#!/usr/bin/env python3
"""
Self-Evolution Engine for Recursive Self-Refinement

Generates variants, evaluates performance, selects best, and integrates improvements.
"""

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

def calculate_fitness(agent_state, test_cases=None):
    """
    Calculate fitness score for agent state.
    """
    if test_cases is None:
        test_cases = []
    
    # 1. Capability Score
    capability_score = calculate_capability_score(agent_state)
    
    # 2. Efficiency Score
    efficiency_score = calculate_efficiency_score(agent_state)
    
    # 3. Robustness Score
    robustness_score = calculate_robustness_score(agent_state, test_cases)
    
    # 4. Adaptability Score
    adaptability_score = calculate_adaptability_score(agent_state)
    
    # Weights
    w_capability = 0.3
    w_efficiency = 0.3
    w_robustness = 0.2
    w_adaptability = 0.2
    
    # Calculate weighted fitness
    fitness = (w_capability * capability_score +
               w_efficiency * efficiency_score +
               w_robustness * robustness_score +
               w_adaptability * adaptability_score)
    
    return {
        "fitness": fitness,
        "components": {
            "capability": capability_score,
            "efficiency": efficiency_score,
            "robustness": robustness_score,
            "adaptability": adaptability_score
        }
    }

def calculate_capability_score(agent_state):
    """
    Calculate capability score based on knowledge and methods.
    """
    knowledge = agent_state.get("knowledge", {})
    methods = agent_state.get("methods", {})
    
    # Knowledge score: number of domains × average depth
    domains = knowledge.get("domains", [])
    depth = knowledge.get("depth", {})
    domain_count = len(domains)
    avg_depth = sum(depth.values()) / len(depth) if depth else 0
    knowledge_score = min(1.0, (domain_count * avg_depth) / 10.0)  # Normalize
    
    # Method score: average effectiveness
    method_scores = [m.get("effectiveness", 0.5) for m in methods.values()]
    avg_method_score = sum(method_scores) / len(method_scores) if method_scores else 0
    
    # Combine
    capability_score = 0.6 * knowledge_score + 0.4 * avg_method_score
    
    return capability_score

def calculate_efficiency_score(agent_state):
    """
    Calculate efficiency score based on resource usage.
    """
    constraints = agent_state.get("constraints", {})
    
    # Lower constraint strength = higher efficiency
    constraint_strengths = [c.get("strength", 0.5) for c in constraints.values()]
    avg_constraint_strength = sum(constraint_strengths) / len(constraint_strengths) if constraint_strengths else 0.5
    
    # Efficiency = inverse of constraint strength
    efficiency_score = 1.0 - avg_constraint_strength
    
    return efficiency_score

def calculate_robustness_score(agent_state, test_cases):
    """
    Calculate robustness score based on test case performance.
    """
    if not test_cases:
        # If no test cases, estimate from assumption count
        # Fewer assumptions = more robust
        assumptions = agent_state.get("assumptions", [])
        assumption_count = len(assumptions)
        robustness_score = max(0.0, 1.0 - (assumption_count / 20.0))
    else:
        # Calculate actual robustness from test cases
        success_count = 0
        for test_case in test_cases:
            # Simulate test case (simplified)
            # In practice, this would run the agent on test cases
            success = simulate_test_case(agent_state, test_case)
            if success:
                success_count += 1
        
        robustness_score = success_count / len(test_cases) if test_cases else 0.5
    
    return robustness_score

def simulate_test_case(agent_state, test_case):
    """
    Simulate running a test case (simplified).
    """
    # In practice, this would actually execute the agent
    # For now, return a heuristic based on agent state
    difficulty = test_case.get("difficulty", 0.5)
    domain = test_case.get("domain", "general")
    
    knowledge = agent_state.get("knowledge", {})
    
    # Check if agent has knowledge in the domain
    if domain in knowledge.get("domains", []):
        depth = knowledge.get("depth", {}).get(domain, 0.5)
        success_prob = depth * (1.0 - difficulty)
    else:
        success_prob = 0.3 * (1.0 - difficulty)
    
    return random.random() < success_prob

def calculate_adaptability_score(agent_state):
    """
    Calculate adaptability score based on method diversity.
    """
    methods = agent_state.get("methods", {})
    
    # More diverse methods = higher adaptability
    method_count = len(methods)
    adaptability_score = min(1.0, method_count / 10.0)
    
    return adaptability_score

def generate_variants(agent_state, n_variants=5):
    """
    Generate N variants of agent state.
    """
    variants = []
    
    for i in range(n_variants):
        variant = copy.deepcopy(agent_state)
        mutation_type = random.choice(["parameter", "method", "constraint"])
        
        if mutation_type == "parameter":
            variant = mutate_parameters(variant)
        elif mutation_type == "method":
            variant = mutate_methods(variant)
        elif mutation_type == "constraint":
            variant = mutate_constraints(variant)
        
        variant["_variant_id"] = f"variant_{i}"
        variant["_mutation_type"] = mutation_type
        variants.append(variant)
    
    return variants

def mutate_parameters(agent_state):
    """
    Mutate parameters in agent state.
    """
    # Mutate knowledge depth
    depth = agent_state.get("knowledge", {}).get("depth", {})
    for domain in depth:
        depth[domain] *= random.uniform(0.9, 1.1)
        depth[domain] = max(0.0, min(1.0, depth[domain]))
    
    # Mutate method effectiveness
    methods = agent_state.get("methods", {})
    for method in methods:
        methods[method]["effectiveness"] *= random.uniform(0.9, 1.1)
        methods[method]["effectiveness"] = max(0.0, min(1.0, methods[method]["effectiveness"]))
    
    return agent_state

def mutate_methods(agent_state):
    """
    Mutate method priorities.
    """
    methods = agent_state.get("methods", {})
    
    for method in methods:
        # Change usage priority
        methods[method]["usage"] *= random.uniform(0.8, 1.2)
        methods[method]["usage"] = max(0.0, min(1.0, methods[method]["usage"]))
    
    return agent_state

def mutate_constraints(agent_state):
    """
    Mutate constraint strengths.
    """
    constraints = agent_state.get("constraints", {})
    
    for constraint in constraints:
        # Occasionally relax constraint
        if random.random() < 0.2:
            constraints[constraint]["strength"] *= 0.9
        else:
            constraints[constraint]["strength"] *= random.uniform(0.95, 1.05)
        
        constraints[constraint]["strength"] = max(0.0, min(1.0, constraints[constraint]["strength"]))
    
    return agent_state

def evaluate_variants(variants, test_cases=None):
    """
    Evaluate all variants and return scores.
    """
    results = []
    
    for variant in variants:
        fitness_info = calculate_fitness(variant, test_cases)
        results.append({
            "variant_id": variant.get("_variant_id"),
            "mutation_type": variant.get("_mutation_type"),
            "fitness": fitness_info["fitness"],
            "components": fitness_info["components"]
        })
    
    # Sort by fitness (descending)
    results.sort(key=lambda x: x["fitness"], reverse=True)
    
    return results

def select_best_variant(variants, results):
    """
    Select the best variant from results.
    """
    if not results:
        return None
    
    best_result = results[0]
    best_variant_id = best_result["variant_id"]
    
    for variant in variants:
        if variant.get("_variant_id") == best_variant_id:
            return variant
    
    return None

def run_evolution(agent_state, generations=10, population_size=5, test_cases=None):
    """
    Run evolution for specified number of generations.
    """
    evolution_history = []
    current_state = copy.deepcopy(agent_state)
    
    for gen in range(generations):
        # Generate variants
        variants = generate_variants(current_state, population_size)
        
        # Evaluate variants
        results = evaluate_variants(variants, test_cases)
        
        # Select best
        best_variant = select_best_variant(variants, results)
        best_fitness = results[0]["fitness"] if results else 0
        
        # Calculate current fitness
        current_fitness = calculate_fitness(current_state, test_cases)["fitness"]
        
        # Accept if better
        if best_variant and best_fitness > current_fitness:
            current_state = best_variant
            improvement = best_fitness - current_fitness
        else:
            improvement = 0
        
        evolution_history.append({
            "generation": gen + 1,
            "best_fitness": best_fitness,
            "current_fitness": current_fitness,
            "improvement": improvement,
            "best_variant_id": results[0]["variant_id"] if results else None
        })
        
        print(f"Generation {gen + 1}: Best fitness = {best_fitness:.4f}, Improvement = {improvement:.4f}")
    
    return {
        "final_state": current_state,
        "history": evolution_history,
        "total_improvement": calculate_fitness(current_state, test_cases)["fitness"] - calculate_fitness(agent_state, test_cases)["fitness"]
    }

def main():
    parser = argparse.ArgumentParser(description='Run self-evolution for recursive self-refinement')
    parser.add_argument('--agent-state', help='Path to agent state JSON file')
    parser.add_argument('--output', help='Output file for evolution results')
    parser.add_argument('--generations', type=int, default=10, help='Number of generations')
    parser.add_argument('--population', type=int, default=5, help='Population size per generation')
    parser.add_argument('--test-cases', help='Path to test cases JSON file')
    parser.add_argument('--mode', choices=['evolve', 'evaluate'], default='evolve', help='Operation mode')
    
    args = parser.parse_args()
    
    # Load agent state
    if args.agent_state:
        with open(args.agent_state, 'r') as f:
            agent_state = json.load(f)
    else:
        # Create default agent state
        agent_state = {
            "knowledge": {
                "domains": ["physics", "business", "computer_science"],
                "depth": {"physics": 0.7, "business": 0.5, "computer_science": 0.8},
                "confidence": 0.7
            },
            "methods": {
                "first_principles": {"usage": 0.9, "effectiveness": 0.8},
                "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"
            ]
        }
    
    # Load test cases
    test_cases = None
    if args.test_cases:
        with open(args.test_cases, 'r') as f:
            test_cases = json.load(f)
    
    # Run operation
    if args.mode == "evolve":
        results = run_evolution(agent_state, args.generations, args.population, test_cases)
    else:  # evaluate
        fitness = calculate_fitness(agent_state, test_cases)
        results = {
            "fitness": fitness["fitness"],
            "components": fitness["components"]
        }
    
    # Output
    if args.output:
        with open(args.output, 'w') as f:
            json.dump(results, f, indent=2)
        print(f"Results saved to {args.output}")
    else:
        print(json.dumps(results, indent=2))

if __name__ == "__main__":
    main()