test_that("nn2poly with list input against precomputed example with single
          output, check also that nn2poly class is given to the output", {
  testing_data <- testing_helper_1()

  # Get the needed data
  object <- testing_data$weights_list
  names(object) <- testing_data$af_string_list
  taylor_orders <- testing_data$taylor_orders

  result <- nn2poly(
    object = object,
    max_order = 3,
    keep_layers = TRUE,
    taylor_orders = taylor_orders
  )

  # Output polynomial order is 2, as no max_order is specified and default is 2
  n_terms <- length(result$layer_3$output$labels)
  order <- length(result$layer_3$output$labels[[n_terms]])
  expect_equal(order, 3)

  # Desired coefficient in output polynomial at layer 2 (element 2*2=4 in list),
  # neuron 1, coefficient "1,1"
  label <- result$layer_2$output$labels[[4]]
  coeff <- result$layer_2$output$values[4,1]
  expect_equal(label,c(1,1))
  expect_equal(round(coeff,4),0.6335)

  # Check class is correctly assigned
  expect_equal(class(result),"nn2poly")
})

test_that("nn2poly with list input against precomputed example with
          default options", {
  testing_data <- testing_helper_1()

  # Get the needed data
  object <- testing_data$weights_list
  names(object) <- testing_data$af_string_list

  result <- nn2poly(
    object = object
  )

  n_terms <- length(result$labels)
  order <- length(result$labels[[n_terms]])
  expect_equal(order, 2)


  label <- result$labels[[4]]
  coeff <- result$values[4,1]
  expect_equal(label,c(1,1))
  expect_equal(round(coeff,4),-4.4295)
})


test_that("nn2poly for a keras.engine.training.Model object", {
  skip_if_not_installed("keras")
  skip_if_not_installed("tensorflow")
  skip_on_cran()

  nn <- keras_test_model()

  result <- nn2poly(nn,
                    taylor_orders = c(2,2,1),
                    max_order = 2)

  expect_equal(result$values[1,1],  0.18148204)
  expect_equal(result$values[3,2], -0.71466625)
  expect_equal(result$labels[[6]], c(2,2))
})

test_that("nn2poly for a nn_module object", {
  skip_if_not_installed("luz")
  skip_if_not_installed("torch")
  skip_on_os("mac") # Runner out of memory
  skip_on_cran()

  testing_data <- testing_helper_2()

  data <- luz_test_data(testing_data)

  fitted <- luz_test_model() %>%
    luz::setup(
      loss = torch::nn_mse_loss(),
      optimizer = torch::optim_adam,
      metrics = list(
        luz::luz_metric_mse()
      )
    ) %>%
    luz::fit(data$train, epochs = 5, valid_data = data$valid, verbose = FALSE)

  result <- nn2poly(fitted,
                    taylor_orders = testing_data$taylor_orders,
                    max_order = 3)

  expect_equal(round(result$values[1,1],2), 0.06)
  expect_equal(result$labels[[7]], c(1,1,1))

})

test_that("nn2poly error when wrong dimensions are given in weights", {
  testing_data <- testing_helper_1()

  # Get the needed data
  object <- testing_data$weights_list
  names(object) <- testing_data$af_string_list

  # Add weights to one layer to create the problem
  object[[2]] <- rbind(object[[2]], c(1,1))

  expect_error(nn2poly(object = object))
})