package vectorized

import (
	"fmt"
	"time"
)

// Real SIMD operations for vectorized execution
// This file demonstrates various approaches to implementing SIMD in Go

// Approach 1: Go Assembly (Most Performance)
// These functions would be implemented in separate .s files

// These would be implemented in simd_amd64.s
// For now, they're stubs that fall back to manual implementations

func simdAddFloat64AVX2(a, b, result []float64) {
	simdAddFloat64Manual(a, b, result)
}

func simdFilterGreaterThanFloat64AVX2(data []float64, threshold float64, selection []int) int {
	return simdFilterGreaterThanManual(data, threshold, selection)
}

func simdSumFloat64AVX2(data []float64) float64 {
	return simdSumFloat64Manual(data)
}

func simdCompareFloat64AVX2(a, b []float64, result []bool) {
	for i := range a {
		result[i] = a[i] > b[i]
	}
}

// Approach 2: CGO with C/C++ SIMD (Good Performance, More Complex)
// Note: CGO implementation commented out to avoid build complexity
// In production, you would implement C functions with AVX2 intrinsics

// Approach 3: Pure Go with Manual Vectorization (Moderate Performance)
// The Go compiler can sometimes auto-vectorize these patterns

// SIMDAddFloat64 adds two slices element-wise using the fastest available method
func SIMDAddFloat64(a, b, result []float64) {
	if len(a) != len(b) || len(a) != len(result) {
		panic("slice lengths must match")
	}

	// Try Go assembly first (highest performance)
	if hasAVX2() {
		simdAddFloat64AVX2(a, b, result)
		return
	}

	// CGO implementation would go here
	// For now, fallback to manual implementation

	// Fallback to manual vectorization (Go compiler may auto-vectorize)
	simdAddFloat64Manual(a, b, result)
}

// SIMDFilterGreaterThan filters elements greater than threshold using SIMD
func SIMDFilterGreaterThan(data []float64, threshold float64, selection []int) int {
	if len(selection) < len(data) {
		panic("selection slice too small")
	}

	// Try Go assembly first
	if hasAVX2() {
		return simdFilterGreaterThanFloat64AVX2(data, threshold, selection)
	}

	// CGO implementation would go here
	// For now, fallback to manual implementation

	// Fallback to manual implementation
	return simdFilterGreaterThanManual(data, threshold, selection)
}

// SIMDSumFloat64 computes sum using SIMD instructions
func SIMDSumFloat64(data []float64) float64 {
	if len(data) == 0 {
		return 0
	}

	// Try Go assembly first
	if hasAVX2() {
		return simdSumFloat64AVX2(data)
	}

	// CGO implementation would go here
	// For now, fallback to manual implementation
	return simdSumFloat64Manual(data)
}

// Manual vectorization implementations (fallbacks)
func simdAddFloat64Manual(a, b, result []float64) {
	// Process 4 elements at a time (manual unrolling for Go compiler)
	i := 0
	for ; i < len(a)-3; i += 4 {
		result[i] = a[i] + b[i]
		result[i+1] = a[i+1] + b[i+1]
		result[i+2] = a[i+2] + b[i+2]
		result[i+3] = a[i+3] + b[i+3]
	}
	
	// Handle remaining elements
	for ; i < len(a); i++ {
		result[i] = a[i] + b[i]
	}
}

func simdFilterGreaterThanManual(data []float64, threshold float64, selection []int) int {
	count := 0
	
	// Process 4 elements at a time
	i := 0
	for ; i < len(data)-3; i += 4 {
		if data[i] > threshold {
			selection[count] = i
			count++
		}
		if data[i+1] > threshold {
			selection[count] = i + 1
			count++
		}
		if data[i+2] > threshold {
			selection[count] = i + 2
			count++
		}
		if data[i+3] > threshold {
			selection[count] = i + 3
			count++
		}
	}
	
	// Handle remaining elements
	for ; i < len(data); i++ {
		if data[i] > threshold {
			selection[count] = i
			count++
		}
	}
	
	return count
}

func simdSumFloat64Manual(data []float64) float64 {
	sum := 0.0
	
	// Process 4 elements at a time
	i := 0
	for ; i < len(data)-3; i += 4 {
		sum += data[i] + data[i+1] + data[i+2] + data[i+3]
	}
	
	// Handle remaining elements
	for ; i < len(data); i++ {
		sum += data[i]
	}
	
	return sum
}

// CPU feature detection
func hasAVX2() bool {
	caps := GetSIMDCapabilities()
	return caps.HasAVX2
}

// High-level vectorized operations for the execution engine
type SIMDOperations struct {
	useNative bool
}

func NewSIMDOperations() *SIMDOperations {
	return &SIMDOperations{
		useNative: hasAVX2(),
	}
}

// VectorizedAdd performs element-wise addition on vectors
func (simd *SIMDOperations) VectorizedAdd(a, b *Vector) (*Vector, error) {
	if a.DataType != FLOAT64 || b.DataType != FLOAT64 {
		return nil, fmt.Errorf("SIMD add only supports FLOAT64")
	}
	
	if a.Length != b.Length {
		return nil, fmt.Errorf("vector lengths must match")
	}
	
	result := NewVector(FLOAT64, a.Length)
	
	aData := a.Data.([]float64)
	bData := b.Data.([]float64)
	resultData := result.Data.([]float64)
	
	SIMDAddFloat64(aData, bData, resultData)
	
	return result, nil
}

// VectorizedFilter performs SIMD-optimized filtering
func (simd *SIMDOperations) VectorizedFilter(data *Vector, threshold float64, op FilterOperator) *SelectionVector {
	if data.DataType != FLOAT64 {
		// Fallback to non-SIMD for other types
		return simd.fallbackFilter(data, threshold, op)
	}
	
	selection := NewSelectionVector(data.Length)
	dataSlice := data.Data.([]float64)
	
	var count int
	switch op {
	case GT:
		count = SIMDFilterGreaterThan(dataSlice, threshold, selection.Indices)
	case LT:
		// Implement SIMD less than
		count = simd.filterLessThan(dataSlice, threshold, selection.Indices)
	default:
		return simd.fallbackFilter(data, threshold, op)
	}
	
	selection.Length = count
	return selection
}

// VectorizedSum computes sum using SIMD
func (simd *SIMDOperations) VectorizedSum(data *Vector) (float64, error) {
	if data.DataType != FLOAT64 {
		return 0, fmt.Errorf("SIMD sum only supports FLOAT64")
	}
	
	dataSlice := data.Data.([]float64)
	return SIMDSumFloat64(dataSlice), nil
}

// Benchmark comparison function
func (simd *SIMDOperations) BenchmarkAddition(size int) (simdTime, scalarTime float64) {
	// Create test data
	a := make([]float64, size)
	b := make([]float64, size)
	result1 := make([]float64, size)
	result2 := make([]float64, size)
	
	for i := 0; i < size; i++ {
		a[i] = float64(i)
		b[i] = float64(i * 2)
	}
	
	// Benchmark SIMD addition
	start := time.Now()
	SIMDAddFloat64(a, b, result1)
	simdTime = float64(time.Since(start).Nanoseconds())
	
	// Benchmark scalar addition
	start = time.Now()
	for i := 0; i < size; i++ {
		result2[i] = a[i] + b[i]
	}
	scalarTime = float64(time.Since(start).Nanoseconds())
	
	return simdTime, scalarTime
}

// Helper functions
func (simd *SIMDOperations) filterLessThan(data []float64, threshold float64, selection []int) int {
	count := 0
	for i, val := range data {
		if val < threshold {
			selection[count] = i
			count++
		}
	}
	return count
}

func (simd *SIMDOperations) fallbackFilter(data *Vector, threshold float64, op FilterOperator) *SelectionVector {
	selection := NewSelectionVector(data.Length)
	count := 0
	
	for i := 0; i < data.Length; i++ {
		var matches bool
		switch data.DataType {
		case FLOAT64:
			val, _ := data.GetFloat64(i)
			matches = simd.compareFloat64(val, threshold, op)
		case INT64:
			val, _ := data.GetInt64(i)
			matches = simd.compareInt64(val, int64(threshold), op)
		}
		
		if matches {
			selection.Indices[count] = i
			count++
		}
	}
	
	selection.Length = count
	return selection
}

func (simd *SIMDOperations) compareFloat64(a, b float64, op FilterOperator) bool {
	switch op {
	case EQ:
		return a == b
	case NE:
		return a != b
	case LT:
		return a < b
	case LE:
		return a <= b
	case GT:
		return a > b
	case GE:
		return a >= b
	default:
		return false
	}
}

func (simd *SIMDOperations) compareInt64(a, b int64, op FilterOperator) bool {
	switch op {
	case EQ:
		return a == b
	case NE:
		return a != b
	case LT:
		return a < b
	case LE:
		return a <= b
	case GT:
		return a > b
	case GE:
		return a >= b
	default:
		return false
	}
}