package imageloader

import (
	"git.andr3h3nriqu3s.com/andr3/fyp/logic/db"
	types "git.andr3h3nriqu3s.com/andr3/fyp/logic/db_types"
	"git.andr3h3nriqu3s.com/andr3/gotch"
	torch "git.andr3h3nriqu3s.com/andr3/gotch/ts"
	"git.andr3h3nriqu3s.com/andr3/gotch/vision"
)

type Dataset struct {
	TrainImages     *torch.Tensor
	TrainLabels     *torch.Tensor
	TestImages      *torch.Tensor
	TestLabels      *torch.Tensor
	TrainImagesSize int
	TestImagesSize  int
	Device          gotch.Device
}

func LoadImagesAndLables(db db.Db, m *types.BaseModel, mode types.DATA_POINT_MODE, classStart int, classEnd int) (imgs, labels *torch.Tensor, count int, err error) {
	train_points, err := m.DataPoints(db, types.DATA_POINT_MODE_TRAINING)
	if err != nil {
		return
	}

	size := int64(classEnd - classStart + 1)

	pimgs := []*torch.Tensor{}
	plabels := []*torch.Tensor{}

	for _, point := range train_points {
		var img, label *torch.Tensor
		img, err = vision.Load(point.Path)
		if err != nil {
			return
		}
		pimgs = append(pimgs, img)

		t_label := make([]int, size)
		if point.Class <= classEnd && point.Class >= classStart {
			t_label[point.Class-classStart] = 1
		}

		label, err = torch.OfSlice(t_label)
		if err != nil {
			return
		}
		plabels = append(plabels, label)
	}

	imgs, err = torch.Concat(pimgs, 0)
	if err != nil {
		return
	}

	labels, err = torch.Stack(plabels, 0)
	if err != nil {
		return
	}

	count = len(pimgs)

	imgs, err = torch.Stack(pimgs, 0)

	labels, err = labels.ToDtype(gotch.Float, false, false, true)
	if err != nil {
		return
	}

	imgs, err = imgs.ToDtype(gotch.Float, false, false, true)
	if err != nil {
		return
	}

	return
}

func NewDataset(db db.Db, m *types.BaseModel, classStart int, classEnd int) (ds *Dataset, err error) {
	trainImages, trainLabels, train_count, err := LoadImagesAndLables(db, m, types.DATA_POINT_MODE_TRAINING, classStart, classEnd)
	if err != nil {
		return
	}

	testImages, testLabels, test_count, err := LoadImagesAndLables(db, m, types.DATA_POINT_MODE_TESTING, classStart, classEnd)
	if err != nil {
		return
	}

	ds = &Dataset{
		TrainImages:     trainImages,
		TrainLabels:     trainLabels,
		TestImages:      testImages,
		TestLabels:      testLabels,
		TrainImagesSize: train_count,
		TestImagesSize:  test_count,
		Device:          gotch.CPU,
	}
	return
}

func (ds *Dataset) To(device gotch.Device) (err error) {
	ds.TrainImages, err = ds.TrainImages.ToDevice(device, ds.TrainImages.DType(), device.IsCuda(), true, true)
	if err != nil {
		return
	}

	ds.TrainLabels, err = ds.TrainLabels.ToDevice(device, ds.TrainLabels.DType(), device.IsCuda(), true, true)
	if err != nil {
		return
	}

	ds.TestImages, err = ds.TestImages.ToDevice(device, ds.TestImages.DType(), device.IsCuda(), true, true)
	if err != nil {
		return
	}

	ds.TestLabels, err = ds.TestLabels.ToDevice(device, ds.TestLabels.DType(), device.IsCuda(), true, true)
	if err != nil {
		return
	}

	ds.Device = device
	return
}

func (ds *Dataset) TestIter(batchSize int64) *torch.Iter2 {
	return torch.MustNewIter2(ds.TestImages, ds.TestLabels, batchSize)
}

func (ds *Dataset) TrainIter(batchSize int64) (iter *torch.Iter2, err error) {

    train_images, err := ds.TrainImages.DetachCopy(false)
    if err != nil {
        return
    }

    train_labels, err := ds.TrainLabels.DetachCopy(false)
    if err != nil {
        return
    }

    iter, err = torch.NewIter2(train_images, train_labels, batchSize)
    if err != nil {
        return
    }

	return
}