import logging
import multiprocessing
import time
from concurrent.futures import ThreadPoolExecutor
from dataclasses import dataclass
from multiprocessing import Pool
from typing import Any, Dict, List, Optional, Tuple, Union

import numpy as np
import sparse
from cirq.circuits.circuit import Circuit
from cirq.devices.line_qubit import LineQubit
from qiskit import QuantumCircuit as QiskitCircuit
from qiskit.quantum_info import Operator, Statevector
from qiskit_aer import AerSimulator
from qiskit_aer.primitives import Estimator as AerEstimator
from qiskit_aer.primitives import Sampler as AerSampler

from .quantum_circuit import QuantumCircuit
from .quantum_gate import QuantumGate

logger = logging.getLogger(__name__)


@dataclass
class ProcessorConfig:
    """Configuration for quantum processor."""

    num_qubits: int = 4
    error_rate: float = 0.001
    decoherence_time: float = 1000.0
    gate_time: float = 0.1
    num_workers: int = 1
    max_depth: int = 100
    optimization_level: int = 1
    use_error_correction: bool = True
    noise_model: str = "depolarizing"
    version: str = "1.1.4"


class QuantumProcessor:
    """Quantum processor implementation."""

    def __init__(self, config: Optional[ProcessorConfig] = None):
        """Initialize quantum processor."""
        self.config = config or ProcessorConfig()
        self.simulator = AerSimulator()
        self.sampler = AerSampler()
        self.estimator = AerEstimator()
        self.circuit = Circuit()
        self.qubits = LineQubit.range(self.config.num_qubits)
        self.error_correction_circuits: Dict[str, QuantumCircuit] = {}
        self.measurement_results: List[Dict[int, int]] = []
        self.noise_models: Dict[str, callable] = {}
        self.error_rates: Dict[int, float] = {}

        # Initialize components
        self._initialize_error_correction()
        self._initialize_noise_models()

    def _initialize_error_correction(self) -> None:
        """Initialize quantum error correction circuits."""
        if not self.config.use_error_correction:
            return

        # Initialize basic error correction codes
        # Surface code for multiple qubits
        self.error_correction_circuits["surface"] = self._create_surface_code()
        # Steane code for 7-qubit error correction
        self.error_correction_circuits["steane"] = self._create_steane_code()

    def _create_surface_code(self) -> QuantumCircuit:
        """Create surface code error correction circuit."""
        circuit = QuantumCircuit(
            self.config.num_qubits + 4
        )  # Additional syndrome qubits

        # Add stabilizer measurements
        for i in range(0, self.config.num_qubits - 1, 2):
            circuit.add_gate("H", [i], None)
            circuit.add_gate("CNOT", [i + 1], [i])
            if i + 2 < self.config.num_qubits:
                circuit.add_gate("CNOT", [i + 2], [i])

        return circuit

    def _create_steane_code(self) -> QuantumCircuit:
        """Create Steane code error correction circuit."""
        circuit = QuantumCircuit(7)  # 7-qubit code

        # Initialize logical zero state
        for i in range(7):
            circuit.add_gate("H", [i], None)

        # Add stabilizer measurements
        stabilizers = [[0, 2, 4, 6], [1, 2, 5, 6], [3, 4, 5, 6]]

        for stabilizer in stabilizers:
            for qubit in stabilizer:
                circuit.add_gate("CNOT", [qubit], [stabilizer[0]])

        return circuit

    def _initialize_noise_models(self) -> None:
        """Initialize noise models for simulation."""
        self.noise_models = {
            "depolarizing": self._apply_depolarizing_noise,
            "amplitude_damping": self._apply_amplitude_damping,
            "phase_damping": self._apply_phase_damping,
        }

    def _apply_depolarizing_noise(self, state: np.ndarray) -> np.ndarray:
        """Apply depolarizing noise to quantum state."""
        p = self.config.error_rate
        if p == 0:
            return state

        rng = np.random.default_rng(seed=42)  # Fixed seed for reproducibility
        noise = rng.random(len(state))
        mask = noise < p
        if np.any(mask):
            # Randomly flip affected amplitudes
            state[mask] = np.exp(2j * np.pi * rng.random(np.sum(mask)))
            # Renormalize
            state /= np.linalg.norm(state)
        return state

    def _apply_amplitude_damping(self, state: np.ndarray) -> np.ndarray:
        """Apply amplitude damping noise."""
        gamma = self.config.error_rate
        if gamma == 0:
            return state

        # Kraus operators for amplitude damping
        K0 = np.array([[1, 0], [0, np.sqrt(1 - gamma)]])
        K1 = np.array([[0, np.sqrt(gamma)], [0, 0]])

        # Apply noise to each qubit
        for _ in range(self.config.num_qubits):
            # Reshape state for single qubit operation
            shape = [2] * self.config.num_qubits
            state = state.reshape(shape)

            # Apply Kraus operators
            new_state = np.tensordot(K0, state, axes=1)
            new_state += np.tensordot(K1, state, axes=1)

            state = new_state.reshape(-1)

        return state / np.linalg.norm(state)

    def _apply_phase_damping(self, state: np.ndarray) -> np.ndarray:
        """Apply phase damping noise."""
        lambda_param = self.config.error_rate
        if lambda_param == 0:
            return state

        # Kraus operators for phase damping
        K0 = np.array([[1, 0], [0, np.sqrt(1 - lambda_param)]])
        K1 = np.array([[0, 0], [0, np.sqrt(lambda_param)]])

        # Apply noise to each qubit
        for _ in range(self.config.num_qubits):
            shape = [2] * self.config.num_qubits
            state = state.reshape(shape)

            new_state = np.tensordot(K0, state, axes=1)
            new_state += np.tensordot(K1, state, axes=1)

            state = new_state.reshape(-1)

        return state / np.linalg.norm(state)

    def prepare_state(self, data: np.ndarray) -> np.ndarray:
        """Prepare quantum state from classical data."""
        # Normalize the data
        normalized = data / np.linalg.norm(data)
        
        # Create quantum circuit
        num_qubits = int(np.ceil(np.log2(len(normalized))))
        circuit = QiskitCircuit(num_qubits)
        
        # Initialize the state
        circuit.initialize(normalized, range(num_qubits))
        
        # Get the state vector
        state = Statevector.from_instruction(circuit)
        return state.data
        
    def measure_correlation(self, quantum_state: np.ndarray) -> float:
        """Measure quantum correlation in the state."""
        # Convert state vector to density matrix
        density_matrix = np.outer(quantum_state, np.conj(quantum_state))
        
        # Calculate quantum mutual information
        # This is a simplified version - in practice you'd want to use
        # more sophisticated measures of quantum correlation
        entropy = -np.trace(density_matrix @ np.log2(density_matrix + 1e-10))
        return float(entropy)
        
    def apply_circuit(self, circuit: QiskitCircuit) -> np.ndarray:
        """Apply quantum circuit and return final state."""
        # If the circuit has measurements, use the sampler
        if circuit.num_clbits > 0:
            job = self.sampler.run(circuits=[circuit], shots=1000)
            result = job.result()
            # Convert counts to normalized state vector
            quasi_dists = result.quasi_dists[0]
            state = np.zeros(2**circuit.num_qubits, dtype=np.complex128)
            for bitstring, prob in quasi_dists.items():
                index = int(bitstring, 2)
                state[index] = np.sqrt(prob)
            return state
            
        # If no measurements, get the state vector directly
        state = Statevector.from_instruction(circuit)
        return state.data
        
    def get_expectation_value(self, circuit: QiskitCircuit, observable: Operator) -> float:
        """Calculate expectation value of an observable."""
        job = self.estimator.run(circuits=[circuit], observables=[observable])
        result = job.result()
        return float(result.values[0])
        
    def optimize_circuit(self, circuit: QiskitCircuit) -> QiskitCircuit:
        """Optimize quantum circuit."""
        from qiskit.transpiler import PassManager
        from qiskit.transpiler.passes import Optimize1qGates, CXCancellation
        
        pm = PassManager([
            Optimize1qGates(),
            CXCancellation()
        ])
        
        return pm.run(circuit)
        
    def add_error_correction(self, circuit: QiskitCircuit) -> QiskitCircuit:
        """Add quantum error correction to circuit."""
        if not self.config.use_error_correction:
            return circuit
            
        # This is a simplified version - in practice you'd want to use
        # proper quantum error correction codes
        
        # Add redundancy
        new_circuit = QiskitCircuit(3 * circuit.num_qubits)
        
        # Replicate each qubit's state
        for i in range(circuit.num_qubits):
            new_circuit.cx(i, i + circuit.num_qubits)
            new_circuit.cx(i, i + 2 * circuit.num_qubits)
            
        # Add error detection
        for i in range(circuit.num_qubits):
            new_circuit.measure_all()
            
        return new_circuit
        
    def correct_errors(self, circuit: QiskitCircuit) -> QiskitCircuit:
        """Apply error correction to circuit results."""
        if not self.config.use_error_correction:
            return circuit
            
        # This is a simplified version - in practice you'd want to use
        # proper error correction techniques
        
        corrected = QiskitCircuit(circuit.num_qubits)
        
        # Add majority voting
        for i in range(circuit.num_qubits // 3):
            base = 3 * i
            corrected.ccx(base, base + 1, base + 2)
            
        return corrected

    def apply_circuit(self, circuit: QuantumCircuit) -> np.ndarray:
        """Apply quantum circuit with error correction and noise simulation.

        Args:
            circuit: QuantumCircuit to apply

        Returns:
            Final quantum state vector
        """
        if len(circuit.gates) > self.config.max_depth:
            raise ValueError(
                f"Circuit depth {len(circuit.gates)} exceeds maximum "
                f"{self.config.max_depth}"
            )

        # Apply gates with noise and error correction
        for gate in circuit.gates:
            # Apply error correction if enabled
            if self.config.use_error_correction:
                self._apply_error_correction(circuit.state)

            # Apply gate
            circuit.apply_gate(gate)

            # Apply noise model
            if self.config.error_rate > 0:
                noise_func = self.noise_models.get(
                    self.config.noise_model, self._apply_depolarizing_noise
                )
                circuit.state.amplitudes = noise_func(circuit.state.amplitudes)

            # Check if decoherence has occurred
            if self._check_decoherence(gate):
                logger.warning("Decoherence detected, state may be unreliable")
                break

        return circuit.get_state()

    def _apply_error_correction(self, quantum_state):
        """Apply quantum error correction to the given quantum state."""
        if not self.config.use_error_correction:
            return quantum_state

        try:
            # Apply stabilizer measurements
            syndrome = self._measure_syndrome(self._select_error_correction_type())

            # Determine correction operations
            correction_ops = self._analyze_and_correct_errors(syndrome)

            # Apply correction operations
            corrected_state = self._apply_correction_operations(
                quantum_state, correction_ops
            )

            return corrected_state
        except Exception as e:
            self.logger.error("Error in quantum error correction", error=str(e))
            return quantum_state

    def _analyze_and_correct_errors(self, syndrome):
        """Analyze syndrome measurements and determine correction operations."""
        correction_ops = []

        for qubit_idx, (error_detected, confidence) in syndrome.items():
            if error_detected and confidence > self.config.error_threshold:
                correction_ops.append(self._determine_correction_operation(qubit_idx))
                self.logger.info(f"Error detected on qubit {qubit_idx}")

        return correction_ops

    def _determine_correction_operation(self, qubit_idx):
        """Determine the appropriate correction operation for a given qubit."""
        # Implementation depends on the error correction code being used
        pass

    def _select_error_correction_type(self) -> str:
        """Select appropriate error correction code based on current conditions."""
        # Analyze current error rates and qubit stability
        error_rates = self._get_current_error_rates()
        qubit_stability = self._get_qubit_stability()

        # Select code based on conditions
        if max(error_rates) > 0.1 or min(qubit_stability) < 0.8:
            return "surface"  # More robust code
        else:
            return "steane"  # More efficient code

    def _measure_syndrome(self, correction_type: str) -> Dict[int, Tuple[int, float]]:
        """Perform enhanced syndrome measurement."""
        syndrome = {}

        if correction_type == "surface":
            # Surface code syndrome measurement
            for i in range(0, self.config.num_qubits - 1, 2):
                # Measure stabilizers
                measurements = self.circuit.measure([i, i + 1])
                syndrome[i] = (
                    sum(measurements.values()) % 2,
                    0.95,
                )  # High confidence
        else:
            # Steane code syndrome measurement
            stabilizers = [[0, 2, 4, 6], [1, 2, 5, 6], [3, 4, 5, 6]]
            for i, stabilizer in enumerate(stabilizers):
                measurements = self.circuit.measure(stabilizer)
                syndrome[i] = (
                    sum(measurements.values()) % 2,
                    0.9,
                )  # Good confidence

        return syndrome

    def _update_error_rates(self) -> None:
        """Update error rates based on correction performance."""
        # Monitor error correction success
        success_rate = self._calculate_correction_success()

        # Adjust error rates based on performance
        for i in range(self.config.num_qubits):
            if success_rate < 0.9:  # Poor correction performance
                self.error_rates[i] *= 1.1  # Increase error rate estimate
            else:
                self.error_rates[i] *= 0.9  # Decrease error rate estimate

    def _check_decoherence(self, gate: "QuantumGate") -> bool:
        """Check if decoherence has occurred based on gate time."""
        total_time = len(self.circuit.gates) * self.config.gate_time
        return total_time > self.config.decoherence_time

    def run_parallel_circuits(self, circuits: List[QuantumCircuit]) -> List[np.ndarray]:
        """Run multiple circuits in parallel using multiprocessing.

        Args:
            circuits: List of quantum circuits to run

        Returns:
            List of final state vectors
        """
        with Pool(self.config.num_workers) as pool:
            results = pool.map(self.apply_circuit, circuits)
        return results

    def get_measurement_statistics(self) -> Dict[str, float]:
        """Get statistics of all measurements."""
        stats: Dict[str, float] = {}
        total_measurements = len(self.measurement_results)

        if total_measurements == 0:
            return stats

        # Compute frequencies of each measurement outcome
        for result in self.measurement_results:
            key = "".join(str(result.get(i, 0)) for i in range(self.config.num_qubits))
            stats[key] = stats.get(key, 0) + 1 / total_measurements

        return stats

    def reset(self) -> None:
        """Reset processor to initial state."""
        self.circuit = Circuit()
        self.qubits = LineQubit.range(self.config.num_qubits)
        self.measurement_results.clear()
        self._initialize_error_correction()

    def __str__(self) -> str:
        """String representation of processor state."""
        return (
            f"QuantumProcessor(num_qubits={self.config.num_qubits}, "
            f"error_rate={self.config.error_rate}, "
            f"noise_model={self.config.noise_model})"
        )

    def _optimize_resource_utilization(self) -> None:
        """Optimize resource utilization with advanced techniques."""
        # 1. Memory Management
        self._optimize_memory_usage()

        # 2. Parallel Processing
        self._optimize_parallel_processing()

        # 3. Resource Allocation
        self._optimize_resource_allocation()

        # 4. Performance Monitoring
        self._monitor_performance()

    def _optimize_memory_usage(self) -> None:
        """Optimize memory usage for quantum state and operations."""
        # Implement memory optimization techniques
        if hasattr(self, "state") and self.state is not None:
            # Compress state vector if possible
            if np.allclose(self.state, np.zeros_like(self.state)):
                self.state = np.zeros(1, dtype=np.complex128)

            # Use sparse representation for large states
            if len(self.state) > 2**20:  # Threshold for sparse representation
                self.state = sparse.csr_matrix(self.state)

        # Clear unused resources
        self._clear_unused_resources()

    def _optimize_parallel_processing(self) -> None:
        """Optimize parallel processing of quantum operations."""
        # Configure parallel processing based on available resources
        num_cores = multiprocessing.cpu_count()
        self.config.num_workers = min(num_cores, self.config.max_workers)

        # Initialize thread pool for parallel operations
        self.thread_pool = ThreadPoolExecutor(max_workers=self.config.num_workers)

        # Configure parallel circuit execution
        self._configure_parallel_circuit_execution()

    def _optimize_resource_allocation(self) -> None:
        """Optimize allocation of quantum and classical resources."""
        # Allocate qubits based on circuit requirements
        self._allocate_qubits()

        # Allocate classical resources
        self._allocate_classical_resources()

        # Balance resource usage
        self._balance_resources()

    def _monitor_performance(self) -> None:
        """Monitor and optimize performance metrics."""
        # Track resource usage
        self._track_resource_usage()

        # Update performance metrics
        self._update_performance_metrics()

        # Optimize based on metrics
        self._optimize_based_on_metrics()

    def _track_resource_usage(self) -> None:
        """Track resource usage and efficiency."""
        # Track memory usage
        memory_usage = self._get_memory_usage()

        # Track CPU usage
        cpu_usage = self._get_cpu_usage()

        # Track qubit utilization
        qubit_utilization = self._get_qubit_utilization()

        # Update resource tracking
        self.resource_metrics = {
            "memory_usage": memory_usage,
            "cpu_usage": cpu_usage,
            "qubit_utilization": qubit_utilization,
            "timestamp": time.time(),
        }

    def _update_performance_metrics(self) -> None:
        """Update performance metrics based on resource usage."""
        # Calculate performance scores
        memory_score = 1.0 - (
            self.resource_metrics["memory_usage"] / self.config.max_memory
        )
        cpu_score = 1.0 - (self.resource_metrics["cpu_usage"] / 100.0)
        qubit_score = self.resource_metrics["qubit_utilization"]

        # Update overall performance metric
        self.performance_metric = (memory_score + cpu_score + qubit_score) / 3.0

    def _optimize_based_on_metrics(self) -> None:
        """Optimize resource usage based on performance metrics."""
        if self.performance_metric < 0.7:  # Performance threshold
            # Trigger optimization routines
            self._optimize_memory_usage()
            self._optimize_parallel_processing()
            self._optimize_resource_allocation()

    def _apply_correction(self, qubit_idx):
        """Apply correction operation to specific qubit."""
        # Apply X gate to correct bit flip
        self.state.apply_gate("X", qubit_idx)

        # Update error tracking
        self._error_history.append(
            {"time": time.time(), "qubit": qubit_idx, "type": "correction"}
        )


def process_quantum_circuit(
    circuit: QuantumCircuit, backend: Optional[str] = None, shots: int = 1024
) -> Dict[str, Any]:
    """
    Process a quantum circuit using the specified backend.

    Args:
        circuit: The quantum circuit to process
        backend: The backend to use for processing
        shots: Number of shots to run the circuit

    Returns:
        Dictionary containing the results
    """
    # ... existing code ...


def analyze_quantum_results(
    results: Dict[str, Any], threshold: float = 0.1, max_iterations: int = 100
) -> Dict[str, Union[float, List[float]]]:
    """
    Analyze quantum computation results with detailed metrics.

    Args:
        results: Dictionary containing quantum computation results
        threshold: Threshold for significance
        max_iterations: Maximum number of iterations for analysis

    Returns:
        Dictionary containing analysis results
    """
    # ... existing code ...


def optimize_quantum_parameters(
    initial_params: List[float], learning_rate: float = 0.01, max_iterations: int = 1000
) -> Dict[str, Union[List[float], float]]:
    """
    Optimize quantum circuit parameters using gradient descent.

    Args:
        initial_params: Initial parameter values
        learning_rate: Learning rate for optimization
        max_iterations: Maximum number of iterations

    Returns:
        Dictionary containing optimized parameters and metrics
    """
    # ... existing code ...
