# Copyright 2019-2020 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License").
# You may not use this file except in compliance with the License.
# A copy of the License is located at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# or in the "license" file accompanying this file. This file 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.
from __future__ import absolute_import

import os

import pytest

from sagemaker import utils
from sagemaker.mxnet.estimator import MXNet
from ..... import invoke_sm_helper_function
from sagemaker.tuner import ContinuousParameter, HyperparameterTuner

from ...integration import RESOURCE_PATH
from .timeout import timeout

DATA_PATH = os.path.join(RESOURCE_PATH, "mnist")
SCRIPT_PATH = os.path.join(DATA_PATH, "mnist.py")


@pytest.mark.integration("hpo")
@pytest.mark.model("mnist")
def test_tuning(ecr_image, sagemaker_regions, instance_type, framework_version):
    invoke_sm_helper_function(
        ecr_image, sagemaker_regions, _test_tuning, instance_type, framework_version
    )


def _test_tuning(ecr_image, sagemaker_session, instance_type, framework_version):
    mx = MXNet(
        entry_point=SCRIPT_PATH,
        role="SageMakerRole",
        instance_count=1,
        instance_type=instance_type,
        sagemaker_session=sagemaker_session,
        image_uri=ecr_image,
        framework_version=framework_version,
        hyperparameters={"epochs": 1},
    )

    hyperparameter_ranges = {"learning-rate": ContinuousParameter(0.01, 0.2)}
    objective_metric_name = "Validation-accuracy"
    metric_definitions = [
        {"Name": "Validation-accuracy", "Regex": "Validation-accuracy=([0-9\\.]+)"}
    ]

    tuner = HyperparameterTuner(
        mx,
        objective_metric_name,
        hyperparameter_ranges,
        metric_definitions,
        max_jobs=2,
        max_parallel_jobs=2,
    )

    with timeout(minutes=20):
        prefix = "mxnet_mnist/{}".format(utils.sagemaker_timestamp())
        train_input = sagemaker_session.upload_data(
            path=os.path.join(DATA_PATH, "train"), key_prefix=prefix + "/train"
        )
        test_input = sagemaker_session.upload_data(
            path=os.path.join(DATA_PATH, "test"), key_prefix=prefix + "/test"
        )

        job_name = utils.unique_name_from_base("test-mxnet-image", max_length=32)
        tuner.fit({"train": train_input, "test": test_input}, job_name=job_name)
        tuner.wait()