# Path: jax_verify/sdp_verify.py

# 
# 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/problem.py
# DEFAULT_DISABLED_DUAL_VARS = ('nu_quad', 'muminus2')
# NECESSARY_DUAL_VARS = ('lam', 'muplus', 'muminus')
#   W0 = W0_orig * jnp.reshape(jnp.sqrt(sigmasq_z), (-1, 1))
#   N = sum([np.prod(np.array(i)) for i in layer_sizes])
# def make_relu_network_lagrangian(dual_vars, params, bounds, obj):
#   def lagrangian(xs_list):
# def relu_robustness_verif_instance_to_sdp(verif_instance):
#   def obj(x_final):
#   def make_inner_lagrangian(dual_vars):
# def make_sdp_verif_instance(verif_instance):
# def make_vae_sdp_verif_instance(params, data_x, bounds):
#   def recon_loss(x_final):
#   def make_inner_lagrangian(dual_vars):
# def make_vae_semantic_spec_params(x, vae_params, classifier_params):
# def get_dual_shapes_and_types(bounds_elided):
# 
# 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 os
import pickle
import jax
import jax.numpy as jnp
import jax_verify
import numpy as np
from absl import app
from absl import flags
from jax_verify import sdp_verify
from jax_verify.extensions.sdp_verify import boundprop_utils
from jax_verify.extensions.sdp_verify import problem
from jax_verify.extensions.sdp_verify import utils
# coding=utf-8
# Copyright 2021 DeepMind Technologies Limited.
#
# 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
r"""Run SDP verification for adversarial robustness specification.

Example launch commands which achieve good results:

CIFAR10 CNN-Mix:
python3 run_sdp_verify.py --model_name=models/cifar10_wongsmall_eps2_mix.pkl \
  --anneal_lengths=30000,30000,30000


# Path: jax_verify/sdp_verify.py

# 
# 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/problem.py
# DEFAULT_DISABLED_DUAL_VARS = ('nu_quad', 'muminus2')
# NECESSARY_DUAL_VARS = ('lam', 'muplus', 'muminus')
#   W0 = W0_orig * jnp.reshape(jnp.sqrt(sigmasq_z), (-1, 1))
#   N = sum([np.prod(np.array(i)) for i in layer_sizes])
# def make_relu_network_lagrangian(dual_vars, params, bounds, obj):
#   def lagrangian(xs_list):
# def relu_robustness_verif_instance_to_sdp(verif_instance):
#   def obj(x_final):
#   def make_inner_lagrangian(dual_vars):
# def make_sdp_verif_instance(verif_instance):
# def make_vae_sdp_verif_instance(params, data_x, bounds):
#   def recon_loss(x_final):
#   def make_inner_lagrangian(dual_vars):
# def make_vae_semantic_spec_params(x, vae_params, classifier_params):
# def get_dual_shapes_and_types(bounds_elided):
# 
# 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: examples/run_sdp_verify.py
import functools
import os
import pickle
import jax
import jax.numpy as jnp
import jax_verify
import numpy as np
from absl import app
from absl import flags
from jax_verify import sdp_verify
from jax_verify.extensions.sdp_verify import boundprop_utils
from jax_verify.extensions.sdp_verify import problem
from jax_verify.extensions.sdp_verify import utils

# coding=utf-8
# Copyright 2021 DeepMind Technologies Limited.
#
# 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
r"""Run SDP verification for adversarial robustness specification.

Example launch commands which achieve good results:

CIFAR10 CNN-Mix:
python3 run_sdp_verify.py --model_name=models/cifar10_wongsmall_eps2_mix.pkl \
  --anneal_lengths=30000,30000,30000


MNIST CNN-Adv: