from collections import defaultdict
import os
import json
import csv

import datasets


_DESCRIPTION = """
A large-scale speech corpus for representation learning, semi-supervised learning and interpretation.
"""

_CITATION = """
@inproceedings{}
"""

_HOMEPAGE = ""

_LICENSE = ""

_ASR_LANGUAGES = [
    "hy"
]
_ASR_ACCENTED_LANGUAGES = [
    ""
]

_LANGUAGES = _ASR_LANGUAGES + _ASR_ACCENTED_LANGUAGES

_BASE_DATA_DIR = "data/"

_N_SHARDS_FILE = _BASE_DATA_DIR + "n_files.json"

_AUDIO_ARCHIVE_PATH = _BASE_DATA_DIR + "{split}/{split}_dataset.tar.gz"

_METADATA_PATH = _BASE_DATA_DIR + "{split}.tsv"



class HySpeech(datasets.GeneratorBasedBuilder):
    """The VoxPopuli dataset."""

    VERSION = datasets.Version("1.1.0")  # TODO: version

    DEFAULT_WRITER_BATCH_SIZE = 256

    def _info(self):
        features = datasets.Features(
            {
                "audio_id": datasets.Value("string"),
                "language": datasets.ClassLabel(names=_LANGUAGES),
                "audio": datasets.Audio(sampling_rate=16_000),
                "raw_text": datasets.Value("string"),
                "normalized_text": datasets.Value("string"),
                "gender": datasets.Value("string"),  # TODO: ClassVar?
                "speaker_id": datasets.Value("string"),
                "is_gold_transcript": datasets.Value("bool"),
                "accent": datasets.Value("string"),
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        # Define paths to your train, dev, and test data
        data_dir = "data/"
        train_data_dir = os.path.join(data_dir, "train")
        dev_data_dir = os.path.join(data_dir, "dev")
        test_data_dir = os.path.join(data_dir, "test")

        # Load metadata files for train, dev, and test
        train_metadata_path = os.path.join(data_dir, "train.tsv")
        dev_metadata_path = os.path.join(data_dir, "dev.tsv")
        test_metadata_path = os.path.join(data_dir, "test.tsv")

        # Yield split generators for train, dev, and test
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"data_dir": train_data_dir, "metadata_path": train_metadata_path}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"data_dir": dev_data_dir, "metadata_path": dev_metadata_path}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"data_dir": test_data_dir, "metadata_path": test_metadata_path}),
        ]

    def _generate_examples(self, data_dir, metadata_path):
        # Load metadata from TSV file
        with open(metadata_path, "r") as f:
            metadata = csv.DictReader(f, delimiter="\t")

            # Iterate over metadata to yield examples
            for row in metadata:
                audio_id = row["audio_id"]
                audio_path = os.path.join(data_dir, row["audio_path"])  # Adjust column name accordingly
                # Load audio file and yield example
                with open(audio_path, "rb") as audio_file:
                    yield audio_id, {
                        "audio_id": audio_id,
                        "language": row["language"],  # Adjust column name accordingly
                        "audio": {"path": audio_path, "bytes": audio_file.read()},
                        # Add other metadata fields as needed
                    }