# set_seed ----

test_that("set_seed returns correct value if seed is a value", {
  seed <- 1.909
  seed_int <- 1

  RNGkind("default")
  rng_state <- set_seed(seed)
  attr(seed_int, "kind") <- list("Mersenne-Twister", "Inversion", "Rejection")
  expect_equal(rng_state, seed_int)

  RNGkind("Super-Duper")
  rng_state <- set_seed(seed)
  attr(seed_int, "kind") <- list("Super-Duper", "Inversion", "Rejection")
  expect_equal(rng_state, seed_int)

  RNGkind("default")
})

test_that("set_seed returns correct value if seed is NULL", {
  seed <- NULL

  RNGkind("default")
  rng_state <- set_seed(seed)
  expect_equal(rng_state, .Random.seed)

  RNGkind("Super-Duper")
  rng_state <- set_seed(seed)
  expect_equal(rng_state, .Random.seed)

  RNGkind("default")
})

# get_result_list ----

test_that("get_result_list returns correct value", {
  res <- get_result_list(mean, 2, NULL, FALSE, 5)
  expect_equal(res, list(1, 2))

  res <- get_result_list(length, 2, NULL, FALSE, 5)
  expect_equal(res, list(1, 1))

  expect_error(get_result_list(length, 2, NULL, 5, 5))
  expect_error(get_result_list(length, 2, NULL, FALSE, 0))
})


test_that("h_simulations_output_format returns object as expected", {
  data_test <- new("Data", nGrid = 3L, doseGrid = c(1, 3, 5))
  dose <- 20
  fit <- data.frame(
    middle = c(0.2, 0.7),
    lower = c(0.1, 0.5),
    upper = c(0.3, 0.4)
  )
  stop <- list(list(
    "Number of cohorts is 10 and thus reached the prespecified minimum number 3"
  ))
  report_results <- c(TRUE, TRUE, TRUE, TRUE, TRUE)
  names(report_results) <- c(NA, NA, NA, NA, NA)
  additional_stats <- list()

  result_list_test <- list(list(
    data = data_test,
    dose = dose,
    fit = fit,
    stop = stop,
    report_results = report_results,
    additional_stats = additional_stats
  ))

  simulations_output <- h_simulations_output_format(result_list_test)

  expect_equal(simulations_output$dataList[[1]], data_test)
  expect_equal(simulations_output$recommendedDoses, dose)
  expect_equal(simulations_output$fitList[[1]], fit)
  expect_equal(
    simulations_output$stop_matrix,
    do.call(rbind, lapply(result_list_test, "[[", "report_results"))
  )
})


test_that("h_this_truth returns correct results for given dose", {
  args <- NULL
  args <- as.data.frame(args)
  nArgs <- max(nrow(args), 1L)
  iterSim <- 5
  this_args <- args[(iterSim - 1) %% nArgs + 1, , drop = FALSE]

  model <- LogisticLogNormal(
    mean = c(-0.85, 1),
    cov = matrix(c(1, -0.5, -0.5, 1), nrow = 2),
    ref_dose = 56
  )

  my_truth <- probFunction(model, alpha0 = 7, alpha1 = 8)

  result <- h_this_truth(30, this_args, my_truth)
  expect_equal(result, 0.8815056)
})


test_that("h_determine_dlts returns correctly updated data object for default condition", {
  data <- new("Data", nGrid = 3L, doseGrid = c(2, 3, 5))
  dose <- 3
  prob <- 0
  size <- 1

  result <- h_determine_dlts(
    data = data,
    dose = dose,
    prob = prob,
    cohort_size = size,
    first_separate = FALSE
  )

  expected_result <- data <- new(
    "Data",
    x = 3,
    y = 0L,
    nGrid = 3L,
    doseGrid = c(2, 3, 5),
    xLevel = 2L,
    placebo = FALSE,
    ID = 1L,
    cohort = 1L,
    nObs = 1L
  )

  expect_valid(result, "Data")
  expect_equal(result, expected_result)
})


test_that("h_determine_dlts returns correctly updated data object for
          first_separate = TRUE & DLT for first patient", {
  data <- new("Data", nGrid = 3L, doseGrid = c(2, 3, 5))
  dose <- 3
  prob <- 1
  size <- 2

  result <- h_determine_dlts(
    data = data,
    dose = dose,
    prob = prob,
    cohort_size = size,
    first_separate = TRUE
  )

  expected_result <- data <- new(
    "Data",
    x = 3,
    y = 1L,
    nGrid = 3L,
    doseGrid = c(2, 3, 5),
    xLevel = 2L,
    placebo = FALSE,
    ID = 1L,
    cohort = 1L,
    nObs = 1L
  )

  expect_s4_class(result, "Data")
  expect_equal(result, expected_result)
})


test_that("h_determine_dlts returns correctly updated data object for first_separate = TRUE
          for no DLT for first patient", {
  data <- new("Data", nGrid = 3L, doseGrid = c(2, 3, 5))
  dose <- 3
  prob <- 0
  size <- 2

  result <- h_determine_dlts(
    data = data,
    dose = dose,
    prob = prob,
    cohort_size = size,
    first_separate = TRUE
  )

  expected_result <- data <- new(
    "Data",
    x = c(3, 3),
    y = c(0L, 0L),
    nGrid = 3L,
    doseGrid = c(2, 3, 5),
    xLevel = c(2L, 2L),
    placebo = FALSE,
    ID = c(1L, 2L),
    cohort = c(1L, 1L),
    nObs = 2L
  )

  expect_s4_class(result, "Data")
  expect_equal(result, expected_result)
})


test_that("h_determine_dlts returns correctly updated data object for placebo = TRUE", {
  data <- new("Data", nGrid = 3L, doseGrid = c(0.0001, 2, 3), placebo = TRUE)
  dose <- 3
  prob <- 0
  size <- 1
  size_pl <- 1
  prob_pl <- 0

  result <- h_determine_dlts(
    data = data,
    dose = dose,
    prob = prob,
    prob_placebo = prob_pl,
    cohort_size = size,
    cohort_size_placebo = size_pl,
    dose_grid = data@doseGrid[1],
    first_separate = FALSE
  )

  expected_result <- data <- new(
    "Data",
    x = c(0.0001, 3),
    y = c(0L, 0L),
    nGrid = 3L,
    doseGrid = c(0.0001, 2, 3),
    xLevel = c(1L, 3L),
    placebo = TRUE,
    ID = c(1L, 2L),
    cohort = c(1L, 1L),
    nObs = 2L
  )

  expect_s4_class(result, "Data")
  expect_equal(result, expected_result)
})