# 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
import jax.numpy as jnp
from typing import Tuple
from jax_verify.extensions.sdp_verify import utils
from jax_verify.tests.sdp_verify import test_utils as sdp_test_utils
  """Sample uniformly some point respecting the bounds.

  Args:
    key: Random number generator
    bounds: Tuple containing [lower bound, upper bound]
    nb_points: How many points to sample.
    axis: Which dimension to add to correspond to the number of points.
  Returns:
    points: Points contained between the given bounds.
  """
  lb, ub = bounds
  act_shape = lb.shape
  to_sample_shape = act_shape[:axis] + (nb_points,) + act_shape[axis:]
  unif_samples = jax.random.uniform(key, to_sample_shape)

  broad_lb = jnp.expand_dims(lb, axis)
  broad_ub = jnp.expand_dims(ub, axis)

  bound_range = broad_ub - broad_lb
  return broad_lb + unif_samples * bound_range


def set_up_toy_problem(rng_key, batch_size, architecture):
  key_1, key_2 = jax.random.split(rng_key)
  params = sdp_test_utils.make_mlp_params(architecture, key_2)

  inputs = jax.random.uniform(key_1, (batch_size, architecture[0]))
  eps = 0.1
  lb = jnp.maximum(jnp.minimum(inputs - eps, 1.), 0.)
  ub = jnp.maximum(jnp.minimum(inputs + eps, 1.), 0.)

# 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/test_utils.py
import functools
import jax
import jax.numpy as jnp
from typing import Tuple
from jax_verify.extensions.sdp_verify import utils
from jax_verify.tests.sdp_verify import test_utils as sdp_test_utils

  """Sample uniformly some point respecting the bounds.

  Args:
    key: Random number generator
    bounds: Tuple containing [lower bound, upper bound]
    nb_points: How many points to sample.
    axis: Which dimension to add to correspond to the number of points.
  Returns:
    points: Points contained between the given bounds.
  """
  lb, ub = bounds
  act_shape = lb.shape
  to_sample_shape = act_shape[:axis] + (nb_points,) + act_shape[axis:]
  unif_samples = jax.random.uniform(key, to_sample_shape)

  broad_lb = jnp.expand_dims(lb, axis)
  broad_ub = jnp.expand_dims(ub, axis)

  bound_range = broad_ub - broad_lb
  return broad_lb + unif_samples * bound_range


def set_up_toy_problem(rng_key, batch_size, architecture):
  key_1, key_2 = jax.random.split(rng_key)
  params = sdp_test_utils.make_mlp_params(architecture, key_2)

  inputs = jax.random.uniform(key_1, (batch_size, architecture[0]))
  eps = 0.1
  lb = jnp.maximum(jnp.minimum(inputs - eps, 1.), 0.)
  ub = jnp.maximum(jnp.minimum(inputs + eps, 1.), 0.)

  fun = functools.partial(utils.predict_cnn, params)