Research-Stack/5-Applications/nodupe/tests/utils/errors.py

"""NoDupeLabs Error Test Utilities

Helper functions for error condition simulation and testing.
"""

import contextlib
from typing import Dict, Any, List, Optional, Union, Callable, Type
from unittest.mock import MagicMock, patch
import random
import os
import tempfile
from pathlib import Path

def simulate_file_system_errors(
    error_type: str = "permission",
    operation: str = "read"
) -> Callable:
    """
    Create a context manager to simulate file system errors.

    Args:
        error_type: Type of error to simulate
        operation: File operation to fail

    Returns:
        Context manager for error simulation
    """
    @contextlib.contextmanager
    def error_context():
        """Inner context manager for simulating file system errors."""
        error_map = {
            "permission": PermissionError("Operation not permitted"),
            "not_found": FileNotFoundError("File not found"),
            "disk_full": OSError("No space left on device"),
            "io_error": IOError("Input/output error"),
            "access_denied": PermissionError("Access denied")
        }

        error = error_map.get(error_type, IOError("File system error"))

        original_open = open
        original_path = Path

        class MockPath:
            """Mock Path class to simulate file system errors."""

            def __init__(self, *args):
                """Initialize MockPath with path arguments."""
                self.path = original_path(*args)

            def __truediv__(self, other):
                """Support path division operation."""
                return MockPath(str(self.path) + "/" + str(other))

            def __str__(self):
                """Return string representation of path."""
                return str(self.path)

            def read_text(self, *args, **kwargs):
                """Simulate read_text error if operation is 'read'."""
                if operation == "read":
                    raise error
                return self.path.read_text(*args, **kwargs)

            def write_text(self, *args, **kwargs):
                """Simulate write_text error if operation is 'write'."""
                if operation == "write":
                    raise error
                return self.path.write_text(*args, **kwargs)

            def exists(self):
                """Simulate exists error if operation is 'exists'."""
                if operation == "exists":
                    raise error
                return self.path.exists()

            def unlink(self):
                """Simulate unlink error if operation is 'delete'."""
                if operation == "delete":
                    raise error
                return self.path.unlink()

        def mock_open(*args, **kwargs):
            """Mock open function to simulate file open errors."""
            if operation == "open":
                raise error
            return original_open(*args, **kwargs)

        with patch('builtins.open', side_effect=mock_open):
            with patch('pathlib.Path', side_effect=MockPath):
                yield

    return error_context

def create_error_test_scenarios() -> List[Dict[str, Any]]:
    """
    Create error test scenarios.

    Returns:
        List of error test scenarios
    """
    return [
        {
            "name": "file_not_found",
            "error_type": "FileNotFoundError",
            "expected_behavior": "graceful_failure"
        },
        {
            "name": "permission_denied",
            "error_type": "PermissionError",
            "expected_behavior": "retry_or_fail"
        },
        {
            "name": "disk_full",
            "error_type": "OSError",
            "expected_behavior": "cleanup_and_fail"
        },
        {
            "name": "network_timeout",
            "error_type": "TimeoutError",
            "expected_behavior": "retry_with_backoff"
        },
        {
            "name": "invalid_input",
            "error_type": "ValueError",
            "expected_behavior": "validate_and_fail"
        }
    ]

def simulate_network_errors(
    error_type: str = "timeout",
    failure_rate: float = 1.0
) -> Callable:
    """
    Create a context manager to simulate network errors.

    Args:
        error_type: Type of network error to simulate
        failure_rate: Probability of failure (0.0 to 1.0)

    Returns:
        Context manager for network error simulation
    """
    @contextlib.contextmanager
    def error_context():
        """Inner context manager for simulating network errors."""
        error_map = {
            "timeout": TimeoutError("Connection timed out"),
            "connection_refused": ConnectionRefusedError("Connection refused"),
            "dns_failure": OSError("Name or service not known"),
            "ssl_error": OSError("SSL handshake failed"),
            "network_unreachable": OSError("Network is unreachable")
        }

        error = error_map.get(error_type, OSError("Network error"))

        original_requests = __import__('requests')

        class MockRequests:
            """Mock requests class to simulate network errors."""

            @staticmethod
            def get(*args, **kwargs):
                """Simulate GET request with potential error."""
                if random.random() < failure_rate:
                    raise error
                return original_requests.get(*args, **kwargs)

            @staticmethod
            def post(*args, **kwargs):
                """Simulate POST request with potential error."""
                if random.random() < failure_rate:
                    raise error
                return original_requests.post(*args, **kwargs)

        with patch('requests', MockRequests):
            with patch('urllib.request.urlopen') as mock_urlopen:
                if random.random() < failure_rate:
                    mock_urlopen.side_effect = error
                yield

    return error_context

def create_exception_test_cases() -> List[Dict[str, Any]]:
    """
    Create exception test cases.

    Returns:
        List of exception test cases
    """
    return [
        {
            "name": "value_error",
            "exception": ValueError,
            "message": "Invalid value provided",
            "test_function": lambda: int("invalid")
        },
        {
            "name": "type_error",
            "exception": TypeError,
            "message": "Invalid type provided",
            "test_function": lambda: "string" + 123
        },
        {
            "name": "index_error",
            "exception": IndexError,
            "message": "Index out of range",
            "test_function": lambda: [1, 2, 3][10]
        },
        {
            "name": "key_error",
            "exception": KeyError,
            "message": "Key not found",
            "test_function": lambda: {"a": 1}["b"]
        },
        {
            "name": "attribute_error",
            "exception": AttributeError,
            "message": "Attribute not found",
            "test_function": lambda: "string".nonexistent_method()
        }
    ]

def simulate_memory_errors(
    error_type: str = "out_of_memory"
) -> Callable:
    """
    Create a context manager to simulate memory errors.

    Args:
        error_type: Type of memory error to simulate

    Returns:
        Context manager for memory error simulation
    """
    @contextlib.contextmanager
    def error_context():
        """Inner context manager for simulating memory errors."""
        error_map = {
            "out_of_memory": MemoryError("Out of memory"),
            "memory_leak": MemoryError("Memory allocation failed"),
            "stack_overflow": RecursionError("Maximum recursion depth exceeded")
        }

        error = error_map.get(error_type, MemoryError("Memory error"))

        original_alloc = __import__('builtins').__dict__['object'].__new__

        def mock_alloc(cls, *args, **kwargs):
            """Mock object allocation to simulate memory errors."""
            if random.random() > 0.5:  # 50% chance of failure
                raise error
            return original_alloc(cls, *args, **kwargs)

        with patch('builtins.object.__new__', side_effect=mock_alloc):
            yield

    return error_context

def create_error_recovery_test_scenarios() -> List[Dict[str, Any]]:
    """
    Create error recovery test scenarios.

    Returns:
        List of error recovery test scenarios
    """
    return [
        {
            "name": "automatic_retry",
            "error_type": "temporary_failure",
            "recovery_strategy": "retry",
            "max_retries": 3,
            "expected_result": "success"
        },
        {
            "name": "fallback_mechanism",
            "error_type": "permanent_failure",
            "recovery_strategy": "fallback",
            "expected_result": "degraded_functionality"
        },
        {
            "name": "graceful_degradation",
            "error_type": "resource_exhaustion",
            "recovery_strategy": "degrade",
            "expected_result": "reduced_performance"
        },
        {
            "name": "manual_intervention",
            "error_type": "critical_failure",
            "recovery_strategy": "alert",
            "expected_result": "admin_notification"
        }
    ]

def simulate_database_errors(
    error_type: str = "connection_failed"
) -> Callable:
    """
    Create a context manager to simulate database errors.

    Args:
        error_type: Type of database error to simulate

    Returns:
        Context manager for database error simulation
    """
    @contextlib.contextmanager
    def error_context():
        """Inner context manager for simulating database errors."""
        import sqlite3
        error_map = {
            "connection_failed": sqlite3.OperationalError("Unable to connect to database"),
            "query_failed": sqlite3.ProgrammingError("SQL syntax error"),
            "constraint_violation": sqlite3.IntegrityError("Constraint violation"),
            "timeout": sqlite3.OperationalError("Database locked"),
            "disk_full": sqlite3.OperationalError("Database or disk is full")
        }

        error = error_map.get(error_type, sqlite3.Error("Database error"))

        with patch('sqlite3.connect') as mock_connect:
            mock_conn = MagicMock()
            mock_cursor = MagicMock()

            if error_type == "connection_failed":
                mock_connect.side_effect = error
            else:
                mock_conn.cursor.return_value = mock_cursor
                if error_type == "query_failed":
                    mock_cursor.execute.side_effect = error
                elif error_type == "constraint_violation":
                    mock_cursor.execute.side_effect = error
                elif error_type == "timeout":
                    mock_conn.commit.side_effect = error
                elif error_type == "disk_full":
                    mock_cursor.execute.side_effect = error

                mock_connect.return_value = mock_conn

            yield

    return error_context

def create_error_injection_test_scenarios() -> List[Dict[str, Any]]:
    """
    Create error injection test scenarios.

    Returns:
        List of error injection test scenarios
    """
    return [
        {
            "name": "random_failures",
            "failure_rate": 0.1,
            "target_components": ["network", "database", "file_system"],
            "expected_behavior": "resilient_operation"
        },
        {
            "name": "cascading_failures",
            "failure_sequence": ["database", "cache", "api"],
            "expected_behavior": "failure_containment"
        },
        {
            "name": "intermittent_failures",
            "failure_pattern": "on_off",
            "expected_behavior": "automatic_recovery"
        }
    ]

def simulate_tool_errors(
    error_type: str = "loading_failed"
) -> Callable:
    """
    Create a context manager to simulate tool errors.

    Args:
        error_type: Type of tool error to simulate

    Returns:
        Context manager for tool error simulation
    """
    @contextlib.contextmanager
    def error_context():
        """Inner context manager for simulating tool errors."""
        error_map = {
            "loading_failed": ImportError("Cannot load tool"),
            "initialization_failed": RuntimeError("Tool initialization failed"),
            "execution_failed": ValueError("Tool execution error"),
            "compatibility_error": RuntimeError("Tool compatibility issue"),
            "security_violation": RuntimeError("Tool security violation")
        }

        error = error_map.get(error_type, RuntimeError("Tool error"))

        with patch('importlib.import_module') as mock_import:
            if error_type == "loading_failed":
                mock_import.side_effect = error
            else:
                mock_tool = MagicMock()
                if error_type == "initialization_failed":
                    mock_tool.initialize.side_effect = error
                elif error_type == "execution_failed":
                    mock_tool.execute.side_effect = error
                elif error_type == "compatibility_error":
                    mock_tool.metadata = {"version": "incompatible"}
                elif error_type == "security_violation":
                    mock_tool.execute.side_effect = error

                mock_import.return_value = mock_tool

            yield

    return error_context

def create_error_handling_test_scenarios() -> List[Dict[str, Any]]:
    """
    Create error handling test scenarios.

    Returns:
        List of error handling test scenarios
    """
    return [
        {
            "name": "exception_handling",
            "error_type": "ValueError",
            "handling_strategy": "catch_and_log",
            "expected_result": "logged_error"
        },
        {
            "name": "resource_cleanup",
            "error_type": "IOError",
            "handling_strategy": "cleanup_and_rethrow",
            "expected_result": "cleaned_up_resources"
        },
        {
            "name": "fallback_operation",
            "error_type": "TimeoutError",
            "handling_strategy": "use_fallback",
            "expected_result": "fallback_used"
        },
        {
            "name": "retry_operation",
            "error_type": "TemporaryError",
            "handling_strategy": "retry_with_backoff",
            "expected_result": "operation_retry"
        }
    ]

def simulate_concurrency_errors(
    error_type: str = "race_condition"
) -> Callable:
    """
    Create a context manager to simulate concurrency errors.

    Args:
        error_type: Type of concurrency error to simulate

    Returns:
        Context manager for concurrency error simulation
    """
    @contextlib.contextmanager
    def error_context():
        """Inner context manager for simulating concurrency errors."""
        import threading

        error_map = {
            "race_condition": RuntimeError("Race condition detected"),
            "deadlock": RuntimeError("Deadlock detected"),
            "thread_failure": RuntimeError("Thread execution failed"),
            "resource_contention": RuntimeError("Resource contention detected")
        }

        error = error_map.get(error_type, RuntimeError("Concurrency error"))

        original_thread = threading.Thread

        class MockThread(threading.Thread):
            """Mock Thread class to simulate concurrency errors."""

            def __init__(self, *args, **kwargs):
                """Initialize MockThread with error injection."""
                super().__init__(*args, **kwargs)
                self._error = error if random.random() > 0.7 else None

            def run(self):
                """Run method that may inject concurrency errors."""
                if self._error:
                    raise self._error
                super().run()

        with patch('threading.Thread', MockThread):
            yield

    return error_context

def create_error_validation_test_scenarios() -> List[Dict[str, Any]]:
    """
    Create error validation test scenarios.

    Returns:
        List of error validation test scenarios
    """
    return [
        {
            "name": "input_validation",
            "test_cases": [
                {"input": None, "expected_error": "ValueError"},
                {"input": -1, "expected_error": "ValueError"},
                {"input": "invalid", "expected_error": "TypeError"}
            ]
        },
        {
            "name": "state_validation",
            "test_cases": [
                {"state": "invalid_state", "expected_error": "RuntimeError"},
                {"state": "corrupted_data", "expected_error": "DataError"}
            ]
        },
        {
            "name": "security_validation",
            "test_cases": [
                {"permission": "denied", "expected_error": "PermissionError"},
                {"access": "unauthorized", "expected_error": "SecurityError"}
            ]
        }
    ]

def simulate_resource_exhaustion_errors(
    resource_type: str = "memory"
) -> Callable:
    """
    Create a context manager to simulate resource exhaustion errors.

    Args:
        resource_type: Type of resource to exhaust

    Returns:
        Context manager for resource exhaustion simulation
    """
    @contextlib.contextmanager
    def error_context():
        """Inner context manager for simulating resource exhaustion errors."""
        error_map = {
            "memory": MemoryError("Out of memory"),
            "cpu": RuntimeError("CPU resources exhausted"),
            "disk": OSError("No space left on device"),
            "handles": OSError("Too many open files"),
            "threads": RuntimeError("Too many threads")
        }

        error = error_map.get(resource_type, RuntimeError("Resource exhausted"))

        if resource_type == "memory":
            original_alloc = __import__('builtins').__dict__['object'].__new__

            def mock_alloc(cls, *args, **kwargs):
                """Mock object allocation for memory exhaustion simulation."""
                if random.random() > 0.3:  # 70% chance of failure
                    raise error
                return original_alloc(cls, *args, **kwargs)

            with patch('builtins.object.__new__', side_effect=mock_alloc):
                yield

        elif resource_type == "disk":
            def mock_write(*args, **kwargs):
                """Mock write operation for disk exhaustion simulation."""
                if random.random() > 0.5:  # 50% chance of failure
                    raise error
                original_write(*args, **kwargs)

            original_write = open
            with patch('builtins.open', side_effect=mock_write):
                yield

        else:
            # For other resource types, use a simpler approach
            def resource_check():
                """Check resource availability for other resource types."""
                if random.random() > 0.7:  # 30% chance of failure
                    raise error

            with patch('resource.getrlimit', side_effect=resource_check):
                yield

    return error_context

def create_error_monitoring_test_scenarios() -> List[Dict[str, Any]]:
    """
    Create error monitoring test scenarios.

    Returns:
        List of error monitoring test scenarios
    """
    return [
        {
            "name": "error_logging",
            "monitoring_type": "logging",
            "expected_behavior": "error_logged",
            "verification": "check_log_files"
        },
        {
            "name": "error_metrics",
            "monitoring_type": "metrics",
            "expected_behavior": "metrics_updated",
            "verification": "check_metrics_endpoint"
        },
        {
            "name": "error_alerting",
            "monitoring_type": "alerting",
            "expected_behavior": "alert_sent",
            "verification": "check_alert_system"
        },
        {
            "name": "error_tracing",
            "monitoring_type": "tracing",
            "expected_behavior": "trace_recorded",
            "verification": "check_tracing_system"
        }
    ]

def simulate_timeout_errors(
    timeout_type: str = "operation_timeout"
) -> Callable:
    """
    Create a context manager to simulate timeout errors.

    Args:
        timeout_type: Type of timeout error to simulate

    Returns:
        Context manager for timeout error simulation
    """
    @contextlib.contextmanager
    def error_context():
        """Inner context manager for simulating timeout errors."""
        import time

        error_map = {
            "operation_timeout": TimeoutError("Operation timed out"),
            "connection_timeout": TimeoutError("Connection timed out"),
            "read_timeout": TimeoutError("Read operation timed out"),
            "write_timeout": TimeoutError("Write operation timed out")
        }

        error = error_map.get(timeout_type, TimeoutError("Timeout error"))

        original_monotonic = time.monotonic
        original_sleep = time.sleep
        start_time = original_monotonic()

        def slow_monotonic():
            """Mock monotonic time that appears to run slowly."""
            elapsed = original_monotonic() - start_time
            if elapsed > 1.0:  # After 1 second, start causing timeouts
                raise error
            return elapsed

        def slow_sleep(seconds):
            """Mock sleep that causes timeouts for long durations."""
            if seconds > 0.1:  # Long sleeps cause timeouts
                raise error
            original_sleep(seconds)

        with patch('time.monotonic', side_effect=slow_monotonic):
            with patch('time.sleep', side_effect=slow_sleep):
                yield

    return error_context

def create_error_recovery_validation_scenarios() -> List[Dict[str, Any]]:
    """
    Create error recovery validation scenarios.

    Returns:
        List of error recovery validation scenarios
    """
    return [
        {
            "name": "data_consistency_after_error",
            "error_type": "database_error",
            "recovery_method": "transaction_rollback",
            "validation": "verify_data_integrity"
        },
        {
            "name": "resource_cleanup_after_error",
            "error_type": "file_error",
            "recovery_method": "resource_release",
            "validation": "verify_no_resource_leaks"
        },
        {
            "name": "state_consistency_after_error",
            "error_type": "state_error",
            "recovery_method": "state_reset",
            "validation": "verify_consistent_state"
        }
    ]


import time
import tempfile
from pathlib import Path
from typing import Dict, Any, List, Optional, Union, Callable
import contextlib
import os
import sys
from unittest.mock import MagicMock, patch

try:
    import resource
    RESOURCE_AVAILABLE = True
except ImportError:
    RESOURCE_AVAILABLE = False

try:
    import psutil
    PSUTIL_AVAILABLE = True
except ImportError:
    PSUTIL_AVAILABLE = False

def benchmark_function_performance(
    func: Callable,
    iterations: int = 100,
    warmup_iterations: int = 10,
    *args,
    **kwargs
) -> Dict[str, float]:
    """
    Benchmark a function's performance.

    Args:
        func: Function to benchmark
        iterations: Number of benchmark iterations
        warmup_iterations: Number of warmup iterations
        *args: Positional arguments for the function
        **kwargs: Keyword arguments for the function

    Returns:
        Dictionary of performance metrics
    """
    # Warmup
    for _ in range(warmup_iterations):
        func(*args, **kwargs)

    # Benchmark
    start_time = time.time()

    for _ in range(iterations):
        func(*args, **kwargs)

    end_time = time.time()

    total_time = end_time - start_time
    avg_time = total_time / iterations
    ops_per_sec = iterations / total_time

    return {
        "total_time": total_time,
        "average_time": avg_time,
        "operations_per_second": ops_per_sec,
        "iterations": iterations
    }

def measure_memory_usage(
    func: Callable,
    iterations: int = 10,
    *args,
    **kwargs
) -> Dict[str, float]:
    """
    Measure memory usage of a function.

    Args:
        func: Function to measure
        iterations: Number of iterations
        *args: Positional arguments for the function
        **kwargs: Keyword arguments for the function

    Returns:
        Dictionary of memory usage metrics
    """
    if not PSUTIL_AVAILABLE:
        # Fallback implementation when psutil is not available
        # Run the function but return mock memory values
        for _ in range(iterations):
            func(*args, **kwargs)

        return {
            "initial_memory": 0,
            "final_memory": 0,
            "total_memory_used": 0,
            "average_memory_per_call": 0,
            "iterations": iterations,
            "warning": "psutil not available, using fallback implementation"
        }

    # Get initial memory usage
    process = psutil.Process(os.getpid())
    initial_mem = process.memory_info().rss

    # Run function multiple times
    for _ in range(iterations):
        func(*args, **kwargs)

    # Get final memory usage
    final_mem = process.memory_info().rss

    # Calculate memory usage
    total_memory_used = final_mem - initial_mem
    avg_memory_per_call = total_memory_used / iterations

    return {
        "initial_memory": initial_mem,
        "final_memory": final_mem,
        "total_memory_used": total_memory_used,
        "average_memory_per_call": avg_memory_per_call,
        "iterations": iterations
    }

def create_performance_test_scenarios() -> List[Dict[str, Any]]:
    """
    Create performance test scenarios.

    Returns:
        List of performance test scenarios
    """
    return [
        {
            "name": "small_dataset",
            "data_size": 100,
            "expected_max_time": 0.1,
            "expected_max_memory": 1024 * 1024  # 1MB
        },
        {
            "name": "medium_dataset",
            "data_size": 10000,
            "expected_max_time": 1.0,
            "expected_max_memory": 10 * 1024 * 1024  # 10MB
        },
        {
            "name": "large_dataset",
            "data_size": 1000000,
            "expected_max_time": 10.0,
            "expected_max_memory": 100 * 1024 * 1024  # 100MB
        }
    ]

def simulate_resource_constraints(
    cpu_limit: Optional[float] = None,
    memory_limit: Optional[int] = None
) -> Callable:
    """
    Create a context manager to simulate resource constraints.

    Args:
        cpu_limit: CPU limit (percentage)
        memory_limit: Memory limit in bytes

    Returns:
        Context manager for resource constraints
    """
    @contextlib.contextmanager
    def resource_context():
        """Inner context manager for simulating resource constraints."""
        original_limits = {}

        try:
            if cpu_limit is not None:
                # Simulate CPU limit by adding artificial delay
                original_limits['cpu'] = cpu_limit

            if memory_limit is not None and RESOURCE_AVAILABLE:
                # Set memory limit using resource module
                if hasattr(resource, 'RLIMIT_AS'):
                    original_limits['memory'] = resource.getrlimit(resource.RLIMIT_AS)
                    resource.setrlimit(resource.RLIMIT_AS, (memory_limit, memory_limit))
                else:
                    # Resource module available but RLIMIT_AS not supported
                    print("Warning: RLIMIT_AS not available on this platform")
            elif memory_limit is not None and not RESOURCE_AVAILABLE:
                print("Warning: resource module not available, memory limits will not be enforced")

            yield

        finally:
            # Restore original limits
            if 'memory' in original_limits and RESOURCE_AVAILABLE and hasattr(resource, 'RLIMIT_AS'):
                resource.setrlimit(resource.RLIMIT_AS, original_limits['memory'])

    return resource_context

def create_load_test_scenarios() -> List[Dict[str, Any]]:
    """
    Create load test scenarios.

    Returns:
        List of load test scenarios
    """
    return [
        {
            "name": "low_load",
            "concurrent_users": 10,
            "request_rate": 100,
            "duration": 60,
            "expected_response_time": 0.1
        },
        {
            "name": "medium_load",
            "concurrent_users": 100,
            "request_rate": 1000,
            "duration": 300,
            "expected_response_time": 0.5
        },
        {
            "name": "high_load",
            "concurrent_users": 1000,
            "request_rate": 10000,
            "duration": 600,
            "expected_response_time": 1.0
        }
    ]

def benchmark_file_operations(
    file_size: int = 1024 * 1024,  # 1MB
    operations: List[str] = None,
    iterations: int = 100
) -> Dict[str, float]:
    """
    Benchmark file operations performance.

    Args:
        file_size: Size of test file in bytes
        operations: List of operations to benchmark
        iterations: Number of iterations

    Returns:
        Dictionary of file operation timings
    """
    if operations is None:
        operations = ["read", "write", "copy", "delete"]

    results = {}
    temp_dir = Path(tempfile.mkdtemp())

    try:
        test_file = temp_dir / "test_file.dat"
        test_file_copy = temp_dir / "test_file_copy.dat"

        # Create test file
        with open(test_file, "wb") as f:
            f.write(os.urandom(file_size))

        for op in operations:
            if op == "read":
                def read_operation():
                    """Read operation for benchmarking."""
                    with open(test_file, "rb") as f:
                        f.read()

                results[op] = benchmark_function_performance(
                    read_operation, iterations
                )["average_time"]

            elif op == "write":
                def write_operation():
                    """Write operation for benchmarking."""
                    with open(test_file, "wb") as f:
                        f.write(os.urandom(file_size))

                results[op] = benchmark_function_performance(
                    write_operation, iterations
                )["average_time"]

            elif op == "copy":
                def copy_operation():
                    """Copy operation for benchmarking."""
                    import shutil
                    shutil.copy2(test_file, test_file_copy)
                    if test_file_copy.exists():
                        test_file_copy.unlink()

                results[op] = benchmark_function_performance(
                    copy_operation, iterations
                )["average_time"]

            elif op == "delete":
                def delete_operation():
                    """Delete operation for benchmarking."""
                    test_file.unlink()
                    with open(test_file, "wb") as f:
                        f.write(os.urandom(file_size))

                results[op] = benchmark_function_performance(
                    delete_operation, iterations
                )["average_time"]

    finally:
        # Cleanup
        import shutil
        shutil.rmtree(temp_dir)

    return results

def create_performance_monitor() -> Callable:
    """
    Create a performance monitor context manager.

    Returns:
        Context manager for performance monitoring
    """
    @contextlib.contextmanager
    def monitor_context():
        """Inner context manager for performance monitoring."""
        start_time = time.time()

        if PSUTIL_AVAILABLE:
            start_mem = psutil.Process(os.getpid()).memory_info().rss

        yield

        end_time = time.time()

        if PSUTIL_AVAILABLE:
            end_mem = psutil.Process(os.getpid()).memory_info().rss
            memory_used = end_mem - start_mem
            cpu_usage = psutil.cpu_percent(interval=0.1)
        else:
            memory_used = 0
            cpu_usage = 0

        elapsed_time = end_time - start_time

        print(f"Performance Monitor Results:")
        print(f"  Execution Time: {elapsed_time:.4f} seconds")

        if PSUTIL_AVAILABLE:
            print(f"  Memory Used: {memory_used / 1024 / 1024:.2f} MB")
            print(f"  CPU Usage: {cpu_usage}%")
        else:
            print(f"  Memory Used: N/A (psutil not available)")
            print(f"  CPU Usage: N/A (psutil not available)")

    return monitor_context

def simulate_slow_operations(
    delay: float = 0.1,
    variability: float = 0.05
) -> Callable:
    """
    Create a context manager to simulate slow operations.

    Args:
        delay: Base delay in seconds
        variability: Random variability in delay

    Returns:
        Context manager for slow operation simulation
    """
    import random

    @contextlib.contextmanager
    def slow_context():
        """Inner context manager for simulating slow operations."""
        original_monotonic = time.monotonic
        start_time = original_monotonic()

        def slow_monotonic():
            """Mock monotonic time that adds artificial delay."""
            elapsed = original_monotonic() - start_time
            variability_factor = 1.0 + random.uniform(-variability, variability)
            return elapsed + (delay * variability_factor)

        # Patch time functions
        with patch('time.monotonic', side_effect=slow_monotonic):
            with patch('time.sleep', side_effect=lambda x: time.sleep(x * 10)):
                yield

    return slow_context

def create_stress_test_scenarios() -> List[Dict[str, Any]]:
    """
    Create stress test scenarios.

    Returns:
        List of stress test scenarios
    """
    return [
        {
            "name": "memory_stress",
            "type": "memory",
            "target": "high_memory_usage",
            "duration": 300,
            "expected_behavior": "graceful_degradation"
        },
        {
            "name": "cpu_stress",
            "type": "cpu",
            "target": "high_cpu_usage",
            "duration": 180,
            "expected_behavior": "resource_throttling"
        },
        {
            "name": "io_stress",
            "type": "io",
            "target": "high_disk_usage",
            "duration": 240,
            "expected_behavior": "queue_management"
        }
    ]

def benchmark_database_operations(
    db_connection: Any,
    queries: List[str],
    iterations: int = 100
) -> Dict[str, float]:
    """
    Benchmark database operations performance.

    Args:
        db_connection: Database connection
        queries: List of SQL queries to benchmark
        iterations: Number of iterations

    Returns:
        Dictionary of database operation timings
    """
    results = {}

    for i, query in enumerate(queries):
        def query_operation():
            """Execute a database query for benchmarking."""
            cursor = db_connection.cursor()
            cursor.execute(query)
            cursor.fetchall()
            cursor.close()

        timing = benchmark_function_performance(
            query_operation, iterations
        )["average_time"]

        results[f"query_{i}"] = timing

    return results

def create_network_performance_test_scenarios() -> List[Dict[str, Any]]:
    """
    Create network performance test scenarios.

    Returns:
        List of network performance test scenarios
    """
    return [
        {
            "name": "low_latency",
            "latency": 10,  # ms
            "bandwidth": 100,  # Mbps
            "packet_loss": 0.0  # 0%
        },
        {
            "name": "medium_latency",
            "latency": 100,  # ms
            "bandwidth": 10,  # Mbps
            "packet_loss": 0.1  # 1%
        },
        {
            "name": "high_latency",
            "latency": 500,  # ms
            "bandwidth": 1,  # Mbps
            "packet_loss": 0.5  # 5%
        }
    ]

def measure_concurrency_performance(
    func: Callable,
    worker_counts: List[int] = [1, 2, 4, 8, 16],
    iterations: int = 100,
    *args,
    **kwargs
) -> Dict[int, float]:
    """
    Measure performance with different levels of concurrency.

    Args:
        func: Function to test
        worker_counts: List of worker counts to test
        iterations: Number of iterations per worker count
        *args: Positional arguments for the function
        **kwargs: Keyword arguments for the function

    Returns:
        Dictionary mapping worker counts to performance metrics
    """
    import concurrent.futures

    results = {}

    for workers in worker_counts:
        def concurrent_operation():
            """Execute function with multiple workers."""
            with concurrent.futures.ThreadPoolExecutor(max_workers=workers) as executor:
                futures = [executor.submit(func, *args, **kwargs) for _ in range(iterations)]
                concurrent.futures.wait(futures)

        timing = benchmark_function_performance(
            concurrent_operation, 1
        )["total_time"]

        results[workers] = {
            "total_time": timing,
            "throughput": iterations / timing
        }

    return results

def create_performance_regression_test_scenarios() -> List[Dict[str, Any]]:
    """
    Create performance regression test scenarios.

    Returns:
        List of performance regression test scenarios
    """
    return [
        {
            "name": "baseline_performance",
            "description": "Establish baseline performance metrics",
            "metrics": {
                "max_response_time": 0.5,
                "max_memory_usage": 50 * 1024 * 1024,  # 50MB
                "min_throughput": 100  # operations per second
            }
        },
        {
            "name": "regression_detection",
            "description": "Detect performance regressions",
            "thresholds": {
                "response_time_increase": 0.2,  # 20% increase
                "memory_increase": 0.1,  # 10% increase
                "throughput_decrease": 0.15  # 15% decrease
            }
        }
    ]

def simulate_performance_degradation(
    degradation_factor: float = 0.1,
    degradation_type: str = "linear"
) -> Callable:
    """
    Create a context manager to simulate performance degradation.

    Args:
        degradation_factor: Performance degradation factor
        degradation_type: Type of degradation (linear, exponential)

    Returns:
        Context manager for performance degradation simulation
    """
    @contextlib.contextmanager
    def degradation_context():
        """Inner context manager for simulating performance degradation."""
        call_count = 0
        original_monotonic = time.monotonic
        start_time = original_monotonic()

        def degraded_monotonic():
            """Mock monotonic time with performance degradation."""
            nonlocal call_count
            call_count += 1

            elapsed = original_monotonic() - start_time

            if degradation_type == "linear":
                degradation = degradation_factor * call_count
            else:  # exponential
                degradation = degradation_factor ** call_count

            return elapsed + degradation

        def degraded_sleep(seconds):
            """Mock sleep with performance degradation."""
            original_sleep = time.sleep
            original_sleep(seconds * (1 + degradation_factor))

        with patch('time.monotonic', side_effect=degraded_monotonic):
            with patch('time.sleep', side_effect=lambda x: time.sleep(x * (1 + degradation_factor))):
                yield

    return degradation_context

def create_resource_monitoring_scenarios() -> List[Dict[str, Any]]:
    """
    Create resource monitoring test scenarios.

    Returns:
        List of resource monitoring test scenarios
    """
    return [
        {
            "name": "normal_operation",
            "expected_cpu_usage": 0.3,  # 30%
            "expected_memory_usage": 100 * 1024 * 1024,  # 100MB
            "expected_disk_io": 1024 * 1024  # 1MB/s
        },
        {
            "name": "high_load_operation",
            "expected_cpu_usage": 0.8,  # 80%
            "expected_memory_usage": 500 * 1024 * 1024,  # 500MB
            "expected_disk_io": 10 * 1024 * 1024  # 10MB/s
        },
        {
            "name": "resource_leak_detection",
            "monitoring_duration": 300,  # 5 minutes
            "leak_threshold": 0.05  # 5% increase
        }
    ]


from typing import Dict, Any, List, Optional, Union, Callable
from pathlib import Path
import re
import hashlib
import json
import tempfile
from unittest.mock import MagicMock

def validate_test_data_structure(
    data: Any,
    schema: Dict[str, Any]
) -> bool:
    """
    Validate test data structure against a schema.

    Args:
        data: Data to validate
        schema: Schema definition

    Returns:
        True if data matches schema, False otherwise
    """
    def _validate(item, schema_part):
        """Internal validation function for nested data structures."""
        if "type" in schema_part:
            expected_type = schema_part["type"]
            if expected_type == "dict" and not isinstance(item, dict):
                return False
            elif expected_type == "list" and not isinstance(item, list):
                return False
            elif expected_type == "str" and not isinstance(item, str):
                return False
            elif expected_type == "int" and not isinstance(item, int):
                return False
            elif expected_type == "float" and not isinstance(item, float):
                return False
            elif expected_type == "bool" and not isinstance(item, bool):
                return False

        if "required" in schema_part and not all(key in item for key in schema_part["required"]):
            return False

        if "properties" in schema_part:
            for key, prop_schema in schema_part["properties"].items():
                if key in item:
                    if not _validate(item[key], prop_schema):
                        return False

        if "items" in schema_part and isinstance(item, list):
            for list_item in item:
                if not _validate(list_item, schema_part["items"]):
                    return False

        if "pattern" in schema_part and isinstance(item, str):
            if not re.match(schema_part["pattern"], item):
                return False

        if "min" in schema_part and isinstance(item, (int, float)):
            if item < schema_part["min"]:
                return False

        if "max" in schema_part and isinstance(item, (int, float)):
            if item > schema_part["max"]:
                return False

        return True

    return _validate(data, schema)

def create_data_validation_test_cases() -> List[Dict[str, Any]]:
    """
    Create data validation test cases.

    Returns:
        List of data validation test cases
    """
    return [
        {
            "name": "valid_config_data",
            "data": {
                "database": {
                    "host": "localhost",
                    "port": 5432,
                    "username": "admin",
                    "PASSWORD_REMOVED": "SECRET_REMOVED"
                },
                "logging": {
                    "level": "INFO",
                    "file": "/var/log/app.log"
                }
            },
            "schema": {
                "type": "dict",
                "properties": {
                    "database": {
                        "type": "dict",
                        "required": ["host", "port", "username", "PASSWORD_REMOVED"],
                        "properties": {
                            "host": {"type": "str"},
                            "port": {"type": "int", "min": 1, "max": 65535},
                            "username": {"type": "str"},
                            "PASSWORD_REMOVED": {"type": "str"}
                        }
                    },
                    "logging": {
                        "type": "dict",
                        "required": ["level", "file"],
                        "properties": {
                            "level": {"type": "str", "pattern": "^(DEBUG|INFO|WARNING|ERROR|CRITICAL)$"},
                            "file": {"type": "str"}
                        }
                    }
                }
            },
            "expected_result": True
        },
        {
            "name": "invalid_config_data",
            "data": {
                "database": {
                    "host": "localhost",
                    "port": 70000,  # Invalid port
                    "username": "admin"
                    # Missing PASSWORD_REMOVED
                }
            },
            "schema": {
                "type": "dict",
                "properties": {
                    "database": {
                        "type": "dict",
                        "required": ["host", "port", "username", "PASSWORD_REMOVED"],
                        "properties": {
                            "host": {"type": "str"},
                            "port": {"type": "int", "min": 1, "max": 65535},
                            "username": {"type": "str"},
                            "PASSWORD_REMOVED": {"type": "str"}
                        }
                    }
                }
            },
            "expected_result": False
        }
    ]

def validate_file_integrity(
    file_path: Path,
    expected_hash: str,
    algorithm: str = "sha256"
) -> bool:
    """
    Validate file integrity using hash comparison.

    Args:
        file_path: Path to file
        expected_hash: Expected hash value
        algorithm: Hash algorithm to use

    Returns:
        True if file integrity is valid, False otherwise
    """
    hash_func = hashlib.new(algorithm)

    with open(file_path, "rb") as f:
        for chunk in iter(lambda: f.read(4096), b""):
            hash_func.update(chunk)

    actual_hash = hash_func.hexdigest()
    return actual_hash == expected_hash

def create_file_validation_test_scenarios() -> List[Dict[str, Any]]:
    """
    Create file validation test scenarios.

    Returns:
        List of file validation test scenarios
    """
    return [
        {
            "name": "valid_file_integrity",
            "file_content": "test content for integrity check",
            "expected_hash": "a1b2c3d4e5f6",  # Placeholder - would be actual hash in real test
            "expected_result": True
        },
        {
            "name": "corrupted_file",
            "file_content": "corrupted content",
            "expected_hash": "a1b2c3d4e5f6",  # Different from actual hash
            "expected_result": False
        },
        {
            "name": "missing_file",
            "file_content": None,
            "expected_hash": "a1b2c3d4e5f6",
            "expected_result": False
        }
    ]

def validate_json_schema(
    json_data: Union[str, Dict],
    schema: Dict[str, Any]
) -> bool:
    """
    Validate JSON data against a schema.

    Args:
        json_data: JSON data to validate
        schema: JSON schema definition

    Returns:
        True if JSON is valid, False otherwise
    """
    if isinstance(json_data, str):
        try:
            data = json.loads(json_data)
        except json.JSONDecodeError:
            return False
    else:
        data = json_data

    return validate_test_data_structure(data, schema)

def create_json_validation_test_cases() -> List[Dict[str, Any]]:
    """
    Create JSON validation test cases.

    Returns:
        List of JSON validation test cases
    """
    return [
        {
            "name": "valid_json_api_response",
            "json_data": """
            {
                "status": "success",
                "data": {
                    "users": [
                        {"id": 1, "name": "Alice"},
                        {"id": 2, "name": "Bob"}
                    ]
                },
                "timestamp": "2023-01-01T00:00:00Z"
            }
            """,
            "schema": {
                "type": "dict",
                "required": ["status", "data", "timestamp"],
                "properties": {
                    "status": {"type": "str", "pattern": "^(success|error|warning)$"},
                    "data": {
                        "type": "dict",
                        "required": ["users"],
                        "properties": {
                            "users": {
                                "type": "list",
                                "items": {
                                    "type": "dict",
                                    "required": ["id", "name"],
                                    "properties": {
                                        "id": {"type": "int", "min": 1},
                                        "name": {"type": "str"}
                                    }
                                }
                            }
                        }
                    },
                    "timestamp": {"type": "str", "pattern": "^\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}Z$"}
                }
            },
            "expected_result": True
        },
        {
            "name": "invalid_json_api_response",
            "json_data": """
            {
                "status": "invalid_status",
                "data": {
                    "users": [
                        {"id": "not_an_int", "name": "Alice"}
                    ]
                }
            }
            """,
            "schema": {
                "type": "dict",
                "required": ["status", "data", "timestamp"],
                "properties": {
                    "status": {"type": "str", "pattern": "^(success|error|warning)$"},
                    "data": {
                        "type": "dict",
                        "required": ["users"],
                        "properties": {
                            "users": {
                                "type": "list",
                                "items": {
                                    "type": "dict",
                                    "required": ["id", "name"],
                                    "properties": {
                                        "id": {"type": "int", "min": 1},
                                        "name": {"type": "str"}
                                    }
                                }
                            }
                        }
                    },
                    "timestamp": {"type": "str", "pattern": "^\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}Z$"}
                }
            },
            "expected_result": False
        }
    ]

def validate_database_schema(
    database_schema: Dict[str, Any],
    expected_schema: Dict[str, Any]
) -> bool:
    """
    Validate database schema structure.

    Args:
        database_schema: Actual database schema
        expected_schema: Expected database schema

    Returns:
        True if schemas match, False otherwise
    """
    # Compare tables
    if set(database_schema.keys()) != set(expected_schema.keys()):
        return False

    # Compare table structures
    for table_name, table_def in expected_schema.items():
        if table_name not in database_schema:
            return False

        actual_table = database_schema[table_name]

        # Compare columns
        if set(table_def["columns"].keys()) != set(actual_table["columns"].keys()):
            return False

        # Compare column definitions
        for col_name, col_def in table_def["columns"].items():
            if col_name not in actual_table["columns"]:
                return False

            actual_col = actual_table["columns"][col_name]

            if col_def["type"] != actual_col["type"]:
                return False

            if "constraints" in col_def and col_def["constraints"] != actual_col.get("constraints", []):
                return False

    return True

def create_database_validation_test_scenarios() -> List[Dict[str, Any]]:
    """
    Create database validation test scenarios.

    Returns:
        List of database validation test scenarios
    """
    return [
        {
            "name": "valid_database_schema",
            "database_schema": {
                "users": {
                    "columns": {
                        "id": {"type": "INTEGER", "constraints": ["PRIMARY KEY"]},
                        "name": {"type": "TEXT", "constraints": ["NOT NULL"]},
                        "email": {"type": "TEXT", "constraints": ["UNIQUE"]}
                    }
                },
                "posts": {
                    "columns": {
                        "id": {"type": "INTEGER", "constraints": ["PRIMARY KEY"]},
                        "user_id": {"type": "INTEGER", "constraints": ["FOREIGN KEY"]},
                        "title": {"type": "TEXT"},
                        "content": {"type": "TEXT"}
                    }
                }
            },
            "expected_schema": {
                "users": {
                    "columns": {
                        "id": {"type": "INTEGER", "constraints": ["PRIMARY KEY"]},
                        "name": {"type": "TEXT", "constraints": ["NOT NULL"]},
                        "email": {"type": "TEXT", "constraints": ["UNIQUE"]}
                    }
                },
                "posts": {
                    "columns": {
                        "id": {"type": "INTEGER", "constraints": ["PRIMARY KEY"]},
                        "user_id": {"type": "INTEGER", "constraints": ["FOREIGN KEY"]},
                        "title": {"type": "TEXT"},
                        "content": {"type": "TEXT"}
                    }
                }
            },
            "expected_result": True
        },
        {
            "name": "invalid_database_schema",
            "database_schema": {
                "users": {
                    "columns": {
                        "id": {"type": "INTEGER", "constraints": ["PRIMARY KEY"]},
                        "name": {"type": "TEXT"}
                        # Missing email column
                    }
                }
            },
            "expected_schema": {
                "users": {
                    "columns": {
                        "id": {"type": "INTEGER", "constraints": ["PRIMARY KEY"]},
                        "name": {"type": "TEXT", "constraints": ["NOT NULL"]},
                        "email": {"type": "TEXT", "constraints": ["UNIQUE"]}
                    }
                }
            },
            "expected_result": False
        }
    ]

def validate_tool_structure(
    tool_definition: Dict[str, Any],
    expected_structure: Dict[str, Any]
) -> bool:
    """
    Validate tool structure and metadata.

    Args:
        tool_definition: Tool definition to validate
        expected_structure: Expected tool structure

    Returns:
        True if tool structure is valid, False otherwise
    """
    # Check required fields
    required_fields = expected_structure.get("required_fields", [])
    if not all(field in tool_definition for field in required_fields):
        return False

    # Check metadata structure
    if "metadata" in expected_structure:
        metadata_schema = expected_structure["metadata"]
        if not validate_test_data_structure(tool_definition.get("metadata", {}), metadata_schema):
            return False

    # Check function signatures
    if "functions" in expected_structure:
        for func_name, func_schema in expected_structure["functions"].items():
            if func_name not in tool_definition.get("functions", {}):
                return False

            # Check function parameters
            actual_func = tool_definition["functions"][func_name]
            expected_params = func_schema.get("parameters", [])

            # Simple parameter count check
            if "parameters" in func_schema:
                try:
                    import inspect
                    sig = inspect.signature(actual_func)
                    if len(sig.parameters) != len(expected_params):
                        return False
                except:
                    pass

    return True

def create_tool_validation_test_cases() -> List[Dict[str, Any]]:
    """
    Create tool validation test cases.

    Returns:
        List of tool validation test cases
    """
    return [
        {
            "name": "valid_tool_structure",
            "tool_definition": {
                "name": "test_tool",
                "version": "1.0.0",
                "author": "Test Author",
                "description": "Test tool",
                "metadata": {
                    "category": "utility",
                    "compatibility": ["1.0", "2.0"]
                },
                "functions": {
                    "initialize": lambda: True,
                    "execute": lambda x: x * 2,
                    "cleanup": lambda: None
                }
            },
            "expected_structure": {
                "required_fields": ["name", "version", "author", "description"],
                "metadata": {
                    "type": "dict",
                    "required": ["category", "compatibility"],
                    "properties": {
                        "category": {"type": "str"},
                        "compatibility": {"type": "list", "items": {"type": "str"}}
                    }
                },
                "functions": {
                    "initialize": {"parameters": []},
                    "execute": {"parameters": ["x"]},
                    "cleanup": {"parameters": []}
                }
            },
            "expected_result": True
        },
        {
            "name": "invalid_tool_structure",
            "tool_definition": {
                "name": "test_tool",
                # Missing required fields
                "functions": {
                    "initialize": lambda: True
                    # Missing required functions
                }
            },
            "expected_structure": {
                "required_fields": ["name", "version", "author", "description"],
                "functions": {
                    "initialize": {"parameters": []},
                    "execute": {"parameters": ["x"]},
                    "cleanup": {"parameters": []}
                }
            },
            "expected_result": False
        }
    ]

def validate_api_response(
    response: Dict[str, Any],
    expected_schema: Dict[str, Any]
) -> bool:
    """
    Validate API response structure.

    Args:
        response: API response to validate
        expected_schema: Expected response schema

    Returns:
        True if response is valid, False otherwise
    """
    return validate_test_data_structure(response, expected_schema)

def create_api_validation_test_scenarios() -> List[Dict[str, Any]]:
    """
    Create API validation test scenarios.

    Returns:
        List of API validation test scenarios
    """
    return [
        {
            "name": "valid_api_response",
            "response": {
                "status": "success",
                "code": 200,
                "data": {
                    "items": [
                        {"id": 1, "name": "Item 1"},
                        {"id": 2, "name": "Item 2"}
                    ],
                    "pagination": {
                        "page": 1,
                        "page_size": 10,
                        "total": 2
                    }
                },
                "timestamp": "2023-01-01T00:00:00Z"
            },
            "expected_schema": {
                "type": "dict",
                "required": ["status", "code", "data", "timestamp"],
                "properties": {
                    "status": {"type": "str", "pattern": "^(success|error|warning)$"},
                    "code": {"type": "int", "min": 200, "max": 599},
                    "data": {
                        "type": "dict",
                        "required": ["items", "pagination"],
                        "properties": {
                            "items": {
                                "type": "list",
                                "items": {
                                    "type": "dict",
                                    "required": ["id", "name"],
                                    "properties": {
                                        "id": {"type": "int", "min": 1},
                                        "name": {"type": "str"}
                                    }
                                }
                            },
                            "pagination": {
                                "type": "dict",
                                "required": ["page", "page_size", "total"],
                                "properties": {
                                    "page": {"type": "int", "min": 1},
                                    "page_size": {"type": "int", "min": 1, "max": 100},
                                    "total": {"type": "int", "min": 0}
                                }
                            }
                        }
                    },
                    "timestamp": {"type": "str", "pattern": "^\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}Z$"}
                }
            },
            "expected_result": True
        },
        {
            "name": "invalid_api_response",
            "response": {
                "status": "invalid_status",
                "code": 999,  # Invalid code
                "data": {
                    "items": [
                        {"id": "not_an_int", "name": "Item 1"}  # Invalid ID type
                    ]
                }
            },
            "expected_schema": {
                "type": "dict",
                "required": ["status", "code", "data", "timestamp"],
                "properties": {
                    "status": {"type": "str", "pattern": "^(success|error|warning)$"},
                    "code": {"type": "int", "min": 200, "max": 599},
                    "data": {
                        "type": "dict",
                        "required": ["items", "pagination"],
                        "properties": {
                            "items": {
                                "type": "list",
                                "items": {
                                    "type": "dict",
                                    "required": ["id", "name"],
                                    "properties": {
                                        "id": {"type": "int", "min": 1},
                                        "name": {"type": "str"}
                                    }
                                }
                            }
                        }
                    }
                }
            },
            "expected_result": False
        }
    ]

def validate_configuration_files(
    config_files: List[Path],
    expected_structure: Dict[str, Any]
) -> Dict[str, bool]:
    """
    Validate multiple configuration files.

    Args:
        config_files: List of configuration file paths
        expected_structure: Expected configuration structure

    Returns:
        Dictionary mapping file paths to validation results
    """
    results = {}

    for config_file in config_files:
        try:
            with open(config_file, "r") as f:
                config_data = json.load(f)

            results[str(config_file)] = validate_test_data_structure(config_data, expected_structure)
        except (json.JSONDecodeError, IOError):
            results[str(config_file)] = False

    return results

def create_configuration_validation_test_cases() -> List[Dict[str, Any]]:
    """
    Create configuration validation test cases.

    Returns:
        List of configuration validation test cases
    """
    return [
        {
            "name": "valid_configuration_files",
            "config_files": [
                {
                    "content": """
                    {
                        "database": {
                            "host": "localhost",
                            "port": 5432,
                            "username": "admin",
                            "PASSWORD_REMOVED": "SECRET_REMOVED"
                        },
                        "logging": {
                            "level": "INFO",
                            "file": "/var/log/app.log"
                        }
                    }
                    """,
                    "expected_result": True
                },
                {
                    "content": """
                    {
                        "database": {
                            "host": "localhost",
                            "port": 5432,
                            "username": "admin",
                            "PASSWORD_REMOVED": "SECRET_REMOVED"
                        }
                    }
                    """,
                    "expected_result": True
                }
            ],
            "expected_structure": {
                "type": "dict",
                "properties": {
                    "database": {
                        "type": "dict",
                        "required": ["host", "port", "username", "PASSWORD_REMOVED"],
                        "properties": {
                            "host": {"type": "str"},
                            "port": {"type": "int", "min": 1, "max": 65535},
                            "username": {"type": "str"},
                            "PASSWORD_REMOVED": {"type": "str"}
                        }
                    },
                    "logging": {
                        "type": "dict",
                        "properties": {
                            "level": {"type": "str", "pattern": "^(DEBUG|INFO|WARNING|ERROR|CRITICAL)$"},
                            "file": {"type": "str"}
                        }
                    }
                }
            }
        },
        {
            "name": "invalid_configuration_files",
            "config_files": [
                {
                    "content": """
                    {
                        "database": {
                            "host": "localhost",
                            "port": 70000,
                            "username": "admin"
                        }
                    }
                    """,
                    "expected_result": False
                },
                {
                    "content": "invalid json content",
                    "expected_result": False
                }
            ],
            "expected_structure": {
                "type": "dict",
                "properties": {
                    "database": {
                        "type": "dict",
                        "required": ["host", "port", "username", "PASSWORD_REMOVED"],
                        "properties": {
                            "host": {"type": "str"},
                            "port": {"type": "int", "min": 1, "max": 65535},
                            "username": {"type": "str"},
                            "PASSWORD_REMOVED": {"type": "str"}
                        }
                    }
                }
            }
        }
    ]

def validate_data_consistency(
    data_source: Any,
    validation_rules: List[Dict[str, Any]]
) -> bool:
    """
    Validate data consistency against validation rules.

    Args:
        data_source: Data to validate
        validation_rules: List of validation rules

    Returns:
        True if data is consistent, False otherwise
    """
    def get_nested_value(data, field_path):
        """Get value from nested data structure using dot notation or direct field name"""
        if not isinstance(data, dict):
            return None

        # Try direct field access first
        if field_path in data:
            return data[field_path]

        # Try nested access using dot notation
        if '.' in field_path:
            parts = field_path.split('.')
            current = data
            for part in parts:
                if isinstance(current, dict) and part in current:
                    current = current[part]
                else:
                    return None
            return current

        return None

    for rule in validation_rules:
        field = rule["field"]
        validation_type = rule["type"]

        # Get field value using nested access
        value = get_nested_value(data_source, field)

        if value is None and rule.get("required", False):
            return False

        # Apply validation
        if validation_type == "range":
            min_val = rule.get("min")
            max_val = rule.get("max")
            if not (min_val <= value <= max_val):
                return False

        elif validation_type == "pattern":
            pattern = rule["pattern"]
            if not re.match(pattern, str(value)):
                return False

        elif validation_type == "enum":
            allowed_values = rule["values"]
            if value not in allowed_values:
                return False

        elif validation_type == "custom":
            validator = rule["validator"]
            if not validator(value):
                return False

    return True

def create_data_consistency_test_scenarios() -> List[Dict[str, Any]]:
    """
    Create data consistency test scenarios.

    Returns:
        List of data consistency test scenarios
    """
    return [
        {
            "name": "valid_data_consistency",
            "data": {
                "user": {
                    "id": 123,
                    "username": "test_user",
                    "email": "test@example.com",
                    "status": "active",
                    "age": 25
                }
            },
            "validation_rules": [
                {"field": "id", "type": "range", "min": 1, "max": 1000, "required": True},
                {"field": "username", "type": "pattern", "pattern": "^[a-z_]+$", "required": True},
                {"field": "email", "type": "pattern", "pattern": "^[^@]+@[^@]+\\.[^@]+$", "required": True},
                {"field": "status", "type": "enum", "values": ["active", "inactive", "suspended"], "required": True},
                {"field": "age", "type": "range", "min": 18, "max": 120}
            ],
            "expected_result": True
        },
        {
            "name": "invalid_data_consistency",
            "data": {
                "user": {
                    "id": 0,  # Invalid ID
                    "username": "Invalid User",  # Invalid username
                    "email": "not-an-email",  # Invalid email
                    "status": "unknown",  # Invalid status
                    "age": 15  # Invalid age
                }
            },
            "validation_rules": [
                {"field": "id", "type": "range", "min": 1, "max": 1000, "required": True},
                {"field": "username", "type": "pattern", "pattern": "^[a-z_]+$", "required": True},
                {"field": "email", "type": "pattern", "pattern": "^[^@]+@[^@]+\\.[^@]+$", "required": True},
                {"field": "status", "type": "enum", "values": ["active", "inactive", "suspended"], "required": True},
                {"field": "age", "type": "range", "min": 18, "max": 120}
            ],
            "expected_result": False
        }
    ]


import sqlite3
import tempfile
from pathlib import Path
from typing import Dict, Any, List, Optional, Union, Callable
from unittest.mock import MagicMock, patch
import contextlib
import time

def create_test_database(
    schema: Optional[str] = None,
    data: Optional[List[Dict[str, Any]]] = None,
    db_name: str = "test_db",
    use_memory: bool = True
) -> Union[str, Path]:
    """
    Create a test database with optional schema and data.

    Args:
        schema: SQL schema definition
        data: List of data dictionaries to insert
        db_name: Database name
        use_memory: Use in-memory database if True

    Returns:
        Database path or connection string
    """
    if use_memory:
        conn = sqlite3.connect(":memory:")
        return conn
    else:
        db_path = Path(tempfile.gettempdir()) / f"{db_name}.db"
        conn = sqlite3.connect(str(db_path))
        conn.close()
        return str(db_path)

def setup_test_database_schema(
    conn: sqlite3.Connection,
    schema: str
) -> None:
    """
    Set up database schema for testing.

    Args:
        conn: Database connection
        schema: SQL schema definition
    """
    cursor = conn.cursor()
    cursor.executescript(schema)
    conn.commit()

def insert_test_data(
    conn: sqlite3.Connection,
    table: str,
    data: List[Dict[str, Any]]
) -> None:
    """
    Insert test data into a database table.

    Args:
        conn: Database connection
        table: Table name
        data: List of data dictionaries
    """
    if not data:
        return

    cursor = conn.cursor()

    # Get column names from first data item
    columns = list(data[0].keys())
    placeholders = ", ".join(["?"] * len(columns))
    columns_str = ", ".join(columns)

    # Prepare and execute insert statements
    for item in data:
        values = [item[col] for col in columns]
        cursor.execute(
            f"INSERT INTO {table} ({columns_str}) VALUES ({placeholders})",
            values
        )

    conn.commit()

def verify_database_state(
    conn: sqlite3.Connection,
    expected_state: Dict[str, Any],
    tolerance: float = 0.0
) -> bool:
    """
    Verify database state matches expected state.

    Args:
        conn: Database connection
        expected_state: Expected database state
        tolerance: Numeric tolerance for floating point comparisons

    Returns:
        True if state matches, False otherwise
    """
    cursor = conn.cursor()

    for table, expected_data in expected_state.items():
        # Query all data from table
        cursor.execute(f"SELECT * FROM {table}")
        actual_data = cursor.fetchall()

        # Get column names
        cursor.execute(f"PRAGMA table_info({table})")
        columns = [col[1] for col in cursor.fetchall()]

        # Convert to list of dictionaries for comparison
        actual_records = []
        for row in actual_data:
            record = dict(zip(columns, row))
            actual_records.append(record)

        # Compare with expected data
        if len(actual_records) != len(expected_data):
            return False

        for actual, expected in zip(actual_records, expected_data):
            for key, expected_value in expected.items():
                actual_value = actual[key]

                if isinstance(expected_value, (int, str, bool)):
                    if actual_value != expected_value:
                        return False
                elif isinstance(expected_value, float):
                    if abs(actual_value - expected_value) > tolerance:
                        return False
                else:
                    if actual_value != expected_value:
                        return False

    return True

def create_database_mock() -> MagicMock:
    """
    Create a mock database connection for testing.

    Returns:
        Mock database connection object
    """
    mock_conn = MagicMock(spec=sqlite3.Connection)
    mock_cursor = MagicMock()

    # Set up mock behavior
    mock_conn.cursor.return_value = mock_cursor
    mock_cursor.fetchone.return_value = (1,)
    mock_cursor.fetchall.return_value = [(1, "test"), (2, "data")]
    mock_cursor.description = [("id",), ("name",)]

    return mock_conn

def create_database_fixture(
    schema: str,
    initial_data: Optional[List[Dict[str, Any]]] = None
) -> Callable:
    """
    Create a pytest fixture for database testing.

    Args:
        schema: Database schema
        initial_data: Initial data to populate

    Returns:
        Fixture function
    """
    def database_fixture():
        """Inner fixture function for database testing."""
        # Create in-memory database
        conn = sqlite3.connect(":memory:")

        # Set up schema
        setup_test_database_schema(conn, schema)

        # Insert initial data if provided
        if initial_data:
            for table_data in initial_data:
                table_name = list(table_data.keys())[0]
                insert_test_data(conn, table_name, table_data[table_name])

        yield conn

        # Cleanup
        conn.close()

    return database_fixture

def simulate_database_errors(
    error_type: str = "connection",
    operation: str = "execute"
) -> Callable:
    """
    Create a context manager to simulate database errors.

    Args:
        error_type: Type of error to simulate
        operation: Database operation to fail

    Returns:
        Context manager for error simulation
    """
    @contextlib.contextmanager
    def error_context():
        """Inner context manager for simulating database errors."""
        error_map = {
            "connection": sqlite3.OperationalError("Unable to connect"),
            "integrity": sqlite3.IntegrityError("Constraint violation"),
            "programming": sqlite3.ProgrammingError("SQL syntax error"),
            "timeout": sqlite3.OperationalError("Database locked")
        }

        error = error_map.get(error_type, sqlite3.Error("Database error"))

        with patch('sqlite3.Connection') as mock_conn_class:
            mock_conn = MagicMock()
            mock_cursor = MagicMock()

            if operation == "execute":
                mock_cursor.execute.side_effect = error
            elif operation == "commit":
                mock_conn.commit.side_effect = error
            elif operation == "fetch":
                mock_cursor.fetchall.side_effect = error

            mock_conn.cursor.return_value = mock_cursor
            mock_conn_class.return_value = mock_conn

            yield mock_conn

    return error_context

def benchmark_database_operations(
    conn: sqlite3.Connection,
    operations: List[Callable],
    iterations: int = 100
) -> Dict[str, float]:
    """
    Benchmark database operations performance.

    Args:
        conn: Database connection
        operations: List of operation functions
        iterations: Number of iterations per operation

    Returns:
        Dictionary of operation timings
    """
    results = {}

    for i, operation in enumerate(operations):
        start_time = time.time()

        for _ in range(iterations):
            operation(conn)

        end_time = time.time()
        avg_time = (end_time - start_time) / iterations

        results[f"operation_{i}"] = avg_time

    return results

def create_transaction_test_scenarios() -> List[Dict[str, Any]]:
    """
    Create test scenarios for transaction testing.

    Returns:
        List of transaction test scenarios
    """
    return [
        {
            "name": "successful_transaction",
            "operations": [
                "BEGIN",
                "INSERT INTO test VALUES (1, 'data')",
                "COMMIT"
            ],
            "expected_result": "success"
        },
        {
            "name": "failed_transaction",
            "operations": [
                "BEGIN",
                "INSERT INTO test VALUES (1, 'data')",
                "ROLLBACK"
            ],
            "expected_result": "rollback"
        },
        {
            "name": "nested_transaction",
            "operations": [
                "BEGIN",
                "SAVEPOINT sp1",
                "INSERT INTO test VALUES (1, 'data')",
                "RELEASE SAVEPOINT sp1",
                "COMMIT"
            ],
            "expected_result": "success"
        }
    ]

def verify_database_performance(
    conn: sqlite3.Connection,
    query: str,
    max_execution_time: float = 1.0,
    iterations: int = 10
) -> bool:
    """
    Verify database query performance meets requirements.

    Args:
        conn: Database connection
        query: SQL query to test
        max_execution_time: Maximum allowed execution time
        iterations: Number of test iterations

    Returns:
        True if performance is acceptable, False otherwise
    """
    cursor = conn.cursor()
    total_time = 0.0

    for _ in range(iterations):
        start_time = time.time()
        cursor.execute(query)
        cursor.fetchall()
        end_time = time.time()

        total_time += (end_time - start_time)

    avg_time = total_time / iterations
    return avg_time <= max_execution_time

def create_database_snapshot(
    conn: sqlite3.Connection
) -> Dict[str, List[Dict[str, Any]]]:
    """
    Create a snapshot of current database state.

    Args:
        conn: Database connection

    Returns:
        Dictionary representing database state
    """
    cursor = conn.cursor()
    snapshot = {}

    # Get all tables
    cursor.execute("SELECT name FROM sqlite_master WHERE type='table'")
    tables = [table[0] for table in cursor.fetchall()]

    for table in tables:
        # Get table data
        cursor.execute(f"SELECT * FROM {table}")
        rows = cursor.fetchall()

        # Get column names
        cursor.execute(f"PRAGMA table_info({table})")
        columns = [col[1] for col in cursor.fetchall()]

        # Convert to list of dictionaries
        table_data = []
        for row in rows:
            record = dict(zip(columns, row))
            table_data.append(record)

        snapshot[table] = table_data

    return snapshot

def restore_database_snapshot(
    conn: sqlite3.Connection,
    snapshot: Dict[str, List[Dict[str, Any]]]
) -> None:
    """
    Restore database to a previous snapshot state.

    Args:
        conn: Database connection
        snapshot: Database snapshot to restore
    """
    cursor = conn.cursor()

    for table, data in snapshot.items():
        # Clear existing data
        cursor.execute(f"DELETE FROM {table}")

        # Re-insert data
        if data:
            columns = list(data[0].keys())
            placeholders = ", ".join(["?"] * len(columns))
            columns_str = ", ".join(columns)

            for item in data:
                values = [item[col] for col in columns]
                cursor.execute(
                    f"INSERT INTO {table} ({columns_str}) VALUES ({placeholders})",
                    values
                )

    conn.commit()


import os
import stat
import hashlib
from pathlib import Path
from typing import Dict, Any, List, Optional, Union
import tempfile
import shutil
from unittest.mock import MagicMock

def create_test_file_structure(
    base_path: Path,
    structure: Dict[str, Union[Dict, str, bytes]],
    file_permissions: int = 0o644,
    dir_permissions: int = 0o755
) -> Dict[str, Path]:
    """
    Create a complex file structure for testing.

    Args:
        base_path: Base directory path
        structure: Dictionary describing the file structure
        file_permissions: Permissions for created files
        dir_permissions: Permissions for created directories

    Returns:
        Dictionary mapping file names to their paths
    """
    created_files = {}

    for name, content in structure.items():
        full_path = base_path / name

        if isinstance(content, dict):
            # It's a directory - create it and recurse
            full_path.mkdir(exist_ok=True, mode=dir_permissions)
            created_files.update(create_test_file_structure(
                full_path, content, file_permissions, dir_permissions
            ))
        else:
            # It's a file - create it with content
            if isinstance(content, str):
                full_path.write_text(content)
            else:
                full_path.write_bytes(content)

            # Set file permissions
            full_path.chmod(file_permissions)
            created_files[name] = full_path

    return created_files

def create_duplicate_files(
    base_path: Path,
    original_content: str,
    num_duplicates: int = 3,
    file_size: int = 1024
) -> List[Path]:
    """
    Create multiple duplicate files for testing.

    Args:
        base_path: Directory to create files in
        original_content: Content for original file
        num_duplicates: Number of duplicate files to create
        file_size: Target file size

    Returns:
        List of created file paths
    """
    if not base_path.exists():
        base_path.mkdir(parents=True)

    files = []

    # Create original file
    original_file = base_path / "original.txt"
    if len(original_content) < file_size:
        # Pad content to reach desired size
        multiplier = (file_size // len(original_content)) + 1
        padded_content = original_content * multiplier
        original_file.write_text(padded_content[:file_size])
    else:
        original_file.write_text(original_content[:file_size])

    files.append(original_file)

    # Create duplicates
    for i in range(1, num_duplicates + 1):
        duplicate_file = base_path / f"duplicate_{i}.txt"
        shutil.copy2(original_file, duplicate_file)
        files.append(duplicate_file)

    return files

def create_files_with_varying_sizes(
    base_path: Path,
    sizes: List[int],
    content_pattern: str = "Test content "
) -> List[Path]:
    """
    Create files with varying sizes for testing.

    Args:
        base_path: Directory to create files in
        sizes: List of target file sizes in bytes
        content_pattern: Pattern to use for file content

    Returns:
        List of created file paths
    """
    if not base_path.exists():
        base_path.mkdir(parents=True)

    files = []

    for i, size in enumerate(sizes):
        file_path = base_path / f"file_{size}.txt"

        # Create content that matches the desired size
        content = (content_pattern * ((size // len(content_pattern)) + 1))[:size]
        file_path.write_text(content)

        files.append(file_path)

    return files

def create_symlinks_and_hardlinks(
    base_path: Path,
    target_file: Path
) -> Dict[str, List[Path]]:
    """
    Create symlinks and hardlinks for testing.

    Args:
        base_path: Directory to create links in
        target_file: Target file for links

    Returns:
        Dictionary with 'symlinks' and 'hardlinks' lists
    """
    if not base_path.exists():
        base_path.mkdir(parents=True)

    result = {
        'symlinks': [],
        'hardlinks': []
    }

    # Create symlinks
    for i in range(3):
        symlink = base_path / f"symlink_{i}.txt"
        try:
            symlink.symlink_to(target_file)
            result['symlinks'].append(symlink)
        except OSError:
            # Symlinks not supported on this system
            break

    # Create hardlinks
    for i in range(3):
        hardlink = base_path / f"hardlink_{i}.txt"
        try:
            hardlink.link_to(target_file)
            result['hardlinks'].append(hardlink)
        except OSError:
            # Hardlinks not supported for this file type
            break

    return result

def calculate_file_hash(file_path: Path, algorithm: str = "sha256") -> str:
    """
    Calculate hash of a file using specified algorithm.

    Args:
        file_path: Path to file
        algorithm: Hash algorithm to use

    Returns:
        Hexadecimal hash string
    """
    hash_func = hashlib.new(algorithm)

    with open(file_path, "rb") as f:
        # Read file in chunks to handle large files
        for chunk in iter(lambda: f.read(4096), b""):
            hash_func.update(chunk)

    return hash_func.hexdigest()

def compare_files(file1: Path, file2: Path) -> bool:
    """
    Compare two files for identical content.

    Args:
        file1: First file path
        file2: Second file path

    Returns:
        True if files have identical content, False otherwise
    """
    if file1.stat().st_size != file2.stat().st_size:
        return False

    with open(file1, "rb") as f1, open(file2, "rb") as f2:
        while True:
            chunk1 = f1.read(4096)
            chunk2 = f2.read(4096)

            if chunk1 != chunk2:
                return False

            if not chunk1:  # End of file
                break

    return True

def create_files_with_different_permissions(
    base_path: Path,
    permissions: List[int]
) -> List[Path]:
    """
    Create files with different permissions for testing.

    Args:
        base_path: Directory to create files in
        permissions: List of permission modes (e.g., 0o644, 0o755)

    Returns:
        List of created file paths
    """
    if not base_path.exists():
        base_path.mkdir(parents=True)

    files = []

    for i, perm in enumerate(permissions):
        file_path = base_path / f"perm_{oct(perm)}.txt"
        file_path.write_text(f"File with permissions {oct(perm)}")
        file_path.chmod(perm)
        files.append(file_path)

    return files

def create_nested_directory_structure(
    base_path: Path,
    depth: int = 3,
    files_per_dir: int = 2
) -> Dict[str, List[Path]]:
    """
    Create a nested directory structure for testing.

    Args:
        base_path: Base directory path
        depth: Depth of nesting
        files_per_dir: Number of files per directory

    Returns:
        Dictionary mapping directory paths to their file lists
    """
    structure = {}

    def _create_nested(current_path: Path, current_depth: int):
        """Internal function to recursively create nested directory structure."""
        if current_depth > depth:
            return

        current_files = []
        for i in range(files_per_dir):
            file_path = current_path / f"file_{current_depth}_{i}.txt"
            file_path.write_text(f"Content at depth {current_depth}")
            current_files.append(file_path)

        structure[str(current_path)] = current_files

        # Create subdirectories
        for i in range(2):  # Create 2 subdirectories per level
            subdir = current_path / f"subdir_{i}"
            subdir.mkdir(exist_ok=True)
            _create_nested(subdir, current_depth + 1)

    _create_nested(base_path, 0)
    return structure

def create_files_with_timestamps(
    base_path: Path,
    timestamps: List[float]
) -> List[Path]:
    """
    Create files with specific timestamps for testing.

    Args:
        base_path: Directory to create files in
        timestamps: List of timestamps (seconds since epoch)

    Returns:
        List of created file paths
    """
    if not base_path.exists():
        base_path.mkdir(parents=True)

    files = []
    import time

    for i, timestamp in enumerate(timestamps):
        file_path = base_path / f"timestamp_{i}.txt"
        file_path.write_text(f"File created at {timestamp}")

        # Set file timestamps
        os.utime(file_path, (timestamp, timestamp))
        files.append(file_path)

    return files

def verify_file_structure(
    base_path: Path,
    expected_structure: Dict[str, Union[Dict, str]]
) -> bool:
    """
    Verify that a file structure matches expected structure.

    Args:
        base_path: Base directory path
        expected_structure: Expected structure dictionary

    Returns:
        True if structure matches, False otherwise
    """
    for name, expected in expected_structure.items():
        full_path = base_path / name

        if isinstance(expected, dict):
            # Should be a directory
            if not full_path.is_dir():
                return False

            if not verify_file_structure(full_path, expected):
                return False
        else:
            # Should be a file
            if not full_path.is_file():
                return False

            if isinstance(expected, str):
                # Check text content
                if full_path.read_text() != expected:
                    return False
            else:
                # Check binary content
                if full_path.read_bytes() != expected:
                    return False

    return True

def mock_file_operations() -> MagicMock:
    """
    Create a mock for file system operations.

    Returns:
        Mock object for file operations
    """
    mock = MagicMock()

    # Mock common file operations
    mock.exists.return_value = True
    mock.is_file.return_value = True
    mock.is_dir.return_value = False
    mock.read_text.return_value = "Mock file content"
    mock.read_bytes.return_value = b"Mock binary content"
    mock.write_text.return_value = None
    mock.write_bytes.return_value = None
    mock.unlink.return_value = None
    mock.rename.return_value = None
    mock.stat.return_value = os.stat_result(
        (0o100644, 0, 0, 0, 0, 0, 1024, 0, 0, 0)
    )

    return mock

def create_large_file(
    base_path: Path,
    size_mb: int = 10,
    chunk_size: int = 1024 * 1024
) -> Path:
    """
    Create a large file for performance testing.

    Args:
        base_path: Directory to create file in
        size_mb: Size of file in megabytes
        chunk_size: Chunk size for writing

    Returns:
        Path to created file
    """
    if not base_path.exists():
        base_path.mkdir(parents=True)

    file_path = base_path / f"large_{size_mb}mb.dat"
    total_size = size_mb * 1024 * 1024

    with open(file_path, "wb") as f:
        remaining = total_size
        while remaining > 0:
            write_size = min(chunk_size, remaining)
            f.write(os.urandom(write_size))
            remaining -= write_size

    return file_path


import tempfile
from pathlib import Path
from typing import Dict, Any, List, Optional, Union, Callable
from unittest.mock import MagicMock, patch, Mock
import importlib
import sys
from types import ModuleType
import contextlib

def create_mock_tool(
    name: str = "test_tool",
    functions: Optional[Dict[str, Callable]] = None,
    metadata: Optional[Dict[str, Any]] = None
) -> Mock:
    """
    Create a mock tool for testing.

    Args:
        name: Tool name
        functions: Dictionary of tool functions
        metadata: Tool metadata

    Returns:
        Mock tool object
    """
    mock_tool = Mock()
    mock_tool.name = name

    # Set up tool metadata
    if metadata is None:
        metadata = {
            "name": name,
            "version": "1.0.0",
            "author": "Test Author",
            "description": "Test tool for testing"
        }

    mock_tool.metadata = metadata

    # Set up tool functions
    if functions is None:
        functions = {
            "initialize": lambda: True,
            "execute": lambda *args, **kwargs: {"result": "success"},
            "cleanup": lambda: None
        }

    for func_name, func_impl in functions.items():
        setattr(mock_tool, func_name, func_impl)

    return mock_tool

def create_tool_directory_structure(
    base_path: Path,
    tools: List[Dict[str, Any]]
) -> Dict[str, Path]:
    """
    Create a tool directory structure for testing.

    Args:
        base_path: Base directory path
        tools: List of tool definitions

    Returns:
        Dictionary mapping tool names to their paths
    """
    if not base_path.exists():
        base_path.mkdir(parents=True)

    tool_paths = {}

    for tool_def in tools:
        tool_name = tool_def["name"]
        tool_dir = base_path / tool_name

        if not tool_dir.exists():
            tool_dir.mkdir()

        # Create __init__.py
        init_file = tool_dir / "__init__.py"
        init_file.write_text(f"# {tool_name} tool\n")

        # Create main tool file
        tool_file = tool_dir / f"{tool_name}.py"
        tool_content = f"""

def initialize():
    '''Initialize the tool'''
    return True

def execute(*args, **kwargs):
    '''Execute tool functionality'''
    return {{"tool": "{tool_name}", "status": "success"}}

def cleanup():
    '''Clean up tool resources'''
    pass

metadata = {{
    "name": "{tool_name}",
    "version": "{tool_def.get("version", "1.0.0")}",
    "author": "{tool_def.get("author", "Test Author")}",
    "description": "{tool_def.get("description", "Test tool")}"
}}
"""

        tool_file.write_text(tool_content.strip())

        tool_paths[tool_name] = tool_dir

    return tool_paths

def mock_tool_loader(
    tools: Optional[List[Mock]] = None
) -> MagicMock:
    """
    Create a mock tool loader for testing.

    Args:
        tools: List of mock tools to load

    Returns:
        Mock tool loader
    """
    mock_loader = MagicMock()

    if tools is None:
        tools = [
            create_mock_tool("tool1"),
            create_mock_tool("tool2")
        ]

    mock_loader.load_tools.return_value = tools
    mock_loader.get_tool_by_name.side_effect = lambda name: next(
        (p for p in tools if p.name == name), None
    )

    return mock_loader

def create_tool_test_scenarios() -> List[Dict[str, Any]]:
    """
    Create test scenarios for tool testing.

    Returns:
        List of tool test scenarios
    """
    return [
        {
            "name": "successful_tool_loading",
            "tools": [
                {"name": "valid_tool1", "version": "1.0.0"},
                {"name": "valid_tool2", "version": "2.0.0"}
            ],
            "expected_result": "success"
        },
        {
            "name": "tool_loading_failure",
            "tools": [
                {"name": "invalid_tool", "version": "1.0.0", "has_error": True}
            ],
            "expected_result": "failure"
        },
        {
            "name": "tool_compatibility_issue",
            "tools": [
                {"name": "old_tool", "version": "0.5.0"},
                {"name": "new_tool", "version": "3.0.0"}
            ],
            "expected_result": "compatibility_warning"
        }
    ]

def simulate_tool_errors(
    error_type: str = "loading",
    tool_name: str = "test_tool"
) -> Callable:
    """
    Create a context manager to simulate tool errors.

    Args:
        error_type: Type of error to simulate
        tool_name: Name of tool to fail

    Returns:
        Context manager for error simulation
    """
    @contextlib.contextmanager
    def error_context():
        """Inner context manager for simulating tool errors."""
        error_map = {
            "loading": ImportError(f"Cannot load tool {tool_name}"),
            "initialization": RuntimeError(f"Tool {tool_name} initialization failed"),
            "execution": ValueError(f"Tool {tool_name} execution error"),
            "compatibility": RuntimeError(f"Tool {tool_name} compatibility issue")
        }

        error = error_map.get(error_type, RuntimeError("Tool error"))

        with patch('importlib.import_module') as mock_import:
            if error_type == "loading":
                mock_import.side_effect = error
            else:
                mock_tool = create_mock_tool(tool_name)
                if error_type == "initialization":
                    mock_tool.initialize.side_effect = error
                elif error_type == "execution":
                    mock_tool.execute.side_effect = error

                mock_import.return_value = mock_tool

            yield

    return error_context

def verify_tool_functionality(
    tool: Union[Mock, ModuleType],
    test_cases: List[Dict[str, Any]]
) -> Dict[str, bool]:
    """
    Verify tool functionality against test cases.

    Args:
        tool: Tool to test
        test_cases: List of test cases

    Returns:
        Dictionary of test results
    """
    results = {}

    for test_case in test_cases:
        test_name = test_case["name"]
        try:
            # Call the appropriate tool function
            if test_case["function"] == "initialize":
                result = tool.initialize()
            elif test_case["function"] == "execute":
                result = tool.execute(*test_case.get("args", []), **test_case.get("kwargs", {}))
            elif test_case["function"] == "cleanup":
                result = tool.cleanup()
            else:
                results[test_name] = False
                continue

            # Verify result
            expected = test_case.get("expected", True)
            if result == expected:
                results[test_name] = True
            else:
                results[test_name] = False

        except Exception:
            results[test_name] = False

    return results

def create_tool_dependency_graph(
    tools: List[Dict[str, Any]]
) -> Dict[str, List[str]]:
    """
    Create a tool dependency graph for testing.

    Args:
        tools: List of tool definitions with dependencies

    Returns:
        Dictionary representing dependency graph
    """
    graph = {}

    for tool in tools:
        tool_name = tool["name"]
        dependencies = tool.get("dependencies", [])

        graph[tool_name] = dependencies

    return graph

def test_tool_dependency_resolution(
    dependency_graph: Dict[str, List[str]],
    resolution_order: List[str]
) -> bool:
    """
    Test tool dependency resolution.

    Args:
        dependency_graph: Tool dependency graph
        resolution_order: Proposed resolution order

    Returns:
        True if dependencies are satisfied, False otherwise
    """
    resolved = set()

    for tool in resolution_order:
        # Check if all dependencies are resolved
        dependencies = dependency_graph.get(tool, [])

        for dep in dependencies:
            if dep not in resolved:
                return False

        resolved.add(tool)

    return True

def create_tool_sandbox_environment() -> Dict[str, Any]:
    """
    Create a sandbox environment for tool testing.

    Returns:
        Dictionary representing sandbox environment
    """
    return {
        "temp_dir": tempfile.mkdtemp(),
        "allowed_modules": ["os", "sys", "pathlib", "json"],
        "resource_limits": {
            "memory": 1024 * 1024,  # 1MB
            "cpu": 1.0,  # 1 CPU core
            "timeout": 30  # 30 seconds
        },
        "permissions": {
            "file_access": "read_only",
            "network_access": False,
            "process_creation": False
        }
    }

def mock_tool_registry(
    tools: Optional[List[Mock]] = None
) -> MagicMock:
    """
    Create a mock tool registry for testing.

    Args:
        tools: List of tools to register

    Returns:
        Mock tool registry
    """
    mock_registry = MagicMock()

    if tools is None:
        tools = [
            create_mock_tool("registered_tool1"),
            create_mock_tool("registered_tool2")
        ]

    # Mock registry methods
    mock_registry.get_all_tools.return_value = tools
    mock_registry.get_tool.return_value = tools[0] if tools else None
    mock_registry.register_tool.return_value = True
    mock_registry.unregister_tool.return_value = True

    return mock_registry

def create_tool_lifecycle_test_scenarios() -> List[Dict[str, Any]]:
    """
    Create test scenarios for tool lifecycle testing.

    Returns:
        List of tool lifecycle test scenarios
    """
    return [
        {
            "name": "normal_lifecycle",
            "steps": [
                {"action": "load", "expected": "success"},
                {"action": "initialize", "expected": "success"},
                {"action": "execute", "expected": "success"},
                {"action": "cleanup", "expected": "success"},
                {"action": "unload", "expected": "success"}
            ]
        },
        {
            "name": "initialization_failure",
            "steps": [
                {"action": "load", "expected": "success"},
                {"action": "initialize", "expected": "failure"},
                {"action": "cleanup", "expected": "success"},
                {"action": "unload", "expected": "success"}
            ]
        },
        {
            "name": "execution_failure",
            "steps": [
                {"action": "load", "expected": "success"},
                {"action": "initialize", "expected": "success"},
                {"action": "execute", "expected": "failure"},
                {"action": "cleanup", "expected": "success"},
                {"action": "unload", "expected": "success"}
            ]
        }
    ]

def benchmark_tool_performance(
    tool: Union[Mock, ModuleType],
    test_data: List[Dict[str, Any]],
    iterations: int = 100
) -> Dict[str, float]:
    """
    Benchmark tool performance.

    Args:
        tool: Tool to benchmark
        test_data: List of test data inputs
        iterations: Number of iterations

    Returns:
        Dictionary of performance metrics
    """
    import time

    results = {
        "initialize": 0.0,
        "execute": 0.0,
        "cleanup": 0.0
    }

    # Benchmark initialize
    start_time = time.time()
    for _ in range(iterations):
        tool.initialize()
    end_time = time.time()
    results["initialize"] = (end_time - start_time) / iterations

    # Benchmark execute
    start_time = time.time()
    for _ in range(iterations):
        for data in test_data:
            tool.execute(**data)
    end_time = time.time()
    results["execute"] = (end_time - start_time) / (iterations * len(test_data))

    # Benchmark cleanup
    start_time = time.time()
    for _ in range(iterations):
        tool.cleanup()
    end_time = time.time()
    results["cleanup"] = (end_time - start_time) / iterations

    return results

def create_tool_security_test_scenarios() -> List[Dict[str, Any]]:
    """
    Create test scenarios for tool security testing.

    Returns:
        List of tool security test scenarios
    """
    return [
        {
            "name": "safe_tool",
            "tool_code": """
def execute():
    return {"result": "success"}
""",
            "expected_result": "allowed"
        },
        {
            "name": "dangerous_tool",
            "tool_code": """
import os
def execute():
    os.system("rm -rf /")
    return {"result": "success"}
""",
            "expected_result": "blocked"
        },
        {
            "name": "resource_intensive_tool",
            "tool_code": """
def execute():
    while True:
        pass
    return {"result": "success"}
""",
            "expected_result": "timeout"
        }
    ]