# coding=utf-8
# 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.
"""Demo for pretrain"""


import os
import json
import datasets


_CITATION = """\
"""

_DESCRIPTION = """\
"""

_LICENSE = "apache-license-2.0"
_HOMEPAGE = "https://github.com/IDEA-CCNL/Fengshenbang-LM"


class Config(datasets.BuilderConfig):
    """BuilderConfig for Demo"""

    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        """
        Args:
            **kwargs: keyword arguments forwarded to super.
        """


class PretrainCorpusDemo(datasets.GeneratorBasedBuilder):

    VERSION = datasets.Version("1.0.0")

    BUILDER_CONFIG_CLASS = Config
    BUILDER_CONFIGS = [
        Config(description=_DESCRIPTION)
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features({
                "text": datasets.Value("string"),
            }),
            homepage=_HOMEPAGE,
            citation=_CITATION,
            license=_LICENSE
        )

    def _split_generators(self, dl_manager):

        files = {
            "test": os.path.join("data", f"train.json"),
            "validation": os.path.join("data", f"train.json"),
            "train": os.path.join("data", f"train.json"),
        }
        data_dir = dl_manager.download_and_extract(files)

        output = []
        test = datasets.SplitGenerator(
            name=datasets.Split.TEST,
            gen_kwargs={
                "filepath": data_dir["test"]
            }
        )
        output.append(test)

        # if os.path.exists(data_dir["validation"]):
        valid = datasets.SplitGenerator(
            name=datasets.Split.VALIDATION,
            gen_kwargs={
                "filepath": data_dir["validation"]
            }
        )
        output.append(valid)

        train = datasets.SplitGenerator(
            name=datasets.Split.TRAIN,
            gen_kwargs={
                "filepath": data_dir["train"]
            }
        )
        output.append(train)

        return output

    def _generate_examples(self, filepath):
        """Yields examples."""
        with open(filepath, encoding="utf-8") as f:
            lines = f.readlines()
            for id_, line in enumerate(lines):
                data = json.loads(line)
                s = {
                    'text': data['text'],
                }
                yield id_, s