"""Dataset loaders for SalTTS training."""

import torch
from torch.utils.data import Dataset, DataLoader
import numpy as np
from pathlib import Path
from typing import Optional, Tuple


class TTSDataset(Dataset):
    """Dataset for TTS training.

    Loads audio, text, and prosody annotations.
    """

    def __init__(
        self,
        data_dir: str,
        split: str = "train",
        sample_rate: int = 24000,
    ):
        """Initialize dataset.

        Args:
            data_dir: Data directory
            split: Dataset split (train/val/test)
            sample_rate: Audio sample rate
        """
        self.data_dir = Path(data_dir)
        self.split = split
        self.sample_rate = sample_rate

        # Load metadata
        self.metadata = self._load_metadata()

    def _load_metadata(self) -> list:
        """Load dataset metadata."""
        # Placeholder - would load from actual metadata file
        return []

    def __len__(self) -> int:
        """Get dataset size."""
        return len(self.metadata)

    def __getitem__(self, idx: int) -> dict:
        """Get dataset item.

        Returns:
            Dictionary containing audio, text, prosody, etc.
        """
        # Placeholder implementation
        return {
            "audio": torch.randn(self.sample_rate * 2),
            "text": torch.randint(0, 100, (50,)),
            "prosody": torch.randn(50, 256),
            "speaker_id": torch.tensor(0),
            "emotion_id": torch.tensor(0),
        }


def get_dataloader(
    data_dir: str,
    split: str = "train",
    batch_size: int = 32,
    num_workers: int = 4,
    shuffle: bool = True,
) -> DataLoader:
    """Get dataloader.

    Args:
        data_dir: Data directory
        split: Dataset split
        batch_size: Batch size
        num_workers: Number of workers
        shuffle: Whether to shuffle

    Returns:
        DataLoader
    """
    dataset = TTSDataset(data_dir, split)
    return DataLoader(
        dataset,
        batch_size=batch_size,
        shuffle=shuffle,
        num_workers=num_workers,
        pin_memory=True,
    )
