test/test_dag_response.py

# coding: utf-8

"""
    Airflow API

    Airflow API. All endpoints located under ``/api/v2`` can be used safely, are stable and backward compatible. Endpoints located under ``/ui`` are dedicated to the UI and are subject to breaking change depending on the need of the frontend. Users should not rely on those but use the public ones instead.  **Filtering with pattern parameters.** Many list endpoints accept ``*_pattern`` and ``*_prefix_pattern`` query parameters. Unless a parameter's own description says otherwise, ``*_pattern`` is a case-insensitive substring match (SQL ``ILIKE '%term%'``) where ``%`` matches any sequence and ``_`` matches any single character (e.g. ``%customer_%``) — convenient, but it cannot use B-tree indexes, so it is slow on large tables. ``*_prefix_pattern`` matches the start of the value, is case-sensitive and index-friendly (prefer it at scale); there ``%`` and ``_`` are literal and trailing non-alphanumeric characters are stripped so the range scan stays index-compatible under locale-aware collations (e.g. ``test_`` matches values starting with ``test``, and ``s3://`` matches ``s3``). In both, ``|`` means OR (e.g. ``dag1|dag2``) and ``~`` matches everything. Regular expressions are not supported by these parameters; regex-capable endpoints expose a separate parameter.

    The version of the OpenAPI document: 2
    Generated by OpenAPI Generator (https://openapi-generator.tech)

    Do not edit the class manually.
"""  # noqa: E501


import unittest

from airflow_client.client.models.dag_response import DAGResponse

class TestDAGResponse(unittest.TestCase):
    """DAGResponse unit test stubs"""

    def setUp(self):
        pass

    def tearDown(self):
        pass

    def make_instance(self, include_optional) -> DAGResponse:
        """Test DAGResponse
            include_optional is a boolean, when False only required
            params are included, when True both required and
            optional params are included """
        # uncomment below to create an instance of `DAGResponse`
        """
        model = DAGResponse()
        if include_optional:
            return DAGResponse(
                allowed_run_types = [
                    'backfill'
                    ],
                bundle_name = '',
                bundle_version = '',
                dag_display_name = '',
                dag_id = '',
                description = '',
                file_token = '',
                fileloc = '',
                has_import_errors = True,
                has_task_concurrency_limits = True,
                is_backfillable = True,
                is_paused = True,
                is_stale = True,
                last_expired = datetime.datetime.strptime('2013-10-20 19:20:30.00', '%Y-%m-%d %H:%M:%S.%f'),
                last_parse_duration = 1.337,
                last_parsed_time = datetime.datetime.strptime('2013-10-20 19:20:30.00', '%Y-%m-%d %H:%M:%S.%f'),
                max_active_runs = 56,
                max_active_tasks = 56,
                max_consecutive_failed_dag_runs = 56,
                next_dagrun_data_interval_end = datetime.datetime.strptime('2013-10-20 19:20:30.00', '%Y-%m-%d %H:%M:%S.%f'),
                next_dagrun_data_interval_start = datetime.datetime.strptime('2013-10-20 19:20:30.00', '%Y-%m-%d %H:%M:%S.%f'),
                next_dagrun_logical_date = datetime.datetime.strptime('2013-10-20 19:20:30.00', '%Y-%m-%d %H:%M:%S.%f'),
                next_dagrun_run_after = datetime.datetime.strptime('2013-10-20 19:20:30.00', '%Y-%m-%d %H:%M:%S.%f'),
                owners = [
                    ''
                    ],
                relative_fileloc = '',
                tags = [
                    airflow_client.client.models.dag_tag_response.DagTagResponse(
                        dag_display_name = '', 
                        dag_id = '', 
                        name = '', )
                    ],
                timetable_description = '',
                timetable_partitioned = True,
                timetable_periodic = True,
                timetable_summary = ''
            )
        else:
            return DAGResponse(
                dag_display_name = '',
                dag_id = '',
                file_token = '',
                fileloc = '',
                has_import_errors = True,
                has_task_concurrency_limits = True,
                is_backfillable = True,
                is_paused = True,
                is_stale = True,
                max_active_tasks = 56,
                max_consecutive_failed_dag_runs = 56,
                owners = [
                    ''
                    ],
                tags = [
                    airflow_client.client.models.dag_tag_response.DagTagResponse(
                        dag_display_name = '', 
                        dag_id = '', 
                        name = '', )
                    ],
                timetable_partitioned = True,
                timetable_periodic = True,
        )
        """

    def testDAGResponse(self):
        """Test DAGResponse"""
        # inst_req_only = self.make_instance(include_optional=False)
        # inst_req_and_optional = self.make_instance(include_optional=True)

if __name__ == '__main__':
    unittest.main()