import numpy as np
import math, cmath, asyncio, functools, itertools
from decimal import Decimal
from fractions import Fraction
from scipy.linalg import expm, norm
from multiprocessing import Pool

class Quantum:
    """
    Triplogonoras-Quantum-Eksponentrix
    Quantum-style AI dengan fitur tambahan superpower:
    - Entanglement, Adaptive Gate, Noise Injection, Multi-Dim Pythagoras, Async Gate
    """
    def __init__(self, qubit_size=4):
        self.qubit_size = qubit_size
        self.state = self.initialize_state()
        self.gates = []
        self.entangled_pairs = []

    def initialize(self):
        state = np.zeros((2 ** self.qubit_size,), dtype=complex)
        state[0] = 1.0
        return state

    # === Gate dengan Adaptive Scaling + Async ===
    async def apply_gate(self, gate, index, adaptive=True):
        I = np.eye(2, dtype=complex)
        ops = [I]*self.qubit_size
        ops[index] = gate
        full_gate = functools.reduce(np.kron, ops)
        if adaptive:
            scale = np.exp(-np.abs(self.state[index]))
            full_gate *= scale
        self.state = full_gate @ self.state
        self.gates.append((gate, index))

    def hadamard_gate(self, index):
        H = 1/np.sqrt(2) * np.array([[1,1],[1,-1]], dtype=complex)
        asyncio.run(self.apply_gate(H, index))

    def pauli_x_gate(self, index):
        X = np.array([[0,1],[1,0]], dtype=complex)
        asyncio.run(self.apply_gate(X, index))

    # === Entanglement Simulator ===
    def entangle(self, q1, q2):
        self.entangled_pairs.append((q1, q2))

    # === Measurement dengan eksponensial + trig + log ===
    def measure(self):
        # Normalisasi amplitudo
        amplitudes = np.exp(self.state)/np.sum(np.exp(self.state))
        # Trig distribusi
        probs = np.abs(np.sin(np.pi*amplitudes/2))**2
        probs /= probs.sum()
        index = np.random.choice(len(self.state), p=probs)
        result = np.binary_repr(index, width=self.qubit_size)
        # Perbarui entangled qubit jika ada
        for q1, q2 in self.entangled_pairs:
            if int(result[q1]) != int(result[q2]):
                result = list(result)
                result[q2] = result[q1]
                result = "".join(result)
        return {"result": result, "probabilities": probs}

    # === Quantum Noise Injection ===
    def add_noise(self, strength=0.01):
        noise = np.random.normal(0, strength, self.state.shape) + 1j*np.random.normal(0,strength,self.state.shape)
        self.state += noise

    # === Multi-Dimensional Pythagoras ===
    def triplepythagoras(self, vectors):
        return np.sqrt(sum(np.linalg.norm(v)**2 for v in vectors))

    # === Reset ===
    def reset(self):
        self.state = self.initialize_state()
        self.gates.clear()
        self.entangled_pairs.clear()

    # === Ringkasan ===
    def summary(self):
        return {
            "qubit_size": self.qubit_size,
            "state": self.state,
            "gates_applied": len(self.gates),
            "entangled_pairs": self.entangled_pairs
        }
