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"""
Example Airflow DAG for Apache Beam operators
"""
from __future__ import annotations

from airflow import models
from airflow.providers.apache.beam.operators.beam import BeamRunJavaPipelineOperator
from airflow.providers.google.cloud.transfers.gcs_to_local import GCSToLocalFilesystemOperator
from tests.system.providers.apache.beam.utils import (
    GCS_INPUT,
    GCS_JAR_SPARK_RUNNER_BUCKET_NAME,
    GCS_JAR_SPARK_RUNNER_OBJECT_NAME,
    START_DATE,
)

with models.DAG(
    "example_beam_native_java_spark_runner",
    schedule=None,  # Override to match your needs
    start_date=START_DATE,
    catchup=False,
    tags=["example"],
) as dag:

    jar_to_local_spark_runner = GCSToLocalFilesystemOperator(
        task_id="jar_to_local_spark_runner",
        bucket=GCS_JAR_SPARK_RUNNER_BUCKET_NAME,
        object_name=GCS_JAR_SPARK_RUNNER_OBJECT_NAME,
        filename="/tmp/beam_wordcount_spark_runner_{{ ds_nodash }}.jar",
    )

    start_java_pipeline_spark_runner = BeamRunJavaPipelineOperator(
        task_id="start_java_pipeline_spark_runner",
        runner="SparkRunner",
        jar="/tmp/beam_wordcount_spark_runner_{{ ds_nodash }}.jar",
        pipeline_options={
            "output": "/tmp/start_java_pipeline_spark_runner",
            "inputFile": GCS_INPUT,
        },
        job_class="org.apache.beam.examples.WordCount",
    )

    jar_to_local_spark_runner >> start_java_pipeline_spark_runner


from tests.system.utils import get_test_run

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