# Path: jaxopt/perturbations.py

# 
# Path: jaxopt/_src/test_util.py
# def ridge_solver(X, y, lam):
# def ridge_solver_jac(X, y, lam, eps=1e-4):
# def lasso_skl(X, y, lam, tol=1e-5, fit_intercept=False):
# def lasso_skl_jac(X, y, lam, tol=1e-5, fit_intercept=False, eps=1e-4):
# def enet_skl(X, y, params_prox, tol=1e-5, fit_intercept=False):
# def enet_skl_jac(X, y, params_prox, tol=1e-5, fit_intercept=False, eps=1e-5):
# def multitask_lasso_skl(X, Y, lam, tol=1e-5):
# def logreg_skl(X, y, lam, tol=1e-5, fit_intercept=False,
#                penalty="l2", multiclass=True):
#   def _reshape_coef(coef):
# def logreg_skl_jac(X, y, lam, tol=1e-5, fit_intercept=False, penalty="l2",
#                    multiclass=True, eps=1e-5):
# def multiclass_linear_svm_skl(X, y, lam, tol=1e-5):
# def multiclass_linear_svm_skl_jac(X, y, lam, tol=1e-5, eps=1e-5):
# def lsq_linear_osp(X, y, bounds, tol=1e-10, max_iter=None):
# def lsq_linear_cube_osp(X, y, l, tol=1e-10, max_iter=None):
# def lsq_linear_cube_osp_jac(X, y, l, eps=1e-5, tol=1e-10, max_iter=None):
# def check_states_have_same_types(state1, state2):
# def _canonicalize_dtype(x64_enabled, dtype):
# def canonicalize_dtype(dtype):
# def _dtype(x):
# def default_tolerance():
# def tolerance(dtype, tol=None):
# def device_under_test():
# def _assert_numpy_allclose(a, b, atol=None, rtol=None, err_msg=''):
# def is_sequence(x):
#   def assertArraysEqual(self, x, y, *, check_dtypes=True, err_msg=''):
#   def assertArraysAllClose(self, x, y, *, check_dtypes=True, atol=None,
#                            rtol=None, err_msg=''):
#   def assertDtypesMatch(self, x, y, *, canonicalize_dtypes=True):
#   def assertAllClose(self, x, y, *, check_dtypes=True, atol=None, rtol=None,
#                      canonicalize_dtypes=True, err_msg=''):
#   XX = jnp.dot(X.T, X)
#   I = jnp.eye(X.shape[1])
# class JaxoptTestCase(parameterized.TestCase):


from absl.testing import absltest
from jaxopt import perturbations
from jaxopt._src import test_util
import jax
import jax.numpy as jnp
      import distrax
      import distrax
    self.num_samples = 1000
    self.sigma = 0.5
    self.rng = jax.random.PRNGKey(0)

  def test_argmax(self):
    """Checks that the perturbed argmax approximates well the softmax."""

    theta_matrix = jnp.array([[-0.5, 0.2, 0.1],
                              [-0.2, 0.1, 0.4]])
    single_argmax = one_hot_argmax(theta_matrix[0])
    test_single_argmax = jnp.array([0., 1., 0.])
    self.assertArraysEqual(single_argmax, test_single_argmax)
    matrix_argmax = one_hot_argmax(theta_matrix)
    test_matrix_argmax = jnp.array([[0., 0., 0.],
                                    [0., 0., 1.]])
    self.assertArraysEqual(matrix_argmax, test_matrix_argmax)
    batch_argmax = jax.vmap(one_hot_argmax)(theta_matrix)
    test_batch_argmax = jnp.array([[0., 1., 0.],
                                   [0., 0., 1.]])
    self.assertArraysEqual(batch_argmax, test_batch_argmax)

    square_norm_fun = lambda x: jnp.sum(x * x, axis=-1)

    theta_batch = jnp.array([[-0.6, 1.9, -0.2, 1.1, -1.0],
                             [-0.6, 1.0, -0.2, 1.8, -1.0]], dtype=jnp.float32)
    one_hot_argmax_batch = jax.jit(jax.vmap(one_hot_argmax))(theta_batch)
    one_hot_test = jnp.array([[0.0, 1.0, 0.0, 0.0, 0.0],
                              [0.0, 0.0, 0.0, 1.0, 0.0]], dtype=jnp.float32)
    self.assertArraysEqual(one_hot_argmax_batch, one_hot_test)


# Path: jaxopt/perturbations.py

# 
# Path: jaxopt/_src/test_util.py
# def ridge_solver(X, y, lam):
# def ridge_solver_jac(X, y, lam, eps=1e-4):
# def lasso_skl(X, y, lam, tol=1e-5, fit_intercept=False):
# def lasso_skl_jac(X, y, lam, tol=1e-5, fit_intercept=False, eps=1e-4):
# def enet_skl(X, y, params_prox, tol=1e-5, fit_intercept=False):
# def enet_skl_jac(X, y, params_prox, tol=1e-5, fit_intercept=False, eps=1e-5):
# def multitask_lasso_skl(X, Y, lam, tol=1e-5):
# def logreg_skl(X, y, lam, tol=1e-5, fit_intercept=False,
#                penalty="l2", multiclass=True):
#   def _reshape_coef(coef):
# def logreg_skl_jac(X, y, lam, tol=1e-5, fit_intercept=False, penalty="l2",
#                    multiclass=True, eps=1e-5):
# def multiclass_linear_svm_skl(X, y, lam, tol=1e-5):
# def multiclass_linear_svm_skl_jac(X, y, lam, tol=1e-5, eps=1e-5):
# def lsq_linear_osp(X, y, bounds, tol=1e-10, max_iter=None):
# def lsq_linear_cube_osp(X, y, l, tol=1e-10, max_iter=None):
# def lsq_linear_cube_osp_jac(X, y, l, eps=1e-5, tol=1e-10, max_iter=None):
# def check_states_have_same_types(state1, state2):
# def _canonicalize_dtype(x64_enabled, dtype):
# def canonicalize_dtype(dtype):
# def _dtype(x):
# def default_tolerance():
# def tolerance(dtype, tol=None):
# def device_under_test():
# def _assert_numpy_allclose(a, b, atol=None, rtol=None, err_msg=''):
# def is_sequence(x):
#   def assertArraysEqual(self, x, y, *, check_dtypes=True, err_msg=''):
#   def assertArraysAllClose(self, x, y, *, check_dtypes=True, atol=None,
#                            rtol=None, err_msg=''):
#   def assertDtypesMatch(self, x, y, *, canonicalize_dtypes=True):
#   def assertAllClose(self, x, y, *, check_dtypes=True, atol=None, rtol=None,
#                      canonicalize_dtypes=True, err_msg=''):
#   XX = jnp.dot(X.T, X)
#   I = jnp.eye(X.shape[1])
# class JaxoptTestCase(parameterized.TestCase):


# Path: tests/perturbations_test.py
from absl.testing import absltest
from jaxopt import perturbations
from jaxopt._src import test_util
import jax
import jax.numpy as jnp
      import distrax
      import distrax

    self.num_samples = 1000
    self.sigma = 0.5
    self.rng = jax.random.PRNGKey(0)

  def test_argmax(self):
    """Checks that the perturbed argmax approximates well the softmax."""

    theta_matrix = jnp.array([[-0.5, 0.2, 0.1],
                              [-0.2, 0.1, 0.4]])
    single_argmax = one_hot_argmax(theta_matrix[0])
    test_single_argmax = jnp.array([0., 1., 0.])
    self.assertArraysEqual(single_argmax, test_single_argmax)
    matrix_argmax = one_hot_argmax(theta_matrix)
    test_matrix_argmax = jnp.array([[0., 0., 0.],
                                    [0., 0., 1.]])
    self.assertArraysEqual(matrix_argmax, test_matrix_argmax)
    batch_argmax = jax.vmap(one_hot_argmax)(theta_matrix)
    test_batch_argmax = jnp.array([[0., 1., 0.],
                                   [0., 0., 1.]])
    self.assertArraysEqual(batch_argmax, test_batch_argmax)

    square_norm_fun = lambda x: jnp.sum(x * x, axis=-1)

    theta_batch = jnp.array([[-0.6, 1.9, -0.2, 1.1, -1.0],
                             [-0.6, 1.0, -0.2, 1.8, -1.0]], dtype=jnp.float32)
    one_hot_argmax_batch = jax.jit(jax.vmap(one_hot_argmax))(theta_batch)
    one_hot_test = jnp.array([[0.0, 1.0, 0.0, 0.0, 0.0],
                              [0.0, 0.0, 0.0, 1.0, 0.0]], dtype=jnp.float32)
    self.assertArraysEqual(one_hot_argmax_batch, one_hot_test)


    pert_argmax_fun = jax.jit(perturbations.make_perturbed_argmax(