# Copyright (c) OpenMMLab. All rights reserved.
# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT!
"""Client and server classes corresponding to protobuf-defined services."""
import grpc

import inference_pb2 as inference__pb2


class InferenceStub(object):
    """The inference service definition."""

    def __init__(self, channel):
        """Constructor.

        Args:
            channel: A grpc.Channel.
        """
        self.Echo = channel.unary_unary(
            '/mmdeploy.Inference/Echo',
            request_serializer=inference__pb2.Empty.SerializeToString,
            response_deserializer=inference__pb2.Reply.FromString,
        )
        self.Init = channel.unary_unary(
            '/mmdeploy.Inference/Init',
            request_serializer=inference__pb2.Model.SerializeToString,
            response_deserializer=inference__pb2.Reply.FromString,
        )
        self.OutputNames = channel.unary_unary(
            '/mmdeploy.Inference/OutputNames',
            request_serializer=inference__pb2.Empty.SerializeToString,
            response_deserializer=inference__pb2.Names.FromString,
        )
        self.Inference = channel.unary_unary(
            '/mmdeploy.Inference/Inference',
            request_serializer=inference__pb2.TensorList.SerializeToString,
            response_deserializer=inference__pb2.Reply.FromString,
        )
        self.Destroy = channel.unary_unary(
            '/mmdeploy.Inference/Destroy',
            request_serializer=inference__pb2.Empty.SerializeToString,
            response_deserializer=inference__pb2.Reply.FromString,
        )


class InferenceServicer(object):
    """The inference service definition."""

    def Echo(self, request, context):
        """Missing associated documentation comment in .proto file."""
        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
        context.set_details('Method not implemented!')
        raise NotImplementedError('Method not implemented!')

    def Init(self, request, context):
        """Init Model with model file."""
        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
        context.set_details('Method not implemented!')
        raise NotImplementedError('Method not implemented!')

    def OutputNames(self, request, context):
        """Get output names."""
        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
        context.set_details('Method not implemented!')
        raise NotImplementedError('Method not implemented!')

    def Inference(self, request, context):
        """Inference with inputs."""
        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
        context.set_details('Method not implemented!')
        raise NotImplementedError('Method not implemented!')

    def Destroy(self, request, context):
        """Destroy handle."""
        context.set_code(grpc.StatusCode.UNIMPLEMENTED)
        context.set_details('Method not implemented!')
        raise NotImplementedError('Method not implemented!')


def add_InferenceServicer_to_server(servicer, server):
    rpc_method_handlers = {
        'Echo':
        grpc.unary_unary_rpc_method_handler(
            servicer.Echo,
            request_deserializer=inference__pb2.Empty.FromString,
            response_serializer=inference__pb2.Reply.SerializeToString,
        ),
        'Init':
        grpc.unary_unary_rpc_method_handler(
            servicer.Init,
            request_deserializer=inference__pb2.Model.FromString,
            response_serializer=inference__pb2.Reply.SerializeToString,
        ),
        'OutputNames':
        grpc.unary_unary_rpc_method_handler(
            servicer.OutputNames,
            request_deserializer=inference__pb2.Empty.FromString,
            response_serializer=inference__pb2.Names.SerializeToString,
        ),
        'Inference':
        grpc.unary_unary_rpc_method_handler(
            servicer.Inference,
            request_deserializer=inference__pb2.TensorList.FromString,
            response_serializer=inference__pb2.Reply.SerializeToString,
        ),
        'Destroy':
        grpc.unary_unary_rpc_method_handler(
            servicer.Destroy,
            request_deserializer=inference__pb2.Empty.FromString,
            response_serializer=inference__pb2.Reply.SerializeToString,
        ),
    }
    generic_handler = grpc.method_handlers_generic_handler(
        'mmdeploy.Inference', rpc_method_handlers)
    server.add_generic_rpc_handlers((generic_handler, ))


# This class is part of an EXPERIMENTAL API.
class Inference(object):
    """The inference service definition."""

    @staticmethod
    def Echo(request,
             target,
             options=(),
             channel_credentials=None,
             call_credentials=None,
             insecure=False,
             compression=None,
             wait_for_ready=None,
             timeout=None,
             metadata=None):
        return grpc.experimental.unary_unary(
            request, target, '/mmdeploy.Inference/Echo',
            inference__pb2.Empty.SerializeToString,
            inference__pb2.Reply.FromString, options, channel_credentials,
            insecure, call_credentials, compression, wait_for_ready, timeout,
            metadata)

    @staticmethod
    def Init(request,
             target,
             options=(),
             channel_credentials=None,
             call_credentials=None,
             insecure=False,
             compression=None,
             wait_for_ready=None,
             timeout=None,
             metadata=None):
        return grpc.experimental.unary_unary(
            request, target, '/mmdeploy.Inference/Init',
            inference__pb2.Model.SerializeToString,
            inference__pb2.Reply.FromString, options, channel_credentials,
            insecure, call_credentials, compression, wait_for_ready, timeout,
            metadata)

    @staticmethod
    def OutputNames(request,
                    target,
                    options=(),
                    channel_credentials=None,
                    call_credentials=None,
                    insecure=False,
                    compression=None,
                    wait_for_ready=None,
                    timeout=None,
                    metadata=None):
        return grpc.experimental.unary_unary(
            request, target, '/mmdeploy.Inference/OutputNames',
            inference__pb2.Empty.SerializeToString,
            inference__pb2.Names.FromString, options, channel_credentials,
            insecure, call_credentials, compression, wait_for_ready, timeout,
            metadata)

    @staticmethod
    def Inference(request,
                  target,
                  options=(),
                  channel_credentials=None,
                  call_credentials=None,
                  insecure=False,
                  compression=None,
                  wait_for_ready=None,
                  timeout=None,
                  metadata=None):
        return grpc.experimental.unary_unary(
            request, target, '/mmdeploy.Inference/Inference',
            inference__pb2.TensorList.SerializeToString,
            inference__pb2.Reply.FromString, options, channel_credentials,
            insecure, call_credentials, compression, wait_for_ready, timeout,
            metadata)

    @staticmethod
    def Destroy(request,
                target,
                options=(),
                channel_credentials=None,
                call_credentials=None,
                insecure=False,
                compression=None,
                wait_for_ready=None,
                timeout=None,
                metadata=None):
        return grpc.experimental.unary_unary(
            request, target, '/mmdeploy.Inference/Destroy',
            inference__pb2.Empty.SerializeToString,
            inference__pb2.Reply.FromString, options, channel_credentials,
            insecure, call_credentials, compression, wait_for_ready, timeout,
            metadata)