import unittest
from unittest import mock

import logging
import numpy as np
import time

from ConfigSpace import Configuration, ConfigurationSpace
from ConfigSpace.hyperparameters import UniformIntegerHyperparameter

from smac.intensification.abstract_racer import RunInfoIntent
from smac.intensification.successive_halving import SuccessiveHalving, _SuccessiveHalving
from smac.runhistory.runhistory import RunHistory, RunInfo, RunValue
from smac.scenario.scenario import Scenario
from smac.stats.stats import Stats
from smac.tae import StatusType
from smac.tae.execute_func import ExecuteTAFuncDict
from smac.utils.io.traj_logging import TrajLogger

from .test_eval_utils import eval_challenger


def get_config_space():
    cs = ConfigurationSpace()
    cs.add_hyperparameter(UniformIntegerHyperparameter(name='a',
                                                       lower=0,
                                                       upper=100))
    cs.add_hyperparameter(UniformIntegerHyperparameter(name='b',
                                                       lower=0,
                                                       upper=100))
    return cs


def target_from_run_info(RunInfo):
    value_from_config = sum([a for a in RunInfo.config.get_dictionary().values()])
    return RunValue(
        cost=value_from_config,
        time=0.5,
        status=StatusType.SUCCESS,
        starttime=time.time(),
        endtime=time.time() + 1,
        additional_info={}
    )


class TestSuccessiveHalving(unittest.TestCase):

    def setUp(self):
        unittest.TestCase.setUp(self)

        self.rh = RunHistory()
        self.cs = get_config_space()
        self.config1 = Configuration(self.cs,
                                     values={'a': 7, 'b': 11})
        self.config2 = Configuration(self.cs,
                                     values={'a': 13, 'b': 17})
        self.config3 = Configuration(self.cs,
                                     values={'a': 0, 'b': 7})
        self.config4 = Configuration(self.cs,
                                     values={'a': 29, 'b': 31})

        self.scen = Scenario({"cutoff_time": 2, 'cs': self.cs,
                              "run_obj": 'runtime',
                              "output_dir": ''})
        self.stats = Stats(scenario=self.scen)
        self.stats.start_timing()

        # Create the base object
        self.SH = SuccessiveHalving(
            stats=self.stats,
            traj_logger=TrajLogger(output_dir=None, stats=self.stats),
            rng=np.random.RandomState(12345),
            deterministic=False,
            run_obj_time=False,
            instances=[1, 2, 3, 4, 5],
            n_seeds=2,
            initial_budget=2,
            max_budget=5,
            eta=2,
        )

    def test_initialization(self):
        """Makes sure that a proper _SH is created"""

        # We initialize the SH with zero intensifier_instances
        self.assertEqual(len(self.SH.intensifier_instances), 0)

        # Add an instance to check the _SH initialization
        self.assertTrue(self.SH._add_new_instance(num_workers=1))

        # Parameters properly passed to _SH
        self.assertEqual(len(self.SH.intensifier_instances[0].inst_seed_pairs), 10)
        self.assertEqual(self.SH.intensifier_instances[0].initial_budget, 2)
        self.assertEqual(self.SH.intensifier_instances[0].max_budget, 5)

    def test_process_results_via_sourceid(self):
        """Makes sure source id is honored when deciding
        which _SH will consume the result/run_info"""
        # Mock the _SH so we can make sure the correct item is passed
        for i in range(10):
            self.SH.intensifier_instances[i] = mock.Mock()

        # randomly create run_infos and push into SH. Then we will make
        # sure they got properly allocated
        for i in np.random.choice(list(range(10)), 30):
            run_info = RunInfo(
                config=self.config1,
                instance=0,
                instance_specific="0",
                cutoff=None,
                seed=0,
                capped=False,
                budget=0.0,
                source_id=i,
            )

            # make sure results aren't messed up via magic variable
            # That is we check only the proper _SH has this
            magic = time.time()

            result = RunValue(
                cost=1,
                time=0.5,
                status=StatusType.SUCCESS,
                starttime=1,
                endtime=2,
                additional_info=magic
            )
            self.SH.process_results(
                run_info=run_info,
                incumbent=None,
                run_history=self.rh,
                time_bound=None,
                result=result,
                log_traj=False
            )

            # Check the call arguments of each sh instance and make sure
            # it is the correct one

            # First the expected one
            self.assertEqual(
                self.SH.intensifier_instances[i].process_results.call_args[1]['run_info'], run_info)
            self.assertEqual(
                self.SH.intensifier_instances[i].process_results.call_args[1]['result'], result)
            all_other_run_infos, all_other_results = [], []
            for j in range(len(self.SH.intensifier_instances)):
                # Skip the expected _SH
                if i == j:
                    continue
                if self.SH.intensifier_instances[j].process_results.call_args is None:
                    all_other_run_infos.append(None)
                else:
                    all_other_run_infos.append(
                        self.SH.intensifier_instances[j].process_results.call_args[1]['run_info'])
                    all_other_results.append(
                        self.SH.intensifier_instances[j].process_results.call_args[1]['result'])
            self.assertNotIn(run_info, all_other_run_infos)
            self.assertNotIn(result, all_other_results)

    def test_get_next_run_single_SH(self):
        """Makes sure that a single _SH returns a valid config"""

        challengers = [self.config1, self.config2, self.config3, self.config4]
        for i in range(30):
            intent, run_info = self.SH.get_next_run(
                challengers=challengers,
                incumbent=None, chooser=None, run_history=self.rh,
                num_workers=1,
            )

            # Regenerate challenger list
            challengers = [c for c in challengers if c != run_info.config]

            if intent == RunInfoIntent.WAIT:
                break

            # Add the config to self.rh in order to make SH aware that this
            # config/instance was launched
            self.rh.add(
                config=run_info.config,
                cost=10,
                time=0.0,
                status=StatusType.RUNNING,
                instance_id=run_info.instance,
                seed=run_info.seed,
                budget=run_info.budget,
            )

        # We should not create more _SH intensifier_instances
        self.assertEqual(len(self.SH.intensifier_instances), 1)

        # We are running with:
        # 'all_budgets': array([2.5, 5. ]) -> 2 intensifier_instances per config top
        # 'n_configs_in_stage': [2.0, 1.0],
        # This means we run int(2.5) + 2.0 = 4 runs before waiting
        self.assertEqual(i, 4)

    def test_get_next_run_dual_SH(self):
        """Makes sure that two  _SH can properly coexist and tag
        run_info properly"""

        # Everything here will be tested with a single _SH
        challengers = [self.config1, self.config2, self.config3, self.config4]
        for i in range(30):
            intent, run_info = self.SH.get_next_run(
                challengers=challengers,
                incumbent=None, chooser=None, run_history=self.rh,
                num_workers=2,
            )

            # Regenerate challenger list
            challengers = [c for c in challengers if c != run_info.config]

            # Add the config to self.rh in order to make SH aware that this
            # config/instance was launched
            if intent == RunInfoIntent.WAIT:
                break
            self.rh.add(
                config=run_info.config,
                cost=10,
                time=0.0,
                status=StatusType.RUNNING,
                instance_id=run_info.instance,
                seed=run_info.seed,
                budget=run_info.budget,
            )

        # We create a second sh intensifier_instances as after 4 runs, the _SH
        # number zero needs to wait
        self.assertEqual(len(self.SH.intensifier_instances), 2)

        # We are running with:
        # 'all_budgets': array([2.5, 5. ]) -> 2 intensifier_instances per config top
        # 'n_configs_in_stage': [2.0, 1.0],
        # This means we run int(2.5) + 2.0 = 4 runs before waiting
        # But we have 2 successive halvers now!
        self.assertEqual(i, 8)

    def test_add_new_instance(self):
        """Test whether we can add a _SH and when we should not"""

        # By default we do not create a _SH
        # test adding the first instance!
        self.assertEqual(len(self.SH.intensifier_instances), 0)
        self.assertTrue(self.SH._add_new_instance(num_workers=1))
        self.assertEqual(len(self.SH.intensifier_instances), 1)
        self.assertIsInstance(self.SH.intensifier_instances[0], _SuccessiveHalving)
        # A second call should not add a new _SH
        self.assertFalse(self.SH._add_new_instance(num_workers=1))

        # We try with 2 _SH active

        # We effectively return true because we added a new _SH
        self.assertTrue(self.SH._add_new_instance(num_workers=2))

        self.assertEqual(len(self.SH.intensifier_instances), 2)
        self.assertIsInstance(self.SH.intensifier_instances[1], _SuccessiveHalving)

        # Trying to add a third one should return false
        self.assertFalse(self.SH._add_new_instance(num_workers=2))
        self.assertEqual(len(self.SH.intensifier_instances), 2)

    def _exhaust_run_and_get_incumbent(self, sh, rh, num_workers=2):
        """
        Runs all provided configs on all intensifier_instances and return the incumbent
        as a nice side effect runhistory/stats are properly filled
        """
        challengers = [self.config1, self.config2, self.config3, self.config4]
        incumbent = None
        for i in range(100):
            try:
                intent, run_info = sh.get_next_run(
                    challengers=challengers,
                    incumbent=None, chooser=None, run_history=rh,
                    num_workers=num_workers,
                )
            except ValueError as e:
                # Get configurations until you run out of them
                print(e)
                break

            # Regenerate challenger list
            challengers = [c for c in challengers if c != run_info.config]

            if intent == RunInfoIntent.WAIT:
                break

            result = target_from_run_info(run_info)
            rh.add(
                config=run_info.config,
                cost=result.cost,
                time=result.time,
                status=result.status,
                instance_id=run_info.instance,
                seed=run_info.seed,
                budget=run_info.budget,
            )
            incumbent, inc_perf = sh.process_results(
                run_info=run_info,
                incumbent=incumbent,
                run_history=rh,
                time_bound=100.0,
                result=result,
                log_traj=False,
            )
        return incumbent, inc_perf

    def test_parallel_same_as_serial_SH(self):
        """Makes sure we behave the same as a serial run at the end"""

        # Get the run_history for a _SH run:
        rh = RunHistory()
        stats = Stats(scenario=self.scen)
        stats.start_timing()
        _SH = _SuccessiveHalving(
            stats=stats,
            traj_logger=TrajLogger(output_dir=None, stats=stats),
            rng=np.random.RandomState(12345),
            deterministic=False,
            run_obj_time=False,
            instances=[1, 2, 3, 4, 5],
            n_seeds=2,
            initial_budget=2,
            max_budget=5,
            eta=2,
        )
        incumbent, inc_perf = self._exhaust_run_and_get_incumbent(_SH, rh)

        # Just to make sure nothing has changed from the _SH side to make
        # this check invalid:
        # We add config values, so config 3 with 0 and 7 should be the lesser cost
        self.assertEqual(incumbent, self.config3)
        self.assertEqual(inc_perf, 7.0)

        # Do the same for SH, but have multiple _SH in there
        # _add_new_instance returns true if it was able to add a new _SH
        # We call this method twice because we want 2 workers
        self.assertTrue(self.SH._add_new_instance(num_workers=2))
        self.assertTrue(self.SH._add_new_instance(num_workers=2))
        incumbent_psh, inc_perf_psh = self._exhaust_run_and_get_incumbent(self.SH, self.rh)
        self.assertEqual(incumbent, incumbent_psh)

        # This makes sure there is a single incumbent in SH
        self.assertEqual(inc_perf, inc_perf_psh)

        # We don't want to loose any configuration, and particularly
        # we want to make sure the values of _SH to SH match
        self.assertEqual(len(self.rh.data), len(rh.data))

        # We are comparing exhausted single vs parallel successive
        # halving runs. The number and type of configs should be the same
        # and is enforced as a dictionary key argument check. The number
        # of runs will be different ParallelSuccesiveHalving has 2 _SH intensifier_instances
        # yet we make sure that after exhaustion, the budgets a config was run
        # should match
        configs_sh_rh = {}
        for k, v in rh.data.items():
            config_sh = rh.ids_config[k.config_id]
            if config_sh not in configs_sh_rh:
                configs_sh_rh[config_sh] = []
            if v.cost not in configs_sh_rh[config_sh]:
                configs_sh_rh[config_sh].append(v.cost)
        configs_psh_rh = {}
        for k, v in self.rh.data.items():
            config_psh = self.rh.ids_config[k.config_id]
            if config_psh not in configs_psh_rh:
                configs_psh_rh[config_psh] = []
            if v.cost not in configs_psh_rh[config_psh]:
                configs_psh_rh[config_psh].append(v.cost)

        # If this dictionaries are equal it means we have all configs
        # and the values track the numbers and actual cost!
        self.assertDictEqual(configs_sh_rh, configs_psh_rh)


class Test_SuccessiveHalving(unittest.TestCase):

    def setUp(self):
        unittest.TestCase.setUp(self)

        self.rh = RunHistory()
        self.cs = get_config_space()
        self.config1 = Configuration(self.cs,
                                     values={'a': 0, 'b': 100})
        self.config2 = Configuration(self.cs,
                                     values={'a': 100, 'b': 0})
        self.config3 = Configuration(self.cs,
                                     values={'a': 100, 'b': 100})
        self.config4 = Configuration(self.cs,
                                     values={'a': 0, 'b': 0})

        self.scen = Scenario({"cutoff_time": 2, 'cs': self.cs,
                              "run_obj": 'runtime',
                              "output_dir": ''})
        self.stats = Stats(scenario=self.scen)
        self.stats.start_timing()

        self.logger = logging.getLogger(self.__module__ + "." + self.__class__.__name__)

    def test_init_1(self):
        """
            test parameter initializations for successive halving - instance as budget
        """
        intensifier = _SuccessiveHalving(
            stats=self.stats,
            traj_logger=TrajLogger(output_dir=None, stats=self.stats),
            rng=np.random.RandomState(12345), deterministic=False, run_obj_time=False,
            instances=[1, 2, 3], n_seeds=2, initial_budget=None, max_budget=None, eta=2)

        self.assertEqual(len(intensifier.inst_seed_pairs), 6)  # since instance-seed pairs
        self.assertEqual(len(intensifier.instances), 3)
        self.assertEqual(intensifier.initial_budget, 1)
        self.assertEqual(intensifier.max_budget, 6)
        self.assertListEqual(intensifier.n_configs_in_stage, [4.0, 2.0, 1.0])
        self.assertTrue(intensifier.instance_as_budget)
        self.assertTrue(intensifier.repeat_configs)

    def test_init_2(self):
        """
            test parameter initialiations for successive halving - real-valued budget
        """
        intensifier = _SuccessiveHalving(
            stats=self.stats,
            traj_logger=TrajLogger(output_dir=None, stats=self.stats),
            rng=np.random.RandomState(12345), deterministic=True, run_obj_time=False,
            instances=[1], initial_budget=1, max_budget=10, eta=2)

        self.assertEqual(len(intensifier.inst_seed_pairs), 1)  # since instance-seed pairs
        self.assertEqual(intensifier.initial_budget, 1)
        self.assertEqual(intensifier.max_budget, 10)
        self.assertListEqual(intensifier.n_configs_in_stage, [8.0, 4.0, 2.0, 1.0])
        self.assertListEqual(list(intensifier.all_budgets), [1.25, 2.5, 5., 10.])
        self.assertFalse(intensifier.instance_as_budget)
        self.assertFalse(intensifier.repeat_configs)

    def test_init_3(self):
        """
            test parameter initialiations for successive halving - real-valued budget, high initial budget
        """
        intensifier = _SuccessiveHalving(
            stats=self.stats,
            traj_logger=TrajLogger(output_dir=None, stats=self.stats),
            rng=np.random.RandomState(12345), deterministic=True, run_obj_time=False,
            instances=[1], initial_budget=9, max_budget=10, eta=2)

        self.assertEqual(len(intensifier.inst_seed_pairs), 1)  # since instance-seed pairs
        self.assertEqual(intensifier.initial_budget, 9)
        self.assertEqual(intensifier.max_budget, 10)
        self.assertListEqual(intensifier.n_configs_in_stage, [1.0])
        self.assertListEqual(list(intensifier.all_budgets), [10.])
        self.assertFalse(intensifier.instance_as_budget)
        self.assertFalse(intensifier.repeat_configs)

    def test_init_4(self):
        """
            test wrong parameter initializations for successive halving
        """

        # runtime as budget (no param provided)
        with self.assertRaisesRegex(ValueError,
                                    "requires parameters initial_budget and max_budget for intensification!"):
            _SuccessiveHalving(
                stats=self.stats,
                traj_logger=TrajLogger(output_dir=None, stats=self.stats),
                rng=np.random.RandomState(12345), deterministic=True, run_obj_time=False,
                cutoff=10, instances=[1])

        # eta < 1
        with self.assertRaisesRegex(ValueError, "eta must be greater than 1"):
            _SuccessiveHalving(
                stats=self.stats,
                traj_logger=TrajLogger(output_dir=None, stats=self.stats),
                rng=np.random.RandomState(12345), deterministic=True, run_obj_time=False,
                cutoff=10, instances=[1], eta=0)

        # max budget > instance-seed pairs
        with self.assertRaisesRegex(ValueError,
                                    "Max budget cannot be greater than the number of instance-seed pairs"):
            _SuccessiveHalving(
                stats=self.stats,
                traj_logger=TrajLogger(output_dir=None, stats=self.stats),
                rng=np.random.RandomState(12345), deterministic=True, run_obj_time=False,
                cutoff=10, instances=[1, 2, 3], initial_budget=1, max_budget=5, n_seeds=1)

    def test_top_k_1(self):
        """
            test _top_k() for configs with same instance-seed-budget keys
        """
        intensifier = _SuccessiveHalving(
            stats=self.stats, traj_logger=None,
            rng=np.random.RandomState(12345),
            instances=[1, 2], initial_budget=1)
        self.rh.add(config=self.config1, cost=1, time=1,
                    status=StatusType.SUCCESS, instance_id=1, seed=None,
                    additional_info=None)
        self.rh.add(config=self.config1, cost=1, time=1,
                    status=StatusType.SUCCESS, instance_id=2, seed=None,
                    additional_info=None)
        self.rh.add(config=self.config2, cost=2, time=2,
                    status=StatusType.SUCCESS, instance_id=1, seed=None,
                    additional_info=None)
        self.rh.add(config=self.config2, cost=2, time=2,
                    status=StatusType.SUCCESS, instance_id=2, seed=None,
                    additional_info=None)
        self.rh.add(config=self.config3, cost=3, time=3,
                    status=StatusType.SUCCESS, instance_id=1, seed=None,
                    additional_info=None)
        self.rh.add(config=self.config3, cost=3, time=3,
                    status=StatusType.SUCCESS, instance_id=2, seed=None,
                    additional_info=None)
        self.rh.add(config=self.config4, cost=0.5, time=0.5,
                    status=StatusType.SUCCESS, instance_id=1, seed=None,
                    additional_info=None)
        self.rh.add(config=self.config4, cost=0.5, time=0.5,
                    status=StatusType.SUCCESS, instance_id=2, seed=None,
                    additional_info=None)
        conf = intensifier._top_k(configs=[self.config1, self.config2, self.config3, self.config4],
                                  k=2, run_history=self.rh)

        # Check that config4 is also before config1 (as it has the lower cost)
        self.assertEqual(conf, [self.config4, self.config1])

    def test_top_k_2(self):
        """
            test _top_k() for configs with different instance-seed-budget keys
        """
        intensifier = _SuccessiveHalving(
            stats=self.stats, traj_logger=None,
            rng=np.random.RandomState(12345),
            instances=[1, 2], initial_budget=1)
        self.rh.add(config=self.config1, cost=1, time=1,
                    status=StatusType.SUCCESS, instance_id=1, seed=None,
                    additional_info=None)
        self.rh.add(config=self.config2, cost=10, time=10,
                    status=StatusType.SUCCESS, instance_id=2, seed=None,
                    additional_info=None)

        with self.assertRaisesRegex(ValueError, 'Cannot compare configs'):
            intensifier._top_k(configs=[self.config2, self.config1, self.config3],
                               k=1, run_history=self.rh)

    def test_top_k_3(self):
        """
            test _top_k() for not enough configs to generate for the next budget
        """
        intensifier = _SuccessiveHalving(
            stats=self.stats, traj_logger=None,
            rng=np.random.RandomState(12345),
            instances=[1], initial_budget=1)
        self.rh.add(config=self.config1, cost=1, time=1,
                    status=StatusType.SUCCESS, instance_id=1, seed=None,
                    additional_info=None)
        self.rh.add(config=self.config2, cost=1, time=1,
                    status=StatusType.CRASHED, instance_id=1, seed=None,
                    additional_info=None)
        configs = intensifier._top_k(configs=[self.config1], k=2, run_history=self.rh)

        # top_k should return whatever configuration is possible
        self.assertEqual(configs, [self.config1])

    def test_top_k_4(self):
        """
            test _top_k() for not enough configs to generate for the next budget
        """
        intensifier = _SuccessiveHalving(
            stats=self.stats, traj_logger=None, run_obj_time=False,
            rng=np.random.RandomState(12345), eta=2, num_initial_challengers=4,
            instances=[1], initial_budget=1, max_budget=10)
        intensifier._update_stage(self.rh)
        self.rh.add(config=self.config1, cost=1, time=1,
                    status=StatusType.SUCCESS, instance_id=1, seed=None, budget=1,
                    additional_info=None)
        self.rh.add(config=self.config2, cost=1, time=1,
                    status=StatusType.DONOTADVANCE, instance_id=1, seed=None, budget=1,
                    additional_info=None)
        self.rh.add(config=self.config3, cost=1, time=1,
                    status=StatusType.DONOTADVANCE, instance_id=1, seed=None, budget=1,
                    additional_info=None)
        self.rh.add(config=self.config4, cost=1, time=1,
                    status=StatusType.DONOTADVANCE, instance_id=1, seed=None, budget=1,
                    additional_info=None)
        intensifier.success_challengers.add(self.config1)
        intensifier.fail_challengers.add(self.config2)
        intensifier.fail_challengers.add(self.config3)
        intensifier.fail_challengers.add(self.config4)
        intensifier._update_stage(self.rh)
        self.assertEqual(intensifier.fail_chal_offset, 1)  # we miss one challenger for this round
        configs = intensifier._top_k(configs=[self.config1], k=2, run_history=self.rh)
        self.assertEqual(configs, [self.config1])

        self.rh.add(config=self.config1, cost=1, time=1,
                    status=StatusType.DONOTADVANCE, instance_id=1, seed=None,
                    budget=intensifier.all_budgets[1], additional_info=None)
        intensifier.fail_challengers.add(self.config2)
        intensifier._update_stage(self.rh)
        self.assertEqual(intensifier.stage, 0)  # going back, since there are not enough to advance

    def test_get_next_run_1(self):
        """
            test get_next_run for a presently running configuration
        """
        def target(x):
            return 1

        taf = ExecuteTAFuncDict(ta=target, stats=self.stats, run_obj='quality')

        taf.runhistory = self.rh

        intensifier = _SuccessiveHalving(
            stats=self.stats, traj_logger=None,
            rng=np.random.RandomState(12345), deterministic=True, run_obj_time=False,
            cutoff=1, instances=[1, 2], initial_budget=1, max_budget=2, eta=2)

        # next challenger from a list
        intent, run_info = intensifier.get_next_run(
            challengers=[self.config1],
            chooser=None,
            run_history=self.rh,
            incumbent=None,
        )
        self.assertEqual(run_info.config, self.config1)
        self.assertTrue(intensifier.new_challenger)

        # until evaluated, does not pick new challenger
        intent, run_info = intensifier.get_next_run(
            challengers=[self.config2],
            chooser=None,
            run_history=self.rh,
            incumbent=None,
        )
        self.assertEqual(run_info.config, self.config1)
        self.assertEqual(intensifier.running_challenger, run_info.config)
        self.assertFalse(intensifier.new_challenger)

        # evaluating configuration
        self.assertIsNotNone(run_info.config)
        result = eval_challenger(run_info, taf, self.stats, self.rh)
        inc, inc_value = intensifier.process_results(
            run_info=run_info,
            incumbent=None,
            run_history=self.rh,
            time_bound=np.inf,
            result=result,
            log_traj=False,
        )
        intent, run_info = intensifier.get_next_run(
            challengers=[self.config2],
            chooser=None,
            incumbent=inc,
            run_history=self.rh
        )
        self.assertEqual(run_info.config, self.config2)
        self.assertEqual(len(intensifier.success_challengers), 1)
        self.assertTrue(intensifier.new_challenger)

    def test_get_next_run_2(self):
        """
            test get_next_run for higher stages of SH iteration
        """
        intensifier = _SuccessiveHalving(
            stats=self.stats, traj_logger=None,
            rng=np.random.RandomState(12345), deterministic=True, run_obj_time=False,
            cutoff=1, instances=[1], initial_budget=1, max_budget=2, eta=2)

        intensifier._update_stage(run_history=None)
        intensifier.stage += 1
        intensifier.configs_to_run = [self.config1]

        # next challenger should come from configs to run
        intent, run_info = intensifier.get_next_run(
            challengers=None,
            chooser=None,
            run_history=self.rh,
            incumbent=None,
        )
        self.assertEqual(run_info.config, self.config1)
        self.assertEqual(len(intensifier.configs_to_run), 0)
        self.assertFalse(intensifier.new_challenger)

    def test_update_stage(self):
        """
            test update_stage - initializations for all tracking variables
        """
        intensifier = _SuccessiveHalving(
            stats=self.stats, traj_logger=None,
            rng=np.random.RandomState(12345), deterministic=True, run_obj_time=False,
            cutoff=1, instances=[1], initial_budget=1, max_budget=2, eta=2)

        # first stage update
        intensifier._update_stage(run_history=None)

        self.assertEqual(intensifier.stage, 0)
        self.assertEqual(intensifier.sh_iters, 0)
        self.assertEqual(intensifier.running_challenger, None)
        self.assertEqual(intensifier.success_challengers, set())

        # higher stages
        self.rh.add(self.config1, 1, 1, StatusType.SUCCESS)
        self.rh.add(self.config2, 2, 2, StatusType.SUCCESS)
        intensifier.success_challengers = {self.config1, self.config2}
        intensifier._update_stage(run_history=self.rh)

        self.assertEqual(intensifier.stage, 1)
        self.assertEqual(intensifier.sh_iters, 0)
        self.assertEqual(intensifier.configs_to_run, [self.config1])

        # next iteration
        intensifier.success_challengers = {self.config1}
        intensifier._update_stage(run_history=self.rh)

        self.assertEqual(intensifier.stage, 0)
        self.assertEqual(intensifier.sh_iters, 1)
        self.assertIsInstance(intensifier.configs_to_run, list)
        self.assertEqual(len(intensifier.configs_to_run), 0)

    @unittest.mock.patch.object(_SuccessiveHalving, '_top_k')
    def test_update_stage_2(self, top_k_mock):
        """
            test update_stage - everything good is in state do not advance
        """

        intensifier = _SuccessiveHalving(
            stats=self.stats, traj_logger=None,
            rng=np.random.RandomState(12345), deterministic=True, run_obj_time=False,
            cutoff=1, initial_budget=1, max_budget=4, eta=2, instances=None)

        # update variables
        intensifier._update_stage(run_history=None)

        intensifier.success_challengers.add(self.config1)
        intensifier.success_challengers.add(self.config2)
        intensifier.do_not_advance_challengers.add(self.config3)
        intensifier.do_not_advance_challengers.add(self.config4)

        top_k_mock.return_value = [self.config1, self.config3]

        # Test that we update the stage as there is one configuration advanced to the next budget
        self.assertEqual(intensifier.stage, 0)
        intensifier._update_stage(run_history=None)
        self.assertEqual(intensifier.stage, 1)
        self.assertEqual(intensifier.configs_to_run, [self.config1])
        self.assertEqual(intensifier.fail_chal_offset, 1)
        self.assertEqual(len(intensifier.success_challengers), 0)
        self.assertEqual(len(intensifier.do_not_advance_challengers), 0)

        intensifier = _SuccessiveHalving(
            stats=self.stats, traj_logger=None,
            rng=np.random.RandomState(12345), deterministic=True, run_obj_time=False,
            cutoff=1, initial_budget=1, max_budget=4, eta=2, instances=None)

        # update variables
        intensifier._update_stage(run_history=None)

        intensifier.success_challengers.add(self.config1)
        intensifier.success_challengers.add(self.config2)
        intensifier.do_not_advance_challengers.add(self.config3)
        intensifier.do_not_advance_challengers.add(self.config4)

        top_k_mock.return_value = [self.config3, self.config4]

        # Test that we update the stage as there is no configuration advanced to the next budget
        self.assertEqual(intensifier.stage, 0)
        intensifier._update_stage(run_history=None)
        self.assertEqual(intensifier.stage, 0)
        self.assertEqual(intensifier.configs_to_run, [])
        self.assertEqual(intensifier.fail_chal_offset, 0)
        self.assertEqual(len(intensifier.success_challengers), 0)
        self.assertEqual(len(intensifier.do_not_advance_challengers), 0)

        top_k_mock.return_value = []

    def test_eval_challenger_1(self):
        """
           test eval_challenger with quality objective & real-valued budget
        """

        def target(x: Configuration, instance: str, seed: int, budget: float):
            return 0.1 * budget

        taf = ExecuteTAFuncDict(ta=target, stats=self.stats, run_obj='quality')
        taf.runhistory = self.rh

        intensifier = _SuccessiveHalving(
            stats=self.stats,
            traj_logger=TrajLogger(output_dir=None, stats=self.stats),
            rng=np.random.RandomState(12345), deterministic=True, run_obj_time=False,
            cutoff=1, instances=[None], initial_budget=0.25, max_budget=0.5, eta=2)
        intensifier._update_stage(run_history=None)

        self.rh.add(config=self.config1, cost=1, time=1, status=StatusType.SUCCESS,
                    seed=0, budget=0.5)
        self.rh.add(config=self.config2, cost=1, time=1, status=StatusType.SUCCESS,
                    seed=0, budget=0.25)
        self.rh.add(config=self.config3, cost=2, time=1, status=StatusType.SUCCESS,
                    seed=0, budget=0.25)

        intensifier.success_challengers = {self.config2, self.config3}
        intensifier._update_stage(run_history=self.rh)

        intent, run_info = intensifier.get_next_run(
            challengers=[self.config1],
            chooser=None,
            incumbent=self.config1,
            run_history=self.rh
        )
        result = eval_challenger(run_info, taf, self.stats, self.rh)
        inc, inc_value = intensifier.process_results(
            run_info=run_info,
            incumbent=self.config1,
            run_history=self.rh,
            time_bound=np.inf,
            result=result,
        )

        self.assertEqual(inc, self.config2)
        self.assertEqual(inc_value, 0.05)
        self.assertEqual(list(self.rh.data.keys())[-1][0], self.rh.config_ids[self.config2])
        self.assertEqual(self.stats.inc_changed, 1)

    def test_eval_challenger_2(self):
        """
           test eval_challenger with runtime objective and adaptive capping
        """

        def target(x: Configuration, instance: str):
            if x['a'] == 100 or instance == 2:
                time.sleep(1.5)
            return (x['a'] + 1) / 1000.

        taf = ExecuteTAFuncDict(ta=target, stats=self.stats, run_obj="runtime")
        taf.runhistory = self.rh

        intensifier = _SuccessiveHalving(
            stats=self.stats,
            traj_logger=TrajLogger(output_dir=None, stats=self.stats),
            rng=np.random.RandomState(12345), deterministic=True, cutoff=1,
            instances=[1, 2], initial_budget=1, max_budget=2, eta=2, instance_order=None)

        # config1 should be executed successfully and selected as incumbent
        intent, run_info = intensifier.get_next_run(
            challengers=[self.config1],
            chooser=None,
            incumbent=None,
            run_history=self.rh
        )
        result = eval_challenger(run_info, taf, self.stats, self.rh)
        inc, inc_value = intensifier.process_results(
            run_info=run_info,
            incumbent=None,
            run_history=self.rh,
            time_bound=np.inf,
            result=result,
        )
        self.assertEqual(run_info.config, self.config1)
        self.assertEqual(self.stats.inc_changed, 1)

        # config2 should be capped and config1 should still be the incumbent

        intent, run_info = intensifier.get_next_run(
            challengers=[self.config2],
            chooser=None,
            incumbent=inc,
            run_history=self.rh
        )
        result = eval_challenger(run_info, taf, self.stats, self.rh)
        inc, inc_value = intensifier.process_results(
            run_info=run_info,
            incumbent=inc,
            run_history=self.rh,
            time_bound=np.inf,
            result=result,
        )
        self.assertEqual(inc, self.config1)
        self.assertEqual(self.stats.inc_changed, 1)
        self.assertEqual(list(self.rh.data.values())[1][2], StatusType.CAPPED)

        # config1 is selected for the next stage and allowed to timeout since this is the 1st run for this instance
        intent, run_info = intensifier.get_next_run(
            challengers=[],
            chooser=None,
            incumbent=inc,
            run_history=self.rh
        )
        result = eval_challenger(run_info, taf, self.stats, self.rh)
        inc, inc_value = intensifier.process_results(
            run_info=run_info,
            incumbent=inc,
            run_history=self.rh,
            time_bound=np.inf,
            result=result,
        )
        self.assertEqual(inc, self.config1)
        self.assertEqual(list(self.rh.data.values())[2][2], StatusType.TIMEOUT)

    @mock.patch.object(_SuccessiveHalving, '_top_k')
    def test_eval_challenger_capping(self, patch):
        """
            test eval_challenger with adaptive capping and all configurations capped/crashed
        """

        def target(x):
            if x['b'] == 100:
                time.sleep(1.5)
            if x['a'] == 100:
                raise ValueError('You shall not pass')
            return (x['a'] + 1) / 1000.

        taf = ExecuteTAFuncDict(ta=target, stats=self.stats, run_obj="runtime",
                                abort_on_first_run_crash=False)
        taf.runhistory = self.rh

        intensifier = _SuccessiveHalving(
            stats=self.stats,
            traj_logger=TrajLogger(output_dir=None, stats=self.stats),
            rng=np.random.RandomState(12345), deterministic=True, cutoff=1,
            instances=[1, 2], initial_budget=1, max_budget=2, eta=2, instance_order=None)

        for i in range(2):
            self.rh.add(config=self.config1, cost=.001, time=0.001,
                        status=StatusType.SUCCESS, instance_id=i + 1, seed=0,
                        additional_info=None)

        # provide configurations
        intent, run_info = intensifier.get_next_run(
            challengers=[self.config2],
            chooser=None,
            incumbent=self.config1,
            run_history=self.rh
        )
        result = eval_challenger(run_info, taf, self.stats, self.rh)
        inc, inc_value = intensifier.process_results(
            run_info=run_info,
            incumbent=self.config1,
            run_history=self.rh,
            time_bound=np.inf,
            result=result,
        )
        self.assertEqual(inc, self.config1)
        self.assertEqual(list(self.rh.data.values())[2][2], StatusType.CRASHED)
        self.assertEqual(len(intensifier.success_challengers), 0)

        # provide configurations
        intent, run_info = intensifier.get_next_run(
            challengers=[self.config3],
            chooser=None,
            incumbent=self.config1,
            run_history=self.rh
        )
        result = eval_challenger(run_info, taf, self.stats, self.rh)
        inc, inc_value = intensifier.process_results(
            run_info=run_info,
            incumbent=self.config1,
            run_history=self.rh,
            time_bound=np.inf,
            result=result,
        )
        self.assertEqual(inc, self.config1)
        self.assertEqual(self.stats.inc_changed, 0)
        self.assertEqual(list(self.rh.data.values())[3][2], StatusType.CAPPED)
        self.assertEqual(len(intensifier.success_challengers), 0)
        # top_k() should not be called since all configs were capped
        self.assertFalse(patch.called)

        # now the SH iteration should begin a new iteration since all configs were capped!
        self.assertEqual(intensifier.sh_iters, 1)
        self.assertEqual(intensifier.stage, 0)

        # should raise an error as this is a new iteration but no configs were provided
        with self.assertRaisesRegex(ValueError, 'No configurations/chooser provided.'):
            intent, run_info = intensifier.get_next_run(
                challengers=None,
                chooser=None,
                incumbent=self.config1,
                run_history=self.rh
            )

    def test_eval_challenger_capping_2(self):
        """
            test eval_challenger for adaptive capping with all but one configuration capped
        """
        def target(x):
            if x['a'] + x['b'] > 0:
                time.sleep(1.5)
            return x['a']

        taf = ExecuteTAFuncDict(ta=target, stats=self.stats, run_obj="runtime")
        taf.runhistory = self.rh

        intensifier = _SuccessiveHalving(
            stats=self.stats,
            traj_logger=TrajLogger(output_dir=None, stats=self.stats),
            rng=np.random.RandomState(12345), deterministic=False, cutoff=1,
            instances=[1, 2], n_seeds=2, initial_budget=1, max_budget=4, eta=2, instance_order=None)

        # first configuration run
        intent, run_info = intensifier.get_next_run(
            challengers=[self.config4],
            chooser=None,
            incumbent=None,
            run_history=self.rh
        )
        result = eval_challenger(run_info, taf, self.stats, self.rh)
        inc, inc_value = intensifier.process_results(
            run_info=run_info,
            incumbent=None,
            run_history=self.rh,
            time_bound=np.inf,
            result=result,
        )
        self.assertEqual(inc, self.config4)

        # remaining 3 runs should be capped
        for i in [self.config1, self.config2, self.config3]:
            intent, run_info = intensifier.get_next_run(
                challengers=[i],
                chooser=None,
                incumbent=inc,
                run_history=self.rh
            )
            result = eval_challenger(run_info, taf, self.stats, self.rh)
            inc, inc_value = intensifier.process_results(
                run_info=run_info,
                incumbent=inc,
                run_history=self.rh,
                time_bound=np.inf,
                result=result,
            )

        self.assertEqual(inc, self.config4)
        self.assertEqual(list(self.rh.data.values())[1][2], StatusType.CAPPED)
        self.assertEqual(list(self.rh.data.values())[2][2], StatusType.CAPPED)
        self.assertEqual(list(self.rh.data.values())[3][2], StatusType.CAPPED)
        self.assertEqual(intensifier.stage, 1)
        self.assertEqual(intensifier.fail_chal_offset, 1)  # 2 configs expected, but 1 failure

        # run next stage - should run only 1 configuration since other 3 were capped
        # 1 runs for config1
        intent, run_info = intensifier.get_next_run(
            challengers=[],
            chooser=None,
            incumbent=inc,
            run_history=self.rh
        )
        self.assertEqual(run_info.config, self.config4)
        result = eval_challenger(run_info, taf, self.stats, self.rh)
        inc, inc_value = intensifier.process_results(
            run_info=run_info,
            incumbent=inc,
            run_history=self.rh,
            time_bound=np.inf,
            result=result,
        )
        self.assertEqual(intensifier.stage, 2)

        # run next stage with only config1
        # should go to next iteration since no more configurations left
        for _ in range(2):
            intent, run_info = intensifier.get_next_run(
                challengers=[],
                chooser=None,
                incumbent=inc,
                run_history=self.rh
            )
            self.assertEqual(run_info.config, self.config4)
            result = eval_challenger(run_info, taf, self.stats, self.rh)
            inc, inc_value = intensifier.process_results(
                run_info=run_info,
                incumbent=inc,
                run_history=self.rh,
                time_bound=np.inf,
                result=result,
            )

        self.assertEqual(inc, self.config4)
        self.assertEqual(len(self.rh.get_runs_for_config(self.config4, only_max_observed_budget=True)), 4)
        self.assertEqual(intensifier.sh_iters, 1)
        self.assertEqual(intensifier.stage, 0)

        with self.assertRaisesRegex(ValueError, 'No configurations/chooser provided.'):
            intensifier.get_next_run(
                challengers=[],
                chooser=None,
                incumbent=inc,
                run_history=self.rh
            )

    def test_eval_challenger_3(self):
        """
            test eval_challenger for updating to next stage and shuffling instance order every run
        """

        def target(x: Configuration, instance: str):
            return (x['a'] + int(instance)) / 1000.

        taf = ExecuteTAFuncDict(ta=target, stats=self.stats, run_obj="quality")
        taf.runhistory = self.rh

        intensifier = _SuccessiveHalving(
            stats=self.stats,
            traj_logger=TrajLogger(output_dir=None, stats=self.stats),
            rng=np.random.RandomState(12345), run_obj_time=False,
            instances=[0, 1], instance_order='shuffle', eta=2,
            deterministic=True, cutoff=1)

        intensifier._update_stage(run_history=self.rh)

        self.assertEqual(intensifier.inst_seed_pairs, [(0, 0), (1, 0)])

        intent, run_info = intensifier.get_next_run(
            challengers=[self.config1],
            chooser=None,
            incumbent=None,
            run_history=self.rh
        )
        result = eval_challenger(run_info, taf, self.stats, self.rh)
        inc, inc_value = intensifier.process_results(
            run_info=run_info,
            incumbent=None,
            run_history=self.rh,
            time_bound=np.inf,
            result=result,
        )

        self.assertEqual(inc, self.config1)
        self.assertEqual(len(self.rh.get_runs_for_config(self.config1, only_max_observed_budget=True)), 1)
        self.assertEqual(intensifier.configs_to_run, [])
        self.assertEqual(intensifier.stage, 0)

        intent, run_info = intensifier.get_next_run(
            challengers=[self.config2],
            chooser=None,
            incumbent=inc,
            run_history=self.rh
        )
        result = eval_challenger(run_info, taf, self.stats, self.rh)
        inc, inc_value = intensifier.process_results(
            run_info=run_info,
            incumbent=inc,
            run_history=self.rh,
            time_bound=np.inf,
            result=result,
        )

        self.assertEqual(inc, self.config1)
        self.assertEqual(len(self.rh.get_runs_for_config(self.config2, only_max_observed_budget=True)), 1)
        self.assertEqual(intensifier.configs_to_run, [self.config1])  # Incumbent is promoted to the next stage
        self.assertEqual(intensifier.stage, 1)

        intent, run_info = intensifier.get_next_run(
            challengers=[self.config3],
            chooser=None,
            incumbent=inc,
            run_history=self.rh
        )
        result = eval_challenger(run_info, taf, self.stats, self.rh)
        inc, inc_value = intensifier.process_results(
            run_info=run_info,
            incumbent=inc,
            run_history=self.rh,
            time_bound=np.inf,
            result=result,
        )

        self.assertEqual(inc, self.config1)

        self.assertEqual(len(self.rh.get_runs_for_config(self.config1, only_max_observed_budget=True)), 2)
        self.assertEqual(intensifier.sh_iters, 1)
        self.assertEqual(self.stats.inc_changed, 1)

        # For the 2nd SH iteration, we should still be able to run the old configurations again
        # since instance order is "shuffle"

        self.assertEqual(intensifier.inst_seed_pairs, [(1, 0), (0, 0)])

        intent, run_info = intensifier.get_next_run(
            challengers=[self.config2],
            chooser=None,
            incumbent=inc,
            run_history=self.rh
        )
        result = eval_challenger(run_info, taf, self.stats, self.rh)
        inc, inc_value = intensifier.process_results(
            run_info=run_info,
            incumbent=inc,
            run_history=self.rh,
            time_bound=np.inf,
            result=result,
        )

        self.assertEqual(run_info.config, self.config2)
        self.assertEqual(len(self.rh.get_runs_for_config(self.config2, only_max_observed_budget=True)), 2)

    def test_incumbent_selection_default(self):
        """
            test _compare_config for default incumbent selection design (highest budget so far)
        """
        intensifier = _SuccessiveHalving(
            stats=self.stats, traj_logger=None,
            rng=np.random.RandomState(12345), run_obj_time=False,
            instances=[1], initial_budget=1, max_budget=2, eta=2)
        intensifier.stage = 0

        # SH considers challenger as incumbent in first run in eval_challenger
        self.rh.add(config=self.config1, cost=1, time=1,
                    status=StatusType.SUCCESS, instance_id=1, seed=None,
                    additional_info=None, budget=1)
        inc = intensifier._compare_configs(challenger=self.config1, incumbent=self.config1,
                                           run_history=self.rh, log_traj=False)
        self.assertEqual(inc, self.config1)
        self.rh.add(config=self.config1, cost=1, time=1,
                    status=StatusType.SUCCESS, instance_id=1, seed=None,
                    additional_info=None, budget=2)
        inc = intensifier._compare_configs(challenger=self.config1, incumbent=inc, run_history=self.rh, log_traj=False)
        self.assertEqual(inc, self.config1)

        # Adding a worse configuration
        self.rh.add(config=self.config2, cost=2, time=2,
                    status=StatusType.SUCCESS, instance_id=1, seed=None,
                    additional_info=None, budget=1)
        inc = intensifier._compare_configs(challenger=self.config2, incumbent=inc, run_history=self.rh, log_traj=False)
        self.assertEqual(inc, self.config1)
        self.rh.add(config=self.config2, cost=2, time=2,
                    status=StatusType.SUCCESS, instance_id=1, seed=None,
                    additional_info=None, budget=2)
        inc = intensifier._compare_configs(challenger=self.config2, incumbent=inc, run_history=self.rh, log_traj=False)
        self.assertEqual(inc, self.config1)

        # Adding a better configuration, but the incumbent will only be changed on budget=2
        self.rh.add(config=self.config3, cost=0.5, time=3,
                    status=StatusType.SUCCESS, instance_id=1, seed=None,
                    additional_info=None, budget=1)
        inc = intensifier._compare_configs(challenger=self.config3, incumbent=inc, run_history=self.rh, log_traj=False)
        self.assertEqual(inc, self.config1)
        self.rh.add(config=self.config3, cost=0.5, time=3,
                    status=StatusType.SUCCESS, instance_id=1, seed=None,
                    additional_info=None, budget=2)
        inc = intensifier._compare_configs(challenger=self.config3, incumbent=inc, run_history=self.rh, log_traj=False)
        self.assertEqual(inc, self.config3)

        # Test that the state is only based on the runhistory
        intensifier = _SuccessiveHalving(
            stats=self.stats, traj_logger=None,
            rng=np.random.RandomState(12345),
            instances=[1], initial_budget=1)
        intensifier.stage = 0

        # Adding a better configuration, but the incumbent will only be changed on budget=2
        self.rh.add(config=self.config4, cost=0.1, time=3,
                    status=StatusType.SUCCESS, instance_id=1, seed=None,
                    additional_info=None, budget=1)
        inc = intensifier._compare_configs(challenger=self.config4, incumbent=inc, run_history=self.rh, log_traj=False)
        self.assertEqual(inc, self.config3)
        self.rh.add(config=self.config4, cost=0.1, time=3,
                    status=StatusType.SUCCESS, instance_id=1, seed=None,
                    additional_info=None, budget=2)
        inc = intensifier._compare_configs(challenger=self.config4, incumbent=inc, run_history=self.rh, log_traj=False)
        self.assertEqual(inc, self.config4)

    def test_incumbent_selection_designs(self):
        """
            test _compare_config with different incumbent selection designs
        """

        # select best on any budget
        intensifier = _SuccessiveHalving(
            stats=self.stats, traj_logger=None,
            rng=np.random.RandomState(12345), run_obj_time=False,
            instances=[1], initial_budget=1, max_budget=2, eta=2, incumbent_selection='any_budget')
        intensifier.stage = 0

        self.rh.add(config=self.config1, instance_id=1, seed=None, budget=1,
                    cost=0.5, time=1, status=StatusType.SUCCESS, additional_info=None)
        self.rh.add(config=self.config1, instance_id=1, seed=None, budget=2,
                    cost=10, time=1, status=StatusType.SUCCESS, additional_info=None)
        self.rh.add(config=self.config2, instance_id=1, seed=None, budget=2,
                    cost=5, time=1, status=StatusType.SUCCESS, additional_info=None)

        # incumbent should be config1, since it has the best performance in one of the budgets
        inc = intensifier._compare_configs(incumbent=self.config2, challenger=self.config1,
                                           run_history=self.rh, log_traj=False)
        self.assertEqual(self.config1, inc)
        # if config1 is incumbent already, it shouldn't change
        inc = intensifier._compare_configs(incumbent=self.config1, challenger=self.config2,
                                           run_history=self.rh, log_traj=False)
        self.assertEqual(self.config1, inc)

        # select best on highest budget only
        intensifier = _SuccessiveHalving(
            stats=self.stats, traj_logger=None,
            rng=np.random.RandomState(12345), run_obj_time=False,
            instances=[1], initial_budget=1, max_budget=4, eta=2, incumbent_selection='highest_budget')
        intensifier.stage = 0

        # incumbent should not change, since there is no run on the highest budget,
        # though config3 is run on a higher budget
        self.rh.add(config=self.config3, instance_id=1, seed=None, budget=2,
                    cost=0.5, time=1, status=StatusType.SUCCESS, additional_info=None)
        self.rh.add(config=self.config4, instance_id=1, seed=None, budget=1,
                    cost=5, time=1, status=StatusType.SUCCESS, additional_info=None)
        inc = intensifier._compare_configs(incumbent=self.config4, challenger=self.config3,
                                           run_history=self.rh, log_traj=False)
        self.assertEqual(self.config4, inc)
        self.assertEqual(self.stats.inc_changed, 0)

        # incumbent changes to config3 since that is run on the highest budget
        self.rh.add(config=self.config3, instance_id=1, seed=None, budget=4,
                    cost=10, time=1, status=StatusType.SUCCESS, additional_info=None)
        inc = intensifier._compare_configs(incumbent=self.config4, challenger=self.config3,
                                           run_history=self.rh, log_traj=False)
        self.assertEqual(self.config3, inc)

    def test_count_running_instances_for_challenger(self):
        """
        Makes sure we return the proper number of running configs
        """
        def target(x):
            return 1
        taf = ExecuteTAFuncDict(ta=target, stats=self.stats, run_obj='quality')

        taf.runhistory = self.rh
        # select best on any budget
        intensifier = _SuccessiveHalving(
            stats=self.stats, traj_logger=None,
            rng=np.random.RandomState(12345), run_obj_time=False,
            instances=list(range(20)), initial_budget=3, max_budget=10, eta=2)

        # Get and process the first instance pair of config 1
        intent, run_info = intensifier.get_next_run(
            challengers=[self.config1, self.config2, self.config3, self.config4],
            chooser=None,
            run_history=self.rh,
            incumbent=None,
        )

        # Add a check for empty run history
        self.assertEqual(len(self.rh.data), 0)
        self.assertEqual(intensifier._count_running_instances_for_challenger(self.rh), 0)

        result = eval_challenger(run_info, taf, self.stats, self.rh)
        inc, inc_value = intensifier.process_results(
            run_info=run_info,
            incumbent=self.config1,
            run_history=self.rh,
            time_bound=np.inf,
            result=result,
            log_traj=False,
        )
        self.assertEqual(intensifier._count_running_instances_for_challenger(self.rh), 0)

        # This will get us the second instance of config 1
        intent, run_info = intensifier.get_next_run(
            challengers=[self.config2, self.config3, self.config4],
            chooser=None,
            run_history=self.rh,
            incumbent=None,
        )

        # Mark this run_info object as running and make sure that the intensifier can
        # access it
        self.rh.add(config=run_info.config,
                    instance_id=run_info.instance,
                    seed=run_info.seed,
                    budget=run_info.budget,
                    cost=10,
                    time=1,
                    status=StatusType.RUNNING,
                    additional_info=None)
        self.assertEqual(intensifier._count_running_instances_for_challenger(self.rh), 1)

        # Now, when the intensifier is requested, to provide a new run,
        # it should provide a new instance, not the same one
        new_intent, new_run_info = intensifier.get_next_run(
            challengers=[self.config2, self.config3, self.config4],
            chooser=None,
            run_history=self.rh,
            incumbent=None,
        )
        self.assertEqual(new_run_info.config, run_info.config)
        self.assertNotEqual(new_run_info.instance, run_info.instance)

        # Add another running instance
        self.rh.add(config=new_run_info.config,
                    instance_id=new_run_info.instance,
                    seed=new_run_info.seed,
                    budget=new_run_info.budget,
                    cost=10,
                    time=1,
                    status=StatusType.RUNNING,
                    additional_info=None)
        self.assertEqual(intensifier._count_running_instances_for_challenger(self.rh), 2)

        # Get a new config to gain more confidence
        newer_intent, newer_run_info = intensifier.get_next_run(
            challengers=[self.config2, self.config3, self.config4],
            chooser=None,
            run_history=self.rh,
            incumbent=None,
        )
        self.assertNotIn(newer_run_info, [new_run_info, run_info])

    def test_launched_all_configs_for_current_stage(self):
        """
        This check makes sure we can identify when all the current runs
        (config/instance/seed) pairs for a given stage have been launched
        """
        def target(x):
            return 1
        taf = ExecuteTAFuncDict(ta=target, stats=self.stats, run_obj='quality')

        taf.runhistory = self.rh
        # select best on any budget
        intensifier = _SuccessiveHalving(
            stats=self.stats, traj_logger=None,
            rng=np.random.RandomState(12345), run_obj_time=False,
            instances=list(range(10)), initial_budget=2, max_budget=10, eta=2)

        # So there are 2 instances per config.
        # self.stage=0
        # self.n_configs_in_stage=[4.0, 2.0, 1.0]
        # self.all_budgets=[ 2.5  5.  10. ]
        total_configs_in_stage = 4
        instances_per_stage = 2

        # get all configs and add them to the dict
        run_tracker = {}
        challengers = [self.config1, self.config2, self.config3, self.config4]
        for i in range(total_configs_in_stage * instances_per_stage):
            intent, run_info = intensifier.get_next_run(
                challengers=challengers,
                chooser=None,
                run_history=self.rh,
                incumbent=None,
            )

            # All this runs are valid for this stage
            self.assertEqual(intent, RunInfoIntent.RUN)

            # Remove from the challengers, the launched configs
            challengers = [c for c in challengers if c != run_info.config]
            run_tracker[(run_info.config, run_info.instance, run_info.seed)] = False
            self.rh.add(config=run_info.config,
                        instance_id=run_info.instance,
                        seed=run_info.seed,
                        budget=run_info.budget,
                        cost=10,
                        time=1,
                        status=StatusType.RUNNING,
                        additional_info=None)

        # This will get us the second instance of config 1
        intent, run_info = intensifier.get_next_run(
            challengers=[self.config2, self.config3, self.config4],
            chooser=None,
            run_history=self.rh,
            incumbent=None,
        )

        # We have launched all runs, that are expected for this stage
        # not registered any, so for sure we have to wait
        # For all runs to be completed before moving to the next stage
        self.assertEqual(intent, RunInfoIntent.WAIT)

    def _exhaust_stage_execution(self, intensifier, taf, challengers, incumbent):
        """
        Exhaust configuration/instances seed and returns the
        run_info that were not launched.

        The idea with this procedure is to emulate the fact that some
        configurations will finish while others won't. We need to be
        robust against this scenario
        """
        pending_processing = []
        stage = 0 if not hasattr(intensifier, 'stage') else intensifier.stage
        curr_budget = intensifier.all_budgets[stage]
        prev_budget = int(intensifier.all_budgets[stage - 1]) if stage > 0 else 0
        if intensifier.instance_as_budget:
            total_runs = int(curr_budget - prev_budget) * int(
                intensifier.n_configs_in_stage[stage])
            toggle = np.random.choice([True, False], total_runs).tolist()
            while not np.any(toggle) or not np.any(np.invert(toggle)):
                # make sure we have both true and false!
                toggle = np.random.choice([True, False], total_runs).tolist()
        else:
            # If we directly use the budget, then there are no instances to wait
            # But we still want to mimic pending configurations. That is, we don't
            # advance to the next stage until all configurations are done for a given
            # budget.
            # Here if we do not launch a configuration because toggle was false, is
            # like this configuration never exited as there is only 1 instance in this
            # and if toggle is false, it is never run. So we cannot do a random toggle
            toggle = [False, True, False, True]

        while True:
            intent, run_info = intensifier.get_next_run(
                challengers=challengers,
                chooser=None,
                run_history=self.rh,
                incumbent=incumbent,
            )

            # Update the challengers
            challengers = [c for c in challengers if c != run_info.config]

            if intent == RunInfoIntent.WAIT:
                break

            # Add this configuration as running
            self.rh.add(config=run_info.config,
                        instance_id=run_info.instance,
                        seed=run_info.seed,
                        budget=run_info.budget,
                        cost=1000,
                        time=1000,
                        status=StatusType.RUNNING,
                        additional_info=None)

            if toggle.pop():
                result = eval_challenger(run_info, taf, self.stats, self.rh,
                                         force_update=True)
                incumbent, inc_value = intensifier.process_results(
                    run_info=run_info,
                    incumbent=incumbent,
                    run_history=self.rh,
                    time_bound=np.inf,
                    result=result,
                    log_traj=False,
                )
            else:
                pending_processing.append(run_info)

            # In case a iteration is done, break
            # This happens if the configs per stage is 1
            if intensifier.iteration_done:
                break

        return pending_processing, incumbent

    def test_iteration_done_only_when_all_configs_processed_instance_as_budget(self):
        """
        Makes sure that iteration done for a given stage is asserted ONLY after all
        configurations AND instances are completed, when instance is used as budget
        """
        def target(x):
            return 1
        taf = ExecuteTAFuncDict(ta=target, stats=self.stats, run_obj='quality')

        taf.runhistory = self.rh
        # select best on any budget
        intensifier = _SuccessiveHalving(
            stats=self.stats, traj_logger=None,
            rng=np.random.RandomState(12345), run_obj_time=False,
            deterministic=True,
            instances=list(range(5)), initial_budget=2, max_budget=5, eta=2)

        # we want to test instance as budget
        self.assertTrue(intensifier.instance_as_budget)

        # Run until there are no more configurations to be proposed
        # Skip running some configurations to emulate the fact that runs finish on different time
        # We need this because there was a bug where not all instances had finished, yet
        # the SH instance assumed all configurations finished
        challengers = [self.config1, self.config2, self.config3, self.config4]
        incumbent = None
        pending_processing, incumbent = self._exhaust_stage_execution(intensifier, taf,
                                                                      challengers, incumbent)

        # We have configurations pending, so iteration should NOT be done
        self.assertFalse(intensifier.iteration_done)

        # Make sure we launched all configurations we were meant to:
        # all_budgets=[2.5 5. ] n_configs_in_stage=[2.0, 1.0]
        # We need 2 configurations in the run history
        configurations = set([k.config_id for k, v in self.rh.data.items()])
        self.assertEqual(configurations, {1, 2})
        # We need int(2.5) instances in the run history per config
        config_inst_seed = set([k for k, v in self.rh.data.items()])
        self.assertEqual(len(config_inst_seed), 4)

        # Go to the last stage. Notice that iteration should not be done
        # as we are in stage 1 out of 2
        for run_info in pending_processing:
            result = eval_challenger(run_info, taf, self.stats, self.rh,
                                     force_update=True)
            incumbent, inc_value = intensifier.process_results(
                run_info=run_info,
                incumbent=self.config1,
                run_history=self.rh,
                time_bound=np.inf,
                result=result,
                log_traj=False,
            )
        self.assertFalse(intensifier.iteration_done)

        # we transition to stage 1, where the budget is 5
        self.assertEqual(intensifier.stage, 1)

        pending_processing, incumbent = self._exhaust_stage_execution(intensifier, taf,
                                                                      challengers, incumbent)

        # Because budget is 5, BUT we previously ran 2 instances in stage 0
        # we expect that the run history will be populated with 3 new instances for 1
        # config more 4 (stage0, 2 config on 2 instances) + 3 (stage1, 1 config 3 instances) = 7
        config_inst_seed = [k for k, v in self.rh.data.items()]
        self.assertEqual(len(config_inst_seed), 7)

        # All new runs should be on the same config
        self.assertEqual(len(set([c.config_id for c in config_inst_seed[4:]])), 1)
        # We need 3 new instance seed pairs
        self.assertEqual(len(set(config_inst_seed[4:])), 3)

        # because there are configurations pending, no iteration should be done
        self.assertFalse(intensifier.iteration_done)

        # Finish the pending runs
        for run_info in pending_processing:
            result = eval_challenger(run_info, taf, self.stats, self.rh,
                                     force_update=True)
            incumbent, inc_value = intensifier.process_results(
                run_info=run_info,
                incumbent=incumbent,
                run_history=self.rh,
                time_bound=np.inf,
                result=result,
                log_traj=False,
            )

        # Finally, all stages are done, so iteration should be done!!
        self.assertTrue(intensifier.iteration_done)

    def test_iteration_done_only_when_all_configs_processed_no_instance_as_budget(self):
        """
        Makes sure that iteration done for a given stage is asserted ONLY after all
        configurations AND instances are completed, when instance is NOT used as budget
        """
        def target(x):
            return 1
        taf = ExecuteTAFuncDict(ta=target, stats=self.stats, run_obj='quality')

        taf.runhistory = self.rh
        # select best on any budget
        intensifier = _SuccessiveHalving(
            stats=self.stats, traj_logger=None,
            rng=np.random.RandomState(12345), run_obj_time=False,
            deterministic=True,
            instances=[0], initial_budget=2, max_budget=5, eta=2)

        # we do not want to test instance as budget
        self.assertFalse(intensifier.instance_as_budget)

        # Run until there are no more configurations to be proposed
        # Skip running some configurations to emulate the fact that runs finish on different time
        # We need this because there was a bug where not all instances had finished, yet
        # the SH instance assumed all configurations finished
        challengers = [self.config1, self.config2, self.config3, self.config4]
        incumbent = None
        pending_processing, incumbent = self._exhaust_stage_execution(intensifier, taf,
                                                                      challengers, incumbent)

        # We have configurations pending, so iteration should NOT be done
        self.assertFalse(intensifier.iteration_done)

        # Make sure we launched all configurations we were meant to:
        # all_budgets=[2.5 5. ] n_configs_in_stage=[2.0, 1.0]
        # We need 2 configurations in the run history
        configurations = set([k.config_id for k, v in self.rh.data.items()])
        self.assertEqual(configurations, {1, 2})
        # There is only one instance always -- so we only have 2 configs for 1 instances each
        config_inst_seed = set([k for k, v in self.rh.data.items()])
        self.assertEqual(len(config_inst_seed), 2)

        # Go to the last stage. Notice that iteration should not be done
        # as we are in stage 1 out of 2
        for run_info in pending_processing:
            result = eval_challenger(run_info, taf, self.stats, self.rh,
                                     force_update=True)
            incumbent, inc_value = intensifier.process_results(
                run_info=run_info,
                incumbent=incumbent,
                run_history=self.rh,
                time_bound=np.inf,
                result=result,
                log_traj=False,
            )
        self.assertFalse(intensifier.iteration_done)

        # we transition to stage 1, where the budget is 5
        self.assertEqual(intensifier.stage, 1)

        pending_processing, incumbent = self._exhaust_stage_execution(intensifier, taf,
                                                                      challengers, incumbent)

        # The next configuration per stage is just one (n_configs_in_stage=[2.0, 1.0])
        # We ran previously 2 configs and with this new, we should have 3 total
        config_inst_seed = [k for k, v in self.rh.data.items()]
        self.assertEqual(len(config_inst_seed), 3)

        # Because it is only 1 config, the iteration is completed
        self.assertTrue(intensifier.iteration_done)

        # We make sure the proper budget got allocated on the whole run:
        # all_budgets=[2.5 5. ]
        # We ran 2 configs in small budget and 1 in full budget
        self.assertEqual(
            [k.budget for k in self.rh.data.keys()],
            [2.5, 2.5, 5]
        )


class Test__SuccessiveHalving(unittest.TestCase):

    def test_budget_initialization(self):
        """
            Check computing budgets (only for non-instance cases)
        """
        intensifier = _SuccessiveHalving(
            stats=None, traj_logger=None,
            rng=np.random.RandomState(12345), deterministic=True, run_obj_time=False,
            instances=None, initial_budget=1, max_budget=81, eta=3
        )
        self.assertListEqual([1, 3, 9, 27, 81], intensifier.all_budgets.tolist())
        self.assertListEqual([81, 27, 9, 3, 1], intensifier.n_configs_in_stage)

        to_check = [
            # minb, maxb, eta, n_configs_in_stage, all_budgets
            [1, 81, 3, [81, 27, 9, 3, 1], [1, 3, 9, 27, 81]],
            [1, 600, 3, [243, 81, 27, 9, 3, 1],
             [2.469135, 7.407407, 22.222222, 66.666666, 200, 600]],
            [1, 100, 10, [100, 10, 1], [1, 10, 100]],
            [0.001, 1, 3, [729, 243, 81, 27, 9, 3, 1],
             [0.001371, 0.004115, 0.012345, 0.037037, 0.111111, 0.333333, 1.0]],
            [1, 1000, 3, [729, 243, 81, 27, 9, 3, 1],
             [1.371742, 4.115226, 12.345679, 37.037037, 111.111111, 333.333333, 1000.0]],
            [0.001, 100, 10, [100000, 10000, 1000, 100, 10, 1],
             [0.001, 0.01, 0.1, 1.0, 10.0, 100.0]],
        ]

        for minb, maxb, eta, n_configs_in_stage, all_budgets in to_check:
            intensifier._init_sh_params(initial_budget=minb,
                                        max_budget=maxb,
                                        eta=eta,
                                        _all_budgets=None,
                                        _n_configs_in_stage=None,
                                        )
            comp_budgets = intensifier.all_budgets.tolist()
            comp_configs = intensifier.n_configs_in_stage

            self.assertEqual(len(all_budgets), len(comp_budgets))
            self.assertEqual(comp_budgets[-1], maxb)
            np.testing.assert_array_almost_equal(all_budgets, comp_budgets, decimal=5)

            self.assertEqual(comp_configs[-1], 1)
            self.assertEqual(len(n_configs_in_stage), len(comp_configs))
            np.testing.assert_array_almost_equal(n_configs_in_stage, comp_configs, decimal=5)


if __name__ == "__main__":
    unittest.main()
