# Copyright 2017-2020 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://aws.amazon.com/apache2.0/
#
# 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
import sagemaker
from sagemaker.tensorflow import TensorFlow
from sagemaker.utils import unique_name_from_base

RESOURCE_PATH = os.path.join(os.path.dirname(__file__), "..", "..", "resources")


@pytest.mark.skip_generic
def test_distributed_training_horovod(
    sagemaker_session, instance_type, image_uri, tmpdir, framework_version
):

    mpi_options = "-verbose -x orte_base_help_aggregate=0"
    estimator = TensorFlow(
        entry_point=os.path.join(RESOURCE_PATH, "mnist", "horovod_mnist.py"),
        role="SageMakerRole",
        instance_type=instance_type,
        instance_count=2,
        image_uri=image_uri,
        framework_version=framework_version,
        py_version="py3",
        hyperparameters={
            "sagemaker_mpi_enabled": True,
            "sagemaker_mpi_custom_mpi_options": mpi_options,
            "sagemaker_mpi_num_of_processes_per_host": 1,
        },
        sagemaker_session=sagemaker_session,
    )

    estimator.fit(job_name=unique_name_from_base("test-tf-horovod"))

    model_data_source = sagemaker.local.data.get_data_source_instance(
        estimator.model_data, sagemaker_session
    )

    for filename in model_data_source.get_file_list():
        assert os.path.basename(filename) == "model.tar.gz"


@pytest.mark.skip_generic
def test_distributed_training_horovod_with_env_vars(
    sagemaker_session, instance_type, image_uri, tmpdir, framework_version
):

    mpi_options = "-verbose -x orte_base_help_aggregate=0"
    estimator = TensorFlow(
        entry_point=os.path.join(RESOURCE_PATH, "hvdbasic", "train_hvd_env_vars.py"),
        role="SageMakerRole",
        instance_type=instance_type,
        instance_count=2,
        image_uri=image_uri,
        framework_version=framework_version,
        py_version="py3",
        hyperparameters={
            "sagemaker_mpi_enabled": True,
            "sagemaker_mpi_custom_mpi_options": mpi_options,
            "sagemaker_mpi_num_of_processes_per_host": 2,
        },
        sagemaker_session=sagemaker_session,
    )

    estimator.fit(job_name=unique_name_from_base("test-tf-horovod-env-vars"))