import os
import stat
import time

from app.core.airflow.task import *
from app.core.airflow.af_util import get_job_path
from app.schemas import AirflowJob


class AirflowJobSubmitter:

    @staticmethod
    def submit_dag(item: AirflowJob):
        m_compilers = {'python': PythonTaskCompiler,
                       'datax': DataXTaskCompiler,
                       'sparks': SparksTaskCompiler,
                       'java': JavaTaskCompiler}

        nodes = [m_compilers[task.task_type](item=task).translate(job_id=item.id, task_mode=item.job_mode or 1) for task
                 in item.tasks if
                 task.task_type != 'sparks']
        spark_nodes = [SparksTaskCompiler(item=task).translate(job_id=item.id, task_mode=item.job_mode or 1) for task in
                       item.tasks if
                       task.task_type == 'sparks']
        edges = []
        for edge in item.dependence:
            edges.append({"source_operator_name": f'op_{edge[0]}',
                          "target_operator_name": f'op_{edge[1]}'})
        #
        # m_interval = {
        #     "None": "None",
        #     "@once": "@once",
        #     "0 * * * *": "@hourly",
        #     "0 0 * * *": "@daily",
        #     "0 0 * * 0": "@weekly",
        #     "0 0 1 * *": "@monthly",
        #     "0 0 1 1 *": "@yearly"
        # }

        # print(f" image pull key is : {config.get('K8S', 'image_pull_key')}")
        parameters = {'nodes': nodes, 'spark_nodes': spark_nodes, 'edges': edges, 'dag_id': f'dag_{item.id}',
                      'user_name': item.user_id, 'job_id': item.id, 'trigger_status': bool(item.trigger_status),
                      'interval': item.cron if item.cron != 'None' else None,
                      'af_backend_uri': config.get('AF_BACKEND', 'uri'),
                      'image_pull_key': config.get('K8S', 'image_pull_key', fallback=None),
                      'enable_notify': True
                      }
        AirflowJobSubmitter.generate_dag_on_airflow(parameters=parameters, save_path=get_job_path(job_id=item.id))

    @staticmethod
    def generate_dag_on_airflow(parameters, save_path):
        env = Environment(
            loader=PackageLoader('app.core.airflow'),
            autoescape=select_autoescape()
        )
        template = env.get_template("dag_template.py.jinja2")
        dag_content = template.render(parameters)
        print(f'finish build:{dag_content}')
        with open(save_path, "w") as fh:
            fh.write(dag_content)
        os.chmod(save_path, stat.S_IRWXO | stat.S_IRWXG | stat.S_IRWXU)
        print(f'write dag to {save_path}')

    @staticmethod
    def auto_submit_data_transfer():
        job_id = 0
        user_id = 0
        spark_task_demo = SparksTaskCompiler(item=None)
        spark_nodes = [
            {
                "sub_nodes": [{
                    "name": 'read_and_save',
                    "id": 0,
                    "image": spark_task_demo.default_image,
                    "cmds": ['/bin/bash', '-c', spark_task_demo.cmd_str(name='spark_data_transfer')],
                    "env": {"SCRIPT": spark_task_demo.render_spark_script(
                        parameters={"hive_metastore_uris": config.get('HIVE_METASTORE', 'uris')},
                        template_file="data_transfer_dag_template.py.jinja2")
                    },
                }],
                "edges": [],
                "name": 'data_save',
                "desc": 'task for data saving',
                "id": 0,
            }

        ]
        parameters = {'nodes': [], 'spark_nodes': spark_nodes, 'edges': [], 'dag_id': f'dag_{job_id}',
                      'user_name': user_id, 'job_id': job_id, 'trigger_status': False,
                      'interval': None,
                      'af_backend_uri': config.get('AF_BACKEND', 'uri'),
                      'image_pull_key': config.get('K8S', 'image_pull_key', fallback=None),
                      'enable_notify': False
                      }

        AirflowJobSubmitter.generate_dag_on_airflow(parameters=parameters, save_path=get_job_path(job_id=job_id))
        print('create data transfer job success!')


    @staticmethod
    def auto_submit_requirements_install():
        job_id = -1
        user_id = 0
        cmds = []
        if config.get('K8S', 'enable_kerberos', fallback=None) in ['true', "True", True]:
            principal = config.get('HIVE', 'principal', fallback=None)
            cmds.append( f"kinit -kt /workspace/conf/user.keytab {principal}")
        cmds.append("cd /workspace")
        cmds.append("./py37/bin/pip3 install {{ dag_run.conf.get('requirements_str','numpy') }} -i https://pypi.douban.com/simple/")
        cmds.append("zip -q -r /tmp/py37.zip py37")
        cmds.append("hdfs dfs -put -f /tmp/py37.zip {{dag_run.conf.get('target_path','/tmp/basic.zip')}}  ")
        py_task_demo = PythonTaskCompiler(item=AirflowTask(id=0, task_type="python", user_id=user_id, cmd_parameters="",
                                                           create_time=0, update_time=0, name="requirements_install",
                                                           file_urls=[], script="", cmd=' && '.join(cmds), envs={},
                                                           run_image=SparksTaskCompiler(item=None).default_image))

        parameters = {'nodes': [py_task_demo.translate(job_id=job_id, task_mode=1)], 'spark_nodes': [], 'edges': [],
                      'dag_id': f'dag_{job_id}',
                      'user_name': user_id, 'job_id': job_id, 'trigger_status': False,
                      'interval': None,
                      'af_backend_uri': config.get('AF_BACKEND', 'uri'),
                      'image_pull_key': config.get('K8S', 'image_pull_key', fallback=None),
                      'enable_notify': False
                      }

        AirflowJobSubmitter.generate_dag_on_airflow(parameters=parameters, save_path=get_job_path(job_id=job_id))
        print('create requirements_install success!')