import numpy as np
import qrcode
from qrcode.image.svg import SvgFillImage
from scipy import integrate, linalg, special, fft
from scipy.optimize import minimize, differential_evolution
import hashlib
import zlib
from typing import Tuple, List, Dict, Optional
import math
import cmath

class QuantumQRProcessor:
    def __init__(self, hilbert_dim=1024, entanglement_depth=8):
        self.H = hilbert_dim
        self.E = entanglement_depth
        self.psi = np.random.randn(self.H) + 1j*np.random.randn(self.H)
        self.psi /= np.linalg.norm(self.psi)
        self.rho = np.outer(self.psi, np.conj(self.psi))
        self.U = self._construct_unitary_evolution()
        
    def _construct_unitary_evolution(self):
        H_op = np.random.randn(self.H, self.H) + 1j*np.random.randn(self.H, self.H)
        H_op = (H_op + np.conj(H_op.T)) / 2
        eigenvals, eigenvecs = linalg.eigh(H_op)
        U = eigenvecs @ np.diag(np.exp(-1j*eigenvals*0.1)) @ np.conj(eigenvecs.T)
        return U
    
    def apply_quantum_encoding(self, data: str) -> np.ndarray:
        data_hash = hashlib.sha3_512(data.encode()).digest()
        data_array = np.frombuffer(data_hash, dtype=np.uint8)
        
        phi = np.zeros(self.H, dtype=complex)
        for i, byte in enumerate(data_array):
            if i >= self.H:
                break
            phase = 2 * np.pi * byte / 255.0
            phi[i] = np.exp(1j * phase)
        
        phi /= np.linalg.norm(phi) + 1e-15
        
        for _ in range(self.E):
            phi = self.U @ phi
            phi = self._apply_nonlinear_transformation(phi)
            phi /= np.linalg.norm(phi) + 1e-15
        
        return phi
    
    def _apply_nonlinear_transformation(self, state: np.ndarray) -> np.ndarray:
        abs_state = np.abs(state)
        phase = np.angle(state)
        
        transformed_abs = np.tanh(abs_state * 3.0) * np.exp(-abs_state**2 / 2)
        transformed_phase = phase + np.sin(phase * 5) * 0.1
        
        return transformed_abs * np.exp(1j * transformed_phase)
    
    def compute_entanglement_entropy(self, state: np.ndarray) -> float:
        rho = np.outer(state, np.conj(state))
        eigenvals = linalg.eigvalsh(rho)
        eigenvals = eigenvals[eigenvals > 1e-15]
        S = -np.sum(eigenvals * np.log2(eigenvals + 1e-15))
        return S
    
    def apply_error_correction_via_stabilizers(self, encoded_state: np.ndarray) -> np.ndarray:
        n_stabilizers = self.H // 4
        stabilizers = []
        
        for i in range(n_stabilizers):
            Z = np.zeros((self.H, self.H), dtype=complex)
            X = np.zeros((self.H, self.H), dtype=complex)
            
            indices = np.random.choice(self.H, 4, replace=False)
            for idx in indices:
                Z[idx, idx] = (-1)**np.random.randint(2)
                if idx < self.H - 1:
                    X[idx, idx+1] = 1
                    X[idx+1, idx] = 1
            
            stabilizers.append(Z @ X + X @ Z)
        
        corrected = encoded_state.copy()
        for S in stabilizers:
            syndrome = S @ corrected
            correction = np.sign(np.real(syndrome)) * 0.01
            corrected += correction
            corrected /= np.linalg.norm(corrected) + 1e-15
        
        return corrected
    
    def decode_to_qr_data(self, quantum_state: np.ndarray, original_data: str) -> str:
        phases = np.angle(quantum_state)
        bytes_encoded = ((phases + np.pi) / (2 * np.pi) * 255).astype(np.uint8)
        
        reconstructed_hash = bytes_encoded.tobytes()[:64]
        
        checksum = hashlib.sha256(original_data.encode()).hexdigest()[:16]
        enhanced_data = f"{original_data}|QEC:{checksum}"
        
        return enhanced_data
    
    def generate_quantum_enhanced_qr(self, data: str, filename: str = 'quantum_qr.svg'):
        quantum_state = self.apply_quantum_encoding(data)
        corrected_state = self.apply_error_correction_via_stabilizers(quantum_state)
        enhanced_data = self.decode_to_qr_data(corrected_state, data)
        
        S_ent = self.compute_entanglement_entropy(corrected_state)
        fidelity = np.abs(np.vdot(quantum_state, corrected_state))**2
        
        qr = qrcode.QRCode(version=None, error_correction=qrcode.constants.ERROR_CORRECT_H, box_size=18, border=4)
        qr.add_data(enhanced_data)
        qr.make(fit=True)
        
        img = qr.make_image(image_factory=SvgFillImage, fill_color='#000000', back_color='#FFFFFF')
        img.save(filename)
        
        return {
            'filename': filename,
            'entanglement_entropy': S_ent,
            'state_fidelity': fidelity,
            'hilbert_dimension': self.H,
            'encoded_checksum': hashlib.sha256(enhanced_data.encode()).hexdigest()[:16]
        }

class RelativisticQRTransform:
    def __init__(self, c=299792458, resolution=2048):
        self.c = c
        self.res = resolution
        self.gamma_factor = lambda v: 1 / np.sqrt(1 - (v/self.c)**2 + 1e-15)
        
    def lorentz_transform(self, data_vector: np.ndarray, velocity: float) -> np.ndarray:
        gamma = self.gamma_factor(velocity)
        beta = velocity / self.c
        
        L = np.array([
            [gamma, -gamma*beta, 0, 0],
            [-gamma*beta, gamma, 0, 0],
            [0, 0, 1, 0],
            [0, 0, 0, 1]
        ])
        
        extended_data = np.zeros(4)
        extended_data[:min(len(data_vector), 4)] = data_vector[:min(len(data_vector), 4)]
        
        transformed = L @ extended_data
        return transformed
    
    def compute_schwarzschild_metric(self, r: float, M: float = 1.989e30) -> np.ndarray:
        G = 6.67430e-11
        rs = 2 * G * M / self.c**2
        
        if r <= rs:
            r = rs * 1.01
        
        g = np.zeros((4, 4))
        g[0, 0] = -(1 - rs/r)
        g[1, 1] = 1 / (1 - rs/r)
        g[2, 2] = r**2
        g[3, 3] = r**2 * np.sin(np.pi/4)**2
        
        return g
    
    def apply_curved_spacetime_encoding(self, data: str) -> np.ndarray:
        data_bytes = np.frombuffer(hashlib.blake2b(data.encode()).digest(), dtype=np.uint8)
        
        field = np.zeros((self.res, self.res))
        for i, byte in enumerate(data_bytes):
            if i >= self.res:
                break
            x = i % self.res
            y = i // self.res % self.res
            field[y, x] = byte / 255.0
        
        x = np.linspace(-10, 10, self.res)
        y = np.linspace(-10, 10, self.res)
        X, Y = np.meshgrid(x, y)
        R = np.sqrt(X**2 + Y**2) + 0.1
        
        rs = 2.0
        g00 = -(1 - rs/R)
        
        curved_field = field * np.abs(g00)
        
        return curved_field
    
    def generate_relativistic_qr(self, data: str, velocity: float = 0.5, filename: str = 'relativistic_qr.svg'):
        data_vec = np.frombuffer(data.encode(), dtype=np.uint8)[:4].astype(float)
        transformed_vec = self.lorentz_transform(data_vec, velocity * self.c)
        
        curved_field = self.apply_curved_spacetime_encoding(data)
        
        field_hash = hashlib.sha256(curved_field.tobytes()).hexdigest()[:32]
        enhanced_data = f"{data}|RLTVSTC:{field_hash}"
        
        qr = qrcode.QRCode(version=None, error_correction=qrcode.constants.ERROR_CORRECT_H, box_size=16, border=4)
        qr.add_data(enhanced_data)
        qr.make(fit=True)
        
        img = qr.make_image(image_factory=SvgFillImage, fill_color='#000000', back_color='#FFFFFF')
        img.save(filename)
        
        return {
            'filename': filename,
            'lorentz_gamma': self.gamma_factor(velocity * self.c),
            'spacetime_curvature': np.mean(np.abs(curved_field)),
            'field_checksum': field_hash
        }

class FieldTheoryQREncoder:
    def __init__(self, lattice_size=256):
        self.L = lattice_size
        self.lattice = np.zeros((self.L, self.L, self.L, 4), dtype=complex)
        self.gauge_fields = self._initialize_gauge_fields()
        
    def _initialize_gauge_fields(self):
        A = []
        for mu in range(4):
            A_mu = np.random.randn(self.L, self.L, self.L) + 1j*np.random.randn(self.L, self.L, self.L)
            A.append(A_mu)
        return A
    
    def compute_field_strength_tensor(self, x, y, z, mu, nu):
        A = self.gauge_fields
        
        dx = 1 if x < self.L - 1 else 0
        dy = 1 if y < self.L - 1 else 0
        dz = 1 if z < self.L - 1 else 0
        
        F_mu_nu = (A[mu][x, y, z] - A[mu][(x+dx)%self.L, (y+dy)%self.L, (z+dz)%self.L]) - \
                  (A[nu][x, y, z] - A[nu][(x+dx)%self.L, (y+dy)%self.L, (z+dz)%self.L])
        
        return F_mu_nu
    
    def yang_mills_action(self):
        S = 0
        for x in range(0, self.L, 8):
            for y in range(0, self.L, 8):
                for z in range(0, self.L, 8):
                    for mu in range(4):
                        for nu in range(mu+1, 4):
                            F = self.compute_field_strength_tensor(x, y, z, mu, nu)
                            S += np.abs(F)**2
        return S / (self.L**3)
    
    def higgs_mechanism_encoding(self, data: str) -> np.ndarray:
        v = 246.0
        lambda_h = 0.129
        
        phi = np.zeros((self.L, self.L, self.L), dtype=complex)
        data_hash = hashlib.sha3_256(data.encode()).digest()
        data_arr = np.frombuffer(data_hash, dtype=np.uint8)
        
        for i, byte in enumerate(data_arr):
            if i >= self.L:
                break
            x = i % self.L
            y = (i // self.L) % self.L
            z = (i // (self.L*self.L)) % self.L
            
            phase = 2 * np.pi * byte / 255.0
            phi[x, y, z] = v * np.exp(1j * phase)
        
        V_higgs = lambda_h * (np.abs(phi)**2 - v**2)**2
        
        return V_higgs
    
    def generate_field_theory_qr(self, data: str, filename: str = 'field_theory_qr.svg'):
        V_higgs = self.higgs_mechanism_encoding(data)
        S_ym = self.yang_mills_action()
        
        total_action = S_ym + np.sum(V_higgs) / (self.L**3)
        
        action_hash = hashlib.sha512(str(total_action).encode()).hexdigest()[:32]
        enhanced_data = f"{data}|GAUGE:{action_hash}"
        
        qr = qrcode.QRCode(version=None, error_correction=qrcode.constants.ERROR_CORRECT_H, box_size=17, border=4)
        qr.add_data(enhanced_data)
        qr.make(fit=True)
        
        img = qr.make_image(image_factory=SvgFillImage, fill_color='#000000', back_color='#FFFFFF')
        img.save(filename)
        
        return {
            'filename': filename,
            'yang_mills_action': float(S_ym),
            'higgs_vev': 246.0,
            'total_action': float(total_action),
            'gauge_checksum': action_hash
        }
