text: # Copyright 2020 Google LLC
  #
  # 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
  #
  #     https://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 os
  from uuid import uuid4
  
  import pytest
  
  import create_batch_prediction_job_tabular_forecasting_sample
  import helpers
  
  PROJECT_ID = os.getenv("BUILD_SPECIFIC_GCLOUD_PROJECT")
  LOCATION = "us-central1"
  MODEL_ID = "8531330622239539200"  # Permanent restaurant rating model
  DISPLAY_NAME = f"temp_create_batch_prediction_tabular_forecasting_test_{uuid4()}"
  GCS_SOURCE_URI = "gs://cloud-samples-data/ai-platform/covid/bigquery-public-covid-nyt-us-counties-train.csv"
  GCS_OUTPUT_URI = "gs://ucaip-samples-test-output/"
  PREDICTIONS_FORMAT = "csv"
  
  
  @pytest.fixture(scope="function", autouse=True)
  def teardown(teardown_batch_prediction_job):
      yield
  
  
  @pytest.mark.skip(reason="https://github.com/googleapis/java-aiplatform/issues/420")
  # Creating AutoML Tabular Forecasting Classification batch prediction job
  def test_create_batch_prediction_job_tabular_forecasting_sample(capsys, shared_state):
  
      model_name = f"projects/{PROJECT_ID}/locations/{LOCATION}/models/{MODEL_ID}"
  
      create_batch_prediction_job_tabular_forecasting_sample.create_batch_prediction_job_tabular_forecasting_sample(
          project=PROJECT_ID,
          display_name=DISPLAY_NAME,
          model_name=model_name,
          gcs_source_uri=GCS_SOURCE_URI,
          gcs_destination_output_uri_prefix=GCS_OUTPUT_URI,
          predictions_format=PREDICTIONS_FORMAT,
      )
  
      out, _ = capsys.readouterr()
  
      # Save resource name of the newly created batch prediction job
      shared_state["batch_prediction_job_name"] = helpers.get_name(out)
  