# 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


import pandas as pd
import json
import os
import shutil

import datasets


# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {French Fiscal texts},
author={Stan Bienaives
},
year={2022}
}
"""


_DESCRIPTION = """\
  This dataset is an extraction from the OPENDATA/JADE. A list of case laws from the French court "Conseil d'Etat".
"""

_HOMEPAGE = "echanges.dila.gouv.fr"


_LICENSE = """/
  This dataset is licensed under Creative Commons Attribution 4.0 International License.
"""


_URLS = {
  "jade": "https://wisenlp.s3.eu-west-1.amazonaws.com/jade.parquet"
}


_SEED = 42

class FrenchOpenFiscalTexts(datasets.GeneratorBasedBuilder):
    """
    This is the main class for the dataset.
    """
    VERSION = datasets.Version("1.1.0")

    def _info(self):
      features = datasets.Features(
        {
          # "file": datasets.Value("string"),
          "title": datasets.Value("string"),
          "content": datasets.Value("string"),
          "summary": datasets.Value("string"),
          "solution": datasets.Value("string"),
          "numero": datasets.Value("string"),
          "publi_receuil": datasets.Value("string"),
          "date": datasets.Value("string"),
        }
      )

      return datasets.DatasetInfo(
        # This is the description that will appear on the datasets page.
        description=_DESCRIPTION,
        features=features,
        homepage=_HOMEPAGE,
        license=_LICENSE,
      )

    def _split_generators(self, dl_manager):
      # self.data_dir = dl_manager.download_and_extract(_URLS["jade"])

      print(dl_manager.download_config)
      df = pd.read_parquet(dl_manager.download(_URLS["jade"]))

      print(len(df))
      print(df.head())



      train = df.sample(frac=0.8,random_state=_SEED)
      test = df.drop(train.index)

      return [
        datasets.SplitGenerator(
          name=datasets.Split.TRAIN,
          gen_kwargs={"dataframe": train},
        ),
        datasets.SplitGenerator(
          name=datasets.Split.TEST,
          gen_kwargs={"dataframe": test},
        ),
      ]

    def _generate_examples(self, dataframe):
      """
      This function returns the examples in the raw (text) form.
      """
      for index, row in dataframe.iterrows():
        yield index, {
          # "file": row["file"],
          "title": row["title"],
          "content": row["content"],
          "summary": row["summary"],
          "solution": row["solution"],
          "numero": row["numero"],
          "publi_receuil": row["publi_receuil"],
          "date": row["date"],
        }