text: from typing import Any
  from typing import Dict
  from typing import Optional
  from typing import Sequence
  from typing import Union
  
  import optuna
  from optuna._experimental import experimental
  from optuna._imports import try_import
  
  
  with try_import() as _imports:
      import wandb
  
  
  @experimental("2.9.0")
  class WeightsAndBiasesCallback(object):
      """Callback to track Optuna trials with Weights & Biases.
  
      This callback enables tracking of Optuna study in
      Weights & Biases. The study is tracked as a single experiment
      run, where all suggested hyperparameters and optimized metrics
      are logged and plotted as a function of optimizer steps.
  
      .. note::
          User needs to be logged in to Weights & Biases before
          using this callback in online mode. For more information, please
          refer to `wandb setup <https://docs.wandb.ai/quickstart#1-set-up-wandb>`_.
  
      .. note::
          Users who want to run multiple Optuna studies within the same process
          should call ``wandb.finish()`` between subsequent calls to
          ``study.optimize()``. Calling ``wandb.finish()`` is not necessary
          if you are running one Optuna study per process.
  
      .. note::
          To ensure correct trial order in Weights & Biases, this callback
          should only be used with ``study.optimize(n_jobs=1)``.
  
      Example:
  
          Add Weights & Biases callback to Optuna optimization.
  
          .. code::
  
              import optuna
              from optuna.integration.wandb import WeightsAndBiasesCallback
  
  
              def objective(trial):
                  x = trial.suggest_float("x", -10, 10)
                  return (x - 2) ** 2
  
  
              wandb_kwargs = {"project": "my-project"}
              wandbc = WeightsAndBiasesCallback(wandb_kwargs=wandb_kwargs)
  
  
              study = optuna.create_study(study_name="my_study")
              study.optimize(objective, n_trials=10, callbacks=[wandbc])
  
      Args:
          metric_name:
              Name assigned to optimized metric. In case of multi-objective optimization,
              list of names can be passed. Those names will be assigned
              to metrics in the order returned by objective function.
              If single name is provided, or this argument is left to default value,
              it will be broadcasted to each objective with a number suffix in order
              returned by objective function e.g. two objectives and default metric name
              will be logged as ``value_0`` and ``value_1``.
          wandb_kwargs:
              Set of arguments passed when initializing Weights & Biases run.
              Please refer to `Weights & Biases API documentation
              <https://docs.wandb.ai/ref/python/init>`_ for more details.
  
      Raises:
          :exc:`ValueError`:
              If there are missing or extra metric names in multi-objective optimization.
          :exc:`TypeError`:
              When metric names are not passed as sequence.
      """
  
      def __init__(
          self,
          metric_name: Union[str, Sequence[str]] = "value",
          wandb_kwargs: Optional[Dict[str, Any]] = None,
      ) -> None:
  
          _imports.check()
  
          if not isinstance(metric_name, Sequence):
              raise TypeError(
                  "Expected metric_name to be string or sequence of strings, got {}.".format(
                      type(metric_name)
                  )
              )
  
          self._metric_name = metric_name
          self._wandb_kwargs = wandb_kwargs or {}
  
          self._initialize_run()
  
      def __call__(self, study: optuna.study.Study, trial: optuna.trial.FrozenTrial) -> None:
  
          if isinstance(self._metric_name, str):
              if len(trial.values) > 1:
                  # Broadcast default name for multi-objective optimization.
                  names = ["{}_{}".format(self._metric_name, i) for i in range(len(trial.values))]
  
              else:
                  names = [self._metric_name]
  
          else:
              if len(self._metric_name) != len(trial.values):
                  raise ValueError(
                      "Running multi-objective optimization "
                      "with {} objective values, but {} names specified. "
                      "Match objective values and names, or use default broadcasting.".format(
                          len(trial.values), len(self._metric_name)
                      )
                  )
  
              else:
                  names = [*self._metric_name]
  
          metrics = {name: value for name, value in zip(names, trial.values)}
          attributes = {"direction": [d.name for d in study.directions]}
  
          wandb.config.update(attributes)
          wandb.log({**trial.params, **metrics}, step=trial.number)
  
      def _initialize_run(self) -> None:
          """Initializes Weights & Biases run."""
  
          wandb.init(**self._wandb_kwargs)
  