#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2024. Huawei Technologies Co.,Ltd. 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.
# 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 sys
import time
import multiprocessing
import logging

import grpc
import numpy as np
import tensorflow as tf
from input_config import config
from tensorflow_serving.apis import predict_pb2, prediction_service_pb2_grpc

logging.basicConfig(
    level=logging.INFO,  # set log level（DEBUG/INFO/WARNING/ERROR/CRITICAL）
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
)


class PredictModelGrpc():
    def __init__(
        self,
        model_name,
        inputs,
        input_types,
        output_name,
        stream_num=1,
        socket="xxx.xxx.xxx.xxx:8500",
    ):
        self.socket = socket
        self.model_name = model_name
        self.inputs = inputs
        self.input_types = input_types
        self.output_name = output_name
        self.request, self.stub = self.__get_request()
        self.stream_num = stream_num
        self.request_count_queue = multiprocessing.Queue()
        self.start_count_flag = multiprocessing.Value("i", 0)

    def inference(self):
        for name in self.inputs:
            self.request.inputs[name].CopyFrom(
                tf.make_tensor_proto(self.inputs[name], dtype=self.input_types[name])
            )
        
        # warm up server
        result = self.stub.Predict.future(self.request, 1000.0)
        try:
            response = result.result() # response is returned when status OK
            logging.debug(f"Status = OK")
        except grpc.RpcError as e:
            logging.error(f"request failed, status = {e.code()}")
            sys.exit(1)
        
        # start multiprocessing
        processes = []
        for _ in range(self.stream_num):
            worker = multiprocessing.Process(target=self.predict_worker)
            processes.append(worker)

        for worker in processes:
            worker.start()

        # count successed request after 10 sec
        time.sleep(10)

        # count successed request for 30 sec
        start_time = time.time()
        self.start_count_flag.value = 1
        time.sleep(30)
        end_time = time.time()
        self.start_count_flag.value = 0

        for worker in processes:
            worker.join()

        request_counts = []
        while not self.request_count_queue.empty():
            request_counts.append(self.request_count_queue.get())
        total_requests = sum(request_counts)
        time_total = (end_time - start_time)
        request_per_sec = total_requests / time_total
        logging.info("successed request in total: ", total_requests)
        logging.info("time cost in total: {:.2f}".format(time_total))
        logging.info("throughput(requests per second): {:.1f}".format(request_per_sec))

    def predict_worker(self):
        # warm up process
        result = self.stub.Predict.future(self.request, 1000.0)
        try:
            response = result.result() # response is returned when status OK
            logging.debug(f"Status = OK")
        except grpc.RpcError as e:
            pass

        request_count = 0
        for _ in range(100):
            result = self.stub.Predict.future(self.request, 1000.0)
            try:
                response = result.result() # response is returned when status OK
                logging.debug(f"Status = OK")
                if self.start_count_flag.value == 1:
                    request_count = request_count + 1

            except grpc.RpcError as e:
                logging.error(f"status = {e.code()}")
            
        self.request_count_queue.put(request_count)

    def __get_request(self):
        channel = grpc.insecure_channel(
            self.socket,
            options=[
                ("grpc.max_send_message_length", 1024 * 1024 * 1024),
                ("grpc.max_receive_message_length", 1024 * 1024 * 1024),
            ],
        )
        stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)
        request = predict_pb2.PredictRequest()
        request.model_spec.name = self.model_name
        request.model_spec.signature_name = "serving_default"

        return request, stub


FIELD_TYPE = "dtype"
FIELD_SHAPE = "shape"


def gen_inputs():
    inputs = {}
    input_types = {}
    for name in config:
        input_types[name] = config[name][FIELD_TYPE]
        if config[name][FIELD_TYPE] == tf.int32:
            inputs[name] = np.random.randint(0, 100, size=config[name][FIELD_SHAPE])
        elif config[name][FIELD_TYPE] == tf.float32:
            inputs[name] = np.random.randint(0, 2, size=config[name][FIELD_SHAPE]) * 1.0
    return inputs, input_types


if __name__ == "__main__":
    input_datas, types = gen_inputs()

    if len(sys.argv) < 2:
        model = PredictModelGrpc(
            model_name="saved_model",
            inputs=input_datas,
            input_types=types,
            output_name="",
            socket="127.0.0.1:9999",
        )
    else:
        model = PredictModelGrpc(
            model_name="saved_model",
            inputs=input_datas,
            input_types=types,
            output_name="",
            stream_num=int(sys.argv[1]),
            socket="127.0.0.1:9999",
        )

    model.inference()