code: # Copyright 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.
  from __future__ import absolute_import
  
  import os
  
  import numpy as np
  import pytest
  
  from sagemaker import NTM, NTMModel, Predictor
  from sagemaker.amazon.common import read_records
  from sagemaker.serverless import ServerlessInferenceConfig
  from sagemaker.utils import unique_name_from_base
  from tests.integ import DATA_DIR, TRAINING_DEFAULT_TIMEOUT_MINUTES
  from tests.integ.timeout import timeout, timeout_and_delete_endpoint_by_name
  from tests.integ.record_set import prepare_record_set_from_local_files
  
  
  @pytest.mark.release
  @pytest.mark.skip(
      reason="This test has always failed, but the failure was masked by a bug. "
      "This test should be fixed. Details in https://github.com/aws/sagemaker-python-sdk/pull/968"
  )
  def test_ntm(sagemaker_session, cpu_instance_type):
      job_name = unique_name_from_base("ntm")
  
      with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
          data_path = os.path.join(DATA_DIR, "ntm")
          data_filename = "nips-train_1.pbr"
  
          with open(os.path.join(data_path, data_filename), "rb") as f:
              all_records = read_records(f)
  
          # all records must be same
          feature_num = int(all_records[0].features["values"].float32_tensor.shape[0])
  
          ntm = NTM(
              role="SageMakerRole",
              instance_count=1,
              instance_type=cpu_instance_type,
              num_topics=10,
              sagemaker_session=sagemaker_session,
          )
  
          record_set = prepare_record_set_from_local_files(
              data_path, ntm.data_location, len(all_records), feature_num, sagemaker_session
          )
          ntm.fit(records=record_set, job_name=job_name)
  
      with timeout_and_delete_endpoint_by_name(job_name, sagemaker_session):
          model = NTMModel(ntm.model_data, role="SageMakerRole", sagemaker_session=sagemaker_session)
          predictor = model.deploy(1, cpu_instance_type, endpoint_name=job_name)
  
          predict_input = np.random.rand(1, feature_num)
          result = predictor.predict(predict_input)
  
          assert len(result) == 1
          for record in result:
              assert record.label["topic_weights"] is not None
  
  
  def test_ntm_serverless_inference(sagemaker_session, cpu_instance_type):
      job_name = unique_name_from_base("ntm-serverless")
  
      with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
          data_path = os.path.join(DATA_DIR, "ntm")
          data_filename = "nips-train_1.pbr"
  
          with open(os.path.join(data_path, data_filename), "rb") as f:
              all_records = read_records(f)
  
          # all records must be same
          feature_num = int(all_records[0].features["values"].float32_tensor.shape[0])
  
          ntm = NTM(
              role="SageMakerRole",
              instance_count=1,
              instance_type=cpu_instance_type,
              num_topics=10,
              sagemaker_session=sagemaker_session,
          )
  
          record_set = prepare_record_set_from_local_files(
              data_path, ntm.data_location, len(all_records), feature_num, sagemaker_session
          )
          ntm.fit(records=record_set, job_name=job_name)
  
      with timeout_and_delete_endpoint_by_name(job_name, sagemaker_session):
          model = NTMModel(ntm.model_data, role="SageMakerRole", sagemaker_session=sagemaker_session)
          predictor = model.deploy(
              serverless_inference_config=ServerlessInferenceConfig(), endpoint_name=job_name
          )
          assert isinstance(predictor, Predictor)
  
apis: numpy.random.rand