"""
Pipeline Components Implementation
"""
from typing import Dict, List, Any, Optional
import json
import duckdb
from config import get_hf_token

class RemoteStoragePipeline:
    """Manages remote storage and caching of model data using HuggingFace storage"""
    def __init__(self, db_url: str = "hf://datasets/Fred808/helium/storage.json"):
        self.cache = {}
        self.db_url = db_url
        self.conn = self._init_db_connection()
        self._init_tables()

    def _init_db_connection(self) -> 'duckdb.DuckDBPyConnection':
        """Initialize database connection with local storage"""
        db_dir = "db/pipeline"
        os.makedirs(db_dir, exist_ok=True)
        
        db_path = os.path.join(db_dir, "pipeline.db")
        
        try:
            # Connect to local database
            conn = duckdb.connect(db_path)
            
            # Initialize extensions
            conn.execute("INSTALL httpfs;")
            conn.execute("LOAD httpfs;")
            
            logging.info(f"Connected to local database: {db_path}")
            return conn
            
        except Exception as e:
            logging.warning(f"Failed to connect to local database: {e}")
            logging.info("Using in-memory database instead")
            return duckdb.connect(":memory:")
        
    def _init_tables(self):
        """Initialize database tables"""
        self.conn.execute("""
            CREATE TABLE IF NOT EXISTS persistent_storage (
                key VARCHAR PRIMARY KEY,
                data BLOB
            )
        """)
        
    def store(self, key: str, data: Any) -> None:
        """Store data with persistence"""
        # Store in cache
        self.cache[key] = data
        
        # Store in remote storage
        self.conn.execute("""
            INSERT OR REPLACE INTO persistent_storage (key, data)
            VALUES (?, ?)
        """, [key, json.dumps(data).encode()])
        
    def load(self, key: str) -> Optional[Any]:
        """Load data with caching"""
        # Check cache first
        if key in self.cache:
            return self.cache[key]
            
        # Try loading from remote storage
        result = self.conn.execute("""
            SELECT data FROM persistent_storage
            WHERE key = ?
        """, [key]).fetchone()
        
        if result:
            data = json.loads(result[0].decode())
            self.cache[key] = data
            return data
            
        return None

class WeightLoadingPipeline:
    """Handles model weight loading and organization"""
    def __init__(self):
        self.weight_map = {}
        self.loaded_weights = set()
        
    def load_weights(self, model_path: str, memory_manager) -> Dict[str, int]:
        """
        Load weights into VGPU memory
        Returns: Dict mapping layer names to weight addresses
        """
        weight_addresses = {}
        
        # Load weight manifest
        with open(f"{model_path}/weights.json", 'r') as f:
            weight_manifest = json.load(f)
            
        # Load each weight tensor
        for layer_name, weight_info in weight_manifest.items():
            # Allocate tensor in VGPU memory
            weight_tensor = self._load_weight_file(
                f"{model_path}/weights/{weight_info['file']}"
            )
            
            addr = memory_manager.allocate_tensor(
                weight_info['shape'],
                weight_info['dtype']
            )
            
            # Transfer weight data to VGPU
            memory_manager.write_tensor(addr, weight_tensor)
            weight_addresses[layer_name] = addr
            
        return weight_addresses
        
    def _load_weight_file(self, file_path: str) -> bytes:
        """Load raw weight data from file"""
        with open(file_path, 'rb') as f:
            return f.read()

class ArchitectureLoadingPipeline:
    """Handles model architecture loading and parsing"""
    def load_architecture(self, model_path: str) -> Dict[str, Any]:
        """
        Load model architecture definition
        Returns: Dict containing layer configs and connections
        """
        with open(f"{model_path}/architecture.json", 'r') as f:
            return json.load(f)
            
    def validate_architecture(self, arch_config: Dict[str, Any]) -> bool:
        """Validate architecture configuration"""
        required_keys = {'layers', 'connections', 'input_shape', 'output_shape'}
        if not all(key in arch_config for key in required_keys):
            return False
            
        # Validate layer definitions
        for layer in arch_config['layers']:
            if not self._validate_layer(layer):
                return False
                
        return True
        
    def _validate_layer(self, layer: Dict[str, Any]) -> bool:
        """Validate individual layer configuration"""
        required_layer_keys = {'name', 'type', 'shape'}
        return all(key in layer for key in required_layer_keys)

class VGPUMemoryManager:
    """Manages VGPU memory allocation and organization"""
    def __init__(self):
        self.memory_pools = {}
        self.tensor_map = {}
        
    def initialize_pools(self, arch_config: Dict[str, Any]) -> None:
        """Initialize memory pools based on architecture"""
        # Calculate total memory needed for weights
        weight_memory = self._calculate_weight_memory(arch_config)
        
        # Calculate memory needed for activations
        activation_memory = self._calculate_activation_memory(arch_config)
        
        # Initialize pools
        self.memory_pools['weights'] = self._create_pool(weight_memory)
        self.memory_pools['activations'] = self._create_pool(activation_memory)
        self.memory_pools['temporary'] = self._create_pool(activation_memory * 2)
        
    def _calculate_weight_memory(self, arch_config: Dict[str, Any]) -> int:
        """Calculate total memory needed for weights"""
        total_memory = 0
        for layer in arch_config['layers']:
            if 'weights' in layer:
                total_memory += self._calculate_tensor_size(layer['weights']['shape'])
        return total_memory
        
    def _calculate_activation_memory(self, arch_config: Dict[str, Any]) -> int:
        """Calculate memory needed for activations"""
        max_activation_size = 0
        for layer in arch_config['layers']:
            activation_size = self._calculate_tensor_size(layer['output_shape'])
            max_activation_size = max(max_activation_size, activation_size)
        return max_activation_size
        
    def _calculate_tensor_size(self, shape: List[int]) -> int:
        """Calculate size in bytes for tensor shape"""
        elements = 1
        for dim in shape:
            elements *= dim
        return elements * 4  # Assuming float32

class ExecutionEngine:
    """Handles model execution and tensor operations"""
    def __init__(self):
        self.tensor_core = None
        self.stream_manager = None
        
    def initialize(self, arch_config: Dict[str, Any],
                  weight_addresses: Dict[str, int],
                  stream_config: Dict[str, Any]) -> None:
        """Initialize execution engine with model configuration"""
        self.arch_config = arch_config
        self.weight_addresses = weight_addresses
        self.stream_config = stream_config
        
    def run_inference(self, input_address: int,
                     stream_pipeline) -> int:
        """
        Run model inference
        Returns: Address of output tensor
        """
        current_address = input_address
        
        # Process each layer
        for layer in self.arch_config['layers']:
            # Get weight addresses for layer
            layer_weights = self._get_layer_weights(layer['name'])
            
            # Get output tensor address
            output_address = self._allocate_output_tensor(layer['output_shape'])
            
            # Execute layer operation
            stream_pipeline.execute_op(
                layer['type'],
                current_address,
                layer_weights,
                output_address
            )
            
            current_address = output_address
            
        return current_address
        
    def _get_layer_weights(self, layer_name: str) -> Dict[str, int]:
        """Get weight addresses for layer"""
        return {
            name: addr
            for name, addr in self.weight_addresses.items()
            if name.startswith(f"{layer_name}/")
        }
        
    def _allocate_output_tensor(self, shape: List[int]) -> int:
        """Allocate tensor for layer output"""
        return self.tensor_core.allocate_tensor(shape, 'float32')
