"""
Memsaur Quantum Memory Implementation

This module implements the Memsaur quantum memory technology that provides persistent
quantum state storage with extended coherence times. It serves as the foundation for
quantum memory management in the Stochastic-Memsaur integration.

The implementation is based on tensor networks and provides:
1. Extended quantum coherence times
2. Persistent state storage
3. Quantum-classical memory interfaces
4. Hierarchical memory architecture
"""

import numpy as np
import logging
import time
import uuid
from typing import Dict, List, Any, Optional, Union, Tuple
from dataclasses import dataclass, field
import threading
from datetime import datetime, timedelta
from enum import Enum

# Try to import tensor network implementation
try:
    from run_tensor_network_on_ibm import QuantumTensorNetwork
    HAS_TENSOR_NETWORK = True
except ImportError:
    HAS_TENSOR_NETWORK = False
    logging.warning("QuantumTensorNetwork not found, using simulated tensor networks")

logger = logging.getLogger(__name__)

class MemoryStorageType(Enum):
    """Types of memory storage available in the Memsaur system."""
    TENSOR_NETWORK = "tensor_network"
    QUANTUM_STATE = "quantum_state"
    STOCHASTIC_TRAJECTORIES = "stochastic_trajectories"
    CLASSICAL_DATA = "classical_data"

@dataclass
class MemoryMetadata:
    """Metadata associated with a quantum memory allocation."""
    id: str
    allocation_time: str
    expiration_time: str
    size: int
    storage_type: MemoryStorageType
    coherence_metrics: Dict[str, Any] = field(default_factory=dict)
    access_count: int = 0
    last_access_time: str = ""

@dataclass
class MemoryHandle:
    """Handle to a quantum memory allocation."""
    id: str
    storage_type: MemoryStorageType
    size: int
    
    def __str__(self):
        return f"MemoryHandle(id={self.id}, type={self.storage_type.value}, size={self.size})"

class MemoryStatus:
    """Status of the quantum memory system."""
    READY = "ready"
    BUSY = "busy"
    ERROR = "error"
    UNAVAILABLE = "unavailable"

class CoherenceExtender:
    """
    Implements quantum coherence extension techniques.
    
    This class provides methods to extend the coherence time of quantum states
    using advanced tensor network techniques and error mitigation.
    """
    
    def __init__(self, extension_factor: float = 3.0):
        """
        Initialize the coherence extender.
        
        Args:
            extension_factor: Factor by which to extend coherence time
        """
        self.extension_factor = extension_factor
        logger.info(f"Initialized CoherenceExtender 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 where the actual coherence extension would happen
        # For now, we'll simulate the effect by updating the expiration time
        
        # Calculate new expiration time
        if 'expiration_time' in quantum_state:
            try:
                expiration_dt = datetime.fromisoformat(quantum_state['expiration_time'])
                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 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()
        
        # Update coherence metrics
        if 'coherence_metrics' not in quantum_state:
            quantum_state['coherence_metrics'] = {}
            
        quantum_state['coherence_metrics'].update({
            'extension_factor': self.extension_factor,
            'extended_at': datetime.now().isoformat(),
            'extension_time_ms': (time.time() - start_time) * 1000,
            'estimated_fidelity': self._estimate_fidelity(quantum_state)
        })
        
        logger.info(f"Coherence extended, new expiration: {quantum_state['expiration_time']}")
        return quantum_state
    
    def _estimate_fidelity(self, quantum_state: Dict[str, Any]) -> float:
        """
        Estimate the fidelity of a quantum state after coherence extension.
        
        Args:
            quantum_state: Quantum state to analyze
            
        Returns:
            Estimated fidelity (0.0-1.0)
        """
        # In a real implementation, this would implement a model to estimate fidelity
        # based on various factors. For now, we'll use a simple decay model.
        
        base_fidelity = 0.99
        
        # Get current coherence metrics if available
        metrics = quantum_state.get('coherence_metrics', {})
        extension_count = metrics.get('extension_count', 0) + 1
        
        # Simple decay model
        decay_factor = 0.99  # 1% loss per extension
        fidelity = base_fidelity * (decay_factor ** extension_count)
        
        # Update extension count
        metrics['extension_count'] = extension_count
        quantum_state['coherence_metrics'] = metrics
        
        return fidelity

class TensorNetworkMemory:
    """
    Implements quantum memory using tensor networks.
    
    This class provides methods to store and manipulate quantum states using
    tensor network representations for improved coherence time and efficiency.
    """
    
    def __init__(self, network_size: int = 8):
        """
        Initialize the tensor network memory.
        
        Args:
            network_size: Size of the tensor network (number of nodes)
        """
        self.network_size = network_size
        self.coherence_extender = CoherenceExtender()
        
        # Initialize tensor network if available
        if HAS_TENSOR_NETWORK:
            self.tensor_network = QuantumTensorNetwork(size=network_size)
            logger.info(f"Initialized QuantumTensorNetwork with size {network_size}")
        else:
            # Simulate tensor network functionality
            self.tensor_network = None
            logger.warning("QuantumTensorNetwork not available, using simulation")
    
    def encode_state(self, quantum_state: Dict[str, Any]) -> Dict[str, Any]:
        """
        Encode a quantum state into tensor network format for storage.
        
        Args:
            quantum_state: Quantum state to encode
            
        Returns:
            Encoded quantum state in tensor network format
        """
        start_time = time.time()
        encoded_state = quantum_state.copy()
        
        # In a real implementation, this would perform the actual tensor network encoding
        # For now, we'll simulate by adding tensor network metadata
        
        encoded_state['tensor_network'] = {
            'bond_dimension': min(2**4, 2**quantum_state.get('size', 1)),
            'num_tensors': quantum_state.get('size', 1),
            'encoding_time_ms': (time.time() - start_time) * 1000,
            'encoding_timestamp': datetime.now().isoformat()
        }
        
        logger.info(f"Encoded quantum state into tensor network format in {encoded_state['tensor_network']['encoding_time_ms']:.2f}ms")
        return encoded_state
    
    def decode_state(self, encoded_state: Dict[str, Any]) -> Dict[str, Any]:
        """
        Decode a quantum state from tensor network format.
        
        Args:
            encoded_state: Encoded quantum state in tensor network format
            
        Returns:
            Decoded quantum state
        """
        start_time = time.time()
        decoded_state = encoded_state.copy()
        
        # In a real implementation, this would perform the actual tensor network decoding
        # For now, we'll simulate by adding decoding metadata
        
        if 'tensor_network' in decoded_state:
            decoded_state['tensor_network']['decoding_time_ms'] = (time.time() - start_time) * 1000
            decoded_state['tensor_network']['decoding_timestamp'] = datetime.now().isoformat()
        
        logger.info(f"Decoded quantum state from tensor network format")
        return decoded_state

class QuantumMemoryManager:
    """
    Quantum memory manager implementing the Memsaur technology.
    
    This class provides a complete memory management system for quantum states,
    including allocation, storage, retrieval, and coherence extension.
    """
    
    def __init__(self, 
                 network_size: int = 8,
                 coherence_extension_factor: float = 3.0,
                 memory_timeout: int = 3600):  # 1 hour default timeout
        """
        Initialize the quantum memory manager.
        
        Args:
            network_size: Size of the tensor network
            coherence_extension_factor: Factor by which to extend coherence time
            memory_timeout: Default memory timeout in seconds
        """
        self.network_size = network_size
        self.coherence_extension_factor = coherence_extension_factor
        self.memory_timeout = memory_timeout
        
        # Initialize components
        self.tensor_memory = TensorNetworkMemory(network_size=network_size)
        self.coherence_extender = CoherenceExtender(extension_factor=coherence_extension_factor)
        
        # Memory storage - maps handle IDs to quantum states
        self.memory_map = {}
        
        # Memory metadata - maps handle IDs to metadata
        self.metadata_map = {}
        
        # Active memory handles
        self.active_handles = set()
        
        # Memory manager status
        self.status = MemoryStatus.READY
        
        # Initialize memory management background thread
        self._start_memory_management()
        
        logger.info(f"Initialized QuantumMemoryManager with network_size={network_size}, " 
                  f"coherence_extension_factor={coherence_extension_factor}")
    
    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.
        
        # For now, we'll just set up the thread but not actually start it
        self._memory_management_thread = threading.Thread(
            target=self._memory_management_task,
            daemon=True
        )
        # self._memory_management_thread.start()
    
    def _memory_management_task(self):
        """Memory management background task."""
        logger.info("Memory management task started")
        
        while True:
            try:
                # Check for expired memory allocations
                now = datetime.now()
                expired_handles = []
                
                for handle_id, metadata in self.metadata_map.items():
                    if metadata.expiration_time:
                        try:
                            expiration = datetime.fromisoformat(metadata.expiration_time)
                            if now > expiration:
                                expired_handles.append(handle_id)
                        except (ValueError, TypeError):
                            pass
                
                # Release expired handles
                for handle_id in expired_handles:
                    logger.info(f"Auto-releasing expired memory handle {handle_id}")
                    self._release_internal(handle_id)
                
                # Sleep before next check
                time.sleep(10)  # Check every 10 seconds
            except Exception as e:
                logger.error(f"Error in memory management task: {str(e)}")
                time.sleep(30)  # Sleep longer on error
    
    def _generate_handle_id(self) -> str:
        """Generate a unique handle ID."""
        return str(uuid.uuid4())
    
    def _create_memory_handle(self, size: int, storage_type: MemoryStorageType) -> MemoryHandle:
        """
        Create a new memory handle.
        
        Args:
            size: Size of the allocation
            storage_type: Type of storage
            
        Returns:
            New memory handle
        """
        handle_id = self._generate_handle_id()
        
        # Create handle
        handle = MemoryHandle(
            id=handle_id,
            storage_type=storage_type,
            size=size
        )
        
        return handle
    
    def _create_metadata(self, handle: MemoryHandle) -> MemoryMetadata:
        """
        Create metadata for a memory handle.
        
        Args:
            handle: Memory handle
            
        Returns:
            Memory metadata
        """
        now = datetime.now()
        expiration = now + timedelta(seconds=self.memory_timeout)
        
        metadata = MemoryMetadata(
            id=handle.id,
            allocation_time=now.isoformat(),
            expiration_time=expiration.isoformat(),
            size=handle.size,
            storage_type=handle.storage_type,
            coherence_metrics={},
            access_count=0,
            last_access_time=now.isoformat()
        )
        
        return metadata
    
    def allocate(self, size: int, storage_type: MemoryStorageType = MemoryStorageType.TENSOR_NETWORK) -> MemoryHandle:
        """
        Allocate quantum memory.
        
        Args:
            size: Size of the allocation (number of qubits)
            storage_type: Type of storage to use
            
        Returns:
            Memory handle for the allocated memory
        """
        logger.info(f"Allocating quantum memory of size {size} with storage type {storage_type.value}")
        
        # Create memory handle
        handle = self._create_memory_handle(size, storage_type)
        
        # Create metadata
        metadata = self._create_metadata(handle)
        
        # Initialize empty state
        quantum_state = {
            'handle_id': handle.id,
            'size': size,
            'data': None,
            'storage_type': storage_type.value
        }
        
        # Store in memory maps
        self.memory_map[handle.id] = quantum_state
        self.metadata_map[handle.id] = metadata
        self.active_handles.add(handle.id)
        
        logger.info(f"Allocated memory with handle {handle.id}")
        return handle
    
    def store(self, handle: MemoryHandle, data: Any) -> bool:
        """
        Store quantum data in allocated memory.
        
        Args:
            handle: Memory handle
            data: Quantum data to store
            
        Returns:
            True if successful, False otherwise
        """
        if not self._is_valid_handle(handle):
            logger.warning(f"Invalid memory handle: {handle}")
            return False
        
        logger.info(f"Storing data in memory with handle {handle.id}")
        
        try:
            # Get the quantum state
            quantum_state = self.memory_map.get(handle.id)
            if not quantum_state:
                logger.warning(f"Memory handle {handle.id} not found")
                return False
            
            # Update the data
            quantum_state['data'] = data
            
            # Encode based on storage type
            if handle.storage_type == MemoryStorageType.TENSOR_NETWORK:
                quantum_state = self.tensor_memory.encode_state(quantum_state)
            
            # Update the memory map
            self.memory_map[handle.id] = quantum_state
            
            # Update metadata
            metadata = self.metadata_map.get(handle.id)
            if metadata:
                metadata.access_count += 1
                metadata.last_access_time = datetime.now().isoformat()
            
            logger.info(f"Successfully stored data in memory with handle {handle.id}")
            return True
        except Exception as e:
            logger.error(f"Error storing data: {str(e)}")
            return False
    
    def retrieve(self, handle: MemoryHandle) -> Any:
        """
        Retrieve quantum data from memory.
        
        Args:
            handle: Memory handle
            
        Returns:
            Retrieved quantum data or None if unsuccessful
        """
        if not self._is_valid_handle(handle):
            logger.warning(f"Invalid memory handle: {handle}")
            return None
        
        logger.info(f"Retrieving data from memory with handle {handle.id}")
        
        try:
            # Get the quantum state
            quantum_state = self.memory_map.get(handle.id)
            if not quantum_state:
                logger.warning(f"Memory handle {handle.id} not found")
                return None
            
            # Decode if necessary
            if handle.storage_type == MemoryStorageType.TENSOR_NETWORK:
                quantum_state = self.tensor_memory.decode_state(quantum_state)
            
            # Update metadata
            metadata = self.metadata_map.get(handle.id)
            if metadata:
                metadata.access_count += 1
                metadata.last_access_time = datetime.now().isoformat()
            
            logger.info(f"Successfully retrieved data from memory with handle {handle.id}")
            return quantum_state.get('data')
        except Exception as e:
            logger.error(f"Error retrieving data: {str(e)}")
            return None
    
    def release(self, handle: MemoryHandle) -> bool:
        """
        Release allocated memory.
        
        Args:
            handle: Memory handle
            
        Returns:
            True if successful, False otherwise
        """
        if not self._is_valid_handle(handle):
            logger.warning(f"Invalid memory handle: {handle}")
            return False
        
        logger.info(f"Releasing memory with handle {handle.id}")
        
        return self._release_internal(handle.id)
    
    def _release_internal(self, handle_id: str) -> bool:
        """
        Internal method to release memory by handle ID.
        
        Args:
            handle_id: Memory handle ID
            
        Returns:
            True if successful, False otherwise
        """
        try:
            # Remove from maps
            if handle_id in self.memory_map:
                del self.memory_map[handle_id]
            
            if handle_id in self.metadata_map:
                del self.metadata_map[handle_id]
            
            if handle_id in self.active_handles:
                self.active_handles.remove(handle_id)
            
            logger.info(f"Successfully released memory with handle {handle_id}")
            return True
        except Exception as e:
            logger.error(f"Error releasing memory: {str(e)}")
            return False
    
    def extend_coherence(self, handle: MemoryHandle) -> bool:
        """
        Extend the coherence time of stored quantum data.
        
        Args:
            handle: Memory handle
            
        Returns:
            True if successful, False otherwise
        """
        if not self._is_valid_handle(handle):
            logger.warning(f"Invalid memory handle: {handle}")
            return False
        
        logger.info(f"Extending coherence for memory with handle {handle.id}")
        
        try:
            # Get the quantum state
            quantum_state = self.memory_map.get(handle.id)
            if not quantum_state:
                logger.warning(f"Memory handle {handle.id} not found")
                return False
            
            # Apply coherence extension
            extended_state = self.coherence_extender.extend_coherence(quantum_state)
            
            # Update the memory map
            self.memory_map[handle.id] = extended_state
            
            # Update metadata
            metadata = self.metadata_map.get(handle.id)
            if metadata and 'expiration_time' in extended_state:
                metadata.expiration_time = extended_state['expiration_time']
                metadata.coherence_metrics = extended_state.get('coherence_metrics', {})
            
            logger.info(f"Successfully extended coherence for memory with handle {handle.id}")
            return True
        except Exception as e:
            logger.error(f"Error extending coherence: {str(e)}")
            return False
    
    def _is_valid_handle(self, handle: MemoryHandle) -> bool:
        """
        Check if a memory handle is valid.
        
        Args:
            handle: Memory handle
            
        Returns:
            True if valid, False otherwise
        """
        if not handle or not handle.id:
            return False
        
        return handle.id in self.active_handles
    
    def get_memory_status(self) -> Dict[str, Any]:
        """
        Get the status of the memory system.
        
        Returns:
            Dictionary with memory status information
        """
        num_allocations = len(self.active_handles)
        total_size = sum(metadata.size for metadata in self.metadata_map.values())
        
        status = {
            'status': self.status,
            'active_allocations': num_allocations,
            'total_allocated_size': total_size,
            'network_size': self.network_size,
            'coherence_extension_factor': self.coherence_extension_factor,
            'timestamp': datetime.now().isoformat()
        }
        
        return status 