from airflow.models import DAG

from utils.gcp import bigquery_etl_query


def merge_params(min_param, max_param, additional_params):
    parameters = (
        f"min_sample_id:INT64:{min_param}",
        f"max_sample_id:INT64:{max_param}",
    )

    if additional_params is not None:
        parameters += additional_params

    return parameters


def repeated_subdag(
    parent_dag_name,
    child_dag_name,
    default_args,
    schedule_interval,
    billing_project_id,
    table_project_id,
    dataset_id,
    fully_qualified_dataset_id,
    additional_params=None,
    num_partitions=5,
    date_partition_parameter="submission_date",
    docker_image="gcr.io/moz-fx-data-airflow-prod-88e0/bigquery-etl:latest",
    parallel=False,
):
    dag = DAG(
        f"{parent_dag_name}.{child_dag_name}",
        default_args=default_args,
        schedule_interval=schedule_interval,
    )

    # This task runs first and replaces the relevant partition, followed
    # by the next tasks that append to the same partition of the same table.
    NUM_SAMPLE_IDS = 100
    PARTITION_SIZE = NUM_SAMPLE_IDS // num_partitions

    if NUM_SAMPLE_IDS % num_partitions != 0:
        raise ValueError(
            f"Number of partitions must be a divisor "
            f"of the number of sample ids ({NUM_SAMPLE_IDS})"
        )

    task_0 = bigquery_etl_query(
        reattach_on_restart=True,
        task_id=f"{child_dag_name}_0",
        destination_table=f"{child_dag_name}_v1",
        dataset_id=fully_qualified_dataset_id,
        sql_file_path=f"sql/{table_project_id}/{dataset_id}/{child_dag_name}_v1/query.sql",
        project_id=billing_project_id,
        depends_on_past=True,
        parameters=merge_params(0, PARTITION_SIZE - 1, additional_params),
        date_partition_parameter=date_partition_parameter,
        arguments=("--replace",),
        dag=dag,
        docker_image=docker_image,
    )

    upstream_task = task_0

    for partition in range(1, num_partitions):
        min_param = partition * PARTITION_SIZE
        max_param = min_param + PARTITION_SIZE - 1

        task = bigquery_etl_query(
            reattach_on_restart=True,
            task_id=f"{child_dag_name}_{partition}",
            destination_table=f"{child_dag_name}_v1",
            dataset_id=fully_qualified_dataset_id,
            sql_file_path=f"sql/{table_project_id}/{dataset_id}/{child_dag_name}_v1/query.sql",
            project_id=billing_project_id,
            depends_on_past=True,
            parameters=merge_params(min_param, max_param, additional_params),
            date_partition_parameter=date_partition_parameter,
            arguments=(
                "--append_table",
                "--noreplace",
            ),
            dag=dag,
            docker_image=docker_image,
        )
        upstream_task >> task
        if not parallel:
            upstream_task = task

    return dag