# 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/temp.csv"

}


# 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):
        print("info called")
        features = datasets.Features(
              {"docket_agency": datasets.Value("string"),
               "docket_title": datasets.Value("string"),
               "docket_date": datasets.Value("string"),
               "comment_id": datasets.Value("string"),
               "comment_date": datasets.Value("string"),
               "comment_url": datasets.Value("string"),
               "comment_title": datasets.Value("string"),
               "commenter_name": datasets.Value("string"),
               "comment_length": datasets.Value("int64"),
               "comment_text": 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"),
            # Homepage of the dataset for documentaxtion
            homepage=_HOMEPAGE
        )

    def _split_generators(self, dl_manager):
        print("split generators called")
        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
        # 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
        urls = _URLS["url"]
        data_dir = dl_manager.download_and_extract(urls) 
        print("urls accessed")
        print(data_dir)
       #print("File path:", os.path.join(data_dir, "train.csv"))
        return [datasets.SplitGenerator(name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": data_dir,
                    #"split": "train",
                },),]
    
    #method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filepath):
        print("generate examples called")
        with open(filepath, encoding="utf-8") as f:
            reader = csv.DictReader(f)
            for key, row in enumerate(reader):
                yield key, {
                    "docket_agency": row["docket_agency"],
                    "docket_title": row["docket_title"],
                    "docket_date": row["docket_date"],
                    "comment_id": row["comment_id"],
                    "comment_date": row["comment_date"],
                    "comment_url": row["comment_url"],
                    "comment_title": row["comment_title"],
                    "commenter_name": row["commenter_name"],
                    "comment_length": int(row["comment_length"]),
                    "comment_text": row["comment_text"],
                }