#  Copyright 2018 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 .ag_tools import AutoGluon
from .local_mode_utils import assert_output_files_exist
from .. import RESOURCE_PATH


@pytest.mark.integration("ag_local")
@pytest.mark.processor("cpu")
@pytest.mark.model("autogluon")
def test_autogluon_local_cpu(
    docker_image, sagemaker_local_session, instance_type, framework_version, tmpdir
):
    _test_autogluon_local(
        "cpu", docker_image, sagemaker_local_session, instance_type, framework_version, tmpdir
    )


@pytest.mark.integration("ag_local")
@pytest.mark.processor("gpu")
@pytest.mark.model("autogluon")
@pytest.mark.skip_cpu
def test_autogluon_local_gpu(
    docker_image, sagemaker_local_session, instance_type, framework_version, tmpdir
):
    _test_autogluon_local(
        "gpu", docker_image, sagemaker_local_session, instance_type, framework_version, tmpdir
    )


@pytest.mark.integration("ag_local")
@pytest.mark.processor("gpu")
@pytest.mark.model("autogluon")
@pytest.mark.skip_cpu
def test_autogluon_local_vision_gpu(
    docker_image, sagemaker_local_session, instance_type, framework_version, tmpdir
):
    ag = AutoGluon(
        entry_point=os.path.join(RESOURCE_PATH, "scripts", "train_cv.py"),
        role="SageMakerRole",
        instance_count=1,
        instance_type=instance_type,
        sagemaker_session=sagemaker_local_session,
        image_uri=docker_image,
        framework_version=framework_version,
        output_path="file://{}".format(tmpdir),
    )

    data_path = os.path.join(RESOURCE_PATH, "data")
    s3_prefix = "integ-test-data/autogluon"
    config_input = sagemaker_local_session.upload_data(
        path=os.path.join(data_path, "config", "config.vision.gpu.yaml"), key_prefix=s3_prefix
    )
    ag.fit({"config": config_input})

    model_success_files = {
        "model": ["predictor.pkl"],
        "output": ["success"],
    }

    for directory, files in model_success_files.items():
        assert_output_files_exist(str(tmpdir), directory, files)


def _test_autogluon_local(
    device, docker_image, sagemaker_local_session, instance_type, framework_version, tmpdir
):
    ag = AutoGluon(
        entry_point=os.path.join(RESOURCE_PATH, "scripts", "train_tab.py"),
        role="SageMakerRole",
        instance_count=1,
        instance_type=instance_type,
        sagemaker_session=sagemaker_local_session,
        image_uri=docker_image,
        framework_version=framework_version,
        output_path="file://{}".format(tmpdir),
    )

    data_path = os.path.join(RESOURCE_PATH, "data")
    s3_prefix = "integ-test-data/autogluon"
    train_input = sagemaker_local_session.upload_data(
        path=os.path.join(data_path, "training", f"train.{device}.csv"), key_prefix=s3_prefix
    )
    eval_input = sagemaker_local_session.upload_data(
        path=os.path.join(data_path, "evaluation", f"eval.{device}.csv"), key_prefix=s3_prefix
    )
    config_input = sagemaker_local_session.upload_data(
        path=os.path.join(data_path, "config", f"config.{device}.yaml"), key_prefix=s3_prefix
    )

    ag.fit({"config": config_input, "train": train_input, "test": eval_input})

    model_success_files = {
        "model": ["learner.pkl", "predictor.pkl"],
        "output": ["success"],
    }

    for directory, files in model_success_files.items():
        assert_output_files_exist(str(tmpdir), directory, files)