from pathlib import Path
import datasets
from datasets import (
    Features,
    SplitGenerator,
)
import pandas as pd

DATA_ARCHIVE = "data.zip"
TABLE_ARCHIVE = "tables.zip"

NAMES = [
    "all",
    "asm",
    "bgc",
    "bht",
    "ckb",
    "ewe",
    "guj",
    "ibo",
    "kan",
    "lin",
    "luo",
    "mal",
    "mar",
    "nag",
    "nde",
    "nlx",
    "pan",
    "peg",
    "tam",
    "tel",
    "tw-akuapem",
    "tw-asante",
    "ukr",
    "urd",
    "vie",
    "yor",
    
]

DESCRIPTION = """"""
CITATION = """@misc{keller2024speechtaximultilingualsemanticspeech,
      title={SpeechTaxi: On Multilingual Semantic Speech Classification}, 
      author={Lennart Keller and Goran Glavaš},
      year={2024},
      eprint={2409.06372},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2409.06372}, 
}"""
HOMPAGE = "https://huggingface.co/datasets/LennartKeller/SpeechTaxi"

class SpeechTaxiConfig(datasets.BuilderConfig):
    def __init__(
        self, name, description, citation, homepage
    ):
        super().__init__(
            name=name,
            version=datasets.Version("0.0.1"),
            description=description,
        )
        self.name = name
        self.description = description
        self.citation = citation
        self.homepage = homepage

def get_config(name):
    return SpeechTaxiConfig(name=name, description=DESCRIPTION, citation=CITATION, homepage=HOMPAGE)

class SpeechTaxi(datasets.GeneratorBasedBuilder):
    BUILDER_CONFIGS = [get_config(name) for name in NAMES]
    BUILDER_CONFIG_CLASS = SpeechTaxiConfig

    def _info(self):
        return datasets.DatasetInfo(
            description=DESCRIPTION,
            features=Features(
                {
                    "verse_ref": datasets.features.Value("string"),
                    "text_en": datasets.features.Value("string"),
                    "language": datasets.features.Value("string"),
                    "transcription": datasets.features.Value("string"),
                    "transcription_romanized": datasets.features.Value("string"),
                    "label": datasets.features.ClassLabel(
                        names=[
                            "Recommendation",
                            "Faith",
                            "Description",
                            "Sin",
                            "Grace",
                            "Violence",
                        ]
                    ),
                    "audio": datasets.features.Audio(sampling_rate=16_000),
                    "transcription_mms-zeroshot-300m": datasets.features.Value("string"),
                    "transcription_whisper-large-v3": datasets.features.Value("string"),
                }
            ),
            supervised_keys=None,
            homepage=HOMPAGE,
            citation=CITATION,
        )
    def _split_generators(self, dl_manager):
        table_dir = dl_manager.download_and_extract(TABLE_ARCHIVE)
        # Make sure data is downloaded 
        data_dir = dl_manager.download_and_extract(DATA_ARCHIVE)
        return [
            SplitGenerator(name="train", gen_kwargs={"split": "train", "table_dir": table_dir, "data_dir": data_dir}),
            SplitGenerator(name="test", gen_kwargs={"split": "test", "table_dir": table_dir, "data_dir": data_dir}),
            SplitGenerator(name="dev", gen_kwargs={"split": "dev", "table_dir": table_dir, "data_dir": data_dir}),
            # Add more splits as necessary
        ]
    
    def _generate_examples(self, split, table_dir, data_dir):
        table_dir = Path(table_dir)
        data_dir = Path(data_dir)
        idx = 0
        if self.config.name != "all":
            table_files = [table_dir / f"{self.config.name}.tsv"]
        else:
            table_files = sorted(table_dir.glob("*.tsv"))

        for table_file in table_files:
            # Load the table
            df = pd.read_table(table_file)
            df["language"] = table_file.stem
            df = df.query("split == @split").reset_index(drop=True)

            for _, row in df.iterrows():
                row["audio"] = str(data_dir / row["audio"])
                yield idx, row.to_dict()
                idx += 1