#!/usr/bin/env python3
"""
BioQL Quantum Error Correction with Qualtran
Implements QEC codes and factorization algorithms
"""

import sympy
from typing import Optional, Dict, Any, Tuple
from dataclasses import dataclass

try:
    from qualtran.bloqs.cryptography.rsa import ModExp
    from qualtran.resource_counting import get_cost_value, QECGatesCost
    from qualtran.drawing import show_bloq
    QUALTRAN_AVAILABLE = True
except ImportError:
    QUALTRAN_AVAILABLE = False
    ModExp = None
    QECGatesCost = None

try:
    import qiskit
    from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister
    QISKIT_AVAILABLE = True
except ImportError:
    QISKIT_AVAILABLE = False

from .logger import get_logger

logger = get_logger(__name__)


@dataclass
class QECResult:
    """Results from QEC analysis."""

    success: bool
    algorithm: str
    qec_gates_cost: Optional[Dict[str, int]]
    num_logical_qubits: int
    num_physical_qubits: int  # With error correction overhead
    code_distance: int
    error_rate_physical: float
    error_rate_logical: float  # After QEC
    accuracy_improvement: float  # Percentage
    metadata: Optional[Dict[str, Any]] = None
    error_message: Optional[str] = None


@dataclass
class RSAFactorizationResult:
    """Results from RSA factorization analysis."""

    success: bool
    number_to_factor: int
    base: int
    modulus: int
    exp_bitsize: int
    x_bitsize: int
    qec_gates_cost: Optional[Dict[str, int]]
    estimated_time_hours: Optional[float]
    num_logical_qubits: int
    num_physical_qubits: int
    error_message: Optional[str] = None


class QuantumErrorCorrection:
    """
    Quantum Error Correction engine using Qualtran.

    Implements:
    - Surface codes
    - Steane code [[7,1,3]]
    - Repetition codes
    - QEC gates cost analysis
    - RSA factorization with ModExp
    """

    # QEC Code parameters
    QEC_CODES = {
        'repetition_3': {
            'logical_qubits': 1,
            'physical_qubits': 3,
            'code_distance': 3,
            'error_correction': 0.1  # Can correct 10% error rate
        },
        'steane_7_1_3': {
            'logical_qubits': 1,
            'physical_qubits': 7,
            'code_distance': 3,
            'error_correction': 0.01  # Can correct 1% error rate
        },
        'surface_15_1_3': {
            'logical_qubits': 1,
            'physical_qubits': 15,
            'code_distance': 3,
            'error_correction': 0.001  # Can correct 0.1% error rate
        },
        'surface_49_1_7': {
            'logical_qubits': 1,
            'physical_qubits': 49,
            'code_distance': 7,
            'error_correction': 0.0001  # Can correct 0.01% error rate
        }
    }

    def __init__(self):
        """Initialize QEC engine."""
        if not QUALTRAN_AVAILABLE:
            raise ImportError("Qualtran not available. Install: pip install qualtran")

        logger.info("Quantum Error Correction engine initialized")

    def analyze_qec_cost(
        self,
        algorithm: str,
        num_logical_qubits: int,
        qec_code: str = 'surface_15_1_3',
        physical_error_rate: float = 0.001
    ) -> QECResult:
        """
        Analyze QEC requirements for an algorithm.

        Args:
            algorithm: Algorithm name
            num_logical_qubits: Number of logical qubits needed
            qec_code: QEC code to use
            physical_error_rate: Physical qubit error rate

        Returns:
            QECResult with overhead and accuracy analysis
        """
        try:
            if qec_code not in self.QEC_CODES:
                raise ValueError(f"Unknown QEC code: {qec_code}")

            code_params = self.QEC_CODES[qec_code]

            # Calculate physical qubit overhead
            physical_qubits_per_logical = code_params['physical_qubits']
            total_physical_qubits = num_logical_qubits * physical_qubits_per_logical

            # Calculate logical error rate after QEC
            code_distance = code_params['code_distance']
            logical_error_rate = self._calculate_logical_error_rate(
                physical_error_rate,
                code_distance
            )

            # Calculate accuracy improvement
            accuracy_before = (1 - physical_error_rate) * 100
            accuracy_after = (1 - logical_error_rate) * 100
            improvement = accuracy_after - accuracy_before

            logger.info(f"QEC Analysis for {algorithm}")
            logger.info(f"Logical qubits: {num_logical_qubits}")
            logger.info(f"Physical qubits: {total_physical_qubits}")
            logger.info(f"Physical error rate: {physical_error_rate:.4f}")
            logger.info(f"Logical error rate: {logical_error_rate:.6f}")
            logger.info(f"Accuracy improvement: {improvement:.2f}%")

            return QECResult(
                success=True,
                algorithm=algorithm,
                qec_gates_cost=None,  # Would be filled by actual Qualtran analysis
                num_logical_qubits=num_logical_qubits,
                num_physical_qubits=total_physical_qubits,
                code_distance=code_distance,
                error_rate_physical=physical_error_rate,
                error_rate_logical=logical_error_rate,
                accuracy_improvement=improvement,
                metadata={
                    'qec_code': qec_code,
                    'overhead_factor': physical_qubits_per_logical
                }
            )

        except Exception as e:
            logger.error(f"QEC analysis failed: {e}")
            return QECResult(
                success=False,
                algorithm=algorithm,
                qec_gates_cost=None,
                num_logical_qubits=num_logical_qubits,
                num_physical_qubits=0,
                code_distance=0,
                error_rate_physical=physical_error_rate,
                error_rate_logical=1.0,
                accuracy_improvement=0.0,
                error_message=str(e)
            )

    def _calculate_logical_error_rate(
        self,
        physical_error: float,
        code_distance: int
    ) -> float:
        """
        Calculate logical error rate after QEC.

        Uses threshold theorem: p_logical ≈ p_physical^((d+1)/2)
        """
        # Simplified model - real QEC is more complex
        threshold = 0.01  # Typical surface code threshold
        if physical_error > threshold:
            return physical_error  # No improvement above threshold

        # Exponential suppression with code distance
        logical_error = physical_error ** ((code_distance + 1) / 2)
        return logical_error

    def rsa_modexp_cost_analysis(
        self,
        base: int = 4,
        modulus: int = 15,
        exp_bitsize: int = 3,
        x_bitsize_symbolic: bool = True
    ) -> RSAFactorizationResult:
        """
        Analyze RSA ModExp QEC gates cost.

        Example from Qualtran docs:
            >>> n = sympy.Symbol('n')
            >>> modexp = ModExp(base=4, mod=15, exp_bitsize=3, x_bitsize=n)
            >>> cost = get_cost_value(modexp, QECGatesCost())
            >>> print(cost)

        Args:
            base: Base for modular exponentiation
            modulus: Modulus
            exp_bitsize: Exponent bit size
            x_bitsize_symbolic: Use symbolic x_bitsize (for analysis)

        Returns:
            RSAFactorizationResult with QEC cost analysis
        """
        try:
            if not QUALTRAN_AVAILABLE:
                raise ImportError("Qualtran not available")

            logger.info(f"Analyzing RSA ModExp: {base}^x mod {modulus}")

            # Create ModExp bloq
            if x_bitsize_symbolic:
                n = sympy.Symbol('n')
                x_bitsize = n
            else:
                x_bitsize = modulus.bit_length()

            modexp = ModExp(
                base=base,
                mod=modulus,
                exp_bitsize=exp_bitsize,
                x_bitsize=x_bitsize
            )

            # Get QEC gates cost
            cost = get_cost_value(modexp, QECGatesCost())

            # Extract cost breakdown
            cost_dict = {}
            if hasattr(cost, '__dict__'):
                cost_dict = {
                    str(k): int(v) if isinstance(v, (int, sympy.Integer)) else str(v)
                    for k, v in cost.__dict__.items()
                    if not k.startswith('_')
                }

            logger.info(f"QEC Gates Cost: {cost}")

            # Estimate logical qubits
            num_logical_qubits = exp_bitsize + 2 * (x_bitsize if isinstance(x_bitsize, int) else 10)

            # Estimate physical qubits with QEC (surface code)
            # Typical: 1000 physical qubits per logical qubit
            num_physical_qubits = num_logical_qubits * 1000

            # Estimate execution time (very rough)
            # Assuming 1 microsecond per gate, surface code overhead
            total_gates = sum(v for v in cost_dict.values() if isinstance(v, int))
            estimated_hours = (total_gates * 1e-6 * 1000) / 3600  # Gate time * overhead / 3600

            logger.info(f"Estimated logical qubits: {num_logical_qubits}")
            logger.info(f"Estimated physical qubits: {num_physical_qubits}")
            logger.info(f"Estimated time: {estimated_hours:.2f} hours")

            return RSAFactorizationResult(
                success=True,
                number_to_factor=modulus,
                base=base,
                modulus=modulus,
                exp_bitsize=exp_bitsize,
                x_bitsize=x_bitsize if isinstance(x_bitsize, int) else -1,
                qec_gates_cost=cost_dict,
                estimated_time_hours=estimated_hours,
                num_logical_qubits=num_logical_qubits,
                num_physical_qubits=num_physical_qubits
            )

        except Exception as e:
            logger.error(f"RSA ModExp analysis failed: {e}")
            return RSAFactorizationResult(
                success=False,
                number_to_factor=modulus,
                base=base,
                modulus=modulus,
                exp_bitsize=exp_bitsize,
                x_bitsize=-1,
                qec_gates_cost=None,
                estimated_time_hours=None,
                num_logical_qubits=0,
                num_physical_qubits=0,
                error_message=str(e)
            )

    def create_repetition_code_circuit(self, num_qubits: int = 3) -> Optional[Any]:
        """
        Create a simple repetition code circuit for error correction.

        Args:
            num_qubits: Number of qubits (must be odd, typically 3 or 5)

        Returns:
            Qiskit QuantumCircuit with repetition code
        """
        if not QISKIT_AVAILABLE:
            logger.error("Qiskit not available")
            return None

        if num_qubits % 2 == 0:
            raise ValueError("num_qubits must be odd for repetition code")

        try:
            # Create circuit: encoding + noise + decoding
            qr = QuantumRegister(num_qubits, 'q')
            ar = QuantumRegister(num_qubits - 1, 'ancilla')  # Syndrome qubits
            cr = ClassicalRegister(1, 'c')
            circuit = QuantumCircuit(qr, ar, cr)

            # Encoding: spread logical |ψ⟩ across physical qubits
            # |ψ⟩ → |ψψψ⟩
            for i in range(1, num_qubits):
                circuit.cx(qr[0], qr[i])

            circuit.barrier()

            # Syndrome measurement (detect errors)
            for i in range(num_qubits - 1):
                circuit.cx(qr[i], ar[i])
                circuit.cx(qr[i+1], ar[i])
                circuit.measure(ar[i], cr[0])  # Simplified - would need more classical bits

            circuit.barrier()

            # Majority vote decoding
            # In practice, use syndrome to correct errors

            logger.info(f"Created repetition code circuit with {num_qubits} qubits")
            return circuit

        except Exception as e:
            logger.error(f"Circuit creation failed: {e}")
            return None


def quick_qec_demo() -> Tuple[QECResult, RSAFactorizationResult]:
    """
    Quick demonstration of QEC capabilities.

    Returns:
        Tuple of (QEC analysis, RSA ModExp analysis)

    Example:
        >>> qec_result, rsa_result = quick_qec_demo()
        >>> print(f"Accuracy improvement: {qec_result.accuracy_improvement}%")
        >>> print(f"RSA factorization cost: {rsa_result.qec_gates_cost}")
    """
    qec = QuantumErrorCorrection()

    # Analyze QEC for VQE algorithm
    qec_result = qec.analyze_qec_cost(
        algorithm='VQE_H2',
        num_logical_qubits=4,
        qec_code='surface_15_1_3',
        physical_error_rate=0.001
    )

    # Analyze RSA factorization
    rsa_result = qec.rsa_modexp_cost_analysis(
        base=4,
        modulus=15,
        exp_bitsize=3,
        x_bitsize_symbolic=True
    )

    return qec_result, rsa_result
