import { performance } from 'perf_hooks';
import chalk from 'chalk';
import Table from 'cli-table3';
import stats from 'stats-lite';
import fs from 'fs';
import path from 'path';
import { fileURLToPath } from 'url';

const __dirname = path.dirname(fileURLToPath(import.meta.url));

class TraditionalSystemSimulator {
    constructor() {
        this.knowledgeBase = this.initializeKnowledgeBase();
    }

    initializeKnowledgeBase() {
        const kb = new Map();
        for (let i = 0; i < 1000; i++) {
            kb.set(`entity_${i}`, {
                properties: Array(10).fill(0).map((_, j) => `prop_${j}`),
                relations: Array(5).fill(0).map((_, j) => `entity_${(i + j + 1) % 1000}`)
            });
        }
        return kb;
    }

    simulateGPT4Reasoning(query, complexity = 'simple') {
        const baseLatencies = {
            simple: { min: 150, max: 300, typical: 200 },
            moderate: { min: 300, max: 500, typical: 400 },
            complex: { min: 500, max: 800, typical: 650 }
        };

        const latency = baseLatencies[complexity];
        const start = performance.now();

        const networkLatency = 20 + Math.random() * 30;
        const processingTime = latency.min + Math.random() * (latency.max - latency.min);
        const totalTime = networkLatency + processingTime;

        const simulatedDelay = () => {
            const iterations = Math.floor(totalTime * 1000);
            let sum = 0;
            for (let i = 0; i < iterations; i++) {
                sum += Math.sqrt(i);
            }
            return sum;
        };

        simulatedDelay();

        const end = performance.now();
        const actualTime = end - start;

        return {
            system: 'GPT-4',
            query,
            complexity,
            simulatedTime: totalTime,
            actualTime,
            breakdown: {
                network: networkLatency,
                processing: processingTime
            }
        };
    }

    simulateNeuralTheoremProver(theorem) {
        const baseLatency = 200 + Math.random() * 1800;
        const start = performance.now();

        const steps = Math.floor(Math.random() * 50) + 10;
        const stepTime = baseLatency / steps;

        const prove = () => {
            let proof = [];
            for (let i = 0; i < steps; i++) {
                const iterations = Math.floor(stepTime * 1000);
                let sum = 0;
                for (let j = 0; j < iterations; j++) {
                    sum += Math.log(j + 1) * Math.sin(j);
                }
                proof.push(`Step ${i}: ${sum}`);
            }
            return proof;
        };

        const proof = prove();
        const end = performance.now();

        return {
            system: 'Neural Theorem Prover',
            theorem,
            steps,
            baseLatency,
            actualTime: end - start,
            proof: proof.length
        };
    }

    simulateOWLReasoner(ontology, reasonerType = 'Pellet') {
        const reasonerLatencies = {
            'Pellet': { min: 50, max: 300, typical: 150 },
            'HermiT': { min: 80, max: 500, typical: 250 }
        };

        const latency = reasonerLatencies[reasonerType];
        const start = performance.now();

        const classify = () => {
            const classificationTime = latency.min + Math.random() * (latency.max - latency.min);
            const iterations = Math.floor(classificationTime * 800);

            const classes = new Set();
            const properties = new Set();

            for (let i = 0; i < iterations; i++) {
                if (i % 100 === 0) {
                    classes.add(`Class_${i}`);
                }
                if (i % 50 === 0) {
                    properties.add(`Property_${i}`);
                }
                Math.sqrt(i) * Math.log(i + 1);
            }

            return {
                classes: classes.size,
                properties: properties.size,
                time: classificationTime
            };
        };

        const result = classify();
        const end = performance.now();

        return {
            system: `OWL Reasoner (${reasonerType})`,
            ontology,
            classification: result,
            actualTime: end - start
        };
    }

    simulatePrologSystem(query) {
        const baseLatency = 5 + Math.random() * 45;
        const start = performance.now();

        const unify = () => {
            const unificationSteps = Math.floor(Math.random() * 100) + 20;
            const stepTime = baseLatency / unificationSteps;

            let bindings = new Map();
            for (let i = 0; i < unificationSteps; i++) {
                const iterations = Math.floor(stepTime * 500);
                for (let j = 0; j < iterations; j++) {
                    Math.pow(j, 0.5) * Math.cos(j);
                }
                bindings.set(`Var_${i}`, `Value_${i}`);
            }

            return bindings;
        };

        const bindings = unify();
        const end = performance.now();

        return {
            system: 'Prolog',
            query,
            unifications: bindings.size,
            baseLatency,
            actualTime: end - start
        };
    }

    simulateRuleEngine(rules, engineType = 'CLIPS') {
        const engineLatencies = {
            'CLIPS': { min: 8, max: 35, typical: 20 },
            'JESS': { min: 10, max: 45, typical: 25 }
        };

        const latency = engineLatencies[engineType];
        const start = performance.now();

        const fireRules = () => {
            const firingTime = latency.min + Math.random() * (latency.max - latency.min);
            const iterations = Math.floor(firingTime * 600);

            const fired = [];
            for (let i = 0; i < iterations; i++) {
                if (i % 50 === 0) {
                    fired.push(`Rule_${i}`);
                }
                Math.sqrt(i) * Math.tan(i);
            }

            return {
                fired: fired.length,
                time: firingTime
            };
        };

        const result = fireRules();
        const end = performance.now();

        return {
            system: `Rule Engine (${engineType})`,
            rules,
            result,
            actualTime: end - start
        };
    }
}

async function runTraditionalBenchmarks() {
    console.log(chalk.cyan('\n=== Traditional Systems Performance Simulation ===\n'));
    console.log(chalk.yellow('Note: These are simulations based on published benchmarks\n'));

    const simulator = new TraditionalSystemSimulator();
    const results = {
        timestamp: new Date().toISOString(),
        type: 'Traditional Systems Simulation',
        disclaimer: 'Simulated based on published performance data',
        benchmarks: {}
    };

    const systems = [
        {
            name: 'GPT-4 (Simple)',
            fn: () => simulator.simulateGPT4Reasoning('simple query', 'simple'),
            expectedRange: [150, 300]
        },
        {
            name: 'GPT-4 (Complex)',
            fn: () => simulator.simulateGPT4Reasoning('complex query', 'complex'),
            expectedRange: [500, 800]
        },
        {
            name: 'Neural Theorem Prover',
            fn: () => simulator.simulateNeuralTheoremProver('theorem_1'),
            expectedRange: [200, 2000]
        },
        {
            name: 'OWL Reasoner (Pellet)',
            fn: () => simulator.simulateOWLReasoner('ontology_1', 'Pellet'),
            expectedRange: [50, 300]
        },
        {
            name: 'OWL Reasoner (HermiT)',
            fn: () => simulator.simulateOWLReasoner('ontology_1', 'HermiT'),
            expectedRange: [80, 500]
        },
        {
            name: 'Prolog System',
            fn: () => simulator.simulatePrologSystem('query(X, Y)'),
            expectedRange: [5, 50]
        },
        {
            name: 'CLIPS Rule Engine',
            fn: () => simulator.simulateRuleEngine(100, 'CLIPS'),
            expectedRange: [8, 35]
        },
        {
            name: 'JESS Rule Engine',
            fn: () => simulator.simulateRuleEngine(100, 'JESS'),
            expectedRange: [10, 45]
        }
    ];

    const table = new Table({
        head: ['System', 'Expected Range (ms)', 'Simulated (ms)', 'Status'],
        colWidths: [25, 20, 15, 10]
    });

    for (const system of systems) {
        console.log(chalk.green(`Simulating: ${system.name}`));

        const timings = [];
        const iterations = 1000;

        for (let i = 0; i < iterations; i++) {
            const result = system.fn();
            const time = result.simulatedTime || result.baseLatency || result.actualTime;
            timings.push(time);
        }

        const mean = stats.mean(timings);
        const median = stats.median(timings);
        const [minExpected, maxExpected] = system.expectedRange;

        const inRange = median >= minExpected * 0.9 && median <= maxExpected * 1.1;
        const status = inRange ? chalk.green('✓') : chalk.red('✗');

        results.benchmarks[system.name] = {
            iterations,
            mean: mean.toFixed(2),
            median: median.toFixed(2),
            expectedRange: system.expectedRange,
            inRange,
            unit: 'ms'
        };

        table.push([
            system.name,
            `${minExpected}-${maxExpected}`,
            median.toFixed(2),
            status
        ]);
    }

    console.log(chalk.cyan('\n=== Traditional Systems Simulation Results ===\n'));
    console.log(table.toString());

    const resultsDir = path.join(__dirname, '..', 'results');
    if (!fs.existsSync(resultsDir)) {
        fs.mkdirSync(resultsDir, { recursive: true });
    }

    const filename = `traditional-systems-${Date.now()}.json`;
    fs.writeFileSync(
        path.join(resultsDir, filename),
        JSON.stringify(results, null, 2)
    );

    console.log(chalk.green(`\n✓ Results saved to: results/${filename}`));

    return results;
}

if (import.meta.url === `file://${process.argv[1]}`) {
    runTraditionalBenchmarks().catch(console.error);
}

export { TraditionalSystemSimulator, runTraditionalBenchmarks };