# Path: optax/_src/stochastic_gradient_estimators.py
# def score_function_jacobians(
#     function: Callable[[chex.Array], float],
#     params: base.Params,
#     dist_builder: Callable[..., Any],
#     rng: chex.PRNGKey,
#     num_samples: int) -> Sequence[chex.Array]:
#   def surrogate(params):
# def pathwise_jacobians(
#     function: Callable[[chex.Array], float],
#     params: base.Params,
#     dist_builder: Callable[..., Any],
#     rng: chex.PRNGKey,
#     num_samples: int) -> Sequence[chex.Array]:
#   def surrogate(params):
# def measure_valued_jacobians(
#     function: Callable[[chex.Array], float],
#     params: base.Params,
#     dist_builder: Callable[..., Any],
#     rng: chex.PRNGKey,
#     num_samples: int,
#     coupling: bool = True) -> Sequence[chex.Array]:
# def measure_valued_estimation_mean(
#     function: Callable[[chex.Array], float],
#     dist: Any,
#     rng: chex.PRNGKey,
#     num_samples: int,
#     coupling: bool = True) -> chex.Array:
# def measure_valued_estimation_std(
#     function: Callable[[chex.Array], float],
#     dist: Any,
#     rng: chex.PRNGKey,
#     num_samples: int,
#     coupling: bool = True) -> chex.Array:
# 
# Path: optax/_src/utils.py
# def tile_second_to_last_dim(a):
# def canonicalize_dtype(dtype):
# def cast_tree(tree, dtype):
# def set_diags(a, new_diags):
# def multi_normal(loc, log_scale):
#   def __init__(self, loc, log_scale):
#   def sample(self, shape, seed):
#   def log_prob(self, x):
#   def log_scale(self):
#   def params(self):
# def _scale_gradient(inputs: chex.Array, scale: float)  -> chex.ArrayTree:
# def _scale_gradient_fwd(inputs, scale):
# def _scale_gradient_bwd(scale, g):
# def scale_gradient(inputs: chex.ArrayTree, scale: float) -> chex.ArrayTree:
# class MultiNormalDiagFromLogScale():


from absl.testing import absltest
from absl.testing import parameterized
from optax._src import stochastic_gradient_estimators as sge
from optax._src import utils
import chex
import jax
import jax.numpy as jnp
import numpy as np
              ('nocoupling', False),
          ],
          named=True))
  def testNonPolynomialFunctionConsistencyWithPathwise(self, effective_mean,
                                                       effective_log_scale,
                                                       function, coupling):
    num_samples = 10**5
    rng = jax.random.PRNGKey(1)
    measure_rng, pathwise_rng = jax.random.split(rng)

    mean = jnp.array(effective_mean, dtype=jnp.float32)
    log_scale = jnp.array(effective_log_scale, dtype=jnp.float32)
    data_dims = len(effective_mean)

    measure_valued_jacobians = _measure_valued_variant(self.variant)(
        function, [mean, log_scale],
        utils.multi_normal, measure_rng, num_samples, coupling)

    measure_valued_mean_jacobians = measure_valued_jacobians[0]
    chex.assert_shape(measure_valued_mean_jacobians, (num_samples, data_dims))
    measure_valued_mean_grads = np.mean(measure_valued_mean_jacobians, axis=0)

    measure_valued_log_scale_jacobians = measure_valued_jacobians[1]
    chex.assert_shape(
        measure_valued_log_scale_jacobians, (num_samples, data_dims))
    measure_valued_log_scale_grads = np.mean(
        measure_valued_log_scale_jacobians, axis=0)

    pathwise_jacobians = _estimator_variant(
        self.variant, sge.pathwise_jacobians)(function, [mean, log_scale],

# Path: optax/_src/stochastic_gradient_estimators.py
# def score_function_jacobians(
#     function: Callable[[chex.Array], float],
#     params: base.Params,
#     dist_builder: Callable[..., Any],
#     rng: chex.PRNGKey,
#     num_samples: int) -> Sequence[chex.Array]:
#   def surrogate(params):
# def pathwise_jacobians(
#     function: Callable[[chex.Array], float],
#     params: base.Params,
#     dist_builder: Callable[..., Any],
#     rng: chex.PRNGKey,
#     num_samples: int) -> Sequence[chex.Array]:
#   def surrogate(params):
# def measure_valued_jacobians(
#     function: Callable[[chex.Array], float],
#     params: base.Params,
#     dist_builder: Callable[..., Any],
#     rng: chex.PRNGKey,
#     num_samples: int,
#     coupling: bool = True) -> Sequence[chex.Array]:
# def measure_valued_estimation_mean(
#     function: Callable[[chex.Array], float],
#     dist: Any,
#     rng: chex.PRNGKey,
#     num_samples: int,
#     coupling: bool = True) -> chex.Array:
# def measure_valued_estimation_std(
#     function: Callable[[chex.Array], float],
#     dist: Any,
#     rng: chex.PRNGKey,
#     num_samples: int,
#     coupling: bool = True) -> chex.Array:
# 
# Path: optax/_src/utils.py
# def tile_second_to_last_dim(a):
# def canonicalize_dtype(dtype):
# def cast_tree(tree, dtype):
# def set_diags(a, new_diags):
# def multi_normal(loc, log_scale):
#   def __init__(self, loc, log_scale):
#   def sample(self, shape, seed):
#   def log_prob(self, x):
#   def log_scale(self):
#   def params(self):
# def _scale_gradient(inputs: chex.Array, scale: float)  -> chex.ArrayTree:
# def _scale_gradient_fwd(inputs, scale):
# def _scale_gradient_bwd(scale, g):
# def scale_gradient(inputs: chex.ArrayTree, scale: float) -> chex.ArrayTree:
# class MultiNormalDiagFromLogScale():


# Path: optax/_src/stochastic_gradient_estimators_test.py
from absl.testing import absltest
from absl.testing import parameterized
from optax._src import stochastic_gradient_estimators as sge
from optax._src import utils
import chex
import jax
import jax.numpy as jnp
import numpy as np

              ('nocoupling', False),
          ],
          named=True))
  def testNonPolynomialFunctionConsistencyWithPathwise(self, effective_mean,
                                                       effective_log_scale,
                                                       function, coupling):
    num_samples = 10**5
    rng = jax.random.PRNGKey(1)
    measure_rng, pathwise_rng = jax.random.split(rng)

    mean = jnp.array(effective_mean, dtype=jnp.float32)
    log_scale = jnp.array(effective_log_scale, dtype=jnp.float32)
    data_dims = len(effective_mean)

    measure_valued_jacobians = _measure_valued_variant(self.variant)(
        function, [mean, log_scale],
        utils.multi_normal, measure_rng, num_samples, coupling)

    measure_valued_mean_jacobians = measure_valued_jacobians[0]
    chex.assert_shape(measure_valued_mean_jacobians, (num_samples, data_dims))
    measure_valued_mean_grads = np.mean(measure_valued_mean_jacobians, axis=0)

    measure_valued_log_scale_jacobians = measure_valued_jacobians[1]
    chex.assert_shape(
        measure_valued_log_scale_jacobians, (num_samples, data_dims))
    measure_valued_log_scale_grads = np.mean(
        measure_valued_log_scale_jacobians, axis=0)

    pathwise_jacobians = _estimator_variant(
        self.variant, sge.pathwise_jacobians)(function, [mean, log_scale],

                                              utils.multi_normal, pathwise_rng,