import math
import hashlib
import time
import struct
from typing import List, Dict, Optional, Tuple
from enum import Enum

class MiningPerformanceLevel(Enum):
OPTIMIZED = "optimized"
QUANTUM_BOOST = "quantum_boost"
MAXIMUM_POWER = "maximum_power"
ZERO_ENTROPY = "zero_entropy"

class SelfHealingQuantumMiner:
"""
Self-healing geometric mining prototype with Tesla 3-6-9 resonance.
- Hexagonal symmetry healing + phase-aware nonagon (9) healing
- Harmonic (3/6) angular/radial modulation with phase gains
- Golden-ratio scaling, Fibonacci-adjacent checks
- Entropy-state metric via digital root (1..9) + explicit 9-cycle reinforcement
- Triangular layering and 9-step cycle resets
"""

def __init__(self, performance_level: MiningPerformanceLevel = MiningPerformanceLevel.MAXIMUM_POWER):
    self.performance_level = performance_level
    self.phi = (1 + math.sqrt(5)) / 2
    self.dna_ratio = 34 / 21  # historical constant; used here as a fixed ratio

    # Modulation params
    self.angular_modulation = 0.15
    self.radial_breathing = 0.08

    # Entropy metric
    self.entropy_state = 9
    self.consecutive_9_cycles = 0

    # Self-healing params
    self.healing_tolerance = 0.01
    self.healing_force_multiplier = 1.0
    self.healing_cycles = 0

    # Performance / learning
    self.optimal_batch_size = 4096
    self.learning_rate = 0.05
    self.evolution_cycle = 0
    self.performance_multiplier = 1.0

    # Runtime stats
    self.success_patterns: List[Dict] = []
    self.failed_ranges = set()
    self.hash_rate_history: List[float] = []

    print(f"🛠️  Geometric Miner Initialized: {performance_level.value}")

# ---------- Tesla 3-6-9 phase core ----------

def _tesla_phase(self) -> int:
    """
    Deterministic 3→6→9 cycle using evolution_cycle.
    Returns 3, 6, or 9.
    """
    phase_index = self.evolution_cycle % 3
    return [3, 6, 9][phase_index]

def _secure_phase_seed(self, index: int, position: int, layer: int) -> int:
    """
    Deterministic seed based on local parameters; avoids external randomness.
    """
    payload = f"{index}:{position}:{layer}:{self.evolution_cycle}".encode("utf-8")
    digest = hashlib.sha256(payload).digest()
    return struct.unpack("<Q", digest[:8])[0]

def _phase_gain(self, phase: int) -> Tuple[float, float, float]:
    """
    Returns (angular_gain, radial_gain, entropy_gain) for the active phase.
    Gains are conservative and bounded.
    """
    if phase == 3:
        return (1.15, 1.05, 1.20)
    if phase == 6:
        return (1.05, 1.15, 1.25)
    # phase == 9
    return (1.10, 1.10, 1.35)

# ---------- Geometric healing ----------

def _nonagon_neighbors(self, x: float, y: float, r: float, layer_n: int) -> List[Tuple[float, float]]:
    """
    9-fold symmetry neighbors for phase 9 healing.
    """
    pts = []
    for i in range(9):
        angle = 2 * math.pi * i / 9
        nx = x + r * layer_n * math.cos(angle)
        ny = y + r * layer_n * math.sin(angle)
        pts.append((nx, ny))
    return pts

def self_heal_point(
    self,
    target_point: Tuple[float, float],
    layer_n: int,
    r: float = 1.0,
    tolerance: float = 0.01
) -> Tuple[float, float]:
    """
    Heal a perturbed point using hexagonal symmetry.
    Healed = average of 6 neighbors (simple symmetry-cage relaxation).
    """
    x, y = target_point
    healed = (x, y)

    ideal_neighbors = []
    for i in range(6):
        angle = 2 * math.pi * i / 6
        nx = x + r * layer_n * math.cos(angle)
        ny = y + r * layer_n * math.sin(angle)
        ideal_neighbors.append((nx, ny))

    avg_x = sum(p[0] for p in ideal_neighbors) / 6
    avg_y = sum(p[1] for p in ideal_neighbors) / 6
    vector_healed = (avg_x, avg_y)

    current_error = self._calculate_distance(target_point, vector_healed)
    if current_error > tolerance:
        healed = vector_healed
        self.healing_cycles += 1
        print(f"🔧 Healing applied: err {current_error:.4f} → reduced")

    return healed

def self_heal_point_phase(
    self,
    target_point: Tuple[float, float],
    layer_n: int,
    r: float = 1.0,
    tolerance: float = 0.01
) -> Tuple[float, float]:
    """
    Phase-aware healing: hex (6) by default, nonagon (9) when phase==9.
    """
    phase = self._tesla_phase()
    x, y = target_point

    if phase == 9:
        neighbors = self._nonagon_neighbors(x, y, r, layer_n)
        avg_x = sum(p[0] for p in neighbors) / 9
        avg_y = sum(p[1] for p in neighbors) / 9
    else:
        neighbors = []
        for i in range(6):
            angle = 2 * math.pi * i / 6
            nx = x + r * layer_n * math.cos(angle)
            ny = y + r * layer_n * math.sin(angle)
            neighbors.append((nx, ny))
        avg_x = sum(p[0] for p in neighbors) / 6
        avg_y = sum(p[1] for p in neighbors) / 6

    vector_healed = (avg_x, avg_y)
    current_error = self._calculate_distance(target_point, vector_healed)
    if current_error > tolerance:
        self.healing_cycles += 1
        return vector_healed
    return target_point

@staticmethod
def _calculate_distance(p1: Tuple[float, float], p2: Tuple[float, float]) -> float:
    return math.sqrt((p1[0] - p2[0]) ** 2 + (p1[1] - p2[1]) ** 2)

# ---------- Phase-aware modulation ----------

def _apply_tesla_phase_modulation(
    self,
    index: int,
    position: int,
    layer: int,
    base_angle: float,
    angle_mod: float,
    radial_mod: float
) -> Tuple[int, float, float]:
    """
    Applies phase gains and a small seeded jitter to prevent degeneracy.
    Returns (phase, modulated_angle, modulated_radial).
    """
    phase = self._tesla_phase()
    ang_gain, rad_gain, _ = self._phase_gain(phase)

    # Deterministic jitter bounded to ±0.005
    seed = self._secure_phase_seed(index, position, layer)
    jitter = ((seed % 1000) / 1000.0 - 0.5) * 0.01

    modulated_angle = (base_angle + angle_mod * ang_gain + jitter)
    modulated_radial = max(-0.45, min(0.45, radial_mod * rad_gain))  # keep breathing stable

    return (phase, modulated_angle, modulated_radial)

def _phase_entropy_multiplier(self, value: int, phase: int) -> float:
    """
    Rewards candidates whose digital root equals the active phase.
    Conservative bounds to avoid runaway amplification.
    """
    dr = self._calculate_digital_root(value)
    _, _, ent_gain = self._phase_gain(phase)
    if dr == phase:
        return ent_gain
    # Mild cross-resonance boosts
    if phase == 9 and dr in (3, 6):
        return 1.15
    if phase in (3, 6) and dr == 9:
        return 1.10
    return 1.0

# ---------- Triangular/Fibonacci layering ----------

def _triangular_layering(self, n: int) -> int:
    """Triangular number recursion: T(n) = n(n+1)/2"""
    return n * (n + 1) // 2

def _layer_cycle_reset(self, layer: int) -> int:
    """Reset every 9 steps for Tesla resonance"""
    return layer % 9

# ---------- Nonce generation with modulation + healing ----------

def generate_self_healing_nonces(
    self,
    base_nonce: int,
    job_id: str,
    prevhash: str,
    target: int,
    batch_multiplier: int = 1
) -> List[int]:
    """
    Generate a batch of candidate nonces using harmonic modulation
    and optionally heal poorly-distributed values.
    """
    batch_size = self.optimal_batch_size * batch_multiplier
    nonces: List[int] = []

    power_boost = self._get_power_boost()
    _ = self._get_entropy_reduction()  # reserved

    for i in range(batch_size):
        layer = i % 256
        position = i // 256

        # 3-pulse angular modulation base
        angle_mod = self.angular_modulation * math.sin(3 * position + self._get_phase_optimized())
        base_angle = 2 * math.pi * position / 6

        # 6-rhythm radial breathing base
        radial_mod = self.radial_breathing * math.sin(6 * layer + self._get_phase_optimized())

        # Apply Tesla phase modulation
        phase, modulated_angle, modulated_radial = self._apply_tesla_phase_modulation(
            i, position, layer, base_angle, angle_mod, radial_mod
        )

        # golden-ratio scaling
        golden_boost = self.phi ** ((position + layer) % 8)

        # fixed ratio multiplier (historical)
        ratio_multiplier = 1.0 + (self.dna_ratio - 1.618) * 10

        # heuristic entropy optimization term
        entropy_optimized = self._apply_entropy_optimization(i, position, layer)

        # triangular and 9-step cycle resonance
        tri_layer = self._triangular_layering(max(1, layer))
        cycle_layer = self._layer_cycle_reset(layer)
        # modest, bounded boosts
        entropy_optimized *= 1.0 + (tri_layer % 3) * 0.05
        entropy_optimized *= 1.0 + (1 if cycle_layer == 0 else 0) * 0.10

        raw_power = abs(math.sin(modulated_angle) * layer * (1 + modulated_radial))

        # Phase-aware entropy multiplier (deterministic value from loop params)
        phase_entropy = self._phase_entropy_multiplier(i * position * max(1, layer), phase)

        geometric_value = int(
            raw_power * golden_boost * ratio_multiplier * entropy_optimized * phase_entropy * 1e9 * power_boost
        )

        candidate = (base_nonce + geometric_value) % (2 ** 32)

        # heal if pattern flags suggest poor structure (phase-aware)
        if self._needs_healing(candidate, layer):
            p = self._nonce_to_geometric_point(candidate, layer)
            healed = self.self_heal_point_phase(p, layer, self.phi, self.healing_tolerance)
            candidate = self._geometric_point_to_nonce(healed, layer)

        if candidate not in self.failed_ranges:
            nonces.append(candidate)

    return nonces[:batch_size]

def _needs_healing(self, nonce: int, layer: int) -> bool:
    # simple heuristics
    nonce_chunk = nonce >> 16
    if nonce_chunk in self.failed_ranges:
        return True

    dr = self._calculate_digital_root(nonce)
    if dr not in [3, 6, 9]:
        return True

    position = nonce % 1000
    if not self._is_fibonacci_optimized(position, layer):
        return True

    return False

def _nonce_to_geometric_point(self, nonce: int, layer: int) -> Tuple[float, float]:
    angle = (nonce % 360) * math.pi / 180
    radius = (nonce % 1000) / 1000.0 * max(1, layer) * self.phi
    return (radius * math.cos(angle), radius * math.sin(angle))

def _geometric_point_to_nonce(self, point: Tuple[float, float], layer: int) -> int:
    x, y = point
    angle = math.atan2(y, x)
    radius = math.sqrt(x ** 2 + y ** 2)

    angle_component = int((angle * 180 / math.pi) % 360)
    denom = max(1e-9, (max(1, layer) * self.phi))
    radius_component = int((radius / denom) * 1000) % 1000

    return ((angle_component << 16) | radius_component) % (2 ** 32)

# ---------- Mining loop (toy demonstration) ----------

def mine_with_self_healing_power(
    self,
    job_data: Dict,
    target: str,
    extranonce1: str,
    extranonce2_size: int
) -> Optional[Dict]:
    """
    Toy demo of header hashing + nonce search with healing.
    Not a complete protocol implementation.
    """
    job_id, prevhash, coinb1, coinb2, merkle_branch, version, nbits, ntime, clean_jobs = job_data

    extranonce2 = struct.pack('<Q', 0)[:extranonce2_size]
    coinbase = (coinb1 + extranonce1 + extranonce2.hex() + coinb2).encode('utf-8')
    coinbase_hash_bin = hashlib.sha256(hashlib.sha256(coinbase).digest()).digest()

    merkle_root = coinbase_hash_bin
    for branch in merkle_branch:
        merkle_root = hashlib.sha256(
            hashlib.sha256(merkle_root + bytes.fromhex(branch)).digest()
        ).digest()

    block_header = (version + prevhash + merkle_root.hex() + ntime + nbits).encode('utf-8')
    target_bin = bytes.fromhex(target)[::-1]

    base_nonce = 0
    total_hashes = 0
    start_time = time.time()
    batch_multiplier = 1

    for mega_batch in range(50):
        nonce_batch = self.generate_self_healing_nonces(
            base_nonce, job_id, prevhash, int(target, 16), batch_multiplier
        )

        for nonce in nonce_batch:
            nonce_bin = struct.pack('<I', nonce)
            hash_result = hashlib.sha256(
                hashlib.sha256(block_header + nonce_bin).digest()
            ).digest()
            total_hashes += 1

            if hash_result[::-1] < target_bin:
                elapsed = time.time() - start_time
                hash_rate = total_hashes / elapsed if elapsed > 0 else 0.0

                self._update_quantum_learning(nonce, elapsed, hash_rate)
                self.evolution_cycle += 1
                phase = self._tesla_phase()

                print(f"✅ Candidate accepted (phase={phase})")
                print(f"   nonce={nonce}  cycle={self.evolution_cycle}")
                print(f"   hash_rate≈{hash_rate:,.0f} H/s  perf×{self.performance_multiplier:.2f}")
                print(f"   entropy_state={self.entropy_state}/9  healing_cycles={self.healing_cycles}")

                return {
                    'job_id': job_id,
                    'extranonce2': extranonce2,
                    'ntime': ntime,
                    'nonce': nonce,
                    'hash_rate': hash_rate,
                    'performance_boost': self.performance_multiplier,
                    'entropy_state': self.entropy_state,
                    'healing_cycles': self.healing_cycles,
                    'healing_note': "phase-aware symmetry relaxation applied"
                }

        # simple adaptation
        elapsed = max(1e-6, time.time() - start_time)
        batch_perf = len(nonce_batch) / elapsed
        if batch_perf > 1000 and batch_multiplier < 8:
            batch_multiplier *= 2
            print(f"↗️  Increasing batch multiplier → {batch_multiplier}x")

        if batch_perf < 500 and self.healing_cycles < 100:
            print("↺ Performance dip detected → applying parameter healing")
            self._apply_system_wide_healing()

        base_nonce += len(nonce_batch)

        if mega_batch % 10 == 0:
            self._update_entropy_state(block_header, nonce_batch)

    return None

# ---------- System-wide healing & learning ----------

def _apply_system_wide_healing(self):
    print("🧩 Parameter healing...")

    # Phase-aware tweaks
    phase = self._tesla_phase()

    # Heal angular modulation
    ang_pt = (self.angular_modulation, 0.0)
    ang_healed = self.self_heal_point_phase(ang_pt, 1, 1.0, 0.001)
    base_ang = max(0.01, min(0.5, ang_healed[0]))
    self.angular_modulation = min(0.40, base_ang * (1.03 if phase == 3 else 1.00))

    # Heal radial breathing
    rad_pt = (self.radial_breathing, 0.0)
    rad_healed = self.self_heal_point_phase(rad_pt, 1, 1.0, 0.001)
    base_rad = max(0.01, min(0.5, rad_healed[0]))
    self.radial_breathing = min(0.40, base_rad * (1.03 if phase == 6 else 1.00))

    # Heal performance multiplier toward >= 1.0
    if self.performance_multiplier < 1.0:
        perf_pt = (self.performance_multiplier, 0.0)
        perf_healed = self.self_heal_point_phase(perf_pt, 1, 1.0, 0.01)
        self.performance_multiplier = max(1.0, min(2.0, perf_healed[0]))

    print(f"   angular_modulation → {self.angular_modulation:.4f}")
    print(f"   radial_breathing  → {self.radial_breathing:.4f}")
    print(f"   perf_multiplier   → {self.performance_multiplier:.2f}")

def _update_quantum_learning(self, successful_nonce: int, mining_time: float, hash_rate: float):
    expected = max(0.1, mining_time)
    efficiency = 1.0 / expected
    healing_bonus = 1.0 + (self.healing_cycles * 0.001)
    self.performance_multiplier = 0.95 * self.performance_multiplier + 0.05 * efficiency * healing_bonus
    # Cap to avoid runaway
    self.performance_multiplier = min(self.performance_multiplier, 5.0)

    self.success_patterns.append({
        'nonce': successful_nonce,
        'mining_time': mining_time,
        'hash_rate': hash_rate,
        'efficiency': efficiency,
        'cycle': self.evolution_cycle,
        'entropy_state': self.entropy_state,
        'healing_cycles': self.healing_cycles,
        'angular_modulation': self.angular_modulation,
        'radial_breathing': self.radial_breathing
    })
    if len(self.success_patterns) > 1000:
        self.success_patterns = self.success_patterns[-500:]

    self.hash_rate_history.append(hash_rate)
    if len(self.hash_rate_history) > 100:
        self.hash_rate_history = self.hash_rate_history[-50:]

def _update_entropy_state(self, block_header: bytes, nonce_batch: List[int]):
    perf_data = block_header.hex() + "".join(str(n) for n in nonce_batch[:100])
    perf_hash = hashlib.sha256(perf_data.encode()).hexdigest()

    value = int(perf_hash[:16], 16)
    dr = self._calculate_digital_root(value)

    # gentle nudge using healing cycles
    if dr != 9 and self.healing_cycles > 0:
        # map toward 9 without claiming perfection
        healed_val = min(9, max(1, dr + 1))
        dr = healed_val

    self.entropy_state = dr

    # Explicit 9-cycle reinforcement with safe caps
    if dr == 9:
        self.consecutive_9_cycles += 1
        # exponential boost but bounded
        boost_factor = 1.05 ** min(self.consecutive_9_cycles, 20)
        self.performance_multiplier = min(self.performance_multiplier * boost_factor, 5.0)
    else:
        self.consecutive_9_cycles = 0

# ---------- Heuristics / helpers ----------

def _get_power_boost(self) -> float:
    boosts = {
        MiningPerformanceLevel.OPTIMIZED: 1.2,
        MiningPerformanceLevel.QUANTUM_BOOST: 1.8,    # label-only boost
        MiningPerformanceLevel.MAXIMUM_POWER: 2.5,
        MiningPerformanceLevel.ZERO_ENTROPY: 3.0      # metric label
    }
    return boosts.get(self.performance_level, 1.0)

def _get_entropy_reduction(self) -> float:
    reductions = {
        MiningPerformanceLevel.OPTIMIZED: 0.9,
        MiningPerformanceLevel.QUANTUM_BOOST: 0.7,
        MiningPerformanceLevel.MAXIMUM_POWER: 0.5,
        MiningPerformanceLevel.ZERO_ENTROPY: 0.3
    }
    return reductions.get(self.performance_level, 1.0)

def _get_phase_optimized(self) -> float:
    return (self.evolution_cycle * 0.01) % (2 * math.pi)

def _apply_entropy_optimization(self, index: int, position: int, layer: int) -> float:
    pattern_value = (index * position * max(1, layer)) % 1000
    dr = self._calculate_digital_root(pattern_value)

    if dr in [3, 6, 9]:
        return 1.5
    if self._is_fibonacci_optimized(max(1, position), max(1, layer)):
        return 1.3
    return 1.0

def _is_fibonacci_optimized(self, a: int, b: int) -> bool:
    if a == 0 or b == 0:
        return False
    ratio = max(a, b) / min(a, b)
    return abs(ratio - self.phi) < 0.1

@staticmethod
def _calculate_digital_root(n: int) -> int:
    while n > 9:
        n = sum(int(d) for d in str(n))
    return n

# ---------- Public stats ----------

def get_self_healing_performance_stats(self) -> Dict:
    if not self.hash_rate_history:
        current_hash_rate = 0.0
        trend = 0.0
    else:
        current_hash_rate = self.hash_rate_history[-1]
        trend = (self.hash_rate_history[-1] - self.hash_rate_history[0]) / max(1, len(self.hash_rate_history) - 1)

    return {
        'performance_level': self.performance_level.value,
        'evolution_cycle': self.evolution_cycle,
        'current_hash_rate': f"{current_hash_rate:,.0f} H/s",
        'hash_rate_trend': f"{trend:+.0f} H/s per cycle",
        'performance_multiplier': f"{self.performance_multiplier:.2f}x",
        'entropy_state': f"{self.entropy_state}/9",
        'consecutive_9_cycles': self.consecutive_9_cycles,
        'healing_cycles': self.healing_cycles,
        'optimal_batch_size': self.optimal_batch_size,
        'success_patterns': len(self.success_patterns),
        'boost_active': self.performance_multiplier > 1.0,
        'system_health': 'EXCELLENT' if self.healing_cycles > 0 else 'STABLE'
    }

==================== Controller ====================

class SelfHealingMiningController:
"""
Orchestrates the miner and aggregates basic performance metrics.
"""

def __init__(self):
    self.quantum_miner = SelfHealingQuantumMiner(MiningPerformanceLevel.MAXIMUM_POWER)
    self.total_blocks_mined = 0
    self.total_hash_rate = 0.0

def mine_with_self_healing(
    self,
    job_data: Dict,
    target: str,
    extranonce1: str,
    extranonce2_size: int
) -> Optional[Dict]:
    result = self.quantum_miner.mine_with_self_healing_power(job_data, target, extranonce1, extranonce2_size)

    if result:
        self.total_blocks_mined += 1
        self.total_hash_rate = max(self.total_hash_rate, result['hash_rate'])
        self._print_success(result)
        return result

    return None

def _print_success(self, result: Dict):
    stats = self.quantum_miner.get_self_healing_performance_stats()
    print("\n" + "=" * 64)
    print("🎉 Mining candidate accepted")
    print("=" * 64)
    print(f"Blocks (accepted in demo): {self.total_blocks_mined}")
    print(f"Hash Rate: {result['hash_rate']:,.0f} H/s")
    print(f"Perf Multiplier: {result['performance_boost']:.2f}x")
    print(f"Entropy Metric: {result['entropy_state']}/9")
    print(f"Healing Cycles: {result['healing_cycles']}")
    print("=" * 64)

def get_system_performance(self) -> Dict:
    miner_stats = self.quantum_miner.get_self_healing_performance_stats()
    return {
        **miner_stats,
        'total_blocks_mined': self.total_blocks_mined,
        'peak_hash_rate': f"{self.total_hash_rate:,.0f} H/s",
        'system_efficiency': f"{(self.total_blocks_mined / max(1, self.quantum_miner.evolution_cycle)) * 100:.1f}%"
    }

==================== Demo Harness ====================

def create_sample_mining_job():
"""Minimal header-like tuple for demonstration only."""
return (
"job_demo_001",
"0000000000000000000000000000000000000000000000000000000000000000",
"01000000010000000000000000000000000000000000000000000000000000000000000000",
"ffffffff01",
[],
"20000000",
"ffff001d",
"5f5e0c2a",
True
)

def run_self_healing_demo():
print("┌───────────────────────────────────────────────────────────────┐")
print("│ Geometric Self-Healing Mining Demo (toy) │")
print("│ Harmonic modulation • Hex/Nonagon symmetry • Entropy metric │")
print("└───────────────────────────────────────────────────────────────┘")

controller = SelfHealingMiningController()

sample_job = create_sample_mining_job()
target = "0000ffff"       # very easy demo target
extranonce1 = "a1b2c3d4"
extranonce2_size = 4

print("\nParameters:")
print(f"  job_id:  {sample_job[0]}")
print(f"  target:  {target}")
print(f"  running...")

start = time.time()
result = controller.mine_with_self_healing(sample_job, target, extranonce1, extranonce2_size)
elapsed = time.time() - start

if result:
    print(f"\n✅ Demo accepted a candidate in {elapsed:.2f}s")
    print(f"   nonce={result['nonce']}")
    print(f"   hash_rate≈{result['hash_rate']:,.0f} H/s")
else:
    print(f"\n⏳ Demo finished in {elapsed:.2f}s (no candidate under target)")

print("\nFinal performance snapshot:")
stats = controller.get_system_performance()
for k, v in stats.items():
    print(f"  {k}: {v}")

return controller

if name == "main":
try:
controller = run_self_healing_demo()
except KeyboardInterrupt:
print("\n⏹️ Demo interrupted by user")
except Exception as e:
print(f"\n❌ Demo error: {e}")