//go:build darwin && arm64

package metal

import (
	"math"
)

// Sampler transforms logits into a sampled token index.
type Sampler interface {
	Sample(logits *Array) *Array
}

// newSampler creates a composable sampler chain from the given parameters.
// Order: TopP -> MinP -> TopK -> Temperature -> categorical sample.
func newSampler(temp, topP, minP float32, topK int) Sampler {
	if temp == 0 {
		return greedy{}
	}

	var samplers []Sampler
	if topP > 0 && topP < 1 {
		samplers = append(samplers, TopP(topP))
	}
	if minP > 0 {
		samplers = append(samplers, MinPSampler(minP))
	}
	if topK > 0 {
		samplers = append(samplers, TopKSampler(topK))
	}
	samplers = append(samplers, Temperature(temp))
	return chain(samplers)
}

// chain applies a sequence of samplers, then samples from the result.
type chain []Sampler

func (c chain) Sample(logits *Array) *Array {
	for _, s := range c {
		logits = s.Sample(logits)
	}
	// Final categorical sample from log-probabilities
	return RandomCategorical(logits)
}

// greedy returns the argmax token.
type greedy struct{}

func (greedy) Sample(logits *Array) *Array {
	return Argmax(logits, -1, false)
}

// Temperature scales logits by 1/temp.
type Temperature float32

func (t Temperature) Sample(logits *Array) *Array {
	return MulScalar(logits, 1.0/float32(t))
}

// TopKSampler masks all but the top-k logits.
type TopKSampler int

func (k TopKSampler) Sample(logits *Array) *Array {
	neg := Negative(logits)
	mask := Argpartition(neg, int(k)-1, -1)
	// Slice the indices beyond top-k
	mask = SliceAxis(mask, -1, int32(k), int32(logits.Dim(-1)))
	return PutAlongAxis(logits, mask, FromValue(float32(math.Inf(-1))), -1)
}

// TopP implements nucleus sampling (cumulative probability threshold).
type TopP float32

func (p TopP) Sample(logits *Array) *Array {
	// TODO: full nucleus sampling requires cumsum which mlx-c doesn't expose directly.
	// For now, pass through. TopK + Temperature covers most use cases.
	return logits
}

// MinPSampler masks tokens below min_p * max_prob.
type MinPSampler float32

func (p MinPSampler) Sample(logits *Array) *Array {
	// For now, pass through — MinP is an optimization over TopP.
	// Full implementation requires finding max prob and masking below threshold.
	return logits
}