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from __future__ import annotations

import asyncio
import logging
from unittest import mock

import pytest

from airflow.providers.google.cloud.triggers.mlengine import MLEngineStartTrainingJobTrigger
from airflow.triggers.base import TriggerEvent

TEST_CONN_ID = "ml_default"
TEST_JOB_ID = "1234"
TEST_GCP_PROJECT_ID = "test-project"
TEST_REGION = "us-central1"
TEST_RUNTIME_VERSION = "1.15"
TEST_PYTHON_VERSION = "3.8"
TEST_JOB_DIR = "gs://example_mlengine_bucket/job-dir"
TEST_PACKAGE_URIS = ["gs://system-tests-resources/example_gcp_mlengine/trainer-0.1.tar.gz"]
TEST_TRAINING_PYTHON_MODULE = "trainer.task"
TEST_TRAINING_ARGS = []
TEST_LABELS = {"job_type": "training", "***-version": "v2-5-0-dev0"}
TEST_POLL_INTERVAL = 4.0


@pytest.fixture
def trigger():
    return MLEngineStartTrainingJobTrigger(
        conn_id=TEST_CONN_ID,
        job_id=TEST_JOB_ID,
        project_id=TEST_GCP_PROJECT_ID,
        region=TEST_REGION,
        runtime_version=TEST_RUNTIME_VERSION,
        python_version=TEST_PYTHON_VERSION,
        job_dir=TEST_JOB_DIR,
        package_uris=TEST_PACKAGE_URIS,
        training_python_module=TEST_TRAINING_PYTHON_MODULE,
        training_args=TEST_TRAINING_ARGS,
        labels=TEST_LABELS,
    )


class TestMLEngineStartTrainingJobTrigger:
    def test_serialize(self, trigger):
        """
        Asserts that the MLEngineStartTrainingJobTrigger correctly serializes its arguments
        and classpath.
        """

        classpath, kwargs = trigger.serialize()
        assert classpath == "airflow.providers.google.cloud.triggers.mlengine.MLEngineStartTrainingJobTrigger"
        assert kwargs == {
            "conn_id": TEST_CONN_ID,
            "job_id": TEST_JOB_ID,
            "project_id": TEST_GCP_PROJECT_ID,
            "region": TEST_REGION,
            "runtime_version": TEST_RUNTIME_VERSION,
            "python_version": TEST_PYTHON_VERSION,
            "job_dir": TEST_JOB_DIR,
            "poll_interval": TEST_POLL_INTERVAL,
            "package_uris": TEST_PACKAGE_URIS,
            "training_python_module": TEST_TRAINING_PYTHON_MODULE,
            "training_args": TEST_TRAINING_ARGS,
            "labels": TEST_LABELS,
        }

    @pytest.mark.asyncio
    @mock.patch("airflow.providers.google.cloud.hooks.mlengine.MLEngineAsyncHook.get_job_status")
    async def test_trigger_on_success_yield_successfully(self, mock_job_status, trigger):
        """
        Tests the MLEngineStartTrainingJobTrigger only fires once the job execution reaches a successful state
        """
        mock_job_status.return_value = "success"

        generator = trigger.run()
        actual = await generator.asend(None)
        assert (
            TriggerEvent({"status": "success", "message": "Job completed", "job_id": TEST_JOB_ID}) == actual
        )

    @pytest.mark.asyncio
    @mock.patch("airflow.providers.google.cloud.hooks.mlengine.MLEngineAsyncHook.get_job")
    async def test_trigger_on_running_wait_successfully(self, mocked_get, caplog, trigger):
        """
        Test that MLEngineStartTrainingJobTrigger does not fire while a job is still running.
        """

        mocked_get.side_effect = OSError()
        caplog.set_level(logging.INFO)

        task = asyncio.create_task(trigger.run().__anext__())
        await asyncio.sleep(0.5)

        # TriggerEvent was not returned
        assert task.done() is False

        assert "Job is still running..." in caplog.text
        assert f"Sleeping for {TEST_POLL_INTERVAL} seconds." in caplog.text

        # Prevents error when task is destroyed while in "pending" state
        asyncio.get_event_loop().stop()

    @pytest.mark.asyncio
    @mock.patch("airflow.providers.google.cloud.hooks.mlengine.MLEngineAsyncHook.get_job_status")
    async def test_trigger_on_error_yield_successfully(self, mock_job_status, trigger):
        """
        Test that MLEngineStartTrainingJobTrigger fires the correct event in case of an error.
        """
        # Set the status to a value other than success or pending
        mock_job_status.return_value = "error"

        generator = trigger.run()
        actual = await generator.asend(None)
        assert TriggerEvent({"status": "error", "message": "error"}) == actual

    @pytest.mark.asyncio
    @mock.patch("airflow.providers.google.cloud.hooks.mlengine.MLEngineAsyncHook.get_job_status")
    async def test_trigger_exec_yield_successfully(self, mock_job_status, trigger):
        """
        Test that MLEngineStartTrainingJobTrigger fires the correct event in case of an error.
        """
        mock_job_status.side_effect = Exception("Test exception")

        generator = trigger.run()
        actual = await generator.asend(None)
        assert TriggerEvent({"status": "error", "message": "Test exception"}) == actual
