import os
import sys
import unittest

import ray
from ray.rllib import _register_all

from ray import tune
from ray.tune import TuneError, register_trainable
from ray.tune.ray_trial_executor import RayTrialExecutor
from ray.tune.schedulers import TrialScheduler, FIFOScheduler
from ray.tune.trial import Trial
from ray.tune.trial_runner import TrialRunner
from ray.tune.resources import Resources
from ray.tune.suggest import BasicVariantGenerator


class TrialRunnerTest(unittest.TestCase):
    def setUp(self):
        _register_all()  # re-register the evicted objects

    def tearDown(self):
        ray.shutdown()

    def testTrialStatus(self):
        ray.init(num_cpus=2)

        trial = Trial("__fake")
        trial_executor = RayTrialExecutor()
        self.assertEqual(trial.status, Trial.PENDING)
        trial_executor.start_trial(trial)
        self.assertEqual(trial.status, Trial.RUNNING)
        trial_executor.stop_trial(trial)
        self.assertEqual(trial.status, Trial.TERMINATED)
        trial_executor.stop_trial(trial, error=True)
        self.assertEqual(trial.status, Trial.ERROR)

    def testExperimentTagTruncation(self):
        ray.init(num_cpus=2)

        def train(config, reporter):
            reporter(timesteps_total=1)

        trial_executor = RayTrialExecutor()
        register_trainable("f1", train)

        experiments = {
            "foo": {
                "run": "f1",
                "config": {
                    "a" * 50: tune.sample_from(lambda spec: 5.0 / 7),
                    "b" * 50: tune.sample_from(lambda spec: "long" * 40)
                },
            }
        }

        for name, spec in experiments.items():
            trial_generator = BasicVariantGenerator()
            trial_generator.add_configurations({name: spec})
            while not trial_generator.is_finished():
                trial = trial_generator.next_trial()
                if not trial:
                    break
                trial_executor.start_trial(trial)
                self.assertLessEqual(len(os.path.basename(trial.logdir)), 200)
                trial_executor.stop_trial(trial)

    def testExtraResources(self):
        ray.init(num_cpus=4, num_gpus=2)
        runner = TrialRunner()
        kwargs = {
            "stopping_criterion": {
                "training_iteration": 1
            },
            "resources": Resources(cpu=1, gpu=0, extra_cpu=3, extra_gpu=1),
        }
        trials = [Trial("__fake", **kwargs), Trial("__fake", **kwargs)]
        for t in trials:
            runner.add_trial(t)

        runner.step()
        self.assertEqual(trials[0].status, Trial.RUNNING)
        self.assertEqual(trials[1].status, Trial.PENDING)

        runner.step()
        self.assertEqual(trials[0].status, Trial.TERMINATED)
        self.assertEqual(trials[1].status, Trial.PENDING)

    def testCustomResources(self):
        ray.init(num_cpus=4, num_gpus=2, resources={"a": 2})
        runner = TrialRunner()
        kwargs = {
            "stopping_criterion": {
                "training_iteration": 1
            },
            "resources": Resources(cpu=1, gpu=0, custom_resources={"a": 2}),
        }
        trials = [Trial("__fake", **kwargs), Trial("__fake", **kwargs)]
        for t in trials:
            runner.add_trial(t)

        runner.step()
        self.assertEqual(trials[0].status, Trial.RUNNING)
        self.assertEqual(trials[1].status, Trial.PENDING)

        runner.step()
        self.assertEqual(trials[0].status, Trial.TERMINATED)
        self.assertEqual(trials[1].status, Trial.PENDING)

    def testExtraCustomResources(self):
        ray.init(num_cpus=4, num_gpus=2, resources={"a": 2})
        runner = TrialRunner()
        kwargs = {
            "stopping_criterion": {
                "training_iteration": 1
            },
            "resources": Resources(
                cpu=1, gpu=0, extra_custom_resources={"a": 2}),
        }
        trials = [Trial("__fake", **kwargs), Trial("__fake", **kwargs)]
        for t in trials:
            runner.add_trial(t)

        runner.step()
        self.assertEqual(trials[0].status, Trial.RUNNING)
        self.assertEqual(trials[1].status, Trial.PENDING)

        runner.step()
        self.assertTrue(sum(t.status == Trial.RUNNING for t in trials) < 2)
        self.assertEqual(trials[0].status, Trial.TERMINATED)
        self.assertEqual(trials[1].status, Trial.PENDING)

    def testCustomResources2(self):
        ray.init(num_cpus=4, num_gpus=2, resources={"a": 2})
        runner = TrialRunner()
        resource1 = Resources(cpu=1, gpu=0, extra_custom_resources={"a": 2})
        self.assertTrue(runner.has_resources(resource1))
        resource2 = Resources(cpu=1, gpu=0, custom_resources={"a": 2})
        self.assertTrue(runner.has_resources(resource2))
        resource3 = Resources(cpu=1, gpu=0, custom_resources={"a": 3})
        self.assertFalse(runner.has_resources(resource3))
        resource4 = Resources(cpu=1, gpu=0, extra_custom_resources={"a": 3})
        self.assertFalse(runner.has_resources(resource4))

    def testFractionalGpus(self):
        ray.init(num_cpus=4, num_gpus=1)
        runner = TrialRunner()
        kwargs = {
            "resources": Resources(cpu=1, gpu=0.5),
        }
        trials = [
            Trial("__fake", **kwargs),
            Trial("__fake", **kwargs),
            Trial("__fake", **kwargs),
            Trial("__fake", **kwargs)
        ]
        for t in trials:
            runner.add_trial(t)

        for _ in range(10):
            runner.step()

        self.assertEqual(trials[0].status, Trial.RUNNING)
        self.assertEqual(trials[1].status, Trial.RUNNING)
        self.assertEqual(trials[2].status, Trial.PENDING)
        self.assertEqual(trials[3].status, Trial.PENDING)

    def testResourceNumericalError(self):
        resource = Resources(cpu=0.99, gpu=0.99, custom_resources={"a": 0.99})
        small_resource = Resources(
            cpu=0.33, gpu=0.33, custom_resources={"a": 0.33})
        for i in range(3):
            resource = Resources.subtract(resource, small_resource)
        self.assertTrue(resource.is_nonnegative())

    def testResourceScheduler(self):
        ray.init(num_cpus=4, num_gpus=1)
        runner = TrialRunner()
        kwargs = {
            "stopping_criterion": {
                "training_iteration": 1
            },
            "resources": Resources(cpu=1, gpu=1),
        }
        trials = [Trial("__fake", **kwargs), Trial("__fake", **kwargs)]
        for t in trials:
            runner.add_trial(t)

        runner.step()
        self.assertEqual(trials[0].status, Trial.RUNNING)
        self.assertEqual(trials[1].status, Trial.PENDING)

        runner.step()
        self.assertEqual(trials[0].status, Trial.TERMINATED)
        self.assertEqual(trials[1].status, Trial.PENDING)

        runner.step()
        self.assertEqual(trials[0].status, Trial.TERMINATED)
        self.assertEqual(trials[1].status, Trial.RUNNING)

        runner.step()
        self.assertEqual(trials[0].status, Trial.TERMINATED)
        self.assertEqual(trials[1].status, Trial.TERMINATED)

    def testMultiStepRun(self):
        ray.init(num_cpus=4, num_gpus=2)
        runner = TrialRunner()
        kwargs = {
            "stopping_criterion": {
                "training_iteration": 5
            },
            "resources": Resources(cpu=1, gpu=1),
        }
        trials = [Trial("__fake", **kwargs), Trial("__fake", **kwargs)]
        for t in trials:
            runner.add_trial(t)

        runner.step()
        self.assertEqual(trials[0].status, Trial.RUNNING)
        self.assertEqual(trials[1].status, Trial.PENDING)

        runner.step()
        self.assertEqual(trials[0].status, Trial.RUNNING)
        self.assertEqual(trials[1].status, Trial.RUNNING)

        runner.step()
        self.assertEqual(trials[0].status, Trial.RUNNING)
        self.assertEqual(trials[1].status, Trial.RUNNING)

        runner.step()
        self.assertEqual(trials[0].status, Trial.RUNNING)
        self.assertEqual(trials[1].status, Trial.RUNNING)

    def testMultiStepRun2(self):
        """Checks that runner.step throws when overstepping."""
        ray.init(num_cpus=1)
        runner = TrialRunner()
        kwargs = {
            "stopping_criterion": {
                "training_iteration": 2
            },
            "resources": Resources(cpu=1, gpu=0),
        }
        trials = [Trial("__fake", **kwargs)]
        for t in trials:
            runner.add_trial(t)

        runner.step()
        self.assertEqual(trials[0].status, Trial.RUNNING)

        runner.step()
        self.assertEqual(trials[0].status, Trial.RUNNING)

        runner.step()
        self.assertEqual(trials[0].status, Trial.TERMINATED)
        self.assertRaises(TuneError, runner.step)

    def testChangeResources(self):
        """Checks that resource requirements can be changed on fly."""
        ray.init(num_cpus=2)

        class ChangingScheduler(FIFOScheduler):
            def on_trial_result(self, trial_runner, trial, result):
                if result["training_iteration"] == 1:
                    executor = trial_runner.trial_executor
                    executor.stop_trial(trial)
                    trial.update_resources(2, 0)
                    executor.start_trial(trial)
                return TrialScheduler.CONTINUE

        runner = TrialRunner(scheduler=ChangingScheduler())
        kwargs = {
            "stopping_criterion": {
                "training_iteration": 2
            },
            "resources": Resources(cpu=1, gpu=0),
        }
        trials = [Trial("__fake", **kwargs)]
        for t in trials:
            runner.add_trial(t)

        runner.step()
        self.assertEqual(trials[0].status, Trial.RUNNING)
        self.assertEqual(runner.trial_executor._committed_resources.cpu, 1)
        self.assertRaises(ValueError, lambda: trials[0].update_resources(2, 0))

        runner.step()
        self.assertEqual(trials[0].status, Trial.RUNNING)
        self.assertEqual(runner.trial_executor._committed_resources.cpu, 2)

    def testQueueFilling(self):
        ray.init(num_cpus=4)

        def f1(config):
            for i in range(10):
                yield i

        tune.register_trainable("f1", f1)

        search_alg = BasicVariantGenerator()
        search_alg.add_configurations({
            "foo": {
                "run": "f1",
                "num_samples": 100,
                "config": {
                    "a": tune.sample_from(lambda spec: 5.0 / 7),
                    "b": tune.sample_from(lambda spec: "long" * 40)
                },
                "resources_per_trial": {
                    "cpu": 2
                }
            }
        })

        runner = TrialRunner(search_alg=search_alg)

        runner.step()
        runner.step()
        runner.step()
        self.assertEqual(len(runner._trials), 3)

        runner.step()
        self.assertEqual(len(runner._trials), 3)

        self.assertEqual(runner._trials[0].status, Trial.RUNNING)
        self.assertEqual(runner._trials[1].status, Trial.RUNNING)
        self.assertEqual(runner._trials[2].status, Trial.PENDING)


if __name__ == "__main__":
    import pytest
    sys.exit(pytest.main(["-v", __file__]))
