text: # Copyright 2020 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  """Dataclasses for optimization configs.
  
  This file define the dataclass for optimization configs (OptimizationConfig).
  It also has two helper functions get_optimizer_config, and get_lr_config from
  an OptimizationConfig class.
  """
  from typing import Optional
  
  import dataclasses
  
  from official.modeling.hyperparams import base_config
  from official.modeling.hyperparams import oneof
  from official.modeling.optimization.configs import learning_rate_config as lr_cfg
  from official.modeling.optimization.configs import optimizer_config as opt_cfg
  
  
  @dataclasses.dataclass
  class OptimizerConfig(oneof.OneOfConfig):
    """Configuration for optimizer.
  
    Attributes:
      type: 'str', type of optimizer to be used, on the of fields below.
      sgd: sgd optimizer config.
      adam: adam optimizer config.
      adamw: adam with weight decay.
      lamb: lamb optimizer.
      rmsprop: rmsprop optimizer.
    """
    type: Optional[str] = None
    sgd: opt_cfg.SGDConfig = opt_cfg.SGDConfig()
    adam: opt_cfg.AdamConfig = opt_cfg.AdamConfig()
    adamw: opt_cfg.AdamWeightDecayConfig = opt_cfg.AdamWeightDecayConfig()
    lamb: opt_cfg.LAMBConfig = opt_cfg.LAMBConfig()
    rmsprop: opt_cfg.RMSPropConfig = opt_cfg.RMSPropConfig()
  
  
  @dataclasses.dataclass
  class LrConfig(oneof.OneOfConfig):
    """Configuration for lr schedule.
  
    Attributes:
      type: 'str', type of lr schedule to be used, on the of fields below.
      constant: constant learning rate config.
      stepwise: stepwise learning rate config.
      exponential: exponential learning rate config.
      polynomial: polynomial learning rate config.
      cosine: cosine learning rate config.
      power: step^power learning rate config.
      power_linear: learning rate config of step^power followed by
        step^power*linear.
    """
    type: Optional[str] = None
    constant: lr_cfg.ConstantLrConfig = lr_cfg.ConstantLrConfig()
    stepwise: lr_cfg.StepwiseLrConfig = lr_cfg.StepwiseLrConfig()
    exponential: lr_cfg.ExponentialLrConfig = lr_cfg.ExponentialLrConfig()
    polynomial: lr_cfg.PolynomialLrConfig = lr_cfg.PolynomialLrConfig()
    cosine: lr_cfg.CosineLrConfig = lr_cfg.CosineLrConfig()
    power: lr_cfg.DirectPowerLrConfig = lr_cfg.DirectPowerLrConfig()
    power_linear: lr_cfg.PowerAndLinearDecayLrConfig = (
        lr_cfg.PowerAndLinearDecayLrConfig())
  
  
  @dataclasses.dataclass
  class WarmupConfig(oneof.OneOfConfig):
    """Configuration for lr schedule.
  
    Attributes:
      type: 'str', type of warmup schedule to be used, on the of fields below.
      linear: linear warmup config.
      polynomial: polynomial warmup config.
    """
    type: Optional[str] = None
    linear: lr_cfg.LinearWarmupConfig = lr_cfg.LinearWarmupConfig()
    polynomial: lr_cfg.PolynomialWarmupConfig = lr_cfg.PolynomialWarmupConfig()
  
  
  @dataclasses.dataclass
  class OptimizationConfig(base_config.Config):
    """Configuration for optimizer and learning rate schedule.
  
    Attributes:
      optimizer: optimizer oneof config.
      ema: optional exponential moving average optimizer config, if specified,
        ema optimizer will be used.
      learning_rate: learning rate oneof config.
      warmup: warmup oneof config.
    """
    optimizer: OptimizerConfig = OptimizerConfig()
    ema: Optional[opt_cfg.EMAConfig] = None
    learning_rate: LrConfig = LrConfig()
    warmup: WarmupConfig = WarmupConfig()
  