# 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.
"""This dataset consists of a toy subset of 8834 (5000 training + 3834 testing) TeX files found in the arXiv subset of MathPile, used for testing. Each document is split using LaTeX-specific characters for recursive character text splitting with ~4k token window and ~1.5k token overlaps. You should not use this dataset. Training and testing sets are already split."""

import json
import os

import datasets

_CITATION = """\
@article{wang2023mathpile,
  title={Generative AI for Math: Part I -- MathPile: A Billion-Token-Scale Pretraining Corpus for Math},
  author={Wang, Zengzhi and Xia, Rui and Liu, Pengfei},
  journal={arXiv preprint arXiv:2312.17120},
  year={2023}
}
"""

_DESCRIPTION = """\
This dataset consists of a toy subset of 8834 (5000 training + 3834 testing) TeX files found in the arXiv subset of MathPile, used for testing. Each document is split using LaTeX-specific characters for recursive character text splitting with ~4k token window and ~1.5k token overlaps. You should not use this dataset.
"""

_HOMEPAGE = "https://huggingface.co/datasets/aluncstokes/mathpile_arxiv_subset_tiny"


_LICENSE = "CC BY-NC-SA 4.0"

# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URLS = {"first_domain": "https://huggingface.co/datasets/GAIR/MathPile"}


class MathpileArxivSubsetTiny(datasets.GeneratorBasedBuilder):
    """This dataset consists of a toy subset of 8834 (5000 training + 3834 testing) TeX files found in the arXiv subset of MathPile, used for testing. Each document is split using LaTeX-specific characters for recursive character text splitting with ~4k token window and ~1.5k token overlaps. You should not use this dataset"""

    VERSION = datasets.Version("0.2")

    # This is an example of a dataset with multiple configurations.
    # If you don't want/need to define several sub-sets in your dataset,
    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

    # If you need to make complex sub-parts in the datasets with configurable options
    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
    # BUILDER_CONFIG_CLASS = MyBuilderConfig

    # You will be able to load one or the other configurations in the following list with
    # data = datasets.load_dataset('my_dataset', 'first_domain')
    # data = datasets.load_dataset('my_dataset', 'second_domain')

    DEFAULT_CONFIG_NAME = ""  # It's not mandatory to have a default configuration. Just use one if it make sense.

    def _info(self):
        if self.config.name == "":
            features = datasets.Features(
                {
                    "set": datasets.Value("string"),
                    "id": datasets.Value("string"),
                    "chunk_text": datasets.Value("long_string"),
                    "chunk_num_tokens": datasets.Value("uint32"),
                    "document_num_tokens": datasets.Value("uint32"),
                    "document_language": datasets.Value("string"),
                }
            )

        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,  # Here we define them above because they are different between the two configurations
            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
            # supervised_keys=("sentence", "label"),
            # 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):
        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
        urls = _URLS[self.config.name]
        data_dir = dl_manager.download_and_extract(urls)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": os.path.join(data_dir, "train_chunked.jsonl"),
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": os.path.join(data_dir, "test_chunked.jsonl"),
                    "split": "test",
                },
            ),
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def generate_examples(self, filepath, split):
        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
        with open(filepath, encoding="utf-8") as f:
            for key, row in enumerate(f):
                data = json.loads(row)
                # Yields examples as (key, example) tuples
                yield key, {"text": data["chunk_text"]}