# Path: optax/_src/privacy.py
# class DifferentiallyPrivateAggregateState(NamedTuple):
# def differentially_private_aggregate(
#     l2_norm_clip: float,
#     noise_multiplier: float,
#     seed: int
# ) -> base.GradientTransformation:
#   def init_fn(params):
#   def update_fn(updates, state, params=None):


from absl.testing import absltest
from absl.testing import parameterized
from optax._src import privacy
import chex
import jax
import jax.numpy as jnp
    # we can easily compute what the values should be:
    expected_val = jnp.mean(jnp.arange(self.batch_size) / divisors)
    expected_tree = jax.tree_map(
        lambda p: jnp.broadcast_to(expected_val, p.shape), self.params)

    for _ in range(3):
      updates, state = update_fn(self.per_eg_grads, state, self.params)
      chex.assert_tree_all_close(updates, expected_tree, rtol=2e-7)

  @chex.all_variants
  @parameterized.parameters((3.0, 2.0), (1.0, 5.0), (100.0, 4.0), (1.0, 90.0))
  def test_noise_multiplier(self, l2_norm_clip, noise_multiplier):
    """Standard dev. of noise should be l2_norm_clip * noise_multiplier."""
    dp_agg = privacy.differentially_private_aggregate(
        l2_norm_clip=l2_norm_clip,
        noise_multiplier=noise_multiplier,
        seed=1337)
    state = dp_agg.init(None)
    update_fn = self.variant(dp_agg.update)
    expected_std = l2_norm_clip * noise_multiplier

    grads = [jnp.ones((1, 100, 100))]  # batch size 1
    for _ in range(3):
      updates, state = update_fn(grads, state)
      chex.assert_tree_all_close(expected_std,
                                 jnp.std(updates[0]),
                                 atol=0.1 * expected_std)

  def test_aggregated_updates_as_input_fails(self):
    """Expect per-example gradients as input to this transform."""

# Path: optax/_src/privacy.py
# class DifferentiallyPrivateAggregateState(NamedTuple):
# def differentially_private_aggregate(
#     l2_norm_clip: float,
#     noise_multiplier: float,
#     seed: int
# ) -> base.GradientTransformation:
#   def init_fn(params):
#   def update_fn(updates, state, params=None):


# Path: optax/_src/privacy_test.py
from absl.testing import absltest
from absl.testing import parameterized
from optax._src import privacy
import chex
import jax
import jax.numpy as jnp

    # we can easily compute what the values should be:
    expected_val = jnp.mean(jnp.arange(self.batch_size) / divisors)
    expected_tree = jax.tree_map(
        lambda p: jnp.broadcast_to(expected_val, p.shape), self.params)

    for _ in range(3):
      updates, state = update_fn(self.per_eg_grads, state, self.params)
      chex.assert_tree_all_close(updates, expected_tree, rtol=2e-7)

  @chex.all_variants
  @parameterized.parameters((3.0, 2.0), (1.0, 5.0), (100.0, 4.0), (1.0, 90.0))
  def test_noise_multiplier(self, l2_norm_clip, noise_multiplier):
    """Standard dev. of noise should be l2_norm_clip * noise_multiplier."""
    dp_agg = privacy.differentially_private_aggregate(
        l2_norm_clip=l2_norm_clip,
        noise_multiplier=noise_multiplier,
        seed=1337)
    state = dp_agg.init(None)
    update_fn = self.variant(dp_agg.update)
    expected_std = l2_norm_clip * noise_multiplier

    grads = [jnp.ones((1, 100, 100))]  # batch size 1
    for _ in range(3):
      updates, state = update_fn(grads, state)
      chex.assert_tree_all_close(expected_std,
                                 jnp.std(updates[0]),
                                 atol=0.1 * expected_std)

  def test_aggregated_updates_as_input_fails(self):
    """Expect per-example gradients as input to this transform."""

    dp_agg = privacy.differentially_private_aggregate(l2_norm_clip=0.1,