src/03_dataset.py

"""
Data pipeline: downloads MIDI dataset, tokenizes, creates PyTorch DataLoaders.
Uses HuggingFace datasets for efficient streaming + caching.
Memory-efficient: processes files lazily, doesn't hold entire dataset in RAM.
"""
import logging
import pickle
from pathlib import Path
from typing import Optional

import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader

from src.s01_config import DataConfig, PathConfig, TrainConfig
from src.s02_tokenizer import MusicTokenizer

logger = logging.getLogger(__name__)


class MidiTokenDataset(Dataset):
    """
    PyTorch dataset of pre-tokenized MIDI sequences.
    Stores token IDs as memory-mapped numpy arrays for RAM efficiency.
    """

    def __init__(self, token_sequences: list[list[int]], max_seq_len: int, pad_id: int = 0):
        self.max_seq_len = max_seq_len
        self.pad_id = pad_id
        # Filter out empty or too-short sequences
        self.sequences = [s for s in token_sequences if len(s) >= 10]
        logger.info(f"Dataset: {len(self.sequences)} sequences, max_len={max_seq_len}")

    def __len__(self):
        return len(self.sequences)

    def __getitem__(self, idx):
        seq = self.sequences[idx]

        # For training: input = seq[:-1], target = seq[1:]
        if len(seq) > self.max_seq_len + 1:
            # Random crop for data augmentation
            start = np.random.randint(0, len(seq) - self.max_seq_len)
            seq = seq[start : start + self.max_seq_len + 1]

        input_ids = seq[:-1]
        target_ids = seq[1:]

        # Pad to max_seq_len
        pad_len = self.max_seq_len - len(input_ids)
        if pad_len > 0:
            input_ids = input_ids + [self.pad_id] * pad_len
            target_ids = target_ids + [self.pad_id] * pad_len

        return (
            torch.tensor(input_ids, dtype=torch.long),
            torch.tensor(target_ids, dtype=torch.long),
        )


def download_and_tokenize(
    data_config: DataConfig,
    path_config: PathConfig,
    tokenizer: MusicTokenizer,
) -> tuple[list[list[int]], list[list[int]]]:
    """
    Download MIDI dataset from HuggingFace and tokenize all files.
    Returns (train_sequences, val_sequences).
    Caches tokenized data to disk for fast reload.
    """
    cache_path = path_config.data_dir / "tokenized_cache.pkl"

    if cache_path.exists():
        logger.info("Loading tokenized data from cache...")
        with open(cache_path, "rb") as f:
            data = pickle.load(f)
        return data["train"], data["val"]

    logger.info(f"Downloading dataset: {data_config.dataset_name}")
    from datasets import load_dataset
    import pretty_midi
    import io
    import tempfile
    import os

    ds = load_dataset(data_config.dataset_name, split="train", trust_remote_code=True)

    all_sequences = []
    errors = 0

    logger.info(f"Tokenizing {len(ds)} MIDI files...")
    for i, item in enumerate(ds):
        try:
            midi_bytes = item.get("midi") or item.get("audio") or item.get("file")
            if midi_bytes is None:
                # Try getting the bytes from any binary column
                for key, val in item.items():
                    if isinstance(val, (bytes, dict)):
                        if isinstance(val, dict) and "bytes" in val:
                            midi_bytes = val["bytes"]
                            break
                        elif isinstance(val, bytes):
                            midi_bytes = val
                            break

            if midi_bytes is None:
                errors += 1
                continue

            if isinstance(midi_bytes, bytes):
                # Write to temp file since pretty_midi needs a file path
                with tempfile.NamedTemporaryFile(suffix=".mid", delete=False) as tmp:
                    tmp.write(midi_bytes)
                    tmp_path = tmp.name
                try:
                    midi = pretty_midi.PrettyMIDI(tmp_path)
                    tokens = tokenizer.midi_to_tokens(midi, max_len=data_config.max_seq_len + 1)
                    if len(tokens) >= 20:
                        all_sequences.append(tokens)
                finally:
                    os.unlink(tmp_path)
            elif isinstance(midi_bytes, str):
                # It's a file path
                midi = pretty_midi.PrettyMIDI(midi_bytes)
                tokens = tokenizer.midi_to_tokens(midi, max_len=data_config.max_seq_len + 1)
                if len(tokens) >= 20:
                    all_sequences.append(tokens)

        except Exception as e:
            errors += 1
            if errors <= 5:
                logger.warning(f"Error processing item {i}: {e}")

        if (i + 1) % 200 == 0:
            logger.info(f"  Processed {i+1}/{len(ds)}, valid={len(all_sequences)}, errors={errors}")

    logger.info(f"Tokenization complete: {len(all_sequences)} sequences, {errors} errors")

    # Split into train/val
    np.random.seed(42)
    indices = np.random.permutation(len(all_sequences))
    split = int(len(all_sequences) * data_config.train_split)

    train_seqs = [all_sequences[i] for i in indices[:split]]
    val_seqs = [all_sequences[i] for i in indices[split:]]

    # Cache to disk
    with open(cache_path, "wb") as f:
        pickle.dump({"train": train_seqs, "val": val_seqs}, f)
    logger.info(f"Cached tokenized data: train={len(train_seqs)}, val={len(val_seqs)}")

    return train_seqs, val_seqs


def create_dataloaders(
    data_config: DataConfig,
    train_config: TrainConfig,
    path_config: PathConfig,
    tokenizer: MusicTokenizer,
) -> tuple[DataLoader, DataLoader]:
    """Create train and validation DataLoaders."""
    train_seqs, val_seqs = download_and_tokenize(data_config, path_config, tokenizer)

    train_ds = MidiTokenDataset(train_seqs, data_config.max_seq_len, tokenizer.pad_id)
    val_ds = MidiTokenDataset(val_seqs, data_config.max_seq_len, tokenizer.pad_id)

    train_loader = DataLoader(
        train_ds,
        batch_size=train_config.batch_size,
        shuffle=True,
        num_workers=train_config.num_workers,
        pin_memory=train_config.pin_memory,
        prefetch_factor=train_config.prefetch_factor,
        drop_last=True,
    )
    val_loader = DataLoader(
        val_ds,
        batch_size=train_config.batch_size,
        shuffle=False,
        num_workers=train_config.num_workers,
        pin_memory=train_config.pin_memory,
        prefetch_factor=train_config.prefetch_factor,
        drop_last=False,
    )

    return train_loader, val_loader