import os
import pandas as pd
import glob

import datasets

_CITATION = """\
@misc{shmidman2022introducing,
      title={Introducing BEREL: BERT Embeddings for Rabbinic-Encoded Language}, 
      author={Avi Shmidman and Joshua Guedalia and Shaltiel Shmidman and Cheyn Shmuel Shmidman and Eli Handel and Moshe Koppel},
      year={2022},
      eprint={2208.01875},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
"""

_DESCRIPTION = """\
This Dataset is a Hugging Face Reflection of the Sefaria Database.
The following is the description of Sefaria from the sefaria.org website:

Sefaria is home to 3,000 years of Jewish texts. We are a non-profit organization 
offering free access to texts, translations, and commentaries so that everyone 
can participate in the ongoing process of studying, interpreting, and creating Torah
"""

_HOMEPAGE = "https://github.com/Sefaria/Sefaria-Export"
_LICENSE = "Each text is licensed separately, so there is no overall license for this repository."

class Sefaria(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.0.3")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="english", version=VERSION, description="This configuration contains only English texts"),
        datasets.BuilderConfig(name="hebrew", version=VERSION, description="This configuration contains only Hebrew texts"),
        datasets.BuilderConfig(name="all", version=VERSION, description="This configuration contains both English and Hebrew texts"),
    ]

    DEFAULT_CONFIG_NAME = "all"

    def _info(self):
        features = datasets.Features(
            {
                "text": datasets.Value("string"),
                "meta": datasets.Value("string")
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )
    data_urls = {
    "english": "https://huggingface.co/datasets/mehdie/sefaria/raw/main/data/*_english.parquet",
    "hebrew": "https://huggingface.co/datasets/mehdie/sefaria/raw/main/data/*_hebrew.parquet",
    "all": "https://huggingface.co/datasets/mehdie/sefaria/raw/main/data/*.parquet"
    }

    def _split_generators(self, dl_manager):
        # Download and extract the dataset files
        if self.config.name in ["english", "hebrew"]:
            data_dir = dl_manager.download_and_extract(data_urls[self.config.name])
        else:
            data_dir = dl_manager.download_and_extract(data_urls["all"])

        # Find the files in the extracted directory
        filepaths = glob.glob(os.path.join(data_dir, "*.parquet"))

        print(f"Filepaths found: {filepaths}")  # Add this line to print the filepaths
        
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepaths": filepaths,
                },
            ),
        ]

    def _generate_examples(self, filepaths):
        for filepath in filepaths:
            df = pd.read_parquet(filepath)
            print(f"Reading file: {filepath}, number of rows: {len(df)}")  # Add this line to print the number of row
            for idx, row in df.iterrows():
                yield idx, {
                    "text": row["text"],
                    "meta": row["meta"],
                }