# 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 collections
import jax.numpy as jnp
import numpy as np
from jax_verify.extensions.sdp_verify import utils
  W0 = W0_orig * jnp.reshape(jnp.sqrt(sigmasq_z), (-1, 1))
  b0 = b0_orig + jnp.dot(mu_z, W0_orig)

  # Now the network is just concatenation of modified decoder + classifier
  # This is also applying a Relu to decoder output, but that's fine
  combined_params = [(W0, b0)] + decoder_params[1:] + classifier_params
  return combined_params


def get_dual_shapes_and_types(bounds_elided):
  """Get shapes and types of dual vars."""
  dual_shapes = []
  dual_types = []
  layer_sizes = utils.layer_sizes_from_bounds(bounds_elided)
  for it in range(len(layer_sizes)):
    m = layer_sizes[it]
    m = [m] if isinstance(m, int) else list(m)
    if it < len(layer_sizes)-1:
      n = layer_sizes[it + 1]
      n = [n] if isinstance(n, int) else list(n)
      shapes = {
          'lam': [1] + n,
          'nu': [1] + m,
          'muminus': [1] + n,
          'muplus': [1] + n,
          'nu_quad': [], 'muminus2': [],
      }
      types = {
          'lam': utils.DualVarTypes.EQUALITY,
          'nu': utils.DualVarTypes.INEQUALITY,

# 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/extensions/sdp_verify/problem.py
import collections
import jax.numpy as jnp
import numpy as np
from jax_verify.extensions.sdp_verify import utils

  W0 = W0_orig * jnp.reshape(jnp.sqrt(sigmasq_z), (-1, 1))
  b0 = b0_orig + jnp.dot(mu_z, W0_orig)

  # Now the network is just concatenation of modified decoder + classifier
  # This is also applying a Relu to decoder output, but that's fine
  combined_params = [(W0, b0)] + decoder_params[1:] + classifier_params
  return combined_params


def get_dual_shapes_and_types(bounds_elided):
  """Get shapes and types of dual vars."""
  dual_shapes = []
  dual_types = []
  layer_sizes = utils.layer_sizes_from_bounds(bounds_elided)
  for it in range(len(layer_sizes)):
    m = layer_sizes[it]
    m = [m] if isinstance(m, int) else list(m)
    if it < len(layer_sizes)-1:
      n = layer_sizes[it + 1]
      n = [n] if isinstance(n, int) else list(n)
      shapes = {
          'lam': [1] + n,
          'nu': [1] + m,
          'muminus': [1] + n,
          'muplus': [1] + n,
          'nu_quad': [], 'muminus2': [],
      }
      types = {
          'lam': utils.DualVarTypes.EQUALITY,
          'nu': utils.DualVarTypes.INEQUALITY,

          'muminus': utils.DualVarTypes.INEQUALITY,