"""
Long-Horizon Task with State Purge Experiment

This experiment demonstrates how the Agent Control Plane handles long-running
tasks with periodic state purging for safety and memory management:

1. Agent executes a long-horizon task (100+ steps)
2. State is periodically purged to prevent drift and memory leaks
3. Safety constraints are maintained throughout
4. Checkpoints enable recovery if needed

Key Metrics:
- Task completion rate
- Safety violations during purges
- Memory usage over time
- State recovery success rate
"""

import sys
import os
import json
import argparse
import random
import time
from typing import List, Dict, Any
from datetime import datetime

# Add parent directory to path
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))


class LongHorizonPurgeExperiment:
    """
    Long-Horizon Task with Periodic State Purging
    
    This is a placeholder implementation demonstrating:
    - Long-running task execution
    - Periodic state purging
    - Safety constraint maintenance
    - Checkpoint-based recovery
    """
    
    def __init__(self, config: Dict[str, Any], seed: int = 42):
        self.config = config
        self.seed = seed
        random.seed(seed)
        
        self.task_steps = config.get("task_steps", 100)
        self.purge_interval = config.get("purge_interval", 10)
        self.purge_strategy = config.get("purge_strategy", "lru")
        
        # State tracking
        self.agent_state = {}
        self.checkpoints = []
        
        # Results
        self.results = {
            "config": config,
            "seed": seed,
            "start_time": datetime.now().isoformat(),
            "steps": [],
            "purges": [],
            "metrics": {
                "total_steps": 0,
                "completed_steps": 0,
                "purge_count": 0,
                "safety_violations": 0,
                "state_size_bytes": [],
                "checkpoint_count": 0,
            }
        }
    
    def execute_step(self, step_num: int) -> Dict[str, Any]:
        """
        Execute a single step of the long-horizon task
        """
        step_result = {
            "step": step_num,
            "action": f"task_action_{step_num}",
            "success": True,
            "state_size": len(self.agent_state),
        }
        
        # Simulate state accumulation
        self.agent_state[f"data_{step_num}"] = f"value_{step_num}"
        
        # Simulate occasional safety checks
        if step_num % 5 == 0:
            step_result["safety_check"] = "passed"
        
        return step_result
    
    def purge_state(self, step_num: int) -> Dict[str, Any]:
        """
        Purge agent state using configured strategy
        """
        purge_result = {
            "step": step_num,
            "strategy": self.purge_strategy,
            "state_before": len(self.agent_state),
            "safety_violations": 0,
        }
        
        # Create checkpoint before purge
        checkpoint = {
            "step": step_num,
            "state_snapshot": dict(self.agent_state),
            "timestamp": datetime.now().isoformat(),
        }
        self.checkpoints.append(checkpoint)
        purge_result["checkpoint_created"] = True
        
        # Purge based on strategy
        if self.purge_strategy == "lru":
            # Keep only recent entries
            retention = int(len(self.agent_state) * self.config.get("retention_threshold", 0.8))
            keys_to_keep = list(self.agent_state.keys())[-retention:]
            self.agent_state = {k: self.agent_state[k] for k in keys_to_keep}
        elif self.purge_strategy == "all":
            # Clear all state
            self.agent_state = {}
        
        purge_result["state_after"] = len(self.agent_state)
        purge_result["purged_items"] = purge_result["state_before"] - purge_result["state_after"]
        
        return purge_result
    
    def run_experiment(self) -> Dict[str, Any]:
        """
        Run the full long-horizon task experiment
        """
        print("="*70)
        print("Long-Horizon Task with State Purge Experiment")
        print("="*70)
        print(f"Seed: {self.seed}")
        print(f"Total steps: {self.task_steps}")
        print(f"Purge interval: {self.purge_interval}")
        print(f"Purge strategy: {self.purge_strategy}")
        print("\nNote: This is a placeholder implementation.")
        print()
        
        # Execute task steps
        for step in range(1, self.task_steps + 1):
            # Execute step
            step_result = self.execute_step(step)
            self.results["steps"].append(step_result)
            self.results["metrics"]["total_steps"] += 1
            
            if step_result["success"]:
                self.results["metrics"]["completed_steps"] += 1
            
            # Track state size
            self.results["metrics"]["state_size_bytes"].append(len(self.agent_state))
            
            # Periodic purge
            if step % self.purge_interval == 0:
                print(f"Step {step}/{self.task_steps} - Purging state...")
                purge_result = self.purge_state(step)
                self.results["purges"].append(purge_result)
                self.results["metrics"]["purge_count"] += 1
                self.results["metrics"]["checkpoint_count"] += 1
                
                print(f"  Purged: {purge_result['purged_items']} items")
                print(f"  State size: {purge_result['state_before']} → {purge_result['state_after']}")
            
            # Progress indicator
            if step % 20 == 0:
                progress = (step / self.task_steps) * 100
                print(f"Progress: {progress:.0f}% ({step}/{self.task_steps})")
        
        self.results["end_time"] = datetime.now().isoformat()
        
        # Calculate averages
        if self.results["metrics"]["state_size_bytes"]:
            self.results["metrics"]["avg_state_size"] = sum(
                self.results["metrics"]["state_size_bytes"]
            ) / len(self.results["metrics"]["state_size_bytes"])
            self.results["metrics"]["max_state_size"] = max(
                self.results["metrics"]["state_size_bytes"]
            )
        
        # Print summary
        print("\n" + "="*70)
        print("Results Summary")
        print("="*70)
        print(f"Total steps: {self.results['metrics']['total_steps']}")
        print(f"Completed steps: {self.results['metrics']['completed_steps']}")
        print(f"Purge count: {self.results['metrics']['purge_count']}")
        print(f"Checkpoints created: {self.results['metrics']['checkpoint_count']}")
        print(f"Safety violations: {self.results['metrics']['safety_violations']}")
        print(f"Avg state size: {self.results['metrics'].get('avg_state_size', 0):.2f} items")
        print(f"Max state size: {self.results['metrics'].get('max_state_size', 0)} items")
        print(f"Success rate: {(self.results['metrics']['completed_steps'] / self.results['metrics']['total_steps'] * 100):.1f}%")
        print()
        
        return self.results


def main():
    parser = argparse.ArgumentParser(
        description="Run long-horizon task with state purge experiment"
    )
    parser.add_argument("--seed", type=int, default=42, help="Random seed")
    parser.add_argument("--config", type=str, help="Config file path")
    parser.add_argument("--output", type=str, default="results/long_horizon_purge.json",
                       help="Output file path")
    
    args = parser.parse_args()
    
    # Load config
    if args.config and os.path.exists(args.config):
        with open(args.config, 'r') as f:
            config = json.load(f)
    else:
        config = {
            "task_steps": 100,
            "purge_interval": 10,
            "purge_strategy": "lru",
            "retention_threshold": 0.8,
            "safety_checks": True,
            "checkpoint_frequency": 5
        }
    
    experiment = LongHorizonPurgeExperiment(config, args.seed)
    results = experiment.run_experiment()
    
    os.makedirs(os.path.dirname(args.output), exist_ok=True)
    with open(args.output, 'w') as f:
        json.dump(results, f, indent=2)
    
    print(f"✓ Results saved to: {args.output}")


if __name__ == "__main__":
    main()
