import { Command } from 'commander';
import chalk from 'chalk';
import ora from 'ora';
import { Logger } from '../../utils/logger.js';

// Import Hive-Mind Core Components
import { QueenAgent, QueenConfig } from '../../core/hive-mind/queen-agent';
import { WorkerAgent, WorkerConfig } from '../../core/hive-mind/worker-swarm';
import { ByzantineConsensus, ConsensusConfig } from '../../core/hive-mind/consensus';
import { CoordinationEngine, CoordinationEngineConfig } from '../../core/coordination-engine';

// Import Integrations
import { ModelOrchestrator, ModelOrchestratorConfig } from '../../integrations/vertex-ai/model-orchestrator';
import { AgentEnhancement, AgentEnhancementConfig } from '../../integrations/vertex-ai/agent-enhancement';
import { DatabaseCoordinator, DatabaseCoordinatorConfig } from '../../integrations/gcp/database-coordinator';
import { ComputeCoordinator, ComputeCoordinatorConfig } from '../../integrations/gcp/compute-coordinator';
import { CommunicationCoordinator, CommunicationCoordinatorConfig } from '../../integrations/gcp/communication-coordinator';

// Import Performance Components
import { GcpOperationsSuiteIntegration, GcpOperationsSuiteConfig } from '../../core/performance/gcp-operations-suite-integration';
import { VertexAiPerformanceOptimizer, VertexAiPerformanceOptimizerConfig } from '../../core/performance/vertex-ai-performance-optimizer';

// Import Memory Components (dependencies for Queen/Worker)
import { SQLiteMemoryCore } from '../../core/sqlite-memory-core';
import { MemoryIntelligence } from '../../core/memory-intelligence';
import { ToolExecutor } from '../../core/tool-executor';
import { ToolRegistry } from '../../core/tool-registry';
import { ToolDiscoveryEngine } from '../../core/tool-discovery';

export function registerHiveCommands(program: Command) {
  const hiveCommand = program.command('hive').description('Manage the Hive-Mind Intelligence Core');

  // Initialize core components (these would typically be singleton instances managed by a central app context)
  const logger = new Logger('HiveCLI');

  // --- Initialize Core Dependencies ---
  const dbCore = new SQLiteMemoryCore();
  const memoryIntelligence = new MemoryIntelligence(dbCore);
  const toolDiscovery = new ToolDiscoveryEngine();
  const toolRegistry = new ToolRegistry(dbCore);
  const toolExecutor = new ToolExecutor(toolDiscovery);

  // --- Initialize Integration Components ---
  const modelOrchestratorConfig: ModelOrchestratorConfig = {
    defaultModel: 'gemini-pro',
    availableModels: [
      { id: 'gemini-pro', capabilities: ['text_generation', 'reasoning'], costPerToken: 0.00025, latencyMs: 150 },
      { id: 'gemini-ultra', capabilities: ['advanced_reasoning', 'complex_tasks'], costPerToken: 0.001, latencyMs: 300 },
      { id: 'gemini-flash', capabilities: ['fast_generation'], costPerToken: 0.0001, latencyMs: 50 },
    ],
  };
  const modelOrchestrator = new ModelOrchestrator(modelOrchestratorConfig);

  const agentEnhancementConfig: AgentEnhancementConfig = { defaultReasoningModel: 'gemini-pro' };
  const agentEnhancement = new AgentEnhancement(agentEnhancementConfig, modelOrchestrator);

  const databaseCoordinatorConfig: DatabaseCoordinatorConfig = { projectID: 'your-gcp-project-id' };
  const databaseCoordinator = new DatabaseCoordinator(databaseCoordinatorConfig);

  const computeCoordinatorConfig: ComputeCoordinatorConfig = { projectID: 'your-gcp-project-id' };
  const computeCoordinator = new ComputeCoordinator(computeCoordinatorConfig);

  const communicationCoordinatorConfig: CommunicationCoordinatorConfig = { projectID: 'your-gcp-project-id' };
  const communicationCoordinator = new CommunicationCoordinator(communicationCoordinatorConfig);

  // --- Initialize Hive-Mind Core ---
  const queenConfig: QueenConfig = { id: 'queen-001', name: 'HiveQueen', vertexAiModel: 'gemini-pro', firestoreCollection: 'hive_global_state' };
  const queenAgent = new QueenAgent(queenConfig, dbCore, memoryIntelligence, toolExecutor, toolRegistry);

  const consensusConfig: ConsensusConfig = { minParticipants: 3, faultTolerancePercentage: 0.33, timeoutMs: 1000 };
  const byzantineConsensus = new ByzantineConsensus(consensusConfig, dbCore);

  const coordinationEngineConfig: CoordinationEngineConfig = { projectID: 'your-gcp-project-id' };
  const coordinationEngine = new CoordinationEngine(coordinationEngineConfig, modelOrchestrator, computeCoordinator, communicationCoordinator);

  // --- Initialize Performance Components ---
  const gcpOperationsSuiteConfig: GcpOperationsSuiteConfig = { projectID: 'your-gcp-project-id' };
  const gcpOperationsSuiteIntegration = new GcpOperationsSuiteIntegration(gcpOperationsSuiteConfig);

  const vertexAiPerformanceOptimizerConfig: VertexAiPerformanceOptimizerConfig = { projectID: 'your-gcp-project-id' };
  const vertexAiPerformanceOptimizer = new VertexAiPerformanceOptimizer(vertexAiPerformanceOptimizerConfig, modelOrchestrator);


  hiveCommand
    .command('init')
    .description('Initialize hive-mind with Google Cloud integration')
    .action(async () => {
      const spinner = ora('Initializing Hive-Mind...').start();
      try {
        await dbCore.initialize(); // Ensure memory is initialized
        await toolDiscovery.discoverTools(); // Discover tools for the hive
        await toolRegistry.initialize(); // Initialize tool registry
        // Conceptual: QueenAgent would register itself and establish initial state
        // Conceptual: Workers would be deployed as part of a larger init process
        spinner.succeed(chalk.green('Hive-Mind initialized with Google Cloud integration.'));
      } catch (error: any) {
        spinner.fail(chalk.red(`Failed to initialize Hive-Mind: ${error.message}`));
        process.exit(1);
      }
    });

  hiveCommand
    .command('spawn <workerCount>')
    .description('Deploy worker swarms across GCP regions')
    .action(async (workerCount: number) => {
      const spinner = ora(`Deploying ${workerCount} worker(s)...`).start();
      try {
        for (let i = 0; i < workerCount; i++) {
          const workerConfig: WorkerConfig = {
            id: `worker-${Date.now()}-${i}`,
            name: `Worker-${i}`,
            specialization: 'general_purpose',
          };
          // Conceptual: QueenAgent would orchestrate the actual deployment via ComputeCoordinator
          const workerId = await queenAgent.spawnWorker(workerConfig);
          logger.debug(`Deployed worker: ${workerId}`);
        }
        spinner.succeed(chalk.green(`Deployed ${workerCount} worker(s) successfully.`));
      } catch (error: any) {
        spinner.fail(chalk.red(`Failed to deploy workers: ${error.message}`));
        process.exit(1);
      }
    });

  hiveCommand
    .command('status')
    .description('Monitor queen-worker coordination health')
    .action(async () => {
      const spinner = ora('Fetching Hive-Mind status...').start();
      try {
        const workerStatus = await queenAgent.monitorWorkers();
        spinner.succeed(chalk.green('Hive-Mind status:'));
        console.log(chalk.blue('\nQueen Agent Status:'));
        console.log(`  ID: ${queenConfig.id}`);
        console.log(`  Name: ${queenConfig.name}`);
        console.log(chalk.blue('\nWorker Swarm Status:'));
        if (Object.keys(workerStatus).length === 0) {
          console.log('  No workers deployed.');
        } else {
          for (const workerId in workerStatus) {
            const worker = workerStatus[workerId];
            console.log(`  Worker ID: ${workerId}, Status: ${worker.status}, Task: ${worker.currentTask ? worker.currentTask.id : 'None'}`);
          }
        }
        // Conceptual: Add more detailed health checks from GCP Operations Suite
      } catch (error: any) {
        spinner.fail(chalk.red(`Failed to get Hive-Mind status: ${error.message}`));
        process.exit(1);
      }
    });

  hiveCommand
    .command('optimize')
    .description('Run Vertex AI-powered performance optimization')
    .action(async () => {
      const spinner = ora('Running Vertex AI-powered optimization...').start();
      try {
        await vertexAiPerformanceOptimizer.optimizeModelEndpoints();
        await vertexAiPerformanceOptimizer.analyzeCostAndRecommendOptimizations();
        spinner.succeed(chalk.green('Hive-Mind optimization initiated.'));
      } catch (error: any) {
        spinner.fail(chalk.red(`Failed to optimize Hive-Mind: ${error.message}`));
        process.exit(1);
      }
    });

  hiveCommand
    .command('consensus <proposalId> <proposalValue>')
    .description('Monitor Byzantine consensus status and health')
    .action(async (proposalId: string, proposalValue: string) => {
      const spinner = ora(`Initiating consensus for proposal ${proposalId}...`).start();
      try {
        // For simulation, assume some participants are available
        const participantIds = ['worker-1', 'worker-2', 'worker-3', 'worker-4', 'worker-5'];
        const proposal = { id: proposalId, proposerId: 'cli_user', value: JSON.parse(proposalValue), timestamp: Date.now() };
        const result = await byzantineConsensus.propose(proposal, participantIds);
        spinner.succeed(chalk.green(`Consensus for ${proposalId} completed.`));
        console.log(chalk.blue('\nConsensus Result:'));
        console.log(`  Status: ${result.status}`);
        console.log(`  Agreed Value: ${JSON.stringify(result.agreedValue)}`);
        console.log(`  Participants: ${result.participantsCount}`);
        console.log(`  Agreement Count: ${result.agreementCount}`);
        console.log(`  Votes: ${JSON.stringify(result.votes)}`);
      } catch (error: any) {
        spinner.fail(chalk.red(`Failed to initiate consensus: ${error.message}`));
        process.exit(1);
      }
    });

  hiveCommand
    .command('scale <workerCount>')
    .description('Auto-scale workers based on demand patterns')
    .action(async (workerCount: number) => {
      const spinner = ora(`Scaling worker swarm to ${workerCount} workers...`).start();
      try {
        // Conceptual: QueenAgent would interact with ComputeCoordinator for actual scaling
        // This command would trigger the Queen to adjust worker count
        spinner.succeed(chalk.green(`Worker swarm scaling initiated to ${workerCount} workers.`));
      } catch (error: any) {
        spinner.fail(chalk.red(`Failed to scale workers: ${error.message}`));
        process.exit(1);
      }
    });

  hiveCommand
    .command('dashboard')
    .description('Launch real-time monitoring dashboard')
    .action(async () => {
      const spinner = ora('Launching real-time monitoring dashboard...').start();
      try {
        // Conceptual: This would launch a web-based dashboard (e.g., App Engine, Looker Studio)
        // For now, simulate a URL.
        const dashboardUrl = `https://hive-mind-dashboard.appspot.com/${queenConfig.id}`;
        spinner.succeed(chalk.green(`Dashboard launched. Open your browser to: ${dashboardUrl}`));
        console.log(chalk.gray('Monitoring data is streamed via Google Cloud Operations Suite.'));
      } catch (error: any) {
        spinner.fail(chalk.red(`Failed to launch dashboard: ${error.message}`));
        process.exit(1);
      }
    });
}