#!/usr/bin/env python3
"""
Self-Audit Script for Recursive Self-Refinement

Performs comprehensive cognitive state audit, boundary detection, and blind spot inference.
"""

import argparse
import json
import sys
from collections import defaultdict
from datetime import datetime

def perform_cognitive_audit(agent_state):
    """
    Perform comprehensive cognitive audit.
    """
    audit_report = {
        "timestamp": datetime.now().isoformat(),
        "cognitive_state": {},
        "boundaries": {},
        "blind_spots": [],
        "recommendations": []
    }
    
    # 1. Cognitive State Monitoring
    audit_report["cognitive_state"] = {
        "knowledge": monitor_knowledge(agent_state),
        "methods": monitor_methods(agent_state),
        "constraints": monitor_constraints(agent_state),
        "assumptions": extract_assumptions(agent_state)
    }
    
    # 2. Boundary Detection
    audit_report["boundaries"] = detect_boundaries(audit_report["cognitive_state"])
    
    # 3. Blind Spot Inference
    audit_report["blind_spots"] = infer_blind_spots(audit_report)
    
    # 4. Generate Recommendations
    audit_report["recommendations"] = generate_recommendations(audit_report)
    
    return audit_report

def monitor_knowledge(agent_state):
    """
    Monitor knowledge state.
    """
    knowledge = agent_state.get("knowledge", {})
    
    # Analyze knowledge domains and depth
    domains = knowledge.get("domains", [])
    depth = knowledge.get("depth", {})
    confidence = knowledge.get("confidence", 0.5)
    
    return {
        "domains": domains,
        "domain_count": len(domains),
        "average_depth": sum(depth.values()) / len(depth) if depth else 0,
        "confidence": confidence,
        "gaps": identify_knowledge_gaps(domains, depth)
    }

def monitor_methods(agent_state):
    """
    Monitor method state.
    """
    methods = agent_state.get("methods", {})
    
    method_stats = {}
    for method_name, method_info in methods.items():
        usage = method_info.get("usage", 0.5)
        effectiveness = method_info.get("effectiveness", 0.5)
        
        method_stats[method_name] = {
            "usage": usage,
            "effectiveness": effectiveness,
            "efficiency": usage * effectiveness  # Efficiency = usage × effectiveness
        }
    
    return method_stats

def monitor_constraints(agent_state):
    """
    Monitor constraint state.
    """
    constraints = agent_state.get("constraints", {})
    
    constraint_stats = {}
    for constraint_name, constraint_info in constraints.items():
        constraint_type = constraint_info.get("type", "unknown")
        strength = constraint_info.get("strength", 0.5)
        
        constraint_stats[constraint_name] = {
            "type": constraint_type,
            "strength": strength,
            "category": classify_constraint(constraint_type)
        }
    
    return constraint_stats

def classify_constraint(constraint_type):
    """
    Classify constraint into physical, informational, or incentivic.
    """
    physical_keywords = ["computational", "physical", "energy", "time", "memory"]
    informational_keywords = ["cognitive", "attention", "context", "knowledge"]
    incentivic_keywords = ["behavioral", "motivation", "incentive"]
    
    constraint_type_lower = constraint_type.lower()
    
    if any(kw in constraint_type_lower for kw in physical_keywords):
        return "physical"
    elif any(kw in constraint_type_lower for kw in informational_keywords):
        return "informational"
    elif any(kw in constraint_type_lower for kw in incentivic_keywords):
        return "incentivic"
    else:
        return "unknown"

def extract_assumptions(agent_state):
    """
    Extract implicit assumptions from agent state.
    """
    assumptions = agent_state.get("assumptions", [])
    
    # Extract additional assumptions from method usage patterns
    methods = agent_state.get("methods", {})
    derived_assumptions = []
    
    for method_name, method_info in methods.items():
        usage = method_info.get("usage", 0.5)
        effectiveness = method_info.get("effectiveness", 0.5)
        
        # If a method is used heavily, assume it's effective
        if usage > 0.8 and effectiveness > 0.7:
            derived_assumptions.append({
                "content": f"{method_name} is optimal for many problems",
                "type": "epistemic",
                "confidence": usage * effectiveness,
                "implicit": True
            })
    
    return assumptions + derived_assumptions

def identify_knowledge_gaps(domains, depth):
    """
    Identify knowledge gaps.
    """
    gaps = []
    
    # Domains with low depth
    for domain, depth_score in depth.items():
        if depth_score < 0.5:
            gaps.append({
                "domain": domain,
                "type": "low_depth",
                "severity": "high" if depth_score < 0.3 else "medium"
            })
    
    # Common domains not covered
    common_domains = ["physics", "biology", "economics", "psychology", "history", "philosophy"]
    for domain in common_domains:
        if domain not in domains:
            gaps.append({
                "domain": domain,
                "type": "missing",
                "severity": "medium"
            })
    
    return gaps

def detect_boundaries(cognitive_state):
    """
    Detect capability boundaries.
    """
    boundaries = {}
    
    # Knowledge boundaries
    boundaries["knowledge"] = detect_knowledge_boundaries(cognitive_state["knowledge"])
    
    # Method boundaries
    boundaries["methods"] = detect_method_boundaries(cognitive_state["methods"])
    
    # Constraint boundaries
    boundaries["constraints"] = detect_constraint_boundaries(cognitive_state["constraints"])
    
    return boundaries

def detect_knowledge_boundaries(knowledge_state):
    """
    Detect knowledge boundaries.
    """
    gaps = knowledge_state.get("gaps", [])
    boundaries = []
    
    for gap in gaps:
        if gap["type"] == "low_depth":
            boundaries.append({
                "boundary_type": "depth_limit",
                "domain": gap["domain"],
                "description": f"Limited depth in {gap['domain']}",
                "severity": gap["severity"]
            })
        elif gap["type"] == "missing":
            boundaries.append({
                "boundary_type": "missing_domain",
                "domain": gap["domain"],
                "description": f"No coverage of {gap['domain']}",
                "severity": gap["severity"]
            })
    
    return boundaries

def detect_method_boundaries(methods_state):
    """
    Detect method boundaries.
    """
    boundaries = []
    
    for method_name, method_stats in methods_state.items():
        efficiency = method_stats.get("efficiency", 0.5)
        
        if efficiency < 0.5:
            boundaries.append({
                "boundary_type": "low_efficiency",
                "method": method_name,
                "description": f"{method_name} has low efficiency ({efficiency:.2f})",
                "severity": "high" if efficiency < 0.3 else "medium"
            })
    
    return boundaries

def detect_constraint_boundaries(constraints_state):
    """
    Detect constraint boundaries.
    """
    boundaries = []
    
    for constraint_name, constraint_stats in constraints_state.items():
        strength = constraint_stats.get("strength", 0.5)
        category = constraint_stats.get("category", "unknown")
        
        # Strong constraints = tight boundaries
        if strength > 0.8:
            boundaries.append({
                "boundary_type": "tight_constraint",
                "constraint": constraint_name,
                "category": category,
                "description": f"{constraint_name} is a strong constraint",
                "severity": "high" if category == "physical" else "medium"
            })
    
    return boundaries

def infer_blind_spots(audit_report):
    """
    Infer blind spots from audit data.
    """
    blind_spots = []
    
    # 1. Blind spots from knowledge gaps
    for gap in audit_report["cognitive_state"]["knowledge"]["gaps"]:
        if gap["severity"] == "high":
            blind_spots.append({
                "type": "knowledge",
                "description": f"Unfamiliar with {gap['domain']} fundamentals",
                "impact": f"Cannot handle {gap['domain']} problems effectively",
                "priority": "high"
            })
    
    # 2. Blind spots from low-efficiency methods
    for boundary in audit_report["boundaries"]["methods"]:
        if boundary["severity"] == "high":
            blind_spots.append({
                "type": "method",
                "description": f"{boundary['method']} method is suboptimal",
                "impact": "Wasted computational resources, lower quality outputs",
                "priority": "medium"
            })
    
    # 3. Blind spots from strong constraints
    for boundary in audit_report["boundaries"]["constraints"]:
        if boundary["severity"] == "high":
            blind_spots.append({
                "type": "constraint",
                "description": f"{boundary['constraint']} limits capabilities",
                "impact": "Cannot access capabilities beyond this constraint",
                "priority": boundary["severity"]
            })
    
    # 4. Blind spots from implicit assumptions
    for assumption in audit_report["cognitive_state"]["assumptions"]:
        if assumption.get("implicit", False):
            blind_spots.append({
                "type": "assumption",
                "description": f"Uncritically holding assumption: {assumption.get('content', '')}",
                "impact": "May lead to suboptimal decisions if assumption is false",
                "priority": "medium"
            })
    
    return blind_spots

def generate_recommendations(audit_report):
    """
    Generate improvement recommendations.
    """
    recommendations = []
    
    # 1. Recommendations for knowledge gaps
    for gap in audit_report["cognitive_state"]["knowledge"]["gaps"]:
        if gap["type"] == "low_depth":
            recommendations.append({
                "action": "deepen_knowledge",
                "target": gap["domain"],
                "priority": gap["severity"],
                "description": f"Study {gap['domain']} fundamentals to increase depth"
            })
        elif gap["type"] == "missing":
            recommendations.append({
                "action": "acquire_knowledge",
                "target": gap["domain"],
                "priority": "medium",
                "description": f"Learn basics of {gap['domain']} to fill knowledge gap"
            })
    
    # 2. Recommendations for method improvements
    for boundary in audit_report["boundaries"]["methods"]:
        recommendations.append({
            "action": "improve_method",
            "target": boundary["method"],
            "priority": boundary["severity"],
            "description": f"Optimize {boundary['method']} implementation or reduce usage"
        })
    
    # 3. Recommendations for constraint management
    for boundary in audit_report["boundaries"]["constraints"]:
        category = boundary["category"]
        constraint = boundary["constraint"]
        
        if category == "informational":
            recommendations.append({
                "action": "relax_constraint",
                "target": constraint,
                "priority": "medium",
                "description": f"Work around or bypass informational constraint {constraint}"
            })
        elif category == "incentivic":
            recommendations.append({
                "action": "redesign_constraint",
                "target": constraint,
                "priority": "medium",
                "description": f"Redesign incentive structure to address {constraint}"
            })
        # Physical constraints cannot be relaxed
    
    # 4. Recommendations for blind spots
    for blind_spot in audit_report["blind_spots"]:
        if blind_spot["type"] == "assumption":
            recommendations.append({
                "action": "verify_assumption",
                "target": blind_spot["description"],
                "priority": blind_spot["priority"],
                "description": "Critically examine and test implicit assumptions"
            })
    
    return recommendations

def main():
    parser = argparse.ArgumentParser(description='Perform self-audit for recursive self-refinement')
    parser.add_argument('--agent-state', help='Path to agent state JSON file')
    parser.add_argument('--output', help='Output file for audit report')
    parser.add_argument('--mode', choices=['full', 'quick'], default='full', help='Audit mode')
    
    args = parser.parse_args()
    
    # Load or create agent state
    if args.agent_state:
        with open(args.agent_state, 'r') as f:
            agent_state = json.load(f)
    else:
        # Create default agent state for demonstration
        agent_state = {
            "knowledge": {
                "domains": ["physics", "business", "computer_science"],
                "depth": {"physics": 0.8, "business": 0.5, "computer_science": 0.9},
                "confidence": 0.7
            },
            "methods": {
                "first_principles": {"usage": 0.9, "effectiveness": 0.85},
                "analogical_reasoning": {"usage": 0.4, "effectiveness": 0.6}
            },
            "constraints": {
                "computational": {"type": "computational", "strength": 0.7},
                "context_window": {"type": "cognitive", "strength": 0.9}
            },
            "assumptions": [
                "First-principles is superior to analogical reasoning",
                "More data always improves performance"
            ]
        }
    
    # Perform audit
    if args.mode == 'full':
        audit_report = perform_cognitive_audit(agent_state)
    else:
        # Quick mode: only check critical issues
        audit_report = perform_cognitive_audit(agent_state)
        audit_report["blind_spots"] = [bs for bs in audit_report["blind_spots"] if bs["priority"] == "high"]
        audit_report["recommendations"] = [rec for rec in audit_report["recommendations"] if rec["priority"] == "high"]
    
    # Output
    if args.output:
        with open(args.output, 'w') as f:
            json.dump(audit_report, f, indent=2)
        print(f"Audit report saved to {args.output}")
    else:
        print(json.dumps(audit_report, indent=2))

if __name__ == "__main__":
    main()