text: from typing import Any, Dict, Optional
  
  import numpy as np
  
  from catalyst.core.logger import ILogger
  from catalyst.settings import SETTINGS
  
  if SETTINGS.wandb_required:
      import wandb
  
  
  class WandbLogger(ILogger):
      """Wandb logger for parameters, metrics, images and other artifacts.
  
      W&B documentation: https://docs.wandb.com
  
      Args:
          Project: Name of the project in W&B to log to.
          name: Name of the run in W&B to log to.
          config: Configuration Dictionary for the experiment.
          entity: Name of W&B entity(team) to log to.
  
      Python API examples:
  
      .. code-block:: python
  
          from catalyst import dl
  
          runner = dl.SupervisedRunner()
          runner.train(
              ...,
              loggers={"wandb": dl.WandbLogger(project="wandb_test", name="expeirment_1")}
          )
  
      .. code-block:: python
  
          from catalyst import dl
  
          class CustomRunner(dl.IRunner):
              # ...
  
              def get_loggers(self):
                  return {
                      "console": dl.ConsoleLogger(),
                      "wandb": dl.WandbLogger(project="wandb_test", name="experiment_1")
                  }
  
              # ...
  
          runner = CustomRunner().run()
  
      Config API example:
  
      .. code-block:: yaml
  
          loggers:
              wandb:
                  _target_: WandbLogger
                  project: test_exp
                  name: test_run
          ...
  
      Hydra API example:
  
      .. code-block:: yaml
  
          loggers:
              wandb:
                  _target_: catalyst.dl.WandbLogger
                  project: test_exp
                  name: test_run
          ...
      """
  
      def __init__(
          self, project: str, name: Optional[str] = None, entity: Optional[str] = None,
      ) -> None:
          self.project = project
          self.name = name
          self.entity = entity
          self.run = wandb.init(
              project=self.project, name=self.name, entity=self.entity, allow_val_change=True
          )
  
      def _log_metrics(self, metrics: Dict[str, float], step: int, loader_key: str, prefix=""):
          for key, value in metrics.items():
              self.run.log({f"{key}_{prefix}/{loader_key}": value}, step=step)
  
      def log_metrics(
          self,
          metrics: Dict[str, Any],
          scope: str = None,
          # experiment info
          run_key: str = None,
          global_epoch_step: int = 0,
          global_batch_step: int = 0,
          global_sample_step: int = 0,
          # stage info
          stage_key: str = None,
          stage_epoch_len: int = 0,
          stage_epoch_step: int = 0,
          stage_batch_step: int = 0,
          stage_sample_step: int = 0,
          # loader info
          loader_key: str = None,
          loader_batch_len: int = 0,
          loader_sample_len: int = 0,
          loader_batch_step: int = 0,
          loader_sample_step: int = 0,
      ) -> None:
          """Logs batch and epoch metrics to wandb."""
          if scope == "batch":
              metrics = {k: float(v) for k, v in metrics.items()}
              self._log_metrics(
                  metrics=metrics, step=global_epoch_step, loader_key=loader_key, prefix="batch"
              )
          elif scope == "loader":
              self._log_metrics(
                  metrics=metrics, step=global_epoch_step, loader_key=loader_key, prefix="epoch",
              )
          elif scope == "epoch":
              loader_key = "_epoch_"
              per_loader_metrics = metrics[loader_key]
              self._log_metrics(
                  metrics=per_loader_metrics,
                  step=global_epoch_step,
                  loader_key=loader_key,
                  prefix="epoch",
              )
  
      def log_image(
          self,
          tag: str,
          image: np.ndarray,
          scope: str = None,
          # experiment info
          run_key: str = None,
          global_epoch_step: int = 0,
          global_batch_step: int = 0,
          global_sample_step: int = 0,
          # stage info
          stage_key: str = None,
          stage_epoch_len: int = 0,
          stage_epoch_step: int = 0,
          stage_batch_step: int = 0,
          stage_sample_step: int = 0,
          # loader info
          loader_key: str = None,
          loader_batch_len: int = 0,
          loader_sample_len: int = 0,
          loader_batch_step: int = 0,
          loader_sample_step: int = 0,
      ) -> None:
          """Logs image to the logger."""
          self.run.log(
              {f"{tag}_scope_{scope}_epoch_{global_epoch_step}.png": wandb.Image(image)},
              step=global_epoch_step,
          )
  
      def log_hparams(
          self,
          hparams: Dict,
          scope: str = None,
          # experiment info
          run_key: str = None,
          stage_key: str = None,
      ) -> None:
          """Logs hyperparameters to the logger."""
          self.run.config.update(hparams)
  
      def flush_log(self) -> None:
          """Flushes the logger."""
          pass
  
      def close_log(self, scope: str = None) -> None:
          """Closes the logger."""
          if scope is None or scope == "experiment":
              self.run.finish()
  
  
  __all__ = ["WandbLogger"]
  