"""
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 os
import pickle
import signal
from pathlib import Path
from typing import Optional

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


class _TimeoutError(Exception):
    pass


def _timeout_handler(signum, frame):
    raise _TimeoutError("File processing timed out")

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}")
    import pretty_midi
    import subprocess
    import glob

    # Fast git clone instead of slow per-file snapshot_download
    midi_dir = path_config.data_dir / "midi_files"
    if not midi_dir.exists():
        logger.info("Cloning MIDI dataset repo (faster than per-file download)...")
        subprocess.run(
            ["git", "clone", "--depth", "1",
             f"https://huggingface.co/datasets/{data_config.dataset_name}",
             str(midi_dir)],
            check=True,
        )
    else:
        logger.info(f"Using cached MIDI files from {midi_dir}")

    # Find all MIDI files
    midi_files = sorted(
        glob.glob(f"{midi_dir}/**/*.mid", recursive=True)
        + glob.glob(f"{midi_dir}/**/*.midi", recursive=True)
        + glob.glob(f"{midi_dir}/**/*.MID", recursive=True)
    )
    logger.info(f"Found {len(midi_files)} MIDI files")

    all_sequences = []
    errors = 0

    for i, midi_path in enumerate(midi_files):
        try:
            # Skip files > 100KB (large orchestral pieces cause slow processing)
            if os.path.getsize(midi_path) > 100_000:
                errors += 1
                continue
            # Skip git LFS pointer files
            with open(midi_path, "rb") as f:
                header = f.read(20)
            if header.startswith(b"version https://git"):
                errors += 1
                continue
            # 30s timeout per file to avoid hangs on complex MIDI
            old_handler = signal.signal(signal.SIGALRM, _timeout_handler)
            signal.alarm(30)
            try:
                midi = pretty_midi.PrettyMIDI(midi_path)
                tokens = tokenizer.midi_to_tokens(midi, max_len=data_config.max_seq_len + 1)
                if len(tokens) >= 20:
                    all_sequences.append(tokens)
            finally:
                signal.alarm(0)
                signal.signal(signal.SIGALRM, old_handler)
        except (_TimeoutError, Exception) as e:
            errors += 1
            if errors <= 10:
                logger.warning(f"Error processing {midi_path}: {e}")

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