# 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.
# See the License for the specific language governing permissions and
# limitations under the License.
# TODO: Address all TODOs and remove all explanatory comments
"""TODO: Add a description here."""


import csv
import json
import os
import datasets


_DESCRIPTION = """\
United States governmental agencies often make proposed regulations open to the public for comment. 
This project will use Regulation.gov public API to aggregate and clean public comments for dockets 
related to Medication Assisted Treatment for Opioid Use Disorders. 

The dataset will contain docket metadata, docket text-content, comment metadata, and comment text-content. 
"""

_HOMEPAGE = "https://www.regulations.gov/"


# TODO: Add link to the official dataset URLs here
# 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 = {"url": "https://huggingface.co./datasets/ro-h/regulatory_comments/raw/main/docket_comments.json"}


# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
class RegComments(datasets.GeneratorBasedBuilder):

    VERSION = datasets.Version("1.1.0")

    # 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')

    #my_dataset[comment_id] = dict(
    #            comment_url = comment['links']['self'], 
    #            comment_text = comment_data['data']['attributes']['comment'],
    #            commenter_name = comment_data['data']['attributes'].get('firstName', '') + " " + comment_data['data']['attributes'].get('lastName', '')
    #        ) #use pandas
    
    def _info(self):
        features = datasets.Features({
            "id": datasets.Value("string"),
            "title": datasets.Value("string"),
            "context": datasets.Value("string"),
            "comments": datasets.Sequence({
                "text": datasets.Value("string"),
                "comment_id": datasets.Value("string"),
                "comment_url": datasets.Value("string"),
                "comment_date": datasets.Value("string"),
                "comment_title": datasets.Value("string"),
                "commenter_fname": datasets.Value("string"),
                "commenter_lname": datasets.Value("string"),
                "comment_length": datasets.Value("int32")
            })
        })

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE
        )

    def _split_generators(self, dl_manager):
        print("split generators called")
        # URLS should point to where your dataset is located
        urls = _URLS["url"]
        data_dir = dl_manager.download_and_extract(urls)
        print("urls accessed")
        print(data_dir)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": data_dir,
                },
            ),
        ]
    
    def _generate_examples(self, filepath):
        """This function returns the examples in the raw (text) form."""
        key = 0
        with open(filepath, 'r', encoding='utf-8') as f:
            data = json.load(f)
            for docket in data:
                docket_id = docket["id"]
                docket_title = docket["title"]
                docket_context = docket["context"]
                comments = []
                for comment in docket["comments"]:
                    comment_data = {
                        "text": comment["text"],
                        "comment_id": comment["comment_id"],
                        "comment_url": comment["comment_url"],
                        "comment_date": comment["comment_date"],
                        "comment_title": comment["comment_title"],
                        "commenter_fname": comment["commenter_fname"],
                        "commenter_lname": comment["commenter_lname"],
                        "comment_length": comment["comment_length"]
                    }
                    comments.append(comment_data)

                yield key, {
                    "id": docket_id,
                    "title": docket_title,
                    "context": docket_context,
                    "comments": comments
                }
                key += 1