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"""Example Airflow DAG for Google Vertex AI service testing Custom Jobs operations."""

from __future__ import annotations

import os
from datetime import datetime

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

from airflow.models.dag import DAG
from airflow.providers.google.cloud.operators.gcs import (
    GCSCreateBucketOperator,
    GCSDeleteBucketOperator,
    GCSSynchronizeBucketsOperator,
)
from airflow.providers.google.cloud.operators.vertex_ai.custom_job import (
    CreateCustomContainerTrainingJobOperator,
    DeleteCustomTrainingJobOperator,
)
from airflow.providers.google.cloud.operators.vertex_ai.dataset import (
    CreateDatasetOperator,
    DeleteDatasetOperator,
)

try:
    from airflow.sdk import TriggerRule
except ImportError:
    # Compatibility for Airflow < 3.1
    from airflow.utils.trigger_rule import TriggerRule  # type: ignore[no-redef,attr-defined]

ENV_ID = os.environ.get("SYSTEM_TESTS_ENV_ID", "default")
PROJECT_ID = os.environ.get("SYSTEM_TESTS_GCP_PROJECT", "default")
DAG_ID = "vertex_ai_custom_job_operations"
REGION = "us-central1"
CONTAINER_DISPLAY_NAME = f"train-housing-container-{ENV_ID}"
MODEL_DISPLAY_NAME = f"container-housing-model-{ENV_ID}"

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

DATA_SAMPLE_GCS_OBJECT_NAME = "vertex-ai/california_housing_train.csv"


def TABULAR_DATASET(bucket_name):
    return {
        "display_name": f"tabular-dataset-{ENV_ID}",
        "metadata_schema_uri": schema.dataset.metadata.tabular,
        "metadata": ParseDict(
            {"input_config": {"gcs_source": {"uri": [f"gs://{bucket_name}/{DATA_SAMPLE_GCS_OBJECT_NAME}"]}}},
            Value(),
        ),
    }


CUSTOM_CONTAINER_URI = "us-central1-docker.pkg.dev/airflow-system-tests-resources/system-tests/housing"
MODEL_SERVING_CONTAINER_URI = "us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-2:latest"
REPLICA_COUNT = 1
MACHINE_TYPE = "n1-standard-4"
ACCELERATOR_TYPE = "ACCELERATOR_TYPE_UNSPECIFIED"
ACCELERATOR_COUNT = 0
TRAINING_FRACTION_SPLIT = 0.7
TEST_FRACTION_SPLIT = 0.15
VALIDATION_FRACTION_SPLIT = 0.15


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

    move_data_files = GCSSynchronizeBucketsOperator(
        task_id="move_files_to_bucket",
        source_bucket=RESOURCE_DATA_BUCKET,
        source_object="vertex-ai/california-housing-data",
        destination_bucket=CUSTOM_CONTAINER_GCS_BUCKET_NAME,
        destination_object="vertex-ai",
        recursive=True,
    )

    create_tabular_dataset = CreateDatasetOperator(
        task_id="tabular_dataset",
        dataset=TABULAR_DATASET(CUSTOM_CONTAINER_GCS_BUCKET_NAME),
        region=REGION,
        project_id=PROJECT_ID,
    )
    tabular_dataset_id = create_tabular_dataset.output["dataset_id"]

    # [START how_to_cloud_vertex_ai_create_custom_container_training_job_operator]
    create_custom_container_training_job = CreateCustomContainerTrainingJobOperator(
        task_id="custom_container_task",
        staging_bucket=f"gs://{CUSTOM_CONTAINER_GCS_BUCKET_NAME}",
        display_name=CONTAINER_DISPLAY_NAME,
        container_uri=CUSTOM_CONTAINER_URI,
        model_serving_container_image_uri=MODEL_SERVING_CONTAINER_URI,
        # run params
        dataset_id=tabular_dataset_id,
        command=["python3", "task.py"],
        model_display_name=MODEL_DISPLAY_NAME,
        replica_count=REPLICA_COUNT,
        machine_type=MACHINE_TYPE,
        accelerator_type=ACCELERATOR_TYPE,
        accelerator_count=ACCELERATOR_COUNT,
        training_fraction_split=TRAINING_FRACTION_SPLIT,
        validation_fraction_split=VALIDATION_FRACTION_SPLIT,
        test_fraction_split=TEST_FRACTION_SPLIT,
        region=REGION,
        project_id=PROJECT_ID,
    )
    # [END how_to_cloud_vertex_ai_create_custom_container_training_job_operator]

    # [START how_to_cloud_vertex_ai_create_custom_container_training_job_operator_deferrable]
    create_custom_container_training_job_deferrable = CreateCustomContainerTrainingJobOperator(
        task_id="custom_container_task_deferrable",
        staging_bucket=f"gs://{CUSTOM_CONTAINER_GCS_BUCKET_NAME}",
        display_name=f"{CONTAINER_DISPLAY_NAME}-def",
        container_uri=CUSTOM_CONTAINER_URI,
        model_serving_container_image_uri=MODEL_SERVING_CONTAINER_URI,
        # run params
        dataset_id=tabular_dataset_id,
        command=["python3", "task.py"],
        model_display_name=f"{MODEL_DISPLAY_NAME}-def",
        replica_count=REPLICA_COUNT,
        machine_type=MACHINE_TYPE,
        accelerator_type=ACCELERATOR_TYPE,
        accelerator_count=ACCELERATOR_COUNT,
        training_fraction_split=TRAINING_FRACTION_SPLIT,
        validation_fraction_split=VALIDATION_FRACTION_SPLIT,
        test_fraction_split=TEST_FRACTION_SPLIT,
        region=REGION,
        project_id=PROJECT_ID,
        deferrable=True,
    )
    # [END how_to_cloud_vertex_ai_create_custom_container_training_job_operator_deferrable]

    delete_custom_training_job = DeleteCustomTrainingJobOperator(
        task_id="delete_custom_training_job",
        training_pipeline_id="{{ task_instance.xcom_pull(task_ids='custom_container_task', "
        "key='training_id') }}",
        custom_job_id="{{ task_instance.xcom_pull(task_ids='custom_container_task', key='custom_job_id') }}",
        region=REGION,
        project_id=PROJECT_ID,
        trigger_rule=TriggerRule.ALL_DONE,
    )

    delete_custom_training_job_deferrable = DeleteCustomTrainingJobOperator(
        task_id="delete_custom_training_job_deferrable",
        training_pipeline_id="{{ task_instance.xcom_pull(task_ids='custom_container_task_deferrable', "
        "key='training_id') }}",
        custom_job_id="{{ task_instance.xcom_pull(task_ids='custom_container_task_deferrable', "
        "key='custom_job_id') }}",
        region=REGION,
        project_id=PROJECT_ID,
        trigger_rule=TriggerRule.ALL_DONE,
    )

    delete_tabular_dataset = DeleteDatasetOperator(
        task_id="delete_tabular_dataset",
        dataset_id=tabular_dataset_id,
        region=REGION,
        project_id=PROJECT_ID,
        trigger_rule=TriggerRule.ALL_DONE,
    )
    delete_bucket = GCSDeleteBucketOperator(
        task_id="delete_bucket",
        bucket_name=CUSTOM_CONTAINER_GCS_BUCKET_NAME,
        trigger_rule=TriggerRule.ALL_DONE,
    )

    (
        # TEST SETUP
        create_bucket
        >> move_data_files
        >> create_tabular_dataset
        # TEST BODY
        >> [create_custom_container_training_job, create_custom_container_training_job_deferrable]
        # TEST TEARDOWN
        >> delete_custom_training_job
        >> delete_custom_training_job_deferrable
        >> delete_tabular_dataset
        >> delete_bucket
    )

    # ### Everything below this line is not part of example ###
    # ### Just for system tests purpose ###
    from tests_common.test_utils.watcher import watcher

    # This test needs watcher in order to properly mark success/failure
    # when "tearDown" task with trigger rule is part of the DAG
    list(dag.tasks) >> watcher()

from tests_common.test_utils.system_tests import get_test_run  # noqa: E402

# Needed to run the example DAG with pytest (see: contributing-docs/testing/system_tests.rst)
test_run = get_test_run(dag)
