process_report_dates <- function(report_list_raw, cover_raw) {
  cover_dates <-
    cover_raw |>
    distinct() |>
    select(
      report_id,
      cover_start_date = date_from,
      cover_end_date = date_to,
      cover_date_filed = date_filed
    )

  report_list_raw |>
    left_join(reporting_schedule(), by = join_by(year, doc_name)) |>
    select(
      report_id, sboe_id, year, doc_name, amended,
      contains("received_"),
      matches("(sboe_)?(start|end)_date")
    ) |>
    left_join(cover_dates, by = "report_id") |>
    mutate(across(matches("received|date"), na_if_obviously_wrong_date)) |>
    mutate(
      # If the received date isn't after at least one of the report or cover date, don't believe it
      received_image = received_image |> na_if_not_after_one_of(sboe_start_date, cover_start_date),
      received_data = received_data |> na_if_not_after_one_of(sboe_start_date, cover_start_date)
    )
}


na_if_obviously_wrong_date <- function(x) {
  x[x > today()] <- NA_Date_
  x[x < ymd("2016-01-01")] <- NA_Date_
  x
}

na_if_not_after_one_of <- function(x, ...) {
  others <- list(...)
  is_after <- function(x, y) {
    ret <- x >= y
    ret[is.na(ret)] <- FALSE
    ret
  }

  allow <- purrr::map(others, is_after, x = x) |> purrr::reduce(`|`)
  x[!allow] <- NA_Date_
  x
}

mean_date_scalar <- function(x, y) {
  if (is.na(x) && is.na(y)) {
    return(NA_Date_)
  }
  mean(c(x, y), na.rm = TRUE)
}