text: import warnings
  
  import attr
  import cattr
  from typing import Dict, Optional, List, Any, DefaultDict, Mapping, Tuple, Union
  from enum import Enum
  import collections
  import argparse
  import abc
  import numpy as np
  import math
  
  from mlagents.trainers.cli_utils import StoreConfigFile, DetectDefault, parser
  from mlagents.trainers.cli_utils import load_config
  from mlagents.trainers.exception import TrainerConfigError, TrainerConfigWarning
  
  from mlagents_envs import logging_util
  from mlagents_envs.side_channel.environment_parameters_channel import (
      EnvironmentParametersChannel,
  )
  
  logger = logging_util.get_logger(__name__)
  
  
  def check_and_structure(key: str, value: Any, class_type: type) -> Any:
      attr_fields_dict = attr.fields_dict(class_type)
      if key not in attr_fields_dict:
          raise TrainerConfigError(
              f"The option {key} was specified in your YAML file for {class_type.__name__}, but is invalid."
          )
      # Apply cattr structure to the values
      return cattr.structure(value, attr_fields_dict[key].type)
  
  
  def strict_to_cls(d: Mapping, t: type) -> Any:
      if not isinstance(d, Mapping):
          raise TrainerConfigError(f"Unsupported config {d} for {t.__name__}.")
      d_copy: Dict[str, Any] = {}
      d_copy.update(d)
      for key, val in d_copy.items():
          d_copy[key] = check_and_structure(key, val, t)
      return t(**d_copy)
  
  
  def defaultdict_to_dict(d: DefaultDict) -> Dict:
      return {key: cattr.unstructure(val) for key, val in d.items()}
  
  
  @attr.s(auto_attribs=True)
  class ExportableSettings:
      def as_dict(self):
          return cattr.unstructure(self)
  
  
  class EncoderType(Enum):
      SIMPLE = "simple"
      NATURE_CNN = "nature_cnn"
      RESNET = "resnet"
  
  
  class ScheduleType(Enum):
      CONSTANT = "constant"
      LINEAR = "linear"
  
  
  @attr.s(auto_attribs=True)
  class NetworkSettings:
      @attr.s
      class MemorySettings:
          sequence_length: int = attr.ib(default=64)
          memory_size: int = attr.ib(default=128)
  
          @memory_size.validator
          def _check_valid_memory_size(self, attribute, value):
              if value <= 0:
                  raise TrainerConfigError(
                      "When using a recurrent network, memory size must be greater than 0."
                  )
              elif value % 2 != 0:
                  raise TrainerConfigError(
                      "When using a recurrent network, memory size must be divisible by 2."
                  )
  
      normalize: bool = False
      hidden_units: int = 128
      num_layers: int = 2
      vis_encode_type: EncoderType = EncoderType.SIMPLE
      memory: Optional[MemorySettings] = None
  
  
  @attr.s(auto_attribs=True)
  class BehavioralCloningSettings:
      demo_path: str
      steps: int = 0
      strength: float = 1.0
      samples_per_update: int = 0
      # Setting either of these to None will allow the Optimizer
      # to decide these parameters, based on Trainer hyperparams
      num_epoch: Optional[int] = None
      batch_size: Optional[int] = None
  
  
  @attr.s(auto_attribs=True)
  class HyperparamSettings:
      batch_size: int = 1024
      buffer_size: int = 10240
      learning_rate: float = 3.0e-4
      learning_rate_schedule: ScheduleType = ScheduleType.CONSTANT
  
  
  @attr.s(auto_attribs=True)
  class PPOSettings(HyperparamSettings):
      beta: float = 5.0e-3
      epsilon: float = 0.2
      lambd: float = 0.95
      num_epoch: int = 3
      learning_rate_schedule: ScheduleType = ScheduleType.LINEAR
  
  
  @attr.s(auto_attribs=True)
  class SACSettings(HyperparamSettings):
      batch_size: int = 128
      buffer_size: int = 50000
      buffer_init_steps: int = 0
      tau: float = 0.005
      steps_per_update: float = 1
      save_replay_buffer: bool = False
      init_entcoef: float = 1.0
      reward_signal_steps_per_update: float = attr.ib()
  
      @reward_signal_steps_per_update.default
      def _reward_signal_steps_per_update_default(self):
          return self.steps_per_update
  
  
  # INTRINSIC REWARD SIGNALS #############################################################
  class RewardSignalType(Enum):
      EXTRINSIC: str = "extrinsic"
      GAIL: str = "gail"
      CURIOSITY: str = "curiosity"
  
      def to_settings(self) -> type:
          _mapping = {
              RewardSignalType.EXTRINSIC: RewardSignalSettings,
              RewardSignalType.GAIL: GAILSettings,
              RewardSignalType.CURIOSITY: CuriositySettings,
          }
          return _mapping[self]
  
  
  @attr.s(auto_attribs=True)
  class RewardSignalSettings:
      gamma: float = 0.99
      strength: float = 1.0
  
      @staticmethod
      def structure(d: Mapping, t: type) -> Any:
          """
          Helper method to structure a Dict of RewardSignalSettings class. Meant to be registered with
          cattr.register_structure_hook() and called with cattr.structure(). This is needed to handle
          the special Enum selection of RewardSignalSettings classes.
          """
          if not isinstance(d, Mapping):
              raise TrainerConfigError(f"Unsupported reward signal configuration {d}.")
          d_final: Dict[RewardSignalType, RewardSignalSettings] = {}
          for key, val in d.items():
              enum_key = RewardSignalType(key)
              t = enum_key.to_settings()
              d_final[enum_key] = strict_to_cls(val, t)
          return d_final
  
  
  @attr.s(auto_attribs=True)
  class GAILSettings(RewardSignalSettings):
      encoding_size: int = 64
      learning_rate: float = 3e-4
      use_actions: bool = False
      use_vail: bool = False
      demo_path: str = attr.ib(kw_only=True)
  
  
  @attr.s(auto_attribs=True)
  class CuriositySettings(RewardSignalSettings):
      encoding_size: int = 64
      learning_rate: float = 3e-4
  
  
  # SAMPLERS #############################################################################
  class ParameterRandomizationType(Enum):
      UNIFORM: str = "uniform"
      GAUSSIAN: str = "gaussian"
      MULTIRANGEUNIFORM: str = "multirangeuniform"
      CONSTANT: str = "constant"
  
      def to_settings(self) -> type:
          _mapping = {
              ParameterRandomizationType.UNIFORM: UniformSettings,
              ParameterRandomizationType.GAUSSIAN: GaussianSettings,
              ParameterRandomizationType.MULTIRANGEUNIFORM: MultiRangeUniformSettings,
              ParameterRandomizationType.CONSTANT: ConstantSettings
              # Constant type is handled if a float is provided instead of a config
          }
          return _mapping[self]
  
  
  @attr.s(auto_attribs=True)
  class ParameterRandomizationSettings(abc.ABC):
      seed: int = parser.get_default("seed")
  
      @staticmethod
      def structure(
          d: Union[Mapping, float], t: type
      ) -> "ParameterRandomizationSettings":
          """
          Helper method to a ParameterRandomizationSettings class. Meant to be registered with
          cattr.register_structure_hook() and called with cattr.structure(). This is needed to handle
          the special Enum selection of ParameterRandomizationSettings classes.
          """
          if isinstance(d, (float, int)):
              return ConstantSettings(value=d)
          if not isinstance(d, Mapping):
              raise TrainerConfigError(
                  f"Unsupported parameter randomization configuration {d}."
              )
          if "sampler_type" not in d:
              raise TrainerConfigError(
                  f"Sampler configuration does not contain sampler_type : {d}."
              )
          if "sampler_parameters" not in d:
              raise TrainerConfigError(
                  f"Sampler configuration does not contain sampler_parameters : {d}."
              )
          enum_key = ParameterRandomizationType(d["sampler_type"])
          t = enum_key.to_settings()
          return strict_to_cls(d["sampler_parameters"], t)
  
      @staticmethod
      def unstructure(d: "ParameterRandomizationSettings") -> Mapping:
          """
          Helper method to a ParameterRandomizationSettings class. Meant to be registered with
          cattr.register_unstructure_hook() and called with cattr.unstructure().
          """
          _reversed_mapping = {
              UniformSettings: ParameterRandomizationType.UNIFORM,
              GaussianSettings: ParameterRandomizationType.GAUSSIAN,
              MultiRangeUniformSettings: ParameterRandomizationType.MULTIRANGEUNIFORM,
              ConstantSettings: ParameterRandomizationType.CONSTANT,
          }
          sampler_type: Optional[str] = None
          for t, name in _reversed_mapping.items():
              if isinstance(d, t):
                  sampler_type = name.value
          sampler_parameters = attr.asdict(d)
          return {"sampler_type": sampler_type, "sampler_parameters": sampler_parameters}
  
      @abc.abstractmethod
      def apply(self, key: str, env_channel: EnvironmentParametersChannel) -> None:
          """
          Helper method to send sampler settings over EnvironmentParametersChannel
          Calls the appropriate sampler type set method.
          :param key: environment parameter to be sampled
          :param env_channel: The EnvironmentParametersChannel to communicate sampler settings to environment
          """
          pass
  
  
  @attr.s(auto_attribs=True)
  class ConstantSettings(ParameterRandomizationSettings):
      value: float = 0.0
  
      def apply(self, key: str, env_channel: EnvironmentParametersChannel) -> None:
          """
          Helper method to send sampler settings over EnvironmentParametersChannel
          Calls the constant sampler type set method.
          :param key: environment parameter to be sampled
          :param env_channel: The EnvironmentParametersChannel to communicate sampler settings to environment
          """
          env_channel.set_float_parameter(key, self.value)
  
  
  @attr.s(auto_attribs=True)
  class UniformSettings(ParameterRandomizationSettings):
      min_value: float = attr.ib()
      max_value: float = 1.0
  
      @min_value.default
      def _min_value_default(self):
          return 0.0
  
      @min_value.validator
      def _check_min_value(self, attribute, value):
          if self.min_value > self.max_value:
              raise TrainerConfigError(
                  "Minimum value is greater than maximum value in uniform sampler."
              )
  
      def apply(self, key: str, env_channel: EnvironmentParametersChannel) -> None:
          """
          Helper method to send sampler settings over EnvironmentParametersChannel
          Calls the uniform sampler type set method.
          :param key: environment parameter to be sampled
          :param env_channel: The EnvironmentParametersChannel to communicate sampler settings to environment
          """
          env_channel.set_uniform_sampler_parameters(
              key, self.min_value, self.max_value, self.seed
          )
  
  
  @attr.s(auto_attribs=True)
  class GaussianSettings(ParameterRandomizationSettings):
      mean: float = 1.0
      st_dev: float = 1.0
  
      def apply(self, key: str, env_channel: EnvironmentParametersChannel) -> None:
          """
          Helper method to send sampler settings over EnvironmentParametersChannel
          Calls the gaussian sampler type set method.
          :param key: environment parameter to be sampled
          :param env_channel: The EnvironmentParametersChannel to communicate sampler settings to environment
          """
          env_channel.set_gaussian_sampler_parameters(
              key, self.mean, self.st_dev, self.seed
          )
  
  
  @attr.s(auto_attribs=True)
  class MultiRangeUniformSettings(ParameterRandomizationSettings):
      intervals: List[Tuple[float, float]] = attr.ib()
  
      @intervals.default
      def _intervals_default(self):
          return [[0.0, 1.0]]
  
      @intervals.validator
      def _check_intervals(self, attribute, value):
          for interval in self.intervals:
              if len(interval) != 2:
                  raise TrainerConfigError(
                      f"The sampling interval {interval} must contain exactly two values."
                  )
              min_value, max_value = interval
              if min_value > max_value:
                  raise TrainerConfigError(
                      f"Minimum value is greater than maximum value in interval {interval}."
                  )
  
      def apply(self, key: str, env_channel: EnvironmentParametersChannel) -> None:
          """
          Helper method to send sampler settings over EnvironmentParametersChannel
          Calls the multirangeuniform sampler type set method.
          :param key: environment parameter to be sampled
          :param env_channel: The EnvironmentParametersChannel to communicate sampler settings to environment
          """
          env_channel.set_multirangeuniform_sampler_parameters(
              key, self.intervals, self.seed
          )
  
  
  # ENVIRONMENT PARAMETERS ###############################################################
  @attr.s(auto_attribs=True)
  class CompletionCriteriaSettings:
      """
      CompletionCriteriaSettings contains the information needed to figure out if the next
      lesson must start.
      """
  
      class MeasureType(Enum):
          PROGRESS: str = "progress"
          REWARD: str = "reward"
  
      measure: MeasureType = attr.ib(default=MeasureType.REWARD)
      behavior: str = attr.ib(default="")
      min_lesson_length: int = 0
      signal_smoothing: bool = True
      threshold: float = attr.ib(default=0.0)
      require_reset: bool = False
  
      @threshold.validator
      def _check_threshold_value(self, attribute, value):
          """
          Verify that the threshold has a value between 0 and 1 when the measure is
          PROGRESS
          """
          if self.measure == self.MeasureType.PROGRESS:
              if self.threshold > 1.0:
                  raise TrainerConfigError(
                      "Threshold for next lesson cannot be greater than 1 when the measure is progress."
                  )
              if self.threshold < 0.0:
                  raise TrainerConfigError(
                      "Threshold for next lesson cannot be negative when the measure is progress."
                  )
  
      def need_increment(
          self, progress: float, reward_buffer: List[float], smoothing: float
      ) -> Tuple[bool, float]:
          """
          Given measures, this method returns a boolean indicating if the lesson
          needs to change now, and a float corresponding to the new smoothed value.
          """
          # Is the min number of episodes reached
          if len(reward_buffer) < self.min_lesson_length:
              return False, smoothing
          if self.measure == CompletionCriteriaSettings.MeasureType.PROGRESS:
              if progress > self.threshold:
                  return True, smoothing
          if self.measure == CompletionCriteriaSettings.MeasureType.REWARD:
              if len(reward_buffer) < 1:
                  return False, smoothing
              measure = np.mean(reward_buffer)
              if math.isnan(measure):
                  return False, smoothing
              if self.signal_smoothing:
                  measure = 0.25 * smoothing + 0.75 * measure
                  smoothing = measure
              if measure > self.threshold:
                  return True, smoothing
          return False, smoothing
  
  
  @attr.s(auto_attribs=True)
  class Lesson:
      """
      Gathers the data of one lesson for one environment parameter including its name,
      the condition that must be fullfiled for the lesson to be completed and a sampler
      for the environment parameter. If the completion_criteria is None, then this is
      the last lesson in the curriculum.
      """
  
      value: ParameterRandomizationSettings
      name: str
      completion_criteria: Optional[CompletionCriteriaSettings] = attr.ib(default=None)
  
  
  @attr.s(auto_attribs=True)
  class EnvironmentParameterSettings:
      """
      EnvironmentParameterSettings is an ordered list of lessons for one environment
      parameter.
      """
  
      curriculum: List[Lesson]
  
      @staticmethod
      def _check_lesson_chain(lessons, parameter_name):
          """
          Ensures that when using curriculum, all non-terminal lessons have a valid
          CompletionCriteria, and that the terminal lesson does not contain a CompletionCriteria.
          """
          num_lessons = len(lessons)
          for index, lesson in enumerate(lessons):
              if index < num_lessons - 1 and lesson.completion_criteria is None:
                  raise TrainerConfigError(
                      f"A non-terminal lesson does not have a completion_criteria for {parameter_name}."
                  )
              if index == num_lessons - 1 and lesson.completion_criteria is not None:
                  warnings.warn(
                      f"Your final lesson definition contains completion_criteria for {parameter_name}."
                      f"It will be ignored.",
                      TrainerConfigWarning,
                  )
  
      @staticmethod
      def structure(d: Mapping, t: type) -> Dict[str, "EnvironmentParameterSettings"]:
          """
          Helper method to structure a Dict of EnvironmentParameterSettings class. Meant
          to be registered with cattr.register_structure_hook() and called with
          cattr.structure().
          """
          if not isinstance(d, Mapping):
              raise TrainerConfigError(
                  f"Unsupported parameter environment parameter settings {d}."
              )
          d_final: Dict[str, EnvironmentParameterSettings] = {}
          for environment_parameter, environment_parameter_config in d.items():
              if (
                  isinstance(environment_parameter_config, Mapping)
                  and "curriculum" in environment_parameter_config
              ):
                  d_final[environment_parameter] = strict_to_cls(
                      environment_parameter_config, EnvironmentParameterSettings
                  )
                  EnvironmentParameterSettings._check_lesson_chain(
                      d_final[environment_parameter].curriculum, environment_parameter
                  )
              else:
                  sampler = ParameterRandomizationSettings.structure(
                      environment_parameter_config, ParameterRandomizationSettings
                  )
                  d_final[environment_parameter] = EnvironmentParameterSettings(
                      curriculum=[
                          Lesson(
                              completion_criteria=None,
                              value=sampler,
                              name=environment_parameter,
                          )
                      ]
                  )
          return d_final
  
  
  # TRAINERS #############################################################################
  @attr.s(auto_attribs=True)
  class SelfPlaySettings:
      save_steps: int = 20000
      team_change: int = attr.ib()
  
      @team_change.default
      def _team_change_default(self):
          # Assign team_change to about 4x save_steps
          return self.save_steps * 5
  
      swap_steps: int = 2000
      window: int = 10
      play_against_latest_model_ratio: float = 0.5
      initial_elo: float = 1200.0
  
  
  class TrainerType(Enum):
      PPO: str = "ppo"
      SAC: str = "sac"
  
      def to_settings(self) -> type:
          _mapping = {TrainerType.PPO: PPOSettings, TrainerType.SAC: SACSettings}
          return _mapping[self]
  
  
  @attr.s(auto_attribs=True)
  class TrainerSettings(ExportableSettings):
      trainer_type: TrainerType = TrainerType.PPO
      hyperparameters: HyperparamSettings = attr.ib()
  
      @hyperparameters.default
      def _set_default_hyperparameters(self):
          return self.trainer_type.to_settings()()
  
      network_settings: NetworkSettings = attr.ib(factory=NetworkSettings)
      reward_signals: Dict[RewardSignalType, RewardSignalSettings] = attr.ib(
          factory=lambda: {RewardSignalType.EXTRINSIC: RewardSignalSettings()}
      )
      init_path: Optional[str] = None
      keep_checkpoints: int = 5
      checkpoint_interval: int = 500000
      max_steps: int = 500000
      time_horizon: int = 64
      summary_freq: int = 50000
      threaded: bool = True
      self_play: Optional[SelfPlaySettings] = None
      behavioral_cloning: Optional[BehavioralCloningSettings] = None
  
      cattr.register_structure_hook(
          Dict[RewardSignalType, RewardSignalSettings], RewardSignalSettings.structure
      )
  
      @network_settings.validator
      def _check_batch_size_seq_length(self, attribute, value):
          if self.network_settings.memory is not None:
              if (
                  self.network_settings.memory.sequence_length
                  > self.hyperparameters.batch_size
              ):
                  raise TrainerConfigError(
                      "When using memory, sequence length must be less than or equal to batch size. "
                  )
  
      @staticmethod
      def dict_to_defaultdict(d: Dict, t: type) -> DefaultDict:
          return collections.defaultdict(
              TrainerSettings, cattr.structure(d, Dict[str, TrainerSettings])
          )
  
      @staticmethod
      def structure(d: Mapping, t: type) -> Any:
          """
          Helper method to structure a TrainerSettings class. Meant to be registered with
          cattr.register_structure_hook() and called with cattr.structure().
          """
          if not isinstance(d, Mapping):
              raise TrainerConfigError(f"Unsupported config {d} for {t.__name__}.")
          d_copy: Dict[str, Any] = {}
          d_copy.update(d)
  
          for key, val in d_copy.items():
              if attr.has(type(val)):
                  # Don't convert already-converted attrs classes.
                  continue
              if key == "hyperparameters":
                  if "trainer_type" not in d_copy:
                      raise TrainerConfigError(
                          "Hyperparameters were specified but no trainer_type was given."
                      )
                  else:
                      d_copy[key] = strict_to_cls(
                          d_copy[key], TrainerType(d_copy["trainer_type"]).to_settings()
                      )
              elif key == "max_steps":
                  d_copy[key] = int(float(val))
                  # In some legacy configs, max steps was specified as a float
              else:
                  d_copy[key] = check_and_structure(key, val, t)
          return t(**d_copy)
  
  
  # COMMAND LINE #########################################################################
  @attr.s(auto_attribs=True)
  class CheckpointSettings:
      run_id: str = parser.get_default("run_id")
      initialize_from: Optional[str] = parser.get_default("initialize_from")
      load_model: bool = parser.get_default("load_model")
      resume: bool = parser.get_default("resume")
      force: bool = parser.get_default("force")
      train_model: bool = parser.get_default("train_model")
      inference: bool = parser.get_default("inference")
  
  
  @attr.s(auto_attribs=True)
  class EnvironmentSettings:
      env_path: Optional[str] = parser.get_default("env_path")
      env_args: Optional[List[str]] = parser.get_default("env_args")
      base_port: int = parser.get_default("base_port")
      num_envs: int = attr.ib(default=parser.get_default("num_envs"))
      seed: int = parser.get_default("seed")
  
      @num_envs.validator
      def validate_num_envs(self, attribute, value):
          if value > 1 and self.env_path is None:
              raise ValueError("num_envs must be 1 if env_path is not set.")
  
  
  @attr.s(auto_attribs=True)
  class EngineSettings:
      width: int = parser.get_default("width")
      height: int = parser.get_default("height")
      quality_level: int = parser.get_default("quality_level")
      time_scale: float = parser.get_default("time_scale")
      target_frame_rate: int = parser.get_default("target_frame_rate")
      capture_frame_rate: int = parser.get_default("capture_frame_rate")
      no_graphics: bool = parser.get_default("no_graphics")
  
  
  @attr.s(auto_attribs=True)
  class RunOptions(ExportableSettings):
      behaviors: DefaultDict[str, TrainerSettings] = attr.ib(
          factory=lambda: collections.defaultdict(TrainerSettings)
      )
      env_settings: EnvironmentSettings = attr.ib(factory=EnvironmentSettings)
      engine_settings: EngineSettings = attr.ib(factory=EngineSettings)
      environment_parameters: Optional[Dict[str, EnvironmentParameterSettings]] = None
      checkpoint_settings: CheckpointSettings = attr.ib(factory=CheckpointSettings)
  
      # These are options that are relevant to the run itself, and not the engine or environment.
      # They will be left here.
      debug: bool = parser.get_default("debug")
      # Strict conversion
      cattr.register_structure_hook(EnvironmentSettings, strict_to_cls)
      cattr.register_structure_hook(EngineSettings, strict_to_cls)
      cattr.register_structure_hook(CheckpointSettings, strict_to_cls)
      cattr.register_structure_hook(
          Dict[str, EnvironmentParameterSettings], EnvironmentParameterSettings.structure
      )
      cattr.register_structure_hook(Lesson, strict_to_cls)
      cattr.register_structure_hook(
          ParameterRandomizationSettings, ParameterRandomizationSettings.structure
      )
      cattr.register_unstructure_hook(
          ParameterRandomizationSettings, ParameterRandomizationSettings.unstructure
      )
      cattr.register_structure_hook(TrainerSettings, TrainerSettings.structure)
      cattr.register_structure_hook(
          DefaultDict[str, TrainerSettings], TrainerSettings.dict_to_defaultdict
      )
      cattr.register_unstructure_hook(collections.defaultdict, defaultdict_to_dict)
  
      @staticmethod
      def from_argparse(args: argparse.Namespace) -> "RunOptions":
          """
          Takes an argparse.Namespace as specified in `parse_command_line`, loads input configuration files
          from file paths, and converts to a RunOptions instance.
          :param args: collection of command-line parameters passed to mlagents-learn
          :return: RunOptions representing the passed in arguments, with trainer config, curriculum and sampler
            configs loaded from files.
          """
          argparse_args = vars(args)
          config_path = StoreConfigFile.trainer_config_path
  
          # Load YAML
          configured_dict: Dict[str, Any] = {
              "checkpoint_settings": {},
              "env_settings": {},
              "engine_settings": {},
          }
          if config_path is not None:
              configured_dict.update(load_config(config_path))
  
          # Use the YAML file values for all values not specified in the CLI.
          for key in configured_dict.keys():
              # Detect bad config options
              if key not in attr.fields_dict(RunOptions):
                  raise TrainerConfigError(
                      "The option {} was specified in your YAML file, but is invalid.".format(
                          key
                      )
                  )
          # Override with CLI args
          # Keep deprecated --load working, TODO: remove
          argparse_args["resume"] = argparse_args["resume"] or argparse_args["load_model"]
          for key, val in argparse_args.items():
              if key in DetectDefault.non_default_args:
                  if key in attr.fields_dict(CheckpointSettings):
                      configured_dict["checkpoint_settings"][key] = val
                  elif key in attr.fields_dict(EnvironmentSettings):
                      configured_dict["env_settings"][key] = val
                  elif key in attr.fields_dict(EngineSettings):
                      configured_dict["engine_settings"][key] = val
                  else:  # Base options
                      configured_dict[key] = val
          return RunOptions.from_dict(configured_dict)
  
      @staticmethod
      def from_dict(options_dict: Dict[str, Any]) -> "RunOptions":
          return cattr.structure(options_dict, RunOptions)
  