# Copyright 2022 The KServe Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License 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.

import os

import pytest
from kubernetes import client
from kubernetes.client import V1ResourceRequirements

from kserve import (
    KServeClient,
    V1beta1InferenceService,
    V1beta1InferenceServiceSpec,
    V1beta1ModelFormat,
    V1beta1ModelSpec,
    V1beta1PredictorSpec,
    constants
)

from ..common.utils import KSERVE_TEST_NAMESPACE, predict


@pytest.mark.helm
def test_sklearn_kserve():
    service_name = "isvc-sklearn-kserve"

    predictor = V1beta1PredictorSpec(
        min_replicas=1,
        model=V1beta1ModelSpec(
            model_format=V1beta1ModelFormat(
                name="sklearn",
            ),
            runtime="kserve-mlserver",
            storage_uri="gs://seldon-models/sklearn/mms/lr_model",
            protocol_version="v2",
            resources=V1ResourceRequirements(
                requests={"cpu": "50m", "memory": "128Mi"},
                limits={"cpu": "100m", "memory": "512Mi"},
            ),
        ),
    )

    isvc = V1beta1InferenceService(
        api_version=constants.KSERVE_V1BETA1,
        kind=constants.KSERVE_KIND,
        metadata=client.V1ObjectMeta(
            name=service_name, namespace=KSERVE_TEST_NAMESPACE
        ),
        spec=V1beta1InferenceServiceSpec(predictor=predictor),
    )

    kserve_client = KServeClient(
        config_file=os.environ.get("KUBECONFIG", "~/.kube/config"))
    kserve_client.create(isvc)
    kserve_client.wait_isvc_ready(
        service_name, namespace=KSERVE_TEST_NAMESPACE)

    res = predict(service_name, "./data/iris_input_v2.json",
                  protocol_version="v2")
    assert res["outputs"][0]["data"] == [1, 1]

    kserve_client.delete(service_name, KSERVE_TEST_NAMESPACE)
