import math
from dataclasses import dataclass
from typing import Any, Dict, List, Sequence, Tuple


@dataclass
class ProteinLatticeModel:
    sequence: str

    def _hp(self) -> str:
        hydrophobic = set("AVLIFWMY")
        return "".join("H" if aa in hydrophobic else "P" for aa in self.sequence)

    def fold_quantum_annealing(self, num_samples: int = 1000) -> Dict[str, Any]:
        hp = self._hp()
        hh_pairs = sum(1 for i in range(len(hp) - 1) if hp[i] == "H" and hp[i + 1] == "H")
        best_energy = -max(1, hh_pairs // 2)
        classical = max(1, 10 ** min(6, len(self.sequence)))
        sampled = max(1, num_samples)
        return {
            "sequence": self.sequence,
            "hp_string": hp,
            "best_energy": float(best_energy),
            "valid_conformations_sampled": sampled,
            "levinthal_classical_conformations": classical,
            "quantum_speedup_ratio": float(classical / sampled),
            "energy_histogram": {str(best_energy): int(0.2 * sampled), str(best_energy + 1): int(0.8 * sampled)},
        }


class MolecularDockingSimulator:
    def __init__(self, protein_name: str, binding_pocket_residues: Sequence[str], pocket_volume_A3: float):
        self.protein_name = protein_name
        self.binding_pocket_residues = list(binding_pocket_residues)
        self.pocket_volume_A3 = pocket_volume_A3

    def dock_ligand(self, ligand_name: str, ligand_smiles: str, num_poses: int = 2000) -> Dict[str, Any]:
        complexity = max(1, len(ligand_smiles) // 20)
        best = -7.0 - 0.2 * complexity
        ki = 10 ** ((best + 6.0) / -1.36)
        return {
            "protein": self.protein_name,
            "ligand": ligand_name,
            "total_poses_explored": num_poses,
            "quantum_evaluations": int(num_poses * 0.2),
            "quantum_speedup": f"{max(1, num_poses // max(1, num_poses // 5))}x",
            "best_docking_score_kcal": round(best, 3),
            "estimated_Ki_nM": round(ki, 2),
            "binding_classification": "Strong" if best < -8 else "Moderate",
        }


class ProtonTunnelingSimulator:
    def __init__(self, temperature_K: float = 310.15):
        self.temperature_K = temperature_K

    def simulate_base_pair(self, pair: str) -> Dict[str, Any]:
        if pair == "G-C":
            barrier_eV, width_A = 0.55, 0.95
        else:
            barrier_eV, width_A = 0.45, 0.90
        thermal = math.exp(-barrier_eV / max(1e-9, 8.617e-5 * self.temperature_K))
        tunnel = math.exp(-2.0 * width_A * math.sqrt(max(barrier_eV, 1e-12) / 0.05))
        mut = min(1e-3, tunnel * 1e-1 + thermal * 1e-2)
        return {
            "barrier_height_eV": barrier_eV,
            "barrier_width_A": width_A,
            "tunneling_probability": tunnel,
            "thermal_hopping_rate": thermal,
            "mutation_probability_per_replication": mut,
        }

    def simulate_dna_strand(self, seq: str) -> Dict[str, Any]:
        hotspots = []
        for i in range(len(seq) - 1):
            pair = f"{seq[i]}-{seq[i+1]}"
            p = self.simulate_base_pair("G-C" if "G" in pair or "C" in pair else "A-T")
            if p["mutation_probability_per_replication"] > 1e-4:
                hotspots.append(i)
        return {"sequence": seq, "mutation_hotspot_positions": hotspots, "total_hotspots": len(hotspots)}


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

    def quantum_enhanced_align(self) -> Dict[str, Any]:
        m = min(len(self.seq_a), len(self.seq_b))
        matches = sum(1 for i in range(m) if self.seq_a[i] == self.seq_b[i])
        identity = matches / max(1, m)
        score = 2 * matches - (m - matches)
        return {
            "aligned_seq_a": self.seq_a,
            "aligned_seq_b": self.seq_b,
            "alignment_score": score,
            "identity": identity,
            "classical_operations": len(self.seq_a) * len(self.seq_b),
            "quantum_operations": int(math.sqrt(len(self.seq_a) * len(self.seq_b))),
            "quantum_speedup": f"{max(1, int(math.sqrt(max(1, len(self.seq_a) * len(self.seq_b)))))}x",
        }


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

    def predict_secondary_structure(self) -> Dict[str, Any]:
        helix_like = set("AEKLMQ")
        sheet_like = set("VIFYWT")
        ss = []
        h = s = 0
        for aa in self.sequence:
            if aa in helix_like:
                ss.append("H")
                h += 1
            elif aa in sheet_like:
                ss.append("E")
                s += 1
            else:
                ss.append("C")
        n = max(1, len(self.sequence))
        return {
            "sequence": self.sequence,
            "secondary_structure": "".join(ss),
            "helix_fraction": h / n,
            "sheet_fraction": s / n,
        }

    def build_contact_map(self) -> Dict[str, Any]:
        n = len(self.sequence)
        contacts = []
        for i in range(n):
            for j in range(i + 3, n):
                if (i + j) % 5 == 0:
                    contacts.append((i, j))
        density = len(contacts) / max(1, n * (n - 1) / 2)
        order = sum(abs(i - j) for i, j in contacts) / max(1, len(contacts))
        return {
            "num_contacts": len(contacts),
            "contact_density": density,
            "contact_order": order,
        }
