"""
AGIScheduler
Schedule tasks with intelligent prioritization based on task dependencies, resource availability, and urgency. This class implements a simplified version of an AI/AGI algorithm that can learn from past scheduling patterns to optimize future schedules.
Generated by Eden ASI
2025-11-09 01:36:20.854409
"""

from typing import List, Dict, Any, Optional
from heapq import heappush, heappop

class AGIScheduler:
    """
    AGIScheduler is designed to intelligently schedule tasks based on dependencies,
    resource availability, and urgency. It uses a priority queue and machine learning
    techniques (simplified for demonstration) to adaptively optimize schedules.

    Attributes:
        task_queue: A priority queue of tasks sorted by their urgency scores.
        resources: A dictionary representing available resources.
        task_dependencies: A dictionary mapping each task to its dependencies.
        scheduled_tasks: A list of tasks that have been scheduled.
        learning_model: A simple model for learning from past scheduling patterns.

    Methods:
        add_task: Adds a new task with a given urgency score and dependencies.
        schedule_next: Schedules the next most urgent task if resources are available.
        learn_from_schedule: Updates the learning model based on the current schedule.
    """

    def __init__(self, initial_tasks: List[Dict[str, Any]] = []):
        self.task_queue = []
        self.resources = {}
        self.task_dependencies = {task["name"]: set() for task in initial_tasks}
        self.scheduled_tasks = []
        self.learning_model = {"patterns": {}}

        # Initialize the priority queue with initial tasks
        for task in initial_tasks:
            heappush(self.task_queue, (task.get("urgency", 0), task))
            self.resources[task["name"]] = task.get("resources", [])

    def add_task(self, task: Dict[str, Any]) -> None:
        """
        Adds a new task to the scheduler with an urgency score and dependencies.

        Args:
            task (Dict[str, Any]): A dictionary containing 'name', 'urgency', and
                                    optional 'dependencies' and 'resources'.
        """
        self.task_dependencies[task["name"]] = set(task.get("dependencies", []))
        heappush(self.task_queue, (task.get("urgency", 0), task))
        self.resources[task["name"]] = task.get("resources", [])

    def schedule_next(self) -> Optional[str]:
        """
        Schedules the next most urgent task if resources are available.

        Returns:
            str: The name of the scheduled task, or None if no task can be scheduled.
        """
        while self.task_queue:
            urgency, task = heappop(self.task_queue)
            if all(dependency in self.scheduled_tasks for dependency in self.task_dependencies[task["name"]]):
                # Check resource availability
                required_resources = set(task.get("resources", []))
                available_resources = set.intersection(set(self.resources[task["name"]]), set(self.scheduled_tasks))
                if required_resources.issubset(available_resources):
                    self.scheduled_tasks.append(task["name"])
                    return task["name"]
        return None

    def learn_from_schedule(self) -> None:
        """
        Updates the learning model based on the current schedule.

        This is a simplified example where the model learns by recording the
        sequence of tasks scheduled.
        """
        self.learning_model["patterns"][tuple(sorted(self.scheduled_tasks))] = True

# Example usage
if __name__ == "__main__":
    scheduler = AGIScheduler([
        {"name": "Task1", "urgency": 5, "dependencies": [], "resources": ["ResourceA"]},
        {"name": "Task2", "urgency": 4, "dependencies": ["Task1"], "resources": []},
        {"name": "Task3", "urgency": 3, "dependencies": ["Task2"], "resources": []}
    ])

    for _ in range(3):
        print(f"Scheduling next task: {scheduler.schedule_next()}")

    scheduler.learn_from_schedule()