# 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 json
import logging
import os

import numpy as np
import pytest
from kubernetes.client import (V1ContainerPort, V1ObjectMeta,
                               V1ResourceRequirements)

from kserve import (KServeClient, V1beta1InferenceService,
                    V1beta1InferenceServiceSpec, V1beta1ModelFormat,
                    V1beta1ModelSpec, V1beta1PaddleServerSpec,
                    V1beta1PredictorSpec, constants)

from ..common.utils import KSERVE_TEST_NAMESPACE, predict, predict_grpc

logging.basicConfig(level=logging.INFO)


@pytest.mark.slow
def test_paddle():
    predictor = V1beta1PredictorSpec(
        min_replicas=1,
        paddle=V1beta1PaddleServerSpec(
            storage_uri="gs://kfserving-examples/models/paddle/resnet",
            resources=V1ResourceRequirements(
                requests={"cpu": "200m", "memory": "256Mi"},
                limits={"cpu": "200m", "memory": "1Gi"},
            )
        )
    )

    service_name = 'isvc-paddle'
    isvc = V1beta1InferenceService(
        api_version=constants.KSERVE_V1BETA1,
        kind=constants.KSERVE_KIND,
        metadata=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)
    try:
        kserve_client.wait_isvc_ready(service_name, namespace=KSERVE_TEST_NAMESPACE, timeout_seconds=720)
    except RuntimeError as e:
        pods = kserve_client.core_api.list_namespaced_pod(KSERVE_TEST_NAMESPACE,
                                                          label_selector='serving.kserve.io/inferenceservice={}'.format(
                                                              service_name))
        for pod in pods.items:
            logging.info(pod)
        raise e

    res = predict(service_name, './data/jay.json')
    assert np.argmax(res["predictions"][0]) == 17

    kserve_client.delete(service_name, KSERVE_TEST_NAMESPACE)


@pytest.mark.slow
def test_paddle_runtime():
    predictor = V1beta1PredictorSpec(
        min_replicas=1,
        model=V1beta1ModelSpec(
            model_format=V1beta1ModelFormat(
                name="paddle",
            ),
            storage_uri="gs://kfserving-examples/models/paddle/resnet",
            resources=V1ResourceRequirements(
                requests={"cpu": "200m", "memory": "256Mi"},
                limits={"cpu": "200m", "memory": "1Gi"},
            )
        )
    )

    service_name = 'isvc-paddle-runtime'
    isvc = V1beta1InferenceService(
        api_version=constants.KSERVE_V1BETA1,
        kind=constants.KSERVE_KIND,
        metadata=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)
    try:
        kserve_client.wait_isvc_ready(service_name, namespace=KSERVE_TEST_NAMESPACE, timeout_seconds=720)
    except RuntimeError as e:
        pods = kserve_client.core_api.list_namespaced_pod(KSERVE_TEST_NAMESPACE,
                                                          label_selector='serving.kserve.io/inferenceservice={}'.format(
                                                              service_name))
        for pod in pods.items:
            logging.info(pod)
        raise e

    res = predict(service_name, './data/jay.json')
    assert np.argmax(res["predictions"][0]) == 17

    kserve_client.delete(service_name, KSERVE_TEST_NAMESPACE)


@pytest.mark.slow
def test_paddle_v2_kserve():
    predictor = V1beta1PredictorSpec(
        min_replicas=1,
        model=V1beta1ModelSpec(
            model_format=V1beta1ModelFormat(
                name="paddle",
            ),
            runtime="kserve-paddleserver",
            storage_uri="gs://kfserving-examples/models/paddle/resnet",
            resources=V1ResourceRequirements(
                requests={"cpu": "200m", "memory": "256Mi"},
                limits={"cpu": "200m", "memory": "1Gi"},
            )
        )
    )

    service_name = 'isvc-paddle-v2-kserve'
    isvc = V1beta1InferenceService(
        api_version=constants.KSERVE_V1BETA1,
        kind=constants.KSERVE_KIND,
        metadata=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)
    try:
        kserve_client.wait_isvc_ready(service_name, namespace=KSERVE_TEST_NAMESPACE, timeout_seconds=720)
    except RuntimeError as e:
        pods = kserve_client.core_api.list_namespaced_pod(KSERVE_TEST_NAMESPACE,
                                                          label_selector='serving.kserve.io/inferenceservice={}'.format(
                                                              service_name))
        for pod in pods.items:
            logging.info(pod)
        raise e

    res = predict(service_name, './data/jay-v2.json', protocol_version="v2")
    assert np.argmax(res["outputs"][0]["data"]) == 17

    kserve_client.delete(service_name, KSERVE_TEST_NAMESPACE)


@pytest.mark.slow
def test_paddle_v2_grpc():
    service_name = "isvc-paddle-v2-grpc"
    model_name = "paddle"
    predictor = V1beta1PredictorSpec(
        min_replicas=1,
        model=V1beta1ModelSpec(
            model_format=V1beta1ModelFormat(
                name="paddle",
            ),
            runtime="kserve-paddleserver",
            storage_uri="gs://kfserving-examples/models/paddle/resnet",
            resources=V1ResourceRequirements(
                requests={"cpu": "200m", "memory": "256Mi"},
                limits={"cpu": "200m", "memory": "1Gi"},
            ),
            ports=[
                V1ContainerPort(
                    container_port=8081,
                    name="h2c",
                    protocol="TCP"
                )],
            args=["--model_name", model_name]
        )
    )

    isvc = V1beta1InferenceService(api_version=constants.KSERVE_V1BETA1,
                                   kind=constants.KSERVE_KIND,
                                   metadata=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)
    try:
        kserve_client.wait_isvc_ready(service_name, namespace=KSERVE_TEST_NAMESPACE, timeout_seconds=720)
    except RuntimeError as e:
        pods = kserve_client.core_api.list_namespaced_pod(KSERVE_TEST_NAMESPACE,
                                                          label_selector='serving.kserve.io/inferenceservice={}'.format(
                                                              service_name))
        for pod in pods.items:
            logging.info(pod)
        raise e

    json_file = open("./data/jay-v2-grpc.json")
    payload = json.load(json_file)["inputs"]
    response = predict_grpc(service_name=service_name,
                            payload=payload, model_name=model_name)
    prediction = list(response.outputs[0].contents.fp32_contents)
    assert np.argmax(prediction) == 17

    kserve_client.delete(service_name, KSERVE_TEST_NAMESPACE)
