"""
Memsaur Technology Adapter

This module provides an adapter for integrating quantum memory management (Memsaur)
technology into the unified API. It leverages tensor networks for persistent quantum
state management and coherence extension.
"""

import logging
import time
import uuid
from datetime import datetime, timedelta
from typing import Dict, List, Any, Optional, Union, Tuple, Set
import numpy as np

# Import the base interfaces
from ..unified_api import QuantumMemoryManager, MemoryHandle, MemoryConfig

# Import the implementations we're adapting
try:
    # Assume the implementation is now within quantum_toolkit
    from quantum_toolkit.tensor_network_lib.run_tensor_network_on_ibm import QuantumTensorNetwork
    HAS_TENSOR_NETWORK = True
except ImportError:
    HAS_TENSOR_NETWORK = False
    logging.warning("QuantumTensorNetwork not found, using simulation")

# Configure logging
logger = logging.getLogger(__name__)


class QuantumCoherenceExtender:
    """
    Quantum coherence extension module.
    
    This class implements techniques to extend the coherence time of quantum states
    using tensor network representations and error mitigation.
    """
    
    def __init__(self, extension_factor: float = 2.0):
        """
        Initialize the quantum coherence extender.
        
        Args:
            extension_factor: Factor by which to extend coherence time
        """
        self.extension_factor = extension_factor
        logger.info(f"Initialized QuantumCoherenceExtender with extension_factor={extension_factor}")
    
    def extend_coherence(self, quantum_state: Dict[str, Any]) -> Dict[str, Any]:
        """
        Extend the coherence of a quantum state.
        
        Args:
            quantum_state: Quantum state data to extend
            
        Returns:
            Extended quantum state with updated coherence metrics
        """
        logger.info(f"Extending coherence of quantum state")
        start_time = time.time()
        
        # This is a placeholder implementation that would be replaced
        # with actual quantum circuit execution in the full implementation
        
        # Calculate new expiration time
        original_expiration = quantum_state.get('expiration_time')
        if original_expiration:
            try:
                # If expiration is provided as a datetime string
                expiration_dt = datetime.fromisoformat(original_expiration)
                current_dt = datetime.now()
                time_diff = (expiration_dt - current_dt).total_seconds()
                
                # Apply extension factor
                new_time_diff = time_diff * self.extension_factor
                new_expiration = current_dt + timedelta(seconds=new_time_diff)
                quantum_state['expiration_time'] = new_expiration.isoformat()
            except (ValueError, TypeError):
                # If expiration is not in the expected format, use default extension
                quantum_state['expiration_time'] = (datetime.now() + 
                                                  timedelta(hours=1 * self.extension_factor)).isoformat()
        else:
            # If no expiration time is provided, set a default
            quantum_state['expiration_time'] = (datetime.now() + 
                                              timedelta(hours=1 * self.extension_factor)).isoformat()
        
        # Add coherence metrics
        quantum_state['coherence_metrics'] = quantum_state.get('coherence_metrics', {})
        quantum_state['coherence_metrics']['extension_factor'] = self.extension_factor
        quantum_state['coherence_metrics']['extended_at'] = datetime.now().isoformat()
        quantum_state['coherence_metrics']['extension_time_ms'] = (time.time() - start_time) * 1000
        
        logger.info(f"Coherence extended, new expiration: {quantum_state['expiration_time']}")
        return quantum_state
    
    def calculate_coherence_metrics(self, quantum_state: Dict[str, Any]) -> Dict[str, float]:
        """
        Calculate coherence metrics for a quantum state.
        
        Args:
            quantum_state: Quantum state to analyze
            
        Returns:
            Dictionary of coherence metrics
        """
        # This would implement actual coherence metrics calculation
        # Currently a placeholder implementation
        
        metrics = {
            'coherence_time': 3600 * self.extension_factor,  # seconds
            'fidelity': 0.95 - (0.1 / self.extension_factor),
            'purity': 0.92 - (0.05 / self.extension_factor)
        }
        
        return metrics


class MemsaurAdapter(QuantumMemoryManager):
    """
    Adapter for Memsaur quantum memory technology, connecting the unified API
    to tensor network implementations for quantum memory management.
    """
    
    def __init__(self, config: MemoryConfig):
        """
        Initialize the Memsaur adapter.
        
        Args:
            config: Configuration for quantum memory management
        """
        logger.info("Initializing MemsaurAdapter")
        self.config = config
        
        # Memory storage - maps handle IDs to quantum states
        self.memory_map = {}
        
        # Set of active memory handles
        self.active_handles = set()
        
        # Initialize the coherence extender
        self.coherence_extender = QuantumCoherenceExtender(
            extension_factor=config.coherence_extension_factor
        )
        
        # Initialize tensor network if available
        if HAS_TENSOR_NETWORK:
            self.tensor_network = QuantumTensorNetwork(size=config.network_size)
            logger.info(f"Initialized QuantumTensorNetwork with size {config.network_size}")
        else:
            self.tensor_network = None
            logger.warning("QuantumTensorNetwork not available, memory operations will be simulated")
            
        # Start memory management background thread
        self._start_memory_management()
    
    def _start_memory_management(self):
        """Start background memory management thread."""
        logger.info("Starting memory management background processes")
        # In a full implementation, this would start a thread for garbage collection,
        # checking expiration times, etc.
    
    def _generate_memory_handle(self, size: int) -> MemoryHandle:
        """
        Generate a new memory handle.
        
        Args:
            size: Size of the allocated memory
            
        Returns:
            New memory handle
        """
        handle_id = str(uuid.uuid4())
        allocation_time = datetime.now().isoformat()
        expiration_time = (datetime.now() + 
                          timedelta(seconds=self.config.memory_timeout)).isoformat()
        
        return MemoryHandle(
            id=handle_id,
            allocation_time=allocation_time,
            size=size,
            expiration_time=expiration_time
        )
    
    def _check_handle_active(self, memory_handle: MemoryHandle) -> bool:
        """
        Check if a memory handle is active and valid.
        
        Args:
            memory_handle: Memory handle to check
            
        Returns:
            True if handle is active, False otherwise
        """
        if not memory_handle or not memory_handle.id:
            return False
        
        if memory_handle.id not in self.active_handles:
            return False
            
        # Check expiration if provided
        if memory_handle.expiration_time:
            try:
                expiration = datetime.fromisoformat(memory_handle.expiration_time)
                if datetime.now() > expiration:
                    logger.warning(f"Memory handle {memory_handle.id} has expired")
                    return False
            except (ValueError, TypeError):
                pass
                
        return True
    
    def allocate(self, state_description: Dict[str, Any]) -> MemoryHandle:
        """
        Allocate quantum memory using tensor network.
        
        Args:
            state_description: Description of the quantum state to allocate
            
        Returns:
            Memory handle for the allocated memory
        """
        logger.info(f"Allocating quantum memory")
        start_time = time.time()
        
        # Extract size from state description
        size = state_description.get('size', self.config.network_size)
        
        # Generate memory handle
        handle = self._generate_memory_handle(size)
        
        # Initialize quantum state
        quantum_state = {
            'handle_id': handle.id,
            'allocation_time': handle.allocation_time,
            'expiration_time': handle.expiration_time,
            'size': size,
            'data': state_description.get('data'),
            'coherence_metrics': {}
        }
        
        # Store in memory map
        self.memory_map[handle.id] = quantum_state
        self.active_handles.add(handle.id)
        
        if self.tensor_network:
            # If using real tensor network, prepare the memory
            try:
                # This would be replaced with actual tensor network operations
                quantum_state['network_params'] = {
                    'contractions': [(i, i+1) for i in range(size-1)],
                    'rotations': [0.1 * i for i in range(size)],
                    'entanglement': 'linear'
                }
                
                logger.info(f"Prepared tensor network for memory handle {handle.id}")
            except Exception as e:
                logger.error(f"Error preparing tensor network: {str(e)}")
        
        # Calculate initial coherence metrics
        quantum_state['coherence_metrics'] = self.coherence_extender.calculate_coherence_metrics(quantum_state)
        
        logger.info(f"Allocated quantum memory with handle {handle.id} in {time.time() - start_time:.2f} seconds")
        return handle
    
    def store(self, memory_handle: MemoryHandle, quantum_data: Any) -> bool:
        """
        Store quantum data in tensor network memory.
        
        Args:
            memory_handle: Handle for the allocated memory
            quantum_data: Data to store in the memory
            
        Returns:
            True if storage successful, False otherwise
        """
        logger.info(f"Storing data in quantum memory {memory_handle.id if memory_handle else 'None'}")
        start_time = time.time()
        
        # Check if handle is valid
        if not self._check_handle_active(memory_handle):
            logger.error(f"Invalid or expired memory handle: {memory_handle.id if memory_handle else 'None'}")
            return False
        
        # Get the quantum state
        quantum_state = self.memory_map.get(memory_handle.id)
        if not quantum_state:
            logger.error(f"Memory handle {memory_handle.id} not found in memory map")
            return False
        
        # Update the quantum state with new data
        quantum_state['data'] = quantum_data
        quantum_state['last_modified'] = datetime.now().isoformat()
        
        if self.tensor_network:
            # If using real tensor network, update the memory
            try:
                # This would be replaced with actual tensor network operations
                # For now, we just update the network parameters
                if isinstance(quantum_data, dict) and 'tensor_params' in quantum_data:
                    quantum_state['network_params'] = quantum_data['tensor_params']
                
                logger.info(f"Updated tensor network for memory handle {memory_handle.id}")
            except Exception as e:
                logger.error(f"Error updating tensor network: {str(e)}")
                return False
        
        logger.info(f"Stored data in quantum memory {memory_handle.id} in {time.time() - start_time:.2f} seconds")
        return True
    
    def retrieve(self, memory_handle: MemoryHandle) -> Any:
        """
        Retrieve quantum data from tensor network memory.
        
        Args:
            memory_handle: Handle for the allocated memory
            
        Returns:
            Retrieved quantum data
        """
        logger.info(f"Retrieving data from quantum memory {memory_handle.id if memory_handle else 'None'}")
        start_time = time.time()
        
        # Check if handle is valid
        if not self._check_handle_active(memory_handle):
            logger.error(f"Invalid or expired memory handle: {memory_handle.id if memory_handle else 'None'}")
            return None
        
        # Get the quantum state
        quantum_state = self.memory_map.get(memory_handle.id)
        if not quantum_state:
            logger.error(f"Memory handle {memory_handle.id} not found in memory map")
            return None
        
        # Extend coherence if needed
        current_time = datetime.now()
        try:
            expiration = datetime.fromisoformat(quantum_state['expiration_time'])
            time_remaining = (expiration - current_time).total_seconds()
            
            # If less than 30% of time remains, extend coherence
            if time_remaining < 0.3 * self.config.memory_timeout:
                logger.info(f"Extending coherence for memory handle {memory_handle.id}")
                quantum_state = self.coherence_extender.extend_coherence(quantum_state)
                self.memory_map[memory_handle.id] = quantum_state
        except (ValueError, TypeError, KeyError):
            pass
        
        if self.tensor_network:
            # If using real tensor network, retrieve the memory
            try:
                # This would be replaced with actual tensor network operations
                # For now, we just return the stored data with some additional metrics
                retrieval_data = {
                    'data': quantum_state['data'],
                    'coherence_metrics': quantum_state['coherence_metrics'],
                    'retrieval_time': time.time() - start_time
                }
                
                logger.info(f"Retrieved data from tensor network for memory handle {memory_handle.id}")
                return retrieval_data
            except Exception as e:
                logger.error(f"Error retrieving from tensor network: {str(e)}")
                return None
        else:
            # Return the stored data directly
            retrieval_data = {
                'data': quantum_state['data'],
                'coherence_metrics': quantum_state['coherence_metrics'],
                'retrieval_time': time.time() - start_time
            }
            
            logger.info(f"Retrieved data from quantum memory {memory_handle.id} in {time.time() - start_time:.2f} seconds")
            return retrieval_data
    
    def release(self, memory_handle: MemoryHandle) -> bool:
        """
        Release tensor network quantum memory.
        
        Args:
            memory_handle: Handle for the allocated memory
            
        Returns:
            True if release successful, False otherwise
        """
        logger.info(f"Releasing quantum memory {memory_handle.id if memory_handle else 'None'}")
        start_time = time.time()
        
        # Check if handle exists
        if not memory_handle or not memory_handle.id:
            logger.error("Invalid memory handle")
            return False
        
        # Check if handle is in active handles
        if memory_handle.id not in self.active_handles:
            logger.warning(f"Memory handle {memory_handle.id} not found in active handles")
            return False
        
        try:
            # Remove from memory map
            if memory_handle.id in self.memory_map:
                del self.memory_map[memory_handle.id]
            
            # Remove from active handles
            self.active_handles.remove(memory_handle.id)
            
            logger.info(f"Released quantum memory {memory_handle.id} in {time.time() - start_time:.2f} seconds")
            return True
        except Exception as e:
            logger.error(f"Error releasing memory: {str(e)}")
            return False
    
    def extend_coherence_time(self, memory_handle: MemoryHandle) -> bool:
        """
        Explicitly extend the coherence time of a quantum memory.
        
        Args:
            memory_handle: Handle for the allocated memory
            
        Returns:
            True if extension successful, False otherwise
        """
        logger.info(f"Extending coherence time for memory {memory_handle.id if memory_handle else 'None'}")
        
        # Check if handle is valid
        if not self._check_handle_active(memory_handle):
            logger.error(f"Invalid or expired memory handle: {memory_handle.id if memory_handle else 'None'}")
            return False
        
        # Get the quantum state
        quantum_state = self.memory_map.get(memory_handle.id)
        if not quantum_state:
            logger.error(f"Memory handle {memory_handle.id} not found in memory map")
            return False
        
        try:
            # Extend coherence
            quantum_state = self.coherence_extender.extend_coherence(quantum_state)
            
            # Update memory map
            self.memory_map[memory_handle.id] = quantum_state
            
            logger.info(f"Extended coherence time for memory {memory_handle.id}")
            return True
        except Exception as e:
            logger.error(f"Error extending coherence time: {str(e)}")
            return False
    
    def get_memory_status(self) -> Dict[str, Any]:
        """
        Get status information about the quantum memory system.
        
        Returns:
            Dictionary with memory status information
        """
        status = {
            'active_handles': len(self.active_handles),
            'total_memory_allocated': sum(self.memory_map[h_id]['size'] for h_id in self.active_handles) 
                                     if self.active_handles else 0,
            'average_coherence': 0.0,
            'memory_utilization': 0.0
        }
        
        # Calculate average coherence
        if self.active_handles:
            coherence_values = []
            for h_id in self.active_handles:
                if h_id in self.memory_map:
                    coherence_metrics = self.memory_map[h_id].get('coherence_metrics', {})
                    if 'fidelity' in coherence_metrics:
                        coherence_values.append(coherence_metrics['fidelity'])
            
            if coherence_values:
                status['average_coherence'] = sum(coherence_values) / len(coherence_values)
            
            # Calculate memory utilization
            status['memory_utilization'] = (status['total_memory_allocated'] / 
                                          (self.config.network_size * 100)) * 100
        
        return status 