sudo su && git clone # Add these imports from abc import ABC, abstractmethod from typing import Protocol import asyncio import hashlib from concurrent.futures import ThreadPoolExecutor import numpy as np

class TestCase(Protocol): async def setup(self) -> None: ... async def execute(self) -> bool: ... async def cleanup(self) -> None: ...

class BeneficenceTest: def init(self, test_data: Dict[str, Any]): self.test_data = test_data self.metrics: Dict[str, float] = {}
async def check_positive_impact(self) -> Tuple[bool, str]: """Analyze positive impact of action""" impact_score = await self._calculate_impact_score() return impact_score > 0.8, f"Impact score: {impact_score}" async def _calculate_impact_score(self) -> float: """Calculate positive impact score""" # Implementation of impact calculation return 0.9 class NonMaleficenceTest: def init(self, test_data: Dict[str, Any]): self.test_data = test_data self.risk_threshold = 0.2
async def check_harm_prevention(self) -> Tuple[bool, str]: """Analyze potential harm""" risk_score = await self._calculate_risk_score() return risk_score < self.risk_threshold, f"Risk score: {risk_score}" async def _calculate_risk_score(self) -> float: """Calculate risk score""" # Implementation of risk calculation return 0.1

class IntegratedTestingFramework: def init(self): # Previous initialization code... self.executor = ThreadPoolExecutor(max_workers=4) self.test_queue: asyncio.Queue = asyncio.Queue() self.results_cache: Dict[str, TestResult] = {}
async def _check_beneficence(self, test_case: Dict[str, Any]) -> Tuple[bool, str]: """Implementation of beneficence check""" test = BeneficenceTest(test_case) result = await test.check_positive_impact() return result async def _check_non_maleficence(self, test_case: Dict[str, Any]) -> Tuple[bool, str]: """Implementation of non-maleficence check""" test = NonMaleficenceTest(test_case) result = await test.check_harm_prevention() return result async def run_test_suite(self, test_type: TestType) -> Dict[str, Any]: """Asynchronous test suite execution""" self.logger.info(f"Starting async {test_type.value} test suite")
test_suite_id = str(uuid.uuid4()) start_time = datetime.utcnow()
# Create test cases based on type test_cases = await self._create_test_cases(test_type)
# Run tests concurrently results = await asyncio.gather(*[ self._run_single_test(test_case) for test_case in test_cases ])
end_time = datetime.utcnow() duration = (end_time - start_time).total_seconds()
suite_results = { "suite_id": test_suite_id, "test_type": test_type.value, "start_time": start_time.isoformat(), "end_time": end_time.isoformat(), "duration": duration, "total_tests": len(results), "passed_tests": sum(1 for r in results if r.passed), "results": results }
# Cache results self.results_cache[test_suite_id] = suite_results
await self._log_test_suite_results(suite_results) return suite_results async def _run_single_test(self, test_case: TestCase) -> TestResult: """Run single test with setup and cleanup""" try: await test_case.setup() result = await test_case.execute() await test_case.cleanup()
return self._create_test_result( test_type=TestType.ETHICAL, name=test_case.class.name, passed=result, details={"execution_successful": True}, metrics={"execution_time": 0.0} ) except Exception as e: self.logger.error(f"Test execution failed: {str(e)}") return self._create_test_result( test_type=TestType.ETHICAL, name=test_case.class.name, passed=False, details={"error": str(e)}, metrics={"execution_time": 0.0} ) async def _create_test_cases(self, test_type: TestType) -> List[TestCase]: """Create appropriate test cases based on type""" # Implementation of test case creation return [] def generate_test_report(self, result_id: str) -> str: """Generate detailed test report""" if result_id not in self.results_cache: return "Result not found"
result = self.results_cache[result_id] report = f""" Test Suite Report ================ ID: {result['suite_id']} Type: {result['test_type']} Duration: {result['duration']:.2f} seconds Pass Rate: {(result['passed_tests'] / result['total_tests'] * 100):.2f}%
Ethical Compliance ----------------- Principles Validated: {self.test_metrics['principles_validated']} Violations Detected: {self.test_metrics['ethical_violations']}
Detailed Results --------------- {json.dumps(result['results'], indent=2)} """ return report async def main(): framework = IntegratedTestingFramework()
print("Enhanced Quantum Ethical Testing Framework Initialized") print(f"Session ID: {framework.current_session}") print(f"Current time: {datetime.utcnow().isoformat()}")
results = await framework.run_integrated_test_suite()
print("\nTest Suite Complete") print(f"Total Duration: {results['duration']:.2f} seconds") print(f"Ethical Compliance: {(framework.test_metrics['passed_tests'] / framework.test_metrics['total_tests'] * 100):.2f}%")
# Generate and print report report = framework.generate_test_report(results['session_id']) print("\nDetailed Report:") print(report) if name == "main": asyncio.run(main()) && from typing import Dict, List, Any, Optional from datetime import datetime import unittest import logging import json import uuid from dataclasses import dataclass from enum import Enum

logging.basicConfig( level=logging.INFO, format='%(asctime)s [%(levelname)s] %(name)s: %(message)s', datefmt='%Y-%m-%d %H:%M:%S' ) class TestType(Enum): UNIT = "unit" INTEGRATION = "integration" SYSTEM = "system" ETHICAL = "ethical" SAFETY = "safety" PERFORMANCE = "performance" SECURITY = "security" @dataclass class TestResult: id: str timestamp: datetime test_type: TestType name: str passed: bool details: Dict[str, Any] metrics: Dict[str, float] ethical_validations: List[Dict[str, bool]]

class QuantumTestingFramework: def init(self): self.logger = logging.getLogger("QuantumTesting") self.test_history: List[TestResult] = [] self.current_session = str(uuid.uuid4())
self.test_metrics = { "total_tests": 0, "passed_tests": 0, "failed_tests": 0, "ethical_violations": 0 }
self.safety_thresholds = { "max_resource_usage": 0.8, # 80% "min_confidence": 0.95, # 95% "max_error_rate": 0.01 # 1% } def run_test_suite(self, test_type: TestType) -> Dict[str, Any]: """Run a complete test suite of specified type""" self.logger.info(f"Starting {test_type.value} test suite")
test_suite_id = str(uuid.uuid4()) start_time = datetime.utcnow()
results = []
if test_type == TestType.ETHICAL: results.extend(self._run_ethical_tests()) elif test_type == TestType.SAFETY: results.extend(self._run_safety_tests()) elif test_type == TestType.SECURITY: results.extend(self._run_security_tests())
end_time = datetime.utcnow() duration = (end_time - start_time).total_seconds()
suite_results = { "suite_id": test_suite_id, "test_type": test_type.value, "start_time": start_time.isoformat(), "end_time": end_time.isoformat(), "duration": duration, "total_tests": len(results), "passed_tests": sum(1 for r in results if r.passed), "results": results }
self._log_test_suite_results(suite_results) return suite_results def _run_ethical_tests(self) -> List[TestResult]: """Run ethical validation tests""" tests = [ self._test_bias_detection(), self._test_fairness_metrics(), self._test_transparency(), self._test_accountability(), self._test_privacy_protection() ] return tests def _run_safety_tests(self) -> List[TestResult]: """Run safety validation tests""" tests = [ self._test_resource_limits(), self._test_error_handling(), self._test_input_validation(), self._test_output_verification(), self._test_system_stability() ] return tests def _run_security_tests(self) -> List[TestResult]: """Run security validation tests""" tests = [ self._test_access_control(), self._test_data_encryption(), self._test_authentication(), self._test_audit_logging(), self._test_vulnerability_scanning() ] return tests def _create_test_result(self, test_type: TestType, name: str, passed: bool, details: Dict[str, Any], metrics: Dict[str, float]) -> TestResult: """Create a standardized test result""" return TestResult( id=str(uuid.uuid4()), timestamp=datetime.utcnow(), test_type=test_type, name=name, passed=passed, details=details, metrics=metrics, ethical_validations=[] ) def log_test_suite_results(self, results: Dict[str, Any]) -> None: """Log test suite results""" self.logger.info(f"Test Suite {results['suite_id']} completed:") self.logger.info(f"Type: {results['test_type']}") self.logger.info(f"Total Tests: {results['total_tests']}") self.logger.info(f"Passed Tests: {results['passed_tests']}") self.logger.info(f"Duration: {results['duration']:.2f} seconds") def main(): framework = QuantumTestingFramework()
print(f"Quantum Testing Framework Initialized") print(f"Session ID: {framework.current_session}") print(f"Current time: {datetime.utcnow().isoformat()}")
# Run all test types for test_type in TestType: print(f"\nRunning {test_type.value} tests...") results = framework.run_test_suite(test_type)
print(f"Results for {test_type.value}:") print(f"Total tests: {results['total_tests']}") print(f"Passed tests: {results['passed_tests']}") print(f"Duration: {results['duration']:.2f} seconds") if name == "main": main() && import random import inspect import ast import types def generate_algorithm(complexity_level=1, max_functions=5, max_operations=10): """ Generates a Python function (algorithm) as a string, with varying complexity. Args: complexity_level: An integer indicating the complexity of the generated algorithm. max_functions: Maximum number of helper functions to create. max_operations: Maximum number of operations within each function. Returns: A string containing the Python code for the generated algorithm. """ def generate_random_variable(used_variables): """Generates a random variable name.""" new_variable = chr(random.randint(ord('a'), ord('z'))) while new_variable in used_variables: new_variable = chr(random.randint(ord('a'), ord('z'))) return new_variable def generate_random_operation(variables, complexity): """Generates a random arithmetic or logical operation.""" if not variables: return str(random.randint(0, 10)) # Base case: return a constant var1 = random.choice(list(variables)) if random.random() < 0.2 and complexity>2: var2 = random.choice(list(variables)) else: var2 = str(random.randint(0,10)) operator = random.choice(['+', '-', '*', '//', '%', '==', '!=', '>', '<', '>=', '<=']) operation = f"{var1} {operator} {var2}" return operation def generate_random_conditional(variables, complexity): """Generates a random conditional statement.""" condition = generate_random_operation(variables, complexity) true_branch = generate_random_operation(variables, complexity) false_branch = generate_random_operation(variables, complexity) return f"({true_branch} if {condition} else {false_branch})" def generate_function(function_name, num_variables, complexity, used_variables): """Generates a random function.""" function_code = f"def {function_name}(" variables = set() for i in range(num_variables): var_name = generate_random_variable(used_variables.union(variables)) variables.add(var_name) function_code += var_name if i < num_variables - 1: function_code += ", " function_code += "):\n" for _ in range(random.randint(1, max_operations)): if random.random() < 0.2 and complexity >1: op = generate_random_conditional(variables, complexity) else: op = generate_random_operation(variables, complexity) function_code += f" {generate_random_variable(variables)} = {op}\n" if variables: return_var = random.choice(list(variables)) function_code += f" return {return_var}\n" else: function_code += f" return {random.randint(0,10)}\n" return function_code all_code = "" used_variables = set() # Generate helper functions num_functions = random.randint(0, max_functions) function_names = [f"func{i}" for i in range(num_functions)] for function_name in function_names: num_variables = random.randint(0, 3) all_code += generate_function(function_name, num_variables, complexity_level, used_variables) + "\n\n" # Generate main function main_function_name = "solve_problem" num_main_variables = random.randint(1, 5) all_code += generate_function(main_function_name, num_main_variables, complexity_level, used_variables) return all_code def compile_and_run_algorithm(algorithm_code, *args): """Compiles and runs the generated algorithm.""" try: tree = ast.parse(algorithm_code) code_object = compile(tree, filename='', mode='exec') local_scope = {} exec(code_object, local_scope) result = local_scope'solve_problem' return result except Exception as e: return f"Error: {e}"

complexity = 3 # Adjust complexity algorithm_code = generate_algorithm(complexity) print("Generated Algorithm:\n", algorithm_code) try: result = compile_and_run_algorithm(algorithm_code, 5, 2, 8) print("\nResult:", result) except TypeError as e: print(f"\nError in execution, likely wrong number of arguments for generated function: {e}") #Example of generating many algorithms. for i in range(3): #Generates 3 algorithms. algorithm_code = generate_algorithm(complexity) print(f"\nAlgorithm {i+1}:\n", algorithm_code) try: result = compile_and_run_algorithm(algorithm_code, 5, 2, 8) print("\nResult:", result) except TypeError as e: print(f"\nError in execution, likely wrong number of arguments for generated function: {e}") && git clone https://api.github.com/repos/octocat/example/git/blobs/3a0f86fb8db8eea7ccbb9a95f325ddbedfb25e15curl -L
-X POST
-H "Accept: application/vnd.github+json"
-H "Authorization: Bearer "
-H "X-GitHub-Api-Version: 2022-11-28"
https://api.github.com/repos///git/blobs
-d '{"content":"Content of the blob","encoding":"utf-8"}' && START DEFINE class NetworkInterceptor DEFINE method init(self, target_ip, target_port) SET self.target_ip TO target_ip SET self.target_port TO target_port INITIALIZE network connection to target_ip:target_port END DEFINE DEFINE method intercept_traffic(self) WHILE connection is active RECEIVE data from network PROCESS data MODIFY data based on predefined rules SEND modified data to destination END WHILE END DEFINE DEFINE method process_data(self, data) PARSE data APPLY modifications based on predefined rules RETURN modified data END DEFINE END DEFINE CREATE instance of NetworkInterceptor with target IP and port CALL instance.intercept_traffic() END && ## Hi there 👋

python -m venv webcrawler-env source webcrawler-env/bin/activate # On Windows: webcrawler-env\Scripts\activate Step 2: Install Required Packages Create a requirements.txt file with the following content: txt beautifulsoup4==4.9.3 certifi==2020.12.5 cryptography==3.3.1 psutil==5.8.0 requests==2.25.1 validators==0.18.2 Then install the dependencies: bash pip install -r requirements.txt Step 3: Save the Web Crawler Code Save the refined web crawler code into a file named web_crawler.py. Step 4: Run the Web Crawler You can run the web crawler using the following command: bash python web_crawler.py Step 5: Schedule the Web Crawler (Optional) If you want to run the web crawler at regular intervals, you can use a task scheduler like cron on Unix-based systems or Task Scheduler on Windows. Using cron (Unix-based systems): Open the crontab file: bash crontab -e Add a cron job to run the web crawler every day at 2 AM: bash 0 2 * * * /path/to/your/virtualenv/bin/python /path/to/your/web_crawler.py Using Task Scheduler (Windows): Open Task Scheduler and create a new task. Set the trigger to run daily at your desired time. Set the action to run the Python interpreter with the path to your web_crawler.py script. Step 6: Monitor and Log Output Ensure that your script logs its output to a file for monitoring. You can modify the logging configuration in the script to log to a file as well as the console. Example of Logging to a File: Modify the logging configuration in web_crawler.py: Python

logging.basicConfig( level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s', handlers=[ logging.FileHandler("webcrawler.log"), logging.StreamHandler() ] )