context("fitted-shared-methods")

set_cmdstan_path()
fits <- list()
fits[["sample"]] <- testing_fit("logistic", method = "sample",
                                seed = 123, save_latent_dynamics = TRUE)
fits[["variational"]] <- testing_fit("logistic", method = "variational",
                                     seed = 123, save_latent_dynamics = TRUE)
fits[["optimize"]] <- testing_fit("logistic", method = "optimize", seed = 123)
fits[["laplace"]] <- testing_fit("logistic", method = "laplace", seed = 123)
fit_bern <- testing_fit("bernoulli", method = "sample", seed = 123)
fits[["generate_quantities"]] <- testing_fit("bernoulli_ppc", method = "generate_quantities", fitted_params = fit_bern, seed = 123)
all_methods <- c("sample", "optimize", "laplace", "variational", "generate_quantities")


test_that("*_files() methods return the right number of paths", {
  for (method in all_methods) {
    expect_length(fits[[method]]$output_files(), fits[[method]]$num_procs())
    expect_length(fits[[method]]$data_file(), 1)
    if (method %in% c("sample", "variational")) {
      expect_length(fits[[method]]$latent_dynamics_files(), fits[[method]]$num_procs())
    }
  }
})

test_that("saving csv output files works", {
  for (method in all_methods) {
    fit <- fits[[method]]
    old_paths <- fit$output_files()
    checkmate::expect_file_exists(old_paths, extension = "csv")

    expect_message(
      paths <- fit$save_output_files(tempdir(), basename = "testing-output"),
      paste("Moved", fit$num_procs(), "files and set internal paths")
    )
    checkmate::expect_file_exists(paths, extension = "csv")
    expect_true(all(file.size(paths) > 0))

    should_match <- paste0("testing-output-",
                           base::format(Sys.time(), "%Y%m%d%H%M"),
                           "-",
                           seq_len(fit$num_procs()))
    for (j in seq_along(paths)) {
      expect_match(paths[j], should_match[j])
    }

    expect_false(any(file.exists(old_paths)))
    expect_equal(fit$output_files(), paths)
  }
})

test_that("saving diagnostic csv output works", {
  for (method in all_methods) {
    fit <- fits[[method]]
    if (!(method %in% c("sample", "variational"))) {
      expect_error(
        fit$save_latent_dynamics_files(),
        "No latent dynamics files found. Set 'save_latent_dynamics=TRUE' when fitting the model",
        fixed = TRUE
      )
      next
    }

    old_paths <- fit$latent_dynamics_files()
    checkmate::expect_file_exists(old_paths, extension = "csv")

    expect_message(
      paths <- fit$save_latent_dynamics_files(tempdir(), basename = "testing-output"),
      paste("Moved", fit$num_procs(), "files and set internal paths")
    )
    checkmate::expect_file_exists(paths, extension = "csv")
    expect_true(all(file.size(paths) > 0))

    should_match <- paste0("testing-output-diagnostic-",
                           base::format(Sys.time(), "%Y%m%d%H%M"),
                           "-",
                           seq_len(fit$num_procs()))

    for (j in seq_along(paths)) {
      expect_match(paths[j], should_match[j])
    }

    expect_false(any(file.exists(old_paths)))
    expect_equal(fit$latent_dynamics_files(), paths)
  }
})

test_that("saving data file works", {
  for (method in all_methods) {
    fit <- fits[[method]]
    old_path <- fit$data_file()
    checkmate::expect_file_exists(old_path, extension = "json")

    expect_message(
      path <- fit$save_data_file(tempdir(), basename = NULL,
                                 timestamp = FALSE, random = FALSE),
      "Moved data file and set internal path"
    )
    checkmate::expect_file_exists(path, extension = "json")
    expect_true(file.size(path) > 0)
    if(method == "generate_quantities") {
      expect_equal(basename(path), "bernoulli_ppc.json")
    } else {
      expect_equal(basename(path), "logistic.json")
    }
    expect_false(file.exists(old_path))
    expect_equal(fit$data_file(), path)
  }
})

test_that("cmdstan_summary() and cmdstan_diagnose() work correctly", {
  for (method in all_methods) {
    fit <- fits[[method]]
    if (method %in% c("optimize", "laplace")) {
      expect_error(fit$cmdstan_summary(), "Not available")
      expect_error(fit$cmdstan_diagnose(), "Not available")
    } else if (method == "generate_quantities") {
      expect_error(fit$cmdstan_summary(), "Not available for generate_quantities method")
      expect_error(fit$cmdstan_diagnose(), "Not available for generate_quantities method")
    } else if (method == "sample") {
      expect_output(fit$cmdstan_summary(), "Inference for Stan model")
      expect_output(fit$cmdstan_diagnose(), "Processing complete")
    }
  }
})

test_that("draws() method returns a 'draws' object", {
  for (method in all_methods) {
    fit <- fits[[method]]
    draws <- fit$draws()
    if (method == "generate_quantities") {
      expect_type(draws, "integer")
    } else {
      expect_type(draws, "double")
    }
    expect_s3_class(draws, "draws")

    if (method != "sample") {
      expect_warning(
        fit$draws(inc_warmup = TRUE),
        "'inc_warmup' is ignored except when used with CmdStanMCMC objects"
      )
    }
  }
})

test_that("as_draws() is equivalent to draws()", {
  for (method in all_methods) {
    fit <- fits[[method]]
    expect_identical(as_draws(fit), fit$draws())

    if (method != "generate_quantities") {
      expect_identical(
        as_draws(fit, variables = c("beta[1]", "alpha")),
        fit$draws(variables = c("beta[1]", "alpha"))
      )
    }
  }
})

test_that("save_object() method works", {
  for (method in all_methods) {
    fit <- fits[[method]]
    temp_rds_file <- tempfile(fileext = ".RDS")
    fit$save_object(temp_rds_file)
    fit2 <- readRDS(temp_rds_file)
    expect_identical(fit2$summary(), fit$summary())
    expect_identical(fit2$return_codes(), fit$return_codes())
  }

  # check after garbage collection too
  temp_rds_file <- tempfile(fileext = ".RDS")
  fit <- testing_fit("logistic", method = "sample", seed = 123)
  fit$save_object(temp_rds_file)
  s <- fit$summary()
  rm(fit); gc()
  fit <- readRDS(temp_rds_file)
  expect_identical(fit$summary(), s)
})

test_that("save_object() method works with profiles", {
  mod <- testing_model("logistic_profiling")
  utils::capture.output(
    fit <- mod$sample(data = testing_data("logistic"), refresh = 0, seed = 123)
  )
  # check after garbage collection too
  temp_rds_file <- tempfile(fileext = ".RDS")
  fit$save_object(temp_rds_file)
  s <- fit$profiles()
  rm(fit); gc()
  fit <- readRDS(temp_rds_file)
  expect_identical(fit$profiles(), s)
})

test_that("metadata() returns list", {
  for (method in all_methods) {
    fit <- fits[[method]]
    expect_type(fit$metadata(), "list")
    expect_equal(fit$metadata()$method, method)
  }
})

test_that("init() errors if no inits specified", {
  # only checking for errors, correct behavior tested in test-model-init.R
  for (method in all_methods) {
    fit <- fits[[method]]
    expect_error(fit$init(), "Can't find initial values files")
  }
})

test_that("return_codes method works properly", {
  expect_equal(fits[["variational"]]$return_codes(), 0)
  expect_equal(fits[["optimize"]]$return_codes(), 0)
  expect_equal(fits[["laplace"]]$return_codes(), 0)
  expect_equal(fits[["sample"]]$return_codes(), c(0,0,0,0))
  expect_equal(fits[["generate_quantities"]]$return_codes(), c(0,0,0,0))

  # non-zero
  non_zero <- testing_fit("schools", method = "optimize", seed = 123)
  expect_gt(non_zero$return_codes(), 0)
})

test_that("output and latent dynamics files are cleaned up correctly", {
  for (method in c("sample", "variational")) {
    fit <- testing_fit("logistic", method = method, seed = 123, save_latent_dynamics = TRUE)
    out_files <- fit$output_files()
    latent_dynamics_files <- fit$latent_dynamics_files()
    expect_true(all(file.exists(out_files)))
    expect_true(all(file.exists(latent_dynamics_files)))
    rm(fit)
    gc()
    expect_true(!any(file.exists(out_files)))
    expect_true(!any(file.exists(latent_dynamics_files)))

    fit <- testing_fit("logistic", method = method, seed = 123, save_latent_dynamics = TRUE)
    fit$save_output_files(dir = tempdir())
    out_files <- fit$output_files()
    latent_dynamics_files <- fit$latent_dynamics_files()
    expect_true(all(file.exists(out_files)))
    expect_true(all(file.exists(latent_dynamics_files)))
    rm(fit)
    gc()
    expect_true(all(file.exists(out_files)))
    expect_true(!any(file.exists(latent_dynamics_files)))
    file.remove(out_files)

    fit <- testing_fit("logistic", method = method, seed = 123, save_latent_dynamics = TRUE)
    fit$save_latent_dynamics_files(dir = tempdir())
    out_files <- fit$output_files()
    latent_dynamics_files <- fit$latent_dynamics_files()
    expect_true(all(file.exists(out_files)))
    expect_true(all(file.exists(latent_dynamics_files)))
    rm(fit)
    gc()
    expect_true(!any(file.exists(out_files)))
    expect_true(all(file.exists(latent_dynamics_files)))
    file.remove(latent_dynamics_files)

    fit <- testing_fit("logistic", method = method, seed = 123, save_latent_dynamics = TRUE)
    fit$save_output_files(dir = tempdir())
    fit$save_latent_dynamics_files(dir = tempdir())
    out_files <- fit$output_files()
    latent_dynamics_files <- fit$latent_dynamics_files()
    expect_true(all(file.exists(out_files)))
    expect_true(all(file.exists(latent_dynamics_files)))
    rm(fit)
    gc()
    expect_true(all(file.exists(out_files)))
    expect_true(all(file.exists(latent_dynamics_files)))
    file.remove(out_files)
    file.remove(latent_dynamics_files)
  }
})

test_that("CmdStanArgs erorrs if idx is out of proc_ids range", {
  data_file <- test_path("resources", "data", "bernoulli.data.json")
  mod <- testing_model("bernoulli")
  arg <- CmdStanArgs$new(
    method_args = SampleArgs$new(),
    model_name = "bernoulli",
    exe_file = mod$exe_file(),
    data_file = data_file,
    proc_ids = c(1,2,3,4)
  )
  expect_error(
    arg$compose_all_args(idx = 5),
    "Index \\(5\\) exceeds number of CmdStan processes \\(4\\)."
  )
})

test_that("no output with refresh = 0", {
  mod <- testing_model("logistic")
  data_list <- testing_data("logistic")
  output <- utils::capture.output(tmp <- mod$variational(data = data_list, seed = 123))
  expect_gt(length(output), 1)
  output <- utils::capture.output(tmp <- mod$optimize(data = data_list, seed = 123))
  expect_gt(length(output), 1)
  output <- utils::capture.output(tmp <- mod$sample(data = data_list, chains = 1, seed = 123))
  expect_gt(length(output), 1)

  output <- utils::capture.output(tmp <- mod$variational(data = data_list, refresh = 0, seed = 123))
  expect_equal(length(output), 1)
  output <- utils::capture.output(tmp <- mod$optimize(data = data_list, refresh = 0, seed = 123))
  expect_equal(length(output), 1)
  output <- utils::capture.output(tmp <- mod$sample(data = data_list, refresh = 0, chains = 1, seed = 123))
  expect_equal(length(output), 3)
})

test_that("sig_figs works with all methods", {
  m <- "data {
    int<lower=0> N;
    int<lower=0> K;
    array[N] int<lower=0,upper=1> y;
    matrix[N, K] X;
  }
  parameters {
    real alpha;
    vector[K] beta;
  }
  model {
    target += normal_lpdf(alpha | 0, 1);
    target += normal_lpdf(beta | 0, 1);
    target += bernoulli_logit_glm_lpmf(y | X, alpha, beta);
  }
  generated quantities {
    real p2 = 0.12;
    real p5 = 0.12345;
    real p9 = 0.123456789;
  }"
  mod <- cmdstan_model(write_stan_file(m))
  utils::capture.output(
    sample <- mod$sample(sig_figs = 2, refresh = 0, data = testing_data("logistic"))
  )
  expect_equal(
    as.numeric(posterior::subset_draws(sample$draws(), variable = c("p2","p5", "p9"), iteration = 1, chain = 1)),
    c(0.12, 0.12, 0.12)
  )
  utils::capture.output(
    sample <- mod$sample(sig_figs = 5, refresh = 0, data = testing_data("logistic"))
  )
  expect_equal(
    as.numeric(posterior::subset_draws(sample$draws(), variable = c("p2","p5", "p9"), iteration = 1, chain = 1)),
    c(0.12, 0.12345, 0.12346)
  )
  utils::capture.output(
    sample <- mod$sample(sig_figs = 10, refresh = 0, data = testing_data("logistic"))
  )
  expect_equal(
    as.numeric(posterior::subset_draws(sample$draws(), variable = c("p2","p5", "p9"), iteration = 1, chain = 1)),
    c(0.12, 0.12345, 0.123456789)
  )
  utils::capture.output(
    variational <- mod$variational(sig_figs = 2, refresh = 0, data = testing_data("logistic"))
  )
  expect_equal(
    as.numeric(posterior::subset_draws(variational$draws(), variable = c("p2","p5", "p9"), iteration = 1, chain = 1)),
    c(0.12, 0.12, 0.12)
  )
  utils::capture.output(
    variational <- mod$variational(sig_figs = 5, refresh = 0, data = testing_data("logistic"))
  )
  expect_equal(
    as.numeric(posterior::subset_draws(variational$draws(), variable = c("p2","p5", "p9"), iteration = 1, chain = 1)),
    c(0.12, 0.12345, 0.12346)
  )
  utils::capture.output(
    variational <- mod$variational(sig_figs = 10, refresh = 0, data = testing_data("logistic"))
  )
  expect_equal(
    as.numeric(posterior::subset_draws(variational$draws(), variable = c("p2","p5", "p9"), iteration = 1, chain = 1)),
    c(0.12, 0.12345, 0.123456789)
  )
  utils::capture.output(
    gq <- mod$generate_quantities(fitted_params = sample, sig_figs = 2, data = testing_data("logistic"))
  )
  expect_equal(
    as.numeric(posterior::subset_draws(gq$draws(), variable = c("p2","p5", "p9"), iteration = 1, chain = 1)),
    c(0.12, 0.12, 0.12)
  )
  utils::capture.output(
    gq <- mod$generate_quantities(fitted_params = sample, sig_figs = 5, data = testing_data("logistic"))
  )
  expect_equal(
    as.numeric(posterior::subset_draws(gq$draws(), variable = c("p2","p5", "p9"), iteration = 1, chain = 1)),
    c(0.12, 0.12345, 0.12346)
  )
  utils::capture.output(
    gq <- mod$generate_quantities(fitted_params = sample, sig_figs = 10, data = testing_data("logistic"))
  )
  expect_equal(
    as.numeric(posterior::subset_draws(gq$draws(), variable = c("p2","p5", "p9"), iteration = 1, chain = 1)),
    c(0.12, 0.12345, 0.123456789)
  )
  utils::capture.output(
    opt <- mod$optimize(sig_figs = 2, refresh = 0, data = testing_data("logistic"), seed = 123)
  )
  expect_equal(
    as.numeric(opt$mle()[c("p2","p5", "p9")]),
    c(0.12, 0.12, 0.12)
  )
  utils::capture.output(
    opt <- mod$optimize(sig_figs = 5, refresh = 0, data = testing_data("logistic"), seed = 123)
  )
  expect_equal(
    as.numeric(opt$mle()[c("p2","p5", "p9")]),
    c(0.12, 0.12345, 0.12346)
  )
  utils::capture.output(
    opt <- mod$optimize(sig_figs = 10, refresh = 0, data = testing_data("logistic"), seed = 123)
  )
  expect_equal(
    as.numeric(opt$mle()[c("p2","p5", "p9")]),
    c(0.12, 0.12345, 0.123456789)
  )
})

test_that("draws are returned for model with spaces", {
  m <- "
  parameters {
    real y;
  }
  model {
    y ~ std_normal();
  }
  generated quantities {
    real two_y = 2 * y;
  }
  "
  f <- write_stan_file(m, basename = "file with spaces")
  expect_equal(basename(f), "file with spaces.stan")
  mod <- cmdstan_model(f)
  utils::capture.output(
    fit_sample <- mod$sample(seed = 123, iter_sampling = 1000, chains = 1)
  )
  expect_equal(dim(fit_sample$draws()), c(1000, 1, 3))
  utils::capture.output(
    fit <- mod$variational(seed = 123, output_samples = 1000)
  )
  expect_equal(dim(fit$draws()), c(1000, 4))
  utils::capture.output(
    fit <- mod$optimize(seed = 123)
  )
  expect_equal(length(fit$mle()), 2)

  utils::capture.output(
    fit <- mod$generate_quantities(fitted_params = fit_sample, seed = 123)
  )
  expect_equal(dim(fit$draws()), c(1000, 1, 1))
})

test_that("sampling with inits works with include_paths", {
  stan_program_w_include <- testing_stan_file("bernoulli_include")
  exe <- cmdstan_ext(strip_ext(stan_program_w_include))
  if(file.exists(exe)) {
    file.remove(exe)
  }

    mod_w_include <- cmdstan_model(stan_file = stan_program_w_include, quiet = FALSE,
                                   include_paths = test_path("resources", "stan"))

  data_list <- list(N = 10, y = c(0,1,0,0,0,0,0,0,0,1))

  fit <- mod_w_include$sample(
    data = data_list,
    seed = 123,
    chains = 4,
    parallel_chains = 4,
    refresh = 500,
    init = list(list(theta = 0.25), list(theta = 0.25), list(theta = 0.25), list(theta = 0.25))
  )
})

test_that("CmdStanModel created with exe_file works", {
  stan_program <- testing_stan_file("bernoulli")
  data_list <- testing_data("bernoulli")

  mod <- cmdstan_model(stan_file = stan_program)
  mod_exe <- cmdstan_model(exe_file = mod$exe_file())

  utils::capture.output(
    fit_optimize <- mod$optimize(data = data_list, seed = 123)
  )
  utils::capture.output(
    fit_optimize_exe <- mod_exe$optimize(data = data_list, seed = 123)
  )
  expect_equal(fit_optimize$mle(), fit_optimize_exe$mle())

  utils::capture.output(
    fit_laplace <- mod$laplace(data = data_list, seed = 123)
  )
  utils::capture.output(
    fit_laplace_exe <- mod_exe$laplace(data = data_list, seed = 123)
  )
  expect_equal(fit_laplace$draws(), fit_laplace_exe$draws())

  utils::capture.output(
    fit_variational <- mod$variational(data = data_list, seed = 123)
  )
  utils::capture.output(
    fit_variational_exe <- mod_exe$variational(data = data_list, seed = 123)
  )
  expect_equal(fit_variational$draws(), fit_variational_exe$draws())

  utils::capture.output(
    fit_sample <- mod$sample(data = data_list, chains = 1, seed = 123)
  )
  utils::capture.output(
    fit_sample_exe <- mod_exe$sample(data = data_list, chains = 1, seed = 123)
  )
  expect_equal(fit_sample$draws(), fit_sample_exe$draws())

  stan_ppc_program <- testing_stan_file("bernoulli_ppc")
  data_ppc_list <- testing_data("bernoulli_ppc")

  mod_bern_ppc <- cmdstan_model(stan_file = stan_ppc_program)
  mod_bern_ppc_exe <- cmdstan_model(exe_file = mod_bern_ppc$exe_file())

  utils::capture.output(
    fit_generate_quantities <- mod_bern_ppc$generate_quantities(fitted_params = fit_sample, data = data_list, seed = 123)
  )
  utils::capture.output(
    fit_generate_quantities_exe <- mod_bern_ppc_exe$generate_quantities(fitted_params = fit_sample, data = data_list, seed = 123)
  )
  expect_equal(fit_generate_quantities$draws(), fit_generate_quantities_exe$draws())

  utils::capture.output(
    fit_diagnose <- mod$diagnose(data = data_list, seed = 123)
  )
  utils::capture.output(
    fit_diagnose_exe <- mod_exe$diagnose(data = data_list, seed = 123)
  )
  expect_equal(fit_diagnose$gradients(), fit_diagnose_exe$gradients())
})

test_that("code() works with all fitted model objects", {
  code_ans <- readLines(testing_stan_file("logistic"))
  for (method in c("sample", "optimize", "laplace", "variational")) {
    expect_identical(fits[[method]]$code(), code_ans)
  }
  code_ans_gq <- readLines(testing_stan_file("bernoulli_ppc"))
  expect_identical(fits[["generate_quantities"]]$code(), code_ans_gq)
})

test_that("code() warns if model not created with Stan file", {
  stan_program <- testing_stan_file("bernoulli")
  mod <- testing_model("bernoulli")
  mod_exe <- cmdstan_model(exe_file = mod$exe_file())
  fit_exe <- mod_exe$sample(data = list(N = 10, y = c(0, 1, 0, 1, 0, 1, 0, 1, 0, 1)),
                            refresh = 0)
  expect_warning(
    expect_null(fit_exe$code()),
    "'$code()' will return NULL because the 'CmdStanModel' was not created with a Stan file",
    fixed = TRUE
  )
})
