# 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 json
import os
from urllib.parse import urlparse

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

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

DATA_FILE = "07.csv"
DATA_PATH = os.path.join(MNIST_PATH, "images", DATA_FILE)


@pytest.mark.skip("Known issue: https://github.com/aws/deep-learning-containers/issues/586")
@pytest.mark.integration("batch_transform")
@pytest.mark.model("mnist")
def test_batch_transform(
    ecr_image, sagemaker_regions, instance_type, framework_version, skip_neuron_containers
):
    invoke_sm_helper_function(
        ecr_image,
        sagemaker_regions,
        _test_batch_transform_function,
        instance_type,
        framework_version,
    )


def _test_batch_transform_function(ecr_image, sagemaker_session, instance_type, framework_version):
    s3_prefix = "mxnet-serving/mnist"
    model_data = sagemaker_session.upload_data(path=MODEL_PATH, key_prefix=s3_prefix)
    model = MXNetModel(
        model_data,
        "SageMakerRole",
        SCRIPT_PATH,
        image_uri=ecr_image,
        framework_version=framework_version,
        sagemaker_session=sagemaker_session,
    )

    transformer = model.transformer(1, instance_type)
    with timeout.timeout_and_delete_model_with_transformer(
        transformer, sagemaker_session, minutes=20
    ):
        input_data = sagemaker_session.upload_data(path=DATA_PATH, key_prefix=s3_prefix)

        job_name = utils.unique_name_from_base("test-mxnet-serving-batch")
        transformer.transform(input_data, content_type="text/csv", job_name=job_name)
        transformer.wait()

    prediction = _transform_result(sagemaker_session.boto_session, transformer.output_path)
    assert prediction == 7


def _transform_result(boto_session, output_path):
    s3 = boto_session.resource("s3", region_name=boto_session.region_name)

    parsed_url = urlparse(output_path)
    bucket_name = parsed_url.netloc
    prefix = parsed_url.path[1:]

    output_obj = s3.Object(bucket_name, "{}/{}.out".format(prefix, DATA_FILE))
    output = output_obj.get()["Body"].read().decode("utf-8")

    probabilities = json.loads(output)[0]
    return probabilities.index(max(probabilities))