# Copyright 2020 The HuggingFace Datasets Authors, the initial dataset script creator (Nouha Drizi),
# the current dataset script contributor (Abbas Ghaddar).
#
# 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.
"""CHARP: Conversation History AwaReness Probing for Knowledge-grounded Dialogue Systems"""


import json

import datasets
from datasets import NamedSplit

# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@article{ghaddar2024charp,
      title={CHARP: Conversation History AwaReness Probing for Knowledge-grounded Dialogue Systems}, 
      author={Abbas Ghaddar and David Alfonso-Hermelo and Philippe Langlais and Mehdi Rezagholizadeh and Boxing Chen and Prasanna Parthasarathi},
      year={2024},
      eprint={2405.15110},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
"""

# You can copy an official description
_DESCRIPTION = """\
CHARP is a testbed, designed for evaluating supposedly non-hallucinatory models abilities to reason over the conversational history of knowledge-grounded dialogue systems. 
"""

_LICENSE = "MIT"

# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URLS = {
    "eCHARP": "data/eCHARP.json",
    "hCHARP": "data/hCHARP.json"
}

class CHARPDataset(datasets.GeneratorBasedBuilder):
    """CHARP is a new benchmark for evaluating contextual history reasoning abilities of knowledge-grounded dialogue systems."""

    VERSION = datasets.Version("1.0.0")

    # This is an example of a dataset with multiple configurations.
    # If you don't want/need to define several sub-sets in your dataset,
    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

    # If you need to make complex sub-parts in the datasets with configurable options
    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
    # BUILDER_CONFIG_CLASS = MyBuilderConfig

    # You will be able to load one or the other configurations in the following list with
    # data = datasets.load_dataset('my_dataset', 'first_domain')
    # data = datasets.load_dataset('my_dataset', 'second_domain')
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="plain_text", version=VERSION, description="Plain text"),
    ]

    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(
            {
                "row_idx": datasets.Value("int32"),
                "history": datasets.features.Sequence(datasets.Value("string")),
                "knowledge": datasets.Value("string"),
                "response": datasets.Value("string"),

            }
        )

        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, uncomment supervised_keys line below and
            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
            # supervised_keys=("sentence", "label"),
            # License for the dataset if available
            license=_LICENSE,
            # Citation for the dataset
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
        downloaded_files = dl_manager.download_and_extract(_URLS)

        split_dict = {
            "eCHARP": NamedSplit("eCHARP"),
            "hCHARP": NamedSplit("hCHARP")
        }

        return [
            datasets.SplitGenerator(
                name=split_dict.get(split, split),
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": downloaded_file,
                    "split": split,
                },
            )
            for split, downloaded_file in sorted(downloaded_files.items(), key=lambda x: x[0])
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filepath, split):
        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
        with open(filepath, encoding="utf-8") as f:
            rows = json.load(f)
            print(type(rows))
            key = 0
            for row in rows:
                print(row)
                yield key, {
                    "row_idx": row["row_idx"],
                    "history": row["history"],
                    "knowledge": row["knowledge"],
                    "response": row["response"]
                }
                key += 1