# 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 import MXNetModel

from test.test_utils import get_framework_and_version_from_tag
from ..... import invoke_sm_helper_function

from ...integration import EI_SUPPORTED_REGIONS, RESOURCE_PATH
from ...integration.sagemaker.timeout import timeout_and_delete_endpoint_by_name

DEFAULT_HANDLER_PATH = os.path.join(RESOURCE_PATH, "default_handlers")
MODEL_PATH = os.path.join(DEFAULT_HANDLER_PATH, "model.tar.gz")
DEFAULT_SCRIPT_PATH = os.path.join(DEFAULT_HANDLER_PATH, "model", "code", "eia_module.py")


@pytest.fixture(autouse=True)
def skip_if_no_accelerator(accelerator_type):
    if accelerator_type is None:
        pytest.skip("Skipping because accelerator type was not provided")


@pytest.fixture(autouse=True)
def skip_if_non_supported_ei_region(region):
    if region not in EI_SUPPORTED_REGIONS:
        pytest.skip("EI is not supported in {}".format(region))


@pytest.mark.processor("eia")
@pytest.mark.integration("elastic_inference")
@pytest.mark.model("linear_regression")
@pytest.mark.skip_if_non_supported_ei_region()
@pytest.mark.skip_if_no_accelerator()
def test_elastic_inference(
    ecr_image,
    sagemaker_regions,
    instance_type,
    accelerator_type,
    framework_version,
    skip_neuron_containers,
):
    invoke_sm_helper_function(
        ecr_image,
        sagemaker_regions,
        _test_elastic_inference_function,
        instance_type,
        accelerator_type,
        framework_version,
    )


def _test_elastic_inference_function(
    ecr_image, sagemaker_session, instance_type, accelerator_type, framework_version
):
    entry_point = DEFAULT_SCRIPT_PATH
    image_framework, image_framework_version = get_framework_and_version_from_tag(ecr_image)
    if image_framework_version == "1.5.1":
        entry_point = os.path.join(DEFAULT_HANDLER_PATH, "model", "code", "empty_module.py")

    endpoint_name = utils.unique_name_from_base("test-mxnet-ei")

    with timeout_and_delete_endpoint_by_name(
        endpoint_name=endpoint_name, sagemaker_session=sagemaker_session, minutes=20
    ):
        prefix = "mxnet-serving/default-handlers"
        model_data = sagemaker_session.upload_data(path=MODEL_PATH, key_prefix=prefix)
        model = MXNetModel(
            model_data=model_data,
            entry_point=entry_point,
            role="SageMakerRole",
            image_uri=ecr_image,
            framework_version=framework_version,
            sagemaker_session=sagemaker_session,
        )

        predictor = model.deploy(
            initial_instance_count=1,
            instance_type=instance_type,
            accelerator_type=accelerator_type,
            endpoint_name=endpoint_name,
        )

        output = predictor.predict([[1, 2]])
        assert [[4.9999918937683105]] == output