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
  
  from google.protobuf import json_format
  from google.protobuf.struct_pb2 import Value
  import pytest
  
  import cancel_training_pipeline_sample
  import helpers
  
  PROJECT_ID = os.getenv("BUILD_SPECIFIC_GCLOUD_PROJECT")
  LOCATION = "us-central1"
  DATASET_ID = "1084241610289446912"  # Permanent 50 Flowers Dataset
  DISPLAY_NAME = f"temp_create_training_pipeline_test_{uuid4()}"
  TRAINING_DEFINITION_GCS_PATH = "gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_image_classification_1.0.0.yaml"
  
  
  @pytest.fixture(autouse=True)
  def setup(shared_state, pipeline_client):
      training_task_inputs = json_format.ParseDict({}, Value())
      training_pipeline = pipeline_client.create_training_pipeline(
          parent=f"projects/{PROJECT_ID}/locations/{LOCATION}",
          training_pipeline={
              "display_name": DISPLAY_NAME,
              "training_task_definition": TRAINING_DEFINITION_GCS_PATH,
              "training_task_inputs": training_task_inputs,
              "input_data_config": {"dataset_id": DATASET_ID},
              "model_to_upload": {"display_name": f"Temp Model for {DISPLAY_NAME}"},
          },
      )
  
      shared_state["training_pipeline_name"] = training_pipeline.name
  
      yield
  
  
  @pytest.fixture(autouse=True)
  def teardown(shared_state, pipeline_client):
      yield
  
      pipeline_client.delete_training_pipeline(
          name=shared_state["training_pipeline_name"]
      )
  
  
  def test_ucaip_generated_cancel_training_pipeline_sample(
      capsys, shared_state, pipeline_client
  ):
      # Run cancel pipeline sample
      training_pipeline_id = shared_state["training_pipeline_name"].split("/")[-1]
  
      cancel_training_pipeline_sample.cancel_training_pipeline_sample(
          project=PROJECT_ID, training_pipeline_id=training_pipeline_id
      )
  
      # Waiting for training pipeline to be in CANCELLED state, otherwise raise error
      helpers.wait_for_job_state(
          get_job_method=pipeline_client.get_training_pipeline,
          name=shared_state["training_pipeline_name"],
      )
  