import datasets
from datasets import DatasetBuilder, DatasetInfo
import pandas as pd

class TMDataset(datasets.GeneratorBasedBuilder):
    def __init__(self):
        self.downloads = [
            "https://huggingface.co/datasets/Locutusque/TM-DATA/resolve/main/combined_datasets_batch_1.parquet",
            "https://huggingface.co/datasets/Locutusque/TM-DATA/resolve/main/combined_datasets_batch_10.parquet",
            "https://huggingface.co/datasets/Locutusque/TM-DATA/resolve/main/combined_datasets_batch_11.parquet",
            "https://huggingface.co/datasets/Locutusque/TM-DATA/resolve/main/combined_datasets_batch_12.parquet",
            "https://huggingface.co/datasets/Locutusque/TM-DATA/resolve/main/combined_datasets_batch_13.parquet",
            "https://huggingface.co/datasets/Locutusque/TM-DATA/resolve/main/combined_datasets_batch_2.parquet",
            "https://huggingface.co/datasets/Locutusque/TM-DATA/resolve/main/combined_datasets_batch_3.parquet",
            "https://huggingface.co/datasets/Locutusque/TM-DATA/resolve/main/combined_datasets_batch_4.parquet",
            "https://huggingface.co/datasets/Locutusque/TM-DATA/resolve/main/combined_datasets_batch_5.parquet",
            "https://huggingface.co/datasets/Locutusque/TM-DATA/resolve/main/combined_datasets_batch_6.parquet",
            "https://huggingface.co/datasets/Locutusque/TM-DATA/resolve/main/combined_datasets_batch_7.parquet",
            "https://huggingface.co/datasets/Locutusque/TM-DATA/resolve/main/combined_datasets_batch_8.parquet",
            "https://huggingface.co/datasets/Locutusque/TM-DATA/resolve/main/combined_datasets_batch_9.parquet",

        ]
    VERSION = datasets.Version("1.0.0")

    def _info(self):
       
        features = datasets.Features({
            "text": datasets.Value("string"),
        })
        
        return datasets.DatasetInfo(
            description="Combination of text completion datasets",
            features=features,
            supervised_keys=None
        )

    def _split_generators(self, dl_manager):

        data_dir = dl_manager.download_and_extract(self.downloads)
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_dir})
        ]

    def _generate_examples(self, filepath):
        for file in filepath: 
            df = pd.read_parquet(file)
            
            for idx, row in df.iterrows():
                yield idx, {
                    "text": row["text"],
                }