# Results of some packs ---------------------------------------------------
input_data_pack_out <- tibble::tibble("rule__1" = TRUE, "nrow" = FALSE)
input_group_pack_out <- tibble::tibble(
  "vs" = c(0, 0, 1, 1),
  "am" = c(0, 1, 0, 1),
  "n_low" = c(TRUE, FALSE, FALSE, FALSE),
  "n_high" = c(TRUE, TRUE, TRUE, TRUE)
)
input_col_pack_out <- tibble::tibble(
  "vs_._.rule__1" = TRUE,
  "am_._.rule__1" = FALSE,
  "cyl_._.not_outlier" = TRUE,
  "vs_._.not_outlier" = TRUE
)
input_row_pack_out <- tibble::tibble(
  "row_rule__1" = rep(TRUE, 2),
  "._.rule__2" = c(TRUE, FALSE)
) %>%
  keyholder::assign_keys(tibble::tibble(.id = c(1, 3)))
input_cell_pack_out <- tibble::tibble(
  "vs_._.rule__1" = rep(TRUE, 2),
  "am_._.rule__1" = rep(FALSE, 2),
  "cyl_._.not_outlier" = c(TRUE, FALSE),
  "vs_._.not_outlier" = c(TRUE, FALSE)
) %>%
  keyholder::assign_keys(tibble::tibble(.id = c(1, 4)))


# Exposure data -----------------------------------------------------------
input_packs <- list(
  data = data_packs(
    . %>%
      dplyr::summarise(
        nrow_low = nrow(.) > 10,
        nrow_high = nrow(.) < 20,
        ncol_low = ncol(.) > 5,
        ncol_high = ncol(.) < 10
      )
  )[[1]],
  group = group_packs(
    . %>%
      dplyr::group_by(vs, am) %>%
      dplyr::summarise(n_low = dplyr::n() > 10, n_high = dplyr::n() < 15) %>%
      dplyr::ungroup(),
    .group_vars = c("vs", "am"),
    .group_sep = "."
  )[[1]],
  col = col_packs(
    . %>%
      dplyr::summarise_if(
        rlang::is_integerish,
        rules(tot_sum = sum(.) > 100)
      )
  )[[1]],
  row = row_packs(
    . %>%
      dplyr::transmute(row_sum = rowSums(.)) %>%
      dplyr::transmute(
        outlier_sum = abs(row_sum - mean(row_sum)) / sd(row_sum) < 1
      ) %>%
      dplyr::slice(15:1)
  )[[1]],
  cell = cell_packs(
    . %>%
      dplyr::transmute_if(
        Negate(rlang::is_integerish),
        rules(abs(. - mean(.)) / sd(.) < 2)
      )
  )[[1]],
  col_other = col_packs(
    . %>%
      dplyr::summarise_if(
        rlang::is_integerish,
        rules(
          tot_sum = sum(.) > 100,
          .prefix = "_._"
        )
      )
  )[[1]],
  cell_other = cell_packs(
    . %>%
      dplyr::transmute_if(
        Negate(rlang::is_integerish),
        rules(abs(. - mean(.)) / sd(.) < 2, .prefix = "_._")
      )
  )[[1]]
)
input_remove_obeyers <- c(
  data = TRUE,
  group = FALSE,
  col = FALSE,
  row = TRUE,
  cell = TRUE
)
input_reports <- list(
  data = tibble::tibble(
    rule = c("nrow_high", "ncol_high"),
    var = rep(".all", 2),
    id = rep(0L, 2),
    value = rep(FALSE, 2)
  ),
  group = tibble::tibble(
    rule = rep(c("n_low", "n_high"), each = 4),
    var = rep(c("0.0", "0.1", "1.0", "1.1"), times = 2),
    id = rep(0L, 8),
    value = c(TRUE, FALSE, FALSE, FALSE, TRUE, TRUE, TRUE, TRUE)
  ),
  col = tibble::tibble(
    rule = rep("tot_sum", 6),
    var = c("cyl", "hp", "vs", "am", "gear", "carb"),
    id = rep(0L, 6),
    value = c(TRUE, TRUE, FALSE, FALSE, TRUE, FALSE)
  ),
  row = tibble::tibble(
    rule = rep("outlier_sum", 2),
    var = rep(".all", 2),
    id = c(15L, 7L),
    value = rep(FALSE, 2)
  ),
  cell = tibble::tibble(
    rule = rep("rule__1", 7),
    var = c("mpg", "mpg", "drat", "wt", "wt", "wt", "qsec"),
    id = c(18L, 20L, 19L, 15L, 16L, 17L, 9L),
    value = rep(FALSE, 7)
  )
)

# Construction of exposure data
add_pack_name_to_single_report <- function(.report, .pack_name) {
  res <- .report
  res[["pack"]] <- rep(.pack_name, nrow(.report))

  res[, c("pack", colnames(.report))] %>% add_class("ruler_report")
}

single_exposure_inds <- c(
  "data",
  "cell",
  "col",
  "col",
  "data",
  "row",
  "data",
  "group"
)
exposure_names <- c(
  "data_dims",
  "cell_not_outlier",
  "col_proper_sums",
  "new_col_proper_sums",
  "new_data_pack",
  "row_not_outlier",
  "another_data_pack",
  "first_group_pack"
)

input_single_exposures <- mapply(
  new_single_exposure,
  # `unname()` is needed to ensure that input vectors have no names. Otherwise
  # there can be issues with `dplyr::bind_rows()` (powered by
  # `vctrs::vec_rbind()`) removing those names but 'tibble'>=3.0.0 keeping them.
  unname(input_packs[single_exposure_inds]),
  unname(input_remove_obeyers[single_exposure_inds]),
  unname(input_reports[single_exposure_inds]),
  SIMPLIFY = FALSE
) %>%
  setNames(exposure_names)

input_exposures <- mapply(
  new_exposure,
  mapply(
    new_packs_info,
    exposure_names,
    # `unname()` is needed to ensure that input vectors have no names
    lapply(unname(input_packs[single_exposure_inds]), list),
    unname(input_remove_obeyers[single_exposure_inds]),
    SIMPLIFY = FALSE
  ),
  mapply(
    add_pack_name_to_single_report,
    # `unname()` is needed to ensure that input vectors have no names
    unname(input_reports[single_exposure_inds]),
    exposure_names,
    SIMPLIFY = FALSE
  ),
  SIMPLIFY = FALSE
) %>%
  setNames(exposure_names)

exposure_ref_inds <- c("col", "col", "cell", "data", "data", "row", "group")
exposure_ref_pack_names <- c(
  "col_pack_n1",
  "col_pack_n2",
  "cell_pack_n1",
  "data_pack_n1",
  "data_pack_n2",
  "row_pack_n1",
  "group_pack_n1"
)
input_exposure_ref <- new_exposure(
  new_packs_info(
    exposure_ref_pack_names,
    # `unname()` is needed to ensure that input vectors have no names
    unname(input_packs[exposure_ref_inds]),
    unname(input_remove_obeyers[exposure_ref_inds])
  ),
  mapply(
    add_pack_name_to_single_report,
    # `unname()` is needed to ensure that input vectors have no names
    unname(input_reports[exposure_ref_inds]),
    exposure_ref_pack_names,
    SIMPLIFY = FALSE
  ) %>%
    dplyr::bind_rows() %>%
    as_report(.validate = FALSE)
)