# 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.
"""Cleaned Indonesian split of the KoPI corpus."""
import json
import glob
import gzip
import textwrap
import datasets
import zstandard as zstd
logger = datasets.logging.get_logger(__name__)

_CITATION = """
"""
_DESCRIPTION = """\
"""
_HOMEPAGE = "https://huggingface.co/datasets/munggok/KoPI"
_LICENSE = "CC0"
_BASE_URL = {
    "train":"https://huggingface.co/datasets/munggok/KoPI/resolve/main/raw/kopi-{index:012d}.json.zst",
    "val":"https://huggingface.co/datasets/munggok/KoPI/resolve/main/raw/kopi-val-{index:012d}.json.zst"

}
_CONFIGS = {
    "tiny": {"train": 10, "validation": 1},
    "small": {"train": 30, "validation": 2},
    "medium": {"train": 55, "validation": 2},
    "large": {"train": 75, "validation": 3},
    "full": {"train": 107, "validation": 4}
}
class KoPIConfig(datasets.BuilderConfig):
    """BuilderConfig for the Clean KoPI corpus."""
    def __init__(self, **kwargs):
        """BuilderConfig for Clean KoPI corpus.
        Args:
            **kwargs: keyword arguments forwarded to super.
        """
        super().__init__(**kwargs)
class KoPI(datasets.GeneratorBasedBuilder):
    """KoPI corpus."""
    BUILDER_CONFIGS = [
        KoPIConfig(
            name="tiny",
            version=datasets.Version("1.0.0"),
            description=textwrap.dedent(
                f"""\
                Tiny version only using 10 shard 
                """
            )
        ),
        KoPIConfig(
            name="small",
            version=datasets.Version("1.0.0"),
            description=textwrap.dedent(
                f"""\
                small version only using 30 shard 
                """
            )
        ),
        KoPIConfig(
            name="medium",
            version=datasets.Version("1.0.0"),
            description=textwrap.dedent(
                f"""\
                medion version only using 50 shard 
                """
            )
        ),
        KoPIConfig(
            name="large",
            version=datasets.Version("1.0.0"),
            description=textwrap.dedent(
                f"""\
                large version only using 75 shard 
                """
            )
        ),
        KoPIConfig(
            name="full",
            version=datasets.Version("1.0.0"),
            description=textwrap.dedent(
                f"""\
                The full cleaned version of KoPI corpus.
                Estimated size of compressed files: 53GB
                """
            )
        )
    ]
    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "text": datasets.Value("string"),
                    "url": datasets.Value("string"),
                    "timestamp": datasets.Value("string"),
                    "meta": datasets.Value("string"),
                }
            ),
            supervised_keys=None,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )
    def _split_generators(self, dl_manager):
        train = [_BASE_URL["train"].format(index=k + 1) for k in range(107)][0:_CONFIGS[self.config.name]['train']]
        validation =  [_BASE_URL["val"].format(index=k + 108) for k in range(4)][0:_CONFIGS[self.config.name]['validation']]
        train_downloaded_files = dl_manager.download(train)
        validation_downloaded_files = dl_manager.download(validation)
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": train_downloaded_files}),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION, gen_kwargs={"filepaths": validation_downloaded_files}
            ),
        ]
    def _generate_examples(self, filepaths):
        """This function returns the examples in the raw (text) form by iterating on all the files."""
        id_ = 0
        for filepath in filepaths:
            logger.info(f"Generating examples from {filepath}")
            with zstd.open(open(filepath, "rb"), "rt", encoding="utf-8") as f:
                for line in f:
                    if line:
                        example = json.loads(line)
                        if example.get('meta') is not None:
                            yield id_, {'text':example['text'],'url':example['url'],'timestamp':example['timestamp'],'meta': example['meta']}
                            id_ += 1
                        else:
                            yield id_, {'text':example['text'],'url':example['url'],'timestamp':example['timestamp'],'meta': "None"}
                            id_ += 1