#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you 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
#
#   http://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.

# mypy ignore arg types (for templated fields)
# type: ignore[arg-type]

"""
Example Airflow DAG for Google Vertex AI service testing Auto ML operations.
"""
from __future__ import annotations

import os
from datetime import datetime

from google.cloud.aiplatform import schema
from google.protobuf.struct_pb2 import Value

from airflow import models
from airflow.providers.google.cloud.operators.gcs import (
    GCSCreateBucketOperator,
    GCSDeleteBucketOperator,
    GCSSynchronizeBucketsOperator,
)
from airflow.providers.google.cloud.operators.vertex_ai.auto_ml import (
    CreateAutoMLImageTrainingJobOperator,
    DeleteAutoMLTrainingJobOperator,
)
from airflow.providers.google.cloud.operators.vertex_ai.dataset import (
    CreateDatasetOperator,
    DeleteDatasetOperator,
    ImportDataOperator,
)
from airflow.utils.trigger_rule import TriggerRule

ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID", "default")
PROJECT_ID = os.environ.get("SYSTEM_TESTS_GCP_PROJECT", "default")
DAG_ID = "example_vertex_ai_auto_ml_operations"
REGION = "us-central1"
IMAGE_DISPLAY_NAME = f"auto-ml-image-{ENV_ID}"
MODEL_DISPLAY_NAME = f"auto-ml-image-model-{ENV_ID}"

RESOURCE_DATA_BUCKET = "airflow-system-tests-resources"
IMAGE_GCS_BUCKET_NAME = f"bucket_image_{DAG_ID}_{ENV_ID}".replace("_", "-")

IMAGE_DATASET = {
    "display_name": f"image-dataset-{ENV_ID}",
    "metadata_schema_uri": schema.dataset.metadata.image,
    "metadata": Value(string_value="image-dataset"),
}
IMAGE_DATA_CONFIG = [
    {
        "import_schema_uri": schema.dataset.ioformat.image.single_label_classification,
        "gcs_source": {"uris": [f"gs://{IMAGE_GCS_BUCKET_NAME}/vertex-ai/image-dataset.csv"]},
    },
]


with models.DAG(
    f"{DAG_ID}_image_training_job",
    schedule="@once",
    start_date=datetime(2021, 1, 1),
    catchup=False,
    tags=["example", "vertex_ai", "auto_ml"],
) as dag:
    create_bucket = GCSCreateBucketOperator(
        task_id="create_bucket",
        bucket_name=IMAGE_GCS_BUCKET_NAME,
        storage_class="REGIONAL",
        location=REGION,
    )

    move_dataset_file = GCSSynchronizeBucketsOperator(
        task_id="move_dataset_to_bucket",
        source_bucket=RESOURCE_DATA_BUCKET,
        source_object="vertex-ai/datasets",
        destination_bucket=IMAGE_GCS_BUCKET_NAME,
        destination_object="vertex-ai",
        recursive=True,
    )

    create_image_dataset = CreateDatasetOperator(
        task_id="image_dataset",
        dataset=IMAGE_DATASET,
        region=REGION,
        project_id=PROJECT_ID,
    )
    image_dataset_id = create_image_dataset.output["dataset_id"]

    import_image_dataset = ImportDataOperator(
        task_id="import_image_data",
        dataset_id=image_dataset_id,
        region=REGION,
        project_id=PROJECT_ID,
        import_configs=IMAGE_DATA_CONFIG,
    )

    # [START how_to_cloud_vertex_ai_create_auto_ml_image_training_job_operator]
    create_auto_ml_image_training_job = CreateAutoMLImageTrainingJobOperator(
        task_id="auto_ml_image_task",
        display_name=IMAGE_DISPLAY_NAME,
        dataset_id=image_dataset_id,
        prediction_type="classification",
        multi_label=False,
        model_type="CLOUD",
        training_fraction_split=0.6,
        validation_fraction_split=0.2,
        test_fraction_split=0.2,
        budget_milli_node_hours=8000,
        model_display_name=MODEL_DISPLAY_NAME,
        disable_early_stopping=False,
        region=REGION,
        project_id=PROJECT_ID,
    )
    # [END how_to_cloud_vertex_ai_create_auto_ml_image_training_job_operator]

    delete_auto_ml_image_training_job = DeleteAutoMLTrainingJobOperator(
        task_id="delete_auto_ml_training_job",
        training_pipeline_id="{{ task_instance.xcom_pull(task_ids='auto_ml_image_task', "
        "key='training_id') }}",
        region=REGION,
        project_id=PROJECT_ID,
        trigger_rule=TriggerRule.ALL_DONE,
    )

    delete_image_dataset = DeleteDatasetOperator(
        task_id="delete_image_dataset",
        dataset_id=image_dataset_id,
        region=REGION,
        project_id=PROJECT_ID,
        trigger_rule=TriggerRule.ALL_DONE,
    )
    delete_bucket = GCSDeleteBucketOperator(
        task_id="delete_bucket",
        bucket_name=IMAGE_GCS_BUCKET_NAME,
        trigger_rule=TriggerRule.ALL_DONE,
    )

    (
        # TEST SETUP
        [
            create_bucket >> move_dataset_file,
            create_image_dataset,
        ]
        >> import_image_dataset
        # TEST BODY
        >> create_auto_ml_image_training_job
        # TEST TEARDOWN
        >> delete_auto_ml_image_training_job
        >> delete_image_dataset
        >> delete_bucket
    )


from tests.system.utils import get_test_run  # noqa: E402

# Needed to run the example DAG with pytest (see: tests/system/README.md#run_via_pytest)
test_run = get_test_run(dag)

3796e9f2c9c4399885c2c2a4f7d8977c0902451c
<mask token>

<mask token>
with models.DAG(f'{DAG_ID}_image_training_job', schedule='@once',
    start_date=datetime(2021, 1, 1), catchup=False, tags=['example',
    'vertex_ai', 'auto_ml']) as dag:
    create_bucket = GCSCreateBucketOperator(task_id='create_bucket',
        bucket_name=IMAGE_GCS_BUCKET_NAME, storage_class='REGIONAL',
        location=REGION)
    move_dataset_file = GCSSynchronizeBucketsOperator(task_id=
        'move_dataset_to_bucket', source_bucket=RESOURCE_DATA_BUCKET,
        source_object='vertex-ai/datasets', destination_bucket=
        IMAGE_GCS_BUCKET_NAME, destination_object='vertex-ai', recursive=True)
    create_image_dataset = CreateDatasetOperator(task_id='image_dataset',
        dataset=IMAGE_DATASET, region=REGION, project_id=PROJECT_ID)
    image_dataset_id = create_image_dataset.output['dataset_id']
    import_image_dataset = ImportDataOperator(task_id='import_image_data',
        dataset_id=image_dataset_id, region=REGION, project_id=PROJECT_ID,
        import_configs=IMAGE_DATA_CONFIG)
    create_auto_ml_image_training_job = CreateAutoMLImageTrainingJobOperator(
        task_id='auto_ml_image_task', display_name=IMAGE_DISPLAY_NAME,
        dataset_id=image_dataset_id, prediction_type='classification',
        multi_label=False, model_type='CLOUD', training_fraction_split=0.6,
        validation_fraction_split=0.2, test_fraction_split=0.2,
        budget_milli_node_hours=8000, model_display_name=MODEL_DISPLAY_NAME,
        disable_early_stopping=False, region=REGION, project_id=PROJECT_ID)
    delete_auto_ml_image_training_job = DeleteAutoMLTrainingJobOperator(task_id
        ='delete_auto_ml_training_job', training_pipeline_id=
        "{{ task_instance.xcom_pull(task_ids='auto_ml_image_task', key='training_id') }}"
        , region=REGION, project_id=PROJECT_ID, trigger_rule=TriggerRule.
        ALL_DONE)
    delete_image_dataset = DeleteDatasetOperator(task_id=
        'delete_image_dataset', dataset_id=image_dataset_id, region=REGION,
        project_id=PROJECT_ID, trigger_rule=TriggerRule.ALL_DONE)
    delete_bucket = GCSDeleteBucketOperator(task_id='delete_bucket',
        bucket_name=IMAGE_GCS_BUCKET_NAME, trigger_rule=TriggerRule.ALL_DONE)
    [create_bucket >> move_dataset_file, create_image_dataset
        ] >> import_image_dataset >> create_auto_ml_image_training_job >> delete_auto_ml_image_training_job >> delete_image_dataset >> delete_bucket
<mask token>

<mask token>
ENV_ID = os.environ.get('SYSTEM_TESTS_ENV_ID', 'default')
PROJECT_ID = os.environ.get('SYSTEM_TESTS_GCP_PROJECT', 'default')
DAG_ID = 'example_vertex_ai_auto_ml_operations'
REGION = 'us-central1'
IMAGE_DISPLAY_NAME = f'auto-ml-image-{ENV_ID}'
MODEL_DISPLAY_NAME = f'auto-ml-image-model-{ENV_ID}'
RESOURCE_DATA_BUCKET = 'airflow-system-tests-resources'
IMAGE_GCS_BUCKET_NAME = f'bucket_image_{DAG_ID}_{ENV_ID}'.replace('_', '-')
IMAGE_DATASET = {'display_name': f'image-dataset-{ENV_ID}',
    'metadata_schema_uri': schema.dataset.metadata.image, 'metadata': Value
    (string_value='image-dataset')}
IMAGE_DATA_CONFIG = [{'import_schema_uri': schema.dataset.ioformat.image.
    single_label_classification, 'gcs_source': {'uris': [
    f'gs://{IMAGE_GCS_BUCKET_NAME}/vertex-ai/image-dataset.csv']}}]
with models.DAG(f'{DAG_ID}_image_training_job', schedule='@once',
    start_date=datetime(2021, 1, 1), catchup=False, tags=['example',
    'vertex_ai', 'auto_ml']) as dag:
    create_bucket = GCSCreateBucketOperator(task_id='create_bucket',
        bucket_name=IMAGE_GCS_BUCKET_NAME, storage_class='REGIONAL',
        location=REGION)
    move_dataset_file = GCSSynchronizeBucketsOperator(task_id=
        'move_dataset_to_bucket', source_bucket=RESOURCE_DATA_BUCKET,
        source_object='vertex-ai/datasets', destination_bucket=
        IMAGE_GCS_BUCKET_NAME, destination_object='vertex-ai', recursive=True)
    create_image_dataset = CreateDatasetOperator(task_id='image_dataset',
        dataset=IMAGE_DATASET, region=REGION, project_id=PROJECT_ID)
    image_dataset_id = create_image_dataset.output['dataset_id']
    import_image_dataset = ImportDataOperator(task_id='import_image_data',
        dataset_id=image_dataset_id, region=REGION, project_id=PROJECT_ID,
        import_configs=IMAGE_DATA_CONFIG)
    create_auto_ml_image_training_job = CreateAutoMLImageTrainingJobOperator(
        task_id='auto_ml_image_task', display_name=IMAGE_DISPLAY_NAME,
        dataset_id=image_dataset_id, prediction_type='classification',
        multi_label=False, model_type='CLOUD', training_fraction_split=0.6,
        validation_fraction_split=0.2, test_fraction_split=0.2,
        budget_milli_node_hours=8000, model_display_name=MODEL_DISPLAY_NAME,
        disable_early_stopping=False, region=REGION, project_id=PROJECT_ID)
    delete_auto_ml_image_training_job = DeleteAutoMLTrainingJobOperator(task_id
        ='delete_auto_ml_training_job', training_pipeline_id=
        "{{ task_instance.xcom_pull(task_ids='auto_ml_image_task', key='training_id') }}"
        , region=REGION, project_id=PROJECT_ID, trigger_rule=TriggerRule.
        ALL_DONE)
    delete_image_dataset = DeleteDatasetOperator(task_id=
        'delete_image_dataset', dataset_id=image_dataset_id, region=REGION,
        project_id=PROJECT_ID, trigger_rule=TriggerRule.ALL_DONE)
    delete_bucket = GCSDeleteBucketOperator(task_id='delete_bucket',
        bucket_name=IMAGE_GCS_BUCKET_NAME, trigger_rule=TriggerRule.ALL_DONE)
    [create_bucket >> move_dataset_file, create_image_dataset
        ] >> import_image_dataset >> create_auto_ml_image_training_job >> delete_auto_ml_image_training_job >> delete_image_dataset >> delete_bucket
<mask token>
test_run = get_test_run(dag)

<mask token>
from __future__ import annotations
import os
from datetime import datetime
from google.cloud.aiplatform import schema
from google.protobuf.struct_pb2 import Value
from airflow import models
from airflow.providers.google.cloud.operators.gcs import GCSCreateBucketOperator, GCSDeleteBucketOperator, GCSSynchronizeBucketsOperator
from airflow.providers.google.cloud.operators.vertex_ai.auto_ml import CreateAutoMLImageTrainingJobOperator, DeleteAutoMLTrainingJobOperator
from airflow.providers.google.cloud.operators.vertex_ai.dataset import CreateDatasetOperator, DeleteDatasetOperator, ImportDataOperator
from airflow.utils.trigger_rule import TriggerRule
ENV_ID = os.environ.get('SYSTEM_TESTS_ENV_ID', 'default')
PROJECT_ID = os.environ.get('SYSTEM_TESTS_GCP_PROJECT', 'default')
DAG_ID = 'example_vertex_ai_auto_ml_operations'
REGION = 'us-central1'
IMAGE_DISPLAY_NAME = f'auto-ml-image-{ENV_ID}'
MODEL_DISPLAY_NAME = f'auto-ml-image-model-{ENV_ID}'
RESOURCE_DATA_BUCKET = 'airflow-system-tests-resources'
IMAGE_GCS_BUCKET_NAME = f'bucket_image_{DAG_ID}_{ENV_ID}'.replace('_', '-')
IMAGE_DATASET = {'display_name': f'image-dataset-{ENV_ID}',
    'metadata_schema_uri': schema.dataset.metadata.image, 'metadata': Value
    (string_value='image-dataset')}
IMAGE_DATA_CONFIG = [{'import_schema_uri': schema.dataset.ioformat.image.
    single_label_classification, 'gcs_source': {'uris': [
    f'gs://{IMAGE_GCS_BUCKET_NAME}/vertex-ai/image-dataset.csv']}}]
with models.DAG(f'{DAG_ID}_image_training_job', schedule='@once',
    start_date=datetime(2021, 1, 1), catchup=False, tags=['example',
    'vertex_ai', 'auto_ml']) as dag:
    create_bucket = GCSCreateBucketOperator(task_id='create_bucket',
        bucket_name=IMAGE_GCS_BUCKET_NAME, storage_class='REGIONAL',
        location=REGION)
    move_dataset_file = GCSSynchronizeBucketsOperator(task_id=
        'move_dataset_to_bucket', source_bucket=RESOURCE_DATA_BUCKET,
        source_object='vertex-ai/datasets', destination_bucket=
        IMAGE_GCS_BUCKET_NAME, destination_object='vertex-ai', recursive=True)
    create_image_dataset = CreateDatasetOperator(task_id='image_dataset',
        dataset=IMAGE_DATASET, region=REGION, project_id=PROJECT_ID)
    image_dataset_id = create_image_dataset.output['dataset_id']
    import_image_dataset = ImportDataOperator(task_id='import_image_data',
        dataset_id=image_dataset_id, region=REGION, project_id=PROJECT_ID,
        import_configs=IMAGE_DATA_CONFIG)
    create_auto_ml_image_training_job = CreateAutoMLImageTrainingJobOperator(
        task_id='auto_ml_image_task', display_name=IMAGE_DISPLAY_NAME,
        dataset_id=image_dataset_id, prediction_type='classification',
        multi_label=False, model_type='CLOUD', training_fraction_split=0.6,
        validation_fraction_split=0.2, test_fraction_split=0.2,
        budget_milli_node_hours=8000, model_display_name=MODEL_DISPLAY_NAME,
        disable_early_stopping=False, region=REGION, project_id=PROJECT_ID)
    delete_auto_ml_image_training_job = DeleteAutoMLTrainingJobOperator(task_id
        ='delete_auto_ml_training_job', training_pipeline_id=
        "{{ task_instance.xcom_pull(task_ids='auto_ml_image_task', key='training_id') }}"
        , region=REGION, project_id=PROJECT_ID, trigger_rule=TriggerRule.
        ALL_DONE)
    delete_image_dataset = DeleteDatasetOperator(task_id=
        'delete_image_dataset', dataset_id=image_dataset_id, region=REGION,
        project_id=PROJECT_ID, trigger_rule=TriggerRule.ALL_DONE)
    delete_bucket = GCSDeleteBucketOperator(task_id='delete_bucket',
        bucket_name=IMAGE_GCS_BUCKET_NAME, trigger_rule=TriggerRule.ALL_DONE)
    [create_bucket >> move_dataset_file, create_image_dataset
        ] >> import_image_dataset >> create_auto_ml_image_training_job >> delete_auto_ml_image_training_job >> delete_image_dataset >> delete_bucket
from tests.system.utils import get_test_run
test_run = get_test_run(dag)

#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you 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
#
#   http://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.

# mypy ignore arg types (for templated fields)
# type: ignore[arg-type]

"""
Example Airflow DAG for Google Vertex AI service testing Auto ML operations.
"""
from __future__ import annotations

import os
from datetime import datetime

from google.cloud.aiplatform import schema
from google.protobuf.struct_pb2 import Value

from airflow import models
from airflow.providers.google.cloud.operators.gcs import (
    GCSCreateBucketOperator,
    GCSDeleteBucketOperator,
    GCSSynchronizeBucketsOperator,
)
from airflow.providers.google.cloud.operators.vertex_ai.auto_ml import (
    CreateAutoMLImageTrainingJobOperator,
    DeleteAutoMLTrainingJobOperator,
)
from airflow.providers.google.cloud.operators.vertex_ai.dataset import (
    CreateDatasetOperator,
    DeleteDatasetOperator,
    ImportDataOperator,
)
from airflow.utils.trigger_rule import TriggerRule

ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID", "default")
PROJECT_ID = os.environ.get("SYSTEM_TESTS_GCP_PROJECT", "default")
DAG_ID = "example_vertex_ai_auto_ml_operations"
REGION = "us-central1"
IMAGE_DISPLAY_NAME = f"auto-ml-image-{ENV_ID}"
MODEL_DISPLAY_NAME = f"auto-ml-image-model-{ENV_ID}"

RESOURCE_DATA_BUCKET = "airflow-system-tests-resources"
IMAGE_GCS_BUCKET_NAME = f"bucket_image_{DAG_ID}_{ENV_ID}".replace("_", "-")

IMAGE_DATASET = {
    "display_name": f"image-dataset-{ENV_ID}",
    "metadata_schema_uri": schema.dataset.metadata.image,
    "metadata": Value(string_value="image-dataset"),
}
IMAGE_DATA_CONFIG = [
    {
        "import_schema_uri": schema.dataset.ioformat.image.single_label_classification,
        "gcs_source": {"uris": [f"gs://{IMAGE_GCS_BUCKET_NAME}/vertex-ai/image-dataset.csv"]},
    },
]


with models.DAG(
    f"{DAG_ID}_image_training_job",
    schedule="@once",
    start_date=datetime(2021, 1, 1),
    catchup=False,
    tags=["example", "vertex_ai", "auto_ml"],
) as dag:
    create_bucket = GCSCreateBucketOperator(
        task_id="create_bucket",
        bucket_name=IMAGE_GCS_BUCKET_NAME,
        storage_class="REGIONAL",
        location=REGION,
    )

    move_dataset_file = GCSSynchronizeBucketsOperator(
        task_id="move_dataset_to_bucket",
        source_bucket=RESOURCE_DATA_BUCKET,
        source_object="vertex-ai/datasets",
        destination_bucket=IMAGE_GCS_BUCKET_NAME,
        destination_object="vertex-ai",
        recursive=True,
    )

    create_image_dataset = CreateDatasetOperator(
        task_id="image_dataset",
        dataset=IMAGE_DATASET,
        region=REGION,
        project_id=PROJECT_ID,
    )
    image_dataset_id = create_image_dataset.output["dataset_id"]

    import_image_dataset = ImportDataOperator(
        task_id="import_image_data",
        dataset_id=image_dataset_id,
        region=REGION,
        project_id=PROJECT_ID,
        import_configs=IMAGE_DATA_CONFIG,
    )

    # [START how_to_cloud_vertex_ai_create_auto_ml_image_training_job_operator]
    create_auto_ml_image_training_job = CreateAutoMLImageTrainingJobOperator(
        task_id="auto_ml_image_task",
        display_name=IMAGE_DISPLAY_NAME,
        dataset_id=image_dataset_id,
        prediction_type="classification",
        multi_label=False,
        model_type="CLOUD",
        training_fraction_split=0.6,
        validation_fraction_split=0.2,
        test_fraction_split=0.2,
        budget_milli_node_hours=8000,
        model_display_name=MODEL_DISPLAY_NAME,
        disable_early_stopping=False,
        region=REGION,
        project_id=PROJECT_ID,
    )
    # [END how_to_cloud_vertex_ai_create_auto_ml_image_training_job_operator]

    delete_auto_ml_image_training_job = DeleteAutoMLTrainingJobOperator(
        task_id="delete_auto_ml_training_job",
        training_pipeline_id="{{ task_instance.xcom_pull(task_ids='auto_ml_image_task', "
        "key='training_id') }}",
        region=REGION,
        project_id=PROJECT_ID,
        trigger_rule=TriggerRule.ALL_DONE,
    )

    delete_image_dataset = DeleteDatasetOperator(
        task_id="delete_image_dataset",
        dataset_id=image_dataset_id,
        region=REGION,
        project_id=PROJECT_ID,
        trigger_rule=TriggerRule.ALL_DONE,
    )
    delete_bucket = GCSDeleteBucketOperator(
        task_id="delete_bucket",
        bucket_name=IMAGE_GCS_BUCKET_NAME,
        trigger_rule=TriggerRule.ALL_DONE,
    )

    (
        # TEST SETUP
        [
            create_bucket >> move_dataset_file,
            create_image_dataset,
        ]
        >> import_image_dataset
        # TEST BODY
        >> create_auto_ml_image_training_job
        # TEST TEARDOWN
        >> delete_auto_ml_image_training_job
        >> delete_image_dataset
        >> delete_bucket
    )


from tests.system.utils import get_test_run  # noqa: E402

# Needed to run the example DAG with pytest (see: tests/system/README.md#run_via_pytest)
test_run = get_test_run(dag)
