# Use sparingly to minimize AWS costs.
# And afterwards, manually verify that all the buckets are gone.
tar_test("dynamic branch references have complete path info", {
  skip_if_no_aws()
  s3 <- paws.storage::s3()
  bucket_name <- random_bucket_name()
  s3$create_bucket(Bucket = bucket_name)
  on.exit(aws_s3_delete_bucket(bucket_name))
  code <- substitute({
    library(ggplot2)
    library(targets)
    library(tarchetypes)
    library(tibble)
    tar_option_set(
      repository = "aws",
      resources = tar_resources(
        aws = tar_resources_aws(
          prefix = "_targets",
          bucket = bucket_name
        )
      )
    )
    # From
    # https://github.com/wjschne/spiro/blob/ \
    #   87f73ec37ceb0a7a9d09856ada8ae28d587a2ebd/R/spirograph.R
    # Adapted under the CC0 1.0 Universal license:
    # https://github.com/wjschne/spiro/blob/ \
    #   87f73ec37ceb0a7a9d09856ada8ae28d587a2ebd/LICENSE.md
    spirograph_points <- function(fixed_radius, cycling_radius) {
      t <- seq(1, 30 * pi, length.out = 1e4)
      diff <- (fixed_radius - cycling_radius)
      ratio <- diff / cycling_radius
      x <- diff * cos(t) + cos(t * ratio)
      y <- diff * sin(t) - sin(t * ratio)
      tibble(
        x = x,
        y = y,
        fixed_radius = fixed_radius,
        cycling_radius = cycling_radius
      )
    }
    plot_spirographs <- function(points) {
      label <- "fixed_radius = %s, cycling_radius = %s"
      points$parameters <- sprintf(
        label,
        points$fixed_radius,
        points$cycling_radius
      )
      ggplot(points) +
        geom_point(aes(x = x, y = y, color = parameters), size = 0.1) +
        facet_wrap(~parameters) +
        theme_gray(16) +
        guides(color = "none")
    }
    list(
      tar_target(fixed_radius, sample.int(n = 10, size = 2)),
      tar_target(cycling_radius, sample.int(n = 10, size = 2)),
      tar_target(
        points,
        spirograph_points(fixed_radius, cycling_radius),
        pattern = map(fixed_radius, cycling_radius)
      ),
      tar_target(
        single_plot,
        plot_spirographs(points),
        pattern = map(points),
        iteration = "list"
      ),
      tar_target(
        combined_plot,
        plot_spirographs(points)
      )
    )
  }, env = list(bucket_name = bucket_name))
  do.call(tar_script, list(code = code))
  for (index in seq_len(2L)) {
    tar_make(reporter = "silent")
    expect_s3_class(tar_read(combined_plot), "ggplot")
  }
})