Error "lexer.lua:117: bad argument #3 to 'set_styling' (number expected, got nil)"

Thanks for creating a beautiful and almost perfect editor. I am relatively new to textadept, but have been using it for some weeks now. This is an error that appeared on the development branch (version 12.0 beta), NOT the stable branch. The error appeared upon opening a Julia file (see code sample below). Below are the error messages in "[Output Buffer]" tab. Other simple Julia file can be opened without error with correct lexer formatting. Thanks. \textadept/core/lexer.lua:117: bad argument #3 to 'set_styling' (number expected, got nil)

Sample Julia code

# https://discourse.julialang.org/t/optimization-with-jump-an-affine-expression-enters-a-nonlinear-constraint-dynamic-discrete-choice/54230/2
# https://discourse.julialang.org/t/forwarddiff-using-on-functions-that-are-defined-on-floats64/18226/2

using JuMP, Ipopt, Random
# generate fake data
N = 100  # number of periods
β0 = 1.0  # regression parameter
x = randn(MersenneTwister(1234), N)  # exog. regressor
ξ0 = 0.25 * randn(MersenneTwister(2345), N)  # good-specific iid shock; in fact E[ξ] ≜ 0
u0 = x * β0 .+ ξ0  # mean utility of good 1
q = rand(MersenneTwister(3456), N)  # probability of winning when choosing good 1 (good 2 always has q = 1, u = 0)
v, V = zeros(N), zeros(N)  # initialize cvf(1), emax(1)
for i=1:N
    v = q .* u0 + (1 .- q) .* vcat(V[2:end], 0.0)  # solve cvf(1) for all t
    V = log.(1 .+ exp.(v))  # solve emax for all t
end

s0 = exp.(v) ./(1 .+ exp.(v))  # generate "observed" market shares

# code up MPEC
function obj(ξ...)
    sum(ξ[i] .^ 2 for i=1:N)
end

function getMeanUtilities(β, ξ::Vararg{TT, NN} where NN) where {TT <: Union{Real, VariableRef}}        
    # recover mean utilities
    u = Vector{TT}(undef, N)

    for i=1:N
        u[i] = x[i] * β + ξ[i]
    end
    u
end

function getShares(u::Vararg{TT, NN} where NN) where {TT <: Union{Real, VariableRef}}

    # # initialize and recover cvf
    v = Vector{TT}(undef, N)
    V = Vector{TT}(undef, N)
    s = Vector{TT}(undef, N)

    for i=1:N
        v[i] = q[i] * u[i]
        V[i] = log(1 + exp(v[i]))
    end

    tol = 1e-14
    dist = 1e3
    v_ = similar(v)

    while dist > tol
        v_ = copy(v)
        for i=1:N
            v[i] = q[i] * u[i] + (1-q[i]) * (i == N ? 0.0 : V[i+1])
            V[i] = log(1 + exp(v[i]))
        end
        dist = norm(v_ - v)
    end

    for i=1:N
        s[i] = exp(v[i])/(1 + exp(v[i]))
    end
    s
end

# now define model
"""
 memoize(foo::Function, n_outputs::Int)

 Take a function `foo` and return a vector of length `n_outputs`, where each
 element is a function that returns the `i`'th output of `foo`.

 To avoid duplication of work, cache the most-recent evaluations of `foo`.
 Because `foo_i` is auto-differentiated with ForwardDiff, our cache needs to
 work when `x` is a `Float64` and a `ForwardDiff.Dual`.
 """
function memoize(foo::Function, n_outputs::Int)
    last_x, last_f = nothing, nothing
    last_dx, last_dfdx = nothing, nothing
    function foo_i(i, x::T...) where {T <: Real}
        if T == Float64
            if x != last_x
                last_x, last_f = x, foo(x...)
            end
            return last_f[i]::T
        else
            if x != last_dx
                last_dx, last_dfdx = x, foo(x...)
            end
            return last_dfdx[i]::T
        end
    end
    return [(x...) -> foo_i(i, x...) for i = 1:n_outputs]
end

# memoize mean utility and share functions
memoized_u = memoize(getMeanUtilities, N)
memoized_s = memoize(getShares, N)

# run model
function runModel(u_functions, s_functions)
    model = Model(Ipopt.Optimizer)
    @variables(model, begin
               β
               ξ[1:N]
               u[1:N]
               end
               )
    for (i, f) in enumerate(u_functions)
        f_sym = Symbol("f_$(i)")
        register(model, f_sym, N+1, f; autodiff = true)
        add_NL_constraint(model, :($(u[i]) == $(f_sym)($(β), $(ξ...))))
    end

    for (i, s) in enumerate(s_functions)
        s_sym = Symbol("s_$(i)")
        register(model, s_sym, N, s; autodiff = true)
        add_NL_constraint(model, :($(s0[i]) == $(s_sym)($(u...))))
    end

    @constraint(model, β <= 3.0)
    @constraint(model, β >= -3.0)

    register(model, :obj, N, obj; autodiff=true)
    @NLobjective(model, Min, obj(ξ...))
    set_optimizer_attribute(model, "max_cpu_time", 420.0)

    optimize!(model)
    @show value(β)
    # return model
end

b = runModel(memoized_u, memoized_s)

orbitalquark / textadept

Error "lexer.lua:117: bad argument #3 to 'set_styling' (number expected, got nil)" #424