# coding=utf-8
# Copyright 2022 esCorpius authors
# The code required to produce and load this dataset is licensed under MIT License.
# The code samples included in this dataset keep their own licenses, which can be retrieved via their metadata.
# 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.

# Please note that the dataset release is still work in progress.

"""The esCorpius dataset."""

import json

import datasets

from pathlib import Path


_CITATION = """\
@misc{TODO
}
"""

_DESCRIPTION = """\
Spanish dataset
"""  # TODO: expand

_HOMEPAGE = "https://huggingface.co/datasets/LHF/escorpius"

_LICENSE = "CC BY-NC-ND 4.0"

_URL = ""  


_FEATURES = datasets.Features(
    {
        "id": datasets.Value("string"),
        "text": datasets.Value("string"),
        "url_warc": datasets.Value("string"),
        "url": datasets.Value("string")

    }
)


class EsCorpiusConfig(datasets.BuilderConfig):
    """BuilderConfig for esCorpius."""

    def __init__(self, *args, **kwargs):
        """BuilderConfig for The Pile.
        Args:
            **kwargs: keyword arguments forwarded to super.
        """
        super().__init__(
            *args,
            **kwargs,
        )


class EsCorpius(datasets.GeneratorBasedBuilder):
    """The esCorpius dataset."""

    BUILDER_CONFIGS = [
        EsCorpiusConfig(
            name="esCorpius",
            version=datasets.Version("1.0.1"),
            description="Spanish dataset"
        ),
    ]

    def _info(self):
        """Give information and typings for the dataset."""
        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,
            # If there's a common (input, target) tuple from the features,
            # specify them here. They'll be used if as_supervised=True in
            # builder.as_dataset.
            supervised_keys=None,
            # Homepage of the dataset for documentation
            homepage=_HOMEPAGE,
            # License for the dataset if available
            license=_LICENSE,
            # Citation for the dataset
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        urls_to_download = [
                'es_corpus.jsonl.aa',
                'es_corpus.jsonl.ab',
                'es_corpus.jsonl.ac',
                'es_corpus.jsonl.ad',
                'es_corpus.jsonl.ae',
                'es_corpus.jsonl.af',
                'es_corpus.jsonl.ag',
                'es_corpus.jsonl.ah',
                'es_corpus.jsonl.ai',
                'es_corpus.jsonl.aj',
                'es_corpus.jsonl.ak',
                'es_corpus.jsonl.al',
                'es_corpus.jsonl.am',
                'es_corpus.jsonl.an',
                'es_corpus.jsonl.ao',
                'es_corpus.jsonl.ap',
                'es_corpus.jsonl.aq',
                'es_corpus.jsonl.ar',
                'es_corpus.jsonl.as',
                'es_corpus.jsonl.at',
                'es_corpus.jsonl.au',
                'es_corpus.jsonl.av',
                'es_corpus.jsonl.aw',
                'es_corpus.jsonl.ax',
                'es_corpus.jsonl.ay',
                'es_corpus.jsonl.az',
                'es_corpus.jsonl.ba',
                'es_corpus.jsonl.bb',
                'es_corpus.jsonl.bc',
                'es_corpus.jsonl.bd',
                'es_corpus.jsonl.be',
                'es_corpus.jsonl.bf',
                'es_corpus.jsonl.bg'
             ]
        # urls_to_download = [urls_to_download[-1]]  # testing
        downloaded_files = dl_manager.download_and_extract(urls_to_download)

        return [

            datasets.SplitGenerator(name='train',
                                    gen_kwargs={"files": downloaded_files}),
        ]

    def _generate_examples(self, files):
        """Yield examples as (key, example) tuples."""
        key = 0

        for path in files:
            with open(path, "r", encoding="utf-8") as f:
                for row in f:
                    data = json.loads(row)
                    yield key, data
                    key += 1