test_that("check_tf_version works", {
  skip_if_not(check_tf_version())

  # record the true version and forge an old version
  true_version <- tf$`__version__`
  tf$`__version__` <- "0.9.0" # nolint

  expect_snapshot(error = TRUE,
    check_tf_version("error")
  )
  expect_snapshot_warning(
    check_tf_version("warn")
  )
  expect_snapshot(
    check_tf_version("message")
  )

  # reset the true version
  tf$`__version__` <- true_version # nolint
})


test_that(".onLoad runs", {
  skip_if_not(check_tf_version())

  expect_ok(greta:::.onLoad())
})

test_that("tensorflow coercion works", {
  skip_if_not(check_tf_version())

  float <- greta:::tf_as_float(1)
  integer <- greta:::tf_as_integer(1)
  logical <- greta:::tf_as_logical(1)

  float_type <- options()$greta_tf_float
  expect_equal(float$dtype$name, float_type)
  expect_equal(integer$dtype$name, "int32")
  expect_equal(logical$dtype$name, "bool")
})

test_that("all_greta_arrays works", {
  skip_if_not(check_tf_version())
  env <- new.env()

  env$a <- normal(0, 1)
  env$b <- as_data(rnorm(10))
  env$c <- env$a * env$b

  array_list <- greta:::all_greta_arrays(env)
  array_list_nodata <- greta:::all_greta_arrays(env, include_data = FALSE)

  expect_identical(names(array_list), c("a", "b", "c"))
  expect_identical(names(array_list_nodata), c("a", "c"))
})

test_that("greta_model objects print", {
  skip_if_not(check_tf_version())

  m <- model(normal(0, 1))
  message <- capture_output(print(m))
  expect_equal(message, "greta model")
})

test_that("define and mcmc error informatively", {
  skip_if_not(check_tf_version())


  x <- as_data(randn(10))

  # no model with non-probability density greta arrays
  expect_snapshot(error = TRUE,
    model(variable())
  )

  expect_snapshot(error = TRUE,
    model(x)
  )

  expect_snapshot(error = TRUE,
    model()
  )

  # can't define a model for an unfixed discrete variable
  expect_snapshot(error = TRUE,
    model(bernoulli(0.5))
  )

  # no parameters here, so define or dag should error
  distribution(x) <- normal(0, 1)
  expect_snapshot(error = TRUE,
    model(x)
  )

  # a bad number of cores
  a <- normal(0, 1)
  m <- model(a)
  expect_warning(
    mcmc(m,
         warmup = 1,
         n_samples = 1,
         n_cores = 1000000L,
         verbose = FALSE
    ),
    "cores were requested, but only"
  )

  # can't draw samples of a data greta array
  z <- normal(x, 1)
  m <- model(x, z)
  expect_snapshot(error = TRUE,
    draws <- mcmc(m, verbose = FALSE)
  )
})

test_that("check_dims errors informatively", {
  skip_if_not(check_tf_version())


  a <- ones(3, 3)
  b <- ones(1)
  c <- ones(2, 2)
  d <- ones(2, 2, 2)
  dim1 <- c(3, 3)

  # with one scalar, it should always should work
  expect_equal(
    greta:::check_dims(a, b),
    dim(a)
  )

  # as long as target_dim matches vector dim
  expect_equal(
    greta:::check_dims(a, b, target_dim = dim1),
    dim(a)
  )

  # with both scalar, it should always should work
  expect_equal(
    greta:::check_dims(b, b),
    dim(b)
  )

  # with two differently shaped arrays it shouldn't
  expect_snapshot(error = TRUE,
    greta:::check_dims(a, c)
  )

  # with two scalars and a target dimension, just return the target dimension
  expect_equal(
    greta:::check_dims(b, b, target_dim = dim1),
    dim1
  )
})

test_that("disjoint graphs are checked", {
  skip_if_not(check_tf_version())


  # if the target nodes aren't related, they sould be checked separately

  a <- uniform(0, 1)
  b <- normal(a, 2)

  # c is unrelated and has no density
  c <- variable()

  expect_snapshot(error = TRUE,
    m <- model(a, b, c)
  )

  # d is unrelated and known
  d <- as_data(randn(3))
  distribution(d) <- normal(0, 1)
  expect_snapshot(error = TRUE,
    m <- model(a, b, d)
  )

})

test_that("plotting models doesn't error", {
  skip_if_not(check_tf_version())


  a <- uniform(0, 1)

  m <- model(a)

  expect_ok(plot(m))
})

test_that("structures work correctly", {
  skip_if_not(check_tf_version())


  a <- ones(2, 2)
  b <- zeros(2)
  c <- greta_array(3, dim = c(2, 2, 2))

  expect_identical(grab(a), array(1, dim = c(2, 2)))
  expect_identical(grab(b), array(0, dim = c(2, 1)))
  expect_identical(grab(c), array(3, dim = c(2, 2, 2)))
})

test_that("cleanly() handles TF errors nicely", {
  skip_if_not(check_tf_version())


  inversion_stop <- function() {
    stop("this non-invertible thing is not invertible")
  }

  cholesky_stop <- function() {
    stop("Cholesky decomposition was not successful")
  }

  other_stop <- function() {
    stop("Fetchez la vache!")
  }

  expect_s3_class(cleanly(inversion_stop()), "error")
  expect_s3_class(cleanly(cholesky_stop()), "error")
  expect_snapshot(error = TRUE,
    cleanly(other_stop())
  )

})

test_that("double precision works for all jacobians", {
  skip_if_not(check_tf_version())

  none <- normal(0, 1)
  expect_ok(model(none, precision = "double"))

  high <- normal(0, 1, truncation = c(-1, Inf))
  expect_ok(model(high, precision = "double"))

  low <- normal(0, 1, truncation = c(-Inf, 1))
  expect_ok(model(low, precision = "double"))

  both <- normal(0, 1, truncation = c(-1, 1))
  expect_ok(model(both, precision = "double"))

  correlation_matrix <- lkj_correlation(1)
  expect_ok(model(correlation_matrix, precision = "double"))

  covariance_matrix <- wishart(3, diag(2))
  expect_ok(model(covariance_matrix, precision = "double"))
})

test_that("module works", {
  mod <- module(mean,
    functions = module(
      sum,
      exp,
      log
    )
  )

  # returns a list
  expect_true(inherits(mod, "list"))
  expect_true(inherits(mod$functions, "list"))

  # all elements named, and reordered
  expect_identical(names(mod), c("functions", "mean"))
  expect_identical(names(mod$functions), c("exp", "log", "sum"))
})