tidy occurrences of varname_leaves as well

src/model_utils.jl

@@ -81,7 +81,7 @@ function varname_in_chain!(
     # We use `VarName{sym}()` so that the resulting leaf `vn` only contains the tail of the optic.
     # This way we can use `getoptic(vn)` to extract the value from `x` and use `getoptic(vn) ∘ vn_parent`
     # to extract the value from the `chain`.
-    for vn in varname_leaves(VarName{sym}(), x)
+    for vn in AbstractPPL.varname_leaves(VarName{sym}(), x)
         # Update `out`, possibly in place, and return.
         l = AbstractPPL.getoptic(vn)
         varname_in_chain!(x, l ∘ vn_parent, chain, chain_idx, iteration_idx, out)
@@ -107,7 +107,7 @@ function values_from_chain(
     # This way we can use `getoptic(vn)` to extract the value from `x` and use `getoptic(vn) ∘ vn_parent`
     # to extract the value from the `chain`.
     out = similar(x)
-    for vn in varname_leaves(VarName{sym}(), x)
+    for vn in AbstractPPL.varname_leaves(VarName{sym}(), x)
         # Update `out`, possibly in place, and return.
         l = AbstractPPL.getoptic(vn)
         out = Accessors.set(

src/test_utils.jl

@@ -11,7 +11,7 @@ using Bijectors: Bijectors
 using Accessors: Accessors
 
 # For backwards compat.
-using DynamicPPL: varname_leaves, update_values!!
+using DynamicPPL: update_values!!
 
 include("test_utils/model_interface.jl")
 include("test_utils/models.jl")

src/test_utils/sampler.jl

@@ -51,7 +51,7 @@ function test_sampler(
         for vn in filter(varnames_filter, varnames(model))
             # We want to compare elementwise which can be achieved by
             # extracting the leaves of the `VarName` and the corresponding value.
-            for vn_leaf in varname_leaves(vn, get(target_values, vn))
+            for vn_leaf in AbstractPPL.varname_leaves(vn, get(target_values, vn))
                 target_value = get(target_values, vn_leaf)
                 chain_mean_value = marginal_mean_of_samples(chain, vn_leaf)
                 @test chain_mean_value ≈ target_value atol = atol rtol = rtol

test/contexts.jl

@@ -93,7 +93,7 @@ Base.IteratorEltype(::Type{<:AbstractContext}) = Base.EltypeUnknown()
                 # here to split up arrays which could potentially have some,
                 # but not all, elements being `missing`.
                 conditioned_vns = mapreduce(
-                    p -> DynamicPPL.TestUtils.varname_leaves(p.first, p.second),
+                    p -> AbstractPPL.varname_leaves(p.first, p.second),
                     vcat,
                     pairs(conditioned_values),
                 )

test/model.jl

@@ -71,7 +71,7 @@ const GDEMO_DEFAULT = DynamicPPL.TestUtils.demo_assume_observe_literal()
             chain_sym_map = Dict{Symbol,Symbol}()
             for vn_parent in keys(var_info)
                 sym = DynamicPPL.getsym(vn_parent)
-                vn_children = DynamicPPL.varname_leaves(vn_parent, var_info[vn_parent])
+                vn_children = AbstractPPL.varname_leaves(vn_parent, var_info[vn_parent])
                 for vn_child in vn_children
                     chain_sym_map[Symbol(vn_child)] = sym
                 end

test/model_utils.jl

@@ -6,11 +6,11 @@
             chain = make_chain_from_prior(model, 10)
             for (i, d) in enumerate(value_iterator_from_chain(model, chain))
                 for vn in keys(d)
-                    val = DynamicPPL.getvalue(d, vn)
+                    val = AbstractPPL.getvalue(d, vn)
                     # Because value_iterator_from_chain groups varnames with
                     # the same parent symbol, we have to ungroup them here
-                    for vn_leaf in DynamicPPL.varname_leaves(vn, val)
-                        val_leaf = DynamicPPL.getvalue(d, vn_leaf)
+                    for vn_leaf in AbstractPPL.varname_leaves(vn, val)
+                        val_leaf = AbstractPPL.getvalue(d, vn_leaf)
                         @test val_leaf == chain[i, Symbol(vn_leaf), 1]
                     end
                 end