text: # Copyright 2018-2020 Amazon.com, Inc. or its affiliates. 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. A copy of
  # the License is located at
  #
  #     http://aws.amazon.com/apache2.0/
  #
  # or in the "license" file accompanying this file. This file 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.
  """Placeholder docstring"""
  from __future__ import absolute_import
  
  import logging
  
  import sagemaker
  from sagemaker import image_uris
  from sagemaker.deserializers import CSVDeserializer
  from sagemaker.fw_utils import model_code_key_prefix
  from sagemaker.model import FrameworkModel, MODEL_SERVER_WORKERS_PARAM_NAME
  from sagemaker.predictor import Predictor
  from sagemaker.serializers import LibSVMSerializer
  from sagemaker.xgboost.defaults import XGBOOST_NAME
  from sagemaker.xgboost.utils import validate_py_version, validate_framework_version
  
  logger = logging.getLogger("sagemaker")
  
  
  class XGBoostPredictor(Predictor):
      """A Predictor for inference against XGBoost Endpoints.
  
      This is able to serialize Python lists, dictionaries, and numpy arrays to xgb.DMatrix
      for XGBoost inference.
      """
  
      def __init__(self, endpoint_name, sagemaker_session=None):
          """Initialize an ``XGBoostPredictor``.
  
          Args:
              endpoint_name (str): The name of the endpoint to perform inference on.
              sagemaker_session (sagemaker.session.Session): Session object which manages
                  interactions with Amazon SageMaker APIs and any other AWS services needed.
                  If not specified, the estimator creates one using the default AWS configuration
                  chain.
          """
          super(XGBoostPredictor, self).__init__(
              endpoint_name, sagemaker_session, LibSVMSerializer(), CSVDeserializer()
          )
  
  
  class XGBoostModel(FrameworkModel):
      """An XGBoost SageMaker ``Model`` that can be deployed to a SageMaker ``Endpoint``."""
  
      _framework_name = XGBOOST_NAME
  
      def __init__(
          self,
          model_data,
          role,
          entry_point,
          framework_version,
          image_uri=None,
          py_version="py3",
          predictor_cls=XGBoostPredictor,
          model_server_workers=None,
          **kwargs
      ):
          """Initialize an XGBoostModel.
  
          Args:
              model_data (str): The S3 location of a SageMaker model data ``.tar.gz`` file.
              role (str): An AWS IAM role (either name or full ARN). The Amazon SageMaker training
                  jobs and APIs that create Amazon SageMaker endpoints use this role to access
                  training data and model artifacts. After the endpoint is created, the inference
                  code might use the IAM role, if it needs to access an AWS resource.
              entry_point (str): Path (absolute or relative) to the Python source file which should
                  be executed  as the entry point to model hosting. If ``source_dir`` is specified,
                  then ``entry_point`` must point to a file located at the root of ``source_dir``.
              image_uri (str): A Docker image URI (default: None). If not specified, a default image
                  for XGBoost is be used.
              py_version (str): Python version you want to use for executing your model training code
                  (default: 'py3').
              framework_version (str): XGBoost version you want to use for executing your model
                  training code.
              predictor_cls (callable[str, sagemaker.session.Session]): A function to call to create
                  a predictor with an endpoint name and SageMaker ``Session``.
                  If specified, ``deploy()`` returns the result of invoking this function on the
                  created endpoint name.
              model_server_workers (int): Optional. The number of worker processes used by the
                  inference server. If None, server will use one worker per vCPU.
              **kwargs: Keyword arguments passed to the superclass
                  :class:`~sagemaker.model.FrameworkModel` and, subsequently, its
                  superclass :class:`~sagemaker.model.Model`.
  
          .. tip::
  
              You can find additional parameters for initializing this class at
              :class:`~sagemaker.model.FrameworkModel` and
              :class:`~sagemaker.model.Model`.
          """
          super(XGBoostModel, self).__init__(
              model_data, image_uri, role, entry_point, predictor_cls=predictor_cls, **kwargs
          )
  
          self.py_version = py_version
          self.framework_version = framework_version
          self.model_server_workers = model_server_workers
  
          validate_py_version(py_version)
          validate_framework_version(framework_version)
  
      def prepare_container_def(self, instance_type=None, accelerator_type=None):
          """Return a container definition with framework configuration
          set in model environment variables.
  
          Args:
              instance_type (str): The EC2 instance type to deploy this Model to.
              accelerator_type (str): The Elastic Inference accelerator type to deploy to the
              instance for loading and making inferences to the model. This parameter is
                  unused because accelerator types are not supported by XGBoostModel.
  
          Returns:
              dict[str, str]: A container definition object usable with the CreateModel API.
          """
          deploy_image = self.image_uri
          if not deploy_image:
              deploy_image = self.serving_image_uri(
                  self.sagemaker_session.boto_region_name, instance_type
              )
  
          deploy_key_prefix = model_code_key_prefix(self.key_prefix, self.name, deploy_image)
          self._upload_code(deploy_key_prefix)
          deploy_env = dict(self.env)
          deploy_env.update(self._framework_env_vars())
  
          if self.model_server_workers:
              deploy_env[MODEL_SERVER_WORKERS_PARAM_NAME.upper()] = str(self.model_server_workers)
          return sagemaker.container_def(deploy_image, self.model_data, deploy_env)
  
      def serving_image_uri(self, region_name, instance_type):
          """Create a URI for the serving image.
  
          Args:
              region_name (str): AWS region where the image is uploaded.
              instance_type (str): SageMaker instance type. Must be a CPU instance type.
  
          Returns:
              str: The appropriate image URI based on the given parameters.
          """
          return image_uris.retrieve(
              self._framework_name,
              region_name,
              version=self.framework_version,
              instance_type=instance_type,
          )
  