package nn

import (
	"errors"
	"math"

	"makarna/pkg/backend/cpu"
)

// Softmax applies softmax normalization in-place.
// Dispatch order: AVX-512 -> AVX2 -> scalar fallback.
func Softmax(x *cpu.Tensor) error {
	data := x.DataFloat32()
	if len(data) == 0 {
		return nil
	}

	softmaxInplace(data)
	var sum float32
	for _, v := range data {
		if v < 0 || math.IsNaN(float64(v)) || math.IsInf(float64(v), 0) {
			return errors.New("softmax produced invalid value")
		}
		sum += v
	}
	if sum <= 0 || math.IsNaN(float64(sum)) || math.IsInf(float64(sum), 0) {
		return errors.New("softmax produced invalid sum")
	}
	d := float32(math.Abs(float64(sum - 1)))
	if d > 1e-3 {
		inv := 1 / sum
		for i := range data {
			data[i] *= inv
		}
	}
	return nil
}

func softmaxInplace(data []float32) {
	if len(data) == 0 {
		return
	}
	switch {
	case hasSoftmaxAVX512 && cpu.SupportsAVX512() && len(data) >= 16:
		softmaxAVX512(data)
	case hasSoftmaxAVX2 && cpu.SupportsAVX2() && len(data) >= 8:
		softmaxAVX2(data)
	default:
		softmaxScalar(data)
	}
}

func softmaxScalar(data []float32) {
	maxVal := float32(-math.MaxFloat32)
	for _, v := range data {
		if v > maxVal {
			maxVal = v
		}
	}

	var sum float32
	for i, v := range data {
		ev := float32(math.Exp(float64(v - maxVal)))
		data[i] = ev
		sum += ev
	}

	inv := 1.0 / sum
	for i := range data {
		data[i] *= inv
	}
}