# 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/test_utils.py
# def _init_fc_layer(key, n_in, n_out):
# def _init_conv_layer(key, n_h, n_w, n_cout, n_cin):
# def make_mlp_params(layer_sizes, key):
# def make_cnn_params(layer_sizes, key):
# def make_toy_verif_instance(seed=None, label=None, target_label=None, nn='mlp'):
# def make_mlp_layer_from_conv_layer(layer_params, input_bounds):
# def make_mlp_verif_instance_from_cnn(verif_instance):
#   W = random.normal(k1, (n_in, n_out))/ n_in
#   W = random.normal(k1, (n_h, n_w, n_cin, n_cout))


import functools
import jax.numpy as jnp
import jax.random as random
from absl.testing import absltest
from absl.testing import parameterized
from jax_verify.extensions.sdp_verify import utils
from jax_verify.tests.sdp_verify import test_utils

    self.check_fun_extract(fun_to_extract, example_inputs)

  def test_mlp_extract(self):
    """Test that weights from a MLP can be extracted."""
    key = random.PRNGKey(0)
    k1, k2 = random.split(key)

    input_sizes = (5,)
    layer_sizes = (5, 8, 5)
    mlp_params = test_utils.make_mlp_params(layer_sizes, k1)

    fun_to_extract = functools.partial(utils.predict_mlp, mlp_params)
    example_inputs = random.normal(k2, input_sizes)
    self.check_fun_extract(fun_to_extract, example_inputs)

  def test_mlp_withpreproc(self):
    """Test extraction of weights from a MLP with input preprocessing."""
    key = random.PRNGKey(0)
    k1, k2, k3, k4 = random.split(key, num=4)

    input_sizes = (5,)
    layer_sizes = (5, 8, 5)
    mlp_params = test_utils.make_mlp_params(layer_sizes, k1)
    example_inputs = random.normal(k2, input_sizes)
    input_mean = random.normal(k3, input_sizes)
    input_std = random.normal(k4, input_sizes)

    def fun_to_extract(inputs):
      inp = (inputs - input_mean) / input_std

# 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/test_utils.py
# def _init_fc_layer(key, n_in, n_out):
# def _init_conv_layer(key, n_h, n_w, n_cout, n_cin):
# def make_mlp_params(layer_sizes, key):
# def make_cnn_params(layer_sizes, key):
# def make_toy_verif_instance(seed=None, label=None, target_label=None, nn='mlp'):
# def make_mlp_layer_from_conv_layer(layer_params, input_bounds):
# def make_mlp_verif_instance_from_cnn(verif_instance):
#   W = random.normal(k1, (n_in, n_out))/ n_in
#   W = random.normal(k1, (n_h, n_w, n_cin, n_cout))


# Path: jax_verify/tests/sdp_verify/test_utilfuns.py
import functools
import jax.numpy as jnp
import jax.random as random
from absl.testing import absltest
from absl.testing import parameterized
from jax_verify.extensions.sdp_verify import utils
from jax_verify.tests.sdp_verify import test_utils


    self.check_fun_extract(fun_to_extract, example_inputs)

  def test_mlp_extract(self):
    """Test that weights from a MLP can be extracted."""
    key = random.PRNGKey(0)
    k1, k2 = random.split(key)

    input_sizes = (5,)
    layer_sizes = (5, 8, 5)
    mlp_params = test_utils.make_mlp_params(layer_sizes, k1)

    fun_to_extract = functools.partial(utils.predict_mlp, mlp_params)
    example_inputs = random.normal(k2, input_sizes)
    self.check_fun_extract(fun_to_extract, example_inputs)

  def test_mlp_withpreproc(self):
    """Test extraction of weights from a MLP with input preprocessing."""
    key = random.PRNGKey(0)
    k1, k2, k3, k4 = random.split(key, num=4)

    input_sizes = (5,)
    layer_sizes = (5, 8, 5)
    mlp_params = test_utils.make_mlp_params(layer_sizes, k1)
    example_inputs = random.normal(k2, input_sizes)
    input_mean = random.normal(k3, input_sizes)
    input_std = random.normal(k4, input_sizes)

    def fun_to_extract(inputs):
      inp = (inputs - input_mean) / input_std

      return utils.predict_mlp(mlp_params, inp)