#!/bin/bash
# MCP-ZERO Agent Template Generator
# Creates agent templates and training infrastructure
# Implements offline-first resilience with single connection attempt pattern

# Generate default agent template
generate_default_agent() {
  local templates_dir="$1"
  
  echo "Generating default agent template..."
  
  cat > "${templates_dir}/default.json" << EOF
{
  "agent": {
    "name": "default-agent",
    "description": "Default agent template with offline-first resilience",
    "version": "1.0.0",
    "created_at": "$(date -Iseconds)",
    "offline_capable": true
  },
  "model": {
    "type": "transformer",
    "path": "../models/default",
    "local_fallback_path": "../models/fallback/default_lite",
    "quantized": true
  },
  "runtime": {
    "memory_limit_mb": 512,
    "timeout_seconds": 30,
    "max_tokens": 1024
  },
  "offline_resilience": {
    "enabled": true,
    "connection_attempts": 1,
    "permanent_fallback": true,
    "lock_file_path": ".mcp/locks/agent_offline.lock"
  },
  "processing": {
    "batch_size": 32,
    "parallel_workers": 2,
    "cache_results": true
  }
}
EOF

  echo "Default agent template generated successfully"
}

# Generate traffic analysis agent template
generate_traffic_agent() {
  local templates_dir="$1"
  local specialized_dir="${templates_dir}/specialized"
  
  echo "Generating traffic analysis agent template..."
  
  mkdir -p "${specialized_dir}"
  
  cat > "${specialized_dir}/traffic_analysis.json" << EOF
{
  "agent": {
    "name": "traffic-analysis",
    "description": "Traffic analysis agent with video processing capabilities",
    "version": "1.0.0",
    "created_at": "$(date -Iseconds)",
    "offline_capable": true
  },
  "model": {
    "type": "computer_vision",
    "path": "../models/vision/traffic_detection",
    "local_fallback_path": "../models/vision/traffic_detection_lite",
    "quantized": true
  },
  "runtime": {
    "memory_limit_mb": 1024,
    "timeout_seconds": 60,
    "gpu_enabled": true,
    "fallback_to_cpu": true
  },
  "offline_resilience": {
    "enabled": true,
    "connection_attempts": 1,
    "permanent_fallback": true,
    "lock_file_path": ".mcp/locks/traffic_agent_offline.lock"
  },
  "processing": {
    "batch_size": 16,
    "frame_skip": 2,
    "min_confidence": 0.65,
    "cache_results": true
  },
  "video": {
    "max_resolution": "1080p",
    "supported_formats": ["mp4", "avi", "mkv"],
    "preprocessing": {
      "denoise": true,
      "stabilize": false
    }
  }
}
EOF

  echo "Traffic analysis agent template generated successfully"
}

# Generate language agent template
generate_language_agent() {
  local templates_dir="$1"
  local specialized_dir="${templates_dir}/specialized"
  
  echo "Generating language processing agent template..."
  
  mkdir -p "${specialized_dir}"
  
  cat > "${specialized_dir}/language_processing.json" << EOF
{
  "agent": {
    "name": "language-processing",
    "description": "Natural language processing agent with offline capabilities",
    "version": "1.0.0",
    "created_at": "$(date -Iseconds)",
    "offline_capable": true
  },
  "model": {
    "type": "transformer",
    "path": "../models/language/llm_medium",
    "local_fallback_path": "../models/language/llm_small",
    "quantized": true
  },
  "runtime": {
    "memory_limit_mb": 2048,
    "timeout_seconds": 30,
    "max_tokens": 2048
  },
  "offline_resilience": {
    "enabled": true,
    "connection_attempts": 1,
    "permanent_fallback": true,
    "lock_file_path": ".mcp/locks/language_agent_offline.lock"
  },
  "processing": {
    "temperature": 0.7,
    "top_p": 0.9,
    "cache_results": true
  },
  "language": {
    "supported": ["en", "es", "fr", "de"],
    "default": "en",
    "translation": {
      "enabled": true,
      "fallback_to_english": true
    }
  }
}
EOF

  echo "Language processing agent template generated successfully"
}

# Generate agent training script
generate_training_script() {
  local training_dir="$1"
  local scripts_dir="${training_dir}/scripts"
  
  echo "Generating agent training script..."
  
  mkdir -p "${scripts_dir}"
  
  cat > "${scripts_dir}/train_agent.py" << EOF
#!/usr/bin/env python3
"""
MCP-ZERO Agent Training Script
Implements offline-first resilience pattern with single connection attempt
"""
import os
import sys
import json
import time
import argparse
import logging
from pathlib import Path
from datetime import datetime

logging.basicConfig(level=logging.INFO, format='%(asctime)s [%(levelname)s] %(name)s: %(message)s')
logger = logging.getLogger('agent-trainer')

class OfflineFirstTrainer:
    """Agent trainer with offline-first resilience pattern"""
    
    def __init__(self, agent_template, dataset_path, output_dir, offline_mode=False):
        """Initialize the trainer with offline-first resilience"""
        self.agent_template = agent_template
        self.dataset_path = dataset_path
        self.output_dir = Path(output_dir)
        self.offline_mode = offline_mode
        self.lock_file = None
        
        # Create output directory
        self.output_dir.mkdir(parents=True, exist_ok=True)
        
        # Load agent template
        with open(agent_template, 'r') as f:
            self.config = json.load(f)
        
        # Set up offline mode if specified
        if offline_mode:
            self._setup_offline_mode("Initialized in offline mode")
    
    def _setup_offline_mode(self, reason):
        """Set up permanent offline mode with lock file"""
        logger.warning(f"Setting up offline mode: {reason}")
        self.offline_mode = True
        
        # Create lock file path from config
        lock_path = self.config.get("offline_resilience", {}).get(
            "lock_file_path", ".mcp/locks/agent_offline.lock")
        
        # Ensure directory exists
        Path(lock_path).parent.mkdir(parents=True, exist_ok=True)
        
        # Create lock file
        lock_data = {
            "created_at": datetime.now().isoformat(),
            "reason": reason,
            "immutable": True,
            "agent": self.config.get("agent", {}).get("name", "unknown")
        }
        
        with open(lock_path, 'w') as f:
            json.dump(lock_data, f, indent=2)
        
        # Make it read-only
        os.chmod(lock_path, 0o444)
        self.lock_file = lock_path
        
        logger.info(f"Created offline lock file at {lock_path}")
    
    def check_connection(self):
        """Single connection attempt pattern"""
        if self.offline_mode:
            logger.info("Already in offline mode, skipping connection check")
            return False
        
        try:
            logger.info("Attempting to connect to training server...")
            # Simulate connection attempt
            time.sleep(1)
            
            # This would be an actual connection check in production
            # If connection fails, switch to offline mode permanently
            if os.environ.get("MCP_FORCE_OFFLINE", "false").lower() == "true":
                raise ConnectionError("Forced offline mode")
                
            logger.info("Connection successful")
            return True
        except Exception as e:
            logger.warning(f"Connection failed: {str(e)}")
            self._setup_offline_mode(f"Connection failed: {str(e)}")
            return False
    
    def train(self):
        """Train the agent with offline-first resilience"""
        # First check connection - single attempt
        online_mode = self.check_connection()
        
        if online_mode:
            logger.info("Training in online mode with full resources")
            # In a real implementation, this would use distributed training
            self._train_online()
        else:
            logger.info("Training in offline mode with local resources")
            self._train_offline()
    
    def _train_online(self):
        """Train with online resources"""
        logger.info("Starting online training process")
        # Simulate training process
        for i in range(5):
            logger.info(f"Online training epoch {i+1}/5...")
            time.sleep(1)
        
        # Save the model
        self._save_model("full")
    
    def _train_offline(self):
        """Train with offline, local resources"""
        logger.info("Starting offline training process")
        # Simulate training process with reduced capabilities
        for i in range(3):
            logger.info(f"Offline training epoch {i+1}/3...")
            time.sleep(0.5)
        
        # Save the model
        self._save_model("lite")
    
    def _save_model(self, model_type):
        """Save the trained model"""
        model_path = self.output_dir / f"trained_model_{model_type}"
        model_path.mkdir(parents=True, exist_ok=True)
        
        # Save model info
        model_info = {
            "agent": self.config.get("agent", {}).get("name", "unknown"),
            "trained_at": datetime.now().isoformat(),
            "offline_mode": self.offline_mode,
            "model_type": model_type
        }
        
        with open(model_path / "model_info.json", 'w') as f:
            json.dump(model_info, f, indent=2)
        
        logger.info(f"Model saved to {model_path}")
        
        # Create a zero-size model file for demonstration
        with open(model_path / "model.bin", 'w') as f:
            pass
            
        return str(model_path)

def main():
    """Main entry point with argument parsing"""
    parser = argparse.ArgumentParser(description="MCP-ZERO Agent Training Script")
    parser.add_argument("--template", required=True, help="Path to agent template JSON")
    parser.add_argument("--dataset", required=True, help="Path to training dataset")
    parser.add_argument("--output", required=True, help="Output directory for trained model")
    parser.add_argument("--offline", action="store_true", help="Force offline mode")
    
    args = parser.parse_args()
    
    # Initialize trainer
    trainer = OfflineFirstTrainer(
        agent_template=args.template,
        dataset_path=args.dataset,
        output_dir=args.output,
        offline_mode=args.offline
    )
    
    # Run training
    trainer.train()
    
    logger.info("Training complete")
    return 0

if __name__ == "__main__":
    sys.exit(main())
EOF

  chmod +x "${scripts_dir}/train_agent.py"
  echo "Agent training script generated successfully"
}

# Generate agent integration script
generate_agent_integration() {
  local training_dir="$1"
  local scripts_dir="${training_dir}/scripts"
  
  echo "Generating agent integration script..."
  
  mkdir -p "${scripts_dir}"
  
  cat > "${scripts_dir}/integrate_agent.py" << EOF
#!/usr/bin/env python3
"""
MCP-ZERO Agent Integration Script
Integrates trained agents with the MCP-ZERO system
Implements offline-first resilience pattern
"""
import os
import sys
import json
import time
import shutil
import argparse
import logging
from pathlib import Path
from datetime import datetime

logging.basicConfig(level=logging.INFO, format='%(asctime)s [%(levelname)s] %(name)s: %(message)s')
logger = logging.getLogger('agent-integration')

class OfflineFirstIntegrator:
    """Agent integrator with offline-first resilience pattern"""
    
    def __init__(self, agent_path, target_dir, config_dir, offline_mode=False):
        """Initialize the integrator with offline-first resilience"""
        self.agent_path = Path(agent_path)
        self.target_dir = Path(target_dir)
        self.config_dir = Path(config_dir)
        self.offline_mode = offline_mode
        
        # Ensure directories exist
        self.target_dir.mkdir(parents=True, exist_ok=True)
        self.config_dir.mkdir(parents=True, exist_ok=True)
        
        # Check for offline lock file
        lock_path = self.config_dir / "agent_offline.lock"
        if lock_path.exists():
            logger.warning("Offline lock file detected, forcing offline mode")
            self.offline_mode = True
    
    def _create_offline_lock(self, reason):
        """Create an offline lock file"""
        lock_path = self.config_dir / "agent_offline.lock"
        
        lock_data = {
            "created_at": datetime.now().isoformat(),
            "reason": reason,
            "immutable": True,
            "agent": self.agent_path.name
        }
        
        with open(lock_path, 'w') as f:
            json.dump(lock_data, f, indent=2)
        
        # Make it read-only
        os.chmod(lock_path, 0o444)
        logger.info(f"Created offline lock file at {lock_path}")
    
    def check_connection(self):
        """Single connection attempt pattern"""
        if self.offline_mode:
            logger.info("Already in offline mode, skipping connection check")
            return False
        
        try:
            logger.info("Attempting to connect to MCP server...")
            # Simulate connection attempt
            time.sleep(1)
            
            # This would be an actual connection check in production
            if os.environ.get("MCP_FORCE_OFFLINE", "false").lower() == "true":
                raise ConnectionError("Forced offline mode")
                
            logger.info("Connection successful")
            return True
        except Exception as e:
            logger.warning(f"Connection failed: {str(e)}")
            self._create_offline_lock(f"Connection failed: {str(e)}")
            self.offline_mode = True
            return False
    
    def integrate(self):
        """Integrate the agent with offline-first resilience"""
        # First check connection - single attempt
        online_mode = self.check_connection()
        
        # Load agent info
        try:
            agent_info_path = self.agent_path / "model_info.json"
            with open(agent_info_path, 'r') as f:
                agent_info = json.load(f)
        except (FileNotFoundError, json.JSONDecodeError):
            logger.error("Invalid agent directory, missing model_info.json")
            return False
        
        # Create integration config
        integration_config = {
            "agent": agent_info.get("agent", "unknown-agent"),
            "integrated_at": datetime.now().isoformat(),
            "offline_mode": self.offline_mode,
            "model_type": agent_info.get("model_type", "unknown"),
            "online_available": online_mode
        }
        
        # Save integration config
        config_path = self.config_dir / f"{agent_info.get('agent', 'agent')}_config.json"
        with open(config_path, 'w') as f:
            json.dump(integration_config, f, indent=2)
        
        # Copy agent files
        target_agent_dir = self.target_dir / agent_info.get("agent", "unknown-agent")
        target_agent_dir.mkdir(parents=True, exist_ok=True)
        
        for file_path in self.agent_path.glob("*"):
            if file_path.is_file():
                shutil.copy2(file_path, target_agent_dir / file_path.name)
        
        logger.info(f"Agent integrated at {target_agent_dir}")
        return True

def main():
    """Main entry point with argument parsing"""
    parser = argparse.ArgumentParser(description="MCP-ZERO Agent Integration Script")
    parser.add_argument("--agent", required=True, help="Path to trained agent directory")
    parser.add_argument("--target", required=True, help="Target directory for agent integration")
    parser.add_argument("--config", required=True, help="Configuration directory")
    parser.add_argument("--offline", action="store_true", help="Force offline mode")
    
    args = parser.parse_args()
    
    # Initialize integrator
    integrator = OfflineFirstIntegrator(
        agent_path=args.agent,
        target_dir=args.target,
        config_dir=args.config,
        offline_mode=args.offline
    )
    
    # Run integration
    if integrator.integrate():
        logger.info("Agent integration completed successfully")
        return 0
    else:
        logger.error("Agent integration failed")
        return 1

if __name__ == "__main__":
    sys.exit(main())
EOF

  chmod +x "${scripts_dir}/integrate_agent.py"
  echo "Agent integration script generated successfully"
}

# Generate sample training dataset
generate_sample_dataset() {
  local training_dir="$1"
  local datasets_dir="${training_dir}/datasets"
  
  echo "Generating sample training dataset..."
  
  mkdir -p "${datasets_dir}/basic_samples"
  mkdir -p "${datasets_dir}/traffic_samples"
  
  # Create a simple metadata file for samples
  cat > "${datasets_dir}/basic_samples/metadata.json" << EOF
{
  "name": "Basic Training Samples",
  "description": "Sample data for basic agent training",
  "version": "1.0.0",
  "created_at": "$(date -Iseconds)",
  "samples": 100,
  "offline_compatible": true,
  "format": "json"
}
EOF

  cat > "${datasets_dir}/traffic_samples/metadata.json" << EOF
{
  "name": "Traffic Analysis Samples",
  "description": "Sample traffic video frames for training",
  "version": "1.0.0",
  "created_at": "$(date -Iseconds)",
  "samples": 50,
  "frames_per_sample": 30,
  "resolution": "640x480",
  "offline_compatible": true,
  "format": "numpy"
}
EOF

  echo "Sample datasets generated successfully"
}

# Generate agent README
generate_agent_readme() {
  local agents_dir="$1"
  
  echo "Generating agent README..."
  
  cat > "${agents_dir}/README.md" << EOF
# MCP-ZERO Agent System

This directory contains agent templates, training data, and models for the MCP-ZERO system.
All agents implement the offline-first resilience pattern with single connection attempt.

## Directory Structure

- **templates/**: Agent template definitions
  - **specialized/**: Task-specific agent templates
- **training/**: Training data and scripts
  - **datasets/**: Training datasets
  - **scripts/**: Training and integration scripts
- **models/**: Trained agent models

## Offline-First Resilience

All agents implement the offline-first resilience pattern:

1. Single connection attempt before falling back to offline mode
2. Immutable lock files for audit trail
3. Local processing capability when external services are unavailable

## Training New Agents

To train a new agent:

\`\`\`bash
cd training/scripts
./train_agent.py --template ../../templates/default.json --dataset ../datasets/basic_samples --output ../../models/new_agent
\`\`\`

## Integrating Agents

To integrate a trained agent with the MCP-ZERO system:

\`\`\`bash
cd training/scripts
./integrate_agent.py --agent ../../models/new_agent --target ../../models/deployed --config ../../../.mcp/config
\`\`\`

## Lock Files

Offline lock files are created in the \`.mcp/locks/\` directory when agents permanently switch to offline mode.
These files are immutable and provide an audit trail for offline mode transitions.
EOF

  echo "Agent README generated successfully"
}

# Generate agent model directory structure
generate_model_structure() {
  local agents_dir="$1"
  local models_dir="${agents_dir}/models"
  
  echo "Generating model directory structure..."
  
  mkdir -p "${models_dir}/fallback"
  mkdir -p "${models_dir}/vision"
  mkdir -p "${models_dir}/language"
  
  # Create empty model files for demonstration
  touch "${models_dir}/fallback/default_lite.bin"
  touch "${models_dir}/vision/traffic_detection_lite.bin"
  touch "${models_dir}/language/llm_small.bin"
  
  echo "Model directory structure generated successfully"
}

# Generate all agent templates and infrastructure
generate_all_agents() {
  local agents_dir="$1"
  local templates_dir="${agents_dir}/templates"
  local training_dir="${agents_dir}/training"
  
  # Create directories
  mkdir -p "${templates_dir}/specialized"
  mkdir -p "${training_dir}/datasets"
  mkdir -p "${training_dir}/scripts"
  
  # Generate agent templates
  generate_default_agent "${templates_dir}"
  generate_traffic_agent "${templates_dir}"
  generate_language_agent "${templates_dir}"
  
  # Generate training infrastructure
  generate_training_script "${training_dir}"
  generate_agent_integration "${training_dir}"
  generate_sample_dataset "${training_dir}"
  
  # Generate model directory structure
  generate_model_structure "${agents_dir}"
  
  # Generate README
  generate_agent_readme "${agents_dir}"
  
  echo "All agent templates and infrastructure generated successfully"
}

# If script is executed directly, show usage
if [[ "${BASH_SOURCE[0]}" == "${0}" ]]; then
  echo "This script is intended to be sourced by burn.sh"
  echo "Usage: source $(basename "$0")"
fi
