# 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.
#
# PEP 723 compliant inline script metadata (not yet widely supported)
# /// script
# requires-python = ">=3.9"
# dependencies = [
#   "apache-airflow-client",
#   "rich",
# ]
# ///

from __future__ import annotations

import sys
import time
import uuid

import airflow_client.client
import pytest

from tests_common.test_utils.api_client_helpers import generate_access_token

try:
    # If you have rich installed, you will have nice colored output of the API responses
    from rich import print
except ImportError:
    print("Output will not be colored. Please install rich to get colored output: `pip install rich`")
    pass
from airflow_client.client.api import config_api, dag_api, dag_run_api, task_api
from airflow_client.client.models.trigger_dag_run_post_body import TriggerDAGRunPostBody

# The client must use the authentication and authorization parameters
# in accordance with the API server security policy.
# Examples for each auth method are provided below, use the example that
# satisfies your auth use case.
#
# The example below use the default FabAuthManager, in case your airflow api server use a different
# auth manager for instance AwsAuthManagerUser or SimpleAuthManager make sure to generate the token with
# appropriate AuthManager.
# This is defined in the `[api]` section of your `airflow.cfg`:
#
# auth_manager = airflow.api_fastapi.auth.managers.simple.simple_auth_manager.SimpleAuthManager
#
# Make sure that your user/name are configured properly - using the user/password that has admin
# privileges in Airflow

# Used to initialize FAB and the auth manager, necessary for creating the token.


access_token = generate_access_token("admin", "admin", "localhost:8080")
configuration = airflow_client.client.Configuration(host="http://localhost:8080", access_token=access_token)

# Make sure in the [core] section, the  `load_examples` config is set to True in your airflow.cfg
# or AIRFLOW__CORE__LOAD_EXAMPLES environment variable set to True
DAG_ID = "example_simplest_dag"


# Enter a context with an instance of the API client
@pytest.mark.execution_timeout(400)
def test_python_client():
    with airflow_client.client.ApiClient(configuration) as api_client:
        errors = False

        print("[blue]Getting DAG list")
        max_retries = 10
        while max_retries > 0:
            try:
                dag_api_instance = dag_api.DAGApi(api_client)
                api_response = dag_api_instance.get_dags()
            except airflow_client.client.OpenApiException as e:
                print(f"[red]Exception when calling DagAPI->get_dags: {e}\n")
                errors = True
                time.sleep(6)
                max_retries -= 1
            else:
                print("[green]Getting DAG list successful")
                break

        print("[blue]Getting Tasks for a DAG")
        try:
            task_api_instance = task_api.TaskApi(api_client)
            api_response = task_api_instance.get_tasks(DAG_ID)
            print(api_response)
        except airflow_client.client.exceptions.OpenApiException as e:
            print(f"[red]Exception when calling DagAPI->get_tasks: {e}\n")
            errors = True
        else:
            print("[green]Getting Tasks successful")

        print("[blue]Triggering a DAG run")
        dag_run_api_instance = dag_run_api.DagRunApi(api_client)
        try:
            # Create a DAGRun object (no dag_id should be specified because it is read-only property of DAGRun)
            # dag_run id is generated randomly to allow multiple executions of the script
            dag_run = TriggerDAGRunPostBody(
                dag_run_id="some_test_run_" + uuid.uuid4().hex,
                logical_date=None,
            )
            api_response = dag_run_api_instance.trigger_dag_run(DAG_ID, dag_run)
            print(api_response)
        except airflow_client.client.exceptions.OpenApiException as e:
            print(f"[red]Exception when calling DAGRunAPI->post_dag_run: {e}\n")
            errors = True
        else:
            print("[green]Posting DAG Run successful")

        # Get current configuration. Note, this is disabled by default with most installation.
        # You need to set `expose_config = True` in Airflow configuration in order to retrieve configuration.
        conf_api_instance = config_api.ConfigApi(api_client)
        try:
            api_response = conf_api_instance.get_config()
            print(api_response)
        except airflow_client.client.OpenApiException as e:
            if "Your Airflow administrator chose" in str(e):
                print(
                    "[yellow]You need to set `expose_config = True` in Airflow configuration"
                    " in order to retrieve configuration."
                )
                print("[bright_blue]This is OK. Exposing config is disabled by default.")
            else:
                print(f"[red]Exception when calling DAGRunAPI->post_dag_run: {e}\n")
                errors = True
        else:
            print("[green]Config retrieved successfully")

        if errors:
            print("\n[red]There were errors while running the script - see above for details")
            sys.exit(1)
        else:
            print("\n[green]Everything went well")


if __name__ == "__main__":
    test_python_client()