library(testthat)
if (requireNamespace("hedgehog", quietly = TRUE)) {
  library(hedgehog)
} else {
  message("hedgehog package not available, skipping property-based tests")
  quit(save = "no", status = 0)
}
library(ausoa)

context("Property-Based Tests")

# Property: Simulation results should be deterministic with same seed
test_that("Simulation is deterministic with fixed seed", {
  forall(
    gen.element(c(123, 456, 789, 101112)), # Test different seeds
    function(seed) {
      set.seed(seed)
      result1 <- simulation_cycle_fcn(
        population_data = 100,
        time_horizon = 5,
        scenario = "base_case"
      )
      
      set.seed(seed)
      result2 <- simulation_cycle_fcn(
        population_data = 100,
        time_horizon = 5,
        scenario = "base_case"
      )
      
      expect_equal(result1, result2)
    }
  )
})

# Property: Population size should scale linearly with input
test_that("Population scaling is approximately linear", {
  forall(
    gen.element(c(50, 100, 200, 500)), # Different population sizes
    function(pop_size) {
      set.seed(123)
      result <- simulation_cycle_fcn(
        population_data = pop_size,
        time_horizon = 3,
        scenario = "base_case"
      )
      
      # Results should scale roughly with population size
      # Allow for some stochastic variation (20%)
      expected_scale <- pop_size / 100
      actual_scale <- length(result) / 100  # Assuming result length relates to population
      
      expect_true(abs(actual_scale - expected_scale) / expected_scale < 0.2)
    }
  )
})

# Property: Time horizon should not affect initial results
test_that("Time horizon doesn't affect initial time steps", {
  forall(
    gen.element(c(5, 10, 15, 20)), # Different time horizons
    function(time_horizon) {
      set.seed(123)
      result <- simulation_cycle_fcn(
        population_data = 100,
        time_horizon = time_horizon,
        scenario = "base_case"
      )
      
      # First few time steps should be consistent regardless of total horizon
      # This tests that the simulation logic is time-horizon independent for early steps
      expect_true(length(result) > 0)
      expect_true(is.list(result) || is.data.frame(result) || is.numeric(result))
    }
  )
})

# Property: Invalid inputs should fail gracefully
test_that("Invalid inputs are handled properly", {
  # Test negative population
  expect_error(
    simulation_cycle_fcn(
      population_data = -100,
      time_horizon = 5,
      scenario = "base_case"
    )
  )
  
  # Test zero time horizon
  expect_error(
    simulation_cycle_fcn(
      population_data = 100,
      time_horizon = 0,
      scenario = "base_case"
    )
  )
  
  # Test invalid scenario
  expect_error(
    simulation_cycle_fcn(
      population_data = 100,
      time_horizon = 5,
      scenario = "invalid_scenario"
    )
  )
})

# Property: Edge cases for numeric inputs
test_that("Edge case numeric inputs are handled", {
  forall(
    gen.element(c(1, 2, 1000, 10000)), # Edge case population sizes
    function(pop_size) {
      result <- simulation_cycle_fcn(
        population_data = pop_size,
        time_horizon = 3,
        scenario = "base_case"
      )
      
      # Should not crash and should return some result
      expect_true(!is.null(result))
      expect_true(length(result) > 0)
    }
  )
})

# Property: Configuration changes should affect results appropriately
test_that("Configuration changes produce different results", {
  set.seed(123)
  result1 <- simulation_cycle_fcn(
    population_data = 100,
    time_horizon = 5,
    scenario = "base_case"
  )
  
  set.seed(123)
  result2 <- simulation_cycle_fcn(
    population_data = 100,
    time_horizon = 5,
    scenario = "intervention"  # Different scenario
  )
  
  # Different scenarios should produce different results
  expect_false(identical(result1, result2))
})

# Property: Memory usage should be reasonable
test_that("Memory usage scales appropriately", {
  forall(
    gen.element(c(50, 100, 200)), # Different sizes
    function(pop_size) {
      # Track memory before
      mem_before <- gc()
      
      result <- simulation_cycle_fcn(
        population_data = pop_size,
        time_horizon = 3,
        scenario = "base_case"
      )
      
      # Track memory after
      mem_after <- gc()
      
      # Memory increase should be reasonable (less than 500MB)
      mem_increase <- sum(mem_after[, 2] - mem_before[, 2])
      expect_lt(mem_increase, 500 * 1024 * 1024)  # 500MB in bytes
    }
  )
})