content: #  Copyright (c) ZenML GmbH 2022. 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.
  
  import click
  from pipelines.deployment_pipelines.deployment_inference_pipeline import (
      mlflow_deployment_inference_pipeline,
  )
  from pipelines.deployment_pipelines.deployment_training_pipeline import (
      mlflow_train_deploy_pipeline,
  )
  from pipelines.registry_pipelines.registry_inference_pipeline import (
      mlflow_registry_inference_pipeline,
  )
  from pipelines.registry_pipelines.registry_training_pipeline import (
      mlflow_registry_training_pipeline,
  )
  from pipelines.tracking_pipeline.tracking_pipeline import (
      mlflow_tracking_pipeline,
  )
  
  
  @click.command()
  @click.option(
      "--type",
      default="tracking",
      help="The type of MLflow example to run.",
      type=click.Choice(["tracking", "registry", "deployment"]),
  )
  def main(type: str) -> None:
      if type == "tracking":
          mlflow_tracking_pipeline()
      elif type == "registry":
          mlflow_registry_training_pipeline()
          mlflow_registry_inference_pipeline()
      elif type == "deployment":
          mlflow_train_deploy_pipeline()
          mlflow_deployment_inference_pipeline()
      else:
          raise NotImplementedError(
              f"MLflow example type {type} not implemented."
          )
  
  
  if __name__ == "__main__":
      main()
  