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  #  Licensed under the Apache License, Version 2.0 (the "License");
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  #
  #       http://www.apache.org/licenses/LICENSE-2.0
  #
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  """Implementation of the BentoML bento builder step."""
  
  import importlib
  import os
  from typing import Any, Dict, List, Optional
  
  import bentoml
  from bentoml import bentos
  from bentoml._internal.bento import bento
  
  from zenml import get_step_context, step
  from zenml.artifacts.unmaterialized_artifact import UnmaterializedArtifact
  from zenml.artifacts.utils import load_artifact_from_response
  from zenml.integrations.bentoml.constants import DEFAULT_BENTO_FILENAME
  from zenml.logger import get_logger
  from zenml.utils import source_utils
  
  logger = get_logger(__name__)
  
  
  @step
  def bento_builder_step(
      model: UnmaterializedArtifact,
      model_name: str,
      model_type: str,
      service: str,
      version: Optional[str] = None,
      labels: Optional[Dict[str, str]] = None,
      description: Optional[str] = None,
      include: Optional[List[str]] = None,
      exclude: Optional[List[str]] = None,
      python: Optional[Dict[str, Any]] = None,
      docker: Optional[Dict[str, Any]] = None,
      working_dir: Optional[str] = None,
  ) -> bento.Bento:
      """Build a BentoML Model and Bento bundle.
  
      This steps takes a model artifact of a trained or loaded ML model in a
      previous step and save it with BentoML, then build a BentoML bundle.
  
      Args:
          model: the model to be packaged.
          model_name: the name of the model to be packaged.
          model_type: the type of the model.
          service: the name of the BentoML service to be deployed.
          version: the version of the model if given.
          labels: the labels of the model if given.
          description: the description of the model if given.
          include: the files to be included in the BentoML bundle.
          exclude: the files to be excluded from the BentoML bundle.
          python: dictionary for configuring Bento's python dependencies,
          docker: dictionary for configuring Bento's docker image.
          working_dir: the working directory of the BentoML bundle.
  
      Returns:
          the BentoML Bento object.
      """
      context = get_step_context()
  
      # save the model and bento uri as part of the bento labels
      labels = labels or {}
      labels["model_uri"] = model.uri
      labels["bento_uri"] = os.path.join(
          context.get_output_artifact_uri(), DEFAULT_BENTO_FILENAME
      )
  
      # Load the model from the model artifact
      model = load_artifact_from_response(model)
  
      # Save the model to a BentoML model based on the model type
      try:
          module = importlib.import_module(f".{model_type}", "bentoml")
          module.save_model(model_name, model, labels=labels)
      except importlib.metadata.PackageNotFoundError:
          bentoml.picklable_model.save_model(
              model_name,
              model,
          )
  
      # Build the BentoML bundle
      bento = bentos.build(
          service=service,
          models=[model_name],
          version=version,
          labels=labels,
          description=description,
          include=include,
          exclude=exclude,
          python=python,
          docker=docker,
          build_ctx=working_dir or source_utils.get_source_root(),
      )
  
      # Return the BentoML Bento bundle
      return bento
  