// presets.go — 7 個系統預設模型（B8）。
//
// 設計決策（使用者拍板「簡單版」）：
//   - metadata 寫死成 Go 常數，**不進 DB**。理由：preset 是固定公用資料，不該有 owner
//     隔離、不該能被刪、也無 seed 重複問題（多實例 / 重啟結果一致）。
//   - 對應的 .nef 檔打包進 visionA-backend image（assets/preset-models/{id}.nef），
//     download 走 visionA 自己的 storage handler（簡單版不簽 token，preset 公用、本不需授權）。
//
// 資料來源：local-tool/server/data/models.json（7 筆 metadata + nef 檔）。對映到 Model
// struct 既有欄位；models.json 有但 Model 無對應欄位的 metadata（quantization / license /
// version / author / taskType / categories / thumbnail）暫不落地（見 B8 回報）。
//
// StorageKey 格式 `models/preset/{id}.nef`：對 download handler 而言只是「preset 識別 +
// 對外 URL 檔名」用途，實際檔案由 PresetAssetsDir（api 層）提供，不經 LocalFS storage root。
package model

import "time"

// presetCreatedAt 是 7 個 preset 的固定建立時間（對齊 models.json 的 createdAt）。
// 寫死成常數讓 List/Get 回應在重啟 / 多實例間穩定（不用 time.Now()，避免每次不同）。
var presetCreatedAt = time.Date(2024, 1, 1, 0, 0, 0, 0, time.UTC)

// presetStorageKey 依 preset id 組出對外用的 storage key（檔名穩定、URL 穩定）。
func presetStorageKey(id string) string {
	return "models/preset/" + id + ".nef"
}

// presetModels 是寫死的 7 個系統預設模型（公用、無 owner、不可刪）。
//
// 欄位對映（models.json → Model）：
//   - name/description/framework 直接對映
//   - InputShape：inputSize{width,height} → []int{1, 3, height, width}（NCHW，對齊
//     conversion 端與 PG INT[] 既有慣例 [1,3,H,W]）。preset 皆 RGB 影像模型，batch=1、channel=3。
//   - Classes：labels 直接對映
//   - TargetChip：supportedHardware[0]（每筆只支援單一晶片）
//   - FileSize：modelSize
//   - Source = SourcePreset、OwnerUserID = ""（公用，非任何 user）
//   - StorageKey = models/preset/{id}.nef（不設 FAAObjectKey；download 走 visionA 自己）
var presetModels = []*Model{
	{
		ID:          "kl520-yolov5-detection",
		Name:        "YOLOv5 Detection (KL520)",
		Description: "YOLOv5 object detection model compiled for Kneron KL520. No upsample variant optimized for NPU inference at 640x640 resolution.",
		StorageKey:  presetStorageKey("kl520-yolov5-detection"),
		FileSize:    7200000,
		TargetChip:  "KL520",
		InputShape:  []int{1, 3, 640, 640},
		Classes:     []string{"person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light"},
		Framework:   "NEF",
		Source:      SourcePreset,
		CreatedAt:   presetCreatedAt,
		UpdatedAt:   presetCreatedAt,
		UploadedAt:  &presetCreatedAt,
	},
	{
		ID:          "kl520-fcos-detection",
		Name:        "FCOS Detection (KL520)",
		Description: "FCOS (Fully Convolutional One-Stage) object detection with DarkNet53s backbone, compiled for KL520. Anchor-free detection at 512x512.",
		StorageKey:  presetStorageKey("kl520-fcos-detection"),
		FileSize:    8900000,
		TargetChip:  "KL520",
		InputShape:  []int{1, 3, 512, 512},
		Classes:     []string{"person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light"},
		Framework:   "NEF",
		Source:      SourcePreset,
		CreatedAt:   presetCreatedAt,
		UpdatedAt:   presetCreatedAt,
		UploadedAt:  &presetCreatedAt,
	},
	{
		ID:          "kl520-ssd-face-detection",
		Name:        "SSD Face Detection (KL520)",
		Description: "SSD-based face detection with landmark localization, compiled for KL520. Lightweight model suitable for face detection and alignment tasks.",
		StorageKey:  presetStorageKey("kl520-ssd-face-detection"),
		FileSize:    1000000,
		TargetChip:  "KL520",
		InputShape:  []int{1, 3, 240, 320},
		Classes:     []string{"face"},
		Framework:   "NEF",
		Source:      SourcePreset,
		CreatedAt:   presetCreatedAt,
		UpdatedAt:   presetCreatedAt,
		UploadedAt:  &presetCreatedAt,
	},
	{
		ID:          "kl520-tiny-yolov3",
		Name:        "Tiny YOLOv3 (KL520)",
		Description: "Tiny YOLOv3 object detection model compiled for KL520. Compact and fast model for general-purpose multi-object detection on edge devices.",
		StorageKey:  presetStorageKey("kl520-tiny-yolov3"),
		FileSize:    9400000,
		TargetChip:  "KL520",
		InputShape:  []int{1, 3, 416, 416},
		Classes:     []string{"person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light"},
		Framework:   "NEF",
		Source:      SourcePreset,
		CreatedAt:   presetCreatedAt,
		UpdatedAt:   presetCreatedAt,
		UploadedAt:  &presetCreatedAt,
	},
	{
		ID:          "kl720-yolov5-detection",
		Name:        "YOLOv5 Detection (KL720)",
		Description: "YOLOv5 object detection model compiled for Kneron KL720. No upsample variant optimized for KL720 NPU inference at 640x640 resolution with USB 3.0 throughput.",
		StorageKey:  presetStorageKey("kl720-yolov5-detection"),
		FileSize:    10168348,
		TargetChip:  "KL720",
		InputShape:  []int{1, 3, 640, 640},
		Classes:     []string{"person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light"},
		Framework:   "NEF",
		Source:      SourcePreset,
		CreatedAt:   presetCreatedAt,
		UpdatedAt:   presetCreatedAt,
		UploadedAt:  &presetCreatedAt,
	},
	{
		ID:          "kl720-resnet18-classification",
		Name:        "ImageNet Classification ResNet18 (KL720)",
		Description: "ResNet18-based image classification compiled for KL720. Supports 1000 ImageNet categories with fast inference via USB 3.0.",
		StorageKey:  presetStorageKey("kl720-resnet18-classification"),
		FileSize:    12826804,
		TargetChip:  "KL720",
		InputShape:  []int{1, 3, 224, 224},
		Classes:     []string{"airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck"},
		Framework:   "NEF",
		Source:      SourcePreset,
		CreatedAt:   presetCreatedAt,
		UpdatedAt:   presetCreatedAt,
		UploadedAt:  &presetCreatedAt,
	},
	{
		ID:          "kl720-fcos-detection",
		Name:        "FCOS Detection (KL720)",
		Description: "FCOS (Fully Convolutional One-Stage) object detection with DarkNet53s backbone, compiled for KL720. Anchor-free detection at 512x512.",
		StorageKey:  presetStorageKey("kl720-fcos-detection"),
		FileSize:    13004640,
		TargetChip:  "KL720",
		InputShape:  []int{1, 3, 512, 512},
		Classes:     []string{"person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light"},
		Framework:   "NEF",
		Source:      SourcePreset,
		CreatedAt:   presetCreatedAt,
		UpdatedAt:   presetCreatedAt,
		UploadedAt:  &presetCreatedAt,
	},
}

// PresetModels 回傳所有系統預設模型的深拷貝（避免呼叫端意外改到共用常數）。
//
// 回傳順序固定（與宣告順序一致），讓 List 回應穩定、測試可確定性斷言。
func PresetModels() []*Model {
	out := make([]*Model, 0, len(presetModels))
	for _, m := range presetModels {
		out = append(out, clonePreset(m))
	}
	return out
}

// PresetByID 依 id 取單一 preset 的深拷貝；非 preset id 回 (nil, false)。
func PresetByID(id string) (*Model, bool) {
	for _, m := range presetModels {
		if m.ID == id {
			return clonePreset(m), true
		}
	}
	return nil, false
}

// IsPresetID 判斷 id 是否為系統預設模型 id。
func IsPresetID(id string) bool {
	_, ok := PresetByID(id)
	return ok
}

// clonePreset 深拷貝一筆 preset（含 slice 欄位），避免外部修改污染共用常數。
func clonePreset(m *Model) *Model {
	cp := *m
	if m.InputShape != nil {
		cp.InputShape = append([]int(nil), m.InputShape...)
	}
	if m.Classes != nil {
		cp.Classes = append([]string(nil), m.Classes...)
	}
	// UploadedAt 指向共用的 presetCreatedAt；複製一份新指標避免別名共享。
	if m.UploadedAt != nil {
		t := *m.UploadedAt
		cp.UploadedAt = &t
	}
	// preset 是公用模型，語意等同全平台可見（visibility=public）。
	// 在此統一標記，preset 宣告區不必逐筆設 Visibility。
	cp.Visibility = VisibilityPublic
	return &cp
}