\n# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# 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.

# Lint as: python3
"""CC-NEWS-ES-titles: Title generation from CC-NEWS in Spanish."""


import json

import datasets
from datasets.tasks import Summarization


logger = datasets.logging.get_logger(__name__)


_CITATION = """ """
_DESCRIPTION = ""
_HOMEPAGE = ""

_LICENSE = ""

_URL = "https://huggingface.co/datasets/LeoCordoba/CC-NEWS-ES-titles/resolve/main/"
_URLS = {
    "train": _URL + "train.jsonl",
    "test": _URL + "test.jsonl",
    "eval": _URL + "eval.jsonl"
}

class CCNewsESTitlesConfig(datasets.BuilderConfig):
    """BuilderConfig for CCNewsESTitles."""

    def __init__(self, **kwargs):
        """BuilderConfig for CCNewsESTitles.
        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(CCNewsESTitlesConfig, self).__init__(**kwargs)
        
class CCNewsESTitles(datasets.GeneratorBasedBuilder):
    """Title generation dataset in Spanish from CC-NEWS"""
    VERSION = datasets.Version("1.0.0")
    
    BUILDER_CONFIGS = [
        CCNewsESTitlesConfig(
        ),
    ]
    
    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "text": datasets.Value("string"),
                    "output_text": datasets.Value("string")
                }
            ),
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )
    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        
        train = dl_manager.download_and_extract(_URLS["train"])
        eval_ = dl_manager.download_and_extract(_URLS["eval"])
        test = dl_manager.download_and_extract(_URLS["test"])
        
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": eval_}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test})
        ]

                
    def _generate_examples(self, filepath):
        logger.info("generating examples from = %s", filepath)
        data = []
        with open(filepath) as f:
            for line in f:
                data.append(json.loads(line))

            for idx, obs in enumerate(data):
                yield idx, obs