# Path: disentanglement_lib/evaluation/metrics/utils.py
# def generate_batch_factor_code(ground_truth_data, representation_function,
#                                num_points, random_state, batch_size):
# def split_train_test(observations, train_percentage):
# def obtain_representation(observations, representation_function, batch_size):
# def discrete_mutual_info(mus, ys):
# def discrete_entropy(ys):
# def make_discretizer(target, num_bins=gin.REQUIRED,
#                      discretizer_fn=gin.REQUIRED):
# def _histogram_discretize(target, num_bins=gin.REQUIRED):
# def normalize_data(data, mean=None, stddev=None):
# def make_predictor_fn(predictor_fn=gin.REQUIRED):
# def logistic_regression_cv():
# def gradient_boosting_classifier():


from disentanglement_lib.evaluation.metrics import utils
from six.moves import range
import numpy as np
import gin.tf
    blacklist=["ground_truth_data", "representation_function", "random_state",
               "artifact_dir"])
def compute_downstream_task(ground_truth_data,
                            representation_function,
                            random_state,
                            artifact_dir=None,
                            num_train=gin.REQUIRED,
                            num_test=gin.REQUIRED,
                            batch_size=16):
  """Computes loss of downstream task.

  Args:
    ground_truth_data: GroundTruthData to be sampled from.
    representation_function: Function that takes observations as input and
      outputs a dim_representation sized representation for each observation.
    random_state: Numpy random state used for randomness.
    artifact_dir: Optional path to directory where artifacts can be saved.
    num_train: Number of points used for training.
    num_test: Number of points used for testing.
    batch_size: Batch size for sampling.

  Returns:
    Dictionary with scores.
  """
  del artifact_dir
  scores = {}
  for train_size in num_train:
    mus_train, ys_train = utils.generate_batch_factor_code(
        ground_truth_data, representation_function, train_size, random_state,
        batch_size)

# Path: disentanglement_lib/evaluation/metrics/utils.py
# def generate_batch_factor_code(ground_truth_data, representation_function,
#                                num_points, random_state, batch_size):
# def split_train_test(observations, train_percentage):
# def obtain_representation(observations, representation_function, batch_size):
# def discrete_mutual_info(mus, ys):
# def discrete_entropy(ys):
# def make_discretizer(target, num_bins=gin.REQUIRED,
#                      discretizer_fn=gin.REQUIRED):
# def _histogram_discretize(target, num_bins=gin.REQUIRED):
# def normalize_data(data, mean=None, stddev=None):
# def make_predictor_fn(predictor_fn=gin.REQUIRED):
# def logistic_regression_cv():
# def gradient_boosting_classifier():


# Path: disentanglement_lib/evaluation/metrics/downstream_task.py
from disentanglement_lib.evaluation.metrics import utils
from six.moves import range
import numpy as np
import gin.tf

    blacklist=["ground_truth_data", "representation_function", "random_state",
               "artifact_dir"])
def compute_downstream_task(ground_truth_data,
                            representation_function,
                            random_state,
                            artifact_dir=None,
                            num_train=gin.REQUIRED,
                            num_test=gin.REQUIRED,
                            batch_size=16):
  """Computes loss of downstream task.

  Args:
    ground_truth_data: GroundTruthData to be sampled from.
    representation_function: Function that takes observations as input and
      outputs a dim_representation sized representation for each observation.
    random_state: Numpy random state used for randomness.
    artifact_dir: Optional path to directory where artifacts can be saved.
    num_train: Number of points used for training.
    num_test: Number of points used for testing.
    batch_size: Batch size for sampling.

  Returns:
    Dictionary with scores.
  """
  del artifact_dir
  scores = {}
  for train_size in num_train:
    mus_train, ys_train = utils.generate_batch_factor_code(
        ground_truth_data, representation_function, train_size, random_state,
        batch_size)

    mus_test, ys_test = utils.generate_batch_factor_code(