command_args <- commandArgs(trailingOnly = FALSE)
script_path <- sub("^--file=", "", grep("^--file=", command_args, value = TRUE)[1])
script_dir <- dirname(normalizePath(script_path, winslash = "/"))
project_root <- normalizePath(file.path(script_dir, "..", ".."), winslash = "/")
library_path <- file.path(project_root, ".r-library", "4.6")
.libPaths(c(library_path, .libPaths()))

results <- list(
  r_version = R.version.string,
  library_path = library_path,
  imports = list(),
  tests = list()
)

packages <- c(
  "survey", "srvyr", "mirt", "lavaan", "semTools", "psych",
  "data.table", "jsonlite", "arrow", "haven", "readr", "dplyr",
  "tidyr", "purrr", "stringr", "digest", "renv", "targets", "withr"
)

for (package in packages) {
  status <- tryCatch({
    loadNamespace(package)
    list(status = "passed", version = as.character(packageVersion(package)))
  }, error = function(error) {
    list(status = "failed", detail = conditionMessage(error))
  })
  results$imports[[package]] <- status
}

run_test <- function(name, expression) {
  results$tests[[name]] <<- tryCatch({
    detail <- force(expression)
    list(status = "passed", detail = detail)
  }, error = function(error) {
    list(status = "failed", detail = conditionMessage(error))
  })
}

run_test("survey_weighted_mean", {
  frame <- data.frame(
    value = c(1, 2, 3, 4, 5, 6),
    strata = c(1, 1, 1, 2, 2, 2),
    psu = c(1, 2, 3, 4, 5, 6),
    weight = c(1, 2, 1, 2, 1, 2)
  )
  design <- survey::svydesign(~psu, strata = ~strata, weights = ~weight, data = frame)
  estimate <- unname(coef(survey::svymean(~value, design))[[1]])
  list(estimate = estimate, finite = is.finite(estimate))
})

run_test("lavaan_cfa", {
  set.seed(20260920)
  latent <- rnorm(180)
  frame <- data.frame(
    x1 = 0.8 * latent + rnorm(180, sd = 0.4),
    x2 = 0.7 * latent + rnorm(180, sd = 0.5),
    x3 = 0.9 * latent + rnorm(180, sd = 0.3)
  )
  fit <- lavaan::cfa("factor =~ x1 + x2 + x3", data = frame)
  list(converged = lavaan::lavInspect(fit, "converged"))
})

run_test("mirt_2pl", {
  set.seed(20260920)
  sample_size <- 500
  item_count <- 8
  theta <- rnorm(sample_size)
  discrimination <- seq(0.8, 1.5, length.out = item_count)
  difficulty <- seq(-1.2, 1.2, length.out = item_count)
  probabilities <- vapply(
    seq_len(item_count),
    function(index) plogis(discrimination[index] * (theta - difficulty[index])),
    numeric(sample_size)
  )
  responses <- matrix(
    rbinom(length(probabilities), 1, as.vector(probabilities)),
    nrow = sample_size,
    ncol = item_count
  )
  colnames(responses) <- paste0("item", seq_len(ncol(responses)))
  fit <- mirt::mirt(
    responses,
    1,
    itemtype = "2PL",
    verbose = FALSE,
    technical = list(NCYCLES = 1000)
  )
  converged <- isTRUE(fit@OptimInfo$converged)
  if (!converged) stop("mirt 2PL fit did not converge")
  list(converged = converged, items = ncol(responses), respondents = nrow(responses))
})

run_test("arrow_parquet", {
  cache_dir <- file.path(project_root, ".cache", "r-smoke")
  dir.create(cache_dir, recursive = TRUE, showWarnings = FALSE)
  path <- file.path(cache_dir, "arrow-smoke.parquet")
  arrow::write_parquet(data.frame(id = 1:3, value = c("a", "b", "c")), path)
  restored <- arrow::read_parquet(path)
  list(rows = nrow(restored), columns = ncol(restored))
})

output_path <- file.path(script_dir, "r-smoke-test-result.json")
writeLines(
  jsonlite::toJSON(results, pretty = TRUE, auto_unbox = TRUE, null = "null"),
  output_path,
  useBytes = TRUE
)
cat(readLines(output_path, warn = FALSE), sep = "\n")

failed_imports <- names(Filter(function(item) item$status != "passed", results$imports))
failed_tests <- names(Filter(function(item) item$status != "passed", results$tests))
quit(status = if (length(failed_imports) || length(failed_tests)) 1L else 0L)