package main

import (
	"git.andr3h3nriqu3s.com/andr3/gotch"

	dbtypes "git.andr3h3nriqu3s.com/andr3/fyp/logic/db_types"
	"git.andr3h3nriqu3s.com/andr3/fyp/logic/models/train/torch"

	//my_nn "git.andr3h3nriqu3s.com/andr3/fyp/logic/models/train/torch/nn"

	torch "git.andr3h3nriqu3s.com/andr3/gotch/ts"
	"github.com/charmbracelet/log"
)

func main() {

	log.Info("Hello world")

	m := train.BuildModel([]*dbtypes.Layer{
		&dbtypes.Layer{
			LayerType: dbtypes.LAYER_INPUT,
			Shape:     "[ 3, 28, 28 ]",
		},
		&dbtypes.Layer{
			LayerType: dbtypes.LAYER_FLATTEN,
		},
		&dbtypes.Layer{
			LayerType: dbtypes.LAYER_DENSE,
			Shape:     "[ 27 ]",
		},
		&dbtypes.Layer{
			LayerType: dbtypes.LAYER_DENSE,
			Shape:     "[ 18 ]",
		},
		//		&dbtypes.Layer{
		//			LayerType: dbtypes.LAYER_DENSE,
		//			Shape:     "[ 9 ]",
		//		},
	}, 0, true)

	//var err error

	d := gotch.CudaIfAvailable()

	log.Info("device", "d", d)

	m.To(d)

	var count = 0

//	vars1 := m.Vs.Variables()
//
//    for k, v := range vars1 {
//        ones := torch.MustOnes(v.MustSize(), gotch.Float, d)
//        v := ones.MustSetRequiresGrad(true, false)
//        v.MustDrop()
//        ones.RetainGrad(false)
//
//        m.Vs.UpdateVarTensor(k, ones, true)
//        m.Refresh()
//    }
//
//	opt, err := my_nn.DefaultAdamConfig().Build(m.Vs, 0.001)
//	if err != nil {
//		return
//	}

    log.Info("start")

	for count < 100 {

		ones := torch.MustOnes([]int64{1, 3, 28, 28}, gotch.Float, d)
		// ones = ones.MustSetRequiresGrad(true, true)
		// ones.RetainGrad(false)

		res := m.ForwardT(ones, true)
		//res = res.MustSetRequiresGrad(true, true)
		//res.RetainGrad(false)

		outs := torch.MustZeros([]int64{1, 18}, gotch.Float, d)

		loss, err := res.BinaryCrossEntropyWithLogits(outs, &torch.Tensor{}, &torch.Tensor{}, 2, false)
		if err != nil {
			log.Fatal(err)
		}
	//	loss = loss.MustSetRequiresGrad(true, true)

		//opt.ZeroGrad()

		log.Info("loss", "loss", loss.Float64Values())

		loss.MustBackward()

		//opt.Step()

		// log.Info(mean.MustGrad(false).Float64Values())
		//ones_grad = ones.MustGrad(true).MustMax(true).Float64Values()[0]
	//	log.Info(res.MustGrad(true).MustMax(true).Float64Values())

//		log.Info(ones_grad)

		vars := m.Vs.Variables()

		for k, v := range vars {
			log.Info("[grad check]", "k", k, "grad", v.MustGrad(false).MustMax(true).Float64Values())
		}

		m.Debug()

		outs.MustDrop()

		count += 1

		log.Fatal("grad zero")

	}

	log.Warn("out")

}