package convert

import (
	"cmp"
	"encoding/json"
	"io/fs"
	"slices"
	"strings"

	"github.com/ollama/ollama/fs/ggml"
)

type qwen3VLModel struct {
	qwen3Model `json:"text_config"`

	VisionModel struct {
		Depth                  uint32  `json:"depth"`
		HiddenSize             uint32  `json:"hidden_size"`
		NumHeads               uint32  `json:"num_heads"`
		InChannels             uint32  `json:"in_channels"`
		PatchSize              uint32  `json:"patch_size"`
		SpatialMergeSize       uint32  `json:"spatial_merge_size"`
		WindowSize             uint32  `json:"window_size"`
		RMSNormEps             float32 `json:"layer_norm_epsilon"`
		RopeTheta              float32 `json:"rope_theta"`
		TemporalPatchSize      uint32  `json:"temporal_patch_size"`
		DeepstackVisualIndexes []int32 `json:"deepstack_visual_indexes"`

		Size struct {
			ShortestEdge uint32 `json:"shortest_edge"`
			LongestEdge  uint32 `json:"longest_edge"`
		} `json:"size"`

		ImageMean []float32 `json:"image_mean"`
		ImageStd  []float32 `json:"image_std"`
	} `json:"vision_config"`
}

func (m *qwen3VLModel) parseMore(fsys fs.FS) error {
	bts, err := fs.ReadFile(fsys, "preprocessor_config.json")
	if err != nil {
		return err
	}

	return json.Unmarshal(bts, &m.VisionModel)
}

func (m *qwen3VLModel) KV(t *Tokenizer) ggml.KV {
	kv := m.qwen3Model.KV(t)

	arch := "qwen3vl"
	if m.NumExperts > 0 {
		arch += "moe"
	}
	// override architecture
	kv["general.architecture"] = arch

	kv["vision.block_count"] = cmp.Or(m.VisionModel.Depth, 32)
	kv["vision.embedding_length"] = m.VisionModel.HiddenSize
	kv["vision.attention.head_count"] = cmp.Or(m.VisionModel.NumHeads, 16)
	kv["vision.num_channels"] = m.VisionModel.InChannels
	kv["vision.patch_size"] = cmp.Or(m.VisionModel.PatchSize, 14)
	kv["vision.spatial_merge_size"] = cmp.Or(m.VisionModel.SpatialMergeSize, 2)
	kv["vision.attention.layer_norm_epsilon"] = cmp.Or(m.VisionModel.RMSNormEps, 1e-6)
	kv["vision.rope.freq_base"] = cmp.Or(m.VisionModel.RopeTheta, 1e4)
	kv["vision.temporal_patch_size"] = cmp.Or(m.VisionModel.TemporalPatchSize, 2)
	kv["vision.deepstack_visual_indexes"] = m.VisionModel.DeepstackVisualIndexes

	kv["vision.shortest_edge"] = m.VisionModel.Size.ShortestEdge
	kv["vision.longest_edge"] = m.VisionModel.Size.LongestEdge

	kv["vision.image_mean"] = m.VisionModel.ImageMean
	kv["vision.image_std"] = m.VisionModel.ImageStd

	return kv
}

func (m *qwen3VLModel) Tensors(ts []Tensor) []*ggml.Tensor {
	var rest []Tensor
	var out []*ggml.Tensor
	for _, t := range ts {
		switch {
		case strings.Contains(t.Name(), "attn_qkv"):
			out = append(out, slices.Collect(splitDim(t, 0,
				split{Replacer: strings.NewReplacer("attn_qkv", "attn_q")},
				split{Replacer: strings.NewReplacer("attn_qkv", "attn_k")},
				split{Replacer: strings.NewReplacer("attn_qkv", "attn_v")},
			))...)
		case strings.Contains(t.Name(), "patch_embed") && strings.HasSuffix(t.Name(), "weight"):
			shape := t.Shape()
			out = append(out, &ggml.Tensor{
				Name:     t.Name(),
				Kind:     t.Kind(),
				Shape:    append([]uint64{shape[0] * shape[1]}, shape[2:]...),
				WriterTo: t,
			})
		default:
			rest = append(rest, t)
		}
	}

	return append(m.qwen3Model.Tensors(rest), out...)
}

func (m *qwen3VLModel) Replacements() []string {
	return append(
		m.qwen3Model.Replacements(),
		"model.language_", "",
		"model.visual", "v",
		"patch_embed.proj", "patch_embed",
		"blocks", "blk",
		"attn.qkv", "attn_qkv",
		"attn.proj", "attn_out",
		"deepstack_merger_list", "deepstack_merger",
	)
}