Make string interpolations lazy

This is to appease JET and get all the paths ready for trimming.

Author: RomeoV

ext/NonlinearSolvePETScExt.jl

@@ -35,7 +35,7 @@ function SciMLBase.__solve(
         end
 
         if petsclibidx === nothing
-            error("No compatible PETSc library found for element type $(T). Pass in a \
+            error(lazy"No compatible PETSc library found for element type $(T). Pass in a \
                    custom `petsclib` via `PETScSNES(; petsclib = <petsclib>, ....)`.")
         end
         petsclib = PETSc.petsclibs[petsclibidx]

ext/NonlinearSolveSIAMFANLEquationsExt.jl

@@ -18,7 +18,7 @@ function siamfanlequations_retcode_mapping(sol)
     elseif sol.errcode == -1
         return ReturnCode.Default
     else
-        error("Unknown SIAMFANLEquations return code: $(sol.errcode)")
+        error(lazy"Unknown SIAMFANLEquations return code: $(sol.errcode)")
     end
 end
 

lib/NonlinearSolveBase/src/abstract_types.jl

@@ -336,7 +336,7 @@ function Base.show(io::IO, ::MIME"text/plain", cache::AbstractNonlinearSolveCach
 end
 
 function show_nonlinearsolve_cache(io::IO, cache::AbstractNonlinearSolveCache, indent = 0)
-    println(io, "$(nameof(typeof(cache)))(")
+    println(io, lazy"$(nameof(typeof(cache)))(")
     show_nonlinearsolve_algorithm(
         io,
         cache.alg,

lib/NonlinearSolveBase/src/autodiff.jl

@@ -23,11 +23,11 @@ function select_forward_mode_autodiff(
     if warn_check_mode && !(ADTypes.mode(ad) isa ADTypes.ForwardMode) &&
        !(ADTypes.mode(ad) isa ADTypes.ForwardOrReverseMode) &&
        !is_finite_differences_backend(ad)
-        @warn "The chosen AD backend $(ad) is not a forward mode AD. Use with caution."
+        @warn lazy"The chosen AD backend $(ad) is not a forward mode AD. Use with caution."
     end
     if incompatible_backend_and_problem(prob, ad)
         adₙ = select_forward_mode_autodiff(prob, nothing; warn_check_mode)
-        @warn "The chosen AD backend `$(ad)` does not support the chosen problem. This \
+        @warn lazy"The chosen AD backend `$(ad)` does not support the chosen problem. This \
                could be because the backend package for the chosen AD isn't loaded. After \
                running autodiff selection detected `$(adₙ)` as a potential forward mode \
                backend."
@@ -50,11 +50,11 @@ function select_reverse_mode_autodiff(
     if warn_check_mode && !(ADTypes.mode(ad) isa ADTypes.ReverseMode) &&
        !(ADTypes.mode(ad) isa ADTypes.ForwardOrReverseMode) &&
        !is_finite_differences_backend(ad)
-        @warn "The chosen AD backend $(ad) is not a reverse mode AD. Use with caution."
+        @warn lazy"The chosen AD backend $(ad) is not a reverse mode AD. Use with caution."
     end
     if incompatible_backend_and_problem(prob, ad)
         adₙ = select_reverse_mode_autodiff(prob, nothing; warn_check_mode)
-        @warn "The chosen AD backend `$(ad)` does not support the chosen problem. This \
+        @warn lazy"The chosen AD backend `$(ad)` does not support the chosen problem. This \
                could be because the backend package for the chosen AD isn't loaded. After \
                running autodiff selection detected `$(adₙ)` as a potential reverse mode \
                backend."
@@ -75,7 +75,7 @@ end
 function select_jacobian_autodiff(prob::AbstractNonlinearProblem, ad::AbstractADType)
     if incompatible_backend_and_problem(prob, ad)
         adₙ = select_jacobian_autodiff(prob, nothing)
-        @warn "The chosen AD backend `$(ad)` does not support the chosen problem. This \
+        @warn lazy"The chosen AD backend `$(ad)` does not support the chosen problem. This \
                could be because the backend package for the chosen AD isn't loaded. After \
                running autodiff selection detected `$(adₙ)` as a potential jacobian \
                backend."

lib/NonlinearSolveBase/src/descent/damped_newton.jl

@@ -215,7 +215,7 @@ function InternalAPI.solve!(
             end
             b = cache.Jᵀfu_cache
         else
-            error("Unknown Mode: $(cache.mode).")
+            error(lazy"Unknown Mode: $(cache.mode).")
         end
     end
 

lib/NonlinearSolveBase/src/jacobian.jl

@@ -234,7 +234,7 @@ function construct_concrete_adtype(f::NonlinearFunction, ad::AbstractADType)
         else
             if sparse_or_structured_prototype(f.jac_prototype)
                 if !(sparsity_detector isa NoSparsityDetector)
-                    @warn "`jac_prototype` is a sparse matrix but sparsity = $(f.sparsity) \
+                    @warn lazy"`jac_prototype` is a sparse matrix but sparsity = $(f.sparsity) \
                            has also been specified. Ignoring sparsity field and using \
                            `jac_prototype` sparsity."
                 end
@@ -248,7 +248,7 @@ function construct_concrete_adtype(f::NonlinearFunction, ad::AbstractADType)
 end
 
 function construct_concrete_adtype(::NonlinearFunction, ad::AutoSparse)
-    error("Specifying a sparse AD type for Nonlinear Problems was removed in v4. \
+    error(lazy"Specifying a sparse AD type for Nonlinear Problems was removed in v4. \
            Instead use the `sparsity`, `jac_prototype`, and `colorvec` to specify \
            the right sparsity pattern and coloring algorithm. Ignoring the sparsity \
            detection algorithm and coloring algorithm present in $(ad).")

lib/NonlinearSolveBase/src/polyalg.jl

@@ -92,12 +92,12 @@ function Base.show(io::IO, ::MIME"text/plain", cache::NonlinearSolvePolyAlgorith
     println(io, "NonlinearSolvePolyAlgorithmCache with \
                  $(Utils.unwrap_val(cache.static_length)) algorithms:")
     best_alg = ifelse(cache.best == -1, "nothing", cache.best)
-    println(io, "    Best Algorithm: $(best_alg)")
+    println(io, lazy"    Best Algorithm: $(best_alg)")
     println(
-        io, "    Current Algorithm: [$(cache.current) / $(Utils.unwrap_val(cache.static_length))]"
+        io, lazy"    Current Algorithm: [$(cache.current) / $(Utils.unwrap_val(cache.static_length))]"
     )
-    println(io, "    nsteps: $(cache.nsteps)")
-    println(io, "    retcode: $(cache.retcode)")
+    println(io, lazy"    nsteps: $(cache.nsteps)")
+    println(io, lazy"    retcode: $(cache.retcode)")
     print(io, "    Current Cache: ")
     NonlinearSolveBase.show_nonlinearsolve_cache(io, cache.caches[cache.current], 4)
 end

lib/NonlinearSolveBase/src/utils.jl

@@ -22,7 +22,7 @@ end
 
 function Base.convert(::Type{AbstractArray}, A::Pinv)
     hasmethod(pinv, Tuple{typeof(A.J)}) && return pinv(A.J)
-    @warn "`pinv` not defined for $(typeof(A.J)). Jacobian will not be inverted when \
+    @warn lazy"`pinv` not defined for $(typeof(A.J)). Jacobian will not be inverted when \
            tracing." maxlog=1
     return A.J
 end

lib/NonlinearSolveBase/src/wrappers.jl

@@ -4,7 +4,7 @@ function assert_extension_supported_termination_condition(
     no_termination_condition = termination_condition === nothing
     no_termination_condition && return nothing
     abs_norm_supported && termination_condition isa AbsNormTerminationMode && return nothing
-    throw(AssertionError("`$(nameof(typeof(alg)))` does not support termination conditions!"))
+    throw(AssertionError(lazy"`$(nameof(typeof(alg)))` does not support termination conditions!"))
 end
 
 function construct_extension_function_wrapper(

lib/NonlinearSolveSciPy/src/NonlinearSolveSciPy.jl

@@ -43,9 +43,12 @@ function SciPyLeastSquares(; method::String = "trf", loss::String = "linear")
     valid_methods = ("trf", "dogbox", "lm")
     valid_losses = ("linear", "soft_l1", "huber", "cauchy", "arctan")
     method in valid_methods ||
-        throw(ArgumentError("Invalid method: $method. Valid methods are: $(join(valid_methods, ", "))"))
+        throw(ArgumentError(
+            lazy"Invalid method: $method. Valid methods are: $(join(valid_methods, ", "))"))
     loss in valid_losses ||
-        throw(ArgumentError("Invalid loss: $loss. Valid loss functions are: $(join(valid_losses, ", "))"))
+        throw(ArgumentError(
+            lazy"Invalid loss: $loss. Valid loss functions are: $(join(valid_losses, ",
+            "))"))
     return SciPyLeastSquares(method, loss, :SciPyLeastSquares)
 end