How to find the number of samples that are picked in each boostrap of stratified bootstrap in pROC?

Question is regarding the roc function of pROC package. Package link: https://www.rdocumentation.org/packages/pROC/versions/1.18.5/topics/roc. Paper link https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3068975/.

I am plotting confidence intervals on my ROC plot:

pROC::roc(as.factor(df$classes),
                   df$score ,
                   plot=TRUE,
                   ci = TRUE,
                   ci.method= 'bootstrap',
                   auc.polygon=TRUE,
                   max.auc.polygon=TRUE,
                   print.auc=TRUE)

I understand from documentation that when using the ci.method as 'bootstrap', stratified bootstrapping takes place. How to find the subsampling percentage used while bootstrapping? Is it 80% of total data, 70% or something else? Can we specify it?

The paper quotes, "Bootstrap is stratified by default; in this case the same number of case and control observations than in the original sample will be selected in each bootstrap replicate.". I think they meant same proportion of case and control observations in each replicate. However, what percentage of subsampling takes place is not mentioned anywhere.

If I am interpretting it wrong please correct me.

Bootstrapping by definition is resampling of the whole data with replacement.

If ci is TRUE and ci.method is "bootstrap", then the roc function eventually calls stratified.ci.auc, which looks like this:

pROC:::stratified.ci.auc

function (n, roc) 
{
    controls <- sample(roc$controls, replace = TRUE)
    cases <- sample(roc$cases, replace = TRUE)
    thresholds <- roc_utils_thresholds(c(cases, controls), roc$direction)
    perfs <- roc$fun.sesp(thresholds = thresholds, controls = controls, 
        cases = cases, direction = roc$direction)
    roc$sensitivities <- perfs$se
    roc$specificities <- perfs$sp
    auc.roc(roc, partial.auc = attr(roc$auc, "partial.auc"), 
        partial.auc.focus = attr(roc$auc, "partial.auc.focus"), 
        partial.auc.correct = attr(roc$auc, "partial.auc.correct"), 
        allow.invalid.partial.auc.correct = TRUE)
}

The first two lines tell you that the stratified samples use 100% of the cases and controls because no size argument is given.

> sample(1:10, replace=TRUE)
# [1]  1 10  6  2  7  3  3  6  4  6

Note that n in the function above is the number of bootstrap samples, not the size of the samples. The default is 2000 (boot.n).