TOS Hash Algorithm

TOS-Hash(input, salt, difficulty, ai_factor) =
    Finalize(
        AI-Enhance(
            Argon2-Hybrid(input, salt, memory_cost, time_cost),
            ai_factor
        )
    )

use sha3::{Sha3_256, Digest};
use argon2::{Argon2, Config};
 
pub struct TOSHasher {
    memory_cost: u32,
    time_cost: u32,
    parallelism: u32,
    ai_factor: f64,
}
 
impl TOSHasher {
    pub fn new(difficulty: u64, ai_factor: f64) -> Self {
        // Calculate parameters based on difficulty
        let memory_cost = (difficulty / 1000).max(1024) as u32; // Min 1MB
        let time_cost = (difficulty / 10000).max(3) as u32;     // Min 3 iterations
        let parallelism = num_cpus::get() as u32;
 
        Self {
            memory_cost,
            time_cost,
            parallelism,
            ai_factor,
        }
    }
 
    pub fn hash(&self, input: &[u8], salt: &[u8]) -> [u8; 32] {
        // Phase 1: Memory-hard hashing with Argon2
        let argon2_config = Config {
            variant: argon2::Variant::Argon2id,
            version: argon2::Version::Version13,
            mem_cost: self.memory_cost,
            time_cost: self.time_cost,
            lanes: self.parallelism,
            secret: &[],
            ad: &[],
            hash_length: 32,
        };
 
        let argon2_hash = argon2::hash_raw(input, salt, &argon2_config)
            .expect("Argon2 hashing failed");
 
        // Phase 2: AI-enhanced processing
        let ai_enhanced = self.ai_enhance(&argon2_hash);
 
        // Phase 3: Final SHA-3 processing
        let mut hasher = Sha3_256::new();
        hasher.update(&ai_enhanced);
        hasher.update(salt);
        hasher.update(&self.ai_factor.to_le_bytes());
 
        let result = hasher.finalize();
        let mut output = [0u8; 32];
        output.copy_from_slice(&result);
        output
    }
 
    fn ai_enhance(&self, input: &[u8]) -> Vec<u8> {
        // AI-enhancement algorithm
        let mut enhanced = input.to_vec();
        let ai_iterations = (self.ai_factor * 1000.0) as usize;
 
        for i in 0..ai_iterations {
            // Simulate AI-mining complexity
            enhanced = self.neural_transform(&enhanced, i);
        }
 
        enhanced
    }
 
    fn neural_transform(&self, data: &[u8], iteration: usize) -> Vec<u8> {
        // Simplified neural network-inspired transformation
        let mut output = Vec::with_capacity(data.len());
 
        for (idx, &byte) in data.iter().enumerate() {
            let weight = (iteration as u8).wrapping_mul(idx as u8);
            let transformed = byte.wrapping_add(weight).wrapping_mul(157); // Prime number
            output.push(transformed);
        }
 
        // Non-linear activation function
        for byte in &mut output {
            *byte = if *byte > 127 {
                (*byte).wrapping_mul(3)
            } else {
                (*byte).wrapping_div(2)
            };
        }
 
        output
    }
}

import hashlib
import numpy as np
from typing import Tuple, List
import time
 
class TOSMiner:
    def __init__(self, difficulty: int, ai_mining: bool = False):
        self.difficulty = difficulty
        self.ai_mining = ai_mining
        self.target = (2 ** 256) // difficulty
 
    def mine_block(self, block_data: bytes, max_nonce: int = 2**32) -> Tuple[int, bytes]:
        """Mine a block using TOS Hash algorithm"""
 
        for nonce in range(max_nonce):
            # Prepare mining input
            mining_input = block_data + nonce.to_bytes(8, 'little')
            salt = self._generate_salt(nonce)
 
            # Calculate TOS Hash
            if self.ai_mining:
                hash_result = self._tos_hash_ai(mining_input, salt)
            else:
                hash_result = self._tos_hash_standard(mining_input, salt)
 
            # Check if hash meets difficulty target
            hash_int = int.from_bytes(hash_result, 'big')
            if hash_int < self.target:
                return nonce, hash_result
 
        raise ValueError("Failed to find valid nonce")
 
    def _tos_hash_standard(self, input_data: bytes, salt: bytes) -> bytes:
        """Standard TOS Hash implementation"""
        # Phase 1: Memory-hard hashing
        memory_cost = max(1024, self.difficulty // 1000)
        iterations = max(3, self.difficulty // 10000)
 
        # Simulate Argon2-like memory-hard function
        state = self._memory_hard_hash(input_data, salt, memory_cost, iterations)
 
        # Phase 2: Final hashing
        hasher = hashlib.sha3_256()
        hasher.update(state)
        hasher.update(salt)
 
        return hasher.digest()
 
    def _tos_hash_ai(self, input_data: bytes, salt: bytes) -> bytes:
        """AI-enhanced TOS Hash implementation"""
        # Standard hash first
        standard_hash = self._tos_hash_standard(input_data, salt)
 
        # AI enhancement
        ai_factor = min(2.5, 1.0 + (self.difficulty / 1000000))
        enhanced_hash = self._ai_enhancement(standard_hash, ai_factor)
 
        # Final processing
        hasher = hashlib.sha3_256()
        hasher.update(enhanced_hash)
        hasher.update(b'AI-MINING')
 
        return hasher.digest()
 
    def _memory_hard_hash(self, data: bytes, salt: bytes, mem_cost: int, iterations: int) -> bytes:
        """Memory-hard hashing function"""
        # Initialize memory block
        memory_size = mem_cost * 1024  # Convert to bytes
        memory_block = bytearray(memory_size)
 
        # Fill initial memory
        hasher = hashlib.blake2b()
        hasher.update(data)
        hasher.update(salt)
        initial_hash = hasher.digest()
 
        # Expand to fill memory
        for i in range(0, memory_size, 64):
            chunk_hasher = hashlib.blake2b()
            chunk_hasher.update(initial_hash)
            chunk_hasher.update(i.to_bytes(8, 'little'))
            chunk_hash = chunk_hasher.digest()
 
            end_idx = min(i + 64, memory_size)
            memory_block[i:end_idx] = chunk_hash[:end_idx-i]
 
        # Perform iterations
        for iteration in range(iterations):
            for i in range(0, memory_size - 64, 64):
                # Random access pattern
                index = int.from_bytes(memory_block[i:i+8], 'little') % (memory_size // 64)
                target_idx = index * 64
 
                # XOR operation
                for j in range(64):
                    if i + j < memory_size and target_idx + j < memory_size:
                        memory_block[i + j] ^= memory_block[target_idx + j]
 
        # Final hash of memory
        final_hasher = hashlib.sha3_256()
        final_hasher.update(memory_block)
        return final_hasher.digest()
 
    def _ai_enhancement(self, data: bytes, ai_factor: float) -> bytes:
        """AI-enhancement algorithm"""
        # Convert to numpy array for processing
        data_array = np.frombuffer(data, dtype=np.uint8)
 
        # Simulate neural network processing
        enhanced = data_array.astype(np.float32)
 
        # Apply multiple transformation layers
        num_layers = int(ai_factor * 3)
        for layer in range(num_layers):
            # Weight matrix (simplified)
            weights = np.random.RandomState(layer).normal(0, 1, (len(enhanced), len(enhanced)))
 
            # Matrix multiplication
            enhanced = np.dot(weights, enhanced)
 
            # Activation function (ReLU variant)
            enhanced = np.maximum(0.1 * enhanced, enhanced)
 
            # Normalization
            enhanced = enhanced / np.max(np.abs(enhanced)) * 255
 
        # Convert back to bytes
        result = enhanced.astype(np.uint8).tobytes()
 
        # Ensure output is 32 bytes
        hasher = hashlib.sha3_256()
        hasher.update(result)
        return hasher.digest()
 
    def _generate_salt(self, nonce: int) -> bytes:
        """Generate deterministic salt from nonce"""
        salt_hasher = hashlib.sha3_256()
        salt_hasher.update(b'TOS-NETWORK-SALT')
        salt_hasher.update(nonce.to_bytes(8, 'little'))
        salt_hasher.update(self.difficulty.to_bytes(8, 'little'))
        return salt_hasher.digest()[:64]  # 64-byte salt

class TOSAIMiner {
  constructor(modelPath, difficulty) {
    this.model = new TensorFlowModel(modelPath);
    this.difficulty = difficulty;
    this.aiComplexity = this.calculateAIComplexity(difficulty);
  }
 
  async mineWithAI(blockData, maxIterations = 1000000) {
    const startTime = Date.now();
 
    for (let nonce = 0; nonce < maxIterations; nonce++) {
      // Prepare input for AI processing
      const input = this.prepareAIInput(blockData, nonce);
 
      // Run through AI model
      const aiOutput = await this.model.predict(input);
 
      // Enhance with AI predictions
      const enhancedHash = this.enhanceWithAI(input, aiOutput);
 
      // Check if result meets difficulty
      if (this.meetsTarget(enhancedHash)) {
        const solutionTime = Date.now() - startTime;
        return {
          nonce,
          hash: enhancedHash,
          aiMetrics: {
            modelAccuracy: aiOutput.confidence,
            processingTime: solutionTime,
            aiComplexity: this.aiComplexity
          }
        };
      }
    }
 
    throw new Error('AI mining failed to find solution');
  }
 
  prepareAIInput(blockData, nonce) {
    // Convert block data to AI-compatible format
    const combined = Buffer.concat([blockData, Buffer.from([nonce])]);
 
    // Normalize to [-1, 1] range for neural network
    const normalized = Array.from(combined).map(byte => (byte - 127.5) / 127.5);
 
    // Pad or truncate to fixed size (e.g., 1024 features)
    const fixedSize = 1024;
    const padded = normalized.slice(0, fixedSize);
    while (padded.length < fixedSize) {
      padded.push(0);
    }
 
    return padded;
  }
 
  enhanceWithAI(input, aiOutput) {
    // Combine original input with AI predictions
    const enhanced = new Uint8Array(32);
 
    for (let i = 0; i < 32; i++) {
      const inputByte = Math.floor((input[i] + 1) * 127.5);
      const aiPrediction = Math.floor((aiOutput.predictions[i] + 1) * 127.5);
 
      // Weighted combination
      const weight = aiOutput.confidence;
      enhanced[i] = Math.floor(inputByte * (1 - weight) + aiPrediction * weight);
    }
 
    // Final hash
    return crypto.subtle.digest('SHA-256', enhanced);
  }
 
  calculateAIComplexity(difficulty) {
    // Higher difficulty requires more complex AI processing
    return Math.min(2.5, 1.0 + Math.log10(difficulty) / 10);
  }
 
  meetsTarget(hash) {
    const hashInt = BigInt('0x' + Buffer.from(hash).toString('hex'));
    const target = BigInt(2) ** BigInt(256) / BigInt(this.difficulty);
    return hashInt < target;
  }
}

// CUDA implementation for GPU mining
__global__ void tos_hash_kernel(
    uint8_t* input_data,
    uint8_t* output_hashes,
    uint32_t* nonces,
    uint64_t difficulty,
    uint32_t batch_size
) {
    uint32_t idx = blockIdx.x * blockDim.x + threadIdx.x;
 
    if (idx >= batch_size) return;
 
    // Local memory for each thread
    uint8_t local_memory[2048];
    uint8_t hash_result[32];
 
    // Calculate nonce for this thread
    uint32_t nonce = nonces[idx];
 
    // Prepare input with nonce
    uint8_t mining_input[1024];
    memcpy(mining_input, input_data, 1016);
    *((uint32_t*)(mining_input + 1016)) = nonce;
 
    // Phase 1: Memory-hard computation
    tos_memory_hard_gpu(mining_input, local_memory, difficulty);
 
    // Phase 2: AI enhancement (simplified for GPU)
    tos_ai_enhance_gpu(local_memory, hash_result, difficulty);
 
    // Phase 3: Final hash
    sha3_256_gpu(hash_result, &output_hashes[idx * 32]);
}
 
// Host function to launch GPU mining
void mine_tos_gpu(
    uint8_t* block_data,
    uint64_t difficulty,
    uint32_t threads_per_block = 256,
    uint32_t num_blocks = 1024
) {
    uint32_t batch_size = threads_per_block * num_blocks;
 
    // Allocate GPU memory
    uint8_t *d_input, *d_output;
    uint32_t *d_nonces;
 
    cudaMalloc(&d_input, 1024);
    cudaMalloc(&d_output, batch_size * 32);
    cudaMalloc(&d_nonces, batch_size * sizeof(uint32_t));
 
    // Copy input data
    cudaMemcpy(d_input, block_data, 1024, cudaMemcpyHostToDevice);
 
    // Generate nonce array
    uint32_t* nonces = new uint32_t[batch_size];
    for (uint32_t i = 0; i < batch_size; i++) {
        nonces[i] = rand();
    }
    cudaMemcpy(d_nonces, nonces, batch_size * sizeof(uint32_t), cudaMemcpyHostToDevice);
 
    // Launch kernel
    tos_hash_kernel<<<num_blocks, threads_per_block>>>(
        d_input, d_output, d_nonces, difficulty, batch_size
    );
 
    // Copy results back
    uint8_t* results = new uint8_t[batch_size * 32];
    cudaMemcpy(results, d_output, batch_size * 32, cudaMemcpyDeviceToHost);
 
    // Check for valid solutions
    check_solutions(results, nonces, difficulty, batch_size);
 
    // Cleanup
    cudaFree(d_input);
    cudaFree(d_output);
    cudaFree(d_nonces);
    delete[] nonces;
    delete[] results;
}

#include <immintrin.h>  // AVX2 intrinsics
#include <thread>
#include <vector>
 
class TOSHashCPU {
private:
    uint64_t difficulty;
    uint32_t num_threads;
 
public:
    TOSHashCPU(uint64_t diff) : difficulty(diff) {
        num_threads = std::thread::hardware_concurrency();
    }
 
    // AVX2-optimized hash function
    void hash_avx2(const uint8_t* input, uint8_t* output) {
        __m256i state[8];
 
        // Load input into AVX2 registers
        for (int i = 0; i < 8; i++) {
            state[i] = _mm256_loadu_si256((__m256i*)(input + i * 32));
        }
 
        // Memory-hard processing with SIMD
        for (uint32_t round = 0; round < difficulty / 1000; round++) {
            memory_hard_round_avx2(state);
        }
 
        // AI enhancement with SIMD
        ai_enhance_avx2(state);
 
        // Final hash
        finalize_hash_avx2(state, output);
    }
 
    // Multi-threaded mining
    std::pair<uint32_t, std::vector<uint8_t>> mine_parallel(
        const std::vector<uint8_t>& block_data
    ) {
        std::atomic<bool> found{false};
        std::atomic<uint32_t> result_nonce{0};
        std::vector<uint8_t> result_hash(32);
 
        std::vector<std::thread> threads;
 
        for (uint32_t t = 0; t < num_threads; t++) {
            threads.emplace_back([&, t]() {
                uint32_t start_nonce = t * (UINT32_MAX / num_threads);
                uint32_t end_nonce = (t + 1) * (UINT32_MAX / num_threads);
 
                for (uint32_t nonce = start_nonce; nonce < end_nonce && !found; nonce++) {
                    std::vector<uint8_t> input = block_data;
 
                    // Append nonce
                    for (int i = 0; i < 4; i++) {
                        input.push_back((nonce >> (i * 8)) & 0xFF);
                    }
 
                    // Calculate hash
                    std::vector<uint8_t> hash(32);
                    hash_avx2(input.data(), hash.data());
 
                    // Check if valid
                    if (check_difficulty(hash)) {
                        found = true;
                        result_nonce = nonce;
                        result_hash = hash;
                        break;
                    }
                }
            });
        }
 
        // Wait for threads
        for (auto& thread : threads) {
            thread.join();
        }
 
        if (!found) {
            throw std::runtime_error("Mining failed");
        }
 
        return {result_nonce, result_hash};
    }
 
private:
    void memory_hard_round_avx2(__m256i state[8]) {
        // SIMD memory-hard computation
        for (int i = 0; i < 8; i++) {
            // Mixing operations using AVX2
            state[i] = _mm256_add_epi32(state[i], state[(i + 1) % 8]);
            state[i] = _mm256_xor_si256(state[i], _mm256_slli_epi32(state[i], 13));
            state[i] = _mm256_add_epi32(state[i], state[(i + 2) % 8]);
        }
    }
 
    void ai_enhance_avx2(__m256i state[8]) {
        // Simplified AI enhancement with SIMD
        __m256i weights = _mm256_set1_epi32(0x9E3779B9);  // Golden ratio
 
        for (int round = 0; round < 4; round++) {
            for (int i = 0; i < 8; i++) {
                // Neural network-inspired transformation
                state[i] = _mm256_mullo_epi32(state[i], weights);
                state[i] = _mm256_add_epi32(state[i], _mm256_srli_epi32(state[i], 16));
            }
        }
    }
 
    void finalize_hash_avx2(__m256i state[8], uint8_t* output) {
        // Combine all state into final hash
        __m256i combined = state[0];
        for (int i = 1; i < 8; i++) {
            combined = _mm256_xor_si256(combined, state[i]);
        }
 
        // Store result
        _mm256_storeu_si256((__m256i*)output, combined);
    }
 
    bool check_difficulty(const std::vector<uint8_t>& hash) {
        // Convert to big integer and compare with target
        uint64_t target = UINT64_MAX / difficulty;
        uint64_t hash_val = 0;
 
        for (int i = 0; i < 8; i++) {
            hash_val = (hash_val << 8) | hash[i];
        }
 
        return hash_val < target;
    }
};

def analyze_tos_hash_properties():
    """Analyze cryptographic properties of TOS Hash"""
 
    properties = {
        'preimage_resistance': test_preimage_resistance(),
        'second_preimage_resistance': test_second_preimage_resistance(),
        'collision_resistance': test_collision_resistance(),
        'avalanche_effect': test_avalanche_effect(),
        'uniformity': test_output_uniformity(),
        'ai_enhancement_security': test_ai_security()
    }
 
    return properties
 
def test_preimage_resistance(samples=10000):
    """Test resistance to preimage attacks"""
    successful_attacks = 0
 
    for _ in range(samples):
        # Generate random target hash
        target = os.urandom(32)
 
        # Try to find preimage (simplified test)
        for attempt in range(1000):
            candidate = os.urandom(64)
            if tos_hash(candidate) == target:
                successful_attacks += 1
                break
 
    # Should be approximately 0 for good hash function
    return successful_attacks / samples
 
def test_avalanche_effect(samples=1000):
    """Test avalanche effect (small input change -> large output change)"""
    total_bit_changes = 0
 
    for _ in range(samples):
        # Original input
        input1 = os.urandom(64)
        hash1 = tos_hash(input1)
 
        # Flip one bit
        input2 = bytearray(input1)
        bit_pos = random.randint(0, len(input2) * 8 - 1)
        byte_pos = bit_pos // 8
        bit_in_byte = bit_pos % 8
        input2[byte_pos] ^= (1 << bit_in_byte)
 
        hash2 = tos_hash(bytes(input2))
 
        # Count different bits
        xor_result = int.from_bytes(hash1, 'big') ^ int.from_bytes(hash2, 'big')
        bit_changes = bin(xor_result).count('1')
        total_bit_changes += bit_changes
 
    # Should be close to 50% (128 bits out of 256)
    average_changes = total_bit_changes / samples
    return average_changes / 256  # Normalize to percentage

{
  "tos_mining_protocol": {
    "version": "1.0",
    "methods": {
      "get_work": {
        "description": "Request mining work from pool",
        "parameters": {
          "worker_id": "string",
          "ai_mining_capable": "boolean",
          "gpu_count": "integer"
        },
        "returns": {
          "block_template": "hex_string",
          "difficulty": "integer",
          "ai_factor": "float",
          "target": "hex_string"
        }
      },
      "submit_work": {
        "description": "Submit mining solution",
        "parameters": {
          "worker_id": "string",
          "nonce": "integer",
          "hash": "hex_string",
          "ai_metrics": "object"
        },
        "returns": {
          "accepted": "boolean",
          "reason": "string"
        }
      }
    }
  }
}

package main
 
import (
    "crypto/rand"
    "encoding/hex"
    "encoding/json"
    "fmt"
    "net/http"
    "time"
)
 
type TOSMiningPool struct {
    difficulty    uint64
    workers      map[string]*Worker
    blockTemplate []byte
    aiEnabled    bool
}
 
type Worker struct {
    ID           string
    HashRate     float64
    AICapable    bool
    LastActivity time.Time
}
 
type WorkRequest struct {
    WorkerID       string `json:"worker_id"`
    AIMiningCapable bool   `json:"ai_mining_capable"`
    GPUCount       int    `json:"gpu_count"`
}
 
type WorkResponse struct {
    BlockTemplate string  `json:"block_template"`
    Difficulty    uint64  `json:"difficulty"`
    AIFactor      float64 `json:"ai_factor"`
    Target        string  `json:"target"`
}
 
func (pool *TOSMiningPool) GetWork(w http.ResponseWriter, r *http.Request) {
    var req WorkRequest
    json.NewDecoder(r.Body).Decode(&req)
 
    // Update worker info
    worker := pool.workers[req.WorkerID]
    if worker == nil {
        worker = &Worker{
            ID:        req.WorkerID,
            AICapable: req.AIMiningCapable,
        }
        pool.workers[req.WorkerID] = worker
    }
    worker.LastActivity = time.Now()
 
    // Calculate AI factor based on worker capabilities
    aiFactor := 1.0
    if req.AIMiningCapable && pool.aiEnabled {
        aiFactor = 1.0 + float64(req.GPUCount)*0.1
    }
 
    // Calculate target from difficulty
    target := calculateTarget(pool.difficulty)
 
    response := WorkResponse{
        BlockTemplate: hex.EncodeToString(pool.blockTemplate),
        Difficulty:    pool.difficulty,
        AIFactor:      aiFactor,
        Target:        hex.EncodeToString(target[:]),
    }
 
    json.NewEncoder(w).Encode(response)
}
 
type SubmitRequest struct {
    WorkerID  string                 `json:"worker_id"`
    Nonce     uint64                 `json:"nonce"`
    Hash      string                 `json:"hash"`
    AIMetrics map[string]interface{} `json:"ai_metrics"`
}
 
type SubmitResponse struct {
    Accepted bool   `json:"accepted"`
    Reason   string `json:"reason"`
}
 
func (pool *TOSMiningPool) SubmitWork(w http.ResponseWriter, r *http.Request) {
    var req SubmitRequest
    json.NewDecoder(r.Body).Decode(&req)
 
    // Verify hash
    hashBytes, _ := hex.DecodeString(req.Hash)
    if pool.verifyHash(hashBytes, req.Nonce) {
        // Valid solution
        response := SubmitResponse{
            Accepted: true,
            Reason:   "Valid solution",
        }
 
        // Update worker stats
        if worker := pool.workers[req.WorkerID]; worker != nil {
            worker.HashRate = pool.calculateHashRate(worker, req.AIMetrics)
        }
 
        json.NewEncoder(w).Encode(response)
    } else {
        response := SubmitResponse{
            Accepted: false,
            Reason:   "Invalid hash",
        }
        json.NewEncoder(w).Encode(response)
    }
}
 
func (pool *TOSMiningPool) verifyHash(hash []byte, nonce uint64) bool {
    // Verify that the submitted hash meets the difficulty target
    target := calculateTarget(pool.difficulty)
 
    // Convert hash to big integer for comparison
    hashInt := bytesToBigInt(hash)
    targetInt := bytesToBigInt(target[:])
 
    return hashInt.Cmp(targetInt) < 0
}
 
func calculateTarget(difficulty uint64) [32]byte {
    // Calculate mining target from difficulty
    maxTarget := [32]byte{}
    for i := range maxTarget {
        maxTarget[i] = 0xFF
    }
 
    // Target = MaxTarget / Difficulty
    return divideByteArray(maxTarget, difficulty)
}

import unittest
import hashlib
import time
from tos_hash import TOSHasher, TOSMiner
 
class TestTOSHash(unittest.TestCase):
 
    def setUp(self):
        self.hasher = TOSHasher(difficulty=1000000, ai_factor=1.0)
        self.test_data = b"TOS Network Test Block Data"
        self.test_salt = b"A" * 64
 
    def test_deterministic_output(self):
        """Hash should be deterministic for same inputs"""
        hash1 = self.hasher.hash(self.test_data, self.test_salt)
        hash2 = self.hasher.hash(self.test_data, self.test_salt)
        self.assertEqual(hash1, hash2)
 
    def test_avalanche_effect(self):
        """Small input change should cause large output change"""
        hash1 = self.hasher.hash(self.test_data, self.test_salt)
 
        # Change one bit
        modified_data = bytearray(self.test_data)
        modified_data[0] ^= 1
        hash2 = self.hasher.hash(bytes(modified_data), self.test_salt)
 
        # Count different bits
        xor_result = int.from_bytes(hash1, 'big') ^ int.from_bytes(hash2, 'big')
        different_bits = bin(xor_result).count('1')
 
        # Should be close to 50% (128 out of 256 bits)
        self.assertGreater(different_bits, 100)
        self.assertLess(different_bits, 156)
 
    def test_difficulty_scaling(self):
        """Higher difficulty should require more work"""
        easy_hasher = TOSHasher(difficulty=1000, ai_factor=1.0)
        hard_hasher = TOSHasher(difficulty=1000000, ai_factor=1.0)
 
        # Time easy mining
        start_time = time.time()
        easy_miner = TOSMiner(1000)
        easy_nonce, _ = easy_miner.mine_block(self.test_data, max_nonce=100000)
        easy_time = time.time() - start_time
 
        # Time hard mining (limited iterations for test)
        start_time = time.time()
        hard_miner = TOSMiner(10000)  # Reduced for test
        try:
            hard_nonce, _ = hard_miner.mine_block(self.test_data, max_nonce=100000)
            hard_time = time.time() - start_time
 
            # Hard mining should take longer
            self.assertGreater(hard_time, easy_time)
        except ValueError:
            # Expected to fail with high difficulty and limited iterations
            pass
 
    def test_ai_enhancement(self):
        """AI factor should affect hash output"""
        standard_hasher = TOSHasher(difficulty=1000000, ai_factor=1.0)
        ai_hasher = TOSHasher(difficulty=1000000, ai_factor=2.0)
 
        hash1 = standard_hasher.hash(self.test_data, self.test_salt)
        hash2 = ai_hasher.hash(self.test_data, self.test_salt)
 
        self.assertNotEqual(hash1, hash2)
 
    def test_salt_dependency(self):
        """Different salts should produce different hashes"""
        salt1 = b"A" * 64
        salt2 = b"B" * 64
 
        hash1 = self.hasher.hash(self.test_data, salt1)
        hash2 = self.hasher.hash(self.test_data, salt2)
 
        self.assertNotEqual(hash1, hash2)
 
    def test_output_size(self):
        """Hash output should always be 32 bytes"""
        for i in range(100):
            test_input = f"test_{i}".encode()
            hash_output = self.hasher.hash(test_input, self.test_salt)
            self.assertEqual(len(hash_output), 32)
 
    def test_performance_benchmarks(self):
        """Performance should meet minimum requirements"""
        start_time = time.time()
        iterations = 1000
 
        for i in range(iterations):
            self.hasher.hash(f"test_{i}".encode(), self.test_salt)
 
        total_time = time.time() - start_time
        hashes_per_second = iterations / total_time
 
        # Should achieve at least 100 hashes per second on modern hardware
        self.assertGreater(hashes_per_second, 100)
 
if __name__ == '__main__':
    unittest.main()

use serde::{Deserialize, Serialize};
use std::time::{SystemTime, UNIX_EPOCH};
 
#[derive(Serialize, Deserialize, Clone)]
pub struct Block {
    pub header: BlockHeader,
    pub transactions: Vec<Transaction>,
}
 
#[derive(Serialize, Deserialize, Clone)]
pub struct BlockHeader {
    pub previous_hash: [u8; 32],
    pub merkle_root: [u8; 32],
    pub timestamp: u64,
    pub difficulty: u64,
    pub nonce: u64,
    pub ai_factor: f64,
    pub hash: [u8; 32],
}
 
impl Block {
    pub fn new(
        previous_hash: [u8; 32],
        transactions: Vec<Transaction>,
        difficulty: u64,
        ai_factor: f64,
    ) -> Self {
        let merkle_root = calculate_merkle_root(&transactions);
        let timestamp = SystemTime::now()
            .duration_since(UNIX_EPOCH)
            .unwrap()
            .as_secs();
 
        let header = BlockHeader {
            previous_hash,
            merkle_root,
            timestamp,
            difficulty,
            nonce: 0,
            ai_factor,
            hash: [0; 32],
        };
 
        Self {
            header,
            transactions,
        }
    }
 
    pub fn mine(&mut self) -> Result<(), String> {
        let hasher = TOSHasher::new(self.header.difficulty, self.header.ai_factor);
        let target = self.calculate_target();
 
        for nonce in 0..u64::MAX {
            self.header.nonce = nonce;
            let hash = self.calculate_hash(&hasher);
 
            if self.meets_target(&hash, &target) {
                self.header.hash = hash;
                return Ok(());
            }
        }
 
        Err("Mining failed".to_string())
    }
 
    fn calculate_hash(&self, hasher: &TOSHasher) -> [u8; 32] {
        let header_bytes = bincode::serialize(&self.header).unwrap();
        let salt = self.generate_salt();
        hasher.hash(&header_bytes, &salt)
    }
 
    fn generate_salt(&self) -> [u8; 64] {
        let mut salt = [0u8; 64];
        let salt_input = format!(
            "{}{}{}",
            self.header.previous_hash.iter().map(|b| format!("{:02x}", b)).collect::<String>(),
            self.header.timestamp,
            self.header.difficulty
        );
 
        let hash = sha256::digest(salt_input.as_bytes());
        salt[..32].copy_from_slice(&hex::decode(hash).unwrap());
        salt[32..].copy_from_slice(&hex::decode(hash).unwrap());
 
        salt
    }
 
    fn calculate_target(&self) -> [u8; 32] {
        let mut target = [0xFFu8; 32];
 
        // Simple target calculation (actual implementation would be more sophisticated)
        let difficulty_bytes = self.header.difficulty.to_be_bytes();
        for (i, &byte) in difficulty_bytes.iter().enumerate() {
            if i < 32 {
                target[i] = target[i].saturating_sub(byte);
            }
        }
 
        target
    }
 
    fn meets_target(&self, hash: &[u8; 32], target: &[u8; 32]) -> bool {
        for (h, t) in hash.iter().zip(target.iter()) {
            if h < t {
                return true;
            } else if h > t {
                return false;
            }
        }
        false
    }
}