# 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 cvxpy as cp
import jax.numpy as jnp
import numpy as np
import scipy
from cvxpy.reductions.solvers.defines import INSTALLED_MI_SOLVERS as MIP_SOLVERS
from jax_verify.extensions.sdp_verify import utils
  # # Pre activations are within bounds
  for (i, pre) in enumerate(pre_activations):
    constraints += [pre <= bounds[i+1].ub_pre]
    constraints += [pre >= bounds[i+1].lb_pre]

  # Set objective over final post-activations
  obj_cp = cp.sum(cp.multiply(obj, post_activations[-1]))

  # Define and solve problem
  problem = cp.Problem(cp.Maximize(obj_cp), constraints)
  # NB: Originally, we used cp.ECOS_BB here, but cvxpy 1.1 drops support,
  # so we just use the first available MIP solver (which is dependent on user
  # installation).
  problem.solve(solver=MIP_SOLVERS[0])

  # Report results
  info = {
      'problem': problem,
      'post': post_activations,
      'pre': pre_activations,
  }
  return obj_cp.value + obj_const, info


def solve_lp_primal_elided(verif_instance):
  """Compute optimal attack loss for MLPs against LP relaxation."""
  assert verif_instance.type == utils.VerifInstanceTypes.MLP_ELIDED
  params, bounds, obj, obj_const = (
      verif_instance.params, verif_instance.bounds, verif_instance.obj,
      verif_instance.const)

# 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/cvxpy_verify.py
import cvxpy as cp
import jax.numpy as jnp
import numpy as np
import scipy
from cvxpy.reductions.solvers.defines import INSTALLED_MI_SOLVERS as MIP_SOLVERS
from jax_verify.extensions.sdp_verify import utils

  # # Pre activations are within bounds
  for (i, pre) in enumerate(pre_activations):
    constraints += [pre <= bounds[i+1].ub_pre]
    constraints += [pre >= bounds[i+1].lb_pre]

  # Set objective over final post-activations
  obj_cp = cp.sum(cp.multiply(obj, post_activations[-1]))

  # Define and solve problem
  problem = cp.Problem(cp.Maximize(obj_cp), constraints)
  # NB: Originally, we used cp.ECOS_BB here, but cvxpy 1.1 drops support,
  # so we just use the first available MIP solver (which is dependent on user
  # installation).
  problem.solve(solver=MIP_SOLVERS[0])

  # Report results
  info = {
      'problem': problem,
      'post': post_activations,
      'pre': pre_activations,
  }
  return obj_cp.value + obj_const, info


def solve_lp_primal_elided(verif_instance):
  """Compute optimal attack loss for MLPs against LP relaxation."""
  assert verif_instance.type == utils.VerifInstanceTypes.MLP_ELIDED
  params, bounds, obj, obj_const = (
      verif_instance.params, verif_instance.bounds, verif_instance.obj,
      verif_instance.const)

  layer_sizes = utils.mlp_layer_sizes(params)