# 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
import requests
from sagemaker.mxnet.model import MXNetModel

from ...integration.local import local_mode_utils
from ...integration import RESOURCE_PATH

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


@pytest.fixture(scope="module")
def predictor(docker_image, sagemaker_local_session, local_instance_type, framework_version):
    model = MXNetModel(
        "file://{}".format(MODEL_PATH),
        "SageMakerRole",
        SCRIPT_PATH,
        image_uri=docker_image,
        framework_version=framework_version,
        sagemaker_session=sagemaker_local_session,
    )

    with local_mode_utils.lock():
        try:
            predictor = model.deploy(1, local_instance_type)
            yield predictor
        finally:
            predictor.delete_endpoint()


@pytest.mark.model("linear_regression")
def test_default_model_fn(predictor):
    input = [[1, 2]]
    output = predictor.predict(input)
    assert [[4.9999918937683105]] == output


@pytest.mark.model("linear_regression")
def test_default_model_fn_content_type(predictor):
    r = requests.post("http://localhost:8080/invocations", json=[[1, 2]])
    assert "application/json" == r.headers["Content-Type"]
    assert [[4.9999918937683105]] == r.json()