text: # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
  #
  # This source code is licensed under the MIT license found in the
  # LICENSE file in the root directory of this source tree.
  
  from .oneshot import SamplingSearch
  from .differentialevolution import DifferentialEvolution
  from .optimizerlib import RandomSearchMaker, SQP, LHSSearch, DE, RandomSearch, MetaRecentering, MetaTuneRecentering  # type: ignore
  from .optimizerlib import (
      MultipleSingleRuns,
      ParametrizedOnePlusOne,
      ParametrizedCMA,
      ConfSplitOptimizer,
      ParametrizedBO,
      EMNA,
      NGOpt10,
  )
  from .optimizerlib import CMA, Chaining, PSO, BO
  
  # DE
  OnePointDE = DifferentialEvolution(crossover="onepoint").set_name("OnePointDE", register=True)
  ParametrizationDE = DifferentialEvolution(crossover="parametrization").set_name(
      "ParametrizationDE", register=True
  )
  MiniDE = DifferentialEvolution(initialization="gaussian", scale="mini").set_name("MiniDE", register=True)
  MiniLhsDE = DifferentialEvolution(initialization="LHS", scale="mini").set_name("MiniLhsDE", register=True)
  MiniQrDE = DifferentialEvolution(initialization="QR", scale="mini").set_name("MiniQrDE", register=True)
  AlmostRotationInvariantDEAndBigPop = DifferentialEvolution(crossover=0.9, popsize="dimension").set_name(
      "AlmostRotationInvariantDEAndBigPop", register=True
  )
  BPRotationInvariantDE = DifferentialEvolution(crossover=1.0, popsize="large").set_name(
      "BPRotationInvariantDE", register=True
  )
  
  # CMA
  MilliCMA = ParametrizedCMA(scale=1e-3).set_name("MilliCMA", register=True)
  MicroCMA = ParametrizedCMA(scale=1e-6).set_name("MicroCMA", register=True)
  FCMAs03 = ParametrizedCMA(fcmaes=True, scale=0.3).set_name("FCMAs03", register=True)
  FCMAp13 = ParametrizedCMA(fcmaes=True, scale=0.1, popsize=13).set_name("FCMAp13", register=True)
  ECMA = ParametrizedCMA(elitist=True).set_name("ECMA", register=True)
  
  # OnePlusOne
  FastGADiscreteOnePlusOne = ParametrizedOnePlusOne(mutation="fastga").set_name(
      "FastGADiscreteOnePlusOne", register=True
  )
  DoubleFastGAOptimisticNoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(
      noise_handling="optimistic", mutation="doublefastga"
  ).set_name("DoubleFastGAOptimisticNoisyDiscreteOnePlusOne", register=True)
  FastGAOptimisticNoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(
      noise_handling="optimistic", mutation="fastga"
  ).set_name("FastGAOptimisticNoisyDiscreteOnePlusOne", register=True)
  FastGANoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(noise_handling="random", mutation="fastga").set_name(
      "FastGANoisyDiscreteOnePlusOne", register=True
  )
  PortfolioOptimisticNoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(
      noise_handling="optimistic", mutation="portfolio"
  ).set_name("PortfolioOptimisticNoisyDiscreteOnePlusOne", register=True)
  PortfolioNoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(
      noise_handling="random", mutation="portfolio"
  ).set_name("PortfolioNoisyDiscreteOnePlusOne", register=True)
  RecombiningOptimisticNoisyDiscreteOnePlusOne = ParametrizedOnePlusOne(
      crossover=True, mutation="discrete", noise_handling="optimistic"
  ).set_name("RecombiningOptimisticNoisyDiscreteOnePlusOne", register=True)
  
  # BO
  RBO = ParametrizedBO(initialization="random").set_name("RBO", register=True)
  QRBO = ParametrizedBO(initialization="Hammersley").set_name("QRBO", register=True)
  MidQRBO = ParametrizedBO(initialization="Hammersley", middle_point=True).set_name("MidQRBO", register=True)
  LBO = ParametrizedBO(initialization="LHS").set_name("LBO", register=True)
  
  # EMNA
  IsoEMNA = EMNA(naive=False).set_name("IsoEMNA", register=True)
  NaiveAnisoEMNA = EMNA(isotropic=False).set_name("NaiveAnisoEMNA", register=True)
  AnisoEMNA = EMNA(naive=False, isotropic=False).set_name("AnisoEMNA", register=True)
  IsoEMNATBPSA = EMNA(naive=False, population_size_adaptation=True).set_name("IsoEMNATBPSA", register=True)
  NaiveIsoEMNATBPSA = EMNA(population_size_adaptation=True).set_name("NaiveIsoEMNATBPSA", register=True)
  AnisoEMNATBPSA = EMNA(naive=False, isotropic=False, population_size_adaptation=True).set_name(
      "AnisoEMNATBPSA", register=True
  )
  NaiveAnisoEMNATBPSA = EMNA(isotropic=False, population_size_adaptation=True).set_name(
      "NaiveAnisoEMNATBPSA", register=True
  )
  
  # Recentering
  MetaCauchyRecentering = SamplingSearch(
      cauchy=True, autorescale=True, sampler="Hammersley", scrambled=True
  ).set_name("MetaCauchyRecentering", register=True)
  # Chaining
  ChainCMASQP = Chaining([CMA, SQP], ["half"]).set_name("ChainCMASQP", register=True)
  ChainCMASQP.no_parallelization = True
  ChainDEwithR = Chaining([RandomSearch, DE], ["num_workers"]).set_name("ChainDEwithR", register=True)
  ChainDEwithRsqrt = Chaining([RandomSearch, DE], ["sqrt"]).set_name("ChainDEwithRsqrt", register=True)
  ChainDEwithRdim = Chaining([RandomSearch, DE], ["dimension"]).set_name("ChainDEwithRdim", register=True)
  ChainDEwithR30 = Chaining([RandomSearch, DE], [30]).set_name("ChainDEwithR30", register=True)
  ChainDEwithLHS = Chaining([LHSSearch, DE], ["num_workers"]).set_name("ChainDEwithLHS", register=True)
  ChainDEwithLHSsqrt = Chaining([LHSSearch, DE], ["sqrt"]).set_name("ChainDEwithLHSsqrt", register=True)
  ChainDEwithLHSdim = Chaining([LHSSearch, DE], ["dimension"]).set_name("ChainDEwithLHSdim", register=True)
  ChainDEwithLHS30 = Chaining([LHSSearch, DE], [30]).set_name("ChainDEwithLHS30", register=True)
  ChainDEwithMetaRecentering = Chaining([MetaRecentering, DE], ["num_workers"]).set_name(
      "ChainDEwithMetaRecentering", register=True
  )
  ChainDEwithMetaRecenteringsqrt = Chaining([MetaRecentering, DE], ["sqrt"]).set_name(
      "ChainDEwithMetaRecenteringsqrt", register=True
  )
  ChainDEwithMetaRecenteringdim = Chaining([MetaRecentering, DE], ["dimension"]).set_name(
      "ChainDEwithMetaRecenteringdim", register=True
  )
  ChainDEwithMetaRecentering30 = Chaining([MetaRecentering, DE], [30]).set_name(
      "ChainDEwithMetaRecentering30", register=True
  )
  ChainBOwithMetaTuneRecentering = Chaining([MetaTuneRecentering, BO], ["num_workers"]).set_name(
      "ChainBOwithMetaTuneRecentering", register=True
  )
  ChainBOwithMetaTuneRecenteringsqrt = Chaining([MetaTuneRecentering, BO], ["sqrt"]).set_name(
      "ChainBOwithMetaTuneRecenteringsqrt", register=True
  )
  ChainBOwithMetaTuneRecenteringdim = Chaining([MetaTuneRecentering, BO], ["dimension"]).set_name(
      "ChainBOwithMetaTuneRecenteringdim", register=True
  )
  ChainBOwithMetaTuneRecentering30 = Chaining([MetaTuneRecentering, BO], [30]).set_name(
      "ChainBOwithMetaTuneRecentering30", register=True
  )
  
  ChainDEwithMetaTuneRecentering = Chaining([MetaTuneRecentering, DE], ["num_workers"]).set_name(
      "ChainDEwithMetaTuneRecentering", register=True
  )
  ChainDEwithMetaTuneRecenteringsqrt = Chaining([MetaTuneRecentering, DE], ["sqrt"]).set_name(
      "ChainDEwithMetaTuneRecenteringsqrt", register=True
  )
  ChainDEwithMetaTuneRecenteringdim = Chaining([MetaTuneRecentering, DE], ["dimension"]).set_name(
      "ChainDEwithMetaTuneRecenteringdim", register=True
  )
  ChainDEwithMetaTuneRecentering30 = Chaining([MetaTuneRecentering, DE], [30]).set_name(
      "ChainDEwithMetaTuneRecentering30", register=True
  )
  
  
  ChainBOwithR = Chaining([RandomSearch, BO], ["num_workers"]).set_name("ChainBOwithR", register=True)
  ChainBOwithRsqrt = Chaining([RandomSearch, BO], ["sqrt"]).set_name("ChainBOwithRsqrt", register=True)
  ChainBOwithRdim = Chaining([RandomSearch, BO], ["dimension"]).set_name("ChainBOwithRdim", register=True)
  ChainBOwithR30 = Chaining([RandomSearch, BO], [30]).set_name("ChainBOwithR30", register=True)
  ChainBOwithLHS30 = Chaining([LHSSearch, BO], [30]).set_name("ChainBOwithLHS30", register=True)
  ChainBOwithLHSsqrt = Chaining([LHSSearch, BO], ["sqrt"]).set_name("ChainBOwithLHSsqrt", register=True)
  ChainBOwithLHSdim = Chaining([LHSSearch, BO], ["dimension"]).set_name("ChainBOwithLHSdim", register=True)
  ChainBOwithLHS = Chaining([LHSSearch, BO], ["num_workers"]).set_name("ChainBOwithLHS", register=True)
  ChainBOwithMetaRecentering30 = Chaining([MetaRecentering, BO], [30]).set_name(
      "ChainBOwithMetaRecentering30", register=True
  )
  ChainBOwithMetaRecenteringsqrt = Chaining([MetaRecentering, BO], ["sqrt"]).set_name(
      "ChainBOwithMetaRecenteringsqrt", register=True
  )
  ChainBOwithMetaRecenteringdim = Chaining([MetaRecentering, BO], ["dimension"]).set_name(
      "ChainBOwithMetaRecenteringdim", register=True
  )
  ChainBOwithMetaRecentering = Chaining([MetaRecentering, BO], ["num_workers"]).set_name(
      "ChainBOwithMetaRecentering", register=True
  )
  
  ChainPSOwithR = Chaining([RandomSearch, PSO], ["num_workers"]).set_name("ChainPSOwithR", register=True)
  ChainPSOwithRsqrt = Chaining([RandomSearch, PSO], ["sqrt"]).set_name("ChainPSOwithRsqrt", register=True)
  ChainPSOwithRdim = Chaining([RandomSearch, PSO], ["dimension"]).set_name("ChainPSOwithRdim", register=True)
  ChainPSOwithR30 = Chaining([RandomSearch, PSO], [30]).set_name("ChainPSOwithR30", register=True)
  ChainPSOwithLHS30 = Chaining([LHSSearch, PSO], [30]).set_name("ChainPSOwithLHS30", register=True)
  ChainPSOwithLHSsqrt = Chaining([LHSSearch, PSO], ["sqrt"]).set_name("ChainPSOwithLHSsqrt", register=True)
  ChainPSOwithLHSdim = Chaining([LHSSearch, PSO], ["dimension"]).set_name("ChainPSOwithLHSdim", register=True)
  ChainPSOwithLHS = Chaining([LHSSearch, PSO], ["num_workers"]).set_name("ChainPSOwithLHS", register=True)
  ChainPSOwithMetaRecentering30 = Chaining([MetaRecentering, PSO], [30]).set_name(
      "ChainPSOwithMetaRecentering30", register=True
  )
  ChainPSOwithMetaRecenteringsqrt = Chaining([MetaRecentering, PSO], ["sqrt"]).set_name(
      "ChainPSOwithMetaRecenteringsqrt", register=True
  )
  ChainPSOwithMetaRecenteringdim = Chaining([MetaRecentering, PSO], ["dimension"]).set_name(
      "ChainPSOwithMetaRecenteringdim", register=True
  )
  ChainPSOwithMetaRecentering = Chaining([MetaRecentering, PSO], ["num_workers"]).set_name(
      "ChainPSOwithMetaRecentering", register=True
  )
  
  ChainCMAwithR = Chaining([RandomSearch, CMA], ["num_workers"]).set_name("ChainCMAwithR", register=True)
  ChainCMAwithRsqrt = Chaining([RandomSearch, CMA], ["sqrt"]).set_name("ChainCMAwithRsqrt", register=True)
  ChainCMAwithRdim = Chaining([RandomSearch, CMA], ["dimension"]).set_name("ChainCMAwithRdim", register=True)
  ChainCMAwithR30 = Chaining([RandomSearch, CMA], [30]).set_name("ChainCMAwithR30", register=True)
  ChainCMAwithLHS30 = Chaining([LHSSearch, CMA], [30]).set_name("ChainCMAwithLHS30", register=True)
  ChainCMAwithLHSsqrt = Chaining([LHSSearch, CMA], ["sqrt"]).set_name("ChainCMAwithLHSsqrt", register=True)
  ChainCMAwithLHSdim = Chaining([LHSSearch, CMA], ["dimension"]).set_name("ChainCMAwithLHSdim", register=True)
  ChainCMAwithLHS = Chaining([LHSSearch, CMA], ["num_workers"]).set_name("ChainCMAwithLHS", register=True)
  ChainCMAwithMetaRecentering30 = Chaining([MetaRecentering, CMA], [30]).set_name(
      "ChainCMAwithMetaRecentering30", register=True
  )
  ChainCMAwithMetaRecenteringsqrt = Chaining([MetaRecentering, CMA], ["sqrt"]).set_name(
      "ChainCMAwithMetaRecenteringsqrt", register=True
  )
  ChainCMAwithMetaRecenteringdim = Chaining([MetaRecentering, CMA], ["dimension"]).set_name(
      "ChainCMAwithMetaRecenteringdim", register=True
  )
  ChainCMAwithMetaRecentering = Chaining([MetaRecentering, CMA], ["num_workers"]).set_name(
      "ChainCMAwithMetaRecentering", register=True
  )
  
  # Random search
  Zero = RandomSearchMaker(scale=0.0).set_name("Zero", register=True)
  StupidRandom = RandomSearchMaker(stupid=True).set_name("StupidRandom", register=True)
  CauchyRandomSearch = RandomSearchMaker(sampler="cauchy").set_name("CauchyRandomSearch", register=True)
  RandomScaleRandomSearch = RandomSearchMaker(scale="random", middle_point=True).set_name(
      "RandomScaleRandomSearch", register=True
  )
  RandomScaleRandomSearchPlusMiddlePoint = RandomSearchMaker(scale="random", middle_point=True).set_name(
      "RandomScaleRandomSearchPlusMiddlePoint", register=True
  )
  
  # quasi-random
  RescaleScrHammersleySearch = SamplingSearch(sampler="Hammersley", scrambled=True, rescaled=True).set_name(
      "RescaleScrHammersleySearch", register=True
  )
  AvgHammersleySearch = SamplingSearch(sampler="Hammersley", recommendation_rule="average_of_best").set_name(
      "AvgHammersleySearch", register=True
  )
  AvgHammersleySearchPlusMiddlePoint = SamplingSearch(
      sampler="Hammersley", middle_point=True, recommendation_rule="average_of_best"
  ).set_name("AvgHammersleySearchPlusMiddlePoint", register=True)
  HCHAvgRandomSearch = SamplingSearch(sampler="Random", recommendation_rule="average_of_hull_best").set_name(
      "HCHAvgRandomSearch", register=True
  )
  AvgRandomSearch = SamplingSearch(sampler="Random", recommendation_rule="average_of_best").set_name(
      "AvgRandomSearch", register=True
  )
  
  # Recommendation rule = average of k best, with k = n / 2^d.
  TEAvgScrHammersleySearchPlusMiddlePoint = SamplingSearch(
      scrambled=True, sampler="Hammersley", middle_point=True, recommendation_rule="average_of_exp_best"
  ).set_name("TEAvgScrHammersleySearchPlusMiddlePoint", register=True)
  TEAvgScrHammersleySearch = SamplingSearch(
      sampler="Hammersley", scrambled=True, recommendation_rule="average_of_exp_best"
  ).set_name("TEAvgScrHammersleySearch", register=True)
  TEAvgRandomSearch = SamplingSearch(sampler="Random", recommendation_rule="average_of_exp_best").set_name(
      "TEAvgRandomSearch", register=True
  )
  TEAvgCauchyScrHammersleySearch = SamplingSearch(
      cauchy=True, sampler="Hammersley", scrambled=True, recommendation_rule="average_of_exp_best"
  ).set_name("TEAvgCauchyScrHammersleySearch", register=True)
  TEAvgLHSSearch = SamplingSearch(sampler="LHS", recommendation_rule="average_of_exp_best").set_name(
      "TEAvgLHSSearch", register=True
  )
  TEAvgCauchyLHSSearch = SamplingSearch(
      sampler="LHS", cauchy=True, recommendation_rule="average_of_exp_best"
  ).set_name("TEAvgCauchyLHSSearch", register=True)
  
  # Recommendation rule = by convex hull.
  HCHAvgScrHaltonSearch = SamplingSearch(scrambled=True, recommendation_rule="average_of_hull_best").set_name(
      "HCHAvgScrHaltonSearch", register=True
  )
  HCHAvgScrHaltonSearchPlusMiddlePoint = SamplingSearch(
      middle_point=True, scrambled=True, recommendation_rule="average_of_hull_best"
  ).set_name("HCHAvgScrHaltonSearchPlusMiddlePoint", register=True)
  HCHAvgScrHammersleySearchPlusMiddlePoint = SamplingSearch(
      scrambled=True, sampler="Hammersley", middle_point=True, recommendation_rule="average_of_hull_best"
  ).set_name("HCHAvgScrHammersleySearchPlusMiddlePoint", register=True)
  HCHAvgLargeHammersleySearch = SamplingSearch(
      scale=100.0, sampler="Hammersley", recommendation_rule="average_of_hull_best"
  ).set_name("HCHAvgLargeHammersleySearch", register=True)
  HCHAvgScrHammersleySearch = SamplingSearch(
      sampler="Hammersley", scrambled=True, recommendation_rule="average_of_hull_best"
  ).set_name("HCHAvgScrHammersleySearch", register=True)
  HCHAvgCauchyScrHammersleySearch = SamplingSearch(
      cauchy=True, sampler="Hammersley", scrambled=True, recommendation_rule="average_of_hull_best"
  ).set_name("HCHAvgCauchyScrHammersleySearch", register=True)
  HCHAvgLHSSearch = SamplingSearch(sampler="LHS", recommendation_rule="average_of_hull_best").set_name(
      "HCHAvgLHSSearch", register=True
  )
  HCHAvgCauchyLHSSearch = SamplingSearch(
      sampler="LHS", cauchy=True, recommendation_rule="average_of_hull_best"
  ).set_name("HCHAvgCauchyLHSSearch", register=True)
  
  # Split on top of competence map.
  MetaNGOpt10 = ConfSplitOptimizer(
      multivariate_optimizer=NGOpt10, monovariate_optimizer=NGOpt10, non_deterministic_descriptor=False
  ).set_name("MetaNGOpt10", register=True)
  
  # Multiple single runs for multi-objective optimization.
  NGOptSingle9 = MultipleSingleRuns(num_single_runs=9).set_name("NGOptSingle9", register=True)
  NGOptSingle16 = MultipleSingleRuns(num_single_runs=16).set_name("NGOptSingle16", register=True)
  NGOptSingle25 = MultipleSingleRuns(num_single_runs=25).set_name("NGOptSingle25", register=True)
  