# Path: disentanglement_lib/data/ground_truth/dummy_data.py
# class IdentityObservationsData(ground_truth_data.GroundTruthData):
# class DummyData(ground_truth_data.GroundTruthData):
#   def num_factors(self):
#   def observation_shape(self):
#   def factors_num_values(self):
#   def sample_factors(self, num, random_state):
#   def sample_observations_from_factors(self, factors, random_state):
#   def factor_names(self):
#   def num_factors(self):
#   def factors_num_values(self):
#   def observation_shape(self):
#   def sample_factors(self, num, random_state):
#   def sample_observations_from_factors(self, factors, random_state):
# 
# Path: disentanglement_lib/evaluation/metrics/irs.py
# def compute_irs(ground_truth_data,
#                 representation_function,
#                 random_state,
#                 artifact_dir=None,
#                 diff_quantile=0.99,
#                 num_train=gin.REQUIRED,
#                 batch_size=gin.REQUIRED):
# def _drop_constant_dims(ys):
# def scalable_disentanglement_score(gen_factors, latents, diff_quantile=0.99):


from absl.testing import absltest
from disentanglement_lib.data.ground_truth import dummy_data
from disentanglement_lib.evaluation.metrics import irs
import numpy as np
import gin.tf
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function


def _identity_discretizer(target, num_bins):
  del num_bins
  return target


class IrsTest(absltest.TestCase):

  def test_metric(self):
    gin.bind_parameter("discretizer.discretizer_fn", _identity_discretizer)
    gin.bind_parameter("discretizer.num_bins", 10)

    ground_truth_data = dummy_data.IdentityObservationsData()
    representation_function = lambda x: np.array(x, dtype=np.float64)
    random_state = np.random.RandomState(0)
    scores = irs.compute_irs(ground_truth_data, representation_function,
                             random_state, None, 0.99, 3000, 3000)
    self.assertBetween(scores["IRS"], 0.9, 1.0)

  def test_bad_metric(self):
    gin.bind_parameter("discretizer.discretizer_fn", _identity_discretizer)
    gin.bind_parameter("discretizer.num_bins", 10)

    ground_truth_data = dummy_data.IdentityObservationsData()
    representation_function = lambda x: np.zeros_like(x, dtype=np.float64)
    random_state = np.random.RandomState(0)

# Path: disentanglement_lib/data/ground_truth/dummy_data.py
# class IdentityObservationsData(ground_truth_data.GroundTruthData):
# class DummyData(ground_truth_data.GroundTruthData):
#   def num_factors(self):
#   def observation_shape(self):
#   def factors_num_values(self):
#   def sample_factors(self, num, random_state):
#   def sample_observations_from_factors(self, factors, random_state):
#   def factor_names(self):
#   def num_factors(self):
#   def factors_num_values(self):
#   def observation_shape(self):
#   def sample_factors(self, num, random_state):
#   def sample_observations_from_factors(self, factors, random_state):
# 
# Path: disentanglement_lib/evaluation/metrics/irs.py
# def compute_irs(ground_truth_data,
#                 representation_function,
#                 random_state,
#                 artifact_dir=None,
#                 diff_quantile=0.99,
#                 num_train=gin.REQUIRED,
#                 batch_size=gin.REQUIRED):
# def _drop_constant_dims(ys):
# def scalable_disentanglement_score(gen_factors, latents, diff_quantile=0.99):


# Path: disentanglement_lib/evaluation/metrics/irs_test.py
from absl.testing import absltest
from disentanglement_lib.data.ground_truth import dummy_data
from disentanglement_lib.evaluation.metrics import irs
import numpy as np
import gin.tf

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function


def _identity_discretizer(target, num_bins):
  del num_bins
  return target


class IrsTest(absltest.TestCase):

  def test_metric(self):
    gin.bind_parameter("discretizer.discretizer_fn", _identity_discretizer)
    gin.bind_parameter("discretizer.num_bins", 10)

    ground_truth_data = dummy_data.IdentityObservationsData()
    representation_function = lambda x: np.array(x, dtype=np.float64)
    random_state = np.random.RandomState(0)
    scores = irs.compute_irs(ground_truth_data, representation_function,
                             random_state, None, 0.99, 3000, 3000)
    self.assertBetween(scores["IRS"], 0.9, 1.0)

  def test_bad_metric(self):
    gin.bind_parameter("discretizer.discretizer_fn", _identity_discretizer)
    gin.bind_parameter("discretizer.num_bins", 10)

    ground_truth_data = dummy_data.IdentityObservationsData()
    representation_function = lambda x: np.zeros_like(x, dtype=np.float64)
    random_state = np.random.RandomState(0)

    scores = irs.compute_irs(ground_truth_data, representation_function,