"""
Enhanced Workflow Controller for N8N

This module provides enhanced functionality to control N8N workflows with better
error handling and alternative methods for workflow management.
"""

import logging
import subprocess
import time
from typing import List, Dict, Any, Optional
from .n8n_client import N8NClient

logger = logging.getLogger(__name__)

class EnhancedWorkflowController:
    """Enhanced controller for managing N8N workflow states with multiple approaches."""
    
    def __init__(self, client: Optional[N8NClient] = None):
        """
        Initialize the enhanced workflow controller.
        
        Args:
            client: N8N client instance. If None, creates a new one.
        """
        self.client = client or N8NClient()
        self._original_states = {}
    
    def force_refresh_workflow(self, workflow_id: str) -> Dict[str, Any]:
        """
        Force refresh a workflow by downloading the latest version.
        
        Args:
            workflow_id: ID of the workflow to refresh
            
        Returns:
            Fresh workflow data
        """
        try:
            # Add cache-busting timestamp to force fresh data
            import time
            cache_buster = int(time.time())
            
            workflow = self.client.get_workflow(workflow_id)
            
            logger.info(f"Force refreshed workflow {workflow_id} at {cache_buster}")
            
            return {
                'success': True,
                'workflow': workflow,
                'refreshed_at': cache_buster,
                'last_updated': workflow.get('updatedAt')
            }
            
        except Exception as e:
            logger.error(f"Failed to force refresh workflow {workflow_id}: {e}")
            return {
                'success': False,
                'error': str(e),
                'workflow_id': workflow_id
            }
    
    def stop_workflows_via_docker(self, exclude_ids: Optional[List[str]] = None) -> Dict[str, Any]:
        """
        Alternative method to stop workflows by restarting N8N container.
        This forces all workflows to inactive state.
        
        Args:
            exclude_ids: Workflows to reactivate after restart
            
        Returns:
            Result of the operation
        """
        exclude_ids = exclude_ids or []
        
        try:
            print("⚠️ WARNING: This will restart the N8N container")
            print("All workflows will be stopped, then excluded ones reactivated")
            
            # Store current active workflows
            workflows = self.client.list_workflows()
            currently_active = []
            
            for wf in workflows:
                if wf.get('active', False):
                    currently_active.append({
                        'id': wf.get('id'),
                        'name': wf.get('name', 'Unknown')
                    })
            
            print(f"Currently active workflows: {len(currently_active)}")
            
            # Restart container (this stops all workflows)
            print("🔄 Restarting N8N container...")
            result = subprocess.run(
                ["docker", "restart", "n8n-n8n-1"],
                capture_output=True,
                text=True,
                timeout=60
            )
            
            if result.returncode == 0:
                print("✅ Container restarted successfully")
                
                # Wait for N8N to start
                print("⏱️ Waiting for N8N to start...")
                time.sleep(20)
                
                # Reactivate excluded workflows
                reactivated = []
                failed_reactivation = []
                
                for workflow_id in exclude_ids:
                    try:
                        workflow = self.client.get_workflow(workflow_id)
                        updated_workflow = {**workflow, 'active': True}
                        self.client.update_workflow(workflow_id, updated_workflow)
                        
                        reactivated.append({
                            'id': workflow_id,
                            'name': workflow.get('name', 'Unknown')
                        })
                        
                    except Exception as e:
                        failed_reactivation.append({
                            'id': workflow_id,
                            'error': str(e)
                        })
                
                return {
                    'success': True,
                    'method': 'docker_restart',
                    'previously_active': currently_active,
                    'reactivated': reactivated,
                    'failed_reactivation': failed_reactivation,
                    'message': 'Container restarted, workflows reset'
                }
                
            else:
                return {
                    'success': False,
                    'error': f"Docker restart failed: {result.stderr}",
                    'method': 'docker_restart'
                }
                
        except Exception as e:
            return {
                'success': False,
                'error': str(e),
                'method': 'docker_restart'
            }
    
    def isolate_workflow_for_debugging(self, debug_workflow_id: str) -> Dict[str, Any]:
        """
        Isolate a specific workflow for debugging by stopping all others.
        Uses multiple approaches for maximum effectiveness.
        
        Args:
            debug_workflow_id: ID of the workflow to keep active for debugging
            
        Returns:
            Result of isolation process
        """
        print(f"🔧 ISOLATING WORKFLOW FOR DEBUGGING: {debug_workflow_id}")
        print("=" * 60)
        
        # Step 1: Get current state
        try:
            workflows = self.client.list_workflows()
            active_workflows = [w for w in workflows if w.get('active', False)]
            
            print(f"Currently active workflows: {len(active_workflows)}")
            for wf in active_workflows:
                name = wf.get('name', 'Unknown')
                wf_id = wf.get('id')
                marker = "🎯 DEBUG" if wf_id == debug_workflow_id else "🔴 TO STOP"
                print(f"  {marker} {name} ({wf_id})")
            
        except Exception as e:
            print(f"❌ Failed to get workflow list: {e}")
            return {'success': False, 'error': str(e)}
        
        # Step 2: Try API method first
        print(f"\\n🔄 Attempting API-based workflow stopping...")
        api_result = self._stop_workflows_api(exclude_ids=[debug_workflow_id])
        
        if api_result['stopped_count'] > 0:
            print(f"✅ API method successful: {api_result['stopped_count']} workflows stopped")
            return {
                'success': True,
                'method': 'api',
                'result': api_result,
                'isolated_workflow': debug_workflow_id
            }
        
        # Step 3: If API fails, offer docker method
        print(f"⚠️ API method failed or incomplete")
        print(f"Stopped: {api_result['stopped_count']}, Failed: {api_result['failed_count']}")
        
        if api_result['failed_count'] > 0:
            print("\\n💡 Alternative: Use Docker restart method?")
            print("This will restart N8N container and stop ALL workflows,")
            print(f"then reactivate only the debug workflow: {debug_workflow_id}")
            
            return {
                'success': False,
                'method': 'api_failed',
                'api_result': api_result,
                'suggestion': 'use_docker_restart',
                'docker_command': f"controller.stop_workflows_via_docker(['{debug_workflow_id}'])"
            }
        
        return {
            'success': True,
            'method': 'api_partial',
            'result': api_result,
            'isolated_workflow': debug_workflow_id
        }
    
    def _stop_workflows_api(self, exclude_ids: Optional[List[str]] = None) -> Dict[str, Any]:
        """Internal API-based workflow stopping."""
        exclude_ids = exclude_ids or []
        workflows = self.client.list_workflows()
        
        stopped = []
        failed = []
        skipped = []
        
        for workflow in workflows:
            workflow_id = workflow.get('id')
            workflow_name = workflow.get('name', 'Unknown')
            is_active = workflow.get('active', False)
            
            if workflow_id in exclude_ids:
                skipped.append({
                    'id': workflow_id,
                    'name': workflow_name,
                    'reason': 'excluded'
                })
                continue
            
            if not is_active:
                skipped.append({
                    'id': workflow_id,
                    'name': workflow_name,
                    'reason': 'already_inactive'
                })
                continue
            
            # Store original state
            self._original_states[workflow_id] = {
                'active': is_active,
                'name': workflow_name
            }
            
            try:
                updated_workflow = {**workflow, 'active': False}
                self.client.update_workflow(workflow_id, updated_workflow)
                
                stopped.append({
                    'id': workflow_id,
                    'name': workflow_name,
                    'was_active': is_active
                })
                logger.info(f"Stopped workflow: {workflow_name} ({workflow_id})")
                
            except Exception as e:
                failed.append({
                    'id': workflow_id,
                    'name': workflow_name,
                    'error': str(e)
                })
                logger.error(f"Failed to stop workflow {workflow_name}: {e}")
        
        return {
            'stopped': stopped,
            'failed': failed,
            'skipped': skipped,
            'stopped_count': len(stopped),
            'failed_count': len(failed),
            'skipped_count': len(skipped)
        }
    
    def verify_workflow_isolation(self, debug_workflow_id: str) -> Dict[str, Any]:
        """
        Verify that only the debug workflow is active.
        
        Args:
            debug_workflow_id: ID of the workflow that should be active
            
        Returns:
            Verification result
        """
        try:
            workflows = self.client.list_workflows()
            active_workflows = [w for w in workflows if w.get('active', False)]
            
            debug_workflow_active = False
            other_active_workflows = []
            
            for wf in active_workflows:
                wf_id = wf.get('id')
                wf_name = wf.get('name', 'Unknown')
                
                if wf_id == debug_workflow_id:
                    debug_workflow_active = True
                else:
                    other_active_workflows.append({
                        'id': wf_id,
                        'name': wf_name
                    })
            
            is_isolated = debug_workflow_active and len(other_active_workflows) == 0
            
            return {
                'success': True,
                'is_isolated': is_isolated,
                'debug_workflow_active': debug_workflow_active,
                'other_active_count': len(other_active_workflows),
                'other_active_workflows': other_active_workflows,
                'total_active': len(active_workflows)
            }
            
        except Exception as e:
            return {
                'success': False,
                'error': str(e)
            }

def create_enhanced_workflow_controller():
    """Create an enhanced workflow controller instance."""
    return EnhancedWorkflowController()