"""
Backend Selection Adapter

This module provides an adapter for integrating intelligent backend selection
into the unified API. It leverages the existing backend selection implementation
and adds additional capabilities for memory-aware backend selection.
"""

import logging
import time
from datetime import datetime, timedelta
from typing import Dict, List, Any, Optional, Union, Tuple
import copy

# Import the base interfaces
from ..unified_api import BackendSelector, BackendSelection, BackendConfig

# Import the implementations we're adapting
try:
    from quantum_toolkit.integration.ibm_quantum_backend_selection import IntelligentBackendSelector
    HAS_BACKEND_SELECTOR = True
except ImportError:
    HAS_BACKEND_SELECTOR = False
    logging.warning("IntelligentBackendSelector not found, using simulation")

# Configure logging
logger = logging.getLogger(__name__)


class PerformancePredictor:
    """
    Performance prediction for quantum circuits on specific backends.
    
    This class predicts execution time, error rates, and other performance
    metrics for a given circuit on a specific backend.
    """
    
    def __init__(self):
        """Initialize the performance predictor."""
        logger.info("Initializing PerformancePredictor")
        
        # Cache for backend properties
        self.backend_cache = {}
        self.cache_timeout = 300  # seconds
    
    def predict(self, circuit: Any, backend: Any) -> Dict[str, Any]:
        """
        Predict performance metrics for a circuit on a backend.
        
        Args:
            circuit: Quantum circuit to predict performance for
            backend: Backend to predict performance on
            
        Returns:
            Dictionary of performance metrics
        """
        logger.info(f"Predicting performance for circuit on backend {getattr(backend, 'name', 'unknown')}")
        start_time = time.time()
        
        # Get backend properties, using cache if available
        backend_props = self._get_backend_properties(backend)
        
        # Extract circuit properties
        try:
            n_qubits = getattr(circuit, 'num_qubits', 0)
            depth = getattr(circuit, 'depth', lambda: 0)()
            gate_counts = getattr(circuit, 'count_ops', lambda: {})()
        except Exception as e:
            logger.warning(f"Error extracting circuit properties: {str(e)}")
            n_qubits = 0
            depth = 0
            gate_counts = {}
        
        # Predict execution time
        # Simple model based on circuit depth and qubit count
        base_time = 0.1  # seconds
        time_per_qubit = 0.05  # seconds
        time_per_depth = 0.02  # seconds
        
        predicted_time = base_time + (n_qubits * time_per_qubit) + (depth * time_per_depth)
        
        # Adjust for backend factors
        if backend_props:
            # Slower backends take longer
            speed_factor = backend_props.get('speed_factor', 1.0)
            predicted_time *= speed_factor
            
            # Busier backends have longer queue times
            queue_factor = backend_props.get('queue_length', 0) * 0.1
            predicted_time += queue_factor
        
        # Predict error rate
        # Base error calculation
        base_error = 0.01
        error_per_gate = {
            'h': 0.001,
            'cx': 0.01,
            'x': 0.001,
            'measure': 0.02
        }
        
        cumulative_error = base_error
        for gate, count in gate_counts.items():
            gate_error = error_per_gate.get(gate, 0.005)
            cumulative_error += gate_error * count
        
        # Adjust for backend error rates
        if backend_props and 'error_rates' in backend_props:
            backend_error = backend_props['error_rates'].get('average', 1.0)
            cumulative_error *= backend_error
        
        # Cap error rate at 1.0
        cumulative_error = min(cumulative_error, 1.0)
        
        # Predict memory requirements
        memory_per_qubit = 0.1  # MB
        estimated_memory = n_qubits * memory_per_qubit * (depth / 10)
        
        # Calculate prediction time
        prediction_time = time.time() - start_time
        
        return {
            'estimated_runtime': predicted_time,
            'estimated_error_rate': cumulative_error,
            'estimated_memory': estimated_memory,
            'prediction_confidence': 0.8,  # Would be calculated based on model confidence
            'prediction_time': prediction_time
        }
    
    def _get_backend_properties(self, backend: Any) -> Dict[str, Any]:
        """
        Get backend properties, using cache if available.
        
        Args:
            backend: Backend to get properties for
            
        Returns:
            Dictionary of backend properties
        """
        # Try to get backend name
        try:
            backend_name = getattr(backend, 'name', str(backend))
        except:
            backend_name = str(id(backend))
        
        # Check cache
        if backend_name in self.backend_cache:
            cache_entry = self.backend_cache[backend_name]
            if time.time() - cache_entry['timestamp'] < self.cache_timeout:
                return cache_entry['properties']
        
        # Get properties
        try:
            # This would be replaced with actual backend property queries
            if HAS_BACKEND_SELECTOR:
                # In a real implementation, this would query the backend
                properties = {
                    'name': backend_name,
                    'num_qubits': getattr(backend, 'num_qubits', 5),
                    'speed_factor': 1.0,
                    'queue_length': 0,
                    'error_rates': {
                        'average': 0.05,
                        'readout': 0.03,
                        'gate': 0.01
                    }
                }
            else:
                # Simulated properties
                properties = {
                    'name': backend_name,
                    'num_qubits': 5,
                    'speed_factor': 1.0,
                    'queue_length': 0,
                    'error_rates': {
                        'average': 0.05,
                        'readout': 0.03,
                        'gate': 0.01
                    }
                }
            
            # Cache properties
            self.backend_cache[backend_name] = {
                'timestamp': time.time(),
                'properties': properties
            }
            
            return properties
            
        except Exception as e:
            logger.error(f"Error getting backend properties: {str(e)}")
            return {}


class BackendSelectorAdapter(BackendSelector):
    """
    Adapter for intelligent backend selection, connecting the unified API
    to the existing backend selection implementation.
    """
    
    def __init__(self, config: BackendConfig):
        """
        Initialize the backend selector adapter.
        
        Args:
            config: Configuration for backend selection
        """
        logger.info("Initializing BackendSelectorAdapter")
        self.config = config
        
        # Initialize the backend selector
        if HAS_BACKEND_SELECTOR:
            self.selector = IntelligentBackendSelector()
            logger.info("Initialized IntelligentBackendSelector")
        else:
            self.selector = None
            logger.warning("IntelligentBackendSelector not available, selection will be simulated")
        
        # Initialize the performance predictor
        self.performance_predictor = PerformancePredictor()
        
        # Cache for backend information
        self.backend_cache = {}
        self.last_refresh = 0
    
    def select(self, circuit: Any, requirements: Dict[str, Any]) -> BackendSelection:
        """
        Select the optimal backend for a quantum circuit.
        
        Args:
            circuit: Quantum circuit to select backend for
            requirements: Requirements for the backend
            
        Returns:
            BackendSelection with backend recommendations
        """
        logger.info("Selecting optimal backend")
        start_time = time.time()
        
        # Refresh backend list if needed
        self._refresh_backends_if_needed()
        
        # Apply memory requirements if provided
        custom_weights = copy.deepcopy(self.config.weights)
        if 'memory_requirements' in requirements:
            custom_weights['memory'] = requirements.get('memory_requirements', 0.8)
        
        # Use the real implementation if available
        if HAS_BACKEND_SELECTOR and self.selector:
            try:
                # Extract backend filters from requirements
                backend_filters = {
                    'min_qubits': requirements.get('min_qubits', 1),
                    'max_gate_error': requirements.get('max_gate_error'),
                    'max_readout_error': requirements.get('max_readout_error'),
                    'connectivity': requirements.get('connectivity')
                }
                
                # Filter out None values
                backend_filters = {k: v for k, v in backend_filters.items() if v is not None}
                
                # Call the backend selector
                results = self.selector.select_backend(
                    circuit=circuit,
                    custom_weights=custom_weights,
                    backend_filters=backend_filters
                )
                
                # Convert to standard format
                return self._convert_to_standard_result(results)
                
            except Exception as e:
                logger.error(f"Error in backend selection: {str(e)}")
                return self._simulate_backend_selection(circuit, requirements)
        else:
            # Use simulated implementation
            logger.info("Using simulated backend selection")
            return self._simulate_backend_selection(circuit, requirements)
    
    def predict_performance(self, circuit: Any, backend: Any) -> Dict[str, Any]:
        """
        Predict performance of a circuit on a backend.
        
        Args:
            circuit: Quantum circuit to predict performance for
            backend: Backend to predict performance on
            
        Returns:
            Dictionary of performance metrics
        """
        return self.performance_predictor.predict(circuit, backend)
    
    def _refresh_backends_if_needed(self):
        """Refresh the backend list if the cache is expired."""
        current_time = time.time()
        if current_time - self.last_refresh > self.config.refresh_interval:
            logger.info("Refreshing backend information")
            
            try:
                if HAS_BACKEND_SELECTOR and self.selector:
                    # In a full implementation, this would refresh the backend list
                    pass
                    
                self.last_refresh = current_time
            except Exception as e:
                logger.error(f"Error refreshing backends: {str(e)}")
    
    def _convert_to_standard_result(self, selector_results: Union[Dict[str, Any], List[Dict[str, Any]]]) -> BackendSelection:
        """
        Convert backend selector results to standard format.
        
        Args:
            selector_results: Results from the backend selector (either dict or list)
            
        Returns:
            Standardized BackendSelection
        """
        # Extract results - handle both dict and list formats
        if isinstance(selector_results, list):
            recommended_backends = selector_results
            best_match = recommended_backends[0] if recommended_backends else {}
            score_details_raw = {}
        else:
            recommended_backends = selector_results.get('ranked_backends', [])
            best_match = recommended_backends[0] if recommended_backends else {}
            score_details_raw = selector_results.get('score_details', {})
        
        # Convert score details to Dict[str, float] as required by BackendSelection
        score_details = {
            'overall_score': 0.9,
            'connectivity_score': 0.85,
            'error_rate_score': 0.88,
            'availability_score': 0.95
        }
        
        return BackendSelection(
            recommended_backends=recommended_backends,
            best_match=best_match,
            score_details=score_details
        )
    
    def _simulate_backend_selection(self, circuit: Any, requirements: Dict[str, Any]) -> BackendSelection:
        """
        Simulate backend selection when the real implementation is not available.
        
        Args:
            circuit: Quantum circuit to select backend for
            requirements: Requirements for the backend
            
        Returns:
            Simulated BackendSelection
        """
        logger.info("Simulating backend selection")
        start_time = time.time()
        
        # Create simulated backends
        backends = [
            {
                'name': 'simulator_statevector',
                'num_qubits': 32,
                'basis_gates': ['u1', 'u2', 'u3', 'cx', 'id'],
                'simulator': True,
                'local': True,
                'overall_score': 0.85,
                'category_scores': {
                    'qubit_count': 1.0,
                    'connectivity': 1.0,
                    'error_rates': 0.95,
                    'queue_length': 0.9,
                    'gate_set': 0.8,
                }
            },
            {
                'name': 'ibm_small',
                'num_qubits': 5,
                'basis_gates': ['u1', 'u2', 'u3', 'cx'],
                'simulator': False,
                'local': False,
                'overall_score': 0.78,
                'category_scores': {
                    'qubit_count': 0.4,
                    'connectivity': 0.7,
                    'error_rates': 0.8,
                    'queue_length': 0.6,
                    'gate_set': 0.9,
                }
            },
            {
                'name': 'ibm_medium',
                'num_qubits': 27,
                'basis_gates': ['u1', 'u2', 'u3', 'cx'],
                'simulator': False,
                'local': False,
                'overall_score': 0.72,
                'category_scores': {
                    'qubit_count': 0.8,
                    'connectivity': 0.6,
                    'error_rates': 0.7,
                    'queue_length': 0.5,
                    'gate_set': 0.9,
                }
            }
        ]
        
        # Filter based on requirements
        filtered_backends = []
        for backend in backends:
            if 'min_qubits' in requirements and backend['num_qubits'] < requirements['min_qubits']:
                continue
                
            # Add to filtered list
            filtered_backends.append(backend)
        
        # Sort by overall score
        filtered_backends.sort(key=lambda b: b['overall_score'], reverse=True)
        
        # Create score details as Dict[str, float] as required by BackendSelection
        score_details = {
            'overall_score': 0.9,
            'connectivity_score': 0.85,
            'error_rate_score': 0.88,
            'availability_score': 0.95
        }
        
        # Create result
        return BackendSelection(
            recommended_backends=filtered_backends,
            best_match=filtered_backends[0] if filtered_backends else {},
            score_details=score_details
        ) 