"""
Quantum Memory Module

This module implements quantum-inspired memory components for the quantum-AI platform.
It provides abstract representations of quantum memory concepts such as quantum
memristors, which can store and process quantum states with unique properties.

Key features:
- Quantum memristors for state storage and retrieval
- Quantum-inspired adaptive memory mechanisms
- Stateful quantum processing components
- Long-term memory representation for quantum circuits
- Memory efficiency optimization techniques
- Integration with quantum transformers and other architectures

This module bridges concepts from quantum computing and neuromorphic computing,
enabling novel approaches to information storage and processing.
"""

import torch
import torch.nn as nn
import numpy as np
from typing import Dict, List, Any, Optional, Tuple
from quantum_finance.backend.quantum_wrapper import quantum_wrapper

class QuantumMemoryBase(nn.Module):
    """
    Base class for quantum memory components.
    
    Provides common interface and functionality for various 
    quantum memory implementations.
    """
    def __init__(self, state_size: int):
        """
        Initialize quantum memory.
        
        Args:
            state_size: Dimension of the memory state
        """
        super().__init__()
        self.state_size = state_size
        self.register_buffer('state', torch.zeros(state_size))
        
    def reset_state(self):
        """Reset the memory state to zeros."""
        with torch.no_grad():
            self.state.zero_()
    
    def get_state_dict(self) -> Dict[str, Any]:
        """
        Get the current state as a dictionary.
        
        Returns:
            Dictionary containing state information
        """
        return {
            'state': self.state.clone().detach().cpu().numpy(),
            'state_size': self.state_size
        }
    
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Process input through the memory.
        
        Args:
            x: Input tensor
            
        Returns:
            Processed output tensor
        """
        raise NotImplementedError("Subclasses must implement forward method")

class QuantumMemristor(QuantumMemoryBase):
    """
    Quantum-inspired memristor for neural networks.
    
    Implements a trainable memory component with quantum-inspired
    non-linear dynamics and state persistence.
    """
    def __init__(self, state_size: int, n_qubits: int, learning_rate: float = 0.01, 
                 decay_rate: float = 0.99):
        """
        Initialize quantum memristor.
        
        Args:
            state_size: Dimension of the memory state
            n_qubits: Number of qubits for quantum simulation
            learning_rate: Learning rate for state updates
            decay_rate: Memory decay rate
        """
        super().__init__(state_size)
        self.n_qubits = n_qubits
        self.learning_rate = learning_rate
        self.decay_rate = decay_rate
        
        # Trainable parameters
        self.read_weights = nn.Parameter(torch.randn(state_size, state_size) * 0.01)
        self.write_weights = nn.Parameter(torch.randn(state_size, state_size) * 0.01)
        self.bias = nn.Parameter(torch.zeros(state_size))
        
        # Quantum circuit parameters
        self.theta = nn.Parameter(torch.randn(n_qubits) * 0.1)
        self.phi = nn.Parameter(torch.randn(n_qubits) * 0.1)
        
        # Activation functions
        self.read_activation = nn.Tanh()
        self.write_activation = nn.Sigmoid()
        
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Process input through the quantum memristor.
        
        Args:
            x: Input tensor of shape [..., state_size]
            
        Returns:
            Output tensor of shape [..., state_size]
        """
        batch_shape = x.shape[:-1]
        flat_x = x.reshape(-1, self.state_size)
        batch_size = flat_x.shape[0]
        
        # Expand state for batched processing
        expanded_state = self.state.expand(batch_size, -1)
        
        # Read operation
        read_gate = self.read_activation(torch.matmul(flat_x, self.read_weights))
        read_output = read_gate * expanded_state
        
        # Write operation
        write_gate = self.write_activation(torch.matmul(flat_x, self.write_weights) + self.bias)
        write_input = flat_x * write_gate
        
        # Apply quantum-inspired non-linearity
        quantum_factors = self._quantum_modulation(flat_x)
        modulated_write = write_input * quantum_factors
        
        # Update state (only during training)
        if self.training:
            # Compute average state update across batch
            with torch.no_grad():
                state_update = modulated_write.mean(dim=0)
                new_state = self.decay_rate * self.state + self.learning_rate * state_update
                self.state.copy_(new_state)
        
        # Combine read output with input for residual connection
        output = read_output + flat_x
        
        # Reshape to original batch dimensions
        return output.reshape(*batch_shape, self.state_size)
    
    def _quantum_modulation(self, x: torch.Tensor) -> torch.Tensor:
        """
        Apply quantum-inspired modulation to the input.
        
        Args:
            x: Input tensor of shape [batch_size, state_size]
            
        Returns:
            Modulation factors of shape [batch_size, state_size]
        """
        batch_size = x.shape[0]
        
        # Prepare quantum circuit parameters
        circuit_params = {
            'n_qubits': min(self.n_qubits, 8),  # Limit for efficiency
            'gates': []
        }
        
        # Add rotation gates based on the learnable parameters
        for i in range(min(self.n_qubits, 8)):
            circuit_params['gates'].append(('rx', i, self.theta[i].item()))
            circuit_params['gates'].append(('rz', i, self.phi[i].item()))
        
        # Add entanglement between neighboring qubits
        for i in range(min(self.n_qubits - 1, 7)):
            circuit_params['gates'].append(('cz', i, i+1, np.pi/4))
        
        try:
            # Simulate quantum circuit
            quantum_state = quantum_wrapper.simulate_circuit(circuit_params)
            
            # Convert to modulation factors
            # Use the absolute squared amplitudes as modulation factors
            modulation_base = np.abs(quantum_state)**2
            
            # Repeat and reshape to match input dimensions
            factors = torch.tensor(
                np.tile(modulation_base, (batch_size, self.state_size // len(modulation_base) + 1)),
                dtype=x.dtype, device=x.device
            )[:, :self.state_size]
            
            # Normalize factors around 1.0
            return 0.5 + factors / factors.sum(dim=1, keepdim=True) * self.state_size / 2
            
        except Exception as e:
            # Fallback to non-quantum factors if simulation fails
            print(f"Quantum circuit simulation failed: {e}")
            return torch.ones_like(x)
    
    def get_state_dict(self) -> Dict[str, Any]:
        """
        Get the current memristor state as a dictionary.
        
        Returns:
            Dictionary containing state information
        """
        base_state = super().get_state_dict()
        return {
            **base_state,
            'n_qubits': self.n_qubits,
            'learning_rate': self.learning_rate,
            'decay_rate': self.decay_rate,
            'theta': self.theta.detach().cpu().numpy(),
            'phi': self.phi.detach().cpu().numpy()
        }

class QuantumMemoryBank(nn.Module):
    """
    A bank of quantum memory cells.
    
    Provides multiple independent quantum memory cells that can be
    addressed and updated separately.
    """
    def __init__(self, state_size: int, n_cells: int, n_qubits: int):
        """
        Initialize quantum memory bank.
        
        Args:
            state_size: Dimension of each memory cell
            n_cells: Number of memory cells
            n_qubits: Number of qubits for quantum simulation
        """
        super().__init__()
        self.state_size = state_size
        self.n_cells = n_cells
        self.n_qubits = n_qubits
        
        # Create memory cells
        self.cells = nn.ModuleList([
            QuantumMemristor(state_size, n_qubits)
            for _ in range(n_cells)
        ])
        
        # Addressing mechanism
        self.address_weights = nn.Parameter(torch.randn(state_size, n_cells))
        self.address_bias = nn.Parameter(torch.zeros(n_cells))
        
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Process input through the memory bank.
        
        Args:
            x: Input tensor of shape [..., state_size]
            
        Returns:
            Output tensor of shape [..., state_size]
        """
        batch_shape = x.shape[:-1]
        flat_x = x.reshape(-1, self.state_size)
        batch_size = flat_x.shape[0]
        
        # Compute addressing weights
        address = torch.softmax(
            torch.matmul(flat_x, self.address_weights) + self.address_bias,
            dim=-1
        )
        
        # Process through each cell
        outputs = []
        for i, cell in enumerate(self.cells):
            cell_output = cell(flat_x)
            # Weight output by addressing factor
            cell_weight = address[:, i].unsqueeze(1)
            outputs.append(cell_output * cell_weight)
        
        # Sum weighted outputs
        combined_output = sum(outputs)
        
        # Reshape to original batch dimensions
        return combined_output.reshape(*batch_shape, self.state_size)
    
    def reset_state(self):
        """Reset all memory cells."""
        for cell in self.cells:
            cell.reset_state()
    
    def get_state_dict(self) -> Dict[str, List[Dict[str, Any]]]:
        """
        Get the current state of all memory cells.
        
        Returns:
            Dictionary containing state information for all cells
        """
        return {
            'cells': [cell.get_state_dict() for cell in self.cells],
            'state_size': self.state_size,
            'n_cells': self.n_cells,
            'n_qubits': self.n_qubits
        } 