# 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.numpy as jnp
import jax_verify
import numpy as np
from absl import app
from absl import flags
from absl import logging
from jax_verify.extensions.sdp_verify import utils
CNN_PATH = 'models/mnist_wongsmall_eps_10_adv.pkl'
ALL_BOUNDPROP_METHODS = (
    jax_verify.interval_bound_propagation,
    jax_verify.forward_fastlin_bound_propagation,
    jax_verify.backward_fastlin_bound_propagation,
    jax_verify.ibpforwardfastlin_bound_propagation,
    jax_verify.forward_crown_bound_propagation,
    jax_verify.backward_crown_bound_propagation,
    jax_verify.crownibp_bound_propagation,
)

flags.DEFINE_string('model', 'mlp', 'mlp or cnn')
flags.DEFINE_string('boundprop_method', '',
                    'Any boundprop method, such as `interval_bound_propagation`'
                    ' `forward_fastlin_bound_propagation` or '
                    ' `crown_bound_propagation`.'
                    'Empty string defaults to IBP.')
FLAGS = flags.FLAGS


def load_model(model_name):
  """Load model parameters and prediction function."""
  # Choose appropriate prediction function
  if model_name == 'mlp':
    model_path = MLP_PATH
    def model_fn(params, inputs):
      inputs = np.reshape(inputs, (inputs.shape[0], -1))
      return utils.predict_mlp(params, inputs)
  elif model_name == 'cnn':
    model_path = CNN_PATH

# 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: examples/run_boundprop.py
import functools
import pickle
import jax.numpy as jnp
import jax_verify
import numpy as np
from absl import app
from absl import flags
from absl import logging
from jax_verify.extensions.sdp_verify import utils

CNN_PATH = 'models/mnist_wongsmall_eps_10_adv.pkl'
ALL_BOUNDPROP_METHODS = (
    jax_verify.interval_bound_propagation,
    jax_verify.forward_fastlin_bound_propagation,
    jax_verify.backward_fastlin_bound_propagation,
    jax_verify.ibpforwardfastlin_bound_propagation,
    jax_verify.forward_crown_bound_propagation,
    jax_verify.backward_crown_bound_propagation,
    jax_verify.crownibp_bound_propagation,
)

flags.DEFINE_string('model', 'mlp', 'mlp or cnn')
flags.DEFINE_string('boundprop_method', '',
                    'Any boundprop method, such as `interval_bound_propagation`'
                    ' `forward_fastlin_bound_propagation` or '
                    ' `crown_bound_propagation`.'
                    'Empty string defaults to IBP.')
FLAGS = flags.FLAGS


def load_model(model_name):
  """Load model parameters and prediction function."""
  # Choose appropriate prediction function
  if model_name == 'mlp':
    model_path = MLP_PATH
    def model_fn(params, inputs):
      inputs = np.reshape(inputs, (inputs.shape[0], -1))
      return utils.predict_mlp(params, inputs)
  elif model_name == 'cnn':
    model_path = CNN_PATH

    model_fn = utils.predict_cnn