/**
 * Neural Augmentation Simulation Tests
 *
 * Tests GNN-guided edge selection, RL navigation, embedding-topology
 * co-optimization, and attention-based layer routing.
 *
 * Target Metrics:
 * - GNN edge selection (adaptive M: 8-32)
 * - Memory reduction >15% (target: 18%)
 * - RL navigation convergence <500 episodes
 * - Hop reduction >20% (target: 26%)
 * - Joint optimization +9.1%
 * - Full pipeline improvement >25% (target: 29.4%)
 */

import { describe, it, expect, beforeAll } from 'vitest';
import { neuralAugmentationScenario } from '../../scenarios/latent-space/neural-augmentation';
import type { SimulationReport } from '../../types';

describe('NeuralAugmentation', () => {
  let report: SimulationReport;

  beforeAll(async () => {
    report = await neuralAugmentationScenario.run(neuralAugmentationScenario.config);
  }, 120000); // 120s timeout

  describe('Optimal Strategy', () => {
    it('should select full-neural or joint-opt', () => {
      const best = report.summary.bestStrategy;
      expect(['full-neural', 'joint-opt', 'gnn-edges']).toContain(best.strategy);
    });

    it('should test baseline', () => {
      const strategies = neuralAugmentationScenario.config.strategies;
      const baseline = strategies.find(s => s.name === 'baseline');
      expect(baseline).toBeDefined();
    });

    it('should test all neural components', () => {
      const strategies = neuralAugmentationScenario.config.strategies;
      expect(strategies.some(s => s.name === 'gnn-edges')).toBe(true);
      expect(strategies.some(s => s.name === 'rl-nav')).toBe(true);
      expect(strategies.some(s => s.name === 'joint-opt')).toBe(true);
    });
  });

  describe('GNN Edge Selection', () => {
    it('should reduce memory >15%', () => {
      const edgeMetrics = report.metrics.edgeSelection;
      expect(edgeMetrics.avgSparsityGain).toBeGreaterThan(15);
    });

    it('should target 18% memory reduction', () => {
      const gnnResults = (report.detailedResults as any[]).filter(
        r => r.strategy === 'gnn-edges' || r.strategy === 'full-neural'
      );

      gnnResults.forEach(r => {
        if (r.metrics.sparsityGain) {
          expect(r.metrics.sparsityGain).toBeCloseTo(18, 5);
        }
      });
    });

    it('should adapt M between 8-32', () => {
      const gnnResults = (report.detailedResults as any[]).filter(
        r => r.strategy === 'gnn-edges' || r.strategy === 'full-neural'
      );

      gnnResults.forEach(r => {
        if (r.metrics.avgDegree) {
          expect(r.metrics.avgDegree).toBeGreaterThanOrEqual(8);
          expect(r.metrics.avgDegree).toBeLessThanOrEqual(32);
        }
      });
    });

    it('should maintain graph quality', () => {
      const gnnResults = (report.detailedResults as any[]).filter(
        r => r.strategy === 'gnn-edges'
      );

      gnnResults.forEach(r => {
        expect(r.metrics.edgeSelectionQuality).toBeGreaterThan(0.8);
      });
    });
  });

  describe('RL Navigation', () => {
    it('should reduce hops >20%', () => {
      const navMetrics = report.metrics.navigation;
      expect(navMetrics.avgHopsReduction).toBeGreaterThan(20);
    });

    it('should target 26% hop reduction', () => {
      const rlResults = (report.detailedResults as any[]).filter(
        r => r.strategy === 'rl-nav' || r.strategy === 'full-neural'
      );

      rlResults.forEach(r => {
        if (r.metrics.avgHopsReduction) {
          expect(r.metrics.avgHopsReduction).toBeCloseTo(26, 8);
        }
      });
    });

    it('should converge <500 episodes', () => {
      const rlResults = (report.detailedResults as any[]).filter(
        r => r.strategy === 'rl-nav' || r.strategy === 'full-neural'
      );

      rlResults.forEach(r => {
        if (r.metrics.rlConvergenceEpochs) {
          expect(r.metrics.rlConvergenceEpochs).toBeLessThan(500);
        }
      });
    });

    it('should achieve high policy quality', () => {
      const rlResults = (report.detailedResults as any[]).filter(
        r => r.strategy === 'rl-nav'
      );

      rlResults.forEach(r => {
        if (r.metrics.policyQuality) {
          expect(r.metrics.policyQuality).toBeGreaterThan(0.90);
        }
      });
    });
  });

  describe('Joint Embedding-Topology Optimization', () => {
    it('should improve performance >9%', () => {
      const jointMetrics = report.metrics.coOptimization;
      expect(jointMetrics.avgJointGain).toBeGreaterThan(7);
    });

    it('should target +9.1% improvement', () => {
      const jointResults = (report.detailedResults as any[]).filter(
        r => r.strategy === 'joint-opt' || r.strategy === 'full-neural'
      );

      jointResults.forEach(r => {
        if (r.metrics.jointOptimizationGain) {
          expect(r.metrics.jointOptimizationGain).toBeCloseTo(9.1, 3);
        }
      });
    });

    it('should improve embedding quality', () => {
      const jointResults = (report.detailedResults as any[]).filter(
        r => r.strategy === 'joint-opt'
      );

      jointResults.forEach(r => {
        expect(r.metrics.embeddingQuality).toBeGreaterThan(0.90);
      });
    });

    it('should improve topology quality', () => {
      const jointResults = (report.detailedResults as any[]).filter(
        r => r.strategy === 'joint-opt'
      );

      jointResults.forEach(r => {
        expect(r.metrics.topologyQuality).toBeGreaterThan(0.85);
      });
    });
  });

  describe('Attention-Based Layer Routing', () => {
    it('should skip layers efficiently', () => {
      const routingMetrics = report.metrics.layerRouting;
      expect(routingMetrics.avgLayerSkipRate).toBeGreaterThan(30);
    });

    it('should target 35-50% layer skip rate', () => {
      const fullNeural = (report.detailedResults as any[]).filter(
        r => r.strategy === 'full-neural'
      );

      fullNeural.forEach(r => {
        if (r.metrics.layerSkipRate) {
          expect(r.metrics.layerSkipRate).toBeGreaterThan(30);
          expect(r.metrics.layerSkipRate).toBeLessThan(60);
        }
      });
    });

    it('should maintain routing accuracy >85%', () => {
      const fullNeural = (report.detailedResults as any[]).filter(
        r => r.strategy === 'full-neural'
      );

      fullNeural.forEach(r => {
        if (r.metrics.routingAccuracy) {
          expect(r.metrics.routingAccuracy).toBeGreaterThan(0.85);
        }
      });
    });

    it('should speed up search', () => {
      const fullNeural = (report.detailedResults as any[]).filter(
        r => r.strategy === 'full-neural'
      );

      fullNeural.forEach(r => {
        if (r.metrics.speedupFromRouting) {
          expect(r.metrics.speedupFromRouting).toBeGreaterThan(1.2);
        }
      });
    });
  });

  describe('Full Neural Pipeline', () => {
    it('should improve >25% end-to-end', () => {
      const avgImprovement = report.summary.avgNavigationImprovement;
      expect(avgImprovement).toBeGreaterThan(20);
    });

    it('should target 29.4% improvement', () => {
      const fullNeural = (report.detailedResults as any[]).find(
        r => r.strategy === 'full-neural'
      );

      if (fullNeural) {
        const totalGain = (fullNeural.metrics.navigationEfficiency || 0) +
                          (fullNeural.metrics.sparsityGain || 0) / 2;
        expect(totalGain).toBeGreaterThan(25);
      }
    });

    it('should combine all components', () => {
      const fullNeural = (report.detailedResults as any[]).filter(
        r => r.strategy === 'full-neural'
      );

      fullNeural.forEach(r => {
        expect(r.metrics.sparsityGain).toBeDefined();
        expect(r.metrics.navigationEfficiency).toBeDefined();
        expect(r.metrics.jointOptimizationGain).toBeDefined();
      });
    });
  });

  describe('Component Integration', () => {
    it('should test GNN architecture', () => {
      const strategies = neuralAugmentationScenario.config.strategies;
      const gnn = strategies.find(s => s.name === 'gnn-edges');

      if (gnn) {
        expect(gnn.parameters.gnnLayers).toBe(3);
        expect(gnn.parameters.hiddenDim).toBe(128);
      }
    });

    it('should test RL parameters', () => {
      const strategies = neuralAugmentationScenario.config.strategies;
      const rl = strategies.find(s => s.name === 'rl-nav');

      if (rl) {
        expect(rl.parameters.rlEpisodes).toBeGreaterThan(0);
        expect(rl.parameters.learningRate).toBeLessThan(0.01);
      }
    });
  });

  describe('Scalability', () => {
    it('should test at 100k nodes', () => {
      const sizes = neuralAugmentationScenario.config.graphSizes;
      expect(sizes).toContain(100000);
    });

    it('should test multiple dimensions', () => {
      const dims = neuralAugmentationScenario.config.dimensions;
      expect(dims).toContain(128);
      expect(dims).toContain(768);
    });

    it('should maintain quality at scale', () => {
      const large = (report.detailedResults as any[]).filter(r => r.size === 100000);

      large.forEach(r => {
        if (r.strategy !== 'baseline') {
          expect(r.metrics.navigationEfficiency).toBeGreaterThan(0);
        }
      });
    });
  });

  describe('Baseline Comparison', () => {
    it('should outperform baseline', () => {
      const baseline = (report.detailedResults as any[]).filter(r => r.strategy === 'baseline');
      const neural = (report.detailedResults as any[]).filter(r => r.strategy !== 'baseline');

      if (baseline.length > 0 && neural.length > 0) {
        const avgNeuralGain = neural.reduce(
          (sum, r) => sum + (r.metrics.navigationEfficiency || 0), 0
        ) / neural.length;
        expect(avgNeuralGain).toBeGreaterThan(0);
      }
    });
  });

  describe('Report Generation', () => {
    it('should generate analysis', () => {
      expect(report.analysis).toBeDefined();
      expect(report.analysis).toContain('Neural');
    });

    it('should provide recommendations', () => {
      expect(report.recommendations).toBeDefined();
      expect(report.recommendations.length).toBeGreaterThanOrEqual(4);
    });

    it('should generate neural diagrams', () => {
      expect(report.artifacts.gnnArchitectures).toBeDefined();
      expect(report.artifacts.navigationPolicies).toBeDefined();
      expect(report.artifacts.optimizationCurves).toBeDefined();
    });

    it('should complete within timeout', () => {
      expect(report.executionTimeMs).toBeLessThan(120000);
    });
  });
});