utils/load_model_weights.py

"""
Functions for downloading model weights from Hugging Face repositories.
"""
import os
import sys
import time
import logging
import traceback
import torch  # Add missing torch import
from pathlib import Path
from typing import Dict, Optional, Tuple, List, Any, Union
from urllib.error import HTTPError
from huggingface_hub import hf_hub_download, HfFileSystem, HfApi

# Add the current directory to Python's path to ensure modules are found
sys.path.append(os.path.dirname(os.path.abspath(__file__)))

# Configure Logging
logger = logging.getLogger(__name__)  # Fix typo: getLOgger -> getLogger

# Try local direct import first with fallback to a minimal version
try:
    from model_repo_config import get_repo_config
    logger.info("Successfully imported model_repo_config")
except ImportError:
    logger.warning("model_repo_config module not found, using minimal implementation")
    
    # Define minimal version inline as fallback
    class MinimalRepoConfig:
        """Minimal repository config for fallback"""
        def __init__(self):
            self.repo_id = "EvolphTech/Weights"
            self.cache_dir = "/tmp/tlm_cache"
            self.weight_locations = ["Wildnerve-tlm01-0.05Bx12.bin", "model.bin", "pytorch_model.bin"]
            self.snn_weight_locations = ["stdp_model_epoch_30.bin", "snn_model.bin"] 
            self.default_repo = "EvolphTech/Weights"
            self.alternative_paths = ["Wildnerve/tlm-0.05Bx12", "Wildnerve/tlm", "EvolphTech/Checkpoints"]
            logger.info("Using minimal repository config")
            
        def get_auth_token(self):
            """Get authentication token from environment"""
            return os.environ.get("HF_TOKEN") or os.environ.get("HF_API_TOKEN")
            
        def save_download_status(self, success, files):
            """Minimal implementation that just logs"""
            logger.info(f"Download status: success={success}, files={len(files) if files else 0}")
    
    def get_repo_config():
        """Get minimal repository config"""
        return MinimalRepoConfig()

# Only set if not already set
if not os.environ.get("HF_TOKEN"):
    os.environ["HF_TOKEN"] = "your_token_here"  # Replace with your actual token

# Configure logging
logger = logging.getLogger(__name__)

def verify_token():
    """Verify the HF token is available and properly formatted."""
    token = os.environ.get("HF_TOKEN", os.environ.get("HF_API_TOKEN"))
    if token:
        token_length = len(token)
        token_preview = token[:5] + "..." + token[-5:] if token_length > 10 else "too_short"
        logger.info(f"HF Token found: length={token_length}, preview={token_preview}")
        
        # Test if token works against a public Hugging Face API endpoint
        try:
            import requests
            headers = {"Authorization": f"Bearer {token}"}
            test_url = "https://huggingface.co/api/whoami"
            response = requests.get(test_url, headers=headers, timeout=10)
            if response.status_code == 200:
                user_info = response.json()
                logger.info(f"Token validated for user: {user_info.get('name', 'unknown')}")
                return True
            else:
                logger.warning(f"Token validation failed: {response.status_code} - {response.text[:100]}")
        except Exception as e:
            logger.warning(f"Error testing token: {e}")
        
        # Even if test fails, return True if we have a token
        return True
    else:
        logger.error("❌ HF Token not found in environment variables!")
        return False

# Call this early in the script or application startup
token_verified = verify_token()

def verify_repository(repo_id: str, token: Optional[str] = None) -> Tuple[bool, List[str]]:
    """
    Verify that a repository exists and is accessible.
    
    Args:
        repo_id: Repository ID to verify
        token: Optional Hugging Face API token
        
    Returns:
        (success, files): Tuple of success flag and list of files
    """
    try:
        # Try to list the repository contents
        api = HfApi()
        logger.info(f"Verifying access to repository: {repo_id}")
        
        try:
            files = api.list_repo_files(repo_id, token=token)
            logger.info(f"Repository {repo_id} is accessible")
            logger.info(f"Found {len(files)} files in repository")
            return True, files
            
        except Exception as e:
            error_msg = str(e).lower()
            
            if "not found" in error_msg or "404" in error_msg:
                logger.error(f"Repository {repo_id} not found. Please check the name.")
                return False, []
            elif "unauthorized" in error_msg or "permission" in error_msg or "401" in error_msg:
                if token:
                    logger.error(f"Authentication failed for repository {repo_id} despite token")
                else:
                    logger.error(f"No token provided for private repository {repo_id}")
                return False, []
            else:
                logger.error(f"Error accessing repository {repo_id}: {e}")
                return False, []
    except Exception as e:
        logger.error(f"Unexpected error verifying repository {repo_id}: {e}")
        return False, []

def download_file(repo_id: str, file_path: str, cache_dir: str, token: Optional[str] = None) -> Optional[str]:
    """
    Download a file from a Hugging Face repository with retry logic.
    
    Args:
        repo_id: Repository ID
        file_path: Path to the file within the repository
        cache_dir: Directory to save the file
        token: Optional Hugging Face API token
        
    Returns:
        Path to the downloaded file if successful, None otherwise
    """
    max_retries = 3
    for attempt in range(1, max_retries + 1):
        try:
            logger.info(f"Downloading {file_path} from {repo_id} (attempt {attempt}/{max_retries})...")
            local_path = hf_hub_download(
                repo_id=repo_id,
                filename=file_path,
                cache_dir=cache_dir,
                force_download=attempt > 1,
                token=token
            )
            logger.info(f"Successfully downloaded {file_path} to {local_path}")
            return local_path
        except Exception as e:
            logger.warning(f"Failed to download {file_path} from {repo_id} (attempt {attempt}/{max_retries}): {e}")
            if attempt == max_retries:
                return None
            time.sleep(1)  # Wait before retry

def check_for_local_weights():
    """Check if weights are available locally"""
    # First check if we've already found weights (avoid redundant checks)
    if os.environ.get("MODEL_WEIGHTS_FOUND") == "true" or os.environ.get("USING_LOCAL_WEIGHTS") == "true":
        logger.info("Using previously found local weights")
        return True
    
    # Check for transformer weights
    transformer_weights = os.environ.get("TLM_TRANSFORMER_WEIGHTS")
    if transformer_weights and os.path.exists(transformer_weights):
        logger.info(f"Found transformer weights locally at: {transformer_weights}")
        
        # Check for SNN weights
        snn_weights = os.environ.get("TLM_SNN_WEIGHTS")
        if snn_weights and os.path.exists(snn_weights):
            logger.info(f"Found SNN weights locally at: {snn_weights}")
            
        # Set environment variable to indicate weights are found
        os.environ["MODEL_WEIGHTS_FOUND"] = "true"
        os.environ["USING_LOCAL_WEIGHTS"] = "true"
        return True
    
    # Check common paths for transformer weights
    transformer_paths = [
        "/app/Weights/Transformer/Wildnerve-tlm01-0.05Bx12.bin",
        "/app/Weights/Wildnerve-tlm01-0.05Bx12.bin", 
        "/app/weights/Wildnerve-tlm01-0.05Bx12.bin",
        "./Weights/Transformer/Wildnerve-tlm01-0.05Bx12.bin",
        "./Weights/Wildnerve-tlm01-0.05Bx12.bin"
    ]
    
    for path in transformer_paths:
        if os.path.exists(path):
            logger.info(f"Found transformer weights at: {path}")
            os.environ["TLM_TRANSFORMER_WEIGHTS"] = path
            os.environ["MODEL_WEIGHTS_FOUND"] = "true"
            
            # Check for SNN weights
            snn_paths = [
                "/app/Weights/SNN/stdp_model_epoch_30.bin",
                "/app/Weights/stdp_model_epoch_30.bin",
                "/app/weights/stdp_model_epoch_30.bin",
                "./Weights/SNN/stdp_model_epoch_30.bin",
                "./Weights/stdp_model_epoch_30.bin"
            ]
            
            for snn_path in snn_paths:
                if os.path.exists(snn_path):
                    logger.info(f"Found SNN weights at: {snn_path}")
                    os.environ["TLM_SNN_WEIGHTS"] = snn_path
                    break
                    
            return True
    
    return False

def load_model_weights(model=None):
    """Load model weights from local files or download from repository."""
    # Check for local model weights first
    logger.info("Checking for local model weights...")
    if check_for_local_weights():
        logger.info("Using local weights, skipping repository download")
        return {
            "transformer": os.environ.get("TLM_TRANSFORMER_WEIGHTS"),
            "snn": os.environ.get("TLM_SNN_WEIGHTS")
        }

    # Only attempt to download if no local weights
    logger.info("No local weights found, attempting to download from repository")
    
    # Get repository configuration
    config = get_repo_config()
    repo_id_base = config.repo_id
    cache_dir = config.cache_dir
    sub_dir = None
    
    return download_model_files(repo_id_base, sub_dir, cache_dir)

def download_model_files(repo_id_base: str, sub_dir: Optional[str] = None, 
                     cache_dir: Optional[str] = None) -> Dict[str, str]:
    """
    Download model files from a Hugging Face repository.
    
    Args:
        repo_id_base: Base repository ID
        sub_dir: Optional subdirectory within the repository
        cache_dir: Optional cache directory
        
    Returns:
        Dictionary of downloaded files (file_type: local_path)
    """
    # Get global configuration
    config = get_repo_config()
    
    # Use provided cache_dir or fall back to config's cache_dir
    cache_dir = cache_dir or config.cache_dir
    
    # Get authentication token if available
    token = config.get_auth_token()
    
    # Dictionary to store downloaded file paths
    downloaded_files = {}
    
    # FIRST: Check if weights exist locally in the current directory or app directory
    local_weight_paths = [
        "./Wildnerve-tlm01-0.05Bx12.bin",
        "./weights/Wildnerve-tlm01-0.05Bx12.bin",
        "./pytorch_model.bin",
        "./model.bin",
        "/app/Wildnerve-tlm01-0.05Bx12.bin",  # For HF Spaces environment
        "/app/weights/Wildnerve-tlm01-0.05Bx12.bin",
        "/app/pytorch_model.bin"
    ]
    
    # Look for local weights first
    logger.info("Checking for local model weights...")
    for weight_path in local_weight_paths:
        if os.path.exists(weight_path):
            logger.info(f"Found local weights: {weight_path}")
            downloaded_files["transformer"] = weight_path
            # Try to find a config file too
            local_config_paths = [
                os.path.join(os.path.dirname(weight_path), "config.json"),
                "./config.json",
                "/app/config.json"
            ]
            for config_path in local_config_paths:
                if os.path.exists(config_path):
                    downloaded_files["config"] = config_path
                    break
            
            # Set environment variables
            os.environ["TLM_TRANSFORMER_WEIGHTS"] = downloaded_files["transformer"]
            if "config" in downloaded_files:
                os.environ["TLM_CONFIG_PATH"] = downloaded_files["config"]
                
            # Return early since we found local weights
            logger.info(f"Using local weights: {weight_path}")
            return downloaded_files
    
    # If no local weights, continue with normal HF download procedure
    logger.info("No local weights found, attempting to download from repository")
    
    # Create full repository path (with subdir if provided)
    repo_id = repo_id_base
    if sub_dir:
        # Remove any trailing slashes from repo_id and leading slashes from sub_dir
        repo_id = repo_id_base.rstrip('/') + '/' + sub_dir.lstrip('/')
    
    # First try the primary Wildnerve model repository
    wildnerve_repo = "Wildnerve/tlm-0.05Bx12"
    logger.info(f"Trying primary Wildnerve model repository: {wildnerve_repo}")
    
    success, files = verify_repository(wildnerve_repo, token)
    if success:
        repo_id = wildnerve_repo
    else:
        # Verify repository exists and is accessible
        success, files = verify_repository(repo_id, token)
        if not success:
            # Try alternatives
            logger.info(f"Primary repository {repo_id} not accessible, trying alternatives")
            
            # Try Wildnerve model repo variants first
            wildnerve_variants = ["Wildnerve/tlm", "EvolphTech/Checkpoints"]
            for wildnerve_alt in wildnerve_variants:
                logger.info(f"Trying Wildnerve alternative: {wildnerve_alt}")
                success, files = verify_repository(wildnerve_alt, token)
                if success:
                    repo_id = wildnerve_alt
                    break
                    
            # If still not successful, try other fallbacks
            if not success:
                for alt_repo in config.alternative_paths:
                    logger.info(f"Trying alternative repository: {alt_repo}")
                    success, files = verify_repository(alt_repo, token)
                    if success:
                        repo_id = alt_repo
                        break
                    
            # Use default if all alternatives fail
            if not success:
                repo_id = config.default_repo
                success, files = verify_repository(repo_id, token)
            
    # Dictionary to store downloaded file paths
    downloaded_files = {}
    
    # Download configuration if available
    try:
        logger.info(f"Downloading config from {repo_id}...")
        config_path = download_file(repo_id, "config.json", cache_dir, token)
        if config_path:
            downloaded_files["config"] = config_path
        else:
            logger.warning("Will use default config values")
    except Exception as e:
        logger.warning(f"Error downloading config: {e}")
    
    # Download transformer weights
    logger.info(f"Downloading transformer weights from {repo_id}...")
    transformer_path = None
    
    # First try the specific Wildnerve model file name
    wildnerve_paths = ["Wildnerve-tlm01-0.05Bx12.bin", "model.bin", "pytorch_model.bin"]
    for path in wildnerve_paths:
        logger.info(f"Trying Wildnerve model path: {path}")
        transformer_path = download_file(repo_id, path, cache_dir, token)
        if transformer_path:
            downloaded_files["transformer"] = transformer_path
            break
    
    # If that doesn't work, try the standard paths
    if not transformer_path:
        for path in config.weight_locations:
            transformer_path = download_file(repo_id, path, cache_dir, token)
            if transformer_path:
                downloaded_files["transformer"] = transformer_path
                break
            logger.info(f"Trying path: {path}")
    
    if not transformer_path:
        logger.warning("No transformer weights found, trying public BERT model as fallback")
        try:
            # Try to download BERT weights
            transformer_path = download_file(config.default_repo, "pytorch_model.bin", cache_dir, token)
            if transformer_path:
                downloaded_files["transformer"] = transformer_path
                logger.info("Successfully downloaded fallback BERT model")
            else:
                # Additional fallbacks to try
                for alt_repo in ["bert-base-uncased", "distilbert-base-uncased"]:
                    transformer_path = download_file(alt_repo, "pytorch_model.bin", cache_dir, token)
                    if transformer_path:
                        downloaded_files["transformer"] = transformer_path
                        logger.info(f"Successfully downloaded fallback model from {alt_repo}")
                        break
        except Exception as e:
            logger.error(f"Failed to download fallback model: {e}")
    
    # Download SNN weights if transformer weights were found
    if "transformer" in downloaded_files:
        logger.info(f"Downloading SNN weights from {repo_id}...")
        snn_path = None
        
        for path in config.snn_weight_locations:
            snn_path = download_file(repo_id, path, cache_dir, token)
            if snn_path:
                downloaded_files["snn"] = snn_path
                break
            logger.info(f"Trying path: {path}")
    
    # Set environment variables for other modules to use
    if "transformer" in downloaded_files:
        os.environ["TLM_TRANSFORMER_WEIGHTS"] = downloaded_files["transformer"]
    if "snn" in downloaded_files:
        os.environ["TLM_SNN_WEIGHTS"] = downloaded_files["snn"]
    
    # Save download status
    config.save_download_status(bool(downloaded_files), downloaded_files)
    
    return downloaded_files

def find_expanded_weights(base_weight_path, target_dim=768):
    """
    Find expanded weights in various potential locations based on the base weight path.
    
    Args:
        base_weight_path: Path to the original weights file
        target_dim: Target embedding dimension to look for
        
    Returns:
        Path to expanded weights if found, otherwise None
    """
    if not base_weight_path:
        return None
        
    base_name = os.path.basename(base_weight_path)
    base_stem, ext = os.path.splitext(base_name)
    expanded_name = f"{base_stem}_expanded_{target_dim}{ext}"
    
    # Check in common writable directories
    common_dirs = [
        "/tmp",
        "/tmp/tlm_data",
        os.environ.get("TLM_DATA_DIR", "/tmp/tlm_data")
    ]
    
    # Also check the original directory
    original_dir = os.path.dirname(base_weight_path)
    if original_dir:
        common_dirs.append(original_dir)
    
    # Check each location
    for directory in common_dirs:
        if not directory:
            continue
            
        expanded_path = os.path.join(directory, expanded_name)
        if os.path.exists(expanded_path):
            logger.info(f"Found expanded weights at {expanded_path}")
            return expanded_path
    
    # Check just the base filename for absolute paths
    if os.path.exists(expanded_name):
        return expanded_name
    
    return None

def load_weights_into_model(model, weights_path: str, strict: bool = False) -> bool:
    """
    Load weights from a file into a model.
    
    Args:
        model: The model to load weights into
        weights_path: Path to the weights file
        strict: Whether to strictly enforce that the keys in the weights file match the model
        
    Returns:
        bool: True if weights were successfully loaded, False otherwise
    """
    try:
        logger.info(f"Loading weights from {weights_path}")
        
        # Try expanded weights first
        expanded_path = find_expanded_weights(weights_path)
        if expanded_path:
            logger.info(f"Using expanded weights: {expanded_path}")
            weights_path = expanded_path
        
        # Load the state dictionary
        state_dict = torch.load(weights_path, map_location="cpu")
        
        # If state_dict has nested structure, extract the actual model weights
        if isinstance(state_dict, dict) and "model_state_dict" in state_dict:
            state_dict = state_dict["model_state_dict"]
        elif isinstance(state_dict, dict) and "state_dict" in state_dict:
            state_dict = state_dict["state_dict"]
            
        # Special handling for Wildnerve-tlm01-0.05Bx12 model
        if "Wildnerve-tlm01" in str(model.__class__):
            logger.info("Detected Wildnerve-tlm01 model, applying special weight loading")
            
            # Check if keys need to be remapped
            model_keys = dict(model.named_parameters())
            state_dict_keys = set(state_dict.keys())
            
            # Check key alignment
            if not any(k in state_dict_keys for k in model_keys.keys()):
                logger.info("Wildnerve model keys don't match state dict keys, attempting remapping")
                
                # Create mapping for common Wildnerve model patterns
                key_mappings = {
                    "embedding.weight": ["embeddings.word_embeddings.weight", "embedding.weight", "word_embeddings.weight"],
                    "pos_encoder.pe": ["position_embeddings.weight", "pos_encoder.pe", "pe"],
                    "transformer_encoder": ["encoder.layer", "transformer.encoder", "transformer_encoder"],
                    "classifier.weight": ["output.weight", "classifier.weight", "lm_head.weight"],
                    "classifier.bias": ["output.bias", "classifier.bias", "lm_head.bias"]
                }
                
                # Apply mappings
                adapted_state_dict = {}
                for target_key, source_keys in key_mappings.items():
                    for source_key in source_keys:
                        for sd_key in state_dict_keys:
                            if source_key in sd_key:
                                if target_key not in model_keys:
                                    # Find a target key that's close enough
                                    for mk in model_keys:
                                        if target_key.split('.')[0] in mk:
                                            adapted_state_dict[mk] = state_dict[sd_key]
                                            break
                                else:
                                    adapted_state_dict[target_key] = state_dict[sd_key]
                
                # Try to load the remapped weights
                if adapted_state_dict:
                    logger.info(f"Attempting to load with {len(adapted_state_dict)} remapped keys")
                    try:
                        missing_keys, unexpected_keys = model.load_state_dict(adapted_state_dict, strict=False)
                        logger.info(f"Loaded remapped weights with {len(missing_keys)} missing keys and {len(unexpected_keys)} unexpected keys")
                        return True
                    except Exception as e:
                        logger.error(f"Error loading remapped weights: {e}")
                
        # Special handling for transformer models from Hugging Face
        if all(k.startswith("bert.") or k.startswith("roberta.") or k.startswith("model.") for k in state_dict.keys()):
            # Try to adapt the state dict keys to match our model
            logger.info("Adapting pretrained Hugging Face transformer weights")
            adapted_state_dict = {}
            
            # Map expected model keys to state dict keys
            key_mappings = {
                # Common mappings for transformer models
                "embedding.weight": ["embeddings.word_embeddings.weight", "bert.embeddings.word_embeddings.weight"],
                "pos_encoder.pe": ["embeddings.position_embeddings.weight", "bert.embeddings.position_embeddings.weight"],
                "transformer_encoder": ["encoder.layer", "bert.encoder.layer"],
                "classifier.weight": ["cls.predictions.decoder.weight", "bert.pooler.dense.weight"],
                "classifier.bias": ["cls.predictions.decoder.bias", "bert.pooler.dense.bias"]
            }
            
            # Try to map keys from state dict to model
            model_keys = dict(model.named_parameters())
            
            # First try exact matches
            for target_key, source_keys in key_mappings.items():
                for source_key in source_keys:
                    if source_key in state_dict:
                        adapted_state_dict[target_key] = state_dict[source_key]
                        break
            
            # If we have very few matches, try partial matches
            if len(adapted_state_dict) < len(model_keys) * 0.1:
                logger.info("Using partial key matching for weights")
                for model_key in model_keys:
                    for sd_key in state_dict:
                        # Skip keys already matched
                        if model_key in adapted_state_dict:
                            continue
                            
                        # Try to find common substrings in the key names
                        key_parts = model_key.split('.')
                        sd_parts = sd_key.split('.')
                        
                        # Check for common parts like "attention", "layer", etc.
                        common_parts = set(key_parts) & set(sd_parts)
                        if len(common_parts) > 0:
                            adapted_state_dict[model_key] = state_dict[sd_key]
                            break
            
            # If we still don't have many matches, try direct loading with non-strict mode
            if len(adapted_state_dict) < len(model_keys) * 0.5:
                logger.warning(f"Could not adapt many keys ({len(adapted_state_dict)}/{len(model_keys)})")
                logger.warning("Attempting to load original state dict with non-strict mode")
                try:
                    # Load with non-strict mode to allow partial loading
                    missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)
                    logger.info(f"Loaded weights with {len(missing_keys)} missing keys and {len(unexpected_keys)} unexpected keys")
                    return True
                except Exception as e:
                    logger.error(f"Error loading original state dict: {e}")
                    return False
            else:
                # Load adapted state dict
                logger.info(f"Loading adapted state dict with {len(adapted_state_dict)} keys")
                try:
                    missing_keys, unexpected_keys = model.load_state_dict(adapted_state_dict, strict=False)
                    logger.info(f"Loaded weights with {len(missing_keys)} missing keys and {len(unexpected_keys)} unexpected keys")
                    return True
                except Exception as e:
                    logger.error(f"Error loading adapted state dict: {e}")
                    return False
        else:
            # Standard loading
            try:
                missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=strict)
                logger.info(f"Loaded weights with {len(missing_keys)} missing keys and {len(unexpected_keys)} unexpected keys")
                return True
            except Exception as e:
                logger.error(f"Error loading state dict: {e}")
                
                # Try non-strict loading if strict failed
                if strict:
                    logger.info("Attempting non-strict loading")
                    try:
                        missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)
                        logger.info(f"Loaded weights with {len(missing_keys)} missing keys and {len(unexpected_keys)} unexpected keys")
                        return True
                    except Exception as ne:
                        logger.error(f"Non-strict loading also failed: {ne}")
                
                return False
    except Exception as e:
        logger.error(f"Failed to load weights: {e}")
        return False

def list_model_files(repo_id: str, token: Optional[str] = None) -> List[str]:
    """
    List model files in a repository.
    
    Args:
        repo_id: Repository ID
        token: Optional Hugging Face API token
        
    Returns:
        List of file paths
    """
    try:
        api = HfApi()
        files = api.list_repo_files(repo_id, token=token)
        
        # Filter for model files
        model_files = [f for f in files if f.endswith('.bin') or f.endswith('.pt') or f.endswith('.pth')]
        logger.info(f"Found {len(model_files)} model files in {repo_id}")
        
        return model_files
    except Exception as e:
        logger.error(f"Error listing model files in {repo_id}: {e}")
        return []

if __name__ == "__main__":
    # Configure logging
    logging.basicConfig(level=logging.INFO)
    
    # Get arguments
    import argparse
    parser = argparse.ArgumentParser(description="Download model weights")
    parser.add_argument("--repo-id", type=str, default=None, help="Repository ID")
    parser.add_argument("--sub-dir", type=str, default=None, help="Subdirectory within repository")
    parser.add_argument("--cache-dir", type=str, default=None, help="Cache directory")
    args = parser.parse_args()
    
    # Download model files
    repo_id = args.repo_id or os.environ.get("MODEL_REPO") or get_repo_config().repo_id
    result = download_model_files(repo_id, args.sub_dir, args.cache_dir)
    
    # Print results
    print(f"\nDownload Results:")
    if "transformer" in result:
        print(f"Transformer weights: {result['transformer']}")
    else:
        print(f"⚠️ No transformer weights downloaded")
        
    if "snn" in result:
        print(f"SNN weights: {result['snn']}")
    else:
        print(f"⚠️ No SNN weights downloaded")