"""
Local simulator for CRISPR-QAI energy estimation

Uses Ising-like Hamiltonian to model gRNA-DNA interactions:
- Each nucleotide → qubit
- Rotations encode sequence features
- ZZ couplings model base-pair affinity

No external quantum hardware required.
"""

import time
from typing import Dict, List, Any, Optional
import numpy as np

from .base import QuantumEngine


class LocalSimulatorEngine(QuantumEngine):
    """
    Built-in quantum simulator for CRISPR energy calculations

    Uses simplified Ising model:
    H = Σ h_i Z_i + Σ J_ij Z_i Z_j

    Where:
    - h_i = angle encoding for nucleotide i
    - J_ij = coupling strength between nucleotides i, j
    """

    def __init__(self, shots: int = 1000, seed: Optional[int] = None):
        """
        Initialize local simulator

        Args:
            shots: Number of measurements
            seed: Random seed for reproducibility
        """
        super().__init__(backend_name="local_simulator", shots=shots)
        self.seed = seed
        if seed is not None:
            np.random.seed(seed)
        self.validated = True  # Always available

    def run_energy_estimation(
        self,
        angles: List[float],
        coupling_strength: float = 1.0,
        metadata: Optional[Dict[str, Any]] = None
    ) -> Dict[str, Any]:
        """
        Simulate quantum energy estimation

        Args:
            angles: Rotation angles encoding gRNA sequence (radians)
            coupling_strength: Coupling between adjacent qubits
            metadata: Optional guide metadata

        Returns:
            Energy estimation results
        """
        start_time = time.time()

        # Validate inputs
        self._validate_angles(angles)

        num_qubits = len(angles)

        # Build Ising Hamiltonian
        # H = Σ h_i Z_i + Σ J_ij Z_i Z_j
        h_fields = np.cos(angles)  # Single-qubit terms

        # Simulate measurements
        counts = self._simulate_measurements(h_fields, coupling_strength)

        # Calculate energy expectation
        energy_estimate = self._calculate_energy(counts, h_fields, coupling_strength)

        # Calculate confidence
        confidence = self._calculate_confidence(counts)

        runtime = time.time() - start_time

        return {
            'energy_estimate': float(energy_estimate),
            'confidence': float(confidence),
            'runtime_seconds': runtime,
            'backend': self.backend_name,
            'shots': self.shots,
            'num_qubits': num_qubits,
            'metadata': metadata or {}
        }

    def validate_backend(self) -> bool:
        """
        Validate simulator (always available)

        Returns:
            True (simulator is always ready)
        """
        self.validated = True
        return True

    def _simulate_measurements(
        self,
        h_fields: np.ndarray,
        coupling_strength: float
    ) -> Dict[str, int]:
        """
        Simulate quantum measurements using Gibbs sampling

        Args:
            h_fields: Local field strengths
            coupling_strength: Qubit-qubit coupling

        Returns:
            Measurement counts {bitstring: count}
        """
        num_qubits = len(h_fields)
        counts = {}

        # Effective temperature for sampling
        beta = 1.0  # Inverse temperature

        for _ in range(self.shots):
            # Sample initial state
            state = np.random.choice([-1, 1], size=num_qubits)

            # Metropolis-Hastings for thermal state
            for _ in range(10):  # Thermalization steps
                i = np.random.randint(num_qubits)

                # Calculate energy change from flipping qubit i
                delta_E = 2 * state[i] * h_fields[i]

                # Add coupling terms
                if i > 0:
                    delta_E += 2 * coupling_strength * state[i] * state[i-1]
                if i < num_qubits - 1:
                    delta_E += 2 * coupling_strength * state[i] * state[i+1]

                # Accept flip with probability exp(-β * ΔE)
                if delta_E < 0 or np.random.random() < np.exp(-beta * delta_E):
                    state[i] *= -1

            # Convert to bitstring (1 → '1', -1 → '0')
            bitstring = ''.join('1' if s == 1 else '0' for s in state)
            counts[bitstring] = counts.get(bitstring, 0) + 1

        return counts

    def _calculate_energy(
        self,
        counts: Dict[str, int],
        h_fields: np.ndarray,
        coupling_strength: float
    ) -> float:
        """
        Calculate energy expectation from measurement counts

        Args:
            counts: Measurement results
            h_fields: Local field strengths
            coupling_strength: Qubit-qubit coupling

        Returns:
            Expected energy value
        """
        total_shots = sum(counts.values())
        energy = 0.0

        for bitstring, count in counts.items():
            # Convert bitstring to spin configuration
            spins = np.array([1 if b == '1' else -1 for b in bitstring])

            # Calculate energy for this configuration
            # H = Σ h_i Z_i + Σ J_ij Z_i Z_j
            config_energy = np.dot(h_fields, spins)

            for i in range(len(spins) - 1):
                config_energy += coupling_strength * spins[i] * spins[i+1]

            energy += (count / total_shots) * config_energy

        return energy
