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import time
import jax
from jax import numpy as jnp
import numpy as np
N=4096
flops_ish = N*N*2*N
for i in range(5):
    A = np.random.rand(N,N).astype(np.float64)
    B = np.random.rand(N,N).astype(np.float64)
    t1 = time.monotonic()

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using Gadfly
import Cairo, Fontconfig
using Printf
using FFMPEG

#################################### This is pretty much the same code as in https://github.com/JuliaPlots/Plots.jl/blob/master/src/animation.jl
struct Animation
    dir::String
    frames::Vector{String}
    kwargs::Iterators.Pairs

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### A Pluto.jl notebook ###
# v0.12.21

using Markdown
using InteractiveUtils

# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).
macro bind(def, element)
    quote
        local el = $(esc(element))

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using MacroTools
using Symbolics

function parse(strexpr)
    expr = Meta.parse(strexpr)
    freevars = Set()
    expr = MacroTools.postwalk(x -> begin
        if isa(x, Symbol) && !isdefined(Base, x)
            push!(freevars, Variable(x))
            Expr(:call, Variable, QuoteNode(x))

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using LinearAlgebra, BenchmarkTools, SparseArrays, SymbolicUtils, Symbolics

## Problem setup
const z = zeros;

wx = Float64[ 0 -1  0;
              1  0  0;
              0  0  0;];

Ak_1 = [ wx           -I   zeros(3,12);

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using LinearAlgebra
using BenchmarkTools
using SparseArrays

# To exploit the sparsity of the matrix as much as possible, pre-compile the multiplication operation
# to skip the matrix entries look-up. Since the matrix is sparse this might generate only a few operations
# which are all inlined.
# Note that this only works with small matrices since otherwise the size of the function will just be to
# large.
# Note that this could also be avoided, but this is another topic.

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