh', '76y6rb', '7r9ouo', '8192oe', '83l9nm', '8agt1s', '8clb74', '8k6lcb')            
        elif split == 'test':
            partition_sources = ('3qc12m', '3ud5z9', '53u5j7', '5emjyw', '5pfq1r', '5t1h6y', '60il0b', '67c2zf', '6jqtkm', '6nz7dy', '6szxwj', '6tm5kp')

        with open(filepath, 'r', encoding="utf-8") as dastfile:
            for line in dastfile:
                instances = self.process_block(line.strip())
                for instance in instances:
                    if instance['source_id'] in partition_sources:
                        yield instance['id'], instance