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"""Example DAG demonstrating the usage of the Classic branching Python operators.

It is showcasing the basic BranchPythonOperator and its sisters BranchExternalPythonOperator
and BranchPythonVirtualenvOperator."""
from __future__ import annotations

import random
import sys
import tempfile
from pathlib import Path

import pendulum

from airflow.models.dag import DAG
from airflow.operators.empty import EmptyOperator
from airflow.operators.python import (
    BranchExternalPythonOperator,
    BranchPythonOperator,
    BranchPythonVirtualenvOperator,
    ExternalPythonOperator,
    PythonOperator,
    PythonVirtualenvOperator,
)
from airflow.utils.edgemodifier import Label
from airflow.utils.trigger_rule import TriggerRule

PATH_TO_PYTHON_BINARY = sys.executable

with DAG(
    dag_id="example_branch_operator",
    start_date=pendulum.datetime(2021, 1, 1, tz="UTC"),
    catchup=False,
    schedule="@daily",
    tags=["example", "example2"],
    orientation="TB",
) as dag:
    run_this_first = EmptyOperator(
        task_id="run_this_first",
    )

    options = ["a", "b", "c", "d"]

    # Example branching on standard Python tasks

    # [START howto_operator_branch_python]
    branching = BranchPythonOperator(
        task_id="branching",
        python_callable=lambda: f"branch_{random.choice(options)}",
    )
    # [END howto_operator_branch_python]
    run_this_first >> branching

    join = EmptyOperator(
        task_id="join",
        trigger_rule=TriggerRule.NONE_FAILED_MIN_ONE_SUCCESS,
    )

    for option in options:
        t = PythonOperator(
            task_id=f"branch_{option}",
            python_callable=lambda: print("Hello World"),
        )

        empty_follow = EmptyOperator(
            task_id="follow_" + option,
        )

        # Label is optional here, but it can help identify more complex branches
        branching >> Label(option) >> t >> empty_follow >> join

    # Example the same with external Python calls

    # [START howto_operator_branch_ext_py]
    def branch_with_external_python(choices):
        import random

        return f"ext_py_{random.choice(choices)}"

    branching_ext_py = BranchExternalPythonOperator(
        task_id="branching_ext_python",
        python=PATH_TO_PYTHON_BINARY,
        python_callable=branch_with_external_python,
        op_args=[options],
    )
    # [END howto_operator_branch_ext_py]
    join >> branching_ext_py

    join_ext_py = EmptyOperator(
        task_id="join_ext_python",
        trigger_rule=TriggerRule.NONE_FAILED_MIN_ONE_SUCCESS,
    )

    def hello_world_with_external_python():
        print("Hello World from external Python")

    for option in options:
        t = ExternalPythonOperator(
            task_id=f"ext_py_{option}",
            python=PATH_TO_PYTHON_BINARY,
            python_callable=hello_world_with_external_python,
        )

        # Label is optional here, but it can help identify more complex branches
        branching_ext_py >> Label(option) >> t >> join_ext_py

    # Example the same with Python virtual environments

    # [START howto_operator_branch_virtualenv]
    # Note: Passing a caching dir allows to keep the virtual environment over multiple runs
    #       Run the example a second time and see that it re-uses it and is faster.
    VENV_CACHE_PATH = Path(tempfile.gettempdir())

    def branch_with_venv(choices):
        import random

        import numpy as np

        print(f"Some numpy stuff: {np.arange(6)}")
        return f"venv_{random.choice(choices)}"

    branching_venv = BranchPythonVirtualenvOperator(
        task_id="branching_venv",
        requirements=["numpy~=1.24.4"],
        venv_cache_path=VENV_CACHE_PATH,
        python_callable=branch_with_venv,
        op_args=[options],
    )
    # [END howto_operator_branch_virtualenv]
    join_ext_py >> branching_venv

    join_venv = EmptyOperator(
        task_id="join_venv",
        trigger_rule=TriggerRule.NONE_FAILED_MIN_ONE_SUCCESS,
    )

    def hello_world_with_venv():
        import numpy as np

        print(f"Hello World with some numpy stuff: {np.arange(6)}")

    for option in options:
        t = PythonVirtualenvOperator(
            task_id=f"venv_{option}",
            requirements=["numpy~=1.24.4"],
            venv_cache_path=VENV_CACHE_PATH,
            python_callable=hello_world_with_venv,
        )

        # Label is optional here, but it can help identify more complex branches
        branching_venv >> Label(option) >> t >> join_venv