# 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
    crown_ubs = utils.flatten([b.ub_pre for b in bounds[1:]])

    max_idx = crown_lbs.shape[0]
    np.random.seed(0)
    test_idxs = np.random.randint(max_idx, size=num_idxs_to_test)

    @jax.jit
    def fwd(x):
      _, acts = utils.predict_cnn(params, jnp.expand_dims(x, 0),
                                  include_preactivations=True)
      return acts

    get_act = lambda x, idx: utils.flatten(fwd(x), backend=jnp)[idx]

    print('Number of activations:', crown_lbs.shape[0])
    print('Bound shape', [b.lb.shape for b in bounds])
    print('Activation shape', [a.shape for a in fwd(x)])
    assert utils.flatten(fwd(x)).shape == crown_lbs.shape, (
        f'bad shape {crown_lbs.shape}, {utils.flatten(fwd(x)).shape}')

    for idx in test_idxs:
      nom = get_act(x, idx)
      crown_lb = crown_lbs[idx]
      crown_ub = crown_ubs[idx]

      adv_loss = lambda x: get_act(x, idx)  # pylint: disable=cell-var-from-loop
      x_lb = utils.pgd(adv_loss, x, eps, 50, 0.01)
      fgsm_lb = get_act(x_lb, idx)

      adv_loss = lambda x: -get_act(x, idx)  # pylint: disable=cell-var-from-loop

# 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

    crown_ubs = utils.flatten([b.ub_pre for b in bounds[1:]])

    max_idx = crown_lbs.shape[0]
    np.random.seed(0)
    test_idxs = np.random.randint(max_idx, size=num_idxs_to_test)

    @jax.jit
    def fwd(x):
      _, acts = utils.predict_cnn(params, jnp.expand_dims(x, 0),
                                  include_preactivations=True)
      return acts

    get_act = lambda x, idx: utils.flatten(fwd(x), backend=jnp)[idx]

    print('Number of activations:', crown_lbs.shape[0])
    print('Bound shape', [b.lb.shape for b in bounds])
    print('Activation shape', [a.shape for a in fwd(x)])
    assert utils.flatten(fwd(x)).shape == crown_lbs.shape, (
        f'bad shape {crown_lbs.shape}, {utils.flatten(fwd(x)).shape}')

    for idx in test_idxs:
      nom = get_act(x, idx)
      crown_lb = crown_lbs[idx]
      crown_ub = crown_ubs[idx]

      adv_loss = lambda x: get_act(x, idx)  # pylint: disable=cell-var-from-loop
      x_lb = utils.pgd(adv_loss, x, eps, 50, 0.01)
      fgsm_lb = get_act(x_lb, idx)

      adv_loss = lambda x: -get_act(x, idx)  # pylint: disable=cell-var-from-loop

      x_ub = utils.pgd(adv_loss, x, eps, 50, 0.01)