text: # Copyright 2017-2018 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
  
  from sagemaker.predictor import RealTimePredictor
  from sagemaker import Object2Vec, Object2VecModel
  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
  
  FEATURE_NUM = None
  
  
  def test_object2vec(sagemaker_session):
      job_name = unique_name_from_base('object2vec')
  
      with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
          data_path = os.path.join(DATA_DIR, 'object2vec')
          data_filename = 'train.jsonl'
  
          with open(os.path.join(data_path, data_filename), 'r') as f:
              num_records = len(f.readlines())
  
          object2vec = Object2Vec(
              role='SageMakerRole',
              train_instance_count=1,
              train_instance_type='ml.c4.xlarge',
              epochs=3,
              enc0_max_seq_len=20,
              enc0_vocab_size=45000,
              enc_dim=16,
              num_classes=3,
              negative_sampling_rate=0,
              comparator_list='hadamard,concat,abs_diff',
              tied_token_embedding_weight=False,
              token_embedding_storage_type='dense',
              sagemaker_session=sagemaker_session)
  
          record_set = prepare_record_set_from_local_files(data_path, object2vec.data_location,
                                                           num_records, FEATURE_NUM,
                                                           sagemaker_session)
  
          object2vec.fit(records=record_set, job_name=job_name)
  
      with timeout_and_delete_endpoint_by_name(job_name, sagemaker_session):
          model = Object2VecModel(object2vec.model_data, role='SageMakerRole',
                                  sagemaker_session=sagemaker_session)
          predictor = model.deploy(1, 'ml.c4.xlarge', endpoint_name=job_name)
          assert isinstance(predictor, RealTimePredictor)
  
          predict_input = {'instances': [{"in0": [354, 623], "in1": [16]}]}
  
          result = predictor.predict(predict_input)
  
          assert len(result) == 1
          for record in result:
              assert record.label["scores"] is not None
  