# Path: jax_verify/extensions/sdp_verify/boundprop_utils.py
# def boundprop(params, x, epsilon, input_bounds, boundprop_type,
#               **extra_boundprop_kwargs):
# def _crown_ibp_boundprop(params, x, epsilon, input_bounds):
#   def get_layer_act(layer_idx, inputs):
# def _nonconvex_boundprop(params, x, epsilon, input_bounds,
#                          nonconvex_boundprop_steps=100,
#                          nonconvex_boundprop_nodes=128):
# 
# Path: jax_verify/extensions/sdp_verify/utils.py
# class SdpDualVerifInstance(_SdpDualVerifInstance):
# class VerifInstanceTypes(enum.Enum):
# class DualVarTypes(enum.Enum):
# def mlp_layer_sizes(params):
# def nn_layer_sizes(params):
# def layer_sizes_from_bounds(bounds):
# def predict_mlp(params, inputs):
# def fwd(inputs, layer_params):
# def predict_cnn(params, inputs, include_preactivations=False):
# def get_network_activs(params, x):
# def get_layer_params(fun_to_extract, example_input):
#   def _get_const_input_arg(eqn):
# def boundprop(params, bounds_in):
# def init_bound(x, epsilon, input_bounds=(0., 1.), add_batch_dim=True):
# def ibp_bound_elided(verif_instance):
# def ibp_bound_nonelided(verif_instance):
# def make_relu_robust_verif_instance(
#     params, bounds=None, target_label=1, label=2, input_bounds=None):
# def make_relu_robust_verif_instance_elided(
#     params, bounds=None, input_bounds=None):
# def output_size_and_verif_type(params, bounds):
# def elide_params(params, label, target_label, op_size):
# def preprocess_cifar(image, inception_preprocess=False, perturbation=False):
# def preprocessed_cifar_eps_and_input_bounds(
#     shape=(32, 32, 3), epsilon=2/255, inception_preprocess=False):
# def adv_objective(model_fn, x, label, target_label):
# def fgsm_single(model_fn, x, label, target_label, epsilon, num_steps,
#                 step_size, input_bounds=(0., 1.)):
# def untargeted_margin_loss(logits, labels):
# def pgd_default(model_fn, x, label, epsilon, num_steps, step_size,
#                 input_bounds=(0., 1.)):
# def pgd(adv_loss, x_init, epsilon, num_steps, step_size, input_bounds=(0., 1.)):
# def scale_by_variable_opt(multipliers):
#   def init_fn(params):
#   def update_fn(updates, _, params=None):
# def flatten(pytree, backend=np):
# def unflatten_like(a, pytree):
# def structure_like(tree1, tree2):
#   MLP_ELIDED = 'mlp_elided'
#   CNN_ELIDED = 'cnn_elided'
#   EQUALITY = 'equality'
#   INEQUALITY = 'inequality'


import functools
import pickle
import jax
import jax.numpy as jnp
import jax_verify
import numpy as np
from absl.testing import absltest
from absl.testing import parameterized
from jax_verify.extensions.sdp_verify import boundprop_utils
from jax_verify.extensions.sdp_verify import utils
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://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.

# Lint as: python3
"""Tests for crown_boundprop.py."""



class BoundpropTest(parameterized.TestCase):

  def test_crown_boundprop(self):
    """Test CROWN bounds vs FGSM on Wong-Small MNIST CNN."""
    crown_boundprop = functools.partial(boundprop_utils.boundprop,
                                        boundprop_type='crown_ibp')
    self._test_boundprop(crown_boundprop)

  def test_nonconvex_boundprop(self):
    """Test Nonconvex bounds vs FGSM on Wong-Small MNIST CNN."""
    # Minimal test, since this already takes 70s.
    nonconvex_boundprop = functools.partial(

# Path: jax_verify/extensions/sdp_verify/boundprop_utils.py
# def boundprop(params, x, epsilon, input_bounds, boundprop_type,
#               **extra_boundprop_kwargs):
# def _crown_ibp_boundprop(params, x, epsilon, input_bounds):
#   def get_layer_act(layer_idx, inputs):
# def _nonconvex_boundprop(params, x, epsilon, input_bounds,
#                          nonconvex_boundprop_steps=100,
#                          nonconvex_boundprop_nodes=128):
# 
# Path: jax_verify/extensions/sdp_verify/utils.py
# class SdpDualVerifInstance(_SdpDualVerifInstance):
# class VerifInstanceTypes(enum.Enum):
# class DualVarTypes(enum.Enum):
# def mlp_layer_sizes(params):
# def nn_layer_sizes(params):
# def layer_sizes_from_bounds(bounds):
# def predict_mlp(params, inputs):
# def fwd(inputs, layer_params):
# def predict_cnn(params, inputs, include_preactivations=False):
# def get_network_activs(params, x):
# def get_layer_params(fun_to_extract, example_input):
#   def _get_const_input_arg(eqn):
# def boundprop(params, bounds_in):
# def init_bound(x, epsilon, input_bounds=(0., 1.), add_batch_dim=True):
# def ibp_bound_elided(verif_instance):
# def ibp_bound_nonelided(verif_instance):
# def make_relu_robust_verif_instance(
#     params, bounds=None, target_label=1, label=2, input_bounds=None):
# def make_relu_robust_verif_instance_elided(
#     params, bounds=None, input_bounds=None):
# def output_size_and_verif_type(params, bounds):
# def elide_params(params, label, target_label, op_size):
# def preprocess_cifar(image, inception_preprocess=False, perturbation=False):
# def preprocessed_cifar_eps_and_input_bounds(
#     shape=(32, 32, 3), epsilon=2/255, inception_preprocess=False):
# def adv_objective(model_fn, x, label, target_label):
# def fgsm_single(model_fn, x, label, target_label, epsilon, num_steps,
#                 step_size, input_bounds=(0., 1.)):
# def untargeted_margin_loss(logits, labels):
# def pgd_default(model_fn, x, label, epsilon, num_steps, step_size,
#                 input_bounds=(0., 1.)):
# def pgd(adv_loss, x_init, epsilon, num_steps, step_size, input_bounds=(0., 1.)):
# def scale_by_variable_opt(multipliers):
#   def init_fn(params):
#   def update_fn(updates, _, params=None):
# def flatten(pytree, backend=np):
# def unflatten_like(a, pytree):
# def structure_like(tree1, tree2):
#   MLP_ELIDED = 'mlp_elided'
#   CNN_ELIDED = 'cnn_elided'
#   EQUALITY = 'equality'
#   INEQUALITY = 'inequality'


# Path: jax_verify/tests/sdp_verify/boundprop_utils_test.py
import functools
import pickle
import jax
import jax.numpy as jnp
import jax_verify
import numpy as np
from absl.testing import absltest
from absl.testing import parameterized
from jax_verify.extensions.sdp_verify import boundprop_utils
from jax_verify.extensions.sdp_verify import utils

#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://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.

# Lint as: python3
"""Tests for crown_boundprop.py."""



class BoundpropTest(parameterized.TestCase):

  def test_crown_boundprop(self):
    """Test CROWN bounds vs FGSM on Wong-Small MNIST CNN."""
    crown_boundprop = functools.partial(boundprop_utils.boundprop,
                                        boundprop_type='crown_ibp')
    self._test_boundprop(crown_boundprop)

  def test_nonconvex_boundprop(self):
    """Test Nonconvex bounds vs FGSM on Wong-Small MNIST CNN."""
    # Minimal test, since this already takes 70s.
    nonconvex_boundprop = functools.partial(

        boundprop_utils.boundprop, boundprop_type='nonconvex',