# Copyright 2018-2020 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 ...integration import RESOURCE_PATH
from .timeout import timeout

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


@pytest.mark.usefixtures("feature_smdebug_present")
@pytest.mark.integration("smdebug")
@pytest.mark.model("mnist")
@pytest.mark.skip_py2_containers
def test_training(ecr_image, sagemaker_regions, instance_type, instance_count, framework_version):
    invoke_sm_helper_function(
        ecr_image,
        sagemaker_regions,
        _test_training,
        instance_type,
        instance_count,
        framework_version,
    )


def _test_training(ecr_image, sagemaker_session, instance_type, instance_count, framework_version):
    hyperparameters = {
        "random_seed": True,
        "num_steps": 50,
        "smdebug_path": "/tmp/ml/output/tensors",
        "epochs": 1,
    }

    mx = MXNet(
        entry_point=SCRIPT_PATH,
        role="SageMakerRole",
        instance_count=instance_count,
        instance_type=instance_type,
        sagemaker_session=sagemaker_session,
        image_uri=ecr_image,
        framework_version=framework_version,
        hyperparameters=hyperparameters,
    )

    with timeout(minutes=15):
        prefix = "mxnet_mnist_gluon_basic_hook_demo/{}".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")
        mx.fit({"train": train_input, "test": test_input}, job_name=job_name)