import json
import pandas as pd
import datasets
import os

logger = datasets.logging.get_logger(__name__)

_CITATION = """

@inproceedings{chen-etal-2021-dialogsum,

  title={{D}ialog{S}um: {A} Real-Life Scenario Dialogue Summarization Dataset},

  author={Chen, Yulong and Liu, Yang  and Chen, Liang  and Zhang, Yue},

  journal={arXiv preprint arXiv:1911.12237},

  year={2021},

  booktitle ={Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021"},

  month = {aug},

  address = {Online},

  publisher = {Association for Computational Linguistics},

  url = {https://aclanthology.org/2021.findings-acl.449},

  doi = {10.18653/v1/2021.findings-acl.449},

  pages = {5062--5074}

}

"""



_DESCRIPTION = """

DialogSUM Corpus contains 13460 chat dialogues with manually annotated

summaries.

There are two features:

  - dialogue: text of dialogue.

  - summary: human written summary of the dialogue.

  - topic: one liner summary of the dialogue.

  - id: id of a example.

"""



_HOMEPAGE = "hhttps://aclanthology.org/2021.findings-acl.449"



_LICENSE = "CC BY-NC-ND 4.0"



_URL = "https://huggingface.co/datasets/knkarthick/dialogsum_reformat/tree/main/"


#_URL = "https://huggingface.co/datasets/knkarthick/dialogsum_reformat/resolve/main/"


_URLS = {
    "train": _URL + "train.json",
    "test": _URL + "test.json",
    "val": _URL + "val.json",
}

class Dialogsum(datasets.GeneratorBasedBuilder):
	"""DialogSum Corpus dataset."""

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="dialogsum_reformat",
            version=datasets.Version("1.0.0", ""),
            description="DialogSum Corpus dataset",
        ),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "dialogue": datasets.Value("string"),
                    "summary": datasets.Value("string"),
                    "topic": datasets.Value("string"),
                }
            ),
            # No default supervised_keys (as we have to pass both question
            # and context as input).
            supervised_keys=None,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        downloaded_files = dl_manager.download_and_extract(_URLS)
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["test"]}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["val"]}),
        ]

    def _generate_examples(self, filepath, split):
        """This function returns the examples in the raw (text) form."""
        logger.info("generating examples from = %s", filepath)
        key = 0
        with open(os.path.join(filepath, split)) as f :
    		data = json.load(f)

        for info in data :
            dialogue_id = info['id']
            dialogue_name = info['dialogue']
            dialogue_summary = info['summary']
            dialogue_topic = info['topic']

            yield key, {
                "id" : dialogue_id,
                "dialogue" : dialogue_name,
                "summary" : dialogue_summary,
                "topic" : dialogue_topic,
            }
            key += 1