ARIMA forecasts don't work for x weekly, monthly and quarterly data where x > 1

See reprex below.

The calculation of fc_start seems to be wrong.

fc_start <- object$tsp$range[2]+round(as.numeric(diff(object$tsp$range)+1)/nrow(object$est), 6)

From what I can tell it's doing:
fc_start <- <last index value> + (<time diff between start and end> + 1 ) / <no of samples>
while it should be:
fc_start <- <last index value> + <time diff between start and end> / <no of intervals>
or, equivalently:
fc_start <- <last index value> + <time diff between start and end> / (<no of samples> - 1)

data <- tsibble::tsibble(
    week = (lubridate::today() + lubridate::period(weeks = seq(1, 9, 4))) |>
        tsibble::yearweek(),
    number = 1:3,
    index = week
)
data |>
    fabletools::model(arima = fable::ARIMA(number ~ pdq() + PDQ())) |>
    fabletools::forecast(h = 1)
#> Error in `mutate()`:
#> ! Problem while computing `arima = (function (object, ...) ...`.
#> Caused by error in `forecast()`:
#> ! Forecasts from an ARIMA model must start one step beyond the end of the trained data.

#> Backtrace:
#>      ▆
#>   1. ├─fabletools::forecast(...)
#>   2. ├─fabletools:::forecast.mdl_df(...)
#>   3. │ └─dplyr::mutate_at(...)
#>   4. │   ├─dplyr::mutate(.tbl, !!!funs)
#>   5. │   └─dplyr:::mutate.data.frame(.tbl, !!!funs)
#>   6. │     └─dplyr:::mutate_cols(.data, dplyr_quosures(...), caller_env = caller_env())
#>   7. │       ├─base::withCallingHandlers(...)
#>   8. │       └─mask$eval_all_mutate(quo)
#>   9. ├─fabletools (local) `<fn>`(...)
#>  10. └─fabletools:::forecast.lst_mdl(...)
#>  11.   └─fabletools:::mapply_maybe_parallel(...)
#>  12.     └─base::mapply(FUN = .f, ..., MoreArgs = MoreArgs, SIMPLIFY = SIMPLIFY)
#>  13.       ├─fabletools (local) `<fn>`(dots[[1L]][[1L]], dots[[2L]][[1L]], h = 1, point_forecast = `<named list>`)
#>  14.       └─fabletools:::forecast.mdl_ts(...)
#>  15.         ├─fabletools::forecast(...)
#>  16.         └─fable:::forecast.ARIMA(object$fit, new_data, specials = specials, times = times, ...)
#>  17.           └─rlang::abort("Forecasts from an ARIMA model must start one step beyond the end of the trained data.")

^{Created on 2023-04-13 with reprex v2.0.2}

Edit: wrap text in code to prevent angle brackets being swallowed
Edit: add missing term to calculation interpretation

Start with the fc_start calculation shown in the issue and reproduce the failure using the provided tsibble, ARIMA model, and forecast call. Trace why the forecast does not start one step beyond the trained data, then verify that weekly, monthly, and quarterly data with x > 1 forecast successfully.