import { createHash } from 'crypto';
import { prisma, VoiceEnrollment, VoiceAnalysis } from '@shieldai/db';
import {
  voicePrintEnv,
  AnalysisJobStatus,
  DetectionType,
  audioPreprocessingConfig,
  voicePrintFeatureFlags,
} from './voiceprint.config';
import { checkFlag } from './voiceprint.feature-flags';
import { logger } from './logger';
import { EmbeddingService as ModularEmbeddingService } from './embedding.service';
import { FAISSIndex as ModularFAISSIndex } from './faiss.index';

// Alias for backwards compatibility
const EmbeddingService = ModularEmbeddingService;
const FAISSIndex = ModularFAISSIndex;

// Audio preprocessing service
export class AudioPreprocessor {
  /**
   * Normalize audio to 16kHz mono with VAD and noise reduction.
   * Returns preprocessing metadata and the processed audio buffer.
   */
  async preprocess(
    audioBuffer: Buffer,
    options?: {
      sourceSampleRate?: number;
      channels?: number;
    }
  ): Promise<{
    buffer: Buffer;
    metadata: {
      sampleRate: number;
      channels: number;
      duration: number;
      format: string;
    };
  }> {
    const duration = this.estimateDuration(audioBuffer, options?.sourceSampleRate ?? 44100);

    if (duration < voicePrintEnv.ENROLLMENT_MIN_DURATION_SEC) {
      throw new Error(
        `Audio too short: ${duration.toFixed(1)}s < ${voicePrintEnv.ENROLLMENT_MIN_DURATION_SEC}s minimum`
      );
    }

    if (duration > voicePrintEnv.ENROLLMENT_MAX_DURATION_SEC) {
      throw new Error(
        `Audio too long: ${duration.toFixed(1)}s > ${voicePrintEnv.ENROLLMENT_MAX_DURATION_SEC}s maximum`
      );
    }

    // TODO: Integrate with Python librosa/torchaudio for actual preprocessing
    // For MVP, return original buffer with target metadata
    return {
      buffer: audioBuffer,
      metadata: {
        sampleRate: audioPreprocessingConfig.sampleRate,
        channels: audioPreprocessingConfig.channels,
        duration,
        format: 'wav',
      },
    };
  }

  /**
   * Apply Voice Activity Detection to remove silence segments.
   */
  async applyVAD(buffer: Buffer): Promise<Buffer> {
    // TODO: Integrate with Python webrtcvad or silero-vad
    // For MVP, return original buffer
    return buffer;
  }

  /**
   * Estimate audio duration from buffer size and sample rate.
   */
  private estimateDuration(
    buffer: Buffer,
    sampleRate: number
  ): number {
    const bytesPerSample = 2;
    const channels = 1;
    const samples = buffer.length / (bytesPerSample * channels);
    return samples / sampleRate;
  }
}

// Voice enrollment service
export class VoiceEnrollmentService {
  /**
   * Enroll a new voice profile from audio data.
   */
  async enroll(
    userId: string,
    name: string,
    audioBuffer: Buffer
  ): Promise<VoiceEnrollment> {
    const preprocessor = new AudioPreprocessor();
    const processed = await preprocessor.preprocess(audioBuffer);

    const embeddingService = new EmbeddingService();
    const embedding = await embeddingService.extract(processed.buffer);
    const voiceHash = this.computeEmbeddingHash(embedding);

    const enrollment = await prisma.voiceEnrollment.create({
      data: {
        userId,
        name,
        voiceHash,
        audioMetadata: {
          ...processed.metadata,
          embeddingDimensions: embedding.length,
          enrollmentTimestamp: new Date().toISOString(),
        },
      },
    });

    // Index in FAISS for similarity search
    const faissIndex = new FAISSIndex();
    await faissIndex.add(enrollment.id, embedding);

    return enrollment;
  }

  /**
   * List all enrollments for a user.
   */
  async listEnrollments(
    userId: string,
    options?: {
      isActive?: boolean;
      limit?: number;
      offset?: number;
    }
  ): Promise<VoiceEnrollment[]> {
    return prisma.voiceEnrollment.findMany({
      where: {
        userId,
        ...(options?.isActive !== undefined && { isActive: options.isActive }),
      },
      orderBy: { createdAt: 'desc' },
      take: options?.limit ?? 50,
      skip: options?.offset ?? 0,
    });
  }

  /**
   * Get a single enrollment by ID.
   */
  async getEnrollment(
    enrollmentId: string,
    userId: string
  ): Promise<VoiceEnrollment | null> {
    return prisma.voiceEnrollment.findFirst({
      where: {
        id: enrollmentId,
        userId,
      },
    });
  }

  /**
   * Remove (deactivate) an enrollment.
   */
  async removeEnrollment(
    enrollmentId: string,
    userId: string
  ): Promise<VoiceEnrollment> {
    const enrollment = await this.getEnrollment(enrollmentId, userId);
    if (!enrollment) {
      throw new Error('Enrollment not found');
    }

    const faissIndex = new FAISSIndex();
    await faissIndex.remove(enrollmentId);

    return prisma.voiceEnrollment.update({
      where: { id: enrollmentId },
      data: { isActive: false },
    });
  }

  /**
   * Search for similar enrollments using FAISS.
   */
  async findSimilar(
    embedding: number[],
    topK: number = 5
  ): Promise<Array<{ enrollment: VoiceEnrollment; similarity: number }>> {
    const faissIndex = new FAISSIndex();
    const results = await faissIndex.search(embedding, topK);

    const enrollmentIds = results.map((r) => r.id);
    const enrollments = await prisma.voiceEnrollment.findMany({
      where: { id: { in: enrollmentIds } },
    });
    const enrollmentMap = new Map(enrollments.map((e) => [e.id, e]));

    return results
      .map((r) => ({
        enrollment: enrollmentMap.get(r.id),
        similarity: r.similarity,
      }))
      .filter((r): r is { enrollment: VoiceEnrollment; similarity: number } => r.enrollment !== undefined);
  }

  private computeEmbeddingHash(embedding: number[]): string {
    const content = embedding.map((v) => v.toFixed(6)).join(',');
    return `vp_${createHash('sha256').update(content).digest('hex').slice(0, 16)}_${embedding.length}`;
  }
}

// Audio analysis service
export class AnalysisService {
  /**
   * Analyze a single audio file for synthetic voice detection.
   */
  async analyze(
    userId: string,
    audioBuffer: Buffer,
    options?: {
      enrollmentId?: string;
      audioUrl?: string;
    }
  ): Promise<VoiceAnalysis> {
    const preprocessor = new AudioPreprocessor();
    const processed = await preprocessor.preprocess(audioBuffer);

    const audioHash = this.computeAudioHash(audioBuffer);

    const embeddingService = new EmbeddingService();
    const analysisResult = await embeddingService.analyze(processed.buffer);

    const isSynthetic = analysisResult.confidence >= voicePrintEnv.SYNTHETIC_THRESHOLD;

    const voiceAnalysis = await prisma.voiceAnalysis.create({
      data: {
        userId,
        enrollmentId: options?.enrollmentId,
        audioHash,
        isSynthetic,
        confidence: analysisResult.confidence,
        analysisResult: {
          ...analysisResult,
          processedMetadata: processed.metadata,
          analysisTimestamp: new Date().toISOString(),
          modelVersion: 'ecapa-tdnn-v1-mock',
        },
        audioUrl: options?.audioUrl ?? '',
      },
    });

    return voiceAnalysis;
  }

  /**
   * Get analysis result by ID.
   */
  async getResult(
    analysisId: string,
    userId: string
  ): Promise<VoiceAnalysis | null> {
    return prisma.voiceAnalysis.findFirst({
      where: {
        id: analysisId,
        userId,
      },
    });
  }

  /**
   * Get analysis history for a user.
   */
  async getHistory(
    userId: string,
    options?: {
      limit?: number;
      offset?: number;
      isSynthetic?: boolean;
    }
  ): Promise<VoiceAnalysis[]> {
    return prisma.voiceAnalysis.findMany({
      where: {
        userId,
        ...(options?.isSynthetic !== undefined && { isSynthetic: options.isSynthetic }),
      },
      orderBy: { createdAt: 'desc' },
      take: options?.limit ?? 50,
      skip: options?.offset ?? 0,
    });
  }

  private computeAudioHash(buffer: Buffer): string {
    return `audio_${createHash('sha256').update(buffer).digest('hex').slice(0, 16)}`;
  }
}

// Batch analysis service
export class BatchAnalysisService {
  private readonly maxConcurrency = 5;

  /**
   * Analyze multiple audio files in a batch with parallel processing.
   * Uses Promise.allSettled with concurrency control for better performance.
   */
  async analyzeBatch(
    userId: string,
    files: Array<{
      name: string;
      buffer: Buffer;
      audioUrl?: string;
    }>,
    options?: {
      enrollmentId?: string;
    }
  ): Promise<{
    jobId: string;
    results: VoiceAnalysis[];
    summary: {
      total: number;
      synthetic: number;
      natural: number;
      failed: number;
    };
  }> {
    if (files.length > voicePrintEnv.BATCH_MAX_FILES) {
      throw new Error(
        `Batch too large: ${files.length} > ${voicePrintEnv.BATCH_MAX_FILES} max`
      );
    }

    const jobId = `batch_${Date.now()}_${Math.random().toString(36).slice(2, 8)}`;
    logger.info('Starting batch analysis', { 
      jobId, 
      userId, 
      totalFiles: files.length,
      enrollmentId: options?.enrollmentId 
    });

    const analysisService = new AnalysisService();
    const results: VoiceAnalysis[] = [];
    const errors: Array<{ name: string; error: string }> = [];
    let synthetic = 0;
    let natural = 0;

    // Process with concurrency control using chunked Promise.allSettled
    const processChunk = async (chunk: typeof files) => {
      const promises = chunk.map(async (file) => {
        try {
          const result = await analysisService.analyze(userId, file.buffer, {
            enrollmentId: options?.enrollmentId,
            audioUrl: file.audioUrl,
          });
          return { success: true as const, result, name: file.name };
        } catch (error) {
          const message = error instanceof Error ? error.message : 'Analysis failed';
          return { success: false as const, error: message, name: file.name };
        }
      });

      const outcomes = await Promise.allSettled(promises);

      for (const outcome of outcomes) {
        if (outcome.status === 'fulfilled') {
          if (outcome.value.success && outcome.value.result) {
            results.push(outcome.value.result);
            if (outcome.value.result.isSynthetic) {
              synthetic++;
            } else {
              natural++;
            }
          } else if (!outcome.value.success) {
            errors.push({ name: outcome.value.name, error: outcome.value.error });
          }
        }
      }
    };

    // Process files in chunks for concurrency control
    for (let i = 0; i < files.length; i += this.maxConcurrency) {
      const chunk = files.slice(i, i + this.maxConcurrency);
      await processChunk(chunk);
    }

    const failed = errors.length;

    // TODO: P3-2 - Persist batch jobId to database once schema is fixed
    // Schema errors need to be resolved first (AnalysisJob relation issues)
    logger.info('Batch analysis completed', { 
      jobId, 
      successfulResults: results.length, 
      failedCount: failed,
      synthetic,
      natural
    });

    return {
      jobId,
      results,
      summary: {
        total: files.length,
        synthetic,
        natural,
        failed,
      },
    };
  }
}

// Re-export improved modular implementations
export { EmbeddingService } from './embedding.service';
export { FAISSIndex } from './faiss.index';

// Export singleton instances for backwards compatibility
export const audioPreprocessor = new AudioPreprocessor();
export const voiceEnrollmentService = new VoiceEnrollmentService();
export const analysisService = new AnalysisService();
export const batchAnalysisService = new BatchAnalysisService();
export const embeddingService = new EmbeddingService();