"""
Optimized checkpoint utilities for faster saving/loading.

Features:
- Async checkpoint saving (non-blocking)
- Compression (gzip) for smaller files
- Incremental checkpoints (only save changed weights)
- Checkpoint validation
"""

import gzip
import logging
from concurrent.futures import ThreadPoolExecutor
from pathlib import Path
from typing import Any, Dict, Optional
import torch
import torch.nn as nn

logger = logging.getLogger(__name__)

# Global thread pool for async operations
_executor = ThreadPoolExecutor(max_workers=2)


def save_checkpoint_async(
    checkpoint_data: Dict[str, Any],
    checkpoint_path: Path,
    compress: bool = True,
    validate: bool = True,
) -> None:
    """
    Save checkpoint asynchronously (non-blocking).

    Args:
        checkpoint_data: Checkpoint data dict
        checkpoint_path: Path to save checkpoint
        compress: Whether to compress checkpoint (gzip)
        validate: Whether to validate checkpoint after saving
    """
    checkpoint_path.parent.mkdir(parents=True, exist_ok=True)

    def _save():
        try:
            if compress:
                # Save compressed
                with gzip.open(f"{checkpoint_path}.gz", "wb") as f:
                    torch.save(checkpoint_data, f)
                logger.debug(f"Saved compressed checkpoint to {checkpoint_path}.gz")
            else:
                # Save uncompressed
                torch.save(checkpoint_data, checkpoint_path)
                logger.debug(f"Saved checkpoint to {checkpoint_path}")

            if validate:
                # Validate by loading
                if compress:
                    with gzip.open(f"{checkpoint_path}.gz", "rb") as f:
                        _ = torch.load(f)
                else:
                    _ = torch.load(checkpoint_path)
                logger.debug(f"Validated checkpoint: {checkpoint_path}")

        except Exception as e:
            logger.error(f"Error saving checkpoint asynchronously: {e}")

    # Submit to thread pool (non-blocking)
    _executor.submit(_save)


def save_checkpoint_compressed(
    checkpoint_data: Dict[str, Any],
    checkpoint_path: Path,
    compression_level: int = 6,
) -> Path:
    """
    Save checkpoint with compression.

    Args:
        checkpoint_data: Checkpoint data dict
        checkpoint_path: Path to save checkpoint
        compression_level: Gzip compression level (0-9)

    Returns:
        Path to saved checkpoint (with .gz extension)
    """
    checkpoint_path.parent.mkdir(parents=True, exist_ok=True)
    compressed_path = checkpoint_path.with_suffix(checkpoint_path.suffix + ".gz")

    # Save compressed
    with gzip.open(compressed_path, "wb", compresslevel=compression_level) as f:
        torch.save(checkpoint_data, f)

    original_size = sum(
        p.stat().st_size
        for p in checkpoint_path.parent.glob(checkpoint_path.name)
        if p != compressed_path
    )
    compressed_size = compressed_path.stat().st_size
    compression_ratio = (1 - compressed_size / original_size) * 100 if original_size > 0 else 0

    logger.info(
        f"Saved compressed checkpoint: {compressed_path} "
        f"({compressed_size / 1024 / 1024:.2f} MB, "
        f"{compression_ratio:.1f}% compression)"
    )

    return compressed_path


def load_checkpoint_compressed(checkpoint_path: Path) -> Dict[str, Any]:
    """
    Load compressed checkpoint.

    Args:
        checkpoint_path: Path to checkpoint (with or without .gz extension)

    Returns:
        Checkpoint data dict
    """
    # Try compressed first
    if checkpoint_path.suffix == ".gz":
        compressed_path = checkpoint_path
    else:
        compressed_path = checkpoint_path.with_suffix(checkpoint_path.suffix + ".gz")

    if compressed_path.exists():
        with gzip.open(compressed_path, "rb") as f:
            checkpoint = torch.load(f, map_location="cpu")
        logger.info(f"Loaded compressed checkpoint from {compressed_path}")
        return checkpoint

    # Fallback to uncompressed
    if checkpoint_path.exists():
        checkpoint = torch.load(checkpoint_path, map_location="cpu")
        logger.info(f"Loaded checkpoint from {checkpoint_path}")
        return checkpoint

    raise FileNotFoundError(f"Checkpoint not found: {checkpoint_path}")


def save_incremental_checkpoint(
    model: nn.Module,
    optimizer,
    scheduler,
    epoch: int,
    loss: float,
    checkpoint_path: Path,
    base_checkpoint_path: Optional[Path] = None,
    save_full_every: int = 10,
) -> Path:
    """
    Save incremental checkpoint (only changed weights).

    Args:
        model: Model to save
        optimizer: Optimizer state
        scheduler: Scheduler state
        epoch: Current epoch
        loss: Current loss
        checkpoint_path: Path to save checkpoint
        base_checkpoint_path: Path to base checkpoint (for diff)
        save_full_every: Save full checkpoint every N epochs

    Returns:
        Path to saved checkpoint
    """
    checkpoint_path.parent.mkdir(parents=True, exist_ok=True)

    # Save full checkpoint periodically
    if base_checkpoint_path is None or epoch % save_full_every == 0:
        checkpoint_data = {
            "epoch": epoch,
            "model_state_dict": model.state_dict(),
            "optimizer_state_dict": optimizer.state_dict(),
            "scheduler_state_dict": scheduler.state_dict(),
            "loss": loss,
            "is_full": True,
        }
        torch.save(checkpoint_data, checkpoint_path)
        logger.info(f"Saved full checkpoint to {checkpoint_path}")
        return checkpoint_path

    # Save incremental checkpoint (diff from base)
    if base_checkpoint_path and base_checkpoint_path.exists():
        base_checkpoint = torch.load(base_checkpoint_path, map_location="cpu")
        base_state = base_checkpoint.get("model_state_dict", {})

        current_state = model.state_dict()
        diff_state = {}

        # Only save changed parameters
        for key, value in current_state.items():
            if key not in base_state or not torch.equal(value, base_state[key]):
                diff_state[key] = value

        checkpoint_data = {
            "epoch": epoch,
            "model_state_dict": diff_state,  # Only differences
            "optimizer_state_dict": optimizer.state_dict(),
            "scheduler_state_dict": scheduler.state_dict(),
            "loss": loss,
            "is_full": False,
            "base_checkpoint": str(base_checkpoint_path),
        }
        torch.save(checkpoint_data, checkpoint_path)
        logger.info(
            f"Saved incremental checkpoint to {checkpoint_path} "
            f"({len(diff_state)}/{len(current_state)} parameters changed)"
        )
        return checkpoint_path

    # Fallback to full checkpoint
    checkpoint_data = {
        "epoch": epoch,
        "model_state_dict": model.state_dict(),
        "optimizer_state_dict": optimizer.state_dict(),
        "scheduler_state_dict": scheduler.state_dict(),
        "loss": loss,
        "is_full": True,
    }
    torch.save(checkpoint_data, checkpoint_path)
    logger.info(f"Saved full checkpoint to {checkpoint_path}")
    return checkpoint_path


def load_incremental_checkpoint(
    model: nn.Module,
    checkpoint_path: Path,
    device: str = "cpu",
) -> Dict[str, Any]:
    """
    Load incremental checkpoint (applies diff to base).

    Args:
        model: Model to load weights into
        checkpoint_path: Path to incremental checkpoint
        device: Device to load on

    Returns:
        Checkpoint data dict
    """
    checkpoint = torch.load(checkpoint_path, map_location=device)

    if checkpoint.get("is_full", True):
        # Full checkpoint
        model.load_state_dict(checkpoint["model_state_dict"])
        logger.info(f"Loaded full checkpoint from {checkpoint_path}")
        return checkpoint

    # Incremental checkpoint - need to load base first
    base_checkpoint_path = Path(checkpoint.get("base_checkpoint", ""))
    if not base_checkpoint_path.exists():
        logger.warning(
            f"Base checkpoint not found: {base_checkpoint_path}. "
            "Loading incremental checkpoint as-is."
        )
        model.load_state_dict(checkpoint["model_state_dict"], strict=False)
        return checkpoint

    # Load base checkpoint
    base_checkpoint = torch.load(base_checkpoint_path, map_location=device)
    base_state = base_checkpoint.get("model_state_dict", {})

    # Apply diff
    diff_state = checkpoint["model_state_dict"]
    full_state = base_state.copy()
    full_state.update(diff_state)

    model.load_state_dict(full_state)
    logger.info(
        f"Loaded incremental checkpoint from {checkpoint_path} "
        f"(applied to base: {base_checkpoint_path})"
    )

    return checkpoint


def validate_checkpoint(checkpoint_path: Path) -> bool:
    """
    Validate checkpoint file integrity.

    Args:
        checkpoint_path: Path to checkpoint

    Returns:
        True if valid, False otherwise
    """
    try:
        if checkpoint_path.suffix == ".gz":
            with gzip.open(checkpoint_path, "rb") as f:
                checkpoint = torch.load(f, map_location="cpu")
        else:
            checkpoint = torch.load(checkpoint_path, map_location="cpu")

        # Check required keys
        required_keys = ["epoch", "model_state_dict"]
        if not all(key in checkpoint for key in required_keys):
            logger.error(f"Checkpoint missing required keys: {required_keys}")
            return False

        # Check state dict is valid
        if not isinstance(checkpoint["model_state_dict"], dict):
            logger.error("Checkpoint model_state_dict is not a dict")
            return False

        logger.info(f"Checkpoint validated: {checkpoint_path}")
        return True

    except Exception as e:
        logger.error(f"Checkpoint validation failed: {e}")
        return False


def get_checkpoint_size(checkpoint_path: Path) -> Dict[str, float]:
    """
    Get checkpoint file size information.

    Args:
        checkpoint_path: Path to checkpoint

    Returns:
        Dict with size information (bytes, mb, etc.)
    """
    sizes = {}

    # Check compressed version
    compressed_path = checkpoint_path.with_suffix(checkpoint_path.suffix + ".gz")
    if compressed_path.exists():
        sizes["compressed_bytes"] = compressed_path.stat().st_size
        sizes["compressed_mb"] = sizes["compressed_bytes"] / 1024 / 1024

    # Check uncompressed version
    if checkpoint_path.exists():
        sizes["uncompressed_bytes"] = checkpoint_path.stat().st_size
        sizes["uncompressed_mb"] = sizes["uncompressed_bytes"] / 1024 / 1024

        if "compressed_bytes" in sizes:
            sizes["compression_ratio"] = (
                1 - sizes["compressed_bytes"] / sizes["uncompressed_bytes"]
            ) * 100

    return sizes