/**
 * Distance functions for vector similarity calculations
 * Optimized for Node.js 23.11+ using enhanced array methods
 * GPU-accelerated versions available for high-performance computing
 */

import { DistanceFunction, Vector } from '../coreTypes.js'
import { executeInThread } from './workerUtils.js'
import { isThreadingAvailable } from './environment.js'

/**
 * Calculates the Euclidean distance between two vectors
 * Lower values indicate higher similarity
 * Optimized using array methods for Node.js 23.11+
 */
export const euclideanDistance: DistanceFunction = (
  a: Vector,
  b: Vector
): number => {
  if (a.length !== b.length) {
    throw new Error('Vectors must have the same dimensions')
  }

  // Use array.reduce for better performance in Node.js 23.11+
  const sum = a.reduce((acc, val, i) => {
    const diff = val - b[i]
    return acc + diff * diff
  }, 0)

  return Math.sqrt(sum)
}

/**
 * Calculates the cosine distance between two vectors
 * Lower values indicate higher similarity
 * Range: 0 (identical) to 2 (opposite)
 * Optimized using array methods for Node.js 23.11+
 */
export const cosineDistance: DistanceFunction = (
  a: Vector,
  b: Vector
): number => {
  if (a.length !== b.length) {
    throw new Error('Vectors must have the same dimensions')
  }

  // Use array.reduce to calculate all values in a single pass
  const { dotProduct, normA, normB } = a.reduce(
    (acc, val, i) => {
      return {
        dotProduct: acc.dotProduct + val * b[i],
        normA: acc.normA + val * val,
        normB: acc.normB + b[i] * b[i]
      }
    },
    { dotProduct: 0, normA: 0, normB: 0 }
  )

  if (normA === 0 || normB === 0) {
    return 2 // Maximum distance for zero vectors
  }

  const similarity = dotProduct / (Math.sqrt(normA) * Math.sqrt(normB))
  // Convert cosine similarity (-1 to 1) to distance (0 to 2)
  return 1 - similarity
}

/**
 * Calculates the Manhattan (L1) distance between two vectors
 * Lower values indicate higher similarity
 * Optimized using array methods for Node.js 23.11+
 */
export const manhattanDistance: DistanceFunction = (
  a: Vector,
  b: Vector
): number => {
  if (a.length !== b.length) {
    throw new Error('Vectors must have the same dimensions')
  }

  // Use array.reduce for better performance in Node.js 23.11+
  return a.reduce((sum, val, i) => sum + Math.abs(val - b[i]), 0)
}

/**
 * Calculates the dot product similarity between two vectors
 * Higher values indicate higher similarity
 * Converted to a distance metric (lower is better)
 * Optimized using array methods for Node.js 23.11+
 */
export const dotProductDistance: DistanceFunction = (
  a: Vector,
  b: Vector
): number => {
  if (a.length !== b.length) {
    throw new Error('Vectors must have the same dimensions')
  }

  // Use array.reduce for better performance in Node.js 23.11+
  const dotProduct = a.reduce((sum, val, i) => sum + val * b[i], 0)

  // Convert to a distance metric (lower is better)
  return -dotProduct
}

/**
 * Batch distance calculation
 * Uses TensorFlow.js with CPU backend for optimized performance
 *
 * @param queryVector The query vector to compare against all vectors
 * @param vectors Array of vectors to compare against
 * @param distanceFunction The distance function to use
 * @returns Promise resolving to array of distances
 */
export async function calculateDistancesBatch(
  queryVector: Vector,
  vectors: Vector[],
  distanceFunction: DistanceFunction = euclideanDistance
): Promise<number[]> {
  // For small batches, use the standard distance function
  if (vectors.length < 10) {
    return vectors.map((vector) => distanceFunction(queryVector, vector))
  }

  try {
    // Function to be executed in a worker thread
    const distanceCalculator = async (args: {
      queryVector: Vector
      vectors: Vector[]
      distanceFnString: string
    }) => {
      const { queryVector, vectors, distanceFnString } = args

      // Use TensorFlow.js with CPU processing
      const useTensorFlow = async () => {
        // TensorFlow.js will use its default EPSILON value

        // Use the importTensorFlow function if available (in worker context)
        // or directly import TensorFlow.js (in main thread)
        let tf
        if (
          typeof self !== 'undefined' &&
          typeof self.importTensorFlow === 'function'
        ) {
          // In worker context, use the importTensorFlow function
          tf = await self.importTensorFlow()
        } else {
          // Dynamically import TensorFlow.js core module and backends
          tf = await import('@tensorflow/tfjs-core')

          // Import CPU backend
          await import('@tensorflow/tfjs-backend-cpu')

          // Set CPU as the backend
          await tf.setBackend('cpu')
        }

        // Convert vectors to tensors
        const queryTensor = tf.tensor2d([queryVector])
        const vectorsTensor = tf.tensor2d(vectors)

        let distances: number[]

        // Calculate distances based on the distance function type
        if (distanceFnString.includes('euclideanDistance')) {
          // Euclidean distance using GPU-optimized operations
          // Formula: sqrt(sum((a - b)^2))
          const expanded = tf.sub(
            (queryTensor as any).expandDims(1),
            (vectorsTensor as any).expandDims(0)
          )
          const squaredDiff = tf.square(expanded)
          const sumSquaredDiff = tf.sum(squaredDiff, -1)
          const distancesTensor = tf.sqrt(sumSquaredDiff)
          distances = (await (distancesTensor as any)
            .squeeze()
            .array()) as number[]

          // Clean up tensors
          queryTensor.dispose()
          vectorsTensor.dispose()
          expanded.dispose()
          squaredDiff.dispose()
          sumSquaredDiff.dispose()
          distancesTensor.dispose()
        } else if (distanceFnString.includes('cosineDistance')) {
          // Cosine distance using GPU-optimized operations
          // Formula: 1 - (a·b / (||a|| * ||b||))
          const dotProduct = tf.matMul(
            queryTensor,
            (vectorsTensor as any).transpose()
          )

          const queryNorm = tf.norm(queryTensor, 2, 1)
          const vectorsNorm = tf.norm(vectorsTensor, 2, 1)

          const normProduct = tf.outerProduct(
            queryNorm as any,
            vectorsNorm as any
          )
          const cosineSimilarity = tf.div(dotProduct, normProduct)
          const distancesTensor = tf.sub(tf.scalar(1), cosineSimilarity)

          distances = (await (distancesTensor as any)
            .squeeze()
            .array()) as number[]

          // Clean up tensors
          queryTensor.dispose()
          vectorsTensor.dispose()
          dotProduct.dispose()
          queryNorm.dispose()
          vectorsNorm.dispose()
          normProduct.dispose()
          cosineSimilarity.dispose()
          distancesTensor.dispose()
        } else if (distanceFnString.includes('manhattanDistance')) {
          // Manhattan distance using GPU-optimized operations
          // Formula: sum(|a - b|)
          const diff = tf.sub(
            (queryTensor as any).expandDims(1),
            (vectorsTensor as any).expandDims(0)
          )
          const absDiff = tf.abs(diff)
          const distancesTensor = tf.sum(absDiff, -1)

          distances = (await (distancesTensor as any)
            .squeeze()
            .array()) as number[]

          // Clean up tensors
          queryTensor.dispose()
          vectorsTensor.dispose()
          diff.dispose()
          absDiff.dispose()
          distancesTensor.dispose()
        } else if (distanceFnString.includes('dotProductDistance')) {
          // Dot product distance using GPU-optimized operations
          // Formula: -sum(a * b)
          const dotProduct = tf.matMul(
            queryTensor,
            (vectorsTensor as any).transpose()
          )
          const distancesTensor = tf.neg(dotProduct)

          distances = (await (distancesTensor as any)
            .squeeze()
            .array()) as number[]

          // Clean up tensors
          queryTensor.dispose()
          vectorsTensor.dispose()
          dotProduct.dispose()
          distancesTensor.dispose()
        } else {
          // For unknown distance functions, fall back to direct CPU implementation
          throw new Error(
            'Unsupported distance function for TensorFlow optimization'
          )
        }

        return {
          distances
        }
      }

      // Try to use TensorFlow.js with CPU optimization
      try {
        return await useTensorFlow()
      } catch (error) {
        // Fall back to direct CPU implementation if TensorFlow.js fails
        // Recreate the distance function from its string representation
        const distanceFunction = new Function(
          'return ' + distanceFnString
        )() as DistanceFunction

        // Calculate distances for all vectors
        const distances = vectors.map((vector) =>
          distanceFunction(queryVector, vector)
        )

        return {
          distances
        }
      }
    }

    // Threading is not available, so we'll always use the main thread implementation
    // This comment is kept for clarity about the removed code

    // If threading is not available or failed, calculate distances in the main thread
    return vectors.map((vector) => distanceFunction(queryVector, vector))
  } catch (error) {
    // If anything fails, fall back to the standard distance function
    console.error('Batch distance calculation failed:', error)
    return vectors.map((vector) => distanceFunction(queryVector, vector))
  }
}