"""
Quantum Circuit Optimization Module

This module provides functions and classes for optimizing quantum circuits by:
1. Reducing circuit depth
2. Cancelling and simplifying gates
3. Implementing circuit reuse and caching strategies
4. Optimizing transpilation for specific backends

The optimization techniques aim to improve circuit performance, reduce execution time,
and enhance fidelity when running on real quantum hardware.
"""

import logging
from typing import Dict, List, Optional, Tuple, Union, Callable
import numpy as np

# Import quantum-specific libraries
try:
    from qiskit import QuantumCircuit, transpile
    from qiskit.transpiler import PassManager
    from qiskit.transpiler.passes import Optimize1qGates
    from qiskit.converters import circuit_to_gate  # For circuit decomposition
    
    QISKIT_AVAILABLE = True
except ImportError:
    QISKIT_AVAILABLE = False
    logging.warning("Qiskit not available. Some optimization functions will be limited.")

# Configure logging
logger = logging.getLogger(__name__)

class CircuitOptimizer:
    """Class for optimizing quantum circuits using various techniques."""
    
    def __init__(self, optimization_level: int = 3, backend = None):
        """
        Initialize the circuit optimizer.
        
        Args:
            optimization_level: Level of optimization to apply (0-3, where 3 is most aggressive)
            backend: Optional backend to target optimizations for
        """
        self.optimization_level = optimization_level
        self.backend = backend
        self.circuit_cache = {}  # Cache for storing optimized circuits
        
        # Validate dependencies
        if not QISKIT_AVAILABLE and self.optimization_level > 1:
            logger.warning("Reducing optimization_level to 1 due to missing Qiskit dependency")
            self.optimization_level = 1
    
    def optimize_circuit(self, circuit: 'QuantumCircuit') -> 'QuantumCircuit':
        """
        Apply optimization techniques to a quantum circuit.
        
        Args:
            circuit: The quantum circuit to optimize
            
        Returns:
            An optimized version of the input circuit
        """
        # Check cache first
        circuit_key = self._get_circuit_hash(circuit)
        if circuit_key in self.circuit_cache:
            logger.info(f"Using cached optimized circuit for {circuit_key}")
            return self.circuit_cache[circuit_key]
        
        # Apply optimizations based on level
        if QISKIT_AVAILABLE:
            optimized = self._apply_qiskit_optimization(circuit)
        else:
            # Fall back to basic optimizations if Qiskit not available
            optimized = self._apply_basic_optimization(circuit)
        
        # Store in cache
        self.circuit_cache[circuit_key] = optimized
        
        return optimized
    
    def _apply_qiskit_optimization(self, circuit: 'QuantumCircuit') -> 'QuantumCircuit':
        """Apply Qiskit-based optimizations to the circuit."""
        logger.info(f"Applying Qiskit optimization level {self.optimization_level}")
        
        if self.optimization_level == 0:
            # No optimization, just return the original circuit
            return circuit
        
        # Use Qiskit's transpile function with appropriate optimization level
        optimized = transpile(
            circuit, 
            backend=self.backend,
            optimization_level=self.optimization_level
        )
        
        # For highest optimization level, apply additional custom optimizations
        if self.optimization_level == 3:
            optimized = self._apply_additional_optimizations(optimized)
            
        return optimized
    
    def _apply_basic_optimization(self, circuit: 'QuantumCircuit') -> 'QuantumCircuit':
        """Apply basic optimizations without Qiskit dependency."""
        # Implementation of basic optimizations
        # This is a placeholder for non-Qiskit implementations
        logger.warning("Using limited optimization capabilities without Qiskit")
        return circuit
    
    def _apply_additional_optimizations(self, circuit: 'QuantumCircuit') -> 'QuantumCircuit':
        """Apply custom additional optimizations beyond standard Qiskit transpilation."""
        # Placeholder for additional custom optimizations
        # Examples include special-case optimizations or domain-specific optimizations
        return circuit
    
    def _get_circuit_hash(self, circuit: 'QuantumCircuit') -> str:
        """Generate a unique hash for a circuit to use as cache key."""
        if QISKIT_AVAILABLE:
            # Use Qiskit's circuit properties for hashing
            return f"{circuit.name}_{circuit.num_qubits}_{len(circuit.data)}"
        else:
            # Fall back to generic object hash
            return str(hash(circuit))


def reduce_circuit_depth(circuit: 'QuantumCircuit') -> 'QuantumCircuit':
    """
    Reduce the depth of a quantum circuit by optimizing gate sequences.
    
    Args:
        circuit: Input quantum circuit
        
    Returns:
        Optimized quantum circuit with reduced depth
    """
    if not QISKIT_AVAILABLE:
        logger.warning("Qiskit required for circuit depth reduction")
        return circuit
    
    # Create a custom pass manager for depth reduction
    pm = PassManager()
    
    # These passes are generally available in all Qiskit versions
    pm.append(Optimize1qGates())  # Combine single-qubit gates
    
    optimized_circuit = pm.run(circuit)
    
    initial_depth = circuit.depth()
    final_depth = optimized_circuit.depth()
    
    logger.info(f"Circuit depth reduced from {initial_depth} to {final_depth}")
    
    return optimized_circuit


def cancel_gates(circuit: 'QuantumCircuit') -> 'QuantumCircuit':
    """
    Cancel adjacent gates that result in identity operations.
    
    Args:
        circuit: Input quantum circuit
        
    Returns:
        Optimized quantum circuit with canceled gates
    """
    if not QISKIT_AVAILABLE:
        logger.warning("Qiskit required for gate cancellation")
        return circuit
    
    # Use transpile which includes cancellation passes at level 1+
    logger.info("Applying transpile level 1 for gate cancellation")
    return transpile(circuit, optimization_level=1)


def optimize_for_backend(circuit: 'QuantumCircuit', backend) -> 'QuantumCircuit':
    """
    Optimize a circuit specifically for a given backend's architecture.
    
    Args:
        circuit: Input quantum circuit
        backend: The backend to optimize for
        
    Returns:
        Optimized circuit for the specific backend
    """
    if not QISKIT_AVAILABLE:
        logger.warning("Qiskit required for backend-specific optimization")
        return circuit
    
    # Use transpile with backend-specific optimization
    optimized = transpile(
        circuit,
        backend=backend,
        optimization_level=3,  # Use highest optimization level
        layout_method='sabre',  # Advanced layout method
        routing_method='sabre'  # Advanced routing method
    )
    
    return optimized


# Dictionary for caching optimized circuits
_circuit_cache: Dict[str, 'QuantumCircuit'] = {}

def get_cached_circuit(circuit_key: str) -> Optional['QuantumCircuit']:
    """Retrieve a circuit from the cache if it exists."""
    return _circuit_cache.get(circuit_key)

def add_to_cache(circuit_key: str, circuit: 'QuantumCircuit') -> None:
    """Add a circuit to the optimization cache."""
    _circuit_cache[circuit_key] = circuit
    
    # Log cache statistics
    cache_size = len(_circuit_cache)
    logger.debug(f"Circuit cache now contains {cache_size} entries") 