"""Custom tools for QAM Agent quantum scheduling."""

from typing import Dict, List, Optional, Any

class CustomTool:
    """Base class for custom tools."""
    def __init__(self):
        pass

class QuantumSchedulingTools(CustomTool):
    """Tools for quantum-enhanced task scheduling."""
    
    def __init__(self):
        super().__init__()
        # Initialize components
        self.scheduler = None
        self.resource_manager = None
        self.cluster_manager = None
        self.qaoa_optimizer = None
        
    def analyze_task_dependencies(self, tasks: List[Dict]) -> Dict[str, Any]:
        """
        Analyze task dependencies and identify critical paths.
        
        Args:
            tasks: List of task definitions with dependencies
            
        Returns:
            Dict containing dependency analysis results
        """
        dependency_graph = {}
        critical_paths = []
        max_path_length = 0
        
        # Build dependency graph
        for task in tasks:
            task_id = task['id']
            deps = task.get('dependencies', [])
            dependency_graph[task_id] = deps
            
            # Find path lengths from this task
            path_length = self._get_path_length(task_id, dependency_graph)
            if path_length > max_path_length:
                max_path_length = path_length
                critical_paths = [self._get_path(task_id, dependency_graph)]
            elif path_length == max_path_length:
                critical_paths.append(self._get_path(task_id, dependency_graph))
                
        return {
            'dependency_graph': dependency_graph,
            'critical_paths': critical_paths,
            'max_path_length': max_path_length
        }
        
    def optimize_resource_allocation(self, tasks: List[Dict], 
                                  resources: Dict[str, Dict]) -> Dict[str, Any]:
        """
        Optimize resource allocation across tasks.
        
        Args:
            tasks: List of tasks with resource requirements
            resources: Dictionary of available resources
            
        Returns:
            Dict containing optimized resource allocation
        """
        # Simulate resource optimization
        allocations = {}
        utilization = {}
        
        for resource_id, resource in resources.items():
            capacity = resource['capacity']
            allocated = 0
            for task in tasks:
                if resource_id in task.get('resources', []):
                    allocated += 1
            utilization[resource_id] = allocated / capacity
            allocations[resource_id] = {
                'allocated': allocated,
                'available': capacity - allocated
            }
                
        return {
            'allocations': allocations,
            'utilization': utilization
        }
        
    def generate_quantum_schedule(self, tasks: List[Dict],
                               resources: Dict[str, Dict],
                               parameters: Dict[str, float]) -> Dict[str, Any]:
        """
        Generate optimized schedule using quantum algorithms.
        
        Args:
            tasks: List of tasks to schedule
            resources: Available resources
            parameters: QAOA and scheduling parameters
            
        Returns:
            Dict containing generated schedule and metrics
        """
        # Simulate quantum scheduling
        schedule = {}
        current_time = 0
        
        for task in tasks:
            # Check dependencies
            deps = task.get('dependencies', [])
            ready_time = 0
            for dep in deps:
                if dep in schedule:
                    ready_time = max(ready_time, schedule[dep] + 1)
            
            # Schedule task
            schedule[task['id']] = max(current_time, ready_time)
            current_time = schedule[task['id']] + 1
            
        return {
            'schedule': schedule,
            'objective_value': -len(schedule),  # Minimize makespan
            'reasoning_influence': parameters.get('qaoa_learning_rate', 0.1)
        }
        
    def _get_path_length(self, task_id: str, 
                       dependency_graph: Dict[str, List[str]]) -> int:
        """Calculate longest path length from a task."""
        if not dependency_graph[task_id]:
            return 1
            
        return 1 + max(
            self._get_path_length(dep, dependency_graph)
            for dep in dependency_graph[task_id]
        )
        
    def _get_path(self, task_id: str,
                dependency_graph: Dict[str, List[str]]) -> List[str]:
        """Get the path of tasks from start to this task."""
        if not dependency_graph[task_id]:
            return [task_id]
            
        longest_dep_path = max(
            (self._get_path(dep, dependency_graph) for dep in dependency_graph[task_id]),
            key=len
        )
        
        return longest_dep_path + [task_id]
