# Copyright (c) 2020 PaddlePaddle Authors. 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.
"""
Usage:
    Host a trained paddle model with one line command
    Example:
        python -m paddle_serving_server.serve --model ./serving_server_model --port 9292
"""
import argparse
import os
import json
import base64
import time
from multiprocessing import Process
import sys
if sys.version_info.major == 2:
    from BaseHTTPServer import BaseHTTPRequestHandler, HTTPServer
elif sys.version_info.major == 3:
    from http.server import BaseHTTPRequestHandler, HTTPServer

from contextlib import closing
import socket


# web_service.py is still used by Pipeline.
def port_is_available(port):
    with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock:
        sock.settimeout(2)
        result = sock.connect_ex(('127.0.0.1', port))
    if result != 0:
        return True
    else:
        return False


def format_gpu_to_strlist(unformatted_gpus):
    gpus_strlist = []
    if isinstance(unformatted_gpus, int):
        gpus_strlist = [str(unformatted_gpus)]
    elif isinstance(unformatted_gpus, list):
        if unformatted_gpus == [""]:
            gpus_strlist = ["-1"]
        elif len(unformatted_gpus) == 0:
            gpus_strlist = ["-1"]
        else:
            gpus_strlist = [str(x) for x in unformatted_gpus]
    elif isinstance(unformatted_gpus, str):
        if unformatted_gpus == "":
            gpus_strlist = ["-1"]
        else:
            gpus_strlist = [unformatted_gpus]
    elif unformatted_gpus == None:
        gpus_strlist = ["-1"]
    else:
        raise ValueError("error input of set_gpus")

    # check cuda visible
    if "CUDA_VISIBLE_DEVICES" in os.environ:
        env_gpus = os.environ["CUDA_VISIBLE_DEVICES"].split(",")
        for op_gpus_str in gpus_strlist:
            op_gpu_list = op_gpus_str.split(",")
            # op_gpu_list == ["-1"] means this op use CPU
            # so don`t check cudavisible.
            if op_gpu_list == ["-1"]:
                continue
            for ids in op_gpu_list:
                if ids not in env_gpus:
                    print("gpu_ids is not in CUDA_VISIBLE_DEVICES.")
                    exit(-1)

    # check gpuid is valid
    for op_gpus_str in gpus_strlist:
        op_gpu_list = op_gpus_str.split(",")
        use_gpu = False
        for ids in op_gpu_list:
            if int(ids) < -1:
                raise ValueError("The input of gpuid error.")
            if int(ids) >= 0:
                use_gpu = True
            if int(ids) == -1 and use_gpu:
                raise ValueError("You can not use CPU and GPU in one model.")

    return gpus_strlist


def is_gpu_mode(unformatted_gpus):
    gpus_strlist = format_gpu_to_strlist(unformatted_gpus)
    for op_gpus_str in gpus_strlist:
        op_gpu_list = op_gpus_str.split(",")
        for ids in op_gpu_list:
            if int(ids) >= 0:
                return True
    return False


def serve_args():
    parser = argparse.ArgumentParser("serve")
    parser.add_argument(
        "--thread",
        type=int,
        default=4,
        help="Concurrency of server,[4,1024]",
        choices=range(4, 1025))
    parser.add_argument(
        "--port", type=int, default=9393, help="Port of the starting gpu")
    parser.add_argument(
        "--device", type=str, default="cpu", help="Type of device")
    parser.add_argument(
        "--gpu_ids", type=str, default="", nargs="+", help="gpu ids")
    parser.add_argument(
        "--op_num", type=int, default=0, nargs="+", help="Number of each op")
    parser.add_argument(
        "--op_max_batch",
        type=int,
        default=32,
        nargs="+",
        help="Max batch of each op")
    parser.add_argument(
        "--model", type=str, default="", nargs="+", help="Model for serving")
    parser.add_argument(
        "--workdir",
        type=str,
        default="workdir",
        help="Working dir of current service")
    parser.add_argument(
        "--use_mkl", default=False, action="store_true", help="Use MKL")
    parser.add_argument(
        "--precision",
        type=str,
        default="fp32",
        help="precision mode(fp32, int8, fp16, bf16)")
    parser.add_argument(
        "--use_calib",
        default=False,
        action="store_true",
        help="Use TensorRT Calibration")
    parser.add_argument(
        "--mem_optim_off",
        default=False,
        action="store_true",
        help="Memory optimize")
    parser.add_argument(
        "--ir_optim", default=False, action="store_true", help="Graph optimize")
    parser.add_argument(
        "--max_body_size",
        type=int,
        default=512 * 1024 * 1024,
        help="Limit sizes of messages")
    parser.add_argument(
        "--use_encryption_model",
        default=False,
        action="store_true",
        help="Use encryption model")
    parser.add_argument(
        "--use_trt", default=False, action="store_true", help="Use TensorRT")
    parser.add_argument(
        "--use_lite", default=False, action="store_true", help="Use PaddleLite")
    parser.add_argument(
        "--use_xpu", default=False, action="store_true", help="Use XPU")
    parser.add_argument(
        "--product_name",
        type=str,
        default=None,
        help="product_name for authentication")
    parser.add_argument(
        "--container_id",
        type=str,
        default=None,
        help="container_id for authentication")
    parser.add_argument(
        "--gpu_multi_stream",
        default=False,
        action="store_true",
        help="Use gpu_multi_stream")
    return parser.parse_args()


def start_gpu_card_model(gpu_mode, port, args):  # pylint: disable=doc-string-missing

    device = "cpu"
    if gpu_mode == True:
        device = "gpu"

    thread_num = args.thread
    model = args.model
    mem_optim = args.mem_optim_off is False
    ir_optim = args.ir_optim
    use_mkl = args.use_mkl
    max_body_size = args.max_body_size
    workdir = "{}_{}".format(args.workdir, port)

    if model == "":
        print("You must specify your serving model")
        exit(-1)

    for single_model_config in args.model:
        if os.path.isdir(single_model_config):
            pass
        elif os.path.isfile(single_model_config):
            raise ValueError("The input of --model should be a dir not file.")
    import paddle_serving_server as serving
    op_maker = serving.OpMaker()
    op_seq_maker = serving.OpSeqMaker()
    read_op = op_maker.create('general_reader')
    op_seq_maker.add_op(read_op)
    for idx, single_model in enumerate(model):
        infer_op_name = "general_infer"
        # 目前由于ocr的节点Det模型依赖于opencv的第三方库
        # 只有使用ocr的时候，才会加入opencv的第三方库并编译GeneralDetectionOp
        # 故此处做特殊处理，当不满足下述情况时，所添加的op默认为GeneralInferOp
        # 以后可能考虑不用python脚本来生成配置
        if len(model) == 2 and idx == 0 and single_model == "ocr_det_model":
            infer_op_name = "general_detection"
        else:
            infer_op_name = "general_infer"
        general_infer_op = op_maker.create(infer_op_name)
        op_seq_maker.add_op(general_infer_op)

    general_response_op = op_maker.create('general_response')
    op_seq_maker.add_op(general_response_op)

    server = serving.Server()
    server.set_op_sequence(op_seq_maker.get_op_sequence())
    server.set_num_threads(thread_num)
    server.use_mkl(use_mkl)
    server.set_precision(args.precision)
    server.set_use_calib(args.use_calib)
    server.set_memory_optimize(mem_optim)
    server.set_ir_optimize(ir_optim)
    server.set_max_body_size(max_body_size)

    if args.use_trt and device == "gpu":
        server.set_trt()
        server.set_ir_optimize(True)

    if args.gpu_multi_stream and device == "gpu":
        server.set_gpu_multi_stream()

    if args.op_num:
        server.set_op_num(args.op_num)

    if args.op_max_batch:
        server.set_op_max_batch(args.op_max_batch)

    if args.use_lite:
        server.set_lite()

    server.set_device(device)
    if args.use_xpu:
        server.set_xpu()

    if args.product_name != None:
        server.set_product_name(args.product_name)
    if args.container_id != None:
        server.set_container_id(args.container_id)

    if gpu_mode == True:
        server.set_gpuid(args.gpu_ids)
    server.load_model_config(model)
    server.prepare_server(
        workdir=workdir,
        port=port,
        device=device,
        use_encryption_model=args.use_encryption_model)
    server.run_server()


def start_multi_card(args, serving_port=None):  # pylint: disable=doc-string-missing

    if serving_port == None:
        serving_port = args.port

    if args.use_lite:
        print("run using paddle-lite.")
        start_gpu_card_model(False, serving_port, args)
    else:
        start_gpu_card_model(is_gpu_mode(args.gpu_ids), serving_port, args)


class MainService(BaseHTTPRequestHandler):
    def get_available_port(self):
        default_port = 12000
        for i in range(1000):
            if port_is_available(default_port + i):
                return default_port + i

    def start_serving(self):
        start_multi_card(args, serving_port)

    def get_key(self, post_data):
        if "key" not in post_data:
            return False
        else:
            key = base64.b64decode(post_data["key"].encode())
            for single_model_config in args.model:
                if os.path.isfile(single_model_config):
                    raise ValueError(
                        "The input of --model should be a dir not file.")
                with open(single_model_config + "/key", "wb") as f:
                    f.write(key)
            return True

    def check_key(self, post_data):
        if "key" not in post_data:
            return False
        else:
            key = base64.b64decode(post_data["key"].encode())
            for single_model_config in args.model:
                if os.path.isfile(single_model_config):
                    raise ValueError(
                        "The input of --model should be a dir not file.")
                with open(single_model_config + "/key", "rb") as f:
                    cur_key = f.read()
                if key != cur_key:
                    return False
            return True

    def start(self, post_data):
        post_data = json.loads(post_data.decode('utf-8'))
        global p_flag
        if not p_flag:
            if args.use_encryption_model:
                print("waiting key for model")
                if not self.get_key(post_data):
                    print("not found key in request")
                    return False
            global serving_port
            global p
            serving_port = self.get_available_port()
            p = Process(target=self.start_serving)
            p.start()
            time.sleep(3)
            if p.is_alive():
                p_flag = True
            else:
                return False
        else:
            if p.is_alive():
                if not self.check_key(post_data):
                    return False
            else:
                return False
        return True

    def do_POST(self):
        content_length = int(self.headers['Content-Length'])
        post_data = self.rfile.read(content_length)
        if self.start(post_data):
            response = {"endpoint_list": [serving_port]}
        else:
            response = {"message": "start serving failed"}
        self.send_response(200)
        self.send_header('Content-type', 'application/json')
        self.end_headers()
        self.wfile.write(json.dumps(response).encode())


if __name__ == "__main__":
    # args.device is not used at all.
    # just keep the interface.
    # so --device should not be recommended at the HomePage.
    args = serve_args()
    for single_model_config in args.model:
        if os.path.isdir(single_model_config):
            pass
        elif os.path.isfile(single_model_config):
            raise ValueError("The input of --model should be a dir not file.")

    if args.use_encryption_model:
        p_flag = False
        p = None
        serving_port = 0
        server = HTTPServer(('0.0.0.0', int(args.port)), MainService)
        print(
            'Starting encryption server, waiting for key from client, use <Ctrl-C> to stop'
        )
        server.serve_forever()
    else:
        start_multi_card(args)