"""
Circuit Cutting Adapter

This module provides an adapter for integrating circuit cutting and distribution
into the unified API. It leverages the existing circuit cutting implementation
and adds features for execution and reconstruction.
"""

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

# Import the base interfaces
from ..unified_api import CircuitCutter, CircuitCuttingConfig

# Import the implementations we're adapting
try:
    from ibm_quantum_circuit_cutting import QuantumCircuitCutter, Subcircuit as IBMSubcircuit
    HAS_CIRCUIT_CUTTER = True
except ImportError:
    HAS_CIRCUIT_CUTTER = False
    logging.warning("QuantumCircuitCutter not found, using simulation")

# Configure logging
logger = logging.getLogger(__name__)


class Subcircuit:
    """
    Representation of a subcircuit resulting from cutting.
    
    This class stores a subcircuit along with its metadata for
    tracking its relationship to the original circuit.
    """
    
    def __init__(self, circuit: Any, id: str, cuts: List[Dict[str, Any]]):
        """
        Initialize a subcircuit.
        
        Args:
            circuit: The subcircuit
            id: Unique identifier for the subcircuit
            cuts: List of cut information for this subcircuit
        """
        self.circuit = circuit
        self.id = id
        self.cuts = cuts
        
    def __repr__(self) -> str:
        """String representation of the subcircuit."""
        num_qubits = getattr(self.circuit, 'num_qubits', 0)
        return f"Subcircuit(id={self.id}, qubits={num_qubits}, cuts={len(self.cuts)})"


class SubcircuitResult:
    """
    Result from executing a subcircuit.
    
    This class stores the result of executing a subcircuit,
    including counts or statevector.
    """
    
    def __init__(self, subcircuit_id: str, counts: Optional[Dict[str, int]] = None, 
                statevector: Optional[List[complex]] = None, metadata: Optional[Dict[str, Any]] = None):
        """
        Initialize a subcircuit result.
        
        Args:
            subcircuit_id: ID of the subcircuit this result is for
            counts: Measurement counts (for shots-based execution)
            statevector: Final statevector (for statevector simulation)
            metadata: Additional metadata about the execution
        """
        self.subcircuit_id = subcircuit_id
        self.counts = counts if counts is not None else {}
        self.statevector = statevector
        self.metadata = metadata if metadata is not None else {}
        
    def __repr__(self) -> str:
        """String representation of the subcircuit result."""
        result_type = "counts" if self.counts else "statevector" if self.statevector else "unknown"
        return f"SubcircuitResult(id={self.subcircuit_id}, type={result_type})"
    
    def get_probability(self, bitstring: str) -> float:
        """
        Get the probability of a specific measurement outcome.
        
        Args:
            bitstring: Measurement outcome as a binary string
            
        Returns:
            Probability of observing this outcome
        """
        if self.counts:
            total_shots = sum(self.counts.values())
            if total_shots > 0:
                return self.counts.get(bitstring, 0) / total_shots
        return 0.0


class CircuitCutterAdapter(CircuitCutter):
    """
    Adapter for circuit cutting technology, connecting the unified API
    to the existing circuit cutting implementation.
    """
    
    def __init__(self, config: CircuitCuttingConfig):
        """
        Initialize the circuit cutter adapter.
        
        Args:
            config: Configuration for circuit cutting
        """
        logger.info("Initializing CircuitCutterAdapter")
        self.config = config
        
        # Initialize the circuit cutter
        if HAS_CIRCUIT_CUTTER:
            self.cutter = QuantumCircuitCutter(
                max_subcircuit_width=config.max_subcircuit_width,
                max_cuts=config.max_cuts
            )
            logger.info(f"Initialized QuantumCircuitCutter with max_width={config.max_subcircuit_width}, max_cuts={config.max_cuts}")
        else:
            self.cutter = None
            logger.warning("QuantumCircuitCutter not available, cutting will be simulated")
    
    def cut(self, circuit: Any, strategy: Dict[str, Any]) -> List[Subcircuit]:
        """
        Cut circuit into smaller subcircuits.
        
        Args:
            circuit: Quantum circuit to cut
            strategy: Strategy for circuit cutting
            
        Returns:
            List of subcircuits
        """
        logger.info("Cutting quantum circuit")
        start_time = time.time()
        
        # Extract strategy parameters
        method = strategy.get('method', self.config.default_method)
        max_subcircuit_width = strategy.get('max_subcircuit_width', self.config.max_subcircuit_width)
        max_cuts = strategy.get('max_cuts', self.config.max_cuts)
        partition_params = strategy.get('partition_params', {})
        
        # Use the real implementation if available
        if HAS_CIRCUIT_CUTTER and self.cutter:
            try:
                # Call the circuit cutter
                ibm_subcircuits = self.cutter.cut_circuit(
                    circuit=circuit,
                    method=method,
                    partition_kwargs=partition_params
                )
                
                # Convert to our Subcircuit objects
                subcircuits = []
                for i, ibm_subcircuit in enumerate(ibm_subcircuits):
                    # Extract cut information
                    cuts = []
                    for cut_info in ibm_subcircuit.cuts:
                        cut_dict = {
                            'qubit_index': cut_info.qubit_index,
                            'gate_index': cut_info.gate_index,
                            'subcircuit_index': cut_info.subcircuit_index,
                            'neighbor_subcircuit': cut_info.neighbor_subcircuit
                        }
                        cuts.append(cut_dict)
                    
                    # Create our subcircuit object
                    subcircuit = Subcircuit(
                        circuit=ibm_subcircuit.circuit,
                        id=f"sub_{i}",
                        cuts=cuts
                    )
                    subcircuits.append(subcircuit)
                
                logger.info(f"Circuit cutting completed in {time.time() - start_time:.2f} seconds, {len(subcircuits)} subcircuits")
                return subcircuits
                
            except Exception as e:
                logger.error(f"Error in circuit cutting: {str(e)}")
                return self._simulate_circuit_cutting(circuit, strategy)
        
        # Fall back to simulation if the real implementation is not available
        return self._simulate_circuit_cutting(circuit, strategy)
    
    def execute(self, subcircuits: List[Subcircuit], backend: Any) -> List[SubcircuitResult]:
        """
        Execute subcircuits on the specified backend.
        
        Args:
            subcircuits: List of subcircuits to execute
            backend: Backend to execute on
            
        Returns:
            List of subcircuit results
        """
        logger.info(f"Executing {len(subcircuits)} subcircuits")
        start_time = time.time()
        
        results = []
        for subcircuit in subcircuits:
            logger.info(f"Executing subcircuit {subcircuit.id}")
            
            try:
                # In a full implementation, this would actually execute on the backend
                # For now, we generate simulated results
                
                # Create simulated counts
                import random
                counts = {}
                n_qubits = getattr(subcircuit.circuit, 'num_qubits', 3)
                for _ in range(100):  # 100 random shots
                    bitstring = ''.join(['0' if random.random() > 0.5 else '1' for _ in range(n_qubits)])
                    counts[bitstring] = counts.get(bitstring, 0) + 1
                
                # Create subcircuit result
                result = SubcircuitResult(
                    subcircuit_id=subcircuit.id,
                    counts=counts,
                    metadata={
                        'backend': getattr(backend, 'name', 'simulator'),
                        'execution_time': random.uniform(0.1, 1.0),
                        'shots': 100
                    }
                )
                
                results.append(result)
                
            except Exception as e:
                logger.error(f"Error executing subcircuit {subcircuit.id}: {str(e)}")
                # Add empty result on error
                results.append(SubcircuitResult(subcircuit_id=subcircuit.id))
        
        logger.info(f"Subcircuit execution completed in {time.time() - start_time:.2f} seconds")
        return results
    
    def reconstruct(self, results: List[SubcircuitResult]) -> Dict[str, Any]:
        """
        Reconstruct full circuit result from subcircuit results.
        
        Args:
            results: List of subcircuit results
            
        Returns:
            Reconstructed circuit result
        """
        logger.info(f"Reconstructing result from {len(results)} subcircuit results")
        start_time = time.time()
        
        # In a full implementation, this would use tensor network techniques
        # for proper reconstruction of the full circuit results
        
        # For now, we just combine counts in a simple way
        combined_counts = {}
        
        # Check if all results have counts
        all_have_counts = all(bool(result.counts) for result in results)
        
        if all_have_counts:
            # For simplicity, we'll just combine the most probable bitstrings
            # This is a placeholder for the real reconstruction algorithm
            for result in results:
                # Get the most probable bitstring
                if result.counts:
                    most_probable = max(result.counts.items(), key=lambda x: x[1])
                    combined_counts[f"{result.subcircuit_id}_{most_probable[0]}"] = most_probable[1]
        
        # Calculate total execution time
        total_exec_time = sum(
            result.metadata.get('execution_time', 0) for result in results
        )
        
        # Create reconstructed result
        reconstructed = {
            'counts': combined_counts,
            'method': 'simple_combination',  # would be a more sophisticated method in reality
            'reconstruction_time': time.time() - start_time,
            'subcircuit_count': len(results),
            'total_execution_time': total_exec_time,
            'success': all_have_counts
        }
        
        logger.info(f"Result reconstruction completed in {time.time() - start_time:.2f} seconds")
        return reconstructed
    
    def _simulate_circuit_cutting(self, circuit: Any, strategy: Dict[str, Any]) -> List[Subcircuit]:
        """
        Simulate circuit cutting when the real implementation is not available.
        
        Args:
            circuit: Quantum circuit to cut
            strategy: Strategy for circuit cutting
            
        Returns:
            List of simulated subcircuits
        """
        logger.info("Simulating circuit cutting")
        
        # Extract circuit size
        try:
            n_qubits = getattr(circuit, 'num_qubits', 10)
        except:
            n_qubits = 10
        
        # Determine number of subcircuits
        max_width = strategy.get('max_subcircuit_width', self.config.max_subcircuit_width)
        num_subcircuits = max(1, (n_qubits + max_width - 1) // max_width)  # Ceiling division
        
        subcircuits = []
        for i in range(num_subcircuits):
            # Calculate subcircuit size
            start_qubit = i * max_width
            end_qubit = min(start_qubit + max_width, n_qubits)
            size = end_qubit - start_qubit
            
            # Create simulated cut points
            cuts = []
            if i > 0:  # Has a cut on the left
                cuts.append({
                    'type': 'qubit_cut',
                    'position': start_qubit - 0.5,
                    'connected_to': f"sub_{i-1}"
                })
            
            if i < num_subcircuits - 1:  # Has a cut on the right
                cuts.append({
                    'type': 'qubit_cut',
                    'position': end_qubit - 0.5,
                    'connected_to': f"sub_{i+1}"
                })
            
            # Create a simulated subcircuit
            # In a real implementation, this would be a proper quantum circuit object
            from collections import namedtuple
            SimCircuit = namedtuple('SimCircuit', ['num_qubits', 'name'])
            sim_circuit = SimCircuit(num_qubits=size, name=f"subcircuit_{i}")
            
            subcircuit = Subcircuit(
                circuit=sim_circuit,
                id=f"sub_{i}",
                cuts=cuts
            )
            subcircuits.append(subcircuit)
        
        logger.info(f"Simulated cutting of {n_qubits}-qubit circuit into {len(subcircuits)} subcircuits")
        return subcircuits 