#
# 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 logging
import time
import requests
import portforward
from kubernetes import client
from kserve import (
    constants,
    KServeClient,
    V1beta1InferenceService,
    V1beta1InferenceServiceSpec,
    V1beta1PredictorSpec,
    V1beta1SKLearnSpec,
)
from kubernetes.client import V1ResourceRequirements
from ..common.utils import KSERVE_TEST_NAMESPACE
from ..common.utils import predict

logging.basicConfig(level=logging.INFO)

ENABLE_METRIC_AGG = "serving.kserve.io/enable-metric-aggregation"
METRICS_AGG_PORT = 9088
METRICS_PATH = "metrics"


def test_qpext_kserve():
    # test the qpext using the sklearn predictor
    service_name = "isvc-sklearn-v2"
    predictor = V1beta1PredictorSpec(
        min_replicas=1,
        sklearn=V1beta1SKLearnSpec(
            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,
            # set the metric aggregation annotation to true
            annotations={ENABLE_METRIC_AGG: "true"},
        ),
        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]

    send_metrics_request(kserve_client, service_name)
    kserve_client.delete(service_name, KSERVE_TEST_NAMESPACE)


def send_metrics_request(kserve_client, service_name):
    time.sleep(10)
    pods = kserve_client.core_api.list_namespaced_pod(KSERVE_TEST_NAMESPACE,
                                                      label_selector='serving.kserve.io/inferenceservice={}'.
                                                      format(service_name))
    pod_name = ""
    for pod in pods.items:
        # get a pod name
        pod_name = pod.metadata.name
        break

    url = f"http://localhost:{METRICS_AGG_PORT}/{METRICS_PATH}"
    with portforward.forward(KSERVE_TEST_NAMESPACE, pod_name, METRICS_AGG_PORT, METRICS_AGG_PORT):
        logging.info(f"metrics request url: {url}")
        response = requests.get(url)
        logging.info(f"response: {response}, content: {response.content}")
        logging.info("Got response code %s, content %s", response.status_code, response.content)

        assert response.status_code == 200
        assert len(response.content) > 0
