import datasets
import pandas as pd
import os
import ast


class WABADBuilderConfig(datasets.BuilderConfig):
    def __init__(self, location_dir, **kwargs):
        super().__init__(**kwargs)
        self.location_dir = location_dir

class WABADDataset(datasets.GeneratorBasedBuilder):
    BUILDER_CONFIGS = [
        WABADBuilderConfig(name="BAM", location_dir="BAM", description="BAM location"),
        WABADBuilderConfig(name="ARD", location_dir="ARD", description="ARD location"),
        # add more locations here
    ]

    def _info(self):
        return datasets.DatasetInfo(
            features=datasets.Features({
                "audio": datasets.Audio(), 
                "labels": datasets.Sequence(datasets.Value("int32")),
                "site_ID": datasets.Value("string"),
                # any other metadata
            })
        )

    def _split_generators(self, dl_manager):
        config_dir = self.config.location_dir
        
        # Use the correct dataset URL format and download specific files
        train_url = f"https://huggingface.co/datasets/benmcewen/WABAD/resolve/main/{config_dir}/{config_dir}_metadata_train.parquet"
        test_url = f"https://huggingface.co/datasets/benmcewen/WABAD/resolve/main/{config_dir}/{config_dir}_metadata_test.parquet"
        
        # Download the parquet files
        train_file = dl_manager.download(train_url)
        test_file = dl_manager.download(test_url)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "parquet_file": train_file,
                    "config_dir": config_dir
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "parquet_file": test_file,
                    "config_dir": config_dir
                },
            )
        ]

    def _generate_examples(self, parquet_file, config_dir):
        df = pd.read_parquet(parquet_file)
        for idx, row in df.iterrows():
            # Download audio file on demand
            audio_url = f"https://huggingface.co/datasets/benmcewen/WABAD/resolve/main/{config_dir}/audio/{os.path.basename(row['filepath'])}"

            labels = row["labels"]
            if isinstance(labels, str):
                labels = ast.literal_eval(labels) 
            
            yield idx, { 
                "audio": {"path": audio_url, "bytes": None},  # Let datasets handle the download
                "labels": labels,
                "site_ID": row.get("site_ID", self.config.name),
                # other metadata
            }