text: # Copyright Contributors to the Pyro project.
  # SPDX-License-Identifier: Apache-2.0
  
  
  from numpy.testing import assert_allclose
  import pytest
  
  from jax import random
  from jax.flatten_util import ravel_pytree
  import jax.numpy as jnp
  from jax.test_util import check_eq
  from jax.tree_util import tree_flatten, tree_multimap
  
  import numpyro
  import numpyro.distributions as dist
  from numpyro.util import check_model_guide_match, fori_collect, format_shapes, soft_vmap
  
  
  def test_fori_collect_thinning():
      def f(x):
          return x + 1.0
  
      actual2 = fori_collect(0, 9, f, jnp.array([-1]), thinning=2)
      expected2 = jnp.array([[2], [4], [6], [8]])
      check_eq(actual2, expected2)
  
      actual3 = fori_collect(0, 9, f, jnp.array([-1]), thinning=3)
      expected3 = jnp.array([[2], [5], [8]])
      check_eq(actual3, expected3)
  
      actual4 = fori_collect(0, 9, f, jnp.array([-1]), thinning=4)
      expected4 = jnp.array([[4], [8]])
      check_eq(actual4, expected4)
  
      actual5 = fori_collect(12, 37, f, jnp.array([-1]), thinning=5)
      expected5 = jnp.array([[16], [21], [26], [31], [36]])
      check_eq(actual5, expected5)
  
  
  def test_fori_collect():
      def f(x):
          return {"i": x["i"] + x["j"], "j": x["i"] - x["j"]}
  
      a = {"i": jnp.array([0.0]), "j": jnp.array([1.0])}
      expected_tree = {"i": jnp.array([[0.0], [2.0]])}
      actual_tree = fori_collect(1, 3, f, a, transform=lambda a: {"i": a["i"]})
      check_eq(actual_tree, expected_tree)
  
  
  @pytest.mark.parametrize("progbar", [False, True])
  def test_fori_collect_return_last(progbar):
      def f(x):
          x["i"] = x["i"] + 1
          return x
  
      tree, init_state = fori_collect(
          2,
          4,
          f,
          {"i": 0},
          transform=lambda a: {"i": a["i"]},
          return_last_val=True,
          progbar=progbar,
      )
      expected_tree = {"i": jnp.array([3, 4])}
      expected_last_state = {"i": jnp.array(4)}
      check_eq(init_state, expected_last_state)
      check_eq(tree, expected_tree)
  
  
  @pytest.mark.parametrize(
      "pytree",
      [
          {"a": jnp.array(0.0), "b": jnp.array([[1.0, 2.0], [3.0, 4.0]])},
          {"a": jnp.array(0), "b": jnp.array([[1, 2], [3, 4]])},
          {"a": jnp.array(0), "b": jnp.array([[1.0, 2.0], [3.0, 4.0]])},
          {"a": 0.0, "b": jnp.array([[1.0, 2.0], [3.0, 4.0]])},
          {"a": False, "b": jnp.array([[1.0, 2.0], [3.0, 4.0]])},
          [False, True, 0.0, jnp.array([[1.0, 2.0], [3.0, 4.0]])],
      ],
  )
  def test_ravel_pytree(pytree):
      flat, unravel_fn = ravel_pytree(pytree)
      unravel = unravel_fn(flat)
      tree_flatten(tree_multimap(lambda x, y: assert_allclose(x, y), unravel, pytree))
      assert all(
          tree_flatten(
              tree_multimap(
                  lambda x, y: jnp.result_type(x) == jnp.result_type(y), unravel, pytree
              )
          )[0]
      )
  
  
  @pytest.mark.parametrize("batch_shape", [(), (1,), (10,), (3, 4)])
  @pytest.mark.parametrize("chunk_size", [None, 1, 5, 16])
  def test_soft_vmap(batch_shape, chunk_size):
      def f(x):
          return {
              k: ((v[..., None] * jnp.ones(4)) if k == "a" else ~v) for k, v in x.items()
          }
  
      xs = {"a": jnp.ones(batch_shape + (4,)), "b": jnp.zeros(batch_shape).astype(bool)}
      ys = soft_vmap(f, xs, len(batch_shape), chunk_size)
      assert set(ys.keys()) == {"a", "b"}
      assert_allclose(ys["a"], xs["a"][..., None] * jnp.ones(4))
      assert_allclose(ys["b"], ~xs["b"])
  
  
  def test_format_shapes():
      data = jnp.arange(100)
  
      def model_test():
          mean = numpyro.param("mean", jnp.zeros(len(data)))
          scale = numpyro.sample("scale", dist.Normal(0, 1).expand([3]).to_event(1))
          scale = scale.sum()
          with numpyro.plate("data", len(data), subsample_size=10) as ind:
              batch = data[ind]
              mean_batch = mean[ind]
              numpyro.sample("x", dist.Normal(mean_batch, scale), obs=batch)
  
      with numpyro.handlers.seed(rng_seed=0), numpyro.handlers.trace() as t:
          model_test()
  
      assert (
          format_shapes(t) == "Trace Shapes:         \n"
          " Param Sites:         \n"
          "         mean    100  \n"
          "Sample Sites:         \n"
          "   scale dist      | 3\n"
          "        value      | 3\n"
          "   data plate 10   |  \n"
          "       x dist 10   |  \n"
          "        value 10   |  "
      )
      assert (
          format_shapes(t, compute_log_prob=True) == "Trace Shapes:         \n"
          " Param Sites:         \n"
          "         mean    100  \n"
          "Sample Sites:         \n"
          "   scale dist      | 3\n"
          "        value      | 3\n"
          "     log_prob      |  \n"
          "   data plate 10   |  \n"
          "       x dist 10   |  \n"
          "        value 10   |  \n"
          "     log_prob 10   |  "
      )
      assert (
          format_shapes(t, compute_log_prob=lambda site: site["name"] == "scale")
          == "Trace Shapes:         \n"
          " Param Sites:         \n"
          "         mean    100  \n"
          "Sample Sites:         \n"
          "   scale dist      | 3\n"
          "        value      | 3\n"
          "     log_prob      |  \n"
          "   data plate 10   |  \n"
          "       x dist 10   |  \n"
          "        value 10   |  "
      )
      assert (
          format_shapes(t, last_site="data") == "Trace Shapes:         \n"
          " Param Sites:         \n"
          "         mean    100  \n"
          "Sample Sites:         \n"
          "   scale dist      | 3\n"
          "        value      | 3\n"
          "   data plate 10   |  "
      )
  
  
  def test_check_model_guide_match():
      def _run_svi(model, guide):
          adam = numpyro.optim.Adam(1e-3)
          svi = numpyro.infer.SVI(model, guide, adam, numpyro.infer.Trace_ELBO())
          svi.run(random.PRNGKey(42), num_steps=50)
  
      def _run_svi_check_warnings(model, guide, expected_string):
          with pytest.warns(UserWarning, match=expected_string) as ws:
              _run_svi(model, guide)
              assert len(ws) == 1
              assert expected_string in str(ws[0].message)
  
      def _create_traces_check_error_string(model, guide, expected_string):
          model_trace = numpyro.handlers.trace(
              numpyro.handlers.seed(model, rng_seed=42)
          ).get_trace()
          guide_trace = numpyro.handlers.trace(
              numpyro.handlers.seed(guide, rng_seed=42)
          ).get_trace()
          with pytest.raises(ValueError, match=expected_string):
              check_model_guide_match(model_trace, guide_trace)
  
      # 1. Auxiliary vars in the model
      def model():
          numpyro.sample("x", dist.Normal())
  
      def guide():
          numpyro.sample("x", dist.Normal(), infer={"is_auxiliary": True})
  
      _run_svi_check_warnings(model, guide, "Found auxiliary vars in the model")
  
      # 2. Non-auxiliary vars in guide but not model
      def model():
          numpyro.sample("x1", dist.Normal())
  
      def guide():
          numpyro.sample("x1", dist.Normal())
          numpyro.sample("x2", dist.Normal())
  
      _run_svi_check_warnings(
          model, guide, "Found non-auxiliary vars in guide but not model"
      )
  
      # 3. Vars in model but not guide
      def model():
          numpyro.sample("x1", dist.Normal())
          numpyro.sample("x2", dist.Normal())
  
      def guide():
          numpyro.sample("x1", dist.Normal())
  
      _run_svi_check_warnings(model, guide, "Found vars in model but not guide")
  
      # 4. Check event_dims agree
      def model():
          numpyro.sample("x", dist.MultivariateNormal(jnp.zeros(4), jnp.identity(4)))
  
      def guide():
          numpyro.sample("x", dist.Normal().expand((3, 5)))
  
      _create_traces_check_error_string(
          model, guide, "Model and guide event_dims disagree"
      )
  
      # 5. Check shapes agree
      def model():
          numpyro.sample("x", dist.Normal().expand((3, 2)))
  
      def guide():
          numpyro.sample("x", dist.Normal().expand((3, 5)))
  
      _create_traces_check_error_string(model, guide, "Model and guide shapes disagree")
  
      # 6. Check subsample sites introduced by plate
      def model():
          numpyro.sample("x", dist.Normal().expand((10,)))
  
      def guide():
          with numpyro.handlers.plate("data", 100, subsample_size=10):
              numpyro.sample("x", dist.Normal())
  
      _run_svi_check_warnings(
          model, guide, "Found plate statements in guide but not model"
      )
  