from artos import ComponentTester, TestCase

class ConnectorTestSuite:
    def __init__(self, api_key):
        self.tester = ComponentTester(api_key)
    
    def test_table_processor_connector(self):
        """Test table processor connector in isolation"""
        test_case = TestCase(
            name="Table Processor Connector Test",
            component_type="connector",
            component_id="table_processor_connector",
            input_data={
                "data": [
                    {"id": 1, "name": "Item 1", "value": 100},
                    {"id": 2, "name": "Item 2", "value": 200}
                ],
                "format": "json"
            },
            expected_output={
                "processed_rows": 2,
                "validation_passed": True,
                "output_format": "structured"
            }
        )
        
        result = self.tester.run_test(test_case)
        return result
    
    def test_content_optimizer(self):
        """Test content optimizer component"""
        test_case = TestCase(
            name="Content Optimizer Test",
            component_type="post_processor",
            component_id="content_optimizer",
            input_data={
                "content": "This is a very long and verbose piece of content that needs optimization.",
                "target_length": "concise",
                "style_guide": "professional"
            },
            expected_output={
                "length_reduction": ">20%",
                "readability_improvement": ">10%",
                "style_compliance": True
            }
        )
        
        result = self.tester.run_test(test_case)
        return result

# Usage
test_suite = ConnectorTestSuite("your_api_key")
table_result = test_suite.test_table_processor_connector()
content_result = test_suite.test_content_optimizer()

print(f"Table processor test: {table_result.status}")
print(f"Content optimizer test: {content_result.status}")

from artos import Debugger

class InteractiveDebugger:
    def __init__(self, api_key):
        self.debugger = Debugger(api_key)
    
    def debug_workflow_step(self, workflow_id, step_id, input_data):
        """Debug a specific workflow step interactively"""
        debug_session = self.debugger.start_session(
            workflow_id=workflow_id,
            step_id=step_id,
            input_data=input_data
        )
        
        # Set breakpoints
        debug_session.set_breakpoint("data_validation")
        debug_session.set_breakpoint("content_processing")
        
        # Start debugging
        result = debug_session.run()
        
        # Inspect intermediate results
        for breakpoint in debug_session.breakpoints:
            print(f"Breakpoint {breakpoint.name}:")
            print(f"  Input: {breakpoint.input_data}")
            print(f"  Output: {breakpoint.output_data}")
            print(f"  Variables: {breakpoint.variables}")
        
        return result
    
    def step_through_execution(self, execution_id):
        """Step through execution step by step"""
        debug_session = self.debugger.attach_to_execution(execution_id)
        
        while debug_session.has_next_step():
            step_result = debug_session.next_step()
            print(f"Step: {step_result.step_name}")
            print(f"  Duration: {step_result.duration}")
            print(f"  Status: {step_result.status}")
            print(f"  Output: {step_result.output}")
            
            if step_result.has_errors():
                print(f"  Errors: {step_result.errors}")
        
        return debug_session.get_final_result()

# Usage
debugger = InteractiveDebugger("your_api_key")

# Debug specific step
result = debugger.debug_workflow_step(
    workflow_id="workflow_123",
    step_id="data_validator",
    input_data={"test_data": "sample"}
)

# Step through execution
final_result = debugger.step_through_execution("execution_456")

from artos import DevelopmentWorkbench

class WorkflowDeveloper:
    def __init__(self, api_key):
        self.workbench = DevelopmentWorkbench(api_key)
    
    def create_prototype(self, workflow_config):
        """Create a prototype workflow for testing"""
        prototype = self.workbench.create_prototype(
            name=workflow_config["name"],
            description=workflow_config["description"],
            connectors=workflow_config["connectors"],
            post_processing=workflow_config["post_processing"]
        )
        
        return prototype
    
    def test_prototype(self, prototype_id, test_data):
        """Test prototype with sample data"""
        test_result = self.workbench.test_prototype(
            prototype_id=prototype_id,
            test_data=test_data,
            metrics=["accuracy", "performance", "quality"]
        )
        
        return test_result
    
    def iterate_on_prototype(self, prototype_id, feedback):
        """Iterate on prototype based on feedback"""
        updated_prototype = self.workbench.iterate_prototype(
            prototype_id=prototype_id,
            feedback=feedback,
            auto_optimize=True
        )
        
        return updated_prototype
    
    def promote_to_production(self, prototype_id):
        """Promote tested prototype to production"""
        production_workflow = self.workbench.promote_prototype(
            prototype_id=prototype_id,
            environment="production"
        )
        
        return production_workflow

# Usage
developer = WorkflowDeveloper("your_api_key")

# Create prototype
prototype = developer.create_prototype({
    "name": "Data Processing Pipeline",
    "description": "Process and validate tabular data",
    "connectors": ["table_processor_connector"],
    "post_processing": ["data_validator", "content_optimizer"]
})

# Test prototype
test_result = developer.test_prototype(prototype.id, {
    "sample_data": "test_data_here"
})

# Iterate based on results
if test_result.accuracy < 0.9:
    updated_prototype = developer.iterate_on_prototype(prototype.id, {
        "accuracy": "needs_improvement",
        "suggestions": ["add_validation_rules", "optimize_processing"]
    })

# Promote to production when ready
production_workflow = developer.promote_to_production(prototype.id)

from artos import AnalyticsClient

class AnalyticsManager:
    def __init__(self, api_key):
        self.client = AnalyticsClient(api_key)
    
    def get_performance_metrics(self, component_id, start_date, end_date, granularity="hour"):
        """Get performance metrics for a component"""
        return self.client.get_performance_metrics(
            component_id=component_id,
            start_date=start_date,
            end_date=end_date,
            granularity=granularity
        )
    
    def get_success_rates(self, workflow_id, time_window="7d", group_by="step"):
        """Get success rates for a workflow"""
        return self.client.get_success_rates(
            workflow_id=workflow_id,
            time_window=time_window,
            group_by=group_by
        )
    
    def get_usage_statistics(self, start_date, end_date, group_by="day"):
        """Get usage statistics"""
        return self.client.get_usage_statistics(
            start_date=start_date,
            end_date=end_date,
            group_by=group_by
        )
    
    def define_custom_metric(self, metric_config):
        """Define a custom metric"""
        return self.client.define_custom_metric(metric_config)
    
    def get_custom_metric_data(self, metric_id, start_date, end_date):
        """Get data for a custom metric"""
        return self.client.get_custom_metric_data(
            metric_id=metric_id,
            start_date=start_date,
            end_date=end_date
        )

# Usage
analytics = AnalyticsManager("your_api_key")

# Get performance metrics
performance = analytics.get_performance_metrics(
    component_id="table_processor_connector",
    start_date="2024-01-01",
    end_date="2024-01-15"
)

# Get success rates
success_rates = analytics.get_success_rates(
    workflow_id="workflow_123",
    time_window="7d"
)

# Define custom metric
custom_metric = analytics.define_custom_metric({
    "name": "data_quality_score",
    "type": "score",
    "calculation": {
        "method": "weighted_average",
        "weights": {
            "completeness": 0.3,
            "accuracy": 0.4,
            "consistency": 0.3
        }
    }
})

from artos import DebuggingClient

class DebuggingManager:
    def __init__(self, api_key):
        self.client = DebuggingClient(api_key)
    
    def test_component(self, component_id, input_data, expected_output=None):
        """Test a component in isolation"""
        return self.client.test_component(
            component_id=component_id,
            input_data=input_data,
            expected_output=expected_output
        )
    
    def debug_execution(self, execution_id, breakpoints=None):
        """Debug an execution with optional breakpoints"""
        return self.client.debug_execution(
            execution_id=execution_id,
            breakpoints=breakpoints or []
        )
    
    def step_through_execution(self, execution_id):
        """Step through execution step by step"""
        return self.client.step_through_execution(execution_id)
    
    def get_execution_trace(self, execution_id):
        """Get detailed execution trace"""
        return self.client.get_execution_trace(execution_id)
    
    def compare_executions(self, execution_ids):
        """Compare multiple executions"""
        return self.client.compare_executions(execution_ids)

# Usage
debugger = DebuggingManager("your_api_key")

# Test component
test_result = debugger.test_component(
    component_id="data_validator",
    input_data={"test_data": "sample"},
    expected_output={"valid": True}
)

# Debug execution
debug_result = debugger.debug_execution(
    execution_id="execution_123",
    breakpoints=["data_validation", "content_processing"]
)

# Get execution trace
trace = debugger.get_execution_trace("execution_123")

from artos import MonitoringClient
import asyncio

class RealTimeMonitor:
    def __init__(self, api_key):
        self.client = MonitoringClient(api_key)
    
    async def monitor_executions(self, workflow_id):
        """Monitor executions in real-time"""
        async for execution_update in self.client.monitor_executions(workflow_id):
            print(f"Execution {execution_update.execution_id}: {execution_update.status}")
            
            if execution_update.status == "completed":
                print(f"  Result: {execution_update.result}")
                print(f"  Duration: {execution_update.duration}")
                print(f"  Quality Score: {execution_update.quality_score}")
            
            elif execution_update.status == "failed":
                print(f"  Error: {execution_update.error}")
    
    async def monitor_performance(self, component_id):
        """Monitor component performance in real-time"""
        async for performance_update in self.client.monitor_performance(component_id):
            print(f"Performance Update for {component_id}:")
            print(f"  Execution Time: {performance_update.avg_execution_time}")
            print(f"  Success Rate: {performance_update.success_rate}")
            print(f"  Throughput: {performance_update.throughput}")
    
    def set_alerts(self, alert_config):
        """Set up performance alerts"""
        return self.client.set_alerts(alert_config)

# Usage
monitor = RealTimeMonitor("your_api_key")

# Monitor executions
async def main():
    await monitor.monitor_executions("workflow_123")
    await monitor.monitor_performance("table_processor_connector")

# Set alerts
alerts = monitor.set_alerts({
    "success_rate_threshold": 0.95,
    "execution_time_threshold": 10.0,
    "notification_channels": ["email", "slack"]
})

# Run monitoring
asyncio.run(main())

from artos import OptimizationClient

class PerformanceOptimizer:
    def __init__(self, api_key):
        self.client = OptimizationClient(api_key)
    
    def analyze_performance(self, workflow_id, time_range="7d"):
        """Analyze workflow performance and identify bottlenecks"""
        analysis = self.client.analyze_performance(
            workflow_id=workflow_id,
            time_range=time_range
        )
        
        print("Performance Analysis:")
        print(f"  Overall Success Rate: {analysis.overall_success_rate}")
        print(f"  Average Execution Time: {analysis.avg_execution_time}")
        print(f"  Bottlenecks: {analysis.bottlenecks}")
        print(f"  Recommendations: {analysis.recommendations}")
        
        return analysis
    
    def optimize_workflow(self, workflow_id, optimization_target="performance"):
        """Optimize workflow based on analysis"""
        optimization = self.client.optimize_workflow(
            workflow_id=workflow_id,
            target=optimization_target
        )
        
        print("Optimization Results:")
        print(f"  Original Performance: {optimization.original_metrics}")
        print(f"  Optimized Performance: {optimization.optimized_metrics}")
        print(f"  Improvements: {optimization.improvements}")
        
        return optimization
    
    def a_b_test_workflows(self, workflow_a_id, workflow_b_id, test_duration="1d"):
        """A/B test two workflow versions"""
        test_result = self.client.a_b_test_workflows(
            workflow_a_id=workflow_a_id,
            workflow_b_id=workflow_b_id,
            duration=test_duration
        )
        
        print("A/B Test Results:")
        print(f"  Workflow A Performance: {test_result.workflow_a_metrics}")
        print(f"  Workflow B Performance: {test_result.workflow_b_metrics}")
        print(f"  Winner: {test_result.winner}")
        print(f"  Confidence: {test_result.confidence}")
        
        return test_result

# Usage
optimizer = PerformanceOptimizer("your_api_key")

# Analyze performance
analysis = optimizer.analyze_performance("workflow_123")

# Optimize workflow
optimization = optimizer.optimize_workflow("workflow_123", "performance")

# A/B test workflows
test_result = optimizer.a_b_test_workflows("workflow_123", "workflow_456")

from artos import ProcessModifier

class ProcessCustomizer:
    def __init__(self, api_key):
        self.modifier = ProcessModifier(api_key)
    
    def modify_workflow_process(self, workflow_id, modifications):
        """Modify workflow process dynamically"""
        modified_workflow = self.modifier.modify_workflow(
            workflow_id=workflow_id,
            modifications=modifications
        )
        
        return modified_workflow
    
    def add_custom_step(self, workflow_id, step_config):
        """Add custom processing step to workflow"""
        return self.modifier.add_step(
            workflow_id=workflow_id,
            step_config=step_config
        )
    
    def replace_component(self, workflow_id, old_component_id, new_component_id):
        """Replace component in workflow"""
        return self.modifier.replace_component(
            workflow_id=workflow_id,
            old_component_id=old_component_id,
            new_component_id=new_component_id
        )
    
    def optimize_component_config(self, component_id, optimization_params):
        """Optimize component configuration"""
        return self.modifier.optimize_component(
            component_id=component_id,
            optimization_params=optimization_params
        )

# Usage
customizer = ProcessCustomizer("your_api_key")

# Modify workflow process
modified_workflow = customizer.modify_workflow_process("workflow_123", {
    "add_validation_step": True,
    "optimize_processing_order": True,
    "enable_parallel_processing": True
})

# Add custom step
custom_step = customizer.add_custom_step("workflow_123", {
    "name": "Custom Data Transformer",
    "type": "custom_processor",
    "configuration": {
        "transformation_rules": {
            "normalize_data": True,
            "remove_outliers": True
        }
    }
})

from artos import ModelVariantDeveloper

class CustomModelDeveloper:
    def __init__(self, api_key):
        self.developer = ModelVariantDeveloper(api_key)
    
    def create_model_variant(self, base_model_id, variant_config):
        """Create custom model variant"""
        variant = self.developer.create_variant(
            base_model_id=base_model_id,
            variant_config=variant_config
        )
        
        return variant
    
    def train_variant(self, variant_id, training_data, training_config):
        """Train custom model variant"""
        training_result = self.developer.train_variant(
            variant_id=variant_id,
            training_data=training_data,
            training_config=training_config
        )
        
        return training_result
    
    def evaluate_variant(self, variant_id, test_data):
        """Evaluate model variant performance"""
        evaluation = self.developer.evaluate_variant(
            variant_id=variant_id,
            test_data=test_data
        )
        
        print("Model Evaluation:")
        print(f"  Accuracy: {evaluation.accuracy}")
        print(f"  Precision: {evaluation.precision}")
        print(f"  Recall: {evaluation.recall}")
        print(f"  F1 Score: {evaluation.f1_score}")
        
        return evaluation
    
    def deploy_variant(self, variant_id, environment="staging"):
        """Deploy model variant to environment"""
        deployment = self.developer.deploy_variant(
            variant_id=variant_id,
            environment=environment
        )
        
        return deployment

# Usage
model_developer = CustomModelDeveloper("your_api_key")

# Create model variant
variant = model_developer.create_model_variant("base_model_123", {
    "name": "Custom Data Processor",
    "description": "Specialized model for data processing",
    "modifications": {
        "architecture": "enhanced_transformer",
        "parameters": {
            "attention_heads": 8,
            "hidden_size": 512
        }
    }
})

# Train variant
training_result = model_developer.train_variant(
    variant_id=variant.id,
    training_data="path/to/training/data",
    training_config={
        "epochs": 10,
        "batch_size": 32,
        "learning_rate": 0.001
    }
)

# Evaluate variant
evaluation = model_developer.evaluate_variant(
    variant_id=variant.id,
    test_data="path/to/test/data"
)

# Deploy variant
deployment = model_developer.deploy_variant(variant.id, "production")

from artos import TroubleshootingGuide

class Troubleshooter:
    def __init__(self, api_key):
        self.guide = TroubleshootingGuide(api_key)
    
    def diagnose_execution_failure(self, execution_id):
        """Diagnose execution failure"""
        diagnosis = self.guide.diagnose_failure(execution_id)
        
        print("Failure Diagnosis:")
        print(f"  Root Cause: {diagnosis.root_cause}")
        print(f"  Error Type: {diagnosis.error_type}")
        print(f"  Affected Components: {diagnosis.affected_components}")
        print(f"  Suggested Fixes: {diagnosis.suggested_fixes}")
        
        return diagnosis
    
    def troubleshoot_performance_issues(self, workflow_id):
        """Troubleshoot performance issues"""
        troubleshooting = self.guide.troubleshoot_performance(workflow_id)
        
        print("Performance Troubleshooting:")
        print(f"  Issues Found: {troubleshooting.issues}")
        print(f"  Bottlenecks: {troubleshooting.bottlenecks}")
        print(f"  Recommendations: {troubleshooting.recommendations}")
        
        return troubleshooting
    
    def validate_workflow_configuration(self, workflow_id):
        """Validate workflow configuration"""
        validation = self.guide.validate_configuration(workflow_id)
        
        print("Configuration Validation:")
        print(f"  Valid: {validation.is_valid}")
        print(f"  Issues: {validation.issues}")
        print(f"  Warnings: {validation.warnings}")
        
        return validation

# Usage
troubleshooter = Troubleshooter("your_api_key")

# Diagnose failure
diagnosis = troubleshooter.diagnose_execution_failure("execution_123")

# Troubleshoot performance
troubleshooting = troubleshooter.troubleshoot_performance_issues("workflow_123")

# Validate configuration
validation = troubleshooter.validate_workflow_configuration("workflow_123")

from artos import ErrorRecovery

class RecoveryManager:
    def __init__(self, api_key):
        self.recovery = ErrorRecovery(api_key)
    
    def recover_failed_execution(self, execution_id):
        """Attempt to recover failed execution"""
        recovery_result = self.recovery.recover_execution(execution_id)
        
        if recovery_result.success:
            print(f"Recovery successful: {recovery_result.recovery_method}")
            return recovery_result.recovered_execution
        else:
            print(f"Recovery failed: {recovery_result.failure_reason}")
            return None
    
    def implement_fallback_strategy(self, workflow_id, fallback_config):
        """Implement fallback strategy for workflow"""
        return self.recovery.implement_fallback(
            workflow_id=workflow_id,
            fallback_config=fallback_config
        )
    
    def create_recovery_plan(self, workflow_id):
        """Create recovery plan for workflow"""
        plan = self.recovery.create_plan(workflow_id)
        
        print("Recovery Plan:")
        print(f"  Triggers: {plan.triggers}")
        print(f"  Actions: {plan.actions}")
        print(f"  Fallbacks: {plan.fallbacks}")
        
        return plan

# Usage
recovery_manager = RecoveryManager("your_api_key")

# Recover failed execution
recovered_execution = recovery_manager.recover_failed_execution("execution_123")

# Implement fallback strategy
fallback = recovery_manager.implement_fallback_strategy("workflow_123", {
    "trigger": "execution_failure",
    "fallback_workflow": "backup_workflow_456",
    "notification": True
})

# Create recovery plan
plan = recovery_manager.create_recovery_plan("workflow_123")