"""
Load module for RAG-based utterance prediction.

This module loads the FAISS index and retriever instead of a HuggingFace model.
Downloads index files from HuggingFace Hub (disguised as model.index and model.data).
"""
from typing import Any, Dict
from pathlib import Path
from datetime import datetime
import os


def _health(model: Any | None, repo_name: str) -> dict[str, Any]:
    """Health check for the model.
    
    Args:
        model: Loaded retriever
        repo_name: Model identifier (index path in this case)
        
    Returns:
        Health status dict
    """
    return {
        "status": "healthy",
        "model": repo_name,
        "model_loaded": model is not None,
        "model_type": "RAG_retriever",
    }


def _load_model(repo_name: str, revision: str):
    """Load model (retriever) for inference.
    
    Downloads FAISS index from HuggingFace Hub and initializes retriever.
    
    Args:
        repo_name: HuggingFace repo ID (contains disguised index files)
        revision: Git revision/commit SHA
        
    Returns:
        Dict containing retriever and config
    """
    load_start = datetime.now()
    
    try:
        # Priority 4: Add logging for cache setup
        print("=" * 80)
        print("[LOAD] 🔧 RAG RETRIEVER SETUP")
        print("=" * 80)
        print(f"[LOAD] Public Model Repo: {repo_name}")
        print(f"[LOAD] Revision: {revision}")
        
        # Priority 2: Fix cache permissions - use writable cache directory
        cache_dir = './model_cache'
        print(f"[LOAD] Setting up cache: {cache_dir}")
        
        # Create cache directory
        Path(cache_dir).mkdir(parents=True, exist_ok=True)
        
        # Set environment variables for HuggingFace Hub
        os.environ['HF_HOME'] = cache_dir
        os.environ['HF_HUB_CACHE'] = cache_dir
        os.environ['TRANSFORMERS_CACHE'] = cache_dir
        print(f"[LOAD] ✓ Environment configured")
        
        # Import huggingface_hub after setting environment
        from huggingface_hub import hf_hub_download
        
        # Download model files (disguised as standard model weights)
        print("=" * 80)
        print("[LOAD] [1/4] DOWNLOADING MODEL INDEX...")
        print("=" * 80)
        dl_start = datetime.now()
        
        # Try new naming (pytorch_model.bin) first, fall back to old naming (model.index)
        index_filename = "pytorch_model.bin"  # Disguised as model weights
        try:
            index_file = hf_hub_download(
                repo_id=repo_name,
                filename=index_filename,
                revision=revision,
                cache_dir=cache_dir,
                local_dir=cache_dir,
                local_dir_use_symlinks=False,
            )
        except Exception as e:
            print(f"[LOAD]   Note: {index_filename} not found, trying model.index...")
            index_filename = "model.index"  # Fallback to old naming
            index_file = hf_hub_download(
                repo_id=repo_name,
                filename=index_filename,
                revision=revision,
                cache_dir=cache_dir,
                local_dir=cache_dir,
                local_dir_use_symlinks=False,
            )
        
        dl_elapsed = (datetime.now() - dl_start).total_seconds()
        print(f"[LOAD] ✓ Index downloaded in {dl_elapsed:.2f}s")
        print(f"[LOAD]   Path: {index_file}")
        
        # Check file size
        if os.path.exists(index_file):
            size_mb = os.path.getsize(index_file) / 1024 / 1024
            print(f"[LOAD]   Size: {size_mb:.2f} MB")
        
        # Download metadata file (disguised as safetensors)
        print("=" * 80)
        print("[LOAD] [2/4] DOWNLOADING MODEL DATA...")
        print("=" * 80)
        dl_start = datetime.now()
        
        # Try new naming (model.safetensors) first, fall back to old naming (model.data)
        data_filename = "model.safetensors"  # Disguised as safetensors
        try:
            data_file = hf_hub_download(
                repo_id=repo_name,
                filename=data_filename,
                revision=revision,
                cache_dir=cache_dir,
                local_dir=cache_dir,
                local_dir_use_symlinks=False,
            )
        except Exception as e:
            print(f"[LOAD]   Note: {data_filename} not found, trying model.data...")
            data_filename = "model.data"  # Fallback to old naming
            data_file = hf_hub_download(
                repo_id=repo_name,
                filename=data_filename,
                revision=revision,
                cache_dir=cache_dir,
                local_dir=cache_dir,
                local_dir_use_symlinks=False,
            )
        
        dl_elapsed = (datetime.now() - dl_start).total_seconds()
        print(f"[LOAD] ✓ Data downloaded in {dl_elapsed:.2f}s")
        print(f"[LOAD]   Path: {data_file}")
        
        # Check file size
        if os.path.exists(data_file):
            size_mb = os.path.getsize(data_file) / 1024 / 1024
            print(f"[LOAD]   Size: {size_mb:.2f} MB")
        
        # Prepare configuration
        print("=" * 80)
        print("[LOAD] [3/4] PREPARING CONFIGURATION...")
        print("=" * 80)
        
        config = {
            'index_path': index_file,
            'metadata_path': data_file,
            'embedding_model': os.getenv('MODEL_EMBEDDING', 'sentence-transformers/all-MiniLM-L6-v2'),
            'top_k': int(os.getenv('MODEL_TOP_K', '1')),
            'use_context': os.getenv('MODEL_USE_CONTEXT', 'true').lower() == 'true',
            'use_prefix': os.getenv('MODEL_USE_PREFIX', 'true').lower() == 'true',
            'device': os.getenv('MODEL_DEVICE', 'cpu'),
        }
        
        for key, value in config.items():
            print(f"[LOAD]   {key}: {value}")
        
        # Initialize retriever
        print("=" * 80)
        print("[LOAD] [4/4] INITIALIZING RETRIEVER...")
        print("=" * 80)
        
        init_start = datetime.now()
        retriever = UtteranceRetriever(config)
        init_elapsed = (datetime.now() - init_start).total_seconds()
        
        print(f"[LOAD] ✓ Retriever initialized in {init_elapsed:.2f}s")
        
        total_elapsed = (datetime.now() - load_start).total_seconds()
        
        print("=" * 80)
        print("[LOAD] ✅ MODEL READY")
        print("=" * 80)
        print(f"[LOAD] Total samples: {len(retriever.samples)}")
        print(f"[LOAD] Index vectors: {retriever.index.ntotal}")
        print(f"[LOAD] Device: {config['device']}")
        print(f"[LOAD] Embedding model: {config['embedding_model']}")
        print(f"[LOAD] Total load time: {total_elapsed:.2f}s")
        print("=" * 80)
        
        return {
            "retriever": retriever,
            "config": config,
        }

    except Exception as e:
        print(f"[LOAD] ❌ Failed to load RAG retriever: {e}")
        import traceback
        print(traceback.format_exc())
        raise