Test_Lux.jl

### A Pluto.jl notebook ###
# v0.19.46

using Markdown
using InteractiveUtils

# ╔═╡ a8c3c330-857e-11ef-0186-5bfd830a2dda
using Lux,Random,DifferentiationInterface,Optimisers,PlutoUI

# ╔═╡ 39dc2709-f486-4302-aa07-8fdef5fa1c2a
using Plots

# ╔═╡ a479a319-4d88-45b2-ab01-2094e01964a5
begin
	using CUDA,LuxCUDA,cuDNN;
	CUDA.allowscalar(false)
	
	const gdev = gpu_device()
end

# ╔═╡ c5d53dab-450a-4321-b6f5-b63dc178af85
using OrdinaryDiffEq

# ╔═╡ 30919ee2-b80c-431d-bdc2-084f7ade5a74
using ComponentArrays,SciMLSensitivity

# ╔═╡ 2f80af3f-ca63-44a5-b8a7-371225f9a245
import Flux,Enzyme,Zygote,Tracker,FiniteDiff

# ╔═╡ 4a0e8889-5b9a-4f3e-8a3d-6fee90a9ac45
PlutoUI.TableOfContents()

# ╔═╡ 50e8fc34-e9e4-4205-b163-1174fb5bd86f
begin
	const bckZ = AutoZygote()
	const bckE = AutoEnzyme()
	const bckT = AutoTracker()
	const bckF = AutoFiniteDiff()
end

# ╔═╡ 16961e40-2773-4ef4-ad11-c9ec25b18136
md"# MLP"

# ╔═╡ 9be0d35e-edf7-4316-92cf-7a2d893788e4
begin
	x = rand(1,1000).*2 .- 1
	f(x) = x^2
	y = f.(x)
end

# ╔═╡ f0f75bee-0cf5-4748-8fd0-6e5dbfaa1c9c
scatter(x',y')

# ╔═╡ dbe3542c-e895-41c7-8efe-e0313705e5ca
begin
	rng = Random.default_rng()
	Random.seed!(rng, 0)
	relu(x) = max(0.0,x)
	model = Chain(
		Dense(1,16,relu),
		Dense(16,16,relu),
		Dense(16,1)
	)
	ps,st = Lux.setup(rng,model)
end

# ╔═╡ 9dc3cf82-4e54-44d4-8e10-af843c55dea5
function loss(model,x,ps,st,y)
	y_pred,_ = Lux.apply(model,x,ps,st)
	Flux.mse(y_pred,y)
end

# ╔═╡ ab4a1464-a55b-473a-adb0-b23c6378be3b
begin
	struct MyDescent{T<:Number} <: AbstractRule
		lr ::T
	end
	MyDescent(;lr) = MyDescent(lr)
	Optimisers.init(o::MyDescent, x::AbstractArray) = nothing
	function Optimisers.apply!(o::MyDescent, state, x, dx)
	  return state, dx * o.lr
	end
end

# ╔═╡ 93508fd6-742a-4e48-ac35-9ca58be22f52
begin
	lr = 0.01
	rule = MyDescent(lr)
	opt = Optimisers.setup(rule,ps)
end

# ╔═╡ 6fd37d15-14c7-4b0e-a1dd-89021d7e18f7
function train_model!(model,x,ps,st,y,opt;epoch = 16)
	data = Flux.DataLoader((x,y),batchsize = 6,shuffle = true)
	for i in 1:epoch
		l = loss(model,x,ps,st,y)
		@info "i = $i, L = $l"
		for (d1,d2) in data
			g = gradient(ps->loss(model,d1,ps,st,d2),bckZ,ps)
			Optimisers.update!(opt,ps,g)
		end
	end
	ps,opt
end

# ╔═╡ b749ade8-b45e-4ba8-97e6-4389467fc31e
@time train_model!(model,x,ps,st,y,opt)

# ╔═╡ 92485a90-9583-4742-8d8a-562e450459af
y_pred = Lux.apply(model,x,ps,st)[1]

# ╔═╡ 845ab080-ce5b-4e4a-be5f-6458acd0f84a
begin
	scatter(x',y_pred')
	scatter!(x',y')
end

# ╔═╡ 4f391006-3e2f-40d7-9a4b-932ce8df2ea6
md"# ODE PINN"

# ╔═╡ 3c90b1ac-ec75-4134-b957-6db29d22869a
begin
	t = rand(1,1000)
	u0() = 1.0
	f(u,t) = -t*u # u'(t) = -u
	uex(t) = exp(-t^2/2)
end

# ╔═╡ 12c5125f-0e89-4a1c-add4-f78d3f234d3f
begin
	rngODE = Random.default_rng()
	Random.seed!(rngODE, 1)
	sig(x) = 1/(1+exp(-x))
	modelODE = Lux.Chain(
		Lux.Dense(1,16,sig),
		Lux.Dense(16,16,sig),
		Lux.Dense(16,16,sig),
		Lux.Dense(16,1)
	)
	psODE,stODE = Lux.setup(rngODE,modelODE)
	psODE = f64(psODE)
end

# ╔═╡ 3da62a8e-4649-4add-a7bc-373f6c62687b
function PINNODE(u_net,t,F)
	dudt = gradient(sum ∘ u_net,bckZ,t)
	return sum(abs2,dudt .- F)
end

# ╔═╡ 85b92e37-6158-4c99-90c3-4496ba82f0d0
der_t = gradient(t->sum(first(modelODE(t,psODE,stODE))),bckZ,t)

# ╔═╡ 46f88fc8-2934-479d-b661-dcc1f58d67b4
begin
	lrODE = 0.1
	ruleODE = Adam(lr)
	optODE = Optimisers.setup(ruleODE,psODE)
end

# ╔═╡ 2fce1e21-5327-4cf1-bcaf-87ac4c6c01a0
y_predODE,_ = modelODE(t,psODE,stODE)

# ╔═╡ 20c4cbe2-7e7b-441a-a1cc-9a9676539aad
begin
scatter(t',y_predODE')
	scatter!(t',uex.(t'))
end

# ╔═╡ 7c7efbf6-ec51-4f57-9763-3fea9739e17d
md"# PDE PINN"

# ╔═╡ e1e216ea-a912-457e-b529-4b3b60232e52
begin
	xxx = range(0.0,1.0,30) 
	ttt = range(0.0,1.0,30) 
	xtp = Iterators.product(ttt,xxx) |> collect |> x-> x[:]
	u0(x) = exp(-50*(x-0.5)^2)
	xt = [ t[k] for k in 1:2, t in xtp] |> gdev
end

# ╔═╡ 4070761d-d214-4774-a8b8-be65454bd9c5
function lossODE(model,t,ps,st,der_t)
	u_net = Lux.StatefulLuxLayer{true}(model,ps,st)
	y_pred,_ = Lux.apply(model,t,ps,st)
	y0,_ = Lux.apply(model,zero(t),ps,st)
	inc = sum((y0 .- u0()).^2)*2/length(t)
	ode = PINNODE(u_net,t, f.(y_pred,t)) 
	0.5*inc + 0.5*ode
end

# ╔═╡ 51c78f47-0b0a-4b1a-b839-73ac9bc25616
lossODE(modelODE,t,psODE,stODE,der_t)

# ╔═╡ 623d8c5e-062a-4f29-b56f-c38259dc9039
 g=gradient(ps->lossODE(modelODE,t,ps,stODE,der_t),bckT,psODE)

# ╔═╡ db60a2cc-b1ef-45c9-be1d-6da9172423f0
Optimisers.update!(optODE,psODE,g)

# ╔═╡ d6a9f9c9-40cf-45df-9c2a-54d2cb9f3dd6
function train_modelODE!(model,t,ps,st,opt,der_t;epoch = 16)
	for i in 1:epoch
		l = lossODE(model,t,ps,st,der_t)
		@info "i = $i, L = $l"
		g = gradient(ps->lossODE(model,t,ps,st,der_t),bckZ,ps)
		Optimisers.update!(opt,ps,g)
	end
	ps,opt
end

# ╔═╡ d84f7063-5f3d-4c8e-82c0-3884014e7792
@time train_modelODE!(modelODE,t,psODE,stODE,optODE,der_t;epoch = 100)

# ╔═╡ 5ff6ffcd-96d9-4a0e-ae03-50e0e7c6a357
begin
	rngPDE = Random.default_rng()
	Random.seed!(rngPDE, 3)
	modelPDE = Lux.Chain(
		Lux.Dense(2,64,Lux.relu),
		Lux.Dense(64,64,Lux.relu),
		Lux.Dense(64,64,Lux.relu),
		Lux.Dense(64,1)
	)
	psPDE,stPDE = Lux.setup(rngPDE,modelPDE) |> gdev
	# psPDE = f64(psPDE)
end

# ╔═╡ 8cd6dba2-5fb2-4894-b4da-9d259a7eee0f
function PDENN(u_net,u_pred,xt)
	dudxt = gradient(sum ∘ u_net, bckZ,xt)
	dudt = dudxt[1:1,:]
	dudx = dudxt[2:2,:]
	return sum(abs2,dudt.+u_pred.*dudx)./size(dudt,2)
end

# ╔═╡ f4bffde6-c803-4d83-ae59-971332a3fbba
function lossPDE(model,xt,ps,st,der_xt)
	u_net = StatefulLuxLayer{true}(model, ps, st)
	u_pred,_ = Lux.apply(model,xt,ps,st)
	y0,_ = Lux.apply(model,xt.*cu([0,1]),ps,st)
	yx0,_ = Lux.apply(model,xt.*cu([1,0]),ps,st)
	inc = sum((y0 .- u0.(xt[2:2,:])).^2)*2/length(y0)
	incx = sum(yx0.^2)*2/length(y0)
	pde = PDENN(u_net,u_pred,xt)
	0.25*inc + 0.5*pde+0.25*incx
end

# ╔═╡ 9b6418b5-986b-412c-a37c-cfede2daf94a
der_xt = gradient(xt->sum(first(modelPDE(xt,psPDE,stPDE))),bckZ,xt)

# ╔═╡ e9e50392-9a6d-4bf3-b7ee-bc1a349ca345
lossPDE(modelPDE,xt,psPDE,stPDE,der_xt)

# ╔═╡ f9ce3cf4-1fbc-4cb6-be5e-fe70a9a7c199

function train_modelPDE!(model,xt,ps,st,opt,der_xt,lr;epoch = 16)
	dtf = Flux.DataLoader(xt,batchsize = 32,shuffle = true) |> gdev
	lrs = lr
	Optimisers.adjust!(opt,lrs)
	for i in 1:epoch
		l = lossPDE(model,xt,ps,st,der_xt)
		@info "i = $i, L = $l"
		for d1 in dtf
			g = gradient(ps->lossPDE(model,d1,ps,st,der_xt),bckZ,ps)
			Optimisers.update!(opt,ps,g)
		end
		if i %20 == 0 && lrs >1e-6
			lrs /=1.5 
			Optimisers.adjust!(opt,lrs)
		end
	end
	ps,opt
end

# ╔═╡ 53b90677-a2b9-453c-92e3-d01c527f8046
begin
	lrPDE = 5e-4
	rulePDE = Adam(lr)
	optPDE = Optimisers.setup(rulePDE,psPDE)
end

# ╔═╡ 1bcc021b-adff-4da7-bdf9-f4bd64e36cd4
import TidierData,DataFrames

# ╔═╡ 8ee2227f-c3be-41d2-bcb4-9e643d6080b1
begin
	@time train_modelPDE!(modelPDE,xt,psPDE,stPDE,optPDE,der_xt,lrPDE;epoch = 100)
	xx = range(0.0,1.0,100)
	yy = range(0.0,1.0,100)
	xtppp = Iterators.product(xx,yy) |> collect |> x-> x[:]
	xtpp = [i[k] for k in 1:2, i in xtppp ] |> gdev
	modd = Lux.testmode(StatefulLuxLayer{true}(modelPDE,psPDE,stPDE))
	val = modd(xtpp)
	xx,yy,zz=collect.((xtpp[1,:],xtpp[2,:],val[1,:]))
	df = DataFrames.DataFrame(:t => xx, :x => yy, :u => zz)
	res = TidierData.@group_by(df,t)
	nothing
end

# ╔═╡ fe34a91b-191b-45ea-a2d2-1bf1913c1990
@gif for dfi in res
	plot(dfi.x,dfi.u)
end

# ╔═╡ 732817b4-31f4-42f7-9626-a6eb991ef71d
md"# Neural ODE"

# ╔═╡ fd8a0f5e-14fb-4c62-b2f6-03169794da6f
md"## Create false datas"

# ╔═╡ 44d53fb6-2f14-4022-9b89-9f575d276a23
function true_ode(u,p,t)
	-u # u'(t) = -u(t)
end

# ╔═╡ 64e99313-9193-47b4-b65e-e8d0cc9df231
begin
	const dt = 0.01
	const tspan = (0.0,1.0)
	const u00 = [2.0]
	pODE = (0.0)
end

# ╔═╡ 7dfe52e7-86d6-48c4-b7e0-779845459839
prob = ODEProblem(true_ode,u00,tspan,pODE)

# ╔═╡ 70df0386-2ca4-4628-96c8-97218d5223c7
sol = solve(prob,Tsit5(),dt = 0.01,saveat=0.01)

# ╔═╡ 53bc33db-d430-4d6a-ab91-f0329058972e
Array(sol)

# ╔═╡ 8c94e1d2-1591-4715-a808-d1b656496568
plot(sol.t,Array(sol)')

# ╔═╡ 26d7f0d8-eb25-45db-98e1-c11f974683dd
md"## Find back evolution"

# ╔═╡ 5b49f487-27e9-4142-8f50-9d2f641e5caa
begin
	const nnODE = Chain(
		Dense(1=>16,relu),
		Dense(16=>16,relu),
		Dense(16=>1)
	)
	nnps, nnst = Lux.setup(Random.default_rng(), nnODE) |> f64 
end

# ╔═╡ f1ef3a6e-b9ba-4b0e-afaa-1eb8242d964d
params = ComponentArray(nnps)

# ╔═╡ 938fdc9a-2454-4bfe-a6d9-9d6c4ba79cbc
const nnODE_model = StatefulLuxLayer{true}(nnODE, nothing, nnst)

# ╔═╡ 804a8cca-905f-4d1e-b697-c22deb1c970c
nnODE_model([1.0],params)

# ╔═╡ 663414b5-3c7d-4d39-a79e-468f8339e944
function learn_ode(u,nn_param,t)
	return nnODE_model(u,nn_param)
end

# ╔═╡ e2f8caf1-1835-44b6-994b-5360fdd7fb56
probnn = ODEProblem(learn_ode,u00,tspan,params)

# ╔═╡ b3af787b-4aa2-4f75-b26d-661a1b0bdbbe
solnn = solve(probnn,Tsit5(),dt = dt,saveat = 0.01,adaptive = false)

# ╔═╡ 9df7777f-0910-4888-8de9-362c93e7ef9b
plot(solnn.t,Array(solnn)')

# ╔═╡ aca658f0-5ab6-4574-9e2a-541fa92e3ceb
md"## Let's train it !"

# ╔═╡ 388a11bc-bf5b-4517-8c61-5f1a8f381338
function lossnn(theta,true_sol)
	probnn = ODEProblem(learn_ode,u00,tspan,theta)
    pred = Array(solve(probnn, Tsit5(); saveat=0.01, dt = 0.01, adaptive=false))
    return sum(abs2,pred.-true_sol)
end

# ╔═╡ afc1bb19-c906-4134-b0ca-7c63c07ea49d
lossnn(params,Array(sol))

# ╔═╡ b8f2faa8-85c1-4236-84db-747e4226d54c
gradient(params -> lossnn(params,Array(sol)),bckZ,params)

# ╔═╡ 10412a01-4016-4702-98c7-1aa6f7d1059f
begin
	optnn = Optimisers.setup(Adam(0.1),params)
end

# ╔═╡ 120877e0-679c-448d-99be-77c87edbe9bd
for i in 1:32
	gin = gradient(params -> lossnn(params,Array(sol)),bckZ,params)
	Optimisers.update!(optnn,params,gin)
	@info lossnn(params,Array(sol))
end

# ╔═╡ 6074b5dd-5ea9-4c1f-81bb-9663fc04fa02
solnn_t = solve(probnn,Tsit5(),dt = dt,saveat = 0.01,adaptive = false)

# ╔═╡ 367f75bf-52a7-4ceb-b492-47391f1ff834
begin
	plot(solnn_t.t,Array(solnn_t)')
	plot!(solnn_t.t,t->u00[1]*exp.(-t))
end

# ╔═╡ ed3f388c-afbe-499d-a3ca-32da1a17dcc8
u02 = [3]

# ╔═╡ 080fbca2-f466-41ff-997b-7ed64d14e2df
probnn2 = ODEProblem(learn_ode,u02,tspan,params)

# ╔═╡ 1100da66-b475-42d8-86de-2f26083942ad
solnn_t2 = solve(probnn2,Tsit5(),dt = dt,saveat = 0.01,adaptive = false)

# ╔═╡ 4d3f2cc8-d30d-44a2-bc6a-e3f067205bbc
begin
	plot(solnn_t2.t,Array(solnn_t2)')
	plot!(solnn_t2.t,t->u02[1]*exp.(-t))
end

# ╔═╡ 00000000-0000-0000-0000-000000000001
PLUTO_PROJECT_TOML_CONTENTS = """
[deps]
CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
ComponentArrays = "b0b7db55-cfe3-40fc-9ded-d10e2dbeff66"
DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
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Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
cuDNN = "02a925ec-e4fe-4b08-9a7e-0d78e3d38ccd"

[compat]
CUDA = "~5.5.2"
ComponentArrays = "~0.15.17"
DataFrames = "~1.7.0"
DifferentiationInterface = "~0.6.7"
Enzyme = "~0.13.7"
FiniteDiff = "~2.26.0"
Flux = "~0.14.21"
Lux = "~1.1.0"
LuxCUDA = "~0.3.3"
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OrdinaryDiffEq = "~6.89.0"
Plots = "~1.40.8"
PlutoUI = "~0.7.60"
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TidierData = "~0.16.2"
Tracker = "~0.2.35"
Zygote = "~0.6.71"
cuDNN = "~1.4.0"
"""

# ╔═╡ 00000000-0000-0000-0000-000000000002
PLUTO_MANIFEST_TOML_CONTENTS = """
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git-tree-sha1 = "8fdda4c692503d44d04a0603d9ac0982054635f9"
uuid = "14d82f49-176c-5ed1-bb49-ad3f5cbd8c74"
version = "0.1.1+0"

[[deps.Xorg_libxcb_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "XSLT_jll", "Xorg_libXau_jll", "Xorg_libXdmcp_jll", "Xorg_libpthread_stubs_jll"]
git-tree-sha1 = "bcd466676fef0878338c61e655629fa7bbc69d8e"
uuid = "c7cfdc94-dc32-55de-ac96-5a1b8d977c5b"
version = "1.17.0+0"

[[deps.Xorg_libxkbfile_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_libX11_jll"]
git-tree-sha1 = "730eeca102434283c50ccf7d1ecdadf521a765a4"
uuid = "cc61e674-0454-545c-8b26-ed2c68acab7a"
version = "1.1.2+0"

[[deps.Xorg_xcb_util_cursor_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_xcb_util_image_jll", "Xorg_xcb_util_jll", "Xorg_xcb_util_renderutil_jll"]
git-tree-sha1 = "04341cb870f29dcd5e39055f895c39d016e18ccd"
uuid = "e920d4aa-a673-5f3a-b3d7-f755a4d47c43"
version = "0.1.4+0"

[[deps.Xorg_xcb_util_image_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "0fab0a40349ba1cba2c1da699243396ff8e94b97"
uuid = "12413925-8142-5f55-bb0e-6d7ca50bb09b"
version = "0.4.0+1"

[[deps.Xorg_xcb_util_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxcb_jll"]
git-tree-sha1 = "e7fd7b2881fa2eaa72717420894d3938177862d1"
uuid = "2def613f-5ad1-5310-b15b-b15d46f528f5"
version = "0.4.0+1"

[[deps.Xorg_xcb_util_keysyms_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "d1151e2c45a544f32441a567d1690e701ec89b00"
uuid = "975044d2-76e6-5fbe-bf08-97ce7c6574c7"
version = "0.4.0+1"

[[deps.Xorg_xcb_util_renderutil_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "dfd7a8f38d4613b6a575253b3174dd991ca6183e"
uuid = "0d47668e-0667-5a69-a72c-f761630bfb7e"
version = "0.3.9+1"

[[deps.Xorg_xcb_util_wm_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "e78d10aab01a4a154142c5006ed44fd9e8e31b67"
uuid = "c22f9ab0-d5fe-5066-847c-f4bb1cd4e361"
version = "0.4.1+1"

[[deps.Xorg_xkbcomp_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_libxkbfile_jll"]
git-tree-sha1 = "330f955bc41bb8f5270a369c473fc4a5a4e4d3cb"
uuid = "35661453-b289-5fab-8a00-3d9160c6a3a4"
version = "1.4.6+0"

[[deps.Xorg_xkeyboard_config_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_xkbcomp_jll"]
git-tree-sha1 = "691634e5453ad362044e2ad653e79f3ee3bb98c3"
uuid = "33bec58e-1273-512f-9401-5d533626f822"
version = "2.39.0+0"

[[deps.Xorg_xtrans_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl"]
git-tree-sha1 = "e92a1a012a10506618f10b7047e478403a046c77"
uuid = "c5fb5394-a638-5e4d-96e5-b29de1b5cf10"
version = "1.5.0+0"

[[deps.Zlib_jll]]
deps = ["Libdl"]
uuid = "83775a58-1f1d-513f-b197-d71354ab007a"
version = "1.2.13+1"

[[deps.Zstd_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl"]
git-tree-sha1 = "555d1076590a6cc2fdee2ef1469451f872d8b41b"
uuid = "3161d3a3-bdf6-5164-811a-617609db77b4"
version = "1.5.6+1"

[[deps.Zygote]]
deps = ["AbstractFFTs", "ChainRules", "ChainRulesCore", "DiffRules", "Distributed", "FillArrays", "ForwardDiff", "GPUArrays", "GPUArraysCore", "IRTools", "InteractiveUtils", "LinearAlgebra", "LogExpFunctions", "MacroTools", "NaNMath", "PrecompileTools", "Random", "Requires", "SparseArrays", "SpecialFunctions", "Statistics", "ZygoteRules"]
git-tree-sha1 = "f2f85ad73ca67b5d3c94239b0fde005e0fe2d900"
uuid = "e88e6eb3-aa80-5325-afca-941959d7151f"
version = "0.6.71"

    [deps.Zygote.extensions]
    ZygoteColorsExt = "Colors"
    ZygoteDistancesExt = "Distances"
    ZygoteTrackerExt = "Tracker"

    [deps.Zygote.weakdeps]
    Colors = "5ae59095-9a9b-59fe-a467-6f913c188581"
    Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
    Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c"

[[deps.ZygoteRules]]
deps = ["ChainRulesCore", "MacroTools"]
git-tree-sha1 = "27798139afc0a2afa7b1824c206d5e87ea587a00"
uuid = "700de1a5-db45-46bc-99cf-38207098b444"
version = "0.2.5"

[[deps.cuDNN]]
deps = ["CEnum", "CUDA", "CUDA_Runtime_Discovery", "CUDNN_jll"]
git-tree-sha1 = "4b3ac62501ca73263eaa0d034c772f13c647fba6"
uuid = "02a925ec-e4fe-4b08-9a7e-0d78e3d38ccd"
version = "1.4.0"

[[deps.demumble_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl"]
git-tree-sha1 = "6498e3581023f8e530f34760d18f75a69e3a4ea8"
uuid = "1e29f10c-031c-5a83-9565-69cddfc27673"
version = "1.3.0+0"

[[deps.eudev_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "gperf_jll"]
git-tree-sha1 = "431b678a28ebb559d224c0b6b6d01afce87c51ba"
uuid = "35ca27e7-8b34-5b7f-bca9-bdc33f59eb06"
version = "3.2.9+0"

[[deps.fzf_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl"]
git-tree-sha1 = "936081b536ae4aa65415d869287d43ef3cb576b2"
uuid = "214eeab7-80f7-51ab-84ad-2988db7cef09"
version = "0.53.0+0"

[[deps.gperf_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "3516a5630f741c9eecb3720b1ec9d8edc3ecc033"
uuid = "1a1c6b14-54f6-533d-8383-74cd7377aa70"
version = "3.1.1+0"

[[deps.libaom_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl"]
git-tree-sha1 = "1827acba325fdcdf1d2647fc8d5301dd9ba43a9d"
uuid = "a4ae2306-e953-59d6-aa16-d00cac43593b"
version = "3.9.0+0"

[[deps.libass_jll]]
deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "HarfBuzz_jll", "JLLWrappers", "Libdl", "Zlib_jll"]
git-tree-sha1 = "e17c115d55c5fbb7e52ebedb427a0dca79d4484e"
uuid = "0ac62f75-1d6f-5e53-bd7c-93b484bb37c0"
version = "0.15.2+0"

[[deps.libblastrampoline_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "8e850b90-86db-534c-a0d3-1478176c7d93"
version = "5.11.0+0"

[[deps.libdecor_jll]]
deps = ["Artifacts", "Dbus_jll", "JLLWrappers", "Libdl", "Libglvnd_jll", "Pango_jll", "Wayland_jll", "xkbcommon_jll"]
git-tree-sha1 = "9bf7903af251d2050b467f76bdbe57ce541f7f4f"
uuid = "1183f4f0-6f2a-5f1a-908b-139f9cdfea6f"
version = "0.2.2+0"

[[deps.libevdev_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "141fe65dc3efabb0b1d5ba74e91f6ad26f84cc22"
uuid = "2db6ffa8-e38f-5e21-84af-90c45d0032cc"
version = "1.11.0+0"

[[deps.libfdk_aac_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl"]
git-tree-sha1 = "8a22cf860a7d27e4f3498a0fe0811a7957badb38"
uuid = "f638f0a6-7fb0-5443-88ba-1cc74229b280"
version = "2.0.3+0"

[[deps.libinput_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "eudev_jll", "libevdev_jll", "mtdev_jll"]
git-tree-sha1 = "ad50e5b90f222cfe78aa3d5183a20a12de1322ce"
uuid = "36db933b-70db-51c0-b978-0f229ee0e533"
version = "1.18.0+0"

[[deps.libpng_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Zlib_jll"]
git-tree-sha1 = "b70c870239dc3d7bc094eb2d6be9b73d27bef280"
uuid = "b53b4c65-9356-5827-b1ea-8c7a1a84506f"
version = "1.6.44+0"

[[deps.libvorbis_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Ogg_jll", "Pkg"]
git-tree-sha1 = "490376214c4721cdaca654041f635213c6165cb3"
uuid = "f27f6e37-5d2b-51aa-960f-b287f2bc3b7a"
version = "1.3.7+2"

[[deps.mtdev_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "814e154bdb7be91d78b6802843f76b6ece642f11"
uuid = "009596ad-96f7-51b1-9f1b-5ce2d5e8a71e"
version = "1.1.6+0"

[[deps.nghttp2_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d"
version = "1.59.0+0"

[[deps.oneTBB_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl"]
git-tree-sha1 = "7d0ea0f4895ef2f5cb83645fa689e52cb55cf493"
uuid = "1317d2d5-d96f-522e-a858-c73665f53c3e"
version = "2021.12.0+0"

[[deps.p7zip_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0"
version = "17.4.0+2"

[[deps.x264_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "4fea590b89e6ec504593146bf8b988b2c00922b2"
uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a"
version = "2021.5.5+0"

[[deps.x265_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "ee567a171cce03570d77ad3a43e90218e38937a9"
uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76"
version = "3.5.0+0"

[[deps.xkbcommon_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll", "Wayland_protocols_jll", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"]
git-tree-sha1 = "9c304562909ab2bab0262639bd4f444d7bc2be37"
uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd"
version = "1.4.1+1"
"""

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