# 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 tempfile

import pytest
from sagemaker import utils
from sagemaker.mxnet.model import MXNetModel

from ..... import invoke_sm_helper_function
from ...integration import RESOURCE_PATH
from ...integration.sagemaker import timeout

GLUONNLP_PATH = os.path.join(RESOURCE_PATH, "gluonnlp")
SCRIPT_PATH = os.path.join(GLUONNLP_PATH, "bert.py")


@pytest.mark.integration("gluonnlp")
@pytest.mark.model("bert_sst")
@pytest.mark.skip_py2_containers
@pytest.mark.skip_eia_containers
def test_gluonnlp(
    ecr_image, sagemaker_regions, instance_type, framework_version, skip_neuron_containers
):
    invoke_sm_helper_function(
        ecr_image, sagemaker_regions, _test_gluonnlp_function, instance_type, framework_version
    )


def _test_gluonnlp_function(ecr_image, sagemaker_session, instance_type, framework_version):
    import urllib.request

    tmpdir = tempfile.mkdtemp()
    tmpfile = os.path.join(tmpdir, "bert_sst.tar.gz")
    urllib.request.urlretrieve(
        "https://aws-dlc-sample-models.s3.amazonaws.com/bert_sst/bert_sst.tar.gz", tmpfile
    )

    prefix = "gluonnlp-serving/default-handlers"
    model_data = sagemaker_session.upload_data(path=tmpfile, key_prefix=prefix)

    model = MXNetModel(
        model_data,
        "SageMakerRole",
        SCRIPT_PATH,
        image_uri=ecr_image,
        py_version="py3",
        framework_version=framework_version,
        sagemaker_session=sagemaker_session,
    )

    endpoint_name = utils.unique_name_from_base("test-mxnet-gluonnlp")
    with timeout.timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
        predictor = model.deploy(1, instance_type, endpoint_name=endpoint_name)

        output = predictor.predict(["Positive sentiment", "Negative sentiment"])
        assert [1, 0] == output