import json
import os

import datasets

_CITATION="""\
@article{azerbayev2023llemma,
    title={Llemma: an open language model for mathematics},
    author={Zhangir Azerbayev and Hailey Schoelkopf and Keiran Paster and Marco Dos Santos and Stephen McAleer and Albert Q. Jiang and Jia Deng and Stella Biderman and Sean Welleck},
    eprint={xyz.xyz},
    archivePrefix={arXiv}
    year={2023}
}
"""

_DESCRIPTION = """\
A dataset of high quality mathematical text. """
_HOMEPAGE = "https://github.com/EleutherAI/math-lm"


SPLITS = ("train", "validation", "test")

_DATA_PATHS = {
        "redpajama-arxiv": {
            {split: [f'redpajama-arxiv/{split}/arXiv_{str(i).zfill(3)}.jsonl.gz' for i in range(100)]}
            for split in SPLITS        
        },
        "open-web-math": {
            {split: [f'open-web-math/{split}/shard-{str(i).zfill(4)}.jsonl.gz' for i in range(63)]}
            for split in SPLITS
        },
        "algebraic-stack": {
            {
                split: [
                    os.path.join(f"algebraic-stack/{split}", filename)
                    for filename in os.listdir(f"algebraic-stack/{split}")
                ]
            }
            for split in SPLITS
        }
}

class ProofPile2Config(datasets.BuilderConfig):
    """BuilderConfig for RedPajama sample."""

    def __init__(self, *args, subsets, **kwargs):
        """BuilderConfig for ProofPile2.
        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(ProofPile2Config, self).__init__(**kwargs)
        self.subsets = subsets


class ProofPile2(datasets.GeneratorBasedBuilder):
    """A large dataset of mathematical text."""
    VERSION = datasets.Version("1.0.0")

    # 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')
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name='default',
            subsets=list(_DATA_PATHS.keys()),
            version=VERSION,
            description="All subsets"
        ),
        datasets.BuilderConfig(
            name='redpajama-arxiv',
            subsets=["redpajama-arxiv"],
            version=VERSION,
            description="ArXiv subset"
        ),
        datasets.BuilderConfig(
            name='open-web-math',
            subsets=['open-web-math'],
            version=VERSION,
            description="OpenWebMath"
        ),
        datasets.BuilderConfig(
            name='algebraic-stack',
            subsets=['algebraic-stack'],
            version=VERSION,
            description="Code subset"
        ),
    ]


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

    def _split_generators(self, dl_manager):
        return [ 
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "data_files": map(dl_manager.download_and_extract, [*_DATA_PATHS[subset]["train"] for subset in self.config.subsets]),
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION, 
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "data_files": map(dl_manager.download_and_extract, [*_DATA_PATHS[subset]["validation"] for subset in self.config.subsets]),
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST, 
                gen_kwargs={
                    "data_files": map(dl_manager.download_and_extract, [*_DATA_PATHS[subset]["test"] for subset in self.config.subsets]),
                }, 
            ), 
        ]
    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, data_files):
        key = 0 
        for name in data_files: 
            with open(name) as f: 
                instances = [json.loads(x) for x in f.readlines() if x]
            for instance in instances: 
                yield key, {"text": instance["text"], "meta": json.dumps(instance["meta"])}
                key += 1