package qwen3vl

import (
	"cmp"
	"math"
	"slices"
	"strings"

	"github.com/ollama/ollama/fs"
	"github.com/ollama/ollama/kvcache"
	"github.com/ollama/ollama/ml"
	"github.com/ollama/ollama/ml/nn"
	"github.com/ollama/ollama/model"
)

type TextOptions struct {
	hiddenSize,
	numHeads,
	numKVHeads,
	keyLength,
	valueLength int

	eps,
	ropeBase,
	ropeScale float32
	mropeSections []int

	numExperts, numExpertsUsed int
	normTopKProb               bool

	inverseFrequenciesCache []float32
}

func (o TextOptions) headDim() int {
	return cmp.Or(o.keyLength, o.valueLength, o.hiddenSize/o.numHeads)
}

type TextAttention struct {
	Query     *nn.Linear  `gguf:"attn_q"`
	QueryNorm *nn.RMSNorm `gguf:"attn_q_norm"`
	Key       *nn.Linear  `gguf:"attn_k"`
	KeyNorm   *nn.RMSNorm `gguf:"attn_k_norm"`
	Value     *nn.Linear  `gguf:"attn_v"`
	Output    *nn.Linear  `gguf:"attn_output"`
}

func (sa *TextAttention) Forward(ctx ml.Context, hiddenStates, cos, sin ml.Tensor, cache kvcache.Cache, opts *TextOptions) ml.Tensor {
	batchSize := hiddenStates.Dim(1)

	query := sa.Query.Forward(ctx, hiddenStates)
	key := sa.Key.Forward(ctx, hiddenStates)
	value := sa.Value.Forward(ctx, hiddenStates)

	query = query.Reshape(ctx, opts.headDim(), opts.numHeads, batchSize)
	key = key.Reshape(ctx, opts.headDim(), opts.numKVHeads, batchSize)
	value = value.Reshape(ctx, opts.headDim(), opts.numKVHeads, batchSize)

	query = sa.QueryNorm.Forward(ctx, query, opts.eps)
	key = sa.KeyNorm.Forward(ctx, key, opts.eps)

	query = applyRotaryPositionalEmbedding(ctx, query, cos, sin)
	key = applyRotaryPositionalEmbedding(ctx, key, cos, sin)

	attention := nn.Attention(ctx, query, key, value, 1./math.Sqrt(float64(opts.headDim())), cache)
	attention = attention.Reshape(ctx, attention.Dim(0)*attention.Dim(1), batchSize)
	return sa.Output.Forward(ctx, attention)
}

type TextMLP interface {
	Forward(ml.Context, ml.Tensor, *TextOptions) ml.Tensor
}

type sparse struct {
	Router *nn.Linear      `gguf:"ffn_gate_inp"`
	Gate   *nn.LinearBatch `gguf:"ffn_gate_exps"`
	Up     *nn.LinearBatch `gguf:"ffn_up_exps"`
	Down   *nn.LinearBatch `gguf:"ffn_down_exps"`
}

func (mlp *sparse) Forward(ctx ml.Context, hiddenStates ml.Tensor, opts *TextOptions) ml.Tensor {
	hiddenDim, sequenceLength, batchSize := hiddenStates.Dim(0), hiddenStates.Dim(1), hiddenStates.Dim(2)
	hiddenStates = hiddenStates.Reshape(ctx, hiddenDim, sequenceLength*batchSize)
	routerLogits := mlp.Router.Forward(ctx, hiddenStates)

	routingWeights := routerLogits.Softmax(ctx)
	selectedExperts := routingWeights.TopK(ctx, opts.numExpertsUsed)
	routingWeights = routingWeights.Reshape(ctx, 1, opts.numExperts, hiddenStates.Dim(1)).Rows(ctx, selectedExperts)
	if opts.normTopKProb {
		routingWeights = routingWeights.Reshape(ctx, opts.numExpertsUsed, hiddenStates.Dim(1))
		routingWeights = routingWeights.Div(ctx, routingWeights.SumRows(ctx))
		routingWeights = routingWeights.Reshape(ctx, 1, opts.numExpertsUsed, hiddenStates.Dim(1))
	}

	hiddenStates = hiddenStates.Reshape(ctx, hiddenStates.Dim(0), 1, hiddenStates.Dim(1))

	hiddenStates = mlp.Gate.Forward(ctx, hiddenStates, selectedExperts).SILU(ctx, mlp.Up.Forward(ctx, hiddenStates, selectedExperts))

	experts := mlp.Down.Forward(ctx, hiddenStates, selectedExperts)
	experts = experts.Mul(ctx, routingWeights)

	nextStates := experts.View(ctx, 0, experts.Dim(0), experts.Stride(2), experts.Dim(2))
	for i := 1; i < opts.numExpertsUsed; i++ {
		nextStates = nextStates.Add(ctx, experts.View(ctx, i*experts.Stride(1), experts.Dim(0), experts.Stride(2), experts.Dim(2)))
	}

	return nextStates
}

type dense struct {
	Gate *nn.Linear `gguf:"ffn_gate"`
	Up   *nn.Linear `gguf:"ffn_up"`
	Down *nn.Linear `gguf:"ffn_down"`
}

func (mlp *dense) Forward(ctx ml.Context, hiddenStates ml.Tensor, _ *TextOptions) ml.Tensor {
	hiddenStates = mlp.Gate.Forward(ctx, hiddenStates).SILU(ctx, mlp.Up.Forward(ctx, hiddenStates))
	return mlp.Down.Forward(ctx, hiddenStates)
}

type TextLayer struct {
	AttentionNorm *nn.RMSNorm `gguf:"attn_norm"`
	*TextAttention

	MLPNorm *nn.RMSNorm `gguf:"ffn_norm"`
	TextMLP
}

func (d *TextLayer) Forward(ctx ml.Context, hiddenStates, cos, sin, outputs ml.Tensor, cache kvcache.Cache, opts *TextOptions) ml.Tensor {
	residual := hiddenStates
	hiddenStates = d.AttentionNorm.Forward(ctx, hiddenStates, opts.eps)
	hiddenStates = d.TextAttention.Forward(ctx, hiddenStates, cos, sin, cache, opts)

	if outputs != nil {
		hiddenStates = hiddenStates.Rows(ctx, outputs)
		residual = residual.Rows(ctx, outputs)
	}

	hiddenStates = hiddenStates.Add(ctx, residual)

	residual = hiddenStates
	hiddenStates = d.MLPNorm.Forward(ctx, hiddenStates, opts.eps)
	hiddenStates = d.TextMLP.Forward(ctx, hiddenStates, opts)
	return hiddenStates.Add(ctx, residual)
}

type TextModel struct {
	TokenEmbedding *nn.Embedding `gguf:"token_embd"`
	OutputNorm     *nn.RMSNorm   `gguf:"output_norm"`
	Output         *nn.Linear    `gguf:"output,alt:token_embd"`

	Layers []TextLayer `gguf:"blk"`

	Options *TextOptions
}

func (m *TextModel) rotaryEmbedding(ctx ml.Context, positions ml.Tensor) (_, _ ml.Tensor) {
	positions = positions.Reshape(ctx, 1, positions.Dim(0), positions.Dim(1))
	if len(m.Options.inverseFrequenciesCache) == 0 {
		m.Options.inverseFrequenciesCache = make([]float32, m.Options.headDim()/2)
		for i := range m.Options.inverseFrequenciesCache {
			frequency := float32(math.Pow(float64(m.Options.ropeBase), float64(i*2)/float64(m.Options.headDim())))
			m.Options.inverseFrequenciesCache[i] = 1 / frequency
		}
	}

	inverseFrequencies := ctx.Input().FromFloats(m.Options.inverseFrequenciesCache, 1, len(m.Options.inverseFrequenciesCache))

	positions = positions.Cast(ctx, ml.DTypeF32)
	frequencies := inverseFrequencies.Mulmat(ctx, positions)

	interleaved := frequencies.View(ctx,
		0, frequencies.Dim(0),
		frequencies.Stride(1), frequencies.Dim(1),
	)

	for _, i := range []int{1, 2} {
		args := []int{
			i * frequencies.Stride(0), 1,
			3 * frequencies.Stride(0), m.Options.mropeSections[i],
			frequencies.Stride(1), frequencies.Dim(1),
		}

		ctx.Forward(frequencies.View(ctx, i*frequencies.Stride(2)+args[0], args[1:]...).
			Copy(ctx, interleaved.View(ctx, args[0], args[1:]...)))
	}

	interleaved = interleaved.Concat(ctx, interleaved, 0)
	interleaved = interleaved.Reshape(ctx, interleaved.Dim(0), 1, interleaved.Dim(1), interleaved.Dim(2))
	return interleaved.Cos(ctx), interleaved.Sin(ctx)
}

var _ model.Model = (*Model)(nil)

func newTextModel(c fs.Config) *TextModel {
	layers := make([]TextLayer, c.Uint("block_count"))
	for i := range layers {
		if strings.HasSuffix(c.String("general.architecture"), "moe") {
			layers[i].TextMLP = &sparse{}
		} else {
			layers[i].TextMLP = &dense{}
		}
	}

	m := TextModel{
		Layers: layers,
		Options: &TextOptions{
			hiddenSize:     int(c.Uint("embedding_length")),
			numHeads:       int(c.Uint("attention.head_count")),
			numKVHeads:     int(c.Uint("attention.head_count_kv")),
			keyLength:      int(c.Uint("attention.key_length")),
			valueLength:    int(c.Uint("attention.value_length")),
			eps:            c.Float("attention.layer_norm_rms_epsilon"),
			ropeBase:       c.Float("rope.freq_base"),
			ropeScale:      c.Float("rope.scaling.factor", 1),
			numExperts:     int(c.Uint("expert_count")),
			numExpertsUsed: int(c.Uint("expert_used_count")),
			normTopKProb:   c.Bool("norm_top_k_prob", true),
			mropeSections: slices.Collect(func(yield func(int) bool) {
				for _, section := range c.Ints("mrope_sections", []int32{24, 20, 20}) {
					if !yield(int(section)) {
						return
					}
				}
			}),
		},
	}

	return &m
}