# 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 jax
import jax.numpy as jnp
import jax.random as random
import numpy as np
from jax_verify.extensions.sdp_verify import utils
def make_toy_verif_instance(seed=None, label=None, target_label=None, nn='mlp'):
  """Mainly used for unit testing."""
  key = jax.random.PRNGKey(0) if seed is None else jax.random.PRNGKey(seed)
  if nn == 'mlp':
    layer_sizes = '5, 5, 5'
    layer_sizes = np.fromstring(layer_sizes, dtype=int, sep=',')
    params = make_mlp_params(layer_sizes, key)
    inp_shape = (1, layer_sizes[0])
  else:
    if nn == 'cnn_simple':
      pad = 'VALID'
      # Input and filter size match -> filter is applied at just one location.

    else:
      pad = 'SAME'
      # Input is padded on right/bottom to form 3x3 input

    layer_sizes = [(1, 2, 2, 1), {
        'n_h': 2,
        'n_w': 2,
        'n_cout': 2,
        'padding': pad,
        'stride': 1,
        'n_cin': 1
    }, 3]
    inp_shape = layer_sizes[0]
    params = make_cnn_params(layer_sizes, key)

  bounds = utils.boundprop(
      params,

# 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
import jax
import jax.numpy as jnp
import jax.random as random
import numpy as np
from jax_verify.extensions.sdp_verify import utils

def make_toy_verif_instance(seed=None, label=None, target_label=None, nn='mlp'):
  """Mainly used for unit testing."""
  key = jax.random.PRNGKey(0) if seed is None else jax.random.PRNGKey(seed)
  if nn == 'mlp':
    layer_sizes = '5, 5, 5'
    layer_sizes = np.fromstring(layer_sizes, dtype=int, sep=',')
    params = make_mlp_params(layer_sizes, key)
    inp_shape = (1, layer_sizes[0])
  else:
    if nn == 'cnn_simple':
      pad = 'VALID'
      # Input and filter size match -> filter is applied at just one location.

    else:
      pad = 'SAME'
      # Input is padded on right/bottom to form 3x3 input

    layer_sizes = [(1, 2, 2, 1), {
        'n_h': 2,
        'n_w': 2,
        'n_cout': 2,
        'padding': pad,
        'stride': 1,
        'n_cin': 1
    }, 3]
    inp_shape = layer_sizes[0]
    params = make_cnn_params(layer_sizes, key)

  bounds = utils.boundprop(
      params,

      utils.IntBound(lb=np.zeros(inp_shape),