# 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
#     https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Tests for jax_perturbations.perturbations."""





def one_hot_argmax(inputs: jnp.array) -> jnp.array:
  """An argmax one-hot function for arbitrary shapes."""
  inputs_flat = jnp.reshape(inputs, (-1))
  flat_one_hot = jax.nn.one_hot(jnp.argmax(inputs_flat), inputs_flat.shape[0])
  return jnp.reshape(flat_one_hot, inputs.shape)


def ranks(inputs: jnp.array) -> jnp.array:
  return jnp.argsort(jnp.argsort(inputs, axis=-1), axis=-1)


def top_k_hots(values, k):
  n = values.shape[0]
  return jax.nn.one_hot(jnp.argsort(values)[-k:], n)



# 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

#     https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Tests for jax_perturbations.perturbations."""





def one_hot_argmax(inputs: jnp.array) -> jnp.array:
  """An argmax one-hot function for arbitrary shapes."""
  inputs_flat = jnp.reshape(inputs, (-1))
  flat_one_hot = jax.nn.one_hot(jnp.argmax(inputs_flat), inputs_flat.shape[0])
  return jnp.reshape(flat_one_hot, inputs.shape)


def ranks(inputs: jnp.array) -> jnp.array:
  return jnp.argsort(jnp.argsort(inputs, axis=-1), axis=-1)


def top_k_hots(values, k):
  n = values.shape[0]
  return jax.nn.one_hot(jnp.argsort(values)[-k:], n)



class PerturbationsArgmaxTest(test_util.JaxoptTestCase):