// +build cgo
// +build arm64

package vectorized

// ARM64 SIMD implementations (stub for now)
// Real implementation would use NEON intrinsics

// SIMDAddFloat64CGO performs vectorized addition (ARM64 stub)
func SIMDAddFloat64CGO(a, b, result []float64) {
	// Fallback to manual implementation on ARM64
	simdAddFloat64Manual(a, b, result)
}

// SIMDMultiplyFloat64CGO performs vectorized multiplication (ARM64 stub)
func SIMDMultiplyFloat64CGO(a, b, result []float64) {
	// Fallback to manual implementation
	for i := range a {
		result[i] = a[i] * b[i]
	}
}

// SIMDSubtractFloat64CGO performs vectorized subtraction (ARM64 stub)
func SIMDSubtractFloat64CGO(a, b, result []float64) {
	for i := range a {
		result[i] = a[i] - b[i]
	}
}

// SIMDDivideFloat64CGO performs vectorized division (ARM64 stub)
func SIMDDivideFloat64CGO(a, b, result []float64) {
	for i := range a {
		result[i] = a[i] / b[i]
	}
}

// SIMDFilterGreaterThanFloat64CGO performs vectorized filtering (ARM64 stub)
func SIMDFilterGreaterThanFloat64CGO(data []float64, threshold float64, selection []int) int {
	return simdFilterGreaterThanManual(data, threshold, selection)
}

// SIMDFilterLessThanFloat64CGO performs vectorized less-than filtering
func SIMDFilterLessThanFloat64CGO(data []float64, threshold float64, selection []int) int {
	count := 0
	for i, val := range data {
		if val < threshold {
			selection[count] = i
			count++
		}
	}
	return count
}

// SIMDFilterEqualFloat64CGO performs vectorized equality filtering
func SIMDFilterEqualFloat64CGO(data []float64, threshold float64, selection []int) int {
	count := 0
	for i, val := range data {
		if val == threshold {
			selection[count] = i
			count++
		}
	}
	return count
}

// SIMDSumFloat64CGO computes sum (ARM64 stub)
func SIMDSumFloat64CGO(data []float64) float64 {
	return simdSumFloat64Manual(data)
}

// SIMDMinFloat64CGO finds minimum (ARM64 stub)
func SIMDMinFloat64CGO(data []float64) float64 {
	if len(data) == 0 {
		return 0
	}
	min := data[0]
	for _, v := range data[1:] {
		if v < min {
			min = v
		}
	}
	return min
}

// SIMDMaxFloat64CGO finds maximum (ARM64 stub)
func SIMDMaxFloat64CGO(data []float64) float64 {
	if len(data) == 0 {
		return 0
	}
	max := data[0]
	for _, v := range data[1:] {
		if v > max {
			max = v
		}
	}
	return max
}

// SIMDCompareEqualFloat64CGO performs vectorized equality comparison
func SIMDCompareEqualFloat64CGO(a, b []float64, result []bool) {
	for i := range a {
		result[i] = a[i] == b[i]
	}
}

// Integer operations

// SIMDAddInt64CGO performs vectorized addition for int64
func SIMDAddInt64CGO(a, b, result []int64) {
	for i := range a {
		result[i] = a[i] + b[i]
	}
}

// SIMDFilterGreaterThanInt64CGO performs vectorized filtering for int64
func SIMDFilterGreaterThanInt64CGO(data []int64, threshold int64, selection []int) int {
	count := 0
	for i, val := range data {
		if val > threshold {
			selection[count] = i
			count++
		}
	}
	return count
}

// SIMDSumInt64CGO computes sum for int64 arrays
func SIMDSumInt64CGO(data []int64) int64 {
	sum := int64(0)
	for _, val := range data {
		sum += val
	}
	return sum
}

// High-level interface functions

// AutoSIMDAddFloat64 automatically selects the best SIMD implementation
func AutoSIMDAddFloat64(a, b, result []float64) {
	SIMDAddFloat64CGO(a, b, result)
}

// AutoSIMDFilterGreaterThan automatically selects the best filtering implementation
func AutoSIMDFilterGreaterThan(data []float64, threshold float64, selection []int) int {
	return SIMDFilterGreaterThanFloat64CGO(data, threshold, selection)
}

// AutoSIMDSum automatically selects the best sum implementation
func AutoSIMDSum(data []float64) float64 {
	return SIMDSumFloat64CGO(data)
}