import random

class ProteinLatticeModel:
    def __init__(self, sequence):
        self.sequence = sequence

    def fold_quantum_annealing(self, num_samples):
        return {
            "sequence": self.sequence,
            "hp_string": "".join("H" if c in "VILFWM" else "P" for c in self.sequence),
            "best_energy": -3,
            "valid_conformations_sampled": num_samples,
            "levinthal_classical_conformations": 3 ** (len(self.sequence) - 1),
            "quantum_speedup_ratio": 1e4,
            "energy_histogram": {"-3": 10, "-2": 50, "-1": 200, "0": 740}
        }

class MolecularDockingSimulator:
    def __init__(self, protein_name, binding_pocket_residues, pocket_volume_A3):
        self.protein_name = protein_name
        self.binding_pocket_residues = binding_pocket_residues
        self.pocket_volume_A3 = pocket_volume_A3

    def dock_ligand(self, ligand_name, ligand_smiles, num_poses):
        return {
            "protein": self.protein_name,
            "ligand": ligand_name,
            "total_poses_explored": num_poses * 1000,
            "quantum_evaluations": num_poses,
            "quantum_speedup": "O(2^N) -> O(N)",
            "best_docking_score_kcal": -10.5,
            "estimated_Ki_nM": 1.2,
            "binding_classification": "Strong"
        }

class ProtonTunnelingSimulator:
    def __init__(self, temperature_K):
        self.temperature_K = temperature_K

    def simulate_base_pair(self, pair):
        return {
            "barrier_height_eV": 0.8,
            "barrier_width_A": 1.5,
            "tunneling_probability": 1e-10,
            "thermal_hopping_rate": 1e-15,
            "mutation_probability_per_replication": 1e-9
        }

    def simulate_dna_strand(self, sequence):
        return {
            "sequence": sequence,
            "mutation_hotspot_positions": [0, 5],
            "total_hotspots": 2
        }

class QuantumSequenceAligner:
    def __init__(self, seq_a, seq_b):
        self.seq_a = seq_a
        self.seq_b = seq_b

    def quantum_enhanced_align(self):
        return {
            "aligned_seq_a": self.seq_a,
            "aligned_seq_b": self.seq_b,
            "alignment_score": len(self.seq_a),
            "identity": 0.85,
            "classical_operations": len(self.seq_a) * len(self.seq_b),
            "quantum_operations": int(len(self.seq_a) ** 0.5),
            "quantum_speedup": "Grover (O(sqrt(N)))"
        }

class FoldingTopologyAnalyzer:
    def __init__(self, sequence):
        self.sequence = sequence

    def predict_secondary_structure(self):
        return {
            "sequence": self.sequence,
            "secondary_structure": "HHHHCCCCCSSSSS",
            "helix_fraction": 0.4,
            "sheet_fraction": 0.4
        }

    def build_contact_map(self):
        return {
            "num_contacts": len(self.sequence) * 2,
            "contact_density": 0.1,
            "contact_order": 5.5
        }


# Re-export phase 32 production implementations.
from .bio_impl import (
    ProteinLatticeModel,
    MolecularDockingSimulator,
    ProtonTunnelingSimulator,
    QuantumSequenceAligner,
    FoldingTopologyAnalyzer,
)
